diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
new file mode 100644
index 00000000..b66486d2
--- /dev/null
+++ b/.github/workflows/ci.yml
@@ -0,0 +1,64 @@
+name: CI
+
+on:
+ pull_request:
+ push:
+ branches: [master]
+
+permissions:
+ contents: read
+
+concurrency:
+ group: ci-${{ github.workflow }}-${{ github.ref }}
+ cancel-in-progress: true
+
+jobs:
+ python:
+ name: Python ${{ matrix.python-version }}
+ runs-on: ubuntu-latest
+ strategy:
+ fail-fast: false
+ matrix:
+ python-version: ["3.10", "3.13"]
+ steps:
+ - name: Check out source
+ uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # v5.0.0
+ - name: Set up Python
+ uses: actions/setup-python@a309ff8b426b58ec0e2a45f0f869d46889d02405 # v6.2.0
+ with:
+ python-version: ${{ matrix.python-version }}
+ cache: pip
+ - name: Install test dependencies
+ run: python -m pip install --disable-pip-version-check ".[test,data]"
+ - name: Run offline gate
+ run: python -m pytest -m "not live and not reproduction"
+
+ dss:
+ name: Python 3.13 with pinned DSS
+ runs-on: ubuntu-latest
+ steps:
+ - name: Check out source
+ uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # v5.0.0
+ - name: Check out DSS
+ uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # v5.0.0
+ with:
+ repository: bc1cindy/dense-subset-sum
+ ref: d8099bc540c9de7d0f3c9f095e8b2aad8f9d8c8d
+ path: .ci/dense-subset-sum
+ - name: Set up Python
+ uses: actions/setup-python@a309ff8b426b58ec0e2a45f0f869d46889d02405 # v6.2.0
+ with:
+ python-version: "3.13"
+ cache: pip
+ - name: Select DSS minimum Rust toolchain
+ run: |
+ rustup toolchain install 1.88.0 --profile minimal
+ rustup default 1.88.0
+ - name: Install project and DSS
+ run: |
+ python -m pip install --disable-pip-version-check ".[test,data]"
+ python -m pip install --disable-pip-version-check .ci/dense-subset-sum
+ - name: Verify DSS revision
+ run: python -c 'import dss; assert dss.__rev__ == "d8099bc540c9de7d0f3c9f095e8b2aad8f9d8c8d"'
+ - name: Run offline gate with DSS
+ run: python -m pytest -m "not live and not reproduction"
diff --git a/.github/workflows/reproduction.yml b/.github/workflows/reproduction.yml
new file mode 100644
index 00000000..5c2d608c
--- /dev/null
+++ b/.github/workflows/reproduction.yml
@@ -0,0 +1,33 @@
+name: Evidence reproduction
+
+on:
+ workflow_dispatch:
+ schedule:
+ - cron: "17 4 * * 1"
+
+permissions:
+ contents: read
+
+concurrency:
+ group: evidence-reproduction
+ cancel-in-progress: false
+
+jobs:
+ bundles:
+ name: Reproduce committed bundles
+ runs-on: macos-14
+ timeout-minutes: 120
+ steps:
+ - name: Check out source
+ uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # v5.0.0
+ - name: Set up Python
+ uses: actions/setup-python@a309ff8b426b58ec0e2a45f0f869d46889d02405 # v6.2.0
+ with:
+ python-version: "3.13.7"
+ cache: pip
+ - name: Install test dependencies
+ run: python -m pip install --disable-pip-version-check ".[test,data]"
+ - name: Verify committed bundle contents
+ run: python -m pytest tests/test_committed_bundles.py::test_every_committed_bundle_is_complete_and_content_verified
+ - name: Reproduce all committed bundles
+ run: python -m pytest -m reproduction tests/test_committed_bundles.py
diff --git a/.gitignore b/.gitignore
index 67f4bce0..9896f8d3 100644
--- a/.gitignore
+++ b/.gitignore
@@ -3,10 +3,25 @@ __pycache__/
*.pyc
.cache/
.blkcache/
+.blkcache-live/
*.ndjson
!catalog/entities.example.ndjson
slice*.json
+!catalog/runs/slice-*.json
+!results/artifacts/slice-*.json
+!releases/slice-*.bundle.json
slice*.err
+!catalog/datasets/slice-a-channels-2016-v1.json
day-*.json
day-*.err
decluster.egg-info/
+
+# logs de coleta/análise (efêmeros)
+*.log
+*.done
+
+# épocas comprimidas da coleta multi-epoch (dados locais grandes)
+epoch_*.ndjson.gz
+data/epochs_*/
+# Planning notes and specs: working material, not part of the published record.
+docs/superpowers/
diff --git a/PAPER.md b/PAPER.md
index 7a747546..115bd620 100644
--- a/PAPER.md
+++ b/PAPER.md
@@ -1,29 +1,37 @@
# Fingerprint-Aware Probabilistic De-anonymization of Bitcoin Transaction Graphs
-*Every empirical number below is reproducible from this repository:
-`decluster/library.py` (measured bits), `results/RESULTS-bigquery.txt` (calibration on a
-~105k uniform whole-chain sample), `results/RESULTS-fingerprint-validation.md` (attribution AUC ≈0.93
-on 4,000 same-wallet + 4,000 random pairs drawn from a witness-bearing mainnet cache),
-`results/RESULTS-graph-deanon.md` (structural de-anon across five eras), and
-`results/RESULTS-wp4.md` (the same-owner-labelled case study). Scope: a fingerprint-aware
-clustering **method**, validated at mainnet scale without an archival node; the one thing
-left is a whole-chain **entity-reduction rate** (§10).*
+*Reproducibility is tracked per result rather than asserted for this manuscript as a whole.
+Canonical runs declare code, data, parameters, environment, outputs and limitations in
+`catalog/runs`; `results/generated` is derived from those artifacts. Historical `RESULTS-*`
+documents without a run manifest remain evidence records, not independently reproducible results.
+Public cold-start reproduction also remains pending until every required blob has an immutable
+public location and an independent mirror.*
+
+*Two consequences of that policy are load-bearing enough to state up front. **The headline
+attribution figure is not one number.** The growing 165,832-transaction cache behind the AUC 0.933
+of earlier revisions was not preserved; the reproducible levels are **0.9459** on the committed
+600-transaction fixture and **0.9244** on the preserved 22,112-transaction cache, and this paper
+names which one it means each time (§5). **That preserved cache is single-era** — heights
+800,000–965,220, Taproot only — so no multi-era claim about it is supported by data on disk.
+Separately, where a figure below is measured but does not yet carry a run manifest, it says so
+rather than borrowing the authority of one.*
## Abstract
-On-chain Bitcoin privacy is an emergent property of the **transaction graph**, not of
+On-chain Bitcoin privacy is an emergent property of the transaction graph, not of
any single transaction. The transaction **graph is primary**: in general the amount channel is, on our reading, the
-**most defeatable** layer — net-settlement and deliberately underdetermined values silence it —
+most defeatable layer — net-settlement and deliberately underdetermined values silence it —
and, following the cited framework (`tx-graph-anonymity-sets`, §05), we treat the amount channel as
-*embedded within* the graph model. Two regimes are **exceptions** to that defeatability, and both are
+*embedded within* the graph model. Two regimes are exceptions to that defeatability, and both are
our reading rather than the framework's, which embeds the channel without carving either out. The
-first is **decidability**: where the amount structure is decidable — a sparse subset-sum instance, or
+first is decidability: where the amount structure is decidable — a sparse subset-sum instance, or
a plausible round partition — the amounts become the *locally* decisive de-anonymization signal,
ahead of any wallet-software fingerprint (the amounts in use are a fingerprint of their own kind),
-decisive only where a plausible partition survives. The second is **dominance**: where one
-participant's input exceeds what every other participant brought to a round, conservation forces
-outputs onto them by arithmetic, with no partition required to survive and no client model invoked
-(§10). The two do not overlap — the first partitions and abstains under ambiguity, the second never
+decisive only where a plausible partition survives. The second is dominance: where one
+participant's input exceeds what every other participant brought to a round, conservation traces the
+funding of outputs to them by arithmetic, with no partition required to survive and no client model
+invoked — value provenance, not ownership, since a participant who settles obligations inside the
+round sends their satoshi into a counterparty's output (§9, §10). The two do not overlap — the first partitions and abstains under ambiguity, the second never
partitions at all. A transaction that merges unrelated parties can be re-partitioned into per-owner
subtransactions by subtracting a contributed input from an output and testing whether the implied
payment "makes sense" (a round number, under the unnecessary-input heuristic), or, for a general
@@ -43,25 +51,28 @@ rather than κ_c. This amount-channel κ_c is a distinct object from the (ε, δ
fingerprint and ancestry feature spaces (the record-linkage precondition, where *sparse* means
de-anonymizable — the opposite sign for privacy); the amount density neither measures nor implies
it, and the two are never composed into one "density". A second question survives it: conservation asks not
-which participant owns each output but what the others could have afforded, and forces ownership
-whenever one participant's input exceeds the rest of the round — arithmetic on the transaction alone,
+which participant owns each output but what the others could have afforded, and forces the *funding*
+of outputs onto one participant whenever their input exceeds the rest of the round — arithmetic on the
+transaction alone,
reported beside the engine rather than inside it — it appears in the fused per-transaction view
(`report(known_input=…)`) on the same footing as the pairwise leak and topology terms, absent rather
than assumed when the participant's input is not supplied (§1, §10,
-`results/RESULTS-conservation.md`). Wallet fingerprints are a **corroborating** layer
-on top of the decidable case. We present a **partition-refinement clustering** that fuses signed bits of evidence over the labeled
+`results/RESULTS-conservation.md`). Wallet fingerprints are a corroborating layer
+on top of the decidable case. We present a partition-refinement clustering that fuses signed bits of evidence over the labeled
transaction multigraph — refusing false merges rather than only growing them (the cluster-collapse
-failure of a single union-find). Its dominant, most robust term is the **Narayanan–Shmatikov
-cluster-level counterparty structure** — the graph itself; onto it fuse the **amount-based
-subtransaction re-partition** and the **fingerprint** weight-of-evidence (Fellegi–Sunter, summed over
+failure of a single union-find). Its dominant, most robust term is the Narayanan–Shmatikov
+cluster-level counterparty structure — the graph itself; onto it fuse the amount-based
+subtransaction re-partition and the fingerprint weight-of-evidence (a Newcombe frequency-rarity
+weight `−log₂p` with a clamped mismatch penalty — not a fitted Fellegi–Sunter model, §4 — summed over
the least-correlated axis subset, which curbs rather than removes the independence-violating
double-counting §8 notes).
-We build a curated **library of fingerprints with evidence** (23 measured axes across the
+We build a curated **library of fingerprints with evidence** (23 catalogued axes, 22 of them active
+on stored data (§3), across the
chain-observable transaction-construction surface, calibrated on unbiased real-chain
samples — 16 construction axes on a whole-chain sample, and 7 on mempool samples (5 witness + OP_RETURN +
a block-feerate broadcast-time axis) — anchored to chain-proven examples) and, on a real mainnet
merged transaction whose correct owner-partition is known, show the intended false merge
-is re-partitioned by the amount structure and **again** by the fingerprints — the two
+is re-partitioned by the amount structure and again by the fingerprints — the two
signals fuse and agree (the engine's standalone refuse is corroboration-gated; §4/§9). We show that a merge's ~1.6 bits of structural ambiguity cannot
survive the identifying structure an established cluster carries — measured at a median
~33 bits per cluster on a real slice, a lower bound on the >100 whole-chain figure — and that
@@ -69,11 +80,11 @@ fingerprint uniformity is necessary but far from sufficient for collaborative-tr
This measurement is instrumental: the same per-axis bits, read as a penalty instead of a
same-owner link, define a construction-side cost function — so quantifying the attack is
the prerequisite for the defense, shaping transactions that no longer carry these tells.
-Beyond clustering, the same graph yields a coin's **provenance anonymity set** — its distribution over
+Beyond clustering, the same graph yields a coin's provenance anonymity set — its distribution over
ancestral origins from an absorbing random walk, whose min-entropy is a lower bound on the coin's
-provenance **entropy** (§04; the §06 reading of that entropy as a lower bound on graph cuts is noted but,
-per §11, not adopted for this absorber-model entropy). It is fused with the same-owner beliefs,
-node-bounded for deep coinjoins, and weighted by a subset-sum path count (§07), exposed as one callable
+provenance entropy (§04; the §06 reading of that entropy as a lower bound on graph cuts is noted but,
+per §12, not adopted for this absorber-model entropy). It is fused with the same-owner beliefs and
+node-bounded for deep coinjoins (§07), exposed as one callable
pipeline; every reading is a lower bound / weight-of-evidence, never a privacy score (§8).
## 1. Introduction & thesis
@@ -103,9 +114,9 @@ entity. Collaborative transactions that deliberately merge unrelated parties vio
hoping a chain analyst will merge the parties into one entity. We show this hope is
quantitatively misplaced. A two-in/two-out merge adds at most **log₂3 ≈ 1.6 bits** of
ambiguity, while an established cluster in a social-transaction graph carries far more
-identifying structure. We measure it: on a real connected slice, **every** co-spend cluster's
+identifying structure. We measure it: on a real connected slice, every co-spend cluster's
counterparty-rarity bits sum past the merge's 1.6 (median **~33 bits**, ~12% already exceed
-100) — the Narayanan–Shmatikov accumulation computed directly (`results/RESULTS-cluster-bits.md`).
+100) — the Narayanan–Shmatikov accumulation computed directly (`results/generated/cluster-bits-v1.md`).
The **>100-bit** figure is the whole-chain order of magnitude; a slice undercounts each
cluster's counterparties, so the measured median is a lower bound (§10). An analyst needs only
~2 of those bits to override the merge, and every cluster supplies an order of magnitude more.
@@ -128,10 +139,10 @@ More inputs buy possibilities, not privacy.
Our contribution is the *combination engine* plus the *evidence library* that make this
concrete: heuristics and fingerprints are fused as signed bits on a weighted graph, and
-a **partition-refinement** pass over those bits (`cluster_refined`) admits *negative* edges, so it
-can **refuse** a co-spent merge — the refinement lattice, not the merge-only union-find that can only
+a partition-refinement pass over those bits (`cluster_refined`) admits *negative* edges, so it
+can refuse a co-spent merge — the refinement lattice, not the merge-only union-find that can only
*grow*, never *refuse*. It is order-independent (a synchronous fixed-point) yet keeps the
-Narayanan–Shmatikov **cluster-level** counterparty accumulation. It is the only fingerprint-aware
+Narayanan–Shmatikov cluster-level counterparty accumulation. It is the only fingerprint-aware
engine; `cluster_naive` is the merge-only BlockSci baseline it is compared against.
This offensive result is a means, not an end: the de-anonymization engine is the
@@ -149,11 +160,11 @@ Three facts defeat it, in order of importance:
the unnecessary-input heuristic, the partition is likely. For a 2-in/2-out merge the
per-owner balance is automatic, so the discriminator is the payment's round-ness, and
the number of plausible partitions is small — often one clean answer. This
- re-partitions the merge **before any fingerprint** (§6). *Round-ness is a heuristic, not
+ re-partitions the merge before any fingerprint (§6). *Round-ness is a heuristic, not
a proof:* it needs a value model (a round number in fiat at the time, not just in sats),
and a market-priced payment may carry no round-number signal at all — so the amount
channel is strong where a plausible round partition exists (as in §6) and silent where
- it does not. In the fused engine this round-ness re-partition **corroborates** a fingerprint
+ it does not. In the fused engine this round-ness re-partition corroborates a fingerprint
disagreement rather than refusing a co-spend on its own (§4, §9): it identifies the likely
split, but the standalone refuse is gated on the fingerprint also disagreeing.
1. **The backward channel (fingerprints).** A collaborative construction can coerce
@@ -162,24 +173,47 @@ Three facts defeat it, in order of importance:
Those carry wallet-specific fingerprints an analyst reads to corroborate the amount
partition.
2. **The bit asymmetry.** Even with perfectly uniform fingerprints and ambiguous amounts,
- the merge's ~1.6 bits are dwarfed by the prior clustering evidence — measured at a median
- ~33 bits per cluster on a real slice, a lower bound on >100 whole-chain (`results/RESULTS-cluster-bits.md`); the
- partition is decidable *without needing the merged transaction at all*.
+ the merge's ~1.6 bits are dwarfed by the prior clustering evidence — a median of 29.0 bits per
+ cluster on the committed cache, where every one of the 2538 clusters clears the merge's
+ contribution (`results/generated/cluster-bits-v1.md`); the cache is block-sampled, so that median
+ is a floor. The partition is decidable *without needing the merged transaction at all*.
+
+**What the fusion measurably adds.** The engine is what makes those channels one decision rather
+than a list of opinions, and `results/generated/fused-engine-v1.md` runs it over committed
+transactions as a ladder. On 335 co-spends spanning 753 funding transactions, the merge-only
+baseline leaves 697 clusters and refuses nothing; the fingerprint channel adds 7,560 links the
+co-spend missed and declines 19 of the merges the baseline takes. The two remaining channels move in
+opposite directions, which is what fusing them is for: cluster-level topology corroborates merges
+the fingerprint alone objected to, taking refusals to 11, and provenance-disjointness cuts pairs
+that survived it, taking them to 22. The amount channel moves nothing on that slice, and the reason
+is the gating stated above rather than a failure of the channel — it is refuse-only and speaks only
+where the fingerprint already disagrees.
## 3. The fingerprint library (evidence)
We catalog the chain-observable transaction-construction axes (nSequence, nLockTime,
input/output ordering, change script type, tx version, coin-selection/UIH, low-R grinding,
-SIGHASH type, **fee-rate**, **input script type**), grouping seven reference
+SIGHASH type, fee-rate, input script type), grouping seven reference
integrations per axis (`catalog/tx-construction-matrix.md`). Each axis carries an
-**extractor**, **measured bits**, and a **chain-proven example** (`decluster/library.py`);
-the library carries **23 measured axes** in total (the base set plus the granular additions
-in §7 — input-type presence, nested segwit, pubkey compression, multisig, OP_RETURN, output
+extractor, measured bits, and a chain-proven example (`decluster/library.py`);
+the library carries 23 catalogued axes / 22 active on stored data (the base set plus the granular
+additions in §7 — input-type presence, nested segwit, pubkey compression, multisig, OP_RETURN, output
encoding, the change relations, and a block-feerate broadcast-time axis): the 16 structural
axes calibrated on the whole-chain BigQuery sample, and 7 on mempool samples (5 witness + OP_RETURN +
broadcast-time; §5).
-Bits are estimated from an **unbiased** mainnet sample (§5). Representative
+**One catalogued axis is inert on any stored dataset.** `locktime_vs_broadcast` reads a
+`_bc` annotation that only `broadcast.annotate_broadcast` writes, and only at fetch time, so on a
+stored record it returns `na` for every transaction. Measured on the preserved 22,112-transaction
+cache: `Counter({'na': 22112})`, zero transactions carrying the annotation. `na` is not among the
+axis's `bits` values, so the abstention predicate skips it — the axis is *inert*, not merely
+constant, and contributes exactly nothing to any published pair score. It is declared in code
+(`library.ANNOTATION_ONLY_AXES`). Every number in this paper drawn from a stored dataset is therefore
+a **22-axis** number; we keep the axis catalogued because it scores under live annotation
+(§7, `results/RESULTS-broadcast.md`). Where this paper says "23-axis" for a scored figure, read
+"23 catalogued, 22 scored".
+
+Bits are estimated from an unbiased mainnet sample (§5). Representative
values (bits per matching value; higher = rarer = stronger link):
| Axis | value | bits/match |
@@ -198,11 +232,11 @@ values (bits per matching value; higher = rarer = stronger link):
(Rare values are now estimable: the whole-chain sample surfaces high-bit tells like the
Cake-style `seq_0x01_other` nSequence at 8.9 bits and mixed input types at 6.0 bits.)
-Ordering tells (input/output) are **n-conditional**: a sorted set arises by chance with
-probability `1/n!` (½ at n=2, ⅙ at n=3), so the engine brands `bip69` only at **n≥4** and
+Ordering tells (input/output) are n-conditional: a sorted set arises by chance with
+probability `1/n!` (½ at n=2, ⅙ at n=3), so the engine brands `bip69` only at n≥4 and
abstains (`small_n`, no link) at n≤3 — the `3.00` above is the software-rarity link weight for
the reliable n≥4 case, not a per-tx claim at small n. (As a *change* predictor rather than a link,
-this ordering axis validates as **real but low-coverage** — it resolves fewer cases than the
+this ordering axis validates as real but low-coverage — it resolves fewer cases than the
round-number baseline at comparable precision; §7.)
**BIP-69 byte-order correction.** BIP-69 orders inputs by the prevout txid in *internal* (reversed)
@@ -212,7 +246,9 @@ agrees with the true order only ~1.5% of the time — so it was branding coincid
display-sorted transactions rather than real BIP-69, measuring noise. Correcting it roughly halves
the `bip69` incidence on the witness cache (0.95%→0.46%), i.e. the true tell is rarer (higher-bit)
than the prior number, and leaves the attribution AUC unchanged within run-to-run variation (≈0.93→
-≈0.95). The `3.00`-bit weight is calibrated on the pre-correction BigQuery sample; the corrected extractor
+≈0.95 — a before/after pair over growing cache snapshots that were not preserved; the two levels
+reproducible today are 0.9459 on the committed fixture and 0.9244 on the preserved cache, §5). The
+`3.00`-bit weight is calibrated on the pre-correction BigQuery sample; the corrected extractor
puts the tell nearer 7–8 bits on the local witness cache, so `3.00` under-weights it — a conservative
placeholder, not an over-claim (recomputing on the structural sample is a pending follow-up, §5).
@@ -237,15 +273,27 @@ The degenerate limit makes the framework's dependence on graph *connectivity* co
subgraph — an unspent coinbase, say — is a cluster of one, assignable only to the miner who found it,
until a collaborative transaction connects it to the rest of the graph.
-Evidence is a signed weight-of-evidence in bits. For a value of frequency `p`, a match
-contributes `-log₂p` toward "same wallet"; a mismatch contributes a negative penalty
-(`Combiner`, Fellegi–Sunter). Per co-spent pair the fingerprint score, the amount-structure weights
+**What the headline scorer is, precisely.** Evidence is a signed weight-of-evidence in bits. For a
+value of frequency `p`, a match contributes `-log₂p` toward "same wallet"; a mismatch contributes a
+clamped negative penalty. That is Newcombe's value-specific frequency weight (1959/1962), scaled
+in the same bits as everything else on the graph — it is *not* a fitted Fellegi–Sunter model, and
+this paper called it one until now. The two are not interchangeable in magnitude: on the
+preserved cache the fitted F-S model's largest *agreement* weight is **+1.3047** bits (`nsequence`)
+and its largest disagreement weight **−11.13** (`locktime`), whereas the rarity weight reaches
+**13.88** bits on a single agreement (`cake_group_c`) — a ratio of ≈10.6× on the agreement side
+(`results/artifacts/fs-temporal-v1.json`). A fitted Fellegi–Sunter model *is* built and used in this repository —
+`decluster/fellegi_sunter.py`, driving the temporal split and the ablation
+(`results/RESULTS-fs-temporal.md`, `results/RESULTS-fs-ablation.md`) — but it is a different object
+from the headline scorer, whose implementation (`rarity_weight_baseline.rarity_score`,
+`combiner.rarity_score`) says so in its own header and raises a `DeprecationWarning` on the legacy
+`fs_score` alias. §12's Fellegi–Sunter entry already credited the frequency weighting to Newcombe;
+the body now agrees with it. Per co-spent pair the fingerprint score, the amount-structure weights
(the 2-in/2-out roundness `amount_refuse_weight`, applied only when the fingerprint also disagrees —
round-ness is a heuristic (§2), so it corroborates a refusal rather than forcing one; and the coinjoin
-de-mix refuse `amount_refuse_demix`, which carries its own uniqueness guard), and the Narayanan–Shmatikov **cluster-level** counterparty-overlap weight
+de-mix refuse `amount_refuse_demix`, which carries its own uniqueness guard), and the Narayanan–Shmatikov cluster-level counterparty-overlap weight
(`cluster_topology_weight` — the rarity-weighted overlap of two clusters' neighbourhoods, scored in
the Newcombe/FS frequency weight `−log₂(share)` that operationalizes the N-S `wt = 1/log|supp|`
-premise (§11), and *not* a per-pair term) are summed, and the partition is refined by
+premise (§12), and *not* a per-pair term) are summed, and the partition is refined by
`cluster_refined`: a union-find that both merges *and* refuses (the refinement lattice, going down
as well as up). A synchronous fixed-point recomputes the cluster-level topology on the growing
entities each round and unions the net-positive pairs until stable — order-independent, and
@@ -270,16 +318,29 @@ conditional-independence double-counting §9 flags. Concretely, on the merged an
merge (−3.16 bits) and recovers the correct partition, whereas scoring the same edge with the
23-axis `LibraryScorer` *resurrects* the false link (**+11.67 bits**, well past the engine's
`link_above=4.0` threshold) and would re-merge the sender into the Cake cluster
-(`results/RESULTS-3v23-engine.md`, `examples/three_vs_23_axes.py` — measured directly, not asserted).
-The per-axis breakdown shows why: the three genuinely discriminating axes still refuse (−10.8 bits
-combined), but 19 low-entropy policy axes that both coins' ordinary SegWit wallets happen to share
-(script type, encoding, sighash, `low_r`, ...) each add a small positive weight, and summing them
-under the Fellegi–Sunter conditional-independence assumption overwhelms the real discriminators. The
-wide-axis model is therefore the right instrument for measuring *attribution* and the wrong one for
-driving *refusal*; the engine uses the narrow, decorrelated set on purpose.
-
-That assumption is the standing objection to the Fellegi–Sunter form, and the reason the
-deep-feature line of work (§11) is held to be the more robust of the two: fewer parameters, no
+(`results/RESULTS-3v23-engine.md`; measured directly by `catalog/runs/anchor-axis-families-v1.json`,
+which scores this edge under all four axis sets on a committed fixture.)
+(One of the 23 axes, `locktime_vs_broadcast`, abstains here and everywhere on stored records; 22 are
+scored. See §3.) The per-axis breakdown shows why: the three genuinely discriminating axes still
+refuse (−10.8 bits combined), but 19 low-entropy policy axes that both coins' ordinary SegWit wallets
+happen to share (script type, encoding, sighash, `low_r`, ...) each add a small positive weight, and
+summing them under the additive kernel's conditional-independence assumption overwhelms the real
+discriminators. The wide-axis model as published is therefore the right instrument for measuring *attribution* and
+the wrong one for driving *refusal*; the engine uses the narrow, decorrelated set on purpose. The
+next paragraph measures exactly how much of that failure is redundancy rather than width.
+
+**How much of that resurrection is double-counting, measured.** The mechanism is not merely argued.
+Dropping one representative per measured correlated cluster — 14 axes rather than 23,
+`fingerprint_validate.decorrelated_scorer()`, the clusters and representatives taken from
+`results/artifacts/fs-ablation-v1.json` — the same Cake↔sender edge scores **+3.59 bits** instead of
++11.67. So **8.07 of the 11.67 bits (69%) are redundant copies of evidence already counted**, and the
+decorrelated wide model lands *below* the engine's `link_above=4.0` threshold: it no longer
+resurrects the false merge. The width was never the problem; the duplication was. The design
+choice above stands, and the reason given for it is now the measured one rather than the argued one.
+
+That assumption is the standing objection to any additive per-field record-linkage form —
+Fellegi–Sunter and this rarity kernel alike — and the reason the
+deep-feature line of work (§12) is held to be the more robust of the two: fewer parameters, no
independence premise, and better behaviour on the sparse high-dimensional vectors this problem
actually produces. The engine does not answer that objection by swapping the weight. `−log₂(share)`
*is* bits — additive, and commensurable with the co-spend prior, the fingerprint weights and the
@@ -296,8 +357,8 @@ Three properties matter for the thesis:
**refuse** a merge — impossible for a monotone union-find.
- **Bit-accounting / priors.** A large established cluster contracts as a unit carrying
its full weight; a merge-strength contrary signal (−3 bits) cannot override an established
- cluster's prior (measured at a median ~33 bits, >100 whole-chain; §1,
- `results/RESULTS-cluster-bits.md`). We verify this as a property test (`high_weight_prior_survives_contrary_fingerprint`);
+ cluster's prior (measured at a median of 29.0 bits on the committed cache; §1,
+ `results/generated/cluster-bits-v1.md`). We verify this as a property test (`high_weight_prior_survives_contrary_fingerprint`);
the fixed-point union-find keeps a large wallet whole as one component (replacing the old hard
"skip large groups"), so large wallets are not silently dropped at scale.
- **Real bits.** `Combiner.from_library` lets the engine score from the measured library bits
@@ -314,57 +375,104 @@ scale). **The 16 construction axes are estimated from a ~105,000-tx uniform samp
witness axes (low-R, SIGHASH, pubkey compression, multisig, nested segwit) *and OP_RETURN* are measured on
a ~3,500-tx mempool sample, since BigQuery's schema carries no witness data (and OP_RETURN is degenerate
in the whole-chain export — all-`none`, 0 bits — so its 4.00 bits come from the mempool sample too).
-That witness snapshot is effectively **SegWit-era**; re-measured across eras it drifts (below).
+That witness snapshot is effectively SegWit-era; re-measured across eras it drifts (below).
Whole-chain calibration notes:
- **nLockTime `zero` is ~74% chain-wide** (whole-chain BigQuery); a recent mempool sample
runs higher (~85%). Chain-wide is the right prior.
-- **UIH is a real signal.** The unnecessary-input heuristic fires on ~8.3% of txs
- (3.6 bits), measured on the whole-chain sample with real top-level input values.
-- Distributions are **non-stationary**: e.g. round fee-rates are ~17% chain-wide but ~9%
+- **UIH is a real signal.** The library's `uih` axis fires on ~8.3% of txs (3.6 bits), measured on
+ the whole-chain sample with real top-level input values. **Which UIH matters here.** That axis is
+ `extractors.x_uih`, a Gibson-style approximation: it brands `uih2` whenever
+ `max(inputs) >= max(outputs)`, non-strict, with no restriction to two outputs and no fee term. It
+ is **not** Ghesmati et al.'s Algorithm 2, which this repository also implements separately
+ (`baselines/unnecessary_input.py`, with the paper's UIH1 / UIH2 / *uncategorized* three-way
+ outcome) and which is deliberately a different observable. Every UIH-derived number in this paper
+ and in `results/RESULTS-amount-channel-survey.md` is the approximation, retained so historical
+ fingerprint artifacts do not shift silently; the paper-faithful categoriser is the one to cite
+ when the claim is about Ghesmati's rates.
+- Distributions are non-stationary: e.g. round fee-rates are ~17% chain-wide but ~9%
in recent blocks — old wallets used round fees more. Chain-wide is the right prior.
-- **Witness bits drift by era** — measured directly on a balanced multi-era sample (180k txs
- split at the SegWit/Taproot activations, `results/RESULTS-witness-era-drift.md`,
- `examples/witness_bits_by_era.py`): `low_r` carries 2.33 bits in the SegWit era but only
- **1.01** in the Taproot era (grinding went mainstream), `pubkey_compression` `compressed`
- falls 2.64 → 0.46, and `nested_segwit` *inverts* 1.88 → 4.01 (P2SH wrappers gave way to
- native bech32, so the wrapper regained rarity). A single bits number per witness axis is
- thus an era-weighted approximation — the library's tracks the SegWit era — and a
- whole-chain model would carry per-era bits for the drifting axes. This does not move the
- validation headline, which scores pairs on the multi-era cache directly, not from these
- per-value bits.
-
-This is a **large representative sample (~105,000 txs)**, not literally every tx; exhaustive
+This is a large representative sample (~105,000 txs), not literally every tx; exhaustive
per-tx measurement would still want the whole chain, but for calibrating fingerprint
frequencies this is publication-grade (rare values become estimable).
**Validation on real data (`results/RESULTS-fingerprint-validation.md`).** Do the calibrated
-bits actually attribute wallets? On **4,000 same-wallet and 4,000 random tx pairs drawn from a
-witness-bearing mainnet cache** (165,832 txs at the reported run; the cache is a growing snapshot,
-so the exact count and AUC drift a little run-to-run), with same-owner labels = address reuse (two
-txs spending the same input address are the same wallet), the canonical 23-axis Fellegi–Sunter
-score ranks same-wallet tx pairs (mean **+13.9 bits**) far above random pairs (mean **−22.4 bits**):
-**AUC 0.933** (0.93–0.95 across re-runs), with a shuffle control at 0.50. The pair draws are sampled
-with replacement, so the effective support is narrower than 4,000 (see the results file). So the
-measured fingerprint model separates same-wallet from random transactions on real data — a
-systematic, quantified result beyond the prior anecdotal spot-checking. (The labels are address
-reuse, so the `input_types` axis matches partly by construction; the signal is spread across all
-axes and the control is clean — see the honest caveats in the results file.)
+bits actually attribute wallets? On **4,000 same-wallet and 4,000 random tx pairs**, with same-owner
+labels = address reuse (two txs spending the same input address are the same wallet), the rarity
+score ranks same-wallet tx pairs far above random ones. **Two reproducible levels, and one that no
+longer is.** The manifest-backed run is the committed **600-transaction fixture**: positive mean
+**+19.65 bits**, negative **−17.41**, **AUC 0.9459**, shuffle control 0.4908
+(`catalog/runs/fingerprint-validation-v1.json`, `results/artifacts/fingerprint-validation-v1.json`).
+The **preserved 22,112-transaction cache** gives **AUC 0.9244**, positive **+15.47** bits, negative
+**−16.58**, shuffle 0.4984 (`results/generated/weight-sensitivity-v1.md`, row `c = 0.95`; the same
+0.92445 appears as the `fixed_0_95` library-scorer AUC in `results/artifacts/em-m-v1.json`). Earlier
+revisions of this paper reported **AUC 0.933 / +13.9 / −22.4 bits** over a **165,832-transaction**
+growing cache. **That cache was not preserved and those figures are not recomputable**; they are an
+evidence record, not a canonical result, and 0.933 is neither of the two levels above. Where an
+argument below needs a headline attribution number, it is the 0.9244 cache figure or the 0.9459
+fixture figure, named as such.
+
+Two scope limits on the preserved cache follow from its contents rather than from the method.
+It spans heights 800,000–965,220 — the Taproot era only, so it cannot support a multi-era claim
+(see the era-drift note above, which is a historical record over a cache snapshot no longer on
+disk). And the pair draws are sampled with replacement, so the effective support is narrower than
+4,000 (see the results file). Subject to those limits the measured fingerprint model separates
+same-wallet from random transactions on real data — a systematic, quantified result beyond the
+prior anecdotal spot-checking.
+
+**How much of that is the label restating itself, measured.** The labels are address reuse, and five
+axes are fixed outright by the shared input address (`input_script_type`, `input_types_present`,
+`nested_segwit`, `pubkey_compression`, `change_address_reuse`). Dropping them
+(`fingerprint_validate.construction_only_scorer()`, 18 axes) on the preserved cache takes AUC
+**0.9244 → 0.9003** and the positive mean **+15.47 → +8.42** bits. So **0.0241 of the AUC is label
+leakage**, and the pure construction-style signal is **≈0.90** — still far above chance, and now
+stated rather than waved at. The standing caveat here was only that the signal is "spread across all
+axes"; this is the number.
+
+**The magnitude is inflated by redundant axes; the ranking is not.** Two of the catalogued axes,
+`input_script_type` and `input_types_present`, are **perfectly correlated on the measured data —
+φ = 1.000 in *both* the match and the non-match class** (`results/artifacts/fs-ablation-v1.json`,
+whose own limitations line says they "are duplicates in this sample"). They are one fact scored
+twice, and they are not alone: the same run clusters the axes at |φ| ≥ 0.6 into four groups. Keeping
+one representative per group — 14 axes rather than 23,
+`fingerprint_validate.decorrelated_scorer()` — on the preserved cache:
+
+| axis set | AUC | positive mean bits | negative mean bits |
+|---|---:|---:|---:|
+| 23 catalogued (22 scored) | 0.9244 | +15.47 | −16.58 |
+| 14 decorrelated | **0.9432** | **+8.72** | −10.24 |
+
+The evidence **magnitude falls by 44%** (+15.47 → +8.72 bits) — the headline bits figure was
+inflated ≈1.77× by double-counted axes — while the **AUC rises by 0.019**. The result is stronger,
+not weaker: the redundant axes were adding bits without adding discrimination. Nor does this hinge on
+which member of each group is kept. Sweeping **all 90** representative choices (5 × 3 × 3 × 2), AUC
+lands in **[0.9299, 0.9439]** — *every* choice beats the 23-axis 0.9244. The sign of the evidence
+survives the correction: on the §4/§6 merged anchor the false edge stays positive at every axis set
+we scored (+11.67 at 23, +4.91 at 18, +3.59 at 14). Its magnitude does not, and neither does its
+position relative to a fixed threshold — at 14 axes that edge falls under the engine's
+`link_above=4.0`. So every bits magnitude in this paper should be read at the decorrelated scale, and
+any threshold calibrated against the 23-axis scale is calibrated against inflated numbers. These
+decorrelated figures are reproducible from the committed cache and the shipped scorers but do **not
+yet carry a run manifest**; what is canonical is the φ = 1.000 pair, the axis clusters and their
+representatives (`fs-ablation-v1`).
**Robustness of the disagreement weights (`results/RESULTS-weight-sensitivity.md`,
-`results/RESULTS-em-m.md`).** The Fellegi–Sunter mismatch weight depends on `c` — the assumed
+`results/RESULTS-em-m.md`).** The mismatch weight depends on `c` — the assumed
probability that the *same* wallet agrees on an axis — which, unlike the agreement frequency `p`, is
not directly measurable without same-owner labels. Two experiments show the verdict does not hinge on
-getting it right. Sweeping a global `c` from 0.60 to 0.99 swings the raw score by ~50 bits (the
-random-pair mean even changes sign) yet moves the **AUC by < 0.01** across the realistic band — the
-*magnitude* of the evidence tracks `c`, the *ranking* does not, because it is the aggregate of 23 axes
-and the mismatch weight is clamped `≤ 0` (a wrong `c` can only soften an axis toward neutral, never
-manufacture a match). Fitting `c` *per axis* by unsupervised EM (Winkler's EM for the FS model,
-Splink-style; `u` fixed at the measured collision) recovers the address-reuse label with **AUC 0.944**
-without being shown it, and yields per-axis `m` ranging from 0.60 to 1.00 — far from the flat 0.95 —
-yet the pair-AUC rises only from 0.933 to 0.937: the same flat plateau, now measured per axis. The
+getting it right. Sweeping a global `c` from 0.60 to 0.99 swings the raw score by ~46 bits on the
+random-pair mean (+8.85 → −36.95, so it changes sign) yet moves the **AUC by 0.00055** across the
+realistic 0.90–0.99 band (0.0161 over the full 0.60–0.99 grid, and not monotone) — the
+*magnitude* of the evidence tracks `c`, the *ranking* does not, because it is an aggregate over many
+axes and the mismatch weight is clamped `≤ 0` (a wrong `c` can only soften an axis toward neutral,
+never manufacture a match). Fitting `c` *per axis* by unsupervised EM (Winkler's EM for the FS model,
+Splink-style; `u` fixed at the measured collision) recovers the address-reuse label with **AUC 0.932**
+without being shown it, and yields per-axis `m` ranging from **0.51 to 1.00** — far from the flat 0.95
+— yet the pair-AUC rises only from **0.92445 to 0.92535** (Δ **+0.0009**): the same flat plateau, now
+measured per axis (`results/artifacts/em-m-v1.json`). The
divergence of the correlated axes' EM `m` from their label-implied value is the expected
-conditional-independence artifact. This establishes the *fingerprint* leg of the
+conditional-independence artifact — and it is visible in the fit: the two φ = 1.000 axes above are
+exactly the ones EM drives to `m = 1.000000`. This establishes the *fingerprint* leg of the
robustness claim; the **graph-topology leg is measured too** (`results/RESULTS-cluster-robustness.md`).
Fusing the counterparty-overlap term into the clustering (`cluster_refined(neigh=…)`, §9) and sweeping the
same `c`, the owner-partition is byte-identical across the whole range (Adjusted Rand Index **1.0**)
@@ -372,23 +480,43 @@ while a fingerprint-only clustering moves at both ends of the sweep — the >100
structure (§1) swamp the per-axis weight uncertainty — fingerprint uniformity is necessary but
graph structure is what decides.
-**Bayesian record linkage vs Fellegi-Sunter (`results/RESULTS-bayes-vs-fs.md`).** The engine's F-S score
-is the point-estimate (plug-in) special case of Bayesian record linkage. We build the Bayesian variant on
-the same per-field likelihood — a light Gibbs over the pair labels and per-axis `m` with a Beta prior,
+**Bayesian record linkage vs Fellegi-Sunter (`results/RESULTS-bayes-vs-fs.md`).** This comparison is
+about a genuine Fellegi–Sunter model — the per-field `m`/`u` form of `decluster/fellegi_sunter.py`,
+not the rarity kernel of §4 — and the F-S point estimate is the plug-in special case of Bayesian
+record linkage. We build the Bayesian variant on the same per-field likelihood — a light Gibbs over the pair labels and per-axis `m` with a Beta prior,
`u` fixed — and compare. (These AUCs are on the classic per-field agree/disagree F-S — `m`/`u` per axis,
-the form both models share — *not* the value-weighted 23-axis scorer of the plateau above; that scorer
+the form both models share — *not* the value-weighted rarity scorer of the plateau above; that scorer
draws most of its discrimination from the `−log2 p` agreement weights, so `m` barely moves it, whereas
the per-field form leans on `m` directly, which is why the same weight now shows a visible effect.)
-Fixing `m` at `0.95` costs both discrimination and calibration (AUC 0.932, ECE
-0.130); a fitted point `m` (EM) and the full posterior are **indistinguishable on the verdict** (AUC
-0.944, ECE ≈ 0.095–0.099) — the tie the theory predicts (and partly structural: sharing the likelihood,
-the three scorers cannot diverge on discrimination). The Bayesian's genuine, exclusive addition is
-*calibrated uncertainty* the point estimate structurally cannot give: a per-axis credible interval for
-`m` (the fixed `0.95` falls outside it on 20 of 23 axes — falsely precise) and a posterior band on
-cluster entity counts — on a clear-structure node set the band collapses to the F-S point (`[4,4]` = 4),
-but on an ambiguous borderline set it is a genuine `[7, 10]` where F-S commits to a single 9: the
-cluster-level uncertainty the point estimate hides, shown. So the Bayesian does not beat a well-tuned
-F-S on the answer; it quantifies the confidence F-S leaves implicit. The roles are therefore fixed,
+On the preserved cache (`results/artifacts/bayes-vs-fs-v1.json`):
+
+| scorer | AUC | ECE |
+|---|---:|---:|
+| F-S, `m` fixed at 0.95 | 0.91625 | **0.1455** |
+| F-S, `m` fitted by EM | 0.93200 | 0.1750 |
+| Bayesian, `m` integrated out | 0.93240 | 0.1936 |
+
+Fixing `m` at `0.95` costs discrimination — 0.916 against 0.932 — and a fitted point `m` (EM) and
+the full posterior are indistinguishable on the verdict (0.93200 vs 0.93240), the tie the theory
+predicts (and partly structural: sharing the likelihood, the three scorers cannot diverge much on
+discrimination). It does **not** cost calibration. The opposite holds, and the sign was reported
+backwards here: the fixed-`0.95` scorer has the best ECE of the three (0.1455), EM
+is worse (0.1750), and the Bayesian is worst (0.1936). `results/RESULTS-bayes-vs-fs.md` states it
+plainly — "Bayesian integration does not improve calibration over EM" — and we state it here.
+Integrating `m` out buys a falsifiable statement of uncertainty, not a better-calibrated score.
+That statement is the Bayesian's genuine, exclusive addition, and it is the one thing the point
+estimate structurally cannot give: a per-axis credible interval for `m`, outside which the flat
+`0.95` falls on **22 of 23 axes** — the lone exception being `locktime_vs_broadcast`, which is inert
+on stored records (§3) and therefore degenerate rather than agreeing. And a posterior band on cluster
+entity counts: on a clear-structure 50-node set the band is `[4, 4]` where the F-S point commits to
+**5**, and on an ambiguous 18-node borderline set it is `[5, 6]` around an F-S point of 5. Earlier
+revisions reported the clear-structure band as collapsing onto the F-S point and the borderline band
+as a wide `[7, 10]` against an F-S 9; neither survives the canonical run. What the numbers actually
+show is narrower and, on the clear set, sharper: the band there excludes the point estimate
+rather than reproducing it, and on the borderline set it merely widens by one around it. So the
+Bayesian does not beat a well-tuned
+F-S on the answer, and does not calibrate better; it quantifies the confidence F-S leaves implicit,
+and occasionally disagrees with it. The roles are therefore fixed,
not interchangeable. F-S is the production engine `cluster_refined` scores in — a cheap additive
bit-weight the partition-refinement fixed point and the graph cost function consume directly — and
the Bayesian runs beside it purely as a calibration audit, never a rival engine. The inversion has no
@@ -398,23 +526,23 @@ one without first collapsing it back to the F-S point.
## 6. Demonstration: a real merged transaction re-partitioned
-Our anchor is a **known merged transaction**: `931d6627` is a confirmed mainnet
-transaction that merges two wallets — a **Cake Wallet receiver** and a **distinct sender
-wallet** — into one common-input group, so the correct owner-partition is known, not
+Our anchor is a known merged transaction: `931d6627` is a confirmed mainnet
+transaction that merges two wallets — a Cake Wallet receiver and a distinct sender
+wallet — into one common-input group, so the correct owner-partition is known, not
inferred. On its ancestry graph (7 coins; inputs 2000 sats sender, 5750 sats Cake
receiver; outputs 791, 6750; fee 209):
- **Amount analysis (primary in the decidable regime, `results/RESULTS-gap1.md`)** re-partitions it:
`6750 − 5750 = 1000` is a round payment, so the receiver is the Cake input and the sender is
- separate — at 1 bit ambiguity, resolved by round-ness, **before any fingerprint**. (This is the
+ separate — at 1 bit ambiguity, resolved by round-ness, before any fingerprint. (This is the
sub-transaction *analysis*; the engine's standalone refuse is corroboration-gated, below.)
-- **Union-find (BlockSci-style)** mis-merges {Cake `0a568e3a`, sender `91106666`} — the
+- Union-find (BlockSci-style) mis-merges {Cake `0a568e3a`, sender `91106666`} — the
exact false link the merge intends.
- **The engine (`cluster_refined`, `results/RESULTS-wp4.md`)** refuses that merge: the
fingerprint evidence scores `−3.1 bits` (`max_ffffffff` vs `seq_0x01_other`), past the
prototype's `−2.0` refuse threshold → the merge is re-partitioned, the sender isolated;
- and the fingerprint layer **adds** the links the co-spend missed (Cake lineage
- `+10.2 bits`, sender funding chain `+5.4 bits` each). The amount channel **corroborates** the same
+ and the fingerprint layer adds the links the co-spend missed (Cake lineage
+ `+10.2 bits`, sender funding chain `+5.4 bits` each). The amount channel corroborates the same
split (the round `1000`-sat re-partition above; the fingerprint disagreement is what licenses its
refuse in the fused engine), so the two signals agree.
Resulting clusters: `{sender}`, `{sender funding chain}`, `{Cake, lineage}` — the correct
@@ -439,7 +567,7 @@ privacy). On the real
| union-find (BlockSci) | 13.8 | 16% |
| fingerprint-aware | **3.7** | 53% |
-The naive common-input view over-reports the anonymity-set size by **~3.7×**; the amount +
+The naive common-input view over-reports the anonymity-set size by ~3.7×; the amount +
fingerprint evidence collapses 15 clusters to 6 and forms a supercluster (53%). This is
the thesis quantified at the graph level — still a modest real graph (19 coins), not a
chain-scale measurement (which needs the whole connected chain, §10).
@@ -447,7 +575,7 @@ chain-scale measurement (which needs the whole connected chain, §10).
**Community-structure de-anonymization (`results/RESULTS-graph-deanon.md`).** Beyond pairwise
evidence, we test the Narayanan–Shmatikov premise directly: does the *structure* of the
transaction graph predict same-owner membership, independent of the co-spend heuristic? On
-a **connected real slice** — blocks 400000–400004, 8 927 txs, 27 962 addresses, 2 463
+a connected real slice — blocks 400000–400004, 8 927 txs, 27 962 addresses, 2 463
entities (`bigquery/graph.sql`, no archival node) — with same-owner labels from transitive
co-spend clusters (itself a heuristic: it over-merges any collaborative transaction in the
slice, so these labels are a near-certain floor for ordinary txs; an independent entity label
@@ -456,10 +584,10 @@ would be stronger, §9) and held-out positives = same-owner pairs that are *not*
Removing the co-spend edges that *define* those labels — scoring by payment structure
alone (common neighbors) — still re-identifies same-owner addresses at **AUC 0.95** on the
2016 slice; the shuffle control lands at 0.50. Graph structure de-anonymizes *beyond* the
-common-input heuristic, on real data. Across **five eras** (2012–2024), swept over graph
+common-input heuristic, on real data. Across five eras (2012–2024), swept over graph
reach *k* (k-hop, hub intermediates excluded; `decluster/graph_deanon.py --depth`). Each
slice is a contiguous block range (partition-pruned by `block_timestamp_month`,
-`bigquery/graph.sql`); **share%** = fraction of same-owner pairs sharing a *direct*
+`bigquery/graph.sql`); share% = fraction of same-owner pairs sharing a *direct*
counterparty:
| Year | blocks | entities | held-out pairs | share% | k=1 | k=2 | k=3 | k=4 |
@@ -471,7 +599,7 @@ counterparty:
| 2024 | 845982–846001 | 2 704 | 897 247 | 48% | **0.74** | 0.88 | 0.95 | 0.97 |
At k=1 the effect is *not* a clean era curve: the 2013 slice falls to chance (0.53) on
-25 944 pairs. The mechanism is exact — 1-hop AUC tracks **share%**, the fraction of
+25 944 pairs. The mechanism is exact — 1-hop AUC tracks share%, the fraction of
same-owner pairs sharing a direct counterparty (96→0.97, 48→0.74, 6→0.53); 2013 is the
service-churn (SatoshiDice) era, where an owner's addresses each touch a *different* service
address. The 2024 slice anchors the modern end: heavy churn (48% share) starves k=1 to
@@ -487,8 +615,8 @@ non-hub component membership, a coarser tell than fine link prediction.) The str
## 7. Coverage of the chain-observable fingerprint surface
The chain-observable fingerprint surface enumerates **~35 granular fingerprints**. We do **not** cover
-all of them; "coverage" here is an honest per-item claim (**✅** = extractor + measured
-bits; **◐** = captured coarsely, not as the granular tell; **❌** = not built).
+all of them; "coverage" here is a per-item claim (✅ = extractor + measured
+bits; ◐ = captured coarsely, not as the granular tell; ❌ = not built).
**Status against the fingerprint checklist:**
@@ -525,13 +653,14 @@ bits; **◐** = captured coarsely, not as the granular tell; **❌** = not built
(the ordering axis validates as a real but low-coverage change signal); a cluster-membership
`findNext` reaches higher raw accuracy but is circular against those labels (§7) and is not used.
-**Honest tally: ~30 of ~35 covered (measured bits), ~3 partial, ~3 not built** — the
-library carries **23 measured axes** (incl. a block-feerate broadcast-time axis, below),
+**Tally: ~30 of ~35 covered (measured bits), ~3 partial, ~3 not built** — the
+library carries **23 catalogued axes** (incl. the block-feerate broadcast-time axis, below, which is
+inert on stored records — 22 are active there, §3),
the structural ones on a whole-chain BigQuery sample (§5). The primary structural signal — the amount / receiver-contribution
-subtransaction re-partition — is covered (§2/§6). **The honest ceiling is
+subtransaction re-partition — is covered (§2/§6). **The ceiling is
~32/35, not 35/35:** the two remaining items are not clean single-transaction chain
-fingerprints — **Coin Control** is a UX behavior no single tx uniquely reveals, and
-**spend-unconfirmed** requires the transaction's ancestry (parent block heights), not the
+fingerprints — Coin Control is a UX behavior no single tx uniquely reveals, and
+spend-unconfirmed requires the transaction's ancestry (parent block heights), not the
transaction alone. The ◐ partials are refinements of axes already covered: output-count and
SegWit-conform were tested against the 23-axis scorer directly and move AUC by only a few
thousandths — an unstable double-counting artifact within the noise floor (the output-count
@@ -540,28 +669,46 @@ axis — so the model correctly leaves both unscored (`results/RESULTS-catalog-a
**Separate tracks, not part of this chain-observable fingerprint checklist:** relay / network-timing
fingerprints and JSON/HTTP serialization. The most consequential of them — the
-**provenance / "deep-feature" channel** — is not out of scope but developed as its own channel in §8:
+provenance / "deep-feature" channel — is not out of scope but developed as its own channel in §8:
the 23 axes here are all *low-dimensional* construction tells
(nSequence, ordering, low-R, …), which standardizing construction can drive toward zero; the
high-dimensional provenance features (where a coin came from — its ancestry signature) are a distinct
and stronger attack surface, the origin of the sparse-high-dimension curse of dimensionality. This
split is measured directly, not assumed: the construction fingerprint behaves as an
-**equivalence-class conditioner** — it sorts a transaction into a construction-style bucket without
-singling one owner out inside it (its N-S rarity-weighted re-identification trails the Fellegi–Sunter
-combiner at picking the true owner from 199 candidates — top-1 0.44–0.51 vs 0.53 — and the
-within-class mean gap sits under `propagate.py`'s θ≈0.5 reference, stable across three independent
-rarity weightings; `results/RESULTS-fingerprint-regime.md`), so the sparse quasi-identifier regime
-that *does* pick one coin out of the crowd lives on the ancestry channel of §8, not on the
-low-dimensional axes.
-(The one timing signal we *do* measure is the **block-feerate
-broadcast-time** estimate — a bound read from on-chain feerate ordering, not network relay — as the
-`locktime_vs_broadcast` axis; `results/RESULTS-broadcast.md`.) The relative clustering-overcount
-diagnostic is **delivered** (§6, `decluster/graph_metric.py`), and the community-structure premise
-(Narayanan–Shmatikov) is now **measured on a real slice** (§6, `decluster/graph_deanon.py`, AUC
-0.95); the full seed-and-extend attack at chain scale over this topology channel still needs
-adjacency infra + labels. (The provenance channel's seed-and-propagate, above, is a separate
-instantiation of the same N-S mechanism — it is built and evaluated on a synthetic fixture plus a preliminary cache-bounded real run already;
-chain-scale validation is the shared pending item, §10.)
+equivalence-class conditioner — it sorts a transaction into a construction-style bucket without
+singling one owner out inside it — but the verdict is **conditional on where the rarity weights come
+from**, and the claim as it stood overstated it. The canonical run
+(`results/artifacts/fingerprint-regime-v1.json`, `results/generated/fingerprint-regime-v1.md`) has
+two weight sources, not three, and they disagree:
+
+| rarity weights | N-S-form top-1 | rarity-combiner top-1 | within-class mean gap |
+|---|---:|---:|---:|
+| `library` (fit elsewhere, out of sample) | 0.680 | 0.730 | 0.393 |
+| `snapshot_measured` (fit on this snapshot) | **0.785** | 0.730 | **0.675** |
+
+With out-of-sample library weights the N-S form trails the combiner at picking the true owner from
+199 candidates (0.680 vs 0.730) and the within-class mean gap sits under `propagate.py`'s θ ≈ 0.5
+reference — the conditioner signature. With weights measured on the snapshot itself the N-S form
+beats the combiner (0.785 vs 0.730) and the gap rises above θ. `results/RESULTS-fingerprint-regime.md`
+says so in its own header: it *falsifies* the historical claim that the conditioner reading is stable
+across weight sources. Those snapshot weights are fitted and evaluated on the same 600-transaction
+selection, so this is evidence of sensitivity to the weighting, not evidence that construction
+fingerprints are universally sparse identifiers. The supportable statement is the conditional one: **the
+conditioner verdict survives only under out-of-sample weights.** Given that, the sparse
+quasi-identifier regime that *does* pick one coin out of the crowd is placed on the ancestry channel
+of §8 rather than on the low-dimensional axes — a placement this run supports at the library weights
+and does not support at the in-sample ones.
+(The one timing signal we *do* model is the block-feerate
+broadcast-time estimate — a bound read from on-chain feerate ordering, not network relay — as the
+`locktime_vs_broadcast` axis; `results/RESULTS-broadcast.md`. It needs a fetch-time annotation, so it
+scores nothing on any stored dataset and contributes to none of the figures here, §3.) The relative
+clustering-overcount
+diagnostic is delivered (§6, `decluster/graph_metric.py`), and a local common-neighbour
+structural-linkage adaptation is measured on a selected real slice (§6,
+`decluster/graph_deanon.py`, AUC 0.95). That experiment is not the seeded cross-view
+Narayanan–Shmatikov attack and its selected weak labels do not establish ownership accuracy. The
+separate N-S baseline and Bitcoin snapshot experiment require supplied seeds; independent seed
+discovery, social-corpus reproduction and chain-scale validation remain pending (§10).
These are named so absence is explicit, not hidden.
### Change identification: validating the ordering against same-owner change labels
@@ -584,7 +731,7 @@ construction axis as a change predictor: change = the output whose onward-spendi
| output order | 0.436 | 0.048 | 0.484 | 0.90 |
| input order | 0.320 | 0.066 | 0.386 | 0.83 |
-Reading *the ordering* honestly: it is a **real but low-coverage** change predictor. `input_order`
+Reading *the ordering*: it is a real but low-coverage change predictor. `input_order`
fires on only 39% of labels (vs 57% for the round-number baseline, 78% for version) and, when it
fires, its precision (0.83) is about the baseline's (0.86); `output_order` fires on 48% at
precision 0.90. So ordering resolves *fewer* cases, not less accurately — the low recall is
@@ -594,19 +741,22 @@ combined tx-level pre↔post score reaches AUC ≈ 0.76 against a shuffle-null
distinction: the 3.00-bit ordering *link* weight and ordering as a *change* signal are different
quantities — the former stands; the latter is real but weak-coverage.
-**A label-robustness caveat.** This ranking does not survive an independent label
-(`results/RESULTS-special-change.md`). Re-run against an *optimal-change* label (the smaller-than-any-input
-output must be change — a value signal disjoint from co-spend) on a multi-epoch sample, all four
-onward-spend axes fall to ~0.60–0.74 precision and `nSequence`/`version` no longer dominate ordering.
-Part is a co-spend-label **selection bias** — that label selects changes whose onward-spender *is* the
-same-wallet reveal transaction, which shares nSequence/version by construction (inflating the numbers
-above); part is epoch / time-gap drift in the multi-epoch sample. Disentangling the two needs a
-contiguous-value slice running both labels on the same transactions (future work). The single-day
-figures above are slice- and label-specific, not a general claim.
+**A label-robustness caveat.** The label above is drawn from co-spend clustering, and that
+construction inflates exactly the axes it then scores: it identifies the change by its onward-spender
+being the same-wallet reveal transaction, and a same-wallet transaction repeats `nSequence` and
+`version` by definition. A value-based label carries no such bias — the output smaller than every
+input must be the change, whatever addresses it uses — and on committed transactions the two
+universal within-transaction heuristics agree with it at 0.81 and 0.90 precision, a cross-check
+neither the fingerprints nor co-spend enter (`results/generated/special-change-labels-v1.md`).
+The per-axis comparison that would settle whether the ranking above survives the label change needs
+each change output's *spender* in the same data, which the block-sampled cache holds for 12 of 1138
+labelled transactions; a contiguous export carrying values at that depth is a collection, not a
+method. So the single-day figures above are slice- and label-specific, and their robustness is
+untested rather than established.
**A circularity caveat.** We also implemented Kappos's cluster-level `findNext` (change = the output
whose onward-spend's construction features are in the input cluster's feature set). Against an M&N
-co-spend label it is **circular** — the change's onward-spender *is* the co-spend reveal transaction,
+co-spend label it is circular — the change's onward-spender *is* the co-spend reveal transaction,
hence a cluster member by construction (verified: change spender is a member 576/578; payment spender
0/226), so "features ∈ cluster set" reduces to the label itself, and nulling the entire construction
fingerprint still scores 0.66. M&N's co-spend label and Kappos's co-spend-cluster `findNext` share
@@ -621,7 +771,7 @@ window is revealed) over one-day clusters. A multi-epoch replication is future w
The fingerprint cells are pinned to the versions this research read (Cake Wallet
`@dc1b369`, 2026-06-10). Several of the Cake divergences the tracking-issue review
-surfaced have since been merged upstream, which **bounds** — but does not erase —
+surfaced have since been merged upstream, which bounds — but does not erase —
the corresponding backward-channel signal: a fix only stops *new* txs from leaking;
every already-confirmed tx built by a pre-fix version still carries the tell, so the
demonstration txs (§6, and Ex.3 `8fb80573…`) remain re-partitionable. Merged fixes,
@@ -648,7 +798,7 @@ Two axes gained a qualitatively different case:
- **Anti-fingerprinting randomization (BTCPay/NBitcoin).** BTCPay is the first
integration that *deliberately blends* rather than emitting a fixed value: NBXplorer
keeps a 5-block window of the joint on-chain fingerprint distribution and, at build
- time, samples **nVersion, nLockTime/fee-sniping, and low-R** to fill any field the
+ time, samples nVersion, nLockTime/fee-sniping, and low-R to fill any field the
caller left unset — so on those axes it carries ~no per-tx partition signal (a
random draw from the real distribution). Its residual fixed tells are structural
and shared with the mainstream cluster (always-RBF `0xFFFFFFFD`, default-P2WPKH
@@ -659,9 +809,9 @@ Two axes gained a qualitatively different case:
static fingerprint value: an adversary who knows the algorithm can *replay* a
suspected cluster's selection and, if a strictly-better coin was left unspent,
argue the coin is mis-assigned to that cluster. This bites only against a
- **deterministic** selector — Cake (greedy, pre-`#3408`), Liana (BnB + deterministic
+ deterministic selector — Cake (greedy, pre-`#3408`), Liana (BnB + deterministic
fallback), a smallest-fit receiver, or Electrum (`CoinChooserPrivacy`, PRNG seeded
- from the candidate UTXO set) — and is defeated by a **randomized** one (Core's
+ from the candidate UTXO set) — and is defeated by a randomized one (Core's
least-waste multi-solver with shuffles and a randomized change target; BTCPay's
stochastic knapsack; BDK's SingleRandomDraw fallback), where a "better" unselected
coin is consistent with normal behaviour. Against the randomized selectors only the
@@ -690,7 +840,7 @@ distinction is the one `intersect.evaluate` now reports as `blind` (§9); applyi
re-run, not a re-derivation. This first rung is only the entropy of the graph-only walk; the full
measurement it feeds — the same-owner fusion, the `max_nodes` node bound for deep coinjoins, the
opt-in `value_weighted` flow rung, the §07 path count, and the partition-posterior check, all as one
-callable pipeline — is developed in the rest of this section. The **deep-feature matching**
+callable pipeline — is developed in the rest of this section. The deep-feature matching
attack — using that sparse ancestry signature as a Narayanan–Shmatikov distinguishing feature to *link*
coins — has a first demonstration too: `provenance_link` (`ancestry.py`) scores the rarity-weighted
overlap of two provenance signatures, and on the merged anchor `931d6627` it independently separates
@@ -699,12 +849,12 @@ fingerprints and amounts of §6. At *graph scale* a first pass is only direction
share nonzero provenance, random pairs zero, but AUC ≈0.52 at depth 3), and a contiguous
value-bearing slice does *worse* — every signature collapses to a single boundary atom (AUC 0.50),
because a tractable-width slice cannot contain multi-hop ancestry (a coin's parents are older than the
-window). This is the load-bearing point: provenance matching is intrinsically a **whole-graph** attack
+window). This is the load-bearing point: provenance matching is intrinsically a whole-graph attack
— shared ancestry lives arbitrarily far back, unlike the few-block-local direct-counterparty structure
of §6 — so the strong graph-scale AUC is gated on whole-connected-graph data (the §10 prevout
stream), a data-scale requirement, not a missing method (`results/RESULTS-ancestry.md`).
-That pairwise signal is now lifted to an entity-level **seed-and-propagate**: `NSPropagator`
+That pairwise signal is now lifted to an entity-level seed-and-propagate: `NSPropagator`
(`decluster/propagate.py`). `entity_signature` aggregates a cluster's member coins' provenance
vectors into one entity-level signature; `propagate_merge` grows a seed labeling by rarity-weighted
signature overlap, gated on an absolute match floor (`min_score`) AND — once a node has ≥3
@@ -714,23 +864,23 @@ re-aggregating and iterating to convergence (multi-hop). A two-channel
`should_split` removes a co-spend edge only when provenance is disjoint (`provenance_link ≈ 0`)
AND the fingerprint diverges — either channel alone leaves the edge intact. Its held-out seed
re-identification (`holdout_reid`: hide a fraction of seed labels, re-derive them from the rest)
-is evaluated on a **synthetic fixture** and a preliminary **cache-bounded real run**
+is evaluated on a synthetic fixture and a preliminary cache-bounded real run
(`results/RESULTS-ns-propagation.md`); the real run mostly truncates at the cache boundary — most
signatures collapse to a single ancestral atom, the whole-graph limit above — so it exercises the
-mechanism on real data but does **not yet establish its strength**; proper chain-scale evaluation pending (§10). The split channel is not only a standalone module: it is now
+mechanism on real data but does not yet establish its strength; proper chain-scale evaluation pending (§10). The split channel is not only a standalone module: it is now
wired into the live engine as `cluster_refined`'s provenance-disjoint refuse term
(`decluster/cluster.py`, `provenance=`) — a co-spent pair is refused when provenance is disjoint
and the fingerprint already disagrees (`fp < 0`), the same fp-gating discipline as the amount
channel (§9).
-Beyond clustering, the system measures a coin's **provenance anonymity set**: the distribution over
+Beyond clustering, the system measures a coin's provenance anonymity set: the distribution over
ancestral origins of the *same* absorbing walk introduced above (`ancestry_entropy`'s first rung), now read
as a set rather than a scalar and exposed as `analyze(txid)`, per output, beside a subjective-fused
-reading. Its min-entropy is the same conservative lower bound on the coin's provenance **entropy** (§04;
+reading. Its min-entropy is the same conservative lower bound on the coin's provenance entropy (§04;
a linear solve, not Monte Carlo); §06 reads such entropy as a lower bound on the graph cuts to
-de-anonymize, but — per §11 — that transfer is not adopted for this absorber-model entropy, so the number
+de-anonymize, but — per §12 — that transfer is not adopted for this absorber-model entropy, so the number
is an entropy bound, not a cut count. (The default weights transitions by subset-sum link probability,
-not §04's coin-value measure; the value-weighted flow rung is the opt-in `value_weighted` — §11.)
+not §04's coin-value measure; the value-weighted flow rung is the opt-in `value_weighted` — §12.)
**Fusion.** Address-reuse self-transfer enters by default as a per-transaction subjective link matrix
that combines with the graph-derived one *before* the absorbing solve (our §04-faithful reading; §04
@@ -744,21 +894,43 @@ robustness argument predicts: intertwined deep structure resists de-anonymizatio
**The node bound.** Deep coinjoin ancestry is exponential in depth (§03: intractable for larger
transactions; the explosion is, in effect, the privacy). A node cap bounds the walk to O(N) fetch/oracle
calls, returning a truncated lower bound in bounded time — ≈5 min on a real 9-in/17-out coinjoin
-(`results/RESULTS-path-count.md`) — where the unbounded walk does not terminate. The bound is honest: it
+(`results/RESULTS-path-count.md`) — where the unbounded walk does not terminate. The bound is stated rather than assumed: it
can only omit origins, never invent them; exact deep resolution is not possible.
-**Path counting (§07).** The path-like anonymity set weights each ancestral origin by the number of
-counterfactual input→output paths reaching it — the subset-sum mapping count `W(E)` of each transaction
-along the way. `W(E)` comes from the `dense-subset-sum` crate's counting dispatcher, which selects by
-density regime among exact enumeration, exact dynamic programming, exact sparse convolution, and the
-Sasamoto saddle-point approximation, and returns the strongest guarantee available: exact where
-tractable, an accurate log-magnitude in the dense regime where exact counting saturates, and an
-off-regime `unknown` (a multiplicity-1 fallback) otherwise. This is the §06/§07 robustness lens — it
-up-weights origins reached through high-multiplicity (ambiguous) transactions. It coincides with the §04
-set at depth 1 always (a transaction's single `W(E)` cancels in the normalization) and, deeper, wherever
-a transaction's values pin a unique mapping (`W(E)=1`, the common case for value-determined coinjoins);
-it diverges only at depth ≥ 2 where genuine subset-sum multiplicity exists. It measures robustness, not
-tractability: it does not enlarge the set of transactions a walk can resolve.
+**Provenance route accumulation (§07 diagnostic).**
+`provenance_route_accumulation.provenance_route_accumulation` weights each
+ancestral origin by link probability alone, summed over every counterfactual input→output route
+reaching it; subset-sum multiplicity does not enter the bound, because `cost.py` declares the amount
+channel refuse-only (§1): it may cut a coin from the graph, never weight one, and folding a mapping
+count in as a per-hop multiplier would do exactly that. The count this walk does not use is still
+read, under the same tier discipline, by the amount channel's own refuse-only cut instead: `W(E)`,
+the `dense-subset-sum` crate's mapping count for a transaction, comes from a **two-tier router,
+`counting.count_w`** (`cost.py:63`), and earlier revisions of this paper described the wrong one — the
+crate's own `dss.w_count` cascade (brute force, dynamic programming, sparse convolution, Sasamoto),
+which `amount_cuts` does not call. The router `amount_cuts` actually uses is radix → sparse:
+brute force is absent (it is the same algorithm as the convolution below its crossover), and the
+Sasamoto saddle point is deliberately **not** a tier, available only when asked for by name. The
+radix tier is an output-only structural diagnostic that answers whether or not the amounts decompose
+into repeated denominations, so the crate now tags its result `Ambiguity::Diagnostic` and
+`counting.guaranteed_log_w` refuses it: `cost.amount_cuts` reads only the tiers guaranteed not to
+overstate — sparse-exact, and the sparse lower bound where exact counting saturates — so a
+transaction whose count comes back refused yields no cut candidates at all rather than a guessed one.
+Overstating `W(E)` there would corroborate a cut the transaction's structure does not support, which
+is the one error direction a refuse-only gate cannot afford.
+
+That gate is strict, and the canonical measurement says how strict
+(`results/artifacts/counting-router-v1.json`, `results/generated/counting-router-v1.md`). Over 1,428
+real multi-input transactions the router resolves **82**: **0 radix-exact, 60 sparse-exact, 22 sparse
+lower-bound, 13 radix-diagnostic (refused), 1,333 unknown**. Called *without* the precondition gate
+the radix tier returns a positive reading **64** times, **51** of them on transactions carrying no
+repeated denomination at all — which is the whole reason the gate exists. Downstream
+(`results/artifacts/amount-per-coin-v1.json`): 318 ungated per-coin cut candidates across 95
+transactions fall to **101 candidates in 62 transactions** after requiring a conservative nonzero
+transaction-level `W(E)`, of which **61 (0 input, 61 output)** carry an exact transaction count
+behind them. The pre-correction figures quoted here and in `RESULTS-counting-methods.md` — 95
+resolved, 533 raw-positive, 514 without precondition, `{3, 63, 66}` exact — are superseded by these. This leaves the construction-side path-count instrument
+with no structural-property term; the limitations section records that as an open gap rather than an
+implied capability.
**The partition posterior as a check.** `cluster_posterior` is the exact-Bayesian same-owner clustering
posterior — a split-merge sampler verified against exact enumeration over the co-spend super-nodes
@@ -771,7 +943,7 @@ the headline anonymity set, and on a real slice it agrees with the clustering en
provenance anonymity set with optional path-count and node bound (`analyze`) → the partition-posterior
check (`cluster_posterior`), over a panic-safe in-process or hang-proof subprocess link oracle. An external
consumer imports the whole pipeline from the top-level namespace; a deterministic offline test suite
-backs the invariants and bounded live measurements report honest numbers under stated limits.
+backs the invariants and bounded live measurements report their numbers under stated limits.
Every reading here is a lower bound / weight-of-evidence under no auxiliary information, never a privacy
score — the same discipline as the clustering half. Read as a penalty rather than a link, the min-entropy
@@ -782,16 +954,34 @@ prerequisite for designing against them.
The provenance signature is a sparse, high-dimensional record — the precondition for the
Narayanan–Shmatikov sparse-dataset attack (cit 19–20). Two measurements close that framing. First
-the **(ε,δ)-sparsity precondition**: on real signatures the ancestry feature space is sparse (a coin's
+the (ε,δ)-sparsity precondition: on real signatures the ancestry feature space is sparse (a coin's
provenance signature typically has no near-twin), while the low-dimensional statistical fingerprint
space is *not* sparse on its own — so the distinguishing signal lives in the structural/ancestry
channel, which is exactly where record linkage bites (`results/RESULTS-def1-sparsity.md`,
`results/RESULTS-ancestry-sparsity.md`; a data-run over unversioned signatures under the conservative
-uniform oracle). Second the **record-linkage attack** itself: stratifying signatures by their own
+uniform oracle). The entity attribute channel is now measured on a 947k-transaction 2016 slice
+and is dense the same way: almost every entity carries a near-twin even at ε = 0.9 (δ(0.9) ≈ 0.99),
+robust to a min-degree filter, so it too fails the precondition (`results/RESULTS-slice-a-channels.md`).
+The channels separate cleanly — ancestry is sparse and de-anonymizes, entity attributes and the
+address graph are dense and do not — which is what tells an attacker which signal to use. Second the
+record-linkage attack itself: stratifying signatures by their own
sparsity and running Algorithm 1B (rarity-weighted overlap, eccentricity gate φ = 1.5) makes the gap
-explicit — **sparse coins are pinned to the exact coin ~95% of the time, dense coins ~20%**, the ~5×
-separation Theorem 2 predicts, with the gate abstaining where a near-twin exists
-(`results/RESULTS-reid.md`, band-pinned on committed `tests/fixtures/reid_sigs.json.gz`). Honest
+explicit. On the canonical run (`results/artifacts/reid-v1.json`,
+`results/generated/reid-v1.md`; 200 records, 165 sparse / 35 dense), with `m` auxiliary origins drawn
+from the target's own signature:
+
+| stratum | m = 4 | m = 8 |
+|---|---:|---:|
+| sparse — pinned to the exact coin | 0.926 | 0.955 |
+| dense — pinned to the exact coin | **0.057** | **0.086** |
+
+Precision is 1.000 in every cell: where the eccentricity gate declares, it is right; the stratum
+difference is entirely in *how often it declares at all*. That is a **~16× separation at m = 4 and
+~11× at m = 8** — much wider than the "~20% dense, ~5× separation" this section used to report. And
+the separation is consistent with Theorem 2 rather than predicted by it: Theorem 2 states a
+de-anonymization condition under sparsity, not a ratio between strata, and no ratio should be
+attributed to it. (`results/RESULTS-reid.md`, band-pinned on committed
+`tests/fixtures/reid_sigs.json.gz`.) The
scope: this is the demonstrated *link* between sparsity and de-anonymization on those signatures, not
a chain-wide rate — the fixture is a depth-bounded, non-representative sample; the representative rate
needs a uniform deep collection (§10).
@@ -801,25 +991,98 @@ needs a uniform deep collection (§10).
The contracted pseudonym graph is, per the framework, a social network (cit 24): two time-separated
views of it should be matchable from a small seed. Contracting two views, seeding a mining-pool /
high-degree correspondence, and propagating (`view_match`) tests the framework's own "*if* the social
-network structure is recoverable" premise. On one-day views a week apart the result is honestly
-**modest and negative**: the cascade does not ignite, attribute conditioners hurt rather than help,
-and the ambiguity-cut partition does not decompose the graph — all against degree-baseline and
-shuffle controls (`results/RESULTS-view-match-2026.md`, `results/RESULTS-graph-shape.md`,
-`results/RESULTS-attribute-conditioning.md`, `results/RESULTS-partition-schemes.md`). The measurement
-is thin — two one-day views, ~5 000 overlapping active clusters — far from the wide-view,
-many-epoch, active-user attack the framework actually specifies (active, consistent users accumulate
-stable representations over time). The result is therefore best read as **underpowered, not a robust
-falsification**; the strong test — wide multi-epoch views over a long period, seeded on high-degree
+network structure is recoverable" premise. On one-day views a week apart the result is
+modest and negative: the cascade does not ignite and the ambiguity-cut partition does not decompose
+the graph — both against degree-baseline and shuffle controls
+(`results/generated/graph-rejoin-2016-v1.md`, `results/generated/partition-schemes-v1.md`). The framework's vertex and edge attributes are built here
+and deliberately kept out of the acceptance gate: a bounded multiplicative conditioner cannot turn a
+zero score positive, but the gate reads the *separation* between the leader and the runner-up, and
+scaling tied candidates by different factors is exactly how that separation is made
+(`decluster/view_match.py`, `tests/test_view_match.py`). Whether they cost accuracy at slice scale is
+not measured — the graph-scale exports this repository ships carry addresses and no construction
+axes, so there is nothing for a conditioner to read. A larger slice reads the same under the
+harness that does not hand the matcher its answer: splitting each cluster along the view boundary into two pseudonyms (`split_clusters_by_view`,
+the incomplete-clustering premise) and asking the matcher to rejoin them from structure. Earlier
+revisions cited "none of 5077 split pairs" from `results/RESULTS-slice-a-channels.md`; that file
+declares itself superseded — its view split leaked, tagging an address by where it was *first* seen,
+which both offered the matcher a wrong-but-structurally-better identity match and silently dropped
+part of the rejoinable population. The corrected construction is
+`results/generated/graph-rejoin-2016-v1.md`, over complete weekly January-2016 views (982,021 and
+1,116,563 transactions) holding 17,431 straddling clusters: at a 10% high-degree seed the
+directed matcher makes **one guess, and it is correct — 1 of 15,688** non-seed pairs; the undirected
+matcher and the 5% seeds guess nothing, and **all four shuffled-seed controls produce zero guesses**.
+One match is not ignition and recall rounds to zero, but it is a different statement from "none": the
+high-confidence path is not inert and it separates from the shuffle arm. What gates it is the
+neighbourhood rather than the transaction count: per-vertex persistence rises from 31% to 38% as the
+view widens from one day to seven and saturates by day three
+(`results/RESULTS-persistence-curve.md`), so a wider window adds relationships without adding
+recurring ones.
+`graph_shape` shows why the structure is not there: the contracted graph is disassortative
+(−0.044 on the canonical snapshot, `results/artifacts/slice-channels-v1.json`) — hubs attaching to
+leaves, the transactional sign, not the positive sign of a social graph. The clustering statistic points the same
+way on committed data and is the sharper of the two: transitivity reads 0.0062 and 0.0001 against a
+configuration-model null of 0.6113 and 0.3976, so the degree sequence alone would produce two orders
+of magnitude more triangles than the graph has (`results/generated/graph-rejoin-2016-v1.md`). The
+degree tail does *not* carry the argument and earlier revisions claimed too much from it: on these
+committed views the exponent is 2.86, inside the social 2–3 range rather than above it, and leaves
+are 4.6–6.0% rather than a majority. Those were properties of a one-day 2026 view, not of contracted
+Bitcoin graphs. The
+limit is the window: a pseudonym's view-A and view-B neighbours are different one-off counterparties,
+and a neighbourhood that does not recur cannot be rejoined — a stable representation needs an entity's
+relationships to repeat across many epochs. The result is therefore **underpowered, not a robust
+falsification**; the strong test — wide multi-epoch views over a multi-year span, seeded on high-degree
entities — is a data-scale requirement (§10), and would land either as a qualified positive on active
users or a robust negative.
-## 9. Limitations (honest)
+### The faithful N-S propagation kernel on real chain: ~1% precision
+
+Everything above is either an adaptation (`view_match.py`, `graph_deanon.py`) or a premise test. The
+paper-faithful 2009 kernel — `decluster/baselines/narayanan_shmatikov.py`, direction-aware
+degree-normalised neighbour votes, eccentricity gate, mandatory reverse match, iterated to
+convergence — has one real-chain run, and earlier revisions of this paper **never reported its
+numbers**. They should have: this is what the attack the programme is named after actually does on
+this graph.
+
+Two adjacent 120-block 2016 windows (heights 391,992–392,111 and 392,112–392,231; 133,576 and 157,724
+transactions), clusters straddling the boundary contracted to a separate pseudonym per view
+(`results/RESULTS-ns-bitcoin.md`, `results/artifacts/ns-bitcoin-v1.json`):
+
+| seed share | θ | declared | correct | precision | coverage |
+|---:|---:|---:|---:|---:|---:|
+| 5% | 0.5 | 248 | 2 | 0.81% | 0.081% |
+| 5% | 1.5 | 246 | 1 | 0.41% | 0.041% |
+| 10% | 0.5 | 450 | 3 | 0.67% | 0.128% |
+| 10% | 1.5 | 448 | 2 | 0.45% | 0.086% |
+| 25% | 0.5 | 779 | 10 | **1.28%** | **0.513%** |
+| 25% | 1.5 | 774 | 9 | 1.16% | 0.462% |
+
+**Precision 0.41%–1.28%, coverage 0.041%–0.513%.** The shuffled-seed control declares comparably
+often and gets **0 correct in all six rows**, so the signal is real and it is tiny. Two further limits
+are structural, not incidental. The independent-label path was tried first and found zero gradeable
+seeds across both views, so every row above is *seed-assisted*: the seeds are sampled from the
+withheld correspondence the run is graded against. And the export carries addresses only — no prevout
+values, no script types — so the amount and fingerprint channels are absent by construction.
+
+Set this beside the AUC 0.95 of §6. Those are not two readings of one thing. The 0.95 is a
+pairwise link-prediction score under weak co-spend labels — evidence that the graph carries
+same-owner structure, which is the *premise* the attack needs; the ~1% is the attack, run to
+completion, on real chain, and told to name a specific counterpart. The paper already says §6 is the
+premise and not the attack (§7, and the `structural_linkage` claim); what was missing was the second
+number. A premise that holds at 0.95 and an attack that lands at 1% is the shape of this
+result, and the gap between them is where the remaining work is.
+
+## 9. Limitations
- **Scope, not scale.** The fingerprint model is validated at mainnet scale (attribution AUC ≈0.93,
- §5) and structural de-anonymization across five eras (§6), both **without an archival node** — but a
- whole-chain **entity-reduction rate** ("X% of all entities collapse") needs the full connected chain
- and is a separate follow-on (§10). The *stronger* N-S form (structure links what co-spend leaves
- separate) needs **independent** entity labels; those are built as self-contained detectors and a
+ §5, at the 0.9244 cache level / 0.9459 fixture level; the 0.933 of earlier revisions rests on a
+ destroyed cache) and structural de-anonymization across five eras (§6), both without an archival
+ node — but a
+ whole-chain entity-reduction rate ("X% of all entities collapse") needs the full connected chain
+ and is a separate follow-on (§10). **And the validated pairwise scores are not attack rates:** the
+ paper-faithful N-S kernel run end to end on real chain lands at **0.41%–1.28% precision** (§8), so
+ every AUC in this paper should be read as evidence that a channel carries signal, never as the rate
+ at which a named counterpart is recovered. The *stronger* N-S form (structure links what co-spend leaves
+ separate) needs independent entity labels; those are built as self-contained detectors and a
same-owner probe disjoint from co-spend (`decluster/entities.py`, `graph_deanon.evaluate_entity`),
demonstrated on real slices with a sharp boundary (`results/RESULTS-entity-deanon.md`): the entity
must sit in an **economic graph with recurring peers** (SatoshiDice re-links at AUC ≈0.72), not a
@@ -841,13 +1104,27 @@ users or a robust negative.
- **Policy-value over-clustering.** A match on a *policy* value (e.g. `locktime=height`)
groups a *class* of wallets, not one; only rare values (`0x01`, `cake_group_c`) are
strongly identifying. Per-axis specificity is modeled but not yet fully calibrated.
-- **Independence assumption.** The engine sums per-axis bits assuming axis independence;
- real intra-wallet correlations (e.g. RBF ↔ locktime) may double-count.
+- **Independence assumption — quantified, not just conceded.** The engine sums per-axis bits assuming
+ axis independence, and real intra-wallet correlations double-count. Two axes,
+ `input_script_type` and `input_types_present`, are **φ = 1.000 in both classes**
+ (`results/artifacts/fs-ablation-v1.json`) — literally one fact scored twice — and four correlated
+ groups exist at |φ| ≥ 0.6. The cost is measured (§5): the headline bits figure is inflated
+ **≈1.77×** (+15.47 → +8.72 bits on the preserved cache when one representative per group is kept),
+ and **69%** of the wide model's score on the §4/§6 merged anchor is redundancy (+11.67 → +3.59).
+ Discrimination is not harmed by the redundancy — removing it *raises* AUC 0.9244 → 0.9432 — so the
+ double-counting damages magnitudes and refusal thresholds, not rankings. The default scorer is
+ deliberately unchanged so published figures stay reproducible; `decorrelated_scorer()` is the
+ corrected reading and every bits magnitude in this paper should be read at that scale.
+- **Label leakage in the attribution figure.** The same-owner label is a shared input address, and
+ five axes are determined by that address outright. Removing them
+ (`construction_only_scorer()`, 18 axes) costs **0.0241 of AUC** — 0.9244 → 0.9003 — so roughly a
+ fortieth of the headline discrimination is the label restating itself. Pure construction style
+ still separates at ≈0.90.
- **Same-software false positives.** Fingerprints separate only *different* wallet software; two
owners sharing a wallet, timezone, and fee policy emit matching fingerprints, so the fingerprint
channel goes silent — and the amount channel does not fill the gap, since its round-ness refuse is
gated on a fingerprint disagreement (§4) that is absent here. The control rests instead on **graph
- topology, scored as a Fellegi–Sunter quasi-identifier**: even with identical fingerprints Alice's
+ topology, scored as a Newcombe/FS rarity quasi-identifier**: even with identical fingerprints Alice's
counterparties differ from Bob's, and few distinguishing relationships suffice (N-S; §6 measures
structure separates owners at AUC 0.95). The engine's cluster-level term — rarity-thresholded so
common hubs count for nothing (`−log₂(share)`, N-S's own `1/log|supp|`) — separates same-owner from
@@ -859,64 +1136,100 @@ users or a robust negative.
- **Topology at chain scale, and a negative result.** Chain-scale seed-and-extend over the whole
graph is future work for this channel (§10; the provenance channel's own seed-and-propagate is built
and cache-bounded-evaluated, §8). A cluster's temporal activity schedule (hour-of-day histogram) was
- tested as a candidate quasi-identifier and reported as a **negative result**: a naive split-half
+ tested as a candidate quasi-identifier and reported as a negative result: a naive split-half
gives AUC 0.92, but a persistence split with matched negatives collapses it to **0.49 (chance)**, so
the schedule does not identify owners in this data (`results/RESULTS-temporal.md`).
+- **A separate bounded structural-property measurement.**
+ `provenance_route_accumulation` (§8)
+ weights ancestral origins by link probability alone; subset-sum multiplicity does not enter, because
+ the amount channel is refuse-only (§1). It therefore measures no structural property of the graph.
+ `disjoint_routes`, evaluated separately by `route-capacity-v1`, now measures coin-disjoint routes
+ and their corresponding minimum cut in the transaction graph, where transactions are vertices and
+ amount-labelled coins are directed edges. It also reports the cut after pruning routes unable to
+ carry the target coin and a value-capacitated maximum-flow reading
+ (`results/generated/route-capacity-v1.md`). These are bounded measurements on one committed slice,
+ not a structural term already composed into provenance accumulation or a chain-scale proof of
+ robust connectivity.
+ This names the most expensive structural notion, not the only one, and the gap should not be read
+ as wider than it is — but "composition, not machinery" holds only for the unqualified
+ conservative reading, and this bullet used to say it flatly. Precisely: how many coins
+ belonging to *other* users already share a user's deep features needs no cut at all, and its
+ primitives are built here (`ancestry.absorber_distribution`, `ancestry.provenance_link`,
+ `intersect.shared_origins`) — for that reading, composition is indeed what is missing. Each way of
+ *qualifying* it, however, needs a primitive and an evaluation appropriate to the qualification:
+ - Qualifying by proximity needs mass resolved by path length, `M_ℓ`.
+ `absorber_distribution` marginalizes length away by construction — it solves the absorbing chain
+ for the boundary distribution — and `collapsed_expected_steps` returns only the mean number of
+ transitions. Neither can answer "how much mass arrives within ℓ hops". The fix looks cheap —
+ iterate `H_ℓ = Q·H_{ℓ−1}` rather than solving to the fixed point, which sums back to
+ `absorber_distribution` — but it is not built, and until it is, a proximity qualification is one
+ this repository cannot express.
+ - Qualifying by flow capacity is now implemented separately from the stochastic transition
+ weights. `flow_capacity` assigns each known coin its recorded value as capacity and computes the
+ maximum value the admitted graph can deliver simultaneously. On the committed slice, 8 of 335
+ measured targets cannot receive their own value from the observed origins. This is still a
+ depth- and data-bounded graph reading: it neither identifies which satoshis actually travelled a
+ route nor supplies the user's antecedent set required to test own-origin robustness.
+ - **Cost is real and omitted.** The conservative reading is one backward walk per candidate input,
+ and `results/RESULTS-path-count.md` measures **292 s (~5 min) for a single coinjoin** at
+ `depth=5, max_nodes=20`; the unbounded walk over the same transaction did not finish in a
+ 25-minute cap. A per-candidate sweep at population scale is a compute question, not a
+ bookkeeping one.
## 10. Future work
The reason this measurement matters is constructive: every bit this paper reads as a link
is, inverted, a bit a wallet must avoid emitting. The offensive engine is the calibration
-instrument for a defensive **cost function** — the bridge to collaborative multi-party
+instrument for a defensive cost function — the bridge to collaborative multi-party
transactions where privacy can be *quantified and designed for* rather than hoped for.
**Two halves, one boundary.** This repository is the *measurement* half — a research instrument
(fingerprint extraction + decluster + record-linkage/ML); taint scores are a poor basis for coin
selection, fighting the fungibility a coordination protocol needs. The *actionable* half a client
-would use to choose among construction proposals is a distinct construction-side **cost function**: it
+would use to choose among construction proposals is a distinct construction-side cost function: it
reads the path-counting provenance anonymity set (§8) — the subset-sum density entering strictly as a
-*cut*, never a positive score — and picks the proposal that improves connectivity. Its leak, topology,
+*cut*, never a positive score. Its leak, topology,
and path-count terms are already wired (`cost.construction_cost`); only their combination into one
scalar (Liebig-min vs weighted) is the open design question, so the combined cost is deferred.
What remains splits into one item that is only scale and several that are separate research.
**Only scale — the whole-chain rate.** The method is validated (§5/§6) but does not yet report a
-whole-chain **entity-reduction rate** over the full connected graph. This needs no archival node — a
+whole-chain entity-reduction rate over the full connected graph. This needs no archival node — a
small-disk node that streams each block's spent prevouts suffices — so the remaining work is an
integration spike over that stream, not new method (the scaled engine lives in a separate `tx-indexer`
crate; the Python prototype here reproduces the method at case-study scale). The one-day
change-identification validation (§7) is likewise only scale.
-**Separate research tracks.** First, the full Narayanan–Shmatikov **seed-and-extend attack** at chain
+**Separate research tracks.** First, the full Narayanan–Shmatikov seed-and-extend attack at chain
scale. The provenance channel's mechanism (`NSPropagator`/`propagate_merge`, §8) is built and
cache-bounded-evaluated (`results/RESULTS-ns-propagation.md`); its real-data strength awaits a
prevout-resolved sample with independent entity labels the co-spend heuristic cannot supply. The
record-linkage and (ε,δ)-sparsity results (§8) need the same kind of representative input — a
-**uniform, deep** ancestry sample rather than the accreted cache — to turn the demonstrated
+uniform, deep ancestry sample rather than the accreted cache — to turn the demonstrated
sparsity→de-anonymization *link* into a chain-wide *rate*; the cross-view social-graph result needs
-**wide, multi-epoch** views over a long period, seeded on high-degree entities, to move its
+wide, multi-epoch views over a long period, seeded on high-degree entities, to move its
underpowered negative to a robust one. A separate
blocker bounds the walk's *depth*: `build_extended_graph(max_nodes=…)` returns real ancestral sets
(24–67 origins on the live slice), but counting `W(E)` does not make the §04 walk tractable to full
coinjoin depth — the exponential is the number of distinct ancestor transactions, and per-transaction
-`W` does not lift it. The tractable route is **sampling**: a valid mapping is a balanced input/output
+`W` does not lift it. The tractable route is sampling: a valid mapping is a balanced input/output
partition, and local moves (split/merge/transfer preserving balance) give the pairwise marginal from
how often two coins share a block, validated below the enumeration guard against the exact marginal
(`pairwise_input_output_prob`, in the dss engine; mixing time, not correctness, is the risk). In a
dense round the marginal comes out near-uniform — amount-privacy turned from a verdict into the entropy
of the link distribution, in the same bits as every channel here.
-Second, cluster-level **topology over the whole connected graph** needs richer features (community
+Second, cluster-level topology over the whole connected graph needs richer features (community
detection, embeddings) beyond the delivered rarity-threshold FP-control (§9). Third, the
-construction-side **cost function** itself — the defensive counterpart, a project in its own right. And
-generalizing the **amount channel** (§2) beyond the delivered de-mix (§1/§6): it already carries two
+construction-side cost function itself — the defensive counterpart, a project in its own right. And
+generalizing the amount channel (§2) beyond the delivered de-mix (§1/§6): it already carries two
secondary readings.
-**Conservation** (`results/RESULTS-conservation.md`) asks not which participant owns each output but
+Conservation (`results/RESULTS-conservation.md`) asks not which participant owns each output but
what the others could afford: on a six-round spine it forces outputs only in the rounds where the
participant holds above ~half the input (fragmenting defeats it, entering at once exposes), and a
-**provenance-overlap ranking** (`results/RESULTS-provenance.md`) ranks rounds by inputs descending from
+provenance-overlap ranking (`results/RESULTS-provenance.md`) ranks rounds by inputs descending from
a known tx — complementary (conservation bites by *value*; overlap peaks where the value share has
collapsed), a cut worth taking, not a test. Both are delivered; a further generalization stays outside
the refuse-only engine, since a channel that may only refuse cannot be handed an attributing argument
@@ -925,28 +1238,62 @@ the refuse-only engine, since a channel that may only refuse cannot be handed an
The three method directions the motivating comment raised are delivered (§5): per-axis weight tuning
(`results/RESULTS-em-m.md`), verdict robustness to those weights (`results/RESULTS-weight-sensitivity.md`,
`results/RESULTS-cluster-robustness.md`), and the Bayesian-vs-Fellegi-Sunter comparison
-(`results/RESULTS-bayes-vs-fs.md`). Still open: a full **partition-level** Bayesian entity resolution
+(`results/RESULTS-bayes-vs-fs.md`). Still open: a full partition-level Bayesian entity resolution
(MCMC over the linkage partition, heavy at scale — the pair-level comparison here is its tractable
-counterpart), and **supervised** record-linkage classifiers (they need labeled same-owner pairs this
+counterpart), and supervised record-linkage classifiers (they need labeled same-owner pairs this
design withholds for validation, so the unsupervised EM/Splink and Bayesian paths are pursued instead).
-## 11. Related work
-
-- **Yuval Kogman (nothingmuch), [*Anonymity Sets on the Transaction Graph*](https://github.com/nothingmuch/tx-graph-anonymity-sets)**: the theoretical framework this paper calibrates empirically — entropic anonymity sets (§6), the sub-transaction and absorber models (§2/§6), and the graph-as-quasi-identifiers argument the topology term realizes (§9). We measure and implement what it models, with one stated divergence: its walk weights transitions by *coin value* — every input satoshi equally likely to have become every output satoshi — whereas `decluster/ancestry.py` weights them by subset-sum link probability, row-normalized. The structure is the framework's (a backward absorbing chain solved as a linear system rather than by Monte Carlo); the transition measure is an approximation of it, flagged as provisional in that module. The value-weighted flow rung it defers to is now implemented as the opt-in `value_weighted` (`analyze(value_weighted=…)` / `build_extended_graph`), with link-probability the default so existing figures stay reproducible. Every figure that rests on the ancestry walk — the provenance signatures, and the intersection results of §9 — inherits that substitution. Two further relations are worth naming rather than leaving implicit. Its entropic treatment proposes entropy for evaluating and comparing apparent privacy across transactions, while noting that transaction-level entropy is the harder figure to interpret; §1 takes that caution further and declines to read entropy as privacy at all, keeping bits as an attacker's weight of evidence only — a narrowing of the framework's use, not an implementation of it. And its *path-like* anonymity set — the counterfactual paths a coin's history admits, and the expansion their intersections give — is the line the path-counting anonymity object continues (built; §8, `decluster/path_count.py`, weighting each origin by the subset-sum path multiplicity `W(E)`); that object is ours, the notion it extends is not. One reading it offers we do *not* take: that an output's entropy lower-bounds the number of edges an adversary must exclude to de-anonymize it. That is attacker-side and so compatible with §1's refusal to read entropy as privacy — but whether it transfers to the absorber-model entropy `ancestry_entropy` computes, which is over a boundary distribution rather than over graph edges, is not established here, so the number is left without it.
+## 11. Conclusion
+
+The graph is primary and the engine is what makes its channels one decision. Refusing a co-spend
+before making it is enough to keep two owners apart when the clusterer owns every merge it made;
+cutting a partition it inherited is the other direction, held to a stricter standard because a cut
+made on absent evidence invents privacy rather than measuring it.
+
+What the measurements establish, at the level they were taken. Construction fingerprints separate
+same-owner coins and the engine declines co-spends the merge-only heuristic takes, on committed
+transactions (§4, `results/generated/fused-engine-v1.md`). Cluster-level topology and
+provenance-disjointness move those refusals in opposite directions, which is the case for fusing
+them rather than reading them in turn. Graph structure predicts same-owner beyond co-spend and
+survives a degree-matched control, so the signal is not an artefact of degree
+(`results/RESULTS-graph-deanon.md`). Sparse feature vectors support record linkage where the vectors
+are sparse, and the sparse-versus-dense gap is the result (`results/generated/reid-v1.md`).
+
+What they do not establish, stated as plainly. Fingerprints are not a sparse quasi-identifier: on the
+committed window, exact-vector classes of size one are a rounding error, so the fingerprint buckets
+and does not single out (`results/generated/fingerprint-sparsity-v1.md`). Seeded propagation across
+two pseudonym views does not ignite at the view widths measured, and neighbourhood persistence — not
+the matcher — is the binding constraint (`results/generated/graph-rejoin-2016-v1.md`,
+`results/RESULTS-persistence-curve.md`). The framework's own positive properties, robust connectivity
+and own-origin robustness, are stated over counterfactual disjoint paths. The bounded route-capacity
+experiment now measures coin-disjoint cuts, value-pruned cuts and maximum flow on one committed
+slice; it finds small cuts as well as high-redundancy cases
+(`results/generated/route-capacity-v1.md`). That is evidence about the structural object, not proof
+of either property: the sample ends mostly at missing parents, and own-origin still lacks a run with
+a user's independently specified antecedent coins. The path-count result remains a different object
+(§9, `results/RESULTS-path-count.md`).
+
+Every measurement above resolves to a canonical artifact. The measurements that do not are listed in
+`tests/test_paper_numbers.py` with the reason each is unavailable, and the list is checked in both
+directions, so a number that gains an artifact has to leave it. `results/REPRODUCIBILITY.md` states
+which evidence state every result document is filed under, and which are not yet filed.
+
+## 12. Related work
+
+- **Yuval Kogman (nothingmuch), [*Anonymity Sets on the Transaction Graph*](https://github.com/nothingmuch/tx-graph-anonymity-sets)**: the theoretical framework this paper calibrates empirically — entropic anonymity sets (§6), the sub-transaction and absorber models (§2/§6), and the graph-as-quasi-identifiers argument the topology term realizes (§9). We measure and implement what it models, with one stated divergence: its walk weights transitions by *coin value* — every input satoshi equally likely to have become every output satoshi — whereas `decluster/ancestry.py` weights them by subset-sum link probability, row-normalized. The structure is the framework's (a backward absorbing chain solved as a linear system rather than by Monte Carlo); the transition measure is an approximation of it, flagged as provisional in that module. The value-weighted flow rung it defers to is now implemented as the opt-in `value_weighted` (`analyze(value_weighted=…)` / `build_extended_graph`), with link-probability the default so existing figures stay reproducible. Every figure that rests on the ancestry walk — the provenance signatures, and the intersection results of §9 — inherits that substitution. Two further relations are worth naming rather than leaving implicit. Its entropic treatment proposes entropy for evaluating and comparing apparent privacy across transactions, while noting that transaction-level entropy is the harder figure to interpret; §1 takes that caution further and declines to read entropy as privacy at all, keeping bits as an attacker's weight of evidence only — a narrowing of the framework's use, not an implementation of it. And its *path-like* anonymity set — the counterfactual paths a coin's history admits, and the expansion their intersections give — is the line the path-counting anonymity object continues (built; §8, `decluster/path_count.py`, weighting each origin by link probability alone — an earlier revision folded in the subset-sum path multiplicity `W(E)` and that term has been withdrawn, §9); that object is ours, the notion it extends is not. One reading it offers we do *not* take: that an output's entropy lower-bounds the number of edges an adversary must exclude to de-anonymize it. That is attacker-side and so compatible with §1's refusal to read entropy as privacy — but whether it transfers to the absorber-model entropy `ancestry_entropy` computes, which is over a boundary distribution rather than over graph edges, is not established here, so the number is left without it.
- **Yuval Kogman (nothingmuch), [*Collaborative Transaction Privacy*](https://gist.github.com/nothingmuch/d84ba390d89b5b08897af2d95009c2a1)**: the failure-mode taxonomy this paper calibrates against — CIOH violation by collaborative transactions, the NS1R / NSNR / net-settlement progression, and the robust-connectivity / own-origin / deep-feature program (§2/§9/§10). It shows how net-settlement with cycles and deliberately underdetermined values can *silence* amount analysis — on our reading the most defeatable layer — which is why our *primary* amount signal is scoped to the decidable regime (§2), and the provenance / deep-feature channel it develops is exactly the one §7 sets aside and §8 develops.
- **Armin Sabouri, [*How Fingerprints Damage PayJoin Privacy*](https://github.com/payjoin/research-docs/blob/main/fingerprints/payjoin.md)** (payjoin/research-docs): the applied payjoin case for this program — it walks real payjoin transactions through the same construction tells this paper measures (low-R, SIGHASH serialization, nSequence, value-conservation/round-number, input ordering/locktime, coin-selection residuals), across intra- and inter-transaction layers, and concludes that "PayJoin's privacy extends only as far as the uniformity of the participating wallets." That is precisely the collaborative-transaction failure our engine quantifies: the merge is refused by the amount structure and again by the fingerprints (§2/§6), and the same per-axis bits, inverted, define the construction-side uniformity a payjoin must reach (§10).
- **Cindy (bc1cindy)**, [*Tracking: chain-observable transaction-level fingerprinting*](https://github.com/payjoin/rust-payjoin/issues/1597) (payjoin/rust-payjoin #1597): the venue for this program and its review discussion — the tracking issue that scopes the fingerprint checklist (§7) this paper measures against.
-- **Maurer, Neudecker & Florian**, *Anonymous CoinJoin Transactions with Arbitrary Values* (2017): the **sub-transaction model** — a transaction with arbitrary amounts can be re-partitioned into the plausible original transactions, and their number and plausibility bound its anonymity. The origin of the amount-based re-partition we take as the *primary* signal (§2/§6).
-- **LaurentMT**, *Boltzmann* (OXT, 2015): operationalized the sub-transaction model as transaction **entropy** `E = log₂N` over the N plausible input→output interpretations. §1 refines this: what bounds anonymity is the *entropy of the distribution* over partitions, not the count `log₂N`.
-- **Fellegi & Sunter**, *A Theory for Record Linkage* (JASA 64(328):1183–1210, 1969; [doi:10.1080/01621459.1969.10501049](https://doi.org/10.1080/01621459.1969.10501049)): the record-linkage weight-of-evidence the engine scores in — an agreement on a value of frequency `p` contributes `−log₂p` bits (§4; this value-specific *frequency-based* weighting is due to Newcombe, 1959/1962); the topology term internalizes counterparty overlap as an FS quasi-identifier, and the rarity threshold is its rarity-weighting of that match (§9).
-- **Winkler**, *Overview of Record Linkage and Current Research Directions* (2005), building on his EM weight-computation work (1988), and **Splink** (UK Ministry of Justice, [moj-analytical-services/splink](https://github.com/moj-analytical-services/splink)): the unsupervised EM that estimates the FS model's per-field agreement probability `m` without same-owner labels, and its open-source reference implementation. Our per-axis `m` fit (§5, `results/RESULTS-em-m.md`) is Splink-style — `u` fixed at the measured collision, the reuse label withheld for validation — and the Bayesian variant (`results/RESULTS-bayes-vs-fs.md`) is its posterior generalization.
-- **Narayanan & Shmatikov**, *Robust De-anonymization of Large Sparse Datasets* (IEEE S&P 2008; [arXiv:cs/0610105](https://arxiv.org/abs/cs/0610105)) and *De-anonymizing Social Networks* (IEEE S&P 2009; [arXiv:0903.3276](https://arxiv.org/abs/0903.3276)): structure alone re-identifies nodes. Their high-dimensional sparsity result — rare structure re-identifies, distinctive attributes rarity-weighted `wt(i) = 1/log|supp(i)|` — is the *premise* our topology term operationalizes (the `−log₂(share)` weight it scores in is the Newcombe/FS frequency weight, §4/§9). §6 tests that premise on a real connected Bitcoin slice — payment-graph structure predicts same-owner at AUC 0.95 beyond co-spend (`results/RESULTS-graph-deanon.md`); the full seed-and-extend attack at chain scale, and richer features (community detection, embeddings), remain future work (§10).
-- **Goldfeder, Kalodner, Reisman & Narayanan**, *When the Cookie Meets the Blockchain: Privacy Risks of Web Payments via Cryptocurrencies* (PoPETs 2018(4):179–199; [arXiv:1708.04748](https://arxiv.org/abs/1708.04748)): the cross-transaction cluster **intersection attack** — a user's coins are linked to a common origin by intersecting the candidate source clusters of coins later shown to be co-held, inherently multi-transaction and graph-wide rather than single-transaction. It is the argument §1 leans on and §9 implements (`decluster/monitor.py` supplies the co-spend occasion, `decluster/intersect.py` narrows the shared origins), under the same discipline as the rest of the engine: the intersection is a *narrowing* handed to `cluster_refined`, never an attribution.
-- **Möser & Narayanan**, *Resurrecting Address Clustering in Bitcoin* (FC 2023; [arXiv:2107.05749](https://arxiv.org/abs/2107.05749)): the non-interactive change-labeling method — the change of a 2-output transaction is revealed when its address is later co-spent with the inputs' cluster — and the "consistent fingerprint" change heuristics (their Table 1, incl. ordered ins/outs). We reproduce their labeling and §2.2 filters, and their per-axis validation, on a real slice (§7; `results/RESULTS-change-id.md`).
+- Maurer, Neudecker & Florian, *Anonymous CoinJoin Transactions with Arbitrary Values* (2017): the sub-transaction model — a transaction with arbitrary amounts can be re-partitioned into the original transactions it could have been, and their number bounds its anonymity. The origin of the amount-based re-partition we take as the *primary* signal (§2/§6). One correction to how earlier revisions of this paper cited it: admissibility in Maurer et al. is binary, not graded. §4.1 requires the values of a sub-transaction's inputs and outputs to cancel exactly, and all admissible mappings are counted as equiprobable — there is no plausibility weight in that model to bound anything. Graded plausibility is a *later* relaxation (Boltzmann widens the net to accommodate fees; §1 goes further and reads the *entropy of the distribution* over partitions rather than the count), and attributing it to Maurer overstates the source.
+- LaurentMT, *Boltzmann* (OXT, 2015): operationalized the sub-transaction model as transaction entropy `E = log₂N` over the N plausible input→output interpretations. §1 refines this: what bounds anonymity is the *entropy of the distribution* over partitions, not the count `log₂N`.
+- Fellegi & Sunter, *A Theory for Record Linkage* (JASA 64(328):1183–1210, 1969; [doi:10.1080/01621459.1969.10501049](https://doi.org/10.1080/01621459.1969.10501049)): the record-linkage weight-of-evidence framework this work sits in. The fitted F-S model — per-field `m`/`u`, estimated by EM — is `decluster/fellegi_sunter.py`, and it drives the temporal split, the ablation, and the Bayesian comparison (§5). The **headline pair scorer is not that model**: an agreement on a value of frequency `p` contributing `−log₂p` bits is the value-specific *frequency-based* weight due to **Newcombe (1959/1962)**, and §4 now says so in the body rather than only here. The topology term internalizes counterparty overlap as a rarity quasi-identifier on the same `−log₂(share)` scale, and the rarity threshold is its rarity-weighting of that match (§9).
+- Winkler, *Overview of Record Linkage and Current Research Directions* (2005), building on his EM weight-computation work (1988), and Splink (UK Ministry of Justice, [moj-analytical-services/splink](https://github.com/moj-analytical-services/splink)): the unsupervised EM that estimates the FS model's per-field agreement probability `m` without same-owner labels, and its open-source reference implementation. Our per-axis `m` fit (§5, `results/RESULTS-em-m.md`) is Splink-style — `u` fixed at the measured collision, the reuse label withheld for validation — and the Bayesian variant (`results/RESULTS-bayes-vs-fs.md`) is its posterior generalization.
+- Narayanan & Shmatikov, *Robust De-anonymization of Large Sparse Datasets* (IEEE S&P 2008; [arXiv:cs/0610105](https://arxiv.org/abs/cs/0610105)) and *De-anonymizing Social Networks* (IEEE S&P 2009; [arXiv:0903.3276](https://arxiv.org/abs/0903.3276)): structure alone re-identifies nodes. Their high-dimensional sparsity result — rare structure re-identifies, distinctive attributes rarity-weighted `wt(i) = 1/log|supp(i)|` — is the *premise* our topology term operationalizes (the `−log₂(share)` weight it scores in is the Newcombe/FS frequency weight, §4/§9). §6 tests that premise on a real connected Bitcoin slice — payment-graph structure predicts same-owner at AUC 0.95 beyond co-spend (`results/RESULTS-graph-deanon.md`). The 2009 propagation kernel itself is implemented faithfully (`decluster/baselines/narayanan_shmatikov.py`) and has been run on two real Bitcoin views, where it reaches **0.41%–1.28% precision** against a shuffled-seed control at zero and finds **no independently gradeable seeds at all** (§8, `results/RESULTS-ns-bitcoin.md`). Independent seed discovery, chain-scale views, and richer features (community detection, embeddings) remain future work (§10).
+- **Goldfeder, Kalodner, Reisman & Narayanan**, *When the Cookie Meets the Blockchain: Privacy Risks of Web Payments via Cryptocurrencies* (PoPETs 2018(4):179–199; [arXiv:1708.04748](https://arxiv.org/abs/1708.04748)): the cross-transaction cluster intersection attack — for each co-held mixed coin, Algorithm 2 collects wallet clusters reachable by join-only backward paths of at most `r` rounds, intersects those sets, and identifies only a unique survivor. That kernel is implemented separately in `decluster/baselines/candidate_set_intersection.py`. §9's `decluster/intersect.py` is an adaptation: `monitor.py` supplies a co-spend occasion, but candidate origins come from a probabilistic walk not restricted to joins, and its narrowing is handed to `cluster_refined`; its real-chain result is not a reproduction of Goldfeder's 2015–2017 JoinMarket experiment.
+- Kelen & Seres, *Towards Measuring the Traceability of Cryptocurrencies* ([arXiv:2211.04259](https://arxiv.org/abs/2211.04259); section numbers below cite **v2, 2024-06-01**, the revision in our corpus): the flow-based traceability and absorbing-chain metrics this paper's provenance walk shares a formalism with. Their ledger-to-graph transforms are implemented as baselines (`decluster/baselines/kelen_seres_graphs.py`) and their expected-steps quantity as `ancestry.kelen_seres_expected_steps`, which converts our collapsed coin-to-parent transition back into the paper's uncollapsed UTXO-graph step count and refuses to report at all on a truncated or depth-capped walk, where no such interpretation exists. Two divergences are local to us rather than inherited: §2.2 sets no restriction on a node's incoming and outgoing amounts and the §2.4 auxiliary node absorbs whatever a node spends without having received, so the stationary transform needs no opening balances — only our *temporal* split transform does, because per-receipt snapshots hold a balance. It is listed in the source catalogue with `obligation: baseline` (`catalog/ctp-sources.json`) and has been implemented since before this section named it.
+- Möser & Narayanan, *Resurrecting Address Clustering in Bitcoin* (FC 2023; [arXiv:2107.05749](https://arxiv.org/abs/2107.05749)): the non-interactive change-labeling method — the change of a 2-output transaction is revealed when its address is later co-spent with the inputs' cluster — and the "consistent fingerprint" change heuristics (their Table 1, incl. ordered ins/outs). We reproduce their labeling and §2.2 filters, and their per-axis validation, on a real slice (§7; `results/RESULTS-change-id.md`).
- **Kappos et al.**, *How to Peel a Million: Validating and Expanding Bitcoin Clusters* (USENIX Security 2022; [paper](https://www.usenix.org/system/files/sec22-kappos.pdf)): change identification by whether an output's onward-spend belongs to the same peel chain — the cluster-feature `findNext` (TFC/AFC/changeC). We implement `findNext` (§7, `change_cluster.py`), but note it cannot be *validated against* an M&N co-spend label: the two share the co-spend-cluster signal, so `findNext` scores that label by construction (§7 circularity caveat). Our label-disjoint validation is the per-axis fingerprint test instead.
-- **Wang et al.**, *Exploring Unconfirmed Transactions for Effective Bitcoin Address Clustering*: the closest model — a clustering-effectiveness paper reporting **entity reduction** by mining unconfirmed/mempool transactions (co-spend on unconfirmed txs +2.3%, three novel mempool heuristics +9.8%, against whole-chain entity counts). We follow the same anonymity-collapse framing but at **case-study scale** (the entropy metric on the merged-transaction graph, §6); a whole-chain measurement needs the whole connected chain, not an archival node (§10).
-- **Ron & Shamir**, *Quantitative Analysis of the Full Bitcoin Transaction Graph* (FC 2013): the first quantitative graph analysis of Bitcoin and the tracking of specific large entities by their on-chain patterns — the lineage of the **special-case** de-anonymization this paper's known-entity catalog and optimal-change labels extend (`catalog/known-entities.md`, §7). The special cases are the next lever (§10).
-- **Dingledine & Mathewson**, *Anonymity Loves Company*: uniformity is a network-effect property — a wallet that de-biases one axis but stands out on another gains nothing. This grounds our recommendation to randomize *between legitimate behaviors* (same distribution), not merely to fix single fingerprints.
-- **Syverson**, *Why I'm Not an Entropist*: caution on the entropy framing; we report bits as weight-of-evidence for pairwise linkage, not as a single anonymity scalar.
-
-
+- **Wang et al.**, *Exploring Unconfirmed Transactions for Effective Bitcoin Address Clustering*: the closest model — a clustering-effectiveness paper reporting entity reduction by mining unconfirmed/mempool transactions (co-spend on unconfirmed txs +2.3%, three novel mempool heuristics +9.8%, against whole-chain entity counts). We follow the same anonymity-collapse framing but at case-study scale (the entropy metric on the merged-transaction graph, §6); a whole-chain measurement needs the whole connected chain, not an archival node (§10).
+- Ron & Shamir, *Quantitative Analysis of the Full Bitcoin Transaction Graph* (FC 2013): the first quantitative graph analysis of Bitcoin and the tracking of specific large entities by their on-chain patterns — the lineage of the special-case de-anonymization this paper's known-entity catalog and optimal-change labels extend (`catalog/known-entities.md`, §7). The special cases are the next lever (§10).
+- Dingledine & Mathewson, *Anonymity Loves Company*: uniformity is a network-effect property — a wallet that de-biases one axis but stands out on another gains nothing. This grounds our recommendation to randomize *between legitimate behaviors* (same distribution), not merely to fix single fingerprints.
+- Syverson, *Why I'm Not an Entropist*: caution on the entropy framing; we report bits as weight-of-evidence for pairwise linkage, not as a single anonymity scalar.
diff --git a/README.md b/README.md
index 9fdbefcf..b395a658 100644
--- a/README.md
+++ b/README.md
@@ -1,8 +1,10 @@
# decluster
-Evidence-weighted **de-anonymization of Bitcoin transactions**. It reads how each
-transaction was built (wallet fingerprints) and how its amounts partition (subtransaction
-structure), scores every signal as **bits of evidence**, and clusters coins by owner.
+An adversarial laboratory for testing **de-anonymization failure modes in Bitcoin
+transactions**. It keeps construction fingerprints, amount models, graph structure,
+attribution and provenance as distinct evidence channels, including explicit abstention and
+cannot-link outcomes. Those channels are model-relative and do not establish true ownership or
+a general privacy score.
Two coins spent in the same transaction are normally assumed to share an owner. Because the
evidence here is *signed*, the clustering can instead **keep them apart** when their
@@ -14,17 +16,27 @@ two people into one).
how it constructs a transaction — nSequence values, script types, signature grinding, and
more. Do those quirks actually identify the wallet? Taking address reuse as the same-owner
label (two transactions spending the same address are the same wallet), the measured
- fingerprint bits rank a *same-wallet* pair of transactions above a *random* pair **93.3%
- of the time** (AUC 0.933) on 166k real mainnet transactions. Shuffle the labels and it
- drops to 0.50 (a coin flip) — so the 0.933 is real signal, not an artifact.
+ fingerprint bits rank a *same-wallet* pair of transactions above a *random* pair **94.6%
+ of the time** on the committed 600-transaction fixture (AUC 0.9459, the manifest-backed run)
+ and **92.4%** on the preserved 22,112-transaction cache (AUC 0.9244). Shuffle the labels and
+ both drop to ~0.50 (a coin flip) — so the separation is real signal, not an artifact. An
+ earlier figure of 0.933 over a 166k-transaction cache is a historical record: that cache was
+ not preserved and the number is not recomputable. Two caveats travel with these: the
+ preserved cache is Taproot-era only (heights 800,000–965,220), and on that cache 0.024 of
+ the AUC is the address-reuse label restating itself — dropping the five axes the shared
+ input address fixes outright takes 0.9244 to 0.9003.
-- **The shape of the payment graph reveals owners too.** Independently of who-spent-with-whom,
- the *structure* of the graph (who pays whom) betrays common ownership — the same effect
- that de-anonymized social networks (Narayanan–Shmatikov). Across five eras (2012–2024),
+- **The shape of the payment graph provides another model-relative signal.** Independently of
+ who-spent-with-whom, recurring counterparty structure can support linkage. The local
+ common-neighbour experiment is not the seeded cross-view Narayanan–Shmatikov attack. Across five eras (2012–2024),
payment-graph structure *alone* predicts whether two addresses share an owner, ranking
same-owner pairs correctly **0.95–0.97 of the time at one hop on the clean eras**, and
**0.97–1.00 across all five eras by four hops** (1.0 = perfect, 0.5 = chance; the churny
- 2013 slice starts near chance at one hop and needs the deeper hops).
+ 2013 slice starts near chance at one hop and needs the deeper hops). That is a *pairwise
+ ranking* score, and it is the premise the attack needs rather than the attack: run to
+ completion on two real Bitcoin views, the faithful 2009 propagation kernel reaches
+ **0.41%–1.28% precision**, against a shuffled-seed control that gets nothing right
+ (`results/RESULTS-ns-bitcoin.md`).
- **It survives a transaction built to fool it.** On a real transaction deliberately
constructed to merge two owners into one (the false link from above), the method keeps
@@ -34,16 +46,64 @@ two people into one).
## Layout
-- `decluster/` — the attacker (the measurement half that runs): extractors, library, combiner, cluster (engine: `cluster_refined`), propagate (entity-level N-S seed-and-propagate via provenance signatures; synthetic evaluation), graph_deanon
-- the construction/cost half (deferred — PAPER §9): `cost` (leak / amount-cut / topology leaf terms + the deferred `construction_cost`), `ancestry` (the absorber-model provenance target; feeds propagate), `report` (fuses the terms on a real tx), `subsetsum`/`coinjoin_demix` (the amount de-mix channel); consumes the `dense-subset-sum` engine (build: `maturin develop`); `cluster_refined` optionally refuses links when provenance and fingerprints diverge
-- chain-analysis channels that select what to ask and read the answer, all outside the engine:
- `conservation` (what the other participants could not have funded — arithmetic on one transaction,
- no client model), `provenance` (which inputs descend from known transactions), `monitor` (watches
- tracked coins for the co-spend an intersection argument needs), `intersect` (the N-ary origin
- intersection, handed to `cluster_refined` to score rather than asserted)
-- `PAPER.md` — the manuscript; `results/` — reproducible outputs; `catalog/`, `bigquery/`
+**The package.** `decluster/` holds the measurement half. Modules are named for the evidence channel
+they carry — `extractors`/`library`/`combiner` for construction fingerprints, `subsetsum`/`counting`/
+`subtransaction` for amounts, `ancestry`/`provenance`/`intersect` for provenance, `views`/
+`view_partition`/`contraction` for the coin graph — and they converge on two objects that travel in
+opposite directions across the partition lattice: `cluster.cluster_refined` ascends, declining a
+co-spend the merge-only heuristic would take, and `declustering.decluster` descends, cutting a
+partition it did not build. `decluster/baselines/` reimplements published algorithms against their papers,
+`decluster/experiments/` holds one module per canonical run, and `decluster/adaptations/` holds the pieces that are
+named adaptations rather than reproductions.
+
+**The evidence chain.** Every published number travels the same path, and each step is checkable:
+
+```
+catalog/datasets/ the data, pinned by digest
+ ↓
+decluster/experiments/ one module per run
+ ↓
+catalog/runs/ the manifest: command, parameters, environment, claim ids, verification
+ ↓
+results/artifacts/ the machine-written result
+ ↓
+results/generated/ the document rendered from it, never edited by hand
+ ↓
+releases/ the evidence bundle, listing every blob by name, size and digest
+ ↓
+artifacts/sha256/ the content-addressed store holding those bytes
+```
+
+`reproduction/` carries one environment and lockfile per bundle, and `sources/` a deterministic
+archive of the code at the revision a run names. `decluster-bundle sync` keeps tree, index and store
+in step; a gate fails if they drift. A run's `verify` recomputes its artifact and compares it byte
+for byte.
+
+**The prose.** `PAPER.md` is the manuscript. `results/` holds both halves of the record: documents
+generated from artifacts, and hand-written `RESULTS-*.md` reports that predate the chain.
+`results/REPRODUCIBILITY.md` files every one of them under an evidence state, and each document
+carries a footer naming its state or saying plainly that none has been assigned.
+
+**The rest.** `bigquery/` holds the extraction recipes, including the ones for collections the
+policy names as still missing. `catalog/ctp-claims.json` and `ctp-sources.json` bind each claim to
+the literature and to the code that implements it. `tests/` is written as gates rather than
+coverage: each file pins a property that has broken once, and says which in its docstring.
Installable (`pip install -e .`), so a protocol-specific client model can consume these primitives
from above without this repository knowing the protocol exists.
-Every number is reproducible. MIT — see `LICENSE`.
+Reproducibility is tracked per result. Every canonical run is bitwise reproducible from the committed store; the exact-oracle audit was the first, and bootstrapping any bundle looks the same:
+
+```console
+decluster-bundle --index releases/exact-oracle-evidence-v1.bundle.json \
+ --store artifacts --root /tmp/decluster-bundle bootstrap
+decluster-bundle --index releases/exact-oracle-evidence-v1.bundle.json \
+ --root /tmp/decluster-bundle --work /tmp/decluster-run reproduce
+```
+
+That environment currently requires CPython 3.13 on macOS arm64. The committed content-addressed
+store makes local cold-start execution possible. Public bootstrap remains pending until every blob
+has a canonical HTTPS location and an independent mirror. Other results retain the guarantees and
+limitations declared in `results/REPRODUCIBILITY.md` and their manifests.
+
+MIT. See `LICENSE`.
diff --git a/artifacts/sha256/00/9538122a9c35006d6ac47a1129317d504fd15ac6861dffaa3c115cc3edc872 b/artifacts/sha256/00/9538122a9c35006d6ac47a1129317d504fd15ac6861dffaa3c115cc3edc872
new file mode 100644
index 00000000..52537129
--- /dev/null
+++ b/artifacts/sha256/00/9538122a9c35006d6ac47a1129317d504fd15ac6861dffaa3c115cc3edc872
@@ -0,0 +1,19 @@
+# N-S 2011 executable coverage
+
+Generated from the canonical experiment artifact. Do not edit manually.
+
+| component | status | limitation |
+|---|---|---|
+| `algorithm1_similarity` | `implemented` | — |
+| `algorithm2_positive_weights` | `implemented` | — |
+| `algorithm2_dummy_weights` | `implemented` | a dummy-incident node term is fixed at zero; the paper states this in prose, not in the printed formula |
+| `annealing` | `partial` | iteration budget, RNG and best-state return are explicit local controls |
+| `two_stage_mapping` | `implemented` | the paper picks an arbitrary unmapped node and stage 1 feeds back, so the ``repr`` order this driver imposes is a result-bearing local choice; injectivity comes from Algorithm 3's 1-1 mapping, not from the propagation section |
+| `confidence_pruning` | `not_reproduced` | an implementation-complete pruning policy is not available |
+| `accepted_mapping_correction` | `not_reproduced` | schedule and conflict resolution are not specified sufficiently |
+| `algorithm3_cascade` | `implemented` | — |
+| `learned_25_feature_model` | `not_reproduced` | the caller supplies an ML score; the published feature producer is absent |
+
+Every non-implemented component is exercised through the refusal gate. The implemented components pass that gate, but the end-to-end paper pipeline remains unreproduced.
+
+This deterministic contract does not reproduce the Flickr/Kaggle corpus, reported metrics, dummy-node convention, pruning policy, correction schedule, or learned model.
diff --git a/artifacts/sha256/04/f9670b4969de9df7ef526818503be374a6b71fa6c2df178db03e7d1c688bb3 b/artifacts/sha256/04/f9670b4969de9df7ef526818503be374a6b71fa6c2df178db03e7d1c688bb3
new file mode 100644
index 00000000..3ef79b37
--- /dev/null
+++ b/artifacts/sha256/04/f9670b4969de9df7ef526818503be374a6b71fa6c2df178db03e7d1c688bb3
@@ -0,0 +1,93 @@
+{
+ "schema_version": 1,
+ "id": "amount-channel-survey-v1",
+ "claim_ids": [
+ "ctp.payjoin.unnecessary_input",
+ "ctp.amount.subtransaction_ambiguity"
+ ],
+ "code": {
+ "revision": "bcd9a3e511860b1b0ff5f3ee6338a2df29ecbfbf",
+ "dirty": false
+ },
+ "command": {
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.amount_channel_summary",
+ "reproduce",
+ "--artifact",
+ "results/artifacts/amount-channel-survey-v1.json",
+ "--markdown",
+ "results/generated/amount-channel-survey-v1.md"
+ ]
+ },
+ "environment": {
+ "python": "3.13.7",
+ "lock_digest": "e0d6a8cada5d383c0aca2bd2a37f28c15721392469c94084128d369c29c5544d",
+ "platform": "macOS-26.6.2-arm64-arm-64bit-Mach-O",
+ "dependencies": []
+ },
+ "datasets": [],
+ "parameters": {
+ "composition": "identity-checked upstream artifacts only",
+ "components": {
+ "amount-local-channels-v1": "210ecb06ecaa32cc4cabe03ced8b85b0882a824dca2c361bd2c5dede7dcccf02",
+ "amount-subset-sum-v1": "240592b5af814fbcd28b06221805b934424170119da5e62e9da0281944377a9d",
+ "amount-per-coin-v1": "7be46858e221d0c942e7fa3ccc93b5eba28cf3a494fd00f07c2d50f2caa473df",
+ "amount-mapping-family-v1": "11f0ec645ea1be334dc7d24fbfc31047b234f335f48b5a514960472c7444b7d6",
+ "amount-pairwise-family-v1": "83f6a9e8501ee4df45f08b8c8ffc2f7265d40bf9add6d3f5093d712657c9dbf5"
+ }
+ },
+ "rng": {
+ "algorithm": null,
+ "seeds": []
+ },
+ "outputs": [
+ {
+ "path": "results/artifacts/amount-channel-survey-v1.json",
+ "bytes": 2589,
+ "sha256": "fcd4ad596d735b7c5a8dba43bc96481946ce5e78c3806932b60c10c865d1dcf8"
+ },
+ {
+ "path": "results/generated/amount-channel-survey-v1.md",
+ "bytes": 1082,
+ "sha256": "2f74b40fb24558a23f162d09da46a5f76a4434c170a78e0deb75eabba5d2ca28"
+ }
+ ],
+ "reproducibility": {
+ "level": "bitwise_reproducible",
+ "availability": "complete"
+ },
+ "verification": {
+ "mode": "exact",
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.amount_channel_summary",
+ "verify",
+ "--artifact",
+ "results/artifacts/amount-channel-survey-v1.json",
+ "--markdown",
+ "results/generated/amount-channel-survey-v1.md"
+ ],
+ "tests": [
+ "tests/test_amount_channel_summary_experiment.py"
+ ],
+ "properties": [
+ "all five component identities are checked before composition",
+ "the summary performs no mechanism calculation",
+ "channels remain separate rather than becoming a scalar score",
+ "DSS claims retain their restricted-family qualification",
+ "the generated Markdown equals the canonical rendering"
+ ],
+ "tolerance": null
+ },
+ "limitations": [
+ "this artifact composes upstream results and performs no mechanism calculation",
+ "component SHA-256 identities are mandatory",
+ "the five components measure different objects and are not combined into one score",
+ "DSS mapping and pairwise claims remain restricted to DSS's mapping family",
+ "the Bitcoin snapshot covers one 137-block interval from 2023",
+ "this summary is not a privacy certificate or CoinScore"
+ ]
+}
diff --git a/artifacts/sha256/06/9d207b44dcbbeaec0fae98262a3fcb76b3fe834834609e67fe883e229f26d3 b/artifacts/sha256/06/9d207b44dcbbeaec0fae98262a3fcb76b3fe834834609e67fe883e229f26d3
new file mode 100644
index 00000000..5728f65a
Binary files /dev/null and b/artifacts/sha256/06/9d207b44dcbbeaec0fae98262a3fcb76b3fe834834609e67fe883e229f26d3 differ
diff --git a/artifacts/sha256/06/ff8b5e8cbcbe3c4c47f6e5b6e5510b591526e40c31802240a33a05ef99ec4a b/artifacts/sha256/06/ff8b5e8cbcbe3c4c47f6e5b6e5510b591526e40c31802240a33a05ef99ec4a
new file mode 100644
index 00000000..5c0f67d8
--- /dev/null
+++ b/artifacts/sha256/06/ff8b5e8cbcbe3c4c47f6e5b6e5510b591526e40c31802240a33a05ef99ec4a
@@ -0,0 +1,13 @@
+{
+ "schema_version": 1,
+ "python": {
+ "implementation": "cpython",
+ "version": "3.13",
+ "system": "Darwin",
+ "machine": "arm64"
+ },
+ "source_archive": "sources/decluster-bcd9a3e-runtime.tar",
+ "project_root": "decluster",
+ "requirements": "reproduction/deterministic-partition/requirements.lock",
+ "dependency_directory": "reproduction/deterministic-partition"
+}
diff --git a/artifacts/sha256/08/376f7020b9d4d70c689748196cb83f7695f9b4e7b526702535e7644901039a b/artifacts/sha256/08/376f7020b9d4d70c689748196cb83f7695f9b4e7b526702535e7644901039a
new file mode 100644
index 00000000..bba1a52b
--- /dev/null
+++ b/artifacts/sha256/08/376f7020b9d4d70c689748196cb83f7695f9b4e7b526702535e7644901039a
@@ -0,0 +1,13 @@
+{
+ "schema_version": 1,
+ "python": {
+ "implementation": "cpython",
+ "version": "3.13",
+ "system": "Darwin",
+ "machine": "arm64"
+ },
+ "source_archive": "sources/decluster-bcd9a3e-runtime.tar",
+ "project_root": "decluster",
+ "requirements": "reproduction/partition-schemes/requirements.lock",
+ "dependency_directory": "reproduction/partition-schemes"
+}
diff --git a/artifacts/sha256/08/47af4617040b548ea9185fc274984825c2000f5549e46a8a7fee39168a5699 b/artifacts/sha256/08/47af4617040b548ea9185fc274984825c2000f5549e46a8a7fee39168a5699
new file mode 100644
index 00000000..0af51f60
--- /dev/null
+++ b/artifacts/sha256/08/47af4617040b548ea9185fc274984825c2000f5549e46a8a7fee39168a5699
@@ -0,0 +1,113 @@
+{
+ "schema_version": 1,
+ "id": "partition-schemes-v1",
+ "claim_ids": [
+ "ctp.social_graph.propagation",
+ "ctp.graph.pseudonym_representation"
+ ],
+ "code": {
+ "revision": "bcd9a3e511860b1b0ff5f3ee6338a2df29ecbfbf",
+ "dirty": false
+ },
+ "command": {
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.partition_schemes",
+ "--dataset",
+ "data/partition-cuts-300k-2016.ndjson.gz",
+ "reproduce",
+ "--artifact",
+ "results/artifacts/partition-schemes-v1.json",
+ "--markdown",
+ "results/generated/partition-schemes-v1.md"
+ ]
+ },
+ "environment": {
+ "python": "3.13.7",
+ "lock_digest": "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855",
+ "platform": "macOS-26.6.2-arm64-arm-64bit-Mach-O",
+ "dependencies": []
+ },
+ "datasets": [
+ {
+ "id": "partition-cuts-300k-2016-v1",
+ "sha256": "44192156107867a305e2f608af9185e4ce22fb88372238c88683f91120b1db16"
+ }
+ ],
+ "parameters": {
+ "core_fraction": 0.01,
+ "min_side": 2,
+ "schemes": [
+ "epoch",
+ "decore",
+ "collapse"
+ ],
+ "seed": 0,
+ "seed_shares": [
+ 0.05,
+ 0.1
+ ],
+ "view_counts": [
+ 2,
+ 3,
+ 4
+ ]
+ },
+ "rng": {
+ "algorithm": "python.random.Random",
+ "seeds": [
+ 0
+ ]
+ },
+ "outputs": [
+ {
+ "path": "results/artifacts/partition-schemes-v1.json",
+ "bytes": 2310,
+ "sha256": "a000b89fd2a3def18ab1336179c2f083690289bd90584e4c9d047d3f4c0241ee"
+ },
+ {
+ "path": "results/generated/partition-schemes-v1.md",
+ "bytes": 2364,
+ "sha256": "57d6d4d6155e20e2ba7537d22e014dc6cd8a78caf82d671fb11fe5318c03cea9"
+ }
+ ],
+ "reproducibility": {
+ "level": "bitwise_reproducible",
+ "availability": "complete"
+ },
+ "verification": {
+ "mode": "exact",
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.partition_schemes",
+ "--dataset",
+ "data/partition-cuts-300k-2016.ndjson.gz",
+ "verify",
+ "--artifact",
+ "results/artifacts/partition-schemes-v1.json",
+ "--markdown",
+ "results/generated/partition-schemes-v1.md"
+ ],
+ "tests": [
+ "tests/test_partition_schemes.py",
+ "tests/test_views.py"
+ ],
+ "properties": [
+ "every scheme returns exactly n_views views",
+ "decore halves the mean straddler degree and the population",
+ "collapse tracks the temporal baseline on both",
+ "a seed share that yields no guess reports no precision rather than zero",
+ "the generated Markdown equals the canonical rendering"
+ ],
+ "tolerance": null
+ },
+ "limitations": [
+ "the matcher recovers a handful of pairs at best, so the comparison rests on the straddler subgraph rather than on precision",
+ "one slice, one era, one 300,000-transaction prefix",
+ "cluster membership is a co-spend label, not wallet ownership",
+ "the export carries addresses but no values, so the collapse detector sees only the shape rule and never the de-mix arm",
+ "the result is not a privacy score"
+ ]
+}
diff --git a/artifacts/sha256/08/93000345e68249911cb8ee6467a47fa6feecd3022f27e2732bcabfb4725f11 b/artifacts/sha256/08/93000345e68249911cb8ee6467a47fa6feecd3022f27e2732bcabfb4725f11
new file mode 100644
index 00000000..b1f44701
Binary files /dev/null and b/artifacts/sha256/08/93000345e68249911cb8ee6467a47fa6feecd3022f27e2732bcabfb4725f11 differ
diff --git a/artifacts/sha256/09/1515f9c7eda9ba6fdff5f571ab005979a76a9f7a19cc7f44e568f1bab2fc7e b/artifacts/sha256/09/1515f9c7eda9ba6fdff5f571ab005979a76a9f7a19cc7f44e568f1bab2fc7e
new file mode 100644
index 00000000..6bb1e77f
--- /dev/null
+++ b/artifacts/sha256/09/1515f9c7eda9ba6fdff5f571ab005979a76a9f7a19cc7f44e568f1bab2fc7e
@@ -0,0 +1,13 @@
+{
+ "schema_version": 1,
+ "python": {
+ "implementation": "cpython",
+ "version": "3.13",
+ "system": "Darwin",
+ "machine": "arm64"
+ },
+ "source_archive": "sources/decluster-bcd9a3e-runtime.tar",
+ "project_root": "decluster",
+ "requirements": "reproduction/fingerprint-sparsity/requirements.lock",
+ "dependency_directory": "reproduction/fingerprint-sparsity"
+}
diff --git a/artifacts/sha256/09/62e258838b34a41978866d73a3a867968271cf8a2e7827db503f341adff78b b/artifacts/sha256/09/62e258838b34a41978866d73a3a867968271cf8a2e7827db503f341adff78b
new file mode 100644
index 00000000..8569bfdd
--- /dev/null
+++ b/artifacts/sha256/09/62e258838b34a41978866d73a3a867968271cf8a2e7827db503f341adff78b
@@ -0,0 +1 @@
+{"collaborative":{"inputs":2,"predicted_merges":1,"reference_conflicts":1},"composition":null,"conclusion":"CIOH merges both co-spends, but only the labelled collaborative fixture conflicts with its supplied ownership partition","experiment":"cioh-collaborative-false-merge-v1","limitations":["the deterministic fixture supplies participant ownership labels","the result demonstrates one false merge, not its chain-wide prevalence","a co-spend alone does not prove common ownership"],"same_owner_control":{"inputs":2,"predicted_merges":1,"reference_conflicts":0},"schema_version":1}
diff --git a/artifacts/sha256/09/d6caac4d26bfc0cb3c0c76152b28d8c853a6162ee054052b42995a5945c765 b/artifacts/sha256/09/d6caac4d26bfc0cb3c0c76152b28d8c853a6162ee054052b42995a5945c765
new file mode 100644
index 00000000..94ccbcc5
--- /dev/null
+++ b/artifacts/sha256/09/d6caac4d26bfc0cb3c0c76152b28d8c853a6162ee054052b42995a5945c765
@@ -0,0 +1,97 @@
+{
+ "schema_version": 1,
+ "id": "fused-engine-v1",
+ "claim_ids": [
+ "ctp.cioh.collaborative_false_merge",
+ "ctp.fingerprint.statistical_linkage"
+ ],
+ "code": {
+ "revision": "bcd9a3e511860b1b0ff5f3ee6338a2df29ecbfbf",
+ "dirty": false
+ },
+ "command": {
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.fused_engine",
+ "--snapshot",
+ "data/fs-blkcache-2026-09-04.tar.gz",
+ "reproduce",
+ "--artifact",
+ "results/artifacts/fused-engine-v1.json",
+ "--markdown",
+ "results/generated/fused-engine-v1.md"
+ ]
+ },
+ "datasets": [
+ {
+ "id": "fs-blkcache-2026-09-04-v1",
+ "sha256": "5a69f6f446dbdfcce09dddc91c653e5d841124d14020b720fdea276315c775a6"
+ }
+ ],
+ "parameters": {
+ "link_above": 4.0,
+ "cospend_prior_bits": 2.0,
+ "prov_refuse_bits": -3.0,
+ "provenance_depth": 1
+ },
+ "rng": {
+ "algorithm": "none",
+ "seeds": []
+ },
+ "reproducibility": {
+ "level": "bitwise_reproducible",
+ "availability": "complete"
+ },
+ "verification": {
+ "mode": "exact",
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.fused_engine",
+ "--snapshot",
+ "data/fs-blkcache-2026-09-04.tar.gz",
+ "verify",
+ "--artifact",
+ "results/artifacts/fused-engine-v1.json",
+ "--markdown",
+ "results/generated/fused-engine-v1.md"
+ ],
+ "tests": [
+ "tests/test_cluster_refined.py",
+ "tests/test_fused_engine_experiment.py"
+ ],
+ "properties": [
+ "the committed snapshot exactly reproduces every ladder row",
+ "the merge-only baseline refuses nothing and the engine refuses on every arm",
+ "cluster-level topology corroborates merges the fingerprint alone objected to",
+ "provenance-disjointness cuts pairs the topology arm kept",
+ "the amount and subset-sum channels are refuse-only and gated behind fingerprint disagreement",
+ "the result remains attacker-side evidence rather than a privacy score"
+ ],
+ "tolerance": null
+ },
+ "environment": {
+ "python": "3.13.7",
+ "lock_digest": "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855",
+ "platform": "macOS-26.6.2-arm64-arm-64bit-Mach-O",
+ "dependencies": []
+ },
+ "limitations": [
+ "The block-cache slice is block-sampled rather than contiguous, so provenance signatures are depth-1.",
+ "Cluster membership here is a co-spend relation, not observed wallet ownership.",
+ "The ladder reports what each channel moves on this slice; it does not estimate chain-wide prevalence."
+ ],
+ "outputs": [
+ {
+ "path": "results/artifacts/fused-engine-v1.json",
+ "bytes": 1019,
+ "sha256": "192890be723f3c135f61aa513c3effcfe426021c3e22e937a555c0970bb4f2d8"
+ },
+ {
+ "path": "results/generated/fused-engine-v1.md",
+ "bytes": 1968,
+ "sha256": "d31d64fb92d20b4d7df3fd88e750f4084dfc89e26807a8fe45b33361ae9b78d6"
+ }
+ ]
+}
diff --git a/artifacts/sha256/0b/1468ea321cefb42d66cb2f6eee78e458aedb2fb0f99c2b58c7da0c7ac7137a b/artifacts/sha256/0b/1468ea321cefb42d66cb2f6eee78e458aedb2fb0f99c2b58c7da0c7ac7137a
new file mode 100644
index 00000000..4024c6a9
--- /dev/null
+++ b/artifacts/sha256/0b/1468ea321cefb42d66cb2f6eee78e458aedb2fb0f99c2b58c7da0c7ac7137a
@@ -0,0 +1,98 @@
+{
+ "schema_version": 1,
+ "id": "route-capacity-v1",
+ "claim_ids": [
+ "ctp.robustness.counterfactual_paths",
+ "ctp.provenance.traceability"
+ ],
+ "code": {
+ "revision": "bcd9a3e511860b1b0ff5f3ee6338a2df29ecbfbf",
+ "dirty": false
+ },
+ "command": {
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.route_capacity",
+ "--snapshot",
+ "data/fs-blkcache-2026-09-04.tar.gz",
+ "reproduce",
+ "--artifact",
+ "results/artifacts/route-capacity-v1.json",
+ "--markdown",
+ "results/generated/route-capacity-v1.md"
+ ]
+ },
+ "datasets": [
+ {
+ "id": "fs-blkcache-2026-09-04-v1",
+ "sha256": "5a69f6f446dbdfcce09dddc91c653e5d841124d14020b720fdea276315c775a6"
+ }
+ ],
+ "parameters": {
+ "depth": 5,
+ "small_cut_at_most": 3,
+ "link_oracle": "value_flow_link_oracle",
+ "capacity_per_coin": 1,
+ "value_threshold": "the target coin's own value"
+ },
+ "rng": {
+ "algorithm": "none",
+ "seeds": []
+ },
+ "reproducibility": {
+ "level": "bitwise_reproducible",
+ "availability": "complete"
+ },
+ "verification": {
+ "mode": "exact",
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.route_capacity",
+ "--snapshot",
+ "data/fs-blkcache-2026-09-04.tar.gz",
+ "verify",
+ "--artifact",
+ "results/artifacts/route-capacity-v1.json",
+ "--markdown",
+ "results/generated/route-capacity-v1.md"
+ ],
+ "tests": [
+ "tests/test_disjoint_routes.py",
+ "tests/test_route_capacity_experiment.py"
+ ],
+ "properties": [
+ "the committed cache exactly reproduces both capacity distributions",
+ "routes share no coin beyond the target, and a shared transaction is not a shared coin",
+ "the origin set overstates redundancy for most coins, which is why the cut is measured",
+ "pruning to routes that could carry the coin raises the single-coin cut, and is reported beside it",
+ "every cut is a lower bound: the boundary the walk stopped at is recorded beside it",
+ "value enters twice: as a threshold a route passes or fails, and as the capacity of a second flow whose cut is the value a separation removes"
+ ],
+ "tolerance": null
+ },
+ "environment": {
+ "python": "3.13.7",
+ "lock_digest": "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855",
+ "platform": "macOS-26.6.2-arm64-arm-64bit-Mach-O",
+ "dependencies": []
+ },
+ "limitations": [
+ "Almost every origin reached is the edge of the committed slice, not a coinbase; a contiguous export can only raise these cuts.",
+ "Routes are counted and, separately, valued; what is not solved is which routes carry how much, the strongly NP-hard k-splittable formulation.",
+ "The walk depth is fixed at 5 transactions."
+ ],
+ "outputs": [
+ {
+ "path": "results/artifacts/route-capacity-v1.json",
+ "bytes": 1445,
+ "sha256": "e2bad958dea37ae3a7eacaa6e0f77fa88b2ad0378d854f31592ce76ecbe353f0"
+ },
+ {
+ "path": "results/generated/route-capacity-v1.md",
+ "bytes": 3907,
+ "sha256": "5602b6166b55f7f7366a7eb9cd5603c95a6d2073bf95828cebd47b5c82504022"
+ }
+ ]
+}
diff --git a/artifacts/sha256/0c/bd76502a1a77f11cc72d8834d4d9f7fc2ba2fffa1bbf33d44c4f075652a47b b/artifacts/sha256/0c/bd76502a1a77f11cc72d8834d4d9f7fc2ba2fffa1bbf33d44c4f075652a47b
new file mode 100644
index 00000000..18677472
--- /dev/null
+++ b/artifacts/sha256/0c/bd76502a1a77f11cc72d8834d4d9f7fc2ba2fffa1bbf33d44c4f075652a47b
@@ -0,0 +1 @@
+{"dataset":"fs-blkcache-2026-09-04-v1","experiment":"em-m-v1","historical_comparison":{"reason":"the preserved 22,112-transaction snapshot is not the historical 165,832-transaction cache","status":"not_reproduced"},"limitations":["address reuse is a weak and partly feature-dependent ownership proxy","the balanced selected sample does not represent population prevalence","EM is fitted and evaluated on the same pair sample","the model assumes conditional independence between correlated axes","u is fixed at the measured collision rate","the small AUC difference is measured without a pre-registered separability verdict","this is attacker-side parameter diagnosis, not CoinScore or a privacy certificate"],"measurement":{"em_minus_fixed_auc":0.0009000000000000119,"fit":{"inferred_pair_fraction":0.6639562138569092,"iterations":21,"posterior_auc_against_address_reuse":0.932},"library_scorer_auc":{"address_reuse_agreement":0.9546,"em":0.92535,"fixed_0_95":0.92445},"pairs":{"negative":4000,"positive":4000},"per_axis":[{"address_reuse_agreement":0.98075,"axis":"nsequence","collision":0.3965560341699342,"em":0.8511166236884449,"fixed_assumption":0.95},{"address_reuse_agreement":0.9995,"axis":"locktime","collision":0.6182534217108192,"em":0.9283368897025305,"fixed_assumption":0.95},{"address_reuse_agreement":0.7663456664977192,"axis":"input_order","collision":0.5282888734301753,"em":0.7688637958822665,"fixed_assumption":0.95},{"address_reuse_agreement":0.5979103471520054,"axis":"output_order","collision":0.3372006451199894,"em":0.505342852993097,"fixed_assumption":0.95},{"address_reuse_agreement":0.81225,"axis":"change_spk","collision":0.378434346868815,"em":0.7426082475264336,"fixed_assumption":0.95},{"address_reuse_agreement":0.99675,"axis":"version","collision":0.5346816519684303,"em":0.8923310281944659,"fixed_assumption":0.95},{"address_reuse_agreement":0.6202450032237267,"axis":"io_shape","collision":0.20372996306959482,"em":0.543342181791182,"fixed_assumption":0.95},{"address_reuse_agreement":0.91475,"axis":"uih","collision":0.8536414295210479,"em":0.9224280927609649,"fixed_assumption":0.95},{"address_reuse_agreement":0.962,"axis":"fee_rate","collision":0.7082946392465704,"em":0.958237482619391,"fixed_assumption":0.95},{"address_reuse_agreement":0.9692269201901426,"axis":"input_script_type","collision":0.3775091158197063,"em":0.999999,"fixed_assumption":0.95},{"address_reuse_agreement":0.79975,"axis":"output_encoding","collision":0.6140442327748129,"em":0.759170939844021,"fixed_assumption":0.95},{"address_reuse_agreement":0.968477703741671,"axis":"input_types_present","collision":0.3773038531919157,"em":0.999999,"fixed_assumption":0.95},{"address_reuse_agreement":0.7465,"axis":"change_index","collision":0.5552437345136181,"em":0.748293185217357,"fixed_assumption":0.95},{"address_reuse_agreement":0.7735,"axis":"change_type_match","collision":0.5842472884656746,"em":0.7720901469099556,"fixed_assumption":0.95},{"address_reuse_agreement":0.75875,"axis":"change_matches_output","collision":0.5601290430217203,"em":0.7588628287261019,"fixed_assumption":0.95},{"address_reuse_agreement":0.867,"axis":"change_address_reuse","collision":0.6502137908886876,"em":0.7980906501052633,"fixed_assumption":0.95},{"address_reuse_agreement":0.72075,"axis":"low_r","collision":0.4773790239598302,"em":0.6905656160166325,"fixed_assumption":0.95},{"address_reuse_agreement":0.9844416562107905,"axis":"sighash","collision":0.45625534504142534,"em":0.9939714861741844,"fixed_assumption":0.95},{"address_reuse_agreement":0.9965,"axis":"op_return","collision":0.886609246290655,"em":0.9780339672131558,"fixed_assumption":0.95},{"address_reuse_agreement":0.99125,"axis":"nested_segwit","collision":0.8027327555564433,"em":0.999999,"fixed_assumption":0.95},{"address_reuse_agreement":0.989,"axis":"pubkey_compression","collision":0.6043962005553807,"em":0.999999,"fixed_assumption":0.95},{"address_reuse_agreement":0.99175,"axis":"multisig","collision":0.8865024296789842,"em":0.9946066511297224,"fixed_assumption":0.95},{"address_reuse_agreement":null,"axis":"locktime_vs_broadcast","collision":0.7563732010755665,"em":0.9,"fixed_assumption":0.95}],"transactions":22112},"parameters":{"fixed_u":true,"pair_cap_per_class":4000,"seed":0},"schema_version":1,"source":{"bytes":91367352,"digest":"91c444bd4e9715d7739ccc9d68c7d9751762e25b647e3eef647c6ec0b48ba90c","files":904,"pattern":".blkcache/*.json","transactions":22112}}
diff --git a/artifacts/sha256/0c/d5877aee284f8a234688c93b586d9f9e556a49f31c2c0605fa53be1d994553 b/artifacts/sha256/0c/d5877aee284f8a234688c93b586d9f9e556a49f31c2c0605fa53be1d994553
new file mode 100644
index 00000000..eafe01ba
--- /dev/null
+++ b/artifacts/sha256/0c/d5877aee284f8a234688c93b586d9f9e556a49f31c2c0605fa53be1d994553
@@ -0,0 +1 @@
+{"capabilities":{"depths_one_through_five_return":true,"deterministic_value_flow_default":true,"offline_transaction_input":true,"subjective_fusion_never_widens":true,"truncation_is_reported":true},"change_fusion":{"fused_min_entropy_bits":0.15200309344505,"provenance_min_entropy_bits":1.0,"sharpened":true},"depth_sweep":{"1":{"fused":{"min_entropy_bits":1.0,"shannon_bits":1.0},"provenance":{"min_entropy_bits":1.0,"n_absorbers":2,"shannon_bits":1.0},"truncated":0},"2":{"fused":{"min_entropy_bits":1.0,"shannon_bits":1.0},"provenance":{"min_entropy_bits":1.0,"n_absorbers":2,"shannon_bits":1.0},"truncated":0},"3":{"fused":{"min_entropy_bits":1.0,"shannon_bits":1.0},"provenance":{"min_entropy_bits":1.0,"n_absorbers":2,"shannon_bits":1.0},"truncated":0},"4":{"fused":{"min_entropy_bits":1.0,"shannon_bits":1.0},"provenance":{"min_entropy_bits":1.0,"n_absorbers":2,"shannon_bits":1.0},"truncated":0},"5":{"fused":{"min_entropy_bits":1.0,"shannon_bits":1.0},"provenance":{"min_entropy_bits":1.0,"n_absorbers":2,"shannon_bits":1.0},"truncated":0}},"experiment":"analyze-contract-v1","fixture":{"targets":[0,1],"transactions":5},"limitations":["the fixture is synthetic and validates the public facade contract","the run does not reproduce the historical live Bitcoin measurements","address reuse is a heuristic subjective signal, not verified ownership","provenance entropy is not a privacy certificate or CoinScore","the path_count response key is retained for API compatibility only"],"node_cap":{"cap_is_observable":true,"capped_truncations":2,"uncapped_truncations":0},"route_diagnostic":{"canonical_meaning":"provenance_route_accumulation","present_when_requested":true},"schema_version":1}
diff --git a/artifacts/sha256/0d/79d8d6b5a601c66c88be9b3432b54ea664492d29c4c3b14224547337f6802b b/artifacts/sha256/0d/79d8d6b5a601c66c88be9b3432b54ea664492d29c4c3b14224547337f6802b
new file mode 100644
index 00000000..1e9f8a7c
--- /dev/null
+++ b/artifacts/sha256/0d/79d8d6b5a601c66c88be9b3432b54ea664492d29c4c3b14224547337f6802b
@@ -0,0 +1,11 @@
+# Path-count implementation contract
+
+Generated from the canonical experiment artifact. Do not edit manually.
+
+The ancestry and path-count distributions are identical on the fixed two-hop DAG: `{'origin-a:0': 0.62, 'origin-b:0': 0.38}`.
+
+The resulting min-entropy is 0.689660 bits and Shannon entropy is 0.958042 bits. Changing the supplied count oracle from log W = 0 to log W = ln(100) leaves the distribution unchanged: `True`.
+
+Route accumulation is implemented: `True`. Edge-disjoint path enumeration, plausible-flow capacity and k-routes are implemented: `True`, `True`, `True`.
+
+This is a provenance-route diagnostic, not CTP robust connectivity or CoinScore.
diff --git a/artifacts/sha256/0e/1d146555a0913181370e4a0145cb38bebd5f43ee938fdfbd2a18e30cd4fa81 b/artifacts/sha256/0e/1d146555a0913181370e4a0145cb38bebd5f43ee938fdfbd2a18e30cd4fa81
new file mode 100644
index 00000000..0cea789e
--- /dev/null
+++ b/artifacts/sha256/0e/1d146555a0913181370e4a0145cb38bebd5f43ee938fdfbd2a18e30cd4fa81
@@ -0,0 +1 @@
+{"dataset":"fs-blkcache-2026-09-04-v1","experiment":"bayes-vs-fs-v1","historical_comparison":{"reason":"the preserved snapshot produces materially different metrics from the historical Markdown","status":"not_reproduced"},"limitations":["address reuse is a weak and partly feature-dependent ownership proxy","the balanced positive-negative sample does not represent population prevalence","parameters are fitted and evaluated on the same selected pair sample","the model assumes conditional independence between correlated axes","u is fixed at the measured collision rate","cluster intervals propagate pair probabilities independently and are not a joint partition posterior","borderline nodes are purpose-selected and neither cluster diagnostic is chain-wide","this is attacker-side record-linkage evidence, not CoinScore or a privacy certificate"],"measurement":{"cluster_diagnostics":{"borderline_selected":{"bayesian_interval":[5,6],"bayesian_mean":5.1,"fellegi_sunter_em_point":5,"nodes":18},"clear_reuse_selected":{"bayesian_interval":[4,4],"bayesian_mean":4.0,"fellegi_sunter_em_point":5,"nodes":50}},"discrimination":[{"auc":0.91625,"ece":0.1455409397971025,"scorer":"fellegi_sunter_fixed_0_95"},{"auc":0.932,"ece":0.17495151311570117,"scorer":"fellegi_sunter_em"},{"auc":0.9324,"ece":0.19361081891398324,"scorer":"bayesian_integrated_m"}],"pairs":{"negative":4000,"positive":4000},"per_axis":[{"address_reuse_agreement":0.98075,"axis":"nsequence","bayesian_interval":[0.840150880343455,0.8628984222144184],"bayesian_mean":0.8515079092642986,"em":0.8511166236884449,"fixed_assumption":0.95},{"address_reuse_agreement":0.9995,"axis":"locktime","bayesian_interval":[0.9205149638049283,0.9362289436339398],"bayesian_mean":0.928540982600861,"em":0.9283368897025305,"fixed_assumption":0.95},{"address_reuse_agreement":0.7663456664977192,"axis":"input_order","bayesian_interval":[0.7570527059144246,0.7810204570943552],"bayesian_mean":0.7690051838326389,"em":0.7688637958822665,"fixed_assumption":0.95},{"address_reuse_agreement":0.5979103471520054,"axis":"output_order","bayesian_interval":[0.49114500214381523,0.5226795512270594],"bayesian_mean":0.5067359631072426,"em":0.505342852993097,"fixed_assumption":0.95},{"address_reuse_agreement":0.81225,"axis":"change_spk","bayesian_interval":[0.7305384941742514,0.7567678791954454],"bayesian_mean":0.7432305080011159,"em":0.7426082475264336,"fixed_assumption":0.95},{"address_reuse_agreement":0.99675,"axis":"version","bayesian_interval":[0.8832826521602977,0.901738602981178],"bayesian_mean":0.8926588742579719,"em":0.8923310281944659,"fixed_assumption":0.95},{"address_reuse_agreement":0.6202450032237267,"axis":"io_shape","bayesian_interval":[0.5299867254244497,0.5591938873274206],"bayesian_mean":0.5443446651145338,"em":0.543342181791182,"fixed_assumption":0.95},{"address_reuse_agreement":0.91475,"axis":"uih","bayesian_interval":[0.9149446971257557,0.929534684092982],"bayesian_mean":0.9223228677360467,"em":0.9224280927609649,"fixed_assumption":0.95},{"address_reuse_agreement":0.962,"axis":"fee_rate","bayesian_interval":[0.9521508270326697,0.9640104900671612],"bayesian_mean":0.9581086254848944,"em":0.958237482619391,"fixed_assumption":0.95},{"address_reuse_agreement":0.9692269201901426,"axis":"input_script_type","bayesian_interval":[0.9990753925119412,0.9999810927800344],"bayesian_mean":0.9997148315169824,"em":0.999999,"fixed_assumption":0.95},{"address_reuse_agreement":0.79975,"axis":"output_encoding","bayesian_interval":[0.7476950377823447,0.7711174968380187],"bayesian_mean":0.7595834325762767,"em":0.759170939844021,"fixed_assumption":0.95},{"address_reuse_agreement":0.968477703741671,"axis":"input_types_present","bayesian_interval":[0.9991201328463265,0.9999787923769962],"bayesian_mean":0.9997174763723115,"em":0.999999,"fixed_assumption":0.95},{"address_reuse_agreement":0.7465,"axis":"change_index","bayesian_interval":[0.7370393439869989,0.760540685629775],"bayesian_mean":0.7487954288075545,"em":0.748293185217357,"fixed_assumption":0.95},{"address_reuse_agreement":0.7735,"axis":"change_type_match","bayesian_interval":[0.7606357696452869,0.7839364183290227],"bayesian_mean":0.772562813508951,"em":0.7720901469099556,"fixed_assumption":0.95},{"address_reuse_agreement":0.75875,"axis":"change_matches_output","bayesian_interval":[0.7480545281505018,0.7714700717194043],"bayesian_mean":0.7595003280180012,"em":0.7588628287261019,"fixed_assumption":0.95},{"address_reuse_agreement":0.867,"axis":"change_address_reuse","bayesian_interval":[0.7868597034350518,0.8099432456634836],"bayesian_mean":0.7984189990320301,"em":0.7980906501052633,"fixed_assumption":0.95},{"address_reuse_agreement":0.72075,"axis":"low_r","bayesian_interval":[0.6774173950186996,0.7031816820496115],"bayesian_mean":0.6909236430993329,"em":0.6905656160166325,"fixed_assumption":0.95},{"address_reuse_agreement":0.9844416562107905,"axis":"sighash","bayesian_interval":[0.9912459574921524,0.9956258640950216],"bayesian_mean":0.9936670185191641,"em":0.9939714861741844,"fixed_assumption":0.95},{"address_reuse_agreement":0.9965,"axis":"op_return","bayesian_interval":[0.9732293760348502,0.9818529412250564],"bayesian_mean":0.9778435652962806,"em":0.9780339672131558,"fixed_assumption":0.95},{"address_reuse_agreement":0.99125,"axis":"nested_segwit","bayesian_interval":[0.9990801570277706,0.9999803235896076],"bayesian_mean":0.9997114947506842,"em":0.999999,"fixed_assumption":0.95},{"address_reuse_agreement":0.989,"axis":"pubkey_compression","bayesian_interval":[0.9991437808766463,0.9999813035347956],"bayesian_mean":0.9997338094193687,"em":0.999999,"fixed_assumption":0.95},{"address_reuse_agreement":0.99175,"axis":"multisig","bayesian_interval":[0.9921771007183748,0.9961713681110425],"bayesian_mean":0.9943503142346654,"em":0.9946066511297224,"fixed_assumption":0.95},{"address_reuse_agreement":null,"axis":"locktime_vs_broadcast","bayesian_interval":[0.5985319437765807,0.9881793640631306],"bayesian_mean":0.8513292960792788,"em":0.9,"fixed_assumption":0.95}],"transactions":22112},"parameters":{"beta_prior":[8.5,1.5],"gibbs_burn":500,"gibbs_samples":2000,"pair_cap_per_class":4000,"seed":0},"schema_version":1,"source":{"bytes":91367352,"digest":"91c444bd4e9715d7739ccc9d68c7d9751762e25b647e3eef647c6ec0b48ba90c","files":904,"pattern":".blkcache/*.json","transactions":22112}}
diff --git a/artifacts/sha256/0e/d138db94e84b2ec2b4d07c7831a20d4fb3be59ae6b5d52685983df92a6cc02 b/artifacts/sha256/0e/d138db94e84b2ec2b4d07c7831a20d4fb3be59ae6b5d52685983df92a6cc02
new file mode 100644
index 00000000..29ba12ab
--- /dev/null
+++ b/artifacts/sha256/0e/d138db94e84b2ec2b4d07c7831a20d4fb3be59ae6b5d52685983df92a6cc02
@@ -0,0 +1 @@
+{"dataset":"graph-deanon-2016-v1","experiment":"graph-deanon-controls-v1","limitations":["cluster membership is a co-spend label, not wallet ownership","one selected 2016 fixture, not a chain-wide sample","the degree-matched control changes the negative population, not the positives","a fall under matching bounds how much of the AUC was degree asymmetry; it does not establish what the remainder is","neither number is a re-identification rate or a privacy score"],"measurement":{"addresses":27962,"clusters_with_two_or_more":2463,"published_negative_pairs":5000,"shuffled_label_auc":0.49935,"transactions":8927,"views":{"full":{"degree_matched_auc":0.9783676882879442,"degree_only_auc_under_matching":0.5478469739055631,"fall_under_matching":0.012807311712055847,"matched_negative_pairs":221289,"positive_pairs":267578,"published_auc":0.991175},"payment":{"degree_matched_auc":0.9153923094980694,"degree_only_auc_under_matching":0.5000018280610434,"fall_under_matching":0.035257690501930616,"matched_negative_pairs":267577,"positive_pairs":267578,"published_auc":0.95065}}},"parameters":{"negative_sampling_control":"degree-matched to each positive","negative_sampling_published":"uniform over non-co-spent pairs, capped","score":"rarity-weighted shared-neighbour structure","seed":0},"schema_version":1}
diff --git a/artifacts/sha256/0f/ef54735522470634c696ec026c31c86dff4d4dae23da956b49d8f89eb3a508 b/artifacts/sha256/0f/ef54735522470634c696ec026c31c86dff4d4dae23da956b49d8f89eb3a508
new file mode 100644
index 00000000..7f401f7c
--- /dev/null
+++ b/artifacts/sha256/0f/ef54735522470634c696ec026c31c86dff4d4dae23da956b49d8f89eb3a508
@@ -0,0 +1 @@
+{"experiment":"counting-fee-sensitivity-v1","fixture":{"inputs_sat":[400000,300000,300000],"output_total_before_fee_sat":1000000},"limitations":["this is a deterministic sensitivity fixture, not a population estimate","the transaction-level subset-sum count and fee-aware per-coin count are different objects","an absent per-coin index is a refusal to measure, not zero ambiguity","these diagnostics are not mapping entropy, a privacy score, or CoinScore"],"observations":[{"cascade":{"count":2,"kind":"exact","log_w":0.6931471805599453,"method":"brute"},"fee_sat":0,"measured_input_indices":[0,1,2],"outputs_sat":[600000,400000],"per_coin_log_w":{"0":0.6931471805599453,"1":0.6931471805599453,"2":0.6931471805599453},"router":{"count":2,"kind":"exact","log_w":0.6931471805599453,"method":"sparse"}},{"cascade":{"count":1,"kind":"exact","log_w":0.0,"method":"brute"},"fee_sat":1,"measured_input_indices":[1,2],"outputs_sat":[600000,399999],"per_coin_log_w":{"1":0.0,"2":0.0},"router":{"count":1,"kind":"exact","log_w":0.0,"method":"sparse"}},{"cascade":{"count":1,"kind":"exact","log_w":0.0,"method":"brute"},"fee_sat":10,"measured_input_indices":[],"outputs_sat":[600000,399990],"per_coin_log_w":{},"router":{"count":1,"kind":"exact","log_w":0.0,"method":"sparse"}},{"cascade":{"count":1,"kind":"exact","log_w":0.0,"method":"brute"},"fee_sat":100,"measured_input_indices":[],"outputs_sat":[600000,399900],"per_coin_log_w":{},"router":{"count":1,"kind":"exact","log_w":0.0,"method":"sparse"}},{"cascade":{"count":1,"kind":"exact","log_w":0.0,"method":"brute"},"fee_sat":1000,"measured_input_indices":[],"outputs_sat":[600000,399000],"per_coin_log_w":{},"router":{"count":1,"kind":"exact","log_w":0.0,"method":"sparse"}},{"cascade":{"count":1,"kind":"exact","log_w":0.0,"method":"brute"},"fee_sat":4956,"measured_input_indices":[],"outputs_sat":[600000,395044],"per_coin_log_w":{},"router":{"count":1,"kind":"exact","log_w":0.0,"method":"sparse"}}],"schema_version":1}
diff --git a/artifacts/sha256/11/f0ec645ea1be334dc7d24fbfc31047b234f335f48b5a514960472c7444b7d6 b/artifacts/sha256/11/f0ec645ea1be334dc7d24fbfc31047b234f335f48b5a514960472c7444b7d6
new file mode 100644
index 00000000..18cf9900
--- /dev/null
+++ b/artifacts/sha256/11/f0ec645ea1be334dc7d24fbfc31047b234f335f48b5a514960472c7444b7d6
@@ -0,0 +1 @@
+{"corpus":"public Bitcoin transaction snapshot","experiment":"amount-mapping-family-v1","limitations":["DSS enumerates a strict sub-family of the full balanced mappings","entropy is uniform over that restricted family, not a posterior over ownership","certain links are certain only within the restricted family","refusal, timeout, and execution failure are distinct outcomes","the sample is the first complete multi-input transactions in one 137-block snapshot","this diagnostic is not a privacy score or CoinScore"],"parameters":{"cap":250,"wall_seconds_per_transaction":3.0},"report":{"outcomes":{"answered":223,"failed":0,"refused":26,"timed_out":1},"population":{"selected_transactions":250,"selection":"first complete multi-input transactions in source order"},"restricted_family":{"certain_input_output_links":1471,"entropy_bins_bits":{"above_four":0,"above_zero_to_four":2,"zero":221},"non_derived_mappings":227}},"schema_version":1}
diff --git a/artifacts/sha256/12/23ce1528c4136d78fb727cc9bc81043af15d47562b7d215973a37b386fbaf4 b/artifacts/sha256/12/23ce1528c4136d78fb727cc9bc81043af15d47562b7d215973a37b386fbaf4
new file mode 100644
index 00000000..62252247
--- /dev/null
+++ b/artifacts/sha256/12/23ce1528c4136d78fb727cc9bc81043af15d47562b7d215973a37b386fbaf4
@@ -0,0 +1 @@
+{"components":[{"component":"algorithm1_similarity","limitation":null,"status":"implemented"},{"component":"algorithm2_positive_weights","limitation":null,"status":"implemented"},{"component":"algorithm2_dummy_weights","limitation":"a dummy-incident node term is fixed at zero; the paper states this in prose, not in the printed formula","status":"implemented"},{"component":"annealing","limitation":"iteration budget, RNG and best-state return are explicit local controls","status":"partial"},{"component":"two_stage_mapping","limitation":"the paper picks an arbitrary unmapped node and stage 1 feeds back, so the ``repr`` order this driver imposes is a result-bearing local choice; injectivity comes from Algorithm 3's 1-1 mapping, not from the propagation section","status":"implemented"},{"component":"confidence_pruning","limitation":"an implementation-complete pruning policy is not available","status":"not_reproduced"},{"component":"accepted_mapping_correction","limitation":"schedule and conflict resolution are not specified sufficiently","status":"not_reproduced"},{"component":"algorithm3_cascade","limitation":null,"status":"implemented"},{"component":"learned_25_feature_model","limitation":"the caller supplies an ML score; the published feature producer is absent","status":"not_reproduced"}],"composition":null,"conclusion":"implemented components do not constitute the complete published pipeline","end_to_end_reproduced":false,"experiment":"ns-link-prediction-coverage-v2","implemented_gate":{"accepted":true,"components":["algorithm1_similarity","algorithm2_positive_weights","algorithm2_dummy_weights","two_stage_mapping","algorithm3_cascade"]},"limitations":["the published Flickr/Kaggle corpus and reported metrics are not reproduced","the zero convention for dummy-incident terms is read from the paper's prose, not from its formula","annealing uses explicit local reproducibility controls","confidence pruning, accepted-mapping correction and the learned 25-feature model are not reproduced"],"paper":"Narayanan-Shi-Rubinstein-2011","refusal_controls":{"accepted_mapping_correction":"N-S 2011 pipeline components unavailable: accepted_mapping_correction=not_reproduced","annealing":"N-S 2011 pipeline components unavailable: annealing=partial","confidence_pruning":"N-S 2011 pipeline components unavailable: confidence_pruning=not_reproduced","learned_25_feature_model":"N-S 2011 pipeline components unavailable: learned_25_feature_model=not_reproduced"},"schema_version":1}
diff --git a/artifacts/sha256/13/269f33b9bee5d6af9fca19bf98bbdd2408f018cfef9d8a53467f84ff2b51e5 b/artifacts/sha256/13/269f33b9bee5d6af9fca19bf98bbdd2408f018cfef9d8a53467f84ff2b51e5
new file mode 100644
index 00000000..354cd75f
--- /dev/null
+++ b/artifacts/sha256/13/269f33b9bee5d6af9fca19bf98bbdd2408f018cfef9d8a53467f84ff2b51e5
@@ -0,0 +1,90 @@
+{
+ "schema_version": 1,
+ "id": "statistical-disclosure-v1",
+ "claim_ids": [
+ "ctp.disclosure.statistical_longitudinal"
+ ],
+ "code": {
+ "revision": "bcd9a3e511860b1b0ff5f3ee6338a2df29ecbfbf",
+ "dirty": false
+ },
+ "command": {
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.statistical_disclosure",
+ "reproduce",
+ "--artifact",
+ "results/artifacts/statistical-disclosure-v1.json",
+ "--markdown",
+ "results/generated/statistical-disclosure-v1.md"
+ ]
+ },
+ "environment": {
+ "python": "3.13.7",
+ "lock_digest": "e0d6a8cada5d383c0aca2bd2a37f28c15721392469c94084128d369c29c5544d",
+ "platform": "macOS-26.6.2-arm64-arm-64bit-Mach-O",
+ "dependencies": []
+ },
+ "datasets": [],
+ "parameters": {
+ "batch_size": 4,
+ "correspondents": 4,
+ "observations_per_scenario": 4,
+ "scenarios": [
+ "persistent-correspondent",
+ "background-matched-control"
+ ]
+ },
+ "rng": {
+ "algorithm": null,
+ "seeds": []
+ },
+ "outputs": [
+ {
+ "path": "results/artifacts/statistical-disclosure-v1.json",
+ "bytes": 1210,
+ "sha256": "1a2ac2247a71136681b765e368b88e2686b4070039efd37b0a262a8ce6e29515"
+ },
+ {
+ "path": "results/generated/statistical-disclosure-v1.md",
+ "bytes": 682,
+ "sha256": "f2c946337645978c160b5ff30321be082e46576608a54aae761d3e137ffd74b7"
+ }
+ ],
+ "reproducibility": {
+ "level": "bitwise_reproducible",
+ "availability": "complete"
+ },
+ "verification": {
+ "mode": "exact",
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.statistical_disclosure",
+ "verify",
+ "--artifact",
+ "results/artifacts/statistical-disclosure-v1.json",
+ "--markdown",
+ "results/generated/statistical-disclosure-v1.md"
+ ],
+ "tests": [
+ "tests/test_statistical_disclosure.py",
+ "tests/test_statistical_disclosure_experiment.py"
+ ],
+ "properties": [
+ "the estimator implements equation 9.2 for the declared threshold-mix model",
+ "repeated target-active rounds recover the supplied persistent correspondent",
+ "the background-matched control has no unique top correspondent",
+ "correspondent evidence is not converted to Bitcoin ownership attribution",
+ "the generated Markdown equals the canonical rendering"
+ ],
+ "tolerance": null
+ },
+ "limitations": [
+ "deterministic synthetic threshold-mix fixture",
+ "target-active rounds and the background distribution are supplied",
+ "the fixture does not reproduce the cited paper's empirical simulations",
+ "the observation model is not a direct model of the Bitcoin transaction graph"
+ ]
+}
diff --git a/artifacts/sha256/13/e61965b56600be3e2ebcf51518ff0ba0b90b97428d10f5b11d4ca86cc1b34e b/artifacts/sha256/13/e61965b56600be3e2ebcf51518ff0ba0b90b97428d10f5b11d4ca86cc1b34e
new file mode 100644
index 00000000..b02c479b
--- /dev/null
+++ b/artifacts/sha256/13/e61965b56600be3e2ebcf51518ff0ba0b90b97428d10f5b11d4ca86cc1b34e
@@ -0,0 +1,13 @@
+{
+ "schema_version": 1,
+ "python": {
+ "implementation": "cpython",
+ "version": "3.13",
+ "system": "Darwin",
+ "machine": "arm64"
+ },
+ "source_archive": "sources/decluster-bcd9a3e-runtime.tar",
+ "project_root": "decluster",
+ "requirements": "reproduction/same-block-linkage/requirements.lock",
+ "dependency_directory": "reproduction/same-block-linkage"
+}
diff --git a/artifacts/sha256/14/2f9dc03f39ab280a77e07c6f4e85b04d5e77b411a9c3cdb476d955280d6cfe b/artifacts/sha256/14/2f9dc03f39ab280a77e07c6f4e85b04d5e77b411a9c3cdb476d955280d6cfe
new file mode 100644
index 00000000..219fd192
--- /dev/null
+++ b/artifacts/sha256/14/2f9dc03f39ab280a77e07c6f4e85b04d5e77b411a9c3cdb476d955280d6cfe
@@ -0,0 +1 @@
+{"experiment":"conservation-round-three-v1","fixture":{"inputs":454,"known_participant_input":4712126860,"outputs":502,"total_input":9000971062,"txid":"80f11c778a2f486ffc55b4e8665a94971ae9c350ac53969e9efcbf90478cbf90"},"interpretation":"forced provenance of value under conservation","largest_forced_denomination":{"forced":2,"forced_value":1549681956,"margin":360201666,"present":7,"value":774840978},"limitations":["the fixture is a reduced representation of one mainnet transaction","the run does not reproduce the surrounding six-round chain","the known participant input is supplied by an external forward walk","conservation establishes provenance of value, not output ownership","net settlement invalidates an ownership inference from this bound","the result is not a privacy score or 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diff --git a/artifacts/sha256/15/e383eb072739fae9ec3a31cd1d9feb4ba9d24c345046049b082fe69f3b6407 b/artifacts/sha256/15/e383eb072739fae9ec3a31cd1d9feb4ba9d24c345046049b082fe69f3b6407
new file mode 100644
index 00000000..55cd21d7
--- /dev/null
+++ b/artifacts/sha256/15/e383eb072739fae9ec3a31cd1d9feb4ba9d24c345046049b082fe69f3b6407
@@ -0,0 +1,86 @@
+{
+ "schema_version": 1,
+ "id": "cioh-collaborative-false-merge-v1",
+ "claim_ids": [
+ "ctp.cioh.collaborative_false_merge"
+ ],
+ "code": {
+ "revision": "bcd9a3e511860b1b0ff5f3ee6338a2df29ecbfbf",
+ "dirty": false
+ },
+ "command": {
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.cioh_collaborative",
+ "reproduce",
+ "--artifact",
+ "results/artifacts/cioh-collaborative-false-merge-v1.json",
+ "--markdown",
+ "results/generated/cioh-collaborative-false-merge-v1.md"
+ ]
+ },
+ "environment": {
+ "python": "3.13.7",
+ "lock_digest": "e0d6a8cada5d383c0aca2bd2a37f28c15721392469c94084128d369c29c5544d",
+ "platform": "macOS-26.6.2-arm64-arm-64bit-Mach-O",
+ "dependencies": []
+ },
+ "datasets": [],
+ "parameters": {
+ "collaborative_inputs": 2,
+ "same_owner_control_inputs": 2,
+ "ownership_partition": "supplied_fixture_labels"
+ },
+ "rng": {
+ "algorithm": null,
+ "seeds": []
+ },
+ "outputs": [
+ {
+ "path": "results/artifacts/cioh-collaborative-false-merge-v1.json",
+ "bytes": 583,
+ "sha256": "0962e258838b34a41978866d73a3a867968271cf8a2e7827db503f341adff78b"
+ },
+ {
+ "path": "results/generated/cioh-collaborative-false-merge-v1.md",
+ "bytes": 550,
+ "sha256": "556b2c5c9355032d65b0939613b33d01d0aa3daa2d89c3b3143d8110ba6b2a8a"
+ }
+ ],
+ "reproducibility": {
+ "level": "bitwise_reproducible",
+ "availability": "complete"
+ },
+ "verification": {
+ "mode": "exact",
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.cioh_collaborative",
+ "verify",
+ "--artifact",
+ "results/artifacts/cioh-collaborative-false-merge-v1.json",
+ "--markdown",
+ "results/generated/cioh-collaborative-false-merge-v1.md"
+ ],
+ "tests": [
+ "tests/test_cioh_collaborative.py",
+ "tests/test_cioh_collaborative_experiment.py",
+ "tests/test_cluster_refined.py"
+ ],
+ "properties": [
+ "CIOH merges both inputs in the labelled collaborative fixture",
+ "the predicted merge conflicts with the fixture's distinct participant labels",
+ "the same-owner control produces the same CIOH merge without a reference conflict",
+ "co-spend evidence remains distinct from the ownership conclusion",
+ "the generated Markdown equals the canonical rendering"
+ ],
+ "tolerance": null
+ },
+ "limitations": [
+ "deterministic synthetic fixture",
+ "participant ownership labels are supplied",
+ "the run does not estimate chain-wide prevalence or infer real ownership"
+ ]
+}
diff --git a/artifacts/sha256/16/ec556ac6d28f5a6b29f6e6378b4f0ea0d2975a706ba6af1f24e18f5a77a6cd b/artifacts/sha256/16/ec556ac6d28f5a6b29f6e6378b4f0ea0d2975a706ba6af1f24e18f5a77a6cd
new file mode 100644
index 00000000..f28d335c
--- /dev/null
+++ b/artifacts/sha256/16/ec556ac6d28f5a6b29f6e6378b4f0ea0d2975a706ba6af1f24e18f5a77a6cd
@@ -0,0 +1,13 @@
+{
+ "schema_version": 1,
+ "python": {
+ "implementation": "cpython",
+ "version": "3.13",
+ "system": "Darwin",
+ "machine": "arm64"
+ },
+ "source_archive": "sources/decluster-bcd9a3e-runtime.tar",
+ "project_root": "decluster",
+ "requirements": "reproduction/ancestry-sparsity/requirements.lock",
+ "dependency_directory": "reproduction/ancestry-sparsity"
+}
diff --git a/artifacts/sha256/17/ee62e307d34660be2c4f50382a63ba044d0ad67f0674873e304d0b8203dc1c b/artifacts/sha256/17/ee62e307d34660be2c4f50382a63ba044d0ad67f0674873e304d0b8203dc1c
new file mode 100644
index 00000000..57b7a296
--- /dev/null
+++ b/artifacts/sha256/17/ee62e307d34660be2c4f50382a63ba044d0ad67f0674873e304d0b8203dc1c
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diff --git a/artifacts/sha256/17/fa4bb1843852a63936de74e550eb5f293822b0a3b16b33e3b527e5f57e8ff5 b/artifacts/sha256/17/fa4bb1843852a63936de74e550eb5f293822b0a3b16b33e3b527e5f57e8ff5
new file mode 100644
index 00000000..d11e32b8
--- /dev/null
+++ b/artifacts/sha256/17/fa4bb1843852a63936de74e550eb5f293822b0a3b16b33e3b527e5f57e8ff5
@@ -0,0 +1,14 @@
+# Temporal Fellegi–Sunter evaluation
+
+Generated from the canonical experiment artifact. Do not edit manually.
+
+| quantity | value |
+|---|---:|
+| transactions | 22112 |
+| train transactions | 15100 |
+| test transactions | 7012 |
+| Fellegi–Sunter AUC | 0.982688 |
+| rarity baseline AUC | 0.934485 |
+| AUC difference | 0.048202 |
+
+Labels are based on address reuse and are not independent ownership labels.
diff --git a/artifacts/sha256/19/1c8c2f082ac84af71b28a70169207d785243a276cf5b6e472ae6f5588a65a6 b/artifacts/sha256/19/1c8c2f082ac84af71b28a70169207d785243a276cf5b6e472ae6f5588a65a6
new file mode 100644
index 00000000..f62ccda9
Binary files /dev/null and b/artifacts/sha256/19/1c8c2f082ac84af71b28a70169207d785243a276cf5b6e472ae6f5588a65a6 differ
diff --git a/artifacts/sha256/19/2890be723f3c135f61aa513c3effcfe426021c3e22e937a555c0970bb4f2d8 b/artifacts/sha256/19/2890be723f3c135f61aa513c3effcfe426021c3e22e937a555c0970bb4f2d8
new file mode 100644
index 00000000..36271839
--- /dev/null
+++ b/artifacts/sha256/19/2890be723f3c135f61aa513c3effcfe426021c3e22e937a555c0970bb4f2d8
@@ -0,0 +1 @@
+{"cospend_baseline":{"added_links":0,"arm":"co-spend union-find","groups":697,"largest_group":5,"refused_cospends":0},"dataset":"fs-blkcache-2026-09-04-v1","experiment":"fused-engine-v1","ladder":[{"added_links":7560,"arm":"fingerprint","groups":508,"largest_group":87,"refused_cospends":19},{"added_links":7560,"arm":"fingerprint+amount","groups":508,"largest_group":87,"refused_cospends":19},{"added_links":7560,"arm":"fingerprint+amount+topology","groups":511,"largest_group":88,"refused_cospends":11},{"added_links":7560,"arm":"fingerprint+amount+topology+provenance","groups":514,"largest_group":87,"refused_cospends":22},{"added_links":7560,"arm":"all five channels","groups":514,"largest_group":87,"refused_cospends":22}],"population":{"cospend_transactions":335,"nodes":753,"transactions":22112,"two_in_two_out_cospends":81},"schema_version":1,"source":{"bytes":91367352,"digest":"91c444bd4e9715d7739ccc9d68c7d9751762e25b647e3eef647c6ec0b48ba90c","files":904,"pattern":".blkcache/*.json","transactions":22112}}
diff --git a/artifacts/sha256/1a/2ac2247a71136681b765e368b88e2686b4070039efd37b0a262a8ce6e29515 b/artifacts/sha256/1a/2ac2247a71136681b765e368b88e2686b4070039efd37b0a262a8ce6e29515
new file mode 100644
index 00000000..45e16414
--- /dev/null
+++ b/artifacts/sha256/1a/2ac2247a71136681b765e368b88e2686b4070039efd37b0a262a8ce6e29515
@@ -0,0 +1 @@
+{"background_matched_control":{"batch_size":4,"observations":4,"scores":{"alice":0.25,"bob":0.25,"carol":0.25,"dave":0.25},"top_margin":0.0,"unique_top_correspondent":null},"composition":null,"conclusion":"repeated target-active rounds recover a persistent correspondent in the threshold-mix model","experiment":"statistical-disclosure-v1","limitations":["deterministic synthetic threshold-mix fixture","the model assumes one target message and batch_size minus one background messages per round","the result ranks persistent correspondents and does not attribute Bitcoin ownership","the fixture does not estimate real-world attack frequency or confidence","the estimator is implemented; the paper's applicability and efficiency section is not — neither the signal-to-noise condition that decides whether the attack is possible at all, nor the bound on observations needed for a stated confidence. Those are the part of the source that speaks to a rate, so no claim about how fast certainty is gained is covered here"],"persistent_correspondent":{"batch_size":4,"observations":4,"scores":{"alice":1.0,"bob":0.0,"carol":0.0,"dave":0.0},"top_margin":1.0,"unique_top_correspondent":"alice"},"schema_version":1}
diff --git a/artifacts/sha256/1d/1346f377c8cfa1bb859d63ab7da11099629dddaf61b166da4006361b2d7355 b/artifacts/sha256/1d/1346f377c8cfa1bb859d63ab7da11099629dddaf61b166da4006361b2d7355
new file mode 100644
index 00000000..48bc6eec
--- /dev/null
+++ b/artifacts/sha256/1d/1346f377c8cfa1bb859d63ab7da11099629dddaf61b166da4006361b2d7355
@@ -0,0 +1,90 @@
+{
+ "schema_version": 1,
+ "id": "pseudonym-graph-contract-v1",
+ "claim_ids": [
+ "ctp.graph.pseudonym_representation"
+ ],
+ "code": {
+ "revision": "bcd9a3e511860b1b0ff5f3ee6338a2df29ecbfbf",
+ "dirty": false
+ },
+ "command": {
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.pseudonym_graph_contract",
+ "reproduce",
+ "--artifact",
+ "results/artifacts/pseudonym-graph-contract-v1.json",
+ "--markdown",
+ "results/generated/pseudonym-graph-contract-v1.md"
+ ]
+ },
+ "environment": {
+ "python": "3.13.7",
+ "lock_digest": "e0d6a8cada5d383c0aca2bd2a37f28c15721392469c94084128d369c29c5544d",
+ "platform": "macOS-26.6.2-arm64-arm-64bit-Mach-O",
+ "dependencies": []
+ },
+ "datasets": [],
+ "parameters": {
+ "addresses": 5,
+ "declared_clusters": 2,
+ "transactions": 5,
+ "axes": false
+ },
+ "rng": {
+ "algorithm": null,
+ "seeds": []
+ },
+ "outputs": [
+ {
+ "path": "results/artifacts/pseudonym-graph-contract-v1.json",
+ "bytes": 850,
+ "sha256": "ab830f2e56b40e961015fda9c9685ddcab4feff1e486bde6ee825ea4db9d33a6"
+ },
+ {
+ "path": "results/generated/pseudonym-graph-contract-v1.md",
+ "bytes": 761,
+ "sha256": "f0533dbe40ddfa8af55d31fbb1e53b2f0a925582a7567670496717e786b0c827"
+ }
+ ],
+ "reproducibility": {
+ "level": "bitwise_reproducible",
+ "availability": "complete"
+ },
+ "verification": {
+ "mode": "exact",
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.pseudonym_graph_contract",
+ "verify",
+ "--artifact",
+ "results/artifacts/pseudonym-graph-contract-v1.json",
+ "--markdown",
+ "results/generated/pseudonym-graph-contract-v1.md"
+ ],
+ "tests": [
+ "tests/test_pseudonym_graph_contract.py",
+ "tests/test_pseudonym_graph_contract_experiment.py",
+ "tests/test_views.py"
+ ],
+ "properties": [
+ "members of each declared cluster contract into one vertex",
+ "an address absent from the lookup remains a singleton pseudonym",
+ "opposite transfer directions remain separate",
+ "parallel transfers fold into count, total value and height span attributes",
+ "self-transfers remain vertex activity rather than relational edges",
+ "vertices are not presented as users without an independently complete clustering",
+ "the generated Markdown equals the canonical rendering"
+ ],
+ "tolerance": null
+ },
+ "limitations": [
+ "deterministic synthetic contraction fixture",
+ "the partial clustering lookup is supplied",
+ "parallel transfers are folded into an attributed edge",
+ "the run validates representation rather than deanonymization success"
+ ]
+}
diff --git a/artifacts/sha256/1d/46dcccb51cd8f1a1d89481db4df45599caaaf8569f9e8e2de37e81e0e5d122 b/artifacts/sha256/1d/46dcccb51cd8f1a1d89481db4df45599caaaf8569f9e8e2de37e81e0e5d122
new file mode 100644
index 00000000..13e0b2a5
Binary files /dev/null and b/artifacts/sha256/1d/46dcccb51cd8f1a1d89481db4df45599caaaf8569f9e8e2de37e81e0e5d122 differ
diff --git a/artifacts/sha256/1d/59757c7033be2e7e983b1b7a17c1cc9673b4084dcd7bc15bff5dce9aec9153 b/artifacts/sha256/1d/59757c7033be2e7e983b1b7a17c1cc9673b4084dcd7bc15bff5dce9aec9153
new file mode 100644
index 00000000..533751a9
--- /dev/null
+++ b/artifacts/sha256/1d/59757c7033be2e7e983b1b7a17c1cc9673b4084dcd7bc15bff5dce9aec9153
@@ -0,0 +1,13 @@
+{
+ "schema_version": 1,
+ "python": {
+ "implementation": "cpython",
+ "version": "3.13",
+ "system": "Darwin",
+ "machine": "arm64"
+ },
+ "source_archive": "sources/decluster-bcd9a3e-runtime.tar",
+ "project_root": "decluster",
+ "requirements": "reproduction/route-capacity/requirements.lock",
+ "dependency_directory": "reproduction/route-capacity"
+}
diff --git a/artifacts/sha256/1d/5afe106a2bb84edd7925ca4ed213d9f5bc3c35349d48dac47c6051492543ed b/artifacts/sha256/1d/5afe106a2bb84edd7925ca4ed213d9f5bc3c35349d48dac47c6051492543ed
new file mode 100644
index 00000000..3771e936
--- /dev/null
+++ b/artifacts/sha256/1d/5afe106a2bb84edd7925ca4ed213d9f5bc3c35349d48dac47c6051492543ed
@@ -0,0 +1,37 @@
+# Rejoining a cluster split across two weekly views
+
+Generated from the canonical experiment artifact. Do not edit manually.
+
+Two consecutive 2016 weekly windows of 982,021 and 1,116,563 transactions. 17,431 clusters straddle the boundary and survive contraction in both views, at a mean internal degree of 2.866.
+
+| statistic | view A | view B |
+|---|---:|---:|
+| vertices | 588,843 | 638,436 |
+| edges | 1,430,236 | 1,561,102 |
+| mean degree | 4.7874 | 4.8199 |
+| degree 1 share | 0.0464 | 0.0600 |
+| transitivity | 0.0062 | 0.0001 |
+| configuration transitivity | 0.6113 | 0.3976 |
+| assortativity | -0.0764 | -0.0782 |
+| tail exponent | 2.8613 | 2.8337 |
+
+The framework's premise is that a contracted view is a social network in the sense its matching algorithm needs. On this measurement it is not, on the two axes that decide it. Clustering sits at 0.0062 and 0.0001 against a configuration-model null of 0.6113 and 0.3976 — the degree distribution alone would produce far more triangles than the graph has. Degree correlation is -0.0764 and -0.0782: hubs attach to leaves, which is what transaction graphs do and the opposite of what a social graph does. A matcher that routes through a vertex's neighbourhood is asking that neighbourhood to be distinctive and stable, and neither statistic says it is.
+
+| seed share | matcher | arm | guesses | correct | precision |
+|---:|---|---|---:|---:|---:|
+| 5% | undirected | seeded | 0 | 0 | — |
+| 5% | undirected | shuffled | 0 | 0 | — |
+| 5% | directed | seeded | 0 | 0 | — |
+| 5% | directed | shuffled | 0 | 0 | — |
+| 10% | undirected | seeded | 0 | 0 | — |
+| 10% | undirected | shuffled | 0 | 0 | — |
+| 10% | directed | seeded | 1 | 1 | 1.000 |
+| 10% | directed | shuffled | 0 | 0 | — |
+
+At the widest seeding the matcher is offered 15,688 non-seed pairs. Across every seeded arm it guesses 1 and gets 1 right; the shuffled-seed arms guess 0. A cascade that carried itself would show each seed unlocking more than itself, and it does not at these view widths. One match is not ignition, and it is a different statement from none.
+
+Cluster membership here is a co-spend relation rather than observed wallet ownership, and the seeds are supplied. The result is what propagation does once a foothold exists, not whether one can be found.
+
+## Reproducibility / provenance
+
+State 1 in `results/REPRODUCIBILITY.md`: band-pinned on committed data — the artifact is recomputed from the committed weekly epoch graphs and compared byte for byte.
diff --git a/artifacts/sha256/1d/ef26d56c2c64f5c7e0ce9208628a6c335981811c47d219c7f92ef019096f57 b/artifacts/sha256/1d/ef26d56c2c64f5c7e0ce9208628a6c335981811c47d219c7f92ef019096f57
new file mode 100644
index 00000000..8c13a392
--- /dev/null
+++ b/artifacts/sha256/1d/ef26d56c2c64f5c7e0ce9208628a6c335981811c47d219c7f92ef019096f57
@@ -0,0 +1,9 @@
+# Subtransaction de-mix diagnostic
+
+Generated from the canonical experiment artifact. Do not edit manually.
+
+The preserved JoinMarket transaction has 12 inputs and 21 outputs; the amount identity recovers 8 inputs.
+
+The CoinJoin-adjacent cache contains 36 transactions. Among 25 width-eligible transactions, 22 trigger the heuristic.
+
+This cache has no ordinary-transaction labels and cannot establish specificity. The historical Wasabi result is not reproduced. This is attacker-side evidence, not a privacy score.
diff --git a/artifacts/sha256/1f/adeece80a936b04473a7e235eafc37e835d21445a2b462cac7d067896c10e9 b/artifacts/sha256/1f/adeece80a936b04473a7e235eafc37e835d21445a2b462cac7d067896c10e9
new file mode 100644
index 00000000..3493951a
--- /dev/null
+++ b/artifacts/sha256/1f/adeece80a936b04473a7e235eafc37e835d21445a2b462cac7d067896c10e9
@@ -0,0 +1 @@
+{"experiment":"change-consolidation-v1","limitations":["synthetic fixture","the report narrows candidate owners and does not identify a real person","a collaborative co-spend invalidates the common-ownership premise"],"result":{"candidates_after":["alice"],"candidates_before":2,"composition":null,"outcome":"candidate_narrowing"},"scenario":{"candidate_owners":[["alice","bob"],["alice","carol"]],"change_outputs":[["payment-1",1],["payment-2",1]],"payments":["payment-1","payment-2"]},"schema_version":1}
diff --git a/artifacts/sha256/1f/d2b0ba7a1927f48d06213bc33d61c4b33bc1cca1e1dce8c85fb1b74c984828 b/artifacts/sha256/1f/d2b0ba7a1927f48d06213bc33d61c4b33bc1cca1e1dce8c85fb1b74c984828
new file mode 100644
index 00000000..ce8d7e92
--- /dev/null
+++ b/artifacts/sha256/1f/d2b0ba7a1927f48d06213bc33d61c4b33bc1cca1e1dce8c85fb1b74c984828
@@ -0,0 +1,87 @@
+{
+ "schema_version": 1,
+ "id": "counting-methods-v1",
+ "claim_ids": [
+ "ctp.amount.subtransaction_ambiguity"
+ ],
+ "code": {
+ "revision": "bcd9a3e511860b1b0ff5f3ee6338a2df29ecbfbf",
+ "dirty": false
+ },
+ "command": {
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.counting_methods_summary",
+ "reproduce",
+ "--artifact",
+ "results/artifacts/counting-methods-v1.json",
+ "--markdown",
+ "results/generated/counting-methods-v1.md"
+ ]
+ },
+ "environment": {
+ "python": "3.13.7",
+ "lock_digest": "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855",
+ "platform": "macOS-26.6.2-arm64-arm-64bit-Mach-O",
+ "dependencies": []
+ },
+ "datasets": [],
+ "parameters": {
+ "composition": "identity-checked upstream artifacts only",
+ "components": {
+ "counting-router-v1": "731164f77a75776fc91a9c5bc8bce16700e6084e44c75d7e83510ac8afec1ce5",
+ "counting-fee-sensitivity-v1": "0fef54735522470634c696ec026c31c86dff4d4dae23da956b49d8f89eb3a508"
+ }
+ },
+ "rng": {
+ "algorithm": null,
+ "seeds": []
+ },
+ "outputs": [
+ {
+ "path": "results/artifacts/counting-methods-v1.json",
+ "bytes": 3003,
+ "sha256": "81d48cf4f7b71eb62354cfd589a40fdfc6171a54b0c9b7a768bd45198e7a1312"
+ },
+ {
+ "path": "results/generated/counting-methods-v1.md",
+ "bytes": 795,
+ "sha256": "7783ece95b71bd11cacf9b6f350b7d22c87c54a46ca7e75b3c5dbeb62e5d091d"
+ }
+ ],
+ "reproducibility": {
+ "level": "bitwise_reproducible",
+ "availability": "complete"
+ },
+ "verification": {
+ "mode": "exact",
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.counting_methods_summary",
+ "verify",
+ "--artifact",
+ "results/artifacts/counting-methods-v1.json",
+ "--markdown",
+ "results/generated/counting-methods-v1.md"
+ ],
+ "tests": [
+ "tests/test_counting_methods_summary_experiment.py"
+ ],
+ "properties": [
+ "both component identities are checked before composition",
+ "the summary performs no counting calculation",
+ "non-reproducible historical measurements remain explicitly excluded",
+ "the generated Markdown equals the canonical rendering"
+ ],
+ "tolerance": null
+ },
+ "limitations": [
+ "this artifact composes upstream results and performs no counting calculation",
+ "component SHA-256 identities are mandatory",
+ "runtime benchmarks require a separate benchmark protocol",
+ "an L-estimator comparison requires a preserved generator and seeds",
+ "the counting objects remain diagnostics and are not a privacy score or CoinScore"
+ ]
+}
diff --git a/artifacts/sha256/20/f7a7c2cd588b7aa6013b72b66e38ca77f12e9959b8cab32890d9e6bf3cb06e b/artifacts/sha256/20/f7a7c2cd588b7aa6013b72b66e38ca77f12e9959b8cab32890d9e6bf3cb06e
new file mode 100644
index 00000000..90315513
--- /dev/null
+++ b/artifacts/sha256/20/f7a7c2cd588b7aa6013b72b66e38ca77f12e9959b8cab32890d9e6bf3cb06e
@@ -0,0 +1,103 @@
+{
+ "schema_version": 1,
+ "id": "unnecessary-input-v2",
+ "claim_ids": [
+ "ctp.payjoin.unnecessary_input"
+ ],
+ "code": {
+ "revision": "bcd9a3e511860b1b0ff5f3ee6338a2df29ecbfbf",
+ "dirty": false
+ },
+ "command": {
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.unnecessary_input",
+ "reproduce",
+ "--artifact",
+ "results/artifacts/unnecessary-input-v2.json",
+ "--markdown",
+ "results/generated/unnecessary-input-v2.md"
+ ]
+ },
+ "environment": {
+ "python": "3.13.7",
+ "lock_digest": "e0d6a8cada5d383c0aca2bd2a37f28c15721392469c94084128d369c29c5544d",
+ "platform": "macOS-26.6.2-arm64-arm-64bit-Mach-O",
+ "dependencies": []
+ },
+ "datasets": [],
+ "parameters": {
+ "definition": "blockstream_fee_aware",
+ "definition_semantic_version": 2,
+ "minimum_inputs": 2,
+ "exact_outputs": 2,
+ "amount_domain": "positive unsigned 64-bit integers",
+ "fixtures": [
+ "jointly-funded-uih2",
+ "fee-aware-uih1",
+ "equality-boundary",
+ "observational-equivalence",
+ "ns1r-out-of-scope",
+ "nsnr-out-of-scope",
+ "net-settlement-cycle"
+ ]
+ },
+ "rng": {
+ "algorithm": null,
+ "seeds": []
+ },
+ "outputs": [
+ {
+ "path": "results/artifacts/unnecessary-input-v2.json",
+ "bytes": 2387,
+ "sha256": "a8e17f91020b0cbdc12bc2cb2652e2ffa63b5b5dcc70af6dfc3d4a73925278e0"
+ },
+ {
+ "path": "results/generated/unnecessary-input-v2.md",
+ "bytes": 1071,
+ "sha256": "52cb79757b649e63cd24161b0f416eeebde8c871133f6cb338db7252786d525a"
+ }
+ ],
+ "reproducibility": {
+ "level": "bitwise_reproducible",
+ "availability": "complete"
+ },
+ "verification": {
+ "mode": "exact",
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.unnecessary_input",
+ "verify",
+ "--artifact",
+ "results/artifacts/unnecessary-input-v2.json",
+ "--markdown",
+ "results/generated/unnecessary-input-v2.md"
+ ],
+ "tests": [
+ "tests/test_unnecessary_input_baseline.py",
+ "tests/test_unnecessary_input_failure_mode.py",
+ "tests/test_collaborative_forms.py",
+ "tests/test_unnecessary_input_experiment.py"
+ ],
+ "properties": [
+ "the fee-aware BlockStream UIH1/UIH2 definition is identified and versioned",
+ "definition boundaries and Gibson/BlockSci comparison predicates are explicit",
+ "NS1R and NSNR controls remain outside the two-output definition",
+ "one positive UIH observation preserves unilateral, PayJoin and net-settlement worlds",
+ "the result infers no ownership, PayJoin identity, payment output or composition",
+ "a gross settlement cycle can change while nonzero net balances remain equal",
+ "the generated Markdown equals the canonical rendering"
+ ],
+ "tolerance": null
+ },
+ "limitations": [
+ "deterministic diagnostic fixtures rather than a representative chain sample",
+ "the published longitudinal dataset and reported UIH rates are not reproduced",
+ "a positive unnecessary-input predicate does not identify PayJoin or ownership",
+ "protocol form, participant roles and ownership require independent evidence",
+ "the paper-faithful classification is scoped to transactions with more than one input and exactly two outputs",
+ "the result is not a privacy score or privacy certificate"
+ ]
+}
diff --git a/artifacts/sha256/21/0ecb06ecaa32cc4cabe03ced8b85b0882a824dca2c361bd2c5dede7dcccf02 b/artifacts/sha256/21/0ecb06ecaa32cc4cabe03ced8b85b0882a824dca2c361bd2c5dede7dcccf02
new file mode 100644
index 00000000..043277b4
--- /dev/null
+++ b/artifacts/sha256/21/0ecb06ecaa32cc4cabe03ced8b85b0882a824dca2c361bd2c5dede7dcccf02
@@ -0,0 +1 @@
+{"corpus":"public Bitcoin transaction snapshot","experiment":"amount-local-channels-v1","limitations":["the snapshot covers one 137-block interval from 2023","no recognized JoinMarket round appears in the measured population","roundness ties show abstention, not ownership truth","the conservation probe assumes the largest input is the known participant","this run excludes subset-sum, DSS, mapping entropy, and score composition"],"report":{"coinjoin":{"demixed":0,"shape_detected":28},"conservation":{"forced_output_value":1353,"known_participant":"largest input value"},"population":{"complete_amounts":5491,"multi_input":1428,"transactions":5491,"two_input_two_output":486},"subtransaction_roundness":{"ranked":483,"top_tied":330},"unnecessary_input":{"classes":{"none":943,"uih2":485},"fires":485}},"schema_version":1}
diff --git a/artifacts/sha256/21/14e568b91808536c94ddb307a4688e3be32720bcb768ef9bb790ef673d0fe5 b/artifacts/sha256/21/14e568b91808536c94ddb307a4688e3be32720bcb768ef9bb790ef673d0fe5
new file mode 100644
index 00000000..1762a003
--- /dev/null
+++ b/artifacts/sha256/21/14e568b91808536c94ddb307a4688e3be32720bcb768ef9bb790ef673d0fe5
@@ -0,0 +1 @@
+{"experiment":"intersection-cluster-fixture-v1","fixture":{"seeds":[["left",0],["right",0]],"transactions":{"join":{"vin":[{"prevout":{"value":10},"txid":"left","vout":0},{"prevout":{"value":10},"txid":"right","vout":0}],"vout":[{"value":20}]},"left":{"vin":[{"prevout":{"value":5},"txid":"origin","vout":0},{"prevout":{"value":5},"txid":"left-source","vout":0}],"vout":[{"value":10}]},"left-source":{"vin":[{"is_coinbase":true}],"vout":[{"value":5}]},"origin":{"vin":[{"is_coinbase":true}],"vout":[{"value":5},{"value":5}]},"right":{"vin":[{"prevout":{"value":5},"txid":"origin","vout":1},{"prevout":{"value":5},"txid":"right-source","vout":0}],"vout":[{"value":10}]},"right-source":{"vin":[{"is_coinbase":true}],"vout":[{"value":5}]}}},"limitations":["synthetic fixture","narrowing is conditional on the observed co-spend","the fixture does not run the clustering verdict"],"parameters":{"ancestry_depth":2,"cluster_key":"creating transaction id","monitor_depth":1},"result":{"results":[{"candidate":{"outpoints":[["left",0],["right",0]],"seeds":[["left",0],["right",0]],"shape":[2,1],"txid":"join"},"narrowing":{"blind":false,"blind_cause":null,"branches":2,"collapsed":1,"collapsed_bits":1.0,"scored":false,"shared":[["origin",0.5,1.0]],"sizes":[2,2],"truncated":[0,0],"truncated_causes":[{"node_capped":0,"oracle_refused":0,"unattributed":0,"zero_link_mass":0},{"node_capped":0,"oracle_refused":0,"unattributed":0,"zero_link_mass":0}],"txid":"join"}}],"walk":{"candidates":[{"outpoints":[["left",0],["right",0]],"seeds":[["left",0],["right",0]],"shape":[2,1],"txid":"join"}],"frontier":[{"depth":1,"outpoint":["join",0],"reason":"unspent","seed":["left",0]},{"depth":1,"outpoint":["join",0],"reason":"unspent","seed":["right",0]}]}},"schema_version":1,"summary":{"blind":false,"branches":2,"candidates_after":1,"candidates_before":2,"collapsed":1,"scored":false}}
diff --git a/artifacts/sha256/23/7ae9efb8eb6819bcb593914f66f8cfc71ae63186f5b5ecc5b28a590981541e b/artifacts/sha256/23/7ae9efb8eb6819bcb593914f66f8cfc71ae63186f5b5ecc5b28a590981541e
new file mode 100644
index 00000000..023e9bf0
--- /dev/null
+++ b/artifacts/sha256/23/7ae9efb8eb6819bcb593914f66f8cfc71ae63186f5b5ecc5b28a590981541e
@@ -0,0 +1,13 @@
+{
+ "schema_version": 1,
+ "python": {
+ "implementation": "cpython",
+ "version": "3.13",
+ "system": "Darwin",
+ "machine": "arm64"
+ },
+ "source_archive": "sources/decluster-bcd9a3e-runtime.tar",
+ "project_root": "decluster",
+ "requirements": "reproduction/ns-link-prediction-coverage/requirements.lock",
+ "dependency_directory": "reproduction/ns-link-prediction-coverage"
+}
diff --git a/artifacts/sha256/24/0592b5af814fbcd28b06221805b934424170119da5e62e9da0281944377a9d b/artifacts/sha256/24/0592b5af814fbcd28b06221805b934424170119da5e62e9da0281944377a9d
new file mode 100644
index 00000000..3bd0d82f
--- /dev/null
+++ b/artifacts/sha256/24/0592b5af814fbcd28b06221805b934424170119da5e62e9da0281944377a9d
@@ -0,0 +1 @@
+{"corpus":"public Bitcoin transaction snapshot","experiment":"amount-subset-sum-v1","limitations":["W(E) is a per-target subset count, not a Maurer mapping count","the whole-transaction count is fee-blind","exact zero means no exact subset hit and supplies no ambiguity evidence","transactions above sixteen inputs are reported as unknown","only exact and lower-bound tiers can support a conservative lower bound","this run does not compute pairwise links, entropy, or CoinScore"],"report":{"outcomes":{"by_kind":{"exact":1303,"unknown":125},"exact_zero":1223,"guaranteed_nonzero_log_w":80},"population":{"maximum_inputs":16,"multi_input":1428}},"schema_version":1}
diff --git a/artifacts/sha256/24/28a32ec1827bfa9b9a2f44756effc968c537413640927a009be00cee38b733 b/artifacts/sha256/24/28a32ec1827bfa9b9a2f44756effc968c537413640927a009be00cee38b733
new file mode 100644
index 00000000..3fdd4bfc
--- /dev/null
+++ b/artifacts/sha256/24/28a32ec1827bfa9b9a2f44756effc968c537413640927a009be00cee38b733
@@ -0,0 +1 @@
+{"claim":"observable transaction data do not by themselves identify the collaborative form, participant allocation, payment graph or ownership","conclusion":"identical on-chain amounts remain compatible with an ordinary spend and several two-party PayJoin protocols; counterparty knowledge must not be assigned to an external observer","experiment":"collaborative-forms-v1","forms":[{"counterparty_knowledge":[],"external_onchain_knowledge":["input and output amounts","transaction shape and script-visible construction features","participant allocation is not observed","payment amount is not observed"],"form":"ordinary_two_input","limitations":["ownership is latent even when the transaction is actually unilateral"],"observation":{"inputs_sat":[90,60],"outputs_sat":[110,40]},"participants":1,"transport":"ordinary wallet transaction"},{"counterparty_knowledge":["own inputs and outputs","the only other party's remaining inputs and outputs by elimination","negotiated payment amount"],"external_onchain_knowledge":["input and output amounts","transaction shape and script-visible construction features","participant allocation is not observed","payment amount is not observed"],"form":"p2ep","limitations":["the same on-chain observation is compatible with an ordinary single-owner spend","two-party PayJoin does not provide privacy from the counterparty"],"observation":{"inputs_sat":[90,60],"outputs_sat":[110,40]},"participants":2,"transport":"Pay-to-EndPoint negotiation"},{"counterparty_knowledge":["own inputs and outputs","the only other party's remaining inputs and outputs by elimination","negotiated payment amount"],"external_onchain_knowledge":["input and output amounts","transaction shape and script-visible construction features","participant allocation is not observed","payment amount is not observed"],"form":"bip79_bustapay","limitations":["the same on-chain observation is compatible with an ordinary single-owner spend","two-party PayJoin does not provide privacy from the counterparty"],"observation":{"inputs_sat":[90,60],"outputs_sat":[110,40]},"participants":2,"transport":"Bustapay template negotiation"},{"counterparty_knowledge":["own inputs and outputs","the only other party's remaining inputs and outputs by elimination","negotiated payment amount"],"external_onchain_knowledge":["input and output amounts","transaction shape and script-visible construction features","participant allocation is not observed","payment amount is not observed"],"form":"bip78_sync_payjoin","limitations":["the same on-chain observation is compatible with an ordinary single-owner spend","two-party PayJoin does not provide privacy from the counterparty"],"observation":{"inputs_sat":[90,60],"outputs_sat":[110,40]},"participants":2,"transport":"synchronous PayJoin negotiation"},{"counterparty_knowledge":["own inputs and outputs","the only other party's remaining inputs and outputs by elimination","negotiated payment amount"],"external_onchain_knowledge":["input and output amounts","transaction shape and script-visible construction features","participant allocation is not observed","payment amount is not observed"],"form":"bip77_async_payjoin","limitations":["the same on-chain observation is compatible with an ordinary single-owner spend","two-party PayJoin does not provide privacy from the counterparty"],"observation":{"inputs_sat":[90,60],"outputs_sat":[110,40]},"participants":2,"transport":"asynchronous PayJoin negotiation"},{"counterparty_knowledge":["the receiver knows each negotiated payment and sender change","the receiver may not know which input belongs to which sender"],"external_onchain_knowledge":["amounts and transaction shape","sender allocation is not observed"],"form":"many_senders_one_receiver","limitations":["receiver consolidation and sibling-output spending can create fingerprints"],"observation":{"inputs_sat":[40,50,60,90],"outputs_sat":[160,30,30,20]},"participants":4,"transport":"many senders negotiate with one receiver"},{"counterparty_knowledge":["own subtransaction and negotiated payment","other pairings remain latent"],"external_onchain_knowledge":["amounts and transaction shape","payment pairing is not observed"],"form":"many_senders_many_receivers","limitations":["one payment per subtransaction leaves amount-based plausibility signals"],"observation":{"inputs_sat":[90,15,40,30,45,35],"outputs_sat":[85,25,50,40,45,10]},"participants":6,"transport":"paired many-sender many-receiver negotiation"},{"counterparty_knowledge":["own obligations and contributions","other parties' gross obligations remain latent"],"external_onchain_knowledge":["net on-chain balances","participant count may remain latent"],"form":"net_settlement_with_cycles","limitations":["cycles make gross payment amounts and obligation count unidentifiable","community structure and auxiliary information remain observable over time"],"observation":{"inputs_sat":[60,20,50],"outputs_sat":[40,50,40]},"participants":null,"transport":"multiparty settlement of mutual obligations"}],"limitations":["the participant allocations and payment relationships are supplied model possibilities","protocol deployment frequency and empirical indistinguishability are not measured","transport differences are not treated as on-chain observations","the matrix is not ownership attribution or a privacy score"],"schema_version":1,"two_party_onchain_equivalence":{"distinct_transports":5,"forms":["ordinary_two_input","p2ep","bip79_bustapay","bip78_sync_payjoin","bip77_async_payjoin"],"observation":{"inputs_sat":[90,60],"outputs_sat":[110,40]}}}
diff --git a/artifacts/sha256/24/761bab6b86a2730ee79c657686cc9dd9733a98daaf4e6be2a2a258df3952fd b/artifacts/sha256/24/761bab6b86a2730ee79c657686cc9dd9733a98daaf4e6be2a2a258df3952fd
new file mode 100644
index 00000000..1c198a7b
--- /dev/null
+++ b/artifacts/sha256/24/761bab6b86a2730ee79c657686cc9dd9733a98daaf4e6be2a2a258df3952fd
@@ -0,0 +1,105 @@
+{
+ "schema_version": 1,
+ "id": "counting-fee-sensitivity-v1",
+ "claim_ids": [
+ "ctp.amount.subtransaction_ambiguity"
+ ],
+ "code": {
+ "revision": "bcd9a3e511860b1b0ff5f3ee6338a2df29ecbfbf",
+ "dirty": false
+ },
+ "command": {
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.counting_fee_sensitivity",
+ "reproduce",
+ "--artifact",
+ "results/artifacts/counting-fee-sensitivity-v1.json",
+ "--markdown",
+ "results/generated/counting-fee-sensitivity-v1.md"
+ ]
+ },
+ "environment": {
+ "python": "3.13.7",
+ "lock_digest": "5ee1d70afd72fdb4ba3f01843e85e7ecd9a6d3e2a52abeb14c346a2143e3c186",
+ "platform": "macOS-26.6.2-arm64-arm-64bit-Mach-O",
+ "dependencies": [
+ {
+ "name": "dss",
+ "version": "0.1.0",
+ "source": "https://github.com/bc1cindy/dense-subset-sum",
+ "revision": "d8099bc540c9de7d0f3c9f095e8b2aad8f9d8c8d",
+ "lock_sha256": "f17a02f09da28dcc603901580d695466c44e1d1a0063e110d3faa375ae6066ca",
+ "license": "MIT",
+ "redistribution": "allowed",
+ "editable": false
+ }
+ ]
+ },
+ "datasets": [],
+ "parameters": {
+ "inputs_sat": [
+ 400000,
+ 300000,
+ 300000
+ ],
+ "fees_sat": [
+ 0,
+ 1,
+ 10,
+ 100,
+ 1000,
+ 4956
+ ]
+ },
+ "rng": {
+ "algorithm": null,
+ "seeds": []
+ },
+ "outputs": [
+ {
+ "path": "results/artifacts/counting-fee-sensitivity-v1.json",
+ "bytes": 1968,
+ "sha256": "0fef54735522470634c696ec026c31c86dff4d4dae23da956b49d8f89eb3a508"
+ },
+ {
+ "path": "results/generated/counting-fee-sensitivity-v1.md",
+ "bytes": 526,
+ "sha256": "df9d25a028a5f5c9f7527923cd354a674865b3012d71b23405c08a6ec643a84b"
+ }
+ ],
+ "reproducibility": {
+ "level": "bitwise_reproducible",
+ "availability": "complete"
+ },
+ "verification": {
+ "mode": "exact",
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.counting_fee_sensitivity",
+ "verify",
+ "--artifact",
+ "results/artifacts/counting-fee-sensitivity-v1.json",
+ "--markdown",
+ "results/generated/counting-fee-sensitivity-v1.md"
+ ],
+ "tests": [
+ "tests/test_counting_fee_sensitivity_experiment.py",
+ "tests/test_counting.py"
+ ],
+ "properties": [
+ "transaction-level and per-coin counting remain separate objects",
+ "unmeasured per-coin inputs are absent rather than assigned zero",
+ "the generated Markdown equals the canonical rendering"
+ ],
+ "tolerance": null
+ },
+ "limitations": [
+ "this is a deterministic sensitivity fixture, not a population estimate",
+ "the transaction-level subset-sum count and fee-aware per-coin count are different objects",
+ "an absent per-coin index is a refusal to measure, not zero ambiguity",
+ "these diagnostics are not mapping entropy, a privacy score, or CoinScore"
+ ]
+}
diff --git a/artifacts/sha256/24/f7126ab3dd2235c85ea0c7fafc2e0faa8d86617d07967409d8448b75c3a0dd b/artifacts/sha256/24/f7126ab3dd2235c85ea0c7fafc2e0faa8d86617d07967409d8448b75c3a0dd
new file mode 100644
index 00000000..7a6aeb34
--- /dev/null
+++ b/artifacts/sha256/24/f7126ab3dd2235c85ea0c7fafc2e0faa8d86617d07967409d8448b75c3a0dd
@@ -0,0 +1,13 @@
+{
+ "schema_version": 1,
+ "python": {
+ "implementation": "cpython",
+ "version": "3.13",
+ "system": "Darwin",
+ "machine": "arm64"
+ },
+ "source_archive": "sources/decluster-bcd9a3e-runtime.tar",
+ "project_root": "decluster",
+ "requirements": "reproduction/fused-engine/requirements.lock",
+ "dependency_directory": "reproduction/fused-engine"
+}
diff --git a/artifacts/sha256/26/c9df4ad45681ed78c142f6551c0ca7e1ee3a1a6a49ab9f19853d7c545a3b14 b/artifacts/sha256/26/c9df4ad45681ed78c142f6551c0ca7e1ee3a1a6a49ab9f19853d7c545a3b14
new file mode 100644
index 00000000..899025f1
--- /dev/null
+++ b/artifacts/sha256/26/c9df4ad45681ed78c142f6551c0ca7e1ee3a1a6a49ab9f19853d7c545a3b14
@@ -0,0 +1,13 @@
+{
+ "schema_version": 1,
+ "python": {
+ "implementation": "cpython",
+ "version": "3.13",
+ "system": "Darwin",
+ "machine": "arm64"
+ },
+ "source_archive": "sources/decluster-bcd9a3e-runtime.tar",
+ "project_root": "decluster",
+ "requirements": "reproduction/cluster-bits/requirements.lock",
+ "dependency_directory": "reproduction/cluster-bits"
+}
diff --git a/artifacts/sha256/27/1fa7e6601871b7fad9ac6e50b23a5f164d4a0ca30bc88c7629828b4858789b b/artifacts/sha256/27/1fa7e6601871b7fad9ac6e50b23a5f164d4a0ca30bc88c7629828b4858789b
new file mode 100644
index 00000000..c3bcd86d
--- /dev/null
+++ b/artifacts/sha256/27/1fa7e6601871b7fad9ac6e50b23a5f164d4a0ca30bc88c7629828b4858789b
@@ -0,0 +1,12 @@
+# Fingerprint ranking regime
+
+Generated from the canonical experiment artifact. Do not edit manually.
+
+Transactions: 600.
+
+| weights | N-S-form AUC | F-S AUC | N-S-form top-1 | F-S top-1 | within-class mean gap |
+|---|---:|---:|---:|---:|---:|
+| library | 0.920700 | 0.939150 | 0.680000 | 0.730000 | 0.392591 |
+| snapshot_measured | 0.953150 | 0.939150 | 0.785000 | 0.730000 | 0.675116 |
+
+The historical claim that the conditioner interpretation is stable across weight sources does not reproduce on this fixture. Snapshot-measured weights outperform the F-S top-1 baseline and have a larger within-class mean gap, but they are fitted and evaluated on the same selected data. This is evidence of sensitivity, not evidence that fingerprints are universal sparse identifiers.
diff --git a/artifacts/sha256/27/6412ce4ca84ae4c2bb1b85404f9d99ea46687207a6edb02ea99ae5656e1468 b/artifacts/sha256/27/6412ce4ca84ae4c2bb1b85404f9d99ea46687207a6edb02ea99ae5656e1468
new file mode 100644
index 00000000..9d4577f3
--- /dev/null
+++ b/artifacts/sha256/27/6412ce4ca84ae4c2bb1b85404f9d99ea46687207a6edb02ea99ae5656e1468
@@ -0,0 +1,13 @@
+{
+ "schema_version": 1,
+ "python": {
+ "implementation": "cpython",
+ "version": "3.13",
+ "system": "Darwin",
+ "machine": "arm64"
+ },
+ "source_archive": "sources/decluster-bcd9a3e-runtime.tar",
+ "project_root": "decluster",
+ "requirements": "reproduction/boltzmann-fee-audit/requirements.lock",
+ "dependency_directory": "reproduction/boltzmann-fee-audit"
+}
diff --git a/artifacts/sha256/29/1e644b50440507ebfef198dcbb0e8916fff39e7814380a22c20291d63c9360 b/artifacts/sha256/29/1e644b50440507ebfef198dcbb0e8916fff39e7814380a22c20291d63c9360
new file mode 100644
index 00000000..85984c79
--- /dev/null
+++ b/artifacts/sha256/29/1e644b50440507ebfef198dcbb0e8916fff39e7814380a22c20291d63c9360
@@ -0,0 +1,99 @@
+{
+ "schema_version": 1,
+ "id": "anchor-axis-families-v1",
+ "claim_ids": [
+ "ctp.fingerprint.statistical_linkage"
+ ],
+ "code": {
+ "revision": "bcd9a3e511860b1b0ff5f3ee6338a2df29ecbfbf",
+ "dirty": false
+ },
+ "command": {
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.anchor_axis_families",
+ "--dataset",
+ "tests/fixtures/merged_anchor_931d6627.json",
+ "reproduce",
+ "--artifact",
+ "results/artifacts/anchor-axis-families-v1.json",
+ "--markdown",
+ "results/generated/anchor-axis-families-v1.md"
+ ]
+ },
+ "environment": {
+ "python": "3.13.7",
+ "lock_digest": "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855",
+ "platform": "macOS-26.6.2-arm64-arm-64bit-Mach-O",
+ "dependencies": []
+ },
+ "datasets": [
+ {
+ "id": "merged-anchor-931d6627-v1",
+ "sha256": "e0e687d23f9417ace6364372fa8d72ea1bb4f83dabaf08d08d2523423e247911"
+ }
+ ],
+ "parameters": {
+ "link_above": 4.0,
+ "families": [
+ "engine_three_axis",
+ "catalogued",
+ "construction_only",
+ "decorrelated"
+ ]
+ },
+ "rng": {
+ "algorithm": null,
+ "seeds": []
+ },
+ "outputs": [
+ {
+ "path": "results/artifacts/anchor-axis-families-v1.json",
+ "bytes": 3152,
+ "sha256": "d465f3640860d0b51f20c6506ca7ae3715c89afb67e4e41b34a5dfc697276505"
+ },
+ {
+ "path": "results/generated/anchor-axis-families-v1.md",
+ "bytes": 1451,
+ "sha256": "4ce04704b838583333871b5d98883ceac02728ccfcea4c430b3ee7d49f708400"
+ }
+ ],
+ "reproducibility": {
+ "level": "bitwise_reproducible",
+ "availability": "complete"
+ },
+ "verification": {
+ "mode": "exact",
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.anchor_axis_families",
+ "--dataset",
+ "tests/fixtures/merged_anchor_931d6627.json",
+ "verify",
+ "--artifact",
+ "results/artifacts/anchor-axis-families-v1.json",
+ "--markdown",
+ "results/generated/anchor-axis-families-v1.md"
+ ],
+ "tests": [
+ "tests/test_anchor_axis_families.py",
+ "tests/test_wp4.py"
+ ],
+ "properties": [
+ "the three-axis engine refuses the edge and the catalogued library links it",
+ "the decorrelated family lands below link_above",
+ "an abstaining axis contributes no bits and is counted apart",
+ "the generated Markdown equals the canonical rendering"
+ ],
+ "tolerance": null
+ },
+ "limitations": [
+ "one named edge, chosen because the design decision was argued on it",
+ "the same-owner reading of this edge is the paper's, not an independent label",
+ "a score past link_above is what the engine would do, not what it does: the engine scores three axes",
+ "the library weights come from a population that was not preserved; library-calibration-v1 measures how far they sit from the one that was",
+ "the result is not a privacy score"
+ ]
+}
diff --git a/artifacts/sha256/2d/6a16cb098df8648a8964f46d50daab59b8f1d04a37e6a5b471ac10f7f64599 b/artifacts/sha256/2d/6a16cb098df8648a8964f46d50daab59b8f1d04a37e6a5b471ac10f7f64599
new file mode 100644
index 00000000..ca0913b5
--- /dev/null
+++ b/artifacts/sha256/2d/6a16cb098df8648a8964f46d50daab59b8f1d04a37e6a5b471ac10f7f64599
@@ -0,0 +1,13 @@
+# Public analysis facade contract
+
+Generated from the canonical experiment artifact. Do not edit manually.
+
+The deterministic fixture returns at depths 1 through 5 with the default nominal-value flow oracle.
+
+The change target's min-entropy changes from 1.000000 to 0.152003 bits after the address-reuse signal. Sharpening was observed: `True`.
+
+The uncapped walk reports 0 truncations and the one-node cap reports 2. The cap is observable: `True`.
+
+The opt-in `path_count` response field is a compatibility name for `provenance_route_accumulation`; it is not robust connectivity or k-routes.
+
+This run validates an API contract on a synthetic graph. It does not reproduce the historical live Bitcoin measurements and does not certify privacy.
diff --git a/artifacts/sha256/2d/f3e2c35dd3f8dcd0b4e510ccfe7907f96a0e5eb505a7b6f2daffb43994421c b/artifacts/sha256/2d/f3e2c35dd3f8dcd0b4e510ccfe7907f96a0e5eb505a7b6f2daffb43994421c
new file mode 100644
index 00000000..4de3bc21
--- /dev/null
+++ b/artifacts/sha256/2d/f3e2c35dd3f8dcd0b4e510ccfe7907f96a0e5eb505a7b6f2daffb43994421c
@@ -0,0 +1,117 @@
+{
+ "schema_version": 1,
+ "id": "amount-pairwise-family-v1",
+ "claim_ids": [
+ "ctp.amount.subtransaction_ambiguity"
+ ],
+ "code": {
+ "revision": "bcd9a3e511860b1b0ff5f3ee6338a2df29ecbfbf",
+ "dirty": false
+ },
+ "command": {
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.amount_pairwise_family",
+ "--dataset",
+ "data/amount-channel-812695-812831-v1.json",
+ "--cap",
+ "300",
+ "--wall-seconds",
+ "3.0",
+ "reproduce",
+ "--artifact",
+ "results/artifacts/amount-pairwise-family-v1.json",
+ "--markdown",
+ "results/generated/amount-pairwise-family-v1.md"
+ ]
+ },
+ "environment": {
+ "python": "3.13.7",
+ "lock_digest": "5ee1d70afd72fdb4ba3f01843e85e7ecd9a6d3e2a52abeb14c346a2143e3c186",
+ "platform": "macOS-26.6.2-arm64-arm-64bit-Mach-O",
+ "dependencies": [
+ {
+ "name": "dss",
+ "version": "0.1.0",
+ "source": "https://github.com/bc1cindy/dense-subset-sum",
+ "revision": "d8099bc540c9de7d0f3c9f095e8b2aad8f9d8c8d",
+ "lock_sha256": "f17a02f09da28dcc603901580d695466c44e1d1a0063e110d3faa375ae6066ca",
+ "license": "MIT",
+ "redistribution": "allowed",
+ "editable": false
+ }
+ ]
+ },
+ "datasets": [
+ {
+ "id": "amount-channel-812695-812831-v1",
+ "sha256": "83b4391f78d07a2fa85a67218d5ae4420d8965da3c95af577592e9ec1850104d"
+ }
+ ],
+ "parameters": {
+ "cap": 300,
+ "selection": "first complete multi-input transactions in source order",
+ "wall_seconds_per_transaction": 3.0
+ },
+ "rng": {
+ "algorithm": null,
+ "seeds": []
+ },
+ "outputs": [
+ {
+ "path": "results/artifacts/amount-pairwise-family-v1.json",
+ "bytes": 1027,
+ "sha256": "83f6a9e8501ee4df45f08b8c8ffc2f7265d40bf9add6d3f5093d712657c9dbf5"
+ },
+ {
+ "path": "results/generated/amount-pairwise-family-v1.md",
+ "bytes": 499,
+ "sha256": "aeaabdc282b4aed6946c6054b95e8b5dd9c20e50eca467de49827e453d5353df"
+ }
+ ],
+ "reproducibility": {
+ "level": "bitwise_reproducible",
+ "availability": "complete"
+ },
+ "verification": {
+ "mode": "exact",
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.amount_pairwise_family",
+ "--dataset",
+ "data/amount-channel-812695-812831-v1.json",
+ "--cap",
+ "300",
+ "--wall-seconds",
+ "3.0",
+ "verify",
+ "--artifact",
+ "results/artifacts/amount-pairwise-family-v1.json",
+ "--markdown",
+ "results/generated/amount-pairwise-family-v1.md"
+ ],
+ "tests": [
+ "tests/test_amount_pairwise_family_experiment.py",
+ "tests/test_oracle_audit.py"
+ ],
+ "properties": [
+ "refusal, wall timeout, and execution failure remain distinct",
+ "row support is labeled as internal to DSS's restricted family",
+ "singleton support is not presented as a global ownership proof",
+ "the selected population and ordering are explicit",
+ "the generated Markdown equals the canonical rendering"
+ ],
+ "tolerance": null
+ },
+ "limitations": [
+ "the matrix is a uniform marginal over DSS's restricted mapping family",
+ "a singleton-support row is not a globally deterministic ownership link",
+ "the exact-oracle audit found false certainties outside the restricted family",
+ "refusal, timeout, and execution failure are distinct outcomes",
+ "wall-time outcomes are resource- and platform-dependent",
+ "the sample is the first complete multi-input transactions in one 137-block snapshot",
+ "this diagnostic is not a privacy score or CoinScore"
+ ]
+}
diff --git a/artifacts/sha256/2e/75b1330181e7cbf8539a357e2bd3ad650c4d2d207c7a47ec687e991e24a622 b/artifacts/sha256/2e/75b1330181e7cbf8539a357e2bd3ad650c4d2d207c7a47ec687e991e24a622
new file mode 100644
index 00000000..7c525c79
--- /dev/null
+++ b/artifacts/sha256/2e/75b1330181e7cbf8539a357e2bd3ad650c4d2d207c7a47ec687e991e24a622
@@ -0,0 +1 @@
+{"corpus":"public Bitcoin transaction snapshot","experiment":"amount-per-coin-v1","limitations":["DSS per-coin log_w is a knee-truncated diagnostic, not an exact mapping count","unreachable coins are missing measurements and are never candidates","the transaction gate and per-coin diagnostic measure different objects","an exact transaction count does not make a per-coin candidate exact","input and output indices have separate identities","candidates are refuse signals, not ownership proofs or privacy scores"],"parameters":{"cut_threshold_natural_log":1.0},"report":{"per_coin_density":{"finite_log_w":{"maximum":19.40651597114107,"median":3.8066624897703196,"minimum":0.0},"reachable":{"input":316,"output":972,"total":1288},"unreachable":22338},"population":{"coins":23626,"inputs":17520,"outputs":6106,"transactions":1428},"transaction_gated":{"candidate_transactions":62,"candidates":{"input":18,"output":83,"total":101},"candidates_with_exact_transaction_count":{"input":0,"output":61,"total":61},"resolved_transactions":82},"ungated_diagnostic":{"candidate_transactions":95,"candidates":{"input":60,"output":258,"total":318}}},"schema_version":1}
diff --git a/artifacts/sha256/2f/74b40fb24558a23f162d09da46a5f76a4434c170a78e0deb75eabba5d2ca28 b/artifacts/sha256/2f/74b40fb24558a23f162d09da46a5f76a4434c170a78e0deb75eabba5d2ca28
new file mode 100644
index 00000000..c78b167e
--- /dev/null
+++ b/artifacts/sha256/2f/74b40fb24558a23f162d09da46a5f76a4434c170a78e0deb75eabba5d2ca28
@@ -0,0 +1,21 @@
+# Amount-channel survey
+
+Generated only from five SHA-256-pinned canonical artifacts. Do not edit manually.
+
+The snapshot contains 5,491 transactions and 1,428 complete multi-input transactions.
+
+## Independent local channels
+
+CoinJoin shape fires 28 times and de-mix resolves 0. Unnecessary-input fires 485 times. Roundness ranks 483 2×2 transactions, with 330 top ties. The conservation probe forces an output value 1,353 times under its declared largest-input assumption.
+
+## Subset-sum diagnostics
+
+Transaction W(E) returns 1,303 exact and 125 unknown outcomes; 80 expose a usable conservative nonzero log W.
+
+Per-coin density yields 318 raw candidates. After the transaction gate, 101 remain in 62 transactions.
+
+## Restricted DSS mapping family
+
+Mapping analysis answers 223 of 250 transactions; 221 answered families have zero-bit entropy. Pairwise analysis returns 914 rows, including 367 singleton-support rows.
+
+Entropy, certainty and support in the final section apply only to DSS's restricted mapping family. The components remain separate and are not a privacy score.
diff --git a/artifacts/sha256/2f/e5d3f085bb7b93ba2d275b442bb31f6ba8c1e95ca670c726474f2c874b0eea b/artifacts/sha256/2f/e5d3f085bb7b93ba2d275b442bb31f6ba8c1e95ca670c726474f2c874b0eea
new file mode 100644
index 00000000..a1bde19c
--- /dev/null
+++ b/artifacts/sha256/2f/e5d3f085bb7b93ba2d275b442bb31f6ba8c1e95ca670c726474f2c874b0eea
@@ -0,0 +1,14 @@
+# Construction-fingerprint pair separation
+
+Generated from the canonical experiment artifact. Do not edit manually.
+
+Transactions: 600; positive draws: 4000; negative draws: 4000.
+
+| metric | value |
+|---|---:|
+| positive mean bits | 19.646924 |
+| negative mean bits | -17.413052 |
+| AUC | 0.945875 |
+| shuffled-label AUC | 0.490825 |
+
+The canonical fingerprint scorer separates address-reuse-labelled pairs from random pairs on this selected snapshot. Address reuse is a weak and partly feature-dependent label, so this is pair-ranking evidence, not wallet attribution or a privacy score.
diff --git a/artifacts/sha256/30/6d898c560316cac38ca3e74df461e17ae37d2b4aa7006d628710ef94e59f42 b/artifacts/sha256/30/6d898c560316cac38ca3e74df461e17ae37d2b4aa7006d628710ef94e59f42
new file mode 100644
index 00000000..64a2a299
--- /dev/null
+++ b/artifacts/sha256/30/6d898c560316cac38ca3e74df461e17ae37d2b4aa7006d628710ef94e59f42
@@ -0,0 +1 @@
+{"adversary":"counterparty with private transaction records","consolidated_return":{"candidates_after":["alice"],"composition":null,"observations":2,"outcome":"candidate_narrowing"},"experiment":"eve-alice-eve-v1","isolated_return":{"candidates_after":[],"composition":null,"observations":1,"outcome":"inconclusive"},"limitations":["synthetic fixture","behavioral priors and temporal patterns are not modeled","candidate narrowing is not identity attribution","a collaborative deposit invalidates the common-ownership premise"],"schema_version":1}
diff --git a/artifacts/sha256/30/cb1732263d29dcca85159c6528705dfe7c9fa24451b795b7b3a41f9afbc447 b/artifacts/sha256/30/cb1732263d29dcca85159c6528705dfe7c9fa24451b795b7b3a41f9afbc447
new file mode 100644
index 00000000..82cb1b7c
--- /dev/null
+++ b/artifacts/sha256/30/cb1732263d29dcca85159c6528705dfe7c9fa24451b795b7b3a41f9afbc447
@@ -0,0 +1,18 @@
+# Exact-oracle audit
+
+Generated from the canonical experiment artifact. Do not edit manually.
+
+| quantity | value |
+|---|---:|
+| transactions | 507 |
+| link-matrix entries | 6228 |
+| entries above exact | 1714 |
+| entries below exact | 4104 |
+| spurious deterministic links | 945 |
+| missed deterministic links | 0 |
+| mapping-count undercounts | 491 |
+| mapping-count overcounts | 0 |
+
+Link-matrix bound direction: `neither`.
+
+DSS: `0.1.0` at revision `d8099bc540c9de7d0f3c9f095e8b2aad8f9d8c8d`.
diff --git a/artifacts/sha256/31/4b5660d08b84013f07692bdc207f791807dbb9f335ca10ade57dd7eb7a2fc5 b/artifacts/sha256/31/4b5660d08b84013f07692bdc207f791807dbb9f335ca10ade57dd7eb7a2fc5
new file mode 100644
index 00000000..e6c22907
--- /dev/null
+++ b/artifacts/sha256/31/4b5660d08b84013f07692bdc207f791807dbb9f335ca10ade57dd7eb7a2fc5
@@ -0,0 +1,100 @@
+{
+ "schema_version": 1,
+ "id": "amount-local-channels-v1",
+ "claim_ids": [
+ "ctp.payjoin.unnecessary_input",
+ "ctp.amount.subtransaction_ambiguity"
+ ],
+ "code": {
+ "revision": "bcd9a3e511860b1b0ff5f3ee6338a2df29ecbfbf",
+ "dirty": false
+ },
+ "command": {
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.amount_local_channels",
+ "--dataset",
+ "data/amount-channel-812695-812831-v1.json",
+ "reproduce",
+ "--artifact",
+ "results/artifacts/amount-local-channels-v1.json",
+ "--markdown",
+ "results/generated/amount-local-channels-v1.md"
+ ]
+ },
+ "environment": {
+ "python": "3.13.7",
+ "lock_digest": "e0d6a8cada5d383c0aca2bd2a37f28c15721392469c94084128d369c29c5544d",
+ "platform": "macOS-26.6.2-arm64-arm-64bit-Mach-O",
+ "dependencies": []
+ },
+ "datasets": [
+ {
+ "id": "amount-channel-812695-812831-v1",
+ "sha256": "83b4391f78d07a2fa85a67218d5ae4420d8965da3c95af577592e9ec1850104d"
+ }
+ ],
+ "parameters": {
+ "minimum_height": 812695,
+ "maximum_height": 812831,
+ "known_participant_probe": "largest input value"
+ },
+ "rng": {
+ "algorithm": null,
+ "seeds": []
+ },
+ "outputs": [
+ {
+ "path": "results/artifacts/amount-local-channels-v1.json",
+ "bytes": 825,
+ "sha256": "210ecb06ecaa32cc4cabe03ced8b85b0882a824dca2c361bd2c5dede7dcccf02"
+ },
+ {
+ "path": "results/generated/amount-local-channels-v1.md",
+ "bytes": 436,
+ "sha256": "4f4ea247a4bbca8cbe1ef24bc7b2ae070576d046c1f04a62bb8d528118fe1c28"
+ }
+ ],
+ "reproducibility": {
+ "level": "bitwise_reproducible",
+ "availability": "complete"
+ },
+ "verification": {
+ "mode": "exact",
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.amount_local_channels",
+ "--dataset",
+ "data/amount-channel-812695-812831-v1.json",
+ "verify",
+ "--artifact",
+ "results/artifacts/amount-local-channels-v1.json",
+ "--markdown",
+ "results/generated/amount-local-channels-v1.md"
+ ],
+ "tests": [
+ "tests/test_amount_channel_snapshot.py",
+ "tests/test_amount_local_channels_experiment.py",
+ "tests/test_coinjoin_demix.py",
+ "tests/test_conservation.py"
+ ],
+ "properties": [
+ "each local channel is reported separately",
+ "CoinJoin shape and successful de-mix are distinct counts",
+ "roundness ties are abstentions rather than ownership labels",
+ "the conservation probe records its known-participant assumption",
+ "DSS, mapping entropy, subset-sum, and composition are excluded",
+ "the generated Markdown equals the canonical rendering"
+ ],
+ "tolerance": null
+ },
+ "limitations": [
+ "the snapshot covers one 137-block interval from 2023",
+ "no recognized JoinMarket round appears in the measured population",
+ "roundness ties show abstention, not ownership truth",
+ "the conservation probe assumes the largest input is the known participant",
+ "this run excludes subset-sum, DSS, mapping entropy, and score composition"
+ ]
+}
diff --git a/artifacts/sha256/35/42b1199c220f49bd923306dcd1493ed5e7e677ff6fdabd1816256eb7021934 b/artifacts/sha256/35/42b1199c220f49bd923306dcd1493ed5e7e677ff6fdabd1816256eb7021934
new file mode 100644
index 00000000..15ace718
--- /dev/null
+++ b/artifacts/sha256/35/42b1199c220f49bd923306dcd1493ed5e7e677ff6fdabd1816256eb7021934
@@ -0,0 +1 @@
+{"experiment":"descent-vs-ascent-v1","limitations":["the snapshot contains six 2016 blocks and is not a chain-wide sample","blocks above max_block get the conservative pass, which cuts only where no pair objects and is not the best cut","signals are counted tells, not calibrated bits, so the thresholds are not likelihoods","the two passes are compared as partitions; neither is checked against same-owner labels","the fingerprint and roundness channels are absent here, as they are in the merge pass","the snapshot recipe is unavailable; the dataset publisher authorized redistribution on 2026-09-04"],"measurement":{"addresses":12341,"addresses_in_exactly_searched_blocks":7748,"ascent_blocks":3282,"ascent_refines_inherited":true,"descent":[{"agreement_with_ascent":{"identical":false,"joined_by_both":597699,"joined_only_by_ascent":0,"joined_only_by_descent":3014},"blocks_approximated":84,"blocks_cut":4,"blocks_left_whole":3178,"cut_below":-1.0,"reaches_ascent":false,"refines_inherited":true,"resulting_blocks":3261,"severed_weight":-823.0},{"agreement_with_ascent":{"identical":false,"joined_by_both":597699,"joined_only_by_ascent":0,"joined_only_by_descent":3837},"blocks_approximated":84,"blocks_cut":0,"blocks_left_whole":3182,"cut_below":-2.0,"reaches_ascent":false,"refines_inherited":true,"resulting_blocks":3182,"severed_weight":0},{"agreement_with_ascent":{"identical":false,"joined_by_both":597699,"joined_only_by_ascent":0,"joined_only_by_descent":3837},"blocks_approximated":84,"blocks_cut":0,"blocks_left_whole":3182,"cut_below":-4.0,"reaches_ascent":false,"refines_inherited":true,"resulting_blocks":3182,"severed_weight":0}],"evidence_reach":{"approximated_blocks_carrying_evidence":5,"joined_pairs":601536,"joined_pairs_in_exactly_searched_blocks":7669,"negative_pairs_in_approximated_blocks":1054,"negative_pairs_in_exactly_searched_blocks":0,"separated_by_ascent_only":3837,"separated_by_ascent_only_in_exactly_searched_blocks":0,"signalled_pairs":1054},"exactly_searched_blocks":3098,"inherited_blocks":3182,"largest_inherited_block":648,"transactions":12000},"parameters":{"channels":["coinjoin shape refuses every pair among the inputs","de-mix partition joins a participant's own inputs and refuses across participants","unnecessary input and mixed input types each refuse once"],"cospend_prior":0.0,"max_block":9,"signal_unit":"one published tell against a pairing","thresholds":[-1.0,-2.0,-4.0]},"schema_version":1}
diff --git a/artifacts/sha256/36/b8c85941d3a3ab382c1e3a14166226a5608f37176503294679b342179c1498 b/artifacts/sha256/36/b8c85941d3a3ab382c1e3a14166226a5608f37176503294679b342179c1498
new file mode 100644
index 00000000..e513c681
--- /dev/null
+++ b/artifacts/sha256/36/b8c85941d3a3ab382c1e3a14166226a5608f37176503294679b342179c1498
@@ -0,0 +1,18 @@
+# Global fingerprint-weight sensitivity
+
+Generated from the canonical experiment artifact. Do not edit manually.
+
+| consistency | positive mean bits | negative mean bits | AUC | shuffled AUC |
+|---:|---:|---:|---:|---:|
+| 0.60 | +23.056019 | +8.852878 | 0.908400 | 0.498700 |
+| 0.70 | +22.073064 | +5.593042 | 0.915200 | 0.499300 |
+| 0.80 | +20.615851 | +0.741083 | 0.921300 | 0.499550 |
+| 0.90 | +18.064538 | -7.809367 | 0.924050 | 0.498300 |
+| 0.95 | +15.465038 | -16.582117 | 0.924450 | 0.498400 |
+| 0.99 | +9.429186 | -36.951812 | 0.923900 | 0.499950 |
+
+AUC range from 0.90 through 0.99: 0.000550. Monotone non-decreasing: false.
+
+That flag reads the measured sequence, and the sequence does dip. The largest dip is 0.000550, against a standard error of 0.003101 at 4000 pairs per class — 0.18 standard errors. So the sweep does not support monotone improvement, and it does not establish a reversal either: over this band the AUC is flat within what the sample resolves.
+
+Evidence magnitudes are the part that moves. Address reuse is a weak label; this is not a privacy score.
diff --git a/artifacts/sha256/37/1e5cc5e6b3fd5de038e2fc75f7454a5cc59ce0a42c8189685921b9dc7acd43 b/artifacts/sha256/37/1e5cc5e6b3fd5de038e2fc75f7454a5cc59ce0a42c8189685921b9dc7acd43
new file mode 100644
index 00000000..51e9e1a9
Binary files /dev/null and b/artifacts/sha256/37/1e5cc5e6b3fd5de038e2fc75f7454a5cc59ce0a42c8189685921b9dc7acd43 differ
diff --git a/artifacts/sha256/37/acfce8b0222153c62882ce707326cf1f68dd9af0be998106a71272ab8c0d59 b/artifacts/sha256/37/acfce8b0222153c62882ce707326cf1f68dd9af0be998106a71272ab8c0d59
new file mode 100644
index 00000000..26962ef9
--- /dev/null
+++ b/artifacts/sha256/37/acfce8b0222153c62882ce707326cf1f68dd9af0be998106a71272ab8c0d59
@@ -0,0 +1,13 @@
+{
+ "schema_version": 1,
+ "python": {
+ "implementation": "cpython",
+ "version": "3.13",
+ "system": "Darwin",
+ "machine": "arm64"
+ },
+ "source_archive": "sources/decluster-bcd9a3e-runtime.tar",
+ "project_root": "decluster",
+ "requirements": "reproduction/fs-temporal/requirements.lock",
+ "dependency_directory": "reproduction/fs-temporal"
+}
diff --git a/artifacts/sha256/37/c8af2a0e43b47144cda0c37f7926ddc7ebb7448b6e46eeb4b37e49b8d0361c b/artifacts/sha256/37/c8af2a0e43b47144cda0c37f7926ddc7ebb7448b6e46eeb4b37e49b8d0361c
new file mode 100644
index 00000000..9399ffbb
--- /dev/null
+++ b/artifacts/sha256/37/c8af2a0e43b47144cda0c37f7926ddc7ebb7448b6e46eeb4b37e49b8d0361c
@@ -0,0 +1,86 @@
+{
+ "schema_version": 1,
+ "id": "eve-alice-eve-v1",
+ "claim_ids": [
+ "ctp.provenance.traceability"
+ ],
+ "code": {
+ "revision": "bcd9a3e511860b1b0ff5f3ee6338a2df29ecbfbf",
+ "dirty": false
+ },
+ "command": {
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.eve_alice_eve",
+ "reproduce",
+ "--artifact",
+ "results/artifacts/eve-alice-eve-v1.json",
+ "--markdown",
+ "results/generated/eve-alice-eve-v1.md"
+ ]
+ },
+ "environment": {
+ "python": "3.13.7",
+ "lock_digest": "e0d6a8cada5d383c0aca2bd2a37f28c15721392469c94084128d369c29c5544d",
+ "platform": "macOS-26.6.2-arm64-arm-64bit-Mach-O",
+ "dependencies": []
+ },
+ "datasets": [],
+ "parameters": {
+ "isolated_return_inputs": 1,
+ "consolidated_return_inputs": 2,
+ "adversary": "counterparty with private transaction records"
+ },
+ "rng": {
+ "algorithm": null,
+ "seeds": []
+ },
+ "outputs": [
+ {
+ "path": "results/artifacts/eve-alice-eve-v1.json",
+ "bytes": 548,
+ "sha256": "306d898c560316cac38ca3e74df461e17ae37d2b4aa7006d628710ef94e59f42"
+ },
+ {
+ "path": "results/generated/eve-alice-eve-v1.md",
+ "bytes": 417,
+ "sha256": "89e9239416d8f40ed38c5d36b2eb2430d726fd0e7fdcf56fbecc37bdf5bd2808"
+ }
+ ],
+ "reproducibility": {
+ "level": "bitwise_reproducible",
+ "availability": "complete"
+ },
+ "verification": {
+ "mode": "exact",
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.eve_alice_eve",
+ "verify",
+ "--artifact",
+ "results/artifacts/eve-alice-eve-v1.json",
+ "--markdown",
+ "results/generated/eve-alice-eve-v1.md"
+ ],
+ "tests": [
+ "tests/test_eve_alice_eve.py",
+ "tests/test_eve_alice_eve_experiment.py"
+ ],
+ "properties": [
+ "one ambiguous return remains inconclusive",
+ "two consolidated returns narrow the shared candidate customer",
+ "private counterparty records remain distinct from public observables",
+ "candidate narrowing is not represented as identity attribution",
+ "the generated Markdown equals the canonical rendering"
+ ],
+ "tolerance": null
+ },
+ "limitations": [
+ "synthetic fixture",
+ "candidate customers and ancestry are supplied rather than inferred",
+ "behavioral priors and temporal patterns are not modeled",
+ "the run does not measure a Bitcoin deanonymization rate"
+ ]
+}
diff --git a/artifacts/sha256/37/d0f5348a1fbb852a826d28b09ed7ce09184da149616c9fd5816e127058764e b/artifacts/sha256/37/d0f5348a1fbb852a826d28b09ed7ce09184da149616c9fd5816e127058764e
new file mode 100644
index 00000000..20915b01
--- /dev/null
+++ b/artifacts/sha256/37/d0f5348a1fbb852a826d28b09ed7ce09184da149616c9fd5816e127058764e
@@ -0,0 +1,16 @@
+# Receiver-side change read-off in a many-senders PayJoin
+
+Generated from the canonical experiment artifact. Do not edit manually.
+
+The receiver knows its own coins and the payment negotiated with each sender, so a candidate change output must satisfy `input - payment == change`. One sender's constraint rarely settles it; the perfect matchings propagate it across senders.
+
+| scenario | senders | matchings | distinct readings | unique reading | unanimous links |
+|---|---:|---:|---:|---|---:|
+| consolidating-receiver | 3 | 2 | 1 | yes | 1 |
+| evenly-spaced-control | 3 | 2 | 2 | no | 0 |
+
+Across 2 scenarios, 5 senders were ambiguous on their own constraint and propagation narrowed 2 of them; 1 scenario reached a single reading of the amounts.
+
+Matchings and readings are counted separately on purpose. Two matchings that assign different output indices but the same amounts are one reading, and the reading is what the receiver learns — the consolidating scenario has two matchings and one reading.
+
+Both scenarios are deterministic fixtures and the negotiated payments are supplied rather than inferred. An external observer holds neither those payments nor the receiver's own coins, so this is a counterparty capability. A unique reading of the amounts is not ownership attribution and is not a privacy score.
diff --git a/artifacts/sha256/39/bb12560c4c96749910324b85097938a7efe57e45ce380127bc7783c978f182 b/artifacts/sha256/39/bb12560c4c96749910324b85097938a7efe57e45ce380127bc7783c978f182
new file mode 100644
index 00000000..7b409c49
--- /dev/null
+++ b/artifacts/sha256/39/bb12560c4c96749910324b85097938a7efe57e45ce380127bc7783c978f182
@@ -0,0 +1 @@
+{"corpus":"adjacent public Bitcoin block windows","experiment":"ns-bitcoin-v1","limitations":["no independent gradeable seeds were found in these windows","the measured sweep uses seeds sampled from withheld correspondence","operational precision is approximately one percent","the observation windows are not representative of the whole chain","this does not reproduce the Twitter, Flickr, or LiveJournal experiments"],"report":{"independent_entity_seeds":{"entities":[{"bitmex":33,"satoshidice":5},{"bitmex":73,"satoshidice":12}],"found":0,"gradeable":0,"labelled_vertices":[23,47],"verdict":"fewer than two gradeable independent seeds; the independent attack is not identifiable on these views and the sweep below is seed-assisted"},"runs":[{"accepted_per_round":[101,65,34,17,10,8,8,4,1],"attack":{"correct":2,"coverage":0.0008100445524503848,"declared":248,"declared_with_an_image":17,"eligible":2469,"precision":0.008064516129032258,"precision_where_an_image_exists":0.11764705882352941},"overlap":{"edge_overlap":0.46734397677793904,"left_internal_edges":2756,"recurring_edges":1288,"vertices":2599},"rounds":9,"seed_fraction":0.05001923816852635,"seed_only_control":{"correct":0,"coverage":0.0,"declared":0,"eligible":2469,"precision":null},"seed_provenance":"withheld-correspondence-sample","seed_size":130,"separability":{"attack_only":2,"beats_shuffled":null,"min_correct_gain":1,"p":0.5,"shuffled_only":0},"shuffled_accepted_per_round":[87,49,23,14,11,3,10,2],"shuffled_seed_control":{"correct":0,"coverage":0.0,"declared":199,"declared_with_an_image":19,"eligible":2469,"fixed_points":0,"precision":0.0,"precision_where_an_image_exists":0.0},"theta":0.5},{"accepted_per_round":[99,65,34,17,10,8,8,4,1],"attack":{"correct":1,"coverage":0.0004050222762251924,"declared":246,"declared_with_an_image":16,"eligible":2469,"precision":0.0040650406504065045,"precision_where_an_image_exists":0.0625},"overlap":{"edge_overlap":0.46734397677793904,"left_internal_edges":2756,"recurring_edges":1288,"vertices":2599},"rounds":9,"seed_fraction":0.05001923816852635,"seed_only_control":{"correct":0,"coverage":0.0,"declared":0,"eligible":2469,"precision":null},"seed_provenance":"withheld-correspondence-sample","seed_size":130,"separability":{"attack_only":1,"beats_shuffled":null,"min_correct_gain":1,"p":1.0,"shuffled_only":0},"shuffled_accepted_per_round":[87,49,23,14,11,3,10,2],"shuffled_seed_control":{"correct":0,"coverage":0.0,"declared":199,"declared_with_an_image":19,"eligible":2469,"fixed_points":0,"precision":0.0,"precision_where_an_image_exists":0.0},"theta":1.5},{"accepted_per_round":[182,142,55,25,22,14,8,2],"attack":{"correct":3,"coverage":0.0012825994014536127,"declared":450,"declared_with_an_image":32,"eligible":2339,"precision":0.006666666666666667,"precision_where_an_image_exists":0.09375},"overlap":{"edge_overlap":0.46734397677793904,"left_internal_edges":2756,"recurring_edges":1288,"vertices":2599},"rounds":8,"seed_fraction":0.1000384763370527,"seed_only_control":{"correct":0,"coverage":0.0,"declared":0,"eligible":2339,"precision":null},"seed_provenance":"withheld-correspondence-sample","seed_size":260,"separability":{"attack_only":3,"beats_shuffled":null,"min_correct_gain":1,"p":0.25,"shuffled_only":0},"shuffled_accepted_per_round":[143,86,52,32,25,8,1,2,1,1],"shuffled_seed_control":{"correct":0,"coverage":0.0,"declared":351,"declared_with_an_image":23,"eligible":2339,"fixed_points":0,"precision":0.0,"precision_where_an_image_exists":0.0},"theta":0.5},{"accepted_per_round":[180,142,55,25,22,14,8,2],"attack":{"correct":2,"coverage":0.0008550662676357417,"declared":448,"declared_with_an_image":31,"eligible":2339,"precision":0.004464285714285714,"precision_where_an_image_exists":0.06451612903225806},"overlap":{"edge_overlap":0.46734397677793904,"left_internal_edges":2756,"recurring_edges":1288,"vertices":2599},"rounds":8,"seed_fraction":0.1000384763370527,"seed_only_control":{"correct":0,"coverage":0.0,"declared":0,"eligible":2339,"precision":null},"seed_provenance":"withheld-correspondence-sample","seed_size":260,"separability":{"attack_only":2,"beats_shuffled":null,"min_correct_gain":1,"p":0.5,"shuffled_only":0},"shuffled_accepted_per_round":[143,86,52,32,25,8,1,2,1,1],"shuffled_seed_control":{"correct":0,"coverage":0.0,"declared":351,"declared_with_an_image":23,"eligible":2339,"fixed_points":0,"precision":0.0,"precision_where_an_image_exists":0.0},"theta":1.5},{"accepted_per_round":[410,207,91,34,15,11,4,1,5,1],"attack":{"correct":10,"coverage":0.00513083632632119,"declared":779,"declared_with_an_image":49,"eligible":1949,"precision":0.012836970474967908,"precision_where_an_image_exists":0.20408163265306123},"overlap":{"edge_overlap":0.46734397677793904,"left_internal_edges":2756,"recurring_edges":1288,"vertices":2599},"rounds":10,"seed_fraction":0.2500961908426318,"seed_only_control":{"correct":0,"coverage":0.0,"declared":0,"eligible":1949,"precision":null},"seed_provenance":"withheld-correspondence-sample","seed_size":650,"separability":{"attack_only":10,"beats_shuffled":"a","min_correct_gain":1,"p":0.001953125,"shuffled_only":0},"shuffled_accepted_per_round":[284,135,67,22,6,1,2],"shuffled_seed_control":{"correct":0,"coverage":0.0,"declared":517,"declared_with_an_image":22,"eligible":1949,"fixed_points":2,"precision":0.0,"precision_where_an_image_exists":0.0},"theta":0.5},{"accepted_per_round":[405,207,91,34,15,11,4,1,5,1],"attack":{"correct":9,"coverage":0.004617752693689072,"declared":774,"declared_with_an_image":47,"eligible":1949,"precision":0.011627906976744186,"precision_where_an_image_exists":0.19148936170212766},"overlap":{"edge_overlap":0.46734397677793904,"left_internal_edges":2756,"recurring_edges":1288,"vertices":2599},"rounds":10,"seed_fraction":0.2500961908426318,"seed_only_control":{"correct":0,"coverage":0.0,"declared":0,"eligible":1949,"precision":null},"seed_provenance":"withheld-correspondence-sample","seed_size":650,"separability":{"attack_only":9,"beats_shuffled":"a","min_correct_gain":1,"p":0.00390625,"shuffled_only":0},"shuffled_accepted_per_round":[284,135,67,22,6,1,2],"shuffled_seed_control":{"correct":0,"coverage":0.0,"declared":517,"declared_with_an_image":22,"eligible":1949,"fixed_points":2,"precision":0.0,"precision_where_an_image_exists":0.0},"theta":1.5}],"views":{"clustered_addresses":613448,"construction":"adjacent disjoint height windows, boundary-split clustering","left":{"edges":163567,"heights":[391992,392111],"path":"data/ns-bitcoin-2016-v1/left-391992-392111.ndjson.gz","transactions":133576,"vertices":88066},"min_degree":2,"right":{"edges":189919,"heights":[392112,392231],"path":"data/ns-bitcoin-2016-v1/right-392112-392231.ndjson.gz","transactions":157724,"vertices":104328},"split_clusters":8030,"split_frac":1.0},"withheld_correspondence":{"edge_overlap":0.46734397677793904,"left_internal_edges":2756,"recurring_edges":1288,"vertices":2599}},"schema_version":1}
diff --git a/artifacts/sha256/3c/aca166d157fb1a1b7b8c995d126bf47ff653a6b7eb01b416b1cef7e254e02f b/artifacts/sha256/3c/aca166d157fb1a1b7b8c995d126bf47ff653a6b7eb01b416b1cef7e254e02f
new file mode 100644
index 00000000..b68e27db
--- /dev/null
+++ b/artifacts/sha256/3c/aca166d157fb1a1b7b8c995d126bf47ff653a6b7eb01b416b1cef7e254e02f
@@ -0,0 +1,9 @@
+# Transaction-level subset-sum W(E)
+
+Generated from the canonical experiment artifact. Do not edit manually.
+
+Of 1,428 multi-input transactions, 1,303 return `exact` and 125 return `unknown`.
+
+Among the exact results, 1,223 find no exact subset hit. Only 80 results expose a usable nonzero conservative log W.
+
+W(E) is not a mapping count, pairwise-link oracle, entropy, or CoinScore.
diff --git a/artifacts/sha256/3e/2fe2add4d7c3b3a015eb780c467551d8a5b444f5c97af310c759de2651e092 b/artifacts/sha256/3e/2fe2add4d7c3b3a015eb780c467551d8a5b444f5c97af310c759de2651e092
new file mode 100644
index 00000000..42c4cd9c
--- /dev/null
+++ b/artifacts/sha256/3e/2fe2add4d7c3b3a015eb780c467551d8a5b444f5c97af310c759de2651e092
@@ -0,0 +1,13 @@
+{
+ "schema_version": 1,
+ "python": {
+ "implementation": "cpython",
+ "version": "3.13",
+ "system": "Darwin",
+ "machine": "arm64"
+ },
+ "source_archive": "sources/decluster-bcd9a3e-runtime.tar",
+ "project_root": "decluster",
+ "requirements": "reproduction/slice-channels/requirements.lock",
+ "dependency_directory": "reproduction/slice-channels"
+}
diff --git a/artifacts/sha256/3f/75d9660582dd7ef44324734b6a0ec7497981305d2bdab917bcc976d76c4e4c b/artifacts/sha256/3f/75d9660582dd7ef44324734b6a0ec7497981305d2bdab917bcc976d76c4e4c
new file mode 100644
index 00000000..5caac6a5
--- /dev/null
+++ b/artifacts/sha256/3f/75d9660582dd7ef44324734b6a0ec7497981305d2bdab917bcc976d76c4e4c
@@ -0,0 +1 @@
+{"bits":{"max":21338.4716,"median":29.0476,"median_topk_floor":29.0476,"p90":68.6917},"dataset":"fs-blkcache-2026-09-04-v1","experiment":"cluster-bits-v1","parameters":{"graph":"payment edges only, co-spend excluded","merge_ambiguity_bits":1.6,"topk":5},"population":{"clusters":2538,"clusters_with_at_most_topk_counterparties":2345,"transactions":22112},"schema_version":1,"share_at_least":{"1.6":1.0,"10":0.978723,"100":0.052403,"50":0.165091},"source":{"bytes":91367352,"digest":"91c444bd4e9715d7739ccc9d68c7d9751762e25b647e3eef647c6ec0b48ba90c","files":904,"pattern":".blkcache/*.json","transactions":22112}}
diff --git a/artifacts/sha256/3f/d9f9bf57e30f4b92851852efa24e4af2201c705c39391d9451b68689005434 b/artifacts/sha256/3f/d9f9bf57e30f4b92851852efa24e4af2201c705c39391d9451b68689005434
new file mode 100644
index 00000000..b7067b77
--- /dev/null
+++ b/artifacts/sha256/3f/d9f9bf57e30f4b92851852efa24e4af2201c705c39391d9451b68689005434
@@ -0,0 +1,14 @@
+# Cluster-level provenance intersection fixture
+
+Generated from the canonical experiment artifact. Do not edit manually.
+
+| quantity | value |
+|---|---:|
+| branches | 2 |
+| candidates before | 2 |
+| candidates after | 1 |
+| candidates removed | 1 |
+| blind | `false` |
+| independently scored | `false` |
+
+This is a synthetic, conditional narrowing. It is not an ownership attribution.
diff --git a/artifacts/sha256/40/6ce2d7d0a04f7c73281bce6b6a781012a8765e079693f010a0154e926c6a6c b/artifacts/sha256/40/6ce2d7d0a04f7c73281bce6b6a781012a8765e079693f010a0154e926c6a6c
new file mode 100644
index 00000000..8c03f26b
--- /dev/null
+++ b/artifacts/sha256/40/6ce2d7d0a04f7c73281bce6b6a781012a8765e079693f010a0154e926c6a6c
@@ -0,0 +1 @@
+[{"txid": "91b718402dc93d89fba20f78a9f0df9efdcd5aea3e403b90fb0f09fd6ff4b27f", "version": 1, "locktime": 0, "vin": [{"txid": "92ec14bccbd2473a4965ad21ba96d000a6314681901770ceca75c7c8dde4fc96", "vout": 0, "prevout": {"scriptpubkey": "00149cd8ff261bfb702b69b82a0ea23d8f6ed6422c33", "scriptpubkey_asm": "OP_0 OP_PUSHBYTES_20 9cd8ff261bfb702b69b82a0ea23d8f6ed6422c33", "scriptpubkey_type": "v0_p2wpkh", "scriptpubkey_address": "bc1qnnv07fsmldczk6dc9g82y0v0dmtyytpnecthwj", "value": 1595697}, "scriptsig": "", "scriptsig_asm": "", "witness": ["3045022100b98e24ef4f723df6ded96743df901925083ed384c69b69ac601b3a31d342045502200a24d46d255f0a7bf4e68d172bc64e58a36747ebc9f9f04c44f4eabd328411bd01", "02589fd870a91828d35a72edcf8917bdd409475182f6003940da2dc5e58cf491c4"], "is_coinbase": false, "sequence": 4294967293}], "vout": [{"scriptpubkey": "001494fd951ac52eea8ab6b28de0230137af2d43d240", "scriptpubkey_asm": "OP_0 OP_PUSHBYTES_20 94fd951ac52eea8ab6b28de0230137af2d43d240", "scriptpubkey_type": "v0_p2wpkh", "scriptpubkey_address": "bc1qjn7e2xk99m4g4d4j3hszxqfh4uk585jq5m6dt7", "value": 1590000}, {"scriptpubkey": "6a34436c323a746f3a555344542854524f4e293a54444e58567736745864483348776e646a7670796853526d434363354461336e7351", "scriptpubkey_asm": "OP_RETURN OP_PUSHBYTES_52 436c323a746f3a555344542854524f4e293a54444e58567736745864483348776e646a7670796853526d434363354461336e7351", "scriptpubkey_type": "op_return", "value": 0}], "size": 255, "weight": 690, "sigops": 1, "fee": 5697, "status": {"confirmed": true, "block_height": 960609, "block_hash": "00000000000000000000a03afa531e4da7c9d2f4a967eabe69229a45ea1f8fa0", "block_time": 1785611389}}, {"txid": "593bbcb1ae200c16efe46fb989c18da88d17ad1c8704283d6440f7a1cd3e0920", "version": 1, "locktime": 0, "vin": [{"txid": "958b4956d016f9e21dc7eeadfa64468091c6db4d7edd4cdbb6bd50ee60388526", "vout": 0, "prevout": {"scriptpubkey": "00149cd8ff261bfb702b69b82a0ea23d8f6ed6422c33", "scriptpubkey_asm": "OP_0 OP_PUSHBYTES_20 9cd8ff261bfb702b69b82a0ea23d8f6ed6422c33", "scriptpubkey_type": "v0_p2wpkh", "scriptpubkey_address": "bc1qnnv07fsmldczk6dc9g82y0v0dmtyytpnecthwj", "value": 3853783}, "scriptsig": "", "scriptsig_asm": "", "witness": ["3045022100e606fe82b5b47dbd265561a0fdb2d2019ba2777f73fc7e5ebd291c8aef59a80a02202db69c859e3f874102457b5e6a8c339d36c072fcb58f0505002a755269e7327901", "02589fd870a91828d35a72edcf8917bdd409475182f6003940da2dc5e58cf491c4"], "is_coinbase": false, "sequence": 4294967293}], "vout": [{"scriptpubkey": "001494fd951ac52eea8ab6b28de0230137af2d43d240", "scriptpubkey_asm": "OP_0 OP_PUSHBYTES_20 94fd951ac52eea8ab6b28de0230137af2d43d240", "scriptpubkey_type": "v0_p2wpkh", "scriptpubkey_address": "bc1qjn7e2xk99m4g4d4j3hszxqfh4uk585jq5m6dt7", "value": 3850000}, {"scriptpubkey": "6a347a734e3a746f3a555344542854524f4e293a54444e58567736745864483348776e646a7670796853526d434363354461336e7351", "scriptpubkey_asm": "OP_RETURN OP_PUSHBYTES_52 7a734e3a746f3a555344542854524f4e293a54444e58567736745864483348776e646a7670796853526d434363354461336e7351", "scriptpubkey_type": "op_return", "value": 0}], "size": 255, "weight": 690, "sigops": 1, "fee": 3783, "status": {"confirmed": true, "block_height": 961329, "block_hash": "00000000000000000002024f8204a700dc7b41b099f264997f1d5b606afdbe9c", "block_time": 1786042680}}, {"txid": "b5c1e3bb2041528f2b5e3f18a9e41e1722fea9fddba5ba166f949c28a00fb43b", "version": 2, "locktime": 0, "vin": [{"txid": "0010726ca349df4d57d1464b193c2d0c4ac34fd27e734a883d21a8c414ab01a5", "vout": 1, "prevout": {"scriptpubkey": "001483cd10f5f009a703da99102ce8f8d76fb4a0b2a4", "scriptpubkey_asm": "OP_0 OP_PUSHBYTES_20 83cd10f5f009a703da99102ce8f8d76fb4a0b2a4", "scriptpubkey_type": "v0_p2wpkh", "scriptpubkey_address": "bc1qs0x3pa0spxns8k5ezqkw37xhd762pv4ycwpa39", "value": 92667467}, "scriptsig": "", "scriptsig_asm": "", "witness": ["304402206faa62faa96381a7938db7af52a0665dfa94b815b76b3ab10653fd929590666402205fcb9d5f9683eb49bae7b2d851f36b20b542a5e7d7b86e7f8361f1707215484801", "024374ec923b4d2908d2b083d0a6788e78de2f3f185622fd8f7713112718c513e7"], "is_coinbase": false, "sequence": 4294967293}], "vout": [{"scriptpubkey": "76a914252a1c5ecc482b03852c9b6dfd3fe08ec1dbf7b188ac", "scriptpubkey_asm": "OP_DUP OP_HASH160 OP_PUSHBYTES_20 252a1c5ecc482b03852c9b6dfd3fe08ec1dbf7b1 OP_EQUALVERIFY OP_CHECKSIG", "scriptpubkey_type": "p2pkh", "scriptpubkey_address": "14PWT1Ww9BJMdub3KSpnQtwdYSdeJ7gjeX", "value": 317900}, {"scriptpubkey": "001483cd10f5f009a703da99102ce8f8d76fb4a0b2a4", "scriptpubkey_asm": "OP_0 OP_PUSHBYTES_20 83cd10f5f009a703da99102ce8f8d76fb4a0b2a4", "scriptpubkey_type": "v0_p2wpkh", "scriptpubkey_address": "bc1qs0x3pa0spxns8k5ezqkw37xhd762pv4ycwpa39", "value": 92347553}], "size": 225, "weight": 573, "sigops": 5, "fee": 2014, "status": {"confirmed": true, "block_height": 961404, "block_hash": "0000000000000000000063f74770e996b3ce9c42673092efa682b36cfebf3e59", "block_time": 1786093346}}, {"txid": "7d0a8c6dd1f4a712ec3ca07beb32af10a7686ea995e5856fb03d2d32a872eda6", "version": 2, "locktime": 0, "vin": [{"txid": "b5c1e3bb2041528f2b5e3f18a9e41e1722fea9fddba5ba166f949c28a00fb43b", "vout": 1, "prevout": {"scriptpubkey": "001483cd10f5f009a703da99102ce8f8d76fb4a0b2a4", "scriptpubkey_asm": "OP_0 OP_PUSHBYTES_20 83cd10f5f009a703da99102ce8f8d76fb4a0b2a4", "scriptpubkey_type": "v0_p2wpkh", "scriptpubkey_address": "bc1qs0x3pa0spxns8k5ezqkw37xhd762pv4ycwpa39", "value": 92347553}, "scriptsig": "", "scriptsig_asm": "", "witness": ["304402207907983250d02f1f77010cb644612de54b24fd4e9a7614679c6104d9204fdf53022043af5795bb55caffd97a65e3dfc1a6bf8d8e0105f477cd6ac1516c9dcc7a024a01", "024374ec923b4d2908d2b083d0a6788e78de2f3f185622fd8f7713112718c513e7"], "is_coinbase": false, "sequence": 4294967293}], "vout": [{"scriptpubkey": "76a914346e406210d3854705588ec3fde1911a4c3733ec88ac", "scriptpubkey_asm": 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OP_PUSHBYTES_20 8d9351be66d9254bba40c0147c933ce9fb93b19b", "scriptpubkey_type": "v0_p2wpkh", "scriptpubkey_address": "bc1q3kf4r0nxmyj5hwjqcq28eyeua8ae8vvm8spx7s", "value": 2492900}, {"scriptpubkey": "001467290541e23d0150f943417323852556935ad282", "scriptpubkey_asm": "OP_0 OP_PUSHBYTES_20 67290541e23d0150f943417323852556935ad282", "scriptpubkey_type": "v0_p2wpkh", "scriptpubkey_address": "bc1qvu5s2s0z85q4p72rg9ej8pf926f4455zk2lyq2", "value": 265000}, {"scriptpubkey": "001495275189786f70beb742af60aabaec362e67c7b4", "scriptpubkey_asm": "OP_0 OP_PUSHBYTES_20 95275189786f70beb742af60aabaec362e67c7b4", "scriptpubkey_type": "v0_p2wpkh", "scriptpubkey_address": "bc1qj5n4rztcdactad6z4as24whvxchx03a5c0j43h", "value": 3863300}, {"scriptpubkey": "76a914b62fd9b1859632c195704ce3100ac2a6a07ea5b488ac", "scriptpubkey_asm": "OP_DUP OP_HASH160 OP_PUSHBYTES_20 b62fd9b1859632c195704ce3100ac2a6a07ea5b4 OP_EQUALVERIFY OP_CHECKSIG", "scriptpubkey_type": "p2pkh", "scriptpubkey_address": "1HcKJcf8gzouX4JD2k19KkSxGkgEwqcJ46", "value": 17500}, {"scriptpubkey": "0014341c0786bb4f5d1b066cb39b2de1b1fa9cacb99d", "scriptpubkey_asm": "OP_0 OP_PUSHBYTES_20 341c0786bb4f5d1b066cb39b2de1b1fa9cacb99d", "scriptpubkey_type": "v0_p2wpkh", "scriptpubkey_address": "bc1qxswq0p4mfaw3kpnvkwdjmcd3l2w2ewva40wa0l", "value": 60800}, {"scriptpubkey": "001477a7f480d7200db982e3c6017ef5e6434c6053c6", "scriptpubkey_asm": "OP_0 OP_PUSHBYTES_20 77a7f480d7200db982e3c6017ef5e6434c6053c6", "scriptpubkey_type": "v0_p2wpkh", "scriptpubkey_address": "bc1qw7nlfqxhyqxmnqhrccqhaa0xgdxxq57xz38w49", "value": 1228500}, {"scriptpubkey": "00140f4b4fc2a893d015b0c8fd2b6d0ef3a955470240", "scriptpubkey_asm": "OP_0 OP_PUSHBYTES_20 0f4b4fc2a893d015b0c8fd2b6d0ef3a955470240", "scriptpubkey_type": "v0_p2wpkh", "scriptpubkey_address": "bc1qpa95ls4gj0gptvxgl54k6rhn4925wqjqaqf4dr", "value": 60000}, {"scriptpubkey": "00143a0b4c440665b327426c8581f2a282c6f39af1e1", "scriptpubkey_asm": "OP_0 OP_PUSHBYTES_20 3a0b4c440665b327426c8581f2a282c6f39af1e1", "scriptpubkey_type": "v0_p2wpkh", "scriptpubkey_address": "bc1q8g95c3qxvkejwsnvskql9g5zcmee4u0pvg9r4t", "value": 22000}, {"scriptpubkey": "0014fefbe364255c36b03f8e5228dede07005ff61284", "scriptpubkey_asm": "OP_0 OP_PUSHBYTES_20 fefbe364255c36b03f8e5228dede07005ff61284", "scriptpubkey_type": "v0_p2wpkh", "scriptpubkey_address": "bc1qlma7xep9tsmtq0uw2g5dahs8qp0lvy5ym9xhuj", "value": 12467300}, {"scriptpubkey": "00147a498404b05f69d56caecdea4acf07e2a9efc621", "scriptpubkey_asm": "OP_0 OP_PUSHBYTES_20 7a498404b05f69d56caecdea4acf07e2a9efc621", "scriptpubkey_type": "v0_p2wpkh", "scriptpubkey_address": "bc1q0fycgp9sta5a2m9weh4y4nc8u257l33p5tzykc", "value": 360273172}], "size": 1167, "weight": 3372, "sigops": 8, "fee": 15930, "status": {"confirmed": true, "block_height": 964785, "block_hash": "00000000000000000000064bacd7e77b24ea2fb4b66051c3aeb664778122b415", "block_time": 1788122967}}]
\ No newline at end of file
diff --git a/artifacts/sha256/40/71e62e0ef642c4f762d585ed5849f67fd10d4dbe10a9433cec8de1d90866b6 b/artifacts/sha256/40/71e62e0ef642c4f762d585ed5849f67fd10d4dbe10a9433cec8de1d90866b6
new file mode 100644
index 00000000..c560881d
--- /dev/null
+++ b/artifacts/sha256/40/71e62e0ef642c4f762d585ed5849f67fd10d4dbe10a9433cec8de1d90866b6
@@ -0,0 +1 @@
+{"composition":null,"conclusion":"joint perfect matching resolves a round that independent per-message maxima cannot assign bijectively","experiment":"perfect-matching-disclosure-v1","joint_assignment":{"joint_likelihood":0.19200000000000003,"messages":3,"naive_is_bijective":false,"naive_receiver_positions":[0,0,2],"optimal_assignment_count":1,"optimal_assignments":[[0,1,2]],"unique_assignment_recovered":true},"limitations":["deterministic synthetic threshold-mix fixture","the profile-estimation phase is supplied and tested separately by statistical disclosure","the result links messages within one round and does not attribute Bitcoin ownership","the cited paper's simulations and success rates are not reproduced"],"schema_version":1,"uniform_profile_control":{"joint_likelihood":0.037037037037037035,"messages":3,"naive_is_bijective":false,"naive_receiver_positions":[0,0,0],"optimal_assignment_count":6,"optimal_assignments":[[0,1,2],[0,2,1],[1,0,2],[1,2,0],[2,0,1],[2,1,0]],"unique_assignment_recovered":false}}
diff --git a/artifacts/sha256/42/810b4fefa21a5a33f9cf17d0aa750718ad7c5ad563fa8edb7ae3ebcb70f8ba b/artifacts/sha256/42/810b4fefa21a5a33f9cf17d0aa750718ad7c5ad563fa8edb7ae3ebcb70f8ba
new file mode 100644
index 00000000..a50791c5
--- /dev/null
+++ b/artifacts/sha256/42/810b4fefa21a5a33f9cf17d0aa750718ad7c5ad563fa8edb7ae3ebcb70f8ba
@@ -0,0 +1,13 @@
+{
+ "schema_version": 1,
+ "python": {
+ "implementation": "cpython",
+ "version": "3.13",
+ "system": "Darwin",
+ "machine": "arm64"
+ },
+ "source_archive": "sources/decluster-bcd9a3e-runtime.tar",
+ "project_root": "decluster",
+ "requirements": "reproduction/entity-deanon/requirements.lock",
+ "dependency_directory": "reproduction/entity-deanon"
+}
diff --git a/artifacts/sha256/44/12fde285276c27d56c446ec47b792cbbb0e5c78112d60bbde78023baa6de9f b/artifacts/sha256/44/12fde285276c27d56c446ec47b792cbbb0e5c78112d60bbde78023baa6de9f
new file mode 100644
index 00000000..8aeab21d
--- /dev/null
+++ b/artifacts/sha256/44/12fde285276c27d56c446ec47b792cbbb0e5c78112d60bbde78023baa6de9f
@@ -0,0 +1,98 @@
+{
+ "schema_version": 1,
+ "id": "subtx-demix-v1",
+ "claim_ids": [
+ "ctp.amount.subtransaction_ambiguity"
+ ],
+ "code": {
+ "revision": "bcd9a3e511860b1b0ff5f3ee6338a2df29ecbfbf",
+ "dirty": false
+ },
+ "command": {
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.subtx_demix",
+ "--snapshot",
+ "data/subtx-demix-cache-2026-09-04.tar.gz",
+ "reproduce",
+ "--artifact",
+ "results/artifacts/subtx-demix-v1.json",
+ "--markdown",
+ "results/generated/subtx-demix-v1.md"
+ ]
+ },
+ "environment": {
+ "python": "3.13.7",
+ "lock_digest": "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855",
+ "platform": "macOS-26.6.2-arm64-arm-64bit-Mach-O",
+ "dependencies": []
+ },
+ "datasets": [
+ {
+ "id": "subtx-demix-cache-2026-09-04-v1",
+ "sha256": "191c8c2f082ac84af71b28a70169207d785243a276cf5b6e472ae6f5588a65a6"
+ }
+ ],
+ "parameters": {
+ "fee_cap_sats": 2000,
+ "minimum_equal_outputs": 3,
+ "eligible_input_range": [
+ 3,
+ 60
+ ]
+ },
+ "rng": {
+ "algorithm": null,
+ "seeds": []
+ },
+ "outputs": [
+ {
+ "path": "results/artifacts/subtx-demix-v1.json",
+ "bytes": 1140,
+ "sha256": "4485f64416d0839e876f4333c8d86e1bbb4ea8ab72c68b3f067812be6983a600"
+ },
+ {
+ "path": "results/generated/subtx-demix-v1.md",
+ "bytes": 516,
+ "sha256": "1def26d56c2c64f5c7e0ce9208628a6c335981811c47d219c7f92ef019096f57"
+ }
+ ],
+ "reproducibility": {
+ "level": "bitwise_reproducible",
+ "availability": "complete"
+ },
+ "verification": {
+ "mode": "exact",
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.subtx_demix",
+ "--snapshot",
+ "data/subtx-demix-cache-2026-09-04.tar.gz",
+ "verify",
+ "--artifact",
+ "results/artifacts/subtx-demix-v1.json",
+ "--markdown",
+ "results/generated/subtx-demix-v1.md"
+ ],
+ "tests": [
+ "tests/test_coinjoin_demix.py",
+ "tests/test_amount_refuse_demix.py",
+ "tests/test_subtx_demix_experiment.py"
+ ],
+ "properties": [
+ "the frozen cache reproduces the JoinMarket amount assignments",
+ "the result distinguishes amount identities from independent maker labels",
+ "the unlabelled CoinJoin-adjacent cache is refused as a specificity population",
+ "the absent historical Wasabi corpus is explicitly not reproduced"
+ ],
+ "tolerance": null
+ },
+ "limitations": [
+ "the cache has no ordinary-transaction labels",
+ "maker identities are not independently labeled",
+ "the historical Wasabi corpus is unavailable",
+ "fee cap and denomination detection are local modeling choices"
+ ]
+}
diff --git a/artifacts/sha256/44/192156107867a305e2f608af9185e4ce22fb88372238c88683f91120b1db16 b/artifacts/sha256/44/192156107867a305e2f608af9185e4ce22fb88372238c88683f91120b1db16
new file mode 100644
index 00000000..e4448fe0
Binary files /dev/null and b/artifacts/sha256/44/192156107867a305e2f608af9185e4ce22fb88372238c88683f91120b1db16 differ
diff --git a/artifacts/sha256/44/85f64416d0839e876f4333c8d86e1bbb4ea8ab72c68b3f067812be6983a600 b/artifacts/sha256/44/85f64416d0839e876f4333c8d86e1bbb4ea8ab72c68b3f067812be6983a600
new file mode 100644
index 00000000..50f4c27e
--- /dev/null
+++ b/artifacts/sha256/44/85f64416d0839e876f4333c8d86e1bbb4ea8ab72c68b3f067812be6983a600
@@ -0,0 +1 @@
+{"dataset":"subtx-demix-cache-2026-09-04-v1","experiment":"subtx-demix-v1","limitations":["the cache is CoinJoin-adjacent and has no ordinary-transaction labels","22 of 25 eligible cached transactions trigger, so this population cannot support a specificity claim","maker identities are inferred from an amount identity rather than independently labeled","the six historical Wasabi rounds are not preserved as a dataset","the fixed fee cap and most-common-output detector are local modeling choices","this is attacker-side de-mix evidence, not CoinScore or a privacy certificate"],"measurement":{"cache_json_files":37,"cache_transactions":36,"eligible_transactions":25,"eligible_with_any_recovery":22,"joinmarket":{"fees":[458,413,623,559,636,973,687,191],"inputs":12,"outputs":21,"recovered_changes":[113283033,2089662830,765432353,10187122,90781833,8045121,100823618,8125627],"recovered_inputs":[0,1,3,6,7,8,9,11],"txid":"0cb4870cf2dfa3877851088c673d163ae3c20ebcd6505c0be964d8fbcc856bbf"}},"schema_version":1,"verdicts":{"joinmarket_fixture":"reproduced","ordinary_transaction_specificity":"not_tested","wasabi_rounds":"not_reproduced"}}
diff --git a/artifacts/sha256/46/46ed52e648da2690331616a7d43c379f2c8ef24f3c0e1f1040d87c5c3b80b4 b/artifacts/sha256/46/46ed52e648da2690331616a7d43c379f2c8ef24f3c0e1f1040d87c5c3b80b4
new file mode 100644
index 00000000..50b26747
--- /dev/null
+++ b/artifacts/sha256/46/46ed52e648da2690331616a7d43c379f2c8ef24f3c0e1f1040d87c5c3b80b4
@@ -0,0 +1,13 @@
+{
+ "schema_version": 1,
+ "python": {
+ "implementation": "cpython",
+ "version": "3.13",
+ "system": "Darwin",
+ "machine": "arm64"
+ },
+ "source_archive": "sources/decluster-bcd9a3e-runtime.tar",
+ "project_root": "decluster",
+ "requirements": "reproduction/perfect-matching-disclosure/requirements.lock",
+ "dependency_directory": "reproduction/perfect-matching-disclosure"
+}
diff --git a/artifacts/sha256/46/5dbf1548bcab18f0fd6ef01a940093bac7b35ec93a431549a8dd66389daefc b/artifacts/sha256/46/5dbf1548bcab18f0fd6ef01a940093bac7b35ec93a431549a8dd66389daefc
new file mode 100644
index 00000000..dc126f38
--- /dev/null
+++ b/artifacts/sha256/46/5dbf1548bcab18f0fd6ef01a940093bac7b35ec93a431549a8dd66389daefc
@@ -0,0 +1 @@
+{"dependencies":{"dss_revision":"d8099bc540c9de7d0f3c9f095e8b2aad8f9d8c8d","dss_version":"0.1.0"},"experiment":"exact-oracle-audit-v1","parameters":{"alphabet":[1,2,3,4],"max_coins":8,"min_side":2,"tolerance":1e-09},"report":{"comparisons":{"deterministic_links":{"agreements":179,"bound_direction":"upper","cases":507,"compared":507,"exceeds_exact":[{"inputs":[1,1],"missed_links":[],"outputs":[1,1],"spurious_links":[[0,0],[1,1]]},{"inputs":[2,2],"missed_links":[],"outputs":[2,2],"spurious_links":[[0,0],[1,1]]},{"inputs":[3,3],"missed_links":[],"outputs":[3,3],"spurious_links":[[0,0],[1,1]]},{"inputs":[4,4],"missed_links":[],"outputs":[4,4],"spurious_links":[[0,0],[1,1]]},{"inputs":[1,2],"missed_links":[],"outputs":[1,1,1],"spurious_links":[[0,2],[1,0],[1,1]]},{"inputs":[1,3],"missed_links":[],"outputs":[1,1,2],"spurious_links":[[0,1],[1,0]]},{"inputs":[1,4],"missed_links":[],"outputs":[1,1,3],"spurious_links":[[0,1],[1,0]]},{"inputs":[2,3],"missed_links":[],"outputs":[1,2,2],"spurious_links":[[0,2],[1,1]]},{"inputs":[2,4],"missed_links":[],"outputs":[2,2,2],"spurious_links":[[0,2],[1,0],[1,1]]},{"inputs":[3,4],"missed_links":[],"outputs":[1,3,3],"spurious_links":[[0,2],[1,1]]},{"inputs":[1,3],"missed_links":[],"outputs":[1,1,1,1],"spurious_links":[[0,3],[1,0],[1,1],[1,2]]},{"inputs":[1,4],"missed_links":[],"outputs":[1,1,1,2],"spurious_links":[[0,2],[1,0],[1,1]]}],"exceeds_exact_cases":328,"missed_links":0,"object_note":"the (input, output) pairs every mapping agrees on — over dss's family against the oracle's; counting.link_matrix's docstring reads a unit row to consumers as 'the amounts settle that assignment on their own', which is the claim a spurious link makes","overcounts":328,"refusals":0,"relation":"restriction","spurious_links":945,"undercounts":0},"deterministic_links_finest_only":{"agreements":179,"bound_direction":"upper","cases":507,"compared":507,"exceeds_exact":[{"inputs":[1,1],"missed_links":[],"outputs":[1,1],"spurious_links":[[0,0],[1,1]]},{"inputs":[2,2],"missed_links":[],"outputs":[2,2],"spurious_links":[[0,0],[1,1]]},{"inputs":[3,3],"missed_links":[],"outputs":[3,3],"spurious_links":[[0,0],[1,1]]},{"inputs":[4,4],"missed_links":[],"outputs":[4,4],"spurious_links":[[0,0],[1,1]]},{"inputs":[1,2],"missed_links":[],"outputs":[1,1,1],"spurious_links":[[0,2],[1,0],[1,1]]},{"inputs":[1,3],"missed_links":[],"outputs":[1,1,2],"spurious_links":[[0,1],[1,0]]},{"inputs":[1,4],"missed_links":[],"outputs":[1,1,3],"spurious_links":[[0,1],[1,0]]},{"inputs":[2,3],"missed_links":[],"outputs":[1,2,2],"spurious_links":[[0,2],[1,1]]},{"inputs":[2,4],"missed_links":[],"outputs":[2,2,2],"spurious_links":[[0,2],[1,0],[1,1]]},{"inputs":[3,4],"missed_links":[],"outputs":[1,3,3],"spurious_links":[[0,2],[1,1]]},{"inputs":[1,3],"missed_links":[],"outputs":[1,1,1,1],"spurious_links":[[0,3],[1,0],[1,1],[1,2]]},{"inputs":[1,4],"missed_links":[],"outputs":[1,1,1,2],"spurious_links":[[0,2],[1,0],[1,1]]}],"exceeds_exact_cases":328,"missed_links":0,"object_note":"as deterministic_links, but the oracle side keeps only the maximal-refinement mappings","overcounts":328,"refusals":0,"relation":"restriction","spurious_links":945,"undercounts":0},"link_matrix":{"agreements":16,"bound_direction":"neither","cases":507,"compared":507,"disagreements":491,"entries":6228,"entries_above_exact":1714,"entries_agreeing":410,"entries_below_exact":4104,"exceeds_exact":[{"entries_above_exact":2,"inputs":[1,1],"max_absolute_error":0.6666666666666666,"outputs":[1,1]},{"entries_above_exact":2,"inputs":[2,2],"max_absolute_error":0.6666666666666666,"outputs":[2,2]},{"entries_above_exact":2,"inputs":[3,3],"max_absolute_error":0.6666666666666666,"outputs":[3,3]},{"entries_above_exact":2,"inputs":[4,4],"max_absolute_error":0.6666666666666666,"outputs":[4,4]},{"entries_above_exact":3,"inputs":[1,2],"max_absolute_error":0.75,"outputs":[1,1,1]},{"entries_above_exact":2,"inputs":[1,3],"max_absolute_error":0.6666666666666666,"outputs":[1,1,2]},{"entries_above_exact":2,"inputs":[1,4],"max_absolute_error":0.6666666666666666,"outputs":[1,1,3]},{"entries_above_exact":2,"inputs":[2,3],"max_absolute_error":0.6666666666666666,"outputs":[1,2,2]},{"entries_above_exact":3,"inputs":[2,4],"max_absolute_error":0.75,"outputs":[2,2,2]},{"entries_above_exact":2,"inputs":[3,4],"max_absolute_error":0.6666666666666666,"outputs":[1,3,3]},{"entries_above_exact":4,"inputs":[1,3],"max_absolute_error":0.8,"outputs":[1,1,1,1]},{"entries_above_exact":3,"inputs":[1,4],"max_absolute_error":0.75,"outputs":[1,1,1,2]}],"exceeds_exact_cases":440,"max_absolute_error":0.8333333333333334,"max_relative_error":2.0000000000000004,"object_note":"P(input i and output j share a block): the same quantity both sides, but dss marginalizes over its own restricted family (verify_dss_marginal_family), so it bounds the oracle's marginal in neither direction","refusals":0,"relation":"restriction","shape_mismatches":0,"undefined_relative_errors":0},"link_matrix_finest_only":{"agreements":68,"bound_direction":"neither","cases":507,"compared":507,"disagreements":439,"entries":6228,"entries_above_exact":1811,"entries_agreeing":2314,"entries_below_exact":2103,"exceeds_exact":[{"entries_above_exact":2,"inputs":[1,1],"max_absolute_error":0.5,"outputs":[1,1]},{"entries_above_exact":2,"inputs":[2,2],"max_absolute_error":0.5,"outputs":[2,2]},{"entries_above_exact":2,"inputs":[3,3],"max_absolute_error":0.5,"outputs":[3,3]},{"entries_above_exact":2,"inputs":[4,4],"max_absolute_error":0.5,"outputs":[4,4]},{"entries_above_exact":3,"inputs":[1,2],"max_absolute_error":0.6666666666666667,"outputs":[1,1,1]},{"entries_above_exact":2,"inputs":[1,3],"max_absolute_error":0.5,"outputs":[1,1,2]},{"entries_above_exact":2,"inputs":[1,4],"max_absolute_error":0.5,"outputs":[1,1,3]},{"entries_above_exact":2,"inputs":[2,3],"max_absolute_error":0.5,"outputs":[1,2,2]},{"entries_above_exact":3,"inputs":[2,4],"max_absolute_error":0.6666666666666667,"outputs":[2,2,2]},{"entries_above_exact":2,"inputs":[3,4],"max_absolute_error":0.5,"outputs":[1,3,3]},{"entries_above_exact":4,"inputs":[1,3],"max_absolute_error":0.75,"outputs":[1,1,1,1]},{"entries_above_exact":3,"inputs":[1,4],"max_absolute_error":0.6666666666666667,"outputs":[1,1,1,2]}],"exceeds_exact_cases":439,"max_absolute_error":0.8,"max_relative_error":4.0,"object_note":"as link_matrix, but the oracle side keeps only the maximal-refinement mappings","refusals":0,"relation":"restriction","shape_mismatches":0,"undefined_relative_errors":0},"mapping_count":{"agreements":16,"bound_direction":"lower","cases":507,"compared":507,"exceeds_exact":[],"exceeds_exact_cases":0,"max_absolute_error":107,"max_relative_error":0.92,"object_note":"mappings both sides, over dss's refinement-maximal family against every index-level balanced set partition","overcounts":0,"refusals":0,"relation":"restriction","undefined_relative_errors":0,"undercounts":491},"mapping_entropy_bits":{"agreements":16,"bound_direction":"lower","cases":507,"compared":507,"exceeds_exact":[],"exceeds_exact_cases":0,"max_absolute_error":3.6438561897747244,"max_relative_error":1.0,"object_note":"both are log2 of a mapping count under a uniform prior, over the two families named for mapping_count","overcounts":0,"refusals":0,"relation":"restriction","undefined_relative_errors":0,"undercounts":491}},"family":{"alphabet":[1,2,3,4],"canonical_form":"sorted input multiset, sorted output multiset; scale not folded","fee":0,"max_coins":8,"min_side":2,"size":507},"flags":["link_matrix: 440 case(s) where the approximation exceeds the exact oracle","link_matrix: neither a lower nor an upper bound — errs in both directions","deterministic_links: 328 case(s) where the approximation exceeds the exact oracle","deterministic_links: 945 link(s) reported as certain that the exact oracle leaves ambiguous","link_matrix_finest_only: 439 case(s) where the approximation exceeds the exact oracle","link_matrix_finest_only: neither a lower nor an upper bound — errs in both directions","deterministic_links_finest_only: 328 case(s) where the approximation exceeds the exact oracle","deterministic_links_finest_only: 945 link(s) reported as certain that the exact oracle leaves ambiguous"],"identifications":{"count_w_router_and_saddle_point":{"cases_is_dense_reads_as_dense":443,"kinds":{"exact":486,"lower_bound":5,"unknown":16},"methods":{"none":16,"sparse":491},"note":"The radix tier never wins on this family: it returns zero, `_resolved` reads that as no answer, and the route falls through to sparse. The saddle point declines every case here even though `is_dense` reads most of them as Dense, which is the optimistic-gate discrepancy its own docstring predicts, now measured.","object":"count_w routes to radix or sparse; the sparse answer is the same subset-sum solution count W(E), so the router is a different object from |M| for the same reason w_count is","relation":"different-object","saddle_point_answers":0,"sparse_answers":491,"sparse_answers_matching_the_subset_sum_rederivation":491},"per_coin_density":{"cases_where_exact_input_marginals_vary":339,"coin_invariant_cases":497,"coin_varying_cases":10,"note":"log_w is a natural log while the oracle's entropy is in bits, and the quantity is a subset-sum density rather than a link marginal, so no entrywise comparison is made. The reportable fact is structural: where its value is identical for every coin of a transaction it cannot express per-coin variation that the exact marginals do show.","object":"per-coin log_w (natural log) from the fee-aware density path, not a probability","refusals":0,"relation":"different-object"},"radix_mappings":{"input_independence_witnesses":[],"note":"The call does not take the inputs, so one answer stands for every transaction sharing an output multiset while the exact mapping count differs across them; and the answer moves when every coin is multiplied by a constant, which the exact mapping count cannot. A quantity blind to a parameter the target depends on, and sensitive to one it does not, is not a bound on the target.","object":"permutations of repeated output denominations; a function of the outputs alone","output_multisets_where_radix_applies":25,"output_multisets_where_radix_returned_a_nonzero_count":0,"output_multisets_whose_radix_count_moved_under_scaling":19,"relation":"different-object","scale":1000,"scale_dependence_witnesses":[{"exact_mapping_counts":[7,21,27,29],"outputs":[1,1,2,2,2],"radix_count":0,"scaled_exact_mapping_counts":[7,21,27,29],"scaled_outputs":[1000,1000,2000,2000,2000],"scaled_radix_count":8},{"exact_mapping_counts":[28],"outputs":[1,1,3,3,3],"radix_count":0,"scaled_exact_mapping_counts":[28],"scaled_outputs":[1000,1000,3000,3000,3000],"scaled_radix_count":2},{"exact_mapping_counts":[4,8,11,16,25,30,53],"outputs":[1,2,2,2],"radix_count":0,"scaled_exact_mapping_counts":[4,8,11,16,25,30,53],"scaled_outputs":[1000,2000,2000,2000],"scaled_radix_count":7},{"exact_mapping_counts":[13,20,27],"outputs":[1,2,2,2,2],"radix_count":0,"scaled_exact_mapping_counts":[13,20,27],"scaled_outputs":[1000,2000,2000,2000,2000],"scaled_radix_count":25},{"exact_mapping_counts":[18,19],"outputs":[1,2,2,2,3],"radix_count":0,"scaled_exact_mapping_counts":[18,19],"scaled_outputs":[1000,2000,2000,2000,3000],"scaled_radix_count":9},{"exact_mapping_counts":[18],"outputs":[1,2,2,2,4],"radix_count":0,"scaled_exact_mapping_counts":[18],"scaled_outputs":[1000,2000,2000,2000,4000],"scaled_radix_count":13},{"exact_mapping_counts":[10],"outputs":[1,2,3,3,3],"radix_count":0,"scaled_exact_mapping_counts":[10],"scaled_outputs":[1000,2000,3000,3000,3000],"scaled_radix_count":4},{"exact_mapping_counts":[7,9,13,16,23,53],"outputs":[1,3,3,3],"radix_count":0,"scaled_exact_mapping_counts":[7,9,13,16,23,53],"scaled_outputs":[1000,3000,3000,3000],"scaled_radix_count":1},{"exact_mapping_counts":[4,16,53],"outputs":[1,4,4,4],"radix_count":0,"scaled_exact_mapping_counts":[4,16,53],"scaled_outputs":[1000,4000,4000,4000],"scaled_radix_count":1},{"exact_mapping_counts":[1,4,10,16,40],"outputs":[2,2,2],"radix_count":0,"scaled_exact_mapping_counts":[1,4,10,16,40],"scaled_outputs":[2000,2000,2000],"scaled_radix_count":6},{"exact_mapping_counts":[5,7,17,27,131],"outputs":[2,2,2,2],"radix_count":0,"scaled_exact_mapping_counts":[5,7,17,27,131],"scaled_outputs":[2000,2000,2000,2000],"scaled_radix_count":24},{"exact_mapping_counts":[11,56],"outputs":[2,2,2,2,2],"radix_count":0,"scaled_exact_mapping_counts":[11,56],"scaled_outputs":[2000,2000,2000,2000,2000],"scaled_radix_count":120}]},"subtransaction_ambiguity_bits":{"cases_above_exact_entropy":6,"cases_below_exact_entropy":6,"cases_equal_to_exact_entropy":0,"note":"Its own docstring already calls this a count diagnostic and not a privacy quantity. The count admits partitions that do not conserve value, so it is neither a bound on nor an estimate of the oracle's mapping entropy; the tallies above describe how the two numbers happen to order, not a bound.","object":"log2 of the number of (receiver input, receiver output) pairs with a positive implied payment; no block is required to balance","refusals":4,"relation":"different-object","two_in_two_out_cases":16},"w_count":{"cases_where_w_equals_exact_mapping_count":34,"exact_answers_checked":507,"object":"non-empty proper input subsets whose sum is a proper output-subset sum, W(E)","rederivation":"decluster.baselines.oracle_audit._subset_sum_solutions","rederivation_examples":[],"rederivation_mismatches":0,"relation":"different-object","verdict":"W(E) identified: dss.w_count reproduces the independent subset-sum re-derivation on 507/507 exact answers. It is not the mapping count, so this module reports the object mismatch and refuses to score it as an under/overcount."}},"restriction_evidence":{"dss_marginal_family":{"cases_checked":507,"cases_where_the_dss_family_is_strictly_smaller_than_the_oracle_family":491,"counterexamples":[],"entries_are_multiples_of_one_over_n_non_derived":507,"unit_entries_equal_the_reported_deterministic_links":507,"verdict":"pairwise_link_prob is the uniform marginal over dss's own family: quantized to 1/n_non_derived on 507/507 cases and unit-valued exactly on the deterministic links it reports on 507/507. That family is strictly smaller than the oracle's on 491 cases, so the matrix, the count and the certain-link set are all restrictions and none is a bound in either direction."},"finest_selection_sensitivity":{"cases":507,"cases_where_the_two_selections_differ":106,"max_block_count":{"missed_links":108,"spurious_links":942},"note":"The audit's cross-check uses refinement-maximal selection, the maximal elements of the refinement order, because that is what 'finest' means on a poset of partitions. Under max-block-count selection — a strict subset, so a smaller oracle family and more oracle certainties, a reading strictly more generous to the approximation — the spurious count falls and certain links begin to read as missed. The primary deterministic_links probe uses neither: it runs against the full oracle family.","refinement_maximal":{"missed_links":0,"spurious_links":945}},"mapping_count_mechanism":{"all_equal_value_cases":12,"all_equal_value_cases_answering_one":0,"cases":507,"different_from_the_finest_oracle_mapping_count":0,"equal_to_the_finest_oracle_mapping_count":507,"examples_where_it_differs":[],"note":"The current DSS count equals the independent refinement-maximal mapping count on the entire bounded family. Equal-value permutations do not collapse in general: `[1,3,4,4] -> [3,3,3,3]` answers 4, and those readings differ only in which equal-valued output stands alone. Agreement on this family is measured, not universal."}},"tolerance":1e-09},"schema_version":1,"summary":{"dss_family_strictly_smaller_cases":491,"dss_marginal_cases_checked":507,"dss_marginal_quantized_cases":507,"dss_marginal_unit_entries_match_deterministic_links":507,"dss_rev":"d8099bc540c9de7d0f3c9f095e8b2aad8f9d8c8d","dss_version":"0.1.0","family_size":507,"finest_only_entries_above_exact":1811,"finest_only_entries_below_exact":2103,"finest_only_missed_deterministic_links":0,"finest_only_spurious_deterministic_links":945,"finest_selection_divergent_cases":106,"link_matrix_bound_direction":"neither","link_matrix_entries":6228,"link_matrix_entries_above_exact":1714,"link_matrix_entries_below_exact":4104,"mapping_count_overcounts":0,"mapping_count_undercounts":491,"max_block_count_missed_deterministic_links":108,"max_block_count_spurious_deterministic_links":942,"missed_deterministic_links":0,"spurious_deterministic_links":945,"w_count_rederivation_mismatches":0}}
diff --git a/artifacts/sha256/46/7a51db69c377bb05e49c5ac60f680a9821e0ea888dde636d75697d78713e6c b/artifacts/sha256/46/7a51db69c377bb05e49c5ac60f680a9821e0ea888dde636d75697d78713e6c
new file mode 100644
index 00000000..4ca98348
--- /dev/null
+++ b/artifacts/sha256/46/7a51db69c377bb05e49c5ac60f680a9821e0ea888dde636d75697d78713e6c
@@ -0,0 +1 @@
+{"dataset":"fs-blkcache-2026-09-04-v1","experiment":"weight-sensitivity-v1","historical_comparison":{"not_supported":"monotone improvement: the measured sequence dips, but by a fifth of one standard error, which does not establish a reversal either","preserved":"AUC changes little from consistency 0.90 through 0.99","status":"not_supported"},"limitations":["address reuse is a weak and partly feature-dependent ownership proxy","the sweep varies one global value rather than fitting each axis","the same selected sample is reused at every grid point","the snapshot is not representative of the whole chain","AUC stability does not imply calibrated evidence magnitudes","the sweep resolves differences of about one standard error; smaller ones are reported and are not readable as an ordering","this is attacker-side sensitivity analysis, not CoinScore or a privacy certificate"],"measurement":{"auc_is_monotone_non_decreasing":false,"auc_standard_error":0.003100965514608813,"largest_decrement":0.0005499999999999394,"largest_decrement_in_standard_errors":0.17736411366358648,"pair_cap_per_class":4000,"realistic_band_auc_range":0.0005499999999999394,"rows":[{"auc":0.9084,"consistency":0.6,"n_neg":4000,"n_pos":4000,"neg_mean":8.852877813661172,"pos_mean":23.05601893431654,"shuffle_auc":0.4987},{"auc":0.9152,"consistency":0.7,"n_neg":4000,"n_pos":4000,"neg_mean":5.593042362354659,"pos_mean":22.073064493220734,"shuffle_auc":0.4993},{"auc":0.9213,"consistency":0.8,"n_neg":4000,"n_pos":4000,"neg_mean":0.7410834698818792,"pos_mean":20.615851379901276,"shuffle_auc":0.49955},{"auc":0.92405,"consistency":0.9,"n_neg":4000,"n_pos":4000,"neg_mean":-7.809367238531249,"pos_mean":18.064538046462225,"shuffle_auc":0.4983},{"auc":0.92445,"consistency":0.95,"n_neg":4000,"n_pos":4000,"neg_mean":-16.582117238531236,"pos_mean":15.465038046462228,"shuffle_auc":0.4984},{"auc":0.9239,"consistency":0.99,"n_neg":4000,"n_pos":4000,"neg_mean":-36.95181193295434,"pos_mean":9.429185963802531,"shuffle_auc":0.49995}],"seed":0,"transactions":22112},"schema_version":1,"source":{"bytes":91367352,"digest":"91c444bd4e9715d7739ccc9d68c7d9751762e25b647e3eef647c6ec0b48ba90c","files":904,"pattern":".blkcache/*.json","transactions":22112}}
diff --git a/artifacts/sha256/46/8eca979874c64b1be24609afaf3f8b2256d40e9b99ef62efc46dd189b9614a b/artifacts/sha256/46/8eca979874c64b1be24609afaf3f8b2256d40e9b99ef62efc46dd189b9614a
new file mode 100644
index 00000000..a75b0e1b
--- /dev/null
+++ b/artifacts/sha256/46/8eca979874c64b1be24609afaf3f8b2256d40e9b99ef62efc46dd189b9614a
@@ -0,0 +1,13 @@
+{
+ "schema_version": 1,
+ "python": {
+ "implementation": "cpython",
+ "version": "3.13",
+ "system": "Darwin",
+ "machine": "arm64"
+ },
+ "source_archive": "sources/decluster-bcd9a3e-runtime.tar",
+ "project_root": "decluster",
+ "requirements": "reproduction/path-count-contract/requirements.lock",
+ "dependency_directory": "dependencies"
+}
diff --git a/artifacts/sha256/47/3b7a0f83805809411d4691f18103fbb74f0587a3a5b6988b776da46f43ea25 b/artifacts/sha256/47/3b7a0f83805809411d4691f18103fbb74f0587a3a5b6988b776da46f43ea25
new file mode 100644
index 00000000..bc795cb7
--- /dev/null
+++ b/artifacts/sha256/47/3b7a0f83805809411d4691f18103fbb74f0587a3a5b6988b776da46f43ea25
@@ -0,0 +1 @@
+{"dataset":"fs-blkcache-2026-09-04-v1","experiment":"scorer-families-v1","limitations":["address reuse is a weak and partly feature-dependent ownership proxy","the axis clusters and the address-determined set are measured on this same cache","one selected snapshot is reused for all three families and is not chain-wide","a higher AUC on fewer axes does not establish that the dropped axes carry no signal","evidence magnitudes are rarity bits under a fixed consistency, not calibrated likelihoods","this is attacker-side analysis, not a privacy score"],"measurement":{"auc_rises_as_axes_are_dropped":true,"consistency":0.95,"magnitude_fall_from_catalogued_to_decorrelated":6.740208748616556,"pair_cap_per_class":4000,"rows":[{"auc":0.92445,"axes":23,"dropped":[],"family":"catalogued","n_neg":4000,"n_pos":4000,"neg_mean":-16.582117238531236,"pos_mean":15.465038046462228,"shuffle_auc":0.4984},{"auc":0.90035,"axes":18,"dropped":["change_address_reuse","input_script_type","input_types_present","nested_segwit","pubkey_compression"],"family":"construction_only","n_neg":4000,"n_pos":4000,"neg_mean":-13.94129774262037,"pos_mean":8.419667321669898,"shuffle_auc":0.499725},{"auc":0.9432,"axes":14,"dropped":["change_matches_output","change_type_match","input_order","input_types_present","nested_segwit","output_encoding","output_order","pubkey_compression","sighash"],"family":"decorrelated","n_neg":4000,"n_pos":4000,"neg_mean":-10.236736430032744,"pos_mean":8.724829297845671,"shuffle_auc":0.49715}],"seed":0},"schema_version":1,"source":{"bytes":91367352,"digest":"91c444bd4e9715d7739ccc9d68c7d9751762e25b647e3eef647c6ec0b48ba90c","files":904,"pattern":".blkcache/*.json","transactions":22112}}
diff --git a/artifacts/sha256/48/19e2c1bcf1509f2a774a7fb3a910b59effc83b09f1b2351b36418523a084af b/artifacts/sha256/48/19e2c1bcf1509f2a774a7fb3a910b59effc83b09f1b2351b36418523a084af
new file mode 100644
index 00000000..e9750371
--- /dev/null
+++ b/artifacts/sha256/48/19e2c1bcf1509f2a774a7fb3a910b59effc83b09f1b2351b36418523a084af
@@ -0,0 +1,104 @@
+{
+ "schema_version": 1,
+ "id": "weight-sensitivity-v1",
+ "claim_ids": [
+ "ctp.fingerprint.statistical_linkage"
+ ],
+ "code": {
+ "revision": "bcd9a3e511860b1b0ff5f3ee6338a2df29ecbfbf",
+ "dirty": false
+ },
+ "command": {
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.weight_sensitivity",
+ "--snapshot",
+ "data/fs-blkcache-2026-09-04.tar.gz",
+ "reproduce",
+ "--artifact",
+ "results/artifacts/weight-sensitivity-v1.json",
+ "--markdown",
+ "results/generated/weight-sensitivity-v1.md"
+ ]
+ },
+ "environment": {
+ "python": "3.13.7",
+ "lock_digest": "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855",
+ "platform": "macOS-26.6.2-arm64-arm-64bit-Mach-O",
+ "dependencies": []
+ },
+ "datasets": [
+ {
+ "id": "fs-blkcache-2026-09-04-v1",
+ "sha256": "5a69f6f446dbdfcce09dddc91c653e5d841124d14020b720fdea276315c775a6"
+ }
+ ],
+ "parameters": {
+ "grid": [
+ 0.6,
+ 0.7,
+ 0.8,
+ 0.9,
+ 0.95,
+ 0.99
+ ],
+ "pair_cap_per_class": 4000,
+ "seed": 0
+ },
+ "rng": {
+ "algorithm": "python.random.MersenneTwister",
+ "seeds": [
+ 0
+ ]
+ },
+ "outputs": [
+ {
+ "path": "results/artifacts/weight-sensitivity-v1.json",
+ "bytes": 2222,
+ "sha256": "467a51db69c377bb05e49c5ac60f680a9821e0ea888dde636d75697d78713e6c"
+ },
+ {
+ "path": "results/generated/weight-sensitivity-v1.md",
+ "bytes": 1084,
+ "sha256": "36b8c85941d3a3ab382c1e3a14166226a5608f37176503294679b342179c1498"
+ }
+ ],
+ "reproducibility": {
+ "level": "bitwise_reproducible",
+ "availability": "complete"
+ },
+ "verification": {
+ "mode": "exact",
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.weight_sensitivity",
+ "--snapshot",
+ "data/fs-blkcache-2026-09-04.tar.gz",
+ "verify",
+ "--artifact",
+ "results/artifacts/weight-sensitivity-v1.json",
+ "--markdown",
+ "results/generated/weight-sensitivity-v1.md"
+ ],
+ "tests": [
+ "tests/test_fingerprint_ns.py",
+ "tests/test_weight_sensitivity_experiment.py"
+ ],
+ "properties": [
+ "the preserved snapshot exactly reproduces every sweep row",
+ "each grid point uses the same seeded weak-label pair sample",
+ "the local AUC range is distinguished from evidence-magnitude sensitivity",
+ "the preserved snapshot falsifies the historical monotonicity claim",
+ "the result remains attacker-side evidence rather than a privacy score"
+ ],
+ "tolerance": null
+ },
+ "limitations": [
+ "address reuse is a weak and partly feature-dependent ownership proxy",
+ "the sweep varies one global value rather than fitting each axis",
+ "the same selected sample is reused at every grid point",
+ "the snapshot is not representative of the whole chain"
+ ]
+}
diff --git a/artifacts/sha256/48/1c68b6c5f923e565fda6c71f636a00dd1ada3a212068853420a7f0de5b2b26 b/artifacts/sha256/48/1c68b6c5f923e565fda6c71f636a00dd1ada3a212068853420a7f0de5b2b26
new file mode 100644
index 00000000..3b2130eb
--- /dev/null
+++ b/artifacts/sha256/48/1c68b6c5f923e565fda6c71f636a00dd1ada3a212068853420a7f0de5b2b26
@@ -0,0 +1,106 @@
+{
+ "schema_version": 1,
+ "id": "fingerprint-catalog-axes-v1",
+ "claim_ids": [
+ "ctp.fingerprint.statistical_linkage"
+ ],
+ "code": {
+ "revision": "bcd9a3e511860b1b0ff5f3ee6338a2df29ecbfbf",
+ "dirty": false
+ },
+ "command": {
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.fingerprint_catalog_axes",
+ "--dataset",
+ "tests/fixtures/fingerprint_blkcache_sample.json",
+ "reproduce",
+ "--artifact",
+ "results/artifacts/fingerprint-catalog-axes-v1.json",
+ "--markdown",
+ "results/generated/fingerprint-catalog-axes-v1.md"
+ ]
+ },
+ "environment": {
+ "python": "3.13.7",
+ "lock_digest": "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855",
+ "platform": "macOS-26.6.2-arm64-arm-64bit-Mach-O",
+ "dependencies": []
+ },
+ "datasets": [
+ {
+ "id": "fingerprint-blkcache-sample-v1",
+ "sha256": "406ce2d7d0a04f7c73281bce6b6a781012a8765e079693f010a0154e926c6a6c"
+ }
+ ],
+ "parameters": {
+ "pair_draw_cap": 4000,
+ "seed": 0,
+ "probability_source": "same frozen snapshot",
+ "same_wallet_consistency": 0.95,
+ "rarity_floor_count": 1000,
+ "diagnostic_axes": [
+ "output_count",
+ "segwit_serialization"
+ ]
+ },
+ "rng": {
+ "algorithm": "python.random.Random",
+ "seeds": [
+ 0
+ ]
+ },
+ "outputs": [
+ {
+ "path": "results/artifacts/fingerprint-catalog-axes-v1.json",
+ "bytes": 1883,
+ "sha256": "f103911399f995ac300dcca04af29eab65cb3962d436915c9cb3f931b81f9e4a"
+ },
+ {
+ "path": "results/generated/fingerprint-catalog-axes-v1.md",
+ "bytes": 802,
+ "sha256": "773bd7f24736da999200cd9e3ef18b99875ed1f62801496bf8add9f96ff11149"
+ }
+ ],
+ "reproducibility": {
+ "level": "bitwise_reproducible",
+ "availability": "partial"
+ },
+ "verification": {
+ "mode": "exact",
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.fingerprint_catalog_axes",
+ "--dataset",
+ "tests/fixtures/fingerprint_blkcache_sample.json",
+ "verify",
+ "--artifact",
+ "results/artifacts/fingerprint-catalog-axes-v1.json",
+ "--markdown",
+ "results/generated/fingerprint-catalog-axes-v1.md"
+ ],
+ "tests": [
+ "tests/test_fingerprint_catalog_axes_experiment.py",
+ "tests/test_fingerprint_validation_experiment.py",
+ "tests/test_fingerprint_auc.py"
+ ],
+ "properties": [
+ "all configurations score the same sampled positive and negative pairs",
+ "diagnostic probabilities are measured from the declared snapshot",
+ "output count and SegWit serialization remain outside the production library",
+ "the non-additive AUC result can be falsified",
+ "the generated Markdown equals the canonical rendering"
+ ],
+ "tolerance": null
+ },
+ "limitations": [
+ "address reuse is a weak and partly feature-dependent same-wallet proxy",
+ "diagnostic probabilities are fitted and evaluated on the same selected snapshot",
+ "the 600-transaction snapshot is not either historical 166k or 180k cache",
+ "AUC deltas on one snapshot do not establish conditional dependence",
+ "the axes remain excluded from the production fingerprint library",
+ "the result measures pair ranking and is not an ownership certificate or privacy score"
+ ]
+}
diff --git a/artifacts/sha256/48/25de7e64906f0d708bdcbd3bd23836675ff168166c5ff06fc0ea25d95afb38 b/artifacts/sha256/48/25de7e64906f0d708bdcbd3bd23836675ff168166c5ff06fc0ea25d95afb38
new file mode 100644
index 00000000..4f1efe81
--- /dev/null
+++ b/artifacts/sha256/48/25de7e64906f0d708bdcbd3bd23836675ff168166c5ff06fc0ea25d95afb38
@@ -0,0 +1 @@
+{"adversary":"payment-graph common-neighbour scorer with independent entity detector","bitmex":{"entity_clusters":1,"payment_auc":0.4974,"positive_mean_shared_neighbours":0.006713271559731377,"positive_pairs":3488761,"transactions":9698},"experiment":"entity-deanon-v1","interpretation":{"bitmex":"null control with hub-and-spoke deposit structure","satoshidice":"positive control with recurring counterparties"},"limitations":["the fixtures are selected entity-centred subgraphs, not uniform chain samples","vanity-prefix detectors label the fixtures and are not general entity attribution","the run evaluates common-neighbour structure, not the full Narayanan-Shmatikov algorithm","the contrast does not establish a universal property of gambling services or exchanges","upstream data licensing is undetermined; the dataset publisher authorized redistribution on 2026-09-04","the result is not a privacy score or CoinScore"],"satoshidice":{"entity_clusters":1,"payment_auc":0.689075,"positive_mean_shared_neighbours":7.948717948717949,"positive_pairs":780,"transactions":15635},"schema_version":1}
diff --git a/artifacts/sha256/48/e21384adfffae4ad26fbc3d90bca4bd461c30af7c9804927739ba3b54a7ec6 b/artifacts/sha256/48/e21384adfffae4ad26fbc3d90bca4bd461c30af7c9804927739ba3b54a7ec6
new file mode 100644
index 00000000..9f3e4628
--- /dev/null
+++ b/artifacts/sha256/48/e21384adfffae4ad26fbc3d90bca4bd461c30af7c9804927739ba3b54a7ec6
@@ -0,0 +1,41 @@
+# Does the matcher beat degree alone
+
+Generated from the canonical experiment artifact. Do not edit manually.
+
+Two consecutive 2016 weekly windows of 982,021 and 1,116,563 transactions, contracted into 588,843 and 638,436 pseudonyms. View B's labels are hidden, so 90,145 vertices are known to be present in both views and the correspondence between them is not. Seeds are drawn from the 54,486 of those whose degree sits in [3, 100] on both sides — below that a neighbourhood identifies nobody, and above it propagation refuses to route through the vertex at all, so a seed outside the band is a seed the matcher cannot spend.
+
+At the 10% seeding, precision against the eccentricity each match won by:
+
+| eccentricity | matched | uncontested | correct | precision | degree class alone | margin |
+|---:|---:|---:|---:|---:|---:|---:|
+| ≥ 0 | 623 | 2 | 376 | 0.604 | 0.31078 | +0.293 |
+| ≥ 1 | 577 | 2 | 362 | 0.627 | 0.32650 | +0.301 |
+| ≥ 2 | 515 | 2 | 334 | 0.649 | 0.34301 | +0.306 |
+| ≥ 3 | 426 | 2 | 281 | 0.660 | 0.37982 | +0.280 |
+| ≥ 5 | 283 | 2 | 197 | 0.696 | 0.48597 | +0.210 |
+| ≥ 10 | 129 | 2 | 95 | 0.736 | 0.66658 | +0.070 |
+
+**The matcher beats degree, and least where it is most sure.** Overall it is +0.293 against what degree alone is worth. That margin does not widen as the matcher grows confident; it collapses, to +0.070 at eccentricity 10. Precision does rise with confidence (0.604 to 0.736), which is the property the framework's argument wants — but the degree baseline rises faster (0.311 to 0.667), because the matches the matcher is surest of are the ones whose partner has a rare degree. The high-confidence band is where the matcher is *hardest* to distinguish from reading the degrees off.
+
+The uncontested column is the matches where only one candidate materialised at all. The matcher records those with infinite eccentricity and skips the gate rather than computing it, so they clear every floor: a high band is only evidence about *standing clear of rivals* to the extent it is not made of them.
+
+The degree-class column is what degree is worth with no algorithm attached: one over the number of B-vertices sharing the true partner's degree. It is handed that degree, which a degree-only adversary would have to guess, so it bounds such an adversary from above rather than approximating one. Classes here run to 225,915 vertices with a median of 119542.
+
+Every arm, with the shuffled-seed control beside each seeded one:
+
+| seed share | arm | seeds | guesses | correct | precision |
+|---:|---|---:|---:|---:|---:|
+| 5% | seeded | 2724 | 571 | 309 | 0.541 |
+| 5% | shuffled | 2724 | 15 | 0 | 0.000 |
+| 10% | seeded | 5448 | 623 | 376 | 0.604 |
+| 10% | shuffled | 5448 | 17 | 3 | 0.176 |
+
+The seeded arm returns 623 graded matches. A shuffled seed is the same matcher on the same graphs with the correspondence destroyed, and what it returns is the share of this that survives without the seeds meaning anything.
+
+This is the complete-clustering premise: the matcher is told which vertices appear in both views and asked only which is which. The split-cluster premise, where a straddling entity carries a separate pseudonym per view and the matcher must discover that they are one, is measured beside it (`results/generated/graph-rejoin-2016-v1.md`) and is the harder question.
+
+Cluster membership is a co-spend relation rather than observed wallet ownership, the seeds are supplied, and this is two windows of one era. None of it is a privacy score.
+
+## Reproducibility / provenance
+
+State 1 in `results/REPRODUCIBILITY.md`: band-pinned on committed data — the artifact is recomputed from the committed weekly epoch graphs and compared byte for byte.
diff --git a/artifacts/sha256/49/0a0f4e7b3452093915bd7dc44ef393048912ff21ead36fdf9be74736f832a9 b/artifacts/sha256/49/0a0f4e7b3452093915bd7dc44ef393048912ff21ead36fdf9be74736f832a9
new file mode 100644
index 00000000..3cf35dbf
--- /dev/null
+++ b/artifacts/sha256/49/0a0f4e7b3452093915bd7dc44ef393048912ff21ead36fdf9be74736f832a9
@@ -0,0 +1 @@
+{"dataset":"fs-blkcache-2026-09-04-v1","experiment":"fs-temporal-v1","limitations":["address reuse supplies weak labels rather than independent ownership labels","the block sample is not representative of the whole chain","Beta smoothing is a local modeling choice","input_script_type and input_types_present are duplicates in this sample"],"report":{"comparison":{"auc_delta_fs_minus_rarity":0.04820243749999997,"same_test_pairs":true},"fellegi_sunter":{"auc":0.9826876875,"calibration":{"brier":0.042522807237584934,"ece_10":0.04074478440106778,"prior":0.5,"scope":"balanced weak-label case-control sample; not population calibration"},"coverage":0.92525,"false_negative":190,"false_positive":179,"link":3989,"non_link":3413,"precision":0.9551265981448984,"recall":0.9525,"review":598,"selective_accuracy":0.9729802756011888,"true_positive":3810},"fields":{"change_address_reuse":{"agreement_weight":0.8448879604406226,"disagreement_weight":-6.940895773005728,"m":0.9963759060234941,"u":0.5547363159210198},"change_index":{"agreement_weight":0.18977281207184551,"disagreement_weight":-0.37675504681503236,"m":0.7076980754811297,"u":0.6204698825293676},"change_matches_output":{"agreement_weight":0.1896845028611041,"disagreement_weight":-0.3706671683587266,"m":0.7039490127468133,"u":0.6172206948262934},"change_spk":{"agreement_weight":0.713547710609539,"disagreement_weight":-0.7247814865451978,"m":0.6258443832874656,"u":0.38165160642570284},"change_type_match":{"agreement_weight":0.17798556590662828,"disagreement_weight":-0.36860118740568565,"m":0.7149462634341415,"u":0.6319670082479381},"fee_rate":{"agreement_weight":0.14006134752928193,"disagreement_weight":-2.5068738036461737,"m":0.9806298425393651,"u":0.8899025243689078},"input_order":{"agreement_weight":0.3832981302346271,"disagreement_weight":-2.4791910706732754,"m":0.9514848950332822,"u":0.7294871794871794},"input_script_type":{"agreement_weight":0.9875116262398764,"disagreement_weight":-7.308146908572327,"m":0.9968624497991968,"u":0.5027645136969088},"input_types_present":{"agreement_weight":0.957708795210742,"disagreement_weight":-10.326607434034601,"m":0.9996220710506425,"u":0.5146793716198815},"io_shape":{"agreement_weight":1.0655985201111102,"disagreement_weight":-2.8221868491832205,"m":0.9208096590909091,"u":0.43993928881179534},"locktime":{"agreement_weight":0.47436200357597175,"disagreement_weight":-11.131213905086362,"m":0.9998750312421895,"u":0.7196950762309423},"low_r":{"agreement_weight":1.2135314204224876,"disagreement_weight":-1.9823186605894445,"m":0.8384153961509623,"u":0.3615346163459135},"multisig":{"agreement_weight":0.17800012470299587,"disagreement_weight":-7.061467632717484,"m":0.9991252186953262,"u":0.8831542114471382},"nested_segwit":{"agreement_weight":0.2255569214885073,"disagreement_weight":-6.286634764892701,"m":0.9981254686328418,"u":0.853661584603849},"nsequence":{"agreement_weight":1.3046728246726353,"disagreement_weight":-6.203140907207537,"m":0.9918770307423144,"u":0.40152461884528867},"op_return":{"agreement_weight":0.12656396128366987,"disagreement_weight":-9.394462694610407,"m":0.9998750312421895,"u":0.9158960259935016},"output_encoding":{"agreement_weight":0.47621333229506774,"disagreement_weight":-0.4952647123164627,"m":0.5929767558110473,"u":0.42626843289177707},"output_order":{"agreement_weight":1.0333148510366172,"disagreement_weight":-4.3557824560112,"m":0.9744117647058823,"u":0.4760841836734694},"pubkey_compression":{"agreement_weight":0.7999286571848719,"disagreement_weight":-8.566054038171039,"m":0.998875281179705,"u":0.5737315671082229},"sighash":{"agreement_weight":0.7031137358990198,"disagreement_weight":-9.24853346156753,"m":0.9993651599796851,"u":0.6138551165146909},"uih":{"agreement_weight":0.15673463410048158,"disagreement_weight":-2.1580795759520517,"m":0.9711322169457636,"u":0.8711572106973257},"version":{"agreement_weight":0.9933337731517751,"disagreement_weight":-8.261371689946062,"m":0.9983754061484629,"u":0.5014996250937266}},"label_provenance":{"independently_verified":false,"kind":"weak_labels","negative":"sampled pair with no observed shared input address","positive":"distinct transactions sharing an observed input address"},"pairs":{"test_negative":4000,"test_positive":4000,"train_negative":4000,"train_positive":4000},"rarity_baseline":{"auc":0.93448525},"source":{"bytes":91367352,"digest":"91c444bd4e9715d7739ccc9d68c7d9751762e25b647e3eef647c6ec0b48ba90c","files":904,"pattern":".blkcache/*.json","transactions":22112},"split":{"cutoff":961653,"strategy":"strict_block_height","test_height":[961654,965220],"test_transactions":7012,"train_height":[800000,961653],"train_transactions":15100}},"schema_version":1}
diff --git a/artifacts/sha256/4a/9fbb5bb1a6fff2f9302794836fb2f161d6d2ccb43984e77ca5c3213a24e438 b/artifacts/sha256/4a/9fbb5bb1a6fff2f9302794836fb2f161d6d2ccb43984e77ca5c3213a24e438
new file mode 100644
index 00000000..df82c95c
--- /dev/null
+++ b/artifacts/sha256/4a/9fbb5bb1a6fff2f9302794836fb2f161d6d2ccb43984e77ca5c3213a24e438
@@ -0,0 +1,13 @@
+{
+ "schema_version": 1,
+ "python": {
+ "implementation": "cpython",
+ "version": "3.13",
+ "system": "Darwin",
+ "machine": "arm64"
+ },
+ "source_archive": "sources/decluster-bcd9a3e-runtime.tar",
+ "project_root": "decluster",
+ "requirements": "reproduction/entropy-insufficiency/requirements.lock",
+ "dependency_directory": "reproduction/entropy-insufficiency"
+}
diff --git a/artifacts/sha256/4a/b4ef604b3debc1f517be5ecf12247e3adb5faea75c1b00b77375a03371ac0b b/artifacts/sha256/4a/b4ef604b3debc1f517be5ecf12247e3adb5faea75c1b00b77375a03371ac0b
new file mode 100644
index 00000000..f831fa7f
--- /dev/null
+++ b/artifacts/sha256/4a/b4ef604b3debc1f517be5ecf12247e3adb5faea75c1b00b77375a03371ac0b
@@ -0,0 +1,13 @@
+{
+ "schema_version": 1,
+ "python": {
+ "implementation": "cpython",
+ "version": "3.13",
+ "system": "Darwin",
+ "machine": "arm64"
+ },
+ "source_archive": "sources/decluster-bcd9a3e-runtime.tar",
+ "project_root": "decluster",
+ "requirements": "reproduction/coinjoin-sudoku-nominal/requirements.lock",
+ "dependency_directory": "reproduction/coinjoin-sudoku-nominal"
+}
diff --git a/artifacts/sha256/4b/0ff865d3d37f4842d1f83e157ac381aee5829d9ae4d82bb7c7486d3109face b/artifacts/sha256/4b/0ff865d3d37f4842d1f83e157ac381aee5829d9ae4d82bb7c7486d3109face
new file mode 100644
index 00000000..b6d986d0
--- /dev/null
+++ b/artifacts/sha256/4b/0ff865d3d37f4842d1f83e157ac381aee5829d9ae4d82bb7c7486d3109face
@@ -0,0 +1,13 @@
+{
+ "schema_version": 1,
+ "python": {
+ "implementation": "cpython",
+ "version": "3.13",
+ "system": "Darwin",
+ "machine": "arm64"
+ },
+ "source_archive": "sources/decluster-bcd9a3e-runtime.tar",
+ "project_root": "decluster",
+ "requirements": "reproduction/pseudonym-graph-contract/requirements.lock",
+ "dependency_directory": "reproduction/pseudonym-graph-contract"
+}
diff --git a/artifacts/sha256/4c/17dbcfbd009a7b0be1282730800cce1cb6e53ee80b170b0c653fc51cfa94af b/artifacts/sha256/4c/17dbcfbd009a7b0be1282730800cce1cb6e53ee80b170b0c653fc51cfa94af
new file mode 100644
index 00000000..1dd883ba
--- /dev/null
+++ b/artifacts/sha256/4c/17dbcfbd009a7b0be1282730800cce1cb6e53ee80b170b0c653fc51cfa94af
@@ -0,0 +1,106 @@
+{
+ "schema_version": 1,
+ "id": "fingerprint-sparsity-v1",
+ "claim_ids": [
+ "ctp.sparse_dataset.feature_uniqueness",
+ "ctp.fingerprint.statistical_linkage"
+ ],
+ "code": {
+ "revision": "bcd9a3e511860b1b0ff5f3ee6338a2df29ecbfbf",
+ "dirty": false
+ },
+ "command": {
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.fingerprint_sparsity",
+ "--dataset",
+ "tests/fixtures/lumen_explorer_data.json",
+ "reproduce",
+ "--artifact",
+ "results/artifacts/fingerprint-sparsity-v1.json",
+ "--markdown",
+ "results/generated/fingerprint-sparsity-v1.md"
+ ]
+ },
+ "environment": {
+ "python": "3.13.7",
+ "lock_digest": "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855",
+ "platform": "macOS-26.6.2-arm64-arm-64bit-Mach-O",
+ "dependencies": []
+ },
+ "datasets": [
+ {
+ "id": "lumen-explorer-data-v1",
+ "sha256": "5337d4bdc574726a2cb4e28f329f16709b497479a7493882bd38d77875660ac9"
+ }
+ ],
+ "parameters": {
+ "vector_width": 12,
+ "scope": "whole_window",
+ "bucket_boundaries": [
+ 1,
+ 2,
+ 10,
+ 100,
+ 1000,
+ 10000,
+ 100000
+ ]
+ },
+ "rng": {
+ "algorithm": null,
+ "seeds": []
+ },
+ "outputs": [
+ {
+ "path": "results/artifacts/fingerprint-sparsity-v1.json",
+ "bytes": 1429,
+ "sha256": "923000f70f5eaaf7349a0715d3ed83c382d4b414d28c0e8aa617103871f9ec79"
+ },
+ {
+ "path": "results/generated/fingerprint-sparsity-v1.md",
+ "bytes": 694,
+ "sha256": "9740d4ad0c64d229df372b81499ae33f1dd54c097a4e26a2fccbee73b80bec6d"
+ }
+ ],
+ "reproducibility": {
+ "level": "bitwise_reproducible",
+ "availability": "partial"
+ },
+ "verification": {
+ "mode": "exact",
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.fingerprint_sparsity",
+ "--dataset",
+ "tests/fixtures/lumen_explorer_data.json",
+ "verify",
+ "--artifact",
+ "results/artifacts/fingerprint-sparsity-v1.json",
+ "--markdown",
+ "results/generated/fingerprint-sparsity-v1.md"
+ ],
+ "tests": [
+ "tests/test_fingerprint_sparsity.py",
+ "tests/test_fingerprint_sparsity_experiment.py",
+ "tests/test_lumen_survey_fixture.py"
+ ],
+ "properties": [
+ "class-size buckets are weighted by transactions rather than distinct vectors",
+ "every preserved conditional partition covers the declared transaction total",
+ "all preserved conditional partitions reconstruct the same global histogram",
+ "the generated Markdown equals the canonical rendering"
+ ],
+ "tolerance": null
+ },
+ "limitations": [
+ "the fixture contains aggregate histograms rather than transaction-level vectors",
+ "the 19-axis and per-epoch distributions are not preserved and are not reproduced",
+ "exact-vector class size is not nearest-neighbour sparsity in a general feature metric",
+ "the result concerns per-transaction vectors, not cluster feature distributions",
+ "the scan ends at height 962720 and differs by 6219 records from the cataloged raw export",
+ "the dataset publisher authorized redistribution of the aggregate fixture on 2026-09-04"
+ ]
+}
diff --git a/artifacts/sha256/4c/e04704b838583333871b5d98883ceac02728ccfcea4c430b3ee7d49f708400 b/artifacts/sha256/4c/e04704b838583333871b5d98883ceac02728ccfcea4c430b3ee7d49f708400
new file mode 100644
index 00000000..cc58131e
--- /dev/null
+++ b/artifacts/sha256/4c/e04704b838583333871b5d98883ceac02728ccfcea4c430b3ee7d49f708400
@@ -0,0 +1,18 @@
+# The merged anchor's false edge under four axis sets
+
+Generated from the canonical experiment artifact. Do not edit manually.
+
+One edge — the sender's funding transaction against the Cake one, on anchor `931d6627`. The engine's refusal threshold is `link_above = 4.0`.
+
+| axis set | axes | bits | verdict | past link_above |
+|---|---:|---:|---|---|
+| engine three axis | 3 | -3.16 | refuse | no |
+| catalogued | 23 | +11.67 | link | yes |
+| construction only | 18 | +4.91 | link | yes |
+| decorrelated | 14 | +3.59 | link | no |
+
+The catalogued model resurrects the edge at +11.67 bits, well past the threshold. Dropping one representative per correlated cluster leaves +3.59 — **8.07 bits of the score were copies of evidence already counted**, and what remains sits below the threshold, so the wide model's failure here is duplication rather than width.
+
+Of the 22 axes that scored, 3 argue against the pairing (-10.82 bits between them) and 19 argue for it (+22.49); 1 abstain. The discriminating axes still refuse — they are outvoted by low-entropy policy axes that two ordinary wallets share, summed under a kernel that assumes they are independent.
+
+This is one edge, chosen because the design decision was argued on it, and its same-owner reading is the paper's rather than an independent label. The library weights come from a population that was not preserved; `library-calibration-v1` measures how far they sit from the one that was.
diff --git a/artifacts/sha256/4c/e4305b41875df32aeb08d7f91d28ad50212fd86542383477b97a84824c8feb b/artifacts/sha256/4c/e4305b41875df32aeb08d7f91d28ad50212fd86542383477b97a84824c8feb
new file mode 100644
index 00000000..0b7f2afa
--- /dev/null
+++ b/artifacts/sha256/4c/e4305b41875df32aeb08d7f91d28ad50212fd86542383477b97a84824c8feb
@@ -0,0 +1,96 @@
+{
+ "schema_version": 1,
+ "id": "ancestry-contract-v1",
+ "claim_ids": [
+ "ctp.provenance.traceability"
+ ],
+ "code": {
+ "revision": "bcd9a3e511860b1b0ff5f3ee6338a2df29ecbfbf",
+ "dirty": false
+ },
+ "command": {
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.ancestry_contract",
+ "reproduce",
+ "--artifact",
+ "results/artifacts/ancestry-contract-v1.json",
+ "--markdown",
+ "results/generated/ancestry-contract-v1.md"
+ ]
+ },
+ "environment": {
+ "python": "3.13.7",
+ "lock_digest": "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855",
+ "platform": "macOS-26.6.2-arm64-arm-64bit-Mach-O",
+ "dependencies": []
+ },
+ "datasets": [],
+ "parameters": {
+ "fixture_transactions": 3,
+ "depth": 2,
+ "input_values": [
+ 5,
+ 5
+ ],
+ "max_nodes_control": 1,
+ "probability_round_digits": 15
+ },
+ "rng": {
+ "algorithm": null,
+ "seeds": []
+ },
+ "outputs": [
+ {
+ "path": "results/artifacts/ancestry-contract-v1.json",
+ "bytes": 1252,
+ "sha256": "5c2be778959f5ab96df33af132a1cebc213375d43725969ba0a6cfec2063d533"
+ },
+ {
+ "path": "results/generated/ancestry-contract-v1.md",
+ "bytes": 783,
+ "sha256": "f14fa583584595c85f81b7cb4a84072cbbec64150d8eedb38e77b18b015d2ef9"
+ }
+ ],
+ "reproducibility": {
+ "level": "bitwise_reproducible",
+ "availability": "complete"
+ },
+ "verification": {
+ "mode": "exact",
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.ancestry_contract",
+ "verify",
+ "--artifact",
+ "results/artifacts/ancestry-contract-v1.json",
+ "--markdown",
+ "results/generated/ancestry-contract-v1.md"
+ ],
+ "tests": [
+ "tests/test_ancestry.py",
+ "tests/test_ancestry_adapter.py",
+ "tests/test_value_flow_ancestry.py",
+ "tests/test_provenance_link.py",
+ "tests/test_ancestry_contract_experiment.py"
+ ],
+ "properties": [
+ "nominal-value transition mass reaches the two source coins equally",
+ "collapsed expected steps convert exactly to the uncollapsed paper graph",
+ "complete and truncated ancestry are different typed states",
+ "node caps and oracle refusals remain separate truncation causes",
+ "identical and disjoint provenance signatures have different overlap",
+ "the generated Markdown equals the canonical rendering"
+ ],
+ "tolerance": null
+ },
+ "limitations": [
+ "the fixture is synthetic and validates mechanism contracts",
+ "the nominal-value model does not infer ownership or payment allocation",
+ "provenance overlap is model-relative evidence, not attribution",
+ "the run does not reproduce the historical DSS or live Bitcoin measurements",
+ "the outputs are not robust connectivity, a privacy certificate or CoinScore"
+ ]
+}
diff --git a/artifacts/sha256/4d/9b3871ab258304c97c371f9ff870763abe9ec28df040bd47e085646582c5b2 b/artifacts/sha256/4d/9b3871ab258304c97c371f9ff870763abe9ec28df040bd47e085646582c5b2
new file mode 100644
index 00000000..21a73d15
--- /dev/null
+++ b/artifacts/sha256/4d/9b3871ab258304c97c371f9ff870763abe9ec28df040bd47e085646582c5b2
@@ -0,0 +1 @@
+{"advisory_group_examples":{"invalid_2_plus_3_to_1_plus_2":0,"valid_2_plus_3_to_1_plus_4":1},"composition":null,"conclusion":"the published mechanism accepts the advisory's balanced example; a separately labelled maximal/uniform adaptation recovers the SX control's one-third symmetry","experiment":"coinjoin-sudoku-nominal-v1","limitations":["this is an independent reconstruction, not parity with the unavailable historical analyzer","largest-group selection, uniform weighting and the fee pseudo-output split are local semantics, not published analyzer rules","the marginals are a function of the local fee unit: at one 30,000-sat unit the same control reads 1.0 instead of one third","digit skipping and the reported 2014 SharedCoin grouping result are not reproduced","amount relations within this family are not ownership attribution"],"local_adaptation":{"fee_model":"the fee split into explicit equal pseudo-outputs of one fee unit","purpose":"reconstruct the SX symmetry argument under explicit semantics","selection":"valid interpretations with the largest group count","weighting":"uniform over indexed selected groupings"},"published_mechanism":{"balance":"exact integer sum per paired group","fee_constraint":"fee is a multiple of a constant, currently 0.0001 BTC","selection":"all valid equal-sum groupings"},"schema_version":1,"sharedcoin_historical_target":{"reason":"the analyzer and complete output-label semantics were not published","reported_grouped_inputs_percent":69,"reported_grouped_outputs_percent":53,"status":"not_reproduced","txid":"0e0337bdf930eba3b082fdfbd30944b18e03f0f810ae531443161f897a4d3db0"},"source":{"advisory":"https://www.coinjoinsudoku.com/advisory/","historical_analyzer_available":false,"repository_revision":"afbd2658b4ac9e0711b12c1bd4ab228ea5dd2499"},"sx_symmetric_control":{"all_equal_sum_groupings":64,"fee_sat":30000,"fee_unit_sat":10000,"inputs_sat":[1010000,1010000,1010000],"local_uniform_real_output_link_probabilities":[[0.3333333333333333,0.3333333333333333,0.3333333333333333],[0.3333333333333333,0.3333333333333333,0.3333333333333333],[0.3333333333333333,0.3333333333333333,0.3333333333333333]],"outputs_sat":[1000000,1000000,1000000],"selected_groupings_collapsing_fee_permutations":6,"selected_indexed_groupings":36,"txid":"9256332b9ca52cbcb06f57296dfd982d8da3f7d4696b4c10cf9bb93dae6edf58"}}
diff --git a/artifacts/sha256/4e/dbf73853fc768a48a91518be8d5ee6e03c0f513163f351e1accf0bc52b46b5 b/artifacts/sha256/4e/dbf73853fc768a48a91518be8d5ee6e03c0f513163f351e1accf0bc52b46b5
new file mode 100644
index 00000000..800fab60
--- /dev/null
+++ b/artifacts/sha256/4e/dbf73853fc768a48a91518be8d5ee6e03c0f513163f351e1accf0bc52b46b5
@@ -0,0 +1,29 @@
+# What a cluster carries against what a merge hides
+
+Generated from the canonical experiment artifact. Do not edit manually.
+
+2538 co-spend clusters in 22112 committed transactions, each scored by the rarity of the external counterparties it pays or is paid by. Co-spend edges are excluded, so the identifier is not the thing that built the cluster.
+
+| quantity | bits |
+|---|---:|
+| median cluster | 29.0 |
+| p90 cluster | 68.7 |
+| largest cluster | 21338.5 |
+| median, five rarest counterparties only | 29.0 |
+
+| threshold | share of clusters at or above |
+|---:|---:|
+| 1.6 bits | 100.0% |
+| 10 bits | 97.9% |
+| 50 bits | 16.5% |
+| 100 bits | 5.2% |
+
+The asymmetry the argument needs is the first row against 1.6: a merged transaction contributes about 1.6 bits of ambiguity, and 100.0% of these clusters already carry at least that much, at a median of 29.0.
+
+The five-rarest floor does not test that on this cache: 92% of these clusters have at most 5 counterparties, so the restriction usually keeps the whole set and its median (29.0) repeats the one above it. A slice with richer neighbourhoods is what would make that floor say something about dependence between counterparties; here it says only that the clusters are small.
+
+The cache is block-sampled rather than contiguous, so a cluster's counterparties are undercounted relative to a connected slice and every figure here is a floor. That is the direction the argument can use: a floor above the merge's contribution settles the comparison, and a larger slice can only raise it.
+
+## Reproducibility / provenance
+
+State 1 in `results/REPRODUCIBILITY.md`: band-pinned on committed data — the artifact is recomputed from `data/fs-blkcache-2026-09-04.tar.gz` and compared byte for byte.
diff --git a/artifacts/sha256/4f/4ea247a4bbca8cbe1ef24bc7b2ae070576d046c1f04a62bb8d528118fe1c28 b/artifacts/sha256/4f/4ea247a4bbca8cbe1ef24bc7b2ae070576d046c1f04a62bb8d528118fe1c28
new file mode 100644
index 00000000..14d50732
--- /dev/null
+++ b/artifacts/sha256/4f/4ea247a4bbca8cbe1ef24bc7b2ae070576d046c1f04a62bb8d528118fe1c28
@@ -0,0 +1,11 @@
+# Local amount-channel observations
+
+Generated from the canonical experiment artifact. Do not edit manually.
+
+The snapshot contains 5,491 transactions and 1,428 complete multi-input transactions.
+
+CoinJoin shape fires 28 times; de-mix resolves 0. Unnecessary-input fires 485 times.
+
+The 2×2 roundness model ranks 483 transactions and ties at the top for 330.
+
+This run excludes subset-sum, DSS, mapping entropy, and score composition.
diff --git a/artifacts/sha256/51/974b565765a0d17aacce40a3ec2f250ca807d10e2641032be600b487f97b7c b/artifacts/sha256/51/974b565765a0d17aacce40a3ec2f250ca807d10e2641032be600b487f97b7c
new file mode 100644
index 00000000..dee4ee6d
--- /dev/null
+++ b/artifacts/sha256/51/974b565765a0d17aacce40a3ec2f250ca807d10e2641032be600b487f97b7c
@@ -0,0 +1,111 @@
+{
+ "schema_version": 1,
+ "id": "entity-deanon-v1",
+ "claim_ids": [
+ "ctp.social_graph.structural_linkage"
+ ],
+ "code": {
+ "revision": "bcd9a3e511860b1b0ff5f3ee6338a2df29ecbfbf",
+ "dirty": false
+ },
+ "command": {
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.entity_deanon",
+ "--satoshidice-dataset",
+ "tests/fixtures/entity_satoshidice_2013.ndjson.gz",
+ "--bitmex-dataset",
+ "tests/fixtures/entity_bitmex_2019.ndjson.gz",
+ "reproduce",
+ "--artifact",
+ "results/artifacts/entity-deanon-v1.json",
+ "--markdown",
+ "results/generated/entity-deanon-v1.md"
+ ]
+ },
+ "environment": {
+ "python": "3.13.7",
+ "lock_digest": "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855",
+ "platform": "macOS-26.6.2-arm64-arm-64bit-Mach-O",
+ "dependencies": []
+ },
+ "datasets": [
+ {
+ "id": "entity-satoshidice-2013-v1",
+ "sha256": "6cac465a5eca59633fbb3e6f99b83146b9b0e590adf95801a303ccd815699846"
+ },
+ {
+ "id": "entity-bitmex-2019-v1",
+ "sha256": "699c87235bce17a43a8befe2c412dd7bea61ffe3dd772abe7addcbae5a4b8ea8"
+ }
+ ],
+ "parameters": {
+ "seed": 0,
+ "score": "payment-graph common neighbours",
+ "labels": {
+ "satoshidice": "1dice vanity-prefix detector",
+ "bitmex": "3BMEX/bc1qmex vanity-prefix detector"
+ }
+ },
+ "rng": {
+ "algorithm": "python.random.Random",
+ "seeds": [
+ 0
+ ]
+ },
+ "outputs": [
+ {
+ "path": "results/artifacts/entity-deanon-v1.json",
+ "bytes": 1100,
+ "sha256": "4825de7e64906f0d708bdcbd3bd23836675ff168166c5ff06fc0ea25d95afb38"
+ },
+ {
+ "path": "results/generated/entity-deanon-v1.md",
+ "bytes": 557,
+ "sha256": "9a3fdc6c79bc5c94cc687b5a41ebff8c663ecf3358cb080883e93cd30d94d850"
+ }
+ ],
+ "reproducibility": {
+ "level": "bitwise_reproducible",
+ "availability": "partial"
+ },
+ "verification": {
+ "mode": "exact",
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.entity_deanon",
+ "--satoshidice-dataset",
+ "tests/fixtures/entity_satoshidice_2013.ndjson.gz",
+ "--bitmex-dataset",
+ "tests/fixtures/entity_bitmex_2019.ndjson.gz",
+ "verify",
+ "--artifact",
+ "results/artifacts/entity-deanon-v1.json",
+ "--markdown",
+ "results/generated/entity-deanon-v1.md"
+ ],
+ "tests": [
+ "tests/test_entity_deanon.py",
+ "tests/test_entity_deanon_real.py",
+ "tests/test_entity_deanon_experiment.py",
+ "tests/test_entities.py"
+ ],
+ "properties": [
+ "both entity-labelled fixtures are evaluated with a fixed seed",
+ "SatoshiDice remains a positive common-neighbour control on its selected subgraph",
+ "BitMEX remains a chance-level null control on its selected subgraph",
+ "the generated Markdown equals the canonical rendering"
+ ],
+ "tolerance": null
+ },
+ "limitations": [
+ "the fixtures are selected entity-centred subgraphs, not uniform chain samples",
+ "vanity-prefix detectors label the fixtures and are not general entity attribution",
+ "the run evaluates common-neighbour structure, not the full Narayanan-Shmatikov algorithm",
+ "the contrast does not establish a universal property of gambling services or exchanges",
+ "upstream data licensing is undetermined; the dataset publisher authorized redistribution on 2026-09-04",
+ "the result is not a privacy score or CoinScore"
+ ]
+}
diff --git a/artifacts/sha256/51/f184dc02945a1eda4b864a53c6097325f0ee2961a85cc06fa3c9a96802a2c3 b/artifacts/sha256/51/f184dc02945a1eda4b864a53c6097325f0ee2961a85cc06fa3c9a96802a2c3
new file mode 100644
index 00000000..51e8f2a9
--- /dev/null
+++ b/artifacts/sha256/51/f184dc02945a1eda4b864a53c6097325f0ee2961a85cc06fa3c9a96802a2c3
@@ -0,0 +1,13 @@
+{
+ "schema_version": 1,
+ "python": {
+ "implementation": "cpython",
+ "version": "3.13",
+ "system": "Darwin",
+ "machine": "arm64"
+ },
+ "source_archive": "sources/decluster-bcd9a3e-runtime.tar",
+ "project_root": "decluster",
+ "requirements": "reproduction/weight-sensitivity/requirements.lock",
+ "dependency_directory": "reproduction/weight-sensitivity"
+}
diff --git a/artifacts/sha256/52/34bb250121404ed4ace83f3f0ddbb3a02204b9eb0e792d9b74f3a85b143b2e b/artifacts/sha256/52/34bb250121404ed4ace83f3f0ddbb3a02204b9eb0e792d9b74f3a85b143b2e
new file mode 100644
index 00000000..e68c18e4
--- /dev/null
+++ b/artifacts/sha256/52/34bb250121404ed4ace83f3f0ddbb3a02204b9eb0e792d9b74f3a85b143b2e
@@ -0,0 +1 @@
+{"confidence":{"0.05":[{"correct":309,"degree_class_expectation":0.305945,"eccentricity_at_least":0.0,"matched":571,"precision":0.541156,"uncontested":1},{"correct":298,"degree_class_expectation":0.318516,"eccentricity_at_least":1.0,"matched":535,"precision":0.557009,"uncontested":1},{"correct":273,"degree_class_expectation":0.32722,"eccentricity_at_least":2.0,"matched":481,"precision":0.567568,"uncontested":1},{"correct":220,"degree_class_expectation":0.337568,"eccentricity_at_least":3.0,"matched":399,"precision":0.551378,"uncontested":1},{"correct":157,"degree_class_expectation":0.366016,"eccentricity_at_least":5.0,"matched":288,"precision":0.545139,"uncontested":1},{"correct":65,"degree_class_expectation":0.498519,"eccentricity_at_least":10.0,"matched":117,"precision":0.555556,"uncontested":1}],"0.1":[{"correct":376,"degree_class_expectation":0.310781,"eccentricity_at_least":0.0,"matched":623,"precision":0.603531,"uncontested":2},{"correct":362,"degree_class_expectation":0.3265,"eccentricity_at_least":1.0,"matched":577,"precision":0.627383,"uncontested":2},{"correct":334,"degree_class_expectation":0.343012,"eccentricity_at_least":2.0,"matched":515,"precision":0.648544,"uncontested":2},{"correct":281,"degree_class_expectation":0.379815,"eccentricity_at_least":3.0,"matched":426,"precision":0.659624,"uncontested":2},{"correct":197,"degree_class_expectation":0.485975,"eccentricity_at_least":5.0,"matched":283,"precision":0.696113,"uncontested":2},{"correct":95,"degree_class_expectation":0.666584,"eccentricity_at_least":10.0,"matched":129,"precision":0.736434,"uncontested":2}]},"datasets":["epoch-graph-2016-391104-392111-v1","epoch-graph-2016-392112-393119-v1"],"experiment":"match-controls-v1","matching":[{"arm":"seeded","correct":309,"guesses":571,"precision":0.541156,"seed_share":0.05,"seeds":2724},{"arm":"shuffled","correct":0,"guesses":15,"precision":0.0,"seed_share":0.05,"seeds":2724},{"arm":"seeded","correct":376,"guesses":623,"precision":0.603531,"seed_share":0.1,"seeds":5448},{"arm":"shuffled","correct":3,"guesses":17,"precision":0.176471,"seed_share":0.1,"seeds":5448}],"population":{"degree_class_size_max":225915,"degree_class_size_median":119542,"matchable_in_both_views":90145,"seedable":54486,"transactions":[982021,1116563],"vertices":[588843,638436],"views":["391104-392111.epochgraph.xz","392112-393119.epochgraph.xz"]},"schema_version":1}
diff --git a/artifacts/sha256/52/8615b5a852021840539b65f22324da7e77ae72085a9d0adb9082beac236eb0 b/artifacts/sha256/52/8615b5a852021840539b65f22324da7e77ae72085a9d0adb9082beac236eb0
new file mode 100644
index 00000000..c74d4bc6
--- /dev/null
+++ b/artifacts/sha256/52/8615b5a852021840539b65f22324da7e77ae72085a9d0adb9082beac236eb0
@@ -0,0 +1,111 @@
+{
+ "schema_version": 1,
+ "id": "slice-channels-v1",
+ "claim_ids": [
+ "ctp.sparse_dataset.feature_uniqueness"
+ ],
+ "code": {
+ "revision": "bcd9a3e511860b1b0ff5f3ee6338a2df29ecbfbf",
+ "dirty": false
+ },
+ "command": {
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.slice_channels",
+ "--dataset",
+ "tests/fixtures/slice_a_channels_2016.ndjson.gz",
+ "reproduce",
+ "--artifact",
+ "results/artifacts/slice-channels-v1.json",
+ "--markdown",
+ "results/generated/slice-channels-v1.md"
+ ]
+ },
+ "environment": {
+ "python": "3.13.7",
+ "lock_digest": "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855",
+ "platform": "macOS-26.6.2-arm64-arm-64bit-Mach-O",
+ "dependencies": []
+ },
+ "datasets": [
+ {
+ "id": "slice-a-channels-2016-v1",
+ "sha256": "66104889f8b79f2938edb6666f16dfa19a74c68cd8016832b4f2a5e03b11865f"
+ }
+ ],
+ "parameters": {
+ "refuse_guard": true,
+ "query_records": 1200,
+ "background_records": 12000,
+ "seed": 0,
+ "minimum_degrees": [
+ 2,
+ 20
+ ],
+ "epsilons": [
+ 0.5,
+ 0.9,
+ 0.99
+ ]
+ },
+ "rng": {
+ "algorithm": "python.random.Random",
+ "seeds": [
+ 0
+ ]
+ },
+ "outputs": [
+ {
+ "path": "results/artifacts/slice-channels-v1.json",
+ "bytes": 1564,
+ "sha256": "588992cc86b2689ea0d409e8d228e3cfb83fc15246731b57f21ad8bf63d1dc64"
+ },
+ {
+ "path": "results/generated/slice-channels-v1.md",
+ "bytes": 1043,
+ "sha256": "c01256072550fd633edd1fa113f1435e9be9323f4ef9a95a6452648464f4fd3d"
+ }
+ ],
+ "reproducibility": {
+ "level": "bitwise_reproducible",
+ "availability": "partial"
+ },
+ "verification": {
+ "mode": "exact",
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.slice_channels",
+ "--dataset",
+ "tests/fixtures/slice_a_channels_2016.ndjson.gz",
+ "verify",
+ "--artifact",
+ "results/artifacts/slice-channels-v1.json",
+ "--markdown",
+ "results/generated/slice-channels-v1.md"
+ ],
+ "tests": [
+ "tests/test_slice_a_channels.py",
+ "tests/test_slice_channels_experiment.py",
+ "tests/test_def1_sparsity.py",
+ "tests/test_graph_shape.py"
+ ],
+ "properties": [
+ "seeded vertex sampling uses a sorted population",
+ "graph statistics use stable vertex and neighbour order",
+ "density is measured separately for minimum degree 2 and 20",
+ "the generated Markdown equals the canonical rendering"
+ ],
+ "tolerance": null
+ },
+ "limitations": [
+ "the snapshot carries none of the four axes, so the similarity measured here is structural",
+ "the snapshot contains six 2016 blocks and is not a chain-wide sample",
+ "the run does not reproduce historical measurements on larger unpreserved slices",
+ "dense attributes do not prove resistance to other linkage channels",
+ "negative assortativity does not by itself determine graph-matching performance",
+ "the snapshot recipe is unavailable; the dataset publisher authorized redistribution on 2026-09-04",
+ "the result is not a privacy score or CoinScore"
+ ]
+}
diff --git a/artifacts/sha256/52/cb79757b649e63cd24161b0f416eeebde8c871133f6cb338db7252786d525a b/artifacts/sha256/52/cb79757b649e63cd24161b0f416eeebde8c871133f6cb338db7252786d525a
new file mode 100644
index 00000000..8a20ee81
--- /dev/null
+++ b/artifacts/sha256/52/cb79757b649e63cd24161b0f416eeebde8c871133f6cb338db7252786d525a
@@ -0,0 +1,20 @@
+# Fee-aware unnecessary-input diagnostic
+
+Generated from the canonical experiment artifact. Do not edit manually.
+
+Definition: `blockstream_fee_aware` v2.
+Its paper domain is transactions with at least two inputs and exactly two outputs.
+
+| case | classification | fee | Gibson flags |
+|---|---|---:|---|
+| jointly funded uih2 | uih2 | 1 | `[False, False]` |
+| fee aware uih1 | uih1 | 1 | `[True, False]` |
+| equality boundary | uih2 | 1 | `[False, True]` |
+
+The observational-equivalence fixture is classified `uih2`, yet remains `inconclusive` across 3 declared latent forms. It infers no ownership, PayJoin identity, payment output, or composition.
+
+NS1R and NSNR controls are outside the two-output definition: `out_of_scope` and `out_of_scope`.
+
+The cycle control preserves the nonzero net balances while changing gross obligations from 500 to 1400. Therefore the gross payment graph is not identified by the on-chain net amounts.
+
+This is a deterministic diagnostic reproduction, not a PayJoin detector, ownership classifier, prevalence estimate, or privacy score.
diff --git a/artifacts/sha256/52/d987e063a086bb8c973e429988eaf0f3f4185be6c81cfd9175922caf73f781 b/artifacts/sha256/52/d987e063a086bb8c973e429988eaf0f3f4185be6c81cfd9175922caf73f781
new file mode 100644
index 00000000..914239d2
--- /dev/null
+++ b/artifacts/sha256/52/d987e063a086bb8c973e429988eaf0f3f4185be6c81cfd9175922caf73f781
@@ -0,0 +1,89 @@
+{
+ "schema_version": 1,
+ "id": "coinjoin-sudoku-nominal-v1",
+ "claim_ids": [
+ "ctp.amount.subtransaction_ambiguity"
+ ],
+ "code": {
+ "revision": "bcd9a3e511860b1b0ff5f3ee6338a2df29ecbfbf",
+ "dirty": false
+ },
+ "command": {
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.coinjoin_sudoku",
+ "reproduce",
+ "--artifact",
+ "results/artifacts/coinjoin-sudoku-nominal-v1.json",
+ "--markdown",
+ "results/generated/coinjoin-sudoku-nominal-v1.md"
+ ]
+ },
+ "environment": {
+ "python": "3.13.7",
+ "lock_digest": "e0d6a8cada5d383c0aca2bd2a37f28c15721392469c94084128d369c29c5544d",
+ "platform": "macOS-26.6.2-arm64-arm-64bit-Mach-O",
+ "dependencies": []
+ },
+ "datasets": [],
+ "parameters": {
+ "amount_domain": "positive integers",
+ "fee_unit_sat": 10000,
+ "max_coins": 12,
+ "published_selection": "all exact equal-sum groupings",
+ "local_selection": "largest group count",
+ "local_weighting": "uniform over indexed selected groupings"
+ },
+ "rng": {
+ "algorithm": null,
+ "seeds": []
+ },
+ "outputs": [
+ {
+ "path": "results/artifacts/coinjoin-sudoku-nominal-v1.json",
+ "bytes": 2351,
+ "sha256": "4d9b3871ab258304c97c371f9ff870763abe9ec28df040bd47e085646582c5b2"
+ },
+ {
+ "path": "results/generated/coinjoin-sudoku-nominal-v1.md",
+ "bytes": 1048,
+ "sha256": "a59e6e2efcc94cbacad687363da37526efd7469930721966f619f28fb2a39fc2"
+ }
+ ],
+ "reproducibility": {
+ "level": "bitwise_reproducible",
+ "availability": "complete"
+ },
+ "verification": {
+ "mode": "exact",
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.coinjoin_sudoku",
+ "verify",
+ "--artifact",
+ "results/artifacts/coinjoin-sudoku-nominal-v1.json",
+ "--markdown",
+ "results/generated/coinjoin-sudoku-nominal-v1.md"
+ ],
+ "tests": [
+ "tests/test_coinjoin_sudoku.py",
+ "tests/test_coinjoin_sudoku_experiment.py"
+ ],
+ "properties": [
+ "the advisory's positive and negative equal-sum examples are reproduced",
+ "the SX control has one-third marginals only under separately labelled local selection and weighting",
+ "indexed fee pseudo-output permutations remain distinguishable from fee-collapsed interpretations",
+ "the historical SharedCoin percentages remain explicitly not reproduced",
+ "the generated Markdown equals the canonical rendering"
+ ],
+ "tolerance": null
+ },
+ "limitations": [
+ "independent reconstruction because the historical analyzer was not published",
+ "largest-group selection, fee pseudo-outputs and uniform weighting are local semantics",
+ "digit skipping and the reported SharedCoin grouping result are not reproduced",
+ "conditional amount relations are not ownership attribution or a privacy score"
+ ]
+}
diff --git a/artifacts/sha256/53/12e64b368dc7c1d34931365f3f3a8b818b57197f938d7c085b687939411226 b/artifacts/sha256/53/12e64b368dc7c1d34931365f3f3a8b818b57197f938d7c085b687939411226
new file mode 100644
index 00000000..cd8ddc57
--- /dev/null
+++ b/artifacts/sha256/53/12e64b368dc7c1d34931365f3f3a8b818b57197f938d7c085b687939411226
@@ -0,0 +1,13 @@
+{
+ "schema_version": 1,
+ "python": {
+ "implementation": "cpython",
+ "version": "3.13",
+ "system": "Darwin",
+ "machine": "arm64"
+ },
+ "source_archive": "sources/decluster-bcd9a3e-runtime.tar",
+ "project_root": "decluster",
+ "requirements": "reproduction/graph-rejoin-2016/requirements.lock",
+ "dependency_directory": "dependencies"
+}
diff --git a/artifacts/sha256/53/37d4bdc574726a2cb4e28f329f16709b497479a7493882bd38d77875660ac9 b/artifacts/sha256/53/37d4bdc574726a2cb4e28f329f16709b497479a7493882bd38d77875660ac9
new file mode 100644
index 00000000..89031976
--- /dev/null
+++ b/artifacts/sha256/53/37d4bdc574726a2cb4e28f329f16709b497479a7493882bd38d77875660ac9
@@ -0,0 +1 @@
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Wallet (cake_wallet =100,000":74176481,"exactly 1":8960},"class_size_shares":{"1,000-9,999":0.062489097765443986,"10,000-99,999":0.16119688797490683,"10-99":0.003513363809647509,"100-999":0.01666768962381462,"2-9":0.0005220035230603434,">=100,000":0.7555196958091439,"exactly 1":9.126149398284247e-05},"scope":"whole_window","share_below_10":0.0006132650170431859,"share_below_100":0.004126628826690695,"vector_width":12},"schema_version":1,"window":{"end_height":962720,"epochs":158,"start_height":939969,"transactions":98179414}}
diff --git a/artifacts/sha256/93/f48cdcee4ccb7def3bdc525593ecece258438e9a3fa4be9ca5b6bb913d537e b/artifacts/sha256/93/f48cdcee4ccb7def3bdc525593ecece258438e9a3fa4be9ca5b6bb913d537e
new file mode 100644
index 00000000..a1812c7f
--- /dev/null
+++ b/artifacts/sha256/93/f48cdcee4ccb7def3bdc525593ecece258438e9a3fa4be9ca5b6bb913d537e
@@ -0,0 +1 @@
+{"ancestry_distribution":{"origin-a:0":0.62,"origin-b:0":0.38},"count_oracle_invariance":{"distributions_equal":true,"high_log_w":4.605170185988092,"low_log_w":0.0},"entropy":{"min_bits":0.6896598793878495,"shannon_bits":0.9580420222262995},"experiment":"path-count-contract-v1","fixture":{"depth":6,"target":"target:0","transactions":4},"implemented_capabilities":{"edge_disjoint_path_enumeration":true,"k_routes":true,"plausible_flow_capacity":true,"provenance_route_accumulation":true},"limitations":["the fixture is synthetic and tests implementation semantics","the function accumulates link-probability mass over ancestry routes","count_oracle is retained for compatibility and does not affect the result","the function does not measure CTP robust connectivity or own-origin robustness","the output is not a privacy score or CoinScore"],"path_count_distribution":{"origin-a:0":0.62,"origin-b:0":0.38},"schema_version":1}
diff --git a/artifacts/sha256/94/705cad4143dff0eabbdffa177873f17ed59aa9c6de8826b92980f5154101e2 b/artifacts/sha256/94/705cad4143dff0eabbdffa177873f17ed59aa9c6de8826b92980f5154101e2
new file mode 100644
index 00000000..a0169b15
--- /dev/null
+++ b/artifacts/sha256/94/705cad4143dff0eabbdffa177873f17ed59aa9c6de8826b92980f5154101e2
@@ -0,0 +1,12 @@
+# Perfect-matching disclosure
+
+Generated from the canonical experiment artifact. Do not edit manually.
+
+| scenario | naive assignment | naive bijective | optimal matchings | unique optimum |
+|---|---|---:|---:|---:|
+| joint assignment | [0, 0, 2] | false | 1 | true |
+| uniform-profile control | [0, 0, 0] | false | 6 | false |
+
+The joint fixture has the unique sender-to-receiver position assignment [0, 1, 2]. Independent maxima reuse receiver 0 and therefore do not form a matching. The uniform control retains all six perfect matchings.
+
+Profiles are supplied. This fixture tests the defining within-round PMDA objective, not longitudinal profile estimation, Bitcoin value flow, or ownership attribution.
diff --git a/artifacts/sha256/94/88b2af4c14230b3ded135f778fdaca270cfb91843e74d15a32991db09414ea b/artifacts/sha256/94/88b2af4c14230b3ded135f778fdaca270cfb91843e74d15a32991db09414ea
new file mode 100644
index 00000000..32e9549b
--- /dev/null
+++ b/artifacts/sha256/94/88b2af4c14230b3ded135f778fdaca270cfb91843e74d15a32991db09414ea
@@ -0,0 +1,118 @@
+{
+ "schema_version": 1,
+ "id": "ns-bitcoin-v1",
+ "claim_ids": [
+ "ctp.social_graph.propagation"
+ ],
+ "code": {
+ "revision": "bcd9a3e511860b1b0ff5f3ee6338a2df29ecbfbf",
+ "dirty": false
+ },
+ "command": {
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.ns_bitcoin",
+ "--left",
+ "data/ns-bitcoin-2016-v1/left-391992-392111.ndjson.gz",
+ "--right",
+ "data/ns-bitcoin-2016-v1/right-392112-392231.ndjson.gz",
+ "reproduce",
+ "--artifact",
+ "results/artifacts/ns-bitcoin-v1.json",
+ "--markdown",
+ "results/generated/ns-bitcoin-v1.md"
+ ]
+ },
+ "environment": {
+ "python": "3.13.7",
+ "lock_digest": "e0d6a8cada5d383c0aca2bd2a37f28c15721392469c94084128d369c29c5544d",
+ "platform": "macOS-26.6.2-arm64-arm-64bit-Mach-O",
+ "dependencies": []
+ },
+ "datasets": [
+ {
+ "id": "ns-bitcoin-left-2016-v1",
+ "sha256": "069d207b44dcbbeaec0fae98262a3fcb76b3fe834834609e67fe883e229f26d3"
+ },
+ {
+ "id": "ns-bitcoin-right-2016-v1",
+ "sha256": "1d46dcccb51cd8f1a1d89481db4df45599caaaf8569f9e8e2de37e81e0e5d122"
+ }
+ ],
+ "parameters": {
+ "boundary": 392112,
+ "blocks": 120,
+ "min_degree": 2,
+ "split_fraction": 1.0,
+ "seed_fractions": [
+ 0.05,
+ 0.1,
+ 0.25
+ ],
+ "theta": [
+ 0.5,
+ 1.5
+ ]
+ },
+ "rng": {
+ "algorithm": "python.random.MersenneTwister",
+ "seeds": [
+ 7
+ ]
+ },
+ "outputs": [
+ {
+ "path": "results/artifacts/ns-bitcoin-v1.json",
+ "bytes": 6912,
+ "sha256": "39bb12560c4c96749910324b85097938a7efe57e45ce380127bc7783c978f182"
+ },
+ {
+ "path": "results/generated/ns-bitcoin-v1.md",
+ "bytes": 416,
+ "sha256": "ce6ae206e95d2be86e8ea13a9161a26a0a6566787abf396a5eeffbb23efa3fcb"
+ }
+ ],
+ "reproducibility": {
+ "level": "bitwise_reproducible",
+ "availability": "complete"
+ },
+ "verification": {
+ "mode": "exact",
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.ns_bitcoin",
+ "--left",
+ "data/ns-bitcoin-2016-v1/left-391992-392111.ndjson.gz",
+ "--right",
+ "data/ns-bitcoin-2016-v1/right-392112-392231.ndjson.gz",
+ "verify",
+ "--artifact",
+ "results/artifacts/ns-bitcoin-v1.json",
+ "--markdown",
+ "results/generated/ns-bitcoin-v1.md"
+ ],
+ "tests": [
+ "tests/test_ns_social_baseline.py",
+ "tests/test_ns_bitcoin.py",
+ "tests/test_ns_bitcoin_experiment.py"
+ ],
+ "properties": [
+ "the exact Bitcoin windows reproduce the reported observation populations",
+ "the propagation report is stable across Python hash iteration order",
+ "the run records that no independent gradeable seed was found",
+ "the seed-assisted sweep is not presented as an independently seeded attack",
+ "the result is not attributed to the published social-network corpora",
+ "the generated Markdown equals the canonical rendering"
+ ],
+ "tolerance": null
+ },
+ "limitations": [
+ "no independent gradeable seeds were found in these windows",
+ "the measured sweep uses seeds sampled from withheld correspondence",
+ "operational precision is approximately one percent",
+ "the observation windows are not representative of the whole chain",
+ "this does not reproduce the Twitter, Flickr, or LiveJournal experiments"
+ ]
+}
diff --git a/artifacts/sha256/94/9d2d9e890818c5ec890484b13750ed6855e6538c9a45c3c5c4b3daf2454a5b b/artifacts/sha256/94/9d2d9e890818c5ec890484b13750ed6855e6538c9a45c3c5c4b3daf2454a5b
new file mode 100644
index 00000000..cf46049b
--- /dev/null
+++ b/artifacts/sha256/94/9d2d9e890818c5ec890484b13750ed6855e6538c9a45c3c5c4b3daf2454a5b
@@ -0,0 +1,37 @@
+# The fingerprint library against the population that is still here
+
+Generated from the canonical experiment artifact. Do not edit manually.
+
+The shipped bits were measured on a ~105k uniform whole-chain sample that no longer exists. This measures the same quantity on the 22112 transactions of the committed block cache. It is not a reproduction — the populations differ by construction — and the only thing it establishes is how far apart they land.
+
+| axis | values | shown by the cache | mean divergence | max |
+|---|---:|---:|---:|---:|
+| nsequence | 6 | 6 | 2.21 | 4.66 |
+| locktime | 5 | 2 | 0.68 | 1.17 |
+| input_order | 4 | 4 | 2.80 | 6.85 |
+| output_order | 4 | 4 | 1.55 | 4.30 |
+| change_spk | 6 | 6 | 1.62 | 2.99 |
+| version | 3 | 3 | 1.44 | 3.14 |
+| io_shape | 4 | 4 | 0.53 | 1.62 |
+| uih | 2 | 2 | 0.13 | 0.25 |
+| fee_rate | 3 | 2 | 0.82 | 1.45 |
+| input_script_type | 6 | 6 | 1.38 | 2.66 |
+| output_encoding | 5 | 4 | 1.71 | 2.56 |
+| input_types_present | 7 | 7 | 1.76 | 3.85 |
+| change_index | 3 | 2 | 1.74 | 2.05 |
+| change_type_match | 3 | 2 | 2.24 | 2.75 |
+| change_matches_output | 3 | 2 | 1.93 | 2.38 |
+| change_address_reuse | 2 | 2 | 0.36 | 0.52 |
+| low_r | 3 | 2 | 1.44 | 1.62 |
+| sighash | 5 | 4 | 0.83 | 1.59 |
+| op_return | 2 | 2 | 0.23 | 0.44 |
+| nested_segwit | 2 | 2 | 0.24 | 0.43 |
+| pubkey_compression | 2 | 1 | 1.86 | 1.86 |
+| multisig | 2 | 2 | 0.02 | 0.03 |
+| locktime_vs_broadcast | 4 | 0 | n/a | n/a |
+
+Across the 22 axes the cache can compare, the mean divergence is **1.25 bits** and the largest single one is **6.85 bits**, on `input_order`. For scale, the catalogued scorer's whole positive mean on this same cache is about 15 bits.
+
+15 of 86 published values never appear in the cache, so they cannot be checked against it at all.
+
+None of this says the shipped table is wrong. It says the part of it a reader can verify is the part measured here, and that the rest moves by bits rather than by decimals when the population changes. Whether to recalibrate on this cache is a separate decision, and `fingerprint-regime-v1` already reports what changes when the weights are fitted to a snapshot instead of read from the library.
diff --git a/artifacts/sha256/97/40d4ad0c64d229df372b81499ae33f1dd54c097a4e26a2fccbee73b80bec6d b/artifacts/sha256/97/40d4ad0c64d229df372b81499ae33f1dd54c097a4e26a2fccbee73b80bec6d
new file mode 100644
index 00000000..1df40f5a
--- /dev/null
+++ b/artifacts/sha256/97/40d4ad0c64d229df372b81499ae33f1dd54c097a4e26a2fccbee73b80bec6d
@@ -0,0 +1,20 @@
+# Fingerprint exact-vector class sizes
+
+Generated from the canonical experiment artifact. Do not edit manually.
+
+Window: blocks 939969 through 962720; 98179414 transactions in 158 epochs.
+
+| class size | transaction share |
+|---|---:|
+| exactly 1 | 0.009% |
+| 2-9 | 0.052% |
+| 10-99 | 0.351% |
+| 100-999 | 1.667% |
+| 1,000-9,999 | 6.249% |
+| 10,000-99,999 | 16.120% |
+| >=100,000 | 75.552% |
+
+Share below 10: 0.061%.
+Share below 100: 0.413%.
+
+All preserved conditional partitions reconstruct the same 12-axis whole-window histogram. The unavailable 19-axis and per-epoch columns are excluded. Exact-vector equivalence classes do not establish general metric sparsity or successful attribution.
diff --git a/artifacts/sha256/97/ecbaa0035044c51f4d5717148ae6f2671d8b1d33ad4c6950801a651bc0db53 b/artifacts/sha256/97/ecbaa0035044c51f4d5717148ae6f2671d8b1d33ad4c6950801a651bc0db53
new file mode 100644
index 00000000..85068854
--- /dev/null
+++ b/artifacts/sha256/97/ecbaa0035044c51f4d5717148ae6f2671d8b1d33ad4c6950801a651bc0db53
@@ -0,0 +1,29 @@
+# Ascending and descending over the same slice
+
+Generated from the canonical experiment artifact. Do not edit manually.
+
+Two passes read the same 12000 transactions and 12341 addresses. The ascending pass starts from the discrete partition and declines a merge the spending transaction argues against. The descending pass is handed the common-input-ownership partition and may only cut it, from the same refusal channels with the co-spend prior removed.
+
+| pass | blocks | blocks cut | separations the ascent makes and this does not |
+|---|---:|---:|---:|
+| common-input ownership, inherited | 3182 | n/a | 3837 |
+| refusing merge pass, ascending | 3282 | n/a | 0 |
+| descent at cut_below -1 | 3261 | 4 | 3014 |
+| descent at cut_below -2 | 3182 | 0 | 3837 |
+| descent at cut_below -4 | 3182 | 0 | 3837 |
+
+At the loosest bar the descent recovers **823 of the 3837** separations the ascent makes, cutting 4 blocks and never separating a pair the ascent keeps together. Every signal on this slice is a single tell, so a bar of two or more clears nothing: the threshold is doing real work rather than sitting below the data.
+
+| quantity | pairs |
+|---|---:|
+| joined by the inherited partition | 601536 |
+| carrying evidence against the pairing | 1054 |
+| joined inside an exactly searched block | 7669 |
+| evidence inside an exactly searched block | 0 |
+| evidence inside a block the conservative pass handled | 1054 |
+
+Evidence against a pairing reaches 0.18% of the joined pairs, and all of it lies inside 5 of the 84 blocks that exceed the exact bound of 9 addresses. Not one of the 3098 blocks the exact search reaches contains a pair these channels argue against, so there it correctly did nothing. The heavy tail is where the evidence lives and where an exact search cannot go, which is why the conservative pass exists.
+
+What the two directions do with identical evidence is still not symmetric, and the remaining 3014 pairs are the measure of it. A refusal at merge time separates two addresses without holding any evidence about that pair: the merge is simply not made, and transitivity never carries through it. A cut has to argue about the boundary it names, against blocks reaching 648 addresses. Descending is strictly more expensive than not ascending, and this slice says how much.
+
+These counts describe one six-block slice under two channels, compare the passes as partitions rather than against same-owner labels, and do not establish how either pass behaves where the evidence is denser.
diff --git a/artifacts/sha256/98/4f0373600a455203cf1bccb44b5f8768368deaf41baf081f940a406775bd0c b/artifacts/sha256/98/4f0373600a455203cf1bccb44b5f8768368deaf41baf081f940a406775bd0c
new file mode 100644
index 00000000..efb74c38
--- /dev/null
+++ b/artifacts/sha256/98/4f0373600a455203cf1bccb44b5f8768368deaf41baf081f940a406775bd0c
@@ -0,0 +1,13 @@
+# Pair-level Bayesian and Fellegi–Sunter comparison
+
+Generated from the canonical experiment artifact. Do not edit manually.
+
+Transactions: 22112; positive pairs: 4000; negative pairs: 4000.
+
+| scorer | AUC | ECE |
+|---|---:|---:|
+| fellegi_sunter_fixed_0_95 | 0.916250 | 0.145541 |
+| fellegi_sunter_em | 0.932000 | 0.174952 |
+| bayesian_integrated_m | 0.932400 | 0.193611 |
+
+The preserved snapshot does not reproduce the metrics in the former handwritten report. These values are the canonical rerun. Address reuse is a weak label, calibration is on a balanced selected sample, and the cluster bands are independent-edge diagnostics rather than a posterior over ownership partitions.
diff --git a/artifacts/sha256/98/bae4be7cf0f6e5ec0d99b8663c5d783b3a61203beb028ebbdf6134ba7ff5b0 b/artifacts/sha256/98/bae4be7cf0f6e5ec0d99b8663c5d783b3a61203beb028ebbdf6134ba7ff5b0
new file mode 100644
index 00000000..646bf091
--- /dev/null
+++ b/artifacts/sha256/98/bae4be7cf0f6e5ec0d99b8663c5d783b3a61203beb028ebbdf6134ba7ff5b0
@@ -0,0 +1,117 @@
+{
+ "schema_version": 1,
+ "id": "amount-mapping-family-v1",
+ "claim_ids": [
+ "ctp.amount.subtransaction_ambiguity"
+ ],
+ "code": {
+ "revision": "bcd9a3e511860b1b0ff5f3ee6338a2df29ecbfbf",
+ "dirty": false
+ },
+ "command": {
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.amount_mapping_family",
+ "--dataset",
+ "data/amount-channel-812695-812831-v1.json",
+ "--cap",
+ "250",
+ "--wall-seconds",
+ "3.0",
+ "reproduce",
+ "--artifact",
+ "results/artifacts/amount-mapping-family-v1.json",
+ "--markdown",
+ "results/generated/amount-mapping-family-v1.md"
+ ]
+ },
+ "environment": {
+ "python": "3.13.7",
+ "lock_digest": "5ee1d70afd72fdb4ba3f01843e85e7ecd9a6d3e2a52abeb14c346a2143e3c186",
+ "platform": "macOS-26.6.2-arm64-arm-64bit-Mach-O",
+ "dependencies": [
+ {
+ "name": "dss",
+ "version": "0.1.0",
+ "source": "https://github.com/bc1cindy/dense-subset-sum",
+ "revision": "d8099bc540c9de7d0f3c9f095e8b2aad8f9d8c8d",
+ "lock_sha256": "f17a02f09da28dcc603901580d695466c44e1d1a0063e110d3faa375ae6066ca",
+ "license": "MIT",
+ "redistribution": "allowed",
+ "editable": false
+ }
+ ]
+ },
+ "datasets": [
+ {
+ "id": "amount-channel-812695-812831-v1",
+ "sha256": "83b4391f78d07a2fa85a67218d5ae4420d8965da3c95af577592e9ec1850104d"
+ }
+ ],
+ "parameters": {
+ "cap": 250,
+ "selection": "first complete multi-input transactions in source order",
+ "wall_seconds_per_transaction": 3.0
+ },
+ "rng": {
+ "algorithm": null,
+ "seeds": []
+ },
+ "outputs": [
+ {
+ "path": "results/artifacts/amount-mapping-family-v1.json",
+ "bytes": 942,
+ "sha256": "11f0ec645ea1be334dc7d24fbfc31047b234f335f48b5a514960472c7444b7d6"
+ },
+ {
+ "path": "results/generated/amount-mapping-family-v1.md",
+ "bytes": 559,
+ "sha256": "73afd1c112bcec43f16464f0589d4107d33a76c4085bd508d00aadc703366c0b"
+ }
+ ],
+ "reproducibility": {
+ "level": "bitwise_reproducible",
+ "availability": "complete"
+ },
+ "verification": {
+ "mode": "exact",
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.amount_mapping_family",
+ "--dataset",
+ "data/amount-channel-812695-812831-v1.json",
+ "--cap",
+ "250",
+ "--wall-seconds",
+ "3.0",
+ "verify",
+ "--artifact",
+ "results/artifacts/amount-mapping-family-v1.json",
+ "--markdown",
+ "results/generated/amount-mapping-family-v1.md"
+ ],
+ "tests": [
+ "tests/test_amount_mapping_family_experiment.py",
+ "tests/test_counting.py",
+ "tests/test_oracle_audit.py"
+ ],
+ "properties": [
+ "refusal, wall timeout, and execution failure remain distinct",
+ "entropy is labeled as uniform over DSS's restricted family",
+ "certain links are not presented as global ownership proofs",
+ "the selected population and ordering are explicit",
+ "the generated Markdown equals the canonical rendering"
+ ],
+ "tolerance": null
+ },
+ "limitations": [
+ "DSS enumerates a strict sub-family of the full balanced mappings",
+ "entropy is uniform over that restricted family, not a posterior over ownership",
+ "certain links are certain only within the restricted family",
+ "refusal, timeout, and execution failure are distinct outcomes",
+ "the sample is the first complete multi-input transactions in one 137-block snapshot",
+ "this diagnostic is not a privacy score or CoinScore"
+ ]
+}
diff --git a/artifacts/sha256/98/f644c679624361308823a76a3b1621f4effaedece2784fe3e9b6883a4384b6 b/artifacts/sha256/98/f644c679624361308823a76a3b1621f4effaedece2784fe3e9b6883a4384b6
new file mode 100644
index 00000000..5ebd584c
--- /dev/null
+++ b/artifacts/sha256/98/f644c679624361308823a76a3b1621f4effaedece2784fe3e9b6883a4384b6
@@ -0,0 +1,98 @@
+{
+ "schema_version": 1,
+ "id": "boltzmann-fee-audit-v1",
+ "claim_ids": [
+ "ctp.amount.subtransaction_ambiguity"
+ ],
+ "code": {
+ "revision": "bcd9a3e511860b1b0ff5f3ee6338a2df29ecbfbf",
+ "dirty": false
+ },
+ "command": {
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.boltzmann_fee_audit",
+ "--dataset",
+ "data/boltzmann-fee-audit-v1.json",
+ "reproduce",
+ "--artifact",
+ "results/artifacts/boltzmann-fee-audit-v1.json",
+ "--markdown",
+ "results/generated/boltzmann-fee-audit-v1.md"
+ ]
+ },
+ "environment": {
+ "python": "3.13.7",
+ "lock_digest": "e0d6a8cada5d383c0aca2bd2a37f28c15721392469c94084128d369c29c5544d",
+ "platform": "macOS-26.6.2-arm64-arm-64bit-Mach-O",
+ "dependencies": []
+ },
+ "datasets": [
+ {
+ "id": "boltzmann-fee-audit-v1",
+ "sha256": "17ee62e307d34660be2c4f50382a63ba044d0ad67f0674873e304d0b8203dc1c"
+ }
+ ],
+ "parameters": {
+ "cap": 300,
+ "max_coins": 8
+ },
+ "rng": {
+ "algorithm": null,
+ "seeds": []
+ },
+ "outputs": [
+ {
+ "path": "results/artifacts/boltzmann-fee-audit-v1.json",
+ "bytes": 52813,
+ "sha256": "9cf09b7659cf0f8f3e429623a903708c450a2c1605035cc4f45f6e1dcb34d2b3"
+ },
+ {
+ "path": "results/generated/boltzmann-fee-audit-v1.md",
+ "bytes": 523,
+ "sha256": "f4e6c0afd79425e8259d4112190a5df3b3eee81344cd7023f786c4671264e112"
+ }
+ ],
+ "reproducibility": {
+ "level": "bitwise_reproducible",
+ "availability": "complete"
+ },
+ "verification": {
+ "mode": "exact",
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.boltzmann_fee_audit",
+ "--dataset",
+ "data/boltzmann-fee-audit-v1.json",
+ "verify",
+ "--artifact",
+ "results/artifacts/boltzmann-fee-audit-v1.json",
+ "--markdown",
+ "results/generated/boltzmann-fee-audit-v1.md"
+ ],
+ "tests": [
+ "tests/test_boltzmann_fee_snapshot.py",
+ "tests/test_boltzmann_fee_audit.py",
+ "tests/test_boltzmann_fee_experiment.py",
+ "tests/test_boltzmann_reference_parity.py"
+ ],
+ "properties": [
+ "the internal snapshot reproduces all 300 historical per-transaction rows",
+ "exact zero-fee and local fee-allocation mappings remain separate",
+ "the all-coins interpretation is not presented as de-mixing evidence",
+ "fee roundness remains observational rather than a ranking model",
+ "general external-tool parity is not claimed",
+ "the generated Markdown equals the canonical rendering"
+ ],
+ "tolerance": null
+ },
+ "limitations": [
+ "the selected population is deterministic but not chain-representative",
+ "the all-coins interpretation makes fee-tolerant acceptance non-informative",
+ "fee roundness is reported but is not a competing probability model",
+ "this audit measures the local fee-allocation model, not general parity with Boltzmann",
+ "separate tests cover selected official Boltzmann vectors and modes"
+ ]
+}
diff --git a/artifacts/sha256/9a/3fdc6c79bc5c94cc687b5a41ebff8c663ecf3358cb080883e93cd30d94d850 b/artifacts/sha256/9a/3fdc6c79bc5c94cc687b5a41ebff8c663ecf3358cb080883e93cd30d94d850
new file mode 100644
index 00000000..13479356
--- /dev/null
+++ b/artifacts/sha256/9a/3fdc6c79bc5c94cc687b5a41ebff8c663ecf3358cb080883e93cd30d94d850
@@ -0,0 +1,10 @@
+# Entity-labelled graph-structure observations
+
+Generated from the canonical experiment artifact. Do not edit manually.
+
+| snapshot | transactions | positive pairs | payment AUC | mean shared neighbours |
+|---|---:|---:|---:|---:|
+| SatoshiDice | 15635 | 780 | 0.689075 | 7.948718 |
+| BitMEX | 9698 | 3488761 | 0.497400 | 0.006713 |
+
+The selected SatoshiDice subgraph is a positive control and the selected BitMEX subgraph is a null control. This contrast is snapshot-specific and is not general entity attribution or the full Narayanan–Shmatikov attack.
diff --git a/artifacts/sha256/9c/4ac11aa3a1014907528b5ed8b324c0a9b1995a0d7a70f2c6963c49bc8e797f b/artifacts/sha256/9c/4ac11aa3a1014907528b5ed8b324c0a9b1995a0d7a70f2c6963c49bc8e797f
new file mode 100644
index 00000000..c14f9869
--- /dev/null
+++ b/artifacts/sha256/9c/4ac11aa3a1014907528b5ed8b324c0a9b1995a0d7a70f2c6963c49bc8e797f
@@ -0,0 +1 @@
+{"experiment":"reid-v1","limitations":["the 200-record snapshot is selected and is not a uniform chain sample","the signature-generation recipe and original comparison population are unavailable","the target identifier is a record identity, not wallet ownership","the auxiliary origins are sampled from the target signature rather than observed independently","this is an adaptation, not a reproduction of the published Netflix experiment","the result does not establish a chain-wide deanonymization rate or privacy score","upstream data licensing is undetermined; the dataset publisher authorized redistribution on 2026-09-04"],"measurement":{"n":200,"n_dense":35,"n_sparse":165,"rows":[{"attackable":162,"declare_rate":0.9259259259259259,"declared":150,"exact":150,"exact_rate":0.9259259259259259,"m":4,"precision":1.0,"stratum":"sparse"},{"attackable":156,"declare_rate":0.9551282051282052,"declared":149,"exact":149,"exact_rate":0.9551282051282052,"m":8,"precision":1.0,"stratum":"sparse"},{"attackable":35,"declare_rate":0.05714285714285714,"declared":2,"exact":2,"exact_rate":0.05714285714285714,"m":4,"precision":1.0,"stratum":"dense"},{"attackable":35,"declare_rate":0.08571428571428572,"declared":3,"exact":3,"exact_rate":0.08571428571428572,"m":8,"precision":1.0,"stratum":"dense"}]},"method":{"auxiliary_origins":[4,8],"dense_at_least_cosine":0.9,"description":"ancestry-signature record-linkage adaptation","eccentricity_threshold":1.5,"seed":0,"sparse_below_cosine":0.5},"schema_version":1}
diff --git a/artifacts/sha256/9c/72972433e75a8f7c0db0774daaf853664bd4218f8128a03cfa4bc7799efa07 b/artifacts/sha256/9c/72972433e75a8f7c0db0774daaf853664bd4218f8128a03cfa4bc7799efa07
new file mode 100644
index 00000000..7ec8182e
--- /dev/null
+++ b/artifacts/sha256/9c/72972433e75a8f7c0db0774daaf853664bd4218f8128a03cfa4bc7799efa07
@@ -0,0 +1,13 @@
+{
+ "schema_version": 1,
+ "python": {
+ "implementation": "cpython",
+ "version": "3.13",
+ "system": "Darwin",
+ "machine": "arm64"
+ },
+ "source_archive": "sources/decluster-bcd9a3e-runtime.tar",
+ "project_root": "decluster",
+ "requirements": "reproduction/reid/requirements.lock",
+ "dependency_directory": "reproduction/reid"
+}
diff --git a/artifacts/sha256/9c/f09b7659cf0f8f3e429623a903708c450a2c1605035cc4f45f6e1dcb34d2b3 b/artifacts/sha256/9c/f09b7659cf0f8f3e429623a903708c450a2c1605035cc4f45f6e1dcb34d2b3
new file mode 100644
index 00000000..b1db8a69
--- /dev/null
+++ b/artifacts/sha256/9c/f09b7659cf0f8f3e429623a903708c450a2c1605035cc4f45f6e1dcb34d2b3
@@ -0,0 +1 @@
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diff --git a/artifacts/sha256/9e/36c5f5337bfda4db92e2f52d2db42d82ddf368cbb8aa3c8bdf2e18d821de8f b/artifacts/sha256/9e/36c5f5337bfda4db92e2f52d2db42d82ddf368cbb8aa3c8bdf2e18d821de8f
new file mode 100644
index 00000000..94d810e1
--- /dev/null
+++ b/artifacts/sha256/9e/36c5f5337bfda4db92e2f52d2db42d82ddf368cbb8aa3c8bdf2e18d821de8f
@@ -0,0 +1,21 @@
+# Projected attribute drift across epochs
+
+Generated from the canonical experiment artifact. Do not edit manually.
+
+Window: 158 epochs of 144 blocks, covering 98179414 transactions.
+
+| axis projection | TV@1 | TV@7 | TV@30 | TV@120 | weekly gain |
+|---|---:|---:|---:|---:|---:|
+| input_order | 0.008 | 0.008 | 0.011 | 0.013 | 0.83 |
+| input_types | 0.036 | 0.045 | 0.082 | 0.175 | 0.99 |
+| low_r | 0.038 | 0.052 | 0.090 | 0.155 | 1.04 |
+| nlocktime | 0.009 | 0.011 | 0.013 | 0.018 | 0.86 |
+| nsequence | 0.040 | 0.044 | 0.074 | 0.077 | 0.88 |
+| op_return | 0.071 | 0.072 | 0.103 | 0.147 | 0.82 |
+| output_order | 0.031 | 0.035 | 0.057 | 0.127 | 0.90 |
+| output_structure | 0.025 | 0.026 | 0.037 | 0.050 | 0.82 |
+| sighash | 0.024 | 0.034 | 0.064 | 0.089 | 1.07 |
+| uncompressed_pubkey | 0.025 | 0.035 | 0.066 | 0.093 | 1.06 |
+| version | 0.029 | 0.026 | 0.042 | 0.070 | 0.75 |
+
+Each row uses the explicitly preserved values plus one combined residual category. This reproduces every rounded historical cell for nlocktime and low_r. The version projection yields 0.070 rather than the historical 0.071 at gap 120. These projections are not replacements for unavailable full-distribution rows or the historical 70/85 volume-coupling summary.
diff --git a/artifacts/sha256/9e/f88ed2230876552f3f18ebf4e1bf462e7bd401160a5d287de443abc03f7415 b/artifacts/sha256/9e/f88ed2230876552f3f18ebf4e1bf462e7bd401160a5d287de443abc03f7415
new file mode 100644
index 00000000..308b6a6c
--- /dev/null
+++ b/artifacts/sha256/9e/f88ed2230876552f3f18ebf4e1bf462e7bd401160a5d287de443abc03f7415
@@ -0,0 +1 @@
+{"balance_model":"exact per-block conservation","composition":null,"conclusion":"the forced example has one non-derived mapping and four unanimous links, while the underdetermined example has three mappings and no unanimous link","experiment":"deterministic-partition-v1","forced_partition":{"inputs":[1,3,20,50],"non_derived_mappings":1,"outputs":[4,70],"unanimous_links":[[0,0],[1,0],[2,1],[3,1]]},"limitations":["certainty applies only within the declared exact non-derived mapping family","the examples are scaled integer renderings of the CTP amount fixtures","fees, contextual priors and net-settlement cycles are not modeled","unanimous mapping links are not ownership attribution"],"mapping_family":"Maurer non-derived mappings","schema_version":1,"underdetermined_control":{"inputs":[1,2,3,4,5],"non_derived_mappings":3,"outputs":[7,8],"unanimous_links":[]}}
diff --git a/artifacts/sha256/9f/297f42ac2839bb8ac9af90ee6eba8696ca09c617f343763354d277af822c4b b/artifacts/sha256/9f/297f42ac2839bb8ac9af90ee6eba8696ca09c617f343763354d277af822c4b
new file mode 100644
index 00000000..ea7b9ff4
--- /dev/null
+++ b/artifacts/sha256/9f/297f42ac2839bb8ac9af90ee6eba8696ca09c617f343763354d277af822c4b
@@ -0,0 +1,13 @@
+{
+ "schema_version": 1,
+ "python": {
+ "implementation": "cpython",
+ "version": "3.13",
+ "system": "Darwin",
+ "machine": "arm64"
+ },
+ "source_archive": "sources/decluster-bcd9a3e-runtime.tar",
+ "project_root": "decluster",
+ "requirements": "reproduction/change-consolidation/requirements.lock",
+ "dependency_directory": "reproduction/change-consolidation"
+}
diff --git a/artifacts/sha256/a0/00b89fd2a3def18ab1336179c2f083690289bd90584e4c9d047d3f4c0241ee b/artifacts/sha256/a0/00b89fd2a3def18ab1336179c2f083690289bd90584e4c9d047d3f4c0241ee
new file mode 100644
index 00000000..d5044274
--- /dev/null
+++ b/artifacts/sha256/a0/00b89fd2a3def18ab1336179c2f083690289bd90584e4c9d047d3f4c0241ee
@@ -0,0 +1 @@
+{"dataset":"slice-a-channels-2016-v1","experiment":"partition-schemes-v1","limitations":["the matcher recovers a handful of pairs at best, so the comparison rests on the straddler subgraph rather than on precision","one slice, one era, one 300,000-transaction prefix","cluster membership is a co-spend label, not wallet ownership","the export carries addresses but no values, so the collapse detector sees only the shape rule and never the de-mix arm","the result is not a privacy score"],"measurement":{"any_scheme_ignited_the_matcher":true,"clusters":71986,"collapse_costs_over_epoch":4989,"schemes":[{"boundary_transactions":0,"edges_among_straddlers":1910,"mean_straddler_degree":1.449648711943794,"scheme":"epoch","seeded":[{"correct":0,"guesses":0,"precision":null,"recall":0.0,"seed_share":0.05,"seeds":128},{"correct":1,"guesses":1,"precision":1.0,"recall":0.0004336513443191674,"seed_share":0.1,"seeds":256}],"straddlers_with_a_straddling_neighbour":1145,"straddling_entities":2562,"view_sizes":[137354,162646]},{"boundary_transactions":117371,"edges_among_straddlers":360,"mean_straddler_degree":0.5486862442040186,"scheme":"decore","seeded":[{"correct":0,"guesses":0,"precision":null,"recall":0.0,"seed_share":0.05,"seeds":64},{"correct":0,"guesses":0,"precision":null,"recall":0.0,"seed_share":0.1,"seeds":129}],"straddlers_with_a_straddling_neighbour":366,"straddling_entities":1294,"view_sizes":[82820,99809]},{"boundary_transactions":4989,"edges_among_straddlers":1941,"mean_straddler_degree":1.4944794952681388,"scheme":"collapse","seeded":[{"correct":0,"guesses":0,"precision":null,"recall":0.0,"seed_share":0.05,"seeds":126},{"correct":1,"guesses":1,"precision":1.0,"recall":0.0004380201489268506,"seed_share":0.1,"seeds":253}],"straddlers_with_a_straddling_neighbour":1129,"straddling_entities":2536,"view_sizes":[135045,159966]}],"transactions":300000,"view_counts":{"collapse":{"2":[135045,159966],"3":[89861,93060,112090],"4":[69302,65459,76829,83421]},"decore":{"2":[82820,99809],"3":[55231,57214,70184],"4":[42474,40182,46904,53069]},"epoch":{"2":[137354,162646],"3":[91349,94577,114074],"4":[70469,66597,78032,84902]}}},"parameters":{"core_fraction":0.01,"min_side":2,"schemes":["epoch","decore","collapse"],"seed":0,"seed_shares":[0.05,0.1],"view_counts":[2,3,4]},"schema_version":1}
diff --git a/artifacts/sha256/a0/02dd84d74d51b0106a6bccd4c625300d838becbfc1edff36b7cfd46eda682f b/artifacts/sha256/a0/02dd84d74d51b0106a6bccd4c625300d838becbfc1edff36b7cfd46eda682f
new file mode 100644
index 00000000..d889d3ac
--- /dev/null
+++ b/artifacts/sha256/a0/02dd84d74d51b0106a6bccd4c625300d838becbfc1edff36b7cfd46eda682f
@@ -0,0 +1,9 @@
+# Counting-router observations
+
+Generated from the canonical experiment artifact. Do not edit manually.
+
+The current router evaluates 1,428 transactions. It resolves 82: 0 radix exact, 60 sparse exact, and 22 sparse lower bound. The remaining 1,333 are unknown.
+
+The radix precondition applies to 20 transactions. Calling radix without that gate returns a positive reading 64 times, including 51 outside its precondition.
+
+Sasamoto is not a router tier. These counts are not mapping entropy or CoinScore.
diff --git a/artifacts/sha256/a0/b3a5b051590a9020d39be8ada5eb616e0d7cf283317a03598d78d6dcd58133 b/artifacts/sha256/a0/b3a5b051590a9020d39be8ada5eb616e0d7cf283317a03598d78d6dcd58133
new file mode 100644
index 00000000..7e4df47b
--- /dev/null
+++ b/artifacts/sha256/a0/b3a5b051590a9020d39be8ada5eb616e0d7cf283317a03598d78d6dcd58133
@@ -0,0 +1,13 @@
+{
+ "schema_version": 1,
+ "python": {
+ "implementation": "cpython",
+ "version": "3.13",
+ "system": "Darwin",
+ "machine": "arm64"
+ },
+ "source_archive": "sources/decluster-bcd9a3e-runtime.tar",
+ "project_root": "decluster",
+ "requirements": "reproduction/fingerprint-regime/requirements.lock",
+ "dependency_directory": "reproduction/fingerprint-regime"
+}
diff --git a/artifacts/sha256/a2/b2220ee30872e47a96ad15e92ee63e611af1ee3b9784f12c6d96d228463fcb b/artifacts/sha256/a2/b2220ee30872e47a96ad15e92ee63e611af1ee3b9784f12c6d96d228463fcb
new file mode 100644
index 00000000..4bc13780
--- /dev/null
+++ b/artifacts/sha256/a2/b2220ee30872e47a96ad15e92ee63e611af1ee3b9784f12c6d96d228463fcb
@@ -0,0 +1,13 @@
+{
+ "schema_version": 1,
+ "python": {
+ "implementation": "cpython",
+ "version": "3.13",
+ "system": "Darwin",
+ "machine": "arm64"
+ },
+ "source_archive": "sources/decluster-bcd9a3e-runtime.tar",
+ "project_root": "decluster",
+ "requirements": "reproduction/match-controls/requirements.lock",
+ "dependency_directory": "dependencies"
+}
diff --git a/artifacts/sha256/a3/23b5b930835a020812272aa8df80af7c922a116b3def6bca2ca4715e94d466 b/artifacts/sha256/a3/23b5b930835a020812272aa8df80af7c922a116b3def6bca2ca4715e94d466
new file mode 100644
index 00000000..f19e4170
--- /dev/null
+++ b/artifacts/sha256/a3/23b5b930835a020812272aa8df80af7c922a116b3def6bca2ca4715e94d466
@@ -0,0 +1 @@
+{"brittle_overlap":{"entropy_bits":2.0,"initial_candidates":4,"observations":2,"surviving_candidates":1},"composition":null,"conclusion":"equal point entropy does not determine longitudinal intersection resistance","equal_initial_entropy":true,"experiment":"entropy-insufficiency-v1","limitations":["synthetic counterexample","equal point entropy does not imply equal future candidate-set overlap","the fixture falsifies sufficiency but does not estimate real-world attack frequency","the result is not a privacy score or certificate"],"overlapping_crowd":{"entropy_bits":2.0,"initial_candidates":4,"observations":2,"surviving_candidates":2},"schema_version":1}
diff --git a/artifacts/sha256/a3/dcc60fca5f8b42eaeef22f5e8f723c34d7f118ee2b54f5787fd103e4b6d390 b/artifacts/sha256/a3/dcc60fca5f8b42eaeef22f5e8f723c34d7f118ee2b54f5787fd103e4b6d390
new file mode 100644
index 00000000..d6155c30
--- /dev/null
+++ b/artifacts/sha256/a3/dcc60fca5f8b42eaeef22f5e8f723c34d7f118ee2b54f5787fd103e4b6d390
@@ -0,0 +1,13 @@
+{
+ "schema_version": 1,
+ "python": {
+ "implementation": "cpython",
+ "version": "3.13",
+ "system": "Darwin",
+ "machine": "arm64"
+ },
+ "source_archive": "sources/decluster-bcd9a3e-runtime.tar",
+ "project_root": "decluster",
+ "requirements": "reproduction/counting-fee-sensitivity/requirements.lock",
+ "dependency_directory": "dependencies"
+}
diff --git a/artifacts/sha256/a4/4b51e219a392466a3d3d92f039f6d07cd72e93f435eb9502db73cdae71bf24 b/artifacts/sha256/a4/4b51e219a392466a3d3d92f039f6d07cd72e93f435eb9502db73cdae71bf24
new file mode 100644
index 00000000..a492506f
--- /dev/null
+++ b/artifacts/sha256/a4/4b51e219a392466a3d3d92f039f6d07cd72e93f435eb9502db73cdae71bf24
@@ -0,0 +1,86 @@
+{
+ "schema_version": 1,
+ "id": "intersection-cluster-fixture-v1",
+ "claim_ids": [
+ "ctp.intersection.longitudinal"
+ ],
+ "code": {
+ "revision": "bcd9a3e511860b1b0ff5f3ee6338a2df29ecbfbf",
+ "dirty": false
+ },
+ "command": {
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.intersection_fixture",
+ "reproduce",
+ "--artifact",
+ "results/artifacts/intersection-cluster-fixture-v1.json",
+ "--markdown",
+ "results/generated/intersection-cluster-fixture-v1.md"
+ ]
+ },
+ "environment": {
+ "python": "3.13.7",
+ "lock_digest": "c7d402576511073ed4fcf076c77ffa6b3be124e0a2ef996d2da1cd3b02fbe541",
+ "platform": "macOS-26.6.2-arm64-arm-64bit-Mach-O",
+ "dependencies": []
+ },
+ "datasets": [],
+ "parameters": {
+ "ancestry_depth": 2,
+ "cluster_key": "creating transaction id",
+ "monitor_depth": 1
+ },
+ "rng": {
+ "algorithm": null,
+ "seeds": []
+ },
+ "outputs": [
+ {
+ "path": "results/artifacts/intersection-cluster-fixture-v1.json",
+ "bytes": 1869,
+ "sha256": "2114e568b91808536c94ddb307a4688e3be32720bcb768ef9bb790ef673d0fe5"
+ },
+ {
+ "path": "results/generated/intersection-cluster-fixture-v1.md",
+ "bytes": 385,
+ "sha256": "3fd9f9bf57e30f4b92851852efa24e4af2201c705c39391d9451b68689005434"
+ }
+ ],
+ "reproducibility": {
+ "level": "bitwise_reproducible",
+ "availability": "complete"
+ },
+ "verification": {
+ "mode": "exact",
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.intersection_fixture",
+ "verify",
+ "--artifact",
+ "results/artifacts/intersection-cluster-fixture-v1.json",
+ "--markdown",
+ "results/generated/intersection-cluster-fixture-v1.md"
+ ],
+ "tests": [
+ "tests/test_intersection_experiment.py",
+ "tests/test_intersection_adapter.py",
+ "tests/test_intersection_pipeline.py"
+ ],
+ "properties": [
+ "the stored artifact equals a fresh execution of the synthetic fixture",
+ "the generated Markdown equals the canonical rendering",
+ "the two valid UTXO branches narrow two antecedent clusters to one shared cluster",
+ "the narrowing remains explicitly conditional and unscored"
+ ],
+ "tolerance": null
+ },
+ "limitations": [
+ "synthetic fixture",
+ "the creating transaction id is used as the fixture's antecedent cluster label",
+ "narrowing is conditional on the observed co-spend",
+ "the fixture does not run the clustering verdict"
+ ]
+}
diff --git a/artifacts/sha256/a5/9e6e2efcc94cbacad687363da37526efd7469930721966f619f28fb2a39fc2 b/artifacts/sha256/a5/9e6e2efcc94cbacad687363da37526efd7469930721966f619f28fb2a39fc2
new file mode 100644
index 00000000..099fd4ce
--- /dev/null
+++ b/artifacts/sha256/a5/9e6e2efcc94cbacad687363da37526efd7469930721966f619f28fb2a39fc2
@@ -0,0 +1,13 @@
+# CoinJoin Sudoku nominal reconstruction
+
+Generated from the canonical experiment artifact. Do not edit manually.
+
+The independently implemented nominal mechanism retains exact equal-sum input/output groupings. The cited repository does not contain the historical analyzer, so this result does not claim code parity.
+
+| primary-source fixture | retained mappings | representative input/output relation |
+|---|---:|---:|
+| SX symmetric control | 36 indexed / 6 fee-collapsed | 0.333333 |
+
+The advisory's larger SharedCoin target `0e0337bdf930eba3b082fdfbd30944b18e03f0f810ae531443161f897a4d3db0` remains **not reproduced**: its analyzer and complete output-label semantics were not published.
+
+The source-backed layer enumerates all exact equal-sum groupings. The displayed marginal comes from a separate local adaptation: explicit 10,000-sat fee pseudo-outputs, largest-group selection, and uniform weighting. Those rules are not attributed to the unavailable analyzer, and the resulting conditional amount relations are not ownership attribution.
diff --git a/artifacts/sha256/a7/9df3ae2ac536f6c6b303f8a5dab73a4b6d53884832b4354db58104449406d0 b/artifacts/sha256/a7/9df3ae2ac536f6c6b303f8a5dab73a4b6d53884832b4354db58104449406d0
new file mode 100644
index 00000000..5e088e13
--- /dev/null
+++ b/artifacts/sha256/a7/9df3ae2ac536f6c6b303f8a5dab73a4b6d53884832b4354db58104449406d0
@@ -0,0 +1,13 @@
+{
+ "schema_version": 1,
+ "python": {
+ "implementation": "cpython",
+ "version": "3.13",
+ "system": "Darwin",
+ "machine": "arm64"
+ },
+ "source_archive": "sources/decluster-bcd9a3e-runtime.tar",
+ "project_root": "decluster",
+ "requirements": "reproduction/bayes-vs-fs/requirements.lock",
+ "dependency_directory": "reproduction/bayes-vs-fs"
+}
diff --git a/artifacts/sha256/a8/76480ac3f573258d6d94e03fb6a31c7270fa2df1d71fa027e49519b243462c b/artifacts/sha256/a8/76480ac3f573258d6d94e03fb6a31c7270fa2df1d71fa027e49519b243462c
new file mode 100644
index 00000000..f8c49efd
--- /dev/null
+++ b/artifacts/sha256/a8/76480ac3f573258d6d94e03fb6a31c7270fa2df1d71fa027e49519b243462c
@@ -0,0 +1,13 @@
+{
+ "schema_version": 1,
+ "python": {
+ "implementation": "cpython",
+ "version": "3.13",
+ "system": "Darwin",
+ "machine": "arm64"
+ },
+ "source_archive": "sources/decluster-bcd9a3e-runtime.tar",
+ "project_root": "decluster",
+ "requirements": "reproduction/graph-deanon-controls/requirements.lock",
+ "dependency_directory": "reproduction/graph-deanon-controls"
+}
diff --git a/artifacts/sha256/a8/e17f91020b0cbdc12bc2cb2652e2ffa63b5b5dcc70af6dfc3d4a73925278e0 b/artifacts/sha256/a8/e17f91020b0cbdc12bc2cb2652e2ffa63b5b5dcc70af6dfc3d4a73925278e0
new file mode 100644
index 00000000..37bcc21b
--- /dev/null
+++ b/artifacts/sha256/a8/e17f91020b0cbdc12bc2cb2652e2ffa63b5b5dcc70af6dfc3d4a73925278e0
@@ -0,0 +1 @@
+{"boundary_cases":{"equality_boundary":{"blocksci_strict_uih1":false,"change_output_index":null,"fee":1,"gibson_flags":[false,true],"inputs":[10,2],"outputs":[9,2],"reason":null,"removed_input_index":1,"status":"uih2"},"fee_aware_uih1":{"blocksci_strict_uih1":true,"change_output_index":1,"fee":1,"gibson_flags":[true,false],"inputs":[10,2],"outputs":[10,1],"reason":null,"removed_input_index":1,"status":"uih1"},"jointly_funded_uih2":{"blocksci_strict_uih1":false,"change_output_index":null,"fee":1,"gibson_flags":[false,false],"inputs":[6,6,6],"outputs":[10,7],"reason":null,"removed_input_index":0,"status":"uih2"}},"conclusion":"the fee-aware predicate classifies a two-output amount shape; it does not identify PayJoin, ownership, participant roles, or gross obligations","cycle_control":{"cyclic_gross_amount":1400,"cyclic_nonzero_net_balances":{"alice":-500,"bob":500},"simple_gross_amount":500,"simple_nonzero_net_balances":{"alice":-500,"bob":500}},"diagnostic":{"definition":"blockstream_fee_aware","paper_domain":{"amounts":"positive unsigned 64-bit integers","exact_outputs":2,"minimum_inputs":2},"semantic_version":2},"experiment":"unnecessary-input-v2","limitations":["a positive predicate does not identify PayJoin","no participant role, ownership relation, payment output, or gross payment is inferred","ordinary consolidation and collaborative settlement share the same observable","the fixture demonstrates non-identifiability, not real-world prevalence"],"observational_equivalence":{"classification":"uih2","composition":null,"decision":"inconclusive","fee":100,"ownership_inferred":false,"payjoin_identified":false,"payment_output_identified":false,"possible_forms":["ordinary_single_owner_consolidation","two_party_payjoin","net_settlement_with_cycles"]},"schema_version":1,"scope_controls":{"many_senders_many_receivers":{"blocksci_strict_uih1":null,"change_output_index":null,"fee":null,"gibson_flags":null,"inputs":[90,15,40,30,45,35],"outputs":[85,25,50,40,45,10],"reason":"requires at least two inputs and exactly two outputs","removed_input_index":null,"status":"out_of_scope"},"many_senders_one_receiver":{"blocksci_strict_uih1":null,"change_output_index":null,"fee":null,"gibson_flags":null,"inputs":[40,50,60,90],"outputs":[160,30,30,20],"reason":"requires at least two inputs and exactly two outputs","removed_input_index":null,"status":"out_of_scope"}}}
diff --git a/artifacts/sha256/a9/5843b58e37348f45520a64411a733e85e7a832310c7a23780a267d928d6ee9 b/artifacts/sha256/a9/5843b58e37348f45520a64411a733e85e7a832310c7a23780a267d928d6ee9
new file mode 100644
index 00000000..61f48588
--- /dev/null
+++ b/artifacts/sha256/a9/5843b58e37348f45520a64411a733e85e7a832310c7a23780a267d928d6ee9
@@ -0,0 +1,13 @@
+{
+ "schema_version": 1,
+ "python": {
+ "implementation": "cpython",
+ "version": "3.13",
+ "system": "Darwin",
+ "machine": "arm64"
+ },
+ "source_archive": "sources/decluster-bcd9a3e-runtime.tar",
+ "project_root": "decluster",
+ "requirements": "reproduction/amount-local-channels/requirements.lock",
+ "dependency_directory": "reproduction/amount-local-channels"
+}
diff --git a/artifacts/sha256/aa/d3f3edfc2be53ca885f780347f09c29ed031598a0cb70520e01f8f3d59fc49 b/artifacts/sha256/aa/d3f3edfc2be53ca885f780347f09c29ed031598a0cb70520e01f8f3d59fc49
new file mode 100644
index 00000000..4c505aa9
--- /dev/null
+++ b/artifacts/sha256/aa/d3f3edfc2be53ca885f780347f09c29ed031598a0cb70520e01f8f3d59fc49
@@ -0,0 +1,90 @@
+{
+ "schema_version": 1,
+ "id": "ns1r-change-readoff-v1",
+ "claim_ids": [
+ "ctp.payjoin.receiver_change_readoff"
+ ],
+ "code": {
+ "revision": "bcd9a3e511860b1b0ff5f3ee6338a2df29ecbfbf",
+ "dirty": false
+ },
+ "command": {
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.ns1r_change_readoff",
+ "reproduce",
+ "--artifact",
+ "results/artifacts/ns1r-change-readoff-v1.json",
+ "--markdown",
+ "results/generated/ns1r-change-readoff-v1.md"
+ ]
+ },
+ "environment": {
+ "python": "3.13.7",
+ "lock_digest": "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855",
+ "platform": "macOS-26.6.2-arm64-arm-64bit-Mach-O",
+ "dependencies": []
+ },
+ "datasets": [],
+ "parameters": {
+ "scenarios": [
+ "consolidating-receiver",
+ "evenly-spaced-control"
+ ],
+ "max_senders": 8,
+ "fees_modelled": false
+ },
+ "rng": {
+ "algorithm": null,
+ "seeds": []
+ },
+ "outputs": [
+ {
+ "path": "results/artifacts/ns1r-change-readoff-v1.json",
+ "bytes": 2317,
+ "sha256": "d2c40b646533f67e4e256f62260b183e6aa4e2e3e2888d0e8d41da54374ca1f1"
+ },
+ {
+ "path": "results/generated/ns1r-change-readoff-v1.md",
+ "bytes": 1321,
+ "sha256": "37d0f5348a1fbb852a826d28b09ed7ce09184da149616c9fd5816e127058764e"
+ }
+ ],
+ "reproducibility": {
+ "level": "bitwise_reproducible",
+ "availability": "complete"
+ },
+ "verification": {
+ "mode": "exact",
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.ns1r_change_readoff",
+ "verify",
+ "--artifact",
+ "results/artifacts/ns1r-change-readoff-v1.json",
+ "--markdown",
+ "results/generated/ns1r-change-readoff-v1.md"
+ ],
+ "tests": [
+ "tests/test_ns1r_change_readoff.py",
+ "tests/test_ns1r_change_readoff_experiment.py"
+ ],
+ "properties": [
+ "a reading counts distinct amounts, not distinct index matchings",
+ "the control scenario keeps two readings and no unanimous link",
+ "the receiver's negotiated payments are inputs, never inferred",
+ "the generated Markdown equals the canonical rendering"
+ ],
+ "tolerance": null
+ },
+ "limitations": [
+ "the scenarios are deterministic fixtures, not sampled transactions",
+ "the receiver's knowledge of each negotiated payment is supplied, not inferred",
+ "an external observer holds neither the payments nor the receiver's own coins",
+ "a unique reading of the amounts is not ownership attribution",
+ "the enumeration is bounded at eight senders and does not model fees",
+ "the result is not a privacy score"
+ ]
+}
diff --git a/artifacts/sha256/ab/1cf300ab292309a6fd3e509b671a174f668313421f1540c1563dff65288686 b/artifacts/sha256/ab/1cf300ab292309a6fd3e509b671a174f668313421f1540c1563dff65288686
new file mode 100644
index 00000000..83521b4f
--- /dev/null
+++ b/artifacts/sha256/ab/1cf300ab292309a6fd3e509b671a174f668313421f1540c1563dff65288686
@@ -0,0 +1,101 @@
+{
+ "schema_version": 1,
+ "id": "ancestry-sparsity-v1",
+ "claim_ids": [
+ "ctp.sparse_dataset.feature_uniqueness"
+ ],
+ "code": {
+ "revision": "bcd9a3e511860b1b0ff5f3ee6338a2df29ecbfbf",
+ "dirty": false
+ },
+ "command": {
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.ancestry_sparsity",
+ "reproduce",
+ "--dataset",
+ "tests/fixtures/reid_sigs.json.gz",
+ "--artifact",
+ "results/artifacts/ancestry-sparsity-v1.json",
+ "--markdown",
+ "results/generated/ancestry-sparsity-v1.md"
+ ]
+ },
+ "environment": {
+ "python": "3.13.7",
+ "lock_digest": "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855",
+ "platform": "macOS-26.6.2-arm64-arm-64bit-Mach-O",
+ "dependencies": []
+ },
+ "datasets": [
+ {
+ "id": "reid-signatures-v1",
+ "sha256": "dbc4214fb499a96327326c4ea31341a76878c1867bfa3ccc57df88d850cdaa6a"
+ }
+ ],
+ "parameters": {
+ "similarity": "cosine",
+ "epsilons": [
+ 0.5,
+ 0.9,
+ 0.99
+ ],
+ "nearest_neighbour_scope": "all other records in the frozen snapshot",
+ "stored_top_values": "excluded"
+ },
+ "rng": {
+ "algorithm": null,
+ "seeds": []
+ },
+ "outputs": [
+ {
+ "path": "results/artifacts/ancestry-sparsity-v1.json",
+ "bytes": 768,
+ "sha256": "c0c417c8efc84acfcb88c465123e910aad0af32d55fd074fc3729091ce748693"
+ },
+ {
+ "path": "results/generated/ancestry-sparsity-v1.md",
+ "bytes": 665,
+ "sha256": "83c424ce8501bffc44b4e0af0ccdf37b6e11aaf8c355be0cfca81659d542b77a"
+ }
+ ],
+ "reproducibility": {
+ "level": "bitwise_reproducible",
+ "availability": "complete"
+ },
+ "verification": {
+ "mode": "exact",
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.ancestry_sparsity",
+ "verify",
+ "--dataset",
+ "tests/fixtures/reid_sigs.json.gz",
+ "--artifact",
+ "results/artifacts/ancestry-sparsity-v1.json",
+ "--markdown",
+ "results/generated/ancestry-sparsity-v1.md"
+ ],
+ "tests": [
+ "tests/test_ancestry_sparsity_experiment.py",
+ "tests/test_def1_sparsity.py"
+ ],
+ "properties": [
+ "nearest-neighbour similarities are recomputed from signature vectors",
+ "the sparse inverted-index implementation agrees with pairwise cosine",
+ "stored top values from the unavailable comparison population are excluded",
+ "the result is scoped to the frozen 200-record snapshot",
+ "the generated Markdown equals the canonical rendering"
+ ],
+ "tolerance": null
+ },
+ "limitations": [
+ "the 200-record frozen snapshot is selected and is not a uniform chain sample",
+ "the signature-generation recipe and original comparison population are unavailable",
+ "the run measures sparsity within this snapshot only",
+ "sparsity is an attack precondition, not proof of successful attribution",
+ "the run does not establish a chain-wide population rate"
+ ]
+}
diff --git a/artifacts/sha256/ab/830f2e56b40e961015fda9c9685ddcab4feff1e486bde6ee825ea4db9d33a6 b/artifacts/sha256/ab/830f2e56b40e961015fda9c9685ddcab4feff1e486bde6ee825ea4db9d33a6
new file mode 100644
index 00000000..da1d61e3
--- /dev/null
+++ b/artifacts/sha256/ab/830f2e56b40e961015fda9c9685ddcab4feff1e486bde6ee825ea4db9d33a6
@@ -0,0 +1 @@
+{"composition":null,"conclusion":"partial contraction preserves singleton pseudonyms, direction and folded transfer attributes","edges":[{"first_height":100,"last_height":140,"source":"cluster-a","target":"cluster-x","transfers":3,"value":600},{"first_height":120,"last_height":120,"source":"cluster-x","target":"cluster-a","transfers":1,"value":50},{"first_height":150,"last_height":150,"source":"z","target":"cluster-a","transfers":1,"value":10}],"experiment":"pseudonym-graph-contract-v1","limitations":["deterministic synthetic fixture","the partial clustering lookup is supplied","vertices represent pseudonyms unless clustering completeness is independently established","the report validates representation invariants, not deanonymization success"],"schema_version":1,"self_transfers":{"cluster-a":1},"vertices":["cluster-a","cluster-x","z"]}
diff --git a/artifacts/sha256/ae/aabdc282b4aed6946c6054b95e8b5dd9c20e50eca467de49827e453d5353df b/artifacts/sha256/ae/aabdc282b4aed6946c6054b95e8b5dd9c20e50eca467de49827e453d5353df
new file mode 100644
index 00000000..aef12ea4
--- /dev/null
+++ b/artifacts/sha256/ae/aabdc282b4aed6946c6054b95e8b5dd9c20e50eca467de49827e453d5353df
@@ -0,0 +1,9 @@
+# DSS pairwise restricted-family diagnostic
+
+Generated from the canonical experiment artifact. Do not edit manually.
+
+The run selects 300 transactions containing 3,384 input rows. 271 transactions answer, 27 refuse, 2 time out, and 0 fail.
+
+The answered matrices return 914 rows, of which 914 have nonempty support and 367 have singleton support.
+
+Singleton support means only that every mapping in DSS's restricted family selects the same output. It is not a globally deterministic ownership link.
diff --git a/artifacts/sha256/b1/19c9d6d6782e6fca449004b11096cba93a9dc37b631b27fd42954e66db3618 b/artifacts/sha256/b1/19c9d6d6782e6fca449004b11096cba93a9dc37b631b27fd42954e66db3618
new file mode 100644
index 00000000..efaf3f24
--- /dev/null
+++ b/artifacts/sha256/b1/19c9d6d6782e6fca449004b11096cba93a9dc37b631b27fd42954e66db3618
@@ -0,0 +1 @@
+{"corpus":"deterministic planted-view fixtures","experiment":"link-prediction-v1","limitations":["synthetic corpus","seeds are sampled from the withheld correspondence","this is not a reproduction of the 2011 paper or Kaggle/Flickr results","the measured advantage transfers links observed by the auxiliary 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diff --git a/artifacts/sha256/b1/626d150fb4bb82933f561cd92700634f9eccf93a3d88b306801ab1536f849e b/artifacts/sha256/b1/626d150fb4bb82933f561cd92700634f9eccf93a3d88b306801ab1536f849e
new file mode 100644
index 00000000..c71dc3b6
--- /dev/null
+++ b/artifacts/sha256/b1/626d150fb4bb82933f561cd92700634f9eccf93a3d88b306801ab1536f849e
@@ -0,0 +1,99 @@
+{
+ "schema_version": 1,
+ "id": "fs-ablation-v1",
+ "claim_ids": [
+ "ctp.fingerprint.statistical_linkage"
+ ],
+ "code": {
+ "revision": "bcd9a3e511860b1b0ff5f3ee6338a2df29ecbfbf",
+ "dirty": false
+ },
+ "command": {
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.fs_ablation",
+ "--snapshot",
+ "data/fs-blkcache-2026-09-04.tar.gz",
+ "reproduce",
+ "--artifact",
+ "results/artifacts/fs-ablation-v1.json",
+ "--markdown",
+ "results/generated/fs-ablation-v1.md"
+ ]
+ },
+ "environment": {
+ "python": "3.13.7",
+ "lock_digest": "e0d6a8cada5d383c0aca2bd2a37f28c15721392469c94084128d369c29c5544d",
+ "platform": "macOS-26.6.2-arm64-arm-64bit-Mach-O",
+ "dependencies": []
+ },
+ "datasets": [
+ {
+ "id": "fs-blkcache-2026-09-04-v1",
+ "sha256": "5a69f6f446dbdfcce09dddc91c653e5d841124d14020b720fdea276315c775a6"
+ }
+ ],
+ "parameters": {
+ "ablation_cap": 4000,
+ "association_cap": 8000,
+ "train_fraction": 0.7,
+ "seed": 0,
+ "cluster_threshold": 0.6
+ },
+ "rng": {
+ "algorithm": "python.random.MersenneTwister",
+ "seeds": [
+ 0
+ ]
+ },
+ "outputs": [
+ {
+ "path": "results/artifacts/fs-ablation-v1.json",
+ "bytes": 7359,
+ "sha256": "9056280af77748e314ac525b2a215a1924a5600e1713021db076fd86072e15cf"
+ },
+ {
+ "path": "results/generated/fs-ablation-v1.md",
+ "bytes": 460,
+ "sha256": "d74c2d1442294fd09f2dde8c1ce43ca44982ec38c99ea2b079c381e9c181502d"
+ }
+ ],
+ "reproducibility": {
+ "level": "bitwise_reproducible",
+ "availability": "complete"
+ },
+ "verification": {
+ "mode": "exact",
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.fs_ablation",
+ "--snapshot",
+ "data/fs-blkcache-2026-09-04.tar.gz",
+ "verify",
+ "--artifact",
+ "results/artifacts/fs-ablation-v1.json",
+ "--markdown",
+ "results/generated/fs-ablation-v1.md"
+ ],
+ "tests": [
+ "tests/test_fs_ablation.py",
+ "tests/test_fs_ablation_experiment.py"
+ ],
+ "properties": [
+ "the snapshot reproduces the complete historical ablation JSON",
+ "association is computed conditionally within weak-label classes",
+ "group ablation is distinct from leaving one representative",
+ "duplicated fingerprint fields remain an explicit limitation",
+ "the generated Markdown equals the canonical rendering"
+ ],
+ "tolerance": null
+ },
+ "limitations": [
+ "address reuse provides weak labels rather than independent ownership labels",
+ "the block sample is not representative of the whole chain",
+ "the association statistic is descriptive rather than causal",
+ "two fingerprint fields are duplicates in this sample"
+ ]
+}
diff --git a/artifacts/sha256/b2/4a645e8459ddc4883e20f01469eb5d68509d9bfbbc55912403cd6e165c74fd b/artifacts/sha256/b2/4a645e8459ddc4883e20f01469eb5d68509d9bfbbc55912403cd6e165c74fd
new file mode 100644
index 00000000..4b79dbc3
--- /dev/null
+++ b/artifacts/sha256/b2/4a645e8459ddc4883e20f01469eb5d68509d9bfbbc55912403cd6e165c74fd
@@ -0,0 +1,108 @@
+{
+ "schema_version": 1,
+ "id": "descent-vs-ascent-v1",
+ "claim_ids": [
+ "ctp.cioh.collaborative_false_merge"
+ ],
+ "code": {
+ "revision": "bcd9a3e511860b1b0ff5f3ee6338a2df29ecbfbf",
+ "dirty": false
+ },
+ "command": {
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.descent_vs_ascent",
+ "--dataset",
+ "tests/fixtures/slice_a_channels_2016.ndjson.gz",
+ "reproduce",
+ "--artifact",
+ "results/artifacts/descent-vs-ascent-v1.json",
+ "--markdown",
+ "results/generated/descent-vs-ascent-v1.md"
+ ]
+ },
+ "environment": {
+ "python": "3.13.7",
+ "lock_digest": "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855",
+ "platform": "macOS-26.6.2-arm64-arm-64bit-Mach-O",
+ "dependencies": []
+ },
+ "datasets": [
+ {
+ "id": "slice-a-channels-2016-v1",
+ "sha256": "66104889f8b79f2938edb6666f16dfa19a74c68cd8016832b4f2a5e03b11865f"
+ }
+ ],
+ "parameters": {
+ "channels": [
+ "coinjoin shape refuses every pair among the inputs",
+ "de-mix partition joins a participant's own inputs and refuses across participants",
+ "unnecessary input and mixed input types each refuse once"
+ ],
+ "cospend_prior": 0.0,
+ "max_block": 9,
+ "signal_unit": "one published tell against a pairing",
+ "thresholds": [
+ -1.0,
+ -2.0,
+ -4.0
+ ]
+ },
+ "rng": {
+ "algorithm": "none",
+ "seeds": []
+ },
+ "outputs": [
+ {
+ "path": "results/artifacts/descent-vs-ascent-v1.json",
+ "bytes": 2448,
+ "sha256": "3542b1199c220f49bd923306dcd1493ed5e7e677ff6fdabd1816256eb7021934"
+ },
+ {
+ "path": "results/generated/descent-vs-ascent-v1.md",
+ "bytes": 2504,
+ "sha256": "97ecbaa0035044c51f4d5717148ae6f2671d8b1d33ad4c6950801a651bc0db53"
+ }
+ ],
+ "reproducibility": {
+ "level": "bitwise_reproducible",
+ "availability": "partial"
+ },
+ "verification": {
+ "mode": "exact",
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.descent_vs_ascent",
+ "--dataset",
+ "tests/fixtures/slice_a_channels_2016.ndjson.gz",
+ "verify",
+ "--artifact",
+ "results/artifacts/descent-vs-ascent-v1.json",
+ "--markdown",
+ "results/generated/descent-vs-ascent-v1.md"
+ ],
+ "tests": [
+ "tests/test_descent_vs_ascent.py",
+ "tests/test_declustering.py",
+ "tests/test_partition.py"
+ ],
+ "properties": [
+ "the descending pass returns a meet with the partition it was given, so it can only refine it",
+ "the co-spend contributes no weight; only the refusal channels do",
+ "blocks above the search bound are left whole and counted as unsearched",
+ "two equally supported cuts leave the block whole",
+ "the generated Markdown equals the canonical rendering"
+ ],
+ "tolerance": null
+ },
+ "limitations": [
+ "the snapshot contains six 2016 blocks and is not a chain-wide sample",
+ "the exact search is skipped on blocks above max_block, and those blocks are left whole",
+ "signals are counted tells, not calibrated bits, so the thresholds are not likelihoods",
+ "the two passes are compared as partitions; neither is checked against same-owner labels",
+ "the fingerprint and roundness channels are absent here, as they are in the merge pass",
+ "the snapshot recipe is unavailable; the dataset publisher authorized redistribution on 2026-09-04"
+ ]
+}
diff --git a/artifacts/sha256/b6/1bed4b86094fcc8dd8382a5ff585a89aae8c1f9c6e459b64b0bcd3b904dd67 b/artifacts/sha256/b6/1bed4b86094fcc8dd8382a5ff585a89aae8c1f9c6e459b64b0bcd3b904dd67
new file mode 100644
index 00000000..b1cecf64
--- /dev/null
+++ b/artifacts/sha256/b6/1bed4b86094fcc8dd8382a5ff585a89aae8c1f9c6e459b64b0bcd3b904dd67
@@ -0,0 +1 @@
+{"experiment":"equal-output-consolidation-v1","limitations":["synthetic fixture","the report demonstrates conditional narrowing, not an identity","a collaborative consolidation would invalidate the co-ownership premise"],"result":{"candidates_after":["alice"],"candidates_before":3,"composition":null,"outcome":"candidate_narrowing"},"scenario":{"candidate_sets":[["alice","bob","carol"],["alice","dave","erin"]],"outputs":[["coinjoin-1",0],["coinjoin-2",0]]},"schema_version":1}
diff --git a/artifacts/sha256/b6/96148f41f66df0756a3f7dbac1c1e53d0ad7d97c14bb194bcfadb4602c5c02 b/artifacts/sha256/b6/96148f41f66df0756a3f7dbac1c1e53d0ad7d97c14bb194bcfadb4602c5c02
new file mode 100644
index 00000000..e64a821c
--- /dev/null
+++ b/artifacts/sha256/b6/96148f41f66df0756a3f7dbac1c1e53d0ad7d97c14bb194bcfadb4602c5c02
@@ -0,0 +1,20 @@
+# Collaborative transaction observer-knowledge matrix
+
+Generated from the canonical experiment artifact. Do not edit manually.
+
+The same two-input/two-output observation is compatible with an ordinary transaction and four PayJoin protocol forms. This does not identify which protocol form occurred.
+
+| form | participants | external on-chain facts | counterparty facts |
+|---|---:|---|---|
+| ordinary_two_input | 1 | input and output amounts; transaction shape and script-visible construction features; participant allocation is not observed; payment amount is not observed | not applicable |
+| p2ep | 2 | input and output amounts; transaction shape and script-visible construction features; participant allocation is not observed; payment amount is not observed | own inputs and outputs; the only other party's remaining inputs and outputs by elimination; negotiated payment amount |
+| bip79_bustapay | 2 | input and output amounts; transaction shape and script-visible construction features; participant allocation is not observed; payment amount is not observed | own inputs and outputs; the only other party's remaining inputs and outputs by elimination; negotiated payment amount |
+| bip78_sync_payjoin | 2 | input and output amounts; transaction shape and script-visible construction features; participant allocation is not observed; payment amount is not observed | own inputs and outputs; the only other party's remaining inputs and outputs by elimination; negotiated payment amount |
+| bip77_async_payjoin | 2 | input and output amounts; transaction shape and script-visible construction features; participant allocation is not observed; payment amount is not observed | own inputs and outputs; the only other party's remaining inputs and outputs by elimination; negotiated payment amount |
+| many_senders_one_receiver | 4 | amounts and transaction shape; sender allocation is not observed | the receiver knows each negotiated payment and sender change; the receiver may not know which input belongs to which sender |
+| many_senders_many_receivers | 6 | amounts and transaction shape; payment pairing is not observed | own subtransaction and negotiated payment; other pairings remain latent |
+| net_settlement_with_cycles | latent | net on-chain balances; participant count may remain latent | own obligations and contributions; other parties' gross obligations remain latent |
+
+The shared observation is inputs `[90, 60]` and outputs `[110, 40]`. Protocol transport, participant allocation and negotiated payments are not added to the external observer's on-chain knowledge.
+
+All allocations and relationships in this fixture are supplied possible worlds. The result is not ownership attribution, a deployment-frequency measurement or a privacy score.
diff --git a/artifacts/sha256/b9/69d36360c6ce8b8711b73427d3e486ff094aa321e7aa3c677a975ee06b8ca5 b/artifacts/sha256/b9/69d36360c6ce8b8711b73427d3e486ff094aa321e7aa3c677a975ee06b8ca5
new file mode 100644
index 00000000..acc0afd3
--- /dev/null
+++ b/artifacts/sha256/b9/69d36360c6ce8b8711b73427d3e486ff094aa321e7aa3c677a975ee06b8ca5
@@ -0,0 +1,13 @@
+{
+ "schema_version": 1,
+ "python": {
+ "implementation": "cpython",
+ "version": "3.13",
+ "system": "Darwin",
+ "machine": "arm64"
+ },
+ "source_archive": "sources/decluster-bcd9a3e-runtime.tar",
+ "project_root": "decluster",
+ "requirements": "reproduction/conservation-round-three/requirements.lock",
+ "dependency_directory": "reproduction/conservation-round-three"
+}
diff --git a/artifacts/sha256/b9/96da99b648bd4c977778dd376dd884ca0a1d82e00abf897812e8200b5e571c b/artifacts/sha256/b9/96da99b648bd4c977778dd376dd884ca0a1d82e00abf897812e8200b5e571c
new file mode 100644
index 00000000..2089846f
--- /dev/null
+++ b/artifacts/sha256/b9/96da99b648bd4c977778dd376dd884ca0a1d82e00abf897812e8200b5e571c
@@ -0,0 +1,13 @@
+{
+ "schema_version": 1,
+ "python": {
+ "implementation": "cpython",
+ "version": "3.13",
+ "system": "Darwin",
+ "machine": "arm64"
+ },
+ "source_archive": "sources/decluster-bcd9a3e-runtime.tar",
+ "project_root": "decluster",
+ "requirements": "reproduction/eve-alice-eve/requirements.lock",
+ "dependency_directory": "reproduction/eve-alice-eve"
+}
diff --git a/artifacts/sha256/bc/61069fbe1d8d62a4007abcde98b4909a0628362ceb6c9a00056bf35decd722 b/artifacts/sha256/bc/61069fbe1d8d62a4007abcde98b4909a0628362ceb6c9a00056bf35decd722
new file mode 100644
index 00000000..71d7cb7a
--- /dev/null
+++ b/artifacts/sha256/bc/61069fbe1d8d62a4007abcde98b4909a0628362ceb6c9a00056bf35decd722
@@ -0,0 +1,95 @@
+{
+ "schema_version": 1,
+ "id": "library-calibration-v1",
+ "claim_ids": [
+ "ctp.fingerprint.statistical_linkage"
+ ],
+ "code": {
+ "revision": "bcd9a3e511860b1b0ff5f3ee6338a2df29ecbfbf",
+ "dirty": false
+ },
+ "command": {
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.library_calibration",
+ "--snapshot",
+ "data/fs-blkcache-2026-09-04.tar.gz",
+ "reproduce",
+ "--artifact",
+ "results/artifacts/library-calibration-v1.json",
+ "--markdown",
+ "results/generated/library-calibration-v1.md"
+ ]
+ },
+ "environment": {
+ "python": "3.13.7",
+ "lock_digest": "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855",
+ "platform": "macOS-26.6.2-arm64-arm-64bit-Mach-O",
+ "dependencies": []
+ },
+ "datasets": [
+ {
+ "id": "fs-blkcache-2026-09-04-v1",
+ "sha256": "5a69f6f446dbdfcce09dddc91c653e5d841124d14020b720fdea276315c775a6"
+ }
+ ],
+ "parameters": {
+ "bits": "minus the base-2 log of a value's share of the scored population",
+ "measured_population": "the committed block cache",
+ "published_population": "~105k uniform whole-chain sample via BigQuery, not preserved"
+ },
+ "rng": {
+ "algorithm": null,
+ "seeds": []
+ },
+ "outputs": [
+ {
+ "path": "results/artifacts/library-calibration-v1.json",
+ "bytes": 17737,
+ "sha256": "5c807a848e9a04cd41065e096af472df0ea568a88792bdd9027bbad58c9064c5"
+ },
+ {
+ "path": "results/generated/library-calibration-v1.md",
+ "bytes": 2173,
+ "sha256": "949d2d9e890818c5ec890484b13750ed6855e6538c9a45c3c5c4b3daf2454a5b"
+ }
+ ],
+ "reproducibility": {
+ "level": "bitwise_reproducible",
+ "availability": "partial"
+ },
+ "verification": {
+ "mode": "exact",
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.library_calibration",
+ "--snapshot",
+ "data/fs-blkcache-2026-09-04.tar.gz",
+ "verify",
+ "--artifact",
+ "results/artifacts/library-calibration-v1.json",
+ "--markdown",
+ "results/generated/library-calibration-v1.md"
+ ],
+ "tests": [
+ "tests/test_library_calibration.py",
+ "tests/test_wp2.py"
+ ],
+ "properties": [
+ "a value the cache never shows is counted, never estimated",
+ "abstentions and unreadable records are counted apart from the scored population",
+ "every axis the library catalogues is walked, including the ones with no comparison",
+ "the generated Markdown equals the canonical rendering"
+ ],
+ "tolerance": null
+ },
+ "limitations": [
+ "this is not a reproduction of the published calibration; the population it used is gone",
+ "the cache is block-sampled and era-skewed, so a divergence is expected and its size is the only thing measured here",
+ "a value the cache never shows is counted, not estimated",
+ "recalibrating the library on this cache is a separate decision this run does not take",
+ "divergence in bits is not an error rate and is not a privacy score"
+ ]
+}
diff --git a/artifacts/sha256/bc/d7c6a58c1206d78841bceba862f9b9eabd9b978040bcf717a5967f0355df29 b/artifacts/sha256/bc/d7c6a58c1206d78841bceba862f9b9eabd9b978040bcf717a5967f0355df29
new file mode 100644
index 00000000..d8743f82
--- /dev/null
+++ b/artifacts/sha256/bc/d7c6a58c1206d78841bceba862f9b9eabd9b978040bcf717a5967f0355df29
@@ -0,0 +1,12 @@
+# Amount-constrained deterministic partition
+
+Generated from the canonical experiment artifact. Do not edit manually.
+
+| fixture | non-derived mappings | unanimous input-output links |
+|---|---:|---:|
+| forced partition | 1 | 4 |
+| underdetermined control | 3 | 0 |
+
+The forced fixture is 0.1 + 0.3 = 0.4 and 2 + 5 = 7, scaled by ten. The control is the CTP's three-way underdetermined 0.7/0.8 example, also scaled by ten.
+
+Every statement is conditional on exact per-block conservation and the non-derived mapping family. Fees, contextual priors and net-settlement cycles can change admissibility.
diff --git a/artifacts/sha256/bc/dc8536c62ad14080bbb68f199425afe173b1eb9782b9c41bbc88b2558a9bf1 b/artifacts/sha256/bc/dc8536c62ad14080bbb68f199425afe173b1eb9782b9c41bbc88b2558a9bf1
new file mode 100644
index 00000000..fd8d5927
--- /dev/null
+++ b/artifacts/sha256/bc/dc8536c62ad14080bbb68f199425afe173b1eb9782b9c41bbc88b2558a9bf1
@@ -0,0 +1,112 @@
+{
+ "schema_version": 1,
+ "id": "graph-rejoin-2016-v1",
+ "claim_ids": [
+ "ctp.social_graph.propagation",
+ "ctp.graph.pseudonym_representation"
+ ],
+ "code": {
+ "revision": "bcd9a3e511860b1b0ff5f3ee6338a2df29ecbfbf",
+ "dirty": false
+ },
+ "command": {
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.graph_rejoin_2016",
+ "--epochs",
+ "data/epochs-2016-weekly-v1/391104-392111.epochgraph.xz",
+ "data/epochs-2016-weekly-v1/392112-393119.epochgraph.xz",
+ "reproduce",
+ "--artifact",
+ "results/artifacts/graph-rejoin-2016-v1.json",
+ "--markdown",
+ "results/generated/graph-rejoin-2016-v1.md"
+ ]
+ },
+ "datasets": [
+ {
+ "id": "epoch-graph-2016-391104-392111-v1",
+ "sha256": "cd129cd4bb225ea7a6733dab891b20272d62d1b0f3a6caf07a4209f9f2368015"
+ },
+ {
+ "id": "epoch-graph-2016-392112-393119-v1",
+ "sha256": "0893000345e68249911cb8ee6467a47fa6feecd3022f27e2732bcabfb4725f11"
+ }
+ ],
+ "parameters": {
+ "min_degree": 2,
+ "seed_shares": [
+ 0.05,
+ 0.1
+ ],
+ "split_fraction": 1.0,
+ "matcher": {
+ "revisit": true,
+ "min_common": 4
+ },
+ "seed": 0
+ },
+ "rng": {
+ "algorithm": "python.random.MersenneTwister",
+ "seeds": [
+ 0
+ ]
+ },
+ "reproducibility": {
+ "level": "bitwise_reproducible",
+ "availability": "complete"
+ },
+ "verification": {
+ "mode": "exact",
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.graph_rejoin_2016",
+ "--epochs",
+ "data/epochs-2016-weekly-v1/391104-392111.epochgraph.xz",
+ "data/epochs-2016-weekly-v1/392112-393119.epochgraph.xz",
+ "verify",
+ "--artifact",
+ "results/artifacts/graph-rejoin-2016-v1.json",
+ "--markdown",
+ "results/generated/graph-rejoin-2016-v1.md"
+ ],
+ "tests": [
+ "tests/test_graph_rejoin_experiment.py",
+ "tests/test_epoch_graph.py"
+ ],
+ "properties": [
+ "the committed epoch graphs exactly reproduce every arm",
+ "each seeded arm runs beside a shuffled-seed arm on the same graphs",
+ "the shuffled-seed arms recover nothing",
+ "the cascade does not ignite, stated as a bound a future run can break",
+ "cluster membership is a co-spend relation, not observed wallet ownership",
+ "the seeds are supplied, so this measures propagation rather than finding a foothold"
+ ],
+ "tolerance": null
+ },
+ "environment": {
+ "python": "3.13.7",
+ "lock_digest": "efdf4055dba10bfff2541ccaf3668be030c7caed44c61650540cc106393e9636",
+ "platform": "macOS-26.6.2-arm64-arm-64bit-Mach-O",
+ "dependencies": []
+ },
+ "limitations": [
+ "Seeds are handed to the matcher; no independently derived seed is claimed.",
+ "Cluster membership is a co-spend relation rather than observed wallet ownership.",
+ "Two weekly views at one width; the persistence curve argues width is the untested lever."
+ ],
+ "outputs": [
+ {
+ "path": "results/artifacts/graph-rejoin-2016-v1.json",
+ "bytes": 1777,
+ "sha256": "ec27b8a3dc36b06482b86cdcb556038d88b73f3595c1e37d78867c5ce159841c"
+ },
+ {
+ "path": "results/generated/graph-rejoin-2016-v1.md",
+ "bytes": 2507,
+ "sha256": "1d5afe106a2bb84edd7925ca4ed213d9f5bc3c35349d48dac47c6051492543ed"
+ }
+ ]
+}
diff --git a/artifacts/sha256/bf/35f273b248388772cb2b34c69ab3cc9ba261e494455ddcf41a570ff3736e1d b/artifacts/sha256/bf/35f273b248388772cb2b34c69ab3cc9ba261e494455ddcf41a570ff3736e1d
new file mode 100644
index 00000000..b96474cb
--- /dev/null
+++ b/artifacts/sha256/bf/35f273b248388772cb2b34c69ab3cc9ba261e494455ddcf41a570ff3736e1d
@@ -0,0 +1,11 @@
+# DSS per-coin amount diagnostic
+
+Generated from the canonical experiment artifact. Do not edit manually.
+
+The run evaluates 23,626 coins in 1,428 complete multi-input transactions. DSS returns a finite per-coin reading for 1,288 coins and no reading for 22,338.
+
+Before the transaction gate, 318 candidates occur in 95 transactions: 60 inputs and 258 outputs.
+
+After requiring a conservative nonzero transaction-level W(E), 101 candidates remain in 62 transactions. An exact transaction count records the gate's provenance; it does not make the per-coin reading exact.
+
+These are refuse-only diagnostics, not ownership proofs, privacy scores, or CoinScore.
diff --git a/artifacts/sha256/c0/1256072550fd633edd1fa113f1435e9be9323f4ef9a95a6452648464f4fd3d b/artifacts/sha256/c0/1256072550fd633edd1fa113f1435e9be9323f4ef9a95a6452648464f4fd3d
new file mode 100644
index 00000000..f5ebcc80
--- /dev/null
+++ b/artifacts/sha256/c0/1256072550fd633edd1fa113f1435e9be9323f4ef9a95a6452648464f4fd3d
@@ -0,0 +1,14 @@
+# Attribute density and graph shape on a six-block slice
+
+Generated from the canonical experiment artifact. Do not edit manually.
+
+Transactions: 12000; clusters: 3179; contracted vertices: 26500.
+
+| minimum degree | sampled records | delta(0.5) | delta(0.9) | delta(0.99) |
+|---:|---:|---:|---:|---:|
+| 2 | 1200 | 1.000000 | 0.997500 | 0.983333 |
+| 20 | 89 | n/a | 0.876404 | n/a |
+
+Degree assortativity: -0.043697.
+
+This export carries none of the four axes — every one of the 12000 transactions abstains on version, input order, locktime and fee rate — so the vectors compared here are structural: degree, transaction count, self-transfers and the neighbour-degree histogram. On those alone the space is dense, and markedly less so among the higher-degree vertices (0.876 against 0.998 at the same threshold), which is where a matcher would start. The contracted graph is disassortative. These observations do not reproduce the historical larger slices, do not measure attribute density, and do not establish graph-matching performance.
diff --git a/artifacts/sha256/c0/c417c8efc84acfcb88c465123e910aad0af32d55fd074fc3729091ce748693 b/artifacts/sha256/c0/c417c8efc84acfcb88c465123e910aad0af32d55fd074fc3729091ce748693
new file mode 100644
index 00000000..ea5ba68a
--- /dev/null
+++ b/artifacts/sha256/c0/c417c8efc84acfcb88c465123e910aad0af32d55fd074fc3729091ce748693
@@ -0,0 +1 @@
+{"experiment":"ancestry-sparsity-v1","limitations":["the 200-record frozen snapshot is selected and is not a uniform chain sample","the signature-generation recipe and original comparison population are unavailable","the run measures sparsity within this snapshot only","sparsity is an attack precondition, not proof of successful attribution","the run does not establish a chain-wide population rate"],"nearest_neighbour_cosine":{"median":0.02591278115565,"survival":{"0.5":0.175,"0.9":0.175,"0.99":0.145}},"schema_version":1,"snapshot":{"dimensions":{"maximum":11436,"median":5887.0,"minimum":3},"records":200},"stored_top_context":{"different_from_snapshot_recomputation":155,"interpretation":"stored values were computed in an unavailable comparison population"}}
diff --git a/artifacts/sha256/c3/d9e1a17478109d3ac8e2da9234ae97339e88b78336542c73496dc41f617562 b/artifacts/sha256/c3/d9e1a17478109d3ac8e2da9234ae97339e88b78336542c73496dc41f617562
new file mode 100644
index 00000000..4feb2711
--- /dev/null
+++ b/artifacts/sha256/c3/d9e1a17478109d3ac8e2da9234ae97339e88b78336542c73496dc41f617562
@@ -0,0 +1,13 @@
+{
+ "schema_version": 1,
+ "python": {
+ "implementation": "cpython",
+ "version": "3.13",
+ "system": "Darwin",
+ "machine": "arm64"
+ },
+ "source_archive": "sources/decluster-bcd9a3e-runtime.tar",
+ "project_root": "decluster",
+ "requirements": "reproduction/link-prediction/requirements.lock",
+ "dependency_directory": "reproduction/link-prediction"
+}
diff --git a/artifacts/sha256/c4/06f31e466277ca77f2fd66349e06710a298b4ff93835636237c0c3d05e5b9e b/artifacts/sha256/c4/06f31e466277ca77f2fd66349e06710a298b4ff93835636237c0c3d05e5b9e
new file mode 100644
index 00000000..e5731b65
--- /dev/null
+++ b/artifacts/sha256/c4/06f31e466277ca77f2fd66349e06710a298b4ff93835636237c0c3d05e5b9e
@@ -0,0 +1,101 @@
+{
+ "schema_version": 1,
+ "id": "fingerprint-validation-v1",
+ "claim_ids": [
+ "ctp.fingerprint.statistical_linkage"
+ ],
+ "code": {
+ "revision": "bcd9a3e511860b1b0ff5f3ee6338a2df29ecbfbf",
+ "dirty": false
+ },
+ "command": {
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.fingerprint_validation",
+ "--dataset",
+ "tests/fixtures/fingerprint_blkcache_sample.json",
+ "reproduce",
+ "--artifact",
+ "results/artifacts/fingerprint-validation-v1.json",
+ "--markdown",
+ "results/generated/fingerprint-validation-v1.md"
+ ]
+ },
+ "environment": {
+ "python": "3.13.7",
+ "lock_digest": "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855",
+ "platform": "macOS-26.6.2-arm64-arm-64bit-Mach-O",
+ "dependencies": []
+ },
+ "datasets": [
+ {
+ "id": "fingerprint-blkcache-sample-v1",
+ "sha256": "406ce2d7d0a04f7c73281bce6b6a781012a8765e079693f010a0154e926c6a6c"
+ }
+ ],
+ "parameters": {
+ "positive_pair_draw_cap": 4000,
+ "negative_pair_draw_cap": 4000,
+ "seed": 0,
+ "same_wallet_consistency": 0.95,
+ "rarity_floor_count": 1000
+ },
+ "rng": {
+ "algorithm": "python.random.Random",
+ "seeds": [
+ 0
+ ]
+ },
+ "outputs": [
+ {
+ "path": "results/artifacts/fingerprint-validation-v1.json",
+ "bytes": 1062,
+ "sha256": "81e9af1328a344323e7a86b219711cbe9ee1428e3bbce4b1995681b9ae35b669"
+ },
+ {
+ "path": "results/generated/fingerprint-validation-v1.md",
+ "bytes": 590,
+ "sha256": "2fe5d3f085bb7b93ba2d275b442bb31f6ba8c1e95ca670c726474f2c874b0eea"
+ }
+ ],
+ "reproducibility": {
+ "level": "bitwise_reproducible",
+ "availability": "partial"
+ },
+ "verification": {
+ "mode": "exact",
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.fingerprint_validation",
+ "--dataset",
+ "tests/fixtures/fingerprint_blkcache_sample.json",
+ "verify",
+ "--artifact",
+ "results/artifacts/fingerprint-validation-v1.json",
+ "--markdown",
+ "results/generated/fingerprint-validation-v1.md"
+ ],
+ "tests": [
+ "tests/test_fingerprint_auc.py",
+ "tests/test_fingerprint_validate.py",
+ "tests/test_fingerprint_validation_experiment.py"
+ ],
+ "properties": [
+ "the canonical 23-axis scorer is used",
+ "positive and negative pairs are sampled with a fixed seed",
+ "the shuffled-label control is emitted beside the observed AUC",
+ "the generated Markdown equals the canonical rendering"
+ ],
+ "tolerance": null
+ },
+ "limitations": [
+ "address reuse is a weak same-wallet label and shares script information with the features",
+ "positive pairs are draws with replacement rather than independent observations",
+ "random negative pairs can include transactions controlled by the same wallet",
+ "the 600-transaction snapshot is selected and is not a chain-wide sample",
+ "the source recipe is unavailable; the dataset publisher authorized redistribution on 2026-09-04",
+ "the result measures pair ranking and is not an ownership certificate or privacy score"
+ ]
+}
diff --git a/artifacts/sha256/c4/33cfc5c966ba7af33ea595cad134862726168033c2029d8b11574319cf0fc8 b/artifacts/sha256/c4/33cfc5c966ba7af33ea595cad134862726168033c2029d8b11574319cf0fc8
new file mode 100644
index 00000000..018d853c
--- /dev/null
+++ b/artifacts/sha256/c4/33cfc5c966ba7af33ea595cad134862726168033c2029d8b11574319cf0fc8
@@ -0,0 +1,12 @@
+# Fixed-denomination preparation fingerprint
+
+Generated from the canonical experiment artifact. Do not edit manually.
+
+| population | transactions | fingerprint matches |
+|---|---:|---:|
+| synthetic preparations | 3 | 3 |
+| arity-matched controls | 3 | 0 |
+
+The rule observes one exact-denomination predecessor output consumed by the declared next CoinJoin. Controls have the same input and output counts.
+
+This construction-level fixture does not measure prevalence and does not prove that a matched predecessor is unilateral or commonly owned.
diff --git a/artifacts/sha256/c6/7e75c5167951c159e3521acc977d4c57234fb3a9bad1a5892a51f111d1dcd6 b/artifacts/sha256/c6/7e75c5167951c159e3521acc977d4c57234fb3a9bad1a5892a51f111d1dcd6
new file mode 100644
index 00000000..a502dbc0
--- /dev/null
+++ b/artifacts/sha256/c6/7e75c5167951c159e3521acc977d4c57234fb3a9bad1a5892a51f111d1dcd6
@@ -0,0 +1,13 @@
+{
+ "schema_version": 1,
+ "python": {
+ "implementation": "cpython",
+ "version": "3.13",
+ "system": "Darwin",
+ "machine": "arm64"
+ },
+ "source_archive": "sources/decluster-bcd9a3e-runtime.tar",
+ "project_root": "decluster",
+ "requirements": "reproduction/ns-bitcoin/requirements.lock",
+ "dependency_directory": "reproduction/ns-bitcoin"
+}
diff --git a/artifacts/sha256/c6/a1627950079d61a18b7d54bfc4e6894644ac732f01096f45634f9e1af95fad b/artifacts/sha256/c6/a1627950079d61a18b7d54bfc4e6894644ac732f01096f45634f9e1af95fad
new file mode 100644
index 00000000..792affc0
--- /dev/null
+++ b/artifacts/sha256/c6/a1627950079d61a18b7d54bfc4e6894644ac732f01096f45634f9e1af95fad
@@ -0,0 +1,101 @@
+{
+ "schema_version": 1,
+ "id": "special-change-labels-v1",
+ "claim_ids": [
+ "ctp.fingerprint.statistical_linkage",
+ "ctp.payjoin.unnecessary_input"
+ ],
+ "code": {
+ "revision": "bcd9a3e511860b1b0ff5f3ee6338a2df29ecbfbf",
+ "dirty": false
+ },
+ "command": {
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.special_change_labels",
+ "--snapshot",
+ "data/fs-blkcache-2026-09-04.tar.gz",
+ "reproduce",
+ "--artifact",
+ "results/artifacts/special-change-labels-v1.json",
+ "--markdown",
+ "results/generated/special-change-labels-v1.md"
+ ]
+ },
+ "datasets": [
+ {
+ "id": "fs-blkcache-2026-09-04-v1",
+ "sha256": "5a69f6f446dbdfcce09dddc91c653e5d841124d14020b720fdea276315c775a6"
+ }
+ ],
+ "parameters": {
+ "labels": [
+ "address_reuse",
+ "optimal_change",
+ "round_number",
+ "type_match"
+ ],
+ "predictors": [
+ "address_reuse",
+ "round_number"
+ ]
+ },
+ "rng": {
+ "algorithm": "none",
+ "seeds": []
+ },
+ "reproducibility": {
+ "level": "bitwise_reproducible",
+ "availability": "complete"
+ },
+ "verification": {
+ "mode": "exact",
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.special_change_labels",
+ "--snapshot",
+ "data/fs-blkcache-2026-09-04.tar.gz",
+ "verify",
+ "--artifact",
+ "results/artifacts/special-change-labels-v1.json",
+ "--markdown",
+ "results/generated/special-change-labels-v1.md"
+ ],
+ "tests": [
+ "tests/test_special_change_labels_experiment.py"
+ ],
+ "properties": [
+ "two universal heuristics agree with a label that reads neither addresses nor roundness",
+ "address reuse and type match are one label, forced to agree rather than corroborating",
+ "the onward-spend arm is absent because the cache holds too few spenders to measure it",
+ "a predictor scored against its own label is reported as self-agreement"
+ ],
+ "tolerance": null
+ },
+ "environment": {
+ "python": "3.13.7",
+ "lock_digest": "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855",
+ "platform": "macOS-26.6.2-arm64-arm-64bit-Mach-O",
+ "dependencies": []
+ },
+ "limitations": [
+ "The optimal-change label is the no-unnecessary-input assumption read as a label, and it fails on transactions that carry one.",
+ "The cache is block-sampled, so the per-axis onward-spend arm the original document reported is not measured here at all.",
+ "Coverage is a property of this cache: address reuse is far more common in it than in a multi-epoch sample, so the coverage column does not transfer.",
+ "Agreement between labels bounds their consistency, not their correctness; all four can be wrong together on a transaction with an unnecessary input."
+ ],
+ "outputs": [
+ {
+ "path": "results/artifacts/special-change-labels-v1.json",
+ "bytes": 2491,
+ "sha256": "88fcdc14d41aa1f51e0e2f359d1ee6242bd69fa15ac9d9413e879970b2b5b581"
+ },
+ {
+ "path": "results/generated/special-change-labels-v1.md",
+ "bytes": 3337,
+ "sha256": "568aa47aac4b0f8e26a7d2b498b3f3bef80709555fe16507169540cb53f822f5"
+ }
+ ]
+}
diff --git a/artifacts/sha256/c7/d402576511073ed4fcf076c77ffa6b3be124e0a2ef996d2da1cd3b02fbe541 b/artifacts/sha256/c7/d402576511073ed4fcf076c77ffa6b3be124e0a2ef996d2da1cd3b02fbe541
new file mode 100644
index 00000000..3e1593f0
--- /dev/null
+++ b/artifacts/sha256/c7/d402576511073ed4fcf076c77ffa6b3be124e0a2ef996d2da1cd3b02fbe541
@@ -0,0 +1 @@
+# No third-party dependencies.
diff --git a/artifacts/sha256/c8/b550fce0a96bb39a2fd46318aaf30d2eb786adc5acd96a752877b6d10c98e1 b/artifacts/sha256/c8/b550fce0a96bb39a2fd46318aaf30d2eb786adc5acd96a752877b6d10c98e1
new file mode 100644
index 00000000..b66e03dd
--- /dev/null
+++ b/artifacts/sha256/c8/b550fce0a96bb39a2fd46318aaf30d2eb786adc5acd96a752877b6d10c98e1
@@ -0,0 +1,13 @@
+{
+ "schema_version": 1,
+ "python": {
+ "implementation": "cpython",
+ "version": "3.13",
+ "system": "Darwin",
+ "machine": "arm64"
+ },
+ "source_archive": "sources/decluster-bcd9a3e-runtime.tar",
+ "project_root": "decluster",
+ "requirements": "reproduction/fingerprint-catalog-axes/requirements.lock",
+ "dependency_directory": "reproduction/fingerprint-catalog-axes"
+}
diff --git a/artifacts/sha256/c8/c6d92b22c9486a08ce2eecaf5164959831534f8e953d4a5d3dbcf6f95ad5cb b/artifacts/sha256/c8/c6d92b22c9486a08ce2eecaf5164959831534f8e953d4a5d3dbcf6f95ad5cb
new file mode 100644
index 00000000..191c9670
--- /dev/null
+++ b/artifacts/sha256/c8/c6d92b22c9486a08ce2eecaf5164959831534f8e953d4a5d3dbcf6f95ad5cb
@@ -0,0 +1,15 @@
+# Per-axis Fellegi–Sunter EM diagnostic
+
+Generated from the canonical experiment artifact. Do not edit manually.
+
+Transactions: 22112; EM iterations: 21; inferred selected-pair fraction: 0.663956.
+
+| parameter source | LibraryScorer AUC |
+|---|---:|
+| fixed 0.95 | 0.924450 |
+| unsupervised EM | 0.925350 |
+| address-reuse agreement | 0.954600 |
+
+EM minus fixed AUC: +0.000900. This difference is descriptive; no pre-registered separability verdict was run.
+
+The historical 165,832-transaction table is not reproduced by the preserved 22,112-transaction snapshot. Address reuse is a weak label, and this is an attacker-side parameter diagnostic rather than a privacy score.
diff --git a/artifacts/sha256/c9/7e7d4989b8c9c2bffbe807a20928853290fd0c69c5a290d850087e8147126b b/artifacts/sha256/c9/7e7d4989b8c9c2bffbe807a20928853290fd0c69c5a290d850087e8147126b
new file mode 100644
index 00000000..957b6fc4
--- /dev/null
+++ b/artifacts/sha256/c9/7e7d4989b8c9c2bffbe807a20928853290fd0c69c5a290d850087e8147126b
@@ -0,0 +1,89 @@
+{
+ "schema_version": 1,
+ "id": "provenance-decay-v1",
+ "claim_ids": [
+ "ctp.provenance.traceability"
+ ],
+ "code": {
+ "revision": "bcd9a3e511860b1b0ff5f3ee6338a2df29ecbfbf",
+ "dirty": false
+ },
+ "command": {
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.provenance_decay",
+ "reproduce",
+ "--artifact",
+ "results/artifacts/provenance-decay-v1.json",
+ "--markdown",
+ "results/generated/provenance-decay-v1.md"
+ ]
+ },
+ "environment": {
+ "python": "3.13.7",
+ "lock_digest": "e0d6a8cada5d383c0aca2bd2a37f28c15721392469c94084128d369c29c5544d",
+ "platform": "macOS-26.6.2-arm64-arm-64bit-Mach-O",
+ "dependencies": []
+ },
+ "datasets": [],
+ "parameters": {
+ "candidate_origins": 3,
+ "removed_auxiliaries": 1,
+ "controls": [
+ "bridge",
+ "peripheral"
+ ]
+ },
+ "rng": {
+ "algorithm": null,
+ "seeds": []
+ },
+ "outputs": [
+ {
+ "path": "results/artifacts/provenance-decay-v1.json",
+ "bytes": 502,
+ "sha256": "879dc6c49619b18d267fd30510472da99e341b6a3eebef6d6ab65faf319c2e9c"
+ },
+ {
+ "path": "results/generated/provenance-decay-v1.md",
+ "bytes": 357,
+ "sha256": "e09b7649b43e9238da355048e2205281c00f63c1e08b230c2a3588b7efb94ab8"
+ }
+ ],
+ "reproducibility": {
+ "level": "bitwise_reproducible",
+ "availability": "complete"
+ },
+ "verification": {
+ "mode": "exact",
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.provenance_decay",
+ "verify",
+ "--artifact",
+ "results/artifacts/provenance-decay-v1.json",
+ "--markdown",
+ "results/generated/provenance-decay-v1.md"
+ ],
+ "tests": [
+ "tests/test_provenance_decay.py",
+ "tests/test_provenance_decay_experiment.py"
+ ],
+ "properties": [
+ "bridge and peripheral controls eliminate the same number of candidates",
+ "only bridge removal increases the connected-component count",
+ "additive elimination and structural fracture remain separate evidence",
+ "component growth is not represented as an exponential-rate measurement",
+ "the generated Markdown equals the canonical rendering"
+ ],
+ "tolerance": null
+ },
+ "limitations": [
+ "synthetic fixture",
+ "candidate origins, graph edges, and auxiliary labels are supplied",
+ "flow plausibility and private adversary information are not modeled",
+ "the run does not establish an exponential deanonymization rate"
+ ]
+}
diff --git a/artifacts/sha256/c9/b77a5f6843ad0ae4302bad33527bf5a0e629b0aaeea75a1b9134ec5f35d945 b/artifacts/sha256/c9/b77a5f6843ad0ae4302bad33527bf5a0e629b0aaeea75a1b9134ec5f35d945
new file mode 100644
index 00000000..ff1acacd
--- /dev/null
+++ b/artifacts/sha256/c9/b77a5f6843ad0ae4302bad33527bf5a0e629b0aaeea75a1b9134ec5f35d945
@@ -0,0 +1,13 @@
+{
+ "schema_version": 1,
+ "python": {
+ "implementation": "cpython",
+ "version": "3.13",
+ "system": "Darwin",
+ "machine": "arm64"
+ },
+ "source_archive": "sources/decluster-bcd9a3e-runtime.tar",
+ "project_root": "decluster",
+ "requirements": "reproduction/denomination-preparation/requirements.lock",
+ "dependency_directory": "reproduction/denomination-preparation"
+}
diff --git a/artifacts/sha256/c9/c39fae7ca9dcd8a467c6018ee29801fb34169e527051ba8787ce5d8dee6f91 b/artifacts/sha256/c9/c39fae7ca9dcd8a467c6018ee29801fb34169e527051ba8787ce5d8dee6f91
new file mode 100644
index 00000000..131f6567
--- /dev/null
+++ b/artifacts/sha256/c9/c39fae7ca9dcd8a467c6018ee29801fb34169e527051ba8787ce5d8dee6f91
@@ -0,0 +1,13 @@
+{
+ "schema_version": 1,
+ "python": {
+ "implementation": "cpython",
+ "version": "3.13",
+ "system": "Darwin",
+ "machine": "arm64"
+ },
+ "source_archive": "sources/decluster-bcd9a3e-runtime.tar",
+ "project_root": "decluster",
+ "requirements": "reproduction/counting-methods/requirements.lock",
+ "dependency_directory": "dependencies"
+}
diff --git a/artifacts/sha256/c9/e5be5be79a133176e262656057d2396728d7e7b84ad29c69a3f9982fd3b769 b/artifacts/sha256/c9/e5be5be79a133176e262656057d2396728d7e7b84ad29c69a3f9982fd3b769
new file mode 100644
index 00000000..430db234
--- /dev/null
+++ b/artifacts/sha256/c9/e5be5be79a133176e262656057d2396728d7e7b84ad29c69a3f9982fd3b769
@@ -0,0 +1,87 @@
+{
+ "schema_version": 1,
+ "id": "path-count-contract-v1",
+ "claim_ids": [
+ "ctp.robustness.counterfactual_paths",
+ "ctp.provenance.traceability"
+ ],
+ "code": {
+ "revision": "bcd9a3e511860b1b0ff5f3ee6338a2df29ecbfbf",
+ "dirty": false
+ },
+ "command": {
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.path_count_contract",
+ "reproduce",
+ "--artifact",
+ "results/artifacts/path-count-contract-v1.json",
+ "--markdown",
+ "results/generated/path-count-contract-v1.md"
+ ]
+ },
+ "environment": {
+ "python": "3.13.7",
+ "lock_digest": "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855",
+ "platform": "macOS-26.6.2-arm64-arm-64bit-Mach-O",
+ "dependencies": []
+ },
+ "datasets": [],
+ "parameters": {
+ "fixture_transactions": 4,
+ "depth": 6,
+ "probability_round_digits": 15
+ },
+ "rng": {
+ "algorithm": null,
+ "seeds": []
+ },
+ "outputs": [
+ {
+ "path": "results/artifacts/path-count-contract-v1.json",
+ "bytes": 927,
+ "sha256": "93f48cdcee4ccb7def3bdc525593ecece258438e9a3fa4be9ca5b6bb913d537e"
+ },
+ {
+ "path": "results/generated/path-count-contract-v1.md",
+ "bytes": 667,
+ "sha256": "0d79d8d6b5a601c66c88be9b3432b54ea664492d29c4c3b14224547337f6802b"
+ }
+ ],
+ "reproducibility": {
+ "level": "bitwise_reproducible",
+ "availability": "complete"
+ },
+ "verification": {
+ "mode": "exact",
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.path_count_contract",
+ "verify",
+ "--artifact",
+ "results/artifacts/path-count-contract-v1.json",
+ "--markdown",
+ "results/generated/path-count-contract-v1.md"
+ ],
+ "tests": [
+ "tests/test_path_count_contract_experiment.py",
+ "tests/test_path_count.py"
+ ],
+ "properties": [
+ "route accumulation equals the ancestry absorption distribution on the fixture",
+ "changing count_oracle does not change the result",
+ "edge-disjoint paths, plausible-flow capacity and k-routes are present and declared as such; the path count is none of them",
+ "the generated Markdown equals the canonical rendering"
+ ],
+ "tolerance": null
+ },
+ "limitations": [
+ "the fixture is synthetic and tests implementation semantics",
+ "the function accumulates link-probability mass over ancestry routes",
+ "count_oracle is retained for compatibility and does not affect the result",
+ "the function does not measure CTP robust connectivity or own-origin robustness",
+ "the output is not a privacy score or CoinScore"
+ ]
+}
diff --git a/artifacts/sha256/cd/129cd4bb225ea7a6733dab891b20272d62d1b0f3a6caf07a4209f9f2368015 b/artifacts/sha256/cd/129cd4bb225ea7a6733dab891b20272d62d1b0f3a6caf07a4209f9f2368015
new file mode 100644
index 00000000..b54235bb
Binary files /dev/null and b/artifacts/sha256/cd/129cd4bb225ea7a6733dab891b20272d62d1b0f3a6caf07a4209f9f2368015 differ
diff --git a/artifacts/sha256/cd/ff9d047a346eebb4a19b63254f7801dd2b4b0e7963c09b624c04bb98849891 b/artifacts/sha256/cd/ff9d047a346eebb4a19b63254f7801dd2b4b0e7963c09b624c04bb98849891
new file mode 100644
index 00000000..aec4ac3c
--- /dev/null
+++ b/artifacts/sha256/cd/ff9d047a346eebb4a19b63254f7801dd2b4b0e7963c09b624c04bb98849891
@@ -0,0 +1,13 @@
+{
+ "schema_version": 1,
+ "python": {
+ "implementation": "cpython",
+ "version": "3.13",
+ "system": "Darwin",
+ "machine": "arm64"
+ },
+ "source_archive": "sources/decluster-bcd9a3e-runtime.tar",
+ "project_root": "decluster",
+ "requirements": "reproduction/analyze-contract/requirements.lock",
+ "dependency_directory": "reproduction/analyze-contract"
+}
diff --git a/artifacts/sha256/ce/6ae206e95d2be86e8ea13a9161a26a0a6566787abf396a5eeffbb23efa3fcb b/artifacts/sha256/ce/6ae206e95d2be86e8ea13a9161a26a0a6566787abf396a5eeffbb23efa3fcb
new file mode 100644
index 00000000..949732cd
--- /dev/null
+++ b/artifacts/sha256/ce/6ae206e95d2be86e8ea13a9161a26a0a6566787abf396a5eeffbb23efa3fcb
@@ -0,0 +1,9 @@
+# N-S propagation on Bitcoin views
+
+Generated from the canonical experiment artifact. Do not edit manually.
+
+The two windows contain 133,576 and 157,724 transactions. No independent gradeable seed was found.
+
+The seed-assisted sweep's highest precision is 1.2837%, with coverage 0.5131%.
+
+The sweep does not establish an independently seeded attack and is not a reproduction of the published social-network corpora.
diff --git a/artifacts/sha256/d1/ae4828869980634e3739f926f61bb3530a43b31f018f0e8fb6a6ce865eb50a b/artifacts/sha256/d1/ae4828869980634e3739f926f61bb3530a43b31f018f0e8fb6a6ce865eb50a
new file mode 100644
index 00000000..b6ce7fa0
--- /dev/null
+++ b/artifacts/sha256/d1/ae4828869980634e3739f926f61bb3530a43b31f018f0e8fb6a6ce865eb50a
@@ -0,0 +1,12 @@
+# Point entropy is not a longitudinal privacy certificate
+
+Generated from the canonical experiment artifact. Do not edit manually.
+
+| scenario | initial entropy | initial candidates | observations | survivors |
+|---|---:|---:|---:|---:|
+| brittle overlap | 2.0 bits | 4 | 2 | 1 |
+| overlapping crowd | 2.0 bits | 4 | 2 | 2 |
+
+The initial posteriors are identical. Different overlap between later candidate sets produces different residual ambiguity.
+
+This synthetic counterexample falsifies sufficiency. It does not estimate attack frequency or certify either scenario's privacy.
diff --git a/artifacts/sha256/d1/ef62e9e4acfc662f989f9e17f2f84ccc1cdfd873ad929e7c3b6e154ecad594 b/artifacts/sha256/d1/ef62e9e4acfc662f989f9e17f2f84ccc1cdfd873ad929e7c3b6e154ecad594
new file mode 100644
index 00000000..781e3062
--- /dev/null
+++ b/artifacts/sha256/d1/ef62e9e4acfc662f989f9e17f2f84ccc1cdfd873ad929e7c3b6e154ecad594
@@ -0,0 +1,112 @@
+{
+ "schema_version": 1,
+ "id": "match-controls-v1",
+ "claim_ids": [
+ "ctp.social_graph.propagation",
+ "ctp.social_graph.structural_linkage"
+ ],
+ "code": {
+ "revision": "bcd9a3e511860b1b0ff5f3ee6338a2df29ecbfbf",
+ "dirty": false
+ },
+ "command": {
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.match_controls",
+ "--epochs",
+ "data/epochs-2016-weekly-v1/391104-392111.epochgraph.xz",
+ "data/epochs-2016-weekly-v1/392112-393119.epochgraph.xz",
+ "reproduce",
+ "--artifact",
+ "results/artifacts/match-controls-v1.json",
+ "--markdown",
+ "results/generated/match-controls-v1.md"
+ ]
+ },
+ "datasets": [
+ {
+ "id": "epoch-graph-2016-391104-392111-v1",
+ "sha256": "cd129cd4bb225ea7a6733dab891b20272d62d1b0f3a6caf07a4209f9f2368015"
+ },
+ {
+ "id": "epoch-graph-2016-392112-393119-v1",
+ "sha256": "0893000345e68249911cb8ee6467a47fa6feecd3022f27e2732bcabfb4725f11"
+ }
+ ],
+ "parameters": {
+ "seed_shares": [
+ 0.05,
+ 0.1
+ ],
+ "eccentricity_bands": [
+ 1.0,
+ 2.0,
+ 3.0,
+ 5.0,
+ 10.0
+ ],
+ "minimum_contracted_degree": 2,
+ "harness": "complete clustering: both views hold the same pseudonyms, view B relabelled"
+ },
+ "rng": {
+ "algorithm": "python.random.MersenneTwister",
+ "seeds": [
+ 0
+ ]
+ },
+ "reproducibility": {
+ "level": "bitwise_reproducible",
+ "availability": "complete"
+ },
+ "verification": {
+ "mode": "exact",
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.match_controls",
+ "--epochs",
+ "data/epochs-2016-weekly-v1/391104-392111.epochgraph.xz",
+ "data/epochs-2016-weekly-v1/392112-393119.epochgraph.xz",
+ "verify",
+ "--artifact",
+ "results/artifacts/match-controls-v1.json",
+ "--markdown",
+ "results/generated/match-controls-v1.md"
+ ],
+ "tests": [
+ "tests/test_match_controls_experiment.py"
+ ],
+ "properties": [
+ "the matcher is scored against the exact degree-class expectation, not only against chance",
+ "a shuffled-seed arm runs beside every seeded arm",
+ "precision is reported by the eccentricity each match won by",
+ "the degree-class baseline is handed the true partner's degree, so it bounds a degree-only adversary from above"
+ ],
+ "tolerance": null
+ },
+ "environment": {
+ "python": "3.13.7",
+ "lock_digest": "efdf4055dba10bfff2541ccaf3668be030c7caed44c61650540cc106393e9636",
+ "platform": "macOS-26.6.2-arm64-arm-64bit-Mach-O",
+ "dependencies": []
+ },
+ "limitations": [
+ "Seeds are handed to the matcher; no independently derived seed is claimed.",
+ "Cluster membership is a co-spend relation rather than observed wallet ownership.",
+ "This is the complete-clustering premise: the matchable population is given, and only the correspondence is hidden. The split-cluster premise is the harder question and is measured by graph-rejoin-2016-v1.",
+ "Two weekly views at one width, one era."
+ ],
+ "outputs": [
+ {
+ "path": "results/artifacts/match-controls-v1.json",
+ "bytes": 2390,
+ "sha256": "5234bb250121404ed4ace83f3f0ddbb3a02204b9eb0e792d9b74f3a85b143b2e"
+ },
+ {
+ "path": "results/generated/match-controls-v1.md",
+ "bytes": 3705,
+ "sha256": "48e21384adfffae4ad26fbc3d90bca4bd461c30af7c9804927739ba3b54a7ec6"
+ }
+ ]
+}
diff --git a/artifacts/sha256/d2/17a986ff347a097a653a79d12c829172f82874893e488b058b2b2def366750 b/artifacts/sha256/d2/17a986ff347a097a653a79d12c829172f82874893e488b058b2b2def366750
new file mode 100644
index 00000000..a8a06cf2
--- /dev/null
+++ b/artifacts/sha256/d2/17a986ff347a097a653a79d12c829172f82874893e488b058b2b2def366750
@@ -0,0 +1,105 @@
+{
+ "schema_version": 1,
+ "id": "fingerprint-regime-v1",
+ "claim_ids": [
+ "ctp.fingerprint.statistical_linkage",
+ "ctp.sparse_dataset.feature_uniqueness"
+ ],
+ "code": {
+ "revision": "bcd9a3e511860b1b0ff5f3ee6338a2df29ecbfbf",
+ "dirty": false
+ },
+ "command": {
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.fingerprint_regime",
+ "--dataset",
+ "tests/fixtures/fingerprint_blkcache_sample.json",
+ "reproduce",
+ "--artifact",
+ "results/artifacts/fingerprint-regime-v1.json",
+ "--markdown",
+ "results/generated/fingerprint-regime-v1.md"
+ ]
+ },
+ "environment": {
+ "python": "3.13.7",
+ "lock_digest": "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855",
+ "platform": "macOS-26.6.2-arm64-arm-64bit-Mach-O",
+ "dependencies": []
+ },
+ "datasets": [
+ {
+ "id": "fingerprint-blkcache-sample-v1",
+ "sha256": "406ce2d7d0a04f7c73281bce6b6a781012a8765e079693f010a0154e926c6a6c"
+ }
+ ],
+ "parameters": {
+ "pair_draw_cap": 2000,
+ "candidate_node_cap": 200,
+ "seed": 0,
+ "weight_sources": [
+ "library",
+ "snapshot_measured"
+ ]
+ },
+ "rng": {
+ "algorithm": "python.random.Random",
+ "seeds": [
+ 0
+ ]
+ },
+ "outputs": [
+ {
+ "path": "results/artifacts/fingerprint-regime-v1.json",
+ "bytes": 1290,
+ "sha256": "6569cd89853bd99678a8de4153b3a01d0a8d7a61b7ab718b2a0fba015537d863"
+ },
+ {
+ "path": "results/generated/fingerprint-regime-v1.md",
+ "bytes": 774,
+ "sha256": "271fa7e6601871b7fad9ac6e50b23a5f164d4a0ca30bc88c7629828b4858789b"
+ }
+ ],
+ "reproducibility": {
+ "level": "bitwise_reproducible",
+ "availability": "partial"
+ },
+ "verification": {
+ "mode": "exact",
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.fingerprint_regime",
+ "--dataset",
+ "tests/fixtures/fingerprint_blkcache_sample.json",
+ "verify",
+ "--artifact",
+ "results/artifacts/fingerprint-regime-v1.json",
+ "--markdown",
+ "results/generated/fingerprint-regime-v1.md"
+ ],
+ "tests": [
+ "tests/test_fingerprint_ns.py",
+ "tests/test_fingerprint_auc.py",
+ "tests/test_fingerprint_regime_experiment.py"
+ ],
+ "properties": [
+ "the same sampled pairs and candidate set feed both scorers",
+ "library and snapshot-measured weights remain separate rows",
+ "the non-reproduction of the historical stable-conditioner claim is tested",
+ "the generated Markdown equals the canonical rendering"
+ ],
+ "tolerance": null
+ },
+ "limitations": [
+ "address reuse is a weak and partly feature-dependent owner proxy",
+ "candidate identities are reuse-group labels rather than wallet ownership",
+ "the 600-transaction snapshot is selected and is not a chain-wide sample",
+ "the snapshot-measured weights are fitted and evaluated on the same data",
+ "the Lumen weighting and historical growing-cache population are not reproduced",
+ "mean eccentricity is descriptive and is not an acceptance rate",
+ "the result does not establish that fingerprints are universally sparse or dense"
+ ]
+}
diff --git a/artifacts/sha256/d2/c40b646533f67e4e256f62260b183e6aa4e2e3e2888d0e8d41da54374ca1f1 b/artifacts/sha256/d2/c40b646533f67e4e256f62260b183e6aa4e2e3e2888d0e8d41da54374ca1f1
new file mode 100644
index 00000000..a17bdccf
--- /dev/null
+++ b/artifacts/sha256/d2/c40b646533f67e4e256f62260b183e6aa4e2e3e2888d0e8d41da54374ca1f1
@@ -0,0 +1 @@
+{"claim":"a receiver who knows each negotiated payment can propagate the change constraint across senders, and the propagation can leave one reading of the amounts where no single sender's constraint does","conclusion":"propagation resolved the consolidating receiver to a single reading and left the evenly spaced control with two; the read-off is a receiver capability, not an external-observer one","experiment":"ns1r-change-readoff-v1","limitations":["the scenarios are deterministic fixtures, not sampled transactions","the receiver's knowledge of each negotiated payment is supplied, not inferred","an external observer holds neither the payments nor the receiver's own coins","a unique reading of the amounts is not ownership attribution","the enumeration is bounded at eight senders and does not model fees","the result is not a privacy score"],"measurement":{"locally_ambiguous_senders":5,"scenarios":2,"senders_narrowed_by_propagation":2,"with_a_unique_reading":1},"scenarios":[{"distinct_readings":1,"feasible_candidates":{"Alice":[1,2],"Bob":[1,2],"Carol":[3]},"identifier":"consolidating-receiver","inputs_sat":[40,50,60,90],"local_candidates":{"Alice":[1,2],"Bob":[1,2,3],"Carol":[3]},"matchings":2,"outputs_sat":[160,30,30,20],"payments_sat":[{"payment":20,"sender":"Alice"},{"payment":30,"sender":"Bob"},{"payment":70,"sender":"Carol"}],"reading_is_unique":true,"readings":[[{"change_sat":30,"input_sat":50,"sender":"Alice"},{"change_sat":30,"input_sat":60,"sender":"Bob"},{"change_sat":20,"input_sat":90,"sender":"Carol"}]],"unanimous_links":[[3,3]]},{"distinct_readings":2,"feasible_candidates":{"Alice":[1,3],"Bob":[1,2],"Carol":[2,3]},"identifier":"evenly-spaced-control","inputs_sat":[20,40,50,60],"local_candidates":{"Alice":[1,3],"Bob":[1,2],"Carol":[1,2,3]},"matchings":2,"outputs_sat":[80,30,20,40],"payments_sat":[{"payment":10,"sender":"Alice"},{"payment":30,"sender":"Bob"},{"payment":20,"sender":"Carol"}],"reading_is_unique":false,"readings":[[{"change_sat":30,"input_sat":40,"sender":"Alice"},{"change_sat":20,"input_sat":50,"sender":"Bob"},{"change_sat":40,"input_sat":60,"sender":"Carol"}],[{"change_sat":40,"input_sat":50,"sender":"Alice"},{"change_sat":30,"input_sat":60,"sender":"Bob"},{"change_sat":20,"input_sat":40,"sender":"Carol"}]],"unanimous_links":[]}],"schema_version":1}
diff --git a/artifacts/sha256/d3/1d64fb92d20b4d7df3fd88e750f4084dfc89e26807a8fe45b33361ae9b78d6 b/artifacts/sha256/d3/1d64fb92d20b4d7df3fd88e750f4084dfc89e26807a8fe45b33361ae9b78d6
new file mode 100644
index 00000000..fc2e2f00
--- /dev/null
+++ b/artifacts/sha256/d3/1d64fb92d20b4d7df3fd88e750f4084dfc89e26807a8fe45b33361ae9b78d6
@@ -0,0 +1,26 @@
+# What each channel adds when the engine fuses them
+
+Generated from the canonical experiment artifact. Do not edit manually.
+
+22112 committed transactions hold 335 co-spends over 753 funding transactions. The engine judges those co-spends; the merge-only baseline takes every one of them.
+
+| arm | groups | refused co-spends | added links | largest group |
+|---|---:|---:|---:|---:|
+| co-spend union-find | 697 | 0 | 0 | 5 |
+| fingerprint | 508 | 19 | 7560 | 87 |
+| fingerprint+amount | 508 | 19 | 7560 | 87 |
+| fingerprint+amount+topology | 511 | 11 | 7560 | 88 |
+| fingerprint+amount+topology+provenance | 514 | 22 | 7560 | 87 |
+| all five channels | 514 | 22 | 7560 | 87 |
+
+The fingerprint channel is what moves the partition: it adds 7560 links the co-spend missed and takes the largest group from 5 to 87. Refusal is the smaller effect and the one the thesis is about — 19 co-spends declined that the baseline merges.
+
+Topology and provenance pull in opposite directions, which is the point of fusing them. Adding the cluster-level topology takes refusals from 19 to 11: it corroborates merges the fingerprint alone objected to. Adding provenance-disjointness takes them to 22, cutting pairs whose ancestry does not overlap.
+
+The amount channel and the subset-sum de-mix move nothing here, and the reason is structural rather than empirical: both are refuse-only and gated behind fingerprint disagreement, so they can only speak on the pairs the fingerprint already objects to. The slice holds 81 two-input two-output co-spends, the only shape the roundness re-partition judges.
+
+The provenance signature here is depth-1 — a node's in-slice funders — because the slice is block-sampled rather than contiguous. A deeper walk needs a contiguous export.
+
+## Reproducibility / provenance
+
+State 1 in `results/REPRODUCIBILITY.md`: band-pinned on committed data — the artifact is recomputed from `data/fs-blkcache-2026-09-04.tar.gz` and compared byte for byte.
diff --git a/artifacts/sha256/d4/65f3640860d0b51f20c6506ca7ae3715c89afb67e4e41b34a5dfc697276505 b/artifacts/sha256/d4/65f3640860d0b51f20c6506ca7ae3715c89afb67e4e41b34a5dfc697276505
new file mode 100644
index 00000000..06c2980c
--- /dev/null
+++ b/artifacts/sha256/d4/65f3640860d0b51f20c6506ca7ae3715c89afb67e4e41b34a5dfc697276505
@@ -0,0 +1 @@
+{"anchor":"931d6627","dataset":"merged-anchor-931d6627-v1","edge":{"cake":"0a568e3ae6fa6bf34ce8925266ac2cdb1668c723980398d9c613d67d72b39729","sender":"91106666451dc43a0e3f78b325764251e205b39d7e9498948885678616ba719a"},"experiment":"anchor-axis-families-v1","limitations":["one named edge, chosen because the design decision was argued on it","the same-owner reading of this edge is the paper's, not an independent label","a score past link_above is what the engine would do, not what it does: the engine scores three axes","the library weights come from a population that was not preserved; library-calibration-v1 measures how far they sit from the one that was","the result is not a privacy score"],"measurement":{"catalogued_axes_abstaining":1,"catalogued_axes_scored":22,"families":{"catalogued":{"axes":23,"bits":11.665618785391802,"past_link_above":true,"verdict":"link"},"construction_only":{"axes":18,"bits":4.905618785391801,"past_link_above":true,"verdict":"link"},"decorrelated":{"axes":14,"bits":3.593522416485701,"past_link_above":false,"verdict":"link"},"engine_three_axis":{"axes":3,"bits":-3.1632198124512647,"past_link_above":false,"verdict":"refuse"}},"link_above":4.0,"negative_axes":3,"negative_bits_total":-10.824381214608199,"per_axis":[{"axis":"nsequence","bits":-3.593219812451265,"cake":"seq_0x01_other","sender":"max_ffffffff"},{"axis":"locktime","bits":0.43,"cake":"zero","sender":"zero"},{"axis":"input_order","bits":-3.237903631093901,"cake":"single","sender":"shuffle"},{"axis":"output_order","bits":1.72,"cake":"single","sender":"single"},{"axis":"change_spk","bits":3.42,"cake":"uniform_v0_p2wpkh","sender":"uniform_v0_p2wpkh"},{"axis":"version","bits":1.42,"cake":"v2","sender":"v2"},{"axis":"io_shape","bits":-3.9932577710630337,"cake":"1in-1out","sender":"3in-1out"},{"axis":"uih","bits":0.12000000000000002,"cake":"none","sender":"none"},{"axis":"fee_rate","bits":0.28,"cake":"precise","sender":"precise"},{"axis":"input_script_type","bits":2.18,"cake":"uniform_v0_p2wpkh","sender":"uniform_v0_p2wpkh"},{"axis":"output_encoding","bits":2.93,"cake":"bech32","sender":"bech32"},{"axis":"input_types_present","bits":2.18,"cake":"v0_p2wpkh","sender":"v0_p2wpkh"},{"axis":"change_index","bits":0.4800000000000001,"cake":"na","sender":"na"},{"axis":"change_type_match","bits":0.4800000000000001,"cake":"na","sender":"na"},{"axis":"change_matches_output","bits":0.4800000000000001,"cake":"na","sender":"na"},{"axis":"change_address_reuse","bits":0.37000000000000005,"cake":"none","sender":"none"},{"axis":"low_r","bits":2.3,"cake":"low_r","sender":"low_r"},{"axis":"sighash","bits":1.49,"cake":"all","sender":"all"},{"axis":"op_return","bits":0.08999999999999997,"cake":"none","sender":"none"},{"axis":"nested_segwit","bits":0.17000000000000004,"cake":"none","sender":"none"},{"axis":"pubkey_compression","bits":1.86,"cake":"compressed","sender":"compressed"},{"axis":"multisig","bits":0.08999999999999997,"cake":"none","sender":"none"},{"axis":"locktime_vs_broadcast","bits":null,"cake":"na","sender":"na"}],"positive_axes":19,"positive_bits_total":22.490000000000002,"redundancy_bits":8.0720963689061},"schema_version":1}
diff --git a/artifacts/sha256/d5/86f8c571b001c9bc1536f6d3eecc861bfa91a5dd55a74643eca519b4c3027b b/artifacts/sha256/d5/86f8c571b001c9bc1536f6d3eecc861bfa91a5dd55a74643eca519b4c3027b
new file mode 100644
index 00000000..ff7cdab6
Binary files /dev/null and b/artifacts/sha256/d5/86f8c571b001c9bc1536f6d3eecc861bfa91a5dd55a74643eca519b4c3027b differ
diff --git a/artifacts/sha256/d6/d3271af502f104cdacfa244288e27273da2214e42b0c515d467d49dde0f34f b/artifacts/sha256/d6/d3271af502f104cdacfa244288e27273da2214e42b0c515d467d49dde0f34f
new file mode 100644
index 00000000..2923798d
--- /dev/null
+++ b/artifacts/sha256/d6/d3271af502f104cdacfa244288e27273da2214e42b0c515d467d49dde0f34f
@@ -0,0 +1,99 @@
+{
+ "schema_version": 1,
+ "id": "em-m-v1",
+ "claim_ids": [
+ "ctp.fingerprint.statistical_linkage"
+ ],
+ "code": {
+ "revision": "bcd9a3e511860b1b0ff5f3ee6338a2df29ecbfbf",
+ "dirty": false
+ },
+ "command": {
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.em_m",
+ "--snapshot",
+ "data/fs-blkcache-2026-09-04.tar.gz",
+ "reproduce",
+ "--artifact",
+ "results/artifacts/em-m-v1.json",
+ "--markdown",
+ "results/generated/em-m-v1.md"
+ ]
+ },
+ "environment": {
+ "python": "3.13.7",
+ "lock_digest": "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855",
+ "platform": "macOS-26.6.2-arm64-arm-64bit-Mach-O",
+ "dependencies": []
+ },
+ "datasets": [
+ {
+ "id": "fs-blkcache-2026-09-04-v1",
+ "sha256": "5a69f6f446dbdfcce09dddc91c653e5d841124d14020b720fdea276315c775a6"
+ }
+ ],
+ "parameters": {
+ "pair_cap_per_class": 4000,
+ "seed": 0,
+ "fixed_u": true
+ },
+ "rng": {
+ "algorithm": "python.random.MersenneTwister",
+ "seeds": [
+ 0
+ ]
+ },
+ "outputs": [
+ {
+ "path": "results/artifacts/em-m-v1.json",
+ "bytes": 4442,
+ "sha256": "0cbd76502a1a77f11cc72d8834d4d9f7fc2ba2fffa1bbf33d44c4f075652a47b"
+ },
+ {
+ "path": "results/generated/em-m-v1.md",
+ "bytes": 676,
+ "sha256": "c8c6d92b22c9486a08ce2eecaf5164959831534f8e953d4a5d3dbcf6f95ad5cb"
+ }
+ ],
+ "reproducibility": {
+ "level": "bitwise_reproducible",
+ "availability": "complete"
+ },
+ "verification": {
+ "mode": "exact",
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.em_m",
+ "--snapshot",
+ "data/fs-blkcache-2026-09-04.tar.gz",
+ "verify",
+ "--artifact",
+ "results/artifacts/em-m-v1.json",
+ "--markdown",
+ "results/generated/em-m-v1.md"
+ ],
+ "tests": [
+ "tests/test_fs_em.py",
+ "tests/test_em_m_experiment.py"
+ ],
+ "properties": [
+ "the preserved snapshot exactly reproduces the canonical artifact",
+ "EM receives no pair labels during fitting",
+ "all scorer variants use the same selected pair population",
+ "the address-reuse comparison is identified as a weak label rather than an oracle",
+ "the AUC delta is reported without an untested direction verdict",
+ "the historical 165,832-transaction result is explicitly not reproduced"
+ ],
+ "tolerance": null
+ },
+ "limitations": [
+ "address reuse is a weak and partly feature-dependent ownership proxy",
+ "the balanced selected sample does not represent population prevalence",
+ "EM is fitted and evaluated on the same pair sample",
+ "conditional independence is violated by correlated axes",
+ "the result is attacker-side evidence and is not CoinScore"
+ ]
+}
diff --git a/artifacts/sha256/d7/0f8db59d561ae8093fb970b0d058426ca9a2de8354e22a0db8894d93a3ee48 b/artifacts/sha256/d7/0f8db59d561ae8093fb970b0d058426ca9a2de8354e22a0db8894d93a3ee48
new file mode 100644
index 00000000..69e9292e
--- /dev/null
+++ b/artifacts/sha256/d7/0f8db59d561ae8093fb970b0d058426ca9a2de8354e22a0db8894d93a3ee48
@@ -0,0 +1,13 @@
+{
+ "schema_version": 1,
+ "python": {
+ "implementation": "cpython",
+ "version": "3.13",
+ "system": "Darwin",
+ "machine": "arm64"
+ },
+ "source_archive": "sources/decluster-bcd9a3e-runtime.tar",
+ "project_root": "decluster",
+ "requirements": "reproduction/ns1r-change-readoff/requirements.lock",
+ "dependency_directory": "reproduction/ns1r-change-readoff"
+}
diff --git a/artifacts/sha256/d7/4c2d1442294fd09f2dde8c1ce43ca44982ec38c99ea2b079c381e9c181502d b/artifacts/sha256/d7/4c2d1442294fd09f2dde8c1ce43ca44982ec38c99ea2b079c381e9c181502d
new file mode 100644
index 00000000..16af456f
--- /dev/null
+++ b/artifacts/sha256/d7/4c2d1442294fd09f2dde8c1ce43ca44982ec38c99ea2b079c381e9c181502d
@@ -0,0 +1,14 @@
+# Fellegi–Sunter conditional-dependence ablation
+
+Generated from the canonical experiment artifact. Do not edit manually.
+
+| quantity | value |
+|---|---:|
+| transactions | 22112 |
+| baseline AUC | 0.982688 |
+| dependent field groups | 4 |
+| most dependent fields | `input_script_type`, `input_types_present` |
+| phi among matches | 1.000000 |
+| phi among non-matches | 1.000000 |
+
+Association is measured conditionally on weak-label class and is not causal.
diff --git a/artifacts/sha256/d9/7dc9b05d9afef5932ac79ee8ca1b3854bf9298467e039c4df4dd961de37ad2 b/artifacts/sha256/d9/7dc9b05d9afef5932ac79ee8ca1b3854bf9298467e039c4df4dd961de37ad2
new file mode 100644
index 00000000..a49f97bd
--- /dev/null
+++ b/artifacts/sha256/d9/7dc9b05d9afef5932ac79ee8ca1b3854bf9298467e039c4df4dd961de37ad2
@@ -0,0 +1,110 @@
+{
+ "schema_version": 1,
+ "id": "deterministic-partition-v1",
+ "claim_ids": [
+ "ctp.amount.deterministic_partition"
+ ],
+ "code": {
+ "revision": "bcd9a3e511860b1b0ff5f3ee6338a2df29ecbfbf",
+ "dirty": false
+ },
+ "command": {
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.deterministic_partition",
+ "reproduce",
+ "--artifact",
+ "results/artifacts/deterministic-partition-v1.json",
+ "--markdown",
+ "results/generated/deterministic-partition-v1.md"
+ ]
+ },
+ "environment": {
+ "python": "3.13.7",
+ "lock_digest": "e0d6a8cada5d383c0aca2bd2a37f28c15721392469c94084128d369c29c5544d",
+ "platform": "macOS-26.6.2-arm64-arm-64bit-Mach-O",
+ "dependencies": []
+ },
+ "datasets": [],
+ "parameters": {
+ "balance_model": "exact_per_block_conservation",
+ "mapping_family": "maurer_non_derived",
+ "forced_inputs": [
+ 1,
+ 3,
+ 20,
+ 50
+ ],
+ "forced_outputs": [
+ 4,
+ 70
+ ],
+ "control_inputs": [
+ 1,
+ 2,
+ 3,
+ 4,
+ 5
+ ],
+ "control_outputs": [
+ 7,
+ 8
+ ],
+ "amount_scale": 10
+ },
+ "rng": {
+ "algorithm": null,
+ "seeds": []
+ },
+ "outputs": [
+ {
+ "path": "results/artifacts/deterministic-partition-v1.json",
+ "bytes": 868,
+ "sha256": "9ef88ed2230876552f3f18ebf4e1bf462e7bd401160a5d287de443abc03f7415"
+ },
+ {
+ "path": "results/generated/deterministic-partition-v1.md",
+ "bytes": 599,
+ "sha256": "bcd7c6a58c1206d78841bceba862f9b9eabd9b978040bcf717a5967f0355df29"
+ }
+ ],
+ "reproducibility": {
+ "level": "bitwise_reproducible",
+ "availability": "complete"
+ },
+ "verification": {
+ "mode": "exact",
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.deterministic_partition",
+ "verify",
+ "--artifact",
+ "results/artifacts/deterministic-partition-v1.json",
+ "--markdown",
+ "results/generated/deterministic-partition-v1.md"
+ ],
+ "tests": [
+ "tests/test_deterministic_partition.py",
+ "tests/test_deterministic_partition_experiment.py",
+ "tests/test_reference_subtransactions.py",
+ "tests/test_boltzmann_reference_parity.py"
+ ],
+ "properties": [
+ "the forced CTP example has one exact non-derived mapping",
+ "four input-output links occur in every mapping of the forced family",
+ "the underdetermined CTP control has three exact non-derived mappings",
+ "the underdetermined control has no unanimous input-output link",
+ "certainty remains explicitly conditional on the declared mapping model",
+ "the generated Markdown equals the canonical rendering"
+ ],
+ "tolerance": null
+ },
+ "limitations": [
+ "paper examples scaled to integer units",
+ "exact per-block conservation",
+ "fees, contextual priors and net-settlement cycles are outside the model",
+ "unanimous mapping links are not ownership attribution"
+ ]
+}
diff --git a/artifacts/sha256/da/73f701f86ec0df9807bbf41b2b3b588d9c04628df743a567d4083b67052921 b/artifacts/sha256/da/73f701f86ec0df9807bbf41b2b3b588d9c04628df743a567d4083b67052921
new file mode 100644
index 00000000..c448b099
--- /dev/null
+++ b/artifacts/sha256/da/73f701f86ec0df9807bbf41b2b3b588d9c04628df743a567d4083b67052921
@@ -0,0 +1,13 @@
+{
+ "schema_version": 1,
+ "python": {
+ "implementation": "cpython",
+ "version": "3.13",
+ "system": "Darwin",
+ "machine": "arm64"
+ },
+ "source_archive": "sources/decluster-bcd9a3e-runtime.tar",
+ "project_root": "decluster",
+ "requirements": "reproduction/amount-subset-sum/requirements.lock",
+ "dependency_directory": "dependencies"
+}
diff --git a/artifacts/sha256/da/e3809d31d7563af2e3ee2a2710df2f0415ca649bdc716d9e9ee35254d05729 b/artifacts/sha256/da/e3809d31d7563af2e3ee2a2710df2f0415ca649bdc716d9e9ee35254d05729
new file mode 100644
index 00000000..94a4563d
--- /dev/null
+++ b/artifacts/sha256/da/e3809d31d7563af2e3ee2a2710df2f0415ca649bdc716d9e9ee35254d05729
@@ -0,0 +1,13 @@
+{
+ "schema_version": 1,
+ "python": {
+ "implementation": "cpython",
+ "version": "3.13",
+ "system": "Darwin",
+ "machine": "arm64"
+ },
+ "source_archive": "sources/decluster-bcd9a3e-runtime.tar",
+ "project_root": "decluster",
+ "requirements": "reproduction/ancestry-contract/requirements.lock",
+ "dependency_directory": "reproduction/ancestry-contract"
+}
diff --git a/artifacts/sha256/da/f028172a667fe766a85b4697340352a2b006a3c1540d222556bb7e4b1af30c b/artifacts/sha256/da/f028172a667fe766a85b4697340352a2b006a3c1540d222556bb7e4b1af30c
new file mode 100644
index 00000000..e26928ef
--- /dev/null
+++ b/artifacts/sha256/da/f028172a667fe766a85b4697340352a2b006a3c1540d222556bb7e4b1af30c
@@ -0,0 +1,106 @@
+{
+ "schema_version": 1,
+ "id": "bayes-vs-fs-v1",
+ "claim_ids": [
+ "ctp.fingerprint.statistical_linkage"
+ ],
+ "code": {
+ "revision": "bcd9a3e511860b1b0ff5f3ee6338a2df29ecbfbf",
+ "dirty": false
+ },
+ "command": {
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.bayes_vs_fs",
+ "--snapshot",
+ "data/fs-blkcache-2026-09-04.tar.gz",
+ "reproduce",
+ "--artifact",
+ "results/artifacts/bayes-vs-fs-v1.json",
+ "--markdown",
+ "results/generated/bayes-vs-fs-v1.md"
+ ]
+ },
+ "environment": {
+ "python": "3.13.7",
+ "lock_digest": "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855",
+ "platform": "macOS-26.6.2-arm64-arm-64bit-Mach-O",
+ "dependencies": []
+ },
+ "datasets": [
+ {
+ "id": "fs-blkcache-2026-09-04-v1",
+ "sha256": "5a69f6f446dbdfcce09dddc91c653e5d841124d14020b720fdea276315c775a6"
+ }
+ ],
+ "parameters": {
+ "pair_cap_per_class": 4000,
+ "seed": 0,
+ "gibbs_samples": 2000,
+ "gibbs_burn": 500,
+ "beta_prior": [
+ 8.5,
+ 1.5
+ ]
+ },
+ "rng": {
+ "algorithm": "python.random.MersenneTwister",
+ "seeds": [
+ 0,
+ 1
+ ]
+ },
+ "outputs": [
+ {
+ "path": "results/artifacts/bayes-vs-fs-v1.json",
+ "bytes": 6334,
+ "sha256": "0e1d146555a0913181370e4a0145cb38bebd5f43ee938fdfbd2a18e30cd4fa81"
+ },
+ {
+ "path": "results/generated/bayes-vs-fs-v1.md",
+ "bytes": 687,
+ "sha256": "984f0373600a455203cf1bccb44b5f8768368deaf41baf081f940a406775bd0c"
+ }
+ ],
+ "reproducibility": {
+ "level": "bitwise_reproducible",
+ "availability": "complete"
+ },
+ "verification": {
+ "mode": "exact",
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.bayes_vs_fs",
+ "--snapshot",
+ "data/fs-blkcache-2026-09-04.tar.gz",
+ "verify",
+ "--artifact",
+ "results/artifacts/bayes-vs-fs-v1.json",
+ "--markdown",
+ "results/generated/bayes-vs-fs-v1.md"
+ ],
+ "tests": [
+ "tests/test_fs_bayes.py",
+ "tests/test_bayes_vs_fs_experiment.py"
+ ],
+ "properties": [
+ "the preserved snapshot exactly reproduces the canonical artifact",
+ "all three scorers use the same pair population and per-axis evidence",
+ "the supervised agreement estimate is identified as an address-reuse weak label",
+ "the former handwritten values are explicitly recorded as not reproduced",
+ "cluster bands are distinguished from a joint ownership-partition posterior",
+ "the generated Markdown equals the canonical rendering"
+ ],
+ "tolerance": null
+ },
+ "limitations": [
+ "address reuse is a weak and partly feature-dependent ownership proxy",
+ "the balanced selected sample does not represent population prevalence",
+ "parameters are fitted and evaluated on the same selected pairs",
+ "the model assumes conditionally independent axes and fixes collision rates",
+ "cluster bands independently sample pair edges and are not a joint partition posterior",
+ "the result is attacker-side evidence and is not CoinScore"
+ ]
+}
diff --git a/artifacts/sha256/db/c4214fb499a96327326c4ea31341a76878c1867bfa3ccc57df88d850cdaa6a b/artifacts/sha256/db/c4214fb499a96327326c4ea31341a76878c1867bfa3ccc57df88d850cdaa6a
new file mode 100644
index 00000000..d738f494
Binary files /dev/null and b/artifacts/sha256/db/c4214fb499a96327326c4ea31341a76878c1867bfa3ccc57df88d850cdaa6a differ
diff --git a/artifacts/sha256/db/ce88680db02fc287600d87f6674273450a524b7d9f364bbfafc91a39348ef1 b/artifacts/sha256/db/ce88680db02fc287600d87f6674273450a524b7d9f364bbfafc91a39348ef1
new file mode 100644
index 00000000..743b5ef0
--- /dev/null
+++ b/artifacts/sha256/db/ce88680db02fc287600d87f6674273450a524b7d9f364bbfafc91a39348ef1
@@ -0,0 +1,13 @@
+{
+ "schema_version": 1,
+ "python": {
+ "implementation": "cpython",
+ "version": "3.13",
+ "system": "Darwin",
+ "machine": "arm64"
+ },
+ "source_archive": "sources/decluster-bcd9a3e-runtime.tar",
+ "project_root": "decluster",
+ "requirements": "reproduction/library-calibration/requirements.lock",
+ "dependency_directory": "reproduction/library-calibration"
+}
diff --git a/artifacts/sha256/de/b852af4f2f29ea7b6f65105e1417c4a75220291f3a281fdc08ef805a280f6a b/artifacts/sha256/de/b852af4f2f29ea7b6f65105e1417c4a75220291f3a281fdc08ef805a280f6a
new file mode 100644
index 00000000..623c5ddc
--- /dev/null
+++ b/artifacts/sha256/de/b852af4f2f29ea7b6f65105e1417c4a75220291f3a281fdc08ef805a280f6a
@@ -0,0 +1,13 @@
+{
+ "schema_version": 1,
+ "python": {
+ "implementation": "cpython",
+ "version": "3.13",
+ "system": "Darwin",
+ "machine": "arm64"
+ },
+ "source_archive": "sources/decluster-bcd9a3e-runtime.tar",
+ "project_root": "decluster",
+ "requirements": "reproduction/candidate-set-intersection/requirements.lock",
+ "dependency_directory": "reproduction/candidate-set-intersection"
+}
diff --git a/artifacts/sha256/df/9d25a028a5f5c9f7527923cd354a674865b3012d71b23405c08a6ec643a84b b/artifacts/sha256/df/9d25a028a5f5c9f7527923cd354a674865b3012d71b23405c08a6ec643a84b
new file mode 100644
index 00000000..6ddbc8c0
--- /dev/null
+++ b/artifacts/sha256/df/9d25a028a5f5c9f7527923cd354a674865b3012d71b23405c08a6ec643a84b
@@ -0,0 +1,16 @@
+# Counting fee sensitivity
+
+Generated from the canonical experiment artifact. Do not edit manually.
+
+| fee (sat) | transaction count | per-coin inputs measured |
+|---:|---:|---:|
+| 0 | 2 | 3 |
+| 1 | 1 | 2 |
+| 10 | 1 | 0 |
+| 100 | 1 | 0 |
+| 1,000 | 1 | 0 |
+| 4,956 | 1 | 0 |
+
+The transaction-level result remains defined after the per-coin interface stops measuring any input. The two interfaces therefore must not be substituted for one another.
+
+This fixture is a mechanism check, not a population estimate or privacy score.
diff --git a/artifacts/sha256/df/9db81e8a356751d544586debd5402f3ab0ebf191ce8ab7a8d09b2dd914b12a b/artifacts/sha256/df/9db81e8a356751d544586debd5402f3ab0ebf191ce8ab7a8d09b2dd914b12a
new file mode 100644
index 00000000..18db1ada
--- /dev/null
+++ b/artifacts/sha256/df/9db81e8a356751d544586debd5402f3ab0ebf191ce8ab7a8d09b2dd914b12a
@@ -0,0 +1,103 @@
+{
+ "schema_version": 1,
+ "id": "reid-v1",
+ "claim_ids": [
+ "ctp.sparse_dataset.feature_uniqueness"
+ ],
+ "code": {
+ "revision": "bcd9a3e511860b1b0ff5f3ee6338a2df29ecbfbf",
+ "dirty": false
+ },
+ "command": {
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.reid",
+ "--dataset",
+ "tests/fixtures/reid_sigs.json.gz",
+ "reproduce",
+ "--artifact",
+ "results/artifacts/reid-v1.json",
+ "--markdown",
+ "results/generated/reid-v1.md"
+ ]
+ },
+ "environment": {
+ "python": "3.13.7",
+ "lock_digest": "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855",
+ "platform": "macOS-26.6.2-arm64-arm-64bit-Mach-O",
+ "dependencies": []
+ },
+ "datasets": [
+ {
+ "id": "reid-signatures-v1",
+ "sha256": "dbc4214fb499a96327326c4ea31341a76878c1867bfa3ccc57df88d850cdaa6a"
+ }
+ ],
+ "parameters": {
+ "auxiliary_origins": [
+ 4,
+ 8
+ ],
+ "eccentricity_threshold": 1.5,
+ "sparse_below_cosine": 0.5,
+ "dense_at_least_cosine": 0.9
+ },
+ "rng": {
+ "algorithm": "python.random.Random",
+ "seeds": [
+ 0
+ ]
+ },
+ "outputs": [
+ {
+ "path": "results/artifacts/reid-v1.json",
+ "bytes": 1504,
+ "sha256": "9c4ac11aa3a1014907528b5ed8b324c0a9b1995a0d7a70f2c6963c49bc8e797f"
+ },
+ {
+ "path": "results/generated/reid-v1.md",
+ "bytes": 795,
+ "sha256": "685acc5dc71a32295f04409c3e11b310934dbba6b44d4bd709255767f6b47c20"
+ }
+ ],
+ "reproducibility": {
+ "level": "bitwise_reproducible",
+ "availability": "partial"
+ },
+ "verification": {
+ "mode": "exact",
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.reid",
+ "--dataset",
+ "tests/fixtures/reid_sigs.json.gz",
+ "verify",
+ "--artifact",
+ "results/artifacts/reid-v1.json",
+ "--markdown",
+ "results/generated/reid-v1.md"
+ ],
+ "tests": [
+ "tests/test_reid.py",
+ "tests/test_reid_experiment.py"
+ ],
+ "properties": [
+ "seeded sampling is invariant to serialized object order",
+ "sparse and dense strata are recomputed from the snapshot",
+ "all denominators and rates are emitted in the structured artifact",
+ "the generated Markdown equals the canonical rendering"
+ ],
+ "tolerance": null
+ },
+ "limitations": [
+ "the 200-record snapshot is selected and is not a uniform chain sample",
+ "the signature-generation recipe and original comparison population are unavailable",
+ "the target identifier is a record identity, not wallet ownership",
+ "the auxiliary origins are sampled from the target signature rather than observed independently",
+ "this is an adaptation, not a reproduction of the published Netflix experiment",
+ "the result does not establish a chain-wide deanonymization rate or privacy score",
+ "upstream data licensing is undetermined; the dataset publisher authorized redistribution on 2026-09-04"
+ ]
+}
diff --git a/artifacts/sha256/e0/9b7649b43e9238da355048e2205281c00f63c1e08b230c2a3588b7efb94ab8 b/artifacts/sha256/e0/9b7649b43e9238da355048e2205281c00f63c1e08b230c2a3588b7efb94ab8
new file mode 100644
index 00000000..1cc4aaa1
--- /dev/null
+++ b/artifacts/sha256/e0/9b7649b43e9238da355048e2205281c00f63c1e08b230c2a3588b7efb94ab8
@@ -0,0 +1,10 @@
+# Provenance decay failure modes
+
+Generated from the canonical experiment artifact. Do not edit manually.
+
+| removal | candidates | structural outcome | components |
+|---|---:|---|---:|
+| bridge | 3 → 2 | `graph_fracture` | 1 → 2 |
+| peripheral | 3 → 2 | `inconclusive` | unchanged |
+
+Equal additive elimination does not imply equal structural decay.
diff --git a/artifacts/sha256/e0/d6a8cada5d383c0aca2bd2a37f28c15721392469c94084128d369c29c5544d b/artifacts/sha256/e0/d6a8cada5d383c0aca2bd2a37f28c15721392469c94084128d369c29c5544d
new file mode 100644
index 00000000..cb70631a
--- /dev/null
+++ b/artifacts/sha256/e0/d6a8cada5d383c0aca2bd2a37f28c15721392469c94084128d369c29c5544d
@@ -0,0 +1 @@
+# no third-party dependencies
diff --git a/artifacts/sha256/e0/e687d23f9417ace6364372fa8d72ea1bb4f83dabaf08d08d2523423e247911 b/artifacts/sha256/e0/e687d23f9417ace6364372fa8d72ea1bb4f83dabaf08d08d2523423e247911
new file mode 100644
index 00000000..ad1b7adc
--- /dev/null
+++ b/artifacts/sha256/e0/e687d23f9417ace6364372fa8d72ea1bb4f83dabaf08d08d2523423e247911
@@ -0,0 +1,134 @@
+{
+ "anchor": "931d6627",
+ "description": "the two funding transactions of the merged anchor's false Cake<->sender edge",
+ "transactions": {
+ "cake": {
+ "fee": 220,
+ "locktime": 0,
+ "sigops": 1,
+ "size": 191,
+ "status": {
+ "block_hash": "000000000000000000016a5b353443db3179d515d081621c402ae37055c294c5",
+ "block_height": 956926,
+ "block_time": 1783348132,
+ "confirmed": true
+ },
+ "txid": "0a568e3ae6fa6bf34ce8925266ac2cdb1668c723980398d9c613d67d72b39729",
+ "version": 2,
+ "vin": [
+ {
+ "is_coinbase": false,
+ "prevout": {
+ "scriptpubkey": "00141306bbe37c68f426779dff2854901861ad01f99e",
+ "scriptpubkey_address": "bc1qzvrthcmudr6zvaualu59fyqcvxksr7v7lsldyn",
+ "scriptpubkey_asm": "OP_0 OP_PUSHBYTES_20 1306bbe37c68f426779dff2854901861ad01f99e",
+ "scriptpubkey_type": "v0_p2wpkh",
+ "value": 5970
+ },
+ "scriptsig": "",
+ "scriptsig_asm": "",
+ "sequence": 1,
+ "txid": "be2e362095f78c0e2761e0a04a1116d5ea9f119d2a55a5f1d1ee8edc3db9640a",
+ "vout": 0,
+ "witness": [
+ "304402207dd1e423ddff3064c4af0d4d0966f4785fc322e9ed8e1a97578c019c85db593b0220689609d7468df2ef99ab64d01447db89d256949d8fde3842310e845eaed0b2fc01",
+ "02f2f5bcf89d6d469eb9fdefe388f5b3492511c8cbadf826d9f81f377292935b6d"
+ ]
+ }
+ ],
+ "vout": [
+ {
+ "scriptpubkey": "001433a4fc41c991e1b3b9a9b75584ba9456eb722b54",
+ "scriptpubkey_address": "bc1qxwj0cswfj8sm8wdfka2cfw552m4hy265e52mk7",
+ "scriptpubkey_asm": "OP_0 OP_PUSHBYTES_20 33a4fc41c991e1b3b9a9b75584ba9456eb722b54",
+ "scriptpubkey_type": "v0_p2wpkh",
+ "value": 5750
+ }
+ ],
+ "weight": 437
+ },
+ "sender": {
+ "fee": 1000,
+ "locktime": 0,
+ "sigops": 3,
+ "size": 487,
+ "status": {
+ "block_hash": "00000000000000000000de1da2589c16d06a4bbadd8014cec42172880b612696",
+ "block_height": 956298,
+ "block_time": 1782961565,
+ "confirmed": true
+ },
+ "txid": "91106666451dc43a0e3f78b325764251e205b39d7e9498948885678616ba719a",
+ "version": 2,
+ "vin": [
+ {
+ "is_coinbase": false,
+ "prevout": {
+ "scriptpubkey": "0014fb31eb03eed8e7a7f69ccb41fc31d0e8922fa65d",
+ "scriptpubkey_address": "bc1qlvc7kqlwmrn60a5uedqlcvwsazfzlfja0slrf5",
+ "scriptpubkey_asm": "OP_0 OP_PUSHBYTES_20 fb31eb03eed8e7a7f69ccb41fc31d0e8922fa65d",
+ "scriptpubkey_type": "v0_p2wpkh",
+ "value": 1000
+ },
+ "scriptsig": "",
+ "scriptsig_asm": "",
+ "sequence": 4294967295,
+ "txid": "5b97102cbed69f796756ba4bd9a268c95d8ca53f9151602d561ff58686d7be98",
+ "vout": 1,
+ "witness": [
+ "304402203b3b0fb15ecd23e1b44a0eb761301555f5ce0cb6267f9b11a40140b2b6b0daff02207cf32f84046f6e581db4dde7c344dc1ea0ff8a503fe23bb24329574741c60e6601",
+ "0281486ad6f4f820131f5a28af49ace57d0b3dfd922b9f991361f3b7a2ed654399"
+ ]
+ },
+ {
+ "is_coinbase": false,
+ "prevout": {
+ "scriptpubkey": "00147a06420787862c6b3cc620d1ce44ba06b00dd4a6",
+ "scriptpubkey_address": "bc1q0gryypu8sckxk0xxyrguu396q6cqm49xc2uxcp",
+ "scriptpubkey_asm": "OP_0 OP_PUSHBYTES_20 7a06420787862c6b3cc620d1ce44ba06b00dd4a6",
+ "scriptpubkey_type": "v0_p2wpkh",
+ "value": 1000
+ },
+ "scriptsig": "",
+ "scriptsig_asm": "",
+ "sequence": 4294967295,
+ "txid": "89300d3b2cc8e6ee8238a482c5b9aa696d9b09e1862c5c4153f8a0019fbae2c8",
+ "vout": 0,
+ "witness": [
+ "3044022017340a704b2f1c6cc0555a5cce4caedfc6c8b53b6940d4487db238624ff65c760220727b9e13aaa0a62c722f71709996e3bf0efabd56a9a0a8bca1772b4bf1005b6101",
+ "02d70eb70310ab37bf889fc48ea17b6704f68875dc8edf62af8ec7859f23c45d34"
+ ]
+ },
+ {
+ "is_coinbase": false,
+ "prevout": {
+ "scriptpubkey": "0014a23dae2ae9e270305a85bff8497ba8ef7abec890",
+ "scriptpubkey_address": "bc1q5g76u2hfufcrqk59hluyj7agaaatajysscqp2e",
+ "scriptpubkey_asm": "OP_0 OP_PUSHBYTES_20 a23dae2ae9e270305a85bff8497ba8ef7abec890",
+ "scriptpubkey_type": "v0_p2wpkh",
+ "value": 1000
+ },
+ "scriptsig": "",
+ "scriptsig_asm": "",
+ "sequence": 4294967295,
+ "txid": "b8c2bd603566490fd6d0418136b1302321b1e7f5b20a818be34e55ee823f0519",
+ "vout": 0,
+ "witness": [
+ "3044022025dba6ad73cd1a24ed52cdb2922b26808f9a72db0096f3421d972d815123a2560220733550d87fd64c36858704d31d53d1d9356d447c397a9b4f99fae07ea6c252c301",
+ "02dcadae98b40e036b8827239019f91d38652496badc08617d3cdae91fc3ffdbc5"
+ ]
+ }
+ ],
+ "vout": [
+ {
+ "scriptpubkey": "0014c92e76fa901418c59e3752584a306fbbdd8d39e6",
+ "scriptpubkey_address": "bc1qeyh8d75szsvvt83h2fvy5vr0h0wc6w0xw63qx8",
+ "scriptpubkey_asm": "OP_0 OP_PUSHBYTES_20 c92e76fa901418c59e3752584a306fbbdd8d39e6",
+ "scriptpubkey_type": "v0_p2wpkh",
+ "value": 2000
+ }
+ ],
+ "weight": 979
+ }
+ }
+}
diff --git a/artifacts/sha256/e2/68a63274e4fb554a2ef06112b755e2155fa61bc6f8bc51de7a7f72f1a3bfa9 b/artifacts/sha256/e2/68a63274e4fb554a2ef06112b755e2155fa61bc6f8bc51de7a7f72f1a3bfa9
new file mode 100644
index 00000000..92612e25
--- /dev/null
+++ b/artifacts/sha256/e2/68a63274e4fb554a2ef06112b755e2155fa61bc6f8bc51de7a7f72f1a3bfa9
@@ -0,0 +1,98 @@
+{
+ "schema_version": 1,
+ "id": "fs-temporal-v1",
+ "claim_ids": [
+ "ctp.fingerprint.statistical_linkage"
+ ],
+ "code": {
+ "revision": "bcd9a3e511860b1b0ff5f3ee6338a2df29ecbfbf",
+ "dirty": false
+ },
+ "command": {
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.fs_temporal",
+ "--snapshot",
+ "data/fs-blkcache-2026-09-04.tar.gz",
+ "reproduce",
+ "--artifact",
+ "results/artifacts/fs-temporal-v1.json",
+ "--markdown",
+ "results/generated/fs-temporal-v1.md"
+ ]
+ },
+ "environment": {
+ "python": "3.13.7",
+ "lock_digest": "e0d6a8cada5d383c0aca2bd2a37f28c15721392469c94084128d369c29c5544d",
+ "platform": "macOS-26.6.2-arm64-arm-64bit-Mach-O",
+ "dependencies": []
+ },
+ "datasets": [
+ {
+ "id": "fs-blkcache-2026-09-04-v1",
+ "sha256": "5a69f6f446dbdfcce09dddc91c653e5d841124d14020b720fdea276315c775a6"
+ }
+ ],
+ "parameters": {
+ "cap": 4000,
+ "train_fraction": 0.7,
+ "seed": 0,
+ "label_kind": "address-reuse weak labels"
+ },
+ "rng": {
+ "algorithm": "python.random.MersenneTwister",
+ "seeds": [
+ 0
+ ]
+ },
+ "outputs": [
+ {
+ "path": "results/artifacts/fs-temporal-v1.json",
+ "bytes": 4694,
+ "sha256": "490a0f4e7b3452093915bd7dc44ef393048912ff21ead36fdf9be74736f832a9"
+ },
+ {
+ "path": "results/generated/fs-temporal-v1.md",
+ "bytes": 408,
+ "sha256": "17fa4bb1843852a63936de74e550eb5f293822b0a3b16b33e3b527e5f57e8ff5"
+ }
+ ],
+ "reproducibility": {
+ "level": "bitwise_reproducible",
+ "availability": "complete"
+ },
+ "verification": {
+ "mode": "exact",
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.fs_temporal",
+ "--snapshot",
+ "data/fs-blkcache-2026-09-04.tar.gz",
+ "verify",
+ "--artifact",
+ "results/artifacts/fs-temporal-v1.json",
+ "--markdown",
+ "results/generated/fs-temporal-v1.md"
+ ],
+ "tests": [
+ "tests/test_fs_temporal.py",
+ "tests/test_fs_temporal_experiment.py"
+ ],
+ "properties": [
+ "the snapshot reproduces the complete historical JSON report",
+ "the temporal split trains strictly before it tests",
+ "Fellegi-Sunter and rarity baseline score the same test pairs",
+ "weak-label and sampling limitations remain explicit",
+ "the generated Markdown equals the canonical rendering"
+ ],
+ "tolerance": null
+ },
+ "limitations": [
+ "address reuse provides weak labels rather than independent ownership labels",
+ "the block sample is not representative of the whole chain",
+ "Beta smoothing is a local modeling choice",
+ "two fingerprint fields are duplicates in this sample"
+ ]
+}
diff --git a/artifacts/sha256/e2/bad958dea37ae3a7eacaa6e0f77fa88b2ad0378d854f31592ce76ecbe353f0 b/artifacts/sha256/e2/bad958dea37ae3a7eacaa6e0f77fa88b2ad0378d854f31592ce76ecbe353f0
new file mode 100644
index 00000000..fa85ccb0
--- /dev/null
+++ b/artifacts/sha256/e2/bad958dea37ae3a7eacaa6e0f77fa88b2ad0378d854f31592ce76ecbe353f0
@@ -0,0 +1 @@
+{"boundary_causes":{"coinbase":0,"depth_capped":48,"fetch_missing":5707},"capacity":{"1":32,"10":4,"11":4,"119":1,"12":2,"129":1,"14":1,"16":1,"17":3,"18":2,"182":1,"19":1,"193":1,"198":1,"2":121,"20":1,"21":1,"22":1,"24":1,"26":1,"27":1,"3":33,"30":1,"32":1,"33":1,"36":1,"4":37,"41":1,"45":1,"5":30,"52":1,"53":1,"55":3,"56":1,"58":1,"59":1,"6":12,"60":5,"61":1,"7":6,"77":1,"8":8,"84":1,"85":1,"89":1,"9":3},"capacity_carrying_the_coin":{"0":128,"1":70,"11":1,"13":1,"16":2,"198":1,"2":57,"21":1,"23":1,"24":1,"29":1,"3":18,"32":1,"33":1,"34":1,"36":1,"4":18,"5":10,"6":10,"69":1,"7":2,"77":1,"8":6,"9":1},"dataset":"fs-blkcache-2026-09-04-v1","experiment":"route-capacity-v1","origin_excess":{"0":134,"1":57,"10":3,"11":2,"12":1,"134":1,"14":1,"17":2,"18":2,"196":1,"2":31,"21":1,"24":2,"25":1,"251":1,"254":1,"259":1,"26":1,"3":19,"33":1,"34":1,"36":1,"4":15,"41":1,"46":1,"5":11,"6":13,"7":12,"8":4,"9":11,"90":1,"92":1},"parameters":{"depth":5,"link_oracle":"value_flow_link_oracle","small_cut_at_most":3},"population":{"measured":335,"targets":335},"readings":{"cut_at_most_small":186,"cut_of_one":32,"cut_of_one_carrying_the_coin":70,"largest_cut":198,"no_route_carries_the_coin":128,"origin_set_overstates":201,"origins_cannot_deliver_the_value":8},"schema_version":1,"source":{"bytes":91367352,"digest":"91c444bd4e9715d7739ccc9d68c7d9751762e25b647e3eef647c6ec0b48ba90c","files":904,"pattern":".blkcache/*.json","transactions":22112}}
diff --git a/artifacts/sha256/e3/1f92d4b3f11374b96f7dbc90caeeb67f4a269d428164e28ab228c5eb577100 b/artifacts/sha256/e3/1f92d4b3f11374b96f7dbc90caeeb67f4a269d428164e28ab228c5eb577100
new file mode 100644
index 00000000..0a14767d
--- /dev/null
+++ b/artifacts/sha256/e3/1f92d4b3f11374b96f7dbc90caeeb67f4a269d428164e28ab228c5eb577100
@@ -0,0 +1,90 @@
+{
+ "schema_version": 1,
+ "id": "denomination-preparation-v1",
+ "claim_ids": [
+ "ctp.amount.denomination_preparation_fingerprint"
+ ],
+ "code": {
+ "revision": "bcd9a3e511860b1b0ff5f3ee6338a2df29ecbfbf",
+ "dirty": false
+ },
+ "command": {
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.denomination_preparation",
+ "reproduce",
+ "--artifact",
+ "results/artifacts/denomination-preparation-v1.json",
+ "--markdown",
+ "results/generated/denomination-preparation-v1.md"
+ ]
+ },
+ "environment": {
+ "python": "3.13.7",
+ "lock_digest": "e0d6a8cada5d383c0aca2bd2a37f28c15721392469c94084128d369c29c5544d",
+ "platform": "macOS-26.6.2-arm64-arm-64bit-Mach-O",
+ "dependencies": []
+ },
+ "datasets": [],
+ "parameters": {
+ "denomination": 100000,
+ "preparations": 3,
+ "controls": 3,
+ "control_matching": [
+ "input_count",
+ "output_count"
+ ]
+ },
+ "rng": {
+ "algorithm": null,
+ "seeds": []
+ },
+ "outputs": [
+ {
+ "path": "results/artifacts/denomination-preparation-v1.json",
+ "bytes": 1299,
+ "sha256": "6102106a1ac39bdfb1f668a7c90772f01c29c214a1fb2a76088403165dbe4ce2"
+ },
+ {
+ "path": "results/generated/denomination-preparation-v1.md",
+ "bytes": 546,
+ "sha256": "c433cfc5c966ba7af33ea595cad134862726168033c2029d8b11574319cf0fc8"
+ }
+ ],
+ "reproducibility": {
+ "level": "bitwise_reproducible",
+ "availability": "complete"
+ },
+ "verification": {
+ "mode": "exact",
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.denomination_preparation",
+ "verify",
+ "--artifact",
+ "results/artifacts/denomination-preparation-v1.json",
+ "--markdown",
+ "results/generated/denomination-preparation-v1.md"
+ ],
+ "tests": [
+ "tests/test_denomination_preparation.py",
+ "tests/test_denomination_preparation_experiment.py"
+ ],
+ "properties": [
+ "all three synthetic preparations expose one exact denomination consumed by the next CoinJoin",
+ "all three arity-matched controls decline the fingerprint",
+ "transaction shape is preserved as descriptive evidence rather than ownership evidence",
+ "the result states that unilateral construction, rarity and prevalence are not established",
+ "the generated Markdown equals the canonical rendering"
+ ],
+ "tolerance": null
+ },
+ "limitations": [
+ "deterministic synthetic corpus",
+ "the next CoinJoin and its denomination are supplied",
+ "controls match arity but not a real-world population distribution",
+ "the CTP qualification has no separately cited empirical experiment"
+ ]
+}
diff --git a/artifacts/sha256/e3/b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855 b/artifacts/sha256/e3/b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855
new file mode 100644
index 00000000..e69de29b
diff --git a/artifacts/sha256/e4/a9048fa27e28732af5449d9b9a2098f401b203c793d79763d0be685fb86660 b/artifacts/sha256/e4/a9048fa27e28732af5449d9b9a2098f401b203c793d79763d0be685fb86660
new file mode 100644
index 00000000..5e237525
--- /dev/null
+++ b/artifacts/sha256/e4/a9048fa27e28732af5449d9b9a2098f401b203c793d79763d0be685fb86660
@@ -0,0 +1,17 @@
+{
+ "schema_version": 1,
+ "txid": "80f11c778a2f486ffc55b4e8665a94971ae9c350ac53969e9efcbf90478cbf90",
+ "total_input": 9000971062,
+ "known_participant_input": 4712126860,
+ "outputs": [
+ [774840978, 7], [387420489, 2], [100000000, 8], [50853104, 1],
+ [50000000, 4], [43046721, 6], [40000000, 1], [33554432, 11],
+ [28697814, 9], [20000000, 13], [16777216, 5], [14348907, 1],
+ [14149484, 1], [10000000, 16], [5000000, 15], [3188646, 24],
+ [2097152, 17], [1594323, 27], [1062882, 23], [531441, 22],
+ [354294, 14], [262144, 29], [200000, 14], [131072, 15],
+ [118098, 15], [100000, 24], [99267, 1], [65536, 20],
+ [59049, 4], [50000, 21], [39366, 24], [32768, 16],
+ [20000, 18], [16384, 11], [15403, 1], [13122, 30], [10000, 32]
+ ]
+}
diff --git a/artifacts/sha256/e5/26fe9de74072d739715146683f97591a292df0e5bd45224e458b7e335a574b b/artifacts/sha256/e5/26fe9de74072d739715146683f97591a292df0e5bd45224e458b7e335a574b
new file mode 100644
index 00000000..f6b7a3dc
--- /dev/null
+++ b/artifacts/sha256/e5/26fe9de74072d739715146683f97591a292df0e5bd45224e458b7e335a574b
@@ -0,0 +1,102 @@
+{
+ "schema_version": 1,
+ "id": "link-prediction-v1",
+ "claim_ids": [
+ "ctp.social_graph.link_prediction"
+ ],
+ "code": {
+ "revision": "bcd9a3e511860b1b0ff5f3ee6338a2df29ecbfbf",
+ "dirty": false
+ },
+ "command": {
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.link_prediction",
+ "reproduce",
+ "--artifact",
+ "results/artifacts/link-prediction-v1.json",
+ "--markdown",
+ "results/generated/link-prediction-v1.md"
+ ]
+ },
+ "environment": {
+ "python": "3.13.7",
+ "lock_digest": "e0d6a8cada5d383c0aca2bd2a37f28c15721392469c94084128d369c29c5544d",
+ "platform": "macOS-26.6.2-arm64-arm-64bit-Mach-O",
+ "dependencies": []
+ },
+ "datasets": [],
+ "parameters": {
+ "vertices": 80,
+ "groups": 8,
+ "fixture_seeds": [
+ 0,
+ 1,
+ 2,
+ 3,
+ 4
+ ],
+ "seed_fraction": 0.25,
+ "view_edge_keep_probability": 0.9,
+ "holdout_fraction": 0.25,
+ "declaration_cutoff": 2
+ },
+ "rng": {
+ "algorithm": "python.random.MersenneTwister",
+ "seeds": [
+ 0,
+ 1,
+ 2,
+ 3,
+ 4
+ ]
+ },
+ "outputs": [
+ {
+ "path": "results/artifacts/link-prediction-v1.json",
+ "bytes": 17650,
+ "sha256": "b119c9d6d6782e6fca449004b11096cba93a9dc37b631b27fd42954e66db3618"
+ },
+ {
+ "path": "results/generated/link-prediction-v1.md",
+ "bytes": 502,
+ "sha256": "7858a3c1059896469a5858a9dfc69b66bf2090cd0668db66270e590344bc9fc0"
+ }
+ ],
+ "reproducibility": {
+ "level": "bitwise_reproducible",
+ "availability": "complete"
+ },
+ "verification": {
+ "mode": "exact",
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.link_prediction",
+ "verify",
+ "--artifact",
+ "results/artifacts/link-prediction-v1.json",
+ "--markdown",
+ "results/generated/link-prediction-v1.md"
+ ],
+ "tests": [
+ "tests/test_link_prediction.py",
+ "tests/test_link_prediction_experiment.py"
+ ],
+ "properties": [
+ "the full canonical report equals the historical JSON measurements",
+ "five deterministic fixtures contain attack, three controls, and a ceiling",
+ "the result is limited to planted views and retained-correspondence seeds",
+ "the result is not attributed to the paper's datasets or metrics",
+ "the generated Markdown equals the canonical rendering"
+ ],
+ "tolerance": null
+ },
+ "limitations": [
+ "synthetic planted-view corpus",
+ "seeds are sampled from the withheld correspondence",
+ "the measured advantage transfers links observed by the auxiliary view",
+ "this does not reproduce the 2011 paper or Kaggle/Flickr experiments"
+ ]
+}
diff --git a/artifacts/sha256/e8/a52583e2ff9e64f455f32270bdea2e2d5b7dabf24d9fa3b89c3d96b4d3631d b/artifacts/sha256/e8/a52583e2ff9e64f455f32270bdea2e2d5b7dabf24d9fa3b89c3d96b4d3631d
new file mode 100644
index 00000000..5d88e56b
--- /dev/null
+++ b/artifacts/sha256/e8/a52583e2ff9e64f455f32270bdea2e2d5b7dabf24d9fa3b89c3d96b4d3631d
@@ -0,0 +1,13 @@
+{
+ "schema_version": 1,
+ "python": {
+ "implementation": "cpython",
+ "version": "3.13",
+ "system": "Darwin",
+ "machine": "arm64"
+ },
+ "source_archive": "sources/decluster-bcd9a3e-runtime.tar",
+ "project_root": "decluster",
+ "requirements": "reproduction/attribute-drift/requirements.lock",
+ "dependency_directory": "reproduction/attribute-drift"
+}
diff --git a/artifacts/sha256/e9/444ee4febc3605c618f25a5c7a9dd1aff6d02126861da00a9786b8f5dad955 b/artifacts/sha256/e9/444ee4febc3605c618f25a5c7a9dd1aff6d02126861da00a9786b8f5dad955
new file mode 100644
index 00000000..aad47038
--- /dev/null
+++ b/artifacts/sha256/e9/444ee4febc3605c618f25a5c7a9dd1aff6d02126861da00a9786b8f5dad955
@@ -0,0 +1,94 @@
+{
+ "schema_version": 1,
+ "id": "analyze-contract-v1",
+ "claim_ids": [
+ "ctp.provenance.traceability"
+ ],
+ "code": {
+ "revision": "bcd9a3e511860b1b0ff5f3ee6338a2df29ecbfbf",
+ "dirty": false
+ },
+ "command": {
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.analyze_contract",
+ "reproduce",
+ "--artifact",
+ "results/artifacts/analyze-contract-v1.json",
+ "--markdown",
+ "results/generated/analyze-contract-v1.md"
+ ]
+ },
+ "environment": {
+ "python": "3.13.7",
+ "lock_digest": "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855",
+ "platform": "macOS-26.6.2-arm64-arm-64bit-Mach-O",
+ "dependencies": []
+ },
+ "datasets": [],
+ "parameters": {
+ "fixture_transactions": 5,
+ "depths": [
+ 1,
+ 2,
+ 3,
+ 4,
+ 5
+ ],
+ "probability_round_digits": 15,
+ "max_nodes_control": 1
+ },
+ "rng": {
+ "algorithm": null,
+ "seeds": []
+ },
+ "outputs": [
+ {
+ "path": "results/artifacts/analyze-contract-v1.json",
+ "bytes": 1697,
+ "sha256": "0cd5877aee284f8a234688c93b586d9f9e556a49f31c2c0605fa53be1d994553"
+ },
+ {
+ "path": "results/generated/analyze-contract-v1.md",
+ "bytes": 744,
+ "sha256": "2d6a16cb098df8648a8964f46d50daab59b8f1d04a37e6a5b471ac10f7f64599"
+ }
+ ],
+ "reproducibility": {
+ "level": "bitwise_reproducible",
+ "availability": "complete"
+ },
+ "verification": {
+ "mode": "exact",
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.analyze_contract",
+ "verify",
+ "--artifact",
+ "results/artifacts/analyze-contract-v1.json",
+ "--markdown",
+ "results/generated/analyze-contract-v1.md"
+ ],
+ "tests": [
+ "tests/test_analyze.py",
+ "tests/test_analyze_contract_experiment.py"
+ ],
+ "properties": [
+ "the public facade returns at depths one through five on the fixed graph",
+ "subjective fusion does not widen provenance entropy and sharpens the address-reuse target",
+ "the node cap reports truncation rather than hiding it",
+ "the opt-in path_count field is identified as provenance route accumulation",
+ "the generated Markdown equals the canonical rendering"
+ ],
+ "tolerance": null
+ },
+ "limitations": [
+ "the fixture is synthetic and validates the public facade contract",
+ "the run does not reproduce the historical live Bitcoin measurements",
+ "address reuse is a heuristic subjective signal, not verified ownership",
+ "provenance entropy is not a privacy certificate or CoinScore",
+ "the path_count response key is retained for API compatibility only"
+ ]
+}
diff --git a/artifacts/sha256/eb/1bd191d66405751de2e39fe90e3c80c4a4c7d4a993440ea6e82ec6d0389654 b/artifacts/sha256/eb/1bd191d66405751de2e39fe90e3c80c4a4c7d4a993440ea6e82ec6d0389654
new file mode 100644
index 00000000..e9194713
--- /dev/null
+++ b/artifacts/sha256/eb/1bd191d66405751de2e39fe90e3c80c4a4c7d4a993440ea6e82ec6d0389654
@@ -0,0 +1,17 @@
+# The same kernel under three axis sets
+
+Generated from the canonical experiment artifact. Do not edit manually.
+
+One population of 22112 transactions, one pair sample (4000 per class, seed 0), one consistency (0.95). Only the axis set changes.
+
+| family | axes | AUC | positive mean bits | negative mean bits |
+|---|---:|---:|---:|---:|
+| catalogued | 23 | 0.9244 | +15.47 | -16.58 |
+| construction only | 18 | 0.9003 | +8.42 | -13.94 |
+| decorrelated | 14 | 0.9432 | +8.72 | -10.24 |
+
+Dropping the redundant axes costs **6.74 bits** of positive evidence and raises the AUC (0.9244 to 0.9432). The wide model scores several correlated axes as if they were separate facts; the additive rarity kernel has no conditional-independence correction, so the magnitude it reports is inflated while its ranking is not.
+
+The construction-only family drops the axes the shared input address fixes outright, which is the part of the score that is the same-owner label restating itself rather than construction style.
+
+The axis clusters and the address-determined set were measured on this same cache, so the families are not an out-of-sample test. A higher AUC on fewer axes does not show the dropped axes carry no signal, and none of these numbers is a privacy score.
diff --git a/artifacts/sha256/ec/27b8a3dc36b06482b86cdcb556038d88b73f3595c1e37d78867c5ce159841c b/artifacts/sha256/ec/27b8a3dc36b06482b86cdcb556038d88b73f3595c1e37d78867c5ce159841c
new file mode 100644
index 00000000..d07afbba
--- /dev/null
+++ b/artifacts/sha256/ec/27b8a3dc36b06482b86cdcb556038d88b73f3595c1e37d78867c5ce159841c
@@ -0,0 +1 @@
+{"datasets":["epoch-graph-2016-391104-392111-v1","epoch-graph-2016-392112-393119-v1"],"experiment":"graph-rejoin-2016-v1","matching":[{"arm":"seeded","correct":0,"guesses":0,"matcher":"undirected","precision":null,"seed_share":0.05,"seeds":871},{"arm":"shuffled","correct":0,"guesses":0,"matcher":"undirected","precision":null,"seed_share":0.05,"seeds":871},{"arm":"seeded","correct":0,"guesses":0,"matcher":"directed","precision":null,"seed_share":0.05,"seeds":871},{"arm":"shuffled","correct":0,"guesses":0,"matcher":"directed","precision":null,"seed_share":0.05,"seeds":871},{"arm":"seeded","correct":0,"guesses":0,"matcher":"undirected","precision":null,"seed_share":0.1,"seeds":1743},{"arm":"shuffled","correct":0,"guesses":0,"matcher":"undirected","precision":null,"seed_share":0.1,"seeds":1743},{"arm":"seeded","correct":1,"guesses":1,"matcher":"directed","precision":1.0,"seed_share":0.1,"seeds":1743},{"arm":"shuffled","correct":0,"guesses":0,"matcher":"directed","precision":null,"seed_share":0.1,"seeds":1743}],"population":{"mean_internal_degree":2.865584,"non_seed_pairs_at_widest_seeding":15688,"pairs_to_rejoin":17431,"transactions":[982021,1116563],"views":["391104-392111.epochgraph.xz","392112-393119.epochgraph.xz"]},"schema_version":1,"shape":[{"assortativity":-0.076395,"configuration_transitivity":0.611338,"degree_1_share":0.046367,"edges":1430236,"mean_degree":4.787371,"second_moment":6289.511374,"tail_exponent":2.861302,"transitivity":0.006241,"transitivity_ratio":0.01021,"vertices":588843},{"assortativity":-0.078161,"configuration_transitivity":0.397562,"degree_1_share":0.060031,"edges":1561102,"mean_degree":4.819922,"second_moment":5335.974889,"tail_exponent":2.833692,"transitivity":0.000132,"transitivity_ratio":0.000331,"vertices":638436}]}
diff --git a/artifacts/sha256/ed/295d199f30f71bb6ac773ec3ef10ed9021f618ba39e25bb99983cebeee44ea b/artifacts/sha256/ed/295d199f30f71bb6ac773ec3ef10ed9021f618ba39e25bb99983cebeee44ea
new file mode 100644
index 00000000..5a3de7fb
--- /dev/null
+++ b/artifacts/sha256/ed/295d199f30f71bb6ac773ec3ef10ed9021f618ba39e25bb99983cebeee44ea
@@ -0,0 +1,13 @@
+{
+ "schema_version": 1,
+ "python": {
+ "implementation": "cpython",
+ "version": "3.13",
+ "system": "Darwin",
+ "machine": "arm64"
+ },
+ "source_archive": "sources/decluster-bcd9a3e-runtime.tar",
+ "project_root": "decluster",
+ "requirements": "reproduction/subtx-demix/requirements.lock",
+ "dependency_directory": "reproduction/subtx-demix"
+}
diff --git a/artifacts/sha256/ef/3e1af858be24e45a7490c78cd6c3f4a65d1ef984d9a91cbfc7320eda0d505e b/artifacts/sha256/ef/3e1af858be24e45a7490c78cd6c3f4a65d1ef984d9a91cbfc7320eda0d505e
new file mode 100644
index 00000000..4d4b6381
--- /dev/null
+++ b/artifacts/sha256/ef/3e1af858be24e45a7490c78cd6c3f4a65d1ef984d9a91cbfc7320eda0d505e
@@ -0,0 +1,91 @@
+{
+ "schema_version": 1,
+ "id": "perfect-matching-disclosure-v1",
+ "claim_ids": [
+ "ctp.disclosure.perfect_matching"
+ ],
+ "code": {
+ "revision": "bcd9a3e511860b1b0ff5f3ee6338a2df29ecbfbf",
+ "dirty": false
+ },
+ "command": {
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.perfect_matching_disclosure",
+ "reproduce",
+ "--artifact",
+ "results/artifacts/perfect-matching-disclosure-v1.json",
+ "--markdown",
+ "results/generated/perfect-matching-disclosure-v1.md"
+ ]
+ },
+ "environment": {
+ "python": "3.13.7",
+ "lock_digest": "e0d6a8cada5d383c0aca2bd2a37f28c15721392469c94084128d369c29c5544d",
+ "platform": "macOS-26.6.2-arm64-arm-64bit-Mach-O",
+ "dependencies": []
+ },
+ "datasets": [],
+ "parameters": {
+ "messages_per_round": 3,
+ "objective": "maximum joint sender-profile likelihood",
+ "scenarios": [
+ "joint-assignment",
+ "uniform-profile-control"
+ ]
+ },
+ "rng": {
+ "algorithm": null,
+ "seeds": []
+ },
+ "outputs": [
+ {
+ "path": "results/artifacts/perfect-matching-disclosure-v1.json",
+ "bytes": 1024,
+ "sha256": "4071e62e0ef642c4f762d585ed5849f67fd10d4dbe10a9433cec8de1d90866b6"
+ },
+ {
+ "path": "results/generated/perfect-matching-disclosure-v1.md",
+ "bytes": 709,
+ "sha256": "94705cad4143dff0eabbdffa177873f17ed59aa9c6de8826b92980f5154101e2"
+ }
+ ],
+ "reproducibility": {
+ "level": "bitwise_reproducible",
+ "availability": "complete"
+ },
+ "verification": {
+ "mode": "exact",
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.perfect_matching_disclosure",
+ "verify",
+ "--artifact",
+ "results/artifacts/perfect-matching-disclosure-v1.json",
+ "--markdown",
+ "results/generated/perfect-matching-disclosure-v1.md"
+ ],
+ "tests": [
+ "tests/test_perfect_matching_disclosure.py",
+ "tests/test_perfect_matching_disclosure_experiment.py"
+ ],
+ "properties": [
+ "the objective maximizes the product of sender-profile edge probabilities through their logarithms",
+ "every candidate assignment is a perfect matching over the observed message nodes",
+ "joint optimization resolves a round whose independent maxima reuse one receiver",
+ "uniform profiles retain all six matchings and produce an inconclusive outcome",
+ "zero-probability edges are excluded",
+ "the result is not represented as value-flow ancestry or Bitcoin ownership attribution",
+ "the generated Markdown equals the canonical rendering"
+ ],
+ "tolerance": null
+ },
+ "limitations": [
+ "deterministic synthetic threshold-mix fixture",
+ "sender profiles are supplied rather than estimated from a longitudinal trace",
+ "the diagnostic enumerator is limited to eight messages",
+ "the paper's simulations and success rates are not reproduced"
+ ]
+}
diff --git a/artifacts/sha256/ef/df4055dba10bfff2541ccaf3668be030c7caed44c61650540cc106393e9636 b/artifacts/sha256/ef/df4055dba10bfff2541ccaf3668be030c7caed44c61650540cc106393e9636
new file mode 100644
index 00000000..70a022a3
--- /dev/null
+++ b/artifacts/sha256/ef/df4055dba10bfff2541ccaf3668be030c7caed44c61650540cc106393e9636
@@ -0,0 +1 @@
+numpy==2.5.1 --hash=sha256:6165343f81b56ef8f514f396989e529b61d9dc709b99421b07e9f3e698e2287d
diff --git a/artifacts/sha256/f0/533dbe40ddfa8af55d31fbb1e53b2f0a925582a7567670496717e786b0c827 b/artifacts/sha256/f0/533dbe40ddfa8af55d31fbb1e53b2f0a925582a7567670496717e786b0c827
new file mode 100644
index 00000000..3340c8c2
--- /dev/null
+++ b/artifacts/sha256/f0/533dbe40ddfa8af55d31fbb1e53b2f0a925582a7567670496717e786b0c827
@@ -0,0 +1,15 @@
+# Partial-clustering pseudonym graph
+
+Generated from the canonical experiment artifact. Do not edit manually.
+
+Vertices: cluster-a, cluster-x, z.
+
+| source | target | transfers | value | height span |
+|---|---|---:|---:|---|
+| cluster-a | cluster-x | 3 | 600 | 100–140 |
+| cluster-x | cluster-a | 1 | 50 | 120–120 |
+| z | cluster-a | 1 | 10 | 150–150 |
+
+The unknown address `z` remains a singleton pseudonym. Parallel transfers from `cluster-a` to `cluster-x` are folded into one directed edge with count, total value and height span. The reverse direction remains a separate edge; change within `cluster-a` is recorded as a self-transfer rather than a relationship.
+
+The supplied clustering is partial. These vertices are pseudonyms, not verified users.
diff --git a/artifacts/sha256/f1/03911399f995ac300dcca04af29eab65cb3962d436915c9cb3f931b81f9e4a b/artifacts/sha256/f1/03911399f995ac300dcca04af29eab65cb3962d436915c9cb3f931b81f9e4a
new file mode 100644
index 00000000..5178135c
--- /dev/null
+++ b/artifacts/sha256/f1/03911399f995ac300dcca04af29eab65cb3962d436915c9cb3f931b81f9e4a
@@ -0,0 +1 @@
+{"diagnostic_axis_probabilities":{"output_count":{"1":0.15333333333333332,"2":0.695,"3":0.07333333333333333,"4plus":0.07833333333333334},"segwit_serialization":{"non_segwit":0.10666666666666667,"segwit":0.8933333333333333}},"experiment":"fingerprint-catalog-axes-v1","limitations":["address reuse is a weak and partly feature-dependent same-wallet proxy","diagnostic probabilities are fitted and evaluated on the same selected snapshot","the 600-transaction snapshot is not either historical 166k or 180k cache","AUC deltas on one snapshot do not establish conditional dependence","the axes remain excluded from the production fingerprint library","the result measures pair ranking and is not an ownership certificate or privacy score"],"measurement":{"negative_pair_draws":4000,"positive_pair_draws":4000,"rows":[{"added_axes":[],"auc":0.945875,"auc_delta_from_library":0.0,"configuration":"library","negative_mean":-17.41305201591386,"positive_mean":19.646924430510953,"shuffled_auc":0.490825},{"added_axes":["output_count"],"auc":0.94515,"auc_delta_from_library":-0.0007249999999999757,"configuration":"plus_output_count","negative_mean":-18.67177489730795,"positive_mean":20.29629417013815,"shuffled_auc":0.490675},{"added_axes":["segwit_serialization"],"auc":0.948475,"auc_delta_from_library":0.0025999999999999357,"configuration":"plus_segwit_serialization","negative_mean":-17.60219163488365,"positive_mean":20.120861983380525,"shuffled_auc":0.489875},{"added_axes":["output_count","segwit_serialization"],"auc":0.948,"auc_delta_from_library":0.0021249999999999325,"configuration":"plus_both","negative_mean":-18.860914516277745,"positive_mean":20.770231723007722,"shuffled_auc":0.490075}],"transactions":600},"parameters":{"pair_draw_cap":4000,"probability_source":"same frozen snapshot","rarity_floor_count":1000,"same_wallet_consistency":0.95,"seed":0},"schema_version":1}
diff --git a/artifacts/sha256/f1/4fa583584595c85f81b7cb4a84072cbbec64150d8eedb38e77b18b015d2ef9 b/artifacts/sha256/f1/4fa583584595c85f81b7cb4a84072cbbec64150d8eedb38e77b18b015d2ef9
new file mode 100644
index 00000000..b560913a
--- /dev/null
+++ b/artifacts/sha256/f1/4fa583584595c85f81b7cb4a84072cbbec64150d8eedb38e77b18b015d2ef9
@@ -0,0 +1,11 @@
+# Nominal-value ancestry contract
+
+Generated from the canonical experiment artifact. Do not edit manually.
+
+The complete walk reaches `{'left:0': 0.5, 'right:0': 0.5}` with Shannon entropy 1.000000 bits and 4.0 steps in the uncollapsed paper graph.
+
+A one-node bound returns `TruncatedAncestry` with causes `{'oracle_refused': 0, 'node_capped': 2, 'zero_link_mass': 0, 'unattributed': 0}`. Oracle refusal returns `TruncatedAncestry` with causes `{'oracle_refused': 1, 'node_capped': 0, 'zero_link_mass': 0, 'unattributed': 0}`.
+
+Provenance overlap is 1.0 for the same signature and 0.0 for a disjoint signature.
+
+This measures model-relative provenance. It does not infer ownership, certify privacy, measure robust connectivity or reproduce the historical live Bitcoin observations.
diff --git a/artifacts/sha256/f2/62f6d54b704e668903d60c9aa1aa100517d94f70e6efe967795e3a85912388 b/artifacts/sha256/f2/62f6d54b704e668903d60c9aa1aa100517d94f70e6efe967795e3a85912388
new file mode 100644
index 00000000..a947a933
--- /dev/null
+++ b/artifacts/sha256/f2/62f6d54b704e668903d60c9aa1aa100517d94f70e6efe967795e3a85912388
@@ -0,0 +1,86 @@
+{
+ "schema_version": 1,
+ "id": "change-consolidation-v1",
+ "claim_ids": [
+ "ctp.intersection.longitudinal"
+ ],
+ "code": {
+ "revision": "bcd9a3e511860b1b0ff5f3ee6338a2df29ecbfbf",
+ "dirty": false
+ },
+ "command": {
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.change_consolidation",
+ "reproduce",
+ "--artifact",
+ "results/artifacts/change-consolidation-v1.json",
+ "--markdown",
+ "results/generated/change-consolidation-v1.md"
+ ]
+ },
+ "environment": {
+ "python": "3.13.7",
+ "lock_digest": "e0d6a8cada5d383c0aca2bd2a37f28c15721392469c94084128d369c29c5544d",
+ "platform": "macOS-26.6.2-arm64-arm-64bit-Mach-O",
+ "dependencies": []
+ },
+ "datasets": [],
+ "parameters": {
+ "candidate_owners_per_change": 2,
+ "payments": 2,
+ "later_observation": "change-output co-spend"
+ },
+ "rng": {
+ "algorithm": null,
+ "seeds": []
+ },
+ "outputs": [
+ {
+ "path": "results/artifacts/change-consolidation-v1.json",
+ "bytes": 507,
+ "sha256": "1fadeece80a936b04473a7e235eafc37e835d21445a2b462cac7d067896c10e9"
+ },
+ {
+ "path": "results/generated/change-consolidation-v1.md",
+ "bytes": 394,
+ "sha256": "f86ec49a75e9ec73d7c5a662e76252056ccf1a9f4de9a13dc77d935d490ad47f"
+ }
+ ],
+ "reproducibility": {
+ "level": "bitwise_reproducible",
+ "availability": "complete"
+ },
+ "verification": {
+ "mode": "exact",
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.change_consolidation",
+ "verify",
+ "--artifact",
+ "results/artifacts/change-consolidation-v1.json",
+ "--markdown",
+ "results/generated/change-consolidation-v1.md"
+ ],
+ "tests": [
+ "tests/test_change_consolidation.py",
+ "tests/test_change_consolidation_experiment.py"
+ ],
+ "properties": [
+ "both payment change candidates remain ambiguous before consolidation",
+ "the later co-spend narrows the candidate owner shared by both payments",
+ "disjoint candidate-owner sets produce no shared candidate",
+ "change identification and co-ownership remain explicit assumptions",
+ "the generated Markdown equals the canonical rendering"
+ ],
+ "tolerance": null
+ },
+ "limitations": [
+ "synthetic fixture",
+ "change outputs and candidate owners are supplied rather than inferred",
+ "the linkage requires correct change identification and common ownership",
+ "the run does not measure a Bitcoin deanonymization rate"
+ ]
+}
diff --git a/artifacts/sha256/f2/c946337645978c160b5ff30321be082e46576608a54aae761d3e137ffd74b7 b/artifacts/sha256/f2/c946337645978c160b5ff30321be082e46576608a54aae761d3e137ffd74b7
new file mode 100644
index 00000000..ed04d2cd
--- /dev/null
+++ b/artifacts/sha256/f2/c946337645978c160b5ff30321be082e46576608a54aae761d3e137ffd74b7
@@ -0,0 +1,12 @@
+# Longitudinal statistical disclosure
+
+Generated from the canonical experiment artifact. Do not edit manually.
+
+| scenario | rounds | batch size | estimated target distribution | unique top |
+|---|---:|---:|---|---|
+| persistent correspondent | 4 | 4 | alice=1.00, bob=0.00, carol=0.00, dave=0.00 | alice |
+| background-matched control | 4 | 4 | alice=0.25, bob=0.25, carol=0.25, dave=0.25 | none |
+
+The persistent fixture recovers Alice. The control remains tied because the target follows the same distribution as the background.
+
+This is a deterministic reproduction of the threshold-mix estimator. It does not attribute Bitcoin ownership or measure real-world attack frequency.
diff --git a/artifacts/sha256/f4/6ec5b7198ed4fce4e4ff1e76d27a454ddd01f60911efe7abb7ef416d70fad8 b/artifacts/sha256/f4/6ec5b7198ed4fce4e4ff1e76d27a454ddd01f60911efe7abb7ef416d70fad8
new file mode 100644
index 00000000..f6e63384
--- /dev/null
+++ b/artifacts/sha256/f4/6ec5b7198ed4fce4e4ff1e76d27a454ddd01f60911efe7abb7ef416d70fad8
@@ -0,0 +1,87 @@
+{
+ "schema_version": 1,
+ "id": "candidate-set-intersection-v1",
+ "claim_ids": [
+ "ctp.intersection.longitudinal"
+ ],
+ "code": {
+ "revision": "bcd9a3e511860b1b0ff5f3ee6338a2df29ecbfbf",
+ "dirty": false
+ },
+ "command": {
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.candidate_set_intersection",
+ "reproduce",
+ "--artifact",
+ "results/artifacts/candidate-set-intersection-v1.json",
+ "--markdown",
+ "results/generated/candidate-set-intersection-v1.md"
+ ]
+ },
+ "environment": {
+ "python": "3.13.7",
+ "lock_digest": "c7d402576511073ed4fcf076c77ffa6b3be124e0a2ef996d2da1cd3b02fbe541",
+ "platform": "macOS-26.6.2-arm64-arm-64bit-Mach-O",
+ "dependencies": []
+ },
+ "datasets": [],
+ "parameters": {
+ "universe_size": 64,
+ "scenarios": [
+ "converging",
+ "stalled",
+ "contradictory"
+ ]
+ },
+ "rng": {
+ "algorithm": null,
+ "seeds": []
+ },
+ "outputs": [
+ {
+ "path": "results/artifacts/candidate-set-intersection-v1.json",
+ "bytes": 2127,
+ "sha256": "57ec942ab96fa6a4da7335d4c936391b163f850a3018fe4d043dae377067493e"
+ },
+ {
+ "path": "results/generated/candidate-set-intersection-v1.md",
+ "bytes": 454,
+ "sha256": "7967116277698ee234507683274cd315b18c44503209d76d51d304aa9dd2a816"
+ }
+ ],
+ "reproducibility": {
+ "level": "bitwise_reproducible",
+ "availability": "complete"
+ },
+ "verification": {
+ "mode": "exact",
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.candidate_set_intersection",
+ "verify",
+ "--artifact",
+ "results/artifacts/candidate-set-intersection-v1.json",
+ "--markdown",
+ "results/generated/candidate-set-intersection-v1.md"
+ ],
+ "tests": [
+ "tests/test_candidate_set_intersection.py",
+ "tests/test_candidate_set_intersection_experiment.py"
+ ],
+ "properties": [
+ "the stored artifact equals a fresh execution of all three deterministic scenarios",
+ "a singleton survivor is the only identifying outcome",
+ "an empty intersection is a refusal with no narrowing score",
+ "the generated Markdown equals the canonical rendering"
+ ],
+ "tolerance": null
+ },
+ "limitations": [
+ "synthetic fixture",
+ "candidate sets are supplied rather than inferred from chain data",
+ "the run does not reproduce the paper's JoinMarket dataset or empirical rates"
+ ]
+}
diff --git a/artifacts/sha256/f4/e6c0afd79425e8259d4112190a5df3b3eee81344cd7023f786c4671264e112 b/artifacts/sha256/f4/e6c0afd79425e8259d4112190a5df3b3eee81344cd7023f786c4671264e112
new file mode 100644
index 00000000..bda45d32
--- /dev/null
+++ b/artifacts/sha256/f4/e6c0afd79425e8259d4112190a5df3b3eee81344cd7023f786c4671264e112
@@ -0,0 +1,9 @@
+# Exact conservation versus explicit fee allocation
+
+Generated from the canonical experiment artifact. Do not edit manually.
+
+The bounded population contains 300 transactions. Exact zero-fee conservation admits mappings for 0; the local fee-allocation model admits mappings for 300.
+
+Only 37 transactions admit more than the all-coins interpretation. This is the informative comparison.
+
+This run is not a claim of general parity with the external Boltzmann tool. Selected official vectors and modes are tested separately.
diff --git a/artifacts/sha256/f6/9fd4aee1cf9612c215036e309b41733aee070c8fa85ac437fabb3aec2defba b/artifacts/sha256/f6/9fd4aee1cf9612c215036e309b41733aee070c8fa85ac437fabb3aec2defba
new file mode 100644
index 00000000..85910f67
--- /dev/null
+++ b/artifacts/sha256/f6/9fd4aee1cf9612c215036e309b41733aee070c8fa85ac437fabb3aec2defba
@@ -0,0 +1,13 @@
+{
+ "schema_version": 1,
+ "python": {
+ "implementation": "cpython",
+ "version": "3.13",
+ "system": "Darwin",
+ "machine": "arm64"
+ },
+ "source_archive": "sources/decluster-bcd9a3e-runtime.tar",
+ "project_root": "decluster",
+ "requirements": "reproduction/anchor-axis-families/requirements.lock",
+ "dependency_directory": "reproduction/anchor-axis-families"
+}
diff --git a/artifacts/sha256/f7/4f9345bd1b8c6690b424466e9426614dd237c277cac432d2e2f5092a82debb b/artifacts/sha256/f7/4f9345bd1b8c6690b424466e9426614dd237c277cac432d2e2f5092a82debb
new file mode 100644
index 00000000..09c59143
--- /dev/null
+++ b/artifacts/sha256/f7/4f9345bd1b8c6690b424466e9426614dd237c277cac432d2e2f5092a82debb
@@ -0,0 +1,111 @@
+{
+ "schema_version": 1,
+ "id": "counting-router-v1",
+ "claim_ids": [
+ "ctp.amount.subtransaction_ambiguity"
+ ],
+ "code": {
+ "revision": "bcd9a3e511860b1b0ff5f3ee6338a2df29ecbfbf",
+ "dirty": false
+ },
+ "command": {
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.counting_router",
+ "--dataset",
+ "data/amount-channel-812695-812831-v1.json",
+ "reproduce",
+ "--artifact",
+ "results/artifacts/counting-router-v1.json",
+ "--markdown",
+ "results/generated/counting-router-v1.md"
+ ]
+ },
+ "environment": {
+ "python": "3.13.7",
+ "lock_digest": "5ee1d70afd72fdb4ba3f01843e85e7ecd9a6d3e2a52abeb14c346a2143e3c186",
+ "platform": "macOS-26.6.2-arm64-arm-64bit-Mach-O",
+ "dependencies": [
+ {
+ "name": "dss",
+ "version": "0.1.0",
+ "source": "https://github.com/bc1cindy/dense-subset-sum",
+ "revision": "d8099bc540c9de7d0f3c9f095e8b2aad8f9d8c8d",
+ "lock_sha256": "f17a02f09da28dcc603901580d695466c44e1d1a0063e110d3faa375ae6066ca",
+ "license": "MIT",
+ "redistribution": "allowed",
+ "editable": false
+ }
+ ]
+ },
+ "datasets": [
+ {
+ "id": "amount-channel-812695-812831-v1",
+ "sha256": "83b4391f78d07a2fa85a67218d5ae4420d8965da3c95af577592e9ec1850104d"
+ }
+ ],
+ "parameters": {
+ "knee": 5,
+ "route": [
+ "radix",
+ "sparse",
+ "none"
+ ]
+ },
+ "rng": {
+ "algorithm": null,
+ "seeds": []
+ },
+ "outputs": [
+ {
+ "path": "results/artifacts/counting-router-v1.json",
+ "bytes": 897,
+ "sha256": "7c0764b7f39ad8795b4e349cff0903f68b7edcbc3f0d75cc6691a138813239b7"
+ },
+ {
+ "path": "results/generated/counting-router-v1.md",
+ "bytes": 505,
+ "sha256": "a002dd84d74d51b0106a6bccd4c625300d838becbfc1edff36b7cfd46eda682f"
+ }
+ ],
+ "reproducibility": {
+ "level": "bitwise_reproducible",
+ "availability": "complete"
+ },
+ "verification": {
+ "mode": "exact",
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.counting_router",
+ "--dataset",
+ "data/amount-channel-812695-812831-v1.json",
+ "verify",
+ "--artifact",
+ "results/artifacts/counting-router-v1.json",
+ "--markdown",
+ "results/generated/counting-router-v1.md"
+ ],
+ "tests": [
+ "tests/test_counting_router_experiment.py",
+ "tests/test_counting.py"
+ ],
+ "properties": [
+ "router outcomes preserve method and guarantee kind",
+ "radix is measured both with and without its required precondition",
+ "zero and unknown outcomes do not count as ambiguity evidence",
+ "Sasamoto remains outside the router",
+ "the generated Markdown equals the canonical rendering"
+ ],
+ "tolerance": null
+ },
+ "limitations": [
+ "W(E) is a transaction-level subset-sum diagnostic, not a mapping count",
+ "raw radix output is audited only to test its required precondition",
+ "zero-count and unknown outcomes provide no ambiguity evidence",
+ "Sasamoto remains an explicit estimator and is not a router tier",
+ "timings and synthetic L-estimator comparisons are outside this run",
+ "this diagnostic is not a privacy score or CoinScore"
+ ]
+}
diff --git a/artifacts/sha256/f7/fc02308fdabd3e8c82cd2ee3ab0276e4beaad96be2cd8224e069ed87ae55c5 b/artifacts/sha256/f7/fc02308fdabd3e8c82cd2ee3ab0276e4beaad96be2cd8224e069ed87ae55c5
new file mode 100644
index 00000000..1c82b332
--- /dev/null
+++ b/artifacts/sha256/f7/fc02308fdabd3e8c82cd2ee3ab0276e4beaad96be2cd8224e069ed87ae55c5
@@ -0,0 +1,13 @@
+{
+ "schema_version": 1,
+ "python": {
+ "implementation": "cpython",
+ "version": "3.13",
+ "system": "Darwin",
+ "machine": "arm64"
+ },
+ "source_archive": "sources/decluster-bcd9a3e-runtime.tar",
+ "project_root": "decluster",
+ "requirements": "reproduction/intersection-cluster-fixture/requirements.lock",
+ "dependency_directory": "reproduction/intersection-cluster-fixture"
+}
diff --git a/artifacts/sha256/f8/6ec49a75e9ec73d7c5a662e76252056ccf1a9f4de9a13dc77d935d490ad47f b/artifacts/sha256/f8/6ec49a75e9ec73d7c5a662e76252056ccf1a9f4de9a13dc77d935d490ad47f
new file mode 100644
index 00000000..303ba88c
--- /dev/null
+++ b/artifacts/sha256/f8/6ec49a75e9ec73d7c5a662e76252056ccf1a9f4de9a13dc77d935d490ad47f
@@ -0,0 +1,12 @@
+# Change-consolidation failure mode
+
+Generated from the canonical experiment artifact. Do not edit manually.
+
+| quantity | value |
+|---|---:|
+| candidate owners before consolidation | 2 |
+| candidate owners after consolidation | 1 |
+| surviving candidates | `alice` |
+| outcome | `candidate_narrowing` |
+
+The linkage requires correct change identification and a valid common-ownership premise.
diff --git a/artifacts/sha256/fc/d4ad596d735b7c5a8dba43bc96481946ce5e78c3806932b60c10c865d1dcf8 b/artifacts/sha256/fc/d4ad596d735b7c5a8dba43bc96481946ce5e78c3806932b60c10c865d1dcf8
new file mode 100644
index 00000000..56ef5dde
--- /dev/null
+++ b/artifacts/sha256/fc/d4ad596d735b7c5a8dba43bc96481946ce5e78c3806932b60c10c865d1dcf8
@@ -0,0 +1 @@
+{"experiment":"amount-channel-survey-v1","limitations":["this artifact composes upstream results and performs no mechanism calculation","component SHA-256 identities are mandatory","the five components measure different objects and are not combined into one score","DSS mapping and pairwise claims remain restricted to DSS's mapping family","the Bitcoin snapshot covers one 137-block interval from 2023","this summary is not a privacy certificate or CoinScore"],"report":{"components":{"local":{"experiment":"amount-local-channels-v1","sha256":"210ecb06ecaa32cc4cabe03ced8b85b0882a824dca2c361bd2c5dede7dcccf02"},"mapping_family":{"experiment":"amount-mapping-family-v1","sha256":"11f0ec645ea1be334dc7d24fbfc31047b234f335f48b5a514960472c7444b7d6"},"pairwise_family":{"experiment":"amount-pairwise-family-v1","sha256":"83f6a9e8501ee4df45f08b8c8ffc2f7265d40bf9add6d3f5093d712657c9dbf5"},"per_coin":{"experiment":"amount-per-coin-v1","sha256":"2e75b1330181e7cbf8539a357e2bd3ad650c4d2d207c7a47ec687e991e24a622"},"subset_sum":{"experiment":"amount-subset-sum-v1","sha256":"240592b5af814fbcd28b06221805b934424170119da5e62e9da0281944377a9d"}},"local_channels":{"coinjoin":{"demixed":0,"shape_detected":28},"conservation":{"forced_output_value":1353,"known_participant":"largest input value"},"subtransaction_roundness":{"ranked":483,"top_tied":330},"unnecessary_input":{"classes":{"none":943,"uih2":485},"fires":485}},"mapping_family":{"outcomes":{"answered":223,"failed":0,"refused":26,"timed_out":1},"restricted_family":{"certain_input_output_links":1471,"entropy_bins_bits":{"above_four":0,"above_zero_to_four":2,"zero":221},"non_derived_mappings":227}},"pairwise_family":{"outcomes":{"answered":271,"failed":0,"refused":27,"timed_out":2},"restricted_family_rows":{"full_output_support":898,"nonempty_support":914,"returned":914,"singleton_support":367}},"per_coin_candidates":{"transaction_gated":{"candidate_transactions":62,"candidates":{"input":18,"output":83,"total":101},"candidates_with_exact_transaction_count":{"input":0,"output":61,"total":61},"resolved_transactions":82},"ungated":{"candidate_transactions":95,"candidates":{"input":60,"output":258,"total":318}}},"per_coin_density":{"finite_log_w":{"maximum":19.40651597114107,"median":3.8066624897703196,"minimum":0.0},"reachable":{"input":316,"output":972,"total":1288},"unreachable":22338},"population":{"complete_amounts":5491,"multi_input":1428,"transactions":5491,"two_input_two_output":486},"transaction_subset_sum":{"by_kind":{"exact":1303,"unknown":125},"exact_zero":1223,"guaranteed_nonzero_log_w":80}},"schema_version":1}
diff --git a/artifacts/sha256/fe/a0440206848a1f12d87c01960840eaf5dd933559a4ddf30bc70ca8505e5774 b/artifacts/sha256/fe/a0440206848a1f12d87c01960840eaf5dd933559a4ddf30bc70ca8505e5774
new file mode 100644
index 00000000..fa080bc3
--- /dev/null
+++ b/artifacts/sha256/fe/a0440206848a1f12d87c01960840eaf5dd933559a4ddf30bc70ca8505e5774
@@ -0,0 +1,16 @@
+# Graph-structure linkage and the degree-matched control
+
+Generated from the canonical experiment artifact. Do not edit manually.
+
+8927 transactions, 27962 addresses, 2463 clusters of two or more. Positives are same-cluster pairs that do not co-spend; the score is rarity-weighted shared-neighbour structure.
+
+| view | published AUC | degree-matched AUC | fall | degree-only AUC under matching |
+|---|---:|---:|---:|---:|
+| full | 0.9912 | 0.9784 | 0.0128 | 0.5478 |
+| payment | 0.9506 | 0.9154 | 0.0353 | 0.5000 |
+
+The payment-graph headline falls 0.0353 under degree-matched negatives, from 0.9506 to 0.9154. Under that control a score made only of the pair's degree sum reads 0.5000 — chance, by construction — so the remaining separation is not the asymmetry the published sampling leaves in.
+
+The shuffled-label control reads 0.4994, which is what says the score is measuring the labels at all.
+
+Cluster membership here is a co-spend label rather than wallet ownership, the fixture is one selected 2016 slice, and a fall under matching bounds how much of the AUC was degree asymmetry without establishing what the remainder is. Neither number is a re-identification rate or a privacy score.
diff --git a/bigquery/README.md b/bigquery/README.md
index 2a4c849a..a0c60a42 100644
--- a/bigquery/README.md
+++ b/bigquery/README.md
@@ -26,7 +26,7 @@ re-implementing the fingerprint logic in SQL.
5. Send the output back — the `library.py` bits, `RESULTS-wp1a.md`, and `PAPER.md` §5 get
updated to the large-scale numbers.
-## What scales here vs what doesn't (honest)
+## What scales here vs what doesn't
- **Scales (BigQuery, ~500k+):** nSequence, nLockTime, version, fee-rate, input/output
order, change script type, input script type + presence, output encoding, OP_RETURN,
@@ -80,7 +80,7 @@ just produces wrong numbers.
Verified while building it: values are satoshis; 17 of 22 axes fire correctly on the
export. Of the five that do not, `low_r`, `sighash` and `pubkey_compression` abstain
-honestly as `na`, but **`multisig` and `nested_segwit` report `none`**, a false negative
+as `na`, but **`multisig` and `nested_segwit` report `none`**, a false negative
rather than a missing value. Exclude those two rather than trusting them.
### Scale of the result
@@ -108,6 +108,6 @@ a parsed epoch rather than a quadratic blow-up, so the fix if it binds is a stre
loader, not a different data structure.
Those figures are for `graph_deanon.build`, the address-level probe. The contraction the
-cross-view pipeline actually runs, `views.contract`, is lighter: **800 MB** for a 144-block
+cross-view pipeline actually runs, `contraction.contract`, is lighter: **800 MB** for a 144-block
view, 653 MB with attributes off, and 624 MB for *both* views once vertices below transfer
count two are dropped.
diff --git a/bigquery/slice_gate.sql b/bigquery/slice_gate.sql
index a24f6c04..68c677cb 100644
--- a/bigquery/slice_gate.sql
+++ b/bigquery/slice_gate.sql
@@ -12,6 +12,7 @@
-- (largest gap 962010-962489), so a slice must stay below that.
-- === 1. spanning material: how much survives from one view to the other ===
+WITH tx AS (
SELECT
IF(block_number <= 940112, 'A', 'B') AS epoch,
`hash` AS txid,
@@ -39,6 +40,7 @@ SELECT
(SELECT COUNT(*) FROM a_side JOIN b_side USING (a)) AS addrs_in_both
-- === 2. degree and cluster-material of the spanning addresses ===
+WITH tx AS (
SELECT
IF(block_number <= 940112, 'A', 'B') AS epoch,
`hash` AS txid,
@@ -77,6 +79,7 @@ SELECT epoch,
FROM deg GROUP BY epoch ORDER BY epoch
-- === 3. seed supply: is the high-degree population the same in both views? ===
+WITH tx AS (
SELECT IF(block_number <= 940112, 'A', 'B') AS epoch, `hash` AS txid,
ARRAY(SELECT DISTINCT a FROM UNNEST(inputs) i, UNNEST(i.addresses) a) AS in_addr,
ARRAY(SELECT DISTINCT a FROM UNNEST(outputs) o, UNNEST(o.addresses) a) AS out_addr
diff --git a/catalog/ctp-claims.json b/catalog/ctp-claims.json
new file mode 100644
index 00000000..4ccdb0b8
--- /dev/null
+++ b/catalog/ctp-claims.json
@@ -0,0 +1,382 @@
+{
+ "schema_version": 1,
+ "claims": [
+ {
+ "id": "ctp.cioh.collaborative_false_merge",
+ "title": "Collaborative inputs can make CIOH merge distinct owners",
+ "scope": "qualitative_failure_mode",
+ "sources": [
+ "cioh-wp",
+ "reid-harrigan",
+ "maxwell",
+ "androulaki",
+ "meiklejohn",
+ "harrigan-fretter"
+ ],
+ "mechanism": "compare naive co-spend clustering with an explicitly supplied collaborative ownership partition",
+ "implementation": "decluster.cluster.cluster_cospends via decluster.failure_modes.cioh_collaborative",
+ "evidence_level": "unit_fixture",
+ "status": "supported_at_evidence_level",
+ "limitations": [
+ "The deterministic fixture supplies participant ownership labels",
+ "The fixture demonstrates the failure mode but does not estimate its chain-wide prevalence",
+ "A co-spend alone does not establish common ownership"
+ ]
+ },
+ {
+ "id": "ctp.payjoin.receiver_change_readoff",
+ "title": "A PayJoin receiver can read off senders' change by propagating the payments it negotiated",
+ "scope": "qualitative_failure_mode",
+ "sources": [
+ "ns1r",
+ "payjoin-entropy",
+ "forms"
+ ],
+ "mechanism": "enumerate the perfect matchings of the per-sender constraint input - payment == change and count distinct readings of the amounts",
+ "implementation": "decluster.failure_modes.ns1r_change_readoff via decluster.experiments.ns1r_change_readoff",
+ "evidence_level": "unit_fixture",
+ "status": "supported_at_evidence_level",
+ "limitations": [
+ "The scenarios are deterministic fixtures, not sampled transactions",
+ "The receiver's knowledge of each negotiated payment is supplied, not inferred",
+ "An external observer holds neither the payments nor the receiver's own coins",
+ "A unique reading of the amounts is not ownership attribution",
+ "The enumeration is bounded at eight senders and does not model fees"
+ ]
+ },
+ {
+ "id": "ctp.forms.observer_knowledge",
+ "title": "Collaborative forms expose different information to external observers and counterparties",
+ "scope": "model_limit",
+ "sources": [
+ "forms",
+ "p2ep",
+ "bip79",
+ "bip78",
+ "bip77",
+ "ns1r"
+ ],
+ "mechanism": "compare on-chain observations separately from protocol-counterparty knowledge across ordinary spends, two-party PayJoin, NS1R, NSNR and net settlement",
+ "implementation": "decluster.failure_modes.collaborative_forms",
+ "evidence_level": "unit_fixture",
+ "status": "supported_at_evidence_level",
+ "limitations": [
+ "The matrix models representative CTP amount examples rather than protocol wire implementations",
+ "P2EP, BIP79, BIP78 and BIP77 transport differences are not treated as on-chain evidence",
+ "Participant allocations and payment graphs are supplied model possibilities, not inferred ownership",
+ "Protocol deployment frequencies and empirical indistinguishability are not measured"
+ ]
+ },
+ {
+ "id": "ctp.payjoin.unnecessary_input",
+ "title": "Unnecessary-input conditions can expose unusual transaction structure",
+ "scope": "qualitative_failure_mode",
+ "sources": [
+ "p2ep",
+ "bip78",
+ "uih-adamisz",
+ "uih-ghesmati"
+ ],
+ "mechanism": "classify two-output transactions with the fee-aware BlockStream UIH1/UIH2 rules, then preserve observationally equivalent unilateral and collaborative interpretations",
+ "implementation": "decluster.baselines.unnecessary_input and decluster.failure_modes.unnecessary_input; decluster.extractors.x_uih remains a legacy Gibson-like approximation",
+ "evidence_level": "unit_fixture",
+ "status": "supported_at_evidence_level",
+ "limitations": [
+ "A positive unnecessary-input predicate does not identify PayJoin or ownership",
+ "Ordinary consolidation, NS1R, NSNR and net settlement may satisfy the predicate",
+ "Cycles make gross payment amounts unidentifiable from net on-chain balances",
+ "Protocol form, participant roles and ownership require independent evidence",
+ "The paper-faithful baseline is scoped to transactions with more than one input and exactly two outputs",
+ "The published longitudinal dataset and reported rates are not reproduced"
+ ]
+ },
+ {
+ "id": "ctp.fingerprint.statistical_linkage",
+ "title": "Construction fingerprints can support clustering",
+ "scope": "qualitative_failure_mode",
+ "sources": [
+ "moser-narayanan",
+ "kappos",
+ "sabouri"
+ ],
+ "mechanism": "out-of-sample comparison-vector linkage",
+ "implementation": "decluster.fingerprint_validate.LibraryScorer, exercised by decluster.experiments.fingerprint_validation, fingerprint_regime, em_m, weight_sensitivity and bayes_vs_fs; decluster.fellegi_sunter is the separate fitted comparator used by fs_temporal and fs_ablation",
+ "evidence_level": "bitcoin_snapshot_experiment",
+ "status": "supported_at_evidence_level",
+ "limitations": [
+ "Address reuse is a weak label",
+ "Local sample is not chain-wide"
+ ]
+ },
+ {
+ "id": "ctp.social_graph.propagation",
+ "title": "Seeded mappings can propagate across overlapping pseudonym graphs",
+ "scope": "qualitative_failure_mode",
+ "sources": [
+ "ns-netflix",
+ "ns-retro",
+ "ns-social"
+ ],
+ "mechanism": "seeded cross-view vertex matching",
+ "implementation": "decluster.baselines.ns_social_attack",
+ "evidence_level": "bitcoin_snapshot_experiment",
+ "status": "partially_supported",
+ "limitations": [
+ "Propagation is exercised rather than established: the canonical run finds one rejoin in 15,688 candidate pairs, with every shuffled-seed control at zero",
+ "Bitcoin seeds are derived from retained correspondence",
+ "Published social datasets are not reproduced"
+ ]
+ },
+ {
+ "id": "ctp.social_graph.structural_linkage",
+ "title": "Recurring counterparty structure can link pseudonyms beyond co-spend",
+ "scope": "qualitative_failure_mode",
+ "sources": [
+ "reid-harrigan",
+ "ns-social"
+ ],
+ "mechanism": "compare rarity-aware or common-neighbour structure under an independent entity label",
+ "implementation": "decluster.graph_deanon.evaluate_entity",
+ "evidence_level": "bitcoin_snapshot_experiment",
+ "status": "partially_supported",
+ "limitations": [
+ "Seeded N-S propagation is not exercised here; the paired fixtures score common neighbours",
+ "The current paired fixtures evaluate common neighbours, not seeded N-S propagation",
+ "Vanity-prefix labels and entity-centred subgraphs are selected",
+ "The contrast is not representative of services or the chain"
+ ]
+ },
+ {
+ "id": "ctp.social_graph.link_prediction",
+ "title": "A cross-view mapping can support prediction of missing links",
+ "scope": "qualitative_failure_mode",
+ "sources": [
+ "ns-linkpred"
+ ],
+ "mechanism": "mapping-assisted link prediction over incomplete views",
+ "implementation": "decluster.baselines.ns_link_prediction_2011",
+ "evidence_level": "synthetic_experiment",
+ "status": "partially_supported",
+ "limitations": [
+ "Confidence pruning and accepted-mapping correction are not implemented",
+ "The zero convention for dummy-incident terms is read from the paper's prose, not from its formula",
+ "Pruning, revisitation and learned feature model are incomplete"
+ ]
+ },
+ {
+ "id": "ctp.amount.subtransaction_ambiguity",
+ "title": "Values can leave few plausible subtransaction mappings",
+ "scope": "qualitative_failure_mode",
+ "sources": [
+ "sudoku",
+ "boltzmann",
+ "maurer",
+ "diaz",
+ "serjantov-danezis",
+ "syverson"
+ ],
+ "mechanism": "enumerate mappings and measure their link distribution",
+ "implementation": "decluster.baselines.maurer plus the separately named decluster.baselines.coinjoin_sudoku nominal reconstruction",
+ "evidence_level": "paper_example",
+ "status": "partially_supported",
+ "limitations": [
+ "Maurer requires exact conservation",
+ "The historical CoinJoin Sudoku analyzer was not published, so only its nominal controls are reconstructed",
+ "Some Boltzmann modes remain incomplete",
+ "Entropy alone is not a privacy certificate"
+ ]
+ },
+ {
+ "id": "ctp.amount.denomination_preparation_fingerprint",
+ "title": "Preparing fixed-denomination inputs can create a unilateral transaction fingerprint",
+ "scope": "qualitative_failure_mode",
+ "sources": [
+ "tx0",
+ "maxwell"
+ ],
+ "mechanism": "compare denomination-preparation transaction shapes with an explicitly defined control population",
+ "implementation": "decluster.failure_modes.denomination_preparation",
+ "evidence_level": "unit_fixture",
+ "status": "supported_at_evidence_level",
+ "limitations": [
+ "The deterministic fixture identifies an exact-denomination output consumed by the declared next CoinJoin",
+ "Controls match input and output arity but are not a sampled real-world population",
+ "The CTP statement is a qualification without an independent cited experiment",
+ "A match does not establish unilateral construction, common ownership, rarity or prevalence"
+ ]
+ },
+ {
+ "id": "ctp.amount.deterministic_partition",
+ "title": "Amount constraints can force links shared by every feasible CoinJoin partition",
+ "scope": "qualitative_failure_mode",
+ "sources": [
+ "sudoku",
+ "boltzmann",
+ "maurer"
+ ],
+ "mechanism": "enumerate a declared mapping family and return only links present in every feasible mapping",
+ "implementation": "decluster.baselines.maurer via decluster.failure_modes.deterministic_partition; CoinJoin Sudoku remains a separate nominal reconstruction",
+ "evidence_level": "paper_example",
+ "status": "supported_at_evidence_level",
+ "limitations": [
+ "The identified deterministic-partition reproduction uses Maurer's exact non-derived mapping family, not the unavailable CoinJoin Sudoku analyzer",
+ "Certainty is conditional on exact per-block conservation and the declared mapping family",
+ "Fees, contextual priors and net-settlement cycles can change admissibility",
+ "Unanimous mapping links are not ownership attribution"
+ ]
+ },
+ {
+ "id": "ctp.graph.pseudonym_representation",
+ "title": "Partial clusters induce an attributed directed multigraph rather than a complete user network",
+ "scope": "model_limit",
+ "sources": [
+ "reid-harrigan",
+ "minor-nitpick",
+ "multigraph-nitpick",
+ "ns-social"
+ ],
+ "mechanism": "contract only declared cluster edges while preserving transfer direction, multiplicity and attributes",
+ "implementation": "decluster.views.contract via decluster.adaptations.pseudonym_graph",
+ "evidence_level": "unit_fixture",
+ "status": "supported_at_evidence_level",
+ "limitations": [
+ "The deterministic fixture supplies the partial clustering lookup",
+ "Parallel transfers are folded into one directed edge carrying count, value and height span rather than separate edge objects",
+ "The fixture validates representation invariants but not social-graph deanonymization",
+ "Vertices remain pseudonyms unless clustering completeness is independently established"
+ ]
+ },
+ {
+ "id": "ctp.provenance.traceability",
+ "title": "Candidate-origin ambiguity can decay under value-flow and auxiliary knowledge",
+ "scope": "qualitative_failure_mode",
+ "sources": [
+ "kelen-seres",
+ "goldfeder"
+ ],
+ "mechanism": "propagate model-relative origins and distinguish candidate elimination from graph fracture",
+ "implementation": "decluster.kelen_seres_value_flow and decluster.failure_modes.provenance_decay",
+ "evidence_level": "paper_example",
+ "status": "supported_at_evidence_level",
+ "limitations": [
+ "Full published datasets are not reproduced",
+ "The synthetic graph-fracture fixture does not establish an exponential deanonymization rate",
+ "Value-flow traceability and ownership attribution remain distinct"
+ ]
+ },
+ {
+ "id": "ctp.disclosure.statistical_longitudinal",
+ "title": "Repeated observations can disclose persistent sender behavior",
+ "scope": "qualitative_failure_mode",
+ "sources": [
+ "danezis"
+ ],
+ "mechanism": "accumulate observations across rounds and estimate a persistent sender profile",
+ "implementation": "decluster.failure_modes.statistical_disclosure",
+ "evidence_level": "unit_fixture",
+ "status": "supported_at_evidence_level",
+ "limitations": [
+ "The deterministic fixture reproduces the threshold-mix estimator and a background-matched control",
+ "Target activity and the background distribution are supplied",
+ "The mix-network observation model is not a Bitcoin ownership model",
+ "The cited empirical experiments and real-world attack rates are not reproduced"
+ ]
+ },
+ {
+ "id": "ctp.disclosure.perfect_matching",
+ "title": "Profile-weighted perfect matching can jointly de-anonymize a mixing round",
+ "scope": "qualitative_failure_mode",
+ "sources": [
+ "troncoso"
+ ],
+ "mechanism": "estimate sender profiles across observed rounds, then maximize the joint probability of a bijective sender-message to receiver-message assignment within one round",
+ "implementation": "decluster.failure_modes.perfect_matching_disclosure",
+ "evidence_level": "unit_fixture",
+ "status": "supported_at_evidence_level",
+ "limitations": [
+ "The deterministic fixture reproduces the joint maximum-weight perfect-matching objective and a tied control",
+ "Profiles are supplied; their longitudinal estimation is represented by the separate statistical-disclosure failure mode",
+ "The exact enumerator is bounded to small fixtures rather than implementing a scalable assignment solver",
+ "The paper's empirical simulations and success rates are not reproduced",
+ "This mechanism is neither value-flow ancestry nor Bitcoin ownership attribution"
+ ]
+ },
+ {
+ "id": "ctp.intersection.longitudinal",
+ "title": "Linked post-CoinJoin coins can narrow pre-CoinJoin origin clusters",
+ "scope": "qualitative_failure_mode",
+ "sources": [
+ "goldfeder",
+ "scroll-intersection"
+ ],
+ "mechanism": "intersect cluster-lifted candidate-origin sets across observations",
+ "implementation": "decluster.baselines.candidate_set_intersection",
+ "evidence_level": "unit_fixture",
+ "status": "partially_supported",
+ "limitations": [
+ "JoinMarket detection and recursive clustering are not reproduced",
+ "Historical purchase experiment is not reproduced"
+ ]
+ },
+ {
+ "id": "ctp.robustness.counterfactual_paths",
+ "title": "Robust privacy requires multiple plausible counterfactual paths",
+ "scope": "defensive_criterion",
+ "sources": [
+ "flow",
+ "proximity",
+ "goldfeder"
+ ],
+ "mechanism": "enumerate amount-plausible edge-disjoint paths with temporal qualifications",
+ "implementation": "decluster.disjoint_routes measures the cut; decluster.path_count via decluster.experiments.path_count_contract accumulates route mass, and decluster.provenance_route_accumulation is the separately named route-accumulation alias",
+ "evidence_level": "unit_fixture",
+ "status": "partially_supported",
+ "limitations": [
+ "Plausible-flow capacity is computed as a maximum flow over coin values; what remains unsolved is the allocation, not the capacity",
+ "Own-origin robustness is not exercised: the corpus states that property relative to a privileged counterparty that is itself the origin, and `flow_capacity` takes an `origins` argument that would express it, but both runs pass the whole absorber set instead of a user's own antecedent coins. What is measured here is the general robust-connectivity form",
+ "Value enters as a threshold \u2014 a route either can or cannot carry the amount \u2014 not as an allocation; choosing which routes carry how much is the k-splittable problem and is not solved here",
+ "The cut is measured against the boundary a bounded walk stopped at, so a deeper walk can only move it up"
+ ]
+ },
+ {
+ "id": "ctp.entropy.not_sufficient",
+ "title": "Point-in-time entropy is not a sufficient privacy certificate",
+ "scope": "model_limit",
+ "sources": [
+ "diaz",
+ "serjantov-danezis",
+ "syverson",
+ "goldfeder"
+ ],
+ "mechanism": "contrast equal entropy with different future intersection or auxiliary-information outcomes",
+ "implementation": "decluster.failure_modes.entropy_insufficiency",
+ "evidence_level": "unit_fixture",
+ "status": "supported_at_evidence_level",
+ "limitations": [
+ "The paired counterexample is synthetic",
+ "The fixture falsifies sufficiency but does not measure real-world attack frequency"
+ ]
+ },
+ {
+ "id": "ctp.sparse_dataset.feature_uniqueness",
+ "title": "Sparse feature vectors can support record-linkage deanonymization",
+ "scope": "qualitative_failure_mode",
+ "sources": [
+ "ns-netflix",
+ "ns-retro"
+ ],
+ "mechanism": "measure nearest-neighbour similarity before applying auxiliary-information record linkage",
+ "implementation": "decluster.experiments.ancestry_sparsity and decluster.experiments.reid",
+ "evidence_level": "bitcoin_snapshot_experiment",
+ "status": "partially_supported",
+ "limitations": [
+ "The linkage is not measured against an independently observed auxiliary: origins are sampled from each target's own stored signature",
+ "The frozen signature snapshot contains 200 selected records",
+ "The signature-generation recipe and original comparison population were not preserved",
+ "Auxiliary origins are sampled from each target signature rather than independently observed",
+ "Record identity is not wallet ownership",
+ "The published Netflix corpus is not reproduced"
+ ]
+ }
+ ]
+}
diff --git a/catalog/ctp-sources.json b/catalog/ctp-sources.json
new file mode 100644
index 00000000..e4569c39
--- /dev/null
+++ b/catalog/ctp-sources.json
@@ -0,0 +1,50 @@
+{
+ "schema_version": 1,
+ "ctp_revision": "51c2ed8f2c2fa817ce44fd662e6f4b5e83577042",
+ "sources": [
+ {"id":"cioh-wp","title":"Bitcoin: A Peer-to-Peer Electronic Cash System","url":"https://bitcoin.org/bitcoin.pdf","kind":"paper","obligation":"fixture","note":"Historical source for the common-input-ownership observation."},
+ {"id":"cioh-scroll","title":"The Scroll #2: Wallet Clustering Basics","url":"https://spiralbtc.substack.com/p/the-scroll-2-wallet-clustering-basics","kind":"informal","obligation":"context","note":"Informal introduction, not a primary algorithm specification."},
+ {"id":"forms","title":"The Scroll #5: The Many Faces of CoinJoins","url":"https://spiralbtc.substack.com/p/the-scroll-5-the-many-faces-of-coinjoins","kind":"informal","obligation":"fixture","note":"Transaction-form taxonomy that is explicitly non-exhaustive."},
+ {"id":"payjoin-entropy","title":"CTP note on two-party PayJoin entropy","url":null,"kind":"internal_note","obligation":"metric","note":"Internal quantitative claim requiring an executable derivation."},
+ {"id":"reid-harrigan","title":"An Analysis of Anonymity in the Bitcoin System","url":"https://arxiv.org/abs/1107.4524","kind":"paper","obligation":"baseline","note":"Introduces clustering and the user-network representation."},
+ {"id":"ron-shamir","title":"Quantitative Analysis of the Full Bitcoin Transaction Graph","url":"https://eprint.iacr.org/2012/584","kind":"paper","obligation":"context","note":"Empirical transaction-graph analysis and dataset context."},
+ {"id":"androulaki","title":"Evaluating User Privacy in Bitcoin","url":"https://eprint.iacr.org/2012/596","kind":"paper","obligation":"baseline","note":"Threat model and evaluation of clustering heuristics."},
+ {"id":"meiklejohn","title":"A Fistful of Bitcoins: Characterizing Payments Among Men with No Names","url":"https://cseweb.ucsd.edu/~smeiklejohn/files/imc13.pdf","kind":"paper","obligation":"baseline","note":"Clustering, service attribution and empirical validation."},
+ {"id":"nick","title":"Data-Driven De-Anonymization in Bitcoin","url":"https://jonasnick.github.io/papers/thesis.pdf","kind":"paper","obligation":"context","note":"Catalogues data-driven Bitcoin deanonymization methods."},
+ {"id":"harrigan-fretter","title":"The Unreasonable Effectiveness of Address Clustering","url":"https://arxiv.org/abs/1605.06369","kind":"paper","obligation":"baseline","note":"Evaluates the effectiveness and reach of address clustering."},
+ {"id":"moser-narayanan","title":"Resurrecting Address Clustering in Bitcoin","url":"https://arxiv.org/abs/2107.05749","kind":"paper","obligation":"baseline","note":"Modern clustering recovery and validation context."},
+ {"id":"kappos","title":"How to Peel a Million: Validating and Expanding Bitcoin Clusters","url":"https://arxiv.org/abs/2205.13882","kind":"paper","obligation":"baseline","note":"Cluster expansion and label-quality methodology."},
+ {"id":"scroll-history","title":"The Scroll #3: A Brief History of Wallet Clustering","url":"https://spiralbtc.substack.com/p/the-scroll-3-a-brief-history-of-wallet","kind":"informal","obligation":"context","note":"Narrative history, not a primary algorithm specification."},
+ {"id":"p2ep","title":"Improving Privacy Using Pay-to-EndPoint","url":"https://blockstream.com/2018/08/08/en-improving-privacy-using-pay-to-endpoint/","kind":"protocol","obligation":"fixture","note":"PayJoin antecedent and two-party transaction form."},
+ {"id":"bip79","title":"BIP 79: Bustapay","url":"https://github.com/bitcoin/bips/blob/master/bip-0079.mediawiki","kind":"protocol","obligation":"fixture","note":"Collaborative payment protocol and observables."},
+ {"id":"bip78","title":"BIP 78: A Simple Payjoin Proposal","url":"https://github.com/bitcoin/bips/blob/master/bip-0078.mediawiki","kind":"protocol","obligation":"fixture","note":"Synchronous PayJoin protocol and counterparty model."},
+ {"id":"bip77","title":"BIP 77: Async Payjoin","url":"https://github.com/bitcoin/bips/blob/master/bip-0077.md","kind":"protocol","obligation":"fixture","note":"Asynchronous PayJoin protocol and observables."},
+ {"id":"uih-adamisz","title":"PayJoin unnecessary-input heuristic writeup","url":"https://gist.github.com/AdamISZ/4551b947789d3216bacfcb7af25e029e","kind":"informal","obligation":"baseline","note":"Informal specification and examples for unnecessary-input analysis."},
+ {"id":"uih-ghesmati","title":"Unnecessary Input Heuristics & PayJoin Transactions","url":"https://eprint.iacr.org/2022/589","kind":"paper","obligation":"baseline","note":"Academic mechanism and evaluation of unnecessary-input heuristics."},
+ {"id":"boltzmann","title":"Boltzmann link-probability matrix","url":"https://gist.github.com/LaurentMT/d361bca6dc52868573a2","kind":"implementation","obligation":"baseline","note":"Reference definition for link-probability matrix behavior."},
+ {"id":"ns1r","title":"rust-payjoin PR 923: remove experimental NS1R","url":"https://github.com/payjoin/rust-payjoin/pull/923","kind":"implementation","obligation":"context","note":"Deployment-status discussion, not an attack specification."},
+ {"id":"maxwell","title":"CoinJoin: Bitcoin privacy for the real world","url":"https://bitcointalk.org/index.php?topic=279249","kind":"protocol","obligation":"fixture","note":"Original CoinJoin proposal and CIOH threat motivation."},
+ {"id":"maurer","title":"Anonymous CoinJoin Transactions with Arbitrary Values","url":"https://www.researchgate.net/publication/318128387_Anonymous_CoinJoin_Transactions_with_Arbitrary_Values","kind":"paper","obligation":"baseline","note":"Exact balanced subtransaction model."},
+ {"id":"ns-netflix","title":"Robust De-anonymization of Large Sparse Datasets","url":"https://arxiv.org/abs/cs/0610105","kind":"paper","obligation":"baseline","note":"Sparsity, auxiliary information and record deanonymization."},
+ {"id":"ns-retro","title":"Robust De-anonymization of Large Sparse Datasets: a Decade Later","url":"https://www.cs.princeton.edu/~arvindn/publications/de-anonymization-retrospective.pdf","kind":"paper","obligation":"qualification","note":"Retrospective scope and external-validity qualifications."},
+ {"id":"ns-social","title":"De-anonymizing Social Networks","url":"https://arxiv.org/abs/0903.3276","kind":"paper","obligation":"baseline","note":"Seeded cross-view graph matching and propagation."},
+ {"id":"minor-nitpick","title":"CTP graph-minor qualification","url":null,"kind":"internal_note","obligation":"qualification","note":"The contracted structure mixes directed and undirected relations."},
+ {"id":"multigraph-nitpick","title":"CTP multigraph qualification","url":null,"kind":"internal_note","obligation":"qualification","note":"Parallel payments may instead be represented by rich edge attributes."},
+ {"id":"ns-linkpred","title":"Link Prediction by De-anonymization","url":"https://arxiv.org/abs/1102.4374","kind":"paper","obligation":"baseline","note":"Mapping-assisted link prediction across incomplete graphs."},
+ {"id":"sabouri","title":"How Wallet Fingerprints Damage Payjoin Privacy","url":"https://payjoin.org/blog/2026/03/25/wallet-fingerprints-payjoin-privacy/","kind":"informal","obligation":"baseline","note":"PayJoin construction-fingerprint threat and measurements."},
+ {"id":"goldfeder","title":"When the Cookie Meets the Blockchain","url":"https://arxiv.org/abs/1708.04748","kind":"paper","obligation":"baseline","note":"Longitudinal CoinJoin candidate-set intersection."},
+ {"id":"danezis","title":"Statistical Disclosure Attacks","url":"https://www.freehaven.net/anonbib/cache/statistical-disclosure.pdf","kind":"paper","obligation":"baseline","note":"Statistical disclosure over repeated observations."},
+ {"id":"troncoso","title":"Perfect Matching Disclosure Attacks","url":"https://www.freehaven.net/anonbib/cache/troncoso-pet2008.pdf","kind":"paper","obligation":"baseline","note":"Perfect-matching disclosure and longitudinal conditions."},
+ {"id":"scroll-intersection","title":"The Scroll #4: Intersection Attacks","url":"https://spiralbtc.substack.com/p/the-scroll-4-intersection-attacks","kind":"informal","obligation":"context","note":"Accessible explanation, not the primary source."},
+ {"id":"kelen-seres","title":"Towards Measuring the Traceability of Cryptocurrencies","url":"https://arxiv.org/abs/2211.04259","kind":"paper","obligation":"baseline","note":"Flow-based traceability and absorbing-chain metrics. Section numbers cite arXiv v2 (2024-06-01); v1 numbers the same material differently."},
+ {"id":"flow","title":"CTP plausible-flow qualification","url":null,"kind":"internal_note","obligation":"qualification","note":"A graph path counts only when its value flow is plausible."},
+ {"id":"diaz","title":"Towards Measuring Anonymity","url":"https://www.freehaven.net/anonbib/cache/Diaz02.pdf","kind":"paper","obligation":"metric","note":"Anonymity metrics require an explicit probability distribution."},
+ {"id":"serjantov-danezis","title":"Towards an Information Theoretic Metric for Anonymity","url":"https://bib.mixnetworks.org/pdf/serjantov2002towards.pdf","kind":"paper","obligation":"metric","note":"Information-theoretic anonymity measurement."},
+ {"id":"tx0","title":"CTP denomination-preparation qualification","url":null,"kind":"internal_note","obligation":"qualification","note":"Pre-CoinJoin denomination preparation can itself be fingerprintable."},
+ {"id":"sudoku","title":"CoinJoin Sudoku","url":"https://www.coinjoinsudoku.com/advisory/","kind":"informal","obligation":"baseline","note":"The advisory specifies equal-sum grouping, but the cited repository never published the analyzer; only an independent nominal reconstruction is possible."},
+ {"id":"radix","title":"Radix decomposition notebook","url":"https://colab.research.google.com/drive/1We_FvfX_Ob9BapFW3X_By9vTtxUrt3pm","kind":"implementation","obligation":"fixture","note":"Constructive denomination and sumset analysis, not an attack oracle or mapping-count bound; immutable export identity is recorded separately and redistribution awaits an explicit license."},
+ {"id":"syverson","title":"Why I'm not an entropist","url":"https://www.freehaven.net/anonbib/cache/entropist.pdf","kind":"paper","obligation":"qualification","note":"Entropy alone is not a sufficient privacy certificate."},
+ {"id":"scam-exchanges","title":"CTP commercial-censorship aside","url":null,"kind":"internal_note","obligation":"no_code_required","note":"Normative aside with no algorithmic implementation requirement."},
+ {"id":"proximity","title":"CTP temporal-proximity qualification","url":null,"kind":"internal_note","obligation":"qualification","note":"Counterfactual path plausibility also depends on time and hop count."}
+ ]
+}
diff --git a/catalog/datasets/amount-channel-812695-812831-v1.json b/catalog/datasets/amount-channel-812695-812831-v1.json
new file mode 100644
index 00000000..20b98350
--- /dev/null
+++ b/catalog/datasets/amount-channel-812695-812831-v1.json
@@ -0,0 +1,16 @@
+{
+ "schema_version": 1,
+ "id": "amount-channel-812695-812831-v1",
+ "title": "Amount-channel Bitcoin transaction snapshot",
+ "kind": "immutable_snapshot",
+ "schema": "bitcoin-transaction-array-v1",
+ "format": "json",
+ "content": {"bytes": 8028513, "sha256": "83b4391f78d07a2fa85a67218d5ae4420d8965da3c95af577592e9ec1850104d"},
+ "locations": {"local_path": "data/amount-channel-812695-812831-v1.json", "canonical": null, "mirrors": []},
+ "source": {"provider": "project-provided public Bitcoin on-chain export", "snapshot_time": "2026-09-04", "recipe": "decluster/amount_channel_snapshot.py", "recipe_sha256": "409a90613dc6ce7fc49ab3fa12a78f94b70e145a2a9a6b5d097c1db5f1fb14fd"},
+ "license": {"data": "redistribution authorized by snapshot provider", "recipe": "MIT"},
+ "redistribution": "allowed",
+ "sensitivity": "public_on_chain",
+ "normalization": {"code_revision": null, "parameters": {"minimum_height": 812695, "maximum_height": 812831, "records": 5491}, "parents": []},
+ "determinism": {"ordering": "source transaction order", "rng_algorithm": null, "rng_seed": null}
+}
diff --git a/catalog/datasets/boltzmann-fee-audit-v1.json b/catalog/datasets/boltzmann-fee-audit-v1.json
new file mode 100644
index 00000000..06dff8e3
--- /dev/null
+++ b/catalog/datasets/boltzmann-fee-audit-v1.json
@@ -0,0 +1,16 @@
+{
+ "schema_version": 1,
+ "id": "boltzmann-fee-audit-v1",
+ "title": "Fee-allocation audit transaction population",
+ "kind": "immutable_snapshot",
+ "schema": "bitcoin-transaction-array-v1",
+ "format": "json",
+ "content": {"bytes": 309025, "sha256": "17ee62e307d34660be2c4f50382a63ba044d0ad67f0674873e304d0b8203dc1c"},
+ "locations": {"local_path": "data/boltzmann-fee-audit-v1.json", "canonical": null, "mirrors": []},
+ "source": {"provider": "project-provided public Bitcoin on-chain export", "snapshot_time": "2026-09-04", "recipe": "decluster/boltzmann_fee_snapshot.py", "recipe_sha256": "e881ede4c7e0ea530f2853fd9aec87ba7650149a2d98986ed3fa0f98b191ccec"},
+ "license": {"data": "redistribution authorized by snapshot provider", "recipe": "MIT"},
+ "redistribution": "allowed",
+ "sensitivity": "public_on_chain",
+ "normalization": {"code_revision": null, "parameters": {"cap": 300, "max_coins": 8, "records": 300}, "parents": []},
+ "determinism": {"ordering": "first eligible transactions in source order", "rng_algorithm": null, "rng_seed": null}
+}
diff --git a/catalog/datasets/conservation-round-three-v1.json b/catalog/datasets/conservation-round-three-v1.json
new file mode 100644
index 00000000..90752108
--- /dev/null
+++ b/catalog/datasets/conservation-round-three-v1.json
@@ -0,0 +1,42 @@
+{
+ "schema_version": 1,
+ "id": "conservation-round-three-v1",
+ "title": "Reduced conservation fixture for mainnet round three",
+ "kind": "git_fixture",
+ "schema": "conservation-output-histogram-v1",
+ "format": "json",
+ "content": {
+ "bytes": 758,
+ "sha256": "e4a9048fa27e28732af5449d9b9a2098f401b203c793d79763d0be685fb86660"
+ },
+ "locations": {
+ "local_path": "tests/fixtures/conservation_round_three.json",
+ "canonical": null,
+ "mirrors": []
+ },
+ "source": {
+ "provider": "project-provided Bitcoin mainnet transaction reduction",
+ "snapshot_time": "2026-09-04",
+ "recipe": null,
+ "recipe_sha256": null
+ },
+ "license": {
+ "data": "redistribution authorized by fixture provider",
+ "recipe": null
+ },
+ "redistribution": "allowed",
+ "sensitivity": "public_on_chain",
+ "normalization": {
+ "code_revision": "700c0aeeac7503c34827245d3473de52ba74be36",
+ "parameters": {
+ "txid": "80f11c778a2f486ffc55b4e8665a94971ae9c350ac53969e9efcbf90478cbf90",
+ "representation": "total input plus output-value histogram"
+ },
+ "parents": []
+ },
+ "determinism": {
+ "ordering": "output values in descending order",
+ "rng_algorithm": null,
+ "rng_seed": null
+ }
+}
diff --git a/catalog/datasets/entity-bitmex-2019-v1.json b/catalog/datasets/entity-bitmex-2019-v1.json
new file mode 100644
index 00000000..c1cdf89f
--- /dev/null
+++ b/catalog/datasets/entity-bitmex-2019-v1.json
@@ -0,0 +1 @@
+{"schema_version":1,"id":"entity-bitmex-2019-v1","title":"BitMEX 2019 null-control fixture","kind":"git_fixture","schema":"bitcoin-address-graph-ndjson-v1","format":"ndjson.gz","content":{"bytes":2523417,"sha256":"699c87235bce17a43a8befe2c412dd7bea61ffe3dd772abe7addcbae5a4b8ea8"},"locations":{"local_path":"tests/fixtures/entity_bitmex_2019.ndjson.gz","canonical":"https://github.com/bc1cindy/decluster/raw/678ecb5fa4681679c378d8adef7bd1c4f2800f37/tests/fixtures/entity_bitmex_2019.ndjson.gz","mirrors":[]},"source":{"provider":"decluster fixture export","snapshot_time":"2026-08-31T03:42:16Z","recipe":null,"recipe_sha256":null},"license":{"data":"upstream license undetermined; redistribution authorized by the dataset publisher on 2026-09-04","recipe":null},"redistribution":"allowed","sensitivity":"public_auxiliary","normalization":{"code_revision":"678ecb5fa4681679c378d8adef7bd1c4f2800f37","parameters":{},"parents":[]},"determinism":{"ordering":"canonical byte order as stored","rng_algorithm":null,"rng_seed":null}}
diff --git a/catalog/datasets/entity-satoshidice-2013-v1.json b/catalog/datasets/entity-satoshidice-2013-v1.json
new file mode 100644
index 00000000..1da62363
--- /dev/null
+++ b/catalog/datasets/entity-satoshidice-2013-v1.json
@@ -0,0 +1 @@
+{"schema_version":1,"id":"entity-satoshidice-2013-v1","title":"SatoshiDice 2013 positive-control fixture","kind":"git_fixture","schema":"bitcoin-address-graph-ndjson-v1","format":"ndjson.gz","content":{"bytes":1315051,"sha256":"6cac465a5eca59633fbb3e6f99b83146b9b0e590adf95801a303ccd815699846"},"locations":{"local_path":"tests/fixtures/entity_satoshidice_2013.ndjson.gz","canonical":"https://github.com/bc1cindy/decluster/raw/554e72131df9b28aa2a90b09184a159a841d3077/tests/fixtures/entity_satoshidice_2013.ndjson.gz","mirrors":[]},"source":{"provider":"decluster fixture export","snapshot_time":"2026-08-31T02:54:00Z","recipe":null,"recipe_sha256":null},"license":{"data":"upstream license undetermined; redistribution authorized by the dataset publisher on 2026-09-04","recipe":null},"redistribution":"allowed","sensitivity":"public_auxiliary","normalization":{"code_revision":"554e72131df9b28aa2a90b09184a159a841d3077","parameters":{},"parents":[]},"determinism":{"ordering":"canonical byte order as stored","rng_algorithm":null,"rng_seed":null}}
diff --git a/catalog/datasets/epoch-graph-2016-391104-392111-v1.json b/catalog/datasets/epoch-graph-2016-391104-392111-v1.json
new file mode 100644
index 00000000..4a3ec187
--- /dev/null
+++ b/catalog/datasets/epoch-graph-2016-391104-392111-v1.json
@@ -0,0 +1,46 @@
+{
+ "schema_version": 1,
+ "id": "epoch-graph-2016-391104-392111-v1",
+ "title": "2016 weekly address graph, blocks 391104-392111",
+ "kind": "immutable_snapshot",
+ "schema": "decluster-epoch-graph-v1",
+ "format": "epochgraph.xz",
+ "content": {
+ "bytes": 10422968,
+ "sha256": "cd129cd4bb225ea7a6733dab891b20272d62d1b0f3a6caf07a4209f9f2368015"
+ },
+ "locations": {
+ "local_path": "data/epochs-2016-weekly-v1/391104-392111.epochgraph.xz",
+ "canonical": null,
+ "mirrors": []
+ },
+ "source": {
+ "provider": "derived from a project-held BigQuery export of the public chain",
+ "snapshot_time": "2026-08",
+ "recipe": "decluster/epoch_graph.py",
+ "recipe_sha256": "8d9f0b23b80b1ce8548c57001cb590a7a1b816f5046ef07b74febcd94930eee2"
+ },
+ "license": {
+ "data": "public on-chain data; redistribution authorized by the exporter",
+ "recipe": "MIT"
+ },
+ "redistribution": "allowed",
+ "sensitivity": "public_on_chain",
+ "normalization": {
+ "code_revision": "5890be00cedaa288bf72457cf84a5834ad97a573",
+ "parameters": {
+ "transactions": 982021,
+ "dropped": "txid, which no consumer reads",
+ "addresses": "replaced by integers in one namespace shared by every window, so an id means the same address in each; the engine's output is invariant under any bijective renaming (tests/test_epoch_graph.py)",
+ "kept_verbatim": "addresses a name-emitting detector matches by prefix",
+ "compression": "lzma preset 6",
+ "namespace": "epochs-2016-weekly-v1"
+ },
+ "parents": []
+ },
+ "determinism": {
+ "ordering": "export record order",
+ "rng_algorithm": null,
+ "rng_seed": null
+ }
+}
diff --git a/catalog/datasets/epoch-graph-2016-392112-393119-v1.json b/catalog/datasets/epoch-graph-2016-392112-393119-v1.json
new file mode 100644
index 00000000..38bc7078
--- /dev/null
+++ b/catalog/datasets/epoch-graph-2016-392112-393119-v1.json
@@ -0,0 +1,46 @@
+{
+ "schema_version": 1,
+ "id": "epoch-graph-2016-392112-393119-v1",
+ "title": "2016 weekly address graph, blocks 392112-393119",
+ "kind": "immutable_snapshot",
+ "schema": "decluster-epoch-graph-v1",
+ "format": "epochgraph.xz",
+ "content": {
+ "bytes": 12928408,
+ "sha256": "0893000345e68249911cb8ee6467a47fa6feecd3022f27e2732bcabfb4725f11"
+ },
+ "locations": {
+ "local_path": "data/epochs-2016-weekly-v1/392112-393119.epochgraph.xz",
+ "canonical": null,
+ "mirrors": []
+ },
+ "source": {
+ "provider": "derived from a project-held BigQuery export of the public chain",
+ "snapshot_time": "2026-08",
+ "recipe": "decluster/epoch_graph.py",
+ "recipe_sha256": "8d9f0b23b80b1ce8548c57001cb590a7a1b816f5046ef07b74febcd94930eee2"
+ },
+ "license": {
+ "data": "public on-chain data; redistribution authorized by the exporter",
+ "recipe": "MIT"
+ },
+ "redistribution": "allowed",
+ "sensitivity": "public_on_chain",
+ "normalization": {
+ "code_revision": "5890be00cedaa288bf72457cf84a5834ad97a573",
+ "parameters": {
+ "transactions": 1116563,
+ "dropped": "txid, which no consumer reads",
+ "addresses": "replaced by integers in one namespace shared by every window, so an id means the same address in each; the engine's output is invariant under any bijective renaming (tests/test_epoch_graph.py)",
+ "kept_verbatim": "addresses a name-emitting detector matches by prefix",
+ "compression": "lzma preset 6",
+ "namespace": "epochs-2016-weekly-v1"
+ },
+ "parents": []
+ },
+ "determinism": {
+ "ordering": "export record order",
+ "rng_algorithm": null,
+ "rng_seed": null
+ }
+}
diff --git a/catalog/datasets/epoch-graph-2016-393120-394127-v1.json b/catalog/datasets/epoch-graph-2016-393120-394127-v1.json
new file mode 100644
index 00000000..ce21e9af
--- /dev/null
+++ b/catalog/datasets/epoch-graph-2016-393120-394127-v1.json
@@ -0,0 +1,46 @@
+{
+ "schema_version": 1,
+ "id": "epoch-graph-2016-393120-394127-v1",
+ "title": "2016 weekly address graph, blocks 393120-394127",
+ "kind": "immutable_snapshot",
+ "schema": "decluster-epoch-graph-v1",
+ "format": "epochgraph.xz",
+ "content": {
+ "bytes": 16193076,
+ "sha256": "314376777d4c596497aa6c8b3369916bb752873a911c9786aae2b71a408c0d11"
+ },
+ "locations": {
+ "local_path": "data/epochs-2016-weekly-v1/393120-394127.epochgraph.xz",
+ "canonical": null,
+ "mirrors": []
+ },
+ "source": {
+ "provider": "derived from a project-held BigQuery export of the public chain",
+ "snapshot_time": "2026-08",
+ "recipe": "decluster/epoch_graph.py",
+ "recipe_sha256": "8d9f0b23b80b1ce8548c57001cb590a7a1b816f5046ef07b74febcd94930eee2"
+ },
+ "license": {
+ "data": "public on-chain data; redistribution authorized by the exporter",
+ "recipe": "MIT"
+ },
+ "redistribution": "allowed",
+ "sensitivity": "public_on_chain",
+ "normalization": {
+ "code_revision": "5890be00cedaa288bf72457cf84a5834ad97a573",
+ "parameters": {
+ "transactions": 1357379,
+ "dropped": "txid, which no consumer reads",
+ "addresses": "replaced by integers in one namespace shared by every window, so an id means the same address in each; the engine's output is invariant under any bijective renaming (tests/test_epoch_graph.py)",
+ "kept_verbatim": "addresses a name-emitting detector matches by prefix",
+ "compression": "lzma preset 6",
+ "namespace": "epochs-2016-weekly-v1"
+ },
+ "parents": []
+ },
+ "determinism": {
+ "ordering": "export record order",
+ "rng_algorithm": null,
+ "rng_seed": null
+ }
+}
diff --git a/catalog/datasets/epoch-graph-2016-394128-395135-v1.json b/catalog/datasets/epoch-graph-2016-394128-395135-v1.json
new file mode 100644
index 00000000..56ca992e
--- /dev/null
+++ b/catalog/datasets/epoch-graph-2016-394128-395135-v1.json
@@ -0,0 +1,46 @@
+{
+ "schema_version": 1,
+ "id": "epoch-graph-2016-394128-395135-v1",
+ "title": "2016 weekly address graph, blocks 394128-395135",
+ "kind": "immutable_snapshot",
+ "schema": "decluster-epoch-graph-v1",
+ "format": "epochgraph.xz",
+ "content": {
+ "bytes": 15600012,
+ "sha256": "75b54d557a95439b72b6b1795a0e45dd287b5c7d6bb925fc4684c2d861877efb"
+ },
+ "locations": {
+ "local_path": "data/epochs-2016-weekly-v1/394128-395135.epochgraph.xz",
+ "canonical": null,
+ "mirrors": []
+ },
+ "source": {
+ "provider": "derived from a project-held BigQuery export of the public chain",
+ "snapshot_time": "2026-08",
+ "recipe": "decluster/epoch_graph.py",
+ "recipe_sha256": "8d9f0b23b80b1ce8548c57001cb590a7a1b816f5046ef07b74febcd94930eee2"
+ },
+ "license": {
+ "data": "public on-chain data; redistribution authorized by the exporter",
+ "recipe": "MIT"
+ },
+ "redistribution": "allowed",
+ "sensitivity": "public_on_chain",
+ "normalization": {
+ "code_revision": "5890be00cedaa288bf72457cf84a5834ad97a573",
+ "parameters": {
+ "transactions": 1310613,
+ "dropped": "txid, which no consumer reads",
+ "addresses": "replaced by integers in one namespace shared by every window, so an id means the same address in each; the engine's output is invariant under any bijective renaming (tests/test_epoch_graph.py)",
+ "kept_verbatim": "addresses a name-emitting detector matches by prefix",
+ "compression": "lzma preset 6",
+ "namespace": "epochs-2016-weekly-v1"
+ },
+ "parents": []
+ },
+ "determinism": {
+ "ordering": "export record order",
+ "rng_algorithm": null,
+ "rng_seed": null
+ }
+}
diff --git a/catalog/datasets/epoch-graph-2016-395136-396143-v1.json b/catalog/datasets/epoch-graph-2016-395136-396143-v1.json
new file mode 100644
index 00000000..6de020d1
--- /dev/null
+++ b/catalog/datasets/epoch-graph-2016-395136-396143-v1.json
@@ -0,0 +1,46 @@
+{
+ "schema_version": 1,
+ "id": "epoch-graph-2016-395136-396143-v1",
+ "title": "2016 weekly address graph, blocks 395136-396143",
+ "kind": "immutable_snapshot",
+ "schema": "decluster-epoch-graph-v1",
+ "format": "epochgraph.xz",
+ "content": {
+ "bytes": 13650936,
+ "sha256": "0331c77b8254eb8f6ffce6037e0b9b8dd8c23db3aa81b30a9af1f688d174db36"
+ },
+ "locations": {
+ "local_path": "data/epochs-2016-weekly-v1/395136-396143.epochgraph.xz",
+ "canonical": null,
+ "mirrors": []
+ },
+ "source": {
+ "provider": "derived from a project-held BigQuery export of the public chain",
+ "snapshot_time": "2026-08",
+ "recipe": "decluster/epoch_graph.py",
+ "recipe_sha256": "8d9f0b23b80b1ce8548c57001cb590a7a1b816f5046ef07b74febcd94930eee2"
+ },
+ "license": {
+ "data": "public on-chain data; redistribution authorized by the exporter",
+ "recipe": "MIT"
+ },
+ "redistribution": "allowed",
+ "sensitivity": "public_on_chain",
+ "normalization": {
+ "code_revision": "5890be00cedaa288bf72457cf84a5834ad97a573",
+ "parameters": {
+ "transactions": 1170182,
+ "dropped": "txid, which no consumer reads",
+ "addresses": "replaced by integers in one namespace shared by every window, so an id means the same address in each; the engine's output is invariant under any bijective renaming (tests/test_epoch_graph.py)",
+ "kept_verbatim": "addresses a name-emitting detector matches by prefix",
+ "compression": "lzma preset 6",
+ "namespace": "epochs-2016-weekly-v1"
+ },
+ "parents": []
+ },
+ "determinism": {
+ "ordering": "export record order",
+ "rng_algorithm": null,
+ "rng_seed": null
+ }
+}
diff --git a/catalog/datasets/fingerprint-blkcache-sample-v1.json b/catalog/datasets/fingerprint-blkcache-sample-v1.json
new file mode 100644
index 00000000..2da53378
--- /dev/null
+++ b/catalog/datasets/fingerprint-blkcache-sample-v1.json
@@ -0,0 +1 @@
+{"schema_version":1,"id":"fingerprint-blkcache-sample-v1","title":"Transaction-construction fingerprint sample","kind":"git_fixture","schema":"bitcoin-transaction-array-v1","format":"json","content":{"bytes":1701974,"sha256":"406ce2d7d0a04f7c73281bce6b6a781012a8765e079693f010a0154e926c6a6c"},"locations":{"local_path":"tests/fixtures/fingerprint_blkcache_sample.json","canonical":"https://github.com/bc1cindy/decluster/raw/2f1bbae5ac4923ff7b799b25be0dd66b4ec3f692/tests/fixtures/fingerprint_blkcache_sample.json","mirrors":[]},"source":{"provider":"decluster blkcache sample export","snapshot_time":"2026-08-31T02:40:25Z","recipe":null,"recipe_sha256":null},"license":{"data":"upstream license undetermined; redistribution authorized by the dataset publisher on 2026-09-04","recipe":null},"redistribution":"allowed","sensitivity":"public_on_chain","normalization":{"code_revision":"2f1bbae5ac4923ff7b799b25be0dd66b4ec3f692","parameters":{},"parents":[]},"determinism":{"ordering":"canonical byte order as stored","rng_algorithm":null,"rng_seed":null}}
diff --git a/catalog/datasets/fs-blkcache-2026-09-04-v1.json b/catalog/datasets/fs-blkcache-2026-09-04-v1.json
new file mode 100644
index 00000000..d184b99d
--- /dev/null
+++ b/catalog/datasets/fs-blkcache-2026-09-04-v1.json
@@ -0,0 +1,44 @@
+{
+ "schema_version": 1,
+ "id": "fs-blkcache-2026-09-04-v1",
+ "title": "Fellegi-Sunter block transaction snapshot",
+ "kind": "immutable_snapshot",
+ "schema": "mempool-space-block-transaction-pages-v1",
+ "format": "tar.gz",
+ "content": {
+ "bytes": 33352455,
+ "sha256": "5a69f6f446dbdfcce09dddc91c653e5d841124d14020b720fdea276315c775a6"
+ },
+ "locations": {
+ "local_path": "data/fs-blkcache-2026-09-04.tar.gz",
+ "canonical": null,
+ "mirrors": []
+ },
+ "source": {
+ "provider": "project-provided public Bitcoin on-chain snapshot",
+ "snapshot_time": "2026-09-04",
+ "recipe": null,
+ "recipe_sha256": null
+ },
+ "license": {
+ "data": "redistribution authorized by snapshot provider",
+ "recipe": null
+ },
+ "redistribution": "allowed",
+ "sensitivity": "public_on_chain",
+ "normalization": {
+ "code_revision": null,
+ "parameters": {
+ "files": 904,
+ "archive_path": ".blkcache",
+ "compression": "gzip -n -9",
+ "historical_directory_digest": "91c444bd4e9715d7739ccc9d68c7d9751762e25b647e3eef647c6ec0b48ba90c"
+ },
+ "parents": []
+ },
+ "determinism": {
+ "ordering": "lexicographic archive path order",
+ "rng_algorithm": null,
+ "rng_seed": null
+ }
+}
diff --git a/catalog/datasets/graph-deanon-2016-v1.json b/catalog/datasets/graph-deanon-2016-v1.json
new file mode 100644
index 00000000..6870b44f
--- /dev/null
+++ b/catalog/datasets/graph-deanon-2016-v1.json
@@ -0,0 +1 @@
+{"schema_version":1,"id":"graph-deanon-2016-v1","title":"Bitcoin graph-structure fixture from 2016","kind":"git_fixture","schema":"bitcoin-address-graph-ndjson-v1","format":"ndjson.gz","content":{"bytes":1405865,"sha256":"68690ce8689378321e4187b111c7b4300f88e5bb405a25dcb67125cd0c739c5b"},"locations":{"local_path":"tests/fixtures/graph_deanon_2016.ndjson.gz","canonical":"https://github.com/bc1cindy/decluster/raw/2fdbd9573c12540012ffef83fb25114120d27ef6/tests/fixtures/graph_deanon_2016.ndjson.gz","mirrors":[]},"source":{"provider":"decluster graph fixture export","snapshot_time":"2026-08-31T02:48:43Z","recipe":null,"recipe_sha256":null},"license":{"data":"upstream license undetermined; redistribution authorized by the dataset publisher on 2026-09-04","recipe":null},"redistribution":"allowed","sensitivity":"public_on_chain","normalization":{"code_revision":"2fdbd9573c12540012ffef83fb25114120d27ef6","parameters":{},"parents":[]},"determinism":{"ordering":"canonical byte order as stored","rng_algorithm":null,"rng_seed":null}}
diff --git a/catalog/datasets/lumen-explorer-data-v1.json b/catalog/datasets/lumen-explorer-data-v1.json
new file mode 100644
index 00000000..0f7b8eb1
--- /dev/null
+++ b/catalog/datasets/lumen-explorer-data-v1.json
@@ -0,0 +1 @@
+{"schema_version":1,"id":"lumen-explorer-data-v1","title":"Lumen explorer aggregate fixture","kind":"git_fixture","schema":"lumen-explorer-aggregate-v1","format":"json","content":{"bytes":432353,"sha256":"5337d4bdc574726a2cb4e28f329f16709b497479a7493882bd38d77875660ac9"},"locations":{"local_path":"tests/fixtures/lumen_explorer_data.json","canonical":"https://github.com/bc1cindy/decluster/raw/103b13efc9e32569642983d7038f365dcb0c09c6/tests/fixtures/lumen_explorer_data.json","mirrors":[]},"source":{"provider":"Lumen explorer public aggregate","snapshot_time":"2026-08-31T02:40:25Z","recipe":null,"recipe_sha256":null},"license":{"data":"upstream license undetermined; redistribution authorized by the dataset publisher on 2026-09-04","recipe":null},"redistribution":"allowed","sensitivity":"public_auxiliary","normalization":{"code_revision":"103b13efc9e32569642983d7038f365dcb0c09c6","parameters":{},"parents":[]},"determinism":{"ordering":"canonical byte order as stored","rng_algorithm":null,"rng_seed":null}}
diff --git a/catalog/datasets/merged-anchor-931d6627-v1.json b/catalog/datasets/merged-anchor-931d6627-v1.json
new file mode 100644
index 00000000..0f4bde55
--- /dev/null
+++ b/catalog/datasets/merged-anchor-931d6627-v1.json
@@ -0,0 +1,39 @@
+{
+ "schema_version": 1,
+ "id": "merged-anchor-931d6627-v1",
+ "title": "The merged anchor's two funding transactions",
+ "kind": "git_fixture",
+ "schema": "esplora-transaction-pair-v1",
+ "format": "json",
+ "content": {
+ "bytes": 5326,
+ "sha256": "e0e687d23f9417ace6364372fa8d72ea1bb4f83dabaf08d08d2523423e247911"
+ },
+ "locations": {
+ "local_path": "tests/fixtures/merged_anchor_931d6627.json",
+ "canonical": null,
+ "mirrors": []
+ },
+ "source": {
+ "provider": "mempool.space transaction records",
+ "snapshot_time": null,
+ "recipe": "the two transactions PAPER.md names on this anchor, as fetched",
+ "recipe_sha256": null
+ },
+ "determinism": {
+ "ordering": "the two records sorted by key as fetched",
+ "rng_algorithm": null,
+ "rng_seed": null
+ },
+ "license": {
+ "data": "public on-chain transaction records; no upstream licence attaches",
+ "recipe": null
+ },
+ "normalization": {
+ "code_revision": "67191517916bec3fc02d4bb5702dc3a92fa869ff",
+ "parameters": {},
+ "parents": []
+ },
+ "redistribution": "allowed",
+ "sensitivity": "public_on_chain"
+}
diff --git a/catalog/datasets/ns-bitcoin-left-2016-v1.json b/catalog/datasets/ns-bitcoin-left-2016-v1.json
new file mode 100644
index 00000000..e1da3b50
--- /dev/null
+++ b/catalog/datasets/ns-bitcoin-left-2016-v1.json
@@ -0,0 +1,16 @@
+{
+ "schema_version": 1,
+ "id": "ns-bitcoin-left-2016-v1",
+ "title": "N-S Bitcoin left observation window",
+ "kind": "immutable_snapshot",
+ "schema": "bitcoin-address-flow-ndjson-v1",
+ "format": "ndjson.gz",
+ "content": {"bytes": 23234649, "sha256": "069d207b44dcbbeaec0fae98262a3fcb76b3fe834834609e67fe883e229f26d3"},
+ "locations": {"local_path": "data/ns-bitcoin-2016-v1/left-391992-392111.ndjson.gz", "canonical": null, "mirrors": []},
+ "source": {"provider": "project-provided public Bitcoin on-chain snapshot", "snapshot_time": "2026-09-04", "recipe": "decluster/ns_bitcoin_snapshot.py", "recipe_sha256": "0b05957e99c38e9f56bc390437b3ca220018c3d873f6c3b60c649983b06d5605"},
+ "license": {"data": "redistribution authorized by snapshot provider", "recipe": "MIT"},
+ "redistribution": "allowed",
+ "sensitivity": "public_on_chain",
+ "normalization": {"code_revision": null, "parameters": {"minimum_height": 391992, "maximum_height": 392111, "records": 133576, "gzip_mtime": 0}, "parents": []},
+ "determinism": {"ordering": "source record order", "rng_algorithm": null, "rng_seed": null}
+}
diff --git a/catalog/datasets/ns-bitcoin-right-2016-v1.json b/catalog/datasets/ns-bitcoin-right-2016-v1.json
new file mode 100644
index 00000000..df5cd53b
--- /dev/null
+++ b/catalog/datasets/ns-bitcoin-right-2016-v1.json
@@ -0,0 +1,16 @@
+{
+ "schema_version": 1,
+ "id": "ns-bitcoin-right-2016-v1",
+ "title": "N-S Bitcoin right observation window",
+ "kind": "immutable_snapshot",
+ "schema": "bitcoin-address-flow-ndjson-v1",
+ "format": "ndjson.gz",
+ "content": {"bytes": 26043862, "sha256": "1d46dcccb51cd8f1a1d89481db4df45599caaaf8569f9e8e2de37e81e0e5d122"},
+ "locations": {"local_path": "data/ns-bitcoin-2016-v1/right-392112-392231.ndjson.gz", "canonical": null, "mirrors": []},
+ "source": {"provider": "project-provided public Bitcoin on-chain snapshot", "snapshot_time": "2026-09-04", "recipe": "decluster/ns_bitcoin_snapshot.py", "recipe_sha256": "0b05957e99c38e9f56bc390437b3ca220018c3d873f6c3b60c649983b06d5605"},
+ "license": {"data": "redistribution authorized by snapshot provider", "recipe": "MIT"},
+ "redistribution": "allowed",
+ "sensitivity": "public_on_chain",
+ "normalization": {"code_revision": null, "parameters": {"minimum_height": 392112, "maximum_height": 392231, "records": 157724, "gzip_mtime": 0}, "parents": []},
+ "determinism": {"ordering": "source record order", "rng_algorithm": null, "rng_seed": null}
+}
diff --git a/catalog/datasets/partition-cuts-300k-2016-v1.json b/catalog/datasets/partition-cuts-300k-2016-v1.json
new file mode 100644
index 00000000..eee10009
--- /dev/null
+++ b/catalog/datasets/partition-cuts-300k-2016-v1.json
@@ -0,0 +1,41 @@
+{
+ "schema_version": 1,
+ "id": "partition-cuts-300k-2016-v1",
+ "title": "The 300,000-transaction 2016 prefix the partition-cut comparison was measured on",
+ "kind": "git_fixture",
+ "schema": "bitcoin-address-graph-ndjson-v1",
+ "format": "ndjson.gz",
+ "content": {
+ "bytes": 52278431,
+ "sha256": "44192156107867a305e2f608af9185e4ce22fb88372238c88683f91120b1db16"
+ },
+ "locations": {
+ "local_path": "data/partition-cuts-300k-2016.ndjson.gz",
+ "canonical": null,
+ "mirrors": []
+ },
+ "source": {
+ "provider": "decluster multi-epoch export",
+ "snapshot_time": null,
+ "recipe": "the first 300,000 transactions of epoch_2016_01_391104-392111.ndjson.gz, recompressed with mtime 0 at level 9",
+ "recipe_sha256": null
+ },
+ "determinism": {
+ "ordering": "the export's own line order, truncated at 300,000",
+ "rng_algorithm": null,
+ "rng_seed": null
+ },
+ "license": {
+ "data": "public on-chain transaction records; no upstream licence attaches",
+ "recipe": null
+ },
+ "normalization": {
+ "code_revision": "096fe63467cae2ea18a1160e1af5925e0f3e75b1",
+ "parameters": {
+ "transactions": 300000
+ },
+ "parents": []
+ },
+ "redistribution": "allowed",
+ "sensitivity": "public_on_chain"
+}
diff --git a/catalog/datasets/reid-signatures-v1.json b/catalog/datasets/reid-signatures-v1.json
new file mode 100644
index 00000000..3526486c
--- /dev/null
+++ b/catalog/datasets/reid-signatures-v1.json
@@ -0,0 +1 @@
+{"schema_version":1,"id":"reid-signatures-v1","title":"Ancestry-signature fixture for sparse matching","kind":"git_fixture","schema":"ancestry-signatures-json-v1","format":"json.gz","content":{"bytes":2051632,"sha256":"dbc4214fb499a96327326c4ea31341a76878c1867bfa3ccc57df88d850cdaa6a"},"locations":{"local_path":"tests/fixtures/reid_sigs.json.gz","canonical":"https://github.com/bc1cindy/decluster/raw/495874b5063bd83fc24e32eec1ebeb933471f6b9/tests/fixtures/reid_sigs.json.gz","mirrors":[]},"source":{"provider":"decluster ancestry-signature export","snapshot_time":"2026-08-31T02:40:25Z","recipe":null,"recipe_sha256":null},"license":{"data":"upstream license undetermined; redistribution authorized by the dataset publisher on 2026-09-04","recipe":null},"redistribution":"allowed","sensitivity":"public_on_chain","normalization":{"code_revision":"495874b5063bd83fc24e32eec1ebeb933471f6b9","parameters":{},"parents":[]},"determinism":{"ordering":"canonical byte order as stored","rng_algorithm":null,"rng_seed":null}}
diff --git a/catalog/datasets/slice-a-channels-2016-v1.json b/catalog/datasets/slice-a-channels-2016-v1.json
new file mode 100644
index 00000000..ad9b353c
--- /dev/null
+++ b/catalog/datasets/slice-a-channels-2016-v1.json
@@ -0,0 +1 @@
+{"schema_version":1,"id":"slice-a-channels-2016-v1","title":"Bitcoin channel-comparison fixture from 2016","kind":"git_fixture","schema":"bitcoin-address-graph-ndjson-v1","format":"ndjson.gz","content":{"bytes":1837504,"sha256":"66104889f8b79f2938edb6666f16dfa19a74c68cd8016832b4f2a5e03b11865f"},"locations":{"local_path":"tests/fixtures/slice_a_channels_2016.ndjson.gz","canonical":"https://github.com/bc1cindy/decluster/raw/43d3faaab07f6703361c300fb6d01a916bb063f0/tests/fixtures/slice_a_channels_2016.ndjson.gz","mirrors":[]},"source":{"provider":"decluster channel fixture export","snapshot_time":"2026-09-03T13:17:00Z","recipe":null,"recipe_sha256":null},"license":{"data":"upstream license undetermined; redistribution authorized by the dataset publisher on 2026-09-04","recipe":null},"redistribution":"allowed","sensitivity":"public_on_chain","normalization":{"code_revision":"43d3faaab07f6703361c300fb6d01a916bb063f0","parameters":{},"parents":[]},"determinism":{"ordering":"canonical byte order as stored","rng_algorithm":null,"rng_seed":null}}
diff --git a/catalog/datasets/subtx-demix-cache-2026-09-04-v1.json b/catalog/datasets/subtx-demix-cache-2026-09-04-v1.json
new file mode 100644
index 00000000..89f066b8
--- /dev/null
+++ b/catalog/datasets/subtx-demix-cache-2026-09-04-v1.json
@@ -0,0 +1,16 @@
+{
+ "schema_version": 1,
+ "id": "subtx-demix-cache-2026-09-04-v1",
+ "title": "CoinJoin-adjacent transaction cache for de-mix diagnostics",
+ "kind": "immutable_snapshot",
+ "schema": "mempool-space-transaction-json-v1",
+ "format": "tar.gz",
+ "content": {"bytes": 114994, "sha256": "191c8c2f082ac84af71b28a70169207d785243a276cf5b6e472ae6f5588a65a6"},
+ "locations": {"local_path": "data/subtx-demix-cache-2026-09-04.tar.gz", "canonical": null, "mirrors": []},
+ "source": {"provider": "project-provided public Bitcoin on-chain cache", "snapshot_time": "2026-09-04", "recipe": null, "recipe_sha256": null},
+ "license": {"data": "redistribution authorized by snapshot provider", "recipe": null},
+ "redistribution": "allowed",
+ "sensitivity": "public_on_chain",
+ "normalization": {"code_revision": null, "parameters": {"archive_path": ".cache", "json_files": 39, "compression": "gzip -n -9"}, "parents": []},
+ "determinism": {"ordering": "archive order as preserved", "rng_algorithm": null, "rng_seed": null}
+}
diff --git a/catalog/lumen-vectors-2026.json b/catalog/lumen-vectors-2026.json
new file mode 100644
index 00000000..8c18f75c
--- /dev/null
+++ b/catalog/lumen-vectors-2026.json
@@ -0,0 +1,41 @@
+{
+ "artifact": "939969-962721.csv.zst",
+ "sha256": "9ac60be6175dc7f804e508c33edce3f84fbe58de8cd3296729f841ed779ebaa1",
+ "row_count": 98185633,
+ "start_height": 939969,
+ "end_height": 962721,
+ "columns": [
+ "txid",
+ "version",
+ "nsequence",
+ "nlocktime",
+ "locktime_offset",
+ "input_order",
+ "output_order",
+ "output_structure",
+ "input_types",
+ "output_types",
+ "low_r",
+ "sighash",
+ "uncompressed_pubkey",
+ "op_return",
+ "input_subtype",
+ "low_s",
+ "ecdsa_sigs",
+ "input_age",
+ "feerate_bucket",
+ "round_feerate",
+ "change_position",
+ "change_type"
+ ],
+ "comparison_aggregate": {
+ "source": "https://github.com/fungi-protocol/lumen-fingerprints/blob/main/docs/src/explorer-data.json",
+ "fixture": "tests/fixtures/lumen_explorer_data.json",
+ "fixture_sha256": "5337d4bdc574726a2cb4e28f329f16709b497479a7493882bd38d77875660ac9",
+ "transaction_count": 98179414,
+ "start_height": 939969,
+ "end_height": 962720,
+ "delta": 6219,
+ "status": "unreconciled; adjacent end heights are not proof of cause"
+ }
+}
diff --git a/catalog/runs/amount-channel-survey-v1.json b/catalog/runs/amount-channel-survey-v1.json
new file mode 100644
index 00000000..3ef79b37
--- /dev/null
+++ b/catalog/runs/amount-channel-survey-v1.json
@@ -0,0 +1,93 @@
+{
+ "schema_version": 1,
+ "id": "amount-channel-survey-v1",
+ "claim_ids": [
+ "ctp.payjoin.unnecessary_input",
+ "ctp.amount.subtransaction_ambiguity"
+ ],
+ "code": {
+ "revision": "bcd9a3e511860b1b0ff5f3ee6338a2df29ecbfbf",
+ "dirty": false
+ },
+ "command": {
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.amount_channel_summary",
+ "reproduce",
+ "--artifact",
+ "results/artifacts/amount-channel-survey-v1.json",
+ "--markdown",
+ "results/generated/amount-channel-survey-v1.md"
+ ]
+ },
+ "environment": {
+ "python": "3.13.7",
+ "lock_digest": "e0d6a8cada5d383c0aca2bd2a37f28c15721392469c94084128d369c29c5544d",
+ "platform": "macOS-26.6.2-arm64-arm-64bit-Mach-O",
+ "dependencies": []
+ },
+ "datasets": [],
+ "parameters": {
+ "composition": "identity-checked upstream artifacts only",
+ "components": {
+ "amount-local-channels-v1": "210ecb06ecaa32cc4cabe03ced8b85b0882a824dca2c361bd2c5dede7dcccf02",
+ "amount-subset-sum-v1": "240592b5af814fbcd28b06221805b934424170119da5e62e9da0281944377a9d",
+ "amount-per-coin-v1": "7be46858e221d0c942e7fa3ccc93b5eba28cf3a494fd00f07c2d50f2caa473df",
+ "amount-mapping-family-v1": "11f0ec645ea1be334dc7d24fbfc31047b234f335f48b5a514960472c7444b7d6",
+ "amount-pairwise-family-v1": "83f6a9e8501ee4df45f08b8c8ffc2f7265d40bf9add6d3f5093d712657c9dbf5"
+ }
+ },
+ "rng": {
+ "algorithm": null,
+ "seeds": []
+ },
+ "outputs": [
+ {
+ "path": "results/artifacts/amount-channel-survey-v1.json",
+ "bytes": 2589,
+ "sha256": "fcd4ad596d735b7c5a8dba43bc96481946ce5e78c3806932b60c10c865d1dcf8"
+ },
+ {
+ "path": "results/generated/amount-channel-survey-v1.md",
+ "bytes": 1082,
+ "sha256": "2f74b40fb24558a23f162d09da46a5f76a4434c170a78e0deb75eabba5d2ca28"
+ }
+ ],
+ "reproducibility": {
+ "level": "bitwise_reproducible",
+ "availability": "complete"
+ },
+ "verification": {
+ "mode": "exact",
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.amount_channel_summary",
+ "verify",
+ "--artifact",
+ "results/artifacts/amount-channel-survey-v1.json",
+ "--markdown",
+ "results/generated/amount-channel-survey-v1.md"
+ ],
+ "tests": [
+ "tests/test_amount_channel_summary_experiment.py"
+ ],
+ "properties": [
+ "all five component identities are checked before composition",
+ "the summary performs no mechanism calculation",
+ "channels remain separate rather than becoming a scalar score",
+ "DSS claims retain their restricted-family qualification",
+ "the generated Markdown equals the canonical rendering"
+ ],
+ "tolerance": null
+ },
+ "limitations": [
+ "this artifact composes upstream results and performs no mechanism calculation",
+ "component SHA-256 identities are mandatory",
+ "the five components measure different objects and are not combined into one score",
+ "DSS mapping and pairwise claims remain restricted to DSS's mapping family",
+ "the Bitcoin snapshot covers one 137-block interval from 2023",
+ "this summary is not a privacy certificate or CoinScore"
+ ]
+}
diff --git a/catalog/runs/amount-local-channels-v1.json b/catalog/runs/amount-local-channels-v1.json
new file mode 100644
index 00000000..e6c22907
--- /dev/null
+++ b/catalog/runs/amount-local-channels-v1.json
@@ -0,0 +1,100 @@
+{
+ "schema_version": 1,
+ "id": "amount-local-channels-v1",
+ "claim_ids": [
+ "ctp.payjoin.unnecessary_input",
+ "ctp.amount.subtransaction_ambiguity"
+ ],
+ "code": {
+ "revision": "bcd9a3e511860b1b0ff5f3ee6338a2df29ecbfbf",
+ "dirty": false
+ },
+ "command": {
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.amount_local_channels",
+ "--dataset",
+ "data/amount-channel-812695-812831-v1.json",
+ "reproduce",
+ "--artifact",
+ "results/artifacts/amount-local-channels-v1.json",
+ "--markdown",
+ "results/generated/amount-local-channels-v1.md"
+ ]
+ },
+ "environment": {
+ "python": "3.13.7",
+ "lock_digest": "e0d6a8cada5d383c0aca2bd2a37f28c15721392469c94084128d369c29c5544d",
+ "platform": "macOS-26.6.2-arm64-arm-64bit-Mach-O",
+ "dependencies": []
+ },
+ "datasets": [
+ {
+ "id": "amount-channel-812695-812831-v1",
+ "sha256": "83b4391f78d07a2fa85a67218d5ae4420d8965da3c95af577592e9ec1850104d"
+ }
+ ],
+ "parameters": {
+ "minimum_height": 812695,
+ "maximum_height": 812831,
+ "known_participant_probe": "largest input value"
+ },
+ "rng": {
+ "algorithm": null,
+ "seeds": []
+ },
+ "outputs": [
+ {
+ "path": "results/artifacts/amount-local-channels-v1.json",
+ "bytes": 825,
+ "sha256": "210ecb06ecaa32cc4cabe03ced8b85b0882a824dca2c361bd2c5dede7dcccf02"
+ },
+ {
+ "path": "results/generated/amount-local-channels-v1.md",
+ "bytes": 436,
+ "sha256": "4f4ea247a4bbca8cbe1ef24bc7b2ae070576d046c1f04a62bb8d528118fe1c28"
+ }
+ ],
+ "reproducibility": {
+ "level": "bitwise_reproducible",
+ "availability": "complete"
+ },
+ "verification": {
+ "mode": "exact",
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.amount_local_channels",
+ "--dataset",
+ "data/amount-channel-812695-812831-v1.json",
+ "verify",
+ "--artifact",
+ "results/artifacts/amount-local-channels-v1.json",
+ "--markdown",
+ "results/generated/amount-local-channels-v1.md"
+ ],
+ "tests": [
+ "tests/test_amount_channel_snapshot.py",
+ "tests/test_amount_local_channels_experiment.py",
+ "tests/test_coinjoin_demix.py",
+ "tests/test_conservation.py"
+ ],
+ "properties": [
+ "each local channel is reported separately",
+ "CoinJoin shape and successful de-mix are distinct counts",
+ "roundness ties are abstentions rather than ownership labels",
+ "the conservation probe records its known-participant assumption",
+ "DSS, mapping entropy, subset-sum, and composition are excluded",
+ "the generated Markdown equals the canonical rendering"
+ ],
+ "tolerance": null
+ },
+ "limitations": [
+ "the snapshot covers one 137-block interval from 2023",
+ "no recognized JoinMarket round appears in the measured population",
+ "roundness ties show abstention, not ownership truth",
+ "the conservation probe assumes the largest input is the known participant",
+ "this run excludes subset-sum, DSS, mapping entropy, and score composition"
+ ]
+}
diff --git a/catalog/runs/amount-mapping-family-v1.json b/catalog/runs/amount-mapping-family-v1.json
new file mode 100644
index 00000000..646bf091
--- /dev/null
+++ b/catalog/runs/amount-mapping-family-v1.json
@@ -0,0 +1,117 @@
+{
+ "schema_version": 1,
+ "id": "amount-mapping-family-v1",
+ "claim_ids": [
+ "ctp.amount.subtransaction_ambiguity"
+ ],
+ "code": {
+ "revision": "bcd9a3e511860b1b0ff5f3ee6338a2df29ecbfbf",
+ "dirty": false
+ },
+ "command": {
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.amount_mapping_family",
+ "--dataset",
+ "data/amount-channel-812695-812831-v1.json",
+ "--cap",
+ "250",
+ "--wall-seconds",
+ "3.0",
+ "reproduce",
+ "--artifact",
+ "results/artifacts/amount-mapping-family-v1.json",
+ "--markdown",
+ "results/generated/amount-mapping-family-v1.md"
+ ]
+ },
+ "environment": {
+ "python": "3.13.7",
+ "lock_digest": "5ee1d70afd72fdb4ba3f01843e85e7ecd9a6d3e2a52abeb14c346a2143e3c186",
+ "platform": "macOS-26.6.2-arm64-arm-64bit-Mach-O",
+ "dependencies": [
+ {
+ "name": "dss",
+ "version": "0.1.0",
+ "source": "https://github.com/bc1cindy/dense-subset-sum",
+ "revision": "d8099bc540c9de7d0f3c9f095e8b2aad8f9d8c8d",
+ "lock_sha256": "f17a02f09da28dcc603901580d695466c44e1d1a0063e110d3faa375ae6066ca",
+ "license": "MIT",
+ "redistribution": "allowed",
+ "editable": false
+ }
+ ]
+ },
+ "datasets": [
+ {
+ "id": "amount-channel-812695-812831-v1",
+ "sha256": "83b4391f78d07a2fa85a67218d5ae4420d8965da3c95af577592e9ec1850104d"
+ }
+ ],
+ "parameters": {
+ "cap": 250,
+ "selection": "first complete multi-input transactions in source order",
+ "wall_seconds_per_transaction": 3.0
+ },
+ "rng": {
+ "algorithm": null,
+ "seeds": []
+ },
+ "outputs": [
+ {
+ "path": "results/artifacts/amount-mapping-family-v1.json",
+ "bytes": 942,
+ "sha256": "11f0ec645ea1be334dc7d24fbfc31047b234f335f48b5a514960472c7444b7d6"
+ },
+ {
+ "path": "results/generated/amount-mapping-family-v1.md",
+ "bytes": 559,
+ "sha256": "73afd1c112bcec43f16464f0589d4107d33a76c4085bd508d00aadc703366c0b"
+ }
+ ],
+ "reproducibility": {
+ "level": "bitwise_reproducible",
+ "availability": "complete"
+ },
+ "verification": {
+ "mode": "exact",
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.amount_mapping_family",
+ "--dataset",
+ "data/amount-channel-812695-812831-v1.json",
+ "--cap",
+ "250",
+ "--wall-seconds",
+ "3.0",
+ "verify",
+ "--artifact",
+ "results/artifacts/amount-mapping-family-v1.json",
+ "--markdown",
+ "results/generated/amount-mapping-family-v1.md"
+ ],
+ "tests": [
+ "tests/test_amount_mapping_family_experiment.py",
+ "tests/test_counting.py",
+ "tests/test_oracle_audit.py"
+ ],
+ "properties": [
+ "refusal, wall timeout, and execution failure remain distinct",
+ "entropy is labeled as uniform over DSS's restricted family",
+ "certain links are not presented as global ownership proofs",
+ "the selected population and ordering are explicit",
+ "the generated Markdown equals the canonical rendering"
+ ],
+ "tolerance": null
+ },
+ "limitations": [
+ "DSS enumerates a strict sub-family of the full balanced mappings",
+ "entropy is uniform over that restricted family, not a posterior over ownership",
+ "certain links are certain only within the restricted family",
+ "refusal, timeout, and execution failure are distinct outcomes",
+ "the sample is the first complete multi-input transactions in one 137-block snapshot",
+ "this diagnostic is not a privacy score or CoinScore"
+ ]
+}
diff --git a/catalog/runs/amount-pairwise-family-v1.json b/catalog/runs/amount-pairwise-family-v1.json
new file mode 100644
index 00000000..4de3bc21
--- /dev/null
+++ b/catalog/runs/amount-pairwise-family-v1.json
@@ -0,0 +1,117 @@
+{
+ "schema_version": 1,
+ "id": "amount-pairwise-family-v1",
+ "claim_ids": [
+ "ctp.amount.subtransaction_ambiguity"
+ ],
+ "code": {
+ "revision": "bcd9a3e511860b1b0ff5f3ee6338a2df29ecbfbf",
+ "dirty": false
+ },
+ "command": {
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.amount_pairwise_family",
+ "--dataset",
+ "data/amount-channel-812695-812831-v1.json",
+ "--cap",
+ "300",
+ "--wall-seconds",
+ "3.0",
+ "reproduce",
+ "--artifact",
+ "results/artifacts/amount-pairwise-family-v1.json",
+ "--markdown",
+ "results/generated/amount-pairwise-family-v1.md"
+ ]
+ },
+ "environment": {
+ "python": "3.13.7",
+ "lock_digest": "5ee1d70afd72fdb4ba3f01843e85e7ecd9a6d3e2a52abeb14c346a2143e3c186",
+ "platform": "macOS-26.6.2-arm64-arm-64bit-Mach-O",
+ "dependencies": [
+ {
+ "name": "dss",
+ "version": "0.1.0",
+ "source": "https://github.com/bc1cindy/dense-subset-sum",
+ "revision": "d8099bc540c9de7d0f3c9f095e8b2aad8f9d8c8d",
+ "lock_sha256": "f17a02f09da28dcc603901580d695466c44e1d1a0063e110d3faa375ae6066ca",
+ "license": "MIT",
+ "redistribution": "allowed",
+ "editable": false
+ }
+ ]
+ },
+ "datasets": [
+ {
+ "id": "amount-channel-812695-812831-v1",
+ "sha256": "83b4391f78d07a2fa85a67218d5ae4420d8965da3c95af577592e9ec1850104d"
+ }
+ ],
+ "parameters": {
+ "cap": 300,
+ "selection": "first complete multi-input transactions in source order",
+ "wall_seconds_per_transaction": 3.0
+ },
+ "rng": {
+ "algorithm": null,
+ "seeds": []
+ },
+ "outputs": [
+ {
+ "path": "results/artifacts/amount-pairwise-family-v1.json",
+ "bytes": 1027,
+ "sha256": "83f6a9e8501ee4df45f08b8c8ffc2f7265d40bf9add6d3f5093d712657c9dbf5"
+ },
+ {
+ "path": "results/generated/amount-pairwise-family-v1.md",
+ "bytes": 499,
+ "sha256": "aeaabdc282b4aed6946c6054b95e8b5dd9c20e50eca467de49827e453d5353df"
+ }
+ ],
+ "reproducibility": {
+ "level": "bitwise_reproducible",
+ "availability": "complete"
+ },
+ "verification": {
+ "mode": "exact",
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.amount_pairwise_family",
+ "--dataset",
+ "data/amount-channel-812695-812831-v1.json",
+ "--cap",
+ "300",
+ "--wall-seconds",
+ "3.0",
+ "verify",
+ "--artifact",
+ "results/artifacts/amount-pairwise-family-v1.json",
+ "--markdown",
+ "results/generated/amount-pairwise-family-v1.md"
+ ],
+ "tests": [
+ "tests/test_amount_pairwise_family_experiment.py",
+ "tests/test_oracle_audit.py"
+ ],
+ "properties": [
+ "refusal, wall timeout, and execution failure remain distinct",
+ "row support is labeled as internal to DSS's restricted family",
+ "singleton support is not presented as a global ownership proof",
+ "the selected population and ordering are explicit",
+ "the generated Markdown equals the canonical rendering"
+ ],
+ "tolerance": null
+ },
+ "limitations": [
+ "the matrix is a uniform marginal over DSS's restricted mapping family",
+ "a singleton-support row is not a globally deterministic ownership link",
+ "the exact-oracle audit found false certainties outside the restricted family",
+ "refusal, timeout, and execution failure are distinct outcomes",
+ "wall-time outcomes are resource- and platform-dependent",
+ "the sample is the first complete multi-input transactions in one 137-block snapshot",
+ "this diagnostic is not a privacy score or CoinScore"
+ ]
+}
diff --git a/catalog/runs/amount-per-coin-v1.json b/catalog/runs/amount-per-coin-v1.json
new file mode 100644
index 00000000..ec704c0f
--- /dev/null
+++ b/catalog/runs/amount-per-coin-v1.json
@@ -0,0 +1,113 @@
+{
+ "schema_version": 1,
+ "id": "amount-per-coin-v1",
+ "claim_ids": [
+ "ctp.amount.subtransaction_ambiguity"
+ ],
+ "code": {
+ "revision": "bcd9a3e511860b1b0ff5f3ee6338a2df29ecbfbf",
+ "dirty": false
+ },
+ "command": {
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.amount_per_coin",
+ "--dataset",
+ "data/amount-channel-812695-812831-v1.json",
+ "--cut-threshold",
+ "1.0",
+ "reproduce",
+ "--artifact",
+ "results/artifacts/amount-per-coin-v1.json",
+ "--markdown",
+ "results/generated/amount-per-coin-v1.md"
+ ]
+ },
+ "environment": {
+ "python": "3.13.7",
+ "lock_digest": "5ee1d70afd72fdb4ba3f01843e85e7ecd9a6d3e2a52abeb14c346a2143e3c186",
+ "platform": "macOS-26.6.2-arm64-arm-64bit-Mach-O",
+ "dependencies": [
+ {
+ "name": "dss",
+ "version": "0.1.0",
+ "source": "https://github.com/bc1cindy/dense-subset-sum",
+ "revision": "d8099bc540c9de7d0f3c9f095e8b2aad8f9d8c8d",
+ "lock_sha256": "f17a02f09da28dcc603901580d695466c44e1d1a0063e110d3faa375ae6066ca",
+ "license": "MIT",
+ "redistribution": "allowed",
+ "editable": false
+ }
+ ]
+ },
+ "datasets": [
+ {
+ "id": "amount-channel-812695-812831-v1",
+ "sha256": "83b4391f78d07a2fa85a67218d5ae4420d8965da3c95af577592e9ec1850104d"
+ }
+ ],
+ "parameters": {
+ "cut_threshold_natural_log": 1.0,
+ "transaction_gate": "guaranteed nonzero W(E)"
+ },
+ "rng": {
+ "algorithm": null,
+ "seeds": []
+ },
+ "outputs": [
+ {
+ "path": "results/artifacts/amount-per-coin-v1.json",
+ "bytes": 1158,
+ "sha256": "2e75b1330181e7cbf8539a357e2bd3ad650c4d2d207c7a47ec687e991e24a622"
+ },
+ {
+ "path": "results/generated/amount-per-coin-v1.md",
+ "bytes": 658,
+ "sha256": "bf35f273b248388772cb2b34c69ab3cc9ba261e494455ddcf41a570ff3736e1d"
+ }
+ ],
+ "reproducibility": {
+ "level": "bitwise_reproducible",
+ "availability": "complete"
+ },
+ "verification": {
+ "mode": "exact",
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.amount_per_coin",
+ "--dataset",
+ "data/amount-channel-812695-812831-v1.json",
+ "--cut-threshold",
+ "1.0",
+ "verify",
+ "--artifact",
+ "results/artifacts/amount-per-coin-v1.json",
+ "--markdown",
+ "results/generated/amount-per-coin-v1.md"
+ ],
+ "tests": [
+ "tests/test_amount_per_coin_experiment.py",
+ "tests/test_cost.py",
+ "tests/test_counting.py"
+ ],
+ "properties": [
+ "unreachable coins remain missing measurements rather than candidates",
+ "input and output coin identities remain distinct",
+ "the transaction-level gate precedes the per-coin diagnostic",
+ "exact transaction counts do not promote per-coin candidates to proofs",
+ "ungated historical and current gated outcomes remain separately reproducible",
+ "the generated Markdown equals the canonical rendering"
+ ],
+ "tolerance": null
+ },
+ "limitations": [
+ "DSS per-coin log_w is a knee-truncated diagnostic, not an exact mapping count",
+ "unreachable coins are missing measurements and are never candidates",
+ "the transaction gate and per-coin diagnostic measure different objects",
+ "an exact transaction count does not make a per-coin candidate exact",
+ "input and output indices have separate identities",
+ "candidates are refuse signals, not ownership proofs or privacy scores"
+ ]
+}
diff --git a/catalog/runs/amount-subset-sum-v1.json b/catalog/runs/amount-subset-sum-v1.json
new file mode 100644
index 00000000..aaa80c26
--- /dev/null
+++ b/catalog/runs/amount-subset-sum-v1.json
@@ -0,0 +1,106 @@
+{
+ "schema_version": 1,
+ "id": "amount-subset-sum-v1",
+ "claim_ids": [
+ "ctp.amount.subtransaction_ambiguity"
+ ],
+ "code": {
+ "revision": "bcd9a3e511860b1b0ff5f3ee6338a2df29ecbfbf",
+ "dirty": false
+ },
+ "command": {
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.amount_subset_sum",
+ "--dataset",
+ "data/amount-channel-812695-812831-v1.json",
+ "reproduce",
+ "--artifact",
+ "results/artifacts/amount-subset-sum-v1.json",
+ "--markdown",
+ "results/generated/amount-subset-sum-v1.md"
+ ]
+ },
+ "environment": {
+ "python": "3.13.7",
+ "lock_digest": "5ee1d70afd72fdb4ba3f01843e85e7ecd9a6d3e2a52abeb14c346a2143e3c186",
+ "platform": "macOS-26.6.2-arm64-arm-64bit-Mach-O",
+ "dependencies": [
+ {
+ "name": "dss",
+ "version": "0.1.0",
+ "source": "https://github.com/bc1cindy/dense-subset-sum",
+ "revision": "d8099bc540c9de7d0f3c9f095e8b2aad8f9d8c8d",
+ "lock_sha256": "f17a02f09da28dcc603901580d695466c44e1d1a0063e110d3faa375ae6066ca",
+ "license": "MIT",
+ "redistribution": "allowed",
+ "editable": false
+ }
+ ]
+ },
+ "datasets": [
+ {
+ "id": "amount-channel-812695-812831-v1",
+ "sha256": "83b4391f78d07a2fa85a67218d5ae4420d8965da3c95af577592e9ec1850104d"
+ }
+ ],
+ "parameters": {
+ "maximum_inputs": 16
+ },
+ "rng": {
+ "algorithm": null,
+ "seeds": []
+ },
+ "outputs": [
+ {
+ "path": "results/artifacts/amount-subset-sum-v1.json",
+ "bytes": 666,
+ "sha256": "240592b5af814fbcd28b06221805b934424170119da5e62e9da0281944377a9d"
+ },
+ {
+ "path": "results/generated/amount-subset-sum-v1.md",
+ "bytes": 385,
+ "sha256": "3caca166d157fb1a1b7b8c995d126bf47ff653a6b7eb01b416b1cef7e254e02f"
+ }
+ ],
+ "reproducibility": {
+ "level": "bitwise_reproducible",
+ "availability": "complete"
+ },
+ "verification": {
+ "mode": "exact",
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.amount_subset_sum",
+ "--dataset",
+ "data/amount-channel-812695-812831-v1.json",
+ "verify",
+ "--artifact",
+ "results/artifacts/amount-subset-sum-v1.json",
+ "--markdown",
+ "results/generated/amount-subset-sum-v1.md"
+ ],
+ "tests": [
+ "tests/test_amount_subset_sum_experiment.py",
+ "tests/test_counting.py"
+ ],
+ "properties": [
+ "exact, lower-bound, and unknown tiers remain distinct",
+ "exact zero is not counted as ambiguity evidence",
+ "only guaranteed nonzero log W is reported as a conservative lower bound",
+ "W(E) is not presented as a mapping count, link oracle, entropy, or CoinScore",
+ "the generated Markdown equals the canonical rendering"
+ ],
+ "tolerance": null
+ },
+ "limitations": [
+ "W(E) is a per-target subset count, not a Maurer mapping count",
+ "the whole-transaction count is fee-blind",
+ "exact zero means no exact subset hit and supplies no ambiguity evidence",
+ "transactions above sixteen inputs are reported as unknown",
+ "only exact and lower-bound tiers can support a conservative lower bound",
+ "this run does not compute pairwise links, entropy, or CoinScore"
+ ]
+}
diff --git a/catalog/runs/analyze-contract-v1.json b/catalog/runs/analyze-contract-v1.json
new file mode 100644
index 00000000..aad47038
--- /dev/null
+++ b/catalog/runs/analyze-contract-v1.json
@@ -0,0 +1,94 @@
+{
+ "schema_version": 1,
+ "id": "analyze-contract-v1",
+ "claim_ids": [
+ "ctp.provenance.traceability"
+ ],
+ "code": {
+ "revision": "bcd9a3e511860b1b0ff5f3ee6338a2df29ecbfbf",
+ "dirty": false
+ },
+ "command": {
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.analyze_contract",
+ "reproduce",
+ "--artifact",
+ "results/artifacts/analyze-contract-v1.json",
+ "--markdown",
+ "results/generated/analyze-contract-v1.md"
+ ]
+ },
+ "environment": {
+ "python": "3.13.7",
+ "lock_digest": "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855",
+ "platform": "macOS-26.6.2-arm64-arm-64bit-Mach-O",
+ "dependencies": []
+ },
+ "datasets": [],
+ "parameters": {
+ "fixture_transactions": 5,
+ "depths": [
+ 1,
+ 2,
+ 3,
+ 4,
+ 5
+ ],
+ "probability_round_digits": 15,
+ "max_nodes_control": 1
+ },
+ "rng": {
+ "algorithm": null,
+ "seeds": []
+ },
+ "outputs": [
+ {
+ "path": "results/artifacts/analyze-contract-v1.json",
+ "bytes": 1697,
+ "sha256": "0cd5877aee284f8a234688c93b586d9f9e556a49f31c2c0605fa53be1d994553"
+ },
+ {
+ "path": "results/generated/analyze-contract-v1.md",
+ "bytes": 744,
+ "sha256": "2d6a16cb098df8648a8964f46d50daab59b8f1d04a37e6a5b471ac10f7f64599"
+ }
+ ],
+ "reproducibility": {
+ "level": "bitwise_reproducible",
+ "availability": "complete"
+ },
+ "verification": {
+ "mode": "exact",
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.analyze_contract",
+ "verify",
+ "--artifact",
+ "results/artifacts/analyze-contract-v1.json",
+ "--markdown",
+ "results/generated/analyze-contract-v1.md"
+ ],
+ "tests": [
+ "tests/test_analyze.py",
+ "tests/test_analyze_contract_experiment.py"
+ ],
+ "properties": [
+ "the public facade returns at depths one through five on the fixed graph",
+ "subjective fusion does not widen provenance entropy and sharpens the address-reuse target",
+ "the node cap reports truncation rather than hiding it",
+ "the opt-in path_count field is identified as provenance route accumulation",
+ "the generated Markdown equals the canonical rendering"
+ ],
+ "tolerance": null
+ },
+ "limitations": [
+ "the fixture is synthetic and validates the public facade contract",
+ "the run does not reproduce the historical live Bitcoin measurements",
+ "address reuse is a heuristic subjective signal, not verified ownership",
+ "provenance entropy is not a privacy certificate or CoinScore",
+ "the path_count response key is retained for API compatibility only"
+ ]
+}
diff --git a/catalog/runs/ancestry-contract-v1.json b/catalog/runs/ancestry-contract-v1.json
new file mode 100644
index 00000000..0b7f2afa
--- /dev/null
+++ b/catalog/runs/ancestry-contract-v1.json
@@ -0,0 +1,96 @@
+{
+ "schema_version": 1,
+ "id": "ancestry-contract-v1",
+ "claim_ids": [
+ "ctp.provenance.traceability"
+ ],
+ "code": {
+ "revision": "bcd9a3e511860b1b0ff5f3ee6338a2df29ecbfbf",
+ "dirty": false
+ },
+ "command": {
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.ancestry_contract",
+ "reproduce",
+ "--artifact",
+ "results/artifacts/ancestry-contract-v1.json",
+ "--markdown",
+ "results/generated/ancestry-contract-v1.md"
+ ]
+ },
+ "environment": {
+ "python": "3.13.7",
+ "lock_digest": "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855",
+ "platform": "macOS-26.6.2-arm64-arm-64bit-Mach-O",
+ "dependencies": []
+ },
+ "datasets": [],
+ "parameters": {
+ "fixture_transactions": 3,
+ "depth": 2,
+ "input_values": [
+ 5,
+ 5
+ ],
+ "max_nodes_control": 1,
+ "probability_round_digits": 15
+ },
+ "rng": {
+ "algorithm": null,
+ "seeds": []
+ },
+ "outputs": [
+ {
+ "path": "results/artifacts/ancestry-contract-v1.json",
+ "bytes": 1252,
+ "sha256": "5c2be778959f5ab96df33af132a1cebc213375d43725969ba0a6cfec2063d533"
+ },
+ {
+ "path": "results/generated/ancestry-contract-v1.md",
+ "bytes": 783,
+ "sha256": "f14fa583584595c85f81b7cb4a84072cbbec64150d8eedb38e77b18b015d2ef9"
+ }
+ ],
+ "reproducibility": {
+ "level": "bitwise_reproducible",
+ "availability": "complete"
+ },
+ "verification": {
+ "mode": "exact",
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.ancestry_contract",
+ "verify",
+ "--artifact",
+ "results/artifacts/ancestry-contract-v1.json",
+ "--markdown",
+ "results/generated/ancestry-contract-v1.md"
+ ],
+ "tests": [
+ "tests/test_ancestry.py",
+ "tests/test_ancestry_adapter.py",
+ "tests/test_value_flow_ancestry.py",
+ "tests/test_provenance_link.py",
+ "tests/test_ancestry_contract_experiment.py"
+ ],
+ "properties": [
+ "nominal-value transition mass reaches the two source coins equally",
+ "collapsed expected steps convert exactly to the uncollapsed paper graph",
+ "complete and truncated ancestry are different typed states",
+ "node caps and oracle refusals remain separate truncation causes",
+ "identical and disjoint provenance signatures have different overlap",
+ "the generated Markdown equals the canonical rendering"
+ ],
+ "tolerance": null
+ },
+ "limitations": [
+ "the fixture is synthetic and validates mechanism contracts",
+ "the nominal-value model does not infer ownership or payment allocation",
+ "provenance overlap is model-relative evidence, not attribution",
+ "the run does not reproduce the historical DSS or live Bitcoin measurements",
+ "the outputs are not robust connectivity, a privacy certificate or CoinScore"
+ ]
+}
diff --git a/catalog/runs/ancestry-sparsity-v1.json b/catalog/runs/ancestry-sparsity-v1.json
new file mode 100644
index 00000000..83521b4f
--- /dev/null
+++ b/catalog/runs/ancestry-sparsity-v1.json
@@ -0,0 +1,101 @@
+{
+ "schema_version": 1,
+ "id": "ancestry-sparsity-v1",
+ "claim_ids": [
+ "ctp.sparse_dataset.feature_uniqueness"
+ ],
+ "code": {
+ "revision": "bcd9a3e511860b1b0ff5f3ee6338a2df29ecbfbf",
+ "dirty": false
+ },
+ "command": {
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.ancestry_sparsity",
+ "reproduce",
+ "--dataset",
+ "tests/fixtures/reid_sigs.json.gz",
+ "--artifact",
+ "results/artifacts/ancestry-sparsity-v1.json",
+ "--markdown",
+ "results/generated/ancestry-sparsity-v1.md"
+ ]
+ },
+ "environment": {
+ "python": "3.13.7",
+ "lock_digest": "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855",
+ "platform": "macOS-26.6.2-arm64-arm-64bit-Mach-O",
+ "dependencies": []
+ },
+ "datasets": [
+ {
+ "id": "reid-signatures-v1",
+ "sha256": "dbc4214fb499a96327326c4ea31341a76878c1867bfa3ccc57df88d850cdaa6a"
+ }
+ ],
+ "parameters": {
+ "similarity": "cosine",
+ "epsilons": [
+ 0.5,
+ 0.9,
+ 0.99
+ ],
+ "nearest_neighbour_scope": "all other records in the frozen snapshot",
+ "stored_top_values": "excluded"
+ },
+ "rng": {
+ "algorithm": null,
+ "seeds": []
+ },
+ "outputs": [
+ {
+ "path": "results/artifacts/ancestry-sparsity-v1.json",
+ "bytes": 768,
+ "sha256": "c0c417c8efc84acfcb88c465123e910aad0af32d55fd074fc3729091ce748693"
+ },
+ {
+ "path": "results/generated/ancestry-sparsity-v1.md",
+ "bytes": 665,
+ "sha256": "83c424ce8501bffc44b4e0af0ccdf37b6e11aaf8c355be0cfca81659d542b77a"
+ }
+ ],
+ "reproducibility": {
+ "level": "bitwise_reproducible",
+ "availability": "complete"
+ },
+ "verification": {
+ "mode": "exact",
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.ancestry_sparsity",
+ "verify",
+ "--dataset",
+ "tests/fixtures/reid_sigs.json.gz",
+ "--artifact",
+ "results/artifacts/ancestry-sparsity-v1.json",
+ "--markdown",
+ "results/generated/ancestry-sparsity-v1.md"
+ ],
+ "tests": [
+ "tests/test_ancestry_sparsity_experiment.py",
+ "tests/test_def1_sparsity.py"
+ ],
+ "properties": [
+ "nearest-neighbour similarities are recomputed from signature vectors",
+ "the sparse inverted-index implementation agrees with pairwise cosine",
+ "stored top values from the unavailable comparison population are excluded",
+ "the result is scoped to the frozen 200-record snapshot",
+ "the generated Markdown equals the canonical rendering"
+ ],
+ "tolerance": null
+ },
+ "limitations": [
+ "the 200-record frozen snapshot is selected and is not a uniform chain sample",
+ "the signature-generation recipe and original comparison population are unavailable",
+ "the run measures sparsity within this snapshot only",
+ "sparsity is an attack precondition, not proof of successful attribution",
+ "the run does not establish a chain-wide population rate"
+ ]
+}
diff --git a/catalog/runs/anchor-axis-families-v1.json b/catalog/runs/anchor-axis-families-v1.json
new file mode 100644
index 00000000..85984c79
--- /dev/null
+++ b/catalog/runs/anchor-axis-families-v1.json
@@ -0,0 +1,99 @@
+{
+ "schema_version": 1,
+ "id": "anchor-axis-families-v1",
+ "claim_ids": [
+ "ctp.fingerprint.statistical_linkage"
+ ],
+ "code": {
+ "revision": "bcd9a3e511860b1b0ff5f3ee6338a2df29ecbfbf",
+ "dirty": false
+ },
+ "command": {
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.anchor_axis_families",
+ "--dataset",
+ "tests/fixtures/merged_anchor_931d6627.json",
+ "reproduce",
+ "--artifact",
+ "results/artifacts/anchor-axis-families-v1.json",
+ "--markdown",
+ "results/generated/anchor-axis-families-v1.md"
+ ]
+ },
+ "environment": {
+ "python": "3.13.7",
+ "lock_digest": "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855",
+ "platform": "macOS-26.6.2-arm64-arm-64bit-Mach-O",
+ "dependencies": []
+ },
+ "datasets": [
+ {
+ "id": "merged-anchor-931d6627-v1",
+ "sha256": "e0e687d23f9417ace6364372fa8d72ea1bb4f83dabaf08d08d2523423e247911"
+ }
+ ],
+ "parameters": {
+ "link_above": 4.0,
+ "families": [
+ "engine_three_axis",
+ "catalogued",
+ "construction_only",
+ "decorrelated"
+ ]
+ },
+ "rng": {
+ "algorithm": null,
+ "seeds": []
+ },
+ "outputs": [
+ {
+ "path": "results/artifacts/anchor-axis-families-v1.json",
+ "bytes": 3152,
+ "sha256": "d465f3640860d0b51f20c6506ca7ae3715c89afb67e4e41b34a5dfc697276505"
+ },
+ {
+ "path": "results/generated/anchor-axis-families-v1.md",
+ "bytes": 1451,
+ "sha256": "4ce04704b838583333871b5d98883ceac02728ccfcea4c430b3ee7d49f708400"
+ }
+ ],
+ "reproducibility": {
+ "level": "bitwise_reproducible",
+ "availability": "complete"
+ },
+ "verification": {
+ "mode": "exact",
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.anchor_axis_families",
+ "--dataset",
+ "tests/fixtures/merged_anchor_931d6627.json",
+ "verify",
+ "--artifact",
+ "results/artifacts/anchor-axis-families-v1.json",
+ "--markdown",
+ "results/generated/anchor-axis-families-v1.md"
+ ],
+ "tests": [
+ "tests/test_anchor_axis_families.py",
+ "tests/test_wp4.py"
+ ],
+ "properties": [
+ "the three-axis engine refuses the edge and the catalogued library links it",
+ "the decorrelated family lands below link_above",
+ "an abstaining axis contributes no bits and is counted apart",
+ "the generated Markdown equals the canonical rendering"
+ ],
+ "tolerance": null
+ },
+ "limitations": [
+ "one named edge, chosen because the design decision was argued on it",
+ "the same-owner reading of this edge is the paper's, not an independent label",
+ "a score past link_above is what the engine would do, not what it does: the engine scores three axes",
+ "the library weights come from a population that was not preserved; library-calibration-v1 measures how far they sit from the one that was",
+ "the result is not a privacy score"
+ ]
+}
diff --git a/catalog/runs/attribute-drift-v1.json b/catalog/runs/attribute-drift-v1.json
new file mode 100644
index 00000000..3242582d
--- /dev/null
+++ b/catalog/runs/attribute-drift-v1.json
@@ -0,0 +1,114 @@
+{
+ "schema_version": 1,
+ "id": "attribute-drift-v1",
+ "claim_ids": [
+ "ctp.fingerprint.statistical_linkage",
+ "ctp.social_graph.propagation"
+ ],
+ "code": {
+ "revision": "bcd9a3e511860b1b0ff5f3ee6338a2df29ecbfbf",
+ "dirty": false
+ },
+ "command": {
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.attribute_drift",
+ "--dataset",
+ "tests/fixtures/lumen_explorer_data.json",
+ "reproduce",
+ "--artifact",
+ "results/artifacts/attribute-drift-v1.json",
+ "--markdown",
+ "results/generated/attribute-drift-v1.md"
+ ]
+ },
+ "environment": {
+ "python": "3.13.7",
+ "lock_digest": "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855",
+ "platform": "macOS-26.6.2-arm64-arm-64bit-Mach-O",
+ "dependencies": []
+ },
+ "datasets": [
+ {
+ "id": "lumen-explorer-data-v1",
+ "sha256": "5337d4bdc574726a2cb4e28f329f16709b497479a7493882bd38d77875660ac9"
+ }
+ ],
+ "parameters": {
+ "epoch_size_blocks": 144,
+ "gaps": [
+ 1,
+ 2,
+ 3,
+ 7,
+ 14,
+ 21,
+ 30,
+ 60,
+ 90,
+ 120
+ ],
+ "cycle_epochs": 7,
+ "cycle_span": 3,
+ "projection": "preserved_values_plus_combined_residual",
+ "minimum_mean_share_for_coupling": 0.01
+ },
+ "rng": {
+ "algorithm": null,
+ "seeds": []
+ },
+ "outputs": [
+ {
+ "path": "results/artifacts/attribute-drift-v1.json",
+ "bytes": 7210,
+ "sha256": "620d6fe56cbfb545dfb2908865f66a18a3fad6a2fa1af96acdda37815ab0f610"
+ },
+ {
+ "path": "results/generated/attribute-drift-v1.md",
+ "bytes": 1241,
+ "sha256": "9e36c5f5337bfda4db92e2f52d2db42d82ddf368cbb8aa3c8bdf2e18d821de8f"
+ }
+ ],
+ "reproducibility": {
+ "level": "bitwise_reproducible",
+ "availability": "partial"
+ },
+ "verification": {
+ "mode": "exact",
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.attribute_drift",
+ "--dataset",
+ "tests/fixtures/lumen_explorer_data.json",
+ "verify",
+ "--artifact",
+ "results/artifacts/attribute-drift-v1.json",
+ "--markdown",
+ "results/generated/attribute-drift-v1.md"
+ ],
+ "tests": [
+ "tests/test_attribute_drift.py",
+ "tests/test_attribute_drift_experiment.py",
+ "tests/test_lumen_survey_fixture.py"
+ ],
+ "properties": [
+ "epoch heights form a contiguous 144-block partition",
+ "template match shares independently reconstruct one transaction volume per epoch",
+ "reconstructed epoch volumes sum to the declared transaction total",
+ "preserved axis shares never exceed one",
+ "the generated Markdown equals the canonical rendering"
+ ],
+ "tolerance": null
+ },
+ "limitations": [
+ "only 20 value series across 11 axes are preserved",
+ "unpreserved values are combined into one residual category per axis",
+ "only projected drift is measured; most full-distribution table rows are historical",
+ "volume is reconstructed from redundant template match shares rather than stored directly",
+ "population drift does not measure per-cluster attribute distinctiveness",
+ "the result is diagnostic and does not produce a clustering score",
+ "the dataset publisher authorized redistribution of the aggregate fixture on 2026-09-04"
+ ]
+}
diff --git a/catalog/runs/bayes-vs-fs-v1.json b/catalog/runs/bayes-vs-fs-v1.json
new file mode 100644
index 00000000..e26928ef
--- /dev/null
+++ b/catalog/runs/bayes-vs-fs-v1.json
@@ -0,0 +1,106 @@
+{
+ "schema_version": 1,
+ "id": "bayes-vs-fs-v1",
+ "claim_ids": [
+ "ctp.fingerprint.statistical_linkage"
+ ],
+ "code": {
+ "revision": "bcd9a3e511860b1b0ff5f3ee6338a2df29ecbfbf",
+ "dirty": false
+ },
+ "command": {
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.bayes_vs_fs",
+ "--snapshot",
+ "data/fs-blkcache-2026-09-04.tar.gz",
+ "reproduce",
+ "--artifact",
+ "results/artifacts/bayes-vs-fs-v1.json",
+ "--markdown",
+ "results/generated/bayes-vs-fs-v1.md"
+ ]
+ },
+ "environment": {
+ "python": "3.13.7",
+ "lock_digest": "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855",
+ "platform": "macOS-26.6.2-arm64-arm-64bit-Mach-O",
+ "dependencies": []
+ },
+ "datasets": [
+ {
+ "id": "fs-blkcache-2026-09-04-v1",
+ "sha256": "5a69f6f446dbdfcce09dddc91c653e5d841124d14020b720fdea276315c775a6"
+ }
+ ],
+ "parameters": {
+ "pair_cap_per_class": 4000,
+ "seed": 0,
+ "gibbs_samples": 2000,
+ "gibbs_burn": 500,
+ "beta_prior": [
+ 8.5,
+ 1.5
+ ]
+ },
+ "rng": {
+ "algorithm": "python.random.MersenneTwister",
+ "seeds": [
+ 0,
+ 1
+ ]
+ },
+ "outputs": [
+ {
+ "path": "results/artifacts/bayes-vs-fs-v1.json",
+ "bytes": 6334,
+ "sha256": "0e1d146555a0913181370e4a0145cb38bebd5f43ee938fdfbd2a18e30cd4fa81"
+ },
+ {
+ "path": "results/generated/bayes-vs-fs-v1.md",
+ "bytes": 687,
+ "sha256": "984f0373600a455203cf1bccb44b5f8768368deaf41baf081f940a406775bd0c"
+ }
+ ],
+ "reproducibility": {
+ "level": "bitwise_reproducible",
+ "availability": "complete"
+ },
+ "verification": {
+ "mode": "exact",
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.bayes_vs_fs",
+ "--snapshot",
+ "data/fs-blkcache-2026-09-04.tar.gz",
+ "verify",
+ "--artifact",
+ "results/artifacts/bayes-vs-fs-v1.json",
+ "--markdown",
+ "results/generated/bayes-vs-fs-v1.md"
+ ],
+ "tests": [
+ "tests/test_fs_bayes.py",
+ "tests/test_bayes_vs_fs_experiment.py"
+ ],
+ "properties": [
+ "the preserved snapshot exactly reproduces the canonical artifact",
+ "all three scorers use the same pair population and per-axis evidence",
+ "the supervised agreement estimate is identified as an address-reuse weak label",
+ "the former handwritten values are explicitly recorded as not reproduced",
+ "cluster bands are distinguished from a joint ownership-partition posterior",
+ "the generated Markdown equals the canonical rendering"
+ ],
+ "tolerance": null
+ },
+ "limitations": [
+ "address reuse is a weak and partly feature-dependent ownership proxy",
+ "the balanced selected sample does not represent population prevalence",
+ "parameters are fitted and evaluated on the same selected pairs",
+ "the model assumes conditionally independent axes and fixes collision rates",
+ "cluster bands independently sample pair edges and are not a joint partition posterior",
+ "the result is attacker-side evidence and is not CoinScore"
+ ]
+}
diff --git a/catalog/runs/boltzmann-fee-audit-v1.json b/catalog/runs/boltzmann-fee-audit-v1.json
new file mode 100644
index 00000000..5ebd584c
--- /dev/null
+++ b/catalog/runs/boltzmann-fee-audit-v1.json
@@ -0,0 +1,98 @@
+{
+ "schema_version": 1,
+ "id": "boltzmann-fee-audit-v1",
+ "claim_ids": [
+ "ctp.amount.subtransaction_ambiguity"
+ ],
+ "code": {
+ "revision": "bcd9a3e511860b1b0ff5f3ee6338a2df29ecbfbf",
+ "dirty": false
+ },
+ "command": {
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.boltzmann_fee_audit",
+ "--dataset",
+ "data/boltzmann-fee-audit-v1.json",
+ "reproduce",
+ "--artifact",
+ "results/artifacts/boltzmann-fee-audit-v1.json",
+ "--markdown",
+ "results/generated/boltzmann-fee-audit-v1.md"
+ ]
+ },
+ "environment": {
+ "python": "3.13.7",
+ "lock_digest": "e0d6a8cada5d383c0aca2bd2a37f28c15721392469c94084128d369c29c5544d",
+ "platform": "macOS-26.6.2-arm64-arm-64bit-Mach-O",
+ "dependencies": []
+ },
+ "datasets": [
+ {
+ "id": "boltzmann-fee-audit-v1",
+ "sha256": "17ee62e307d34660be2c4f50382a63ba044d0ad67f0674873e304d0b8203dc1c"
+ }
+ ],
+ "parameters": {
+ "cap": 300,
+ "max_coins": 8
+ },
+ "rng": {
+ "algorithm": null,
+ "seeds": []
+ },
+ "outputs": [
+ {
+ "path": "results/artifacts/boltzmann-fee-audit-v1.json",
+ "bytes": 52813,
+ "sha256": "9cf09b7659cf0f8f3e429623a903708c450a2c1605035cc4f45f6e1dcb34d2b3"
+ },
+ {
+ "path": "results/generated/boltzmann-fee-audit-v1.md",
+ "bytes": 523,
+ "sha256": "f4e6c0afd79425e8259d4112190a5df3b3eee81344cd7023f786c4671264e112"
+ }
+ ],
+ "reproducibility": {
+ "level": "bitwise_reproducible",
+ "availability": "complete"
+ },
+ "verification": {
+ "mode": "exact",
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.boltzmann_fee_audit",
+ "--dataset",
+ "data/boltzmann-fee-audit-v1.json",
+ "verify",
+ "--artifact",
+ "results/artifacts/boltzmann-fee-audit-v1.json",
+ "--markdown",
+ "results/generated/boltzmann-fee-audit-v1.md"
+ ],
+ "tests": [
+ "tests/test_boltzmann_fee_snapshot.py",
+ "tests/test_boltzmann_fee_audit.py",
+ "tests/test_boltzmann_fee_experiment.py",
+ "tests/test_boltzmann_reference_parity.py"
+ ],
+ "properties": [
+ "the internal snapshot reproduces all 300 historical per-transaction rows",
+ "exact zero-fee and local fee-allocation mappings remain separate",
+ "the all-coins interpretation is not presented as de-mixing evidence",
+ "fee roundness remains observational rather than a ranking model",
+ "general external-tool parity is not claimed",
+ "the generated Markdown equals the canonical rendering"
+ ],
+ "tolerance": null
+ },
+ "limitations": [
+ "the selected population is deterministic but not chain-representative",
+ "the all-coins interpretation makes fee-tolerant acceptance non-informative",
+ "fee roundness is reported but is not a competing probability model",
+ "this audit measures the local fee-allocation model, not general parity with Boltzmann",
+ "separate tests cover selected official Boltzmann vectors and modes"
+ ]
+}
diff --git a/catalog/runs/candidate-set-intersection-v1.json b/catalog/runs/candidate-set-intersection-v1.json
new file mode 100644
index 00000000..f6e63384
--- /dev/null
+++ b/catalog/runs/candidate-set-intersection-v1.json
@@ -0,0 +1,87 @@
+{
+ "schema_version": 1,
+ "id": "candidate-set-intersection-v1",
+ "claim_ids": [
+ "ctp.intersection.longitudinal"
+ ],
+ "code": {
+ "revision": "bcd9a3e511860b1b0ff5f3ee6338a2df29ecbfbf",
+ "dirty": false
+ },
+ "command": {
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.candidate_set_intersection",
+ "reproduce",
+ "--artifact",
+ "results/artifacts/candidate-set-intersection-v1.json",
+ "--markdown",
+ "results/generated/candidate-set-intersection-v1.md"
+ ]
+ },
+ "environment": {
+ "python": "3.13.7",
+ "lock_digest": "c7d402576511073ed4fcf076c77ffa6b3be124e0a2ef996d2da1cd3b02fbe541",
+ "platform": "macOS-26.6.2-arm64-arm-64bit-Mach-O",
+ "dependencies": []
+ },
+ "datasets": [],
+ "parameters": {
+ "universe_size": 64,
+ "scenarios": [
+ "converging",
+ "stalled",
+ "contradictory"
+ ]
+ },
+ "rng": {
+ "algorithm": null,
+ "seeds": []
+ },
+ "outputs": [
+ {
+ "path": "results/artifacts/candidate-set-intersection-v1.json",
+ "bytes": 2127,
+ "sha256": "57ec942ab96fa6a4da7335d4c936391b163f850a3018fe4d043dae377067493e"
+ },
+ {
+ "path": "results/generated/candidate-set-intersection-v1.md",
+ "bytes": 454,
+ "sha256": "7967116277698ee234507683274cd315b18c44503209d76d51d304aa9dd2a816"
+ }
+ ],
+ "reproducibility": {
+ "level": "bitwise_reproducible",
+ "availability": "complete"
+ },
+ "verification": {
+ "mode": "exact",
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.candidate_set_intersection",
+ "verify",
+ "--artifact",
+ "results/artifacts/candidate-set-intersection-v1.json",
+ "--markdown",
+ "results/generated/candidate-set-intersection-v1.md"
+ ],
+ "tests": [
+ "tests/test_candidate_set_intersection.py",
+ "tests/test_candidate_set_intersection_experiment.py"
+ ],
+ "properties": [
+ "the stored artifact equals a fresh execution of all three deterministic scenarios",
+ "a singleton survivor is the only identifying outcome",
+ "an empty intersection is a refusal with no narrowing score",
+ "the generated Markdown equals the canonical rendering"
+ ],
+ "tolerance": null
+ },
+ "limitations": [
+ "synthetic fixture",
+ "candidate sets are supplied rather than inferred from chain data",
+ "the run does not reproduce the paper's JoinMarket dataset or empirical rates"
+ ]
+}
diff --git a/catalog/runs/change-consolidation-v1.json b/catalog/runs/change-consolidation-v1.json
new file mode 100644
index 00000000..a947a933
--- /dev/null
+++ b/catalog/runs/change-consolidation-v1.json
@@ -0,0 +1,86 @@
+{
+ "schema_version": 1,
+ "id": "change-consolidation-v1",
+ "claim_ids": [
+ "ctp.intersection.longitudinal"
+ ],
+ "code": {
+ "revision": "bcd9a3e511860b1b0ff5f3ee6338a2df29ecbfbf",
+ "dirty": false
+ },
+ "command": {
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.change_consolidation",
+ "reproduce",
+ "--artifact",
+ "results/artifacts/change-consolidation-v1.json",
+ "--markdown",
+ "results/generated/change-consolidation-v1.md"
+ ]
+ },
+ "environment": {
+ "python": "3.13.7",
+ "lock_digest": "e0d6a8cada5d383c0aca2bd2a37f28c15721392469c94084128d369c29c5544d",
+ "platform": "macOS-26.6.2-arm64-arm-64bit-Mach-O",
+ "dependencies": []
+ },
+ "datasets": [],
+ "parameters": {
+ "candidate_owners_per_change": 2,
+ "payments": 2,
+ "later_observation": "change-output co-spend"
+ },
+ "rng": {
+ "algorithm": null,
+ "seeds": []
+ },
+ "outputs": [
+ {
+ "path": "results/artifacts/change-consolidation-v1.json",
+ "bytes": 507,
+ "sha256": "1fadeece80a936b04473a7e235eafc37e835d21445a2b462cac7d067896c10e9"
+ },
+ {
+ "path": "results/generated/change-consolidation-v1.md",
+ "bytes": 394,
+ "sha256": "f86ec49a75e9ec73d7c5a662e76252056ccf1a9f4de9a13dc77d935d490ad47f"
+ }
+ ],
+ "reproducibility": {
+ "level": "bitwise_reproducible",
+ "availability": "complete"
+ },
+ "verification": {
+ "mode": "exact",
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.change_consolidation",
+ "verify",
+ "--artifact",
+ "results/artifacts/change-consolidation-v1.json",
+ "--markdown",
+ "results/generated/change-consolidation-v1.md"
+ ],
+ "tests": [
+ "tests/test_change_consolidation.py",
+ "tests/test_change_consolidation_experiment.py"
+ ],
+ "properties": [
+ "both payment change candidates remain ambiguous before consolidation",
+ "the later co-spend narrows the candidate owner shared by both payments",
+ "disjoint candidate-owner sets produce no shared candidate",
+ "change identification and co-ownership remain explicit assumptions",
+ "the generated Markdown equals the canonical rendering"
+ ],
+ "tolerance": null
+ },
+ "limitations": [
+ "synthetic fixture",
+ "change outputs and candidate owners are supplied rather than inferred",
+ "the linkage requires correct change identification and common ownership",
+ "the run does not measure a Bitcoin deanonymization rate"
+ ]
+}
diff --git a/catalog/runs/cioh-collaborative-false-merge-v1.json b/catalog/runs/cioh-collaborative-false-merge-v1.json
new file mode 100644
index 00000000..55cd21d7
--- /dev/null
+++ b/catalog/runs/cioh-collaborative-false-merge-v1.json
@@ -0,0 +1,86 @@
+{
+ "schema_version": 1,
+ "id": "cioh-collaborative-false-merge-v1",
+ "claim_ids": [
+ "ctp.cioh.collaborative_false_merge"
+ ],
+ "code": {
+ "revision": "bcd9a3e511860b1b0ff5f3ee6338a2df29ecbfbf",
+ "dirty": false
+ },
+ "command": {
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.cioh_collaborative",
+ "reproduce",
+ "--artifact",
+ "results/artifacts/cioh-collaborative-false-merge-v1.json",
+ "--markdown",
+ "results/generated/cioh-collaborative-false-merge-v1.md"
+ ]
+ },
+ "environment": {
+ "python": "3.13.7",
+ "lock_digest": "e0d6a8cada5d383c0aca2bd2a37f28c15721392469c94084128d369c29c5544d",
+ "platform": "macOS-26.6.2-arm64-arm-64bit-Mach-O",
+ "dependencies": []
+ },
+ "datasets": [],
+ "parameters": {
+ "collaborative_inputs": 2,
+ "same_owner_control_inputs": 2,
+ "ownership_partition": "supplied_fixture_labels"
+ },
+ "rng": {
+ "algorithm": null,
+ "seeds": []
+ },
+ "outputs": [
+ {
+ "path": "results/artifacts/cioh-collaborative-false-merge-v1.json",
+ "bytes": 583,
+ "sha256": "0962e258838b34a41978866d73a3a867968271cf8a2e7827db503f341adff78b"
+ },
+ {
+ "path": "results/generated/cioh-collaborative-false-merge-v1.md",
+ "bytes": 550,
+ "sha256": "556b2c5c9355032d65b0939613b33d01d0aa3daa2d89c3b3143d8110ba6b2a8a"
+ }
+ ],
+ "reproducibility": {
+ "level": "bitwise_reproducible",
+ "availability": "complete"
+ },
+ "verification": {
+ "mode": "exact",
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.cioh_collaborative",
+ "verify",
+ "--artifact",
+ "results/artifacts/cioh-collaborative-false-merge-v1.json",
+ "--markdown",
+ "results/generated/cioh-collaborative-false-merge-v1.md"
+ ],
+ "tests": [
+ "tests/test_cioh_collaborative.py",
+ "tests/test_cioh_collaborative_experiment.py",
+ "tests/test_cluster_refined.py"
+ ],
+ "properties": [
+ "CIOH merges both inputs in the labelled collaborative fixture",
+ "the predicted merge conflicts with the fixture's distinct participant labels",
+ "the same-owner control produces the same CIOH merge without a reference conflict",
+ "co-spend evidence remains distinct from the ownership conclusion",
+ "the generated Markdown equals the canonical rendering"
+ ],
+ "tolerance": null
+ },
+ "limitations": [
+ "deterministic synthetic fixture",
+ "participant ownership labels are supplied",
+ "the run does not estimate chain-wide prevalence or infer real ownership"
+ ]
+}
diff --git a/catalog/runs/cluster-bits-v1.json b/catalog/runs/cluster-bits-v1.json
new file mode 100644
index 00000000..8dcf6127
--- /dev/null
+++ b/catalog/runs/cluster-bits-v1.json
@@ -0,0 +1,94 @@
+{
+ "schema_version": 1,
+ "id": "cluster-bits-v1",
+ "claim_ids": [
+ "ctp.fingerprint.statistical_linkage",
+ "ctp.cioh.collaborative_false_merge"
+ ],
+ "code": {
+ "revision": "bcd9a3e511860b1b0ff5f3ee6338a2df29ecbfbf",
+ "dirty": false
+ },
+ "command": {
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.cluster_bits",
+ "--snapshot",
+ "data/fs-blkcache-2026-09-04.tar.gz",
+ "reproduce",
+ "--artifact",
+ "results/artifacts/cluster-bits-v1.json",
+ "--markdown",
+ "results/generated/cluster-bits-v1.md"
+ ]
+ },
+ "datasets": [
+ {
+ "id": "fs-blkcache-2026-09-04-v1",
+ "sha256": "5a69f6f446dbdfcce09dddc91c653e5d841124d14020b720fdea276315c775a6"
+ }
+ ],
+ "parameters": {
+ "topk": 5,
+ "merge_ambiguity_bits": 1.6,
+ "graph": "payment edges only, co-spend excluded"
+ },
+ "rng": {
+ "algorithm": "none",
+ "seeds": []
+ },
+ "reproducibility": {
+ "level": "bitwise_reproducible",
+ "availability": "complete"
+ },
+ "verification": {
+ "mode": "exact",
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.cluster_bits",
+ "--snapshot",
+ "data/fs-blkcache-2026-09-04.tar.gz",
+ "verify",
+ "--artifact",
+ "results/artifacts/cluster-bits-v1.json",
+ "--markdown",
+ "results/generated/cluster-bits-v1.md"
+ ],
+ "tests": [
+ "tests/test_cluster_bits_experiment.py"
+ ],
+ "properties": [
+ "the committed cache exactly reproduces the bit distribution",
+ "every cluster clears the merge ambiguity it would have to hide behind",
+ "the identifier excludes the co-spend edges that built the cluster",
+ "the five-rarest floor is reported with the reason it says little on this cache",
+ "the cache is block-sampled, so every figure is a floor"
+ ],
+ "tolerance": null
+ },
+ "environment": {
+ "python": "3.13.7",
+ "lock_digest": "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855",
+ "platform": "macOS-26.6.2-arm64-arm-64bit-Mach-O",
+ "dependencies": []
+ },
+ "limitations": [
+ "The cache is block-sampled rather than contiguous, so counterparties are undercounted and every bit count is a floor.",
+ "A cluster is a co-spend relation, not observed wallet ownership.",
+ "Ninety-two per cent of clusters have at most five counterparties, so the top-k floor repeats the total rather than testing independence."
+ ],
+ "outputs": [
+ {
+ "path": "results/artifacts/cluster-bits-v1.json",
+ "bytes": 613,
+ "sha256": "3f75d9660582dd7ef44324734b6a0ec7497981305d2bdab917bcc976d76c4e4c"
+ },
+ {
+ "path": "results/generated/cluster-bits-v1.md",
+ "bytes": 1749,
+ "sha256": "4edbf73853fc768a48a91518be8d5ee6e03c0f513163f351e1accf0bc52b46b5"
+ }
+ ]
+}
diff --git a/catalog/runs/coinjoin-sudoku-nominal-v1.json b/catalog/runs/coinjoin-sudoku-nominal-v1.json
new file mode 100644
index 00000000..914239d2
--- /dev/null
+++ b/catalog/runs/coinjoin-sudoku-nominal-v1.json
@@ -0,0 +1,89 @@
+{
+ "schema_version": 1,
+ "id": "coinjoin-sudoku-nominal-v1",
+ "claim_ids": [
+ "ctp.amount.subtransaction_ambiguity"
+ ],
+ "code": {
+ "revision": "bcd9a3e511860b1b0ff5f3ee6338a2df29ecbfbf",
+ "dirty": false
+ },
+ "command": {
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.coinjoin_sudoku",
+ "reproduce",
+ "--artifact",
+ "results/artifacts/coinjoin-sudoku-nominal-v1.json",
+ "--markdown",
+ "results/generated/coinjoin-sudoku-nominal-v1.md"
+ ]
+ },
+ "environment": {
+ "python": "3.13.7",
+ "lock_digest": "e0d6a8cada5d383c0aca2bd2a37f28c15721392469c94084128d369c29c5544d",
+ "platform": "macOS-26.6.2-arm64-arm-64bit-Mach-O",
+ "dependencies": []
+ },
+ "datasets": [],
+ "parameters": {
+ "amount_domain": "positive integers",
+ "fee_unit_sat": 10000,
+ "max_coins": 12,
+ "published_selection": "all exact equal-sum groupings",
+ "local_selection": "largest group count",
+ "local_weighting": "uniform over indexed selected groupings"
+ },
+ "rng": {
+ "algorithm": null,
+ "seeds": []
+ },
+ "outputs": [
+ {
+ "path": "results/artifacts/coinjoin-sudoku-nominal-v1.json",
+ "bytes": 2351,
+ "sha256": "4d9b3871ab258304c97c371f9ff870763abe9ec28df040bd47e085646582c5b2"
+ },
+ {
+ "path": "results/generated/coinjoin-sudoku-nominal-v1.md",
+ "bytes": 1048,
+ "sha256": "a59e6e2efcc94cbacad687363da37526efd7469930721966f619f28fb2a39fc2"
+ }
+ ],
+ "reproducibility": {
+ "level": "bitwise_reproducible",
+ "availability": "complete"
+ },
+ "verification": {
+ "mode": "exact",
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.coinjoin_sudoku",
+ "verify",
+ "--artifact",
+ "results/artifacts/coinjoin-sudoku-nominal-v1.json",
+ "--markdown",
+ "results/generated/coinjoin-sudoku-nominal-v1.md"
+ ],
+ "tests": [
+ "tests/test_coinjoin_sudoku.py",
+ "tests/test_coinjoin_sudoku_experiment.py"
+ ],
+ "properties": [
+ "the advisory's positive and negative equal-sum examples are reproduced",
+ "the SX control has one-third marginals only under separately labelled local selection and weighting",
+ "indexed fee pseudo-output permutations remain distinguishable from fee-collapsed interpretations",
+ "the historical SharedCoin percentages remain explicitly not reproduced",
+ "the generated Markdown equals the canonical rendering"
+ ],
+ "tolerance": null
+ },
+ "limitations": [
+ "independent reconstruction because the historical analyzer was not published",
+ "largest-group selection, fee pseudo-outputs and uniform weighting are local semantics",
+ "digit skipping and the reported SharedCoin grouping result are not reproduced",
+ "conditional amount relations are not ownership attribution or a privacy score"
+ ]
+}
diff --git a/catalog/runs/collaborative-forms-v1.json b/catalog/runs/collaborative-forms-v1.json
new file mode 100644
index 00000000..bce2403a
--- /dev/null
+++ b/catalog/runs/collaborative-forms-v1.json
@@ -0,0 +1,107 @@
+{
+ "schema_version": 1,
+ "id": "collaborative-forms-v1",
+ "claim_ids": [
+ "ctp.forms.observer_knowledge"
+ ],
+ "code": {
+ "revision": "bcd9a3e511860b1b0ff5f3ee6338a2df29ecbfbf",
+ "dirty": false
+ },
+ "command": {
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.collaborative_forms",
+ "reproduce",
+ "--artifact",
+ "results/artifacts/collaborative-forms-v1.json",
+ "--markdown",
+ "results/generated/collaborative-forms-v1.md"
+ ]
+ },
+ "environment": {
+ "python": "3.13.7",
+ "lock_digest": "e0d6a8cada5d383c0aca2bd2a37f28c15721392469c94084128d369c29c5544d",
+ "platform": "macOS-26.6.2-arm64-arm-64bit-Mach-O",
+ "dependencies": []
+ },
+ "datasets": [],
+ "parameters": {
+ "forms": [
+ "ordinary_two_input",
+ "p2ep",
+ "bip79_bustapay",
+ "bip78_sync_payjoin",
+ "bip77_async_payjoin",
+ "many_senders_one_receiver",
+ "many_senders_many_receivers",
+ "net_settlement_with_cycles"
+ ],
+ "observers": [
+ "external_onchain_observer",
+ "protocol_counterparty"
+ ],
+ "two_party_observation": {
+ "inputs_sat": [
+ 90,
+ 60
+ ],
+ "outputs_sat": [
+ 110,
+ 40
+ ]
+ }
+ },
+ "rng": {
+ "algorithm": null,
+ "seeds": []
+ },
+ "outputs": [
+ {
+ "path": "results/artifacts/collaborative-forms-v1.json",
+ "bytes": 5574,
+ "sha256": "2428a32ec1827bfa9b9a2f44756effc968c537413640927a009be00cee38b733"
+ },
+ {
+ "path": "results/generated/collaborative-forms-v1.md",
+ "bytes": 2762,
+ "sha256": "b696148f41f66df0756a3f7dbac1c1e53d0ad7d97c14bb194bcfadb4602c5c02"
+ }
+ ],
+ "reproducibility": {
+ "level": "bitwise_reproducible",
+ "availability": "complete"
+ },
+ "verification": {
+ "mode": "exact",
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.collaborative_forms",
+ "verify",
+ "--artifact",
+ "results/artifacts/collaborative-forms-v1.json",
+ "--markdown",
+ "results/generated/collaborative-forms-v1.md"
+ ],
+ "tests": [
+ "tests/test_collaborative_forms.py",
+ "tests/test_collaborative_forms_experiment.py"
+ ],
+ "properties": [
+ "an ordinary spend and four PayJoin protocols share the declared two-input/two-output observation",
+ "protocol transports remain distinct from external on-chain knowledge",
+ "two-party counterparty knowledge is not assigned to an external observer",
+ "NS1R, NSNR and net-settlement limitations remain explicit",
+ "the generated Markdown equals the canonical rendering"
+ ],
+ "tolerance": null
+ },
+ "limitations": [
+ "participant allocations and payment relationships are supplied model possibilities",
+ "protocol deployment frequency and empirical indistinguishability are not measured",
+ "transport differences are not treated as on-chain observations",
+ "the matrix is not protocol detection, ownership attribution or a privacy score"
+ ]
+}
diff --git a/catalog/runs/conservation-round-three-v1.json b/catalog/runs/conservation-round-three-v1.json
new file mode 100644
index 00000000..9fc76b8d
--- /dev/null
+++ b/catalog/runs/conservation-round-three-v1.json
@@ -0,0 +1,98 @@
+{
+ "schema_version": 1,
+ "id": "conservation-round-three-v1",
+ "claim_ids": [
+ "ctp.amount.subtransaction_ambiguity"
+ ],
+ "code": {
+ "revision": "bcd9a3e511860b1b0ff5f3ee6338a2df29ecbfbf",
+ "dirty": false
+ },
+ "command": {
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.conservation_round_three",
+ "--dataset",
+ "tests/fixtures/conservation_round_three.json",
+ "reproduce",
+ "--artifact",
+ "results/artifacts/conservation-round-three-v1.json",
+ "--markdown",
+ "results/generated/conservation-round-three-v1.md"
+ ]
+ },
+ "environment": {
+ "python": "3.13.7",
+ "lock_digest": "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855",
+ "platform": "macOS-26.6.2-arm64-arm-64bit-Mach-O",
+ "dependencies": []
+ },
+ "datasets": [
+ {
+ "id": "conservation-round-three-v1",
+ "sha256": "e4a9048fa27e28732af5449d9b9a2098f401b203c793d79763d0be685fb86660"
+ }
+ ],
+ "parameters": {
+ "known_participant_source": "external forward walk",
+ "fee_treatment": "ignored as a conservative floor",
+ "output_representation": "value histogram"
+ },
+ "rng": {
+ "algorithm": null,
+ "seeds": []
+ },
+ "outputs": [
+ {
+ "path": "results/artifacts/conservation-round-three-v1.json",
+ "bytes": 9263,
+ "sha256": "142f9dc03f39ab280a77e07c6f4e85b04d5e77b411a9c3cdb476d955280d6cfe"
+ },
+ {
+ "path": "results/generated/conservation-round-three-v1.md",
+ "bytes": 558,
+ "sha256": "8e32849b0f691361403dcbe219f436c2542cb0a2ecab24b98b1ed79b90c2a942"
+ }
+ ],
+ "reproducibility": {
+ "level": "bitwise_reproducible",
+ "availability": "complete"
+ },
+ "verification": {
+ "mode": "exact",
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.conservation_round_three",
+ "--dataset",
+ "tests/fixtures/conservation_round_three.json",
+ "verify",
+ "--artifact",
+ "results/artifacts/conservation-round-three-v1.json",
+ "--markdown",
+ "results/generated/conservation-round-three-v1.md"
+ ],
+ "tests": [
+ "tests/test_conservation.py",
+ "tests/test_conservation_round_three.py",
+ "tests/test_conservation_experiment.py"
+ ],
+ "properties": [
+ "the fixture preserves the transaction total and output multiplicities used by conservation",
+ "the largest denomination forces two of seven outputs onto value from the known participant",
+ "the margin to the next boundary is recomputed exactly",
+ "the full prefix curve is emitted rather than selecting its maximum",
+ "the generated Markdown equals the canonical rendering"
+ ],
+ "tolerance": null
+ },
+ "limitations": [
+ "the fixture is a reduced representation of one mainnet transaction",
+ "the run does not reproduce the surrounding six-round chain",
+ "the known participant input is supplied by an external forward walk",
+ "conservation establishes provenance of value, not output ownership",
+ "net settlement invalidates an ownership inference from this bound",
+ "the result is not a privacy score or CoinScore"
+ ]
+}
diff --git a/catalog/runs/counting-fee-sensitivity-v1.json b/catalog/runs/counting-fee-sensitivity-v1.json
new file mode 100644
index 00000000..1c198a7b
--- /dev/null
+++ b/catalog/runs/counting-fee-sensitivity-v1.json
@@ -0,0 +1,105 @@
+{
+ "schema_version": 1,
+ "id": "counting-fee-sensitivity-v1",
+ "claim_ids": [
+ "ctp.amount.subtransaction_ambiguity"
+ ],
+ "code": {
+ "revision": "bcd9a3e511860b1b0ff5f3ee6338a2df29ecbfbf",
+ "dirty": false
+ },
+ "command": {
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.counting_fee_sensitivity",
+ "reproduce",
+ "--artifact",
+ "results/artifacts/counting-fee-sensitivity-v1.json",
+ "--markdown",
+ "results/generated/counting-fee-sensitivity-v1.md"
+ ]
+ },
+ "environment": {
+ "python": "3.13.7",
+ "lock_digest": "5ee1d70afd72fdb4ba3f01843e85e7ecd9a6d3e2a52abeb14c346a2143e3c186",
+ "platform": "macOS-26.6.2-arm64-arm-64bit-Mach-O",
+ "dependencies": [
+ {
+ "name": "dss",
+ "version": "0.1.0",
+ "source": "https://github.com/bc1cindy/dense-subset-sum",
+ "revision": "d8099bc540c9de7d0f3c9f095e8b2aad8f9d8c8d",
+ "lock_sha256": "f17a02f09da28dcc603901580d695466c44e1d1a0063e110d3faa375ae6066ca",
+ "license": "MIT",
+ "redistribution": "allowed",
+ "editable": false
+ }
+ ]
+ },
+ "datasets": [],
+ "parameters": {
+ "inputs_sat": [
+ 400000,
+ 300000,
+ 300000
+ ],
+ "fees_sat": [
+ 0,
+ 1,
+ 10,
+ 100,
+ 1000,
+ 4956
+ ]
+ },
+ "rng": {
+ "algorithm": null,
+ "seeds": []
+ },
+ "outputs": [
+ {
+ "path": "results/artifacts/counting-fee-sensitivity-v1.json",
+ "bytes": 1968,
+ "sha256": "0fef54735522470634c696ec026c31c86dff4d4dae23da956b49d8f89eb3a508"
+ },
+ {
+ "path": "results/generated/counting-fee-sensitivity-v1.md",
+ "bytes": 526,
+ "sha256": "df9d25a028a5f5c9f7527923cd354a674865b3012d71b23405c08a6ec643a84b"
+ }
+ ],
+ "reproducibility": {
+ "level": "bitwise_reproducible",
+ "availability": "complete"
+ },
+ "verification": {
+ "mode": "exact",
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.counting_fee_sensitivity",
+ "verify",
+ "--artifact",
+ "results/artifacts/counting-fee-sensitivity-v1.json",
+ "--markdown",
+ "results/generated/counting-fee-sensitivity-v1.md"
+ ],
+ "tests": [
+ "tests/test_counting_fee_sensitivity_experiment.py",
+ "tests/test_counting.py"
+ ],
+ "properties": [
+ "transaction-level and per-coin counting remain separate objects",
+ "unmeasured per-coin inputs are absent rather than assigned zero",
+ "the generated Markdown equals the canonical rendering"
+ ],
+ "tolerance": null
+ },
+ "limitations": [
+ "this is a deterministic sensitivity fixture, not a population estimate",
+ "the transaction-level subset-sum count and fee-aware per-coin count are different objects",
+ "an absent per-coin index is a refusal to measure, not zero ambiguity",
+ "these diagnostics are not mapping entropy, a privacy score, or CoinScore"
+ ]
+}
diff --git a/catalog/runs/counting-methods-v1.json b/catalog/runs/counting-methods-v1.json
new file mode 100644
index 00000000..ce8d7e92
--- /dev/null
+++ b/catalog/runs/counting-methods-v1.json
@@ -0,0 +1,87 @@
+{
+ "schema_version": 1,
+ "id": "counting-methods-v1",
+ "claim_ids": [
+ "ctp.amount.subtransaction_ambiguity"
+ ],
+ "code": {
+ "revision": "bcd9a3e511860b1b0ff5f3ee6338a2df29ecbfbf",
+ "dirty": false
+ },
+ "command": {
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.counting_methods_summary",
+ "reproduce",
+ "--artifact",
+ "results/artifacts/counting-methods-v1.json",
+ "--markdown",
+ "results/generated/counting-methods-v1.md"
+ ]
+ },
+ "environment": {
+ "python": "3.13.7",
+ "lock_digest": "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855",
+ "platform": "macOS-26.6.2-arm64-arm-64bit-Mach-O",
+ "dependencies": []
+ },
+ "datasets": [],
+ "parameters": {
+ "composition": "identity-checked upstream artifacts only",
+ "components": {
+ "counting-router-v1": "731164f77a75776fc91a9c5bc8bce16700e6084e44c75d7e83510ac8afec1ce5",
+ "counting-fee-sensitivity-v1": "0fef54735522470634c696ec026c31c86dff4d4dae23da956b49d8f89eb3a508"
+ }
+ },
+ "rng": {
+ "algorithm": null,
+ "seeds": []
+ },
+ "outputs": [
+ {
+ "path": "results/artifacts/counting-methods-v1.json",
+ "bytes": 3003,
+ "sha256": "81d48cf4f7b71eb62354cfd589a40fdfc6171a54b0c9b7a768bd45198e7a1312"
+ },
+ {
+ "path": "results/generated/counting-methods-v1.md",
+ "bytes": 795,
+ "sha256": "7783ece95b71bd11cacf9b6f350b7d22c87c54a46ca7e75b3c5dbeb62e5d091d"
+ }
+ ],
+ "reproducibility": {
+ "level": "bitwise_reproducible",
+ "availability": "complete"
+ },
+ "verification": {
+ "mode": "exact",
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.counting_methods_summary",
+ "verify",
+ "--artifact",
+ "results/artifacts/counting-methods-v1.json",
+ "--markdown",
+ "results/generated/counting-methods-v1.md"
+ ],
+ "tests": [
+ "tests/test_counting_methods_summary_experiment.py"
+ ],
+ "properties": [
+ "both component identities are checked before composition",
+ "the summary performs no counting calculation",
+ "non-reproducible historical measurements remain explicitly excluded",
+ "the generated Markdown equals the canonical rendering"
+ ],
+ "tolerance": null
+ },
+ "limitations": [
+ "this artifact composes upstream results and performs no counting calculation",
+ "component SHA-256 identities are mandatory",
+ "runtime benchmarks require a separate benchmark protocol",
+ "an L-estimator comparison requires a preserved generator and seeds",
+ "the counting objects remain diagnostics and are not a privacy score or CoinScore"
+ ]
+}
diff --git a/catalog/runs/counting-router-v1.json b/catalog/runs/counting-router-v1.json
new file mode 100644
index 00000000..09c59143
--- /dev/null
+++ b/catalog/runs/counting-router-v1.json
@@ -0,0 +1,111 @@
+{
+ "schema_version": 1,
+ "id": "counting-router-v1",
+ "claim_ids": [
+ "ctp.amount.subtransaction_ambiguity"
+ ],
+ "code": {
+ "revision": "bcd9a3e511860b1b0ff5f3ee6338a2df29ecbfbf",
+ "dirty": false
+ },
+ "command": {
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.counting_router",
+ "--dataset",
+ "data/amount-channel-812695-812831-v1.json",
+ "reproduce",
+ "--artifact",
+ "results/artifacts/counting-router-v1.json",
+ "--markdown",
+ "results/generated/counting-router-v1.md"
+ ]
+ },
+ "environment": {
+ "python": "3.13.7",
+ "lock_digest": "5ee1d70afd72fdb4ba3f01843e85e7ecd9a6d3e2a52abeb14c346a2143e3c186",
+ "platform": "macOS-26.6.2-arm64-arm-64bit-Mach-O",
+ "dependencies": [
+ {
+ "name": "dss",
+ "version": "0.1.0",
+ "source": "https://github.com/bc1cindy/dense-subset-sum",
+ "revision": "d8099bc540c9de7d0f3c9f095e8b2aad8f9d8c8d",
+ "lock_sha256": "f17a02f09da28dcc603901580d695466c44e1d1a0063e110d3faa375ae6066ca",
+ "license": "MIT",
+ "redistribution": "allowed",
+ "editable": false
+ }
+ ]
+ },
+ "datasets": [
+ {
+ "id": "amount-channel-812695-812831-v1",
+ "sha256": "83b4391f78d07a2fa85a67218d5ae4420d8965da3c95af577592e9ec1850104d"
+ }
+ ],
+ "parameters": {
+ "knee": 5,
+ "route": [
+ "radix",
+ "sparse",
+ "none"
+ ]
+ },
+ "rng": {
+ "algorithm": null,
+ "seeds": []
+ },
+ "outputs": [
+ {
+ "path": "results/artifacts/counting-router-v1.json",
+ "bytes": 897,
+ "sha256": "7c0764b7f39ad8795b4e349cff0903f68b7edcbc3f0d75cc6691a138813239b7"
+ },
+ {
+ "path": "results/generated/counting-router-v1.md",
+ "bytes": 505,
+ "sha256": "a002dd84d74d51b0106a6bccd4c625300d838becbfc1edff36b7cfd46eda682f"
+ }
+ ],
+ "reproducibility": {
+ "level": "bitwise_reproducible",
+ "availability": "complete"
+ },
+ "verification": {
+ "mode": "exact",
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.counting_router",
+ "--dataset",
+ "data/amount-channel-812695-812831-v1.json",
+ "verify",
+ "--artifact",
+ "results/artifacts/counting-router-v1.json",
+ "--markdown",
+ "results/generated/counting-router-v1.md"
+ ],
+ "tests": [
+ "tests/test_counting_router_experiment.py",
+ "tests/test_counting.py"
+ ],
+ "properties": [
+ "router outcomes preserve method and guarantee kind",
+ "radix is measured both with and without its required precondition",
+ "zero and unknown outcomes do not count as ambiguity evidence",
+ "Sasamoto remains outside the router",
+ "the generated Markdown equals the canonical rendering"
+ ],
+ "tolerance": null
+ },
+ "limitations": [
+ "W(E) is a transaction-level subset-sum diagnostic, not a mapping count",
+ "raw radix output is audited only to test its required precondition",
+ "zero-count and unknown outcomes provide no ambiguity evidence",
+ "Sasamoto remains an explicit estimator and is not a router tier",
+ "timings and synthetic L-estimator comparisons are outside this run",
+ "this diagnostic is not a privacy score or CoinScore"
+ ]
+}
diff --git a/catalog/runs/denomination-preparation-v1.json b/catalog/runs/denomination-preparation-v1.json
new file mode 100644
index 00000000..0a14767d
--- /dev/null
+++ b/catalog/runs/denomination-preparation-v1.json
@@ -0,0 +1,90 @@
+{
+ "schema_version": 1,
+ "id": "denomination-preparation-v1",
+ "claim_ids": [
+ "ctp.amount.denomination_preparation_fingerprint"
+ ],
+ "code": {
+ "revision": "bcd9a3e511860b1b0ff5f3ee6338a2df29ecbfbf",
+ "dirty": false
+ },
+ "command": {
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.denomination_preparation",
+ "reproduce",
+ "--artifact",
+ "results/artifacts/denomination-preparation-v1.json",
+ "--markdown",
+ "results/generated/denomination-preparation-v1.md"
+ ]
+ },
+ "environment": {
+ "python": "3.13.7",
+ "lock_digest": "e0d6a8cada5d383c0aca2bd2a37f28c15721392469c94084128d369c29c5544d",
+ "platform": "macOS-26.6.2-arm64-arm-64bit-Mach-O",
+ "dependencies": []
+ },
+ "datasets": [],
+ "parameters": {
+ "denomination": 100000,
+ "preparations": 3,
+ "controls": 3,
+ "control_matching": [
+ "input_count",
+ "output_count"
+ ]
+ },
+ "rng": {
+ "algorithm": null,
+ "seeds": []
+ },
+ "outputs": [
+ {
+ "path": "results/artifacts/denomination-preparation-v1.json",
+ "bytes": 1299,
+ "sha256": "6102106a1ac39bdfb1f668a7c90772f01c29c214a1fb2a76088403165dbe4ce2"
+ },
+ {
+ "path": "results/generated/denomination-preparation-v1.md",
+ "bytes": 546,
+ "sha256": "c433cfc5c966ba7af33ea595cad134862726168033c2029d8b11574319cf0fc8"
+ }
+ ],
+ "reproducibility": {
+ "level": "bitwise_reproducible",
+ "availability": "complete"
+ },
+ "verification": {
+ "mode": "exact",
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.denomination_preparation",
+ "verify",
+ "--artifact",
+ "results/artifacts/denomination-preparation-v1.json",
+ "--markdown",
+ "results/generated/denomination-preparation-v1.md"
+ ],
+ "tests": [
+ "tests/test_denomination_preparation.py",
+ "tests/test_denomination_preparation_experiment.py"
+ ],
+ "properties": [
+ "all three synthetic preparations expose one exact denomination consumed by the next CoinJoin",
+ "all three arity-matched controls decline the fingerprint",
+ "transaction shape is preserved as descriptive evidence rather than ownership evidence",
+ "the result states that unilateral construction, rarity and prevalence are not established",
+ "the generated Markdown equals the canonical rendering"
+ ],
+ "tolerance": null
+ },
+ "limitations": [
+ "deterministic synthetic corpus",
+ "the next CoinJoin and its denomination are supplied",
+ "controls match arity but not a real-world population distribution",
+ "the CTP qualification has no separately cited empirical experiment"
+ ]
+}
diff --git a/catalog/runs/descent-vs-ascent-v1.json b/catalog/runs/descent-vs-ascent-v1.json
new file mode 100644
index 00000000..4b79dbc3
--- /dev/null
+++ b/catalog/runs/descent-vs-ascent-v1.json
@@ -0,0 +1,108 @@
+{
+ "schema_version": 1,
+ "id": "descent-vs-ascent-v1",
+ "claim_ids": [
+ "ctp.cioh.collaborative_false_merge"
+ ],
+ "code": {
+ "revision": "bcd9a3e511860b1b0ff5f3ee6338a2df29ecbfbf",
+ "dirty": false
+ },
+ "command": {
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.descent_vs_ascent",
+ "--dataset",
+ "tests/fixtures/slice_a_channels_2016.ndjson.gz",
+ "reproduce",
+ "--artifact",
+ "results/artifacts/descent-vs-ascent-v1.json",
+ "--markdown",
+ "results/generated/descent-vs-ascent-v1.md"
+ ]
+ },
+ "environment": {
+ "python": "3.13.7",
+ "lock_digest": "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855",
+ "platform": "macOS-26.6.2-arm64-arm-64bit-Mach-O",
+ "dependencies": []
+ },
+ "datasets": [
+ {
+ "id": "slice-a-channels-2016-v1",
+ "sha256": "66104889f8b79f2938edb6666f16dfa19a74c68cd8016832b4f2a5e03b11865f"
+ }
+ ],
+ "parameters": {
+ "channels": [
+ "coinjoin shape refuses every pair among the inputs",
+ "de-mix partition joins a participant's own inputs and refuses across participants",
+ "unnecessary input and mixed input types each refuse once"
+ ],
+ "cospend_prior": 0.0,
+ "max_block": 9,
+ "signal_unit": "one published tell against a pairing",
+ "thresholds": [
+ -1.0,
+ -2.0,
+ -4.0
+ ]
+ },
+ "rng": {
+ "algorithm": "none",
+ "seeds": []
+ },
+ "outputs": [
+ {
+ "path": "results/artifacts/descent-vs-ascent-v1.json",
+ "bytes": 2448,
+ "sha256": "3542b1199c220f49bd923306dcd1493ed5e7e677ff6fdabd1816256eb7021934"
+ },
+ {
+ "path": "results/generated/descent-vs-ascent-v1.md",
+ "bytes": 2504,
+ "sha256": "97ecbaa0035044c51f4d5717148ae6f2671d8b1d33ad4c6950801a651bc0db53"
+ }
+ ],
+ "reproducibility": {
+ "level": "bitwise_reproducible",
+ "availability": "partial"
+ },
+ "verification": {
+ "mode": "exact",
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.descent_vs_ascent",
+ "--dataset",
+ "tests/fixtures/slice_a_channels_2016.ndjson.gz",
+ "verify",
+ "--artifact",
+ "results/artifacts/descent-vs-ascent-v1.json",
+ "--markdown",
+ "results/generated/descent-vs-ascent-v1.md"
+ ],
+ "tests": [
+ "tests/test_descent_vs_ascent.py",
+ "tests/test_declustering.py",
+ "tests/test_partition.py"
+ ],
+ "properties": [
+ "the descending pass returns a meet with the partition it was given, so it can only refine it",
+ "the co-spend contributes no weight; only the refusal channels do",
+ "blocks above the search bound are left whole and counted as unsearched",
+ "two equally supported cuts leave the block whole",
+ "the generated Markdown equals the canonical rendering"
+ ],
+ "tolerance": null
+ },
+ "limitations": [
+ "the snapshot contains six 2016 blocks and is not a chain-wide sample",
+ "the exact search is skipped on blocks above max_block, and those blocks are left whole",
+ "signals are counted tells, not calibrated bits, so the thresholds are not likelihoods",
+ "the two passes are compared as partitions; neither is checked against same-owner labels",
+ "the fingerprint and roundness channels are absent here, as they are in the merge pass",
+ "the snapshot recipe is unavailable; the dataset publisher authorized redistribution on 2026-09-04"
+ ]
+}
diff --git a/catalog/runs/deterministic-partition-v1.json b/catalog/runs/deterministic-partition-v1.json
new file mode 100644
index 00000000..a49f97bd
--- /dev/null
+++ b/catalog/runs/deterministic-partition-v1.json
@@ -0,0 +1,110 @@
+{
+ "schema_version": 1,
+ "id": "deterministic-partition-v1",
+ "claim_ids": [
+ "ctp.amount.deterministic_partition"
+ ],
+ "code": {
+ "revision": "bcd9a3e511860b1b0ff5f3ee6338a2df29ecbfbf",
+ "dirty": false
+ },
+ "command": {
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.deterministic_partition",
+ "reproduce",
+ "--artifact",
+ "results/artifacts/deterministic-partition-v1.json",
+ "--markdown",
+ "results/generated/deterministic-partition-v1.md"
+ ]
+ },
+ "environment": {
+ "python": "3.13.7",
+ "lock_digest": "e0d6a8cada5d383c0aca2bd2a37f28c15721392469c94084128d369c29c5544d",
+ "platform": "macOS-26.6.2-arm64-arm-64bit-Mach-O",
+ "dependencies": []
+ },
+ "datasets": [],
+ "parameters": {
+ "balance_model": "exact_per_block_conservation",
+ "mapping_family": "maurer_non_derived",
+ "forced_inputs": [
+ 1,
+ 3,
+ 20,
+ 50
+ ],
+ "forced_outputs": [
+ 4,
+ 70
+ ],
+ "control_inputs": [
+ 1,
+ 2,
+ 3,
+ 4,
+ 5
+ ],
+ "control_outputs": [
+ 7,
+ 8
+ ],
+ "amount_scale": 10
+ },
+ "rng": {
+ "algorithm": null,
+ "seeds": []
+ },
+ "outputs": [
+ {
+ "path": "results/artifacts/deterministic-partition-v1.json",
+ "bytes": 868,
+ "sha256": "9ef88ed2230876552f3f18ebf4e1bf462e7bd401160a5d287de443abc03f7415"
+ },
+ {
+ "path": "results/generated/deterministic-partition-v1.md",
+ "bytes": 599,
+ "sha256": "bcd7c6a58c1206d78841bceba862f9b9eabd9b978040bcf717a5967f0355df29"
+ }
+ ],
+ "reproducibility": {
+ "level": "bitwise_reproducible",
+ "availability": "complete"
+ },
+ "verification": {
+ "mode": "exact",
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.deterministic_partition",
+ "verify",
+ "--artifact",
+ "results/artifacts/deterministic-partition-v1.json",
+ "--markdown",
+ "results/generated/deterministic-partition-v1.md"
+ ],
+ "tests": [
+ "tests/test_deterministic_partition.py",
+ "tests/test_deterministic_partition_experiment.py",
+ "tests/test_reference_subtransactions.py",
+ "tests/test_boltzmann_reference_parity.py"
+ ],
+ "properties": [
+ "the forced CTP example has one exact non-derived mapping",
+ "four input-output links occur in every mapping of the forced family",
+ "the underdetermined CTP control has three exact non-derived mappings",
+ "the underdetermined control has no unanimous input-output link",
+ "certainty remains explicitly conditional on the declared mapping model",
+ "the generated Markdown equals the canonical rendering"
+ ],
+ "tolerance": null
+ },
+ "limitations": [
+ "paper examples scaled to integer units",
+ "exact per-block conservation",
+ "fees, contextual priors and net-settlement cycles are outside the model",
+ "unanimous mapping links are not ownership attribution"
+ ]
+}
diff --git a/catalog/runs/em-m-v1.json b/catalog/runs/em-m-v1.json
new file mode 100644
index 00000000..2923798d
--- /dev/null
+++ b/catalog/runs/em-m-v1.json
@@ -0,0 +1,99 @@
+{
+ "schema_version": 1,
+ "id": "em-m-v1",
+ "claim_ids": [
+ "ctp.fingerprint.statistical_linkage"
+ ],
+ "code": {
+ "revision": "bcd9a3e511860b1b0ff5f3ee6338a2df29ecbfbf",
+ "dirty": false
+ },
+ "command": {
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.em_m",
+ "--snapshot",
+ "data/fs-blkcache-2026-09-04.tar.gz",
+ "reproduce",
+ "--artifact",
+ "results/artifacts/em-m-v1.json",
+ "--markdown",
+ "results/generated/em-m-v1.md"
+ ]
+ },
+ "environment": {
+ "python": "3.13.7",
+ "lock_digest": "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855",
+ "platform": "macOS-26.6.2-arm64-arm-64bit-Mach-O",
+ "dependencies": []
+ },
+ "datasets": [
+ {
+ "id": "fs-blkcache-2026-09-04-v1",
+ "sha256": "5a69f6f446dbdfcce09dddc91c653e5d841124d14020b720fdea276315c775a6"
+ }
+ ],
+ "parameters": {
+ "pair_cap_per_class": 4000,
+ "seed": 0,
+ "fixed_u": true
+ },
+ "rng": {
+ "algorithm": "python.random.MersenneTwister",
+ "seeds": [
+ 0
+ ]
+ },
+ "outputs": [
+ {
+ "path": "results/artifacts/em-m-v1.json",
+ "bytes": 4442,
+ "sha256": "0cbd76502a1a77f11cc72d8834d4d9f7fc2ba2fffa1bbf33d44c4f075652a47b"
+ },
+ {
+ "path": "results/generated/em-m-v1.md",
+ "bytes": 676,
+ "sha256": "c8c6d92b22c9486a08ce2eecaf5164959831534f8e953d4a5d3dbcf6f95ad5cb"
+ }
+ ],
+ "reproducibility": {
+ "level": "bitwise_reproducible",
+ "availability": "complete"
+ },
+ "verification": {
+ "mode": "exact",
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.em_m",
+ "--snapshot",
+ "data/fs-blkcache-2026-09-04.tar.gz",
+ "verify",
+ "--artifact",
+ "results/artifacts/em-m-v1.json",
+ "--markdown",
+ "results/generated/em-m-v1.md"
+ ],
+ "tests": [
+ "tests/test_fs_em.py",
+ "tests/test_em_m_experiment.py"
+ ],
+ "properties": [
+ "the preserved snapshot exactly reproduces the canonical artifact",
+ "EM receives no pair labels during fitting",
+ "all scorer variants use the same selected pair population",
+ "the address-reuse comparison is identified as a weak label rather than an oracle",
+ "the AUC delta is reported without an untested direction verdict",
+ "the historical 165,832-transaction result is explicitly not reproduced"
+ ],
+ "tolerance": null
+ },
+ "limitations": [
+ "address reuse is a weak and partly feature-dependent ownership proxy",
+ "the balanced selected sample does not represent population prevalence",
+ "EM is fitted and evaluated on the same pair sample",
+ "conditional independence is violated by correlated axes",
+ "the result is attacker-side evidence and is not CoinScore"
+ ]
+}
diff --git a/catalog/runs/entity-deanon-v1.json b/catalog/runs/entity-deanon-v1.json
new file mode 100644
index 00000000..dee4ee6d
--- /dev/null
+++ b/catalog/runs/entity-deanon-v1.json
@@ -0,0 +1,111 @@
+{
+ "schema_version": 1,
+ "id": "entity-deanon-v1",
+ "claim_ids": [
+ "ctp.social_graph.structural_linkage"
+ ],
+ "code": {
+ "revision": "bcd9a3e511860b1b0ff5f3ee6338a2df29ecbfbf",
+ "dirty": false
+ },
+ "command": {
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.entity_deanon",
+ "--satoshidice-dataset",
+ "tests/fixtures/entity_satoshidice_2013.ndjson.gz",
+ "--bitmex-dataset",
+ "tests/fixtures/entity_bitmex_2019.ndjson.gz",
+ "reproduce",
+ "--artifact",
+ "results/artifacts/entity-deanon-v1.json",
+ "--markdown",
+ "results/generated/entity-deanon-v1.md"
+ ]
+ },
+ "environment": {
+ "python": "3.13.7",
+ "lock_digest": "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855",
+ "platform": "macOS-26.6.2-arm64-arm-64bit-Mach-O",
+ "dependencies": []
+ },
+ "datasets": [
+ {
+ "id": "entity-satoshidice-2013-v1",
+ "sha256": "6cac465a5eca59633fbb3e6f99b83146b9b0e590adf95801a303ccd815699846"
+ },
+ {
+ "id": "entity-bitmex-2019-v1",
+ "sha256": "699c87235bce17a43a8befe2c412dd7bea61ffe3dd772abe7addcbae5a4b8ea8"
+ }
+ ],
+ "parameters": {
+ "seed": 0,
+ "score": "payment-graph common neighbours",
+ "labels": {
+ "satoshidice": "1dice vanity-prefix detector",
+ "bitmex": "3BMEX/bc1qmex vanity-prefix detector"
+ }
+ },
+ "rng": {
+ "algorithm": "python.random.Random",
+ "seeds": [
+ 0
+ ]
+ },
+ "outputs": [
+ {
+ "path": "results/artifacts/entity-deanon-v1.json",
+ "bytes": 1100,
+ "sha256": "4825de7e64906f0d708bdcbd3bd23836675ff168166c5ff06fc0ea25d95afb38"
+ },
+ {
+ "path": "results/generated/entity-deanon-v1.md",
+ "bytes": 557,
+ "sha256": "9a3fdc6c79bc5c94cc687b5a41ebff8c663ecf3358cb080883e93cd30d94d850"
+ }
+ ],
+ "reproducibility": {
+ "level": "bitwise_reproducible",
+ "availability": "partial"
+ },
+ "verification": {
+ "mode": "exact",
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.entity_deanon",
+ "--satoshidice-dataset",
+ "tests/fixtures/entity_satoshidice_2013.ndjson.gz",
+ "--bitmex-dataset",
+ "tests/fixtures/entity_bitmex_2019.ndjson.gz",
+ "verify",
+ "--artifact",
+ "results/artifacts/entity-deanon-v1.json",
+ "--markdown",
+ "results/generated/entity-deanon-v1.md"
+ ],
+ "tests": [
+ "tests/test_entity_deanon.py",
+ "tests/test_entity_deanon_real.py",
+ "tests/test_entity_deanon_experiment.py",
+ "tests/test_entities.py"
+ ],
+ "properties": [
+ "both entity-labelled fixtures are evaluated with a fixed seed",
+ "SatoshiDice remains a positive common-neighbour control on its selected subgraph",
+ "BitMEX remains a chance-level null control on its selected subgraph",
+ "the generated Markdown equals the canonical rendering"
+ ],
+ "tolerance": null
+ },
+ "limitations": [
+ "the fixtures are selected entity-centred subgraphs, not uniform chain samples",
+ "vanity-prefix detectors label the fixtures and are not general entity attribution",
+ "the run evaluates common-neighbour structure, not the full Narayanan-Shmatikov algorithm",
+ "the contrast does not establish a universal property of gambling services or exchanges",
+ "upstream data licensing is undetermined; the dataset publisher authorized redistribution on 2026-09-04",
+ "the result is not a privacy score or CoinScore"
+ ]
+}
diff --git a/catalog/runs/entropy-insufficiency-v1.json b/catalog/runs/entropy-insufficiency-v1.json
new file mode 100644
index 00000000..3d8b3239
--- /dev/null
+++ b/catalog/runs/entropy-insufficiency-v1.json
@@ -0,0 +1,91 @@
+{
+ "schema_version": 1,
+ "id": "entropy-insufficiency-v1",
+ "claim_ids": [
+ "ctp.entropy.not_sufficient"
+ ],
+ "code": {
+ "revision": "bcd9a3e511860b1b0ff5f3ee6338a2df29ecbfbf",
+ "dirty": false
+ },
+ "command": {
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.entropy_insufficiency",
+ "reproduce",
+ "--artifact",
+ "results/artifacts/entropy-insufficiency-v1.json",
+ "--markdown",
+ "results/generated/entropy-insufficiency-v1.md"
+ ]
+ },
+ "environment": {
+ "python": "3.13.7",
+ "lock_digest": "e0d6a8cada5d383c0aca2bd2a37f28c15721392469c94084128d369c29c5544d",
+ "platform": "macOS-26.6.2-arm64-arm-64bit-Mach-O",
+ "dependencies": []
+ },
+ "datasets": [],
+ "parameters": {
+ "initial_candidates": 4,
+ "initial_entropy_bits": 2.0,
+ "observations_per_scenario": 2,
+ "scenarios": [
+ "brittle-overlap",
+ "overlapping-crowd"
+ ]
+ },
+ "rng": {
+ "algorithm": null,
+ "seeds": []
+ },
+ "outputs": [
+ {
+ "path": "results/artifacts/entropy-insufficiency-v1.json",
+ "bytes": 662,
+ "sha256": "a323b5b930835a020812272aa8df80af7c922a116b3def6bca2ca4715e94d466"
+ },
+ {
+ "path": "results/generated/entropy-insufficiency-v1.md",
+ "bytes": 580,
+ "sha256": "d1ae4828869980634e3739f926f61bb3530a43b31f018f0e8fb6a6ce865eb50a"
+ }
+ ],
+ "reproducibility": {
+ "level": "bitwise_reproducible",
+ "availability": "complete"
+ },
+ "verification": {
+ "mode": "exact",
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.entropy_insufficiency",
+ "verify",
+ "--artifact",
+ "results/artifacts/entropy-insufficiency-v1.json",
+ "--markdown",
+ "results/generated/entropy-insufficiency-v1.md"
+ ],
+ "tests": [
+ "tests/test_entropy_insufficiency.py",
+ "tests/test_entropy_insufficiency_experiment.py"
+ ],
+ "properties": [
+ "both scenarios begin with the same explicit four-candidate posterior and two bits of entropy",
+ "the brittle observations intersect to one surviving candidate",
+ "the overlapping-crowd observations retain two candidates",
+ "posterior and longitudinal intersection remain separate evidence channels",
+ "the result is not presented as attack frequency, a privacy score, or a certificate",
+ "the generated Markdown equals the canonical rendering"
+ ],
+ "tolerance": null
+ },
+ "limitations": [
+ "synthetic counterexample",
+ "posterior probabilities and later candidate sets are supplied",
+ "the fixture falsifies sufficiency but does not estimate real-world attack frequency",
+ "the result does not reproduce the empirical experiments of the cited papers"
+ ]
+}
diff --git a/catalog/runs/equal-output-consolidation-v1.json b/catalog/runs/equal-output-consolidation-v1.json
new file mode 100644
index 00000000..89c01064
--- /dev/null
+++ b/catalog/runs/equal-output-consolidation-v1.json
@@ -0,0 +1,86 @@
+{
+ "schema_version": 1,
+ "id": "equal-output-consolidation-v1",
+ "claim_ids": [
+ "ctp.intersection.longitudinal"
+ ],
+ "code": {
+ "revision": "bcd9a3e511860b1b0ff5f3ee6338a2df29ecbfbf",
+ "dirty": false
+ },
+ "command": {
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.equal_output_consolidation",
+ "reproduce",
+ "--artifact",
+ "results/artifacts/equal-output-consolidation-v1.json",
+ "--markdown",
+ "results/generated/equal-output-consolidation-v1.md"
+ ]
+ },
+ "environment": {
+ "python": "3.13.7",
+ "lock_digest": "c7d402576511073ed4fcf076c77ffa6b3be124e0a2ef996d2da1cd3b02fbe541",
+ "platform": "macOS-26.6.2-arm64-arm-64bit-Mach-O",
+ "dependencies": []
+ },
+ "datasets": [],
+ "parameters": {
+ "candidate_clusters_per_output": 3,
+ "coinjoin_outputs": 2,
+ "later_observation": "co-spend"
+ },
+ "rng": {
+ "algorithm": null,
+ "seeds": []
+ },
+ "outputs": [
+ {
+ "path": "results/artifacts/equal-output-consolidation-v1.json",
+ "bytes": 480,
+ "sha256": "b61bed4b86094fcc8dd8382a5ff585a89aae8c1f9c6e459b64b0bcd3b904dd67"
+ },
+ {
+ "path": "results/generated/equal-output-consolidation-v1.md",
+ "bytes": 460,
+ "sha256": "5c9d2fdbc1b11a5439d1e588a1ec95f71fec09bb1c064ae7902c123cffd9260b"
+ }
+ ],
+ "reproducibility": {
+ "level": "bitwise_reproducible",
+ "availability": "complete"
+ },
+ "verification": {
+ "mode": "exact",
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.equal_output_consolidation",
+ "verify",
+ "--artifact",
+ "results/artifacts/equal-output-consolidation-v1.json",
+ "--markdown",
+ "results/generated/equal-output-consolidation-v1.md"
+ ],
+ "tests": [
+ "tests/test_equal_output_consolidation.py",
+ "tests/test_equal_output_consolidation_experiment.py"
+ ],
+ "properties": [
+ "both outputs are ambiguous before the consolidation",
+ "intersection retains only the origin cluster shared by both candidate sets",
+ "disjoint candidate sets produce a refusal instead of an identification",
+ "the report preserves the co-ownership premise as a limitation",
+ "the generated Markdown equals the canonical rendering"
+ ],
+ "tolerance": null
+ },
+ "limitations": [
+ "synthetic fixture",
+ "candidate origin clusters are supplied rather than inferred from chain data",
+ "the narrowing is conditional on treating the later co-spend as common ownership",
+ "the run does not measure a Bitcoin deanonymization rate"
+ ]
+}
diff --git a/catalog/runs/eve-alice-eve-v1.json b/catalog/runs/eve-alice-eve-v1.json
new file mode 100644
index 00000000..9399ffbb
--- /dev/null
+++ b/catalog/runs/eve-alice-eve-v1.json
@@ -0,0 +1,86 @@
+{
+ "schema_version": 1,
+ "id": "eve-alice-eve-v1",
+ "claim_ids": [
+ "ctp.provenance.traceability"
+ ],
+ "code": {
+ "revision": "bcd9a3e511860b1b0ff5f3ee6338a2df29ecbfbf",
+ "dirty": false
+ },
+ "command": {
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.eve_alice_eve",
+ "reproduce",
+ "--artifact",
+ "results/artifacts/eve-alice-eve-v1.json",
+ "--markdown",
+ "results/generated/eve-alice-eve-v1.md"
+ ]
+ },
+ "environment": {
+ "python": "3.13.7",
+ "lock_digest": "e0d6a8cada5d383c0aca2bd2a37f28c15721392469c94084128d369c29c5544d",
+ "platform": "macOS-26.6.2-arm64-arm-64bit-Mach-O",
+ "dependencies": []
+ },
+ "datasets": [],
+ "parameters": {
+ "isolated_return_inputs": 1,
+ "consolidated_return_inputs": 2,
+ "adversary": "counterparty with private transaction records"
+ },
+ "rng": {
+ "algorithm": null,
+ "seeds": []
+ },
+ "outputs": [
+ {
+ "path": "results/artifacts/eve-alice-eve-v1.json",
+ "bytes": 548,
+ "sha256": "306d898c560316cac38ca3e74df461e17ae37d2b4aa7006d628710ef94e59f42"
+ },
+ {
+ "path": "results/generated/eve-alice-eve-v1.md",
+ "bytes": 417,
+ "sha256": "89e9239416d8f40ed38c5d36b2eb2430d726fd0e7fdcf56fbecc37bdf5bd2808"
+ }
+ ],
+ "reproducibility": {
+ "level": "bitwise_reproducible",
+ "availability": "complete"
+ },
+ "verification": {
+ "mode": "exact",
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.eve_alice_eve",
+ "verify",
+ "--artifact",
+ "results/artifacts/eve-alice-eve-v1.json",
+ "--markdown",
+ "results/generated/eve-alice-eve-v1.md"
+ ],
+ "tests": [
+ "tests/test_eve_alice_eve.py",
+ "tests/test_eve_alice_eve_experiment.py"
+ ],
+ "properties": [
+ "one ambiguous return remains inconclusive",
+ "two consolidated returns narrow the shared candidate customer",
+ "private counterparty records remain distinct from public observables",
+ "candidate narrowing is not represented as identity attribution",
+ "the generated Markdown equals the canonical rendering"
+ ],
+ "tolerance": null
+ },
+ "limitations": [
+ "synthetic fixture",
+ "candidate customers and ancestry are supplied rather than inferred",
+ "behavioral priors and temporal patterns are not modeled",
+ "the run does not measure a Bitcoin deanonymization rate"
+ ]
+}
diff --git a/catalog/runs/exact-oracle-audit-v1.json b/catalog/runs/exact-oracle-audit-v1.json
new file mode 100644
index 00000000..c969bd4b
--- /dev/null
+++ b/catalog/runs/exact-oracle-audit-v1.json
@@ -0,0 +1,101 @@
+{
+ "schema_version": 1,
+ "id": "exact-oracle-audit-v1",
+ "claim_ids": [
+ "ctp.amount.subtransaction_ambiguity"
+ ],
+ "code": {
+ "revision": "bcd9a3e511860b1b0ff5f3ee6338a2df29ecbfbf",
+ "dirty": false
+ },
+ "command": {
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.exact_oracle_audit",
+ "reproduce",
+ "--artifact",
+ "results/artifacts/exact-oracle-audit-v1.json",
+ "--markdown",
+ "results/generated/exact-oracle-audit-v1.md"
+ ]
+ },
+ "environment": {
+ "python": "3.13.7",
+ "lock_digest": "5ee1d70afd72fdb4ba3f01843e85e7ecd9a6d3e2a52abeb14c346a2143e3c186",
+ "platform": "macOS-26.6.2-arm64-arm-64bit-Mach-O",
+ "dependencies": [
+ {
+ "name": "dss",
+ "version": "0.1.0",
+ "source": "https://github.com/bc1cindy/dense-subset-sum",
+ "revision": "d8099bc540c9de7d0f3c9f095e8b2aad8f9d8c8d",
+ "lock_sha256": "f17a02f09da28dcc603901580d695466c44e1d1a0063e110d3faa375ae6066ca",
+ "license": "MIT",
+ "redistribution": "allowed",
+ "editable": false
+ }
+ ]
+ },
+ "datasets": [],
+ "parameters": {
+ "alphabet": [
+ 1,
+ 2,
+ 3,
+ 4
+ ],
+ "max_coins": 8,
+ "min_side": 2,
+ "tolerance": 1e-09
+ },
+ "rng": {
+ "algorithm": null,
+ "seeds": []
+ },
+ "outputs": [
+ {
+ "path": "results/artifacts/exact-oracle-audit-v1.json",
+ "bytes": 16593,
+ "sha256": "465dbf1548bcab18f0fd6ef01a940093bac7b35ec93a431549a8dd66389daefc"
+ },
+ {
+ "path": "results/generated/exact-oracle-audit-v1.md",
+ "bytes": 497,
+ "sha256": "30cb1732263d29dcca85159c6528705dfe7c9fa24451b795b7b3a41f9afbc447"
+ }
+ ],
+ "reproducibility": {
+ "level": "bitwise_reproducible",
+ "availability": "complete"
+ },
+ "verification": {
+ "mode": "exact",
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.exact_oracle_audit",
+ "verify",
+ "--artifact",
+ "results/artifacts/exact-oracle-audit-v1.json",
+ "--markdown",
+ "results/generated/exact-oracle-audit-v1.md"
+ ],
+ "tests": [
+ "tests/test_exact_oracle_experiment.py",
+ "tests/test_oracle_audit.py"
+ ],
+ "properties": [
+ "the stored artifact equals a fresh exhaustive audit",
+ "the generated Markdown equals the canonical rendering",
+ "declared output sizes and SHA-256 digests match stored bytes"
+ ],
+ "tolerance": null
+ },
+ "limitations": [
+ "zero-fee transactions only",
+ "bounded exhaustive family over values 1 through 4",
+ "the recorded dss revision is MIT licensed, published and identified by commit and Cargo.lock digest",
+ "the bundled environment currently targets CPython 3.13 on macOS arm64"
+ ]
+}
diff --git a/catalog/runs/fingerprint-catalog-axes-v1.json b/catalog/runs/fingerprint-catalog-axes-v1.json
new file mode 100644
index 00000000..3b2130eb
--- /dev/null
+++ b/catalog/runs/fingerprint-catalog-axes-v1.json
@@ -0,0 +1,106 @@
+{
+ "schema_version": 1,
+ "id": "fingerprint-catalog-axes-v1",
+ "claim_ids": [
+ "ctp.fingerprint.statistical_linkage"
+ ],
+ "code": {
+ "revision": "bcd9a3e511860b1b0ff5f3ee6338a2df29ecbfbf",
+ "dirty": false
+ },
+ "command": {
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.fingerprint_catalog_axes",
+ "--dataset",
+ "tests/fixtures/fingerprint_blkcache_sample.json",
+ "reproduce",
+ "--artifact",
+ "results/artifacts/fingerprint-catalog-axes-v1.json",
+ "--markdown",
+ "results/generated/fingerprint-catalog-axes-v1.md"
+ ]
+ },
+ "environment": {
+ "python": "3.13.7",
+ "lock_digest": "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855",
+ "platform": "macOS-26.6.2-arm64-arm-64bit-Mach-O",
+ "dependencies": []
+ },
+ "datasets": [
+ {
+ "id": "fingerprint-blkcache-sample-v1",
+ "sha256": "406ce2d7d0a04f7c73281bce6b6a781012a8765e079693f010a0154e926c6a6c"
+ }
+ ],
+ "parameters": {
+ "pair_draw_cap": 4000,
+ "seed": 0,
+ "probability_source": "same frozen snapshot",
+ "same_wallet_consistency": 0.95,
+ "rarity_floor_count": 1000,
+ "diagnostic_axes": [
+ "output_count",
+ "segwit_serialization"
+ ]
+ },
+ "rng": {
+ "algorithm": "python.random.Random",
+ "seeds": [
+ 0
+ ]
+ },
+ "outputs": [
+ {
+ "path": "results/artifacts/fingerprint-catalog-axes-v1.json",
+ "bytes": 1883,
+ "sha256": "f103911399f995ac300dcca04af29eab65cb3962d436915c9cb3f931b81f9e4a"
+ },
+ {
+ "path": "results/generated/fingerprint-catalog-axes-v1.md",
+ "bytes": 802,
+ "sha256": "773bd7f24736da999200cd9e3ef18b99875ed1f62801496bf8add9f96ff11149"
+ }
+ ],
+ "reproducibility": {
+ "level": "bitwise_reproducible",
+ "availability": "partial"
+ },
+ "verification": {
+ "mode": "exact",
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.fingerprint_catalog_axes",
+ "--dataset",
+ "tests/fixtures/fingerprint_blkcache_sample.json",
+ "verify",
+ "--artifact",
+ "results/artifacts/fingerprint-catalog-axes-v1.json",
+ "--markdown",
+ "results/generated/fingerprint-catalog-axes-v1.md"
+ ],
+ "tests": [
+ "tests/test_fingerprint_catalog_axes_experiment.py",
+ "tests/test_fingerprint_validation_experiment.py",
+ "tests/test_fingerprint_auc.py"
+ ],
+ "properties": [
+ "all configurations score the same sampled positive and negative pairs",
+ "diagnostic probabilities are measured from the declared snapshot",
+ "output count and SegWit serialization remain outside the production library",
+ "the non-additive AUC result can be falsified",
+ "the generated Markdown equals the canonical rendering"
+ ],
+ "tolerance": null
+ },
+ "limitations": [
+ "address reuse is a weak and partly feature-dependent same-wallet proxy",
+ "diagnostic probabilities are fitted and evaluated on the same selected snapshot",
+ "the 600-transaction snapshot is not either historical 166k or 180k cache",
+ "AUC deltas on one snapshot do not establish conditional dependence",
+ "the axes remain excluded from the production fingerprint library",
+ "the result measures pair ranking and is not an ownership certificate or privacy score"
+ ]
+}
diff --git a/catalog/runs/fingerprint-regime-v1.json b/catalog/runs/fingerprint-regime-v1.json
new file mode 100644
index 00000000..a8a06cf2
--- /dev/null
+++ b/catalog/runs/fingerprint-regime-v1.json
@@ -0,0 +1,105 @@
+{
+ "schema_version": 1,
+ "id": "fingerprint-regime-v1",
+ "claim_ids": [
+ "ctp.fingerprint.statistical_linkage",
+ "ctp.sparse_dataset.feature_uniqueness"
+ ],
+ "code": {
+ "revision": "bcd9a3e511860b1b0ff5f3ee6338a2df29ecbfbf",
+ "dirty": false
+ },
+ "command": {
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.fingerprint_regime",
+ "--dataset",
+ "tests/fixtures/fingerprint_blkcache_sample.json",
+ "reproduce",
+ "--artifact",
+ "results/artifacts/fingerprint-regime-v1.json",
+ "--markdown",
+ "results/generated/fingerprint-regime-v1.md"
+ ]
+ },
+ "environment": {
+ "python": "3.13.7",
+ "lock_digest": "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855",
+ "platform": "macOS-26.6.2-arm64-arm-64bit-Mach-O",
+ "dependencies": []
+ },
+ "datasets": [
+ {
+ "id": "fingerprint-blkcache-sample-v1",
+ "sha256": "406ce2d7d0a04f7c73281bce6b6a781012a8765e079693f010a0154e926c6a6c"
+ }
+ ],
+ "parameters": {
+ "pair_draw_cap": 2000,
+ "candidate_node_cap": 200,
+ "seed": 0,
+ "weight_sources": [
+ "library",
+ "snapshot_measured"
+ ]
+ },
+ "rng": {
+ "algorithm": "python.random.Random",
+ "seeds": [
+ 0
+ ]
+ },
+ "outputs": [
+ {
+ "path": "results/artifacts/fingerprint-regime-v1.json",
+ "bytes": 1290,
+ "sha256": "6569cd89853bd99678a8de4153b3a01d0a8d7a61b7ab718b2a0fba015537d863"
+ },
+ {
+ "path": "results/generated/fingerprint-regime-v1.md",
+ "bytes": 774,
+ "sha256": "271fa7e6601871b7fad9ac6e50b23a5f164d4a0ca30bc88c7629828b4858789b"
+ }
+ ],
+ "reproducibility": {
+ "level": "bitwise_reproducible",
+ "availability": "partial"
+ },
+ "verification": {
+ "mode": "exact",
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.fingerprint_regime",
+ "--dataset",
+ "tests/fixtures/fingerprint_blkcache_sample.json",
+ "verify",
+ "--artifact",
+ "results/artifacts/fingerprint-regime-v1.json",
+ "--markdown",
+ "results/generated/fingerprint-regime-v1.md"
+ ],
+ "tests": [
+ "tests/test_fingerprint_ns.py",
+ "tests/test_fingerprint_auc.py",
+ "tests/test_fingerprint_regime_experiment.py"
+ ],
+ "properties": [
+ "the same sampled pairs and candidate set feed both scorers",
+ "library and snapshot-measured weights remain separate rows",
+ "the non-reproduction of the historical stable-conditioner claim is tested",
+ "the generated Markdown equals the canonical rendering"
+ ],
+ "tolerance": null
+ },
+ "limitations": [
+ "address reuse is a weak and partly feature-dependent owner proxy",
+ "candidate identities are reuse-group labels rather than wallet ownership",
+ "the 600-transaction snapshot is selected and is not a chain-wide sample",
+ "the snapshot-measured weights are fitted and evaluated on the same data",
+ "the Lumen weighting and historical growing-cache population are not reproduced",
+ "mean eccentricity is descriptive and is not an acceptance rate",
+ "the result does not establish that fingerprints are universally sparse or dense"
+ ]
+}
diff --git a/catalog/runs/fingerprint-sparsity-v1.json b/catalog/runs/fingerprint-sparsity-v1.json
new file mode 100644
index 00000000..1dd883ba
--- /dev/null
+++ b/catalog/runs/fingerprint-sparsity-v1.json
@@ -0,0 +1,106 @@
+{
+ "schema_version": 1,
+ "id": "fingerprint-sparsity-v1",
+ "claim_ids": [
+ "ctp.sparse_dataset.feature_uniqueness",
+ "ctp.fingerprint.statistical_linkage"
+ ],
+ "code": {
+ "revision": "bcd9a3e511860b1b0ff5f3ee6338a2df29ecbfbf",
+ "dirty": false
+ },
+ "command": {
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.fingerprint_sparsity",
+ "--dataset",
+ "tests/fixtures/lumen_explorer_data.json",
+ "reproduce",
+ "--artifact",
+ "results/artifacts/fingerprint-sparsity-v1.json",
+ "--markdown",
+ "results/generated/fingerprint-sparsity-v1.md"
+ ]
+ },
+ "environment": {
+ "python": "3.13.7",
+ "lock_digest": "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855",
+ "platform": "macOS-26.6.2-arm64-arm-64bit-Mach-O",
+ "dependencies": []
+ },
+ "datasets": [
+ {
+ "id": "lumen-explorer-data-v1",
+ "sha256": "5337d4bdc574726a2cb4e28f329f16709b497479a7493882bd38d77875660ac9"
+ }
+ ],
+ "parameters": {
+ "vector_width": 12,
+ "scope": "whole_window",
+ "bucket_boundaries": [
+ 1,
+ 2,
+ 10,
+ 100,
+ 1000,
+ 10000,
+ 100000
+ ]
+ },
+ "rng": {
+ "algorithm": null,
+ "seeds": []
+ },
+ "outputs": [
+ {
+ "path": "results/artifacts/fingerprint-sparsity-v1.json",
+ "bytes": 1429,
+ "sha256": "923000f70f5eaaf7349a0715d3ed83c382d4b414d28c0e8aa617103871f9ec79"
+ },
+ {
+ "path": "results/generated/fingerprint-sparsity-v1.md",
+ "bytes": 694,
+ "sha256": "9740d4ad0c64d229df372b81499ae33f1dd54c097a4e26a2fccbee73b80bec6d"
+ }
+ ],
+ "reproducibility": {
+ "level": "bitwise_reproducible",
+ "availability": "partial"
+ },
+ "verification": {
+ "mode": "exact",
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.fingerprint_sparsity",
+ "--dataset",
+ "tests/fixtures/lumen_explorer_data.json",
+ "verify",
+ "--artifact",
+ "results/artifacts/fingerprint-sparsity-v1.json",
+ "--markdown",
+ "results/generated/fingerprint-sparsity-v1.md"
+ ],
+ "tests": [
+ "tests/test_fingerprint_sparsity.py",
+ "tests/test_fingerprint_sparsity_experiment.py",
+ "tests/test_lumen_survey_fixture.py"
+ ],
+ "properties": [
+ "class-size buckets are weighted by transactions rather than distinct vectors",
+ "every preserved conditional partition covers the declared transaction total",
+ "all preserved conditional partitions reconstruct the same global histogram",
+ "the generated Markdown equals the canonical rendering"
+ ],
+ "tolerance": null
+ },
+ "limitations": [
+ "the fixture contains aggregate histograms rather than transaction-level vectors",
+ "the 19-axis and per-epoch distributions are not preserved and are not reproduced",
+ "exact-vector class size is not nearest-neighbour sparsity in a general feature metric",
+ "the result concerns per-transaction vectors, not cluster feature distributions",
+ "the scan ends at height 962720 and differs by 6219 records from the cataloged raw export",
+ "the dataset publisher authorized redistribution of the aggregate fixture on 2026-09-04"
+ ]
+}
diff --git a/catalog/runs/fingerprint-validation-v1.json b/catalog/runs/fingerprint-validation-v1.json
new file mode 100644
index 00000000..e5731b65
--- /dev/null
+++ b/catalog/runs/fingerprint-validation-v1.json
@@ -0,0 +1,101 @@
+{
+ "schema_version": 1,
+ "id": "fingerprint-validation-v1",
+ "claim_ids": [
+ "ctp.fingerprint.statistical_linkage"
+ ],
+ "code": {
+ "revision": "bcd9a3e511860b1b0ff5f3ee6338a2df29ecbfbf",
+ "dirty": false
+ },
+ "command": {
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.fingerprint_validation",
+ "--dataset",
+ "tests/fixtures/fingerprint_blkcache_sample.json",
+ "reproduce",
+ "--artifact",
+ "results/artifacts/fingerprint-validation-v1.json",
+ "--markdown",
+ "results/generated/fingerprint-validation-v1.md"
+ ]
+ },
+ "environment": {
+ "python": "3.13.7",
+ "lock_digest": "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855",
+ "platform": "macOS-26.6.2-arm64-arm-64bit-Mach-O",
+ "dependencies": []
+ },
+ "datasets": [
+ {
+ "id": "fingerprint-blkcache-sample-v1",
+ "sha256": "406ce2d7d0a04f7c73281bce6b6a781012a8765e079693f010a0154e926c6a6c"
+ }
+ ],
+ "parameters": {
+ "positive_pair_draw_cap": 4000,
+ "negative_pair_draw_cap": 4000,
+ "seed": 0,
+ "same_wallet_consistency": 0.95,
+ "rarity_floor_count": 1000
+ },
+ "rng": {
+ "algorithm": "python.random.Random",
+ "seeds": [
+ 0
+ ]
+ },
+ "outputs": [
+ {
+ "path": "results/artifacts/fingerprint-validation-v1.json",
+ "bytes": 1062,
+ "sha256": "81e9af1328a344323e7a86b219711cbe9ee1428e3bbce4b1995681b9ae35b669"
+ },
+ {
+ "path": "results/generated/fingerprint-validation-v1.md",
+ "bytes": 590,
+ "sha256": "2fe5d3f085bb7b93ba2d275b442bb31f6ba8c1e95ca670c726474f2c874b0eea"
+ }
+ ],
+ "reproducibility": {
+ "level": "bitwise_reproducible",
+ "availability": "partial"
+ },
+ "verification": {
+ "mode": "exact",
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.fingerprint_validation",
+ "--dataset",
+ "tests/fixtures/fingerprint_blkcache_sample.json",
+ "verify",
+ "--artifact",
+ "results/artifacts/fingerprint-validation-v1.json",
+ "--markdown",
+ "results/generated/fingerprint-validation-v1.md"
+ ],
+ "tests": [
+ "tests/test_fingerprint_auc.py",
+ "tests/test_fingerprint_validate.py",
+ "tests/test_fingerprint_validation_experiment.py"
+ ],
+ "properties": [
+ "the canonical 23-axis scorer is used",
+ "positive and negative pairs are sampled with a fixed seed",
+ "the shuffled-label control is emitted beside the observed AUC",
+ "the generated Markdown equals the canonical rendering"
+ ],
+ "tolerance": null
+ },
+ "limitations": [
+ "address reuse is a weak same-wallet label and shares script information with the features",
+ "positive pairs are draws with replacement rather than independent observations",
+ "random negative pairs can include transactions controlled by the same wallet",
+ "the 600-transaction snapshot is selected and is not a chain-wide sample",
+ "the source recipe is unavailable; the dataset publisher authorized redistribution on 2026-09-04",
+ "the result measures pair ranking and is not an ownership certificate or privacy score"
+ ]
+}
diff --git a/catalog/runs/fs-ablation-v1.json b/catalog/runs/fs-ablation-v1.json
new file mode 100644
index 00000000..c71dc3b6
--- /dev/null
+++ b/catalog/runs/fs-ablation-v1.json
@@ -0,0 +1,99 @@
+{
+ "schema_version": 1,
+ "id": "fs-ablation-v1",
+ "claim_ids": [
+ "ctp.fingerprint.statistical_linkage"
+ ],
+ "code": {
+ "revision": "bcd9a3e511860b1b0ff5f3ee6338a2df29ecbfbf",
+ "dirty": false
+ },
+ "command": {
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.fs_ablation",
+ "--snapshot",
+ "data/fs-blkcache-2026-09-04.tar.gz",
+ "reproduce",
+ "--artifact",
+ "results/artifacts/fs-ablation-v1.json",
+ "--markdown",
+ "results/generated/fs-ablation-v1.md"
+ ]
+ },
+ "environment": {
+ "python": "3.13.7",
+ "lock_digest": "e0d6a8cada5d383c0aca2bd2a37f28c15721392469c94084128d369c29c5544d",
+ "platform": "macOS-26.6.2-arm64-arm-64bit-Mach-O",
+ "dependencies": []
+ },
+ "datasets": [
+ {
+ "id": "fs-blkcache-2026-09-04-v1",
+ "sha256": "5a69f6f446dbdfcce09dddc91c653e5d841124d14020b720fdea276315c775a6"
+ }
+ ],
+ "parameters": {
+ "ablation_cap": 4000,
+ "association_cap": 8000,
+ "train_fraction": 0.7,
+ "seed": 0,
+ "cluster_threshold": 0.6
+ },
+ "rng": {
+ "algorithm": "python.random.MersenneTwister",
+ "seeds": [
+ 0
+ ]
+ },
+ "outputs": [
+ {
+ "path": "results/artifacts/fs-ablation-v1.json",
+ "bytes": 7359,
+ "sha256": "9056280af77748e314ac525b2a215a1924a5600e1713021db076fd86072e15cf"
+ },
+ {
+ "path": "results/generated/fs-ablation-v1.md",
+ "bytes": 460,
+ "sha256": "d74c2d1442294fd09f2dde8c1ce43ca44982ec38c99ea2b079c381e9c181502d"
+ }
+ ],
+ "reproducibility": {
+ "level": "bitwise_reproducible",
+ "availability": "complete"
+ },
+ "verification": {
+ "mode": "exact",
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.fs_ablation",
+ "--snapshot",
+ "data/fs-blkcache-2026-09-04.tar.gz",
+ "verify",
+ "--artifact",
+ "results/artifacts/fs-ablation-v1.json",
+ "--markdown",
+ "results/generated/fs-ablation-v1.md"
+ ],
+ "tests": [
+ "tests/test_fs_ablation.py",
+ "tests/test_fs_ablation_experiment.py"
+ ],
+ "properties": [
+ "the snapshot reproduces the complete historical ablation JSON",
+ "association is computed conditionally within weak-label classes",
+ "group ablation is distinct from leaving one representative",
+ "duplicated fingerprint fields remain an explicit limitation",
+ "the generated Markdown equals the canonical rendering"
+ ],
+ "tolerance": null
+ },
+ "limitations": [
+ "address reuse provides weak labels rather than independent ownership labels",
+ "the block sample is not representative of the whole chain",
+ "the association statistic is descriptive rather than causal",
+ "two fingerprint fields are duplicates in this sample"
+ ]
+}
diff --git a/catalog/runs/fs-temporal-v1.json b/catalog/runs/fs-temporal-v1.json
new file mode 100644
index 00000000..92612e25
--- /dev/null
+++ b/catalog/runs/fs-temporal-v1.json
@@ -0,0 +1,98 @@
+{
+ "schema_version": 1,
+ "id": "fs-temporal-v1",
+ "claim_ids": [
+ "ctp.fingerprint.statistical_linkage"
+ ],
+ "code": {
+ "revision": "bcd9a3e511860b1b0ff5f3ee6338a2df29ecbfbf",
+ "dirty": false
+ },
+ "command": {
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.fs_temporal",
+ "--snapshot",
+ "data/fs-blkcache-2026-09-04.tar.gz",
+ "reproduce",
+ "--artifact",
+ "results/artifacts/fs-temporal-v1.json",
+ "--markdown",
+ "results/generated/fs-temporal-v1.md"
+ ]
+ },
+ "environment": {
+ "python": "3.13.7",
+ "lock_digest": "e0d6a8cada5d383c0aca2bd2a37f28c15721392469c94084128d369c29c5544d",
+ "platform": "macOS-26.6.2-arm64-arm-64bit-Mach-O",
+ "dependencies": []
+ },
+ "datasets": [
+ {
+ "id": "fs-blkcache-2026-09-04-v1",
+ "sha256": "5a69f6f446dbdfcce09dddc91c653e5d841124d14020b720fdea276315c775a6"
+ }
+ ],
+ "parameters": {
+ "cap": 4000,
+ "train_fraction": 0.7,
+ "seed": 0,
+ "label_kind": "address-reuse weak labels"
+ },
+ "rng": {
+ "algorithm": "python.random.MersenneTwister",
+ "seeds": [
+ 0
+ ]
+ },
+ "outputs": [
+ {
+ "path": "results/artifacts/fs-temporal-v1.json",
+ "bytes": 4694,
+ "sha256": "490a0f4e7b3452093915bd7dc44ef393048912ff21ead36fdf9be74736f832a9"
+ },
+ {
+ "path": "results/generated/fs-temporal-v1.md",
+ "bytes": 408,
+ "sha256": "17fa4bb1843852a63936de74e550eb5f293822b0a3b16b33e3b527e5f57e8ff5"
+ }
+ ],
+ "reproducibility": {
+ "level": "bitwise_reproducible",
+ "availability": "complete"
+ },
+ "verification": {
+ "mode": "exact",
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.fs_temporal",
+ "--snapshot",
+ "data/fs-blkcache-2026-09-04.tar.gz",
+ "verify",
+ "--artifact",
+ "results/artifacts/fs-temporal-v1.json",
+ "--markdown",
+ "results/generated/fs-temporal-v1.md"
+ ],
+ "tests": [
+ "tests/test_fs_temporal.py",
+ "tests/test_fs_temporal_experiment.py"
+ ],
+ "properties": [
+ "the snapshot reproduces the complete historical JSON report",
+ "the temporal split trains strictly before it tests",
+ "Fellegi-Sunter and rarity baseline score the same test pairs",
+ "weak-label and sampling limitations remain explicit",
+ "the generated Markdown equals the canonical rendering"
+ ],
+ "tolerance": null
+ },
+ "limitations": [
+ "address reuse provides weak labels rather than independent ownership labels",
+ "the block sample is not representative of the whole chain",
+ "Beta smoothing is a local modeling choice",
+ "two fingerprint fields are duplicates in this sample"
+ ]
+}
diff --git a/catalog/runs/fused-engine-v1.json b/catalog/runs/fused-engine-v1.json
new file mode 100644
index 00000000..94ccbcc5
--- /dev/null
+++ b/catalog/runs/fused-engine-v1.json
@@ -0,0 +1,97 @@
+{
+ "schema_version": 1,
+ "id": "fused-engine-v1",
+ "claim_ids": [
+ "ctp.cioh.collaborative_false_merge",
+ "ctp.fingerprint.statistical_linkage"
+ ],
+ "code": {
+ "revision": "bcd9a3e511860b1b0ff5f3ee6338a2df29ecbfbf",
+ "dirty": false
+ },
+ "command": {
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.fused_engine",
+ "--snapshot",
+ "data/fs-blkcache-2026-09-04.tar.gz",
+ "reproduce",
+ "--artifact",
+ "results/artifacts/fused-engine-v1.json",
+ "--markdown",
+ "results/generated/fused-engine-v1.md"
+ ]
+ },
+ "datasets": [
+ {
+ "id": "fs-blkcache-2026-09-04-v1",
+ "sha256": "5a69f6f446dbdfcce09dddc91c653e5d841124d14020b720fdea276315c775a6"
+ }
+ ],
+ "parameters": {
+ "link_above": 4.0,
+ "cospend_prior_bits": 2.0,
+ "prov_refuse_bits": -3.0,
+ "provenance_depth": 1
+ },
+ "rng": {
+ "algorithm": "none",
+ "seeds": []
+ },
+ "reproducibility": {
+ "level": "bitwise_reproducible",
+ "availability": "complete"
+ },
+ "verification": {
+ "mode": "exact",
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.fused_engine",
+ "--snapshot",
+ "data/fs-blkcache-2026-09-04.tar.gz",
+ "verify",
+ "--artifact",
+ "results/artifacts/fused-engine-v1.json",
+ "--markdown",
+ "results/generated/fused-engine-v1.md"
+ ],
+ "tests": [
+ "tests/test_cluster_refined.py",
+ "tests/test_fused_engine_experiment.py"
+ ],
+ "properties": [
+ "the committed snapshot exactly reproduces every ladder row",
+ "the merge-only baseline refuses nothing and the engine refuses on every arm",
+ "cluster-level topology corroborates merges the fingerprint alone objected to",
+ "provenance-disjointness cuts pairs the topology arm kept",
+ "the amount and subset-sum channels are refuse-only and gated behind fingerprint disagreement",
+ "the result remains attacker-side evidence rather than a privacy score"
+ ],
+ "tolerance": null
+ },
+ "environment": {
+ "python": "3.13.7",
+ "lock_digest": "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855",
+ "platform": "macOS-26.6.2-arm64-arm-64bit-Mach-O",
+ "dependencies": []
+ },
+ "limitations": [
+ "The block-cache slice is block-sampled rather than contiguous, so provenance signatures are depth-1.",
+ "Cluster membership here is a co-spend relation, not observed wallet ownership.",
+ "The ladder reports what each channel moves on this slice; it does not estimate chain-wide prevalence."
+ ],
+ "outputs": [
+ {
+ "path": "results/artifacts/fused-engine-v1.json",
+ "bytes": 1019,
+ "sha256": "192890be723f3c135f61aa513c3effcfe426021c3e22e937a555c0970bb4f2d8"
+ },
+ {
+ "path": "results/generated/fused-engine-v1.md",
+ "bytes": 1968,
+ "sha256": "d31d64fb92d20b4d7df3fd88e750f4084dfc89e26807a8fe45b33361ae9b78d6"
+ }
+ ]
+}
diff --git a/catalog/runs/graph-deanon-controls-v1.json b/catalog/runs/graph-deanon-controls-v1.json
new file mode 100644
index 00000000..3acd2aba
--- /dev/null
+++ b/catalog/runs/graph-deanon-controls-v1.json
@@ -0,0 +1,98 @@
+{
+ "schema_version": 1,
+ "id": "graph-deanon-controls-v1",
+ "claim_ids": [
+ "ctp.social_graph.structural_linkage"
+ ],
+ "code": {
+ "revision": "bcd9a3e511860b1b0ff5f3ee6338a2df29ecbfbf",
+ "dirty": false
+ },
+ "command": {
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.graph_deanon_controls",
+ "--dataset",
+ "tests/fixtures/graph_deanon_2016.ndjson.gz",
+ "reproduce",
+ "--artifact",
+ "results/artifacts/graph-deanon-controls-v1.json",
+ "--markdown",
+ "results/generated/graph-deanon-controls-v1.md"
+ ]
+ },
+ "environment": {
+ "python": "3.13.7",
+ "lock_digest": "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855",
+ "platform": "macOS-26.6.2-arm64-arm-64bit-Mach-O",
+ "dependencies": []
+ },
+ "datasets": [
+ {
+ "id": "graph-deanon-2016-v1",
+ "sha256": "68690ce8689378321e4187b111c7b4300f88e5bb405a25dcb67125cd0c739c5b"
+ }
+ ],
+ "parameters": {
+ "negative_sampling_control": "degree-matched to each positive",
+ "negative_sampling_published": "uniform over non-co-spent pairs, capped",
+ "score": "rarity-weighted shared-neighbour structure",
+ "seed": 0
+ },
+ "rng": {
+ "algorithm": "python.random.Random",
+ "seeds": [
+ 0
+ ]
+ },
+ "outputs": [
+ {
+ "path": "results/artifacts/graph-deanon-controls-v1.json",
+ "bytes": 1308,
+ "sha256": "0ed138db94e84b2ec2b4d07c7831a20d4fb3be59ae6b5d52685983df92a6cc02"
+ },
+ {
+ "path": "results/generated/graph-deanon-controls-v1.md",
+ "bytes": 1200,
+ "sha256": "fea0440206848a1f12d87c01960840eaf5dd933559a4ddf30bc70ca8505e5774"
+ }
+ ],
+ "reproducibility": {
+ "level": "bitwise_reproducible",
+ "availability": "complete"
+ },
+ "verification": {
+ "mode": "exact",
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.graph_deanon_controls",
+ "--dataset",
+ "tests/fixtures/graph_deanon_2016.ndjson.gz",
+ "verify",
+ "--artifact",
+ "results/artifacts/graph-deanon-controls-v1.json",
+ "--markdown",
+ "results/generated/graph-deanon-controls-v1.md"
+ ],
+ "tests": [
+ "tests/test_graph_deanon_controls.py",
+ "tests/test_graph_deanon.py"
+ ],
+ "properties": [
+ "the run is byte-identical across processes, which it was not before the sort",
+ "the degree-only score sits at chance under degree-matched negatives",
+ "the shuffled-label control sits at chance under the published sampling",
+ "the generated Markdown equals the canonical rendering"
+ ],
+ "tolerance": null
+ },
+ "limitations": [
+ "cluster membership is a co-spend label, not wallet ownership",
+ "one selected 2016 fixture, not a chain-wide sample",
+ "the degree-matched control changes the negative population, not the positives",
+ "a fall under matching bounds how much of the AUC was degree asymmetry; it does not establish what the remainder is",
+ "neither number is a re-identification rate or a privacy score"
+ ]
+}
diff --git a/catalog/runs/graph-rejoin-2016-v1.json b/catalog/runs/graph-rejoin-2016-v1.json
new file mode 100644
index 00000000..fd8d5927
--- /dev/null
+++ b/catalog/runs/graph-rejoin-2016-v1.json
@@ -0,0 +1,112 @@
+{
+ "schema_version": 1,
+ "id": "graph-rejoin-2016-v1",
+ "claim_ids": [
+ "ctp.social_graph.propagation",
+ "ctp.graph.pseudonym_representation"
+ ],
+ "code": {
+ "revision": "bcd9a3e511860b1b0ff5f3ee6338a2df29ecbfbf",
+ "dirty": false
+ },
+ "command": {
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.graph_rejoin_2016",
+ "--epochs",
+ "data/epochs-2016-weekly-v1/391104-392111.epochgraph.xz",
+ "data/epochs-2016-weekly-v1/392112-393119.epochgraph.xz",
+ "reproduce",
+ "--artifact",
+ "results/artifacts/graph-rejoin-2016-v1.json",
+ "--markdown",
+ "results/generated/graph-rejoin-2016-v1.md"
+ ]
+ },
+ "datasets": [
+ {
+ "id": "epoch-graph-2016-391104-392111-v1",
+ "sha256": "cd129cd4bb225ea7a6733dab891b20272d62d1b0f3a6caf07a4209f9f2368015"
+ },
+ {
+ "id": "epoch-graph-2016-392112-393119-v1",
+ "sha256": "0893000345e68249911cb8ee6467a47fa6feecd3022f27e2732bcabfb4725f11"
+ }
+ ],
+ "parameters": {
+ "min_degree": 2,
+ "seed_shares": [
+ 0.05,
+ 0.1
+ ],
+ "split_fraction": 1.0,
+ "matcher": {
+ "revisit": true,
+ "min_common": 4
+ },
+ "seed": 0
+ },
+ "rng": {
+ "algorithm": "python.random.MersenneTwister",
+ "seeds": [
+ 0
+ ]
+ },
+ "reproducibility": {
+ "level": "bitwise_reproducible",
+ "availability": "complete"
+ },
+ "verification": {
+ "mode": "exact",
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.graph_rejoin_2016",
+ "--epochs",
+ "data/epochs-2016-weekly-v1/391104-392111.epochgraph.xz",
+ "data/epochs-2016-weekly-v1/392112-393119.epochgraph.xz",
+ "verify",
+ "--artifact",
+ "results/artifacts/graph-rejoin-2016-v1.json",
+ "--markdown",
+ "results/generated/graph-rejoin-2016-v1.md"
+ ],
+ "tests": [
+ "tests/test_graph_rejoin_experiment.py",
+ "tests/test_epoch_graph.py"
+ ],
+ "properties": [
+ "the committed epoch graphs exactly reproduce every arm",
+ "each seeded arm runs beside a shuffled-seed arm on the same graphs",
+ "the shuffled-seed arms recover nothing",
+ "the cascade does not ignite, stated as a bound a future run can break",
+ "cluster membership is a co-spend relation, not observed wallet ownership",
+ "the seeds are supplied, so this measures propagation rather than finding a foothold"
+ ],
+ "tolerance": null
+ },
+ "environment": {
+ "python": "3.13.7",
+ "lock_digest": "efdf4055dba10bfff2541ccaf3668be030c7caed44c61650540cc106393e9636",
+ "platform": "macOS-26.6.2-arm64-arm-64bit-Mach-O",
+ "dependencies": []
+ },
+ "limitations": [
+ "Seeds are handed to the matcher; no independently derived seed is claimed.",
+ "Cluster membership is a co-spend relation rather than observed wallet ownership.",
+ "Two weekly views at one width; the persistence curve argues width is the untested lever."
+ ],
+ "outputs": [
+ {
+ "path": "results/artifacts/graph-rejoin-2016-v1.json",
+ "bytes": 1777,
+ "sha256": "ec27b8a3dc36b06482b86cdcb556038d88b73f3595c1e37d78867c5ce159841c"
+ },
+ {
+ "path": "results/generated/graph-rejoin-2016-v1.md",
+ "bytes": 2507,
+ "sha256": "1d5afe106a2bb84edd7925ca4ed213d9f5bc3c35349d48dac47c6051492543ed"
+ }
+ ]
+}
diff --git a/catalog/runs/intersection-cluster-fixture-v1.json b/catalog/runs/intersection-cluster-fixture-v1.json
new file mode 100644
index 00000000..a492506f
--- /dev/null
+++ b/catalog/runs/intersection-cluster-fixture-v1.json
@@ -0,0 +1,86 @@
+{
+ "schema_version": 1,
+ "id": "intersection-cluster-fixture-v1",
+ "claim_ids": [
+ "ctp.intersection.longitudinal"
+ ],
+ "code": {
+ "revision": "bcd9a3e511860b1b0ff5f3ee6338a2df29ecbfbf",
+ "dirty": false
+ },
+ "command": {
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.intersection_fixture",
+ "reproduce",
+ "--artifact",
+ "results/artifacts/intersection-cluster-fixture-v1.json",
+ "--markdown",
+ "results/generated/intersection-cluster-fixture-v1.md"
+ ]
+ },
+ "environment": {
+ "python": "3.13.7",
+ "lock_digest": "c7d402576511073ed4fcf076c77ffa6b3be124e0a2ef996d2da1cd3b02fbe541",
+ "platform": "macOS-26.6.2-arm64-arm-64bit-Mach-O",
+ "dependencies": []
+ },
+ "datasets": [],
+ "parameters": {
+ "ancestry_depth": 2,
+ "cluster_key": "creating transaction id",
+ "monitor_depth": 1
+ },
+ "rng": {
+ "algorithm": null,
+ "seeds": []
+ },
+ "outputs": [
+ {
+ "path": "results/artifacts/intersection-cluster-fixture-v1.json",
+ "bytes": 1869,
+ "sha256": "2114e568b91808536c94ddb307a4688e3be32720bcb768ef9bb790ef673d0fe5"
+ },
+ {
+ "path": "results/generated/intersection-cluster-fixture-v1.md",
+ "bytes": 385,
+ "sha256": "3fd9f9bf57e30f4b92851852efa24e4af2201c705c39391d9451b68689005434"
+ }
+ ],
+ "reproducibility": {
+ "level": "bitwise_reproducible",
+ "availability": "complete"
+ },
+ "verification": {
+ "mode": "exact",
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.intersection_fixture",
+ "verify",
+ "--artifact",
+ "results/artifacts/intersection-cluster-fixture-v1.json",
+ "--markdown",
+ "results/generated/intersection-cluster-fixture-v1.md"
+ ],
+ "tests": [
+ "tests/test_intersection_experiment.py",
+ "tests/test_intersection_adapter.py",
+ "tests/test_intersection_pipeline.py"
+ ],
+ "properties": [
+ "the stored artifact equals a fresh execution of the synthetic fixture",
+ "the generated Markdown equals the canonical rendering",
+ "the two valid UTXO branches narrow two antecedent clusters to one shared cluster",
+ "the narrowing remains explicitly conditional and unscored"
+ ],
+ "tolerance": null
+ },
+ "limitations": [
+ "synthetic fixture",
+ "the creating transaction id is used as the fixture's antecedent cluster label",
+ "narrowing is conditional on the observed co-spend",
+ "the fixture does not run the clustering verdict"
+ ]
+}
diff --git a/catalog/runs/library-calibration-v1.json b/catalog/runs/library-calibration-v1.json
new file mode 100644
index 00000000..71d7cb7a
--- /dev/null
+++ b/catalog/runs/library-calibration-v1.json
@@ -0,0 +1,95 @@
+{
+ "schema_version": 1,
+ "id": "library-calibration-v1",
+ "claim_ids": [
+ "ctp.fingerprint.statistical_linkage"
+ ],
+ "code": {
+ "revision": "bcd9a3e511860b1b0ff5f3ee6338a2df29ecbfbf",
+ "dirty": false
+ },
+ "command": {
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.library_calibration",
+ "--snapshot",
+ "data/fs-blkcache-2026-09-04.tar.gz",
+ "reproduce",
+ "--artifact",
+ "results/artifacts/library-calibration-v1.json",
+ "--markdown",
+ "results/generated/library-calibration-v1.md"
+ ]
+ },
+ "environment": {
+ "python": "3.13.7",
+ "lock_digest": "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855",
+ "platform": "macOS-26.6.2-arm64-arm-64bit-Mach-O",
+ "dependencies": []
+ },
+ "datasets": [
+ {
+ "id": "fs-blkcache-2026-09-04-v1",
+ "sha256": "5a69f6f446dbdfcce09dddc91c653e5d841124d14020b720fdea276315c775a6"
+ }
+ ],
+ "parameters": {
+ "bits": "minus the base-2 log of a value's share of the scored population",
+ "measured_population": "the committed block cache",
+ "published_population": "~105k uniform whole-chain sample via BigQuery, not preserved"
+ },
+ "rng": {
+ "algorithm": null,
+ "seeds": []
+ },
+ "outputs": [
+ {
+ "path": "results/artifacts/library-calibration-v1.json",
+ "bytes": 17737,
+ "sha256": "5c807a848e9a04cd41065e096af472df0ea568a88792bdd9027bbad58c9064c5"
+ },
+ {
+ "path": "results/generated/library-calibration-v1.md",
+ "bytes": 2173,
+ "sha256": "949d2d9e890818c5ec890484b13750ed6855e6538c9a45c3c5c4b3daf2454a5b"
+ }
+ ],
+ "reproducibility": {
+ "level": "bitwise_reproducible",
+ "availability": "partial"
+ },
+ "verification": {
+ "mode": "exact",
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.library_calibration",
+ "--snapshot",
+ "data/fs-blkcache-2026-09-04.tar.gz",
+ "verify",
+ "--artifact",
+ "results/artifacts/library-calibration-v1.json",
+ "--markdown",
+ "results/generated/library-calibration-v1.md"
+ ],
+ "tests": [
+ "tests/test_library_calibration.py",
+ "tests/test_wp2.py"
+ ],
+ "properties": [
+ "a value the cache never shows is counted, never estimated",
+ "abstentions and unreadable records are counted apart from the scored population",
+ "every axis the library catalogues is walked, including the ones with no comparison",
+ "the generated Markdown equals the canonical rendering"
+ ],
+ "tolerance": null
+ },
+ "limitations": [
+ "this is not a reproduction of the published calibration; the population it used is gone",
+ "the cache is block-sampled and era-skewed, so a divergence is expected and its size is the only thing measured here",
+ "a value the cache never shows is counted, not estimated",
+ "recalibrating the library on this cache is a separate decision this run does not take",
+ "divergence in bits is not an error rate and is not a privacy score"
+ ]
+}
diff --git a/catalog/runs/link-prediction-v1.json b/catalog/runs/link-prediction-v1.json
new file mode 100644
index 00000000..f6b7a3dc
--- /dev/null
+++ b/catalog/runs/link-prediction-v1.json
@@ -0,0 +1,102 @@
+{
+ "schema_version": 1,
+ "id": "link-prediction-v1",
+ "claim_ids": [
+ "ctp.social_graph.link_prediction"
+ ],
+ "code": {
+ "revision": "bcd9a3e511860b1b0ff5f3ee6338a2df29ecbfbf",
+ "dirty": false
+ },
+ "command": {
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.link_prediction",
+ "reproduce",
+ "--artifact",
+ "results/artifacts/link-prediction-v1.json",
+ "--markdown",
+ "results/generated/link-prediction-v1.md"
+ ]
+ },
+ "environment": {
+ "python": "3.13.7",
+ "lock_digest": "e0d6a8cada5d383c0aca2bd2a37f28c15721392469c94084128d369c29c5544d",
+ "platform": "macOS-26.6.2-arm64-arm-64bit-Mach-O",
+ "dependencies": []
+ },
+ "datasets": [],
+ "parameters": {
+ "vertices": 80,
+ "groups": 8,
+ "fixture_seeds": [
+ 0,
+ 1,
+ 2,
+ 3,
+ 4
+ ],
+ "seed_fraction": 0.25,
+ "view_edge_keep_probability": 0.9,
+ "holdout_fraction": 0.25,
+ "declaration_cutoff": 2
+ },
+ "rng": {
+ "algorithm": "python.random.MersenneTwister",
+ "seeds": [
+ 0,
+ 1,
+ 2,
+ 3,
+ 4
+ ]
+ },
+ "outputs": [
+ {
+ "path": "results/artifacts/link-prediction-v1.json",
+ "bytes": 17650,
+ "sha256": "b119c9d6d6782e6fca449004b11096cba93a9dc37b631b27fd42954e66db3618"
+ },
+ {
+ "path": "results/generated/link-prediction-v1.md",
+ "bytes": 502,
+ "sha256": "7858a3c1059896469a5858a9dfc69b66bf2090cd0668db66270e590344bc9fc0"
+ }
+ ],
+ "reproducibility": {
+ "level": "bitwise_reproducible",
+ "availability": "complete"
+ },
+ "verification": {
+ "mode": "exact",
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.link_prediction",
+ "verify",
+ "--artifact",
+ "results/artifacts/link-prediction-v1.json",
+ "--markdown",
+ "results/generated/link-prediction-v1.md"
+ ],
+ "tests": [
+ "tests/test_link_prediction.py",
+ "tests/test_link_prediction_experiment.py"
+ ],
+ "properties": [
+ "the full canonical report equals the historical JSON measurements",
+ "five deterministic fixtures contain attack, three controls, and a ceiling",
+ "the result is limited to planted views and retained-correspondence seeds",
+ "the result is not attributed to the paper's datasets or metrics",
+ "the generated Markdown equals the canonical rendering"
+ ],
+ "tolerance": null
+ },
+ "limitations": [
+ "synthetic planted-view corpus",
+ "seeds are sampled from the withheld correspondence",
+ "the measured advantage transfers links observed by the auxiliary view",
+ "this does not reproduce the 2011 paper or Kaggle/Flickr experiments"
+ ]
+}
diff --git a/catalog/runs/match-controls-v1.json b/catalog/runs/match-controls-v1.json
new file mode 100644
index 00000000..781e3062
--- /dev/null
+++ b/catalog/runs/match-controls-v1.json
@@ -0,0 +1,112 @@
+{
+ "schema_version": 1,
+ "id": "match-controls-v1",
+ "claim_ids": [
+ "ctp.social_graph.propagation",
+ "ctp.social_graph.structural_linkage"
+ ],
+ "code": {
+ "revision": "bcd9a3e511860b1b0ff5f3ee6338a2df29ecbfbf",
+ "dirty": false
+ },
+ "command": {
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.match_controls",
+ "--epochs",
+ "data/epochs-2016-weekly-v1/391104-392111.epochgraph.xz",
+ "data/epochs-2016-weekly-v1/392112-393119.epochgraph.xz",
+ "reproduce",
+ "--artifact",
+ "results/artifacts/match-controls-v1.json",
+ "--markdown",
+ "results/generated/match-controls-v1.md"
+ ]
+ },
+ "datasets": [
+ {
+ "id": "epoch-graph-2016-391104-392111-v1",
+ "sha256": "cd129cd4bb225ea7a6733dab891b20272d62d1b0f3a6caf07a4209f9f2368015"
+ },
+ {
+ "id": "epoch-graph-2016-392112-393119-v1",
+ "sha256": "0893000345e68249911cb8ee6467a47fa6feecd3022f27e2732bcabfb4725f11"
+ }
+ ],
+ "parameters": {
+ "seed_shares": [
+ 0.05,
+ 0.1
+ ],
+ "eccentricity_bands": [
+ 1.0,
+ 2.0,
+ 3.0,
+ 5.0,
+ 10.0
+ ],
+ "minimum_contracted_degree": 2,
+ "harness": "complete clustering: both views hold the same pseudonyms, view B relabelled"
+ },
+ "rng": {
+ "algorithm": "python.random.MersenneTwister",
+ "seeds": [
+ 0
+ ]
+ },
+ "reproducibility": {
+ "level": "bitwise_reproducible",
+ "availability": "complete"
+ },
+ "verification": {
+ "mode": "exact",
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.match_controls",
+ "--epochs",
+ "data/epochs-2016-weekly-v1/391104-392111.epochgraph.xz",
+ "data/epochs-2016-weekly-v1/392112-393119.epochgraph.xz",
+ "verify",
+ "--artifact",
+ "results/artifacts/match-controls-v1.json",
+ "--markdown",
+ "results/generated/match-controls-v1.md"
+ ],
+ "tests": [
+ "tests/test_match_controls_experiment.py"
+ ],
+ "properties": [
+ "the matcher is scored against the exact degree-class expectation, not only against chance",
+ "a shuffled-seed arm runs beside every seeded arm",
+ "precision is reported by the eccentricity each match won by",
+ "the degree-class baseline is handed the true partner's degree, so it bounds a degree-only adversary from above"
+ ],
+ "tolerance": null
+ },
+ "environment": {
+ "python": "3.13.7",
+ "lock_digest": "efdf4055dba10bfff2541ccaf3668be030c7caed44c61650540cc106393e9636",
+ "platform": "macOS-26.6.2-arm64-arm-64bit-Mach-O",
+ "dependencies": []
+ },
+ "limitations": [
+ "Seeds are handed to the matcher; no independently derived seed is claimed.",
+ "Cluster membership is a co-spend relation rather than observed wallet ownership.",
+ "This is the complete-clustering premise: the matchable population is given, and only the correspondence is hidden. The split-cluster premise is the harder question and is measured by graph-rejoin-2016-v1.",
+ "Two weekly views at one width, one era."
+ ],
+ "outputs": [
+ {
+ "path": "results/artifacts/match-controls-v1.json",
+ "bytes": 2390,
+ "sha256": "5234bb250121404ed4ace83f3f0ddbb3a02204b9eb0e792d9b74f3a85b143b2e"
+ },
+ {
+ "path": "results/generated/match-controls-v1.md",
+ "bytes": 3705,
+ "sha256": "48e21384adfffae4ad26fbc3d90bca4bd461c30af7c9804927739ba3b54a7ec6"
+ }
+ ]
+}
diff --git a/catalog/runs/ns-bitcoin-v1.json b/catalog/runs/ns-bitcoin-v1.json
new file mode 100644
index 00000000..32e9549b
--- /dev/null
+++ b/catalog/runs/ns-bitcoin-v1.json
@@ -0,0 +1,118 @@
+{
+ "schema_version": 1,
+ "id": "ns-bitcoin-v1",
+ "claim_ids": [
+ "ctp.social_graph.propagation"
+ ],
+ "code": {
+ "revision": "bcd9a3e511860b1b0ff5f3ee6338a2df29ecbfbf",
+ "dirty": false
+ },
+ "command": {
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.ns_bitcoin",
+ "--left",
+ "data/ns-bitcoin-2016-v1/left-391992-392111.ndjson.gz",
+ "--right",
+ "data/ns-bitcoin-2016-v1/right-392112-392231.ndjson.gz",
+ "reproduce",
+ "--artifact",
+ "results/artifacts/ns-bitcoin-v1.json",
+ "--markdown",
+ "results/generated/ns-bitcoin-v1.md"
+ ]
+ },
+ "environment": {
+ "python": "3.13.7",
+ "lock_digest": "e0d6a8cada5d383c0aca2bd2a37f28c15721392469c94084128d369c29c5544d",
+ "platform": "macOS-26.6.2-arm64-arm-64bit-Mach-O",
+ "dependencies": []
+ },
+ "datasets": [
+ {
+ "id": "ns-bitcoin-left-2016-v1",
+ "sha256": "069d207b44dcbbeaec0fae98262a3fcb76b3fe834834609e67fe883e229f26d3"
+ },
+ {
+ "id": "ns-bitcoin-right-2016-v1",
+ "sha256": "1d46dcccb51cd8f1a1d89481db4df45599caaaf8569f9e8e2de37e81e0e5d122"
+ }
+ ],
+ "parameters": {
+ "boundary": 392112,
+ "blocks": 120,
+ "min_degree": 2,
+ "split_fraction": 1.0,
+ "seed_fractions": [
+ 0.05,
+ 0.1,
+ 0.25
+ ],
+ "theta": [
+ 0.5,
+ 1.5
+ ]
+ },
+ "rng": {
+ "algorithm": "python.random.MersenneTwister",
+ "seeds": [
+ 7
+ ]
+ },
+ "outputs": [
+ {
+ "path": "results/artifacts/ns-bitcoin-v1.json",
+ "bytes": 6912,
+ "sha256": "39bb12560c4c96749910324b85097938a7efe57e45ce380127bc7783c978f182"
+ },
+ {
+ "path": "results/generated/ns-bitcoin-v1.md",
+ "bytes": 416,
+ "sha256": "ce6ae206e95d2be86e8ea13a9161a26a0a6566787abf396a5eeffbb23efa3fcb"
+ }
+ ],
+ "reproducibility": {
+ "level": "bitwise_reproducible",
+ "availability": "complete"
+ },
+ "verification": {
+ "mode": "exact",
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.ns_bitcoin",
+ "--left",
+ "data/ns-bitcoin-2016-v1/left-391992-392111.ndjson.gz",
+ "--right",
+ "data/ns-bitcoin-2016-v1/right-392112-392231.ndjson.gz",
+ "verify",
+ "--artifact",
+ "results/artifacts/ns-bitcoin-v1.json",
+ "--markdown",
+ "results/generated/ns-bitcoin-v1.md"
+ ],
+ "tests": [
+ "tests/test_ns_social_baseline.py",
+ "tests/test_ns_bitcoin.py",
+ "tests/test_ns_bitcoin_experiment.py"
+ ],
+ "properties": [
+ "the exact Bitcoin windows reproduce the reported observation populations",
+ "the propagation report is stable across Python hash iteration order",
+ "the run records that no independent gradeable seed was found",
+ "the seed-assisted sweep is not presented as an independently seeded attack",
+ "the result is not attributed to the published social-network corpora",
+ "the generated Markdown equals the canonical rendering"
+ ],
+ "tolerance": null
+ },
+ "limitations": [
+ "no independent gradeable seeds were found in these windows",
+ "the measured sweep uses seeds sampled from withheld correspondence",
+ "operational precision is approximately one percent",
+ "the observation windows are not representative of the whole chain",
+ "this does not reproduce the Twitter, Flickr, or LiveJournal experiments"
+ ]
+}
diff --git a/catalog/runs/ns-link-prediction-coverage-v2.json b/catalog/runs/ns-link-prediction-coverage-v2.json
new file mode 100644
index 00000000..e8e4955b
--- /dev/null
+++ b/catalog/runs/ns-link-prediction-coverage-v2.json
@@ -0,0 +1,93 @@
+{
+ "schema_version": 1,
+ "id": "ns-link-prediction-coverage-v2",
+ "claim_ids": [
+ "ctp.social_graph.link_prediction"
+ ],
+ "code": {
+ "revision": "bcd9a3e511860b1b0ff5f3ee6338a2df29ecbfbf",
+ "dirty": false
+ },
+ "command": {
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.ns_link_prediction_coverage",
+ "reproduce",
+ "--artifact",
+ "results/artifacts/ns-link-prediction-coverage-v2.json",
+ "--markdown",
+ "results/generated/ns-link-prediction-coverage-v2.md"
+ ]
+ },
+ "environment": {
+ "python": "3.13.7",
+ "lock_digest": "e0d6a8cada5d383c0aca2bd2a37f28c15721392469c94084128d369c29c5544d",
+ "platform": "macOS-26.6.2-arm64-arm-64bit-Mach-O",
+ "dependencies": []
+ },
+ "datasets": [],
+ "parameters": {
+ "coverage_schema": 1,
+ "statuses": [
+ "implemented",
+ "partial",
+ "mathematically_undefined",
+ "not_reproduced"
+ ],
+ "component_count": 9
+ },
+ "rng": {
+ "algorithm": null,
+ "seeds": []
+ },
+ "outputs": [
+ {
+ "path": "results/artifacts/ns-link-prediction-coverage-v2.json",
+ "bytes": 2500,
+ "sha256": "1223ce1528c4136d78fb727cc9bc81043af15d47562b7d215973a37b386fbaf4"
+ },
+ {
+ "path": "results/generated/ns-link-prediction-coverage-v2.md",
+ "bytes": 1522,
+ "sha256": "009538122a9c35006d6ac47a1129317d504fd15ac6861dffaa3c115cc3edc872"
+ }
+ ],
+ "reproducibility": {
+ "level": "bitwise_reproducible",
+ "availability": "complete"
+ },
+ "verification": {
+ "mode": "exact",
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.ns_link_prediction_coverage",
+ "verify",
+ "--artifact",
+ "results/artifacts/ns-link-prediction-coverage-v2.json",
+ "--markdown",
+ "results/generated/ns-link-prediction-coverage-v2.md"
+ ],
+ "tests": [
+ "tests/test_ns_link_prediction_2011.py",
+ "tests/test_ns_link_prediction_coverage_experiment.py"
+ ],
+ "properties": [
+ "every declared pipeline component appears exactly once",
+ "implemented components pass the requirement gate",
+ "partial, undefined and unreproduced components are refused",
+ "dummy zero-weight semantics are not completed with invented smoothing",
+ "the result explicitly denies end-to-end paper reproduction",
+ "the generated Markdown equals the canonical rendering"
+ ],
+ "tolerance": null
+ },
+ "limitations": [
+ "coverage contract rather than a performance experiment",
+ "the Flickr/Kaggle corpus and published metrics are not reproduced",
+ "annealing uses local reproducibility controls",
+ "the zero convention for dummy-incident terms is read from the paper's prose, not from its formula",
+ "confidence pruning, accepted-mapping correction and the learned 25-feature model are not reproduced"
+ ]
+}
diff --git a/catalog/runs/ns1r-change-readoff-v1.json b/catalog/runs/ns1r-change-readoff-v1.json
new file mode 100644
index 00000000..4c505aa9
--- /dev/null
+++ b/catalog/runs/ns1r-change-readoff-v1.json
@@ -0,0 +1,90 @@
+{
+ "schema_version": 1,
+ "id": "ns1r-change-readoff-v1",
+ "claim_ids": [
+ "ctp.payjoin.receiver_change_readoff"
+ ],
+ "code": {
+ "revision": "bcd9a3e511860b1b0ff5f3ee6338a2df29ecbfbf",
+ "dirty": false
+ },
+ "command": {
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.ns1r_change_readoff",
+ "reproduce",
+ "--artifact",
+ "results/artifacts/ns1r-change-readoff-v1.json",
+ "--markdown",
+ "results/generated/ns1r-change-readoff-v1.md"
+ ]
+ },
+ "environment": {
+ "python": "3.13.7",
+ "lock_digest": "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855",
+ "platform": "macOS-26.6.2-arm64-arm-64bit-Mach-O",
+ "dependencies": []
+ },
+ "datasets": [],
+ "parameters": {
+ "scenarios": [
+ "consolidating-receiver",
+ "evenly-spaced-control"
+ ],
+ "max_senders": 8,
+ "fees_modelled": false
+ },
+ "rng": {
+ "algorithm": null,
+ "seeds": []
+ },
+ "outputs": [
+ {
+ "path": "results/artifacts/ns1r-change-readoff-v1.json",
+ "bytes": 2317,
+ "sha256": "d2c40b646533f67e4e256f62260b183e6aa4e2e3e2888d0e8d41da54374ca1f1"
+ },
+ {
+ "path": "results/generated/ns1r-change-readoff-v1.md",
+ "bytes": 1321,
+ "sha256": "37d0f5348a1fbb852a826d28b09ed7ce09184da149616c9fd5816e127058764e"
+ }
+ ],
+ "reproducibility": {
+ "level": "bitwise_reproducible",
+ "availability": "complete"
+ },
+ "verification": {
+ "mode": "exact",
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.ns1r_change_readoff",
+ "verify",
+ "--artifact",
+ "results/artifacts/ns1r-change-readoff-v1.json",
+ "--markdown",
+ "results/generated/ns1r-change-readoff-v1.md"
+ ],
+ "tests": [
+ "tests/test_ns1r_change_readoff.py",
+ "tests/test_ns1r_change_readoff_experiment.py"
+ ],
+ "properties": [
+ "a reading counts distinct amounts, not distinct index matchings",
+ "the control scenario keeps two readings and no unanimous link",
+ "the receiver's negotiated payments are inputs, never inferred",
+ "the generated Markdown equals the canonical rendering"
+ ],
+ "tolerance": null
+ },
+ "limitations": [
+ "the scenarios are deterministic fixtures, not sampled transactions",
+ "the receiver's knowledge of each negotiated payment is supplied, not inferred",
+ "an external observer holds neither the payments nor the receiver's own coins",
+ "a unique reading of the amounts is not ownership attribution",
+ "the enumeration is bounded at eight senders and does not model fees",
+ "the result is not a privacy score"
+ ]
+}
diff --git a/catalog/runs/partition-schemes-v1.json b/catalog/runs/partition-schemes-v1.json
new file mode 100644
index 00000000..0af51f60
--- /dev/null
+++ b/catalog/runs/partition-schemes-v1.json
@@ -0,0 +1,113 @@
+{
+ "schema_version": 1,
+ "id": "partition-schemes-v1",
+ "claim_ids": [
+ "ctp.social_graph.propagation",
+ "ctp.graph.pseudonym_representation"
+ ],
+ "code": {
+ "revision": "bcd9a3e511860b1b0ff5f3ee6338a2df29ecbfbf",
+ "dirty": false
+ },
+ "command": {
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.partition_schemes",
+ "--dataset",
+ "data/partition-cuts-300k-2016.ndjson.gz",
+ "reproduce",
+ "--artifact",
+ "results/artifacts/partition-schemes-v1.json",
+ "--markdown",
+ "results/generated/partition-schemes-v1.md"
+ ]
+ },
+ "environment": {
+ "python": "3.13.7",
+ "lock_digest": "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855",
+ "platform": "macOS-26.6.2-arm64-arm-64bit-Mach-O",
+ "dependencies": []
+ },
+ "datasets": [
+ {
+ "id": "partition-cuts-300k-2016-v1",
+ "sha256": "44192156107867a305e2f608af9185e4ce22fb88372238c88683f91120b1db16"
+ }
+ ],
+ "parameters": {
+ "core_fraction": 0.01,
+ "min_side": 2,
+ "schemes": [
+ "epoch",
+ "decore",
+ "collapse"
+ ],
+ "seed": 0,
+ "seed_shares": [
+ 0.05,
+ 0.1
+ ],
+ "view_counts": [
+ 2,
+ 3,
+ 4
+ ]
+ },
+ "rng": {
+ "algorithm": "python.random.Random",
+ "seeds": [
+ 0
+ ]
+ },
+ "outputs": [
+ {
+ "path": "results/artifacts/partition-schemes-v1.json",
+ "bytes": 2310,
+ "sha256": "a000b89fd2a3def18ab1336179c2f083690289bd90584e4c9d047d3f4c0241ee"
+ },
+ {
+ "path": "results/generated/partition-schemes-v1.md",
+ "bytes": 2364,
+ "sha256": "57d6d4d6155e20e2ba7537d22e014dc6cd8a78caf82d671fb11fe5318c03cea9"
+ }
+ ],
+ "reproducibility": {
+ "level": "bitwise_reproducible",
+ "availability": "complete"
+ },
+ "verification": {
+ "mode": "exact",
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.partition_schemes",
+ "--dataset",
+ "data/partition-cuts-300k-2016.ndjson.gz",
+ "verify",
+ "--artifact",
+ "results/artifacts/partition-schemes-v1.json",
+ "--markdown",
+ "results/generated/partition-schemes-v1.md"
+ ],
+ "tests": [
+ "tests/test_partition_schemes.py",
+ "tests/test_views.py"
+ ],
+ "properties": [
+ "every scheme returns exactly n_views views",
+ "decore halves the mean straddler degree and the population",
+ "collapse tracks the temporal baseline on both",
+ "a seed share that yields no guess reports no precision rather than zero",
+ "the generated Markdown equals the canonical rendering"
+ ],
+ "tolerance": null
+ },
+ "limitations": [
+ "the matcher recovers a handful of pairs at best, so the comparison rests on the straddler subgraph rather than on precision",
+ "one slice, one era, one 300,000-transaction prefix",
+ "cluster membership is a co-spend label, not wallet ownership",
+ "the export carries addresses but no values, so the collapse detector sees only the shape rule and never the de-mix arm",
+ "the result is not a privacy score"
+ ]
+}
diff --git a/catalog/runs/path-count-contract-v1.json b/catalog/runs/path-count-contract-v1.json
new file mode 100644
index 00000000..430db234
--- /dev/null
+++ b/catalog/runs/path-count-contract-v1.json
@@ -0,0 +1,87 @@
+{
+ "schema_version": 1,
+ "id": "path-count-contract-v1",
+ "claim_ids": [
+ "ctp.robustness.counterfactual_paths",
+ "ctp.provenance.traceability"
+ ],
+ "code": {
+ "revision": "bcd9a3e511860b1b0ff5f3ee6338a2df29ecbfbf",
+ "dirty": false
+ },
+ "command": {
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.path_count_contract",
+ "reproduce",
+ "--artifact",
+ "results/artifacts/path-count-contract-v1.json",
+ "--markdown",
+ "results/generated/path-count-contract-v1.md"
+ ]
+ },
+ "environment": {
+ "python": "3.13.7",
+ "lock_digest": "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855",
+ "platform": "macOS-26.6.2-arm64-arm-64bit-Mach-O",
+ "dependencies": []
+ },
+ "datasets": [],
+ "parameters": {
+ "fixture_transactions": 4,
+ "depth": 6,
+ "probability_round_digits": 15
+ },
+ "rng": {
+ "algorithm": null,
+ "seeds": []
+ },
+ "outputs": [
+ {
+ "path": "results/artifacts/path-count-contract-v1.json",
+ "bytes": 927,
+ "sha256": "93f48cdcee4ccb7def3bdc525593ecece258438e9a3fa4be9ca5b6bb913d537e"
+ },
+ {
+ "path": "results/generated/path-count-contract-v1.md",
+ "bytes": 667,
+ "sha256": "0d79d8d6b5a601c66c88be9b3432b54ea664492d29c4c3b14224547337f6802b"
+ }
+ ],
+ "reproducibility": {
+ "level": "bitwise_reproducible",
+ "availability": "complete"
+ },
+ "verification": {
+ "mode": "exact",
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.path_count_contract",
+ "verify",
+ "--artifact",
+ "results/artifacts/path-count-contract-v1.json",
+ "--markdown",
+ "results/generated/path-count-contract-v1.md"
+ ],
+ "tests": [
+ "tests/test_path_count_contract_experiment.py",
+ "tests/test_path_count.py"
+ ],
+ "properties": [
+ "route accumulation equals the ancestry absorption distribution on the fixture",
+ "changing count_oracle does not change the result",
+ "edge-disjoint paths, plausible-flow capacity and k-routes are present and declared as such; the path count is none of them",
+ "the generated Markdown equals the canonical rendering"
+ ],
+ "tolerance": null
+ },
+ "limitations": [
+ "the fixture is synthetic and tests implementation semantics",
+ "the function accumulates link-probability mass over ancestry routes",
+ "count_oracle is retained for compatibility and does not affect the result",
+ "the function does not measure CTP robust connectivity or own-origin robustness",
+ "the output is not a privacy score or CoinScore"
+ ]
+}
diff --git a/catalog/runs/perfect-matching-disclosure-v1.json b/catalog/runs/perfect-matching-disclosure-v1.json
new file mode 100644
index 00000000..4d4b6381
--- /dev/null
+++ b/catalog/runs/perfect-matching-disclosure-v1.json
@@ -0,0 +1,91 @@
+{
+ "schema_version": 1,
+ "id": "perfect-matching-disclosure-v1",
+ "claim_ids": [
+ "ctp.disclosure.perfect_matching"
+ ],
+ "code": {
+ "revision": "bcd9a3e511860b1b0ff5f3ee6338a2df29ecbfbf",
+ "dirty": false
+ },
+ "command": {
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.perfect_matching_disclosure",
+ "reproduce",
+ "--artifact",
+ "results/artifacts/perfect-matching-disclosure-v1.json",
+ "--markdown",
+ "results/generated/perfect-matching-disclosure-v1.md"
+ ]
+ },
+ "environment": {
+ "python": "3.13.7",
+ "lock_digest": "e0d6a8cada5d383c0aca2bd2a37f28c15721392469c94084128d369c29c5544d",
+ "platform": "macOS-26.6.2-arm64-arm-64bit-Mach-O",
+ "dependencies": []
+ },
+ "datasets": [],
+ "parameters": {
+ "messages_per_round": 3,
+ "objective": "maximum joint sender-profile likelihood",
+ "scenarios": [
+ "joint-assignment",
+ "uniform-profile-control"
+ ]
+ },
+ "rng": {
+ "algorithm": null,
+ "seeds": []
+ },
+ "outputs": [
+ {
+ "path": "results/artifacts/perfect-matching-disclosure-v1.json",
+ "bytes": 1024,
+ "sha256": "4071e62e0ef642c4f762d585ed5849f67fd10d4dbe10a9433cec8de1d90866b6"
+ },
+ {
+ "path": "results/generated/perfect-matching-disclosure-v1.md",
+ "bytes": 709,
+ "sha256": "94705cad4143dff0eabbdffa177873f17ed59aa9c6de8826b92980f5154101e2"
+ }
+ ],
+ "reproducibility": {
+ "level": "bitwise_reproducible",
+ "availability": "complete"
+ },
+ "verification": {
+ "mode": "exact",
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.perfect_matching_disclosure",
+ "verify",
+ "--artifact",
+ "results/artifacts/perfect-matching-disclosure-v1.json",
+ "--markdown",
+ "results/generated/perfect-matching-disclosure-v1.md"
+ ],
+ "tests": [
+ "tests/test_perfect_matching_disclosure.py",
+ "tests/test_perfect_matching_disclosure_experiment.py"
+ ],
+ "properties": [
+ "the objective maximizes the product of sender-profile edge probabilities through their logarithms",
+ "every candidate assignment is a perfect matching over the observed message nodes",
+ "joint optimization resolves a round whose independent maxima reuse one receiver",
+ "uniform profiles retain all six matchings and produce an inconclusive outcome",
+ "zero-probability edges are excluded",
+ "the result is not represented as value-flow ancestry or Bitcoin ownership attribution",
+ "the generated Markdown equals the canonical rendering"
+ ],
+ "tolerance": null
+ },
+ "limitations": [
+ "deterministic synthetic threshold-mix fixture",
+ "sender profiles are supplied rather than estimated from a longitudinal trace",
+ "the diagnostic enumerator is limited to eight messages",
+ "the paper's simulations and success rates are not reproduced"
+ ]
+}
diff --git a/catalog/runs/provenance-decay-v1.json b/catalog/runs/provenance-decay-v1.json
new file mode 100644
index 00000000..957b6fc4
--- /dev/null
+++ b/catalog/runs/provenance-decay-v1.json
@@ -0,0 +1,89 @@
+{
+ "schema_version": 1,
+ "id": "provenance-decay-v1",
+ "claim_ids": [
+ "ctp.provenance.traceability"
+ ],
+ "code": {
+ "revision": "bcd9a3e511860b1b0ff5f3ee6338a2df29ecbfbf",
+ "dirty": false
+ },
+ "command": {
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.provenance_decay",
+ "reproduce",
+ "--artifact",
+ "results/artifacts/provenance-decay-v1.json",
+ "--markdown",
+ "results/generated/provenance-decay-v1.md"
+ ]
+ },
+ "environment": {
+ "python": "3.13.7",
+ "lock_digest": "e0d6a8cada5d383c0aca2bd2a37f28c15721392469c94084128d369c29c5544d",
+ "platform": "macOS-26.6.2-arm64-arm-64bit-Mach-O",
+ "dependencies": []
+ },
+ "datasets": [],
+ "parameters": {
+ "candidate_origins": 3,
+ "removed_auxiliaries": 1,
+ "controls": [
+ "bridge",
+ "peripheral"
+ ]
+ },
+ "rng": {
+ "algorithm": null,
+ "seeds": []
+ },
+ "outputs": [
+ {
+ "path": "results/artifacts/provenance-decay-v1.json",
+ "bytes": 502,
+ "sha256": "879dc6c49619b18d267fd30510472da99e341b6a3eebef6d6ab65faf319c2e9c"
+ },
+ {
+ "path": "results/generated/provenance-decay-v1.md",
+ "bytes": 357,
+ "sha256": "e09b7649b43e9238da355048e2205281c00f63c1e08b230c2a3588b7efb94ab8"
+ }
+ ],
+ "reproducibility": {
+ "level": "bitwise_reproducible",
+ "availability": "complete"
+ },
+ "verification": {
+ "mode": "exact",
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.provenance_decay",
+ "verify",
+ "--artifact",
+ "results/artifacts/provenance-decay-v1.json",
+ "--markdown",
+ "results/generated/provenance-decay-v1.md"
+ ],
+ "tests": [
+ "tests/test_provenance_decay.py",
+ "tests/test_provenance_decay_experiment.py"
+ ],
+ "properties": [
+ "bridge and peripheral controls eliminate the same number of candidates",
+ "only bridge removal increases the connected-component count",
+ "additive elimination and structural fracture remain separate evidence",
+ "component growth is not represented as an exponential-rate measurement",
+ "the generated Markdown equals the canonical rendering"
+ ],
+ "tolerance": null
+ },
+ "limitations": [
+ "synthetic fixture",
+ "candidate origins, graph edges, and auxiliary labels are supplied",
+ "flow plausibility and private adversary information are not modeled",
+ "the run does not establish an exponential deanonymization rate"
+ ]
+}
diff --git a/catalog/runs/pseudonym-graph-contract-v1.json b/catalog/runs/pseudonym-graph-contract-v1.json
new file mode 100644
index 00000000..48bc6eec
--- /dev/null
+++ b/catalog/runs/pseudonym-graph-contract-v1.json
@@ -0,0 +1,90 @@
+{
+ "schema_version": 1,
+ "id": "pseudonym-graph-contract-v1",
+ "claim_ids": [
+ "ctp.graph.pseudonym_representation"
+ ],
+ "code": {
+ "revision": "bcd9a3e511860b1b0ff5f3ee6338a2df29ecbfbf",
+ "dirty": false
+ },
+ "command": {
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.pseudonym_graph_contract",
+ "reproduce",
+ "--artifact",
+ "results/artifacts/pseudonym-graph-contract-v1.json",
+ "--markdown",
+ "results/generated/pseudonym-graph-contract-v1.md"
+ ]
+ },
+ "environment": {
+ "python": "3.13.7",
+ "lock_digest": "e0d6a8cada5d383c0aca2bd2a37f28c15721392469c94084128d369c29c5544d",
+ "platform": "macOS-26.6.2-arm64-arm-64bit-Mach-O",
+ "dependencies": []
+ },
+ "datasets": [],
+ "parameters": {
+ "addresses": 5,
+ "declared_clusters": 2,
+ "transactions": 5,
+ "axes": false
+ },
+ "rng": {
+ "algorithm": null,
+ "seeds": []
+ },
+ "outputs": [
+ {
+ "path": "results/artifacts/pseudonym-graph-contract-v1.json",
+ "bytes": 850,
+ "sha256": "ab830f2e56b40e961015fda9c9685ddcab4feff1e486bde6ee825ea4db9d33a6"
+ },
+ {
+ "path": "results/generated/pseudonym-graph-contract-v1.md",
+ "bytes": 761,
+ "sha256": "f0533dbe40ddfa8af55d31fbb1e53b2f0a925582a7567670496717e786b0c827"
+ }
+ ],
+ "reproducibility": {
+ "level": "bitwise_reproducible",
+ "availability": "complete"
+ },
+ "verification": {
+ "mode": "exact",
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.pseudonym_graph_contract",
+ "verify",
+ "--artifact",
+ "results/artifacts/pseudonym-graph-contract-v1.json",
+ "--markdown",
+ "results/generated/pseudonym-graph-contract-v1.md"
+ ],
+ "tests": [
+ "tests/test_pseudonym_graph_contract.py",
+ "tests/test_pseudonym_graph_contract_experiment.py",
+ "tests/test_views.py"
+ ],
+ "properties": [
+ "members of each declared cluster contract into one vertex",
+ "an address absent from the lookup remains a singleton pseudonym",
+ "opposite transfer directions remain separate",
+ "parallel transfers fold into count, total value and height span attributes",
+ "self-transfers remain vertex activity rather than relational edges",
+ "vertices are not presented as users without an independently complete clustering",
+ "the generated Markdown equals the canonical rendering"
+ ],
+ "tolerance": null
+ },
+ "limitations": [
+ "deterministic synthetic contraction fixture",
+ "the partial clustering lookup is supplied",
+ "parallel transfers are folded into an attributed edge",
+ "the run validates representation rather than deanonymization success"
+ ]
+}
diff --git a/catalog/runs/reid-v1.json b/catalog/runs/reid-v1.json
new file mode 100644
index 00000000..18db1ada
--- /dev/null
+++ b/catalog/runs/reid-v1.json
@@ -0,0 +1,103 @@
+{
+ "schema_version": 1,
+ "id": "reid-v1",
+ "claim_ids": [
+ "ctp.sparse_dataset.feature_uniqueness"
+ ],
+ "code": {
+ "revision": "bcd9a3e511860b1b0ff5f3ee6338a2df29ecbfbf",
+ "dirty": false
+ },
+ "command": {
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.reid",
+ "--dataset",
+ "tests/fixtures/reid_sigs.json.gz",
+ "reproduce",
+ "--artifact",
+ "results/artifacts/reid-v1.json",
+ "--markdown",
+ "results/generated/reid-v1.md"
+ ]
+ },
+ "environment": {
+ "python": "3.13.7",
+ "lock_digest": "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855",
+ "platform": "macOS-26.6.2-arm64-arm-64bit-Mach-O",
+ "dependencies": []
+ },
+ "datasets": [
+ {
+ "id": "reid-signatures-v1",
+ "sha256": "dbc4214fb499a96327326c4ea31341a76878c1867bfa3ccc57df88d850cdaa6a"
+ }
+ ],
+ "parameters": {
+ "auxiliary_origins": [
+ 4,
+ 8
+ ],
+ "eccentricity_threshold": 1.5,
+ "sparse_below_cosine": 0.5,
+ "dense_at_least_cosine": 0.9
+ },
+ "rng": {
+ "algorithm": "python.random.Random",
+ "seeds": [
+ 0
+ ]
+ },
+ "outputs": [
+ {
+ "path": "results/artifacts/reid-v1.json",
+ "bytes": 1504,
+ "sha256": "9c4ac11aa3a1014907528b5ed8b324c0a9b1995a0d7a70f2c6963c49bc8e797f"
+ },
+ {
+ "path": "results/generated/reid-v1.md",
+ "bytes": 795,
+ "sha256": "685acc5dc71a32295f04409c3e11b310934dbba6b44d4bd709255767f6b47c20"
+ }
+ ],
+ "reproducibility": {
+ "level": "bitwise_reproducible",
+ "availability": "partial"
+ },
+ "verification": {
+ "mode": "exact",
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.reid",
+ "--dataset",
+ "tests/fixtures/reid_sigs.json.gz",
+ "verify",
+ "--artifact",
+ "results/artifacts/reid-v1.json",
+ "--markdown",
+ "results/generated/reid-v1.md"
+ ],
+ "tests": [
+ "tests/test_reid.py",
+ "tests/test_reid_experiment.py"
+ ],
+ "properties": [
+ "seeded sampling is invariant to serialized object order",
+ "sparse and dense strata are recomputed from the snapshot",
+ "all denominators and rates are emitted in the structured artifact",
+ "the generated Markdown equals the canonical rendering"
+ ],
+ "tolerance": null
+ },
+ "limitations": [
+ "the 200-record snapshot is selected and is not a uniform chain sample",
+ "the signature-generation recipe and original comparison population are unavailable",
+ "the target identifier is a record identity, not wallet ownership",
+ "the auxiliary origins are sampled from the target signature rather than observed independently",
+ "this is an adaptation, not a reproduction of the published Netflix experiment",
+ "the result does not establish a chain-wide deanonymization rate or privacy score",
+ "upstream data licensing is undetermined; the dataset publisher authorized redistribution on 2026-09-04"
+ ]
+}
diff --git a/catalog/runs/route-capacity-v1.json b/catalog/runs/route-capacity-v1.json
new file mode 100644
index 00000000..4024c6a9
--- /dev/null
+++ b/catalog/runs/route-capacity-v1.json
@@ -0,0 +1,98 @@
+{
+ "schema_version": 1,
+ "id": "route-capacity-v1",
+ "claim_ids": [
+ "ctp.robustness.counterfactual_paths",
+ "ctp.provenance.traceability"
+ ],
+ "code": {
+ "revision": "bcd9a3e511860b1b0ff5f3ee6338a2df29ecbfbf",
+ "dirty": false
+ },
+ "command": {
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.route_capacity",
+ "--snapshot",
+ "data/fs-blkcache-2026-09-04.tar.gz",
+ "reproduce",
+ "--artifact",
+ "results/artifacts/route-capacity-v1.json",
+ "--markdown",
+ "results/generated/route-capacity-v1.md"
+ ]
+ },
+ "datasets": [
+ {
+ "id": "fs-blkcache-2026-09-04-v1",
+ "sha256": "5a69f6f446dbdfcce09dddc91c653e5d841124d14020b720fdea276315c775a6"
+ }
+ ],
+ "parameters": {
+ "depth": 5,
+ "small_cut_at_most": 3,
+ "link_oracle": "value_flow_link_oracle",
+ "capacity_per_coin": 1,
+ "value_threshold": "the target coin's own value"
+ },
+ "rng": {
+ "algorithm": "none",
+ "seeds": []
+ },
+ "reproducibility": {
+ "level": "bitwise_reproducible",
+ "availability": "complete"
+ },
+ "verification": {
+ "mode": "exact",
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.route_capacity",
+ "--snapshot",
+ "data/fs-blkcache-2026-09-04.tar.gz",
+ "verify",
+ "--artifact",
+ "results/artifacts/route-capacity-v1.json",
+ "--markdown",
+ "results/generated/route-capacity-v1.md"
+ ],
+ "tests": [
+ "tests/test_disjoint_routes.py",
+ "tests/test_route_capacity_experiment.py"
+ ],
+ "properties": [
+ "the committed cache exactly reproduces both capacity distributions",
+ "routes share no coin beyond the target, and a shared transaction is not a shared coin",
+ "the origin set overstates redundancy for most coins, which is why the cut is measured",
+ "pruning to routes that could carry the coin raises the single-coin cut, and is reported beside it",
+ "every cut is a lower bound: the boundary the walk stopped at is recorded beside it",
+ "value enters twice: as a threshold a route passes or fails, and as the capacity of a second flow whose cut is the value a separation removes"
+ ],
+ "tolerance": null
+ },
+ "environment": {
+ "python": "3.13.7",
+ "lock_digest": "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855",
+ "platform": "macOS-26.6.2-arm64-arm-64bit-Mach-O",
+ "dependencies": []
+ },
+ "limitations": [
+ "Almost every origin reached is the edge of the committed slice, not a coinbase; a contiguous export can only raise these cuts.",
+ "Routes are counted and, separately, valued; what is not solved is which routes carry how much, the strongly NP-hard k-splittable formulation.",
+ "The walk depth is fixed at 5 transactions."
+ ],
+ "outputs": [
+ {
+ "path": "results/artifacts/route-capacity-v1.json",
+ "bytes": 1445,
+ "sha256": "e2bad958dea37ae3a7eacaa6e0f77fa88b2ad0378d854f31592ce76ecbe353f0"
+ },
+ {
+ "path": "results/generated/route-capacity-v1.md",
+ "bytes": 3907,
+ "sha256": "5602b6166b55f7f7366a7eb9cd5603c95a6d2073bf95828cebd47b5c82504022"
+ }
+ ]
+}
diff --git a/catalog/runs/same-block-linkage-v1.json b/catalog/runs/same-block-linkage-v1.json
new file mode 100644
index 00000000..4d901161
--- /dev/null
+++ b/catalog/runs/same-block-linkage-v1.json
@@ -0,0 +1,97 @@
+{
+ "schema_version": 1,
+ "id": "same-block-linkage-v1",
+ "claim_ids": [
+ "ctp.cioh.collaborative_false_merge",
+ "ctp.amount.subtransaction_ambiguity"
+ ],
+ "code": {
+ "revision": "bcd9a3e511860b1b0ff5f3ee6338a2df29ecbfbf",
+ "dirty": false
+ },
+ "command": {
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.same_block_linkage",
+ "--dataset",
+ "data/boltzmann-fee-audit-v1.json",
+ "reproduce",
+ "--artifact",
+ "results/artifacts/same-block-linkage-v1.json",
+ "--markdown",
+ "results/generated/same-block-linkage-v1.md"
+ ]
+ },
+ "environment": {
+ "python": "3.13.7",
+ "lock_digest": "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855",
+ "platform": "macOS-26.6.2-arm64-arm-64bit-Mach-O",
+ "dependencies": []
+ },
+ "datasets": [
+ {
+ "id": "boltzmann-fee-audit-v1",
+ "sha256": "17ee62e307d34660be2c4f50382a63ba044d0ad67f0674873e304d0b8203dc1c"
+ }
+ ],
+ "parameters": {
+ "balance_model": "fee_tolerant",
+ "fee_tolerance": "the transaction's own observed fee",
+ "max_coins": 12
+ },
+ "rng": {
+ "algorithm": null,
+ "seeds": []
+ },
+ "outputs": [
+ {
+ "path": "results/artifacts/same-block-linkage-v1.json",
+ "bytes": 1166,
+ "sha256": "8f569aa3f08e4644bcba4f3cbfd48d4b2d52cafa9ecb4f455d730307700ad30e"
+ },
+ {
+ "path": "results/generated/same-block-linkage-v1.md",
+ "bytes": 1438,
+ "sha256": "7fa983fd3330dcf3c4c8d1d90b7284ae587cbb10f312683d18696e82096fa450"
+ }
+ ],
+ "reproducibility": {
+ "level": "bitwise_reproducible",
+ "availability": "complete"
+ },
+ "verification": {
+ "mode": "exact",
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.same_block_linkage",
+ "--dataset",
+ "data/boltzmann-fee-audit-v1.json",
+ "verify",
+ "--artifact",
+ "results/artifacts/same-block-linkage-v1.json",
+ "--markdown",
+ "results/generated/same-block-linkage-v1.md"
+ ],
+ "tests": [
+ "tests/test_same_block_linkage.py",
+ "tests/test_boltzmann.py"
+ ],
+ "properties": [
+ "p_II is the same-side quantity; p_IO does not cover it",
+ "a diagonal entry is not counted as a pair",
+ "a forced pair is a mapping-family fact, never an ownership finding",
+ "the generated Markdown equals the canonical rendering"
+ ],
+ "tolerance": null
+ },
+ "limitations": [
+ "a block of a value-conserving mapping is an accounting unit, not a wallet",
+ "a collaborative transaction can place two owners in one block, so a forced pair is not an ownership finding",
+ "only transactions with at most max_coins coins are in scope, which excludes the wide consolidations and coinjoins where the question is hardest",
+ "the snapshot is 300 selected transactions and is not a chain-wide sample",
+ "fee tolerance admits mappings exact conservation would refuse, which can only widen the family and lower the forced share",
+ "the result is not a privacy score"
+ ]
+}
diff --git a/catalog/runs/scorer-families-v1.json b/catalog/runs/scorer-families-v1.json
new file mode 100644
index 00000000..e79d6092
--- /dev/null
+++ b/catalog/runs/scorer-families-v1.json
@@ -0,0 +1,103 @@
+{
+ "schema_version": 1,
+ "id": "scorer-families-v1",
+ "claim_ids": [
+ "ctp.fingerprint.statistical_linkage"
+ ],
+ "code": {
+ "revision": "bcd9a3e511860b1b0ff5f3ee6338a2df29ecbfbf",
+ "dirty": false
+ },
+ "command": {
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.scorer_families",
+ "--snapshot",
+ "data/fs-blkcache-2026-09-04.tar.gz",
+ "reproduce",
+ "--artifact",
+ "results/artifacts/scorer-families-v1.json",
+ "--markdown",
+ "results/generated/scorer-families-v1.md"
+ ]
+ },
+ "environment": {
+ "python": "3.13.7",
+ "lock_digest": "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855",
+ "platform": "macOS-26.6.2-arm64-arm-64bit-Mach-O",
+ "dependencies": []
+ },
+ "datasets": [
+ {
+ "id": "fs-blkcache-2026-09-04-v1",
+ "sha256": "5a69f6f446dbdfcce09dddc91c653e5d841124d14020b720fdea276315c775a6"
+ }
+ ],
+ "parameters": {
+ "consistency": 0.95,
+ "pair_cap_per_class": 4000,
+ "seed": 0,
+ "families": [
+ "catalogued",
+ "construction_only",
+ "decorrelated"
+ ]
+ },
+ "rng": {
+ "algorithm": "python.random.MersenneTwister",
+ "seeds": [
+ 0
+ ]
+ },
+ "outputs": [
+ {
+ "path": "results/artifacts/scorer-families-v1.json",
+ "bytes": 1698,
+ "sha256": "473b7a0f83805809411d4691f18103fbb74f0587a3a5b6988b776da46f43ea25"
+ },
+ {
+ "path": "results/generated/scorer-families-v1.md",
+ "bytes": 1257,
+ "sha256": "eb1bd191d66405751de2e39fe90e3c80c4a4c7d4a993440ea6e82ec6d0389654"
+ }
+ ],
+ "reproducibility": {
+ "level": "bitwise_reproducible",
+ "availability": "partial"
+ },
+ "verification": {
+ "mode": "exact",
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.scorer_families",
+ "--snapshot",
+ "data/fs-blkcache-2026-09-04.tar.gz",
+ "verify",
+ "--artifact",
+ "results/artifacts/scorer-families-v1.json",
+ "--markdown",
+ "results/generated/scorer-families-v1.md"
+ ],
+ "tests": [
+ "tests/test_scorer_families.py",
+ "tests/test_fingerprint_validate.py"
+ ],
+ "properties": [
+ "the three families score one pair sample under one seed and one consistency",
+ "only the axis set differs between rows",
+ "the catalogued row reproduces the weight-sensitivity row at the same consistency",
+ "the generated Markdown equals the canonical rendering"
+ ],
+ "tolerance": null
+ },
+ "limitations": [
+ "address reuse is a weak and partly feature-dependent ownership proxy",
+ "the axis clusters and the address-determined set are measured on this same cache",
+ "one selected snapshot is reused for all three families and is not chain-wide",
+ "a higher AUC on fewer axes does not establish that the dropped axes carry no signal",
+ "evidence magnitudes are rarity bits under a fixed consistency, not calibrated likelihoods",
+ "this is attacker-side analysis, not a privacy score"
+ ]
+}
diff --git a/catalog/runs/slice-channels-v1.json b/catalog/runs/slice-channels-v1.json
new file mode 100644
index 00000000..c74d4bc6
--- /dev/null
+++ b/catalog/runs/slice-channels-v1.json
@@ -0,0 +1,111 @@
+{
+ "schema_version": 1,
+ "id": "slice-channels-v1",
+ "claim_ids": [
+ "ctp.sparse_dataset.feature_uniqueness"
+ ],
+ "code": {
+ "revision": "bcd9a3e511860b1b0ff5f3ee6338a2df29ecbfbf",
+ "dirty": false
+ },
+ "command": {
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.slice_channels",
+ "--dataset",
+ "tests/fixtures/slice_a_channels_2016.ndjson.gz",
+ "reproduce",
+ "--artifact",
+ "results/artifacts/slice-channels-v1.json",
+ "--markdown",
+ "results/generated/slice-channels-v1.md"
+ ]
+ },
+ "environment": {
+ "python": "3.13.7",
+ "lock_digest": "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855",
+ "platform": "macOS-26.6.2-arm64-arm-64bit-Mach-O",
+ "dependencies": []
+ },
+ "datasets": [
+ {
+ "id": "slice-a-channels-2016-v1",
+ "sha256": "66104889f8b79f2938edb6666f16dfa19a74c68cd8016832b4f2a5e03b11865f"
+ }
+ ],
+ "parameters": {
+ "refuse_guard": true,
+ "query_records": 1200,
+ "background_records": 12000,
+ "seed": 0,
+ "minimum_degrees": [
+ 2,
+ 20
+ ],
+ "epsilons": [
+ 0.5,
+ 0.9,
+ 0.99
+ ]
+ },
+ "rng": {
+ "algorithm": "python.random.Random",
+ "seeds": [
+ 0
+ ]
+ },
+ "outputs": [
+ {
+ "path": "results/artifacts/slice-channels-v1.json",
+ "bytes": 1564,
+ "sha256": "588992cc86b2689ea0d409e8d228e3cfb83fc15246731b57f21ad8bf63d1dc64"
+ },
+ {
+ "path": "results/generated/slice-channels-v1.md",
+ "bytes": 1043,
+ "sha256": "c01256072550fd633edd1fa113f1435e9be9323f4ef9a95a6452648464f4fd3d"
+ }
+ ],
+ "reproducibility": {
+ "level": "bitwise_reproducible",
+ "availability": "partial"
+ },
+ "verification": {
+ "mode": "exact",
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.slice_channels",
+ "--dataset",
+ "tests/fixtures/slice_a_channels_2016.ndjson.gz",
+ "verify",
+ "--artifact",
+ "results/artifacts/slice-channels-v1.json",
+ "--markdown",
+ "results/generated/slice-channels-v1.md"
+ ],
+ "tests": [
+ "tests/test_slice_a_channels.py",
+ "tests/test_slice_channels_experiment.py",
+ "tests/test_def1_sparsity.py",
+ "tests/test_graph_shape.py"
+ ],
+ "properties": [
+ "seeded vertex sampling uses a sorted population",
+ "graph statistics use stable vertex and neighbour order",
+ "density is measured separately for minimum degree 2 and 20",
+ "the generated Markdown equals the canonical rendering"
+ ],
+ "tolerance": null
+ },
+ "limitations": [
+ "the snapshot carries none of the four axes, so the similarity measured here is structural",
+ "the snapshot contains six 2016 blocks and is not a chain-wide sample",
+ "the run does not reproduce historical measurements on larger unpreserved slices",
+ "dense attributes do not prove resistance to other linkage channels",
+ "negative assortativity does not by itself determine graph-matching performance",
+ "the snapshot recipe is unavailable; the dataset publisher authorized redistribution on 2026-09-04",
+ "the result is not a privacy score or CoinScore"
+ ]
+}
diff --git a/catalog/runs/special-change-labels-v1.json b/catalog/runs/special-change-labels-v1.json
new file mode 100644
index 00000000..792affc0
--- /dev/null
+++ b/catalog/runs/special-change-labels-v1.json
@@ -0,0 +1,101 @@
+{
+ "schema_version": 1,
+ "id": "special-change-labels-v1",
+ "claim_ids": [
+ "ctp.fingerprint.statistical_linkage",
+ "ctp.payjoin.unnecessary_input"
+ ],
+ "code": {
+ "revision": "bcd9a3e511860b1b0ff5f3ee6338a2df29ecbfbf",
+ "dirty": false
+ },
+ "command": {
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.special_change_labels",
+ "--snapshot",
+ "data/fs-blkcache-2026-09-04.tar.gz",
+ "reproduce",
+ "--artifact",
+ "results/artifacts/special-change-labels-v1.json",
+ "--markdown",
+ "results/generated/special-change-labels-v1.md"
+ ]
+ },
+ "datasets": [
+ {
+ "id": "fs-blkcache-2026-09-04-v1",
+ "sha256": "5a69f6f446dbdfcce09dddc91c653e5d841124d14020b720fdea276315c775a6"
+ }
+ ],
+ "parameters": {
+ "labels": [
+ "address_reuse",
+ "optimal_change",
+ "round_number",
+ "type_match"
+ ],
+ "predictors": [
+ "address_reuse",
+ "round_number"
+ ]
+ },
+ "rng": {
+ "algorithm": "none",
+ "seeds": []
+ },
+ "reproducibility": {
+ "level": "bitwise_reproducible",
+ "availability": "complete"
+ },
+ "verification": {
+ "mode": "exact",
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.special_change_labels",
+ "--snapshot",
+ "data/fs-blkcache-2026-09-04.tar.gz",
+ "verify",
+ "--artifact",
+ "results/artifacts/special-change-labels-v1.json",
+ "--markdown",
+ "results/generated/special-change-labels-v1.md"
+ ],
+ "tests": [
+ "tests/test_special_change_labels_experiment.py"
+ ],
+ "properties": [
+ "two universal heuristics agree with a label that reads neither addresses nor roundness",
+ "address reuse and type match are one label, forced to agree rather than corroborating",
+ "the onward-spend arm is absent because the cache holds too few spenders to measure it",
+ "a predictor scored against its own label is reported as self-agreement"
+ ],
+ "tolerance": null
+ },
+ "environment": {
+ "python": "3.13.7",
+ "lock_digest": "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855",
+ "platform": "macOS-26.6.2-arm64-arm-64bit-Mach-O",
+ "dependencies": []
+ },
+ "limitations": [
+ "The optimal-change label is the no-unnecessary-input assumption read as a label, and it fails on transactions that carry one.",
+ "The cache is block-sampled, so the per-axis onward-spend arm the original document reported is not measured here at all.",
+ "Coverage is a property of this cache: address reuse is far more common in it than in a multi-epoch sample, so the coverage column does not transfer.",
+ "Agreement between labels bounds their consistency, not their correctness; all four can be wrong together on a transaction with an unnecessary input."
+ ],
+ "outputs": [
+ {
+ "path": "results/artifacts/special-change-labels-v1.json",
+ "bytes": 2491,
+ "sha256": "88fcdc14d41aa1f51e0e2f359d1ee6242bd69fa15ac9d9413e879970b2b5b581"
+ },
+ {
+ "path": "results/generated/special-change-labels-v1.md",
+ "bytes": 3337,
+ "sha256": "568aa47aac4b0f8e26a7d2b498b3f3bef80709555fe16507169540cb53f822f5"
+ }
+ ]
+}
diff --git a/catalog/runs/statistical-disclosure-v1.json b/catalog/runs/statistical-disclosure-v1.json
new file mode 100644
index 00000000..354cd75f
--- /dev/null
+++ b/catalog/runs/statistical-disclosure-v1.json
@@ -0,0 +1,90 @@
+{
+ "schema_version": 1,
+ "id": "statistical-disclosure-v1",
+ "claim_ids": [
+ "ctp.disclosure.statistical_longitudinal"
+ ],
+ "code": {
+ "revision": "bcd9a3e511860b1b0ff5f3ee6338a2df29ecbfbf",
+ "dirty": false
+ },
+ "command": {
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.statistical_disclosure",
+ "reproduce",
+ "--artifact",
+ "results/artifacts/statistical-disclosure-v1.json",
+ "--markdown",
+ "results/generated/statistical-disclosure-v1.md"
+ ]
+ },
+ "environment": {
+ "python": "3.13.7",
+ "lock_digest": "e0d6a8cada5d383c0aca2bd2a37f28c15721392469c94084128d369c29c5544d",
+ "platform": "macOS-26.6.2-arm64-arm-64bit-Mach-O",
+ "dependencies": []
+ },
+ "datasets": [],
+ "parameters": {
+ "batch_size": 4,
+ "correspondents": 4,
+ "observations_per_scenario": 4,
+ "scenarios": [
+ "persistent-correspondent",
+ "background-matched-control"
+ ]
+ },
+ "rng": {
+ "algorithm": null,
+ "seeds": []
+ },
+ "outputs": [
+ {
+ "path": "results/artifacts/statistical-disclosure-v1.json",
+ "bytes": 1210,
+ "sha256": "1a2ac2247a71136681b765e368b88e2686b4070039efd37b0a262a8ce6e29515"
+ },
+ {
+ "path": "results/generated/statistical-disclosure-v1.md",
+ "bytes": 682,
+ "sha256": "f2c946337645978c160b5ff30321be082e46576608a54aae761d3e137ffd74b7"
+ }
+ ],
+ "reproducibility": {
+ "level": "bitwise_reproducible",
+ "availability": "complete"
+ },
+ "verification": {
+ "mode": "exact",
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.statistical_disclosure",
+ "verify",
+ "--artifact",
+ "results/artifacts/statistical-disclosure-v1.json",
+ "--markdown",
+ "results/generated/statistical-disclosure-v1.md"
+ ],
+ "tests": [
+ "tests/test_statistical_disclosure.py",
+ "tests/test_statistical_disclosure_experiment.py"
+ ],
+ "properties": [
+ "the estimator implements equation 9.2 for the declared threshold-mix model",
+ "repeated target-active rounds recover the supplied persistent correspondent",
+ "the background-matched control has no unique top correspondent",
+ "correspondent evidence is not converted to Bitcoin ownership attribution",
+ "the generated Markdown equals the canonical rendering"
+ ],
+ "tolerance": null
+ },
+ "limitations": [
+ "deterministic synthetic threshold-mix fixture",
+ "target-active rounds and the background distribution are supplied",
+ "the fixture does not reproduce the cited paper's empirical simulations",
+ "the observation model is not a direct model of the Bitcoin transaction graph"
+ ]
+}
diff --git a/catalog/runs/subtx-demix-v1.json b/catalog/runs/subtx-demix-v1.json
new file mode 100644
index 00000000..8aeab21d
--- /dev/null
+++ b/catalog/runs/subtx-demix-v1.json
@@ -0,0 +1,98 @@
+{
+ "schema_version": 1,
+ "id": "subtx-demix-v1",
+ "claim_ids": [
+ "ctp.amount.subtransaction_ambiguity"
+ ],
+ "code": {
+ "revision": "bcd9a3e511860b1b0ff5f3ee6338a2df29ecbfbf",
+ "dirty": false
+ },
+ "command": {
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.subtx_demix",
+ "--snapshot",
+ "data/subtx-demix-cache-2026-09-04.tar.gz",
+ "reproduce",
+ "--artifact",
+ "results/artifacts/subtx-demix-v1.json",
+ "--markdown",
+ "results/generated/subtx-demix-v1.md"
+ ]
+ },
+ "environment": {
+ "python": "3.13.7",
+ "lock_digest": "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855",
+ "platform": "macOS-26.6.2-arm64-arm-64bit-Mach-O",
+ "dependencies": []
+ },
+ "datasets": [
+ {
+ "id": "subtx-demix-cache-2026-09-04-v1",
+ "sha256": "191c8c2f082ac84af71b28a70169207d785243a276cf5b6e472ae6f5588a65a6"
+ }
+ ],
+ "parameters": {
+ "fee_cap_sats": 2000,
+ "minimum_equal_outputs": 3,
+ "eligible_input_range": [
+ 3,
+ 60
+ ]
+ },
+ "rng": {
+ "algorithm": null,
+ "seeds": []
+ },
+ "outputs": [
+ {
+ "path": "results/artifacts/subtx-demix-v1.json",
+ "bytes": 1140,
+ "sha256": "4485f64416d0839e876f4333c8d86e1bbb4ea8ab72c68b3f067812be6983a600"
+ },
+ {
+ "path": "results/generated/subtx-demix-v1.md",
+ "bytes": 516,
+ "sha256": "1def26d56c2c64f5c7e0ce9208628a6c335981811c47d219c7f92ef019096f57"
+ }
+ ],
+ "reproducibility": {
+ "level": "bitwise_reproducible",
+ "availability": "complete"
+ },
+ "verification": {
+ "mode": "exact",
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.subtx_demix",
+ "--snapshot",
+ "data/subtx-demix-cache-2026-09-04.tar.gz",
+ "verify",
+ "--artifact",
+ "results/artifacts/subtx-demix-v1.json",
+ "--markdown",
+ "results/generated/subtx-demix-v1.md"
+ ],
+ "tests": [
+ "tests/test_coinjoin_demix.py",
+ "tests/test_amount_refuse_demix.py",
+ "tests/test_subtx_demix_experiment.py"
+ ],
+ "properties": [
+ "the frozen cache reproduces the JoinMarket amount assignments",
+ "the result distinguishes amount identities from independent maker labels",
+ "the unlabelled CoinJoin-adjacent cache is refused as a specificity population",
+ "the absent historical Wasabi corpus is explicitly not reproduced"
+ ],
+ "tolerance": null
+ },
+ "limitations": [
+ "the cache has no ordinary-transaction labels",
+ "maker identities are not independently labeled",
+ "the historical Wasabi corpus is unavailable",
+ "fee cap and denomination detection are local modeling choices"
+ ]
+}
diff --git a/catalog/runs/unnecessary-input-v2.json b/catalog/runs/unnecessary-input-v2.json
new file mode 100644
index 00000000..90315513
--- /dev/null
+++ b/catalog/runs/unnecessary-input-v2.json
@@ -0,0 +1,103 @@
+{
+ "schema_version": 1,
+ "id": "unnecessary-input-v2",
+ "claim_ids": [
+ "ctp.payjoin.unnecessary_input"
+ ],
+ "code": {
+ "revision": "bcd9a3e511860b1b0ff5f3ee6338a2df29ecbfbf",
+ "dirty": false
+ },
+ "command": {
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.unnecessary_input",
+ "reproduce",
+ "--artifact",
+ "results/artifacts/unnecessary-input-v2.json",
+ "--markdown",
+ "results/generated/unnecessary-input-v2.md"
+ ]
+ },
+ "environment": {
+ "python": "3.13.7",
+ "lock_digest": "e0d6a8cada5d383c0aca2bd2a37f28c15721392469c94084128d369c29c5544d",
+ "platform": "macOS-26.6.2-arm64-arm-64bit-Mach-O",
+ "dependencies": []
+ },
+ "datasets": [],
+ "parameters": {
+ "definition": "blockstream_fee_aware",
+ "definition_semantic_version": 2,
+ "minimum_inputs": 2,
+ "exact_outputs": 2,
+ "amount_domain": "positive unsigned 64-bit integers",
+ "fixtures": [
+ "jointly-funded-uih2",
+ "fee-aware-uih1",
+ "equality-boundary",
+ "observational-equivalence",
+ "ns1r-out-of-scope",
+ "nsnr-out-of-scope",
+ "net-settlement-cycle"
+ ]
+ },
+ "rng": {
+ "algorithm": null,
+ "seeds": []
+ },
+ "outputs": [
+ {
+ "path": "results/artifacts/unnecessary-input-v2.json",
+ "bytes": 2387,
+ "sha256": "a8e17f91020b0cbdc12bc2cb2652e2ffa63b5b5dcc70af6dfc3d4a73925278e0"
+ },
+ {
+ "path": "results/generated/unnecessary-input-v2.md",
+ "bytes": 1071,
+ "sha256": "52cb79757b649e63cd24161b0f416eeebde8c871133f6cb338db7252786d525a"
+ }
+ ],
+ "reproducibility": {
+ "level": "bitwise_reproducible",
+ "availability": "complete"
+ },
+ "verification": {
+ "mode": "exact",
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.unnecessary_input",
+ "verify",
+ "--artifact",
+ "results/artifacts/unnecessary-input-v2.json",
+ "--markdown",
+ "results/generated/unnecessary-input-v2.md"
+ ],
+ "tests": [
+ "tests/test_unnecessary_input_baseline.py",
+ "tests/test_unnecessary_input_failure_mode.py",
+ "tests/test_collaborative_forms.py",
+ "tests/test_unnecessary_input_experiment.py"
+ ],
+ "properties": [
+ "the fee-aware BlockStream UIH1/UIH2 definition is identified and versioned",
+ "definition boundaries and Gibson/BlockSci comparison predicates are explicit",
+ "NS1R and NSNR controls remain outside the two-output definition",
+ "one positive UIH observation preserves unilateral, PayJoin and net-settlement worlds",
+ "the result infers no ownership, PayJoin identity, payment output or composition",
+ "a gross settlement cycle can change while nonzero net balances remain equal",
+ "the generated Markdown equals the canonical rendering"
+ ],
+ "tolerance": null
+ },
+ "limitations": [
+ "deterministic diagnostic fixtures rather than a representative chain sample",
+ "the published longitudinal dataset and reported UIH rates are not reproduced",
+ "a positive unnecessary-input predicate does not identify PayJoin or ownership",
+ "protocol form, participant roles and ownership require independent evidence",
+ "the paper-faithful classification is scoped to transactions with more than one input and exactly two outputs",
+ "the result is not a privacy score or privacy certificate"
+ ]
+}
diff --git a/catalog/runs/weight-sensitivity-v1.json b/catalog/runs/weight-sensitivity-v1.json
new file mode 100644
index 00000000..e9750371
--- /dev/null
+++ b/catalog/runs/weight-sensitivity-v1.json
@@ -0,0 +1,104 @@
+{
+ "schema_version": 1,
+ "id": "weight-sensitivity-v1",
+ "claim_ids": [
+ "ctp.fingerprint.statistical_linkage"
+ ],
+ "code": {
+ "revision": "bcd9a3e511860b1b0ff5f3ee6338a2df29ecbfbf",
+ "dirty": false
+ },
+ "command": {
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.weight_sensitivity",
+ "--snapshot",
+ "data/fs-blkcache-2026-09-04.tar.gz",
+ "reproduce",
+ "--artifact",
+ "results/artifacts/weight-sensitivity-v1.json",
+ "--markdown",
+ "results/generated/weight-sensitivity-v1.md"
+ ]
+ },
+ "environment": {
+ "python": "3.13.7",
+ "lock_digest": "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855",
+ "platform": "macOS-26.6.2-arm64-arm-64bit-Mach-O",
+ "dependencies": []
+ },
+ "datasets": [
+ {
+ "id": "fs-blkcache-2026-09-04-v1",
+ "sha256": "5a69f6f446dbdfcce09dddc91c653e5d841124d14020b720fdea276315c775a6"
+ }
+ ],
+ "parameters": {
+ "grid": [
+ 0.6,
+ 0.7,
+ 0.8,
+ 0.9,
+ 0.95,
+ 0.99
+ ],
+ "pair_cap_per_class": 4000,
+ "seed": 0
+ },
+ "rng": {
+ "algorithm": "python.random.MersenneTwister",
+ "seeds": [
+ 0
+ ]
+ },
+ "outputs": [
+ {
+ "path": "results/artifacts/weight-sensitivity-v1.json",
+ "bytes": 2222,
+ "sha256": "467a51db69c377bb05e49c5ac60f680a9821e0ea888dde636d75697d78713e6c"
+ },
+ {
+ "path": "results/generated/weight-sensitivity-v1.md",
+ "bytes": 1084,
+ "sha256": "36b8c85941d3a3ab382c1e3a14166226a5608f37176503294679b342179c1498"
+ }
+ ],
+ "reproducibility": {
+ "level": "bitwise_reproducible",
+ "availability": "complete"
+ },
+ "verification": {
+ "mode": "exact",
+ "argv": [
+ "python",
+ "-m",
+ "decluster.experiments.weight_sensitivity",
+ "--snapshot",
+ "data/fs-blkcache-2026-09-04.tar.gz",
+ "verify",
+ "--artifact",
+ "results/artifacts/weight-sensitivity-v1.json",
+ "--markdown",
+ "results/generated/weight-sensitivity-v1.md"
+ ],
+ "tests": [
+ "tests/test_fingerprint_ns.py",
+ "tests/test_weight_sensitivity_experiment.py"
+ ],
+ "properties": [
+ "the preserved snapshot exactly reproduces every sweep row",
+ "each grid point uses the same seeded weak-label pair sample",
+ "the local AUC range is distinguished from evidence-magnitude sensitivity",
+ "the preserved snapshot falsifies the historical monotonicity claim",
+ "the result remains attacker-side evidence rather than a privacy score"
+ ],
+ "tolerance": null
+ },
+ "limitations": [
+ "address reuse is a weak and partly feature-dependent ownership proxy",
+ "the sweep varies one global value rather than fitting each axis",
+ "the same selected sample is reused at every grid point",
+ "the snapshot is not representative of the whole chain"
+ ]
+}
diff --git a/catalog/schemas/bundle-manifest.schema.json b/catalog/schemas/bundle-manifest.schema.json
new file mode 100644
index 00000000..2a16ffcc
--- /dev/null
+++ b/catalog/schemas/bundle-manifest.schema.json
@@ -0,0 +1,41 @@
+{
+ "$schema": "https://json-schema.org/draft/2020-12/schema",
+ "$id": "urn:decluster:schema:bundle-manifest:v1",
+ "type": "object",
+ "additionalProperties": false,
+ "required": ["schema_version", "id", "capabilities", "runs", "blobs", "limitations"],
+ "properties": {
+ "schema_version": {"const": 1},
+ "id": {"type": "string", "minLength": 1},
+ "capabilities": {
+ "type": "array", "minItems": 1, "uniqueItems": true,
+ "items": {"enum": ["verify_outputs", "reproduce_runs"]}
+ },
+ "runs": {
+ "type": "array", "minItems": 1, "uniqueItems": true,
+ "items": {"type": "string", "minLength": 1}
+ },
+ "blobs": {
+ "type": "array", "minItems": 1,
+ "items": {
+ "type": "object", "additionalProperties": false,
+ "required": ["name", "role", "bytes", "sha256", "locations"],
+ "properties": {
+ "name": {"type": "string", "minLength": 1},
+ "role": {"enum": ["run_manifest", "dataset", "result", "source", "dependency", "lockfile", "environment"]},
+ "bytes": {"type": "integer", "minimum": 0},
+ "sha256": {"type": "string", "pattern": "^[0-9a-f]{64}$"},
+ "locations": {
+ "type": "object", "additionalProperties": false,
+ "required": ["canonical", "mirrors"],
+ "properties": {
+ "canonical": {"type": ["string", "null"], "pattern": "^https://"},
+ "mirrors": {"type": "array", "uniqueItems": true, "items": {"type": "string", "pattern": "^https://"}}
+ }
+ }
+ }
+ }
+ },
+ "limitations": {"type": "array", "items": {"type": "string", "minLength": 1}}
+ }
+}
diff --git a/catalog/schemas/claims.schema.json b/catalog/schemas/claims.schema.json
new file mode 100644
index 00000000..b1f0f95a
--- /dev/null
+++ b/catalog/schemas/claims.schema.json
@@ -0,0 +1,29 @@
+{
+ "$schema": "https://json-schema.org/draft/2020-12/schema",
+ "$id": "urn:decluster:schema:claims:v1",
+ "type": "object",
+ "additionalProperties": false,
+ "required": ["schema_version", "claims"],
+ "properties": {
+ "schema_version": {"const": 1},
+ "claims": {
+ "type": "array",
+ "items": {
+ "type": "object",
+ "additionalProperties": false,
+ "required": ["id", "title", "scope", "sources", "mechanism", "implementation", "evidence_level", "status", "limitations"],
+ "properties": {
+ "id": {"type": "string", "minLength": 1},
+ "title": {"type": "string", "minLength": 1},
+ "scope": {"enum": ["qualitative_failure_mode", "model_limit", "defensive_criterion"]},
+ "sources": {"type": "array", "minItems": 1, "uniqueItems": true, "items": {"type": "string", "minLength": 1}},
+ "mechanism": {"type": "string", "minLength": 1},
+ "implementation": {"type": ["string", "null"], "minLength": 1},
+ "evidence_level": {"enum": ["unit_fixture", "paper_example", "synthetic_experiment", "published_dataset_reproduction", "bitcoin_snapshot_experiment", "independent_replication"]},
+ "status": {"enum": ["unassessed", "partially_supported", "supported_at_evidence_level", "no_code_required"]},
+ "limitations": {"type": "array", "items": {"type": "string", "minLength": 1}}
+ }
+ }
+ }
+ }
+}
diff --git a/catalog/schemas/ctp-sources.schema.json b/catalog/schemas/ctp-sources.schema.json
new file mode 100644
index 00000000..1ca14909
--- /dev/null
+++ b/catalog/schemas/ctp-sources.schema.json
@@ -0,0 +1,27 @@
+{
+ "$schema": "https://json-schema.org/draft/2020-12/schema",
+ "$id": "urn:decluster:schema:ctp-sources:v1",
+ "type": "object",
+ "additionalProperties": false,
+ "required": ["schema_version", "ctp_revision", "sources"],
+ "properties": {
+ "schema_version": {"const": 1},
+ "ctp_revision": {"type": "string", "minLength": 1},
+ "sources": {
+ "type": "array",
+ "items": {
+ "type": "object",
+ "additionalProperties": false,
+ "required": ["id", "title", "url", "kind", "obligation", "note"],
+ "properties": {
+ "id": {"type": "string", "minLength": 1},
+ "title": {"type": "string", "minLength": 1},
+ "url": {"type": ["string", "null"], "pattern": "^https://"},
+ "kind": {"enum": ["paper", "protocol", "implementation", "informal", "internal_note"]},
+ "obligation": {"enum": ["baseline", "fixture", "metric", "context", "qualification", "no_code_required"]},
+ "note": {"type": "string", "minLength": 1}
+ }
+ }
+ }
+ }
+}
diff --git a/catalog/schemas/dataset-manifest.schema.json b/catalog/schemas/dataset-manifest.schema.json
new file mode 100644
index 00000000..167753be
--- /dev/null
+++ b/catalog/schemas/dataset-manifest.schema.json
@@ -0,0 +1,70 @@
+{
+ "$schema": "https://json-schema.org/draft/2020-12/schema",
+ "$id": "urn:decluster:schema:dataset-manifest:v1",
+ "type": "object",
+ "additionalProperties": false,
+ "required": ["schema_version", "id", "title", "kind", "schema", "format", "content", "locations", "source", "license", "redistribution", "sensitivity", "normalization", "determinism"],
+ "properties": {
+ "schema_version": {"const": 1},
+ "id": {"type": "string", "minLength": 1},
+ "title": {"type": "string", "minLength": 1},
+ "kind": {"enum": ["git_fixture", "immutable_snapshot", "regenerable", "derived"]},
+ "schema": {"type": "string", "minLength": 1},
+ "format": {"type": "string", "minLength": 1},
+ "content": {
+ "type": "object", "additionalProperties": false,
+ "required": ["bytes", "sha256"],
+ "properties": {
+ "bytes": {"type": "integer", "minimum": 0},
+ "sha256": {"type": "string", "pattern": "^[0-9a-f]{64}$"}
+ }
+ },
+ "locations": {
+ "type": "object", "additionalProperties": false,
+ "required": ["local_path", "canonical", "mirrors"],
+ "properties": {
+ "local_path": {"type": "string", "minLength": 1},
+ "canonical": {"type": ["string", "null"], "pattern": "^https://"},
+ "mirrors": {"type": "array", "uniqueItems": true, "items": {"type": "string", "pattern": "^https://"}}
+ }
+ },
+ "source": {
+ "type": "object", "additionalProperties": false,
+ "required": ["provider", "snapshot_time", "recipe", "recipe_sha256"],
+ "properties": {
+ "provider": {"type": "string", "minLength": 1},
+ "snapshot_time": {"type": ["string", "null"], "minLength": 1},
+ "recipe": {"type": ["string", "null"], "minLength": 1},
+ "recipe_sha256": {"type": ["string", "null"], "pattern": "^[0-9a-f]{64}$"}
+ }
+ },
+ "license": {
+ "type": "object", "additionalProperties": false,
+ "required": ["data", "recipe"],
+ "properties": {
+ "data": {"type": "string", "minLength": 1},
+ "recipe": {"type": ["string", "null"], "minLength": 1}
+ }
+ },
+ "redistribution": {"enum": ["allowed", "restricted", "unknown"]},
+ "sensitivity": {"enum": ["synthetic", "public_on_chain", "public_auxiliary", "restricted_auxiliary"]},
+ "normalization": {
+ "type": "object", "additionalProperties": false,
+ "required": ["code_revision", "parameters", "parents"],
+ "properties": {
+ "code_revision": {"type": ["string", "null"], "minLength": 1},
+ "parameters": {"type": "object"},
+ "parents": {"type": "array", "uniqueItems": true, "items": {"type": "string", "minLength": 1}}
+ }
+ },
+ "determinism": {
+ "type": "object", "additionalProperties": false,
+ "required": ["ordering", "rng_algorithm", "rng_seed"],
+ "properties": {
+ "ordering": {"type": "string", "minLength": 1},
+ "rng_algorithm": {"type": ["string", "null"], "minLength": 1},
+ "rng_seed": {"type": ["integer", "null"], "minimum": 0}
+ }
+ }
+ }
+}
diff --git a/catalog/schemas/reproduction-environment.schema.json b/catalog/schemas/reproduction-environment.schema.json
new file mode 100644
index 00000000..e5b0ae4d
--- /dev/null
+++ b/catalog/schemas/reproduction-environment.schema.json
@@ -0,0 +1,25 @@
+{
+ "$schema": "https://json-schema.org/draft/2020-12/schema",
+ "$id": "urn:decluster:schema:reproduction-environment:v1",
+ "type": "object",
+ "additionalProperties": false,
+ "required": ["schema_version", "python", "source_archive", "project_root", "requirements", "dependency_directory"],
+ "properties": {
+ "schema_version": {"const": 1},
+ "python": {
+ "type": "object",
+ "additionalProperties": false,
+ "required": ["implementation", "version", "system", "machine"],
+ "properties": {
+ "implementation": {"const": "cpython"},
+ "version": {"type": "string", "pattern": "^[0-9]+\\.[0-9]+$"},
+ "system": {"type": "string", "minLength": 1},
+ "machine": {"type": "string", "minLength": 1}
+ }
+ },
+ "source_archive": {"type": "string", "minLength": 1},
+ "project_root": {"type": "string", "minLength": 1},
+ "requirements": {"type": "string", "minLength": 1},
+ "dependency_directory": {"type": "string", "minLength": 1}
+ }
+}
diff --git a/catalog/schemas/run-manifest.schema.json b/catalog/schemas/run-manifest.schema.json
new file mode 100644
index 00000000..3ed64027
--- /dev/null
+++ b/catalog/schemas/run-manifest.schema.json
@@ -0,0 +1,116 @@
+{
+ "$schema": "https://json-schema.org/draft/2020-12/schema",
+ "$id": "urn:decluster:schema:run-manifest:v1",
+ "type": "object",
+ "additionalProperties": false,
+ "required": ["schema_version", "id", "claim_ids", "code", "command", "environment", "datasets", "parameters", "rng", "outputs", "reproducibility", "verification", "limitations"],
+ "properties": {
+ "schema_version": {"const": 1},
+ "id": {"type": "string", "minLength": 1},
+ "claim_ids": {"type": "array", "minItems": 1, "uniqueItems": true, "items": {"type": "string", "minLength": 1}},
+ "code": {
+ "type": "object", "additionalProperties": false,
+ "required": ["revision", "dirty"],
+ "properties": {"revision": {"type": "string", "minLength": 1}, "dirty": {"type": "boolean"}}
+ },
+ "command": {
+ "type": "object", "additionalProperties": false,
+ "required": ["argv"],
+ "properties": {"argv": {"type": "array", "minItems": 1, "items": {"type": "string", "minLength": 1}}}
+ },
+ "environment": {
+ "type": "object", "additionalProperties": false,
+ "required": ["python", "lock_digest", "platform", "dependencies"],
+ "properties": {
+ "python": {"type": "string", "minLength": 1},
+ "lock_digest": {"type": ["string", "null"], "pattern": "^[0-9a-f]{64}$"},
+ "platform": {"type": "string", "minLength": 1},
+ "dependencies": {
+ "type": "array", "uniqueItems": true,
+ "items": {
+ "type": "object", "additionalProperties": false,
+ "required": ["name", "version", "source", "revision", "lock_sha256", "license", "redistribution", "editable"],
+ "properties": {
+ "name": {"type": "string", "minLength": 1},
+ "version": {"type": "string", "minLength": 1},
+ "source": {"type": "string", "pattern": "^https://"},
+ "revision": {"type": "string", "minLength": 1},
+ "lock_sha256": {"type": ["string", "null"], "pattern": "^[0-9a-f]{64}$"},
+ "license": {"type": "string", "minLength": 1},
+ "redistribution": {"enum": ["allowed", "restricted", "unknown"]},
+ "editable": {"type": "boolean"}
+ }
+ }
+ }
+ }
+ },
+ "datasets": {
+ "type": "array",
+ "items": {
+ "type": "object", "additionalProperties": false,
+ "required": ["id", "sha256"],
+ "properties": {"id": {"type": "string", "minLength": 1}, "sha256": {"type": "string", "pattern": "^[0-9a-f]{64}$"}}
+ }
+ },
+ "parameters": {"type": "object"},
+ "rng": {
+ "type": "object", "additionalProperties": false,
+ "required": ["algorithm", "seeds"],
+ "properties": {
+ "algorithm": {"type": ["string", "null"], "minLength": 1},
+ "seeds": {"type": "array", "items": {"type": "integer", "minimum": 0}}
+ }
+ },
+ "outputs": {
+ "type": "array", "minItems": 1,
+ "items": {
+ "type": "object", "additionalProperties": false,
+ "required": ["path", "bytes", "sha256"],
+ "properties": {
+ "path": {"type": "string", "minLength": 1},
+ "bytes": {"type": "integer", "minimum": 0},
+ "sha256": {"type": "string", "pattern": "^[0-9a-f]{64}$"}
+ }
+ }
+ },
+ "reproducibility": {
+ "type": "object", "additionalProperties": false,
+ "required": ["level", "availability"],
+ "properties": {
+ "level": {"enum": ["verified", "reexecutable", "bitwise_reproducible", "statistically_reproducible"]},
+ "availability": {"enum": ["complete", "restricted", "partial"]}
+ }
+ },
+ "verification": {
+ "type": "object", "additionalProperties": false,
+ "required": ["mode", "argv", "tests", "properties", "tolerance"],
+ "properties": {
+ "mode": {"enum": ["exact", "tolerance", "statistical"]},
+ "argv": {"type": "array", "minItems": 1, "items": {"type": "string", "minLength": 1}},
+ "tests": {"type": "array", "uniqueItems": true, "items": {"type": "string", "minLength": 1}},
+ "properties": {"type": "array", "uniqueItems": true, "items": {"type": "string", "minLength": 1}},
+ "tolerance": {"type": ["string", "null"], "minLength": 1}
+ },
+ "anyOf": [
+ {"properties": {"tests": {"minItems": 1}}},
+ {"properties": {"properties": {"minItems": 1}}}
+ ]
+ },
+ "limitations": {"type": "array", "items": {"type": "string", "minLength": 1}}
+ },
+ "allOf": [
+ {
+ "if": {"properties": {"verification": {"properties": {"mode": {"const": "exact"}}}}},
+ "then": {"properties": {"verification": {"properties": {"tolerance": {"type": "null"}}}}},
+ "else": {"properties": {"verification": {"properties": {"tolerance": {"type": "string", "minLength": 1}}}}}
+ },
+ {
+ "if": {"properties": {"reproducibility": {"properties": {"level": {"const": "bitwise_reproducible"}}}}},
+ "then": {"properties": {"verification": {"properties": {"mode": {"const": "exact"}}}}}
+ },
+ {
+ "if": {"properties": {"reproducibility": {"properties": {"level": {"const": "statistically_reproducible"}}}}},
+ "then": {"properties": {"verification": {"properties": {"mode": {"const": "statistical"}}}}}
+ }
+ ]
+}
diff --git a/catalog/source-snapshots/radix-notebook-v1.json b/catalog/source-snapshots/radix-notebook-v1.json
new file mode 100644
index 00000000..b53359dc
--- /dev/null
+++ b/catalog/source-snapshots/radix-notebook-v1.json
@@ -0,0 +1,36 @@
+{
+ "schema_version": 1,
+ "id": "radix-notebook-v1",
+ "title": "Small Hamming Weight Denominations for CoinJoins",
+ "canonical_url": "https://colab.research.google.com/drive/1We_FvfX_Ob9BapFW3X_By9vTtxUrt3pm",
+ "retrieval_url": "https://drive.google.com/uc?export=download&id=1We_FvfX_Ob9BapFW3X_By9vTtxUrt3pm",
+ "retrieved_at": "2026-09-05T01:54:48Z",
+ "format": "application/x-ipynb+json; nbformat=4.0",
+ "content": {
+ "bytes": 589558,
+ "sha256": "afaee22d223b442774eeaa50f3da00c97b1fba5c7c300b5d3cde56ddc57ff48f"
+ },
+ "structure": {
+ "cells": 41,
+ "code_cells": 24,
+ "markdown_cells": 17,
+ "display_data_outputs": 9,
+ "execute_result_outputs": 1
+ },
+ "normalization": {
+ "algorithm": "jq-cS-strip-runtime-v1",
+ "tool": "jq 1.8.1",
+ "removed": [
+ ".cells[].outputs",
+ ".cells[].execution_count",
+ ".metadata.colab.authorship_tag",
+ ".metadata.colab.provenance"
+ ],
+ "serialization": "jq -cS with one trailing LF; not RFC 8785 JCS",
+ "source_only_sha256": "a8a9c3d6f70f42c928490ba5974569d6846ff0906cb87481993a3dc6ed2d83f9"
+ },
+ "license": "no explicit license found in the exported notebook",
+ "redistribution": "unknown",
+ "availability": "current remote version only; no immutable mirror; repository redistribution is blocked pending permission",
+ "interpretation": "constructive denomination and sumset diagnostic; not an attack oracle, CoinScore, privacy proof, or universal mapping-count bound"
+}
diff --git a/catalog/tx-construction-matrix.md b/catalog/tx-construction-matrix.md
index 4440ce24..4058b3ca 100644
--- a/catalog/tx-construction-matrix.md
+++ b/catalog/tx-construction-matrix.md
@@ -1,8 +1,7 @@
# Tx-construction fingerprint matrix (issue #1597)
Companion to `research-docs/fingerprints/merged transaction.md` and to the network-level
-harness seed (`docs/superpowers/specs/2026-05-27-fingerprint-verification-harness-design.md`,
-#1586). This is the **chain-level** analog: it audits each integration's
+harness seed (#1586). This is the **chain-level** analog: it audits each integration's
*standard* transaction builder — the code that produces the **prior transactions**
feeding a merged transaction — across 10 observable fingerprint axes, and groups the seven
integrations per axis (the way the issue grouped nSequence into A/B/C).
diff --git a/data/amount-channel-812695-812831-v1.json b/data/amount-channel-812695-812831-v1.json
new file mode 100644
index 00000000..28d031bc
--- /dev/null
+++ b/data/amount-channel-812695-812831-v1.json
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diff --git a/data/boltzmann-fee-audit-v1.json b/data/boltzmann-fee-audit-v1.json
new file mode 100644
index 00000000..57b7a296
--- /dev/null
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diff --git a/data/epochs-2016-weekly-v1/391104-392111.epochgraph.xz b/data/epochs-2016-weekly-v1/391104-392111.epochgraph.xz
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diff --git a/decluster/__init__.py b/decluster/__init__.py
index 115f25b1..8e4f714f 100644
--- a/decluster/__init__.py
+++ b/decluster/__init__.py
@@ -1,13 +1,29 @@
-"""decluster: fingerprint-aware probabilistic de-anonymization of Bitcoin transactions."""
+"""decluster: fingerprint-aware probabilistic de-anonymization of Bitcoin transactions.
+
+Two directions move through the partition refinement lattice, and which one a function travels is
+part of its contract, not an implementation detail:
+
+ `cluster_*` ascends. Starts from the discrete partition and fuses blocks, so it owns every
+ merge it makes and keeps two owners apart by declining to merge them.
+ `decluster_*` descends. Takes a partition it did not build and cuts blocks the evidence says
+ hold more than one owner. It cannot decline a merge that already happened, so it
+ has to argue against one.
+
+`Partition` carries both operations (`meet` descends, `join` ascends) and refuses to combine
+partitions of different ground sets. `cluster.cluster_refined` predates the convention and
+ascends despite its name; `declustering.decluster` is the descending pass.
+"""
from .fetch import fetch_tx
from .extractors import x_nsequence, x_input_order, x_io_shape
from .engine import measure, print_report, sample_recent_txs, locktime_class
-from .combiner import Combiner
+from .rarity_weight_baseline import Combiner
from .cluster import cluster_naive, cluster_refined
+from .partition import Partition
from .conservation import forced_in_round
from .provenance import candidate_coins, rank_by_overlap
from .monitor import walk_frontier
from .intersect import evaluate, score_candidate
from .analyze import analyze, cluster_map, cluster_posterior
-from .oracle import bounded_link_oracle, subprocess_link_oracle
+from .oracle import bounded_dss_link_oracle, bounded_link_oracle, subprocess_link_oracle
from .path_count import path_count_anonymity
+from .provenance_route_accumulation import provenance_route_accumulation
diff --git a/decluster/adaptations/__init__.py b/decluster/adaptations/__init__.py
new file mode 100644
index 00000000..078b8aa5
--- /dev/null
+++ b/decluster/adaptations/__init__.py
@@ -0,0 +1,39 @@
+"""Local adaptations that translate legacy pipelines into domain contracts."""
+
+from .ancestry import (
+ AncestryReport,
+ CompleteAncestry,
+ TruncatedAncestry,
+ TruncationBreakdown,
+ UnobservedAncestry,
+ ancestry_signature_report,
+)
+from .cluster_refined import ClusterRefinementReport, cluster_refined_report
+from .intersection import (
+ BlindCause,
+ BlindIntersection,
+ CompleteIntersection,
+ IntersectionReport,
+ evaluate_ancestry_reports,
+ evaluate_report,
+)
+from .pseudonym_graph import contract_evidence, contract_report
+
+__all__ = [
+ "AncestryReport",
+ "BlindCause",
+ "BlindIntersection",
+ "ClusterRefinementReport",
+ "CompleteAncestry",
+ "CompleteIntersection",
+ "IntersectionReport",
+ "TruncatedAncestry",
+ "TruncationBreakdown",
+ "UnobservedAncestry",
+ "ancestry_signature_report",
+ "cluster_refined_report",
+ "contract_evidence",
+ "contract_report",
+ "evaluate_report",
+ "evaluate_ancestry_reports",
+]
diff --git a/decluster/adaptations/ancestry.py b/decluster/adaptations/ancestry.py
new file mode 100644
index 00000000..f660b41e
--- /dev/null
+++ b/decluster/adaptations/ancestry.py
@@ -0,0 +1,168 @@
+"""Typed results for provenance signatures and bounded ancestry walks."""
+
+from dataclasses import dataclass
+from typing import Any, Callable, Mapping, Optional, TypeAlias, Union
+
+from ..ancestry import TruncationSupport, ancestry_signature_and_truncation
+from ..domain import (
+ AttackReport,
+ CompleteProvenanceMeasured,
+ EvidenceChannel,
+ EvidenceContext,
+ Inconclusive,
+ NotObserved,
+ ProvenanceDistributionEvidence,
+ Subject,
+ SubjectKind,
+ TruncatedProvenanceMeasured,
+)
+
+
+@dataclass(frozen=True)
+class TruncationBreakdown:
+ oracle_refused: int
+ node_capped: int
+ zero_link_mass: int
+ unattributed: int
+
+ def __post_init__(self) -> None:
+ if min(
+ self.oracle_refused,
+ self.node_capped,
+ self.zero_link_mass,
+ self.unattributed,
+ ) < 0:
+ raise ValueError("truncation counts must be non-negative")
+
+ @property
+ def total(self) -> int:
+ return (
+ self.oracle_refused
+ + self.node_capped
+ + self.zero_link_mass
+ + self.unattributed
+ )
+
+
+@dataclass(frozen=True)
+class CompleteAncestry:
+ evidence: ProvenanceDistributionEvidence
+
+
+@dataclass(frozen=True)
+class TruncatedAncestry:
+ evidence: Optional[ProvenanceDistributionEvidence]
+ truncation: TruncationBreakdown
+
+ def __post_init__(self) -> None:
+ if self.truncation.total == 0:
+ raise ValueError("truncated ancestry requires a truncation cause")
+
+
+@dataclass(frozen=True)
+class UnobservedAncestry:
+ reason: str
+
+ def __post_init__(self) -> None:
+ if not self.reason:
+ raise ValueError("unobserved ancestry reason must not be empty")
+
+
+AncestryState: TypeAlias = Union[CompleteAncestry, TruncatedAncestry, UnobservedAncestry]
+
+
+@dataclass(frozen=True)
+class AncestryReport:
+ target: Subject
+ distribution: tuple[tuple[Subject, float], ...]
+ truncation: TruncationBreakdown
+ state: AncestryState
+
+ def as_legacy(self) -> tuple[dict[Any, float], TruncationSupport]:
+ distribution = {subject.identifier: mass for subject, mass in self.distribution}
+ support = TruncationSupport(
+ oracle_refused=self.truncation.oracle_refused,
+ node_capped=self.truncation.node_capped,
+ unattributed=self.truncation.unattributed,
+ zero_link_mass=self.truncation.zero_link_mass,
+ )
+ return distribution, support
+
+ def as_attack_report(self, identifier: str) -> AttackReport:
+ """Expose provenance measurement without interpreting it as privacy."""
+ evidence = getattr(self.state, "evidence", None)
+ channels = (
+ (EvidenceChannel("absorbing_ancestry_walk", (evidence,)),)
+ if evidence is not None
+ else ()
+ )
+ if isinstance(self.state, CompleteAncestry):
+ outcomes = (CompleteProvenanceMeasured(self.state.evidence),)
+ elif isinstance(self.state, TruncatedAncestry):
+ if self.state.evidence is None:
+ outcomes = (
+ Inconclusive((self.target,), "ancestry boundary is entirely truncated"),
+ )
+ else:
+ outcomes = (TruncatedProvenanceMeasured(self.state.evidence),)
+ else:
+ outcomes = (NotObserved("positive-mass ancestry boundary"),)
+ return AttackReport(
+ identifier=identifier,
+ attack="provenance measurement",
+ subjects=(self.target,),
+ channels=channels,
+ outcomes=outcomes,
+ limitations=("this report measures model-relative provenance, not privacy",),
+ )
+
+
+def ancestry_signature_report(
+ target: Any,
+ depth: int = 6,
+ fetch: Optional[Callable[[Any], Mapping[str, Any]]] = None,
+ link_oracle: Optional[Callable[[list[int], list[int]], Any]] = None,
+ max_nodes: Optional[int] = None,
+) -> AncestryReport:
+ """Run one ancestry walk and distinguish complete, truncated, and unseen states."""
+ options = {"depth": depth, "fetch": fetch, "max_nodes": max_nodes}
+ if link_oracle is not None:
+ options["link_oracle"] = link_oracle
+ distribution, support = ancestry_signature_and_truncation(target, **options)
+ target_subject = Subject(SubjectKind.COIN, target)
+ typed_distribution = tuple(
+ (Subject(SubjectKind.COIN, origin), mass)
+ for origin, mass in distribution.items()
+ )
+ truncation = TruncationBreakdown(
+ oracle_refused=support.oracle_refused,
+ node_capped=support.node_capped,
+ zero_link_mass=support.zero_link_mass,
+ unattributed=support.unattributed,
+ )
+ evidence = None
+ if typed_distribution:
+ limitations = ["the distribution is conditional on the selected link model"]
+ if truncation.total:
+ limitations.append(
+ "legacy output counts truncated boundary atoms but does not identify "
+ "them individually"
+ )
+ evidence = ProvenanceDistributionEvidence(
+ subject=target_subject,
+ probabilities=typed_distribution,
+ context=EvidenceContext(
+ adversary="external on-chain observer",
+ observables=("transaction graph", "input-output link model"),
+ hypothesis="the target descends from the reported boundary coins",
+ algorithm="absorbing_ancestry_walk",
+ limitations=tuple(limitations),
+ ),
+ )
+ if truncation.total:
+ state = TruncatedAncestry(evidence, truncation)
+ elif evidence is None:
+ state = UnobservedAncestry("the walk produced no positive-mass boundary")
+ else:
+ state = CompleteAncestry(evidence)
+ return AncestryReport(target_subject, typed_distribution, truncation, state)
diff --git a/decluster/adaptations/cluster_refined.py b/decluster/adaptations/cluster_refined.py
new file mode 100644
index 00000000..03f2a2fd
--- /dev/null
+++ b/decluster/adaptations/cluster_refined.py
@@ -0,0 +1,199 @@
+"""Typed adapter for the legacy ``cluster_refined`` return value.
+
+The legacy engine exposes final groups, refused co-spend pairs, and additional
+fingerprint links. It does not expose an audit trail for every accepted
+co-spend merge, so this adapter intentionally makes no claim about those
+unreported decisions.
+"""
+
+from dataclasses import dataclass
+from typing import Any, Iterable
+
+from ..cluster import (
+ ClusterPairDecision,
+ PairDecisionStatus,
+ cluster_refined_decisions,
+)
+from ..domain import (
+ AttackReport,
+ CannotLinkEvidence,
+ ClusterMerge,
+ Direction,
+ EvidenceContext,
+ EvidenceChannel,
+ MergeRefused,
+ NotObserved,
+ Outcome,
+ OwnershipLikelihoodEvidence,
+ Subject,
+ SubjectKind,
+ TransitiveMembership,
+)
+
+
+LegacyRefusal = tuple[str, str, str, float, float, float]
+LegacyLink = tuple[str, str, float]
+LegacyResult = tuple[list[list[str]], list[LegacyRefusal], list[LegacyLink]]
+
+
+def _transaction(txid: str) -> Subject:
+ return Subject(SubjectKind.TRANSACTION, txid)
+
+
+def _context(algorithm: str, observables: tuple[str, ...]) -> EvidenceContext:
+ return EvidenceContext(
+ adversary="external on-chain observer",
+ observables=observables,
+ hypothesis="the funding transactions represent coins controlled by one owner",
+ algorithm=algorithm,
+ limitations=(),
+ )
+
+
+@dataclass(frozen=True)
+class ClusterRefinementReport:
+ """Typed view of decisions that the legacy engine actually reports."""
+
+ groups: tuple[tuple[str, ...], ...]
+ merge_refusals: tuple[MergeRefused, ...]
+ added_links: tuple[ClusterMerge, ...]
+ pair_outcomes: tuple[Outcome, ...]
+ pair_evidence: tuple[OwnershipLikelihoodEvidence, ...]
+ legacy_refusals: tuple[LegacyRefusal, ...]
+ legacy_links: tuple[LegacyLink, ...]
+
+ def as_legacy(self) -> LegacyResult:
+ """Return the original positional representation without semantic loss."""
+ return (
+ [list(group) for group in self.groups],
+ list(self.legacy_refusals),
+ list(self.legacy_links),
+ )
+
+ def as_attack_report(self, identifier: str) -> AttackReport:
+ """Describe the legacy engine's observable decisions without composition."""
+ reported = self.pair_outcomes + self.added_links
+ outcomes = reported or (NotObserved("merge refusal or additional fingerprint link"),)
+ by_algorithm = {}
+ for outcome in reported:
+ for item in getattr(outcome, "evidence", ()):
+ by_algorithm.setdefault(item.context.algorithm, []).append(item)
+ for item in self.pair_evidence:
+ by_algorithm.setdefault(item.context.algorithm, []).append(item)
+ subjects = tuple(
+ _transaction(node)
+ for node in dict.fromkeys(node for group in self.groups for node in group)
+ )
+ channels = tuple(
+ EvidenceChannel(channel_id, tuple(items))
+ for channel_id, items in sorted(by_algorithm.items())
+ )
+ return AttackReport(
+ identifier=identifier,
+ attack="fingerprint-aware cluster refinement",
+ subjects=subjects,
+ channels=channels,
+ outcomes=outcomes,
+ limitations=(
+ "transitive membership is reported separately from directly evaluated merges",
+ ),
+ )
+
+
+def _numeric_evidence(decision: ClusterPairDecision):
+ subject, target = _transaction(decision.left), _transaction(decision.right)
+ channels = (
+ ("cluster_refined.cospend_prior", "co-spend", decision.cospend_prior_bits),
+ ("cluster_refined.fingerprint", "wallet fingerprint", decision.fingerprint_bits),
+ ("cluster_refined.amount", "transaction amounts", decision.amount_bits),
+ ("cluster_refined.topology", "counterparty topology", decision.topology_bits),
+ ("cluster_refined.provenance", "provenance overlap", decision.provenance_bits),
+ ("cluster_refined.subset_sum", "input-output amounts", decision.subset_sum_bits),
+ )
+ return tuple(
+ OwnershipLikelihoodEvidence(
+ subject,
+ target,
+ bits,
+ _context(algorithm, (observable,)),
+ (
+ Direction.SUPPORTS_COMMON_OWNERSHIP
+ if bits > 0
+ else Direction.OPPOSES_COMMON_OWNERSHIP
+ ),
+ )
+ for algorithm, observable, bits in channels
+ if bits
+ )
+
+
+def _pair_outcome(decision: ClusterPairDecision, evidence):
+ subject, target = _transaction(decision.left), _transaction(decision.right)
+ if decision.status is PairDecisionStatus.DIRECT_MERGE:
+ return ClusterMerge(subject, target, evidence)
+ if decision.status is PairDecisionStatus.TRANSITIVE_MEMBERSHIP:
+ return TransitiveMembership(
+ subject,
+ target,
+ "the pair shares a final component through other accepted relations",
+ )
+ cannot_link = CannotLinkEvidence(
+ subject,
+ target,
+ _context("cluster_refined", ("co-spend",)),
+ "fused evidence did not support the co-spend merge",
+ )
+ return MergeRefused(
+ subject,
+ target,
+ (cannot_link,) + evidence,
+ f"merge refused in spending transaction {decision.spending_txid}; "
+ f"total evidence {decision.total_bits:g} bits",
+ )
+
+
+def _added_link(record: LegacyLink) -> ClusterMerge:
+ left, right, score = record
+ subject, target = _transaction(left), _transaction(right)
+ evidence = OwnershipLikelihoodEvidence(
+ subject,
+ target,
+ score,
+ _context("cluster_refined.fingerprint_link", ("wallet fingerprint",)),
+ Direction.SUPPORTS_COMMON_OWNERSHIP,
+ )
+ return ClusterMerge(subject, target, (evidence,))
+
+
+def cluster_refined_report(
+ nodes: Iterable[str],
+ combiner: Any,
+ **options: Any,
+) -> ClusterRefinementReport:
+ """Run ``cluster_refined`` and expose its reported decisions as domain types.
+
+ ``options`` is passed through unchanged so existing callers can migrate
+ without maintaining a second copy of the engine's evolving keyword API.
+ """
+ node_list = list(nodes)
+ groups, refusals, links, decisions = cluster_refined_decisions(
+ node_list, combiner, **options
+ )
+ legacy_refusals = tuple(tuple(record) for record in refusals)
+ legacy_links = tuple(tuple(record) for record in links)
+ decision_evidence = tuple(_numeric_evidence(decision) for decision in decisions)
+ pair_outcomes = tuple(
+ _pair_outcome(decision, evidence)
+ for decision, evidence in zip(decisions, decision_evidence)
+ )
+ return ClusterRefinementReport(
+ groups=tuple(tuple(group) for group in groups),
+ merge_refusals=tuple(
+ outcome for outcome in pair_outcomes if isinstance(outcome, MergeRefused)
+ ),
+ added_links=tuple(_added_link(record) for record in legacy_links),
+ pair_outcomes=pair_outcomes,
+ pair_evidence=tuple(item for evidence in decision_evidence for item in evidence),
+ legacy_refusals=legacy_refusals,
+ legacy_links=legacy_links,
+ )
diff --git a/decluster/adaptations/intersection.py b/decluster/adaptations/intersection.py
new file mode 100644
index 00000000..5a3677e6
--- /dev/null
+++ b/decluster/adaptations/intersection.py
@@ -0,0 +1,192 @@
+"""Typed boundary for the legacy provenance-intersection evaluator."""
+
+from copy import deepcopy
+from dataclasses import dataclass
+from enum import Enum
+from types import MappingProxyType
+from typing import Any, Callable, Mapping, Optional, TypeAlias, Union
+
+from ..domain import (
+ AttackReport,
+ CandidateNarrowing,
+ EvidenceChannel,
+ EvidenceContext,
+ Inconclusive,
+ IntersectionEvidence,
+ NoSharedCandidates,
+ Subject,
+ SubjectKind,
+)
+from ..intersect import evaluate
+from .ancestry import AncestryReport
+
+
+class BlindCause(str, Enum):
+ ORACLE_REFUSED = "oracle_refused"
+ NODE_CAPPED = "node_capped"
+ ZERO_LINK_MASS = "zero_link_mass"
+ MIXED = "mixed"
+ UNKNOWN = "unknown"
+ NO_OBSERVED_ORIGIN = "no_observed_origin"
+
+
+@dataclass(frozen=True)
+class BranchTruncation:
+ branch: Subject
+ total: int
+ oracle_refused: Optional[int]
+ node_capped: Optional[int]
+ zero_link_mass: Optional[int]
+ unattributed: Optional[int]
+
+
+@dataclass(frozen=True)
+class CompleteIntersection:
+ evidence: IntersectionEvidence
+
+
+@dataclass(frozen=True)
+class BlindIntersection:
+ cause: BlindCause
+ branches: tuple[Subject, ...]
+
+
+IntersectionState: TypeAlias = Union[CompleteIntersection, BlindIntersection]
+
+
+@dataclass(frozen=True)
+class IntersectionReport:
+ transaction: Subject
+ branches: tuple[Subject, ...]
+ sizes: tuple[int, ...]
+ shared: tuple[tuple[Any, float, float], ...]
+ collapsed: int
+ collapsed_bits: Optional[float]
+ truncation: Optional[tuple[BranchTruncation, ...]]
+ state: IntersectionState
+ _legacy: Mapping[str, Any]
+
+ def as_legacy(self) -> dict[str, Any]:
+ """Return a fresh copy of the evaluator's original result."""
+ return deepcopy(dict(self._legacy))
+
+ def as_attack_report(self, identifier: str) -> AttackReport:
+ """Expose narrowing only when every branch was observable."""
+ if isinstance(self.state, CompleteIntersection):
+ channels = (EvidenceChannel("provenance_intersection", (self.state.evidence,)),)
+ if self.state.evidence.candidates_after:
+ outcomes = (CandidateNarrowing(self.state.evidence),)
+ else:
+ outcomes = (NoSharedCandidates(self.state.evidence),)
+ else:
+ channels = ()
+ outcomes = (
+ Inconclusive(
+ self.state.branches,
+ f"intersection is blind: {self.state.cause.value}",
+ ),
+ )
+ return AttackReport(
+ identifier=identifier,
+ attack="provenance intersection",
+ subjects=self.branches,
+ channels=channels,
+ outcomes=outcomes,
+ limitations=("the narrowing is conditional on validation of the observed linkage",),
+ )
+
+
+def _outpoint(value: Any) -> Subject:
+ return Subject(SubjectKind.OUTPOINT, value)
+
+
+def _truncations(branches, totals, causes):
+ if totals is None:
+ return None
+ records = []
+ for branch, total, cause in zip(branches, totals, causes):
+ records.append(
+ BranchTruncation(
+ branch=branch,
+ total=total,
+ oracle_refused=None if cause is None else cause["oracle_refused"],
+ node_capped=None if cause is None else cause["node_capped"],
+ zero_link_mass=None if cause is None else cause["zero_link_mass"],
+ unattributed=None if cause is None else cause["unattributed"],
+ )
+ )
+ return tuple(records)
+
+
+def evaluate_report(
+ candidate: Mapping[str, Any],
+ signature_of: Callable[[Any], Mapping[Any, float]],
+ **options: Any,
+) -> IntersectionReport:
+ """Evaluate an intersection and replace boolean blindness with a typed state."""
+ legacy = evaluate(candidate, signature_of, **options)
+ branches = tuple(_outpoint(value) for value in candidate.get("outpoints", ()))
+ # Without `cluster_of` the surviving origins are the ancestry walk's coins, not wallets.
+ kind = SubjectKind.CLUSTER if options.get("cluster_of") is not None else SubjectKind.COIN
+ shared_subjects = frozenset(
+ Subject(kind, origin) for origin, _mass, _weight in legacy["shared"]
+ )
+ context = EvidenceContext(
+ adversary="external on-chain observer",
+ observables=("candidate provenance sets", "co-spend observation"),
+ hypothesis="linked branches share candidate origin clusters",
+ algorithm="provenance_intersection",
+ limitations=("narrowing is conditional on independent validation of the co-spend",),
+ )
+ evidence = IntersectionEvidence(
+ observations=branches,
+ candidates_before=min(legacy["sizes"]) if legacy["sizes"] else 0,
+ candidates_after=shared_subjects,
+ context=context,
+ )
+ if legacy["blind"]:
+ cause = legacy["blind_cause"] or BlindCause.NO_OBSERVED_ORIGIN.value
+ state = BlindIntersection(BlindCause(cause), branches)
+ else:
+ state = CompleteIntersection(evidence)
+ return IntersectionReport(
+ transaction=Subject(SubjectKind.TRANSACTION, legacy["txid"]),
+ branches=branches,
+ sizes=tuple(legacy["sizes"]),
+ shared=tuple(legacy["shared"]),
+ collapsed=legacy["collapsed"],
+ collapsed_bits=legacy["collapsed_bits"],
+ truncation=_truncations(branches, legacy["truncated"], legacy["truncated_causes"]),
+ state=state,
+ _legacy=MappingProxyType(deepcopy(legacy)),
+ )
+
+
+def evaluate_ancestry_reports(
+ candidate: Mapping[str, Any],
+ ancestry_of: Callable[[Any], AncestryReport],
+ **options: Any,
+) -> IntersectionReport:
+ """Intersect atomic ancestry observations, keeping signature and limits together."""
+ outpoints = tuple(candidate.get("outpoints", ()))
+ observations = {}
+ for outpoint in outpoints:
+ report = ancestry_of(outpoint)
+ if report.target.identifier != outpoint:
+ raise ValueError("ancestry report target does not match requested outpoint")
+ observations[outpoint] = report.as_legacy()
+
+ def signature_of(outpoint):
+ return observations[outpoint][0]
+
+ def truncation_of(outpoint):
+ return observations[outpoint][1]
+
+ if "truncation_of" in options:
+ raise TypeError("truncation_of is derived from ancestry reports")
+ return evaluate_report(
+ candidate,
+ signature_of,
+ truncation_of=truncation_of,
+ **options,
+ )
diff --git a/decluster/adaptations/pseudonym_graph.py b/decluster/adaptations/pseudonym_graph.py
new file mode 100644
index 00000000..d04a50b9
--- /dev/null
+++ b/decluster/adaptations/pseudonym_graph.py
@@ -0,0 +1,77 @@
+"""Typed boundary for the legacy pseudonym-graph contraction."""
+
+from ..domain import (
+ AttackReport,
+ ContractedTransfer,
+ EvidenceChannel,
+ EvidenceContext,
+ PseudonymGraphEvidence,
+ PseudonymGraphConstructed,
+ Subject,
+ SubjectKind,
+)
+from ..contraction import contract
+
+
+def _cluster(identifier) -> Subject:
+ return Subject(SubjectKind.CLUSTER, identifier)
+
+
+def contract_evidence(sample, lookup) -> PseudonymGraphEvidence:
+ """Contract a sample while preserving direction and folded transfer attributes."""
+ graph = contract(sample, lookup=lookup, axes=False)
+ vertices = tuple(
+ _cluster(identifier) for identifier in sorted(graph.vertices, key=repr)
+ )
+ by_identifier = {vertex.identifier: vertex for vertex in vertices}
+ edges = tuple(
+ ContractedTransfer(
+ by_identifier[source],
+ by_identifier[target],
+ attributes["transfers"],
+ attributes["value"],
+ attributes["first"],
+ attributes["last"],
+ )
+ for (source, target), attributes in sorted(
+ graph.edges.items(), key=lambda item: repr(item[0])
+ )
+ )
+ self_transfers = tuple(
+ (by_identifier[identifier], record["self_transfers"])
+ for identifier, record in sorted(graph.vertices.items(), key=lambda item: repr(item[0]))
+ if record["self_transfers"]
+ )
+ return PseudonymGraphEvidence(
+ vertices,
+ edges,
+ self_transfers,
+ EvidenceContext(
+ adversary="observer with a partial clustering of Bitcoin addresses",
+ observables=("directed transactions", "partial address-to-cluster lookup"),
+ hypothesis="partial contraction yields an attributed pseudonym graph, not a complete user network",
+ algorithm="decluster.contraction.contract with structure-only attributes",
+ dataset="deterministic synthetic contraction fixture",
+ limitations=(
+ "the supplied lookup is a clustering hypothesis",
+ "parallel transfers are folded rather than stored as separate edge objects",
+ ),
+ ),
+ )
+
+
+def contract_report(sample, lookup) -> AttackReport:
+ evidence = contract_evidence(sample, lookup)
+ return AttackReport(
+ identifier="ctp.pseudonym-graph-representation",
+ attack="partial-clustering graph contraction",
+ subjects=evidence.vertices,
+ channels=(EvidenceChannel("contracted_graph", (evidence,)),),
+ outcomes=(PseudonymGraphConstructed(evidence),),
+ limitations=(
+ "deterministic synthetic fixture",
+ "the partial clustering lookup is supplied",
+ "vertices represent pseudonyms unless clustering completeness is independently established",
+ "the report validates representation invariants, not deanonymization success",
+ ),
+ )
diff --git a/decluster/amount_channel_snapshot.py b/decluster/amount_channel_snapshot.py
new file mode 100644
index 00000000..9b4f1137
--- /dev/null
+++ b/decluster/amount_channel_snapshot.py
@@ -0,0 +1,38 @@
+"""Build the canonical transaction snapshot used by amount-channel experiments."""
+
+from __future__ import annotations
+
+import argparse
+from pathlib import Path
+
+from .measure import load_ndjson
+from .result_artifacts import write_canonical_json
+
+
+def build_snapshot(source, destination, *, expected_records=None):
+ """Normalize all source transactions while preserving their observed order."""
+ transactions = [transaction for transaction, _height in load_ndjson(source)]
+ if expected_records is not None and len(transactions) != expected_records:
+ raise ValueError(
+ f"expected {expected_records} transactions, found {len(transactions)}"
+ )
+ write_canonical_json(Path(destination), transactions)
+ return len(transactions)
+
+
+def main(argv=None):
+ parser = argparse.ArgumentParser(description=__doc__)
+ parser.add_argument("source")
+ parser.add_argument("destination")
+ parser.add_argument("--expected-records", type=int)
+ args = parser.parse_args(argv)
+ build_snapshot(
+ args.source,
+ args.destination,
+ expected_records=args.expected_records,
+ )
+ return 0
+
+
+if __name__ == "__main__":
+ raise SystemExit(main())
diff --git a/decluster/analyze.py b/decluster/analyze.py
index de8cbd48..45870789 100644
--- a/decluster/analyze.py
+++ b/decluster/analyze.py
@@ -1,17 +1,19 @@
"""Public entry point: fused provenance anonymity-set analysis of a transaction's outputs, §04 of
tx-graph-anonymity-sets. Thin orchestration over the tested walk/fusion stack (ancestry +
anonymity_set) — NO new science. This is the facade an external consumer (e.g. wasabi-model) imports
-to get per-coin provenance anonymity sets robustly at any depth (default 5): an oracle refusal or dss
-panic on a tx it cannot handle truncates that branch (ancestry's existing None-boundary) rather than
-raising, and never fabricates a link. (Errors outside the oracle — a failing `fetch`, a malformed tx
+to get per-coin provenance anonymity sets robustly at any depth (default 5): an oracle refusal (or,
+on the opt-in subset-sum oracle, a dss panic) on a tx it cannot handle truncates that branch
+(ancestry's existing None-boundary) rather than raising, and never fabricates a link. The default
+walk is `ancestry.value_flow_link_oracle`, which takes no dss and refuses only a transaction with no
+positive input value. (Errors outside the oracle — a failing `fetch`, a malformed tx
dict, an out-of-range target — still propagate; wrap the call if the caller must survive those.)"""
from . import cluster as _cluster_mod
from .ancestry import build_extended_graph, absorber_distribution
from .anonymity_set import anonymity_bits, cluster_of_from_tx_groups, provenance_anonymity_fused, \
subjective_oracle_for
-from .combiner import Combiner
+from .rarity_weight_baseline import Combiner
from .partition_model import build_evidence, contract_cospend
-from .path_count import path_count_anonymity
+from .provenance_route_accumulation import provenance_route_accumulation
from .report import _spendable_vouts
from .split_merge import M3_MAX_SUPERNODES, m3_gap_and_samples
@@ -27,20 +29,24 @@ def analyze(tx, targets=None, depth=5, *, fetch=None, link_oracle=None,
target) still propagate — wrap the call if the caller must survive those.
tx: a txid str (fetched via `fetch`) OR a tx dict (offline). targets: vout indices (default all
- spendable outputs). link_oracle default = oracle.bounded_link_oracle() (panic-safe, resolves
- coinjoins). value_weighted = Gap C satoshi-flow weighting. cluster_of = {address: owner} folded
+ spendable outputs). link_oracle default = ancestry.value_flow_link_oracle (nominal-value
+ transitions; no dss). The subset-sum walk is opt-in: pass
+ `oracle.bounded_dss_link_oracle(budget_ms)` explicitly. Both facades of this walk (`analyze`
+ and `report`) take the same default, so they read one walk two ways. value_weighted = Gap C satoshi-flow weighting. cluster_of = {address: owner} folded
into the §04 subjective source. with_origins includes the absorber distribution per target.
max_nodes (default None = uncapped, backward-compatible) is the deep-coinjoin tractability knob:
bounds both walks' cost to O(max_nodes) fetch/oracle calls regardless of depth (a lower bound on
the coin's ambiguity, never an overstatement — see build_extended_graph). path_count (default
- False, backward-compatible) is an opt-in §06/§07 robustness lens: folds in the dss W(E)
- counterfactual path-multiplicity count on top of the §04 link-probability-only walk, at the cost
- of an extra per-tx W(E) count.
+ False, backward-compatible) exposes the provenance-route diagnostic under its legacy response
+ name. It reuses the same link-probability-only walk as §04; no W(E)/subset-sum count is folded in.
+ It is not the §07 robust-connectivity object: it accumulates route mass rather than measuring
+ the cut that object is stated over. `disjoint_routes.cut_size` measures that cut; what neither
+ supplies is plausible-flow capacity, the value a route could actually carry.
Returns {vout: {
"provenance": {"min_entropy", "shannon", "n_absorbers", ["origins": {ancestor: mass}]},
["fused": {"min_entropy", "shannon"}], # when subjective
- ["path_count": {"log_W_paths", "min_entropy", "shannon", "origins_weighted"}], # when path_count
+ ["path_count": {"min_entropy", "shannon", "origins_weighted"}], # compatibility key
"truncated": int, # the provenance (graph) walk's truncation count (oracle-None + max_nodes);
# the fused/path_count walks cap identically under the same max_nodes.
}}. `provenance.min_entropy` is a conservative lower bound on the coin's provenance ENTROPY under no
@@ -52,8 +58,8 @@ def analyze(tx, targets=None, depth=5, *, fetch=None, link_oracle=None,
from .fetch import fetch_tx
fetch = fetch_tx
if link_oracle is None:
- from .oracle import bounded_link_oracle
- link_oracle = bounded_link_oracle()
+ from .ancestry import value_flow_link_oracle
+ link_oracle = value_flow_link_oracle
if isinstance(tx, str):
tx = fetch(tx)
txid = tx["txid"]
@@ -79,10 +85,15 @@ def analyze(tx, targets=None, depth=5, *, fetch=None, link_oracle=None,
entry["fused"] = {"min_entropy": min(fb["min_entropy"], provenance["min_entropy"]),
"shannon": min(fb["shannon"], provenance["shannon"])}
if path_count:
- pc = path_count_anonymity((txid, vout), depth=depth, max_nodes=max_nodes, fetch=fetch,
- link_oracle=link_oracle)
+ pc = provenance_route_accumulation(
+ (txid, vout),
+ depth=depth,
+ max_nodes=max_nodes,
+ fetch=fetch,
+ link_oracle=link_oracle,
+ )
entry["path_count"] = {k: pc[k] for k in
- ("log_W_paths", "min_entropy", "shannon", "origins_weighted")}
+ ("min_entropy", "shannon", "origins_weighted")}
out[vout] = entry
return out
diff --git a/decluster/ancestry.py b/decluster/ancestry.py
index 44942062..79987b78 100644
--- a/decluster/ancestry.py
+++ b/decluster/ancestry.py
@@ -1,12 +1,20 @@
-"""Ancestry-entropy engine (Laplacian-approximation rung). A backward provenance walk over the tx
-graph, weighted by the subset-sum link matrix and solved as an absorbing Markov chain: the coin's
+"""Ancestry-entropy engines. A backward provenance walk over the tx
+graph, solved as an absorbing Markov chain: the coin's
absorption distribution is the harmonic measure of the walk — the solution of the discrete Laplace
-system (I − Q) H = R with absorbing boundary. This is a Laplacian *approximation* of the provenance
-stationary distribution, not an exact or Monte-Carlo computation of it (the link-probability-only
-edge weighting is provisional; see build_extended_graph). Every number is a lower bound on the
-intrinsic graph entropy of the payment's provenance under no auxiliary information — NOT a privacy
-score; subjectively discounted by the reader's threat model."""
+system (I − Q) H = R with absorbing boundary.
+
+Two transition models are intentionally kept separate:
+
+* ``build_value_flow_graph`` implements the nominal-value transition rule: an output walks back to
+ every input of its creating transaction in proportion to that input's value.
+* ``build_extended_graph`` is the experimental subset-sum-link model. It requires a link oracle and
+ may additionally combine subjective evidence. It is not the nominal-value model.
+
+With the nominal-value model, frontier truncation yields a lower bound on that model's provenance
+entropy. This is not a privacy score. The opt-in DSS transition model has no general bound direction;
+its outputs must be read only as estimates conditional on that mapping family."""
import math
+from dataclasses import dataclass
class Graph:
@@ -15,7 +23,79 @@ def __init__(self):
self.transient = [] # interior coins with backward link edges
self.absorbers = [] # boundary coins: coinbase / depth-cutoff / oracle-None
self.edges = {} # coin -> [(next_coin, weight), ...], row-stochastic over next_coin
- self.truncated = 0 # count of coins made absorbers by the oracle-None refusal or the max_nodes cap
+ self.truncated = 0 # total: oracle-None refusals + max_nodes caps + any unnamed cause
+ self.oracle_refused = 0 # the link oracle declined to link the coin's transaction
+ self.node_capped = 0 # the max_nodes bound cut the frontier before any fetch
+ self.zero_link_mass = 0 # the selected output has no positive incoming link mass
+ self.fetch_missing = 0 # the source produced no record for the coin
+ self.unattributed = 0 # truncated by a cause this module does not name
+ self.truncated_coins = {} # coin -> cause, so truncation can be counted, and attributed,
+ # over the mass-carrying boundary
+ self.source_absorbers = set() # fetched coinbase boundaries (real sources)
+ self.depth_capped = set() # artificial boundaries introduced by depth
+ self.values = {} # coin -> satoshis, recorded as the walk reads them
+
+
+ORACLE_REFUSED = "oracle_refused"
+NODE_CAPPED = "node_capped"
+FETCH_MISSING = "fetch_missing" # the source has no record of this coin
+ZERO_LINK_MASS = "zero_link_mass"
+
+
+@dataclass(frozen=True)
+class TruncationSupport:
+ """How much of a signature's mass-carrying boundary is truncation, split by cause.
+
+ The two causes answer different questions and a single total answers neither. `oracle_refused`
+ is the link oracle declining to commit to a link; `node_capped` is the `max_nodes` bound cutting
+ the frontier. A consumer reading "no view" off a total cannot tell which walk limit it hit, and
+ under `value_flow_link_oracle` — which refuses only on zero total input value — the first is
+ practically always zero, so a total silently means the second.
+
+ `unattributed` holds truncation whose cause this module does not name. It stays in `total` —
+ dropping it would under-report blindness — but it is never folded into one of the two names:
+ a consumer that branches on a cause must not be handed one that was never measured.
+
+ `zero_link_mass` is the third measured failure to continue: the selected output's link column
+ has no positive mass. It is kept separate from genuine boundaries such as coinbase and the
+ requested depth cutoff.
+ """
+
+ oracle_refused: int
+ node_capped: int
+ unattributed: int = 0
+ zero_link_mass: int = 0
+
+ def __post_init__(self):
+ if min(self.oracle_refused, self.node_capped, self.unattributed,
+ self.zero_link_mass) < 0:
+ raise ValueError("truncation counts must be non-negative")
+
+ @property
+ def total(self) -> int:
+ return self.oracle_refused + self.node_capped + self.unattributed + self.zero_link_mass
+
+ def __bool__(self) -> bool:
+ # Without this an all-zero support is truthy, so a caller's `if truncated:` — written
+ # against the old bare int — would flip silently instead of failing where `>=` does.
+ return self.total > 0
+
+
+def _truncate(graph, kind, coin, cause):
+ """Make `coin` a boundary absorber and record which walk limit put it there."""
+ kind[coin] = "absorber"
+ graph.truncated += 1
+ graph.truncated_coins[coin] = cause
+ if cause == ORACLE_REFUSED:
+ graph.oracle_refused += 1
+ elif cause == NODE_CAPPED:
+ graph.node_capped += 1
+ elif cause == ZERO_LINK_MASS:
+ graph.zero_link_mass += 1
+ elif cause == FETCH_MISSING:
+ graph.fetch_missing += 1
+ else:
+ graph.unattributed += 1
def _solve(a, b):
@@ -60,6 +140,33 @@ def absorber_distribution(graph, target):
return {graph.absorbers[a]: p for a, p in enumerate(row) if p > 1e-12}
+def collapsed_expected_steps(graph, target):
+ """Expected transitions to this graph's boundary, ``t = (I-Q)^-1 1``."""
+ if target in graph.absorbers:
+ return 0.0
+ t_index = {coin: index for index, coin in enumerate(graph.transient)}
+ nt = len(graph.transient)
+ im_q = [[1.0 if i == j else 0.0 for j in range(nt)] for i in range(nt)]
+ for coin, row in t_index.items():
+ for nxt, weight in graph.edges.get(coin, ()):
+ if nxt in t_index:
+ im_q[row][t_index[nxt]] -= weight
+ return _solve(im_q, [[1.0] for _ in range(nt)])[t_index[target]][0]
+
+
+def kelen_seres_expected_steps(graph, target):
+ """Expected steps in Kelen–Seres' uncollapsed UTXO transaction graph.
+
+ A collapsed coin-to-parent transition represents ``coin -> transaction ->
+ input coin`` (two paper steps). At a real coinbase boundary another two
+ steps reach ``coinbase transaction -> auxiliary source``. Artificial depth
+ or truncation boundaries have no such interpretation and are refused.
+ """
+ if graph.truncated or graph.depth_capped or set(graph.absorbers) != graph.source_absorbers:
+ raise ValueError("expected steps require an untruncated walk reaching only real sources")
+ return 2.0 * (collapsed_expected_steps(graph, target) + 1.0)
+
+
def _is_coinbase(tx):
vin = tx.get("vin", [])
return bool(vin) and vin[0].get("is_coinbase", False)
@@ -77,8 +184,15 @@ def build_extended_graph(target, depth=6, fetch=None, link_oracle=None, value_we
min-entropy cannot rise either — the safe direction for a lower bound on ambiguity (never
overstating it). De-duplicates states by coin identity, so shared ancestors (diamonds) are exact.
- PROVISIONAL modelling choice (spec §9, not settled): edges are the row-stochastic normalisation
- of the link column (`col[i]/sum(col)`), weighting by LINK-PROBABILITY only. Why this is open:
+ `link_oracle` is required, and what it returns decides the transition model. The library's
+ facades (`analyze`, `report`, `provenance_route_accumulation`, `ancestry_entropy`,
+ `anonymity_set`) now
+ pass `value_flow_link_oracle`; the paragraph below describes the subset-sum oracle, which is
+ opt-in.
+
+ PROVISIONAL modelling choice (spec §9, not settled) FOR THE SUBSET-SUM ORACLE: edges are the
+ row-stochastic normalisation of the link column (`col[i]/sum(col)`), weighting by
+ LINK-PROBABILITY only. Why this is open:
normalising the column this way can discard effective-value / fee-plausibility constraints (the
upstream §05 caution — e.g. Wasabi-1 clustering information lives in the coins' effective values),
and the theory's provenance measure is satoshi-weighted, not link-weighted. `L` already encodes
@@ -86,29 +200,31 @@ def build_extended_graph(target, depth=6, fetch=None, link_oracle=None, value_we
flow rung. `L` is uniform over non-derived mappings (the crate's definition), not
multiplicity-weighted.
- `value_weighted` (opt-in, default False) implements that deferred flow rung: the link column is
- scaled by each input's satoshi value before normalising (`col[i]*in_val_i / Σ_k col[k]*in_val_k`),
- so a coin's provenance mass tilts toward its larger-value parents. Default stays link-probability-
- only so existing results remain reproducible.
+ `value_weighted` (opt-in, default False) is an experimental hybrid: the link column is scaled by
+ each input's satoshi value before normalising (`col[i]*in_val_i / Σ_k col[k]*in_val_k`). It is
+ neither the plain subset-sum-link model nor the nominal-value transition model. Use
+ `build_value_flow_graph` for the latter. The default stays link-probability-only so existing
+ experimental results remain reproducible.
`subjective_oracle` implements post-04's subjective matrix that combines with the graph-derived
link matrix, applied at the link level before the absorbing solve (the §04-faithful fusion);
default None keeps the graph-only walk.
- `max_nodes` (default None = no cap, reproduces prior behavior exactly) bounds the walk's cost to
- O(max_nodes) fetch/oracle calls regardless of depth -- the tractability lever for deep coinjoins,
- where exponential ancestor fan-out can otherwise hang. Once the graph has committed max_nodes
- coins (graph size = len(kind), the count already classified transient or absorber), every further
- dequeued coin is made a truncation absorber WITHOUT fetching or oracle-linking it, using the same
- boundary semantics as an oracle-None refusal (kind[coin]="absorber", g.truncated += 1, its parents
- are never enqueued). BFS expands nearest coins first, so truncation falls on the deepest, least
- informative frontier, and the resulting absorber_distribution is a conservative LOWER BOUND:
- unexpanded ancestry is lumped into truncation, which can only add origins later, never remove.
- The bound is on fetch/oracle COST (<= max_nodes); the final coin count can exceed max_nodes by up
- to the one BFS frontier layer already queued when the cap trips (those extra coins are cheap
- no-fetch truncation absorbers). A coin that would have resolved to a genuine coinbase/depth-cutoff
- origin but is dequeued after the cap trips is folded into truncation instead — conservative (its
- mass becomes a truncation atom, never an invented origin)."""
+ `max_nodes` (default None = no cap, reproduces prior behavior exactly) bounds the walk's cost
+ to O(max_nodes) fetch/oracle calls regardless of depth -- the tractability lever for deep
+ coinjoins, where exponential ancestor fan-out can otherwise hang. Once the graph has committed
+ max_nodes coins (graph size = len(kind), the count already classified transient or absorber),
+ every further dequeued coin is made a truncation absorber WITHOUT fetching or oracle-linking
+ it, using the same boundary semantics as an oracle-None refusal (kind[coin]="absorber", counted
+ in g.truncated and g.node_capped, its parents are never enqueued). BFS expands nearest coins
+ first, so truncation falls on the deepest, least informative frontier, and the resulting
+ absorber_distribution is a conservative LOWER BOUND: unexpanded ancestry is lumped into
+ truncation, which can only add origins later, never remove. The bound is on fetch/oracle COST
+ (<= max_nodes); the final coin count can exceed max_nodes by up to the one BFS frontier layer
+ already queued when the cap trips (those extra coins are cheap no-fetch truncation absorbers).
+ A coin that would have resolved to a genuine coinbase/depth-cutoff origin but is dequeued after
+ the cap trips is folded into truncation instead — conservative (its mass becomes a truncation
+ atom, never an invented origin)."""
g = Graph()
kind = {} # coin -> "transient" | "absorber"
queue = [(target, depth)] # BFS from target: first dequeue = largest remaining depth
@@ -119,18 +235,29 @@ def build_extended_graph(target, depth=6, fetch=None, link_oracle=None, value_we
continue
seen.add(coin)
if max_nodes is not None and len(kind) >= max_nodes:
- kind[coin] = "absorber"; g.truncated += 1; continue # node cap reached: truncate, no fetch
+ _truncate(g, kind, coin, NODE_CAPPED); continue # node cap reached: truncate, no fetch
txid, vout = coin
tx = fetch(txid)
+ if tx is None:
+ # A slice holds what it holds. A parent the source cannot produce is a boundary the
+ # walk stops at, the same shape as a depth cutoff — not a reason to abandon the walk,
+ # which would report nothing for every coin whose ancestry leaves the sample.
+ _truncate(g, kind, coin, FETCH_MISSING); continue
if _is_coinbase(tx):
- kind[coin] = "absorber"; continue
+ kind[coin] = "absorber"; g.source_absorbers.add(coin); continue
if d <= 0:
- kind[coin] = "absorber"; continue # depth cutoff
+ kind[coin] = "absorber"; g.depth_capped.add(coin); continue # depth cutoff
in_vals = [v["prevout"]["value"] for v in tx["vin"]]
out_vals = [o["value"] for o in tx["vout"]]
+ # The walk already holds every value it passes; recording them lets a consumer ask what a
+ # route could carry without fetching the chain a second time.
+ g.values[coin] = out_vals[vout] if vout < len(out_vals) else None
+ for vin in tx["vin"]:
+ if vin.get("txid") is not None:
+ g.values.setdefault((vin["txid"], vin["vout"]), vin["prevout"]["value"])
matrix = link_oracle(in_vals, out_vals)
if matrix is None:
- kind[coin] = "absorber"; g.truncated += 1; continue # refuse to fabricate
+ _truncate(g, kind, coin, ORACLE_REFUSED); continue # refuse to fabricate
if subjective_oracle is not None:
sub = subjective_oracle(tx, in_vals, out_vals)
if sub is not None:
@@ -141,7 +268,7 @@ def build_extended_graph(target, depth=6, fetch=None, link_oracle=None, value_we
col = [c * in_vals[i] for i, c in enumerate(col)]
s = sum(col)
if s <= 0:
- kind[coin] = "absorber"; continue # no link info -> boundary
+ _truncate(g, kind, coin, ZERO_LINK_MASS); continue
edges = []
for i, v in enumerate(tx["vin"]):
parent = (v["txid"], v["vout"])
@@ -154,6 +281,75 @@ def build_extended_graph(target, depth=6, fetch=None, link_oracle=None, value_we
return g
+def value_flow_link_oracle(inputs, outputs):
+ """Return the collapsed nominal-value transition matrix.
+
+ In the uncollapsed UTXO graph an output first walks to its creating transaction with
+ probability one. From that transaction it walks to input ``i`` with probability
+ ``inputs[i] / sum(inputs)``. Collapsing the deterministic output-to-transaction step yields the
+ same column for every output. Output values and transaction fees therefore do not alter this
+ transition rule.
+
+ ``None`` is returned for a transaction with no positive input value, because no stochastic row
+ can be constructed without inventing provenance.
+ """
+ total = sum(inputs)
+ if total <= 0:
+ return None
+ probabilities = [value / total for value in inputs]
+ return [[probability for _ in outputs] for probability in probabilities]
+
+
+def build_value_flow_graph(target, depth=6, fetch=None, max_nodes=None):
+ """Build the nominal-value absorbing walk over a UTXO ancestry graph.
+
+ This is deliberately a separate entry point from the subset-sum-link experiment: it never
+ imports or calls ``dss`` and has no amount-link or subjective-evidence weighting.
+ """
+ return build_extended_graph(
+ target,
+ depth=depth,
+ fetch=fetch,
+ link_oracle=value_flow_link_oracle,
+ max_nodes=max_nodes,
+ )
+
+
+def value_flow_signature(target, depth=6, fetch=None, max_nodes=None):
+ """Absorption probabilities produced by the nominal-value transition model."""
+ if fetch is None:
+ from .fetch import fetch_tx
+ fetch = fetch_tx
+ graph = build_value_flow_graph(target, depth=depth, fetch=fetch, max_nodes=max_nodes)
+ return absorber_distribution(graph, target)
+
+
+def value_flow_untraceability(target, depth=6, fetch=None, max_nodes=None):
+ """Shannon untraceability and absorption details for the nominal-value model.
+
+ ``untraceability`` is the Shannon entropy of the source absorption distribution in bits.
+ ``expected_steps`` uses the exact two-step expansion of each collapsed UTXO transition and is
+ reported only when every boundary is a fetched coinbase source; otherwise it is ``None``.
+ """
+ if fetch is None:
+ from .fetch import fetch_tx
+ fetch = fetch_tx
+ graph = build_value_flow_graph(target, depth=depth, fetch=fetch, max_nodes=max_nodes)
+ distribution = absorber_distribution(graph, target)
+ probabilities = list(distribution.values())
+ try:
+ expected_steps = kelen_seres_expected_steps(graph, target)
+ except ValueError:
+ expected_steps = None
+ return {
+ "untraceability": _shannon(probabilities),
+ "distribution": distribution,
+ "n_absorbers": len(probabilities),
+ "truncated": graph.truncated,
+ "expected_steps": expected_steps,
+ }
+
+
# Per-link wall-clock budget for the subset-sum oracle. Coin count is a poor cost predictor
# (same-size calls ranged 54ms-69s in dss's own measurements), so the walk truncates on time,
# admitting the cheap large mixes the old count guard refused and cutting only the genuine
@@ -162,8 +358,10 @@ def build_extended_graph(target, depth=6, fetch=None, link_oracle=None, value_we
def dss_link_oracle(inputs, outputs, budget_ms=DEFAULT_LINK_BUDGET_MS):
- """Default production link oracle: the exact subset-sum pairwise link matrix, bounded by a
- wall-clock budget so a dense mix truncates on time rather than on coin count. Lazy import so
+ """Opt-in subset-sum link oracle; not any facade's default (see
+ `ancestry.value_flow_link_oracle`). Returns the DSS model's pairwise link matrix, bounded by a
+ wall-clock budget so a dense mix truncates on time rather than on coin count. It is not an exact
+ oracle for all plausible transaction interpretations and has no general bound direction. Lazy import so
decluster.ancestry loads without the compiled `dss` module (build: maturin develop)."""
import dss
try:
@@ -183,7 +381,7 @@ def _min_entropy(probs):
return -math.log2(top) if top > 0 else 0.0
-def ancestry_signature(target, depth=6, fetch=None, link_oracle=dss_link_oracle):
+def ancestry_signature(target, depth=6, fetch=None, link_oracle=value_flow_link_oracle):
"""The coin's **provenance signature**: its absorption distribution over ancestral boundary coins —
a sparse, high-dimensional quasi-identifier vector (`{ancestor: mass}`). Same backward walk as
`ancestry_entropy`, exposing the distribution instead of its entropy. This is the feature the
@@ -195,23 +393,56 @@ def ancestry_signature(target, depth=6, fetch=None, link_oracle=dss_link_oracle)
return absorber_distribution(g, target)
-def ancestry_signature_and_truncation(target, depth=6, fetch=None, link_oracle=dss_link_oracle):
- """The signature, plus how many of its absorbers are the oracle refusing rather than an origin.
+def ancestry_signature_and_truncation(
+ target, depth=6, fetch=None, link_oracle=value_flow_link_oracle, max_nodes=None
+):
+ """The signature, plus a `TruncationSupport` over the absorbers that carry its mass.
- Both come from one walk. The count is what makes an *empty* intersection readable: a boundary
+ Both come from one walk. The split is what makes an *empty* intersection readable: a boundary
that is entirely truncation contains no observed origin, so two such signatures fail to overlap
- whatever their provenance is. Without it, "no shared origin" and "no view" are the same answer."""
+ whatever their provenance is. Without it, "no shared origin" and "no view" are the same answer —
+ and without the *cause*, "no view" does not say which limit produced it."""
if fetch is None:
from .fetch import fetch_tx
fetch = fetch_tx
- g = build_extended_graph(target, depth=depth, fetch=fetch, link_oracle=link_oracle)
- return absorber_distribution(g, target), g.truncated
+ g = build_extended_graph(
+ target,
+ depth=depth,
+ fetch=fetch,
+ link_oracle=link_oracle,
+ max_nodes=max_nodes,
+ )
+ sig = absorber_distribution(g, target)
+ return sig, truncated_support(sig, g)
+
+
+def truncated_support(signature, graph):
+ """How many of a signature's *mass-carrying* absorbers are truncation, by cause.
+
+ `graph.truncated` counts every truncated coin, including atoms this target never reaches with
+ any mass. `absorber_distribution` reports only positive-mass absorbers, so comparing the two
+ calls a branch blind while it is still resolving a full-mass origin. Consumers compare
+ `TruncationSupport.total` against `len(signature)`, so both sides must count the same objects.
+ A boundary coin the walk stopped at for a reason this module does not name is counted under
+ `unattributed`.
+ """
+ causes = [graph.truncated_coins[a] for a in signature if a in graph.truncated_coins]
+ # An unrecognised cause stays in the total, under `unattributed`, and is never given one of the
+ # two names: under-reporting truncation lets an empty intersection be read as a refusal, and
+ # naming an unmeasured cause lets a consumer act on a walk limit that never fired.
+ return TruncationSupport(
+ oracle_refused=sum(1 for c in causes if c == ORACLE_REFUSED),
+ node_capped=sum(1 for c in causes if c == NODE_CAPPED),
+ unattributed=sum(1 for c in causes
+ if c not in (ORACLE_REFUSED, NODE_CAPPED, ZERO_LINK_MASS)),
+ zero_link_mass=sum(1 for c in causes if c == ZERO_LINK_MASS),
+ )
def provenance_link(sig_a, sig_b, rarity=None):
"""Narayanan–Shmatikov quasi-identifier overlap of two provenance signatures: the shared ancestral
coins, each scored by the mass it carries in *both* and, optionally, its global rarity
- `wt = 1/log2(support)` (a shared *rare* ancestor is strong same-origin evidence; a shared common
+ `wt = 1/log2(support+1)` (a shared *rare* ancestor is strong same-origin evidence; a shared common
one — a hub coinbase spent by everyone — is weak). `rarity` maps ancestor -> support count; absent,
all shared ancestors weigh equally. Returns a non-negative link score (0 = disjoint provenance)."""
shared = set(sig_a) & set(sig_b)
@@ -225,11 +456,18 @@ def wt(c):
return sum(min(sig_a[c], sig_b[c]) * wt(c) for c in shared)
-def ancestry_entropy(target, depth=6, fetch=None, link_oracle=dss_link_oracle):
- """Lower bound on the intrinsic graph entropy of the target coin's provenance under no auxiliary
- information — NOT a privacy score; subjectively discounted by the reader's threat model. Returns
- Shannon and min-entropy (the conservative, defender-side read) of the provenance distribution
- over the ancestral boundary, the boundary size, and how many coins were truncated (oracle-None)."""
+def ancestry_entropy(target, depth=6, fetch=None, link_oracle=value_flow_link_oracle):
+ """Entropy of the target's model-relative provenance distribution.
+
+ With the default nominal-value model, truncating the frontier gives a lower bound on that model's
+ entropy. No bound direction is promised for an arbitrary supplied oracle; in particular the
+ opt-in DSS pairwise model is diagnostic. This is not a privacy score. Returns Shannon and
+ min-entropy, boundary size and truncation count. `Graph.oracle_refused` and
+ `Graph.node_capped` keep the two principal truncation causes apart.
+
+ Unnormalized bits, after Serjantov and Danezis; Diaz et al.'s degree of anonymity
+ `d = H/H_M` is not adopted, because a truncated walk has no fixed candidate population
+ to put in `H_M`."""
if fetch is None:
from .fetch import fetch_tx
fetch = fetch_tx
diff --git a/decluster/anonymity_set.py b/decluster/anonymity_set.py
index 963b7e86..853e3814 100644
--- a/decluster/anonymity_set.py
+++ b/decluster/anonymity_set.py
@@ -32,12 +32,12 @@ def provenance_anonymity(target, hypotheses, *, depth=6, fetch=None, link_oracle
"""Reweighted provenance distribution for `target`: the graph-derived absorption distribution
(ancestry), multiplicatively narrowed by each subjective hypothesis (name, factors)."""
if base_dist is None:
- from .ancestry import build_extended_graph, absorber_distribution, dss_link_oracle
+ from .ancestry import build_extended_graph, absorber_distribution, value_flow_link_oracle
if fetch is None:
from .fetch import fetch_tx
fetch = fetch_tx
g = build_extended_graph(target, depth=depth, fetch=fetch,
- link_oracle=link_oracle or dss_link_oracle)
+ link_oracle=link_oracle or value_flow_link_oracle)
base_dist = absorber_distribution(g, target)
dist = dict(base_dist)
for _name, factors in hypotheses:
@@ -77,12 +77,12 @@ def provenance_anonymity_fused(target, subjective_oracle, *, depth=6, fetch=None
`reweight` cannot do. `value_weighted` (default False, backward-compatible) passes through to
build_extended_graph's Gap C satoshi-flow weighting. `max_nodes` (default None, backward-
compatible) passes through to build_extended_graph's node-count cap."""
- from .ancestry import build_extended_graph, absorber_distribution, dss_link_oracle
+ from .ancestry import build_extended_graph, absorber_distribution, value_flow_link_oracle
if fetch is None:
from .fetch import fetch_tx
fetch = fetch_tx
g = build_extended_graph(target, depth=depth, fetch=fetch,
- link_oracle=link_oracle or dss_link_oracle,
+ link_oracle=link_oracle or value_flow_link_oracle,
value_weighted=value_weighted,
subjective_oracle=subjective_oracle,
max_nodes=max_nodes)
diff --git a/decluster/archive_snapshot.py b/decluster/archive_snapshot.py
new file mode 100644
index 00000000..097686f9
--- /dev/null
+++ b/decluster/archive_snapshot.py
@@ -0,0 +1,26 @@
+"""Safe extraction of immutable dataset archives."""
+
+from pathlib import Path, PurePosixPath
+import tarfile
+
+
+class UnsafeArchiveError(ValueError):
+ """An archive contains an entry outside the supported regular-file subset."""
+
+
+def extract_tar_gz(archive, destination):
+ """Extract regular files and directories without links or path traversal."""
+ destination = Path(destination)
+ destination.mkdir(parents=True, exist_ok=True)
+ with tarfile.open(archive, mode="r:gz") as source:
+ members = source.getmembers()
+ for member in members:
+ path = PurePosixPath(member.name)
+ if path.is_absolute() or ".." in path.parts:
+ raise UnsafeArchiveError(f"unsafe archive path: {member.name!r}")
+ if not (member.isfile() or member.isdir()):
+ raise UnsafeArchiveError(
+ f"unsupported archive entry: {member.name!r}"
+ )
+ source.extractall(destination, members=members, filter="data")
+ return destination
diff --git a/decluster/attribute_drift.py b/decluster/attribute_drift.py
new file mode 100644
index 00000000..bd2d56c5
--- /dev/null
+++ b/decluster/attribute_drift.py
@@ -0,0 +1,60 @@
+"""Distribution drift and volume coupling across graph-view epochs."""
+
+from __future__ import annotations
+
+import math
+
+GAPS = (1, 2, 3, 7, 14, 21, 30, 60, 90, 120)
+CYCLE = 7
+
+
+def normalise(counts):
+ total = sum(counts.values())
+ return {key: value / total for key, value in counts.items()} if total else {}
+
+
+def total_variation(left, right):
+ return 0.5 * sum(
+ abs(left.get(key, 0.0) - right.get(key, 0.0))
+ for key in set(left) | set(right)
+ )
+
+
+def pearson(left, right):
+ if len(left) != len(right) or not left:
+ raise ValueError("Pearson inputs must have the same non-zero length")
+ mean_left = sum(left) / len(left)
+ mean_right = sum(right) / len(right)
+ numerator = sum(
+ (x - mean_left) * (y - mean_right)
+ for x, y in zip(left, right)
+ )
+ denominator = math.sqrt(
+ sum((x - mean_left) ** 2 for x in left)
+ * sum((y - mean_right) ** 2 for y in right)
+ )
+ return numerator / denominator if denominator else 0.0
+
+
+def drift(series, gaps):
+ return {
+ gap: sum(
+ total_variation(series[index], series[index + gap])
+ for index in range(len(series) - gap)
+ ) / (len(series) - gap)
+ for gap in gaps
+ if 0 < gap < len(series)
+ }
+
+
+def cycle_gain(series, span=3):
+ """Compare mean drift on weekly-cycle gaps with other gaps in the same range."""
+ if isinstance(span, bool) or not isinstance(span, int) or span < 1:
+ raise ValueError("cycle span must be a positive integer")
+ curve = drift(series, range(1, CYCLE * span + 1))
+ on_cycle = [value for gap, value in curve.items() if gap % CYCLE == 0]
+ off_cycle = [value for gap, value in curve.items() if gap % CYCLE]
+ if not on_cycle or not off_cycle:
+ raise ValueError("series is too short for the requested cycle span")
+ baseline = sum(off_cycle) / len(off_cycle)
+ return (sum(on_cycle) / len(on_cycle)) / baseline if baseline else 1.0
diff --git a/decluster/baselines/__init__.py b/decluster/baselines/__init__.py
new file mode 100644
index 00000000..465a2563
--- /dev/null
+++ b/decluster/baselines/__init__.py
@@ -0,0 +1,144 @@
+"""Reference baselines kept separate from experimental heuristics."""
+
+from .maurer import (
+ ExactMapping,
+ exact_non_derived_mappings,
+ exact_subtransaction_mappings,
+ mapping_refines,
+ non_derived_mappings,
+)
+from .boltzmann import (
+ ExactCoinLinkEvidence,
+ ExactLinkAnalysis,
+ ExactLinkCandidate,
+ ExactPerCoinLinkAnalysis,
+ exact_link_analysis,
+ exact_per_coin_link_evidence,
+ fee_tolerant_link_analysis,
+ fee_tolerant_subtransaction_mappings,
+ link_analysis,
+ per_coin_link_evidence,
+)
+from .boltzmann_reference import (
+ BoltzmannReferenceAnalysis,
+ boltzmann_reference_analysis,
+ boltzmann_reference_with_linked_inputs,
+)
+from .candidate_set_intersection import (
+ GoldfederIntersectionResult,
+ IntersectionResult,
+ IntersectionStep,
+ goldfeder_cluster_intersection,
+ intersect_candidate_sets,
+ join_ancestors,
+ narrowing_bits,
+)
+from .kelen_seres_graphs import (
+ AbsorbingTransactionChain,
+ AuxiliarySource,
+ Transfer,
+ WeightedTransactionGraph,
+ absorbing_transaction_chain,
+ stationary_account_graph,
+ temporal_account_graph,
+)
+from .link_prediction import LinkPredictionResult, Prediction
+from .ns_link_prediction_2011 import (
+ CombinedPrediction,
+ SimilarityEvidence,
+ TwoStageMapping,
+ UndefinedAlgorithm2Weight,
+ algorithm2_node_distance,
+ algorithm2_pair_distance,
+ algorithm2_potential,
+ anneal_seed_mapping,
+ combine_predictions,
+ similarity_evidence,
+ stage1_match,
+ stage2_candidates,
+ two_stage_mapping,
+)
+from .oracle_audit import (
+ audit,
+ enumerate_family,
+ equal_value_mapping_count,
+ finest_mappings,
+ identify_objects,
+ manifest_invariants,
+ mapping_count_mechanism,
+ verify_dss_marginal_family,
+)
+from .unnecessary_input import (
+ UIHAnalysis,
+ UIHStatus,
+ analyze_blockstream,
+ analyze_transaction,
+ blocksci_uih1,
+ gibson_flags,
+ optimal_change_candidate,
+)
+
+__all__ = [
+ "AbsorbingTransactionChain",
+ "AuxiliarySource",
+ "BoltzmannReferenceAnalysis",
+ "CombinedPrediction",
+ "SimilarityEvidence",
+ "TwoStageMapping",
+ "UIHAnalysis",
+ "UIHStatus",
+ "UndefinedAlgorithm2Weight",
+ "ExactCoinLinkEvidence",
+ "ExactLinkAnalysis",
+ "ExactLinkCandidate",
+ "ExactMapping",
+ "ExactPerCoinLinkAnalysis",
+ "GoldfederIntersectionResult",
+ "IntersectionResult",
+ "IntersectionStep",
+ "LinkPredictionResult",
+ "Prediction",
+ "Transfer",
+ "WeightedTransactionGraph",
+ "audit",
+ "absorbing_transaction_chain",
+ "algorithm2_node_distance",
+ "algorithm2_pair_distance",
+ "algorithm2_potential",
+ "anneal_seed_mapping",
+ "analyze_blockstream",
+ "analyze_transaction",
+ "boltzmann_reference_analysis",
+ "boltzmann_reference_with_linked_inputs",
+ "blocksci_uih1",
+ "combine_predictions",
+ "similarity_evidence",
+ "stage1_match",
+ "stage2_candidates",
+ "stationary_account_graph",
+ "temporal_account_graph",
+ "two_stage_mapping",
+ "enumerate_family",
+ "equal_value_mapping_count",
+ "exact_link_analysis",
+ "exact_non_derived_mappings",
+ "exact_per_coin_link_evidence",
+ "fee_tolerant_link_analysis",
+ "fee_tolerant_subtransaction_mappings",
+ "exact_subtransaction_mappings",
+ "finest_mappings",
+ "identify_objects",
+ "goldfeder_cluster_intersection",
+ "gibson_flags",
+ "optimal_change_candidate",
+ "intersect_candidate_sets",
+ "join_ancestors",
+ "manifest_invariants",
+ "link_analysis",
+ "mapping_count_mechanism",
+ "mapping_refines",
+ "narrowing_bits",
+ "non_derived_mappings",
+ "per_coin_link_evidence",
+ "verify_dss_marginal_family",
+]
diff --git a/decluster/baselines/boltzmann.py b/decluster/baselines/boltzmann.py
new file mode 100644
index 00000000..18c6b263
--- /dev/null
+++ b/decluster/baselines/boltzmann.py
@@ -0,0 +1,272 @@
+"""Uniform link-probability analysis over explicitly selected balance models.
+
+This is not parity with the Boltzmann tool. ``exact`` is the Maurer-style conservation oracle;
+``fee_tolerant`` permits a non-negative fee allocation across participant blocks, bounded by a
+caller-supplied total tolerance. Roundness is deliberately not an admissibility rule or prior.
+
+Because every block deficit here is non-negative, a block that receives more value than it
+contributes is inexpressible, and that block is exactly the JoinMarket maker: the reference tool
+admits it through a negative-difference bound (``diff >= -fees_maker``). So the intrafee case
+usually cited alongside this mechanism is outside this module, not merely untested.
+``boltzmann_reference`` carries the reference intrafee interval; it is not modelled here.
+
+Source: `boltzmann` in `catalog/ctp-sources.json` — LaurentMT, *Boltzmann* (2015),
+the link-probability matrix this analysis is measured against rather than ported from.
+"""
+
+from dataclasses import dataclass
+from itertools import permutations
+from math import log2
+
+from .maurer import _canonical, _partitions, exact_subtransaction_mappings
+
+
+@dataclass(frozen=True)
+class ExactLinkAnalysis:
+ mappings: tuple
+ matrix: tuple[tuple[float, ...], ...]
+ entropy_bits: float
+ deterministic_links: tuple[tuple[int, int], ...]
+ balance_model: str = "exact"
+ observed_fee: int = 0
+ fee_tolerance: int = 0
+
+
+@dataclass(frozen=True)
+class SameBlockAnalysis:
+ """Same-side link probabilities: Maurer's ``p_II`` and ``p_OO``."""
+
+ mapping_count: int
+ input_matrix: tuple[tuple[float, ...], ...]
+ output_matrix: tuple[tuple[float, ...], ...]
+ balance_model: str = "exact"
+ observed_fee: int = 0
+ fee_tolerance: int = 0
+
+
+@dataclass(frozen=True)
+class ExactLinkCandidate:
+ """One marginal link, with its exact support before division."""
+
+ counterpart_index: int
+ mapping_count: int
+ probability: float
+
+
+@dataclass(frozen=True)
+class ExactCoinLinkEvidence:
+ """Raw per-coin evidence derived from exact mapping membership.
+
+ ``candidate_count`` is the number of counterpart coins linked in at least
+ one exact mapping. ``max_link_probability`` is the largest marginal link
+ probability, not a normalized privacy score. The integer counts and the
+ common ``mapping_count`` keep the calculation independently checkable.
+ """
+
+ role: str
+ index: int
+ value: int
+ mapping_count: int
+ candidates: tuple[ExactLinkCandidate, ...]
+ candidate_count: int
+ max_link_probability: float | None
+
+
+@dataclass(frozen=True)
+class ExactPerCoinLinkAnalysis:
+ """Input- and output-side marginal evidence for one transaction."""
+
+ mapping_count: int
+ inputs: tuple[ExactCoinLinkEvidence, ...]
+ outputs: tuple[ExactCoinLinkEvidence, ...]
+
+
+def _link_counts(mappings, input_count, output_count):
+ counts = [[0] * output_count for _ in range(input_count)]
+ for mapping in mappings:
+ for in_block, out_block in mapping.blocks:
+ for input_index in in_block:
+ for output_index in out_block:
+ counts[input_index][output_index] += 1
+ return tuple(tuple(row) for row in counts)
+
+
+def fee_tolerant_subtransaction_mappings(inputs, outputs, *, fee_tolerance, max_coins=12):
+ """Enumerate mappings whose blocks conserve value after allocating the observed fee.
+
+ For every block, input value must be at least output value; the block deficits necessarily sum
+ to the transaction's observed fee. ``fee_tolerance`` caps that total and must be explicit.
+ This models fee allocation, not amount rounding: no near-equality or roundness preference is
+ introduced. The all-coins interpretation remains present when the transaction fee is allowed.
+ """
+ inputs, outputs = tuple(inputs), tuple(outputs)
+ if not inputs or not outputs:
+ return ()
+ if any(isinstance(value, bool) or not isinstance(value, int) or value < 0
+ for value in inputs + outputs):
+ raise ValueError("coin values must be non-negative integers")
+ if isinstance(fee_tolerance, bool) or not isinstance(fee_tolerance, int) or fee_tolerance < 0:
+ raise ValueError("fee_tolerance must be a non-negative integer")
+ if len(inputs) + len(outputs) > max_coins:
+ raise ValueError("fee-tolerant baseline exceeds max_coins")
+ observed_fee = sum(inputs) - sum(outputs)
+ if observed_fee < 0 or observed_fee > fee_tolerance:
+ return ()
+
+ mappings = set()
+ for in_blocks in _partitions(list(range(len(inputs)))):
+ for out_blocks in _partitions(list(range(len(outputs)))):
+ if len(in_blocks) != len(out_blocks):
+ continue
+ in_sums = [sum(inputs[i] for i in block) for block in in_blocks]
+ for permuted_outputs in permutations(out_blocks):
+ out_sums = [sum(outputs[j] for j in block) for block in permuted_outputs]
+ if all(left >= right for left, right in zip(in_sums, out_sums)):
+ mappings.add(_canonical(tuple(zip(in_blocks, permuted_outputs))))
+ return tuple(sorted(mappings, key=lambda mapping: mapping.blocks))
+
+
+def link_analysis(inputs, outputs, *, max_coins=12, balance_model="exact", fee_tolerance=0):
+ """Compute uniform input-output link probabilities for the exact baseline.
+
+ A link is present when an input and output share a sub-transaction block.
+ This is Maurer's ``p_IO`` over the *full* mapping family, which is how §4.1
+ defines it; the paper's own evaluation in §4.3 instead restricts to the
+ non-derived family, for which see ``oracle_audit.exact_finest_link_matrix``.
+
+ Equal mass per mapping is a modelling choice, not a measurement. It is the
+ choice that makes the resulting entropy largest, and a non-uniform posterior
+ over the same family would leave less privacy than these numbers show.
+ """
+ inputs, outputs = tuple(inputs), tuple(outputs)
+ observed_fee = sum(inputs) - sum(outputs)
+ if balance_model == "exact":
+ if fee_tolerance != 0:
+ raise ValueError("fee_tolerance applies only to balance_model='fee_tolerant'")
+ mappings = exact_subtransaction_mappings(inputs, outputs, max_coins=max_coins)
+ elif balance_model == "fee_tolerant":
+ mappings = fee_tolerant_subtransaction_mappings(
+ inputs, outputs, fee_tolerance=fee_tolerance, max_coins=max_coins
+ )
+ else:
+ raise ValueError("balance_model must be 'exact' or 'fee_tolerant'")
+ if not mappings:
+ return ExactLinkAnalysis((), (), 0.0, (), balance_model, observed_fee, fee_tolerance)
+
+ counts = _link_counts(mappings, len(inputs), len(outputs))
+ total = len(mappings)
+ matrix = tuple(tuple(count / total for count in row) for row in counts)
+ deterministic = tuple(
+ (i, j)
+ for i, row in enumerate(matrix)
+ for j, probability in enumerate(row)
+ if probability == 1.0
+ )
+ return ExactLinkAnalysis(mappings, matrix, log2(total), deterministic,
+ balance_model, observed_fee, fee_tolerance)
+
+
+def _same_block_counts(mappings, size, side):
+ counts = [[0] * size for _ in range(size)]
+ for mapping in mappings:
+ for blocks in mapping.blocks:
+ for first in blocks[side]:
+ for second in blocks[side]:
+ counts[first][second] += 1
+ return tuple(tuple(row) for row in counts)
+
+
+def same_block_probabilities(inputs, outputs, *, max_coins=12, balance_model="exact",
+ fee_tolerance=0):
+ """Compute Maurer's ``p_II`` and ``p_OO`` over the family ``link_analysis`` uses.
+
+ Two coins on the same side are linked when one block holds both. ``p_II`` is
+ the quantity the common-input-ownership heuristic asserts and the one the
+ paper states its own conclusion in, so ``p_IO`` alone does not cover the
+ paper's metrics. Diagonal entries are 1.0 whenever a mapping exists, since a
+ coin always shares its own block.
+ """
+ inputs, outputs = tuple(inputs), tuple(outputs)
+ analysis = link_analysis(inputs, outputs, max_coins=max_coins,
+ balance_model=balance_model, fee_tolerance=fee_tolerance)
+ total = len(analysis.mappings)
+ if not total:
+ return SameBlockAnalysis(0, (), (), balance_model, analysis.observed_fee,
+ fee_tolerance)
+
+ def matrix(size, side):
+ return tuple(
+ tuple(count / total for count in row)
+ for row in _same_block_counts(analysis.mappings, size, side)
+ )
+
+ return SameBlockAnalysis(total, matrix(len(inputs), 0), matrix(len(outputs), 1),
+ balance_model, analysis.observed_fee, fee_tolerance)
+
+
+def exact_link_analysis(inputs, outputs, *, max_coins=12):
+ """Compatibility entry point restricted to exact conservation."""
+ return link_analysis(inputs, outputs, max_coins=max_coins)
+
+
+def fee_tolerant_link_analysis(inputs, outputs, *, fee_tolerance, max_coins=12):
+ """Link analysis under explicit non-negative fee allocation."""
+ return link_analysis(inputs, outputs, max_coins=max_coins,
+ balance_model="fee_tolerant", fee_tolerance=fee_tolerance)
+
+
+def per_coin_link_evidence(inputs, outputs, *, max_coins=12, balance_model="exact",
+ fee_tolerance=0):
+ """Expose per-coin marginals from the exact link matrix without scoring.
+
+ An absent exact mapping is represented explicitly by zero candidates and
+ ``max_link_probability=None``. In particular, fees are not silently
+ balanced or tolerated by this reference baseline.
+ """
+ inputs, outputs = tuple(inputs), tuple(outputs)
+ analysis = link_analysis(inputs, outputs, max_coins=max_coins,
+ balance_model=balance_model, fee_tolerance=fee_tolerance)
+ total = len(analysis.mappings)
+
+ def evidence(role, values, rows):
+ coins = []
+ for index, (value, row) in enumerate(zip(values, rows)):
+ candidates = tuple(
+ ExactLinkCandidate(counterpart_index, count, count / total)
+ for counterpart_index, count in enumerate(row)
+ if count
+ ) if total else ()
+ coins.append(ExactCoinLinkEvidence(
+ role=role,
+ index=index,
+ value=value,
+ mapping_count=total,
+ candidates=candidates,
+ candidate_count=len(candidates),
+ max_link_probability=max(
+ (candidate.probability for candidate in candidates),
+ default=None,
+ ),
+ ))
+ return tuple(coins)
+
+ if total:
+ input_counts = _link_counts(analysis.mappings, len(inputs), len(outputs))
+ output_counts = tuple(
+ tuple(input_counts[i][j] for i in range(len(inputs)))
+ for j in range(len(outputs))
+ )
+ else:
+ input_counts = tuple(() for _ in inputs)
+ output_counts = tuple(() for _ in outputs)
+
+ return ExactPerCoinLinkAnalysis(
+ mapping_count=total,
+ inputs=evidence("input", inputs, input_counts),
+ outputs=evidence("output", outputs, output_counts),
+ )
+
+
+def exact_per_coin_link_evidence(inputs, outputs, *, max_coins=12):
+ """Compatibility entry point restricted to exact conservation."""
+ return per_coin_link_evidence(inputs, outputs, max_coins=max_coins)
diff --git a/decluster/baselines/boltzmann_reference.py b/decluster/baselines/boltzmann_reference.py
new file mode 100644
index 00000000..e3c32b7f
--- /dev/null
+++ b/decluster/baselines/boltzmann_reference.py
@@ -0,0 +1,369 @@
+"""Small, dependency-free port of Boltzmann's aggregate traversal.
+
+The public result deliberately exposes integer multiplicities. They are not
+the same object as Maurer's set of canonical sub-transaction mappings: when a
+transaction pays a fee, Boltzmann's decomposition tree can assign more than one
+occurrence to the same terminal partition.
+
+This implements default ``LINKABILITY`` plus explicit ``MERGE_FEES``,
+``PRECHECK``, known-owner input packing and JoinMarket intrafee bounds. The
+upstream ``MERGE_OUTPUTS`` option is not ported: in the pinned reference
+revision it is documented as unreliable and its packer only examines inputs.
+
+Two upstream layers above the traversal are also absent. Intrafee bounds must
+be supplied by the caller: the reference derives them from a maximum intrafee
+ratio by first testing the transaction for a CoinJoin output pattern and
+estimating the participant count, and neither the test nor the derivation is
+ported, so nothing here decides *whether* a transaction is a CoinJoin or what
+its intrafees would be. Known-owner input groups are likewise given, not
+recovered from shared addresses.
+
+Source: `boltzmann` in `catalog/ctp-sources.json` — LaurentMT, *Boltzmann* (2015).
+This is the port; `baselines.boltzmann` is the analysis built on top of it.
+"""
+
+from collections import defaultdict, deque
+from dataclasses import dataclass
+from math import isfinite
+
+
+@dataclass(frozen=True)
+class BoltzmannReferenceAnalysis:
+ """Counts plus the optional diagnostic state of the pinned reference modes.
+
+ ``precheck_deterministic_links`` uses this module's input-by-output matrix
+ order. Intrafees are the maximum maker receipt and taker payment accepted
+ by the reference matching predicate; they are bounds, not observed fees.
+ """
+
+ combination_count: int
+ link_counts: tuple[tuple[int, ...], ...]
+ probabilities: tuple[tuple[float, ...], ...]
+ observed_fee: int
+ inputs: tuple[int, ...]
+ outputs: tuple[int, ...]
+ merge_fees: bool = False
+ fee_output_index: int | None = None
+ linked_input_groups: tuple[tuple[int, ...], ...] = ()
+ precheck_deterministic_links: tuple[tuple[int, int], ...] = ()
+ intrafees: tuple[float, float] = (0.0, 0.0)
+
+
+def _aggregate_values(values):
+ return tuple(
+ sum(value for index, value in enumerate(values) if mask & (1 << index))
+ for mask in range(1 << len(values))
+ )
+
+
+def _link_counts(input_mask, output_mask, input_count, output_count):
+ return tuple(
+ tuple(int(bool(input_mask & (1 << i)) and bool(output_mask & (1 << o)))
+ for o in range(output_count))
+ for i in range(input_count)
+ )
+
+
+def _add_counts(target, source, multiplier=1):
+ for i, row in enumerate(source):
+ for o, value in enumerate(row):
+ target[i][o] += value * multiplier
+
+
+def _validated_intrafees(intrafees):
+ try:
+ maker, taker = intrafees
+ except (TypeError, ValueError) as exc:
+ raise ValueError("intrafees must contain maker and taker bounds") from exc
+ if any(isinstance(value, bool) or not isinstance(value, (int, float))
+ or not isfinite(value) or value < 0 for value in (maker, taker)):
+ raise ValueError("intrafee bounds must be finite non-negative numbers")
+ return float(maker), float(taker)
+
+
+def boltzmann_reference_analysis(
+ inputs, outputs, *, max_coins=12, merge_fees=False, precheck=False,
+ intrafees=(0, 0),
+):
+ """Reproduce Boltzmann's default aggregate traversal and link matrix.
+
+ Values must be non-negative integers and the observed transaction fee must
+ be non-negative. The bound applies to each side, matching the reference
+ tool's ``max_txos`` guard rather than Maurer's combined-coin bound.
+ ``precheck`` exposes the reference aggregate test. The reference then repacks
+ the deterministic links it finds before running the exhaustive traversal, and
+ this port does not. Across every zero- and positive-fee transaction of up to
+ four coins a side, that repacking changed neither the combination count nor
+ any link count; what it does change is the order of the returned coins, since
+ a pack is reinserted where it sat rather than at its descending-value
+ position. So callers must not read ``inputs`` positionally as reference order
+ under ``precheck``. The two intrafee bounds are accepted only as an explicit
+ hypothesis and disable precheck, as in the pinned implementation.
+ """
+
+ inputs, outputs = tuple(inputs), tuple(outputs)
+ intrafees = _validated_intrafees(intrafees)
+ values = inputs + outputs
+ if not inputs or not outputs:
+ return BoltzmannReferenceAnalysis(
+ 0, (), (), 0, inputs, outputs, merge_fees, None,
+ intrafees=intrafees,
+ )
+ if any(isinstance(value, bool) or not isinstance(value, int) or value < 0
+ for value in values):
+ raise ValueError("coin values must be non-negative integers")
+ fee = sum(inputs) - sum(outputs)
+ if fee < 0:
+ return BoltzmannReferenceAnalysis(
+ 0, (), (), fee, inputs, outputs, merge_fees, None,
+ intrafees=intrafees,
+ )
+ # The reference tool discards null-value txos (OP_RETURN and the like) before
+ # any aggregate is formed, at both of its filter points. Keeping them would
+ # match every aggregate value and inflate the combination count.
+ inputs = tuple(value for value in inputs if value > 0)
+ outputs = tuple(value for value in outputs if value > 0)
+ if not inputs or not outputs:
+ return BoltzmannReferenceAnalysis(
+ 0, (), (), fee, inputs, outputs, merge_fees, None,
+ intrafees=intrafees,
+ )
+
+ output_entries = [(value, False, index) for index, value in enumerate(outputs)]
+ traversal_fee = fee
+ if merge_fees and fee > 0:
+ output_entries.append((fee, True, len(output_entries)))
+ traversal_fee = 0
+ if len(inputs) > max_coins or len(output_entries) > max_coins:
+ raise ValueError("Boltzmann reference baseline exceeds max_coins")
+
+ # TxosLinker sorts both sides by descending value before building its
+ # matrix. Expose that order in the result so asymmetric vectors can be
+ # compared cell-for-cell without an implicit permutation.
+ inputs = tuple(sorted(inputs, reverse=True))
+ output_entries.sort(key=lambda entry: entry[0], reverse=True)
+ outputs = tuple(entry[0] for entry in output_entries)
+ fee_output_index = next(
+ (index for index, entry in enumerate(output_entries) if entry[1]), None
+ )
+
+ in_values = _aggregate_values(inputs)
+ out_values = _aggregate_values(outputs)
+ unique_in_values = sorted(set(in_values))
+ unique_out_values = sorted(set(out_values))
+
+ matching_inputs = []
+ input_value = {}
+ outputs_by_input_value = defaultdict(set)
+ for left in unique_in_values:
+ for right in unique_out_values:
+ difference = left - right
+ if intrafees == (0.0, 0.0) and difference < 0:
+ break
+ maker_fee, taker_fee = intrafees
+ matches = (
+ -maker_fee <= difference <= traversal_fee + taker_fee
+ if intrafees != (0.0, 0.0)
+ else difference <= traversal_fee
+ )
+ if matches:
+ for mask, value in enumerate(in_values):
+ if value == left and mask not in input_value:
+ matching_inputs.append(mask)
+ input_value[mask] = left
+ outputs_by_input_value[left].update(
+ mask for mask, value in enumerate(out_values) if value == right
+ )
+ matching_inputs.sort()
+ deterministic_links = ()
+ if precheck and intrafees == (0.0, 0.0) and matching_inputs:
+ raw_counts = [[0] * len(outputs) for _ in inputs]
+ input_occurrences = [0] * len(inputs)
+ for input_mask in matching_inputs:
+ value = input_value[input_mask]
+ for output_mask in outputs_by_input_value[value]:
+ _add_counts(
+ raw_counts,
+ _link_counts(input_mask, output_mask, len(inputs), len(outputs)),
+ )
+ for index in range(len(inputs)):
+ input_occurrences[index] += int(bool(input_mask & (1 << index)))
+ reference_count = input_occurrences[0]
+ deterministic_links = tuple(
+ (input_index, output_index)
+ for input_index, row in enumerate(raw_counts)
+ for output_index, count in enumerate(row)
+ if count == reference_count
+ )
+ if len(matching_inputs) < 2:
+ return BoltzmannReferenceAnalysis(
+ 0, (), (), fee, inputs, outputs, merge_fees, fee_output_index,
+ precheck_deterministic_links=deterministic_links,
+ intrafees=intrafees,
+ )
+
+ target = matching_inputs[-1]
+ interior = set(matching_inputs[1:-1])
+ decompositions = defaultdict(list)
+ for right in range(target + 1):
+ if right not in interior:
+ continue
+ for left in range(min(right, target - right + 1)):
+ if right & left == 0 and left in interior:
+ decompositions[right + left].append((right, left))
+
+ input_target = (1 << len(inputs)) - 1
+ output_target = (1 << len(outputs)) - 1
+ raw_links = defaultdict(int)
+ initial_outputs = {output_target: {0: (1, 0)}}
+ stack = deque([(0, 0, input_target, initial_outputs)])
+ combination_count = 0
+
+ while stack:
+ index, previous_left, right_input, output_states = stack[-1]
+ choices = decompositions[right_input]
+ next_index = index
+ descended = False
+ for choice_index in range(index, len(choices)):
+ next_index = choice_index
+ left_input = choices[choice_index][1]
+ if left_input > previous_left:
+ next_right_input = choices[choice_index][0]
+ next_states = defaultdict(dict)
+ for right_output, left_outputs in output_states.items():
+ used_output = output_target - right_output
+ parent_count = sum(value[0] for value in left_outputs.values())
+ for left_output in outputs_by_input_value[input_value[left_input]]:
+ if used_output & left_output:
+ continue
+ next_used_output = used_output + left_output
+ next_right_output = output_target - next_used_output
+ if (next_used_output & next_right_output == 0
+ and next_right_output
+ in outputs_by_input_value[input_value[next_right_input]]):
+ next_states[next_right_output][left_output] = (parent_count, 0)
+ stack[-1] = (choice_index + 1, previous_left, right_input, output_states)
+ stack.append((0, left_input, next_right_input, next_states))
+ descended = True
+ break
+ next_index = len(choices)
+ break
+ if descended:
+ continue
+ if next_index <= len(choices) - 1:
+ continue
+
+ _, left_input, right_input, output_states = stack.pop()
+ if not stack:
+ combination_count = output_states[output_target][0][1]
+ continue
+ parent_states = stack[-1][3]
+ for right_output, left_outputs in output_states.items():
+ for left_output, (parent_count, child_count) in left_outputs.items():
+ occurrences = child_count + 1
+ raw_links[(right_input, right_output)] += parent_count
+ raw_links[(left_input, left_output)] += parent_count * occurrences
+ parent_right_output = left_output + right_output
+ for parent_left_output, (count, children) in list(
+ parent_states[parent_right_output].items()):
+ parent_states[parent_right_output][parent_left_output] = (
+ count, children + occurrences
+ )
+
+ combination_count += 1
+ counts = [[0] * len(outputs) for _ in inputs]
+ _add_counts(
+ counts,
+ _link_counts(input_target, output_target, len(inputs), len(outputs)),
+ )
+ for (input_mask, output_mask), multiplicity in raw_links.items():
+ _add_counts(
+ counts,
+ _link_counts(input_mask, output_mask, len(inputs), len(outputs)),
+ multiplicity,
+ )
+ frozen_counts = tuple(tuple(row) for row in counts)
+ probabilities = tuple(
+ tuple(value / combination_count for value in row) for row in frozen_counts
+ )
+ return BoltzmannReferenceAnalysis(
+ combination_count, frozen_counts, probabilities, fee, inputs, outputs,
+ merge_fees, fee_output_index, (), deterministic_links, intrafees,
+ )
+
+
+def _merged_index_groups(groups, size):
+ components = []
+ for raw_group in groups:
+ group = set(raw_group)
+ if not group:
+ continue
+ if any(isinstance(index, bool) or not isinstance(index, int)
+ or not 0 <= index < size for index in group):
+ raise ValueError("linked input index out of range")
+ touching = [component for component in components if component & group]
+ for component in touching:
+ group.update(component)
+ components.remove(component)
+ components.append(group)
+ return tuple(tuple(sorted(group)) for group in sorted(components, key=lambda group: min(group)))
+
+
+def boltzmann_reference_with_linked_inputs(
+ inputs, outputs, linked_inputs, *, max_coins=12, merge_fees=False,
+ precheck=False, intrafees=(0, 0),
+):
+ """Reproduce TxosLinker's input packing and matrix expansion.
+
+ ``linked_inputs`` contains sets of original input indices. Overlapping sets
+ are transitively merged. The aggregate traversal sees one summed input per
+ group; afterward its row is replicated for every original member, exactly
+ as the reference tool unpacks a known-owner input pack.
+ """
+
+ inputs = tuple(inputs)
+ groups = _merged_index_groups(linked_inputs, len(inputs))
+ # Null-value inputs are filtered before the reference packs known owners, so
+ # they join no pack and occupy no row. Caller indices still name the groups.
+ kept = {index for index, value in enumerate(inputs) if value > 0}
+ groups = tuple(
+ kept_group
+ for kept_group in (tuple(i for i in group if i in kept) for group in groups)
+ if kept_group
+ )
+ grouped = {index for group in groups for index in group}
+ entries = [((index,), inputs[index]) for index in sorted(kept - grouped)]
+ entries.extend((group, sum(inputs[index] for index in group)) for group in groups)
+ sorted_entries = sorted(entries, key=lambda entry: entry[1], reverse=True)
+ analysis = boltzmann_reference_analysis(
+ tuple(value for _, value in entries), outputs,
+ max_coins=max_coins, merge_fees=merge_fees, precheck=precheck,
+ intrafees=intrafees,
+ )
+ # The core uses the same stable descending sort as ``sorted_entries``.
+ expanded_rows = []
+ expanded_inputs = []
+ expanded_precheck = []
+ for packed_index, ((members, _), row) in enumerate(
+ zip(sorted_entries, analysis.link_counts)
+ ):
+ for index in members:
+ expanded_inputs.append(inputs[index])
+ expanded_rows.append(row)
+ expanded_precheck.append(packed_index)
+ counts = tuple(expanded_rows)
+ probabilities = tuple(
+ tuple(value / analysis.combination_count for value in row) for row in counts
+ ) if analysis.combination_count else ()
+ deterministic = tuple(
+ (expanded_index, output_index)
+ for expanded_index, packed_index in enumerate(expanded_precheck)
+ for candidate, output_index in analysis.precheck_deterministic_links
+ if candidate == packed_index
+ )
+ return BoltzmannReferenceAnalysis(
+ analysis.combination_count, counts, probabilities, analysis.observed_fee,
+ tuple(expanded_inputs), analysis.outputs, analysis.merge_fees,
+ analysis.fee_output_index, groups, deterministic,
+ analysis.intrafees,
+ )
diff --git a/decluster/baselines/candidate_set_intersection.py b/decluster/baselines/candidate_set_intersection.py
new file mode 100644
index 00000000..334060e4
--- /dev/null
+++ b/decluster/baselines/candidate_set_intersection.py
@@ -0,0 +1,237 @@
+"""Candidate-set intersection: narrowing a coin's origin across co-held observations.
+
+Goldfeder, Kalodner, Reisman and Narayanan describe a cross-transaction intersection attack: a
+coin's plausible origins form a *set*, and coins later shown to be held by the same party must have
+come from a common origin, so what survives is the intersection of their sets. Repeated over
+successive observations the surviving set shrinks, and it can shrink to one.
+
+The paper's Algorithm 2 is implemented below over injected graph and clustering callbacks. Its
+2015--2017 JoinMarket dataset and empirical rates are NOT reproduced, so no measured rate in this
+repository is attributed to Goldfeder et al. In particular this module states no shrink law. The
+reading that each observation cuts the candidate set by a constant factor is not Goldfeder's: the
+intersection paper demonstrates the attack and states no such law. It is not sourced elsewhere
+here either -- the statistical-disclosure line of work bounds the observations an adversary needs
+by a signal-to-noise condition and a confidence interval, not by a constant factor per observation.
+``decluster/intersect.py`` carries the same distinction and this module does not weaken it.
+
+The scope is deliberately narrow. This module takes candidate sets; it does not compute them.
+Where a set comes from -- a backward provenance walk, an address clustering, a wallet's own history
+-- is outside it, and so is deciding that the coins were really co-held. Both are
+``decluster/intersect.py``'s business: that module wires the same mechanism to this repository's
+ancestry walk and subordinates the result to ``cluster_refined``, which can refuse the co-spend the
+whole narrowing is conditional on.
+
+An empty intersection is a refusal, never an identification. Observations that share no candidate
+are inconsistent -- one of them is wrong, or the coins were not co-held -- and the attack has no
+answer. Reporting that as a maximal narrowing would turn the strongest evidence of a broken
+assumption into the strongest claim.
+
+Source: `goldfeder` in `catalog/ctp-sources.json` — the candidate-set intersection this
+narrows with; `scroll-intersection` states the same attack informally.
+"""
+
+from dataclasses import dataclass
+from math import log2
+from typing import Hashable
+
+
+@dataclass(frozen=True)
+class IntersectionStep:
+ """What one observation did to the surviving set."""
+
+ index: int
+ observed: int
+ before: int
+ after: int
+ narrowing_bits: float | None
+
+
+@dataclass(frozen=True)
+class IntersectionResult:
+ """The surviving candidates and the narrowing that produced them.
+
+ ``identified`` is the lone survivor of a consistent run and ``None`` otherwise -- including
+ when the run refused, so a refusal can never be read as an identification. ``inconsistent_at``
+ is the index of the observation that emptied the set, or ``None``. A run with no observations
+ at all has empty ``steps``: it did not refuse, it was never asked.
+ """
+
+ surviving: frozenset
+ steps: tuple[IntersectionStep, ...]
+ universe_size: int | None
+ narrowing_bits: float | None
+ identified: Hashable | None
+ inconsistent_at: int | None
+
+
+@dataclass(frozen=True)
+class GoldfederIntersectionResult:
+ """Algorithm 2's candidate clusters and unique-or-refuse verdict."""
+
+ candidate_sets: tuple[frozenset, ...]
+ surviving: frozenset
+ identified: Hashable | None
+ incorrect_assumptions: bool
+
+
+def join_ancestors(coin, rounds, predecessors_of):
+ """Return coins reachable backwards through at most ``rounds`` joins.
+
+ ``predecessors_of(c)`` must return the inputs of the join transaction that
+ created ``c``, or an empty iterable when that transaction is not a join.
+ The start coin is included by the paper's length-zero-path case.
+ """
+
+ if isinstance(rounds, bool) or not isinstance(rounds, int) or rounds < 0:
+ raise ValueError("rounds must be a non-negative integer")
+ seen = {coin}
+ frontier = {coin}
+ for _ in range(rounds):
+ following = set()
+ for current in frontier:
+ following.update(predecessors_of(current))
+ following -= seen
+ if not following:
+ break
+ seen.update(following)
+ frontier = following
+ return frozenset(seen)
+
+
+def goldfeder_cluster_intersection(coins, rounds, predecessors_of, cluster_of):
+ """Goldfeder et al. Algorithm 2 over an already identified join graph.
+
+ For every mixed coin, collect all coins on join-only backward paths of
+ length at most ``rounds``, lift them to wallet clusters, and intersect the
+ resulting sets. Exactly one survivor is returned as an identification;
+ zero or multiple survivors produce the paper's ``incorrect assumptions``
+ outcome. Join detection and recursive address clustering are explicit
+ injected prerequisites rather than silently replaced by this repository's
+ probabilistic ancestry walk.
+ """
+
+ candidate_sets = tuple(
+ frozenset(cluster_of(ancestor) for ancestor in join_ancestors(
+ coin, rounds, predecessors_of
+ ))
+ for coin in coins
+ )
+ result = intersect_candidate_sets(candidate_sets)
+ identified = result.identified
+ return GoldfederIntersectionResult(
+ candidate_sets=candidate_sets,
+ surviving=result.surviving,
+ identified=identified,
+ incorrect_assumptions=identified is None,
+ )
+
+
+def narrowing_bits(before, after):
+ """``log2(before / after)``, or ``None`` when there is nothing to measure.
+
+ Bits, not a count of removed candidates, because a run's steps start from sets of different
+ sizes. ``None`` for an empty survivor set: an empty intersection is a refusal, not an
+ unbounded narrowing. (``intersect.collapse_bits`` is the same quantity wired to this
+ repository's walk; the duplication keeps this baseline standalone.)
+ """
+ if before <= 0 or after <= 0:
+ return None
+ return log2(before / after)
+
+
+def intersect_candidate_sets(observations, universe_size=None):
+ """Narrow a coin's candidate origins across successive observations of co-held coins.
+
+ ``observations`` is a sequence of candidate sets, one per coin shown to be held with the
+ others; each is any iterable of hashable candidates. ``universe_size``, when known, lets the
+ first observation's own narrowing be counted, since a first set of 8 out of 4096 has already
+ said something; without it the reported bits are explicitly relative to the first observed set
+ and that first step carries no bits.
+
+ Intersection stops at the observation that empties the set. Later observations cannot narrow
+ an empty set, and continuing would let an inconsistent run absorb them silently.
+ """
+ surviving = None
+ steps = []
+ total = 0.0
+ inconsistent_at = None
+
+ for index, observed in enumerate(observations):
+ observed = frozenset(observed)
+ if surviving is None:
+ before = universe_size if universe_size is not None else len(observed)
+ surviving = observed
+ else:
+ before = len(surviving)
+ surviving = surviving & observed
+ bits = narrowing_bits(before, len(surviving))
+ steps.append(IntersectionStep(index, len(observed), before, len(surviving), bits))
+ if bits is not None:
+ total += bits
+ if not surviving:
+ inconsistent_at = index
+ break
+
+ surviving = surviving if surviving is not None else frozenset()
+ identified = None
+ if inconsistent_at is None and len(surviving) == 1:
+ (identified,) = surviving
+ return IntersectionResult(
+ surviving=surviving,
+ steps=tuple(steps),
+ universe_size=universe_size,
+ narrowing_bits=total if surviving else None,
+ identified=identified,
+ inconsistent_at=inconsistent_at,
+ )
+
+
+# --- The results document's fixture, not part of the attack. ---------------------------------
+# A generated family, so `results/RESULTS-candidate-set-intersection.md` reports the mechanism's
+# behaviour on cases a reader can recompute rather than on data that is not in this checkout.
+
+UNIVERSE = 64
+
+
+def scenarios():
+ """Three deterministic longitudinal runs over the same universe: converging, stalled, refusing.
+
+ Candidates are plain integers. Nothing here is a claim about real chain data, and nothing here
+ is taken from the paper -- the shapes are chosen so each of the three outcomes is exhibited
+ once, at sizes a reader can check by hand.
+ """
+ halving = tuple(
+ frozenset(range(UNIVERSE >> k)) for k in range(1, 7)
+ ) # 32, 16, 8, 4, 2, 1 -- every set contains 0
+ repeated = tuple(frozenset(range(8)) for _ in range(4))
+ contradictory = (frozenset(range(8)), frozenset(range(8, 16)), frozenset(range(4)))
+ return (
+ ("converging", halving),
+ ("stalled", repeated),
+ ("contradictory", contradictory),
+ )
+
+
+def manifest_invariants():
+ """Population facts behind the results document, for ``reproducibility.write_manifest``.
+
+ The source digest covers this module's text; it cannot see what the mechanism does with it.
+ The writer and the test both recompute these from ``scenarios()``, so a drifting number fails
+ loudly rather than standing in the document.
+ """
+ runs = {name: intersect_candidate_sets(obs, universe_size=UNIVERSE)
+ for name, obs in scenarios()}
+ return {
+ "universe_size": UNIVERSE,
+ "scenarios": len(runs),
+ "observations_supplied": sum(len(obs) for _, obs in scenarios()),
+ "converging_survivors": len(runs["converging"].surviving),
+ "converging_bits": runs["converging"].narrowing_bits,
+ "converging_identified": runs["converging"].identified,
+ "stalled_survivors": len(runs["stalled"].surviving),
+ "stalled_bits": runs["stalled"].narrowing_bits,
+ "contradictory_survivors": len(runs["contradictory"].surviving),
+ "contradictory_inconsistent_at": runs["contradictory"].inconsistent_at,
+ "identifications": sum(1 for r in runs.values() if r.identified is not None),
+ "refusals": sum(1 for r in runs.values() if r.inconsistent_at is not None),
+ }
diff --git a/decluster/baselines/coinjoin_sudoku.py b/decluster/baselines/coinjoin_sudoku.py
new file mode 100644
index 00000000..82428a47
--- /dev/null
+++ b/decluster/baselines/coinjoin_sudoku.py
@@ -0,0 +1,152 @@
+"""Independent reconstruction of the amount grouping described by CoinJoin Sudoku.
+
+The historical analyzer was not published in the cited repository. This module
+therefore implements only the mechanism stated in the advisory: partition both
+transaction sides into non-empty groups and retain equal-sum pairings. Selection,
+weighting and the fee model are separate, explicitly local adaptations. This module
+does not reproduce the digit-skipping optimization or historical SharedCoin result.
+
+Source: `sudoku` in `catalog/ctp-sources.json`. The original analyzer was never published,
+so this reconstructs the grouping it describes rather than porting it.
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+from itertools import permutations
+
+
+@dataclass(frozen=True)
+class SudokuMapping:
+ """One canonical pairing of input and output index groups."""
+
+ blocks: tuple[tuple[tuple[int, ...], tuple[int, ...]], ...]
+
+
+def _partitions(items):
+ if not items:
+ yield ()
+ return
+ first, *rest = items
+ for partition in _partitions(rest):
+ yield ((first,),) + partition
+ for index in range(len(partition)):
+ blocks = list(partition)
+ blocks[index] = tuple(sorted((first,) + blocks[index]))
+ yield tuple(sorted(blocks, key=lambda block: block[0]))
+
+
+def _fee_outputs(inputs, outputs, fee_unit, max_outputs):
+ fee = sum(inputs) - sum(outputs)
+ if fee < 0:
+ return None
+ if fee == 0:
+ return outputs
+ if fee_unit is None or fee_unit <= 0 or fee % fee_unit:
+ return None
+ fee_count = fee // fee_unit
+ if fee_count > max_outputs - len(outputs):
+ raise ValueError("nominal Sudoku reconstruction exceeds max_coins")
+ return outputs + (fee_unit,) * fee_count
+
+
+def equal_sum_groupings(inputs, outputs, *, fee_unit=None, max_coins=12):
+ """Enumerate every indexed equal-sum grouping described by the advisory.
+
+ ``fee_unit`` is a LOCAL fee model, not a published rule. The advisory states only that the
+ fee is a multiple of a constant (currently 0.0001 BTC) and shows it as a single amount beside
+ the outputs; splitting it into that many equal pseudo-outputs so groups can balance is this
+ module's choice, and it decides the result — the same transaction read with one pseudo-output
+ of the whole fee admits only the trivial grouping. The unavailable analyzer leaves its grouping
+ deduplication semantics unspecified, so equal-valued coins and fee units remain distinct by
+ index here.
+ """
+ inputs, outputs = tuple(inputs), tuple(outputs)
+ if not inputs or not outputs:
+ return ()
+ if not isinstance(max_coins, int) or isinstance(max_coins, bool) or max_coins < 2:
+ raise ValueError("max_coins must be an integer of at least two")
+ if any(
+ not isinstance(value, int) or isinstance(value, bool) or value <= 0
+ for value in inputs + outputs
+ ):
+ raise ValueError("coin values must be positive integers")
+ if len(inputs) + len(outputs) > max_coins:
+ raise ValueError("nominal Sudoku reconstruction exceeds max_coins")
+ balanced_outputs = _fee_outputs(
+ inputs, outputs, fee_unit, max_coins - len(inputs)
+ )
+ if balanced_outputs is None:
+ return ()
+ mappings = set()
+ input_partitions = tuple(_partitions(tuple(range(len(inputs)))))
+ output_partitions = tuple(_partitions(tuple(range(len(balanced_outputs)))))
+ for input_blocks in input_partitions:
+ for output_blocks in output_partitions:
+ if len(input_blocks) != len(output_blocks):
+ continue
+ input_sums = tuple(
+ sum(inputs[index] for index in block) for block in input_blocks
+ )
+ for candidate in permutations(output_blocks):
+ if not all(
+ input_sums[position]
+ == sum(balanced_outputs[index] for index in output_block)
+ for position, output_block in enumerate(candidate)
+ ):
+ continue
+ mapping = SudokuMapping(tuple(sorted(zip(input_blocks, candidate))))
+ mappings.add(mapping)
+ return tuple(sorted(mappings, key=lambda mapping: mapping.blocks))
+
+
+def maximally_separated_groupings(inputs, outputs, *, fee_unit=None, max_coins=12):
+ """Local adaptation retaining groupings with the largest participant count."""
+ mappings = equal_sum_groupings(
+ inputs, outputs, fee_unit=fee_unit, max_coins=max_coins
+ )
+ if not mappings:
+ return ()
+ largest = max(len(mapping.blocks) for mapping in mappings)
+ return tuple(mapping for mapping in mappings if len(mapping.blocks) == largest)
+
+
+def uniform_link_probabilities(inputs, outputs, *, fee_unit=None, max_coins=12):
+ """Local uniform marginals over maximally separated indexed groupings."""
+ mappings = maximally_separated_groupings(
+ inputs, outputs, fee_unit=fee_unit, max_coins=max_coins
+ )
+ if not mappings:
+ return ()
+ denominator = len(mappings)
+ return tuple(
+ tuple(
+ sum(
+ any(
+ input_index in input_block and output_index in output_block
+ for input_block, output_block in mapping.blocks
+ )
+ for mapping in mappings
+ )
+ / denominator
+ for output_index in range(len(outputs))
+ )
+ for input_index in range(len(inputs))
+ )
+
+
+def collapse_fee_permutations(mappings, real_output_count):
+ """Collapse mappings differing only in indexed pseudo-output identities."""
+ return {
+ tuple(
+ sorted(
+ (
+ input_block,
+ tuple(index for index in output_block if index < real_output_count),
+ sum(index >= real_output_count for index in output_block),
+ )
+ for input_block, output_block in mapping.blocks
+ )
+ )
+ for mapping in mappings
+ }
diff --git a/decluster/baselines/kelen_seres_graphs.py b/decluster/baselines/kelen_seres_graphs.py
new file mode 100644
index 00000000..a9fdd7bc
--- /dev/null
+++ b/decluster/baselines/kelen_seres_graphs.py
@@ -0,0 +1,164 @@
+"""Ledger-to-graph transformations from Kelen and Seres, Section 3.
+
+Section 2.2 sets no restrictions on a node's incoming and outgoing amounts, and the
+auxiliary source of Section 2.4 absorbs whatever a node spends without having received.
+So the paper's model does not need opening balances, and `stationary_account_graph`
+does not ask for them.
+
+`temporal_account_graph` does, and the reason is local to that transform rather than
+inherited from the paper: splitting an account into per-receipt snapshots makes each
+snapshot hold a balance, and the carry edge into the next snapshot is only defined when
+the spend does not exceed it. Requiring the balances up front also keeps pre-window state
+from being back-derived out of the observed transfers.
+
+Source: `kelen-seres` in `catalog/ctp-sources.json` — Kelen & Seres, *Towards Measuring
+the Traceability of Cryptocurrencies*, Section 3.
+"""
+
+from collections import defaultdict
+from dataclasses import dataclass
+from typing import Hashable, Iterable
+
+
+Account = Hashable
+Node = Hashable
+
+
+@dataclass(frozen=True)
+class Transfer:
+ identifier: Hashable
+ source: Account
+ destination: Account
+ amount: int
+ timestamp: int
+
+
+@dataclass(frozen=True)
+class WeightedTransactionGraph:
+ nodes: frozenset[Node]
+ edges: dict[tuple[Node, Node], int]
+ balances: dict[Account, int]
+
+
+@dataclass(frozen=True)
+class AbsorbingTransactionChain:
+ transient: tuple[Node, ...]
+ absorbers: tuple[Node, ...]
+ transitions: dict[Node, tuple[tuple[Node, float], ...]]
+
+
+@dataclass(frozen=True)
+class AuxiliarySource:
+ node: Node
+
+
+def absorbing_transaction_chain(graph: WeightedTransactionGraph):
+ """Materialize Section 2.4 auxiliary sources and reversed transitions.
+
+ For every node whose observed outgoing value exceeds its incoming value,
+ an auxiliary predecessor supplies exactly that surplus. Reversing the
+ augmented edges and normalizing by total incoming value implements
+ Equation 1. Auxiliary nodes receive absorbing self-loops.
+ """
+
+ incoming = defaultdict(int)
+ outgoing = defaultdict(int)
+ predecessors = defaultdict(list)
+ for (source, destination), amount in graph.edges.items():
+ if isinstance(amount, bool) or not isinstance(amount, int) or amount <= 0:
+ raise ValueError("graph edge weights must be positive integers")
+ outgoing[source] += amount
+ incoming[destination] += amount
+ predecessors[destination].append((source, amount))
+
+ absorbers = []
+ for node in sorted(graph.nodes, key=repr):
+ surplus = max(outgoing[node] - incoming[node], 0)
+ if surplus:
+ auxiliary = AuxiliarySource(node)
+ absorbers.append(auxiliary)
+ predecessors[node].append((auxiliary, surplus))
+ incoming[node] += surplus
+
+ transitions = {}
+ for node in sorted(graph.nodes, key=repr):
+ total = incoming[node]
+ if total:
+ transitions[node] = tuple(
+ (previous, amount / total)
+ for previous, amount in sorted(predecessors[node], key=lambda row: repr(row[0]))
+ )
+ for absorber in absorbers:
+ transitions[absorber] = ((absorber, 1.0),)
+ transient = tuple(sorted(graph.nodes, key=repr))
+ return AbsorbingTransactionChain(transient, tuple(absorbers), transitions)
+
+
+def _validated(transfers: Iterable[Transfer]):
+ transfers = tuple(transfers)
+ if any(isinstance(tx.amount, bool) or not isinstance(tx.amount, int) or tx.amount <= 0
+ for tx in transfers):
+ raise ValueError("transfer amounts must be positive integers")
+ if len({tx.identifier for tx in transfers}) != len(transfers):
+ raise ValueError("transfer identifiers must be unique")
+ return transfers
+
+
+def stationary_account_graph(transfers: Iterable[Transfer], *, opening_balances=None):
+ """Aggregate transfers between stable account vertices, ignoring time."""
+
+ transfers = _validated(transfers)
+ balances = dict(opening_balances or {})
+ edges = defaultdict(int)
+ nodes = set(balances)
+ for tx in transfers:
+ nodes.update((tx.source, tx.destination))
+ edges[(tx.source, tx.destination)] += tx.amount
+ balances[tx.source] = balances.get(tx.source, 0) - tx.amount
+ balances[tx.destination] = balances.get(tx.destination, 0) + tx.amount
+ return WeightedTransactionGraph(frozenset(nodes), dict(edges), balances)
+
+
+def temporal_account_graph(transfers: Iterable[Transfer], *, opening_balances):
+ """Split an account whenever it receives, carrying its prior balance.
+
+ Transfer order is ``(timestamp, repr(identifier))``. A payment leaves the
+ sender's current snapshot and enters a newly created destination snapshot;
+ the destination's preceding non-zero balance is carried to that snapshot.
+ Consequently a reverse walk from an earlier payment cannot cross into a
+ receipt that happened later.
+
+ `opening_balances` is required, and a snapshot spending more than it holds raises:
+ the carry edge needs a non-negative remainder. This is a condition of the temporal
+ split, not of the paper's graph model.
+ """
+
+ transfers = _validated(transfers)
+ balances = dict(opening_balances)
+ accounts = set(balances)
+ for tx in transfers:
+ accounts.update((tx.source, tx.destination))
+ balances.setdefault(tx.source, 0)
+ balances.setdefault(tx.destination, 0)
+ if any(isinstance(value, bool) or not isinstance(value, int) or value < 0
+ for value in balances.values()):
+ raise ValueError("opening balances must be non-negative integers")
+
+ generation = {account: 0 for account in accounts}
+ nodes = {(account, 0) for account in accounts}
+ edges = defaultdict(int)
+ for tx in sorted(transfers, key=lambda row: (row.timestamp, repr(row.identifier))):
+ if balances[tx.source] < tx.amount:
+ raise ValueError(f"insufficient opening/history balance for {tx.source!r}")
+ source_node = (tx.source, generation[tx.source])
+ previous_destination = (tx.destination, generation[tx.destination])
+ balances[tx.source] -= tx.amount
+ generation[tx.destination] += 1
+ destination_node = (tx.destination, generation[tx.destination])
+ nodes.add(destination_node)
+ prior_balance = balances[tx.destination]
+ if prior_balance:
+ edges[(previous_destination, destination_node)] += prior_balance
+ edges[(source_node, destination_node)] += tx.amount
+ balances[tx.destination] += tx.amount
+ return WeightedTransactionGraph(frozenset(nodes), dict(edges), balances)
diff --git a/decluster/baselines/link_prediction.py b/decluster/baselines/link_prediction.py
new file mode 100644
index 00000000..c6272497
--- /dev/null
+++ b/decluster/baselines/link_prediction.py
@@ -0,0 +1,265 @@
+"""Link prediction driven by de-anonymization, next to the same predictor without it.
+
+The mechanism, stated so it can be checked: run the faithful 2009 propagation
+(:mod:`decluster.baselines.narayanan_shmatikov`) between a target view and an auxiliary view,
+pull the auxiliary view's adjacency back onto target vertices through the mapping it returns,
+and score candidate pairs on the enlarged neighbourhoods. The structure-only arm applies the
+*same* score to the target view alone. The two arms therefore differ in one thing only —
+whether the de-anonymized auxiliary view is available — which is what makes the comparison a
+statement about de-anonymization rather than about two different predictors.
+
+**This is not a reproduction of the 2011 Narayanan--Shi--Rubinstein link-prediction result.**
+The paper combines simulated-annealing weighted graph matching for seed discovery, deterministic
+and candidate-set de-anonymization, unanimous candidate voting, and a 25-feature random forest
+trained partly on de-anonymized test cases. This module implements none of that composite. It is
+a smaller local experiment in transferring auxiliary adjacency through a supplied mapping; no
+number or parameter here may be attributed to the paper. The paper's Algorithm 3 combiner now
+lives separately in :mod:`decluster.baselines.ns_link_prediction_2011`; its full reproduction
+remains open.
+
+Scope, narrowly: no vertex attributes, no timestamps, no edge values, no supervised model, no
+seed discovery. The mapping is direction-aware because propagation is; the prediction is
+undirected, because a common-neighbour score is. Seeds are supplied by the caller together
+with the provenance string that says where they came from, since a mapping propagated from
+seeds drawn out of a withheld correspondence measures something different from one propagated
+from independently observed labels, and the difference is not visible in the scores.
+
+The graph interface is the structural subset :class:`contraction.PseudonymGraph` exposes —
+``vertices``, ``_in`` and ``_out`` — the same subset the propagation consumes.
+
+Source: `ns-linkpred` in `catalog/ctp-sources.json` — Narayanan, Shi & Rubinstein,
+*Link Prediction by De-anonymization* (2011). The propagation it drives is `ns-social`.
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+from itertools import combinations
+from typing import Hashable, Iterable, Mapping, Sequence
+
+from ..graph_deanon import exact_auc, structural_score
+from ..reproducibility import separable
+from .ns_social_attack import propagate
+
+Vertex = Hashable
+Pair = tuple[Vertex, Vertex]
+
+
+def neighbourhoods(graph) -> dict[Vertex, set[Vertex]]:
+ """Undirected adjacency of a directed view, one set per vertex."""
+
+ return {v: set(graph._out.get(v, ())) | set(graph._in.get(v, ()))
+ for v in graph.vertices}
+
+
+def transferred_neighbourhoods(target, auxiliary, mapping: Mapping[Vertex, Vertex]):
+ """Target adjacency enlarged by every auxiliary edge the mapping can pull back.
+
+ An auxiliary edge is transferable only when *both* of its endpoints are images of target
+ vertices; an auxiliary vertex the mapping never claimed has no target identity, so its
+ edges say nothing about the target view. Returns `(neighbourhoods, new_pairs)` where
+ `new_pairs` counts the undirected pairs the transfer added that the target did not
+ already hold — the size of the evidence the arm is being given, which is zero when
+ propagation mapped too little for any auxiliary edge to land inside the mapped set.
+ """
+
+ neighbours = neighbourhoods(target)
+ preimage = {image: node for node, image in mapping.items()}
+ new_pairs = 0
+ for image, node in preimage.items():
+ for other_image in auxiliary._out.get(image, ()):
+ other = preimage.get(other_image)
+ if other is None or other == node:
+ continue
+ if other not in neighbours.setdefault(node, set()):
+ new_pairs += 1
+ neighbours[node].add(other)
+ neighbours.setdefault(other, set()).add(node)
+ return neighbours, new_pairs
+
+
+def score(neighbours: Mapping[Vertex, set], u: Vertex, v: Vertex) -> int:
+ """Common neighbours over closed neighbourhoods.
+
+ On a pair the graph does not join this is exactly the classic common-neighbour count
+ (`graph_deanon.structural_score`). On a pair it does join it is that count plus two,
+ because each endpoint then appears in the other's closed neighbourhood. That term is
+ what carries a *directly* transferred edge: without it the arm would throw away the
+ auxiliary view's plainest statement, "these two transacted". In the structure-only arm
+ it is identically zero, since the pairs being scored are by construction the ones the
+ residual target view does not join.
+ """
+
+ left = neighbours.get(u, frozenset()) | {u}
+ right = neighbours.get(v, frozenset()) | {v}
+ return len(left & right)
+
+
+def open_score(neighbours: Mapping[Vertex, set], u: Vertex, v: Vertex) -> int:
+ """`score` with the closed term ablated: literally `graph_deanon.structural_score`.
+
+ The whole result rests on the closed term, so the ablation is a parameter rather than an
+ argument: pass it as `scorer` and the de-anonymization arm keeps the transferred adjacency
+ but is no longer allowed to read a transferred edge as evidence for the pair it joins.
+ `RESULTS-link-prediction.md` publishes what happens then, which is that the arm's advantage
+ does not shrink, it disappears.
+ """
+
+ return structural_score(u, v, neighbours)
+
+
+def candidate_pairs(graph) -> list[Pair]:
+ """Every unordered pair of vertices the view does not already join.
+
+ Quadratic in the vertex count and deliberately uncapped: it is for controlled runs, and a
+ caller working on a real view should pass the candidate set its own observation policy
+ selects rather than materialise this.
+ """
+
+ neighbours = neighbourhoods(graph)
+ return [(u, v) for u, v in combinations(sorted(graph.vertices, key=repr), 2)
+ if v not in neighbours.get(u, ())]
+
+
+@dataclass(frozen=True)
+class Prediction:
+ """A run of the attack: the mapping it propagated and what it predicts from it."""
+
+ mapping: dict[Vertex, Vertex]
+ rounds: int
+ transferred_pairs: int
+ candidates: tuple[Pair, ...]
+ scores: tuple[int, ...]
+
+ def ranked(self) -> list[tuple[Pair, int]]:
+ """Candidates highest score first; ties keep the order they were supplied in."""
+
+ return sorted(zip(self.candidates, self.scores), key=lambda item: -item[1])
+
+ def declared(self, cutoff: int) -> list[Pair]:
+ return [pair for pair, value in zip(self.candidates, self.scores) if value >= cutoff]
+
+
+def predict(target, auxiliary, seeds: Mapping[Vertex, Vertex], candidates: Iterable[Pair], *,
+ seed_provenance: str, theta: float = 1.5, scorer=score) -> Prediction:
+ """Propagate, transfer the auxiliary view's edges, score the candidates.
+
+ `seed_provenance` is required and unused by the computation: it is here so a caller cannot
+ obtain a prediction without stating where its seeds came from, which is the fact that
+ decides whether the run is an attack or a demonstration of what an attack would do with an
+ identity it was handed.
+ """
+
+ result = propagate(target, auxiliary, seeds, theta=theta)
+ neighbours, new_pairs = transferred_neighbourhoods(target, auxiliary, result.mapping)
+ candidates = tuple(candidates)
+ return Prediction(mapping=dict(result.mapping), rounds=result.rounds,
+ transferred_pairs=new_pairs, candidates=candidates,
+ scores=tuple(scorer(neighbours, u, v) for u, v in candidates))
+
+
+@dataclass(frozen=True)
+class Arm:
+ """One predictor's showing on the scored pairs, at one cutoff."""
+
+ name: str
+ declared: int
+ correct: int
+ precision: float | None
+ recall: float
+ auc: float | None
+
+
+def _arm(name: str, scores: Sequence[int], labels: Sequence[bool], cutoff: int) -> Arm:
+ declared = [index for index, value in enumerate(scores) if value >= cutoff]
+ correct = sum(1 for index in declared if labels[index])
+ positives = sum(1 for label in labels if label)
+ return Arm(
+ name=name, declared=len(declared), correct=correct,
+ precision=correct / len(declared) if declared else None,
+ recall=correct / positives if positives else 0.0,
+ auc=exact_auc(scores, labels),
+ )
+
+
+def _discordant(left: Sequence[int], right: Sequence[int], labels: Sequence[bool],
+ cutoff: int) -> tuple[int, int]:
+ """Per-pair wins for the paired test: pairs exactly one arm called correctly.
+
+ A call is correct when the declaration at the cutoff agrees with the pair's held-out
+ status, so a false positive counts against an arm exactly as a miss does.
+ """
+
+ wins_left = wins_right = 0
+ for here, there, label in zip(left, right, labels):
+ right_here = (here >= cutoff) == bool(label)
+ right_there = (there >= cutoff) == bool(label)
+ wins_left += right_here and not right_there
+ wins_right += right_there and not right_here
+ return wins_left, wins_right
+
+
+@dataclass(frozen=True)
+class LinkPredictionResult:
+ seed_provenance: str
+ seed_size: int
+ theta: float
+ cutoff: int
+ rounds: int
+ mapping_size: int
+ transferred_pairs: int
+ held_out: int
+ negatives: int
+ deanonymization: Arm
+ structure_only: Arm
+ separability: dict[str, float | int | str | None]
+
+
+def compare(target, auxiliary, seeds: Mapping[Vertex, Vertex], held_out: Iterable[Pair],
+ negatives: Iterable[Pair], *, seed_provenance: str, theta: float = 1.5,
+ cutoff: int = 2, min_correct_gain: int = 1,
+ scorer=score) -> LinkPredictionResult:
+ """Score the same pairs with and without the de-anonymized auxiliary view.
+
+ `held_out` are target links removed from the view before propagation ran; `negatives` are
+ pairs the target view does not join and never did. Both must be absent from `target`, or
+ the arms are being asked to predict something they can read off their own input.
+
+ Both arms use `scorer`, so an ablation of the score changes the comparison and not one side
+ of it. Under the default the de-anonymization arm's score **dominates** the structure-only
+ arm's on every pair — the transfer only ever adds neighbours — so its declaration set is a
+ superset at any cutoff and its recall cannot come out lower. That is a property of the
+ construction and not a finding; what can and does invert is the AUC and the paired
+ discordant count, because the arm pays for the extra declarations in false positives.
+
+ `min_correct_gain` is the pre-registered effect for the paired comparison and the verdict
+ is a *direction* only: "a" says the de-anonymization arm called pairs the structure-only
+ arm got wrong, and says nothing about the level, which precision and recall carry.
+ """
+
+ held_out, negatives = list(held_out), list(negatives)
+ pairs = held_out + negatives
+ labels = [True] * len(held_out) + [False] * len(negatives)
+ structure = neighbourhoods(target)
+ for u, v in pairs:
+ if v in structure.get(u, ()):
+ raise ValueError(f"pair {(u, v)} is already an edge of the target view")
+
+ prediction = predict(target, auxiliary, seeds, pairs, seed_provenance=seed_provenance,
+ theta=theta, scorer=scorer)
+ deanon_scores = list(prediction.scores)
+ structure_scores = [scorer(structure, u, v) for u, v in pairs]
+ wins_deanon, wins_structure = _discordant(deanon_scores, structure_scores, labels, cutoff)
+ verdict, p = separable(wins_deanon, wins_structure, wins_deanon - wins_structure,
+ min_correct_gain)
+ return LinkPredictionResult(
+ seed_provenance=seed_provenance, seed_size=len(seeds), theta=theta, cutoff=cutoff,
+ rounds=prediction.rounds, mapping_size=len(prediction.mapping),
+ transferred_pairs=prediction.transferred_pairs,
+ held_out=len(held_out), negatives=len(negatives),
+ deanonymization=_arm("deanonymization", deanon_scores, labels, cutoff),
+ structure_only=_arm("structure_only", structure_scores, labels, cutoff),
+ separability={"deanonymization_only": wins_deanon, "structure_only": wins_structure,
+ "min_correct_gain": min_correct_gain, "p": p,
+ "beats_structure_only": verdict},
+ )
diff --git a/decluster/baselines/maurer.py b/decluster/baselines/maurer.py
new file mode 100644
index 00000000..4753f3f0
--- /dev/null
+++ b/decluster/baselines/maurer.py
@@ -0,0 +1,121 @@
+"""Exact balanced sub-transaction mappings.
+
+This is the deliberately narrow reference baseline: every participant block must
+balance exactly. Fees, amount tolerances, roundness, and contextual priors are
+outside this module.
+
+Source: `maurer` in `catalog/ctp-sources.json` — Maurer, Neudecker & Florian, *Anonymous
+CoinJoin Transactions with Arbitrary Values* (2017), §4.1-4.2.
+"""
+
+from dataclasses import dataclass
+from itertools import permutations
+
+
+@dataclass(frozen=True)
+class ExactMapping:
+ """One mapping, represented as canonical input/output index blocks."""
+
+ blocks: tuple[tuple[tuple[int, ...], tuple[int, ...]], ...]
+
+
+def _partitions(items):
+ """Yield every set partition once, with canonical block ordering."""
+ if not items:
+ yield ()
+ return
+ first, *rest = items
+ for partition in _partitions(rest):
+ yield ((first,),) + partition
+ for index in range(len(partition)):
+ blocks = list(partition)
+ blocks[index] = tuple(sorted((first,) + blocks[index]))
+ yield tuple(sorted(blocks, key=lambda block: block[0]))
+
+
+def _canonical(blocks):
+ return ExactMapping(tuple(sorted(blocks, key=lambda pair: (pair[0], pair[1]))))
+
+
+def mapping_refines(finer, coarser):
+ """Return whether ``finer`` can produce ``coarser`` by merging blocks.
+
+ Maurer et al. call the coarser mapping *derived*. Containment must hold on
+ both the input and output side of the same paired block; comparing only the
+ number of blocks is insufficient because refinement-maximal mappings need
+ not all have the globally largest block count.
+ """
+
+ return all(
+ any(
+ set(input_block) <= set(other_inputs)
+ and set(output_block) <= set(other_outputs)
+ for other_inputs, other_outputs in coarser.blocks
+ )
+ for input_block, output_block in finer.blocks
+ )
+
+
+def non_derived_mappings(mappings):
+ """Keep mappings that no distinct valid mapping strictly refines.
+
+ This is the family used for the linkability evaluation in Maurer et al.;
+ the paper excludes derived mappings because merging participant blocks adds
+ no new information. ``exact_subtransaction_mappings`` intentionally keeps
+ returning the full family so existing callers do not change semantics.
+ """
+
+ mappings = tuple(mappings)
+ return tuple(
+ mapping
+ for mapping in mappings
+ if not any(
+ other.blocks != mapping.blocks and mapping_refines(other, mapping)
+ for other in mappings
+ )
+ )
+
+
+def exact_non_derived_mappings(inputs, outputs, *, max_coins=12):
+ """Enumerate Maurer et al.'s exact non-derived mapping family."""
+
+ return non_derived_mappings(
+ exact_subtransaction_mappings(inputs, outputs, max_coins=max_coins)
+ )
+
+
+def exact_subtransaction_mappings(inputs, outputs, *, max_coins=12):
+ """Enumerate all exact, non-empty input/output block mappings.
+
+ Each input and output occurs in exactly one block and each block independently
+ conserves value. The all-coins/single-owner interpretation is included. The
+ bound is explicit because exhaustive set-partition enumeration is exponential.
+
+ Values must be integers: the paper's coin domain is satoshis, and binary
+ floating point silently breaks exact conservation. ``0.1 + 0.2 + 0.4`` is
+ not ``0.7``, so a float-valued transaction loses mappings without error.
+ """
+ inputs, outputs = tuple(inputs), tuple(outputs)
+ if not inputs or not outputs:
+ return ()
+ if any(isinstance(value, bool) or not isinstance(value, int) or value < 0
+ for value in inputs + outputs):
+ raise ValueError("coin values must be non-negative integers")
+ if len(inputs) + len(outputs) > max_coins:
+ raise ValueError("exact baseline exceeds max_coins")
+ if sum(inputs) != sum(outputs):
+ return ()
+
+ input_partitions = tuple(_partitions(list(range(len(inputs)))))
+ output_partitions = tuple(_partitions(list(range(len(outputs)))))
+ mappings = set()
+ for in_blocks in input_partitions:
+ for out_blocks in output_partitions:
+ if len(in_blocks) != len(out_blocks):
+ continue
+ in_sums = [sum(inputs[i] for i in block) for block in in_blocks]
+ for permuted_outputs in permutations(out_blocks):
+ if all(in_sums[k] == sum(outputs[j] for j in out_block)
+ for k, out_block in enumerate(permuted_outputs)):
+ mappings.add(_canonical(tuple(zip(in_blocks, permuted_outputs))))
+ return tuple(sorted(mappings, key=lambda mapping: mapping.blocks))
diff --git a/decluster/baselines/narayanan_shmatikov.py b/decluster/baselines/narayanan_shmatikov.py
new file mode 100644
index 00000000..8bfcaac2
--- /dev/null
+++ b/decluster/baselines/narayanan_shmatikov.py
@@ -0,0 +1,313 @@
+"""Narayanan--Shmatikov (2009) social-graph propagation kernel.
+
+This module deliberately contains only the topology-only propagation algorithm. In
+particular, it does not use vertex attributes, edge attributes, hub filters, rarity
+weights, or single-view labels. ``left`` and ``right`` must be distinct directed graph
+views and ``seeds`` is a partial one-to-one correspondence between them.
+
+The graph interface is the small subset exposed by :class:`contraction.PseudonymGraph`:
+``vertices``, ``_in`` and ``_out``. Keeping that interface structural also makes the
+baseline usable by controlled synthetic tests without coupling it to Bitcoin records.
+
+The score, eccentricity gate, direction handling, degree normalization and reverse match follow
+the paper's propagation pseudocode. This is not the complete published attack: seeds are supplied
+instead of found with the paper's clique search. Results from this module therefore test the
+propagation mechanism, not an end-to-end reproduction of the 2009 experiment.
+
+Revisiting is specified further than "occasional correction". The propagation step iterates over
+*every* left vertex, already-mapped ones included, and assigns over any existing image, so
+revisiting is the main loop and not a separate pass; it passes the same eccentricity gate and
+reverse match as a first match; and the complexity argument states the schedule, a node being
+revisited only once its number of already-mapped neighbours has grown by at least one. What the
+paper leaves open is the conflict alone: its score skips every right vertex that is already an
+image, so a revisited node cannot even re-propose the image it currently holds, and nothing says
+what to do when the winner belongs to someone else. :func:`conservative_revisit` is the declared
+local policy for that gap. It is a bounded leave-one-out pass rather than the main loop, and it
+is off by default, so published runs of this module revisited nothing at all where the paper
+revisits throughout.
+
+The eccentricity population has two readings in the paper. The prose scores "a single node in v1
+and each unmapped node in v2"; the pseudocode initializes one score per right vertex and leaves
+the claimed ones at zero, because only the vote loop skips them. ``population`` selects between
+them and defaults to the prose reading, which is the one every published run used.
+
+Source: `ns-social` in `catalog/ctp-sources.json` — Narayanan & Shmatikov,
+*De-anonymizing Social Networks* (2009).
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+from math import fsum, sqrt
+from statistics import pstdev
+from typing import Hashable, Mapping
+
+Vertex = Hashable
+
+
+def eccentricity(scores: Mapping[Vertex, float]) -> float:
+ """Return ``(max - second_max) / population_stddev``.
+
+ Every score handed in belongs to the population, zeros included; which vertices are
+ handed in is the caller's choice of reading (see ``population``). Fewer than two
+ candidates or zero variance cannot establish a unique winner and therefore have
+ eccentricity zero.
+ """
+
+ values = list(scores.values())
+ if len(values) < 2:
+ return 0.0
+ ordered = sorted(values, reverse=True)
+ sigma = pstdev(values)
+ return (ordered[0] - ordered[1]) / sigma if sigma else 0.0
+
+
+def match_scores(left, right, mapping: Mapping[Vertex, Vertex], node: Vertex):
+ """Compute the paper's direction-aware, degree-normalized candidate scores.
+
+ An incoming mapped neighbour votes through incoming edges in the right view and
+ contributes ``1/sqrt(candidate.in_degree)``. Outgoing neighbours analogously use
+ outgoing degree. Already claimed right-hand vertices are excluded.
+ """
+
+ claimed = set(mapping.values())
+ scores = {candidate: 0.0 for candidate in right.vertices if candidate not in claimed}
+
+ for neighbour in sorted(left._in[node], key=repr):
+ image = mapping.get(neighbour)
+ if image is None:
+ continue
+ for candidate in sorted(right._out[image], key=repr):
+ if candidate in scores:
+ degree = len(right._in[candidate])
+ scores[candidate] += 1.0 / sqrt(degree)
+
+ for neighbour in sorted(left._out[node], key=repr):
+ image = mapping.get(neighbour)
+ if image is None:
+ continue
+ for candidate in sorted(right._in[image], key=repr):
+ if candidate in scores:
+ degree = len(right._out[candidate])
+ scores[candidate] += 1.0 / sqrt(degree)
+
+ return scores
+
+
+def _winner(scores: Mapping[Vertex, float], theta: float):
+ """Readable gate over a materialised score dict, the counterpart of :func:`match_scores`.
+
+ :func:`propagate` runs the sparse pair instead; this one exists so that pair has a dense
+ reference to be checked against, which is what ``test_ns_social_baseline`` does with it.
+ """
+
+ if not scores or max(scores.values()) <= 0.0 or eccentricity(scores) < theta:
+ return None
+ best = max(scores.values())
+ winners = [node for node, score in scores.items() if score == best]
+ return winners[0] if len(winners) == 1 else None
+
+
+def _sparse_winner(scores: Mapping[Vertex, float], population: int, theta: float):
+ """Winner with the implicit zero scores included in the candidate population.
+
+ The paper's score has sparse support: only vertices adjacent to an already mapped
+ neighbour can receive a vote. Materialising every zero made a real 17k-by-17k run
+ quadratic without changing either the mean, variance, or eccentricity.
+ """
+
+ if not scores or population < 2:
+ return None
+ ordered = sorted(scores.values(), reverse=True)
+ best = ordered[0]
+ second = ordered[1] if len(ordered) > 1 else 0.0
+ if best <= 0.0 or best == second:
+ return None
+ total = sum(ordered)
+ mean = total / population
+ variance = (sum(value * value for value in ordered) / population) - mean * mean
+ sigma = sqrt(max(0.0, variance))
+ if not sigma or (best - second) / sigma < theta:
+ return None
+ winners = [node for node, score in scores.items() if score == best]
+ return winners[0] if len(winners) == 1 else None
+
+
+def _sparse_match_scores(left, right, mapping, node, population="unmapped"):
+ claimed = set(mapping.values())
+ contributions = {}
+
+ def vote(candidate, degree):
+ if candidate not in claimed and degree:
+ contributions.setdefault(candidate, []).append(1.0 / sqrt(degree))
+
+ for neighbour in left._in[node]:
+ image = mapping.get(neighbour)
+ if image is not None:
+ for candidate in right._out[image]:
+ vote(candidate, len(right._in[candidate]))
+ for neighbour in left._out[node]:
+ image = mapping.get(neighbour)
+ if image is not None:
+ for candidate in right._in[image]:
+ vote(candidate, len(right._out[candidate]))
+ scores = {candidate: fsum(values) for candidate, values in contributions.items()}
+ total = len(right.vertices)
+ return scores, total if population == "all" else total - len(claimed)
+
+
+@dataclass(frozen=True)
+class PropagationResult:
+ mapping: dict[Vertex, Vertex]
+ rounds: int
+ accepted_per_round: tuple[int, ...]
+ revisit_policy: str = "disabled"
+ remapped_per_round: tuple[int, ...] = ()
+
+
+@dataclass(frozen=True)
+class RevisitResult:
+ mapping: dict[Vertex, Vertex]
+ remapped_per_round: tuple[int, ...]
+
+
+def conservative_revisit(
+ left,
+ right,
+ accepted: Mapping[Vertex, Vertex],
+ seeds: Mapping[Vertex, Vertex],
+ *,
+ theta: float = 1.5,
+ max_rounds: int = 5,
+ population: str = "unmapped",
+):
+ """Re-score accepted nodes without changing seeds or stealing claimed images.
+
+ The paper revisits inside its propagation step, under the same gates and on a stated
+ schedule; what it leaves open is what a revisited node may take. Refusing to take a
+ claimed image, freeing a node's own image while it is re-scored, and bounding the pass
+ are this module's answer to that, and therefore an explicit adaptation.
+ """
+
+ if max_rounds < 0:
+ raise ValueError("max_rounds must be non-negative")
+ mapping = dict(accepted)
+ if any(mapping.get(node) != image for node, image in seeds.items()):
+ raise ValueError("accepted mapping must contain every seed unchanged")
+ if len(mapping) != len(set(mapping.values())):
+ raise ValueError("accepted mapping must be one-to-one")
+ if not set(mapping).issubset(left.vertices):
+ raise ValueError("accepted mapping contains a vertex absent from the left view")
+ if not set(mapping.values()).issubset(right.vertices):
+ raise ValueError("accepted mapping contains a vertex absent from the right view")
+ remapped_per_round = []
+ for _ in range(max_rounds):
+ remapped = 0
+ for node in sorted((vertex for vertex in mapping if vertex not in seeds), key=repr):
+ old = mapping.pop(node)
+ scores, size = _sparse_match_scores(left, right, mapping, node, population)
+ candidate = _sparse_winner(scores, size, theta)
+ if candidate is not None:
+ reverse = {image: vertex for vertex, image in mapping.items()}
+ reverse_scores, reverse_size = _sparse_match_scores(
+ right, left, reverse, candidate, population
+ )
+ if _sparse_winner(reverse_scores, reverse_size, theta) != node:
+ candidate = None
+ mapping[node] = old if candidate is None else candidate
+ remapped += candidate is not None and candidate != old
+ if not remapped:
+ break
+ remapped_per_round.append(remapped)
+ return RevisitResult(mapping, tuple(remapped_per_round))
+
+
+def propagate(
+ left,
+ right,
+ seeds: Mapping[Vertex, Vertex],
+ theta: float = 1.5,
+ *,
+ conservative_revisit_rounds: int = 0,
+ population: str = "unmapped",
+):
+ """Propagate a seed mapping to convergence using the 2009 scoring kernel.
+
+ Each proposed match must clear the eccentricity threshold in both directions and
+ must map back to the proposing node. The returned mapping includes the seeds.
+
+ ``population`` is the eccentricity denominator's candidate set: ``"unmapped"`` for the
+ paper's prose, ``"all"`` for its pseudocode, which keeps claimed vertices in at zero.
+ The two differ by the claimed fraction, negligible on a real view and not on a small
+ fixture; every published run of this module used the default.
+ """
+
+ if conservative_revisit_rounds < 0:
+ raise ValueError("conservative_revisit_rounds must be non-negative")
+ if population not in ("unmapped", "all"):
+ raise ValueError("population must be 'unmapped' or 'all'")
+ mapping = dict(seeds)
+ if len(mapping) != len(set(mapping.values())):
+ raise ValueError("seed mapping must be one-to-one")
+ if not set(mapping).issubset(left.vertices):
+ raise ValueError("seed contains a vertex absent from the left view")
+ if not set(mapping.values()).issubset(right.vertices):
+ raise ValueError("seed contains a vertex absent from the right view")
+
+ accepted_per_round = []
+ while True:
+ accepted = 0
+ for node in sorted((v for v in left.vertices if v not in mapping), key=repr):
+ scores, size = _sparse_match_scores(left, right, mapping, node, population)
+ candidate = _sparse_winner(scores, size, theta)
+ if candidate is None:
+ continue
+
+ reverse = {right_node: left_node for left_node, right_node in mapping.items()}
+ reverse_scores, reverse_size = _sparse_match_scores(
+ right, left, reverse, candidate, population
+ )
+ reverse_candidate = _sparse_winner(reverse_scores, reverse_size, theta)
+ if reverse_candidate != node:
+ continue
+ mapping[node] = candidate
+ accepted += 1
+ if not accepted:
+ break
+ accepted_per_round.append(accepted)
+
+ revisit = conservative_revisit(
+ left,
+ right,
+ mapping,
+ seeds,
+ theta=theta,
+ max_rounds=conservative_revisit_rounds,
+ population=population,
+ )
+ policy = (
+ "conservative_leave_one_out" if conservative_revisit_rounds else "disabled"
+ )
+ return PropagationResult(
+ revisit.mapping,
+ len(accepted_per_round),
+ tuple(accepted_per_round),
+ policy,
+ revisit.remapped_per_round,
+ )
+
+
+def evaluate(mapping: Mapping[Vertex, Vertex], truth: Mapping[Vertex, Vertex], seeds=()):
+ """Precision and non-seed coverage against an independently supplied truth map."""
+
+ seed_nodes = set(seeds)
+ declared = {node: image for node, image in mapping.items() if node not in seed_nodes}
+ eligible = {node: image for node, image in truth.items() if node not in seed_nodes}
+ correct = sum(truth.get(node) == image for node, image in declared.items())
+ return {
+ "declared": len(declared),
+ "correct": correct,
+ "eligible": len(eligible),
+ "precision": correct / len(declared) if declared else None,
+ "coverage": correct / len(eligible) if eligible else 0.0,
+ }
diff --git a/decluster/baselines/ns_link_prediction_2011.py b/decluster/baselines/ns_link_prediction_2011.py
new file mode 100644
index 00000000..abd67d22
--- /dev/null
+++ b/decluster/baselines/ns_link_prediction_2011.py
@@ -0,0 +1,533 @@
+"""Components of Narayanan, Shi and Rubinstein (2011), one at a time.
+
+These are paper-faithful components, not the complete published pipeline:
+Algorithm 1's similarity, Algorithm 2's distance and its annealer, the two-stage
+propagation, and the Algorithm 3 cascade, which takes its machine-learning score
+from the caller. Confidence pruning, the correction of accepted mappings and the
+25-feature random forest are absent. :func:`pipeline_coverage` is the machine-
+readable version of that boundary and :func:`require_components` enforces it.
+
+Source: `ns-linkpred` in `catalog/ctp-sources.json` — Narayanan, Shi & Rubinstein,
+*Link Prediction by De-anonymization* (2011).
+"""
+
+from dataclasses import dataclass
+from enum import Enum
+from math import exp, sqrt
+import random
+from typing import Callable, Hashable, Iterable, Mapping
+
+
+Vertex = Hashable
+Pair = tuple[Vertex, Vertex]
+
+
+class ComponentStatus(str, Enum):
+ IMPLEMENTED = "implemented"
+ PARTIAL = "partial"
+ MATHEMATICALLY_UNDEFINED = "mathematically_undefined"
+ NOT_REPRODUCED = "not_reproduced"
+
+
+class PipelineComponent(str, Enum):
+ ALGORITHM1_SIMILARITY = "algorithm1_similarity"
+ ALGORITHM2_POSITIVE_WEIGHTS = "algorithm2_positive_weights"
+ ALGORITHM2_DUMMY_WEIGHTS = "algorithm2_dummy_weights"
+ ANNEALING = "annealing"
+ TWO_STAGE_MAPPING = "two_stage_mapping"
+ CONFIDENCE_PRUNING = "confidence_pruning"
+ ACCEPTED_MAPPING_CORRECTION = "accepted_mapping_correction"
+ ALGORITHM3_CASCADE = "algorithm3_cascade"
+ LEARNED_25_FEATURE_MODEL = "learned_25_feature_model"
+
+
+@dataclass(frozen=True)
+class ComponentCoverage:
+ component: PipelineComponent
+ status: ComponentStatus
+ limitation: str | None = None
+
+
+class IncompletePipelineError(ValueError):
+ """A caller requested components this baseline does not reproduce."""
+
+
+def pipeline_coverage() -> tuple[ComponentCoverage, ...]:
+ """Return the executable boundary of the 2011 baseline.
+
+ ``implemented`` applies only to the named component, not the paper's end-to-end experiment.
+ The annealer is partial because its fixed iteration budget, RNG injection and best-state return
+ are reproducibility controls rather than parameters reported by the paper.
+ """
+
+ return (
+ ComponentCoverage(PipelineComponent.ALGORITHM1_SIMILARITY, ComponentStatus.IMPLEMENTED),
+ ComponentCoverage(
+ PipelineComponent.ALGORITHM2_POSITIVE_WEIGHTS, ComponentStatus.IMPLEMENTED
+ ),
+ ComponentCoverage(
+ PipelineComponent.ALGORITHM2_DUMMY_WEIGHTS,
+ ComponentStatus.IMPLEMENTED,
+ "a dummy-incident node term is fixed at zero; the paper states this in prose, "
+ "not in the printed formula",
+ ),
+ ComponentCoverage(
+ PipelineComponent.ANNEALING,
+ ComponentStatus.PARTIAL,
+ "iteration budget, RNG and best-state return are explicit local controls",
+ ),
+ ComponentCoverage(
+ PipelineComponent.TWO_STAGE_MAPPING,
+ ComponentStatus.IMPLEMENTED,
+ "the paper picks an arbitrary unmapped node and stage 1 feeds back, so the "
+ "``repr`` order this driver imposes is a result-bearing local choice; injectivity "
+ "comes from Algorithm 3's 1-1 mapping, not from the propagation section",
+ ),
+ ComponentCoverage(
+ PipelineComponent.CONFIDENCE_PRUNING,
+ ComponentStatus.NOT_REPRODUCED,
+ "an implementation-complete pruning policy is not available",
+ ),
+ ComponentCoverage(
+ PipelineComponent.ACCEPTED_MAPPING_CORRECTION,
+ ComponentStatus.NOT_REPRODUCED,
+ "schedule and conflict resolution are not specified sufficiently",
+ ),
+ ComponentCoverage(PipelineComponent.ALGORITHM3_CASCADE, ComponentStatus.IMPLEMENTED),
+ ComponentCoverage(
+ PipelineComponent.LEARNED_25_FEATURE_MODEL,
+ ComponentStatus.NOT_REPRODUCED,
+ "the caller supplies an ML score; the published feature producer is absent",
+ ),
+ )
+
+
+def require_components(*components: PipelineComponent) -> None:
+ """Refuse unless every requested component is implemented in this baseline."""
+
+ coverage = {row.component: row for row in pipeline_coverage()}
+ unavailable = [coverage[component] for component in components
+ if coverage[component].status is not ComponentStatus.IMPLEMENTED]
+ if unavailable:
+ details = ", ".join(
+ f"{row.component.value}={row.status.value}" for row in unavailable
+ )
+ raise IncompletePipelineError(f"N-S 2011 pipeline components unavailable: {details}")
+
+
+@dataclass(frozen=True)
+class CombinedPrediction:
+ pair: Pair
+ score: float
+ source: str
+ votes: int | None = None
+
+
+@dataclass(frozen=True)
+class SimilarityEvidence:
+ target: Vertex
+ auxiliary: Vertex
+ score: float
+ common_mapped_neighbours: int
+
+
+class UndefinedAlgorithm2Weight(ValueError):
+ """Algorithm 2's published ratio has no value for the arguments it was given."""
+
+
+@dataclass(frozen=True)
+class TwoStageMapping:
+ deterministic: dict[Vertex, Vertex]
+ candidates: dict[Vertex, tuple[Vertex, ...]]
+ stage1_rounds: int
+
+
+def algorithm2_pair_distance(left: float, right: float, *, alpha=0.5) -> float:
+ """The paper's ``(max(x/y, y/x) - 1)^alpha`` pair distance.
+
+ Only zero against zero is undefined. A zero against a positive weight is an infinite
+ ratio and so an infinite penalty, which is what the printed formula says rather than a
+ convention added here; the weights are in-neighbourhood cosines, so zeros between real
+ nodes are ordinary and a state can legitimately carry infinite potential.
+ """
+
+ if left < 0 or right < 0:
+ raise ValueError("Algorithm 2 weights must be non-negative")
+ if not left and not right:
+ raise UndefinedAlgorithm2Weight(
+ "Algorithm 2 does not define the ratio of two zero weights"
+ )
+ if not left or not right:
+ return float("inf")
+ ratio = max(left / right, right / left)
+ return (ratio - 1) ** alpha
+
+
+def algorithm2_node_distance(
+ target_nodes,
+ auxiliary_nodes,
+ target_dummies,
+ auxiliary_dummies,
+ target_weight,
+ auxiliary_weight,
+ index,
+ *,
+ alpha=0.5,
+ beta=0.5,
+) -> float:
+ """Algorithm 2's distance for one pair of mapped nodes.
+
+ A node incident only on dummies has an all-zero weight vector and so a zero mean, and the
+ paper fixes the resulting term at zero in prose: every edge incident on a dummy has weight
+ zero, which is why "adding k dummy nodes ... has the effect of finding a mapping of size
+ n - k" and why a dummy-to-dummy pair is "sub-optimal ... improvable in 1 step". Both
+ statements are true only when a dummy-incident term costs nothing, so the convention is a
+ reading of the paper rather than an invention. Refusing it instead pins the annealer at
+ zero dummies, where the paper reports output no better than a random permutation.
+
+ The paper's index conditions are per graph — a position is dropped from one vector when it
+ holds a dummy in *that* graph — while ``PairDist(sigmaK[j], sigmaF[j])`` reads both vectors
+ at one ``j``. When the two graphs put their dummies at different positions the vectors line
+ up in length but not in index, and the paper does not say which pairing it means. This
+ implementation pairs by position within each filtered vector, and refuses outright when the
+ lengths do not even match.
+ """
+
+ target_nodes, auxiliary_nodes = tuple(target_nodes), tuple(auxiliary_nodes)
+ if len(target_nodes) != len(auxiliary_nodes):
+ raise ValueError("mapped node sequences must have equal length")
+ if not 0 <= index < len(target_nodes):
+ raise IndexError("mapped-node index out of range")
+ target_dummies, auxiliary_dummies = set(target_dummies), set(auxiliary_dummies)
+ if (target_nodes[index] in target_dummies
+ or auxiliary_nodes[index] in auxiliary_dummies):
+ return 0.0
+ target_vector = tuple(
+ float(target_weight(target_nodes[index], node))
+ for position, node in enumerate(target_nodes)
+ if position != index and node not in target_dummies
+ )
+ auxiliary_vector = tuple(
+ float(auxiliary_weight(auxiliary_nodes[index], node))
+ for position, node in enumerate(auxiliary_nodes)
+ if position != index and node not in auxiliary_dummies
+ )
+ if not target_vector or not auxiliary_vector:
+ raise ValueError("Algorithm 2 requires a non-empty non-dummy weight vector")
+ if len(target_vector) != len(auxiliary_vector):
+ raise ValueError(
+ "unequal numbers of dummies leave the per-graph index conditions of Algorithm 2 "
+ "with no common index to pair on"
+ )
+ target_mean = sum(target_vector) / len(target_vector)
+ auxiliary_mean = sum(auxiliary_vector) / len(auxiliary_vector)
+ if target_mean <= 0 or auxiliary_mean <= 0:
+ raise UndefinedAlgorithm2Weight("Algorithm 2 normalization has a zero mean")
+ normalized = zip(
+ (value / target_mean for value in target_vector),
+ (value / auxiliary_mean for value in auxiliary_vector),
+ )
+ distance = sum(
+ algorithm2_pair_distance(left, right, alpha=alpha)
+ for left, right in normalized
+ )
+ return (target_mean * auxiliary_mean) ** (beta / 2) * distance
+
+
+def algorithm2_potential(
+ target_nodes,
+ auxiliary_nodes,
+ target_dummies,
+ auxiliary_dummies,
+ target_weight,
+ auxiliary_weight,
+ *,
+ alpha=0.5,
+ beta=0.5,
+) -> float:
+ """Sum Algorithm 2's node distance over the current bijection."""
+
+ target_nodes, auxiliary_nodes = tuple(target_nodes), tuple(auxiliary_nodes)
+ return sum(
+ algorithm2_node_distance(
+ target_nodes, auxiliary_nodes, target_dummies, auxiliary_dummies,
+ target_weight, auxiliary_weight, index, alpha=alpha, beta=beta,
+ )
+ for index in range(len(target_nodes))
+ )
+
+
+def anneal_seed_mapping(
+ target_nodes,
+ auxiliary_nodes,
+ target_dummies,
+ auxiliary_dummies,
+ target_weight,
+ auxiliary_weight,
+ *,
+ iterations: int,
+ rng: random.Random,
+):
+ """Paper-specified swap transitions and ``T=1/t, c=20n`` schedule.
+
+ The fixed iteration budget and injected RNG are reproducibility controls,
+ not parameters reported by the paper. The best visited bijection is
+ returned rather than whichever state happens to be last.
+
+ Dummies are supported under the zero convention :func:`algorithm2_node_distance`
+ documents, which is what lets the annealer return a partial mapping of size ``n - k``.
+ """
+
+ target_nodes, auxiliary_nodes = tuple(target_nodes), list(auxiliary_nodes)
+ if len(target_nodes) != len(auxiliary_nodes) or len(target_nodes) < 2:
+ raise ValueError("annealing requires equally sized node sets of size at least two")
+ if iterations < 0:
+ raise ValueError("iterations must be non-negative")
+ rng.shuffle(auxiliary_nodes)
+
+ def potential(order):
+ return algorithm2_potential(
+ target_nodes, order, target_dummies, auxiliary_dummies,
+ target_weight, auxiliary_weight,
+ )
+
+ current = potential(auxiliary_nodes)
+ best_order, best = tuple(auxiliary_nodes), current
+ n = len(target_nodes)
+ for iteration in range(1, iterations + 1):
+ first, second = rng.sample(range(n), 2)
+ auxiliary_nodes[first], auxiliary_nodes[second] = (
+ auxiliary_nodes[second], auxiliary_nodes[first]
+ )
+ candidate = potential(auxiliary_nodes)
+ delta = candidate - current
+ if delta <= 0 or rng.random() < exp(-delta * iteration / (20 * n)):
+ current = candidate
+ if candidate < best:
+ best_order, best = tuple(auxiliary_nodes), candidate
+ else:
+ auxiliary_nodes[first], auxiliary_nodes[second] = (
+ auxiliary_nodes[second], auxiliary_nodes[first]
+ )
+ return dict(zip(target_nodes, best_order)), best
+
+
+def similarity_evidence(
+ target_graph,
+ auxiliary_graph,
+ target: Vertex,
+ auxiliary: Vertex,
+ mapping: Mapping[Vertex, Vertex],
+ *,
+ crawled_target: Iterable[Vertex],
+ crawled_auxiliary: Iterable[Vertex],
+) -> SimilarityEvidence:
+ """Compute the directed, crawl-aware similarity in the paper's Algorithm 1.
+
+ Cosine similarity is undefined when either neighbourhood is empty, and the paper says
+ nothing about that case either. Scoring it ``0.0`` is a declared convention of this
+ module, matching the zero convention :func:`algorithm2_node_distance` takes for the same
+ shape of degeneracy in Algorithm 2: an empty neighbourhood is no evidence, and no
+ evidence must not become a match.
+ """
+
+ crawled_target = set(crawled_target)
+ crawled_auxiliary = set(crawled_auxiliary)
+ mapped_domain = set(mapping)
+ mapped_crawled = {mapping[v] for v in crawled_target if v in mapping}
+
+ target_neighbours = {
+ mapping[v] for v in target_graph._in.get(target, ())
+ if v in mapped_domain and mapping[v] in crawled_auxiliary
+ }
+ auxiliary_neighbours = (
+ set(auxiliary_graph._in.get(auxiliary, ())) & mapped_crawled
+ )
+ if target in crawled_target and auxiliary in crawled_auxiliary:
+ target_neighbours.update(
+ mapping[v] for v in target_graph._out.get(target, ()) if v in mapped_domain
+ )
+ auxiliary_neighbours.update(
+ set(auxiliary_graph._out.get(auxiliary, ()))
+ & {mapping[v] for v in mapped_domain}
+ )
+
+ common = len(target_neighbours & auxiliary_neighbours)
+ denominator = sqrt(len(target_neighbours) * len(auxiliary_neighbours))
+ return SimilarityEvidence(
+ target, auxiliary, common / denominator if denominator else 0.0, common
+ )
+
+
+def stage1_match(evidence: Iterable[SimilarityEvidence], *, k=4, theta=0.5, delta=0.2):
+ """Select the unique high-confidence stage-1 match, or refuse.
+
+ Evidence must concern one target node. The best candidate needs at least
+ ``k`` mapped-neighbour pairs, score ``theta``, and a ``delta`` lead over
+ the runner-up, matching the paper's published parameters by default.
+ """
+
+ ranked = sorted(evidence, key=lambda row: (-row.score, repr(row.auxiliary)))
+ if not ranked:
+ return None
+ if any(row.target != ranked[0].target for row in ranked):
+ raise ValueError("stage1 evidence must describe one target vertex")
+ best = ranked[0]
+ second_score = ranked[1].score if len(ranked) > 1 else 0.0
+ if (best.common_mapped_neighbours >= k and best.score >= theta
+ and best.score - second_score >= delta):
+ return best.auxiliary
+ return None
+
+
+def stage2_candidates(evidence: Iterable[SimilarityEvidence], *, k=3, theta=0.5, limit=3):
+ """Return up to the three best relaxed-stage candidates.
+
+ These candidates are intentionally not fed back into similarity scoring;
+ the caller receives an immutable tuple and must keep it separate from the
+ deterministic stage-1 mapping.
+
+ This is the stage 2 of the propagation section: ``k = 3``, the margin criterion dropped,
+ and the best three candidates at or above ``theta``. The paper's voting coverage figures
+ come from a *different* run of stage 2, preceded by a prune of the de-anonymization
+ output by confidence score and with the "sufficiently similar" criterion eliminated
+ altogether so that far more candidates survive. Neither the prune nor that variant is
+ reproduced here, so no voting coverage number may be read off this function.
+ """
+
+ if limit < 1:
+ raise ValueError("limit must be positive")
+ evidence = tuple(evidence)
+ if evidence and any(row.target != evidence[0].target for row in evidence):
+ raise ValueError("stage2 evidence must describe one target vertex")
+ eligible = [row for row in evidence
+ if row.common_mapped_neighbours >= k and row.score >= theta]
+ eligible.sort(key=lambda row: (-row.score, repr(row.auxiliary)))
+ return tuple(row.auxiliary for row in eligible[:limit])
+
+
+def two_stage_mapping(
+ target_graph,
+ auxiliary_graph,
+ seeds: Mapping[Vertex, Vertex],
+ target_vertices: Iterable[Vertex],
+ auxiliary_vertices: Iterable[Vertex],
+ *,
+ crawled_target: Iterable[Vertex],
+ crawled_auxiliary: Iterable[Vertex],
+ stage1_k=4,
+ stage1_theta=0.5,
+ stage1_delta=0.2,
+ stage2_k=3,
+ stage2_theta=0.5,
+ stage2_limit=3,
+) -> TwoStageMapping:
+ """Run the specified self-feeding stage 1 and non-feeding stage 2.
+
+ Vertex iteration is canonicalized by ``repr`` so "pick an arbitrary node"
+ becomes reproducible. This driver does not claim the paper's unspecified
+ confidence-pruning or occasional correction of accepted mappings.
+ """
+
+ target_vertices = tuple(sorted(set(target_vertices), key=repr))
+ auxiliary_vertices = tuple(sorted(set(auxiliary_vertices), key=repr))
+ crawled_target, crawled_auxiliary = set(crawled_target), set(crawled_auxiliary)
+ mapping = dict(seeds)
+ if not set(mapping) <= set(target_vertices):
+ raise ValueError("seed domain must be part of target_vertices")
+ if not set(mapping.values()) <= set(auxiliary_vertices):
+ raise ValueError("seed images must be part of auxiliary_vertices")
+ if len(set(mapping.values())) != len(mapping):
+ raise ValueError("seed mapping must be one-to-one")
+
+ rounds = 0
+ while True:
+ changed = False
+ used_images = set(mapping.values())
+ for target in target_vertices:
+ if target in mapping:
+ continue
+ unused = [vertex for vertex in auxiliary_vertices
+ if vertex not in used_images]
+ evidence = [
+ similarity_evidence(
+ target_graph, auxiliary_graph, target, auxiliary, mapping,
+ crawled_target=crawled_target,
+ crawled_auxiliary=crawled_auxiliary,
+ )
+ for auxiliary in unused
+ ]
+ match = stage1_match(
+ evidence, k=stage1_k, theta=stage1_theta, delta=stage1_delta
+ )
+ if match is not None:
+ mapping[target] = match
+ used_images.add(match)
+ changed = True
+ if not changed:
+ break
+ rounds += 1
+
+ candidate_sets = {}
+ used = set(mapping.values())
+ for target in target_vertices:
+ if target in mapping:
+ continue
+ evidence = [
+ similarity_evidence(
+ target_graph, auxiliary_graph, target, auxiliary, mapping,
+ crawled_target=crawled_target,
+ crawled_auxiliary=crawled_auxiliary,
+ )
+ for auxiliary in auxiliary_vertices if auxiliary not in used
+ ]
+ candidates = stage2_candidates(
+ evidence, k=stage2_k, theta=stage2_theta, limit=stage2_limit
+ )
+ if candidates:
+ candidate_sets[target] = candidates
+ return TwoStageMapping(mapping, candidate_sets, rounds)
+
+
+def combine_predictions(
+ test_pairs: Iterable[Pair],
+ auxiliary_edges: Iterable[Pair],
+ deterministic_mapping: Mapping[Vertex, Vertex],
+ candidate_mapping: Mapping[Vertex, Iterable[Vertex]],
+ ml_score: Callable[[Pair], float],
+) -> tuple[CombinedPrediction, ...]:
+ """Apply the paper's deterministic / unanimous-vote / ML cascade.
+
+ Auxiliary edges are directed, as in the paper's Flickr graph. Candidate
+ iterables are canonicalized to sets. A unanimous vote examines the full
+ Cartesian product, so duplicate candidates cannot change its outcome.
+ """
+
+ edges = set(auxiliary_edges)
+ candidates = {vertex: frozenset(images)
+ for vertex, images in candidate_mapping.items()}
+ predictions = []
+ for pair in test_pairs:
+ left, right = pair
+ if left in deterministic_mapping and right in deterministic_mapping:
+ edge = (deterministic_mapping[left], deterministic_mapping[right])
+ predictions.append(CombinedPrediction(pair, float(edge in edges), "deanonymized", 1))
+ continue
+
+ left_candidates = candidates.get(left, frozenset())
+ right_candidates = candidates.get(right, frozenset())
+ if left_candidates and right_candidates:
+ votes = tuple(
+ (left_image, right_image) in edges
+ for left_image in left_candidates
+ for right_image in right_candidates
+ )
+ if all(votes):
+ predictions.append(CombinedPrediction(pair, 1.0, "unanimous_vote", len(votes)))
+ continue
+ if not any(votes):
+ predictions.append(CombinedPrediction(pair, 0.0, "unanimous_vote", len(votes)))
+ continue
+
+ predictions.append(CombinedPrediction(pair, float(ml_score(pair)), "machine_learning"))
+ return tuple(predictions)
diff --git a/decluster/baselines/ns_social_attack.py b/decluster/baselines/ns_social_attack.py
new file mode 100644
index 00000000..13d59c21
--- /dev/null
+++ b/decluster/baselines/ns_social_attack.py
@@ -0,0 +1,7 @@
+"""Canonical namespace for the two-view Narayanan--Shmatikov social-graph baseline.
+Source: `ns-social` in `catalog/ctp-sources.json`.
+"""
+
+from .narayanan_shmatikov import PropagationResult, evaluate, propagate
+
+__all__ = ["PropagationResult", "evaluate", "propagate"]
diff --git a/decluster/baselines/oracle_audit.py b/decluster/baselines/oracle_audit.py
new file mode 100644
index 00000000..a5a48e20
--- /dev/null
+++ b/decluster/baselines/oracle_audit.py
@@ -0,0 +1,1038 @@
+"""Production approximations measured against the exact oracle.
+
+`baselines.maurer` enumerates every balanced input/output block mapping of a transaction and
+`baselines.boltzmann` averages over them uniformly. That pair is the exact oracle: exponential,
+zero-fee, and narrow. The production paths — the compiled `dss` extension and the wrappers in
+`decluster.counting`, `decluster.subtransaction` — are approximations of *something*, and nothing in
+this repo measured which something, or in which direction the error runs. This module does, over an
+exhaustively enumerated family of small zero-fee transactions.
+
+Semantics first, comparison second. Comparing two quantities that count different objects
+manufactures a defect that is really a category error, so every `dss` entry point used here is
+identified before it is compared, and the identification is *re-derived in Python* by
+`identify_objects` rather than taken from a docstring:
+
+ exact ``|M|`` index-level balanced set partitions: every input and every output lies in
+ exactly one block, and every block conserves value on its own. The
+ single-block (all coins, one owner) reading is included.
+ ``dss.mapping_analysis`` refinement-maximal mappings on all 507 bounded cases checked here. This
+ is a strict sub-family of ``|M|`` on 491 cases. Equal-value permutations
+ do not collapse in general: ``[1,3,4,4] -> [3,3,3,3]`` answers 4.
+ Agreement with the refinement-maximal oracle is measured on this family,
+ not asserted universally.
+ ``dss.pairwise_link_prob`` the uniform marginal over *that same restricted family*, not over the
+ oracle's. Verified rather than argued, by
+ ``verify_dss_marginal_family``: on 507/507 cases every entry is an exact
+ multiple of ``1 / n_non_derived`` and the entries equal to 1.0 are
+ exactly the ``deterministic_links`` the same call reports. So the
+ marginal, the count and the certain-link set are three outputs of one
+ enumeration and carry one classification: **restriction**. A marginal
+ over a sub-family is neither a lower nor an upper bound on the marginal
+ over the full family, which is what the comparison then measures.
+ ``dss.w_count`` / NOT mappings. Verified by re-derivation: the count is the number of
+ ``w_brute`` / ``w_sparse`` non-empty proper input subsets whose sum equals some proper
+ output-subset sum — the subset-sum solution count ``W(E)``. A set
+ partition is a simultaneous, disjoint, exhaustive choice of many such
+ subsets, so ``W(E)`` and ``|M|`` are different objects and ordering one
+ against the other says nothing about either. Reported as an
+ identification, never as a count disagreement.
+ ``dss.radix_mappings`` a function of the OUTPUT values alone; the inputs are not a parameter.
+ A quantity that cannot see the inputs cannot bound one that depends on
+ them.
+ ``dss.per_coin_density`` per-coin ``log_w`` (natural log, not bits). Measured on this family it
+ is invariant across every coin of a transaction, so it carries none of
+ the per-coin variation the exact marginals do carry.
+ ``subtransaction.subtransactions`` ``ambiguity_bits`` over 2-in/2-out: ``log2`` of the number of
+ (receiver input, receiver output) pairs with a positive implied payment.
+ No block ever has to balance, so it is not a mapping count either.
+
+Zero fee throughout. The oracle has no fee model — it returns no mapping at all when the sides do
+not balance — so nothing here speaks to how any of these behave on a fee-paying transaction, and the
+`dss` link paths in particular balance a fee in as a synthetic extra output when there is one.
+
+The relabelling above costs the finding nothing, and the audit also scores the link matrix against
+the *finest-only* oracle family (mappings no other mapping strictly refines) so the result is
+visibly independent of the oracle's inclusion of the single-block reading: the certain-link
+overclaim is identical under both readings.
+
+Every comparison carries a measured `bound_direction`. "Lower bound" is a claim about all inputs,
+and until something ran, no approximation in this repo had earned it.
+
+Sources: `maurer` and `boltzmann` in `catalog/ctp-sources.json`. The pair is the exact
+oracle the production approximations are measured against; neither is reimplemented here.
+"""
+
+from itertools import combinations, combinations_with_replacement
+
+from .boltzmann import exact_link_analysis, exact_per_coin_link_evidence
+from .maurer import (
+ exact_subtransaction_mappings,
+ non_derived_mappings,
+)
+
+FAMILY_ALPHABET = (1, 2, 3, 4)
+FAMILY_MAX_COINS = 8
+FAMILY_MIN_SIDE = 2
+TOLERANCE = 1e-9
+
+# Disagreements are counted in full; only the worked examples carried in the report are capped, so a
+# 500-case family does not turn the summary into a case dump.
+MAX_EXAMPLES = 12
+
+# `radix_mappings` answers zero below this magnitude and non-zero at or above it, on output sets
+# whose exact mapping count is identical either way. The scale is the demonstration, not a tuning.
+RADIX_SCALE = 1000
+
+# The results document this audit backs, and the source identity recorded for it. There is no data
+# file behind these numbers — the family is generated — so the source is the exact enumerator,
+# and the crate identity travels in the invariants, which is the half `check_manifest` compares.
+MANIFEST_DOC = "RESULTS-exact-oracle-audit.md"
+MANIFEST_SOURCE = "decluster/baselines/maurer.py"
+
+# The comparability verdict attached to every probe. `restriction` means the approximation is the
+# same quantity computed over a strict sub-family of what the oracle enumerates — comparable, so
+# long as the restriction travels with the number, and a bound in neither direction by construction.
+# `different-object` probes are identified and NOT compared. No probe here is over the same family,
+# which is why there is no "same-object" verdict to assign.
+RESTRICTION = "restriction"
+DIFFERENT_OBJECT = "different-object"
+
+
+def enumerate_family(alphabet=FAMILY_ALPHABET, max_coins=FAMILY_MAX_COINS,
+ min_side=FAMILY_MIN_SIDE):
+ """Every balanced `(inputs, outputs)` over `alphabet` with both sides at least `min_side` coins
+ and at most `max_coins` coins in total, each in canonical form exactly once.
+
+ Canonical form is the pair of sorted value multisets: coin order carries no information the
+ oracle reads, and `combinations_with_replacement` emits each multiset once per shape while
+ distinct shapes differ in length, so nothing has to be deduplicated afterwards — a `seen` set
+ here would be dead code, and a test asserts the result carries no duplicate. Scale is
+ deliberately NOT canonicalized — `|M|` is scale-invariant but
+ `radix_mappings` and the density gate are not, so folding `(2,2)/(2,2)` into `(1,1)/(1,1)` would
+ hide exactly the input dependence this audit is looking for.
+ """
+ return [
+ (inputs, outputs)
+ for n_in in range(min_side, max_coins - min_side + 1)
+ for n_out in range(min_side, max_coins - n_in + 1)
+ for inputs in combinations_with_replacement(alphabet, n_in)
+ for outputs in combinations_with_replacement(alphabet, n_out)
+ if sum(outputs) == sum(inputs)
+ ]
+
+
+def _proper_subset_sums(values):
+ """Sums of the non-empty proper subsets of `values`, as a set."""
+ return {
+ sum(values[i] for i in subset)
+ for size in range(1, len(values))
+ for subset in combinations(range(len(values)), size)
+ }
+
+
+def _subset_sum_solutions(inputs, outputs):
+ """`W(E)` re-derived: non-empty proper input subsets whose sum is a proper output-subset sum."""
+ targets = _proper_subset_sums(outputs)
+ return sum(
+ 1
+ for size in range(1, len(inputs))
+ for subset in combinations(range(len(inputs)), size)
+ if sum(inputs[i] for i in subset) in targets
+ )
+
+
+def equal_value_mapping_count(n):
+ """Self-derived closed form for `|M|` of an n-in/n-out join of one repeated value.
+
+ Derivation (mine, from the oracle's own definition — not taken from any paper). When every coin
+ carries the same value, a block of `a` inputs balances exactly the blocks of `a` outputs, so a
+ mapping is: a set partition of the inputs, a set partition of the outputs with the *same*
+ multiset of block sizes, and a size-preserving bijection between the two. Write a shape as
+ sizes with multiplicities `m_s` (so 4 = 2+1+1 has `m_2 = 1, m_1 = 2`). The set partitions of n
+ labelled coins with that shape is the standard
+
+ P(shape) = n! / (prod_s (s!)^{m_s} * prod_s m_s!)
+
+ and the size-preserving bijections number `prod_s m_s!`, since blocks of equal size are
+ interchangeable and blocks of different sizes cannot pair. Hence
+
+ |M|(n) = sum over shapes of P(shape)^2 * prod_s m_s!
+
+ n = 2 gives 1 + 2 = 3 (identity pairing, swapped pairing, single all-coins block), so the
+ canonical equal-value 2-in/2-out join has three mappings and log2(3) = 1.585 bits of entropy.
+ n = 3 gives 16 and n = 4 gives 131. `tests/test_oracle_audit.py` checks the form against the
+ oracle's own enumeration rather than trusting the algebra.
+ """
+ from math import factorial
+
+ def shapes(remaining, largest):
+ if not remaining:
+ yield ()
+ return
+ for size in range(min(remaining, largest), 0, -1):
+ for rest in shapes(remaining - size, size):
+ yield (size,) + rest
+
+ total = 0
+ for shape in shapes(n, n):
+ multiplicity = {}
+ for size in shape:
+ multiplicity[size] = multiplicity.get(size, 0) + 1
+ denominator = 1
+ for size, count in multiplicity.items():
+ denominator *= factorial(size) ** count * factorial(count)
+ partitions = factorial(n) // denominator
+ bijections = 1
+ for count in multiplicity.values():
+ bijections *= factorial(count)
+ total += partitions * partitions * bijections
+ return total
+
+
+def identify_objects(family):
+ """Check, case by case, that `dss.w_count` counts subset-sum solutions and not mappings.
+
+ The claim that two quantities count different objects is the load-bearing one in this module: it
+ is what licenses refusing the comparison instead of publishing a spurious undercount. So it is
+ checked rather than asserted — against an independent Python re-derivation of `W(E)`, and
+ against `|M|` for the cases where the two numbers happen to coincide anyway.
+ """
+ import dss
+
+ checked = mismatches = coincidences = 0
+ examples = []
+ for inputs, outputs in family:
+ count = dss.w_count(list(inputs), list(outputs))
+ if str(count.get("kind", "")).lower() != "exact" or count.get("count") is None:
+ continue
+ checked += 1
+ derived = _subset_sum_solutions(inputs, outputs)
+ exact = len(exact_subtransaction_mappings(inputs, outputs))
+ if count["count"] != derived:
+ mismatches += 1
+ if len(examples) < MAX_EXAMPLES:
+ examples.append({
+ "inputs": list(inputs), "outputs": list(outputs),
+ "dss_w_count": count["count"], "rederived_subset_sum_solutions": derived,
+ })
+ if count["count"] == exact:
+ coincidences += 1
+ return {
+ "object": "non-empty proper input subsets whose sum is a proper output-subset sum, W(E)",
+ "rederivation": "decluster.baselines.oracle_audit._subset_sum_solutions",
+ "exact_answers_checked": checked,
+ "rederivation_mismatches": mismatches,
+ "rederivation_examples": examples,
+ "cases_where_w_equals_exact_mapping_count": coincidences,
+ "verdict": (
+ "W(E) identified: dss.w_count reproduces the independent subset-sum re-derivation on "
+ f"{checked - mismatches}/{checked} exact answers. It is not the mapping count, so this "
+ "module reports the object mismatch and refuses to score it as an under/overcount."
+ ) if checked and not mismatches else (
+ "W(E) NOT identified: the re-derivation disagrees with dss.w_count, so the object "
+ "behind that entry point is still unknown and no verdict about it is published here."
+ ),
+ }
+
+
+def _dss_mapping_analysis(inputs, outputs):
+ import dss
+
+ try:
+ return dss.mapping_analysis(list(inputs), list(outputs), None)
+ except BaseException:
+ return None
+
+
+def approx_mapping_count(inputs, outputs):
+ report = _dss_mapping_analysis(inputs, outputs)
+ return None if report is None else report.get("n_non_derived")
+
+
+def approx_mapping_entropy_bits(inputs, outputs):
+ report = _dss_mapping_analysis(inputs, outputs)
+ return None if report is None else report.get("entropy")
+
+
+def approx_link_matrix(inputs, outputs):
+ from .. import counting
+
+ return counting.link_matrix(list(inputs), list(outputs))
+
+
+def approx_deterministic_links(inputs, outputs):
+ report = _dss_mapping_analysis(inputs, outputs)
+ if report is None:
+ return None
+ return {(int(i), int(j)) for i, j in report.get("deterministic_links", ())}
+
+
+def exact_mapping_count(inputs, outputs):
+ return len(exact_subtransaction_mappings(inputs, outputs))
+
+
+def exact_mapping_entropy_bits(inputs, outputs):
+ return exact_link_analysis(inputs, outputs).entropy_bits
+
+
+def exact_link_matrix(inputs, outputs):
+ matrix = exact_link_analysis(inputs, outputs).matrix
+ return matrix or None
+
+
+def exact_deterministic_links(inputs, outputs):
+ return set(exact_link_analysis(inputs, outputs).deterministic_links)
+
+
+def finest_mappings(mappings):
+ """The mappings no other mapping strictly refines: the refinement order's maximal elements.
+
+ This is what "finest" means on a poset of partitions, and it is the reason to prefer it: a
+ mapping belongs here exactly when nothing splits it further, which is the property the word
+ names. It is NOT chosen for being the strongest reading available to the approximation, and an
+ earlier draft of this docstring claimed that it was, which is inverted. Selecting instead by
+ *maximum block count* keeps a strict subset of these — a strict refinement always has strictly
+ more blocks, so an argmax-by-block-count mapping is always refinement-maximal but not
+ conversely — which is a SMALLER oracle family, hence MORE oracle certainties, hence a reading
+ strictly more generous to the approximation. `finest_selection_sensitivity` measures exactly
+ what that costs, because the difference is not cosmetic: it is where "no certain link is missed"
+ stops holding.
+ """
+ return non_derived_mappings(mappings)
+
+
+def max_block_count_mappings(mappings):
+ """The mappings with the most blocks — a strict subset of `finest_mappings`.
+
+ Kept only so the audit can measure how much the finest-only cross-check depends on which notion
+ of "finest" is used. Not the notion this module compares against; see `finest_mappings`.
+ """
+ mappings = tuple(mappings)
+ if not mappings:
+ return ()
+ most = max(len(mapping.blocks) for mapping in mappings)
+ return tuple(mapping for mapping in mappings if len(mapping.blocks) == most)
+
+
+def _certain_links(mappings, input_count, output_count):
+ if not mappings:
+ return set()
+ counts = [[0] * output_count for _ in range(input_count)]
+ for mapping in mappings:
+ for in_block, out_block in mapping.blocks:
+ for i in in_block:
+ for j in out_block:
+ counts[i][j] += 1
+ return {(i, j) for i, row in enumerate(counts) for j, count in enumerate(row)
+ if count == len(mappings)}
+
+
+def finest_selection_sensitivity(family):
+ """How much the finest-only cross-check depends on the definition of "finest".
+
+ The audit compares against the refinement-maximal mappings. Selecting by maximum block count
+ instead keeps a strict subset, so the oracle family shrinks and its certain links multiply, and
+ the cross-check's "no certain link is missed" is a statement about the first selection only.
+ Measured here on every run rather than described once, because a claim that holds under one
+ definition and fails under another must carry the definition with it.
+ """
+ import dss
+
+ divergent = 0
+ tallies = {
+ "refinement_maximal": {"spurious_links": 0, "missed_links": 0},
+ "max_block_count": {"spurious_links": 0, "missed_links": 0},
+ }
+ for inputs, outputs in family:
+ mappings = exact_subtransaction_mappings(inputs, outputs)
+ if not mappings:
+ continue
+ maximal = finest_mappings(mappings)
+ by_blocks = max_block_count_mappings(mappings)
+ if {m.blocks for m in maximal} != {m.blocks for m in by_blocks}:
+ divergent += 1
+ matrix = dss.pairwise_link_prob(list(inputs), list(outputs), None)
+ if not matrix:
+ continue
+ claimed = {(i, j) for i, row in enumerate(matrix)
+ for j, value in enumerate(row) if value == 1.0}
+ for key, selection in (("refinement_maximal", maximal), ("max_block_count", by_blocks)):
+ certain = _certain_links(selection, len(inputs), len(outputs))
+ tallies[key]["spurious_links"] += len(claimed - certain)
+ tallies[key]["missed_links"] += len(certain - claimed)
+ return {
+ "cases": len(family),
+ "cases_where_the_two_selections_differ": divergent,
+ **tallies,
+ "note": (
+ "The audit's cross-check uses refinement-maximal selection, the maximal elements of "
+ "the refinement order, because that is what 'finest' means on a poset of partitions. "
+ "Under "
+ "max-block-count selection — a strict subset, so a smaller oracle family and more "
+ "oracle certainties, a reading strictly more generous to the approximation — the "
+ "spurious count falls and certain links begin to read as missed. The primary "
+ "deterministic_links probe uses neither: it runs against the full oracle family."
+ ),
+ }
+
+
+def _finest_link_counts(inputs, outputs):
+ mappings = finest_mappings(exact_subtransaction_mappings(inputs, outputs))
+ if not mappings:
+ return None, 0
+ counts = [[0] * len(outputs) for _ in inputs]
+ for mapping in mappings:
+ for in_block, out_block in mapping.blocks:
+ for i in in_block:
+ for j in out_block:
+ counts[i][j] += 1
+ return counts, len(mappings)
+
+
+def exact_finest_link_matrix(inputs, outputs):
+ counts, total = _finest_link_counts(inputs, outputs)
+ if not total:
+ return None
+ return tuple(tuple(count / total for count in row) for row in counts)
+
+
+def exact_finest_deterministic_links(inputs, outputs):
+ counts, total = _finest_link_counts(inputs, outputs)
+ if not total:
+ return set()
+ return {(i, j) for i, row in enumerate(counts) for j, count in enumerate(row)
+ if count == total}
+
+
+def verify_dss_marginal_family(family):
+ """Re-derive, rather than assert, that `pairwise_link_prob` marginalizes dss's OWN family.
+
+ Two checks, both one-liners over the returned matrix, and this is what demotes the matrix from
+ "the same object as the oracle's marginal" to "the same quantity over a restricted family":
+ every entry is an exact multiple of `1 / n_non_derived`, and the entries equal to 1.0 are
+ exactly the `deterministic_links` the same call reports. A marginal, a count and a certain-link
+ set out of one enumeration cannot carry three different comparability verdicts.
+ """
+ import dss
+
+ checked = quantized = links_match = smaller = 0
+ counterexamples = []
+ for inputs, outputs in family:
+ analysis = dss.mapping_analysis(list(inputs), list(outputs), None)
+ matrix = dss.pairwise_link_prob(list(inputs), list(outputs), None)
+ if analysis is None or not matrix or not analysis.get("n_non_derived"):
+ continue
+ checked += 1
+ total = analysis["n_non_derived"]
+ is_quantized = all(abs(value * total - round(value * total)) <= TOLERANCE
+ for row in matrix for value in row)
+ ones = {(i, j) for i, row in enumerate(matrix) for j, value in enumerate(row)
+ if value == 1.0}
+ reported = {(int(i), int(j)) for i, j in analysis.get("deterministic_links", ())}
+ quantized += is_quantized
+ links_match += (ones == reported)
+ if total < exact_mapping_count(inputs, outputs):
+ smaller += 1
+ if not (is_quantized and ones == reported) and len(counterexamples) < MAX_EXAMPLES:
+ counterexamples.append({
+ "inputs": list(inputs), "outputs": list(outputs),
+ "n_non_derived": total, "matrix": [list(row) for row in matrix],
+ "deterministic_links": sorted(reported),
+ })
+ return {
+ "cases_checked": checked,
+ "entries_are_multiples_of_one_over_n_non_derived": quantized,
+ "unit_entries_equal_the_reported_deterministic_links": links_match,
+ "cases_where_the_dss_family_is_strictly_smaller_than_the_oracle_family": smaller,
+ "counterexamples": counterexamples,
+ "verdict": (
+ "pairwise_link_prob is the uniform marginal over dss's own family: quantized to "
+ f"1/n_non_derived on {quantized}/{checked} cases and unit-valued exactly on the "
+ f"deterministic links it reports on {links_match}/{checked}. That family is strictly "
+ f"smaller than the oracle's on {smaller} cases, so the matrix, the count and the "
+ "certain-link set are all restrictions and none is a bound in either direction."
+ ) if checked and quantized == links_match == checked else (
+ "NOT verified: the matrix is not consistent with a uniform marginal over n_non_derived "
+ "mappings on every case, so the restriction claim is withheld."
+ ),
+ }
+
+
+def mapping_count_mechanism(family):
+ """Measure the relation between `n_non_derived` and refinement-maximal mappings."""
+ import dss
+
+ equal_to_finest = differs_from_finest = 0
+ all_equal_value = all_equal_value_answering_one = 0
+ examples = []
+ for inputs, outputs in family:
+ total = dss.mapping_analysis(list(inputs), list(outputs), None)["n_non_derived"]
+ finest = len(finest_mappings(exact_subtransaction_mappings(inputs, outputs)))
+ if total == finest:
+ equal_to_finest += 1
+ else:
+ differs_from_finest += 1
+ if len(examples) < MAX_EXAMPLES:
+ examples.append({
+ "inputs": list(inputs), "outputs": list(outputs),
+ "n_non_derived": total, "finest_oracle_mappings": finest,
+ })
+ if len(set(inputs + outputs)) == 1:
+ all_equal_value += 1
+ all_equal_value_answering_one += (total == 1)
+ return {
+ "cases": len(family),
+ "equal_to_the_finest_oracle_mapping_count": equal_to_finest,
+ "different_from_the_finest_oracle_mapping_count": differs_from_finest,
+ "examples_where_it_differs": examples,
+ "all_equal_value_cases": all_equal_value,
+ "all_equal_value_cases_answering_one": all_equal_value_answering_one,
+ "note": (
+ "The current DSS count equals the independent refinement-maximal mapping count on the "
+ "entire bounded family. Equal-value permutations do not collapse in general: "
+ "`[1,3,4,4] -> [3,3,3,3]` answers 4, and those readings differ only in which "
+ "equal-valued output stands alone. Agreement on this family is measured, not universal."
+ ),
+ }
+
+
+# name -> (kind, relation, object note, exact side, approximate side). `kind` picks the comparison
+# engine; nothing else in this module dispatches on the probe name.
+PROBES = {
+ "mapping_count": (
+ "scalar", RESTRICTION,
+ "mappings both sides, over dss's refinement-maximal family against every index-level "
+ "balanced set partition",
+ exact_mapping_count, approx_mapping_count,
+ ),
+ "mapping_entropy_bits": (
+ "scalar", RESTRICTION,
+ "both are log2 of a mapping count under a uniform prior, over the two families named for "
+ "mapping_count",
+ exact_mapping_entropy_bits, approx_mapping_entropy_bits,
+ ),
+ "link_matrix": (
+ "matrix", RESTRICTION,
+ "P(input i and output j share a block): the same quantity both sides, but dss marginalizes "
+ "over its own restricted family (verify_dss_marginal_family), so it bounds the oracle's "
+ "marginal in neither direction",
+ exact_link_matrix, approx_link_matrix,
+ ),
+ "deterministic_links": (
+ "links", RESTRICTION,
+ "the (input, output) pairs every mapping agrees on — over dss's family against the "
+ "oracle's; counting.link_matrix's docstring reads a unit row to consumers as 'the amounts "
+ "settle that assignment on their own', which is the claim a spurious link makes",
+ exact_deterministic_links, approx_deterministic_links,
+ ),
+ # The same two comparisons against the finest-only oracle view, so the finding is visibly
+ # independent of the oracle's inclusion of coarser readings.
+ "link_matrix_finest_only": (
+ "matrix", RESTRICTION,
+ "as link_matrix, but the oracle side keeps only the maximal-refinement mappings",
+ exact_finest_link_matrix, approx_link_matrix,
+ ),
+ "deterministic_links_finest_only": (
+ "links", RESTRICTION,
+ "as deterministic_links, but the oracle side keeps only the maximal-refinement mappings",
+ exact_finest_deterministic_links, approx_deterministic_links,
+ ),
+}
+
+
+def _bound_direction(under, over, agree):
+ if over and under:
+ return "neither"
+ if over:
+ return "upper"
+ if under:
+ return "lower"
+ return "exact" if agree else "unmeasured"
+
+
+def _note_relative_error(stats, exact, approx):
+ relative = _relative_error(exact, approx)
+ if relative is None:
+ stats["undefined_relative_errors"] += 1
+ else:
+ stats["max_relative_error"] = max(stats["max_relative_error"], relative)
+
+
+def _record_example(stats, item):
+ """Every exceedance is counted; `MAX_EXAMPLES` of them are carried as worked cases."""
+ stats["exceeds_exact_cases"] += 1
+ if len(stats["exceeds_exact"]) < MAX_EXAMPLES:
+ stats["exceeds_exact"].append(item)
+
+
+def _relative_error(exact, approx):
+ """`None` where it is undefined — an exact zero against a non-zero approximation.
+
+ Reporting that as an infinite maximum would put `Infinity` in the JSON and swallow the finite
+ worst case behind it, so undefined entries are counted separately instead.
+ """
+ if exact:
+ return abs(approx - exact) / abs(exact)
+ return 0.0 if approx == 0 else None
+
+
+def _compare_scalar(family, exact_fn, approx_fn, tolerance):
+ stats = {
+ "compared": 0, "refusals": 0, "agreements": 0, "undercounts": 0, "overcounts": 0,
+ "max_absolute_error": 0.0, "max_relative_error": 0.0, "undefined_relative_errors": 0,
+ "exceeds_exact_cases": 0, "exceeds_exact": [],
+ }
+ rows = []
+ for inputs, outputs in family:
+ exact, approx = exact_fn(inputs, outputs), approx_fn(inputs, outputs)
+ row = {"exact": exact, "approx": approx}
+ if approx is None:
+ stats["refusals"] += 1
+ rows.append(row)
+ continue
+ stats["compared"] += 1
+ absolute = abs(approx - exact)
+ row["absolute_error"] = absolute
+ stats["max_absolute_error"] = max(stats["max_absolute_error"], absolute)
+ _note_relative_error(stats, exact, approx)
+ if absolute <= tolerance:
+ stats["agreements"] += 1
+ elif approx < exact:
+ stats["undercounts"] += 1
+ else:
+ stats["overcounts"] += 1
+ _record_example(stats, {
+ "inputs": list(inputs), "outputs": list(outputs), "exact": exact, "approx": approx,
+ })
+ rows.append(row)
+ stats["bound_direction"] = _bound_direction(
+ stats["undercounts"], stats["overcounts"], stats["agreements"])
+ return stats, rows
+
+
+def _compare_matrix(family, exact_fn, approx_fn, tolerance):
+ stats = {
+ "compared": 0, "refusals": 0, "shape_mismatches": 0, "agreements": 0, "disagreements": 0,
+ "entries": 0, "entries_agreeing": 0, "entries_above_exact": 0, "entries_below_exact": 0,
+ "max_absolute_error": 0.0, "max_relative_error": 0.0, "undefined_relative_errors": 0,
+ "exceeds_exact_cases": 0, "exceeds_exact": [],
+ }
+ rows = []
+ for inputs, outputs in family:
+ exact, approx = exact_fn(inputs, outputs), approx_fn(inputs, outputs)
+ row = {}
+ if exact is None or approx is None:
+ stats["refusals"] += 1
+ rows.append(row)
+ continue
+ if len(approx) != len(exact) or any(len(a) != len(e) for a, e in zip(approx, exact)):
+ stats["shape_mismatches"] += 1
+ rows.append({"shape_mismatch": True})
+ continue
+ stats["compared"] += 1
+ above = below = 0
+ worst = 0.0
+ for exact_row, approx_row in zip(exact, approx):
+ for e, a in zip(exact_row, approx_row):
+ stats["entries"] += 1
+ delta = a - e
+ worst = max(worst, abs(delta))
+ _note_relative_error(stats, e, a)
+ if abs(delta) <= tolerance:
+ stats["entries_agreeing"] += 1
+ elif delta > 0:
+ above += 1
+ else:
+ below += 1
+ stats["entries_above_exact"] += above
+ stats["entries_below_exact"] += below
+ stats["max_absolute_error"] = max(stats["max_absolute_error"], worst)
+ row.update({"entries_above_exact": above, "entries_below_exact": below,
+ "max_absolute_error": worst})
+ if above or below:
+ stats["disagreements"] += 1
+ else:
+ stats["agreements"] += 1
+ if above:
+ _record_example(stats, {
+ "inputs": list(inputs), "outputs": list(outputs),
+ "entries_above_exact": above, "max_absolute_error": worst,
+ })
+ rows.append(row)
+ stats["bound_direction"] = _bound_direction(
+ stats["entries_below_exact"], stats["entries_above_exact"], stats["entries_agreeing"])
+ return stats, rows
+
+
+def _compare_links(family, exact_fn, approx_fn, tolerance):
+ """Set comparison of certain links.
+
+ `spurious` is a link the approximation calls certain that the oracle does not: a manufactured
+ de-anonymization, and the only direction of error here that costs a holder privacy they were
+ told they had. `missed` is the opposite — a certainty the oracle finds and the approximation
+ does not, which loses an adversary a link rather than inventing one."""
+ stats = {
+ "compared": 0, "refusals": 0, "agreements": 0, "undercounts": 0, "overcounts": 0,
+ "spurious_links": 0, "missed_links": 0, "exceeds_exact_cases": 0, "exceeds_exact": [],
+ }
+ rows = []
+ for inputs, outputs in family:
+ exact, approx = exact_fn(inputs, outputs), approx_fn(inputs, outputs)
+ if approx is None:
+ stats["refusals"] += 1
+ rows.append({})
+ continue
+ stats["compared"] += 1
+ spurious, missed = approx - exact, exact - approx
+ stats["spurious_links"] += len(spurious)
+ stats["missed_links"] += len(missed)
+ if not spurious and not missed:
+ stats["agreements"] += 1
+ elif spurious:
+ stats["overcounts"] += 1
+ _record_example(stats, {
+ "inputs": list(inputs), "outputs": list(outputs),
+ "spurious_links": sorted(spurious), "missed_links": sorted(missed),
+ })
+ else:
+ stats["undercounts"] += 1
+ rows.append({"exact": len(exact), "approx": len(approx),
+ "spurious": sorted(spurious), "missed": sorted(missed)})
+ stats["bound_direction"] = _bound_direction(
+ stats["undercounts"], stats["overcounts"], stats["agreements"])
+ return stats, rows
+
+
+_ENGINES = {"scalar": _compare_scalar, "matrix": _compare_matrix, "links": _compare_links}
+
+
+def _per_coin_density_identification(family):
+ """Measure the structural property that makes `per_coin_density` a different reading: whether
+ its per-coin `log_w` varies across the coins of one transaction, where the marginals do."""
+ import dss
+
+ coin_invariant = varying = refusals = 0
+ exact_varying = 0
+ for inputs, outputs in family:
+ try:
+ report = dss.per_coin_density(list(inputs), list(outputs))
+ except BaseException:
+ refusals += 1
+ continue
+ values = {c.get("log_w") for c in report.get("coins", ())}
+ if not values:
+ refusals += 1
+ continue
+ if len(values) == 1:
+ coin_invariant += 1
+ else:
+ varying += 1
+ evidence = exact_per_coin_link_evidence(inputs, outputs)
+ if len({c.max_link_probability for c in evidence.inputs}) > 1:
+ exact_varying += 1
+ return {
+ "object": "per-coin log_w (natural log) from the fee-aware density path, not a probability",
+ "coin_invariant_cases": coin_invariant,
+ "coin_varying_cases": varying,
+ "refusals": refusals,
+ "cases_where_exact_input_marginals_vary": exact_varying,
+ "note": (
+ "log_w is a natural log while the oracle's entropy is in bits, and the quantity is a "
+ "subset-sum density rather than a link marginal, so no entrywise comparison is made. "
+ "The reportable fact is structural: where its value is identical for every coin of a "
+ "transaction it cannot express per-coin variation that the exact marginals do show."
+ ),
+ }
+
+
+def _radix_identification(family):
+ """Two structural facts about `radix_mappings`, each demonstrated rather than argued.
+
+ Input independence: the call takes the outputs and a knee, so one answer stands for every
+ transaction sharing an output multiset — and the family contains output multisets whose exact
+ mapping count differs across the inputs that fund them.
+
+ Scale dependence: `|M|` is invariant under multiplying every coin by a constant, because the
+ balance conditions scale with it. `radix_mappings` is not, so the pair (unscaled, scaled) is a
+ witness that the two cannot be the same quantity — no bound argument needed.
+ """
+ import dss
+
+ from .. import counting
+
+ by_outputs = {}
+ for inputs, outputs in family:
+ by_outputs.setdefault(outputs, []).append(inputs)
+
+ applicable = answered = scale_flips = 0
+ witnesses, scale_witnesses = [], []
+ for outputs, input_sets in sorted(by_outputs.items()):
+ if not counting.radix_applies(list(outputs)):
+ continue
+ applicable += 1
+ count = dss.radix_mappings(list(outputs), counting.KNEE).get("count") or 0
+ scaled = tuple(value * RADIX_SCALE for value in outputs)
+ scaled_count = dss.radix_mappings(list(scaled), counting.KNEE).get("count") or 0
+ exacts = {len(exact_subtransaction_mappings(i, outputs)) for i in input_sets}
+ if count:
+ answered += 1
+ if len(exacts) > 1 and len(witnesses) < MAX_EXAMPLES:
+ witnesses.append({
+ "outputs": list(outputs), "radix_count": count,
+ "exact_mapping_counts": sorted(exacts),
+ })
+ if scaled_count != count:
+ scale_flips += 1
+ scaled_exacts = {
+ len(exact_subtransaction_mappings(tuple(v * RADIX_SCALE for v in i), scaled))
+ for i in input_sets
+ }
+ if len(scale_witnesses) < MAX_EXAMPLES:
+ scale_witnesses.append({
+ "outputs": list(outputs), "radix_count": count,
+ "scaled_outputs": list(scaled), "scaled_radix_count": scaled_count,
+ "exact_mapping_counts": sorted(exacts),
+ "scaled_exact_mapping_counts": sorted(scaled_exacts),
+ })
+ return {
+ "object": "permutations of repeated output denominations; a function of the outputs alone",
+ "output_multisets_where_radix_applies": applicable,
+ "output_multisets_where_radix_returned_a_nonzero_count": answered,
+ "input_independence_witnesses": witnesses,
+ "scale": RADIX_SCALE,
+ "output_multisets_whose_radix_count_moved_under_scaling": scale_flips,
+ "scale_dependence_witnesses": scale_witnesses,
+ "note": (
+ "The call does not take the inputs, so one answer stands for every transaction sharing "
+ "an output multiset while the exact mapping count differs across them; and the answer "
+ "moves when every coin is multiplied by a constant, which the exact mapping count "
+ "cannot. A quantity blind to a parameter the target depends on, and sensitive to one "
+ "it does not, is not a bound on the target."
+ ),
+ }
+
+
+def _subtransaction_identification(family):
+ """`subtransaction.subtransactions` on the 2-in/2-out slice: count of positive implied payments,
+ with no balance requirement, so compare it only as an identified different object."""
+ from .. import subtransaction
+
+ from math import log2
+
+ slice_size = exceeds = agrees = below = refusals = 0
+ for inputs, outputs in family:
+ if len(inputs) != 2 or len(outputs) != 2:
+ continue
+ slice_size += 1
+ tx = {
+ "txid": "audit",
+ "vin": [{"txid": f"in{i}", "prevout": {"value": v}} for i, v in enumerate(inputs)],
+ "vout": [{"value": v} for v in outputs],
+ }
+ _, bits = subtransaction.subtransactions(tx)
+ if bits is None:
+ refusals += 1
+ continue
+ exact = log2(max(len(exact_subtransaction_mappings(inputs, outputs)), 1))
+ if abs(bits - exact) <= TOLERANCE:
+ agrees += 1
+ elif bits > exact:
+ exceeds += 1
+ else:
+ below += 1
+ return {
+ "object": "log2 of the number of (receiver input, receiver output) pairs with a positive "
+ "implied payment; no block is required to balance",
+ "two_in_two_out_cases": slice_size,
+ "refusals": refusals,
+ "cases_above_exact_entropy": exceeds,
+ "cases_equal_to_exact_entropy": agrees,
+ "cases_below_exact_entropy": below,
+ "note": (
+ "Its own docstring already calls this a count diagnostic and not a privacy quantity. "
+ "The count admits partitions that do not conserve value, so it is neither a bound on "
+ "nor an estimate of the oracle's mapping entropy; the tallies above describe how the "
+ "two numbers happen to order, not a bound."
+ ),
+ }
+
+
+def _router_identification(family):
+ """`counting.count_w` (the production router) and `counting.saddle_point_log_w`.
+
+ The router's tiers are radix then sparse, and the sparse tier answers the same subset-sum count
+ `w_count` does — checked here against the same re-derivation — so the router inherits that
+ object mismatch rather than acquiring a new one. The saddle point is recorded because its own
+ gate and the gate `counting.is_dense` reports are not the same gate, and the size of that gap is
+ measurable.
+ """
+ from collections import Counter
+
+ from .. import counting
+
+ methods, kinds = Counter(), Counter()
+ sparse_matches_rederivation = sparse_answers = 0
+ saddle_answers = dense_by_is_dense = 0
+ for inputs, outputs in family:
+ routed = counting.count_w(list(inputs), list(outputs))
+ methods[routed.get("method")] += 1
+ kinds[str(routed.get("kind"))] += 1
+ if routed.get("method") == "sparse" and routed.get("count") is not None:
+ sparse_answers += 1
+ sparse_matches_rederivation += (
+ routed["count"] == _subset_sum_solutions(inputs, outputs))
+ saddle_answers += counting.saddle_point_log_w(list(inputs), list(outputs)) is not None
+ dense_by_is_dense += bool(counting.is_dense(list(inputs), list(outputs)))
+ return {
+ "object": "count_w routes to radix or sparse; the sparse answer is the same subset-sum "
+ "solution count W(E), so the router is a different object from |M| for the same "
+ "reason w_count is",
+ "methods": dict(methods),
+ "kinds": dict(kinds),
+ "sparse_answers": sparse_answers,
+ "sparse_answers_matching_the_subset_sum_rederivation": sparse_matches_rederivation,
+ "saddle_point_answers": saddle_answers,
+ "cases_is_dense_reads_as_dense": dense_by_is_dense,
+ "note": (
+ "The radix tier never wins on this family: it returns zero, `_resolved` reads that as "
+ "no answer, and the route falls through to sparse. The saddle point declines every "
+ "case here even though `is_dense` reads most of them as Dense, which is the "
+ "optimistic-gate discrepancy its own docstring predicts, now measured."
+ ),
+ }
+
+
+def audit(family=None, *, tolerance=TOLERANCE, approximations=None, include_cases=True):
+ """Compare every like-for-like approximation against the exact oracle and report disagreements.
+
+ `approximations` overrides individual probe providers by name, which is what makes this
+ falsifiable: an audit that cannot be made to fail is not an audit, so
+ `tests/test_oracle_audit.py` feeds it deliberately wrong counters and asserts the report flags
+ them.
+
+ Returns a JSON-serializable report: `comparisons` (the same quantity both sides, each with a
+ measured `bound_direction`), `identifications` (the entry points that count a different object,
+ each with the evidence for that claim), `restriction_evidence` (the re-derivations behind the
+ `restriction` verdict itself), `cases` (per-case detail), and `flags` (one line per disagreement
+ an approximation would have to answer for before being described as a bound).
+ """
+ family = list(enumerate_family()) if family is None else list(family)
+ overrides = dict(approximations or {})
+
+ comparisons, per_case = {}, {}
+ for name, (kind, relation, note, exact_fn, approx_fn) in PROBES.items():
+ stats, rows = _ENGINES[kind](family, exact_fn, overrides.get(name, approx_fn), tolerance)
+ comparisons[name] = dict(stats, relation=relation, object_note=note, cases=len(family))
+ per_case[name] = rows
+
+ identifications = {
+ "w_count": identify_objects(family),
+ "count_w_router_and_saddle_point": _router_identification(family),
+ "radix_mappings": _radix_identification(family),
+ "per_coin_density": _per_coin_density_identification(family),
+ "subtransaction_ambiguity_bits": _subtransaction_identification(family),
+ }
+ for identification in identifications.values():
+ identification["relation"] = DIFFERENT_OBJECT
+
+ restriction_evidence = {
+ "dss_marginal_family": verify_dss_marginal_family(family),
+ "mapping_count_mechanism": mapping_count_mechanism(family),
+ "finest_selection_sensitivity": finest_selection_sensitivity(family),
+ }
+
+ flags = []
+ for name, stats in comparisons.items():
+ if stats.get("exceeds_exact_cases"):
+ flags.append(
+ f"{name}: {stats['exceeds_exact_cases']} case(s) where the approximation exceeds "
+ f"the exact oracle")
+ if stats.get("bound_direction") == "neither":
+ flags.append(f"{name}: neither a lower nor an upper bound — errs in both directions")
+ if stats.get("spurious_links"):
+ flags.append(
+ f"{name}: {stats['spurious_links']} link(s) reported as certain that the exact "
+ f"oracle leaves ambiguous")
+ if stats.get("shape_mismatches"):
+ flags.append(f"{name}: {stats['shape_mismatches']} shape mismatch(es)")
+
+ report = {
+ "family": {
+ "size": len(family),
+ "alphabet": list(FAMILY_ALPHABET),
+ "max_coins": FAMILY_MAX_COINS,
+ "min_side": FAMILY_MIN_SIDE,
+ "fee": 0,
+ "canonical_form": "sorted input multiset, sorted output multiset; scale not folded",
+ },
+ "tolerance": tolerance,
+ "comparisons": comparisons,
+ "identifications": identifications,
+ "restriction_evidence": restriction_evidence,
+ "flags": flags,
+ }
+ if include_cases:
+ report["cases"] = [
+ dict({"inputs": list(inputs), "outputs": list(outputs)},
+ **{name: per_case[name][index] for name in PROBES})
+ for index, (inputs, outputs) in enumerate(family)
+ ]
+ return report
+
+
+def manifest_invariants(report):
+ """The population facts behind the results document, for `reproducibility.write_manifest`.
+
+ A source digest cannot see any of these: the family is generated rather than read, and the
+ numbers come out of the Rust crate, whose identity the policy asks for and which
+ `fingerprint_source` has no way to record. Both the writer (`examples/exact_oracle_audit.py
+ --manifest`) and the test recompute this from a fresh audit, so a drifting number fails loudly
+ instead of standing in the document.
+ """
+ import dss
+
+ comparisons, identifications = report["comparisons"], report["identifications"]
+ return {
+ "dss_version": dss.__version__,
+ "dss_rev": dss.__rev__,
+ "family_size": report["family"]["size"],
+ "link_matrix_entries": comparisons["link_matrix"]["entries"],
+ "link_matrix_entries_above_exact": comparisons["link_matrix"]["entries_above_exact"],
+ "link_matrix_entries_below_exact": comparisons["link_matrix"]["entries_below_exact"],
+ "link_matrix_bound_direction": comparisons["link_matrix"]["bound_direction"],
+ "spurious_deterministic_links": comparisons["deterministic_links"]["spurious_links"],
+ "missed_deterministic_links": comparisons["deterministic_links"]["missed_links"],
+ "mapping_count_undercounts": comparisons["mapping_count"]["undercounts"],
+ "mapping_count_overcounts": comparisons["mapping_count"]["overcounts"],
+ "w_count_rederivation_mismatches": identifications["w_count"]["rederivation_mismatches"],
+ "finest_only_entries_above_exact":
+ comparisons["link_matrix_finest_only"]["entries_above_exact"],
+ "finest_only_entries_below_exact":
+ comparisons["link_matrix_finest_only"]["entries_below_exact"],
+ "finest_only_spurious_deterministic_links":
+ comparisons["deterministic_links_finest_only"]["spurious_links"],
+ "finest_only_missed_deterministic_links":
+ comparisons["deterministic_links_finest_only"]["missed_links"],
+ # Both halves of the document's "507/507", plus the denominator: pinning only the
+ # quantization half left the other half of the same claim unchecked.
+ "dss_marginal_cases_checked":
+ report["restriction_evidence"]["dss_marginal_family"]["cases_checked"],
+ "dss_marginal_quantized_cases":
+ report["restriction_evidence"]["dss_marginal_family"][
+ "entries_are_multiples_of_one_over_n_non_derived"],
+ "dss_marginal_unit_entries_match_deterministic_links":
+ report["restriction_evidence"]["dss_marginal_family"][
+ "unit_entries_equal_the_reported_deterministic_links"],
+ "finest_selection_divergent_cases":
+ report["restriction_evidence"]["finest_selection_sensitivity"][
+ "cases_where_the_two_selections_differ"],
+ "max_block_count_spurious_deterministic_links":
+ report["restriction_evidence"]["finest_selection_sensitivity"][
+ "max_block_count"]["spurious_links"],
+ "max_block_count_missed_deterministic_links":
+ report["restriction_evidence"]["finest_selection_sensitivity"][
+ "max_block_count"]["missed_links"],
+ "dss_family_strictly_smaller_cases":
+ report["restriction_evidence"]["dss_marginal_family"][
+ "cases_where_the_dss_family_is_strictly_smaller_than_the_oracle_family"],
+ }
diff --git a/decluster/baselines/unnecessary_input.py b/decluster/baselines/unnecessary_input.py
new file mode 100644
index 00000000..87a7b05d
--- /dev/null
+++ b/decluster/baselines/unnecessary_input.py
@@ -0,0 +1,160 @@
+"""Unnecessary-input classifications from Ghesmati et al. §3.
+
+The published comparison is scoped to transactions with more than one input
+and exactly two outputs. A positive classification describes transaction
+shape. It does not identify PayJoin, ownership, or the payment output.
+
+Source: `uih-ghesmati` in `catalog/ctp-sources.json` — Ghesmati et al., §3; `uih-adamisz`
+states the heuristic informally and `bip78` the protocol it applies to.
+"""
+
+from collections.abc import Mapping, Sequence
+from dataclasses import dataclass
+from enum import Enum
+from typing import Any
+
+
+MAX_AMOUNT = 2**64 - 1
+
+
+class UIHStatus(str, Enum):
+ """Outcome of the fee-aware BlockStream classification."""
+
+ UIH1 = "uih1"
+ UIH2 = "uih2"
+ UNCATEGORIZED = "uncategorized"
+ OUT_OF_SCOPE = "out_of_scope"
+ INVALID = "invalid"
+
+
+@dataclass(frozen=True)
+class UIHAnalysis:
+ """A model-relative UIH result with the values needed to audit it."""
+
+ status: UIHStatus
+ fee: int | None = None
+ removed_input_index: int | None = None
+ change_output_index: int | None = None
+ reason: str | None = None
+
+
+def _valid_amounts(values: Sequence[int]) -> bool:
+ return all(type(value) is int and 0 < value <= MAX_AMOUNT for value in values)
+
+
+def analyze_blockstream(inputs: Sequence[int], outputs: Sequence[int]) -> UIHAnalysis:
+ """Apply Algorithm 2 of Ghesmati et al. to a two-output transaction.
+
+ UIH2 holds when removing one occurrence of the smallest input still leaves
+ enough value to fund the largest output and the observed transaction fee.
+ UIH1 holds when the analogous test succeeds only for the smaller output.
+ Algorithm 2 has no closing ``else``: when neither test succeeds the
+ transaction stays uncategorized, which the paper reports as a measured
+ outcome rather than an omission. Ties between smallest inputs use the
+ first index only to make the witness deterministic.
+ """
+
+ if len(inputs) < 2 or len(outputs) != 2:
+ return UIHAnalysis(
+ UIHStatus.OUT_OF_SCOPE,
+ reason="requires at least two inputs and exactly two outputs",
+ )
+ if not _valid_amounts(inputs) or not _valid_amounts(outputs):
+ return UIHAnalysis(
+ UIHStatus.INVALID,
+ reason="amounts must be positive unsigned 64-bit integers",
+ )
+
+ total_in = sum(inputs)
+ total_out = sum(outputs)
+ if total_in > MAX_AMOUNT or total_out > MAX_AMOUNT:
+ return UIHAnalysis(UIHStatus.INVALID, reason="amount sum exceeds unsigned 64-bit range")
+ fee = total_in - total_out
+ if fee < 0:
+ return UIHAnalysis(UIHStatus.INVALID, reason="outputs exceed inputs")
+
+ removed_index = min(range(len(inputs)), key=inputs.__getitem__)
+ remaining = total_in - inputs[removed_index]
+ if remaining >= max(outputs) + fee:
+ return UIHAnalysis(UIHStatus.UIH2, fee=fee, removed_input_index=removed_index)
+ if remaining >= min(outputs) + fee:
+ # this branch reduces to min(out) < min(in) <= max(out), so the outputs
+ # differ and the smaller one is the only optimal-change candidate
+ return UIHAnalysis(
+ UIHStatus.UIH1,
+ fee=fee,
+ removed_input_index=removed_index,
+ change_output_index=min(range(len(outputs)), key=outputs.__getitem__),
+ )
+ return UIHAnalysis(
+ UIHStatus.UNCATEGORIZED,
+ fee=fee,
+ removed_input_index=removed_index,
+ reason="every output is smaller than every input, so neither branch applies",
+ )
+
+
+def blocksci_uih1(inputs: Sequence[int], outputs: Sequence[int]) -> bool | None:
+ """Return BlockSci's strict optimal-change predicate, or ``None`` out of scope."""
+
+ if (
+ len(inputs) < 2
+ or len(outputs) != 2
+ or not _valid_amounts(inputs)
+ or not _valid_amounts(outputs)
+ ):
+ return None
+ return min(outputs) < min(inputs)
+
+
+def optimal_change_candidate(
+ inputs: Sequence[int], outputs: Sequence[int]
+) -> int | None:
+ """Return the unique smaller-than-every-input output using values only.
+
+ This is a candidate under the BlockSci/Gibson UIH1 assumption, not an
+ ownership label. Equal or multiple qualifying outputs are ambiguous.
+ """
+
+ if blocksci_uih1(inputs, outputs) is not True:
+ return None
+ smallest_input = min(inputs)
+ candidates = [index for index, value in enumerate(outputs) if value < smallest_input]
+ return candidates[0] if len(candidates) == 1 else None
+
+
+def gibson_flags(inputs: Sequence[int], outputs: Sequence[int]) -> tuple[bool, bool] | None:
+ """Return the independent UIH1/UIH2 flags from Algorithm 3.
+
+ This older UIH2 predicate is intentionally strict and can miss cases where
+ several retained inputs jointly fund the largest output.
+ """
+
+ if (
+ len(inputs) < 2
+ or len(outputs) != 2
+ or not _valid_amounts(inputs)
+ or not _valid_amounts(outputs)
+ ):
+ return None
+ return min(outputs) < min(inputs), max(inputs) > max(outputs)
+
+
+def analyze_transaction(transaction: Mapping[str, Any]) -> UIHAnalysis:
+ """Extract values from the project's transaction shape and run Algorithm 2.
+
+ Missing values remain an explicit invalid result. This adapter performs no
+ ownership inference and deliberately does not fall back to another UIH
+ definition.
+ """
+
+ try:
+ vin: Sequence[Mapping[str, Any]] = transaction["vin"]
+ vout: Sequence[Mapping[str, Any]] = transaction["vout"]
+ inputs = [item.get("prevout", {}).get("value", item.get("value")) for item in vin]
+ outputs = [item.get("value") for item in vout]
+ except (AttributeError, KeyError, TypeError):
+ return UIHAnalysis(UIHStatus.INVALID, reason="malformed transaction structure")
+ if any(value is None for value in inputs + outputs):
+ return UIHAnalysis(UIHStatus.INVALID, reason="transaction amounts are incomplete")
+ return analyze_blockstream(inputs, outputs)
diff --git a/decluster/bayes_fs_comparison.py b/decluster/bayes_fs_comparison.py
new file mode 100644
index 00000000..21dd6d4c
--- /dev/null
+++ b/decluster/bayes_fs_comparison.py
@@ -0,0 +1,145 @@
+"""Pair-level Bayesian and Fellegi-Sunter fingerprint comparison.
+
+This module measures an attacker's record-linkage evidence. It does not infer a
+joint ownership partition and does not produce a defensive privacy score.
+"""
+
+from __future__ import annotations
+
+from collections import defaultdict
+import random
+
+from .fingerprint_validate import LibraryScorer, reuse_pairs
+from .fs_bayes import ece, gibbs_fit, pair_probs
+from .fs_em import agree_matrix, em_fit, oracle_m
+from .graph_deanon import auc
+from .unionfind import UF
+
+
+def _entity_counts(agreement, mask, u, m_samples, prior, pairs, node_count, seed, cap=200):
+ rng = random.Random(seed)
+ counts = []
+ for m in m_samples[:cap]:
+ probabilities = pair_probs(agreement, mask, m, u, prior)
+ groups = UF(range(node_count))
+ for (left, right), probability in zip(pairs, probabilities):
+ if rng.random() < probability:
+ groups.union(left, right)
+ counts.append(len(groups.groups()))
+ return counts
+
+
+def _cluster_band(transactions, axes, u, m_samples, prior, em_m):
+ node_count = len(transactions)
+ pairs = [(left, right) for left in range(node_count) for right in range(left + 1, node_count)]
+ agreement, mask, _, _ = agree_matrix(
+ [(transactions[left], transactions[right]) for left, right in pairs], axes
+ )
+ counts = sorted(_entity_counts(
+ agreement, mask, u, m_samples, prior, pairs, node_count, seed=0
+ ))
+ probabilities = pair_probs(agreement, mask, em_m, u, 0.5)
+ groups = UF(range(node_count))
+ for (left, right), probability in zip(pairs, probabilities):
+ if probability >= 0.5:
+ groups.union(left, right)
+ return {
+ "bayesian_mean": sum(counts) / len(counts),
+ "bayesian_interval": [
+ counts[int(0.025 * (len(counts) - 1))],
+ counts[int(0.975 * (len(counts) - 1))],
+ ],
+ "fellegi_sunter_em_point": len(groups.groups()),
+ }
+
+
+def _reuse_nodes(transactions, cap=50):
+ from .change_gt import input_addrs
+
+ by_address = {}
+ for transaction in transactions:
+ for address in sorted(input_addrs(transaction)):
+ by_address.setdefault(address, {})[transaction["txid"]] = transaction
+ nodes = []
+ for group in (list(group.values()) for group in by_address.values() if len(group) >= 2):
+ if len(nodes) >= cap:
+ break
+ nodes.extend(group[:3])
+ return nodes[:cap]
+
+
+def _ambiguous_nodes(transactions, axes, m, u, pool_size=300, size=18, seed=1):
+ rng = random.Random(seed)
+ pool = rng.sample(transactions, min(pool_size, len(transactions)))
+ pairs = [(left, right) for left in range(len(pool)) for right in range(left + 1, len(pool))]
+ agreement, mask, _, _ = agree_matrix(
+ [(pool[left], pool[right]) for left, right in pairs], axes
+ )
+ degrees = defaultdict(int)
+ for (left, right), probability in zip(pairs, pair_probs(agreement, mask, m, u, 0.5)):
+ if 0.2 <= probability <= 0.8:
+ degrees[left] += 1
+ degrees[right] += 1
+ candidates = sorted(degrees)
+ candidates.sort(key=degrees.__getitem__)
+ step = max(1, len(candidates) // size)
+ return [pool[index] for index in candidates[::step][:size]]
+
+
+def evaluate(transactions, *, pair_cap=4000, seed=0, n_samples=2000, burn=500):
+ """Evaluate three pair scorers on one balanced weak-label sample."""
+ positive, negative = reuse_pairs(transactions, pair_cap, seed)
+ pairs = positive + negative
+ labels = [1] * len(positive) + [0] * len(negative)
+ axes = LibraryScorer().axes
+ agreement, mask, names, u = agree_matrix(pairs, axes)
+ em = em_fit(agreement, mask, u)
+ label_agreement = oracle_m(agreement, mask, labels)
+ bayes = gibbs_fit(
+ agreement, mask, u, n_samples=n_samples, burn=burn, seed=seed
+ )
+
+ scorers = (
+ ("fellegi_sunter_fixed_0_95", pair_probs(agreement, mask, [0.95] * len(u), u, 0.5)),
+ ("fellegi_sunter_em", pair_probs(agreement, mask, em["m"], u, 0.5)),
+ ("bayesian_integrated_m", bayes["p_match"]),
+ )
+ discrimination = [
+ {
+ "scorer": name,
+ "auc": auc(probabilities[:len(positive)], probabilities[len(positive):], seed),
+ "ece": ece(probabilities, labels),
+ }
+ for name, probabilities in scorers
+ ]
+ per_axis = []
+ for index, name in enumerate(names):
+ per_axis.append({
+ "axis": name,
+ "bayesian_mean": bayes["m_mean"][index],
+ "bayesian_interval": list(bayes["m_ci"][index]),
+ "em": em["m"][index],
+ "address_reuse_agreement": label_agreement[index],
+ "fixed_assumption": 0.95,
+ })
+
+ clear = _reuse_nodes(transactions)
+ ambiguous = _ambiguous_nodes(transactions, axes, em["m"], u)
+ return {
+ "transactions": len(transactions),
+ "pairs": {"positive": len(positive), "negative": len(negative)},
+ "discrimination": discrimination,
+ "per_axis": per_axis,
+ "cluster_diagnostics": {
+ "clear_reuse_selected": {
+ "nodes": len(clear),
+ **_cluster_band(clear, axes, u, bayes["m_samples"], bayes["lam_mean"], em["m"]),
+ },
+ "borderline_selected": {
+ "nodes": len(ambiguous),
+ **_cluster_band(
+ ambiguous, axes, u, bayes["m_samples"], bayes["lam_mean"], em["m"]
+ ),
+ },
+ },
+ }
diff --git a/decluster/blob_locations.py b/decluster/blob_locations.py
new file mode 100644
index 00000000..76c82ba2
--- /dev/null
+++ b/decluster/blob_locations.py
@@ -0,0 +1,120 @@
+"""Give each bundle blob a canonical URL, but only one that actually resolves.
+
+A bundle without locations bootstraps from the local store and nowhere else, so a reader who has
+the checkout can verify it and a reader who has the index cannot. Filling the field in is cheap;
+filling it in *honestly* is the constraint, because a canonical URL names a revision, and a revision
+that has not been pushed is a link that 404s. Declaring one would be the same defect this
+repository keeps finding elsewhere — a record asserting something nobody checked.
+
+So the rule here is content-addressed, not name-addressed: a blob gets a URL only when the file at
+the named revision hashes to the digest the bundle pins, and only when that revision is an ancestor
+of the published branch. Everything else is left null and counted, which is the honest state until
+the branch is pushed.
+
+`--check` reports what could be filled in and what could not, so the gap is visible rather than
+mistaken for a decision.
+"""
+
+from __future__ import annotations
+
+import argparse
+import hashlib
+import json
+import re
+import subprocess
+from pathlib import Path
+
+RAW = "https://github.com/bc1cindy/decluster/raw/{revision}/{path}"
+REVISION = re.compile(r"/raw/([0-9a-f]{40})/(.+)$")
+
+
+def _git(*args, root="."):
+ return subprocess.run(("git", *args), cwd=root, capture_output=True)
+
+
+def is_ancestor(revision, published, root="."):
+ return _git("merge-base", "--is-ancestor", revision, published, root=root).returncode == 0
+
+
+def digest_at(revision, path, root="."):
+ result = _git("show", f"{revision}:{path}", root=root)
+ if result.returncode:
+ return None
+ return hashlib.sha256(result.stdout).hexdigest()
+
+
+def resolvable(blob, revision, published, root="."):
+ """Whether a URL at `revision` would serve exactly the bytes this blob pins."""
+ return (is_ancestor(revision, published, root=root)
+ and digest_at(revision, blob["name"], root=root) == blob["sha256"])
+
+
+def audit(root=".", published="origin/master"):
+ """Every blob, and whether its declared location resolves or one could be declared."""
+ rows = []
+ for index in sorted(Path(root, "releases").glob("*.bundle.json")):
+ for blob in json.loads(index.read_text())["blobs"]:
+ declared = blob["locations"]["canonical"]
+ state = "absent"
+ if declared:
+ match = REVISION.search(declared)
+ state = ("resolves" if match and resolvable(blob, match.group(1), published, root)
+ else "dangling")
+ elif digest_at(published, blob["name"], root=root) == blob["sha256"]:
+ state = "available"
+ rows.append({"bundle": index.stem, "name": blob["name"], "state": state})
+ return rows
+
+
+def apply(root=".", published="origin/master"):
+ """Fill in what resolves at `published`, and clear what does not. Returns the changes."""
+ revision = _git("rev-parse", published, root=root).stdout.decode().strip()
+ changes = []
+ for index in sorted(Path(root, "releases").glob("*.bundle.json")):
+ bundle = json.loads(index.read_text())
+ touched = False
+ for blob in bundle["blobs"]:
+ declared = blob["locations"]["canonical"]
+ fits = digest_at(revision, blob["name"], root=root) == blob["sha256"]
+ if declared:
+ match = REVISION.search(declared)
+ if match and resolvable(blob, match.group(1), published, root):
+ continue # an older public revision still serves it
+ if not fits:
+ blob["locations"]["canonical"] = None
+ changes.append(("cleared", blob["name"]))
+ touched = True
+ continue
+ if fits and not declared:
+ blob["locations"]["canonical"] = RAW.format(revision=revision, path=blob["name"])
+ changes.append(("declared", blob["name"]))
+ touched = True
+ if touched:
+ index.write_text(json.dumps(bundle, indent=2) + "\n")
+ return changes
+
+
+def main(argv=None):
+ parser = argparse.ArgumentParser(description=__doc__.splitlines()[0])
+ parser.add_argument("--root", default=".")
+ parser.add_argument("--published", default="origin/master")
+ parser.add_argument("--check", action="store_true")
+ arguments = parser.parse_args(argv)
+ if arguments.check:
+ rows = audit(arguments.root, arguments.published)
+ counts = {}
+ for row in rows:
+ counts[row["state"]] = counts.get(row["state"], 0) + 1
+ for state in ("resolves", "available", "absent", "dangling"):
+ print(f" {state}: {counts.get(state, 0)}")
+ for row in rows:
+ if row["state"] == "dangling":
+ print(f" dangling {row['bundle']} {row['name']}")
+ return 1 if counts.get("dangling") else 0
+ for kind, name in apply(arguments.root, arguments.published):
+ print(f" {kind} {name}")
+ return 0
+
+
+if __name__ == "__main__":
+ raise SystemExit(main())
diff --git a/decluster/boltzmann_fee_snapshot.py b/decluster/boltzmann_fee_snapshot.py
new file mode 100644
index 00000000..e809b78a
--- /dev/null
+++ b/decluster/boltzmann_fee_snapshot.py
@@ -0,0 +1,59 @@
+"""Build the exact transaction population used by the fee-allocation audit."""
+
+from __future__ import annotations
+
+import argparse
+from pathlib import Path
+
+from .measure import load_ndjson
+from .result_artifacts import write_canonical_json
+
+
+def transaction_values(transaction):
+ inputs = [
+ (item.get("prevout") or {}).get("value")
+ for item in (transaction.get("vin") or [])
+ ]
+ outputs = [item.get("value") for item in (transaction.get("vout") or [])]
+ if (
+ len(inputs) < 2
+ or not outputs
+ or any(value is None for value in inputs + outputs)
+ ):
+ return None
+ return tuple(inputs), tuple(outputs)
+
+
+def build_snapshot(source, destination, *, cap=300, max_coins=8):
+ """Write the first ``cap`` eligible transactions in source order."""
+ if cap < 1:
+ raise ValueError("cap must be positive")
+ if max_coins < 3:
+ raise ValueError("max_coins must allow two inputs and one output")
+ selected = []
+ for transaction, _height in load_ndjson(source):
+ values = transaction_values(transaction)
+ if values is None or sum(map(len, values)) > max_coins:
+ continue
+ selected.append(transaction)
+ if len(selected) == cap:
+ break
+ if len(selected) != cap:
+ raise ValueError(f"source contains only {len(selected)} eligible transactions")
+ write_canonical_json(Path(destination), selected)
+ return len(selected)
+
+
+def main(argv=None):
+ parser = argparse.ArgumentParser(description=__doc__)
+ parser.add_argument("source")
+ parser.add_argument("destination")
+ parser.add_argument("--cap", type=int, default=300)
+ parser.add_argument("--max-coins", type=int, default=8)
+ args = parser.parse_args(argv)
+ build_snapshot(args.source, args.destination, cap=args.cap, max_coins=args.max_coins)
+ return 0
+
+
+if __name__ == "__main__":
+ raise SystemExit(main())
diff --git a/decluster/bundle_cli.py b/decluster/bundle_cli.py
new file mode 100644
index 00000000..8f2d1e56
--- /dev/null
+++ b/decluster/bundle_cli.py
@@ -0,0 +1,136 @@
+"""Thin command-line adapter for evidence bundle verification and bootstrap."""
+
+from __future__ import annotations
+
+import argparse
+import json
+from pathlib import Path
+import sys
+
+from .bundle_sync import synchronise, unresolved
+from .data_manifest import ManifestError
+from .evidence_bundle import (
+ BlobStatus,
+ BundleBootstrapError,
+ BundleReproductionError,
+ ReproductionStatus,
+ assess_reproduction_readiness,
+ bootstrap_bundle,
+ load_bundle,
+ reproduce_bundle,
+ verify_bundle,
+)
+
+
+def _parser():
+ parser = argparse.ArgumentParser(prog="decluster-bundle")
+ parser.add_argument("--index", type=Path)
+ parser.add_argument("--store", type=Path, default=Path("artifacts"))
+ parser.add_argument("--root", type=Path, default=Path.cwd())
+ parser.add_argument("--replace", action="store_true")
+ parser.add_argument("--json", action="store_true", dest="as_json")
+ parser.add_argument("--work", type=Path)
+ parser.add_argument("--check", action="store_true",
+ help="sync: report what is out of step without writing")
+ parser.add_argument("command",
+ choices=("verify", "bootstrap", "readiness", "reproduce", "sync"))
+ return parser
+
+
+def _verification_records(bundle, store):
+ return [{
+ "name": check.blob.name,
+ "status": check.status.value,
+ "bytes": check.blob.content.bytes,
+ "sha256": check.blob.content.sha256,
+ } for check in verify_bundle(bundle, store)]
+
+
+def _bootstrap_records(bundle, store, root, *, replace):
+ return [{
+ "name": result.blob.name,
+ "acquisition": result.acquisition.value,
+ "materialization": result.materialization.value,
+ "path": str(result.path),
+ "source_url": result.source_url,
+ } for result in bootstrap_bundle(bundle, store, root, replace=replace)]
+
+
+def _print(records, as_json):
+ if as_json:
+ print(json.dumps(records, allow_nan=False, separators=(",", ":"), sort_keys=True))
+ return
+ for record in records:
+ if "issues" in record:
+ print(f"{record['bundle']}\t{record['status']}")
+ for issue in record["issues"]:
+ print(f"{issue['kind']}\t{issue['subject']}\t{issue['detail']}")
+ continue
+ state = record.get("status") or (
+ f"{record['acquisition']}/{record['materialization']}"
+ )
+ print(f"{record['name']}\t{state}")
+
+
+def _readiness_record(bundle, store, root):
+ readiness = assess_reproduction_readiness(bundle, store, root)
+ return readiness, {
+ "bundle": bundle.id,
+ "status": readiness.status.value,
+ "issues": [
+ {"kind": issue.kind.value, "subject": issue.subject, "detail": issue.detail}
+ for issue in readiness.issues
+ ],
+ }
+
+
+def main(argv=None):
+ args = _parser().parse_args(argv)
+ if args.command == "sync":
+ changes = synchronise(args.root, write=not args.check)
+ _print([{"name": c.name, "status": f"{c.kind}: {c.detail}"} for c in changes],
+ args.as_json)
+ if unresolved(changes):
+ return 1
+ return 1 if (args.check and changes) else 0
+ if args.index is None:
+ _parser().error("--index is required for this command")
+ try:
+ bundle = load_bundle(args.index)
+ readiness = None
+ if args.command == "verify":
+ records = _verification_records(bundle, args.store)
+ elif args.command == "bootstrap":
+ records = _bootstrap_records(
+ bundle, args.store, args.root, replace=args.replace,
+ )
+ elif args.command == "readiness":
+ readiness, record = _readiness_record(bundle, args.store, args.root)
+ records = [record]
+ else:
+ if args.work is None:
+ parser = _parser()
+ parser.error("--work is required for reproduce")
+ records = [{"name": str(path), "status": "reproduced"}
+ for path in reproduce_bundle(bundle, args.root, args.work)]
+ except ManifestError as exc:
+ print(str(exc), file=sys.stderr)
+ return 2
+ except BundleBootstrapError as exc:
+ print(str(exc), file=sys.stderr)
+ return 1
+ except BundleReproductionError as exc:
+ print(str(exc), file=sys.stderr)
+ return 1
+ _print(records, args.as_json)
+ if args.command == "verify" and any(
+ record["status"] != BlobStatus.VERIFIED.value for record in records
+ ):
+ return 1
+ if readiness is not None and readiness.status is ReproductionStatus.BLOCKED:
+ return 1
+ return 0
+
+
+if __name__ == "__main__":
+ raise SystemExit(main())
diff --git a/decluster/bundle_sync.py b/decluster/bundle_sync.py
new file mode 100644
index 00000000..1d60f1df
--- /dev/null
+++ b/decluster/bundle_sync.py
@@ -0,0 +1,122 @@
+"""Bring the content store and the bundle indexes back in step with the tree.
+
+A bundle pins each blob by name, byte count and digest, and the same bytes are kept in
+`artifacts/sha256/`. Editing a tracked file that a bundle ships therefore has to move three things
+at once, and doing it by hand goes wrong in ways the suite only half catches: a digest updated
+without its byte count still verifies against the store, and a stored blob nobody references stays
+behind until the completeness test notices.
+
+Two rules the procedure has to respect, both learned by breaking them:
+
+ store-only blobs a blob whose name is not in the tree — the built `dss` wheel — keeps the
+ identity it has. It is not missing; it was never a tracked file.
+ manifests first a run manifest pins its own outputs, and a bundle pins the manifest. Refreshing
+ them in the other order leaves the bundle pinning bytes that are already stale
+ again by the time it is written.
+ orphans last a blob is removed from the store only after every index has been rewritten,
+ because until then "referenced" is not yet known.
+"""
+
+from __future__ import annotations
+
+import hashlib
+import json
+from dataclasses import dataclass
+from pathlib import Path
+
+INDEX_DIR = "releases"
+RUN_DIR = "catalog/runs"
+STORE_DIR = "artifacts/sha256"
+
+
+@dataclass(frozen=True)
+class Change:
+ kind: str
+ name: str
+ detail: str
+
+
+def _identity(path):
+ data = path.read_bytes()
+ return len(data), hashlib.sha256(data).hexdigest()
+
+
+def _indexes(root):
+ return sorted(Path(root, INDEX_DIR).glob("*.bundle.json"))
+
+
+def _stored(root):
+ store = Path(root, STORE_DIR)
+ return {p.parent.name + p.name: p for p in store.glob("*/*") if p.is_file()}
+
+
+def synchronise(root=".", *, write=True):
+ """Return the changes the store and indexes need, applying them unless `write` is false."""
+ root = Path(root)
+ changes = []
+
+ for manifest_path in sorted(Path(root, RUN_DIR).glob("*.json")):
+ manifest = json.loads(manifest_path.read_text())
+ touched = False
+ for output in manifest.get("outputs", ()):
+ produced = root / output["path"]
+ if not produced.is_file():
+ changes.append(Change("unavailable", output["path"], "declared output is not here"))
+ continue
+ size, digest = _identity(produced)
+ if (size, digest) == (output["bytes"], output["sha256"]):
+ continue
+ changes.append(Change("output", output["path"],
+ f"{output['bytes']}/{output['sha256'][:12]} -> {size}/{digest[:12]}"))
+ output["bytes"], output["sha256"] = size, digest
+ touched = True
+ if touched and write:
+ manifest_path.write_text(json.dumps(manifest, indent=2) + "\n")
+
+ for index in _indexes(root):
+ bundle = json.loads(index.read_text())
+ touched = False
+ for blob in bundle["blobs"]:
+ source = root / blob["name"]
+ if not source.is_file():
+ continue # store-only blob: its identity stands
+ size, digest = _identity(source)
+ if (size, digest) == (blob["bytes"], blob["sha256"]):
+ continue
+ changes.append(Change("identity", blob["name"],
+ f"{blob['bytes']}/{blob['sha256'][:12]} -> {size}/{digest[:12]}"))
+ blob["bytes"], blob["sha256"] = size, digest
+ touched = True
+ if touched and write:
+ index.write_text(json.dumps(bundle, indent=2) + "\n")
+
+ referenced = {}
+ for index in _indexes(root):
+ for blob in json.loads(index.read_text())["blobs"]:
+ referenced[blob["sha256"]] = blob["name"]
+
+ stored = _stored(root)
+ for digest, name in sorted(referenced.items(), key=lambda item: item[1]):
+ if digest in stored:
+ continue
+ source = root / name
+ if not source.is_file():
+ changes.append(Change("unavailable", name, f"{digest[:12]} is in no store and no tree"))
+ continue
+ changes.append(Change("stored", name, digest[:12]))
+ if write:
+ target = Path(root, STORE_DIR, digest[:2], digest[2:])
+ target.parent.mkdir(parents=True, exist_ok=True)
+ target.write_bytes(source.read_bytes())
+
+ for digest in sorted(set(stored) - set(referenced)):
+ changes.append(Change("orphaned", stored[digest].name, digest[:12]))
+ if write:
+ stored[digest].unlink()
+
+ return tuple(changes)
+
+
+def unresolved(changes):
+ """The changes `synchronise` could not carry out on its own."""
+ return tuple(change for change in changes if change.kind == "unavailable")
diff --git a/decluster/change_gt.py b/decluster/change_gt.py
index 40c356e8..4a09768d 100644
--- a/decluster/change_gt.py
+++ b/decluster/change_gt.py
@@ -9,8 +9,12 @@ def input_addrs(tx):
return {a for v in tx["vin"] if (a := (v.get("prevout") or {}).get("scriptpubkey_address"))}
def union_input_addrs(tx, uf):
- """Union a transaction's input addresses into uf (multi-input co-spend clustering)."""
- ins = list(input_addrs(tx))
+ """Union a transaction's input addresses into uf (multi-input co-spend clustering).
+
+ Sorted, because the union-find root is whichever address is unioned into first. The partition
+ is the same either way, but the root labels the clusters, and downstream sampling walks them in
+ that order — so an unsorted set made a seeded run give a different answer in every process."""
+ ins = sorted(input_addrs(tx))
for a in ins[1:]:
uf.union(ins[0], a)
diff --git a/decluster/change_special.py b/decluster/change_special.py
index 74ad2397..170f5484 100644
--- a/decluster/change_special.py
+++ b/decluster/change_special.py
@@ -1,24 +1,22 @@
-"""Special-case change labels (value-based heuristics, M&N Table 1): near-certain change identification from a single
-signal, used as an INDEPENDENT label to validate fingerprints non-circularly. label_optimal_change
-reads ONLY values — disjoint from co-spend/addresses AND from ordering/nSequence/version — which is
-what breaks the circularity that invalidates cluster findNext against an M&N co-spend label."""
+"""Special-case change candidates used to validate fingerprint signals.
+
+The optimal-change candidate is computed from values only. It remains a
+heuristic conditioned on the absence of unnecessary inputs, not an ownership label.
+"""
from itertools import combinations
from .change_gt import is_candidate, input_addrs, out_addr
from .change_cluster import _addr_type
from .change_validate import axis_rates
def label_optimal_change(tx):
- """Optimal-change / UIH: in a >=2-input 2-output tx, the output smaller than the smallest input
- value MUST be change (else an input was unnecessary). Returns that index when exactly one output
- qualifies, else None. Reads ONLY values."""
- if not is_candidate(tx): return None
+ """Return the values-only UIH1 change candidate, or ``None`` if ambiguous."""
+ from .baselines.unnecessary_input import optimal_change_candidate
+
iv = [v.get("prevout", {}).get("value") for v in tx["vin"]]
- if any(v is None for v in iv) or len(iv) < 2: return None
+ if any(v is None for v in iv): return None
ov = [o.get("value") for o in tx["vout"]]
if any(v is None for v in ov): return None
- smin = min(iv)
- small = [i for i in (0, 1) if ov[i] < smin]
- return small[0] if len(small) == 1 else None
+ return optimal_change_candidate(iv, ov)
def label_address_reuse(tx):
"""Address reuse (self-change): the single output whose address is an input address. NOTE: reuse
diff --git a/decluster/change_validate.py b/decluster/change_validate.py
index 2cf248ee..54cee9c5 100644
--- a/decluster/change_validate.py
+++ b/decluster/change_validate.py
@@ -2,7 +2,7 @@
a shuffle null control (the net that caught the temporal fingerprint), and the universal baseline.
Pure functions with injected fetchers; the slice pipeline (change_slice) drives them."""
from .fetch import fetch_tx, fetch_outspends
-from .combiner import Combiner
+from .rarity_weight_baseline import Combiner
from .change_score import output_score, spending_tx
from .graph_deanon import auc, shuffle_auc
from .extractors import x_input_order, x_output_order, x_nsequence, x_version, x_change_index
@@ -79,4 +79,3 @@ def report(gt, combiner=None, get_tx=fetch_tx, get_outspends=fetch_outspends):
"the combined AUC uses the FS Combiner, which abstains on single/small_n order.",
"the two input_order views are not directly comparable."]
return "\n".join(lines)
-
diff --git a/decluster/cluster.py b/decluster/cluster.py
index ea9019e3..01525941 100644
--- a/decluster/cluster.py
+++ b/decluster/cluster.py
@@ -1,26 +1,47 @@
"""Layer 4 — graph-level clustering: single-heuristic union-find (common-input-ownership)
collapse vs. fingerprint-aware clustering."""
+from dataclasses import dataclass
+from enum import Enum
+
from .fetch import fetch_tx
from .subtransaction import subtransactions, norm
from .unionfind import UF
-def _cospent_pairs(nodes):
+def _cospent_pairs(nodes, fetch=None):
+ fetch = fetch or fetch_tx
"""common-input-ownership: coins co-spent in one tx -> same owner. A node is the txid
that funded a coin; if tx T spends coins funded by F1,F2..., F1,F2 get a same-owner edge.
A bare-coinbase vin (`{"is_coinbase": True}`, no "txid") has no funder to record — skip it,
rather than KeyError on a well-formed coinbase input."""
pairs = []
for t in nodes:
- funders = [vin["txid"] for vin in fetch_tx(t)["vin"] if vin.get("txid") in nodes]
+ funders = [vin["txid"] for vin in fetch(t)["vin"] if vin.get("txid") in nodes]
for i in range(len(funders)):
for j in range(i+1, len(funders)):
pairs.append((funders[i], funders[j], t))
return pairs
-def cluster_naive(nodes):
+def cluster_naive(nodes, fetch=None):
"""pure union-find over common-input-ownership (the BlockSci heuristic)."""
uf = UF(nodes)
- for a, b, _ in _cospent_pairs(nodes): uf.union(a, b)
+ for a, b, _ in _cospent_pairs(nodes, fetch): uf.union(a, b)
+ return uf.groups()
+
+
+def cluster_cospends(nodes, cospends):
+ """Apply CIOH to explicitly supplied co-spend groups.
+
+ This is the data-independent form of :func:`cluster_naive`. Each item in
+ ``cospends`` contains the input coins observed in one transaction. Unknown
+ nodes are ignored, matching the induced-subgraph behavior of
+ :func:`_cospent_pairs`.
+ """
+ known = set(nodes)
+ uf = UF(nodes)
+ for inputs in cospends:
+ members = [node for node in inputs if node in known]
+ for member in members[1:]:
+ uf.union(members[0], member)
return uf.groups()
def cluster_from_index(nodes, lookup):
@@ -33,7 +54,7 @@ def cluster_from_index(nodes, lookup):
groups.setdefault(key, []).append(n)
return list(groups.values())
-def amount_refuse_weight(t, a, b):
+def amount_refuse_weight(t, a, b, fetch=None):
"""Amount refuse weight (<=0) for a 2-in/2-out co-spend of inputs a,b: returns
-(roundness margin) = -(best re-partition's roundness minus the runner-up's). A soft refuse
that grows only when one round re-partition is distinctly more plausible than the alternatives,
@@ -41,7 +62,7 @@ def amount_refuse_weight(t, a, b):
merge. Roundness is a heuristic, not proof (paper §2): refuse-only (it can cut a co-spend from
the graph, never add a positive same-owner term), meant to be corroborated by the fingerprint
and topology channels. 0 out of scope (not a 2-in/2-out co-spend of {a,b}, or no partition)."""
- tx = norm(fetch_tx(t))
+ tx = norm((fetch or fetch_tx)(t))
ins = [v["txid"] for v in tx["vin"]]
if set(ins) != {a, b}:
return 0.0
@@ -82,8 +103,8 @@ def topology_weight(a, b, neigh, cbits=None, disjoint_bits=-1.65, share_cap=12.0
calibrated *weak*: on a real slice P(disjoint|same)=0.32 vs P(disjoint|diff)=1.00 ->
log2 ratio ~= -1.65, so a SINGLE disjoint pair barely moves the score and cannot by itself
overcome a fingerprint match. The refusal the collaborator describes ('enough
- distinguishing relationships') is the ACCUMULATION of these across a whole cluster — an
- N-S cluster-level computation, not this per-pair term (paper §10). NOT called by the engine
+ distinguishing relationships') is the ACCUMULATION of these across a whole cluster. That
+ local aggregation is not the seeded cross-view N-S matcher and not this per-pair term. NOT called by the engine
(`cluster_refined` uses `cluster_topology_weight`); kept as the per-pair illustration. 0 when
either side has too little graph to judge."""
na, nb = neigh.get(a, set()), neigh.get(b, set())
@@ -179,12 +200,46 @@ def build_cospend_lookup(corpus_txs):
COSPEND_PRIOR = 2.0 # common-input-ownership prior in bits; a merge survives iff prior + fp + amt + topo > 0
+
+class PairDecisionStatus(str, Enum):
+ DIRECT_MERGE = "direct_merge"
+ TRANSITIVE_MEMBERSHIP = "transitive_membership"
+ REFUSED = "refused"
+
+
+@dataclass(frozen=True)
+class ClusterPairDecision:
+ left: str
+ right: str
+ spending_txid: str
+ status: PairDecisionStatus
+ cospend_prior_bits: float
+ fingerprint_bits: float
+ amount_bits: float
+ topology_bits: float
+ provenance_bits: float
+ subset_sum_bits: float
+
+ @property
+ def total_bits(self) -> float:
+ return (
+ self.cospend_prior_bits
+ + self.fingerprint_bits
+ + self.amount_bits
+ + self.topology_bits
+ + self.provenance_bits
+ + self.subset_sum_bits
+ )
+
def cluster_refined(nodes, combiner, cospend_prior=COSPEND_PRIOR, link_above=4.0, neigh=None,
amount=True, topo_tau=1.0, subsetsum=False, _ss_fn=None, hubs=None,
provenance=False, signatures=None, rarity=None, prov_refuse_bits=-3.0,
- link_eps=1e-9):
+ link_eps=1e-9, fetch=None, _decision_log=None):
"""The engine (the only fingerprint-aware clusterer; `cluster_naive` is the merge-only BlockSci
- baseline). Order-independent partition refinement that fuses multiple refuse channels:
+ baseline). This pass ASCENDS: it starts from the discrete partition and declines a merge rather
+ than undoing one, so it owns every block it builds and can never cut a block it inherited.
+ Cutting an inherited partition is the other direction and lives in `declustering.decluster`.
+ Order-independent, fusing multiple refuse channels:
co-spend prior, fingerprint, amount (roundness + denomination de-mix), cluster-level topology,
and provenance-disjointness (disjoint ancestry + divergent fingerprint → refuse). Signatures
supplied precomputed by caller; entity-level realization of same signal in propagate.py
@@ -199,20 +254,23 @@ def cluster_refined(nodes, combiner, cospend_prior=COSPEND_PRIOR, link_above=4.0
(`fp >= link_above`). Monotone, so it converges in <= |nodes| rounds. Returns (groups,
refused, linked): refused = (a, b, t, fp, amt, fp+amt+top) for co-spent pairs left split;
linked = (a, b, sc)."""
+ fetch = fetch or fetch_tx
cbits = counterparty_bits(neigh, hubs=hubs) if neigh else None # hubs: known super-cluster addresses forced to 0 bits (never corroborate a merge)
ev = {} # (t, fp, amt) per pair: fp is a pair property (scored
- for a, b, t in _cospent_pairs(nodes): # once); amt kept from the most-refuting co-spend of the pair
+ for a, b, t in _cospent_pairs(nodes, fetch): # once); amt kept from the most-refuting co-spend of the pair
k = (a, b) if a <= b else (b, a)
- fp = ev[k][1] if k in ev else combiner.score(fetch_tx(a), fetch_tx(b))
+ fp = ev[k][1] if k in ev else combiner.score(fetch(a), fetch(b))
# amount channel, mode 1 of 2 (refuse-only): structural roundness re-partition. Roundness is a
# heuristic (paper §2), not proof, so it only refuses a co-spend the fingerprint ALSO disagrees
# on (fp < 0) — it corroborates, never splits a benign single-owner 2-in/2-out round payment on
# its own; the fingerprint-silent case falls to the topology control (§8). Mode 2 is the
# denomination de-mix (`subsetsum=`, below), which carries its own uniqueness guard.
- amt = amount_refuse_weight(t, a, b) if (amount and fp < 0) else 0.0
+ amt = amount_refuse_weight(t, a, b, fetch) if (amount and fp < 0) else 0.0
if k not in ev or amt < ev[k][2]:
ev[k] = (t, fp, amt)
base = {k: cospend_prior + fp + amt for k, (t, fp, amt) in ev.items()}
+ provenance_bits = {k: 0.0 for k in ev}
+ subset_sum_bits = {k: 0.0 for k in ev}
if provenance and signatures is not None:
# Refuse-only provenance channel: disjoint provenance is evidence AGAINST common
# ownership. Gated on fp < 0 like the amount channel, so provenance-disjointness
@@ -224,6 +282,7 @@ def cluster_refined(nodes, combiner, cospend_prior=COSPEND_PRIOR, link_above=4.0
sa, sb = signatures.get(k[0]), signatures.get(k[1])
if sa is not None and sb is not None and provenance_link(sa, sb, rarity) <= link_eps:
base[k] += prov_refuse_bits
+ provenance_bits[k] = prov_refuse_bits
if subsetsum:
resolve = _ss_fn
if resolve is None:
@@ -232,7 +291,8 @@ def cluster_refined(nodes, combiner, cospend_prior=COSPEND_PRIOR, link_above=4.0
def resolve(tx, a, b):
return amount_refuse_demix(tx, a, b) # per-pair de-mix verdict (diff participants -> refuse)
for k, (t, fp, amt) in ev.items():
- base[k] += resolve(fetch_tx(t), k[0], k[1]) # per-pair: amount refuse / forced-link
+ subset_sum_bits[k] = resolve(fetch(t), k[0], k[1])
+ base[k] += subset_sum_bits[k] # per-pair: amount refuse / forced-link
uf = UF(nodes); linked = []
node_list = list(nodes) # links the co-spend MISSED: only NON-co-spent pairs —
for i in range(len(node_list)): # a co-spent pair is decided by the fused fixed-point
@@ -240,9 +300,10 @@ def resolve(tx, a, b):
a, b = node_list[i], node_list[j] # coerced-uniform merge cannot bypass an amount/topology refusal
if ((a, b) if a <= b else (b, a)) in base:
continue
- sc = combiner.score(fetch_tx(a), fetch_tx(b))
+ sc = combiner.score(fetch(a), fetch(b))
if sc >= link_above:
uf.union(a, b); linked.append((a, b, sc))
+ direct_topology = {}
for _ in range(len(node_list)): # synchronous fixed-point; monotone -> <= |nodes| rounds
to_merge = []
for (a, b), be in base.items():
@@ -251,6 +312,7 @@ def resolve(tx, a, b):
top = cluster_topology_weight(uf.group(a), uf.group(b), neigh, cbits, tau=topo_tau) if neigh else 0.0
if be + top > 0:
to_merge.append((a, b))
+ direct_topology[(a, b)] = top
if not to_merge:
break
for a, b in to_merge:
@@ -260,4 +322,45 @@ def resolve(tx, a, b):
if uf.find(a) != uf.find(b):
top = cluster_topology_weight(uf.group(a), uf.group(b), neigh, cbits, tau=topo_tau) if neigh else 0.0
refused.append((a, b, t, fp, amt, fp + amt + top))
+ if _decision_log is not None:
+ for (a, b), (t, fp, amt) in ev.items():
+ if (a, b) in direct_topology:
+ status = PairDecisionStatus.DIRECT_MERGE
+ top = direct_topology[(a, b)]
+ elif uf.find(a) == uf.find(b):
+ status = PairDecisionStatus.TRANSITIVE_MEMBERSHIP
+ top = 0.0
+ else:
+ status = PairDecisionStatus.REFUSED
+ top = (
+ cluster_topology_weight(
+ uf.group(a), uf.group(b), neigh, cbits, tau=topo_tau
+ )
+ if neigh
+ else 0.0
+ )
+ _decision_log.append(
+ ClusterPairDecision(
+ a,
+ b,
+ t,
+ status,
+ cospend_prior,
+ fp,
+ amt,
+ top,
+ provenance_bits[(a, b)],
+ subset_sum_bits[(a, b)],
+ )
+ )
return uf.groups(), refused, linked
+
+
+def cluster_refined_decisions(nodes, combiner, **options):
+ """Return the legacy result plus the evidence breakdown for each co-spend pair."""
+ node_list = list(nodes)
+ decisions = []
+ groups, refused, linked = cluster_refined(
+ node_list, combiner, _decision_log=decisions, **options
+ )
+ return groups, refused, linked, decisions
diff --git a/decluster/combiner.py b/decluster/combiner.py
index 5684bc15..43116437 100644
--- a/decluster/combiner.py
+++ b/decluster/combiner.py
@@ -1,20 +1,30 @@
-"""Layer 3 — Fellegi-Sunter combiner: score(txA,txB) in bits (+ same / - different). This is the
-narrow engine combiner (3 axes: nsequence, locktime, in_order) that `cluster_refined` uses; the full
-23-axis validated model (the AUC≈0.933 headline) is `fingerprint_validate.LibraryScorer`."""
+"""Layer 3 — legacy value-rarity fingerprint combiner.
+
+Agreement is weighted by observed value rarity and disagreement by a supplied
+consistency assumption. Existing callers retain this behavior. The fitted,
+supervised Fellegi--Sunter baseline lives in :mod:`decluster.fellegi_sunter`.
+"""
import math
from collections import Counter
-from .extractors import x_nsequence, x_input_order, locktime_policy
+from .extractors import NA, x_nsequence, x_input_order, locktime_policy
from .engine import sample_recent_txs
+# A fixed three-axis choice, not the output of a selection procedure. Checked afterwards rather
+# than chosen for it: within-class phi over these three peaks at 0.125 (nsequence/locktime,
+# non-match class), so the additive kernel is not double-counting here. In the match class phi is
+# undefined — locktime and in_order agree on every scored match pair, so a marginal is degenerate.
+# That the triple is uncorrelated is a property of this particular triple, not of the method.
AXES = {"nsequence": x_nsequence, "locktime": locktime_policy, "in_order": x_input_order}
_LIB_AXIS = {"nsequence": "nsequence", "in_order": "input_order", "locktime": "locktime"}
-def _never(va, vb): return False
-def _in_order_abstain(va, vb): return bool({"single", "small_n"} & {va, vb})
-_ABSTAIN = {"nsequence": _never, "locktime": _never, "in_order": _in_order_abstain}
+def _absent(va, vb): return NA in (va, vb)
+def _in_order_abstain(va, vb): return _absent(va, vb) or bool({"single", "small_n"} & {va, vb})
+# Every axis abstains where the export does not say; `NA` is never a value two transactions can
+# agree on.
+_ABSTAIN = {"nsequence": _absent, "locktime": _absent, "in_order": _in_order_abstain}
-def fs_score(axes, txA, txB, c, floor_n, explain=False):
- """Fellegi-Sunter kernel over axes = [(name, fn, p, collision, abstain)]: agreement adds
+def rarity_score(axes, txA, txB, c, floor_n, explain=False):
+ """Legacy rarity kernel over axes = [(name, fn, p, collision, abstain)]: agreement adds
-log2(p[value]); a mismatch adds a clamped (<=0) weight; abstain(va, vb) skips the axis.
c is a float, or a dict mapping axis-name -> m per axis (must cover every scored axis)."""
total, rows = 0.0, []
@@ -32,6 +42,7 @@ def fs_score(axes, txA, txB, c, floor_n, explain=False):
total += w; rows.append((name, va, vb, w))
return (total, rows) if explain else total
+
class Combiner:
def __init__(self, sample=None, consistency=0.95):
self.c = consistency
@@ -62,4 +73,4 @@ def from_library(cls, consistency=0.95):
return self
def score(self, txA, txB, explain=False):
- return fs_score(self.axes, txA, txB, self.c, self.floor_n, explain)
+ return rarity_score(self.axes, txA, txB, self.c, self.floor_n, explain)
diff --git a/decluster/conservation.py b/decluster/conservation.py
index f5b64f21..a890fc19 100644
--- a/decluster/conservation.py
+++ b/decluster/conservation.py
@@ -1,17 +1,23 @@
-"""Force ownership of coinjoin outputs by conservation alone.
+"""Trace the value of coinjoin outputs by conservation alone.
If a set of equal outputs is worth more than every other participant brought to
-the round, the excess had to come from the one participant whose input we can
+the round, the excess had to *come from* the one participant whose input we can
name. No model of the client is involved: not the denomination lattice, not the
decomposer, not fee accounting beyond a conservative floor. Just arithmetic on
what the transaction shows.
-That makes this the sharpest tool here and the one with the fewest ways to be
-wrong. It is also refusal-shaped in the sense PAPER §1 requires, though it reads
-as attribution: what it establishes is that the alternative — every one of those
-outputs belonging to somebody else — is impossible. The engine's invariant is
-about the amount channel inventing same-owner *links*; this invents nothing, it
-eliminates the only competing assignment.
+What that establishes is provenance of value, and only that. It is not ownership,
+and the gap between the two is the whole reason multi-party protocols are built:
+under net settlement a participant may both pay and receive inside one round, so
+their satoshi legitimately end up in a counterparty's output and the surplus is
+forced onto their *input* without any output being theirs. The inference to
+ownership needs the extra premise that every participant's sub-transaction is
+self-funded -- true of a plain batch, false of anything that settles obligations,
+and the adversary cannot assume its absence from a protocol that permits it.
+
+So this is refusal-shaped in the sense PAPER §1 requires: it eliminates the
+reading that none of that value came from the named participant. Reporting it as
+"these outputs are theirs" overstates it by exactly one assumption.
The bound is deliberately conservative in one direction. Output fees are ignored,
which understates what the other participants must have paid, so the count
@@ -24,7 +30,7 @@ def others_input(total_input, known_input):
def forced_count(total_input, known_input, output_value, output_count):
- """How many outputs of `output_value` must belong to the known participant.
+ """How many outputs of `output_value` the known participant must have funded.
The others can fund at most `others_input // output_value` of them. Anything
beyond that has no other source. Returns 0 when the others could have funded
@@ -37,11 +43,13 @@ def forced_count(total_input, known_input, output_value, output_count):
def forced_in_round(tx, known_input, min_count=1):
- """Every output value in `tx` that conservation forces onto the known input.
+ """Every output value in `tx` whose funding conservation traces to the known input.
- Returns `[(value, forced, present)]`, largest forced value first: `forced` of
- the `present` outputs at that value cannot belong to anyone else. `min_count`
- filters out values that force fewer than that many.
+ Returns `[(value, forced, present)]`, largest first: `forced` of the `present`
+ outputs at that value could not have been funded by anyone else. That is a claim
+ about where the satoshi came from, not about who holds the outputs -- see the
+ module docstring on why net settlement separates the two. `min_count` filters out
+ values that force fewer than that many.
Empty is the expected result. It takes a participant large relative to the
round for the inequality to bite at all.
@@ -60,7 +68,7 @@ def forced_in_round(tx, known_input, min_count=1):
def forced_value(tx, known_input, min_count=1):
- """Total satoshi conservation forces onto the known input in this round."""
+ """Total satoshi in this round whose funding conservation traces to the known input."""
return sum(value * forced for value, forced, _ in forced_in_round(tx, known_input, min_count))
diff --git a/decluster/contraction.py b/decluster/contraction.py
new file mode 100644
index 00000000..4b2c3db4
--- /dev/null
+++ b/decluster/contraction.py
@@ -0,0 +1,292 @@
+"""Contract a view's clustering into the pseudonym graph the matching algorithm consumes.
+
+The cited framework's construction: coins are vertices, a clustering is a set of undirected
+edges joining same-owner coins, and contracting those edges fuses each cluster into one
+vertex whose residual edges are transfers of bitcoin. With a complete clustering that is a
+user network; with the partial clustering an adversary actually holds it is a *pseudonym*
+graph, and that is the object its matching algorithm consumes.
+
+Two details the construction turns on:
+
+ global clustering the partition is applied to the coin graph, but the clustering that
+ annotates it is computed over the whole slice, then each part is
+ contracted with that same lookup. Clustering each part independently
+ would give the two views different vertex sets by construction.
+ fold, don't stack contraction yields a multigraph, one edge per transfer, but the
+ matching model wants a directed graph carrying one edge per ordered
+ pair with the parallel transfers collected into its attributes.
+
+Vertex attributes are stored as raw counts alongside the view's own base rates, never as
+bare shares: `RESULTS-attribute-drift.md` finds a median 54% of an axis value's variance
+tracks epoch volume, a covariate that moves every vertex together, so an attribute has to
+be read relative to its own view before it crosses the boundary.
+"""
+from collections import Counter, defaultdict
+
+from .extractors import NA, locktime_policy, x_fee_rate, x_input_order, x_version
+from .tx_addrs import in_addrs, out_addrs
+
+# The axes RESULTS-attribute-drift.md finds stable across a one-week gap. The high-drift
+# axes (output types, feerate bucket, change position) are deliberately absent: at 4-8x the
+# drift they do not survive the boundary they would be compared across.
+AXES = {"version": x_version, "input_order": x_input_order,
+ "locktime": locktime_policy, "fee_rate": x_fee_rate}
+
+
+def _fold(store, key, val, height):
+ """Add one transfer to the folded record at `key`, extending its value and height span."""
+ e = store.get(key)
+ if e is None:
+ store[key] = {"transfers": 1, "value": val, "first": height, "last": height}
+ return
+ e["transfers"] += 1
+ e["value"] += val
+ if height < e["first"]:
+ e["first"] = height
+ elif height > e["last"]:
+ e["last"] = height
+
+
+class PseudonymGraph:
+ """Contracted view. Vertices are clusters; `edges[(src, dst)]` is the single directed
+ edge folding every transfer from src to dst, carrying how many there were and how much
+ value moved. Value is kept because connectivity means a plausible flow, not merely a
+ traceable one, so a matcher may discount an edge that only dust created.
+
+ A transfer whose destination is its own source is a loop, which the user network has and
+ which no ordered pair can hold. `self_edges[vid]` folds those under the same schema as an
+ edge, so the value and the height span survive contraction there too; the vertex record's
+ `self_transfers` is the same count, kept because that record is what gets serialised."""
+
+ def __init__(self, axes=True):
+ self.axes = axes # whether vertex records carry the per-axis counters
+ self.vertices = {}
+ self.edges = {}
+ self.self_edges = {} # vid -> the folded loop, same attributes as an edge
+ self.edge_sig = {} # (src, dst) -> the axis values of its first transfer
+ self.base_rates = {axis: Counter() for axis in AXES}
+ self.skipped = Counter() # axis -> transactions it could not be read from
+ self.unattributed = 0 # transactions whose transfers could not be attributed
+ self._out = defaultdict(set)
+ self._in = defaultdict(set)
+
+ def _vertex(self, vid):
+ # Not setdefault: it builds the default on every hit, and this is called once per
+ # (transaction, source). The per-axis Counters are the vertex record's bulk — 681 B
+ # against 191 B without them — so a structure-only run does not allocate them at all.
+ v = self.vertices.get(vid)
+ if v is None:
+ v = {"coins": 0, "txs": 0, "self_transfers": 0}
+ if self.axes:
+ v["axes"] = {axis: Counter() for axis in AXES}
+ self.vertices[vid] = v
+ return v
+
+ def neighbours(self, vid):
+ # Read through `.get`: indexing a defaultdict creates an empty set for every vertex
+ # merely asked about, and a full degree sweep asks about all of them. The union is
+ # written to build the same set the defaultdict form did, in the same order — the
+ # matcher breaks score ties on iteration order, so a different one moves results.
+ out, inn = self._out.get(vid), self._in.get(vid)
+ if out is None and inn is None:
+ return set()
+ return (out or set()) | (inn or set())
+
+ def degree(self, vid):
+ return len(self.neighbours(vid))
+
+ def attribute(self, vid, axis):
+ """The vertex's distribution over an axis, as a lift over this view's own base rate.
+ 1.0 means the vertex looks exactly like its view; the normalisation is what makes
+ the value comparable to a vertex measured in another view."""
+ record = self.vertices[vid]
+ if "axes" not in record: # structure-only view: nothing was counted
+ return {}
+ counts = record["axes"][axis]
+ total = sum(counts.values())
+ base_total = sum(self.base_rates[axis].values())
+ if not total or not base_total:
+ return {}
+ out = {}
+ for value, n in counts.items():
+ base = self.base_rates[axis].get(value, 0) / base_total
+ out[value] = (n / total) / base if base else float("inf")
+ return out
+
+
+def transfer_counts(sample, lookup=None, min_value=0, max_sources=1):
+ """How many transfers each cluster takes part in, in one cheap pass. Distinct degree is
+ at most this, so thresholding on it never drops a vertex that would have cleared the
+ same threshold on degree — which is what makes it safe as a pre-filter. `max_sources`
+ must match the value `contract` will be called with, or the pre-filter counts edges the
+ contraction never creates."""
+ lookup = {} if lookup is None else lookup
+ counts = Counter()
+ for tx, _ in sample:
+ ins = in_addrs(tx)
+ if not ins:
+ continue
+ srcs = {lookup.get(a, a) for a in ins}
+ if len(srcs) > max_sources:
+ continue # unattributable multi-source tx creates no edges
+ for addr, val in out_addrs(tx):
+ if val < min_value:
+ continue
+ dst = lookup.get(addr, addr)
+ for s in srcs:
+ if s != dst:
+ counts[s] += 1
+ counts[dst] += 1
+ return counts
+
+
+def contract_degrees(sample, indices=None, lookup=None, min_value=0, max_sources=1):
+ """The degrees of a contracted view, without contracting it.
+
+ The matcher needs each vertex's degree in the *unfiltered* graph: dropping leaves to fit a
+ wide view in memory lowers the degree of everything they hung off and silently reweights
+ every score. Getting those by building the whole graph and throwing it away costs more than
+ the graph that is kept — on a complete 2016 weekly view, 18.3 s and 1104 MB for a number
+ that needs only the adjacency. This walks the same edges under the same `max_sources` bound
+ and keeps nothing but the neighbour sets, so the degrees are identical by construction.
+
+ Returns `{vertex: degree}`, including the isolated vertices a transaction registers without
+ attributing, which is what `contract(...).degree` over `.vertices` reports.
+ """
+ lookup = {} if lookup is None else lookup
+ out, inn = defaultdict(set), defaultdict(set)
+ isolated = set()
+ pairs = sample if indices is None else (sample[i] for i in indices)
+ for tx, _ in pairs:
+ ins = in_addrs(tx)
+ if not ins:
+ continue
+ srcs = {lookup.get(a, a) for a in ins}
+ isolated |= srcs
+ if len(srcs) > max_sources:
+ continue
+ for addr, val in out_addrs(tx):
+ if val < min_value:
+ continue
+ dst = lookup.get(addr, addr)
+ isolated.add(dst)
+ for s in srcs:
+ if s == dst:
+ continue
+ out[s].add(dst)
+ inn[dst].add(s)
+ degrees = {}
+ for v in isolated:
+ o, i = out.get(v), inn.get(v)
+ if o is None:
+ degrees[v] = 0 if i is None else len(i)
+ else:
+ degrees[v] = len(o) if i is None else len(o | i)
+ return degrees
+
+
+def contract(sample, indices=None, lookup=None, min_value=0, axes=True, keep=None,
+ max_sources=1):
+ """Contract one view: fuse each cluster's coins into a vertex and fold the transfers
+ between clusters into one attributed directed edge per ordered pair, carrying how many
+ transfers it folded, how much value moved, and the block-height span they cover.
+
+ An address absent from `lookup` is its own singleton pseudonym, which is the ordinary
+ case under a partial clustering rather than an error. `min_value` drops transfers below
+ a threshold, so a dust spray does not manufacture a relationship.
+
+ `indices` selects from a materialised sample; passing None instead iterates `sample`
+ directly, so a whole-epoch view can be streamed off disk without holding it in memory.
+ `axes=False` skips the attribute counters, which dominate the vertex record: a matcher
+ run that uses structure alone does not need them and a wide view may not have room.
+
+ A vertex counts its coins rather than listing them; the addresses stay recoverable from
+ the clustering lookup.
+
+ `max_sources` bounds how many distinct source pseudonyms a transaction may have before
+ its transfers are treated as unattributable and contribute no edges. An edge is supposed
+ to be a transfer of bitcoin from one cluster to another; where several pseudonyms fund a
+ transaction, which of them paid which output is exactly what is not observable, and
+ asserting every source-destination pair invents relationships. In a coinjoin it invents
+ the one relationship the construction is defined not to have, since its participants
+ need no economic tie at all. Measured on this slice: 211 transactions with six or more
+ source pseudonyms were generating 3.4 million of 4.4 million pairs, 77% of the graph.
+ Transactions above the bound still register their vertices and their activity.
+
+ `keep` restricts the graph to a vertex set, dropping edges with an endpoint outside it.
+ Paired with `transfer_counts` this excludes the leaves — 57% of a contracted 2026 view
+ sits at degree below two — which propagation can neither match nor bridge through, so
+ the restriction roughly halves the graph. It drops edges and never adds one: the
+ `max_sources` bound is evaluated on the transaction's own sources, before the filter.
+ """
+ lookup = {} if lookup is None else lookup
+ g = PseudonymGraph(axes=axes)
+ pairs = sample if indices is None else (sample[i] for i in indices)
+ for tx, _ in pairs:
+ ins = in_addrs(tx)
+ if not ins:
+ continue
+ height = tx.get("height") or 0
+ srcs = {lookup.get(a, a) for a in ins}
+ # Attributability is a property of the transaction, decided before the view filter
+ # touches it. Intersecting first lets a transaction the contraction refused emit an
+ # edge as soon as `keep` has removed all but one of its sources — and the survivor is
+ # systematically the hub, so the filter invents exactly the relationships the bound
+ # exists to withhold.
+ attributable = len(srcs) <= max_sources
+ if keep is not None:
+ srcs &= keep
+ if not srcs:
+ continue
+ sig = None
+ if axes:
+ sig = []
+ for axis, fn in AXES.items():
+ try:
+ value = fn(tx)
+ except Exception: # a malformed or partial tx, not a bug
+ g.skipped[axis] += 1 # counted, never silent: an axis that
+ continue # dies on every tx must be visible
+ if value == NA: # the export does not say. Recording it as a
+ g.skipped[axis] += 1 # value made every vertex in an address-only
+ continue # slice agree on the axis for free
+ g.base_rates[axis][value] += 1
+ sig.append(value)
+ for s in srcs:
+ g._vertex(s)["axes"][axis][value] += 1
+ sig = tuple(sig)
+ for s in srcs:
+ v = g._vertex(s)
+ v["txs"] += 1
+ v["coins"] += sum(1 for a in ins if lookup.get(a, a) == s)
+ if not attributable:
+ g.unattributed += 1
+ continue
+ for addr, val in out_addrs(tx):
+ if val < min_value:
+ continue
+ dst = lookup.get(addr, addr)
+ if keep is not None and dst not in keep:
+ continue
+ g._vertex(dst)["coins"] += 1
+ for s in srcs:
+ if s == dst:
+ g.vertices[s]["self_transfers"] += 1
+ _fold(g.self_edges, s, val, height)
+ continue
+ key = (s, dst)
+ # Reid and Harrigan label every user-network edge with value *and* time. The
+ # span is what separates a relationship that recurs from a one-off, which is
+ # the property cross-view matching actually depends on.
+ _fold(g.edges, key, val, height)
+ if axes and key not in g.edge_sig:
+ # The framework wants the statistical features to inform the EDGE
+ # attributes too, not only the vertices. Stored as the axis values of
+ # the transfer that created the edge rather than a distribution over
+ # all of them: 93% of edges carry a single transfer, so for almost all
+ # of them the two are the same thing, and a Counter per edge is not
+ # affordable at slice scale.
+ g.edge_sig[key] = sig
+ g._out[s].add(dst)
+ g._in[dst].add(s)
+ return g
diff --git a/decluster/cost.py b/decluster/cost.py
index 35e84ceb..ef7d2229 100644
--- a/decluster/cost.py
+++ b/decluster/cost.py
@@ -1,22 +1,23 @@
-"""Cost-function contract.
+"""Experimental channel contract for adversarial diagnostics.
-Every quantity here is an attacker's weight-of-evidence / lower bound under no auxiliary
-information, never a positive "bits of privacy". Sign discipline: leak and topology may penalise;
-the amount channel is REFUSE-ONLY (it can cut a coin from the graph, never add anonymity).
+The quantities retain their channel-specific interpretation and assumptions. They are not
+interchangeable privacy scores or universal bounds. Sign discipline: leak and topology may
+penalise; the amount channel is refuse-only and can propose cuts but never add anonymity.
"""
from dataclasses import dataclass
from .cluster import cluster_topology_weight, counterparty_bits
from .ancestry import ancestry_entropy
-from .path_count import path_count_anonymity
+from .provenance_route_accumulation import provenance_route_accumulation
@dataclass(frozen=True)
class CutCandidate:
+ role: str
index: int
value: int
log_w: float
- exact: bool = False
+ transaction_count_exact: bool = False
def leak_bits(tx_a, tx_b, combiner):
@@ -24,32 +25,66 @@ def leak_bits(tx_a, tx_b, combiner):
return combiner.score(tx_a, tx_b)
+def _reachable(log_w):
+ """Whether the oracle actually measured this coin, as opposed to failing to reach it.
+
+ Unreachable is spelled as None by some callers and as negative infinity by the dss per-coin
+ path; NaN would be a third spelling. All three mean the same thing and none of them is a low
+ ambiguity."""
+ return log_w is not None and log_w == log_w and log_w != float("-inf")
+
+
def amount_cuts(inputs, outputs, oracle, cut_threshold=1.0, count_oracle=None):
"""Amount-channel linkage CANDIDATES — directional, not a proof. The oracle's per-coin `log_w` is
a knee-truncated lower bound on the coin's ambiguity, so a LOW value only *flags a candidate* (few
small balancing subsets were found); it does not prove low ambiguity, since a real balance may need
more coins than the truncation counts. Refuse-only: returns candidate cuts, never a positive term,
- and a dense coinjoin (high log_w everywhere) yields none. Coins with `log_w is None` (unreachable
- within the truncation) are skipped, not cut.
-
- `count_oracle`, when given, corroborates each cut with the TX-LEVEL exact-count flag
- (`count_oracle(inputs, outputs)["kind"] == "exact"`, matching `counting.w_total`'s shape): when the
- tx's total W(E) is exact, no knee-truncation bites, so the per-coin `log_w` from `oracle` is itself
- untruncated and the cut is RIGOROUS (`exact=True`); when the count is approximate (Sasamoto) or
- off-regime — or `count_oracle` is not given (default `None`) — the per-coin `log_w` stays a lower
- bound and the cut remains a candidate (`exact=False`, today's behavior). dss exposes the exact
- W(E) per TRANSACTION, not per coin, so this is a tx-level corroboration, never a fabricated
- per-coin exact count. `oracle(inputs, outputs)` follows the dss.per_coin_density shape."""
+ and a dense coinjoin (high log_w everywhere) yields none.
+
+ A coin the truncated search could not reach at all is SKIPPED, not cut — "no balancing subset was
+ found" is the absence of a measurement, not a measurement of zero ambiguity. The oracle spells
+ that state as negative infinity rather than None, and on a real slice it is the common case:
+ 98.2% of input coins over 1,428 multi-input transactions, and every input coin in 97% of them.
+ Reading it as a low value would cut nearly every coin in the graph.
+
+ The per-coin reading is the last of three steps and is gated on the one before it: classify the
+ amounts, evaluate the transaction as a whole, and only then apportion across its coins. A
+ transaction whose ambiguity did not resolve at the second step has nothing to apportion, and the
+ per-coin oracle answering anyway is the oracle guessing rather than measuring.
+
+ `count_oracle` performs that second step, defaulting to the routed count. It also corroborates
+ each cut with a TX-LEVEL exact-count flag (`kind == "exact"` with a positive count). This is
+ provenance about the gate only. Transaction W(E) and per-coin density are different objects, so
+ an exact transaction count does not make the coin reading exact or turn the candidate into a
+ proof. dss exposes no exact per-coin W(E). Only the subset-sum tiers can raise the flag: the
+ denominational path answers `diagnostic`, which neither passes the gate above nor reads as
+ exact here. `oracle(inputs, outputs)` follows the dss.per_coin_density shape."""
+ from .counting import count_w, guaranteed_log_w
+ if count_oracle is None:
+ count_oracle = count_w
+ # Step two before step three. Apportioning ambiguity across the coins of a transaction whose
+ # ambiguity as a whole did not resolve is apportioning nothing: measured, a third of the
+ # transactions the per-coin oracle spoke for had no transaction-level reading behind them.
+ whole = count_oracle(list(inputs), list(outputs))
+ if guaranteed_log_w(whole) is None:
+ return []
+ # An exact count of ZERO is the counter completing and finding no balancing subset, which is
+ # what a fee-paying transaction produces under a fee-blind count — the weakest corroboration
+ # there is, not the strongest. Rigour requires the count to have found something.
+ transaction_count_exact = whole.get("kind") == "exact" and (whole.get("count") or 0) > 0
report = oracle(list(inputs), list(outputs))
- exact = count_oracle is not None and count_oracle(list(inputs), list(outputs)).get("kind") == "exact"
- return [CutCandidate(c["index"], c["value"], c["log_w"], exact=exact)
+ return [CutCandidate(c["role"], c["index"], c["value"], c["log_w"],
+ transaction_count_exact=transaction_count_exact)
for c in report["coins"]
- if c["log_w"] is not None and c["log_w"] <= cut_threshold]
+ if _reachable(c["log_w"]) and c["log_w"] <= cut_threshold]
def topology_bits(members_a, members_b, neigh, tau=1.0):
- """Cluster-level N–S counterparty-overlap weight (bits): a shared rare counterparty corroborates
- same owner (+), disjoint neighbourhoods refuse (−). Rarity-weighted, global (field-independent)."""
+ """Local cluster-level counterparty-overlap weight in bits.
+
+ A shared rare counterparty corroborates same owner; disjoint neighbourhoods refuse. This
+ rarity-weighted adaptation is not the seeded cross-view N-S matching algorithm.
+ """
cbits = counterparty_bits(neigh)
return cluster_topology_weight(members_a, members_b, neigh, cbits=cbits, tau=tau)
@@ -58,16 +93,18 @@ def topology_bits(members_a, members_b, neigh, tau=1.0):
# `decluster.report.report` (the per-tx orchestrator). This module holds the leaf terms it composes.
-def construction_cost(leak, topology, target_fn=path_count_anonymity, graph=None, combine=False):
+def construction_cost(
+ leak, topology, target_fn=provenance_route_accumulation, graph=None, combine=False
+):
"""(A) construction objective — the component terms, structured. `target_fn` is the §07
- path-counting target (`path_count.path_count_anonymity`, now wired) or any compatible target
+ provenance-route target (`provenance_route_accumulation`, now wired) or any compatible target
(e.g. `ancestry_entropy`); the missing-target blocker is gone, so this no longer raises by
default. It returns the terms UNCOMBINED: `{"leak": leak, "topology": topology,
"target": target_fn}` — leak/topology as given, `target_fn` itself as the target marker (this
function does not invoke it; composing a scalar isn't its job). The only remaining open design
- question is HOW the three channels combine into one cost (Liebig-min vs weighted; see
- docs/superpowers/specs/2026-07-27-construction-cost-design.md) — pass `combine=True` to hit that
- deferred raise explicitly. No combination formula is invented here."""
+ question is HOW the three channels combine into one cost (Liebig-min vs weighted) — pass
+ `combine=True` to hit that deferred raise explicitly. No combination formula is invented
+ here."""
if combine:
raise NotImplementedError(
"construction-cost channel combination is not defined yet: composing leak/topology/"
@@ -75,8 +112,33 @@ def construction_cost(leak, topology, target_fn=path_count_anonymity, graph=None
return {"leak": leak, "topology": topology, "target": target_fn}
+def boltzmann_oracle(inputs, outputs):
+ """Per-coin ambiguity read off the link matrix, in the shape `amount_cuts` consumes.
+
+ `log_w` here is the log-count of outputs an input could plausibly have funded under dss's
+ mapping family, which puts it on the same footing as the per-coin density oracle it stands
+ beside: a low value means few readings survive *in that family*. Zero does NOT mean the amounts
+ settle the assignment — dss's family is a strict restriction of the exact one, and reading a
+ one-entry row as certain was measured to assert 945 unsettled certainties across 328 of 507
+ transactions (`results/RESULTS-exact-oracle-audit.md`). Unlike the density oracle it answers
+ when the transaction pays a fee, which is nearly always.
+ """
+ import math
+
+ from .counting import link_ambiguity
+
+ ambiguity = link_ambiguity(inputs, outputs)
+ values = list(inputs)
+ return {"coins": [{"role": "in", "index": i, "value": values[i],
+ "log_w": math.log2(n) if n > 0 else None}
+ for i, n in sorted(ambiguity.items()) if i < len(values)]}
+
+
def dss_oracle(inputs, outputs):
- """Default production amount oracle: the dense-subset-sum per-coin density/ambiguity signal.
- Lazy import so decluster.cost loads without the compiled `dss` module (build: maturin develop)."""
+ """Return DSS's model-relative per-coin density diagnostic.
+
+ This adapter is not an exact mapping oracle or a CoinScore. Lazy import keeps
+ :mod:`decluster.cost` importable without the optional compiled ``dss`` module.
+ """
import dss
return dss.per_coin_density(list(inputs), list(outputs))
diff --git a/decluster/counting.py b/decluster/counting.py
index 8a34e996..8f4bd07d 100644
--- a/decluster/counting.py
+++ b/decluster/counting.py
@@ -1,27 +1,358 @@
"""Per-transaction W(E) count provider.
Thin delegate to `dss.w_count` — the crate's feasibility-cascade dispatcher (brute -> dp -> sparse ->
-sasamoto), which picks the counting method by feasibility and returns the best-available guarantee
-plus the method used. Method SELECTION is the dss caller-toolkit's job (done once, in Rust, beside the
+sasamoto), which picks the counting method by feasibility and returns the accepted reading plus the
+method used. Method SELECTION is the dss caller-toolkit's job (done once, in Rust, beside the
regime logic and with cross-validated tests); decluster just consumes the unified result. Counts are
weight-of-evidence, not a score, and an off-regime or panicking call must never fabricate a count.
"""
_UNKNOWN = {"kind": "unknown", "count": None, "log_w": None}
+# The tiers that carry a guarantee of not exceeding the true multiplicity. The saddle-point
+# estimate is not among them: it errs in both directions. Neither is the denominational path,
+# whose `diagnostic` kind counts a different object altogether.
+_GUARANTEED = frozenset({"exact", "lowerbound"})
-def w_total(inputs, outputs, max_size=64):
+
+def guaranteed_log_w(count):
+ """`log_w`, but only from a tier guaranteed not to overstate the count; else None.
+
+ An over-estimate of W(E) inflates whatever consumes it — it credits a coin with counterfactual
+ paths the transaction may not admit, and a reported anonymity built on those is privacy the
+ holder does not have. Undercounting is the safe direction: a path the adversary failed to see
+ is still a path. So an approximate or unrecognised tier is refused here rather than discounted,
+ and the caller falls back to multiplicity 1.
+
+ The denominational path is refused for a second reason, which is not about direction: it counts
+ denomination exchanges among the outputs, never sees the inputs, and moves under a rescaling
+ that leaves the mapping count fixed. A quantity blind to a parameter its supposed target depends
+ on, and sensitive to one it does not, bounds that target in neither direction, so there is no
+ safe direction to fall back on. The crate spells it `diagnostic` for that reason.
+ """
+ kind = str(count.get("kind", "")).strip().lower().replace("_", "").replace("-", "")
+ return count.get("log_w") if kind in _GUARANTEED else None
+
+
+# The cascade's brute branch enumerates *every* distinct output subset sum as a target and, for each,
+# every input subset at every size. Its own width limit is twenty inputs, and that is exactly where
+# the call stops returning: probed per width on a real slice, under a second to ten inputs, 3.4s at
+# sixteen, and past twenty two thirds of the transactions never finished. A hang is not an exception,
+# so the panic guard below cannot catch it, and a Python alarm cannot either, the block being inside
+# Rust — the only bound available is declining the call.
+#
+# Sixteen is the largest width the slice both contains and completes, and it keeps the dense 16-input
+# mix the cascade is pinned to count exactly. Widths seventeen to nineteen do not occur in the
+# measured data, so the cliff sits between 16 and 20 and no more precisely than that.
+#
+# This limit belongs to the whole-transaction count only. `per_coin_log_w` below reaches the same
+# crate through its fee-aware per-coin path, which returns in milliseconds at sixty-seven inputs and
+# needs no such guard.
+MAX_INPUTS = 16
+
+
+def w_total(inputs, outputs, max_size=MAX_INPUTS):
"""Total subset-sum multiplicity W(E) of ONE transaction (a per-target subset count, NOT
Maurer's full matched-partition mapping count |M| nor a mapping-entropy), as {"kind", "count", "log_w",
"method"}. Delegates to `dss.w_count`, which cascades the four exact/approx counting primitives by
feasibility (brute for tiny N, exact DP, sparse convolution, then the Sasamoto saddle-point for the
- Dense regime) and returns the strongest guarantee available — Exact where tractable, a LowerBound
- where the exact count saturates, a LogApprox in the large Dense regime, else unknown. `max_size` is
- accepted for backward compatibility and ignored (the dispatcher self-selects). Lazy `import dss`;
- panic-safe (dss can hard-panic on pathological inputs) — never crash on a count."""
+ Dense regime). Its acceptance order is Exact, then a Dense LogApprox, then a truncated LowerBound,
+ then unknown — magnitude order rather than guarantee order, because a lower bound that saturated
+ far below the true count describes a large dense mix worse than an approximation of its logarithm.
+ `guaranteed_log_w` is what re-imposes the guarantee, on the returned kind.
+
+ `max_size` caps the input count the call is attempted on; above it the answer is `unknown`,
+ which every consumer already reads as multiplicity 1. Undercounting is the safe direction for a
+ refuse-only channel, and a wide transaction is where the count would be least trustworthy
+ anyway. Lazy `import dss`; panic-safe (dss can hard-panic on pathological inputs) — never
+ crash, and never hang, on a count.
+
+ Fee-blind by construction: it asks which input subsets hit an output subset sum *exactly*, and a
+ real transaction pays a fee, so no subset balances and the answer is an exact zero. On a 1,428
+ transaction slice that is 1,223 of the 1,303 exact counts. An exact zero is the counter running
+ to completion and finding nothing, which is the weakest corroboration available, not the
+ strongest — see `cost.amount_cuts`. Use `per_coin_log_w` for a fee-aware reading."""
+ inputs, outputs = list(inputs), list(outputs)
+ if len(inputs) > max_size:
+ return dict(_UNKNOWN)
import dss
try:
- return dss.w_count(list(inputs), list(outputs))
+ return dss.w_count(inputs, outputs)
except BaseException:
return dict(_UNKNOWN)
+
+
+def per_coin_log_w(inputs, outputs):
+ """Per-coin ambiguity from the crate's fee-aware path: `{index: log_w}` over the input coins.
+
+ `w_total` above answers a whole-transaction question, fee-blind and bounded by width. This is
+ the measurement `cost.amount_cuts` already consumes: a knee-truncated lower bound on each coin's
+ own ambiguity, computed against the transaction's actual balance rather than an exact subset
+ hit, and returning in milliseconds at widths where the whole-transaction count does not return
+ at all. Coins the truncation could not reach are absent rather than zero.
+
+ A low value flags a candidate — few small balancing subsets were found — and never proves low
+ ambiguity, since a real balance may need more coins than the truncation counts.
+ """
+ import dss
+
+ try:
+ report = dss.per_coin_density(list(inputs), list(outputs))
+ except BaseException:
+ return {}
+ out = {}
+ for coin in report.get("coins", ()):
+ if coin.get("role") != "in":
+ continue
+ lw = coin.get("log_w")
+ if lw is not None and lw == lw and lw not in (float("inf"), float("-inf")):
+ out[coin["index"]] = lw
+ return out
+
+
+# The crate's own knee for coinjoin-shaped analysis. Bounding the subset size is what makes the
+# convolution safe at width: unbounded, it builds the graded sumset up to N and a 25-input
+# transaction takes minutes; at the knee the same call returns in milliseconds.
+KNEE = 5
+
+
+def count_w(inputs, outputs, knee=KNEE, radix_first=True, sparse_max=50):
+ """Route one transaction to the counting method that suits it, returning the dss shape plus the
+ `method` that answered.
+
+ The order is the one the design calls for, and it deliberately does not include brute force,
+ which is the same algorithm as the convolution below its crossover:
+
+ radix an output-only structural diagnostic, used only where amounts actually decompose
+ into repeated denominations. The crate hands back a number whether or not they do,
+ so that precondition is checked here by `radix_applies`. It is not a bound on the
+ transaction's mapping count, and the crate tags it `diagnostic` so it cannot pass
+ `guaranteed_log_w`. Unguarded it answers on 64 of 1,428 real multi-input
+ transactions, 51 of which carry no repeated denomination.
+ sparse the subset-sum solution count W(E), bounded by `knee`. It is exact where it can be
+ and otherwise a lower bound on W(E), not on the subtransaction mapping count.
+
+ The saddle-point estimator is deliberately not a third tier, and the reason is narrow: it does
+ not fill the holes a truncated lower bound leaves, so chaining it as an automatic upgrade mixes
+ two different readings into one number. That composition was built upstream and withdrawn. It is
+ still available here as `saddle_point_log_w`, to be asked for on purpose — an analyst reading a
+ confirmed transaction may well want a magnitude estimate, and the estimator is useful beyond the
+ construction side.
+
+ Brute force is absent for a different reason: it is the same algorithm as the convolution below
+ its crossover, so it adds a way to be slow and no way to be right.
+
+ `sparse_max` bounds the convolution by input width. The knee alone is nearly enough — it takes
+ a 25-input call from minutes to milliseconds — but one 67-input transaction in the measured
+ slice still ran 81s, so a residual width guard stays.
+
+ Contrast `w_total`, which delegates to the crate's own cascade: that tries brute first and
+ hands the convolution an unbounded subset size, which is why it does not return past twenty
+ inputs. Nothing here is a privacy score; a count that did not resolve reads as `unknown`, and
+ every consumer treats that as multiplicity 1.
+ """
+ inputs, outputs = list(inputs), list(outputs)
+ if not inputs or not outputs:
+ return dict(_UNKNOWN, method="none")
+ import dss
+
+ if radix_first and radix_applies(outputs):
+ r = _resolved(lambda: dss.radix_mappings(outputs, knee))
+ if r is not None:
+ return dict(r, method="radix")
+ if len(inputs) <= sparse_max:
+ r = _resolved(lambda: dss.w_sparse(inputs, outputs, knee))
+ if r is not None:
+ return dict(r, method="sparse")
+ return dict(_UNKNOWN, method="none")
+
+
+def _resolved(call):
+ """The counter's answer, or None when it did not actually resolve anything.
+
+ A tier that completes with a count of zero has found no mapping at all, which is the absence of
+ an answer and not an answer of "no ambiguity" — the same distinction `cost.amount_cuts` draws
+ for an unreachable coin. Treating it as resolved would let the first tier short-circuit the rest
+ on a non-result: the radix path returns zero on 1,364 of 1,428 real multi-input transactions.
+ """
+ try:
+ r = call()
+ except BaseException:
+ return None
+ if str(r.get("kind", "")).lower() == "unknown":
+ return None
+ if (r.get("count") or 0) == 0 and r.get("log_w") is None:
+ return None
+ return r
+
+
+def is_dense(inputs, outputs):
+ """Whether the per-coin gate reads this instance as Dense, as `kappa < kappa_c`.
+
+ This is the *optimistic* reading: the exposed kappa is computed at the best-case L, while the
+ saddle-point's own gate requires kappa < kappa_c at the WORST-case L and calls anything between
+ the two Transitional. So a True here does not promise that the saddle point will answer, and in
+ practice it usually does not. Reported so the discrepancy is visible rather than surprising.
+ """
+ import dss
+
+ try:
+ report = dss.per_coin_density(list(inputs), list(outputs))
+ except BaseException:
+ return None
+ coins = [c for c in report.get("coins", ()) if c.get("role") == "in"]
+ if not coins:
+ return None
+ kappa_c = sum(c["kappa_c"] for c in coins) / len(coins)
+ return report["kappa"] < kappa_c
+
+
+def saddle_point_log_w(inputs, outputs):
+ """`log W(E)` from the saddle point, or None where it does not apply. Not a fallback.
+
+ Deliberately separate from `count_w`'s routing rather than a tier inside it. It estimates a
+ magnitude where the exact tiers return a truncated lower bound, and those are different
+ readings: substituting one for the other silently would let an estimate stand where a guaranteed
+ floor was asked for. Ask for it when an estimate is what you want.
+
+ It applies only in the Dense regime and declines everywhere else, which on real transactions is
+ almost everywhere — see `is_dense`, and note that the gate it applies internally is stricter
+ than the one that function reports.
+ """
+ import dss
+
+ try:
+ report = dss.w_sasamoto(list(inputs), list(outputs))
+ except BaseException:
+ return None
+ if str(report.get("kind", "")).lower() == "unknown":
+ return None
+ return report.get("log_w")
+
+
+# A denomination has to repeat before its permutations bound anything. Three is the same floor the
+# de-mix uses to call a value a mix denomination.
+RADIX_MIN_MULTIPLICITY = 3
+
+# The bases the denominational decomposition is stated over. A value of Hamming weight one in one of
+# them is a single digit times a power of that base -- 5,000,000 in base ten, 2^20 in base two.
+RADIX_BASES = (2, 3, 10)
+
+
+def radix_series(value, bases=RADIX_BASES):
+ """`(base, exponent, digit)` when `value` is a single digit times a power of one of `bases`,
+ else None. That is what "Hamming weight one" names: one non-zero digit in that base."""
+ if value <= 0:
+ return None
+ for base in bases:
+ v, exponent = value, 0
+ while v % base == 0:
+ v //= base
+ exponent += 1
+ if v < base and exponent:
+ return (base, exponent, v)
+ return None
+
+
+def radix_applies(outputs, min_multiplicity=RADIX_MIN_MULTIPLICITY, bases=RADIX_BASES):
+ """Whether the denominational diagnostic is meaningful for these outputs.
+
+ It counts the ways a repeated denomination can be permuted among the participants, so it means
+ nothing unless a denomination actually repeats. The crate computes it either way and leaves this
+ precondition to the caller; unguarded, it answers on 64 of 1,428 real multi-input transactions,
+ 51 of which carry no repeated value at all.
+
+ Following the classification the upstream work describes: separate the values into multiplicity
+ counts per *kind* of value, and take the low-Hamming-weight ones seriously. A value that is not
+ a single digit times a power of one of the bases is not a denomination, however often it
+ repeats, so counting its repeats towards the bound would be counting the wrong thing.
+
+ The gap analysis within a series is the remaining half and stays upstream; it would only narrow
+ this further, never widen it.
+ """
+ counts = {}
+ for value in outputs:
+ series = radix_series(value, bases)
+ if series is not None:
+ counts[value] = counts.get(value, 0) + 1
+ return bool(counts) and max(counts.values()) >= min_multiplicity
+
+
+# The link matrix is the one path here that tolerates a real fee: it reads the transaction's actual
+# balance rather than asking for an exact subset hit, and answers at fees where the counts do not.
+# No budget by default. The crate treats a budget as a *replacement* for its own size guard, and
+# the budget is cooperative — it does not preempt an enumeration that has already blown up, which is
+# why a subprocess variant exists for the walk. Passing none keeps the guard, which refuses the wide
+# transactions instead of running into them.
+DEFAULT_LINK_BUDGET_MS = None
+
+
+def link_matrix(inputs, outputs, budget_ms=DEFAULT_LINK_BUDGET_MS, wall_ms=None):
+ """The link-probability matrix, `m[i][j]` for input `i` against output `j`, or None.
+
+ Rows say which outputs an input could plausibly have funded *under dss's mapping family*, and a
+ row of ones is the case where every output is equally possible.
+
+ A row with one non-zero entry is NOT a settled assignment. dss's family is a strict restriction
+ of the exact balanced-mapping family, and this matrix is the uniform marginal over the smaller
+ one, so it can put zero where the exact marginal is positive. Measured against the exact oracle
+ over a 507-transaction family (`results/RESULTS-exact-oracle-audit.md`), reading a one-entry row
+ as certain asserts 945 certainties the amounts do not settle, across 328 of those 507, while
+ missing no genuinely certain link. The reading is an upper bound on certainty, not a proof of it.
+
+ This is reachable from the amount channel, and unlike the counts it does not go quiet the moment
+ a transaction pays a fee. It is no longer the provenance walk's transition measure: that walk
+ defaults to `ancestry.value_flow_link_oracle` and reaches this only when a caller passes
+ `ancestry.dss_link_oracle` or `oracle.bounded_dss_link_oracle()` explicitly.
+
+ `budget_ms` replaces the crate's size guard rather than joining it, and neither bounds the cost
+ in practice: measured, a single call under the guard alone ran past a minute. Bulk work needs
+ `wall_ms`, which routes through the throwaway-subprocess oracle and kills on a real deadline —
+ the same escape the provenance walk already keeps for this.
+ """
+ if wall_ms is not None:
+ from .oracle import subprocess_link_oracle
+ return subprocess_link_oracle(list(inputs), list(outputs), wall_ms)
+ import dss
+
+ try:
+ return dss.pairwise_link_prob(list(inputs), list(outputs), budget_ms)
+ except BaseException:
+ return None
+
+
+def link_ambiguity(inputs, outputs, budget_ms=DEFAULT_LINK_BUDGET_MS, wall_ms=None):
+ """`{input index: how many outputs it could plausibly have funded}`, or {} when unresolved.
+
+ One means the amounts determine where that input went. The count is what the row of the matrix
+ carries, so it is on the same footing across coins of the same transaction.
+ """
+ matrix = link_matrix(inputs, outputs, budget_ms, wall_ms)
+ if not matrix:
+ return {}
+ return {i: sum(1 for p in row if p > 0) for i, row in enumerate(matrix)}
+
+
+def mapping_entropy(inputs, outputs, budget_ms=DEFAULT_LINK_BUDGET_MS):
+ """Entropy over DSS's restricted mapping family and the links that family agrees on.
+
+ `{"entropy": bits, "n_non_derived": int, "deterministic_links": [(input, output)]}`, or None
+ where the enumeration is refused. The returned distribution is uniform over DSS's non-derived
+ mappings. It is not the full balanced-mapping space and not an ownership posterior. The exact
+ oracle audit shows that certainty in this family can be false in the larger family.
+
+ Fee handling is the same as the link matrix: the fee is balanced in as an extra output before
+ enumerating, so it answers on transactions where an exact-cancellation count cannot. Zero bits
+ means one reading survives in this family; a deterministic link is shared by every mapping in
+ this family. Neither statement is a global ownership proof.
+ """
+ import dss
+
+ try:
+ report = dss.mapping_analysis(list(inputs), list(outputs), budget_ms)
+ except BaseException:
+ return None
+ if not isinstance(report, dict) or report.get("status") != "complete":
+ return None
+ return report
diff --git a/decluster/data_catalog.py b/decluster/data_catalog.py
new file mode 100644
index 00000000..41594582
--- /dev/null
+++ b/decluster/data_catalog.py
@@ -0,0 +1,228 @@
+"""Offline dataset catalog operations used by tests, automation and future UIs."""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+from enum import Enum
+import hashlib
+import os
+from pathlib import Path
+import shutil
+import tempfile
+from urllib.request import urlopen
+
+from .data_manifest import ContentIdentity, DatasetManifest, ManifestError, load_dataset_manifest
+from .result_artifacts import content_identity
+from .evidence_bundle import blob_path
+
+
+class DatasetStatus(str, Enum):
+ VERIFIED = "verified"
+ MISSING = "missing"
+ IDENTITY_MISMATCH = "identity_mismatch"
+
+
+class FetchStatus(str, Enum):
+ ALREADY_AVAILABLE = "already_available"
+ DOWNLOADED = "downloaded"
+
+
+class FetchAttemptStatus(str, Enum):
+ TRANSPORT_ERROR = "transport_error"
+ IDENTITY_MISMATCH = "identity_mismatch"
+
+
+class PrepareStatus(str, Enum):
+ ALREADY_PREPARED = "already_prepared"
+ MATERIALIZED = "materialized"
+
+
+class PrepareFailure(str, Enum):
+ STORE_MISSING = "store_missing"
+ STORE_IDENTITY_MISMATCH = "store_identity_mismatch"
+ DESTINATION_IDENTITY_MISMATCH = "destination_identity_mismatch"
+
+
+@dataclass(frozen=True)
+class DatasetCheck:
+ dataset: DatasetManifest
+ status: DatasetStatus
+ expected: ContentIdentity
+ actual: ContentIdentity | None
+
+
+@dataclass(frozen=True)
+class FetchAttempt:
+ url: str
+ status: FetchAttemptStatus
+
+
+@dataclass(frozen=True)
+class FetchResult:
+ dataset: DatasetManifest
+ status: FetchStatus
+ path: Path
+ source_url: str | None
+
+
+@dataclass(frozen=True)
+class PrepareResult:
+ dataset: DatasetManifest
+ status: PrepareStatus
+ path: Path
+
+
+class DatasetPrepareError(RuntimeError):
+ def __init__(self, dataset_id: str, failure: PrepareFailure, path: Path):
+ self.dataset_id = dataset_id
+ self.failure = failure
+ self.path = path
+ super().__init__(f"{dataset_id}: {failure.value}: {path}")
+
+
+class DatasetFetchError(RuntimeError):
+ def __init__(self, dataset_id: str, attempts: tuple[FetchAttempt, ...]):
+ self.dataset_id = dataset_id
+ self.attempts = attempts
+ detail = ", ".join(f"{attempt.url}={attempt.status.value}" for attempt in attempts)
+ super().__init__(f"{dataset_id}: no verified source available: {detail or 'no locations'}")
+
+
+def load_dataset_catalog(root) -> tuple[DatasetManifest, ...]:
+ """Load the complete catalog deterministically and reject duplicate IDs."""
+ root = Path(root)
+ paths = sorted((root / "catalog" / "datasets").glob("*.json"))
+ if not paths:
+ raise ManifestError(f"{root}: dataset catalog is empty")
+ manifests = tuple(load_dataset_manifest(path) for path in paths)
+ seen = set()
+ for manifest in manifests:
+ if manifest.id in seen:
+ raise ManifestError(f"duplicate dataset id: {manifest.id!r}")
+ seen.add(manifest.id)
+ return manifests
+
+
+def dataset_path(dataset: DatasetManifest, root) -> Path:
+ """Resolve a catalog path while keeping it inside the project root."""
+ root = Path(root).resolve()
+ path = (root / dataset.local_path).resolve()
+ try:
+ path.relative_to(root)
+ except ValueError as exc:
+ raise ManifestError(
+ f"{dataset.id}.locations.local_path: escapes project root"
+ ) from exc
+ return path
+
+
+def dataset_status(dataset: DatasetManifest, root) -> DatasetCheck:
+ expected = dataset.content
+ path = dataset_path(dataset, root)
+ if not path.is_file():
+ return DatasetCheck(dataset, DatasetStatus.MISSING, expected, None)
+ actual = content_identity(path)
+ status = DatasetStatus.VERIFIED if actual == expected else DatasetStatus.IDENTITY_MISMATCH
+ return DatasetCheck(dataset, status, expected, actual)
+
+
+def catalog_status(root) -> tuple[DatasetCheck, ...]:
+ return tuple(dataset_status(dataset, root) for dataset in load_dataset_catalog(root))
+
+
+def require_verified_datasets(root) -> tuple[DatasetManifest, ...]:
+ """Return verified datasets or fail without accepting a partial catalog."""
+ checks = catalog_status(root)
+ failures = [check for check in checks if check.status is not DatasetStatus.VERIFIED]
+ if failures:
+ detail = ", ".join(f"{check.dataset.id}={check.status.value}" for check in failures)
+ raise ManifestError(f"dataset catalog is not fully verified: {detail}")
+ return tuple(check.dataset for check in checks)
+
+
+def _download_candidate(dataset, url, directory, opener):
+ digest = hashlib.sha256()
+ size = 0
+ with tempfile.NamedTemporaryFile(dir=directory, delete=False) as temporary:
+ temporary_path = Path(temporary.name)
+ try:
+ with opener(url) as response:
+ while chunk := response.read(1024 * 1024):
+ size += len(chunk)
+ if size > dataset.content.bytes:
+ return temporary_path, ContentIdentity(size, digest.hexdigest())
+ digest.update(chunk)
+ temporary.write(chunk)
+ temporary.flush()
+ os.fsync(temporary.fileno())
+ except BaseException:
+ temporary_path.unlink(missing_ok=True)
+ raise
+ return temporary_path, ContentIdentity(size, digest.hexdigest())
+
+
+def fetch_dataset(dataset: DatasetManifest, store, *, opener=urlopen) -> FetchResult:
+ """Fetch one dataset into the content store, accepting only its declared bytes."""
+ destination = blob_path(store, dataset.content.sha256)
+ if destination.is_file() and content_identity(destination) == dataset.content:
+ return FetchResult(dataset, FetchStatus.ALREADY_AVAILABLE, destination, None)
+
+ destination.parent.mkdir(parents=True, exist_ok=True)
+ attempts = []
+ urls = tuple(url for url in (dataset.canonical_location, *dataset.mirrors) if url)
+ for url in urls:
+ try:
+ temporary_path, actual = _download_candidate(dataset, url, destination.parent, opener)
+ except (OSError, ValueError):
+ attempts.append(FetchAttempt(url, FetchAttemptStatus.TRANSPORT_ERROR))
+ continue
+ try:
+ if actual != dataset.content:
+ attempts.append(FetchAttempt(url, FetchAttemptStatus.IDENTITY_MISMATCH))
+ continue
+ os.replace(temporary_path, destination)
+ return FetchResult(dataset, FetchStatus.DOWNLOADED, destination, url)
+ finally:
+ temporary_path.unlink(missing_ok=True)
+ raise DatasetFetchError(dataset.id, tuple(attempts))
+
+
+def prepare_dataset(dataset: DatasetManifest, root, store, *, replace=False) -> PrepareResult:
+ """Materialize one verified store blob at its declared local path."""
+ destination = dataset_path(dataset, root)
+ if destination.is_file():
+ if content_identity(destination) == dataset.content:
+ return PrepareResult(dataset, PrepareStatus.ALREADY_PREPARED, destination)
+ if not replace:
+ raise DatasetPrepareError(
+ dataset.id, PrepareFailure.DESTINATION_IDENTITY_MISMATCH, destination,
+ )
+ elif destination.exists():
+ raise DatasetPrepareError(
+ dataset.id, PrepareFailure.DESTINATION_IDENTITY_MISMATCH, destination,
+ )
+
+ source = blob_path(store, dataset.content.sha256)
+ if not source.is_file():
+ raise DatasetPrepareError(dataset.id, PrepareFailure.STORE_MISSING, source)
+ if content_identity(source) != dataset.content:
+ raise DatasetPrepareError(
+ dataset.id, PrepareFailure.STORE_IDENTITY_MISMATCH, source,
+ )
+
+ destination.parent.mkdir(parents=True, exist_ok=True)
+ with tempfile.NamedTemporaryFile(dir=destination.parent, delete=False) as temporary:
+ temporary_path = Path(temporary.name)
+ with source.open("rb") as stream:
+ shutil.copyfileobj(stream, temporary)
+ temporary.flush()
+ os.fsync(temporary.fileno())
+ try:
+ if content_identity(temporary_path) != dataset.content:
+ raise DatasetPrepareError(
+ dataset.id, PrepareFailure.STORE_IDENTITY_MISMATCH, source,
+ )
+ os.replace(temporary_path, destination)
+ finally:
+ temporary_path.unlink(missing_ok=True)
+ return PrepareResult(dataset, PrepareStatus.MATERIALIZED, destination)
diff --git a/decluster/data_cli.py b/decluster/data_cli.py
new file mode 100644
index 00000000..5a723d82
--- /dev/null
+++ b/decluster/data_cli.py
@@ -0,0 +1,104 @@
+"""Thin command-line adapter for the offline dataset catalog API."""
+
+from __future__ import annotations
+
+import argparse
+from dataclasses import asdict
+import json
+from pathlib import Path
+import sys
+
+from .data_catalog import (
+ DatasetFetchError,
+ DatasetPrepareError,
+ DatasetStatus,
+ catalog_status,
+ fetch_dataset,
+ load_dataset_catalog,
+ prepare_dataset,
+)
+from .data_manifest import ManifestError
+
+
+def _parser():
+ parser = argparse.ArgumentParser(prog="decluster-data")
+ parser.add_argument("--root", type=Path, default=Path.cwd())
+ parser.add_argument("--store", type=Path, default=Path("artifacts"))
+ parser.add_argument("--json", action="store_true", dest="as_json")
+ parser.add_argument("--replace", action="store_true")
+ parser.add_argument("command", choices=("list", "status", "verify", "fetch", "prepare"))
+ parser.add_argument("dataset_id", nargs="?")
+ return parser
+
+
+def _records(command, root, *, dataset_id=None, store=None, replace=False):
+ if command == "list":
+ return [{
+ "id": item.id,
+ "kind": item.kind.value,
+ "bytes": item.content.bytes,
+ "sha256": item.content.sha256,
+ } for item in load_dataset_catalog(root)]
+ if command in {"status", "verify"}:
+ return [{
+ "id": check.dataset.id,
+ "status": check.status.value,
+ "expected": asdict(check.expected),
+ "actual": asdict(check.actual) if check.actual is not None else None,
+ } for check in catalog_status(root)]
+ if not dataset_id:
+ raise ManifestError(f"{command}: dataset id is required")
+ matches = [dataset for dataset in load_dataset_catalog(root) if dataset.id == dataset_id]
+ if not matches:
+ raise ManifestError(f"{command}: unknown dataset id {dataset_id!r}")
+ if command == "fetch":
+ result = fetch_dataset(matches[0], store)
+ source_url = result.source_url
+ else:
+ result = prepare_dataset(matches[0], root, store, replace=replace)
+ source_url = None
+ return [{
+ "id": result.dataset.id,
+ "status": result.status.value,
+ "path": str(result.path),
+ "source_url": source_url,
+ }]
+
+
+def _print(records, *, as_json):
+ if as_json:
+ print(json.dumps(records, allow_nan=False, separators=(",", ":"), sort_keys=True))
+ return
+ for record in records:
+ if "expected" in record:
+ print(f"{record['id']}\t{record['status']}")
+ elif "kind" in record:
+ print(f"{record['id']}\t{record['kind']}\t{record['bytes']}")
+ else:
+ print(f"{record['id']}\t{record['status']}\t{record['path']}")
+
+
+def main(argv=None):
+ args = _parser().parse_args(argv)
+ try:
+ store = args.store if args.store.is_absolute() else args.root / args.store
+ records = _records(
+ args.command, args.root, dataset_id=args.dataset_id, store=store,
+ replace=args.replace,
+ )
+ except (DatasetFetchError, DatasetPrepareError) as exc:
+ print(str(exc), file=sys.stderr)
+ return 1
+ except ManifestError as exc:
+ print(str(exc), file=sys.stderr)
+ return 2
+ _print(records, as_json=args.as_json)
+ if args.command == "verify" and any(
+ record["status"] != DatasetStatus.VERIFIED.value for record in records
+ ):
+ return 1
+ return 0
+
+
+if __name__ == "__main__":
+ raise SystemExit(main())
diff --git a/decluster/data_manifest.py b/decluster/data_manifest.py
new file mode 100644
index 00000000..ded5e71f
--- /dev/null
+++ b/decluster/data_manifest.py
@@ -0,0 +1,475 @@
+"""Typed, strict manifests for reproducible datasets and experiment runs.
+
+These readers validate identities and lineage at the boundary. They do not
+download data, inspect the network, or silently repair incomplete manifests.
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+from enum import Enum
+import json
+from pathlib import Path
+import re
+from types import MappingProxyType
+from typing import Mapping
+
+
+class ManifestError(ValueError):
+ """A manifest is incomplete, ambiguous, or internally inconsistent."""
+
+
+class DatasetKind(str, Enum):
+ FIXTURE = "git_fixture"
+ SNAPSHOT = "immutable_snapshot"
+ REGENERABLE = "regenerable"
+ DERIVED = "derived"
+
+
+class Redistribution(str, Enum):
+ ALLOWED = "allowed"
+ RESTRICTED = "restricted"
+ UNKNOWN = "unknown"
+
+
+class Sensitivity(str, Enum):
+ SYNTHETIC = "synthetic"
+ PUBLIC_ON_CHAIN = "public_on_chain"
+ PUBLIC_AUXILIARY = "public_auxiliary"
+ RESTRICTED_AUXILIARY = "restricted_auxiliary"
+
+
+class ReproducibilityLevel(str, Enum):
+ VERIFIED = "verified"
+ REEXECUTABLE = "reexecutable"
+ BITWISE = "bitwise_reproducible"
+ STATISTICAL = "statistically_reproducible"
+
+
+class RunAvailability(str, Enum):
+ COMPLETE = "complete"
+ RESTRICTED = "restricted"
+ PARTIAL = "partial"
+
+
+class VerificationMode(str, Enum):
+ EXACT = "exact"
+ TOLERANCE = "tolerance"
+ STATISTICAL = "statistical"
+
+
+@dataclass(frozen=True)
+class ContentIdentity:
+ bytes: int
+ sha256: str
+
+
+@dataclass(frozen=True)
+class DatasetManifest:
+ id: str
+ title: str
+ kind: DatasetKind
+ schema: str
+ format: str
+ content: ContentIdentity
+ local_path: str
+ canonical_location: str | None
+ mirrors: tuple[str, ...]
+ provider: str
+ snapshot_time: str | None
+ recipe: str | None
+ recipe_sha256: str | None
+ data_license: str
+ recipe_license: str | None
+ redistribution: Redistribution
+ sensitivity: Sensitivity
+ code_revision: str | None
+ parameters: Mapping
+ parents: tuple[str, ...]
+ ordering: str
+ rng_algorithm: str | None
+ rng_seed: int | None
+
+
+@dataclass(frozen=True)
+class DatasetInput:
+ id: str
+ sha256: str
+
+
+@dataclass(frozen=True)
+class DependencyInput:
+ name: str
+ version: str
+ source: str
+ revision: str
+ lock_sha256: str | None
+ license: str
+ redistribution: Redistribution
+ editable: bool
+
+
+@dataclass(frozen=True)
+class RunOutput:
+ path: str
+ bytes: int
+ sha256: str
+
+
+@dataclass(frozen=True)
+class RunVerification:
+ mode: VerificationMode
+ argv: tuple[str, ...]
+ tests: tuple[str, ...]
+ properties: tuple[str, ...]
+ tolerance: str | None
+
+
+@dataclass(frozen=True)
+class RunManifest:
+ id: str
+ claim_ids: tuple[str, ...]
+ code_revision: str
+ dirty: bool
+ argv: tuple[str, ...]
+ python: str
+ lock_digest: str | None
+ platform: str
+ dependencies: tuple[DependencyInput, ...]
+ datasets: tuple[DatasetInput, ...]
+ parameters: Mapping
+ rng_algorithm: str | None
+ rng_seeds: tuple[int, ...]
+ outputs: tuple[RunOutput, ...]
+ reproducibility_level: ReproducibilityLevel
+ availability: RunAvailability
+ verification: RunVerification
+ limitations: tuple[str, ...]
+
+
+DATASET_KEYS = frozenset({
+ "schema_version", "id", "title", "kind", "schema", "format", "content",
+ "locations", "source", "license", "redistribution", "sensitivity",
+ "normalization", "determinism",
+})
+RUN_KEYS = frozenset({
+ "schema_version", "id", "claim_ids", "code", "command", "environment",
+ "datasets", "parameters", "rng", "outputs", "reproducibility", "verification",
+ "limitations",
+})
+SHA256 = re.compile(r"^[0-9a-f]{64}$")
+
+
+def _load(path):
+ try:
+ with Path(path).open(encoding="utf-8") as source:
+ return json.load(source)
+ except (OSError, json.JSONDecodeError) as exc:
+ raise ManifestError(f"{path}: cannot read manifest: {exc}") from exc
+
+
+def _object(value, where, keys=None):
+ if not isinstance(value, dict):
+ raise ManifestError(f"{where}: expected object")
+ if keys is not None and frozenset(value) != frozenset(keys):
+ expected = frozenset(keys)
+ actual = frozenset(value)
+ raise ManifestError(
+ f"{where}: missing={sorted(expected - actual)}, unknown={sorted(actual - expected)}"
+ )
+ return value
+
+
+def _text(value, where, *, nullable=False):
+ if nullable and value is None:
+ return None
+ if not isinstance(value, str) or not value.strip():
+ raise ManifestError(f"{where}: expected non-empty string")
+ return value
+
+
+def _https(value, where):
+ value = _text(value, where, nullable=True)
+ if value is not None and not value.startswith("https://"):
+ raise ManifestError(f"{where}: expected https URL")
+ return value
+
+
+def _integer(value, where, *, minimum=None):
+ if isinstance(value, bool) or not isinstance(value, int):
+ raise ManifestError(f"{where}: expected integer")
+ if minimum is not None and value < minimum:
+ raise ManifestError(f"{where}: expected value >= {minimum}")
+ return value
+
+
+def _digest(value, where, *, nullable=False):
+ value = _text(value, where, nullable=nullable)
+ if value is not None and not SHA256.fullmatch(value):
+ raise ManifestError(f"{where}: expected lowercase SHA-256")
+ return value
+
+
+def _relative_path(value, where):
+ value = _text(value, where)
+ path = Path(value)
+ if path.is_absolute() or ".." in path.parts or path == Path("."):
+ raise ManifestError(f"{where}: expected a safe relative path")
+ return value
+
+
+def _enum(enum_type, value, where):
+ try:
+ return enum_type(value)
+ except (TypeError, ValueError) as exc:
+ raise ManifestError(f"{where}: invalid {enum_type.__name__}: {value!r}") from exc
+
+
+def _strings(value, where, *, nonempty=False, unique=False):
+ if not isinstance(value, list) or (nonempty and not value):
+ qualifier = "non-empty " if nonempty else ""
+ raise ManifestError(f"{where}: expected {qualifier}array")
+ result = tuple(_text(item, f"{where}[{index}]") for index, item in enumerate(value))
+ if unique and len(result) != len(set(result)):
+ raise ManifestError(f"{where}: duplicate value")
+ return result
+
+
+def _array(value, where):
+ if not isinstance(value, list):
+ raise ManifestError(f"{where}: expected array")
+ return value
+
+
+def _freeze_json(value):
+ if isinstance(value, dict):
+ return MappingProxyType({key: _freeze_json(item) for key, item in value.items()})
+ if isinstance(value, list):
+ return tuple(_freeze_json(item) for item in value)
+ return value
+
+
+def load_dataset_manifest(path):
+ where = str(path)
+ raw = _object(_load(path), where, DATASET_KEYS)
+ if raw["schema_version"] != 1:
+ raise ManifestError(f"{where}: unsupported schema_version {raw['schema_version']!r}")
+
+ content = _object(raw["content"], f"{where}.content", {"bytes", "sha256"})
+ locations = _object(
+ raw["locations"], f"{where}.locations", {"local_path", "canonical", "mirrors"}
+ )
+ source = _object(
+ raw["source"], f"{where}.source",
+ {"provider", "snapshot_time", "recipe", "recipe_sha256"},
+ )
+ license_ = _object(raw["license"], f"{where}.license", {"data", "recipe"})
+ normalization = _object(
+ raw["normalization"], f"{where}.normalization",
+ {"code_revision", "parameters", "parents"},
+ )
+ determinism = _object(
+ raw["determinism"], f"{where}.determinism",
+ {"ordering", "rng_algorithm", "rng_seed"},
+ )
+ mirrors = _strings(locations["mirrors"], f"{where}.locations.mirrors", unique=True)
+ for index, mirror in enumerate(mirrors):
+ _https(mirror, f"{where}.locations.mirrors[{index}]")
+ parameters = _object(normalization["parameters"], f"{where}.normalization.parameters")
+ rng_seed = determinism["rng_seed"]
+ if rng_seed is not None:
+ rng_seed = _integer(rng_seed, f"{where}.determinism.rng_seed", minimum=0)
+
+ manifest = DatasetManifest(
+ id=_text(raw["id"], f"{where}.id"),
+ title=_text(raw["title"], f"{where}.title"),
+ kind=_enum(DatasetKind, raw["kind"], f"{where}.kind"),
+ schema=_text(raw["schema"], f"{where}.schema"),
+ format=_text(raw["format"], f"{where}.format"),
+ content=ContentIdentity(
+ bytes=_integer(content["bytes"], f"{where}.content.bytes", minimum=0),
+ sha256=_digest(content["sha256"], f"{where}.content.sha256"),
+ ),
+ local_path=_text(locations["local_path"], f"{where}.locations.local_path"),
+ canonical_location=_https(locations["canonical"], f"{where}.locations.canonical"),
+ mirrors=mirrors,
+ provider=_text(source["provider"], f"{where}.source.provider"),
+ snapshot_time=_text(source["snapshot_time"], f"{where}.source.snapshot_time", nullable=True),
+ recipe=_text(source["recipe"], f"{where}.source.recipe", nullable=True),
+ recipe_sha256=_digest(source["recipe_sha256"], f"{where}.source.recipe_sha256", nullable=True),
+ data_license=_text(license_["data"], f"{where}.license.data"),
+ recipe_license=_text(license_["recipe"], f"{where}.license.recipe", nullable=True),
+ redistribution=_enum(Redistribution, raw["redistribution"], f"{where}.redistribution"),
+ sensitivity=_enum(Sensitivity, raw["sensitivity"], f"{where}.sensitivity"),
+ code_revision=_text(normalization["code_revision"], f"{where}.normalization.code_revision", nullable=True),
+ parameters=_freeze_json(parameters),
+ parents=_strings(normalization["parents"], f"{where}.normalization.parents", unique=True),
+ ordering=_text(determinism["ordering"], f"{where}.determinism.ordering"),
+ rng_algorithm=_text(determinism["rng_algorithm"], f"{where}.determinism.rng_algorithm", nullable=True),
+ rng_seed=rng_seed,
+ )
+ if manifest.kind is DatasetKind.DERIVED and not manifest.parents:
+ raise ManifestError(f"{where}: derived dataset must name at least one parent")
+ if manifest.recipe_sha256 is not None and manifest.recipe is None:
+ raise ManifestError(f"{where}: recipe_sha256 requires recipe")
+ if manifest.rng_seed is not None and manifest.rng_algorithm is None:
+ raise ManifestError(f"{where}: rng_seed requires rng_algorithm")
+ return manifest
+
+
+def load_run_manifest(path, *, claim_ids, datasets):
+ where = str(path)
+ raw = _object(_load(path), where, RUN_KEYS)
+ if raw["schema_version"] != 1:
+ raise ManifestError(f"{where}: unsupported schema_version {raw['schema_version']!r}")
+ code = _object(raw["code"], f"{where}.code", {"revision", "dirty"})
+ command = _object(raw["command"], f"{where}.command", {"argv"})
+ environment = _object(
+ raw["environment"], f"{where}.environment",
+ {"python", "lock_digest", "platform", "dependencies"},
+ )
+ rng = _object(raw["rng"], f"{where}.rng", {"algorithm", "seeds"})
+ reproducibility = _object(
+ raw["reproducibility"], f"{where}.reproducibility", {"level", "availability"}
+ )
+ verification = _object(
+ raw["verification"], f"{where}.verification",
+ {"mode", "argv", "tests", "properties", "tolerance"},
+ )
+ if not isinstance(code["dirty"], bool):
+ raise ManifestError(f"{where}.code.dirty: expected boolean")
+ dependencies = []
+ dependency_names = set()
+ for index, item in enumerate(_array(environment["dependencies"], f"{where}.environment.dependencies")):
+ item_where = f"{where}.environment.dependencies[{index}]"
+ item = _object(item, item_where, {
+ "name", "version", "source", "revision", "lock_sha256", "license",
+ "redistribution", "editable",
+ })
+ name = _text(item["name"], f"{item_where}.name")
+ if name in dependency_names:
+ raise ManifestError(f"{item_where}.name: duplicate {name!r}")
+ dependency_names.add(name)
+ if not isinstance(item["editable"], bool):
+ raise ManifestError(f"{item_where}.editable: expected boolean")
+ dependencies.append(DependencyInput(
+ name=name,
+ version=_text(item["version"], f"{item_where}.version"),
+ source=_https(item["source"], f"{item_where}.source"),
+ revision=_text(item["revision"], f"{item_where}.revision"),
+ lock_sha256=_digest(item["lock_sha256"], f"{item_where}.lock_sha256", nullable=True),
+ license=_text(item["license"], f"{item_where}.license"),
+ redistribution=_enum(Redistribution, item["redistribution"], f"{item_where}.redistribution"),
+ editable=item["editable"],
+ ))
+ run_claims = _strings(raw["claim_ids"], f"{where}.claim_ids", nonempty=True, unique=True)
+ dangling_claims = sorted(set(run_claims) - set(claim_ids))
+ if dangling_claims:
+ raise ManifestError(f"{where}.claim_ids: unknown claim ids {dangling_claims}")
+
+ inputs = []
+ seen_inputs = set()
+ for index, item in enumerate(_array(raw["datasets"], f"{where}.datasets")):
+ item_where = f"{where}.datasets[{index}]"
+ item = _object(item, item_where, {"id", "sha256"})
+ dataset_id = _text(item["id"], f"{item_where}.id")
+ if dataset_id in seen_inputs:
+ raise ManifestError(f"{item_where}.id: duplicate {dataset_id!r}")
+ seen_inputs.add(dataset_id)
+ digest = _digest(item["sha256"], f"{item_where}.sha256")
+ expected = datasets.get(dataset_id)
+ if expected is None:
+ raise ManifestError(f"{item_where}.id: unknown dataset {dataset_id!r}")
+ if expected.content.sha256 != digest:
+ raise ManifestError(f"{item_where}.sha256: does not match dataset manifest")
+ inputs.append(DatasetInput(dataset_id, digest))
+
+ outputs = []
+ seen_outputs = set()
+ for index, item in enumerate(_array(raw["outputs"], f"{where}.outputs")):
+ item_where = f"{where}.outputs[{index}]"
+ item = _object(item, item_where, {"path", "bytes", "sha256"})
+ output_path = _relative_path(item["path"], f"{item_where}.path")
+ if output_path in seen_outputs:
+ raise ManifestError(f"{item_where}.path: duplicate {output_path!r}")
+ seen_outputs.add(output_path)
+ outputs.append(RunOutput(
+ path=output_path,
+ bytes=_integer(item["bytes"], f"{item_where}.bytes", minimum=0),
+ sha256=_digest(item["sha256"], f"{item_where}.sha256"),
+ ))
+ if not outputs:
+ raise ManifestError(f"{where}.outputs: expected non-empty array")
+
+ seeds = rng["seeds"]
+ if not isinstance(seeds, list):
+ raise ManifestError(f"{where}.rng.seeds: expected array")
+ seeds = tuple(_integer(seed, f"{where}.rng.seeds", minimum=0) for seed in seeds)
+ algorithm = _text(rng["algorithm"], f"{where}.rng.algorithm", nullable=True)
+ if seeds and algorithm is None:
+ raise ManifestError(f"{where}.rng: seeds require algorithm")
+
+ level = _enum(
+ ReproducibilityLevel, reproducibility["level"],
+ f"{where}.reproducibility.level",
+ )
+ availability = _enum(
+ RunAvailability, reproducibility["availability"],
+ f"{where}.reproducibility.availability",
+ )
+ mode = _enum(VerificationMode, verification["mode"], f"{where}.verification.mode")
+ verification_argv = _strings(
+ verification["argv"], f"{where}.verification.argv", nonempty=True
+ )
+ verification_tests = _strings(
+ verification["tests"], f"{where}.verification.tests", unique=True
+ )
+ verification_properties = _strings(
+ verification["properties"], f"{where}.verification.properties", unique=True
+ )
+ if not verification_tests and not verification_properties:
+ raise ManifestError(
+ f"{where}.verification: expected at least one test or property"
+ )
+ tolerance = _text(
+ verification["tolerance"], f"{where}.verification.tolerance", nullable=True
+ )
+ if mode is VerificationMode.EXACT and tolerance is not None:
+ raise ManifestError(f"{where}.verification: exact mode forbids tolerance")
+ if mode is not VerificationMode.EXACT and tolerance is None:
+ raise ManifestError(f"{where}.verification: {mode.value} mode requires tolerance")
+ if level is ReproducibilityLevel.BITWISE and mode is not VerificationMode.EXACT:
+ raise ManifestError(
+ f"{where}.reproducibility: bitwise reproducibility requires exact verification"
+ )
+ if level is ReproducibilityLevel.STATISTICAL and mode is not VerificationMode.STATISTICAL:
+ raise ManifestError(
+ f"{where}.reproducibility: statistical reproducibility requires statistical verification"
+ )
+
+ return RunManifest(
+ id=_text(raw["id"], f"{where}.id"),
+ claim_ids=run_claims,
+ code_revision=_text(code["revision"], f"{where}.code.revision"),
+ dirty=code["dirty"],
+ argv=_strings(command["argv"], f"{where}.command.argv", nonempty=True),
+ python=_text(environment["python"], f"{where}.environment.python"),
+ lock_digest=_digest(environment["lock_digest"], f"{where}.environment.lock_digest", nullable=True),
+ platform=_text(environment["platform"], f"{where}.environment.platform"),
+ dependencies=tuple(dependencies),
+ datasets=tuple(inputs),
+ parameters=_freeze_json(_object(raw["parameters"], f"{where}.parameters")),
+ rng_algorithm=algorithm,
+ rng_seeds=seeds,
+ outputs=tuple(outputs),
+ reproducibility_level=level,
+ availability=availability,
+ verification=RunVerification(
+ mode=mode,
+ argv=verification_argv,
+ tests=verification_tests,
+ properties=verification_properties,
+ tolerance=tolerance,
+ ),
+ limitations=_strings(raw["limitations"], f"{where}.limitations"),
+ )
diff --git a/decluster/declustering.py b/decluster/declustering.py
new file mode 100644
index 00000000..ad11a363
--- /dev/null
+++ b/decluster/declustering.py
@@ -0,0 +1,188 @@
+"""The descending pass: cut a partition this module did not build.
+
+A clusterer owns every merge it made, so refusing a merge before making it is enough to keep two
+owners apart. An inherited partition offers no such control — the merges are already there, made
+by a criterion that cannot be inspected — so keeping owners apart requires cutting instead.
+
+Cutting is held to a stricter standard than merging, and deliberately so. Refusing a merge decides
+on evidence the analyst gathered; cutting an inherited block contradicts a claim whose basis is
+unavailable, and a cut made on absent evidence invents privacy rather than measuring it. So
+`cut_below` has no default: the caller states how much aggregate evidence against a pairing is
+enough to overturn someone else's claim, and the number travels in the result.
+
+Blocks larger than `max_block` get the conservative pass instead of the exact one. Leaving them
+whole was honest but useless: on a real slice the whole heavy tail of a common-input clustering sits
+above any bound an exact search can reach, and that is where every pair the evidence argues about
+actually lives. The conservative pass separates only along boundaries nothing blocks — a pair whose
+weight is above the threshold holds its two elements together — which is admissible by construction
+at any size and never claims to be the best cut, only a cut the evidence permits.
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+from itertools import combinations
+
+from .partition import Partition
+from .unionfind import UF
+
+# Bell(9) = 21147 candidates over at most 36 pairs. Past this the exact search stops paying for
+# itself and a sampler is the better instrument.
+MAX_BLOCK = 9
+
+
+@dataclass(frozen=True)
+class BlockOutcome:
+ """What the pass did with one block, and the evidence it acted on."""
+
+ members: frozenset
+ groups: tuple[frozenset, ...]
+ interior_weight: float
+ severed_weight: float
+
+ @property
+ def was_cut(self):
+ return len(self.groups) > 1
+
+
+@dataclass(frozen=True)
+class DeclusterResult:
+ partition: Partition
+ outcomes: tuple[BlockOutcome, ...]
+ approximated: tuple[frozenset, ...]
+ cut_below: float
+
+ @property
+ def cut(self):
+ return tuple(outcome for outcome in self.outcomes if outcome.was_cut)
+
+ def summary(self):
+ return {
+ "blocks": len(self.outcomes),
+ "cut": len(self.cut),
+ "left_whole": len(self.outcomes) - len(self.cut),
+ "approximated": len(self.approximated),
+ "severed_weight": sum(outcome.severed_weight for outcome in self.cut),
+ "cut_below": self.cut_below,
+ }
+
+
+def _set_partitions(items):
+ """Every partition of `items` as a tuple of tuples, in a deterministic order."""
+ if not items:
+ yield ()
+ return
+ first, *rest = items
+ for partition in _set_partitions(rest):
+ yield ((first,), *partition)
+ for index in range(len(partition)):
+ blocks = list(partition)
+ blocks[index] = (first, *blocks[index])
+ yield tuple(blocks)
+
+
+def _pairwise(members, weight):
+ """The weight of every pair in the block, evaluated once."""
+ return {
+ frozenset(pair): weight(*pair) for pair in combinations(sorted(members, key=repr), 2)
+ }
+
+
+def _across(left, right, table):
+ return sum(table[frozenset((a, b))] for a in left for b in right)
+
+
+def _split(candidate, table, cut_below):
+ """Interior and severed weight of a candidate, or None when it cuts on too little evidence.
+
+ Admissibility is checked between whole groups rather than between single pairs: that is what
+ stops one strongly negative pair from carrying a cut the rest of the block argues against.
+ """
+ severed = 0.0
+ for left, right in combinations(candidate, 2):
+ between = _across(left, right, table)
+ if between > cut_below:
+ return None
+ severed += between
+ interior = sum(total for total in table.values()) - severed
+ return interior, severed
+
+
+def _best_cut(members, weight, cut_below):
+ """The admissible partition of `members` holding the most evidence inside its groups.
+
+ A tie between two admissible cuts is resolved by not cutting. Picking either one would name a
+ boundary the evidence does not place, and an arbitrary boundary reads downstream exactly like a
+ measured one.
+ """
+ table = _pairwise(members, weight)
+ whole = (tuple(sorted(members, key=repr)),)
+ best, best_interior, best_severed, ties = whole, sum(table.values()), 0.0, 1
+ for candidate in _set_partitions(list(whole[0])):
+ if len(candidate) == 1:
+ continue
+ scored = _split(candidate, table, cut_below)
+ if scored is None:
+ continue
+ interior, severed = scored
+ if interior > best_interior:
+ best, best_interior, best_severed, ties = candidate, interior, severed, 1
+ elif interior == best_interior:
+ ties += 1
+ if ties > 1:
+ return (frozenset(members),), sum(table.values()), 0.0
+ return tuple(frozenset(group) for group in best), best_interior, best_severed
+
+
+def _conservative_cut(members, weight, cut_below):
+ """Separate only where nothing holds the two sides together.
+
+ A pair whose weight is above `cut_below` blocks any boundary between its elements; the
+ components of those blocking pairs are the finest partition no single pair objects to. Between
+ two components every crossing pair is at or below the threshold, so their aggregate is too, and
+ the result is admissible at any block size. It is not the best cut — the exact search can beat
+ it by severing a blocking pair whose neighbours pay for it — and it never pretends to be.
+ """
+ ordered = sorted(members, key=repr)
+ uf = UF(ordered)
+ for index, a in enumerate(ordered):
+ for b in ordered[index + 1:]:
+ if weight(a, b) > cut_below:
+ uf.union(a, b)
+ groups = tuple(frozenset(group) for group in uf.groups())
+ table = _pairwise(members, weight)
+ severed = sum(
+ _across(left, right, table) for left, right in combinations(groups, 2)
+ )
+ return groups, sum(table.values()) - severed, severed
+
+
+def decluster(partition, weight, *, cut_below, max_block=MAX_BLOCK):
+ """Refine `partition` where the evidence overturns the merges it inherited.
+
+ `weight(a, b)` returns a signed evidence weight for a pair and must **not** include a co-spend
+ prior: the co-spend is the inherited claim under test, not evidence for it. The result is a meet
+ with `partition`, so it can only be finer.
+
+ Blocks at or below `max_block` get the exact search; larger ones get `_conservative_cut`, and
+ the result records which pass decided each block.
+ """
+ if cut_below > 0:
+ raise ValueError("cut_below must be at most zero; a cut needs evidence against a pairing")
+
+ outcomes, approximate, blocks = [], [], []
+ for block in partition.blocks():
+ if len(block) < 2:
+ blocks.append(block)
+ continue
+ if len(block) > max_block:
+ groups, interior, severed = _conservative_cut(block, weight, cut_below)
+ approximate.append(block)
+ else:
+ groups, interior, severed = _best_cut(block, weight, cut_below)
+ outcomes.append(BlockOutcome(block, groups, interior, severed))
+ blocks.extend(groups)
+
+ return DeclusterResult(
+ partition.meet(Partition(blocks)), tuple(outcomes), tuple(approximate), cut_below
+ )
diff --git a/decluster/def1_sparsity.py b/decluster/def1_sparsity.py
index 56b3a98f..b28e925c 100644
--- a/decluster/def1_sparsity.py
+++ b/decluster/def1_sparsity.py
@@ -14,6 +14,14 @@
sample of query clusters is compared against a background sample, and its top similarity
recorded. Reported as an estimate over the sample, with the background size stated.
+The similarity used here is the standard cosine, `dot / (||a|| * ||b||)`. The definition's own
+`Sim` generalises cosine differently: it sums the per-attribute similarities and normalises by
+`|supp(a) union supp(b)|`, so two records agreeing on a few attributes each score lower the more
+attributes either one has. The substitution is local, not a reading of the source, and it changes
+the scale: `cosine` is insensitive to support size, so the epsilon thresholds in `survival` are not
+the source's thresholds. Both fixtures reach the same conclusion under either form, but a result
+quoted against the definition's own epsilon must be recomputed with the union normalisation.
+
Offline, stdlib only.
"""
import math
@@ -21,6 +29,8 @@
def cosine(a, b):
+ """Standard cosine, not the union-of-supports normalisation the definition uses (see module
+ docstring)."""
if not a or not b:
return 0.0
dot = sum(a.get(k, 0.0) * v for k, v in b.items())
@@ -50,8 +60,17 @@ def feature_vector(g, vid):
v[f"tx={_log_bin(node.get('txs', 0))}"] = 1.0
if node.get("self_transfers"):
v[f"self={_log_bin(node['self_transfers'])}"] = 1.0
+ # The neighbour-degree histogram is normalised like every other axis. Left as raw counts
+ # it is the only unbounded component: at degree 50 one bin carries 99.9% of the vector's
+ # norm, so the cosine reads degree similarity and two clusters with wholly disjoint
+ # fingerprints score 0.9996.
+ bins = {}
for nb in g.neighbours(vid):
- v[f"nbdeg={_log_bin(g.degree(nb))}"] = v.get(f"nbdeg={_log_bin(g.degree(nb))}", 0.0) + 1.0
+ key = f"nbdeg={_log_bin(g.degree(nb))}"
+ bins[key] = bins.get(key, 0.0) + 1.0
+ total = sum(bins.values())
+ for key, n in bins.items():
+ v[key] = n / total
return v
@@ -59,7 +78,8 @@ def nearest_similarities(g, query_n=2000, background_n=20000, min_degree=1, seed
"""For each of `query_n` sampled clusters, its highest cosine similarity to any of
`background_n` other sampled clusters. Returns the list of top similarities."""
rng = random.Random(seed)
- verts = [v for v in g.vertices if g.degree(v) >= min_degree]
+ # A seeded sample is reproducible only when its population order is stable too.
+ verts = sorted(v for v in g.vertices if g.degree(v) >= min_degree)
if len(verts) <= 1:
return []
background = rng.sample(verts, min(background_n, len(verts)))
diff --git a/decluster/disjoint_routes.py b/decluster/disjoint_routes.py
new file mode 100644
index 00000000..9b42acdc
--- /dev/null
+++ b/decluster/disjoint_routes.py
@@ -0,0 +1,248 @@
+"""How many edge-disjoint routes join a coin to its candidate origins.
+
+The framework states its two positive properties over this quantity and nothing else. Robust
+connectivity asks that no small cut separate an output from the mass of its candidate origins —
+that the two are joined by *multiple disjoint paths*. Own-origin robustness asks the same of the
+paths back to a user's own antecedents. Both are the size of a minimum cut, and a minimum cut is
+what a maximum flow measures.
+
+`path_count` counts routes weighted by link probability, which is a different number: many routes
+may share the same coin, so a large count says nothing about how few coins a cut would have to
+sever. This module answers the cut question, and only that.
+
+Two modelling choices, both the framework's rather than ours. Routes are **edge**-disjoint, which is
+the reading the specification settled on: paths may pass through a shared transaction but may not
+share a coin, because it is the coin that a seizure or a label removes. And several origins are
+reduced to one by an artificial source joined to each, the standard construction, so `k` is the
+number of routes to the origin *set* rather than to any particular member.
+
+What this does not say. A flow treats value as fungible, and a transaction that mixes inputs does
+not record which satoshi left by which output. A route here is a path the graph permits, not a claim
+that particular value travelled it: `k` bounds how much redundancy a cut has to defeat, and any
+reading beyond that is the taint-transfer question this module refuses to answer.
+
+The quantity is the min cut of a directed graph with unit capacities, which is the connectivity
+Abboud, Georgiadis, Italiano, Krauthgamer, Parotsidis, Trabelsi, Uznański and Wolleb-Graf study in
+*Faster Algorithms for All-Pairs Bounded Min-Cuts* (arXiv:1807.05803). Two things there carry over.
+The vertex-capacitated case reduces to the edge-capacitated one by splitting each vertex, which is
+what puts a coin's single unit of capacity on an arc of its own here. And the useful question is
+k-bounded: which coins have a *small* cut, rather than the exact value where it is large — on the
+committed cache the whole finding sits at k = 1 and k = 2.
+
+`catalog/ctp-sources.json` is the framework's own bibliography, pinned to its footnotes at a named
+revision, so a method reference like this one does not belong in it and is cited here instead.
+"""
+
+from __future__ import annotations
+
+from collections import deque
+
+UNCAPPED = 1 << 30 # a coin's own arc is the only scarce one
+TARGET_VALUE = object() # prune to what the target coin itself is worth
+SOURCE = ("__source__", -1)
+SINK = ("__sink__", -1)
+
+
+class _Network:
+ """A unit-capacity flow network over coins, held as an adjacency list of paired arcs."""
+
+ def __init__(self):
+ self.arcs = [] # (head, capacity) with arcs[i ^ 1] the reverse
+ self.out = {}
+
+ def add(self, tail, head, capacity=1):
+ self.out.setdefault(tail, []).append(len(self.arcs))
+ self.arcs.append([head, capacity])
+ self.out.setdefault(head, []).append(len(self.arcs))
+ self.arcs.append([tail, 0])
+
+ def _levels(self, source, sink):
+ level = {source: 0}
+ queue = deque([source])
+ while queue:
+ node = queue.popleft()
+ for index in self.out.get(node, ()):
+ head, capacity = self.arcs[index]
+ if capacity > 0 and head not in level:
+ level[head] = level[node] + 1
+ queue.append(head)
+ return level if sink in level else None
+
+ def _augment(self, node, sink, level, cursor, limit):
+ if node == sink:
+ return limit
+ arcs = self.out.get(node, ())
+ while cursor[node] < len(arcs):
+ index = arcs[cursor[node]]
+ head, capacity = self.arcs[index]
+ if capacity > 0 and level.get(head, -1) == level[node] + 1:
+ pushed = self._augment(head, sink, level, cursor, min(limit, capacity))
+ if pushed:
+ self.arcs[index][1] -= pushed
+ self.arcs[index ^ 1][1] += pushed
+ return pushed
+ cursor[node] += 1
+ return 0
+
+ def max_flow(self, source, sink):
+ """Dinic. Integral capacities in, integral flow out; with unit capacities that is `k`."""
+ total = 0
+ while (level := self._levels(source, sink)) is not None:
+ cursor = {node: 0 for node in self.out}
+ while (pushed := self._augment(source, sink, level, cursor, float("inf"))):
+ total += pushed
+ return total
+
+ def saturated_routes(self, source, sink, carried):
+ """One route per unit of flow, walked over arcs the flow used and not yet spent.
+
+ `carried` reads the flow on a forward arc, which is what the reverse arc's capacity holds.
+ A route is reported as the coins it passes through, in walk order.
+ """
+ remaining = {i: carried(i) for i in range(0, len(self.arcs), 2) if carried(i) > 0}
+ routes = []
+ while True:
+ path, node = [], source
+ while node != sink:
+ step = next((i for i in self.out.get(node, ()) if remaining.get(i, 0) > 0), None)
+ if step is None:
+ return routes
+ remaining[step] -= 1
+ node = self.arcs[step][0]
+ path.append(node)
+ routes.append([name for name, side in path[:-1] if side == "out"])
+
+ def saturated_flow_routes(self, source, sink, carried):
+ """Decompose an integral flow into ``(amount, route)`` records.
+
+ Unlike ``saturated_routes``, this spends the bottleneck amount at once. Calling the
+ unit-flow decomposer for satoshi capacities would emit one route per satoshi even when
+ every satoshi follows the same path.
+ """
+ remaining = {i: carried(i) for i in range(0, len(self.arcs), 2) if carried(i) > 0}
+ routes = []
+ while True:
+ path, steps, node = [], [], source
+ while node != sink:
+ step = next((i for i in self.out.get(node, ()) if remaining.get(i, 0) > 0), None)
+ if step is None:
+ return routes
+ steps.append(step)
+ node = self.arcs[step][0]
+ path.append(node)
+ amount = min(remaining[step] for step in steps)
+ for step in steps:
+ remaining[step] -= amount
+ routes.append((amount, [name for name, side in path[:-1] if side == "out"]))
+
+
+class _Restricted:
+ """A view of `graph` holding only the coins in `keep`, so reachability is recomputed on them."""
+
+ def __init__(self, graph, keep):
+ self.edges = {coin: [(nxt, w) for nxt, w in graph.edges.get(coin, ()) if nxt in keep]
+ for coin in keep}
+
+
+def _reachable_backwards(graph, target):
+ """The coins a walk from `target` can still reach, so the cut is measured on live ground."""
+ seen, queue = {target}, deque([target])
+ while queue:
+ coin = queue.popleft()
+ for nxt, _weight in graph.edges.get(coin, ()):
+ if nxt not in seen:
+ seen.add(nxt)
+ queue.append(nxt)
+ return seen
+
+
+def route_capacity(graph, target, origins=None, carries=None):
+ """`(k, routes)`: how many edge-disjoint routes join `target` to `origins`.
+
+ `origins` defaults to the graph's absorbers — the boundary the walk stopped at. A coin that no
+ longer reaches the target is dropped rather than counted as an origin it cannot serve.
+
+ `carries` is the amount a route has to be able to deliver, in satoshis, and prunes every coin
+ worth less than it before the cut is measured. Without it a route of a thousand satoshis counts
+ the same as a route that could have supplied the whole coin, which overstates redundancy in the
+ direction that matters: the specification prunes exactly this way — a path worth less than the
+ amount in question may have been removed. `carries=TARGET_VALUE` uses the target's own value,
+ the natural threshold for asking which routes could have produced it.
+ """
+ live = _reachable_backwards(graph, target)
+ if carries is not None:
+ floor = graph.values.get(target) if carries is TARGET_VALUE else carries
+ if floor is not None:
+ values = getattr(graph, "values", {})
+ live = {coin for coin in live
+ if coin == target or (values.get(coin) or 0) >= floor}
+ live = _reachable_backwards(_Restricted(graph, live), target)
+ ends = [coin for coin in (origins if origins is not None else graph.absorbers)
+ if coin in live and coin != target]
+ if not ends:
+ return 0, []
+
+ # A coin is an edge, not a vertex: the specification puts transactions at the vertices and coins
+ # on the arcs between them, because it is a coin that a seizure or a label removes. The graph
+ # here keys state by coin, so each coin is split into an in/out pair joined by the one unit of
+ # capacity it owns; a transition between coins is then uncapacitated. Max flow over that is the
+ # number of coin-disjoint routes, and its min cut is the coins a separation would have to take.
+ inward = lambda coin: (coin, "in")
+ outward = lambda coin: (coin, "out")
+ network = _Network()
+ for coin in live:
+ network.add(inward(coin), outward(coin), 1)
+ for coin in live:
+ for nxt, _weight in graph.edges.get(coin, ()):
+ if nxt in live:
+ network.add(outward(coin), inward(nxt), UNCAPPED)
+ for end in ends:
+ network.add(outward(end), SINK, UNCAPPED)
+ # Every route leaves the target, so the target is the query point rather than a resource
+ # routes compete for: the source joins its outward side, past its own unit of capacity.
+ network.add(SOURCE, outward(target), UNCAPPED)
+
+ k = network.max_flow(SOURCE, SINK)
+ return k, network.saturated_routes(SOURCE, SINK, lambda i: network.arcs[i ^ 1][1])
+
+
+def cut_size(graph, target, origins=None, carries=None):
+ """The minimum number of coins whose removal separates `target` from every origin."""
+ return route_capacity(graph, target, origins, carries)[0]
+
+def flow_capacity(graph, target, origins=None):
+ """`(satoshis, valued_routes)`: the largest value origins could deliver at once.
+
+ The framework states its property over the *mass* of a coin's candidate origins, not only over
+ how many routes reach it. Giving each coin its own value as capacity answers that: the maximum
+ flow is the value the origin set could deliver if every route ran at once, and its minimum cut
+ is the value a separation would have to remove.
+
+ This is still one flow and still polynomial. What it is not is the k-splittable question —
+ the most value carried by at most k routes — which is strongly NP-hard and is a choice about
+ routing rather than a measurement of capacity.
+
+ A coin whose value the walk did not record cannot bound anything, so it is treated as
+ unconstrained rather than as zero: understating capacity here would invent a cut.
+ """
+ live = _reachable_backwards(graph, target)
+ ends = [coin for coin in (origins if origins is not None else graph.absorbers)
+ if coin in live and coin != target]
+ if not ends:
+ return 0, []
+ values = getattr(graph, "values", {})
+ worth = lambda coin: values.get(coin) or UNCAPPED
+ inward, outward = lambda c: (c, "in"), lambda c: (c, "out")
+ network = _Network()
+ for coin in live:
+ network.add(inward(coin), outward(coin), worth(coin))
+ for coin in live:
+ for nxt, _weight in graph.edges.get(coin, ()):
+ if nxt in live:
+ network.add(outward(coin), inward(nxt), UNCAPPED)
+ for end in ends:
+ network.add(outward(end), SINK, UNCAPPED)
+ network.add(SOURCE, outward(target), UNCAPPED)
+ total = network.max_flow(SOURCE, SINK)
+ return total, network.saturated_flow_routes(SOURCE, SINK,
+ lambda i: network.arcs[i ^ 1][1])
diff --git a/decluster/domain/__init__.py b/decluster/domain/__init__.py
new file mode 100644
index 00000000..3a322846
--- /dev/null
+++ b/decluster/domain/__init__.py
@@ -0,0 +1,107 @@
+"""Stable domain contracts shared by attacks, evaluations, and adapters."""
+
+from .evidence import (
+ AbstentionEvidence,
+ CandidateEliminationEvidence,
+ CandidateSetEvidence,
+ CannotLinkEvidence,
+ CoSpendEvidence,
+ CorrespondentDistributionEvidence,
+ ContractedTransfer,
+ Direction,
+ Evidence,
+ EvidenceContext,
+ FlowConstraintEvidence,
+ GraphFractureEvidence,
+ IntersectionEvidence,
+ MappingEvidence,
+ UnanimousMappingLinksEvidence,
+ OwnershipLikelihoodEvidence,
+ PerfectMatchingEvidence,
+ TransactionFingerprintEvidence,
+ UnnecessaryInputEvidence,
+ PartitionPosteriorEvidence,
+ ProvenanceDistributionEvidence,
+ PseudonymGraphEvidence,
+ Subject,
+ SubjectKind,
+)
+from .outcome import (
+ Attribution,
+ AdditiveDecayMeasured,
+ CandidateNarrowing,
+ ConditionalLinksMeasured,
+ ClusterMerge,
+ DeclusterSplit,
+ GraphFractureMeasured,
+ PersistentCorrespondentRanked,
+ PseudonymGraphConstructed,
+ ReferenceOwnershipConflict,
+ MessageAssignmentRecovered,
+ TransactionFingerprintObserved,
+ MergeRefused,
+ Inconclusive,
+ NoSharedCandidates,
+ CompleteProvenanceMeasured,
+ TruncatedProvenanceMeasured,
+ NodeCapped,
+ NotObserved,
+ OracleRefused,
+ Outcome,
+ TransitiveMembership,
+ Unsupported,
+)
+from .report import AttackReport, EvidenceChannel, ExperimentalComposition
+
+__all__ = [
+ "AbstentionEvidence",
+ "AdditiveDecayMeasured",
+ "Attribution",
+ "AttackReport",
+ "CandidateSetEvidence",
+ "CandidateEliminationEvidence",
+ "CandidateNarrowing",
+ "ConditionalLinksMeasured",
+ "CannotLinkEvidence",
+ "CoSpendEvidence",
+ "CorrespondentDistributionEvidence",
+ "ContractedTransfer",
+ "ClusterMerge",
+ "DeclusterSplit",
+ "Direction",
+ "Evidence",
+ "EvidenceContext",
+ "EvidenceChannel",
+ "ExperimentalComposition",
+ "FlowConstraintEvidence",
+ "GraphFractureEvidence",
+ "GraphFractureMeasured",
+ "PersistentCorrespondentRanked",
+ "PseudonymGraphConstructed",
+ "ReferenceOwnershipConflict",
+ "MessageAssignmentRecovered",
+ "TransactionFingerprintObserved",
+ "IntersectionEvidence",
+ "Inconclusive",
+ "MappingEvidence",
+ "UnanimousMappingLinksEvidence",
+ "MergeRefused",
+ "NodeCapped",
+ "NotObserved",
+ "NoSharedCandidates",
+ "OracleRefused",
+ "Outcome",
+ "OwnershipLikelihoodEvidence",
+ "PerfectMatchingEvidence",
+ "TransactionFingerprintEvidence",
+ "UnnecessaryInputEvidence",
+ "PartitionPosteriorEvidence",
+ "ProvenanceDistributionEvidence",
+ "PseudonymGraphEvidence",
+ "CompleteProvenanceMeasured",
+ "TruncatedProvenanceMeasured",
+ "Subject",
+ "SubjectKind",
+ "TransitiveMembership",
+ "Unsupported",
+]
diff --git a/decluster/domain/evidence.py b/decluster/domain/evidence.py
new file mode 100644
index 00000000..ffc36240
--- /dev/null
+++ b/decluster/domain/evidence.py
@@ -0,0 +1,450 @@
+"""Closed evidence types for ownership attacks and partition refinement.
+
+Evidence records observations and model outputs. It does not itself authorize a
+cluster merge, a refusal, or a split; those decisions are represented by outcome
+types in :mod:`decluster.domain.outcome`.
+"""
+
+from dataclasses import dataclass
+from enum import Enum
+from math import isfinite
+from typing import Hashable, Optional, TypeAlias, Union
+
+
+class SubjectKind(str, Enum):
+ COIN = "coin"
+ OUTPOINT = "outpoint"
+ TRANSACTION = "transaction"
+ CLUSTER = "cluster"
+ MAPPING = "mapping"
+ PARTITION = "partition"
+
+
+@dataclass(frozen=True)
+class Subject:
+ kind: SubjectKind
+ identifier: Hashable
+
+ def __post_init__(self) -> None:
+ if self.identifier is None or (
+ isinstance(self.identifier, str) and not self.identifier
+ ):
+ raise ValueError("subject identifier must not be empty")
+ try:
+ hash(self.identifier)
+ except TypeError as exc:
+ raise ValueError("subject identifier must be hashable") from exc
+
+
+class Direction(str, Enum):
+ SUPPORTS_COMMON_OWNERSHIP = "supports_common_ownership"
+ OPPOSES_COMMON_OWNERSHIP = "opposes_common_ownership"
+ DESCRIPTIVE = "descriptive"
+
+
+@dataclass(frozen=True)
+class EvidenceContext:
+ adversary: str
+ observables: tuple[str, ...]
+ hypothesis: str
+ algorithm: str
+ dataset: Optional[str] = None
+ limitations: tuple[str, ...] = ()
+
+ def __post_init__(self) -> None:
+ for label, value in (
+ ("adversary", self.adversary),
+ ("hypothesis", self.hypothesis),
+ ("algorithm", self.algorithm),
+ ):
+ if not value:
+ raise ValueError(f"{label} must not be empty")
+ if not self.observables or any(not item for item in self.observables):
+ raise ValueError("observables must contain non-empty values")
+ if any(not item for item in self.limitations):
+ raise ValueError("limitations must contain non-empty values")
+
+
+def _require_pair(subject: Subject, target: Subject) -> None:
+ if subject == target:
+ raise ValueError("subject and target must be distinct")
+
+
+def _require_finite(value: float, label: str) -> None:
+ if not isfinite(value):
+ raise ValueError(f"{label} must be finite")
+
+
+@dataclass(frozen=True)
+class OwnershipLikelihoodEvidence:
+ subject: Subject
+ target: Subject
+ log2_odds: float
+ context: EvidenceContext
+ direction: Direction
+
+ def __post_init__(self) -> None:
+ _require_pair(self.subject, self.target)
+ _require_finite(self.log2_odds, "log2_odds")
+ if self.direction is Direction.DESCRIPTIVE:
+ raise ValueError("ownership likelihood must have an ownership direction")
+
+
+@dataclass(frozen=True)
+class CannotLinkEvidence:
+ subject: Subject
+ target: Subject
+ context: EvidenceContext
+ reason: str
+
+ def __post_init__(self) -> None:
+ _require_pair(self.subject, self.target)
+ if not self.reason:
+ raise ValueError("cannot-link reason must not be empty")
+
+
+@dataclass(frozen=True)
+class MappingEvidence:
+ inputs: tuple[Subject, ...]
+ outputs: tuple[Subject, ...]
+ mapping_count: int
+ context: EvidenceContext
+
+ def __post_init__(self) -> None:
+ if not self.inputs or not self.outputs:
+ raise ValueError("mapping evidence requires inputs and outputs")
+ if self.mapping_count < 0:
+ raise ValueError("mapping_count must be non-negative")
+
+
+@dataclass(frozen=True)
+class UnanimousMappingLinksEvidence:
+ """Input-output links present in every member of one declared mapping family."""
+
+ inputs: tuple[Subject, ...]
+ outputs: tuple[Subject, ...]
+ mapping_count: int
+ links: tuple[tuple[Subject, Subject], ...]
+ context: EvidenceContext
+
+ def __post_init__(self) -> None:
+ if not self.inputs or not self.outputs or self.mapping_count < 1:
+ raise ValueError("unanimous links require inputs, outputs, and mappings")
+ if len(set(self.inputs)) != len(self.inputs) or len(set(self.outputs)) != len(
+ self.outputs
+ ):
+ raise ValueError("mapping subjects must be unique on each side")
+ allowed_inputs = set(self.inputs)
+ allowed_outputs = set(self.outputs)
+ if len(set(self.links)) != len(self.links):
+ raise ValueError("unanimous mapping links must be unique")
+ if any(left not in allowed_inputs or right not in allowed_outputs for left, right in self.links):
+ raise ValueError("unanimous link endpoints must belong to the mapping")
+
+
+@dataclass(frozen=True)
+class CandidateSetEvidence:
+ subject: Subject
+ candidates: frozenset[Subject]
+ context: EvidenceContext
+
+
+@dataclass(frozen=True)
+class PartitionPosteriorEvidence:
+ subjects: tuple[Subject, ...]
+ entropy_bits: float
+ sampled_partitions: int
+ context: EvidenceContext
+
+ def __post_init__(self) -> None:
+ _require_finite(self.entropy_bits, "entropy_bits")
+ if self.entropy_bits < 0:
+ raise ValueError("entropy_bits must be non-negative")
+ if self.sampled_partitions < 1:
+ raise ValueError("sampled_partitions must be positive")
+
+
+@dataclass(frozen=True)
+class ProvenanceDistributionEvidence:
+ subject: Subject
+ probabilities: tuple[tuple[Subject, float], ...]
+ context: EvidenceContext
+
+ def __post_init__(self) -> None:
+ if not self.probabilities:
+ raise ValueError("provenance distribution must not be empty")
+ total = 0.0
+ origins: set[Subject] = set()
+ for origin, probability in self.probabilities:
+ _require_finite(probability, "probability")
+ if not 0.0 <= probability <= 1.0:
+ raise ValueError("probability must be within [0, 1]")
+ if origin in origins:
+ raise ValueError("provenance origins must be unique")
+ origins.add(origin)
+ total += probability
+ if abs(total - 1.0) > 1e-9:
+ raise ValueError("provenance probabilities must sum to 1")
+
+
+@dataclass(frozen=True)
+class IntersectionEvidence:
+ observations: tuple[Subject, ...]
+ candidates_before: int
+ candidates_after: frozenset[Subject]
+ context: EvidenceContext
+
+ def __post_init__(self) -> None:
+ if len(self.observations) < 2:
+ raise ValueError("intersection requires at least two observations")
+ if self.candidates_before < len(self.candidates_after):
+ raise ValueError("intersection cannot increase its candidate set")
+
+
+@dataclass(frozen=True)
+class FlowConstraintEvidence:
+ subject: Subject
+ target: Subject
+ amount: int
+ unit: str
+ context: EvidenceContext
+
+ def __post_init__(self) -> None:
+ _require_pair(self.subject, self.target)
+ if self.amount < 0:
+ raise ValueError("amount must be non-negative")
+ if not self.unit:
+ raise ValueError("unit must not be empty")
+
+
+@dataclass(frozen=True)
+class AbstentionEvidence:
+ subjects: tuple[Subject, ...]
+ reason: str
+ context: EvidenceContext
+
+ def __post_init__(self) -> None:
+ if not self.subjects:
+ raise ValueError("abstention requires at least one subject")
+ if not self.reason:
+ raise ValueError("abstention reason must not be empty")
+
+
+@dataclass(frozen=True)
+class CandidateEliminationEvidence:
+ subject: Subject
+ candidates_before: frozenset[Subject]
+ eliminated: frozenset[Subject]
+ context: EvidenceContext
+
+ def __post_init__(self) -> None:
+ if not self.candidates_before:
+ raise ValueError("candidate elimination requires an initial set")
+ if not self.eliminated:
+ raise ValueError("candidate elimination requires removed candidates")
+ if not self.eliminated <= self.candidates_before:
+ raise ValueError("eliminated candidates must belong to the initial set")
+
+ @property
+ def candidates_after(self) -> frozenset[Subject]:
+ return self.candidates_before - self.eliminated
+
+
+@dataclass(frozen=True)
+class GraphFractureEvidence:
+ removed: frozenset[Subject]
+ components_before: int
+ components_after: int
+ context: EvidenceContext
+
+ def __post_init__(self) -> None:
+ if not self.removed:
+ raise ValueError("graph fracture requires removed subjects")
+ if self.components_before < 1:
+ raise ValueError("components_before must be positive")
+ if self.components_after <= self.components_before:
+ raise ValueError("graph fracture must increase the component count")
+
+
+@dataclass(frozen=True)
+class CorrespondentDistributionEvidence:
+ """Finite-sample estimate of a target's persistent correspondent distribution.
+
+ Scores are not constrained to ``[0, 1]`` because subtraction of an estimated
+ background distribution can produce negative finite-sample values.
+ """
+
+ target: Subject
+ scores: tuple[tuple[Subject, float], ...]
+ observations: int
+ batch_size: int
+ context: EvidenceContext
+
+ def __post_init__(self) -> None:
+ if not self.scores:
+ raise ValueError("correspondent scores must not be empty")
+ correspondents: set[Subject] = set()
+ total = 0.0
+ for correspondent, score in self.scores:
+ _require_pair(self.target, correspondent)
+ _require_finite(score, "correspondent score")
+ if correspondent in correspondents:
+ raise ValueError("correspondents must be unique")
+ correspondents.add(correspondent)
+ total += score
+ if abs(total - 1.0) > 1e-9:
+ raise ValueError("correspondent scores must sum to one")
+ if self.observations < 1:
+ raise ValueError("observations must be positive")
+ if self.batch_size < 2:
+ raise ValueError("batch_size must be at least two")
+
+
+@dataclass(frozen=True)
+class PerfectMatchingEvidence:
+ """Jointly optimal bijective message assignments for one observed round."""
+
+ senders: tuple[Subject, ...]
+ receivers: tuple[Subject, ...]
+ optimal_assignments: tuple[tuple[tuple[Subject, Subject], ...], ...]
+ log_likelihood: float
+ context: EvidenceContext
+
+ def __post_init__(self) -> None:
+ if len(self.senders) < 2 or len(self.senders) != len(self.receivers):
+ raise ValueError("perfect matching requires equal sides of size at least two")
+ if len(set(self.senders)) != len(self.senders) or len(set(self.receivers)) != len(
+ self.receivers
+ ):
+ raise ValueError("message nodes on each side must be unique")
+ if not self.optimal_assignments:
+ raise ValueError("perfect matching evidence requires an optimum")
+ expected_senders = set(self.senders)
+ expected_receivers = set(self.receivers)
+ for assignment in self.optimal_assignments:
+ if len(assignment) != len(self.senders):
+ raise ValueError("every assignment must cover the round")
+ if {sender for sender, _ in assignment} != expected_senders or {
+ receiver for _, receiver in assignment
+ } != expected_receivers:
+ raise ValueError("every assignment must be a perfect matching")
+ _require_finite(self.log_likelihood, "log_likelihood")
+
+
+@dataclass(frozen=True)
+class TransactionFingerprintEvidence:
+ """Named observable features of one transaction under a declared rule."""
+
+ subject: Subject
+ features: tuple[tuple[str, int], ...]
+ context: EvidenceContext
+
+ def __post_init__(self) -> None:
+ if self.subject.kind is not SubjectKind.TRANSACTION:
+ raise ValueError("transaction fingerprint requires a transaction subject")
+ if not self.features:
+ raise ValueError("transaction fingerprint features must not be empty")
+ names = tuple(name for name, _ in self.features)
+ if any(not name for name in names) or len(set(names)) != len(names):
+ raise ValueError("transaction fingerprint feature names must be unique")
+
+
+@dataclass(frozen=True)
+class UnnecessaryInputEvidence:
+ """A UIH classification without an ownership or protocol-form inference."""
+
+ transaction: Subject
+ definition: str
+ classification: str
+ fee: int
+ removed_input_index: int
+ context: EvidenceContext
+
+ def __post_init__(self) -> None:
+ if self.transaction.kind is not SubjectKind.TRANSACTION:
+ raise ValueError("unnecessary-input evidence requires a transaction")
+ if not self.definition or self.classification not in {"uih1", "uih2"}:
+ raise ValueError("unnecessary-input definition and classification are required")
+ if self.fee < 0 or self.removed_input_index < 0:
+ raise ValueError("fee and removed input index must be non-negative")
+
+
+@dataclass(frozen=True)
+class CoSpendEvidence:
+ """Inputs observed together in one transaction, before an ownership inference."""
+
+ transaction: Subject
+ inputs: tuple[Subject, ...]
+ context: EvidenceContext
+
+ def __post_init__(self) -> None:
+ if self.transaction.kind is not SubjectKind.TRANSACTION:
+ raise ValueError("co-spend evidence requires a transaction subject")
+ if len(self.inputs) < 2 or len(set(self.inputs)) != len(self.inputs):
+ raise ValueError("co-spend evidence requires at least two unique inputs")
+ if any(subject.kind is not SubjectKind.COIN for subject in self.inputs):
+ raise ValueError("co-spend inputs must be coin subjects")
+
+
+@dataclass(frozen=True)
+class ContractedTransfer:
+ source: Subject
+ target: Subject
+ transfers: int
+ value: int
+ first_height: int
+ last_height: int
+
+ def __post_init__(self) -> None:
+ _require_pair(self.source, self.target)
+ if self.transfers < 1:
+ raise ValueError("contracted transfer count must be positive")
+ if self.value < 0:
+ raise ValueError("contracted transfer value must be non-negative")
+ if self.first_height < 0 or self.last_height < self.first_height:
+ raise ValueError("contracted transfer height span is invalid")
+
+
+@dataclass(frozen=True)
+class PseudonymGraphEvidence:
+ vertices: tuple[Subject, ...]
+ edges: tuple[ContractedTransfer, ...]
+ self_transfers: tuple[tuple[Subject, int], ...]
+ context: EvidenceContext
+
+ def __post_init__(self) -> None:
+ if not self.vertices or len(set(self.vertices)) != len(self.vertices):
+ raise ValueError("pseudonym graph requires unique vertices")
+ vertices = set(self.vertices)
+ if any(edge.source not in vertices or edge.target not in vertices for edge in self.edges):
+ raise ValueError("contracted transfer endpoint must be a graph vertex")
+ pairs = tuple((edge.source, edge.target) for edge in self.edges)
+ if len(set(pairs)) != len(pairs):
+ raise ValueError("parallel transfers must be folded into one attributed edge")
+ seen = set()
+ for vertex, count in self.self_transfers:
+ if vertex not in vertices or vertex in seen or count < 1:
+ raise ValueError("self-transfer counts require unique graph vertices")
+ seen.add(vertex)
+
+
+Evidence: TypeAlias = Union[
+ OwnershipLikelihoodEvidence,
+ CannotLinkEvidence,
+ MappingEvidence,
+ UnanimousMappingLinksEvidence,
+ CandidateSetEvidence,
+ PartitionPosteriorEvidence,
+ ProvenanceDistributionEvidence,
+ IntersectionEvidence,
+ FlowConstraintEvidence,
+ AbstentionEvidence,
+ CandidateEliminationEvidence,
+ GraphFractureEvidence,
+ CorrespondentDistributionEvidence,
+ PerfectMatchingEvidence,
+ TransactionFingerprintEvidence,
+ UnnecessaryInputEvidence,
+ CoSpendEvidence,
+ PseudonymGraphEvidence,
+]
diff --git a/decluster/domain/outcome.py b/decluster/domain/outcome.py
new file mode 100644
index 00000000..7327e950
--- /dev/null
+++ b/decluster/domain/outcome.py
@@ -0,0 +1,273 @@
+"""Closed outcomes for adversarial analysis and partition operations."""
+
+from dataclasses import dataclass
+from math import isfinite
+from typing import TypeAlias, Union
+
+from .evidence import (
+ CandidateEliminationEvidence,
+ CoSpendEvidence,
+ Evidence,
+ GraphFractureEvidence,
+ CorrespondentDistributionEvidence,
+ PerfectMatchingEvidence,
+ TransactionFingerprintEvidence,
+ PseudonymGraphEvidence,
+ UnanimousMappingLinksEvidence,
+ IntersectionEvidence,
+ ProvenanceDistributionEvidence,
+ Subject,
+ SubjectKind,
+)
+
+
+def _require_pair(subject: Subject, target: Subject) -> None:
+ if subject == target:
+ raise ValueError("subject and target must be distinct")
+
+
+def _require_evidence(evidence: tuple[Evidence, ...]) -> None:
+ if not evidence:
+ raise ValueError("outcome requires evidence")
+
+
+@dataclass(frozen=True)
+class ClusterMerge:
+ subject: Subject
+ target: Subject
+ evidence: tuple[Evidence, ...]
+
+ def __post_init__(self) -> None:
+ _require_pair(self.subject, self.target)
+ _require_evidence(self.evidence)
+
+
+@dataclass(frozen=True)
+class MergeRefused:
+ subject: Subject
+ target: Subject
+ evidence: tuple[Evidence, ...]
+ reason: str
+
+ def __post_init__(self) -> None:
+ _require_pair(self.subject, self.target)
+ _require_evidence(self.evidence)
+ if not self.reason:
+ raise ValueError("refusal reason must not be empty")
+
+
+@dataclass(frozen=True)
+class DeclusterSplit:
+ original_cluster: Subject
+ refined_clusters: tuple[frozenset[Subject], ...]
+ evidence: tuple[Evidence, ...]
+
+ def __post_init__(self) -> None:
+ if self.original_cluster.kind is not SubjectKind.CLUSTER:
+ raise ValueError("decluster split requires a cluster subject")
+ if len(self.refined_clusters) < 2 or any(not part for part in self.refined_clusters):
+ raise ValueError("decluster split requires at least two non-empty parts")
+ flattened = [subject for part in self.refined_clusters for subject in part]
+ if len(flattened) != len(set(flattened)):
+ raise ValueError("decluster split parts must be disjoint")
+ _require_evidence(self.evidence)
+
+
+@dataclass(frozen=True)
+class Attribution:
+ subject: Subject
+ attributed_to: Subject
+ evidence: tuple[Evidence, ...]
+
+ def __post_init__(self) -> None:
+ _require_pair(self.subject, self.attributed_to)
+ _require_evidence(self.evidence)
+
+
+@dataclass(frozen=True)
+class TransitiveMembership:
+ subject: Subject
+ target: Subject
+ reason: str
+
+ def __post_init__(self) -> None:
+ _require_pair(self.subject, self.target)
+ if not self.reason:
+ raise ValueError("transitive membership reason must not be empty")
+
+
+@dataclass(frozen=True)
+class CandidateNarrowing:
+ evidence: IntersectionEvidence
+
+
+@dataclass(frozen=True)
+class NoSharedCandidates:
+ evidence: IntersectionEvidence
+
+ def __post_init__(self) -> None:
+ if self.evidence.candidates_after:
+ raise ValueError("no-shared-candidates outcome requires an empty intersection")
+
+
+@dataclass(frozen=True)
+class CompleteProvenanceMeasured:
+ evidence: ProvenanceDistributionEvidence
+
+
+@dataclass(frozen=True)
+class TruncatedProvenanceMeasured:
+ evidence: ProvenanceDistributionEvidence
+
+
+@dataclass(frozen=True)
+class Inconclusive:
+ subjects: tuple[Subject, ...]
+ reason: str
+
+ def __post_init__(self) -> None:
+ if not self.subjects:
+ raise ValueError("inconclusive outcome requires subjects")
+ if not self.reason:
+ raise ValueError("inconclusive reason must not be empty")
+
+
+@dataclass(frozen=True)
+class NodeCapped:
+ observed: int
+ limit: int
+
+ def __post_init__(self) -> None:
+ if self.limit < 1 or self.observed <= self.limit:
+ raise ValueError("node cap requires observed > positive limit")
+
+
+@dataclass(frozen=True)
+class OracleRefused:
+ reason: str
+
+ def __post_init__(self) -> None:
+ if not self.reason:
+ raise ValueError("oracle refusal reason must not be empty")
+
+
+@dataclass(frozen=True)
+class Unsupported:
+ feature: str
+ reason: str
+
+ def __post_init__(self) -> None:
+ if not self.feature or not self.reason:
+ raise ValueError("unsupported outcome requires feature and reason")
+
+
+@dataclass(frozen=True)
+class NotObserved:
+ observable: str
+
+ def __post_init__(self) -> None:
+ if not self.observable:
+ raise ValueError("observable must not be empty")
+
+
+@dataclass(frozen=True)
+class AdditiveDecayMeasured:
+ evidence: CandidateEliminationEvidence
+
+
+@dataclass(frozen=True)
+class GraphFractureMeasured:
+ evidence: GraphFractureEvidence
+
+
+@dataclass(frozen=True)
+class PersistentCorrespondentRanked:
+ evidence: CorrespondentDistributionEvidence
+ correspondent: Subject
+ margin: float
+
+ def __post_init__(self) -> None:
+ if self.correspondent not in dict(self.evidence.scores):
+ raise ValueError("ranked correspondent must occur in evidence")
+ if not isfinite(self.margin) or self.margin < 0:
+ raise ValueError("ranking margin must be finite and non-negative")
+
+
+@dataclass(frozen=True)
+class MessageAssignmentRecovered:
+ evidence: PerfectMatchingEvidence
+ assignment: tuple[tuple[Subject, Subject], ...]
+
+ def __post_init__(self) -> None:
+ if len(self.evidence.optimal_assignments) != 1:
+ raise ValueError("recovered assignment requires a unique optimum")
+ if self.assignment != self.evidence.optimal_assignments[0]:
+ raise ValueError("assignment must equal the unique optimum")
+
+
+@dataclass(frozen=True)
+class TransactionFingerprintObserved:
+ evidence: TransactionFingerprintEvidence
+ rule: str
+
+ def __post_init__(self) -> None:
+ if not self.rule:
+ raise ValueError("fingerprint rule must not be empty")
+
+
+@dataclass(frozen=True)
+class PseudonymGraphConstructed:
+ evidence: PseudonymGraphEvidence
+
+
+@dataclass(frozen=True)
+class ConditionalLinksMeasured:
+ """Unanimous links measured within, and only within, a declared mapping model."""
+
+ evidence: UnanimousMappingLinksEvidence
+
+
+@dataclass(frozen=True)
+class ReferenceOwnershipConflict:
+ """A predicted merge conflicts with distinct ownership labels supplied by a fixture."""
+
+ subject: Subject
+ target: Subject
+ evidence: CoSpendEvidence
+ subject_owner: str
+ target_owner: str
+
+ def __post_init__(self) -> None:
+ _require_pair(self.subject, self.target)
+ if self.subject not in self.evidence.inputs or self.target not in self.evidence.inputs:
+ raise ValueError("conflicting subjects must occur in the co-spend")
+ if not self.subject_owner or not self.target_owner:
+ raise ValueError("reference owner labels must not be empty")
+ if self.subject_owner == self.target_owner:
+ raise ValueError("ownership conflict requires distinct reference labels")
+
+
+Outcome: TypeAlias = Union[
+ ClusterMerge,
+ MergeRefused,
+ DeclusterSplit,
+ Attribution,
+ TransitiveMembership,
+ CandidateNarrowing,
+ NoSharedCandidates,
+ CompleteProvenanceMeasured,
+ TruncatedProvenanceMeasured,
+ Inconclusive,
+ NodeCapped,
+ OracleRefused,
+ Unsupported,
+ NotObserved,
+ AdditiveDecayMeasured,
+ GraphFractureMeasured,
+ PersistentCorrespondentRanked,
+ MessageAssignmentRecovered,
+ TransactionFingerprintObserved,
+ PseudonymGraphConstructed,
+ ConditionalLinksMeasured,
+ ReferenceOwnershipConflict,
+]
diff --git a/decluster/domain/report.py b/decluster/domain/report.py
new file mode 100644
index 00000000..e9226d5d
--- /dev/null
+++ b/decluster/domain/report.py
@@ -0,0 +1,80 @@
+"""Structured reports that preserve adversarial evidence by channel."""
+
+from dataclasses import dataclass
+from math import isfinite
+from typing import Optional
+
+from .evidence import Evidence, Subject
+from .outcome import Outcome
+
+
+@dataclass(frozen=True)
+class EvidenceChannel:
+ """Evidence emitted by one independently identifiable mechanism."""
+
+ identifier: str
+ evidence: tuple[Evidence, ...]
+
+ def __post_init__(self) -> None:
+ if not self.identifier:
+ raise ValueError("channel identifier must not be empty")
+ if not self.evidence:
+ raise ValueError("channel must contain evidence")
+
+
+@dataclass(frozen=True)
+class ExperimentalComposition:
+ """Non-normative summary whose inputs remain available in the report."""
+
+ method: str
+ channel_ids: tuple[str, ...]
+ value: float
+ unit: str
+ interpretation: str
+
+ def __post_init__(self) -> None:
+ if not self.method or not self.unit or not self.interpretation:
+ raise ValueError("composition metadata must not be empty")
+ if not self.channel_ids or len(set(self.channel_ids)) != len(self.channel_ids):
+ raise ValueError("composition requires unique channel identifiers")
+ if not isfinite(self.value):
+ raise ValueError("composition value must be finite")
+
+
+@dataclass(frozen=True)
+class AttackReport:
+ """Evidence and outcomes for one adversarial scenario.
+
+ A report is not a privacy certificate. Its channels remain separate even
+ when an experimental composition is attached.
+ """
+
+ identifier: str
+ attack: str
+ subjects: tuple[Subject, ...]
+ channels: tuple[EvidenceChannel, ...]
+ outcomes: tuple[Outcome, ...]
+ limitations: tuple[str, ...]
+ composition: Optional[ExperimentalComposition] = None
+
+ def __post_init__(self) -> None:
+ for label, value in (("identifier", self.identifier), ("attack", self.attack)):
+ if not value:
+ raise ValueError(f"{label} must not be empty")
+ if not self.subjects or len(set(self.subjects)) != len(self.subjects):
+ raise ValueError("report requires unique subjects")
+ channel_ids = tuple(channel.identifier for channel in self.channels)
+ if len(set(channel_ids)) != len(channel_ids):
+ raise ValueError("report channel identifiers must be unique")
+ if not self.outcomes:
+ raise ValueError("report requires at least one outcome")
+ if any(not limitation for limitation in self.limitations):
+ raise ValueError("limitations must contain non-empty values")
+ if self.composition is not None:
+ unknown = set(self.composition.channel_ids) - set(channel_ids)
+ if unknown:
+ raise ValueError(f"composition references unknown channels: {sorted(unknown)}")
+
+ def evidence(self) -> tuple[Evidence, ...]:
+ """Flatten evidence without discarding its channel organization."""
+ return tuple(item for channel in self.channels for item in channel.evidence)
diff --git a/decluster/em_m_comparison.py b/decluster/em_m_comparison.py
new file mode 100644
index 00000000..7570e719
--- /dev/null
+++ b/decluster/em_m_comparison.py
@@ -0,0 +1,61 @@
+"""Unsupervised per-axis Fellegi-Sunter parameter diagnostic."""
+
+from __future__ import annotations
+
+from .fingerprint_validate import LibraryScorer, reuse_pairs
+from .fs_em import agree_matrix, em_fit, oracle_m
+from .graph_deanon import auc
+
+
+def _score_pairs(scorer, positive, negative, seed):
+ positive_scores = [scorer.score(left, right) for left, right in positive]
+ negative_scores = [scorer.score(left, right) for left, right in negative]
+ return auc(positive_scores, negative_scores, seed)
+
+
+def evaluate(transactions, *, pair_cap=4000, seed=0):
+ """Fit EM without labels, then evaluate against an address-reuse proxy."""
+ positive, negative = reuse_pairs(transactions, pair_cap, seed)
+ pairs = positive + negative
+ labels = [1] * len(positive) + [0] * len(negative)
+ axes = LibraryScorer().axes
+ agreement, mask, names, collision = agree_matrix(pairs, axes)
+ fit = em_fit(agreement, mask, collision)
+ label_agreement = oracle_m(agreement, mask, labels)
+
+ per_axis = [
+ {
+ "axis": name,
+ "collision": collision[index],
+ "em": fit["m"][index],
+ "address_reuse_agreement": label_agreement[index],
+ "fixed_assumption": 0.95,
+ }
+ for index, name in enumerate(names)
+ ]
+ em_values = dict(zip(names, fit["m"]))
+ label_values = {
+ name: value if value is not None else 0.95
+ for name, value in zip(names, label_agreement)
+ }
+ pair_auc = {
+ "fixed_0_95": _score_pairs(LibraryScorer(consistency=0.95), positive, negative, seed),
+ "em": _score_pairs(LibraryScorer(consistency=em_values), positive, negative, seed),
+ "address_reuse_agreement": _score_pairs(
+ LibraryScorer(consistency=label_values), positive, negative, seed
+ ),
+ }
+ return {
+ "transactions": len(transactions),
+ "pairs": {"positive": len(positive), "negative": len(negative)},
+ "fit": {
+ "inferred_pair_fraction": fit["lam"],
+ "iterations": fit["n_iter"],
+ "posterior_auc_against_address_reuse": auc(
+ fit["r"][:len(positive)], fit["r"][len(positive):], seed
+ ),
+ },
+ "per_axis": per_axis,
+ "library_scorer_auc": pair_auc,
+ "em_minus_fixed_auc": pair_auc["em"] - pair_auc["fixed_0_95"],
+ }
diff --git a/decluster/epoch_graph.py b/decluster/epoch_graph.py
new file mode 100644
index 00000000..e63f284f
--- /dev/null
+++ b/decluster/epoch_graph.py
@@ -0,0 +1,164 @@
+"""The address graph of a block window, in a form small enough to commit.
+
+The weekly 2016 export ships 977 MB of NDJSON that this repository cannot carry: every file is
+larger than the 100 MB a git host accepts, and one of them is twice that. What the pipeline reads
+from it is narrower than what it stores — `height`, the input addresses and the output addresses.
+The transaction id is never read: contraction spans heights and the address helpers take addresses,
+so 64 hex characters per record travel for nothing.
+
+Addresses are the remaining bulk, and they do not need to be themselves. Clustering asks which
+addresses coincide, not what they are called, and the pipeline's output is invariant under any
+bijective renaming — verified against the committed block cache under both an order-preserving and
+an order-reversing map. So an address becomes an integer, and only the ones a name-emitting detector
+can recognise by prefix keep their text: on the first epoch that is 94 of 1,788,311.
+
+What this costs is honesty about what the file now is. It is a derived dataset, not a chain export:
+a reader cannot recover the addresses from it, and verifies it by running the query and the
+transform rather than by inspecting the bytes.
+"""
+
+from __future__ import annotations
+
+import json
+import lzma
+
+MAGIC = b"decluster/epoch-graph/1\n"
+# The prefixes `entities` matches on. A detector that reads values or scripts cannot fire on this
+# export at all, so keeping the prefix is enough to keep every detector it can carry.
+NAMED_PREFIXES = ("3BMEX", "bc1qmex", "1dice")
+
+
+def _varint(values):
+ out = bytearray()
+ for value in values:
+ value = (value << 1) ^ (value >> 63) if value < 0 else value << 1
+ while value >= 0x80:
+ out.append((value & 0x7F) | 0x80)
+ value >>= 7
+ out.append(value)
+ return bytes(out)
+
+
+def _read_varints(data, count):
+ values, shift, current, index = [], 0, 0, 0
+ for byte in data:
+ current |= (byte & 0x7F) << shift
+ if byte & 0x80:
+ shift += 7
+ continue
+ values.append(-(current + 1) // 2 if current & 1 else current >> 1)
+ shift = current = 0
+ index += 1
+ if index == count:
+ break
+ return values
+
+
+def _sections(blobs):
+ out = bytearray()
+ for blob in blobs:
+ out += len(blob).to_bytes(8, "little") + blob
+ return bytes(out)
+
+
+def _split(data):
+ blobs, offset = [], 0
+ while offset < len(data):
+ size = int.from_bytes(data[offset:offset + 8], "little")
+ offset += 8
+ blobs.append(data[offset:offset + size])
+ offset += size
+ return blobs
+
+
+def in_addresses(transaction):
+ return [address for vin in transaction.get("vin", ())
+ if (address := (vin.get("prevout") or {}).get("scriptpubkey_address"))]
+
+
+def out_addresses(transaction):
+ return [address for vout in transaction.get("vout", ())
+ if (address := vout.get("scriptpubkey_address"))]
+
+
+def encode(transactions, path, *, preset=6, identifiers=None, namespace="single"):
+ """Write the address graph of `transactions` to `path`. Returns what it recorded.
+
+ `identifiers` carries the address-to-integer map. Windows that will be analysed together must
+ share one: an id means nothing on its own, so two files numbered from zero make the same name
+ stand for different addresses, which invents cross-view links rather than only losing them.
+ `encode_many` is the way to get that right; passing `None` here numbers this file alone.
+ """
+ identifiers = {} if identifiers is None else identifiers
+ named = {}
+ heights, ins, outs, stream = [], [], [], []
+ for transaction in transactions:
+ inputs, outputs = in_addresses(transaction), out_addresses(transaction)
+ heights.append(transaction.get("height", 0))
+ ins.append(len(inputs))
+ outs.append(len(outputs))
+ for address in inputs + outputs:
+ identifier = identifiers.get(address)
+ if identifier is None:
+ identifier = identifiers[address] = len(identifiers)
+ if address.startswith(NAMED_PREFIXES):
+ named[identifier] = address
+ stream.append(identifier)
+
+ previous = heights[0] if heights else 0
+ deltas = []
+ for height in heights:
+ deltas.append(height - previous)
+ previous = height
+ payload = _sections([
+ json.dumps({"transactions": len(heights), "addresses": len(identifiers),
+ "namespace": namespace,
+ "first_height": heights[0] if heights else 0}, sort_keys=True).encode(),
+ _varint(deltas), _varint(ins), _varint(outs), _varint(stream),
+ json.dumps(named, sort_keys=True).encode(),
+ ])
+ with open(path, "wb") as sink:
+ sink.write(lzma.compress(MAGIC + payload, preset=preset))
+ return {"transactions": len(heights), "addresses": len(identifiers), "named": len(named)}
+
+
+def decode(path):
+ """Yield one transaction dict per record, in the order `encode` received them."""
+ with open(path, "rb") as source:
+ raw = lzma.decompress(source.read())
+ if not raw.startswith(MAGIC):
+ raise ValueError(f"{path}: not an epoch-graph file")
+ header, deltas, ins, outs, stream, named = _split(raw[len(MAGIC):])
+ meta = json.loads(header)
+ count = meta["transactions"]
+ heights, height = [], meta["first_height"]
+ for delta in _read_varints(deltas, count):
+ height += delta
+ heights.append(height)
+ n_in = _read_varints(ins, count)
+ n_out = _read_varints(outs, count)
+ identifiers = _read_varints(stream, sum(n_in) + sum(n_out))
+ labels = {int(k): v for k, v in json.loads(named).items()}
+ name = lambda identifier: labels.get(identifier) or f"a{identifier:09d}"
+
+ cursor = 0
+ for index in range(count):
+ inputs = identifiers[cursor:cursor + n_in[index]]
+ cursor += n_in[index]
+ outputs = identifiers[cursor:cursor + n_out[index]]
+ cursor += n_out[index]
+ yield {
+ "height": heights[index],
+ "vin": [{"prevout": {"scriptpubkey_address": name(i)}} for i in inputs],
+ "vout": [{"scriptpubkey_address": name(o)} for o in outputs],
+ }
+
+
+def encode_many(sources, targets, *, preset=6, namespace="epochs-2016-weekly-v1"):
+ """Encode several windows into one address namespace, so an id means the same thing in each."""
+ identifiers = {}
+ written = []
+ for source, target in zip(sources, targets):
+ written.append(encode(source(), target, preset=preset,
+ identifiers=identifiers, namespace=namespace))
+ return written, len(identifiers)
diff --git a/decluster/evidence_bundle.py b/decluster/evidence_bundle.py
new file mode 100644
index 00000000..6f9e5761
--- /dev/null
+++ b/decluster/evidence_bundle.py
@@ -0,0 +1,562 @@
+"""Content-addressed, offline evidence bundles.
+
+An index names immutable blobs. Verification never downloads, repairs or
+executes anything; reproduction is a separate capability that an index must
+declare only after its complete environment has been bundled.
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+from enum import Enum
+import json
+import os
+import platform
+from pathlib import Path
+import shutil
+import subprocess
+import sys
+import tarfile
+import tempfile
+from urllib.request import urlopen
+
+from .data_manifest import ContentIdentity, ManifestError, SHA256
+from .data_manifest import Redistribution, load_run_manifest
+from .reference_registry import load_claims, load_sources
+from .result_artifacts import content_identity
+
+
+class BundleCapability(str, Enum):
+ VERIFY_OUTPUTS = "verify_outputs"
+ REPRODUCE_RUNS = "reproduce_runs"
+
+
+class BlobRole(str, Enum):
+ RUN_MANIFEST = "run_manifest"
+ DATASET = "dataset"
+ RESULT = "result"
+ SOURCE = "source"
+ DEPENDENCY = "dependency"
+ LOCKFILE = "lockfile"
+ ENVIRONMENT = "environment"
+
+
+class BlobStatus(str, Enum):
+ VERIFIED = "verified"
+ MISSING = "missing"
+ IDENTITY_MISMATCH = "identity_mismatch"
+
+
+class AcquisitionStatus(str, Enum):
+ ALREADY_AVAILABLE = "already_available"
+ DOWNLOADED = "downloaded"
+
+
+class MaterializationStatus(str, Enum):
+ ALREADY_MATERIALIZED = "already_materialized"
+ MATERIALIZED = "materialized"
+
+
+class ReproductionStatus(str, Enum):
+ READY = "ready"
+ BLOCKED = "blocked"
+
+
+class ReadinessIssueKind(str, Enum):
+ CAPABILITY_MISSING = "capability_missing"
+ BLOB_MISSING = "blob_missing"
+ BLOB_IDENTITY_MISMATCH = "blob_identity_mismatch"
+ REQUIRED_ROLE_MISSING = "required_role_missing"
+ CANONICAL_LOCATION_MISSING = "canonical_location_missing"
+ MIRROR_MISSING = "mirror_missing"
+ RUN_MANIFEST_MISSING = "run_manifest_missing"
+ DEPENDENCY_EDITABLE = "dependency_editable"
+ DEPENDENCY_LICENSE_UNKNOWN = "dependency_license_unknown"
+ DEPENDENCY_REDISTRIBUTION_NOT_ALLOWED = "dependency_redistribution_not_allowed"
+ DEPENDENCY_LOCK_MISSING = "dependency_lock_missing"
+ ENVIRONMENT_LOCK_MISSING = "environment_lock_missing"
+
+
+@dataclass(frozen=True)
+class BundleBlob:
+ name: str
+ role: BlobRole
+ content: ContentIdentity
+ canonical_location: str | None
+ mirrors: tuple[str, ...]
+
+
+@dataclass(frozen=True)
+class EvidenceBundle:
+ id: str
+ capabilities: tuple[BundleCapability, ...]
+ runs: tuple[str, ...]
+ blobs: tuple[BundleBlob, ...]
+ limitations: tuple[str, ...]
+
+
+@dataclass(frozen=True)
+class BlobVerification:
+ blob: BundleBlob
+ status: BlobStatus
+ actual: ContentIdentity | None
+
+
+@dataclass(frozen=True)
+class BootstrapResult:
+ blob: BundleBlob
+ acquisition: AcquisitionStatus
+ materialization: MaterializationStatus
+ path: Path
+ source_url: str | None
+
+
+@dataclass(frozen=True)
+class ReadinessIssue:
+ kind: ReadinessIssueKind
+ subject: str
+ detail: str
+
+
+@dataclass(frozen=True)
+class ReproductionReadiness:
+ status: ReproductionStatus
+ issues: tuple[ReadinessIssue, ...]
+
+
+class BundleBootstrapError(RuntimeError):
+ pass
+
+
+class BundleReproductionError(RuntimeError):
+ pass
+
+
+def blob_path(store, sha256: str) -> Path:
+ if not SHA256.fullmatch(sha256):
+ raise ValueError("expected lowercase SHA-256")
+ return Path(store) / "sha256" / sha256[:2] / sha256[2:]
+
+
+def ingest_blob(source, store) -> ContentIdentity:
+ """Atomically add a file to a content-addressed store without overwriting it."""
+ source = Path(source)
+ identity = content_identity(source)
+ destination = blob_path(store, identity.sha256)
+ destination.parent.mkdir(parents=True, exist_ok=True)
+ if destination.exists():
+ if content_identity(destination) != identity:
+ raise OSError(f"corrupt blob already exists: {destination}")
+ return identity
+ with tempfile.NamedTemporaryFile(dir=destination.parent, delete=False) as temporary:
+ temporary_path = Path(temporary.name)
+ with source.open("rb") as stream:
+ shutil.copyfileobj(stream, temporary)
+ temporary.flush()
+ os.fsync(temporary.fileno())
+ try:
+ if content_identity(temporary_path) != identity:
+ raise OSError("source changed while being ingested")
+ os.replace(temporary_path, destination)
+ except BaseException:
+ temporary_path.unlink(missing_ok=True)
+ raise
+ return identity
+
+
+def _safe_target(root, name):
+ root = Path(root).resolve()
+ target = (root / name).resolve()
+ try:
+ target.relative_to(root)
+ except ValueError as exc:
+ raise ManifestError(f"bundle blob name escapes target root: {name!r}") from exc
+ return target
+
+
+def _https(value, where, *, nullable=False):
+ if nullable and value is None:
+ return None
+ if not isinstance(value, str) or not value.startswith("https://"):
+ raise ManifestError(f"{where}: expected https URL")
+ return value
+
+
+def load_bundle(path) -> EvidenceBundle:
+ where = str(path)
+ try:
+ raw = json.loads(Path(path).read_text(encoding="utf-8"))
+ except (OSError, json.JSONDecodeError) as exc:
+ raise ManifestError(f"{where}: cannot read bundle: {exc}") from exc
+ required = {"schema_version", "id", "capabilities", "runs", "blobs", "limitations"}
+ if not isinstance(raw, dict) or set(raw) != required or raw.get("schema_version") != 1:
+ raise ManifestError(f"{where}: invalid bundle envelope")
+
+ def unique_texts(value, field):
+ if not isinstance(value, list) or not value or any(not isinstance(x, str) or not x for x in value):
+ raise ManifestError(f"{where}.{field}: expected non-empty string array")
+ if len(value) != len(set(value)):
+ raise ManifestError(f"{where}.{field}: duplicate value")
+ return tuple(value)
+
+ try:
+ capabilities = tuple(BundleCapability(x) for x in unique_texts(raw["capabilities"], "capabilities"))
+ except ValueError as exc:
+ raise ManifestError(f"{where}.capabilities: invalid capability") from exc
+ runs = unique_texts(raw["runs"], "runs")
+ limitations = tuple(raw["limitations"])
+ if any(not isinstance(x, str) or not x for x in limitations):
+ raise ManifestError(f"{where}.limitations: expected string array")
+ blobs = []
+ names = set()
+ identities = set()
+ for index, item in enumerate(raw["blobs"]):
+ item_where = f"{where}.blobs[{index}]"
+ expected_keys = {"name", "role", "bytes", "sha256", "locations"}
+ if not isinstance(item, dict) or set(item) != expected_keys:
+ raise ManifestError(f"{item_where}: invalid blob")
+ name, digest, size = item["name"], item["sha256"], item["bytes"]
+ if not isinstance(name, str) or not name or name in names:
+ raise ManifestError(f"{item_where}.name: invalid or duplicate")
+ if not isinstance(size, int) or isinstance(size, bool) or size < 0:
+ raise ManifestError(f"{item_where}.bytes: expected non-negative integer")
+ if not isinstance(digest, str) or not SHA256.fullmatch(digest):
+ raise ManifestError(f"{item_where}.sha256: expected lowercase SHA-256")
+ try:
+ role = BlobRole(item["role"])
+ except ValueError as exc:
+ raise ManifestError(f"{item_where}.role: invalid role") from exc
+ identity = (size, digest)
+ if identity in identities:
+ raise ManifestError(f"{item_where}: duplicate content identity")
+ names.add(name)
+ identities.add(identity)
+ locations = item["locations"]
+ if not isinstance(locations, dict) or set(locations) != {"canonical", "mirrors"}:
+ raise ManifestError(f"{item_where}.locations: invalid locations")
+ canonical = _https(locations["canonical"], f"{item_where}.locations.canonical", nullable=True)
+ mirrors = locations["mirrors"]
+ if not isinstance(mirrors, list) or len(mirrors) != len(set(mirrors)):
+ raise ManifestError(f"{item_where}.locations.mirrors: expected unique array")
+ mirrors = tuple(
+ _https(url, f"{item_where}.locations.mirrors[{position}]")
+ for position, url in enumerate(mirrors)
+ )
+ _safe_target(Path.cwd(), name)
+ blobs.append(BundleBlob(name, role, ContentIdentity(size, digest), canonical, mirrors))
+ if not blobs:
+ raise ManifestError(f"{where}.blobs: expected non-empty array")
+ roles = {blob.role for blob in blobs}
+ if BlobRole.RUN_MANIFEST not in roles:
+ raise ManifestError(f"{where}.blobs: bundle must contain a run manifest")
+ if BundleCapability.REPRODUCE_RUNS in capabilities:
+ required_roles = {BlobRole.SOURCE, BlobRole.LOCKFILE, BlobRole.ENVIRONMENT}
+ missing = sorted(role.value for role in required_roles - roles)
+ if missing:
+ raise ManifestError(f"{where}.blobs: reproduction capability missing roles {missing}")
+ return EvidenceBundle(raw["id"], capabilities, runs, tuple(blobs), limitations)
+
+
+def verify_bundle(bundle: EvidenceBundle, store) -> tuple[BlobVerification, ...]:
+ checks = []
+ for blob in bundle.blobs:
+ path = blob_path(store, blob.content.sha256)
+ if not path.is_file():
+ checks.append(BlobVerification(blob, BlobStatus.MISSING, None))
+ continue
+ actual = content_identity(path)
+ status = BlobStatus.VERIFIED if actual == blob.content else BlobStatus.IDENTITY_MISMATCH
+ checks.append(BlobVerification(blob, status, actual))
+ return tuple(checks)
+
+
+def assess_reproduction_readiness(bundle: EvidenceBundle, store, root) -> ReproductionReadiness:
+ """Report every known obstacle to independent cold-start reproduction.
+
+ This is deliberately stricter than bundle verification. A ready bundle must
+ be downloadable from a canonical location and a mirror, contain the full
+ execution environment, and avoid mutable or legally ambiguous dependencies.
+ """
+ from .data_catalog import load_dataset_catalog
+
+ root = Path(root)
+ issues = []
+
+ def add(kind, subject, detail):
+ issues.append(ReadinessIssue(kind, subject, detail))
+
+ if BundleCapability.REPRODUCE_RUNS not in bundle.capabilities:
+ add(ReadinessIssueKind.CAPABILITY_MISSING, bundle.id, "reproduce_runs")
+
+ checks = verify_bundle(bundle, store)
+ checks_by_name = {check.blob.name: check for check in checks}
+ for check in checks:
+ if check.status is BlobStatus.MISSING:
+ add(ReadinessIssueKind.BLOB_MISSING, check.blob.name, "content-addressed blob")
+ elif check.status is BlobStatus.IDENTITY_MISMATCH:
+ add(ReadinessIssueKind.BLOB_IDENTITY_MISMATCH, check.blob.name, "SHA-256 or size")
+
+ roles = {blob.role for blob in bundle.blobs}
+ for role in sorted(
+ {BlobRole.SOURCE, BlobRole.LOCKFILE, BlobRole.ENVIRONMENT} - roles,
+ key=lambda item: item.value,
+ ):
+ add(ReadinessIssueKind.REQUIRED_ROLE_MISSING, role.value, "bundle blob role")
+
+ for blob in bundle.blobs:
+ if blob.canonical_location is None:
+ add(ReadinessIssueKind.CANONICAL_LOCATION_MISSING, blob.name, "public HTTPS location")
+ if not blob.mirrors:
+ add(ReadinessIssueKind.MIRROR_MISSING, blob.name, "independent HTTPS mirror")
+
+ sources = load_sources(root / "catalog" / "ctp-sources.json")
+ claims = load_claims(root / "catalog" / "ctp-claims.json", {source.id for source in sources})
+ datasets = {dataset.id: dataset for dataset in load_dataset_catalog(root)}
+ run_blobs = {blob.name: blob for blob in bundle.blobs if blob.role is BlobRole.RUN_MANIFEST}
+ for run_id in bundle.runs:
+ name = f"catalog/runs/{run_id}.json"
+ if name not in run_blobs:
+ add(ReadinessIssueKind.RUN_MANIFEST_MISSING, run_id, name)
+ continue
+ if checks_by_name[name].status is not BlobStatus.VERIFIED:
+ continue
+ run = load_run_manifest(
+ blob_path(store, run_blobs[name].content.sha256),
+ claim_ids={claim.id for claim in claims},
+ datasets=datasets,
+ )
+ if run.lock_digest is None:
+ add(ReadinessIssueKind.ENVIRONMENT_LOCK_MISSING, run.id, "environment.lock_digest")
+ for dependency in run.dependencies:
+ subject = f"{run.id}:{dependency.name}"
+ if dependency.editable:
+ add(ReadinessIssueKind.DEPENDENCY_EDITABLE, subject, dependency.source)
+ if dependency.license.strip().lower() in {"unknown", "none", "unlicensed"}:
+ add(ReadinessIssueKind.DEPENDENCY_LICENSE_UNKNOWN, subject, dependency.license)
+ if dependency.redistribution is not Redistribution.ALLOWED:
+ add(
+ ReadinessIssueKind.DEPENDENCY_REDISTRIBUTION_NOT_ALLOWED,
+ subject,
+ dependency.redistribution.value,
+ )
+ if dependency.lock_sha256 is None:
+ add(ReadinessIssueKind.DEPENDENCY_LOCK_MISSING, subject, "dependency lock SHA-256")
+
+ status = ReproductionStatus.READY if not issues else ReproductionStatus.BLOCKED
+ return ReproductionReadiness(status, tuple(issues))
+
+
+def _fetch_blob(blob, store, opener):
+ destination = blob_path(store, blob.content.sha256)
+ if destination.is_file() and content_identity(destination) == blob.content:
+ return AcquisitionStatus.ALREADY_AVAILABLE, destination, None
+ destination.parent.mkdir(parents=True, exist_ok=True)
+ failures = []
+ for url in tuple(x for x in (blob.canonical_location, *blob.mirrors) if x):
+ with tempfile.NamedTemporaryFile(dir=destination.parent, delete=False) as temporary:
+ temporary_path = Path(temporary.name)
+ size = 0
+ try:
+ with opener(url) as response:
+ while chunk := response.read(1024 * 1024):
+ size += len(chunk)
+ if size > blob.content.bytes:
+ break
+ temporary.write(chunk)
+ temporary.flush()
+ os.fsync(temporary.fileno())
+ except (OSError, ValueError):
+ failures.append(f"{url}=transport_error")
+ temporary_path.unlink(missing_ok=True)
+ continue
+ try:
+ if size != blob.content.bytes or content_identity(temporary_path) != blob.content:
+ failures.append(f"{url}=identity_mismatch")
+ continue
+ os.replace(temporary_path, destination)
+ return AcquisitionStatus.DOWNLOADED, destination, url
+ finally:
+ temporary_path.unlink(missing_ok=True)
+ raise BundleBootstrapError(
+ f"{blob.name}: no verified source available: {', '.join(failures) or 'no locations'}"
+ )
+
+
+def _materialize_blob(blob, source, root, *, replace):
+ target = _safe_target(root, blob.name)
+ if target.is_file():
+ if content_identity(target) == blob.content:
+ return MaterializationStatus.ALREADY_MATERIALIZED, target
+ if not replace:
+ raise BundleBootstrapError(f"{blob.name}: destination_identity_mismatch: {target}")
+ elif target.exists():
+ raise BundleBootstrapError(f"{blob.name}: destination_identity_mismatch: {target}")
+ target.parent.mkdir(parents=True, exist_ok=True)
+ with tempfile.NamedTemporaryFile(dir=target.parent, delete=False) as temporary:
+ temporary_path = Path(temporary.name)
+ with source.open("rb") as stream:
+ shutil.copyfileobj(stream, temporary)
+ temporary.flush()
+ os.fsync(temporary.fileno())
+ try:
+ if content_identity(temporary_path) != blob.content:
+ raise BundleBootstrapError(f"{blob.name}: store_identity_mismatch: {source}")
+ os.replace(temporary_path, target)
+ finally:
+ temporary_path.unlink(missing_ok=True)
+ return MaterializationStatus.MATERIALIZED, target
+
+
+def bootstrap_bundle(bundle: EvidenceBundle, store, root, *, opener=urlopen, replace=False):
+ """Acquire and materialize blobs in index order, raising on the first failure."""
+ results = []
+ for blob in bundle.blobs:
+ acquisition, source, source_url = _fetch_blob(blob, store, opener)
+ materialization, target = _materialize_blob(blob, source, root, replace=replace)
+ results.append(BootstrapResult(blob, acquisition, materialization, target, source_url))
+ return tuple(results)
+
+
+def _load_environment(path):
+ where = str(path)
+ try:
+ raw = json.loads(Path(path).read_text(encoding="utf-8"))
+ except (OSError, json.JSONDecodeError) as exc:
+ raise BundleReproductionError(f"{where}: cannot read environment: {exc}") from exc
+ required = {
+ "schema_version", "python", "source_archive", "project_root", "requirements",
+ "dependency_directory",
+ }
+ if not isinstance(raw, dict) or set(raw) != required or raw["schema_version"] != 1:
+ raise BundleReproductionError(f"{where}: invalid reproduction environment")
+ python = raw["python"]
+ if not isinstance(python, dict) or set(python) != {
+ "implementation", "version", "system", "machine",
+ }:
+ raise BundleReproductionError(f"{where}.python: invalid interpreter contract")
+ for field in ("implementation", "version", "system", "machine"):
+ if not isinstance(python[field], str) or not python[field]:
+ raise BundleReproductionError(f"{where}.python.{field}: expected non-empty string")
+ for field in ("source_archive", "project_root", "requirements", "dependency_directory"):
+ if not isinstance(raw[field], str) or not raw[field]:
+ raise BundleReproductionError(f"{where}.{field}: expected non-empty string")
+ return raw
+
+
+def _require_compatible_interpreter(contract):
+ actual = {
+ "implementation": platform.python_implementation().lower(),
+ "version": f"{sys.version_info.major}.{sys.version_info.minor}",
+ "system": platform.system(),
+ "machine": platform.machine(),
+ }
+ if actual != contract:
+ raise BundleReproductionError(
+ f"incompatible interpreter: expected {contract!r}, found {actual!r}"
+ )
+
+
+def _extract_source(archive, destination):
+ destination = Path(destination).resolve()
+ destination.mkdir(parents=True, exist_ok=False)
+ with tarfile.open(archive, mode="r:") as source:
+ for member in source.getmembers():
+ if not (member.isfile() or member.isdir()):
+ raise BundleReproductionError(
+ f"source archive contains unsupported entry: {member.name!r}"
+ )
+ _safe_target(destination, member.name)
+ extraction_options = {"filter": "fully_trusted"} if sys.version_info >= (3, 12) else {}
+ source.extractall(destination, **extraction_options)
+
+
+def _run_checked(argv, *, cwd, env=None):
+ try:
+ subprocess.run(argv, cwd=cwd, env=env, check=True)
+ except (OSError, subprocess.CalledProcessError) as exc:
+ raise BundleReproductionError(f"command failed: {argv!r}") from exc
+
+
+def _resolve_dataset_arguments(argv, manifest, datasets, root):
+ """Resolve declared dataset paths against the materialized bundle root."""
+ replacements = {}
+ for dataset_input in manifest.datasets:
+ dataset = datasets[dataset_input.id]
+ replacements[dataset.local_path] = str(_safe_target(root, dataset.local_path))
+ return [replacements.get(argument, argument) for argument in argv]
+
+
+def reproduce_bundle(bundle: EvidenceBundle, root, work, *, interpreter=sys.executable):
+ """Reexecute every declared run using only materialized bundle contents.
+
+ Network access is disabled at the package-manager layer. Callers performing a cold-start audit
+ should additionally isolate the process from the network at the operating-system level.
+ """
+ if BundleCapability.REPRODUCE_RUNS not in bundle.capabilities:
+ raise BundleReproductionError(f"{bundle.id}: reproduce_runs capability is not declared")
+ root = Path(root).resolve()
+ environments = [blob for blob in bundle.blobs if blob.role is BlobRole.ENVIRONMENT]
+ if len(environments) != 1:
+ raise BundleReproductionError(f"{bundle.id}: expected exactly one environment blob")
+ specification = _load_environment(_safe_target(root, environments[0].name))
+ _require_compatible_interpreter(specification["python"])
+
+ work = Path(work).resolve()
+ if work.exists():
+ raise BundleReproductionError(f"work directory already exists: {work}")
+ work.mkdir(parents=True)
+ source_root = work / "source"
+ _extract_source(_safe_target(root, specification["source_archive"]), source_root)
+ checkout = _safe_target(source_root, specification["project_root"])
+ if not checkout.is_dir():
+ raise BundleReproductionError(f"project root is missing from source archive: {checkout}")
+
+ virtualenv = work / "venv"
+ _run_checked([interpreter, "-m", "venv", str(virtualenv)], cwd=work)
+ python = virtualenv / "bin" / "python"
+ if not python.is_file():
+ raise BundleReproductionError("the declared environment requires a POSIX Python layout")
+ requirements = _safe_target(root, specification["requirements"])
+ dependencies = _safe_target(root, specification["dependency_directory"])
+ _run_checked([
+ str(python), "-m", "pip", "install", "--no-index", "--no-cache-dir",
+ "--disable-pip-version-check", "--require-hashes",
+ "--find-links", str(dependencies), "--requirement", str(requirements),
+ ], cwd=work)
+
+ environment = os.environ.copy()
+ environment["PYTHONPATH"] = str(checkout)
+ sources = load_sources(checkout / "catalog" / "ctp-sources.json")
+ claims = load_claims(
+ checkout / "catalog" / "ctp-claims.json", {source.id for source in sources}
+ )
+ from .data_catalog import load_dataset_catalog
+ datasets = {dataset.id: dataset for dataset in load_dataset_catalog(checkout)}
+ outputs = []
+ for run_id in bundle.runs:
+ manifest = load_run_manifest(
+ _safe_target(root, f"catalog/runs/{run_id}.json"),
+ claim_ids={claim.id for claim in claims},
+ datasets=datasets,
+ )
+ argv = _resolve_dataset_arguments(
+ [str(python), *manifest.argv[1:]], manifest, datasets, root
+ )
+ _run_checked(argv, cwd=checkout, env=environment)
+ verification_argv = _resolve_dataset_arguments(
+ [str(python), *manifest.verification.argv[1:]],
+ manifest,
+ datasets,
+ root,
+ )
+ _run_checked(verification_argv, cwd=checkout, env=environment)
+ for output in manifest.outputs:
+ path = _safe_target(checkout, output.path)
+ actual = content_identity(path)
+ expected = ContentIdentity(output.bytes, output.sha256)
+ if actual != expected:
+ raise BundleReproductionError(
+ f"{run_id}: reproduced output identity mismatch: {output.path}"
+ )
+ outputs.append(path)
+ return tuple(outputs)
diff --git a/decluster/experiments/__init__.py b/decluster/experiments/__init__.py
new file mode 100644
index 00000000..a93a4af9
--- /dev/null
+++ b/decluster/experiments/__init__.py
@@ -0,0 +1 @@
+"""Reproducible experiment entry points built on the analysis kernels."""
diff --git a/decluster/experiments/amount_channel_summary.py b/decluster/experiments/amount_channel_summary.py
new file mode 100644
index 00000000..4e9f3f34
--- /dev/null
+++ b/decluster/experiments/amount_channel_summary.py
@@ -0,0 +1,222 @@
+"""Compose the amount-channel report exclusively from canonical upstream artifacts."""
+
+from __future__ import annotations
+
+import argparse
+import hashlib
+import json
+from pathlib import Path
+
+from ..result_artifacts import canonical_json_bytes, write_canonical_json
+
+EXPERIMENT_ID = "amount-channel-survey-v1"
+COMPONENTS = {
+ "local": (
+ "results/artifacts/amount-local-channels-v1.json",
+ "amount-local-channels-v1",
+ "210ecb06ecaa32cc4cabe03ced8b85b0882a824dca2c361bd2c5dede7dcccf02",
+ ),
+ "subset_sum": (
+ "results/artifacts/amount-subset-sum-v1.json",
+ "amount-subset-sum-v1",
+ "240592b5af814fbcd28b06221805b934424170119da5e62e9da0281944377a9d",
+ ),
+ "per_coin": (
+ "results/artifacts/amount-per-coin-v1.json",
+ "amount-per-coin-v1",
+ "2e75b1330181e7cbf8539a357e2bd3ad650c4d2d207c7a47ec687e991e24a622",
+ ),
+ "mapping_family": (
+ "results/artifacts/amount-mapping-family-v1.json",
+ "amount-mapping-family-v1",
+ "11f0ec645ea1be334dc7d24fbfc31047b234f335f48b5a514960472c7444b7d6",
+ ),
+ "pairwise_family": (
+ "results/artifacts/amount-pairwise-family-v1.json",
+ "amount-pairwise-family-v1",
+ "83f6a9e8501ee4df45f08b8c8ffc2f7265d40bf9add6d3f5093d712657c9dbf5",
+ ),
+}
+
+
+class VerificationError(ValueError):
+ """An input or generated result violates the composition contract."""
+
+
+def _load_component(path, expected_experiment, expected_sha256):
+ source = Path(path)
+ try:
+ payload = source.read_bytes()
+ value = json.loads(payload)
+ except (OSError, json.JSONDecodeError) as exc:
+ raise VerificationError(f"cannot read component {path}: {exc}") from exc
+ digest = hashlib.sha256(payload).hexdigest()
+ if digest != expected_sha256:
+ raise VerificationError(
+ f"component {path} has SHA-256 {digest}, expected {expected_sha256}"
+ )
+ if not isinstance(value, dict) or value.get("experiment") != expected_experiment:
+ raise VerificationError(f"component {path} has the wrong experiment identity")
+ return value
+
+
+def build_report(paths=None):
+ paths = paths or {name: spec[0] for name, spec in COMPONENTS.items()}
+ components = {}
+ identities = {}
+ for name, (_default, experiment, digest) in COMPONENTS.items():
+ path = paths[name]
+ components[name] = _load_component(path, experiment, digest)
+ identities[name] = {"experiment": experiment, "sha256": digest}
+
+ local = components["local"]["report"]
+ subset_sum = components["subset_sum"]["report"]
+ per_coin = components["per_coin"]["report"]
+ mapping = components["mapping_family"]["report"]
+ pairwise = components["pairwise_family"]["report"]
+ return {
+ "components": identities,
+ "population": local["population"],
+ "local_channels": {
+ "coinjoin": local["coinjoin"],
+ "unnecessary_input": local["unnecessary_input"],
+ "subtransaction_roundness": local["subtransaction_roundness"],
+ "conservation": local["conservation"],
+ },
+ "transaction_subset_sum": subset_sum["outcomes"],
+ "per_coin_density": per_coin["per_coin_density"],
+ "per_coin_candidates": {
+ "ungated": per_coin["ungated_diagnostic"],
+ "transaction_gated": per_coin["transaction_gated"],
+ },
+ "mapping_family": {
+ "outcomes": mapping["outcomes"],
+ "restricted_family": mapping["restricted_family"],
+ },
+ "pairwise_family": {
+ "outcomes": pairwise["outcomes"],
+ "restricted_family_rows": pairwise["restricted_family_rows"],
+ },
+ }
+
+
+def build_artifact(paths=None):
+ return {
+ "schema_version": 1,
+ "experiment": EXPERIMENT_ID,
+ "report": build_report(paths),
+ "limitations": [
+ "this artifact composes upstream results and performs no mechanism calculation",
+ "component SHA-256 identities are mandatory",
+ "the five components measure different objects and are not combined into one score",
+ "DSS mapping and pairwise claims remain restricted to DSS's mapping family",
+ "the Bitcoin snapshot covers one 137-block interval from 2023",
+ "this summary is not a privacy certificate or CoinScore",
+ ],
+ }
+
+
+def load_artifact(path):
+ try:
+ with Path(path).open(encoding="utf-8") as source:
+ value = json.load(source)
+ except (OSError, json.JSONDecodeError) as exc:
+ raise VerificationError(f"cannot read artifact {path}: {exc}") from exc
+ if not isinstance(value, dict):
+ raise VerificationError("artifact root must be an object")
+ return value
+
+
+def verify_artifact(artifact, *, paths=None):
+ if artifact.get("schema_version") != 1 or artifact.get("experiment") != EXPERIMENT_ID:
+ raise VerificationError("unsupported amount-channel summary identity")
+ measured = build_artifact(paths)
+ if canonical_json_bytes(artifact) != canonical_json_bytes(measured):
+ raise VerificationError("artifact differs from the canonical component composition")
+ return measured
+
+
+def render_markdown(artifact):
+ report = artifact["report"]
+ population = report["population"]
+ local = report["local_channels"]
+ subset_sum = report["transaction_subset_sum"]
+ per_coin = report["per_coin_candidates"]
+ mapping = report["mapping_family"]
+ pairwise = report["pairwise_family"]
+ return "\n".join([
+ "# Amount-channel survey",
+ "",
+ "Generated only from five SHA-256-pinned canonical artifacts. Do not edit manually.",
+ "",
+ f"The snapshot contains {population['transactions']:,} transactions and "
+ f"{population['multi_input']:,} complete multi-input transactions.",
+ "",
+ "## Independent local channels",
+ "",
+ f"CoinJoin shape fires {local['coinjoin']['shape_detected']:,} times and de-mix resolves "
+ f"{local['coinjoin']['demixed']:,}. Unnecessary-input fires "
+ f"{local['unnecessary_input']['fires']:,} times. Roundness ranks "
+ f"{local['subtransaction_roundness']['ranked']:,} 2×2 transactions, with "
+ f"{local['subtransaction_roundness']['top_tied']:,} top ties. The conservation probe "
+ f"forces an output value {local['conservation']['forced_output_value']:,} times under its "
+ "declared largest-input assumption.",
+ "",
+ "## Subset-sum diagnostics",
+ "",
+ f"Transaction W(E) returns {subset_sum['by_kind'].get('exact', 0):,} exact and "
+ f"{subset_sum['by_kind'].get('unknown', 0):,} unknown outcomes; "
+ f"{subset_sum['guaranteed_nonzero_log_w']:,} expose a usable conservative nonzero log W.",
+ "",
+ f"Per-coin density yields {per_coin['ungated']['candidates']['total']:,} raw candidates. "
+ f"After the transaction gate, {per_coin['transaction_gated']['candidates']['total']:,} "
+ f"remain in {per_coin['transaction_gated']['candidate_transactions']:,} transactions.",
+ "",
+ "## Restricted DSS mapping family",
+ "",
+ f"Mapping analysis answers {mapping['outcomes']['answered']:,} of 250 transactions; "
+ f"{mapping['restricted_family']['entropy_bins_bits']['zero']:,} answered families have "
+ "zero-bit entropy. Pairwise analysis returns "
+ f"{pairwise['restricted_family_rows']['returned']:,} rows, including "
+ f"{pairwise['restricted_family_rows']['singleton_support']:,} singleton-support rows.",
+ "",
+ "Entropy, certainty and support in the final section apply only to DSS's restricted "
+ "mapping family. The components remain separate and are not a privacy score.",
+ "",
+ ])
+
+
+def _parser():
+ parser = argparse.ArgumentParser(description=__doc__)
+ for name, (default, _experiment, _digest) in COMPONENTS.items():
+ parser.add_argument(f"--{name.replace('_', '-')}", default=default)
+ commands = parser.add_subparsers(dest="command", required=True)
+ reproduce = commands.add_parser("reproduce")
+ reproduce.add_argument("--artifact", required=True)
+ reproduce.add_argument("--markdown", required=True)
+ verify = commands.add_parser("verify")
+ verify.add_argument("--artifact", required=True)
+ verify.add_argument("--markdown")
+ return parser
+
+
+def main(argv=None):
+ args = _parser().parse_args(argv)
+ paths = {name: getattr(args, name) for name in COMPONENTS}
+ if args.command == "reproduce":
+ artifact = build_artifact(paths)
+ write_canonical_json(args.artifact, artifact)
+ destination = Path(args.markdown)
+ destination.parent.mkdir(parents=True, exist_ok=True)
+ destination.write_text(render_markdown(artifact), encoding="utf-8")
+ else:
+ artifact = verify_artifact(load_artifact(args.artifact), paths=paths)
+ if args.markdown is not None:
+ actual = Path(args.markdown).read_text(encoding="utf-8")
+ if actual != render_markdown(artifact):
+ raise VerificationError("Markdown differs from canonical rendering")
+ return 0
+
+
+if __name__ == "__main__":
+ raise SystemExit(main())
diff --git a/decluster/experiments/amount_local_channels.py b/decluster/experiments/amount_local_channels.py
new file mode 100644
index 00000000..1291870e
--- /dev/null
+++ b/decluster/experiments/amount_local_channels.py
@@ -0,0 +1,184 @@
+"""Measure local refuse-only amount channels without DSS or mapping enumeration."""
+
+from __future__ import annotations
+
+import argparse
+import json
+from collections import Counter
+from pathlib import Path
+
+from ..coinjoin_demix import coinjoin_demix
+from ..conservation import forced_in_round
+from ..extractors import x_uih
+from ..measure import load_ndjson
+from ..monitor import is_coinjoin
+from ..result_artifacts import canonical_json_bytes, write_canonical_json
+from ..subtransaction import subtransactions
+
+EXPERIMENT_ID = "amount-local-channels-v1"
+DATASET = "data/amount-channel-812695-812831-v1.json"
+
+
+class VerificationError(ValueError):
+ """A stored result differs from a fresh execution."""
+
+
+def _complete(transaction):
+ inputs = transaction.get("vin") or []
+ outputs = transaction.get("vout") or []
+ return (
+ bool(inputs)
+ and all((item.get("prevout") or {}).get("value") is not None for item in inputs)
+ and all(item.get("value") is not None for item in outputs)
+ )
+
+
+def _demixes(transaction):
+ inputs = [item["prevout"]["value"] for item in transaction["vin"]]
+ outputs = [item["value"] for item in transaction.get("vout") or []]
+ assignment = coinjoin_demix(inputs, outputs)
+ return len(set(assignment.values())) >= 2
+
+
+def build_report(dataset=DATASET):
+ transactions = [transaction for transaction, _height in load_ndjson(dataset)]
+ usable = [transaction for transaction in transactions if _complete(transaction)]
+ multi = [transaction for transaction in usable if len(transaction["vin"]) >= 2]
+
+ shape = sum(is_coinjoin(transaction) for transaction in multi)
+ demixed = sum(_demixes(transaction) for transaction in multi)
+ uih = Counter(x_uih(transaction) for transaction in multi)
+
+ two_by_two = [
+ transaction
+ for transaction in usable
+ if len(transaction["vin"]) == 2 and len(transaction.get("vout") or []) == 2
+ ]
+ ranked = 0
+ tied = 0
+ for transaction in two_by_two:
+ readings, _bits = subtransactions(transaction)
+ if not readings:
+ continue
+ ranked += 1
+ tied += len(readings) > 1 and readings[0][1] == readings[1][1]
+
+ forced = 0
+ for transaction in multi:
+ largest_input = max(
+ item["prevout"]["value"] for item in transaction["vin"]
+ )
+ forced += bool(forced_in_round(transaction, largest_input))
+
+ return {
+ "population": {
+ "transactions": len(transactions),
+ "complete_amounts": len(usable),
+ "multi_input": len(multi),
+ "two_input_two_output": len(two_by_two),
+ },
+ "coinjoin": {"shape_detected": shape, "demixed": demixed},
+ "unnecessary_input": {
+ "fires": len(multi) - uih.get("none", 0),
+ "classes": dict(sorted(uih.items())),
+ },
+ "subtransaction_roundness": {"ranked": ranked, "top_tied": tied},
+ "conservation": {
+ "known_participant": "largest input value",
+ "forced_output_value": forced,
+ },
+ }
+
+
+def build_artifact(dataset=DATASET):
+ return {
+ "schema_version": 1,
+ "experiment": EXPERIMENT_ID,
+ "corpus": "public Bitcoin transaction snapshot",
+ "report": build_report(dataset),
+ "limitations": [
+ "the snapshot covers one 137-block interval from 2023",
+ "no recognized JoinMarket round appears in the measured population",
+ "roundness ties show abstention, not ownership truth",
+ "the conservation probe assumes the largest input is the known participant",
+ "this run excludes subset-sum, DSS, mapping entropy, and score composition",
+ ],
+ }
+
+
+def load_artifact(path):
+ try:
+ with Path(path).open(encoding="utf-8") as source:
+ value = json.load(source)
+ except (OSError, json.JSONDecodeError) as exc:
+ raise VerificationError(f"cannot read artifact {path}: {exc}") from exc
+ if not isinstance(value, dict):
+ raise VerificationError("artifact root must be an object")
+ return value
+
+
+def verify_artifact(artifact, *, dataset=DATASET):
+ if artifact.get("schema_version") != 1 or artifact.get("experiment") != EXPERIMENT_ID:
+ raise VerificationError("unsupported local amount-channel artifact identity")
+ measured = build_artifact(dataset)
+ if canonical_json_bytes(artifact) != canonical_json_bytes(measured):
+ raise VerificationError("artifact differs from a fresh experiment execution")
+ return measured
+
+
+def render_markdown(artifact):
+ report = artifact["report"]
+ population = report["population"]
+ return "\n".join([
+ "# Local amount-channel observations",
+ "",
+ "Generated from the canonical experiment artifact. Do not edit manually.",
+ "",
+ f"The snapshot contains {population['transactions']:,} transactions and "
+ f"{population['multi_input']:,} complete multi-input transactions.",
+ "",
+ f"CoinJoin shape fires {report['coinjoin']['shape_detected']:,} times; de-mix "
+ f"resolves {report['coinjoin']['demixed']:,}. Unnecessary-input fires "
+ f"{report['unnecessary_input']['fires']:,} times.",
+ "",
+ f"The 2×2 roundness model ranks {report['subtransaction_roundness']['ranked']:,} "
+ f"transactions and ties at the top for "
+ f"{report['subtransaction_roundness']['top_tied']:,}.",
+ "",
+ "This run excludes subset-sum, DSS, mapping entropy, and score composition.",
+ "",
+ ])
+
+
+def _parser():
+ parser = argparse.ArgumentParser(description=__doc__)
+ parser.add_argument("--dataset", default=DATASET)
+ commands = parser.add_subparsers(dest="command", required=True)
+ reproduce = commands.add_parser("reproduce")
+ reproduce.add_argument("--artifact", required=True)
+ reproduce.add_argument("--markdown", required=True)
+ verify = commands.add_parser("verify")
+ verify.add_argument("--artifact", required=True)
+ verify.add_argument("--markdown")
+ return parser
+
+
+def main(argv=None):
+ args = _parser().parse_args(argv)
+ if args.command == "reproduce":
+ artifact = build_artifact(args.dataset)
+ write_canonical_json(args.artifact, artifact)
+ destination = Path(args.markdown)
+ destination.parent.mkdir(parents=True, exist_ok=True)
+ destination.write_text(render_markdown(artifact), encoding="utf-8")
+ else:
+ artifact = verify_artifact(load_artifact(args.artifact), dataset=args.dataset)
+ if args.markdown is not None:
+ actual = Path(args.markdown).read_text(encoding="utf-8")
+ if actual != render_markdown(artifact):
+ raise VerificationError("Markdown differs from canonical rendering")
+ return 0
+
+
+if __name__ == "__main__":
+ raise SystemExit(main())
diff --git a/decluster/experiments/amount_mapping_family.py b/decluster/experiments/amount_mapping_family.py
new file mode 100644
index 00000000..0d48468a
--- /dev/null
+++ b/decluster/experiments/amount_mapping_family.py
@@ -0,0 +1,227 @@
+"""Measure entropy and certain links inside DSS's restricted mapping family."""
+
+from __future__ import annotations
+
+import argparse
+from collections import Counter
+import json
+import multiprocessing
+from pathlib import Path
+from queue import Empty
+
+from ..measure import load_ndjson
+from ..result_artifacts import canonical_json_bytes, write_canonical_json
+
+EXPERIMENT_ID = "amount-mapping-family-v1"
+DATASET = "data/amount-channel-812695-812831-v1.json"
+DEFAULT_CAP = 250
+DEFAULT_WALL_SECONDS = 3.0
+
+
+class VerificationError(ValueError):
+ """A stored result differs from a fresh execution."""
+
+
+def _worker(inputs, outputs, queue):
+ try:
+ import dss
+
+ queue.put(("answered", dss.mapping_analysis(inputs, outputs, None)))
+ except BaseException as exc:
+ queue.put(("failed", f"{type(exc).__name__}: {exc}"))
+
+
+def _bounded_analysis(inputs, outputs, wall_seconds):
+ context = multiprocessing.get_context("spawn")
+ queue = context.Queue(maxsize=1)
+ process = context.Process(target=_worker, args=(inputs, outputs, queue))
+ process.start()
+ process.join(wall_seconds)
+ if process.is_alive():
+ process.terminate()
+ process.join()
+ queue.close()
+ return "timed_out", None
+ try:
+ status, result = queue.get(timeout=1.0)
+ except Empty:
+ status, result = "failed", None
+ finally:
+ queue.close()
+ if status == "answered" and (
+ result is None or (isinstance(result, dict) and result.get("status") == "refused")
+ ):
+ return "refused", None
+ if status == "answered" and (
+ not isinstance(result, dict) or result.get("status") != "complete"
+ ):
+ return "failed", None
+ return status, result
+
+
+def _transactions(dataset, cap):
+ selected = 0
+ for transaction, _height in load_ndjson(dataset):
+ inputs = transaction.get("vin") or []
+ outputs = transaction.get("vout") or []
+ if (
+ len(inputs) >= 2
+ and all((item.get("prevout") or {}).get("value") is not None for item in inputs)
+ and all(item.get("value") is not None for item in outputs)
+ ):
+ yield transaction
+ selected += 1
+ if selected == cap:
+ return
+
+
+def build_report(dataset=DATASET, cap=DEFAULT_CAP, wall_seconds=DEFAULT_WALL_SECONDS):
+ outcomes = Counter()
+ entropy = Counter()
+ mappings = 0
+ certain_links = 0
+ selected = 0
+ for transaction in _transactions(dataset, cap):
+ selected += 1
+ inputs = [item["prevout"]["value"] for item in transaction["vin"]]
+ outputs = [item["value"] for item in transaction["vout"]]
+ status, analysis = _bounded_analysis(inputs, outputs, wall_seconds)
+ outcomes[status] += 1
+ if status != "answered":
+ continue
+ value = analysis["entropy"]
+ if value == 0.0:
+ entropy["zero"] += 1
+ elif value <= 4.0:
+ entropy["above_zero_to_four"] += 1
+ else:
+ entropy["above_four"] += 1
+ mappings += analysis["n_non_derived"]
+ certain_links += len(analysis["deterministic_links"])
+ return {
+ "population": {
+ "selected_transactions": selected,
+ "selection": "first complete multi-input transactions in source order",
+ },
+ "outcomes": {
+ "answered": outcomes["answered"],
+ "refused": outcomes["refused"],
+ "timed_out": outcomes["timed_out"],
+ "failed": outcomes["failed"],
+ },
+ "restricted_family": {
+ "entropy_bins_bits": {
+ "zero": entropy["zero"],
+ "above_zero_to_four": entropy["above_zero_to_four"],
+ "above_four": entropy["above_four"],
+ },
+ "non_derived_mappings": mappings,
+ "certain_input_output_links": certain_links,
+ },
+ }
+
+
+def build_artifact(dataset=DATASET, cap=DEFAULT_CAP, wall_seconds=DEFAULT_WALL_SECONDS):
+ return {
+ "schema_version": 1,
+ "experiment": EXPERIMENT_ID,
+ "corpus": "public Bitcoin transaction snapshot",
+ "parameters": {"cap": cap, "wall_seconds_per_transaction": wall_seconds},
+ "report": build_report(dataset, cap, wall_seconds),
+ "limitations": [
+ "DSS enumerates a strict sub-family of the full balanced mappings",
+ "entropy is uniform over that restricted family, not a posterior over ownership",
+ "certain links are certain only within the restricted family",
+ "refusal, timeout, and execution failure are distinct outcomes",
+ "the sample is the first complete multi-input transactions in one 137-block snapshot",
+ "this diagnostic is not a privacy score or CoinScore",
+ ],
+ }
+
+
+def load_artifact(path):
+ try:
+ with Path(path).open(encoding="utf-8") as source:
+ value = json.load(source)
+ except (OSError, json.JSONDecodeError) as exc:
+ raise VerificationError(f"cannot read artifact {path}: {exc}") from exc
+ if not isinstance(value, dict):
+ raise VerificationError("artifact root must be an object")
+ return value
+
+
+def verify_artifact(artifact, *, dataset=DATASET, cap=DEFAULT_CAP,
+ wall_seconds=DEFAULT_WALL_SECONDS):
+ if artifact.get("schema_version") != 1 or artifact.get("experiment") != EXPERIMENT_ID:
+ raise VerificationError("unsupported mapping-family artifact identity")
+ measured = build_artifact(dataset, cap, wall_seconds)
+ if canonical_json_bytes(artifact) != canonical_json_bytes(measured):
+ raise VerificationError("artifact differs from a fresh experiment execution")
+ return measured
+
+
+def render_markdown(artifact):
+ report = artifact["report"]
+ outcomes = report["outcomes"]
+ family = report["restricted_family"]
+ bins = family["entropy_bins_bits"]
+ return "\n".join([
+ "# DSS restricted mapping-family diagnostic",
+ "",
+ "Generated from the canonical experiment artifact. Do not edit manually.",
+ "",
+ f"Of {report['population']['selected_transactions']:,} selected transactions, "
+ f"{outcomes['answered']:,} answer, {outcomes['refused']:,} refuse, "
+ f"{outcomes['timed_out']:,} time out, and {outcomes['failed']:,} fail.",
+ "",
+ f"Within the answered restricted families, {bins['zero']:,} have zero-bit entropy, "
+ f"{bins['above_zero_to_four']:,} have more than zero and at most four bits, and "
+ f"{bins['above_four']:,} have more than four bits. The families contain "
+ f"{family['non_derived_mappings']:,} non-derived mappings and "
+ f"{family['certain_input_output_links']:,} input-output links shared by every mapping.",
+ "",
+ "Certainty and entropy apply only to DSS's restricted family, not to the full mapping "
+ "space or an ownership posterior.",
+ "",
+ ])
+
+
+def _parser():
+ parser = argparse.ArgumentParser(description=__doc__)
+ parser.add_argument("--dataset", default=DATASET)
+ parser.add_argument("--cap", type=int, default=DEFAULT_CAP)
+ parser.add_argument("--wall-seconds", type=float, default=DEFAULT_WALL_SECONDS)
+ commands = parser.add_subparsers(dest="command", required=True)
+ reproduce = commands.add_parser("reproduce")
+ reproduce.add_argument("--artifact", required=True)
+ reproduce.add_argument("--markdown", required=True)
+ verify = commands.add_parser("verify")
+ verify.add_argument("--artifact", required=True)
+ verify.add_argument("--markdown")
+ return parser
+
+
+def main(argv=None):
+ args = _parser().parse_args(argv)
+ if args.cap <= 0 or args.wall_seconds <= 0:
+ raise SystemExit("--cap and --wall-seconds must be positive")
+ if args.command == "reproduce":
+ artifact = build_artifact(args.dataset, args.cap, args.wall_seconds)
+ write_canonical_json(args.artifact, artifact)
+ destination = Path(args.markdown)
+ destination.parent.mkdir(parents=True, exist_ok=True)
+ destination.write_text(render_markdown(artifact), encoding="utf-8")
+ else:
+ artifact = verify_artifact(
+ load_artifact(args.artifact), dataset=args.dataset,
+ cap=args.cap, wall_seconds=args.wall_seconds,
+ )
+ if args.markdown is not None:
+ actual = Path(args.markdown).read_text(encoding="utf-8")
+ if actual != render_markdown(artifact):
+ raise VerificationError("Markdown differs from canonical rendering")
+ return 0
+
+
+if __name__ == "__main__":
+ raise SystemExit(main())
diff --git a/decluster/experiments/amount_pairwise_family.py b/decluster/experiments/amount_pairwise_family.py
new file mode 100644
index 00000000..8260260a
--- /dev/null
+++ b/decluster/experiments/amount_pairwise_family.py
@@ -0,0 +1,213 @@
+"""Measure DSS pairwise rows on the Bitcoin amount-channel snapshot."""
+
+from __future__ import annotations
+
+import argparse
+from collections import Counter
+import json
+import multiprocessing
+from pathlib import Path
+from queue import Empty
+
+from .amount_mapping_family import DATASET, _transactions
+from ..result_artifacts import canonical_json_bytes, write_canonical_json
+
+EXPERIMENT_ID = "amount-pairwise-family-v1"
+DEFAULT_CAP = 300
+DEFAULT_WALL_SECONDS = 3.0
+
+
+class VerificationError(ValueError):
+ """A stored result differs from a fresh execution."""
+
+
+def _worker(inputs, outputs, queue):
+ try:
+ import dss
+
+ queue.put(("answered", dss.pairwise_link_prob(inputs, outputs, None)))
+ except BaseException as exc:
+ queue.put(("failed", f"{type(exc).__name__}: {exc}"))
+
+
+def _bounded_matrix(inputs, outputs, wall_seconds):
+ context = multiprocessing.get_context("spawn")
+ queue = context.Queue(maxsize=1)
+ process = context.Process(target=_worker, args=(inputs, outputs, queue))
+ process.start()
+ process.join(wall_seconds)
+ if process.is_alive():
+ process.terminate()
+ process.join()
+ queue.close()
+ return "timed_out", None
+ try:
+ status, result = queue.get(timeout=1.0)
+ except Empty:
+ status, result = "failed", None
+ finally:
+ queue.close()
+ if status == "answered" and result is None:
+ return "refused", None
+ if status == "answered" and not isinstance(result, list):
+ return "failed", None
+ return status, result
+
+
+def build_report(dataset=DATASET, cap=DEFAULT_CAP, wall_seconds=DEFAULT_WALL_SECONDS):
+ outcomes = Counter()
+ selected_transactions = 0
+ selected_input_rows = 0
+ selected_outputs = 0
+ returned_rows = 0
+ nonempty_rows = 0
+ singleton_support_rows = 0
+ full_support_rows = 0
+
+ for transaction in _transactions(dataset, cap):
+ selected_transactions += 1
+ inputs = [item["prevout"]["value"] for item in transaction["vin"]]
+ outputs = [item["value"] for item in transaction["vout"]]
+ selected_input_rows += len(inputs)
+ selected_outputs += len(outputs)
+ status, matrix = _bounded_matrix(inputs, outputs, wall_seconds)
+ outcomes[status] += 1
+ if status != "answered":
+ continue
+ if len(matrix) != len(inputs) or any(len(row) != len(outputs) for row in matrix):
+ outcomes["answered"] -= 1
+ outcomes["failed"] += 1
+ continue
+ returned_rows += len(matrix)
+ for row in matrix:
+ support = sum(probability > 0.0 for probability in row)
+ nonempty_rows += support > 0
+ singleton_support_rows += support == 1
+ full_support_rows += support == len(outputs) and bool(outputs)
+
+ return {
+ "population": {
+ "selected_transactions": selected_transactions,
+ "selected_input_rows": selected_input_rows,
+ "selected_outputs": selected_outputs,
+ "selection": "first complete multi-input transactions in source order",
+ },
+ "outcomes": {
+ "answered": outcomes["answered"],
+ "refused": outcomes["refused"],
+ "timed_out": outcomes["timed_out"],
+ "failed": outcomes["failed"],
+ },
+ "restricted_family_rows": {
+ "returned": returned_rows,
+ "nonempty_support": nonempty_rows,
+ "singleton_support": singleton_support_rows,
+ "full_output_support": full_support_rows,
+ },
+ }
+
+
+def build_artifact(dataset=DATASET, cap=DEFAULT_CAP, wall_seconds=DEFAULT_WALL_SECONDS):
+ return {
+ "schema_version": 1,
+ "experiment": EXPERIMENT_ID,
+ "corpus": "public Bitcoin transaction snapshot",
+ "parameters": {"cap": cap, "wall_seconds_per_transaction": wall_seconds},
+ "report": build_report(dataset, cap, wall_seconds),
+ "limitations": [
+ "the matrix is a uniform marginal over DSS's restricted mapping family",
+ "a singleton-support row is not a globally deterministic ownership link",
+ "the exact-oracle audit found false certainties outside the restricted family",
+ "refusal, timeout, and execution failure are distinct outcomes",
+ "wall-time outcomes are resource- and platform-dependent",
+ "the sample is the first complete multi-input transactions in one 137-block snapshot",
+ "this diagnostic is not a privacy score or CoinScore",
+ ],
+ }
+
+
+def load_artifact(path):
+ try:
+ with Path(path).open(encoding="utf-8") as source:
+ value = json.load(source)
+ except (OSError, json.JSONDecodeError) as exc:
+ raise VerificationError(f"cannot read artifact {path}: {exc}") from exc
+ if not isinstance(value, dict):
+ raise VerificationError("artifact root must be an object")
+ return value
+
+
+def verify_artifact(artifact, *, dataset=DATASET, cap=DEFAULT_CAP,
+ wall_seconds=DEFAULT_WALL_SECONDS):
+ if artifact.get("schema_version") != 1 or artifact.get("experiment") != EXPERIMENT_ID:
+ raise VerificationError("unsupported pairwise-family artifact identity")
+ measured = build_artifact(dataset, cap, wall_seconds)
+ if canonical_json_bytes(artifact) != canonical_json_bytes(measured):
+ raise VerificationError("artifact differs from a fresh experiment execution")
+ return measured
+
+
+def render_markdown(artifact):
+ report = artifact["report"]
+ population = report["population"]
+ outcomes = report["outcomes"]
+ rows = report["restricted_family_rows"]
+ return "\n".join([
+ "# DSS pairwise restricted-family diagnostic",
+ "",
+ "Generated from the canonical experiment artifact. Do not edit manually.",
+ "",
+ f"The run selects {population['selected_transactions']:,} transactions containing "
+ f"{population['selected_input_rows']:,} input rows. {outcomes['answered']:,} transactions "
+ f"answer, {outcomes['refused']:,} refuse, {outcomes['timed_out']:,} time out, and "
+ f"{outcomes['failed']:,} fail.",
+ "",
+ f"The answered matrices return {rows['returned']:,} rows, of which "
+ f"{rows['nonempty_support']:,} have nonempty support and "
+ f"{rows['singleton_support']:,} have singleton support.",
+ "",
+ "Singleton support means only that every mapping in DSS's restricted family selects the "
+ "same output. It is not a globally deterministic ownership link.",
+ "",
+ ])
+
+
+def _parser():
+ parser = argparse.ArgumentParser(description=__doc__)
+ parser.add_argument("--dataset", default=DATASET)
+ parser.add_argument("--cap", type=int, default=DEFAULT_CAP)
+ parser.add_argument("--wall-seconds", type=float, default=DEFAULT_WALL_SECONDS)
+ commands = parser.add_subparsers(dest="command", required=True)
+ reproduce = commands.add_parser("reproduce")
+ reproduce.add_argument("--artifact", required=True)
+ reproduce.add_argument("--markdown", required=True)
+ verify = commands.add_parser("verify")
+ verify.add_argument("--artifact", required=True)
+ verify.add_argument("--markdown")
+ return parser
+
+
+def main(argv=None):
+ args = _parser().parse_args(argv)
+ if args.cap <= 0 or args.wall_seconds <= 0:
+ raise SystemExit("--cap and --wall-seconds must be positive")
+ if args.command == "reproduce":
+ artifact = build_artifact(args.dataset, args.cap, args.wall_seconds)
+ write_canonical_json(args.artifact, artifact)
+ destination = Path(args.markdown)
+ destination.parent.mkdir(parents=True, exist_ok=True)
+ destination.write_text(render_markdown(artifact), encoding="utf-8")
+ else:
+ artifact = verify_artifact(
+ load_artifact(args.artifact), dataset=args.dataset,
+ cap=args.cap, wall_seconds=args.wall_seconds,
+ )
+ if args.markdown is not None:
+ actual = Path(args.markdown).read_text(encoding="utf-8")
+ if actual != render_markdown(artifact):
+ raise VerificationError("Markdown differs from canonical rendering")
+ return 0
+
+
+if __name__ == "__main__":
+ raise SystemExit(main())
diff --git a/decluster/experiments/amount_per_coin.py b/decluster/experiments/amount_per_coin.py
new file mode 100644
index 00000000..5c0dbcae
--- /dev/null
+++ b/decluster/experiments/amount_per_coin.py
@@ -0,0 +1,250 @@
+"""Measure the DSS per-coin amount diagnostic and its transaction-level gate."""
+
+from __future__ import annotations
+
+import argparse
+from collections import Counter
+import json
+import math
+from pathlib import Path
+from statistics import median
+
+from ..cost import _reachable, amount_cuts, dss_oracle
+from ..counting import count_w, guaranteed_log_w
+from ..measure import load_ndjson
+from ..result_artifacts import canonical_json_bytes, write_canonical_json
+
+EXPERIMENT_ID = "amount-per-coin-v1"
+DATASET = "data/amount-channel-812695-812831-v1.json"
+CUT_THRESHOLD = 1.0
+
+
+class VerificationError(ValueError):
+ """A stored result differs from a fresh execution."""
+
+
+def _transactions(dataset):
+ for transaction, _height in load_ndjson(dataset):
+ inputs = transaction.get("vin") or []
+ outputs = transaction.get("vout") or []
+ if (
+ len(inputs) >= 2
+ and all((item.get("prevout") or {}).get("value") is not None for item in inputs)
+ and all(item.get("value") is not None for item in outputs)
+ ):
+ yield transaction
+
+
+def _role(coin):
+ role = coin.get("role")
+ if role == "in":
+ return "input"
+ if role == "out":
+ return "output"
+ raise ValueError(f"unsupported DSS coin role: {role!r}")
+
+
+def build_report(dataset=DATASET, cut_threshold=CUT_THRESHOLD):
+ population = Counter()
+ reachable = Counter()
+ raw_candidates = Counter()
+ gated_candidates = Counter()
+ exact_gate_candidates = Counter()
+ finite_log_w = []
+ raw_candidate_transactions = 0
+ gated_candidate_transactions = 0
+ gate_resolved_transactions = 0
+
+ for transaction in _transactions(dataset):
+ inputs = [item["prevout"]["value"] for item in transaction["vin"]]
+ outputs = [item["value"] for item in transaction["vout"]]
+ population["transactions"] += 1
+ population["input"] += len(inputs)
+ population["output"] += len(outputs)
+
+ per_coin = dss_oracle(inputs, outputs)
+ raw_here = False
+ for coin in per_coin["coins"]:
+ role = _role(coin)
+ log_w = coin.get("log_w")
+ if not _reachable(log_w):
+ continue
+ reachable[role] += 1
+ finite_log_w.append(log_w)
+ if log_w <= cut_threshold:
+ raw_candidates[role] += 1
+ raw_here = True
+ raw_candidate_transactions += raw_here
+
+ whole = count_w(inputs, outputs)
+ if guaranteed_log_w(whole) is None:
+ continue
+ gate_resolved_transactions += 1
+ candidates = amount_cuts(
+ inputs,
+ outputs,
+ lambda _inputs, _outputs, report=per_coin: report,
+ cut_threshold=cut_threshold,
+ count_oracle=lambda _inputs, _outputs, result=whole: result,
+ )
+ gated_candidate_transactions += bool(candidates)
+ for candidate in candidates:
+ role = _role({"role": candidate.role})
+ gated_candidates[role] += 1
+ if candidate.transaction_count_exact:
+ exact_gate_candidates[role] += 1
+
+ total_coins = population["input"] + population["output"]
+ finite = len(finite_log_w)
+ return {
+ "population": {
+ "transactions": population["transactions"],
+ "inputs": population["input"],
+ "outputs": population["output"],
+ "coins": total_coins,
+ },
+ "per_coin_density": {
+ "reachable": {
+ "input": reachable["input"],
+ "output": reachable["output"],
+ "total": finite,
+ },
+ "unreachable": total_coins - finite,
+ "finite_log_w": {
+ "minimum": min(finite_log_w) if finite_log_w else None,
+ "median": median(finite_log_w) if finite_log_w else None,
+ "maximum": max(finite_log_w) if finite_log_w else None,
+ },
+ },
+ "ungated_diagnostic": {
+ "candidate_transactions": raw_candidate_transactions,
+ "candidates": {
+ "input": raw_candidates["input"],
+ "output": raw_candidates["output"],
+ "total": sum(raw_candidates.values()),
+ },
+ },
+ "transaction_gated": {
+ "resolved_transactions": gate_resolved_transactions,
+ "candidate_transactions": gated_candidate_transactions,
+ "candidates": {
+ "input": gated_candidates["input"],
+ "output": gated_candidates["output"],
+ "total": sum(gated_candidates.values()),
+ },
+ "candidates_with_exact_transaction_count": {
+ "input": exact_gate_candidates["input"],
+ "output": exact_gate_candidates["output"],
+ "total": sum(exact_gate_candidates.values()),
+ },
+ },
+ }
+
+
+def build_artifact(dataset=DATASET, cut_threshold=CUT_THRESHOLD):
+ return {
+ "schema_version": 1,
+ "experiment": EXPERIMENT_ID,
+ "corpus": "public Bitcoin transaction snapshot",
+ "parameters": {"cut_threshold_natural_log": cut_threshold},
+ "report": build_report(dataset, cut_threshold),
+ "limitations": [
+ "DSS per-coin log_w is a knee-truncated diagnostic, not an exact mapping count",
+ "unreachable coins are missing measurements and are never candidates",
+ "the transaction gate and per-coin diagnostic measure different objects",
+ "an exact transaction count does not make a per-coin candidate exact",
+ "input and output indices have separate identities",
+ "candidates are refuse signals, not ownership proofs or privacy scores",
+ ],
+ }
+
+
+def load_artifact(path):
+ try:
+ with Path(path).open(encoding="utf-8") as source:
+ value = json.load(source)
+ except (OSError, json.JSONDecodeError) as exc:
+ raise VerificationError(f"cannot read artifact {path}: {exc}") from exc
+ if not isinstance(value, dict):
+ raise VerificationError("artifact root must be an object")
+ return value
+
+
+def verify_artifact(artifact, *, dataset=DATASET, cut_threshold=CUT_THRESHOLD):
+ if artifact.get("schema_version") != 1 or artifact.get("experiment") != EXPERIMENT_ID:
+ raise VerificationError("unsupported per-coin amount artifact identity")
+ measured = build_artifact(dataset, cut_threshold)
+ if canonical_json_bytes(artifact) != canonical_json_bytes(measured):
+ raise VerificationError("artifact differs from a fresh experiment execution")
+ return measured
+
+
+def render_markdown(artifact):
+ report = artifact["report"]
+ population = report["population"]
+ density = report["per_coin_density"]
+ raw = report["ungated_diagnostic"]
+ gated = report["transaction_gated"]
+ return "\n".join([
+ "# DSS per-coin amount diagnostic",
+ "",
+ "Generated from the canonical experiment artifact. Do not edit manually.",
+ "",
+ f"The run evaluates {population['coins']:,} coins in "
+ f"{population['transactions']:,} complete multi-input transactions. DSS returns a "
+ f"finite per-coin reading for {density['reachable']['total']:,} coins and no reading for "
+ f"{density['unreachable']:,}.",
+ "",
+ f"Before the transaction gate, {raw['candidates']['total']:,} candidates occur in "
+ f"{raw['candidate_transactions']:,} transactions: {raw['candidates']['input']:,} inputs "
+ f"and {raw['candidates']['output']:,} outputs.",
+ "",
+ f"After requiring a conservative nonzero transaction-level W(E), "
+ f"{gated['candidates']['total']:,} candidates remain in "
+ f"{gated['candidate_transactions']:,} transactions. An exact transaction count records "
+ "the gate's provenance; it does not make the per-coin reading exact.",
+ "",
+ "These are refuse-only diagnostics, not ownership proofs, privacy scores, or CoinScore.",
+ "",
+ ])
+
+
+def _parser():
+ parser = argparse.ArgumentParser(description=__doc__)
+ parser.add_argument("--dataset", default=DATASET)
+ parser.add_argument("--cut-threshold", type=float, default=CUT_THRESHOLD)
+ commands = parser.add_subparsers(dest="command", required=True)
+ reproduce = commands.add_parser("reproduce")
+ reproduce.add_argument("--artifact", required=True)
+ reproduce.add_argument("--markdown", required=True)
+ verify = commands.add_parser("verify")
+ verify.add_argument("--artifact", required=True)
+ verify.add_argument("--markdown")
+ return parser
+
+
+def main(argv=None):
+ args = _parser().parse_args(argv)
+ if not math.isfinite(args.cut_threshold):
+ raise SystemExit("--cut-threshold must be finite")
+ if args.command == "reproduce":
+ artifact = build_artifact(args.dataset, args.cut_threshold)
+ write_canonical_json(args.artifact, artifact)
+ destination = Path(args.markdown)
+ destination.parent.mkdir(parents=True, exist_ok=True)
+ destination.write_text(render_markdown(artifact), encoding="utf-8")
+ else:
+ artifact = verify_artifact(
+ load_artifact(args.artifact),
+ dataset=args.dataset,
+ cut_threshold=args.cut_threshold,
+ )
+ if args.markdown is not None:
+ actual = Path(args.markdown).read_text(encoding="utf-8")
+ if actual != render_markdown(artifact):
+ raise VerificationError("Markdown differs from canonical rendering")
+ return 0
+
+
+if __name__ == "__main__":
+ raise SystemExit(main())
diff --git a/decluster/experiments/amount_subset_sum.py b/decluster/experiments/amount_subset_sum.py
new file mode 100644
index 00000000..c5484970
--- /dev/null
+++ b/decluster/experiments/amount_subset_sum.py
@@ -0,0 +1,148 @@
+"""Measure transaction-level subset-sum W(E) on the amount-channel snapshot."""
+
+from __future__ import annotations
+
+import argparse
+from collections import Counter
+import json
+from pathlib import Path
+
+from ..counting import MAX_INPUTS, guaranteed_log_w, w_total
+from ..measure import load_ndjson
+from ..result_artifacts import canonical_json_bytes, write_canonical_json
+
+EXPERIMENT_ID = "amount-subset-sum-v1"
+DATASET = "data/amount-channel-812695-812831-v1.json"
+
+
+class VerificationError(ValueError):
+ """A stored result differs from a fresh execution."""
+
+
+def _multi_input_transactions(dataset):
+ for transaction, _height in load_ndjson(dataset):
+ inputs = transaction.get("vin") or []
+ outputs = transaction.get("vout") or []
+ if (
+ len(inputs) >= 2
+ and all((item.get("prevout") or {}).get("value") is not None for item in inputs)
+ and all(item.get("value") is not None for item in outputs)
+ ):
+ yield transaction
+
+
+def build_report(dataset=DATASET):
+ kinds = Counter()
+ guaranteed = 0
+ exact_zero = 0
+ transactions = 0
+ for transaction in _multi_input_transactions(dataset):
+ inputs = [item["prevout"]["value"] for item in transaction["vin"]]
+ outputs = [item["value"] for item in transaction["vout"]]
+ result = w_total(inputs, outputs)
+ kind = str(result.get("kind", "unknown")).lower()
+ kinds[kind] += 1
+ guaranteed += guaranteed_log_w(result) is not None
+ exact_zero += kind == "exact" and (result.get("count") or 0) == 0
+ transactions += 1
+ return {
+ "population": {"multi_input": transactions, "maximum_inputs": MAX_INPUTS},
+ "outcomes": {
+ "by_kind": dict(sorted(kinds.items())),
+ "guaranteed_nonzero_log_w": guaranteed,
+ "exact_zero": exact_zero,
+ },
+ }
+
+
+def build_artifact(dataset=DATASET):
+ return {
+ "schema_version": 1,
+ "experiment": EXPERIMENT_ID,
+ "corpus": "public Bitcoin transaction snapshot",
+ "report": build_report(dataset),
+ "limitations": [
+ "W(E) is a per-target subset count, not a Maurer mapping count",
+ "the whole-transaction count is fee-blind",
+ "exact zero means no exact subset hit and supplies no ambiguity evidence",
+ "transactions above sixteen inputs are reported as unknown",
+ "only exact and lower-bound tiers can support a conservative lower bound",
+ "this run does not compute pairwise links, entropy, or CoinScore",
+ ],
+ }
+
+
+def load_artifact(path):
+ try:
+ with Path(path).open(encoding="utf-8") as source:
+ value = json.load(source)
+ except (OSError, json.JSONDecodeError) as exc:
+ raise VerificationError(f"cannot read artifact {path}: {exc}") from exc
+ if not isinstance(value, dict):
+ raise VerificationError("artifact root must be an object")
+ return value
+
+
+def verify_artifact(artifact, *, dataset=DATASET):
+ if artifact.get("schema_version") != 1 or artifact.get("experiment") != EXPERIMENT_ID:
+ raise VerificationError("unsupported amount subset-sum artifact identity")
+ measured = build_artifact(dataset)
+ if canonical_json_bytes(artifact) != canonical_json_bytes(measured):
+ raise VerificationError("artifact differs from a fresh experiment execution")
+ return measured
+
+
+def render_markdown(artifact):
+ report = artifact["report"]
+ population = report["population"]
+ outcomes = report["outcomes"]
+ return "\n".join([
+ "# Transaction-level subset-sum W(E)",
+ "",
+ "Generated from the canonical experiment artifact. Do not edit manually.",
+ "",
+ f"Of {population['multi_input']:,} multi-input transactions, "
+ f"{outcomes['by_kind'].get('exact', 0):,} return `exact` and "
+ f"{outcomes['by_kind'].get('unknown', 0):,} return `unknown`.",
+ "",
+ f"Among the exact results, {outcomes['exact_zero']:,} find no exact subset hit. "
+ f"Only {outcomes['guaranteed_nonzero_log_w']:,} results expose a usable "
+ "nonzero conservative log W.",
+ "",
+ "W(E) is not a mapping count, pairwise-link oracle, entropy, or CoinScore.",
+ "",
+ ])
+
+
+def _parser():
+ parser = argparse.ArgumentParser(description=__doc__)
+ parser.add_argument("--dataset", default=DATASET)
+ commands = parser.add_subparsers(dest="command", required=True)
+ reproduce = commands.add_parser("reproduce")
+ reproduce.add_argument("--artifact", required=True)
+ reproduce.add_argument("--markdown", required=True)
+ verify = commands.add_parser("verify")
+ verify.add_argument("--artifact", required=True)
+ verify.add_argument("--markdown")
+ return parser
+
+
+def main(argv=None):
+ args = _parser().parse_args(argv)
+ if args.command == "reproduce":
+ artifact = build_artifact(args.dataset)
+ write_canonical_json(args.artifact, artifact)
+ destination = Path(args.markdown)
+ destination.parent.mkdir(parents=True, exist_ok=True)
+ destination.write_text(render_markdown(artifact), encoding="utf-8")
+ else:
+ artifact = verify_artifact(load_artifact(args.artifact), dataset=args.dataset)
+ if args.markdown is not None:
+ actual = Path(args.markdown).read_text(encoding="utf-8")
+ if actual != render_markdown(artifact):
+ raise VerificationError("Markdown differs from canonical rendering")
+ return 0
+
+
+if __name__ == "__main__":
+ raise SystemExit(main())
diff --git a/decluster/experiments/analyze_contract.py b/decluster/experiments/analyze_contract.py
new file mode 100644
index 00000000..7a4ec398
--- /dev/null
+++ b/decluster/experiments/analyze_contract.py
@@ -0,0 +1,210 @@
+"""Exercise the public analysis facade on a deterministic transaction graph."""
+
+from __future__ import annotations
+
+import argparse
+import json
+from pathlib import Path
+
+from ..analyze import analyze
+from ..result_artifacts import canonical_json_bytes, write_canonical_json
+
+EXPERIMENT_ID = "analyze-contract-v1"
+
+
+class VerificationError(ValueError):
+ """A stored result differs from a fresh execution."""
+
+
+TRANSACTIONS = {
+ "target": {
+ "txid": "target",
+ "vin": [
+ {"txid": "left", "vout": 0,
+ "prevout": {"value": 500, "scriptpubkey_address": "owner-left"}},
+ {"txid": "right", "vout": 0, "prevout": {"value": 500}},
+ ],
+ "vout": [
+ {"value": 600, "scriptpubkey_address": "external"},
+ {"value": 399, "scriptpubkey_address": "owner-left"},
+ ],
+ },
+ "left": {
+ "txid": "left",
+ "vin": [{
+ "txid": "origin-left",
+ "vout": 0,
+ "prevout": {"value": 500, "scriptpubkey_address": "owner-left"},
+ }],
+ "vout": [{"value": 500}],
+ },
+ "right": {
+ "txid": "right",
+ "vin": [{"txid": "origin-right", "vout": 0, "prevout": {"value": 500}}],
+ "vout": [{"value": 500}],
+ },
+ "origin-left": {
+ "txid": "origin-left",
+ "vin": [{"is_coinbase": True}],
+ "vout": [{"value": 500}],
+ },
+ "origin-right": {
+ "txid": "origin-right",
+ "vin": [{"is_coinbase": True}],
+ "vout": [{"value": 500}],
+ },
+}
+
+
+def _fetch(txid):
+ return TRANSACTIONS[txid]
+
+
+def _number(value):
+ return round(value, 15)
+
+
+def _summary(entry):
+ provenance = entry["provenance"]
+ return {
+ "provenance": {
+ "min_entropy_bits": _number(provenance["min_entropy"]),
+ "shannon_bits": _number(provenance["shannon"]),
+ "n_absorbers": provenance["n_absorbers"],
+ },
+ "fused": {
+ "min_entropy_bits": _number(entry["fused"]["min_entropy"]),
+ "shannon_bits": _number(entry["fused"]["shannon"]),
+ },
+ "truncated": entry["truncated"],
+ }
+
+
+def build_artifact():
+ depth_sweep = {}
+ for depth in range(1, 6):
+ result = analyze("target", targets=[0], depth=depth, fetch=_fetch)
+ depth_sweep[str(depth)] = _summary(result[0])
+
+ change = analyze("target", targets=[1], depth=1, fetch=_fetch)[1]
+ uncapped = analyze("target", targets=[0], depth=5, fetch=_fetch)[0]
+ capped = analyze("target", targets=[0], depth=5, fetch=_fetch, max_nodes=1)[0]
+ route = analyze(
+ "target", targets=[0], depth=2, fetch=_fetch, subjective=False, path_count=True,
+ )[0]
+
+ return {
+ "schema_version": 1,
+ "experiment": EXPERIMENT_ID,
+ "fixture": {"transactions": len(TRANSACTIONS), "targets": [0, 1]},
+ "depth_sweep": depth_sweep,
+ "change_fusion": {
+ "provenance_min_entropy_bits": _number(change["provenance"]["min_entropy"]),
+ "fused_min_entropy_bits": _number(change["fused"]["min_entropy"]),
+ "sharpened": change["fused"]["min_entropy"] < change["provenance"]["min_entropy"],
+ },
+ "node_cap": {
+ "uncapped_truncations": uncapped["truncated"],
+ "capped_truncations": capped["truncated"],
+ "cap_is_observable": capped["truncated"] > uncapped["truncated"],
+ },
+ "route_diagnostic": {
+ "present_when_requested": "path_count" in route,
+ "canonical_meaning": "provenance_route_accumulation",
+ },
+ "capabilities": {
+ "offline_transaction_input": True,
+ "deterministic_value_flow_default": True,
+ "depths_one_through_five_return": True,
+ "subjective_fusion_never_widens": True,
+ "truncation_is_reported": True,
+ },
+ "limitations": [
+ "the fixture is synthetic and validates the public facade contract",
+ "the run does not reproduce the historical live Bitcoin measurements",
+ "address reuse is a heuristic subjective signal, not verified ownership",
+ "provenance entropy is not a privacy certificate or CoinScore",
+ "the path_count response key is retained for API compatibility only",
+ ],
+ }
+
+
+def load_artifact(path):
+ try:
+ with Path(path).open(encoding="utf-8") as source:
+ value = json.load(source)
+ except (OSError, json.JSONDecodeError) as exc:
+ raise VerificationError(f"cannot read artifact {path}: {exc}") from exc
+ if not isinstance(value, dict):
+ raise VerificationError("artifact root must be an object")
+ return value
+
+
+def verify_artifact(artifact):
+ if artifact.get("schema_version") != 1 or artifact.get("experiment") != EXPERIMENT_ID:
+ raise VerificationError("unsupported analyze-contract artifact identity")
+ measured = build_artifact()
+ if canonical_json_bytes(artifact) != canonical_json_bytes(measured):
+ raise VerificationError("artifact differs from a fresh contract execution")
+ return measured
+
+
+def render_markdown(artifact):
+ change = artifact["change_fusion"]
+ cap = artifact["node_cap"]
+ return "\n".join([
+ "# Public analysis facade contract",
+ "",
+ "Generated from the canonical experiment artifact. Do not edit manually.",
+ "",
+ "The deterministic fixture returns at depths 1 through 5 with the default nominal-value "
+ "flow oracle.",
+ "",
+ f"The change target's min-entropy changes from "
+ f"{change['provenance_min_entropy_bits']:.6f} to "
+ f"{change['fused_min_entropy_bits']:.6f} bits after the address-reuse signal. "
+ f"Sharpening was observed: `{change['sharpened']}`.",
+ "",
+ f"The uncapped walk reports {cap['uncapped_truncations']} truncations and the one-node cap "
+ f"reports {cap['capped_truncations']}. The cap is observable: `{cap['cap_is_observable']}`.",
+ "",
+ "The opt-in `path_count` response field is a compatibility name for "
+ "`provenance_route_accumulation`; it is not robust connectivity or k-routes.",
+ "",
+ "This run validates an API contract on a synthetic graph. It does not reproduce the "
+ "historical live Bitcoin measurements and does not certify privacy.",
+ "",
+ ])
+
+
+def _parser():
+ parser = argparse.ArgumentParser(description=__doc__)
+ commands = parser.add_subparsers(dest="command", required=True)
+ reproduce = commands.add_parser("reproduce")
+ reproduce.add_argument("--artifact", required=True)
+ reproduce.add_argument("--markdown", required=True)
+ verify = commands.add_parser("verify")
+ verify.add_argument("--artifact", required=True)
+ verify.add_argument("--markdown")
+ return parser
+
+
+def main(argv=None):
+ args = _parser().parse_args(argv)
+ if args.command == "reproduce":
+ artifact = build_artifact()
+ write_canonical_json(args.artifact, artifact)
+ destination = Path(args.markdown)
+ destination.parent.mkdir(parents=True, exist_ok=True)
+ destination.write_text(render_markdown(artifact), encoding="utf-8")
+ else:
+ artifact = verify_artifact(load_artifact(args.artifact))
+ if args.markdown is not None:
+ actual = Path(args.markdown).read_text(encoding="utf-8")
+ if actual != render_markdown(artifact):
+ raise VerificationError("Markdown differs from canonical rendering")
+ return 0
+
+
+if __name__ == "__main__":
+ raise SystemExit(main())
diff --git a/decluster/experiments/ancestry_contract.py b/decluster/experiments/ancestry_contract.py
new file mode 100644
index 00000000..71cbd22a
--- /dev/null
+++ b/decluster/experiments/ancestry_contract.py
@@ -0,0 +1,188 @@
+"""Measure the nominal-value provenance and typed ancestry contracts."""
+
+from __future__ import annotations
+
+import argparse
+import json
+from pathlib import Path
+
+from ..adaptations.ancestry import (
+ CompleteAncestry,
+ TruncatedAncestry,
+ ancestry_signature_report,
+)
+from ..ancestry import provenance_link, value_flow_untraceability
+from ..result_artifacts import canonical_json_bytes, write_canonical_json
+
+EXPERIMENT_ID = "ancestry-contract-v1"
+TARGET = ("target", 0)
+
+
+class VerificationError(ValueError):
+ """A stored result differs from a fresh execution."""
+
+
+TRANSACTIONS = {
+ "target": {
+ "vin": [
+ {"txid": "left", "vout": 0, "prevout": {"value": 5}},
+ {"txid": "right", "vout": 0, "prevout": {"value": 5}},
+ ],
+ "vout": [{"value": 9}],
+ },
+ "left": {"vin": [{"is_coinbase": True}], "vout": [{"value": 5}]},
+ "right": {"vin": [{"is_coinbase": True}], "vout": [{"value": 5}]},
+}
+
+
+def _fetch(txid):
+ return TRANSACTIONS[txid]
+
+
+def _distribution(report):
+ return {
+ f"{subject.identifier[0]}:{subject.identifier[1]}": round(mass, 15)
+ for subject, mass in report.distribution
+ }
+
+
+def _truncation(report):
+ return {
+ "oracle_refused": report.truncation.oracle_refused,
+ "node_capped": report.truncation.node_capped,
+ "zero_link_mass": report.truncation.zero_link_mass,
+ "unattributed": report.truncation.unattributed,
+ }
+
+
+def build_artifact():
+ complete = ancestry_signature_report(TARGET, depth=2, fetch=_fetch)
+ capped = ancestry_signature_report(TARGET, depth=2, fetch=_fetch, max_nodes=1)
+ refused = ancestry_signature_report(
+ TARGET, depth=2, fetch=_fetch, link_oracle=lambda _inputs, _outputs: None,
+ )
+ nominal = value_flow_untraceability(TARGET, depth=2, fetch=_fetch)
+ signature, _support = complete.as_legacy()
+ disjoint = {("unrelated", 0): 1.0}
+
+ return {
+ "schema_version": 1,
+ "experiment": EXPERIMENT_ID,
+ "fixture": {"transactions": len(TRANSACTIONS), "target": "target:0", "depth": 2},
+ "complete": {
+ "state": type(complete.state).__name__,
+ "is_complete": isinstance(complete.state, CompleteAncestry),
+ "distribution": _distribution(complete),
+ "truncation": _truncation(complete),
+ "shannon_bits": round(nominal["untraceability"], 15),
+ "expected_paper_steps": nominal["expected_steps"],
+ },
+ "bounded": {
+ "state": type(capped.state).__name__,
+ "is_truncated": isinstance(capped.state, TruncatedAncestry),
+ "truncation": _truncation(capped),
+ },
+ "oracle_refusal": {
+ "state": type(refused.state).__name__,
+ "is_truncated": isinstance(refused.state, TruncatedAncestry),
+ "truncation": _truncation(refused),
+ },
+ "overlap": {
+ "self": provenance_link(signature, signature),
+ "disjoint": provenance_link(signature, disjoint),
+ },
+ "capabilities": {
+ "nominal_value_transition": True,
+ "absorbing_distribution": True,
+ "typed_complete_and_truncated_states": True,
+ "truncation_causes_separated": True,
+ "provenance_overlap_diagnostic": True,
+ },
+ "limitations": [
+ "the fixture is synthetic and validates mechanism contracts",
+ "the nominal-value model does not infer ownership or payment allocation",
+ "provenance overlap is model-relative evidence, not attribution",
+ "the run does not reproduce the historical DSS or live Bitcoin measurements",
+ "the outputs are not robust connectivity, a privacy certificate or CoinScore",
+ ],
+ }
+
+
+def load_artifact(path):
+ try:
+ with Path(path).open(encoding="utf-8") as source:
+ value = json.load(source)
+ except (OSError, json.JSONDecodeError) as exc:
+ raise VerificationError(f"cannot read artifact {path}: {exc}") from exc
+ if not isinstance(value, dict):
+ raise VerificationError("artifact root must be an object")
+ return value
+
+
+def verify_artifact(artifact):
+ if artifact.get("schema_version") != 1 or artifact.get("experiment") != EXPERIMENT_ID:
+ raise VerificationError("unsupported ancestry-contract artifact identity")
+ measured = build_artifact()
+ if canonical_json_bytes(artifact) != canonical_json_bytes(measured):
+ raise VerificationError("artifact differs from a fresh contract execution")
+ return measured
+
+
+def render_markdown(artifact):
+ complete = artifact["complete"]
+ capped = artifact["bounded"]
+ refused = artifact["oracle_refusal"]
+ overlap = artifact["overlap"]
+ return "\n".join([
+ "# Nominal-value ancestry contract",
+ "",
+ "Generated from the canonical experiment artifact. Do not edit manually.",
+ "",
+ f"The complete walk reaches `{complete['distribution']}` with Shannon entropy "
+ f"{complete['shannon_bits']:.6f} bits and "
+ f"{complete['expected_paper_steps']:.1f} steps in the uncollapsed paper graph.",
+ "",
+ f"A one-node bound returns `{capped['state']}` with causes "
+ f"`{capped['truncation']}`. Oracle refusal returns `{refused['state']}` with causes "
+ f"`{refused['truncation']}`.",
+ "",
+ f"Provenance overlap is {overlap['self']:.1f} for the same signature and "
+ f"{overlap['disjoint']:.1f} for a disjoint signature.",
+ "",
+ "This measures model-relative provenance. It does not infer ownership, certify privacy, "
+ "measure robust connectivity or reproduce the historical live Bitcoin observations.",
+ "",
+ ])
+
+
+def _parser():
+ parser = argparse.ArgumentParser(description=__doc__)
+ commands = parser.add_subparsers(dest="command", required=True)
+ reproduce = commands.add_parser("reproduce")
+ reproduce.add_argument("--artifact", required=True)
+ reproduce.add_argument("--markdown", required=True)
+ verify = commands.add_parser("verify")
+ verify.add_argument("--artifact", required=True)
+ verify.add_argument("--markdown")
+ return parser
+
+
+def main(argv=None):
+ args = _parser().parse_args(argv)
+ if args.command == "reproduce":
+ artifact = build_artifact()
+ write_canonical_json(args.artifact, artifact)
+ destination = Path(args.markdown)
+ destination.parent.mkdir(parents=True, exist_ok=True)
+ destination.write_text(render_markdown(artifact), encoding="utf-8")
+ else:
+ artifact = verify_artifact(load_artifact(args.artifact))
+ if args.markdown is not None:
+ actual = Path(args.markdown).read_text(encoding="utf-8")
+ if actual != render_markdown(artifact):
+ raise VerificationError("Markdown differs from canonical rendering")
+ return 0
+
+
+if __name__ == "__main__":
+ raise SystemExit(main())
diff --git a/decluster/experiments/ancestry_sparsity.py b/decluster/experiments/ancestry_sparsity.py
new file mode 100644
index 00000000..3246b64c
--- /dev/null
+++ b/decluster/experiments/ancestry_sparsity.py
@@ -0,0 +1,186 @@
+"""Measure nearest-neighbour sparsity in a frozen ancestry-signature snapshot."""
+
+from __future__ import annotations
+
+import argparse
+import gzip
+import json
+import math
+import statistics
+from pathlib import Path
+
+from ..def1_sparsity import survival
+from ..result_artifacts import canonical_json_bytes, write_canonical_json
+
+EXPERIMENT_ID = "ancestry-sparsity-v1"
+EPSILONS = (0.5, 0.9, 0.99)
+
+
+class VerificationError(ValueError):
+ """A stored result differs from a fresh execution."""
+
+
+def _load_signatures(path):
+ try:
+ with gzip.open(path, "rt", encoding="utf-8") as source:
+ raw = json.load(source)
+ except (OSError, json.JSONDecodeError) as exc:
+ raise VerificationError(f"cannot read signature snapshot {path}: {exc}") from exc
+ if not isinstance(raw, dict) or len(raw) < 2:
+ raise VerificationError("signature snapshot must contain at least two records")
+ signatures = {}
+ stored_tops = {}
+ for coin, record in raw.items():
+ if not isinstance(record, dict) or set(record) != {"sig", "top"}:
+ raise VerificationError(f"record {coin!r} has an invalid schema")
+ signature = record["sig"]
+ if not isinstance(signature, dict) or not signature:
+ raise VerificationError(f"record {coin!r} has no signature")
+ values = {feature: float(value) for feature, value in signature.items()}
+ if any(not math.isfinite(value) or value < 0 for value in values.values()):
+ raise VerificationError(f"record {coin!r} has an invalid signature weight")
+ signatures[str(coin)] = values
+ stored_tops[str(coin)] = float(record["top"])
+ return signatures, stored_tops
+
+
+def _nearest_cosines(signatures):
+ """Return exact nearest-neighbour cosines using a sparse inverted index."""
+ coins = sorted(signatures)
+ normalized = {}
+ postings = {}
+ for coin in coins:
+ signature = signatures[coin]
+ norm = math.sqrt(sum(value * value for value in signature.values()))
+ vector = {feature: value / norm for feature, value in signature.items()} if norm else {}
+ normalized[coin] = vector
+ for feature, value in vector.items():
+ postings.setdefault(feature, []).append((coin, value))
+
+ similarities = {coin: {} for coin in coins}
+ for entries in postings.values():
+ for left_index, (left, left_value) in enumerate(entries):
+ for right, right_value in entries[left_index + 1:]:
+ contribution = left_value * right_value
+ similarities[left][right] = similarities[left].get(right, 0.0) + contribution
+ similarities[right][left] = similarities[right].get(left, 0.0) + contribution
+ return {
+ coin: max(similarities[coin].values(), default=0.0)
+ for coin in coins
+ }
+
+
+def build_artifact(dataset):
+ signatures, stored_tops = _load_signatures(dataset)
+ nearest = _nearest_cosines(signatures)
+ values = list(nearest.values())
+ dimensions = [len(signature) for signature in signatures.values()]
+ curve = survival(values, EPSILONS)
+ return {
+ "schema_version": 1,
+ "experiment": EXPERIMENT_ID,
+ "snapshot": {
+ "records": len(signatures),
+ "dimensions": {
+ "minimum": min(dimensions),
+ "median": statistics.median(dimensions),
+ "maximum": max(dimensions),
+ },
+ },
+ "nearest_neighbour_cosine": {
+ "median": round(statistics.median(values), 15),
+ "survival": {str(epsilon): curve[epsilon] for epsilon in EPSILONS},
+ },
+ "stored_top_context": {
+ "different_from_snapshot_recomputation": sum(
+ abs(nearest[coin] - stored_tops[coin]) > 1e-9 for coin in signatures
+ ),
+ "interpretation": "stored values were computed in an unavailable comparison population",
+ },
+ "limitations": [
+ "the 200-record frozen snapshot is selected and is not a uniform chain sample",
+ "the signature-generation recipe and original comparison population are unavailable",
+ "the run measures sparsity within this snapshot only",
+ "sparsity is an attack precondition, not proof of successful attribution",
+ "the run does not establish a chain-wide population rate",
+ ],
+ }
+
+
+def load_artifact(path):
+ try:
+ with Path(path).open(encoding="utf-8") as source:
+ value = json.load(source)
+ except (OSError, json.JSONDecodeError) as exc:
+ raise VerificationError(f"cannot read artifact {path}: {exc}") from exc
+ if not isinstance(value, dict):
+ raise VerificationError("artifact root must be an object")
+ return value
+
+
+def verify_artifact(artifact, dataset):
+ if artifact.get("schema_version") != 1 or artifact.get("experiment") != EXPERIMENT_ID:
+ raise VerificationError("unsupported ancestry-sparsity artifact identity")
+ measured = build_artifact(dataset)
+ if canonical_json_bytes(artifact) != canonical_json_bytes(measured):
+ raise VerificationError("artifact differs from a fresh snapshot analysis")
+ return measured
+
+
+def render_markdown(artifact):
+ snapshot = artifact["snapshot"]
+ nearest = artifact["nearest_neighbour_cosine"]
+ dimensions = snapshot["dimensions"]
+ curve = nearest["survival"]
+ return "\n".join([
+ "# Ancestry-signature sparsity snapshot",
+ "",
+ "Generated from the canonical experiment artifact. Do not edit manually.",
+ "",
+ f"The frozen snapshot contains {snapshot['records']} signatures. Their feature counts have "
+ f"minimum {dimensions['minimum']}, median {dimensions['median']:.1f} and maximum "
+ f"{dimensions['maximum']}.",
+ "",
+ f"The median recomputed nearest-neighbour cosine is {nearest['median']:.6f}. The fractions "
+ f"with a neighbour above 0.5, 0.9 and 0.99 are {curve['0.5']:.3f}, "
+ f"{curve['0.9']:.3f} and {curve['0.99']:.3f}.",
+ "",
+ f"Stored nearest-neighbour values differ from the self-contained snapshot recomputation for "
+ f"{artifact['stored_top_context']['different_from_snapshot_recomputation']} records and are "
+ "therefore excluded from the result.",
+ "",
+ "This measures an attack precondition within a selected frozen snapshot. It is neither a "
+ "chain-wide estimate nor proof of successful attribution.",
+ "",
+ ])
+
+
+def _parser():
+ parser = argparse.ArgumentParser(description=__doc__)
+ commands = parser.add_subparsers(dest="command", required=True)
+ for name in ("reproduce", "verify"):
+ command = commands.add_parser(name)
+ command.add_argument("--dataset", required=True)
+ command.add_argument("--artifact", required=True)
+ command.add_argument("--markdown", required=True)
+ return parser
+
+
+def main(argv=None):
+ args = _parser().parse_args(argv)
+ if args.command == "reproduce":
+ artifact = build_artifact(args.dataset)
+ write_canonical_json(args.artifact, artifact)
+ destination = Path(args.markdown)
+ destination.parent.mkdir(parents=True, exist_ok=True)
+ destination.write_text(render_markdown(artifact), encoding="utf-8")
+ else:
+ artifact = verify_artifact(load_artifact(args.artifact), args.dataset)
+ actual = Path(args.markdown).read_text(encoding="utf-8")
+ if actual != render_markdown(artifact):
+ raise VerificationError("Markdown differs from canonical rendering")
+ return 0
+
+
+if __name__ == "__main__":
+ raise SystemExit(main())
diff --git a/decluster/experiments/anchor_axis_families.py b/decluster/experiments/anchor_axis_families.py
new file mode 100644
index 00000000..1eaa93f8
--- /dev/null
+++ b/decluster/experiments/anchor_axis_families.py
@@ -0,0 +1,205 @@
+"""Score the merged anchor's false Cake-to-sender edge under every axis set the paper compares.
+
+`PAPER.md` §4 turns on one named edge: the three-axis engine refuses it, the twenty-three-axis
+library resurrects it past the engine's `link_above` threshold, and dropping the redundant axes
+brings it back below. The paper calls that measured rather than asserted, and it was — by a script
+reading two transactions out of a gitignored cache, which is not something a reader can rerun.
+
+So the two transactions are committed as a fixture and the comparison is a run. `scorer-families`
+measures the same three axis sets over a population; this measures them on the one edge the
+argument names, which is a different question: a mean says what the families do on average, and
+this says what they did to the case the design decision was made on.
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+from pathlib import Path
+
+from ..combiner import Combiner
+from ..fingerprint_validate import (
+ LibraryScorer,
+ construction_only_scorer,
+ decorrelated_scorer,
+)
+from ..result_artifacts import canonical_json_bytes, write_canonical_json
+
+EXPERIMENT_ID = "anchor-axis-families-v1"
+DEFAULT_DATASET = "tests/fixtures/merged_anchor_931d6627.json"
+LINK_ABOVE = 4.0
+
+
+class VerificationError(ValueError):
+ """The dataset or stored result violates this experiment's contract."""
+
+
+def _load(path):
+ try:
+ fixture = json.loads(Path(path).read_text(encoding="utf-8"))
+ cake = fixture["transactions"]["cake"]
+ sender = fixture["transactions"]["sender"]
+ except (OSError, json.JSONDecodeError, KeyError, TypeError) as exc:
+ raise VerificationError(f"cannot read anchor fixture {path}: {exc}") from exc
+ if not cake.get("txid") or not sender.get("txid"):
+ raise VerificationError(f"{path}: both anchor transactions must carry a txid")
+ return fixture, cake, sender
+
+
+def _rows(scorer, sender, cake):
+ _score, rows = scorer.score(sender, cake, explain=True)
+ return [
+ {"axis": name, "sender": str(a), "cake": str(b),
+ "bits": None if weight is None else weight}
+ for name, a, b, weight in rows
+ ]
+
+
+def build_artifact(dataset=DEFAULT_DATASET):
+ fixture, cake, sender = _load(dataset)
+ engine = Combiner.from_library()
+ families = {
+ "engine_three_axis": (engine, None),
+ "catalogued": (LibraryScorer(), None),
+ "construction_only": (construction_only_scorer(), None),
+ "decorrelated": (decorrelated_scorer(), None),
+ }
+ scores = {}
+ for name, (scorer, _) in families.items():
+ score = scorer.score(sender, cake)
+ if isinstance(score, tuple):
+ score = score[0]
+ scores[name] = {
+ "axes": len(getattr(scorer, "axes", ())),
+ "bits": score,
+ "verdict": "link" if score > 0 else "refuse",
+ "past_link_above": score > LINK_ABOVE,
+ }
+ catalogued = _rows(LibraryScorer(), sender, cake)
+ positive = [row for row in catalogued if row["bits"] is not None and row["bits"] > 0]
+ negative = [row for row in catalogued if row["bits"] is not None and row["bits"] < 0]
+ return {
+ "schema_version": 1,
+ "experiment": EXPERIMENT_ID,
+ "dataset": "merged-anchor-931d6627-v1",
+ "anchor": fixture["anchor"],
+ "edge": {"sender": sender["txid"], "cake": cake["txid"]},
+ "measurement": {
+ "link_above": LINK_ABOVE,
+ "families": scores,
+ "redundancy_bits": scores["catalogued"]["bits"] - scores["decorrelated"]["bits"],
+ "catalogued_axes_scored": sum(1 for row in catalogued if row["bits"] is not None),
+ "catalogued_axes_abstaining": sum(1 for row in catalogued if row["bits"] is None),
+ "positive_axes": len(positive),
+ "negative_axes": len(negative),
+ "negative_bits_total": sum(row["bits"] for row in negative),
+ "positive_bits_total": sum(row["bits"] for row in positive),
+ "per_axis": catalogued,
+ },
+ "limitations": [
+ "one named edge, chosen because the design decision was argued on it",
+ "the same-owner reading of this edge is the paper's, not an independent label",
+ "a score past link_above is what the engine would do, not what it does: the engine "
+ "scores three axes",
+ "the library weights come from a population that was not preserved; "
+ "library-calibration-v1 measures how far they sit from the one that was",
+ "the result is not a privacy score",
+ ],
+ }
+
+
+def load_artifact(path):
+ try:
+ with Path(path).open(encoding="utf-8") as source:
+ value = json.load(source)
+ except (OSError, json.JSONDecodeError) as exc:
+ raise VerificationError(f"cannot read artifact {path}: {exc}") from exc
+ if not isinstance(value, dict):
+ raise VerificationError("artifact root must be an object")
+ return value
+
+
+def verify_artifact(artifact, dataset=DEFAULT_DATASET):
+ if artifact.get("schema_version") != 1 or artifact.get("experiment") != EXPERIMENT_ID:
+ raise VerificationError("unsupported anchor-axis-family artifact identity")
+ measured = build_artifact(dataset)
+ if canonical_json_bytes(artifact) != canonical_json_bytes(measured):
+ raise VerificationError("artifact differs from a fresh experiment execution")
+ return measured
+
+
+def render_markdown(artifact):
+ measured = artifact["measurement"]
+ families = measured["families"]
+ lines = [
+ f"# The merged anchor's false edge under four axis sets",
+ "",
+ "Generated from the canonical experiment artifact. Do not edit manually.",
+ "",
+ f"One edge — the sender's funding transaction against the Cake one, on anchor "
+ f"`{artifact['anchor']}`. The engine's refusal threshold is `link_above = "
+ f"{measured['link_above']}`.",
+ "",
+ "| axis set | axes | bits | verdict | past link_above |",
+ "|---|---:|---:|---|---|",
+ ]
+ for name in ("engine_three_axis", "catalogued", "construction_only", "decorrelated"):
+ row = families[name]
+ lines.append(
+ f"| {name.replace('_', ' ')} | {row['axes']} | {row['bits']:+.2f} | {row['verdict']} | "
+ f"{'yes' if row['past_link_above'] else 'no'} |"
+ )
+ lines += [
+ "",
+ f"The catalogued model resurrects the edge at {families['catalogued']['bits']:+.2f} bits, "
+ f"well past the threshold. Dropping one representative per correlated cluster leaves "
+ f"{families['decorrelated']['bits']:+.2f} — **{measured['redundancy_bits']:.2f} bits of the "
+ "score were copies of evidence already counted**, and what remains sits below the "
+ "threshold, so the wide model's failure here is duplication rather than width.",
+ "",
+ f"Of the {measured['catalogued_axes_scored']} axes that scored, "
+ f"{measured['negative_axes']} argue against the pairing "
+ f"({measured['negative_bits_total']:+.2f} bits between them) and "
+ f"{measured['positive_axes']} argue for it "
+ f"({measured['positive_bits_total']:+.2f}); "
+ f"{measured['catalogued_axes_abstaining']} abstain. The discriminating axes still refuse — "
+ "they are outvoted by low-entropy policy axes that two ordinary wallets share, summed under "
+ "a kernel that assumes they are independent.",
+ "",
+ "This is one edge, chosen because the design decision was argued on it, and its same-owner "
+ "reading is the paper's rather than an independent label. The library weights come from a "
+ "population that was not preserved; `library-calibration-v1` measures how far they sit from "
+ "the one that was.",
+ "",
+ ]
+ return "\n".join(lines)
+
+
+def _parser():
+ parser = argparse.ArgumentParser(description=__doc__)
+ parser.add_argument("--dataset", default=DEFAULT_DATASET)
+ commands = parser.add_subparsers(dest="command", required=True)
+ for name in ("reproduce", "verify"):
+ command = commands.add_parser(name)
+ command.add_argument("--artifact", required=True)
+ command.add_argument("--markdown", required=True)
+ return parser
+
+
+def main(argv=None):
+ args = _parser().parse_args(argv)
+ if args.command == "reproduce":
+ artifact = build_artifact(args.dataset)
+ write_canonical_json(args.artifact, artifact)
+ destination = Path(args.markdown)
+ destination.parent.mkdir(parents=True, exist_ok=True)
+ destination.write_text(render_markdown(artifact), encoding="utf-8")
+ else:
+ artifact = verify_artifact(load_artifact(args.artifact), args.dataset)
+ if Path(args.markdown).read_text(encoding="utf-8") != render_markdown(artifact):
+ raise VerificationError("Markdown differs from canonical rendering")
+ return 0
+
+
+if __name__ == "__main__":
+ raise SystemExit(main())
diff --git a/decluster/experiments/attribute_drift.py b/decluster/experiments/attribute_drift.py
new file mode 100644
index 00000000..904f99dc
--- /dev/null
+++ b/decluster/experiments/attribute_drift.py
@@ -0,0 +1,237 @@
+"""Measure projected epoch drift from the frozen Lumen aggregate."""
+
+from __future__ import annotations
+
+import argparse
+import json
+from pathlib import Path
+
+from ..attribute_drift import GAPS, cycle_gain, drift, pearson
+from ..result_artifacts import canonical_json_bytes, write_canonical_json
+
+EXPERIMENT_ID = "attribute-drift-v1"
+DEFAULT_DATASET = "tests/fixtures/lumen_explorer_data.json"
+RESIDUAL = "__all_unpreserved_values__"
+
+
+class VerificationError(ValueError):
+ """The aggregate or stored result violates this experiment's contract."""
+
+
+def _load(path):
+ try:
+ with Path(path).open(encoding="utf-8") as source:
+ value = json.load(source)
+ except (OSError, json.JSONDecodeError) as exc:
+ raise VerificationError(f"cannot read Lumen aggregate {path}: {exc}") from exc
+ required = {"window", "totals", "axis_value_series", "template_series"}
+ if not isinstance(value, dict) or not required.issubset(value):
+ raise VerificationError("Lumen aggregate lacks epoch-series fields")
+ return value
+
+
+def _series_points(series, epochs, label):
+ if not isinstance(series, list) or len(series) != epochs:
+ raise VerificationError(f"{label} must contain exactly {epochs} epochs")
+ heights = []
+ shares = []
+ for index, point in enumerate(series):
+ if not isinstance(point, dict) or set(point) != {"start_height", "share"}:
+ raise VerificationError(f"{label}[{index}] has an invalid schema")
+ height, share = point["start_height"], point["share"]
+ if not isinstance(height, int) or not isinstance(share, (int, float)):
+ raise VerificationError(f"{label}[{index}] has invalid values")
+ share = float(share)
+ if not 0.0 <= share <= 1.0:
+ raise VerificationError(f"{label}[{index}] share is outside [0,1]")
+ heights.append(height)
+ shares.append(share)
+ return heights, shares
+
+
+def _volumes(source, epochs, expected_heights):
+ candidates = [[] for _ in range(epochs)]
+ for template, series in sorted(source["template_series"].items()):
+ if not isinstance(series, list) or len(series) != epochs:
+ raise VerificationError(f"template {template!r} has an invalid epoch count")
+ for index, point in enumerate(series):
+ if point.get("start_height") != expected_heights[index]:
+ raise VerificationError(f"template {template!r} has inconsistent heights")
+ matches, share = point.get("matches"), point.get("share")
+ if not isinstance(matches, int) or not isinstance(share, (int, float)):
+ raise VerificationError(f"template {template!r} has invalid observations")
+ if share:
+ volume = round(matches / share)
+ if volume <= 0 or abs(matches / volume - share) > 1e-12:
+ raise VerificationError(f"template {template!r} cannot reconstruct volume")
+ candidates[index].append(volume)
+ volumes = []
+ for index, values in enumerate(candidates):
+ if not values or len(set(values)) != 1:
+ raise VerificationError(f"epoch {index} has missing or inconsistent volume estimates")
+ volumes.append(values[0])
+ return volumes
+
+
+def build_artifact(dataset=DEFAULT_DATASET):
+ source = _load(dataset)
+ epochs = source["window"].get("epochs")
+ start = source["window"].get("start_height")
+ if not isinstance(epochs, int) or epochs < max(GAPS) + 1 or not isinstance(start, int):
+ raise VerificationError("invalid epoch window")
+ expected_heights = [start + 144 * index for index in range(epochs)]
+ if source["window"].get("end_height") != expected_heights[-1] + 143:
+ raise VerificationError("epoch heights do not cover the declared end height")
+ axes = {}
+ selected = {}
+ for axis, values in sorted(source["axis_value_series"].items()):
+ if not isinstance(values, dict) or not values:
+ raise VerificationError(f"axis {axis!r} contains no value series")
+ value_shares = {}
+ for value, points in sorted(values.items()):
+ heights, shares = _series_points(points, epochs, f"{axis}.{value}")
+ if heights != expected_heights:
+ raise VerificationError(f"axis {axis!r} has inconsistent heights")
+ value_shares[value] = shares
+ series = []
+ for index in range(epochs):
+ row = {value: shares[index] for value, shares in value_shares.items()}
+ residual = 1.0 - sum(row.values())
+ if residual < -1e-12:
+ raise VerificationError(f"axis {axis!r} shares exceed one at epoch {index}")
+ row[RESIDUAL] = max(0.0, residual)
+ series.append(row)
+ axes[axis] = series
+ selected[axis] = value_shares
+ volumes = _volumes(source, epochs, expected_heights)
+ if sum(volumes) != source["totals"].get("txs"):
+ raise VerificationError("reconstructed epoch volumes do not equal the transaction total")
+ rows = []
+ for axis, series in axes.items():
+ curve = drift(series, GAPS)
+ rows.append({
+ "axis": axis,
+ "preserved_values": sorted(selected[axis]),
+ "drift": {str(gap): curve[gap] for gap in GAPS},
+ "weekly_gain": cycle_gain(series),
+ })
+ coupling = []
+ for axis, values in selected.items():
+ for value, shares in values.items():
+ mean_share = sum(shares) / epochs
+ if mean_share > 0.01:
+ correlation = pearson(volumes, shares)
+ coupling.append({
+ "axis": axis,
+ "value": value,
+ "mean_share": mean_share,
+ "pearson_r": correlation,
+ "r_squared": correlation * correlation,
+ })
+ return {
+ "schema_version": 1,
+ "experiment": EXPERIMENT_ID,
+ "window": {
+ "start_height": start,
+ "end_height": source["window"].get("end_height"),
+ "epochs": epochs,
+ "transactions": source["totals"].get("txs"),
+ "epoch_size_blocks": 144,
+ },
+ "measurement": {
+ "projection": "preserved_values_plus_combined_residual",
+ "axes": rows,
+ "volume_coupling": coupling,
+ "volume_range": {"minimum": min(volumes), "maximum": max(volumes)},
+ },
+ "limitations": [
+ "only 20 value series across 11 axes are preserved",
+ "unpreserved values are combined into one residual category per axis",
+ "only projected drift is measured; most full-distribution table rows are historical",
+ "volume is reconstructed from redundant template match shares rather than stored directly",
+ "population drift does not measure per-cluster attribute distinctiveness",
+ "the result is diagnostic and does not produce a clustering score",
+ "the dataset publisher authorized redistribution of the aggregate fixture on 2026-09-04",
+ ],
+ }
+
+
+def load_artifact(path):
+ try:
+ with Path(path).open(encoding="utf-8") as source:
+ value = json.load(source)
+ except (OSError, json.JSONDecodeError) as exc:
+ raise VerificationError(f"cannot read artifact {path}: {exc}") from exc
+ if not isinstance(value, dict):
+ raise VerificationError("artifact root must be an object")
+ return value
+
+
+def verify_artifact(artifact, dataset=DEFAULT_DATASET):
+ if artifact.get("schema_version") != 1 or artifact.get("experiment") != EXPERIMENT_ID:
+ raise VerificationError("unsupported attribute-drift artifact identity")
+ measured = build_artifact(dataset)
+ if canonical_json_bytes(artifact) != canonical_json_bytes(measured):
+ raise VerificationError("artifact differs from a fresh projected-drift execution")
+ return measured
+
+
+def render_markdown(artifact):
+ window = artifact["window"]
+ lines = [
+ "# Projected attribute drift across epochs",
+ "",
+ "Generated from the canonical experiment artifact. Do not edit manually.",
+ "",
+ f"Window: {window['epochs']} epochs of {window['epoch_size_blocks']} blocks, covering "
+ f"{window['transactions']} transactions.",
+ "",
+ "| axis projection | TV@1 | TV@7 | TV@30 | TV@120 | weekly gain |",
+ "|---|---:|---:|---:|---:|---:|",
+ ]
+ for row in artifact["measurement"]["axes"]:
+ curve = row["drift"]
+ lines.append(
+ f"| {row['axis']} | {curve['1']:.3f} | {curve['7']:.3f} | "
+ f"{curve['30']:.3f} | {curve['120']:.3f} | {row['weekly_gain']:.2f} |"
+ )
+ lines.extend([
+ "",
+ "Each row uses the explicitly preserved values plus one combined residual category. "
+ "This reproduces every rounded historical cell for nlocktime and low_r. The version "
+ "projection yields 0.070 rather than the historical 0.071 at gap 120. These projections "
+ "are not replacements for unavailable full-distribution rows or the historical 70/85 "
+ "volume-coupling summary.",
+ "",
+ ])
+ return "\n".join(lines)
+
+
+def _parser():
+ parser = argparse.ArgumentParser(description=__doc__)
+ parser.add_argument("--dataset", default=DEFAULT_DATASET)
+ commands = parser.add_subparsers(dest="command", required=True)
+ for name in ("reproduce", "verify"):
+ command = commands.add_parser(name)
+ command.add_argument("--artifact", required=True)
+ command.add_argument("--markdown", required=True)
+ return parser
+
+
+def main(argv=None):
+ args = _parser().parse_args(argv)
+ if args.command == "reproduce":
+ artifact = build_artifact(args.dataset)
+ write_canonical_json(args.artifact, artifact)
+ destination = Path(args.markdown)
+ destination.parent.mkdir(parents=True, exist_ok=True)
+ destination.write_text(render_markdown(artifact), encoding="utf-8")
+ else:
+ artifact = verify_artifact(load_artifact(args.artifact), args.dataset)
+ if Path(args.markdown).read_text(encoding="utf-8") != render_markdown(artifact):
+ raise VerificationError("Markdown differs from canonical rendering")
+ return 0
+
+
+if __name__ == "__main__":
+ raise SystemExit(main())
diff --git a/decluster/experiments/bayes_vs_fs.py b/decluster/experiments/bayes_vs_fs.py
new file mode 100644
index 00000000..65b006fa
--- /dev/null
+++ b/decluster/experiments/bayes_vs_fs.py
@@ -0,0 +1,141 @@
+"""Reproduce the pair-level Bayesian versus Fellegi-Sunter comparison."""
+
+from __future__ import annotations
+
+import argparse
+import json
+from pathlib import Path
+from tempfile import TemporaryDirectory
+
+from ..archive_snapshot import extract_tar_gz
+from ..bayes_fs_comparison import evaluate
+from ..fingerprint_validate import load_blkcache
+from ..reproducibility import fingerprint_source
+from ..result_artifacts import canonical_json_bytes, write_canonical_json
+
+EXPERIMENT_ID = "bayes-vs-fs-v1"
+DATASET_ID = "fs-blkcache-2026-09-04-v1"
+
+
+class VerificationError(ValueError):
+ """The stored result differs from a fresh execution."""
+
+
+def build_artifact(snapshot, *, pair_cap=4000, seed=0, n_samples=2000, burn=500):
+ with TemporaryDirectory(prefix="decluster-bayes-fs-") as temporary:
+ root = extract_tar_gz(snapshot, temporary)
+ cache = root / ".blkcache"
+ transactions = load_blkcache(str(cache))
+ measurement = evaluate(
+ transactions,
+ pair_cap=pair_cap,
+ seed=seed,
+ n_samples=n_samples,
+ burn=burn,
+ )
+ source = fingerprint_source(str(cache / "*.json"))
+ source.update(pattern=".blkcache/*.json", transactions=len(transactions))
+ return {
+ "schema_version": 1,
+ "experiment": EXPERIMENT_ID,
+ "dataset": DATASET_ID,
+ "parameters": {
+ "pair_cap_per_class": pair_cap,
+ "seed": seed,
+ "gibbs_samples": n_samples,
+ "gibbs_burn": burn,
+ "beta_prior": [8.5, 1.5],
+ },
+ "source": source,
+ "measurement": measurement,
+ "historical_comparison": {
+ "status": "not_reproduced",
+ "reason": "the preserved snapshot produces materially different metrics from the historical Markdown",
+ },
+ "limitations": [
+ "address reuse is a weak and partly feature-dependent ownership proxy",
+ "the balanced positive-negative sample does not represent population prevalence",
+ "parameters are fitted and evaluated on the same selected pair sample",
+ "the model assumes conditional independence between correlated axes",
+ "u is fixed at the measured collision rate",
+ "cluster intervals propagate pair probabilities independently and are not a joint partition posterior",
+ "borderline nodes are purpose-selected and neither cluster diagnostic is chain-wide",
+ "this is attacker-side record-linkage evidence, not CoinScore or a privacy certificate",
+ ],
+ }
+
+
+def load_artifact(path):
+ try:
+ with Path(path).open(encoding="utf-8") as source:
+ value = json.load(source)
+ except (OSError, json.JSONDecodeError) as exc:
+ raise VerificationError(f"cannot read artifact {path}: {exc}") from exc
+ if not isinstance(value, dict):
+ raise VerificationError("artifact root must be an object")
+ return value
+
+
+def verify_artifact(artifact, snapshot):
+ if artifact.get("schema_version") != 1 or artifact.get("experiment") != EXPERIMENT_ID:
+ raise VerificationError("unsupported Bayes-versus-FS artifact identity")
+ measured = build_artifact(snapshot)
+ if canonical_json_bytes(artifact) != canonical_json_bytes(measured):
+ raise VerificationError("artifact differs from a fresh experiment execution")
+ return measured
+
+
+def render_markdown(artifact):
+ measurement = artifact["measurement"]
+ lines = [
+ "# Pair-level Bayesian and Fellegi–Sunter comparison",
+ "",
+ "Generated from the canonical experiment artifact. Do not edit manually.",
+ "",
+ f"Transactions: {measurement['transactions']}; positive pairs: "
+ f"{measurement['pairs']['positive']}; negative pairs: {measurement['pairs']['negative']}.",
+ "",
+ "| scorer | AUC | ECE |",
+ "|---|---:|---:|",
+ ]
+ for row in measurement["discrimination"]:
+ lines.append(f"| {row['scorer']} | {row['auc']:.6f} | {row['ece']:.6f} |")
+ lines.extend([
+ "",
+ "The preserved snapshot does not reproduce the metrics in the former handwritten report. "
+ "These values are the canonical rerun. Address reuse is a weak label, calibration is on a "
+ "balanced selected sample, and the cluster bands are independent-edge diagnostics rather "
+ "than a posterior over ownership partitions.",
+ "",
+ ])
+ return "\n".join(lines)
+
+
+def _parser():
+ parser = argparse.ArgumentParser(description=__doc__)
+ parser.add_argument("--snapshot", required=True)
+ commands = parser.add_subparsers(dest="command", required=True)
+ for name in ("reproduce", "verify"):
+ command = commands.add_parser(name)
+ command.add_argument("--artifact", required=True)
+ command.add_argument("--markdown", required=True)
+ return parser
+
+
+def main(argv=None):
+ args = _parser().parse_args(argv)
+ if args.command == "reproduce":
+ artifact = build_artifact(args.snapshot)
+ write_canonical_json(args.artifact, artifact)
+ destination = Path(args.markdown)
+ destination.parent.mkdir(parents=True, exist_ok=True)
+ destination.write_text(render_markdown(artifact), encoding="utf-8")
+ else:
+ artifact = verify_artifact(load_artifact(args.artifact), args.snapshot)
+ if Path(args.markdown).read_text(encoding="utf-8") != render_markdown(artifact):
+ raise VerificationError("Markdown differs from canonical rendering")
+ return 0
+
+
+if __name__ == "__main__":
+ raise SystemExit(main())
diff --git a/decluster/experiments/boltzmann_fee_audit.py b/decluster/experiments/boltzmann_fee_audit.py
new file mode 100644
index 00000000..4bd4edac
--- /dev/null
+++ b/decluster/experiments/boltzmann_fee_audit.py
@@ -0,0 +1,112 @@
+"""Reproduce the bounded exact-versus-fee-allocation audit."""
+
+from __future__ import annotations
+
+import argparse
+import json
+from pathlib import Path
+
+from examples import boltzmann_fee_audit as legacy
+
+from ..result_artifacts import canonical_json_bytes, write_canonical_json
+
+EXPERIMENT_ID = "boltzmann-fee-audit-v1"
+DATASET = "data/boltzmann-fee-audit-v1.json"
+
+
+class VerificationError(ValueError):
+ """A stored result differs from a fresh execution."""
+
+
+def build_artifact(dataset=DATASET):
+ report = legacy.audit(dataset, cap=300, max_coins=8)
+ return {
+ "schema_version": 1,
+ "experiment": EXPERIMENT_ID,
+ "corpus": "bounded public Bitcoin transaction snapshot",
+ "report": report,
+ "limitations": [
+ "the selected population is deterministic but not chain-representative",
+ "the all-coins interpretation makes fee-tolerant acceptance non-informative",
+ "fee roundness is reported but is not a competing probability model",
+ "this audit measures the local fee-allocation model, not general parity with Boltzmann",
+ "separate tests cover selected official Boltzmann vectors and modes",
+ ],
+ }
+
+
+def load_artifact(path):
+ try:
+ with Path(path).open(encoding="utf-8") as source:
+ value = json.load(source)
+ except (OSError, json.JSONDecodeError) as exc:
+ raise VerificationError(f"cannot read artifact {path}: {exc}") from exc
+ if not isinstance(value, dict):
+ raise VerificationError("artifact root must be an object")
+ return value
+
+
+def verify_artifact(artifact, *, dataset=DATASET):
+ if artifact.get("schema_version") != 1 or artifact.get("experiment") != EXPERIMENT_ID:
+ raise VerificationError("unsupported Boltzmann fee-audit artifact identity")
+ measured = build_artifact(dataset)
+ if canonical_json_bytes(artifact) != canonical_json_bytes(measured):
+ raise VerificationError("artifact differs from a fresh experiment execution")
+ return measured
+
+
+def render_markdown(artifact):
+ report = artifact["report"]
+ population = report["population"]
+ outcomes = report["outcomes"]
+ return "\n".join([
+ "# Exact conservation versus explicit fee allocation",
+ "",
+ "Generated from the canonical experiment artifact. Do not edit manually.",
+ "",
+ f"The bounded population contains {population['selected']} transactions. Exact "
+ f"zero-fee conservation admits mappings for {outcomes['exact_with_mapping']}; "
+ f"the local fee-allocation model admits mappings for "
+ f"{outcomes['fee_tolerant_with_mapping']}.",
+ "",
+ f"Only {outcomes['fee_tolerant_with_nontrivial_split']} transactions admit more "
+ "than the all-coins interpretation. This is the informative comparison.",
+ "",
+ "This run is not a claim of general parity with the external Boltzmann tool. "
+ "Selected official vectors and modes are tested separately.",
+ "",
+ ])
+
+
+def _parser():
+ parser = argparse.ArgumentParser(description=__doc__)
+ parser.add_argument("--dataset", default=DATASET)
+ commands = parser.add_subparsers(dest="command", required=True)
+ reproduce = commands.add_parser("reproduce")
+ reproduce.add_argument("--artifact", required=True)
+ reproduce.add_argument("--markdown", required=True)
+ verify = commands.add_parser("verify")
+ verify.add_argument("--artifact", required=True)
+ verify.add_argument("--markdown")
+ return parser
+
+
+def main(argv=None):
+ args = _parser().parse_args(argv)
+ if args.command == "reproduce":
+ artifact = build_artifact(args.dataset)
+ write_canonical_json(args.artifact, artifact)
+ destination = Path(args.markdown)
+ destination.parent.mkdir(parents=True, exist_ok=True)
+ destination.write_text(render_markdown(artifact), encoding="utf-8")
+ else:
+ artifact = verify_artifact(load_artifact(args.artifact), dataset=args.dataset)
+ if args.markdown is not None:
+ actual = Path(args.markdown).read_text(encoding="utf-8")
+ if actual != render_markdown(artifact):
+ raise VerificationError("Markdown differs from canonical rendering")
+ return 0
+
+
+if __name__ == "__main__":
+ raise SystemExit(main())
diff --git a/decluster/experiments/candidate_set_intersection.py b/decluster/experiments/candidate_set_intersection.py
new file mode 100644
index 00000000..cc9f3d1f
--- /dev/null
+++ b/decluster/experiments/candidate_set_intersection.py
@@ -0,0 +1,134 @@
+"""Reproduce the deterministic candidate-set intersection fixture."""
+
+from __future__ import annotations
+
+import argparse
+import json
+from pathlib import Path
+
+from ..baselines import candidate_set_intersection as baseline
+from ..result_artifacts import canonical_json_bytes, write_canonical_json
+
+
+EXPERIMENT_ID = "candidate-set-intersection-v1"
+
+
+class VerificationError(ValueError):
+ """A stored result differs from a fresh execution."""
+
+
+def _serialize_step(step):
+ return {
+ "index": step.index,
+ "observed": step.observed,
+ "before": step.before,
+ "after": step.after,
+ "narrowing_bits": step.narrowing_bits,
+ }
+
+
+def build_artifact():
+ scenarios = []
+ for name, observations in baseline.scenarios():
+ result = baseline.intersect_candidate_sets(
+ observations, universe_size=baseline.UNIVERSE
+ )
+ scenarios.append({
+ "name": name,
+ "observations": [sorted(observation) for observation in observations],
+ "steps": [_serialize_step(step) for step in result.steps],
+ "surviving": sorted(result.surviving),
+ "narrowing_bits": result.narrowing_bits,
+ "identified": result.identified,
+ "inconsistent_at": result.inconsistent_at,
+ })
+ return {
+ "schema_version": 1,
+ "experiment": EXPERIMENT_ID,
+ "parameters": {"universe_size": baseline.UNIVERSE},
+ "scenarios": scenarios,
+ "summary": baseline.manifest_invariants(),
+ "limitations": [
+ "synthetic fixture",
+ "candidate sets are supplied rather than inferred from chain data",
+ "the run validates the abstract intersection mechanism, not JoinMarket detection",
+ "no empirical rate is attributed to Goldfeder et al.",
+ ],
+ }
+
+
+def load_artifact(path):
+ try:
+ with Path(path).open(encoding="utf-8") as source:
+ value = json.load(source)
+ except (OSError, json.JSONDecodeError) as exc:
+ raise VerificationError(f"cannot read artifact {path}: {exc}") from exc
+ if not isinstance(value, dict):
+ raise VerificationError("artifact root must be an object")
+ return value
+
+
+def verify_artifact(artifact):
+ if artifact.get("schema_version") != 1 or artifact.get("experiment") != EXPERIMENT_ID:
+ raise VerificationError("unsupported candidate-set intersection artifact identity")
+ measured = build_artifact()
+ if canonical_json_bytes(artifact) != canonical_json_bytes(measured):
+ raise VerificationError("artifact differs from a fresh fixture execution")
+ return measured
+
+
+def render_markdown(artifact):
+ rows = []
+ for scenario in artifact["scenarios"]:
+ identified = scenario["identified"]
+ rows.append(
+ f"| {scenario['name']} | {len(scenario['surviving'])} | "
+ f"{scenario['narrowing_bits']} | {identified} | "
+ f"{scenario['inconsistent_at']} |"
+ )
+ return "\n".join([
+ "# Candidate-set intersection fixture",
+ "",
+ "Generated from the canonical experiment artifact. Do not edit manually.",
+ "",
+ "| scenario | survivors | narrowing bits | identified | inconsistent at |",
+ "|---|---:|---:|---:|---:|",
+ *rows,
+ "",
+ "This synthetic run validates the abstract intersection mechanism. It does not measure "
+ "JoinMarket or Bitcoin deanonymization rates.",
+ "",
+ ])
+
+
+def _parser():
+ parser = argparse.ArgumentParser(description=__doc__)
+ commands = parser.add_subparsers(dest="command", required=True)
+ reproduce = commands.add_parser("reproduce")
+ reproduce.add_argument("--artifact", required=True)
+ reproduce.add_argument("--markdown", required=True)
+ verify = commands.add_parser("verify")
+ verify.add_argument("--artifact", required=True)
+ verify.add_argument("--markdown")
+ return parser
+
+
+def main(argv=None):
+ args = _parser().parse_args(argv)
+ if args.command == "reproduce":
+ artifact = build_artifact()
+ write_canonical_json(args.artifact, artifact)
+ destination = Path(args.markdown)
+ destination.parent.mkdir(parents=True, exist_ok=True)
+ destination.write_text(render_markdown(artifact), encoding="utf-8")
+ else:
+ artifact = verify_artifact(load_artifact(args.artifact))
+ if args.markdown is not None:
+ actual = Path(args.markdown).read_text(encoding="utf-8")
+ if actual != render_markdown(artifact):
+ raise VerificationError("Markdown differs from canonical rendering")
+ return 0
+
+
+if __name__ == "__main__":
+ raise SystemExit(main())
diff --git a/decluster/experiments/change_consolidation.py b/decluster/experiments/change_consolidation.py
new file mode 100644
index 00000000..427550ef
--- /dev/null
+++ b/decluster/experiments/change_consolidation.py
@@ -0,0 +1,135 @@
+"""Reproduce the CTP retroactive change-consolidation failure mode."""
+
+from __future__ import annotations
+
+import argparse
+import json
+from pathlib import Path
+
+from ..domain import CandidateNarrowing, NoSharedCandidates
+from ..failure_modes import change_consolidation as failure_mode
+from ..result_artifacts import canonical_json_bytes, write_canonical_json
+
+EXPERIMENT_ID = "change-consolidation-v1"
+
+
+class VerificationError(ValueError):
+ """A stored result differs from a fresh execution."""
+
+
+def _outcome_name(outcome):
+ if isinstance(outcome, CandidateNarrowing):
+ return "candidate_narrowing"
+ if isinstance(outcome, NoSharedCandidates):
+ return "no_shared_candidates"
+ raise TypeError(
+ f"unsupported change-consolidation outcome: {type(outcome).__name__}"
+ )
+
+
+def build_artifact():
+ scenario = failure_mode.ctp_example()
+ report = failure_mode.evaluate(scenario)
+ intersection = report.channels[1].evidence[0]
+ return {
+ "schema_version": 1,
+ "experiment": EXPERIMENT_ID,
+ "scenario": {
+ "payments": [payment.identifier for payment in scenario.payments],
+ "change_outputs": [
+ list(output.identifier) for output in scenario.change_outputs
+ ],
+ "candidate_owners": [
+ sorted(owner.identifier for owner in owners)
+ for owners in scenario.candidate_owners
+ ],
+ },
+ "result": {
+ "candidates_before": intersection.candidates_before,
+ "candidates_after": sorted(
+ candidate.identifier for candidate in intersection.candidates_after
+ ),
+ "outcome": _outcome_name(report.outcomes[0]),
+ "composition": report.composition,
+ },
+ "limitations": list(report.limitations),
+ }
+
+
+def load_artifact(path):
+ try:
+ with Path(path).open(encoding="utf-8") as source:
+ value = json.load(source)
+ except (OSError, json.JSONDecodeError) as exc:
+ raise VerificationError(f"cannot read artifact {path}: {exc}") from exc
+ if not isinstance(value, dict):
+ raise VerificationError("artifact root must be an object")
+ return value
+
+
+def verify_artifact(artifact):
+ if (
+ artifact.get("schema_version") != 1
+ or artifact.get("experiment") != EXPERIMENT_ID
+ ):
+ raise VerificationError("unsupported change-consolidation artifact identity")
+ measured = build_artifact()
+ if canonical_json_bytes(artifact) != canonical_json_bytes(measured):
+ raise VerificationError("artifact differs from a fresh failure-mode execution")
+ return measured
+
+
+def render_markdown(artifact):
+ result = artifact["result"]
+ survivors = (
+ ", ".join(f"`{item}`" for item in result["candidates_after"]) or "none"
+ )
+ return "\n".join([
+ "# Change-consolidation failure mode",
+ "",
+ "Generated from the canonical experiment artifact. Do not edit manually.",
+ "",
+ "| quantity | value |",
+ "|---|---:|",
+ f"| candidate owners before consolidation | {result['candidates_before']} |",
+ f"| candidate owners after consolidation | {len(result['candidates_after'])} |",
+ f"| surviving candidates | {survivors} |",
+ f"| outcome | `{result['outcome']}` |",
+ "",
+ "The linkage requires correct change identification and a valid "
+ "common-ownership premise.",
+ "",
+ ])
+
+
+def _parser():
+ parser = argparse.ArgumentParser(description=__doc__)
+ commands = parser.add_subparsers(dest="command", required=True)
+ reproduce = commands.add_parser("reproduce")
+ reproduce.add_argument("--artifact", required=True)
+ reproduce.add_argument("--markdown", required=True)
+ verify = commands.add_parser("verify")
+ verify.add_argument("--artifact", required=True)
+ verify.add_argument("--markdown")
+ return parser
+
+
+def main(argv=None):
+ args = _parser().parse_args(argv)
+ if args.command == "reproduce":
+ artifact = build_artifact()
+ write_canonical_json(args.artifact, artifact)
+ destination = Path(args.markdown)
+ destination.parent.mkdir(parents=True, exist_ok=True)
+ destination.write_text(render_markdown(artifact), encoding="utf-8")
+ else:
+ artifact = verify_artifact(load_artifact(args.artifact))
+ if args.markdown is not None:
+ actual = Path(args.markdown).read_text(encoding="utf-8")
+ if actual != render_markdown(artifact):
+ raise VerificationError("Markdown differs from canonical rendering")
+ return 0
+
+
+if __name__ == "__main__":
+ raise SystemExit(main())
diff --git a/decluster/experiments/cioh_collaborative.py b/decluster/experiments/cioh_collaborative.py
new file mode 100644
index 00000000..67ad7acd
--- /dev/null
+++ b/decluster/experiments/cioh_collaborative.py
@@ -0,0 +1,119 @@
+"""Reproduce the CIOH false merge on a labelled collaborative fixture."""
+
+from __future__ import annotations
+
+import argparse
+import json
+from pathlib import Path
+
+from ..domain import ClusterMerge, ReferenceOwnershipConflict
+from ..failure_modes.cioh_collaborative import evaluate
+from ..result_artifacts import canonical_json_bytes, write_canonical_json
+
+EXPERIMENT_ID = "cioh-collaborative-false-merge-v1"
+
+
+class VerificationError(ValueError):
+ """A stored result differs from a fresh execution."""
+
+
+def _measurement(owner_by_coin):
+ report = evaluate("collaborative-spend", owner_by_coin)
+ return {
+ "inputs": len(owner_by_coin),
+ "predicted_merges": sum(isinstance(item, ClusterMerge) for item in report.outcomes),
+ "reference_conflicts": sum(
+ isinstance(item, ReferenceOwnershipConflict) for item in report.outcomes
+ ),
+ }
+
+
+def build_artifact():
+ collaborative = _measurement({"alice-input": "alice", "bob-input": "bob"})
+ unilateral = _measurement({"alice-input-1": "alice", "alice-input-2": "alice"})
+ return {
+ "schema_version": 1,
+ "experiment": EXPERIMENT_ID,
+ "collaborative": collaborative,
+ "same_owner_control": unilateral,
+ "composition": None,
+ "conclusion": "CIOH merges both co-spends, but only the labelled collaborative fixture conflicts with its supplied ownership partition",
+ "limitations": list(
+ evaluate(
+ "collaborative-spend", {"alice-input": "alice", "bob-input": "bob"}
+ ).limitations
+ ),
+ }
+
+
+def load_artifact(path):
+ try:
+ with Path(path).open(encoding="utf-8") as source:
+ value = json.load(source)
+ except (OSError, json.JSONDecodeError) as exc:
+ raise VerificationError(f"cannot read artifact {path}: {exc}") from exc
+ if not isinstance(value, dict):
+ raise VerificationError("artifact root must be an object")
+ return value
+
+
+def verify_artifact(artifact):
+ if artifact.get("schema_version") != 1 or artifact.get("experiment") != EXPERIMENT_ID:
+ raise VerificationError("unsupported CIOH collaborative artifact identity")
+ measured = build_artifact()
+ if canonical_json_bytes(artifact) != canonical_json_bytes(measured):
+ raise VerificationError("artifact differs from a fresh failure-mode execution")
+ return measured
+
+
+def render_markdown(artifact):
+ collaborative = artifact["collaborative"]
+ control = artifact["same_owner_control"]
+ return "\n".join([
+ "# CIOH false merge in a collaborative transaction",
+ "",
+ "Generated from the canonical experiment artifact. Do not edit manually.",
+ "",
+ "| fixture | inputs | predicted merges | reference conflicts |",
+ "|---|---:|---:|---:|",
+ f"| collaborative | {collaborative['inputs']} | {collaborative['predicted_merges']} | {collaborative['reference_conflicts']} |",
+ f"| same-owner control | {control['inputs']} | {control['predicted_merges']} | {control['reference_conflicts']} |",
+ "",
+ "CIOH observes only the co-spend and therefore merges both pairs. The collaborative fixture's supplied participant labels expose that merge as false.",
+ "",
+ "The labels are part of the deterministic fixture. This result does not estimate chain-wide prevalence or infer real ownership.",
+ "",
+ ])
+
+
+def _parser():
+ parser = argparse.ArgumentParser(description=__doc__)
+ commands = parser.add_subparsers(dest="command", required=True)
+ reproduce = commands.add_parser("reproduce")
+ reproduce.add_argument("--artifact", required=True)
+ reproduce.add_argument("--markdown", required=True)
+ verify = commands.add_parser("verify")
+ verify.add_argument("--artifact", required=True)
+ verify.add_argument("--markdown")
+ return parser
+
+
+def main(argv=None):
+ args = _parser().parse_args(argv)
+ if args.command == "reproduce":
+ artifact = build_artifact()
+ write_canonical_json(args.artifact, artifact)
+ destination = Path(args.markdown)
+ destination.parent.mkdir(parents=True, exist_ok=True)
+ destination.write_text(render_markdown(artifact), encoding="utf-8")
+ else:
+ artifact = verify_artifact(load_artifact(args.artifact))
+ if args.markdown is not None and Path(args.markdown).read_text(
+ encoding="utf-8"
+ ) != render_markdown(artifact):
+ raise VerificationError("Markdown differs from canonical rendering")
+ return 0
+
+
+if __name__ == "__main__":
+ raise SystemExit(main())
diff --git a/decluster/experiments/cluster_bits.py b/decluster/experiments/cluster_bits.py
new file mode 100644
index 00000000..c931277d
--- /dev/null
+++ b/decluster/experiments/cluster_bits.py
@@ -0,0 +1,191 @@
+"""What a co-spend cluster carries as a quasi-identifier, on committed transactions.
+
+The paper's §2 rests on an asymmetry: a merged transaction contributes about 1.6 bits of ambiguity,
+and an established cluster carries far more identifying structure than that, so the partition is
+decidable without the merge. The figure behind it was measured on a slice that no longer exists,
+which left the argument's load-bearing number unverifiable.
+
+The quantity is the Narayanan--Shmatikov accumulation on real transactions. A cluster's identifier
+is the set of external counterparties it pays or is paid by, co-spend edges excluded so the signal
+is not circular and its own members excluded because an intra-cluster edge tells an outsider
+nothing. Each counterparty is worth `-log2(share of nodes touching it)`: a hub everyone touches is
+close to zero, a rare address is many.
+
+The committed cache is block-sampled rather than contiguous, so it holds fewer counterparties per
+cluster than a connected slice would. That makes every bit count here a floor, and the comparison
+the paper needs — against 1.6 — is one a floor can settle.
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+import statistics
+from pathlib import Path
+from tempfile import TemporaryDirectory
+
+from ..archive_snapshot import extract_tar_gz
+from ..cluster import _agg, counterparty_bits
+from ..fingerprint_validate import load_blkcache
+from ..graph_deanon import _clusters, build
+from ..reproducibility import fingerprint_source
+from ..result_artifacts import canonical_json_bytes, write_canonical_json
+
+EXPERIMENT_ID = "cluster-bits-v1"
+MERGE_AMBIGUITY_BITS = 1.6 # the merge's contribution the asymmetry is stated against
+TOPK = 5
+THRESHOLDS = (1.6, 10.0, 50.0, 100.0)
+
+
+class VerificationError(ValueError):
+ """The stored result differs from a fresh execution."""
+
+
+def _rows(transactions):
+ sample = [(tx, 0) for tx in transactions]
+ union, _full, payments, _cospent = build(sample)
+ bits = counterparty_bits(payments)
+ rows = []
+ for members in _clusters(union, payments).values():
+ counterparties = _agg(members, payments) - set(members)
+ if not counterparties:
+ continue
+ per = sorted((bits.get(c, 0.0) for c in counterparties), reverse=True)
+ rows.append({"members": len(members), "counterparties": len(counterparties),
+ "total_bits": sum(per), "topk_bits": sum(per[:TOPK])})
+ return rows
+
+
+def build_artifact(snapshot="data/fs-blkcache-2026-09-04.tar.gz"):
+ with TemporaryDirectory(prefix="decluster-bits-") as temporary:
+ root = extract_tar_gz(snapshot, temporary)
+ cache = root / ".blkcache"
+ transactions = load_blkcache(str(cache))
+ source = fingerprint_source(str(cache / "*.json"))
+ source.update(pattern=".blkcache/*.json", transactions=len(transactions))
+
+ rows = [row for row in _rows(transactions) if row["total_bits"] > 0]
+ totals = sorted(row["total_bits"] for row in rows)
+ floors = sorted(row["topk_bits"] for row in rows)
+ quantile = lambda seq, q: seq[min(int(len(seq) * q), len(seq) - 1)] if seq else 0.0
+ return {
+ "schema_version": 1,
+ "experiment": EXPERIMENT_ID,
+ "dataset": "fs-blkcache-2026-09-04-v1",
+ "source": source,
+ "parameters": {"topk": TOPK, "merge_ambiguity_bits": MERGE_AMBIGUITY_BITS,
+ "graph": "payment edges only, co-spend excluded"},
+ "population": {
+ "transactions": len(transactions), "clusters": len(rows),
+ "clusters_with_at_most_topk_counterparties":
+ sum(1 for row in rows if row["counterparties"] <= TOPK),
+ },
+ "bits": {
+ "median": round(statistics.median(totals), 4) if totals else 0.0,
+ "p90": round(quantile(totals, 0.9), 4),
+ "max": round(max(totals), 4) if totals else 0.0,
+ "median_topk_floor": round(statistics.median(floors), 4) if floors else 0.0,
+ },
+ "share_at_least": {f"{t:g}": round(sum(1 for v in totals if v >= t) / len(totals), 6)
+ for t in THRESHOLDS} if totals else {},
+ }
+
+
+def load_artifact(path):
+ try:
+ with Path(path).open(encoding="utf-8") as source:
+ value = json.load(source)
+ except (OSError, json.JSONDecodeError) as exc:
+ raise VerificationError(f"cannot read artifact {path}: {exc}") from exc
+ if not isinstance(value, dict):
+ raise VerificationError("artifact root must be an object")
+ return value
+
+
+def verify_artifact(artifact, snapshot="data/fs-blkcache-2026-09-04.tar.gz"):
+ if artifact.get("schema_version") != 1 or artifact.get("experiment") != EXPERIMENT_ID:
+ raise VerificationError("unsupported cluster-bits artifact identity")
+ measured = build_artifact(snapshot)
+ if canonical_json_bytes(artifact) != canonical_json_bytes(measured):
+ raise VerificationError("artifact differs from a fresh experiment execution")
+ return measured
+
+
+def render_markdown(artifact):
+ bits, population = artifact["bits"], artifact["population"]
+ share = artifact["share_at_least"]
+ merge = artifact["parameters"]["merge_ambiguity_bits"]
+ lines = [
+ "# What a cluster carries against what a merge hides",
+ "",
+ "Generated from the canonical experiment artifact. Do not edit manually.",
+ "",
+ f"{population['clusters']} co-spend clusters in {population['transactions']} committed "
+ f"transactions, each scored by the rarity of the external counterparties it pays or is paid "
+ f"by. Co-spend edges are excluded, so the identifier is not the thing that built the cluster.",
+ "",
+ "| quantity | bits |",
+ "|---|---:|",
+ f"| median cluster | {bits['median']:.1f} |",
+ f"| p90 cluster | {bits['p90']:.1f} |",
+ f"| largest cluster | {bits['max']:.1f} |",
+ f"| median, five rarest counterparties only | {bits['median_topk_floor']:.1f} |",
+ "",
+ "| threshold | share of clusters at or above |",
+ "|---:|---:|",
+ ]
+ for threshold in sorted(share, key=float):
+ lines.append(f"| {threshold} bits | {share[threshold]:.1%} |")
+ lines.extend([
+ "",
+ f"The asymmetry the argument needs is the first row against {merge}: a merged transaction "
+ f"contributes about {merge} bits of ambiguity, and {share.get(str(merge), 0):.1%} of these "
+ f"clusters already carry at least that much, at a median of {bits['median']:.1f}.",
+ "",
+ f"The five-rarest floor does not test that on this cache: "
+ f"{population['clusters_with_at_most_topk_counterparties'] / population['clusters']:.0%} of "
+ f"these clusters have at most {artifact['parameters']['topk']} counterparties, so the "
+ f"restriction usually keeps the whole set and its median "
+ f"({bits['median_topk_floor']:.1f}) repeats the one above it. A slice with richer "
+ f"neighbourhoods is what would make that floor say something about dependence between "
+ f"counterparties; here it says only that the clusters are small.",
+ "",
+ "The cache is block-sampled rather than contiguous, so a cluster's counterparties are "
+ "undercounted relative to a connected slice and every figure here is a floor. That is the "
+ "direction the argument can use: a floor above the merge's contribution settles the "
+ "comparison, and a larger slice can only raise it.",
+ "",
+ "## Reproducibility / provenance",
+ "",
+ "State 1 in `results/REPRODUCIBILITY.md`: band-pinned on committed data — the artifact is "
+ "recomputed from `data/fs-blkcache-2026-09-04.tar.gz` and compared byte for byte.",
+ ])
+ return "\n".join(lines) + "\n"
+
+
+def _parser():
+ parser = argparse.ArgumentParser(description=__doc__.splitlines()[0])
+ parser.add_argument("command", choices=("reproduce", "verify"))
+ parser.add_argument("--snapshot", default="data/fs-blkcache-2026-09-04.tar.gz")
+ parser.add_argument("--artifact", required=True)
+ parser.add_argument("--markdown", required=True)
+ return parser
+
+
+def main(argv=None):
+ args = _parser().parse_args(argv)
+ if args.command == "reproduce":
+ artifact = build_artifact(args.snapshot)
+ write_canonical_json(args.artifact, artifact)
+ destination = Path(args.markdown)
+ destination.parent.mkdir(parents=True, exist_ok=True)
+ destination.write_text(render_markdown(artifact), encoding="utf-8")
+ else:
+ artifact = verify_artifact(load_artifact(args.artifact), args.snapshot)
+ if Path(args.markdown).read_text(encoding="utf-8") != render_markdown(artifact):
+ raise VerificationError("Markdown differs from canonical rendering")
+ return 0
+
+
+if __name__ == "__main__":
+ raise SystemExit(main())
diff --git a/decluster/experiments/coinjoin_sudoku.py b/decluster/experiments/coinjoin_sudoku.py
new file mode 100644
index 00000000..c67e2ee9
--- /dev/null
+++ b/decluster/experiments/coinjoin_sudoku.py
@@ -0,0 +1,161 @@
+"""Run the independently reconstructed nominal CoinJoin Sudoku mechanism."""
+
+from __future__ import annotations
+
+import argparse
+import json
+from pathlib import Path
+
+from ..baselines.coinjoin_sudoku import (
+ collapse_fee_permutations,
+ equal_sum_groupings,
+ maximally_separated_groupings,
+ uniform_link_probabilities,
+)
+from ..result_artifacts import canonical_json_bytes, write_canonical_json
+
+EXPERIMENT_ID = "coinjoin-sudoku-nominal-v1"
+SOURCE_REVISION = "afbd2658b4ac9e0711b12c1bd4ab228ea5dd2499"
+
+
+class VerificationError(ValueError):
+ """A stored result differs from a fresh nominal reconstruction."""
+
+
+def build_artifact():
+ sx_inputs = (1_010_000,) * 3
+ sx_outputs = (1_000_000,) * 3
+ sx_all = equal_sum_groupings(sx_inputs, sx_outputs, fee_unit=10_000)
+ sx_selected = maximally_separated_groupings(
+ sx_inputs, sx_outputs, fee_unit=10_000
+ )
+ return {
+ "schema_version": 1,
+ "experiment": EXPERIMENT_ID,
+ "source": {
+ "advisory": "https://www.coinjoinsudoku.com/advisory/",
+ "repository_revision": SOURCE_REVISION,
+ "historical_analyzer_available": False,
+ },
+ "published_mechanism": {
+ "balance": "exact integer sum per paired group",
+ "selection": "all valid equal-sum groupings",
+ "fee_constraint": "fee is a multiple of a constant, currently 0.0001 BTC",
+ },
+ "local_adaptation": {
+ "selection": "valid interpretations with the largest group count",
+ "weighting": "uniform over indexed selected groupings",
+ "fee_model": "the fee split into explicit equal pseudo-outputs of one fee unit",
+ "purpose": "reconstruct the SX symmetry argument under explicit semantics",
+ },
+ "advisory_group_examples": {
+ "valid_2_plus_3_to_1_plus_4": len(equal_sum_groupings((2, 3), (1, 4))),
+ "invalid_2_plus_3_to_1_plus_2": len(equal_sum_groupings((2, 3), (1, 2))),
+ },
+ "sx_symmetric_control": {
+ "txid": "9256332b9ca52cbcb06f57296dfd982d8da3f7d4696b4c10cf9bb93dae6edf58",
+ "inputs_sat": list(sx_inputs),
+ "outputs_sat": list(sx_outputs),
+ "fee_sat": 30_000,
+ "fee_unit_sat": 10_000,
+ "all_equal_sum_groupings": len(sx_all),
+ "selected_indexed_groupings": len(sx_selected),
+ "selected_groupings_collapsing_fee_permutations": len(
+ collapse_fee_permutations(sx_selected, len(sx_outputs))
+ ),
+ "local_uniform_real_output_link_probabilities": uniform_link_probabilities(
+ sx_inputs, sx_outputs, fee_unit=10_000
+ ),
+ },
+ "sharedcoin_historical_target": {
+ "txid": "0e0337bdf930eba3b082fdfbd30944b18e03f0f810ae531443161f897a4d3db0",
+ "reported_grouped_inputs_percent": 69,
+ "reported_grouped_outputs_percent": 53,
+ "status": "not_reproduced",
+ "reason": "the analyzer and complete output-label semantics were not published",
+ },
+ "composition": None,
+ "conclusion": "the published mechanism accepts the advisory's balanced example; a separately labelled maximal/uniform adaptation recovers the SX control's one-third symmetry",
+ "limitations": [
+ "this is an independent reconstruction, not parity with the unavailable historical analyzer",
+ "largest-group selection, uniform weighting and the fee pseudo-output split are local semantics, not published analyzer rules",
+ "the marginals are a function of the local fee unit: at one 30,000-sat unit the same control reads 1.0 instead of one third",
+ "digit skipping and the reported 2014 SharedCoin grouping result are not reproduced",
+ "amount relations within this family are not ownership attribution",
+ ],
+ }
+
+
+def load_artifact(path):
+ try:
+ with Path(path).open(encoding="utf-8") as source:
+ value = json.load(source)
+ except (OSError, json.JSONDecodeError) as exc:
+ raise VerificationError(f"cannot read artifact {path}: {exc}") from exc
+ if not isinstance(value, dict):
+ raise VerificationError("artifact root must be an object")
+ return value
+
+
+def verify_artifact(artifact):
+ if artifact.get("schema_version") != 1 or artifact.get("experiment") != EXPERIMENT_ID:
+ raise VerificationError("unsupported CoinJoin Sudoku artifact identity")
+ measured = build_artifact()
+ if canonical_json_bytes(artifact) != canonical_json_bytes(measured):
+ raise VerificationError("artifact differs from a fresh nominal reconstruction")
+ return measured
+
+
+def render_markdown(artifact):
+ sx = artifact["sx_symmetric_control"]
+ probability = sx["local_uniform_real_output_link_probabilities"][0][0]
+ target = artifact["sharedcoin_historical_target"]
+ return "\n".join([
+ "# CoinJoin Sudoku nominal reconstruction",
+ "",
+ "Generated from the canonical experiment artifact. Do not edit manually.",
+ "",
+ "The independently implemented nominal mechanism retains exact equal-sum input/output groupings. The cited repository does not contain the historical analyzer, so this result does not claim code parity.",
+ "",
+ "| primary-source fixture | retained mappings | representative input/output relation |",
+ "|---|---:|---:|",
+ f"| SX symmetric control | {sx['selected_indexed_groupings']} indexed / {sx['selected_groupings_collapsing_fee_permutations']} fee-collapsed | {probability:.6f} |",
+ "",
+ f"The advisory's larger SharedCoin target `{target['txid']}` remains **not reproduced**: its analyzer and complete output-label semantics were not published.",
+ "",
+ "The source-backed layer enumerates all exact equal-sum groupings. The displayed marginal comes from a separate local adaptation: explicit 10,000-sat fee pseudo-outputs, largest-group selection, and uniform weighting. Those rules are not attributed to the unavailable analyzer, and the resulting conditional amount relations are not ownership attribution.",
+ "",
+ ])
+
+
+def _parser():
+ parser = argparse.ArgumentParser(description=__doc__)
+ commands = parser.add_subparsers(dest="command", required=True)
+ reproduce = commands.add_parser("reproduce")
+ reproduce.add_argument("--artifact", required=True)
+ reproduce.add_argument("--markdown", required=True)
+ verify = commands.add_parser("verify")
+ verify.add_argument("--artifact", required=True)
+ verify.add_argument("--markdown")
+ return parser
+
+
+def main(argv=None):
+ args = _parser().parse_args(argv)
+ if args.command == "reproduce":
+ artifact = build_artifact()
+ write_canonical_json(args.artifact, artifact)
+ destination = Path(args.markdown)
+ destination.parent.mkdir(parents=True, exist_ok=True)
+ destination.write_text(render_markdown(artifact), encoding="utf-8")
+ else:
+ artifact = verify_artifact(load_artifact(args.artifact))
+ if args.markdown is not None and Path(args.markdown).read_text(
+ encoding="utf-8"
+ ) != render_markdown(artifact):
+ raise VerificationError("Markdown differs from canonical rendering")
+ return 0
+
+
+if __name__ == "__main__":
+ raise SystemExit(main())
diff --git a/decluster/experiments/collaborative_forms.py b/decluster/experiments/collaborative_forms.py
new file mode 100644
index 00000000..89afc76b
--- /dev/null
+++ b/decluster/experiments/collaborative_forms.py
@@ -0,0 +1,154 @@
+"""Publish the CTP observer-knowledge matrix without inferring protocol or ownership."""
+
+from __future__ import annotations
+
+import argparse
+import json
+from pathlib import Path
+
+from ..failure_modes.collaborative_forms import (
+ CollaborativeForm,
+ Observer,
+ form_matrix,
+ knowledge,
+)
+from ..result_artifacts import canonical_json_bytes, write_canonical_json
+
+EXPERIMENT_ID = "collaborative-forms-v1"
+
+
+class VerificationError(ValueError):
+ """A stored result differs from the executable observer-knowledge matrix."""
+
+
+def _observation(contract):
+ return {
+ "inputs_sat": list(contract.observation.inputs),
+ "outputs_sat": list(contract.observation.outputs),
+ }
+
+
+def _row(contract):
+ return {
+ "form": contract.form.value,
+ "participants": contract.participants,
+ "observation": _observation(contract),
+ "transport": contract.transport,
+ "external_onchain_knowledge": list(knowledge(contract, Observer.EXTERNAL)),
+ "counterparty_knowledge": list(knowledge(contract, Observer.COUNTERPARTY)),
+ "limitations": list(contract.limitations),
+ }
+
+
+def build_artifact():
+ contracts = form_matrix()
+ two_party = (
+ CollaborativeForm.ORDINARY_TWO_INPUT,
+ CollaborativeForm.P2EP,
+ CollaborativeForm.BIP79,
+ CollaborativeForm.BIP78,
+ CollaborativeForm.BIP77,
+ )
+ rows = [_row(contract) for contract in contracts]
+ by_form = {row["form"]: row for row in rows}
+ return {
+ "schema_version": 1,
+ "experiment": EXPERIMENT_ID,
+ "claim": "observable transaction data do not by themselves identify the collaborative form, participant allocation, payment graph or ownership",
+ "two_party_onchain_equivalence": {
+ "forms": [form.value for form in two_party],
+ "observation": by_form[two_party[0].value]["observation"],
+ "distinct_transports": len({
+ by_form[form.value]["transport"] for form in two_party
+ }),
+ },
+ "forms": rows,
+ "conclusion": "identical on-chain amounts remain compatible with an ordinary spend and several two-party PayJoin protocols; counterparty knowledge must not be assigned to an external observer",
+ "limitations": [
+ "the participant allocations and payment relationships are supplied model possibilities",
+ "protocol deployment frequency and empirical indistinguishability are not measured",
+ "transport differences are not treated as on-chain observations",
+ "the matrix is not ownership attribution or a privacy score",
+ ],
+ }
+
+
+def load_artifact(path):
+ try:
+ with Path(path).open(encoding="utf-8") as source:
+ value = json.load(source)
+ except (OSError, json.JSONDecodeError) as exc:
+ raise VerificationError(f"cannot read artifact {path}: {exc}") from exc
+ if not isinstance(value, dict):
+ raise VerificationError("artifact root must be an object")
+ return value
+
+
+def verify_artifact(artifact):
+ if artifact.get("schema_version") != 1 or artifact.get("experiment") != EXPERIMENT_ID:
+ raise VerificationError("unsupported collaborative-forms artifact identity")
+ measured = build_artifact()
+ if canonical_json_bytes(artifact) != canonical_json_bytes(measured):
+ raise VerificationError("artifact differs from the executable observer-knowledge matrix")
+ return measured
+
+
+def render_markdown(artifact):
+ rows = artifact["forms"]
+ equivalent = artifact["two_party_onchain_equivalence"]
+ lines = [
+ "# Collaborative transaction observer-knowledge matrix",
+ "",
+ "Generated from the canonical experiment artifact. Do not edit manually.",
+ "",
+ "The same two-input/two-output observation is compatible with an ordinary transaction and four PayJoin protocol forms. This does not identify which protocol form occurred.",
+ "",
+ "| form | participants | external on-chain facts | counterparty facts |",
+ "|---|---:|---|---|",
+ ]
+ for row in rows:
+ participants = "latent" if row["participants"] is None else row["participants"]
+ external = "; ".join(row["external_onchain_knowledge"])
+ counterparty = "; ".join(row["counterparty_knowledge"]) or "not applicable"
+ lines.append(f"| {row['form']} | {participants} | {external} | {counterparty} |")
+ lines.extend([
+ "",
+ f"The shared observation is inputs `{equivalent['observation']['inputs_sat']}` and outputs `{equivalent['observation']['outputs_sat']}`. Protocol transport, participant allocation and negotiated payments are not added to the external observer's on-chain knowledge.",
+ "",
+ "All allocations and relationships in this fixture are supplied possible worlds. The result is not ownership attribution, a deployment-frequency measurement or a privacy score.",
+ "",
+ ])
+ return "\n".join(lines)
+
+
+def _parser():
+ parser = argparse.ArgumentParser(description=__doc__)
+ commands = parser.add_subparsers(dest="command", required=True)
+ reproduce = commands.add_parser("reproduce")
+ reproduce.add_argument("--artifact", required=True)
+ reproduce.add_argument("--markdown", required=True)
+ verify = commands.add_parser("verify")
+ verify.add_argument("--artifact", required=True)
+ verify.add_argument("--markdown")
+ return parser
+
+
+def main(argv=None):
+ args = _parser().parse_args(argv)
+ if args.command == "reproduce":
+ artifact = build_artifact()
+ write_canonical_json(args.artifact, artifact)
+ destination = Path(args.markdown)
+ destination.parent.mkdir(parents=True, exist_ok=True)
+ destination.write_text(render_markdown(artifact), encoding="utf-8")
+ else:
+ artifact = verify_artifact(load_artifact(args.artifact))
+ if args.markdown is not None and Path(args.markdown).read_text(
+ encoding="utf-8"
+ ) != render_markdown(artifact):
+ raise VerificationError("Markdown differs from canonical rendering")
+ return 0
+
+
+if __name__ == "__main__":
+ raise SystemExit(main())
diff --git a/decluster/experiments/conservation_round_three.py b/decluster/experiments/conservation_round_three.py
new file mode 100644
index 00000000..1346ead0
--- /dev/null
+++ b/decluster/experiments/conservation_round_three.py
@@ -0,0 +1,176 @@
+"""Reproduce the reduced third-round conservation observation."""
+
+from __future__ import annotations
+
+import argparse
+import json
+from pathlib import Path
+
+from ..conservation import forced_in_round, forced_prefixes, forced_value, slack_to_force_one_more
+from ..result_artifacts import canonical_json_bytes, write_canonical_json
+
+EXPERIMENT_ID = "conservation-round-three-v1"
+DATASET = "tests/fixtures/conservation_round_three.json"
+
+
+class VerificationError(ValueError):
+ """A fixture or stored result violates this experiment's contract."""
+
+
+def load_fixture(path=DATASET):
+ try:
+ with Path(path).open(encoding="utf-8") as source:
+ value = json.load(source)
+ except (OSError, json.JSONDecodeError) as exc:
+ raise VerificationError(f"cannot read conservation fixture {path}: {exc}") from exc
+ if not isinstance(value, dict) or value.get("schema_version") != 1:
+ raise VerificationError("unsupported conservation fixture identity")
+ if not isinstance(value.get("txid"), str) or len(value["txid"]) != 64:
+ raise VerificationError("fixture txid must be a 64-character string")
+ for field in ("total_input", "known_participant_input"):
+ if not isinstance(value.get(field), int) or value[field] <= 0:
+ raise VerificationError(f"fixture {field} must be a positive integer")
+ outputs = value.get("outputs")
+ if not isinstance(outputs, list) or not outputs:
+ raise VerificationError("fixture outputs must be a non-empty list")
+ if any(
+ not isinstance(row, list)
+ or len(row) != 2
+ or not all(isinstance(item, int) and item > 0 for item in row)
+ for row in outputs
+ ):
+ raise VerificationError("each output row must contain positive integer value and count")
+ if len({row[0] for row in outputs}) != len(outputs):
+ raise VerificationError("fixture output values must be unique")
+ return value
+
+
+def _transaction(fixture):
+ return {
+ "vin": [{"prevout": {"value": fixture["total_input"]}}],
+ "vout": [
+ {"value": value}
+ for value, count in fixture["outputs"]
+ for _ in range(count)
+ ],
+ }
+
+
+def build_artifact(dataset=DATASET):
+ fixture = load_fixture(dataset)
+ transaction = _transaction(fixture)
+ known_input = fixture["known_participant_input"]
+ forced = forced_in_round(transaction, known_input)
+ prefixes = forced_prefixes(transaction, known_input)
+ if not forced:
+ raise VerificationError("fixture does not produce a conservation observation")
+ value, count, present = forced[0]
+ return {
+ "schema_version": 1,
+ "experiment": EXPERIMENT_ID,
+ "fixture": {
+ "txid": fixture["txid"],
+ "inputs": 454,
+ "outputs": len(transaction["vout"]),
+ "total_input": fixture["total_input"],
+ "known_participant_input": known_input,
+ },
+ "largest_forced_denomination": {
+ "value": value,
+ "forced": count,
+ "present": present,
+ "forced_value": forced_value(transaction, known_input),
+ "margin": slack_to_force_one_more(
+ fixture["total_input"], known_input, value, present
+ ),
+ },
+ "prefix_curve": [
+ {"values": values, "forced_value": amount, "minimum_coins": coins, "pool": pool}
+ for values, amount, coins, pool in prefixes
+ ],
+ "interpretation": "forced provenance of value under conservation",
+ "limitations": [
+ "the fixture is a reduced representation of one mainnet transaction",
+ "the run does not reproduce the surrounding six-round chain",
+ "the known participant input is supplied by an external forward walk",
+ "conservation establishes provenance of value, not output ownership",
+ "net settlement invalidates an ownership inference from this bound",
+ "the result is not a privacy score or CoinScore",
+ ],
+ }
+
+
+def load_artifact(path):
+ try:
+ with Path(path).open(encoding="utf-8") as source:
+ value = json.load(source)
+ except (OSError, json.JSONDecodeError) as exc:
+ raise VerificationError(f"cannot read artifact {path}: {exc}") from exc
+ if not isinstance(value, dict):
+ raise VerificationError("artifact root must be an object")
+ return value
+
+
+def verify_artifact(artifact, *, dataset=DATASET):
+ if artifact.get("schema_version") != 1 or artifact.get("experiment") != EXPERIMENT_ID:
+ raise VerificationError("unsupported conservation artifact identity")
+ measured = build_artifact(dataset)
+ if canonical_json_bytes(artifact) != canonical_json_bytes(measured):
+ raise VerificationError("artifact differs from a fresh experiment execution")
+ return measured
+
+
+def render_markdown(artifact):
+ fixture = artifact["fixture"]
+ forced = artifact["largest_forced_denomination"]
+ return "\n".join([
+ "# Conservation observation for round three",
+ "",
+ "Generated from the canonical experiment artifact. Do not edit manually.",
+ "",
+ f"The reduced mainnet fixture `{fixture['txid']}` contains "
+ f"{fixture['outputs']} outputs. Conservation forces {forced['forced']} of the "
+ f"{forced['present']} outputs worth {forced['value']} sat onto value supplied by "
+ "the known participant.",
+ "",
+ f"The forced value is {forced['forced_value']} sat and the margin to the next "
+ f"boundary is {forced['margin']} sat.",
+ "",
+ "This is provenance of value under conservation, not output ownership, identity "
+ "attribution, a population estimate or CoinScore.",
+ "",
+ ])
+
+
+def _parser():
+ parser = argparse.ArgumentParser(description=__doc__)
+ parser.add_argument("--dataset", default=DATASET)
+ commands = parser.add_subparsers(dest="command", required=True)
+ reproduce = commands.add_parser("reproduce")
+ reproduce.add_argument("--artifact", required=True)
+ reproduce.add_argument("--markdown", required=True)
+ verify = commands.add_parser("verify")
+ verify.add_argument("--artifact", required=True)
+ verify.add_argument("--markdown")
+ return parser
+
+
+def main(argv=None):
+ args = _parser().parse_args(argv)
+ if args.command == "reproduce":
+ artifact = build_artifact(args.dataset)
+ write_canonical_json(args.artifact, artifact)
+ destination = Path(args.markdown)
+ destination.parent.mkdir(parents=True, exist_ok=True)
+ destination.write_text(render_markdown(artifact), encoding="utf-8")
+ else:
+ artifact = verify_artifact(load_artifact(args.artifact), dataset=args.dataset)
+ if args.markdown is not None:
+ actual = Path(args.markdown).read_text(encoding="utf-8")
+ if actual != render_markdown(artifact):
+ raise VerificationError("Markdown differs from canonical rendering")
+ return 0
+
+
+if __name__ == "__main__":
+ raise SystemExit(main())
diff --git a/decluster/experiments/counting_fee_sensitivity.py b/decluster/experiments/counting_fee_sensitivity.py
new file mode 100644
index 00000000..41403bc6
--- /dev/null
+++ b/decluster/experiments/counting_fee_sensitivity.py
@@ -0,0 +1,127 @@
+"""Measure how the counting interfaces react to fees on a fixed transaction shape."""
+
+from __future__ import annotations
+
+import argparse
+import json
+from pathlib import Path
+
+from ..counting import count_w, per_coin_log_w, w_total
+from ..result_artifacts import canonical_json_bytes, write_canonical_json
+
+EXPERIMENT_ID = "counting-fee-sensitivity-v1"
+INPUTS = (400_000, 300_000, 300_000)
+OUTPUT_TOTAL = 1_000_000
+FEES = (0, 1, 10, 100, 1_000, 4_956)
+
+
+class VerificationError(ValueError):
+ """A stored result differs from a fresh execution."""
+
+
+def build_artifact():
+ observations = []
+ for fee in FEES:
+ outputs = (600_000, 400_000 - fee)
+ cascade = w_total(INPUTS, outputs)
+ router = count_w(INPUTS, outputs)
+ per_coin = per_coin_log_w(INPUTS, outputs)
+ observations.append({
+ "fee_sat": fee,
+ "outputs_sat": list(outputs),
+ "cascade": cascade,
+ "router": router,
+ "measured_input_indices": sorted(per_coin),
+ "per_coin_log_w": {str(index): per_coin[index] for index in sorted(per_coin)},
+ })
+ return {
+ "schema_version": 1,
+ "experiment": EXPERIMENT_ID,
+ "fixture": {"inputs_sat": list(INPUTS), "output_total_before_fee_sat": OUTPUT_TOTAL},
+ "observations": observations,
+ "limitations": [
+ "this is a deterministic sensitivity fixture, not a population estimate",
+ "the transaction-level subset-sum count and fee-aware per-coin count are "
+ "different objects",
+ "an absent per-coin index is a refusal to measure, not zero ambiguity",
+ "these diagnostics are not mapping entropy, a privacy score, or CoinScore",
+ ],
+ }
+
+
+def load_artifact(path):
+ try:
+ with Path(path).open(encoding="utf-8") as source:
+ value = json.load(source)
+ except (OSError, json.JSONDecodeError) as exc:
+ raise VerificationError(f"cannot read artifact {path}: {exc}") from exc
+ if not isinstance(value, dict):
+ raise VerificationError("artifact root must be an object")
+ return value
+
+
+def verify_artifact(artifact):
+ if artifact.get("schema_version") != 1 or artifact.get("experiment") != EXPERIMENT_ID:
+ raise VerificationError("unsupported counting-fee-sensitivity artifact identity")
+ measured = build_artifact()
+ if canonical_json_bytes(artifact) != canonical_json_bytes(measured):
+ raise VerificationError("artifact differs from a fresh experiment execution")
+ return measured
+
+
+def render_markdown(artifact):
+ rows = [
+ "# Counting fee sensitivity",
+ "",
+ "Generated from the canonical experiment artifact. Do not edit manually.",
+ "",
+ "| fee (sat) | transaction count | per-coin inputs measured |",
+ "|---:|---:|---:|",
+ ]
+ for observation in artifact["observations"]:
+ rows.append(
+ f"| {observation['fee_sat']:,} | {observation['router']['count']} | "
+ f"{len(observation['measured_input_indices'])} |"
+ )
+ rows.extend([
+ "",
+ "The transaction-level result remains defined after the per-coin interface stops measuring "
+ "any input. The two interfaces therefore must not be substituted for one another.",
+ "",
+ "This fixture is a mechanism check, not a population estimate or privacy score.",
+ "",
+ ])
+ return "\n".join(rows)
+
+
+def _parser():
+ parser = argparse.ArgumentParser(description=__doc__)
+ commands = parser.add_subparsers(dest="command", required=True)
+ reproduce = commands.add_parser("reproduce")
+ reproduce.add_argument("--artifact", required=True)
+ reproduce.add_argument("--markdown", required=True)
+ verify = commands.add_parser("verify")
+ verify.add_argument("--artifact", required=True)
+ verify.add_argument("--markdown")
+ return parser
+
+
+def main(argv=None):
+ args = _parser().parse_args(argv)
+ if args.command == "reproduce":
+ artifact = build_artifact()
+ write_canonical_json(args.artifact, artifact)
+ destination = Path(args.markdown)
+ destination.parent.mkdir(parents=True, exist_ok=True)
+ destination.write_text(render_markdown(artifact), encoding="utf-8")
+ else:
+ artifact = verify_artifact(load_artifact(args.artifact))
+ if args.markdown is not None:
+ actual = Path(args.markdown).read_text(encoding="utf-8")
+ if actual != render_markdown(artifact):
+ raise VerificationError("Markdown differs from canonical rendering")
+ return 0
+
+
+if __name__ == "__main__":
+ raise SystemExit(main())
diff --git a/decluster/experiments/counting_methods_summary.py b/decluster/experiments/counting_methods_summary.py
new file mode 100644
index 00000000..0ed5544c
--- /dev/null
+++ b/decluster/experiments/counting_methods_summary.py
@@ -0,0 +1,173 @@
+"""Compose the counting-method report from canonical experiment artifacts."""
+
+from __future__ import annotations
+
+import argparse
+import hashlib
+import json
+from pathlib import Path
+
+from ..result_artifacts import canonical_json_bytes, write_canonical_json
+
+EXPERIMENT_ID = "counting-methods-v1"
+COMPONENTS = {
+ "router": (
+ "results/artifacts/counting-router-v1.json",
+ "counting-router-v1",
+ "7c0764b7f39ad8795b4e349cff0903f68b7edcbc3f0d75cc6691a138813239b7",
+ ),
+ "fee_sensitivity": (
+ "results/artifacts/counting-fee-sensitivity-v1.json",
+ "counting-fee-sensitivity-v1",
+ "0fef54735522470634c696ec026c31c86dff4d4dae23da956b49d8f89eb3a508",
+ ),
+}
+
+
+class VerificationError(ValueError):
+ """An input or generated result violates the composition contract."""
+
+
+def _load_component(path, expected_experiment, expected_sha256):
+ source = Path(path)
+ try:
+ payload = source.read_bytes()
+ value = json.loads(payload)
+ except (OSError, json.JSONDecodeError) as exc:
+ raise VerificationError(f"cannot read component {path}: {exc}") from exc
+ digest = hashlib.sha256(payload).hexdigest()
+ if digest != expected_sha256:
+ raise VerificationError(
+ f"component {path} has SHA-256 {digest}, expected {expected_sha256}"
+ )
+ if not isinstance(value, dict) or value.get("experiment") != expected_experiment:
+ raise VerificationError(f"component {path} has the wrong experiment identity")
+ return value
+
+
+def build_artifact(paths=None):
+ paths = paths or {name: specification[0] for name, specification in COMPONENTS.items()}
+ loaded = {}
+ identities = {}
+ for name, (_default, experiment, digest) in COMPONENTS.items():
+ loaded[name] = _load_component(paths[name], experiment, digest)
+ identities[name] = {"experiment": experiment, "sha256": digest}
+ return {
+ "schema_version": 1,
+ "experiment": EXPERIMENT_ID,
+ "components": identities,
+ "router": loaded["router"]["report"],
+ "fee_sensitivity": {
+ "fixture": loaded["fee_sensitivity"]["fixture"],
+ "observations": loaded["fee_sensitivity"]["observations"],
+ },
+ "excluded_historical_measurements": [
+ {
+ "measurement": "wall-clock benchmarks",
+ "reason": "no pinned machine and resource protocol supports comparison",
+ },
+ {
+ "measurement": "seven L candidates over 18 synthetic instances",
+ "reason": "the instance-generation recipe and seeds were not preserved",
+ },
+ {
+ "measurement": "obsolete router shares",
+ "reason": "the current canonical router run supersedes earlier revisions",
+ },
+ ],
+ "limitations": [
+ "this artifact composes upstream results and performs no counting calculation",
+ "component SHA-256 identities are mandatory",
+ "runtime benchmarks require a separate benchmark protocol",
+ "an L-estimator comparison requires a preserved generator and seeds",
+ "the counting objects remain diagnostics and are not a privacy score or CoinScore",
+ ],
+ }
+
+
+def load_artifact(path):
+ try:
+ with Path(path).open(encoding="utf-8") as source:
+ value = json.load(source)
+ except (OSError, json.JSONDecodeError) as exc:
+ raise VerificationError(f"cannot read artifact {path}: {exc}") from exc
+ if not isinstance(value, dict):
+ raise VerificationError("artifact root must be an object")
+ return value
+
+
+def verify_artifact(artifact, *, paths=None):
+ if artifact.get("schema_version") != 1 or artifact.get("experiment") != EXPERIMENT_ID:
+ raise VerificationError("unsupported counting-methods summary identity")
+ measured = build_artifact(paths)
+ if canonical_json_bytes(artifact) != canonical_json_bytes(measured):
+ raise VerificationError("artifact differs from the canonical component composition")
+ return measured
+
+
+def render_markdown(artifact):
+ router = artifact["router"]["current_router"]
+ routes = router["by_method_and_kind"]
+ population = artifact["router"]["population"]["multi_input_transactions"]
+ observations = artifact["fee_sensitivity"]["observations"]
+ first_unmeasured = next(
+ row["fee_sat"] for row in observations if not row["measured_input_indices"]
+ )
+ return "\n".join([
+ "# Counting-method observations",
+ "",
+ "Generated only from two SHA-256-pinned canonical artifacts. Do not edit manually.",
+ "",
+ f"The current router evaluates {population:,} complete multi-input transactions. It "
+ f"returns {routes.get('radix:exact', 0):,} radix exact, "
+ f"{routes.get('sparse:exact', 0):,} sparse exact and "
+ f"{routes.get('sparse:lower_bound', 0):,} sparse lower-bound outcomes. "
+ f"It refuses {routes.get('none:unknown', 0):,} transactions.",
+ "",
+ f"In the fixed fee fixture, the per-coin interface first measures no input at "
+ f"{first_unmeasured:,} sat while the transaction router remains defined. These are "
+ "different counting objects and cannot substitute for one another.",
+ "",
+ "Historical wall-clock timings and the 18-case L comparison are excluded because their "
+ "benchmark protocol and synthetic generator were not preserved. Earlier router shares "
+ "are superseded by the current run.",
+ "",
+ "These observations are diagnostics, not mapping entropy, a privacy score or CoinScore.",
+ "",
+ ])
+
+
+def _parser():
+ parser = argparse.ArgumentParser(description=__doc__)
+ for name, (default, _experiment, _digest) in COMPONENTS.items():
+ parser.add_argument(f"--{name.replace('_', '-')}", default=default)
+ commands = parser.add_subparsers(dest="command", required=True)
+ reproduce = commands.add_parser("reproduce")
+ reproduce.add_argument("--artifact", required=True)
+ reproduce.add_argument("--markdown", required=True)
+ verify = commands.add_parser("verify")
+ verify.add_argument("--artifact", required=True)
+ verify.add_argument("--markdown")
+ return parser
+
+
+def main(argv=None):
+ args = _parser().parse_args(argv)
+ paths = {name: getattr(args, name) for name in COMPONENTS}
+ if args.command == "reproduce":
+ artifact = build_artifact(paths)
+ write_canonical_json(args.artifact, artifact)
+ destination = Path(args.markdown)
+ destination.parent.mkdir(parents=True, exist_ok=True)
+ destination.write_text(render_markdown(artifact), encoding="utf-8")
+ else:
+ artifact = verify_artifact(load_artifact(args.artifact), paths=paths)
+ if args.markdown is not None:
+ actual = Path(args.markdown).read_text(encoding="utf-8")
+ if actual != render_markdown(artifact):
+ raise VerificationError("Markdown differs from canonical rendering")
+ return 0
+
+
+if __name__ == "__main__":
+ raise SystemExit(main())
diff --git a/decluster/experiments/counting_router.py b/decluster/experiments/counting_router.py
new file mode 100644
index 00000000..d4941fa3
--- /dev/null
+++ b/decluster/experiments/counting_router.py
@@ -0,0 +1,171 @@
+"""Measure the current radix-to-sparse counting router on the amount snapshot."""
+
+from __future__ import annotations
+
+import argparse
+from collections import Counter
+import json
+from pathlib import Path
+
+from ..counting import KNEE, count_w, guaranteed_log_w, radix_applies
+from ..measure import load_ndjson
+from ..result_artifacts import canonical_json_bytes, write_canonical_json
+
+EXPERIMENT_ID = "counting-router-v1"
+DATASET = "data/amount-channel-812695-812831-v1.json"
+
+
+class VerificationError(ValueError):
+ """A stored result differs from a fresh execution."""
+
+
+def _transactions(dataset):
+ for transaction, _height in load_ndjson(dataset):
+ inputs = transaction.get("vin") or []
+ outputs = transaction.get("vout") or []
+ if (
+ len(inputs) >= 2
+ and all((item.get("prevout") or {}).get("value") is not None for item in inputs)
+ and all(item.get("value") is not None for item in outputs)
+ ):
+ yield transaction
+
+
+def build_report(dataset=DATASET):
+ import dss
+
+ routes = Counter()
+ transactions = 0
+ guaranteed = 0
+ radix_precondition = 0
+ raw_radix_positive = 0
+ raw_radix_positive_without_precondition = 0
+ for transaction in _transactions(dataset):
+ transactions += 1
+ inputs = [item["prevout"]["value"] for item in transaction["vin"]]
+ outputs = [item["value"] for item in transaction["vout"]]
+ applies = radix_applies(outputs)
+ radix_precondition += applies
+ raw_radix = dss.radix_mappings(outputs, KNEE)
+ raw_positive = (raw_radix.get("count") or 0) > 0 or raw_radix.get("log_w") is not None
+ raw_radix_positive += raw_positive
+ raw_radix_positive_without_precondition += raw_positive and not applies
+
+ result = count_w(inputs, outputs)
+ routes[(result["method"], result["kind"])] += 1
+ guaranteed += guaranteed_log_w(result) is not None
+
+ return {
+ "population": {"multi_input_transactions": transactions},
+ "current_router": {
+ "by_method_and_kind": {
+ f"{method}:{kind}": count
+ for (method, kind), count in sorted(routes.items())
+ },
+ "guaranteed_nonzero_log_w": guaranteed,
+ },
+ "radix_precondition_audit": {
+ "precondition_applies": radix_precondition,
+ "raw_positive": raw_radix_positive,
+ "raw_positive_without_precondition": raw_radix_positive_without_precondition,
+ },
+ }
+
+
+def build_artifact(dataset=DATASET):
+ return {
+ "schema_version": 1,
+ "experiment": EXPERIMENT_ID,
+ "corpus": "public Bitcoin transaction snapshot",
+ "parameters": {"knee": KNEE, "route": ["radix", "sparse", "none"]},
+ "report": build_report(dataset),
+ "limitations": [
+ "W(E) is a transaction-level subset-sum diagnostic, not a mapping count",
+ "raw radix output is audited only to test its required precondition",
+ "zero-count and unknown outcomes provide no ambiguity evidence",
+ "Sasamoto remains an explicit estimator and is not a router tier",
+ "timings and synthetic L-estimator comparisons are outside this run",
+ "this diagnostic is not a privacy score or CoinScore",
+ ],
+ }
+
+
+def load_artifact(path):
+ try:
+ with Path(path).open(encoding="utf-8") as source:
+ value = json.load(source)
+ except (OSError, json.JSONDecodeError) as exc:
+ raise VerificationError(f"cannot read artifact {path}: {exc}") from exc
+ if not isinstance(value, dict):
+ raise VerificationError("artifact root must be an object")
+ return value
+
+
+def verify_artifact(artifact, *, dataset=DATASET):
+ if artifact.get("schema_version") != 1 or artifact.get("experiment") != EXPERIMENT_ID:
+ raise VerificationError("unsupported counting-router artifact identity")
+ measured = build_artifact(dataset)
+ if canonical_json_bytes(artifact) != canonical_json_bytes(measured):
+ raise VerificationError("artifact differs from a fresh experiment execution")
+ return measured
+
+
+def render_markdown(artifact):
+ report = artifact["report"]
+ population = report["population"]
+ router = report["current_router"]
+ radix = report["radix_precondition_audit"]
+ routes = router["by_method_and_kind"]
+ return "\n".join([
+ "# Counting-router observations",
+ "",
+ "Generated from the canonical experiment artifact. Do not edit manually.",
+ "",
+ f"The current router evaluates {population['multi_input_transactions']:,} transactions. "
+ f"It resolves {router['guaranteed_nonzero_log_w']:,}: "
+ f"{routes.get('radix:exact', 0):,} radix exact, "
+ f"{routes.get('sparse:exact', 0):,} sparse exact, and "
+ f"{routes.get('sparse:lower_bound', 0):,} sparse lower bound. "
+ f"The remaining {routes.get('none:unknown', 0):,} are unknown.",
+ "",
+ f"The radix precondition applies to {radix['precondition_applies']:,} transactions. "
+ f"Calling radix without that gate returns a positive reading {radix['raw_positive']:,} "
+ f"times, including {radix['raw_positive_without_precondition']:,} outside its precondition.",
+ "",
+ "Sasamoto is not a router tier. These counts are not mapping entropy or CoinScore.",
+ "",
+ ])
+
+
+def _parser():
+ parser = argparse.ArgumentParser(description=__doc__)
+ parser.add_argument("--dataset", default=DATASET)
+ commands = parser.add_subparsers(dest="command", required=True)
+ reproduce = commands.add_parser("reproduce")
+ reproduce.add_argument("--artifact", required=True)
+ reproduce.add_argument("--markdown", required=True)
+ verify = commands.add_parser("verify")
+ verify.add_argument("--artifact", required=True)
+ verify.add_argument("--markdown")
+ return parser
+
+
+def main(argv=None):
+ args = _parser().parse_args(argv)
+ if args.command == "reproduce":
+ artifact = build_artifact(args.dataset)
+ write_canonical_json(args.artifact, artifact)
+ destination = Path(args.markdown)
+ destination.parent.mkdir(parents=True, exist_ok=True)
+ destination.write_text(render_markdown(artifact), encoding="utf-8")
+ else:
+ artifact = verify_artifact(load_artifact(args.artifact), dataset=args.dataset)
+ if args.markdown is not None:
+ actual = Path(args.markdown).read_text(encoding="utf-8")
+ if actual != render_markdown(artifact):
+ raise VerificationError("Markdown differs from canonical rendering")
+ return 0
+
+
+if __name__ == "__main__":
+ raise SystemExit(main())
diff --git a/decluster/experiments/denomination_preparation.py b/decluster/experiments/denomination_preparation.py
new file mode 100644
index 00000000..2077e95a
--- /dev/null
+++ b/decluster/experiments/denomination_preparation.py
@@ -0,0 +1,121 @@
+"""Reproduce a fixed-denomination predecessor fingerprint on paired fixtures."""
+
+from __future__ import annotations
+
+import argparse
+import json
+from pathlib import Path
+
+from ..domain import TransactionFingerprintObserved
+from ..failure_modes.denomination_preparation import evaluate, paired_corpus
+from ..result_artifacts import canonical_json_bytes, write_canonical_json
+
+EXPERIMENT_ID = "denomination-preparation-v1"
+
+
+class VerificationError(ValueError):
+ """A stored result differs from a fresh execution."""
+
+
+def _measurement(observations):
+ reports = tuple(evaluate(observation) for observation in observations)
+ return {
+ "transactions": len(reports),
+ "fingerprint_matches": sum(
+ isinstance(report.outcomes[0], TransactionFingerprintObserved)
+ for report in reports
+ ),
+ "features": [
+ dict(report.channels[0].evidence[0].features) for report in reports
+ ],
+ }
+
+
+def build_artifact():
+ preparations, controls = paired_corpus()
+ return {
+ "schema_version": 1,
+ "experiment": EXPERIMENT_ID,
+ "preparations": _measurement(preparations),
+ "arity_matched_controls": _measurement(controls),
+ "composition": None,
+ "conclusion": "an exact denomination consumed by the next CoinJoin distinguishes the synthetic preparations from arity-matched controls",
+ "limitations": list(evaluate(preparations[0]).limitations),
+ }
+
+
+def load_artifact(path):
+ try:
+ with Path(path).open(encoding="utf-8") as source:
+ value = json.load(source)
+ except (OSError, json.JSONDecodeError) as exc:
+ raise VerificationError(f"cannot read artifact {path}: {exc}") from exc
+ if not isinstance(value, dict):
+ raise VerificationError("artifact root must be an object")
+ return value
+
+
+def verify_artifact(artifact):
+ if artifact.get("schema_version") != 1 or artifact.get("experiment") != EXPERIMENT_ID:
+ raise VerificationError("unsupported denomination-preparation artifact identity")
+ measured = build_artifact()
+ if canonical_json_bytes(artifact) != canonical_json_bytes(measured):
+ raise VerificationError("artifact differs from a fresh failure-mode execution")
+ return measured
+
+
+def render_markdown(artifact):
+ preparations = artifact["preparations"]
+ controls = artifact["arity_matched_controls"]
+ return "\n".join([
+ "# Fixed-denomination preparation fingerprint",
+ "",
+ "Generated from the canonical experiment artifact. Do not edit manually.",
+ "",
+ "| population | transactions | fingerprint matches |",
+ "|---|---:|---:|",
+ f"| synthetic preparations | {preparations['transactions']} | "
+ f"{preparations['fingerprint_matches']} |",
+ f"| arity-matched controls | {controls['transactions']} | "
+ f"{controls['fingerprint_matches']} |",
+ "",
+ "The rule observes one exact-denomination predecessor output consumed by the declared "
+ "next CoinJoin. Controls have the same input and output counts.",
+ "",
+ "This construction-level fixture does not measure prevalence and does not prove that a "
+ "matched predecessor is unilateral or commonly owned.",
+ "",
+ ])
+
+
+def _parser():
+ parser = argparse.ArgumentParser(description=__doc__)
+ commands = parser.add_subparsers(dest="command", required=True)
+ reproduce = commands.add_parser("reproduce")
+ reproduce.add_argument("--artifact", required=True)
+ reproduce.add_argument("--markdown", required=True)
+ verify = commands.add_parser("verify")
+ verify.add_argument("--artifact", required=True)
+ verify.add_argument("--markdown")
+ return parser
+
+
+def main(argv=None):
+ args = _parser().parse_args(argv)
+ if args.command == "reproduce":
+ artifact = build_artifact()
+ write_canonical_json(args.artifact, artifact)
+ destination = Path(args.markdown)
+ destination.parent.mkdir(parents=True, exist_ok=True)
+ destination.write_text(render_markdown(artifact), encoding="utf-8")
+ else:
+ artifact = verify_artifact(load_artifact(args.artifact))
+ if args.markdown is not None:
+ actual = Path(args.markdown).read_text(encoding="utf-8")
+ if actual != render_markdown(artifact):
+ raise VerificationError("Markdown differs from canonical rendering")
+ return 0
+
+
+if __name__ == "__main__":
+ raise SystemExit(main())
diff --git a/decluster/experiments/descent_vs_ascent.py b/decluster/experiments/descent_vs_ascent.py
new file mode 100644
index 00000000..2472e170
--- /dev/null
+++ b/decluster/experiments/descent_vs_ascent.py
@@ -0,0 +1,364 @@
+"""Measure what a descending pass recovers from a clustering it did not build.
+
+The two directions answer different questions with the same evidence. Ascending, an analyst holds
+the discrete partition and declines a merge the spending transaction argues against: nothing is
+ever fused wrongly because nothing is fused without assent. Descending, the analyst is handed a
+partition already fused by common-input ownership and can only cut it, and a cut has to clear a bar
+a refusal never faced — the evidence must argue against the pairing rather than merely fail to
+argue for it.
+
+This experiment runs both over one frozen slice and reports where they disagree. The comparison is
+possible at all only because the refusal channels here are properties of the spending transaction:
+coinjoin shape, the de-mix partition, and the published merge tells. None of them is the bare
+co-spend, which is the inherited claim under test and so contributes nothing to the descent.
+"""
+
+from __future__ import annotations
+
+import argparse
+import gzip
+import json
+from collections import Counter
+from pathlib import Path
+
+from .. import tx_addrs, views
+from ..declustering import MAX_BLOCK, decluster
+from ..monitor import is_coinjoin
+from ..partition import Partition
+from ..result_artifacts import canonical_json_bytes, write_canonical_json
+
+EXPERIMENT_ID = "descent-vs-ascent-v1"
+DEFAULT_DATASET = "tests/fixtures/slice_a_channels_2016.ndjson.gz"
+
+# Thresholds are reported rather than chosen: `decluster` takes no default, and a single number
+# would hide how much of the divergence is the threshold's doing.
+THRESHOLDS = (-1.0, -2.0, -4.0)
+
+
+class VerificationError(ValueError):
+ """The dataset or stored result violates this experiment's contract."""
+
+
+def _load(path):
+ sample = []
+ try:
+ with gzip.open(path, "rt", encoding="utf-8") as source:
+ for line_number, line in enumerate(source, start=1):
+ if not line.strip():
+ continue
+ transaction = json.loads(line)
+ if not isinstance(transaction, dict) or not transaction.get("txid"):
+ raise VerificationError(
+ f"{path}:{line_number}: transaction must be an object with a txid"
+ )
+ sample.append((transaction, 0))
+ except (OSError, json.JSONDecodeError) as exc:
+ raise VerificationError(f"cannot read slice snapshot {path}: {exc}") from exc
+ if not sample:
+ raise VerificationError(f"slice snapshot is empty: {path}")
+ return sample
+
+
+def pair_evidence(sample):
+ """Signed signals per input pair, counting only what argues about the pairing itself.
+
+ One signal is one published tell. A co-spend with nothing against it scores zero: it is the
+ claim the descent is testing, so counting it would let the inherited partition vouch for
+ itself.
+ """
+ signals = Counter()
+ for transaction, _ in sample:
+ addresses = tx_addrs.in_addrs(transaction)
+ distinct = list(dict.fromkeys(addresses))
+ if len(distinct) < 2:
+ continue
+ if is_coinjoin(transaction):
+ verdict = {pair: -1 for pair in _pairs(distinct)}
+ else:
+ participants = views._demix_participants(transaction)
+ if participants is not None:
+ placed = {
+ address: name
+ for name, group in participants.items()
+ for address in group
+ }
+ verdict = {}
+ for pair in _pairs(distinct):
+ together = placed.get(pair[0]) is not None and placed.get(pair[0]) == placed.get(pair[1])
+ verdict[pair] = 1 if together else -1
+ else:
+ objections = views.merge_objections(transaction) or 0
+ if not objections:
+ continue
+ verdict = {pair: -objections for pair in _pairs(distinct)}
+ for pair, value in verdict.items():
+ signals[pair] += value
+ return signals
+
+
+def _pairs(items):
+ return [
+ (a, b) if a <= b else (b, a)
+ for index, a in enumerate(items)
+ for b in items[index + 1:]
+ ]
+
+
+def _weight(signals):
+ return lambda a, b: float(signals.get((a, b) if a <= b else (b, a), 0))
+
+
+def _shared_ground(inherited_lookup, ascent_lookup):
+ """The ascent over the inherited ground set: an address it never merged is a singleton.
+
+ The two passes return lookups of different size — a refused merge leaves its addresses out of
+ the union-find entirely — and the lattice refuses partitions of different ground sets rather
+ than resolving the difference silently.
+ """
+ extra = set(ascent_lookup) - set(inherited_lookup)
+ if extra:
+ raise VerificationError(
+ f"the refusing pass covers {len(extra)} addresses the inherited partition does not"
+ )
+ filled = dict(ascent_lookup)
+ for address in inherited_lookup:
+ filled.setdefault(address, ("_singleton", address))
+ return Partition.from_lookup(filled)
+
+
+def _agreement(left, right):
+ """How the two partitions differ, counted over the pairs each one joins."""
+ joined_left = {pair for block in left.blocks() for pair in _pairs(sorted(block))}
+ joined_right = {pair for block in right.blocks() for pair in _pairs(sorted(block))}
+ return {
+ "joined_by_both": len(joined_left & joined_right),
+ "joined_only_by_descent": len(joined_left - joined_right),
+ "joined_only_by_ascent": len(joined_right - joined_left),
+ "identical": left == right,
+ }
+
+
+def _run(inherited, weight, ascent, cut_below):
+ result = decluster(inherited, weight, cut_below=cut_below, max_block=MAX_BLOCK)
+ summary = result.summary()
+ return {
+ "cut_below": cut_below,
+ "blocks_cut": summary["cut"],
+ "blocks_left_whole": summary["left_whole"],
+ "blocks_approximated": summary["approximated"],
+ "severed_weight": summary["severed_weight"],
+ "resulting_blocks": len(result.partition),
+ "refines_inherited": result.partition.refines(inherited),
+ "reaches_ascent": result.partition.refines(ascent),
+ "agreement_with_ascent": _agreement(result.partition, ascent),
+ }
+
+
+def _evidence_reach(inherited, ascent, signals):
+ """Where the refusal channels actually speak, relative to the blocks they would have to cut."""
+ reach = {
+ "signalled_pairs": len(signals),
+ "joined_pairs": 0,
+ "joined_pairs_in_exactly_searched_blocks": 0,
+ "negative_pairs_in_exactly_searched_blocks": 0,
+ "negative_pairs_in_approximated_blocks": 0,
+ "approximated_blocks_carrying_evidence": 0,
+ "separated_by_ascent_only": 0,
+ "separated_by_ascent_only_in_exactly_searched_blocks": 0,
+ }
+ for block in inherited.blocks():
+ exact = len(block) <= MAX_BLOCK
+ pairs = _pairs(sorted(block))
+ reach["joined_pairs"] += len(pairs)
+ carries = 0
+ for pair in pairs:
+ if signals.get(pair, 0) < 0:
+ carries += 1
+ if not ascent.same_block(*pair):
+ reach["separated_by_ascent_only"] += 1
+ if exact:
+ reach["separated_by_ascent_only_in_exactly_searched_blocks"] += 1
+ if exact:
+ reach["joined_pairs_in_exactly_searched_blocks"] += len(pairs)
+ reach["negative_pairs_in_exactly_searched_blocks"] += carries
+ else:
+ reach["negative_pairs_in_approximated_blocks"] += carries
+ reach["approximated_blocks_carrying_evidence"] += carries > 0
+ return reach
+
+
+def build_artifact(dataset=DEFAULT_DATASET):
+ sample = _load(dataset)
+ inherited_lookup = views.cluster_addresses(sample, refuse=False)
+ ascent_lookup = views.cluster_addresses(sample, refuse=True)
+ inherited = Partition.from_lookup(inherited_lookup)
+ ascent = _shared_ground(inherited_lookup, ascent_lookup)
+ signals = pair_evidence(sample)
+ weight = _weight(signals)
+
+ sizes = Counter(len(block) for block in inherited.blocks())
+ searchable = sum(count for size, count in sizes.items() if size <= MAX_BLOCK)
+ return {
+ "schema_version": 1,
+ "experiment": EXPERIMENT_ID,
+ "measurement": {
+ "transactions": len(sample),
+ "addresses": len(inherited.elements()),
+ "inherited_blocks": len(inherited),
+ "ascent_blocks": len(ascent),
+ "ascent_refines_inherited": ascent.refines(inherited),
+ "largest_inherited_block": max(sizes),
+ "addresses_in_exactly_searched_blocks": sum(
+ size * count for size, count in sizes.items() if size <= MAX_BLOCK
+ ),
+ "exactly_searched_blocks": searchable,
+ "evidence_reach": _evidence_reach(inherited, ascent, signals),
+ "descent": [_run(inherited, weight, ascent, bar) for bar in THRESHOLDS],
+ },
+ "parameters": {
+ "max_block": MAX_BLOCK,
+ "thresholds": list(THRESHOLDS),
+ "signal_unit": "one published tell against a pairing",
+ "cospend_prior": 0.0,
+ "channels": [
+ "coinjoin shape refuses every pair among the inputs",
+ "de-mix partition joins a participant's own inputs and refuses across participants",
+ "unnecessary input and mixed input types each refuse once",
+ ],
+ },
+ "limitations": [
+ "the snapshot contains six 2016 blocks and is not a chain-wide sample",
+ "blocks above max_block get the conservative pass, which cuts only where no pair objects and is not the best cut",
+ "signals are counted tells, not calibrated bits, so the thresholds are not likelihoods",
+ "the two passes are compared as partitions; neither is checked against same-owner labels",
+ "the fingerprint and roundness channels are absent here, as they are in the merge pass",
+ "the snapshot recipe is unavailable; the dataset publisher authorized redistribution on 2026-09-04",
+ ],
+ }
+
+
+def load_artifact(path):
+ try:
+ with Path(path).open(encoding="utf-8") as source:
+ value = json.load(source)
+ except (OSError, json.JSONDecodeError) as exc:
+ raise VerificationError(f"cannot read artifact {path}: {exc}") from exc
+ if not isinstance(value, dict):
+ raise VerificationError("artifact root must be an object")
+ return value
+
+
+def verify_artifact(artifact, dataset=DEFAULT_DATASET):
+ if artifact.get("schema_version") != 1 or artifact.get("experiment") != EXPERIMENT_ID:
+ raise VerificationError("unsupported descent-versus-ascent artifact identity")
+ measured = build_artifact(dataset)
+ if canonical_json_bytes(artifact) != canonical_json_bytes(measured):
+ raise VerificationError("artifact differs from a fresh experiment execution")
+ return measured
+
+
+def render_markdown(artifact):
+ measured = artifact["measurement"]
+ reach = measured["evidence_reach"]
+ runs = measured["descent"]
+ covered = 100.0 * reach["signalled_pairs"] / reach["joined_pairs"]
+ best = min(runs, key=lambda run: run["agreement_with_ascent"]["joined_only_by_descent"])
+ recovered = (reach["separated_by_ascent_only"]
+ - best["agreement_with_ascent"]["joined_only_by_descent"])
+ lines = [
+ "# Ascending and descending over the same slice",
+ "",
+ "Generated from the canonical experiment artifact. Do not edit manually.",
+ "",
+ f"Two passes read the same {measured['transactions']} transactions and "
+ f"{measured['addresses']} addresses. The ascending pass starts from the discrete "
+ "partition and declines a merge the spending transaction argues against. The descending "
+ "pass is handed the common-input-ownership partition and may only cut it, from the same "
+ "refusal channels with the co-spend prior removed.",
+ "",
+ "| pass | blocks | blocks cut | separations the ascent makes and this does not |",
+ "|---|---:|---:|---:|",
+ f"| common-input ownership, inherited | {measured['inherited_blocks']} | n/a | "
+ f"{reach['separated_by_ascent_only']} |",
+ f"| refusing merge pass, ascending | {measured['ascent_blocks']} | n/a | 0 |",
+ ]
+ for run in runs:
+ lines.append(
+ f"| descent at cut_below {run['cut_below']:.0f} | {run['resulting_blocks']} | "
+ f"{run['blocks_cut']} | "
+ f"{run['agreement_with_ascent']['joined_only_by_descent']} |"
+ )
+ lines += [
+ "",
+ f"At the loosest bar the descent recovers **{recovered} of the "
+ f"{reach['separated_by_ascent_only']}** separations the ascent makes, cutting "
+ f"{best['blocks_cut']} blocks and never separating a pair the ascent keeps together. "
+ "Every signal on this slice is a single tell, so a bar of two or more clears nothing: the "
+ "threshold is doing real work rather than sitting below the data.",
+ "",
+ "| quantity | pairs |",
+ "|---|---:|",
+ f"| joined by the inherited partition | {reach['joined_pairs']} |",
+ f"| carrying evidence against the pairing | {reach['signalled_pairs']} |",
+ f"| joined inside an exactly searched block | {reach['joined_pairs_in_exactly_searched_blocks']} |",
+ f"| evidence inside an exactly searched block | {reach['negative_pairs_in_exactly_searched_blocks']} |",
+ f"| evidence inside a block the conservative pass handled | "
+ f"{reach['negative_pairs_in_approximated_blocks']} |",
+ "",
+ f"Evidence against a pairing reaches {covered:.2f}% of the joined pairs, and all of it lies "
+ f"inside {reach['approximated_blocks_carrying_evidence']} of the "
+ f"{measured['inherited_blocks'] - measured['exactly_searched_blocks']} blocks that exceed "
+ f"the exact bound of {artifact['parameters']['max_block']} addresses. Not one of the "
+ f"{measured['exactly_searched_blocks']} blocks the exact search reaches contains a pair "
+ "these channels argue against, so there it correctly did nothing. The heavy tail is where "
+ "the evidence lives and where an exact search cannot go, which is why the conservative pass "
+ "exists.",
+ "",
+ "What the two directions do with identical evidence is still not symmetric, and the "
+ f"remaining {best['agreement_with_ascent']['joined_only_by_descent']} pairs are the "
+ "measure of it. A refusal at merge time separates two addresses without holding any "
+ "evidence about that pair: the merge is simply not made, and transitivity never carries "
+ "through it. A cut has to argue about the boundary it names, against blocks reaching "
+ f"{measured['largest_inherited_block']} addresses. Descending is strictly more expensive "
+ "than not ascending, and this slice says how much.",
+ "",
+ "These counts describe one six-block slice under two channels, compare the passes as "
+ "partitions rather than against same-owner labels, and do not establish how either pass "
+ "behaves where the evidence is denser.",
+ "",
+ ]
+ return "\n".join(lines)
+
+
+def _parser():
+ parser = argparse.ArgumentParser(description=__doc__)
+ parser.add_argument("--dataset", default=DEFAULT_DATASET)
+ commands = parser.add_subparsers(dest="command", required=True)
+ reproduce = commands.add_parser("reproduce")
+ reproduce.add_argument("--artifact", required=True)
+ reproduce.add_argument("--markdown", required=True)
+ verify = commands.add_parser("verify")
+ verify.add_argument("--artifact", required=True)
+ verify.add_argument("--markdown")
+ return parser
+
+
+def main(argv=None):
+ args = _parser().parse_args(argv)
+ if args.command == "reproduce":
+ artifact = build_artifact(args.dataset)
+ write_canonical_json(args.artifact, artifact)
+ destination = Path(args.markdown)
+ destination.parent.mkdir(parents=True, exist_ok=True)
+ destination.write_text(render_markdown(artifact), encoding="utf-8")
+ else:
+ artifact = verify_artifact(load_artifact(args.artifact), args.dataset)
+ if args.markdown is not None:
+ actual = Path(args.markdown).read_text(encoding="utf-8")
+ if actual != render_markdown(artifact):
+ raise VerificationError("Markdown differs from canonical rendering")
+ return 0
+
+
+if __name__ == "__main__":
+ raise SystemExit(main())
diff --git a/decluster/experiments/deterministic_partition.py b/decluster/experiments/deterministic_partition.py
new file mode 100644
index 00000000..4d44b485
--- /dev/null
+++ b/decluster/experiments/deterministic_partition.py
@@ -0,0 +1,118 @@
+"""Reproduce the forced and underdetermined amount examples from the CTP."""
+
+from __future__ import annotations
+
+import argparse
+import json
+from pathlib import Path
+
+from ..failure_modes.deterministic_partition import evaluate
+from ..result_artifacts import canonical_json_bytes, write_canonical_json
+
+EXPERIMENT_ID = "deterministic-partition-v1"
+
+
+class VerificationError(ValueError):
+ """A stored result differs from a fresh execution."""
+
+
+def _measurement(inputs, outputs):
+ report = evaluate(inputs, outputs)
+ evidence = report.channels[0].evidence[0]
+ return {
+ "inputs": list(inputs),
+ "outputs": list(outputs),
+ "non_derived_mappings": evidence.mapping_count,
+ "unanimous_links": [
+ [left.identifier[1], right.identifier[1]] for left, right in evidence.links
+ ],
+ }
+
+
+def build_artifact():
+ forced = _measurement((1, 3, 20, 50), (4, 70))
+ underdetermined = _measurement((1, 2, 3, 4, 5), (7, 8))
+ return {
+ "schema_version": 1,
+ "experiment": EXPERIMENT_ID,
+ "balance_model": "exact per-block conservation",
+ "mapping_family": "Maurer non-derived mappings",
+ "forced_partition": forced,
+ "underdetermined_control": underdetermined,
+ "composition": None,
+ "conclusion": "the forced example has one non-derived mapping and four unanimous links, while the underdetermined example has three mappings and no unanimous link",
+ "limitations": list(evaluate((1, 3, 20, 50), (4, 70)).limitations),
+ }
+
+
+def load_artifact(path):
+ try:
+ with Path(path).open(encoding="utf-8") as source:
+ value = json.load(source)
+ except (OSError, json.JSONDecodeError) as exc:
+ raise VerificationError(f"cannot read artifact {path}: {exc}") from exc
+ if not isinstance(value, dict):
+ raise VerificationError("artifact root must be an object")
+ return value
+
+
+def verify_artifact(artifact):
+ if artifact.get("schema_version") != 1 or artifact.get("experiment") != EXPERIMENT_ID:
+ raise VerificationError("unsupported deterministic-partition artifact identity")
+ measured = build_artifact()
+ if canonical_json_bytes(artifact) != canonical_json_bytes(measured):
+ raise VerificationError("artifact differs from a fresh failure-mode execution")
+ return measured
+
+
+def render_markdown(artifact):
+ forced = artifact["forced_partition"]
+ control = artifact["underdetermined_control"]
+ return "\n".join([
+ "# Amount-constrained deterministic partition",
+ "",
+ "Generated from the canonical experiment artifact. Do not edit manually.",
+ "",
+ "| fixture | non-derived mappings | unanimous input-output links |",
+ "|---|---:|---:|",
+ f"| forced partition | {forced['non_derived_mappings']} | {len(forced['unanimous_links'])} |",
+ f"| underdetermined control | {control['non_derived_mappings']} | {len(control['unanimous_links'])} |",
+ "",
+ "The forced fixture is 0.1 + 0.3 = 0.4 and 2 + 5 = 7, scaled by ten. The control is the CTP's three-way underdetermined 0.7/0.8 example, also scaled by ten.",
+ "",
+ "Every statement is conditional on exact per-block conservation and the non-derived mapping family. Fees, contextual priors and net-settlement cycles can change admissibility.",
+ "",
+ ])
+
+
+def _parser():
+ parser = argparse.ArgumentParser(description=__doc__)
+ commands = parser.add_subparsers(dest="command", required=True)
+ reproduce = commands.add_parser("reproduce")
+ reproduce.add_argument("--artifact", required=True)
+ reproduce.add_argument("--markdown", required=True)
+ verify = commands.add_parser("verify")
+ verify.add_argument("--artifact", required=True)
+ verify.add_argument("--markdown")
+ return parser
+
+
+def main(argv=None):
+ args = _parser().parse_args(argv)
+ if args.command == "reproduce":
+ artifact = build_artifact()
+ write_canonical_json(args.artifact, artifact)
+ destination = Path(args.markdown)
+ destination.parent.mkdir(parents=True, exist_ok=True)
+ destination.write_text(render_markdown(artifact), encoding="utf-8")
+ else:
+ artifact = verify_artifact(load_artifact(args.artifact))
+ if args.markdown is not None and Path(args.markdown).read_text(
+ encoding="utf-8"
+ ) != render_markdown(artifact):
+ raise VerificationError("Markdown differs from canonical rendering")
+ return 0
+
+
+if __name__ == "__main__":
+ raise SystemExit(main())
diff --git a/decluster/experiments/em_m.py b/decluster/experiments/em_m.py
new file mode 100644
index 00000000..55c72136
--- /dev/null
+++ b/decluster/experiments/em_m.py
@@ -0,0 +1,130 @@
+"""Reproduce the per-axis Fellegi-Sunter EM diagnostic."""
+
+from __future__ import annotations
+
+import argparse
+import json
+from pathlib import Path
+from tempfile import TemporaryDirectory
+
+from ..archive_snapshot import extract_tar_gz
+from ..em_m_comparison import evaluate
+from ..fingerprint_validate import load_blkcache
+from ..reproducibility import fingerprint_source
+from ..result_artifacts import canonical_json_bytes, write_canonical_json
+
+EXPERIMENT_ID = "em-m-v1"
+DATASET_ID = "fs-blkcache-2026-09-04-v1"
+
+
+class VerificationError(ValueError):
+ """The stored result differs from a fresh execution."""
+
+
+def build_artifact(snapshot):
+ with TemporaryDirectory(prefix="decluster-em-m-") as temporary:
+ root = extract_tar_gz(snapshot, temporary)
+ cache = root / ".blkcache"
+ transactions = load_blkcache(str(cache))
+ measurement = evaluate(transactions)
+ source = fingerprint_source(str(cache / "*.json"))
+ source.update(pattern=".blkcache/*.json", transactions=len(transactions))
+ return {
+ "schema_version": 1,
+ "experiment": EXPERIMENT_ID,
+ "dataset": DATASET_ID,
+ "parameters": {"pair_cap_per_class": 4000, "seed": 0, "fixed_u": True},
+ "source": source,
+ "measurement": measurement,
+ "historical_comparison": {
+ "status": "not_reproduced",
+ "reason": "the preserved 22,112-transaction snapshot is not the historical 165,832-transaction cache",
+ },
+ "limitations": [
+ "address reuse is a weak and partly feature-dependent ownership proxy",
+ "the balanced selected sample does not represent population prevalence",
+ "EM is fitted and evaluated on the same pair sample",
+ "the model assumes conditional independence between correlated axes",
+ "u is fixed at the measured collision rate",
+ "the small AUC difference is measured without a pre-registered separability verdict",
+ "this is attacker-side parameter diagnosis, not CoinScore or a privacy certificate",
+ ],
+ }
+
+
+def load_artifact(path):
+ try:
+ with Path(path).open(encoding="utf-8") as source:
+ value = json.load(source)
+ except (OSError, json.JSONDecodeError) as exc:
+ raise VerificationError(f"cannot read artifact {path}: {exc}") from exc
+ if not isinstance(value, dict):
+ raise VerificationError("artifact root must be an object")
+ return value
+
+
+def verify_artifact(artifact, snapshot):
+ if artifact.get("schema_version") != 1 or artifact.get("experiment") != EXPERIMENT_ID:
+ raise VerificationError("unsupported EM-m artifact identity")
+ measured = build_artifact(snapshot)
+ if canonical_json_bytes(artifact) != canonical_json_bytes(measured):
+ raise VerificationError("artifact differs from a fresh experiment execution")
+ return measured
+
+
+def render_markdown(artifact):
+ measurement = artifact["measurement"]
+ aucs = measurement["library_scorer_auc"]
+ return "\n".join([
+ "# Per-axis Fellegi–Sunter EM diagnostic",
+ "",
+ "Generated from the canonical experiment artifact. Do not edit manually.",
+ "",
+ f"Transactions: {measurement['transactions']}; EM iterations: "
+ f"{measurement['fit']['iterations']}; inferred selected-pair fraction: "
+ f"{measurement['fit']['inferred_pair_fraction']:.6f}.",
+ "",
+ "| parameter source | LibraryScorer AUC |",
+ "|---|---:|",
+ f"| fixed 0.95 | {aucs['fixed_0_95']:.6f} |",
+ f"| unsupervised EM | {aucs['em']:.6f} |",
+ f"| address-reuse agreement | {aucs['address_reuse_agreement']:.6f} |",
+ "",
+ f"EM minus fixed AUC: {measurement['em_minus_fixed_auc']:+.6f}. This difference is "
+ "descriptive; no pre-registered separability verdict was run.",
+ "",
+ "The historical 165,832-transaction table is not reproduced by the preserved "
+ "22,112-transaction snapshot. Address reuse is a weak label, and this is an "
+ "attacker-side parameter diagnostic rather than a privacy score.",
+ "",
+ ])
+
+
+def _parser():
+ parser = argparse.ArgumentParser(description=__doc__)
+ parser.add_argument("--snapshot", required=True)
+ commands = parser.add_subparsers(dest="command", required=True)
+ for name in ("reproduce", "verify"):
+ command = commands.add_parser(name)
+ command.add_argument("--artifact", required=True)
+ command.add_argument("--markdown", required=True)
+ return parser
+
+
+def main(argv=None):
+ args = _parser().parse_args(argv)
+ if args.command == "reproduce":
+ artifact = build_artifact(args.snapshot)
+ write_canonical_json(args.artifact, artifact)
+ destination = Path(args.markdown)
+ destination.parent.mkdir(parents=True, exist_ok=True)
+ destination.write_text(render_markdown(artifact), encoding="utf-8")
+ else:
+ artifact = verify_artifact(load_artifact(args.artifact), args.snapshot)
+ if Path(args.markdown).read_text(encoding="utf-8") != render_markdown(artifact):
+ raise VerificationError("Markdown differs from canonical rendering")
+ return 0
+
+
+if __name__ == "__main__":
+ raise SystemExit(main())
diff --git a/decluster/experiments/entity_deanon.py b/decluster/experiments/entity_deanon.py
new file mode 100644
index 00000000..823e71ea
--- /dev/null
+++ b/decluster/experiments/entity_deanon.py
@@ -0,0 +1,156 @@
+"""Reproduce the paired entity-labelled graph-structure observations."""
+
+from __future__ import annotations
+
+import argparse
+import gzip
+import json
+from pathlib import Path
+
+from ..entities import detect_bitmex, detect_satoshidice
+from ..graph_deanon import evaluate_entity
+from ..result_artifacts import canonical_json_bytes, write_canonical_json
+
+EXPERIMENT_ID = "entity-deanon-v1"
+SATOSHIDICE_DATASET = "tests/fixtures/entity_satoshidice_2013.ndjson.gz"
+BITMEX_DATASET = "tests/fixtures/entity_bitmex_2019.ndjson.gz"
+
+
+class VerificationError(ValueError):
+ """A dataset or stored result violates this experiment's contract."""
+
+
+def _load_sample(path):
+ sample = []
+ try:
+ with gzip.open(path, "rt", encoding="utf-8") as source:
+ for line_number, line in enumerate(source, start=1):
+ if not line.strip():
+ continue
+ value = json.loads(line)
+ if not isinstance(value, dict):
+ raise VerificationError(f"{path}:{line_number}: transaction must be an object")
+ sample.append((value, 0))
+ except (OSError, json.JSONDecodeError) as exc:
+ raise VerificationError(f"cannot read entity snapshot {path}: {exc}") from exc
+ if not sample:
+ raise VerificationError(f"entity snapshot is empty: {path}")
+ return sample
+
+
+def _measurement(path, labeler):
+ sample = _load_sample(path)
+ measured = evaluate_entity(sample, labeler, seed=0)
+ return {
+ "transactions": len(sample),
+ "entity_clusters": measured["entity_clusters"],
+ "positive_pairs": measured["pos_pairs"],
+ "payment_auc": measured["auc_payment"],
+ "positive_mean_shared_neighbours": measured["pos_mean"],
+ }
+
+
+def build_artifact(
+ satoshidice_dataset=SATOSHIDICE_DATASET,
+ bitmex_dataset=BITMEX_DATASET,
+):
+ return {
+ "schema_version": 1,
+ "experiment": EXPERIMENT_ID,
+ "adversary": "payment-graph common-neighbour scorer with independent entity detector",
+ "satoshidice": _measurement(satoshidice_dataset, detect_satoshidice),
+ "bitmex": _measurement(bitmex_dataset, detect_bitmex),
+ "interpretation": {
+ "satoshidice": "positive control with recurring counterparties",
+ "bitmex": "null control with hub-and-spoke deposit structure",
+ },
+ "limitations": [
+ "the fixtures are selected entity-centred subgraphs, not uniform chain samples",
+ "vanity-prefix detectors label the fixtures and are not general entity attribution",
+ "the run evaluates common-neighbour structure, not the full Narayanan-Shmatikov algorithm",
+ "the contrast does not establish a universal property of gambling services or exchanges",
+ "upstream data licensing is undetermined; the dataset publisher authorized redistribution on 2026-09-04",
+ "the result is not a privacy score or CoinScore",
+ ],
+ }
+
+
+def load_artifact(path):
+ try:
+ with Path(path).open(encoding="utf-8") as source:
+ value = json.load(source)
+ except (OSError, json.JSONDecodeError) as exc:
+ raise VerificationError(f"cannot read artifact {path}: {exc}") from exc
+ if not isinstance(value, dict):
+ raise VerificationError("artifact root must be an object")
+ return value
+
+
+def verify_artifact(artifact, **datasets):
+ if artifact.get("schema_version") != 1 or artifact.get("experiment") != EXPERIMENT_ID:
+ raise VerificationError("unsupported entity-deanon artifact identity")
+ measured = build_artifact(**datasets)
+ if canonical_json_bytes(artifact) != canonical_json_bytes(measured):
+ raise VerificationError("artifact differs from a fresh experiment execution")
+ return measured
+
+
+def render_markdown(artifact):
+ dice = artifact["satoshidice"]
+ bitmex = artifact["bitmex"]
+ return "\n".join([
+ "# Entity-labelled graph-structure observations",
+ "",
+ "Generated from the canonical experiment artifact. Do not edit manually.",
+ "",
+ "| snapshot | transactions | positive pairs | payment AUC | mean shared neighbours |",
+ "|---|---:|---:|---:|---:|",
+ f"| SatoshiDice | {dice['transactions']} | {dice['positive_pairs']} | "
+ f"{dice['payment_auc']:.6f} | {dice['positive_mean_shared_neighbours']:.6f} |",
+ f"| BitMEX | {bitmex['transactions']} | {bitmex['positive_pairs']} | "
+ f"{bitmex['payment_auc']:.6f} | {bitmex['positive_mean_shared_neighbours']:.6f} |",
+ "",
+ "The selected SatoshiDice subgraph is a positive control and the selected BitMEX "
+ "subgraph is a null control. This contrast is snapshot-specific and is not general "
+ "entity attribution or the full Narayanan–Shmatikov attack.",
+ "",
+ ])
+
+
+def _parser():
+ parser = argparse.ArgumentParser(description=__doc__)
+ parser.add_argument("--satoshidice-dataset", default=SATOSHIDICE_DATASET)
+ parser.add_argument("--bitmex-dataset", default=BITMEX_DATASET)
+ commands = parser.add_subparsers(dest="command", required=True)
+ reproduce = commands.add_parser("reproduce")
+ reproduce.add_argument("--artifact", required=True)
+ reproduce.add_argument("--markdown", required=True)
+ verify = commands.add_parser("verify")
+ verify.add_argument("--artifact", required=True)
+ verify.add_argument("--markdown")
+ return parser
+
+
+def main(argv=None):
+ args = _parser().parse_args(argv)
+ datasets = {
+ "satoshidice_dataset": args.satoshidice_dataset,
+ "bitmex_dataset": args.bitmex_dataset,
+ }
+ if args.command == "reproduce":
+ artifact = build_artifact(**datasets)
+ write_canonical_json(args.artifact, artifact)
+ destination = Path(args.markdown)
+ destination.parent.mkdir(parents=True, exist_ok=True)
+ destination.write_text(render_markdown(artifact), encoding="utf-8")
+ else:
+ artifact = verify_artifact(load_artifact(args.artifact), **datasets)
+ if args.markdown is not None:
+ actual = Path(args.markdown).read_text(encoding="utf-8")
+ if actual != render_markdown(artifact):
+ raise VerificationError("Markdown differs from canonical rendering")
+ return 0
+
+
+if __name__ == "__main__":
+ raise SystemExit(main())
diff --git a/decluster/experiments/entropy_insufficiency.py b/decluster/experiments/entropy_insufficiency.py
new file mode 100644
index 00000000..1edf311c
--- /dev/null
+++ b/decluster/experiments/entropy_insufficiency.py
@@ -0,0 +1,125 @@
+"""Reproduce an equal-entropy, different-intersection counterexample."""
+
+from __future__ import annotations
+
+import argparse
+import json
+from pathlib import Path
+
+from ..failure_modes.entropy_insufficiency import evaluate, paired_examples
+from ..result_artifacts import canonical_json_bytes, write_canonical_json
+
+EXPERIMENT_ID = "entropy-insufficiency-v1"
+
+
+class VerificationError(ValueError):
+ """A stored result differs from a fresh execution."""
+
+
+def _measurement(scenario):
+ report = evaluate(scenario)
+ intersection = report.outcomes[0].evidence
+ return {
+ "entropy_bits": scenario.entropy_bits,
+ "initial_candidates": len(scenario.probabilities),
+ "observations": len(scenario.observations),
+ "surviving_candidates": len(intersection.candidates_after),
+ }
+
+
+def build_artifact():
+ brittle, overlapping = paired_examples()
+ brittle_measurement = _measurement(brittle)
+ overlapping_measurement = _measurement(overlapping)
+ return {
+ "schema_version": 1,
+ "experiment": EXPERIMENT_ID,
+ "brittle_overlap": brittle_measurement,
+ "overlapping_crowd": overlapping_measurement,
+ "equal_initial_entropy": (
+ brittle_measurement["entropy_bits"]
+ == overlapping_measurement["entropy_bits"]
+ ),
+ "composition": None,
+ "conclusion": "equal point entropy does not determine longitudinal intersection resistance",
+ "limitations": list(evaluate(brittle).limitations),
+ }
+
+
+def load_artifact(path):
+ try:
+ with Path(path).open(encoding="utf-8") as source:
+ value = json.load(source)
+ except (OSError, json.JSONDecodeError) as exc:
+ raise VerificationError(f"cannot read artifact {path}: {exc}") from exc
+ if not isinstance(value, dict):
+ raise VerificationError("artifact root must be an object")
+ return value
+
+
+def verify_artifact(artifact):
+ if artifact.get("schema_version") != 1 or artifact.get("experiment") != EXPERIMENT_ID:
+ raise VerificationError("unsupported entropy-insufficiency artifact identity")
+ measured = build_artifact()
+ if canonical_json_bytes(artifact) != canonical_json_bytes(measured):
+ raise VerificationError("artifact differs from a fresh failure-mode execution")
+ return measured
+
+
+def render_markdown(artifact):
+ brittle = artifact["brittle_overlap"]
+ overlapping = artifact["overlapping_crowd"]
+ return "\n".join([
+ "# Point entropy is not a longitudinal privacy certificate",
+ "",
+ "Generated from the canonical experiment artifact. Do not edit manually.",
+ "",
+ "| scenario | initial entropy | initial candidates | observations | survivors |",
+ "|---|---:|---:|---:|---:|",
+ f"| brittle overlap | {brittle['entropy_bits']:.1f} bits | "
+ f"{brittle['initial_candidates']} | {brittle['observations']} | "
+ f"{brittle['surviving_candidates']} |",
+ f"| overlapping crowd | {overlapping['entropy_bits']:.1f} bits | "
+ f"{overlapping['initial_candidates']} | {overlapping['observations']} | "
+ f"{overlapping['surviving_candidates']} |",
+ "",
+ "The initial posteriors are identical. Different overlap between later candidate sets "
+ "produces different residual ambiguity.",
+ "",
+ "This synthetic counterexample falsifies sufficiency. It does not estimate attack "
+ "frequency or certify either scenario's privacy.",
+ "",
+ ])
+
+
+def _parser():
+ parser = argparse.ArgumentParser(description=__doc__)
+ commands = parser.add_subparsers(dest="command", required=True)
+ reproduce = commands.add_parser("reproduce")
+ reproduce.add_argument("--artifact", required=True)
+ reproduce.add_argument("--markdown", required=True)
+ verify = commands.add_parser("verify")
+ verify.add_argument("--artifact", required=True)
+ verify.add_argument("--markdown")
+ return parser
+
+
+def main(argv=None):
+ args = _parser().parse_args(argv)
+ if args.command == "reproduce":
+ artifact = build_artifact()
+ write_canonical_json(args.artifact, artifact)
+ destination = Path(args.markdown)
+ destination.parent.mkdir(parents=True, exist_ok=True)
+ destination.write_text(render_markdown(artifact), encoding="utf-8")
+ else:
+ artifact = verify_artifact(load_artifact(args.artifact))
+ if args.markdown is not None:
+ actual = Path(args.markdown).read_text(encoding="utf-8")
+ if actual != render_markdown(artifact):
+ raise VerificationError("Markdown differs from canonical rendering")
+ return 0
+
+
+if __name__ == "__main__":
+ raise SystemExit(main())
diff --git a/decluster/experiments/equal_output_consolidation.py b/decluster/experiments/equal_output_consolidation.py
new file mode 100644
index 00000000..d008200d
--- /dev/null
+++ b/decluster/experiments/equal_output_consolidation.py
@@ -0,0 +1,126 @@
+"""Reproduce the CTP equal-output consolidation failure mode."""
+
+from __future__ import annotations
+
+import argparse
+import json
+from pathlib import Path
+
+from ..domain import CandidateNarrowing, NoSharedCandidates
+from ..failure_modes import equal_output_consolidation as failure_mode
+from ..result_artifacts import canonical_json_bytes, write_canonical_json
+
+
+EXPERIMENT_ID = "equal-output-consolidation-v1"
+
+
+class VerificationError(ValueError):
+ """A stored result differs from a fresh execution."""
+
+
+def _outcome_name(outcome):
+ if isinstance(outcome, CandidateNarrowing):
+ return "candidate_narrowing"
+ if isinstance(outcome, NoSharedCandidates):
+ return "no_shared_candidates"
+ raise TypeError(f"unsupported consolidation outcome: {type(outcome).__name__}")
+
+
+def build_artifact():
+ scenario = failure_mode.ctp_example()
+ report = failure_mode.evaluate(scenario)
+ intersection = report.channels[1].evidence[0]
+ outcome = report.outcomes[0]
+ return {
+ "schema_version": 1,
+ "experiment": EXPERIMENT_ID,
+ "scenario": {
+ "outputs": [list(output.identifier) for output in scenario.outputs],
+ "candidate_sets": [
+ sorted(candidate.identifier for candidate in candidates)
+ for candidates in scenario.candidate_sets
+ ],
+ },
+ "result": {
+ "candidates_before": intersection.candidates_before,
+ "candidates_after": sorted(
+ candidate.identifier for candidate in intersection.candidates_after
+ ),
+ "outcome": _outcome_name(outcome),
+ "composition": report.composition,
+ },
+ "limitations": list(report.limitations),
+ }
+
+
+def load_artifact(path):
+ try:
+ with Path(path).open(encoding="utf-8") as source:
+ value = json.load(source)
+ except (OSError, json.JSONDecodeError) as exc:
+ raise VerificationError(f"cannot read artifact {path}: {exc}") from exc
+ if not isinstance(value, dict):
+ raise VerificationError("artifact root must be an object")
+ return value
+
+
+def verify_artifact(artifact):
+ if artifact.get("schema_version") != 1 or artifact.get("experiment") != EXPERIMENT_ID:
+ raise VerificationError("unsupported equal-output consolidation artifact identity")
+ measured = build_artifact()
+ if canonical_json_bytes(artifact) != canonical_json_bytes(measured):
+ raise VerificationError("artifact differs from a fresh failure-mode execution")
+ return measured
+
+
+def render_markdown(artifact):
+ result = artifact["result"]
+ return "\n".join([
+ "# Equal-output consolidation failure mode",
+ "",
+ "Generated from the canonical experiment artifact. Do not edit manually.",
+ "",
+ "| quantity | value |",
+ "|---|---:|",
+ f"| candidate clusters per output before consolidation | {result['candidates_before']} |",
+ f"| shared candidate clusters after consolidation | {len(result['candidates_after'])} |",
+ f"| surviving cluster | `{result['candidates_after'][0]}` |",
+ f"| outcome | `{result['outcome']}` |",
+ "",
+ "The narrowing is conditional on the later co-spend being evidence of common ownership. "
+ "It is not an attribution to a real person.",
+ "",
+ ])
+
+
+def _parser():
+ parser = argparse.ArgumentParser(description=__doc__)
+ commands = parser.add_subparsers(dest="command", required=True)
+ reproduce = commands.add_parser("reproduce")
+ reproduce.add_argument("--artifact", required=True)
+ reproduce.add_argument("--markdown", required=True)
+ verify = commands.add_parser("verify")
+ verify.add_argument("--artifact", required=True)
+ verify.add_argument("--markdown")
+ return parser
+
+
+def main(argv=None):
+ args = _parser().parse_args(argv)
+ if args.command == "reproduce":
+ artifact = build_artifact()
+ write_canonical_json(args.artifact, artifact)
+ destination = Path(args.markdown)
+ destination.parent.mkdir(parents=True, exist_ok=True)
+ destination.write_text(render_markdown(artifact), encoding="utf-8")
+ else:
+ artifact = verify_artifact(load_artifact(args.artifact))
+ if args.markdown is not None:
+ actual = Path(args.markdown).read_text(encoding="utf-8")
+ if actual != render_markdown(artifact):
+ raise VerificationError("Markdown differs from canonical rendering")
+ return 0
+
+
+if __name__ == "__main__":
+ raise SystemExit(main())
diff --git a/decluster/experiments/eve_alice_eve.py b/decluster/experiments/eve_alice_eve.py
new file mode 100644
index 00000000..ff72e9eb
--- /dev/null
+++ b/decluster/experiments/eve_alice_eve.py
@@ -0,0 +1,139 @@
+"""Reproduce the CTP Eve–Alice–Eve consolidation failure mode."""
+
+from __future__ import annotations
+
+import argparse
+import json
+from pathlib import Path
+
+from ..domain import CandidateNarrowing, Inconclusive
+from ..failure_modes import eve_alice_eve as failure_mode
+from ..result_artifacts import canonical_json_bytes, write_canonical_json
+
+EXPERIMENT_ID = "eve-alice-eve-v1"
+
+
+class VerificationError(ValueError):
+ """A stored result differs from a fresh execution."""
+
+
+def _outcome_name(outcome):
+ if isinstance(outcome, CandidateNarrowing):
+ return "candidate_narrowing"
+ if isinstance(outcome, Inconclusive):
+ return "inconclusive"
+ raise TypeError(f"unsupported Eve-Alice-Eve outcome: {type(outcome).__name__}")
+
+
+def _result(report):
+ outcome = report.outcomes[0]
+ candidates_after = []
+ if isinstance(outcome, CandidateNarrowing):
+ candidates_after = sorted(
+ candidate.identifier for candidate in outcome.evidence.candidates_after
+ )
+ return {
+ "observations": len(report.subjects) - 1,
+ "outcome": _outcome_name(outcome),
+ "candidates_after": candidates_after,
+ "composition": report.composition,
+ }
+
+
+def build_artifact():
+ consolidated = failure_mode.ctp_consolidation_example()
+ isolated = failure_mode.CounterpartyReturnScenario(
+ consolidated.counterparty,
+ consolidated.deposited_inputs[:1],
+ consolidated.candidate_customers[:1],
+ )
+ isolated_report = failure_mode.evaluate(isolated)
+ consolidated_report = failure_mode.evaluate(consolidated)
+ return {
+ "schema_version": 1,
+ "experiment": EXPERIMENT_ID,
+ "adversary": "counterparty with private transaction records",
+ "isolated_return": _result(isolated_report),
+ "consolidated_return": _result(consolidated_report),
+ "limitations": list(consolidated_report.limitations),
+ }
+
+
+def load_artifact(path):
+ try:
+ with Path(path).open(encoding="utf-8") as source:
+ value = json.load(source)
+ except (OSError, json.JSONDecodeError) as exc:
+ raise VerificationError(f"cannot read artifact {path}: {exc}") from exc
+ if not isinstance(value, dict):
+ raise VerificationError("artifact root must be an object")
+ return value
+
+
+def verify_artifact(artifact):
+ if (
+ artifact.get("schema_version") != 1
+ or artifact.get("experiment") != EXPERIMENT_ID
+ ):
+ raise VerificationError("unsupported Eve-Alice-Eve artifact identity")
+ measured = build_artifact()
+ if canonical_json_bytes(artifact) != canonical_json_bytes(measured):
+ raise VerificationError("artifact differs from a fresh failure-mode execution")
+ return measured
+
+
+def render_markdown(artifact):
+ isolated = artifact["isolated_return"]
+ consolidated = artifact["consolidated_return"]
+ survivors = ", ".join(
+ f"`{item}`" for item in consolidated["candidates_after"]
+ )
+ return "\n".join([
+ "# Eve–Alice–Eve consolidation failure mode",
+ "",
+ "Generated from the canonical experiment artifact. Do not edit manually.",
+ "",
+ "| observation | inputs | outcome | surviving candidates |",
+ "|---|---:|---|---|",
+ f"| isolated return | {isolated['observations']} | "
+ f"`{isolated['outcome']}` | none |",
+ f"| consolidated return | {consolidated['observations']} | "
+ f"`{consolidated['outcome']}` | {survivors} |",
+ "",
+ "The counterparty's records strengthen its observation, but candidate "
+ "narrowing is not identity attribution.",
+ "",
+ ])
+
+
+def _parser():
+ parser = argparse.ArgumentParser(description=__doc__)
+ commands = parser.add_subparsers(dest="command", required=True)
+ reproduce = commands.add_parser("reproduce")
+ reproduce.add_argument("--artifact", required=True)
+ reproduce.add_argument("--markdown", required=True)
+ verify = commands.add_parser("verify")
+ verify.add_argument("--artifact", required=True)
+ verify.add_argument("--markdown")
+ return parser
+
+
+def main(argv=None):
+ args = _parser().parse_args(argv)
+ if args.command == "reproduce":
+ artifact = build_artifact()
+ write_canonical_json(args.artifact, artifact)
+ destination = Path(args.markdown)
+ destination.parent.mkdir(parents=True, exist_ok=True)
+ destination.write_text(render_markdown(artifact), encoding="utf-8")
+ else:
+ artifact = verify_artifact(load_artifact(args.artifact))
+ if args.markdown is not None:
+ actual = Path(args.markdown).read_text(encoding="utf-8")
+ if actual != render_markdown(artifact):
+ raise VerificationError("Markdown differs from canonical rendering")
+ return 0
+
+
+if __name__ == "__main__":
+ raise SystemExit(main())
diff --git a/decluster/experiments/exact_oracle_audit.py b/decluster/experiments/exact_oracle_audit.py
new file mode 100644
index 00000000..b0d5816c
--- /dev/null
+++ b/decluster/experiments/exact_oracle_audit.py
@@ -0,0 +1,151 @@
+"""Reproduce and verify the exact-oracle differential audit."""
+
+from __future__ import annotations
+
+import argparse
+import json
+from pathlib import Path
+
+from ..baselines import oracle_audit
+from ..result_artifacts import canonical_json_bytes, write_canonical_json
+
+
+EXPERIMENT_ID = "exact-oracle-audit-v1"
+
+
+class VerificationError(ValueError):
+ """A stored experiment artifact differs from a fresh computation."""
+
+
+def build_artifact(*, max_coins=oracle_audit.FAMILY_MAX_COINS):
+ import dss
+
+ try:
+ dss_revision = dss.require_build_revision()
+ except (AttributeError, RuntimeError) as exc:
+ raise VerificationError(
+ "the exact-oracle artifact requires a DSS build with an embedded Git revision"
+ ) from exc
+ family = oracle_audit.enumerate_family(max_coins=max_coins)
+ report = oracle_audit.audit(family, include_cases=False)
+ return {
+ "schema_version": 1,
+ "experiment": EXPERIMENT_ID,
+ "parameters": {
+ "alphabet": list(oracle_audit.FAMILY_ALPHABET),
+ "max_coins": max_coins,
+ "min_side": oracle_audit.FAMILY_MIN_SIDE,
+ "tolerance": oracle_audit.TOLERANCE,
+ },
+ "dependencies": {"dss_version": dss.__version__, "dss_revision": dss_revision},
+ "summary": oracle_audit.manifest_invariants(report),
+ "report": report,
+ }
+
+
+def load_artifact(path):
+ try:
+ with Path(path).open(encoding="utf-8") as source:
+ artifact = json.load(source)
+ except (OSError, json.JSONDecodeError) as exc:
+ raise VerificationError(f"cannot read artifact {path}: {exc}") from exc
+ if not isinstance(artifact, dict):
+ raise VerificationError("artifact root must be an object")
+ return artifact
+
+
+def verify_artifact(artifact):
+ if artifact.get("schema_version") != 1 or artifact.get("experiment") != EXPERIMENT_ID:
+ raise VerificationError("unsupported exact-oracle artifact identity")
+ parameters = artifact.get("parameters")
+ if not isinstance(parameters, dict) or set(parameters) != {
+ "alphabet", "max_coins", "min_side", "tolerance",
+ }:
+ raise VerificationError("artifact parameters are incomplete or contain unknown fields")
+ expected_fixed = {
+ "alphabet": list(oracle_audit.FAMILY_ALPHABET),
+ "min_side": oracle_audit.FAMILY_MIN_SIDE,
+ "tolerance": oracle_audit.TOLERANCE,
+ }
+ for key, expected in expected_fixed.items():
+ if parameters[key] != expected:
+ raise VerificationError(f"unsupported parameter {key}: {parameters[key]!r}")
+ max_coins = parameters["max_coins"]
+ if isinstance(max_coins, bool) or not isinstance(max_coins, int):
+ raise VerificationError("max_coins must be an integer")
+ if not oracle_audit.FAMILY_MIN_SIDE * 2 <= max_coins <= oracle_audit.FAMILY_MAX_COINS:
+ raise VerificationError("max_coins is outside the supported exact-oracle range")
+ measured = build_artifact(max_coins=max_coins)
+ if canonical_json_bytes(artifact) != canonical_json_bytes(measured):
+ raise VerificationError("artifact differs from a fresh exact-oracle audit")
+ return measured
+
+
+SUMMARY_ROWS = (
+ ("transactions", "family_size"),
+ ("link-matrix entries", "link_matrix_entries"),
+ ("entries above exact", "link_matrix_entries_above_exact"),
+ ("entries below exact", "link_matrix_entries_below_exact"),
+ ("spurious deterministic links", "spurious_deterministic_links"),
+ ("missed deterministic links", "missed_deterministic_links"),
+ ("mapping-count undercounts", "mapping_count_undercounts"),
+ ("mapping-count overcounts", "mapping_count_overcounts"),
+)
+
+
+def render_markdown(artifact):
+ summary = artifact["summary"]
+ lines = [
+ "# Exact-oracle audit", "",
+ "Generated from the canonical experiment artifact. Do not edit manually.", "",
+ "| quantity | value |", "|---|---:|",
+ ]
+ lines.extend(f"| {label} | {summary[key]} |" for label, key in SUMMARY_ROWS)
+ lines.extend([
+ "", f"Link-matrix bound direction: `{summary['link_matrix_bound_direction']}`.", "",
+ f"DSS: `{summary['dss_version']}` at revision `{summary['dss_rev']}`.", "",
+ ])
+ return "\n".join(lines)
+
+
+def _parser():
+ parser = argparse.ArgumentParser(description=__doc__)
+ commands = parser.add_subparsers(dest="command", required=True)
+ run = commands.add_parser("run")
+ run.add_argument("--output", required=True)
+ run.add_argument("--max-coins", type=int, default=oracle_audit.FAMILY_MAX_COINS)
+ verify = commands.add_parser("verify")
+ verify.add_argument("--artifact", required=True)
+ verify.add_argument("--markdown")
+ render = commands.add_parser("render")
+ render.add_argument("--artifact", required=True)
+ render.add_argument("--output", required=True)
+ reproduce = commands.add_parser("reproduce")
+ reproduce.add_argument("--artifact", required=True)
+ reproduce.add_argument("--markdown", required=True)
+ reproduce.add_argument("--max-coins", type=int, default=oracle_audit.FAMILY_MAX_COINS)
+ return parser
+
+
+def main(argv=None):
+ args = _parser().parse_args(argv)
+ if args.command == "run":
+ write_canonical_json(args.output, build_artifact(max_coins=args.max_coins))
+ elif args.command == "verify":
+ artifact = verify_artifact(load_artifact(args.artifact))
+ if (args.markdown is not None
+ and Path(args.markdown).read_text(encoding="utf-8") != render_markdown(artifact)):
+ raise VerificationError("Markdown differs from the canonical artifact rendering")
+ elif args.command == "render":
+ artifact = load_artifact(args.artifact)
+ Path(args.output).write_text(render_markdown(artifact), encoding="utf-8")
+ else:
+ artifact = build_artifact(max_coins=args.max_coins)
+ write_canonical_json(args.artifact, artifact)
+ Path(args.markdown).parent.mkdir(parents=True, exist_ok=True)
+ Path(args.markdown).write_text(render_markdown(artifact), encoding="utf-8")
+ return 0
+
+
+if __name__ == "__main__":
+ raise SystemExit(main())
diff --git a/decluster/experiments/fingerprint_catalog_axes.py b/decluster/experiments/fingerprint_catalog_axes.py
new file mode 100644
index 00000000..d62f9a37
--- /dev/null
+++ b/decluster/experiments/fingerprint_catalog_axes.py
@@ -0,0 +1,226 @@
+"""Ablate two excluded fingerprint axes on a frozen transaction snapshot."""
+
+from __future__ import annotations
+
+import argparse
+from collections import Counter
+import json
+from pathlib import Path
+
+from ..fingerprint_validate import LibraryScorer, reuse_pairs
+from ..graph_deanon import auc, shuffle_auc
+from ..result_artifacts import canonical_json_bytes, write_canonical_json
+
+EXPERIMENT_ID = "fingerprint-catalog-axes-v1"
+DEFAULT_DATASET = "tests/fixtures/fingerprint_blkcache_sample.json"
+PAIR_CAP = 4000
+SEED = 0
+
+
+class VerificationError(ValueError):
+ """The dataset or stored result violates this experiment's contract."""
+
+
+def _output_count(transaction):
+ count = len(transaction["vout"])
+ if count == 1:
+ return "1"
+ if count == 2:
+ return "2"
+ if count == 3:
+ return "3"
+ return "4plus"
+
+
+def _segwit_serialization(transaction):
+ return "segwit" if any(entry.get("witness") for entry in transaction["vin"]) else "non_segwit"
+
+
+DIAGNOSTIC_AXES = {
+ "output_count": _output_count,
+ "segwit_serialization": _segwit_serialization,
+}
+
+
+def _load(path):
+ try:
+ with Path(path).open(encoding="utf-8") as source:
+ transactions = json.load(source)
+ except (OSError, json.JSONDecodeError) as exc:
+ raise VerificationError(f"cannot read transaction snapshot {path}: {exc}") from exc
+ if not isinstance(transactions, list) or len(transactions) < 2:
+ raise VerificationError("transaction snapshot must contain at least two transactions")
+ identifiers = []
+ for index, transaction in enumerate(transactions):
+ if not isinstance(transaction, dict) or not isinstance(transaction.get("txid"), str):
+ raise VerificationError(f"transaction {index} must have a string txid")
+ if not isinstance(transaction.get("vin"), list) or not isinstance(transaction.get("vout"), list):
+ raise VerificationError(f"transaction {index} must have input and output arrays")
+ identifiers.append(transaction["txid"])
+ if len(identifiers) != len(set(identifiers)):
+ raise VerificationError("transaction ids must be unique")
+ return transactions
+
+
+def _probabilities(transactions, extractor):
+ counts = Counter(extractor(transaction) for transaction in transactions)
+ return {value: count / len(transactions) for value, count in sorted(counts.items())}
+
+
+def _scorer(transactions, axes):
+ scorer = LibraryScorer()
+ for name in axes:
+ extractor = DIAGNOSTIC_AXES[name]
+ probabilities = _probabilities(transactions, extractor)
+ collision = sum(probability * probability for probability in probabilities.values())
+ scorer.axes.append((
+ name,
+ extractor,
+ probabilities,
+ collision,
+ lambda left, right, known=probabilities: left not in known and right not in known,
+ ))
+ return scorer
+
+
+def _measure(scorer, positive_pairs, negative_pairs):
+ positive = [scorer.score(left, right) for left, right in positive_pairs]
+ negative = [scorer.score(left, right) for left, right in negative_pairs]
+ return {
+ "positive_mean": sum(positive) / len(positive),
+ "negative_mean": sum(negative) / len(negative),
+ "auc": auc(positive, negative, SEED),
+ "shuffled_auc": shuffle_auc(positive, negative, SEED),
+ }
+
+
+def build_artifact(dataset=DEFAULT_DATASET):
+ transactions = _load(dataset)
+ positive_pairs, negative_pairs = reuse_pairs(transactions, PAIR_CAP, SEED)
+ configurations = (
+ ("library", ()),
+ ("plus_output_count", ("output_count",)),
+ ("plus_segwit_serialization", ("segwit_serialization",)),
+ ("plus_both", ("output_count", "segwit_serialization")),
+ )
+ rows = []
+ base_auc = None
+ for label, axes in configurations:
+ measurement = _measure(_scorer(transactions, axes), positive_pairs, negative_pairs)
+ if base_auc is None:
+ base_auc = measurement["auc"]
+ rows.append({
+ "configuration": label,
+ "added_axes": list(axes),
+ **measurement,
+ "auc_delta_from_library": measurement["auc"] - base_auc,
+ })
+ return {
+ "schema_version": 1,
+ "experiment": EXPERIMENT_ID,
+ "measurement": {
+ "transactions": len(transactions),
+ "positive_pair_draws": len(positive_pairs),
+ "negative_pair_draws": len(negative_pairs),
+ "rows": rows,
+ },
+ "diagnostic_axis_probabilities": {
+ name: _probabilities(transactions, extractor)
+ for name, extractor in DIAGNOSTIC_AXES.items()
+ },
+ "parameters": {
+ "pair_draw_cap": PAIR_CAP,
+ "seed": SEED,
+ "probability_source": "same frozen snapshot",
+ "same_wallet_consistency": 0.95,
+ "rarity_floor_count": 1000,
+ },
+ "limitations": [
+ "address reuse is a weak and partly feature-dependent same-wallet proxy",
+ "diagnostic probabilities are fitted and evaluated on the same selected snapshot",
+ "the 600-transaction snapshot is not either historical 166k or 180k cache",
+ "AUC deltas on one snapshot do not establish conditional dependence",
+ "the axes remain excluded from the production fingerprint library",
+ "the result measures pair ranking and is not an ownership certificate or privacy score",
+ ],
+ }
+
+
+def load_artifact(path):
+ try:
+ with Path(path).open(encoding="utf-8") as source:
+ value = json.load(source)
+ except (OSError, json.JSONDecodeError) as exc:
+ raise VerificationError(f"cannot read artifact {path}: {exc}") from exc
+ if not isinstance(value, dict):
+ raise VerificationError("artifact root must be an object")
+ return value
+
+
+def verify_artifact(artifact, dataset=DEFAULT_DATASET):
+ if artifact.get("schema_version") != 1 or artifact.get("experiment") != EXPERIMENT_ID:
+ raise VerificationError("unsupported fingerprint-catalog-axes artifact identity")
+ measured = build_artifact(dataset)
+ if canonical_json_bytes(artifact) != canonical_json_bytes(measured):
+ raise VerificationError("artifact differs from a fresh axis-ablation execution")
+ return measured
+
+
+def render_markdown(artifact):
+ measurement = artifact["measurement"]
+ lines = [
+ "# Excluded fingerprint-axis ablation",
+ "",
+ "Generated from the canonical experiment artifact. Do not edit manually.",
+ "",
+ f"Transactions: {measurement['transactions']}; positive draws: "
+ f"{measurement['positive_pair_draws']}; negative draws: "
+ f"{measurement['negative_pair_draws']}.",
+ "",
+ "| configuration | AUC | delta from library | shuffled AUC |",
+ "|---|---:|---:|---:|",
+ ]
+ for row in measurement["rows"]:
+ lines.append(
+ f"| {row['configuration']} | {row['auc']:.6f} | "
+ f"{row['auc_delta_from_library']:+.6f} | {row['shuffled_auc']:.6f} |"
+ )
+ lines.extend([
+ "",
+ "On this frozen snapshot, output count reduces AUC, SegWit serialization increases it, "
+ "and adding both yields less than SegWit serialization alone. This is consistent with "
+ "the historical redundancy concern, but one selected snapshot does not prove conditional "
+ "dependence. Neither diagnostic axis is added to the production library.",
+ "",
+ ])
+ return "\n".join(lines)
+
+
+def _parser():
+ parser = argparse.ArgumentParser(description=__doc__)
+ parser.add_argument("--dataset", default=DEFAULT_DATASET)
+ commands = parser.add_subparsers(dest="command", required=True)
+ for name in ("reproduce", "verify"):
+ command = commands.add_parser(name)
+ command.add_argument("--artifact", required=True)
+ command.add_argument("--markdown", required=True)
+ return parser
+
+
+def main(argv=None):
+ args = _parser().parse_args(argv)
+ if args.command == "reproduce":
+ artifact = build_artifact(args.dataset)
+ write_canonical_json(args.artifact, artifact)
+ destination = Path(args.markdown)
+ destination.parent.mkdir(parents=True, exist_ok=True)
+ destination.write_text(render_markdown(artifact), encoding="utf-8")
+ else:
+ artifact = verify_artifact(load_artifact(args.artifact), args.dataset)
+ if Path(args.markdown).read_text(encoding="utf-8") != render_markdown(artifact):
+ raise VerificationError("Markdown differs from canonical rendering")
+ return 0
+
+
+if __name__ == "__main__":
+ raise SystemExit(main())
diff --git a/decluster/experiments/fingerprint_regime.py b/decluster/experiments/fingerprint_regime.py
new file mode 100644
index 00000000..5e0084a5
--- /dev/null
+++ b/decluster/experiments/fingerprint_regime.py
@@ -0,0 +1,153 @@
+"""Reproduce fingerprint-ranking regimes on a frozen transaction snapshot."""
+
+from __future__ import annotations
+
+import argparse
+import json
+from pathlib import Path
+
+from .. import fingerprint_ns
+from ..fingerprint_regime import evaluate
+from ..result_artifacts import canonical_json_bytes, write_canonical_json
+
+EXPERIMENT_ID = "fingerprint-regime-v1"
+DEFAULT_DATASET = "tests/fixtures/fingerprint_blkcache_sample.json"
+
+
+class VerificationError(ValueError):
+ """The dataset or stored result violates this experiment's contract."""
+
+
+def _load(path):
+ try:
+ with Path(path).open(encoding="utf-8") as source:
+ transactions = json.load(source)
+ except (OSError, json.JSONDecodeError) as exc:
+ raise VerificationError(f"cannot read transaction snapshot {path}: {exc}") from exc
+ if not isinstance(transactions, list) or len(transactions) < 2:
+ raise VerificationError("transaction snapshot must contain at least two transactions")
+ txids = []
+ for index, transaction in enumerate(transactions):
+ if not isinstance(transaction, dict) or not isinstance(transaction.get("txid"), str):
+ raise VerificationError(f"transaction {index} must be an object with a string txid")
+ txids.append(transaction["txid"])
+ if len(txids) != len(set(txids)):
+ raise VerificationError("transaction ids must be unique")
+ return transactions
+
+
+def build_artifact(dataset=DEFAULT_DATASET):
+ transactions = _load(dataset)
+ axes = fingerprint_ns.axis_fns()
+ rows = [
+ evaluate(transactions, fingerprint_ns.library_weights(), "library"),
+ evaluate(
+ transactions,
+ fingerprint_ns.measured_weights(transactions, axes),
+ "snapshot_measured",
+ ),
+ ]
+ return {
+ "schema_version": 1,
+ "experiment": EXPERIMENT_ID,
+ "measurement": {"transactions": len(transactions), "rows": rows},
+ "parameters": {
+ "pair_draw_cap": 2000,
+ "candidate_node_cap": 200,
+ "seed": 0,
+ "conditioning_axes": list(fingerprint_ns.CONDITIONING_AXES),
+ },
+ "limitations": [
+ "address reuse is a weak and partly feature-dependent owner proxy",
+ "candidate identities are reuse-group labels rather than wallet ownership",
+ "the 600-transaction snapshot is selected and is not a chain-wide sample",
+ "the snapshot-measured weights are fitted and evaluated on the same data",
+ "the Lumen weighting and historical growing-cache population are not reproduced",
+ "mean eccentricity is descriptive and is not an acceptance rate",
+ "the result does not establish that fingerprints are universally sparse or dense",
+ ],
+ }
+
+
+def load_artifact(path):
+ try:
+ with Path(path).open(encoding="utf-8") as source:
+ value = json.load(source)
+ except (OSError, json.JSONDecodeError) as exc:
+ raise VerificationError(f"cannot read artifact {path}: {exc}") from exc
+ if not isinstance(value, dict):
+ raise VerificationError("artifact root must be an object")
+ return value
+
+
+def verify_artifact(artifact, dataset=DEFAULT_DATASET):
+ if artifact.get("schema_version") != 1 or artifact.get("experiment") != EXPERIMENT_ID:
+ raise VerificationError("unsupported fingerprint-regime artifact identity")
+ measured = build_artifact(dataset)
+ if canonical_json_bytes(artifact) != canonical_json_bytes(measured):
+ raise VerificationError("artifact differs from a fresh experiment execution")
+ return measured
+
+
+def render_markdown(artifact):
+ measured = artifact["measurement"]
+ lines = [
+ "# Fingerprint ranking regime",
+ "",
+ "Generated from the canonical experiment artifact. Do not edit manually.",
+ "",
+ f"Transactions: {measured['transactions']}.",
+ "",
+ "| weights | N-S-form AUC | F-S AUC | N-S-form top-1 | F-S top-1 | within-class mean gap |",
+ "|---|---:|---:|---:|---:|---:|",
+ ]
+ for row in measured["rows"]:
+ lines.append(
+ f"| {row['weight_source']} | {row['ns_auc']:.6f} | {row['fs_auc']:.6f} | "
+ f"{row['ns_top1']:.6f} | {row['fs_top1']:.6f} | "
+ f"{row['within_class_gap_mean']:.6f} |"
+ )
+ lines.extend([
+ "",
+ "The historical claim that the conditioner interpretation is stable across weight "
+ "sources does not reproduce on this fixture. Snapshot-measured weights outperform the "
+ "F-S top-1 baseline and have a larger within-class mean gap, but they are fitted and "
+ "evaluated on the same selected data. This is evidence of sensitivity, not evidence that "
+ "fingerprints are universal sparse identifiers.",
+ "",
+ ])
+ return "\n".join(lines)
+
+
+def _parser():
+ parser = argparse.ArgumentParser(description=__doc__)
+ parser.add_argument("--dataset", default=DEFAULT_DATASET)
+ commands = parser.add_subparsers(dest="command", required=True)
+ reproduce = commands.add_parser("reproduce")
+ reproduce.add_argument("--artifact", required=True)
+ reproduce.add_argument("--markdown", required=True)
+ verify = commands.add_parser("verify")
+ verify.add_argument("--artifact", required=True)
+ verify.add_argument("--markdown")
+ return parser
+
+
+def main(argv=None):
+ args = _parser().parse_args(argv)
+ if args.command == "reproduce":
+ artifact = build_artifact(args.dataset)
+ write_canonical_json(args.artifact, artifact)
+ destination = Path(args.markdown)
+ destination.parent.mkdir(parents=True, exist_ok=True)
+ destination.write_text(render_markdown(artifact), encoding="utf-8")
+ else:
+ artifact = verify_artifact(load_artifact(args.artifact), args.dataset)
+ if args.markdown is not None:
+ actual = Path(args.markdown).read_text(encoding="utf-8")
+ if actual != render_markdown(artifact):
+ raise VerificationError("Markdown differs from canonical rendering")
+ return 0
+
+
+if __name__ == "__main__":
+ raise SystemExit(main())
diff --git a/decluster/experiments/fingerprint_sparsity.py b/decluster/experiments/fingerprint_sparsity.py
new file mode 100644
index 00000000..ec7178f3
--- /dev/null
+++ b/decluster/experiments/fingerprint_sparsity.py
@@ -0,0 +1,162 @@
+"""Reconstruct fingerprint class sizes from the frozen Lumen aggregate."""
+
+from __future__ import annotations
+
+import argparse
+import json
+from pathlib import Path
+
+from ..fingerprint_sparsity import LABELS, reconstruct_histogram
+from ..result_artifacts import canonical_json_bytes, write_canonical_json
+
+EXPERIMENT_ID = "fingerprint-sparsity-v1"
+DEFAULT_DATASET = "tests/fixtures/lumen_explorer_data.json"
+
+
+class VerificationError(ValueError):
+ """The aggregate or stored result violates this experiment's contract."""
+
+
+def _load(path):
+ try:
+ with Path(path).open(encoding="utf-8") as source:
+ value = json.load(source)
+ except (OSError, json.JSONDecodeError) as exc:
+ raise VerificationError(f"cannot read Lumen aggregate {path}: {exc}") from exc
+ if not isinstance(value, dict) or not isinstance(value.get("cond"), dict):
+ raise VerificationError("Lumen aggregate must contain conditional histograms")
+ return value
+
+
+def build_artifact(dataset=DEFAULT_DATASET):
+ source = _load(dataset)
+ expected_total = source.get("totals", {}).get("txs")
+ if isinstance(expected_total, bool) or not isinstance(expected_total, int):
+ raise VerificationError("Lumen aggregate has no integer transaction total")
+ reconstructions = {}
+ for axis in sorted(source["cond"]):
+ try:
+ histogram, total = reconstruct_histogram(source["cond"][axis])
+ except ValueError as exc:
+ raise VerificationError(f"invalid conditional histogram for {axis}: {exc}") from exc
+ if total != expected_total:
+ raise VerificationError(
+ f"conditional histogram for {axis} covers {total}, expected {expected_total}"
+ )
+ reconstructions[axis] = histogram
+ if not reconstructions:
+ raise VerificationError("Lumen aggregate contains no conditional axes")
+ first_axis = next(iter(reconstructions))
+ histogram = reconstructions[first_axis]
+ disagreeing = [axis for axis, candidate in reconstructions.items() if candidate != histogram]
+ if disagreeing:
+ raise VerificationError(f"conditional axes disagree with {first_axis}: {disagreeing}")
+ shares = {label: histogram[label] / expected_total for label in LABELS}
+ return {
+ "schema_version": 1,
+ "experiment": EXPERIMENT_ID,
+ "window": {
+ "start_height": source.get("window", {}).get("start_height"),
+ "end_height": source.get("window", {}).get("end_height"),
+ "epochs": source.get("window", {}).get("epochs"),
+ "transactions": expected_total,
+ },
+ "measurement": {
+ "vector_width": 12,
+ "scope": "whole_window",
+ "class_size_counts": histogram,
+ "class_size_shares": shares,
+ "share_below_10": shares["exactly 1"] + shares["2-9"],
+ "share_below_100": shares["exactly 1"] + shares["2-9"] + shares["10-99"],
+ },
+ "cross_check": {"consistent_conditional_partitions": sorted(reconstructions)},
+ "limitations": [
+ "the fixture contains aggregate histograms rather than transaction-level vectors",
+ "the 19-axis and per-epoch distributions are not preserved and are not reproduced",
+ "exact-vector class size is not nearest-neighbour sparsity in a general feature metric",
+ "the result concerns per-transaction vectors, not cluster feature distributions",
+ "the scan ends at height 962720 and differs by 6219 records from the cataloged raw export",
+ "the dataset publisher authorized redistribution of the aggregate fixture on 2026-09-04",
+ ],
+ }
+
+
+def load_artifact(path):
+ try:
+ with Path(path).open(encoding="utf-8") as source:
+ value = json.load(source)
+ except (OSError, json.JSONDecodeError) as exc:
+ raise VerificationError(f"cannot read artifact {path}: {exc}") from exc
+ if not isinstance(value, dict):
+ raise VerificationError("artifact root must be an object")
+ return value
+
+
+def verify_artifact(artifact, dataset=DEFAULT_DATASET):
+ if artifact.get("schema_version") != 1 or artifact.get("experiment") != EXPERIMENT_ID:
+ raise VerificationError("unsupported fingerprint-sparsity artifact identity")
+ measured = build_artifact(dataset)
+ if canonical_json_bytes(artifact) != canonical_json_bytes(measured):
+ raise VerificationError("artifact differs from a fresh aggregate reconstruction")
+ return measured
+
+
+def render_markdown(artifact):
+ window = artifact["window"]
+ measurement = artifact["measurement"]
+ shares = measurement["class_size_shares"]
+ lines = [
+ "# Fingerprint exact-vector class sizes",
+ "",
+ "Generated from the canonical experiment artifact. Do not edit manually.",
+ "",
+ f"Window: blocks {window['start_height']} through {window['end_height']}; "
+ f"{window['transactions']} transactions in {window['epochs']} epochs.",
+ "",
+ "| class size | transaction share |",
+ "|---|---:|",
+ ]
+ lines.extend(f"| {label} | {shares[label] * 100:.3f}% |" for label in LABELS)
+ lines.extend([
+ "",
+ f"Share below 10: {measurement['share_below_10'] * 100:.3f}%.",
+ f"Share below 100: {measurement['share_below_100'] * 100:.3f}%.",
+ "",
+ "All preserved conditional partitions reconstruct the same 12-axis whole-window "
+ "histogram. The unavailable 19-axis and per-epoch columns are excluded. Exact-vector "
+ "equivalence classes do not establish general metric sparsity or successful attribution.",
+ "",
+ ])
+ return "\n".join(lines)
+
+
+def _parser():
+ parser = argparse.ArgumentParser(description=__doc__)
+ parser.add_argument("--dataset", default=DEFAULT_DATASET)
+ commands = parser.add_subparsers(dest="command", required=True)
+ reproduce = commands.add_parser("reproduce")
+ reproduce.add_argument("--artifact", required=True)
+ reproduce.add_argument("--markdown", required=True)
+ verify = commands.add_parser("verify")
+ verify.add_argument("--artifact", required=True)
+ verify.add_argument("--markdown", required=True)
+ return parser
+
+
+def main(argv=None):
+ args = _parser().parse_args(argv)
+ if args.command == "reproduce":
+ artifact = build_artifact(args.dataset)
+ write_canonical_json(args.artifact, artifact)
+ destination = Path(args.markdown)
+ destination.parent.mkdir(parents=True, exist_ok=True)
+ destination.write_text(render_markdown(artifact), encoding="utf-8")
+ else:
+ artifact = verify_artifact(load_artifact(args.artifact), args.dataset)
+ if Path(args.markdown).read_text(encoding="utf-8") != render_markdown(artifact):
+ raise VerificationError("Markdown differs from canonical rendering")
+ return 0
+
+
+if __name__ == "__main__":
+ raise SystemExit(main())
diff --git a/decluster/experiments/fingerprint_validation.py b/decluster/experiments/fingerprint_validation.py
new file mode 100644
index 00000000..91471185
--- /dev/null
+++ b/decluster/experiments/fingerprint_validation.py
@@ -0,0 +1,143 @@
+"""Reproduce construction-fingerprint separation on a frozen transaction snapshot."""
+
+from __future__ import annotations
+
+import argparse
+import json
+from pathlib import Path
+
+from ..fingerprint_validate import LibraryScorer, evaluate
+from ..result_artifacts import canonical_json_bytes, write_canonical_json
+
+EXPERIMENT_ID = "fingerprint-validation-v1"
+DEFAULT_DATASET = "tests/fixtures/fingerprint_blkcache_sample.json"
+
+
+class VerificationError(ValueError):
+ """The dataset or stored result violates this experiment's contract."""
+
+
+def _load(path):
+ try:
+ with Path(path).open(encoding="utf-8") as source:
+ transactions = json.load(source)
+ except (OSError, json.JSONDecodeError) as exc:
+ raise VerificationError(f"cannot read transaction snapshot {path}: {exc}") from exc
+ if not isinstance(transactions, list) or len(transactions) < 2:
+ raise VerificationError("transaction snapshot must contain at least two transactions")
+ txids = set()
+ for index, transaction in enumerate(transactions):
+ if not isinstance(transaction, dict) or not isinstance(transaction.get("txid"), str):
+ raise VerificationError(f"transaction {index} must be an object with a string txid")
+ if transaction["txid"] in txids:
+ raise VerificationError(f"duplicate transaction id: {transaction['txid']}")
+ txids.add(transaction["txid"])
+ return transactions
+
+
+def build_artifact(dataset=DEFAULT_DATASET):
+ transactions = _load(dataset)
+ measurement = evaluate(transactions, LibraryScorer(), cap=4000, seed=0)
+ return {
+ "schema_version": 1,
+ "experiment": EXPERIMENT_ID,
+ "measurement": {"transactions": len(transactions), **measurement},
+ "parameters": {
+ "positive_pair_draw_cap": 4000,
+ "negative_pair_draw_cap": 4000,
+ "seed": 0,
+ "same_wallet_consistency": 0.95,
+ "rarity_floor_count": 1000,
+ "scorer": "canonical construction-fingerprint library",
+ },
+ "labels": {
+ "positive": "distinct transactions sharing an input address",
+ "negative": "random distinct-transaction pairs",
+ },
+ "limitations": [
+ "address reuse is a weak same-wallet label and shares script information with the features",
+ "positive pairs are draws with replacement rather than independent observations",
+ "random negative pairs can include transactions controlled by the same wallet",
+ "the 600-transaction snapshot is selected and is not a chain-wide sample",
+ "the source recipe is unavailable; the dataset publisher authorized redistribution on 2026-09-04",
+ "the result measures pair ranking and is not an ownership certificate or privacy score",
+ ],
+ }
+
+
+def load_artifact(path):
+ try:
+ with Path(path).open(encoding="utf-8") as source:
+ value = json.load(source)
+ except (OSError, json.JSONDecodeError) as exc:
+ raise VerificationError(f"cannot read artifact {path}: {exc}") from exc
+ if not isinstance(value, dict):
+ raise VerificationError("artifact root must be an object")
+ return value
+
+
+def verify_artifact(artifact, dataset=DEFAULT_DATASET):
+ if artifact.get("schema_version") != 1 or artifact.get("experiment") != EXPERIMENT_ID:
+ raise VerificationError("unsupported fingerprint-validation artifact identity")
+ measured = build_artifact(dataset)
+ if canonical_json_bytes(artifact) != canonical_json_bytes(measured):
+ raise VerificationError("artifact differs from a fresh experiment execution")
+ return measured
+
+
+def render_markdown(artifact):
+ measured = artifact["measurement"]
+ return "\n".join([
+ "# Construction-fingerprint pair separation",
+ "",
+ "Generated from the canonical experiment artifact. Do not edit manually.",
+ "",
+ f"Transactions: {measured['transactions']}; positive draws: {measured['n_pos']}; "
+ f"negative draws: {measured['n_neg']}.",
+ "",
+ "| metric | value |",
+ "|---|---:|",
+ f"| positive mean bits | {measured['pos_mean']:.6f} |",
+ f"| negative mean bits | {measured['neg_mean']:.6f} |",
+ f"| AUC | {measured['auc']:.6f} |",
+ f"| shuffled-label AUC | {measured['shuffle_auc']:.6f} |",
+ "",
+ "The canonical fingerprint scorer separates address-reuse-labelled pairs from random "
+ "pairs on this selected snapshot. Address reuse is a weak and partly feature-dependent "
+ "label, so this is pair-ranking evidence, not wallet attribution or a privacy score.",
+ "",
+ ])
+
+
+def _parser():
+ parser = argparse.ArgumentParser(description=__doc__)
+ parser.add_argument("--dataset", default=DEFAULT_DATASET)
+ commands = parser.add_subparsers(dest="command", required=True)
+ reproduce = commands.add_parser("reproduce")
+ reproduce.add_argument("--artifact", required=True)
+ reproduce.add_argument("--markdown", required=True)
+ verify = commands.add_parser("verify")
+ verify.add_argument("--artifact", required=True)
+ verify.add_argument("--markdown")
+ return parser
+
+
+def main(argv=None):
+ args = _parser().parse_args(argv)
+ if args.command == "reproduce":
+ artifact = build_artifact(args.dataset)
+ write_canonical_json(args.artifact, artifact)
+ destination = Path(args.markdown)
+ destination.parent.mkdir(parents=True, exist_ok=True)
+ destination.write_text(render_markdown(artifact), encoding="utf-8")
+ else:
+ artifact = verify_artifact(load_artifact(args.artifact), args.dataset)
+ if args.markdown is not None:
+ actual = Path(args.markdown).read_text(encoding="utf-8")
+ if actual != render_markdown(artifact):
+ raise VerificationError("Markdown differs from canonical rendering")
+ return 0
+
+
+if __name__ == "__main__":
+ raise SystemExit(main())
diff --git a/decluster/experiments/fs_ablation.py b/decluster/experiments/fs_ablation.py
new file mode 100644
index 00000000..229f269c
--- /dev/null
+++ b/decluster/experiments/fs_ablation.py
@@ -0,0 +1,134 @@
+"""Reproduce the Fellegi-Sunter conditional-dependence ablation."""
+
+from __future__ import annotations
+
+import argparse
+import json
+from pathlib import Path
+from tempfile import TemporaryDirectory
+
+from ..archive_snapshot import extract_tar_gz
+from ..fingerprint_validate import load_blkcache
+from ..fs_ablation import run_ablation_study
+from ..reproducibility import fingerprint_source
+from ..result_artifacts import canonical_json_bytes, write_canonical_json
+
+EXPERIMENT_ID = "fs-ablation-v1"
+
+
+class VerificationError(ValueError):
+ """A stored result differs from a fresh execution."""
+
+
+def _evaluate_snapshot(snapshot):
+ with TemporaryDirectory(prefix="decluster-fs-ablation-") as temporary:
+ cache = extract_tar_gz(snapshot, temporary) / ".blkcache"
+ transactions = load_blkcache(str(cache))
+ report = run_ablation_study(
+ transactions,
+ train_fraction=0.7,
+ ablation_cap=4000,
+ association_cap=8000,
+ seed=0,
+ cluster_threshold=0.6,
+ )
+ report["source"] = fingerprint_source(str(cache / "*.json"))
+ report["source"]["pattern"] = ".blkcache/*.json"
+ report["source"]["transactions"] = len(transactions)
+ return report
+
+
+def build_artifact(snapshot):
+ return {
+ "schema_version": 1,
+ "experiment": EXPERIMENT_ID,
+ "dataset": "fs-blkcache-2026-09-04-v1",
+ "report": _evaluate_snapshot(snapshot),
+ "limitations": [
+ "address reuse supplies weak labels rather than independent ownership "
+ "labels",
+ "the block sample is not representative of the whole chain",
+ "the association statistic is descriptive rather than causal",
+ "input_script_type and input_types_present are duplicates in this sample",
+ ],
+ }
+
+
+def load_artifact(path):
+ try:
+ with Path(path).open(encoding="utf-8") as source:
+ value = json.load(source)
+ except (OSError, json.JSONDecodeError) as exc:
+ raise VerificationError(f"cannot read artifact {path}: {exc}") from exc
+ if not isinstance(value, dict):
+ raise VerificationError("artifact root must be an object")
+ return value
+
+
+def verify_artifact(artifact, snapshot):
+ if (
+ artifact.get("schema_version") != 1
+ or artifact.get("experiment") != EXPERIMENT_ID
+ ):
+ raise VerificationError("unsupported FS ablation artifact identity")
+ measured = build_artifact(snapshot)
+ if canonical_json_bytes(artifact) != canonical_json_bytes(measured):
+ raise VerificationError("artifact differs from a fresh experiment execution")
+ return measured
+
+
+def render_markdown(artifact):
+ report = artifact["report"]
+ association = report["association"]
+ pair = association["most_dependent_pairs"][0]
+ return "\n".join([
+ "# Fellegi–Sunter conditional-dependence ablation",
+ "",
+ "Generated from the canonical experiment artifact. Do not edit manually.",
+ "",
+ "| quantity | value |",
+ "|---|---:|",
+ f"| transactions | {report['source']['transactions']} |",
+ f"| baseline AUC | {report['leave_one_out']['baseline']['auc']:.6f} |",
+ f"| dependent field groups | {len(association['clusters'])} |",
+ f"| most dependent fields | `{'`, `'.join(pair['fields'])}` |",
+ f"| phi among matches | {pair['phi_match']:.6f} |",
+ f"| phi among non-matches | {pair['phi_non_match']:.6f} |",
+ "",
+ "Association is measured conditionally on weak-label class and is not causal.",
+ "",
+ ])
+
+
+def _parser():
+ parser = argparse.ArgumentParser(description=__doc__)
+ parser.add_argument("--snapshot", required=True)
+ commands = parser.add_subparsers(dest="command", required=True)
+ reproduce = commands.add_parser("reproduce")
+ reproduce.add_argument("--artifact", required=True)
+ reproduce.add_argument("--markdown", required=True)
+ verify = commands.add_parser("verify")
+ verify.add_argument("--artifact", required=True)
+ verify.add_argument("--markdown")
+ return parser
+
+
+def main(argv=None):
+ args = _parser().parse_args(argv)
+ if args.command == "reproduce":
+ artifact = build_artifact(args.snapshot)
+ write_canonical_json(args.artifact, artifact)
+ destination = Path(args.markdown)
+ destination.parent.mkdir(parents=True, exist_ok=True)
+ destination.write_text(render_markdown(artifact), encoding="utf-8")
+ else:
+ artifact = verify_artifact(load_artifact(args.artifact), args.snapshot)
+ if args.markdown is not None:
+ actual = Path(args.markdown).read_text(encoding="utf-8")
+ if actual != render_markdown(artifact):
+ raise VerificationError("Markdown differs from canonical rendering")
+ return 0
+
+
+if __name__ == "__main__":
+ raise SystemExit(main())
diff --git a/decluster/experiments/fs_temporal.py b/decluster/experiments/fs_temporal.py
new file mode 100644
index 00000000..64db4196
--- /dev/null
+++ b/decluster/experiments/fs_temporal.py
@@ -0,0 +1,130 @@
+"""Reproduce the temporal Fellegi-Sunter evaluation from its snapshot."""
+
+from __future__ import annotations
+
+import argparse
+import json
+from pathlib import Path
+from tempfile import TemporaryDirectory
+
+from ..archive_snapshot import extract_tar_gz
+from ..fingerprint_validate import load_blkcache
+from ..fs_temporal import evaluate_temporal
+from ..reproducibility import fingerprint_source
+from ..result_artifacts import canonical_json_bytes, write_canonical_json
+
+EXPERIMENT_ID = "fs-temporal-v1"
+
+
+class VerificationError(ValueError):
+ """A stored result differs from a fresh execution."""
+
+
+def _evaluate_snapshot(snapshot):
+ with TemporaryDirectory(prefix="decluster-fs-") as temporary:
+ root = extract_tar_gz(snapshot, temporary)
+ cache = root / ".blkcache"
+ transactions = load_blkcache(str(cache))
+ report = evaluate_temporal(
+ transactions, train_fraction=0.7, cap=4000, seed=0
+ )
+ report["source"] = fingerprint_source(str(cache / "*.json"))
+ report["source"]["pattern"] = ".blkcache/*.json"
+ report["source"]["transactions"] = len(transactions)
+ return report
+
+
+def build_artifact(snapshot):
+ return {
+ "schema_version": 1,
+ "experiment": EXPERIMENT_ID,
+ "dataset": "fs-blkcache-2026-09-04-v1",
+ "report": _evaluate_snapshot(snapshot),
+ "limitations": [
+ "address reuse supplies weak labels rather than independent ownership labels",
+ "the block sample is not representative of the whole chain",
+ "Beta smoothing is a local modeling choice",
+ "input_script_type and input_types_present are duplicates in this sample",
+ ],
+ }
+
+
+def load_artifact(path):
+ try:
+ with Path(path).open(encoding="utf-8") as source:
+ value = json.load(source)
+ except (OSError, json.JSONDecodeError) as exc:
+ raise VerificationError(f"cannot read artifact {path}: {exc}") from exc
+ if not isinstance(value, dict):
+ raise VerificationError("artifact root must be an object")
+ return value
+
+
+def verify_artifact(artifact, snapshot):
+ if (
+ artifact.get("schema_version") != 1
+ or artifact.get("experiment") != EXPERIMENT_ID
+ ):
+ raise VerificationError("unsupported FS temporal artifact identity")
+ measured = build_artifact(snapshot)
+ if canonical_json_bytes(artifact) != canonical_json_bytes(measured):
+ raise VerificationError("artifact differs from a fresh experiment execution")
+ return measured
+
+
+def render_markdown(artifact):
+ report = artifact["report"]
+ fs = report["fellegi_sunter"]
+ rarity = report["rarity_baseline"]
+ split = report["split"]
+ return "\n".join([
+ "# Temporal Fellegi–Sunter evaluation",
+ "",
+ "Generated from the canonical experiment artifact. Do not edit manually.",
+ "",
+ "| quantity | value |",
+ "|---|---:|",
+ f"| transactions | {report['source']['transactions']} |",
+ f"| train transactions | {split['train_transactions']} |",
+ f"| test transactions | {split['test_transactions']} |",
+ f"| Fellegi–Sunter AUC | {fs['auc']:.6f} |",
+ f"| rarity baseline AUC | {rarity['auc']:.6f} |",
+ f"| AUC difference | {report['comparison']['auc_delta_fs_minus_rarity']:.6f} |",
+ "",
+ "Labels are based on address reuse and are not independent ownership labels.",
+ "",
+ ])
+
+
+def _parser():
+ parser = argparse.ArgumentParser(description=__doc__)
+ parser.add_argument("--snapshot", required=True)
+ commands = parser.add_subparsers(dest="command", required=True)
+ reproduce = commands.add_parser("reproduce")
+ reproduce.add_argument("--artifact", required=True)
+ reproduce.add_argument("--markdown", required=True)
+ verify = commands.add_parser("verify")
+ verify.add_argument("--artifact", required=True)
+ verify.add_argument("--markdown")
+ return parser
+
+
+def main(argv=None):
+ args = _parser().parse_args(argv)
+ if args.command == "reproduce":
+ artifact = build_artifact(args.snapshot)
+ write_canonical_json(args.artifact, artifact)
+ destination = Path(args.markdown)
+ destination.parent.mkdir(parents=True, exist_ok=True)
+ destination.write_text(render_markdown(artifact), encoding="utf-8")
+ else:
+ artifact = verify_artifact(load_artifact(args.artifact), args.snapshot)
+ if args.markdown is not None:
+ actual = Path(args.markdown).read_text(encoding="utf-8")
+ if actual != render_markdown(artifact):
+ raise VerificationError("Markdown differs from canonical rendering")
+ return 0
+
+
+if __name__ == "__main__":
+ raise SystemExit(main())
diff --git a/decluster/experiments/fused_engine.py b/decluster/experiments/fused_engine.py
new file mode 100644
index 00000000..690d3354
--- /dev/null
+++ b/decluster/experiments/fused_engine.py
@@ -0,0 +1,204 @@
+"""What each evidence channel adds when the engine fuses them, on committed transactions.
+
+The engine is the thesis: `cluster_refined` refuses a co-spend the merge-only heuristic would take,
+by fusing signed bits from several channels. Every channel had a canonical run of its own and the
+fusion had none — it was exercised by unit tests and by scripts over data this repository does not
+ship, so the object the argument rests on was the one object no published artifact executed.
+
+This runs the engine over the committed block-cache slice as a ladder: co-spend alone, then each
+channel switched on in turn. The quantity of interest is the delta, not the level. A channel that
+moves nothing here has not been shown useless — the slice is block-sampled rather than contiguous,
+and the amount channel in particular is refuse-only and gated behind fingerprint disagreement, so it
+can only speak where the fingerprint already objects.
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+from collections import Counter
+from pathlib import Path
+from tempfile import TemporaryDirectory
+
+from ..archive_snapshot import extract_tar_gz
+from ..cluster import cluster_naive, cluster_refined
+from ..combiner import Combiner
+from ..fingerprint_validate import load_blkcache
+from ..reproducibility import fingerprint_source
+from ..result_artifacts import canonical_json_bytes, write_canonical_json
+
+EXPERIMENT_ID = "fused-engine-v1"
+
+
+class VerificationError(ValueError):
+ """The stored result differs from a fresh execution."""
+
+
+def _slice_inputs(transactions):
+ """Nodes, counterparty sets and depth-1 provenance signatures, all read off the slice."""
+ index = {tx["txid"]: tx for tx in transactions}
+ cospends = [tx for tx in transactions
+ if len([v for v in tx["vin"] if v.get("txid") in index]) >= 2]
+ nodes = sorted({v["txid"] for tx in cospends for v in tx["vin"] if v.get("txid") in index})
+ neighbours = {n: {o.get("scriptpubkey_address") for o in index[n]["vout"]
+ if o.get("scriptpubkey_address")} for n in nodes}
+ signatures = {n: {v["txid"]: 1.0 for v in index[n]["vin"] if v.get("txid")} for n in nodes}
+ rarity = Counter(origin for signature in signatures.values() for origin in signature)
+ return index, cospends, nodes, neighbours, signatures, dict(rarity)
+
+
+def _arm(name, nodes, combiner, fetch, **options):
+ groups, refused, linked = cluster_refined(nodes, combiner, fetch=fetch, **options)
+ return {
+ "arm": name,
+ "groups": len(groups),
+ "refused_cospends": len(refused),
+ "added_links": len(linked),
+ "largest_group": max((len(group) for group in groups), default=0),
+ }
+
+
+def build_artifact(snapshot):
+ with TemporaryDirectory(prefix="decluster-fused-") as temporary:
+ root = extract_tar_gz(snapshot, temporary)
+ cache = root / ".blkcache"
+ transactions = load_blkcache(str(cache))
+ source = fingerprint_source(str(cache / "*.json"))
+ source.update(pattern=".blkcache/*.json", transactions=len(transactions))
+
+ index, cospends, nodes, neighbours, signatures, rarity = _slice_inputs(transactions)
+ fetch = index.__getitem__
+ combiner = Combiner.from_library()
+ topology = {"neigh": neighbours}
+ provenance = {"provenance": True, "signatures": signatures, "rarity": rarity}
+
+ baseline = cluster_naive(nodes, fetch=fetch)
+ ladder = [
+ _arm("fingerprint", nodes, combiner, fetch, amount=False),
+ _arm("fingerprint+amount", nodes, combiner, fetch, amount=True),
+ _arm("fingerprint+amount+topology", nodes, combiner, fetch, amount=True, **topology),
+ _arm("fingerprint+amount+topology+provenance", nodes, combiner, fetch,
+ amount=True, **topology, **provenance),
+ _arm("all five channels", nodes, combiner, fetch,
+ amount=True, **topology, **provenance, subsetsum=True),
+ ]
+ return {
+ "schema_version": 1,
+ "experiment": EXPERIMENT_ID,
+ "dataset": "fs-blkcache-2026-09-04-v1",
+ "source": source,
+ "population": {
+ "transactions": len(transactions),
+ "cospend_transactions": len(cospends),
+ "two_in_two_out_cospends": sum(
+ 1 for tx in cospends if len(tx["vin"]) == 2 and len(tx["vout"]) == 2),
+ "nodes": len(nodes),
+ },
+ "cospend_baseline": {
+ "arm": "co-spend union-find",
+ "groups": len(baseline),
+ "refused_cospends": 0,
+ "added_links": 0,
+ "largest_group": max((len(group) for group in baseline), default=0),
+ },
+ "ladder": ladder,
+ }
+
+
+def load_artifact(path):
+ try:
+ with Path(path).open(encoding="utf-8") as source:
+ value = json.load(source)
+ except (OSError, json.JSONDecodeError) as exc:
+ raise VerificationError(f"cannot read artifact {path}: {exc}") from exc
+ if not isinstance(value, dict):
+ raise VerificationError("artifact root must be an object")
+ return value
+
+
+def verify_artifact(artifact, snapshot):
+ if artifact.get("schema_version") != 1 or artifact.get("experiment") != EXPERIMENT_ID:
+ raise VerificationError("unsupported fused-engine artifact identity")
+ measured = build_artifact(snapshot)
+ if canonical_json_bytes(artifact) != canonical_json_bytes(measured):
+ raise VerificationError("artifact differs from a fresh experiment execution")
+ return measured
+
+
+def render_markdown(artifact):
+ population = artifact["population"]
+ rows = [artifact["cospend_baseline"], *artifact["ladder"]]
+ lines = [
+ "# What each channel adds when the engine fuses them",
+ "",
+ "Generated from the canonical experiment artifact. Do not edit manually.",
+ "",
+ f"{population['transactions']} committed transactions hold {population['cospend_transactions']} "
+ f"co-spends over {population['nodes']} funding transactions. The engine judges those co-spends; "
+ f"the merge-only baseline takes every one of them.",
+ "",
+ "| arm | groups | refused co-spends | added links | largest group |",
+ "|---|---:|---:|---:|---:|",
+ ]
+ for row in rows:
+ lines.append(f"| {row['arm']} | {row['groups']} | {row['refused_cospends']} | "
+ f"{row['added_links']} | {row['largest_group']} |")
+
+ baseline, *ladder = rows
+ fingerprint, _amount, topology, provenance, full = ladder
+ lines.extend([
+ "",
+ f"The fingerprint channel is what moves the partition: it adds {fingerprint['added_links']} "
+ f"links the co-spend missed and takes the largest group from {baseline['largest_group']} to "
+ f"{fingerprint['largest_group']}. Refusal is the smaller effect and the one the thesis is "
+ f"about — {fingerprint['refused_cospends']} co-spends declined that the baseline merges.",
+ "",
+ f"Topology and provenance pull in opposite directions, which is the point of fusing them. "
+ f"Adding the cluster-level topology takes refusals from {fingerprint['refused_cospends']} to "
+ f"{topology['refused_cospends']}: it corroborates merges the fingerprint alone objected to. "
+ f"Adding provenance-disjointness takes them to {provenance['refused_cospends']}, cutting "
+ f"pairs whose ancestry does not overlap.",
+ "",
+ f"The amount channel and the subset-sum de-mix move nothing here, and the reason is "
+ f"structural rather than empirical: both are refuse-only and gated behind fingerprint "
+ f"disagreement, so they can only speak on the pairs the fingerprint already objects to. "
+ f"The slice holds {population['two_in_two_out_cospends']} two-input two-output co-spends, "
+ f"the only shape the roundness re-partition judges.",
+ "",
+ "The provenance signature here is depth-1 — a node's in-slice funders — because the slice is "
+ "block-sampled rather than contiguous. A deeper walk needs a contiguous export.",
+ "",
+ "## Reproducibility / provenance",
+ "",
+ "State 1 in `results/REPRODUCIBILITY.md`: band-pinned on committed data — the artifact is "
+ "recomputed from `data/fs-blkcache-2026-09-04.tar.gz` and compared byte for byte.",
+ ])
+ return "\n".join(lines) + "\n"
+
+
+def _parser():
+ parser = argparse.ArgumentParser(description=__doc__.splitlines()[0])
+ parser.add_argument("command", choices=("reproduce", "verify"))
+ parser.add_argument("--snapshot", default="data/fs-blkcache-2026-09-04.tar.gz")
+ parser.add_argument("--artifact", required=True)
+ parser.add_argument("--markdown", required=True)
+ return parser
+
+
+def main(argv=None):
+ args = _parser().parse_args(argv)
+ if args.command == "reproduce":
+ artifact = build_artifact(args.snapshot)
+ write_canonical_json(args.artifact, artifact)
+ destination = Path(args.markdown)
+ destination.parent.mkdir(parents=True, exist_ok=True)
+ destination.write_text(render_markdown(artifact), encoding="utf-8")
+ else:
+ artifact = verify_artifact(load_artifact(args.artifact), args.snapshot)
+ if Path(args.markdown).read_text(encoding="utf-8") != render_markdown(artifact):
+ raise VerificationError("Markdown differs from canonical rendering")
+ return 0
+
+
+if __name__ == "__main__":
+ raise SystemExit(main())
diff --git a/decluster/experiments/graph_deanon_controls.py b/decluster/experiments/graph_deanon_controls.py
new file mode 100644
index 00000000..b7f45ce7
--- /dev/null
+++ b/decluster/experiments/graph_deanon_controls.py
@@ -0,0 +1,184 @@
+"""Publish the graph-structure AUC together with the control that qualifies it.
+
+`PAPER.md` cites AUC 0.95 for payment-graph structure predicting same-cluster membership, and the
+fixture it comes from was in the dataset catalogue but owned by no run. Publishing the headline
+without its control would be the easier half: `_pairs` samples negatives without matching degree,
+and a same-cluster pair is systematically higher-degree than a random one, so part of any AUC
+measured that way is the sampling rather than shared structure.
+
+So the run reports both, plus the degree-only score — a number that is the pair's degree sum and
+nothing else. Under degree-matched negatives that score is at chance by construction, which is what
+makes it a usable ruler for how much of the headline the asymmetry was carrying.
+"""
+
+from __future__ import annotations
+
+import argparse
+import gzip
+import json
+from pathlib import Path
+
+from .. import graph_deanon
+from ..result_artifacts import canonical_json_bytes, write_canonical_json
+
+EXPERIMENT_ID = "graph-deanon-controls-v1"
+DEFAULT_DATASET = "tests/fixtures/graph_deanon_2016.ndjson.gz"
+SEED = 0
+
+
+class VerificationError(ValueError):
+ """The dataset or stored result violates this experiment's contract."""
+
+
+def _load(path):
+ sample = []
+ try:
+ with gzip.open(path, "rt", encoding="utf-8") as source:
+ for line_number, line in enumerate(source, start=1):
+ if not line.strip():
+ continue
+ transaction = json.loads(line)
+ if not isinstance(transaction, dict) or not transaction.get("txid"):
+ raise VerificationError(
+ f"{path}:{line_number}: transaction must be an object with a txid"
+ )
+ sample.append((transaction, 0))
+ except (OSError, json.JSONDecodeError) as exc:
+ raise VerificationError(f"cannot read graph fixture {path}: {exc}") from exc
+ if not sample:
+ raise VerificationError(f"graph fixture is empty: {path}")
+ return sample
+
+
+def build_artifact(dataset=DEFAULT_DATASET):
+ sample = _load(dataset)
+ published = graph_deanon.evaluate(sample, seed=SEED)
+ matched = graph_deanon.degree_matched_auc(sample, seed=SEED)
+ views = {}
+ for view in ("full", "payment"):
+ views[view] = {
+ "published_auc": published[f"auc_{view}"],
+ "degree_matched_auc": matched[f"auc_{view}"],
+ "degree_only_auc_under_matching": matched[f"degree_only_{view}"],
+ "fall_under_matching": published[f"auc_{view}"] - matched[f"auc_{view}"],
+ "positive_pairs": matched[f"pos_pairs_{view}"],
+ "matched_negative_pairs": matched[f"neg_pairs_{view}"],
+ }
+ return {
+ "schema_version": 1,
+ "experiment": EXPERIMENT_ID,
+ "dataset": "graph-deanon-2016-v1",
+ "measurement": {
+ "transactions": len(sample),
+ "addresses": published["addrs"],
+ "clusters_with_two_or_more": published["clusters_ge2"],
+ "published_negative_pairs": published["neg_pairs"],
+ "shuffled_label_auc": published["auc_shuffle"],
+ "views": views,
+ },
+ "parameters": {
+ "seed": SEED,
+ "negative_sampling_published": "uniform over non-co-spent pairs, capped",
+ "negative_sampling_control": "degree-matched to each positive",
+ "score": "rarity-weighted shared-neighbour structure",
+ },
+ "limitations": [
+ "cluster membership is a co-spend label, not wallet ownership",
+ "one selected 2016 fixture, not a chain-wide sample",
+ "the degree-matched control changes the negative population, not the positives",
+ "a fall under matching bounds how much of the AUC was degree asymmetry; it does not "
+ "establish what the remainder is",
+ "neither number is a re-identification rate or a privacy score",
+ ],
+ }
+
+
+def load_artifact(path):
+ try:
+ with Path(path).open(encoding="utf-8") as source:
+ value = json.load(source)
+ except (OSError, json.JSONDecodeError) as exc:
+ raise VerificationError(f"cannot read artifact {path}: {exc}") from exc
+ if not isinstance(value, dict):
+ raise VerificationError("artifact root must be an object")
+ return value
+
+
+def verify_artifact(artifact, dataset=DEFAULT_DATASET):
+ if artifact.get("schema_version") != 1 or artifact.get("experiment") != EXPERIMENT_ID:
+ raise VerificationError("unsupported graph-deanon-control artifact identity")
+ measured = build_artifact(dataset)
+ if canonical_json_bytes(artifact) != canonical_json_bytes(measured):
+ raise VerificationError("artifact differs from a fresh experiment execution")
+ return measured
+
+
+def render_markdown(artifact):
+ measured = artifact["measurement"]
+ lines = [
+ "# Graph-structure linkage and the degree-matched control",
+ "",
+ "Generated from the canonical experiment artifact. Do not edit manually.",
+ "",
+ f"{measured['transactions']} transactions, {measured['addresses']} addresses, "
+ f"{measured['clusters_with_two_or_more']} clusters of two or more. Positives are "
+ "same-cluster pairs that do not co-spend; the score is rarity-weighted shared-neighbour "
+ "structure.",
+ "",
+ "| view | published AUC | degree-matched AUC | fall | degree-only AUC under matching |",
+ "|---|---:|---:|---:|---:|",
+ ]
+ for view, row in sorted(measured["views"].items()):
+ lines.append(
+ f"| {view} | {row['published_auc']:.4f} | {row['degree_matched_auc']:.4f} | "
+ f"{row['fall_under_matching']:.4f} | {row['degree_only_auc_under_matching']:.4f} |"
+ )
+ payment = measured["views"]["payment"]
+ lines += [
+ "",
+ f"The payment-graph headline falls {payment['fall_under_matching']:.4f} under degree-matched "
+ f"negatives, from {payment['published_auc']:.4f} to {payment['degree_matched_auc']:.4f}. "
+ "Under that control a score made only of the pair's degree sum reads "
+ f"{payment['degree_only_auc_under_matching']:.4f} — chance, by construction — so the "
+ "remaining separation is not the asymmetry the published sampling leaves in.",
+ "",
+ f"The shuffled-label control reads {measured['shuffled_label_auc']:.4f}, which is what says "
+ "the score is measuring the labels at all.",
+ "",
+ "Cluster membership here is a co-spend label rather than wallet ownership, the fixture is "
+ "one selected 2016 slice, and a fall under matching bounds how much of the AUC was degree "
+ "asymmetry without establishing what the remainder is. Neither number is a "
+ "re-identification rate or a privacy score.",
+ "",
+ ]
+ return "\n".join(lines)
+
+
+def _parser():
+ parser = argparse.ArgumentParser(description=__doc__)
+ parser.add_argument("--dataset", default=DEFAULT_DATASET)
+ commands = parser.add_subparsers(dest="command", required=True)
+ for name in ("reproduce", "verify"):
+ command = commands.add_parser(name)
+ command.add_argument("--artifact", required=True)
+ command.add_argument("--markdown", required=True)
+ return parser
+
+
+def main(argv=None):
+ args = _parser().parse_args(argv)
+ if args.command == "reproduce":
+ artifact = build_artifact(args.dataset)
+ write_canonical_json(args.artifact, artifact)
+ destination = Path(args.markdown)
+ destination.parent.mkdir(parents=True, exist_ok=True)
+ destination.write_text(render_markdown(artifact), encoding="utf-8")
+ else:
+ artifact = verify_artifact(load_artifact(args.artifact), args.dataset)
+ if Path(args.markdown).read_text(encoding="utf-8") != render_markdown(artifact):
+ raise VerificationError("Markdown differs from canonical rendering")
+ return 0
+
+
+if __name__ == "__main__":
+ raise SystemExit(main())
diff --git a/decluster/experiments/graph_rejoin_2016.py b/decluster/experiments/graph_rejoin_2016.py
new file mode 100644
index 00000000..adc3ef3e
--- /dev/null
+++ b/decluster/experiments/graph_rejoin_2016.py
@@ -0,0 +1,241 @@
+"""Rejoining a cluster split across two weekly views, on committed transactions.
+
+The measurements this replaces were taken on a 1.1 GB slice that no longer exists, so nobody could
+check them and nobody could take them again. The committed weekly graph carries the same object at
+seven times the size: two consecutive windows, every cluster that straddles their boundary
+contracted under a separate pseudonym on each side, and a matcher asked to rejoin the two halves
+from structure alone.
+
+Two things make the answer readable rather than merely low. The seeds are handed to the matcher, so
+this measures propagation given a foothold rather than an attack that finds one; and a shuffled-seed
+arm runs beside every seeded arm, so a guess rate that survives the shuffle is the graph answering
+the seeds rather than the matcher answering itself.
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+import random
+from pathlib import Path
+
+from .. import epoch_graph as eg
+from ..contraction import contract, contract_degrees
+from ..graph_shape import summary as shape_summary
+from ..result_artifacts import canonical_json_bytes, write_canonical_json
+from ..scale_cluster import cluster_scale_np_stream
+from ..view_match import ViewMatcher
+from ..views import split_clusters_by_view, view_lookup
+
+EXPERIMENT_ID = "graph-rejoin-2016-v1"
+DEFAULT_EPOCHS = ("data/epochs-2016-weekly-v1/391104-392111.epochgraph.xz",
+ "data/epochs-2016-weekly-v1/392112-393119.epochgraph.xz")
+SEED_SHARES = (0.05, 0.10)
+MIN_DEGREE = 2
+
+
+class VerificationError(ValueError):
+ """The stored result differs from a fresh execution."""
+
+
+def _addresses(path):
+ found = set()
+ for record in eg.decode(path):
+ found.update(eg.in_addresses(record))
+ found.update(eg.out_addresses(record))
+ return found
+
+
+def _active(path, lookup):
+ """Contract one view, keeping the vertices a matcher could route through."""
+ stream = lambda: ((record, 0) for record in eg.decode(path))
+ degrees = contract_degrees(stream(), lookup=lookup)
+ keep = {vertex for vertex, degree in degrees.items() if degree >= MIN_DEGREE}
+ graph = contract(stream(), lookup=lookup, axes=False, keep=keep)
+ return graph, {vertex: degrees[vertex] for vertex in keep}
+
+
+def build_artifact(epochs=DEFAULT_EPOCHS, seed=0):
+ left, right = (Path(path) for path in epochs)
+ counts = [sum(1 for _ in eg.decode(path)) for path in (left, right)]
+ clustering = cluster_scale_np_stream(record for path in (left, right)
+ for record in eg.decode(path))
+ left_addresses = _addresses(left)
+ lookup = clustering.map_many(left_addresses)
+ lookup.update(clustering.map_many(_addresses(right)))
+ del clustering
+
+ split, origin = split_clusters_by_view(lookup, left_addresses, frac=1.0,
+ rng=random.Random(seed))
+ del left_addresses
+ graph_a, stat_a = _active(left, view_lookup(lookup, split, "#a"))
+ graph_b, stat_b = _active(right, view_lookup(lookup, split, "#b"))
+ del lookup
+
+ right_side = {v for v in graph_b.vertices if isinstance(v, str) and v.endswith("#b")}
+ truth = {u: f"{origin[u]}#b" for u in graph_a.vertices
+ if isinstance(u, str) and u.endswith("#a") and f"{origin[u]}#b" in right_side}
+ straddlers = set(truth)
+ internal = {u: sum(1 for n in graph_a.neighbours(u) if n in straddlers) for u in straddlers}
+
+ shapes = [{k: (round(v, 6) if isinstance(v, float) else v)
+ for k, v in shape_summary(g, rng=random.Random(seed)).items()}
+ for g in (graph_a, graph_b)]
+ ordered = sorted(truth, key=lambda u: -(graph_a.degree(u) + graph_b.degree(truth[u])))
+ rng = random.Random(seed)
+ arms = []
+ for share in SEED_SHARES:
+ count = max(2, int(len(ordered) * share))
+ seeds = set(ordered[:count])
+ supplied = {u: truth[u] for u in seeds}
+ images = list(supplied.values())
+ rng.shuffle(images)
+ shuffled = dict(zip(supplied, images))
+ for directional in (False, True):
+ for label, mapping in (("seeded", supplied), ("shuffled", shuffled)):
+ matched = ViewMatcher(revisit=True, directional=directional,
+ min_common=4).match(graph_a, graph_b, mapping,
+ stat_a=stat_a, stat_b=stat_b)
+ guesses = {u: w for u, w in matched.items() if u in truth and u not in seeds}
+ correct = sum(1 for u, w in guesses.items() if w == truth[u])
+ arms.append({
+ "seed_share": share, "seeds": count,
+ "matcher": "directed" if directional else "undirected",
+ "arm": label, "guesses": len(guesses), "correct": correct,
+ "precision": round(correct / len(guesses), 6) if guesses else None,
+ })
+ return {
+ "schema_version": 1,
+ "experiment": EXPERIMENT_ID,
+ "datasets": [f"epoch-graph-2016-{Path(p).name.split('.')[0]}-v1" for p in epochs],
+ "population": {
+ # The file names, not the paths given: a cold-start reproduction reads the
+ # datasets from wherever the bundle materialized them, and a path recorded
+ # here would make the artifact differ by where it was run.
+ "views": [left.name, right.name],
+ "transactions": counts,
+ "pairs_to_rejoin": len(truth),
+ "non_seed_pairs_at_widest_seeding":
+ len(truth) - max(int(len(ordered) * share) for share in SEED_SHARES),
+ "mean_internal_degree": round(sum(internal.values()) / len(straddlers), 6)
+ if straddlers else 0.0,
+ },
+ "shape": shapes,
+ "matching": arms,
+ }
+
+
+def load_artifact(path):
+ try:
+ with Path(path).open(encoding="utf-8") as source:
+ value = json.load(source)
+ except (OSError, json.JSONDecodeError) as exc:
+ raise VerificationError(f"cannot read artifact {path}: {exc}") from exc
+ if not isinstance(value, dict):
+ raise VerificationError("artifact root must be an object")
+ return value
+
+
+def verify_artifact(artifact, epochs=DEFAULT_EPOCHS):
+ if artifact.get("schema_version") != 1 or artifact.get("experiment") != EXPERIMENT_ID:
+ raise VerificationError("unsupported graph-rejoin artifact identity")
+ measured = build_artifact(epochs)
+ if canonical_json_bytes(artifact) != canonical_json_bytes(measured):
+ raise VerificationError("artifact differs from a fresh experiment execution")
+ return measured
+
+
+def render_markdown(artifact):
+ population = artifact["population"]
+ lines = [
+ "# Rejoining a cluster split across two weekly views",
+ "",
+ "Generated from the canonical experiment artifact. Do not edit manually.",
+ "",
+ f"Two consecutive 2016 weekly windows of {population['transactions'][0]:,} and "
+ f"{population['transactions'][1]:,} transactions. {population['pairs_to_rejoin']:,} clusters straddle "
+ f"the boundary and survive contraction in both views, at a mean internal degree of "
+ f"{population['mean_internal_degree']:.3f}.",
+ "",
+ "| statistic | view A | view B |",
+ "|---|---:|---:|",
+ ]
+ for field in ("vertices", "edges", "mean_degree", "degree_1_share", "transitivity",
+ "configuration_transitivity", "assortativity", "tail_exponent"):
+ left_value, right_value = (s[field] for s in artifact["shape"])
+ fmt = (lambda v: "—" if v is None else f"{v:,}" if isinstance(v, int) else f"{v:.4f}")
+ lines.append(f"| {field.replace('_', ' ')} | {fmt(left_value)} | {fmt(right_value)} |")
+ left, right = artifact["shape"]
+ lines.extend([
+ "",
+ f"The framework's premise is that a contracted view is a social network in the sense its "
+ f"matching algorithm needs. On this measurement it is not, on the two axes that decide it. "
+ f"Clustering sits at {left['transitivity']:.4f} and {right['transitivity']:.4f} against a "
+ f"configuration-model null of {left['configuration_transitivity']:.4f} and "
+ f"{right['configuration_transitivity']:.4f} — the degree distribution alone would produce "
+ f"far more triangles than the graph has. Degree correlation is "
+ f"{left['assortativity']:+.4f} and {right['assortativity']:+.4f}: hubs attach to leaves, "
+ f"which is what transaction graphs do and the opposite of what a social graph does. A "
+ f"matcher that routes through a vertex's neighbourhood is asking that neighbourhood to be "
+ f"distinctive and stable, and neither statistic says it is.",
+ "",
+ "| seed share | matcher | arm | guesses | correct | precision |",
+ "|---:|---|---|---:|---:|---:|",
+ ])
+ for row in artifact["matching"]:
+ precision = "—" if row["precision"] is None else f"{row['precision']:.3f}"
+ lines.append(f"| {row['seed_share']:.0%} | {row['matcher']} | {row['arm']} | "
+ f"{row['guesses']} | {row['correct']} | {precision} |")
+ seeded = [r for r in artifact["matching"] if r["arm"] == "seeded"]
+ shuffled = [r for r in artifact["matching"] if r["arm"] == "shuffled"]
+ lines.extend([
+ "",
+ f"At the widest seeding the matcher is offered "
+ f"{population['pairs_to_rejoin'] - max(r['seeds'] for r in artifact['matching']):,} "
+ f"non-seed pairs. Across every seeded arm it guesses "
+ f"{sum(r['guesses'] for r in seeded)} and gets {sum(r['correct'] for r in seeded)} right; "
+ f"the shuffled-seed arms guess {sum(r['guesses'] for r in shuffled)}. A cascade that "
+ f"carried itself would show each seed unlocking more than itself, and it does not at these "
+ f"view widths. One match is not ignition, and it is a different statement from none.",
+ "",
+ "Cluster membership here is a co-spend relation rather than observed wallet ownership, and "
+ "the seeds are supplied. The result is what propagation does once a foothold exists, not "
+ "whether one can be found.",
+ "",
+ "## Reproducibility / provenance",
+ "",
+ "State 1 in `results/REPRODUCIBILITY.md`: band-pinned on committed data — the artifact is "
+ "recomputed from the committed weekly epoch graphs and compared byte for byte.",
+ ])
+ return "\n".join(lines) + "\n"
+
+
+def _parser():
+ parser = argparse.ArgumentParser(description=__doc__.splitlines()[0])
+ parser.add_argument("command", choices=("reproduce", "verify"))
+ # The bundle harness rewrites a dataset path it finds in the argv, and only there. Taking the
+ # views as arguments is what lets a cold-start reproduction read the materialized copies rather
+ # than paths relative to a source tree that does not carry the data.
+ parser.add_argument("--epochs", nargs=2, default=list(DEFAULT_EPOCHS))
+ parser.add_argument("--artifact", required=True)
+ parser.add_argument("--markdown", required=True)
+ return parser
+
+
+def main(argv=None):
+ args = _parser().parse_args(argv)
+ if args.command == "reproduce":
+ artifact = build_artifact(tuple(args.epochs))
+ write_canonical_json(args.artifact, artifact)
+ destination = Path(args.markdown)
+ destination.parent.mkdir(parents=True, exist_ok=True)
+ destination.write_text(render_markdown(artifact), encoding="utf-8")
+ else:
+ artifact = verify_artifact(load_artifact(args.artifact), tuple(args.epochs))
+ if Path(args.markdown).read_text(encoding="utf-8") != render_markdown(artifact):
+ raise VerificationError("Markdown differs from canonical rendering")
+ return 0
+
+
+if __name__ == "__main__":
+ raise SystemExit(main())
diff --git a/decluster/experiments/intersection_fixture.py b/decluster/experiments/intersection_fixture.py
new file mode 100644
index 00000000..612c9622
--- /dev/null
+++ b/decluster/experiments/intersection_fixture.py
@@ -0,0 +1,180 @@
+"""Reproduce a synthetic cluster-level provenance intersection."""
+
+from __future__ import annotations
+
+import argparse
+import json
+from pathlib import Path
+
+from examples.intersection_pipeline import run
+
+from ..adaptations.ancestry import ancestry_signature_report
+from ..result_artifacts import canonical_json_bytes, write_canonical_json
+
+
+EXPERIMENT_ID = "intersection-cluster-fixture-v1"
+
+
+class VerificationError(ValueError):
+ """A stored fixture result differs from a fresh execution."""
+
+
+def _fixture():
+ transactions = {
+ "origin": {
+ "vin": [{"is_coinbase": True}],
+ "vout": [{"value": 5}, {"value": 5}],
+ },
+ "left-source": {
+ "vin": [{"is_coinbase": True}],
+ "vout": [{"value": 5}],
+ },
+ "right-source": {
+ "vin": [{"is_coinbase": True}],
+ "vout": [{"value": 5}],
+ },
+ "left": {
+ "vin": [
+ {"txid": "origin", "vout": 0, "prevout": {"value": 5}},
+ {"txid": "left-source", "vout": 0, "prevout": {"value": 5}},
+ ],
+ "vout": [{"value": 10}],
+ },
+ "right": {
+ "vin": [
+ {"txid": "origin", "vout": 1, "prevout": {"value": 5}},
+ {"txid": "right-source", "vout": 0, "prevout": {"value": 5}},
+ ],
+ "vout": [{"value": 10}],
+ },
+ "join": {
+ "vin": [
+ {"txid": "left", "vout": 0, "prevout": {"value": 10}},
+ {"txid": "right", "vout": 0, "prevout": {"value": 10}},
+ ],
+ "vout": [{"value": 20}],
+ },
+ }
+ outspends = {
+ "left": [{"spent": True, "txid": "join"}],
+ "right": [{"spent": True, "txid": "join"}],
+ "join": [{"spent": False}],
+ }
+ return transactions, outspends
+
+
+def build_artifact():
+ transactions, outspends = _fixture()
+ fetch = transactions.__getitem__
+ result = run(
+ seeds=[("left", 0), ("right", 0)],
+ get_tx=fetch,
+ get_outspends=lambda txid: outspends.get(txid, []),
+ ancestry_report_of=lambda outpoint: ancestry_signature_report(
+ outpoint, depth=2, fetch=fetch
+ ),
+ intersection_options={"cluster_of": lambda coin: coin[0]},
+ max_depth=1,
+ )
+ narrowing = result["results"][0]["narrowing"]
+ return {
+ "schema_version": 1,
+ "experiment": EXPERIMENT_ID,
+ "parameters": {
+ "ancestry_depth": 2,
+ "cluster_key": "creating transaction id",
+ "monitor_depth": 1,
+ },
+ "fixture": {
+ "seeds": [["left", 0], ["right", 0]],
+ "transactions": transactions,
+ },
+ "result": result,
+ "summary": {
+ "blind": narrowing["blind"],
+ "branches": narrowing["branches"],
+ "candidates_before": min(narrowing["sizes"]),
+ "candidates_after": len(narrowing["shared"]),
+ "collapsed": narrowing["collapsed"],
+ "scored": narrowing["scored"],
+ },
+ "limitations": [
+ "synthetic fixture",
+ "narrowing is conditional on the observed co-spend",
+ "the fixture does not run the clustering verdict",
+ ],
+ }
+
+
+def load_artifact(path):
+ try:
+ with Path(path).open(encoding="utf-8") as source:
+ value = json.load(source)
+ except (OSError, json.JSONDecodeError) as exc:
+ raise VerificationError(f"cannot read artifact {path}: {exc}") from exc
+ if not isinstance(value, dict):
+ raise VerificationError("artifact root must be an object")
+ return value
+
+
+def verify_artifact(artifact):
+ if artifact.get("schema_version") != 1 or artifact.get("experiment") != EXPERIMENT_ID:
+ raise VerificationError("unsupported intersection artifact identity")
+ measured = build_artifact()
+ if canonical_json_bytes(artifact) != canonical_json_bytes(measured):
+ raise VerificationError("artifact differs from a fresh fixture execution")
+ return measured
+
+
+def render_markdown(artifact):
+ summary = artifact["summary"]
+ return "\n".join([
+ "# Cluster-level provenance intersection fixture",
+ "",
+ "Generated from the canonical experiment artifact. Do not edit manually.",
+ "",
+ "| quantity | value |",
+ "|---|---:|",
+ f"| branches | {summary['branches']} |",
+ f"| candidates before | {summary['candidates_before']} |",
+ f"| candidates after | {summary['candidates_after']} |",
+ f"| candidates removed | {summary['collapsed']} |",
+ f"| blind | `{str(summary['blind']).lower()}` |",
+ f"| independently scored | `{str(summary['scored']).lower()}` |",
+ "",
+ "This is a synthetic, conditional narrowing. It is not an ownership attribution.",
+ "",
+ ])
+
+
+def _parser():
+ parser = argparse.ArgumentParser(description=__doc__)
+ commands = parser.add_subparsers(dest="command", required=True)
+ reproduce = commands.add_parser("reproduce")
+ reproduce.add_argument("--artifact", required=True)
+ reproduce.add_argument("--markdown", required=True)
+ verify = commands.add_parser("verify")
+ verify.add_argument("--artifact", required=True)
+ verify.add_argument("--markdown")
+ return parser
+
+
+def main(argv=None):
+ args = _parser().parse_args(argv)
+ if args.command == "reproduce":
+ artifact = build_artifact()
+ write_canonical_json(args.artifact, artifact)
+ destination = Path(args.markdown)
+ destination.parent.mkdir(parents=True, exist_ok=True)
+ destination.write_text(render_markdown(artifact), encoding="utf-8")
+ else:
+ artifact = verify_artifact(load_artifact(args.artifact))
+ if args.markdown is not None:
+ actual = Path(args.markdown).read_text(encoding="utf-8")
+ if actual != render_markdown(artifact):
+ raise VerificationError("Markdown differs from canonical rendering")
+ return 0
+
+
+if __name__ == "__main__":
+ raise SystemExit(main())
diff --git a/decluster/experiments/library_calibration.py b/decluster/experiments/library_calibration.py
new file mode 100644
index 00000000..56bdf7b2
--- /dev/null
+++ b/decluster/experiments/library_calibration.py
@@ -0,0 +1,226 @@
+"""Measure the fingerprint library's per-axis bits on the one population that is still here.
+
+Every weight in `library.py` is minus the log-share of a value, and the docstring says where the
+shares came from: a ~105k uniform whole-chain BigQuery sample. That export no longer exists, so the
+shipped table cannot be re-derived, and the numbers it feeds — every evidence magnitude the scorer
+reports — rest on a calibration a reader cannot check.
+
+This does not restore that provenance and does not claim to. It measures the same quantity on the
+committed block cache and reports the distance, per value, so the size of the unverifiable part is
+on the record rather than assumed small. The two populations differ by construction — 22k
+block-sampled transactions against 105k uniform ones — so a divergence is expected; what was not
+on the record is how large it is.
+
+A value the cache never shows cannot be calibrated from it at all, and those are counted rather
+than filled in.
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+import math
+from collections import Counter
+from pathlib import Path
+from tempfile import TemporaryDirectory
+
+from .. import engine, extractors, library
+from ..archive_snapshot import extract_tar_gz
+from ..fingerprint_validate import load_blkcache
+from ..reproducibility import fingerprint_source
+from ..result_artifacts import canonical_json_bytes, write_canonical_json
+
+EXPERIMENT_ID = "library-calibration-v1"
+
+
+class VerificationError(ValueError):
+ """The dataset or stored result violates this experiment's contract."""
+
+
+def _extractor(name):
+ function = getattr(extractors, name, None) or getattr(engine, name, None)
+ if function is None:
+ raise VerificationError(f"library names an extractor that does not exist: {name}")
+ return function
+
+
+def _observed(transactions, function):
+ counts = Counter()
+ for transaction in transactions:
+ try:
+ value = function(transaction)
+ except Exception: # a partial record, not a bug in the axis
+ counts["__unreadable__"] += 1
+ continue
+ counts[value] += 1
+ scored = {value: count for value, count in counts.items()
+ if value not in ("__unreadable__", extractors.NA)}
+ return scored, counts["__unreadable__"], counts.get(extractors.NA, 0)
+
+
+def _axis(entry, transactions):
+ scored, unreadable, abstained = _observed(transactions, _extractor(entry["extractor"]))
+ total = sum(scored.values())
+ values, divergences = [], []
+ for value, published in sorted(entry["bits"].items()):
+ seen = scored.get(value, 0)
+ measured = -math.log2(seen / total) if seen and total else None
+ row = {"value": value, "published_bits": published, "observations": seen,
+ "measured_bits": measured}
+ if measured is not None:
+ row["divergence"] = abs(measured - published)
+ divergences.append(row["divergence"])
+ values.append(row)
+ return {
+ "axis": entry["axis"],
+ "extractor": entry["extractor"],
+ "severity": entry["severity"],
+ "scored_transactions": total,
+ "unreadable": unreadable,
+ "abstained": abstained,
+ "values_published": len(entry["bits"]),
+ "values_the_cache_shows": sum(1 for row in values if row["observations"]),
+ "mean_divergence_bits": (sum(divergences) / len(divergences)) if divergences else None,
+ "max_divergence_bits": max(divergences) if divergences else None,
+ "values": values,
+ }
+
+
+def build_artifact(snapshot):
+ with TemporaryDirectory(prefix="decluster-calibration-") as temporary:
+ root = extract_tar_gz(snapshot, temporary)
+ cache = root / ".blkcache"
+ transactions = load_blkcache(str(cache))
+ axes = [_axis(entry, transactions) for entry in library.AXES]
+ source = fingerprint_source(str(cache / "*.json"))
+ source.update(pattern=".blkcache/*.json", transactions=len(transactions))
+ comparable = [axis for axis in axes if axis["mean_divergence_bits"] is not None]
+ uncalibratable = sum(axis["values_published"] - axis["values_the_cache_shows"]
+ for axis in axes)
+ return {
+ "schema_version": 1,
+ "experiment": EXPERIMENT_ID,
+ "dataset": "fs-blkcache-2026-09-04-v1",
+ "source": source,
+ "measurement": {
+ "axes_catalogued": len(axes),
+ "axes_the_cache_can_compare": len(comparable),
+ "values_published": sum(axis["values_published"] for axis in axes),
+ "values_the_cache_never_shows": uncalibratable,
+ "mean_divergence_bits": (
+ sum(axis["mean_divergence_bits"] for axis in comparable) / len(comparable)
+ if comparable else None
+ ),
+ "largest_divergence": max(
+ ((axis["axis"], axis["max_divergence_bits"]) for axis in comparable),
+ key=lambda pair: pair[1], default=None,
+ ),
+ "axes": axes,
+ },
+ "parameters": {
+ "published_population": "~105k uniform whole-chain sample via BigQuery, not preserved",
+ "measured_population": "the committed block cache",
+ "bits": "minus the base-2 log of a value's share of the scored population",
+ },
+ "limitations": [
+ "this is not a reproduction of the published calibration; the population it used is gone",
+ "the cache is block-sampled and era-skewed, so a divergence is expected and its size is "
+ "the only thing measured here",
+ "a value the cache never shows is counted, not estimated",
+ "recalibrating the library on this cache is a separate decision this run does not take",
+ "divergence in bits is not an error rate and is not a privacy score",
+ ],
+ }
+
+
+def load_artifact(path):
+ try:
+ with Path(path).open(encoding="utf-8") as source:
+ value = json.load(source)
+ except (OSError, json.JSONDecodeError) as exc:
+ raise VerificationError(f"cannot read artifact {path}: {exc}") from exc
+ if not isinstance(value, dict):
+ raise VerificationError("artifact root must be an object")
+ return value
+
+
+def verify_artifact(artifact, snapshot):
+ if artifact.get("schema_version") != 1 or artifact.get("experiment") != EXPERIMENT_ID:
+ raise VerificationError("unsupported library-calibration artifact identity")
+ measured = build_artifact(snapshot)
+ if canonical_json_bytes(artifact) != canonical_json_bytes(measured):
+ raise VerificationError("artifact differs from a fresh experiment execution")
+ return measured
+
+
+def render_markdown(artifact):
+ measured = artifact["measurement"]
+ axis, worst = measured["largest_divergence"]
+ lines = [
+ "# The fingerprint library against the population that is still here",
+ "",
+ "Generated from the canonical experiment artifact. Do not edit manually.",
+ "",
+ f"The shipped bits were measured on a ~105k uniform whole-chain sample that no longer "
+ f"exists. This measures the same quantity on the {artifact['source']['transactions']} "
+ "transactions of the committed block cache. It is not a reproduction — the populations "
+ "differ by construction — and the only thing it establishes is how far apart they land.",
+ "",
+ "| axis | values | shown by the cache | mean divergence | max |",
+ "|---|---:|---:|---:|---:|",
+ ]
+ for row in measured["axes"]:
+ mean = "n/a" if row["mean_divergence_bits"] is None else f"{row['mean_divergence_bits']:.2f}"
+ top = "n/a" if row["max_divergence_bits"] is None else f"{row['max_divergence_bits']:.2f}"
+ lines.append(
+ f"| {row['axis']} | {row['values_published']} | {row['values_the_cache_shows']} | "
+ f"{mean} | {top} |"
+ )
+ lines += [
+ "",
+ f"Across the {measured['axes_the_cache_can_compare']} axes the cache can compare, the mean "
+ f"divergence is **{measured['mean_divergence_bits']:.2f} bits** and the largest single one "
+ f"is **{worst:.2f} bits**, on `{axis}`. For scale, the catalogued scorer's whole positive "
+ "mean on this same cache is about 15 bits.",
+ "",
+ f"{measured['values_the_cache_never_shows']} of {measured['values_published']} published "
+ "values never appear in the cache, so they cannot be checked against it at all.",
+ "",
+ "None of this says the shipped table is wrong. It says the part of it a reader can verify "
+ "is the part measured here, and that the rest moves by bits rather than by decimals when "
+ "the population changes. Whether to recalibrate on this cache is a separate decision, and "
+ "`fingerprint-regime-v1` already reports what changes when the weights are fitted to a "
+ "snapshot instead of read from the library.",
+ "",
+ ]
+ return "\n".join(lines)
+
+
+def _parser():
+ parser = argparse.ArgumentParser(description=__doc__)
+ parser.add_argument("--snapshot", required=True)
+ commands = parser.add_subparsers(dest="command", required=True)
+ for name in ("reproduce", "verify"):
+ command = commands.add_parser(name)
+ command.add_argument("--artifact", required=True)
+ command.add_argument("--markdown", required=True)
+ return parser
+
+
+def main(argv=None):
+ args = _parser().parse_args(argv)
+ if args.command == "reproduce":
+ artifact = build_artifact(args.snapshot)
+ write_canonical_json(args.artifact, artifact)
+ destination = Path(args.markdown)
+ destination.parent.mkdir(parents=True, exist_ok=True)
+ destination.write_text(render_markdown(artifact), encoding="utf-8")
+ else:
+ artifact = verify_artifact(load_artifact(args.artifact), args.snapshot)
+ if Path(args.markdown).read_text(encoding="utf-8") != render_markdown(artifact):
+ raise VerificationError("Markdown differs from canonical rendering")
+ return 0
+
+
+if __name__ == "__main__":
+ raise SystemExit(main())
diff --git a/decluster/experiments/link_prediction.py b/decluster/experiments/link_prediction.py
new file mode 100644
index 00000000..3d28a2bf
--- /dev/null
+++ b/decluster/experiments/link_prediction.py
@@ -0,0 +1,118 @@
+"""Reproduce the planted-view link-prediction experiment."""
+
+from __future__ import annotations
+
+import argparse
+import json
+from pathlib import Path
+
+from examples import link_prediction_run as legacy
+
+from ..result_artifacts import canonical_json_bytes, write_canonical_json
+
+EXPERIMENT_ID = "link-prediction-v1"
+
+
+class VerificationError(ValueError):
+ """A stored result differs from a fresh execution."""
+
+
+def build_artifact():
+ args = legacy.build_parser().parse_args([])
+ return {
+ "schema_version": 1,
+ "experiment": EXPERIMENT_ID,
+ "corpus": "deterministic planted-view fixtures",
+ "report": legacy.build_report(args),
+ "limitations": [
+ "synthetic corpus",
+ "seeds are sampled from the withheld correspondence",
+ "this is not a reproduction of the 2011 paper or Kaggle/Flickr results",
+ "the measured advantage transfers links observed by the auxiliary view",
+ ],
+ }
+
+
+def load_artifact(path):
+ try:
+ with Path(path).open(encoding="utf-8") as source:
+ value = json.load(source)
+ except (OSError, json.JSONDecodeError) as exc:
+ raise VerificationError(f"cannot read artifact {path}: {exc}") from exc
+ if not isinstance(value, dict):
+ raise VerificationError("artifact root must be an object")
+ return value
+
+
+def verify_artifact(artifact):
+ if (
+ artifact.get("schema_version") != 1
+ or artifact.get("experiment") != EXPERIMENT_ID
+ ):
+ raise VerificationError("unsupported link-prediction artifact identity")
+ measured = build_artifact()
+ if canonical_json_bytes(artifact) != canonical_json_bytes(measured):
+ raise VerificationError("artifact differs from a fresh experiment execution")
+ return measured
+
+
+def _attack_rows(artifact):
+ return [fixture["runs"][0] for fixture in artifact["report"]["fixtures"]]
+
+
+def render_markdown(artifact):
+ rows = _attack_rows(artifact)
+ lines = [
+ "# Link prediction on planted views",
+ "",
+ "Generated from the canonical experiment artifact. Do not edit manually.",
+ "",
+ "| seed | de-anonymization AUC | structure-only AUC | verdict |",
+ "|---:|---:|---:|---|",
+ ]
+ for fixture, row in zip(artifact["report"]["fixtures"], rows):
+ lines.append(
+ f"| {fixture['fixture_seed']} | {row['deanonymization']['auc']:.6f} | "
+ f"{row['structure_only']['auc']:.6f} | "
+ f"`{row['separability']['beats_structure_only']}` |"
+ )
+ lines.extend([
+ "",
+ "This synthetic result transfers links already observed in the auxiliary view. "
+ "It is not a reproduction of the 2011 paper or its datasets.",
+ "",
+ ])
+ return "\n".join(lines)
+
+
+def _parser():
+ parser = argparse.ArgumentParser(description=__doc__)
+ commands = parser.add_subparsers(dest="command", required=True)
+ reproduce = commands.add_parser("reproduce")
+ reproduce.add_argument("--artifact", required=True)
+ reproduce.add_argument("--markdown", required=True)
+ verify = commands.add_parser("verify")
+ verify.add_argument("--artifact", required=True)
+ verify.add_argument("--markdown")
+ return parser
+
+
+def main(argv=None):
+ args = _parser().parse_args(argv)
+ if args.command == "reproduce":
+ artifact = build_artifact()
+ write_canonical_json(args.artifact, artifact)
+ destination = Path(args.markdown)
+ destination.parent.mkdir(parents=True, exist_ok=True)
+ destination.write_text(render_markdown(artifact), encoding="utf-8")
+ else:
+ artifact = verify_artifact(load_artifact(args.artifact))
+ if args.markdown is not None:
+ actual = Path(args.markdown).read_text(encoding="utf-8")
+ if actual != render_markdown(artifact):
+ raise VerificationError("Markdown differs from canonical rendering")
+ return 0
+
+
+if __name__ == "__main__":
+ raise SystemExit(main())
diff --git a/decluster/experiments/match_controls.py b/decluster/experiments/match_controls.py
new file mode 100644
index 00000000..8330ca60
--- /dev/null
+++ b/decluster/experiments/match_controls.py
@@ -0,0 +1,345 @@
+"""Does the cross-view matcher beat degree alone, and does it know which answers are good.
+
+Two questions the framework's own criterion turns on, and both were measured on a slice that no
+longer exists. The framework does not rest its case on coverage — it says high-confidence links can
+feed other clustering heuristics — so the quantities that matter are whether precision rises with
+the matcher's own confidence, and whether any of it survives the comparison to reading degrees off
+the two views.
+
+A shuffle control excludes chance and nothing else. The likelier alternative is that a matcher on a
+degree-labelled graph recovers degree: a vertex of degree 47 in one view and one of degree 47 in the
+other are an obvious pair with no propagation at all. The decisive control is therefore the exact
+degree-class expectation — one over the number of B-vertices sharing the true partner's degree —
+which is deliberately generous, since it is handed the true partner's degree that a degree-only
+adversary would have to guess.
+
+The harness is the framework's complete-clustering premise: both views hold the same pseudonyms and
+only view B's labels are hidden, so the population is known and the correspondence is not. That is a
+weaker premise than the split-cluster rejoin measured beside it (`graph-rejoin-2016-v1`), and the
+difference is the point — this is what the matcher does when it is told which vertices to match.
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+import random
+from collections import Counter
+from pathlib import Path
+
+from .. import epoch_graph as eg
+from ..contraction import PseudonymGraph, contract, contract_degrees
+from ..result_artifacts import canonical_json_bytes, write_canonical_json
+from ..scale_cluster import cluster_scale_np_stream
+from ..view_match import ViewMatcher
+
+EXPERIMENT_ID = "match-controls-v1"
+DEFAULT_EPOCHS = ("data/epochs-2016-weekly-v1/391104-392111.epochgraph.xz",
+ "data/epochs-2016-weekly-v1/392112-393119.epochgraph.xz")
+SEED_SHARES = (0.05, 0.10)
+BANDS = (1.0, 2.0, 3.0, 5.0, 10.0)
+MIN_DEGREE = 2
+# The band a seed can do work from. Propagation refuses to route through a matched neighbour whose
+# image exceeds the matcher's hub cap, so a hub handed over as a seed is evidence the matcher will
+# not read; and a vertex below three neighbours has too thin a neighbourhood to identify anyone.
+# Seeding by raw degree spends the whole budget on exactly the vertices that carry nothing.
+SEEDABLE = (3, 100)
+
+
+class VerificationError(ValueError):
+ """The stored result differs from a fresh execution."""
+
+
+def _addresses(path):
+ found = set()
+ for record in eg.decode(path):
+ found.update(eg.in_addresses(record))
+ found.update(eg.out_addresses(record))
+ return found
+
+
+def _active(path, lookup):
+ stream = lambda: ((record, 0) for record in eg.decode(path))
+ degrees = contract_degrees(stream(), lookup=lookup)
+ keep = {vertex for vertex, degree in degrees.items() if degree >= MIN_DEGREE}
+ graph = contract(stream(), lookup=lookup, axes=False, keep=keep)
+ return graph, {vertex: degrees[vertex] for vertex in keep}
+
+
+def _pseudonymise(graph, rng):
+ """Relabel a view with opaque ids: the population stays visible, the correspondence does not.
+
+ The source graph is consumed as it is copied. Holding both at full size doubles the peak on a
+ view of this width, which is enough to be killed for memory rather than merely be slow.
+ """
+ names = sorted(graph.vertices, key=repr)
+ rng.shuffle(names)
+ sigma = {vertex: f"p{index}" for index, vertex in enumerate(names)}
+ del names
+ hidden = PseudonymGraph(axes=False)
+ while graph.vertices:
+ vertex, record = graph.vertices.popitem()
+ hidden.vertices[sigma[vertex]] = record
+ while graph.edges:
+ (src, dst), edge = graph.edges.popitem()
+ hidden.edges[(sigma[src], sigma[dst])] = edge
+ hidden._out[sigma[src]].add(sigma[dst])
+ hidden._in[sigma[dst]].add(sigma[src])
+ graph._out.clear()
+ graph._in.clear()
+ return hidden, sigma
+
+
+def _bands(graded, confidence, truth):
+ """Precision by the eccentricity the match won by, against the degree-class expectation.
+
+ `truth` maps a vertex to its image and the size of that image's degree class, so a guess is
+ scored against the first and the baseline is one over the second.
+
+ A match with only one materialised candidate is recorded with infinite eccentricity — the gate
+ is skipped rather than computed, which `view_match` declares as a divergence from the paper.
+ Those clear every floor, so a high band silently mixes "stood clear of its rivals" with "had
+ none". The count is carried per row so the top band can be read for what it is.
+ """
+ rows = []
+ for floor in (0.0,) + BANDS:
+ kept = [u for u in graded if confidence[u][0] >= floor]
+ correct = sum(1 for u in kept if graded[u] == truth[u][0])
+ expectation = [1.0 / truth[u][1] for u in kept]
+ rows.append({
+ "eccentricity_at_least": floor,
+ "matched": len(kept),
+ "correct": correct,
+ "uncontested": sum(1 for u in kept if confidence[u][0] == float("inf")),
+ "precision": round(correct / len(kept), 6) if kept else None,
+ "degree_class_expectation":
+ round(sum(expectation) / len(expectation), 6) if expectation else None,
+ })
+ return rows
+
+
+def build_artifact(epochs=DEFAULT_EPOCHS, seed=0):
+ left, right = (Path(path) for path in epochs)
+ counts = [sum(1 for _ in eg.decode(path)) for path in (left, right)]
+ clustering = cluster_scale_np_stream(record for path in (left, right)
+ for record in eg.decode(path))
+ lookup = clustering.map_many(_addresses(left))
+ lookup.update(clustering.map_many(_addresses(right)))
+ del clustering
+
+ graph_a, stat_a = _active(left, lookup)
+ graph_b, stat_b = _active(right, lookup)
+ del lookup
+
+ rng = random.Random(seed)
+ hidden, sigma = _pseudonymise(graph_b, rng)
+ # `keep` admits a vertex on its degree, but contraction drops one whose counterparties
+ # were all dropped, so the degree map is the wider of the two.
+ stat_hidden = {sigma[vertex]: degree for vertex, degree in stat_b.items()
+ if vertex in sigma}
+ del graph_b, stat_b
+
+ # A vertex is matchable when both views hold it with an edge; degree is read on the hidden view
+ # because that is the population a degree-only adversary would be choosing among.
+ by_degree = Counter(hidden.degree(vertex) for vertex in hidden.vertices)
+ matchable = [vertex for vertex in graph_a.vertices
+ if vertex in sigma and graph_a.degree(vertex) and hidden.degree(sigma[vertex])]
+ truth = {vertex: (sigma[vertex], by_degree[hidden.degree(sigma[vertex])])
+ for vertex in matchable}
+
+ low, high = SEEDABLE
+ seedable = [v for v in matchable
+ if low <= graph_a.degree(v) <= high and low <= hidden.degree(sigma[v]) <= high]
+ ordered = sorted(seedable, key=lambda v: (-(graph_a.degree(v) + hidden.degree(sigma[v])),
+ repr(v)))
+ arms, banded = [], {}
+ for share in SEED_SHARES:
+ count = max(2, int(len(ordered) * share))
+ # Iterate the ranked list, not a set built from it. The seeded mapping does not care — a
+ # mapping is its content — but the shuffled control is built by permuting
+ # `supplied.values()`, so a set's iteration order decides which image each seed is wrongly
+ # given, and Python randomises that per process. The control arm then differs between two
+ # runs of the same input, which is exactly how this was found.
+ chosen = ordered[:count]
+ seeds = set(chosen)
+ supplied = {vertex: sigma[vertex] for vertex in chosen}
+ images = list(supplied.values())
+ rng.shuffle(images)
+ shuffled = dict(zip(supplied, images))
+ for label, mapping in (("seeded", supplied), ("shuffled", shuffled)):
+ matcher = ViewMatcher()
+ matched = matcher.match(graph_a, hidden, dict(mapping),
+ stat_a=stat_a, stat_b=stat_hidden)
+ graded = {u: w for u, w in matched.items() if u in truth and u not in seeds}
+ correct = sum(1 for u, w in graded.items() if w == truth[u][0])
+ arms.append({"seed_share": share, "seeds": count, "arm": label,
+ "guesses": len(graded), "correct": correct,
+ "precision": round(correct / len(graded), 6) if graded else None})
+ if label == "seeded":
+ banded[f"{share:g}"] = _bands(graded, matcher.confidence,
+ {u: truth[u] for u in graded})
+
+ sizes = sorted(truth[vertex][1] for vertex in matchable)
+ return {
+ "schema_version": 1,
+ "experiment": EXPERIMENT_ID,
+ "datasets": [f"epoch-graph-2016-{Path(p).name.split('.')[0]}-v1" for p in epochs],
+ "population": {
+ # The file names, not the paths given: a cold-start reproduction reads the
+ # datasets from wherever the bundle materialized them, and a path recorded
+ # here would make the artifact differ by where it was run.
+ "views": [left.name, right.name],
+ "transactions": counts,
+ "vertices": [len(graph_a.vertices), len(hidden.vertices)],
+ "matchable_in_both_views": len(matchable),
+ "seedable": len(seedable),
+ "degree_class_size_median": sizes[len(sizes) // 2] if sizes else 0,
+ "degree_class_size_max": sizes[-1] if sizes else 0,
+ },
+ "matching": arms,
+ "confidence": banded,
+ }
+
+
+def load_artifact(path):
+ try:
+ with Path(path).open(encoding="utf-8") as source:
+ value = json.load(source)
+ except (OSError, json.JSONDecodeError) as exc:
+ raise VerificationError(f"cannot read artifact {path}: {exc}") from exc
+ if not isinstance(value, dict):
+ raise VerificationError("artifact root must be an object")
+ return value
+
+
+def verify_artifact(artifact, epochs=DEFAULT_EPOCHS):
+ if artifact.get("schema_version") != 1 or artifact.get("experiment") != EXPERIMENT_ID:
+ raise VerificationError("unsupported match-controls artifact identity")
+ measured = build_artifact(epochs)
+ if canonical_json_bytes(artifact) != canonical_json_bytes(measured):
+ raise VerificationError("artifact differs from a fresh experiment execution")
+ return measured
+
+
+def _widest(artifact):
+ return max(artifact["confidence"], key=float)
+
+
+def render_markdown(artifact):
+ population = artifact["population"]
+ widest = _widest(artifact)
+ bands = artifact["confidence"][widest]
+ overall, narrowest = bands[0], bands[-1]
+ lines = [
+ "# Does the matcher beat degree alone",
+ "",
+ "Generated from the canonical experiment artifact. Do not edit manually.",
+ "",
+ f"Two consecutive 2016 weekly windows of {population['transactions'][0]:,} and "
+ f"{population['transactions'][1]:,} transactions, contracted into "
+ f"{population['vertices'][0]:,} and {population['vertices'][1]:,} pseudonyms. View B's "
+ f"labels are hidden, so {population['matchable_in_both_views']:,} vertices are known to be "
+ f"present in both views and the correspondence between them is not. Seeds are drawn from "
+ f"the {population['seedable']:,} of those whose degree sits in "
+ f"[{SEEDABLE[0]}, {SEEDABLE[1]}] on both sides — below that a neighbourhood identifies "
+ f"nobody, and above it propagation refuses to route through the vertex at all, so a seed "
+ f"outside the band is a seed the matcher cannot spend.",
+ "",
+ f"At the {float(widest):.0%} seeding, precision against the eccentricity each match won by:",
+ "",
+ "| eccentricity | matched | uncontested | correct | precision | degree class alone | "
+ "margin |",
+ "|---:|---:|---:|---:|---:|---:|---:|",
+ ]
+ for row in bands:
+ precision = row["precision"]
+ baseline = row["degree_class_expectation"]
+ margin = ("—" if precision is None or baseline is None
+ else f"{precision - baseline:+.3f}")
+ lines.append(
+ f"| ≥ {row['eccentricity_at_least']:g} | {row['matched']} | {row['uncontested']} | "
+ f"{row['correct']} | {'—' if precision is None else f'{precision:.3f}'} | "
+ f"{'—' if baseline is None else f'{baseline:.5f}'} | {margin} |")
+ lines.extend([
+ "",
+ f"**The matcher beats degree, and least where it is most sure.** Overall it is "
+ f"{overall['precision'] - overall['degree_class_expectation']:+.3f} against what degree "
+ f"alone is worth. That margin does not widen as the matcher grows confident; it collapses, "
+ f"to {narrowest['precision'] - narrowest['degree_class_expectation']:+.3f} at eccentricity "
+ f"{narrowest['eccentricity_at_least']:g}. Precision does rise with confidence "
+ f"({overall['precision']:.3f} to {narrowest['precision']:.3f}), which is the property the "
+ f"framework's argument wants — but the degree baseline rises faster "
+ f"({overall['degree_class_expectation']:.3f} to "
+ f"{narrowest['degree_class_expectation']:.3f}), because the matches the matcher is surest "
+ f"of are the ones whose partner has a rare degree. The high-confidence band is where the "
+ f"matcher is *hardest* to distinguish from reading the degrees off.",
+ "",
+ "The uncontested column is the matches where only one candidate materialised at all. The "
+ "matcher records those with infinite eccentricity and skips the gate rather than computing "
+ "it, so they clear every floor: a high band is only evidence about *standing clear of "
+ "rivals* to the extent it is not made of them.",
+ "",
+ "The degree-class column is what degree is worth with no algorithm attached: one over the "
+ "number of B-vertices sharing the true partner's degree. It is handed that degree, which a "
+ "degree-only adversary would have to guess, so it bounds such an adversary from above rather "
+ f"than approximating one. Classes here run to {population['degree_class_size_max']:,} "
+ f"vertices with a median of {population['degree_class_size_median']}.",
+ "",
+ "Every arm, with the shuffled-seed control beside each seeded one:",
+ "",
+ "| seed share | arm | seeds | guesses | correct | precision |",
+ "|---:|---|---:|---:|---:|---:|",
+ ])
+ for row in artifact["matching"]:
+ precision = row["precision"]
+ lines.append(
+ f"| {row['seed_share']:.0%} | {row['arm']} | {row['seeds']} | {row['guesses']} | "
+ f"{row['correct']} | {'—' if precision is None else f'{precision:.3f}'} |")
+ lines.extend([
+ "",
+ f"The seeded arm returns {overall['matched']} graded matches. A shuffled seed is the same "
+ "matcher on the same graphs with the correspondence destroyed, and what it returns is the "
+ "share of this that survives without the seeds meaning anything.",
+ "",
+ "This is the complete-clustering premise: the matcher is told which vertices appear in both "
+ "views and asked only which is which. The split-cluster premise, where a straddling entity "
+ "carries a separate pseudonym per view and the matcher must discover that they are one, is "
+ "measured beside it (`results/generated/graph-rejoin-2016-v1.md`) and is the harder question.",
+ "",
+ "Cluster membership is a co-spend relation rather than observed wallet ownership, the seeds "
+ "are supplied, and this is two windows of one era. None of it is a privacy score.",
+ "",
+ "## Reproducibility / provenance",
+ "",
+ "State 1 in `results/REPRODUCIBILITY.md`: band-pinned on committed data — the artifact is "
+ "recomputed from the committed weekly epoch graphs and compared byte for byte.",
+ ])
+ return "\n".join(lines) + "\n"
+
+
+def _parser():
+ parser = argparse.ArgumentParser(description=__doc__.splitlines()[0])
+ parser.add_argument("command", choices=("reproduce", "verify"))
+ parser.add_argument("--epochs", nargs=2, default=list(DEFAULT_EPOCHS))
+ parser.add_argument("--artifact", required=True)
+ parser.add_argument("--markdown", required=True)
+ return parser
+
+
+def main(argv=None):
+ args = _parser().parse_args(argv)
+ if args.command == "reproduce":
+ artifact = build_artifact(tuple(args.epochs))
+ write_canonical_json(args.artifact, artifact)
+ destination = Path(args.markdown)
+ destination.parent.mkdir(parents=True, exist_ok=True)
+ destination.write_text(render_markdown(artifact), encoding="utf-8")
+ else:
+ artifact = verify_artifact(load_artifact(args.artifact), tuple(args.epochs))
+ if Path(args.markdown).read_text(encoding="utf-8") != render_markdown(artifact):
+ raise VerificationError("Markdown differs from canonical rendering")
+ return 0
+
+
+if __name__ == "__main__":
+ raise SystemExit(main())
diff --git a/decluster/experiments/ns1r_change_readoff.py b/decluster/experiments/ns1r_change_readoff.py
new file mode 100644
index 00000000..b7c4d769
--- /dev/null
+++ b/decluster/experiments/ns1r_change_readoff.py
@@ -0,0 +1,177 @@
+"""Publish the receiver-side change read-off in a many-senders/one-receiver PayJoin.
+
+The receiver knows its own coins and the payment it negotiated with each sender, so every candidate
+(input, change) pair has to satisfy `input - payment == change`. Read one sender at a time the
+constraint usually leaves several candidates; enumerating the perfect matchings propagates it
+across senders and can leave a single reading of the amounts.
+
+The distinction the artifact keeps is between matchings and readings. Two matchings that assign
+different output *indices* but the same output *amounts* are one reading, and a reading is what the
+receiver actually learns. Reporting matchings alone would understate the leak.
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+from pathlib import Path
+
+from ..failure_modes.ns1r_change_readoff import analyze, ns1r_examples
+from ..result_artifacts import canonical_json_bytes, write_canonical_json
+
+EXPERIMENT_ID = "ns1r-change-readoff-v1"
+
+
+class VerificationError(ValueError):
+ """A stored result differs from the executable read-off."""
+
+
+def _scenario(readoff):
+ scenario = readoff.scenario
+ return {
+ "identifier": scenario.identifier,
+ "inputs_sat": list(scenario.inputs),
+ "outputs_sat": list(scenario.outputs),
+ "payments_sat": [{"sender": name, "payment": amount} for name, amount in scenario.payments],
+ "local_candidates": {name: sorted(values) for name, values in readoff.local_candidates},
+ "feasible_candidates": {name: sorted(values) for name, values in readoff.feasible_candidates},
+ "matchings": len(readoff.matchings),
+ "distinct_readings": len(readoff.readings),
+ "reading_is_unique": len(readoff.readings) == 1,
+ "unanimous_links": [list(link) for link in readoff.unanimous_links],
+ "readings": [
+ [{"sender": name, "input_sat": source, "change_sat": target}
+ for name, source, target in reading]
+ for reading in readoff.readings
+ ],
+ }
+
+
+def build_artifact():
+ rows = [_scenario(analyze(scenario)) for scenario in ns1r_examples()]
+ resolved = [row for row in rows if row["reading_is_unique"]]
+ return {
+ "schema_version": 1,
+ "experiment": EXPERIMENT_ID,
+ "claim": "a receiver who knows each negotiated payment can propagate the change constraint "
+ "across senders, and the propagation can leave one reading of the amounts where "
+ "no single sender's constraint does",
+ "scenarios": rows,
+ "measurement": {
+ "scenarios": len(rows),
+ "with_a_unique_reading": len(resolved),
+ "locally_ambiguous_senders": sum(
+ 1 for row in rows for values in row["local_candidates"].values() if len(values) > 1
+ ),
+ "senders_narrowed_by_propagation": sum(
+ 1
+ for row in rows
+ for sender, local in row["local_candidates"].items()
+ if len(row["feasible_candidates"][sender]) < len(local)
+ ),
+ },
+ "conclusion": "propagation resolved the consolidating receiver to a single reading and left "
+ "the evenly spaced control with two; the read-off is a receiver capability, "
+ "not an external-observer one",
+ "limitations": [
+ "the scenarios are deterministic fixtures, not sampled transactions",
+ "the receiver's knowledge of each negotiated payment is supplied, not inferred",
+ "an external observer holds neither the payments nor the receiver's own coins",
+ "a unique reading of the amounts is not ownership attribution",
+ "the enumeration is bounded at eight senders and does not model fees",
+ "the result is not a privacy score",
+ ],
+ }
+
+
+def load_artifact(path):
+ try:
+ with Path(path).open(encoding="utf-8") as source:
+ value = json.load(source)
+ except (OSError, json.JSONDecodeError) as exc:
+ raise VerificationError(f"cannot read artifact {path}: {exc}") from exc
+ if not isinstance(value, dict):
+ raise VerificationError("artifact root must be an object")
+ return value
+
+
+def verify_artifact(artifact):
+ if artifact.get("schema_version") != 1 or artifact.get("experiment") != EXPERIMENT_ID:
+ raise VerificationError("unsupported change-read-off artifact identity")
+ measured = build_artifact()
+ if canonical_json_bytes(artifact) != canonical_json_bytes(measured):
+ raise VerificationError("artifact differs from the executable read-off")
+ return measured
+
+
+def render_markdown(artifact):
+ measured = artifact["measurement"]
+ lines = [
+ "# Receiver-side change read-off in a many-senders PayJoin",
+ "",
+ "Generated from the canonical experiment artifact. Do not edit manually.",
+ "",
+ "The receiver knows its own coins and the payment negotiated with each sender, so a "
+ "candidate change output must satisfy `input - payment == change`. One sender's constraint "
+ "rarely settles it; the perfect matchings propagate it across senders.",
+ "",
+ "| scenario | senders | matchings | distinct readings | unique reading | unanimous links |",
+ "|---|---:|---:|---:|---|---:|",
+ ]
+ for row in artifact["scenarios"]:
+ lines.append(
+ f"| {row['identifier']} | {len(row['payments_sat'])} | {row['matchings']} | "
+ f"{row['distinct_readings']} | {'yes' if row['reading_is_unique'] else 'no'} | "
+ f"{len(row['unanimous_links'])} |"
+ )
+ lines += [
+ "",
+ f"Across {measured['scenarios']} scenarios, {measured['locally_ambiguous_senders']} senders "
+ f"were ambiguous on their own constraint and propagation narrowed "
+ f"{measured['senders_narrowed_by_propagation']} of them; "
+ f"{measured['with_a_unique_reading']} scenario reached a single reading of the amounts.",
+ "",
+ "Matchings and readings are counted separately on purpose. Two matchings that assign "
+ "different output indices but the same amounts are one reading, and the reading is what the "
+ "receiver learns — the consolidating scenario has two matchings and one reading.",
+ "",
+ "Both scenarios are deterministic fixtures and the negotiated payments are supplied rather "
+ "than inferred. An external observer holds neither those payments nor the receiver's own "
+ "coins, so this is a counterparty capability. A unique reading of the amounts is not "
+ "ownership attribution and is not a privacy score.",
+ "",
+ ]
+ return "\n".join(lines)
+
+
+def _parser():
+ parser = argparse.ArgumentParser(description=__doc__)
+ commands = parser.add_subparsers(dest="command", required=True)
+ reproduce = commands.add_parser("reproduce")
+ reproduce.add_argument("--artifact", required=True)
+ reproduce.add_argument("--markdown", required=True)
+ verify = commands.add_parser("verify")
+ verify.add_argument("--artifact", required=True)
+ verify.add_argument("--markdown")
+ return parser
+
+
+def main(argv=None):
+ args = _parser().parse_args(argv)
+ if args.command == "reproduce":
+ artifact = build_artifact()
+ write_canonical_json(args.artifact, artifact)
+ destination = Path(args.markdown)
+ destination.parent.mkdir(parents=True, exist_ok=True)
+ destination.write_text(render_markdown(artifact), encoding="utf-8")
+ else:
+ artifact = verify_artifact(load_artifact(args.artifact))
+ if args.markdown is not None:
+ actual = Path(args.markdown).read_text(encoding="utf-8")
+ if actual != render_markdown(artifact):
+ raise VerificationError("Markdown differs from canonical rendering")
+ return 0
+
+
+if __name__ == "__main__":
+ raise SystemExit(main())
diff --git a/decluster/experiments/ns_bitcoin.py b/decluster/experiments/ns_bitcoin.py
new file mode 100644
index 00000000..4a6cf4b0
--- /dev/null
+++ b/decluster/experiments/ns_bitcoin.py
@@ -0,0 +1,119 @@
+"""Reproduce N-S propagation across adjacent Bitcoin observation windows."""
+
+from __future__ import annotations
+
+import argparse
+import json
+from pathlib import Path
+
+from examples import ns_bitcoin_views as legacy
+
+from ..result_artifacts import canonical_json_bytes, write_canonical_json
+
+EXPERIMENT_ID = "ns-bitcoin-v1"
+LEFT = "data/ns-bitcoin-2016-v1/left-391992-392111.ndjson.gz"
+RIGHT = "data/ns-bitcoin-2016-v1/right-392112-392231.ndjson.gz"
+
+
+class VerificationError(ValueError):
+ """A stored result differs from a fresh execution."""
+
+
+def build_artifact(left=LEFT, right=RIGHT):
+ args = legacy.build_parser().parse_args(["--left", str(left), "--right", str(right)])
+ report = legacy.build_report(args)
+ report["views"]["left"]["path"] = LEFT
+ report["views"]["right"]["path"] = RIGHT
+ return {
+ "schema_version": 1,
+ "experiment": EXPERIMENT_ID,
+ "corpus": "adjacent public Bitcoin block windows",
+ "report": report,
+ "limitations": [
+ "no independent gradeable seeds were found in these windows",
+ "the measured sweep uses seeds sampled from withheld correspondence",
+ "operational precision is approximately one percent",
+ "the observation windows are not representative of the whole chain",
+ "this does not reproduce the Twitter, Flickr, or LiveJournal experiments",
+ ],
+ }
+
+
+def load_artifact(path):
+ try:
+ with Path(path).open(encoding="utf-8") as source:
+ value = json.load(source)
+ except (OSError, json.JSONDecodeError) as exc:
+ raise VerificationError(f"cannot read artifact {path}: {exc}") from exc
+ if not isinstance(value, dict):
+ raise VerificationError("artifact root must be an object")
+ return value
+
+
+def verify_artifact(artifact, *, left=LEFT, right=RIGHT):
+ if artifact.get("schema_version") != 1 or artifact.get("experiment") != EXPERIMENT_ID:
+ raise VerificationError("unsupported N-S Bitcoin artifact identity")
+ measured = build_artifact(left, right)
+ if canonical_json_bytes(artifact) != canonical_json_bytes(measured):
+ raise VerificationError("artifact differs from a fresh experiment execution")
+ return measured
+
+
+def render_markdown(artifact):
+ report = artifact["report"]
+ runs = report["runs"]
+ best = max(runs, key=lambda run: run["attack"]["precision"])
+ return "\n".join([
+ "# N-S propagation on Bitcoin views",
+ "",
+ "Generated from the canonical experiment artifact. Do not edit manually.",
+ "",
+ f"The two windows contain {report['views']['left']['transactions']:,} and "
+ f"{report['views']['right']['transactions']:,} transactions. No independent "
+ "gradeable seed was found.",
+ "",
+ f"The seed-assisted sweep's highest precision is "
+ f"{best['attack']['precision']:.4%}, with coverage "
+ f"{best['attack']['coverage']:.4%}.",
+ "",
+ "The sweep does not establish an independently seeded attack and is not a "
+ "reproduction of the published social-network corpora.",
+ "",
+ ])
+
+
+def _parser():
+ parser = argparse.ArgumentParser(description=__doc__)
+ parser.add_argument("--left", default=LEFT)
+ parser.add_argument("--right", default=RIGHT)
+ commands = parser.add_subparsers(dest="command", required=True)
+ reproduce = commands.add_parser("reproduce")
+ reproduce.add_argument("--artifact", required=True)
+ reproduce.add_argument("--markdown", required=True)
+ verify = commands.add_parser("verify")
+ verify.add_argument("--artifact", required=True)
+ verify.add_argument("--markdown")
+ return parser
+
+
+def main(argv=None):
+ args = _parser().parse_args(argv)
+ if args.command == "reproduce":
+ artifact = build_artifact(args.left, args.right)
+ write_canonical_json(args.artifact, artifact)
+ destination = Path(args.markdown)
+ destination.parent.mkdir(parents=True, exist_ok=True)
+ destination.write_text(render_markdown(artifact), encoding="utf-8")
+ else:
+ artifact = verify_artifact(
+ load_artifact(args.artifact), left=args.left, right=args.right
+ )
+ if args.markdown is not None:
+ actual = Path(args.markdown).read_text(encoding="utf-8")
+ if actual != render_markdown(artifact):
+ raise VerificationError("Markdown differs from canonical rendering")
+ return 0
+
+
+if __name__ == "__main__":
+ raise SystemExit(main())
diff --git a/decluster/experiments/ns_link_prediction_coverage.py b/decluster/experiments/ns_link_prediction_coverage.py
new file mode 100644
index 00000000..273cc2f8
--- /dev/null
+++ b/decluster/experiments/ns_link_prediction_coverage.py
@@ -0,0 +1,159 @@
+"""Reproduce the executable boundary of the N-S 2011 baseline."""
+
+from __future__ import annotations
+
+import argparse
+import json
+from pathlib import Path
+
+from ..baselines.ns_link_prediction_2011 import (
+ ComponentStatus,
+ IncompletePipelineError,
+ PipelineComponent,
+ pipeline_coverage,
+ require_components,
+)
+from ..result_artifacts import canonical_json_bytes, write_canonical_json
+
+
+EXPERIMENT_ID = "ns-link-prediction-coverage-v2"
+
+
+class VerificationError(ValueError):
+ """A stored result differs from a fresh execution."""
+
+
+def _refusal(component: PipelineComponent) -> str:
+ try:
+ require_components(component)
+ except IncompletePipelineError as exc:
+ return str(exc)
+ raise RuntimeError(f"expected {component.value} to be refused")
+
+
+def build_artifact() -> dict:
+ coverage = pipeline_coverage()
+ implemented = tuple(
+ row.component for row in coverage if row.status is ComponentStatus.IMPLEMENTED
+ )
+ require_components(*implemented)
+ unavailable = tuple(
+ row.component for row in coverage if row.status is not ComponentStatus.IMPLEMENTED
+ )
+ return {
+ "schema_version": 1,
+ "experiment": EXPERIMENT_ID,
+ "paper": "Narayanan-Shi-Rubinstein-2011",
+ "components": [
+ {
+ "component": row.component.value,
+ "status": row.status.value,
+ "limitation": row.limitation,
+ }
+ for row in coverage
+ ],
+ "implemented_gate": {
+ "accepted": True,
+ "components": [component.value for component in implemented],
+ },
+ "refusal_controls": {
+ component.value: _refusal(component) for component in unavailable
+ },
+ "end_to_end_reproduced": False,
+ "composition": None,
+ "conclusion": (
+ "implemented components do not constitute the complete published pipeline"
+ ),
+ "limitations": [
+ "the published Flickr/Kaggle corpus and reported metrics are not reproduced",
+ "the zero convention for dummy-incident terms is read from the paper's prose, "
+ "not from its formula",
+ "annealing uses explicit local reproducibility controls",
+ "confidence pruning, accepted-mapping correction and the learned 25-feature model "
+ "are not reproduced",
+ ],
+ }
+
+
+def load_artifact(path: str | Path) -> dict:
+ try:
+ with Path(path).open(encoding="utf-8") as source:
+ value = json.load(source)
+ except (OSError, json.JSONDecodeError) as exc:
+ raise VerificationError(f"cannot read artifact {path}: {exc}") from exc
+ if not isinstance(value, dict):
+ raise VerificationError("artifact root must be an object")
+ return value
+
+
+def verify_artifact(artifact: dict) -> dict:
+ if (artifact.get("schema_version"), artifact.get("experiment")) != (
+ 1,
+ EXPERIMENT_ID,
+ ):
+ raise VerificationError("unsupported N-S coverage artifact identity")
+ measured = build_artifact()
+ if canonical_json_bytes(artifact) != canonical_json_bytes(measured):
+ raise VerificationError("artifact differs from a fresh coverage execution")
+ return measured
+
+
+def render_markdown(artifact: dict) -> str:
+ lines = [
+ "# N-S 2011 executable coverage",
+ "",
+ "Generated from the canonical experiment artifact. Do not edit manually.",
+ "",
+ "| component | status | limitation |",
+ "|---|---|---|",
+ ]
+ lines.extend(
+ f"| `{row['component']}` | `{row['status']}` | {row['limitation'] or '—'} |"
+ for row in artifact["components"]
+ )
+ lines.extend([
+ "",
+ "Every non-implemented component is exercised through the refusal gate. The implemented "
+ "components pass that gate, but the end-to-end paper pipeline remains unreproduced.",
+ "",
+ "This deterministic contract does not reproduce the Flickr/Kaggle corpus, reported "
+ "metrics, dummy-node convention, pruning policy, correction schedule, or learned model.",
+ "",
+ ])
+ return "\n".join(lines)
+
+
+def _parser() -> argparse.ArgumentParser:
+ parser = argparse.ArgumentParser(description=__doc__)
+ commands = parser.add_subparsers(dest="command", required=True)
+ reproduce = commands.add_parser("reproduce")
+ reproduce.add_argument("--artifact", required=True)
+ reproduce.add_argument("--markdown", required=True)
+ verify = commands.add_parser("verify")
+ verify.add_argument("--artifact", required=True)
+ verify.add_argument("--markdown")
+ return parser
+
+
+def main(argv: list[str] | None = None) -> int:
+ args = _parser().parse_args(argv)
+ if args.command == "reproduce":
+ artifact = build_artifact()
+ write_canonical_json(args.artifact, artifact)
+ markdown = Path(args.markdown)
+ markdown.parent.mkdir(parents=True, exist_ok=True)
+ markdown.write_text(render_markdown(artifact), encoding="utf-8")
+ else:
+ artifact = verify_artifact(load_artifact(args.artifact))
+ if args.markdown is not None:
+ try:
+ actual = Path(args.markdown).read_text(encoding="utf-8")
+ except OSError as exc:
+ raise VerificationError(f"cannot read Markdown {args.markdown}: {exc}") from exc
+ if actual != render_markdown(artifact):
+ raise VerificationError("Markdown differs from canonical rendering")
+ return 0
+
+
+if __name__ == "__main__":
+ raise SystemExit(main())
diff --git a/decluster/experiments/partition_schemes.py b/decluster/experiments/partition_schemes.py
new file mode 100644
index 00000000..309db69c
--- /dev/null
+++ b/decluster/experiments/partition_schemes.py
@@ -0,0 +1,307 @@
+"""Compare the partition schemes the framework names, on data a reader has.
+
+The framework calls the temporal cut its own trivial example and asks for one that divides along
+cluster-collapse regions instead, generalised to n views. Both are implemented, and
+`results/RESULTS-partition-cuts.md` reports the comparison over 300,000 transactions, and for a
+while that measurement rested on a 977 MB export nobody else held. It did not need the whole
+export: the run consumes one prefix, and that prefix recompresses to 50 MB, so it is committed and
+this reproduces the published table rather than approximating it.
+
+The number that decides the comparison is not the straddler count but the connectivity among them
+— propagation needs edges *between* straddlers, not merely a population of them — which is why the
+run reports mean straddler degree beside the count.
+"""
+
+from __future__ import annotations
+
+import argparse
+import gzip
+import json
+import random
+from pathlib import Path
+
+from .. import contraction, tx_addrs, view_partition, views
+from ..result_artifacts import canonical_json_bytes, write_canonical_json
+from ..view_match import ViewMatcher
+
+EXPERIMENT_ID = "partition-schemes-v1"
+DEFAULT_DATASET = "data/partition-cuts-300k-2016.ndjson.gz"
+SCHEMES = ("epoch", "decore", "collapse")
+VIEW_COUNTS = (2, 3, 4)
+CORE_FRACTION = 0.01
+MIN_SIDE = 2
+SEED = 0
+SEED_SHARES = (0.05, 0.10)
+
+
+class VerificationError(ValueError):
+ """The dataset or stored result violates this experiment's contract."""
+
+
+def _load(path):
+ sample = []
+ try:
+ with gzip.open(path, "rt", encoding="utf-8") as source:
+ for line_number, line in enumerate(source, start=1):
+ if not line.strip():
+ continue
+ transaction = json.loads(line)
+ if not isinstance(transaction, dict) or not transaction.get("txid"):
+ raise VerificationError(
+ f"{path}:{line_number}: transaction must be an object with a txid"
+ )
+ sample.append((transaction, 0))
+ except (OSError, json.JSONDecodeError) as exc:
+ raise VerificationError(f"cannot read slice snapshot {path}: {exc}") from exc
+ if not sample:
+ raise VerificationError(f"slice snapshot is empty: {path}")
+ return sample
+
+
+def _active(sample, indices, lookup):
+ degrees = contraction.contract_degrees(sample, indices=indices, lookup=lookup)
+ keep = {vertex for vertex, degree in degrees.items() if degree >= 2}
+ graph = contraction.contract(sample, indices=indices, lookup=lookup, axes=False, keep=keep)
+ return graph, {vertex: degrees[vertex] for vertex in keep}
+
+
+def _straddlers(sample, lookup, parts):
+ addresses = set()
+ for index in parts[0]:
+ transaction = sample[index][0]
+ addresses.update(tx_addrs.in_addrs(transaction))
+ addresses.update(address for address, _ in tx_addrs.out_addrs(transaction))
+ split, origin = views.split_clusters_by_view(
+ lookup, addresses, frac=1.0, rng=random.Random(SEED)
+ )
+ left, stat_left = _active(sample, parts[0], views.view_lookup(lookup, split, "#a"))
+ right, stat_right = _active(sample, parts[1], views.view_lookup(lookup, split, "#b"))
+ in_right = {v for v in right.vertices if isinstance(v, str) and v.endswith("#b")}
+ truth = {
+ vertex: f"{origin[vertex]}#b"
+ for vertex in left.vertices
+ if isinstance(vertex, str) and vertex.endswith("#a") and f"{origin[vertex]}#b" in in_right
+ }
+ return left, right, stat_left, stat_right, truth
+
+
+def _scheme(sample, lookup, scheme):
+ parts = view_partition.partition_coins(sample, scheme=scheme, core_frac=CORE_FRACTION,
+ n_views=2, min_side=MIN_SIDE)
+ boundary = len(sample) - sum(len(part) for part in parts)
+ left, right, stat_left, stat_right, truth = _straddlers(sample, lookup, parts[:2])
+ crowd = set(truth)
+ among = sum(1 for source, destination in left.edges
+ if source in crowd and destination in crowd)
+ connected = sum(1 for vertex in crowd
+ if any(other in crowd for other in left.neighbours(vertex)))
+ degree = (sum(1 for v in crowd for other in left.neighbours(v) if other in crowd)
+ / len(crowd)) if crowd else 0.0
+ ranked = sorted(truth, key=lambda v: -(left.degree(v) + right.degree(truth[v])))
+ seeded = []
+ for share in SEED_SHARES:
+ count = max(2, int(len(ranked) * share))
+ seeds = set(ranked[:count])
+ matched = ViewMatcher(revisit=True).match(
+ left, right, {v: truth[v] for v in seeds}, stat_a=stat_left, stat_b=stat_right
+ )
+ guesses = {v: w for v, w in matched.items() if v in truth and v not in seeds}
+ correct = sum(1 for v, w in guesses.items() if w == truth[v])
+ remaining = len(truth) - len(seeds)
+ seeded.append({
+ "seed_share": share, "seeds": len(seeds), "guesses": len(guesses),
+ "correct": correct,
+ "precision": (correct / len(guesses)) if guesses else None,
+ "recall": (correct / remaining) if remaining > 0 else None,
+ })
+ return {
+ "scheme": scheme,
+ "view_sizes": [len(part) for part in parts],
+ "boundary_transactions": boundary,
+ "straddling_entities": len(truth),
+ "edges_among_straddlers": among,
+ "mean_straddler_degree": degree,
+ "straddlers_with_a_straddling_neighbour": connected,
+ "seeded": seeded,
+ }
+
+
+def build_artifact(dataset=DEFAULT_DATASET):
+ sample = _load(dataset)
+ lookup = views.cluster_addresses(sample, refuse=True)
+ rows = [_scheme(sample, lookup, scheme) for scheme in SCHEMES]
+ generalisation = {
+ scheme: {
+ str(count): [len(part) for part in view_partition.partition_coins(
+ sample, scheme=scheme, core_frac=CORE_FRACTION, n_views=count, min_side=MIN_SIDE)]
+ for count in VIEW_COUNTS
+ }
+ for scheme in SCHEMES
+ }
+ by_scheme = {row["scheme"]: row for row in rows}
+ return {
+ "schema_version": 1,
+ "experiment": EXPERIMENT_ID,
+ "dataset": "slice-a-channels-2016-v1",
+ "measurement": {
+ "transactions": len(sample),
+ "clusters": len(set(lookup.values())),
+ "schemes": rows,
+ "view_counts": generalisation,
+ "collapse_costs_over_epoch": (
+ by_scheme["collapse"]["boundary_transactions"]
+ - by_scheme["epoch"]["boundary_transactions"]
+ ),
+ "any_scheme_ignited_the_matcher": any(
+ seed["guesses"] for row in rows for seed in row["seeded"]
+ ),
+ },
+ "parameters": {
+ "schemes": list(SCHEMES),
+ "view_counts": list(VIEW_COUNTS),
+ "core_fraction": CORE_FRACTION,
+ "min_side": MIN_SIDE,
+ "seed": SEED,
+ "seed_shares": list(SEED_SHARES),
+ },
+ "limitations": [
+ "the matcher recovers a handful of pairs at best, so the comparison rests on the "
+ "straddler subgraph rather than on precision",
+ "one slice, one era, one 300,000-transaction prefix",
+ "cluster membership is a co-spend label, not wallet ownership",
+ "the export carries addresses but no values, so the collapse detector sees only the "
+ "shape rule and never the de-mix arm",
+ "the result is not a privacy score",
+ ],
+ }
+
+
+def load_artifact(path):
+ try:
+ with Path(path).open(encoding="utf-8") as source:
+ value = json.load(source)
+ except (OSError, json.JSONDecodeError) as exc:
+ raise VerificationError(f"cannot read artifact {path}: {exc}") from exc
+ if not isinstance(value, dict):
+ raise VerificationError("artifact root must be an object")
+ return value
+
+
+def verify_artifact(artifact, dataset=DEFAULT_DATASET):
+ if artifact.get("schema_version") != 1 or artifact.get("experiment") != EXPERIMENT_ID:
+ raise VerificationError("unsupported partition-scheme artifact identity")
+ measured = build_artifact(dataset)
+ if canonical_json_bytes(artifact) != canonical_json_bytes(measured):
+ raise VerificationError("artifact differs from a fresh experiment execution")
+ return measured
+
+
+def render_markdown(artifact):
+ measured = artifact["measurement"]
+ rows = {row["scheme"]: row for row in measured["schemes"]}
+ lines = [
+ "# Which cut makes two matchable views",
+ "",
+ "Generated from the canonical experiment artifact. Do not edit manually.",
+ "",
+ f"{measured['transactions']} transactions, {measured['clusters']} clusters. Each scheme "
+ "cuts the slice into two views, a straddling entity gets a distinct pseudonym per view, "
+ "and the matcher has to rejoin them from structure alone.",
+ "",
+ "| scheme | boundary txs | pairs to rejoin | straddler edges | mean straddler degree | "
+ "non-isolated | correct @ 10% seed |",
+ "|---|---:|---:|---:|---:|---:|---:|",
+ ]
+ for scheme in ("epoch", "decore", "collapse"):
+ row = rows[scheme]
+ share = (100.0 * row["straddlers_with_a_straddling_neighbour"] / row["straddling_entities"]
+ if row["straddling_entities"] else 0.0)
+ at_ten = next(s for s in row["seeded"] if s["seed_share"] == 0.10)
+ cost = (100.0 * row["boundary_transactions"] / measured["transactions"])
+ lines.append(
+ f"| `{scheme}` | {row['boundary_transactions']} ({cost:.1f}%) | "
+ f"{row['straddling_entities']} | {row['edges_among_straddlers']} | "
+ f"{row['mean_straddler_degree']:.2f} | {share:.1f}% | {at_ten['correct']} |"
+ )
+ epoch, decore, collapse = rows["epoch"], rows["decore"], rows["collapse"]
+ lines += [
+ "",
+ f"**`decore` cuts the wrong thing.** Dropping the busiest "
+ f"{artifact['parameters']['core_fraction']:.0%} of addresses removes "
+ f"{100.0 * decore['boundary_transactions'] / measured['transactions']:.1f}% of the "
+ f"transactions, takes the rejoinable population from {epoch['straddling_entities']} to "
+ f"{decore['straddling_entities']} and the straddler subgraph's mean degree from "
+ f"{epoch['mean_straddler_degree']:.2f} to {decore['mean_straddler_degree']:.2f}. It cuts by "
+ "degree, and degree is where the recurring relationships live.",
+ "",
+ f"**`collapse` is nearly free and does not change the regime.** It costs "
+ f"{100.0 * collapse['boundary_transactions'] / measured['transactions']:.1f}% of the "
+ f"transactions and leaves the population and the degree where the temporal baseline leaves "
+ f"them ({collapse['straddling_entities']} against {epoch['straddling_entities']} pairs, "
+ f"{collapse['mean_straddler_degree']:.2f} against {epoch['mean_straddler_degree']:.2f}). "
+ "It is the cut the framework asks for and it is not the binding constraint.",
+ "",
+ "| scheme | seed | seeds | guesses | correct | precision |",
+ "|---|---:|---:|---:|---:|---:|",
+ ]
+ for scheme in ("epoch", "decore", "collapse"):
+ for seed in rows[scheme]["seeded"]:
+ precision = "n/a" if seed["precision"] is None else f"{seed['precision']:.3f}"
+ lines.append(
+ f"| `{scheme}` | {seed['seed_share']:.0%} | {seed['seeds']} | {seed['guesses']} | "
+ f"{seed['correct']} | {precision} |"
+ )
+ lines += [
+ "",
+ "The matcher recovers a handful of pairs at best, so what separates the schemes here is the "
+ "straddler subgraph and not the precision. A cut that halves the mean straddler degree "
+ "leaves nothing to propagate along, whatever its precision reads on the pairs it does "
+ "return.",
+ "",
+ "Every scheme generalises to n views:",
+ "",
+ "| scheme | 2 | 3 | 4 |",
+ "|---|---|---|---|",
+ ]
+ for scheme in ("epoch", "decore", "collapse"):
+ sizes = measured["view_counts"][scheme]
+ cells = " | ".join("/".join(str(n) for n in sizes[str(count)]) for count in (2, 3, 4))
+ lines.append(f"| `{scheme}` | {cells} |")
+ lines += [
+ "",
+ "Cluster membership here is a co-spend label rather than wallet ownership, the export "
+ "carries no output values so the collapse detector sees only its shape rule, and this is "
+ "one slice of one era. None of it is a privacy score.",
+ "",
+ ]
+ return "\n".join(lines)
+
+
+def _parser():
+ parser = argparse.ArgumentParser(description=__doc__)
+ parser.add_argument("--dataset", default=DEFAULT_DATASET)
+ commands = parser.add_subparsers(dest="command", required=True)
+ for name in ("reproduce", "verify"):
+ command = commands.add_parser(name)
+ command.add_argument("--artifact", required=True)
+ command.add_argument("--markdown", required=True)
+ return parser
+
+
+def main(argv=None):
+ args = _parser().parse_args(argv)
+ if args.command == "reproduce":
+ artifact = build_artifact(args.dataset)
+ write_canonical_json(args.artifact, artifact)
+ destination = Path(args.markdown)
+ destination.parent.mkdir(parents=True, exist_ok=True)
+ destination.write_text(render_markdown(artifact), encoding="utf-8")
+ else:
+ artifact = verify_artifact(load_artifact(args.artifact), args.dataset)
+ if Path(args.markdown).read_text(encoding="utf-8") != render_markdown(artifact):
+ raise VerificationError("Markdown differs from canonical rendering")
+ return 0
+
+
+if __name__ == "__main__":
+ raise SystemExit(main())
diff --git a/decluster/experiments/path_count_contract.py b/decluster/experiments/path_count_contract.py
new file mode 100644
index 00000000..e40f55c8
--- /dev/null
+++ b/decluster/experiments/path_count_contract.py
@@ -0,0 +1,198 @@
+"""Measure the implemented provenance-route accumulation contract."""
+
+from __future__ import annotations
+
+import argparse
+import json
+import math
+from pathlib import Path
+
+from ..ancestry import absorber_distribution, build_extended_graph
+from ..provenance_route_accumulation import provenance_route_accumulation
+from ..result_artifacts import canonical_json_bytes, write_canonical_json
+
+EXPERIMENT_ID = "path-count-contract-v1"
+TARGET = ("target", 0)
+
+
+class VerificationError(ValueError):
+ """A stored result differs from a fresh execution."""
+
+
+def _coinbase(value):
+ return {"vin": [{"is_coinbase": True}], "vout": [{"value": value}]}
+
+
+TRANSACTIONS = {
+ "target": {
+ "vin": [
+ {"txid": "middle", "vout": 0, "prevout": {"value": 3}},
+ {"txid": "middle", "vout": 1, "prevout": {"value": 4}},
+ ],
+ "vout": [{"value": 7}],
+ },
+ "middle": {
+ "vin": [
+ {"txid": "origin-a", "vout": 0, "prevout": {"value": 2}},
+ {"txid": "origin-b", "vout": 0, "prevout": {"value": 5}},
+ ],
+ "vout": [{"value": 2}, {"value": 5}],
+ },
+ "origin-a": _coinbase(2),
+ "origin-b": _coinbase(5),
+}
+
+
+def _fetch(txid):
+ return TRANSACTIONS[txid]
+
+
+def _link_oracle(inputs, outputs):
+ if inputs == [3, 4]:
+ return [[0.6], [0.4]]
+ if inputs == [2, 5]:
+ return [[0.7, 0.5], [0.3, 0.5]]
+ return None
+
+
+def _count_oracle(log_w):
+ def count(_inputs, _outputs):
+ return {"kind": "exact", "count": round(math.exp(log_w)), "log_w": log_w}
+
+ return count
+
+
+def _serialize_distribution(distribution):
+ return {
+ f"{txid}:{index}": round(probability, 15)
+ for (txid, index), probability in sorted(distribution.items())
+ }
+
+
+def build_artifact():
+ graph = build_extended_graph(TARGET, depth=6, fetch=_fetch, link_oracle=_link_oracle)
+ ancestry = absorber_distribution(graph, TARGET)
+ low_count = provenance_route_accumulation(
+ TARGET,
+ depth=6,
+ fetch=_fetch,
+ link_oracle=_link_oracle,
+ count_oracle=_count_oracle(0.0),
+ )
+ high_count = provenance_route_accumulation(
+ TARGET,
+ depth=6,
+ fetch=_fetch,
+ link_oracle=_link_oracle,
+ count_oracle=_count_oracle(math.log(100.0)),
+ )
+ return {
+ "schema_version": 1,
+ "experiment": EXPERIMENT_ID,
+ "fixture": {"transactions": 4, "target": "target:0", "depth": 6},
+ "ancestry_distribution": _serialize_distribution(ancestry),
+ "path_count_distribution": _serialize_distribution(low_count["origins_weighted"]),
+ "entropy": {
+ "min_bits": low_count["min_entropy"],
+ "shannon_bits": low_count["shannon"],
+ },
+ "count_oracle_invariance": {
+ "low_log_w": 0.0,
+ "high_log_w": math.log(100.0),
+ "distributions_equal": (
+ low_count["origins_weighted"] == high_count["origins_weighted"]
+ ),
+ },
+ "implemented_capabilities": {
+ "provenance_route_accumulation": True,
+ "edge_disjoint_path_enumeration": True, # decluster.disjoint_routes
+ "plausible_flow_capacity": True, # disjoint_routes.flow_capacity
+ "k_routes": True,
+ },
+ "limitations": [
+ "the fixture is synthetic and tests implementation semantics",
+ "the function accumulates link-probability mass over ancestry routes",
+ "count_oracle is retained for compatibility and does not affect the result",
+ "the function does not measure CTP robust connectivity or own-origin robustness",
+ "the output is not a privacy score or CoinScore",
+ ],
+ }
+
+
+def load_artifact(path):
+ try:
+ with Path(path).open(encoding="utf-8") as source:
+ value = json.load(source)
+ except (OSError, json.JSONDecodeError) as exc:
+ raise VerificationError(f"cannot read artifact {path}: {exc}") from exc
+ if not isinstance(value, dict):
+ raise VerificationError("artifact root must be an object")
+ return value
+
+
+def verify_artifact(artifact):
+ if artifact.get("schema_version") != 1 or artifact.get("experiment") != EXPERIMENT_ID:
+ raise VerificationError("unsupported path-count-contract artifact identity")
+ measured = build_artifact()
+ if canonical_json_bytes(artifact) != canonical_json_bytes(measured):
+ raise VerificationError("artifact differs from a fresh contract execution")
+ return measured
+
+
+def render_markdown(artifact):
+ capabilities = artifact["implemented_capabilities"]
+ entropy = artifact["entropy"]
+ return "\n".join([
+ "# Path-count implementation contract",
+ "",
+ "Generated from the canonical experiment artifact. Do not edit manually.",
+ "",
+ "The ancestry and path-count distributions are identical on the fixed two-hop DAG: "
+ f"`{artifact['path_count_distribution']}`.",
+ "",
+ f"The resulting min-entropy is {entropy['min_bits']:.6f} bits and Shannon entropy is "
+ f"{entropy['shannon_bits']:.6f} bits. Changing the supplied count oracle from log W = 0 "
+ f"to log W = ln(100) leaves the distribution unchanged: "
+ f"`{artifact['count_oracle_invariance']['distributions_equal']}`.",
+ "",
+ f"Route accumulation is implemented: `{capabilities['provenance_route_accumulation']}`. "
+ "Edge-disjoint path enumeration, plausible-flow capacity and k-routes are implemented: "
+ f"`{capabilities['edge_disjoint_path_enumeration']}`, "
+ f"`{capabilities['plausible_flow_capacity']}`, `{capabilities['k_routes']}`.",
+ "",
+ "This is a provenance-route diagnostic, not CTP robust connectivity or CoinScore.",
+ "",
+ ])
+
+
+def _parser():
+ parser = argparse.ArgumentParser(description=__doc__)
+ commands = parser.add_subparsers(dest="command", required=True)
+ reproduce = commands.add_parser("reproduce")
+ reproduce.add_argument("--artifact", required=True)
+ reproduce.add_argument("--markdown", required=True)
+ verify = commands.add_parser("verify")
+ verify.add_argument("--artifact", required=True)
+ verify.add_argument("--markdown")
+ return parser
+
+
+def main(argv=None):
+ args = _parser().parse_args(argv)
+ if args.command == "reproduce":
+ artifact = build_artifact()
+ write_canonical_json(args.artifact, artifact)
+ destination = Path(args.markdown)
+ destination.parent.mkdir(parents=True, exist_ok=True)
+ destination.write_text(render_markdown(artifact), encoding="utf-8")
+ else:
+ artifact = verify_artifact(load_artifact(args.artifact))
+ if args.markdown is not None:
+ actual = Path(args.markdown).read_text(encoding="utf-8")
+ if actual != render_markdown(artifact):
+ raise VerificationError("Markdown differs from canonical rendering")
+ return 0
+
+
+if __name__ == "__main__":
+ raise SystemExit(main())
diff --git a/decluster/experiments/perfect_matching_disclosure.py b/decluster/experiments/perfect_matching_disclosure.py
new file mode 100644
index 00000000..e1cc9c93
--- /dev/null
+++ b/decluster/experiments/perfect_matching_disclosure.py
@@ -0,0 +1,136 @@
+"""Reproduce the defining PMDA joint-assignment mechanism and a tied control."""
+
+from __future__ import annotations
+
+import argparse
+import json
+from math import exp
+from pathlib import Path
+
+from ..domain import MessageAssignmentRecovered
+from ..failure_modes.perfect_matching_disclosure import evaluate, paired_examples
+from ..result_artifacts import canonical_json_bytes, write_canonical_json
+
+EXPERIMENT_ID = "perfect-matching-disclosure-v1"
+
+
+class VerificationError(ValueError):
+ """A stored result differs from a fresh execution."""
+
+
+def _positions(scenario, assignment):
+ receiver_positions = {receiver: index for index, receiver in enumerate(scenario.receivers)}
+ return [receiver_positions[receiver] for _, receiver in assignment]
+
+
+def _measurement(scenario):
+ report = evaluate(scenario)
+ evidence = report.channels[0].evidence[0]
+ outcome = report.outcomes[0]
+ assignments = getattr(evidence, "optimal_assignments", ())
+ log_likelihood = getattr(evidence, "log_likelihood", None)
+ naive = [max(range(len(row)), key=row.__getitem__) for row in scenario.profile_weights]
+ return {
+ "messages": len(scenario.senders),
+ "naive_receiver_positions": naive,
+ "naive_is_bijective": len(set(naive)) == len(naive),
+ "optimal_assignments": [
+ _positions(scenario, assignment) for assignment in assignments
+ ],
+ "optimal_assignment_count": len(assignments),
+ "joint_likelihood": exp(log_likelihood) if log_likelihood is not None else None,
+ "unique_assignment_recovered": isinstance(outcome, MessageAssignmentRecovered),
+ }
+
+
+def build_artifact():
+ joint, control = paired_examples()
+ return {
+ "schema_version": 1,
+ "experiment": EXPERIMENT_ID,
+ "joint_assignment": _measurement(joint),
+ "uniform_profile_control": _measurement(control),
+ "composition": None,
+ "conclusion": "joint perfect matching resolves a round that independent per-message maxima cannot assign bijectively",
+ "limitations": list(evaluate(joint).limitations),
+ }
+
+
+def load_artifact(path):
+ try:
+ with Path(path).open(encoding="utf-8") as source:
+ value = json.load(source)
+ except (OSError, json.JSONDecodeError) as exc:
+ raise VerificationError(f"cannot read artifact {path}: {exc}") from exc
+ if not isinstance(value, dict):
+ raise VerificationError("artifact root must be an object")
+ return value
+
+
+def verify_artifact(artifact):
+ if artifact.get("schema_version") != 1 or artifact.get("experiment") != EXPERIMENT_ID:
+ raise VerificationError("unsupported perfect-matching artifact identity")
+ measured = build_artifact()
+ if canonical_json_bytes(artifact) != canonical_json_bytes(measured):
+ raise VerificationError("artifact differs from a fresh failure-mode execution")
+ return measured
+
+
+def render_markdown(artifact):
+ joint = artifact["joint_assignment"]
+ control = artifact["uniform_profile_control"]
+ return "\n".join([
+ "# Perfect-matching disclosure",
+ "",
+ "Generated from the canonical experiment artifact. Do not edit manually.",
+ "",
+ "| scenario | naive assignment | naive bijective | optimal matchings | unique optimum |",
+ "|---|---|---:|---:|---:|",
+ f"| joint assignment | {joint['naive_receiver_positions']} | "
+ f"{str(joint['naive_is_bijective']).lower()} | {joint['optimal_assignment_count']} | "
+ f"{str(joint['unique_assignment_recovered']).lower()} |",
+ f"| uniform-profile control | {control['naive_receiver_positions']} | "
+ f"{str(control['naive_is_bijective']).lower()} | {control['optimal_assignment_count']} | "
+ f"{str(control['unique_assignment_recovered']).lower()} |",
+ "",
+ "The joint fixture has the unique sender-to-receiver position assignment [0, 1, 2]. "
+ "Independent maxima reuse receiver 0 and therefore do not form a matching. The uniform "
+ "control retains all six perfect matchings.",
+ "",
+ "Profiles are supplied. This fixture tests the defining within-round PMDA objective, not "
+ "longitudinal profile estimation, Bitcoin value flow, or ownership attribution.",
+ "",
+ ])
+
+
+def _parser():
+ parser = argparse.ArgumentParser(description=__doc__)
+ commands = parser.add_subparsers(dest="command", required=True)
+ reproduce = commands.add_parser("reproduce")
+ reproduce.add_argument("--artifact", required=True)
+ reproduce.add_argument("--markdown", required=True)
+ verify = commands.add_parser("verify")
+ verify.add_argument("--artifact", required=True)
+ verify.add_argument("--markdown")
+ return parser
+
+
+def main(argv=None):
+ args = _parser().parse_args(argv)
+ if args.command == "reproduce":
+ artifact = build_artifact()
+ write_canonical_json(args.artifact, artifact)
+ destination = Path(args.markdown)
+ destination.parent.mkdir(parents=True, exist_ok=True)
+ destination.write_text(render_markdown(artifact), encoding="utf-8")
+ else:
+ artifact = verify_artifact(load_artifact(args.artifact))
+ if args.markdown is not None:
+ actual = Path(args.markdown).read_text(encoding="utf-8")
+ if actual != render_markdown(artifact):
+ raise VerificationError("Markdown differs from canonical rendering")
+ return 0
+
+
+if __name__ == "__main__":
+ raise SystemExit(main())
diff --git a/decluster/experiments/provenance_decay.py b/decluster/experiments/provenance_decay.py
new file mode 100644
index 00000000..ac861e78
--- /dev/null
+++ b/decluster/experiments/provenance_decay.py
@@ -0,0 +1,139 @@
+"""Reproduce additive decay and provenance graph fracture separately."""
+
+from __future__ import annotations
+
+import argparse
+import json
+from pathlib import Path
+
+from ..domain import GraphFractureMeasured, Inconclusive
+from ..failure_modes import provenance_decay as failure_mode
+from ..result_artifacts import canonical_json_bytes, write_canonical_json
+
+EXPERIMENT_ID = "provenance-decay-v1"
+
+
+class VerificationError(ValueError):
+ """A stored result differs from a fresh execution."""
+
+
+def _measurement(report):
+ elimination = report.channels[0].evidence[0]
+ structural = report.outcomes[1]
+ result = {
+ "candidates_before": len(elimination.candidates_before),
+ "candidates_after": len(elimination.candidates_after),
+ "structural_outcome": (
+ "graph_fracture"
+ if isinstance(structural, GraphFractureMeasured)
+ else "inconclusive"
+ ),
+ }
+ if isinstance(structural, GraphFractureMeasured):
+ result["components_before"] = structural.evidence.components_before
+ result["components_after"] = structural.evidence.components_after
+ elif not isinstance(structural, Inconclusive):
+ raise TypeError(f"unsupported structural outcome: {type(structural).__name__}")
+ return result
+
+
+def build_artifact():
+ bridge = failure_mode.ctp_fracture_example()
+ peripheral = next(
+ node for node in bridge.graph_nodes if node.identifier == "left-origin"
+ )
+ control = failure_mode.ProvenanceDecayScenario(
+ bridge.target,
+ bridge.candidates,
+ frozenset({peripheral}),
+ bridge.graph_nodes,
+ bridge.graph_edges,
+ )
+ bridge_report = failure_mode.evaluate(bridge)
+ control_report = failure_mode.evaluate(control)
+ return {
+ "schema_version": 1,
+ "experiment": EXPERIMENT_ID,
+ "bridge_removal": _measurement(bridge_report),
+ "peripheral_removal": _measurement(control_report),
+ "composition": None,
+ "limitations": list(bridge_report.limitations),
+ }
+
+
+def load_artifact(path):
+ try:
+ with Path(path).open(encoding="utf-8") as source:
+ value = json.load(source)
+ except (OSError, json.JSONDecodeError) as exc:
+ raise VerificationError(f"cannot read artifact {path}: {exc}") from exc
+ if not isinstance(value, dict):
+ raise VerificationError("artifact root must be an object")
+ return value
+
+
+def verify_artifact(artifact):
+ if (
+ artifact.get("schema_version") != 1
+ or artifact.get("experiment") != EXPERIMENT_ID
+ ):
+ raise VerificationError("unsupported provenance-decay artifact identity")
+ measured = build_artifact()
+ if canonical_json_bytes(artifact) != canonical_json_bytes(measured):
+ raise VerificationError("artifact differs from a fresh failure-mode execution")
+ return measured
+
+
+def render_markdown(artifact):
+ bridge = artifact["bridge_removal"]
+ control = artifact["peripheral_removal"]
+ return "\n".join([
+ "# Provenance decay failure modes",
+ "",
+ "Generated from the canonical experiment artifact. Do not edit manually.",
+ "",
+ "| removal | candidates | structural outcome | components |",
+ "|---|---:|---|---:|",
+ f"| bridge | {bridge['candidates_before']} → {bridge['candidates_after']} | "
+ f"`{bridge['structural_outcome']}` | "
+ f"{bridge['components_before']} → {bridge['components_after']} |",
+ f"| peripheral | {control['candidates_before']} → "
+ f"{control['candidates_after']} | `{control['structural_outcome']}` | "
+ "unchanged |",
+ "",
+ "Equal additive elimination does not imply equal structural decay.",
+ "",
+ ])
+
+
+def _parser():
+ parser = argparse.ArgumentParser(description=__doc__)
+ commands = parser.add_subparsers(dest="command", required=True)
+ reproduce = commands.add_parser("reproduce")
+ reproduce.add_argument("--artifact", required=True)
+ reproduce.add_argument("--markdown", required=True)
+ verify = commands.add_parser("verify")
+ verify.add_argument("--artifact", required=True)
+ verify.add_argument("--markdown")
+ return parser
+
+
+def main(argv=None):
+ args = _parser().parse_args(argv)
+ if args.command == "reproduce":
+ artifact = build_artifact()
+ write_canonical_json(args.artifact, artifact)
+ destination = Path(args.markdown)
+ destination.parent.mkdir(parents=True, exist_ok=True)
+ destination.write_text(render_markdown(artifact), encoding="utf-8")
+ else:
+ artifact = verify_artifact(load_artifact(args.artifact))
+ if args.markdown is not None:
+ actual = Path(args.markdown).read_text(encoding="utf-8")
+ if actual != render_markdown(artifact):
+ raise VerificationError("Markdown differs from canonical rendering")
+ return 0
+
+
+if __name__ == "__main__":
+ raise SystemExit(main())
diff --git a/decluster/experiments/pseudonym_graph_contract.py b/decluster/experiments/pseudonym_graph_contract.py
new file mode 100644
index 00000000..5da1fb2f
--- /dev/null
+++ b/decluster/experiments/pseudonym_graph_contract.py
@@ -0,0 +1,169 @@
+"""Reproduce the attributed pseudonym-graph contraction contract."""
+
+from __future__ import annotations
+
+import argparse
+import json
+from pathlib import Path
+
+from ..adaptations.pseudonym_graph import contract_report
+from ..result_artifacts import canonical_json_bytes, write_canonical_json
+
+EXPERIMENT_ID = "pseudonym-graph-contract-v1"
+
+
+class VerificationError(ValueError):
+ """A stored result differs from a fresh execution."""
+
+
+def _transaction(identifier, source, outputs, height):
+ return {
+ "txid": identifier,
+ "height": height,
+ "version": 2,
+ "locktime": 0,
+ "fee": 100,
+ "weight": 400,
+ "vin": [{
+ "txid": f"previous-{source}",
+ "vout": 0,
+ "sequence": 0xFFFFFFFF,
+ "prevout": {
+ "value": 10_000,
+ "scriptpubkey_type": "v0_p2wpkh",
+ "scriptpubkey_address": source,
+ },
+ }],
+ "vout": [
+ {
+ "value": value,
+ "scriptpubkey_type": "v0_p2wpkh",
+ "scriptpubkey_address": destination,
+ }
+ for destination, value in outputs
+ ],
+ }
+
+
+def fixture():
+ transactions = (
+ _transaction("forward-1", "a", (("x", 100), ("y", 200)), 100),
+ _transaction("forward-2", "b", (("x", 300),), 140),
+ _transaction("reverse", "x", (("a", 50),), 120),
+ _transaction("self", "a", (("b", 25),), 130),
+ _transaction("singleton", "z", (("a", 10),), 150),
+ )
+ return tuple((transaction, None) for transaction in transactions), {
+ "a": "cluster-a",
+ "b": "cluster-a",
+ "x": "cluster-x",
+ "y": "cluster-x",
+ }
+
+
+def build_artifact():
+ sample, lookup = fixture()
+ report = contract_report(sample, lookup)
+ evidence = report.channels[0].evidence[0]
+ return {
+ "schema_version": 1,
+ "experiment": EXPERIMENT_ID,
+ "vertices": [vertex.identifier for vertex in evidence.vertices],
+ "edges": [
+ {
+ "source": edge.source.identifier,
+ "target": edge.target.identifier,
+ "transfers": edge.transfers,
+ "value": edge.value,
+ "first_height": edge.first_height,
+ "last_height": edge.last_height,
+ }
+ for edge in evidence.edges
+ ],
+ "self_transfers": {
+ vertex.identifier: count for vertex, count in evidence.self_transfers
+ },
+ "composition": None,
+ "conclusion": "partial contraction preserves singleton pseudonyms, direction and folded transfer attributes",
+ "limitations": list(report.limitations),
+ }
+
+
+def load_artifact(path):
+ try:
+ with Path(path).open(encoding="utf-8") as source:
+ value = json.load(source)
+ except (OSError, json.JSONDecodeError) as exc:
+ raise VerificationError(f"cannot read artifact {path}: {exc}") from exc
+ if not isinstance(value, dict):
+ raise VerificationError("artifact root must be an object")
+ return value
+
+
+def verify_artifact(artifact):
+ if artifact.get("schema_version") != 1 or artifact.get("experiment") != EXPERIMENT_ID:
+ raise VerificationError("unsupported pseudonym-graph artifact identity")
+ measured = build_artifact()
+ if canonical_json_bytes(artifact) != canonical_json_bytes(measured):
+ raise VerificationError("artifact differs from a fresh contraction execution")
+ return measured
+
+
+def render_markdown(artifact):
+ rows = [
+ f"| {edge['source']} | {edge['target']} | {edge['transfers']} | "
+ f"{edge['value']} | {edge['first_height']}–{edge['last_height']} |"
+ for edge in artifact["edges"]
+ ]
+ return "\n".join([
+ "# Partial-clustering pseudonym graph",
+ "",
+ "Generated from the canonical experiment artifact. Do not edit manually.",
+ "",
+ f"Vertices: {', '.join(artifact['vertices'])}.",
+ "",
+ "| source | target | transfers | value | height span |",
+ "|---|---|---:|---:|---|",
+ *rows,
+ "",
+ "The unknown address `z` remains a singleton pseudonym. Parallel transfers from "
+ "`cluster-a` to `cluster-x` are folded into one directed edge with count, total value "
+ "and height span. The reverse direction remains a separate edge; change within "
+ "`cluster-a` is recorded as a self-transfer rather than a relationship.",
+ "",
+ "The supplied clustering is partial. These vertices are pseudonyms, not verified users.",
+ "",
+ ])
+
+
+def _parser():
+ parser = argparse.ArgumentParser(description=__doc__)
+ commands = parser.add_subparsers(dest="command", required=True)
+ reproduce = commands.add_parser("reproduce")
+ reproduce.add_argument("--artifact", required=True)
+ reproduce.add_argument("--markdown", required=True)
+ verify = commands.add_parser("verify")
+ verify.add_argument("--artifact", required=True)
+ verify.add_argument("--markdown")
+ return parser
+
+
+def main(argv=None):
+ args = _parser().parse_args(argv)
+ if args.command == "reproduce":
+ artifact = build_artifact()
+ write_canonical_json(args.artifact, artifact)
+ destination = Path(args.markdown)
+ destination.parent.mkdir(parents=True, exist_ok=True)
+ destination.write_text(render_markdown(artifact), encoding="utf-8")
+ else:
+ artifact = verify_artifact(load_artifact(args.artifact))
+ if args.markdown is not None:
+ actual = Path(args.markdown).read_text(encoding="utf-8")
+ if actual != render_markdown(artifact):
+ raise VerificationError("Markdown differs from canonical rendering")
+ return 0
+
+
+if __name__ == "__main__":
+ raise SystemExit(main())
diff --git a/decluster/experiments/reid.py b/decluster/experiments/reid.py
new file mode 100644
index 00000000..a90c015a
--- /dev/null
+++ b/decluster/experiments/reid.py
@@ -0,0 +1,152 @@
+"""Reproduce ancestry-signature record linkage on a frozen Bitcoin-derived snapshot."""
+
+from __future__ import annotations
+
+import argparse
+import gzip
+import json
+from pathlib import Path
+
+from ..reid import stratified_reid
+from ..result_artifacts import canonical_json_bytes, write_canonical_json
+
+EXPERIMENT_ID = "reid-v1"
+DEFAULT_DATASET = "tests/fixtures/reid_sigs.json.gz"
+
+
+class VerificationError(ValueError):
+ """The dataset or stored result violates this experiment's contract."""
+
+
+def _load(path):
+ try:
+ with gzip.open(path, "rt", encoding="utf-8") as source:
+ value = json.load(source)
+ except (OSError, json.JSONDecodeError) as exc:
+ raise VerificationError(f"cannot read signature snapshot {path}: {exc}") from exc
+ if not isinstance(value, dict) or len(value) < 2:
+ raise VerificationError("signature snapshot must contain at least two records")
+ signatures = {}
+ for coin, record in value.items():
+ if not isinstance(coin, str) or not isinstance(record, dict):
+ raise VerificationError("each record must have a string identifier and object value")
+ signature = record.get("sig")
+ if not isinstance(signature, dict) or not signature:
+ raise VerificationError(f"record {coin!r} has no non-empty signature")
+ parsed = {}
+ for origin, weight in signature.items():
+ if not isinstance(origin, str) or not isinstance(weight, (int, float)) or weight < 0:
+ raise VerificationError(f"record {coin!r} has an invalid signature entry")
+ parsed[origin] = float(weight)
+ signatures[coin] = parsed
+ return sorted(signatures), signatures
+
+
+def build_artifact(dataset=DEFAULT_DATASET):
+ coins, signatures = _load(dataset)
+ measured = stratified_reid(coins, signatures, ms=(4, 8), phi=1.5, seed=0)
+ return {
+ "schema_version": 1,
+ "experiment": EXPERIMENT_ID,
+ "method": {
+ "description": "ancestry-signature record-linkage adaptation",
+ "auxiliary_origins": [4, 8],
+ "eccentricity_threshold": 1.5,
+ "seed": 0,
+ "sparse_below_cosine": 0.5,
+ "dense_at_least_cosine": 0.9,
+ },
+ "measurement": measured,
+ "limitations": [
+ "the 200-record snapshot is selected and is not a uniform chain sample",
+ "the signature-generation recipe and original comparison population are unavailable",
+ "the target identifier is a record identity, not wallet ownership",
+ "the auxiliary origins are sampled from the target signature rather than observed independently",
+ "this is an adaptation, not a reproduction of the published Netflix experiment",
+ "the result does not establish a chain-wide deanonymization rate or privacy score",
+ "upstream data licensing is undetermined; the dataset publisher authorized redistribution on 2026-09-04",
+ ],
+ }
+
+
+def load_artifact(path):
+ try:
+ with Path(path).open(encoding="utf-8") as source:
+ value = json.load(source)
+ except (OSError, json.JSONDecodeError) as exc:
+ raise VerificationError(f"cannot read artifact {path}: {exc}") from exc
+ if not isinstance(value, dict):
+ raise VerificationError("artifact root must be an object")
+ return value
+
+
+def verify_artifact(artifact, dataset=DEFAULT_DATASET):
+ if artifact.get("schema_version") != 1 or artifact.get("experiment") != EXPERIMENT_ID:
+ raise VerificationError("unsupported reidentification artifact identity")
+ measured = build_artifact(dataset)
+ if canonical_json_bytes(artifact) != canonical_json_bytes(measured):
+ raise VerificationError("artifact differs from a fresh experiment execution")
+ return measured
+
+
+def render_markdown(artifact):
+ measurement = artifact["measurement"]
+ lines = [
+ "# Ancestry-signature record linkage",
+ "",
+ "Generated from the canonical experiment artifact. Do not edit manually.",
+ "",
+ f"Snapshot records: {measurement['n']} ({measurement['n_sparse']} sparse, "
+ f"{measurement['n_dense']} dense).",
+ "",
+ "| stratum | auxiliary origins | attackable | declared | exact | precision | exact rate |",
+ "|---|---:|---:|---:|---:|---:|---:|",
+ ]
+ for row in measurement["rows"]:
+ lines.append(
+ f"| {row['stratum']} | {row['m']} | {row['attackable']} | {row['declared']} | "
+ f"{row['exact']} | {row['precision']:.6f} | {row['exact_rate']:.6f} |"
+ )
+ lines.extend([
+ "",
+ "The selected snapshot exhibits a large sparse-versus-dense difference under this "
+ "adapted attack. The auxiliary origins come from each target's own stored signature, "
+ "so this demonstrates mechanism behavior under supplied auxiliary information, not "
+ "independent real-world attribution or a chain-wide rate.",
+ "",
+ ])
+ return "\n".join(lines)
+
+
+def _parser():
+ parser = argparse.ArgumentParser(description=__doc__)
+ parser.add_argument("--dataset", default=DEFAULT_DATASET)
+ commands = parser.add_subparsers(dest="command", required=True)
+ reproduce = commands.add_parser("reproduce")
+ reproduce.add_argument("--artifact", required=True)
+ reproduce.add_argument("--markdown", required=True)
+ verify = commands.add_parser("verify")
+ verify.add_argument("--artifact", required=True)
+ verify.add_argument("--markdown")
+ return parser
+
+
+def main(argv=None):
+ args = _parser().parse_args(argv)
+ if args.command == "reproduce":
+ artifact = build_artifact(args.dataset)
+ write_canonical_json(args.artifact, artifact)
+ destination = Path(args.markdown)
+ destination.parent.mkdir(parents=True, exist_ok=True)
+ destination.write_text(render_markdown(artifact), encoding="utf-8")
+ else:
+ artifact = verify_artifact(load_artifact(args.artifact), args.dataset)
+ if args.markdown is not None:
+ actual = Path(args.markdown).read_text(encoding="utf-8")
+ if actual != render_markdown(artifact):
+ raise VerificationError("Markdown differs from canonical rendering")
+ return 0
+
+
+if __name__ == "__main__":
+ raise SystemExit(main())
diff --git a/decluster/experiments/route_capacity.py b/decluster/experiments/route_capacity.py
new file mode 100644
index 00000000..8a238d4f
--- /dev/null
+++ b/decluster/experiments/route_capacity.py
@@ -0,0 +1,221 @@
+"""How small a cut separates a coin from its origins, on committed transactions.
+
+The framework states robust connectivity as a property of a cut: no small cut should separate an
+output from the mass of its candidate origin coins. This experiment measures three bounded readings
+of that object on the committed slice: the coin-disjoint route cut, the cut after pruning routes that
+cannot carry the target coin, and the maximum value the remaining network can deliver. `path_count`
+accumulates route mass, and its own docstring says why that is a different number — many routes may
+run through one coin, so a large count is consistent with a cut of one.
+
+The reading is k-bounded, following the literature on all-pairs bounded min-cuts: what matters is
+which coins have a *small* cut, not the exact value where it is large. A coin with two hundred
+disjoint routes and a coin with fifty are both out of reach; a coin with one is not.
+
+Two bounds the numbers carry. The walk stops at a fixed depth and at coins the slice does not hold,
+so every cut here is a lower bound — a deeper walk can only find more routes, never fewer. The
+value-capacitated reading measures simultaneous deliverable value on the graph the oracle admits; it
+does not identify the real path of particular satoshis, establish own-origin robustness without a
+user's antecedent set, or turn this bounded sample into a general robust-connectivity result.
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+from collections import Counter
+from pathlib import Path
+from tempfile import TemporaryDirectory
+
+from .. import ancestry
+from ..archive_snapshot import extract_tar_gz
+from ..disjoint_routes import TARGET_VALUE, flow_capacity, route_capacity
+from ..fingerprint_validate import load_blkcache
+from ..reproducibility import fingerprint_source
+from ..result_artifacts import canonical_json_bytes, write_canonical_json
+
+EXPERIMENT_ID = "route-capacity-v1"
+DEPTH = 5
+SMALL = 3 # the k-bounded frontier: at or below this, a cut is cheap
+
+
+class VerificationError(ValueError):
+ """The stored result differs from a fresh execution."""
+
+
+def build_artifact(snapshot="data/fs-blkcache-2026-09-04.tar.gz"):
+ with TemporaryDirectory(prefix="decluster-routes-") as temporary:
+ root = extract_tar_gz(snapshot, temporary)
+ cache = root / ".blkcache"
+ transactions = load_blkcache(str(cache))
+ source = fingerprint_source(str(cache / "*.json"))
+ source.update(pattern=".blkcache/*.json", transactions=len(transactions))
+
+ index = {tx["txid"]: tx for tx in transactions}
+ targets = sorted({(tx["txid"], 0) for tx in transactions
+ if sum(1 for vin in tx["vin"] if vin.get("txid") in index) >= 2})
+
+ capacity, plausible, excess, boundary = Counter(), Counter(), Counter(), Counter()
+ measured = short_of_value = 0
+ for target in targets:
+ graph = ancestry.build_extended_graph(
+ target, depth=DEPTH, fetch=index.get,
+ link_oracle=ancestry.value_flow_link_oracle)
+ origins = len(graph.absorbers)
+ if not origins:
+ continue
+ k, _routes = route_capacity(graph, target)
+ payable, _ = route_capacity(graph, target, carries=TARGET_VALUE)
+ deliverable, _ = flow_capacity(graph, target)
+ worth = graph.values.get(target)
+ if worth is not None and deliverable < worth:
+ short_of_value += 1
+ measured += 1
+ capacity[k] += 1
+ plausible[payable] += 1
+ excess[origins - k] += 1
+ boundary["fetch_missing"] += graph.fetch_missing
+ boundary["depth_capped"] += len(graph.depth_capped)
+ boundary["coinbase"] += len(graph.source_absorbers)
+
+ small = sum(count for k, count in capacity.items() if k <= SMALL)
+ overstated = sum(count for gap, count in excess.items() if gap > 0)
+ return {
+ "schema_version": 1,
+ "experiment": EXPERIMENT_ID,
+ "dataset": "fs-blkcache-2026-09-04-v1",
+ "source": source,
+ "parameters": {"depth": DEPTH, "small_cut_at_most": SMALL,
+ "link_oracle": "value_flow_link_oracle"},
+ "population": {"targets": len(targets), "measured": measured},
+ "capacity": {str(k): capacity[k] for k in sorted(capacity)},
+ "capacity_carrying_the_coin": {str(k): plausible[k] for k in sorted(plausible)},
+ "origin_excess": {str(gap): excess[gap] for gap in sorted(excess)},
+ "readings": {
+ "cut_of_one": capacity[1],
+ "cut_of_one_carrying_the_coin": plausible[1],
+ "no_route_carries_the_coin": plausible[0],
+ "origins_cannot_deliver_the_value": short_of_value,
+ "cut_at_most_small": small,
+ "origin_set_overstates": overstated,
+ "largest_cut": max(capacity) if capacity else 0,
+ },
+ "boundary_causes": dict(sorted(boundary.items())),
+ }
+
+
+def load_artifact(path):
+ try:
+ with Path(path).open(encoding="utf-8") as source:
+ value = json.load(source)
+ except (OSError, json.JSONDecodeError) as exc:
+ raise VerificationError(f"cannot read artifact {path}: {exc}") from exc
+ if not isinstance(value, dict):
+ raise VerificationError("artifact root must be an object")
+ return value
+
+
+def verify_artifact(artifact, snapshot="data/fs-blkcache-2026-09-04.tar.gz"):
+ if artifact.get("schema_version") != 1 or artifact.get("experiment") != EXPERIMENT_ID:
+ raise VerificationError("unsupported route-capacity artifact identity")
+ measured = build_artifact(snapshot)
+ if canonical_json_bytes(artifact) != canonical_json_bytes(measured):
+ raise VerificationError("artifact differs from a fresh experiment execution")
+ return measured
+
+
+def render_markdown(artifact):
+ population, readings = artifact["population"], artifact["readings"]
+ share = lambda n: f"{n} ({n / population['measured']:.0%})"
+ lines = [
+ "# How small a cut separates a coin from its origins",
+ "",
+ "Generated from the canonical experiment artifact. Do not edit manually.",
+ "",
+ f"{population['measured']} coins in the committed cache have an ancestry the slice can walk. "
+ f"For each, the number of routes back to its candidate origins that share no coin — the "
+ f"size of the cut that would separate it.",
+ "",
+ "| routes | coins | coins, routes able to carry the coin |",
+ "|---:|---:|---:|",
+ ]
+ carrying = artifact["capacity_carrying_the_coin"]
+ for k in sorted({*artifact["capacity"], *carrying}, key=int):
+ lines.append(f"| {k} | {artifact['capacity'].get(k, 0)} | {carrying.get(k, 0)} |")
+ lines.extend([
+ "",
+ f"**{share(readings['cut_of_one'])} of these coins are separated from their entire ancestry "
+ f"by removing one coin**, and {share(readings['cut_at_most_small'])} by removing at most "
+ f"{artifact['parameters']['small_cut_at_most']}. The framework asks that no small cut do "
+ f"this. At the other end the distribution reaches {readings['largest_cut']} routes, so "
+ f"redundancy varies by orders of magnitude between coins in one slice.",
+ "",
+ f"That count treats every route alike, and the specification does not: a path worth less "
+ f"than the amount in question may have been removed before the question is asked. Pruning "
+ f"each coin's routes to those that could have carried its own value "
+ f"**takes the single-coin cut from {share(readings['cut_of_one'])} to "
+ f"{share(readings['cut_of_one_carrying_the_coin'])}**, and leaves "
+ f"{share(readings['no_route_carries_the_coin'])} with no route able to carry the coin at "
+ f"all. Counting routes without their value overstates redundancy, in the same direction and "
+ f"for the same reason as counting origins without their overlap.",
+ "",
+ f"**Counting origins overstates that redundancy for {share(readings['origin_set_overstates'])} "
+ f"of them.** A coin's origin set says how many places its value could have come from; the cut "
+ f"says how many of those a separation would have to defeat, and the two are not the same "
+ f"number because routes share coins. That difference is the reason this is measured rather "
+ f"than counted.",
+ "",
+ f"Both readings are lower bounds, and one boundary count says how loose. The walk stopped "
+ f"{artifact['boundary_causes']['fetch_missing']} times because the slice holds no record of "
+ f"a parent, against {artifact['boundary_causes']['depth_capped']} times at the depth limit: "
+ f"almost every origin here is the edge of the sample rather than a coinbase or a genuine "
+ f"end of provenance. A cut measured to that edge can only grow on a contiguous export, so "
+ f"these are the cuts the committed data can already show and not a ceiling on redundancy. "
+ f"Read the shape — that a tenth of coins are separated by one coin while others carry "
+ f"{artifact['readings']['largest_cut']} routes — rather than the level.",
+ "",
+ f"**The framework states its property over the mass of a coin's candidate origins, not only "
+ f"over how many routes reach it**, so the same network is solved a second time with each "
+ f"coin's own value as its capacity. The maximum flow is what the origin set could deliver "
+ f"if every route ran at once, and its minimum cut is the value a separation would have to "
+ f"remove. For {share(readings['origins_cannot_deliver_the_value'])} of these coins the "
+ f"origins cannot deliver the coin's own value at all — a shortfall a route count cannot "
+ f"see, because those routes exist and simply are not worth enough together.",
+ "",
+ "That is still one flow and still polynomial. Choosing which routes carry how much — the "
+ "k-splittable formulation — is strongly NP-hard, is a decision about routing rather than a "
+ "measurement of capacity, and is not this one.",
+ "",
+ "## Reproducibility / provenance",
+ "",
+ "State 1 in `results/REPRODUCIBILITY.md`: band-pinned on committed data — the artifact is "
+ "recomputed from `data/fs-blkcache-2026-09-04.tar.gz` and compared byte for byte.",
+ ])
+ return "\n".join(lines) + "\n"
+
+
+def _parser():
+ parser = argparse.ArgumentParser(description=__doc__.splitlines()[0])
+ parser.add_argument("command", choices=("reproduce", "verify"))
+ parser.add_argument("--snapshot", default="data/fs-blkcache-2026-09-04.tar.gz")
+ parser.add_argument("--artifact", required=True)
+ parser.add_argument("--markdown", required=True)
+ return parser
+
+
+def main(argv=None):
+ args = _parser().parse_args(argv)
+ if args.command == "reproduce":
+ artifact = build_artifact(args.snapshot)
+ write_canonical_json(args.artifact, artifact)
+ destination = Path(args.markdown)
+ destination.parent.mkdir(parents=True, exist_ok=True)
+ destination.write_text(render_markdown(artifact), encoding="utf-8")
+ else:
+ artifact = verify_artifact(load_artifact(args.artifact), args.snapshot)
+ if Path(args.markdown).read_text(encoding="utf-8") != render_markdown(artifact):
+ raise VerificationError("Markdown differs from canonical rendering")
+ return 0
+
+
+if __name__ == "__main__":
+ raise SystemExit(main())
diff --git a/decluster/experiments/same_block_linkage.py b/decluster/experiments/same_block_linkage.py
new file mode 100644
index 00000000..7b816afe
--- /dev/null
+++ b/decluster/experiments/same_block_linkage.py
@@ -0,0 +1,213 @@
+"""Measure the quantity common-input ownership asserts, inside the mapping family that admits it.
+
+CIOH says two inputs of one transaction belong to one owner. Maurer's `p_II` says something
+narrower and checkable: across every sub-transaction mapping the amounts admit, how often do two
+inputs land in the same block. The two are not the same claim — a block of a value-conserving
+mapping is an accounting unit, not a wallet, and a collaborative transaction can put two owners in
+one block — but `p_II` bounds what the amounts alone can settle, which is the part CIOH borrows.
+
+`p_IO` is what the link matrix usually reports and it does not cover this: it is the cross-side
+quantity, and the heuristic's assertion is same-side.
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+from collections import Counter
+from pathlib import Path
+
+from ..baselines import boltzmann
+from ..result_artifacts import canonical_json_bytes, write_canonical_json
+
+EXPERIMENT_ID = "same-block-linkage-v1"
+DEFAULT_DATASET = "data/boltzmann-fee-audit-v1.json"
+MAX_COINS = 12
+BALANCE_MODEL = "fee_tolerant"
+
+
+class VerificationError(ValueError):
+ """The dataset or stored result violates this experiment's contract."""
+
+
+def _load(path):
+ try:
+ transactions = json.loads(Path(path).read_text(encoding="utf-8"))
+ except (OSError, json.JSONDecodeError) as exc:
+ raise VerificationError(f"cannot read transaction snapshot {path}: {exc}") from exc
+ if not isinstance(transactions, list) or not transactions:
+ raise VerificationError(f"transaction snapshot must be a non-empty list: {path}")
+ return transactions
+
+
+def _amounts(transaction):
+ inputs = [(v.get("prevout") or {}).get("value") for v in transaction.get("vin", ())]
+ outputs = [o.get("value") for o in transaction.get("vout", ())]
+ if any(value is None for value in inputs + outputs):
+ return None
+ return inputs, outputs
+
+
+def _same_side(matrix, size):
+ return [matrix[i][j] for i in range(size) for j in range(i + 1, size)]
+
+
+def build_artifact(dataset=DEFAULT_DATASET):
+ transactions = _load(dataset)
+ buckets, shapes = Counter(), Counter()
+ input_pairs, forced_input_pairs = 0, 0
+ output_pairs, forced_output_pairs = 0, 0
+ in_scope = analysed = 0
+ for transaction in transactions:
+ amounts = _amounts(transaction)
+ if amounts is None:
+ continue
+ inputs, outputs = amounts
+ if len(inputs) < 2 or len(inputs) + len(outputs) > MAX_COINS:
+ continue
+ in_scope += 1
+ analysis = boltzmann.same_block_probabilities(
+ inputs, outputs, max_coins=MAX_COINS, balance_model=BALANCE_MODEL,
+ fee_tolerance=transaction.get("fee") or 0,
+ )
+ if not analysis.mapping_count:
+ continue
+ analysed += 1
+ same_input = _same_side(analysis.input_matrix, len(inputs))
+ input_pairs += len(same_input)
+ forced_input_pairs += sum(1 for p in same_input if p == 1.0)
+ for probability in same_input:
+ buckets[f"{round(probability, 1):.1f}"] += 1
+ shapes["every input pair forced" if all(p == 1.0 for p in same_input)
+ else "some input pair open"] += 1
+ same_output = _same_side(analysis.output_matrix, len(outputs))
+ output_pairs += len(same_output)
+ forced_output_pairs += sum(1 for p in same_output if p == 1.0)
+ return {
+ "schema_version": 1,
+ "experiment": EXPERIMENT_ID,
+ "dataset": "boltzmann-fee-audit-v1",
+ "measurement": {
+ "transactions": len(transactions),
+ "multi_input_in_scope": in_scope,
+ "with_a_feasible_mapping": analysed,
+ "input_pairs": input_pairs,
+ "input_pairs_forced_together": forced_input_pairs,
+ "input_pairs_forced_apart": sum(count for value, count in buckets.items()
+ if float(value) == 0.0),
+ "output_pairs": output_pairs,
+ "output_pairs_forced_together": forced_output_pairs,
+ "p_ii_distribution": dict(sorted(buckets.items())),
+ "transactions_by_shape": dict(sorted(shapes.items())),
+ },
+ "parameters": {
+ "max_coins": MAX_COINS,
+ "balance_model": BALANCE_MODEL,
+ "fee_tolerance": "the transaction's own observed fee",
+ },
+ "limitations": [
+ "a block of a value-conserving mapping is an accounting unit, not a wallet",
+ "a collaborative transaction can place two owners in one block, so a forced pair is not "
+ "an ownership finding",
+ "only transactions with at most max_coins coins are in scope, which excludes the wide "
+ "consolidations and coinjoins where the question is hardest",
+ "the snapshot is 300 selected transactions and is not a chain-wide sample",
+ "fee tolerance admits mappings exact conservation would refuse, which can only widen "
+ "the family and lower the forced share",
+ "the result is not a privacy score",
+ ],
+ }
+
+
+def load_artifact(path):
+ try:
+ with Path(path).open(encoding="utf-8") as source:
+ value = json.load(source)
+ except (OSError, json.JSONDecodeError) as exc:
+ raise VerificationError(f"cannot read artifact {path}: {exc}") from exc
+ if not isinstance(value, dict):
+ raise VerificationError("artifact root must be an object")
+ return value
+
+
+def verify_artifact(artifact, dataset=DEFAULT_DATASET):
+ if artifact.get("schema_version") != 1 or artifact.get("experiment") != EXPERIMENT_ID:
+ raise VerificationError("unsupported same-block-linkage artifact identity")
+ measured = build_artifact(dataset)
+ if canonical_json_bytes(artifact) != canonical_json_bytes(measured):
+ raise VerificationError("artifact differs from a fresh experiment execution")
+ return measured
+
+
+def render_markdown(artifact):
+ measured = artifact["measurement"]
+ share = 100.0 * measured["input_pairs_forced_together"] / measured["input_pairs"]
+ lines = [
+ "# What the amounts settle about co-spent inputs",
+ "",
+ "Generated from the canonical experiment artifact. Do not edit manually.",
+ "",
+ f"{measured['with_a_feasible_mapping']} of {measured['multi_input_in_scope']} multi-input "
+ f"transactions in scope admit a value-conserving mapping under the transaction's own fee. "
+ f"Across their {measured['input_pairs']} input pairs, Maurer's `p_II` says how often the "
+ "two coins share a block in every mapping the amounts admit.",
+ "",
+ "| p_II | input pairs |",
+ "|---:|---:|",
+ ]
+ for value, count in measured["p_ii_distribution"].items():
+ lines.append(f"| {value} | {count} |")
+ lines += [
+ "",
+ f"**{measured['input_pairs_forced_together']} of {measured['input_pairs']} pairs "
+ f"({share:.1f}%) are forced together** — no admissible mapping separates them — and "
+ f"{measured['input_pairs_forced_apart']} are forced apart. The amounts corroborate the "
+ "co-spend far more often than they contest it here, and on this snapshot they never "
+ "contest it outright.",
+ "",
+ f"On the output side, {measured['output_pairs_forced_together']} of "
+ f"{measured['output_pairs']} pairs are forced together.",
+ "",
+ "**This is not an ownership result, and the gap is the point.** A block of a "
+ "value-conserving mapping is an accounting unit; a collaborative transaction can put two "
+ "owners in one block and satisfy every constraint measured here. What the number bounds is "
+ "what the amounts alone can settle — the part common-input ownership borrows without "
+ "checking.",
+ "",
+ f"Scope is capped at {artifact['parameters']['max_coins']} coins, which excludes the wide "
+ "consolidations and coinjoins where the question is hardest, and the fee-tolerant model "
+ "admits mappings exact conservation would refuse — which can only widen the family and "
+ "lower the forced share. The snapshot is 300 selected transactions and is not chain-wide.",
+ "",
+ ]
+ return "\n".join(lines)
+
+
+def _parser():
+ parser = argparse.ArgumentParser(description=__doc__)
+ parser.add_argument("--dataset", default=DEFAULT_DATASET)
+ commands = parser.add_subparsers(dest="command", required=True)
+ for name in ("reproduce", "verify"):
+ command = commands.add_parser(name)
+ command.add_argument("--artifact", required=True)
+ command.add_argument("--markdown", required=True)
+ return parser
+
+
+def main(argv=None):
+ args = _parser().parse_args(argv)
+ if args.command == "reproduce":
+ artifact = build_artifact(args.dataset)
+ write_canonical_json(args.artifact, artifact)
+ destination = Path(args.markdown)
+ destination.parent.mkdir(parents=True, exist_ok=True)
+ destination.write_text(render_markdown(artifact), encoding="utf-8")
+ else:
+ artifact = verify_artifact(load_artifact(args.artifact), args.dataset)
+ if Path(args.markdown).read_text(encoding="utf-8") != render_markdown(artifact):
+ raise VerificationError("Markdown differs from canonical rendering")
+ return 0
+
+
+if __name__ == "__main__":
+ raise SystemExit(main())
diff --git a/decluster/experiments/scorer_families.py b/decluster/experiments/scorer_families.py
new file mode 100644
index 00000000..2338d4ac
--- /dev/null
+++ b/decluster/experiments/scorer_families.py
@@ -0,0 +1,159 @@
+"""Compare the catalogued, construction-only and decorrelated scorers on one population.
+
+`PAPER.md` reports three rows of AUC and evidence magnitude for the same fingerprint kernel under
+three axis sets, and none of them had a run behind it. The numbers were real — this reproduces them
+— but a number without a chain is a number a reader has to take on trust.
+
+Only the axis set moves: same transactions, same pair sample, same seed, same consistency. That is
+what makes the magnitudes comparable across rows, and it is why the drop from the catalogued family
+reads as redundancy and label leakage rather than as a different measurement.
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+from pathlib import Path
+from tempfile import TemporaryDirectory
+
+from ..archive_snapshot import extract_tar_gz
+from ..fingerprint_validate import axis_families, load_blkcache
+from ..reproducibility import fingerprint_source
+from ..result_artifacts import canonical_json_bytes, write_canonical_json
+
+EXPERIMENT_ID = "scorer-families-v1"
+CONSISTENCY = 0.95
+PAIR_CAP = 4000
+SEED = 0
+
+
+class VerificationError(ValueError):
+ """The stored result differs from a fresh execution."""
+
+
+def build_artifact(snapshot):
+ with TemporaryDirectory(prefix="decluster-families-") as temporary:
+ root = extract_tar_gz(snapshot, temporary)
+ cache = root / ".blkcache"
+ transactions = load_blkcache(str(cache))
+ rows = axis_families(transactions, consistency=CONSISTENCY, cap=PAIR_CAP, seed=SEED)
+ source = fingerprint_source(str(cache / "*.json"))
+ source.update(pattern=".blkcache/*.json", transactions=len(transactions))
+ by_family = {row["family"]: row for row in rows}
+ catalogued, decorrelated = by_family["catalogued"], by_family["decorrelated"]
+ return {
+ "schema_version": 1,
+ "experiment": EXPERIMENT_ID,
+ "dataset": "fs-blkcache-2026-09-04-v1",
+ "source": source,
+ "measurement": {
+ "consistency": CONSISTENCY,
+ "pair_cap_per_class": PAIR_CAP,
+ "seed": SEED,
+ "rows": rows,
+ "magnitude_fall_from_catalogued_to_decorrelated":
+ catalogued["pos_mean"] - decorrelated["pos_mean"],
+ "auc_rises_as_axes_are_dropped": decorrelated["auc"] > catalogued["auc"],
+ },
+ "limitations": [
+ "address reuse is a weak and partly feature-dependent ownership proxy",
+ "the axis clusters and the address-determined set are measured on this same cache",
+ "one selected snapshot is reused for all three families and is not chain-wide",
+ "a higher AUC on fewer axes does not establish that the dropped axes carry no signal",
+ "evidence magnitudes are rarity bits under a fixed consistency, not calibrated likelihoods",
+ "this is attacker-side analysis, not a privacy score",
+ ],
+ }
+
+
+def load_artifact(path):
+ try:
+ with Path(path).open(encoding="utf-8") as source:
+ value = json.load(source)
+ except (OSError, json.JSONDecodeError) as exc:
+ raise VerificationError(f"cannot read artifact {path}: {exc}") from exc
+ if not isinstance(value, dict):
+ raise VerificationError("artifact root must be an object")
+ return value
+
+
+def verify_artifact(artifact, snapshot):
+ if artifact.get("schema_version") != 1 or artifact.get("experiment") != EXPERIMENT_ID:
+ raise VerificationError("unsupported scorer-family artifact identity")
+ measured = build_artifact(snapshot)
+ if canonical_json_bytes(artifact) != canonical_json_bytes(measured):
+ raise VerificationError("artifact differs from a fresh experiment execution")
+ return measured
+
+
+def render_markdown(artifact):
+ measured = artifact["measurement"]
+ rows = {row["family"]: row for row in measured["rows"]}
+ lines = [
+ "# The same kernel under three axis sets",
+ "",
+ "Generated from the canonical experiment artifact. Do not edit manually.",
+ "",
+ f"One population of {artifact['source']['transactions']} transactions, one pair sample "
+ f"({measured['pair_cap_per_class']} per class, seed {measured['seed']}), one consistency "
+ f"({measured['consistency']}). Only the axis set changes.",
+ "",
+ "| family | axes | AUC | positive mean bits | negative mean bits |",
+ "|---|---:|---:|---:|---:|",
+ ]
+ for name in ("catalogued", "construction_only", "decorrelated"):
+ row = rows[name]
+ lines.append(
+ f"| {name.replace('_', ' ')} | {row['axes']} | {row['auc']:.4f} | "
+ f"{row['pos_mean']:+.2f} | {row['neg_mean']:+.2f} |"
+ )
+ fall = measured["magnitude_fall_from_catalogued_to_decorrelated"]
+ lines += [
+ "",
+ f"Dropping the redundant axes costs **{fall:.2f} bits** of positive evidence and "
+ f"{'raises' if measured['auc_rises_as_axes_are_dropped'] else 'lowers'} the AUC "
+ f"({rows['catalogued']['auc']:.4f} to {rows['decorrelated']['auc']:.4f}). The wide model "
+ "scores several correlated axes as if they were separate facts; the additive rarity kernel "
+ "has no conditional-independence correction, so the magnitude it reports is inflated while "
+ "its ranking is not.",
+ "",
+ "The construction-only family drops the axes the shared input address fixes outright, which "
+ "is the part of the score that is the same-owner label restating itself rather than "
+ "construction style.",
+ "",
+ "The axis clusters and the address-determined set were measured on this same cache, so the "
+ "families are not an out-of-sample test. A higher AUC on fewer axes does not show the "
+ "dropped axes carry no signal, and none of these numbers is a privacy score.",
+ "",
+ ]
+ return "\n".join(lines)
+
+
+def _parser():
+ parser = argparse.ArgumentParser(description=__doc__)
+ parser.add_argument("--snapshot", required=True)
+ commands = parser.add_subparsers(dest="command", required=True)
+ for name in ("reproduce", "verify"):
+ command = commands.add_parser(name)
+ command.add_argument("--artifact", required=True)
+ command.add_argument("--markdown", required=True)
+ return parser
+
+
+def main(argv=None):
+ args = _parser().parse_args(argv)
+ if args.command == "reproduce":
+ artifact = build_artifact(args.snapshot)
+ write_canonical_json(args.artifact, artifact)
+ destination = Path(args.markdown)
+ destination.parent.mkdir(parents=True, exist_ok=True)
+ destination.write_text(render_markdown(artifact), encoding="utf-8")
+ else:
+ artifact = verify_artifact(load_artifact(args.artifact), args.snapshot)
+ if Path(args.markdown).read_text(encoding="utf-8") != render_markdown(artifact):
+ raise VerificationError("Markdown differs from canonical rendering")
+ return 0
+
+
+if __name__ == "__main__":
+ raise SystemExit(main())
diff --git a/decluster/experiments/slice_channels.py b/decluster/experiments/slice_channels.py
new file mode 100644
index 00000000..53441286
--- /dev/null
+++ b/decluster/experiments/slice_channels.py
@@ -0,0 +1,187 @@
+"""Reproduce stable attribute-density and graph-shape observations on a frozen slice.
+
+The snapshot is an address-only export: it carries no version, locktime, sequence or fee, so all
+four contraction axes abstain on every transaction and the vertex feature vectors are structural
+alone. The run reports the per-axis skip counts rather than leaving that to be inferred, because an
+earlier revision read the missing fields as values — every vertex then agreed on `version` and
+`locktime` for free, and the similarity those constants added is what the density figures carried.
+"""
+
+from __future__ import annotations
+
+import argparse
+import gzip
+import json
+from pathlib import Path
+
+from .. import contraction, def1_sparsity, graph_shape, views
+from ..result_artifacts import canonical_json_bytes, write_canonical_json
+
+EXPERIMENT_ID = "slice-channels-v1"
+DEFAULT_DATASET = "tests/fixtures/slice_a_channels_2016.ndjson.gz"
+
+
+class VerificationError(ValueError):
+ """The dataset or stored result violates this experiment's contract."""
+
+
+def _load(path):
+ sample = []
+ try:
+ with gzip.open(path, "rt", encoding="utf-8") as source:
+ for line_number, line in enumerate(source, start=1):
+ if not line.strip():
+ continue
+ transaction = json.loads(line)
+ if not isinstance(transaction, dict) or not transaction.get("txid"):
+ raise VerificationError(
+ f"{path}:{line_number}: transaction must be an object with a txid"
+ )
+ sample.append((transaction, 0))
+ except (OSError, json.JSONDecodeError) as exc:
+ raise VerificationError(f"cannot read slice snapshot {path}: {exc}") from exc
+ if not sample:
+ raise VerificationError(f"slice snapshot is empty: {path}")
+ return sample
+
+
+def _density(graph, min_degree, epsilons):
+ tops = def1_sparsity.nearest_similarities(
+ graph,
+ query_n=1200,
+ background_n=12000,
+ min_degree=min_degree,
+ seed=0,
+ )
+ return {
+ "eligible_sample": len(tops),
+ "survival": {str(epsilon): value for epsilon, value in
+ def1_sparsity.survival(tops, epsilons=epsilons).items()},
+ }
+
+
+def build_artifact(dataset=DEFAULT_DATASET):
+ sample = _load(dataset)
+ lookup = views.cluster_addresses(sample, refuse=True)
+ graph = contraction.contract(sample, lookup=lookup, axes=True)
+ shape = graph_shape.summary(graph)
+ return {
+ "schema_version": 1,
+ "experiment": EXPERIMENT_ID,
+ "measurement": {
+ "transactions": len(sample),
+ "clustered_addresses": len(lookup),
+ "clusters": len(set(lookup.values())),
+ "axis_values_recorded": {axis: sum(counts.values())
+ for axis, counts in sorted(graph.base_rates.items())},
+ "axis_transactions_skipped": {axis: graph.skipped.get(axis, 0)
+ for axis in sorted(graph.base_rates)},
+ "density_min_degree_2": _density(graph, 2, (0.5, 0.9, 0.99)),
+ "density_min_degree_20": _density(graph, 20, (0.9,)),
+ "graph_shape": shape,
+ },
+ "parameters": {
+ "refuse_guard": True,
+ "query_records": 1200,
+ "background_records": 12000,
+ "seed": 0,
+ "similarity": "cosine over normalized feature vectors",
+ },
+ "limitations": [
+ "the snapshot carries none of the four axes, so the similarity measured here is structural",
+ "the snapshot contains six 2016 blocks and is not a chain-wide sample",
+ "the run does not reproduce historical measurements on larger unpreserved slices",
+ "dense attributes do not prove resistance to other linkage channels",
+ "negative assortativity does not by itself determine graph-matching performance",
+ "the snapshot recipe is unavailable; the dataset publisher authorized redistribution on 2026-09-04",
+ "the result is not a privacy score or CoinScore",
+ ],
+ }
+
+
+def load_artifact(path):
+ try:
+ with Path(path).open(encoding="utf-8") as source:
+ value = json.load(source)
+ except (OSError, json.JSONDecodeError) as exc:
+ raise VerificationError(f"cannot read artifact {path}: {exc}") from exc
+ if not isinstance(value, dict):
+ raise VerificationError("artifact root must be an object")
+ return value
+
+
+def verify_artifact(artifact, dataset=DEFAULT_DATASET):
+ if artifact.get("schema_version") != 1 or artifact.get("experiment") != EXPERIMENT_ID:
+ raise VerificationError("unsupported slice-channel artifact identity")
+ measured = build_artifact(dataset)
+ if canonical_json_bytes(artifact) != canonical_json_bytes(measured):
+ raise VerificationError("artifact differs from a fresh experiment execution")
+ return measured
+
+
+def render_markdown(artifact):
+ measured = artifact["measurement"]
+ ordinary = measured["density_min_degree_2"]
+ hubs = measured["density_min_degree_20"]
+ shape = measured["graph_shape"]
+ return "\n".join([
+ "# Attribute density and graph shape on a six-block slice",
+ "",
+ "Generated from the canonical experiment artifact. Do not edit manually.",
+ "",
+ f"Transactions: {measured['transactions']}; clusters: {measured['clusters']}; "
+ f"contracted vertices: {shape['vertices']}.",
+ "",
+ "| minimum degree | sampled records | delta(0.5) | delta(0.9) | delta(0.99) |",
+ "|---:|---:|---:|---:|---:|",
+ f"| 2 | {ordinary['eligible_sample']} | {ordinary['survival']['0.5']:.6f} | "
+ f"{ordinary['survival']['0.9']:.6f} | {ordinary['survival']['0.99']:.6f} |",
+ f"| 20 | {hubs['eligible_sample']} | n/a | {hubs['survival']['0.9']:.6f} | n/a |",
+ "",
+ f"Degree assortativity: {shape['assortativity']:.6f}.",
+ "",
+ f"This export carries none of the four axes — every one of the {measured['transactions']} "
+ "transactions abstains on version, input order, locktime and fee rate — so the vectors "
+ "compared here are structural: degree, transaction count, self-transfers and the "
+ "neighbour-degree histogram. On those alone the space is dense, and markedly less so among "
+ f"the higher-degree vertices ({hubs['survival']['0.9']:.3f} against "
+ f"{ordinary['survival']['0.9']:.3f} at the same threshold), which is where a matcher would "
+ "start. The contracted graph is disassortative. These observations do not reproduce the "
+ "historical larger slices, do not measure attribute density, and do not establish "
+ "graph-matching performance.",
+ "",
+ ])
+
+
+def _parser():
+ parser = argparse.ArgumentParser(description=__doc__)
+ parser.add_argument("--dataset", default=DEFAULT_DATASET)
+ commands = parser.add_subparsers(dest="command", required=True)
+ reproduce = commands.add_parser("reproduce")
+ reproduce.add_argument("--artifact", required=True)
+ reproduce.add_argument("--markdown", required=True)
+ verify = commands.add_parser("verify")
+ verify.add_argument("--artifact", required=True)
+ verify.add_argument("--markdown")
+ return parser
+
+
+def main(argv=None):
+ args = _parser().parse_args(argv)
+ if args.command == "reproduce":
+ artifact = build_artifact(args.dataset)
+ write_canonical_json(args.artifact, artifact)
+ destination = Path(args.markdown)
+ destination.parent.mkdir(parents=True, exist_ok=True)
+ destination.write_text(render_markdown(artifact), encoding="utf-8")
+ else:
+ artifact = verify_artifact(load_artifact(args.artifact), args.dataset)
+ if args.markdown is not None:
+ actual = Path(args.markdown).read_text(encoding="utf-8")
+ if actual != render_markdown(artifact):
+ raise VerificationError("Markdown differs from canonical rendering")
+ return 0
+
+
+if __name__ == "__main__":
+ raise SystemExit(main())
diff --git a/decluster/experiments/special_change_labels.py b/decluster/experiments/special_change_labels.py
new file mode 100644
index 00000000..95e217ce
--- /dev/null
+++ b/decluster/experiments/special_change_labels.py
@@ -0,0 +1,246 @@
+"""Change labels that read values and types rather than co-spend, on committed transactions.
+
+Change identification is validated against a label, and a label drawn from co-spend clustering
+validates the fingerprints with the thing the fingerprints are meant to extend. The value-based
+special cases break that circle: the output smaller than every input must be the change, an output
+whose value is a round number is the deliberate payment, and neither reads an address relation.
+
+The figures this replaces were taken on downloads outside the repository, so nobody could check them
+and nobody could take them again. What the committed block cache supports is the offline half: how
+often each within-transaction predictor agrees with each label, and how often the labels agree with
+each other. The onward-spend half needs a transaction's change output to be spent inside the same
+data, which a block-sampled cache almost never holds; the count is recorded here rather than
+asserted away, because it is the reason that arm is absent.
+
+The agreement matrix is where the labels stop being four votes. Reuse of an input address forces the
+output's script type to match an input's, so wherever both labels are defined they cannot disagree —
+a dependence in the labels themselves, not a corroboration between them.
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+from pathlib import Path
+from tempfile import TemporaryDirectory
+
+from ..archive_snapshot import extract_tar_gz
+from ..change_gt import is_candidate
+from ..change_slice import index_slice, slice_fetchers
+from ..change_special import (agreement_matrix, build_gt_special, label_address_reuse,
+ label_optimal_change, label_round_number, label_type_match,
+ within_tx_rates)
+from ..fingerprint_validate import load_blkcache
+from ..reproducibility import fingerprint_source
+from ..result_artifacts import canonical_json_bytes, write_canonical_json
+
+EXPERIMENT_ID = "special-change-labels-v1"
+SNAPSHOT = "data/fs-blkcache-2026-09-04.tar.gz"
+
+LABELS = {"optimal_change": label_optimal_change, "round_number": label_round_number,
+ "type_match": label_type_match, "address_reuse": label_address_reuse}
+
+
+class VerificationError(ValueError):
+ """The stored result differs from a fresh execution."""
+
+
+def _load(snapshot):
+ with TemporaryDirectory(prefix="decluster-special-") as temporary:
+ cache = extract_tar_gz(snapshot, temporary) / ".blkcache"
+ transactions = load_blkcache(str(cache))
+ source = fingerprint_source(str(cache / "*.json"))
+ source.update(pattern=".blkcache/*.json", transactions=len(transactions))
+ return transactions, source
+
+
+def _onward_reach(transactions, labelled):
+ """How many labelled transactions have an output spent inside the same data.
+
+ The per-axis onward-spend test votes for the output whose spender carries the same fingerprint,
+ so it needs the spender. Block sampling puts it outside the cache for almost every transaction,
+ which bounds the arm rather than merely making it noisy.
+ """
+ by_txid, spender, _uf = index_slice(transactions)
+ _get_tx, get_outspends = slice_fetchers(by_txid, spender)
+ reach = {}
+ for name, ground in labelled.items():
+ spends = [[entry["spent"] for entry in get_outspends(record["tx"]["txid"])[:2]]
+ for record in ground]
+ reach[name] = {"labels": len(ground),
+ "one_output_spent": sum(1 for s in spends if any(s)),
+ "both_outputs_spent": sum(1 for s in spends if len(s) == 2 and all(s))}
+ return reach
+
+
+def build_artifact(snapshot=SNAPSHOT):
+ transactions, source = _load(snapshot)
+ labelled = {name: build_gt_special(transactions, labeler)
+ for name, labeler in LABELS.items()}
+
+ within = []
+ for name, ground in sorted(labelled.items()):
+ for predictor, (tpr, fpr, coverage) in sorted(within_tx_rates(ground).items()):
+ decided = tpr + fpr
+ within.append({"label": name, "predictor": predictor,
+ "tpr": round(tpr, 6), "fpr": round(fpr, 6),
+ "coverage": round(coverage, 6),
+ "precision": round(tpr / decided, 6) if decided else None})
+
+ agreement = [{"a": a, "b": b, **counts}
+ for (a, b), counts in sorted(agreement_matrix(labelled).items())]
+
+ return {
+ "schema_version": 1,
+ "experiment": EXPERIMENT_ID,
+ "dataset": "fs-blkcache-2026-09-04-v1",
+ "source": source,
+ "parameters": {"labels": sorted(LABELS), "predictors": ["address_reuse", "round_number"]},
+ "population": {
+ "transactions": len(transactions),
+ "two_output_candidates": sum(1 for tx in transactions if is_candidate(tx)),
+ "labels": {name: len(ground) for name, ground in sorted(labelled.items())},
+ },
+ "within_tx": within,
+ "agreement": agreement,
+ "onward_spend_reach": _onward_reach(transactions, labelled),
+ }
+
+
+def load_artifact(path):
+ try:
+ with Path(path).open(encoding="utf-8") as source:
+ value = json.load(source)
+ except (OSError, json.JSONDecodeError) as exc:
+ raise VerificationError(f"cannot read artifact {path}: {exc}") from exc
+ if not isinstance(value, dict):
+ raise VerificationError("artifact root must be an object")
+ return value
+
+
+def verify_artifact(artifact, snapshot=SNAPSHOT):
+ if artifact.get("schema_version") != 1 or artifact.get("experiment") != EXPERIMENT_ID:
+ raise VerificationError("unsupported special-change artifact identity")
+ measured = build_artifact(snapshot)
+ if canonical_json_bytes(artifact) != canonical_json_bytes(measured):
+ raise VerificationError("artifact differs from a fresh experiment execution")
+ return measured
+
+
+def _row(rows, label, predictor):
+ return next(r for r in rows if r["label"] == label and r["predictor"] == predictor)
+
+
+def render_markdown(artifact):
+ population, within = artifact["population"], artifact["within_tx"]
+ reach = artifact["onward_spend_reach"]
+ counts = population["labels"]
+ forced = next(r for r in artifact["agreement"]
+ if {r["a"], r["b"]} == {"address_reuse", "type_match"})
+ lines = [
+ "# Change labels that do not read co-spend",
+ "",
+ "Generated from the canonical experiment artifact. Do not edit manually.",
+ "",
+ f"{population['transactions']} committed transactions, {population['two_output_candidates']} "
+ f"of them two-output candidates. Each label picks the change output from values or script "
+ f"types alone, so none of them reads the address relation the fingerprints are meant to "
+ f"extend.",
+ "",
+ "| label | transactions it labels |",
+ "|---|---:|",
+ ]
+ lines.extend(f"| {name} | {count} |" for name, count in counts.items())
+ lines.extend([
+ "",
+ "## Within-transaction predictors against each label",
+ "",
+ "| label | predictor | TPR | FPR | coverage | precision |",
+ "|---|---|---:|---:|---:|---:|",
+ ])
+ for row in within:
+ precision = "—" if row["precision"] is None else f"{row['precision']:.3f}"
+ lines.append(f"| {row['label']} | {row['predictor']} | {row['tpr']:.3f} | "
+ f"{row['fpr']:.3f} | {row['coverage']:.3f} | {precision} |")
+ optimal_round = _row(within, "optimal_change", "round_number")
+ optimal_reuse = _row(within, "optimal_change", "address_reuse")
+ lines.extend([
+ "",
+ f"Against the value label, the round-number heuristic is right {optimal_round['precision']:.2f} "
+ f"of the time it commits, on {optimal_round['coverage']:.0%} of the labelled transactions, and "
+ f"address reuse {optimal_reuse['precision']:.2f} on {optimal_reuse['coverage']:.0%}. Two "
+ f"universal heuristics agreeing with a label neither of them reads.",
+ "",
+ "A predictor scored against its own label is self-agreement and not corroboration: the "
+ "round-number rows under the `round_number` label, and the address-reuse rows under "
+ "`address_reuse`, read the same fact twice and are reported only so the table is complete.",
+ "",
+ "## Do the labels agree with each other",
+ "",
+ "| label | label | both label | agree | disagree |",
+ "|---|---|---:|---:|---:|",
+ ])
+ for row in artifact["agreement"]:
+ lines.append(f"| {row['a']} | {row['b']} | {row['both']} | {row['agree']} | "
+ f"{row['disagree']} |")
+ lines.extend([
+ "",
+ f"`address_reuse` and `type_match` agree on all {forced['both']} transactions both label, "
+ "and that is arithmetic rather than evidence: an output reusing an input address carries an "
+ "input's script type, so the type rule can only select the same output or abstain. They are "
+ "one label counted twice, which is the number a corroboration between them would "
+ "double-count.",
+ "",
+ "## The onward-spend arm is not measured here",
+ "",
+ "Voting for the output whose *spender* repeats the transaction's fingerprint needs that "
+ "spender to be in the data. The cache is block-sampled, so it is usually not:",
+ "",
+ "| label | labelled | one output spent inside | both spent inside |",
+ "|---|---:|---:|---:|",
+ ])
+ for name, row in reach.items():
+ lines.append(f"| {name} | {row['labels']} | {row['one_output_spent']} | "
+ f"{row['both_outputs_spent']} |")
+ optimal = reach["optimal_change"]
+ lines.extend([
+ "",
+ f"At {optimal['both_outputs_spent']} transactions with both outputs spent inside the cache, "
+ "the per-axis rates that arm would report are not a measurement. Getting it needs a "
+ "contiguous export deep enough to hold each change output's spender, which is a collection "
+ "and not a method — state 4 in `results/REPRODUCIBILITY.md`.",
+ "",
+ "## Reproducibility / provenance",
+ "",
+ "State 1 in `results/REPRODUCIBILITY.md`: band-pinned on committed data — the artifact is "
+ f"recomputed from `{SNAPSHOT}` and compared byte for byte.",
+ ])
+ return "\n".join(lines) + "\n"
+
+
+def _parser():
+ parser = argparse.ArgumentParser(description=__doc__.splitlines()[0])
+ parser.add_argument("command", choices=("reproduce", "verify"))
+ parser.add_argument("--snapshot", default=SNAPSHOT)
+ parser.add_argument("--artifact", required=True)
+ parser.add_argument("--markdown", required=True)
+ return parser
+
+
+def main(argv=None):
+ args = _parser().parse_args(argv)
+ if args.command == "reproduce":
+ artifact = build_artifact(args.snapshot)
+ write_canonical_json(args.artifact, artifact)
+ destination = Path(args.markdown)
+ destination.parent.mkdir(parents=True, exist_ok=True)
+ destination.write_text(render_markdown(artifact), encoding="utf-8")
+ else:
+ artifact = verify_artifact(load_artifact(args.artifact), args.snapshot)
+ if Path(args.markdown).read_text(encoding="utf-8") != render_markdown(artifact):
+ raise VerificationError("Markdown differs from canonical rendering")
+ return 0
+
+
+if __name__ == "__main__":
+ raise SystemExit(main())
diff --git a/decluster/experiments/statistical_disclosure.py b/decluster/experiments/statistical_disclosure.py
new file mode 100644
index 00000000..e265bb33
--- /dev/null
+++ b/decluster/experiments/statistical_disclosure.py
@@ -0,0 +1,133 @@
+"""Reproduce statistical disclosure and its background-matched control."""
+
+from __future__ import annotations
+
+import argparse
+import json
+from pathlib import Path
+
+from ..domain import PersistentCorrespondentRanked
+from ..failure_modes.statistical_disclosure import evaluate, paired_examples
+from ..result_artifacts import canonical_json_bytes, write_canonical_json
+
+EXPERIMENT_ID = "statistical-disclosure-v1"
+
+
+class VerificationError(ValueError):
+ """A stored result differs from a fresh execution."""
+
+
+def _measurement(scenario):
+ report = evaluate(scenario)
+ evidence = report.channels[0].evidence[0]
+ outcome = report.outcomes[0]
+ return {
+ "batch_size": scenario.batch_size,
+ "observations": len(scenario.rounds),
+ "scores": {
+ correspondent.identifier: score for correspondent, score in evidence.scores
+ },
+ "unique_top_correspondent": (
+ outcome.correspondent.identifier
+ if isinstance(outcome, PersistentCorrespondentRanked)
+ else None
+ ),
+ "top_margin": outcome.margin if isinstance(outcome, PersistentCorrespondentRanked) else 0.0,
+ }
+
+
+def build_artifact():
+ persistent, control = paired_examples()
+ return {
+ "schema_version": 1,
+ "experiment": EXPERIMENT_ID,
+ "persistent_correspondent": _measurement(persistent),
+ "background_matched_control": _measurement(control),
+ "composition": None,
+ "conclusion": "repeated target-active rounds recover a persistent correspondent in the threshold-mix model",
+ "limitations": list(evaluate(persistent).limitations),
+ }
+
+
+def load_artifact(path):
+ try:
+ with Path(path).open(encoding="utf-8") as source:
+ value = json.load(source)
+ except (OSError, json.JSONDecodeError) as exc:
+ raise VerificationError(f"cannot read artifact {path}: {exc}") from exc
+ if not isinstance(value, dict):
+ raise VerificationError("artifact root must be an object")
+ return value
+
+
+def verify_artifact(artifact):
+ if artifact.get("schema_version") != 1 or artifact.get("experiment") != EXPERIMENT_ID:
+ raise VerificationError("unsupported statistical-disclosure artifact identity")
+ measured = build_artifact()
+ if canonical_json_bytes(artifact) != canonical_json_bytes(measured):
+ raise VerificationError("artifact differs from a fresh failure-mode execution")
+ return measured
+
+
+def render_markdown(artifact):
+ persistent = artifact["persistent_correspondent"]
+ control = artifact["background_matched_control"]
+ rows = []
+ for name, measurement in (("persistent correspondent", persistent), ("background-matched control", control)):
+ scores = ", ".join(
+ f"{correspondent}={score:.2f}"
+ for correspondent, score in measurement["scores"].items()
+ )
+ rows.append(
+ f"| {name} | {measurement['observations']} | {measurement['batch_size']} | "
+ f"{scores} | {measurement['unique_top_correspondent'] or 'none'} |"
+ )
+ return "\n".join([
+ "# Longitudinal statistical disclosure",
+ "",
+ "Generated from the canonical experiment artifact. Do not edit manually.",
+ "",
+ "| scenario | rounds | batch size | estimated target distribution | unique top |",
+ "|---|---:|---:|---|---|",
+ *rows,
+ "",
+ "The persistent fixture recovers Alice. The control remains tied because the target "
+ "follows the same distribution as the background.",
+ "",
+ "This is a deterministic reproduction of the threshold-mix estimator. It does not "
+ "attribute Bitcoin ownership or measure real-world attack frequency.",
+ "",
+ ])
+
+
+def _parser():
+ parser = argparse.ArgumentParser(description=__doc__)
+ commands = parser.add_subparsers(dest="command", required=True)
+ reproduce = commands.add_parser("reproduce")
+ reproduce.add_argument("--artifact", required=True)
+ reproduce.add_argument("--markdown", required=True)
+ verify = commands.add_parser("verify")
+ verify.add_argument("--artifact", required=True)
+ verify.add_argument("--markdown")
+ return parser
+
+
+def main(argv=None):
+ args = _parser().parse_args(argv)
+ if args.command == "reproduce":
+ artifact = build_artifact()
+ write_canonical_json(args.artifact, artifact)
+ destination = Path(args.markdown)
+ destination.parent.mkdir(parents=True, exist_ok=True)
+ destination.write_text(render_markdown(artifact), encoding="utf-8")
+ else:
+ artifact = verify_artifact(load_artifact(args.artifact))
+ if args.markdown is not None:
+ actual = Path(args.markdown).read_text(encoding="utf-8")
+ if actual != render_markdown(artifact):
+ raise VerificationError("Markdown differs from canonical rendering")
+ return 0
+
+
+if __name__ == "__main__":
+ raise SystemExit(main())
diff --git a/decluster/experiments/subtx_demix.py b/decluster/experiments/subtx_demix.py
new file mode 100644
index 00000000..c7b70dad
--- /dev/null
+++ b/decluster/experiments/subtx_demix.py
@@ -0,0 +1,169 @@
+"""Reproduce bounded de-mix diagnostics from a frozen CoinJoin-adjacent cache."""
+
+from __future__ import annotations
+
+import argparse
+import json
+from pathlib import Path
+from tempfile import TemporaryDirectory
+
+from ..archive_snapshot import extract_tar_gz
+from ..coinjoin_demix import coinjoin_demix
+from ..result_artifacts import canonical_json_bytes, write_canonical_json
+
+EXPERIMENT_ID = "subtx-demix-v1"
+JOINMARKET_TXID = "0cb4870cf2dfa3877851088c673d163ae3c20ebcd6505c0be964d8fbcc856bbf"
+
+
+class VerificationError(ValueError):
+ """The dataset or stored result violates the experiment contract."""
+
+
+def _load_transactions(cache):
+ transactions = []
+ for path in sorted(Path(cache).glob("*.json")):
+ with path.open(encoding="utf-8") as source:
+ value = json.load(source)
+ if isinstance(value, list) and path.name.endswith(".outspends.json"):
+ continue
+ if not isinstance(value, dict) or not isinstance(value.get("txid"), str):
+ raise VerificationError(f"unrecognized cache object: {path}")
+ transactions.append(value)
+ return transactions
+
+
+def _amounts(transaction):
+ inputs = [entry["prevout"]["value"] for entry in transaction["vin"]]
+ outputs = [entry["value"] for entry in transaction["vout"]]
+ return inputs, outputs
+
+
+def build_artifact(snapshot):
+ with TemporaryDirectory(prefix="decluster-demix-") as temporary:
+ cache = extract_tar_gz(snapshot, temporary) / ".cache"
+ cache_files = len(list(cache.glob("*.json")))
+ transactions = _load_transactions(cache)
+ by_id = {transaction["txid"]: transaction for transaction in transactions}
+ if len(by_id) != len(transactions) or JOINMARKET_TXID not in by_id:
+ raise VerificationError("snapshot must contain unique txids and the declared JoinMarket round")
+ inputs, outputs = _amounts(by_id[JOINMARKET_TXID])
+ assignments = coinjoin_demix(inputs, outputs)
+ eligible = []
+ for transaction in transactions:
+ try:
+ tx_inputs, tx_outputs = _amounts(transaction)
+ except (KeyError, TypeError):
+ continue
+ if 3 <= len(tx_inputs) <= 60 and len(tx_outputs) >= 2:
+ eligible.append({
+ "txid": transaction["txid"],
+ "recovered": len(coinjoin_demix(tx_inputs, tx_outputs)),
+ })
+ return {
+ "schema_version": 1,
+ "experiment": EXPERIMENT_ID,
+ "dataset": "subtx-demix-cache-2026-09-04-v1",
+ "measurement": {
+ "cache_json_files": cache_files,
+ "cache_transactions": len(transactions),
+ "eligible_transactions": len(eligible),
+ "eligible_with_any_recovery": sum(row["recovered"] > 0 for row in eligible),
+ "joinmarket": {
+ "txid": JOINMARKET_TXID,
+ "inputs": len(inputs),
+ "outputs": len(outputs),
+ "recovered_inputs": sorted(assignments),
+ "recovered_changes": [assignments[index] for index in sorted(assignments)],
+ "fees": [
+ max(outputs, key=outputs.count) + assignments[index] - inputs[index]
+ for index in sorted(assignments)
+ ],
+ },
+ },
+ "verdicts": {
+ "joinmarket_fixture": "reproduced",
+ "ordinary_transaction_specificity": "not_tested",
+ "wasabi_rounds": "not_reproduced",
+ },
+ "limitations": [
+ "the cache is CoinJoin-adjacent and has no ordinary-transaction labels",
+ "22 of 25 eligible cached transactions trigger, so this population cannot support a specificity claim",
+ "maker identities are inferred from an amount identity rather than independently labeled",
+ "the six historical Wasabi rounds are not preserved as a dataset",
+ "the fixed fee cap and most-common-output detector are local modeling choices",
+ "this is attacker-side de-mix evidence, not CoinScore or a privacy certificate",
+ ],
+ }
+
+
+def load_artifact(path):
+ try:
+ with Path(path).open(encoding="utf-8") as source:
+ value = json.load(source)
+ except (OSError, json.JSONDecodeError) as exc:
+ raise VerificationError(f"cannot read artifact {path}: {exc}") from exc
+ if not isinstance(value, dict):
+ raise VerificationError("artifact root must be an object")
+ return value
+
+
+def verify_artifact(artifact, snapshot):
+ if artifact.get("schema_version") != 1 or artifact.get("experiment") != EXPERIMENT_ID:
+ raise VerificationError("unsupported subtransaction de-mix artifact identity")
+ measured = build_artifact(snapshot)
+ if canonical_json_bytes(artifact) != canonical_json_bytes(measured):
+ raise VerificationError("artifact differs from a fresh experiment execution")
+ return measured
+
+
+def render_markdown(artifact):
+ measurement = artifact["measurement"]
+ joinmarket = measurement["joinmarket"]
+ return "\n".join([
+ "# Subtransaction de-mix diagnostic",
+ "",
+ "Generated from the canonical experiment artifact. Do not edit manually.",
+ "",
+ f"The preserved JoinMarket transaction has {joinmarket['inputs']} inputs and "
+ f"{joinmarket['outputs']} outputs; the amount identity recovers "
+ f"{len(joinmarket['recovered_inputs'])} inputs.",
+ "",
+ f"The CoinJoin-adjacent cache contains {measurement['cache_transactions']} transactions. "
+ f"Among {measurement['eligible_transactions']} width-eligible transactions, "
+ f"{measurement['eligible_with_any_recovery']} trigger the heuristic.",
+ "",
+ "This cache has no ordinary-transaction labels and cannot establish specificity. "
+ "The historical Wasabi result is not reproduced. This is attacker-side evidence, "
+ "not a privacy score.",
+ "",
+ ])
+
+
+def _parser():
+ parser = argparse.ArgumentParser(description=__doc__)
+ parser.add_argument("--snapshot", required=True)
+ commands = parser.add_subparsers(dest="command", required=True)
+ for name in ("reproduce", "verify"):
+ command = commands.add_parser(name)
+ command.add_argument("--artifact", required=True)
+ command.add_argument("--markdown", required=True)
+ return parser
+
+
+def main(argv=None):
+ args = _parser().parse_args(argv)
+ if args.command == "reproduce":
+ artifact = build_artifact(args.snapshot)
+ write_canonical_json(args.artifact, artifact)
+ destination = Path(args.markdown)
+ destination.parent.mkdir(parents=True, exist_ok=True)
+ destination.write_text(render_markdown(artifact), encoding="utf-8")
+ else:
+ artifact = verify_artifact(load_artifact(args.artifact), args.snapshot)
+ if Path(args.markdown).read_text(encoding="utf-8") != render_markdown(artifact):
+ raise VerificationError("Markdown differs from canonical rendering")
+ return 0
+
+
+if __name__ == "__main__":
+ raise SystemExit(main())
diff --git a/decluster/experiments/unnecessary_input.py b/decluster/experiments/unnecessary_input.py
new file mode 100644
index 00000000..58ebedd5
--- /dev/null
+++ b/decluster/experiments/unnecessary_input.py
@@ -0,0 +1,205 @@
+"""Reproduce the fee-aware unnecessary-input diagnostic and its limits."""
+
+from __future__ import annotations
+
+import argparse
+import json
+from pathlib import Path
+
+from ..baselines.unnecessary_input import (
+ UIHStatus,
+ analyze_blockstream,
+ blocksci_uih1,
+ gibson_flags,
+)
+from ..failure_modes.unnecessary_input import (
+ collaborative_form_examples,
+ cycle_equivalent_obligations,
+ evaluate,
+ net_balances,
+ observationally_equivalent_example,
+)
+from ..result_artifacts import canonical_json_bytes, write_canonical_json
+
+
+EXPERIMENT_ID = "unnecessary-input-v2"
+DEFINITION = "blockstream_fee_aware"
+DEFINITION_VERSION = 2
+
+
+class VerificationError(ValueError):
+ """A stored result differs from a fresh execution."""
+
+
+def _classification(inputs: tuple[int, ...], outputs: tuple[int, ...]) -> dict:
+ analysis = analyze_blockstream(inputs, outputs)
+ return {
+ "inputs": list(inputs),
+ "outputs": list(outputs),
+ "status": analysis.status.value,
+ "fee": analysis.fee,
+ "removed_input_index": analysis.removed_input_index,
+ "change_output_index": analysis.change_output_index,
+ "reason": analysis.reason,
+ "blocksci_strict_uih1": blocksci_uih1(inputs, outputs),
+ "gibson_flags": list(flags) if (flags := gibson_flags(inputs, outputs)) else None,
+ }
+
+
+def build_artifact() -> dict:
+ equivalent = observationally_equivalent_example()
+ report = evaluate(equivalent)
+ evidence = report.channels[0].evidence[0]
+ ns1r, nsnr = collaborative_form_examples()
+ simple, with_cycle = cycle_equivalent_obligations()
+ return {
+ "schema_version": 1,
+ "experiment": EXPERIMENT_ID,
+ "diagnostic": {
+ "definition": DEFINITION,
+ "semantic_version": DEFINITION_VERSION,
+ "paper_domain": {
+ "minimum_inputs": 2,
+ "exact_outputs": 2,
+ "amounts": "positive unsigned 64-bit integers",
+ },
+ },
+ "boundary_cases": {
+ "jointly_funded_uih2": _classification((6, 6, 6), (10, 7)),
+ "fee_aware_uih1": _classification((10, 2), (10, 1)),
+ "equality_boundary": _classification((10, 2), (9, 2)),
+ },
+ "observational_equivalence": {
+ "classification": evidence.classification,
+ "fee": evidence.fee,
+ "possible_forms": [form.value for form in equivalent.possible_forms],
+ "decision": type(report.outcomes[0]).__name__.lower(),
+ "ownership_inferred": False,
+ "payjoin_identified": False,
+ "payment_output_identified": False,
+ "composition": report.composition,
+ },
+ "scope_controls": {
+ ns1r.form.value: _classification(ns1r.inputs, ns1r.outputs),
+ nsnr.form.value: _classification(nsnr.inputs, nsnr.outputs),
+ },
+ "cycle_control": {
+ "simple_nonzero_net_balances": {
+ party: balance for party, balance in net_balances(simple).items() if balance
+ },
+ "cyclic_nonzero_net_balances": {
+ party: balance for party, balance in net_balances(with_cycle).items() if balance
+ },
+ "simple_gross_amount": sum(item.amount for item in simple),
+ "cyclic_gross_amount": sum(item.amount for item in with_cycle),
+ },
+ "conclusion": (
+ "the fee-aware predicate classifies a two-output amount shape; it does not "
+ "identify PayJoin, ownership, participant roles, or gross obligations"
+ ),
+ "limitations": list(report.limitations),
+ }
+
+
+def load_artifact(path: str | Path) -> dict:
+ try:
+ with Path(path).open(encoding="utf-8") as source:
+ value = json.load(source)
+ except (OSError, json.JSONDecodeError) as exc:
+ raise VerificationError(f"cannot read artifact {path}: {exc}") from exc
+ if not isinstance(value, dict):
+ raise VerificationError("artifact root must be an object")
+ return value
+
+
+def verify_artifact(artifact: dict) -> dict:
+ identity = (artifact.get("schema_version"), artifact.get("experiment"))
+ if identity != (1, EXPERIMENT_ID):
+ raise VerificationError("unsupported unnecessary-input artifact identity")
+ measured = build_artifact()
+ if canonical_json_bytes(artifact) != canonical_json_bytes(measured):
+ raise VerificationError("artifact differs from a fresh diagnostic execution")
+ return measured
+
+
+def render_markdown(artifact: dict) -> str:
+ cases = artifact["boundary_cases"]
+ equivalence = artifact["observational_equivalence"]
+ scopes = artifact["scope_controls"]
+ cycle = artifact["cycle_control"]
+ rows = [
+ (name.replace("_", " "), case["status"], case["fee"], case["gibson_flags"])
+ for name, case in cases.items()
+ ]
+ lines = [
+ "# Fee-aware unnecessary-input diagnostic",
+ "",
+ "Generated from the canonical experiment artifact. Do not edit manually.",
+ "",
+ "Definition: "
+ f"`{artifact['diagnostic']['definition']}` "
+ f"v{artifact['diagnostic']['semantic_version']}.",
+ "Its paper domain is transactions with at least two inputs and exactly two outputs.",
+ "",
+ "| case | classification | fee | Gibson flags |",
+ "|---|---|---:|---|",
+ ]
+ lines.extend(
+ f"| {name} | {status} | {fee} | `{flags}` |" for name, status, fee, flags in rows
+ )
+ lines.extend([
+ "",
+ "The observational-equivalence fixture is classified "
+ f"`{equivalence['classification']}`, yet remains `{equivalence['decision']}` across "
+ f"{len(equivalence['possible_forms'])} declared latent forms. It infers no ownership, "
+ "PayJoin identity, payment output, or composition.",
+ "",
+ "NS1R and NSNR controls are outside the two-output definition: "
+ f"`{scopes['many_senders_one_receiver']['status']}` and "
+ f"`{scopes['many_senders_many_receivers']['status']}`.",
+ "",
+ "The cycle control preserves the nonzero net balances while changing gross obligations "
+ f"from {cycle['simple_gross_amount']} to {cycle['cyclic_gross_amount']}. Therefore the "
+ "gross payment graph is not identified by the on-chain net amounts.",
+ "",
+ "This is a deterministic diagnostic reproduction, not a PayJoin detector, ownership "
+ "classifier, prevalence estimate, or privacy score.",
+ "",
+ ])
+ return "\n".join(lines)
+
+
+def _parser() -> argparse.ArgumentParser:
+ parser = argparse.ArgumentParser(description=__doc__)
+ commands = parser.add_subparsers(dest="command", required=True)
+ reproduce = commands.add_parser("reproduce")
+ reproduce.add_argument("--artifact", required=True)
+ reproduce.add_argument("--markdown", required=True)
+ verify = commands.add_parser("verify")
+ verify.add_argument("--artifact", required=True)
+ verify.add_argument("--markdown")
+ return parser
+
+
+def main(argv: list[str] | None = None) -> int:
+ args = _parser().parse_args(argv)
+ if args.command == "reproduce":
+ artifact = build_artifact()
+ write_canonical_json(args.artifact, artifact)
+ destination = Path(args.markdown)
+ destination.parent.mkdir(parents=True, exist_ok=True)
+ destination.write_text(render_markdown(artifact), encoding="utf-8")
+ else:
+ artifact = verify_artifact(load_artifact(args.artifact))
+ if args.markdown is not None:
+ try:
+ actual = Path(args.markdown).read_text(encoding="utf-8")
+ except OSError as exc:
+ raise VerificationError(f"cannot read Markdown {args.markdown}: {exc}") from exc
+ if actual != render_markdown(artifact):
+ raise VerificationError("Markdown differs from canonical rendering")
+ return 0
+
+
+if __name__ == "__main__":
+ raise SystemExit(main())
diff --git a/decluster/experiments/weight_sensitivity.py b/decluster/experiments/weight_sensitivity.py
new file mode 100644
index 00000000..783ecee3
--- /dev/null
+++ b/decluster/experiments/weight_sensitivity.py
@@ -0,0 +1,137 @@
+"""Reproduce the global fingerprint-weight sensitivity sweep."""
+
+from __future__ import annotations
+
+import argparse
+import json
+from pathlib import Path
+from tempfile import TemporaryDirectory
+
+from ..archive_snapshot import extract_tar_gz
+from ..fingerprint_validate import load_blkcache
+from ..reproducibility import fingerprint_source
+from ..result_artifacts import canonical_json_bytes, write_canonical_json
+from ..weight_sensitivity import sweep
+
+EXPERIMENT_ID = "weight-sensitivity-v1"
+
+
+class VerificationError(ValueError):
+ """The stored result differs from a fresh execution."""
+
+
+def build_artifact(snapshot):
+ with TemporaryDirectory(prefix="decluster-weight-") as temporary:
+ root = extract_tar_gz(snapshot, temporary)
+ cache = root / ".blkcache"
+ transactions = load_blkcache(str(cache))
+ measurement = sweep(transactions)
+ source = fingerprint_source(str(cache / "*.json"))
+ source.update(pattern=".blkcache/*.json", transactions=len(transactions))
+ return {
+ "schema_version": 1,
+ "experiment": EXPERIMENT_ID,
+ "dataset": "fs-blkcache-2026-09-04-v1",
+ "source": source,
+ "measurement": measurement,
+ "historical_comparison": {
+ "status": "not_supported",
+ "preserved": "AUC changes little from consistency 0.90 through 0.99",
+ "not_supported": "monotone improvement: the measured sequence dips, but by a fifth of "
+ "one standard error, which does not establish a reversal either",
+ },
+ "limitations": [
+ "address reuse is a weak and partly feature-dependent ownership proxy",
+ "the sweep varies one global value rather than fitting each axis",
+ "the same selected sample is reused at every grid point",
+ "the snapshot is not representative of the whole chain",
+ "AUC stability does not imply calibrated evidence magnitudes",
+ "the sweep resolves differences of about one standard error; smaller ones are reported "
+ "and are not readable as an ordering",
+ "this is attacker-side sensitivity analysis, not CoinScore or a privacy certificate",
+ ],
+ }
+
+
+def load_artifact(path):
+ try:
+ with Path(path).open(encoding="utf-8") as source:
+ value = json.load(source)
+ except (OSError, json.JSONDecodeError) as exc:
+ raise VerificationError(f"cannot read artifact {path}: {exc}") from exc
+ if not isinstance(value, dict):
+ raise VerificationError("artifact root must be an object")
+ return value
+
+
+def verify_artifact(artifact, snapshot):
+ if artifact.get("schema_version") != 1 or artifact.get("experiment") != EXPERIMENT_ID:
+ raise VerificationError("unsupported weight-sensitivity artifact identity")
+ measured = build_artifact(snapshot)
+ if canonical_json_bytes(artifact) != canonical_json_bytes(measured):
+ raise VerificationError("artifact differs from a fresh experiment execution")
+ return measured
+
+
+def render_markdown(artifact):
+ measurement = artifact["measurement"]
+ lines = [
+ "# Global fingerprint-weight sensitivity",
+ "",
+ "Generated from the canonical experiment artifact. Do not edit manually.",
+ "",
+ "| consistency | positive mean bits | negative mean bits | AUC | shuffled AUC |",
+ "|---:|---:|---:|---:|---:|",
+ ]
+ for row in measurement["rows"]:
+ lines.append(
+ f"| {row['consistency']:.2f} | {row['pos_mean']:+.6f} | "
+ f"{row['neg_mean']:+.6f} | {row['auc']:.6f} | {row['shuffle_auc']:.6f} |"
+ )
+ lines.extend([
+ "",
+ f"AUC range from 0.90 through 0.99: {measurement['realistic_band_auc_range']:.6f}. "
+ f"Monotone non-decreasing: {str(measurement['auc_is_monotone_non_decreasing']).lower()}.",
+ "",
+ f"That flag reads the measured sequence, and the sequence does dip. The largest dip is "
+ f"{measurement['largest_decrement']:.6f}, against a standard error of "
+ f"{measurement['auc_standard_error']:.6f} at {measurement['pair_cap_per_class']} pairs per "
+ f"class — {measurement['largest_decrement_in_standard_errors']:.2f} standard errors. So "
+ "the sweep does not support monotone improvement, and it does not establish a reversal "
+ "either: over this band the AUC is flat within what the sample resolves.",
+ "",
+ "Evidence magnitudes are the part that moves. Address reuse is a weak label; this is not a "
+ "privacy score.",
+ "",
+ ])
+ return "\n".join(lines)
+
+
+def _parser():
+ parser = argparse.ArgumentParser(description=__doc__)
+ parser.add_argument("--snapshot", required=True)
+ commands = parser.add_subparsers(dest="command", required=True)
+ for name in ("reproduce", "verify"):
+ command = commands.add_parser(name)
+ command.add_argument("--artifact", required=True)
+ command.add_argument("--markdown", required=True)
+ return parser
+
+
+def main(argv=None):
+ args = _parser().parse_args(argv)
+ if args.command == "reproduce":
+ artifact = build_artifact(args.snapshot)
+ write_canonical_json(args.artifact, artifact)
+ destination = Path(args.markdown)
+ destination.parent.mkdir(parents=True, exist_ok=True)
+ destination.write_text(render_markdown(artifact), encoding="utf-8")
+ else:
+ artifact = verify_artifact(load_artifact(args.artifact), args.snapshot)
+ if Path(args.markdown).read_text(encoding="utf-8") != render_markdown(artifact):
+ raise VerificationError("Markdown differs from canonical rendering")
+ return 0
+
+
+if __name__ == "__main__":
+ raise SystemExit(main())
diff --git a/decluster/extractors.py b/decluster/extractors.py
index 489558ea..2b5d2870 100644
--- a/decluster/extractors.py
+++ b/decluster/extractors.py
@@ -1,9 +1,23 @@
-"""Layer 1 — pure extractors: tx -> categorical fingerprint value."""
-def locktime_policy(tx): return "zero" if tx.get("locktime", 0) == 0 else "height"
+"""Layer 1 — pure extractors: tx -> categorical fingerprint value.
-def seqs(tx): return [v["sequence"] for v in tx["vin"]]
+`NA` is the one value that means "this export does not say", and it is never evidence. An
+address-only export has no version, locktime or sequence, and every committed graph fixture is
+one; reading absence as a value made every transaction in such a slice agree on those axes and
+the scorer credited the agreement. So an extractor that cannot see what it needs returns `NA`,
+and every consumer that scores or counts skips it.
+"""
+NA = "na"
+
+def locktime_policy(tx):
+ locktime = tx.get("locktime")
+ if locktime is None: return NA
+ return "zero" if locktime == 0 else "height"
+
+def seqs(tx): return [v["sequence"] for v in tx.get("vin", ())]
def x_nsequence(tx):
+ vin = tx.get("vin") or ()
+ if not vin or any("sequence" not in v for v in vin): return NA
s = seqs(tx)
if len(s) >= 2 and s[0] == 0x01 and all(v == 0xFFFFFFFF for v in s[1:]):
return "cake_group_c" # [0x01, MAX, ...]: Cake-specific signature
@@ -19,35 +33,57 @@ def _txid_internal(t): # BIP-69 orders by the p
except ValueError: return t.encode() # non-hex synthetic ids (test fixtures): fall back to raw order
def x_input_order(tx):
- o = [(_txid_internal(v["txid"]), v["vout"]) for v in tx["vin"]]
+ vin = tx.get("vin") or ()
+ if not vin or any("txid" not in v or "vout" not in v for v in vin): return NA
+ o = [(_txid_internal(v["txid"]), v["vout"]) for v in vin]
n = len(o)
if n == 1: return "single"
if o != sorted(o): return "shuffle" # not sorted -> not BIP-69 (reliable at any n)
return "bip69" if n >= 4 else "small_n" # sorted: brands BIP-69 only when accidental sort (1/n!) is small; n<=3 is coincidental (1/2, 1/6)
-def x_io_shape(tx): return f"{len(tx['vin'])}in-{len(tx['vout'])}out"
+def x_io_shape(tx):
+ if "vin" not in tx or "vout" not in tx: return NA
+ return f"{len(tx['vin'])}in-{len(tx['vout'])}out"
-def x_version(tx): return f"v{tx.get('version')}"
+def x_version(tx):
+ version = tx.get("version")
+ return NA if version is None else f"v{version}"
def x_output_order(tx):
+ vout = tx.get("vout") or ()
+ if not vout or any("value" not in v for v in vout): return NA
o = [(v["value"], bytes.fromhex(v["scriptpubkey"]) if v.get("scriptpubkey") else b"") # BIP-69: value ascending, then scriptPubKey byte order (breaks equal-value ties); falls back to value-only where raw spk is absent (e.g. the BigQuery export)
- for v in tx["vout"]]
+ for v in vout]
n = len(o)
if n == 1: return "single"
if o != sorted(o): return "unsorted" # not sorted -> reliable at any n
return "sorted_value" if n >= 4 else "small_n" # sorted: only brands at n>=4; n<=3 is coincidental (1/n!)
def x_change_spk_type(tx):
- types = {o["scriptpubkey_type"] for o in tx["vout"]}
+ vout = tx.get("vout") or ()
+ if not vout or any("scriptpubkey_type" not in o for o in vout): return NA
+ types = {o["scriptpubkey_type"] for o in vout}
return f"uniform_{next(iter(types))}" if len(types) == 1 else "mixed"
def x_uih(tx):
- # UIH1: some single input alone exceeds the largest output -> an input was unnecessary
- in_vals = [iv for v in tx["vin"]
+ # Legacy Gibson-like approximation retained so historical fingerprint artifacts do not change
+ # silently. New attack code must use baselines.unnecessary_input.analyze_transaction, whose
+ # fee-aware Algorithm 2 semantics are intentionally a separate observable.
+ # Amounts arrive as strings in some exports; comparing those lexicographically silently
+ # reverses the predicate ("330" > "1000"), so a non-integer amount is refused rather than
+ # ranked. Callers that hold string amounts must coerce before asking.
+ in_vals = [iv for v in tx.get("vin", ())
if (iv := (v.get("prevout") or {}).get("value", v.get("value"))) is not None]
- out_vals = [o["value"] for o in tx["vout"]]
+ out_vals = [o["value"] for o in tx.get("vout", ()) if "value" in o]
if len(in_vals) < 2 or not out_vals: return "none"
- return "uih1" if max(in_vals) >= max(out_vals) else "none"
+ if any(type(value) is not int for value in in_vals + out_vals): return NA
+ return "uih2" if max(in_vals) >= max(out_vals) else "none"
+
+def x_uih_fee_aware(tx):
+ """Paper-faithful UIH1/UIH2 category, including explicit invalid/scope states."""
+ from .baselines.unnecessary_input import analyze_transaction
+
+ return analyze_transaction(tx).status.value
def _witness_sig(vin):
w = vin.get("witness") or []
@@ -58,29 +94,29 @@ def _witness_sig(vin):
def x_low_r(tx):
lens = []
- for v in tx["vin"]:
+ for v in tx.get("vin", ()):
s = _witness_sig(v)
if not s: continue
n = len(s) // 2
if 68 <= n <= 73: lens.append(n) # ECDSA DER incl. sighash byte
- if not lens: return "na"
+ if not lens: return NA
return "low_r" if all(n <= 71 for n in lens) else "not_low_r"
def x_sighash(tx):
cls = set()
- for v in tx["vin"]:
+ for v in tx.get("vin", ()):
s = _witness_sig(v)
if not s: continue
n = len(s) // 2
if n == 64: cls.add("taproot_default")
elif n == 65: cls.add("taproot_explicit")
elif 68 <= n <= 73: cls.add(_SH.get(s[-2:].lower(), "sh_" + s[-2:].lower()))
- if not cls: return "na"
+ if not cls: return NA
return next(iter(cls)) if len(cls) == 1 else "mixed"
def x_fee_rate(tx):
fee, w = tx.get("fee"), tx.get("weight")
- if not fee or not w: return "na"
+ if not fee or not w: return NA
r = fee / (w / 4) # sat/vB
nearest = round(r)
if nearest >= 1 and abs(r - nearest) < 0.01:
@@ -88,13 +124,15 @@ def x_fee_rate(tx):
return "precise" # estimator fractional
def x_input_script_type(tx):
- types = {(v.get("prevout") or {}).get("scriptpubkey_type") for v in tx["vin"]}
+ types = {(v.get("prevout") or {}).get("scriptpubkey_type") for v in tx.get("vin", ())}
types.discard(None)
- if not types: return "na"
+ if not types: return NA
return f"uniform_{next(iter(types))}" if len(types) == 1 else "mixed"
def x_op_return(tx):
- return "has_op_return" if any(o.get("scriptpubkey_type") == "op_return" for o in tx["vout"]) else "none"
+ vout = tx.get("vout") or ()
+ if not vout or any("scriptpubkey_type" not in o for o in vout): return NA
+ return "has_op_return" if any(o["scriptpubkey_type"] == "op_return" for o in vout) else "none"
_ENC = {"p2pkh": "base58", "p2sh": "base58", "v0_p2wpkh": "bech32",
"v0_p2wsh": "bech32", "v1_p2tr": "bech32m"}
@@ -103,7 +141,7 @@ def x_output_encoding(tx):
encs = {_ENC.get(o.get("scriptpubkey_type")) for o in tx["vout"]
if o.get("scriptpubkey_type") != "op_return"}
encs.discard(None)
- if not encs: return "na"
+ if not encs: return NA
return next(iter(encs)) if len(encs) == 1 else "mixed"
def x_input_types_present(tx):
@@ -126,7 +164,7 @@ def x_pubkey_compression(tx):
pk = w[-1]; n = len(pk) // 2
if n == 33 and pk[:2].lower() in ("02", "03"): kinds.add("compressed")
elif n == 65 and pk[:2].lower() == "04": kinds.add("uncompressed")
- if not kinds: return "na"
+ if not kinds: return NA
return next(iter(kinds)) if len(kinds) == 1 else "mixed"
def x_multisig(tx):
@@ -155,19 +193,19 @@ def _change_index(tx):
def x_change_index(tx):
ci = _change_index(tx)
- if ci is None: return "na"
+ if ci is None: return NA
return "first" if ci == 0 else "last" # 2-out: change is idx 0 or 1
def x_change_type_match(tx):
ci = _change_index(tx)
- if ci is None: return "na"
+ if ci is None: return NA
ct = tx["vout"][ci].get("scriptpubkey_type")
itypes = {(v.get("prevout") or {}).get("scriptpubkey_type") for v in tx["vin"]}
return "match_input" if ct in itypes else "mismatch_input"
def x_change_matches_output(tx):
ci = _change_index(tx)
- if ci is None: return "na"
+ if ci is None: return NA
ot = tx["vout"][1 - ci].get("scriptpubkey_type")
ct = tx["vout"][ci].get("scriptpubkey_type")
return "match_output" if ct == ot else "mismatch_output"
@@ -183,11 +221,14 @@ def x_change_address_reuse(tx):
def x_locktime_vs_broadcast(tx):
"""nLocktime vs estimated broadcast height. Reads the tx['_bc'] annotation
- (prev_min/prev_time/incl_time); returns 'na' when unannotated / coinbase."""
+ (prev_min/prev_time/incl_time); returns 'na' when unannotated / coinbase.
+
+ Only `broadcast.annotate_broadcast` writes that annotation, and only at fetch time, so on any
+ stored dataset this returns 'na' for every transaction and the axis carries no evidence."""
bc = tx.get("_bc")
st = tx.get("status") or {}
n = st.get("block_height")
if bc is None or n is None:
- return "na"
+ return NA
win = broadcast_window(tx_feerate(tx), bc["prev_min"], bc["prev_time"], bc["incl_time"])
return locktime_vs_broadcast(tx.get("locktime", 0), n, win)
diff --git a/decluster/failure_modes/__init__.py b/decluster/failure_modes/__init__.py
new file mode 100644
index 00000000..4a68fe7e
--- /dev/null
+++ b/decluster/failure_modes/__init__.py
@@ -0,0 +1,27 @@
+"""Executable scenarios for failure modes described by the CTP model."""
+
+from . import (
+ change_consolidation,
+ collaborative_forms,
+ entropy_insufficiency,
+ equal_output_consolidation,
+ eve_alice_eve,
+ provenance_decay,
+ perfect_matching_disclosure,
+ denomination_preparation,
+ statistical_disclosure,
+ unnecessary_input,
+)
+
+__all__ = [
+ "change_consolidation",
+ "collaborative_forms",
+ "entropy_insufficiency",
+ "equal_output_consolidation",
+ "eve_alice_eve",
+ "provenance_decay",
+ "perfect_matching_disclosure",
+ "denomination_preparation",
+ "statistical_disclosure",
+ "unnecessary_input",
+]
diff --git a/decluster/failure_modes/change_consolidation.py b/decluster/failure_modes/change_consolidation.py
new file mode 100644
index 00000000..6f983ab7
--- /dev/null
+++ b/decluster/failure_modes/change_consolidation.py
@@ -0,0 +1,114 @@
+"""Retroactive linkage of payments when their change outputs are consolidated."""
+
+from dataclasses import dataclass
+
+from ..baselines.candidate_set_intersection import intersect_candidate_sets
+from ..domain import (
+ AttackReport,
+ CandidateNarrowing,
+ CandidateSetEvidence,
+ EvidenceChannel,
+ EvidenceContext,
+ IntersectionEvidence,
+ NoSharedCandidates,
+ Subject,
+ SubjectKind,
+)
+
+
+@dataclass(frozen=True)
+class ChangeConsolidationScenario:
+ """Two prior payments whose candidate change outputs are later co-spent."""
+
+ payments: tuple[Subject, Subject]
+ change_outputs: tuple[Subject, Subject]
+ candidate_owners: tuple[frozenset[Subject], frozenset[Subject]]
+
+ def __post_init__(self) -> None:
+ if len(set(self.payments)) != 2 or len(set(self.change_outputs)) != 2:
+ raise ValueError("payments and change outputs must be distinct")
+ if any(
+ payment.kind is not SubjectKind.TRANSACTION for payment in self.payments
+ ):
+ raise ValueError("payments must be transaction subjects")
+ if any(
+ output.kind is not SubjectKind.OUTPOINT for output in self.change_outputs
+ ):
+ raise ValueError("change outputs must be outpoint subjects")
+ if any(len(owners) < 2 for owners in self.candidate_owners):
+ raise ValueError("each candidate change output must remain ambiguous")
+
+
+def ctp_example() -> ChangeConsolidationScenario:
+ """Return the CTP-style case where Alice's two changes meet later."""
+ alice = Subject(SubjectKind.CLUSTER, "alice")
+ return ChangeConsolidationScenario(
+ payments=(
+ Subject(SubjectKind.TRANSACTION, "payment-1"),
+ Subject(SubjectKind.TRANSACTION, "payment-2"),
+ ),
+ change_outputs=(
+ Subject(SubjectKind.OUTPOINT, ("payment-1", 1)),
+ Subject(SubjectKind.OUTPOINT, ("payment-2", 1)),
+ ),
+ candidate_owners=(
+ frozenset({alice, Subject(SubjectKind.CLUSTER, "bob")}),
+ frozenset({alice, Subject(SubjectKind.CLUSTER, "carol")}),
+ ),
+ )
+
+
+def evaluate(scenario: ChangeConsolidationScenario) -> AttackReport:
+ """Evaluate retroactive linkage conditional on change identification and CIOH."""
+ candidate_context = EvidenceContext(
+ adversary="external on-chain observer",
+ observables=("payment transactions", "candidate change outputs"),
+ hypothesis="each selected output is change owned by one candidate cluster",
+ algorithm="change-candidate construction",
+ dataset="ctp synthetic change-consolidation fixture",
+ limitations=("change identification is supplied by the fixture",),
+ )
+ candidates = tuple(
+ CandidateSetEvidence(output, owners, candidate_context)
+ for output, owners in zip(scenario.change_outputs, scenario.candidate_owners)
+ )
+ surviving = frozenset(
+ intersect_candidate_sets(scenario.candidate_owners).surviving
+ )
+ intersection_context = EvidenceContext(
+ adversary="external on-chain observer",
+ observables=("candidate change outputs", "later co-spend"),
+ hypothesis="identified change outputs in the co-spend share an owner",
+ algorithm="candidate-owner intersection",
+ dataset="ctp synthetic change-consolidation fixture",
+ limitations=(
+ "the inference requires both change identifications and the co-spend "
+ "ownership premise",
+ ),
+ )
+ intersection = IntersectionEvidence(
+ observations=scenario.payments,
+ candidates_before=min(map(len, scenario.candidate_owners)),
+ candidates_after=surviving,
+ context=intersection_context,
+ )
+ outcome = (
+ CandidateNarrowing(intersection)
+ if surviving
+ else NoSharedCandidates(intersection)
+ )
+ return AttackReport(
+ identifier="ctp.change-consolidation",
+ attack="retroactive payment linkage through consolidated change",
+ subjects=scenario.payments + scenario.change_outputs,
+ channels=(
+ EvidenceChannel("change_candidates", candidates),
+ EvidenceChannel("payment_intersection", (intersection,)),
+ ),
+ outcomes=(outcome,),
+ limitations=(
+ "synthetic fixture",
+ "the report narrows candidate owners and does not identify a real person",
+ "a collaborative co-spend invalidates the common-ownership premise",
+ ),
+ )
diff --git a/decluster/failure_modes/cioh_collaborative.py b/decluster/failure_modes/cioh_collaborative.py
new file mode 100644
index 00000000..5edf4863
--- /dev/null
+++ b/decluster/failure_modes/cioh_collaborative.py
@@ -0,0 +1,68 @@
+"""Evaluate CIOH against an explicitly labelled collaborative co-spend."""
+
+from __future__ import annotations
+
+from itertools import combinations
+
+from ..cluster import cluster_cospends
+from ..domain import (
+ AttackReport,
+ ClusterMerge,
+ CoSpendEvidence,
+ EvidenceChannel,
+ EvidenceContext,
+ ReferenceOwnershipConflict,
+ Subject,
+ SubjectKind,
+)
+
+
+def evaluate(transaction_id: str, owner_by_coin: dict[str, str]) -> AttackReport:
+ """Apply CIOH once and expose conflicts with supplied ownership labels.
+
+ The labels are fixture inputs, not inferred identities or chain-wide truth.
+ """
+ if len(owner_by_coin) < 2:
+ raise ValueError("a co-spend requires at least two labelled coins")
+ if any(not coin or not owner for coin, owner in owner_by_coin.items()):
+ raise ValueError("coin and owner labels must not be empty")
+ coins = tuple(owner_by_coin)
+ subjects = tuple(Subject(SubjectKind.COIN, coin) for coin in coins)
+ transaction = Subject(SubjectKind.TRANSACTION, transaction_id)
+ context = EvidenceContext(
+ adversary="external observer applying common-input ownership",
+ observables=("transaction inputs",),
+ hypothesis="all inputs in one transaction share an owner",
+ algorithm="common-input ownership union-find",
+ limitations=("reference owner labels are supplied by the fixture",),
+ )
+ evidence = CoSpendEvidence(transaction=transaction, inputs=subjects, context=context)
+ groups = cluster_cospends(coins, (coins,))
+ cluster_by_coin = {
+ coin: index for index, group in enumerate(groups) for coin in group
+ }
+ outcomes = []
+ for left, right in combinations(subjects, 2):
+ if cluster_by_coin[left.identifier] != cluster_by_coin[right.identifier]:
+ continue
+ outcomes.append(ClusterMerge(left, right, (evidence,)))
+ left_owner = owner_by_coin[left.identifier]
+ right_owner = owner_by_coin[right.identifier]
+ if left_owner != right_owner:
+ outcomes.append(
+ ReferenceOwnershipConflict(
+ left, right, evidence, left_owner, right_owner
+ )
+ )
+ return AttackReport(
+ identifier=f"cioh-collaborative:{transaction_id}",
+ attack="common-input ownership applied to a collaborative transaction",
+ subjects=subjects,
+ channels=(EvidenceChannel("co_spend", (evidence,)),),
+ outcomes=tuple(outcomes),
+ limitations=(
+ "the deterministic fixture supplies participant ownership labels",
+ "the result demonstrates one false merge, not its chain-wide prevalence",
+ "a co-spend alone does not prove common ownership",
+ ),
+ )
diff --git a/decluster/failure_modes/collaborative_forms.py b/decluster/failure_modes/collaborative_forms.py
new file mode 100644
index 00000000..e3d495d5
--- /dev/null
+++ b/decluster/failure_modes/collaborative_forms.py
@@ -0,0 +1,155 @@
+"""Executable CTP matrix of collaborative forms and observer knowledge."""
+
+from dataclasses import dataclass
+from enum import Enum
+
+
+class CollaborativeForm(str, Enum):
+ ORDINARY_TWO_INPUT = "ordinary_two_input"
+ P2EP = "p2ep"
+ BIP79 = "bip79_bustapay"
+ BIP78 = "bip78_sync_payjoin"
+ BIP77 = "bip77_async_payjoin"
+ NS1R = "many_senders_one_receiver"
+ NSNR = "many_senders_many_receivers"
+ NET_SETTLEMENT = "net_settlement_with_cycles"
+
+
+class Observer(str, Enum):
+ EXTERNAL = "external_onchain_observer"
+ COUNTERPARTY = "protocol_counterparty"
+
+
+@dataclass(frozen=True)
+class OnChainObservation:
+ inputs: tuple[int, ...]
+ outputs: tuple[int, ...]
+
+ def __post_init__(self) -> None:
+ if not self.inputs or not self.outputs:
+ raise ValueError("on-chain observation requires inputs and outputs")
+ if any(value <= 0 for value in self.inputs + self.outputs):
+ raise ValueError("on-chain amounts must be positive")
+ if sum(self.outputs) > sum(self.inputs):
+ raise ValueError("outputs cannot exceed inputs")
+
+
+@dataclass(frozen=True)
+class FormContract:
+ form: CollaborativeForm
+ participants: int | None
+ observation: OnChainObservation
+ transport: str
+ external_knowledge: tuple[str, ...]
+ counterparty_knowledge: tuple[str, ...]
+ limitations: tuple[str, ...]
+
+ def __post_init__(self) -> None:
+ if self.participants is not None and self.participants < 1:
+ raise ValueError("participant count must be positive when known")
+ if not self.transport or not self.external_knowledge or not self.limitations:
+ raise ValueError("form contract metadata must not be empty")
+
+
+TWO_PARTY_OBSERVATION = OnChainObservation((90, 60), (110, 40))
+
+
+def form_matrix() -> tuple[FormContract, ...]:
+ """Return protocol/form contracts using the CTP's representative amounts."""
+
+ external_two_party = (
+ "input and output amounts",
+ "transaction shape and script-visible construction features",
+ "participant allocation is not observed",
+ "payment amount is not observed",
+ )
+ counterparty_two_party = (
+ "own inputs and outputs",
+ "the only other party's remaining inputs and outputs by elimination",
+ "negotiated payment amount",
+ )
+ two_party_limit = (
+ "the same on-chain observation is compatible with an ordinary single-owner spend",
+ "two-party PayJoin does not provide privacy from the counterparty",
+ )
+ rows = [
+ FormContract(
+ CollaborativeForm.ORDINARY_TWO_INPUT,
+ 1,
+ TWO_PARTY_OBSERVATION,
+ "ordinary wallet transaction",
+ external_two_party,
+ (),
+ ("ownership is latent even when the transaction is actually unilateral",),
+ )
+ ]
+ for form, transport in (
+ (CollaborativeForm.P2EP, "Pay-to-EndPoint negotiation"),
+ (CollaborativeForm.BIP79, "Bustapay template negotiation"),
+ (CollaborativeForm.BIP78, "synchronous PayJoin negotiation"),
+ (CollaborativeForm.BIP77, "asynchronous PayJoin negotiation"),
+ ):
+ rows.append(
+ FormContract(
+ form,
+ 2,
+ TWO_PARTY_OBSERVATION,
+ transport,
+ external_two_party,
+ counterparty_two_party,
+ two_party_limit,
+ )
+ )
+ rows.extend(
+ (
+ FormContract(
+ CollaborativeForm.NS1R,
+ 4,
+ OnChainObservation((40, 50, 60, 90), (160, 30, 30, 20)),
+ "many senders negotiate with one receiver",
+ ("amounts and transaction shape", "sender allocation is not observed"),
+ (
+ "the receiver knows each negotiated payment and sender change",
+ "the receiver may not know which input belongs to which sender",
+ ),
+ (
+ "receiver consolidation and sibling-output spending can create fingerprints",
+ ),
+ ),
+ FormContract(
+ CollaborativeForm.NSNR,
+ 6,
+ OnChainObservation(
+ (90, 15, 40, 30, 45, 35),
+ (85, 25, 50, 40, 45, 10),
+ ),
+ "paired many-sender many-receiver negotiation",
+ ("amounts and transaction shape", "payment pairing is not observed"),
+ ("own subtransaction and negotiated payment", "other pairings remain latent"),
+ ("one payment per subtransaction leaves amount-based plausibility signals",),
+ ),
+ FormContract(
+ CollaborativeForm.NET_SETTLEMENT,
+ None,
+ OnChainObservation((60, 20, 50), (40, 50, 40)),
+ "multiparty settlement of mutual obligations",
+ ("net on-chain balances", "participant count may remain latent"),
+ ("own obligations and contributions", "other parties' gross obligations remain latent"),
+ (
+ "cycles make gross payment amounts and obligation count unidentifiable",
+ "community structure and auxiliary information remain observable over time",
+ ),
+ ),
+ )
+ )
+ return tuple(rows)
+
+
+def knowledge(contract: FormContract, observer: Observer) -> tuple[str, ...]:
+ """Return only the knowledge available to the declared observer."""
+
+ if observer is Observer.EXTERNAL:
+ return contract.external_knowledge
+ if observer is Observer.COUNTERPARTY:
+ return contract.counterparty_knowledge
+ raise TypeError("observer must be an Observer")
diff --git a/decluster/failure_modes/denomination_preparation.py b/decluster/failure_modes/denomination_preparation.py
new file mode 100644
index 00000000..e7bbc530
--- /dev/null
+++ b/decluster/failure_modes/denomination_preparation.py
@@ -0,0 +1,132 @@
+"""Detect an overt fixed-denomination preparation shape before CoinJoin."""
+
+from dataclasses import dataclass
+
+from ..domain import (
+ AttackReport,
+ EvidenceChannel,
+ EvidenceContext,
+ Inconclusive,
+ Subject,
+ SubjectKind,
+ TransactionFingerprintEvidence,
+ TransactionFingerprintObserved,
+)
+
+RULE = "one exact-denomination output is consumed by the declared next CoinJoin"
+
+
+@dataclass(frozen=True)
+class DenominationPreparationObservation:
+ transaction: Subject
+ input_count: int
+ output_amounts: tuple[int, ...]
+ next_coinjoin_inputs: frozenset[int]
+ denomination: int
+
+ def __post_init__(self) -> None:
+ if self.transaction.kind is not SubjectKind.TRANSACTION:
+ raise ValueError("observation requires a transaction subject")
+ if self.input_count < 1:
+ raise ValueError("input_count must be positive")
+ if not self.output_amounts or any(amount < 0 for amount in self.output_amounts):
+ raise ValueError("output amounts must be non-empty and non-negative")
+ if self.denomination <= 0:
+ raise ValueError("denomination must be positive")
+ if any(index < 0 or index >= len(self.output_amounts) for index in self.next_coinjoin_inputs):
+ raise ValueError("next-CoinJoin output index is outside the transaction")
+
+
+def paired_corpus() -> tuple[
+ tuple[DenominationPreparationObservation, ...],
+ tuple[DenominationPreparationObservation, ...],
+]:
+ denomination = 100_000
+ preparations = tuple(
+ DenominationPreparationObservation(
+ Subject(SubjectKind.TRANSACTION, f"preparation-{index}"),
+ 2,
+ (denomination, change),
+ frozenset({0}),
+ denomination,
+ )
+ for index, change in enumerate((17_000, 23_000, 41_000))
+ )
+ controls = tuple(
+ DenominationPreparationObservation(
+ Subject(SubjectKind.TRANSACTION, f"control-{index}"),
+ 2,
+ (payment, change),
+ frozenset({0}),
+ denomination,
+ )
+ for index, (payment, change) in enumerate(
+ ((93_000, 24_000), (107_000, 16_000), (88_000, 53_000))
+ )
+ )
+ return preparations, controls
+
+
+def fingerprint(observation: DenominationPreparationObservation) -> TransactionFingerprintEvidence:
+ exact_outputs = tuple(
+ index
+ for index, amount in enumerate(observation.output_amounts)
+ if amount == observation.denomination
+ )
+ exact_spent_next = sum(
+ index in observation.next_coinjoin_inputs for index in exact_outputs
+ )
+ context = EvidenceContext(
+ adversary="external on-chain observer",
+ observables=(
+ "predecessor input and output counts",
+ "output amounts",
+ "outputs consumed by the declared next CoinJoin",
+ ),
+ hypothesis="a predecessor transaction visibly prepares the next CoinJoin denomination",
+ algorithm="exact-denomination predecessor shape",
+ dataset="deterministic synthetic preparation and arity-matched controls",
+ limitations=(
+ "the next CoinJoin and its denomination are supplied",
+ "the rule does not establish whether the predecessor was unilateral",
+ ),
+ )
+ return TransactionFingerprintEvidence(
+ observation.transaction,
+ (
+ ("input_count", observation.input_count),
+ ("output_count", len(observation.output_amounts)),
+ ("exact_denomination_outputs", len(exact_outputs)),
+ ("exact_denomination_outputs_spent_next", exact_spent_next),
+ ),
+ context,
+ )
+
+
+def evaluate(observation: DenominationPreparationObservation) -> AttackReport:
+ evidence = fingerprint(observation)
+ features = dict(evidence.features)
+ matched = features["exact_denomination_outputs"] == 1 and features[
+ "exact_denomination_outputs_spent_next"
+ ] == 1
+ outcome = (
+ TransactionFingerprintObserved(evidence, RULE)
+ if matched
+ else Inconclusive(
+ (observation.transaction,),
+ "the declared denomination-preparation fingerprint was not observed",
+ )
+ )
+ return AttackReport(
+ identifier=f"ctp.denomination-preparation.{observation.transaction.identifier}",
+ attack="fixed-denomination preparation fingerprint",
+ subjects=(observation.transaction,),
+ channels=(EvidenceChannel("transaction_shape", (evidence,)),),
+ outcomes=(outcome,),
+ limitations=(
+ "deterministic synthetic corpus",
+ "controls match transaction arity but not a real-world population distribution",
+ "a fingerprint match is not proof of unilateral construction or common ownership",
+ "the CTP qualification has no separately cited empirical experiment",
+ ),
+ )
diff --git a/decluster/failure_modes/deterministic_partition.py b/decluster/failure_modes/deterministic_partition.py
new file mode 100644
index 00000000..fae45b5a
--- /dev/null
+++ b/decluster/failure_modes/deterministic_partition.py
@@ -0,0 +1,79 @@
+"""Amount links shared by every exact non-derived subtransaction mapping."""
+
+from __future__ import annotations
+
+from ..baselines.maurer import exact_non_derived_mappings
+from ..domain import (
+ AttackReport,
+ ConditionalLinksMeasured,
+ EvidenceChannel,
+ EvidenceContext,
+ Subject,
+ SubjectKind,
+ UnanimousMappingLinksEvidence,
+)
+
+
+def evaluate(inputs, outputs, *, max_coins=12) -> AttackReport:
+ """Enumerate Maurer's exact non-derived family and return unanimous links.
+
+ The result is conditional on exact per-block conservation and equal weight
+ across the retained family. It is not a model-independent ownership claim.
+ """
+ input_values = tuple(inputs)
+ output_values = tuple(outputs)
+ mappings = exact_non_derived_mappings(
+ input_values, output_values, max_coins=max_coins
+ )
+ if not mappings:
+ raise ValueError("fixture has no exact non-derived mapping")
+ input_subjects = tuple(
+ Subject(SubjectKind.COIN, ("input", index))
+ for index in range(len(input_values))
+ )
+ output_subjects = tuple(
+ Subject(SubjectKind.COIN, ("output", index))
+ for index in range(len(output_values))
+ )
+ unanimous = []
+ for input_index, input_subject in enumerate(input_subjects):
+ for output_index, output_subject in enumerate(output_subjects):
+ if all(
+ any(
+ input_index in input_block and output_index in output_block
+ for input_block, output_block in mapping.blocks
+ )
+ for mapping in mappings
+ ):
+ unanimous.append((input_subject, output_subject))
+ context = EvidenceContext(
+ adversary="external observer using transaction amounts",
+ observables=("input amounts", "output amounts"),
+ hypothesis="inputs and outputs sharing a participant block are linked",
+ algorithm="Maurer exact non-derived subtransaction mappings",
+ dataset="CTP amount-partition examples scaled to integer units",
+ limitations=(
+ "every participant block must conserve value exactly",
+ "fees and net-settlement cycles are outside this mapping model",
+ ),
+ )
+ evidence = UnanimousMappingLinksEvidence(
+ inputs=input_subjects,
+ outputs=output_subjects,
+ mapping_count=len(mappings),
+ links=tuple(unanimous),
+ context=context,
+ )
+ return AttackReport(
+ identifier="deterministic-partition",
+ attack="amount-constrained subtransaction partitioning",
+ subjects=input_subjects + output_subjects,
+ channels=(EvidenceChannel("exact_non_derived_mappings", (evidence,)),),
+ outcomes=(ConditionalLinksMeasured(evidence),),
+ limitations=(
+ "certainty applies only within the declared exact non-derived mapping family",
+ "the examples are scaled integer renderings of the CTP amount fixtures",
+ "fees, contextual priors and net-settlement cycles are not modeled",
+ "unanimous mapping links are not ownership attribution",
+ ),
+ )
diff --git a/decluster/failure_modes/entropy_insufficiency.py b/decluster/failure_modes/entropy_insufficiency.py
new file mode 100644
index 00000000..d8878719
--- /dev/null
+++ b/decluster/failure_modes/entropy_insufficiency.py
@@ -0,0 +1,122 @@
+"""Paired scenarios where equal point entropy has different longitudinal outcomes."""
+
+from dataclasses import dataclass
+from math import isfinite, log2
+
+from ..domain import (
+ AttackReport,
+ CandidateNarrowing,
+ EvidenceChannel,
+ EvidenceContext,
+ IntersectionEvidence,
+ ProvenanceDistributionEvidence,
+ Subject,
+ SubjectKind,
+)
+
+
+@dataclass(frozen=True)
+class EntropyScenario:
+ identifier: str
+ target: Subject
+ probabilities: tuple[tuple[Subject, float], ...]
+ observations: tuple[tuple[Subject, frozenset[Subject]], ...]
+
+ def __post_init__(self) -> None:
+ if not self.identifier:
+ raise ValueError("scenario identifier must not be empty")
+ if self.target.kind is not SubjectKind.OUTPOINT:
+ raise ValueError("target must be an outpoint")
+ if len(self.observations) < 2:
+ raise ValueError("longitudinal comparison requires at least two observations")
+ candidates = {candidate for candidate, _ in self.probabilities}
+ if not candidates or len(candidates) != len(self.probabilities):
+ raise ValueError("initial distribution must not be empty")
+ probabilities = tuple(probability for _, probability in self.probabilities)
+ if any(not isfinite(probability) or probability < 0.0 for probability in probabilities):
+ raise ValueError("posterior probabilities must be finite and non-negative")
+ if abs(sum(probabilities) - 1.0) > 1e-9:
+ raise ValueError("posterior probabilities must sum to one")
+ if any(not observed or not observed <= candidates for _, observed in self.observations):
+ raise ValueError("observed candidate sets must be non-empty subsets of the posterior")
+
+ @property
+ def entropy_bits(self) -> float:
+ return -sum(
+ probability * log2(probability)
+ for _, probability in self.probabilities
+ if probability > 0.0
+ )
+
+
+def _candidate(identifier: str) -> Subject:
+ return Subject(SubjectKind.CLUSTER, identifier)
+
+
+def paired_examples() -> tuple[EntropyScenario, EntropyScenario]:
+ candidates = tuple(_candidate(identifier) for identifier in ("a", "b", "c", "d"))
+ probabilities = tuple((candidate, 0.25) for candidate in candidates)
+ brittle = EntropyScenario(
+ identifier="brittle-overlap",
+ target=Subject(SubjectKind.OUTPOINT, ("brittle", 0)),
+ probabilities=probabilities,
+ observations=(
+ (Subject(SubjectKind.COIN, "brittle-observation-1"), frozenset(candidates[:2])),
+ (
+ Subject(SubjectKind.COIN, "brittle-observation-2"),
+ frozenset((candidates[0], candidates[2])),
+ ),
+ ),
+ )
+ robust = EntropyScenario(
+ identifier="overlapping-crowd",
+ target=Subject(SubjectKind.OUTPOINT, ("overlapping", 0)),
+ probabilities=probabilities,
+ observations=(
+ (Subject(SubjectKind.COIN, "overlapping-observation-1"), frozenset(candidates[:3])),
+ (
+ Subject(SubjectKind.COIN, "overlapping-observation-2"),
+ frozenset((candidates[0], candidates[1], candidates[3])),
+ ),
+ ),
+ )
+ return brittle, robust
+
+
+def evaluate(scenario: EntropyScenario) -> AttackReport:
+ context = EvidenceContext(
+ adversary="observer who links multiple post-CoinJoin observations",
+ observables=("initial candidate posterior", "candidate sets after later observations"),
+ hypothesis="point entropy alone predicts resistance to longitudinal intersection",
+ algorithm="explicit posterior entropy followed by candidate-set intersection",
+ dataset="CTP synthetic equal-entropy counterexample",
+ limitations=("posterior probabilities and later candidate sets are supplied",),
+ )
+ posterior = ProvenanceDistributionEvidence(
+ scenario.target, scenario.probabilities, context
+ )
+ survivors = frozenset.intersection(
+ *(candidates for _, candidates in scenario.observations)
+ )
+ intersection = IntersectionEvidence(
+ tuple(observation for observation, _ in scenario.observations),
+ len(scenario.probabilities),
+ survivors,
+ context,
+ )
+ return AttackReport(
+ identifier=f"ctp.entropy-insufficiency.{scenario.identifier}",
+ attack="longitudinal intersection after an equal-entropy posterior",
+ subjects=(scenario.target,) + tuple(candidate for candidate, _ in scenario.probabilities),
+ channels=(
+ EvidenceChannel("initial_posterior", (posterior,)),
+ EvidenceChannel("longitudinal_intersection", (intersection,)),
+ ),
+ outcomes=(CandidateNarrowing(intersection),),
+ limitations=(
+ "synthetic counterexample",
+ "equal point entropy does not imply equal future candidate-set overlap",
+ "the fixture falsifies sufficiency but does not estimate real-world attack frequency",
+ "the result is not a privacy score or certificate",
+ ),
+ )
diff --git a/decluster/failure_modes/equal_output_consolidation.py b/decluster/failure_modes/equal_output_consolidation.py
new file mode 100644
index 00000000..cd0c5c5f
--- /dev/null
+++ b/decluster/failure_modes/equal_output_consolidation.py
@@ -0,0 +1,107 @@
+"""Retroactive narrowing after outputs from separate CoinJoins are co-spent."""
+
+from dataclasses import dataclass
+
+from ..baselines.candidate_set_intersection import intersect_candidate_sets
+from ..domain import (
+ AttackReport,
+ CandidateNarrowing,
+ CandidateSetEvidence,
+ EvidenceChannel,
+ EvidenceContext,
+ IntersectionEvidence,
+ NoSharedCandidates,
+ Subject,
+ SubjectKind,
+)
+
+
+@dataclass(frozen=True)
+class ConsolidationScenario:
+ """Two independently ambiguous outputs and their observed consolidation."""
+
+ outputs: tuple[Subject, Subject]
+ candidate_sets: tuple[frozenset[Subject], frozenset[Subject]]
+
+ def __post_init__(self) -> None:
+ if self.outputs[0] == self.outputs[1]:
+ raise ValueError("consolidation requires distinct outputs")
+ if any(len(candidates) < 2 for candidates in self.candidate_sets):
+ raise ValueError("each output must be ambiguous before consolidation")
+
+
+def ctp_example() -> ConsolidationScenario:
+ """Return the minimal CTP-style case whose candidate sets overlap at Alice."""
+ alice = Subject(SubjectKind.CLUSTER, "alice")
+ return ConsolidationScenario(
+ outputs=(
+ Subject(SubjectKind.OUTPOINT, ("coinjoin-1", 0)),
+ Subject(SubjectKind.OUTPOINT, ("coinjoin-2", 0)),
+ ),
+ candidate_sets=(
+ frozenset({
+ alice,
+ Subject(SubjectKind.CLUSTER, "bob"),
+ Subject(SubjectKind.CLUSTER, "carol"),
+ }),
+ frozenset({
+ alice,
+ Subject(SubjectKind.CLUSTER, "dave"),
+ Subject(SubjectKind.CLUSTER, "erin"),
+ }),
+ ),
+ )
+
+
+def evaluate(scenario: ConsolidationScenario) -> AttackReport:
+ """Evaluate narrowing conditional on treating the consolidation as co-ownership."""
+ candidate_context = EvidenceContext(
+ adversary="external on-chain observer",
+ observables=("pre-CoinJoin clusters", "equal-output CoinJoin membership"),
+ hypothesis="each post-CoinJoin output has one of its candidate origin clusters",
+ algorithm="candidate origin construction",
+ dataset="ctp synthetic consolidation fixture",
+ limitations=("candidate sets are supplied by the fixture",),
+ )
+ candidates = tuple(
+ CandidateSetEvidence(output, origins, candidate_context)
+ for output, origins in zip(scenario.outputs, scenario.candidate_sets)
+ )
+
+ result = intersect_candidate_sets(scenario.candidate_sets)
+ intersection_context = EvidenceContext(
+ adversary="external on-chain observer",
+ observables=("candidate origin clusters", "later co-spend"),
+ hypothesis="the co-spent outputs share an owner and therefore an origin cluster",
+ algorithm="candidate set intersection",
+ dataset="ctp synthetic consolidation fixture",
+ limitations=(
+ "narrowing is conditional on the later co-spend being a valid ownership link",
+ ),
+ )
+ intersection = IntersectionEvidence(
+ observations=scenario.outputs,
+ candidates_before=min(map(len, scenario.candidate_sets)),
+ candidates_after=frozenset(result.surviving),
+ context=intersection_context,
+ )
+ outcome = (
+ CandidateNarrowing(intersection)
+ if intersection.candidates_after
+ else NoSharedCandidates(intersection)
+ )
+ return AttackReport(
+ identifier="ctp.equal-output-consolidation",
+ attack="retroactive candidate-set intersection after consolidation",
+ subjects=scenario.outputs,
+ channels=(
+ EvidenceChannel("candidate_origins", candidates),
+ EvidenceChannel("consolidation_intersection", (intersection,)),
+ ),
+ outcomes=(outcome,),
+ limitations=(
+ "synthetic fixture",
+ "the report demonstrates conditional narrowing, not an identity",
+ "a collaborative consolidation would invalidate the co-ownership premise",
+ ),
+ )
diff --git a/decluster/failure_modes/eve_alice_eve.py b/decluster/failure_modes/eve_alice_eve.py
new file mode 100644
index 00000000..5fd83286
--- /dev/null
+++ b/decluster/failure_modes/eve_alice_eve.py
@@ -0,0 +1,223 @@
+"""Eve–Alice–Eve narrowing by an adversarial counterparty.
+
+The writeup's claim about this adversary is not that a second consolidated return adds a
+candidate's worth of information, but that the loss compounds. One intersection cannot show
+that, so `ctp_compounding_example` supplies three returns and a stated candidate universe, and
+the report then carries the two numbers that separate the readings: the bits the successive
+intersections accumulate, and the bits striking one candidate off the list per return would
+have yielded over the same returns. Both are properties of the fixture. Neither is a measured
+shrink rate, and no rate is assumed to obtain them.
+"""
+
+import math
+from dataclasses import dataclass
+from typing import Optional
+
+from ..baselines.candidate_set_intersection import intersect_candidate_sets
+from ..domain import (
+ AttackReport,
+ CandidateNarrowing,
+ CandidateSetEvidence,
+ EvidenceChannel,
+ EvidenceContext,
+ ExperimentalComposition,
+ Inconclusive,
+ IntersectionEvidence,
+ Subject,
+ SubjectKind,
+)
+from ..intersect import accumulate_intersections
+
+
+@dataclass(frozen=True)
+class CounterpartyReturnScenario:
+ """Deposited inputs and customers consistent with each known origin."""
+
+ counterparty: Subject
+ deposited_inputs: tuple[Subject, ...]
+ candidate_customers: tuple[frozenset[Subject], ...]
+ universe_size: Optional[int] = None
+
+ def __post_init__(self) -> None:
+ if self.counterparty.kind is not SubjectKind.CLUSTER:
+ raise ValueError("counterparty must be a cluster subject")
+ if not self.deposited_inputs or len(self.deposited_inputs) != len(
+ self.candidate_customers
+ ):
+ raise ValueError("each deposited input requires a candidate set")
+ if len(set(self.deposited_inputs)) != len(self.deposited_inputs):
+ raise ValueError("deposited inputs must be distinct")
+ if any(item.kind is not SubjectKind.OUTPOINT for item in self.deposited_inputs):
+ raise ValueError("deposited inputs must be outpoint subjects")
+ if any(len(candidates) < 2 for candidates in self.candidate_customers):
+ raise ValueError("each input must remain ambiguous before intersection")
+ if self.universe_size is not None:
+ widest = max(len(candidates) for candidates in self.candidate_customers)
+ if isinstance(self.universe_size, bool) or not isinstance(self.universe_size, int):
+ raise ValueError("universe size must be an integer")
+ if self.universe_size < widest:
+ raise ValueError("universe size must cover the widest candidate set")
+
+
+def ctp_consolidation_example() -> CounterpartyReturnScenario:
+ """Return two independently mixed descendants consistent only with Alice."""
+ alice = Subject(SubjectKind.CLUSTER, "alice")
+ return CounterpartyReturnScenario(
+ counterparty=Subject(SubjectKind.CLUSTER, "eve-atm"),
+ deposited_inputs=(
+ Subject(SubjectKind.OUTPOINT, ("coinjoin-1", 0)),
+ Subject(SubjectKind.OUTPOINT, ("coinjoin-2", 0)),
+ ),
+ candidate_customers=(
+ frozenset({alice, Subject(SubjectKind.CLUSTER, "bob")}),
+ frozenset({alice, Subject(SubjectKind.CLUSTER, "carol")}),
+ ),
+ )
+
+
+def ctp_compounding_example() -> CounterpartyReturnScenario:
+ """Three returns against a stated customer universe, so the accumulation is measurable.
+
+ Two returns show the intersection; they do not distinguish compounding from adding up.
+ Separating those readings needs more than one intersection and a universe to measure the
+ narrowing against, which is what this scenario supplies and `ctp_consolidation_example`
+ deliberately does not.
+ """
+ alice = Subject(SubjectKind.CLUSTER, "alice")
+ bob = Subject(SubjectKind.CLUSTER, "bob")
+ carol = Subject(SubjectKind.CLUSTER, "carol")
+ dave = Subject(SubjectKind.CLUSTER, "dave")
+ return CounterpartyReturnScenario(
+ counterparty=Subject(SubjectKind.CLUSTER, "eve-atm"),
+ deposited_inputs=(
+ Subject(SubjectKind.OUTPOINT, ("coinjoin-1", 0)),
+ Subject(SubjectKind.OUTPOINT, ("coinjoin-2", 0)),
+ Subject(SubjectKind.OUTPOINT, ("coinjoin-3", 0)),
+ ),
+ candidate_customers=(
+ frozenset({alice, bob, carol}),
+ frozenset({alice, carol, dave}),
+ frozenset({alice, bob, dave}),
+ ),
+ universe_size=8,
+ )
+
+
+def evaluate(scenario: CounterpartyReturnScenario) -> AttackReport:
+ """Evaluate candidate narrowing available to the known-origin counterparty."""
+ provenance_context = EvidenceContext(
+ adversary="counterparty with private transaction records",
+ observables=(
+ "dispensed outpoints",
+ "descendant deposits",
+ "CoinJoin ancestry",
+ ),
+ hypothesis="a deposited input belongs to one customer consistent with its ancestry",
+ algorithm="known-origin candidate construction",
+ dataset="ctp synthetic Eve-Alice-Eve fixture",
+ limitations=("candidate customers are supplied by the fixture",),
+ )
+ candidates = tuple(
+ CandidateSetEvidence(deposit, customers, provenance_context)
+ for deposit, customers in zip(
+ scenario.deposited_inputs, scenario.candidate_customers
+ )
+ )
+ subjects = (scenario.counterparty,) + scenario.deposited_inputs
+ if len(scenario.deposited_inputs) == 1:
+ return AttackReport(
+ identifier="ctp.eve-alice-eve",
+ attack="known-origin counterparty return analysis",
+ subjects=subjects,
+ channels=(EvidenceChannel("known_origin_candidates", candidates),),
+ outcomes=(
+ Inconclusive(
+ subjects=scenario.deposited_inputs,
+ reason="one ambiguous return does not identify its customer",
+ ),
+ ),
+ limitations=(
+ "synthetic fixture",
+ "behavioral priors and temporal patterns are not modeled",
+ "candidate ancestry does not prove possession",
+ ),
+ )
+
+ surviving = frozenset(
+ intersect_candidate_sets(scenario.candidate_customers).surviving
+ )
+ intersection_context = EvidenceContext(
+ adversary="counterparty with private transaction records",
+ observables=("known origins", "multiple inputs in one deposit"),
+ hypothesis="the consolidated deposited inputs share a customer",
+ algorithm="known-origin candidate intersection",
+ dataset="ctp synthetic Eve-Alice-Eve fixture",
+ limitations=("common ownership of the deposited inputs is required",),
+ )
+ intersection = IntersectionEvidence(
+ observations=scenario.deposited_inputs,
+ candidates_before=min(map(len, scenario.candidate_customers)),
+ candidates_after=surviving,
+ context=intersection_context,
+ )
+ outcome = (
+ CandidateNarrowing(intersection)
+ if surviving
+ else Inconclusive(subjects, "known-origin candidate sets do not overlap")
+ )
+ return AttackReport(
+ identifier="ctp.eve-alice-eve",
+ attack="known-origin counterparty return intersection",
+ subjects=subjects,
+ channels=(
+ EvidenceChannel("known_origin_candidates", candidates),
+ EvidenceChannel("return_intersection", (intersection,)),
+ ),
+ outcomes=(outcome,),
+ limitations=(
+ "synthetic fixture",
+ "behavioral priors and temporal patterns are not modeled",
+ "candidate narrowing is not identity attribution",
+ "a collaborative deposit invalidates the common-ownership premise",
+ ),
+ composition=_compounding(scenario),
+ )
+
+
+def accumulate(
+ scenario: CounterpartyReturnScenario,
+) -> tuple[frozenset[Subject], float]:
+ """Survivors and accumulated bits over the returns, in order. Requires `universe_size`."""
+ survivors, bits = accumulate_intersections(
+ [[{customer: 1.0 for customer in customers}]
+ for customers in scenario.candidate_customers],
+ universe_size=scenario.universe_size,
+ )
+ return frozenset(survivors), bits
+
+
+def _elimination_baseline_bits(universe_size: int, returns: int) -> float:
+ """Bits from striking one candidate off the list per return — the additive reading."""
+ return math.log2(universe_size / max(universe_size - returns, 1))
+
+
+def _compounding(
+ scenario: CounterpartyReturnScenario,
+) -> Optional[ExperimentalComposition]:
+ """Accumulated narrowing against the one-at-a-time baseline, or None without a universe."""
+ if scenario.universe_size is None:
+ return None
+ _survivors, bits = accumulate(scenario)
+ additive = _elimination_baseline_bits(scenario.universe_size, len(scenario.deposited_inputs))
+ return ExperimentalComposition(
+ method="log2 narrowing accumulated across successive consolidated returns",
+ channel_ids=("return_intersection",),
+ value=bits,
+ unit="bits",
+ interpretation=(
+ f"{bits:.2f} bits over {len(scenario.deposited_inputs)} returns against "
+ f"{additive:.2f} for striking one candidate off a universe of "
+ f"{scenario.universe_size} per return; the gap is what compounding means here, "
+ "and it is a property of this fixture, not a measured rate"
+ ),
+ )
diff --git a/decluster/failure_modes/ns1r_change_readoff.py b/decluster/failure_modes/ns1r_change_readoff.py
new file mode 100644
index 00000000..507e49b8
--- /dev/null
+++ b/decluster/failure_modes/ns1r_change_readoff.py
@@ -0,0 +1,292 @@
+"""Receiver-side change read-off in a many-senders/one-receiver payjoin.
+
+The receiver knows its own coins and each sender's negotiated payment, so every
+candidate (input, change) pair must satisfy ``input - payment == change``. Read
+sender by sender the constraint is often ambiguous; enumerating the perfect
+matchings propagates it across senders and can leave a single reading.
+"""
+
+from dataclasses import dataclass
+from typing import Iterator, Mapping, Sequence
+
+from ..domain import (
+ AbstentionEvidence,
+ AttackReport,
+ CandidateSetEvidence,
+ ConditionalLinksMeasured,
+ EvidenceChannel,
+ EvidenceContext,
+ Inconclusive,
+ Subject,
+ SubjectKind,
+ UnanimousMappingLinksEvidence,
+)
+
+MAX_SENDERS = 8
+
+# Exact value-conserving block mappings are not a usable enumeration here: they
+# require every block to conserve value, whereas the receiver's block absorbs
+# the payments (40 in against 160 out below). The true sub-transaction
+# structure is therefore not a member of that family for any positive payment.
+
+
+@dataclass(frozen=True)
+class ReadOffScenario:
+ """One transaction plus the payment amounts the receiver negotiated."""
+
+ identifier: str
+ inputs: tuple[int, ...]
+ outputs: tuple[int, ...]
+ receiver_input: int
+ receiver_output: int
+ payments: tuple[tuple[str, int], ...]
+
+ def __post_init__(self) -> None:
+ if not self.identifier:
+ raise ValueError("scenario identifier must not be empty")
+ if any(value <= 0 for value in self.inputs + self.outputs):
+ raise ValueError("coin amounts must be positive")
+ # A non-zero fee would need a declared allocation rule between the
+ # sub-transactions; the read-off does not observe one.
+ if sum(self.inputs) != sum(self.outputs):
+ raise ValueError("read-off requires a fee-free conservation model")
+ if not 0 <= self.receiver_input < len(self.inputs):
+ raise ValueError("receiver input index out of range")
+ if not 0 <= self.receiver_output < len(self.outputs):
+ raise ValueError("receiver output index out of range")
+ senders = tuple(name for name, _ in self.payments)
+ if not senders or len(set(senders)) != len(senders):
+ raise ValueError("sender names must be unique and non-empty")
+ if any(not name or amount <= 0 for name, amount in self.payments):
+ raise ValueError("each sender must have a name and a positive payment")
+ if len(senders) > MAX_SENDERS:
+ raise ValueError("read-off enumeration is limited to eight senders")
+ # The form gives each sender exactly one input and one change output.
+ if len(self.inputs) != len(senders) + 1 or len(self.outputs) != len(senders) + 1:
+ raise ValueError("each sender contributes one input and one change output")
+ paid = sum(amount for _, amount in self.payments)
+ if self.outputs[self.receiver_output] != self.inputs[self.receiver_input] + paid:
+ raise ValueError("receiver output must equal its input plus the payments")
+
+ @classmethod
+ def from_payments(
+ cls,
+ identifier: str,
+ inputs: Sequence[int],
+ outputs: Sequence[int],
+ receiver_input: int,
+ receiver_output: int,
+ payments: Mapping[str, int],
+ ) -> "ReadOffScenario":
+ return cls(
+ identifier,
+ tuple(inputs),
+ tuple(outputs),
+ receiver_input,
+ receiver_output,
+ tuple(payments.items()),
+ )
+
+ @property
+ def senders(self) -> tuple[str, ...]:
+ return tuple(name for name, _ in self.payments)
+
+ @property
+ def sender_inputs(self) -> tuple[int, ...]:
+ return tuple(i for i in range(len(self.inputs)) if i != self.receiver_input)
+
+ @property
+ def change_outputs(self) -> tuple[int, ...]:
+ return tuple(i for i in range(len(self.outputs)) if i != self.receiver_output)
+
+
+@dataclass(frozen=True)
+class ChangeReadOff:
+ """Per-sender candidate change before and after propagating the constraint."""
+
+ scenario: ReadOffScenario
+ local_candidates: tuple[tuple[str, frozenset[int]], ...]
+ feasible_candidates: tuple[tuple[str, frozenset[int]], ...]
+ matchings: tuple[tuple[tuple[str, int, int], ...], ...]
+ readings: tuple[tuple[tuple[str, int, int], ...], ...]
+
+ @property
+ def unanimous_links(self) -> tuple[tuple[int, int], ...]:
+ if not self.matchings:
+ return ()
+ shared = set.intersection(
+ *({(source, target) for _, source, target in matching} for matching in self.matchings)
+ )
+ return tuple(sorted(shared))
+
+
+def ns1r_examples() -> tuple[ReadOffScenario, ReadOffScenario]:
+ return (
+ ReadOffScenario(
+ "consolidating-receiver",
+ (40, 50, 60, 90),
+ (160, 30, 30, 20),
+ 0,
+ 0,
+ (("Alice", 20), ("Bob", 30), ("Carol", 70)),
+ ),
+ ReadOffScenario(
+ "evenly-spaced-control",
+ (20, 40, 50, 60),
+ (80, 30, 20, 40),
+ 0,
+ 0,
+ (("Alice", 10), ("Bob", 30), ("Carol", 20)),
+ ),
+ )
+
+
+def _pairs(scenario: ReadOffScenario, payment: int) -> frozenset[tuple[int, int]]:
+ return frozenset(
+ (source, target)
+ for source in scenario.sender_inputs
+ for target in scenario.change_outputs
+ if scenario.inputs[source] - payment == scenario.outputs[target]
+ )
+
+
+def _matchings(
+ scenario: ReadOffScenario,
+ feasible: Sequence[frozenset[tuple[int, int]]],
+ position: int = 0,
+ used_inputs: frozenset[int] = frozenset(),
+ used_outputs: frozenset[int] = frozenset(),
+) -> Iterator[tuple[tuple[str, int, int], ...]]:
+ if position == len(feasible):
+ yield ()
+ return
+ sender = scenario.senders[position]
+ for source, target in sorted(feasible[position]):
+ if source in used_inputs or target in used_outputs:
+ continue
+ for rest in _matchings(
+ scenario,
+ feasible,
+ position + 1,
+ used_inputs | {source},
+ used_outputs | {target},
+ ):
+ yield ((sender, source, target),) + rest
+
+
+def analyze(scenario: ReadOffScenario) -> ChangeReadOff:
+ feasible = [_pairs(scenario, payment) for _, payment in scenario.payments]
+ matchings = tuple(_matchings(scenario, feasible))
+ local = tuple(
+ (sender, frozenset(target for _, target in pairs))
+ for sender, pairs in zip(scenario.senders, feasible)
+ )
+ reachable: dict[str, set[int]] = {sender: set() for sender in scenario.senders}
+ readings = set()
+ for matching in matchings:
+ for sender, _, target in matching:
+ reachable[sender].add(target)
+ readings.add(
+ tuple(
+ (sender, scenario.inputs[source], scenario.outputs[target])
+ for sender, source, target in matching
+ )
+ )
+ return ChangeReadOff(
+ scenario,
+ local,
+ tuple((sender, frozenset(reachable[sender])) for sender in scenario.senders),
+ matchings,
+ tuple(sorted(readings)),
+ )
+
+
+def _sender(name: str) -> Subject:
+ return Subject(SubjectKind.MAPPING, ("sender", name))
+
+
+def _coin(side: str, index: int) -> Subject:
+ return Subject(SubjectKind.COIN, (side, index))
+
+
+def evaluate(scenario: ReadOffScenario) -> AttackReport:
+ analysis = analyze(scenario)
+ senders = tuple(_sender(name) for name in scenario.senders)
+ sources = tuple(_coin("input", index) for index in scenario.sender_inputs)
+ targets = tuple(_coin("output", index) for index in scenario.change_outputs)
+ context = EvidenceContext(
+ adversary="the receiver of a many-senders/one-receiver payjoin",
+ observables=(
+ "input and output amounts",
+ "the receiver's own input and output",
+ "the payment amount negotiated with each sender",
+ ),
+ hypothesis="each sender's change is the remainder of one input after its payment",
+ algorithm="enumeration of the perfect matchings between senders and remainder-consistent input/output pairs",
+ dataset="deterministic synthetic NS1R fixture",
+ limitations=(
+ "the negotiated payments are supplied rather than observed",
+ "the form is assumed to give each sender one input and one change output",
+ "change outputs of equal amount stay interchangeable as coins",
+ ),
+ )
+ local = EvidenceChannel(
+ "per_sender_remainder",
+ tuple(
+ CandidateSetEvidence(
+ _sender(name),
+ frozenset(_coin("output", index) for index in candidates),
+ context,
+ )
+ for name, candidates in analysis.local_candidates
+ ),
+ )
+ if not analysis.matchings:
+ abstention = AbstentionEvidence(
+ senders, "no assignment satisfies every sender's remainder", context
+ )
+ channels = (local, EvidenceChannel("matching_propagation", (abstention,)))
+ outcomes = (Inconclusive(senders, abstention.reason),)
+ else:
+ propagated = EvidenceChannel(
+ "matching_propagation",
+ tuple(
+ CandidateSetEvidence(
+ _sender(name),
+ frozenset(_coin("output", index) for index in candidates),
+ context,
+ )
+ for name, candidates in analysis.feasible_candidates
+ ),
+ )
+ links = UnanimousMappingLinksEvidence(
+ sources,
+ targets,
+ len(analysis.matchings),
+ tuple(
+ (_coin("input", source), _coin("output", target))
+ for source, target in analysis.unanimous_links
+ ),
+ context,
+ )
+ channels = (local, propagated)
+ outcomes = (ConditionalLinksMeasured(links),)
+ if len(analysis.readings) > 1:
+ outcomes += (
+ Inconclusive(
+ senders, "several sender-to-remainder readings survive propagation"
+ ),
+ )
+ return AttackReport(
+ identifier=f"ctp.ns1r-change-readoff.{scenario.identifier}",
+ attack="receiver-side change read-off under declared payment amounts",
+ subjects=senders + sources + targets,
+ channels=channels,
+ outcomes=outcomes,
+ limitations=(
+ "the payment amounts are the receiver's declared knowledge, not an on-chain observable",
+ "a fee would require a declared allocation rule between sub-transactions",
+ "a reading assigns amounts, not coin identity, when change amounts coincide",
+ "the result is relative to the declared model and does not attribute ownership",
+ ),
+ )
diff --git a/decluster/failure_modes/perfect_matching_disclosure.py b/decluster/failure_modes/perfect_matching_disclosure.py
new file mode 100644
index 00000000..67978fcb
--- /dev/null
+++ b/decluster/failure_modes/perfect_matching_disclosure.py
@@ -0,0 +1,177 @@
+"""Profile-weighted perfect-matching disclosure for one threshold-mix round.
+
+Sent and received messages are carried as `SubjectKind.MAPPING`. There is no message kind in
+the vocabulary and none is added for a synthetic fixture; the reuse is notation.
+
+One deliberate divergence from the paper. Its equation 4 linearizes the joint probability by
+edge weights `log(P)`, and it defines `log(0) = -inf` expressly so the assignment algorithm
+still returns a matching -- the paper notes that the substitution "solely prevents numerical
+errors and has no influence on the output M". Here a permutation containing a zero-probability
+pair is dropped instead, and a round in which every permutation contains one abstains rather
+than returning a matching the profiles say is impossible. That is the repository's refuse-only
+posture, not the paper's behaviour, and it changes the output where the paper's does not.
+Weights are natural logs; base is immaterial to the argmax but the reported log-likelihood is
+not on the paper's base-10 scale.
+"""
+
+from dataclasses import dataclass
+from itertools import permutations
+from math import isclose, isfinite, log
+from typing import Optional
+
+from ..domain import (
+ AbstentionEvidence,
+ AttackReport,
+ EvidenceChannel,
+ EvidenceContext,
+ Inconclusive,
+ MessageAssignmentRecovered,
+ PerfectMatchingEvidence,
+ Subject,
+ SubjectKind,
+)
+
+
+@dataclass(frozen=True)
+class PerfectMatchingScenario:
+ identifier: str
+ senders: tuple[Subject, ...]
+ receivers: tuple[Subject, ...]
+ profile_weights: tuple[tuple[float, ...], ...]
+
+ def __post_init__(self) -> None:
+ if not self.identifier:
+ raise ValueError("scenario identifier must not be empty")
+ size = len(self.senders)
+ if size < 2 or len(self.receivers) != size:
+ raise ValueError("scenario requires equal sides of size at least two")
+ if len(set(self.senders)) != size or len(set(self.receivers)) != size:
+ raise ValueError("message nodes on each side must be unique")
+ if len(self.profile_weights) != size or any(
+ len(row) != size for row in self.profile_weights
+ ):
+ raise ValueError("profile matrix must be square and match the round")
+ if any(
+ not isfinite(weight) or not 0.0 <= weight <= 1.0
+ for row in self.profile_weights
+ for weight in row
+ ):
+ raise ValueError("profile weights must be finite probabilities")
+ if size > 8:
+ raise ValueError("diagnostic enumerator is limited to eight messages")
+
+
+@dataclass(frozen=True)
+class PerfectMatchingAnalysis:
+ assignments: tuple[tuple[tuple[Subject, Subject], ...], ...]
+ log_likelihood: Optional[float]
+
+
+def _message(side: str, position: int) -> Subject:
+ return Subject(SubjectKind.MAPPING, (side, position))
+
+
+def paired_examples() -> tuple[PerfectMatchingScenario, PerfectMatchingScenario]:
+ senders = tuple(_message("sender-message", position) for position in range(3))
+ receivers = tuple(_message("receiver-message", position) for position in range(3))
+ return (
+ PerfectMatchingScenario(
+ "joint-assignment",
+ senders,
+ receivers,
+ (
+ (0.60, 0.35, 0.05),
+ (0.55, 0.40, 0.05),
+ (0.05, 0.15, 0.80),
+ ),
+ ),
+ PerfectMatchingScenario(
+ "uniform-profile-control",
+ senders,
+ receivers,
+ (
+ (1.0 / 3.0, 1.0 / 3.0, 1.0 / 3.0),
+ (1.0 / 3.0, 1.0 / 3.0, 1.0 / 3.0),
+ (1.0 / 3.0, 1.0 / 3.0, 1.0 / 3.0),
+ ),
+ ),
+ )
+
+
+def analyze(scenario: PerfectMatchingScenario) -> PerfectMatchingAnalysis:
+ best = None
+ assignments: list[tuple[tuple[Subject, Subject], ...]] = []
+ for receiver_order in permutations(range(len(scenario.receivers))):
+ weights = tuple(
+ scenario.profile_weights[sender_index][receiver_index]
+ for sender_index, receiver_index in enumerate(receiver_order)
+ )
+ if any(weight == 0.0 for weight in weights):
+ continue
+ likelihood = sum(log(weight) for weight in weights)
+ assignment = tuple(
+ (scenario.senders[sender_index], scenario.receivers[receiver_index])
+ for sender_index, receiver_index in enumerate(receiver_order)
+ )
+ if best is None or (
+ likelihood > best and not isclose(likelihood, best, abs_tol=1e-12)
+ ):
+ best = likelihood
+ assignments = [assignment]
+ elif isclose(likelihood, best, abs_tol=1e-12):
+ assignments.append(assignment)
+ return PerfectMatchingAnalysis(tuple(assignments), best)
+
+
+def evaluate(scenario: PerfectMatchingScenario) -> AttackReport:
+ analysis = analyze(scenario)
+ context = EvidenceContext(
+ adversary="global passive observer with estimated sender profiles",
+ observables=("sender-message multiset", "receiver-message multiset", "sender profiles"),
+ hypothesis="one bijective assignment jointly maximizes the round likelihood",
+ algorithm="exact diagnostic enumeration of the PMDA maximum-weight perfect-matching objective",
+ dataset="deterministic synthetic threshold-mix round",
+ limitations=(
+ "sender profiles are supplied rather than estimated from a longitudinal trace",
+ "enumeration is a small-fixture oracle, not the scalable assignment algorithm",
+ "zero-probability pairs abstain here; under the paper's log(0) = -inf convention the"
+ " assignment algorithm still returns a matching",
+ ),
+ )
+ if analysis.log_likelihood is None:
+ evidence = AbstentionEvidence(
+ scenario.senders + scenario.receivers,
+ "no positive-probability perfect matching",
+ context,
+ )
+ outcomes = (Inconclusive(scenario.senders + scenario.receivers, evidence.reason),)
+ else:
+ evidence = PerfectMatchingEvidence(
+ scenario.senders,
+ scenario.receivers,
+ analysis.assignments,
+ analysis.log_likelihood,
+ context,
+ )
+ if len(analysis.assignments) == 1:
+ outcomes = (MessageAssignmentRecovered(evidence, analysis.assignments[0]),)
+ else:
+ outcomes = (
+ Inconclusive(
+ scenario.senders + scenario.receivers,
+ "multiple perfect matchings share the maximum likelihood",
+ ),
+ )
+ return AttackReport(
+ identifier=f"ctp.perfect-matching-disclosure.{scenario.identifier}",
+ attack="profile-weighted perfect-matching disclosure",
+ subjects=scenario.senders + scenario.receivers,
+ channels=(EvidenceChannel("joint_round_assignment", (evidence,)),),
+ outcomes=outcomes,
+ limitations=(
+ "deterministic synthetic threshold-mix fixture",
+ "the profile-estimation phase is supplied and tested separately by statistical disclosure",
+ "the result links messages within one round and does not attribute Bitcoin ownership",
+ "the cited paper's simulations and success rates are not reproduced",
+ ),
+ )
diff --git a/decluster/failure_modes/provenance_decay.py b/decluster/failure_modes/provenance_decay.py
new file mode 100644
index 00000000..fb16f269
--- /dev/null
+++ b/decluster/failure_modes/provenance_decay.py
@@ -0,0 +1,118 @@
+"""Additive candidate decay and structural fracture in provenance graphs."""
+
+from dataclasses import dataclass
+
+from ..domain import (
+ AdditiveDecayMeasured,
+ AttackReport,
+ CandidateEliminationEvidence,
+ EvidenceChannel,
+ EvidenceContext,
+ GraphFractureEvidence,
+ GraphFractureMeasured,
+ Inconclusive,
+ Subject,
+ SubjectKind,
+)
+
+
+@dataclass(frozen=True)
+class ProvenanceDecayScenario:
+ target: Subject
+ candidates: frozenset[Subject]
+ known_auxiliaries: frozenset[Subject]
+ graph_nodes: frozenset[Subject]
+ graph_edges: frozenset[frozenset[Subject]]
+
+ def __post_init__(self) -> None:
+ if self.target.kind is not SubjectKind.OUTPOINT:
+ raise ValueError("target must be an outpoint")
+ if not self.known_auxiliaries or not self.known_auxiliaries <= self.candidates:
+ raise ValueError("known auxiliaries must be candidate origins")
+ if not self.candidates <= self.graph_nodes:
+ raise ValueError("candidate origins must be graph nodes")
+ if any(
+ len(edge) != 2 or not edge <= self.graph_nodes
+ for edge in self.graph_edges
+ ):
+ raise ValueError("edges must connect two declared graph nodes")
+
+
+def ctp_fracture_example() -> ProvenanceDecayScenario:
+ bridge = Subject(SubjectKind.CLUSTER, "known-auxiliary")
+ left = Subject(SubjectKind.CLUSTER, "left-origin")
+ right = Subject(SubjectKind.CLUSTER, "right-origin")
+ return ProvenanceDecayScenario(
+ target=Subject(SubjectKind.OUTPOINT, ("target", 0)),
+ candidates=frozenset({left, bridge, right}),
+ known_auxiliaries=frozenset({bridge}),
+ graph_nodes=frozenset({left, bridge, right}),
+ graph_edges=frozenset({frozenset({left, bridge}), frozenset({bridge, right})}),
+ )
+
+
+def _component_count(nodes, edges):
+ unseen = set(nodes)
+ count = 0
+ while unseen:
+ count += 1
+ frontier = [unseen.pop()]
+ while frontier:
+ current = frontier.pop()
+ neighbors = {
+ next(iter(edge - {current}))
+ for edge in edges
+ if current in edge
+ }
+ discovered = neighbors & unseen
+ unseen -= discovered
+ frontier.extend(discovered)
+ return count
+
+
+def evaluate(scenario: ProvenanceDecayScenario) -> AttackReport:
+ context = EvidenceContext(
+ adversary="observer with newly identified auxiliary entities",
+ observables=("candidate origins", "provenance adjacency", "auxiliary labels"),
+ hypothesis="identified auxiliaries can be excluded from the target's origins",
+ algorithm="candidate elimination and component comparison",
+ dataset="ctp synthetic provenance-fracture fixture",
+ limitations=("graph and auxiliary labels are supplied by the fixture",),
+ )
+ elimination = CandidateEliminationEvidence(
+ scenario.target, scenario.candidates, scenario.known_auxiliaries, context
+ )
+ remaining_nodes = scenario.graph_nodes - scenario.known_auxiliaries
+ remaining_edges = frozenset(
+ edge for edge in scenario.graph_edges if not edge & scenario.known_auxiliaries
+ )
+ before = _component_count(scenario.graph_nodes, scenario.graph_edges)
+ after = _component_count(remaining_nodes, remaining_edges)
+ channels = [EvidenceChannel("candidate_elimination", (elimination,))]
+ outcomes = [AdditiveDecayMeasured(elimination)]
+ if after > before:
+ fracture = GraphFractureEvidence(
+ scenario.known_auxiliaries, before, after, context
+ )
+ channels.append(EvidenceChannel("graph_fracture", (fracture,)))
+ outcomes.append(GraphFractureMeasured(fracture))
+ else:
+ outcomes.append(
+ Inconclusive(
+ (scenario.target,), "auxiliary removal did not fracture the graph"
+ )
+ )
+ return AttackReport(
+ identifier="ctp.provenance-decay",
+ attack="candidate elimination and provenance graph fracture",
+ subjects=(scenario.target,) + tuple(
+ sorted(scenario.graph_nodes, key=lambda subject: str(subject.identifier))
+ ),
+ channels=tuple(channels),
+ outcomes=tuple(outcomes),
+ limitations=(
+ "synthetic fixture",
+ "component growth does not by itself establish exponential deanonymization",
+ "edge plausibility and private adversary information are not modeled",
+ ),
+ )
diff --git a/decluster/failure_modes/statistical_disclosure.py b/decluster/failure_modes/statistical_disclosure.py
new file mode 100644
index 00000000..6bed5b64
--- /dev/null
+++ b/decluster/failure_modes/statistical_disclosure.py
@@ -0,0 +1,163 @@
+"""Longitudinal statistical disclosure in the threshold-mix model.
+
+Mix senders and recipients are carried as `SubjectKind.CLUSTER`. There is no mix-participant
+kind in the vocabulary and none is added for a synthetic fixture; the reuse is notation, and
+nothing here reads a Bitcoin wallet cluster out of it.
+"""
+
+from dataclasses import dataclass
+from math import isfinite
+
+from ..domain import (
+ AttackReport,
+ CorrespondentDistributionEvidence,
+ EvidenceChannel,
+ EvidenceContext,
+ Inconclusive,
+ PersistentCorrespondentRanked,
+ Subject,
+ SubjectKind,
+)
+
+
+@dataclass(frozen=True)
+class StatisticalDisclosureScenario:
+ identifier: str
+ target: Subject
+ correspondents: tuple[Subject, ...]
+ background_distribution: tuple[float, ...]
+ rounds: tuple[tuple[Subject, ...], ...]
+
+ def __post_init__(self) -> None:
+ if not self.identifier:
+ raise ValueError("scenario identifier must not be empty")
+ if len(self.correspondents) < 2 or len(set(self.correspondents)) != len(
+ self.correspondents
+ ):
+ raise ValueError("scenario requires at least two unique correspondents")
+ if self.target in self.correspondents:
+ raise ValueError("target must not be a correspondent")
+ if len(self.background_distribution) != len(self.correspondents):
+ raise ValueError("background distribution must cover every correspondent")
+ if any(
+ not isfinite(probability) or probability < 0.0
+ for probability in self.background_distribution
+ ):
+ raise ValueError("background probabilities must be finite and non-negative")
+ if abs(sum(self.background_distribution) - 1.0) > 1e-9:
+ raise ValueError("background probabilities must sum to one")
+ if not self.rounds:
+ raise ValueError("scenario requires observed rounds")
+ batch_sizes = {len(round_) for round_ in self.rounds}
+ if len(batch_sizes) != 1 or next(iter(batch_sizes)) < 2:
+ raise ValueError("rounds must have one batch size of at least two")
+ allowed = set(self.correspondents)
+ if any(receiver not in allowed for round_ in self.rounds for receiver in round_):
+ raise ValueError("round contains an unknown correspondent")
+
+ @property
+ def batch_size(self) -> int:
+ return len(self.rounds[0])
+
+
+def _correspondent(identifier: str) -> Subject:
+ return Subject(SubjectKind.CLUSTER, identifier)
+
+
+def paired_examples() -> tuple[StatisticalDisclosureScenario, StatisticalDisclosureScenario]:
+ correspondents = tuple(_correspondent(name) for name in ("alice", "bob", "carol", "dave"))
+ target = Subject(SubjectKind.CLUSTER, "observed-sender")
+ background = (0.25, 0.25, 0.25, 0.25)
+ background_messages = (
+ (correspondents[0], correspondents[1], correspondents[2]),
+ (correspondents[1], correspondents[2], correspondents[3]),
+ (correspondents[2], correspondents[3], correspondents[0]),
+ (correspondents[3], correspondents[0], correspondents[1]),
+ )
+ persistent_rounds = tuple(
+ (correspondents[0],) + messages for messages in background_messages
+ )
+ null_rounds = tuple(
+ (correspondents[index],) + messages
+ for index, messages in enumerate(background_messages)
+ )
+ return (
+ StatisticalDisclosureScenario(
+ "persistent-correspondent",
+ target,
+ correspondents,
+ background,
+ persistent_rounds,
+ ),
+ StatisticalDisclosureScenario(
+ "background-matched-control",
+ target,
+ correspondents,
+ background,
+ null_rounds,
+ ),
+ )
+
+
+def estimate(scenario: StatisticalDisclosureScenario) -> CorrespondentDistributionEvidence:
+ counts = {correspondent: 0 for correspondent in scenario.correspondents}
+ for round_ in scenario.rounds:
+ for correspondent in round_:
+ counts[correspondent] += 1
+ denominator = len(scenario.rounds) * scenario.batch_size
+ scores = tuple(
+ (
+ correspondent,
+ scenario.batch_size * counts[correspondent] / denominator
+ - (scenario.batch_size - 1) * background,
+ )
+ for correspondent, background in zip(
+ scenario.correspondents, scenario.background_distribution
+ )
+ )
+ context = EvidenceContext(
+ adversary="global passive observer who knows the target-active rounds",
+ observables=("recipient multiset per round", "batch size", "background distribution"),
+ hypothesis="the target has a persistent correspondent distribution distinct from background",
+ algorithm="Danezis threshold-mix statistical disclosure estimator, equation 2",
+ dataset="deterministic synthetic threshold-mix fixture",
+ limitations=(
+ "target activity and the background distribution are supplied",
+ "the threshold-mix model is not an ownership model for Bitcoin",
+ ),
+ )
+ return CorrespondentDistributionEvidence(
+ scenario.target,
+ scores,
+ len(scenario.rounds),
+ scenario.batch_size,
+ context,
+ )
+
+
+def evaluate(scenario: StatisticalDisclosureScenario) -> AttackReport:
+ evidence = estimate(scenario)
+ ranked = sorted(evidence.scores, key=lambda item: item[1], reverse=True)
+ margin = ranked[0][1] - ranked[1][1]
+ if margin > 1e-12:
+ outcomes = (PersistentCorrespondentRanked(evidence, ranked[0][0], margin),)
+ else:
+ outcomes = (Inconclusive((scenario.target,), "no unique correspondent exceeds background"),)
+ return AttackReport(
+ identifier=f"ctp.statistical-disclosure.{scenario.identifier}",
+ attack="longitudinal statistical disclosure",
+ subjects=(scenario.target,) + scenario.correspondents,
+ channels=(EvidenceChannel("correspondent_distribution", (evidence,)),),
+ outcomes=outcomes,
+ limitations=(
+ "deterministic synthetic threshold-mix fixture",
+ "the model assumes one target message and batch_size minus one background messages per round",
+ "the result ranks persistent correspondents and does not attribute Bitcoin ownership",
+ "the fixture does not estimate real-world attack frequency or confidence",
+ "the estimator is implemented; the paper's applicability and efficiency section is not"
+ " — neither the signal-to-noise condition that decides whether the attack is possible"
+ " at all, nor the bound on observations needed for a stated confidence. Those are the"
+ " part of the source that speaks to a rate, so no claim about how fast certainty is"
+ " gained is covered here",
+ ),
+ )
diff --git a/decluster/failure_modes/unnecessary_input.py b/decluster/failure_modes/unnecessary_input.py
new file mode 100644
index 00000000..473816da
--- /dev/null
+++ b/decluster/failure_modes/unnecessary_input.py
@@ -0,0 +1,170 @@
+"""Show why a positive unnecessary-input predicate does not identify PayJoin."""
+
+from dataclasses import dataclass
+from enum import Enum
+
+from ..baselines.unnecessary_input import UIHStatus, analyze_blockstream
+from ..domain import (
+ AttackReport,
+ EvidenceChannel,
+ EvidenceContext,
+ Inconclusive,
+ Subject,
+ SubjectKind,
+ UnnecessaryInputEvidence,
+)
+
+
+class LatentTransactionForm(str, Enum):
+ """Possible worlds that can share the same on-chain amount observation."""
+
+ ORDINARY_CONSOLIDATION = "ordinary_single_owner_consolidation"
+ TWO_PARTY_PAYJOIN = "two_party_payjoin"
+ NS1R = "many_senders_one_receiver"
+ NSNR = "many_senders_many_receivers"
+ NET_SETTLEMENT = "net_settlement_with_cycles"
+
+
+@dataclass(frozen=True)
+class UnnecessaryInputScenario:
+ transaction: Subject
+ inputs: tuple[int, ...]
+ outputs: tuple[int, int]
+ possible_forms: tuple[LatentTransactionForm, ...]
+
+ def __post_init__(self) -> None:
+ if self.transaction.kind is not SubjectKind.TRANSACTION:
+ raise ValueError("scenario requires a transaction subject")
+ if len(self.inputs) < 2:
+ raise ValueError("scenario requires at least two inputs")
+ if len(set(self.possible_forms)) < 2:
+ raise ValueError("scenario requires at least two distinct latent forms")
+
+
+@dataclass(frozen=True)
+class FormObservation:
+ """A collaborative form and the amounts visible to an external observer."""
+
+ form: LatentTransactionForm
+ inputs: tuple[int, ...]
+ outputs: tuple[int, ...]
+
+
+@dataclass(frozen=True)
+class Obligation:
+ payer: str
+ receiver: str
+ amount: int
+
+ def __post_init__(self) -> None:
+ if not self.payer or not self.receiver or self.payer == self.receiver:
+ raise ValueError("obligation requires distinct named parties")
+ if self.amount <= 0:
+ raise ValueError("obligation amount must be positive")
+
+
+def net_balances(obligations: tuple[Obligation, ...]) -> dict[str, int]:
+ """Return received minus sent for each participant."""
+
+ balances: dict[str, int] = {}
+ for obligation in obligations:
+ balances.setdefault(obligation.payer, 0)
+ balances.setdefault(obligation.receiver, 0)
+ balances[obligation.payer] -= obligation.amount
+ balances[obligation.receiver] += obligation.amount
+ return dict(sorted(balances.items()))
+
+
+def collaborative_form_examples() -> tuple[FormObservation, FormObservation]:
+ """CTP-style NS1R and NSNR shapes outside the paper's two-output scope."""
+
+ return (
+ FormObservation(
+ LatentTransactionForm.NS1R,
+ (40, 50, 60, 90),
+ (160, 30, 30, 20),
+ ),
+ FormObservation(
+ LatentTransactionForm.NSNR,
+ (90, 15, 40, 30, 45, 35),
+ (85, 25, 50, 40, 45, 10),
+ ),
+ )
+
+
+def cycle_equivalent_obligations() -> tuple[
+ tuple[Obligation, ...], tuple[Obligation, ...]
+]:
+ """Two gross obligation graphs with identical observable net balances."""
+
+ simple = (Obligation("alice", "bob", 500),)
+ with_cycle = (
+ Obligation("alice", "bob", 800),
+ Obligation("bob", "carol", 300),
+ Obligation("carol", "alice", 300),
+ )
+ return simple, with_cycle
+
+
+def observationally_equivalent_example() -> UnnecessaryInputScenario:
+ """One amount vector compatible with unilateral and collaborative worlds."""
+
+ return UnnecessaryInputScenario(
+ transaction=Subject(SubjectKind.TRANSACTION, "uih-equivalent-worlds"),
+ inputs=(600, 600, 600),
+ outputs=(1_000, 700),
+ possible_forms=(
+ LatentTransactionForm.ORDINARY_CONSOLIDATION,
+ LatentTransactionForm.TWO_PARTY_PAYJOIN,
+ LatentTransactionForm.NET_SETTLEMENT,
+ ),
+ )
+
+
+def evaluate(scenario: UnnecessaryInputScenario) -> AttackReport:
+ """Classify the amounts while refusing any latent-form or ownership conclusion."""
+
+ analysis = analyze_blockstream(scenario.inputs, scenario.outputs)
+ if analysis.status not in {UIHStatus.UIH1, UIHStatus.UIH2}:
+ raise ValueError("failure-mode scenario must have a valid UIH classification")
+ if analysis.fee is None or analysis.removed_input_index is None:
+ raise RuntimeError("valid UIH analysis omitted its fee or input witness")
+
+ forms = tuple(form.value for form in scenario.possible_forms)
+ context = EvidenceContext(
+ adversary="external observer with transaction amounts and no participant labels",
+ observables=("input amounts", "output amounts", "transaction fee"),
+ hypothesis="a proper input subset can fund an output and the observed fee",
+ algorithm="Ghesmati et al. Algorithm 2 BlockStream UIH1/UIH2",
+ dataset="deterministic observational-equivalence fixture",
+ limitations=(
+ "the same observation is compatible with " + ", ".join(forms),
+ "gross obligations and cycles are not observable from net transaction amounts",
+ ),
+ )
+ evidence = UnnecessaryInputEvidence(
+ transaction=scenario.transaction,
+ definition="blockstream_fee_aware",
+ classification=analysis.status.value,
+ fee=analysis.fee,
+ removed_input_index=analysis.removed_input_index,
+ context=context,
+ )
+ return AttackReport(
+ identifier=f"ctp.unnecessary-input.{scenario.transaction.identifier}",
+ attack="unnecessary-input observational equivalence",
+ subjects=(scenario.transaction,),
+ channels=(EvidenceChannel("unnecessary_input_shape", (evidence,)),),
+ outcomes=(
+ Inconclusive(
+ (scenario.transaction,),
+ "UIH does not distinguish the declared latent transaction forms",
+ ),
+ ),
+ limitations=(
+ "a positive predicate does not identify PayJoin",
+ "no participant role, ownership relation, payment output, or gross payment is inferred",
+ "ordinary consolidation and collaborative settlement share the same observable",
+ "the fixture demonstrates non-identifiability, not real-world prevalence",
+ ),
+ )
diff --git a/decluster/fellegi_sunter.py b/decluster/fellegi_sunter.py
new file mode 100644
index 00000000..6f15d321
--- /dev/null
+++ b/decluster/fellegi_sunter.py
@@ -0,0 +1,233 @@
+"""Supervised Fellegi--Sunter record linkage.
+
+This module implements the classical binary-comparison model. It is deliberately
+separate from :mod:`decluster.combiner`: that module's value-rarity agreement
+score is a useful legacy baseline, but is not a fitted Fellegi--Sunter model.
+"""
+
+from dataclasses import dataclass
+import math
+from typing import Callable, Iterable, Mapping, Optional, Sequence
+
+
+ComparisonVector = Mapping[str, Optional[bool]]
+
+
+@dataclass(frozen=True)
+class ComparisonField:
+ """A named binary comparison; ``None`` means that the field abstains."""
+
+ name: str
+ compare: Callable[[object, object], Optional[bool]]
+
+
+@dataclass(frozen=True)
+class FieldParameters:
+ """Agreement probabilities conditional on match (m) and non-match (u)."""
+
+ m: float
+ u: float
+
+ def __post_init__(self):
+ if not 0.0 < self.m < 1.0 or not 0.0 < self.u < 1.0:
+ raise ValueError("m and u must be strictly between zero and one")
+
+ @property
+ def agreement_weight(self):
+ return math.log2(self.m / self.u)
+
+ @property
+ def disagreement_weight(self):
+ return math.log2((1.0 - self.m) / (1.0 - self.u))
+
+
+def comparison_vector(left, right, fields: Sequence[ComparisonField]):
+ """Build the named agree/disagree/abstain vector for a record pair."""
+
+ out = {}
+ for field in fields:
+ if field.name in out:
+ raise ValueError(f"duplicate comparison field: {field.name}")
+ value = field.compare(left, right)
+ if value is not None and not isinstance(value, bool):
+ raise TypeError(f"comparison {field.name!r} must return bool or None")
+ out[field.name] = value
+ return out
+
+
+def comparison_vectors(pairs: Iterable[tuple], fields: Sequence[ComparisonField]):
+ """Build comparison vectors for an iterable of ``(left, right)`` pairs."""
+
+ return [comparison_vector(left, right, fields) for left, right in pairs]
+
+
+def fit_supervised(vectors: Sequence[ComparisonVector], labels: Sequence[bool], *,
+ alpha=0.5):
+ """Estimate per-field m and u from labelled training pairs.
+
+ ``True`` labels are known matches and ``False`` labels known non-matches.
+ A symmetric Beta(``alpha``, ``alpha``) pseudo-count keeps maximum-likelihood
+ estimates finite for fields with all agreements or all disagreements. An
+ abstention is excluded from that field's denominator.
+ """
+
+ if len(vectors) != len(labels):
+ raise ValueError("vectors and labels must have the same length")
+ if alpha <= 0:
+ raise ValueError("alpha must be positive")
+ names = set()
+ for vector in vectors:
+ names.update(vector)
+ if not names:
+ raise ValueError("at least one comparison field is required")
+
+ params = {}
+ for name in sorted(names):
+ match_agree = match_total = nonmatch_agree = nonmatch_total = 0
+ for vector, label in zip(vectors, labels):
+ value = vector.get(name)
+ if value is None:
+ continue
+ if label:
+ match_total += 1
+ match_agree += bool(value)
+ else:
+ nonmatch_total += 1
+ nonmatch_agree += bool(value)
+ if not match_total or not nonmatch_total:
+ raise ValueError(f"field {name!r} needs active match and non-match observations")
+ m = (match_agree + alpha) / (match_total + 2.0 * alpha)
+ u = (nonmatch_agree + alpha) / (nonmatch_total + 2.0 * alpha)
+ params[name] = FieldParameters(m=m, u=u)
+ return params
+
+
+@dataclass(frozen=True)
+class FellegiSunterModel:
+ """Fitted parameters plus the classical non-link/review/link thresholds."""
+
+ parameters: Mapping[str, FieldParameters]
+ non_link_below: float
+ link_at_or_above: float
+
+ def __post_init__(self):
+ if not self.parameters:
+ raise ValueError("parameters cannot be empty")
+ if self.non_link_below >= self.link_at_or_above:
+ raise ValueError("non-link threshold must be below link threshold")
+
+ def contributions(self, vector: ComparisonVector):
+ """Return each active field's exact log2 likelihood-ratio term."""
+
+ unknown = set(vector) - set(self.parameters)
+ if unknown:
+ raise ValueError(f"unknown comparison fields: {sorted(unknown)}")
+ return {
+ name: (parameter.agreement_weight if vector.get(name) else
+ parameter.disagreement_weight)
+ for name, parameter in self.parameters.items()
+ if vector.get(name) is not None
+ }
+
+ def score(self, vector: ComparisonVector):
+ """Return the sum of field log2 likelihood ratios."""
+
+ return sum(self.contributions(vector).values())
+
+ def classify(self, vector: ComparisonVector):
+ """Classify as ``link``, ``non-link``, or ``review``."""
+
+ score = self.score(vector)
+ if score >= self.link_at_or_above:
+ return "link"
+ if score <= self.non_link_below:
+ return "non-link"
+ return "review"
+
+
+def thresholds_from_error_rates(false_match_rate: float,
+ false_non_match_rate: float):
+ """Return the classical Fellegi--Sunter decision bounds in log2 units.
+
+ ``false_match_rate`` is the tolerated probability of linking a non-match
+ and ``false_non_match_rate`` the tolerated probability of rejecting a
+ match. The likelihood-ratio bounds are converted to the same log2 scale
+ used by :class:`FieldParameters`.
+ """
+ if not 0.0 < false_match_rate < 1.0:
+ raise ValueError("false_match_rate must be strictly between zero and one")
+ if not 0.0 < false_non_match_rate < 1.0:
+ raise ValueError(
+ "false_non_match_rate must be strictly between zero and one"
+ )
+ non_link_below = math.log2(
+ false_non_match_rate / (1.0 - false_match_rate)
+ )
+ link_at_or_above = math.log2(
+ (1.0 - false_non_match_rate) / false_match_rate
+ )
+ if non_link_below >= link_at_or_above:
+ raise ValueError("error rates do not define a non-empty review region")
+ return non_link_below, link_at_or_above
+
+
+def fit_model(vectors: Sequence[ComparisonVector], labels: Sequence[bool], *,
+ non_link_below: float, link_at_or_above: float, alpha=0.5):
+ """Fit parameters on training data and attach caller-selected thresholds."""
+
+ return FellegiSunterModel(
+ fit_supervised(vectors, labels, alpha=alpha),
+ non_link_below=non_link_below,
+ link_at_or_above=link_at_or_above,
+ )
+
+
+def fit_model_for_error_rates(
+ vectors: Sequence[ComparisonVector],
+ labels: Sequence[bool],
+ *,
+ false_match_rate: float,
+ false_non_match_rate: float,
+ alpha=0.5,
+):
+ """Fit a model using the classical bounds implied by error-rate targets."""
+ non_link_below, link_at_or_above = thresholds_from_error_rates(
+ false_match_rate, false_non_match_rate
+ )
+ return fit_model(
+ vectors,
+ labels,
+ non_link_below=non_link_below,
+ link_at_or_above=link_at_or_above,
+ alpha=alpha,
+ )
+
+
+def evaluate(model: FellegiSunterModel, vectors: Sequence[ComparisonVector],
+ labels: Sequence[bool]):
+ """Evaluate an already-fitted model on a separate labelled set.
+
+ Keeping fitting out of this function makes train/test leakage explicit. The
+ returned selective accuracy excludes review decisions and reports their
+ coverage separately.
+ """
+
+ if len(vectors) != len(labels):
+ raise ValueError("vectors and labels must have the same length")
+ decisions = [model.classify(vector) for vector in vectors]
+ decided = [i for i, decision in enumerate(decisions) if decision != "review"]
+ correct = sum(
+ (decisions[i] == "link") == bool(labels[i])
+ for i in decided
+ )
+ n = len(labels)
+ return {
+ "n": n,
+ "link": decisions.count("link"),
+ "non_link": decisions.count("non-link"),
+ "review": decisions.count("review"),
+ "coverage": len(decided) / n if n else 0.0,
+ "selective_accuracy": correct / len(decided) if decided else None,
+ "scores": [model.score(vector) for vector in vectors],
+ "decisions": decisions,
+ }
diff --git a/decluster/fetch.py b/decluster/fetch.py
index ce40ab1d..94e38100 100644
--- a/decluster/fetch.py
+++ b/decluster/fetch.py
@@ -1,7 +1,18 @@
+"""Cached reads of the chain.
+
+`.blkcache` is the published block slice, the one `data/fs-blkcache-*.tar.gz` ships and two result
+manifests fingerprint file-for-file. Nothing here writes to it. A block a live run has to go and
+fetch lands in `.blkcache-live` instead, so running the suite cannot move a source that published
+numbers were measured on. Growing the published slice is then a deliberate act — fold
+`.blkcache-live` into `.blkcache` and re-record the manifests — rather than something a test does
+to you.
+"""
import os, json, urllib.request, time
CACHE = os.path.join(os.path.dirname(__file__), "..", ".cache")
BLK = os.path.join(os.path.dirname(__file__), "..", ".blkcache")
+BLK_LIVE = os.path.join(os.path.dirname(__file__), "..", ".blkcache-live")
os.makedirs(CACHE, exist_ok=True); os.makedirs(BLK, exist_ok=True)
+os.makedirs(BLK_LIVE, exist_ok=True)
API = "https://mempool.space/api"
_LAST = [0.0] # last request timestamp (min interval throttle)
@@ -57,7 +68,11 @@ def fetch_outspends(txid):
return _cached_json(os.path.join(CACHE, txid + ".outspends.json"), f"{API}/tx/{txid}/outspends")
def fetch_block_txs(block_id, index):
- return _cached_json(os.path.join(BLK, f"{block_id}_{index}.json"), f"{API}/block/{block_id}/txs/{index}")
+ name = f"{block_id}_{index}.json"
+ published = os.path.join(BLK, name)
+ if os.path.exists(published):
+ return json.load(open(published))
+ return _cached_json(os.path.join(BLK_LIVE, name), f"{API}/block/{block_id}/txs/{index}")
def recent_blocks():
return _get(f"{API}/v1/blocks")
diff --git a/decluster/fingerprint_ns.py b/decluster/fingerprint_ns.py
index 88c93488..aed0ad4d 100644
--- a/decluster/fingerprint_ns.py
+++ b/decluster/fingerprint_ns.py
@@ -1,7 +1,19 @@
"""Layer 4 — N-S fingerprint channel: a tx's fingerprint as a sparse {(axis,value): bits}
quasi-identifier, scored by rarity-weighted agreement overlap. Reuses propagate.eccentricity
for the acceptance gap. Measures whether fingerprints are sparse quasi-identifiers or
-equivalence-class conditioners (see results/RESULTS-fingerprint-regime.md). Offline."""
+equivalence-class conditioners (see results/RESULTS-fingerprint-regime.md). Offline.
+
+WHAT THIS ACTUALLY IMPLEMENTS. A rarity-weighted agreement overlap between two fingerprints. Two
+departures from the cited algorithm, both deliberate and neither hidden: (1) the feature weight is
+the self-information `-log2(p)` of the value (`library_weights`, `measured_weights`), where the
+paper's similarity weights a shared attribute by `1/log|support|` — the form this repo does keep in
+`ancestry.provenance_link`; a rare value is up-weighted under both, but the two are different
+functions and are not interchangeable. (2) `reid_gap` computes the eccentricity gap and RETURNS it;
+nothing in this module gates on it, so no candidate is ever refused for being a diffuse tie — the
+refusal is left to the caller. There is no seed set, no propagation and no second view here either.
+This repository holds no faithful implementation of the cited algorithm; the module closest to it
+is `decluster.reid`, itself declared an adaptation. (`decluster/baselines/narayanan_shmatikov.py`
+is a different paper: the 2009 social-graph propagation kernel, not this record-matching one.)"""
import json
import math
@@ -28,7 +40,8 @@ def fingerprint_signature(tx, axis_fns, weights):
def fingerprint_link(sig_a, sig_b):
"""Rarity-weighted overlap over agreeing features (same axis AND same value). Agreements
- only — no disagreement penalty (that is the F-S structure this channel steps away from)."""
+ only — no mismatch penalty, which is the one term the rarity combiner adds and this channel
+ drops."""
return sum(w for k, w in sig_a.items() if k in sig_b)
@@ -91,7 +104,7 @@ def equivalence_key(tx, axis_fns, cond_axes=CONDITIONING_AXES):
def reid_gap(query, candidates, axis_fns, weights):
"""Rank candidates by fingerprint_link vs query; return top index, eccentricity gap, top score."""
- from .propagate import eccentricity
+ from .single_view_propagation import eccentricity
q = fingerprint_signature(query, axis_fns, weights)
scores = [fingerprint_link(q, fingerprint_signature(c, axis_fns, weights)) for c in candidates]
if not scores:
diff --git a/decluster/fingerprint_regime.py b/decluster/fingerprint_regime.py
new file mode 100644
index 00000000..5e294b0f
--- /dev/null
+++ b/decluster/fingerprint_regime.py
@@ -0,0 +1,87 @@
+"""Compare two rarity rankings over the same library axes.
+
+Both arms score `-log2(p)` on the same values from the same axes; the only difference is the
+mismatch term. `fingerprint_ns.fingerprint_link` credits agreements only; `LibraryScorer` adds a
+clamped mismatch penalty. Neither is a fitted Fellegi--Sunter model, so this comparison says
+nothing about that method: it isolates the mismatch penalty.
+
+The `ns_*` and `fs_*` result keys are the frozen artifact schema's names for the agreement-only
+and agreement-plus-mismatch-penalty arms respectively, and are kept for that schema alone."""
+
+from . import fingerprint_ns
+from .change_gt import input_addrs
+from .fingerprint_validate import LibraryScorer, reuse_pairs
+from .graph_deanon import auc
+
+
+def build_labeled_nodes(transactions, cap=200):
+ """Assign each transaction to at most one address-reuse group."""
+ by_address = {}
+ for transaction in transactions:
+ for address in sorted(input_addrs(transaction)):
+ by_address.setdefault(address, {})[transaction["txid"]] = transaction
+ nodes = []
+ assigned = set()
+ owner = 0
+ for _, group in sorted(by_address.items()):
+ members = [transaction for txid, transaction in sorted(group.items())
+ if txid not in assigned]
+ if len(members) < 2:
+ continue
+ for transaction in members:
+ assigned.add(transaction["txid"])
+ nodes.append((transaction, owner))
+ owner += 1
+ if len(nodes) >= cap:
+ break
+ return nodes[:cap]
+
+
+def evaluate(transactions, weights, label, pair_cap=2000, node_cap=200):
+ """Evaluate pair ranking and candidate ranking under one weight source."""
+ axes = fingerprint_ns.axis_fns()
+ scorer = LibraryScorer()
+ positives, negatives = reuse_pairs(transactions, cap=pair_cap, seed=0)
+ agreement_only_auc = fingerprint_ns.pairwise_auc(positives, negatives, axes, weights)
+ penalised_auc = auc(
+ [scorer.score(a, b) for a, b in positives],
+ [scorer.score(a, b) for a, b in negatives],
+ 0,
+ )
+ nodes = build_labeled_nodes(transactions, cap=node_cap)
+ within_gaps = []
+ agreement_only_exact = penalised_exact = evaluated = 0
+ for index, (query, owner) in enumerate(nodes):
+ candidates = [item for candidate_index, item in enumerate(nodes) if candidate_index != index]
+ if not candidates:
+ continue
+ candidate_transactions = [transaction for transaction, _ in candidates]
+ ns_result = fingerprint_ns.reid_gap(query, candidate_transactions, axes, weights)
+ evaluated += 1
+ if ns_result["best"] is not None and candidates[ns_result["best"]][1] == owner:
+ agreement_only_exact += 1
+ penalised = [scorer.score(query, transaction) for transaction in candidate_transactions]
+ penalised_best = max(range(len(penalised)), key=penalised.__getitem__)
+ if candidates[penalised_best][1] == owner:
+ penalised_exact += 1
+ key = fingerprint_ns.equivalence_key(query, axes)
+ within_class = [transaction for transaction in candidate_transactions
+ if fingerprint_ns.equivalence_key(transaction, axes) == key]
+ if len(within_class) >= 2:
+ within_gaps.append(
+ fingerprint_ns.reid_gap(query, within_class, axes, weights)["gap"]
+ )
+ return {
+ "weight_source": label,
+ "pair_positive_draws": len(positives),
+ "pair_negative_draws": len(negatives),
+ "candidate_queries": evaluated,
+ "within_class_queries": len(within_gaps),
+ "ns_auc": agreement_only_auc,
+ "fs_auc": penalised_auc,
+ "ns_top1": agreement_only_exact / evaluated if evaluated else 0.0,
+ "fs_top1": penalised_exact / evaluated if evaluated else 0.0,
+ "within_class_gap_mean": (
+ sum(within_gaps) / len(within_gaps) if within_gaps else 0.0
+ ),
+ }
diff --git a/decluster/fingerprint_sparsity.py b/decluster/fingerprint_sparsity.py
new file mode 100644
index 00000000..57c37962
--- /dev/null
+++ b/decluster/fingerprint_sparsity.py
@@ -0,0 +1,61 @@
+"""Exact-vector class sizes for transaction-construction fingerprints."""
+
+from __future__ import annotations
+
+from collections import Counter
+from collections.abc import Mapping
+
+LABELS = (
+ "exactly 1",
+ "2-9",
+ "10-99",
+ "100-999",
+ "1,000-9,999",
+ "10,000-99,999",
+ ">=100,000",
+)
+
+
+def bucket(size: int) -> str:
+ if isinstance(size, bool) or not isinstance(size, int) or size < 1:
+ raise ValueError("class size must be a positive integer")
+ if size == 1:
+ return LABELS[0]
+ for index, upper in enumerate((10, 100, 1_000, 10_000, 100_000), start=1):
+ if size < upper:
+ return LABELS[index]
+ return LABELS[-1]
+
+
+def distribution(counts: Mapping[object, int]):
+ """Return transaction-weighted class-size shares and the transaction count."""
+ histogram = Counter()
+ for size in counts.values():
+ if isinstance(size, bool) or not isinstance(size, int) or size < 1:
+ raise ValueError("vector occurrence counts must be positive integers")
+ histogram[bucket(size)] += size
+ total = sum(histogram.values())
+ if total == 0:
+ raise ValueError("at least one vector occurrence count is required")
+ return {label: histogram.get(label, 0) / total for label in LABELS}, total
+
+
+def reconstruct_histogram(conditional: Mapping[str, Mapping[str, object]]):
+ """Recover a global histogram from a partition of conditional bucket counts."""
+ histogram = Counter()
+ if not conditional:
+ raise ValueError("conditional partition must not be empty")
+ for value, record in conditional.items():
+ if not isinstance(record, Mapping) or not isinstance(record.get("buckets"), Mapping):
+ raise ValueError(f"conditional value {value!r} has no bucket mapping")
+ unknown = set(record["buckets"]) - set(LABELS)
+ if unknown:
+ raise ValueError(f"conditional value {value!r} has unknown buckets: {sorted(unknown)}")
+ for label, count in record["buckets"].items():
+ if isinstance(count, bool) or not isinstance(count, int) or count < 0:
+ raise ValueError(f"bucket {label!r} for {value!r} has an invalid count")
+ histogram[label] += count
+ total = sum(histogram.values())
+ if total == 0:
+ raise ValueError("conditional partition contains no transactions")
+ return {label: histogram.get(label, 0) for label in LABELS}, total
diff --git a/decluster/fingerprint_validate.py b/decluster/fingerprint_validate.py
index eb04502f..5a0bbd06 100644
--- a/decluster/fingerprint_validate.py
+++ b/decluster/fingerprint_validate.py
@@ -3,7 +3,7 @@
and reports AUC. Offline."""
import random
from .graph_deanon import auc, shuffle_auc
-from .combiner import fs_score
+from .rarity_weight_baseline import rarity_score
from .change_gt import input_addrs
@@ -41,15 +41,45 @@ def evaluate(txs, scorer, cap=4000, seed=0):
"auc": auc(pos, neg, seed), "shuffle_auc": shuffle_auc(pos, neg, seed)}
+# Correlated axis clusters measured on the cache (`fs_ablation.dependent_clusters`, |phi| >= 0.6
+# within either class) with the representative each one keeps (`fs_ablation.group_ablation`: the
+# member carrying the largest |agreement_weight| in the train-fitted model). The additive rarity
+# kernel has no conditional-independence correction, so within a cluster it scores one fact
+# several times; keeping a single representative is the decorrelated reading of the same evidence.
+AXIS_CLUSTERS = (
+ (("input_script_type", "input_types_present", "nested_segwit", "pubkey_compression",
+ "sighash"), "input_script_type"),
+ (("change_index", "change_matches_output", "change_type_match"), "change_index"),
+ (("input_order", "io_shape", "output_order"), "io_shape"),
+ (("change_spk", "output_encoding"), "change_spk"),
+)
+
+REDUNDANT_AXES = frozenset(
+ axis for cluster, keep in AXIS_CLUSTERS for axis in cluster if axis != keep)
+
+# Axes the shared input address fixes outright. The same-owner label is that shared address, so
+# agreement on these is partly the label restating itself rather than construction style.
+ADDRESS_DETERMINED_AXES = frozenset({
+ "input_script_type", "input_types_present", "nested_segwit", "pubkey_compression",
+ "change_address_reuse",
+})
+
+
class LibraryScorer:
- """Canonical Fellegi-Sunter pair scorer over ALL library axes (library.AXES) — the witness-bearing
- multi-axis model, using the corrected extractors. Delegates the scoring kernel to combiner.fs_score."""
- def __init__(self, consistency=0.95, floor_n=1000):
+ """Legacy rarity pair scorer over the measured fingerprint axes.
+
+ `drop` excludes named axes before scoring. The default drops nothing and is what every
+ published number uses; `decorrelated_scorer` and `construction_only_scorer` are the two named
+ variants, kept alongside rather than replacing it."""
+ def __init__(self, consistency=0.95, floor_n=1000, drop=()):
from . import extractors, engine, library
self.c = consistency
self.floor_n = floor_n
self.axes = []
+ drop = frozenset(drop)
for a in library.AXES:
+ if a["axis"] in drop:
+ continue
fn = getattr(extractors, a["extractor"], None) or getattr(engine, a["extractor"], None)
if fn is None:
continue
@@ -68,7 +98,41 @@ def __init__(self, consistency=0.95, floor_n=1000):
self.axes.append((a["axis"], fn, p, collision, abstain))
def score(self, txA, txB, explain=False):
- return fs_score(self.axes, txA, txB, self.c, self.floor_n, explain)
+ return rarity_score(self.axes, txA, txB, self.c, self.floor_n, explain)
+
+
+def decorrelated_scorer(consistency=0.95, floor_n=1000):
+ """One representative per correlated cluster: 14 axes rather than 23."""
+ return LibraryScorer(consistency, floor_n, drop=REDUNDANT_AXES)
+
+
+def construction_only_scorer(consistency=0.95, floor_n=1000):
+ """Only the axes the shared-input-address label does not determine: 18 axes rather than 23.
+ Separates construction style from the label restating itself."""
+ return LibraryScorer(consistency, floor_n, drop=ADDRESS_DETERMINED_AXES)
+
+
+def axis_families(txs, consistency=0.95, cap=4000, seed=0):
+ """The three scorers over one pair sample: catalogued, construction-only, decorrelated.
+
+ Same population, same seed, same consistency — only the axis set moves. That is what makes the
+ magnitudes comparable: a drop between families is redundancy or label leakage in the wide
+ model, not a different measurement.
+ """
+ scorers = (
+ ("catalogued", LibraryScorer(consistency, drop=frozenset()), REDUNDANT_AXES | ADDRESS_DETERMINED_AXES),
+ ("construction_only", construction_only_scorer(consistency), ADDRESS_DETERMINED_AXES),
+ ("decorrelated", decorrelated_scorer(consistency), REDUNDANT_AXES),
+ )
+ rows = []
+ for name, scorer, dropped in scorers:
+ rows.append({
+ "family": name,
+ "axes": len(scorer.axes),
+ "dropped": sorted(dropped) if name != "catalogued" else [],
+ **evaluate(txs, scorer, cap=cap, seed=seed),
+ })
+ return rows
def load_blkcache(path=".blkcache"):
diff --git a/decluster/fs_ablation.py b/decluster/fs_ablation.py
new file mode 100644
index 00000000..23490ee4
--- /dev/null
+++ b/decluster/fs_ablation.py
@@ -0,0 +1,334 @@
+"""Ablation of correlated comparison axes in the supervised Fellegi--Sunter model.
+
+`decluster.fellegi_sunter` assumes conditional independence of comparison fields GIVEN match
+status, and sums their log-likelihood weights. Several fingerprint axes plainly violate that
+assumption (fields derived from the same script type, the same signature encoding, or the same
+wallet behaviour move together). This module measures the damage rather than assuming it:
+
+1. Within-class association (`class_conditional_association`): unconditional correlation between
+ two fields is expected even under a perfectly valid FS model, because matches and non-matches
+ differ systematically on every predictive axis -- pooling the two classes induces spurious
+ association (a Simpson's-paradox-style confound). The FS assumption is about association WITHIN
+ each class, so that is what is measured here, separately for the match and non-match class, via
+ the phi coefficient (see `phi_coefficient`).
+2. Leave-one-out ablation (`leave_one_out`): refit and re-evaluate out-of-period with each field
+ removed, on identical held-out pairs, reporting the change in held-out AUC and classification
+ metrics. A field whose removal does not hurt is carrying no independent information.
+3. Group ablation (`group_ablation`): for a cluster of mutually dependent fields, compare removing
+ the whole cluster against removing every member but one representative.
+
+Stdlib only -- no scipy/numpy/pandas.
+"""
+
+from __future__ import annotations
+
+import math
+from itertools import combinations
+
+from .fellegi_sunter import comparison_vectors, fit_model_for_error_rates
+from .fs_temporal import (
+ _classification_metrics,
+ fingerprint_fields,
+ temporal_split,
+ weak_label_pairs,
+)
+from .graph_deanon import exact_auc
+
+__all__ = [
+ "phi_coefficient",
+ "class_conditional_association",
+ "most_dependent_pairs",
+ "dependent_clusters",
+ "leave_one_out",
+ "group_ablation",
+ "run_ablation_study",
+]
+
+
+def phi_coefficient(a_values, b_values):
+ """Matthews/phi correlation between two parallel sequences of booleans.
+
+ ``phi = (n11*n00 - n10*n01) / sqrt((n11+n10)(n01+n00)(n11+n01)(n10+n00))`` where ``nXY`` counts
+ how often the pair took agree-value X for the first field and Y for the second (1 = agree, 0 =
+ disagree). This is exactly the Pearson correlation coefficient specialised to two binary
+ variables. Returns ``None`` when any marginal is degenerate (all-agree or all-disagree on
+ either field), where phi is undefined rather than zero.
+ """
+ n11 = n10 = n01 = n00 = 0
+ for a, b in zip(a_values, b_values):
+ if a and b:
+ n11 += 1
+ elif a and not b:
+ n10 += 1
+ elif not a and b:
+ n01 += 1
+ else:
+ n00 += 1
+ denom = (n11 + n10) * (n01 + n00) * (n11 + n01) * (n10 + n00)
+ if denom == 0:
+ return None
+ return (n11 * n00 - n10 * n01) / math.sqrt(denom)
+
+
+def class_conditional_association(vectors, labels, fields, min_n=30):
+ """Pairwise phi coefficient between comparison fields, WITHIN each class separately.
+
+ Restricts each pair to the pairs where both fields are active (non-``None``) and where the
+ label matches the class being measured. This is deliberately NOT the unconditional correlation
+ over the full sample: the FS conditional-independence assumption is about association given
+ match status, and unconditional correlation would be dominated by the two classes' different
+ base rates on every axis, proving nothing about the assumption FS actually makes.
+
+ Returns ``{"match": {(name_a, name_b): {"phi": float, "n": int}}, "non_match": {...}}``,
+ restricted to pairs with at least ``min_n`` jointly-active observations and a defined phi.
+ Field-name pairs are ordered as given by ``fields`` (alphabetical, since callers use
+ ``fingerprint_fields()``, whose names are already sorted by the underlying axis order).
+ """
+ names = [field.name for field in fields]
+ result = {"match": {}, "non_match": {}}
+ for cls, want in (("match", True), ("non_match", False)):
+ idx = [i for i, label in enumerate(labels) if bool(label) == want]
+ for a, b in combinations(names, 2):
+ av, bv = [], []
+ for i in idx:
+ va, vb = vectors[i].get(a), vectors[i].get(b)
+ if va is None or vb is None:
+ continue
+ av.append(va)
+ bv.append(vb)
+ if len(av) < min_n:
+ continue
+ phi = phi_coefficient(av, bv)
+ if phi is not None:
+ result[cls][(a, b)] = {"phi": phi, "n": len(av)}
+ return result
+
+
+def most_dependent_pairs(association, top_n=10):
+ """Rank field pairs by the larger-magnitude phi seen in either class.
+
+ Returns a list of ``(pair, phi_match, phi_non_match)`` tuples, ``phi_match``/``phi_non_match``
+ each ``None`` when that class had too few observations to score the pair, sorted by
+ ``max(|phi_match|, |phi_non_match|)`` descending.
+ """
+ pairs = set(association["match"]) | set(association["non_match"])
+
+ def rank_key(pair):
+ m = association["match"].get(pair)
+ n = association["non_match"].get(pair)
+ return max(abs(m["phi"]) if m else 0.0, abs(n["phi"]) if n else 0.0)
+
+ ranked = sorted(pairs, key=rank_key, reverse=True)
+ out = []
+ for pair in ranked[:top_n]:
+ m = association["match"].get(pair)
+ n = association["non_match"].get(pair)
+ out.append((pair, m["phi"] if m else None, n["phi"] if n else None))
+ return out
+
+
+def dependent_clusters(association, threshold=0.6):
+ """Connected components of fields joined by an edge when |phi| >= `threshold` in either class.
+
+ `threshold=0.6` (conventionally a "strong" phi coefficient) is a fixed, stated choice, not
+ fitted to the data. Single-linkage connected components can chain transitively (A-B and B-C
+ strong does not mean A-C is), so a returned cluster is a claim about the connectivity structure
+ at this threshold, not that every pair inside it is individually strongly associated -- callers
+ that need that should check `association` directly. Singletons (fields joined to nothing) are
+ dropped; only clusters of two or more fields are returned.
+ """
+ parent = {}
+
+ def find(x):
+ parent.setdefault(x, x)
+ while parent[x] != x:
+ parent[x] = parent[parent[x]]
+ x = parent[x]
+ return x
+
+ def union(a, b):
+ ra, rb = find(a), find(b)
+ if ra != rb:
+ parent[ra] = rb
+
+ for cls in ("match", "non_match"):
+ for (a, b), data in association[cls].items():
+ if abs(data["phi"]) >= threshold:
+ union(a, b)
+
+ groups = {}
+ for field in list(parent):
+ groups.setdefault(find(field), set()).add(field)
+ clusters = [group for group in groups.values() if len(group) > 1]
+ clusters.sort(key=lambda group: (-len(group), sorted(group)))
+ return clusters
+
+
+def _usable_names(vectors, labels, names):
+ """Names with both a match and a non-match observation active, mirroring
+ `fs_temporal._usable_fields` but over plain field names rather than `ComparisonField` objects,
+ since ablation works by deleting keys from already-built comparison vectors."""
+ usable = []
+ for name in names:
+ match = any(label and vector.get(name) is not None
+ for vector, label in zip(vectors, labels))
+ nonmatch = any(not label and vector.get(name) is not None
+ for vector, label in zip(vectors, labels))
+ if match and nonmatch:
+ usable.append(name)
+ return usable
+
+
+def _fit_eval(train_vectors, train_labels, test_vectors, test_labels, drop=frozenset(), *,
+ false_match_rate=0.05, false_non_match_rate=0.05):
+ """Refit on `train_vectors` with the fields in `drop` removed, then score `test_vectors`."""
+ def strip(vector):
+ return {name: value for name, value in vector.items() if name not in drop}
+
+ stripped_train = [strip(vector) for vector in train_vectors]
+ stripped_test = [strip(vector) for vector in test_vectors]
+ names = _usable_names(stripped_train, train_labels,
+ sorted({name for vector in stripped_train for name in vector}))
+ if not names:
+ raise ValueError("no comparison field survives this ablation")
+ train_v = [{name: vector[name] for name in names if name in vector} for vector in stripped_train]
+ test_v = [{name: vector[name] for name in names if name in vector} for vector in stripped_test]
+ model = fit_model_for_error_rates(
+ train_v, train_labels,
+ false_match_rate=false_match_rate, false_non_match_rate=false_non_match_rate,
+ )
+ scores = [model.score(vector) for vector in test_v]
+ decisions = [model.classify(vector) for vector in test_v]
+ return {
+ "fields": names,
+ "model": model,
+ "auc": exact_auc(scores, test_labels),
+ **_classification_metrics(list(test_labels), decisions),
+ }
+
+
+def _split_and_vectorize(txs, train_fraction, cap, seed):
+ train_txs, test_txs, cutoff = temporal_split(txs, train_fraction)
+ train_pairs, train_labels = weak_label_pairs(train_txs, cap, seed)
+ test_pairs, test_labels = weak_label_pairs(test_txs, cap, seed + 1)
+ fields = fingerprint_fields()
+ train_vectors = comparison_vectors(train_pairs, fields)
+ test_vectors = comparison_vectors(test_pairs, fields)
+ return train_vectors, train_labels, test_vectors, test_labels, cutoff
+
+
+def leave_one_out(txs, *, train_fraction=0.7, cap=4000, seed=0,
+ false_match_rate=0.05, false_non_match_rate=0.05):
+ """Refit and re-evaluate out-of-period with each usable field removed, one at a time.
+
+ Uses the identical held-out pairs for every ablation (same temporal split, same weak-label
+ pairs) so `auc_delta` isolates the effect of removing that one field. A field whose removal
+ does not hurt (`auc_delta` near zero or negative) is carrying no independent information once
+ the rest of the model is fit -- exactly what a correlated, redundant axis looks like.
+ """
+ train_vectors, train_labels, test_vectors, test_labels, cutoff = _split_and_vectorize(
+ txs, train_fraction, cap, seed)
+ baseline = _fit_eval(train_vectors, train_labels, test_vectors, test_labels,
+ false_match_rate=false_match_rate, false_non_match_rate=false_non_match_rate)
+ per_field = {}
+ for name in baseline["fields"]:
+ ablated = _fit_eval(train_vectors, train_labels, test_vectors, test_labels, drop={name},
+ false_match_rate=false_match_rate,
+ false_non_match_rate=false_non_match_rate)
+ per_field[name] = {
+ "auc": ablated["auc"],
+ "auc_delta": baseline["auc"] - ablated["auc"],
+ "coverage": ablated["coverage"],
+ "selective_accuracy": ablated["selective_accuracy"],
+ }
+ return {
+ "cutoff": cutoff,
+ "baseline": {k: v for k, v in baseline.items() if k != "model"},
+ "leave_one_out": per_field,
+ }
+
+
+def group_ablation(txs, group, *, train_fraction=0.7, cap=4000, seed=0,
+ false_match_rate=0.05, false_non_match_rate=0.05):
+ """Compare removing a whole correlated `group` against removing every member but one.
+
+ The representative kept is the group member with the largest `|agreement_weight|` in the
+ train-fitted full model -- the single field in the group that alone carries the most
+ discriminative signal, chosen from train-side fit parameters only (no test-side information
+ enters the choice). If the group is genuinely redundant, `remove_group_delta` should exceed
+ `keep_representative_delta` by a wide margin: dropping every correlated copy loses real
+ information, dropping all-but-one loses almost none.
+ """
+ group = set(group)
+ if len(group) < 2:
+ raise ValueError("a group needs at least two fields")
+ train_vectors, train_labels, test_vectors, test_labels, cutoff = _split_and_vectorize(
+ txs, train_fraction, cap, seed)
+ baseline = _fit_eval(train_vectors, train_labels, test_vectors, test_labels,
+ false_match_rate=false_match_rate, false_non_match_rate=false_non_match_rate)
+ missing = group - set(baseline["fields"])
+ if missing:
+ raise ValueError(f"group contains fields absent from the fitted model: {sorted(missing)}")
+ representative = max(group, key=lambda name: abs(baseline["model"].parameters[name].agreement_weight))
+
+ remove_group = _fit_eval(train_vectors, train_labels, test_vectors, test_labels, drop=group,
+ false_match_rate=false_match_rate,
+ false_non_match_rate=false_non_match_rate)
+ keep_representative = _fit_eval(
+ train_vectors, train_labels, test_vectors, test_labels, drop=group - {representative},
+ false_match_rate=false_match_rate, false_non_match_rate=false_non_match_rate)
+ return {
+ "cutoff": cutoff,
+ "group": sorted(group),
+ "representative": representative,
+ "baseline_auc": baseline["auc"],
+ "remove_group_auc": remove_group["auc"],
+ "remove_group_delta": baseline["auc"] - remove_group["auc"],
+ "keep_representative_auc": keep_representative["auc"],
+ "keep_representative_delta": baseline["auc"] - keep_representative["auc"],
+ }
+
+
+def run_ablation_study(txs, *, train_fraction=0.7, ablation_cap=4000,
+ association_cap=8000, seed=0, association_min_n=30,
+ cluster_threshold=0.6,
+ false_match_rate=0.05, false_non_match_rate=0.05):
+ """The full study: within-class association, leave-one-out, and group ablation on every
+ cluster the association step finds. Association is measured on the train side of the same
+ temporal split leave-one-out and group ablation use, with a larger pair cap for statistical
+ power -- it is a diagnostic about the axes' joint distribution, not a held-out performance
+ claim, so it does not need to be evaluated out-of-period, but it is still computed only from
+ data available before the cutoff to keep every number in this module "fit early"."""
+ train_txs, _test_txs, cutoff = temporal_split(txs, train_fraction)
+ fields = fingerprint_fields()
+ assoc_pairs, assoc_labels = weak_label_pairs(train_txs, association_cap, seed)
+ assoc_vectors = comparison_vectors(assoc_pairs, fields)
+ association = class_conditional_association(assoc_vectors, assoc_labels, fields,
+ min_n=association_min_n)
+ top_pairs = most_dependent_pairs(association, top_n=15)
+ clusters = dependent_clusters(association, threshold=cluster_threshold)
+
+ loo = leave_one_out(txs, train_fraction=train_fraction, cap=ablation_cap, seed=seed,
+ false_match_rate=false_match_rate, false_non_match_rate=false_non_match_rate)
+ groups = [
+ group_ablation(txs, cluster, train_fraction=train_fraction, cap=ablation_cap, seed=seed,
+ false_match_rate=false_match_rate, false_non_match_rate=false_non_match_rate)
+ for cluster in clusters
+ ]
+ return {
+ "cutoff": cutoff,
+ "association": {
+ "statistic": "phi coefficient (Pearson correlation of two binary agree/disagree "
+ "indicators), computed within each class separately",
+ "min_n": association_min_n,
+ "cap": association_cap,
+ "most_dependent_pairs": [
+ {"fields": list(pair), "phi_match": phi_match, "phi_non_match": phi_non_match}
+ for pair, phi_match, phi_non_match in top_pairs
+ ],
+ "cluster_threshold": cluster_threshold,
+ "clusters": [sorted(cluster) for cluster in clusters],
+ },
+ "leave_one_out": loo,
+ "group_ablation": groups,
+ }
diff --git a/decluster/fs_bayes.py b/decluster/fs_bayes.py
index d746b9e2..9a264c5d 100644
--- a/decluster/fs_bayes.py
+++ b/decluster/fs_bayes.py
@@ -1,8 +1,9 @@
"""Bayesian (Gibbs) record linkage over the per-axis agree/disagree likelihood — the classic per-field
Fellegi-Sunter form with m_j given a Beta prior and u_j fixed at the measured collision. F-S is the
plug-in (point-m) special case. Offline, deterministic. Consumes the fs_em.agree_matrix (A, mask, u)
-shape; see results/RESULTS-bayes-vs-fs.md. NOTE: this is the axis-level F-S, not combiner.fs_score
-(which is value-specific)."""
+shape; see results/RESULTS-bayes-vs-fs.md. NOTE: this is the axis-level F-S, not the value-specific
+combiner.rarity_score (a rarity baseline, not a
+fitted Fellegi-Sunter model; the fitted model is `decluster/fellegi_sunter.py`)."""
import math
import random
from .fs_em import _clamp # shared clamp for the per-axis F-S probabilities
@@ -10,7 +11,8 @@
def pair_probs(A, mask, m, u, lam):
"""Per-pair P(match) under the per-field naive-Bayes likelihood: log-LR = Σ over active axes of
- a·log(m/u) + (1−a)·log((1−m)/(1−u)); P = λ·LR / (λ·LR + (1−λ)). Axis-level F-S, not fs_score."""
+ a·log(m/u) + (1−a)·log((1−m)/(1−u)); P = λ·LR / (λ·LR + (1−λ)). Axis-level F-S, not the
+value-specific combiner.rarity_score."""
m = [_clamp(v) for v in m]
u = [_clamp(v) for v in u]
k = len(u)
diff --git a/decluster/fs_em.py b/decluster/fs_em.py
index 344129bd..e1fc465e 100644
--- a/decluster/fs_em.py
+++ b/decluster/fs_em.py
@@ -1,6 +1,7 @@
"""Unsupervised EM for the per-axis Fellegi-Sunter m (u fixed at the measured collision), plus a
-supervised reuse-label oracle. Offline, deterministic. See combiner.fs_score for the scoring kernel and
-results/RESULTS-em-m.md for the reading."""
+supervised reuse-label oracle. Offline, deterministic. See combiner.rarity_score for the scoring
+kernel (`combiner.rarity_score`, which was never a fitted Fellegi-Sunter model — the
+fitted model is `decluster/fellegi_sunter.py`) and results/RESULTS-em-m.md for the reading."""
import math
_EPS = 1e-6
diff --git a/decluster/fs_temporal.py b/decluster/fs_temporal.py
new file mode 100644
index 00000000..819492cd
--- /dev/null
+++ b/decluster/fs_temporal.py
@@ -0,0 +1,239 @@
+"""Temporal, weak-label evaluation of fingerprint record linkage.
+
+Address reuse supplies useful labels for an attack experiment, but it is not
+independently verified ownership. This module keeps that limitation in the
+machine-readable result and never fits on transactions from the test period.
+"""
+
+from __future__ import annotations
+
+import math
+import random
+from itertools import combinations
+
+from .change_gt import input_addrs
+from .fellegi_sunter import ComparisonField, comparison_vectors, fit_model_for_error_rates
+from .fingerprint_validate import LibraryScorer
+from .fs_bayes import ece
+from .graph_deanon import exact_auc
+
+
+def _height(tx):
+ return (tx.get("status") or {}).get("block_height")
+
+
+def temporal_split(txs, train_fraction=0.7):
+ """Split at a block-height boundary; equal-height transactions never leak."""
+ if not 0.0 < train_fraction < 1.0:
+ raise ValueError("train_fraction must be strictly between zero and one")
+ if any(_height(tx) is None for tx in txs):
+ raise ValueError("every transaction needs status.block_height")
+ heights = sorted({_height(tx) for tx in txs})
+ if len(heights) < 2:
+ raise ValueError("temporal evaluation needs at least two block heights")
+ index = min(len(heights) - 2, max(0, int(len(heights) * train_fraction) - 1))
+ cutoff = heights[index]
+ train = [tx for tx in txs if _height(tx) <= cutoff]
+ test = [tx for tx in txs if _height(tx) > cutoff]
+ if not train or not test:
+ raise ValueError("temporal split produced an empty partition")
+ return train, test, cutoff
+
+
+def weak_label_pairs(txs, cap=4000, seed=0):
+ """Return balanced address-reuse/random pairs and their weak labels.
+
+ Positives are distinct transactions sharing an input address. Negatives
+ are pairs with no observed shared input address. Collaborative spends,
+ address transfer, and unseen history can make either label wrong, hence the
+ deliberate ``weak`` terminology.
+ """
+ if cap <= 0:
+ raise ValueError("cap must be positive")
+ rng = random.Random(seed)
+ by_addr = {}
+ addresses = {}
+ by_txid = {}
+ for tx in txs:
+ tid = tx.get("txid")
+ if not tid:
+ raise ValueError("every transaction needs a txid")
+ by_txid[tid] = tx
+ aset = frozenset(input_addrs(tx))
+ addresses[tid] = aset
+ for address in sorted(aset):
+ by_addr.setdefault(address, set()).add(tid)
+
+ positive_ids = set()
+ for tids in by_addr.values():
+ for a, b in combinations(sorted(tids), 2):
+ positive_ids.add((a, b))
+ positive_ids = sorted(positive_ids)
+ if len(positive_ids) > cap:
+ positive_ids = rng.sample(positive_ids, cap)
+
+ tids = sorted(by_txid)
+ negative_ids = []
+ seen = set()
+ attempts = 0
+ max_attempts = max(1000, cap * 100)
+ while len(negative_ids) < min(cap, len(positive_ids)) and attempts < max_attempts:
+ attempts += 1
+ if len(tids) < 2:
+ break
+ a, b = sorted(rng.sample(tids, 2))
+ if (a, b) in seen or addresses[a] & addresses[b]:
+ continue
+ seen.add((a, b))
+ negative_ids.append((a, b))
+ n = min(len(positive_ids), len(negative_ids))
+ if not n:
+ raise ValueError("both weak-label classes need at least one pair")
+ positive_ids = positive_ids[:n]
+ negative_ids = negative_ids[:n]
+ pairs = ([(by_txid[a], by_txid[b]) for a, b in positive_ids] +
+ [(by_txid[a], by_txid[b]) for a, b in negative_ids])
+ return pairs, [True] * n + [False] * n
+
+
+def fingerprint_fields():
+ """Build binary comparisons for the same axes as ``LibraryScorer``."""
+ scorer = LibraryScorer()
+ fields = []
+ for name, extractor, probabilities, _collision, _abstain in scorer.axes:
+ active = frozenset(probabilities)
+
+ def compare(left, right, extractor=extractor, active=active):
+ a, b = extractor(left), extractor(right)
+ if a not in active or b not in active:
+ return None
+ return a == b
+
+ fields.append(ComparisonField(name, compare))
+ return fields
+
+
+def _usable_fields(vectors, labels, fields):
+ usable = []
+ for field in fields:
+ match = any(label and vector.get(field.name) is not None
+ for vector, label in zip(vectors, labels))
+ nonmatch = any(not label and vector.get(field.name) is not None
+ for vector, label in zip(vectors, labels))
+ if match and nonmatch:
+ usable.append(field)
+ return usable
+
+
+def _classification_metrics(labels, decisions):
+ linked = [i for i, decision in enumerate(decisions) if decision == "link"]
+ positives = sum(labels)
+ tp = sum(labels[i] for i in linked)
+ decided = [i for i, decision in enumerate(decisions) if decision != "review"]
+ correct = sum((decisions[i] == "link") == labels[i] for i in decided)
+ return {
+ "true_positive": tp,
+ "false_positive": len(linked) - tp,
+ "false_negative": positives - tp,
+ "precision": tp / len(linked) if linked else None,
+ "recall": tp / positives if positives else None,
+ "coverage": len(decided) / len(labels) if labels else 0.0,
+ "selective_accuracy": correct / len(decided) if decided else None,
+ }
+
+
+def _score_metrics(scores, labels):
+ """Exact Mann--Whitney AUC with half credit for ties (`graph_deanon.exact_auc`).
+
+ Deliberately NOT `graph_deanon.auc`, which estimates the same quantity from 20,000 sampled
+ draws: the AUCs in this module are differenced against each other, and a sampling error in the
+ third decimal on each side is a large fraction of the delta. It does mean the numbers here are
+ not directly comparable at three decimals with AUCs published elsewhere in this repository,
+ which came from the estimator."""
+ return {"auc": exact_auc(scores, labels)}
+
+
+def _posterior(score, prior):
+ log_odds = math.log(prior / (1.0 - prior)) + score * math.log(2.0)
+ if log_odds >= 0:
+ return 1.0 / (1.0 + math.exp(-log_odds))
+ exp_odds = math.exp(log_odds)
+ return exp_odds / (1.0 + exp_odds)
+
+
+def evaluate_temporal(txs, *, train_fraction=0.7, cap=4000, seed=0,
+ false_match_rate=0.05, false_non_match_rate=0.05):
+ """Fit early, evaluate late, and compare FS with rarity on identical pairs."""
+ train_txs, test_txs, cutoff = temporal_split(txs, train_fraction)
+ train_pairs, train_labels = weak_label_pairs(train_txs, cap, seed)
+ test_pairs, test_labels = weak_label_pairs(test_txs, cap, seed + 1)
+
+ fields = fingerprint_fields()
+ train_vectors = comparison_vectors(train_pairs, fields)
+ fields = _usable_fields(train_vectors, train_labels, fields)
+ if not fields:
+ raise ValueError("no comparison field has both weak-label classes in training")
+ train_vectors = [{f.name: vector[f.name] for f in fields} for vector in train_vectors]
+ test_vectors = comparison_vectors(test_pairs, fields)
+ model = fit_model_for_error_rates(
+ train_vectors, train_labels,
+ false_match_rate=false_match_rate,
+ false_non_match_rate=false_non_match_rate,
+ )
+
+ fs_scores = [model.score(vector) for vector in test_vectors]
+ decisions = [model.classify(vector) for vector in test_vectors]
+ rarity = LibraryScorer()
+ rarity_scores = [rarity.score(left, right) for left, right in test_pairs]
+ fs_metrics = _score_metrics(fs_scores, test_labels)
+ rarity_metrics = _score_metrics(rarity_scores, test_labels)
+ prior = sum(train_labels) / len(train_labels)
+ probabilities = [_posterior(score, prior) for score in fs_scores]
+ calibration = {
+ "prior": prior,
+ "brier": sum((p - int(y)) ** 2 for p, y in zip(probabilities, test_labels)) /
+ len(test_labels),
+ "ece_10": ece(probabilities, [int(y) for y in test_labels], bins=10),
+ "scope": "balanced weak-label case-control sample; not population calibration",
+ }
+ return {
+ "label_provenance": {
+ "kind": "weak_labels",
+ "positive": "distinct transactions sharing an observed input address",
+ "negative": "sampled pair with no observed shared input address",
+ "independently_verified": False,
+ },
+ "split": {
+ "strategy": "strict_block_height",
+ "cutoff": cutoff,
+ "train_height": [min(map(_height, train_txs)), max(map(_height, train_txs))],
+ "test_height": [min(map(_height, test_txs)), max(map(_height, test_txs))],
+ "train_transactions": len(train_txs),
+ "test_transactions": len(test_txs),
+ },
+ "pairs": {
+ "train_positive": sum(train_labels),
+ "train_negative": len(train_labels) - sum(train_labels),
+ "test_positive": sum(test_labels),
+ "test_negative": len(test_labels) - sum(test_labels),
+ },
+ "fields": {
+ name: {"m": parameter.m, "u": parameter.u,
+ "agreement_weight": parameter.agreement_weight,
+ "disagreement_weight": parameter.disagreement_weight}
+ for name, parameter in model.parameters.items()
+ },
+ "fellegi_sunter": {
+ **fs_metrics,
+ **_classification_metrics(test_labels, decisions),
+ "link": decisions.count("link"),
+ "non_link": decisions.count("non-link"),
+ "review": decisions.count("review"),
+ "calibration": calibration,
+ },
+ "rarity_baseline": rarity_metrics,
+ "comparison": {
+ "same_test_pairs": True,
+ "auc_delta_fs_minus_rarity": fs_metrics["auc"] - rarity_metrics["auc"],
+ },
+ }
diff --git a/decluster/graph_deanon.py b/decluster/graph_deanon.py
index 6daf8490..ee8dbd38 100644
--- a/decluster/graph_deanon.py
+++ b/decluster/graph_deanon.py
@@ -3,7 +3,15 @@
(a collaborative tx in the slice would be a false merge). Held-out positives = same-owner pairs
NOT directly co-spent, scored by common neighbors. `evaluate` runs the 1-hop probe;
`analyze` sweeps graph depth k (hubs excluded) to show structure is deeper under churn.
-usage: python3 -m decluster.graph_deanon [--depth] """
+usage: python3 -m decluster.graph_deanon [--depth]
+
+WHAT THIS ACTUALLY IMPLEMENTS. Common-neighbour link prediction on one graph, scored pairwise
+(`structural_score` = |N(a) ∩ N(b)|). It has no seed set, no vertex correspondence, no propagation
+and no second view — none of the four things the Narayanan--Shmatikov de-anonymization algorithm is
+made of. The name records which paper motivated the probe, not which algorithm runs here. The
+faithful reference implementation is `decluster/baselines/narayanan_shmatikov.py`, and
+`decluster/baselines/link_prediction.py` is where a mapping from it actually drives link
+prediction; this module's `structural_score` is that comparison's structure-only arm."""
import sys, random
from .measure import load_unique
from .unionfind import UF
@@ -60,9 +68,9 @@ def build(sample, dust_guard=False):
in_addr = [a for a in in_addr if a]
out_addr = [o.get("scriptpubkey_address") for o in tx.get("vout", []) if o.get("scriptpubkey_address")]
cospent.add(n, in_addr)
- parts = set(in_addr) | set(out_addr)
- for a in parts:
- neigh_full.setdefault(a, set()).update(parts - {a})
+ parts = sorted(set(in_addr) | set(out_addr)) # insertion order here fixes the order
+ for a in parts: # every seeded sample below walks
+ neigh_full.setdefault(a, set()).update(set(parts) - {a})
for a in in_addr:
neigh_pay.setdefault(a, set()).update(out_addr)
for o in out_addr:
@@ -76,7 +84,18 @@ def structural_score(a, b, neigh):
def auc(pos_scores, neg_scores, seed=0):
- """AUC = P(a positive pair scores above a negative pair)."""
+ """AUC = P(a positive pair scores above a negative pair), ESTIMATED by at most 20,000 sampled
+ (positive, negative) draws with half credit for ties.
+
+ This is a Monte-Carlo estimator, not the exact statistic: it is seeded and so reproducible, but
+ two runs at different seeds differ in the third decimal, and every AUC published from this
+ module and its callers (`fingerprint_validate`, `change_validate`, `fingerprint_ns`, `cluster`,
+ `broadcast`) carries that sampling error. `exact_auc` below computes the same quantity exactly.
+ They are not interchangeable at three decimals, so a number from one should not be compared
+ against a number from the other without saying which produced it.
+
+ Kept as the estimator because the repository's published AUCs were measured with it; new call
+ sites should prefer `exact_auc`."""
rng = random.Random(seed)
if not pos_scores or not neg_scores: return None
trials = min(20000, len(pos_scores) * len(neg_scores))
@@ -87,6 +106,32 @@ def auc(pos_scores, neg_scores, seed=0):
return wins / trials
+def exact_auc(scores, labels):
+ """Exact Mann--Whitney AUC with half credit for ties: the rank-sum statistic, no sampling.
+
+ Takes one score list with a parallel boolean-ish `labels` rather than two lists, because that
+ is the shape an evaluation harness already holds. `None` when either class is empty, matching
+ `auc`. O(n log n) — cheaper than `auc`'s 20,000 draws on any realistic n, and deterministic
+ without a seed. The single implementation of the exact statistic in this repository;
+ `fs_temporal._score_metrics` calls it."""
+ positive = sum(1 for label in labels if label)
+ negative = len(labels) - positive
+ if not positive or not negative:
+ return None
+ ordered = sorted(zip(scores, labels), key=lambda item: item[0])
+ rank_sum = 0.0
+ index = 0
+ while index < len(ordered):
+ end = index + 1
+ while end < len(ordered) and ordered[end][0] == ordered[index][0]:
+ end += 1
+ average_rank = ((index + 1) + end) / 2.0
+ rank_sum += average_rank * sum(1 for _, label in ordered[index:end] if label)
+ index = end
+ statistic = rank_sum - positive * (positive + 1) / 2.0
+ return statistic / (positive * negative)
+
+
def shuffle_auc(pos_scores, neg_scores, seed=0):
"""Shuffle-null control: randomly flip each aligned (pos, neg) pair's label, then AUC -> ~0.5."""
rng = random.Random(seed)
@@ -98,7 +143,13 @@ def shuffle_auc(pos_scores, neg_scores, seed=0):
def _pairs(clusters, neigh, cospent, rng, cap=5000):
- """positives = same cluster but not directly co-spent (held-out); negatives = cross-cluster."""
+ """positives = same cluster but not directly co-spent (held-out); negatives = cross-cluster.
+
+ The two classes are not degree-balanced, and the score reads degree. Positives are *every*
+ intra-cluster pair, so they are quadratic in cluster size and dominated by the largest,
+ highest-degree clusters; negatives are drawn uniformly over cluster *roots*, which gives a
+ two-address cluster the same weight as a thousand-address one. `degree_matched_auc` is the
+ control that says how much of the separation survives once that channel is closed."""
pos, neg = [], []
for members in clusters.values():
for i in range(len(members)):
@@ -114,6 +165,57 @@ def _pairs(clusters, neigh, cospent, rng, cap=5000):
return pos, neg
+def _degree_matched_negatives(clusters, neigh, positives, rng, tries=50):
+ """One cross-cluster pair per positive, matching each endpoint's degree exactly.
+
+ Endpoints are drawn from the addresses that share their positive counterpart's degree, so the
+ negative class carries the positive class's degree distribution and a degree-only score sits
+ at chance. A positive whose degree admits no cross-cluster partner is dropped, which is why
+ the returned list can be shorter than `positives`."""
+ root_of = {a: r for r, members in clusters.items() for a in members}
+ by_degree = {}
+ for a in root_of:
+ by_degree.setdefault(len(neigh.get(a, ())), []).append(a)
+ out = []
+ for a, b in positives:
+ left = by_degree.get(len(neigh.get(a, ())), ())
+ right = by_degree.get(len(neigh.get(b, ())), ())
+ if not left or not right:
+ continue
+ for _ in range(tries):
+ x, y = rng.choice(left), rng.choice(right)
+ if root_of[x] != root_of[y]:
+ out.append((x, y))
+ break
+ return out
+
+
+def degree_matched_auc(sample, seed=0):
+ """`evaluate`'s AUCs recomputed against degree-matched negatives.
+
+ `evaluate` publishes the numbers; this says how much of them is the degree asymmetry
+ `_pairs` documents rather than shared structure. Reported alongside `degree_only_*`, the
+ AUC of a score that is nothing but the pair's degree sum: at chance under this control by
+ construction, and far from it under the published sampling."""
+ uf, neigh_full, neigh_pay, cospent = build(sample)
+ rng = random.Random(seed)
+ out = {}
+ for name, neigh in (("full", neigh_full), ("payment", neigh_pay)):
+ clusters = _clusters(uf, neigh)
+ pos = [(m[i], m[j]) for m in clusters.values()
+ for i in range(len(m)) for j in range(i + 1, len(m))
+ if frozenset((m[i], m[j])) not in cospent]
+ neg = _degree_matched_negatives(clusters, neigh, pos, rng)
+ labels = [True] * len(pos) + [False] * len(neg)
+ pairs = pos + neg
+ out[f"auc_{name}"] = exact_auc(
+ [structural_score(a, b, neigh) for a, b in pairs], labels)
+ out[f"degree_only_{name}"] = exact_auc(
+ [len(neigh.get(a, ())) + len(neigh.get(b, ())) for a, b in pairs], labels)
+ out[f"pos_pairs_{name}"], out[f"neg_pairs_{name}"] = len(pos), len(neg)
+ return out
+
+
def _clusters(uf, neigh):
clusters = {}
for a in list(neigh):
@@ -206,17 +308,20 @@ def evaluate_entity(sample, labeler, seed=0):
when the sample carries no multi-address entity (the usual case unless the slice covers an entity)."""
_uf, _full, neigh_pay, cospent = build(sample)
euf, labeled_all = entity_label_uf(sample, labeler)
- labeled = {a for a in neigh_pay if a in labeled_all}
+ labeled = sorted(a for a in neigh_pay if a in labeled_all)
clusters = {}
for a in labeled:
clusters.setdefault(euf.find(a), []).append(a)
clusters = {r: m for r, m in clusters.items() if len(m) >= 2}
+ for members in clusters.values():
+ members.sort()
rng = random.Random(seed)
pos = [structural_score(m[i], m[j], neigh_pay)
for m in clusters.values()
for i in range(len(m)) for j in range(i + 1, len(m))
if frozenset((m[i], m[j])) not in cospent]
- others = [a for a in neigh_pay if a not in labeled]
+ labeled_set = set(labeled)
+ others = sorted(a for a in neigh_pay if a not in labeled_set)
ent_addrs = list(labeled)
neg = ([structural_score(rng.choice(ent_addrs), rng.choice(others), neigh_pay)
for _ in range(min(5000, max(len(pos), 1)))] if ent_addrs and others else [])
diff --git a/decluster/graph_metric.py b/decluster/graph_metric.py
index a0e35da3..1ef595f7 100644
--- a/decluster/graph_metric.py
+++ b/decluster/graph_metric.py
@@ -1,7 +1,7 @@
"""Clustering-overcount diagnostic (relative, NOT a privacy score): how much the naive co-spend
clustering overstates the attacker's residual uncertainty relative to the fingerprint+amount
clustering. The entropy here is a property of a *clustering partition*, not the intrinsic anonymity
-of any transaction (that is the separate path-counting / k-route estimate). Read only as the
+of any transaction (that is the separate §07 path-counting object). Read only as the
naive-vs-fused ratio; never as an absolute 'this graph has N bits of anonymity'. Not a chain-scale
validation."""
import math
diff --git a/decluster/graph_shape.py b/decluster/graph_shape.py
index db187d5e..14881a7d 100644
--- a/decluster/graph_shape.py
+++ b/decluster/graph_shape.py
@@ -1,5 +1,10 @@
"""Is the contracted pseudonym graph a social network in the sense the matching needs?
+Every walk here orders vertices by `repr`. A contracted view mixes an unsplit cluster's
+integer root with a split one's tagged string, so `<` has no meaning across them; the
+statistics do not depend on the order, only reproducibility does. Sorting them raw raised
+a TypeError, which is why no contracted view had been measured for shape before.
+
The framework asserts it is: the cluster graph under an incomplete clustering "is a social
network per Shmatikov and Narayanan's definition". Everything downstream rests on that, and
it is an assertion rather than a measurement. The algorithms were validated on social
@@ -36,8 +41,8 @@ def assortativity(g):
positive: well-connected people know well-connected people. Technological and
transactional graphs come out negative, hubs attaching to leaves."""
xs, ys = [], []
- for u in g.vertices:
- for v in g.neighbours(u):
+ for u in sorted(g.vertices, key=repr):
+ for v in sorted(g.neighbours(u), key=repr):
xs.append(g.degree(u))
ys.append(g.degree(v)) # each undirected edge is seen from both ends
n = len(xs)
@@ -52,7 +57,7 @@ def assortativity(g):
def transitivity(g, sample=None, rng=None):
"""Global clustering: closed triples over all triples. Sampling vertices bounds the cost
on a large graph; the estimate is over the sampled vertices' triples."""
- verts = list(g.vertices)
+ verts = sorted(g.vertices, key=repr)
if sample and sample < len(verts):
verts = (rng or random.Random(0)).sample(verts, sample)
triples = closed = 0
@@ -62,7 +67,7 @@ def transitivity(g, sample=None, rng=None):
if d < 2:
continue
triples += d * (d - 1) // 2
- nb_list = list(nb)
+ nb_list = sorted(nb, key=repr)
for i in range(len(nb_list)):
ni = g.neighbours(nb_list[i])
for j in range(i + 1, len(nb_list)):
diff --git a/decluster/intersect.py b/decluster/intersect.py
index c9f93fd0..18e45c32 100644
--- a/decluster/intersect.py
+++ b/decluster/intersect.py
@@ -38,7 +38,19 @@ def rarity_weight(ancestor, rarity):
return 1.0 / math.log2(support + 1) if support > 1 else 1.0
-def shared_origins(signatures, rarity=None):
+def cluster_key(ancestor, cluster_of):
+ """The wallet an origin coin belongs to, or the coin itself when it is unclustered.
+
+ Both forms fall back to the coin. A callable that answers `None` for a coin it does not
+ know would otherwise collapse every unclustered origin onto one shared pseudo-cluster, and
+ branches with nothing in common would intersect at full mass — a fabricated attribution,
+ which is the one failure this module must not have.
+ """
+ c = cluster_of(ancestor) if callable(cluster_of) else cluster_of.get(ancestor, ancestor)
+ return ancestor if c is None else c
+
+
+def shared_origins(signatures, rarity=None, cluster_of=None, cluster_rarity=None):
"""Ancestors present in *every* signature, with the mass all of them carry.
`signatures` is a list of `{ancestor: mass}`. Returns
@@ -53,6 +65,22 @@ def shared_origins(signatures, rarity=None):
"""
if not signatures:
return []
+ if cluster_of is not None:
+ # Cluster-lift (the writeup's "the candidate origins are not coins, but rather
+ # clusters", under Robust connectivity, cit-42): coarsen each coin-origin to its
+ # owning cluster, summing masses, and intersect WALLETS rather than coins.
+ def _lift(sig):
+ out = {}
+ for a, m in sig.items():
+ c = cluster_key(a, cluster_of)
+ out[c] = out.get(c, 0) + m
+ return out
+ signatures = [_lift(s) for s in signatures]
+ if rarity is not None and cluster_rarity is None:
+ raise ValueError("cluster_rarity is required when weighting cluster-lifted origins")
+ # Cluster support must be measured over the population, not reconstructed from only the
+ # branches in this intersection. Reusing coin support or local branch counts biases hubs.
+ rarity = cluster_rarity
common = set(signatures[0])
for sig in signatures[1:]:
common &= set(sig)
@@ -73,6 +101,12 @@ def collapse_bits(smallest, n_shared):
`ancestry_entropy`. A raw difference of origins is not comparable across
branches of different sizes; bits are.
+ Unnormalized bits, after Serjantov and Danezis. Diaz et al.'s degree of anonymity
+ `d = H/H_M` is deliberately not adopted: `H_M = log2(N)` needs a fixed population `N`
+ of candidate origins, and a coin's candidate set has no such `N` — the walk's reach,
+ the clustering and the truncation bound all move it. Dividing by a quantity that moves
+ with the measurement would report method changes as privacy changes.
+
`None` when there is nothing to measure: no signature at all, or an empty
intersection. An empty intersection is a refusal — the branches share no
origin — not an unbounded narrowing, and reporting it as infinite bits would
@@ -83,6 +117,38 @@ def collapse_bits(smallest, n_shared):
return math.log2(smallest / n_shared)
+def accumulate_intersections(observations, rarity=None, cluster_of=None, universe_size=None,
+ cluster_rarity=None):
+ """Cross-event accumulation. Each observation is a list of branch signatures (one co-spend /
+ linkage event); intersecting the surviving origin set across successive linked observations is
+ where the compounding comes from — the writeup's "O(log n) observations, each cutting the
+ candidate set by a constant factor" (Robust connectivity). Nothing cited here states that rate.
+ It is not Goldfeder's: the intersection paper demonstrates the attack and states no shrink law.
+ Nor is it the statistical-disclosure literature's, which bounds the observations an adversary
+ needs by a signal-to-noise condition and a confidence interval, not by a constant factor per
+ observation. The bits below are measured off the observations supplied, never assumed from a
+ rate. `universe_size`, when known, includes the first observation's narrowing in `total_bits`;
+ without it the returned bits are explicitly relative to the first observed candidate set.
+ Returns (survivors, total_bits)."""
+ surviving = None
+ total = 0.0
+ for sigs in observations:
+ step = {a for a, _, _ in shared_origins(sigs, rarity, cluster_of, cluster_rarity)}
+ if surviving is None:
+ surviving = step
+ if universe_size is not None and step:
+ first = collapse_bits(universe_size, len(step))
+ if first:
+ total += first
+ continue
+ before = len(surviving)
+ surviving = surviving & step
+ b = collapse_bits(before, len(surviving)) if surviving else None
+ if b:
+ total += b
+ return (surviving or set()), total
+
+
def score_candidate(candidate, cluster_fn, funder_of, signatures=None):
"""Hand a co-spend candidate to the engine and report what it decided.
@@ -100,7 +166,7 @@ def score_candidate(candidate, cluster_fn, funder_of, signatures=None):
`refused` — that is, the engine fused the co-spend prior with fingerprint,
amount and topology and still kept them together. False means the engine
refused a merge the co-spend alone would have made, which is the case the
- whole partition-refinement design exists for.
+ whole refuse-before-merging design exists for.
This is the step that separates an intersection attack from the common-input
heuristic: the co-spend is an input to the decision, not the decision.
@@ -126,7 +192,58 @@ def score_candidate(candidate, cluster_fn, funder_of, signatures=None):
}
-def evaluate(candidate, signature_of, rarity=None, truncation_of=None):
+def _truncation_causes(reported):
+ """One branch's truncation as `(total, causes)`.
+
+ Accepts an `ancestry.TruncationSupport` — the walk limits kept apart — or a bare integer total.
+ A bare total names no cause and is reported with `causes=None` rather than being assigned to
+ one; guessing here is what made `blind` unreadable in the first place. Anything else raises:
+ coercing, say, a float count would round a caller's measurement without telling them.
+ """
+ oracle = getattr(reported, "oracle_refused", None)
+ capped = getattr(reported, "node_capped", None)
+ if oracle is None or capped is None:
+ if isinstance(reported, bool) or not isinstance(reported, int):
+ raise TypeError("truncation_of must return an int total or an ancestry.TruncationSupport, "
+ f"not {type(reported).__name__}")
+ return reported, None
+ unattributed = getattr(reported, "unattributed", 0)
+ zero_link_mass = getattr(reported, "zero_link_mass", 0)
+ return oracle + capped + unattributed + zero_link_mass, {
+ "oracle_refused": oracle, "node_capped": capped,
+ "zero_link_mass": zero_link_mass, "unattributed": unattributed,
+ }
+
+
+def _blind_cause(truncated, causes, sizes):
+ """Which walk limit blinded the blind branches: a cause name, `"mixed"`, `"unknown"` or None.
+
+ `unknown` is a real answer, not a missing one: it says at least one blind branch's cause was
+ never measured — the caller reported a bare total, or a cause this tree does not name. It
+ DOMINATES: an unmeasured cause alongside a measured one is still unknown, never `"mixed"`,
+ because `"mixed"` asserts that both named walk limits fired and one of them was never observed.
+
+ `None` means no blind branch had anything to attribute: every one of them is blind with zero
+ truncation, which is a branch that observed nothing at all rather than one a walk limit cut.
+ `evaluate` also returns `None` when nothing is blind; `blind` tells the two apart.
+ """
+ names = set()
+ for total, cause, size in zip(truncated, causes, sizes):
+ if total < size or total == 0:
+ continue
+ if cause is None:
+ names.add("unknown")
+ continue
+ names |= {name for name, count in cause.items() if count}
+ if "unknown" in names or "unattributed" in names:
+ return "unknown"
+ if not names:
+ return None
+ return names.pop() if len(names) == 1 else "mixed"
+
+
+def evaluate(candidate, signature_of, rarity=None, truncation_of=None,
+ cluster_of=None, cluster_rarity=None):
"""Intersect the origin sets of the coins one co-spend candidate consumes.
`candidate` is an entry from `monitor.walk_frontier`'s `candidates`.
@@ -141,11 +258,48 @@ def evaluate(candidate, signature_of, rarity=None, truncation_of=None):
says how many origins went and the bits say how much that was worth against
branches of different sizes.
- `truncation_of`, when supplied, maps an outpoint to how many of its absorbers are the link
- oracle refusing rather than an origin, and adds `truncated` and `blind`. `blind` is True when
- some branch's boundary is *entirely* truncation: that branch contributes no observed origin, so
- an empty `shared` says the walk could not see, not that the branches came from different places.
- Reading a blind empty as a refusal is the failure mode this field exists to prevent.
+ `truncation_of`, when supplied, maps an outpoint to how much of its boundary is truncation
+ rather than an origin, and adds `truncated`, `truncated_causes`, `blind` and `blind_cause`.
+ `blind` is True when some branch's boundary is *entirely* truncation: that branch contributes no
+ observed origin, so an empty `shared` says the walk could not see, not that the branches came
+ from different places. Reading a blind empty as a refusal is the failure mode this field exists
+ to prevent. The count must be over the absorbers that carry mass —
+ `ancestry.ancestry_signature_and_truncation` reports it that way — since comparing every
+ truncated coin against only the positive-mass boundary calls a branch blind while it is still
+ resolving a full-mass origin.
+
+ A branch is blinded by one of two different walk limits, and `blind` alone does not say which.
+ Pass `ancestry.TruncationSupport` and `truncated_causes` carries the split per branch, with
+ `blind_cause` naming what blinded the blind ones:
+
+ - `"oracle_refused"` — the link oracle declined to link.
+ - `"node_capped"` — the `max_nodes` bound cut the frontier.
+ - `"zero_link_mass"` — the selected output had no positive incoming link mass.
+ - `"mixed"` — more than one measured cause fired across the blind branches.
+ - `"unknown"` — at least one blind branch's cause was not measured: a bare integer total, or a
+ cause this tree does not name (`TruncationSupport.unattributed`). It dominates the named
+ values, so `"mixed"` never covers for a cause nobody observed.
+ - `None` — with `blind` False, nothing was blind, including when `truncation_of` was not
+ supplied at all. With `blind` **True**, a branch is blind with zero truncation: it observed
+ no origins and no walk limit cut it, so there is no cause to name. `blind` is what separates
+ those two `None`s, and a consumer asking "why is this blind" must read it first.
+
+ Under `ancestry.value_flow_link_oracle` — which refuses only on zero total input value — the
+ oracle-refusal half is practically always zero, so a default-oracle walk that comes back blind
+ was blinded by `max_nodes`, and now says so rather than leaving the reader to infer it.
+
+ `blind` mixes units when `cluster_of` is given: `truncated` counts coins the walk never
+ reached, `sizes` counts the wallets those coins lift to, and lifting is many-to-one, so
+ `t >= n` fires earlier than a like-for-like comparison would. The error is one-directional —
+ it over-reports blindness and therefore refuses more — but it is an over-report, not a
+ measurement. Only the coin-level path (`cluster_of` unset) compares like with like.
+
+ `cluster_of` lifts each origin to its owning wallet before intersecting, which is what the
+ writeup asks for: the candidate origins are clusters, not coins, so two branches can overlap
+ without their origin coins being connected on the transaction graph, and the clusters being
+ intersected are the pre-mix ones where privacy is weakest. `sizes` and the narrowing are then
+ reported over wallets. Weighting a lifted intersection needs `cluster_rarity` measured over the
+ population (`propagate.build_cluster_rarity`); coin support does not survive the coarsening.
`scored` is always False. The narrowing is conditional on the co-spend being
genuine, and deciding that is [`score_candidate`], which runs the engine over
@@ -155,16 +309,27 @@ def evaluate(candidate, signature_of, rarity=None, truncation_of=None):
"""
outpoints = list(candidate.get("outpoints", []))
signatures = [signature_of(op) for op in outpoints]
- sizes = [len(s) for s in signatures]
- shared = shared_origins(signatures, rarity)
+ if cluster_of is not None:
+ # Report sizes in the same objects the intersection runs over, or `collapsed` compares
+ # a wallet count against a coin count and reads as a narrowing that never happened.
+ sizes = [len({cluster_key(a, cluster_of) for a in s}) for s in signatures]
+ else:
+ sizes = [len(s) for s in signatures]
+ shared = shared_origins(signatures, rarity, cluster_of, cluster_rarity)
smallest = min(sizes) if sizes else 0
- truncated = [truncation_of(op) for op in outpoints] if truncation_of else None
+ reported = ([_truncation_causes(truncation_of(op)) for op in outpoints]
+ if truncation_of else None)
+ truncated = [total for total, _ in reported] if reported else None
+ causes = [cause for _, cause in reported] if reported else None
+ blind = bool(truncated) and any(t >= n for t, n in zip(truncated, sizes))
return {
"txid": candidate.get("txid"),
"branches": len(outpoints),
"sizes": sizes,
"truncated": truncated,
- "blind": bool(truncated) and any(t >= n for t, n in zip(truncated, sizes)),
+ "truncated_causes": causes,
+ "blind": blind,
+ "blind_cause": _blind_cause(truncated, causes, sizes) if blind else None,
"shared": shared,
"collapsed": max(0, smallest - len(shared)),
"collapsed_bits": collapse_bits(smallest, len(shared)),
diff --git a/decluster/kelen_seres_value_flow.py b/decluster/kelen_seres_value_flow.py
new file mode 100644
index 00000000..2697deb2
--- /dev/null
+++ b/decluster/kelen_seres_value_flow.py
@@ -0,0 +1,7 @@
+"""Canonical namespace for the nominal-value absorbing ancestry walk."""
+
+from .ancestry import (absorber_distribution, build_value_flow_graph, value_flow_link_oracle,
+ value_flow_signature, value_flow_untraceability)
+
+__all__ = ["absorber_distribution", "build_value_flow_graph", "value_flow_link_oracle",
+ "value_flow_signature", "value_flow_untraceability"]
diff --git a/decluster/library.py b/decluster/library.py
index 0e21aad5..f278791e 100644
--- a/decluster/library.py
+++ b/decluster/library.py
@@ -15,7 +15,11 @@
here, because they are not static extracted values: **BTCPay/NBitcoin** samples
version/locktime/low-R from the on-chain distribution, so it lands on the common
values (~0 distinctive bits, no new entry); **coin-selection prediction** is a
-replay technique against a deterministic selector, not a per-tx fingerprint bit."""
+replay technique against a deterministic selector, not a per-tx fingerprint bit.
+
+Twenty-three axes are catalogued here, but only twenty-two are scorable from stored
+transaction records: see ``ANNOTATION_ONLY_AXES``. Two further axes are perfectly
+redundant with each other on measured data: see ``REDUNDANT_AXIS_PAIRS``."""
_EX3 = "8fb80573d8871efee060a34dcb97fd12d5229444b7262b26358cd84912a04a75"
@@ -45,10 +49,14 @@
"chain_proven": None, "bits": {"1in-2out": 1.38, "1in-1out": 2.17,
"2in-2out": 3.95, "3in-2out": 4.31}},
{"axis": "uih", "extractor": "x_uih", "severity": "medium",
- "chain_proven": None, "bits": {"none": 0.12, "uih1": 3.59}}, # measured (real input values)
+ "chain_proven": None, "bits": {"none": 0.12, "uih2": 3.59}}, # measured (real input values)
{"axis": "fee_rate", "extractor": "x_fee_rate", "severity": "medium",
"chain_proven": "16d3fad11242d95da3d12991e176b04cbb474bda95b968b3c4635453d4f9c90e",
"bits": {"precise": 0.28, "round": 2.53, "na": 7.99}},
+ # input_script_type and input_types_present are one fact scored twice: the first is the
+ # single shared type or "mixed", the second the sorted type set. On the measured cache their
+ # agreement indicators have phi = 1.000 in both the match and the non-match class, so any
+ # additive scorer double-counts them. See REDUNDANT_AXIS_PAIRS.
{"axis": "input_script_type", "extractor": "x_input_script_type", "severity": "high",
"chain_proven": "dce69633124d7a3240cc76de5fcc947881f6a140d6d2d0b009f70938136c6bb9",
"bits": {"uniform_p2pkh": 0.87, "uniform_v0_p2wpkh": 2.18, "uniform_p2sh": 2.59,
@@ -92,6 +100,8 @@
{"axis": "multisig", "extractor": "x_multisig", "severity": "medium",
"chain_proven": "065ba81e754450f0f8ae373bf56b0bc3ef454b981f31db9ca92a7280f6ceb623",
"bits": {"none": 0.09, "multisig": 4.02}},
+ # Scorable only against a live broadcast annotation; inert on stored records. See
+ # ANNOTATION_ONLY_AXES.
{"axis": "locktime_vs_broadcast", "extractor": "x_locktime_vs_broadcast", "severity": "medium",
"chain_proven": "0ab4abca70d71f4554baa708a75604c0f05ad43f21f23cb0b25bd3e0e308b129",
# na_loose = abstention (loose bound): non-evidential, weight 0. It is rare (~0.3%), but
@@ -102,6 +112,16 @@
_BY = {a["axis"]: a for a in AXES}
+# `x_locktime_vs_broadcast` reads the `tx["_bc"]` annotation that `broadcast.annotate_broadcast`
+# writes at fetch time. No stored dataset carries it, so the extractor returns "na", which is not
+# a measured value here, so the axis abstains on every pair: 0 of 4,000 pairs scored on the
+# 22,112-tx cache. Report axis counts as "23 catalogued / 22 active" on stored data.
+ANNOTATION_ONLY_AXES = ("locktime_vs_broadcast",)
+
+# Axis pairs measured as perfectly dependent within both classes (phi = 1.000), i.e. one fact
+# scored twice by any additive combiner. See `fingerprint_validate.REDUNDANT_AXES`.
+REDUNDANT_AXIS_PAIRS = (("input_script_type", "input_types_present"),)
+
def bits(axis, value):
"""Evidence bits for (axis, value); None if the axis is unmeasured or the value unknown."""
a = _BY.get(axis)
@@ -110,5 +130,5 @@ def bits(axis, value):
def p_from_bits(bits):
"""{value: p} from a measured-bits dict: p = 2**-bits, dropping 0-bit (abstain) values.
- The value-frequency prior shared by the F-S axis builders (combiner, LibraryScorer)."""
+ The value-frequency prior shared by the rarity axis builders (combiner, LibraryScorer)."""
return {v: 2 ** -b for v, b in (bits or {}).items() if b > 0}
diff --git a/decluster/lumen_data.py b/decluster/lumen_data.py
new file mode 100644
index 00000000..35047808
--- /dev/null
+++ b/decluster/lumen_data.py
@@ -0,0 +1,249 @@
+"""Streaming ingestion and validation for Lumen fingerprint artifacts.
+
+The transaction CSV and explorer JSON deliberately have different types: the former contains
+one fingerprint vector per transaction, while the latter contains only aggregate statistics.
+Keeping their loaders separate prevents aggregate data from accidentally being used as records.
+"""
+
+from __future__ import annotations
+
+import csv
+import hashlib
+import io
+import json
+import re
+from collections import Counter
+from contextlib import contextmanager
+from dataclasses import dataclass
+from pathlib import Path
+from typing import Dict, Iterable, Iterator, Mapping, Optional, Sequence, TextIO, Tuple
+
+
+TXID_COLUMN = "txid"
+_RANGE_RE = re.compile(r"(?P\d+)-(?P\d+)\.csv(?:\.zst)?$")
+
+
+class LumenValidationError(ValueError):
+ """A Lumen artifact does not satisfy its declared manifest."""
+
+
+@dataclass(frozen=True)
+class VectorManifest:
+ """Expected identity and shape of a transaction-level vector artifact."""
+
+ columns: Tuple[str, ...]
+ row_count: Optional[int] = None
+ start_height: Optional[int] = None
+ end_height: Optional[int] = None
+ sha256: Optional[str] = None
+
+ @classmethod
+ def from_mapping(cls, value: Mapping[str, object]) -> "VectorManifest":
+ columns = value.get("columns")
+ if not isinstance(columns, (list, tuple)) or not all(isinstance(x, str) for x in columns):
+ raise LumenValidationError("manifest 'columns' must be a list of strings")
+ return cls(
+ tuple(columns),
+ _optional_int(value, "row_count"),
+ _optional_int(value, "start_height"),
+ _optional_int(value, "end_height"),
+ _optional_sha256(value),
+ )
+
+ @classmethod
+ def from_json(cls, path: str) -> "VectorManifest":
+ with open(path, encoding="utf-8") as stream:
+ value = json.load(stream)
+ if not isinstance(value, dict):
+ raise LumenValidationError("vector manifest must be a JSON object")
+ return cls.from_mapping(value)
+
+
+@dataclass(frozen=True)
+class VectorSummary:
+ row_count: int
+ columns: Tuple[str, ...]
+ axis_counts: Mapping[str, Counter]
+
+
+@dataclass(frozen=True)
+class ExplorerAggregate:
+ """Validated aggregate survey data; it intentionally exposes no row iterator."""
+
+ start_height: int
+ end_height: int
+ epochs: int
+ transaction_count: int
+ defects: int
+ axis_summaries: Mapping[str, object]
+
+
+def _optional_int(value: Mapping[str, object], key: str) -> Optional[int]:
+ item = value.get(key)
+ if item is None:
+ return None
+ if isinstance(item, bool) or not isinstance(item, int):
+ raise LumenValidationError(f"manifest {key!r} must be an integer")
+ return item
+
+
+def _optional_sha256(value: Mapping[str, object]) -> Optional[str]:
+ digest = value.get("sha256")
+ if digest is None:
+ return None
+ if (not isinstance(digest, str) or len(digest) != 64 or
+ any(ch not in "0123456789abcdef" for ch in digest)):
+ raise LumenValidationError("manifest 'sha256' must be a lowercase SHA-256 hex digest")
+ return digest
+
+
+def validate_file_digest(path: str, expected_sha256: str, chunk_size=1024 * 1024) -> None:
+ """Hash the stored artifact bytes and reject a provenance mismatch."""
+ digest = hashlib.sha256()
+ with open(path, "rb") as stream:
+ while chunk := stream.read(chunk_size):
+ digest.update(chunk)
+ actual = digest.hexdigest()
+ if actual != expected_sha256:
+ raise LumenValidationError(
+ f"artifact SHA-256 differs: expected {expected_sha256}, got {actual}"
+ )
+
+
+@contextmanager
+def open_vector_text(path: str) -> Iterator[TextIO]:
+ """Open plain CSV or zstd CSV as a text stream without materialising decompressed data."""
+
+ source = Path(path)
+ if source.suffix != ".zst":
+ with source.open("r", encoding="utf-8", newline="") as stream:
+ yield stream
+ return
+
+ try:
+ import zstandard # type: ignore[import-not-found]
+ except ImportError as exc:
+ raise RuntimeError(
+ "reading .zst vectors requires the optional 'zstandard' package"
+ ) from exc
+
+ with source.open("rb") as compressed:
+ decompressor = zstandard.ZstdDecompressor()
+ with decompressor.stream_reader(compressed) as binary:
+ with io.TextIOWrapper(binary, encoding="utf-8", newline="") as stream:
+ yield stream
+
+
+def iter_vector_rows(stream: TextIO, expected_columns: Sequence[str]) -> Iterator[Dict[str, str]]:
+ """Yield CSV rows and reject missing, reordered, duplicate, or surplus columns."""
+
+ reader = csv.DictReader(stream)
+ actual = tuple(reader.fieldnames or ())
+ expected = tuple(expected_columns)
+ if actual != expected:
+ raise LumenValidationError(f"CSV columns differ: expected {expected!r}, got {actual!r}")
+ if len(set(actual)) != len(actual):
+ raise LumenValidationError("CSV header contains duplicate columns")
+ for line_number, row in enumerate(reader, 2):
+ if None in row or any(value is None for value in row.values()):
+ raise LumenValidationError(f"malformed CSV row at line {line_number}")
+ yield row # one row is live at a time unless the caller retains it
+
+
+def validate_filename_range(path: str, manifest: VectorManifest) -> None:
+ """Check declared heights against a conventional ``START-END.csv.zst`` filename.
+
+ Vector rows have no height column, so this validates artifact provenance rather than making an
+ unsupported claim that heights were derived from CSV content.
+ """
+
+ if manifest.start_height is None and manifest.end_height is None:
+ return
+ match = _RANGE_RE.search(Path(path).name)
+ if match is None:
+ raise LumenValidationError("cannot validate block range: filename has no START-END range")
+ actual = (int(match.group("start")), int(match.group("end")))
+ expected = (manifest.start_height, manifest.end_height)
+ if actual != expected:
+ raise LumenValidationError(f"filename range differs: expected {expected}, got {actual}")
+
+
+def summarize_vector_stream(stream: TextIO, manifest: VectorManifest) -> VectorSummary:
+ """Validate and aggregate a vector CSV in one pass using memory proportional to cardinality."""
+
+ if not manifest.columns or manifest.columns[0] != TXID_COLUMN:
+ raise LumenValidationError("transaction vector schema must begin with 'txid'")
+ counts = {axis: Counter() for axis in manifest.columns[1:]}
+ row_count = 0
+ for row in iter_vector_rows(stream, manifest.columns):
+ row_count += 1
+ for axis, counter in counts.items():
+ counter[row[axis]] += 1
+ if manifest.row_count is not None and row_count != manifest.row_count:
+ raise LumenValidationError(
+ f"CSV row count differs: expected {manifest.row_count}, got {row_count}"
+ )
+ return VectorSummary(row_count, manifest.columns, counts)
+
+
+def summarize_vector_file(path: str, manifest: VectorManifest, *, verify_digest=True) -> VectorSummary:
+ validate_filename_range(path, manifest)
+ if verify_digest and manifest.sha256 is not None:
+ validate_file_digest(path, manifest.sha256)
+ with open_vector_text(path) as stream:
+ return summarize_vector_stream(stream, manifest)
+
+
+def load_explorer_aggregate(path: str) -> ExplorerAggregate:
+ """Load and internally validate Lumen's aggregate explorer artifact."""
+
+ with open(path, encoding="utf-8") as stream:
+ value = json.load(stream)
+ try:
+ window = value["window"]
+ totals = value["totals"]
+ axes = value["axis_summaries"]
+ total = int(totals["txs"])
+ result = ExplorerAggregate(
+ int(window["start_height"]), int(window["end_height"]), int(window["epochs"]),
+ total, int(totals["defects"]), axes,
+ )
+ except (KeyError, TypeError, ValueError) as exc:
+ raise LumenValidationError("invalid explorer aggregate schema") from exc
+ if not isinstance(axes, dict):
+ raise LumenValidationError("axis_summaries must be an object")
+ for axis, summary in axes.items():
+ try:
+ values = summary["values"]
+ count = sum(int(item["count"]) for item in values)
+ distinct = int(summary["distinct_values"])
+ except (KeyError, TypeError, ValueError) as exc:
+ raise LumenValidationError(f"invalid explorer summary for axis {axis!r}") from exc
+ if count != total or distinct != len(values):
+ raise LumenValidationError(f"inconsistent explorer summary for axis {axis!r}")
+ return result
+
+
+def reconcile_counts(
+ vectors: VectorSummary,
+ explorer: ExplorerAggregate,
+ vector_manifest: Optional[VectorManifest] = None,
+) -> Mapping[str, object]:
+ """Describe, but do not guess the cause of, a vector/aggregate count discrepancy."""
+
+ delta = vectors.row_count - explorer.transaction_count
+ same_start = None
+ adjacent_end = None
+ if vector_manifest is not None:
+ if vector_manifest.start_height is not None:
+ same_start = vector_manifest.start_height == explorer.start_height
+ if vector_manifest.end_height is not None:
+ adjacent_end = vector_manifest.end_height == explorer.end_height + 1
+ return {
+ "vector_rows": vectors.row_count,
+ "aggregate_transactions": explorer.transaction_count,
+ "delta": delta,
+ "same_start_height": same_start,
+ "adjacent_end_height": adjacent_end,
+ "requires_txid_or_block_evidence": delta != 0,
+ }
diff --git a/decluster/monitor.py b/decluster/monitor.py
index c1e10038..9c1e0d57 100644
--- a/decluster/monitor.py
+++ b/decluster/monitor.py
@@ -34,15 +34,47 @@
"""
COINJOIN_MIN_PARTICIPANTS = 20
+EQUAL_OUTPUT_MIN_PARTICIPANTS = 3
UNSPENT = "unspent"
COINJOIN = "coinjoin"
DEPTH = "depth"
-def is_coinjoin(tx, min_participants=COINJOIN_MIN_PARTICIPANTS):
- """Many inputs and many outputs — the shape where co-spending stops implying
- common ownership, and so the shape where a forward walk must stop."""
+def equal_output_group(tx, min_participants=EQUAL_OUTPUT_MIN_PARTICIPANTS):
+ """The size of the largest set of outputs sharing one value, when it is big enough to be a
+ mix denomination and the transaction has the inputs to fund it; else 0.
+
+ This is the defining structure of an equal-amount coinjoin — interchangeable outputs of one
+ denomination, one per participant — and it is what the clustering literature detects on
+ (BlockSci's `isCoinjoin`, which Möser and Narayanan use). The size rule below cannot see it:
+ a five-participant Whirlpool round is 5-in/5-out, far under any participant threshold, and
+ refusing to notice it means applying common-input ownership across five unrelated users.
+ """
+ values = [o.get("value") for o in tx.get("vout", []) if o.get("value") is not None]
+ if len(tx.get("vin", [])) < min_participants or not values:
+ return 0
+ counts = {}
+ for v in values:
+ counts[v] = counts.get(v, 0) + 1
+ best = max(counts.values())
+ return best if best >= min_participants else 0
+
+
+def is_coinjoin(tx, min_participants=COINJOIN_MIN_PARTICIPANTS,
+ equal_min=EQUAL_OUTPUT_MIN_PARTICIPANTS):
+ """The shapes where co-spending stops implying common ownership, and so where a forward walk
+ must stop and the clusterer must decline the common-input heuristic.
+
+ Two rules, because one shape does not cover the construction. `equal_min` catches the
+ equal-amount coinjoin by its denomination, at any size. `min_participants` keeps the original
+ many-in/many-out rule, which catches arbitrary-amount mixes that carry no repeated value.
+
+ Neither sees a payjoin: two inputs and two outputs is the shape a payjoin is built to wear,
+ and detecting it needs the amount channel, not a shape rule.
+ """
+ if equal_output_group(tx, equal_min):
+ return True
return (
len(tx.get("vin", [])) >= min_participants
and len(tx.get("vout", [])) >= min_participants
diff --git a/decluster/ns_bitcoin.py b/decluster/ns_bitcoin.py
new file mode 100644
index 00000000..943c2227
--- /dev/null
+++ b/decluster/ns_bitcoin.py
@@ -0,0 +1,245 @@
+"""Evaluation seam for the N--S baseline on contracted Bitcoin views.
+
+The correspondence used for grading is deliberately kept separate from seed discovery.
+An automatic seed is admitted only when an independently observed public entity label has
+exactly one vertex in each view. In particular, degree and the withheld correspondence
+may select an evaluation population, but may not manufacture seeds.
+
+Where a checkout holds no such label on both sides, the paper's own seed-assisted protocol
+is still available: draw the seeds from the withheld correspondence itself. Every result
+therefore carries `seed_provenance`, because the two runs answer different questions. A
+run stamped ``SEED_SAMPLED`` measures how far propagation carries an identity it was
+handed; it is never evidence that the identity could have been found, and reporting it as
+an independent attack would be a false claim about the seeds, not about the algorithm.
+"""
+
+from __future__ import annotations
+
+import random
+from collections import Counter, defaultdict
+from dataclasses import dataclass
+from typing import Hashable, Iterable, Mapping
+
+from .baselines.ns_social_attack import evaluate, propagate
+from .reproducibility import separable
+
+Vertex = Hashable
+
+SEED_INDEPENDENT = "independent-entity-label"
+SEED_SAMPLED = "withheld-correspondence-sample"
+
+
+def unique_entity_seeds(
+ left_labels: Iterable[tuple[str, Vertex]],
+ right_labels: Iterable[tuple[str, Vertex]],
+) -> dict[Vertex, Vertex]:
+ """Return unambiguous cross-view seeds from independently detected entity names.
+
+ Ambiguity is refused, never resolved, on both sides. An entity holding more than one
+ vertex in either view is dropped: repeated vertices are not guessed or paired by degree,
+ which prevents the grading correspondence from leaking into the attack. A *vertex*
+ claimed by more than one entity is dropped for the same reason, and this is the ordinary
+ case rather than the exotic one -- a co-spend cluster holding both a pool address and an
+ exchange deposit address carries two entity labels on one contracted vertex. Resolving
+ such a collision by iteration order would silently drop one label on the left, and on the
+ right would return a non-injective map that `propagate` rejects outright.
+
+ The result is therefore a partial injection: every left vertex appears once, every right
+ vertex appears once.
+ """
+
+ sides = []
+ for observations in (left_labels, right_labels):
+ grouped: dict[str, set[Vertex]] = defaultdict(set)
+ for entity, vertex in observations:
+ grouped[entity].add(vertex)
+ sides.append(grouped)
+ left, right = sides
+ candidates = [
+ (next(iter(vertices)), next(iter(right[entity])))
+ for entity, vertices in left.items()
+ if len(vertices) == 1 and len(right.get(entity, ())) == 1
+ ]
+ left_claims = Counter(u for u, _ in candidates)
+ right_claims = Counter(v for _, v in candidates)
+ return {u: v for u, v in candidates
+ if left_claims[u] == 1 and right_claims[v] == 1}
+
+
+def graph_overlap(left, right, truth: Mapping[Vertex, Vertex]) -> dict[str, float | int]:
+ """Measure overlap of planted vertices and their directed relationships."""
+
+ eligible = {u: v for u, v in truth.items()
+ if u in left.vertices and v in right.vertices}
+ left_edges = {(u, v) for (u, v) in left.edges if u in eligible and v in eligible}
+ # Left-view edges whose image under the correspondence also exists in the right view: still
+ # left-view pairs, which is why they are not named for the right side.
+ recurring = {(u, v) for (u, v) in left_edges
+ if (eligible[u], eligible[v]) in right.edges}
+ return {
+ "vertices": len(eligible),
+ "left_internal_edges": len(left_edges),
+ "recurring_edges": len(recurring),
+ "edge_overlap": len(recurring) / len(left_edges) if left_edges else 0.0,
+ }
+
+
+def eligible_correspondence(left, right, truth: Mapping[Vertex, Vertex]):
+ """The part of the withheld correspondence both views can actually be graded on."""
+
+ return {u: v for u, v in truth.items() if u in left.vertices and v in right.vertices}
+
+
+def sampled_seeds(truth: Mapping[Vertex, Vertex], fraction: float, rng) -> dict[Vertex, Vertex]:
+ """Draw a seed set from the withheld correspondence at `fraction` of its population.
+
+ This is grading information handed to the attack, so a run using it must record
+ ``SEED_SAMPLED``. Sampling is uniform rather than by degree: taking the highest-degree
+ correspondences would seed exactly the vertices propagation finds easiest and report the
+ resulting spread as the algorithm's reach.
+ """
+
+ keys = sorted(truth, key=repr)
+ count = max(2, round(len(keys) * fraction))
+ return {u: truth[u] for u in rng.sample(keys, min(count, len(keys)))}
+
+
+def _shuffle_images(seeds: Mapping[Vertex, Vertex], rng) -> dict[Vertex, Vertex]:
+ """The falsification control: the same seed vertices, wrong images.
+
+ A uniform shuffle of a small seed set can return the identity, and an identity "control"
+ is the attack run twice. Redraw until the permutation moves something; the reported
+ `fixed_points` says how much of the seed set it still left in place.
+ """
+
+ images = list(seeds.values())
+ for _ in range(16):
+ rng.shuffle(images)
+ shuffled = dict(zip(seeds, images))
+ if shuffled != dict(seeds):
+ return shuffled
+ return dict(zip(seeds, images))
+
+
+def _placement(mapping: Mapping[Vertex, Vertex], truth: Mapping[Vertex, Vertex],
+ seeds) -> dict[str, float | int | None]:
+ """Where the declarations landed.
+
+ Two different failures hide behind one precision figure: naming the wrong image for a
+ vertex that has one, and naming any image at all for a vertex the other view does not
+ contain. The algorithm has no abstention for the second, and on disjoint observation
+ windows most of a view is in that class, so the split has to be reported.
+
+ `precision_where_an_image_exists` is a *diagnostic, not an operating point*: the subgroup
+ is selected by membership in the withheld correspondence, which the attacker does not
+ have, so no thresholding available to it can reach that number. It says where the signal
+ is, and must never be quoted as the attack's precision.
+ """
+
+ declared = [u for u in mapping if u not in seeds]
+ gradeable = [u for u in declared if u in truth]
+ return {
+ "declared_with_an_image": len(gradeable),
+ "precision_where_an_image_exists": (
+ sum(mapping[u] == truth[u] for u in gradeable) / len(gradeable)
+ if gradeable else None),
+ }
+
+
+def _discordant(attack: Mapping[Vertex, Vertex], control: Mapping[Vertex, Vertex],
+ truth: Mapping[Vertex, Vertex], seeds) -> tuple[int, int]:
+ """Per-vertex wins for the paired test: cases where exactly one arm was right."""
+
+ wins_attack = wins_control = 0
+ for node, image in truth.items():
+ if node in seeds:
+ continue
+ right_here, right_there = attack.get(node) == image, control.get(node) == image
+ wins_attack += right_here and not right_there
+ wins_control += right_there and not right_here
+ return wins_attack, wins_control
+
+
+@dataclass(frozen=True)
+class BitcoinNSResult:
+ seed_provenance: str
+ seed_size: int
+ seed_fraction: float
+ overlap: dict[str, float | int]
+ attack: dict[str, float | int | None]
+ shuffled_seed_control: dict[str, float | int | None]
+ seed_only_control: dict[str, float | int | None]
+ separability: dict[str, float | int | str | None]
+ theta: float
+ rounds: int
+ accepted_per_round: tuple[int, ...]
+ shuffled_accepted_per_round: tuple[int, ...]
+
+
+def run_bitcoin_views(left, right, truth: Mapping[Vertex, Vertex],
+ seeds: Mapping[Vertex, Vertex], *, theta=1.5, rng_seed=0,
+ seed_provenance: str = SEED_INDEPENDENT, min_correct_gain: int = 1):
+ """Run faithful propagation and two controls against the withheld correspondence.
+
+ `min_correct_gain` is the pre-registered effect for the paired shuffled-seed comparison:
+ the direction under test is "the attack recovers at least this many correspondences the
+ shuffled-seed control does not". It is a direction claim only — a verdict of "a" says
+ the spread is not an artifact of the seed positions, and says nothing about precision,
+ which the reported levels have to carry on their own.
+ """
+
+ truth = eligible_correspondence(left, right, truth)
+ seeds = dict(seeds)
+ if any(truth.get(u) != v for u, v in seeds.items()):
+ raise ValueError("supplied seed conflicts with the withheld correspondence")
+
+ result = propagate(left, right, seeds, theta=theta)
+ attack = evaluate(result.mapping, truth, seeds)
+ attack.update(_placement(result.mapping, truth, seeds))
+
+ shuffled = _shuffle_images(seeds, random.Random(rng_seed))
+ shuffled_result = propagate(left, right, shuffled, theta=theta)
+ shuffled_metrics = evaluate(shuffled_result.mapping, truth, seeds)
+ shuffled_metrics.update(_placement(shuffled_result.mapping, truth, seeds))
+ shuffled_metrics["fixed_points"] = sum(shuffled[u] == v for u, v in seeds.items())
+
+ # With no propagation, a held-out node can never be declared. Keep this explicit so
+ # reports do not compare the attack to a seed count disguised as recovered identity.
+ seed_only = evaluate(seeds, truth, seeds)
+
+ wins_attack, wins_shuffled = _discordant(result.mapping, shuffled_result.mapping,
+ truth, seeds)
+ verdict, p = separable(wins_attack, wins_shuffled, wins_attack - wins_shuffled,
+ min_correct_gain)
+ return BitcoinNSResult(
+ seed_provenance=seed_provenance, seed_size=len(seeds),
+ seed_fraction=len(seeds) / len(truth) if truth else 0.0,
+ overlap=graph_overlap(left, right, truth),
+ attack=attack, shuffled_seed_control=shuffled_metrics,
+ seed_only_control=seed_only,
+ separability={"attack_only": wins_attack, "shuffled_only": wins_shuffled,
+ "min_correct_gain": min_correct_gain, "p": p, "beats_shuffled": verdict},
+ theta=theta, rounds=result.rounds,
+ accepted_per_round=result.accepted_per_round,
+ shuffled_accepted_per_round=shuffled_result.accepted_per_round,
+ )
+
+
+def sweep_bitcoin_views(left, right, truth: Mapping[Vertex, Vertex], fractions, thetas, *,
+ rng_seed=0, min_correct_gain=1):
+ """The seed-assisted sweep: one run per (seed fraction, theta), seeds drawn from the
+ withheld correspondence.
+
+ theta is part of the reported configuration, not a dial turned until a number appears, so
+ every value swept is reported whether or not anything propagated under it.
+ """
+
+ truth = eligible_correspondence(left, right, truth)
+ rows = []
+ for fraction in fractions:
+ seeds = sampled_seeds(truth, fraction, random.Random(rng_seed))
+ for theta in thetas:
+ rows.append(run_bitcoin_views(left, right, truth, seeds, theta=theta,
+ rng_seed=rng_seed, seed_provenance=SEED_SAMPLED,
+ min_correct_gain=min_correct_gain))
+ return rows
diff --git a/decluster/ns_bitcoin_snapshot.py b/decluster/ns_bitcoin_snapshot.py
new file mode 100644
index 00000000..7c9630a8
--- /dev/null
+++ b/decluster/ns_bitcoin_snapshot.py
@@ -0,0 +1,52 @@
+"""Build deterministic compressed snapshots of exact N-S Bitcoin windows."""
+
+from __future__ import annotations
+
+import argparse
+import gzip
+import json
+from pathlib import Path
+import tempfile
+
+
+def build_window(source, destination, *, minimum_height, maximum_height):
+ """Copy records in an inclusive height range to deterministic gzip NDJSON."""
+ if minimum_height > maximum_height:
+ raise ValueError("minimum_height must not exceed maximum_height")
+ destination = Path(destination)
+ destination.parent.mkdir(parents=True, exist_ok=True)
+ count = 0
+ with tempfile.NamedTemporaryFile(dir=destination.parent, delete=False) as raw:
+ temporary = Path(raw.name)
+ with gzip.GzipFile(fileobj=raw, mode="wb", filename="", mtime=0) as target:
+ with gzip.open(source, "rt", encoding="utf-8") as records:
+ for line in records:
+ if not line.strip():
+ continue
+ record = json.loads(line)
+ if minimum_height <= record.get("height", 0) <= maximum_height:
+ target.write(line.encode("utf-8"))
+ count += 1
+ temporary.replace(destination)
+ return count
+
+
+def main(argv=None):
+ parser = argparse.ArgumentParser(description=__doc__)
+ parser.add_argument("source")
+ parser.add_argument("destination")
+ parser.add_argument("--minimum-height", type=int, required=True)
+ parser.add_argument("--maximum-height", type=int, required=True)
+ args = parser.parse_args(argv)
+ count = build_window(
+ args.source,
+ args.destination,
+ minimum_height=args.minimum_height,
+ maximum_height=args.maximum_height,
+ )
+ print(count)
+ return 0
+
+
+if __name__ == "__main__":
+ raise SystemExit(main())
diff --git a/decluster/oracle.py b/decluster/oracle.py
index f21f7b5d..628a9e7c 100644
--- a/decluster/oracle.py
+++ b/decluster/oracle.py
@@ -1,25 +1,38 @@
-"""Robust oracle layer on top of `ancestry.dss_link_oracle`: an in-process, panic-safe default
-(`bounded_link_oracle`) plus an opt-in, hang-proof subprocess oracle (`subprocess_link_oracle`)
-for the rare tx where dss's own `budget_ms` fails to preempt an internal blow-up."""
+"""Robust oracle layer on top of `ancestry.dss_link_oracle`: an in-process, panic-safe wrapper
+(`bounded_dss_link_oracle`) plus an opt-in, hang-proof subprocess oracle (`subprocess_link_oracle`)
+for the rare tx where dss's own `budget_ms` fails to preempt an internal blow-up.
+
+Everything here bounds the SUBSET-SUM oracle (`dss.pairwise_link_prob`). None of it is the default
+walk any more: since the value-flow swap, `analyze`, `report`, `provenance_route_accumulation` and the
+`ancestry` facades all default to `ancestry.value_flow_link_oracle` (nominal-value transitions, no
+dss). The subset-sum walk is reachable only by passing one of these explicitly."""
import multiprocessing as mp
from decluster import ancestry
# A cooperative dss budget that RESOLVES a ~2.3s payment+partial-mix coinjoin; 1500-2000ms
-# truncates such a tx to a point mass -- confirmed on live data. This is the default budget for
-# the public analyze() facade, distinct from ancestry.DEFAULT_LINK_BUDGET_MS (2000).
+# truncates such a tx to a point mass -- confirmed on live data. The budget a caller opting into
+# the subset-sum walk gets by default, distinct from ancestry.DEFAULT_LINK_BUDGET_MS (2000).
DEFAULT_ANALYZE_BUDGET_MS = 6000
-def bounded_link_oracle(budget_ms=DEFAULT_ANALYZE_BUDGET_MS):
- """Build the default in-process link oracle: `(inputs, outputs) -> matrix|None`, bound to
- `budget_ms`. Panic-safe (inherits ancestry.dss_link_oracle's BaseException hardening), no
- subprocess. This is the default oracle the public `analyze()` facade uses."""
+def bounded_dss_link_oracle(budget_ms=DEFAULT_ANALYZE_BUDGET_MS):
+ """Build an in-process SUBSET-SUM link oracle: `(inputs, outputs) -> matrix|None`, wrapping
+ `ancestry.dss_link_oracle` (`dss.pairwise_link_prob`) at `budget_ms`. Panic-safe (inherits that
+ function's BaseException hardening), no subprocess.
+
+ NOT a default. `analyze()`, `report()` and `provenance_route_accumulation()` default to
+ `ancestry.value_flow_link_oracle`; pass this explicitly to take the subset-sum walk instead."""
def link(inputs, outputs):
return ancestry.dss_link_oracle(inputs, outputs, budget_ms)
return link
+# Retained name: `bounded_link_oracle` is the public export existing callers and results documents
+# use. It has always bounded the subset-sum oracle; the clearer name above says so.
+bounded_link_oracle = bounded_dss_link_oracle
+
+
def _link_worker(inputs, outputs, budget_ms, q):
import dss
try:
diff --git a/decluster/partition.py b/decluster/partition.py
new file mode 100644
index 00000000..76d4102b
--- /dev/null
+++ b/decluster/partition.py
@@ -0,0 +1,141 @@
+"""The partition refinement lattice as a first-class object.
+
+Clustering moves up this lattice: union-find fuses blocks and can never split one, so a clusterer
+built that way owns every merge it made. Declustering moves down: it takes a partition it did not
+build and cuts blocks that the evidence says hold more than one owner. The two directions need the
+same object, so it lives here rather than inside either.
+
+`join` divides only where both sides divide, so it is the coarser of the two — the direction a
+merge pass travels. `meet` divides wherever either side divides, so it is the finer — the direction
+a refinement pass travels. Ordering is by refinement: `a <= b` when every block of `a` sits inside
+a block of `b`.
+
+Both operations are defined over one ground set. Combining partitions of different sets is refused
+rather than resolved, because every silent resolution of that mismatch invents an answer.
+"""
+
+from __future__ import annotations
+
+from .unionfind import UF
+
+
+class GroundSetMismatch(ValueError):
+ """Two partitions cover different elements, so no lattice operation between them is defined."""
+
+
+class Partition:
+ """An immutable partition of a finite set, comparable and hashable."""
+
+ __slots__ = ("_blocks", "_index")
+
+ def __init__(self, blocks):
+ collected, index = [], {}
+ for block in blocks:
+ members = frozenset(block)
+ if not members:
+ continue
+ for element in members:
+ if element in index:
+ raise ValueError(f"element in more than one block: {element!r}")
+ index[element] = members
+ collected.append(members)
+ self._blocks = frozenset(collected)
+ self._index = index
+
+ # --- construction ---------------------------------------------------------------------
+
+ @classmethod
+ def from_lookup(cls, mapping):
+ """From `element -> label`, the shape `views.cluster_addresses` returns."""
+ blocks = {}
+ for element, label in mapping.items():
+ blocks.setdefault(label, []).append(element)
+ return cls(blocks.values())
+
+ @classmethod
+ def from_union_find(cls, uf: UF):
+ return cls(uf.groups())
+
+ @classmethod
+ def discrete(cls, elements):
+ """Every element alone: the finest partition, the bottom of the lattice."""
+ return cls([element] for element in elements)
+
+ @classmethod
+ def trivial(cls, elements):
+ """One block: the coarsest partition, the top of the lattice."""
+ elements = frozenset(elements)
+ return cls([elements] if elements else [])
+
+ # --- reading --------------------------------------------------------------------------
+
+ def blocks(self):
+ return self._blocks
+
+ def elements(self):
+ return frozenset(self._index)
+
+ def block(self, element):
+ """The block holding `element`, or None when the partition does not cover it."""
+ return self._index.get(element)
+
+ def same_block(self, a, b):
+ block = self._index.get(a)
+ return block is not None and b in block
+
+ # --- lattice --------------------------------------------------------------------------
+
+ def _require_same_ground(self, other):
+ if self.elements() != other.elements():
+ raise GroundSetMismatch(
+ f"{len(self.elements())} elements against {len(other.elements())}"
+ )
+
+ def meet(self, other):
+ """Divide wherever either side divides: blocks are the non-empty intersections."""
+ self._require_same_ground(other)
+ parts = {}
+ for element, block in self._index.items():
+ parts.setdefault((block, other._index[element]), []).append(element)
+ return Partition(parts.values())
+
+ def join(self, other):
+ """Divide only where both sides divide: the transitive closure of the two relations."""
+ self._require_same_ground(other)
+ uf = UF(self._index)
+ for source in (self, other):
+ for block in source._blocks:
+ members = iter(block)
+ first = next(members)
+ for element in members:
+ uf.union(first, element)
+ return Partition.from_union_find(uf)
+
+ def refines(self, other):
+ """Whether every block here sits inside a block of `other` — this partition is finer."""
+ self._require_same_ground(other)
+ return all(block <= other._index[next(iter(block))] for block in self._blocks)
+
+ # --- protocol -------------------------------------------------------------------------
+
+ __and__ = meet
+ __or__ = join
+ __le__ = refines
+
+ def __lt__(self, other):
+ return self != other and self.refines(other)
+
+ def __eq__(self, other):
+ return isinstance(other, Partition) and self._blocks == other._blocks
+
+ def __hash__(self):
+ return hash(self._blocks)
+
+ def __len__(self):
+ return len(self._blocks)
+
+ def __iter__(self):
+ return iter(self._blocks)
+
+ def __repr__(self):
+ return f"Partition({len(self._blocks)} blocks over {len(self._index)} elements)"
diff --git a/decluster/path_count.py b/decluster/path_count.py
index a9f2b4c5..e5d5a155 100644
--- a/decluster/path_count.py
+++ b/decluster/path_count.py
@@ -1,12 +1,25 @@
-"""§07 path-count anonymity object: provenance over ancestral origins weighted by counterfactual
-subset-sum PATH MULTIPLICITY, distinct from §04's set-size entropy
-(`ancestry.absorber_distribution`, which weighs origins by link-probability mass alone). Reuses
-`ancestry.build_extended_graph`'s walk verbatim (same graph, same `max_nodes` bound) and folds in
-the dss W(E) mapping-count (`counting.w_total`) per edge: how many counterfactual subset-sum
-routes reach each origin, not just how likely the single most-probable one is.
-
-This is a LOWER BOUND / weight-of-evidence, never a privacy score -- exactly like every other
-number in this module family."""
+"""Probability-weighted provenance route accumulation over the ancestry DAG.
+
+The module name is retained for compatibility. This is not a counterfactual k-routes algorithm:
+it does not enumerate edge-disjoint paths, and it does not weight a route by the value it could
+carry. `disjoint_routes` answers the first of those; the second is the value dimension the framework
+states its properties over, and no module here computes it.
+
+The origin distribution is weighted by link probability alone. Subset-sum multiplicity is NOT a
+factor: `cost.py` declares the amount channel refuse-only — it may cut a coin from the graph, never
+weight one — and multiplicity entering as a weight is what that forbids. Historical record: measured
+on a synthetic two-origin DAG the term moved min-entropy from 1.0 bit to 0.137, but that DAG is not
+in this checkout and no artifact preserves it, so the pair is not recomputable from here. The
+direction is beside the point anyway; the objection is that an ambiguity signal was a weight at all.
+The combinatorial sub-transaction literature reads a low mapping count as low privacy, so under that
+reading the weighting was the correct ambiguity signal; the refuse-only contract overrides it, and
+this module does not pretend the two agree.
+
+Multiplicity, not redundancy: many routes may share the same coins, so this says nothing about how
+few of them a cut would sever. `disjoint_routes.cut_size` measures that, and the two disagree on
+three of every five coins in the committed cache: the origin set is larger than the number of routes
+that reach it without sharing a coin.
+"""
import math
from .ancestry import build_extended_graph, _shannon, _min_entropy
@@ -43,52 +56,33 @@ def _topological_order(target, edges):
return order
-def path_count_anonymity(target, *, depth=6, max_nodes=None, fetch=None, link_oracle=None,
- count_oracle=None):
- """§07 path-count anonymity set: provenance over ancestral origins weighted by counterfactual
- subset-sum PATH MULTIPLICITY, from the dss W(E) counts. This is the §07 path-like object (how
- many amount-consistent counterfactual routes reach each origin); it is NOT the §06 robustness
- metric, which is edge-disjoint path count / min-cut (k-routes max-flow) and is a separate,
- not-yet-implemented component — W(E) multiplicity and edge-disjoint connectivity are different
- quantities. Reuses
+def provenance_route_accumulation(
+ target, *, depth=6, max_nodes=None, fetch=None, link_oracle=None, count_oracle=None
+):
+ """Provenance over ancestral origins, weighted by link probability alone. Reuses
`build_extended_graph`'s walk (honoring `max_nodes` for deep-coinjoin tractability) -- does not
- re-walk. Returns {"origins_weighted": {origin: weight}, "log_W_paths": float,
- "min_entropy": float, "shannon": float, "truncated": int}.
-
- Per-edge path multiplicity = link_prob * W(E) of the edge's tx (`g.edges` already stores
- link_prob as the row-stochastic `weight`; `coin.txid` names the tx that produced `coin`, whose
- W(E) scales every edge out of `coin`). Total multiplicity to an origin sums, over every route
- from `target`, the product of edge multiplicities along it -- computed as a forward log-domain
- accumulation over the ancestry DAG (topological order; acyclic -> finite -> converges).
-
- A LOWER BOUND / weight-of-evidence, not a privacy score. When a tx's W(E) is off-regime
- (`log_w` is None) that hop falls back to link-probability weight alone (multiplicity factor 1)
- -- never fabricate a count."""
+ re-walk. Returns {"origins_weighted": {origin: weight}, "min_entropy": float, "shannon": float,
+ "truncated": int}.
+
+ `link_oracle` default = `ancestry.value_flow_link_oracle`, the same walk `analyze`/`report`
+ take; pass `oracle.bounded_dss_link_oracle()` for the opt-in subset-sum walk.
+
+ `count_oracle` is accepted but unused: kept for backward compatibility with callers that still
+ pass one. See the module docstring for why subset-sum multiplicity is not folded in here.
+
+ Total mass to an origin sums, over every route from `target`, the product of edge link
+ probabilities along it -- computed as a forward log-domain accumulation over the ancestry DAG
+ (topological order; acyclic -> finite -> converges)."""
if fetch is None:
from .fetch import fetch_tx
fetch = fetch_tx
if link_oracle is None:
- from .oracle import bounded_link_oracle
- link_oracle = bounded_link_oracle()
- if count_oracle is None:
- from .counting import w_total
- count_oracle = w_total
+ from .ancestry import value_flow_link_oracle
+ link_oracle = value_flow_link_oracle
g = build_extended_graph(target, depth=depth, fetch=fetch, link_oracle=link_oracle,
max_nodes=max_nodes)
- log_w_by_txid = {}
-
- def log_w_of(txid):
- if txid not in log_w_by_txid:
- tx = fetch(txid)
- in_vals = [v["prevout"]["value"] for v in tx["vin"]]
- out_vals = [o["value"] for o in tx["vout"]]
- res = count_oracle(in_vals, out_vals)
- lw = res.get("log_w")
- log_w_by_txid[txid] = 0.0 if lw is None else lw # None -> link-prob-only fallback
- return log_w_by_txid[txid]
-
log_reach = {target: 0.0}
for coin in _topological_order(target, g.edges):
edges_out = g.edges.get(coin)
@@ -97,24 +91,28 @@ def log_w_of(txid):
base = log_reach.get(coin)
if base is None:
continue # unreachable from target in this DAG (shouldn't occur; defensive)
- lw = log_w_of(coin[0])
for source, w in edges_out:
if w <= 0:
continue
- log_edge = math.log(w) + lw
- log_reach[source] = _logsumexp(log_reach.get(source, NEG_INF), base + log_edge)
+ log_reach[source] = _logsumexp(log_reach.get(source, NEG_INF),
+ base + math.log(w))
contrib = {o: log_reach[o] for o in g.absorbers if o in log_reach}
if not contrib:
- return {"origins_weighted": {}, "log_W_paths": NEG_INF,
- "min_entropy": 0.0, "shannon": 0.0, "truncated": g.truncated}
+ return {"origins_weighted": {}, "min_entropy": 0.0, "shannon": 0.0,
+ "truncated": g.truncated}
- log_w_paths = contrib[next(iter(contrib))]
+ total = contrib[next(iter(contrib))]
for lr in list(contrib.values())[1:]:
- log_w_paths = _logsumexp(log_w_paths, lr)
- origins_weighted = {o: math.exp(lr - log_w_paths) for o, lr in contrib.items()}
+ total = _logsumexp(total, lr)
+ origins_weighted = {o: math.exp(lr - total) for o, lr in contrib.items()}
probs = list(origins_weighted.values())
- return {"origins_weighted": origins_weighted, "log_W_paths": log_w_paths,
- "min_entropy": _min_entropy(probs), "shannon": _shannon(probs),
- "truncated": g.truncated}
+ return {"origins_weighted": origins_weighted, "min_entropy": _min_entropy(probs),
+ "shannon": _shannon(probs), "truncated": g.truncated}
+
+
+# Compatibility name. It predates the distinction between probability-weighted ancestry routes and
+# the CTP plausible-flow metric, whose value dimension is still unimplemented. New code should import
+# `provenance_route_accumulation` from its canonical namespace.
+path_count_anonymity = provenance_route_accumulation
diff --git a/decluster/propagate.py b/decluster/propagate.py
index 3031d272..db725bb8 100644
--- a/decluster/propagate.py
+++ b/decluster/propagate.py
@@ -2,7 +2,20 @@
two-channel edge-splitting refinement. Provenance signatures (ancestry.ancestry_signature)
are the sparse quasi-identifier; merge propagation is gated on eccentricity, split
refinement on provenance-disjointness AND fingerprint-divergence. Needs no same-owner
-labels: it propagates from a seed set."""
+labels: it propagates from a seed set.
+
+WHAT THIS ACTUALLY IMPLEMENTS. Seed-and-propagate over provenance SIGNATURES, not over a graph:
+there is no adjacency structure here at all, so no edges vote, nothing is degree-normalised, and
+there is no reverse-match check (a candidate is never required to win from the other side).
+`eccentricity` below is the paper's acceptance gap and is faithful to it, with one caveat: it
+normalises by the standard deviation of the scores actually materialised here, while the baseline's
+gate (`baselines.narayanan_shmatikov._sparse_winner`) normalises over the whole unclaimed candidate
+population, implicit zeros included — a larger population and a different sigma. The two formulas
+agree given the same values; they are not given the same values. Everything `eccentricity` is
+applied to here is signature overlap between a node and the running aggregate of each label's
+members.
+The faithful reference implementation — two views, degree-normalised edge votes, reverse match — is
+`decluster/baselines/narayanan_shmatikov.py`."""
from collections import Counter
import statistics
import random
@@ -19,6 +32,24 @@ def build_rarity(signatures):
return dict(c)
+def build_cluster_rarity(signatures, cluster_of):
+ """cluster -> support count, for weighting a cluster-lifted intersection.
+
+ Coin support cannot be reused once origins are coarsened into wallets: a cluster holding
+ many coins would inherit the support of the rarest of them. It must be recounted over the
+ same population `build_rarity` reads, after lifting — which is why `intersect.shared_origins`
+ refuses to weight a lifted intersection unless it is handed one of these.
+
+ `signatures` is the population corpus, not the branches under test. Counting support from
+ the two or three signatures being intersected makes every shared origin look rare.
+ """
+ from .intersect import cluster_key
+ c = Counter()
+ for sig in signatures:
+ c.update({cluster_key(a, cluster_of) for a in sig})
+ return dict(c)
+
+
def entity_signature(coin_sigs):
"""Aggregate member coins' provenance signatures: sum per ancestor, renormalise to
unit total mass. The entity-level sparse quasi-identifier vector."""
diff --git a/decluster/provenance_route_accumulation.py b/decluster/provenance_route_accumulation.py
new file mode 100644
index 00000000..81b8c28f
--- /dev/null
+++ b/decluster/provenance_route_accumulation.py
@@ -0,0 +1,10 @@
+"""Canonical namespace for probability-weighted ancestry route accumulation.
+
+This is not the CTP robust-connectivity metric — see `disjoint_routes` for the cut it is stated
+over. It neither enumerates edge-disjoint paths nor
+checks whether a path has enough amount capacity to carry a plausible flow.
+"""
+
+from .path_count import provenance_route_accumulation
+
+__all__ = ["provenance_route_accumulation"]
diff --git a/decluster/rarity_weight_baseline.py b/decluster/rarity_weight_baseline.py
new file mode 100644
index 00000000..4e19e0d9
--- /dev/null
+++ b/decluster/rarity_weight_baseline.py
@@ -0,0 +1,9 @@
+"""Canonical namespace for decluster's fixed value-rarity scoring baseline.
+
+This is not a fitted Fellegi--Sunter model. ``decluster.combiner`` remains as a compatibility
+facade; new code should import this module.
+"""
+
+from .combiner import AXES, Combiner, rarity_score
+
+__all__ = ["AXES", "Combiner", "rarity_score"]
diff --git a/decluster/reference_registry.py b/decluster/reference_registry.py
new file mode 100644
index 00000000..68c94133
--- /dev/null
+++ b/decluster/reference_registry.py
@@ -0,0 +1,197 @@
+"""Strict readers for the CTP source and claim registries.
+
+The registries are audit inputs, not free-form documentation. Rejecting unknown
+fields and dangling references prevents a typo from silently removing a source
+from the fidelity matrix.
+"""
+
+from dataclasses import dataclass
+from enum import Enum
+import json
+from pathlib import Path
+
+
+class RegistryError(ValueError):
+ """The registry cannot support an auditable claim graph."""
+
+
+class SourceKind(str, Enum):
+ PAPER = "paper"
+ PROTOCOL = "protocol"
+ IMPLEMENTATION = "implementation"
+ INFORMAL = "informal"
+ INTERNAL_NOTE = "internal_note"
+
+
+class Obligation(str, Enum):
+ BASELINE = "baseline"
+ FIXTURE = "fixture"
+ METRIC = "metric"
+ CONTEXT = "context"
+ QUALIFICATION = "qualification"
+ NO_CODE = "no_code_required"
+
+
+class ClaimScope(str, Enum):
+ FAILURE_MODE = "qualitative_failure_mode"
+ MODEL_LIMIT = "model_limit"
+ DEFENSIVE_CRITERION = "defensive_criterion"
+
+
+class EvidenceLevel(str, Enum):
+ UNIT_FIXTURE = "unit_fixture"
+ PAPER_EXAMPLE = "paper_example"
+ SYNTHETIC = "synthetic_experiment"
+ PUBLISHED_DATASET = "published_dataset_reproduction"
+ BITCOIN_SNAPSHOT = "bitcoin_snapshot_experiment"
+ INDEPENDENT = "independent_replication"
+
+
+class ClaimStatus(str, Enum):
+ UNASSESSED = "unassessed"
+ PARTIAL = "partially_supported"
+ SUPPORTED_AT_LEVEL = "supported_at_evidence_level"
+ NO_CODE = "no_code_required"
+
+
+@dataclass(frozen=True)
+class Source:
+ id: str
+ title: str
+ url: str | None
+ kind: SourceKind
+ obligation: Obligation
+ note: str
+
+
+@dataclass(frozen=True)
+class Claim:
+ id: str
+ title: str
+ scope: ClaimScope
+ sources: tuple[str, ...]
+ mechanism: str
+ implementation: str | None
+ evidence_level: EvidenceLevel
+ status: ClaimStatus
+ limitations: tuple[str, ...]
+
+
+SOURCE_KEYS = frozenset({"id", "title", "url", "kind", "obligation", "note"})
+CLAIM_KEYS = frozenset({
+ "id", "title", "scope", "sources", "mechanism", "implementation",
+ "evidence_level", "status", "limitations",
+})
+
+
+def _object(value, where):
+ if not isinstance(value, dict):
+ raise RegistryError(f"{where}: expected object")
+ return value
+
+
+def _exact_keys(value, expected, where):
+ actual = frozenset(value)
+ if actual != expected:
+ missing = sorted(expected - actual)
+ unknown = sorted(actual - expected)
+ raise RegistryError(f"{where}: missing={missing}, unknown={unknown}")
+
+
+def _text(value, where, *, nullable=False):
+ if nullable and value is None:
+ return None
+ if not isinstance(value, str) or not value.strip():
+ raise RegistryError(f"{where}: expected non-empty string")
+ return value
+
+
+def _enum(enum_type, value, where):
+ try:
+ return enum_type(value)
+ except (TypeError, ValueError) as exc:
+ raise RegistryError(f"{where}: invalid {enum_type.__name__}: {value!r}") from exc
+
+
+def _load(path):
+ try:
+ with Path(path).open(encoding="utf-8") as source:
+ return json.load(source)
+ except (OSError, json.JSONDecodeError) as exc:
+ raise RegistryError(f"{path}: cannot read registry: {exc}") from exc
+
+
+def load_sources(path):
+ root = _object(_load(path), str(path))
+ _exact_keys(root, frozenset({"schema_version", "ctp_revision", "sources"}), str(path))
+ if root["schema_version"] != 1:
+ raise RegistryError(f"{path}: unsupported schema_version {root['schema_version']!r}")
+ _text(root["ctp_revision"], f"{path}.ctp_revision")
+ if not isinstance(root["sources"], list):
+ raise RegistryError(f"{path}.sources: expected array")
+
+ sources = []
+ seen = set()
+ for index, raw in enumerate(root["sources"]):
+ where = f"{path}.sources[{index}]"
+ raw = _object(raw, where)
+ _exact_keys(raw, SOURCE_KEYS, where)
+ source_id = _text(raw["id"], f"{where}.id")
+ if source_id in seen:
+ raise RegistryError(f"{where}.id: duplicate {source_id!r}")
+ seen.add(source_id)
+ url = _text(raw["url"], f"{where}.url", nullable=True)
+ if url is not None and not url.startswith("https://"):
+ raise RegistryError(f"{where}.url: expected https URL")
+ sources.append(Source(
+ id=source_id,
+ title=_text(raw["title"], f"{where}.title"),
+ url=url,
+ kind=_enum(SourceKind, raw["kind"], f"{where}.kind"),
+ obligation=_enum(Obligation, raw["obligation"], f"{where}.obligation"),
+ note=_text(raw["note"], f"{where}.note"),
+ ))
+ return tuple(sources)
+
+
+def load_claims(path, source_ids):
+ root = _object(_load(path), str(path))
+ _exact_keys(root, frozenset({"schema_version", "claims"}), str(path))
+ if root["schema_version"] != 1:
+ raise RegistryError(f"{path}: unsupported schema_version {root['schema_version']!r}")
+ if not isinstance(root["claims"], list):
+ raise RegistryError(f"{path}.claims: expected array")
+
+ known_sources = set(source_ids)
+ claims = []
+ seen = set()
+ for index, raw in enumerate(root["claims"]):
+ where = f"{path}.claims[{index}]"
+ raw = _object(raw, where)
+ _exact_keys(raw, CLAIM_KEYS, where)
+ claim_id = _text(raw["id"], f"{where}.id")
+ if claim_id in seen:
+ raise RegistryError(f"{where}.id: duplicate {claim_id!r}")
+ seen.add(claim_id)
+ if not isinstance(raw["sources"], list) or not raw["sources"]:
+ raise RegistryError(f"{where}.sources: expected non-empty array")
+ sources = tuple(_text(item, f"{where}.sources") for item in raw["sources"])
+ dangling = sorted(set(sources) - known_sources)
+ if dangling:
+ raise RegistryError(f"{where}.sources: unknown source ids {dangling}")
+ if len(sources) != len(set(sources)):
+ raise RegistryError(f"{where}.sources: duplicate source id")
+ if not isinstance(raw["limitations"], list):
+ raise RegistryError(f"{where}.limitations: expected array")
+ claims.append(Claim(
+ id=claim_id,
+ title=_text(raw["title"], f"{where}.title"),
+ scope=_enum(ClaimScope, raw["scope"], f"{where}.scope"),
+ sources=sources,
+ mechanism=_text(raw["mechanism"], f"{where}.mechanism"),
+ implementation=_text(raw["implementation"], f"{where}.implementation", nullable=True),
+ evidence_level=_enum(EvidenceLevel, raw["evidence_level"], f"{where}.evidence_level"),
+ status=_enum(ClaimStatus, raw["status"], f"{where}.status"),
+ limitations=tuple(_text(item, f"{where}.limitations") for item in raw["limitations"]),
+ ))
+ return tuple(claims)
diff --git a/decluster/reid.py b/decluster/reid.py
index 77d0d002..7d8067a9 100644
--- a/decluster/reid.py
+++ b/decluster/reid.py
@@ -1,28 +1,42 @@
-"""Record-linkage de-anonymization stratified by sparsity (Narayanan-Shmatikov 2008,
-Algorithm 1B). Records are coins; each coin's provenance signature is its rarity-weighted
-distribution over ancestral origins.
+"""Record linkage stratified by ancestry-signature sparsity.
-The measurement ties the sparse-dataset attack's *precondition* to its *success*: coins are
-stratified by their own nearest-neighbour cosine (sparse = no near twin, dense = has one),
-and the same Algorithm 1B attack is run on each stratum. Reveal `m` of a target's ancestors
-as auxiliary information, score every candidate by the rarity-weighted provenance overlap
-(`ancestry.provenance_link`, the paper's `wt = 1/log2(support)` scoring), and accept a match
-only when the top score clears the runner-up by the eccentricity gate
-(`propagate.eccentricity`, phi = 1.5 as in Netflix). Sparsity is the stratifier, so no
-same-owner labels are needed and the two measurements are internally consistent. Theorem 2
-predicts sparse records de-anonymize and dense ones do not; this measures that gap directly,
-reusing the same primitives as the rest of the engine."""
+This is an adaptation of the Narayanan-Shmatikov sparse-record model, not a reproduction of
+Algorithm 1B or of the Netflix experiment. Coins are stratified by nearest-neighbour cosine.
+For each target, the attack samples origins from its stored signature, scores candidates by
+rarity-weighted provenance overlap, and accepts only when the score distribution clears an
+eccentricity gate. Exact matches identify records in the frozen fixture, not wallet owners.
+"""
import random
from .ancestry import provenance_link
-from .propagate import build_rarity, eccentricity
+from .single_view_propagation import build_rarity, eccentricity
from .def1_sparsity import cosine
-def top_similarity(coins, sigs):
- """Each coin's highest cosine similarity to any *other* coin: its sparsity coordinate.
- Low = no near twin = sparse (de-anonymizable); high = has a twin = dense (protected)."""
+def top_similarity(coins, sigs, metric="cosine", rarity=None):
+ """Each coin's highest similarity to any *other* coin: its sparsity coordinate. Low = no
+ near twin = sparse (de-anonymizable); high = has a twin = dense (protected).
+
+ `metric="provlink"` measures that similarity in the *same* space the attack scores in
+ (rarity-weighted provenance overlap, normalised by the coin's self-overlap), so the
+ (epsilon,delta) stratification and the de-anonymization are internally consistent — the
+ metric-consistency the Netflix paper assumes. `cosine` (default) preserves the committed
+ fixture."""
+ if metric == "provlink":
+ selfs = {a: provenance_link(sigs[a], sigs[a], rarity) for a in coins}
+ top = {}
+ for i, a in enumerate(coins):
+ sa, best = sigs[a], 0.0
+ for j, b in enumerate(coins):
+ if i == j:
+ continue
+ denom = (selfs[a] * selfs[b]) ** 0.5
+ s = provenance_link(sa, sigs[b], rarity) / denom if denom else 0.0
+ if s > best:
+ best = s
+ top[a] = best
+ return top
top = {}
for i, a in enumerate(coins):
sa, best = sigs[a], 0.0
@@ -38,9 +52,9 @@ def top_similarity(coins, sigs):
return top
-def stratify(coins, sigs, sparse_below=0.5, dense_atleast=0.9):
+def stratify(coins, sigs, sparse_below=0.5, dense_atleast=0.9, metric="cosine", rarity=None):
"""Split coins into sparse and dense strata by their own top-similarity."""
- top = top_similarity(coins, sigs)
+ top = top_similarity(coins, sigs, metric=metric, rarity=rarity)
sparse = [c for c in coins if top[c] < sparse_below]
dense = [c for c in coins if top[c] >= dense_atleast]
return sparse, dense, top
@@ -54,7 +68,8 @@ def reid_attack(subset, coins, sigs, rarity, m, phi=1.5, seed=0):
rng = random.Random(seed)
declared = exact = total = 0
for target in subset:
- supp = list(sigs[target])
+ # Keep seeded sampling independent of JSON insertion order and hash randomisation.
+ supp = sorted(sigs[target])
if len(supp) < m:
continue
aux = {a: sigs[target][a] for a in rng.sample(supp, m)}
@@ -71,18 +86,20 @@ def reid_attack(subset, coins, sigs, rarity, m, phi=1.5, seed=0):
def stratified_reid(coins, sigs, ms=(4, 8), phi=1.5, seed=0,
- sparse_below=0.5, dense_atleast=0.9):
+ sparse_below=0.5, dense_atleast=0.9, metric="cosine"):
"""Full measurement: split into sparse/dense strata, run the Algorithm 1B attack on each
for each aux size in `ms`. Returns {rows, n, n_sparse, n_dense}, where each row is
{stratum, m, declared, exact, precision, declare_rate}. The scientific quantity is the
gap between sparse and dense precision, not the absolute level."""
rarity = build_rarity(sigs.values())
- sparse, dense, _ = stratify(coins, sigs, sparse_below, dense_atleast)
+ sparse, dense, _ = stratify(coins, sigs, sparse_below, dense_atleast, metric=metric, rarity=rarity)
rows = []
for name, subset in (("sparse", sparse), ("dense", dense)):
for m in ms:
dec, exa, tot = reid_attack(subset, coins, sigs, rarity, m, phi, seed)
- rows.append({"stratum": name, "m": m, "declared": dec, "exact": exa,
+ rows.append({"stratum": name, "m": m, "attackable": tot,
+ "declared": dec, "exact": exa,
"precision": exa / dec if dec else 0.0,
- "declare_rate": dec / tot if tot else 0.0})
+ "declare_rate": dec / tot if tot else 0.0,
+ "exact_rate": exa / tot if tot else 0.0})
return {"rows": rows, "n": len(coins), "n_sparse": len(sparse), "n_dense": len(dense)}
diff --git a/decluster/report.py b/decluster/report.py
index 0dd984d8..d440b04c 100644
--- a/decluster/report.py
+++ b/decluster/report.py
@@ -5,10 +5,12 @@
are None (never fabricated). `forced` joins them on the same footing: conservation needs one
participant's input to be known, which the transaction alone does not give. Every number is an
attacker lower bound under no auxiliary information, not a privacy score. By default (`subjective=
-True`) the target's headline is fused_min_entropy — the §04 subjective-fused anonymity set, read as a
-conservative lower bound on the number of graph cuts an adversary needs to de-anonymize the coin (the
-post-06 reading); min_entropy is the graph-only (no-subjective) baseline it can only narrow, never
-widen. The fused reading is the CONSERVATIVE minimum of the graph-only and subjective-fused
+True`) the target's headline is fused_min_entropy — the §04 subjective-fused anonymity set;
+min_entropy is the graph-only (no-subjective) baseline it can only narrow, never widen. It is NOT read
+as a lower bound on the number of graph cuts an adversary needs: the framework offers that reading for
+an output's entropy, and PAPER §11 declines it, because whether it transfers to the absorber-model
+entropy computed here — which is over a boundary distribution rather than over graph edges — is not
+established. The number is reported without it. The fused reading is the CONSERVATIVE minimum of the graph-only and subjective-fused
min-entropy/shannon: subjective evidence can only narrow the anonymity set, never widen it, so a raw
fusion that (via a same-owner boost favoring a graph-minority input) ends up spreading mass instead of
concentrating it is clamped down to the graph-only baseline. This makes fused_min_entropy <= min_entropy
@@ -18,7 +20,7 @@
`forced` is reported here, not scored: it is an attribution, and the engine's amount channel may only
refuse (PAPER §1), so it never reaches `cluster_refined`."""
from .cost import amount_cuts, topology_bits, leak_bits, dss_oracle
-from .ancestry import ancestry_entropy, dss_link_oracle
+from .ancestry import ancestry_entropy, value_flow_link_oracle
from .conservation import forced_in_round
FOOTING = ("attacker lower bounds under no auxiliary information; not a privacy score. target "
@@ -40,7 +42,7 @@ def _spendable_vouts(tx):
def report(tx, combiner=None, neigh=None, entities=None, pair=None,
oracle=None, link_oracle=None, fetch=None, depth=6, targets=None,
- known_input=None, subjective=True, cluster_of=None):
+ known_input=None, subjective=True, cluster_of=None, count_oracle=None):
"""Fused measurement view of a real transaction `tx` (esplora/mempool.space JSON). See module
docstring for the always-vs-conditional term policy and the lower-bound footing. `subjective=True`
(default) adds the §04 subjective-fused readout (fused_min_entropy/fused_shannon) to each target,
@@ -50,14 +52,14 @@ def report(tx, combiner=None, neigh=None, entities=None, pair=None,
if oracle is None:
oracle = dss_oracle
if link_oracle is None:
- link_oracle = dss_link_oracle
+ link_oracle = value_flow_link_oracle
if fetch is None:
from .fetch import fetch_tx
fetch = fetch_tx
txid = tx["txid"]
in_vals = [v["prevout"]["value"] for v in tx["vin"]]
out_vals = [o["value"] for o in tx["vout"]]
- amount = amount_cuts(in_vals, out_vals, oracle)
+ amount = amount_cuts(in_vals, out_vals, oracle, count_oracle=count_oracle)
vouts = targets if targets is not None else _spendable_vouts(tx)
targets_out = {}
for vout in vouts:
@@ -102,8 +104,7 @@ def print_report(rep):
base = (f" target vout {vout}: min_entropy={t['min_entropy']:.3f} bits (graph-only) "
f"shannon={t['shannon']:.3f} absorbers={t['n_absorbers']} truncated={t['truncated']}")
if "fused_min_entropy" in t:
- base += (f" | FUSED min_entropy={t['fused_min_entropy']:.3f} bits "
- "(>= graph cuts to de-anon)")
+ base += f" | FUSED min_entropy={t['fused_min_entropy']:.3f} bits"
print(base)
if rep["forced"] is None:
print(" forced: n/a (no participant input supplied)")
diff --git a/decluster/reproducibility.py b/decluster/reproducibility.py
new file mode 100644
index 00000000..bdb8998c
--- /dev/null
+++ b/decluster/reproducibility.py
@@ -0,0 +1,169 @@
+"""Provenance and separability for published results.
+
+Two instruments, because results make two kinds of claim. A *level* (a survival curve, a rate) is
+pinned by a band and by the data it was measured on; a *direction* (A beats B) is pinned by a paired
+test with a pre-registered effect size. Conflating them is how a published sign inverted with the
+suite green.
+"""
+import glob
+import hashlib
+import json
+import math
+import os
+
+__all__ = ["fingerprint_source", "manifest_path", "write_manifest", "read_manifest", "check_manifest", "separable"]
+
+
+def fingerprint_source(pattern):
+ """Identity of the data behind a result: file count, total bytes, and a digest over the sorted
+ (name, size) list. Never reads contents, so it is usable on multi-GB sources. A source that is
+ not present returns `{"pattern": ..., "missing": True}` rather than raising: absence is the
+ normal case for unversioned data and must be reportable."""
+ paths = sorted(p for p in glob.glob(pattern) if os.path.isfile(p))
+ if not paths:
+ return {"pattern": pattern, "missing": True}
+ total = 0
+ h = hashlib.sha256()
+ for p in paths:
+ size = os.path.getsize(p)
+ total += size
+ h.update(os.path.basename(p).encode()); h.update(b"\0")
+ h.update(str(size).encode()); h.update(b"\n")
+ return {"pattern": pattern, "files": len(paths), "bytes": total, "digest": h.hexdigest()}
+
+
+MANIFEST_DIR = "results/manifests"
+
+
+def _root(root):
+ return root or os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+
+
+def manifest_path(doc, root=None):
+ stem = doc[:-3] if doc.endswith(".md") else doc # suffix strip, not substring removal
+ return os.path.join(_root(root), MANIFEST_DIR, stem + ".json")
+
+
+def _rooted_pattern(pattern, root):
+ """A glob pattern resolved against `root` rather than the caller's CWD, so recording and
+ checking a manifest see the same source regardless of where either runs from. An already-
+ absolute pattern is left alone."""
+ return pattern if os.path.isabs(pattern) else os.path.join(_root(root), pattern)
+
+
+def write_manifest(doc, pattern, invariants, root=None):
+ """Record what a result was measured on: the source's identity, and the population facts the
+ claim depends on — the ones a byte digest cannot see. `pattern` is resolved against `root`
+ (like `check_manifest`) so the identity recorded here is what a later, differently-rooted check
+ will actually see; the pattern stored in the manifest stays as given, so a committed manifest
+ stays portable across checkouts."""
+ path = manifest_path(doc, root)
+ os.makedirs(os.path.dirname(path), exist_ok=True)
+ source = fingerprint_source(_rooted_pattern(pattern, root))
+ source["pattern"] = pattern
+ manifest = {"doc": doc, "source": source, "invariants": dict(invariants)}
+ with open(path, "w") as fh:
+ json.dump(manifest, fh, indent=2, sort_keys=True); fh.write("\n")
+ return manifest
+
+
+def read_manifest(doc, root=None):
+ path = manifest_path(doc, root)
+ if not os.path.exists(path):
+ return None
+ with open(path) as fh:
+ return json.load(fh)
+
+
+def check_manifest(doc, invariants=None, root=None):
+ """`(status, message)` where status is one of:
+
+ `unrecorded` no manifest for this document
+ `absent` the source is not present; nothing was checked
+ `identity-only` the source matches, but no invariants were supplied to compare
+ `partial` source and supplied invariants match, but some recorded invariants were omitted
+ `stale` the source moved, an invariant disagrees, or an unrecorded key was supplied
+ `ok` source and every supplied invariant match
+
+ `identity-only` is not a success. The failure this exists to catch was a source growing while
+ its population degenerated, which a digest cannot see; answering `ok` with no invariant
+ compared would report success on exactly that run.
+ """
+ recorded = read_manifest(doc, root)
+ if recorded is None:
+ return "unrecorded", f"{doc}: no manifest in {MANIFEST_DIR}"
+ live = fingerprint_source(_rooted_pattern(recorded["source"]["pattern"], root))
+ if live.get("missing"):
+ return "absent", f"{doc}: source {live['pattern']} not present; nothing checked"
+ was, now = recorded["source"], live
+ if was.get("digest") != now.get("digest"):
+ return "stale", (f"{doc}: source moved — was {was.get('files')} files / {was.get('bytes')} "
+ f"bytes, now {now['files']} / {now['bytes']}")
+ if not invariants:
+ return "identity-only", (f"{doc}: source unchanged; {len(recorded['invariants'])} recorded "
+ f"invariant(s) NOT checked (none supplied)")
+ extra = sorted(set(invariants) - set(recorded["invariants"]))
+ if extra:
+ return "stale", f"{doc}: invariant(s) not recorded: {', '.join(extra)}"
+ for key, want in invariants.items():
+ got = recorded["invariants"].get(key)
+ if got != want:
+ return "stale", f"{doc}: invariant {key} recorded {got}, measured {want}"
+ missing = sorted(set(recorded["invariants"]) - set(invariants))
+ if missing:
+ return "partial", f"{doc}: source and supplied invariants unchanged; not recomputed: {', '.join(missing)}"
+ return "ok", f"{doc}: source and {len(invariants)} invariant(s) unchanged"
+
+
+EXACT_MAX_TAIL = 2000 # the exact sum costs min(a, b) terms, not a + b: a lopsided discordance
+ # stays exact at any sample size, and only a near-tie reaches the fallback
+
+
+def _binom_cdf_half(k, n):
+ return sum(math.comb(n, i) for i in range(k + 1)) / (2 ** n)
+
+
+def _two_sided_p(a, b):
+ n = a + b
+ if n == 0:
+ return 1.0
+ if min(a, b) <= EXACT_MAX_TAIL:
+ return min(1.0, 2.0 * _binom_cdf_half(min(a, b), n))
+ # Continuity correction. Without it the approximation is anti-conservative on exactly the
+ # pairs that sit against alpha, reporting a separation the exact test refuses.
+ return math.erfc(max(abs(a - b) - 1.0, 0.0) / math.sqrt(n) / math.sqrt(2.0))
+
+
+def separable(wins_a, wins_b, effect, min_effect, alpha=0.05):
+ """Did A beat B, did B beat A, or is the difference noise?
+
+ `wins_a` / `wins_b` are the DISCORDANT counts — cases where exactly one arm was right.
+ Concordant cases carry no information about a difference; excluding them is what makes this
+ the paired test rather than two independent proportions.
+
+ Three gates, all of which must clear for either direction. **The arm must actually have won**
+ on the discordant counts — a function that answers "A" when B won the discordant pairs is the
+ failure this instrument exists to prevent. The p-value answers "could this be chance". `effect`
+ is signed (positive favors A, negative favors B) and `min_effect` is PRE-REGISTERED, answering
+ "is it large enough to matter": without it a large sample makes any difference significant and a
+ small one makes every difference unmeasured. A verdict requires the win-count direction and the
+ effect's sign to agree; a mismatch (e.g. B won the discordant pairs but `effect` was passed
+ positive) is reported as `None`, the same as genuine noise, rather than guessed past to a
+ verdict.
+
+ Returns `(verdict, p)` where verdict is `"a"`, `"b"`, or `None` — never a bare bool, because a
+ decisive inversion (B wins outright, p astronomically small) and true noise (p ≈ 1) are both
+ "not a verdict for A" but are not the same finding, and the historical failure this instrument
+ exists to name was the former, not the latter. `None` is state 5: measured, not separable.
+
+ One-sided in effect: this compares a two-sided p-value against `alpha` while also gating on
+ which arm won, which is a one-sided test at `alpha/2` — conservative and defensible, but worth
+ stating rather than leaving implicit.
+ """
+ p = _two_sided_p(wins_a, wins_b)
+ significant = p < alpha
+ if wins_a > wins_b and effect >= min_effect and significant:
+ return "a", p
+ if wins_b > wins_a and -effect >= min_effect and significant:
+ return "b", p
+ return None, p
diff --git a/decluster/result_artifacts.py b/decluster/result_artifacts.py
new file mode 100644
index 00000000..1d3811af
--- /dev/null
+++ b/decluster/result_artifacts.py
@@ -0,0 +1,88 @@
+"""Canonical result artifacts and side-effect-free identity verification."""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+from enum import Enum
+import hashlib
+import json
+import os
+from pathlib import Path
+import tempfile
+
+from .data_manifest import ContentIdentity, RunManifest
+
+
+class OutputStatus(str, Enum):
+ VERIFIED = "verified"
+ MISSING = "missing"
+ IDENTITY_MISMATCH = "identity_mismatch"
+
+
+@dataclass(frozen=True)
+class OutputVerification:
+ path: str
+ status: OutputStatus
+ expected: ContentIdentity
+ actual: ContentIdentity | None
+
+
+def canonical_json_bytes(value) -> bytes:
+ """Serialize JSON deterministically, rejecting values JSON cannot represent exactly."""
+ return (json.dumps(
+ value,
+ allow_nan=False,
+ ensure_ascii=False,
+ separators=(",", ":"),
+ sort_keys=True,
+ ) + "\n").encode("utf-8")
+
+
+def write_canonical_json(path, value) -> ContentIdentity:
+ """Atomically replace ``path`` with canonical JSON and return its content identity."""
+ destination = Path(path)
+ destination.parent.mkdir(parents=True, exist_ok=True)
+ payload = canonical_json_bytes(value)
+ with tempfile.NamedTemporaryFile(dir=destination.parent, delete=False) as temporary:
+ temporary.write(payload)
+ temporary.flush()
+ os.fsync(temporary.fileno())
+ temporary_path = Path(temporary.name)
+ try:
+ os.replace(temporary_path, destination)
+ except BaseException:
+ temporary_path.unlink(missing_ok=True)
+ raise
+ return ContentIdentity(bytes=len(payload), sha256=hashlib.sha256(payload).hexdigest())
+
+
+def content_identity(path) -> ContentIdentity:
+ source = Path(path)
+ digest = hashlib.sha256()
+ size = 0
+ with source.open("rb") as stream:
+ for chunk in iter(lambda: stream.read(1024 * 1024), b""):
+ size += len(chunk)
+ digest.update(chunk)
+ return ContentIdentity(bytes=size, sha256=digest.hexdigest())
+
+
+def verify_run_outputs(manifest: RunManifest, root) -> tuple[OutputVerification, ...]:
+ """Verify declared output identities without executing the manifest's commands."""
+ root = Path(root).resolve()
+ checks = []
+ for output in manifest.outputs:
+ expected = ContentIdentity(bytes=output.bytes, sha256=output.sha256)
+ path = root / output.path
+ if not path.is_file():
+ checks.append(OutputVerification(
+ path=output.path, status=OutputStatus.MISSING,
+ expected=expected, actual=None,
+ ))
+ continue
+ actual = content_identity(path)
+ status = OutputStatus.VERIFIED if actual == expected else OutputStatus.IDENTITY_MISMATCH
+ checks.append(OutputVerification(
+ path=output.path, status=status, expected=expected, actual=actual,
+ ))
+ return tuple(checks)
diff --git a/decluster/result_inventory.py b/decluster/result_inventory.py
new file mode 100644
index 00000000..c76a7c1e
--- /dev/null
+++ b/decluster/result_inventory.py
@@ -0,0 +1,72 @@
+"""Inventory legacy result documents against executable run manifests."""
+
+from dataclasses import dataclass
+import json
+from pathlib import Path
+
+
+# Some historical documents summarize a narrower canonical run or share one
+# with another document, so their names cannot be inferred from output stems.
+DOCUMENT_RUN_ALIASES = {
+ "RESULTS-analyze.md": "analyze-contract-v1",
+ "RESULTS-ancestry.md": "ancestry-contract-v1",
+ "RESULTS-catalog-axes.md": "fingerprint-catalog-axes-v1",
+ "RESULTS-conservation.md": "conservation-round-three-v1",
+ "RESULTS-path-count.md": "path-count-contract-v1",
+ "RESULTS-path-counting-analysis.md": "path-count-contract-v1",
+ "RESULTS-slice-a-channels.md": "slice-channels-v1",
+}
+
+
+@dataclass(frozen=True)
+class ResultInventoryEntry:
+ document: str
+ status: str
+ legacy_manifest: str | None = None
+ canonical_run: str | None = None
+
+
+def _run_outputs(root: Path) -> dict[str, str]:
+ owners = {}
+ for path in sorted((root / "catalog" / "runs").glob("*.json")):
+ value = json.loads(path.read_text(encoding="utf-8"))
+ for output in value.get("outputs", []):
+ owners[Path(output["path"]).name] = value["id"]
+ return owners
+
+
+def inventory_results(root: Path) -> tuple[ResultInventoryEntry, ...]:
+ """Classify every legacy result without claiming unsupported provenance."""
+ run_outputs = _run_outputs(root)
+ generated_by_stem = {
+ Path(name).stem.removesuffix("-v1"): run_id
+ for name, run_id in run_outputs.items()
+ if name.endswith(".md")
+ }
+ entries = []
+ for document in sorted((root / "results").glob("RESULTS-*.md")):
+ stem = document.stem.removeprefix("RESULTS-")
+ old_manifest = root / "results" / "manifests" / f"{document.stem}.json"
+ canonical_run = DOCUMENT_RUN_ALIASES.get(document.name, generated_by_stem.get(stem))
+ if canonical_run is not None:
+ status = "superseded_by_canonical_run"
+ elif old_manifest.is_file():
+ status = "legacy_manifest_requires_migration"
+ else:
+ status = "historical_result_without_run_manifest"
+ entries.append(ResultInventoryEntry(
+ document=str(document.relative_to(root)),
+ status=status,
+ legacy_manifest=(
+ str(old_manifest.relative_to(root)) if old_manifest.is_file() else None
+ ),
+ canonical_run=canonical_run,
+ ))
+ return tuple(entries)
+
+
+def summarize(entries: tuple[ResultInventoryEntry, ...]) -> dict[str, int]:
+ counts: dict[str, int] = {}
+ for entry in entries:
+ counts[entry.status] = counts.get(entry.status, 0) + 1
+ return dict(sorted(counts.items()))
diff --git a/decluster/results_index.py b/decluster/results_index.py
new file mode 100644
index 00000000..f2452223
--- /dev/null
+++ b/decluster/results_index.py
@@ -0,0 +1,121 @@
+"""Build the index of `results/`, so which documents carry a chain is readable rather than guessed.
+
+Sixty-six hand-written documents and fifty-seven generated ones sit in one directory with no entry
+point. A reader cannot tell, without opening each, which are regenerated from a canonical run and
+which are prose over data the repository does not ship — and that distinction is the whole
+difference between a figure someone can check and one they have to take.
+
+The index is generated, so it cannot drift from the catalogue the way a hand-written list would.
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+from pathlib import Path
+
+INDEX = "results/INDEX.md"
+
+
+def _runs(root):
+ """Which run manifest owns each generated document, and which claims it discharges."""
+ owners = {}
+ for path in sorted(Path(root, "catalog", "runs").glob("*.json")):
+ manifest = json.loads(path.read_text())
+ for output in manifest["outputs"]:
+ if output["path"].endswith(".md"):
+ owners[output["path"]] = (path.stem, tuple(manifest["claim_ids"]))
+ return owners
+
+
+def _legacy(root):
+ return {path.stem + ".md" for path in Path(root, "results", "manifests").glob("*.json")}
+
+
+def _title(path):
+ for line in path.read_text(encoding="utf-8").splitlines():
+ if line.startswith("# "):
+ return line[2:].strip()
+ return path.stem
+
+
+def build(root="."):
+ root = Path(root)
+ owners = _runs(root)
+ legacy = _legacy(root)
+ generated = sorted(Path(root, "results", "generated").glob("*.md"))
+ manual = sorted(Path(root, "results").glob("RESULTS-*.md"))
+
+ lines = [
+ "# What is in `results/`",
+ "",
+ "Generated by `decluster.results_index`. Do not edit manually.",
+ "",
+ f"{len(generated)} generated documents, each the canonical rendering of a run manifest, and "
+ f"{len(manual)} written ones. The difference is what a reader can check: a generated "
+ "document is reproduced byte-for-byte by its run and verified by the reproduction gate, "
+ "while a written one is prose that may rest on data this repository does not ship.",
+ "",
+ "## Generated from a canonical run",
+ "",
+ "| document | run | claims |",
+ "|---|---|---|",
+ ]
+ for path in generated:
+ key = f"results/generated/{path.name}"
+ run, claims = owners.get(key, ("—", ()))
+ lines.append(f"| [`{path.name}`]({path.name and 'generated/' + path.name}) | `{run}` | "
+ f"{', '.join(f'`{c}`' for c in claims) or '—'} |")
+
+ lines += [
+ "",
+ "## Written, with a legacy manifest",
+ "",
+ "These carry a `results/manifests/` fingerprint of the source they were measured on, so a "
+ "moved source is detected even though the numbers are not regenerated.",
+ "",
+ "| document | title |",
+ "|---|---|",
+ ]
+ for path in manual:
+ if path.name in legacy:
+ lines.append(f"| [`{path.name}`]({path.name}) | {_title(path)} |")
+
+ lines += [
+ "",
+ "## Written, no manifest",
+ "",
+ "Prose over measurements whose source is named in the document itself. Several rest on "
+ "exports this repository does not ship; each says so where that is the case.",
+ "",
+ "| document | title |",
+ "|---|---|",
+ ]
+ for path in manual:
+ if path.name not in legacy:
+ lines.append(f"| [`{path.name}`]({path.name}) | {_title(path)} |")
+ lines.append("")
+ return "\n".join(lines)
+
+
+def main(argv=None):
+ parser = argparse.ArgumentParser(description=__doc__.splitlines()[0])
+ parser.add_argument("--root", default=".")
+ parser.add_argument("--check", action="store_true",
+ help="report drift instead of writing")
+ arguments = parser.parse_args(argv)
+ rendered = build(arguments.root)
+ destination = Path(arguments.root, INDEX)
+ if arguments.check:
+ current = destination.read_text(encoding="utf-8") if destination.is_file() else ""
+ if current != rendered:
+ print(f"{INDEX} differs from the catalogue")
+ return 1
+ return 0
+ destination.write_text(rendered, encoding="utf-8")
+ print(f"{INDEX} {len(rendered.splitlines())} lines")
+ return 0
+
+
+if __name__ == "__main__":
+ raise SystemExit(main())
diff --git a/decluster/runtime_snapshot.py b/decluster/runtime_snapshot.py
new file mode 100644
index 00000000..0eac9947
--- /dev/null
+++ b/decluster/runtime_snapshot.py
@@ -0,0 +1,159 @@
+"""The runtime snapshot every evidence bundle pins as its source.
+
+`reproduce` extracts this archive and runs *its* code, so the archive — not the working tree — is
+what a bundle's reproduction actually exercises. An archive that lags the tree makes the gate
+verify a revision nobody ships.
+
+Entries carry a zero timestamp and no ownership, so the same revision always produces the same
+bytes and the digest a bundle pins is a function of content alone.
+
+`--repoint` does the rest of the move: every `reproduction/*/environment.json`, every run manifest's
+`code.revision` and every bundle's source blob name follow the new archive, and the superseded one
+is removed. Done by hand it is four steps in a required order, and skipping any of them leaves a
+record describing a runtime nobody ships. `decluster-bundle sync` then refreshes the identities.
+"""
+
+from __future__ import annotations
+
+import argparse
+import io
+import json
+import subprocess
+import tarfile
+from pathlib import Path
+
+PREFIX = "decluster"
+
+# The reproduction surface: the package and what it reads at run time. Datasets are materialised
+# from the content store instead, and `results/generated` is an output, not an input.
+INCLUDED = (
+ "decluster",
+ "catalog",
+ "examples",
+ "results/artifacts",
+ "tests/fixtures/conservation_round_three.json",
+ "pyproject.toml",
+ "LICENSE",
+)
+
+
+def _git(*args, root):
+ return subprocess.run(
+ ("git", *args), cwd=root, capture_output=True, check=True
+ ).stdout
+
+
+def revision_id(revision, root):
+ return _git("rev-parse", revision, root=root).decode().strip()
+
+
+def tracked_paths(revision, root):
+ """Every file the snapshot covers at `revision`, with its git mode, in a stable order."""
+ listing = _git("ls-tree", "-r", revision, "--", *INCLUDED, root=root).decode()
+ entries = {}
+ for line in listing.splitlines():
+ if not line:
+ continue
+ metadata, path = line.split("\t", 1)
+ entries[path] = 0o755 if metadata.split()[0] == "100755" else 0o644
+ return dict(sorted(entries.items()))
+
+
+def _walk(paths):
+ """Names in archive order: one sorted sequence with each directory before its contents."""
+ directories = set()
+ for path in paths:
+ parts = path.split("/")[:-1]
+ for index in range(len(parts)):
+ directories.add("/".join(parts[: index + 1]))
+ return [(name, name in directories) for name in sorted(directories | set(paths))]
+
+
+def _entry(name, *, directory=False, size=0, mode=0o644):
+ info = tarfile.TarInfo(f"{PREFIX}/{name}" if name else PREFIX)
+ info.type = tarfile.DIRTYPE if directory else tarfile.REGTYPE
+ info.mode = 0o755 if directory else mode
+ info.mtime = 0
+ info.uid = info.gid = 0
+ info.uname = info.gname = ""
+ info.size = size
+ return info
+
+
+def build(revision="HEAD", root=".", out_dir="sources"):
+ """Write `sources/decluster--runtime.tar` for `revision`, deterministically."""
+ root = Path(root).resolve()
+ full = revision_id(revision, root)
+ paths = tracked_paths(full, root)
+ if not paths:
+ raise ValueError(f"no tracked files under {INCLUDED} at {revision}")
+
+ destination = Path(root, out_dir, f"{PREFIX}-{full[:7]}-runtime.tar")
+ destination.parent.mkdir(parents=True, exist_ok=True)
+ with tarfile.open(destination, "w", format=tarfile.USTAR_FORMAT) as archive:
+ archive.addfile(_entry("", directory=True))
+ for name, is_directory in _walk(paths):
+ if is_directory:
+ archive.addfile(_entry(name, directory=True))
+ continue
+ payload = _git("show", f"{full}:{name}", root=root)
+ entry = _entry(name, size=len(payload), mode=paths[name])
+ archive.addfile(entry, io.BytesIO(payload))
+ return destination, full, len(paths)
+
+
+def repoint(root, archive, revision, out_dir="sources"):
+ """Point every record at `archive`, then drop the archives it supersedes."""
+ root = Path(root)
+ name = f"{out_dir}/{archive.name}"
+ moved = []
+ for environment in sorted(root.glob("reproduction/*/environment.json")):
+ record = json.loads(environment.read_text())
+ if record.get("source_archive") == name:
+ continue
+ record["source_archive"] = name
+ environment.write_text(json.dumps(record, indent=2) + "\n")
+ moved.append(str(environment.relative_to(root)))
+ for manifest in sorted(root.glob("catalog/runs/*.json")):
+ record = json.loads(manifest.read_text())
+ if record["code"]["revision"] == revision:
+ continue
+ manifest.write_text(manifest.read_text().replace(
+ f'"revision": "{record["code"]["revision"]}"', f'"revision": "{revision}"', 1))
+ moved.append(str(manifest.relative_to(root)))
+ for index in sorted(root.glob("releases/*.bundle.json")):
+ record = json.loads(index.read_text())
+ touched = False
+ for blob in record["blobs"]:
+ if blob["role"] == "source" and blob["name"] != name:
+ blob["name"] = name
+ touched = True
+ if touched:
+ index.write_text(json.dumps(record, indent=2) + "\n")
+ moved.append(str(index.relative_to(root)))
+ superseded = [p for p in Path(root, out_dir).glob(f"{PREFIX}-*-runtime.tar")
+ if p.name != archive.name]
+ for stale in superseded:
+ stale.unlink()
+ return moved, [p.name for p in superseded]
+
+
+def main(argv=None):
+ parser = argparse.ArgumentParser(description=__doc__.splitlines()[0])
+ parser.add_argument("--revision", default="HEAD")
+ parser.add_argument("--root", default=".")
+ parser.add_argument("--out-dir", default="sources")
+ parser.add_argument("--repoint", action="store_true",
+ help="point every environment, run manifest and bundle at the new archive")
+ arguments = parser.parse_args(argv)
+ path, revision, count = build(arguments.revision, arguments.root, arguments.out_dir)
+ print(f"{path} {revision[:7]} {count} files")
+ if arguments.repoint:
+ moved, superseded = repoint(arguments.root, path, revision, arguments.out_dir)
+ print(f"repointed {len(moved)} records; removed {len(superseded)} superseded archive(s)")
+ print("run `decluster-bundle sync` to refresh the identities")
+ return 0
+
+
+if __name__ == "__main__":
+ raise SystemExit(main())
diff --git a/decluster/scale_cluster.py b/decluster/scale_cluster.py
new file mode 100644
index 00000000..dc44a879
--- /dev/null
+++ b/decluster/scale_cluster.py
@@ -0,0 +1,204 @@
+"""Memory-scalable global CIOH clustering for multi-epoch slices too large to hold in RAM.
+
+`views.cluster_addresses` materialises the whole sample and a string-keyed union-find, which is fine
+for ~1M transactions but not for a multi-month, tens-of-millions-of-address slice on a small machine.
+This streams the transactions from disk and runs union-find over 64-bit address hashes, so memory
+scales with the union-participating address set rather than with the transaction count.
+
+It reproduces the refuse-guarded clustering exactly on the address-only slices this project collects
+(no input/output values, so the de-mix refinement is inert and refusal reduces to skipping the
+coinjoin shape): skip a transaction whose shape is many-in-many-out, otherwise union all of its input
+addresses. `test`-validated to produce the identical partition to `views.cluster_addresses` on a real
+slice.
+"""
+import hashlib
+import json
+import gzip
+from array import array
+
+from .monitor import COINJOIN_MIN_PARTICIPANTS, is_coinjoin
+
+
+def ahash(a):
+ """Address to 64-bit id. Collision probability is negligible at the address counts here."""
+ return int.from_bytes(hashlib.blake2b(a.encode(), digest_size=8).digest(), "big")
+
+
+def stream_txs(paths):
+ for p in paths:
+ opener = gzip.open if str(p).endswith(".gz") else open
+ with opener(p, "rt") as f:
+ for line in f:
+ if line.strip():
+ yield json.loads(line)
+
+
+def _in_addr_hashes(tx):
+ hs = []
+ for v in tx.get("vin", []):
+ a = (v.get("prevout") or {}).get("scriptpubkey_address")
+ if a:
+ hs.append(ahash(a))
+ return hs
+
+
+class ScaleClustering:
+ """Union-find over address hashes. `cid(address)` returns the cluster root (an int) for a
+ union-participating address, or the address itself for a singleton, matching the semantics
+ `contraction.contract` expects from a partial `lookup` (`lookup.get(a, a)`)."""
+
+ def __init__(self, parent):
+ self._parent = parent
+
+ def _find(self, x):
+ p = self._parent
+ r = x
+ while p[r] != r:
+ r = p[r]
+ while p[x] != r:
+ p[x], x = r, p[x]
+ return r
+
+ def get(self, addr, default=None):
+ h = ahash(addr)
+ if h in self._parent:
+ return self._find(h)
+ return default
+
+ def n_clusters(self):
+ return len({self._find(x) for x in self._parent})
+
+ def map_many(self, addresses):
+ """Return address -> cluster id, preserving addresses outside the union-find as singletons."""
+ return {address: self.get(address, address) for address in addresses}
+
+
+def cluster_scale(paths, refuse=True, coinjoin_min=COINJOIN_MIN_PARTICIPANTS):
+ return cluster_scale_stream(stream_txs(paths), refuse=refuse, coinjoin_min=coinjoin_min)
+
+
+def cluster_scale_stream(txs, refuse=True, coinjoin_min=COINJOIN_MIN_PARTICIPANTS):
+ """Dictionary-backed reference implementation over a transaction iterator."""
+ parent = {}
+
+ def find(x):
+ r = x
+ while parent[r] != r:
+ r = parent[r]
+ while parent[x] != r:
+ parent[x], x = r, parent[x]
+ return r
+
+ for tx in txs:
+ if refuse and is_coinjoin(tx, coinjoin_min):
+ continue
+ hs = _in_addr_hashes(tx)
+ if len(hs) < 2:
+ continue
+ for h in hs:
+ if h not in parent:
+ parent[h] = h
+ r0 = find(hs[0])
+ for h in hs[1:]:
+ r = find(h)
+ if r != r0:
+ parent[r] = r0
+ return ScaleClustering(parent)
+
+
+def cluster_scale_np(paths, refuse=True, coinjoin_min=COINJOIN_MIN_PARTICIPANTS):
+ """Dense-integer union-find over 64-bit address hashes, the BlockSci-style compact
+ representation: instead of a Python dict {hash: hash}, map the
+ union-participating hashes to dense indices via a sorted int64 array + searchsorted, and
+ run union-find on an int32 parent array. The final partition is compact, but building the
+ global unique-address table requires retaining an edge buffer; measured end-to-end memory
+ is therefore workload-dependent (about 20% lower at 3M transactions, and slightly higher at
+ 1M). Produces the identical partition (validated in tests)."""
+ return cluster_scale_np_stream(stream_txs(paths), refuse=refuse, coinjoin_min=coinjoin_min)
+
+
+def cluster_scale_np_stream(txs, refuse=True, coinjoin_min=COINJOIN_MIN_PARTICIPANTS,
+ edge_chunk=1_000_000):
+ """Compact clustering over an arbitrary transaction iterator.
+
+ Edges are stored in an interleaved native uint64 buffer rather than Python integer lists.
+ Dense-index lookup is performed a chunk at a time, so the two full-size `searchsorted` index
+ arrays and their accidental `.tolist()` copies never coexist in memory.
+ """
+ import numpy as np
+ edges = array("Q")
+ for tx in txs:
+ if refuse and is_coinjoin(tx, coinjoin_min):
+ continue
+ hs = _in_addr_hashes(tx)
+ if len(hs) < 2:
+ continue
+ h0 = hs[0]
+ for h in hs[1:]:
+ edges.extend((h0, h))
+ if not edges:
+ return NpClustering(np.empty(0, np.uint64), np.empty(0, np.int32))
+ edge_view = np.frombuffer(edges, dtype=np.uint64).reshape(-1, 2)
+ uniq = np.unique(edge_view)
+ parent = np.arange(len(uniq), dtype=np.int32)
+
+ def find(x):
+ r = x
+ while parent[r] != r:
+ r = parent[r]
+ while parent[x] != r:
+ parent[x], x = r, parent[x]
+ return r
+
+ for lo in range(0, len(edge_view), edge_chunk):
+ block = edge_view[lo:lo + edge_chunk]
+ src_idx = np.searchsorted(uniq, block[:, 0])
+ dst_idx = np.searchsorted(uniq, block[:, 1])
+ for a, b in zip(src_idx, dst_idx):
+ ra, rb = find(int(a)), find(int(b))
+ if ra != rb:
+ parent[ra] = rb
+ del src_idx, dst_idx
+ del edge_view, edges
+ root = np.fromiter((find(i) for i in range(len(uniq))), np.int32, len(uniq))
+ return NpClustering(uniq, root)
+
+
+class NpClustering:
+ """`get(address)` -> a stable cluster id (the root's dense index) for a union-participating
+ address, else `default`. Same `contraction.contract` semantics as `ScaleClustering`, backed by a
+ sorted-hash array instead of a dict."""
+
+ def __init__(self, uniq, root):
+ self._u = uniq
+ self._r = root
+
+ def get(self, addr, default=None):
+ import numpy as np
+ if not len(self._u):
+ return default
+ h = ahash(addr)
+ pos = int(np.searchsorted(self._u, h))
+ if pos < len(self._u) and int(self._u[pos]) == h:
+ return int(self._r[pos])
+ return default
+
+ def n_clusters(self):
+ import numpy as np
+ return int(len(np.unique(self._r)))
+
+ def map_many(self, addresses):
+ """Vectorized dense lookup; hashing remains streaming but searchsorted crosses Python once."""
+ import numpy as np
+ addresses = list(addresses)
+ if not addresses or not len(self._u):
+ return {address: address for address in addresses}
+ hashes = np.frombuffer(array("Q", (ahash(address) for address in addresses)),
+ dtype=np.uint64)
+ positions = np.searchsorted(self._u, hashes)
+ out = {}
+ for address, hashed, pos in zip(addresses, hashes, positions):
+ index = int(pos)
+ out[address] = (int(self._r[index])
+ if index < len(self._u) and self._u[index] == hashed else address)
+ return out
diff --git a/decluster/single_view_propagation.py b/decluster/single_view_propagation.py
new file mode 100644
index 00000000..aa550e16
--- /dev/null
+++ b/decluster/single_view_propagation.py
@@ -0,0 +1,12 @@
+"""Canonical namespace for provenance-signature propagation within one view.
+
+It is not the Narayanan--Shmatikov graph attack. ``decluster.propagate`` remains compatible.
+"""
+
+from .propagate import (NSPropagator, build_cluster_rarity, build_rarity, eccentricity,
+ entity_signature, holdout_reid, label_scores, partition_from_assignment,
+ propagate_merge, should_split)
+
+__all__ = ["NSPropagator", "build_cluster_rarity", "build_rarity", "eccentricity",
+ "entity_signature", "holdout_reid", "label_scores", "partition_from_assignment",
+ "propagate_merge", "should_split"]
diff --git a/decluster/source_snapshot.py b/decluster/source_snapshot.py
new file mode 100644
index 00000000..4c643c52
--- /dev/null
+++ b/decluster/source_snapshot.py
@@ -0,0 +1,263 @@
+"""Strict identities for external research sources that cannot be redistributed."""
+
+from __future__ import annotations
+
+import argparse
+from dataclasses import dataclass
+import hashlib
+import json
+import os
+from pathlib import Path
+import subprocess
+import tempfile
+from urllib.request import urlopen
+
+
+class SourceSnapshotError(ValueError):
+ """An external source lock is malformed or does not match supplied bytes."""
+
+
+@dataclass(frozen=True)
+class SourceSnapshot:
+ id: str
+ size_bytes: int
+ sha256: str
+ canonical_url: str
+ retrieval_url: str
+ redistribution: str
+ structure: dict | None
+ source_only_sha256: str | None
+
+
+def load_source_snapshot(path):
+ try:
+ document = json.loads(Path(path).read_text(encoding="utf-8"))
+ except (OSError, json.JSONDecodeError) as exc:
+ raise SourceSnapshotError(f"cannot read source snapshot {path}: {exc}") from exc
+ required = {
+ "schema_version", "id", "title", "canonical_url", "retrieval_url",
+ "retrieved_at", "format", "content", "structure", "normalization",
+ "license", "redistribution", "availability", "interpretation",
+ }
+ if not isinstance(document, dict) or set(document) != required:
+ raise SourceSnapshotError("source snapshot has missing or unknown fields")
+ if document["schema_version"] != 1:
+ raise SourceSnapshotError("unsupported source snapshot schema")
+ content = document["content"]
+ if not isinstance(content, dict) or set(content) != {"bytes", "sha256"}:
+ raise SourceSnapshotError("source snapshot content identity is malformed")
+ digest = content["sha256"]
+ if (
+ not isinstance(content["bytes"], int)
+ or isinstance(content["bytes"], bool)
+ or content["bytes"] < 0
+ or not isinstance(digest, str)
+ or len(digest) != 64
+ or any(character not in "0123456789abcdef" for character in digest)
+ ):
+ raise SourceSnapshotError("source snapshot content identity is invalid")
+ if document["redistribution"] not in {"allowed", "restricted", "unknown"}:
+ raise SourceSnapshotError("invalid redistribution status")
+ for field in ("id", "canonical_url", "retrieval_url"):
+ if not isinstance(document[field], str) or not document[field]:
+ raise SourceSnapshotError(f"source snapshot {field} is invalid")
+ if not document["canonical_url"].startswith("https://") or not document[
+ "retrieval_url"
+ ].startswith("https://"):
+ raise SourceSnapshotError("source snapshot URLs must use HTTPS")
+ for field in ("title", "retrieved_at", "format", "license", "availability", "interpretation"):
+ if not isinstance(document[field], str) or not document[field]:
+ raise SourceSnapshotError(f"source snapshot {field} is invalid")
+ structure = document["structure"]
+ normalization = document["normalization"]
+ if not isinstance(structure, dict) or not isinstance(normalization, dict):
+ raise SourceSnapshotError("source snapshot structure and normalization must be objects")
+ if structure:
+ structure_keys = {
+ "cells", "code_cells", "markdown_cells", "display_data_outputs",
+ "execute_result_outputs",
+ }
+ if set(structure) != structure_keys or any(
+ not isinstance(value, int) or isinstance(value, bool) or value < 0
+ for value in structure.values()
+ ):
+ raise SourceSnapshotError("source snapshot structure is malformed")
+ source_only_sha256 = None
+ if normalization:
+ expected = {
+ "algorithm", "tool", "removed", "serialization", "source_only_sha256"
+ }
+ if set(normalization) != expected:
+ raise SourceSnapshotError("source snapshot normalization is malformed")
+ if normalization["algorithm"] != "jq-cS-strip-runtime-v1":
+ raise SourceSnapshotError("unsupported source normalization algorithm")
+ if normalization["tool"] != "jq 1.8.1":
+ raise SourceSnapshotError("jq normalization must pin version 1.8.1")
+ expected_removed = [
+ ".cells[].outputs",
+ ".cells[].execution_count",
+ ".metadata.colab.authorship_tag",
+ ".metadata.colab.provenance",
+ ]
+ if normalization["removed"] != expected_removed:
+ raise SourceSnapshotError("source normalization removal set is invalid")
+ if normalization["serialization"] != "jq -cS with one trailing LF; not RFC 8785 JCS":
+ raise SourceSnapshotError("source normalization serialization is invalid")
+ source_only_sha256 = normalization["source_only_sha256"]
+ if (
+ not isinstance(source_only_sha256, str)
+ or len(source_only_sha256) != 64
+ or any(character not in "0123456789abcdef" for character in source_only_sha256)
+ ):
+ raise SourceSnapshotError("source-only identity is invalid")
+ return SourceSnapshot(
+ id=document["id"],
+ size_bytes=content["bytes"],
+ sha256=digest,
+ canonical_url=document["canonical_url"],
+ retrieval_url=document["retrieval_url"],
+ redistribution=document["redistribution"],
+ structure=structure or None,
+ source_only_sha256=source_only_sha256,
+ )
+
+
+def verify_source(snapshot, source_path):
+ source_path = Path(source_path)
+ digest = hashlib.sha256()
+ size = 0
+ try:
+ with source_path.open("rb") as source:
+ for chunk in iter(lambda: source.read(1024 * 1024), b""):
+ size += len(chunk)
+ digest.update(chunk)
+ except OSError as exc:
+ raise SourceSnapshotError(f"cannot read source bytes {source_path}: {exc}") from exc
+ if size != snapshot.size_bytes or digest.hexdigest() != snapshot.sha256:
+ raise SourceSnapshotError(
+ f"{snapshot.id}: source identity mismatch: bytes={size}, sha256={digest.hexdigest()}"
+ )
+ return snapshot
+
+
+def verify_normalized_source(snapshot, source_path, *, jq="jq"):
+ """Verify the recorded jq-normalized notebook identity and structure."""
+ if snapshot.source_only_sha256 is None:
+ return snapshot
+ try:
+ notebook = json.loads(Path(source_path).read_text(encoding="utf-8"))
+ except (OSError, UnicodeDecodeError, json.JSONDecodeError) as exc:
+ raise SourceSnapshotError(f"cannot parse notebook source: {exc}") from exc
+ cells = notebook.get("cells")
+ if not isinstance(cells, list) or any(not isinstance(cell, dict) for cell in cells):
+ raise SourceSnapshotError("notebook cells are missing")
+ if any(
+ not isinstance(cell.get("outputs", []), list)
+ or any(not isinstance(output, dict) for output in cell.get("outputs", []))
+ for cell in cells
+ ):
+ raise SourceSnapshotError("notebook outputs are malformed")
+ actual_structure = {
+ "cells": len(cells),
+ "code_cells": sum(cell.get("cell_type") == "code" for cell in cells),
+ "markdown_cells": sum(cell.get("cell_type") == "markdown" for cell in cells),
+ "display_data_outputs": sum(
+ output.get("output_type") == "display_data"
+ for cell in cells
+ for output in cell.get("outputs", ())
+ ),
+ "execute_result_outputs": sum(
+ output.get("output_type") == "execute_result"
+ for cell in cells
+ for output in cell.get("outputs", ())
+ ),
+ }
+ if snapshot.structure != actual_structure:
+ raise SourceSnapshotError(f"{snapshot.id}: notebook structure mismatch")
+ expression = (
+ "del(.cells[].outputs,.cells[].execution_count,"
+ ".metadata.colab.authorship_tag,.metadata.colab.provenance)"
+ )
+ try:
+ version = subprocess.run(
+ [jq, "--version"], check=True, capture_output=True, text=True
+ ).stdout.strip()
+ if version != "jq-1.8.1":
+ raise SourceSnapshotError(f"expected jq-1.8.1, found {version!r}")
+ result = subprocess.run(
+ [jq, "-cS", expression, str(source_path)],
+ check=True,
+ capture_output=True,
+ )
+ except (OSError, subprocess.CalledProcessError) as exc:
+ raise SourceSnapshotError(f"cannot normalize notebook with jq: {exc}") from exc
+ digest = hashlib.sha256(result.stdout).hexdigest()
+ if digest != snapshot.source_only_sha256:
+ raise SourceSnapshotError(f"{snapshot.id}: source-only identity mismatch")
+ return snapshot
+
+
+def _sync_directory(path):
+ descriptor = os.open(path, os.O_RDONLY)
+ try:
+ os.fsync(descriptor)
+ finally:
+ os.close(descriptor)
+
+
+def fetch_source(snapshot, destination, *, opener=urlopen, timeout=30):
+ """Publish a fully verified source atomically; directory durability is best-effort."""
+ destination = Path(destination)
+ destination.parent.mkdir(parents=True, exist_ok=True)
+ with tempfile.NamedTemporaryFile(dir=destination.parent, delete=False) as temporary:
+ temporary_path = Path(temporary.name)
+ digest = hashlib.sha256()
+ size = 0
+ try:
+ try:
+ with opener(snapshot.retrieval_url, timeout=timeout) as response:
+ while chunk := response.read(1024 * 1024):
+ size += len(chunk)
+ if size > snapshot.size_bytes:
+ raise SourceSnapshotError(
+ f"{snapshot.id}: downloaded source exceeds locked size"
+ )
+ digest.update(chunk)
+ temporary.write(chunk)
+ except SourceSnapshotError:
+ raise
+ except (OSError, ValueError) as exc:
+ raise SourceSnapshotError(f"{snapshot.id}: source download failed: {exc}") from exc
+ temporary.flush()
+ os.fsync(temporary.fileno())
+ if size != snapshot.size_bytes or digest.hexdigest() != snapshot.sha256:
+ raise SourceSnapshotError(f"{snapshot.id}: downloaded source identity mismatch")
+ verify_source(snapshot, temporary_path)
+ verify_normalized_source(snapshot, temporary_path)
+ os.replace(temporary_path, destination)
+ try:
+ _sync_directory(destination.parent)
+ except OSError:
+ pass
+ finally:
+ temporary_path.unlink(missing_ok=True)
+ return verify_source(snapshot, destination)
+
+
+def main(argv=None):
+ parser = argparse.ArgumentParser(description=__doc__)
+ parser.add_argument("command", choices=("fetch", "verify"))
+ parser.add_argument("manifest")
+ parser.add_argument("source")
+ args = parser.parse_args(argv)
+ snapshot = load_source_snapshot(args.manifest)
+ if args.command == "fetch":
+ fetch_source(snapshot, args.source)
+ else:
+ verify_source(snapshot, args.source)
+ verify_normalized_source(snapshot, args.source)
+ return 0
+
+
+if __name__ == "__main__":
+ raise SystemExit(main())
diff --git a/decluster/subset_sum_weighted_ancestry.py b/decluster/subset_sum_weighted_ancestry.py
new file mode 100644
index 00000000..a523afd0
--- /dev/null
+++ b/decluster/subset_sum_weighted_ancestry.py
@@ -0,0 +1,18 @@
+"""Opt-in ancestry walk weighted by subset-sum link evidence.
+
+The shared solver remains in ``decluster.ancestry``; this namespace prevents the experimental
+link-weighted walk from being mistaken for the nominal-value default.
+"""
+
+from .ancestry import (DEFAULT_LINK_BUDGET_MS, absorber_distribution, build_extended_graph,
+ dss_link_oracle)
+
+
+def build_graph(target, depth=6, fetch=None, link_oracle=dss_link_oracle, value_weighted=False,
+ subjective_oracle=None, max_nodes=None):
+ return build_extended_graph(target, depth=depth, fetch=fetch, link_oracle=link_oracle,
+ value_weighted=value_weighted,
+ subjective_oracle=subjective_oracle, max_nodes=max_nodes)
+
+
+__all__ = ["DEFAULT_LINK_BUDGET_MS", "absorber_distribution", "build_graph", "dss_link_oracle"]
diff --git a/decluster/subtransaction.py b/decluster/subtransaction.py
index e900e451..ca22bbbd 100644
--- a/decluster/subtransaction.py
+++ b/decluster/subtransaction.py
@@ -5,6 +5,41 @@
Refuse-only: it can cut a co-spend from the graph, never add a positive same-owner link. Roundness
is a heuristic, not proof. ("Structural" is reserved for the graph/provenance attack, not this.)"""
import math
+from dataclasses import dataclass
+from enum import Enum
+
+
+class TransactionModel(str, Enum):
+ """Assumptions under which amount interpretations may be consumed."""
+
+ UNKNOWN = "unknown"
+ RESTRICTED_TWO_PARTY_PAYMENT = "restricted_two_party_payment"
+ NET_SETTLEMENT_ALLOWED = "net_settlement_allowed"
+
+
+class AmountDecisionStatus(str, Enum):
+ INCONCLUSIVE = "inconclusive"
+ DIRECTIONAL_HYPOTHESIS = "directional_hypothesis"
+ OUT_OF_SCOPE = "out_of_scope"
+
+
+@dataclass(frozen=True)
+class AmountInterpretation:
+ implied_net_transfer: int
+ roundness_score: int
+ receiver_input_index: int
+ receiver_output_index: int
+
+
+@dataclass(frozen=True)
+class AmountModelDecision:
+ status: AmountDecisionStatus
+ model: TransactionModel
+ interpretations: tuple[AmountInterpretation, ...]
+ ranked: tuple[AmountInterpretation, ...]
+ refuse: tuple[tuple[str, str], ...] = ()
+ links: tuple[tuple[str, str], ...] = ()
+ reason: str = ""
def roundness(x):
"""how 'designed' the number looks: +k if divisible by 10^k (1000 -> 3, 4750 -> 1)."""
@@ -14,24 +49,47 @@ def roundness(x):
x //= 10; k += 1
return k
+def enumerate_amount_interpretations(tx):
+ """Enumerate positive implied net transfers for every 2-in/2-out allocation."""
+ ins = [(i, v["prevout"]["value"]) for i, v in enumerate(tx["vin"])]
+ outs = [(j, o["value"]) for j, o in enumerate(tx["vout"])]
+ if len(ins) != 2 or len(outs) != 2:
+ return ()
+ return tuple(
+ AmountInterpretation(wr - vr, roundness(wr - vr), ri, ro)
+ for ri, vr in ins
+ for ro, wr in outs
+ if wr - vr > 0
+ )
+
+
+def rank_amount_interpretations(interpretations):
+ """Rank candidates by roundness and then magnitude without selecting truth."""
+ return tuple(
+ sorted(
+ interpretations,
+ key=lambda item: (-item.roundness_score, -item.implied_net_transfer),
+ )
+ )
+
+
def subtransactions(tx):
"""balanced 2-owner partitions of a 2-in/2-out merged transaction, ranked by plausibility.
Returns (ranked, ambiguity_bits); ranked = [(payment, score, r_in_idx, r_out_idx)].
`ambiguity_bits = log2(count)` is a raw *count* diagnostic (Boltzmann-style), NOT a privacy
quantity — what bounds anonymity is the entropy of the *distribution* over partitions, not the
count (paper §10). It is reported for transparency and never fed into clustering as anonymity."""
- ins = [(i, v["prevout"]["value"]) for i, v in enumerate(tx["vin"])]
- outs = [(j, o["value"]) for j, o in enumerate(tx["vout"])]
- if len(ins) != 2 or len(outs) != 2:
- return [], None # scope guard: v1 is 2-in/2-out only
- plausible = []
- for ri, vr in ins:
- for ro, wr in outs:
- payment = wr - vr # receiver adds its input to the payment
- if payment <= 0:
- continue
- plausible.append((payment, roundness(payment), ri, ro))
- plausible.sort(key=lambda t: (-t[1], -t[0]))
+ interpretations = enumerate_amount_interpretations(tx)
+ ranked = rank_amount_interpretations(interpretations)
+ plausible = [
+ (
+ item.implied_net_transfer,
+ item.roundness_score,
+ item.receiver_input_index,
+ item.receiver_output_index,
+ )
+ for item in ranked
+ ]
amb = math.log2(len(plausible)) if plausible else None
return plausible, amb
@@ -41,23 +99,67 @@ def norm(t):
"vin": [{"txid": v["txid"], "prevout": {"value": v["prevout"]["value"]}} for v in t["vin"]],
"vout": [{"value": o["value"]} for o in t["vout"]]}
+def evaluate_amount_model(tx, model):
+ """Evaluate ranked interpretations without exceeding the declared model.
+
+ Unknown and net-settlement-capable models retain every candidate and never
+ emit ownership-directional links or refusals. The restricted model exposes
+ the historical roundness hypothesis for compatibility; it remains a
+ hypothesis rather than an observed payment.
+ """
+ if not isinstance(model, TransactionModel):
+ raise TypeError("model must be a TransactionModel")
+ interpretations = enumerate_amount_interpretations(tx)
+ ranked = rank_amount_interpretations(interpretations)
+ if not ranked:
+ return AmountModelDecision(
+ AmountDecisionStatus.OUT_OF_SCOPE,
+ model,
+ interpretations,
+ ranked,
+ reason="requires a positive interpretation in a 2-in/2-out transaction",
+ )
+ if model in {TransactionModel.UNKNOWN, TransactionModel.NET_SETTLEMENT_ALLOWED}:
+ return AmountModelDecision(
+ AmountDecisionStatus.INCONCLUSIVE,
+ model,
+ interpretations,
+ ranked,
+ reason="roundness cannot identify allocation when the transaction form is not restricted",
+ )
+
+ best = ranked[0]
+ txids = tuple(v["txid"] for v in tx["vin"])
+ sender_input = 1 - best.receiver_input_index
+ sender_output = 1 - best.receiver_output_index
+ return AmountModelDecision(
+ AmountDecisionStatus.DIRECTIONAL_HYPOTHESIS,
+ model,
+ interpretations,
+ ranked,
+ refuse=((txids[sender_input], txids[best.receiver_input_index]),),
+ links=(
+ (txids[best.receiver_input_index], f"{tx['txid']}:{best.receiver_output_index}"),
+ (txids[sender_input], f"{tx['txid']}:{sender_output}"),
+ ),
+ reason="highest-roundness implied net transfer under a restricted two-party model",
+ )
+
+
def partition_signal(tx):
"""structural signal for the combiner: refuse (different-owner inputs) + link
(input -> its output) from the most likely partition. scope guard -> empty."""
- ranked, amb = subtransactions(tx)
- if not ranked:
+ decision = evaluate_amount_model(tx, TransactionModel.RESTRICTED_TWO_PARTY_PAYMENT)
+ amb = math.log2(len(decision.interpretations)) if decision.interpretations else None
+ if decision.status is not AmountDecisionStatus.DIRECTIONAL_HYPOTHESIS:
return {"refuse": [], "link": [], "payment": None, "ambiguity_bits": amb}
- payment, _score, ri, ro = ranked[0]
- txids = [v["txid"] for v in tx["vin"]]
- si = next(i for i in range(2) if i != ri)
- so = next(j for j in range(2) if j != ro)
+ best = decision.ranked[0]
return {
- "refuse": [(txids[si], txids[ri])], # different owners (consumed by the engine)
+ "refuse": list(decision.refuse),
# UNUSED by cluster_refined: amounts REFUSE only, never add a positive same-owner link
# (sub-transaction evidence can cut a coin from the graph, never inflate the score). Kept
# for diagnostics/tests; do not wire into clustering as a positive signal.
- "link": [(txids[ri], f"{tx['txid']}:{ro}"), # receiver -> its output
- (txids[si], f"{tx['txid']}:{so}")], # sender -> its output
- "payment": payment,
+ "link": list(decision.links),
+ "payment": best.implied_net_transfer,
"ambiguity_bits": amb, # count diagnostic, not privacy (see subtransactions)
}
diff --git a/decluster/tx_addrs.py b/decluster/tx_addrs.py
new file mode 100644
index 00000000..011bce90
--- /dev/null
+++ b/decluster/tx_addrs.py
@@ -0,0 +1,16 @@
+"""The two address readings every view construction starts from.
+
+Clustering, the partition schemes and contraction all read a transaction the same way. Kept here
+rather than in any one of them because a private copy in one is the copy the other two quietly
+diverge from.
+"""
+
+
+def in_addrs(tx):
+ return [a for a in (v.get("prevout", {}).get("scriptpubkey_address")
+ for v in tx.get("vin", [])) if a]
+
+
+def out_addrs(tx):
+ return [(o.get("scriptpubkey_address"), o.get("value") or 0)
+ for o in tx.get("vout", []) if o.get("scriptpubkey_address")]
diff --git a/decluster/view_match.py b/decluster/view_match.py
index 2373730b..f131761c 100644
--- a/decluster/view_match.py
+++ b/decluster/view_match.py
@@ -9,8 +9,9 @@
Three guards, each answering something measured rather than assumed:
- hub cap degree in a contracted 2026 view is heavy-tailed, median 2 against a
- maximum above 15 000 (`RESULTS-contraction-2026.md`). A hub is adjacent to
+ hub cap degree in a contracted view is heavy-tailed: mean 4.79 against a second
+ moment of 6289, which forces some vertex above 1300
+ (`results/generated/graph-rejoin-2016-v1.md`). A hub is adjacent to
thousands of candidates and would vote for all of them, so propagation
must not route through one.
eccentricity a match is accepted only when its score stands clear of the runner-up,
@@ -18,11 +19,41 @@
refusal, not a coin flip.
reversibility the match must also win scoring from the other side. Two views need not
agree, and a vertex with no counterpart is meant to stay unmatched.
+
+WHERE THIS DEPARTS FROM THE PAPER. This is the closest module in `decluster/` to the cited
+algorithm, but it is not the faithful implementation of it; that is
+`decluster/baselines/narayanan_shmatikov.py`. Five differences change verdicts:
+
+ * A candidate set of size one is accepted with `eccentricity = float("inf")`
+ (`ViewMatcher._best`), skipping the gate entirely rather than computing a large-but-finite
+ eccentricity. The baseline's live gate (`propagate`'s `_sparse_winner`, not `_winner`) never
+ skips it: given one materialised score it still normalises over the whole unclaimed candidate
+ population and computes a finite eccentricity from that, which clears `theta` or not depending
+ on the population size — not a blanket refusal of a lone candidate.
+ * The imported `propagate.eccentricity` normalises by the standard deviation of the scores that
+ were actually materialised, and `candidate_scores` materialises only vertices that received a
+ vote. The baseline's gate (`_sparse_winner`) normalises over the whole unclaimed candidate
+ population, implicit zeros included, which is a larger population and a different sigma. The
+ two formulas agree given the same values; they are not given the same values.
+ * `candidate_scores` damps by `1/sqrt(conn(image))` — the connectedness of the *matched
+ neighbour's image*, one weight for every candidate that neighbour reaches. The paper divides
+ each vote by the square root of the degree of the *candidate* being voted for, which is what
+ makes it a cosine-like similarity; the baseline does that at
+ `narayanan_shmatikov._sparse_match_scores`. Different quantity, different ranking: the two
+ agree only where every candidate reached by a matched neighbour has that neighbour's degree.
+ * `directional` is off by default, so a vote from a matched neighbour is scored over the
+ undirected neighbourhood. The paper scores in- and out-edges separately and sums the two,
+ which is a strictly finer constraint — a candidate must be on the *same side* of the matched
+ neighbour. Turning it on changes both coverage and precision, and not by a little.
+ * `hubcap` drops any matched neighbour whose connectedness exceeds it, and defaults to 100.
+ The paper has no such filter. The guard answers a measured property of a contracted Bitcoin
+ view rather than anything in the algorithm, so it is a local addition and not a parameter
+ choice.
"""
from collections import Counter
from math import sqrt
-from .propagate import eccentricity
+from .single_view_propagation import eccentricity
def agreement(sa, sb):
@@ -42,11 +73,12 @@ def _condition(weight, agree, alpha):
It can, however, manufacture *eccentricity*, and that is what the gate reads. Scaling
tied candidates by different factors creates the separation the gate tests for, turning
- refusals into accepted matches. `RESULTS-attribute-conditioning.md` measures the cost:
- the margin over a degree-only guess falls monotonically as alpha rises from zero, and
- even alpha 0.05 gives most of the loss away. Both conditioners therefore default to off,
- and they are kept as the instrument that measured this rather than as a feature to
- enable."""
+ refusals into accepted matches on no new structural fact
+ (`test_view_match.test_the_conditioner_cannot_manufacture_a_score_but_does_manufacture_eccentricity`).
+ The gate normalises by the spread of the scores, so its verdict does not depend on how
+ large alpha is, only on the ordering the conditioner imposes: the smallest nonzero
+ setting costs what the largest does. Both conditioners therefore default to off, and they
+ are kept as the instrument that established this rather than as a feature to enable."""
return weight if agree is None else weight * ((1 - alpha) + 2 * alpha * agree)
@@ -72,7 +104,7 @@ def vertex_agreement(src, u, dst, v, axes=None):
def candidate_scores(u, src, dst, mapping, hubcap=100, damping=True, stat=None,
- edge_alpha=0.0):
+ edge_alpha=0.0, directional=False, return_support=False):
"""Score dst-vertices as candidates for src-vertex `u`, by the images of u's already
matched neighbours. Only matched neighbours carry information, so an unmatched
neighbourhood scores nothing and u simply waits for a later round.
@@ -86,18 +118,28 @@ def candidate_scores(u, src, dst, mapping, hubcap=100, damping=True, stat=None,
seeing the degrees the full graph had."""
conn = (lambda v: stat.get(v, 0)) if stat is not None else dst.degree
scores = Counter()
- for n in src.neighbours(u):
- image = mapping.get(n)
- if image is None or conn(image) > hubcap:
- continue
- w = 1.0 / sqrt(conn(image) or 1) if damping else 1.0
- sa = _sig(src, u, n) if edge_alpha else None
- for v in dst.neighbours(image):
- wv = w
- if edge_alpha:
- wv = _condition(w, agreement(sa, _sig(dst, v, image)), edge_alpha)
- scores[v] += wv
- return scores
+ support = Counter()
+ # Preserve the relation to the matched neighbour. If u -> n, a candidate v must satisfy
+ # v -> image(n), so v is an in-neighbour of image(n); the converse holds for n -> u.
+ if directional:
+ sides = [(src._out[u], lambda img: dst._in[img]),
+ (src._in[u], lambda img: dst._out[img])]
+ else:
+ sides = [(src.neighbours(u), lambda img: dst.neighbours(img))]
+ for src_nbrs, dst_nbrs in sides:
+ for n in src_nbrs:
+ image = mapping.get(n)
+ if image is None or conn(image) > hubcap:
+ continue
+ w = 1.0 / sqrt(conn(image) or 1) if damping else 1.0
+ sa = _sig(src, u, n) if edge_alpha else None
+ for v in dst_nbrs(image):
+ wv = w
+ if edge_alpha:
+ wv = _condition(w, agreement(sa, _sig(dst, v, image)), edge_alpha)
+ scores[v] += wv
+ support[v] += 1
+ return (scores, support) if return_support else scores
class ViewMatcher:
@@ -106,7 +148,8 @@ class ViewMatcher:
def __init__(self, theta=0.5, hubcap=100, min_score=0.0, damping=True,
reversible=True, stat=None, edge_alpha=0.0, vertex_alpha=0.0,
- vertex_top=10):
+ vertex_top=10, revisit=False, revisit_rounds=5, directional=False,
+ min_common=1):
self.theta = theta
self.hubcap = hubcap
self.min_score = min_score
@@ -116,6 +159,10 @@ def __init__(self, theta=0.5, hubcap=100, min_score=0.0, damping=True,
self.edge_alpha = edge_alpha # 0 disables the attribute conditioners entirely
self.vertex_alpha = vertex_alpha
self.vertex_top = vertex_top # only the leaders can change the gate's verdict
+ self.directional = directional # NS'09 in/out separated scoring (default: undirected)
+ self.min_common = min_common # paper's high-confidence stage uses k=4
+ self.revisit = revisit # NS'09 self-reinforcing pass: re-score mapped nodes
+ self.revisit_rounds = revisit_rounds
self.confidence = {}
def _best(self, u, src, dst, mapping, detail=False):
@@ -124,8 +171,12 @@ def _best(self, u, src, dst, mapping, detail=False):
won — the framework's value lies in the *high confidence* links, not in coverage,
so a match has to carry how confident it was."""
miss = (None, 0.0, 0.0) if detail else None
- scores = candidate_scores(u, src, dst, mapping, self.hubcap, self.damping,
- self.stat.get(id(dst)), self.edge_alpha)
+ scores, support = candidate_scores(u, src, dst, mapping, self.hubcap, self.damping,
+ self.stat.get(id(dst)), self.edge_alpha,
+ self.directional, return_support=True)
+ if self.min_common > 1:
+ scores = Counter({v: score for v, score in scores.items()
+ if support[v] >= self.min_common})
if self.vertex_alpha and len(scores) >= 2:
for v, _ in scores.most_common(self.vertex_top):
scores[v] = _condition(scores[v],
@@ -157,7 +208,13 @@ def match(self, ga, gb, seed, stat_a=None, stat_b=None):
frontier = {n for u in mapping for n in ga.neighbours(u) if n not in mapping}
while frontier:
nxt, refused, matched = set(), set(), 0
- for u in frontier:
+ # Sorted, because the order decides the outcome and a set's order is not defined.
+ # `_best` denies an image once it is claimed, so whoever is scored first takes it from
+ # everyone contesting it; iterating the set directly makes that hinge on string hashing,
+ # which Python randomises per process. The algorithm does not specify an order, but an
+ # implementation whose result changes between two runs of the same input has not
+ # measured anything. This was found by a canonical run failing to reproduce itself.
+ for u in sorted(frontier, key=repr):
if u in mapping:
continue
v, ecc, sc = self._best(u, ga, gb, mapping, detail=True)
@@ -175,4 +232,92 @@ def match(self, ga, gb, seed, stat_a=None, stat_b=None):
if not matched:
break
frontier = nxt | refused # a refusal can turn into a match once a
- return mapping # rival candidate is claimed elsewhere
+ if self.revisit: # rival candidate is claimed elsewhere
+ self._revisit(ga, gb, mapping, reverse, set(seed))
+ return mapping
+
+ def _revisit(self, ga, gb, mapping, reverse, seeds):
+ """NS'09's self-reinforcing pass: once propagation settles, re-score each mapped
+ (non-seed) vertex against the now-larger mapping. A vertex whose neighbourhood
+ became better resolved can upgrade to a candidate that only became supportable
+ later. A vertex is freed while it is re-scored, so it may reclaim its own image;
+ it never steals another's, so the pass only ever sharpens, never thrashes."""
+ for _ in range(self.revisit_rounds):
+ changed = 0
+ for u in [k for k in mapping if k not in seeds]:
+ old = mapping.pop(u)
+ reverse.pop(old, None)
+ v, ecc, sc = self._best(u, ga, gb, mapping, detail=True)
+ if v is not None and self.reversible \
+ and self._best(v, gb, ga, reverse) != u:
+ v = None
+ if v is None:
+ mapping[u], reverse[old] = old, u # keep the prior match
+ continue
+ mapping[u], reverse[v] = v, u
+ self.confidence[u] = (ecc, sc)
+ if v != old:
+ changed += 1
+ if not changed:
+ break
+
+ def match_candidates(self, ga, gb, seed, top_k=3, stat_a=None, stat_b=None):
+ """The cit-25 (link-prediction) relaxation for incomplete graphs: after the strict
+ match settles, every still-unmatched vertex adjacent to the matched region keeps a
+ *candidate set* of its top-`top_k` images instead of a single accepted match. Stage-2
+ sets do not feed back into propagation; they exist so a link can still be predicted
+ for a vertex the strict matcher refused. Returns (mapping, {vertex: [candidate, ...]})."""
+ mapping = self.match(ga, gb, seed, stat_a, stat_b)
+ candidates = {}
+ frontier = {n for u in mapping for n in ga.neighbours(u) if n not in mapping}
+ for u in sorted(frontier, key=repr):
+ scores = candidate_scores(u, ga, gb, mapping, self.hubcap, self.damping,
+ self.stat.get(id(gb)), self.edge_alpha, self.directional)
+ for taken in mapping.values():
+ scores.pop(taken, None)
+ if scores:
+ candidates[u] = [v for v, _ in scores.most_common(top_k)]
+ return mapping, candidates
+
+
+def predict_link(a, b, gb, mapping, candidates, ml=None):
+ """Predict whether a->b is an edge in the target view, cit-25 Algorithm-3: de-anonymize
+ (DA) when both endpoints are uniquely mapped, else a unanimous vote over their candidate
+ sets, else the `ml` fallback. `ml` is an optional callable (a, b) -> score in [0, 1] (the
+ paper's logistic classifier over neighbourhood features); when absent, the mixed-vote and
+ no-candidate branches abstain (return None) — the high-precision default. Returns 1 (edge),
+ 0 (no edge), a float (ml score), or None (abstain). Unanimous voting overwhelmingly
+ recovers *non-edges*: random pairs rarely share one."""
+ def images(x):
+ return [mapping[x]] if x in mapping else candidates.get(x)
+ ia, ib = images(a), images(b)
+ if not ia or not ib:
+ return ml(a, b) if ml is not None else None
+ votes = [(v in gb._out[u] or v in gb._in[u]) for u in ia for v in ib]
+ if all(votes):
+ return 1
+ if not any(votes):
+ return 0
+ return ml(a, b) if ml is not None else None
+
+
+def find_seeds(ga, gb, top=50):
+ """Experimental seed bootstrap: match vertices whose
+ local signature (own degree + sorted neighbour degrees) is unique and identical in both
+ views, highest degree first. Returns a seed mapping {a-vertex: b-vertex}. This is a strict,
+ local heuristic, not the cited papers' weighted global graph matching; callers must validate
+ its precision before feeding it into propagation."""
+ def sig(g, v):
+ return (g.degree(v), tuple(sorted(g.degree(n) for n in g.neighbours(v))))
+ sa, sb = {}, {}
+ for v in ga.vertices:
+ sa.setdefault(sig(ga, v), []).append(v)
+ for v in gb.vertices:
+ sb.setdefault(sig(gb, v), []).append(v)
+ seeds = {}
+ for key in sorted((k for k in sa if len(sa[k]) == 1 and len(sb.get(k, ())) == 1),
+ key=lambda k: -k[0]):
+ seeds[sa[key][0]] = sb[key][0]
+ if len(seeds) >= top:
+ break
+ return seeds
diff --git a/decluster/view_partition.py b/decluster/view_partition.py
new file mode 100644
index 00000000..2d5d9aa9
--- /dev/null
+++ b/decluster/view_partition.py
@@ -0,0 +1,233 @@
+"""Where to cut the coin graph so the sides can be matched against each other.
+
+Cutting by time is the framework's own trivial example. The schemes after it cut along structure
+instead: `ambiguity_cut` is the opposite of expander decomposition, keeping the well-connected cores
+and spending the ambiguous boundary on the cut, and `collapse` cuts at the merges that would fuse two
+already-substantial clusters. Every scheme takes `n_views`, so none of them is limited to two sides.
+"""
+from collections import Counter
+
+from .monitor import is_coinjoin
+from .tx_addrs import in_addrs, out_addrs
+from .unionfind import UF
+
+
+def partition_coins(sample, scheme="epoch", bounds=None, core_frac=0.01, n_views=2,
+ min_side=2):
+ """Split the sample into views, returning one list of sample indices per view.
+
+ epoch by block height, the trivial partition the framework names as its
+ example. `bounds` is [(lo, hi), ...]; without it the height range is
+ halved.
+ coinjoin_boundary a coinjoin is where co-spending stops implying common ownership, so
+ it is a natural seam: transactions before and after form the views and
+ the coinjoins themselves are the boundary, in no view.
+ ambiguity_cut the framework's preferred scheme, the "opposite" of expander
+ decomposition. See `ambiguity_partition`; note it needs two passes,
+ so `sample` must be a list rather than an iterator here. `n_views`
+ keeps the largest N components, the framework's n > 2 generalization.
+ collapse cut along the cluster-collapse events themselves — the merges that would
+ fuse two already-substantial clusters — then split the survivors by height
+ into `n_views` bands. See `collapse_partition`.
+
+ A cut removes the boundary from the views and keeps the rest; it does not discard the
+ vertices incident to it.
+ """
+ if scheme == "epoch":
+ heights = [tx.get("height") or 0 for tx, _ in sample]
+ if bounds is None:
+ bounds = height_bands(heights, n_views)
+ return [[i for i, h in enumerate(heights) if lo <= h <= hi] for lo, hi in bounds]
+
+ if scheme == "coinjoin_boundary":
+ seam = [i for i, (tx, _) in enumerate(sample) if is_coinjoin(tx)]
+ cut = seam[len(seam) // 2] if seam else len(sample) // 2
+ boundary = set(seam)
+ return [[i for i in range(cut) if i not in boundary],
+ [i for i in range(cut + 1, len(sample)) if i not in boundary]]
+
+ if scheme == "ambiguity_cut":
+ return ambiguity_partition(sample, sample, core_frac=core_frac, n_views=n_views)
+
+ if scheme == "decore":
+ return decore_partition(sample, core_frac=core_frac, bounds=bounds, n_views=n_views)
+
+ if scheme == "collapse":
+ return collapse_partition(sample, min_side=min_side, bounds=bounds, n_views=n_views)
+
+ raise ValueError(f"unknown scheme: {scheme}")
+
+
+def ambiguity_partition(pass_one, pass_two, core_frac=0.01, n_views=2):
+ """The framework's preferred partition, "the opposite of expander decomposition".
+
+ Expander decomposition finds a *sparse* cut and leaves well-connected components. The
+ opposite cuts through the dense core and leaves components that are relatively sparser,
+ which is the regime the matching needs: in a dense region every vertex looks like its
+ neighbours and identity is ambiguous, while a sparse neighbourhood is distinctive.
+
+ The core is the busiest `core_frac` of addresses. Transactions touching it are the cut
+ and join no view; what remains decomposes into connected components, and the largest
+ `n_views` become the views. A cluster whose activity only reached across through the
+ core now appears in two components under separate pseudonyms, which is exactly the
+ correspondence the matcher exists to recover.
+
+ Takes two independent iterators over the same transactions so a slice can be partitioned
+ without being held in memory.
+ """
+ seen = Counter()
+ for tx, _ in pass_one:
+ for a in set(in_addrs(tx)) | {a for a, _ in out_addrs(tx)}:
+ seen[a] += 1
+ if not seen:
+ return [[] for _ in range(n_views)]
+ cut = max(1, int(len(seen) * core_frac))
+ core = {a for a, _ in seen.most_common(cut)}
+
+ uf = UF()
+ members = {}
+ for i, (tx, _) in enumerate(pass_two):
+ addrs = set(in_addrs(tx)) | {a for a, _ in out_addrs(tx)}
+ if not addrs or addrs & core:
+ continue # the cut itself joins no view
+ addrs = sorted(addrs)
+ for a in addrs[1:]:
+ uf.union(addrs[0], a)
+ members[i] = addrs[0]
+ comps = {}
+ for i, anchor in members.items():
+ comps.setdefault(uf.find(anchor), []).append(i)
+ return sorted(comps.values(), key=len, reverse=True)[:n_views]
+
+
+def collapse_boundary(sample, min_side=2):
+ """The transactions whose common-input merge would fuse two already-substantial clusters.
+
+ Returns `(boundary_indices, uf)` — the sample positions of those transactions, and the
+ union-find that results from declining them.
+
+ This is the cut the framework asks for in place of the trivial temporal one: not the busiest
+ addresses (`decore_partition`, which cuts by degree and so removes hubs whether or not they
+ fuse anything) but the *cluster-collapse* events themselves. A merge that attaches a fresh
+ address to one existing cluster only grows it; a merge that joins two clusters of `min_side`
+ or more is the one that destroys the distinction between two entities, and it is exactly what
+ a competent adversary refuses and what the partition should therefore cut along.
+
+ Detection and refusal are the same pass, which makes the result self-consistent: the clusters
+ that survive are the ones the declined merges never joined, so the boundary really is a
+ boundary of the clustering it produces. Order is the sample's own, i.e. block order — a
+ streaming union-find's view of which cluster was already substantial is causal, and reordering
+ the input can move a marginal case either way.
+ """
+ uf = UF()
+ size = {} # root -> addresses under it, maintained alongside the union-find
+ boundary = []
+
+ def root_of(a):
+ r = uf.find(a)
+ size.setdefault(r, 1)
+ return r
+
+ for i, (tx, _) in enumerate(sample):
+ ins = in_addrs(tx)
+ if len(ins) < 2:
+ for a in ins:
+ root_of(a)
+ continue
+ roots = {root_of(a) for a in ins}
+ if len(roots) >= 2 and sum(1 for r in roots if size[r] >= min_side) >= 2:
+ boundary.append(i)
+ continue
+ merged = sum(size[r] for r in roots)
+ first = ins[0]
+ for a in ins[1:]:
+ uf.union(first, a)
+ new_root = uf.find(first)
+ for r in roots:
+ if r != new_root:
+ size.pop(r, None)
+ size[new_root] = merged
+ return boundary, uf
+
+
+def height_bands(heights, n_views):
+ """`n_views` contiguous height bands covering the range, the last one taking the remainder.
+
+ The framework asks for the n > 2 generalisation of every cut, so the temporal axis every
+ scheme falls back on bands the same way. Two schemes used to halve the range whatever
+ `n_views` said, which returned two views for a caller that asked for four and reported
+ nothing.
+ """
+ if n_views < 1:
+ raise ValueError(f"n_views must be at least 1, not {n_views}")
+ lo_h, hi_h = min(heights), max(heights)
+ step = max(1, (hi_h - lo_h + 1) // n_views)
+ bounds, bottom = [], lo_h
+ for index in range(n_views):
+ top = hi_h if index == n_views - 1 else min(bottom + step - 1, hi_h)
+ bounds.append((bottom, top))
+ bottom = top + 1
+ return bounds
+
+
+def collapse_partition(sample, min_side=2, scheme="epoch", bounds=None, n_views=2):
+ """Views cut along cluster-collapse regions rather than along time.
+
+ The collapse transactions leave every view (they are the boundary, in none of them) and the
+ survivors are split on an axis orthogonal to connectivity — block height by default, into
+ `n_views` bands. The orthogonal split is not optional: taking the connected components the cut
+ leaves would give vertex-disjoint views that share no entity, and a matcher with nothing to
+ rejoin. Cut for ambiguity, split for overlap.
+ """
+ if scheme != "epoch":
+ raise ValueError(f"unknown inner scheme for collapse: {scheme}")
+ boundary = set(collapse_boundary(sample, min_side=min_side)[0])
+ kept = [i for i in range(len(sample)) if i not in boundary and in_addrs(sample[i][0])]
+ if not kept:
+ return [[] for _ in range(n_views)]
+ h = {i: (sample[i][0].get("height") or 0) for i in kept}
+ if bounds is None:
+ lo_h, hi_h = min(h.values()), max(h.values())
+ step = max(1, (hi_h - lo_h + 1) // max(1, n_views))
+ bounds = []
+ b = lo_h
+ for k in range(n_views):
+ top = hi_h if k == n_views - 1 else min(b + step - 1, hi_h)
+ bounds.append((b, top))
+ b = top + 1
+ return [[i for i in kept if lo <= h[i] <= hi] for lo, hi in bounds]
+
+
+def decore_partition(sample, core_frac=0.01, scheme="epoch", bounds=None, n_views=2):
+ """The ambiguity-cut reformulated so the views actually overlap.
+
+ `ambiguity_partition` removes the dense core and returns its *connected components*,
+ which are vertex-disjoint by construction: a cluster (defined by co-spend, i.e. address
+ sharing) stays inside one component, so two components share almost no entity and the
+ cross-view matcher has nothing to rejoin. This keeps the "de-core" idea — drop the busiest
+ `core_frac` of addresses as ambiguous noise from *both* views — but takes the view
+ boundary from an axis orthogonal to connectivity (block height by default). An entity
+ active on both sides of that axis then appears in both views, restoring the shared-vertex
+ overlap the matcher needs. Pair with `split_clusters_by_view` so a straddling entity
+ carries a distinct pseudonym per view rather than the trivial identity.
+ """
+ seen = Counter()
+ addrs = []
+ for tx, _ in sample:
+ a = set(in_addrs(tx)) | {x for x, _ in out_addrs(tx)}
+ addrs.append(a)
+ for x in a:
+ seen[x] += 1
+ if not seen:
+ return [[], []]
+ cut = max(1, int(len(seen) * core_frac))
+ core = {a for a, _ in seen.most_common(cut)}
+ kept = [i for i, a in enumerate(addrs) if a and not (a & core)]
+ if scheme == "epoch":
+ h = {i: (sample[i][0].get("height") or 0) for i in kept}
+ if not h:
+ return [[], []]
+ if bounds is None:
+ bounds = height_bands(list(h.values()), n_views)
+ return [[i for i in kept if lo <= h[i] <= hi] for lo, hi in bounds]
+ raise ValueError(f"unknown inner scheme for decore: {scheme}")
diff --git a/decluster/views.py b/decluster/views.py
index c81b8e84..a8b94310 100644
--- a/decluster/views.py
+++ b/decluster/views.py
@@ -1,55 +1,156 @@
-"""Partition the coin graph into views, then contract each into a pseudonym graph.
-
-The cited framework's construction: coins are vertices, a clustering is a set of undirected
-edges joining same-owner coins, and contracting those edges fuses each cluster into one
-vertex whose residual edges are transfers of bitcoin. With a complete clustering that is a
-user network; with the partial clustering an adversary actually holds it is a *pseudonym*
-graph, and that is the object its matching algorithm consumes.
-
-Two details the construction turns on:
-
- global clustering the partition is applied to the coin graph, but the clustering that
- annotates it is computed over the whole slice, then each part is
- contracted with that same lookup. Clustering each part independently
- would give the two views different vertex sets by construction.
- fold, don't stack contraction yields a multigraph, one edge per transfer, but the
- matching model wants a directed graph carrying one edge per ordered
- pair with the parallel transfers collected into its attributes.
-
-Vertex attributes are stored as raw counts alongside the view's own base rates, never as
-bare shares: `RESULTS-attribute-drift.md` finds a median 54% of an axis value's variance
-tracks epoch volume, a covariate that moves every vertex together, so an attribute has to
-be read relative to its own view before it crosses the boundary.
-"""
-from collections import Counter, defaultdict
+"""Cluster addresses into wallets, then split each cluster into one pseudonym per view.
+The clustering is the adversary's partial knowledge: co-spending joins addresses, and
+`merge_objections` lets a channel refuse a join the co-spend would otherwise force.
+`split_clusters_by_view` then models the incomplete-clustering premise the matching algorithm
+assumes — a cluster straddling a view boundary becomes a separate pseudonym on each side, and
+rejoining them is what the matcher is asked to do.
+"""
from .coinjoin_demix import coinjoin_demix
-from .extractors import locktime_policy, x_fee_rate, x_input_order, x_version
+from .extractors import x_uih
from .monitor import is_coinjoin
+from .tx_addrs import in_addrs
from .unionfind import UF
-from .change_gt import union_input_addrs
-# The axes RESULTS-attribute-drift.md finds stable across a one-week gap. The high-drift
-# axes (output types, feerate bucket, change position) are deliberately absent: at 4-8x the
-# drift they do not survive the boundary they would be compared across.
-AXES = {"version": x_version, "input_order": x_input_order,
- "locktime": locktime_policy, "fee_rate": x_fee_rate}
+def _union_tracking_size(uf, size, addrs):
+ """Union a transaction's inputs, keeping a root -> member-count map current.
-def _in_addrs(tx):
- return [a for a in (v.get("prevout", {}).get("scriptpubkey_address")
- for v in tx.get("vin", [])) if a]
+ The doubt gate asks whether a merge would join two established clusters, and answering that
+ by rescanning the union-find once per transaction is quadratic on a real slice.
+ """
+ # Fewer than two distinct addresses is not a merge, and must not touch the union-find:
+ # `find` would register the address as a singleton and put it in the returned lookup, where
+ # the reference implementation leaves it absent.
+ addrs = list(dict.fromkeys(addrs))
+ if len(addrs) < 2:
+ return
+ roots = {uf.find(a) for a in addrs}
+ merged = sum(size.get(r, 1) for r in roots)
+ first = addrs[0]
+ for a in addrs[1:]:
+ uf.union(first, a)
+ new_root = uf.find(first)
+ for r in roots:
+ if r != new_root:
+ size.pop(r, None)
+ size[new_root] = merged
+
+
+def merge_objections(tx):
+ """What the transaction says against reading its inputs as one owner, counted.
+
+ Zero is the conspicuous case: nothing here argues against common-input ownership, so the merge
+ can be made before any doubtful one and its result becomes context for judging those. Each
+ objection is decidable from the spending transaction alone and comes from a published tell:
+
+ unnecessary input some input already covers the largest output, so the others were not
+ needed to fund it and another participant may have contributed one.
+ mixed input types wallets differ in the script types they spend, so inputs that disagree
+ on type have more than one plausible contributor.
+
+ Returns None when the transaction carries neither signal, which is not the same as zero. An
+ address-only export cannot be ranked at all, and reading absence of evidence as absence of
+ objection would put every transaction in the conspicuous tier for free.
+ """
+ vin = tx.get("vin", [])
+ if len(vin) < 2:
+ return 0
+ prevouts = [v.get("prevout") or {} for v in vin]
+ types = {p.get("scriptpubkey_type") for p in prevouts}
+ have_types = None not in types
+ have_values = (all(p.get("value") is not None for p in prevouts)
+ and all(o.get("value") is not None for o in tx.get("vout", [])))
+ if not have_types and not have_values:
+ return None
+ objections = 0
+ if have_values and x_uih(tx) != "none":
+ objections += 1
+ if have_types and len(types) > 1:
+ objections += 1
+ return objections
-def _out_addrs(tx):
- return [(o.get("scriptpubkey_address"), o.get("value") or 0)
- for o in tx.get("vout", []) if o.get("scriptpubkey_address")]
+def merge_order(sample):
+ """Sample positions ordered most conspicuous first, ties keeping their original order.
+ The framework asks for the clustering to begin with the transactions that argue least against
+ themselves and proceed to the next most, so that the doubtful ones are judged against a
+ partition built from unambiguous evidence rather than against whatever the block order
+ happened to supply. Unrankable transactions sort last, since nothing is known in their favour.
-def cluster_addresses(sample, refuse=True):
+ Raises when the sample carries no rankable evidence at all: staging would then be a no-op
+ dressed as a decision, and the caller should know its export cannot support it.
+ """
+ scored = [(merge_objections(tx), i) for i, (tx, _) in enumerate(sample)]
+ # Judge rankability on the transactions the order actually decides about. A single-input
+ # transaction has nothing to merge and so scores zero for free; counting those would let an
+ # address-only export look rankable and stage into a no-op.
+ mergeable = [scored[i][0] for i, (tx, _) in enumerate(sample) if len(tx.get("vin", [])) >= 2]
+ if mergeable and all(objections is None for objections in mergeable):
+ raise ValueError(
+ "no multi-input transaction in this sample carries the input values or script types "
+ "that rank a merge's conspicuousness; an address-only export cannot be staged")
+ ranked = sorted(range(len(scored)),
+ key=lambda k: (scored[k][0] is None, scored[k][0] or 0, k))
+ return ranked
+
+
+def _merge_pass(sample, order, refuse, doubt_min_side):
+ """Apply the merges in `order`, returning the union-find and the positions eligible for a
+ change link — the transactions whose inputs already stood in one cluster when they were
+ reached, which is a property of the order and so is returned with it rather than read
+ back off the finished partition."""
+ uf = UF()
+ size = {} # root -> addresses under it, so the doubt gate stays linear
+ eligible = set()
+ for tx_index in order:
+ tx, _ = sample[tx_index]
+ ins = in_addrs(tx)
+ # Do not bootstrap a change link from the CIOH edge currently under examination.
+ # Require earlier evidence that all inputs already belong to one cluster; otherwise a
+ # collaborative transaction could be mistaken for a unilateral spend.
+ if ins and all(a in uf.p for a in ins) and len({uf.find(a) for a in ins}) == 1:
+ eligible.add(tx_index)
+ if not refuse:
+ _union_tracking_size(uf, size, ins)
+ continue
+ if is_coinjoin(tx):
+ continue
+ if len(ins) < 2:
+ continue
+ parts = _demix_participants(tx)
+ if parts is None:
+ if doubt_min_side and (merge_objections(tx) or 0) > 0:
+ roots = {uf.find(a) for a in ins}
+ if sum(1 for r in roots if size.get(r, 1) >= doubt_min_side) >= 2:
+ continue # would fuse two established clusters on doubted evidence
+ _union_tracking_size(uf, size, ins)
+ continue
+ for group in parts.values():
+ first = group[0]
+ for a in group[1:]:
+ uf.union(first, a)
+ return uf, eligible
+
+
+def cluster_addresses(sample, refuse=True, change_link=False, staged=False,
+ doubt_min_side=0):
"""{address: cluster id} over the WHOLE sample. The lookup is global on purpose: it is
what lets a cluster keep one identity across the partition.
+ `staged` clusters the most conspicuous transactions first (`merge_order`) instead of in block
+ order. On its own that changes nothing — union-find over a fixed set of merges is
+ order-independent, and the one decision that reads the partition built so far, change
+ eligibility, is taken in sample order under either setting — so it is paired with
+ `doubt_min_side`, which is what the order is for: a transaction that argues against itself
+ (`merge_objections` above zero) is declined when its inputs already span two clusters of that
+ size or more, because that merge would fuse two established entities on evidence the
+ transaction itself undermines. The conspicuous merges run first precisely so that "already
+ established" means built from unambiguous evidence rather than from whatever the block order
+ happened to supply. Zero disables the gate. Both need an export carrying input values or
+ script types.
+
`refuse` declines to apply common-input ownership where the transaction itself argues
against it, which is the difference between the framework's competent adversary and the
one whose blind merging collapses clusters. Two rules, both decidable from the spending
@@ -67,24 +168,42 @@ def cluster_addresses(sample, refuse=True):
a two-day slice lie almost entirely outside it, and the roundness channel is gated on
the fingerprint disagreeing, so using it alone would refuse ordinary round payments.
"""
- uf = UF()
- for tx, _ in sample:
- if not refuse:
- union_input_addrs(tx, uf)
- continue
- if is_coinjoin(tx):
- continue
- ins = _in_addrs(tx)
- if len(ins) < 2:
- continue
- parts = _demix_participants(tx)
- if parts is None:
- union_input_addrs(tx, uf)
- continue
- for group in parts.values():
- first = group[0]
- for a in group[1:]:
- uf.union(first, a)
+ if staged and change_link:
+ # Eligibility is read off the partition standing before each transaction, so it is a
+ # property of the order it is read in. Take it in sample order, the same order the
+ # unstaged run takes it in, so that staging changes which merges are judged against
+ # which context and nothing else.
+ _, change_eligible = _merge_pass(sample, range(len(sample)), refuse, doubt_min_side)
+ uf, _ = _merge_pass(sample, merge_order(sample), refuse, doubt_min_side)
+ else:
+ order = merge_order(sample) if staged else range(len(sample))
+ uf, change_eligible = _merge_pass(sample, order, refuse, doubt_min_side)
+ if change_link:
+ # Optimal-change (the unnecessary-input heuristics, cit-15/16), collapse-safe: only link
+ # a *fresh* change address (never seen as an input) so it cannot merge two existing
+ # clusters -- it only extends one. Eligibility above is a prefix condition, so this pass
+ # is sensitive to the sample's own sequence: a different sequence is a different set of
+ # links, and only the staging flag is held not to move it.
+ from .change_special import label_optimal_change
+ from .change_gt import out_addr
+ input_seen = set()
+ for tx, _ in sample:
+ input_seen.update(in_addrs(tx))
+ for tx_index, (tx, _) in enumerate(sample):
+ if tx_index not in change_eligible:
+ continue
+ # Optimal-change is a unilateral-transaction heuristic. When transaction shape or
+ # amount de-mixing indicates multiple participants, input ordering cannot identify
+ # which participant owns the output, so abstain.
+ if is_coinjoin(tx) or _demix_participants(tx) is not None:
+ continue
+ ci = label_optimal_change(tx) # unique change index or None (abstains otherwise)
+ if ci is None:
+ continue
+ change = out_addr(tx, ci)
+ ins = in_addrs(tx)
+ if change and change not in input_seen and ins:
+ uf.union(ins[0], change)
return {a: uf.find(a) for g in uf.groups() for a in g}
@@ -117,9 +236,34 @@ def split_clusters(lookup, frac, rng, min_size=2):
return out, origin
+class _ViewLookup:
+ """One view's read of a split clustering: a straddling cluster answers under this view's
+ own pseudonym, every other cluster under its own id.
+
+ The tag is applied on lookup rather than materialised. Two per-view dicts would be twice
+ the largest structure in a multi-epoch run, and the base map is needed either way.
+ """
+ __slots__ = ("_base", "_split", "_suffix")
+
+ def __init__(self, base, split_cids, suffix):
+ self._base, self._split, self._suffix = base, split_cids, suffix
+
+ def get(self, address, default=None):
+ cid = self._base.get(address)
+ if cid is None:
+ return default
+ # The compact backend keys clusters by dense integer, the dict one by address.
+ return f"{cid}{self._suffix}" if cid in self._split else cid
+
+
+def view_lookup(lookup, split_cids, suffix):
+ """The lookup to contract one view with. `suffix` is "#a" or "#b"."""
+ return _ViewLookup(lookup, split_cids, suffix)
+
+
def split_clusters_by_view(lookup, view_a_addrs, frac, rng):
- """Fragment clusters along the view boundary: the addresses a cluster uses in view A
- become one pseudonym, the rest another.
+ """Choose the clusters that straddle the view boundary, so each view can contract them
+ under its own pseudonym. Returns `(split_cids, {pseudonym: original cluster})`.
`split_clusters` fragments by drawing addresses at random, which leaves both halves
present in both views, so the matcher can satisfy itself with the identity match and
@@ -127,22 +271,27 @@ def split_clusters_by_view(lookup, view_a_addrs, frac, rng):
pseudonym lives on each side, and the only correspondence available is the discovery.
It is also the natural form of the premise, a clustering that has failed to link a
user's activity across time.
+
+ Which side an address falls on cannot decide its tag globally. An address used in *both*
+ windows is in `view_a_addrs`, so tagging by that alone marks it `#a` everywhere and view
+ B ends up holding `C#a` as well as `C#b` — the identity match, structurally the better
+ one, graded wrong. Each view is therefore contracted under its own `view_lookup`, which
+ is also the framework's construction: each epoch is clustered separately.
"""
- members = {}
+ # Record only which side(s) each cluster touches. The previous implementation also built
+ # cid -> [all member addresses], duplicating the largest structure in a multi-epoch run.
+ sides = {}
for addr, cid in lookup.items():
- members.setdefault(cid, []).append(addr)
- out, origin = {}, {}
- for cid, addrs in members.items():
- left = [a for a in addrs if a in view_a_addrs]
- right = [a for a in addrs if a not in view_a_addrs]
- if not left or not right or rng.random() >= frac:
- out.update({a: cid for a in addrs})
+ sides[cid] = sides.get(cid, 0) | (1 if addr in view_a_addrs else 2)
+ split_cids = {cid for cid, side in sides.items() if side == 3 and rng.random() < frac}
+ origin = {}
+ for cid in set(lookup.values()):
+ if cid in split_cids:
+ origin[f"{cid}#a"] = cid
+ origin[f"{cid}#b"] = cid
+ else:
origin[cid] = cid
- continue
- for tag, part in ((f"{cid}#a", left), (f"{cid}#b", right)):
- origin[tag] = cid
- out.update({a: tag for a in part})
- return out, origin
+ return split_cids, origin
def _demix_participants(tx):
@@ -164,244 +313,3 @@ def _demix_participants(tx):
if addr:
groups.setdefault(participant, []).append(addr)
return {k: v for k, v in groups.items() if v} or None
-
-
-def partition_coins(sample, scheme="epoch", bounds=None, core_frac=0.01):
- """Split the sample into views, returning one list of sample indices per view.
-
- epoch by block height, the trivial partition the framework names as its
- example. `bounds` is [(lo, hi), ...]; without it the height range is
- halved.
- coinjoin_boundary a coinjoin is where co-spending stops implying common ownership, so
- it is a natural seam: transactions before and after form the views and
- the coinjoins themselves are the boundary, in no view.
- ambiguity_cut the framework's preferred scheme, the "opposite" of expander
- decomposition. See `ambiguity_partition`; note it needs two passes,
- so `sample` must be a list rather than an iterator here.
-
- A cut removes the boundary from the views and keeps the rest; it does not discard the
- vertices incident to it.
- """
- if scheme == "epoch":
- heights = [tx.get("height") or 0 for tx, _ in sample]
- if bounds is None:
- mid = (min(heights) + max(heights)) // 2
- bounds = [(min(heights), mid), (mid + 1, max(heights))]
- return [[i for i, h in enumerate(heights) if lo <= h <= hi] for lo, hi in bounds]
-
- if scheme == "coinjoin_boundary":
- seam = [i for i, (tx, _) in enumerate(sample) if is_coinjoin(tx)]
- cut = seam[len(seam) // 2] if seam else len(sample) // 2
- boundary = set(seam)
- return [[i for i in range(cut) if i not in boundary],
- [i for i in range(cut + 1, len(sample)) if i not in boundary]]
-
- if scheme == "ambiguity_cut":
- return ambiguity_partition(sample, sample, core_frac=core_frac)
-
- raise ValueError(f"unknown scheme: {scheme}")
-
-
-def ambiguity_partition(pass_one, pass_two, core_frac=0.01, n_views=2):
- """The framework's preferred partition, "the opposite of expander decomposition".
-
- Expander decomposition finds a *sparse* cut and leaves well-connected components. The
- opposite cuts through the dense core and leaves components that are relatively sparser,
- which is the regime the matching needs: in a dense region every vertex looks like its
- neighbours and identity is ambiguous, while a sparse neighbourhood is distinctive.
-
- The core is the busiest `core_frac` of addresses. Transactions touching it are the cut
- and join no view; what remains decomposes into connected components, and the largest
- `n_views` become the views. A cluster whose activity only reached across through the
- core now appears in two components under separate pseudonyms, which is exactly the
- correspondence the matcher exists to recover.
-
- Takes two independent iterators over the same transactions so a slice can be partitioned
- without being held in memory.
- """
- seen = Counter()
- for tx, _ in pass_one:
- for a in set(_in_addrs(tx)) | {a for a, _ in _out_addrs(tx)}:
- seen[a] += 1
- if not seen:
- return [[] for _ in range(n_views)]
- cut = max(1, int(len(seen) * core_frac))
- core = {a for a, _ in seen.most_common(cut)}
-
- uf = UF()
- members = {}
- for i, (tx, _) in enumerate(pass_two):
- addrs = set(_in_addrs(tx)) | {a for a, _ in _out_addrs(tx)}
- if not addrs or addrs & core:
- continue # the cut itself joins no view
- addrs = sorted(addrs)
- for a in addrs[1:]:
- uf.union(addrs[0], a)
- members[i] = addrs[0]
- comps = {}
- for i, anchor in members.items():
- comps.setdefault(uf.find(anchor), []).append(i)
- return sorted(comps.values(), key=len, reverse=True)[:n_views]
-
-
-class PseudonymGraph:
- """Contracted view. Vertices are clusters; `edges[(src, dst)]` is the single directed
- edge folding every transfer from src to dst, carrying how many there were and how much
- value moved. Value is kept because connectivity means a plausible flow, not merely a
- traceable one, so a matcher may discount an edge that only dust created."""
-
- def __init__(self):
- self.vertices = {}
- self.edges = {}
- self.edge_sig = {} # (src, dst) -> the axis values of its first transfer
- self.base_rates = {axis: Counter() for axis in AXES}
- self.skipped = Counter() # axis -> transactions it could not be read from
- self.unattributed = 0 # transactions whose transfers could not be attributed
- self._out = defaultdict(set)
- self._in = defaultdict(set)
-
- def _vertex(self, vid):
- return self.vertices.setdefault(
- vid, {"coins": 0, "txs": 0, "self_transfers": 0,
- "axes": {axis: Counter() for axis in AXES}})
-
- def neighbours(self, vid):
- return self._out[vid] | self._in[vid]
-
- def degree(self, vid):
- return len(self.neighbours(vid))
-
- def attribute(self, vid, axis):
- """The vertex's distribution over an axis, as a lift over this view's own base rate.
- 1.0 means the vertex looks exactly like its view; the normalisation is what makes
- the value comparable to a vertex measured in another view."""
- counts = self.vertices[vid]["axes"][axis]
- total = sum(counts.values())
- base_total = sum(self.base_rates[axis].values())
- if not total or not base_total:
- return {}
- out = {}
- for value, n in counts.items():
- base = self.base_rates[axis].get(value, 0) / base_total
- out[value] = (n / total) / base if base else float("inf")
- return out
-
-
-def transfer_counts(sample, lookup=None, min_value=0, max_sources=1):
- """How many transfers each cluster takes part in, in one cheap pass. Distinct degree is
- at most this, so thresholding on it never drops a vertex that would have cleared the
- same threshold on degree — which is what makes it safe as a pre-filter. `max_sources`
- must match the value `contract` will be called with, or the pre-filter counts edges the
- contraction never creates."""
- lookup = {} if lookup is None else lookup
- counts = Counter()
- for tx, _ in sample:
- ins = _in_addrs(tx)
- if not ins:
- continue
- srcs = {lookup.get(a, a) for a in ins}
- if len(srcs) > max_sources:
- g.unattributed += 1
- continue
- for addr, val in _out_addrs(tx):
- if val < min_value:
- continue
- dst = lookup.get(addr, addr)
- for s in srcs:
- if s != dst:
- counts[s] += 1
- counts[dst] += 1
- return counts
-
-
-def contract(sample, indices=None, lookup=None, min_value=0, axes=True, keep=None,
- max_sources=1):
- """Contract one view: fuse each cluster's coins into a vertex and fold the transfers
- between clusters into one attributed directed edge per ordered pair.
-
- An address absent from `lookup` is its own singleton pseudonym, which is the ordinary
- case under a partial clustering rather than an error. `min_value` drops transfers below
- a threshold, so a dust spray does not manufacture a relationship.
-
- `indices` selects from a materialised sample; passing None instead iterates `sample`
- directly, so a whole-epoch view can be streamed off disk without holding it in memory.
- `axes=False` skips the attribute counters, which dominate the vertex record: a matcher
- run that uses structure alone does not need them and a wide view may not have room.
-
- A vertex counts its coins rather than listing them; the addresses stay recoverable from
- the clustering lookup.
-
- `max_sources` bounds how many distinct source pseudonyms a transaction may have before
- its transfers are treated as unattributable and contribute no edges. An edge is supposed
- to be a transfer of bitcoin from one cluster to another; where several pseudonyms fund a
- transaction, which of them paid which output is exactly what is not observable, and
- asserting every source-destination pair invents relationships. In a coinjoin it invents
- the one relationship the construction is defined not to have, since its participants
- need no economic tie at all. Measured on this slice: 211 transactions with six or more
- source pseudonyms were generating 3.4 million of 4.4 million pairs, 77% of the graph.
- Transactions above the bound still register their vertices and their activity.
-
- `keep` restricts the graph to a vertex set, dropping edges with an endpoint outside it.
- Paired with `transfer_counts` this excludes the leaves — 57% of a contracted 2026 view
- sits at degree below two — which propagation can neither match nor bridge through, so
- the restriction leaves the matching unchanged while roughly halving the graph.
- """
- lookup = {} if lookup is None else lookup
- g = PseudonymGraph()
- pairs = sample if indices is None else (sample[i] for i in indices)
- for tx, _ in pairs:
- ins = _in_addrs(tx)
- if not ins:
- continue
- srcs = {lookup.get(a, a) for a in ins}
- if keep is not None:
- srcs &= keep
- if not srcs:
- continue
- sig = None
- if axes:
- sig = []
- for axis, fn in AXES.items():
- try:
- value = fn(tx)
- except Exception: # a malformed or partial tx, not a bug
- g.skipped[axis] += 1 # counted, never silent: an axis that
- continue # dies on every tx must be visible
- g.base_rates[axis][value] += 1
- sig.append(value)
- for s in srcs:
- g._vertex(s)["axes"][axis][value] += 1
- sig = tuple(sig)
- for s in srcs:
- v = g._vertex(s)
- v["txs"] += 1
- v["coins"] += sum(1 for a in ins if lookup.get(a, a) == s)
- if len(srcs) > max_sources:
- g.unattributed += 1
- continue
- for addr, val in _out_addrs(tx):
- if val < min_value:
- continue
- dst = lookup.get(addr, addr)
- if keep is not None and dst not in keep:
- continue
- g._vertex(dst)["coins"] += 1
- for s in srcs:
- if s == dst:
- g.vertices[s]["self_transfers"] += 1
- continue
- key = (s, dst)
- e = g.edges.setdefault(key, {"transfers": 0, "value": 0})
- e["transfers"] += 1
- e["value"] += val
- if axes and key not in g.edge_sig:
- # The framework wants the statistical features to inform the EDGE
- # attributes too, not only the vertices. Stored as the axis values of
- # the transfer that created the edge rather than a distribution over
- # all of them: 93% of edges carry a single transfer, so for almost all
- # of them the two are the same thing, and a Counter per edge is not
- # affordable at slice scale.
- g.edge_sig[key] = sig
- g._out[s].add(dst)
- g._in[dst].add(s)
- return g
diff --git a/decluster/weight_sensitivity.py b/decluster/weight_sensitivity.py
new file mode 100644
index 00000000..1f70fc92
--- /dev/null
+++ b/decluster/weight_sensitivity.py
@@ -0,0 +1,58 @@
+"""Sensitivity of attacker-side fingerprint evidence to one global weight."""
+
+from __future__ import annotations
+
+import math
+
+from .fingerprint_validate import LibraryScorer, evaluate
+
+GRID = (0.60, 0.70, 0.80, 0.90, 0.95, 0.99)
+
+
+def sweep(transactions, *, pair_cap=4000, seed=0):
+ """Evaluate one deterministic pair sample at each global consistency value."""
+ rows = []
+ for consistency in GRID:
+ measurement = evaluate(
+ transactions,
+ LibraryScorer(consistency=consistency),
+ cap=pair_cap,
+ seed=seed,
+ )
+ rows.append({"consistency": consistency, **measurement})
+ decrements = [left["auc"] - right["auc"]
+ for left, right in zip(rows, rows[1:]) if left["auc"] > right["auc"]]
+ largest = max(decrements, default=0.0)
+ error = auc_standard_error(max(row["auc"] for row in rows), pair_cap, pair_cap)
+ return {
+ "transactions": len(transactions),
+ "pair_cap_per_class": pair_cap,
+ "seed": seed,
+ "rows": rows,
+ "realistic_band_auc_range": max(row["auc"] for row in rows[3:])
+ - min(row["auc"] for row in rows[3:]),
+ "auc_is_monotone_non_decreasing": all(
+ left["auc"] <= right["auc"] for left, right in zip(rows, rows[1:])
+ ),
+ # A dip in the measured sequence is a fact about these numbers; whether it says anything
+ # about the underlying ordering depends on how it compares to what the sample resolves.
+ "auc_standard_error": error,
+ "largest_decrement": largest,
+ "largest_decrement_in_standard_errors": (largest / error) if error else None,
+ }
+
+def auc_standard_error(auc, positives, negatives):
+ """Hanley-McNeil standard error of an AUC, so a difference can be read against its resolution.
+
+ Without it a sweep reports that the measured sequence dips and a reader has no way to tell a
+ real reversal from sampling noise. On this grid the largest dip is a fifth of one standard
+ error, which does not falsify a monotone ordering; it fails to support one.
+ """
+ if not positives or not negatives:
+ return None
+ q1 = auc / (2 - auc)
+ q2 = 2 * auc ** 2 / (1 + auc)
+ variance = (auc * (1 - auc)
+ + (positives - 1) * (q1 - auc ** 2)
+ + (negatives - 1) * (q2 - auc ** 2)) / (positives * negatives)
+ return math.sqrt(max(variance, 0.0))
diff --git a/decluster/weighted_path_count.py b/decluster/weighted_path_count.py
new file mode 100644
index 00000000..d5b8e0b4
--- /dev/null
+++ b/decluster/weighted_path_count.py
@@ -0,0 +1,8 @@
+"""Canonical namespace for probability-weighted ancestry path accumulation.
+
+``decluster.path_count`` remains as a compatibility facade for existing callers.
+"""
+
+from .path_count import path_count_anonymity, provenance_route_accumulation
+
+__all__ = ["path_count_anonymity", "provenance_route_accumulation"]
diff --git a/dependencies/numpy-2.5.1-cp313-cp313-macosx_14_0_arm64.whl b/dependencies/numpy-2.5.1-cp313-cp313-macosx_14_0_arm64.whl
new file mode 100644
index 00000000..6b2f6f25
Binary files /dev/null and b/dependencies/numpy-2.5.1-cp313-cp313-macosx_14_0_arm64.whl differ
diff --git a/docs/reference-fidelity-audit.md b/docs/reference-fidelity-audit.md
new file mode 100644
index 00000000..b757a4fe
--- /dev/null
+++ b/docs/reference-fidelity-audit.md
@@ -0,0 +1,56 @@
+# Primary-source fidelity audit
+
+Checked 2026-09-04 against the primary texts and reference implementation linked below; the
+anonymity-metric and mixnet rows (Danezis, Troncoso, Serjantov–Danezis, Díaz, Syverson) were added
+2026-09-05. “Kernel” means the defining calculation is executable; “reproduction” additionally
+requires the paper's input construction, policy choices and expected output.
+
+Every source carrying an `obligation` in `catalog/ctp-sources.json` must have a row here, including
+the ones the repository honours by *declining* to implement something — an undeclared correct
+refusal is indistinguishable from an oversight.
+
+| Reference | Primary source | Local status | What remains |
+|---|---|---|---|
+| Fellegi–Sunter (1969) | [paper](https://nhis.ipums.org/nhis/resources/Fellegi69.pdf) | Kernel implemented: comparison vectors, conditional `m/u` log-likelihood weights, three-way decision and held-out evaluation. | The local Beta smoothing is an explicit estimation choice, not a paper result. Keep value-rarity scoring named separately. |
+| Narayanan–Shmatikov sparse data (2008) | [paper](https://arxiv.org/pdf/cs/0610105) | Rarity-weighted record matching is an adapted application. | Do not call Bitcoin AUCs a reproduction of Netflix Algorithm 1B; its auxiliary-information model, `1/log |supp(i)|`, eccentricity and exponential score distribution form one contract. |
+| Narayanan–Shmatikov social graphs (2009) | [paper](https://arxiv.org/pdf/0903.3276) | Directional, degree-normalized propagation score, eccentricity and reverse match are implemented and run on synthetic and Bitcoin views. A bounded leave-one-out revisit pass preserves seeds and bijection, but is explicitly a local policy because the paper does not specify its schedule or conflict resolution. | Seed clique discovery and a reproducible paper-defined correction policy are missing; Twitter/Flickr/LiveJournal results are not reproduced. The module is a propagation kernel, not the full attack. |
+| Narayanan–Shi–Rubinstein link prediction (2011) | [paper](https://arxiv.org/pdf/1102.4374) | Algorithm 1's crawl-aware directed cosine, both threshold policies, a reproducible two-stage driver with stage-1 feedback and non-feeding stage-2 candidates, Algorithm 3's deterministic/vote/ML cascade, and Algorithm 2's positive-weight distance plus swap/cooling annealer are executable. A typed coverage gate prevents these components from being promoted to an end-to-end reproduction. A smaller local adjacency-transfer experiment remains separately named. | Partial. The pseudocode's ratio is undefined for zero weights even though dummy incident weights are zero; the implementation refuses that case instead of inventing smoothing. Annealing has explicit local reproducibility controls. Revisiting/correction, confidence pruning and the 25-feature random forest remain not reproduced. |
+| Maurer–Neudecker–Florian (2017) | [paper](https://m.flrn.cc/academic/2017_CoinJoin.pdf) | Exact mappings and the paper's non-derived selection are executable; Figure 2 (`21,12,36,28 → 25,8,50,14`) is a regression fixture. | Reproduce the output-splitting experiments/figures if their empirical claim is needed. Fees are deliberately outside this model. |
+| LaurentMT / Boltzmann | [metrics](https://gist.github.com/LaurentMT/e758767ca4038ac40aaf), [LPM](https://gist.github.com/LaurentMT/d361bca6dc52868573a2), [reference code](https://github.com/Samourai-Wallet/boltzmann) | The dependency-free port reproduces default `LINKABILITY`, fee multiplicity, asymmetric vectors and `MERGE_FEES`. Known-owner input groups are transitively packed, traversed and expanded in reference order. `PRECHECK` exposes the aggregate deterministic-link probe without changing the exhaustive result. Explicit maker/taker intrafee bounds reproduce the reference matching interval and disable precheck. The older set-valued fee model remains separately named. | There is no reliable `MERGE_OUTPUTS` contract to port from revision `ed0b649c`: upstream marks it work in progress, warns that results are not always reliable, and the shared packer only searches inputs. Preserve this as an upstream limitation unless a corrected normative revision is selected. |
+| Goldfeder et al. (2018) | [paper](https://arxiv.org/pdf/1708.04748) | Algorithm 2's core is executable: join-only ancestry to `r`, address-cluster lift, intersection, unique-or-refuse. | JoinMarket detection, recursive clustering, 2015–2017 simulation and 21-purchase validation are not reproduced. `decluster/intersect.py` remains an adapted probabilistic pipeline, not Algorithm 2. |
+| Kelen–Seres (2022) | [paper](https://arxiv.org/pdf/2211.04259) — local corpus is **v2 (2024-06-01)**; v1 renumbers these sections | Equation 1 transitions, auxiliary-source materialization, absorption probabilities, Shannon untraceability, `t=N1` expected steps, and stationary/temporal account transforms are implemented. Figure 2's `5:3:2 → .5:.3:.2` transition is pinned. The temporal transform splits on receipts, carries prior balance and requires explicit pre-window balances. | Tables 1–2 are not reproduced. The published Bitcoin table requires its full chain interval, not a depth-limited walk. |
+| Danezis, *Statistical Disclosure Attacks* (2003) | [paper](https://www.freehaven.net/anonbib/cache/statistical-disclosure.pdf) | Section 3's threshold-mix estimator is executable on a deterministic fixture with a background-matched control (`failure_modes/statistical_disclosure.py`). Target activity and the background distribution are supplied, not estimated. | **§3.1 is not implemented.** The CTP sentence this source carries (`decluster.md:675-677`) is about the *rate* at which clustering certainty is gained and about eventual revelation — and §3.1 is the only part of the paper that quantifies either: the signal-to-noise condition `m < N/(b−1)` for the attack to apply at all, and the bound on observations `t` for a stated confidence (`l=2` → 95%, `l=3` → 99%). Neither the SNR condition nor the rounds-to-confidence bound is computed anywhere in the repository. `baselines/candidate_set_intersection.py` now names both in prose — correctly, to deny a constant-factor shrink law — so §3.1 is cited as the right source while remaining unimplemented. Until one of the two is computed, no claim about *how fast* disclosure proceeds has a local basis. |
+| Troncoso et al., *Perfect Matching Disclosure Attacks* (2008) | [paper](https://www.freehaven.net/anonbib/cache/troncoso-pet2008.pdf) | PMDA's joint maximum-weight perfect-matching objective is executable as an exact enumerator over one round, with a tied uniform-profile control (`failure_modes/perfect_matching_disclosure.py`). The divergence from the paper's `log(0) = −inf` convention — abstaining where the paper still returns a matching — is declared in the module docstring and in the report limitations. | **NSDA, the paper's other contribution, is not implemented** (zero occurrences in the source tree). Sender profiles are supplied as fixture data; their longitudinal estimation is delegated to the statistical-disclosure module and is therefore subject to that row's gap. The solver is brute-force `itertools.permutations` capped at 8 messages, not a scalable assignment algorithm — so the paper's empirical simulations and success rates are out of reach by construction, not merely unrun. |
+| Serjantov–Danezis, *Towards an Information Theoretic Metric for Anonymity* (2002) | [paper](https://bib.mixnetworks.org/pdf/serjantov2002towards.pdf) | **Adopted.** Every entropy in the repository is the paper's non-normalized effective-anonymity-set size in bits — `−Σ p log₂ p`, or `log₂(count)` under a uniform prior (`graph_metric.partition_entropy` and `effective_cluster_count`, `boltzmann.exact_link_analysis`, `candidate_set_intersection.narrowing_bits`, `oracle_audit.approx_mapping_entropy_bits` and `exact_mapping_entropy_bits`, `failure_modes.entropy_insufficiency.entropy_bits`, `failure_modes.eve_alice_eve`'s returns-to-bits conversion). This is the correct choice for the repository's purpose: it stays on a bit scale that composes with the rest of the evidence apparatus. | **Not attributed anywhere.** No entropy-producing module names this paper, and after the removal of the unsustainable "Danezis and Serjantov's statistical-disclosure result" line from `candidate_set_intersection.py`, the source tree contains **zero** mentions of Serjantov — the single remaining hit is `statistical_disclosure.py:122`, which is Danezis alone and a different paper. A reader cannot tell from the code which metric convention is in force or why. |
+| Díaz, Seys, Claessens & Preneel, *Towards Measuring Anonymity* (2002) | [paper](https://www.freehaven.net/anonbib/cache/Diaz02.pdf) | The paper's underlying requirement — an explicit probability distribution before any anonymity number — is honoured: every entropy site takes a stated distribution or an explicit uniform prior. | **The paper's actual metric, the normalized degree of anonymity `d = H/H_M`, is never computed** — there is no division by a maximum entropy anywhere in `decluster/**/*.py`. Declining it is the right call given PAPER §1: normalizing by `H_M` produces a 0–1 number that reads as a privacy score, which is exactly what the repository refuses to emit. **The reasoning is now written where the entropies are produced** — `decluster/intersect.py:104-108` (`H_M = log2(N)` needs a fixed candidate population `N`, and a coin's candidate set has none; "dividing by a quantity that moves with the measurement would report method changes as privacy changes") and `decluster/ancestry.py:452-454` (a truncated walk has no fixed population to put in `H_M`). The source carries `obligation: metric` in `catalog/ctp-sources.json`, and the non-adoption is no longer indistinguishable from an oversight. |
+| Syverson, *Why I'm not an entropist* | [paper](https://www.freehaven.net/anonbib/cache/entropist.pdf) — **no body in the local corpus**, reference only | **Obligation met.** The refusal to read entropy as privacy holds across the repository: a full sweep for privacy-score phrasing returns denials at every hit — `ancestry.py:14,448`, `anonymity_set.py:5`, `domain/report.py:48`, eleven `experiments/*.py` modules, the matching `catalog/runs/*.json` limitation strings, and a dedicated falsifying fixture (`ctp.entropy.not_sufficient` → `failure_modes/entropy_insufficiency.py`). No mechanism in the repository is sold as a privacy proof. | The paper itself is not in the corpus — only the footnote (`decluster.md:1457`) and a passing mention inside Scroll #4 (`decluster.md:22377`). The obligation is discharged against the *thesis* as summarized in the CTP, not against a local reading of the text. Anyone extending the argument beyond "entropy is not sufficient" must open the primary source first. |
+
+## Consequences for the plan
+
+1. Maurer's “paper cases” is no longer wholly blocked: the defining Figure 2 case and non-derived
+ semantics are now tested. Its experimental output-splitting section remains separate work.
+2. Goldfeder's abstract intersection cell is now an Algorithm 2 kernel, but the existing real-chain
+ `intersect.py` measurement cannot be relabeled as the paper's reproduction.
+3. Boltzmann's default aggregate-traversal multiplicity is now reproduced separately. The
+ set-valued fee model still intentionally disagrees and must not be described as Boltzmann;
+ `PRECHECK` and the intrafee matching predicate are now explicit. `MERGE_OUTPUTS` remains open
+ because the pinned upstream implementation does not supply reliable semantics to reproduce.
+4. Kelen–Seres' UTXO transition, explicit source nodes, absorption distribution, expected absorption
+ time and account/time transforms are executable. “Kelen–Seres complete” remains false without
+ the paper's data-scale table reproduction.
+5. The 2011 cell is partial: Algorithms 1 and 3, both threshold policies, and Algorithm 2 on its
+ mathematically defined positive-weight domain are pinned. Dummy-node zero semantics, the
+ revisiting/pruning policies and learned-score producer remain open. Its actual composite is
+ materially different from `baselines/link_prediction.py`.
+6. The five anonymity-metric and mixnet rows added 2026-09-05 were previously absent although all
+ five carry an `obligation` in `catalog/ctp-sources.json`. Two are clean — Syverson's is met, and
+ Serjantov–Danezis' metric is adopted — but neither was written down, so the repository was
+ getting no credit for choices it had actually made correctly. The two real gaps are Danezis §3.1,
+ which is the only quantitative basis for the CTP's claim about the *rate* of disclosure, and the
+ undeclared non-adoption of Díaz's `d = H/H_M`. Both are documentation-shaped, not code-shaped:
+ §3.1 is a closed-form condition and a closed-form bound on `t`, and the Díaz item needs one
+ sentence saying the normalization is refused because a 0–1 number reads as a privacy score.
+7. Kelen–Seres' section citations are only well-defined against a revision. The local corpus block
+ is arXiv **v2 (2024-06-01)**; v1 carries a different title and numbers the same material §3.4 and
+ §4. `kelen_seres_graphs.py`'s "Section 2.4" and "Section 3" are correct under v2. Per-source
+ revision and block SHA-256 are now recorded in the corpus `provenance.md`.
diff --git a/examples/amount_channel_survey.py b/examples/amount_channel_survey.py
new file mode 100644
index 00000000..6f4276df
--- /dev/null
+++ b/examples/amount_channel_survey.py
@@ -0,0 +1,117 @@
+"""What each arm of the amount channel actually says on a real slice.
+
+The channel has four arms and, until now, no measurement of how often any of them speaks on real
+transactions: the coinjoin de-mix, the unnecessary-input heuristic, the 2-in/2-out sub-transaction
+re-partition, and the subset-sum multiplicity W(E). Each is refuse-only, so what matters is the
+*abstention rate* — an arm that never fires cannot be carrying the weight its prose implies.
+
+Needs an export with prevout values and script types; the graph-scale exports carry neither.
+
+usage: python3 examples/amount_channel_survey.py [max_txs]
+"""
+import json
+import os
+import sys
+from collections import Counter
+
+sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+from decluster import views
+from decluster.coinjoin_demix import coinjoin_demix
+from decluster.conservation import forced_in_round
+from decluster.counting import guaranteed_log_w, w_total
+from decluster.extractors import x_uih
+from decluster.monitor import is_coinjoin
+from decluster.subtransaction import subtransactions
+
+
+def load(path, cap):
+ """Read either NDJSON or a JSON array, coercing the numeric fields some exports stringify."""
+ raw = open(path).read().lstrip()
+ rows = json.loads(raw) if raw.startswith("[") else [
+ json.loads(line) for line in raw.splitlines() if line.strip()]
+
+ def num(x):
+ return int(x) if x is not None else None
+
+ out = []
+ for tx in rows[:cap]:
+ if tx.get("height") is not None:
+ tx["height"] = num(tx["height"])
+ for v in tx.get("vin") or []:
+ p = v.get("prevout") or {}
+ if p.get("value") is not None:
+ p["value"] = num(p["value"])
+ for o in tx.get("vout") or []:
+ if o.get("value") is not None:
+ o["value"] = num(o["value"])
+ out.append(tx)
+ return out
+
+
+def complete(tx):
+ """Whether the amount channel can read this transaction at all."""
+ vin = tx.get("vin") or []
+ return bool(vin) and all((v.get("prevout") or {}).get("value") is not None for v in vin) \
+ and all(o.get("value") is not None for o in tx.get("vout") or [])
+
+
+def main(path, cap=10 ** 9):
+ txs = load(path, cap)
+ usable = [t for t in txs if complete(t)]
+ multi = [t for t in usable if len(t["vin"]) >= 2]
+ print(f"{path}: {len(txs):,} txs, {len(usable):,} with complete amounts, "
+ f"{len(multi):,} multi-input", flush=True)
+
+ demixed = shape = 0
+ for tx in multi:
+ if is_coinjoin(tx):
+ shape += 1
+ if views._demix_participants(tx) is not None:
+ demixed += 1
+ print(f"\ncoinjoin detector fires on {shape:,} ({shape / len(multi):.3%} of multi-input)")
+ print(f"de-mix resolves >=2 participants on {demixed:,} "
+ f"({demixed / len(multi):.3%}) — the arm's real contribution to refusal")
+
+ uih = Counter(x_uih(t) for t in multi)
+ fires = len(multi) - uih.get("none", 0)
+ print(f"\nunnecessary-input heuristic fires on {fires:,} ({fires / len(multi):.2%})")
+
+ two = [t for t in usable if len(t["vin"]) == 2 and len(t.get("vout") or []) == 2]
+ decided = amb = 0
+ for tx in two:
+ ranked, bits = subtransactions(tx)
+ if not ranked:
+ continue
+ decided += 1
+ if len(ranked) > 1 and ranked[0][1] == ranked[1][1]:
+ amb += 1 # top two equally round: the re-partition does not decide
+ print(f"\n2-in/2-out transactions: {len(two):,}; the re-partition ranks {decided:,} of them, "
+ f"and {amb:,} ({amb / decided if decided else 0:.2%}) tie at the top")
+
+ kinds = Counter()
+ guaranteed = 0
+ for tx in multi[:2000]:
+ res = w_total([v["prevout"]["value"] for v in tx["vin"]],
+ [o["value"] for o in tx["vout"]])
+ kinds[str(res.get("kind"))] += 1
+ if guaranteed_log_w(res) is not None:
+ guaranteed += 1
+ counted = sum(kinds.values())
+ print(f"\nsubset-sum W(E) over {counted:,} multi-input transactions:")
+ for kind, n in kinds.most_common():
+ print(f" {kind:>12}: {n:>6,} {n / counted:>7.2%}")
+ print(f" usable as a lower bound (exact or lower-bound tier): {guaranteed:,} "
+ f"({guaranteed / counted:.2%})")
+
+ forced_any = 0
+ for tx in multi[:2000]:
+ largest = max(v["prevout"]["value"] for v in tx["vin"])
+ if forced_in_round(tx, largest):
+ forced_any += 1
+ print(f"\nconservation forces at least one output value on {forced_any:,} of "
+ f"{min(len(multi), 2000):,} multi-input transactions, taking the largest input as the "
+ f"known participant ({forced_any / min(len(multi), 2000):.2%})")
+
+
+if __name__ == "__main__":
+ main(sys.argv[1], int(sys.argv[2]) if len(sys.argv) > 2 else 10 ** 9)
diff --git a/examples/analyze_ambiguity.py b/examples/analyze_ambiguity.py
new file mode 100644
index 00000000..8f345333
--- /dev/null
+++ b/examples/analyze_ambiguity.py
@@ -0,0 +1,89 @@
+"""Faithful local social-graph test: the framework's *ambiguity-cut* partition (the opposite
+of expander decomposition) on a single slice, matched with the full matcher (NS'09 revisit).
+
+Unlike an epoch split, ambiguity-cut works within one slice: it removes the dense core and
+leaves relatively sparse components, so a cluster whose activity crossed the core appears in
+two components with different neighbourhoods. Re-linking those is the de-anonymization, and
+the same-owner labels are natural (one global cluster id present in two components) rather than an
+artificial split. Fits a small machine on a ~1M-transaction slice.
+
+usage: python3 examples/analyze_ambiguity.py [max_txs] [n_views] [core_frac]
+"""
+import sys
+import os
+import json
+import gzip
+import random
+
+sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+from decluster import contraction, graph_shape, view_partition, views
+from decluster.view_match import ViewMatcher
+
+
+def load(path, cap):
+ op = gzip.open(path, "rt") if path.endswith(".gz") else open(path)
+ sample = []
+ with op as f:
+ for line in f:
+ if len(sample) >= cap:
+ break
+ if line.strip():
+ sample.append((json.loads(line), 0))
+ return sample
+
+
+def active_graph(sample, indices, lookup):
+ full = contraction.contract(sample, indices=indices, lookup=lookup, axes=False)
+ keep = {v for v in full.vertices if full.degree(v) >= 2}
+ del full
+ return contraction.contract(sample, indices=indices, lookup=lookup, axes=False, keep=keep)
+
+
+def main(path, cap=1_000_000, n_views=2, core_frac=0.01):
+ print(f"loading up to {cap} txs from {path}", flush=True)
+ sample = load(path, cap)
+ print(f" {len(sample)} txs", flush=True)
+
+ print("clustering (CIOH, refuse-guarded)...", flush=True)
+ lookup = views.cluster_addresses(sample, refuse=True)
+ print(f" {len(set(lookup.values()))} clusters over {len(lookup)} addresses", flush=True)
+
+ print(f"ambiguity-cut partition (core_frac={core_frac}, n_views={n_views})...", flush=True)
+ parts = view_partition.partition_coins(sample, scheme="ambiguity_cut", n_views=n_views, core_frac=core_frac)
+ print(f" {len(parts)} components, sizes {[len(p) for p in parts]}", flush=True)
+
+ graphs = []
+ for k, idxs in enumerate(parts):
+ g = active_graph(sample, idxs, lookup)
+ a = graph_shape.assortativity(g)
+ a = f"{a:.3f}" if isinstance(a, (int, float)) else "n/a"
+ print(f" component {k}: active V={sum(1 for _ in g.vertices)} assort={a}", flush=True)
+ graphs.append(g)
+
+ rng = random.Random(0)
+ for i in range(len(graphs)):
+ for j in range(i + 1, len(graphs)):
+ ga, gb = graphs[i], graphs[j]
+ overlap = sorted(set(ga.vertices) & set(gb.vertices))
+ print(f"\n== components {i} <-> {j}: {len(overlap)} clusters cross the cut ==", flush=True)
+ if len(overlap) < 20:
+ print(" too few to evaluate", flush=True)
+ continue
+ keys = sorted(overlap, key=lambda v: -(ga.degree(v) + gb.degree(v)))
+ for sf in (0.05, 0.10):
+ sn = max(2, int(len(keys) * sf))
+ seeds = set(keys[:sn])
+ m = ViewMatcher(revisit=True).match(ga, gb, {v: v for v in seeds})
+ guesses = {u: w for u, w in m.items() if u in set(overlap) and u not in seeds}
+ correct = sum(1 for u, w in guesses.items() if w == u)
+ prec = correct / len(guesses) if guesses else 0.0
+ rec = correct / (len(overlap) - len(seeds)) if len(overlap) > len(seeds) else 0.0
+ print(f" seed {sf:.0%} ({sn}): guessed {len(guesses)} correct {correct} "
+ f"precision {prec:.3f} recall {rec:.3f}", flush=True)
+
+
+if __name__ == "__main__":
+ main(sys.argv[1],
+ int(sys.argv[2]) if len(sys.argv) > 2 else 1_000_000,
+ int(sys.argv[3]) if len(sys.argv) > 3 else 2,
+ float(sys.argv[4]) if len(sys.argv) > 4 else 0.01)
diff --git a/examples/analyze_decore.py b/examples/analyze_decore.py
new file mode 100644
index 00000000..17b9b3f2
--- /dev/null
+++ b/examples/analyze_decore.py
@@ -0,0 +1,115 @@
+"""Local social-graph test across partition schemes, so the cut criterion can be compared rather
+than assumed.
+
+Every scheme produces two or more views of one slice, `split_clusters_by_view` gives a straddling
+entity a distinct pseudonym per view, and the matcher must rejoin them from structure alone. The
+matcher runs with `revisit=True` and is fed the UNFILTERED degrees (`stat_a`/`stat_b`) so the
+active-user pre-filter stays neutral (the code documents a ~0.09 precision loss otherwise).
+
+ epoch the trivial temporal cut, the framework's own example of a weak one.
+ decore drop the busiest `core_frac` of addresses as ambiguous noise, then split by height.
+ collapse cut along the cluster-collapse events themselves -- the merges that would fuse two
+ already-substantial clusters -- then split by height.
+
+usage: python3 examples/analyze_decore.py [max_txs] [--scheme S] [--n-views N]
+"""
+import sys
+import os
+import json
+import gzip
+import random
+
+sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+from decluster import contraction, graph_shape, tx_addrs, view_partition, views
+from decluster.view_match import ViewMatcher
+
+
+def load(path, cap):
+ op = gzip.open(path, "rt") if path.endswith(".gz") else open(path)
+ s = []
+ with op as f:
+ for line in f:
+ if len(s) >= cap:
+ break
+ if line.strip():
+ s.append((json.loads(line), 0))
+ return s
+
+
+def contract_with_stat(sample, indices, lookup):
+ """Degrees first, then contract once keeping only the active (degree>=2) vertices.
+ Returns (active_graph, {vertex: unfiltered_degree})."""
+ degrees = contraction.contract_degrees(sample, indices=indices, lookup=lookup)
+ keep = {v for v, d in degrees.items() if d >= 2}
+ stat = {v: degrees[v] for v in keep}
+ del degrees
+ return contraction.contract(sample, indices=indices, lookup=lookup, axes=False, keep=keep), stat
+
+
+def main(path, cap=1_000_000, core_frac=0.01, scheme="collapse", n_views=2, min_side=2):
+ print(f"loading up to {cap} txs from {path}", flush=True)
+ sample = load(path, cap)
+ print(f" {len(sample)} txs", flush=True)
+
+ lookup = views.cluster_addresses(sample, refuse=True)
+ print(f" {len(set(lookup.values()))} clusters over {len(lookup)} addresses", flush=True)
+
+ parts = view_partition.partition_coins(sample, scheme=scheme, core_frac=core_frac,
+ n_views=n_views, min_side=min_side)
+ dropped = len(sample) - sum(len(p) for p in parts)
+ print(f" scheme={scheme}: views {[len(p) for p in parts]} txs, {dropped} at the boundary",
+ flush=True)
+ parts = parts[:2] # the matcher compares a pair; n>2 reports the split only
+ aA = set()
+ for i in parts[0]:
+ tx = sample[i][0]
+ aA.update(tx_addrs.in_addrs(tx))
+ aA.update(a for a, _ in tx_addrs.out_addrs(tx))
+
+ rng = random.Random(0)
+ split_cids, origin = views.split_clusters_by_view(lookup, aA, frac=1.0, rng=rng)
+
+ ga, stat_a = contract_with_stat(sample, parts[0], views.view_lookup(lookup, split_cids, "#a"))
+ gb, stat_b = contract_with_stat(sample, parts[1], views.view_lookup(lookup, split_cids, "#b"))
+ assort = graph_shape.assortativity(ga)
+ assort = f"{assort:.3f}" if isinstance(assort, (int, float)) else "n/a"
+ Bb = {v for v in gb.vertices if isinstance(v, str) and v.endswith("#b")}
+ truth = {u: f"{origin[u]}#b" for u in ga.vertices
+ if isinstance(u, str) and u.endswith("#a") and f"{origin[u]}#b" in Bb}
+ # go/no-go metric (cit-24): propagation needs edges AMONG the straddlers, not just their count
+ strad = set(truth)
+ edges_between = sum(1 for (s, d) in ga.edges if s in strad and d in strad)
+ with_nbr = sum(1 for u in strad if any(n in strad for n in ga.neighbours(u)))
+ mean_deg = (sum(1 for u in strad for n in ga.neighbours(u) if n in strad) / len(strad)) if strad else 0
+ print(f" split pseudonym pairs to rejoin: {len(truth)} [A] V={sum(1 for _ in ga.vertices)} assort={assort}", flush=True)
+ print(f" straddler subgraph: {edges_between} edges, mean straddler-degree {mean_deg:.2f}, "
+ f"{with_nbr}/{len(truth)} non-isolated (propagation needs this dense)", flush=True)
+ if len(truth) < 20:
+ print(" too few to evaluate", flush=True)
+ return
+
+ keys = sorted(truth, key=lambda u: -(ga.degree(u) + gb.degree(truth[u])))
+ for sf in (0.05, 0.10):
+ sn = max(2, int(len(keys) * sf))
+ seeds = set(keys[:sn])
+ m = ViewMatcher(revisit=True).match(ga, gb, {u: truth[u] for u in seeds},
+ stat_a=stat_a, stat_b=stat_b)
+ guesses = {u: w for u, w in m.items() if u in truth and u not in seeds}
+ correct = sum(1 for u, w in guesses.items() if w == truth[u])
+ prec = correct / len(guesses) if guesses else 0.0
+ rec = correct / (len(truth) - len(seeds)) if len(truth) > len(seeds) else 0.0
+ print(f" seed {sf:.0%} ({sn}): guessed {len(guesses)} correct {correct} "
+ f"precision {prec:.3f} recall {rec:.3f}", flush=True)
+
+
+if __name__ == "__main__":
+ import argparse
+ ap = argparse.ArgumentParser()
+ ap.add_argument("path")
+ ap.add_argument("max_txs", nargs="?", type=int, default=1_000_000)
+ ap.add_argument("--scheme", default="collapse", choices=("epoch", "decore", "collapse"))
+ ap.add_argument("--core-frac", type=float, default=0.01)
+ ap.add_argument("--n-views", type=int, default=2)
+ ap.add_argument("--min-side", type=int, default=2)
+ a = ap.parse_args()
+ main(a.path, a.max_txs, a.core_frac, a.scheme, a.n_views, a.min_side)
diff --git a/examples/analyze_multiepoch.py b/examples/analyze_multiepoch.py
new file mode 100644
index 00000000..027709be
--- /dev/null
+++ b/examples/analyze_multiepoch.py
@@ -0,0 +1,258 @@
+"""Cross-view re-identification across many temporal epochs, sized for a small machine. Each monthly
+file is split by block height into `weeks` sub-epochs; for each consecutive pair the pair is clustered
+on its own (CIOH over just the two windows, streamed), the clusters are split along the pair boundary
+into a view-A and a view-B pseudonym (`split_clusters_by_view`, the incomplete-clustering premise),
+each view is contracted, and the matcher must rejoin the two from structure alone. Contracts are freed
+as soon as their degree filter is read, so peak memory is one full plus one pruned graph. Reports
+precision/recall per pair, so the trend across the year is visible.
+
+usage: python3 examples/analyze_multiepoch.py epoch_A.ndjson [epoch_B.ndjson ...]
+"""
+import sys
+import os
+import json
+import gzip
+import random
+import resource
+import time
+
+
+def _open(path):
+ return gzip.open(path, "rt") if path.endswith(".gz") else open(path)
+
+sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+from decluster import contraction, graph_shape, tx_addrs, views
+from decluster.view_match import ViewMatcher, find_seeds
+from decluster.scale_cluster import cluster_scale_np_stream
+from decluster.monitor import COINJOIN_MIN_PARTICIPANTS
+from decluster.entities import detect_mining_pool
+from decluster.ns_bitcoin import run_bitcoin_views, unique_entity_seeds
+
+
+def stream_window(spec):
+ path, lo, hi = spec[:3]
+ cap = spec[3] if len(spec) > 3 else None
+ emitted = 0
+ with _open(path) as f:
+ for line in f:
+ if not line.strip():
+ continue
+ tx = json.loads(line)
+ h = tx.get("height", 0)
+ if lo <= h <= hi:
+ yield tx, 0
+ emitted += 1
+ if cap is not None and emitted >= cap:
+ break
+
+
+def height_range(path):
+ lo, hi = None, None
+ with _open(path) as f:
+ for line in f:
+ if not line.strip():
+ continue
+ h = json.loads(line).get("height", 0)
+ lo = h if lo is None else min(lo, h)
+ hi = h if hi is None else max(hi, h)
+ return lo, hi
+
+
+def windows_for(path, weeks, cap=None):
+ lo, hi = height_range(path)
+ step = max(1, (hi - lo + 1) // weeks)
+ out = []
+ b = lo
+ while b <= hi:
+ out.append((path, b, min(b + step - 1, hi), cap))
+ b += step
+ return out
+
+
+def cluster_windows(specs, coinjoin_min=COINJOIN_MIN_PARTICIPANTS):
+ txs = (tx for spec in specs for tx, _ in stream_window(spec))
+ return cluster_scale_np_stream(txs, coinjoin_min=coinjoin_min)
+
+
+def addrs_of(spec):
+ s = set()
+ for tx, _ in stream_window(spec):
+ s.update(tx_addrs.in_addrs(tx))
+ s.update(a for a, _v in tx_addrs.out_addrs(tx))
+ return s
+
+
+def stage(label, started):
+ rss = resource.getrusage(resource.RUSAGE_SELF).ru_maxrss
+ print(f" stage {label}: {time.monotonic() - started:.1f}s, max_rss={rss / 1e6:.1f} MB",
+ flush=True)
+ return time.monotonic()
+
+
+def _active_contract(spec, split):
+ degrees = contraction.contract_degrees(stream_window(spec), lookup=split) # unfiltered, for the matcher
+ keep = {v for v, d in degrees.items() if d >= 2}
+ # The matcher only ever asks about vertices that survived the filter, so the dropped
+ # leaves' degrees are dead weight — on a complete weekly view, two thirds of the map.
+ stat = {v: degrees[v] for v in keep}
+ del degrees
+ return contraction.contract(stream_window(spec), lookup=split, axes=False, keep=keep), stat
+
+
+def independent_pool_labels(spec, lookup):
+ """Public coinbase self-labels; no withheld cross-view mapping is consulted."""
+ labels = []
+ for tx, _ in stream_window(spec):
+ hit = detect_mining_pool(tx)
+ if hit and hit["payout_addr"]:
+ address = hit["payout_addr"]
+ labels.append((hit["entity"], lookup.get(address, address)))
+ return labels
+
+
+def pair(sa, sb, rng, frac=1.0, controls=False, auto_seeds=False,
+ faithful_ns=False):
+ mark = time.monotonic()
+ glob = cluster_windows([sa, sb])
+ mark = stage("cluster", mark)
+ aA = addrs_of(sa)
+ mark = stage("view-a-addresses", mark)
+ look = glob.map_many(aA)
+ aB = addrs_of(sb)
+ look.update(glob.map_many(aB))
+ del aB
+ mark = stage("global-lookup", mark)
+ split_cids, origin = views.split_clusters_by_view(look, aA, frac=frac, rng=rng)
+ del aA, glob
+ mark = stage("split", mark)
+ # Each view contracts under its own tag, so a cluster split across the boundary appears
+ # only as #a in view A and only as #b in view B.
+ lookup_a = views.view_lookup(look, split_cids, "#a")
+ lookup_b = views.view_lookup(look, split_cids, "#b")
+ ga, stat_a = _active_contract(sa, lookup_a)
+ mark = stage("contract-a", mark)
+ gb, stat_b = _active_contract(sb, lookup_b)
+ mark = stage("contract-b", mark)
+ Bb = {v for v in gb.vertices if isinstance(v, str) and v.endswith("#b")}
+ truth = {u: f"{origin[u]}#b" for u in ga.vertices
+ if isinstance(u, str) and u.endswith("#a") and f"{origin[u]}#b" in Bb}
+ straddlers = set(truth)
+ internal_degree = {u: sum(1 for n in ga.neighbours(u) if n in straddlers)
+ for u in straddlers}
+ internal_edges = sum(internal_degree.values()) / 2
+ mean_internal = (sum(internal_degree.values()) / len(straddlers)
+ if straddlers else 0.0)
+ nonisolated = sum(degree > 0 for degree in internal_degree.values())
+ recurring_support = {}
+ for u, image in truth.items():
+ mapped_a_neighbours = {truth[n] for n in ga.neighbours(u) if n in truth}
+ recurring_support[u] = len(mapped_a_neighbours & set(gb.neighbours(image)))
+ mean_support = (sum(recurring_support.values()) / len(recurring_support)
+ if recurring_support else 0.0)
+ support_one = sum(value >= 1 for value in recurring_support.values())
+ support_four = sum(value >= 4 for value in recurring_support.values())
+ # Does any relationship in this graph repeat at all? The cross-view attack needs neighbourhoods
+ # that recur; if an edge almost never folds more than one transfer, there is nothing to recur.
+ def repeat_profile(g, keys):
+ keys = [k for k in keys if k in g.edges]
+ if not keys:
+ return 0, 0, 0.0
+ multi = [g.edges[k] for k in keys if g.edges[k]["transfers"] > 1]
+ span = sum(e["last"] - e["first"] for e in multi) / len(multi) if multi else 0.0
+ return len(keys), len(multi), span
+
+ all_e, all_multi, all_span = repeat_profile(ga, list(ga.edges))
+ strad_keys = [(u, v) for (u, v) in ga.edges if u in straddlers and v in straddlers]
+ st_e, st_multi, st_span = repeat_profile(ga, strad_keys)
+ a = graph_shape.assortativity(ga)
+ a = f"{a:.3f}" if isinstance(a, (int, float)) else "n/a"
+ print(f" pairs to rejoin {len(truth)} [A] V={sum(1 for _ in ga.vertices)} assort={a}", flush=True)
+ print(f" straddler subgraph: edges={internal_edges:.0f} mean_degree={mean_internal:.3f} "
+ f"nonisolated={nonisolated}/{len(straddlers)}", flush=True)
+ print(f" cross-view recurring support: mean={mean_support:.3f} "
+ f">=1={support_one}/{len(straddlers)} >=4={support_four}/{len(straddlers)}", flush=True)
+ print(f" repeat relationships [A]: all edges {all_multi}/{all_e} fold >1 transfer "
+ f"({all_multi / all_e if all_e else 0:.2%}, mean span {all_span:.0f} blocks); "
+ f"straddler edges {st_multi}/{st_e} "
+ f"({st_multi / st_e if st_e else 0:.2%}, mean span {st_span:.0f})", flush=True)
+ if len(truth) < 20:
+ print(" too few to evaluate", flush=True)
+ return
+ if faithful_ns:
+ # Seed discovery is kept causally upstream of `truth`: the public entity name and
+ # each view's own clustering are the only inputs. Ambiguous entity-to-vertex labels
+ # abstain rather than being paired with the withheld correspondence.
+ seeds = unique_entity_seeds(independent_pool_labels(sa, lookup_a),
+ independent_pool_labels(sb, lookup_b))
+ measurable = {u: v for u, v in seeds.items() if truth.get(u) == v}
+ conflicts = len(seeds) - len(measurable)
+ print(f" faithful N-S independent seeds: {len(seeds)} "
+ f"({len(measurable)} agree with withheld correspondence, {conflicts} conflicts)",
+ flush=True)
+ if conflicts:
+ print(" faithful N-S not run: independently derived seed conflicts with grading map",
+ flush=True)
+ elif not seeds:
+ print(" faithful N-S not identifiable: no unique public entity spans both views",
+ flush=True)
+ else:
+ result = run_bitcoin_views(ga, gb, truth, seeds)
+ print(f" faithful N-S: {result}", flush=True)
+ keys = sorted(truth, key=lambda u: -(ga.degree(u) + gb.degree(truth[u])))
+ if auto_seeds:
+ automatic = find_seeds(ga, gb)
+ measurable = {u: v for u, v in automatic.items() if u in truth}
+ auto_correct = sum(v == truth[u] for u, v in measurable.items())
+ print(f" automatic seeds: {len(automatic)} total, {len(measurable)} measurable, "
+ f"{auto_correct} correct", flush=True)
+ for sf in (0.05, 0.10):
+ sn = max(2, int(len(keys) * sf))
+ seeds = set(keys[:sn])
+ seed_map = {u: truth[u] for u in seeds}
+ shuffled_images = list(seed_map.values())
+ rng.shuffle(shuffled_images)
+ shuffled = dict(zip(seed_map, shuffled_images))
+ configurations = ((False, "truth", seed_map), (False, "shuffle", shuffled),
+ (True, "truth", seed_map), (True, "shuffle", shuffled)) \
+ if controls else ((True, "truth", seed_map),)
+ for directional, label, supplied in configurations:
+ m = ViewMatcher(revisit=True, directional=directional, min_common=4).match(
+ ga, gb, supplied, stat_a=stat_a, stat_b=stat_b)
+ guesses = {u: w for u, w in m.items() if u in truth and u not in seeds}
+ correct = sum(1 for u, w in guesses.items() if w == truth[u])
+ prec = correct / len(guesses) if guesses else 0.0
+ rec = correct / (len(truth) - len(seeds)) if len(truth) > len(seeds) else 0.0
+ print(f" seed {sf:.0%} ({sn}) {label} directional={directional}: "
+ f"guessed {len(guesses)} correct {correct} precision {prec:.3f} "
+ f"recall {rec:.3f}", flush=True)
+
+
+def main(weeks, files, cap=None, controls=False, auto_seeds=False, faithful_ns=False):
+ specs = []
+ for f in files:
+ specs.extend(windows_for(f, weeks, cap))
+ print(f"{len(specs)} sub-epochs over {len(files)} files ({weeks}/file)", flush=True)
+ for i in range(len(specs) - 1):
+ sa, sb = specs[i], specs[i + 1]
+ print(f"\n== pair {i}: {os.path.basename(sa[0])}[{sa[1]}-{sa[2]}] -> "
+ f"{os.path.basename(sb[0])}[{sb[1]}-{sb[2]}] ==", flush=True)
+ pair(sa, sb, random.Random(0), controls=controls, auto_seeds=auto_seeds,
+ faithful_ns=faithful_ns)
+
+
+if __name__ == "__main__":
+ import argparse
+ parser = argparse.ArgumentParser()
+ parser.add_argument("weeks", type=int)
+ parser.add_argument("files", nargs="+")
+ parser.add_argument("--max-txs", type=int, default=None,
+ help="cap transactions per sub-epoch for staged local validation")
+ parser.add_argument("--controls", action="store_true",
+ help="also run undirected and shuffled-seed controls")
+ parser.add_argument("--auto-seeds", action="store_true",
+ help="run the costly experimental exact-signature seed bootstrap")
+ parser.add_argument("--faithful-ns", action="store_true",
+ help="run topology-only N-S with unique public-entity seeds")
+ args = parser.parse_args()
+ main(args.weeks, args.files, args.max_txs, args.controls, args.auto_seeds,
+ args.faithful_ns)
diff --git a/examples/analyze_slice_a.py b/examples/analyze_slice_a.py
new file mode 100644
index 00000000..9e32495b
--- /dev/null
+++ b/examples/analyze_slice_a.py
@@ -0,0 +1,100 @@
+"""Run the two Collection-A measurements on a real, committed-scale mainnet slice:
+
+ 1. Entity-level (epsilon,delta)-sparsity (Def 1). Cluster addresses (CIOH, refuse-guarded),
+ contract the whole slice into a pseudonym graph, and measure the survival curve of each
+ entity's nearest-neighbour cosine. A sparse space stays near zero until epsilon is high:
+ the precondition Theorem 2 turns into de-anonymizability.
+
+ 2. Cross-view re-identification (the social-graph channel, cit 24). Split the slice into two
+ epoch views, contract each, seed a fraction of the entities present in both, and let the
+ structural/attribute matcher propagate. Precision/recall are reported *alongside* the
+ graph_shape diagnostic of each view, so a weak match reads as "the contracted graph is not
+ social enough here", not as an unexplained failure.
+
+usage: python3 examples/analyze_slice_a.py slice_a_2016.ndjson [max_lines]
+"""
+import sys
+import os
+import json
+import random
+
+sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+from decluster import contraction, def1_sparsity, graph_shape, view_partition, views
+from decluster.view_match import ViewMatcher
+
+
+def load(path, cap=None):
+ sample = []
+ with open(path) as f:
+ for line in f:
+ if cap and len(sample) >= cap:
+ break
+ try:
+ tx = json.loads(line)
+ except Exception:
+ continue
+ sample.append((tx, int(tx.get("height", 0))))
+ return sample
+
+
+def _f(x, d=3):
+ return f"{x:.{d}f}" if isinstance(x, (int, float)) else "n/a"
+
+
+def shape_line(g, tag):
+ s = graph_shape.summary(g)
+ return (f" [{tag}] V={s['vertices']} E={s['edges']} ={_f(s['mean_degree'],2)} "
+ f"deg1={_f(s['degree_1_share'])} assort={_f(s['assortativity'])} "
+ f"trans={_f(s['transitivity'],4)} cfg={_f(s['configuration_transitivity'],4)} "
+ f"ratio={_f(s['transitivity_ratio'],2)} tail={_f(s['tail_exponent'],2)}")
+
+
+def main(path, cap=None):
+ print(f"loading {path}" + (f" (cap {cap})" if cap else ""), flush=True)
+ sample = load(path, cap)
+ print(f" {len(sample)} txs", flush=True)
+
+ print("clustering addresses (CIOH, refuse-guarded)...", flush=True)
+ lookup = views.cluster_addresses(sample, refuse=True)
+ print(f" {len(set(lookup.values()))} clusters over {len(lookup)} addresses", flush=True)
+
+ # ---- 1. entity-level (epsilon, delta)-sparsity ----
+ print("\n== Def 1: entity-level sparsity ==", flush=True)
+ g_full = contraction.contract(sample, lookup=lookup, axes=True)
+ print(f" contracted: {len(list(g_full.vertices))} vertices", flush=True)
+ print(shape_line(g_full, "full"), flush=True)
+ tops = def1_sparsity.nearest_similarities(g_full, query_n=2000, background_n=20000, min_degree=1, seed=0)
+ surv = def1_sparsity.survival(tops)
+ print(f" nearest-neighbour survival delta(epsilon) over {len(tops)} entities:", flush=True)
+ for e in sorted(surv):
+ print(f" epsilon={e:>4}: delta={surv[e]:.3f}", flush=True)
+
+ # ---- 2. cross-view re-identification ----
+ print("\n== Social graph: cross-view re-identification (epoch split) ==", flush=True)
+ parts = view_partition.partition_coins(sample, scheme="epoch")
+ ga = contraction.contract(sample, indices=parts[0], lookup=lookup, axes=True)
+ gb = contraction.contract(sample, indices=parts[1], lookup=lookup, axes=True)
+ va, vb = set(ga.vertices), set(gb.vertices)
+ overlap = sorted(va & vb)
+ print(f" view A={len(va)} vtx view B={len(vb)} vtx overlap={len(overlap)} entities", flush=True)
+ print(shape_line(ga, "A"), flush=True)
+ print(shape_line(gb, "B"), flush=True)
+ if len(overlap) < 20:
+ print(" overlap too small to matcher-evaluate", flush=True)
+ return
+ rng = random.Random(0)
+ for seed_frac in (0.05, 0.10):
+ seed_n = max(2, int(len(overlap) * seed_frac))
+ seeds = set(rng.sample(overlap, seed_n))
+ seed_map = {v: v for v in seeds}
+ m = ViewMatcher().match(ga, gb, seed_map)
+ guesses = {u: w for u, w in m.items() if u not in seeds}
+ correct = sum(1 for u, w in guesses.items() if w == u)
+ prec = correct / len(guesses) if guesses else 0.0
+ rec = correct / (len(overlap) - len(seeds)) if len(overlap) > len(seeds) else 0.0
+ print(f" seed {seed_frac:.0%} ({seed_n}): guessed {len(guesses)} correct {correct} "
+ f"precision {prec:.3f} recall {rec:.3f}", flush=True)
+
+
+if __name__ == "__main__":
+ main(sys.argv[1], int(sys.argv[2]) if len(sys.argv) > 2 else None)
diff --git a/examples/attribute_ablation.py b/examples/attribute_ablation.py
index f2de15f6..beb1944e 100644
--- a/examples/attribute_ablation.py
+++ b/examples/attribute_ablation.py
@@ -19,7 +19,8 @@
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from decluster.view_match import ViewMatcher
-from decluster.views import cluster_addresses, contract
+from decluster.contraction import contract
+from decluster.views import cluster_addresses
from examples.view_match_run import pseudonymise, stream
ECC = 5.0
diff --git a/examples/attribute_drift.py b/examples/attribute_drift.py
index 2ee76ca5..bd16b989 100644
--- a/examples/attribute_drift.py
+++ b/examples/attribute_drift.py
@@ -20,25 +20,15 @@
import json
import sys
-GAPS = (1, 2, 3, 7, 14, 21, 30, 60, 90, 120)
-CYCLE = 7
-
-
-def normalise(counts):
- total = sum(counts.values())
- return {k: v / total for k, v in counts.items()} if total else {}
-
-
-def total_variation(p, q):
- return 0.5 * sum(abs(p.get(k, 0.0) - q.get(k, 0.0)) for k in set(p) | set(q))
-
-
-def pearson(xs, ys):
- n = len(xs)
- mx, my = sum(xs) / n, sum(ys) / n
- num = sum((x - mx) * (y - my) for x, y in zip(xs, ys))
- den = (sum((x - mx) ** 2 for x in xs) * sum((y - my) ** 2 for y in ys)) ** 0.5
- return num / den if den else 0.0
+from decluster.attribute_drift import (
+ CYCLE,
+ GAPS,
+ cycle_gain,
+ drift,
+ normalise,
+ pearson,
+ total_variation,
+)
def load(path):
@@ -51,24 +41,6 @@ def load(path):
return volume, axes
-def drift(series, gaps):
- return {g: sum(total_variation(series[i], series[i + g])
- for i in range(len(series) - g)) / (len(series) - g)
- for g in gaps if g < len(series)}
-
-
-def cycle_gain(series, span=3):
- """Mean drift at multiples of the weekly cycle vs at every other gap in the same
- range. Below 1.0 means same-weekday epochs really are more comparable."""
- curve = drift(series, range(1, CYCLE * span + 1))
- on = [v for g, v in curve.items() if g % CYCLE == 0]
- off = [v for g, v in curve.items() if g % CYCLE]
- baseline = sum(off) / len(off) if off else 0.0
- if not baseline:
- return 1.0
- return (sum(on) / len(on)) / baseline
-
-
def main(path):
volume, axes = load(path)
n = len(volume)
diff --git a/examples/bayes_vs_fs.py b/examples/bayes_vs_fs.py
index 93439f12..a51bb636 100644
--- a/examples/bayes_vs_fs.py
+++ b/examples/bayes_vs_fs.py
@@ -1,145 +1,13 @@
-"""Bayesian (Gibbs) vs Fellegi-Sunter comparison on decluster's per-axis evidence. On the same reuse
-pairs, scores three ways under the shared per-field likelihood — F-S(0.95), F-S(EM), Bayesian(integrated
-m) — and reports AUC (tie control), ECE (calibration), the per-axis m posterior vs EM/oracle/0.95, and a
-cluster-level entity-count band in two regimes: clear address-reuse structure (band collapses, Bayesian =
-F-S) and ambiguous borderline structure (a genuine band — the uncertainty a point estimate hides). Offline.
-Run from repo root: python3 examples/bayes_vs_fs.py. See results/RESULTS-bayes-vs-fs.md."""
-import sys
-import os
-import random
-
-sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
-
-
-def _auc_split(probs, n_pos):
- from decluster.graph_deanon import auc
- return auc(probs[:n_pos], probs[n_pos:], 0)
-
-
-def _entity_counts(A, mask, u, m_samples, lam, node_pairs, n_nodes, seed, cap=200):
- """For a subsample of m draws, sample edges ~ pair P(match) and count connected components."""
- from decluster.fs_bayes import pair_probs
- from decluster.unionfind import UF
- rng = random.Random(seed)
- counts = []
- for m in m_samples[:cap]:
- pm = pair_probs(A, mask, m, u, lam)
- uf = UF(range(n_nodes))
- for (i, j), p in zip(node_pairs, pm):
- if rng.random() < p:
- uf.union(i, j)
- counts.append(len(uf.groups()))
- return counts
-
-
-def _cluster_band(node_txs, axes, u, m_samples, lam, em_m):
- """Bayesian entity-count posterior band (mean, 2.5%, 97.5%) over node_txs, vs the F-S(EM) point
- partition (edges thresholded at 0.5)."""
- from decluster.fs_em import agree_matrix
- from decluster.fs_bayes import pair_probs
- from decluster.unionfind import UF
- n = len(node_txs)
- node_pairs = [(i, j) for i in range(n) for j in range(i + 1, n)]
- nA, nmask, _, _ = agree_matrix([(node_txs[i], node_txs[j]) for (i, j) in node_pairs], axes)
- c = sorted(_entity_counts(nA, nmask, u, m_samples, lam, node_pairs, n, seed=0))
- band = (sum(c) / len(c), c[int(0.025 * (len(c) - 1))], c[int(0.975 * (len(c) - 1))])
- pm = pair_probs(nA, nmask, em_m, u, 0.5)
- uf = UF(range(n))
- for (i, j), p in zip(node_pairs, pm):
- if p >= 0.5:
- uf.union(i, j)
- return band, len(uf.groups())
-
+"""Print the canonical Bayesian versus Fellegi-Sunter comparison."""
-def _reuse_nodes(txs, cap=50):
- """Clear-structure node set: coins from address-reuse groups (bimodal P(match) -> confident partition)."""
- from decluster.change_gt import input_addrs
- by_addr = {}
- for tx in txs:
- for a in sorted(input_addrs(tx)): # sorted -> PYTHONHASHSEED-independent
- by_addr.setdefault(a, {})[tx["txid"]] = tx
- groups = [list(g.values()) for g in by_addr.values() if len(g) >= 2]
- node_txs = []
- for g in groups:
- if len(node_txs) >= cap:
- break
- node_txs.extend(g[:3])
- return node_txs[:cap]
-
-
-def _ambiguous_nodes(txs, axes, m, u, pool_n=300, size=18, seed=1):
- """Ambiguous-structure node set: coins whose pairwise fingerprint agreement is *borderline*
- (intermediate P(match)) — the regime where the owner-partition is genuinely uncertain. Chosen sparse
- (low intermediate-degree) to sit near the percolation threshold, so the entity-count posterior is
- non-degenerate. Deterministic under seed + PYTHONHASHSEED-independent sampling."""
- from decluster.fs_em import agree_matrix
- from decluster.fs_bayes import pair_probs
- from collections import defaultdict
- rng = random.Random(seed)
- pool = rng.sample(txs, min(pool_n, len(txs)))
- n = len(pool)
- pA, pmask, _, _ = agree_matrix([(pool[i], pool[j]) for i in range(n) for j in range(i + 1, n)], axes)
- pm = pair_probs(pA, pmask, m, u, 0.5)
- deg = defaultdict(int)
- k = 0
- for i in range(n):
- for j in range(i + 1, n):
- if 0.2 <= pm[k] <= 0.8:
- deg[i] += 1
- deg[j] += 1
- k += 1
- cand = sorted(deg, key=lambda z: deg[z]) # sparse (low intermediate-degree) first
- step = max(1, len(cand) // size)
- return [pool[i] for i in cand[::step][:size]]
-
-
-def main(cap=4000):
- from decluster.fingerprint_validate import load_blkcache, reuse_pairs, LibraryScorer
- from decluster.fs_em import agree_matrix, em_fit, oracle_m
- from decluster.fs_bayes import gibbs_fit, pair_probs, ece
-
- txs = load_blkcache()
- pos, neg = reuse_pairs(txs, cap=cap, seed=0)
- pairs = pos + neg
- labels = [1] * len(pos) + [0] * len(neg)
- axes = LibraryScorer().axes
- A, mask, names, u = agree_matrix(pairs, axes)
-
- em = em_fit(A, mask, u)
- om = oracle_m(A, mask, labels)
- bayes = gibbs_fit(A, mask, u)
-
- p_fs95 = pair_probs(A, mask, [0.95] * len(u), u, 0.5)
- p_fsem = pair_probs(A, mask, em["m"], u, 0.5)
- p_bayes = bayes["p_match"]
-
- print("# Bayesian (Gibbs) vs Fellegi-Sunter on %d txs; %d+%d reuse pairs (per-field likelihood)\n"
- % (len(txs), len(pos), len(neg)))
- print("# AUC (tie control — same evidence):")
- for name, p in (("F-S(0.95)", p_fs95), ("F-S(EM)", p_fsem), ("Bayesian", p_bayes)):
- print(" %-10s AUC %.4f ECE %.4f" % (name, _auc_split(p, len(pos)), ece(p, labels)))
+from pathlib import Path
+import sys
- print("\n# per-axis m: Bayesian posterior (mean [95%% CI]) vs EM vs oracle vs assumed")
- print("%-22s %-20s %8s %8s %8s" % ("axis", "Bayesian mean[CI]", "EM", "oracle", "assumed"))
- for i, nm in enumerate(names):
- lo, hi = bayes["m_ci"][i]
- oj = ("%.3f" % om[i]) if om[i] is not None else "n/a"
- flag = " <-- 0.95 outside CI" if not (lo <= 0.95 <= hi) else ""
- print("%-22s %.3f[%.3f,%.3f] %8.3f %8s %8.2f%s"
- % (nm, bayes["m_mean"][i], lo, hi, em["m"][i], oj, 0.95, flag))
+sys.path.insert(0, str(Path(__file__).resolve().parents[1]))
- # cluster-level: F-S gives a point; the Bayesian gives a posterior band. Two regimes.
- print("\n# cluster-level entity count (F-S = a point; Bayesian = a posterior band over %d draws):"
- % min(200, len(bayes["m_samples"])))
- rn = _reuse_nodes(txs)
- rb, rb_fs = _cluster_band(rn, axes, u, bayes["m_samples"], bayes["lam_mean"], em["m"])
- print(" clear structure (%2d reuse-linked nodes): Bayesian mean %.1f band [%d, %d] F-S(EM) point %d"
- % (len(rn), rb[0], rb[1], rb[2], rb_fs))
- an = _ambiguous_nodes(txs, axes, em["m"], u)
- ab, ab_fs = _cluster_band(an, axes, u, bayes["m_samples"], bayes["lam_mean"], em["m"])
- print(" ambiguous structure (%2d borderline nodes): Bayesian mean %.1f band [%d, %d] F-S(EM) point %d"
- % (len(an), ab[0], ab[1], ab[2], ab_fs))
+from decluster.experiments.bayes_vs_fs import build_artifact, render_markdown
if __name__ == "__main__":
- main()
+ snapshot = Path(__file__).resolve().parents[1] / "data" / "fs-blkcache-2026-09-04.tar.gz"
+ print(render_markdown(build_artifact(snapshot)), end="")
diff --git a/examples/boltzmann_fee_audit.py b/examples/boltzmann_fee_audit.py
new file mode 100644
index 00000000..cb900a44
--- /dev/null
+++ b/examples/boltzmann_fee_audit.py
@@ -0,0 +1,121 @@
+"""Compare exact conservation, explicit fee allocation, and roundness on local transactions.
+
+This is a measurement of decluster's local bounded enumerator, not parity with Boltzmann.
+usage: python3 examples/boltzmann_fee_audit.py [sample.ndjson] [--cap 300] [--manifest]
+"""
+import argparse
+import json
+import os
+import sys
+from collections import Counter
+
+sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+from decluster import reproducibility
+from decluster.baselines.boltzmann import exact_link_analysis, fee_tolerant_link_analysis
+from decluster.measure import load_ndjson
+from decluster.subtransaction import roundness
+
+ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+DOC = "RESULTS-boltzmann-fee-audit.md"
+
+
+def values(tx):
+ vin = tx.get("vin") or []
+ vout = tx.get("vout") or []
+ inputs = [(item.get("prevout") or {}).get("value") for item in vin]
+ outputs = [item.get("value") for item in vout]
+ if len(inputs) < 2 or not outputs or any(value is None for value in inputs + outputs):
+ return None
+ return tuple(inputs), tuple(outputs)
+
+
+def audit(path, cap=300, max_coins=8):
+ selected = []
+ for tx, _height in load_ndjson(path):
+ pair = values(tx)
+ if pair is None or sum(map(len, pair)) > max_coins:
+ continue
+ selected.append((tx.get("txid"), *pair))
+ if len(selected) == cap:
+ break
+
+ rows = []
+ for txid, inputs, outputs in selected:
+ fee = sum(inputs) - sum(outputs)
+ exact = exact_link_analysis(inputs, outputs, max_coins=max_coins)
+ tolerant = (fee_tolerant_link_analysis(inputs, outputs, fee_tolerance=fee,
+ max_coins=max_coins)
+ if fee >= 0 else None)
+ rows.append({
+ "txid": txid,
+ "inputs": len(inputs), "outputs": len(outputs), "fee": fee,
+ "fee_roundness": roundness(fee),
+ "exact_mappings": len(exact.mappings),
+ "fee_tolerant_mappings": len(tolerant.mappings) if tolerant is not None else None,
+ })
+
+ fees = [row["fee"] for row in rows]
+ return {
+ "configuration": {"cap": cap, "max_coins": max_coins},
+ "population": {
+ "selected": len(rows),
+ "nonnegative_fee": sum(fee >= 0 for fee in fees),
+ "positive_fee": sum(fee > 0 for fee in fees),
+ "round_fee": sum(row["fee_roundness"] > 0 for row in rows),
+ },
+ "outcomes": {
+ "exact_with_mapping": sum(row["exact_mappings"] > 0 for row in rows),
+ "fee_tolerant_with_mapping": sum((row["fee_tolerant_mappings"] or 0) > 0
+ for row in rows),
+ "fee_tolerant_with_nontrivial_split": sum(
+ (row["fee_tolerant_mappings"] or 0) > 1 for row in rows
+ ),
+ "max_fee_tolerant_mappings": max(
+ (row["fee_tolerant_mappings"] or 0 for row in rows), default=0
+ ),
+ "round_fee_and_nontrivial_split": sum(
+ row["fee_roundness"] > 0 and (row["fee_tolerant_mappings"] or 0) > 1
+ for row in rows
+ ),
+ "mapping_count_relation": dict(sorted(Counter(
+ "unavailable" if row["fee_tolerant_mappings"] is None else
+ "equal" if row["exact_mappings"] == row["fee_tolerant_mappings"] else
+ "fee_tolerant_more" if row["fee_tolerant_mappings"] > row["exact_mappings"] else
+ "exact_more"
+ for row in rows
+ ).items())),
+ },
+ "rows": rows,
+ }
+
+
+def invariants(report):
+ return {**report["configuration"], **report["population"], **report["outcomes"]}
+
+
+def parser():
+ out = argparse.ArgumentParser()
+ out.add_argument("path", nargs="?", default="sample.ndjson")
+ out.add_argument("--cap", type=int, default=300)
+ out.add_argument("--max-coins", type=int, default=8)
+ # No default path: the canonical output belongs to a run manifest, and an example that
+ # writes into results/ by default is how five unowned files got there.
+ out.add_argument("--out", default=None)
+ out.add_argument("--manifest", action="store_true")
+ return out
+
+
+def main(argv=None):
+ args = parser().parse_args(argv)
+ report = audit(os.path.join(ROOT, args.path), args.cap, args.max_coins)
+ print(json.dumps(report, indent=2, sort_keys=True))
+ if args.out:
+ with open(os.path.join(ROOT, args.out), "w") as handle:
+ json.dump(report, handle, indent=2, sort_keys=True)
+ handle.write("\n")
+ if args.manifest:
+ reproducibility.write_manifest(DOC, args.path, invariants(report), root=ROOT)
+
+
+if __name__ == "__main__":
+ main()
diff --git a/examples/bq_chunk_download.py b/examples/bq_chunk_download.py
new file mode 100644
index 00000000..24315e77
--- /dev/null
+++ b/examples/bq_chunk_download.py
@@ -0,0 +1,64 @@
+"""Download a wide BigQuery slice WITHOUT billing/GCS, by chunking into small block ranges that each
+fit under the `bq query` download limit, then concatenating to one ndjson. For Collection A (wide
+multi-epoch slices → social graph / def1). Free-tier query only (no bq extract, no bucket).
+
+usage: python3 examples/bq_chunk_download.py
+e.g.: python3 examples/bq_chunk_download.py 2016-02-01 400000 400600 20 slice_a.ndjson
+Resumable: appends; a re-run with the same out re-fetches only missing chunks (dedup by txid on load).
+"""
+import sys
+import os
+import json
+import subprocess
+
+TEMPLATE = """SELECT `hash` AS txid, block_number AS height,
+ ARRAY(SELECT AS STRUCT STRUCT(i.addresses[SAFE_OFFSET(0)] AS scriptpubkey_address) AS prevout FROM UNNEST(inputs) AS i) AS vin,
+ ARRAY(SELECT AS STRUCT o.addresses[SAFE_OFFSET(0)] AS scriptpubkey_address FROM UNNEST(outputs) AS o) AS vout
+FROM `bigquery-public-data.crypto_bitcoin.transactions`
+WHERE block_timestamp_month = '{month}' AND block_number BETWEEN {lo} AND {hi} AND NOT is_coinbase"""
+
+
+def run_chunk(month, lo, hi):
+ q = TEMPLATE.format(month=month, lo=lo, hi=hi)
+ raw = subprocess.run(["bq", "query", "--use_legacy_sql=false", "--format=json",
+ "--max_rows=500000", q], capture_output=True, text=True, timeout=110)
+ if raw.returncode != 0:
+ raise RuntimeError(raw.stderr[:200])
+ return json.loads(raw.stdout) if raw.stdout.strip() else []
+
+
+def main(month, lo, hi, chunk, out):
+ seen = set()
+ if os.path.exists(out):
+ for line in open(out):
+ try:
+ seen.add(json.loads(line)["txid"])
+ except Exception:
+ pass
+ print(f"resume: {len(seen)} txs already downloaded", flush=True)
+ with open(out, "a") as f:
+ b = lo
+ while b <= hi:
+ top = min(b + chunk - 1, hi)
+ try:
+ rows = run_chunk(month, b, top)
+ except Exception as e:
+ print(f" blocks {b}-{top}: FAILED ({e}); shrink chunk if this repeats", flush=True)
+ b = top + 1
+ continue
+ new = 0
+ for r in rows:
+ if r["txid"] in seen:
+ continue
+ r["height"] = int(r["height"])
+ f.write(json.dumps(r) + "\n")
+ seen.add(r["txid"])
+ new += 1
+ f.flush()
+ print(f" blocks {b}-{top}: +{new} txs ({len(seen)} total)", flush=True)
+ b = top + 1
+ print(f"done: {len(seen)} txs in {out}", flush=True)
+
+
+if __name__ == "__main__":
+ main(sys.argv[1], int(sys.argv[2]), int(sys.argv[3]), int(sys.argv[4]), sys.argv[5])
diff --git a/examples/cluster_scale.py b/examples/cluster_scale.py
deleted file mode 100644
index 8a82d74d..00000000
--- a/examples/cluster_scale.py
+++ /dev/null
@@ -1,64 +0,0 @@
-"""Offline scale measurement of the whole-corpus merge-only baseline (cluster.build_cospend_lookup):
-entity reduction over the value-carrying downloads + a window-local vs whole-corpus contrast. No
-network. Run from anywhere: python3 examples/cluster_scale.py
-Reads ~/Downloads/bquxjob_*.json. See results/RESULTS-cluster-scale.md."""
-import glob
-import os
-import sys
-
-sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
-
-
-def _load_all_txs():
- from decluster.measure import load_unique
- paths = sorted(glob.glob(os.path.expanduser("~/Downloads/bquxjob_*.json")))
- return [tx for tx, _ in load_unique(paths)]
-
-
-def _groups(lookup):
- g = {}
- for node, cid in lookup.items():
- g.setdefault(cid, []).append(node)
- return list(g.values())
-
-
-def main():
- from decluster.cluster import build_cospend_lookup
- from decluster.graph_metric import partition_entropy, effective_cluster_count, largest_cluster_frac
- txs = _load_all_txs()
- print("# corpus: %d unique txs from the downloads\n" % len(txs))
-
- lk = build_cospend_lookup(txs)
- g = _groups(lk)
- print("# whole-corpus multi-input baseline:")
- print(" funders=%d -> entities=%d (%.1f%% collapse) entropy=%.2f bits eff_clusters=%.1f largest=%.4f"
- % (len(lk), len(g), 100 * (1 - len(g) / len(lk)), partition_entropy(g),
- effective_cluster_count(g), largest_cluster_frac(g)))
-
- print("\n# scale curve (corpus fraction -> entities/funder; contiguous chains collapse, scattered ones don't):")
- print("%-8s %10s %10s %10s %12s" % ("frac", "txs", "funders", "entities", "ent/funder"))
- for frac in (0.1, 0.25, 0.5, 0.75, 1.0):
- sub = txs[:max(1, int(len(txs) * frac))]
- ls = build_cospend_lookup(sub); gs = _groups(ls)
- print("%-8.2f %10d %10d %10d %12.4f"
- % (frac, len(sub), len(ls), len(gs), (len(gs) / len(ls)) if ls else 0.0))
-
- window = txs[:max(1, len(txs) // 10)]
- lk_win = build_cospend_lookup(window)
- win_funders = set(lk_win)
-
- def entities_over(funders, lookup):
- cids = {}
- for f in funders:
- cids.setdefault(lookup.get(f, ("_solo", f)), []).append(f)
- return len(cids)
-
- e_local = entities_over(win_funders, lk_win)
- e_whole = entities_over(win_funders, lk)
- print("\n# window-local vs whole-corpus baseline on the SAME %d window funders:" % len(win_funders))
- print(" window-local=%d whole-corpus=%d (%d extra merges from co-spends outside the window)"
- % (e_local, e_whole, e_local - e_whole))
-
-
-if __name__ == "__main__":
- main()
diff --git a/examples/collect_2016_epochs.py b/examples/collect_2016_epochs.py
new file mode 100644
index 00000000..4c95405f
--- /dev/null
+++ b/examples/collect_2016_epochs.py
@@ -0,0 +1,39 @@
+"""Collect the twelve contiguous monthly epochs of 2016 for the multi-epoch cross-view test, one
+`collect_month` run each (one BigQuery scan per month, free paginated download). Month-level resume:
+a completed month writes an `.done` marker and is skipped on restart, so an interrupted or
+quota-paused run continues where it left off (the free-tier scan budget resets monthly).
+
+usage: python3 examples/collect_2016_epochs.py [out_dir]
+"""
+import sys
+import os
+import subprocess
+
+sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+from examples.collect_month import main as collect_month
+
+# (month, lo_block, hi_block) from MIN/MAX block_number per 2016 month partition
+MONTHS = [
+ ("2016-01", 391182, 396048), ("2016-02", 396049, 400600), ("2016-03", 400601, 405178),
+ ("2016-04", 405179, 409637), ("2016-05", 409638, 414257), ("2016-06", 414258, 418722),
+ ("2016-07", 418723, 423087), ("2016-08", 423088, 427736), ("2016-09", 427737, 432283),
+ ("2016-10", 432284, 436827), ("2016-11", 436828, 441340), ("2016-12", 441341, 446032),
+]
+
+
+def main(out_dir="."):
+ for month, lo, hi in MONTHS:
+ out = os.path.join(out_dir, f"epoch_{month.replace('-', '_')}.ndjson")
+ if os.path.exists(out + ".done"):
+ print(f"[{month}] already complete, skipping", flush=True)
+ continue
+ print(f"[{month}] collecting -> {out}", flush=True)
+ collect_month(month, lo, hi, out)
+ subprocess.run(["gzip", "-f", out]) # compress in place (~5x); analysis reads .gz
+ print(f"[{month}] compressed -> {out}.gz", flush=True)
+ open(out + ".done", "w").close()
+ print("all epochs collected", flush=True)
+
+
+if __name__ == "__main__":
+ main(sys.argv[1] if len(sys.argv) > 1 else ".")
diff --git a/examples/collect_ancestry.py b/examples/collect_ancestry.py
index fa3271d5..cde471cd 100644
--- a/examples/collect_ancestry.py
+++ b/examples/collect_ancestry.py
@@ -11,6 +11,7 @@
import os
import json
import random
+import multiprocessing as mp
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from decluster.fetch import _get_text, fetch_block_txs, fetch_tx, API
@@ -48,7 +49,20 @@ def signature(coin, depth, max_nodes):
return absorber_distribution(g, coin)
-def main(lo, hi, n, out, seed=0, depth=6, max_nodes=600):
+
+
+def _worker(args):
+ txid, vout, depth, max_nodes = args
+ try:
+ sig = signature((txid, vout), depth, max_nodes)
+ if not sig:
+ return None
+ return (f"{txid}:{vout}", {(a if isinstance(a, str) else ":".join(map(str, a))): round(m, 8) for a, m in sig.items()})
+ except Exception:
+ return None
+
+
+def main(lo, hi, n, out, seed=0, depth=6, max_nodes=600, workers=6):
done = set()
if os.path.exists(out):
for line in open(out):
@@ -58,28 +72,29 @@ def main(lo, hi, n, out, seed=0, depth=6, max_nodes=600):
pass
print(f"resume: {len(done)} already collected", flush=True)
coins = sample_coins(lo, hi, n, seed)
+ print(f"sampling done: {len(coins)} candidate coins", flush=True)
+ todo = [(t, v, depth, max_nodes) for (t, v) in coins if f"{t}:{v}" not in done]
+ print(f"collecting up to {n} signatures from {len(todo)} candidates, {workers} workers, depth {depth}...", flush=True)
kept = len(done)
with open(out, "a") as f:
- for j, (txid, vout) in enumerate(coins):
- if kept >= n:
- break
- key = f"{txid}:{vout}"
- if key in done:
- continue
- try:
- sig = signature((txid, vout), depth, max_nodes)
- except Exception:
- continue
- if not sig:
- continue
- f.write(json.dumps({"coin": key, "sig": { (a if isinstance(a,str) else ":".join(map(str,a))): round(m, 8) for a, m in sig.items() }}) + "\n")
- f.flush()
- kept += 1
- if kept % 25 == 0:
- print(f"{kept}/{n} collected (scanned {j})", flush=True)
+ with mp.Pool(workers) as pool:
+ for res in pool.imap_unordered(_worker, todo):
+ if kept >= n:
+ pool.terminate()
+ break
+ if res is None:
+ continue
+ key, sig = res
+ f.write(json.dumps({"coin": key, "sig": sig}) + "\n")
+ f.flush()
+ kept += 1
+ if kept % 5 == 0:
+ print(f"{kept}/{n} collected", flush=True)
print(f"done: {kept} signatures in {out}", flush=True)
if __name__ == "__main__":
main(int(sys.argv[1]), int(sys.argv[2]), int(sys.argv[3]), sys.argv[4],
- int(sys.argv[5]) if len(sys.argv) > 5 else 0)
+ int(sys.argv[5]) if len(sys.argv) > 5 else 0,
+ workers=int(sys.argv[6]) if len(sys.argv) > 6 else 1,
+ max_nodes=int(sys.argv[7]) if len(sys.argv) > 7 else 600)
diff --git a/examples/collect_month.py b/examples/collect_month.py
new file mode 100644
index 00000000..5ae3ded2
--- /dev/null
+++ b/examples/collect_month.py
@@ -0,0 +1,76 @@
+"""Download one contiguous month of the address-level tx graph WITHOUT billing, efficiently: one
+BigQuery scan of the month partition into a destination table, then paginate the download from that
+table with `bq head` (tabledata.list, free, no re-scan). This replaces the per-chunk downloader, whose
+block-range filter still scans the whole month partition on every chunk.
+
+usage: python3 examples/collect_month.py
+Emits {txid, height, vin:[{prevout:{scriptpubkey_address}}], vout:[{scriptpubkey_address}]} per line,
+the shape the clustering/contraction reads. Resumable at month granularity (skips a complete file).
+"""
+import sys
+import os
+import json
+import subprocess
+
+TABLE = "decluster_scratch.epoch_tmp"
+TEMPLATE = """SELECT `hash` AS txid, block_number AS height,
+ ARRAY(SELECT AS STRUCT STRUCT(i.addresses[SAFE_OFFSET(0)] AS scriptpubkey_address) AS prevout FROM UNNEST(inputs) AS i) AS vin,
+ ARRAY(SELECT AS STRUCT o.addresses[SAFE_OFFSET(0)] AS scriptpubkey_address FROM UNNEST(outputs) AS o) AS vout
+FROM `bigquery-public-data.crypto_bitcoin.transactions`
+WHERE block_timestamp_month = '{month}-01' AND block_number BETWEEN {lo} AND {hi} AND NOT is_coinbase"""
+
+
+def materialize(month, lo, hi):
+ q = TEMPLATE.format(month=month, lo=lo, hi=hi)
+ r = subprocess.run(["bq", "query", "--use_legacy_sql=false", f"--destination_table={TABLE}",
+ "--replace=true", "--format=none", q], capture_output=True, text=True, timeout=900)
+ if r.returncode != 0:
+ raise RuntimeError(r.stderr[:400])
+
+
+def table_count():
+ r = subprocess.run(["bq", "query", "--use_legacy_sql=false", "--format=csv",
+ f"SELECT COUNT(*) FROM {TABLE}"], capture_output=True, text=True, timeout=180)
+ return int(r.stdout.strip().splitlines()[-1])
+
+
+def page(offset, n, attempts=5):
+ last = ""
+ for i in range(attempts):
+ try:
+ r = subprocess.run(["bq", "head", "-n", str(n), "--start_row", str(offset),
+ "--format=json", TABLE], capture_output=True, text=True, timeout=300)
+ except subprocess.TimeoutExpired:
+ last = "timeout"
+ print(f" page@{offset} timeout, retry {i + 1}/{attempts}", flush=True)
+ continue
+ if r.returncode != 0:
+ last = r.stderr[:200]
+ print(f" page@{offset} err ({last}), retry {i + 1}/{attempts}", flush=True)
+ continue
+ return json.loads(r.stdout) if r.stdout.strip() else []
+ raise RuntimeError(f"page@{offset} failed after {attempts}: {last}")
+
+
+def main(month, lo, hi, out):
+ print(f"materializing {month} blocks {lo}-{hi} (one scan)...", flush=True)
+ materialize(month, lo, hi)
+ total = table_count()
+ print(f" {total} rows materialized; paginating (free reads)...", flush=True)
+ n, got = 25000, 0
+ with open(out, "w") as f:
+ while got < total:
+ rows = page(got, n)
+ if not rows:
+ break
+ for r in rows:
+ r["height"] = int(r["height"])
+ f.write(json.dumps(r) + "\n")
+ got += len(rows)
+ f.flush()
+ print(f" {got}/{total}", flush=True)
+ print(f"done: {got} txs in {out}", flush=True)
+
+
+if __name__ == "__main__":
+ main(sys.argv[1], int(sys.argv[2]), int(sys.argv[3]), sys.argv[4])
diff --git a/examples/contract_slice.py b/examples/contract_slice.py
index 66e7cae1..03b61ca8 100644
--- a/examples/contract_slice.py
+++ b/examples/contract_slice.py
@@ -10,7 +10,7 @@
from collections import Counter
from decluster.unionfind import UF
-from decluster.views import AXES, contract
+from decluster.contraction import AXES, contract
from decluster.change_gt import union_input_addrs
diff --git a/examples/em_m_fit.py b/examples/em_m_fit.py
index fe63902c..d9159c34 100644
--- a/examples/em_m_fit.py
+++ b/examples/em_m_fit.py
@@ -1,54 +1,13 @@
-"""EM per-axis m: fit the Fellegi-Sunter m per axis by unsupervised EM over the reuse_pairs mixture
-(labels withheld), and compare to the assumed 0.95 and the held-out reuse-label oracle. Reports: the
-per-axis table, whether EM alone recovers the label (AUC of the posterior), and the pair-AUC with per-
-axis fitted m vs 0.95 vs oracle. Offline. Run from repo root: python3 examples/em_m_fit.py [cap].
-See results/RESULTS-em-m.md."""
-import sys
-import os
-
-sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
-
-
-def main(cap=4000):
- from decluster.fingerprint_validate import load_blkcache, reuse_pairs, evaluate, LibraryScorer
- from decluster.fs_em import agree_matrix, em_fit, oracle_m
- from decluster.graph_deanon import auc
+"""Print the canonical per-axis Fellegi-Sunter EM diagnostic."""
- txs = load_blkcache()
- pos, neg = reuse_pairs(txs, cap=cap, seed=0)
- pairs = pos + neg
- labels = [1] * len(pos) + [0] * len(neg)
- axes = LibraryScorer().axes
- A, mask, names, u = agree_matrix(pairs, axes)
-
- fit = em_fit(A, mask, u) # unsupervised: labels NOT passed
- om = oracle_m(A, mask, labels) # supervised oracle
- m_em = fit["m"]
-
- print("# EM per-axis m on %d witness-bearing txs; %d+%d pairs (labels withheld from EM)\n"
- % (len(txs), len(pos), len(neg)))
- print("# lambda (match fraction EM inferred, ~0.5 by enrichment): %.3f iters=%d"
- % (fit["lam"], fit["n_iter"]))
- print("\n%-22s %8s %8s %8s %8s" % ("axis", "u", "m_EM", "oracle", "assumed"))
- for name, uj, me, oj in zip(names, u, m_em, om):
- print("%-22s %8.3f %8.3f %8s %8.2f"
- % (name, uj, me, ("%.3f" % oj) if oj is not None else "n/a", 0.95))
+from pathlib import Path
+import sys
- r_pos = fit["r"][:len(pos)]
- r_neg = fit["r"][len(pos):]
- print("\n# does EM alone recover the reuse label? AUC of the posterior r: %.4f"
- % auc(r_pos, r_neg, 0))
+sys.path.insert(0, str(Path(__file__).resolve().parents[1]))
- m_dict = {name: me for name, me in zip(names, m_em)}
- o_dict = {name: (oj if oj is not None else 0.95) for name, oj in zip(names, om)}
- a_base = evaluate(txs, LibraryScorer(consistency=0.95), cap=cap, seed=0)["auc"]
- a_em = evaluate(txs, LibraryScorer(consistency=m_dict), cap=cap, seed=0)["auc"]
- a_or = evaluate(txs, LibraryScorer(consistency=o_dict), cap=cap, seed=0)["auc"]
- print("\n# pair-AUC by per-axis m source:")
- print(" assumed 0.95 : %.4f" % a_base)
- print(" EM-fitted : %.4f" % a_em)
- print(" oracle : %.4f" % a_or)
+from decluster.experiments.em_m import build_artifact, render_markdown
if __name__ == "__main__":
- main(int(sys.argv[1]) if len(sys.argv) > 1 else 4000)
+ snapshot = Path(__file__).resolve().parents[1] / "data" / "fs-blkcache-2026-09-04.tar.gz"
+ print(render_markdown(build_artifact(snapshot)), end="")
diff --git a/examples/exact_oracle_audit.py b/examples/exact_oracle_audit.py
new file mode 100644
index 00000000..f1ef2b99
--- /dev/null
+++ b/examples/exact_oracle_audit.py
@@ -0,0 +1,49 @@
+"""Run the production approximations against the exact oracle and print the disagreement report.
+
+The full family is every balanced zero-fee `(inputs, outputs)` over the values 1..4 with at least
+two coins a side and at most eight coins in total, each sorted value multiset appearing once. Each
+`dss` entry point is identified before it is compared, and the entry points that count a different
+object (`w_count`, `radix_mappings`, `per_coin_density`, `subtransaction.subtransactions`) are
+reported as
+identifications, never scored as under- or overcounts. See `decluster/baselines/oracle_audit.py`.
+
+Exits non-zero when the report carries a flag, so an approximation that starts exceeding the oracle
+fails a pipeline rather than printing quietly.
+
+usage: python3 examples/exact_oracle_audit.py [max_coins] [--cases] [--manifest]
+ python3 examples/exact_oracle_audit.py | python3 -m json.tool
+"""
+import json
+import os
+import sys
+
+sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+from decluster import reproducibility
+from decluster.baselines import oracle_audit
+
+ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+
+
+def main(argv):
+ include_cases = "--cases" in argv
+ sizes = [a for a in argv[1:] if not a.startswith("--")]
+ max_coins = int(sizes[0]) if sizes else oracle_audit.FAMILY_MAX_COINS
+ family = oracle_audit.enumerate_family(max_coins=max_coins)
+ report = oracle_audit.audit(family, include_cases=include_cases)
+ if "--manifest" in argv:
+ # Only the full family backs the published document; a reduced run must not overwrite it.
+ if max_coins != oracle_audit.FAMILY_MAX_COINS:
+ sys.stderr.write("refusing to record a manifest for a reduced family\n")
+ return 2
+ reproducibility.write_manifest(
+ oracle_audit.MANIFEST_DOC, oracle_audit.MANIFEST_SOURCE,
+ oracle_audit.manifest_invariants(report), root=ROOT)
+ path = reproducibility.manifest_path(oracle_audit.MANIFEST_DOC, ROOT)
+ sys.stderr.write(f"wrote {path}\n")
+ json.dump(report, sys.stdout, indent=2, sort_keys=True)
+ sys.stdout.write("\n")
+ return 1 if report["flags"] else 0
+
+
+if __name__ == "__main__":
+ sys.exit(main(sys.argv))
diff --git a/examples/fellegi_sunter_temporal.py b/examples/fellegi_sunter_temporal.py
new file mode 100644
index 00000000..443e3196
--- /dev/null
+++ b/examples/fellegi_sunter_temporal.py
@@ -0,0 +1,50 @@
+"""Emit a machine-readable temporal FS evaluation; no narrative result is hand-written."""
+
+import argparse
+import hashlib
+import json
+from pathlib import Path
+import sys
+
+sys.path.insert(0, str(Path(__file__).resolve().parents[1]))
+
+from decluster.fingerprint_validate import load_blkcache
+from decluster.fs_temporal import evaluate_temporal
+from decluster.reproducibility import fingerprint_source
+
+
+def source_identity(source):
+ if source.is_dir():
+ return fingerprint_source(str(source / "*.json"))
+ digest = hashlib.sha256()
+ with source.open("rb") as stream:
+ for chunk in iter(lambda: stream.read(1024 * 1024), b""):
+ digest.update(chunk)
+ return {"path": str(source), "bytes": source.stat().st_size,
+ "sha256": digest.hexdigest()}
+
+
+def main():
+ parser = argparse.ArgumentParser()
+ parser.add_argument("source", nargs="?", default=".blkcache",
+ help="JSON transaction list, or a .blkcache directory")
+ parser.add_argument("--cap", type=int, default=4000)
+ parser.add_argument("--train-fraction", type=float, default=0.7)
+ parser.add_argument("--seed", type=int, default=0)
+ args = parser.parse_args()
+ source = Path(args.source)
+ if source.is_dir():
+ txs = load_blkcache(str(source))
+ else:
+ with source.open() as stream:
+ txs = json.load(stream)
+ result = evaluate_temporal(txs, train_fraction=args.train_fraction,
+ cap=args.cap, seed=args.seed)
+ result["source"] = source_identity(source)
+ result["source"]["transactions"] = len(txs)
+ json.dump(result, sys.stdout, indent=2, sort_keys=True)
+ sys.stdout.write("\n")
+
+
+if __name__ == "__main__":
+ main()
diff --git a/examples/fingerprint_ns.py b/examples/fingerprint_ns.py
index bd30ba82..3e9560e2 100644
--- a/examples/fingerprint_ns.py
+++ b/examples/fingerprint_ns.py
@@ -8,68 +8,8 @@
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from decluster import fingerprint_ns as fns
-from decluster.fingerprint_validate import load_blkcache, reuse_pairs, LibraryScorer
-from decluster.change_gt import input_addrs
-from decluster.graph_deanon import auc
-
-
-def build_labeled_nodes(txs, cap=200, seed=0):
- """One owner_id per address-reuse group; each node a tx (deterministic, order-stable).
- A tx whose inputs reuse more than one address is assigned to exactly one owner - the
- first qualifying group in sorted-address order - so a txid never appears twice."""
- by_addr = {}
- for tx in txs:
- for a in sorted(input_addrs(tx)):
- by_addr.setdefault(a, {})[tx["txid"]] = tx
- nodes, assigned, owner = [], set(), 0
- for addr, g in sorted(by_addr.items()):
- members = [tx for txid, tx in sorted(g.items()) if txid not in assigned]
- if len(members) < 2:
- continue
- for tx in members:
- assigned.add(tx["txid"])
- nodes.append((tx, owner))
- owner += 1
- if len(nodes) >= cap:
- break
- return nodes[:cap]
-
-
-def run_regime(txs, weights, label):
- axis_fns = fns.axis_fns()
- scorer = LibraryScorer()
- pos, neg = reuse_pairs(txs, cap=2000, seed=0)
-
- ns_auc = fns.pairwise_auc(pos, neg, axis_fns, weights)
- fs_auc = auc([scorer.score(a, b) for a, b in pos],
- [scorer.score(a, b) for a, b in neg], 0)
-
- nodes = build_labeled_nodes(txs)
- within_gaps, ns_correct, fs_correct, n = [], 0, 0, 0
- for i, (q, owner) in enumerate(nodes):
- others = [(t, o) for j, (t, o) in enumerate(nodes) if j != i]
- if not others:
- continue
- cand_txs = [t for t, _ in others]
- r = fns.reid_gap(q, cand_txs, axis_fns, weights)
- n += 1
- if r["best"] is not None and others[r["best"]][1] == owner:
- ns_correct += 1
- # F-S top-1 on the same candidate set
- fs_scores = [scorer.score(q, t) for t in cand_txs]
- fs_best = max(range(len(fs_scores)), key=lambda k: fs_scores[k])
- if others[fs_best][1] == owner:
- fs_correct += 1
- # within-class gap: candidates sharing q's equivalence key
- qk = fns.equivalence_key(q, axis_fns)
- wc = [t for t in cand_txs if fns.equivalence_key(t, axis_fns) == qk]
- if len(wc) >= 2:
- within_gaps.append(fns.reid_gap(q, wc, axis_fns, weights)["gap"])
-
- return {"label": label, "ns_auc": ns_auc, "fs_auc": fs_auc,
- "ns_top1": ns_correct / n if n else 0.0, "fs_top1": fs_correct / n if n else 0.0,
- "within_gap_mean": (sum(within_gaps) / len(within_gaps)) if within_gaps else 0.0,
- "within_n": len(within_gaps)}
+from decluster.fingerprint_validate import load_blkcache
+from decluster.fingerprint_regime import build_labeled_nodes, evaluate as run_regime
def main():
@@ -89,7 +29,7 @@ def main():
for label, w in sources:
r = run_regime(txs, w, label)
print("%-10s %8.4f %8.4f %9.3f %9.3f %13.3f"
- % (r["label"], r["ns_auc"], r["fs_auc"], r["ns_top1"], r["fs_top1"], r["within_gap_mean"]))
+ % (r["weight_source"], r["ns_auc"], r["fs_auc"], r["ns_top1"], r["fs_top1"], r["within_class_gap_mean"]))
if __name__ == "__main__":
diff --git a/examples/fingerprint_sparsity.py b/examples/fingerprint_sparsity.py
index e538265f..34521ca8 100644
--- a/examples/fingerprint_sparsity.py
+++ b/examples/fingerprint_sparsity.py
@@ -16,27 +16,7 @@
import sys
from collections import Counter
-LABELS = ["exactly 1", "2-9", "10-99", "100-999", "1,000-9,999",
- "10,000-99,999", ">=100,000"]
-
-
-def bucket(size):
- if size == 1:
- return LABELS[0]
- for i, hi in enumerate((10, 100, 1_000, 10_000, 100_000), start=1):
- if size < hi:
- return LABELS[i]
- return LABELS[-1]
-
-
-def distribution(counts):
- """counts: vector -> occurrences. Returns bucket -> share of TRANSACTIONS (not vectors)
- landing in a class of that size, so the reading is "a transaction's own crowd"."""
- out = Counter()
- for size in counts.values():
- out[bucket(size)] += size
- total = sum(out.values())
- return {label: out.get(label, 0) / total for label in LABELS}, total
+from decluster.fingerprint_sparsity import LABELS, bucket, distribution
def render(name, dist, total):
diff --git a/examples/fs_ablation.py b/examples/fs_ablation.py
new file mode 100644
index 00000000..d706819a
--- /dev/null
+++ b/examples/fs_ablation.py
@@ -0,0 +1,45 @@
+"""Emit a machine-readable FS ablation study; no narrative result is hand-written."""
+
+import argparse
+import json
+from pathlib import Path
+import sys
+
+sys.path.insert(0, str(Path(__file__).resolve().parents[1]))
+
+from decluster.fingerprint_validate import load_blkcache
+from decluster.fs_ablation import run_ablation_study
+from decluster.reproducibility import fingerprint_source
+
+
+def main():
+ parser = argparse.ArgumentParser()
+ parser.add_argument("source", nargs="?", default=".blkcache",
+ help="JSON transaction list, or a .blkcache directory")
+ parser.add_argument("--ablation-cap", type=int, default=4000)
+ parser.add_argument("--association-cap", type=int, default=8000)
+ parser.add_argument("--train-fraction", type=float, default=0.7)
+ parser.add_argument("--seed", type=int, default=0)
+ parser.add_argument("--cluster-threshold", type=float, default=0.6)
+ args = parser.parse_args()
+ source = Path(args.source)
+ if source.is_dir():
+ txs = load_blkcache(str(source))
+ source_identity = fingerprint_source(str(source / "*.json"))
+ else:
+ with source.open() as stream:
+ txs = json.load(stream)
+ source_identity = {"path": str(source)}
+ result = run_ablation_study(
+ txs, train_fraction=args.train_fraction, ablation_cap=args.ablation_cap,
+ association_cap=args.association_cap, seed=args.seed,
+ cluster_threshold=args.cluster_threshold,
+ )
+ result["source"] = source_identity
+ result["source"]["transactions"] = len(txs)
+ json.dump(result, sys.stdout, indent=2, sort_keys=True)
+ sys.stdout.write("\n")
+
+
+if __name__ == "__main__":
+ main()
diff --git a/examples/fused_report.py b/examples/fused_report.py
index c13fac14..f174a080 100644
--- a/examples/fused_report.py
+++ b/examples/fused_report.py
@@ -5,11 +5,16 @@
Run from the decluster repo root:
python -m examples.fused_report [txid]
-A dense coinjoin exceeds dss's LINK_GUARD, so pairwise_link_prob returns None and the target walk
-truncates at the coinjoin itself (target becomes its own absorber, shannon 0, truncated 1) — the
-correct refuse behaviour, not a bug. Pass a smaller txid to see a non-trivial provenance walk."""
+Runs the report twice, to contrast the two link oracles. The first arm pins the subset-sum link
+oracle explicitly (report()'s default is now nominal value flow). A dense coinjoin exceeds dss's
+LINK_GUARD, so pairwise_link_prob returns None and the target walk truncates at the coinjoin itself
+(target becomes its own absorber, shannon 0, truncated 1) — the correct refuse behaviour, not a bug.
+The second arm omits `link_oracle`, taking report()'s default `value_flow_link_oracle`: it never
+refuses on width, so the same coinjoin walks past the truncation point instead. Pass a smaller txid
+to see a non-trivial provenance walk on the subset-sum arm too."""
import sys
from decluster.fetch import fetch_tx
+from decluster.ancestry import dss_link_oracle
from decluster import report as R
# A real mainnet coinjoin (285 outputs); bounded to one output for a fast, readable demo.
@@ -25,8 +30,15 @@ def main(argv):
return 1
txid = argv[1] if len(argv) > 1 else DEFAULT_TXID
tx = fetch_tx(txid)
- rep = R.report(tx, targets=[0]) # one output, real dss oracles
+ # One output, real dss oracles on both channels. The link oracle is named rather than
+ # inherited: report()'s default moved to nominal value flow, which does not refuse on width,
+ # so inheriting it would silently replace the truncation this demo is written around.
+ print("-- subset-sum link oracle (pinned) --")
+ rep = R.report(tx, targets=[0], link_oracle=dss_link_oracle)
R.print_report(rep)
+ print("-- value-flow link oracle (report()'s default; link_oracle omitted) --")
+ rep_default = R.report(tx, targets=[0])
+ R.print_report(rep_default)
return 0
diff --git a/examples/graph_shape_run.py b/examples/graph_shape_run.py
index 5cdbfc01..9103b001 100644
--- a/examples/graph_shape_run.py
+++ b/examples/graph_shape_run.py
@@ -9,7 +9,8 @@
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from decluster.graph_shape import summary
-from decluster.views import cluster_addresses, contract
+from decluster.contraction import contract
+from decluster.views import cluster_addresses
from examples.view_match_run import stream
diff --git a/examples/intersection_pipeline.py b/examples/intersection_pipeline.py
index 47d9e74a..c2ba732d 100644
--- a/examples/intersection_pipeline.py
+++ b/examples/intersection_pipeline.py
@@ -20,6 +20,7 @@
"""
from decluster.fetch import fetch_outspends, fetch_tx
+from decluster.adaptations.intersection import evaluate_ancestry_reports
from decluster.intersect import evaluate, score_candidate
from decluster.monitor import summarise, walk_frontier
@@ -55,10 +56,26 @@ def default_signature_of(depth=SIGNATURE_DEPTH):
mix has hundreds of parents per hop. It is a floor either way: the walk
truncates at any transaction the link oracle refuses, which every large mix
is, and reports that it did.
+
+ The oracle is named, not inherited: the walk's default moved to nominal
+ value flow, which never refuses, so inheriting it would drop both the
+ truncation this floor rests on and the walk that RESULTS-intersection.md
+ reports.
"""
- from decluster.ancestry import ancestry_signature_and_truncation
+ from decluster.ancestry import ancestry_signature_and_truncation, dss_link_oracle
+
+ return lambda outpoint: ancestry_signature_and_truncation(
+ outpoint, depth=depth, link_oracle=dss_link_oracle)
+
+
+def default_ancestry_report_of(depth=SIGNATURE_DEPTH):
+ """Atomic ancestry observation used by the typed intersection path."""
+ from decluster.adaptations.ancestry import ancestry_signature_report
+ from decluster.ancestry import dss_link_oracle
- return lambda outpoint: ancestry_signature_and_truncation(outpoint, depth=depth)
+ return lambda outpoint: ancestry_signature_report(
+ outpoint, depth=depth, link_oracle=dss_link_oracle
+ )
def default_cluster_fn():
@@ -88,22 +105,41 @@ def cluster_fn(nodes, signatures=None):
def run(seeds=None, get_tx=fetch_tx, get_outspends=fetch_outspends,
- signature_of=None, cluster_fn=None, max_depth=3):
+ signature_of=None, ancestry_report_of=None, cluster_fn=None, max_depth=3,
+ intersection_options=None):
"""Walk, intersect, and score. Returns one dict per co-spend candidate.
- `signature_of` returns `(signature, truncated)` for an outpoint — the origin set and how much of
- its boundary is the link oracle refusing rather than an origin. Both are needed: without the
- second, an empty intersection cannot be told from a walk that could not see. `cluster_fn` takes
- `(nodes, signatures)`. Both are injected so the pipeline can be exercised without a network walk;
- production passes `default_signature_of()` and `default_cluster_fn()`.
+ `ancestry_report_of` is the preferred interface: one typed result binds a
+ signature to the truncation observed by the same walk. `signature_of` is the
+ compatibility interface and returns `(signature, truncated)` for an outpoint — the origin set and, as an
+ `ancestry.TruncationSupport`, how much of its boundary is truncation rather than an origin and
+ which walk limit produced it. Both are needed: without the second, an empty intersection cannot
+ be told from a walk that could not see, and without its split a blind branch does not say
+ whether the oracle refused or the node cap bit. `cluster_fn` takes
+ `(nodes, signatures)`. The functions are injected so the pipeline can be exercised without a
+ network walk; production uses `default_ancestry_report_of()` and `default_cluster_fn()`.
"""
+ if signature_of is not None and ancestry_report_of is not None:
+ raise ValueError("provide signature_of or ancestry_report_of, not both")
seeds = default_seeds(get_tx) if seeds is None else seeds
walked = walk_frontier(seeds, get_tx, get_outspends, max_depth=max_depth)
results = []
for candidate in walked["candidates"]:
entry = {"candidate": candidate}
sigs = None
- if signature_of is not None:
+ if ancestry_report_of is not None:
+ ancestry_reports = {
+ op: ancestry_report_of(op) for op in candidate.get("outpoints", [])
+ }
+ sigs = {
+ op: report.as_legacy()[0] for op, report in ancestry_reports.items()
+ }
+ entry["narrowing"] = evaluate_ancestry_reports(
+ candidate,
+ ancestry_reports.__getitem__,
+ **(intersection_options or {}),
+ ).as_legacy()
+ elif signature_of is not None:
# Computed once: the narrowing reads them, and the engine's
# provenance channel is offered the same ones rather than a
# second walk.
@@ -122,7 +158,10 @@ def run(seeds=None, get_tx=fetch_tx, get_outspends=fetch_outspends,
def main():
- out = run(signature_of=default_signature_of(), cluster_fn=default_cluster_fn())
+ out = run(
+ ancestry_report_of=default_ancestry_report_of(),
+ cluster_fn=default_cluster_fn(),
+ )
print(f"seeds: {len(default_seeds())}")
print(f"walk: {summarise(out['walk'])}")
for entry in out["walk"]["frontier"]:
diff --git a/examples/link_prediction_run.py b/examples/link_prediction_run.py
new file mode 100644
index 00000000..6d866616
--- /dev/null
+++ b/examples/link_prediction_run.py
@@ -0,0 +1,289 @@
+"""De-anonymization-driven link prediction against structure-only prediction, on planted views.
+
+The corpus is synthetic and generated here, deterministically from each `--fixture-seeds`
+value: a directed community graph is drawn once, each view keeps an independent random subset
+of its edges, and a share of the target view's links is held out. Nothing about it is a
+Bitcoin measurement, and the numbers it produces are numbers about this fixture. Real 2016
+views behave very differently — see `results/RESULTS-ns-bitcoin.md`, where the same propagation
+found zero usable independent seeds.
+
+Five arms per fixture seed, all scored on identical held-out links:
+
+ attack seeds drawn from the withheld correspondence, real auxiliary view
+ independent-aux an auxiliary view drawn from its own graph, so the correspondence the
+ seeds assert is not a correspondence at all and every transferred edge
+ is noise
+ deranged-seeds the real auxiliary view, seeds pointing at the wrong vertices
+ stripped-aux the real auxiliary view with every held-out link deleted from it, which
+ leaves the mapping intact and removes only the evidence it transfers
+ ceiling the whole withheld correspondence as the seed set, which leaves
+ propagation nothing to do; a diagnostic, not an operating point
+
+`--scorer open` ablates the closed-neighbourhood term from *both* arms, which is the sensitivity
+the result turns on.
+
+usage: python3 examples/link_prediction_run.py [--out PATH] [--manifest]
+"""
+import argparse
+import json
+import os
+import random
+import sys
+from dataclasses import asdict
+
+sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+from decluster import reproducibility
+from decluster.baselines import link_prediction as lp
+from decluster.baselines.narayanan_shmatikov import evaluate
+from decluster.ns_bitcoin import SEED_SAMPLED
+from decluster.contraction import PseudonymGraph
+
+ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+MANIFEST_DOC = "RESULTS-link-prediction.md"
+MANIFEST_SOURCE = "examples/link_prediction_run.py"
+
+SEED_UNRELATED = "unrelated-auxiliary-control"
+SEED_DERANGED = "deranged-correspondence-control"
+SEED_STRIPPED = "withheld-correspondence-sample-on-a-stripped-auxiliary"
+SEED_CEILING = "withheld-correspondence-as-the-mapping"
+
+SCORERS = {"closed": lp.score, "open": lp.open_score}
+
+
+def directed_graph(vertices, edges):
+ graph = PseudonymGraph(axes=False)
+ for vertex in vertices:
+ graph._vertex(vertex)
+ for source, target in edges:
+ graph.edges[(source, target)] = {"transfers": 1, "value": 1}
+ graph._out[source].add(target)
+ graph._in[target].add(source)
+ return graph
+
+
+def community_edges(vertices, groups, p_in, p_out, rng):
+ """A planted-partition digraph: dense inside a group, sparse across.
+
+ Communities are the point of the fixture, not decoration. On a graph with no community
+ structure a common-neighbour predictor has nothing to find, and the structure-only arm
+ would lose to anything at all; giving it real signal is what makes the comparison worth
+ running.
+ """
+ size = vertices // groups
+ return [(u, v) for u in range(vertices) for v in range(vertices)
+ if u != v and rng.random() < (p_in if u // size == v // size else p_out)]
+
+
+def planted_views(args, rng):
+ """Two partial views of one activity graph, plus what was withheld from the target."""
+ edges = community_edges(args.vertices, args.groups, args.p_in, args.p_out, rng)
+ target_edges = [e for e in edges if rng.random() < args.keep]
+ auxiliary_edges = [e for e in edges if rng.random() < args.keep]
+
+ # Hold out whole links, both directions: leaving the reverse edge in place would make a
+ # "held-out" pair one the target view still joins, and the structure-only arm would be
+ # scored on a pair it can read off its own input.
+ links = sorted({tuple(sorted(e)) for e in target_edges})
+ held = set(rng.sample(links, round(len(links) * args.holdout)))
+ residual = [e for e in target_edges if tuple(sorted(e)) not in held]
+
+ target = directed_graph([f"a{i}" for i in range(args.vertices)],
+ [(f"a{u}", f"a{v}") for u, v in residual])
+ auxiliary = directed_graph([f"b{i}" for i in range(args.vertices)],
+ [(f"b{u}", f"b{v}") for u, v in auxiliary_edges])
+ correspondence = {f"a{i}": f"b{i}" for i in range(args.vertices)}
+ held_out = [(f"a{u}", f"a{v}") for u, v in sorted(held)]
+ negatives = sample_negatives(args, links, held, rng)
+ return {
+ "target": target, "auxiliary": auxiliary, "correspondence": correspondence,
+ "held_out": held_out, "negatives": negatives,
+ "activity_edges": len(edges), "target_edges": len(target_edges),
+ "auxiliary_edges": len(auxiliary_edges), "residual_edges": len(residual),
+ "held_out_links_in_auxiliary": sum(
+ 1 for link in held if link in {tuple(sorted(e)) for e in auxiliary_edges}),
+ }
+
+
+def sample_negatives(args, links, held, rng):
+ """Pairs the activity graph never joined, at `--negatives-per-positive` of the held-out set.
+
+ Uniform over vertex pairs, so the negatives are mostly cross-community. That is a property
+ of the fixture worth stating rather than tuning away: a negative set drawn inside the
+ communities would be harder for both arms.
+ """
+ joined = set(links)
+ negatives, seen = [], set()
+ while len(negatives) < args.negatives_per_positive * len(held):
+ pair = tuple(sorted((rng.randrange(args.vertices), rng.randrange(args.vertices))))
+ if pair[0] == pair[1] or pair in joined or pair in seen:
+ continue
+ seen.add(pair)
+ negatives.append((f"a{pair[0]}", f"a{pair[1]}"))
+ return negatives
+
+
+def sampled_seeds(correspondence, fraction, rng):
+ keys = sorted(correspondence)
+ return {u: correspondence[u] for u in rng.sample(keys, round(len(keys) * fraction))}
+
+
+def deranged(seeds, rng):
+ """The same seed vertices with the wrong images; redrawn until nothing is left in place."""
+ images = list(seeds.values())
+ for _ in range(64):
+ rng.shuffle(images)
+ candidate = dict(zip(seeds, images))
+ if all(candidate[u] != v for u, v in seeds.items()):
+ return candidate
+ return dict(zip(seeds, images))
+
+
+def without_links(graph, links, correspondence):
+ """The auxiliary view with the target's held-out links deleted, both directions.
+
+ The mapping propagation finds is left to move as it will — an auxiliary view that never saw
+ those links is a coherent counterfactual view, not the same view with a hole punched in the
+ scoring — and what is removed is exactly the evidence the transfer would carry.
+ """
+ banned = {frozenset((correspondence[u], correspondence[v])) for u, v in links}
+ return directed_graph(graph.vertices,
+ [e for e in graph.edges if frozenset(e) not in banned])
+
+
+def unrelated_auxiliary(args, rng):
+ """An auxiliary view of a different activity graph, under the same vertex names."""
+ return directed_graph([f"b{i}" for i in range(args.vertices)],
+ [(f"b{u}", f"b{v}") for u, v in
+ community_edges(args.vertices, args.groups, args.p_in, args.p_out, rng)])
+
+
+def _row(name, provenance, result, mapping_quality=None):
+ row = {"arm": name, **asdict(result)}
+ if mapping_quality is not None:
+ row["mapping_against_withheld_correspondence"] = mapping_quality
+ row["seed_provenance"] = provenance
+ return row
+
+
+def run_fixture(args, fixture_seed):
+ rng = random.Random(fixture_seed)
+ views = planted_views(args, rng)
+ target, auxiliary = views["target"], views["auxiliary"]
+ correspondence = views["correspondence"]
+ held_out, negatives = views["held_out"], views["negatives"]
+ seeds = sampled_seeds(correspondence, args.seed_fraction, rng)
+
+ scorer = SCORERS[args.scorer]
+
+ def compare(aux, seed_map, provenance):
+ return lp.compare(target, aux, seed_map, held_out, negatives,
+ seed_provenance=provenance, theta=args.theta, cutoff=args.cutoff,
+ scorer=scorer)
+
+ attack = compare(auxiliary, seeds, SEED_SAMPLED)
+ mapping = lp.predict(target, auxiliary, seeds, held_out,
+ seed_provenance=SEED_SAMPLED, theta=args.theta).mapping
+ quality = evaluate(mapping, correspondence, seeds)
+ # How much of the arm's reach is the auxiliary view simply handing over the missing link,
+ # rather than the enlarged neighbourhoods implying it.
+ enlarged, _ = lp.transferred_neighbourhoods(target, auxiliary, mapping)
+ direct = sum(1 for u, v in held_out if v in enlarged.get(u, ()))
+
+ control_rng = random.Random(fixture_seed + 1000)
+ unrelated = compare(unrelated_auxiliary(args, control_rng), seeds, SEED_UNRELATED)
+ scrambled = compare(auxiliary, deranged(seeds, control_rng), SEED_DERANGED)
+ stripped = compare(without_links(auxiliary, held_out, correspondence), seeds, SEED_STRIPPED)
+ # Seeding with the whole correspondence leaves propagation nothing to do, so this row is the
+ # transfer's ceiling under a perfect mapping. It is a diagnostic and not an operating point:
+ # the correspondence is withheld, and no attacker can seed with it.
+ ceiling = compare(auxiliary, correspondence, SEED_CEILING)
+
+ return {
+ "fixture_seed": fixture_seed,
+ "views": {k: v for k, v in views.items()
+ if k not in ("target", "auxiliary", "correspondence", "held_out", "negatives")},
+ "held_out_links": len(held_out), "negative_pairs": len(negatives),
+ "held_out_links_transferred_directly": direct,
+ "runs": [_row("attack", SEED_SAMPLED, attack, quality),
+ _row("independent-auxiliary-control", SEED_UNRELATED, unrelated),
+ _row("deranged-seed-control", SEED_DERANGED, scrambled),
+ _row("stripped-auxiliary-control", SEED_STRIPPED, stripped),
+ _row("perfect-mapping-ceiling", SEED_CEILING, ceiling)],
+ }
+
+
+def manifest_invariants(report):
+ """The population and headline facts a byte digest of the generator cannot see."""
+ config = report["configuration"]
+ attacks = [f["runs"][0] for f in report["fixtures"]]
+ return {
+ **config,
+ "fixtures": len(report["fixtures"]),
+ "held_out_links": [f["held_out_links"] for f in report["fixtures"]],
+ "auc_deanonymization": [round(a["deanonymization"]["auc"], 6) for a in attacks],
+ "auc_structure_only": [round(a["structure_only"]["auc"], 6) for a in attacks],
+ "recall_deanonymization": [round(a["deanonymization"]["recall"], 6) for a in attacks],
+ "recall_structure_only": [round(a["structure_only"]["recall"], 6) for a in attacks],
+ "beats_structure_only": [a["separability"]["beats_structure_only"] for a in attacks],
+ "held_out_links_transferred_directly": [f["held_out_links_transferred_directly"]
+ for f in report["fixtures"]],
+ "control_verdicts": sorted({run["separability"]["beats_structure_only"]
+ for f in report["fixtures"] for run in f["runs"]
+ if run["arm"].endswith("-control")}, key=repr),
+ }
+
+
+def configuration(args):
+ return {"vertices": args.vertices, "groups": args.groups, "p_in": args.p_in,
+ "p_out": args.p_out, "keep": args.keep, "holdout": args.holdout,
+ "negatives_per_positive": args.negatives_per_positive,
+ "seed_fraction": args.seed_fraction, "theta": args.theta, "cutoff": args.cutoff,
+ "scorer": args.scorer, "fixture_seeds": list(args.fixture_seeds)}
+
+
+def build_report(args):
+ return {"configuration": configuration(args),
+ "fixtures": [run_fixture(args, seed) for seed in args.fixture_seeds]}
+
+
+def build_parser():
+ parser = argparse.ArgumentParser()
+ parser.add_argument("--vertices", type=int, default=80)
+ parser.add_argument("--groups", type=int, default=8)
+ parser.add_argument("--p-in", type=float, default=0.18)
+ parser.add_argument("--p-out", type=float, default=0.012)
+ parser.add_argument("--keep", type=float, default=0.9,
+ help="probability a view observes any one activity edge")
+ parser.add_argument("--holdout", type=float, default=0.25,
+ help="share of the target view's links removed and predicted")
+ parser.add_argument("--negatives-per-positive", type=int, default=4)
+ parser.add_argument("--seed-fraction", type=float, default=0.25,
+ help="seeds drawn from the withheld correspondence")
+ parser.add_argument("--theta", type=float, default=1.5)
+ parser.add_argument("--cutoff", type=int, default=2,
+ help="score at which a candidate is declared a link")
+ parser.add_argument("--scorer", choices=sorted(SCORERS), default="closed",
+ help="'open' ablates the closed-neighbourhood term from both arms")
+ parser.add_argument("--fixture-seeds", type=int, nargs="+", default=[0, 1, 2, 3, 4])
+ parser.add_argument("--out", default=None)
+ parser.add_argument("--manifest", nargs="?", const=MANIFEST_DOC, default=None)
+ return parser
+
+
+def main(argv=None):
+ args = build_parser().parse_args(argv)
+ report = build_report(args)
+ print(json.dumps(report, indent=2, sort_keys=True))
+ if args.out:
+ with open(os.path.join(ROOT, args.out), "w") as fh:
+ json.dump(report, fh, indent=2, sort_keys=True)
+ fh.write("\n")
+ if args.manifest:
+ reproducibility.write_manifest(args.manifest, MANIFEST_SOURCE,
+ manifest_invariants(report), root=ROOT)
+ sys.stderr.write(f"wrote {reproducibility.manifest_path(args.manifest, ROOT)}\n")
+ return 0
+
+
+if __name__ == "__main__":
+ sys.exit(main())
diff --git a/examples/match_controls.py b/examples/match_controls.py
index 77cc3dbe..949bbf99 100644
--- a/examples/match_controls.py
+++ b/examples/match_controls.py
@@ -24,7 +24,8 @@
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from decluster.view_match import ViewMatcher
-from decluster.views import cluster_addresses, contract
+from decluster.contraction import contract
+from decluster.views import cluster_addresses
from examples.view_match_run import pseudonymise, stream
ECC = 5.0
diff --git a/examples/ns_bitcoin_views.py b/examples/ns_bitcoin_views.py
new file mode 100644
index 00000000..49e75f13
--- /dev/null
+++ b/examples/ns_bitcoin_views.py
@@ -0,0 +1,260 @@
+"""Narayanan--Shmatikov propagation across two real Bitcoin observation windows.
+
+Two views, and why an adversary holds both
+------------------------------------------
+The two views are two adjacent block-height windows of the public chain, meeting at
+`--boundary`: view A is the `--blocks` blocks before it, view B the `--blocks` blocks after.
+Nothing about that is privileged — anyone can read both windows — and that is the point: the
+adversary's *observation* is complete and it is the *clustering* that is partial. A cluster
+whose activity spans the boundary is contracted under a separate pseudonym in each view
+(`views.split_clusters_by_view`), which is the framework's premise made concrete: one owner,
+two pseudonyms, unlinked, and the only correspondence available to the attacker is the one
+propagation can rediscover from topology. Contracting each view under its own lookup is
+also what removes the trivial escape, since no pseudonym string appears on both sides.
+
+The withheld correspondence is `{C#a: C#b}` over the straddling clusters. It grades; it
+does not participate in view construction or scoring.
+
+Observation policy, stated because it changes the population: the clustering is global CIOH
+over both windows with the coinjoin shape refused (`scale_cluster.cluster_scale_stream`,
+which is what this address-only export supports), and each view keeps the vertices whose
+degree *in its own view* is at least `--min-degree`. The degree filter is per-view and
+never consults the other view or the correspondence. Degree below two is a vertex
+propagation can neither match nor bridge through.
+
+usage: python3 examples/ns_bitcoin_views.py [--blocks 120] [--min-degree 2] ...
+"""
+import argparse
+import gzip
+import json
+import os
+import random
+import sys
+import time
+from collections import Counter
+from dataclasses import asdict
+
+sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+
+from decluster import contraction, ns_bitcoin, reproducibility, tx_addrs, views
+from decluster.entities import detect_bitmex, detect_mining_pool, detect_satoshidice
+from decluster.scale_cluster import cluster_scale_stream
+
+ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+EPOCH_GLOB = "data/epochs_2016_weekly/*.ndjson.gz"
+DEFAULT_LEFT = "data/epochs_2016_weekly/epoch_2016_01_391104-392111.ndjson.gz"
+DEFAULT_RIGHT = "data/epochs_2016_weekly/epoch_2016_01_392112-393119.ndjson.gz"
+DEFAULT_BOUNDARY = 392112 # first height of the second weekly epoch
+
+
+def load_window(path, lo, hi):
+ """The window's transactions, held once. A weekly epoch is 500 MB of JSON and the run
+ needs four passes over each window, so the window is materialised and the epoch is not."""
+ out = []
+ with gzip.open(path, "rt") as fh:
+ for line in fh:
+ if not line.strip():
+ continue
+ tx = json.loads(line)
+ if lo <= tx.get("height", 0) <= hi:
+ out.append(tx)
+ return out
+
+
+def window_size(path, lo, hi):
+ """How many transactions the window holds, without parsing the records.
+
+ `height` is the first field of every line of this export, so the count a manifest check
+ needs costs a decompress rather than 291k JSON parses. A line not in that shape falls back
+ to the parser rather than being miscounted.
+ """
+ prefix = '{"height": '
+ total = 0
+ with gzip.open(path, "rt") as fh:
+ for line in fh:
+ if line.startswith(prefix):
+ height = int(line[len(prefix):line.index(",", len(prefix))])
+ elif line.strip():
+ height = json.loads(line).get("height", 0)
+ else:
+ continue
+ total += lo <= height <= hi
+ return total
+
+
+def independent_entity_labels(window, lookup):
+ """`(entity, vertex)` pairs from detectors that read the chain alone.
+
+ No curated address list and no cross-view information: a mining pool names itself in its
+ coinbase tag, BitMEX and SatoshiDice in an address vanity prefix. Whether any of these
+ resolves to exactly one vertex per view is the question `unique_entity_seeds` answers,
+ and on this export it is allowed to answer "none".
+
+ These are the *name-emitting* detectors this export can carry at all. `entities` has two
+ more, and neither can fire here: `detect_bip47_notification` reads an OP_RETURN
+ scriptPubKey and `detect_dust_fanout` reads output values, and this export carries only
+ `scriptpubkey_address`. The behaviour proxies (`detect_consolidation`, `detect_batching`)
+ are excluded by kind, not by schema: they name a pattern, not an entity, so they cannot
+ supply a one-to-one cross-view label.
+ """
+ labels = []
+ for tx in window:
+ for hit in detect_bitmex(tx) + detect_satoshidice(tx):
+ address = hit["address"]
+ labels.append((hit["entity"], lookup.get(address, address)))
+ pool = detect_mining_pool(tx)
+ if pool and pool["payout_addr"]:
+ labels.append((pool["entity"], lookup.get(pool["payout_addr"],
+ pool["payout_addr"])))
+ return labels
+
+
+def build_views(left_window, right_window, min_degree, split_frac, rng):
+ """Global clustering, boundary split, and one contracted view per window."""
+ clustering = cluster_scale_stream(tx for w in (left_window, right_window) for tx in w)
+ addresses = []
+ for window in (left_window, right_window):
+ seen = set()
+ for tx in window:
+ seen.update(tx_addrs.in_addrs(tx))
+ seen.update(a for a, _ in tx_addrs.out_addrs(tx))
+ addresses.append(seen)
+ lookup = clustering.map_many(addresses[0] | addresses[1])
+ split_cids, origin = views.split_clusters_by_view(lookup, addresses[0],
+ frac=split_frac, rng=rng)
+ graphs, view_lookups = [], []
+ for window, suffix in ((left_window, "#a"), (right_window, "#b")):
+ per_view = views.view_lookup(lookup, split_cids, suffix)
+ degrees = contraction.contract_degrees(((tx, 0) for tx in window), lookup=per_view)
+ keep = {v for v, d in degrees.items() if d >= min_degree}
+ del degrees
+ graphs.append(contraction.contract(((tx, 0) for tx in window), lookup=per_view,
+ axes=False, keep=keep))
+ view_lookups.append(per_view)
+ left, right = graphs
+ right_pseudonyms = {v for v in right.vertices
+ if isinstance(v, str) and v.endswith("#b")}
+ correspondence = {u: f"{origin[u]}#b" for u in left.vertices
+ if isinstance(u, str) and u.endswith("#a")
+ and f"{origin[u]}#b" in right_pseudonyms}
+ return left, right, correspondence, view_lookups, len(lookup), len(split_cids)
+
+
+def run_configuration(args):
+ """The knobs the published populations were measured under, as manifest invariants."""
+ return {"left_path": args.left, "right_path": args.right, "boundary": args.boundary,
+ "blocks": args.blocks, "min_degree": args.min_degree,
+ "split_frac": args.split_frac, "seed_fractions": list(args.fractions),
+ "thetas": list(args.thetas), "rng_seed": args.seed}
+
+
+def build_report(args):
+ """Compute the scientific report without volatile execution metadata."""
+ rng = random.Random(args.seed)
+ left_lo, left_hi = args.boundary - args.blocks, args.boundary - 1
+ right_lo, right_hi = args.boundary, args.boundary + args.blocks - 1
+ left_window = load_window(os.path.join(ROOT, args.left), left_lo, left_hi)
+ right_window = load_window(os.path.join(ROOT, args.right), right_lo, right_hi)
+
+ left, right, correspondence, view_lookups, addresses, split_cids = build_views(
+ left_window, right_window, args.min_degree, args.split_frac, rng)
+
+ labels = [independent_entity_labels(w, lk) for w, lk
+ in ((left_window, view_lookups[0]), (right_window, view_lookups[1]))]
+ independent = ns_bitcoin.unique_entity_seeds(*labels)
+ # An independent label may name a vertex the correspondence does not grade (an entity that
+ # never straddled the boundary), which is not a conflict but is also not measurable.
+ gradeable = {u: v for u, v in independent.items() if correspondence.get(u) == v}
+
+ overlap = ns_bitcoin.graph_overlap(left, right, correspondence)
+ report = {
+ "views": {
+ "construction": "adjacent disjoint height windows, boundary-split clustering",
+ "left": {"path": args.left, "heights": [left_lo, left_hi],
+ "transactions": len(left_window),
+ "vertices": len(left.vertices), "edges": len(left.edges)},
+ "right": {"path": args.right, "heights": [right_lo, right_hi],
+ "transactions": len(right_window),
+ "vertices": len(right.vertices), "edges": len(right.edges)},
+ "min_degree": args.min_degree, "split_frac": args.split_frac,
+ "clustered_addresses": addresses, "split_clusters": split_cids,
+ },
+ "withheld_correspondence": {"vertices": len(correspondence), **overlap},
+ "independent_entity_seeds": {
+ "found": len(independent), "gradeable": len(gradeable),
+ "labelled_vertices": [len({v for _, v in side}) for side in labels],
+ "entities": [dict(Counter(entity for entity, _ in side)) for side in labels],
+ },
+ "runs": [],
+ }
+
+ if len(gradeable) >= 2:
+ report["runs"].append(asdict(ns_bitcoin.run_bitcoin_views(
+ left, right, correspondence, gradeable, theta=args.thetas[0],
+ rng_seed=args.seed, seed_provenance=ns_bitcoin.SEED_INDEPENDENT)))
+ else:
+ report["independent_entity_seeds"]["verdict"] = (
+ "fewer than two gradeable independent seeds; the independent attack is not "
+ "identifiable on these views and the sweep below is seed-assisted")
+
+ for result in ns_bitcoin.sweep_bitcoin_views(left, right, correspondence,
+ args.fractions, args.thetas,
+ rng_seed=args.seed):
+ report["runs"].append(asdict(result))
+ return report
+
+
+def main(args):
+ started = time.monotonic()
+ report = build_report(args)
+ report["seconds"] = round(time.monotonic() - started, 1)
+
+ print(json.dumps(report, indent=2, sort_keys=True))
+ if args.out:
+ with open(os.path.join(ROOT, args.out), "w") as fh:
+ json.dump(report, fh, indent=2, sort_keys=True)
+ fh.write("\n")
+ if args.manifest:
+ # Two kinds of invariant, because only one of them is cheap to check. The
+ # configuration and the window transaction counts are recomputable in a few seconds
+ # (`tests/test_ns_bitcoin.py` does exactly that); the contracted populations are not,
+ # and are recorded as identity-only. The configuration is what makes the second kind
+ # meaningful: a changed `--min-degree` or `--blocks` default would detach every
+ # published population number from the code, and leaves the epoch bytes untouched.
+ reproducibility.write_manifest(args.manifest, EPOCH_GLOB, {
+ **run_configuration(args),
+ "left_transactions": len(left_window), "right_transactions": len(right_window),
+ "left_vertices": len(left.vertices), "right_vertices": len(right.vertices),
+ "left_edges": len(left.edges), "right_edges": len(right.edges),
+ "correspondence_vertices": len(correspondence),
+ "left_internal_edges": overlap["left_internal_edges"],
+ "recurring_edges": overlap["recurring_edges"],
+ "edge_overlap": round(overlap["edge_overlap"], 6),
+ "independent_entity_seeds": len(independent),
+ "seed_counts": sorted({run["seed_size"] for run in report["runs"]}),
+ }, root=ROOT)
+
+
+def build_parser():
+ parser = argparse.ArgumentParser()
+ parser.add_argument("--left", default=DEFAULT_LEFT)
+ parser.add_argument("--right", default=DEFAULT_RIGHT)
+ parser.add_argument("--boundary", type=int, default=DEFAULT_BOUNDARY)
+ parser.add_argument("--blocks", type=int, default=120,
+ help="height span of each view")
+ parser.add_argument("--min-degree", type=int, default=2,
+ help="per-view degree floor; the stated observation policy")
+ parser.add_argument("--split-frac", type=float, default=1.0,
+ help="fraction of straddling clusters split into two pseudonyms")
+ parser.add_argument("--fractions", type=float, nargs="+", default=[0.05, 0.10, 0.25],
+ help="seed fractions of the withheld correspondence")
+ parser.add_argument("--thetas", type=float, nargs="+", default=[0.5, 1.5],
+ help="eccentricity thresholds; part of the reported configuration")
+ parser.add_argument("--seed", type=int, default=7)
+ parser.add_argument("--out", default=None) # see boltzmann_fee_audit: no default path
+ parser.add_argument("--manifest", default="RESULTS-ns-bitcoin.md")
+ return parser
+
+
+if __name__ == "__main__":
+ main(build_parser().parse_args())
diff --git a/examples/ns_propagation.py b/examples/ns_propagation.py
index 7fa261dd..80fcaebb 100644
--- a/examples/ns_propagation.py
+++ b/examples/ns_propagation.py
@@ -37,13 +37,19 @@ def score(self, a, b): return 3.0
def run(txs, seeds, depth=6):
"""Real wiring: build per-node provenance signatures and rarity, then summarise.
- `txs` maps node -> representative tx dict; `seeds` maps node -> label."""
- from decluster.ancestry import ancestry_signature
+ `txs` maps node -> representative tx dict; `seeds` maps node -> label.
+
+ The link oracle is named rather than inherited: the walk's default moved to nominal value flow,
+ and RESULTS-ns-propagation.md reports the subset-sum walk (its cache harness pins the same
+ oracle explicitly).
+ """
+ from decluster.ancestry import ancestry_signature, dss_link_oracle
from decluster.propagate import build_rarity, entity_signature
node_sigs = {}
for node, tx in txs.items():
coin = (tx["txid"], 0)
- node_sigs[node] = entity_signature([ancestry_signature(coin, depth=depth)])
+ node_sigs[node] = entity_signature(
+ [ancestry_signature(coin, depth=depth, link_oracle=dss_link_oracle)])
rarity = build_rarity(node_sigs.values())
node_txs = {node: tx for node, tx in txs.items()}
return run_on_signatures(node_sigs, seeds, node_txs, rarity,
diff --git a/examples/partition_compare.py b/examples/partition_compare.py
index 010b871d..a96dc6dc 100644
--- a/examples/partition_compare.py
+++ b/examples/partition_compare.py
@@ -15,7 +15,9 @@
from decluster.monitor import is_coinjoin
from decluster.view_match import ViewMatcher
-from decluster.views import ambiguity_partition, cluster_addresses, contract
+from decluster.contraction import contract
+from decluster.view_partition import ambiguity_partition
+from decluster.views import cluster_addresses
from examples.view_match_run import pseudonymise, stream
ECC = 5.0
diff --git a/examples/path_count_live.py b/examples/path_count_live.py
index 33f5c680..da23b216 100644
--- a/examples/path_count_live.py
+++ b/examples/path_count_live.py
@@ -1,9 +1,9 @@
"""Live end-to-end proof of the two things task 5/6 shipped: (a) `max_nodes` tames the deep-coinjoin
`analyze(depth=5, path_count=True)` call that `RESULTS-analyze.md` (b) found hung past a 25-minute
wall cap uncapped — bounded, it returns fast with a truncated LOWER BOUND, never a crash; (b) the §07
-path-count object (`decluster.path_count.path_count_anonymity`) is a genuinely different
-(multiplicity-weighted / robustness) lens from §04's set-size entropy on the SAME tractable envelope
-(§07 does not extend it — see `RESULTS-path-counting-analysis.md`).
+provenance-route object (`decluster.provenance_route_accumulation`) walks the SAME tractable envelope as
+§04's set-size entropy, weighted by link probability alone (§07 does not extend the envelope either —
+see `RESULTS-path-counting-analysis.md`).
This is a live, non-deterministic `examples/` harness (network + subprocess-capable oracle
internals) — NOT part of the asserted test suite. Live-only imports (`decluster.fetch`,
@@ -27,8 +27,7 @@ def bounded_coinjoin(round_txid=None, depth=5, max_nodes=400, budget_ms=3000):
`try/except BaseException` — crashes must stay 0 (the panic-safe oracle + max_nodes cap should
make an escaped exception unreachable).
- Returns {"txid", "secs", "crashed": bool, "n_origins", "min_entropy", "truncated",
- "path_log_W": float}."""
+ Returns {"txid", "secs", "crashed": bool, "n_origins", "min_entropy", "truncated"}."""
from decluster import analyze, bounded_link_oracle
from decluster.fetch import fetch_tx
from examples.anonymity_set_scale import seed_targets
@@ -49,13 +48,11 @@ def bounded_coinjoin(round_txid=None, depth=5, max_nodes=400, budget_ms=3000):
secs = time.monotonic() - t0
r0 = result[0]
prov = r0["provenance"]
- pc = r0.get("path_count") or {}
entry.update({
"secs": secs,
"n_origins": prov["n_absorbers"],
"min_entropy": prov["min_entropy"],
"truncated": r0["truncated"],
- "path_log_W": pc.get("log_W_paths"),
})
except BaseException as e:
# the panic-safe oracle + max_nodes cap should make this unreachable -- crashes must stay 0.
@@ -67,13 +64,15 @@ def bounded_coinjoin(round_txid=None, depth=5, max_nodes=400, budget_ms=3000):
def sweep_vs_s04(txid="00264b9175b14c6a783610ed39da33a2717a4494bfefee08d8e5cdd7e7ebc23f",
depths=(1, 2, 3, 4, 5), budget_ms=3000):
- """§04 vs §07 contrast on a NARROW (tractable) tx across depths: for each depth, run
+ """§04 vs §07 agreement on a NARROW (tractable) tx across depths: for each depth, run
`analyze(txid, depth=depth, path_count=True)` once and report the §04 provenance
- (min_entropy/n_origins, link-probability/set-size) beside the §07 path_count
- (min_entropy/log_W_paths, multiplicity-weighted/robustness) -- the SAME envelope, two different
- lenses, showing they need not agree.
+ (min_entropy/n_origins, link-probability/set-size) beside the §07 path_count min_entropy.
+ With subset-sum multiplicity out of the §07 walk and the graph's edges row-stochastic, the two
+ are the same quantity: `s04_min_entropy` and `s07_min_entropy` should match at every depth (a
+ real run measured `{OP: 0.5, OQ: 0.25, OR: 0.25}` against `{0.4999999999999999, ...}` --
+ float-noise apart, identical), not diverge -- see `results/RESULTS-path-count.md`.
- Returns [{"depth", "n_origins", "s04_min_entropy", "s07_min_entropy", "log_W_paths",
+ Returns [{"depth", "n_origins", "s04_min_entropy", "s07_min_entropy",
"truncated", "crashed"}]."""
from decluster import analyze, bounded_link_oracle
from decluster.fetch import fetch_tx
@@ -91,7 +90,6 @@ def sweep_vs_s04(txid="00264b9175b14c6a783610ed39da33a2717a4494bfefee08d8e5cdd7e
"n_origins": prov["n_absorbers"],
"s04_min_entropy": prov["min_entropy"],
"s07_min_entropy": pc.get("min_entropy"),
- "log_W_paths": pc.get("log_W_paths"),
"truncated": e["truncated"],
})
except BaseException as ex:
diff --git a/examples/profile_scale.py b/examples/profile_scale.py
new file mode 100644
index 00000000..4e82ef4b
--- /dev/null
+++ b/examples/profile_scale.py
@@ -0,0 +1,36 @@
+"""Profile one clustering backend over a bounded prefix of a local epoch.
+
+Use `/usr/bin/time -l` around this command to capture peak resident memory.
+
+usage: python3 examples/profile_scale.py BACKEND MAX_TXS EPOCH.ndjson[.gz]
+BACKEND is `dict` or `numpy`.
+"""
+import itertools
+import json
+import os
+import resource
+import sys
+import time
+
+sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+from decluster.scale_cluster import (cluster_scale_np_stream, cluster_scale_stream,
+ stream_txs)
+
+
+def main(backend, max_txs, path):
+ txs = itertools.islice(stream_txs([path]), max_txs)
+ started = time.monotonic()
+ if backend == "dict":
+ clustering = cluster_scale_stream(txs)
+ elif backend == "numpy":
+ clustering = cluster_scale_np_stream(txs)
+ else:
+ raise SystemExit("BACKEND must be dict or numpy")
+ print(json.dumps({"backend": backend, "max_txs": max_txs,
+ "clusters": clustering.n_clusters(),
+ "elapsed_seconds": round(time.monotonic() - started, 3),
+ "max_rss_bytes": resource.getrusage(resource.RUSAGE_SELF).ru_maxrss}))
+
+
+if __name__ == "__main__":
+ main(sys.argv[1], int(sys.argv[2]), sys.argv[3])
diff --git a/examples/reid_definitive.py b/examples/reid_definitive.py
new file mode 100644
index 00000000..e6666996
--- /dev/null
+++ b/examples/reid_definitive.py
@@ -0,0 +1,79 @@
+"""Netflix-faithful definitive reid on a UNIFORM ancestry sample (from collect_ancestry.py).
+Reports, per Narayanan-Shmatikov: the (epsilon,delta)-sparsity survival curve (App E / Fig 12),
+Algorithm 1B de-anonymization rate as a function of aux size m (Fig 1), stratified by the coins'
+own sparsity, and the not-in-sample false-positive control (Fig 2: remove the target, expect abstain).
+
+usage: python3 examples/reid_definitive.py
+"""
+import sys
+import os
+import json
+import random
+
+sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+from decluster.reid import reid_attack, top_similarity
+from decluster.propagate import build_rarity
+from decluster.def1_sparsity import survival
+from decluster.ancestry import provenance_link
+
+
+def load(path):
+ coins, sigs = [], {}
+ for line in open(path):
+ d = json.loads(line)
+ coins.append(d["coin"]); sigs[d["coin"]] = {a: float(m) for a, m in d["sig"].items()}
+ return coins, sigs
+
+
+def not_in_sample_control(coins, sigs, rarity, m=8, trials=200, seed=0):
+ """Fig 2: reveal a target's aux but REMOVE it from the candidate set; a faithful attack should
+ ABSTAIN (no confident match) rather than mis-attribute. Returns the abstain rate."""
+ rng = random.Random(seed)
+ from decluster.propagate import eccentricity
+ abst = tot = 0
+ pool = [c for c in coins if len(sigs[c]) >= m]
+ for c in rng.sample(pool, min(trials, len(pool))):
+ aux = {a: sigs[c][a] for a in rng.sample(list(sigs[c]), m)}
+ others = [x for x in coins if x != c]
+ scores = [provenance_link(aux, sigs[x], rarity) for x in others]
+ if len(scores) < 2:
+ continue
+ tot += 1
+ if eccentricity(scores) < 1.5: # gate abstains -> correct (target absent)
+ abst += 1
+ return abst / tot if tot else None
+
+
+def main(path):
+ coins, sigs = load(path)
+ rarity = build_rarity(sigs.values())
+ print(f"N={len(coins)} coins | median dims={sorted(len(s) for s in sigs.values())[len(sigs)//2]}", flush=True)
+
+ tops = list(top_similarity(coins, sigs).values())
+ print("\n(epsilon,delta)-sparsity survival (fraction with a neighbour > epsilon):")
+ for eps, frac in survival(tops).items():
+ print(f" epsilon={eps}: delta={frac:.3f}")
+
+ sparse = [c for c in coins if _top(c, coins, sigs) < 0.5]
+ dense = [c for c in coins if _top(c, coins, sigs) >= 0.9]
+ print(f"\nstrata: sparse(top<0.5)={len(sparse)} dense(top>=0.9)={len(dense)}")
+ print("\nAlgorithm 1B de-anon rate vs aux size m (exact-hit / attackable):")
+ print(f"{'m':>3}{'sparse':>10}{'dense':>10}")
+ for m in (2, 4, 6, 8):
+ ds, es, ts = reid_attack(sparse, coins, sigs, rarity, m)
+ dd, ed, td = reid_attack(dense, coins, sigs, rarity, m)
+ print(f"{m:>3}{(es/ts if ts else 0):>10.3f}{(ed/td if td else 0):>10.3f}", flush=True)
+
+ ctrl = not_in_sample_control(coins, sigs, rarity)
+ print(f"\nnot-in-sample control (abstain rate, want high): {ctrl:.3f}" if ctrl else "\nnot-in-sample control: n/a")
+
+
+_TOPCACHE = {}
+def _top(c, coins, sigs):
+ if not _TOPCACHE:
+ _TOPCACHE.update(top_similarity(coins, sigs))
+ return _TOPCACHE[c]
+
+
+if __name__ == "__main__":
+ main(sys.argv[1])
diff --git a/examples/rejoin_run.py b/examples/rejoin_run.py
index 225486ff..ad39a7ba 100644
--- a/examples/rejoin_run.py
+++ b/examples/rejoin_run.py
@@ -24,9 +24,9 @@
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from decluster.view_match import ViewMatcher
-from decluster.views import (cluster_addresses, contract, split_clusters,
- split_clusters_by_view)
-from decluster.views import _in_addrs, _out_addrs
+from decluster.contraction import contract
+from decluster.views import cluster_addresses, split_clusters, split_clusters_by_view, view_lookup
+from decluster.tx_addrs import in_addrs, out_addrs
from examples.view_match_run import pseudonymise, stream
ECC = 5.0
@@ -40,17 +40,19 @@ def main(path, boundary, frac=0.5, n_seeds=400, how="boundary"):
for tx, _ in stream(path, 0, boundary):
seen.update(_in_addrs(tx))
seen.update(a for a, _ in _out_addrs(tx))
- lookup, origin = split_clusters_by_view(base, seen, frac, rng)
- tail = "#a"
+ split_cids, origin = split_clusters_by_view(base, seen, frac, rng)
+ la = view_lookup(base, split_cids, "#a")
+ lb = view_lookup(base, split_cids, "#b")
+ n_split = len(split_cids)
else:
lookup, origin = split_clusters(base, frac, rng)
- tail = "#0"
- n_split = sum(1 for t in set(lookup.values()) if t.endswith(tail))
+ la = lb = lookup
+ n_split = sum(1 for t in set(lookup.values()) if t.endswith("#0"))
print(f"clustering: {len(set(base.values())):,} clusters, {n_split:,} split "
f"({how})\n")
- ga = contract(stream(path, 0, boundary), lookup=lookup, axes=False)
- gbt = contract(stream(path, boundary + 1, 10 ** 9), lookup=lookup, axes=False)
+ ga = contract(stream(path, 0, boundary), lookup=la, axes=False)
+ gbt = contract(stream(path, boundary + 1, 10 ** 9), lookup=lb, axes=False)
gb, sigma = pseudonymise(gbt, rng)
unsigma = {v: k for k, v in sigma.items()}
diff --git a/examples/special_change_validation.py b/examples/special_change_validation.py
index 82a2930f..f793625d 100644
--- a/examples/special_change_validation.py
+++ b/examples/special_change_validation.py
@@ -4,8 +4,9 @@
per label (no network), and onward-spend per-axis per label (live mempool.space fetch, sampled). The
per-axis rate compared ACROSS the four independent labels is the label-robustness check.
Caveat: the within-tx `round_number` predictor against the round_number LABEL is near-circular (both
-read roundness) — read that cell as self-agreement, not corroboration. The single-label Phase-1 tables
-are in results/RESULTS-special-change.md; a full triangulation results doc awaits a run.
+read roundness) — read that cell as self-agreement, not corroboration. The offline half of this runs as a
+canonical experiment over committed transactions (decluster.experiments.special_change_labels); what
+is left here is the onward-spend half, which needs a fetcher.
Run from repo root: python3 examples/special_change_validation.py [n_onward]
Reads the value-carrying downloads in ~/Downloads/bquxjob_*.json."""
import glob
diff --git a/examples/split_epochs.py b/examples/split_epochs.py
new file mode 100644
index 00000000..52c3dae0
--- /dev/null
+++ b/examples/split_epochs.py
@@ -0,0 +1,84 @@
+"""Split monthly NDJSON epochs into seekable block-height chunks.
+
+Each input is decompressed once. Outputs retain NDJSON gzip format so every existing reader can
+consume them, while a multi-epoch run no longer rescans an entire month for each sub-window.
+
+usage: python3 examples/split_epochs.py OUTPUT_DIR INPUT... [--blocks 1008]
+"""
+import argparse
+import gzip
+import hashlib
+import json
+import os
+from pathlib import Path
+
+
+def _open(path, mode="rt"):
+ return gzip.open(path, mode) if str(path).endswith(".gz") else open(path, mode)
+
+
+def _sha256(path):
+ digest = hashlib.sha256()
+ with open(path, "rb") as f:
+ for block in iter(lambda: f.read(1024 * 1024), b""):
+ digest.update(block)
+ return digest.hexdigest()
+
+
+def split_file(source, output_dir, blocks):
+ stem = Path(source).name.removesuffix(".gz").removesuffix(".ndjson")
+ writers = {}
+ records = {}
+ try:
+ with _open(source) as incoming:
+ for line in incoming:
+ if not line.strip():
+ continue
+ tx = json.loads(line)
+ height = int(tx.get("height") or 0)
+ bucket = height // blocks
+ if bucket not in writers:
+ lo = bucket * blocks
+ hi = lo + blocks - 1
+ path = os.path.join(output_dir, f"{stem}_{lo}-{hi}.ndjson.gz")
+ writers[bucket] = gzip.open(path, "wt")
+ records[bucket] = {"source": str(source), "path": path,
+ "height_lo": height, "height_hi": height,
+ "transactions": 0}
+ writers[bucket].write(line)
+ record = records[bucket]
+ record["height_lo"] = min(record["height_lo"], height)
+ record["height_hi"] = max(record["height_hi"], height)
+ record["transactions"] += 1
+ finally:
+ for writer in writers.values():
+ writer.close()
+ ordered = [records[bucket] for bucket in sorted(records)]
+ for record in ordered:
+ record["sha256"] = _sha256(record["path"])
+ return ordered
+
+
+def main():
+ parser = argparse.ArgumentParser()
+ parser.add_argument("output_dir")
+ parser.add_argument("inputs", nargs="+")
+ parser.add_argument("--blocks", type=int, default=1008,
+ help="block heights per chunk (1008 is approximately one week)")
+ args = parser.parse_args()
+ if args.blocks <= 0:
+ parser.error("--blocks must be positive")
+ os.makedirs(args.output_dir, exist_ok=True)
+ records = []
+ for source in args.inputs:
+ records.extend(split_file(source, args.output_dir, args.blocks))
+ manifest = {"blocks_per_chunk": args.blocks, "chunks": records}
+ manifest_path = os.path.join(args.output_dir, "manifest.json")
+ with open(manifest_path, "w") as f:
+ json.dump(manifest, f, indent=2)
+ f.write("\n")
+ print(f"wrote {len(records)} chunks and {manifest_path}")
+
+
+if __name__ == "__main__":
+ main()
diff --git a/examples/view_match_run.py b/examples/view_match_run.py
index 769132c5..366f39fe 100644
--- a/examples/view_match_run.py
+++ b/examples/view_match_run.py
@@ -17,7 +17,7 @@
from decluster.entities import detect_mining_pool
from decluster.unionfind import UF
from decluster.view_match import ViewMatcher
-from decluster.views import PseudonymGraph, contract
+from decluster.contraction import PseudonymGraph, contract
def stream(path, lo=None, hi=None):
diff --git a/examples/weight_sensitivity.py b/examples/weight_sensitivity.py
index b447c4bb..d105501a 100644
--- a/examples/weight_sensitivity.py
+++ b/examples/weight_sensitivity.py
@@ -1,26 +1,13 @@
-"""Weight-sensitivity sweep: how robust is the fingerprint-pair AUC to the assumed disagreement-weight
-parameter c (= the same-wallet self-agreement rate m, cravado at 0.95 and not fitted — the epistemically
-uncertain knob)? Sweeps c and re-measures on the SAME seeded pairs, so only the weight changes. Offline.
-Run from repo root: python3 examples/weight_sensitivity.py [cap]. See results/RESULTS-weight-sensitivity.md."""
-import sys
-import os
-
-sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+"""Print the canonical global fingerprint-weight sensitivity sweep."""
-C_GRID = (0.60, 0.70, 0.80, 0.90, 0.95, 0.99)
+from pathlib import Path
+import sys
+sys.path.insert(0, str(Path(__file__).resolve().parents[1]))
-def main(cap=4000):
- from decluster.fingerprint_validate import load_blkcache, evaluate, LibraryScorer
- txs = load_blkcache()
- print("# weight sensitivity on %d witness-bearing txs (.blkcache); same seeded pairs across all c\n"
- % len(txs))
- print("%-5s %10s %10s %8s %9s" % ("c", "pos_mean", "neg_mean", "AUC", "shuffle"))
- for c in C_GRID:
- r = evaluate(txs, LibraryScorer(consistency=c), cap=cap, seed=0)
- print("%-5.2f %+10.2f %+10.2f %8.4f %9.4f"
- % (c, r["pos_mean"], r["neg_mean"], r["auc"], r["shuffle_auc"]))
+from decluster.experiments.weight_sensitivity import build_artifact, render_markdown
if __name__ == "__main__":
- main(int(sys.argv[1]) if len(sys.argv) > 1 else 4000)
+ snapshot = Path(__file__).resolve().parents[1] / "data" / "fs-blkcache-2026-09-04.tar.gz"
+ print(render_markdown(build_artifact(snapshot)), end="")
diff --git a/pyproject.toml b/pyproject.toml
index 5b667c23..daddcd49 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -6,7 +6,33 @@ build-backend = "setuptools.build_meta"
name = "decluster"
version = "0.1.0"
description = "Evidence-weighted de-anonymization of Bitcoin transactions"
-requires-python = ">=3.9"
+requires-python = ">=3.10"
+# PEP 639: the expression and `license-files` replace the license classifier, and setuptools
+# refuses the pair.
+license = "MIT"
+license-files = ["LICENSE"]
-[tool.setuptools]
-packages = ["decluster"]
+[project.scripts]
+decluster-data = "decluster.data_cli:main"
+decluster-bundle = "decluster.bundle_cli:main"
+decluster-source = "decluster.source_snapshot:main"
+
+[project.optional-dependencies]
+data = ["zstandard>=0.23"]
+test = ["pytest>=8"]
+# The exact subset-sum counter. It is a Rust extension built from a pinned revision rather than a
+# release on an index, so it is named by that revision: the same one `catalog/runs` declares and
+# the evidence bundles ship a wheel of. Absent it, the tests that need it skip.
+exact = ["dss @ git+https://github.com/bc1cindy/dense-subset-sum@d8099bc540c9de7d0f3c9f095e8b2aad8f9d8c8d"]
+# Imported lazily by scale_cluster, fs_ablation and ancestry; nothing outside those paths needs it.
+scale = ["numpy>=1.24"]
+
+[tool.setuptools.packages.find]
+include = ["decluster", "decluster.*"]
+
+[tool.pytest.ini_options]
+addopts = "-m 'not live'"
+markers = [
+ "live: requires external network services and is excluded from the offline suite",
+ "reproduction: materializes and reexecutes evidence bundles in their pinned environment",
+]
diff --git a/releases/amount-channel-survey-v1.bundle.json b/releases/amount-channel-survey-v1.bundle.json
new file mode 100644
index 00000000..a9a59516
--- /dev/null
+++ b/releases/amount-channel-survey-v1.bundle.json
@@ -0,0 +1,128 @@
+{
+ "schema_version": 1,
+ "id": "amount-channel-survey-v1",
+ "capabilities": [
+ "verify_outputs",
+ "reproduce_runs"
+ ],
+ "runs": [
+ "amount-channel-survey-v1"
+ ],
+ "blobs": [
+ {
+ "name": "catalog/runs/amount-channel-survey-v1.json",
+ "role": "run_manifest",
+ "bytes": 3149,
+ "sha256": "04f9670b4969de9df7ef526818503be374a6b71fa6c2df178db03e7d1c688bb3",
+ "locations": {
+ "canonical": null,
+ "mirrors": []
+ }
+ },
+ {
+ "name": "results/artifacts/amount-local-channels-v1.json",
+ "role": "result",
+ "bytes": 825,
+ "sha256": "210ecb06ecaa32cc4cabe03ced8b85b0882a824dca2c361bd2c5dede7dcccf02",
+ "locations": {
+ "canonical": null,
+ "mirrors": []
+ }
+ },
+ {
+ "name": "results/artifacts/amount-subset-sum-v1.json",
+ "role": "result",
+ "bytes": 666,
+ "sha256": "240592b5af814fbcd28b06221805b934424170119da5e62e9da0281944377a9d",
+ "locations": {
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+ }
+ },
+ {
+ "name": "results/artifacts/amount-per-coin-v1.json",
+ "role": "result",
+ "bytes": 1158,
+ "sha256": "2e75b1330181e7cbf8539a357e2bd3ad650c4d2d207c7a47ec687e991e24a622",
+ "locations": {
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+ "mirrors": []
+ }
+ },
+ {
+ "name": "results/artifacts/amount-mapping-family-v1.json",
+ "role": "result",
+ "bytes": 942,
+ "sha256": "11f0ec645ea1be334dc7d24fbfc31047b234f335f48b5a514960472c7444b7d6",
+ "locations": {
+ "canonical": null,
+ "mirrors": []
+ }
+ },
+ {
+ "name": "results/artifacts/amount-pairwise-family-v1.json",
+ "role": "result",
+ "bytes": 1027,
+ "sha256": "83f6a9e8501ee4df45f08b8c8ffc2f7265d40bf9add6d3f5093d712657c9dbf5",
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+ }
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+ "locations": {
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+ "mirrors": []
+ }
+ },
+ {
+ "name": "sources/decluster-bcd9a3e-runtime.tar",
+ "role": "source",
+ "bytes": 2447360,
+ "sha256": "d586f8c571b001c9bc1536f6d3eecc861bfa91a5dd55a74643eca519b4c3027b",
+ "locations": {
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+ "mirrors": []
+ }
+ },
+ {
+ "name": "reproduction/amount-channel-survey/requirements.lock",
+ "role": "lockfile",
+ "bytes": 30,
+ "sha256": "e0d6a8cada5d383c0aca2bd2a37f28c15721392469c94084128d369c29c5544d",
+ "locations": {
+ "canonical": null,
+ "mirrors": []
+ }
+ },
+ {
+ "name": "reproduction/amount-channel-survey/environment.json",
+ "role": "environment",
+ "bytes": 378,
+ "sha256": "6626bcb82fd68b9d75ae85635897122384cca4ce2ae37e62e46bcfd1af40bfce",
+ "locations": {
+ "canonical": null,
+ "mirrors": []
+ }
+ }
+ ],
+ "limitations": [
+ "The bundled environment currently targets CPython 3.13 on macOS arm64.",
+ "The bundle composes pinned upstream artifacts and does not contain their raw dataset.",
+ "Public bootstrap remains unavailable until every blob has a canonical HTTPS location and an independent mirror."
+ ]
+}
diff --git a/releases/amount-local-channels-v1.bundle.json b/releases/amount-local-channels-v1.bundle.json
new file mode 100644
index 00000000..ad31f99c
--- /dev/null
+++ b/releases/amount-local-channels-v1.bundle.json
@@ -0,0 +1,87 @@
+{
+ "schema_version": 1,
+ "id": "amount-local-channels-v1",
+ "capabilities": [
+ "verify_outputs",
+ "reproduce_runs"
+ ],
+ "runs": [
+ "amount-local-channels-v1"
+ ],
+ "blobs": [
+ {
+ "name": "catalog/runs/amount-local-channels-v1.json",
+ "role": "run_manifest",
+ "bytes": 3064,
+ "sha256": "314b5660d08b84013f07692bdc207f791807dbb9f335ca10ade57dd7eb7a2fc5",
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+ "mirrors": []
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+ "role": "dataset",
+ "bytes": 8028513,
+ "sha256": "83b4391f78d07a2fa85a67218d5ae4420d8965da3c95af577592e9ec1850104d",
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+ "role": "result",
+ "bytes": 825,
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+ }
+ },
+ {
+ "name": "sources/decluster-bcd9a3e-runtime.tar",
+ "role": "source",
+ "bytes": 2447360,
+ "sha256": "d586f8c571b001c9bc1536f6d3eecc861bfa91a5dd55a74643eca519b4c3027b",
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+ }
+ },
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+ "name": "reproduction/amount-local-channels/requirements.lock",
+ "role": "lockfile",
+ "bytes": 30,
+ "sha256": "e0d6a8cada5d383c0aca2bd2a37f28c15721392469c94084128d369c29c5544d",
+ "locations": {
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+ "mirrors": []
+ }
+ },
+ {
+ "name": "reproduction/amount-local-channels/environment.json",
+ "role": "environment",
+ "bytes": 378,
+ "sha256": "a95843b58e37348f45520a64411a733e85e7a832310c7a23780a267d928d6ee9",
+ "locations": {
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+ "mirrors": []
+ }
+ }
+ ],
+ "limitations": [
+ "The bundled environment currently targets CPython 3.13 on macOS arm64.",
+ "Public bootstrap remains unavailable until every blob has a canonical HTTPS location and an independent mirror."
+ ]
+}
diff --git a/releases/amount-mapping-family-v1.bundle.json b/releases/amount-mapping-family-v1.bundle.json
new file mode 100644
index 00000000..110e9ae0
--- /dev/null
+++ b/releases/amount-mapping-family-v1.bundle.json
@@ -0,0 +1,97 @@
+{
+ "schema_version": 1,
+ "id": "amount-mapping-family-v1",
+ "capabilities": [
+ "verify_outputs",
+ "reproduce_runs"
+ ],
+ "runs": [
+ "amount-mapping-family-v1"
+ ],
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+ }
+ }
+ ],
+ "limitations": [
+ "The bundled environment currently targets CPython 3.13 on macOS arm64.",
+ "Public bootstrap remains unavailable until every blob has a canonical HTTPS location and an independent mirror."
+ ]
+}
diff --git a/releases/amount-pairwise-family-v1.bundle.json b/releases/amount-pairwise-family-v1.bundle.json
new file mode 100644
index 00000000..f80f376e
--- /dev/null
+++ b/releases/amount-pairwise-family-v1.bundle.json
@@ -0,0 +1,98 @@
+{
+ "schema_version": 1,
+ "id": "amount-pairwise-family-v1",
+ "capabilities": [
+ "verify_outputs",
+ "reproduce_runs"
+ ],
+ "runs": [
+ "amount-pairwise-family-v1"
+ ],
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+ }
+ }
+ ],
+ "limitations": [
+ "The bundled environment currently targets CPython 3.13 on macOS arm64.",
+ "Wall-time outcomes are resource-dependent even in the pinned environment.",
+ "Public bootstrap remains unavailable until every blob has a canonical HTTPS location and an independent mirror."
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diff --git a/releases/weight-sensitivity-v1.bundle.json b/releases/weight-sensitivity-v1.bundle.json
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+ "role": "result",
+ "bytes": 1084,
+ "sha256": "36b8c85941d3a3ab382c1e3a14166226a5608f37176503294679b342179c1498",
+ "locations": {
+ "canonical": null,
+ "mirrors": []
+ }
+ },
+ {
+ "name": "sources/decluster-bcd9a3e-runtime.tar",
+ "role": "source",
+ "bytes": 2447360,
+ "sha256": "d586f8c571b001c9bc1536f6d3eecc861bfa91a5dd55a74643eca519b4c3027b",
+ "locations": {
+ "canonical": null,
+ "mirrors": []
+ }
+ },
+ {
+ "name": "reproduction/weight-sensitivity/requirements.lock",
+ "role": "lockfile",
+ "bytes": 0,
+ "sha256": "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855",
+ "locations": {
+ "canonical": null,
+ "mirrors": []
+ }
+ },
+ {
+ "name": "reproduction/weight-sensitivity/environment.json",
+ "role": "environment",
+ "bytes": 372,
+ "sha256": "51f184dc02945a1eda4b864a53c6097325f0ee2961a85cc06fa3c9a96802a2c3",
+ "locations": {
+ "canonical": null,
+ "mirrors": []
+ }
+ }
+ ],
+ "limitations": [
+ "The bundled environment currently targets CPython 3.13 on macOS arm64.",
+ "The public on-chain snapshot is redistributed with provider authorization.",
+ "The historical 165,832-transaction cache is unavailable and is not reproduced.",
+ "Public bootstrap remains incomplete until every blob has a canonical HTTPS location and an independent mirror."
+ ]
+}
diff --git a/reproduction/amount-channel-survey/environment.json b/reproduction/amount-channel-survey/environment.json
new file mode 100644
index 00000000..6a4c8036
--- /dev/null
+++ b/reproduction/amount-channel-survey/environment.json
@@ -0,0 +1,13 @@
+{
+ "schema_version": 1,
+ "python": {
+ "implementation": "cpython",
+ "version": "3.13",
+ "system": "Darwin",
+ "machine": "arm64"
+ },
+ "source_archive": "sources/decluster-bcd9a3e-runtime.tar",
+ "project_root": "decluster",
+ "requirements": "reproduction/amount-channel-survey/requirements.lock",
+ "dependency_directory": "reproduction/amount-channel-survey"
+}
diff --git a/reproduction/amount-channel-survey/requirements.lock b/reproduction/amount-channel-survey/requirements.lock
new file mode 100644
index 00000000..cb70631a
--- /dev/null
+++ b/reproduction/amount-channel-survey/requirements.lock
@@ -0,0 +1 @@
+# no third-party dependencies
diff --git a/reproduction/amount-local-channels/environment.json b/reproduction/amount-local-channels/environment.json
new file mode 100644
index 00000000..61f48588
--- /dev/null
+++ b/reproduction/amount-local-channels/environment.json
@@ -0,0 +1,13 @@
+{
+ "schema_version": 1,
+ "python": {
+ "implementation": "cpython",
+ "version": "3.13",
+ "system": "Darwin",
+ "machine": "arm64"
+ },
+ "source_archive": "sources/decluster-bcd9a3e-runtime.tar",
+ "project_root": "decluster",
+ "requirements": "reproduction/amount-local-channels/requirements.lock",
+ "dependency_directory": "reproduction/amount-local-channels"
+}
diff --git a/reproduction/amount-local-channels/requirements.lock b/reproduction/amount-local-channels/requirements.lock
new file mode 100644
index 00000000..cb70631a
--- /dev/null
+++ b/reproduction/amount-local-channels/requirements.lock
@@ -0,0 +1 @@
+# no third-party dependencies
diff --git a/reproduction/amount-mapping-family/environment.json b/reproduction/amount-mapping-family/environment.json
new file mode 100644
index 00000000..e263df62
--- /dev/null
+++ b/reproduction/amount-mapping-family/environment.json
@@ -0,0 +1,13 @@
+{
+ "schema_version": 1,
+ "python": {
+ "implementation": "cpython",
+ "version": "3.13",
+ "system": "Darwin",
+ "machine": "arm64"
+ },
+ "source_archive": "sources/decluster-bcd9a3e-runtime.tar",
+ "project_root": "decluster",
+ "requirements": "reproduction/amount-mapping-family/requirements.lock",
+ "dependency_directory": "dependencies"
+}
diff --git a/reproduction/amount-mapping-family/requirements.lock b/reproduction/amount-mapping-family/requirements.lock
new file mode 100644
index 00000000..6fa079eb
--- /dev/null
+++ b/reproduction/amount-mapping-family/requirements.lock
@@ -0,0 +1 @@
+dss==0.1.0 --hash=sha256:371e5cc5e6b3fd5de038e2fc75f7454a5cc59ce0a42c8189685921b9dc7acd43
diff --git a/reproduction/amount-pairwise-family/environment.json b/reproduction/amount-pairwise-family/environment.json
new file mode 100644
index 00000000..c5ccf022
--- /dev/null
+++ b/reproduction/amount-pairwise-family/environment.json
@@ -0,0 +1,13 @@
+{
+ "schema_version": 1,
+ "python": {
+ "implementation": "cpython",
+ "version": "3.13",
+ "system": "Darwin",
+ "machine": "arm64"
+ },
+ "source_archive": "sources/decluster-bcd9a3e-runtime.tar",
+ "project_root": "decluster",
+ "requirements": "reproduction/amount-pairwise-family/requirements.lock",
+ "dependency_directory": "dependencies"
+}
diff --git a/reproduction/amount-pairwise-family/requirements.lock b/reproduction/amount-pairwise-family/requirements.lock
new file mode 100644
index 00000000..6fa079eb
--- /dev/null
+++ b/reproduction/amount-pairwise-family/requirements.lock
@@ -0,0 +1 @@
+dss==0.1.0 --hash=sha256:371e5cc5e6b3fd5de038e2fc75f7454a5cc59ce0a42c8189685921b9dc7acd43
diff --git a/reproduction/amount-per-coin/environment.json b/reproduction/amount-per-coin/environment.json
new file mode 100644
index 00000000..52f8f847
--- /dev/null
+++ b/reproduction/amount-per-coin/environment.json
@@ -0,0 +1,13 @@
+{
+ "schema_version": 1,
+ "python": {
+ "implementation": "cpython",
+ "version": "3.13",
+ "system": "Darwin",
+ "machine": "arm64"
+ },
+ "source_archive": "sources/decluster-bcd9a3e-runtime.tar",
+ "project_root": "decluster",
+ "requirements": "reproduction/amount-per-coin/requirements.lock",
+ "dependency_directory": "dependencies"
+}
diff --git a/reproduction/amount-per-coin/requirements.lock b/reproduction/amount-per-coin/requirements.lock
new file mode 100644
index 00000000..6fa079eb
--- /dev/null
+++ b/reproduction/amount-per-coin/requirements.lock
@@ -0,0 +1 @@
+dss==0.1.0 --hash=sha256:371e5cc5e6b3fd5de038e2fc75f7454a5cc59ce0a42c8189685921b9dc7acd43
diff --git a/reproduction/amount-subset-sum/environment.json b/reproduction/amount-subset-sum/environment.json
new file mode 100644
index 00000000..c448b099
--- /dev/null
+++ b/reproduction/amount-subset-sum/environment.json
@@ -0,0 +1,13 @@
+{
+ "schema_version": 1,
+ "python": {
+ "implementation": "cpython",
+ "version": "3.13",
+ "system": "Darwin",
+ "machine": "arm64"
+ },
+ "source_archive": "sources/decluster-bcd9a3e-runtime.tar",
+ "project_root": "decluster",
+ "requirements": "reproduction/amount-subset-sum/requirements.lock",
+ "dependency_directory": "dependencies"
+}
diff --git a/reproduction/amount-subset-sum/requirements.lock b/reproduction/amount-subset-sum/requirements.lock
new file mode 100644
index 00000000..6fa079eb
--- /dev/null
+++ b/reproduction/amount-subset-sum/requirements.lock
@@ -0,0 +1 @@
+dss==0.1.0 --hash=sha256:371e5cc5e6b3fd5de038e2fc75f7454a5cc59ce0a42c8189685921b9dc7acd43
diff --git a/reproduction/analyze-contract/environment.json b/reproduction/analyze-contract/environment.json
new file mode 100644
index 00000000..aec4ac3c
--- /dev/null
+++ b/reproduction/analyze-contract/environment.json
@@ -0,0 +1,13 @@
+{
+ "schema_version": 1,
+ "python": {
+ "implementation": "cpython",
+ "version": "3.13",
+ "system": "Darwin",
+ "machine": "arm64"
+ },
+ "source_archive": "sources/decluster-bcd9a3e-runtime.tar",
+ "project_root": "decluster",
+ "requirements": "reproduction/analyze-contract/requirements.lock",
+ "dependency_directory": "reproduction/analyze-contract"
+}
diff --git a/reproduction/analyze-contract/requirements.lock b/reproduction/analyze-contract/requirements.lock
new file mode 100644
index 00000000..e69de29b
diff --git a/reproduction/ancestry-contract/environment.json b/reproduction/ancestry-contract/environment.json
new file mode 100644
index 00000000..94a4563d
--- /dev/null
+++ b/reproduction/ancestry-contract/environment.json
@@ -0,0 +1,13 @@
+{
+ "schema_version": 1,
+ "python": {
+ "implementation": "cpython",
+ "version": "3.13",
+ "system": "Darwin",
+ "machine": "arm64"
+ },
+ "source_archive": "sources/decluster-bcd9a3e-runtime.tar",
+ "project_root": "decluster",
+ "requirements": "reproduction/ancestry-contract/requirements.lock",
+ "dependency_directory": "reproduction/ancestry-contract"
+}
diff --git a/reproduction/ancestry-contract/requirements.lock b/reproduction/ancestry-contract/requirements.lock
new file mode 100644
index 00000000..e69de29b
diff --git a/reproduction/ancestry-sparsity/environment.json b/reproduction/ancestry-sparsity/environment.json
new file mode 100644
index 00000000..f28d335c
--- /dev/null
+++ b/reproduction/ancestry-sparsity/environment.json
@@ -0,0 +1,13 @@
+{
+ "schema_version": 1,
+ "python": {
+ "implementation": "cpython",
+ "version": "3.13",
+ "system": "Darwin",
+ "machine": "arm64"
+ },
+ "source_archive": "sources/decluster-bcd9a3e-runtime.tar",
+ "project_root": "decluster",
+ "requirements": "reproduction/ancestry-sparsity/requirements.lock",
+ "dependency_directory": "reproduction/ancestry-sparsity"
+}
diff --git a/reproduction/ancestry-sparsity/requirements.lock b/reproduction/ancestry-sparsity/requirements.lock
new file mode 100644
index 00000000..e69de29b
diff --git a/reproduction/anchor-axis-families/environment.json b/reproduction/anchor-axis-families/environment.json
new file mode 100644
index 00000000..85910f67
--- /dev/null
+++ b/reproduction/anchor-axis-families/environment.json
@@ -0,0 +1,13 @@
+{
+ "schema_version": 1,
+ "python": {
+ "implementation": "cpython",
+ "version": "3.13",
+ "system": "Darwin",
+ "machine": "arm64"
+ },
+ "source_archive": "sources/decluster-bcd9a3e-runtime.tar",
+ "project_root": "decluster",
+ "requirements": "reproduction/anchor-axis-families/requirements.lock",
+ "dependency_directory": "reproduction/anchor-axis-families"
+}
diff --git a/reproduction/anchor-axis-families/requirements.lock b/reproduction/anchor-axis-families/requirements.lock
new file mode 100644
index 00000000..cb70631a
--- /dev/null
+++ b/reproduction/anchor-axis-families/requirements.lock
@@ -0,0 +1 @@
+# no third-party dependencies
diff --git a/reproduction/attribute-drift/environment.json b/reproduction/attribute-drift/environment.json
new file mode 100644
index 00000000..5d88e56b
--- /dev/null
+++ b/reproduction/attribute-drift/environment.json
@@ -0,0 +1,13 @@
+{
+ "schema_version": 1,
+ "python": {
+ "implementation": "cpython",
+ "version": "3.13",
+ "system": "Darwin",
+ "machine": "arm64"
+ },
+ "source_archive": "sources/decluster-bcd9a3e-runtime.tar",
+ "project_root": "decluster",
+ "requirements": "reproduction/attribute-drift/requirements.lock",
+ "dependency_directory": "reproduction/attribute-drift"
+}
diff --git a/reproduction/attribute-drift/requirements.lock b/reproduction/attribute-drift/requirements.lock
new file mode 100644
index 00000000..e69de29b
diff --git a/reproduction/bayes-vs-fs/environment.json b/reproduction/bayes-vs-fs/environment.json
new file mode 100644
index 00000000..5e088e13
--- /dev/null
+++ b/reproduction/bayes-vs-fs/environment.json
@@ -0,0 +1,13 @@
+{
+ "schema_version": 1,
+ "python": {
+ "implementation": "cpython",
+ "version": "3.13",
+ "system": "Darwin",
+ "machine": "arm64"
+ },
+ "source_archive": "sources/decluster-bcd9a3e-runtime.tar",
+ "project_root": "decluster",
+ "requirements": "reproduction/bayes-vs-fs/requirements.lock",
+ "dependency_directory": "reproduction/bayes-vs-fs"
+}
diff --git a/reproduction/bayes-vs-fs/requirements.lock b/reproduction/bayes-vs-fs/requirements.lock
new file mode 100644
index 00000000..e69de29b
diff --git a/reproduction/boltzmann-fee-audit/environment.json b/reproduction/boltzmann-fee-audit/environment.json
new file mode 100644
index 00000000..9d4577f3
--- /dev/null
+++ b/reproduction/boltzmann-fee-audit/environment.json
@@ -0,0 +1,13 @@
+{
+ "schema_version": 1,
+ "python": {
+ "implementation": "cpython",
+ "version": "3.13",
+ "system": "Darwin",
+ "machine": "arm64"
+ },
+ "source_archive": "sources/decluster-bcd9a3e-runtime.tar",
+ "project_root": "decluster",
+ "requirements": "reproduction/boltzmann-fee-audit/requirements.lock",
+ "dependency_directory": "reproduction/boltzmann-fee-audit"
+}
diff --git a/reproduction/boltzmann-fee-audit/requirements.lock b/reproduction/boltzmann-fee-audit/requirements.lock
new file mode 100644
index 00000000..cb70631a
--- /dev/null
+++ b/reproduction/boltzmann-fee-audit/requirements.lock
@@ -0,0 +1 @@
+# no third-party dependencies
diff --git a/reproduction/candidate-set-intersection/environment.json b/reproduction/candidate-set-intersection/environment.json
new file mode 100644
index 00000000..623c5ddc
--- /dev/null
+++ b/reproduction/candidate-set-intersection/environment.json
@@ -0,0 +1,13 @@
+{
+ "schema_version": 1,
+ "python": {
+ "implementation": "cpython",
+ "version": "3.13",
+ "system": "Darwin",
+ "machine": "arm64"
+ },
+ "source_archive": "sources/decluster-bcd9a3e-runtime.tar",
+ "project_root": "decluster",
+ "requirements": "reproduction/candidate-set-intersection/requirements.lock",
+ "dependency_directory": "reproduction/candidate-set-intersection"
+}
diff --git a/reproduction/candidate-set-intersection/requirements.lock b/reproduction/candidate-set-intersection/requirements.lock
new file mode 100644
index 00000000..3e1593f0
--- /dev/null
+++ b/reproduction/candidate-set-intersection/requirements.lock
@@ -0,0 +1 @@
+# No third-party dependencies.
diff --git a/reproduction/change-consolidation/environment.json b/reproduction/change-consolidation/environment.json
new file mode 100644
index 00000000..ea7b9ff4
--- /dev/null
+++ b/reproduction/change-consolidation/environment.json
@@ -0,0 +1,13 @@
+{
+ "schema_version": 1,
+ "python": {
+ "implementation": "cpython",
+ "version": "3.13",
+ "system": "Darwin",
+ "machine": "arm64"
+ },
+ "source_archive": "sources/decluster-bcd9a3e-runtime.tar",
+ "project_root": "decluster",
+ "requirements": "reproduction/change-consolidation/requirements.lock",
+ "dependency_directory": "reproduction/change-consolidation"
+}
diff --git a/reproduction/change-consolidation/requirements.lock b/reproduction/change-consolidation/requirements.lock
new file mode 100644
index 00000000..cb70631a
--- /dev/null
+++ b/reproduction/change-consolidation/requirements.lock
@@ -0,0 +1 @@
+# no third-party dependencies
diff --git a/reproduction/cioh-collaborative/environment.json b/reproduction/cioh-collaborative/environment.json
new file mode 100644
index 00000000..70e171bd
--- /dev/null
+++ b/reproduction/cioh-collaborative/environment.json
@@ -0,0 +1,13 @@
+{
+ "schema_version": 1,
+ "python": {
+ "implementation": "cpython",
+ "version": "3.13",
+ "system": "Darwin",
+ "machine": "arm64"
+ },
+ "source_archive": "sources/decluster-bcd9a3e-runtime.tar",
+ "project_root": "decluster",
+ "requirements": "reproduction/cioh-collaborative/requirements.lock",
+ "dependency_directory": "reproduction/cioh-collaborative"
+}
diff --git a/reproduction/cioh-collaborative/requirements.lock b/reproduction/cioh-collaborative/requirements.lock
new file mode 100644
index 00000000..cb70631a
--- /dev/null
+++ b/reproduction/cioh-collaborative/requirements.lock
@@ -0,0 +1 @@
+# no third-party dependencies
diff --git a/reproduction/cluster-bits/environment.json b/reproduction/cluster-bits/environment.json
new file mode 100644
index 00000000..899025f1
--- /dev/null
+++ b/reproduction/cluster-bits/environment.json
@@ -0,0 +1,13 @@
+{
+ "schema_version": 1,
+ "python": {
+ "implementation": "cpython",
+ "version": "3.13",
+ "system": "Darwin",
+ "machine": "arm64"
+ },
+ "source_archive": "sources/decluster-bcd9a3e-runtime.tar",
+ "project_root": "decluster",
+ "requirements": "reproduction/cluster-bits/requirements.lock",
+ "dependency_directory": "reproduction/cluster-bits"
+}
diff --git a/reproduction/cluster-bits/requirements.lock b/reproduction/cluster-bits/requirements.lock
new file mode 100644
index 00000000..cb70631a
--- /dev/null
+++ b/reproduction/cluster-bits/requirements.lock
@@ -0,0 +1 @@
+# no third-party dependencies
diff --git a/reproduction/coinjoin-sudoku-nominal/environment.json b/reproduction/coinjoin-sudoku-nominal/environment.json
new file mode 100644
index 00000000..f831fa7f
--- /dev/null
+++ b/reproduction/coinjoin-sudoku-nominal/environment.json
@@ -0,0 +1,13 @@
+{
+ "schema_version": 1,
+ "python": {
+ "implementation": "cpython",
+ "version": "3.13",
+ "system": "Darwin",
+ "machine": "arm64"
+ },
+ "source_archive": "sources/decluster-bcd9a3e-runtime.tar",
+ "project_root": "decluster",
+ "requirements": "reproduction/coinjoin-sudoku-nominal/requirements.lock",
+ "dependency_directory": "reproduction/coinjoin-sudoku-nominal"
+}
diff --git a/reproduction/coinjoin-sudoku-nominal/requirements.lock b/reproduction/coinjoin-sudoku-nominal/requirements.lock
new file mode 100644
index 00000000..cb70631a
--- /dev/null
+++ b/reproduction/coinjoin-sudoku-nominal/requirements.lock
@@ -0,0 +1 @@
+# no third-party dependencies
diff --git a/reproduction/collaborative-forms/environment.json b/reproduction/collaborative-forms/environment.json
new file mode 100644
index 00000000..259178d5
--- /dev/null
+++ b/reproduction/collaborative-forms/environment.json
@@ -0,0 +1,13 @@
+{
+ "schema_version": 1,
+ "python": {
+ "implementation": "cpython",
+ "version": "3.13",
+ "system": "Darwin",
+ "machine": "arm64"
+ },
+ "source_archive": "sources/decluster-bcd9a3e-runtime.tar",
+ "project_root": "decluster",
+ "requirements": "reproduction/collaborative-forms/requirements.lock",
+ "dependency_directory": "reproduction/collaborative-forms"
+}
diff --git a/reproduction/collaborative-forms/requirements.lock b/reproduction/collaborative-forms/requirements.lock
new file mode 100644
index 00000000..cb70631a
--- /dev/null
+++ b/reproduction/collaborative-forms/requirements.lock
@@ -0,0 +1 @@
+# no third-party dependencies
diff --git a/reproduction/conservation-round-three/environment.json b/reproduction/conservation-round-three/environment.json
new file mode 100644
index 00000000..acc0afd3
--- /dev/null
+++ b/reproduction/conservation-round-three/environment.json
@@ -0,0 +1,13 @@
+{
+ "schema_version": 1,
+ "python": {
+ "implementation": "cpython",
+ "version": "3.13",
+ "system": "Darwin",
+ "machine": "arm64"
+ },
+ "source_archive": "sources/decluster-bcd9a3e-runtime.tar",
+ "project_root": "decluster",
+ "requirements": "reproduction/conservation-round-three/requirements.lock",
+ "dependency_directory": "reproduction/conservation-round-three"
+}
diff --git a/reproduction/conservation-round-three/requirements.lock b/reproduction/conservation-round-three/requirements.lock
new file mode 100644
index 00000000..e69de29b
diff --git a/reproduction/counting-fee-sensitivity/environment.json b/reproduction/counting-fee-sensitivity/environment.json
new file mode 100644
index 00000000..d6155c30
--- /dev/null
+++ b/reproduction/counting-fee-sensitivity/environment.json
@@ -0,0 +1,13 @@
+{
+ "schema_version": 1,
+ "python": {
+ "implementation": "cpython",
+ "version": "3.13",
+ "system": "Darwin",
+ "machine": "arm64"
+ },
+ "source_archive": "sources/decluster-bcd9a3e-runtime.tar",
+ "project_root": "decluster",
+ "requirements": "reproduction/counting-fee-sensitivity/requirements.lock",
+ "dependency_directory": "dependencies"
+}
diff --git a/reproduction/counting-fee-sensitivity/requirements.lock b/reproduction/counting-fee-sensitivity/requirements.lock
new file mode 100644
index 00000000..6fa079eb
--- /dev/null
+++ b/reproduction/counting-fee-sensitivity/requirements.lock
@@ -0,0 +1 @@
+dss==0.1.0 --hash=sha256:371e5cc5e6b3fd5de038e2fc75f7454a5cc59ce0a42c8189685921b9dc7acd43
diff --git a/reproduction/counting-methods/environment.json b/reproduction/counting-methods/environment.json
new file mode 100644
index 00000000..131f6567
--- /dev/null
+++ b/reproduction/counting-methods/environment.json
@@ -0,0 +1,13 @@
+{
+ "schema_version": 1,
+ "python": {
+ "implementation": "cpython",
+ "version": "3.13",
+ "system": "Darwin",
+ "machine": "arm64"
+ },
+ "source_archive": "sources/decluster-bcd9a3e-runtime.tar",
+ "project_root": "decluster",
+ "requirements": "reproduction/counting-methods/requirements.lock",
+ "dependency_directory": "dependencies"
+}
diff --git a/reproduction/counting-methods/requirements.lock b/reproduction/counting-methods/requirements.lock
new file mode 100644
index 00000000..e69de29b
diff --git a/reproduction/counting-router/environment.json b/reproduction/counting-router/environment.json
new file mode 100644
index 00000000..9becdf84
--- /dev/null
+++ b/reproduction/counting-router/environment.json
@@ -0,0 +1,13 @@
+{
+ "schema_version": 1,
+ "python": {
+ "implementation": "cpython",
+ "version": "3.13",
+ "system": "Darwin",
+ "machine": "arm64"
+ },
+ "source_archive": "sources/decluster-bcd9a3e-runtime.tar",
+ "project_root": "decluster",
+ "requirements": "reproduction/counting-router/requirements.lock",
+ "dependency_directory": "dependencies"
+}
diff --git a/reproduction/counting-router/requirements.lock b/reproduction/counting-router/requirements.lock
new file mode 100644
index 00000000..6fa079eb
--- /dev/null
+++ b/reproduction/counting-router/requirements.lock
@@ -0,0 +1 @@
+dss==0.1.0 --hash=sha256:371e5cc5e6b3fd5de038e2fc75f7454a5cc59ce0a42c8189685921b9dc7acd43
diff --git a/reproduction/denomination-preparation/environment.json b/reproduction/denomination-preparation/environment.json
new file mode 100644
index 00000000..ff1acacd
--- /dev/null
+++ b/reproduction/denomination-preparation/environment.json
@@ -0,0 +1,13 @@
+{
+ "schema_version": 1,
+ "python": {
+ "implementation": "cpython",
+ "version": "3.13",
+ "system": "Darwin",
+ "machine": "arm64"
+ },
+ "source_archive": "sources/decluster-bcd9a3e-runtime.tar",
+ "project_root": "decluster",
+ "requirements": "reproduction/denomination-preparation/requirements.lock",
+ "dependency_directory": "reproduction/denomination-preparation"
+}
diff --git a/reproduction/denomination-preparation/requirements.lock b/reproduction/denomination-preparation/requirements.lock
new file mode 100644
index 00000000..cb70631a
--- /dev/null
+++ b/reproduction/denomination-preparation/requirements.lock
@@ -0,0 +1 @@
+# no third-party dependencies
diff --git a/reproduction/descent-vs-ascent/environment.json b/reproduction/descent-vs-ascent/environment.json
new file mode 100644
index 00000000..46e4b6ff
--- /dev/null
+++ b/reproduction/descent-vs-ascent/environment.json
@@ -0,0 +1,13 @@
+{
+ "schema_version": 1,
+ "python": {
+ "implementation": "cpython",
+ "version": "3.13",
+ "system": "Darwin",
+ "machine": "arm64"
+ },
+ "source_archive": "sources/decluster-bcd9a3e-runtime.tar",
+ "project_root": "decluster",
+ "requirements": "reproduction/descent-vs-ascent/requirements.lock",
+ "dependency_directory": "reproduction/descent-vs-ascent"
+}
diff --git a/reproduction/descent-vs-ascent/requirements.lock b/reproduction/descent-vs-ascent/requirements.lock
new file mode 100644
index 00000000..cb70631a
--- /dev/null
+++ b/reproduction/descent-vs-ascent/requirements.lock
@@ -0,0 +1 @@
+# no third-party dependencies
diff --git a/reproduction/deterministic-partition/environment.json b/reproduction/deterministic-partition/environment.json
new file mode 100644
index 00000000..5c0f67d8
--- /dev/null
+++ b/reproduction/deterministic-partition/environment.json
@@ -0,0 +1,13 @@
+{
+ "schema_version": 1,
+ "python": {
+ "implementation": "cpython",
+ "version": "3.13",
+ "system": "Darwin",
+ "machine": "arm64"
+ },
+ "source_archive": "sources/decluster-bcd9a3e-runtime.tar",
+ "project_root": "decluster",
+ "requirements": "reproduction/deterministic-partition/requirements.lock",
+ "dependency_directory": "reproduction/deterministic-partition"
+}
diff --git a/reproduction/deterministic-partition/requirements.lock b/reproduction/deterministic-partition/requirements.lock
new file mode 100644
index 00000000..cb70631a
--- /dev/null
+++ b/reproduction/deterministic-partition/requirements.lock
@@ -0,0 +1 @@
+# no third-party dependencies
diff --git a/reproduction/em-m/environment.json b/reproduction/em-m/environment.json
new file mode 100644
index 00000000..88aa9d92
--- /dev/null
+++ b/reproduction/em-m/environment.json
@@ -0,0 +1,13 @@
+{
+ "schema_version": 1,
+ "python": {
+ "implementation": "cpython",
+ "version": "3.13",
+ "system": "Darwin",
+ "machine": "arm64"
+ },
+ "source_archive": "sources/decluster-bcd9a3e-runtime.tar",
+ "project_root": "decluster",
+ "requirements": "reproduction/em-m/requirements.lock",
+ "dependency_directory": "reproduction/em-m"
+}
diff --git a/reproduction/em-m/requirements.lock b/reproduction/em-m/requirements.lock
new file mode 100644
index 00000000..e69de29b
diff --git a/reproduction/entity-deanon/environment.json b/reproduction/entity-deanon/environment.json
new file mode 100644
index 00000000..a50791c5
--- /dev/null
+++ b/reproduction/entity-deanon/environment.json
@@ -0,0 +1,13 @@
+{
+ "schema_version": 1,
+ "python": {
+ "implementation": "cpython",
+ "version": "3.13",
+ "system": "Darwin",
+ "machine": "arm64"
+ },
+ "source_archive": "sources/decluster-bcd9a3e-runtime.tar",
+ "project_root": "decluster",
+ "requirements": "reproduction/entity-deanon/requirements.lock",
+ "dependency_directory": "reproduction/entity-deanon"
+}
diff --git a/reproduction/entity-deanon/requirements.lock b/reproduction/entity-deanon/requirements.lock
new file mode 100644
index 00000000..e69de29b
diff --git a/reproduction/entropy-insufficiency/environment.json b/reproduction/entropy-insufficiency/environment.json
new file mode 100644
index 00000000..df82c95c
--- /dev/null
+++ b/reproduction/entropy-insufficiency/environment.json
@@ -0,0 +1,13 @@
+{
+ "schema_version": 1,
+ "python": {
+ "implementation": "cpython",
+ "version": "3.13",
+ "system": "Darwin",
+ "machine": "arm64"
+ },
+ "source_archive": "sources/decluster-bcd9a3e-runtime.tar",
+ "project_root": "decluster",
+ "requirements": "reproduction/entropy-insufficiency/requirements.lock",
+ "dependency_directory": "reproduction/entropy-insufficiency"
+}
diff --git a/reproduction/entropy-insufficiency/requirements.lock b/reproduction/entropy-insufficiency/requirements.lock
new file mode 100644
index 00000000..cb70631a
--- /dev/null
+++ b/reproduction/entropy-insufficiency/requirements.lock
@@ -0,0 +1 @@
+# no third-party dependencies
diff --git a/reproduction/equal-output-consolidation/environment.json b/reproduction/equal-output-consolidation/environment.json
new file mode 100644
index 00000000..a8430930
--- /dev/null
+++ b/reproduction/equal-output-consolidation/environment.json
@@ -0,0 +1,13 @@
+{
+ "schema_version": 1,
+ "python": {
+ "implementation": "cpython",
+ "version": "3.13",
+ "system": "Darwin",
+ "machine": "arm64"
+ },
+ "source_archive": "sources/decluster-bcd9a3e-runtime.tar",
+ "project_root": "decluster",
+ "requirements": "reproduction/equal-output-consolidation/requirements.lock",
+ "dependency_directory": "reproduction/equal-output-consolidation"
+}
diff --git a/reproduction/equal-output-consolidation/requirements.lock b/reproduction/equal-output-consolidation/requirements.lock
new file mode 100644
index 00000000..3e1593f0
--- /dev/null
+++ b/reproduction/equal-output-consolidation/requirements.lock
@@ -0,0 +1 @@
+# No third-party dependencies.
diff --git a/reproduction/eve-alice-eve/environment.json b/reproduction/eve-alice-eve/environment.json
new file mode 100644
index 00000000..2089846f
--- /dev/null
+++ b/reproduction/eve-alice-eve/environment.json
@@ -0,0 +1,13 @@
+{
+ "schema_version": 1,
+ "python": {
+ "implementation": "cpython",
+ "version": "3.13",
+ "system": "Darwin",
+ "machine": "arm64"
+ },
+ "source_archive": "sources/decluster-bcd9a3e-runtime.tar",
+ "project_root": "decluster",
+ "requirements": "reproduction/eve-alice-eve/requirements.lock",
+ "dependency_directory": "reproduction/eve-alice-eve"
+}
diff --git a/reproduction/eve-alice-eve/requirements.lock b/reproduction/eve-alice-eve/requirements.lock
new file mode 100644
index 00000000..cb70631a
--- /dev/null
+++ b/reproduction/eve-alice-eve/requirements.lock
@@ -0,0 +1 @@
+# no third-party dependencies
diff --git a/reproduction/exact-oracle/environment.json b/reproduction/exact-oracle/environment.json
new file mode 100644
index 00000000..a4ee7fe1
--- /dev/null
+++ b/reproduction/exact-oracle/environment.json
@@ -0,0 +1,13 @@
+{
+ "schema_version": 1,
+ "python": {
+ "implementation": "cpython",
+ "version": "3.13",
+ "system": "Darwin",
+ "machine": "arm64"
+ },
+ "source_archive": "sources/decluster-bcd9a3e-runtime.tar",
+ "project_root": "decluster",
+ "requirements": "reproduction/exact-oracle/requirements.lock",
+ "dependency_directory": "dependencies"
+}
diff --git a/reproduction/exact-oracle/requirements.lock b/reproduction/exact-oracle/requirements.lock
new file mode 100644
index 00000000..6fa079eb
--- /dev/null
+++ b/reproduction/exact-oracle/requirements.lock
@@ -0,0 +1 @@
+dss==0.1.0 --hash=sha256:371e5cc5e6b3fd5de038e2fc75f7454a5cc59ce0a42c8189685921b9dc7acd43
diff --git a/reproduction/fingerprint-catalog-axes/environment.json b/reproduction/fingerprint-catalog-axes/environment.json
new file mode 100644
index 00000000..b66e03dd
--- /dev/null
+++ b/reproduction/fingerprint-catalog-axes/environment.json
@@ -0,0 +1,13 @@
+{
+ "schema_version": 1,
+ "python": {
+ "implementation": "cpython",
+ "version": "3.13",
+ "system": "Darwin",
+ "machine": "arm64"
+ },
+ "source_archive": "sources/decluster-bcd9a3e-runtime.tar",
+ "project_root": "decluster",
+ "requirements": "reproduction/fingerprint-catalog-axes/requirements.lock",
+ "dependency_directory": "reproduction/fingerprint-catalog-axes"
+}
diff --git a/reproduction/fingerprint-catalog-axes/requirements.lock b/reproduction/fingerprint-catalog-axes/requirements.lock
new file mode 100644
index 00000000..e69de29b
diff --git a/reproduction/fingerprint-regime/environment.json b/reproduction/fingerprint-regime/environment.json
new file mode 100644
index 00000000..7e4df47b
--- /dev/null
+++ b/reproduction/fingerprint-regime/environment.json
@@ -0,0 +1,13 @@
+{
+ "schema_version": 1,
+ "python": {
+ "implementation": "cpython",
+ "version": "3.13",
+ "system": "Darwin",
+ "machine": "arm64"
+ },
+ "source_archive": "sources/decluster-bcd9a3e-runtime.tar",
+ "project_root": "decluster",
+ "requirements": "reproduction/fingerprint-regime/requirements.lock",
+ "dependency_directory": "reproduction/fingerprint-regime"
+}
diff --git a/reproduction/fingerprint-regime/requirements.lock b/reproduction/fingerprint-regime/requirements.lock
new file mode 100644
index 00000000..e69de29b
diff --git a/reproduction/fingerprint-sparsity/environment.json b/reproduction/fingerprint-sparsity/environment.json
new file mode 100644
index 00000000..6bb1e77f
--- /dev/null
+++ b/reproduction/fingerprint-sparsity/environment.json
@@ -0,0 +1,13 @@
+{
+ "schema_version": 1,
+ "python": {
+ "implementation": "cpython",
+ "version": "3.13",
+ "system": "Darwin",
+ "machine": "arm64"
+ },
+ "source_archive": "sources/decluster-bcd9a3e-runtime.tar",
+ "project_root": "decluster",
+ "requirements": "reproduction/fingerprint-sparsity/requirements.lock",
+ "dependency_directory": "reproduction/fingerprint-sparsity"
+}
diff --git a/reproduction/fingerprint-sparsity/requirements.lock b/reproduction/fingerprint-sparsity/requirements.lock
new file mode 100644
index 00000000..e69de29b
diff --git a/reproduction/fingerprint-validation/environment.json b/reproduction/fingerprint-validation/environment.json
new file mode 100644
index 00000000..8af57cbd
--- /dev/null
+++ b/reproduction/fingerprint-validation/environment.json
@@ -0,0 +1,13 @@
+{
+ "schema_version": 1,
+ "python": {
+ "implementation": "cpython",
+ "version": "3.13",
+ "system": "Darwin",
+ "machine": "arm64"
+ },
+ "source_archive": "sources/decluster-bcd9a3e-runtime.tar",
+ "project_root": "decluster",
+ "requirements": "reproduction/fingerprint-validation/requirements.lock",
+ "dependency_directory": "reproduction/fingerprint-validation"
+}
diff --git a/reproduction/fingerprint-validation/requirements.lock b/reproduction/fingerprint-validation/requirements.lock
new file mode 100644
index 00000000..e69de29b
diff --git a/reproduction/fs-ablation/environment.json b/reproduction/fs-ablation/environment.json
new file mode 100644
index 00000000..a54de114
--- /dev/null
+++ b/reproduction/fs-ablation/environment.json
@@ -0,0 +1,13 @@
+{
+ "schema_version": 1,
+ "python": {
+ "implementation": "cpython",
+ "version": "3.13",
+ "system": "Darwin",
+ "machine": "arm64"
+ },
+ "source_archive": "sources/decluster-bcd9a3e-runtime.tar",
+ "project_root": "decluster",
+ "requirements": "reproduction/fs-ablation/requirements.lock",
+ "dependency_directory": "reproduction/fs-ablation"
+}
diff --git a/reproduction/fs-ablation/requirements.lock b/reproduction/fs-ablation/requirements.lock
new file mode 100644
index 00000000..cb70631a
--- /dev/null
+++ b/reproduction/fs-ablation/requirements.lock
@@ -0,0 +1 @@
+# no third-party dependencies
diff --git a/reproduction/fs-temporal/environment.json b/reproduction/fs-temporal/environment.json
new file mode 100644
index 00000000..26962ef9
--- /dev/null
+++ b/reproduction/fs-temporal/environment.json
@@ -0,0 +1,13 @@
+{
+ "schema_version": 1,
+ "python": {
+ "implementation": "cpython",
+ "version": "3.13",
+ "system": "Darwin",
+ "machine": "arm64"
+ },
+ "source_archive": "sources/decluster-bcd9a3e-runtime.tar",
+ "project_root": "decluster",
+ "requirements": "reproduction/fs-temporal/requirements.lock",
+ "dependency_directory": "reproduction/fs-temporal"
+}
diff --git a/reproduction/fs-temporal/requirements.lock b/reproduction/fs-temporal/requirements.lock
new file mode 100644
index 00000000..cb70631a
--- /dev/null
+++ b/reproduction/fs-temporal/requirements.lock
@@ -0,0 +1 @@
+# no third-party dependencies
diff --git a/reproduction/fused-engine/environment.json b/reproduction/fused-engine/environment.json
new file mode 100644
index 00000000..7a6aeb34
--- /dev/null
+++ b/reproduction/fused-engine/environment.json
@@ -0,0 +1,13 @@
+{
+ "schema_version": 1,
+ "python": {
+ "implementation": "cpython",
+ "version": "3.13",
+ "system": "Darwin",
+ "machine": "arm64"
+ },
+ "source_archive": "sources/decluster-bcd9a3e-runtime.tar",
+ "project_root": "decluster",
+ "requirements": "reproduction/fused-engine/requirements.lock",
+ "dependency_directory": "reproduction/fused-engine"
+}
diff --git a/reproduction/fused-engine/requirements.lock b/reproduction/fused-engine/requirements.lock
new file mode 100644
index 00000000..cb70631a
--- /dev/null
+++ b/reproduction/fused-engine/requirements.lock
@@ -0,0 +1 @@
+# no third-party dependencies
diff --git a/reproduction/graph-deanon-controls/environment.json b/reproduction/graph-deanon-controls/environment.json
new file mode 100644
index 00000000..f8c49efd
--- /dev/null
+++ b/reproduction/graph-deanon-controls/environment.json
@@ -0,0 +1,13 @@
+{
+ "schema_version": 1,
+ "python": {
+ "implementation": "cpython",
+ "version": "3.13",
+ "system": "Darwin",
+ "machine": "arm64"
+ },
+ "source_archive": "sources/decluster-bcd9a3e-runtime.tar",
+ "project_root": "decluster",
+ "requirements": "reproduction/graph-deanon-controls/requirements.lock",
+ "dependency_directory": "reproduction/graph-deanon-controls"
+}
diff --git a/reproduction/graph-deanon-controls/requirements.lock b/reproduction/graph-deanon-controls/requirements.lock
new file mode 100644
index 00000000..cb70631a
--- /dev/null
+++ b/reproduction/graph-deanon-controls/requirements.lock
@@ -0,0 +1 @@
+# no third-party dependencies
diff --git a/reproduction/graph-rejoin-2016/environment.json b/reproduction/graph-rejoin-2016/environment.json
new file mode 100644
index 00000000..cd8ddc57
--- /dev/null
+++ b/reproduction/graph-rejoin-2016/environment.json
@@ -0,0 +1,13 @@
+{
+ "schema_version": 1,
+ "python": {
+ "implementation": "cpython",
+ "version": "3.13",
+ "system": "Darwin",
+ "machine": "arm64"
+ },
+ "source_archive": "sources/decluster-bcd9a3e-runtime.tar",
+ "project_root": "decluster",
+ "requirements": "reproduction/graph-rejoin-2016/requirements.lock",
+ "dependency_directory": "dependencies"
+}
diff --git a/reproduction/graph-rejoin-2016/requirements.lock b/reproduction/graph-rejoin-2016/requirements.lock
new file mode 100644
index 00000000..70a022a3
--- /dev/null
+++ b/reproduction/graph-rejoin-2016/requirements.lock
@@ -0,0 +1 @@
+numpy==2.5.1 --hash=sha256:6165343f81b56ef8f514f396989e529b61d9dc709b99421b07e9f3e698e2287d
diff --git a/reproduction/intersection-cluster-fixture/environment.json b/reproduction/intersection-cluster-fixture/environment.json
new file mode 100644
index 00000000..1c82b332
--- /dev/null
+++ b/reproduction/intersection-cluster-fixture/environment.json
@@ -0,0 +1,13 @@
+{
+ "schema_version": 1,
+ "python": {
+ "implementation": "cpython",
+ "version": "3.13",
+ "system": "Darwin",
+ "machine": "arm64"
+ },
+ "source_archive": "sources/decluster-bcd9a3e-runtime.tar",
+ "project_root": "decluster",
+ "requirements": "reproduction/intersection-cluster-fixture/requirements.lock",
+ "dependency_directory": "reproduction/intersection-cluster-fixture"
+}
diff --git a/reproduction/intersection-cluster-fixture/requirements.lock b/reproduction/intersection-cluster-fixture/requirements.lock
new file mode 100644
index 00000000..3e1593f0
--- /dev/null
+++ b/reproduction/intersection-cluster-fixture/requirements.lock
@@ -0,0 +1 @@
+# No third-party dependencies.
diff --git a/reproduction/library-calibration/environment.json b/reproduction/library-calibration/environment.json
new file mode 100644
index 00000000..743b5ef0
--- /dev/null
+++ b/reproduction/library-calibration/environment.json
@@ -0,0 +1,13 @@
+{
+ "schema_version": 1,
+ "python": {
+ "implementation": "cpython",
+ "version": "3.13",
+ "system": "Darwin",
+ "machine": "arm64"
+ },
+ "source_archive": "sources/decluster-bcd9a3e-runtime.tar",
+ "project_root": "decluster",
+ "requirements": "reproduction/library-calibration/requirements.lock",
+ "dependency_directory": "reproduction/library-calibration"
+}
diff --git a/reproduction/library-calibration/requirements.lock b/reproduction/library-calibration/requirements.lock
new file mode 100644
index 00000000..cb70631a
--- /dev/null
+++ b/reproduction/library-calibration/requirements.lock
@@ -0,0 +1 @@
+# no third-party dependencies
diff --git a/reproduction/link-prediction/environment.json b/reproduction/link-prediction/environment.json
new file mode 100644
index 00000000..4feb2711
--- /dev/null
+++ b/reproduction/link-prediction/environment.json
@@ -0,0 +1,13 @@
+{
+ "schema_version": 1,
+ "python": {
+ "implementation": "cpython",
+ "version": "3.13",
+ "system": "Darwin",
+ "machine": "arm64"
+ },
+ "source_archive": "sources/decluster-bcd9a3e-runtime.tar",
+ "project_root": "decluster",
+ "requirements": "reproduction/link-prediction/requirements.lock",
+ "dependency_directory": "reproduction/link-prediction"
+}
diff --git a/reproduction/link-prediction/requirements.lock b/reproduction/link-prediction/requirements.lock
new file mode 100644
index 00000000..cb70631a
--- /dev/null
+++ b/reproduction/link-prediction/requirements.lock
@@ -0,0 +1 @@
+# no third-party dependencies
diff --git a/reproduction/match-controls/environment.json b/reproduction/match-controls/environment.json
new file mode 100644
index 00000000..4bc13780
--- /dev/null
+++ b/reproduction/match-controls/environment.json
@@ -0,0 +1,13 @@
+{
+ "schema_version": 1,
+ "python": {
+ "implementation": "cpython",
+ "version": "3.13",
+ "system": "Darwin",
+ "machine": "arm64"
+ },
+ "source_archive": "sources/decluster-bcd9a3e-runtime.tar",
+ "project_root": "decluster",
+ "requirements": "reproduction/match-controls/requirements.lock",
+ "dependency_directory": "dependencies"
+}
diff --git a/reproduction/match-controls/requirements.lock b/reproduction/match-controls/requirements.lock
new file mode 100644
index 00000000..70a022a3
--- /dev/null
+++ b/reproduction/match-controls/requirements.lock
@@ -0,0 +1 @@
+numpy==2.5.1 --hash=sha256:6165343f81b56ef8f514f396989e529b61d9dc709b99421b07e9f3e698e2287d
diff --git a/reproduction/ns-bitcoin/environment.json b/reproduction/ns-bitcoin/environment.json
new file mode 100644
index 00000000..a502dbc0
--- /dev/null
+++ b/reproduction/ns-bitcoin/environment.json
@@ -0,0 +1,13 @@
+{
+ "schema_version": 1,
+ "python": {
+ "implementation": "cpython",
+ "version": "3.13",
+ "system": "Darwin",
+ "machine": "arm64"
+ },
+ "source_archive": "sources/decluster-bcd9a3e-runtime.tar",
+ "project_root": "decluster",
+ "requirements": "reproduction/ns-bitcoin/requirements.lock",
+ "dependency_directory": "reproduction/ns-bitcoin"
+}
diff --git a/reproduction/ns-bitcoin/requirements.lock b/reproduction/ns-bitcoin/requirements.lock
new file mode 100644
index 00000000..cb70631a
--- /dev/null
+++ b/reproduction/ns-bitcoin/requirements.lock
@@ -0,0 +1 @@
+# no third-party dependencies
diff --git a/reproduction/ns-link-prediction-coverage/environment.json b/reproduction/ns-link-prediction-coverage/environment.json
new file mode 100644
index 00000000..023e9bf0
--- /dev/null
+++ b/reproduction/ns-link-prediction-coverage/environment.json
@@ -0,0 +1,13 @@
+{
+ "schema_version": 1,
+ "python": {
+ "implementation": "cpython",
+ "version": "3.13",
+ "system": "Darwin",
+ "machine": "arm64"
+ },
+ "source_archive": "sources/decluster-bcd9a3e-runtime.tar",
+ "project_root": "decluster",
+ "requirements": "reproduction/ns-link-prediction-coverage/requirements.lock",
+ "dependency_directory": "reproduction/ns-link-prediction-coverage"
+}
diff --git a/reproduction/ns-link-prediction-coverage/requirements.lock b/reproduction/ns-link-prediction-coverage/requirements.lock
new file mode 100644
index 00000000..cb70631a
--- /dev/null
+++ b/reproduction/ns-link-prediction-coverage/requirements.lock
@@ -0,0 +1 @@
+# no third-party dependencies
diff --git a/reproduction/ns1r-change-readoff/environment.json b/reproduction/ns1r-change-readoff/environment.json
new file mode 100644
index 00000000..69e9292e
--- /dev/null
+++ b/reproduction/ns1r-change-readoff/environment.json
@@ -0,0 +1,13 @@
+{
+ "schema_version": 1,
+ "python": {
+ "implementation": "cpython",
+ "version": "3.13",
+ "system": "Darwin",
+ "machine": "arm64"
+ },
+ "source_archive": "sources/decluster-bcd9a3e-runtime.tar",
+ "project_root": "decluster",
+ "requirements": "reproduction/ns1r-change-readoff/requirements.lock",
+ "dependency_directory": "reproduction/ns1r-change-readoff"
+}
diff --git a/reproduction/ns1r-change-readoff/requirements.lock b/reproduction/ns1r-change-readoff/requirements.lock
new file mode 100644
index 00000000..cb70631a
--- /dev/null
+++ b/reproduction/ns1r-change-readoff/requirements.lock
@@ -0,0 +1 @@
+# no third-party dependencies
diff --git a/reproduction/partition-schemes/environment.json b/reproduction/partition-schemes/environment.json
new file mode 100644
index 00000000..bba1a52b
--- /dev/null
+++ b/reproduction/partition-schemes/environment.json
@@ -0,0 +1,13 @@
+{
+ "schema_version": 1,
+ "python": {
+ "implementation": "cpython",
+ "version": "3.13",
+ "system": "Darwin",
+ "machine": "arm64"
+ },
+ "source_archive": "sources/decluster-bcd9a3e-runtime.tar",
+ "project_root": "decluster",
+ "requirements": "reproduction/partition-schemes/requirements.lock",
+ "dependency_directory": "reproduction/partition-schemes"
+}
diff --git a/reproduction/partition-schemes/requirements.lock b/reproduction/partition-schemes/requirements.lock
new file mode 100644
index 00000000..cb70631a
--- /dev/null
+++ b/reproduction/partition-schemes/requirements.lock
@@ -0,0 +1 @@
+# no third-party dependencies
diff --git a/reproduction/path-count-contract/environment.json b/reproduction/path-count-contract/environment.json
new file mode 100644
index 00000000..a75b0e1b
--- /dev/null
+++ b/reproduction/path-count-contract/environment.json
@@ -0,0 +1,13 @@
+{
+ "schema_version": 1,
+ "python": {
+ "implementation": "cpython",
+ "version": "3.13",
+ "system": "Darwin",
+ "machine": "arm64"
+ },
+ "source_archive": "sources/decluster-bcd9a3e-runtime.tar",
+ "project_root": "decluster",
+ "requirements": "reproduction/path-count-contract/requirements.lock",
+ "dependency_directory": "dependencies"
+}
diff --git a/reproduction/path-count-contract/requirements.lock b/reproduction/path-count-contract/requirements.lock
new file mode 100644
index 00000000..e69de29b
diff --git a/reproduction/perfect-matching-disclosure/environment.json b/reproduction/perfect-matching-disclosure/environment.json
new file mode 100644
index 00000000..50b26747
--- /dev/null
+++ b/reproduction/perfect-matching-disclosure/environment.json
@@ -0,0 +1,13 @@
+{
+ "schema_version": 1,
+ "python": {
+ "implementation": "cpython",
+ "version": "3.13",
+ "system": "Darwin",
+ "machine": "arm64"
+ },
+ "source_archive": "sources/decluster-bcd9a3e-runtime.tar",
+ "project_root": "decluster",
+ "requirements": "reproduction/perfect-matching-disclosure/requirements.lock",
+ "dependency_directory": "reproduction/perfect-matching-disclosure"
+}
diff --git a/reproduction/perfect-matching-disclosure/requirements.lock b/reproduction/perfect-matching-disclosure/requirements.lock
new file mode 100644
index 00000000..cb70631a
--- /dev/null
+++ b/reproduction/perfect-matching-disclosure/requirements.lock
@@ -0,0 +1 @@
+# no third-party dependencies
diff --git a/reproduction/provenance-decay/environment.json b/reproduction/provenance-decay/environment.json
new file mode 100644
index 00000000..70eb9958
--- /dev/null
+++ b/reproduction/provenance-decay/environment.json
@@ -0,0 +1,13 @@
+{
+ "schema_version": 1,
+ "python": {
+ "implementation": "cpython",
+ "version": "3.13",
+ "system": "Darwin",
+ "machine": "arm64"
+ },
+ "source_archive": "sources/decluster-bcd9a3e-runtime.tar",
+ "project_root": "decluster",
+ "requirements": "reproduction/provenance-decay/requirements.lock",
+ "dependency_directory": "reproduction/provenance-decay"
+}
diff --git a/reproduction/provenance-decay/requirements.lock b/reproduction/provenance-decay/requirements.lock
new file mode 100644
index 00000000..cb70631a
--- /dev/null
+++ b/reproduction/provenance-decay/requirements.lock
@@ -0,0 +1 @@
+# no third-party dependencies
diff --git a/reproduction/pseudonym-graph-contract/environment.json b/reproduction/pseudonym-graph-contract/environment.json
new file mode 100644
index 00000000..b6d986d0
--- /dev/null
+++ b/reproduction/pseudonym-graph-contract/environment.json
@@ -0,0 +1,13 @@
+{
+ "schema_version": 1,
+ "python": {
+ "implementation": "cpython",
+ "version": "3.13",
+ "system": "Darwin",
+ "machine": "arm64"
+ },
+ "source_archive": "sources/decluster-bcd9a3e-runtime.tar",
+ "project_root": "decluster",
+ "requirements": "reproduction/pseudonym-graph-contract/requirements.lock",
+ "dependency_directory": "reproduction/pseudonym-graph-contract"
+}
diff --git a/reproduction/pseudonym-graph-contract/requirements.lock b/reproduction/pseudonym-graph-contract/requirements.lock
new file mode 100644
index 00000000..cb70631a
--- /dev/null
+++ b/reproduction/pseudonym-graph-contract/requirements.lock
@@ -0,0 +1 @@
+# no third-party dependencies
diff --git a/reproduction/reid/environment.json b/reproduction/reid/environment.json
new file mode 100644
index 00000000..7ec8182e
--- /dev/null
+++ b/reproduction/reid/environment.json
@@ -0,0 +1,13 @@
+{
+ "schema_version": 1,
+ "python": {
+ "implementation": "cpython",
+ "version": "3.13",
+ "system": "Darwin",
+ "machine": "arm64"
+ },
+ "source_archive": "sources/decluster-bcd9a3e-runtime.tar",
+ "project_root": "decluster",
+ "requirements": "reproduction/reid/requirements.lock",
+ "dependency_directory": "reproduction/reid"
+}
diff --git a/reproduction/reid/requirements.lock b/reproduction/reid/requirements.lock
new file mode 100644
index 00000000..e69de29b
diff --git a/reproduction/route-capacity/environment.json b/reproduction/route-capacity/environment.json
new file mode 100644
index 00000000..533751a9
--- /dev/null
+++ b/reproduction/route-capacity/environment.json
@@ -0,0 +1,13 @@
+{
+ "schema_version": 1,
+ "python": {
+ "implementation": "cpython",
+ "version": "3.13",
+ "system": "Darwin",
+ "machine": "arm64"
+ },
+ "source_archive": "sources/decluster-bcd9a3e-runtime.tar",
+ "project_root": "decluster",
+ "requirements": "reproduction/route-capacity/requirements.lock",
+ "dependency_directory": "reproduction/route-capacity"
+}
diff --git a/reproduction/route-capacity/requirements.lock b/reproduction/route-capacity/requirements.lock
new file mode 100644
index 00000000..cb70631a
--- /dev/null
+++ b/reproduction/route-capacity/requirements.lock
@@ -0,0 +1 @@
+# no third-party dependencies
diff --git a/reproduction/same-block-linkage/environment.json b/reproduction/same-block-linkage/environment.json
new file mode 100644
index 00000000..b02c479b
--- /dev/null
+++ b/reproduction/same-block-linkage/environment.json
@@ -0,0 +1,13 @@
+{
+ "schema_version": 1,
+ "python": {
+ "implementation": "cpython",
+ "version": "3.13",
+ "system": "Darwin",
+ "machine": "arm64"
+ },
+ "source_archive": "sources/decluster-bcd9a3e-runtime.tar",
+ "project_root": "decluster",
+ "requirements": "reproduction/same-block-linkage/requirements.lock",
+ "dependency_directory": "reproduction/same-block-linkage"
+}
diff --git a/reproduction/same-block-linkage/requirements.lock b/reproduction/same-block-linkage/requirements.lock
new file mode 100644
index 00000000..cb70631a
--- /dev/null
+++ b/reproduction/same-block-linkage/requirements.lock
@@ -0,0 +1 @@
+# no third-party dependencies
diff --git a/reproduction/scorer-families/environment.json b/reproduction/scorer-families/environment.json
new file mode 100644
index 00000000..5f2428d2
--- /dev/null
+++ b/reproduction/scorer-families/environment.json
@@ -0,0 +1,13 @@
+{
+ "schema_version": 1,
+ "python": {
+ "implementation": "cpython",
+ "version": "3.13",
+ "system": "Darwin",
+ "machine": "arm64"
+ },
+ "source_archive": "sources/decluster-bcd9a3e-runtime.tar",
+ "project_root": "decluster",
+ "requirements": "reproduction/scorer-families/requirements.lock",
+ "dependency_directory": "reproduction/scorer-families"
+}
diff --git a/reproduction/scorer-families/requirements.lock b/reproduction/scorer-families/requirements.lock
new file mode 100644
index 00000000..e69de29b
diff --git a/reproduction/slice-channels/environment.json b/reproduction/slice-channels/environment.json
new file mode 100644
index 00000000..42c4cd9c
--- /dev/null
+++ b/reproduction/slice-channels/environment.json
@@ -0,0 +1,13 @@
+{
+ "schema_version": 1,
+ "python": {
+ "implementation": "cpython",
+ "version": "3.13",
+ "system": "Darwin",
+ "machine": "arm64"
+ },
+ "source_archive": "sources/decluster-bcd9a3e-runtime.tar",
+ "project_root": "decluster",
+ "requirements": "reproduction/slice-channels/requirements.lock",
+ "dependency_directory": "reproduction/slice-channels"
+}
diff --git a/reproduction/slice-channels/requirements.lock b/reproduction/slice-channels/requirements.lock
new file mode 100644
index 00000000..e69de29b
diff --git a/reproduction/special-change-labels/environment.json b/reproduction/special-change-labels/environment.json
new file mode 100644
index 00000000..910892ba
--- /dev/null
+++ b/reproduction/special-change-labels/environment.json
@@ -0,0 +1,13 @@
+{
+ "schema_version": 1,
+ "python": {
+ "implementation": "cpython",
+ "version": "3.13",
+ "system": "Darwin",
+ "machine": "arm64"
+ },
+ "source_archive": "sources/decluster-bcd9a3e-runtime.tar",
+ "project_root": "decluster",
+ "requirements": "reproduction/special-change-labels/requirements.lock",
+ "dependency_directory": "reproduction/special-change-labels"
+}
diff --git a/reproduction/special-change-labels/requirements.lock b/reproduction/special-change-labels/requirements.lock
new file mode 100644
index 00000000..cb70631a
--- /dev/null
+++ b/reproduction/special-change-labels/requirements.lock
@@ -0,0 +1 @@
+# no third-party dependencies
diff --git a/reproduction/statistical-disclosure/environment.json b/reproduction/statistical-disclosure/environment.json
new file mode 100644
index 00000000..daf432e8
--- /dev/null
+++ b/reproduction/statistical-disclosure/environment.json
@@ -0,0 +1,13 @@
+{
+ "schema_version": 1,
+ "python": {
+ "implementation": "cpython",
+ "version": "3.13",
+ "system": "Darwin",
+ "machine": "arm64"
+ },
+ "source_archive": "sources/decluster-bcd9a3e-runtime.tar",
+ "project_root": "decluster",
+ "requirements": "reproduction/statistical-disclosure/requirements.lock",
+ "dependency_directory": "reproduction/statistical-disclosure"
+}
diff --git a/reproduction/statistical-disclosure/requirements.lock b/reproduction/statistical-disclosure/requirements.lock
new file mode 100644
index 00000000..cb70631a
--- /dev/null
+++ b/reproduction/statistical-disclosure/requirements.lock
@@ -0,0 +1 @@
+# no third-party dependencies
diff --git a/reproduction/subtx-demix/environment.json b/reproduction/subtx-demix/environment.json
new file mode 100644
index 00000000..5a3de7fb
--- /dev/null
+++ b/reproduction/subtx-demix/environment.json
@@ -0,0 +1,13 @@
+{
+ "schema_version": 1,
+ "python": {
+ "implementation": "cpython",
+ "version": "3.13",
+ "system": "Darwin",
+ "machine": "arm64"
+ },
+ "source_archive": "sources/decluster-bcd9a3e-runtime.tar",
+ "project_root": "decluster",
+ "requirements": "reproduction/subtx-demix/requirements.lock",
+ "dependency_directory": "reproduction/subtx-demix"
+}
diff --git a/reproduction/subtx-demix/requirements.lock b/reproduction/subtx-demix/requirements.lock
new file mode 100644
index 00000000..e69de29b
diff --git a/reproduction/unnecessary-input/environment.json b/reproduction/unnecessary-input/environment.json
new file mode 100644
index 00000000..23839aa3
--- /dev/null
+++ b/reproduction/unnecessary-input/environment.json
@@ -0,0 +1,13 @@
+{
+ "schema_version": 1,
+ "python": {
+ "implementation": "cpython",
+ "version": "3.13",
+ "system": "Darwin",
+ "machine": "arm64"
+ },
+ "source_archive": "sources/decluster-bcd9a3e-runtime.tar",
+ "project_root": "decluster",
+ "requirements": "reproduction/unnecessary-input/requirements.lock",
+ "dependency_directory": "reproduction/unnecessary-input"
+}
diff --git a/reproduction/unnecessary-input/requirements.lock b/reproduction/unnecessary-input/requirements.lock
new file mode 100644
index 00000000..cb70631a
--- /dev/null
+++ b/reproduction/unnecessary-input/requirements.lock
@@ -0,0 +1 @@
+# no third-party dependencies
diff --git a/reproduction/weight-sensitivity/environment.json b/reproduction/weight-sensitivity/environment.json
new file mode 100644
index 00000000..51e8f2a9
--- /dev/null
+++ b/reproduction/weight-sensitivity/environment.json
@@ -0,0 +1,13 @@
+{
+ "schema_version": 1,
+ "python": {
+ "implementation": "cpython",
+ "version": "3.13",
+ "system": "Darwin",
+ "machine": "arm64"
+ },
+ "source_archive": "sources/decluster-bcd9a3e-runtime.tar",
+ "project_root": "decluster",
+ "requirements": "reproduction/weight-sensitivity/requirements.lock",
+ "dependency_directory": "reproduction/weight-sensitivity"
+}
diff --git a/reproduction/weight-sensitivity/requirements.lock b/reproduction/weight-sensitivity/requirements.lock
new file mode 100644
index 00000000..e69de29b
diff --git a/results/INDEX.md b/results/INDEX.md
new file mode 100644
index 00000000..6f95529e
--- /dev/null
+++ b/results/INDEX.md
@@ -0,0 +1,139 @@
+# What is in `results/`
+
+Generated by `decluster.results_index`. Do not edit manually.
+
+63 generated documents, each the canonical rendering of a run manifest, and 52 written ones. The difference is what a reader can check: a generated document is reproduced byte-for-byte by its run and verified by the reproduction gate, while a written one is prose that may rest on data this repository does not ship.
+
+## Generated from a canonical run
+
+| document | run | claims |
+|---|---|---|
+| [`amount-channel-survey-v1.md`](generated/amount-channel-survey-v1.md) | `amount-channel-survey-v1` | `ctp.payjoin.unnecessary_input`, `ctp.amount.subtransaction_ambiguity` |
+| [`amount-local-channels-v1.md`](generated/amount-local-channels-v1.md) | `amount-local-channels-v1` | `ctp.payjoin.unnecessary_input`, `ctp.amount.subtransaction_ambiguity` |
+| [`amount-mapping-family-v1.md`](generated/amount-mapping-family-v1.md) | `amount-mapping-family-v1` | `ctp.amount.subtransaction_ambiguity` |
+| [`amount-pairwise-family-v1.md`](generated/amount-pairwise-family-v1.md) | `amount-pairwise-family-v1` | `ctp.amount.subtransaction_ambiguity` |
+| [`amount-per-coin-v1.md`](generated/amount-per-coin-v1.md) | `amount-per-coin-v1` | `ctp.amount.subtransaction_ambiguity` |
+| [`amount-subset-sum-v1.md`](generated/amount-subset-sum-v1.md) | `amount-subset-sum-v1` | `ctp.amount.subtransaction_ambiguity` |
+| [`analyze-contract-v1.md`](generated/analyze-contract-v1.md) | `analyze-contract-v1` | `ctp.provenance.traceability` |
+| [`ancestry-contract-v1.md`](generated/ancestry-contract-v1.md) | `ancestry-contract-v1` | `ctp.provenance.traceability` |
+| [`ancestry-sparsity-v1.md`](generated/ancestry-sparsity-v1.md) | `ancestry-sparsity-v1` | `ctp.sparse_dataset.feature_uniqueness` |
+| [`anchor-axis-families-v1.md`](generated/anchor-axis-families-v1.md) | `anchor-axis-families-v1` | `ctp.fingerprint.statistical_linkage` |
+| [`attribute-drift-v1.md`](generated/attribute-drift-v1.md) | `attribute-drift-v1` | `ctp.fingerprint.statistical_linkage`, `ctp.social_graph.propagation` |
+| [`bayes-vs-fs-v1.md`](generated/bayes-vs-fs-v1.md) | `bayes-vs-fs-v1` | `ctp.fingerprint.statistical_linkage` |
+| [`boltzmann-fee-audit-v1.md`](generated/boltzmann-fee-audit-v1.md) | `boltzmann-fee-audit-v1` | `ctp.amount.subtransaction_ambiguity` |
+| [`candidate-set-intersection-v1.md`](generated/candidate-set-intersection-v1.md) | `candidate-set-intersection-v1` | `ctp.intersection.longitudinal` |
+| [`change-consolidation-v1.md`](generated/change-consolidation-v1.md) | `change-consolidation-v1` | `ctp.intersection.longitudinal` |
+| [`cioh-collaborative-false-merge-v1.md`](generated/cioh-collaborative-false-merge-v1.md) | `cioh-collaborative-false-merge-v1` | `ctp.cioh.collaborative_false_merge` |
+| [`cluster-bits-v1.md`](generated/cluster-bits-v1.md) | `cluster-bits-v1` | `ctp.fingerprint.statistical_linkage`, `ctp.cioh.collaborative_false_merge` |
+| [`coinjoin-sudoku-nominal-v1.md`](generated/coinjoin-sudoku-nominal-v1.md) | `coinjoin-sudoku-nominal-v1` | `ctp.amount.subtransaction_ambiguity` |
+| [`collaborative-forms-v1.md`](generated/collaborative-forms-v1.md) | `collaborative-forms-v1` | `ctp.forms.observer_knowledge` |
+| [`conservation-round-three-v1.md`](generated/conservation-round-three-v1.md) | `conservation-round-three-v1` | `ctp.amount.subtransaction_ambiguity` |
+| [`counting-fee-sensitivity-v1.md`](generated/counting-fee-sensitivity-v1.md) | `counting-fee-sensitivity-v1` | `ctp.amount.subtransaction_ambiguity` |
+| [`counting-methods-v1.md`](generated/counting-methods-v1.md) | `counting-methods-v1` | `ctp.amount.subtransaction_ambiguity` |
+| [`counting-router-v1.md`](generated/counting-router-v1.md) | `counting-router-v1` | `ctp.amount.subtransaction_ambiguity` |
+| [`denomination-preparation-v1.md`](generated/denomination-preparation-v1.md) | `denomination-preparation-v1` | `ctp.amount.denomination_preparation_fingerprint` |
+| [`descent-vs-ascent-v1.md`](generated/descent-vs-ascent-v1.md) | `descent-vs-ascent-v1` | `ctp.cioh.collaborative_false_merge` |
+| [`deterministic-partition-v1.md`](generated/deterministic-partition-v1.md) | `deterministic-partition-v1` | `ctp.amount.deterministic_partition` |
+| [`em-m-v1.md`](generated/em-m-v1.md) | `em-m-v1` | `ctp.fingerprint.statistical_linkage` |
+| [`entity-deanon-v1.md`](generated/entity-deanon-v1.md) | `entity-deanon-v1` | `ctp.social_graph.structural_linkage` |
+| [`entropy-insufficiency-v1.md`](generated/entropy-insufficiency-v1.md) | `entropy-insufficiency-v1` | `ctp.entropy.not_sufficient` |
+| [`equal-output-consolidation-v1.md`](generated/equal-output-consolidation-v1.md) | `equal-output-consolidation-v1` | `ctp.intersection.longitudinal` |
+| [`eve-alice-eve-v1.md`](generated/eve-alice-eve-v1.md) | `eve-alice-eve-v1` | `ctp.provenance.traceability` |
+| [`exact-oracle-audit-v1.md`](generated/exact-oracle-audit-v1.md) | `exact-oracle-audit-v1` | `ctp.amount.subtransaction_ambiguity` |
+| [`fingerprint-catalog-axes-v1.md`](generated/fingerprint-catalog-axes-v1.md) | `fingerprint-catalog-axes-v1` | `ctp.fingerprint.statistical_linkage` |
+| [`fingerprint-regime-v1.md`](generated/fingerprint-regime-v1.md) | `fingerprint-regime-v1` | `ctp.fingerprint.statistical_linkage`, `ctp.sparse_dataset.feature_uniqueness` |
+| [`fingerprint-sparsity-v1.md`](generated/fingerprint-sparsity-v1.md) | `fingerprint-sparsity-v1` | `ctp.sparse_dataset.feature_uniqueness`, `ctp.fingerprint.statistical_linkage` |
+| [`fingerprint-validation-v1.md`](generated/fingerprint-validation-v1.md) | `fingerprint-validation-v1` | `ctp.fingerprint.statistical_linkage` |
+| [`fs-ablation-v1.md`](generated/fs-ablation-v1.md) | `fs-ablation-v1` | `ctp.fingerprint.statistical_linkage` |
+| [`fs-temporal-v1.md`](generated/fs-temporal-v1.md) | `fs-temporal-v1` | `ctp.fingerprint.statistical_linkage` |
+| [`fused-engine-v1.md`](generated/fused-engine-v1.md) | `fused-engine-v1` | `ctp.cioh.collaborative_false_merge`, `ctp.fingerprint.statistical_linkage` |
+| [`graph-deanon-controls-v1.md`](generated/graph-deanon-controls-v1.md) | `graph-deanon-controls-v1` | `ctp.social_graph.structural_linkage` |
+| [`graph-rejoin-2016-v1.md`](generated/graph-rejoin-2016-v1.md) | `graph-rejoin-2016-v1` | `ctp.social_graph.propagation`, `ctp.graph.pseudonym_representation` |
+| [`intersection-cluster-fixture-v1.md`](generated/intersection-cluster-fixture-v1.md) | `intersection-cluster-fixture-v1` | `ctp.intersection.longitudinal` |
+| [`library-calibration-v1.md`](generated/library-calibration-v1.md) | `library-calibration-v1` | `ctp.fingerprint.statistical_linkage` |
+| [`link-prediction-v1.md`](generated/link-prediction-v1.md) | `link-prediction-v1` | `ctp.social_graph.link_prediction` |
+| [`match-controls-v1.md`](generated/match-controls-v1.md) | `match-controls-v1` | `ctp.social_graph.propagation`, `ctp.social_graph.structural_linkage` |
+| [`ns-bitcoin-v1.md`](generated/ns-bitcoin-v1.md) | `ns-bitcoin-v1` | `ctp.social_graph.propagation` |
+| [`ns-link-prediction-coverage-v2.md`](generated/ns-link-prediction-coverage-v2.md) | `ns-link-prediction-coverage-v2` | `ctp.social_graph.link_prediction` |
+| [`ns1r-change-readoff-v1.md`](generated/ns1r-change-readoff-v1.md) | `ns1r-change-readoff-v1` | `ctp.payjoin.receiver_change_readoff` |
+| [`partition-schemes-v1.md`](generated/partition-schemes-v1.md) | `partition-schemes-v1` | `ctp.social_graph.propagation`, `ctp.graph.pseudonym_representation` |
+| [`path-count-contract-v1.md`](generated/path-count-contract-v1.md) | `path-count-contract-v1` | `ctp.robustness.counterfactual_paths`, `ctp.provenance.traceability` |
+| [`perfect-matching-disclosure-v1.md`](generated/perfect-matching-disclosure-v1.md) | `perfect-matching-disclosure-v1` | `ctp.disclosure.perfect_matching` |
+| [`provenance-decay-v1.md`](generated/provenance-decay-v1.md) | `provenance-decay-v1` | `ctp.provenance.traceability` |
+| [`pseudonym-graph-contract-v1.md`](generated/pseudonym-graph-contract-v1.md) | `pseudonym-graph-contract-v1` | `ctp.graph.pseudonym_representation` |
+| [`reid-v1.md`](generated/reid-v1.md) | `reid-v1` | `ctp.sparse_dataset.feature_uniqueness` |
+| [`route-capacity-v1.md`](generated/route-capacity-v1.md) | `route-capacity-v1` | `ctp.robustness.counterfactual_paths`, `ctp.provenance.traceability` |
+| [`same-block-linkage-v1.md`](generated/same-block-linkage-v1.md) | `same-block-linkage-v1` | `ctp.cioh.collaborative_false_merge`, `ctp.amount.subtransaction_ambiguity` |
+| [`scorer-families-v1.md`](generated/scorer-families-v1.md) | `scorer-families-v1` | `ctp.fingerprint.statistical_linkage` |
+| [`slice-channels-v1.md`](generated/slice-channels-v1.md) | `slice-channels-v1` | `ctp.sparse_dataset.feature_uniqueness` |
+| [`special-change-labels-v1.md`](generated/special-change-labels-v1.md) | `special-change-labels-v1` | `ctp.fingerprint.statistical_linkage`, `ctp.payjoin.unnecessary_input` |
+| [`statistical-disclosure-v1.md`](generated/statistical-disclosure-v1.md) | `statistical-disclosure-v1` | `ctp.disclosure.statistical_longitudinal` |
+| [`subtx-demix-v1.md`](generated/subtx-demix-v1.md) | `subtx-demix-v1` | `ctp.amount.subtransaction_ambiguity` |
+| [`unnecessary-input-v2.md`](generated/unnecessary-input-v2.md) | `unnecessary-input-v2` | `ctp.payjoin.unnecessary_input` |
+| [`weight-sensitivity-v1.md`](generated/weight-sensitivity-v1.md) | `weight-sensitivity-v1` | `ctp.fingerprint.statistical_linkage` |
+
+## Written, with a legacy manifest
+
+These carry a `results/manifests/` fingerprint of the source they were measured on, so a moved source is detected even though the numbers are not regenerated.
+
+| document | title |
+|---|---|
+| [`RESULTS-boltzmann-fee-audit.md`](RESULTS-boltzmann-fee-audit.md) | Exact conservation versus explicit fee allocation |
+| [`RESULTS-candidate-set-intersection.md`](RESULTS-candidate-set-intersection.md) | Candidate-set intersection: the mechanism, and what is not claimed for it |
+| [`RESULTS-fs-ablation.md`](RESULTS-fs-ablation.md) | Ablation of correlated axes in the Fellegi-Sunter model |
+| [`RESULTS-fs-temporal.md`](RESULTS-fs-temporal.md) | Fellegi-Sunter, fit early / scored late, on the real transaction cache |
+| [`RESULTS-link-prediction.md`](RESULTS-link-prediction.md) | De-anonymization-driven link prediction, on planted views |
+| [`RESULTS-ns-bitcoin.md`](RESULTS-ns-bitcoin.md) | Narayanan-Shmatikov propagation on two real Bitcoin views |
+
+## Written, no manifest
+
+Prose over measurements whose source is named in the document itself. Several rest on exports this repository does not ship; each says so where that is the case.
+
+| document | title |
+|---|---|
+| [`RESULTS-3v23-engine.md`](RESULTS-3v23-engine.md) | 3-axis engine vs. 23-axis LibraryScorer on the merged-transaction anchor (real number for the §4 claim) |
+| [`RESULTS-amount-channel-survey.md`](RESULTS-amount-channel-survey.md) | What the amount channel actually says on a real slice |
+| [`RESULTS-analyze.md`](RESULTS-analyze.md) | `analyze()` — systematic-callability validation (real numbers) |
+| [`RESULTS-ancestry-sparsity.md`](RESULTS-ancestry-sparsity.md) | The sparse signal lives in ancestry, not in graph topology |
+| [`RESULTS-ancestry.md`](RESULTS-ancestry.md) | Provenance-entropy channel on real data — the absorber-model rung |
+| [`RESULTS-anonymity-set-scale.md`](RESULTS-anonymity-set-scale.md) | §04 provenance anonymity set at scale — live-fetch resolved subsample |
+| [`RESULTS-anonymity-set.md`](RESULTS-anonymity-set.md) | Provenance anonymity set (M1) — graph-only entropy, subjective decay, and the F-S collapse |
+| [`RESULTS-attribute-drift.md`](RESULTS-attribute-drift.md) | Attribute stability across epochs |
+| [`RESULTS-bayes-vs-fs.md`](RESULTS-bayes-vs-fs.md) | Pair-level Bayesian and Fellegi–Sunter comparison |
+| [`RESULTS-broadcast.md`](RESULTS-broadcast.md) | Broadcast-time fingerprint — coverage and calibration |
+| [`RESULTS-catalog-axes.md`](RESULTS-catalog-axes.md) | Two Möser–Narayanan tells, tested against the 23-axis model |
+| [`RESULTS-change-id.md`](RESULTS-change-id.md) | Change-id validation — validating the ordering axis (M&N labels + fingerprint tests) |
+| [`RESULTS-cluster-robustness.md`](RESULTS-cluster-robustness.md) | Clustering weight-robustness: does graph topology stabilise the partition against weight changes? |
+| [`RESULTS-conservation.md`](RESULTS-conservation.md) | Conservation: forcing coinjoin outputs onto a known participant |
+| [`RESULTS-conspicuous-order.md`](RESULTS-conspicuous-order.md) | Does clustering the conspicuous transactions first change anything? |
+| [`RESULTS-counting-methods.md`](RESULTS-counting-methods.md) | Which counting method, and which L |
+| [`RESULTS-def1-sparsity.md`](RESULTS-def1-sparsity.md) | (epsilon, delta)-sparsity of cluster features: the attack's own precondition, measured |
+| [`RESULTS-e2e.md`](RESULTS-e2e.md) | End-to-end pipeline validation — Tier-1 (offline, asserted) + Tier-2 (live) |
+| [`RESULTS-em-m.md`](RESULTS-em-m.md) | Per-axis Fellegi–Sunter EM diagnostic |
+| [`RESULTS-entity-deanon.md`](RESULTS-entity-deanon.md) | Entity-labelled strong-N-S probe on real data — a positive result and its boundary |
+| [`RESULTS-entropy.md`](RESULTS-entropy.md) | Clustering-overcount diagnostic (relative) |
+| [`RESULTS-era-sweep.md`](RESULTS-era-sweep.md) | Is the pseudonym graph a social network in any era? |
+| [`RESULTS-exact-oracle-audit.md`](RESULTS-exact-oracle-audit.md) | The approximations against the exact oracle — measured |
+| [`RESULTS-fingerprint-regime.md`](RESULTS-fingerprint-regime.md) | Fingerprint N-S regime test: conditioner or sparse quasi-identifier? |
+| [`RESULTS-fingerprint-sparsity.md`](RESULTS-fingerprint-sparsity.md) | Fingerprint anonymity sets at survey scale |
+| [`RESULTS-fingerprint-validation.md`](RESULTS-fingerprint-validation.md) | Canonical fingerprint model: validation on real witness-bearing transactions |
+| [`RESULTS-gap1.md`](RESULTS-gap1.md) | Gap #1 — amount / subtransaction inference (the primary signal) |
+| [`RESULTS-graph-deanon.md`](RESULTS-graph-deanon.md) | Community-structure de-anonymization (Narayanan–Shmatikov, on real chain data) |
+| [`RESULTS-intersection.md`](RESULTS-intersection.md) | Intersection: exercising the channel on a real co-spend |
+| [`RESULTS-ns-propagation.md`](RESULTS-ns-propagation.md) | N-S seed-and-propagate driver |
+| [`RESULTS-partition-cuts.md`](RESULTS-partition-cuts.md) | Which cut makes two matchable views — temporal, de-core, or cluster-collapse |
+| [`RESULTS-partition-posterior.md`](RESULTS-partition-posterior.md) | Partition posterior by split-merge MCMC (M3) — calibration, ablation, coarsening hierarchy |
+| [`RESULTS-path-count.md`](RESULTS-path-count.md) | Bounded walk + §07 path-count — real numbers |
+| [`RESULTS-path-counting-analysis.md`](RESULTS-path-counting-analysis.md) | Is dss what's needed for §07 path counting, and is it exposed/integrated in decluster? |
+| [`RESULTS-persistence-curve.md`](RESULTS-persistence-curve.md) | Does widening the view recover the neighbourhoods the matcher needs? |
+| [`RESULTS-provenance.md`](RESULTS-provenance.md) | Provenance overlap: what ranking by descended inputs does and does not isolate |
+| [`RESULTS-refusing-clusterer.md`](RESULTS-refusing-clusterer.md) | Does declining to apply CIOH blindly change the 2026 picture? |
+| [`RESULTS-reid.md`](RESULTS-reid.md) | Ancestry-signature record linkage |
+| [`RESULTS-slice-a-channels.md`](RESULTS-slice-a-channels.md) | The entity-attribute and social-graph channels on a real slice |
+| [`RESULTS-slice-gate-2026.md`](RESULTS-slice-gate-2026.md) | Go/no-go gate for a 2026 cross-view slice |
+| [`RESULTS-subtx-demix.md`](RESULTS-subtx-demix.md) | Subtransaction de-mix diagnostic |
+| [`RESULTS-temporal.md`](RESULTS-temporal.md) | Cluster temporal fingerprint — when is a cluster active |
+| [`RESULTS-topology.md`](RESULTS-topology.md) | Graph-topology weight — counterparty overlap as a Fellegi–Sunter quasi-identifier |
+| [`RESULTS-weight-sensitivity.md`](RESULTS-weight-sensitivity.md) | Global fingerprint-weight sensitivity |
+| [`RESULTS-wp1a.md`](RESULTS-wp1a.md) | WP1a — recalibrated evidence bits (real mainnet, unbiased) |
+| [`RESULTS-wp4.md`](RESULTS-wp4.md) | WP4 — merged transaction backward-channel demonstration (real bits) |
diff --git a/results/REPRODUCIBILITY.md b/results/REPRODUCIBILITY.md
index a6537730..0ef40af4 100644
--- a/results/REPRODUCIBILITY.md
+++ b/results/REPRODUCIBILITY.md
@@ -1,8 +1,14 @@
# Reproducibility policy
-Every claim in `results/` falls into one of four states. This is the single index; per-doc
+Every claim in `results/` falls into one of five states. This is the single index; per-doc
"Reproducibility / provenance" footers point back here.
+A document that has not been read and filed says so in its own footer — "Not yet filed against
+`results/REPRODUCIBILITY.md`" — rather than carrying a guessed state. That is not a sixth state; it
+is the absence of one, said out loud so a reader is never left to infer a level from silence.
+`tests/test_results_provenance_footer.py` requires every document to answer, and caps how many may
+answer this way.
+
## The rule
A claim's **mechanism** (the algorithm) is proven by a unit test on small/synthetic input. A claim's
@@ -20,13 +26,23 @@ survives subsampling** can it be pinned as a **band** on a committed fixture.
| fingerprint clustering separates same-owner (FS + NS AUC) | `tests/fixtures/fingerprint_blkcache_sample.json` | `test_fingerprint_auc.py` |
| intersection attack narrows candidates | seeds in-test | `test_intersection_real.py` |
| coinjoin de-mix recovers makers | amounts inlined | `test_coinjoin_demix.py`, `test_amount_refuse_demix.py` |
-| conservation forces ownership | tx inlined | `test_conservation_round_three.py` |
+| conservation traces output funding | tx inlined | `test_conservation_round_three.py` |
| §04 anonymity set at scale | `results/scale_output.json` | `test_anonymity_set_scale.py` |
| amount density gate κ < κ_c orientation | synthetic | `test_cost.py` |
| W(E) multiplicity / path-count object | synthetic | `test_counting.py`, `test_path_count.py` |
| survey metadata + consistency | `tests/fixtures/lumen_explorer_data.json` | `test_lumen_survey_fixture.py` |
| graph structural de-anon (payment/full AUC) | `tests/fixtures/graph_deanon_2016.ndjson.gz` | `test_graph_deanon_real.py` |
+| approximations vs the exact oracle: the `dss` link matrix bounds it in neither direction (`RESULTS-exact-oracle-audit.md`) | canonical run `catalog/runs/exact-oracle-audit-v1.json`; generated family, artifact and Markdown are recomputed exactly | `test_oracle_audit.py`, `test_exact_oracle_experiment.py` |
| known-entity de-anon (SatoshiDice positive + BitMEX null control) | `entity_satoshidice_2013.ndjson.gz`, `entity_bitmex_2019.ndjson.gz` | `test_entity_deanon_real.py` |
+| entity-attribute space is dense not sparse (Def-1 negative) + graph disassortative | `tests/fixtures/slice_a_channels_2016.ndjson.gz` | `test_slice_a_channels.py` |
+| Fellegi-Sunter beats the fixed-rarity baseline out-of-period (the direction `RESULTS-fs-temporal.md` publishes) | `tests/fixtures/fingerprint_blkcache_sample.json` | `test_fs_temporal.py::test_fellegi_sunter_beats_the_rarity_baseline_out_of_period` |
+| the fused engine: what each evidence channel adds over co-spend alone (`results/generated/fused-engine-v1.md`) | canonical run `catalog/runs/fused-engine-v1.json` over `data/fs-blkcache-2026-09-04.tar.gz` | `test_fused_engine_experiment.py` |
+| neighbourhood persistence against view width (`RESULTS-persistence-curve.md`) | `tests/fixtures/persistence_curve.json` | `test_state3_promotion.py` |
+| slice go/no-go: spanning material and seed supply (`RESULTS-slice-gate-2026.md`) | `tests/fixtures/slice_gate.json` | `test_state3_promotion.py` |
+| candidate-set intersection narrows, stalls or refuses (`RESULTS-candidate-set-intersection.md`) | none needed — the 3-scenario family is generated, not sampled | `test_candidate_set_intersection.py` |
+| a cluster's identifying structure against the merge's ambiguity (`results/generated/cluster-bits-v1.md`) | canonical run `catalog/runs/cluster-bits-v1.json` over `data/fs-blkcache-2026-09-04.tar.gz` | `test_cluster_bits_experiment.py` |
+| change labels that read values and types rather than co-spend (`results/generated/special-change-labels-v1.md`) | canonical run `catalog/runs/special-change-labels-v1.json` over `data/fs-blkcache-2026-09-04.tar.gz` | `test_special_change_labels_experiment.py` |
+| what the transactions argue against themselves, and what refusal and the doubt gate do to the partition (`RESULTS-conspicuous-order.md`) | `data/amount-channel-812695-812831-v1.json` | `test_conspicuous_order_slice.py` |
## 2. Mechanism unit-tested, headline number is a data-run (proven algorithm, labelled number)
@@ -37,27 +53,272 @@ subsampled fixture would assert a different number.
| Claim family | Mechanism test | Data source |
|---|---|---|
| refusal shares (declining CIOH) | `test_cluster_refined.py`, `test_cluster.py` | `slice_2026.ndjson` |
-| cross-view matcher precision vs baseline | `test_view_match.py` | `slice_2026.ndjson` |
+| cross-view matcher precision vs baseline **(direction — owes migration, see state 5)** | `test_view_match.py` | `slice_2026.ndjson` |
| (ε,δ) feature sparsity survival | `test_def1_sparsity.py` | `slice_2026.ndjson` |
-| fingerprint-regime / bayes-vs-fs / em-m / weight-sensitivity | `test_fingerprint_ns.py`, `test_fs_bayes.py`, `test_fs_em.py` | `.blkcache/` |
-| cluster-bits, graph-shape, contraction | `test_cluster_bits.py`, `test_graph_shape.py`, `test_views.py` | `slice_2026.ndjson` |
+| fingerprint-regime | `test_fingerprint_ns.py` | `.blkcache/` |
+| global fingerprint-weight sensitivity (`RESULTS-weight-sensitivity.md`; exact snapshot rerun; historical monotonicity falsified) | `test_fingerprint_ns.py`, `test_weight_sensitivity_experiment.py` | `data/fs-blkcache-2026-09-04.tar.gz` |
+| per-axis EM diagnostic (`RESULTS-em-m.md`; exact snapshot rerun; historical 165,832-transaction table not reproduced) | `test_fs_em.py`, `test_em_m_experiment.py` | `data/fs-blkcache-2026-09-04.tar.gz` |
+| pair-level Bayesian vs Fellegi-Sunter (`RESULTS-bayes-vs-fs.md`; exact snapshot rerun; historical handwritten table not reproduced) | `test_fs_bayes.py`, `test_bayes_vs_fs_experiment.py` | `data/fs-blkcache-2026-09-04.tar.gz` |
+| graph-shape, contraction | `test_graph_shape.py`, `test_views.py` | `slice_2026.ndjson` |
| entropy overcount, provenance overlap | `test_metric.py`, `test_provenance*.py` | live / `.cache/` |
+| Fellegi-Sunter fit early / scored late — the reported AUCs, thresholds and calibration (`RESULTS-fs-temporal.md`; manifest + recomputed invariants; the *direction* is pinned in state 1 above) | `test_fs_temporal.py` | `.blkcache/` |
+| N-S propagation on contracted Bitcoin views — the reported precision, coverage and controls (`RESULTS-ns-bitcoin.md`; manifest + recomputed configuration; the direction at 5% / 10% seeding is **state 5**, at 25% it separates) | `test_ns_bitcoin.py`, `test_ns_social_baseline.py` | `epoch_2016_*.ndjson.gz` |
| broadcast timing, temporal | `test_broadcast.py` | live |
+| exact versus explicit fee allocation (`RESULTS-boltzmann-fee-audit.md`; manifest + recomputed invariants) | `test_boltzmann_fee_audit.py` | `data/amount-channel-812695-812831-v1.json` |
## 3. Reproducible once a small BigQuery window is committed
Run the query, commit the small output as a fixture, and the number becomes band-pinnable (state 1).
+`tests/test_state3_promotion.py` holds one slot per row below: each skips with the query to run and
+the path to write, so a promotion is one commit rather than a commit plus a test nobody wrote yet.
| Claim | Query | Notes |
|---|---|---|
-| slice-gate spanning material | `bigquery/slice_gate.sql` | aggregate, few rows |
-| persistence curve | `bigquery/persistence_curve.sql` | 5-row aggregate |
-| change-id validation | `bigquery/slice.sql` | 1-day labelled slice |
-| fingerprint frequency calibration | `bigquery/sample_small.sql` | already sized small |
+| fingerprint frequency calibration | `bigquery/sample_small.sql` | output is 15k rows, but the `TABLESAMPLE` carries no partition filter and scans the whole table |
+
+Two rows left this state on 2026-09-07, both promoted to state 1 above: the slice gate and the
+persistence curve. One row left it in the other direction. **Change-id validation is not a small
+window.** `bigquery/slice.sql` covers 2024-06-01, which is 739,889 non-coinbase transactions — the
+count `test_paper_numbers.py` already carries as unbacked. It cannot become a committed fixture as
+filed, so it belongs in state 4, below, and is recorded there.
## 4. Needs a new representative collection (no small fixture substitutes)
+- **change-id validation** — moved here from state 3 on 2026-09-07. The window `bigquery/slice.sql`
+ names holds 739,889 transactions; a smaller one reveals fewer change labels, so it measures
+ something else rather than the same claim more cheaply.
+
- **ancestry (ε,δ)-sparsity** — needs deep contiguous ancestry connectivity; a small window has ~1%
in-file parents. The definitive run is a deep contiguous export at dss-oracle depth.
- **definitive reid** — the chain-wide de-anonymization *rate* (not the link, which is pinned in
state 1) needs a uniform chain sample at real depth with the dss oracle, an API collection.
+- **the amount channel at graph scale** — the de-mix arm of the refusing clusterer, the
+ unnecessary-input axis, conservation and the conspicuousness ranking all need prevout **values**
+ and **script types**. The graph-scale exports (`epoch_2016_*`, the committed graph fixtures)
+ carry addresses only. They are measurable today on
+ `data/amount-channel-812695-812831-v1.json` (5,491 txs, blocks
+ 812,695-812,831, complete) and `.blkcache/` (22,571 txs, of which 22,112 are not coinbase; complete, but block-sampled rather than
+ contiguous); what is missing is a *graph-scale* contiguous export carrying them, which is a
+ re-collection of the existing query with two more columns rather than new method.
+
+## 5. Measured, not separable
+
+The number exists and the run is recorded, but the *direction* it is read for is not established:
+`decluster.reproducibility.separable` does not clear all three of its gates — the discordant win
+count favoring one arm, the pre-registered significance threshold, and the pre-registered minimum
+effect size — on a committed fixture. The document reports the value and states that the direction
+is not established.
+
+This state exists because a published comparison inverted its sign while the suite stayed green — and
+the instructive part is *where* it was filed. It was not an unasserted data-run. It was state 1: a
+committed green test (`test_multiplicity_upweights_higher_w_e_origin`) on a synthetic fixture, and
+the fixture had been built to exhibit the very direction the test asserted. A fixture constructed
+from the same belief as the claim cannot refute it, so the assertion held while the claim was wrong.
+
+**Being asserted is not the same as being tested.** For a direction claim, state 1 counts only if
+the fixture could have shown the opposite — the claim has to be falsifiable by the data it is pinned
+on, not merely consistent with data chosen to display it. Where no such fixture exists, the
+filing is state 5: report the value, decline the direction.
+
+A direction filed under state 2 is worse still, since by policy nothing there is asserted at all.
+One row of the state-2 table remains a direction claim by its own name — "cross-view matcher precision
+vs baseline" — and is flagged in that table as owing a migration to state 1 or 5. It is
+recorded rather than quietly relabelled, because the number each reports is real; it is the direction
+read off it that is not yet established.
+
+**Filed under state 5 (2026-09-03).** `RESULTS-ns-bitcoin.md` is the first document to use this
+state, and it splits itself across it: `decluster/ns_bitcoin.py` calls `separable` on the paired
+discordant win counts, and the document files its 5% and 10% seeding rows as **state 5** — measured,
+not separable — while its 25% row clears all three gates. That is the intended shape of the state:
+one run, two verdicts, each named. The cross-view matcher row flagged above still owes its migration.
+
+### A measurement whose data is gone
+
+The five states all assume the data still exists: state 1 re-asserts it, state 2 regenerates from
+it, states 3 and 4 collect it, state 5 has it and declines the direction. A measurement whose source
+cannot be recovered fits none of them, and the corpus already names that condition rather than
+needing a sixth state for it — `fonte indisponível`, the source could not be examined.
+
+A document in that condition does not support a claim. Its numbers are a record of what was once
+measured, and `PAPER.md` must not cite them as evidence. The alternative is the reversal of the
+burden of proof this work exists to argue against: an attack asserted on a figure nobody can check
+asks for the same credit it denies a vendor. So the resolution is not a label but a decision — read
+the document, and either measure it again on data that exists, which produces a new result rather
+than a restoration, or remove it.
+
+Three were removed on 2026-09-08 under that rule: an ancestry cross-view feasibility note and a
+cluster-scale reading, neither cited by the paper, and the witness-axis era drift, whose paper
+paragraph went with it. Git keeps them; the published surface does not.
+
+Three more went on the same rule, and they show both of its outcomes. The cluster-bits reading and
+the special-case-label cross-check were measured again on the committed block cache and became state
+1 above; what the cache cannot carry — the per-axis onward-spend arm, which needs each change
+output's spender in the same data — is absent from the new run and named there rather than carried
+over. The attribute-conditioning result had no such path: its conditioners read construction axes
+per edge, and every graph-scale export this repository ships carries addresses only, so there is
+nothing at that scale for them to read. What survives it is the mechanism, which needed no slice —
+the conditioner cannot create a score but does create the eccentricity the gate reads, and that is
+now decided by `tests/test_view_match.py` instead of argued from a table. Re-measuring is not
+restoring: the new numbers are new, and the paper cites those.
+
+### Manifests
+
+`results/manifests/` holds one file per migrated document, and that directory listing — not a count
+in this paragraph, which went stale as soon as the 2026-09 wave filed several at once — is the
+current inventory. The rule below is the standing one; as the
+remaining state-2 and state-5 results are migrated, each should carry
+`results/manifests/.json`, recording the source's identity (files, bytes, digest) and the
+population invariants the claim depends on — the facts a byte digest cannot see.
+`tests/test_results_manifests.py` checks every manifest that exists against its live source and
+skips with a message when the source is absent, or when the manifest's invariants were not
+recomputed; it also requires every `RESULTS-*.md` to either carry a manifest or be named in that
+test's `NOT_YET_MIGRATED` allowlist, so a document cannot silently fall outside both. For a number
+produced by the Rust crate, a manifest's identity is `dss.__version__` / `dss.__rev__`, its lock
+digest, parameters and seeds rather than a data path. `catalog/runs/exact-oracle-audit-v1.json` is
+the first manifest using that contract.
+
+### Evidence bundles
+
+`releases/exact-oracle-evidence-v1.bundle.json` is the first bundle with both `verify_outputs` and
+`reproduce_runs`. It contains a minimal source archive, an identified DSS wheel, a hash-locked
+requirement and an environment contract. The cold-start test materializes those blobs into an empty
+root, creates a new virtual environment without consulting a package index, reexecutes the audit and
+compares both outputs byte for byte.
+
+This does not yet establish public or platform-independent reproduction. The environment targets
+CPython 3.13 on macOS arm64, and the bundle has no public HTTPS origin or independent mirror. The
+readiness command reports those distribution blockers explicitly.
+
+
+## Known contradictions (2026-09-03)
+
+Filed, not fixed. The 2026-09 attack-faithfulness wave changed the provenance walk's default link
+oracle from the subset-sum link matrix (`ancestry.dss_link_oracle`, `dss.pairwise_link_prob`) to the
+nominal-value transition rule (`ancestry.value_flow_link_oracle`), and measured the subset-sum
+oracle against an exact one (`RESULTS-exact-oracle-audit.md`). Prose alignment across `PAPER.md` and
+`README.md` is a later phase's work. Until then, this is the
+list of what the tree disagrees with, so no reader has to discover it by running something.
+
+**The default oracle moved; these still describe the old one.**
+
+- `PAPER.md:683-684` — "`ancestry_entropy` … a backward walk weighted by the subset-sum link matrix
+ (`dss.pairwise_link_prob`)". `ancestry_entropy`'s default is now `value_flow_link_oracle`; the
+ subset-sum walk is opt-in.
+- `PAPER.md:734-735` — "The default weights transitions by subset-sum link probability, not §04's
+ coin-value measure; the value-weighted flow rung is the opt-in `value_weighted`". Inverted on both
+ halves: the default *is* now the coin-value measure, and `value_weighted` is a hybrid that scales
+ the *link* column by input value — it is not the flow rung. The flow rung is
+ `ancestry.value_flow_link_oracle` / `build_value_flow_graph`.
+- `PAPER.md:962` (the `tx-graph-anonymity-sets` citation footnote) — "`decluster/ancestry.py`
+ weights them by subset-sum link probability, row-normalized … with link-probability the default so
+ existing figures stay reproducible", and the "one stated divergence" framing built on it. The
+ divergence is no longer the default; the figures that rest on it are the ones listed below.
+- `results/RESULTS-ancestry.md`, `RESULTS-intersection.md`, `RESULTS-ns-propagation.md`,
+ `RESULTS-analyze.md`, `RESULTS-path-count.md` — each now carries a dated correction note at its
+ head naming the oracle that produced its numbers. Their bodies are otherwise unrevised, and none
+ has been re-measured under value flow.
+
+**Nothing measures the new default.** No `results/` document reports a measurement made under
+`ancestry.value_flow_link_oracle` on real data. `examples/fused_report.py` now runs a second arm
+under it (added here) so the walk is at least runnable and visibly contrasted with the pinned
+subset-sum arm, but that arm prints a demo, not a recorded result — the default walk every facade
+now takes is still unmeasured in this tree.
+
+**The exact-oracle audit falsified a reading these still carry.**
+
+- `results/RESULTS-amount-channel-survey.md` — "369 rows admit exactly one output — a deterministic
+ link, the amounts settling the assignment on their own". Corrected by a dated note at its head;
+ the body sentence is unrevised. `decluster/counting.py`'s and `decluster/cost.py`'s docstrings
+ have been amended at source.
+
+**Naming compatibility after the algorithm split.** New code has explicit canonical namespaces;
+the historical module names remain as compatibility facades so existing imports do not break:
+
+- `decluster.rarity_weight_baseline` — fixed value-rarity scoring, not fitted FS.
+- `decluster.single_view_propagation` — provenance-signature propagation without a graph.
+- `decluster.baselines.ns_social_attack` — the faithful two-view seeded graph propagation.
+- `decluster.subset_sum_weighted_ancestry` — opt-in subset-sum-link ancestry.
+- `decluster.kelen_seres_value_flow` — nominal-value absorbing flow.
+- `decluster.weighted_path_count` — probability-weighted route accumulation.
+
+**Boltzmann fee contract.** `decluster.baselines.boltzmann.link_analysis` keeps
+`balance_model="exact"` and `balance_model="fee_tolerant"` separate. The latter requires an
+explicit integer `fee_tolerance`, permits only non-negative per-block deficits, and records the
+observed fee and selected model in its result. Roundness does not affect mapping admission or
+probability. This closes the local fee-allocation mechanism only. Official no-fee vectors
+A/B/C/P2/P3 match. On P3-with-fees, the set-valued model reports 19 unique mappings and per-input
+counts `10/9/9`; a separate default-`LINKABILITY` aggregate-traversal port reproduces the tool's 28
+combinations and `14/13/13`.
+`MERGE_FEES` is separately pinned: it appends the observed fee as a typed synthetic output, runs
+exact conservation, and preserves the fee output's position after the reference's descending-value
+sort.
+Known-owner input groups are also a separate mode: overlapping index groups are merged transitively,
+replaced by summed inputs for traversal, and expanded back in the tool's order. This reproduces the
+reference's row replication without treating equal-valued inputs as the same object. `PRECHECK`
+now reports the deterministic input-output cells found by the reference's aggregate probe while
+leaving the exhaustive matrix authoritative. Explicit JoinMarket maker/taker bounds widen the
+matching interval exactly as in the pinned reference and disable precheck. These bounds are
+hypotheses, not observed fees. `MERGE_OUTPUTS` is not silently repaired: revision `ed0b649c` marks
+the option unreliable and its shared packer only examines inputs, so it does not define a stable
+baseline contract.
+
+The compatibility modules still disclose why their old names must not be used as evidence of a
+reference algorithm:
+
+- `decluster/graph_deanon.py` — common-neighbour link prediction; no seeds, mapping, propagation or
+ second view. `README.md:37` lists it under the attacker modules without that qualification.
+- `decluster/propagate.py` — signature propagation with no graph, no edges and no reverse match.
+ `README.md:37` calls it "entity-level N-S seed-and-propagate".
+- `decluster/fingerprint_ns.py` — `-log2(p)` weights where the paper uses `1/log|support|`, and an
+ eccentricity that is computed and returned but never gated on.
+- `decluster/view_match.py` — accepts a lone candidate at infinite eccentricity, skipping the
+ faithful baseline's live gate (`_sparse_winner`) entirely rather than computing a
+ large-but-finite value from it, and normalises eccentricity over the materialised scores where
+ that gate normalises over the whole candidate population.
+
+**A published attack name rests on the mechanism, not on an adapted walk.** Goldfeder et al.'s
+primary text is now checked. `goldfeder_cluster_intersection` implements Algorithm 2's join-only
+backward paths bounded by `r`, wallet-cluster lift, intersection and unique-or-refuse verdict over
+injected graph/clustering callbacks. The JoinMarket detector, recursive address clustering and
+2015–2017 experiments remain unreproduced. `decluster/intersect.py` is not that baseline: it wires
+intersection to a probabilistic ancestry walk that is not restricted to join paths, with rarity
+weighting, truncation reporting and subordination to `cluster_refined` — additions that are ours.
+
+**Two AUC estimators.** `graph_deanon.auc` samples at most 20,000 draws; `graph_deanon.exact_auc`
+(now the single exact implementation, called by `fs_temporal._score_metrics`) is exact Mann--Whitney.
+Every AUC published outside `RESULTS-fs-temporal.md` — including `README.md:18`'s 0.933 — came from
+the sampled estimator and carries third-decimal sampling error. The two are disclosed side by side
+in `graph_deanon.auc`'s docstring and in `RESULTS-fs-temporal.md`; migrating the older call sites to
+`exact_auc` would move published numbers and is deliberately not done here.
+
+## Deferred findings (2026-09-03)
+
+Recorded so they are not lost. Each was filed unfixed; a struck entry was repaired later in the same
+wave and says what remains open.
+
+- ~~**`check_manifest` does not check that enough was checked.**~~ **Repaired (2026-09-03).**
+ A complete recomputation must supply exactly the manifest's invariant keys. Missing keys now
+ return `partial`, and newly measured keys return `stale`, both with the unmatched names; a partial
+ recomputation can no longer return `ok`.
+- **Redundant re-enumeration in `baselines/oracle_audit.py`.** The exact mapping family is
+ enumerated, and per-coin quantities recomputed, more times than the report needs. Exponential
+ work, so the constant factor is felt — but the numbers are unaffected. Performance only.
+- ~~**`g.truncated` and `intersect`'s `blind` field are structurally degenerate under the value-flow
+ default.**~~ **Repaired (2026-09-03), not re-measured.** The two causes are now counted apart:
+ `ancestry.Graph` carries `oracle_refused` and `node_capped` alongside the `truncated` total,
+ `ancestry.truncated_support` returns a `TruncationSupport` over the mass-carrying boundary, and
+ `intersect.evaluate` adds `truncated_causes` and `blind_cause` — `"oracle_refused"`,
+ `"node_capped"`, `"zero_link_mass"`, `"mixed"` when multiple measured causes fired, and
+ `"unknown"` when any blind branch's cause was not
+ measured (a bare total, or `TruncationSupport.unattributed`), and `None` in two distinct states
+ that `blind` tells apart: nothing was blind, or a branch is blind with zero truncation because it
+ observed nothing at all. `"unknown"` dominates the named values, so an unmeasured cause is never
+ laundered into `"mixed"`. A blind branch now names the walk limit that blinded it instead of
+ leaving the reader to infer it from which oracle was in use. What is *not* fixed: no `results/`
+ document has been re-measured under the value-flow default, so `RESULTS-intersection.md`'s counts
+ remain the subset-sum walk's bare totals — its blind three-branch run re-reads as `"unknown"`, and
+ its non-blind four-branch run as `None`, which is not a cause at all.
+- ~~**`TruncationSupport` is not a complete account of "no view".**~~ **Repaired (2026-09-03).**
+ A zero-sum link column is now recorded as `zero_link_mass`, carried through
+ `TruncationSupport`, and exposed by `intersect.evaluate`; it is no longer indistinguishable from
+ coinbase or the requested depth cutoff.
diff --git a/results/RESULTS-3v23-engine.md b/results/RESULTS-3v23-engine.md
index 8799fd32..f5e7874f 100644
--- a/results/RESULTS-3v23-engine.md
+++ b/results/RESULTS-3v23-engine.md
@@ -56,9 +56,9 @@ defaults* (`change_spk`, `input_script_type`, `output_encoding`, `low_r`, `pubke
every low-entropy policy axis. Those agreements are individually small (+0.09 to +3.42 bits each) but
numerous and positively correlated, and summing them under the (false) Fellegi-Sunter conditional-
independence assumption overwhelms the three real discriminators, flipping the total from −3.16 to
-+11.67 bits — a **false LINK** that would re-merge the sender into the Cake cluster.
++11.67 bits — a false LINK that would re-merge the sender into the Cake cluster.
-## Honest limits
+## Limits
- **One anchor, not a distribution.** Existence demonstration on the same real merged transaction as
§6/WP4, not a swept statistic — consistent with `RESULTS-weight-sensitivity.md`'s broader finding that
@@ -68,3 +68,7 @@ independence assumption overwhelms the three real discriminators, flipping the t
identical `fs_score` kernel, so the sign of this single-edge score is exactly what `cluster_refined`
would act on for this pair (a positive score here clears the engine's default `link_above` threshold
used in `RESULTS-wp4.md`).
+
+## Reproducibility / provenance
+
+State 2 in `results/REPRODUCIBILITY.md`: the mechanism is pinned; the reported numbers come from a data-run over the `.cache/` coins `examples/three_vs_23_axes.py` reads, which is not committed, and are not asserted.
diff --git a/results/RESULTS-amount-channel-survey.md b/results/RESULTS-amount-channel-survey.md
new file mode 100644
index 00000000..b338bd7c
--- /dev/null
+++ b/results/RESULTS-amount-channel-survey.md
@@ -0,0 +1,155 @@
+# What the amount channel actually says on a real slice
+
+> Historical record. The canonical replacement is generated from five independently verified
+> artifacts and registered at `catalog/runs/amount-channel-survey-v1.json`. Use
+> `results/generated/amount-channel-survey-v1.md` for the current result.
+
+> **Correction (2026-09-03) — "a deterministic link" is measured false.**
+> Below, "369 rows admit exactly one output — a deterministic link, the amounts settling the
+> assignment on their own" is the reading `counting.link_matrix`'s docstring handed consumers at the
+> time. `results/RESULTS-exact-oracle-audit.md` has since measured that reading against an exact
+> mapping oracle and found it wrong in one direction: `dss.pairwise_link_prob` is the uniform
+> marginal over dss's own, strictly smaller mapping family, and asserting certainty from a
+> single-non-zero row produces **945 spurious certainties across 328 of 507 transactions** (while
+> missing no genuinely certain link). A one-entry row means *dss's family* admits one output, not
+> that the amounts settle the assignment. The 369 count itself is unaffected — it is a count of rows
+> — but the sentence attached to it is not a licence to read those 369 as settled. The docstrings in
+> `decluster/counting.py` and `decluster/cost.py` have been amended accordingly.
+
+## Verdict
+
+Run against real transactions for the first time, the channel had three defects and one arm that
+never speaks. The worst: `cost.amount_cuts` was cutting every coin it could not measure, because
+the per-coin oracle spells "unreachable" as negative infinity while the guard tested for `None`.
+That is 98.2% of input coins. With the sentinel handled, the channel cuts 1.35% of coins on 6.65% of
+transactions, which is what a refuse-only channel should look like.
+
+## Data
+
+`data/amount-channel-812695-812831-v1.json`: 5,491 transactions over blocks 812,695–812,831, all
+carrying prevout values and script types, 1,428 of them multi-input. The graph-scale exports carry addresses only, which is why
+none of this had been measured before.
+
+## Each arm, and how often it speaks
+
+| arm | fires on | of |
+|---|---:|---|
+| coinjoin shape detector | 28 (1.96%) | multi-input transactions |
+| **de-mix into ≥2 participants** | **0 (0.00%)** | multi-input transactions |
+| unnecessary-input heuristic | 485 (33.96%) | multi-input transactions |
+| 2-in/2-out re-partition, top rank tied | 330 (68.32%) | the 483 it ranks |
+| amount cuts (after the fix below) | 318 coins (1.35%) | 23,626 coins |
+
+**The de-mix arm is silent.** Zero of 1,428. It looks for a fixed mix denomination appearing three
+or more times with a uniquely matching change output, which is the JoinMarket shape; ordinary
+traffic does not have it. The refusing clusterer's de-mix arm therefore contributes nothing on this
+data, and the refusal reduces to the shape rule.
+
+**The re-partition does not decide.** Of the 2-in/2-out transactions it ranks, more than two thirds
+have their top two readings tied on roundness. The "decidable regime" is a minority of the shape the
+engine treats as its primary case.
+
+## Three defects, in order of severity
+
+**1. Unreachable coins were being cut.** `amount_cuts` documents that a coin the truncated search
+cannot reach is "skipped, not cut", and guards it with `log_w is not None`. The dss per-coin path
+spells that state as `-inf`, so the guard never fired and `-inf <= cut_threshold` sent every such
+coin into the cut list. Measured: 17,204 of 17,520 input coins (98.2%) are `-inf`, and 1,385 of
+1,428 transactions (97.0%) have every input coin in that state. This is live code —
+`report.report` calls `amount_cuts` — so the fused per-transaction view was refusing nearly every
+coin it saw. Now: 318 cuts, 1.35% of coins.
+
+**2. An exact count of zero was corroborating cuts as rigorous.** `amount_cuts` promotes a cut when
+the transaction-level count comes back `exact`. The whole-transaction count is fee-blind — it asks
+which input subsets hit an output subset sum *exactly*, and a fee-paying transaction has none — so
+it completes at zero on 1,223 of the 1,303 exact counts. Zero is the counter finding nothing, the
+weakest corroboration available. Rigour now requires the count to have found something.
+
+**3. The whole-transaction count does not return above twenty inputs.** The cascade's brute branch
+enumerates every distinct output subset sum as a target and, for each, every input subset at every
+size; its own width limit is twenty, which is exactly where the call stops returning. Probed per
+width in separate processes: under a second to ten inputs, 3.4s at sixteen, and past twenty two
+thirds of the transactions never finished. A hang is not an exception, so the module's panic guard
+could not catch it, and a Python alarm cannot either — the block is inside Rust. The call is now
+declined above the largest width measured to complete.
+
+The crate's fee-aware per-coin path has no such limit: it returns in 0.008s at sixty inputs and
+0.136s at sixty-seven, the widths where the whole-transaction count hangs. That path was already
+what `cost.py` used; only `counting.py` was on the other one.
+
+## What this does not show
+
+The per-coin path answers, but on this slice it mostly answers "unreachable" — 98.2%. So the
+correction is about not *mistaking* that for a cut, and the channel remains quiet rather than
+becoming informative. Of 316 coins with a finite measurement, the median is 4.20 and 60 sit at or
+below the cut threshold.
+
+## Scope
+
+One slice, 137 blocks, one era, 2023. The de-mix's silence is a statement about ordinary traffic,
+not about JoinMarket rounds, of which this slice contains none the detector recognises.
+
+## Reproducibility / provenance
+
+Per `results/REPRODUCIBILITY.md`, state 2: **mechanism unit-tested, headline number is a data-run.**
+The sentinel handling, the zero-count corroboration and the width guard are pinned in
+`tests/test_cost.py` and `tests/test_counting.py`. The tables here are regenerated by
+`examples/amount_channel_survey.py` over `data/amount-channel-812695-812831-v1.json`, which is
+committed, and are not asserted as pinned values.
+
+
+## The fee-tolerant reading was already in the building
+
+The counts ask for an exact subset hit and a fee-paying transaction has none. The link-probability
+matrix does not: it reads the transaction's actual balance, and it is already exposed and already
+used — as the transition measure of the provenance walk. Nothing in the amount channel was reaching
+for it.
+
+Wired as a per-coin oracle and measured over 300 transactions (3,384 input coins):
+
+| per-coin oracle | coins measured | |
+|---|---:|---:|
+| density, exact-hit | 348 | 10.28% |
+| **link matrix** | **947** | **27.98%** |
+
+Of the 947, 369 rows admit exactly one output — a deterministic link, the amounts settling the
+assignment on their own, which is the refuse signal the channel exists to emit.
+
+Two costs come with it. The matrix enumeration is exponential and its budget is cooperative: a
+single call under the crate's own size guard was measured running past a minute, so bulk use has to
+go through the throwaway-subprocess oracle the provenance walk already keeps for exactly this. And
+the crate can hard-panic on pathological inputs, which the wrapper catches.
+
+It is offered as `cost.boltzmann_oracle` beside the existing one rather than replacing it. The two
+answer different questions — how many balancing subsets, against how many outputs an input could
+have funded — and swapping them silently would move every number that rests on the old one.
+
+
+## The entropy the writeup asks for, measured
+
+"if the probabilities over the partitions are far from uniform this amounts to relatively little
+privacy. This is typically quantified in terms of entropy." Everything above counts readings; this
+is how concentrated they are, and it was implemented in the crate but reachable only from Rust. It
+is now bound to Python and returned alongside the links every mapping agrees on, from the one
+enumeration that produces both.
+
+Over 250 transactions, each in its own process on a three-second deadline, 12 seconds total:
+
+| | transactions | |
+|---|---:|---:|
+| answered | 223 | 89.2% |
+| refused (size guard) | 26 | 10.4% |
+| timed out | 1 | 0.4% |
+
+| mapping entropy | transactions | |
+|---|---:|---:|
+| **0 bits — one reading, every coin pinned** | **222** | **99.6%** |
+| 1–4 bits | 1 | 0.4% |
+
+1,473 deterministic input-output links across the 223. Answering on 89% against the counts' 6% is
+the fee handling: the fee is balanced in as an extra output before enumerating rather than being
+required to vanish.
+
+The reading is the writeup's own about unilateral transactions — "the amounts strongly suggest one
+way of partitioning the transaction" — now measured rather than asserted. Ordinary traffic carries
+no mapping ambiguity at all, which is what makes the 0.4% that does worth looking at.
diff --git a/results/RESULTS-analyze.md b/results/RESULTS-analyze.md
index c2eeb74b..258b961e 100644
--- a/results/RESULTS-analyze.md
+++ b/results/RESULTS-analyze.md
@@ -1,5 +1,21 @@
# `analyze()` — systematic-callability validation (real numbers)
+> **Canonical status (2026-09-04).** The deterministic public-facade contract is now generated by
+> `decluster.experiments.analyze_contract` and registered in
+> `catalog/runs/analyze-contract-v1.json`. It covers depths 1 through 5, the nominal-value default,
+> conservative subjective fusion, explicit truncation and the compatibility meaning of the
+> `path_count` response field. The live Bitcoin timings and sample statistics below remain historical:
+> their original network snapshots, timing protocol and complete inputs were not preserved, so they
+> are not represented as a bitwise-reproducible run.
+
+> **Correction (2026-09-03) — `analyze()`'s default oracle has moved.**
+> The runs below pass `bounded_link_oracle(3000)` / `bounded_link_oracle(5000)` explicitly, so their
+> numbers remain reproducible as written. What is no longer true is the prose calling that oracle
+> `analyze()`'s *default*: the default is now `ancestry.value_flow_link_oracle`, the nominal-value
+> transition rule, which takes no dss at all. `oracle.bounded_link_oracle` (also spelled
+> `bounded_dss_link_oracle`) still bounds the subset-sum oracle and is now opt-in. The "In-process
+> bounded oracle" limit below therefore describes the opt-in path, not the default one.
+
`examples/analyze_live.py` exercises the PUBLIC `analyze()` facade (`decluster/analyze.py`, exported
from `decluster`) — the entry point an external consumer (e.g. `wasabi-model`) imports to get per-coin
§04 fused provenance anonymity sets. This file reports what actually happens when you call it on real
@@ -30,7 +46,7 @@ The right test (one tx, each depth), live-fetched, narrow (non-coinjoin) ancestr
Reading it:
- **Runs and returns at every depth 1→5**, tractably (<6 s each), **0 crashes, 0 truncations** — the
systematic-callability proof, in the clean single-transaction form.
-- **The provenance set grows with depth** (3 → 10 → 14 origins) then **converges** at depth 4–5: the
+- **The provenance set grows with depth** (3 → 10 → 14 origins) then converges at depth 4–5: the
narrow ancestry is fully resolved, so deeper walks add nothing. Depth genuinely resolves more
ancestral origins where they exist.
- `min_entropy` stays 1.585 (= log₂3) even as `n_origins` grows to 14, because min-entropy is the
@@ -41,7 +57,7 @@ Reading it:
## (b) Depth-5 over **coinjoin** ancestry is computationally intractable (§03), and that is the point
Running `analyze()` at `depth=5` over real Wasabi-scale coinjoin round targets
-(`analyze_live`, 8 targets, `budget_ms=6000`) did **not** complete in a 25-minute wall cap — it did
+(`analyze_live`, 8 targets, `budget_ms=6000`) did not complete in a 25-minute wall cap — it did
not crash, it did not finish. Even `depth=2` on a single wide coinjoin exceeds minutes. Root cause is
inherent, not a bug: the ancestry graph fans out exponentially (each coinjoin ≈ 10 parents, each a
coinjoin → thousands of bounded-oracle calls). This is exactly the refs' §03 caveat —
@@ -61,28 +77,26 @@ transaction carries an address-reuse self-transfer (`depth=1`): **34 / 90 (38%)
(`fused < graph`), reduction **max 2.25 bits, mean 0.98 bits**. So the §04 fusion demonstrably
narrows the anonymity set on real transactions.
-**Deep (ancestral) fusion** — sharpening from a same-owner link found back in the ancestry rather than
+Deep (ancestral) fusion — sharpening from a same-owner link found back in the ancestry rather than
in the target's own tx — is rare: a depth-sweep over 11 real targets with no own-tx reuse produced
-**0** cases of deep fusion emerging or growing with depth. This is consistent with §06/robustness
+0 cases of deep fusion emerging or growing with depth. This is consistent with §06/robustness
(*"the combinatorial explosion of graph based features … rendered mostly inert"*): a single ancestral
same-owner link narrows only the mass routed through that one ancestor, rarely enough to move the
worst-case min-entropy. Reported as theory alignment, not a fusion failure — the fusion *mechanism* is
independently demonstrated (the 38% own-tx figure above; the controlled fixture in `RESULTS-e2e.md`
lowering 1.0 → 0.304 bits; `tests/test_analyze.py`'s conservative-clamp + sharpening tests).
-## §07 path counting — now shipped, opt-in via `analyze(path_count=True)`
+## Provenance route accumulation, opt-in via `analyze(path_count=True)`
-Depth-5 over coinjoins is intractable because the walk enumerates the ancestral graph explicitly. The
-refs' §07 (*path-like anonymity set*) is the alternative object — counting the counterfactual *paths*
-through the graph. This is now BUILT: `decluster.path_count.path_count_anonymity` (a concrete §07
-instance: the provenance distribution weighted by per-edge path multiplicity `W(E)`), reachable via
-`analyze(path_count=True)`, backed by `dss.w_count` (the crate's feasibility-cascade count dispatcher:
-brute → dp → sparse → sasamoto). Real numbers and its honest limits (it does not extend the tractable
-envelope; it adds the §06/§07 robustness lens) are in `results/RESULTS-path-count.md`. The original
-motivating analysis — "is dss sufficient, and what did decluster expose at the time" — is in
-`results/RESULTS-path-counting-analysis.md` (now superseded by the shipped object).
+`analyze(path_count=True)` retains its public response key for compatibility, but the implementation
+is canonically named `provenance_route_accumulation`. It accumulates link-probability mass over the
+same ancestry routes and does not use `dss.w_count`. It neither extends the tractable envelope nor
+implements the CTP robust-connectivity contract. In particular, it does not enumerate edge-disjoint
+paths, impose plausible-flow capacity or implement k-routes. The executable distinction is in
+`catalog/runs/path-count-contract-v1.json`; the original motivating analysis remains historical in
+`results/RESULTS-path-counting-analysis.md`.
-## Honest limits
+## Limits
- **In-process bounded oracle.** `analyze()`'s default `bounded_link_oracle` is panic-safe but
cooperative (dss's own `budget_ms` check), not an OS-level wall kill — so a pathological ancestor
diff --git a/results/RESULTS-ancestry-crossview-feasibility.md b/results/RESULTS-ancestry-crossview-feasibility.md
deleted file mode 100644
index f9c611ad..00000000
--- a/results/RESULTS-ancestry-crossview-feasibility.md
+++ /dev/null
@@ -1,55 +0,0 @@
-# Can ancestry feed the cross-view matcher? Feasibility, and a correction
-
-**Why this was proposed.** The cross-view social-graph matcher built this session scores on
-pseudonym-graph neighbourhood topology, and `RESULTS-graph-shape.md` found that topology is
-not a social network on this slice. The framework calls structural features "more
-importantly" the source of uniqueness, meaning ancestry / deep features. The proposal was to
-feed those into the matcher, on the theory that the negative result might be about the chosen
-feature rather than the graph.
-
-**Two findings kill the proposal as stated, before any build.**
-
-## The slice is too shallow for ancestry
-
-An ancestry signature walks backward through funding transactions; its power comes from
-reaching depth 6 or so, where the set of origin coins becomes a sparse quasi-identifier. On
-`slice_2026.ndjson`, of 2 190 883 non-coinbase inputs, **53.6 % have their funder inside the
-slice**, which caps the average backward walk at **~2.2 hops** before it truncates on a
-missing funder. At that depth the signature is mostly truncation absorbers, not the sparse
-high-dimensional vector the attack needs. The ancestry channel is blocked on the same
-one-hop-back-and-deeper export the fingerprint channel needs (`RESULTS-refusing-clusterer.md`),
-only more so, since it wants many hops rather than one.
-
-## Ancestry belongs to a different attack, already built
-
-The proposal conflated two of the three cited N-S papers. **Cit. 24 (social graph
-de-anonymization)** matches two graphs by neighbourhood — the cross-view matcher this session
-built. **Cit. 19–20 (sparse dataset / Netflix)** matches records by sparse feature-vector
-overlap — which is exactly what ancestry signatures are, and what `decluster/propagate.py`
-already implements via `provenance_link`, measured in `RESULTS-ns-propagation.md`
-(re-identification rate 0.154 on a bounded real cache sample, 1.0 on the synthetic fixture).
-
-So there is no missing cross-view ancestry experiment to build. Ancestry is the sparse-dataset
-attack, and that attack exists and is measured; it is a separate paradigm from the social-graph
-cross-view matching, and grafting it onto the matcher as a vertex or edge attribute would hit
-the same gate problem `RESULTS-attribute-conditioning.md` already measured — a weak per-vertex
-signal that manufactures eccentricity and adds error — on top of being depth-starved here.
-
-## Where this leaves the three N-S papers on this data
-
-| paper | attack | status |
-|---|---|---|
-| cit. 19–20 sparse dataset | record linkage on provenance signatures | built (`propagate.py`), measured (`ns-propagation`); richer measurement blocked on chain depth |
-| cit. 24 social graph | cross-view neighbourhood matching | built (`view_match.py`), measured; precondition fails on this slice (`graph-shape`) |
-| cit. 25 link prediction | matching on incomplete graphs | not built; would predict edges that are absent rather than censored, so poorly motivated here |
-
-## Honest recommendation
-
-Nothing cheap remains on this slice. Both open levers — a deeper contiguous export for the
-ancestry channel, and a wider-view export for the social-graph channel — need new data, and
-the persistence curve already bounds the second's expected gain. The one measurement that could
-still overturn the social-graph verdict cheaply would be a *different era or a longer contiguous
-window*, but that is a data-collection decision, not a code one. The correct state to stop in is:
-the social-graph attack is answered negatively for this regime with a mechanism, the sparse-dataset
-attack is a separate already-measured channel, and further progress on either is gated on an export,
-not on missing implementation.
diff --git a/results/RESULTS-ancestry-sparsity.md b/results/RESULTS-ancestry-sparsity.md
index 4a5dab6f..31d5e328 100644
--- a/results/RESULTS-ancestry-sparsity.md
+++ b/results/RESULTS-ancestry-sparsity.md
@@ -1,9 +1,17 @@
# The sparse signal lives in ancestry, not in graph topology
+> **Canonical status (2026-09-04).** A self-contained analysis of the preserved 200-signature
+> snapshot is generated by `decluster.experiments.ancestry_sparsity` and registered in
+> `catalog/runs/ancestry-sparsity-v1.json`. It recomputes nearest-neighbour similarities from the
+> signatures and deliberately ignores the stored `top` values, because their original comparison
+> population is unavailable. The 1.000-coin cache result and statistical-fingerprint comparison
+> below remain historical and are not reproduced by the canonical run.
+
**Why this reconciles the whole session.** The social-graph matching attack did not cascade
-(`RESULTS-view-match-2026.md`) and the pseudonym graph turned out not to be a social network
-(`RESULTS-graph-shape.md`). But the framework's claim is specific: "cluster features, both
-statistical and **more importantly structural**, are very likely to be unique." Structural
+(`results/generated/graph-rejoin-2016-v1.md`) and the pseudonym graph turned out not to be a
+social network by the two statistics that decide it (same run: clustering far below its
+configuration null, and negative degree correlation). But the framework's claim is specific: "cluster features, both
+statistical and more importantly structural, are very likely to be unique." Structural
here means ancestry / deep features. This measures whether *that* space — not the graph
topology, not the statistical fingerprints — is sparse in the sparse-dataset attack's own
sense, which is the precondition the authors define as decisive.
@@ -16,7 +24,7 @@ over `decluster/ancestry.py` signatures.
## Result
-Signatures: median **4 797 dimensions** (max 11 511) — high-dimensional, as the attack
+Signatures: median 4 797 dimensions (max 11 511) — high-dimensional, as the attack
requires.
| epsilon | ancestry (deep features) | statistical (fingerprints) |
@@ -28,22 +36,22 @@ requires.
## Reading
-**The ancestry feature space is sparse; the statistical one is not.** Two thirds of coins
-have no ancestry near-twin above similarity 0.5, and the median coin's closest match is only
-0.20 — the Netflix-like regime the sparse-dataset attack needs. The statistical fingerprint
-space is the opposite, 89 % with a near-identical twin. The framework's exact claim — that
-the *structural* features are the unique ones, "more importantly" than the statistical — is
-confirmed on real data, by the authors' own operational definition of sparsity.
+The original reading concluded that ancestry is sparse while the statistical space is not. That
+comparison is not retained as a canonical claim because neither the 1.000-record ancestry population
+nor the statistical comparison input was preserved. The canonical 200-record snapshot does exhibit
+low nearest-neighbour similarity internally, but a selected subset can become artificially sparse
+when neighbours from the original comparison population are removed.
**This reconciles the social-graph negative rather than contradicting it.** There are two
distinct N-S attacks, and this data separates them cleanly:
-- the **social-graph** attack (cit. 24) matches two graphs by neighbourhood topology. Its
+- the social-graph attack (cit. 24) matches two graphs by neighbourhood topology. Its
strong form needs the pseudonym graph to be a social network, and it is not
- (`RESULTS-graph-shape.md`), so the cascade does not ignite across 2013–2026; its modest
+ (`results/generated/graph-rejoin-2016-v1.md`), so the cascade does not ignite across 2013–2026;
+ its modest
form — a few high-confidence links that feed clustering — does fire, which is what the
framework claims for it.
-- the **sparse-dataset** attack (cit. 19–20) matches records by sparse feature-vector
+- the sparse-dataset attack (cit. 19–20) matches records by sparse feature-vector
overlap. Its precondition is (epsilon, delta)-sparsity of the feature space, and the
ancestry space *has* it. This attack is the one `propagate.py` already runs
(re-identification 0.154 on a bounded cache sample).
@@ -57,19 +65,16 @@ result.
## The caveat that bounds this, and points at the definitive run
-The signatures here use a **uniform** link oracle (each input equally likely to fund each
-output), not the real subset-sum `dss_link_oracle`, and depth 4 with `max_nodes=60`, to fit
-the per-coin cost. A uniform oracle makes signatures *coarser* than the real ones — the
-subset-sum weights would discriminate more. So the direction of the bias is conservative:
-the true ancestry space is at least this sparse, probably more. A result of "sparse" under
-a coarse oracle is therefore strong. The definitive measurement uses the real dss oracle at
-depth 6 over a contiguous slice, which is what the deep contiguous export unblocks — now the
-clearly highest-value collection, because it sharpens the one space the framework calls most
-important and this run shows is the sparse one.
+The historical signatures used a uniform link oracle, depth 4 and `max_nodes=60`. There is no valid
+general ordering from that model to DSS sparsity: changing transition weights can increase or
+decrease pairwise similarity, and DSS itself has no general bound direction against the complete
+mapping oracle. Therefore “the true ancestry space is at least this sparse” is withdrawn. A stronger
+measurement requires a preserved sampling frame, signature-generation recipe and comparison
+population before changing the link model or depth.
## Scope
-1 000 coins from an accreted cache (not a uniform chain sample), uniform oracle, depth 4.
-The survival curve is a sampled estimate. It establishes the *sign* of the result — ancestry
-is sparse where statistics are not — robustly; the precise delta values await the dss-oracle
-run.
+The historical measurement used 1.000 coins from an accreted cache, a uniform oracle and depth 4.
+Its survival curve cannot be reexecuted from the current repository. The canonical result covers
+only the frozen 200-record snapshot and does not establish either the sign of a population-level
+comparison or a direction of bias relative to DSS.
diff --git a/results/RESULTS-ancestry.md b/results/RESULTS-ancestry.md
index bc1ce845..95e257ec 100644
--- a/results/RESULTS-ancestry.md
+++ b/results/RESULTS-ancestry.md
@@ -1,12 +1,28 @@
# Provenance-entropy channel on real data — the absorber-model rung
+> **Canonical status (2026-09-04).** The deterministic nominal-value provenance contract is now
+> generated by `decluster.experiments.ancestry_contract` and registered in
+> `catalog/runs/ancestry-contract-v1.json`. It covers the absorbing distribution, the exact
+> collapsed-to-paper step conversion, typed complete/truncated outcomes, separate truncation causes
+> and model-relative provenance overlap. All DSS, mempool.space, WP4, cache, AUC and contiguous-slice
+> numbers below remain historical because their complete input snapshots and execution protocol were
+> not preserved. They are not part of the canonical run.
+
+**Scope: which walk these figures are.** Every figure below is the subset-sum link oracle
+(`ancestry.dss_link_oracle`, `dss.pairwise_link_prob`). Reproducing them takes
+`link_oracle=decluster.ancestry.dss_link_oracle`, which the command at the foot of this file passes.
+The headline — provenance as a near-deterministic quasi-identifier, Shannon median ~0.00 bits —
+stands for that walk. The nominal-value transition rule (`ancestry.value_flow_link_oracle`), which
+never refuses and never truncates on transaction width, is a different walk and is not measured here.
+`results/RESULTS-exact-oracle-audit.md` measures how far the subset-sum oracle sits from an exact one.
The `ancestry_entropy` engine (`decluster/ancestry.py`) measures the **provenance / deep-feature
channel** that the framework (`tx-graph-anonymity-sets`, the absorber / random-walk model) centres:
-a backward walk over the transaction graph, edge-weighted by the exact subset-sum link matrix
+a backward walk over the transaction graph, edge-weighted by the DSS model's subset-sum link matrix
(`dss.pairwise_link_prob`), solved as an absorbing Markov chain — the coin's absorption distribution
over its ancestral boundary is the harmonic measure of the walk. Its Shannon / min-entropy is a
-**lower bound** on the intrinsic graph entropy of the payment's provenance under no auxiliary
-information (never a privacy score; the conservative, defender-side read is min-entropy).
+model-relative provenance entropy. The exact-oracle audit later showed that this DSS pairwise matrix
+has no general lower- or upper-bound direction, so these historical values are not conservative
+certificates and are never privacy scores.
Run in `./.venv` (the native `dss` module is built there), `fetch_tx` resolving ancestry over
mempool.space.
@@ -27,29 +43,28 @@ mass onto fewer boundary coins.)
**Spectrum on 8 ordinary sampled coins (1–3 in/out), depth 4:**
- Shannon **median ≈ 0.00 bits**, range **[0.00, 2.94]**; min-entropy median 0.00.
-- Most coins (boundary = 1) resolve to a **single** ancestral origin — provenance fully determined,
+- Most coins (boundary = 1) resolve to a single ancestral origin — provenance fully determined,
zero ambiguity.
- The high end (2.94 bits over a **30-coin** boundary) is a coin that passed through a fan-out — the
only regime with meaningful provenance ambiguity.
## Reading
-This is exactly the framework's thesis measured on-chain: "**every coin is sparsely represented**" in
-the exponential quasi-identifier space of ancestry — for typical coins the provenance lower bound is
-~0 bits, i.e. the origin is (near-)determined, so ancestry is a **strong** identifier. Only coins
-routed through genuine fan-out/mixing accumulate provenance entropy. Because every figure is a
-conservative lower bound (truncation on an oracle-`None`, and the depth cutoff, can only *understate*
-ambiguity), the true identifiability is at least this strong.
+The original reading treated this as an on-chain measurement of sparse provenance. That conclusion
+is not retained as a canonical claim. Frontier truncation can only coarsen a distribution conditional
+on a fixed transition model, but the DSS pairwise transition itself has no general bound direction.
+The sample can illustrate the selected model; it cannot establish that typical coins have
+near-determined provenance or that real identifiability is at least this strong.
## Deep-feature matching — provenance as a linking quasi-identifier
-The entropy above is per-coin; the *matching* attack scores **pairs**. `ancestry_signature` exposes a
+The entropy above is per-coin; the *matching* attack scores pairs. `ancestry_signature` exposes a
coin's provenance vector and `provenance_link` scores the Narayanan–Shmatikov overlap of two vectors —
shared ancestral mass, optionally rarity-weighted (`wt = 1/log2(support)`, so a shared *rare* ancestor
is strong same-origin evidence and a shared hub coinbase is weak). Mechanism validated in
`tests/test_provenance_link.py`.
-On the **WP4 merged anchor** (`931d6627`, which fuses a Cake-receiver lineage and a distinct-sender
+On the WP4 merged anchor (`931d6627`, which fuses a Cake-receiver lineage and a distinct-sender
lineage), the pairwise link matrix (depth 4):
| | cake_in | sender_in |
@@ -57,7 +72,7 @@ lineage), the pairwise link matrix (depth 4):
| **cake_in** | 1.000 | **0.000** |
| **sender_in** | 0.000 | 1.000 |
-The two merged parties have **disjoint provenance** (link 0.000) — a *third* independent channel, after
+The two merged parties have disjoint provenance (link 0.000) — a *third* independent channel, after
the fingerprints (§6, −3.1 bits) and the amounts (§6, round re-partition), that separates the owners
the common-input merge tried to combine. (Same-tx sibling outputs `merged_o0/o1` link at 1.0, as
expected — they share the full input ancestry. Cross-*generation* terms are not comparable here: two
@@ -80,11 +95,11 @@ The **sign is right** — random pairs share *zero* provenance, same-owner pairs
AUC is near chance because at depth 3 on a *fragmented* sample most same-owner pairs also reach no
shared ancestor.
-We then tried the obvious fix — a **contiguous value-bearing slice** (2019, blocks 581000–581025,
+We then tried the obvious fix — a contiguous value-bearing slice (2019, blocks 581000–581025,
64,744 txs; in-memory fetch with an out-of-slice boundary stub) at depth 5. It is *worse*, and the
-reason is the load-bearing finding: every signature collapses to a **single** boundary atom
+reason is the load-bearing finding: every signature collapses to a single boundary atom
(mean support = 1.0), so pos and neg both ≈0 and AUC = 0.50. A tractable-width slice (~25 blocks ≈ 4
-hours) **cannot contain multi-hop ancestry** — a coin's parents are almost always older than the
+hours) cannot contain multi-hop ancestry — a coin's parents are almost always older than the
window, so the first backward hop already exits the slice and truncates. Keeping depth-5 ancestry
in-slice would need a slice spanning the ancestry timespan (weeks of blocks), i.e. essentially the
whole connected graph.
@@ -100,11 +115,13 @@ on new method.
## Boundary — what this rung is and is not
-Built and measured: the absorber-model provenance **entropy** (per-coin ambiguity lower bound), the
-pairwise **matching** (`provenance_link`, mechanism-tested; WP4 lineage separation at 0.000), and a
-first **graph-scale** pass (weak/directional, AUC 0.52 — depth+connectivity bound). The provisional
+Historically measured: absorber-model provenance entropy conditional on the DSS transition, the
+pairwise matching (`provenance_link`, mechanism-tested; WP4 lineage separation at 0.000), and a
+first graph-scale pass (weak/directional, AUC 0.52 — depth+connectivity bound). The provisional
edge weighting is link-probability-only (satoshi-flow value-weighting deferred;
`ancestry.build_extended_graph`).
-Reproduce: `./.venv/bin/python -c "from decluster.ancestry import ancestry_entropy; from decluster
-import fetch_tx; print(ancestry_entropy(('931d6627f7b63491cbc2e6d860dc630537385fd9ee3171f2013b64e6a143a4e4',0), depth=6, fetch=fetch_tx))"`.
+Reproduce (the `link_oracle` argument is required now that the default has moved to value flow —
+see the correction at the head of this file): `./.venv/bin/python -c "from decluster.ancestry import
+ancestry_entropy, dss_link_oracle; from decluster import fetch_tx;
+print(ancestry_entropy(('931d6627f7b63491cbc2e6d860dc630537385fd9ee3171f2013b64e6a143a4e4',0), depth=6, fetch=fetch_tx, link_oracle=dss_link_oracle))"`.
diff --git a/results/RESULTS-anonymity-set-scale.md b/results/RESULTS-anonymity-set-scale.md
index 9a68b3e3..4bf6fc3e 100644
--- a/results/RESULTS-anonymity-set-scale.md
+++ b/results/RESULTS-anonymity-set-scale.md
@@ -1,6 +1,6 @@
# §04 provenance anonymity set at scale — live-fetch resolved subsample
-`examples/anonymity_set_scale.py` walks real targets with **live ancestry fetch** (mempool.space,
+`examples/anonymity_set_scale.py` walks real targets with live ancestry fetch (mempool.space,
not the shallow cache-only walk), keeps the ones whose graph-only walk actually branches, and on that
non-coinjoin subsample measures the §04 subjective-fusion sharpening and the `value_weighted` (Gap C)
ablation. Live network run; the walk uses the hard-bounded subset-sum oracle (a killed subprocess
@@ -30,7 +30,7 @@ returns `None` = the truncation boundary, not a fabricated link). Numbers below
`7787e8b6` (2 inputs, 15 absorbers): **graph 3.096 → fused 2.268 bits (−0.83)**. This confirms the
§04 mechanism (Task-2 fixture, 1.0→0.304) on real branching walks, not just a synthetic fixture.
-3. **Coverage is thin (3/15) — the honest limit.** The self-contained subjective signal
+3. **Coverage is thin (3/15), and that is the limit.** The self-contained subjective signal
(address-reuse self-transfer) fires on few non-coinjoin txs. The *strong* per-tx same-owner signal
is coinjoin **demix** — which lives on exactly the coinjoin txs the oracle truncates. So full §04
coverage is gated by the oracle, not by the signal design.
@@ -38,7 +38,7 @@ returns `None` = the truncation boundary, not a fabricated link). Numbers below
## The Gap-C result — `value_weighted` walk sharpens sharply (the strong finding)
The satoshi-flow-weighted walk (`value_weighted=True`, the deferred §04 flow rung) reduced entropy
-**substantially on most resolved targets**, independent of the subjective signal's coverage:
+substantially on most resolved targets, independent of the subjective signal's coverage:
| target | graph bits | value_weighted bits |
|---|---|---|
@@ -55,7 +55,7 @@ walk concentrates the origin distribution markedly**, more than the subjective f
sample. It was implemented opt-in (default off) precisely because it changes the numbers this much;
this run is the first real-data measurement of that effect.
-## Honest limits
+## Limits
- **Non-coinjoin subsample bias.** The resolved set excludes coinjoin-heavy coins — their ancestry
truncates because the exact subset-sum oracle can't evaluate dense mixes. A live probe found ~7/8
@@ -63,9 +63,9 @@ this run is the first real-data measurement of that effect.
(peel-chain-ish) provenance, and the population is skewed toward simpler coins.
- **The ceiling is the oracle, not the data.** Full-population §04 validation (including coinjoins,
where the subjective demix signal is strongest) is blocked until the subset-sum oracle handles dense
- coinjoins — the "Radics special case" (see `docs/superpowers/specs/2026-08-08-dss-radics-fast-path-design.md`).
+ coinjoins — the "Radics special case".
- **Conservative-lower-bound discipline.** Every entropy here is a lower bound / weight-of-evidence,
- not a privacy score. Same-owner labels (not "ground truth") drive the address-reuse signal.
+ not a privacy score. Same-owner labels drive the address-reuse signal.
- **Single live run.** Network variability means the exact target set and numbers are not bit-for-bit
reproducible; the qualitative findings (deep walks resolve, §04 sharpens when covered, value_weighted
sharpens strongly) are robust.
@@ -74,8 +74,7 @@ this run is the first real-data measurement of that effect.
The numbers above predate the dss fix. The `dss` crate now recognizes dense coinjoins structurally
(≥2 output denominations each repeated ≥3×, covering ≥half the outputs — the transcript's "Radix"
-case) and returns a **uniform link matrix** for them instead of timing out (see
-`docs/superpowers/specs/2026-08-08-dss-radix-matrix-and-expose-design.md`). Measured effect:
+case) and returns a uniform link matrix for them instead of timing out. Measured effect:
- **Mechanism (direct `dss.pairwise_link_prob`):** real 100- and 200-input coinjoins now return a
uniform matrix in **0.00 s** (before: hung > 8 s → the harness's bounded oracle killed it → `None`
@@ -85,12 +84,17 @@ case) and returns a **uniform link matrix** for them instead of timing out (see
collapsed to a point mass before the fix); `372fd5cb…` → 8 absorbers / 3.0 bits. This is the win:
the coinjoin-ancestry truncation that dominated the earlier collapse is gone.
- **What still collapses is different and correct:** the remaining collapsed targets are
- **consolidations** (many inputs → few / distinct outputs, e.g. 203-in / few-out), not coinjoins. The
+ consolidations (many inputs → few / distinct outputs, e.g. 203-in / few-out), not coinjoins. The
recognizer correctly does **not** fire on them (no repeated denominations); their subset-sum is
- genuinely hard, so truncation there is the honest answer, not a bug.
+ genuinely hard, so truncation there is the answer, not a bug.
**Caveat narrowed:** the "ceiling is the oracle" limit above no longer applies to coinjoins — those
-now resolve. It narrows to **consolidation-style** (many-in / few-distinct-out) transactions, whose
+now resolve. It narrows to consolidation-style (many-in / few-distinct-out) transactions, whose
exact subset-sum remains intractable and is out of scope for the Radix fast path. The dense matrix is
-still the crude **uniform** approximation (max ambiguity); the exact radix-structured per-(i,o) matrix
+still the crude uniform approximation (max ambiguity); the exact radix-structured per-(i,o) matrix
is a deferred refinement.
+
+## Reproducibility / provenance
+
+State 1 in `results/REPRODUCIBILITY.md`: band-pinned on committed data — `results/scale_output.json`,
+asserted by `tests/test_anonymity_set_scale.py`.
diff --git a/results/RESULTS-anonymity-set.md b/results/RESULTS-anonymity-set.md
index a2509359..8d0bf79a 100644
--- a/results/RESULTS-anonymity-set.md
+++ b/results/RESULTS-anonymity-set.md
@@ -23,7 +23,7 @@ One case (a 27-input tx) hard-panicked the native code (`assertion failed: set.l
`pyo3_runtime.PanicException`) rather than hanging or returning `None`.
So `examples/anonymity_set.py`'s `hard_bounded_link_oracle` runs each link computation in a
-throwaway subprocess and kills it (`terminate`, then `kill`) on a **1.2s wall-clock deadline** —
+throwaway subprocess and kills it (`terminate`, then `kill`) on a 1.2s wall-clock deadline —
an OS-level bound that works regardless of what the native code does internally, and also absorbs
the panic case cleanly. A kill or a panic both return `None`, exactly `ancestry`'s own
oracle-refusal boundary — `build_extended_graph` already truncates cleanly on `None`, so no
@@ -58,7 +58,7 @@ The 2 targets that *do* resolve real branching (both small, un-truncated, exact
| `4817b7a896…` (3-in tx) | 3 | **1.585 bits** |
Both are a real 4-way / 3-way tie: the backward walk resolves the coin's provenance to several
-boundary coins with exactly equal mass (`0.25` each / `0.333` each) — this **is** the §04
+boundary coins with exactly equal mass (`0.25` each / `0.333` each) — this is the §04
anonymity set for these two coins under no auxiliary information: an attacker constrained to the
graph alone cannot do better than guessing uniformly among 4 (resp. 3) candidate origins.
@@ -84,12 +84,12 @@ For each of the 2 resolved targets, three hypotheses are folded in via `decay`,
| `796c742b14…` | 2.000 | 2.000 | **0.379** | **0.000** | yes |
| `4817b7a896…` | 1.585 | 1.585 | **0.263** | **0.000** | yes |
-Both traces are monotone non-increasing end to end, and both end in an **exact 0-bit point mass**
+Both traces are monotone non-increasing end to end, and both end in an exact 0-bit point mass
— (b) and (c) both hold on real data.
-**Honest reading of step 1 (flat in both cases):** the `provenance_overlap` hypothesis contributed
+**Reading of step 1 (flat in both cases):** the `provenance_overlap` hypothesis contributed
*zero* narrowing for either target — not because the mechanism is broken, but because each
-reference coin's own bounded-depth signature had already collapsed to a **single** boundary atom
+reference coin's own bounded-depth signature had already collapsed to a single boundary atom
(1 and 4 absorbers respectively — checked: `reference_n_absorbers` = 1 and 4), and that atom did
not coincide with any of the target's own absorbers within depth 2
(`h1_kept_graph_argmax=False` for both — the graph's own top candidate wasn't even in the
@@ -101,7 +101,7 @@ signal is real but weak and partial") — here it shows up directly in a subject
built from that signal. Steps 2–3 do not depend on the reference at all (they narrow around
whatever step 1 leaves as the leading candidate), so they demonstrate (b)/(c) cleanly regardless.
-## Honest limits
+## Limits
- **Bounded slice.** All 34 pairs and both resolved walks are over this checkout's `.cache/`
(~1,900 tx JSON files) at depth 2 — not a chain-scale claim. 94% collapse here; a live,
@@ -128,7 +128,8 @@ whatever step 1 leaves as the leading candidate), so they demonstrate (b)/(c) cl
what a confidence-1 hypothesis structurally does to the distribution (the F-S special case), not
a claim that either coin's origin is actually known with certainty.
- Same-owner labels throughout are the address-reuse heuristic already validated (independent of
- co-spend) in `RESULTS-ns-propagation.md` — "same-owner labels," not ground truth.
+ co-spend) in `RESULTS-ns-propagation.md` — same-owner labels, which are an address-reuse
+ heuristic rather than ownership.
## §04-faithful fusion: link-level (pre-solve) vs the post-solve `reweight` baseline
@@ -188,7 +189,7 @@ Same 34 targets as section (a). For each: graph-only min-entropy vs §04-fused m
| — of which fired a real pin | 1 / 16 |
The two targets that resolve real graph-only branching (section (a): `796c742b14…` at 2.000 bits,
-`4817b7a896…` at 1.585 bits) are **unchanged under fusion** — the fused walk visits exactly one
+`4817b7a896…` at 1.585 bits) are unchanged under fusion — the fused walk visits exactly one
interior tx for each (the target's own top-level tx) before hitting its boundary, and neither is a
clean 2-output tx, so `_change_index` abstains and no pin is ever available to route mass through.
@@ -197,9 +198,9 @@ boundary atom under the graph-only walk (0 bits) — a single-parent chain with
sharpen: boosting the one live link column by 9x is a no-op when that column already has exactly
one nonzero entry.
-### Honest reading
+### Reading
-- **Real numbers, not fabricated — and the honest result on this slice is null.** 0/34 targets show
+- **The result on this slice is null.** 0/34 targets show
measurable §04 sharpening from this concrete signal, and the signal itself fires on only 1 of 34
targets' own reachable interior txs. This is not a contradiction of the mechanism test above (which
proves the sharpening happens whenever the oracle fires on a branching walk) — it is a coverage/
@@ -220,4 +221,9 @@ one nonzero entry.
`coinjoin_demix` pairs, when they cleanly yield input↔output links) would very likely surface real
sharpening that this particular slice's `_change_index`-only signal did not.
- Same-owner labels throughout are the address-reuse heuristic already validated (independent of
- co-spend) in `RESULTS-ns-propagation.md` — "same-owner labels," not ground truth.
+ co-spend) in `RESULTS-ns-propagation.md` — same-owner labels, which are an address-reuse
+ heuristic rather than ownership.
+
+## Reproducibility / provenance
+
+State 2 in `results/REPRODUCIBILITY.md`: the mechanism is pinned; the reported numbers come from a data-run over the `.cache/` slice `examples/anonymity_set.py` reads, which is not committed, and are not asserted.
diff --git a/results/RESULTS-attribute-conditioning.md b/results/RESULTS-attribute-conditioning.md
deleted file mode 100644
index 053ea656..00000000
--- a/results/RESULTS-attribute-conditioning.md
+++ /dev/null
@@ -1,85 +0,0 @@
-# Wiring the attributes in, and why they come back out
-
-**Why ask.** The framework has statistical feature distributions reducing to attributes on
-the vertices, with *"both those and the structural features"* informing the edge attributes.
-Until now the matcher used neither: it was purely structural, and the whole attribute
-apparatus, including the per-epoch normalisation calibrated in `RESULTS-attribute-drift.md`,
-had no consumer. This wires both in and measures them.
-
-**What was built.** Each edge stores the axis values of the transfer that created it (93 %
-of edges carry one transfer, so for nearly all of them that is the whole distribution; a
-`Counter` per edge does not fit at slice scale). Two bounded multiplicative conditioners:
-one comparing the A-edge (u, n) against the B-edge (v, image) when evidence arrives through
-a matched neighbour, one comparing the two vertices' attribute lifts. Half agreement is
-neutral, so neither can turn a zero score into a positive one.
-
-**Data.** `slice_2026.ndjson`, refusing clusterer, seed 400, 3.9 / 4.0 million edge
-signatures. Every configuration scored against the degree-class baseline recomputed on its
-own matched set.
-
-## Result
-
-At alpha 0.5:
-
-| configuration | matched | precision | at ecc ≥ 5 | precision | degree baseline | margin |
-|---|---:|---:|---:|---:|---:|---:|
-| structure only | 143 | 0.559 | 56 | 0.679 | 0.578 | **+0.101** |
-| + edge attributes | 370 | 0.322 | 78 | 0.513 | 0.516 | −0.003 |
-| + vertex attributes | 426 | 0.211 | 115 | 0.409 | 0.557 | −0.149 |
-| + both | 765 | 0.184 | 176 | 0.352 | 0.484 | −0.132 |
-
-*(Re-run on the graph corrected in `RESULTS-graph-shape.md`, with 525k edge signatures in
-place of the earlier inflated 3.9M. The pattern is unchanged and slightly sharper: every
-conditioner is negative, the loss grows monotonically, and the mechanism below is
-independent of the edge-attribution bug.)*
-
-Sweeping the edge conditioner's strength (separate run, see the note on variance):
-
-| edge alpha | links at ecc ≥ 5 | precision | degree baseline | margin |
-|---:|---:|---:|---:|---:|
-| 0.00 | 52 | 0.654 | 0.356 | **+0.298** |
-| 0.05 | 88 | 0.455 | 0.217 | +0.238 |
-| 0.10 | 85 | 0.447 | 0.218 | +0.229 |
-| 0.25 | 84 | 0.476 | 0.264 | +0.212 |
-
-## Reading
-
-**The attributes hurt, and there is no setting at which they do not.** The margin over a
-degree-only guess is monotone decreasing in alpha from zero. The largest single loss is
-between alpha 0 and alpha 0.05, so it is not the size of the perturbation that costs, it is
-that there is a perturbation at all.
-
-**The mechanism is manufactured eccentricity, and it invalidates the design argument.** The
-conditioner was justified on the grounds that a bounded multiplicative factor cannot create
-a match where structure found nothing, because zero times anything is zero. That holds for
-the *score*. The gate does not read the score; it reads the separation between the leader
-and the runner-up. Scaling tied candidates by different factors creates exactly that
-separation. The signature is in the counts: matches rise from 143 to 765 while precision
-falls from 0.559 to 0.184. The attributes are not pointing at the wrong vertex, they are
-converting refusals into errors.
-
-**This was predictable from measurements already in hand.** 0.4 % of transactions sit in a
-fingerprint class below 100 over the whole window, 2.35 % at single-epoch scope. A signal
-that weak cannot break ties without contributing more noise than information. The earlier
-conclusion that attributes should condition rather than score was right; implementing the
-conditioner at the point where the decision is taken was not.
-
-**So the correct implementation is to build them and keep them out of the gate.** That is
-different from not having them, and the difference matters for the framework's model: the
-vertex and edge attributes it specifies do exist here, are computed, and are measured to be
-too weak to participate in the matching decision on this data. Both conditioners default to
-off and are retained as the instrument that established this.
-
-## Untested alternative
-
-Applying the attributes *after* the gate, to rank confidence rather than to decide
-acceptance, cannot manufacture eccentricity because the accept-or-refuse verdict is already
-fixed. It would not change which matches are made, only their order, and whether that
-sharpens the high-confidence band is not answered here.
-
-## Note on variance
-
-The two tables come from separate runs and their degree baselines differ, 0.542 against
-0.356, because the random pseudonym labelling differs. `RESULTS-match-controls.md` records
-the same effect. Comparisons within a table are internally consistent, which is what each
-table is for; the two tables should not be read against each other.
diff --git a/results/RESULTS-attribute-drift.md b/results/RESULTS-attribute-drift.md
index c3f2a146..c0e07693 100644
--- a/results/RESULTS-attribute-drift.md
+++ b/results/RESULTS-attribute-drift.md
@@ -1,5 +1,15 @@
# Attribute stability across epochs
+> A reproducible projected-drift analysis is defined by
+> `catalog/runs/attribute-drift-v1.json`. Its structured artifact is
+> `results/artifacts/attribute-drift-v1.json`, with canonical rendering in
+> `results/generated/attribute-drift-v1.md`. The preserved aggregate contains 20 value series
+> across 11 axes. Each reproduced distribution therefore consists of those values plus one
+> combined residual category. This reproduces every rounded cell below for `nlocktime` and
+> `low_r`; `version` yields 0.070 rather than 0.071 at gap 120. The other full-distribution rows
+> and the 70/85 volume-coupling summary remain historical. The projected result diagnoses
+> population drift and is neither a clustering score nor a per-cluster distinctiveness result.
+
**Claim tested.** Cross-view matching contracts two epochs of the coin graph into two
views of the pseudonym graph, then compares vertices between them. Statistical
fingerprint distributions are proposed as vertex attributes. Does an attribute mean the
diff --git a/results/RESULTS-bayes-vs-fs.md b/results/RESULTS-bayes-vs-fs.md
index fe5c1eb6..939d632b 100644
--- a/results/RESULTS-bayes-vs-fs.md
+++ b/results/RESULTS-bayes-vs-fs.md
@@ -1,110 +1,23 @@
-# Bayesian record linkage vs Fellegi-Sunter: what does the point estimate cost?
-
-**Claim tested.** decluster's engine is Fellegi-Sunter — a per-pair verdict with a *point* estimate of
-the per-axis same-wallet agreement rate `m`. F-S is the plug-in special case of Bayesian record linkage,
-which puts a prior on `m` and integrates the uncertainty. We compare them on decluster's own evidence.
-(The full Bayesian record-linkage program — a hierarchical model with a posterior over the *linkage
-partition* itself, Larsen's "Hierarchical Bayesian Record Linkage," `fingerprints.md`— is MCMC over
-partitions, "computationally prohibitive" at scale per the same corpus. This comparison uses a tractable
-**pair-level** Gibbs, a deliberate retreat from that full-partition MCMC; the cluster-level band below is
-the cheap propagation of the pair posterior, not the joint-partition posterior.)
-
-**Method.** On 8000 reuse pairs (4000+4000, `cap=4000`), three scorers under the **same per-field
-naive-Bayes likelihood** (`m_j`, `u_j = collision`; NOT the value-specific `combiner.fs_score`):
-F-S(0.95) point, F-S(EM) point, and Bayesian (a light Gibbs over `(z, m)`, `Beta(8.5,1.5)` prior, `u`
-fixed, `p_match` integrating the `m` posterior). Calibration (ECE) is evaluated against reuse labels;
-the labels are not used to fit.
-
-## Result
-
-**Discrimination and calibration.**
-
-| scorer | AUC | ECE |
-|---|---:|---:|
-| F-S(0.95) | 0.9324 | 0.1302 |
-| F-S(EM) | 0.9438 | 0.0947 |
-| Bayesian | 0.9443 | 0.0989 |
-
-The AUC is a tie: F-S(EM) 0.9438 and Bayesian 0.9443 are indistinguishable (same evidence), so the
-Bayesian's value is not discrimination — calibration is where it pays off: ECE drops sharply from the
-assumed-`m` F-S(0.95) (0.1302) to both the fitted F-S(EM) (0.0947) and the Bayesian (0.0989), which are
-essentially equally well calibrated, roughly 25–27% better than fixing every axis at 0.95. The AUC tie is
-partly *structural*, not just empirical: all three scorers share the same per-field product likelihood,
-so they cannot diverge on discrimination — holding the likelihood fixed is exactly what isolates the
-point-vs-Bayesian question (the treatment of `m`), which is calibration and uncertainty, not ranking.
-
-**Per-axis `m`: the Bayesian posterior vs the point estimates.**
-
-| axis | Bayesian mean [95% CI] | EM | oracle | assumed |
-|---|---|---:|---:|---:|
-| nsequence | 0.945 [0.937, 0.953] | 0.946 | 0.979 | 0.95 |
-| locktime | 0.987 [0.982, 0.991] | 0.987 | 0.996 | 0.95 |
-| input_order | 0.819 [0.806, 0.830] | 0.819 | 0.811 | 0.95 |
-| output_order | 0.605 [0.589, 0.622] | 0.604 | 0.622 | 0.95 |
-| change_spk | 0.836 [0.824, 0.847] | 0.836 | 0.852 | 0.95 |
-| version | 0.972 [0.966, 0.977] | 0.972 | 0.993 | 0.95 |
-| io_shape | 0.616 [0.599, 0.635] | 0.615 | 0.605 | 0.95 |
-| uih | 0.888 [0.879, 0.898] | 0.889 | 0.880 | 0.95 |
-| fee_rate | 0.926 [0.919, 0.934] | 0.926 | 0.928 | 0.95 |
-| input_script_type | 1.000 [0.999, 1.000] | 1.000 | 0.983 | 0.95 |
-| output_encoding | 0.890 [0.881, 0.900] | 0.890 | 0.893 | 0.95 |
-| input_types_present | 1.000 [0.999, 1.000] | 1.000 | 0.982 | 0.95 |
-| change_index | 0.746 [0.733, 0.760] | 0.746 | 0.750 | 0.95 |
-| change_type_match | 0.782 [0.770, 0.795] | 0.782 | 0.784 | 0.95 |
-| change_matches_output | 0.767 [0.755, 0.779] | 0.767 | 0.768 | 0.95 |
-| change_address_reuse | 0.845 [0.833, 0.855] | 0.845 | 0.872 | 0.95 |
-| low_r | 0.862 [0.851, 0.872] | 0.862 | 0.865 | 0.95 |
-| sighash | 0.996 [0.993, 0.997] | 0.996 | 0.984 | 0.95 |
-| op_return | 0.945 [0.937, 0.953] | 0.945 | 0.986 | 0.95 |
-| nested_segwit | 0.999 [0.998, 1.000] | 0.999 | 0.996 | 0.95 |
-| pubkey_compression | 1.000 [0.999, 1.000] | 1.000 | 0.995 | 0.95 |
-| multisig | 0.993 [0.990, 0.995] | 0.993 | 0.989 | 0.95 |
-| locktime_vs_broadcast | 0.848 [0.596, 0.988] | 0.900 | n/a | 0.95 |
-
-With 8000 pairs most axes are sharply identified — CIs a few thousandths wide — and on 20 of the 23
-axes the assumed 0.95 falls *outside* the 95% CI, i.e. the point estimate is falsely precise at a value
-the data reject. The reject runs both ways: `output_order` (0.605), `io_shape` (0.616), `change_index`
-(0.746), `change_matches_output` (0.767) and `change_type_match` (0.782) agree far *less* often within a
-wallet than 0.95 assumes, while `input_script_type`, `input_types_present`, `pubkey_compression`
-(≈1.000), `nested_segwit` (0.999), `sighash` (0.996) agree *more*. Only `locktime_vs_broadcast` is
-poorly identified — its CI [0.596, 0.988] is the sole *wide* one (`nsequence` and `op_return` contain
-0.95 merely because their posterior means sit a hair above it) — because that
-axis is rarely active, so few pairs constrain its `m`; there the honest posterior is "we don't know,"
-exactly the state fixing `m` at 0.95 (or EM's 0.900 point) hides. This axis-by-axis gap is the epistemic
-cost of a point estimate, made explicit. **One caveat on the clamped axes:** the tight `[0.999, 1.000]`
-CIs on `input_script_type`/`input_types_present` are sharp around a value the oracle puts at 0.983/0.982
-— i.e. tightly identified but *biased upward* by the conditional-independence inflation (same artifact as
-the EM, `RESULTS-em-m.md`); "sharply identified" there means sharp, not necessarily correct. The Bayesian
-means track EM to within a thousandth throughout, confirming F-S is the plug-in: same location, added
-uncertainty.
-
-**Cluster-level uncertainty (the Bayesian's unique output).** F-S gives a point entity count; the
-Bayesian gives a *posterior band*. Two regimes make the difference concrete:
-
-- **Clear structure** (50 address-reuse-linked coins): the band collapses to **[4, 4]**, coincident with
- the F-S point (4). When the same-owner structure is unambiguous, the point estimate loses nothing.
-- **Ambiguous structure** (18 coins whose pairwise fingerprint links are borderline — intermediate
- P(match), chosen near the percolation threshold): the band is **[7, 10]** (mean 8.6) while the F-S
- point commits to a single **9**. The honest answer is "these 18 coins are 7–10 owners"; only the
- Bayesian carries that spread, and the point estimate hides it.
-
-This is the calibrated cluster-level uncertainty the comment's Bayesian ask implies — *shown*, not just
-asserted, once the node set is genuinely uncertain.
-
-Reproduce: `python3 examples/bayes_vs_fs.py`.
-
-## Honest limits
-
-- **The AUC tie is the finding, not a null.** F-S and Bayesian share the evidence; the Bayesian adds
- calibrated uncertainty, not discrimination — F-S is its plug-in special case.
-- **Axis-level, not the value-specific `fs_score`.** The comparison uses the per-field F-S both models
- share, to isolate point-vs-Bayesian; decluster's shipped scorer weights agreement by value rarity, an
- orthogonal enrichment.
-- **Conditional independence** (per-axis terms multiplied) biases the `m` posterior on correlated axes
- the same way the EM is biased — cross-check against the oracle (`RESULTS-em-m.md`).
-- **`u` fixed; `λ` from the enriched 50/50 pair mix; cluster sets are bounded**, not chain scale — an
- illustrative propagation of uncertainty, like the other case-study artifacts. The ambiguous-node band
- `[7, 10]` is on a purpose-selected borderline set (18 nodes near the percolation threshold); a
- strongly-structured set collapses it to a point (the clear-structure `[4, 4]`).
-- **Prior sensitivity.** The `Beta(8.5,1.5)` prior (mean ≈ 0.85) is a modeling choice, anchored above
- chance to keep match the higher-agreement class; it is exposed as a parameter.
+# Pair-level Bayesian and Fellegi–Sunter comparison
+
+Canonical run: `catalog/runs/bayes-vs-fs-v1.json`.
+
+This experiment compares three attacker-side pair-linkage scorers on 4,000
+address-reuse pairs and 4,000 sampled non-reuse pairs from the preserved
+22,112-transaction snapshot. The executable table is generated at
+`results/generated/bayes-vs-fs-v1.md`; the full per-axis values and cluster
+diagnostics are in `results/artifacts/bayes-vs-fs-v1.json`.
+
+The preserved data do not reproduce the former handwritten numbers. The
+canonical rerun reports AUC 0.9163 for fixed `m=0.95`, 0.9320 for EM, and
+0.9324 for Bayesian integration. Its ECE values are 0.1455, 0.1750 and 0.1936
+respectively. The AUCs are quoted to four places because a balanced sample of this
+size resolves no further, which is also why the 0.0004 between EM and Bayesian
+integration carries nothing; the ECE gap does. On this selected balanced sample,
+Bayesian integration does not improve calibration over EM.
+
+Address reuse is a weak, partly feature-dependent label. The model fits and
+evaluates on the same selected pairs, assumes conditional independence between
+correlated axes, and fixes `u` at the measured collision rate. The entity-count
+bands independently sample pair edges; they are not a posterior over ownership
+partitions. This is attack evidence, not CoinScore or a privacy certificate.
diff --git a/results/RESULTS-boltzmann-fee-audit.md b/results/RESULTS-boltzmann-fee-audit.md
new file mode 100644
index 00000000..d09a1be0
--- /dev/null
+++ b/results/RESULTS-boltzmann-fee-audit.md
@@ -0,0 +1,64 @@
+# Exact conservation versus explicit fee allocation
+
+**Finding.** On a deterministic bounded sample of 300 ordinary multi-input transactions, all 300
+pay a positive fee, so the exact-conservation baseline admits no mapping. The local fee-allocation
+model admits the whole-transaction interpretation in all 300, but only **37** admit more than that
+trivial single-block interpretation. The largest family has **192** mappings.
+
+This is a measurement of `decluster.baselines.boltzmann`, not a claim of general parity with the
+external Boltzmann tool. Separate reference tests now cover selected official vectors and modes.
+The population measurements below remain specific to the local fee-allocation model.
+
+## Protocol
+
+Source: `data/amount-channel-812695-812831-v1.json`. Scan in file order and select the first 300 transactions satisfying all of:
+
+- at least two inputs and one output;
+- every input prevout and output has an integer value;
+- at most eight input-plus-output coins, the declared exhaustive-enumeration bound.
+
+For each selected transaction:
+
+1. `exact_link_analysis` requires every participant block to balance exactly.
+2. `fee_tolerant_link_analysis` permits a non-negative deficit in each block and sets the total
+ tolerance to the transaction's observed fee, `sum(inputs) - sum(outputs)`.
+3. Fee roundness is recorded independently as trailing decimal zeros. It does not admit, remove or
+ weight a mapping.
+
+| measurement | result |
+|---|---:|
+| selected transactions | 300 |
+| positive-fee transactions | 300 |
+| exact model with any mapping | 0 |
+| fee-tolerant model with any mapping | 300 |
+| fee-tolerant model with more than the all-coins mapping | 37 |
+| maximum fee-tolerant mappings | 192 |
+| round fees | 65 |
+| round fee and nontrivial split | 10 |
+
+The 300/300 fee-tolerant result is not evidence of de-mixing: the all-coins interpretation is
+always admissible once the observed fee is allowed. The informative count here is 37/300, where at
+least one participant split exists in addition to that trivial interpretation. Conversely, zero
+exact mappings does not mean the transactions have no plausible ownership structure; it means an
+exact zero-fee conservation rule cannot consume ordinary fee-paying transactions.
+
+Roundness is deliberately not compared as a competing probability model. Ten transactions happen
+to have both a round fee and a nontrivial split, but this run neither establishes association nor
+uses roundness to rank the mappings.
+
+## Reproducibility
+
+Run:
+
+```sh
+.venv/bin/python examples/boltzmann_fee_audit.py --manifest
+```
+
+The complete per-transaction output is `results/artifacts/boltzmann-fee-audit-v1.json`, produced by
+`catalog/runs/boltzmann-fee-audit-v1.json`. `results/boltzmann-fee-audit.json` is kept beside it as
+the frozen historical report: `tests/test_boltzmann_fee_experiment.py` asserts the canonical run
+still reproduces it exactly, which is a guard the canonical artifact cannot give itself.
+The manifest fingerprints
+`data/amount-channel-812695-812831-v1.json` and pins the configuration, population counts and outcomes
+above. Tests recompute the report from that committed source rather than skipping when it is
+absent. The official-tool parity cell remains open.
diff --git a/results/RESULTS-broadcast.md b/results/RESULTS-broadcast.md
index 2f0cea58..0c4fd9bd 100644
--- a/results/RESULTS-broadcast.md
+++ b/results/RESULTS-broadcast.md
@@ -37,7 +37,7 @@ construction**; we report coverage and calibrate the axis bits, and claim nothin
| na_loose | 0.28% | **0 (abstain)** |
| backdated | 0.14% | 9.48 |
-`na_loose` is an **abstention**, so its stored weight in `library.py` is **0**, not the raw
+`na_loose` is an abstention, so its stored weight in `library.py` is 0, not the raw
−log2(share)=8.48 the calibrator prints from rarity. Rarity is not owner-linkage: "both txs
waited during congestion" is a market-wide condition, so scoring co-abstention as 8.48 bits
would forge a false same-owner link if the axis were fed to the combiner.
@@ -51,7 +51,7 @@ wallet would encode is ~N−1 — one block inside the ±100 `matches` tolerance
The value over the plain `locktime` axis (which compares locktime to the *inclusion* height)
is shown by `tests/test_broadcast.py::test_deconfounds_plain_locktime`. A tx with
-`locktime=700` included 200 blocks later at height 900, on a **loose** bound (it waited):
+`locktime=700` included 200 blocks later at height 900, on a loose bound (it waited):
| axis | verdict |
|---|---|
@@ -66,8 +66,13 @@ it. This is shown as a unit case; in the 712-tx live sample the de-confounding i
exercised (2 `na_loose`, 1 `backdated`), so the effect is demonstrated, not yet measured at
population scale.
-## Honest limits
+## Limits
- No known broadcast time; only the bound is guaranteed.
- Loose for low-feerate txs during congestion (they wait many blocks) → `na_loose`.
- The cluster temporal fingerprint built on this estimate (activity schedule / timezone) was
tested separately and is a **null** under proper controls (`results/RESULTS-temporal.md`).
+
+## Reproducibility / provenance
+
+State 2 in `results/REPRODUCIBILITY.md`: the mechanism is pinned by `tests/test_broadcast.py`; the
+reported numbers come from a live data-run and are not asserted.
diff --git a/results/RESULTS-candidate-set-intersection.md b/results/RESULTS-candidate-set-intersection.md
new file mode 100644
index 00000000..e67bdb8d
--- /dev/null
+++ b/results/RESULTS-candidate-set-intersection.md
@@ -0,0 +1,136 @@
+# Candidate-set intersection: the mechanism, and what is not claimed for it
+
+`decluster/baselines/candidate_set_intersection.py` implements the cross-transaction intersection
+attack described by Goldfeder, Kalodner, Reisman and Narayanan: a coin's plausible origins form a
+*set*, coins later shown to be held by the same party must have come from a common origin, and what
+survives is the intersection of their sets.
+
+## What this document does not claim
+
+The primary text is now available and Algorithm 2's graph-independent core is implemented. The
+paper's JoinMarket dataset, figures and empirical rates are not reproduced, and no measured
+rate on this page is attributed to Goldfeder et al. The kernel cell is closed; empirical parity is
+not.
+
+The baseline also states no shrink law. The reading that each observation cuts the candidate set
+by a constant factor is not Goldfeder's — the intersection paper demonstrates the attack and states
+no such law — and it is not sourced anywhere else here either. It was previously credited on this
+page to "Danezis and Serjantov's statistical-disclosure result", which does not exist: statistical
+disclosure is Danezis alone, and the one paper by both authors that could have carried it, *Statistical
+Disclosure or Intersection Attacks on Anonymity Systems* (IH 2004), was obtained and read in full. It
+is Bayesian throughout and states no constant-factor law; its §7 says in the authors' own words that
+analytic representations "would allow us to calculate the number of rounds", which is to say they did
+not have them. The statistical-disclosure line of work bounds the observations an adversary needs by a
+signal-to-noise condition and a confidence interval, not by a constant factor per observation.
+`decluster/intersect.py` and `decluster/baselines/candidate_set_intersection.py` carry the same
+distinction, and the numbers below are properties of a constructed family, not of any observed shrink
+rate.
+
+## Scope
+
+The generic primitive takes candidate sets. `goldfeder_cluster_intersection` additionally constructs
+them at Algorithm 2's abstraction boundary: injected join-only predecessor edges, a bound `r`, and
+an injected wallet-cluster function. Join detection, recursive address clustering, and the auxiliary
+observation that the coins are co-held remain caller responsibilities. The baseline imports no
+ancestry walk, fetcher, or engine from this repository.
+
+Three answers are possible, and the third matters most:
+
+| answer | shape | reported as |
+|---|---|---|
+| identified | exactly one candidate survives a consistent run | `identified` = that candidate |
+| narrowed | more than one survives | a set; `identified` is `None` |
+| refused | the intersection is empty | `inconsistent_at`, `identified` `None`, bits `None` |
+
+An empty intersection means the observations contradict each other — a candidate set is wrong, or
+the coins were not co-held. It is a refusal, never an identification, and it is never reported as an
+unbounded narrowing: `narrowing_bits` comes back `None` rather than infinite, so the strongest
+evidence that the premise broke cannot be read as the strongest claim. A run that hits a
+contradiction also stops there, rather than absorbing later observations that cannot narrow an empty
+set.
+
+## The constructed family
+
+`candidate_set_intersection.scenarios()` is a deterministic family of **3** longitudinal runs over
+one universe of `universe_size` = **64** candidates, **13** observations supplied in total. It is a
+fixture for this document, not a claim about chain data, and the sizes are chosen so a reader can
+check every row by hand.
+
+### converging — successive observations narrow to one
+
+Six observations, each half the size of the last, all containing candidate `0`.
+
+| observation | observed | before | after | bits |
+|---:|---:|---:|---:|---:|
+| 0 | 32 | 64 | 32 | 1.0 |
+| 1 | 16 | 32 | 16 | 1.0 |
+| 2 | 8 | 16 | 8 | 1.0 |
+| 3 | 4 | 8 | 4 | 1.0 |
+| 4 | 2 | 4 | 2 | 1.0 |
+| 5 | 1 | 2 | 1 | 1.0 |
+
+`converging_survivors` = **1**, `converging_identified` = **0**, `converging_bits` = **6.0**. This is
+the attack's whole shape: no single observation names the origin, and together they do.
+
+### stalled — observations that do not converge
+
+Four observations of the same 8 candidates.
+
+| observation | observed | before | after | bits |
+|---:|---:|---:|---:|---:|
+| 0 | 8 | 64 | 8 | 3.0 |
+| 1 | 8 | 8 | 8 | 0.0 |
+| 2 | 8 | 8 | 8 | 0.0 |
+| 3 | 8 | 8 | 8 | 0.0 |
+
+`stalled_survivors` = **8**, `stalled_bits` = **3.0** — all of it from knowing the universe, none of
+it from repetition. `identified` is `None`: eight survivors identify nobody, and the result stays a
+set rather than being rounded to a name.
+
+The first row is the reason `universe_size` is an input at all. A first set of 8 out of 64 has
+already said 3 bits; without the universe there is nothing to measure that against, and the run says
+so by carrying no bits for its first step rather than inventing a baseline for it.
+
+### contradictory — the refusal
+
+| observation | observed | before | after | bits |
+|---:|---:|---:|---:|---:|
+| 0 | 8 | 64 | 8 | 3.0 |
+| 1 | 8 | 8 | 0 | — |
+
+`contradictory_survivors` = **0**, `contradictory_inconsistent_at` = **1**, and
+`narrowing_bits` is `None`. A third observation was supplied and was never consumed: the run stops
+at the contradiction.
+
+Across the family: `identifications` = **1**, `refusals` = **1**.
+
+## Relationship to `decluster/intersect.py`
+
+`decluster/intersect.py` stays, unrenamed, and is not replaced by this baseline. The two are
+different objects:
+
+- This module is the published mechanism in outline, standalone, over abstract candidate sets.
+- `intersect.py` is this repository's wiring of that mechanism: origin sets from the backward
+ absorbing walk, rarity weighting, cluster-lift, truncation and blindness reporting, and
+ subordination to `cluster_refined`. Those additions are this repository's, not the paper's.
+
+The primary text has now been checked. `goldfeder_cluster_intersection` implements Algorithm 2's
+join-only backward paths bounded by `r`, wallet-cluster lift, intersection, and unique-or-refuse
+verdict over injected join-graph and clustering callbacks. The 2015–2017 JoinMarket simulation and
+its empirical rates have not been reproduced. `decluster/intersect.py` is a different, adapted
+pipeline: it uses a probabilistic ancestry walk that is not restricted to join paths and adds engine
+gating, rarity weights and blindness accounting.
+
+## Reproduce
+
+```python
+from decluster.baselines import candidate_set_intersection as csi
+
+for name, observations in csi.scenarios():
+ print(name, csi.intersect_candidate_sets(observations, universe_size=csi.UNIVERSE))
+print(csi.manifest_invariants())
+```
+
+`tests/test_candidate_set_intersection.py` recomputes every invariant above and checks it against
+`results/manifests/RESULTS-candidate-set-intersection.json`, so a drifting number fails loudly
+instead of standing on this page.
diff --git a/results/RESULTS-catalog-axes.md b/results/RESULTS-catalog-axes.md
index 3d5975e8..8ed028ca 100644
--- a/results/RESULTS-catalog-axes.md
+++ b/results/RESULTS-catalog-axes.md
@@ -1,12 +1,27 @@
# Two Möser–Narayanan tells, tested against the 23-axis model
+> A reproducible ablation on the frozen 600-transaction snapshot is defined by
+> `catalog/runs/fingerprint-catalog-axes-v1.json`. Its structured artifact is
+> `results/artifacts/fingerprint-catalog-axes-v1.json`, with canonical rendering in
+> `results/generated/fingerprint-catalog-axes-v1.md`. It uses one pair population for all four
+> configurations. On this snapshot, output count decreases AUC by 0.000725, SegWit serialization
+> increases it by 0.002600, and both together increase it by 0.002125, less than SegWit alone. This
+> is consistent with the historical redundancy concern but does not prove conditional dependence.
+> The two historical cache snapshots below were not preserved and are not reproduced.
+
The record-linkage corpus (`fingerprints.md`, Table 4) lists two construction fingerprints
-the paper's axis catalog (§8) marks ◐ — **output count** ("more than two outputs is less
-likely an ordinary wallet") and **SegWit-conform** ("a segwit-capable wallet is forced to
+the paper's axis catalog (§8) marks ◐ — output count ("more than two outputs is less
+likely an ordinary wallet") and SegWit-conform ("a segwit-capable wallet is forced to
non-segwit serialization when no input is segwit"). This measures whether adding them to the
-canonical 23-axis Fellegi–Sunter scorer buys anything. It does not — and the *shape* of the
+canonical 23-axis rarity scorer buys anything. It does not — and the *shape* of the
non-result is the point.
+(The scorer is `fingerprint_validate.LibraryScorer`, called the Fellegi-Sunter scorer here until
+now. It is not: agreement carries Newcombe's `-log2(p)` rarity weight and disagreement a clamped
+penalty from an assumed consistency, with no fitted `m`/`u`. The fitted Fellegi-Sunter baseline is
+`decluster/fellegi_sunter.py` and is a different object. Nothing measured below changes — only what
+it is called.)
+
## Probes
Two throwaway single-tx extractors (not shipped — they earn no place in the library, see
@@ -26,7 +41,7 @@ Bits = `−log2(share)` on the local `.blkcache` (~166k): output_count `{2: 0.51
## Result — measured on two cache snapshots
Absolute AUCs are `.blkcache`-specific (the cache is a local, growing artifact — see
-`RESULTS-fingerprint-validation.md`); the **deltas** are the finding, and they are not even
+`RESULTS-fingerprint-validation.md`); the deltas are the finding, and they are not even
stable across two cache sizes on the same seeded pairs:
| adding to the 23-axis scorer | Δ AUC @ 166k cache | Δ AUC @ 180k cache |
@@ -47,22 +62,27 @@ moving, not the axes.)
- **The bigger magnitude comes from the *more redundant* axis.** `segwit_serialization` is
nearly determined by `input_script_type` (+ `nested_segwit`), yet it moves the AUC more
(~±0.002) than `output_count`. In a naïve-Bayes product-of-axes scorer, adding an axis
- correlated with one already present **re-counts the same evidence**: same-wallet pairs share
+ correlated with one already present re-counts the same evidence: same-wallet pairs share
their input script type by construction (address-reuse label → same type), so counting
"both are segwit" on top of "both are `v0_p2wpkh`" inflates the positive-pair scores. A
redundant axis producing the larger swing is the signature of that double-count.
- **Not additive.** Both together ≤ segwit alone on *both* snapshots: `output_count` adds
essentially nothing once `segwit_serialization` is in. Independent signals would add up.
-- **Within noise.** Every delta above is smaller than the EM per-axis-`m` refinement
- (+0.0035, `RESULTS-em-m.md`) and deep inside the weight-sensitivity band (AUC moves ~0.04
- across the realistic `c` sweep, `RESULTS-weight-sensitivity.md`).
+- **Within noise, though by less than was claimed.** Every delta above is smaller than the EM
+ per-axis-`m` refinement (+0.0035, `RESULTS-em-m.md`). The weight-sensitivity comparison used to
+ read "AUC moves ~0.04 across the realistic `c` sweep", which overstates the floor by about 2.5x:
+ `results/generated/weight-sensitivity-v1.md` puts the full 0.60-0.99 grid at **0.0161**
+ (0.9084 to 0.9245) and the realistic 0.90-0.99 band at **0.00055**. Read against the band that
+ actually brackets the shipped `c=0.95`, the +0.0026 and +0.0020 deltas are *larger* than the
+ weight sensitivity, not deep inside it. What keeps them out of the model is the sign flip and the
+ non-additivity above, not a noise floor wide enough to swallow them.
## Conclusion
-The canonical 23-axis model **correctly excludes** both. `output_count` is subsumed by
+The canonical 23-axis model correctly excludes both. `output_count` is subsumed by
`io_shape`; `segwit_serialization` by `input_script_type` + `nested_segwit`. Folding them in
would violate the scorer's conditional-independence assumption and buy only a
-double-counting artifact within the noise floor — so they stay out, and the library ships no
+double-counting artifact whose contribution is not even sign-stable — so they stay out, and the library ships no
extractor that no result consumes. `fingerprints.md` does not cover the taproot
script-tree-depth-from-round-fee leak (a separate derived signal) or "change is always
bech32" (a cross-transaction consistency claim, not a single-tx bit); both remain out of
diff --git a/results/RESULTS-change-id.md b/results/RESULTS-change-id.md
index a48b4089..56ddc3b0 100644
--- a/results/RESULTS-change-id.md
+++ b/results/RESULTS-change-id.md
@@ -1,18 +1,18 @@
# Change-id validation — validating the ordering axis (M&N labels + fingerprint tests)
Slice: `bigquery/slice.sql`, 1 day = 2024-06-01 (blocks 845982–846122, 739,889 txs).
-Same-owner change labels: the change output revealed by multi-input **cluster membership** (M&N),
-then filtered by M&N §2.2 (`build_gt_slice_mn`) — **fresh change** (change address appears as an
-output only in T, an in-slice reuse proxy for M&N's "change already known at creation") and the
-**>10% two-change cluster** exclusion.
+Same-owner change labels: the change output revealed by multi-input cluster membership (M&N),
+then filtered by M&N §2.2 (`build_gt_slice_mn`) — fresh change (change address appears as an
+output only in T, an in-slice reuse proxy for M&N's "change already known at creation") and the >10% two-change cluster
+exclusion.
**Raw labels: 1045 → n = 578** (467 dropped as in-slice reused-change; 0 by the two-change rule).
`M&N supercluster / tag-collapse removal (Mt.Gox) needs whole-chain tags and is NOT applied.`
## The valid result: per-axis change prediction (M&N Table 4 style; bootstrap B=2000)
-Each axis votes "change = the output whose onward-spending tx **agrees with T on that axis**" — a
-construction-fingerprint agreement between T and its output's spender, **disjoint** from the
+Each axis votes "change = the output whose onward-spending tx agrees with T on that axis" — a
+construction-fingerprint agreement between T and its output's spender, disjoint from the
address-graph label. `coverage` = fraction of labels where the axis fires (else it abstains);
`prec.` = precision when it fires (TPR / coverage).
@@ -24,7 +24,7 @@ address-graph label. `coverage` = fraction of labels where the axis fires (else
| version | 0.779 [0.744, 0.811] | 0.000 | 0.779 | 1.00 |
| change_index (round-number baseline) | 0.486 [0.446, 0.524] | 0.080 | 0.566 | 0.86 |
-Combined tx-level pre↔post **AUC = 0.759 [0.716, 0.802]** vs shuffle-null ≈ 0.50 [0.44, 0.56] — the
+Combined tx-level pre↔post AUC = 0.759 [0.716, 0.802] vs shuffle-null ≈ 0.50 [0.44, 0.56] — the
change's onward-spend shares T's construction fingerprint more than the payment's does (a genuine,
label-disjoint signal; the shuffle control randomizes the pos/neg direction and collapses to ~0.5).
@@ -39,23 +39,24 @@ coverage axes (version/nSequence) — another face of the fast-wallet skew. The
- **The ordering axis validates as a real but LOW-COVERAGE change signal.** `input_order` fires on
only 39% of labels (vs 57% baseline, 78% version) and, when it fires, its precision (0.83) is
about the round-number baseline (0.86). `output_order` fires on 48% at precision 0.90. So ordering
- resolves **fewer** cases, not less accurately — its low TPR is mostly low coverage, not low
- precision. This is the honest answer to "validating the ordering": as a change predictor it is
+ resolves fewer cases, not less accurately — its low TPR is mostly low coverage, not low
+ precision. This is the answer to "validating the ordering": as a change predictor it is
real but low-coverage and no better than the round-number baseline in precision.
- **`nsequence` / `version` are the strong single tells here** — near-perfect precision at high
coverage on this slice: same-owner onward-spends reuse the wallet's sequence/version ~77–78%.
-- The fresh-change filter removed ~45% of raw labels (an **in-slice** reuse proxy for M&N's
+- The fresh-change filter removed ~45% of raw labels (an in-slice reuse proxy for M&N's
reused-change removal — not identical to M&N's whole-chain filter, and this share is slice-local,
not comparable to M&N's 28.4%).
-**This ranking is NOT robust to the label or the epoch** (`results/RESULTS-special-change.md`). Against
-an *independent, value-based* label (optimal-change, disjoint from co-spend) on a multi-epoch sample,
-all four onward-spend axes fall to ~0.60–0.74 precision and `nSequence`/`version` no longer dominate
-the ordering axes. Part of that is a co-spend-label **selection bias** — the co-spend label selects
-changes whose onward-spender *is* the reveal tx, a same-wallet transaction that shares nSequence/version
-by construction (inflating the numbers above); part is epoch / onward-spend time-gap drift in the
-multi-epoch sample. Disentangling the two needs a contiguous-value slice running both labels on the
-same transactions (Phase 2). Read the single-day numbers above as slice-and-label-specific, not general.
+**This ranking is not established as robust to the label.** The co-spend label selects changes whose
+onward-spender *is* the reveal transaction, a same-wallet transaction that shares nSequence/version by
+construction, so it inflates exactly the two axes that lead the table. An independent value-based
+label (optimal-change, disjoint from co-spend) carries no such bias, and against it the two
+within-transaction heuristics agree at 0.81 and 0.90 precision
+(`results/generated/special-change-labels-v1.md`). The per-axis comparison that would settle the
+ranking needs each change output's spender in the same data; the committed cache holds it for 12 of
+1138 labelled transactions, so that arm awaits a contiguous export carrying values at depth. Read the
+single-day numbers above as slice-and-label-specific, not general.
## The circular non-result: cluster `findNext` (reported ONLY as a circularity demonstration)
@@ -63,7 +64,7 @@ We also implemented Kappos's cluster-level `findNext` (change = the output at th
index whose onward-spending tx's features are in the cluster's `TFC` set). **Against an M&N co-spend
label this is circular and is NOT evidence for any fingerprint:**
-- The change output's onward-spender is, by construction, the co-spend **reveal** tx — a cluster
+- The change output's onward-spender is, by construction, the co-spend reveal tx — a cluster
member. Verified on this slice: the change spender is a cluster member **576/578**; the payment
spender is a member **0/226**. So `features ∈ cluster TFC` is trivially true for the change, and
the check collapses to *cluster membership = the label itself*.
@@ -85,3 +86,9 @@ pre↔post test above (T vs its output's spender). `change_cluster.py` is kept f
- Kappos's address-type check (AFC) is approximated from address prefixes (`_addr_type`), not the
raw script type; on this 2024 slice it does not help (segwit/taproot homogeneity — Kappos's own
caveat), so the per-axis fingerprint test, not AFC, carries the validation.
+
+## Reproducibility / provenance
+
+State 4 in `results/REPRODUCIBILITY.md`: the window `bigquery/slice.sql` names holds 739,889 non-coinbase
+transactions, so it cannot become a committed fixture; a smaller window reveals fewer change labels
+and measures something else. Moved here from state 3 on 2026-09-07.
diff --git a/results/RESULTS-cluster-bits.md b/results/RESULTS-cluster-bits.md
deleted file mode 100644
index 63104afd..00000000
--- a/results/RESULTS-cluster-bits.md
+++ /dev/null
@@ -1,55 +0,0 @@
-# Anchoring the ">100 bits": a real cluster's identifying structure, measured
-
-§1 argues a merge's ~1.6 bits of ambiguity cannot survive the **>100 bits** an established
-cluster carries — an order-of-magnitude figure inherited from Narayanan–Shmatikov, previously
-*not measured*. This measures it directly, instantiating the N-S accumulation that
-`cluster.topology_weight`'s docstring names.
-
-## Method
-
-On a real connected slice, each cluster (co-spend-linked addresses) carries, as its structural
-quasi-identifier, its set of distinct **external payment-graph counterparties** (co-spend
-edges excluded — non-circular; and the cluster's own members excluded — an intra-cluster edge
-is not a quasi-identifier to an outsider). Each counterparty contributes
-`−log2(share of nodes touching it)` bits (`counterparty_bits`): a hub everyone touches ≈ 0
-bits, a rare private address many. The cluster's **structural information content** is their
-sum — the N-S accumulation ("dozens of sparse attributes, each a few bits") on real Bitcoin
-(`examples/cluster_bits.py`).
-
-## Result — 150k-tx connected subset of `slice.json`, 5,864 clusters (≥2 addresses)
-
-| quantity | value |
-|---|---:|
-| median cluster bits | **32.7** |
-| p90 cluster bits | 126.2 |
-| max cluster bits (a hub/exchange) | ~29,200 |
-| clusters ≥ 1.6 bits (the merge's ambiguity) | **100.0%** |
-| clusters ≥ 10 bits | 98.4% |
-| clusters ≥ 50 bits | 22.7% |
-| clusters ≥ 100 bits | **11.5%** |
-
-## Reading
-
-- **Every cluster clears the merge.** All 5,864 clusters carry ≥ 1.6 bits — the exact quantity
- a 2-in/2-out merge adds. The median carries **~33 bits, ~20× the merge**; the asymmetry §1
- asserts is measured, not just argued. An analyst needs only ~2 bits to override the merge and
- every cluster supplies an order of magnitude more.
-- **The ">100" is reached, even truncated.** 11.5% of clusters already exceed 100 bits on a
- *150k-tx subset*. A subset sees only a fraction of each cluster's true counterparties, so this
- **undercounts** the whole-chain figure — the real per-cluster bits are higher, and the
- order-of-magnitude ">100" is the whole-chain expectation (§9).
-- **Not an independence artifact.** The median top-5-counterparty sum equals the median total
- (32.7), i.e. the median cluster has ≤ 5 counterparties: its bits come from a handful of *rare*
- counterparties, not from summing hundreds of weak-but-correlated ones. So the naïve-independence
- caveat (Σ overstates unique-ID bits when counterparties correlate) does not inflate the median.
-
-## Honest limits
-
-- **Structural content, not proof of unique ID.** `Σ −log2(share)` is the identifying
- information *content*; correlated counterparties mean the effective unique-identification bits
- are fewer than the raw sum for large clusters. The top-5 floor and the small median cluster
- size bound this for typical clusters; hub/exchange super-clusters (the ~29,200 tail) are where
- the raw sum most overstates.
-- **Slice, not whole chain.** A connected subset truncates counterparty sets, so the figures are
- a **lower bound** on the whole-chain per-cluster bits (§9). Reproduce:
- `python3 -m examples.cluster_bits slice.json 150000`.
diff --git a/results/RESULTS-cluster-robustness.md b/results/RESULTS-cluster-robustness.md
index b180de86..1e2e3e6a 100644
--- a/results/RESULTS-cluster-robustness.md
+++ b/results/RESULTS-cluster-robustness.md
@@ -1,7 +1,7 @@
# Clustering weight-robustness: does graph topology stabilise the partition against weight changes?
-**Claim tested.** The collaborator's part-3 conjecture is robustness "with enough fingerprints **and graph
-structure**." The fingerprint leg is shown in `RESULTS-weight-sensitivity.md` / `RESULTS-em-m.md`. This
+**Claim tested.** The collaborator's part-3 conjecture is robustness "with enough fingerprints and graph
+structure." The fingerprint leg is shown in `RESULTS-weight-sensitivity.md` / `RESULTS-em-m.md`. This
tests the graph leg at the clustering level: does fusing the topology term (`cluster_refined(neigh=…)`,
calibrated in `RESULTS-topology.md`) make the owner-*partition* stable under a fingerprint-weight (`c`)
sweep, where a fingerprint-only clustering moves?
@@ -35,7 +35,7 @@ holds in this controlled scenario.
Reproduce: `python3 examples/cluster_robustness.py`.
-## Honest limits
+## Limits
- **Constructed scenario, not chain scale.** Like the paper's `931d6627` existence demos, this isolates
the mechanism on a graph we build; it is not a whole-chain rate. Real-data confirmation on the
@@ -44,10 +44,15 @@ Reproduce: `python3 examples/cluster_robustness.py`.
`LibraryScorer` of the pair-AUC validation.
- **`cluster_refined` is case-study scale** (`fetch_tx` per pair, O(n²) linking); the scenario is sized
accordingly.
-- **The topology signal here is counterparty-overlap, narrower than "graph connectivity."** The fused
- term is a rarity-weighted shared-counterparty overlap (`cluster_topology_weight`) — a graph-derived
- quasi-identifier (shared *edges* in the payment graph), the intersection-attack intuition. It is *not*
- the connectivity / average-distance / max-flow of the whole graph that the design conversation frames
- as the substance of robustness; those are a stronger, separate topological measure (future work). What
- this shows is "a rare shared counterparty dominates the fingerprint weight," which is one concrete way
- graph structure swamps the per-axis uncertainty, not the full connectivity argument.
+- **The topology signal here is counterparty-overlap.** The fused term is a rarity-weighted
+ shared-counterparty overlap (`cluster_topology_weight`) — a graph-derived quasi-identifier (shared
+ *edges* in the payment graph), the intersection-attack intuition. What this shows is "a rare shared
+ counterparty dominates the fingerprint weight," which is one concrete way graph structure swamps the
+ per-axis uncertainty.
+
+## Reproducibility / provenance
+
+Not yet filed against `results/REPRODUCIBILITY.md`. The document demonstrates the mechanism on a
+graph it builds and says so — "it is not a whole-chain rate" — but no test was found that asserts
+the numbers below, so neither state 1 nor state 2 fits on the evidence available. Read them as
+unclassified until someone names the test.
diff --git a/results/RESULTS-cluster-scale.md b/results/RESULTS-cluster-scale.md
deleted file mode 100644
index 5101a99d..00000000
--- a/results/RESULTS-cluster-scale.md
+++ /dev/null
@@ -1,78 +0,0 @@
-# Cluster-scale — the whole-corpus merge-only baseline, measured offline
-
-The faithful merge-only baseline (`cluster.build_cospend_lookup` + `cluster_from_index`, wired into
-`graph_metric.overcount_report` via `baseline_lookup`) computes multi-input clustering over the **whole
-loaded corpus**, not just the evaluation sample. This measures its **scale** and checks the premise
-that whole-corpus membership is a stronger, fairer baseline than the sample-local `cluster_naive`.
-Offline — no network. Code: `decluster/cluster.py`, `decluster/graph_metric.py`.
-
-**Corpus:** the value-carrying BigQuery downloads (`~/Downloads/bquxjob_*.json`), **133,672 unique
-transactions** (a scattered multi-epoch sample, not a contiguous chain — this matters below).
-
-## Whole-corpus multi-input baseline
-
-| funders | entities | collapse | entropy (bits) | eff. anon. set | largest-cluster frac |
-|---|---|---|---|---|---|
-| 318,433 | 99,957 | **68.6%** | 13.21 | 9,501 | 0.072 |
-
-The common-input-ownership heuristic collapses 318k coin-funders into ~100k entities in seconds,
-entirely offline — the machinery scales. The largest cluster already holds **7.2% of all funders** (a
-supercluster, the usual exchange/service signature).
-
-## Scale curve — entities per funder as the corpus grows
-
-| corpus fraction | txs | funders | entities | entities/funder |
-|---|---|---|---|---|
-| 0.10 | 13,367 | 63,870 | 12,754 | 0.200 |
-| 0.25 | 33,418 | 110,565 | 22,818 | 0.206 |
-| 0.50 | 66,836 | 165,052 | 51,978 | 0.315 |
-| 0.75 | 100,254 | 264,201 | 83,943 | 0.318 |
-| 1.00 | 133,672 | 318,433 | 99,957 | 0.314 |
-
-**Honest reading:** the entities/funder ratio does **not** fall monotonically as the corpus grows — it
-rises from ~0.20 to ~0.31. On a scattered value-sample, each added chunk introduces more *new,
-not-yet-co-spent* funders than it does new merges, so per-funder collapse dilutes rather than
-compounds. On a **contiguous** corpus (a block window, or the eventual whole-chain source) the curve
-would behave differently, because co-spends accumulate on the same funder population. Do not read this
-as "more data always collapses more" — that holds for contiguous chain coverage, not for a scattered
-sample.
-
-## Window-local vs whole-corpus — the branch's premise
-
-The point of `build_cospend_lookup` is to drop `cluster_naive`'s `if vin["txid"] in nodes` sample gate,
-so the baseline sees co-spends **outside** the evaluation window. On the **same 63,870 funders** that
-appear in a 10%-window:
-
-| baseline | entities |
-|---|---|
-| window-local (co-spends inside the window only) | 12,754 |
-| whole-corpus (all co-spends) | **12,705** |
-
-Whole-corpus merges **49 more** funder-groups — co-spends the window-local baseline structurally
-misses. The premise holds **directionally**: the whole-corpus baseline is strictly stronger. But the
-magnitude here is **small (0.4%)**, again because the downloads are scattered: cross-window co-spends
-among this particular sample are rare. The faithful-baseline advantage this branch enables becomes
-material only on a **contiguous / whole-chain** source, where a coin's true cluster genuinely extends
-far beyond any window.
-
-## Scope & caveats
-
-- **Baseline only.** The `cluster_refined` (decluster signed) side of `overcount_report` is `O(n^2)`
- pairwise and calls `fetch_tx` (network), so the baseline-vs-fused comparison at scale is **not** run
- here — it awaits a local raw-tx source (esplora / Core txindex) to be cheap. This doc measures the
- merge-only baseline's scale and faithfulness, which is what `build_cospend_lookup` newly enables.
-- **Whole-downloads, not whole-chain.** The lookup is faithful to the *loaded corpus*; a funder never
- co-spent within it stays a singleton, so the true whole-chain baseline (which would merge more) is a
- lower bound on collapse. The full effect awaits the whole-chain source.
-- **Node = funder txid** (a coin-granularity simplification decluster already makes); pass evaluation
- nodes as a **set** (deduped) — `cluster_from_index` counts duplicates while the fused side's
- union-find dedups, so a duplicated node list would desync the two partitions.
-
-## Conclusion
-
-The whole-corpus merge-only baseline computes at scale offline (133k txs -> 100k entities in seconds)
-and is measurably stronger than the sample-local baseline (49 extra merges on the shared funders),
-confirming the branch's premise directionally. The magnitude of the whole-chain advantage — and the
-baseline-vs-fused comparison — is bounded here by the scattered, network-free sample; both grow on a
-contiguous / whole-chain source, which is the next data-layer step (local esplora), not a change to
-this method.
diff --git a/results/RESULTS-conservation.md b/results/RESULTS-conservation.md
index e2ebb41a..1264c2d1 100644
--- a/results/RESULTS-conservation.md
+++ b/results/RESULTS-conservation.md
@@ -1,5 +1,11 @@
# Conservation: forcing coinjoin outputs onto a known participant
+> **Executable scope, 2026-09-04.** The round-three arithmetic, complete output
+> histogram, prefix curve and boundary margin are now generated by
+> `catalog/runs/conservation-round-three-v1.json`. That run does not reproduce
+> the surrounding six-round forward walk. The remaining round history below is
+> retained as a historical account and must not be read as bundle-verified.
+
`decluster/conservation.py`. If a set of outputs is worth more than every other
participant brought to the round, the excess had to come from the one participant
whose input we can name. No client model is involved — not the denomination
@@ -93,7 +99,7 @@ forced_prefixes(tx, 4712126860) # the curve above
## Relation to the amount channel as measured elsewhere
-`RESULTS-subtx-demix.md` reports that the labelled dense coinjoins recover **0**
+`RESULTS-subtx-demix.md` reports that the labelled dense coinjoins recover 0
participants, and PAPER §9 reads that as those rounds being amount-private. Both
remain true of the de-mix, which looks for `input = mix + change − fee` and finds
no unique match in dense denomination tiers.
diff --git a/results/RESULTS-conspicuous-order.md b/results/RESULTS-conspicuous-order.md
new file mode 100644
index 00000000..c9ff75ac
--- /dev/null
+++ b/results/RESULTS-conspicuous-order.md
@@ -0,0 +1,103 @@
+# Does clustering the conspicuous transactions first change anything?
+
+## Verdict
+
+No. On a contiguous 137-block slice with complete transaction data, ordering the merges by how
+little each argues against itself produces a partition identical to block order. What does
+change the partition is the state-dependent gate the ordering was supposed to feed: declining a
+doubted merge that would fuse two already-established clusters takes the slice from 868 to 1,117
+clusters. The ordering is inert; the gate is not.
+
+## Data
+
+`data/amount-channel-812695-812831-v1.json`: 5,491 transactions over blocks 812,695–812,831, every
+one carrying prevout values and script types, 1,428 of them multi-input. This is the committed
+contiguous slice that supports the amount channel — the graph-scale exports (`epoch_2016_*`, the committed graph
+fixtures) carry addresses only, and the fingerprint survey is aggregate rather than per-transaction.
+
+## What the transactions say against themselves
+
+`merge_objections` counts the published tells that argue against reading a transaction's inputs as
+one owner: an input that already covers the largest output (the unnecessary-input heuristics), and
+inputs that disagree on script type. Over the 1,428 multi-input transactions:
+
+| objections | transactions | share |
+|---:|---:|---:|
+| 0 | 855 | 59.9% |
+| 1 | 512 | 35.9% |
+| 2 | 61 | 4.3% |
+| unrankable | 0 | — |
+
+> **Superseded numbers, and how far the error reached.** An earlier revision published 513 / 820 / 95
+> here. It was computed over an export storing every amount as a JSON string, so the comparison in
+> `x_uih` ran lexicographically — `"330" > "1000"` is true as text and false as a number — and 51.3%
+> of the UIH verdicts changed once the amounts were read as integers. `x_uih` now refuses a
+> non-integer amount instead of ranking it, so this cannot recur silently.
+>
+> That revision also stated that the partition tables below were unaffected. They were not: the two
+> doubt-gate rows are recomputed here as well, and they move a great deal, because the gate only
+> declines a merge that the transaction argues against and under string amounts *every* multi-input
+> transaction appeared to argue against itself.
+
+**A majority of multi-input transactions is unambiguous** — 59.9% raise no objection at all, so
+blind common-input ownership merges the rest regardless. That is the quantity the refusal exists to
+act on.
+
+## What each rule does to the partition
+
+| configuration | clusters | largest | addresses clustered |
+|---|---:|---:|---:|
+| naive CIOH | 889 | 996 | 9,960 |
+| refuse (shape + de-mix), block order | 868 | 996 | 9,109 |
+| refuse, conspicuous-first | 868 | 996 | 9,109 |
+| refuse + doubt gate, block order | 869 | 996 | 9,009 |
+| refuse + doubt gate, conspicuous-first | 869 | 996 | 9,009 |
+
+- Refusal changes the partition: yes (889 → 868)
+- The doubt gate barely changes it: 868 → 869, one cluster. The earlier 1,117 was the string-amount
+ reading, where every multi-input transaction raised an objection and the gate therefore had a
+ candidate to decline almost everywhere. Read on the amounts as numbers, the gate is nearly inert
+ on this slice
+- The order changes it: no — the two gated runs are identical, and so are the two ungated ones
+
+The largest cluster is 996 addresses in every configuration, including naive. Nothing here prevents
+it, which says it is assembled from transactions that raise no objection — a genuinely conspicuous
+entity, or one whose collapse happens through evidence none of these rules examines.
+
+## Why the order is inert
+
+Union-find over a fixed set of merges is order-independent, so ordering can only matter through a
+decision that reads the partition built so far. The refusal rules are not such a decision: the
+coinjoin shape and the de-mix both read the spending transaction alone.
+
+`cluster_addresses` does contain one, though, and it is worth naming rather than passing over.
+Change-link eligibility asks whether a transaction's inputs *already* stood in one cluster when it
+was reached, which is a property of the partition-so-far and therefore of the order. It is inert
+here for a different reason than the refusal rules are: `_merge_pass` takes that eligibility in
+sample order under both settings (`views.py:171-177`), so staging changes which merges are judged
+against which context and nothing else. The independence holds by construction, not because no
+order-sensitive decision exists — read the other way, this section would be claiming something the
+function does not support.
+
+Adding a state-dependent decision to the *merging* — the doubt gate — makes the partition move, but
+not with the order, because at a two-address threshold a cluster becomes "established" almost
+immediately and the verdict is the same whenever it is taken.
+
+That is a property of this gate, not a refutation of the idea. A gate whose threshold sat higher, or
+which read something that accumulates more slowly than cluster size, would be order-sensitive. The
+measurement says the *cheap* version of the idea buys nothing, not that staging is worthless.
+
+## Scope
+
+One slice, 137 blocks, one era. The doubt gate's threshold sweep was run under the string-amount
+reading and is not carried over; what is measured here is the threshold of two. The largest-cluster figure is stable across every configuration,
+which limits what this slice can say about cluster collapse — nothing here produces one.
+
+## Reproducibility / provenance
+
+State 1 in `results/REPRODUCIBILITY.md`: band-pinned on committed data. `merge_objections`,
+`merge_order` and the ordering's refusal on an unrankable export are pinned in
+`tests/test_views.py`; the tables above are recomputed from
+`data/amount-channel-812695-812831-v1.json` and asserted in
+`tests/test_conspicuous_order_slice.py`, so a change that moves them fails rather than republishing
+quietly.
diff --git a/results/RESULTS-contraction-2026.md b/results/RESULTS-contraction-2026.md
deleted file mode 100644
index 6cc752d4..00000000
--- a/results/RESULTS-contraction-2026.md
+++ /dev/null
@@ -1,67 +0,0 @@
-# Contracting a 2026 slice into two pseudonym-graph views
-
-**What this builds.** The cited framework's construction: contract the cluster-annotated
-coin graph so each cluster becomes one vertex, leaving transfers as the residual edges.
-Under a partial clustering the result is a pseudonym graph, the object the matching
-algorithm consumes. Contraction yields a multigraph; the parallel transfers between one
-ordered pair are folded into a single attributed directed edge, per the model's own
-footnote.
-
-**Data.** `slice_2026.ndjson`: blocks 939 969–940 112 (view A) and 940 977–941 120
-(view B), one week apart, 830 770 transactions. Two streaming passes, 44 s.
-`examples/contract_slice.py`.
-
-## Result
-
-Global clustering over the whole slice: **633 933 addresses in 55 850 clusters** of two or
-more, largest 30 304 addresses.
-
-| | view A | view B |
-|---|---:|---:|
-| vertices | 395 547 | 389 908 |
-| edges | 534 652 | 515 022 |
-| mean degree | 2.68 | 2.62 |
-| median / p90 / max degree | 1 / 3 / 15 638 | 1 / 3 / 13 408 |
-| edges folding more than one transfer | 38 257 (7.2 %) | 36 251 (7.0 %) |
-
-**Vertices present and linked in both views: 33 033.** Their mean degree in A is **8.68**
-(median 2, p90 5), and **5 468** hold degree ≥ 3 in *both* views.
-
-## Reading
-
-**Contraction roughly doubles degree, on the population where that matters.**
-`RESULTS-slice-gate-2026.md` measured mean out-degree 4.81 at address level among the
-45 105 spanning addresses; the same population after contraction reads 8.68. The
-whole-graph mean of 2.68 is *lower* than 4.81 and does not contradict it: it averages over
-360 000-odd singleton pseudonyms, addresses the partial clustering never touched, which are
-mostly degree-1 leaves. The two numbers describe different populations and only the
-spanning one is the comparison the gate was making.
-
-**The matchable population is 5 468 vertices, not 33 033.** Degree ≥ 3 in both views is the
-condition under which a vertex has a neighbourhood distinctive enough for propagation to
-work on. Everything above that is spanning material; this is the part with structure.
-
-**Folding is doing real but minority work.** 7 % of ordered pairs carry more than one
-transfer, so most cluster relationships in a single day are one-shot. The attributes that
-folding preserves (how many transfers, how much value) therefore separate a small,
-presumably more meaningful subset of edges rather than describing the typical one.
-
-**Degree is heavy-tailed and the tail is dangerous.** Median 2 against a maximum of 15 638,
-alongside a 30 304-address cluster, means a handful of hubs dominate. `graph_deanon` already
-refuses to expand through nodes above degree 100 to avoid small-world collapse; the matcher
-needs the same guard, or propagation will route through a hub and match everything to
-everything.
-
-## Scope, and one deliberate simplification to remove
-
-The clustering here is **naive common-input union-find**, which is precisely the adversary
-the framework calls incompetent. That simplification has since been measured rather than
-left standing: `RESULTS-refusing-clusterer.md` finds a refusing clusterer changes 0.33 % of
-merge decisions on this slice and leaves the 30 304-address cluster byte-identical, so the
-suspicion above is narrowed rather than confirmed. The refusal that would bear on it is the
-fingerprint channel, which compares *funding* transactions lying outside a two-day slice.
-
-Vertex attributes are stored as raw counts against each view's own base rates, never as
-bare shares, because a median 54 % of an axis value's variance tracks epoch volume
-(`RESULTS-attribute-drift.md`). No matching is performed here; this builds and measures the
-object a matcher would run on.
diff --git a/results/RESULTS-counting-methods.md b/results/RESULTS-counting-methods.md
new file mode 100644
index 00000000..e0619cd0
--- /dev/null
+++ b/results/RESULTS-counting-methods.md
@@ -0,0 +1,226 @@
+# Which counting method, and which L
+
+> Historical record. The current canonical result is
+> `catalog/runs/counting-methods-v1.json`, rendered as
+> `results/generated/counting-methods-v1.md`. The historical timings, L comparison and router
+> shares below are not current canonical measurements.
+
+## Verdict
+
+The whole-transaction cascade was picking the one method that adds nothing and handing the next one
+an unbounded subset size, which is why it stopped returning past twenty inputs. Bounding the
+convolution by the crate's own knee takes a 25-input call from 246.8s to 0.0032s. The
+saddle-point estimator is not a missing tier here: it answers a pre-output question and this is a
+post-output analyzer. And on the regime gate that decides when it applies, only one of the seven L
+candidates recovers the truth — `max(A)`, at 18 of 18 against instances whose L is known.
+
+## The order, and what each is for
+
+| method | role | in this module |
+|---|---|---|
+| brute force | the convolution below its crossover | **absent** — same algorithm, only a way to be slow |
+| radix | fast path for denominated shapes; reads the output side alone | first |
+| sparse convolution | the general post-output path, bounded by the knee | second |
+| saddle point | a magnitude estimate where the exact tiers return a truncated floor | reachable on purpose, never a tier |
+
+## Why the cascade did not return
+
+`w_count` tries brute first up to twenty inputs, and brute enumerates every distinct output subset
+sum as a target and every input subset at every size. Past twenty it falls to the convolution — but
+passes `max_size = inputs.len()`, so the graded sumset is built to full width. The crate has a knee
+of 5 for coinjoin-shaped analysis and its per-coin path uses it; only the whole-transaction cascade
+does not.
+
+| inputs | `max_size = n_in` | `max_size = knee` |
+|---:|---|---|
+| 25 | 246.8 s | 0.0032 s |
+| 60 | does not return | 0.0004 s |
+| 67 | does not return | 81.4 s |
+
+Five orders of magnitude, and the cause is the bound rather than the algorithm. It is not a complete
+fix — one 67-input transaction still runs 81s — so a residual width guard stays.
+
+## Routing the real slice
+
+Over 1,428 multi-input transactions with complete amounts, 9.6s total, slowest single call 0.296s
+(the cascade does not finish at all):
+
+| resolved by | share |
+|---|---:|
+| radix, exact | 37.32% |
+| sparse, exact | 2.10% |
+| sparse, lower bound | 0.28% |
+| nothing resolved | 60.29% |
+
+A tier that completes with a count of zero has found no mapping, which is the absence of an answer
+rather than an answer of no ambiguity. The radix path returns one on 63% of these transactions, so
+accepting it would let the first tier short-circuit the rest on a non-result.
+
+## Which L
+
+The model draws each value uniformly from {1, …, L}, so L is a property of the value distribution
+and `max(A)` is its natural estimator. Tested against instances generated with a known L, scoring
+each candidate on whether it recovers the regime that L implies — 10 dense and 8 sparse, so a
+candidate cannot win by always answering the same way:
+
+| L | agreement |
+|---|---:|
+| **max(A)** | **18/18 (100%)** |
+| sum(A) | 13/18 (72%) |
+| max(A)², sum(aᵢ²), sum(A)² | 8/18 (44%) — never say dense |
+| MAX_MONEY/N | 6/18 (33%) |
+| MAX_MONEY | 5/18 (28%) |
+
+κ rises with L while κ_c falls, so overestimating L pushes an instance toward sparse twice over. The
+three quadratic candidates never return dense at all, and MAX_MONEY/N is undefined on three of the
+sparse cases.
+
+This matters because the regime gate reads dense only when κ < κ_c at the worst-case L
+(MAX_MONEY/N) and calls everything between best and worst transitional. With a candidate that is
+wrong two thirds of the time and biased toward sparse, dense becomes close to unreachable —
+measured, the estimator declines on every transaction in the slice and on synthetic instances at
+κ = 0.026 against κ_c = 0.121.
+
+That is an observation about the upstream gate, not a change to it: those files are held as the
+canonical statement of the model. It is recorded here because it explains what the estimator does
+rather than leaving it looking broken.
+
+## Why the estimator is separate rather than absent
+
+Pre-output and post-output is the wrong axis to sort the methods by. It separates the two *sides* —
+constructing a transaction versus reading one that exists — and on the construction side every
+method is pre-output by definition. It does not say which method an analyst may use.
+
+What the upstream correction actually rules out is narrower: the estimator does not fill the holes a
+truncated lower bound leaves, so chaining it as an automatic upgrade merges a guarantee and an
+estimate into one number. The pipeline that did that was withdrawn, and the prescribed shape is a
+public function the caller invokes deliberately, checking convergence itself. The transcript is
+explicit that the estimator "is more useful than just in the cost function", which is the analytic
+side.
+
+So it is not a tier in `count_w`, and it is available as `saddle_point_log_w` for a caller who wants
+a magnitude rather than a floor. An earlier revision of this document removed it outright on the
+pre/post-output reading; that was too strong.
+
+## Scope
+
+One slice, 137 blocks, 2023. The L comparison is on synthetic instances drawn from a known L, which
+is the only way to know the answer being estimated; real transaction values are not drawn uniformly from any
+range, so the estimator question there is a modelling choice rather than an estimation one.
+
+## Reproducibility / provenance
+
+Per `results/REPRODUCIBILITY.md`, state 2: **mechanism unit-tested, headline number is a data-run.**
+The routing order, the zero-count rule and the estimator's absence from the post-output path are
+pinned in `tests/test_counting.py`. The timings and the L comparison are regenerated over
+`data/amount-channel-812695-812831-v1.json` and synthetic draws, and are not asserted.
+
+
+## Is any of this the model the writeup asks for?
+
+Only partly, and the writeup says so itself. On the two combinatorial models it names:
+
+> "In Maurer et al, the values of the inputs and the outputs must exactly cancel out, **which is not
+> sufficiently general for real world analyses**. Boltzmann casts a wider net, by allowing these to
+> vary somewhat, accounting for fees."
+
+Every counting path reachable here applies the exact-cancellation criterion. Holding the shape fixed
+and moving only the fee:
+
+| | whole-tx count | per-coin path |
+|---|---|---|
+| fee = 0 | exact, 0 | 3 coins measured |
+| fee = 1,000 sats | exact, 0 | **0 coins measured** |
+
+Sweeping the fee locates the tolerance precisely: three coins measured at a fee of zero, one at a
+fee of **1 satoshi**, none from **10** upward. So the machinery does carry a tolerance — it is
+calibrated for exact arithmetic, three orders of magnitude below what a transaction actually pays.
+Real fees on the slice run to a median of 4,956 sats. So the fee-blindness is not a wiring defect to
+be fixed downstream; it is the criterion the writeup names as insufficient, and it accounts for the
+exact-zero on 86% of transactions and the unreachable per-coin reading on 98.2% of input coins. The
+tolerance needed by this old measurement was absent when it was produced.
+
+> **Correction (2026-09-03).** `decluster.baselines.boltzmann` now exposes a separate
+> `fee_tolerant` balance model. It allocates the observed non-negative transaction fee across
+> participant blocks and requires an explicit total `fee_tolerance`; the original `exact` path and
+> every number in this document remain unchanged. Roundness is neither an admissibility rule nor a
+> prior in that baseline. This is a local mechanism, **not parity with the Boltzmann tool**, whose
+> cases and implementation are not available in this checkout. This table has not been re-measured
+> with the new model.
+
+Two further gaps between the object and the writeup's framing, both of which the paper already
+states: what it counts is subset-sum multiplicity, not Maurer's matched-partition count; and the
+quantity the writeup says bounds anonymity is the *entropy of the distribution* over partitions,
+not a count of them.
+
+### The denominational bound, and the precondition nobody was checking
+
+The denominational path is not a different quantity — it is a cheap lower bound on the same mapping
+count, obtained by counting how a repeated denomination permutes among participants. But that bound
+means nothing unless a denomination actually repeats, and the crate returns a number either way. The
+upstream classifier work states the division explicitly: it delivers the per-series multiplicity
+counts and leaves "the gap/density check ... to the lower-bound consumer that uses it". This module
+is that consumer, and it was not checking.
+
+Unguarded, the path answers on 64 of 1,428 real multi-input transactions — and **51 of those
+(79.7%) carry no repeated output value at all**. It is now gated on the precondition, which is the
+same three-fold floor the de-mix uses to call a value a mix denomination.
+
+Those two figures were 533 and 514 (96.4%) before the mapping count itself was corrected
+(`115b4e55:results/artifacts/counting-router-v1.json`). The
+collapse from 533 to 64 is the same defect seen from the other side: almost every "answer" the
+ungated path returned was a permutation of a denomination the transaction does not contain, so it
+was counting a multiplicity that was not there rather than counting it without a licence. Current
+values are `raw_positive` 64 and `raw_positive_without_precondition` 51 in
+`results/artifacts/counting-router-v1.json`, rendered in `results/generated/counting-router-v1.md`.
+
+| revision | multiplicity live | why |
+|---|---:|---|
+| the crate cascade | 5.60% | stalls past twenty inputs, exact-zero on the rest |
+| router, denominational path ungated | 39.71% | 94% of it invalid — the precondition was unmet |
+| router, denominational path removed | 6.37% | over-corrected: threw away the valid cases too |
+| **router, precondition checked** | **6.86%** | sparse on 5.25%, denominational on the 1.61% that are denominated |
+
+The middle two rows are both wrong, in opposite directions, and are recorded because the first is
+the more tempting number and the second is the more comfortable one.
+
+**Every share in that table predates the mapping-count correction and is superseded.** They were
+measured when the radix path returned a positive reading 533 times; it now returns one 64 times, and
+the exact tier it used to fill is empty. The current router resolves **82 of 1,428 transactions
+(5.74%)** — 0 radix exact, 60 sparse exact, 22 sparse lower bound, 1,333 refused — per
+`results/artifacts/counting-router-v1.json`. The rows above are kept as a record of the two failure
+directions, not as current shares, and the generated report says the same: "Earlier router shares
+are superseded by the current run."
+
+
+## The sequence, and making each step depend on the one before
+
+The guidance for this work states an order: classify the amounts, then evaluate the transaction as a
+whole, and only then score per output — the last explicitly deferred, and to be reached with "a few
+lookups in a precomputed table" rather than by a separate oracle call.
+
+decluster had all three, wired in parallel rather than in sequence:
+
+| step | where | was it grounded? |
+|---|---|---|
+| 1. classify radix-ness | `counting.radix_applies` | not checked at all — the bound was taken on 64 transactions, 51 of which had no denomination |
+| 2. evaluate the tx as a whole | `counting.count_w` | routed through the crate cascade, which stalls past twenty inputs |
+| 3. score per coin | `cost.amount_cuts` | ran independently of step 2 |
+
+Step three running independently is measurable. Over the slice, the per-coin oracle produced cuts for
+95 transactions and the transaction-level reading resolved for 82 — but they agree on only 62.
+Thirty-three transactions were being cut on a per-coin reading that the transaction-level evaluation
+had found nothing to support, and twenty that did resolve got no cut because the truncation could
+not reach their coins.
+
+Gating the third step on the second closes that:
+
+| | transactions cut | cuts | share of coins |
+|---|---:|---:|---:|
+| ungated | 95 | 318 | 1.35% |
+| **gated on step 2** | **62** | **101** | **0.43%** |
+
+The remaining 62 are the transactions where the amounts classify, the transaction as a whole
+resolves, and the coins are individually reachable — which is what a per-coin score was supposed to
+mean. That the number is small is the finding, not a failure of the plumbing: it is the same 6%
+ceiling the fee-blind criterion imposes, now reached through a chain where each link is earned.
diff --git a/results/RESULTS-def1-sparsity.md b/results/RESULTS-def1-sparsity.md
index 1132ebea..1ff866c1 100644
--- a/results/RESULTS-def1-sparsity.md
+++ b/results/RESULTS-def1-sparsity.md
@@ -1,5 +1,24 @@
# (epsilon, delta)-sparsity of cluster features: the attack's own precondition, measured
+> **Correction, 2 Sep 2026 — the table below was measured with a mis-scaled feature vector.**
+> `feature_vector` normalised every fingerprint axis to sum to 1.0 but accumulated the
+> neighbour-degree histogram as a raw count, so at degree 50 that one key carried 99.9% of the
+> vector's norm and the cosine read degree similarity rather than feature similarity: two clusters
+> with wholly disjoint fingerprint axes scored 0.9996. The histogram is now normalised like every
+> other axis.
+>
+> The conclusion survives and strengthens. Re-measured on the committed fixture
+> (`tests/fixtures/slice_a_channels_2016.ndjson.gz`, 12 000 txs, 3 179 clusters), the corrected
+> curve is δ(0.5)=1.000, δ(0.9)=1.000, δ(0.99)=0.990 against a documented 1.000 / 0.996 / 0.968 —
+> *less* sparse than recorded, not more. Restricting the query set to hubs weakens it only mildly
+> (min_degree 20: δ(0.9)=0.921, median top-similarity 0.999), so the density is not an artefact of
+> the one-transaction clusters that dominate the population. See the degree-stratified table in
+> `RESULTS-slice-a-channels.md`.
+>
+> The `slice_2026.ndjson` table below has **not** been re-run — that data is not committed and is
+> not present locally. Treat its specific values as superseded pending a re-run; the reading it
+> supports is unchanged.
+
**Why this is load-bearing.** The sparse-dataset de-anonymization the framework builds on
defines its precondition precisely: a record space is (epsilon, delta)-sparse if a random
record has probability at most delta of having any other record more than epsilon-similar
@@ -25,11 +44,11 @@ query. `slice_2026.ndjson`, refusing clusterer, one view. `decluster/def1_sparsi
| 0.95 | 0.929 | 0.896 |
| 0.99 | 0.889 | 0.731 |
-Median nearest-neighbour similarity: **1.000**.
+Median nearest-neighbour similarity: 1.000.
The Netflix Figure-1 baseline the method comes from: the vast majority of records had no
peer above similarity 0.5. Here every cluster has a peer above 0.5, and ~89 % have a near
-identical twin above 0.99. The space is maximally **non-sparse**.
+identical twin above 0.99. The space is maximally non-sparse.
## Reading
@@ -43,9 +62,9 @@ sparse-dataset attack, and on this feature set it fails.
vector here is low-dimensional: four fingerprint axes and a handful of coarse structural
bins. The paper's sparsity comes from *high* dimensionality — hundreds to thousands of
observations per record. The framework locates the sparse, unique signal in the
-**structural / deep features** (ancestry), which are the high-dimensional ones. Those are
-absent from this vector, because ancestry walks reach only ~2.2 hops on a two-day slice
-(`RESULTS-ancestry-crossview-feasibility.md`). So this result confirms the conceded half
+structural / deep features (ancestry), which are the high-dimensional ones. Those are
+absent from this vector, because an ancestry walk over a slice this shallow reaches too few
+hops to carry them. So this result confirms the conceded half
and leaves the load-bearing half — is the *ancestry* feature space (epsilon, delta)-sparse?
— unmeasured.
@@ -57,7 +76,8 @@ define as decisive: the (epsilon, delta)-sparsity survival curve over ancestry s
If that space is non-sparse too, the attack's precondition fails on the feature the
framework calls most important, and the negative is complete and clean. If it is sparse,
then the attack's failure on this graph is the surprising result, and the reason lies in
-the graph's topology (`RESULTS-graph-shape.md`), not its feature sparsity — a genuinely
+the graph's topology (`results/generated/graph-rejoin-2016-v1.md`), not its feature sparsity — a
+genuinely
interesting split. Either outcome is a real finding; neither can be had without the export.
## Scope
@@ -69,7 +89,7 @@ space and does not speak to the high-dimensional one.
## Reproducibility / provenance
-The sparsity **mechanism** (cosine similarity, nearest-neighbour survival curve, its
-monotonicity) is unit-tested in `tests/test_def1_sparsity.py`. The specific **(epsilon, delta)
-survival table** here is a **data-run** over the feature vectors built from unversioned local
-data, and is **not** pinned as asserted values in a unit test.
+The sparsity mechanism (cosine similarity, nearest-neighbour survival curve, its
+monotonicity) is unit-tested in `tests/test_def1_sparsity.py`. The specific (epsilon, delta)
+survival table here is a data-run over the feature vectors built from unversioned local
+data, and is not pinned as asserted values in a unit test.
diff --git a/results/RESULTS-e2e.md b/results/RESULTS-e2e.md
index 1614dbfc..faa78543 100644
--- a/results/RESULTS-e2e.md
+++ b/results/RESULTS-e2e.md
@@ -44,7 +44,7 @@ Both assert the same three invariants, on a synthetic and on a real-cache slice
3. **cluster_refined ran for real** — `>= 2` clustered nodes, `>= 1` cluster, and at least one group
merges more than one node (not every node its own singleton).
-Full suite: `.venv/bin/pytest -q` → **454 passed** (Tier-2's live-only imports are deferred inside
+Full suite: `.venv/bin/pytest -q` → 454 passed (Tier-2's live-only imports are deferred inside
`run_live`'s function body, so the offline import path and the rest of the suite never touch the
network — see the docstring in `examples/e2e.py`).
@@ -57,7 +57,7 @@ identical composition against a real slice:
txids drawn from local tx history — reusing that module's seed/slice approach, not a new one); tx
dicts and `cluster_refined`'s fetch dependency both bind to the real `decluster.fetch.fetch_tx`
(mempool.space, on-disk cached to `.cache/`). The seed targets here turn out to be
- **payment+partial-mix** transactions (e.g. 9-in / 17-out with one denomination repeated ~9× beside
+ payment+partial-mix transactions (e.g. 9-in / 17-out with one denomination repeated ~9× beside
a large payment output), not fully-dense equal-value coinjoins.
- **Bounded oracle.** The link oracle is `examples.anonymity_set.hard_bounded_link_oracle` — the dss
subset-sum call run in a throwaway subprocess and killed on a wall-clock deadline (`wall_ms`), so a
@@ -66,8 +66,8 @@ identical composition against a real slice:
below and were both verified during this run:
- **The exact subset-sum path needs adequate wall time.** A payment+partial-mix tx of the shape
above resolves to a real (non-uniform) link matrix in **~2.3 s** of dss compute; the historical
- `wall_ms=1500` default killed it at ~1.8 s → `None` → the target collapsed to a **point mass**
- (`n_absorbers=1`). Raising `wall_ms` to 6000 lets these resolve. (The dss **radix** fast path is a
+ `wall_ms=1500` default killed it at ~1.8 s → `None` → the target collapsed to a point mass
+ (`n_absorbers=1`). Raising `wall_ms` to 6000 lets these resolve. (The dss radix fast path is a
separate, O(n) short-circuit that returns a *uniform* matrix in ~0 s — but only for
*fully*-dense coinjoins with ≥2 denominations each repeated ≥3× over ≥half the outputs; the seed
targets here do not meet that structural test, so they take the exact path, not radix.)
@@ -79,8 +79,8 @@ identical composition against a real slice:
`max_targets` caps how many of that slice's txs get a full `report()` walk (the wall-clock cost
driver); `depth` caps the ancestry walk (with a ~9× coinjoin fan-out, `depth=2` keeps a full sweep
to minutes under the bounded oracle).
-- **Collects**, beyond `run_offline`'s keys: `n_targets_attempted` / `n_targets_resolved` (the
- strict resolution count — targets whose graph-only walk finished with **zero** oracle-refusal
+- Collects, beyond `run_offline`'s keys: `n_targets_attempted` / `n_targets_resolved` (the
+ strict resolution count — targets whose graph-only walk finished with zero oracle-refusal
truncations), and `m3_coassignment_agreement` (fraction of M3 super-node pairs where the split-merge
sampler's posterior P(same partition) `> 0.5` agrees with whether `cluster_refined` placed their
representative addresses in the same cluster — a cross-check between the exact-Bayesian partition
@@ -120,18 +120,18 @@ Per-target (graph-only == fused for every one):
| `372fd5cb…` | 24 | 3.000 | 5 |
| `50447deb…` | 39 | 3.585 | 8 |
-Reading it honestly:
+Reading it:
- **Coinjoin ancestry resolves on live data.** Given adequate oracle wall time, all six
payment+partial-mix targets resolve to real anonymity sets of 24–45 ancestral origins (2.6–3.6
bits of min-entropy) — where an under-budgeted oracle collapses each to a single point
(`n_absorbers=1`, 0 bits). This is the §04 anonymity set, live. `n_targets_resolved=0` is the
- **strict** metric (zero truncations anywhere in the walk); every target still hits 1–8 deep
- coinjoin ancestors the bounded oracle refuses, so each set is a **lower bound** — more oracle time
+ strict metric (zero truncations anywhere in the walk); every target still hits 1–8 deep
+ coinjoin ancestors the bounded oracle refuses, so each set is a lower bound — more oracle time
would only add origins, never remove them.
-- **Fusion did not sharpen here, and that is honest.** `fused == graph` for all six: no subjective
+- **Fusion did not sharpen here.** `fused == graph` for all six: no subjective
same-owner link (address-reuse self-transfer or `cluster_pairs`) fell inside these particular
- coinjoin-ancestry walks, so the §04 fusion had nothing to narrow. The fusion **mechanism** is the
+ coinjoin-ancestry walks, so the §04 fusion had nothing to narrow. The fusion mechanism is the
thing under test, and it is demonstrated separately by the Tier-1 controlled fixture (a
concentrating same-owner oracle lowers provenance min-entropy 1.0 → 0.304 bits) and asserted by the
conservative-clamp invariant; the live slice simply lacks a subjective-covered target. Coverage of
@@ -148,7 +148,7 @@ Reading it honestly:
input prevout addresses; the offline e2e test now asserts the cluster source actually fires, as a
regression guard.)
-## Honest limits
+## Limits
- **Bounded slice.** Both tiers cap the slice size (`cap_total`, `M3_MAX_SUPERNODES=5`,
`max_targets`) — this validates the pipeline's plumbing and mechanism on a real but small slice,
@@ -166,3 +166,8 @@ Reading it honestly:
by design (same posture as `results/RESULTS-anonymity-set-scale.md`).
- **Conservative-lower-bound footing.** Every entropy number here (Tier-1 or Tier-2) is an attacker
lower bound / weight-of-evidence under no auxiliary information, not a privacy score.
+
+## Reproducibility / provenance
+
+State 2 in `results/REPRODUCIBILITY.md`: the mechanism is pinned; the reported numbers come from a data-run over `.cache`, which is not committed, and are not asserted.
+Tier-1 is asserted by `tests/test_e2e.py`; Tier-2 fetches live and is not.
diff --git a/results/RESULTS-em-m.md b/results/RESULTS-em-m.md
index fe7f4406..b6f7f5ca 100644
--- a/results/RESULTS-em-m.md
+++ b/results/RESULTS-em-m.md
@@ -1,83 +1,20 @@
-# EM per-axis `m`: does the data-driven self-agreement rate match the assumed 0.95?
-
-**Claim tested.** The Fellegi-Sunter combiner assumes a single same-wallet self-agreement rate
-`c = 0.95` for every axis's disagreement weight (`decluster/combiner.py`), because `m` is not directly
-measurable without same-owner labels — the epistemic challenge. This fits `m` per axis by **unsupervised
-EM** (Winkler's EM for the F-S model), with `u` fixed at the measured `collision`, over the `reuse_pairs`
-mixture with the labels **withheld**. The address-reuse label is held out as an oracle.
-
-**Data.** 165832 witness-bearing transactions (`.blkcache`); 4000 reuse-positive + 4000 random pairs
-(`seed=0`, PYTHONHASHSEED-independent). EM never sees the labels; `λ ≈ 0.571` reflects the ~50%
-enrichment of the pair sample, not the corpus match rate.
-
-## Result
-
-**Per-axis m: EM vs oracle vs assumed.**
-
-| axis | u (collision) | m_EM | oracle (reuse) | assumed |
-|---|---:|---:|---:|---:|
-| nsequence | 0.397 | 0.946 | 0.979 | 0.95 |
-| locktime | 0.618 | 0.987 | 0.996 | 0.95 |
-| input_order | 0.528 | 0.819 | 0.811 | 0.95 |
-| output_order | 0.337 | 0.604 | 0.622 | 0.95 |
-| change_spk | 0.378 | 0.836 | 0.852 | 0.95 |
-| version | 0.535 | 0.972 | 0.993 | 0.95 |
-| io_shape | 0.204 | 0.615 | 0.605 | 0.95 |
-| uih | 0.854 | 0.889 | 0.880 | 0.95 |
-| fee_rate | 0.708 | 0.926 | 0.928 | 0.95 |
-| input_script_type | 0.378 | 1.000 | 0.983 | 0.95 |
-| output_encoding | 0.614 | 0.890 | 0.893 | 0.95 |
-| input_types_present | 0.377 | 1.000 | 0.982 | 0.95 |
-| change_index | 0.555 | 0.746 | 0.750 | 0.95 |
-| change_type_match | 0.584 | 0.782 | 0.784 | 0.95 |
-| change_matches_output | 0.560 | 0.767 | 0.768 | 0.95 |
-| change_address_reuse | 0.650 | 0.845 | 0.872 | 0.95 |
-| low_r | 0.477 | 0.862 | 0.865 | 0.95 |
-| sighash | 0.456 | 0.996 | 0.984 | 0.95 |
-| op_return | 0.887 | 0.945 | 0.986 | 0.95 |
-| nested_segwit | 0.803 | 0.999 | 0.996 | 0.95 |
-| pubkey_compression | 0.604 | 1.000 | 0.995 | 0.95 |
-| multisig | 0.887 | 0.993 | 0.989 | 0.95 |
-| locktime_vs_broadcast | 0.756 | 0.900 | n/a | 0.95 |
-
-**Does EM alone recover the label?** Posterior `r` separates reuse-positives from random pairs with
-AUC **0.9438** — unsupervised EM recovers the reuse-label structure without ever being shown which pairs
-share an input address. (This 0.9438 is the per-pair naive-Bayes posterior `r`; the pair-AUC table just
-below, 0.9333–0.9462, is a *different* scorer — the 23-axis `LibraryScorer` with each `m` source plugged
-into `consistency` — so the two AUCs are not the same measurement.)
-
-**Pair-AUC by per-axis m source.**
-
-| m source | AUC |
-|---|---:|
-| assumed 0.95 | 0.9333 |
-| EM-fitted (per axis) | 0.9368 |
-| oracle (per axis) | 0.9462 |
-
-The per-axis fitted m values spread widely — from 0.604 (output_order) to 1.000 (input_script_type,
-input_types_present, pubkey_compression) — confirming that the flat 0.95 assumption is inaccurate for
-most axes. The pair-AUC improvement from EM-fitted m (0.9368) over the assumed scalar (0.9333) is modest
-but consistent with the weight-sensitivity plateau documented in `RESULTS-weight-sensitivity.md`: the
-combiner is relatively robust to m shifts in the middle of the score distribution, so even a substantially
-different per-axis m yields only a small AUC gain. The oracle m achieves 0.9462, under a point above
-EM (0.9368), indicating there is headroom that unsupervised EM does not fully capture. For correlated axes —
-input_script_type and input_types_present both clamp to 1.000 in m_EM while the oracle sits at 0.983 and
-0.982 — the upward bias is the expected conditional-independence artifact: EM treats them as independent
-evidence and inflates their joint m to compensate. The divergence between m_EM and oracle for those axes
-is the independence-violation signal, not noise. Axes with low self-agreement (output_order 0.604,
-io_shape 0.615, change_spk 0.836) are structurally noisier across wallets regardless of reuse labels;
-EM correctly identifies them as weak discriminators.
-
-Reproduce: `python3 examples/em_m_fit.py`.
-
-## Honest limits
-
-- **Conditional independence is assumed and partly violated.** EM's E-step multiplies per-axis
- likelihoods as if axes were independent given match. Correlated axes (input script type, low-R,
- encoding) bias their `m_EM`; the gap from the oracle is exactly how that shows up — read the divergent
- rows as the independence-violation signal, not as noise.
-- **`λ ≈ 0.571` is an artifact of the enriched population**, not the corpus match rate.
-- **Same label/sampling caveats as the base validation** (address-reuse labels share script type by
- construction; positives sampled with replacement) — see `RESULTS-fingerprint-validation.md`.
-- **Snapshot.** Figures wobble as `.blkcache` grows; the pattern (does EM recover the oracle; does the
- AUC stay flat) is the result.
+# Per-axis Fellegi–Sunter EM diagnostic
+
+Canonical run: `catalog/runs/em-m-v1.json`.
+
+The executable report is `results/generated/em-m-v1.md`, and the per-axis
+measurements are in `results/artifacts/em-m-v1.json`. EM is fitted without pair
+labels; address reuse is used afterward as a weak comparison label.
+
+On the preserved 22,112-transaction snapshot, the LibraryScorer AUC is 0.9244
+with fixed `m=0.95`, 0.9254 with EM-fitted per-axis values, and 0.9546 with
+address-reuse agreement values. The EM-minus-fixed difference is +0.0009 — below
+what this sample resolves, which is the point of the sentence that follows.
+It is descriptive, not an established improvement, because no pre-registered
+separability test was run.
+
+The former 165,832-transaction table cannot be reproduced from the preserved
+snapshot. Address reuse is a weak and partly feature-dependent proxy; fitting
+and evaluation use the same selected pair sample; correlated axes violate the
+conditional-independence assumption. This diagnoses attacker-side linkage
+parameters. It is not CoinScore or a privacy certificate.
diff --git a/results/RESULTS-entity-deanon.md b/results/RESULTS-entity-deanon.md
index d89cce41..b8cb14fc 100644
--- a/results/RESULTS-entity-deanon.md
+++ b/results/RESULTS-entity-deanon.md
@@ -1,9 +1,15 @@
# Entity-labelled strong-N-S probe on real data — a positive result and its boundary
+> **Executable scope, 2026-09-04.** The paired committed subgraphs are now
+> recalculated by `catalog/runs/entity-deanon-v1.json`. The canonical run
+> supports a snapshot-specific common-neighbour positive control and null
+> control. It does not implement seeded N-S propagation, reproduce the full
+> BigQuery slices or justify the older phrase “strong N-S claim”.
+
The independent entity-label path (`decluster/entities.py` → `graph_deanon.entity_label_uf` /
-`evaluate_entity`) tests the *strong* Narayanan–Shmatikov claim on **real BigQuery slices**: *does
+`evaluate_entity`) tests the *strong* Narayanan–Shmatikov claim on real BigQuery slices: *does
payment-graph structure re-link same-entity addresses that co-spend leaves separate?*, using an
-**independent** label (a vanity-prefix entity detector, disjoint from co-spend). The answer depends
+independent label (a vanity-prefix entity detector, disjoint from co-spend). The answer depends
entirely on the entity's *structure*, and the two cases below make the boundary precise.
## Positive — SatoshiDice (a service with recurring counterparties): AUC ≈ 0.72
@@ -18,8 +24,8 @@ house addresses (a small fixed set, one per bet ratio, heavily reused).
| k=2 | 0.71 |
| k=3 | 0.73 |
-Mean shared neighbours **7.95** between same-SatoshiDice pairs. Structure re-links the house addresses
-**beyond co-spend**, robustly across depth — the strong N-S claim, demonstrated on real data with an
+Mean shared neighbours 7.95 between same-SatoshiDice pairs. Structure re-links the house addresses
+beyond co-spend, robustly across depth — the strong N-S claim, demonstrated on real data with an
independent label. The mechanism is exactly the recurring-counterparty economics of a gambling
service: the same bettors play across several house addresses, so those addresses share a large
common-neighbour population that common-neighbours link prediction recovers.
@@ -35,24 +41,24 @@ common-neighbour population that common-neighbours link prediction recovers.
| PAYMENT-only (strong N-S), k=1 | 0.502 |
| PAYMENT-only, k=2 / k=3 | 0.500 / 0.514 |
-Mean shared neighbours ~0.007; **zero** of ~3.5M pairs directly co-spent in-window. No signal at any
+Mean shared neighbours ~0.007; zero of ~3.5M pairs directly co-spent in-window. No signal at any
depth.
## What separates them — the boundary condition for the strong claim
Independent entity labels are *necessary* but not *sufficient*: the entity must sit in an
-**economic graph with recurring peers**.
-- **SatoshiDice** is such a graph — a fixed set of house addresses transacting with a *returning*
+economic graph with recurring peers.
+- SatoshiDice is such a graph — a fixed set of house addresses transacting with a *returning*
bettor population → dense shared neighbourhoods → recoverable (0.72).
-- **BitMEX** is a **hub-and-spoke star** — each deposit address links only to its own distinct
+- BitMEX is a hub-and-spoke star — each deposit address links only to its own distinct
depositor, and the shared hot wallet is reached only through consolidation that is a co-spend
(excluded) or out of window → no shared structure (0.50).
-- **Mining pools** (checked on the 2019 range) fail for a related reason: a pool's coinbase pays only
+- Mining pools (checked on the 2019 range) fail for a related reason: a pool's coinbase pays only
2–4 outputs to a near-single reused payout address, and its recurring counterparties (miners) are a
hop downstream in separate distribution txs — not a multi-address cluster with shared *direct*
neighbours.
-**Consequence for the program.** The strong N-S claim is now **empirically demonstrated** (SatoshiDice,
+**Consequence for the program.** The strong N-S claim is now empirically demonstrated (SatoshiDice,
0.72) — and, importantly, *without external data*: it used a vanity-prefix detector on an entity
already named in `catalog/known-entities.md`. A curated address list (`catalog/entities.ndjson`) is
still what would unlock the *non-detectable* named entities (Mt. Gox, Binance, generic exchanges), but
diff --git a/results/RESULTS-entropy.md b/results/RESULTS-entropy.md
index 269e9a70..4918a7fe 100644
--- a/results/RESULTS-entropy.md
+++ b/results/RESULTS-entropy.md
@@ -3,11 +3,11 @@
Reproduce: `python3 examples/metric_demo.py`. Diagnostic in `decluster/graph_metric.py`,
guarded by `tests/test_metric.py`.
-This is a **relative** diagnostic — how much the naive co-spend clustering *overstates* the
-attacker's residual uncertainty versus the fingerprint+amount clustering — **not** an absolute
+This is a relative diagnostic — how much the naive co-spend clustering *overstates* the
+attacker's residual uncertainty versus the fingerprint+amount clustering — not an absolute
"bits of anonymity" of any transaction. The intrinsic anonymity of a payment (interpretations
-under no auxiliary information) is the separate path-counting / k-route estimate, not this
-partition entropy.
+under no auxiliary information) is the separate §07 path-counting object, not this partition
+entropy.
## The diagnostic
For a clustering of N coins into groups, the Shannon entropy of the cluster-size distribution
@@ -30,10 +30,15 @@ cluster grows from 16% to 53% (a supercluster forming). This is the paper's thes
at the graph level: the naive common-input view *overstates* the attacker's residual
uncertainty once fingerprints and amount structure are accounted for.
-## Honest caveats
+## Caveats
- **Not chain-scale.** 19 coins, one merged transaction's depth-6 ancestry (capped at 60,
fetched via mempool.space). A statistically meaningful chain-wide measurement needs an
archival Bitcoin Core node (Floresta cannot provide it — its Utreexo model keeps no
historical blocks). The metric is the contribution; the graph size is data-limited.
- The fingerprint-aware clustering inherits the round-number change-id and the
per-axis measured bits (the library-measured bits, decluster/library.py).
+
+## Reproducibility / provenance
+
+State 2 in `results/REPRODUCIBILITY.md`: the mechanism is pinned; the reported numbers come from a data-run over live data and `.cache/`, which is not committed, and are not asserted.
+The diagnostic itself is guarded by `tests/test_metric.py`.
diff --git a/results/RESULTS-era-sweep.md b/results/RESULTS-era-sweep.md
index cf90b086..66ac541b 100644
--- a/results/RESULTS-era-sweep.md
+++ b/results/RESULTS-era-sweep.md
@@ -1,10 +1,10 @@
# Is the pseudonym graph a social network in any era?
-**Why sweep eras.** `RESULTS-graph-shape.md` found the 2026 pseudonym graph is not a social
-network — clustering far below chance, disassortative — which explains the social-graph
-attack's failure. But that could be a property of the 2026 chain (segwit, taproot, privacy
+**Why sweep eras.** A contracted pseudonym graph is not a social network — clustering far below
+chance, disassortative (`results/generated/graph-rejoin-2016-v1.md`) — which explains the
+social-graph attack's failure. But that could be a property of the 2026 chain (segwit, taproot, privacy
tooling, little address reuse) rather than of Bitcoin graphs generally. The framework's claim
-is about "today's transaction graph"; the honest test is whether the precondition ever held.
+is about "today's transaction graph"; the test that decides it is whether the precondition ever held.
2016 is the sharp case: heavy address reuse, and the era where `RESULTS-graph-deanon.md`
measured structural same-owner prediction at AUC 0.95.
@@ -69,3 +69,7 @@ estimate (20 000 vertices). The window sizes differ in tx count across eras (cha
the configuration-model null normalises for density, which is why the ratio is the comparable
quantity. 2022 and 2025 would extend the curve; the four points already settle the
qualitative question.
+
+## Reproducibility / provenance
+
+State 2 in `results/REPRODUCIBILITY.md`: the mechanism is pinned; the reported numbers come from a data-run over one contiguous ~2-day window per era, which is not committed, and are not asserted.
diff --git a/results/RESULTS-exact-oracle-audit.md b/results/RESULTS-exact-oracle-audit.md
new file mode 100644
index 00000000..ca621632
--- /dev/null
+++ b/results/RESULTS-exact-oracle-audit.md
@@ -0,0 +1,279 @@
+# The approximations against the exact oracle — measured
+
+Canonical run: `catalog/runs/exact-oracle-audit-v1.json`. Machine-readable
+results live in `results/artifacts/exact-oracle-audit-v1.json`; the concise
+generated view is `results/generated/exact-oracle-audit-v1.md`. The narrative
+below interprets that run and is not the source of its numeric results.
+
+`decluster/baselines/maurer.py` + `boltzmann.py` are an exact, exponential oracle: every balanced
+input/output block mapping of a transaction, and the uniform marginal link matrix over them. The
+production paths use the compiled `dss` extension and the wrappers in `decluster/counting.py`,
+`decluster/subtransaction.py`. Nothing in this repo had measured whether those agree with the oracle.
+`decluster/baselines/oracle_audit.py` now does; `examples/exact_oracle_audit.py` emits the report as
+JSON and exits non-zero when it carries a flag.
+
+**Headline.** `dss.pairwise_link_prob` is the uniform marginal over dss's own mapping family,
+which is a strict restriction of the oracle's, and a marginal over a sub-family is a bound on the
+marginal over the full family in neither direction. Measured: over 6,228 entries it sits
+above the exact marginal on 1,714 and below it on 4,104, agreeing on 410. And because
+`decluster/counting.py`'s own `link_matrix` docstring tells consumers that "a row with one non-zero
+entry is a deterministic link — the amounts settle that assignment on their own", the production path
+asserts **945 certainties in 328 of 507 transactions that the amounts do not settle**, while
+missing no *certain* link. That count is measured against the full oracle family, which is the
+most conservative reading available: any smaller family has more certain links and so fewer spurious
+ones. Dropping the oracle's coarser readings (refinement-maximal selection) leaves it unchanged at
+945 / 0; selecting the finest mappings by block count instead — a strictly more generous reading
+for the approximation — gives 942 / 108. Both are measured every run.
+
+## The family
+
+Exhaustive, deterministic, zero-fee: every balanced `(inputs, outputs)` over the value alphabet
+`{1, 2, 3, 4}` with at least 2 coins a side and at most 8 coins in total, each in canonical form
+(the pair of sorted value multisets) exactly once — the enumeration emits each multiset once per
+shape, so no deduplication pass is involved and a test asserts the result carries no duplicate.
+
+| quantity | value |
+|---|---|
+| transactions in the family | **507** |
+| distinct (inputs, outputs) shapes | 15, from 2×2 to 6×2 and 2×6 (largest cell: 4×4, 119 cases) |
+| exact mappings enumerated over the family | 7,930 |
+| link-matrix entries compared | 6,228 |
+| fee | **0** — the oracle has no fee model |
+| wall (full family, all probes) | ~4 s on one machine, 6.4 s on another; the test file ~7-9 s. Machine-dependent, so deliberately not a manifest invariant |
+
+Scale is deliberately *not* folded into the canonical form: `|M|` is scale-invariant, but
+`radix_mappings` and the density gate are not, and folding would have hidden that.
+
+## Semantics first: what each entry point actually counts
+
+Comparing two quantities that count different objects manufactures a defect that is really a
+category error. So every entry point is identified before it is compared, and every identification
+below is *re-derived in Python* rather than read off a docstring.
+
+| entry point | object | relation to the oracle |
+|---|---|---|
+| `exact_subtransaction_mappings` | index-level balanced set partitions, coarser readings included | the oracle |
+| `dss.mapping_analysis` `n_non_derived` | refinement-maximal mappings on this bounded audit, a strict sub-family of the full oracle | **restriction** |
+| `dss.pairwise_link_prob` | the uniform marginal over *that same restricted family* | **restriction** |
+| `dss.mapping_analysis` `deterministic_links` | the certain links of *that same restricted family* | **restriction** |
+| `dss.w_count` / `w_brute` / `w_sparse` | non-empty **proper input subsets whose sum is a proper output-subset sum**: the subset-sum solution count `W(E)` | **different object** |
+| `counting.count_w` (the production router) | radix, else sparse — and the sparse answer is `W(E)` | **different object** |
+| `dss.radix_mappings` | permutations of repeated output denominations; a function of the outputs alone | **different object** |
+| `dss.per_coin_density` | per-coin `log_w` (natural log), a subset-sum density | **different object** |
+| `subtransaction.subtransactions` `ambiguity_bits` | log2 of the (receiver input, receiver output) pairs with a positive implied payment; nothing has to balance | **different object** |
+
+### The matrix, the count and the certain links are one enumeration
+
+An earlier draft of this document called the count a "restriction" and the matrix "the same object".
+That cannot both be true, and it is falsified in one line. Checked on all 507/507 transactions
+(`verify_dss_marginal_family`):
+
+- every entry of `dss.pairwise_link_prob` is an exact multiple of `1 / n_non_derived`;
+- the entries equal to 1.0 are exactly the `deterministic_links` the same call reports.
+
+So all three are outputs of one enumeration over one family, and that family is **strictly smaller
+than the oracle's on 491 of 507** cases. They therefore carry one verdict — restriction — and the
+consequence is not a caveat but the finding: a marginal over a sub-family bounds the marginal over
+the full family in neither direction, which is exactly what the entrywise tallies below measure.
+
+### What `n_non_derived` measures
+
+The current DSS revision exposes refinement-maximal mappings. Its count agrees with the independent
+oracle's refinement-maximal count on all 507 cases in this bounded family. This is a measured
+agreement, not a proof for every transaction.
+
+Equal-value permutations do not collapse in general: `[1,3,4,4] → [3,3,3,3]` answers 4, and
+those four readings differ only in which equal-valued output stands alone. The refinement-maximal
+family is strictly smaller than the full oracle family on 491 of 507 cases. The matrix and its
+certain links therefore remain marginals over a restriction of the full family.
+
+`W(E)` was identified, not assumed: `dss.w_count` reproduces an independent Python re-derivation of
+that subset-sum count on 507/507 exact answers (0 mismatches). A set partition is a
+simultaneous, disjoint, exhaustive choice of many such subsets, so `W(E)` and `|M|` count different
+things — they coincide by accident on 34 of the 507 cases. Ordering one against the other would say
+nothing about either, so this audit reports the object mismatch and **declines to score `w_count` as
+an under- or overcount**. `w_total`'s own docstring already says as much ("a per-target subset count,
+NOT Maurer's full matched-partition mapping count"); what is new here is the verification.
+
+## The comparisons
+
+Every row is the same quantity on both sides, computed over two different mapping families, so
+"bound direction" is a measurement and never a licence. The first four rows score against the full
+oracle family; the two `_finest_only` rows are the robustness reading described below, and there
+"finest" means refinement-maximal — the choice matters and is measured, see
+"None missed is a statement about that definition".
+
+| probe | relation | cases | agree | approx < exact | approx > exact | max abs error | measured bound direction |
+|---|---|---|---|---|---|---|---|
+| `mapping_count` | restriction | 507 | 16 | **491** | 0 | 107 mappings | **lower** |
+| `mapping_entropy_bits` | restriction | 507 | 16 | **491** | 0 | **3.64 bits** | **lower** |
+| `link_matrix` (entrywise) | restriction | 507 tx / 6,228 entries | 410 entries | 4,104 entries | **1,714 entries** (in **440** transactions) | **0.833** | **neither** |
+| `deterministic_links` | restriction | 507 | 179 | 0 | **328** | 945 spurious links | **upper** |
+| `link_matrix_finest_only` | restriction | 507 tx / 6,228 entries | 2,314 entries | 2,103 entries | **1,811 entries** (in **439** transactions) | **0.800** | **neither** |
+| `deterministic_links_finest_only` | restriction | 507 | 179 | 0 | **328** | 945 spurious links | **upper** |
+
+Reading the two link rows: the **440** (and 439) are transactions carrying at least one entry above
+the oracle's — the number the audit's `flags` reports, since a transaction is where a consumer reads
+the matrix. The **179** agreements on `deterministic_links` are the transactions where the two
+certain-link sets match exactly.
+
+"Measured bound direction" is measured on *this* family only. `lower` here means no case in these 507
+exceeded the oracle; it is not a proof for all transactions, and it says nothing at all about a
+fee-paying one.
+
+### The link matrix errs in both directions
+
+Only 16 of 507 transactions agree on every entry.
+
+| transaction | `\|M\|` (exact) | exact marginal | `dss.pairwise_link_prob` |
+|---|---|---|---|
+| `[1,1,1] → [1,1,1]` | 16 | 0.5 everywhere | identity: 1.0 on the diagonal, 0.0 off it |
+| `[1,1,1,1] → [1,1,1,1]` | 131 | 0.405 everywhere | identity |
+| `[1,3,4,4] → [3,3,3,3]` | 5 | 0.8 / 0.4 rows | 10 entries at 1.0, 6 at 0.0 |
+| `[1,4] → [1,1,1,1,1]` | 6 | 0.333 / 0.833 rows | 5 entries at 1.0, 5 at 0.0 |
+
+Both directions of error are present in a single case, which is why no monotone description survives.
+Of the two, the one that matters is the overclaim: an entry the approximation calls 1.0 where the
+oracle says 0.4 asserts a link the amounts do not settle, and `counting.link_matrix`'s docstring
+hands exactly that reading to consumers. 945 such assertions of certainty stand across 328
+transactions, and there is no case in the family where the approximation *misses* a link the oracle
+calls certain — on certainty the error is one-directional, in exactly the direction that costs a
+holder privacy they were told they had. (The 4,104 entries *below* the oracle are the same matrix
+being under-confident about merely probable links; both are true, of different quantities.)
+
+**First, note what the 945 is measured against.** The `deterministic_links` probe runs against the
+full oracle family — every balanced set partition, coarser readings included. That is the largest
+family available and therefore the most conservative reading of the overclaim: any sub-family has
+*more* certain links, so restricting the oracle can only reduce the spurious count. The primary
+finding never calls `finest_mappings` and nothing below changes it.
+
+**The cross-check, and the definition it depends on.** As a robustness reading, the audit also scores
+against the oracle's *finest-only* family — here refinement-maximal: the mappings no other
+mapping strictly refines. Under it the entry tallies move (1,811 above, 2,103 below, 2,314 agreeing)
+and the verdict does not: still neither bound, still **945** spurious certain links, still none
+missed.
+
+**"None missed" is a statement about that definition, and only that one.** Selecting the finest
+mappings by *maximum block count* instead keeps a strict subset of the refinement-maximal ones — a
+strict refinement always has more blocks, so an argmax-by-block-count mapping is always
+refinement-maximal but not conversely. That is a smaller oracle family, hence more oracle
+certainties, hence a reading strictly *more generous* to the approximation, and under it the tally
+becomes **942 spurious and 108 missed**. The two selections differ on
+**106 of 507** cases. Refinement-maximal is kept because it is what "finest" means on a poset of
+partitions — not because it is the strongest reading; it is the weaker of the two for the
+approximation. Both tallies are measured on every run
+(`restriction_evidence.finest_selection_sensitivity`) and both are manifest invariants, so the
+sensitivity cannot quietly stop being true.
+
+The mechanism is visible in the equal-value cases. Three coins of equal value in and three out admit
+16 balanced mappings and 4.000 bits of mapping entropy; the refinement-maximal family has six
+mappings and the resulting matrix still breaks the value symmetry by index. Coin
+order carries no information, so an index-diagonal answer to a value-symmetric transaction is the
+symmetry being broken by the enumeration order rather than by the amounts.
+
+### The mapping count is a restriction, and a severe one
+
+`n_non_derived` agrees with `|M|` on 16 of 507 cases, and every one of those 16 is a transaction with
+exactly one mapping. Where the transaction is ambiguous at all, the two numbers part company: the
+largest understatement in this family is 3.64 bits. That is the
+undercounting direction, which is the safe one for a refuse-only channel, and `counting.py` already
+argues for preferring it. The finding is its size under the declared bounded family.
+
+## Identifications, not comparisons
+
+**`radix_mappings` is input-blind and scale-dependent.** The call takes the outputs and a knee; the
+inputs are not a parameter. `counting.radix_applies` is true for 25 of the family's output multisets,
+and on all 25 the crate returns a count of **0** at these magnitudes — so on this family the guarded
+route never produces a usable bound. Multiply every coin by 1,000 and the count becomes non-zero on
+**all 25** while the exact mapping count is unchanged, e.g.
+
+| outputs | radix count | ×1000 outputs | radix count | `\|M\|` (either scale) |
+|---|---|---|---|---|
+| `[1,1,2,2,2]` | 0 | `[1000,1000,2000,2000,2000]` | 22 | 7 / 21 / 27 / 29, by inputs |
+| `[1,2,2,2]` | 0 | `[1000,2000,2000,2000]` | 19 | 4 / 8 / 11 / 16 / 25 / 30 / 53, by inputs |
+
+`|M|` cannot move under a common rescaling — the balance conditions scale with it — and it does
+differ across the inputs that fund one output multiset. A quantity blind to a parameter the target
+depends on, and sensitive to one it does not, is not a bound on the target.
+
+**`per_coin_density` is coin-invariant where the exact marginals are not.** Its per-coin `log_w` is
+identical for every coin of the transaction in **497 of 507** cases, while the exact per-coin maximum
+link probability varies across the input coins in **339 of 507**. It is also a natural log, not bits.
+No entrywise comparison is offered, because it is not the same object; the reportable fact is
+structural — where it is flat it cannot express variation the oracle does show.
+
+**`counting.count_w` (the production router) inherits `w_count`'s object, and the saddle point never
+answers.** The router's radix tier never wins on this family — it returns zero, `_resolved` reads
+that as no answer, and the route falls through to sparse on 491 of 507 cases (16 resolve to nothing).
+Every one of those 491 sparse answers equals the same `W(E)` re-derivation, so the router is a
+different object from `|M|` for exactly the reason `w_count` is; 486 come back `exact` and 5
+`lower_bound`. `counting.saddle_point_log_w` declines **all 507**, while `counting.is_dense` reads
+**443** of them as Dense — the optimistic-gate discrepancy that function's own docstring predicts,
+now measured rather than expected.
+
+**`subtransaction.subtransactions` `ambiguity_bits`** covers the 16 two-in/two-out cases. It abstains
+on 4 (no positive implied payment), and on the remaining 12 it lands above the exact mapping
+entropy 6 times and below it 6 times, never equal. Its count admits partitions that do not
+conserve value, so this is not a bound in either direction — its own docstring already calls it a
+count diagnostic and not a privacy quantity, and that reading is confirmed here.
+
+## Self-derived canonical cases
+
+No number here is attributed to any paper: none of the source texts is in this checkout, so a
+citation would be fabricated. The canonical cases below are derived from the oracle's own definition
+and checked against its enumeration in `tests/test_oracle_audit.py`.
+
+For an *n*-in/*n*-out join of one repeated value, a block of *a* inputs balances exactly the blocks of
+*a* outputs, so a mapping is: a set partition of the inputs, a set partition of the outputs with the
+same multiset of block sizes, and a size-preserving bijection between them. Writing a shape as sizes
+with multiplicities `m_s`, the set partitions of that shape number `n! / (Π_s (s!)^{m_s} · Π_s m_s!)`
+and the size-preserving bijections number `Π_s m_s!`, so
+
+ |M|(n) = Σ over shapes of P(shape)² · Π_s m_s!
+
+| n | derivation | `\|M\|` | entropy |
+|---|---|---|---|
+| 2 | 1 (one block) + 2 (two singletons, 2 pairings) | **3** | log2 3 = **1.585 bits** |
+| 3 | 1 + 9 + 6 | **16** | **4.000 bits** |
+| 4 | 1 + 16 + 18 + 72 + 24 | **131** | **7.033 bits** |
+| 5 | — | **1,496** | 10.547 bits |
+
+So the canonical equal-value 2-in/2-out join has exactly three mappings — the identity pairing, the
+swapped pairing, and the single all-coins block — and log2(3) bits of mapping entropy, with no
+deterministic link at all. `dss.pairwise_link_prob` answers that case with an identity matrix.
+
+## What this does and does not establish
+
+- Zero-fee only. Every case here balances exactly; the oracle returns no mapping otherwise. The `dss`
+ link paths balance a real fee in as a synthetic extra output, and nothing here measures that path.
+- Small only: at most 8 coins, values in `{1,2,3,4}`. Nothing extrapolates to a 16-input mix, which is
+ where these approximations are actually used and where the oracle cannot follow.
+- No approximation in this repo may be described as a lower bound on the strength of this run. Two
+ are measured as under-only *on this family* (`mapping_count`, `mapping_entropy_bits`); the link
+ matrix is measured as neither, and that is a finding, not a caveat.
+- The enumerator behind `n_non_derived` is **not** specified here. This audit sees the count, the
+ marginal and the certain-link set — not the mappings — so it reports what that family demonstrably
+ is not, and stops there.
+- `decluster.oracle`'s subprocess link path and `counting.link_matrix`'s `budget_ms` / `wall_ms`
+ refusal behaviour are unprobed: nothing in this family is large enough to trigger a refusal, so
+ every call answered and the refusal branch went unmeasured.
+
+## Reproducibility / provenance
+
+The generated family, parameters, dependency identity and complete measurements are stored in the
+canonical artifact. The run manifest records its content hash, source revision, environment,
+limitations and exact verification command. `tests/test_oracle_audit.py` recomputes the artifact and
+checks the generated Markdown, while `tests/test_data_manifest.py` verifies the recorded output
+identities. Numeric claims therefore do not depend on parsing this narrative.
+
+Reproduce with:
+
+ .venv/bin/python -m decluster.experiments.exact_oracle_audit reproduce \
+ --artifact results/artifacts/exact-oracle-audit-v1.json \
+ --markdown results/generated/exact-oracle-audit-v1.md
+ .venv/bin/python -m decluster.experiments.exact_oracle_audit verify \
+ --artifact results/artifacts/exact-oracle-audit-v1.json \
+ --markdown results/generated/exact-oracle-audit-v1.md
+
+The verifier recomputes the full experiment from the artifact's recorded parameters and requires
+byte-for-byte equality for both machine-readable and generated presentation artifacts.
diff --git a/results/RESULTS-fingerprint-regime.md b/results/RESULTS-fingerprint-regime.md
index 8dd0096c..22cc0bd8 100644
--- a/results/RESULTS-fingerprint-regime.md
+++ b/results/RESULTS-fingerprint-regime.md
@@ -1,16 +1,25 @@
# Fingerprint N-S regime test: conditioner or sparse quasi-identifier?
+> The reproducible 600-transaction comparison is recalculated by
+> `catalog/runs/fingerprint-regime-v1.json`. Its structured artifact is
+> `results/artifacts/fingerprint-regime-v1.json`, with canonical rendering in
+> `results/generated/fingerprint-regime-v1.md`. It falsifies the historical assertion below that
+> the conditioner verdict is stable across weight sources: snapshot-measured weights produce
+> higher N-S-form top-1 than F-S and a within-class mean gap above 0.5. Those weights are fitted and
+> evaluated on the same selected snapshot, so this demonstrates sensitivity rather than universal
+> fingerprint sparsity. The cache-scale and Lumen rows below remain historical.
+
**Claim tested.** Does the per-tx fingerprint (`decluster/fingerprint_ns.py`: sparse
`{(axis,value): bits}` signature, agreement-overlap link, `propagate.eccentricity` acceptance
-gap) behave as a **sparse quasi-identifier** — like ancestry — that can pick one individual out
-of a crowd, or as a **low-cardinality equivalence-class conditioner**: it groups same-construction
+gap) behave as a sparse quasi-identifier — like ancestry — that can pick one individual out
+of a crowd, or as a low-cardinality equivalence-class conditioner: it groups same-construction
transactions together but cannot further resolve *within* a group? The tx-graph anonymity-set
theory (posts 02/03 of the anonymity-set series) predicts the latter for construction-style
fingerprints and reserves the sparse-quasi-identifier regime for the ancestry channel
(`propagate.py`, post 03).
**Data.** 8,864 transactions from the local block-tx cache (`.blkcache`; this cache grows over
-time, see Honest limits). Same-owner labels = address reuse (two txs spending the same input
+time, see Limits). Same-owner labels = address reuse (two txs spending the same input
address). `build_labeled_nodes` turns every reuse group of size ≥2 into one `owner_id`, capped at
200 nodes, assigning each tx to exactly one owner even when its inputs reuse more than one address
(the first qualifying group in sorted-address order; every other group it also belongs to is
@@ -25,9 +34,19 @@ skipped) — 200 labeled nodes, each a distinct txid, over 57 distinct owners (a
prior, path given via the `LUMEN_PRIOR` environment variable, falling back to `library` per
feature; the source is omitted entirely, not silently duplicated, if `LUMEN_PRIOR` is unset or
the file doesn't exist).
-- **F-S** (`decluster.fingerprint_validate.LibraryScorer`): the canonical Fellegi-Sunter combiner
- (agreement bits and clamped mismatch penalties over the same 23 axes) — fixed across all rows
- since it doesn't consume the N-S rarity source.
+- **F-S** (`decluster.fingerprint_validate.LibraryScorer`): agreement bits and clamped mismatch
+ penalties over the same 23 axes — fixed across all rows since it doesn't consume the N-S rarity
+ source.
+
+**What the two arms actually differ in, since the labels overstate it.** The `FS_` columns are not
+fitted Fellegi-Sunter and the `NS_` columns are not the 2009 propagation algorithm; the key names
+are kept because they are written verbatim into
+`results/artifacts/fingerprint-regime-v1.json`. Both arms sum `-log2(p)` rarity weights over the
+same axes from the same shares. The **only** structural difference is that `LibraryScorer` adds a
+clamped penalty when two axis values disagree and `fingerprint_link` does not. So the comparison
+below isolates the mismatch penalty, and nothing more: it supports no conclusion about the
+Narayanan--Shmatikov method, and none about Fellegi-Sunter, whose fitted `m`/`u` baseline
+(`decluster/fellegi_sunter.py`) is not in this experiment at all.
Two measurements per source:
@@ -76,7 +95,7 @@ no surprise to chase here. The sparse N-S regime — where a signature *does* pi
crowd — lives on the ancestry channel (`propagate.py`, see `results/RESULTS-ns-propagation.md`),
not on construction-style fingerprints.
-## Honest limits
+## Limits
- **The `.blkcache` is a growing local artifact, not a fixed dataset.** Absolute numbers here are
specific to this checkout's cache at measurement time (8,864 txs; an earlier run against 8,129
@@ -91,7 +110,7 @@ not on construction-style fingerprints.
not purely a property of the fingerprint model.
- **`measured` is fit and evaluated on the same txs it's scored against** — its NS_AUC (0.935) is
optimistic relative to an out-of-sample rarity fit; `library` (0.890, fit elsewhere) is the more
- honest read of the conditioner effect's size.
+ read of the conditioner effect's size.
- **θ≈0.5 is cited from `propagate.py`'s driver default for scale, not re-derived here** — the
claim is the within-class gap sits at or under a threshold used elsewhere in this pipeline, not
a formally re-fitted cutoff for this exact channel. The `measured` source's gap (0.366) is the
diff --git a/results/RESULTS-fingerprint-sparsity.md b/results/RESULTS-fingerprint-sparsity.md
index 74188d24..2bc62c6b 100644
--- a/results/RESULTS-fingerprint-sparsity.md
+++ b/results/RESULTS-fingerprint-sparsity.md
@@ -1,5 +1,14 @@
# Fingerprint anonymity sets at survey scale
+> The 12-axis whole-window column is recalculated from the preserved aggregate by
+> `catalog/runs/fingerprint-sparsity-v1.json`. Its structured artifact is
+> `results/artifacts/fingerprint-sparsity-v1.json`, with canonical rendering in
+> `results/generated/fingerprint-sparsity-v1.md`. All conditional partitions preserved in the
+> aggregate reconstruct the same histogram. The 19-axis and single-epoch columns below remain
+> historical because their source vectors or histograms were not preserved. The reproduced result
+> measures exact-vector equivalence classes, not general nearest-neighbour sparsity, cluster
+> distributions or successful attribution.
+
**Claim tested.** The Narayanan–Shmatikov results require a *sparse* feature space: most
records have no close neighbours. Wallet-construction fingerprints are the obvious
candidate feature space on Bitcoin. Are they sparse?
@@ -58,8 +67,8 @@ transaction — which is precisely why the channel buckets rather than identifie
## Scope
-This measures the **per-transaction** vector. It says nothing about the distinctiveness of
-a **cluster's feature distribution**: a cluster of fifty transactions carries a
+This measures the per-transaction vector. It says nothing about the distinctiveness of
+a cluster's feature distribution: a cluster of fifty transactions carries a
distribution over vectors, which can be far more distinctive than any single vector, and
measuring it requires a clustering rather than a survey. That quantity is the one the
structural argument actually rests on, and it is measured separately.
diff --git a/results/RESULTS-fingerprint-validation.md b/results/RESULTS-fingerprint-validation.md
index 9a3176d7..6b2f7821 100644
--- a/results/RESULTS-fingerprint-validation.md
+++ b/results/RESULTS-fingerprint-validation.md
@@ -1,7 +1,15 @@
# Canonical fingerprint model: validation on real witness-bearing transactions
+> The reproducible 600-transaction observation is recalculated by
+> `catalog/runs/fingerprint-validation-v1.json`. Its structured artifact is
+> `results/artifacts/fingerprint-validation-v1.json`, with canonical rendering in
+> `results/generated/fingerprint-validation-v1.md`. The 165.832-transaction figures below are
+> historical because that growing cache snapshot was not preserved. Address reuse remains a weak,
+> partly feature-dependent label, so the canonical result is pair-ranking evidence rather than
+> wallet attribution or a privacy score.
+
**Claim tested.** Does the 23-axis fingerprint library (`decluster/library.py`) tell same-wallet
-transactions apart from random ones — on real witness-bearing data? Scored on the **canonical** path
+transactions apart from random ones — on real witness-bearing data? Scored on the canonical path
(`LibraryScorer` over all library axes, using the same extractors the rest of the pipeline uses), not
the former `rust_bridge` mirror.
@@ -11,9 +19,15 @@ including the witness ones: low-R, uncompressed pubkey, taproot sighash — are
**Method.** Same-owner labels = address reuse: two transactions that spend the same input address are
the same wallet (near-certain). Positives = tx pairs sharing an input address (4,000 sampled);
-negatives = random tx pairs (4,000). Score = the Fellegi-Sunter weight of evidence in bits
+negatives = random tx pairs (4,000). Score = the rarity weight of evidence in bits
(`fingerprint_validate.LibraryScorer`, summing agreement bits / clamped mismatch weights over the 23
-library axes). Metric = AUC, with a shuffle control.
+library axes). Metric = AUC, with a shuffle control. **`LibraryScorer` is not Fellegi-Sunter**, and
+was described as such here: agreement is Newcombe's frequency-based rarity weight `-log2(p)` taken
+from the library's measured shares, and disagreement is a clamped penalty from an assumed
+consistency, neither of them a fitted `log2(m/u)`. `decluster/combiner.py` says so of itself, and
+its `fs_score` entry point now raises a `DeprecationWarning` naming the same thing. The fitted,
+supervised Fellegi-Sunter baseline is `decluster/fellegi_sunter.py`, and it is not what produced any
+number on this page.
## Result
@@ -38,10 +52,10 @@ cache grows — the signal, not the third decimal, is the result.)
now-removed `rust_bridge` mirror whose low-R and BIP-69 extractors were defective (a high-R signature
was misclassified as low-R; a coincidental small-n input sort was branded BIP-69) — both of which could
inflate the separation. Recomputed on the canonical path — with the correct length-based `x_low_r`, the
-`n>=4`-gated `x_input_order`, and `x_uih` reading `prevout.value` — the honest figure is **0.933**. The
+`n>=4`-gated `x_input_order`, and `x_uih` reading `prevout.value` — the figure that survives is **0.933**. The
signal holds; the model is no longer measured through a buggy, unmaintained parallel implementation.
-## Honest limits
+## Limits
- **Labels are address reuse.** Two txs spending the same address are the same wallet, but they also
share that address's script type — so the `input_script_type` / `low_r` / type axes match partly by
@@ -65,6 +79,6 @@ signal holds; the model is no longer measured through a buggy, unmaintained para
The chain-scale AUC (~0.93 over the full `.blkcache`) is a population statistic and is a data-run,
not a unit-asserted value. The load-bearing claim — the library scorer separates same-owner from
-random pairs with strong AUC and a shuffle control at chance — is pinned as a **band** on a committed
+random pairs with strong AUC and a shuffle control at chance — is pinned as a band on a committed
600-tx witness-bearing sample: `tests/fixtures/fingerprint_blkcache_sample.json`,
`tests/test_fingerprint_auc.py` (asserts AUC ≥ 0.88, shuffle ∈ [0.40, 0.60]).
diff --git a/results/RESULTS-fs-ablation.md b/results/RESULTS-fs-ablation.md
new file mode 100644
index 00000000..9d5d6383
--- /dev/null
+++ b/results/RESULTS-fs-ablation.md
@@ -0,0 +1,196 @@
+# Ablation of correlated axes in the Fellegi-Sunter model
+
+**What this document is.** `decluster/fellegi_sunter.py` assumes conditional independence of
+comparison fields given match status, and sums their log2 likelihood-ratio weights. Several
+fingerprint axes in `decluster/library.py` plainly violate that (fields derived from the same
+script type, the same signature encoding, or the same wallet behaviour move together). This
+document measures the damage on the same real transaction cache and split used in
+`results/RESULTS-fs-temporal.md`, rather than assuming it. It does not change that document's
+headline (+0.0482 AUC over the rarity baseline); it asks a different question about the same model.
+
+**Sample.** Same `.blkcache/` slice as `RESULTS-fs-temporal.md`: 22,112 transactions, heights
+800,000-965,220, strict block-height split at cutoff 961,653 (15,100 train / 7,012 test
+transactions). Same caveats apply: a block-sampled, non-contiguous slice; weak address-reuse
+labels, not independently verified same-owner labels.
+
+**Method.** `examples/fs_ablation.py .blkcache --seed 0`; the canonical output is
+`results/artifacts/fs-ablation-v1.json`. `results/fs-ablation.json` stays beside it as the frozen
+historical report — `tests/test_fs_ablation_experiment.py` asserts the canonical run still
+reproduces it, which is a guard the canonical artifact cannot give itself.
+Implemented in `decluster/fs_ablation.py`, stdlib only.
+
+## 1. Within-class association, not unconditional correlation
+
+Unconditional correlation between two comparison fields is expected even under a perfectly valid
+FS model: matches and non-matches differ systematically on every predictive axis, so pooling the
+two classes induces spurious association between any two fields that are each individually
+predictive (a Simpson's-paradox-style confound by class). The FS assumption is about association
+*within* each class, so that is what is measured: the phi coefficient (the Pearson correlation
+of two binary agree/disagree indicators; `phi = (n11*n00 - n10*n01) / sqrt((n11+n10)(n01+n00)(n11+n01)(n10+n00))`,
+`decluster.fs_ablation.phi_coefficient`), computed separately within the match class and within the
+non-match class, over comparison vectors built by `fs_temporal.fingerprint_fields()` on 8,000
+train-side weak-labelled pairs (`association_cap=8000`, restricted to field pairs with at least 30
+jointly-active observations in that class).
+
+**Most dependent pairs** (by the larger of |phi_match|, |phi_non_match|):
+
+| field a | field b | phi (match) | phi (non-match) |
+|---|---|---:|---:|
+| input_script_type | input_types_present | 1.000 | 1.000 |
+| change_index | change_type_match | 0.977 | 0.950 |
+| change_type_match | change_matches_output | 0.951 | 0.949 |
+| change_index | change_matches_output | 0.948 | 0.944 |
+| input_order | io_shape | 0.924 | 0.348 |
+| output_order | io_shape | 0.446 | 0.884 |
+| input_types_present | pubkey_compression | n/a (<30 in match) | 0.871 |
+| input_script_type | pubkey_compression | 0.122 | 0.857 |
+| input_types_present | sighash | 0.632 | 0.780 |
+| input_script_type | nested_segwit | 0.778 | 0.415 |
+| input_script_type | sighash | 0.301 | 0.776 |
+| sighash | pubkey_compression | 0.547 | 0.714 |
+| change_spk | output_encoding | 0.690 | 0.711 |
+
+`input_script_type`/`input_types_present` are perfectly dependent in both classes (phi = 1.000):
+on this population one is exactly a function of the other, so the FS model is currently double-
+counting a single fact as two independent log-likelihood terms. The `change_index` /
+`change_type_match` / `change_matches_output` triple and the `input_script_type`-adjacent
+script/signature-encoding group are both strongly (phi > 0.6-0.98) associated within class as well.
+
+**Clusters** (connected components at |phi| >= 0.6 in either class, a fixed, stated threshold, not
+fitted to the data; single-linkage chaining means membership is a claim about connectivity at this
+threshold, not that every internal pair is individually that strong):
+
+1. `input_script_type`, `input_types_present`, `nested_segwit`, `pubkey_compression`, `sighash`
+2. `change_index`, `change_matches_output`, `change_type_match`
+3. `input_order`, `io_shape`, `output_order`
+4. `change_spk`, `output_encoding`
+
+## 2. Leave-one-out ablation
+
+Each usable field removed one at a time, model refit on train, scored on the identical held-out
+test pairs `RESULTS-fs-temporal.md` uses (`--cap 4000 --seed 0`, baseline AUC 0.9827, matching that
+document). `auc_delta` is baseline AUC minus the AUC with that one field removed: positive means
+removing it hurt, negative means removing it *improved* held-out AUC.
+
+| field | AUC without it | auc_delta | coverage | selective accuracy |
+|---|---:|---:|---:|---:|
+| nsequence | 0.9707 | **+0.01195** | 0.895 | 0.9620 |
+| version | 0.9737 | +0.00898 | 0.898 | 0.9621 |
+| change_address_reuse | 0.9775 | +0.00520 | 0.903 | 0.9637 |
+| low_r | 0.9792 | +0.00352 | 0.930 | 0.9712 |
+| locktime | 0.9799 | +0.00275 | 0.907 | 0.9682 |
+| op_return | 0.9812 | +0.00146 | 0.922 | 0.9714 |
+| output_order | 0.9820 | +0.00070 | 0.918 | 0.9709 |
+| fee_rate | 0.9823 | +0.00037 | 0.926 | 0.9721 |
+| input_order | 0.9824 | +0.00025 | 0.931 | 0.9714 |
+| multisig | 0.9827 | +0.00002 | 0.925 | 0.9735 |
+| pubkey_compression | 0.9827 | -0.00005 | 0.919 | 0.9750 |
+| uih | 0.9827 | -0.00005 | 0.929 | 0.9727 |
+| nested_segwit | 0.9827 | -0.00006 | 0.921 | 0.9738 |
+| input_types_present | 0.9828 | -0.00008 | 0.916 | 0.9750 |
+| change_matches_output | 0.9828 | -0.00011 | 0.927 | 0.9728 |
+| change_index | 0.9828 | -0.00013 | 0.928 | 0.9726 |
+| change_type_match | 0.9828 | -0.00014 | 0.927 | 0.9729 |
+| sighash | 0.9829 | -0.00020 | 0.921 | 0.9745 |
+| input_script_type | 0.9831 | -0.00044 | 0.916 | 0.9753 |
+| output_encoding | 0.9836 | -0.00092 | 0.926 | 0.9726 |
+| change_spk | 0.9837 | -0.00103 | 0.924 | 0.9730 |
+| io_shape | 0.9840 | **-0.00129** | 0.924 | 0.9724 |
+
+**Reading it.** 12 of 22 fields have `auc_delta` <= 0: removing them individually does not hurt
+held-out AUC, and for four of them (`io_shape`, `change_spk`, `output_encoding`,
+`input_script_type`) it measurably helps. This is a majority-not-universal pattern, not a clean
+rule: 11 of those 12 are members of a cluster from step 1 (`change_index`, `change_matches_output`,
+`change_spk`, `change_type_match`, `input_script_type`, `input_types_present`, `io_shape`,
+`nested_segwit`, `output_encoding`, `pubkey_compression`, `sighash`) — exactly what a correlated,
+redundant axis looks like: on its own it looks harmless or even actively noisy to keep, because a
+correlated partner already carries the same signal (and the partner's own m/u estimate absorbs the
+discriminative content, so the removed field's disagreement penalty was mostly adding calibration
+noise). The twelfth, `uih`, is **not** in any cluster at the 0.6 threshold — its strongest
+association is phi 0.49 (match) / 0.25 (non-match) with `io_shape`, and phi 0.40 (match) / 0.44
+(non-match) with `input_order`, both real but sub-threshold. My best read is that `uih`'s -0.00005
+is mostly measurement noise: it is tied for the smallest-magnitude change in the whole table
+(alongside `multisig`'s +0.00002 and `pubkey_compression`'s -0.00005), an order of magnitude below
+the smallest effect I'd call real (`locktime` at +0.0028). The sub-threshold correlation to the
+`io_shape` cluster is a plausible partial explanation if the effect is not pure noise, but nothing
+here distinguishes the two, and at this magnitude it does not matter which is true.
+
+Correlation membership does not fully predict the sign either: two members of the
+`input_order`/`io_shape`/`output_order` cluster — `input_order` (+0.00025) and `output_order`
+(+0.00070) — have small *positive* deltas, i.e. removing them individually costs a little rather
+than nothing. Counting the full 13-field union of the four clusters against this table: 11 of 13
+clustered fields have `auc_delta` <= 0, and 2 do not. Section 3 below shows this same cluster is
+the one place where group ablation does not tell the clean redundancy story either.
+
+The fields whose removal hurts most (`nsequence` +0.0120, `version` +0.0090, `change_address_reuse`
++0.0052, `low_r` +0.0035, `locktime` +0.0028) are none of them in a cluster from step 1 — each is
+carrying information no other field supplies. That direction of the claim (informative ⇒
+uncorrelated) holds without exception on this population; the reverse (correlated ⇒
+uninformative-alone) holds for most but not all of the clustered fields, as above.
+
+## 3. Group ablation
+
+For each cluster from step 1: remove every member, versus remove every member but one
+representative (the group member with the largest `|agreement_weight|` in the train-fitted full
+model — chosen from train-side parameters only, no test-side information enters the choice).
+
+| cluster | representative | remove-group delta | keep-representative delta |
+|---|---|---:|---:|
+| input_script_type, input_types_present, nested_segwit, pubkey_compression, sighash | input_script_type | **+0.00338** | -0.00007 |
+| change_index, change_matches_output, change_type_match | change_index | **+0.00044** | -0.00009 |
+| input_order, io_shape, output_order | io_shape | +0.00155 | +0.00112 |
+| change_spk, output_encoding | change_spk | -0.00115 | -0.00092 |
+
+**Reading it.** For the two strongest clusters (the script/signature-encoding group and the
+change-position group), removing the whole cluster costs roughly the discriminative content of
+the group's best member; keeping just that one representative recovers essentially all of it
+(delta near zero, in fact very slightly negative — the fifth field's disagreement penalty was
+adding a hair of noise rather than signal). This is the direction
+`tests/test_fs_ablation.py::test_removing_the_whole_correlated_cluster_hurts_more_than_keeping_one_representative`
+pins on the 5-field cluster, across six seeds, through `reproducibility.separable`.
+
+Not every cluster tells the same story. For `input_order`/`io_shape`/`output_order`, both actions
+cost a small positive amount (remove-group > keep-rep, same direction, smaller margin). For
+`change_spk`/`output_encoding`, *both* deltas are negative: this correlated pair is net noise on
+this population, and dropping it entirely beats dropping all-but-one. A blanket rule ("always keep
+one representative per cluster") would be wrong here; the summary that survives is that correlated
+clusters are redundant with each other, not that every correlated field is individually valuable.
+
+## Limits
+
+- **Does not change the fs-temporal headline.** `RESULTS-fs-temporal.md`'s +0.0482 AUC delta over
+ the rarity baseline is measured with all 22 fields present, as published; nothing here
+ contradicts it. This document is about which of those 22 fields are carrying independent
+ information, not about whether FS beats the rarity baseline.
+- **Same weak labels, same slice.** Everything inherited from `RESULTS-fs-temporal.md`'s limits
+ section applies here unchanged: block-sampled non-contiguous population, weak address-reuse
+ labels (not independently verified same-owner labels), one out-of-period split.
+- **Cluster threshold is a stated choice, not derived from the data.** `threshold=0.6` on the phi
+ coefficient is a fixed, conventional "strong association" cutoff. Connected components can chain
+ transitively through single-linkage; the 5-field cluster is one connected component at this
+ threshold, not a claim that every pair inside it individually exceeds 0.6 (`sighash`-`nested_segwit`,
+ for instance, is not in the top-15 pairs table above).
+- **Representative selection uses the full model's fitted weights, which include the very
+ correlation being ablated.** Picking the group member with the largest train-side
+ `|agreement_weight|` is a reasonable proxy for "carries the group's signal alone," but it is not
+ an independent measurement — a field's own m/u estimate is itself shaped by the correlated
+ behaviour of the other axes it was fit alongside.
+- **Leave-one-out changes are small relative to the FS-vs-rarity delta.** The largest single-field
+ effect (`nsequence`, +0.012) is a quarter of the fs-temporal headline delta (+0.048); this
+ analysis is about internal redundancy among FS's own inputs, not a comparably large finding on
+ its own.
+
+## Reproducibility / provenance
+
+Manifest: `results/manifests/RESULTS-fs-ablation.json`, written by
+`decluster.reproducibility.write_manifest` over `.blkcache/*.json` (904 files, byte digest) with
+the population invariants this claim depends on: transaction count, split cutoff and train/test
+transaction counts, the cluster threshold and the four clusters it produces, the single most
+dependent field pair with its phi in each class, and the 22-field baseline AUC.
+`tests/test_fs_ablation.py::test_manifest_invariants_are_recomputed_and_match_results_fs_ablation`
+recomputes every one of those from `.blkcache` on each test run and asserts `check_manifest`
+returns `ok`, not just `identity-only`. It skips cleanly, naming what went unrecomputed, on a
+checkout without `.blkcache`.
+
+Reproduce with the command in `catalog/runs/fs-ablation-v1.json`, which writes
+`results/artifacts/fs-ablation-v1.json`.
diff --git a/results/RESULTS-fs-temporal.md b/results/RESULTS-fs-temporal.md
new file mode 100644
index 00000000..82c2155a
--- /dev/null
+++ b/results/RESULTS-fs-temporal.md
@@ -0,0 +1,203 @@
+# Fellegi-Sunter, fit early / scored late, on the real transaction cache
+
+**Sample limit stated up front.** `.blkcache/` holds 904 cached block-page files covering 22,112
+distinct, complete (non-coinbase, fully-cached-input) transactions across 458 block heights
+(800,000-965,220) — a block-sampled, non-contiguous slice, not a chain-wide or time-representative
+population. Every number below is measured on exactly that slice; none of it supports a claim about
+Bitcoin transactions in general, and it is not padded with data from any other source.
+
+**Claim tested.** `decluster/fellegi_sunter.py` (supervised m/u Fellegi-Sunter, thresholds set from
+tolerated error rates) and `decluster/fs_temporal.py` (block-height train/test split, address-reuse
+weak labels, held-out evaluation against the repo's legacy value-rarity scorer — `combiner.rarity_score`,
+exposed here through `LibraryScorer`, and never a Fellegi-Sunter model itself) exist and were unit-tested
+on a synthetic fixture, but had never been run end to end on real cached transactions. This document is
+that run.
+
+**Method.** `examples/fellegi_sunter_temporal.py .blkcache --cap 4000 --seed 0 --train-fraction 0.7`
+(the canonical run is `results/artifacts/fs-temporal-v1.json`, and `results/fs-temporal.json` is the
+frozen historical report a test holds it against; every number below comes from that run
+except the uncapped pair-pool row in the Split table, marked with a footnote). Transactions are
+split at a block-height cutoff, strictly — the model is
+fit only on pairs drawn from the train side and scored only on pairs drawn from the test side. Labels are
+weak: a positive pair is two distinct transactions that share an observed input address (the same
+address-reuse signal `fingerprint_validate.py` already uses); a negative pair shares no observed input
+address. These are not independently verified same-owner labels — collaborative spends, address
+transfer, and unseen history can flip either class, which is exactly why `label_provenance` in the JSON
+carries `"independently_verified": false` rather than the word this repo does not use.
+
+## Split
+
+| | value |
+|---|---:|
+| transactions loaded | 22,112 |
+| height range | 800,000 - 965,220 (458 distinct heights) |
+| split strategy | strict block-height cutoff, `train_fraction=0.7` |
+| cutoff height | 961,653 |
+| train transactions | 15,100 (heights 800,000-961,653) |
+| test transactions | 7,012 (heights 961,654-965,220) |
+| train pairs (capped) | 4,000 positive / 4,000 negative |
+| test pairs (capped) | 4,000 positive / 4,000 negative |
+| positive pairs available before capping* | 153,122 (train), 64,521 (test) |
+
+\* Not in the artifact — `weak_label_pairs` only reports the capped counts it actually drew.
+These are the uncapped population sizes, recomputed directly from `.blkcache` (same split, same
+`weak_label_pairs`, `cap=10**7` so no positive-side sampling ever triggers) and pinned in
+`results/manifests/RESULTS-fs-temporal.json`. `tests/test_fs_temporal.py`'s
+`test_manifest_invariants_are_recomputed_and_match_results_fs_temporal` recomputes them on every
+test run and asserts the manifest is `ok` against that live recomputation, not just `identity-only`
+against an unchanged byte digest.
+
+## Fitted m/u and weights (train side, `alpha=0.5` pseudo-count)
+
+Tolerated error rates `false_match_rate=0.05`, `false_non_match_rate=0.05` give thresholds
+`non_link_below = -4.2479`, `link_at_or_above = +4.2479` in log2 likelihood-ratio units
+(`decluster.fellegi_sunter.thresholds_from_error_rates`).
+
+| field | m | u | agreement weight | disagreement weight |
+|---|---:|---:|---:|---:|
+| change_address_reuse | 0.9964 | 0.5547 | 0.845 | -6.941 |
+| change_index | 0.7077 | 0.6205 | 0.190 | -0.377 |
+| change_matches_output | 0.7039 | 0.6172 | 0.190 | -0.371 |
+| change_spk | 0.6258 | 0.3817 | 0.714 | -0.725 |
+| change_type_match | 0.7149 | 0.6320 | 0.178 | -0.369 |
+| fee_rate | 0.9806 | 0.8899 | 0.140 | -2.507 |
+| input_order | 0.9515 | 0.7295 | 0.383 | -2.479 |
+| input_script_type | 0.9969 | 0.5028 | 0.988 | -7.308 |
+| input_types_present | 0.9996 | 0.5147 | 0.958 | -10.327 |
+| io_shape | 0.9208 | 0.4399 | 1.066 | -2.822 |
+| locktime | 0.9999 | 0.7197 | 0.474 | -11.131 |
+| low_r | 0.8384 | 0.3615 | 1.214 | -1.982 |
+| multisig | 0.9991 | 0.8832 | 0.178 | -7.061 |
+| nested_segwit | 0.9981 | 0.8537 | 0.226 | -6.287 |
+| nsequence | 0.9919 | 0.4015 | 1.305 | -6.203 |
+| op_return | 0.9999 | 0.9159 | 0.127 | -9.394 |
+| output_encoding | 0.5930 | 0.4263 | 0.476 | -0.495 |
+| output_order | 0.9744 | 0.4761 | 1.033 | -4.356 |
+| pubkey_compression | 0.9989 | 0.5737 | 0.800 | -8.566 |
+| sighash | 0.9994 | 0.6139 | 0.703 | -9.249 |
+| uih | 0.9711 | 0.8712 | 0.157 | -2.158 |
+| version | 0.9984 | 0.5015 | 0.993 | -8.261 |
+
+All 22 comparison fields had both weak-label classes active in training and were used; none were
+dropped for lacking observations on this slice.
+
+## Held-out performance (test side, identical pairs for both scorers)
+
+| metric | Fellegi-Sunter | rarity baseline (`LibraryScorer`) |
+|---|---:|---:|
+| AUC | 0.9827 | 0.9345 |
+| **AUC delta (FS - rarity)** | **+0.0482** | |
+
+Both AUCs above are the exact Mann--Whitney statistic with tie half-credit
+(`graph_deanon.exact_auc`, called through `fs_temporal._score_metrics`), on the identical held-out
+pairs. Note when comparing across documents: most other AUCs in `results/` — including the
+`LibraryScorer` AUC in `RESULTS-fingerprint-validation.md` — come from `graph_deanon.auc`, which
+*estimates* the same quantity from at most 20,000 sampled draws and carries sampling error in the
+third decimal. The exact one is used here because the headline is a difference between two AUCs,
+where two independent third-decimal errors are a large fraction of a +0.048 delta. The 0.9345 above
+and any sampled-estimator figure for the same scorer are therefore not the same measurement to three
+decimals, and neither is wrong.
+
+FS decisions at the 0.05/0.05 thresholds: 3,989 link, 3,413 non-link, 598 review (coverage 92.5%).
+
+| metric | value |
+|---|---:|
+| precision (of links) | 0.9551 |
+| recall (of positives) | 0.9525 |
+| coverage (decided / total) | 0.9253 |
+| selective accuracy (of decided) | 0.9730 |
+
+**Calibration** (case-control caveat: the test set is a balanced 50/50 weak-label sample, not the
+population base rate, so Brier and ECE describe calibration *on this sample*, not population risk):
+Brier 0.0425, ECE (10 bins) 0.0407, prior used for the posterior transform 0.50.
+
+## Headline finding
+
+**On this out-of-period test split, Fellegi-Sunter beats the rarity baseline: AUC 0.9827 vs 0.9345, a
+delta of +0.0482, on the identical held-out pairs.** The rarity baseline is already strong (it was built
+from the same axes), so the gap is a modest but real improvement from fitting per-axis m/u instead of
+scoring value rarity alone — not a large-margin win.
+
+## Seed sensitivity
+
+Same command, `--cap 4000 --train-fraction 0.7`, five seeds (seed also reseeds the test-side weak
+labels via `seed+1` inside `evaluate_temporal`):
+
+| seed | FS AUC | rarity AUC | delta |
+|---:|---:|---:|---:|
+| 0 | 0.9827 | 0.9345 | 0.0482 |
+| 1 | 0.9815 | 0.9351 | 0.0464 |
+| 2 | 0.9818 | 0.9348 | 0.0471 |
+| 3 | 0.9789 | 0.9327 | 0.0462 |
+| 4 | 0.9812 | 0.9359 | 0.0453 |
+
+Delta ranges 0.0453-0.0482 across five seeds (spread 0.003, about 6% of the mean delta): FS beats
+rarity on every seed tried, by a consistent margin. The direction survives resampling on this slice;
+it is not a lucky draw at seed 0.
+
+## Cap and train-fraction sensitivity
+
+| run | cap | train_fraction | FS AUC | rarity AUC | delta |
+|---|---:|---:|---:|---:|---:|
+| `--cap 2000` | 2000 | 0.7 | 0.9837 | 0.9310 | 0.0527 |
+| `--cap 4000` (canonical) | 4000 | 0.7 | 0.9827 | 0.9345 | 0.0482 |
+| `--cap 8000` | 8000 | 0.7 | 0.9805 | 0.9318 | 0.0487 |
+| `--train-fraction 0.5` | 4000 | 0.5 | 0.9795 | 0.9280 | 0.0515 |
+| `--train-fraction 0.6` | 4000 | 0.6 | 0.9810 | 0.9354 | 0.0456 |
+| `--train-fraction 0.8` | 4000 | 0.8 | 0.9778 | 0.9307 | 0.0471 |
+
+The delta stays positive and in a narrow 0.046-0.053 band across every cap and split tried; nothing
+here suggests it is an artifact of one particular sample size or cutoff choice.
+
+## Limits
+
+- **904 files, 22,112 transactions, one non-contiguous slice.** This is a small-sample measurement of
+ one attack instance, not a population claim about Bitcoin traffic generally.
+- **Weak labels, not independently verified ownership.** Address reuse is a real same-owner signal but
+ not proof: two distinct owners can reuse an address handed to them (payment requests, exchange
+ deposit addresses), and unseen history can hide a real link inside a "negative" pair. `label_provenance`
+ in the JSON marks this explicitly (`"independently_verified": false`).
+- **Correlated axes on both sides.** FS's per-field product likelihood and the rarity baseline's scorer
+ are built from the same fingerprint axes (`LibraryScorer.axes`), so the two are not independent
+ measurements of the same population statistic — the delta reflects what per-axis m/u fitting adds over
+ fixed-rarity scoring on this same evidence, not an unrelated method beating a naive one. The 22
+ comparison fields fitted above are not 22 independent pieces of evidence either — one pair,
+ `input_script_type` and `input_types_present`, is functionally one field on this population
+ (phi coefficient 1.000 within both the match and non-match class); see
+ `results/RESULTS-fs-ablation.md` for the full within-class dependence measurement and the
+ leave-one-out/group ablation this document does not attempt.
+- **"Fit early / scored late" is asymmetric between the two arms.** It holds strictly for the FS
+ arm: `fit_model_for_error_rates` sees only train-side pairs, drawn from below the height cutoff.
+ It does not hold for the rarity baseline. `LibraryScorer`'s per-axis value shares are
+ `decluster/library.py`'s fixed priors, whose own provenance note records a uniform ~105k
+ whole-chain BigQuery sample plus ~3.5k recent mempool.space transactions — neither restricted to
+ below the height cutoff, and both plausibly covering the test window. The baseline is therefore
+ not held out from the test period in the way FS is. The asymmetry runs *against*
+ the headline — the baseline is if anything advantaged — which makes the +0.0482 delta a
+ conservative reading rather than an optimistic one. It is stated because "fit early / scored late"
+ in this document's own title reads as a property of the comparison, and it is a property of one
+ arm.
+- **Calibration is case-control.** Brier/ECE are computed on a balanced 50/50 sample; they describe how
+ well-calibrated the posterior transform is on that sample, not on Bitcoin's true match/non-match base
+ rate.
+- **No fix to `decluster/fs_temporal.py` was needed.** The module ran cleanly against the real cache on
+ the first attempt across every cap/seed/train-fraction combination tried above; nothing in this run
+ exposed a bug.
+
+## Reproducibility / provenance
+
+Manifest: `results/manifests/RESULTS-fs-temporal.json`, written by
+`decluster.reproducibility.write_manifest` over `.blkcache/*.json` (904 files, recorded as a byte
+digest) with the population invariants this claim depends on: transaction count (22,112), height range
+(800,000-965,220, 458 distinct heights), the split cutoff and train/test transaction counts, and the
+uncapped positive-pair pool sizes on each side of the split (153,122 train, 64,521 test) — the facts a
+byte digest of `.blkcache/` cannot see. `tests/test_results_manifests.py`'s generic walker only
+confirms the source is unchanged (`identity-only`, since it never recomputes any doc's invariants
+itself); `tests/test_fs_temporal.py::test_manifest_invariants_are_recomputed_and_match_results_fs_temporal`
+is the one that actually recomputes every invariant above from `.blkcache` and asserts `check_manifest`
+returns `ok` against that live recomputation. It skips cleanly, naming what went unrecomputed, on a
+checkout without `.blkcache`.
+
+Reproduce the canonical run with the command in `catalog/runs/fs-temporal-v1.json`. The example
+behind it is `.venv/bin/python examples/fellegi_sunter_temporal.py .blkcache --cap 4000 --seed 0
+--train-fraction 0.7` (the other rows above substitute the named flag), which writes to stdout.
diff --git a/results/RESULTS-gap1.md b/results/RESULTS-gap1.md
index 3f59d439..cba2b8ac 100644
--- a/results/RESULTS-gap1.md
+++ b/results/RESULTS-gap1.md
@@ -24,7 +24,7 @@ survive `payment > 0`:
| **in1 (Cake 5750) → out1 (6750)** | **1000** | **3** ✓ |
| in0 (sender 2000) → out1 (6750) | 4750 | 1 |
-`ambiguity_bits = log2(2) = 1.0`. Roundness picks **payment = 1000** → the most-likely
+`ambiguity_bits = log2(2) = 1.0`. Roundness picks payment = 1000 → the most-likely
partition:
- **REFUSE** `91106666` (sender) ↔ `0a568e3a` (Cake): the amount says the two inputs are
@@ -33,20 +33,20 @@ partition:
This is exactly the amount move: subtract the receiver's contributed input from its
output → a low-hanging round payment (1000). The "number of plausible partitions is a lot
-lower than we think": here it is **1 bit**, and roundness resolves it.
+lower than we think": here it is 1 bit, and roundness resolves it.
## Two independent signals agree
| signal | verdict on sender↔Cake |
|---|---|
-| **amount** (this result) | REFUSE, payment 1000, 1 bit ambiguity |
-| **fingerprint** (WP4, `results/RESULTS-wp4.md`) | REFUSE, −3.1 bits (`max_ffffffff` vs `seq_0x01`) |
+| amount (this result) | REFUSE, payment 1000, 1 bit ambiguity |
+| fingerprint (WP4, `results/RESULTS-wp4.md`) | REFUSE, −3.1 bits (`max_ffffffff` vs `seq_0x01`) |
The amount is the primary structure (the cake); the fingerprint confirms (the cherry).
On this merged transaction they agree on the same re-partition.
-## Honest caveats
+## Caveats
- **Roundness is a prior, not proof.** A market-price payment can be non-round; the score
- is a likelihood weight, and `ambiguity_bits` reports honestly when amount does not
+ is a likelihood weight, and `ambiguity_bits` reports when amount does not
disambiguate (here it is 1 bit, not 0).
- **2-in/2-out only.** General n-party subtransaction matching is deferred; other shapes
return a scope guard (no silent wrong answer).
@@ -57,3 +57,8 @@ On this merged transaction they agree on the same re-partition.
`partition_signal(tx)` is the integration-ready output; wiring it into the combiner as a
structural weight (so amount and fingerprint fuse) is a documented follow-up.
+
+## Reproducibility / provenance
+
+State 2 in `results/REPRODUCIBILITY.md`: the mechanism is pinned; the reported numbers come from a data-run over `.cache/`, which is not committed, and are not asserted.
+The mechanism is guarded by `tests/test_gap1.py`.
diff --git a/results/RESULTS-graph-deanon.md b/results/RESULTS-graph-deanon.md
index e9824f0b..043b2eb3 100644
--- a/results/RESULTS-graph-deanon.md
+++ b/results/RESULTS-graph-deanon.md
@@ -25,19 +25,38 @@ Blocks 400000–400004 (2016), full slice:
| Graph used for structure | AUC | Reading |
|---|---:|---|
| **FULL** (co-spend + payment edges) | **0.992** | structure aligns with entity boundaries |
-| **PAYMENT-ONLY** (co-spend edges removed) | **0.950** | ← the honest test: structure de-anonymizes *independently* of the clustering heuristic |
+| **PAYMENT-ONLY** (co-spend edges removed) | **0.950** | ← the test that removes the circularity: structure de-anonymizes *independently* of the clustering heuristic |
| **SHUFFLE** (entity labels randomized) | **0.500** | control: the signal is not a sampling artifact |
The confound: the FULL graph shares its co-spend edges with the heuristic that *defines*
those labels, so its 0.992 is partly circular. Removing those edges — scoring pairs by
-**payment** relationships only — still yields **AUC 0.950**. The shuffle control lands at
+payment relationships only — still yields AUC 0.950. The shuffle control lands at
0.500, confirming the effect is real structure, not the pair-sampling.
+**Addition, 7 Sep 2026 — the second control, and a determinism fix.** Two things were missing.
+
+First, the numbers above were **not reproducible**: `union_input_addrs` took its union-find root
+from an unsorted set, so the cluster roots — and the seeded sampling that walks them — came out
+differently in every process. Three runs of this same fixture and seed gave 0.9905, 0.9908 and
+0.9910. Sorting the two sets that fix downstream order makes the run byte-identical across
+processes; the stable values are **0.9912 (full)** and **0.9507 (payment-only)**, so the figures
+above survive at the precision they are quoted to.
+
+Second, the shuffle control says the score reads the labels, but it says nothing about *how*. The
+published negatives are drawn uniformly over cluster roots while positives are every intra-cluster
+pair, so the positive class is systematically higher-degree — and the score reads degree. Under
+degree-matched negatives the payment-only AUC falls to **0.9156**, and a score made of nothing
+but the pair's degree sum sits at **0.5000**, at chance by construction. So about **0.035 of the
+0.9507** was the sampling asymmetry, and the rest is not.
+
+Both are published as `catalog/runs/graph-deanon-controls-v1.json`, which is also what now owns the
+`graph-deanon-2016-v1` fixture.
+
### Across five eras: the mechanism, not a clean curve
Payment-only AUC on five connected slices, swept over graph reach *k* (k-hop common
neighbors, hub intermediates excluded — `decluster/graph_deanon.py --depth`).
-**share%** = fraction of same-owner pairs sharing a *direct* counterparty. Each slice is a
+share% = fraction of same-owner pairs sharing a *direct* counterparty. Each slice is a
contiguous block range (partition-pruned by `block_timestamp_month`, `bigquery/graph.sql`):
| Year | blocks | entities | held-out pairs | share% | k=1 | k=2 | k=3 | k=4 |
@@ -55,7 +74,7 @@ non-informative 1.00; a meaningful 2025 point needs that supercluster excluded f
Two findings. **(1) At k=1 the effect is not a clean era/reuse curve**: the 2013 slice
drops to chance (0.53) on a *substantial* 25 944 pairs — not a small-sample fluke. The
-diagnostic explains it: 1-hop AUC tracks **share%** monotonically (96%→0.97, 91%→0.95,
+diagnostic explains it: 1-hop AUC tracks share% monotonically (96%→0.97, 91%→0.95,
65%→0.83, 48%→0.74, 6%→0.53). 2013 is the SatoshiDice / service-churn era — an owner's addresses each
touch a *different* service address, so only 6% share a direct counterparty and the 1-hop
feature is starved.
@@ -70,13 +89,13 @@ Narayanan–Shmatikov claim: structure de-anonymizes across every era tested.
Why deeper reach does not collapse to chance (the usual small-world objection): hub
counterparties (degree > 100, i.e. services/exchanges that connect everyone) are excluded
as intermediates, so k-hop follows only *personal* edges and the graph stays fragmented
-into per-owner regions. **Honest caveat:** as k grows, that hub-excluded reachability
+into per-owner regions. Caveat: as k grows, that hub-excluded reachability
approaches "same non-hub-connected region," a coarser statement than fine link
prediction — legitimate entity recovery without co-spend, but at k=4 it is closer to
component membership than to a pairwise structural tell. (2023's 1.00 also rests on only
111 pairs.)
-## Honest limits
+## Limits
- **One slice, one era.** 5 blocks of 2016. A multi-era / larger connected graph would
strengthen (and possibly weaken, for modern low-reuse txs) the number. This is a
diff --git a/results/RESULTS-graph-shape.md b/results/RESULTS-graph-shape.md
deleted file mode 100644
index 9bf0ee67..00000000
--- a/results/RESULTS-graph-shape.md
+++ /dev/null
@@ -1,86 +0,0 @@
-# Is the pseudonym graph a social network?
-
-**Why this is the question under all the others.** The framework asserts that the
-contracted cluster graph, under an incomplete clustering, "is a social network per
-Shmatikov and Narayanan's definition." Everything the matching does rests on that: the N-S
-algorithms were validated on social graphs, and a social graph is heavy-tailed, clustered
-far above chance, and assortative — a vertex sits in a tight neighbourhood of well-connected
-vertices, which is what lets its neighbours identify it. This is the one premise of the
-whole programme that had been asserted rather than measured.
-
-**Data.** `slice_2026.ndjson`, refusing clusterer, the two contracted views. Each statistic
-is reported against a configuration-model null with the same degree sequence, because the
-raw value alone conflates structure with mere density; the null answers "more than the
-degrees alone would produce?". `decluster/graph_shape.py`.
-
-## Result
-
-| statistic | view A | view B | a social graph |
-|---|---:|---:|---|
-| vertices | 403 963 | 394 989 | |
-| edges | 525 010 | 506 447 | |
-| mean degree | 2.57 | 2.54 | higher |
-| degree-1 share | 0.583 | 0.594 | low |
-| transitivity (clustering) | 0.0030 | 0.0005 | high |
-| configuration null | 0.428 | 0.355 | |
-| **clustering / null** | **0.007** | **0.001** | **≫ 1** |
-| assortativity | −0.072 | −0.074 | positive |
-| degree tail exponent | 3.90 | 3.94 | 2 – 3 |
-
-## Reading
-
-**It is not a social network, on every axis at once.**
-
-- **Clustering is ~140× and ~700× *below* chance.** A real social graph clusters far above
- what its degree sequence forces; this one clusters more than two orders of magnitude
- *under* it. Its friends-of-friends are systematically not friends. This is the community
- structure the framework's argument depends on, and it is absent — worse than absent, it is
- actively suppressed relative to a random graph with the same degrees.
-- **The graph is mildly disassortative** (−0.07), the sign transactional and technological
- graphs carry, not the positive sign of a social graph. Its hubs attach to leaves.
-- **The degree tail is too steep** (3.9 against the social 2–3), and **58 % of vertices are
- leaves.** The heavy tail of well-connected nodes that N-S propagation rides is thin here.
-
-**This explains the whole series of negatives at once, at the level of a missing
-precondition.** The frontier collapsed (`RESULTS-view-match-2026.md`), the ambiguity cut
-would not decompose the graph (`RESULTS-partition-schemes.md`), attributes only added error
-(`RESULTS-attribute-conditioning.md`), and the rejoin almost never fired
-(`RESULTS-rejoin.md`). These are not four independent disappointments. They are what a
-matching algorithm designed for social graphs does when handed a graph that is nearly a tree
-of one-shot transfers: there is no community structure to propagate through, because at this
-timescale the graph does not have any. To be exact about scope, what fails here is the
-*cascade* — the strong form that would recover most of the graph. The matcher still returns a
-few high-confidence links above a degree baseline (`RESULTS-match-controls.md`), which is the
-modest outcome the framework actually claims for a single run; those are not among the
-"negatives" above. It is the runaway propagation, not all output, that the missing structure
-denies.
-
-**And it says which of the remaining levers can and cannot help.** A wider view raises degree
-but does not manufacture clustering where transfers are genuinely one-shot; the persistence
-curve already showed the recurrence rate saturating at 38 %. Link prediction assumes a true
-graph whose edges are censored, but these edges are not censored, they are absent, so
-predicting them would invent economic activity that did not happen. Neither lever supplies
-the missing precondition. The precondition is a property of the transaction graph itself, and
-the only thing that changes it is the transaction graph becoming more interwoven — which is
-exactly the defence the framework argues for in its closing section.
-
-## What would change the verdict
-
-The measurement is of two-day views in one month under one clusterer. The precondition could
-hold at a longer timescale if recurring economic relationships accumulate faster than the
-graph grows, but the persistence curve is evidence against that, and the clustering deficit
-here is so large that closing it by a factor of a hundred-plus is a different regime, not a
-tuning. A denser clusterer (the fingerprint channel, which needs a one-hop-back export) would
-raise degree and could raise clustering; whether it moves the ratio above 1 is the one open
-empirical question this leaves, and it is a sharper question than "try a wider view".
-
-## Note on a bug this measurement caught
-
-The first run of this reported 3.9 million edges and mean degree 19, four times the truth.
-`contract` was asserting an edge from every source pseudonym to every output, which is a
-no-op under the naive clusterer (one source per transaction) but explodes under the refusing
-one: 211 coinjoins with six or more source pseudonyms generated 77 % of the edges, inventing
-in a coinjoin the very relationship the construction is defined not to have. Fixed by treating
-a multi-source transaction's transfers as unattributable. Every earlier result run on the
-refusing clusterer stood on that inflated graph and is being re-checked; the results on the
-naive clusterer, and the address-level and no-contraction measurements, are unaffected.
diff --git a/results/RESULTS-intersection.md b/results/RESULTS-intersection.md
index 5d70cb52..0dddca8d 100644
--- a/results/RESULTS-intersection.md
+++ b/results/RESULTS-intersection.md
@@ -1,5 +1,34 @@
# Intersection: exercising the channel on a real co-spend
+**Scope: which walk these signatures are.** Every signature below is the subset-sum link oracle
+(`ancestry.dss_link_oracle`), which `examples/intersection_pipeline.py` names explicitly, so the
+reproduce block runs the walk measured here. Read "What the origin sets are, and are not" with that
+in mind: it contrasts this walk with a value-weighted one, and that walk is a callable object
+(`ancestry.value_flow_link_oracle` / `build_value_flow_graph`). Whether it intersects where this one
+does not is not measured. `results/RESULTS-exact-oracle-audit.md` measures how far the subset-sum
+oracle sits from an exact one.
+> **Correction (2026-09-03) — the `truncated` counts below name no cause, because none was
+> recorded.**
+> `evaluate` now separates the two limits that can truncate a branch: an oracle that refused to link,
+> and a walk cut off at `max_nodes`. It reports them per branch in `truncated_causes` and names what
+> blinded a blind branch in `blind_cause`. Every count on this page predates that split and is a
+> bare total, so re-running from these numbers yields `truncated_causes` entries of `None`
+> throughout. The three-branch run below is blind, and its `blind_cause` comes back `"unknown"` —
+> measured blindness, unmeasured cause. The four-branch run is **not** blind, so its `blind_cause`
+> is `None` for the ordinary reason; `tests/test_intersection_real.py` pins both. As measured, the
+> cause of the three-branch blindness was the subset-sum oracle refusing on transaction width, which
+> is what the prose below describes; that is a statement about the walk that was run, not a value
+> this document recorded. Nothing here has been re-measured.
+
+> **Correction (2026-09-03) — what the attack name rests on.**
+> Goldfeder et al.'s Algorithm 2 has now been checked from the primary text and its graph-independent
+> core is separated as `decluster/baselines/candidate_set_intersection.py`
+> (`results/RESULTS-candidate-set-intersection.md`); `decluster/intersect.py`, which produced
+> everything below, is this repository's wiring of that mechanism to the backward provenance walk and
+> to `cluster_refined`, with additions — probabilistic non-join ancestry, rarity weighting,
+> truncation and blindness
+> reporting — that are ours and not the paper's.
+
`decluster/monitor.py` → `decluster/intersect.py` → `cluster_refined`, run end to end on mainnet
rather than on fixtures. The question is not whether an entity falls out — it does not — but whether
each stage does on real data what it does in the tests, and what the stages say to each other.
@@ -74,12 +103,12 @@ candidate 5cce9a7fa309eabd shape (19, 20) branches 4
engine believed=False, 3 refusals
```
-Two things change. The empty intersection is now a **result**: every branch contributes observed
+Two things change. The empty intersection is now a result: every branch contributes observed
origins, so "no shared ancestor at depth 3" is what the walk found, not what it failed to see.
-And **the engine refused**. The co-spend is a +2.0-bit prior toward one owner; three of the four
+And the engine refused. The co-spend is a +2.0-bit prior toward one owner; three of the four
funders agree at +6.82 bits of fingerprint while the fourth disagrees at −4.06 against each of them,
-and the fused score goes negative on exactly those three pairs. The partition comes out **4/1** — the
+and the fused score goes negative on exactly those three pairs. The partition comes out 4/1 — the
odd funder split out of a transaction that spent its coin alongside the others.
| pair | fingerprint | gate `fp < 0` |
@@ -99,7 +128,7 @@ empty intersection, and the 4/1 refusal — as data, so they are checked without
## What the origin sets are, and are not
Every number above that involves a signature rests on `ancestry.ancestry_signature`, whose edge
-weighting is a **stated approximation** of the framework it implements. The theory's walk weights a
+weighting is a stated approximation of the framework it implements. The theory's walk weights a
transition by coin value — each input satoshi equally likely to have become each output satoshi. This
walk weights by subset-sum link probability, row-normalized, which `decluster/ancestry.py` flags as
provisional and defers to the flow rung.
@@ -138,3 +167,8 @@ from examples.intersection_pipeline import run, default_signature_of, default_cl
run(seeds=seeds, signature_of=default_signature_of(),
cluster_fn=default_cluster_fn(), max_depth=0)
```
+
+## Reproducibility / provenance
+
+State 1 in `results/REPRODUCIBILITY.md`: band-pinned on committed data — the policy files this claim
+as "intersection attack narrows candidates", seeds in-test, asserted by `tests/test_intersection_real.py`.
diff --git a/results/RESULTS-link-prediction.md b/results/RESULTS-link-prediction.md
new file mode 100644
index 00000000..6f38bb52
--- /dev/null
+++ b/results/RESULTS-link-prediction.md
@@ -0,0 +1,263 @@
+# De-anonymization-driven link prediction, on planted views
+
+**What was measured, in one sentence.** At 90% view overlap and 25% handed-over seeds,
+de-anonymization recovers held-out links the structure-only arm cannot, **entirely by copying
+edges the auxiliary view had already observed**; it adds no predictive signal of its own, and
+the certification disappears below roughly 90% overlap or 15% seeding.
+
+> **This is not a reproduction of the 2011 link-prediction paper.** That paper is *Link Prediction
+> by De-anonymization*, by Narayanan, Shi and Rubinstein — not Narayanan and Shmatikov, who are
+> the authors of the 2009 de-anonymization paper this one builds on, and to whom it was previously
+> misattributed here. Its text is in the corpus and
+> `decluster/baselines/ns_link_prediction_2011.py` implements components of it, so the earlier claim
+> that "that paper is not in this checkout" is obsolete; what runs *below*, however, is not that
+> module. What is implemented below is the mechanism the title describes — a mapping obtained by
+> de-anonymization, used as the feature that predicts links — and nothing about the paper's feature
+> set, its parameters, its dataset or its numbers is reproduced. **No number or parameter below may
+> be attributed to Narayanan, Shi and Rubinstein.** The paper-case reproduction stays an
+> open cell of the fidelity matrix; what this document closes is the weaker cell "the repo has
+> an implementation of the mechanism, measured against the obvious alternative".
+
+> **Synthetic corpus, and seed-assisted.** Every view below is generated by
+> `examples/link_prediction_run.py` from a planted-partition graph. Nothing here is a Bitcoin
+> measurement and none of these rates transfer to a real view: on two real 2016 views the same
+> propagation found zero usable independent seeds and, seeded from the withheld
+> correspondence at 25%, declared 779 correspondences at 1.28% precision
+> (`results/RESULTS-ns-bitcoin.md`). The seeds here are likewise drawn from the withheld
+> correspondence — every attack row is stamped `seed_provenance:
+> "withheld-correspondence-sample"` in `results/artifacts/link-prediction-v1.json` (held against the
+> frozen `results/link-prediction.json` by a test) — so this measures what
+> the mechanism does with an identity it was *handed*, on a fixture built to be tractable.
+
+## What runs
+
+`decluster/baselines/link_prediction.py`. Given a target view (the one whose missing links
+are wanted) and an auxiliary view of overlapping activity:
+
+1. `baselines.narayanan_shmatikov.propagate` maps target vertices to auxiliary vertices from a
+ seed correspondence — the faithful 2009 algorithm, imported, not reimplemented.
+2. Every auxiliary edge whose both endpoints are images under that mapping is pulled back
+ onto the target vertices it came from. An auxiliary vertex the mapping never claimed has no
+ target identity, so its edges are dropped rather than guessed at.
+3. Candidate pairs are scored on the enlarged neighbourhoods and ranked.
+
+The comparison arm is the same score on the target view alone: common neighbours, which is
+what `graph_deanon.structural_score` computes. The two arms differ in one thing — whether the
+de-anonymized auxiliary view is available — which is what makes the result a statement about
+de-anonymization rather than about two different predictors.
+
+**The score, stated because the comparison turns on it.** `score(u, v)` is the number of common
+neighbours over *closed* neighbourhoods: the plain common-neighbour count on a pair the graph
+does not join, plus two on a pair it does. That second term is what carries a directly
+transferred edge — the auxiliary view's plainest statement, "these two transacted" — and
+without it the arm would discard exactly the evidence de-anonymization buys. In the
+structure-only arm the term is identically zero, because the pairs being scored are by
+construction the ones the residual target view does not join.
+
+**The whole result rests on that term, and the ablation is published rather than argued.**
+`--scorer open` replaces the score with the open-neighbourhood count — literally
+`graph_deanon.structural_score`, in both arms — so the de-anonymization arm keeps every
+transferred edge in its adjacency but may no longer read a transferred edge as evidence for the
+pair it joins. Then, over the same five fixture seeds: mean AUC 0.5791 for the de-anonymization
+arm against 0.5817 for structure only, a gap of **-0.003**, and **0 of 5** certified at cutoff
+1, 2 or 3. At cutoff 1 two of the five seeds are certified the *other* way (verdict `b`,
+p = 0.049 and 0.039); at cutoff 3 neither arm declares anything at all. So the second-order
+effect of the enlarged neighbourhoods is not small, it is nil to negative: without the
+closed term there is no result here. The term is still the right modelling choice — a
+pulled-back auxiliary edge is a genuine observation, and a predictor that threw it away would
+be a strange one — but a reader should know the finding lives on it.
+`tests/test_link_prediction.py::test_ablating_the_closed_term_removes_the_result_rather_than_shrinking_it`
+holds the ablation in the suite.
+
+## The fixture
+
+80 vertices in 8 communities; a directed edge is drawn with probability 0.18 inside a community
+and 0.012 across. Each view independently observes each activity edge with probability 0.9. A
+quarter of the target view's links are then removed, both directions, and are the held-out
+links; negatives are four times as many pairs the activity graph never joined. Seeds are 25% of
+the correspondence. `theta = 1.5`, declaration cutoff 2. Five fixture seeds, 0-4.
+
+The communities are not decoration: on a structureless graph a common-neighbour predictor has
+nothing to find and the structure-only arm would lose to anything at all. Here it has real
+signal (its AUC is 0.55-0.63, above chance) and still loses.
+
+| fixture seed | activity edges | target / auxiliary edges | residual target edges | held-out links | of those, in the auxiliary | negatives |
+|---|---|---|---|---|---|---|
+| 0 | 205 | 189 / 183 | 143 | 46 | 44 | 184 |
+| 1 | 202 | 180 / 176 | 134 | 42 | 38 | 168 |
+| 2 | 203 | 185 / 178 | 141 | 43 | 40 | 172 |
+| 3 | 199 | 186 / 173 | 139 | 44 | 39 | 176 |
+| 4 | 182 | 170 / 166 | 128 | 40 | 38 | 160 |
+
+Propagation from 20 seeds maps 54-64 of the 80 target vertices at 0.91-1.00 precision against
+the withheld correspondence (coverage 0.52-0.73 of the non-seed vertices).
+
+## Does de-anonymization beat structure-only prediction? At the stated cutoff, yes
+
+Cutoff 2, all five fixture seeds. AUC is `graph_deanon.exact_auc` — the exact Mann--Whitney
+statistic, not the sampling estimator `graph_deanon.auc`.
+
+| fixture seed | arm | declared | correct | precision | recall | AUC | discordant wins | p | verdict |
+|---|---|---|---|---|---|---|---|---|---|
+| 0 | de-anonymization | 30 | 24 | 0.800 | 0.522 | 0.7537 | 21 | 0.0025 | **a** |
+| 0 | structure only | 4 | 3 | 0.750 | 0.065 | 0.5929 | 5 | | |
+| 1 | de-anonymization | 15 | 14 | 0.933 | 0.333 | 0.7287 | 14 | 0.00012 | **a** |
+| 1 | structure only | 1 | 0 | 0.000 | 0.000 | 0.5944 | 0 | | |
+| 2 | de-anonymization | 24 | 18 | 0.750 | 0.419 | 0.7223 | 17 | 0.0026 | **a** |
+| 2 | structure only | 4 | 1 | 0.250 | 0.023 | 0.5460 | 3 | | |
+| 3 | de-anonymization | 23 | 18 | 0.783 | 0.409 | 0.7353 | 13 | 0.0018 | **a** |
+| 3 | structure only | 9 | 5 | 0.556 | 0.114 | 0.6288 | 1 | | |
+| 4 | de-anonymization | 21 | 18 | 0.857 | 0.450 | 0.7023 | 16 | 0.0044 | **a** |
+| 4 | structure only | 2 | 2 | 1.000 | 0.050 | 0.5466 | 3 | | |
+
+The verdict is `reproducibility.separable` on the paired counts: pairs exactly one arm
+called correctly, where a false positive counts against an arm exactly as a miss does, with a
+pre-registered effect of one pair. It is a direction claim only. The level is what precision
+and recall carry, and the level is modest: at best 24 of 46 held-out links recovered.
+
+**The recall comparison cannot invert, and is a level rather than a finding.** The transfer only
+ever *adds* neighbours, so the de-anonymization score is greater than or equal to the
+structure-only score on every pair (0 violations over the 1,075 scored pairs, asserted in
+`test_the_deanonymization_score_dominates_the_structure_only_score_on_every_pair`), its
+declaration set is a superset at any cutoff, and its recall is therefore never lower by
+construction. What is free to invert, and does invert in the controls below, is the AUC and the
+paired discordant count — the arm pays for its extra declarations in false positives. Read the
+direction off those two, never off the recall column.
+
+**AUC gain +0.11 to +0.18; recall 0.33-0.52 against 0.00-0.11.** Precision is the one column
+where the structure-only arm sometimes reads higher (1.000 on fixture seed 4), and it does so on
+two declarations against twenty-one; a precision computed on one to nine declarations is not
+a comparable level, which is why the direction is taken from the paired test and not from this
+column.
+
+### Where the win comes from, since it is not a better structural predictor
+
+Held-out links the mapping pulled back directly — the auxiliary view simply handing over the
+missing edge: 22, 13, 17, 16, 18 across the five fixture seeds, against 24, 14, 18, 18, 18
+correct declarations. Within one or two pairs, **every correct declaration is a link the
+auxiliary view had already observed and the mapping carried across**. The enlarged
+neighbourhoods contribute almost nothing beyond that.
+
+That is the mechanism's actual shape, and it is worth being blunt about: de-anonymization here
+does not predict links better, it transfers observations that identity resolution makes
+addressable. Anything that breaks the transfer — a mapping that is wrong, an auxiliary view
+that did not see the link — removes the entire advantage, which is exactly what the controls
+below show. The residual predictive contribution is not "almost nothing": deleting the held-out
+links from the auxiliary view and changing nothing else leaves a mean AUC gap of **+0.000** and
+**0 of 5** certified, so it is exactly nothing.
+
+### The direction depends on the cutoff, and that is not a footnote
+
+Rerun with `--cutoff 1` (any evidence at all declares a link):
+
+| fixture seed | de-anon precision / recall | structure precision / recall | discordant wins | p | verdict |
+|---|---|---|---|---|---|
+| 0 | 0.459 / 0.609 | 0.378 / 0.304 | 14 vs 10 | 0.54 | none |
+| 1 | 0.511 / 0.548 | 0.429 / 0.286 | 11 vs 6 | 0.33 | none |
+| 2 | 0.453 / 0.558 | 0.333 / 0.186 | 16 vs 13 | 0.71 | none |
+| 3 | 0.558 / 0.545 | 0.500 / 0.341 | 9 vs 4 | 0.27 | none |
+| 4 | 0.435 / 0.500 | 0.308 / 0.200 | 12 vs 8 | 0.50 | none |
+
+The AUCs are unchanged — AUC does not depend on a cutoff — so the ranking is better at every
+threshold, on all five seeds. The declaration is not certified at cutoff 1: the extra recall
+is bought with enough extra false positives that the paired test returns no verdict on this
+fixture size. Both cutoffs are pre-stated here; cutoff 2 is the operating point the headline
+table reports, and it is stated up front rather than selected after the fact.
+
+## Controls
+
+Three, per fixture seed, scored on the identical held-out links (the structure-only column is
+bit-identical across all rows of a fixture, which `tests/test_link_prediction.py` asserts):
+
+* **independent auxiliary** — an auxiliary view drawn from its own activity graph, so the
+ correspondence the seeds assert is not a correspondence and every transferred edge is noise.
+* **deranged seeds** — the real auxiliary view, seeds pointing at the wrong vertices.
+* **stripped auxiliary** — the real auxiliary view with every held-out link deleted from it and
+ nothing else changed. This is the sharp one: the correspondence is real, the propagation runs
+ on a coherent view, and the only thing missing is the evidence the transfer carries. Mean AUC
+ gap +0.000 (per seed -0.010, -0.009, +0.009, +0.007, +0.003).
+
+None reaches a verdict on any fixture seed (`beats_structure_only: null` in all fifteen control
+rows). Their de-anonymization AUC is 0.544-0.638 against the structure-only arm's 0.546-0.629,
+and on several seeds the noisy transfer makes the arm *worse* than not having it. This is the
+falsification the fixture exists to allow: an arm that won here regardless of whether the
+auxiliary view corresponded to anything would be measuring the score's slack, not
+de-anonymization.
+
+## The certified verdict lives in a narrow window, and the defaults sit at its favourable end
+
+Every row below is the mean AUC gap over the same five fixture seeds with everything else at
+its published value, and the count of seeds the paired test certifies. The published
+configuration is marked.
+
+| `--keep` (per-view observation rate) | mean gap | certified | | `--seed-fraction` | mean gap | certified |
+|---|---|---|---|---|---|---|
+| 0.4 | +0.009 | 0/5 | | 0.05 | +0.008 | 0/5 |
+| 0.5 | +0.031 | 0/5 | | 0.10 | +0.030 | 0/5 |
+| 0.6 | +0.048 | 0/5 | | 0.15 | +0.092 | 2/5 |
+| 0.7 | +0.072 | 3/5 | | **0.25** (published) | **+0.147** | **5/5** |
+| 0.8 | +0.064 | 1/5 | | 0.40 | +0.235 | 5/5 |
+| **0.9** (published) | **+0.147** | **5/5** | | | | |
+| 1.0 | +0.297 | 5/5 | | | | |
+
+**At `--keep 0.8` the verdict is already gone (1 of 5).** The gap stays positive all the way
+down to 40% overlap but stops being separable somewhere between 0.7 and 0.9, and the same
+happens below about 15% seeding. Denser communities also remove it: `--p-in 0.45` gives +0.055
+at 2/5 and `--p-in 0.60` gives +0.010 at 0/5 (published value 0.18, +0.147 at 5/5), because a
+common-neighbour predictor gets stronger as the communities thicken while the transfer does
+not. Two axes are robust in sign but not uniformly in certification: `--holdout` 0.1/0.25/0.5/0.75
+gives +0.265, +0.147, +0.078, +0.037 at 5/5, 5/5, 4/5, 5/5, and `--groups` 4/8/16 gives +0.187,
++0.147, +0.128 at 3/5, 5/5, 4/5.
+
+So the reading of the headline table is not "de-anonymization beats structure-only
+prediction" but "at 90% view overlap and 25% handed-over seeds it does, on this fixture" —
+and both of those are generous settings chosen before the sweep, not found by it. Real views are
+nowhere near either: `RESULTS-ns-bitcoin.md` measures 46.7% edge overlap and zero obtainable
+independent seeds.
+
+## The ceiling, which is a diagnostic and not an operating point
+
+Seeding with the whole withheld correspondence leaves propagation nothing to do, so the run
+measures the transfer under a perfect mapping: recall 0.929-0.975, AUC 0.950-0.986, all five
+seeds separable at p from 2.9e-11 to 2.0e-06. No attacker can seed with the withheld
+correspondence, so this is not a rate anyone can reach; it is a decomposition. It says the
+binding constraint on the attack row is propagation coverage (54-64 of 80 vertices mapped),
+not the transfer, and that a fixture where propagation carried further would close most of the
+gap between 0.52 and 0.95 recall.
+
+## What this does not show
+
+* Nothing about real Bitcoin views. The corpus is planted, the overlap is engineered at 90% per
+ view, and the seeds are handed over. `RESULTS-ns-bitcoin.md` is the real-view measurement and
+ it is far bleaker.
+* Nothing about the 2011 paper's numbers, features or dataset — see the banner. Component-level fidelity to Narayanan, Shi and Rubinstein is `decluster/baselines/ns_link_prediction_2011.py`'s business, recorded in `catalog/runs/ns-link-prediction-coverage-v2.json`, not here.
+* Nothing about undirected-versus-directed prediction: the mapping is direction-aware because
+ propagation is, the prediction is undirected because a common-neighbour score is, and no
+ variant that predicts edge direction was measured.
+* Nothing about attributes, values or time. The arms see topology only.
+* Nothing outside the window the sensitivity table maps: at 80% view overlap or 15% seeding the
+ same experiment produces a positive gap that the paired test refuses to certify, and the
+ document would then have had to say so.
+
+## Reproducing
+
+```
+python3 examples/link_prediction_run.py --out link-prediction.json # the table above
+python3 examples/link_prediction_run.py --cutoff 1 # the cutoff sweep
+python3 examples/link_prediction_run.py --scorer open # the ablation
+python3 examples/link_prediction_run.py --keep 0.8 # a sensitivity row
+python3 examples/link_prediction_run.py --seed-fraction 0.15 # another
+python3 -m pytest tests/test_link_prediction.py
+```
+
+Every sensitivity row is one such run: read `fixtures[*].runs[0]` for the attack arm, take
+`deanonymization.auc - structure_only.auc` for the gap and
+`separability.beats_structure_only == "a"` for the certification.
+
+`results/manifests/RESULTS-link-prediction.json` records the fixture configuration and every
+published AUC, recall and verdict against the identity of the generator that produced them;
+`tests/test_link_prediction.py::test_manifest_invariants_are_recomputed_and_match_results_link_prediction`
+recomputes them, so a change in the mechanism that moves a published number fails the suite
+rather than leaving this document behind. Regenerate the manifest with
+`python3 examples/link_prediction_run.py --manifest` after any deliberate change to the
+generator.
diff --git a/results/RESULTS-match-confidence.md b/results/RESULTS-match-confidence.md
deleted file mode 100644
index 0364dfb7..00000000
--- a/results/RESULTS-match-confidence.md
+++ /dev/null
@@ -1,63 +0,0 @@
-# Precision against the matcher's own confidence
-
-**Why ask.** `RESULTS-view-match-2026.md` reported that the propagation never becomes
-self-sustaining and treated that as the headline. The framework anticipates exactly this
-and does not rest its case on coverage: *"a proliferation of pseudonyms may limit the
-effectiveness of this approach for any particular run of the propagation algorithm, **high
-confidence links obtained by this method can feed into other clustering heuristics**."*
-The criterion it actually states is whether the matcher produces high-confidence links, and
-that had never been measured: precision was only ever reported in aggregate.
-
-**Method.** Every accepted match now records the eccentricity it won by, `(best − second) /
-stdev` over the candidate scores, the same gap the gate tests. Matches are then stratified
-by it. Same slice, refusing clusterer, seed 400, 141 graded matches. Wilson 95 % intervals,
-because the interesting bins are small.
-
-## Result
-
-| | correct / matched | precision | 95 % interval |
-|---|---:|---:|---|
-| all matches | 81 / 141 | 0.574 | 0.49 – 0.65 |
-| eccentricity ≥ 1 | 76 / 131 | 0.580 | 0.49 – 0.66 |
-| eccentricity ≥ 2 | 65 / 110 | 0.591 | 0.50 – 0.68 |
-| eccentricity ≥ 3 | 61 / 91 | 0.670 | 0.57 – 0.76 |
-| eccentricity ≥ 5 | 40 / 55 | 0.727 | 0.60 – 0.83 |
-| eccentricity ≥ 10 | 10 / 10 | 1.000 | 0.72 – 1.00 |
-| top 10 by eccentricity | 9 / 10 | 0.900 | 0.60 – 0.98 |
-| top 20 by eccentricity | 17 / 20 | 0.850 | 0.64 – 0.95 |
-
-## Reading
-
-**Read every figure here against the degree baseline, not against zero.**
-`RESULTS-match-controls.md` measures what degree alone is worth on the same matched
-vertices: 0.428 overall and 0.499 above eccentricity 5. The matcher's margin over that is
-about fifteen and eighteen points respectively, which is what the numbers below actually
-buy. The shuffle control used up to this point excluded chance and not this.
-
-**Precision is monotone in the matcher's own confidence, across the whole range.** It rises
-from 0.574 over all matches to 0.727 at eccentricity 5 and 10 of 10 above eccentricity 10.
-The matcher knows which of its answers are good, which is the property the framework's
-argument needs and the aggregate figure hides.
-
-**A high-confidence subset exists, and it is small.** 55 links at eccentricity ≥ 5 out of
-33 033 spanning vertices. Read as coverage that is negligible; read as evidence it is not,
-because the framework's use for these links is to feed other clustering heuristics, where
-a small number of reliable links is worth more than many unreliable ones.
-
-**Translated into the weight such a channel would carry**, a link at precision 0.727 is
-worth about 1.4 bits of evidence for common ownership, and one at 0.9 about 3.2 bits. Those
-are the same order as the fingerprint channel's per-axis contributions, so the output is
-usable as a channel rather than merely suggestive. That is the concrete case for wiring the
-matcher back into the clusterer.
-
-**The top bin is too small to pin down.** Ten of ten is consistent with anything above 0.72.
-The claim this supports is that precision keeps rising with confidence, not that the top
-band is error-free. Distinguishing those needs more matches, which means either a wider
-slice or a partition scheme that yields more.
-
-## Consequence for the earlier reading
-
-`RESULTS-view-match-2026.md` framed the absent cascade as the result. That was measured
-correctly but judged against a stricter criterion than the source states, and this
-supersedes that emphasis: coverage does not ignite, and the links that *are* produced carry
-graded, usable confidence. Both are true and the second was missing.
diff --git a/results/RESULTS-match-controls.md b/results/RESULTS-match-controls.md
deleted file mode 100644
index 77b7a739..00000000
--- a/results/RESULTS-match-controls.md
+++ /dev/null
@@ -1,83 +0,0 @@
-# Negative controls: does the matcher beat degree alone?
-
-**Why ask.** Every result so far was defended with a shuffle control, which rules out
-chance. It does not rule out the likelier alternative: that the matcher recovers nothing
-but degree. A vertex of degree 47 in one view and one of degree 47 in the other are an
-obvious pair with no propagation at all, so beating chance is a low bar when the graphs are
-degree-labelled. Until this is excluded, "precision 0.68 at eccentricity 5" is not evidence
-that structure carries identity.
-
-**Method.** All controls are scored on the *same* A-vertices the matcher matched, so the
-comparison is of scoring rules rather than of populations. The decisive one is the **degree
-class** expectation: for each matched vertex, one over the number of B-vertices sharing its
-true partner's degree. That is exactly what degree is worth, with no algorithm attached, and
-it is deliberately generous — it is handed the true partner's degree, which a real
-degree-only adversary would have to guess from the other view, so it upper-bounds that
-adversary rather than approximating it.
-
-**Data.** `slice_2026.ndjson`, refusing clusterer, epoch partition, seed 400. 138 graded
-matches, 50 at eccentricity ≥ 5.
-
-## Result
-
-| control | all matched | at eccentricity ≥ 5 |
-|---|---:|---:|
-| **matcher** | **0.561** | **0.700** |
-| shuffle | 0.000 | 0.000 |
-| degree rank | 0.087 | 0.170 |
-| nearest degree, greedy | 0.000 | 0.000 |
-| **degree class, exact upper bound** | **0.440** | **0.553** |
-
-B-vertices sharing the true partner's degree: median 8, minimum 1, maximum 82 185.
-148 graded matches, 60 at eccentricity ≥ 5.
-
-*(Re-run on the graph corrected in `RESULTS-graph-shape.md`. The earlier figures — 0.580 /
-0.680 for the matcher, 0.428 / 0.499 for the baseline — ran on a graph with four times too
-many edges, almost all of them on hubs the matcher already discards, so every conclusion
-holds and the numbers move within the run-to-run band. The margin over degree is +0.12
-overall and +0.15 in the high-confidence band.)*
-
-## Reading
-
-**The matcher wins, by less than the shuffle control implied.** Against an upper bound of
-0.428 overall and 0.499 in the high-confidence band, it reaches 0.561 and 0.700. The
-structural propagation is worth roughly twelve points overall and fifteen in the band
-where the framework locates the value. That is a real margin against a generous baseline,
-and it is a long way from the near-zero baseline a shuffle control suggests.
-
-**Degree is already most of the way there, on this population.** The median matched vertex
-has a true partner whose degree is shared by only eight vertices in a view of 390 000. The
-matcher matches where degree is nearly a quasi-identifier in its own right, which is a
-selection effect worth naming rather than a coincidence: the vertices propagation can act on
-are the ones with distinctive neighbourhoods, and a distinctive neighbourhood implies a
-distinctive degree.
-
-**The two naive degree strategies are weak algorithms, not bounds.** Degree rank scores
-0.095 and greedy nearest-degree scores 0.000, because both must break ties among as many as
-82 247 equal-degree vertices arbitrarily. They are reported for completeness; the exact
-class expectation is the baseline that matters, and it is far stronger than either.
-
-**One earlier control was degenerate and should not have been reported as one.** The
-"seed-only" row in previous runs scores zero by construction, because the seed is excluded
-from grading. It measured nothing.
-
-**Run-to-run variation is real and was not previously stated.** This run reports 0.680 at
-eccentricity ≥ 5 where an earlier one, differing only in the random pseudonym labelling,
-reported 0.727 on 55 links. Both sit inside the Wilson interval given there (0.60–0.83), so
-they are consistent, but the point estimate moves by about five points from relabelling
-alone. Single-run point estimates at these counts should be read as a band, not a number.
-
-## Consequence
-
-`RESULTS-match-confidence.md` reported precision rising with confidence and translated it
-into an evidence weight for feeding a clustering channel. That translation has to be taken
-against this baseline, not against zero: the *additional* evidence the matcher supplies over
-reading degrees off is what a clustering channel could claim, and it is smaller than the raw
-precision suggests. Whether the residual margin is worth a channel is now a quantitative
-question with a measured answer rather than an assumed one.
-
-## Scope
-
-One slice, one partition scheme, one seed size. The degree-class baseline is computed on the
-matched set, which is the fair comparison for "is the matcher adding anything" and is *not*
-an estimate of what degree alone would achieve across the whole graph.
diff --git a/results/RESULTS-ns-bitcoin.md b/results/RESULTS-ns-bitcoin.md
new file mode 100644
index 00000000..c6a6593f
--- /dev/null
+++ b/results/RESULTS-ns-bitcoin.md
@@ -0,0 +1,262 @@
+# Narayanan-Shmatikov propagation on two real Bitcoin views
+
+> **This is a seed-assisted experiment, not an independent attack.** The seeds below were drawn
+> from the withheld correspondence — the same map the run is graded against. Every row is stamped
+> `seed_provenance: "withheld-correspondence-sample"` in `results/artifacts/ns-bitcoin-v1.json`. It measures how
+> far propagation carries an identity it was *handed*; it is not evidence that the identity could
+> have been found. The independent-label path was tried first and yielded zero usable seeds
+> (below), which is why the fallback is what is reported.
+
+**Sample limit stated up front.** Two adjacent 120-block windows of the 2016 chain: heights
+391,992-392,111 (133,576 transactions) and 392,112-392,231 (157,724 transactions), read from
+`data/epochs_2016_weekly/`. That is roughly 40 hours of chain, not a chain-wide or
+time-representative population, and the export carries **addresses only** — no prevout values and
+no script types — so the clustering below is plain common-input ownership with the coinjoin shape
+refused, and the amount and fingerprint channels are absent by construction, not by choice.
+
+**Which algorithm.** `decluster/baselines/narayanan_shmatikov.py`: the 2009 topology-only
+propagation scoring kernel — direction-aware degree-normalised neighbour votes, eccentricity over the
+unclaimed candidate population including its implicit zeros, a mandatory reverse match, iterated
+to convergence. The population is a genuine ambiguity in the source rather than a choice made
+against it: the paper's prose scores "each unmapped node in v2", while its pseudocode two pages
+later initialises one score per right vertex and leaves the claimed ones at zero. Both readings are
+implemented (`population="unmapped"` / `"all"`); the default, and what is run below, is the prose.
+Seeds are supplied rather than found by the paper's clique search, and accepted nodes are not
+revisited/remapped as the paper's prose specifies. No vertex attributes, no edge attributes, no hub
+filter, no rarity weights.
+
+The repo's older cross-view numbers are not this attack's and none are reused here, but they come
+from two different places and were previously credited to one. The cross-view numbers — including
+the "one match in 15,688" figure (`results/generated/graph-rejoin-2016-v1.md`) — come from
+`decluster/view_match.py`, a different, experimental matcher. `RESULTS-graph-deanon.md` comes
+from `decluster/graph_deanon.py`, which is not a matcher at all: it is common-neighbour link
+prediction scored pairwise on a single graph, with no seed set, no vertex correspondence, no
+propagation and no second view.
+
+## The two views, and why one adversary holds both
+
+| | view A | view B |
+|---|---|---|
+| source | `epoch_2016_01_391104-392111.ndjson.gz` | `epoch_2016_01_392112-393119.ndjson.gz` |
+| heights | 391,992-392,111 | 392,112-392,231 |
+| transactions | 133,576 | 157,724 |
+| vertices (degree >= 2) | 88,066 | 104,328 |
+| edges | 163,567 | 189,919 |
+
+Both windows are public chain, so *observation* is not what is partial here — the clustering
+is. The two windows are clustered together (one global common-input lookup over both, so a cluster
+keeps one identity across the boundary), and the 8,030 clusters whose addresses straddle the
+boundary are then contracted under a **separate pseudonym in each view**
+(`views.split_clusters_by_view`): `C#a` in A, `C#b` in B. That is the framework's premise made
+concrete — one owner, two pseudonyms, unlinked — and the adversary who holds both views is anyone
+who observed the chain across a clustering discontinuity, which is the ordinary condition of an
+incomplete clustering, not a privileged position. Because each view is contracted under its own
+lookup, **no pseudonym string appears in both views**, so the trivial identity match does not
+exist and the only correspondence available is one propagation has to rediscover.
+
+Stated observation policy, because it sets the population: 613,448 addresses were clustered; each
+view keeps the vertices whose degree in its own view is at least 2. The filter is per-view and
+consults neither the other view nor the correspondence; degree below 2 is a vertex propagation can
+neither match nor bridge through.
+
+Withheld correspondence — it takes no part in view construction, in seeding the independent
+path, or in any score the algorithm computes. It is used for grading, for drawing the seed-assisted
+seeds below (which is why those rows are stamped as such), and for selecting the subgroup in the
+"had an image" diagnostic further down, which is why that diagnostic is not an operating point.
+2,599 `C#a -> C#b` pairs, which is **2.95% of view A's vertices**.
+Among those 2,599, view A holds 2,756 internal edges and 1,288 of them recur in view B — a
+**46.7% edge overlap**. So the structure the attack needs does partly recur; what does not is
+presence: 97% of view A has no image in view B at all.
+
+## The independent-seed path, tried first, yielded nothing
+
+`ns_bitcoin.unique_entity_seeds` admits a seed only when an independently detected entity name
+resolves to exactly one vertex in each view. Three of `decluster/entities.py`'s name-emitting
+detectors were run over both windows (`detect_bitmex`, `detect_satoshidice`, `detect_mining_pool`).
+The other two cannot fire on this export at all, and that is a schema fact, not an omission:
+`detect_bip47_notification` reads an OP_RETURN scriptPubKey and `detect_dust_fanout` reads output
+values, while every `vout` record here carries exactly one field, `scriptpubkey_address`. The
+behaviour proxies (`detect_consolidation`, `detect_batching`) are excluded by kind rather than by
+schema — they name a pattern, not an entity, so they cannot supply a one-to-one cross-view label.
+
+| | view A | view B |
+|---|---|---|
+| BitMEX vanity-prefix hits | 33 | 73 |
+| SatoshiDice `1dice` hits | 5 | 12 |
+| distinct vertices labelled | 23 | 47 |
+| entities unambiguous on **both** sides | **0** | |
+
+The mining-pool detector cannot fire at all: it reads the coinbase scriptSig tag, which this
+address-only export does not carry. Neither surviving detector names a single vertex per view — a
+service's deposit addresses land in many clusters — so zero independent seeds were admitted,
+and zero of them would have been gradeable. The seam refuses to pair an ambiguous label by degree,
+which is exactly what stops the grading map from leaking in through the back door. The independent
+attack is therefore not identifiable on these views with the labels this export supports, and
+everything below is seed-assisted. That qualifier is the whole claim: with coinbase scriptSig tags
+or a populated `catalog/entities.ndjson` the seed set might be non-empty, and this run says nothing
+about that case.
+
+## Seed-assisted sweep
+
+`sweep_bitcoin_views`, seeds drawn uniformly (not by degree) from the withheld correspondence.
+`theta` is part of the reported configuration: both a lowered value (0.5) and the module's default
+(1.5, `decluster/baselines/narayanan_shmatikov.py:132`) are reported for every seed fraction, in
+both cases whether or not anything propagated.
+
+| seed frac | seeds | theta | rounds | declared | of those, had an image | correct | precision | precision where an image exists† | held-out | coverage |
+|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|
+| 5% | 130 | 0.5 | 9 | 248 | 17 | 2 | 0.81% | 11.8% | 2,469 | 0.081% |
+| 5% | 130 | 1.5 | 9 | 246 | 16 | 1 | 0.41% | 6.3% | 2,469 | 0.041% |
+| 10% | 260 | 0.5 | 8 | 450 | 32 | 3 | 0.67% | 9.4% | 2,339 | 0.128% |
+| 10% | 260 | 1.5 | 8 | 448 | 31 | 2 | 0.45% | 6.5% | 2,339 | 0.086% |
+| 25% | 650 | 0.5 | 10 | 779 | 49 | 10 | 1.28% | 20.4% | 1,949 | 0.513% |
+| 25% | 650 | 1.5 | 10 | 774 | 47 | 9 | 1.16% | 19.1% | 1,949 | 0.462% |
+
+† Not an operating point. That subgroup is selected by membership in the withheld
+correspondence. The attacker cannot tell which of its 779 declarations are among the 49, so no
+threshold available to it reaches 20.4%; the column says where the signal is, not what the attack
+achieves. The attack's precision is the column to its left.
+
+`results/artifacts/ns-bitcoin-v1.json` carries the full accepted-per-round series for the attack and the
+shuffled control in every one of the six configurations; only the round *count* is tabulated here.
+
+Propagation does spread — 8 to 10 rounds, front-loaded (at 25%/0.5: 410 accepted in round one,
+then 207, 91, 34, ...). The headline is therefore not "nothing propagated". It is that almost
+everything it propagated was wrong.
+
+## Controls
+
+| control | 5%/0.5 | 5%/1.5 | 10%/0.5 | 10%/1.5 | 25%/0.5 | 25%/1.5 |
+|---|---:|---:|---:|---:|---:|---:|
+| shuffled-seed: declared | 199 | 199 | 351 | 351 | 517 | 517 |
+| shuffled-seed: **correct** | 0 | 0 | 0 | 0 | 0 | 0 |
+| shuffled-seed: seeds left in place | 0 | 0 | 0 | 0 | 2 | 2 |
+| seed-only: declared | 0 | 0 | 0 | 0 | 0 | 0 |
+| paired discordant (attack / shuffled) | 2 / 0 | 1 / 0 | 3 / 0 | 2 / 0 | 10 / 0 | 9 / 0 |
+| `separable` p | 0.500 | 1.000 | 0.250 | 0.500 | **0.0020** | **0.0039**|
+| direction verdict | none | none | none | none | **attack** | **attack** |
+
+The **shuffled-seed control recovers nothing in any configuration**, while propagating on a
+similar scale (199-517 declarations). So the attack's correct matches are not an artifact of
+seed positions. The **seed-only control declares 0** everywhere, so no number above is a seed
+count in disguise.
+
+The direction test is `decluster.reproducibility.separable` on the paired discordant counts, with
+the pre-registered effect `min_correct_gain = 1` — the direction under test is "the attack recovers
+at least one correspondence the shuffled-seed control does not", a direction claim only, which
+says nothing about precision. At 5% and 10% seeding the sign favours the attack every time but the
+discordant counts (1-3) are too small for a two-sided sign test to clear 0.05: **measured, not
+separable** (state 5 of `results/REPRODUCIBILITY.md`). At 25% seeding it separates, p = 0.002 and
+p = 0.004; six configurations were tested and these two clear Bonferroni at 0.05/6 = 0.0083, so the
+conclusion survives the multiplicity.
+
+**One assumption is worth stating rather than leaving in the arithmetic.** The sign test treats the
+10 discordant vertices as independent exchangeable trials. Propagation is a cascade — round one
+accepts 410 matches and every later round is conditioned on them — so one correct early match can
+produce several correlated wins, and the effective number of independent trials is at most 10 and
+plausibly fewer. The p-value is computed exactly as `separable` specifies and the *direction* is
+sound (the control recovered nothing at all, in any configuration); its precise magnitude is not,
+which is why the finding is stated as "beats the control" rather than as a calibrated confidence.
+
+## The two failure modes, separated
+
+Precision of ~1% invites the reading "the algorithm guesses". The `declared_with_an_image` column
+says otherwise, and the split matters:
+
+- **Most declarations are on vertices that have no image at all.** At 25%/0.5, 779 declarations,
+ of which **730 were on vertices view B does not contain**. Those cannot be right. The 2009
+ algorithm has no abstention for "this vertex is not in the other graph" — its own setting was two
+ heavily overlapping views of one social graph — and on two disjoint observation windows 97% of a
+ view is in that class.
+- **Where an image does exist, the attack is doing real work.** 10 of 49 (20.4%) at 25%/0.5;
+ 9 of 47 (19.1%) at theta 1.5, against a shuffled control that declared on 22 vertices with
+ images and got **0** of them. That comparison is the whole argument, and it is like-for-like.
+ Again: this subgroup is chosen using the withheld correspondence, so 20.4% is a statement about
+ where the signal lives, not a precision the attacker can operate at.
+
+So the statement of the mechanism is: on real Bitcoin views the eccentricity test does carry
+some real signal about *which* vertex a straddling pseudonym is, and carries essentially none about
+*whether* a pseudonym straddles at all. Overall precision is dominated by the second.
+
+## What this establishes, and what it does not
+
+- **Does**: the faithful 2009 algorithm runs at real scale (88k x 104k vertices, about five
+ minutes for the whole six-configuration sweep) and, handed a quarter of the correspondence,
+ recovers 0.5% of the held-out remainder at 1.2% precision, beating a shuffled-seed control that
+ recovers none in any configuration (p = 0.002, subject to the cascade-dependence caveat above).
+- **Does not**: any independent de-anonymization claim. The seeds are grading information. With
+ the independently observable entity labels this export supports, the seed set is **empty** and
+ the attack cannot start — which is itself the more consequential finding for a real adversary
+ than any row of the sweep.
+- **Does not**: any claim about seed fractions between 25% and 100%, other windows, other epochs,
+ or a clustering built with the amount and fingerprint channels this export cannot supply.
+
+## Limits
+
+- **One boundary, one epoch pair.** Six configurations over a single window pair. The coverage
+ trend in seed fraction (0.08% -> 0.13% -> 0.51%) has a rising numerator *and* a falling
+ denominator, because seeding more of the correspondence leaves less of it held out: it is
+ 2/2,469, then 3/2,339, then 10/1,949. Read the numerators alongside it. Three points, one
+ boundary.
+- **theta barely matters here.** 0.5 and 1.5 differ by one or two matches. The eccentricity
+ threshold is not the binding constraint; presence in both views is.
+- **The 20.4% figure conditions on information the attacker does not have.** It is measured on
+ the declarations that landed on a vertex with an image, a subgroup selected from the withheld
+ correspondence. There is no thresholding, ranking or abstention rule available to the attacker
+ that isolates that subgroup, so the figure cannot be turned into an operating point and must not
+ be quoted as this attack's precision.
+- **The three seed sets are not nested.** `random.sample` changes algorithm at k = 650, so the 25%
+ set shares only 251 of the 10% set's 260 seeds. The seed-fraction series is three draws, not a
+ superset chain.
+- **`split_frac = 1.0`.** Every straddling cluster is split, which maximises the correspondence
+ population. A partial split would leave some straddlers un-pseudonymised and is not measured.
+- **Uniform seed sampling.** Seeding the highest-degree correspondences instead would seed exactly
+ the vertices propagation finds easiest and report the resulting spread as reach; that is why the
+ sampling is uniform, and it means these numbers are not the best case a seed-choosing adversary
+ could arrange.
+- **Address-only export.** No coinbase tags (so no mining-pool labels), no values, no script types.
+ The absent independent-seed path is a property of this export as much as of the chain.
+- **No fix to `decluster/contraction.py` or `decluster/baselines/narayanan_shmatikov.py` was needed.**
+ Both ran against real data unmodified; nothing in this run exposed a bug in either.
+
+## Reproducibility / provenance
+
+Manifest: `results/manifests/RESULTS-ns-bitcoin.json`, written by
+`decluster.reproducibility.write_manifest` over `data/epochs_2016_weekly/*.ndjson.gz` (5 files,
+1,024,227,001 bytes, recorded as a byte digest) with two kinds of invariant, because only one kind
+is cheap to re-derive:
+
+- **Recomputed and asserted** by `tests/test_ns_bitcoin.py::test_manifest_invariants_are_recomputed_and_match_results_ns_bitcoin`
+ (2.4 s, skips with a named message when `data/epochs_2016_weekly/` is absent): the run
+ configuration — `left_path`, `right_path`, `boundary` (392,112), `blocks` (120), `min_degree`
+ (2), `split_frac` (1.0), `seed_fractions`, `thetas`, `rng_seed` — re-derived from the driver's
+ own argparse defaults, and the two window transaction counts (133,576 and 157,724) read straight
+ off the compressed epochs. The failure this closes is the one a digest cannot see: change a
+ `--min-degree` or `--blocks` default and the epoch bytes are untouched, so the digest still
+ matches while every published population number detaches from the code that produced it. That
+ test now fails loudly instead.
+- **Identity-only, not checked**: the contracted populations — vertices and edges per view
+ (88,066/163,567 and 104,328/189,919), correspondence size (2,599), its internal and recurring
+ edge counts (2,756 and 1,288) and their ratio (0.467344), the independent entity-seed count (0),
+ and the seed-set sizes swept (130, 260, 650). Recomputing these means rebuilding two
+ 100k-vertex views over 291,300 transactions, which is minutes, not seconds, and so belongs in
+ the driver rather than the suite. They are pinned by the recomputed configuration above plus the
+ source digest, which is weaker than a recomputation and is stated here as such.
+
+`tests/test_results_manifests.py` additionally checks the source digest on every run and reports
+`identity-only` there, which is the policy's intended loud skip, not a pass.
+
+Mechanism tests: `tests/test_ns_bitcoin.py` (seed admission, sampled-seed provenance and
+reproducibility, the sweep grid, the paired separability verdict including the case where a real
+advantage is too small to separate, the declaration split above, and view construction on a tiny
+fixture) and `tests/test_ns_social_baseline.py` for the algorithm itself. Per
+`results/REPRODUCIBILITY.md`, the numbers in this document are a **state 2** data-run over
+unversioned data; the direction claim at 5% and 10% seeding is **state 5**, measured and not
+separable, and at 25% seeding it clears all three gates of `separable`.
+
+Reproduce: `.venv/bin/python examples/ns_bitcoin_views.py` (defaults are the canonical run:
+`--blocks 120 --min-degree 2 --split-frac 1.0 --fractions 0.05 0.10 0.25 --thetas 0.5 1.5
+--seed 7 --out results/ns-bitcoin.json`), which rewrites the frozen historical report a test holds
+the canonical run against. Single process, peak RSS
+about 1 GB; 283 s and 345 s on two runs of the identical command, which reproduced every number in
+this document exactly.
diff --git a/results/RESULTS-ns-propagation.md b/results/RESULTS-ns-propagation.md
index 2c060550..e537f23e 100644
--- a/results/RESULTS-ns-propagation.md
+++ b/results/RESULTS-ns-propagation.md
@@ -1,20 +1,30 @@
# N-S seed-and-propagate driver
+> **Correction (2026-09-03) — which walk produced the signatures.**
+> Every provenance signature below came from the subset-sum link oracle
+> (`ancestry.dss_link_oracle`): `examples/ns_propagation_cache_run.py` has always pinned it, and
+> `examples/ns_propagation.run` inherited it as `ancestry_signature`'s default. That default is now
+> `ancestry.value_flow_link_oracle` — the nominal-value transition rule, which never refuses on
+> transaction width. `examples/ns_propagation.run` had silently inherited the new default; it now
+> names `dss_link_oracle` explicitly, so both entry points run the walk measured here. Nothing below
+> has been re-measured under value flow. `results/RESULTS-exact-oracle-audit.md` measures how far the
+> subset-sum oracle sits from an exact one.
+
`examples/ns_propagation.py` wires `decluster.propagate` (build_rarity, entity_signature,
NSPropagator, holdout_reid, partition_from_assignment) into a single `run_on_signatures(...)`
summary: seed-label re-identification rate, whole-partition bits (entropy) before vs. after
merge propagation, and the split channel's refined-group count.
-This file now has two runs. The **real cache-bounded run** (below) replaces the previously-deferred
+This file now has two runs. The real cache-bounded run (below) replaces the previously-deferred
chain-data measurement, computed entirely offline against this checkout's `.cache/` (~7,000 cached
-tx JSON files) — no network. The original **synthetic smoke run** is kept underneath it for the
+tx JSON files) — no network. The original synthetic smoke run is kept underneath it for the
wiring-level regression check it still serves.
## Real cache-bounded run
`examples/ns_propagation_cache_run.py` (offline, no `decluster/propagate.py` or `decluster/cluster.py`
changes) draws a bounded real sample from `.cache/`, computes real `ancestry_signature`s via a
-**cache-only fetch** (`cache_fetch_tx`: reads `.cache/{txid}.json`; a MISS returns a coinbase-shaped
+cache-only fetch (`cache_fetch_tx`: reads `.cache/{txid}.json`; a MISS returns a coinbase-shaped
boundary marker, which `ancestry.build_extended_graph`'s existing `_is_coinbase` check already treats
as an absorber — no core-code change needed, and the walk never touches the network), and runs both
N-S channels plus the deferred union-find baseline.
@@ -31,7 +41,7 @@ correctly —
22 clusters (20 pairs, 2 triples), 46 member coins → feeds `seed_labels` for
`propagate`/`holdout_reid`.
-Total sample: **150 coins** (nodes), capped so the run finishes in under 20s.
+Total sample: 150 coins (nodes), capped so the run finishes in under 20s.
### Ancestry: mostly boundary, not mostly resolved — but not a total collapse either
@@ -45,14 +55,14 @@ Total sample: **150 coins** (nodes), capped so the run finishes in under 20s.
| total cache-miss truncations (oracle-`None` refusals) across all 150 walks | 63 |
Unlike the prior contiguous-slice attempt in `results/RESULTS-ancestry.md` (§7 of `PAPER.md`: "every
-signature collapses to a **single** boundary atom... a tractable-width slice cannot contain
-multi-hop ancestry"), this sample does **not** collapse universally — 43% of coins resolve real
+signature collapses to a single boundary atom... a tractable-width slice cannot contain
+multi-hop ancestry"), this sample does not collapse universally — 43% of coins resolve real
branching within the cache, because the sample was deliberately built from co-spend/address-reuse
*linked* subgraphs rather than a random contiguous block range, so a meaningful fraction of nodes'
immediate parents (or grandparents) happen to already be cached too. But the majority (57%) still
hit the cache boundary within 0–1 hops, exactly the mechanism `PAPER.md` §7 describes: a bounded
cache is a bounded graph window, and most coins' ancestry reaches outside it almost immediately.
-**Honest reading: the cache-bounded ancestry signal is real but weak and partial, not the
+**Reading: the cache-bounded ancestry signal is real but weak and partial, not the
near-deterministic quasi-identifier `RESULTS-ancestry.md` found with live network access at small
scale — a bounded, offline slice recovers some of the effect, not most of it.**
@@ -65,7 +75,7 @@ scale — a bounded, offline slice recovers some of the effect, not most of it.*
| partition_bits_after (merge propagation; 57 coins labeled) | 4.368 |
| n_refined_groups (split channel, over 40 cospend clusters) | 46 |
-- **reid_rate = 0.154**, versus **1.0** on the synthetic fixture below — the honest gap between a
+- **reid_rate = 0.154**, versus **1.0** on the synthetic fixture below — the gap between a
clean hand-built signal and a real, cache-truncated one. `holdout_reid` hid 13 of the 46
address-reuse-labeled coins and re-derived their label from provenance overlap with the rest;
only 2 recovered. Consistent with the ancestry finding above: 57% of signatures are
@@ -78,7 +88,7 @@ scale — a bounded, offline slice recovers some of the effect, not most of it.*
weaker signal.
**Caveat on the split channel (`n_refined_groups`, 40 → 46 groups, i.e. 6 splits):** `should_split`
-requires provenance-disjointness **and** fingerprint divergence together, so a split is not provenance
+requires provenance-disjointness and fingerprint divergence together, so a split is not provenance
alone. But of the 58 within-cluster co-spend pairs checked, **24 (41%) have both members' signatures
collapsed to a single (necessarily different) boundary atom** — meaning `provenance_link` reads them
as "disjoint" not because their true provenance is known to differ, but because the cache boundary
@@ -96,14 +106,14 @@ evidence from this run should be read as weak/inflated, not trusted at face valu
| **union-find** (`cluster.cluster_naive`, common-input-ownership only) | 100 | 6.517 |
| **N-S refined** (split channel's 46 groups + 110 untouched coins as singletons, full 150-coin partition) | 107 | 6.620 |
-Over this sample, the refined partition has **more** clusters and **higher** entropy than the raw
+Over this sample, the refined partition has more clusters and higher entropy than the raw
union-find baseline — i.e. it fragmented slightly further, not less. This is the opposite direction
from the engine's usual role (`cluster_refined` normally *reduces* overcount relative to
-`cluster_naive`, see `RESULTS-cluster-scale.md`/`graph_metric.overcount_report`): here the split
+`cluster_naive`, see `graph_metric.overcount_report`): here the split
channel (`NSPropagator.refine`, the provenance-aware analogue) removed a few co-spend edges
(6 of 40 clusters split), and — per the caveat above — a meaningful fraction of those removals rest
on a provenance-disjointness signal that the cache boundary made spuriously easy to satisfy. Read
-honestly: on this small, cache-bounded, boundary-corrupted sample, the split channel is not shown to
+it: on this small, cache-bounded, boundary-corrupted sample, the split channel is not shown to
improve on the union-find baseline; a live-network run (real multi-hop provenance, not a truncated
cache) is needed before drawing a directional conclusion either way.
@@ -183,7 +193,7 @@ target). 10 nodes total, `theta=0.5`, `min_score=0.1`, `refuse_below=-2.0` (driv
Not computed *on this synthetic fixture* — `cluster.cluster_naive(nodes)` clusters by
common-input-ownership and needs `fetch_tx` over real txids; it has no meaningful reading over
-synthetic node labels like `A1`/`u1`. **Now computed for real** in the "Real cache-bounded run"
+synthetic node labels like `A1`/`u1`. Now computed for real in the "Real cache-bounded run"
section above (100 clusters / 6.517 bits vs. the refined partition's 107 / 6.620).
## Chain-data reproduction — done (cache-bounded); network run still open
@@ -191,9 +201,13 @@ section above (100 clusters / 6.517 bits vs. the refined partition's 107 / 6.620
The "Real cache-bounded run" above supersedes the original plan here: it ran the full pipeline
(real `ancestry_signature`, `build_rarity`, `NSPropagator.propagate`/`.refine`, `holdout_reid`,
and the `cluster.cluster_naive` baseline) against this checkout's `.cache/`, entirely offline. The
-one thing it does *not* give is a **network** run with unbounded ancestry depth — a real fetch
+one thing it does *not* give is a network run with unbounded ancestry depth — a real fetch
over `mempool.space` was previously abandoned mid-task as too slow (a single depth-2 walk from a
19-input coin took ~24s in that environment, rate-limited to ~5 req/s with backoff) and remains a
possible follow-up if a much larger multi-hop-connected cache (or a live, budgeted network session)
becomes available; `examples/ns_propagation.run(txs, seeds, depth=6)` is still the entry point for
that (`fetch=fetch_tx`, network-backed) once such a budget exists.
+
+## Reproducibility / provenance
+
+State 2 in `results/REPRODUCIBILITY.md`: the mechanism is pinned; the reported numbers come from a data-run over the cache-bounded slice `examples/ns_propagation_cache_run.py` builds, which is not committed, and are not asserted.
diff --git a/results/RESULTS-partition-cuts.md b/results/RESULTS-partition-cuts.md
new file mode 100644
index 00000000..496c2996
--- /dev/null
+++ b/results/RESULTS-partition-cuts.md
@@ -0,0 +1,128 @@
+# Which cut makes two matchable views — temporal, de-core, or cluster-collapse
+
+## Verdict
+
+The framework names the temporal cut as its own trivial example and asks for a cut along
+cluster-collapse regions instead. Both are now implemented and measured on the same slice against
+the same matcher. The result is a **clean negative for the cut criterion and a clear positive for
+retiring `decore`**: cutting along collapse events costs 1.7% of the transactions and leaves every
+figure within noise of the temporal baseline, while the previous "clever" scheme, `decore`, is
+actively harmful. The one partition choice that does move the precondition is the number of views.
+
+## The three cuts
+
+All three produce two or more views of one slice; `split_clusters_by_view` then gives a straddling
+cluster a distinct pseudonym per view and the matcher must rejoin them from structure alone.
+
+| scheme | what it removes |
+|---|---|
+| `epoch` | nothing — splits by block height |
+| `decore` | the busiest `core_frac` of addresses, then splits the survivors by height |
+| `collapse` | the transactions whose common-input merge would fuse two clusters that are each already `min_side` or larger, then splits the survivors by height |
+
+`collapse` detects and refuses in one pass, so the clusters that survive are the ones the declined
+merges never joined: the boundary is a boundary of the clustering it produces. A merge that attaches
+a fresh address to one cluster only grows it and is never a collapse.
+
+## Result — 300,000 transactions, blocks 391182–392111
+
+| scheme | boundary txs | pairs to rejoin | straddler edges | mean straddler degree | non-isolated | correct @ 10% seed |
+|---|---:|---:|---:|---:|---:|---:|
+| `epoch` | 0 | 2,562 | 1,910 | 1.45 | 44.7% | 1 |
+| `decore` | **117,371 (39.1%)** | 1,294 | 360 | **0.55** | 28.3% | 0 |
+| `collapse` | 4,989 (1.7%) | 2,536 | 1,941 | **1.49** | 44.5% | 1 |
+
+**`decore` cuts the wrong thing.** Dropping the busiest 1% of addresses removes 39% of the
+transactions, halves the rejoinable population and takes the straddler subgraph's mean degree from
+1.45 to 0.55 — from marginal to hopeless. It cuts by degree, and degree is where the recurring
+relationships live: the hubs it discards as ambiguous noise are carrying most of the structure the
+matcher propagates along. It should not be used.
+
+**`collapse` is nearly free and slightly better than doing nothing.** 4,989 collapse events in
+300,000 transactions, and removing them leaves the population intact and the straddler degree
+marginally higher than the temporal baseline (1.49 against 1.45) — consistent with collapse merges
+being noise that dilutes genuine neighbourhoods. But it does not change the regime.
+
+## Threshold sweep — the effect is flat
+
+| `min_side` | boundary txs | pairs | mean straddler degree | correct @ 10% |
+|---:|---:|---:|---:|---:|
+| 2 | 4,989 | 2,536 | 1.49 | 1 |
+| 3 | 4,264 | 2,536 | 1.37 | 1 |
+| 5 | 3,220 | 2,542 | 1.38 | 1 |
+| 10 | 1,948 | 2,546 | 1.40 | 1 |
+| 25 | 488 | 2,562 | 1.44 | 1 |
+
+Non-monotonic between 1.37 and 1.49 against a 1.45 baseline, converging on the baseline as the
+threshold rises and fewer transactions are cut. That is noise in which transactions happen to be
+removed, not a signal from the criterion. The matching result is identical at every setting,
+including at no cut at all.
+
+The reason is prevalence: on this data a collapse event is 0.16%–1.7% of transactions. The
+framework's hypothesis — that cutting along collapse regions leaves sparser, more matchable
+components — presupposes that collapse regions are a substantial structuring feature of the graph.
+Here they are not, so the cut has almost nothing to act on. That is a property of the data, and it
+would be worth re-testing on an era with heavy coinjoin activity, where collapse events are common.
+
+## Number of views — the one choice that moves the precondition
+
+`collapse`, `min_side=2`, splitting the survivors into narrower height bands:
+
+| views | pairs to rejoin | straddler edges | mean straddler degree | non-isolated | correct @ 10% |
+|---:|---:|---:|---:|---:|---:|
+| 2 | 2,536 | 1,941 | 1.49 | 44.5% | 1 |
+| 3 | 1,760 | 1,529 | **1.70** | 47.4% | 1 |
+| 4 | 1,371 | 1,238 | **1.77** | 48.9% | 0 |
+
+Narrowing the views raises mean straddler degree by 19% and the non-isolated share by four points.
+The mechanism is selection: a cluster that straddles two *narrow* adjacent windows is one that was
+active in both, and consistently active entities have denser neighbourhoods. It is the first
+partition choice measured here that moves the precondition in the right direction.
+
+It does not convert. The population falls faster than the density rises — 2,536 to 1,371 — and by
+four views the single correct rejoin is gone. Density and population trade against each other along
+this axis, and at this scale the trade is not favourable.
+
+## Reading
+
+The cut criterion is not the binding constraint. Three different cuts, a five-point threshold sweep
+and a three-point view-count sweep all land on the same matching outcome, while the one scheme that
+differs does so by destroying the signal. What separates a matchable pair of views from an
+unmatchable one on this data is not where the boundary is drawn but whether the same economic
+neighbourhood recurs across it, and that is a property of the underlying activity
+(`results/generated/graph-rejoin-2016-v1.md`, `RESULTS-persistence-curve.md`).
+
+`decore` is retired as a recommendation. `collapse` replaces it as the non-trivial cut, on the
+grounds that it costs almost nothing and is the construction the framework actually asks for — not
+on the grounds that it was measured to help, because it was not.
+
+## Scope
+
+One slice, one era, one 300k-transaction prefix. The straddler-degree differences between `epoch`
+and `collapse` are small enough that they should not be read as an ordering. The n>2 result is a
+single sweep at one `min_side`. The export carries addresses but no output values, so the collapse
+detector sees only the shape rule and never the de-mix arm.
+
+**Addition, 7 Sep 2026 — the table above is now reproducible.** It came from
+`data/epochs_2016_weekly/`, 977 MB this repository does not ship, so none of it could be checked by
+anyone else. It never needed the whole export: the run consumes one prefix, that prefix recompresses
+to 50 MB, and it is committed as `data/partition-cuts-300k-2016.ndjson.gz`.
+`catalog/runs/partition-schemes-v1.json` reproduces every figure above — 2,562 / 1,294 / 2,536 pairs,
+1,910 / 360 / 1,941 straddler edges, 1.45 / 0.55 / 1.49 mean degree, one correct pair at a 10% seed
+for `epoch` and `collapse` and none for `decore`.
+
+Two things the canonical run states that this document did not. A seed share that returns no guess
+reports no precision, not zero: three of the six seed/scheme cells make no guess at all, and
+printing 0.000 there would read as a measured tie between schemes rather than as an absence of
+measurement. And `epoch` and `decore` had been ignoring `n_views` — they halved the height range
+whatever was asked, so the framework's n > 2 generalisation silently applied to one scheme of three.
+All three now band the range into the number of views requested.
+
+## Reproducibility / provenance
+
+Per `results/REPRODUCIBILITY.md`, state 2: **mechanism unit-tested, headline number is a data-run.**
+`collapse_boundary` and `collapse_partition` are pinned in `tests/test_views.py` (the merge of two
+substantial clusters is cut, a fresh address joining one cluster is not, the boundary lands in no
+view, and n>2 partitions the sample without loss or duplication). The tables here are regenerated by
+`examples/analyze_decore.py --scheme {epoch,decore,collapse}` over `data/epochs_2016_weekly/`, which
+is local and unversioned, and are not asserted.
diff --git a/results/RESULTS-partition-posterior.md b/results/RESULTS-partition-posterior.md
index 57f21960..28912dba 100644
--- a/results/RESULTS-partition-posterior.md
+++ b/results/RESULTS-partition-posterior.md
@@ -31,7 +31,7 @@ checkout's real `.cache/` — no fabricated or hand-picked figures. Full run log
| slice build time | 67.6s |
Small and heavily class-imbalanced (2 positive pairs out of 21 labeled). **Illustrative, not
-conclusive** — see honest limits below. The label pairs come from address-reuse groups that
+conclusive** — see the limits below. The label pairs come from address-reuse groups that
survived the `max_supernodes` truncation (which prioritizes labeled singles first, so this is
already the best label coverage obtainable at this slice size from the current cache).
@@ -57,12 +57,12 @@ uncertainty-reduction that channel is responsible for):
| categorical (fingerprint axes) | **9.590 bits** |
| provenance (N-S link factor, beta=1.0, depth=1, bounded slice) | **0.000 bits** |
-**Honest reading:** on this slice, the categorical fingerprint channel carries essentially all of
+**Reading:** on this slice, the categorical fingerprint channel carries essentially all of
the entropy-reducing evidence — dropping it collapses the posterior from near-certainty about the
partition (0.86 bits, effectively locked at K=5) to a nearly uninformative spread over K=2..9
-(10.45 bits). The provenance channel contributed **zero** marginal bits here: `drop_provenance`
+(10.45 bits). The provenance channel contributed zero marginal bits here: `drop_provenance`
is bit-for-bit identical to the full run. This is a real, not a rounding-noise, null result for
-this slice — see honest limits for why (depth=1 + the bounded-dimension link oracle sharply limits
+this slice — see the limits for why (depth=1 + the bounded-dimension link oracle sharply limits
how much real provenance signal reaches the evidence at this scale).
## Beta sweep (`beta_sweep`, betas = 0.0, 0.5, 1.0, 2.0)
@@ -80,7 +80,7 @@ slice, scaling it by `beta` has no measurable effect on the posterior, so ECE do
`beta`. Not evidence that beta calibration is unnecessary in general — it is evidence that this
particular bounded slice does not exercise the provenance channel enough to calibrate it.
-**Headline run** (full evidence, selected beta=0.0, 2 chains, 2000/600): ECE = **0.23884**
+Headline run (full evidence, selected beta=0.0, 2 chains, 2000/600): ECE = 0.23884
(consistent with the sweep's single-chain 1500/500 estimate at beta=0.0, 0.23857 — small MC
difference from the different chain/iteration count, not a discrepancy).
@@ -110,7 +110,7 @@ branch (`anonymity-set-s04-fusion`) and now exists: `anonymity_set.py` provides
absorbing solve — see `RESULTS-anonymity-set.md`'s "§04-faithful fusion" section for the real
graph-only-vs-fused min-entropy numbers on this cache's same-owner pairs).
-The M1 row of the coarsening-hierarchy table above is still reported as **N/A** — not because the
+The M1 row of the coarsening-hierarchy table above is still reported as N/A — not because the
module is missing, but because M1 reports a per-target min-entropy lower bound (a weight-of-evidence
floor), not the kind of per-pair match-probability this table's ECE metric calibrates against
same-owner labels. Computing an ECE-comparable number for M1 would need its own calibration
@@ -129,7 +129,7 @@ the same sampler settings (single chain, 1500/500):
| real evidence | 0.7362 |
| shuffled evidence (null) | 0.7622 |
-**Honest reading: the separation is real but weak (0.736 vs 0.762, ~0.03 bits) on this tiny
+**Reading: the separation is real but weak (0.736 vs 0.762, ~0.03 bits) on this tiny
15-super-node slice** — not the sharp "collapses to the prior" separation the design spec
anticipates for a slice with more signal. Two contributing factors, both consistent with the rest
of this run: (1) the categorical channel — which the ablation above shows carries essentially all
@@ -139,7 +139,7 @@ chance leave a few super-nodes closely matched, especially with only 15 elements
sharpen this separation either. This shuffle-null is not strong evidence of a well-separated
signal at this scale; it is weak-but-directionally-correct evidence, reported as such.
-## Honest limits
+## Limits
1. **The N-S provenance channel is a pseudo-likelihood, not a probability.** `beta * link(i,j)`
scales a rarity-weighted overlap score into the log-posterior; it is weight-of-evidence, a
@@ -153,7 +153,7 @@ signal at this scale; it is weak-but-directionally-correct evidence, reported as
tx in this slice is CoinJoin-scale (vin=277, vout=325); the exact subset-sum solver's own
documented range is 54ms-69s per call (see `ancestry.dss_link_oracle`'s docstring), and its
`budget_ms` argument is advisory, not a hard preemptive cap. To keep this harness offline and
- tractable, `examples.partition_posterior._bounded_link_oracle` refuses (same honest
+ tractable, `examples.partition_posterior._bounded_link_oracle` refuses (the same
oracle-None truncation semantics `build_extended_graph` already uses) on any tx with more than
24 inputs or outputs, and the walk depth is capped at 1 hop. This is very likely why the
provenance channel measured **zero** marginal bits above: a depth-1, dimension-bounded walk
@@ -168,7 +168,7 @@ signal at this scale; it is weak-but-directionally-correct evidence, reported as
is over all 105 pairs** (the full pairwise set), not restricted to the labeled subset — reported
separately in the coarsening-hierarchy table's notes column to avoid conflating the two.
-Same-owner labels (not "ground truth") throughout — the address-reuse heuristic used for labels
+Same-owner labels throughout — the address-reuse heuristic used for labels
here is a standard, near-certain but not infallible same-owner signal (a collaborative or shared
tx would produce a false merge), independent of the co-spend signal the sampler's super-nodes are
built from.
@@ -177,3 +177,7 @@ built from.
`build_slice(cap_total=50, depth=1, max_supernodes=15)` then `channel_ablation`, `beta_sweep`,
`shuffle_null`, and `fs_bayes_pair_marginal` as this run did (~8 minutes total, offline, no
network). Deterministic under `seed=0`.
+
+## Reproducibility / provenance
+
+State 2 in `results/REPRODUCIBILITY.md`: the mechanism is pinned; the reported numbers come from a data-run over the bounded `.cache` slice `examples.partition_posterior.build_slice` builds, which is not committed, and are not asserted.
diff --git a/results/RESULTS-partition-schemes.md b/results/RESULTS-partition-schemes.md
deleted file mode 100644
index 8c558987..00000000
--- a/results/RESULTS-partition-schemes.md
+++ /dev/null
@@ -1,80 +0,0 @@
-# Comparing partition schemes
-
-**Why ask.** The framework names the epoch partition as its trivial example and proposes a
-better one: *"the adversary decomposes the cluster graph along a high ambiguity cut, such
-that each component is relatively sparser, and then matching these components."* Epoch was
-the only scheme ever run. `RESULTS-match-confidence.md` supplies the criterion to compare
-on: not coverage, but how many links a scheme yields above an eccentricity threshold, since
-those are the links the framework says feed other clustering heuristics.
-
-**Schemes.** *epoch*, split by block height. *coinjoin seam*, the same split with
-coinjoin-shaped transactions removed from both sides, since a coinjoin is where co-spending
-stops implying common ownership. *ambiguity cut*, read literally as the opposite of expander
-decomposition: remove the densest region, which is where every vertex looks like its
-neighbours and identity is most ambiguous, and take the connected components it leaves.
-
-**Data.** `slice_2026.ndjson`, refusing clusterer, seed 400, threshold eccentricity ≥ 5.
-
-## Result
-
-| scheme | views | spanning | seedable | matched | correct | links at ecc ≥ 5 | correct | precision |
-|---|---|---:|---:|---:|---:|---:|---:|---:|
-| epoch | 403 963 / 394 989 | 32 857 | 5 124 | 147 | 83 | 57 | 40 | 0.702 |
-| coinjoin seam | 389 263 / 383 115 | 32 843 | 5 118 | 147 | 83 | **58** | **41** | **0.707** |
-| ambiguity cut | 195 197 / 352 | **1** | 0 | 0 | 0 | 0 | 0 | — |
-
-*(Re-run on the graph corrected in `RESULTS-graph-shape.md`. The ambiguity cut's structural
-failure is unchanged, as expected: whether multi-source transfers assert edges does not
-affect whether the graph decomposes. Epoch and coinjoin-seam precision moved by under two
-points and remain indistinguishable.)*
-
-Sweeping the fraction of addresses treated as the dense core:
-
-| core fraction | transactions cut | largest component | second | second / largest |
-|---:|---:|---:|---:|---:|
-| 0.1 % | 564 578 (68 %) | 266 490 | 411 | 0.0015 |
-| 0.5 % | 677 985 (82 %) | 152 478 | 929 | 0.0061 |
-| 1 % | 715 481 (86 %) | 115 961 | 209 | 0.0018 |
-| 5 % | 788 214 (95 %) | 42 917 | 344 | 0.0080 |
-| 10 % | 816 391 (98 %) | 13 982 | 1 107 | 0.079 |
-| 25 % | 831 566 (99.98 %) | 113 | 53 | 0.469 |
-
-## Reading
-
-**The ambiguity cut fails, and not because the threshold was wrong.** No fraction produces
-two comparable components. The ratio of second to largest stays near zero until the graph
-has been destroyed: at 25 % the components are finally comparable, and the largest holds
-113 transactions out of 831 770. This is the ordinary behaviour of a scale-free graph, which
-keeps one giant component through hub removal and then crumbles to dust rather than
-splitting in two.
-
-**Removing even a tenth of a percent of addresses cuts 68 % of transactions.** The busiest
-addresses touch most of the graph, so the cut is never a thin boundary; it swallows the data
-before it separates anything.
-
-**And there is a tension underneath the arithmetic.** A partition into *disconnected*
-components cannot have entities spanning it, because a cluster with coins in two components
-would join them. The correspondence the matcher recovers is exactly an entity appearing in
-both views, so decomposing into components destroys the thing being looked for. One
-spanning cluster out of 33 114 is that argument made concrete. The epoch partition works
-precisely because it is *not* a graph decomposition: it is a time slice, and one entity is
-in both slices with different neighbourhoods.
-
-This does not settle what the source intends, and the sentence admits more than one reading.
-What is measured here is that the literal reading, a decomposition into sparser components
-matched against each other, is not available on this graph at any threshold, for a
-structural reason rather than a tuning one.
-
-**The coinjoin seam and epoch are indistinguishable**, 58 links against 57 and precision
-0.707 against 0.702. At these counts the gap is noise and should not be read as a ranking. It is worth noting
-only because it is the framework's own prescription for the cautious adversary, declining
-CIOH inside a coinjoin while still matching the graphs on either side, and it costs nothing:
-it does the same work on 5 % fewer vertices.
-
-## Scope
-
-One operationalisation of ambiguity, density, chosen because the framework's own analogy is
-to expander decomposition. Other signals may behave differently, and the amount-side
-ambiguity the source discusses at length is only defined for 2-in/2-out transactions, 5.6 %
-of this slice, too thin to cut on. The structural objection above is not specific to the
-signal: it applies to any reading that partitions into disconnected components.
diff --git a/results/RESULTS-path-count.md b/results/RESULTS-path-count.md
index 182cc980..a0624257 100644
--- a/results/RESULTS-path-count.md
+++ b/results/RESULTS-path-count.md
@@ -1,8 +1,19 @@
# Bounded walk + §07 path-count — real numbers
-`examples/path_count_live.py` exercises the two shipped capabilities on real data: the **bounded walk**
-(`max_nodes`, makes deep coinjoin `analyze()` tractable) and the **§07 path-count** object (the
-multiplicity/robustness lens). Honest numbers below, with the nuances the runs surfaced.
+> Historical record. The current executable contract is
+> `catalog/runs/path-count-contract-v1.json`, rendered as
+> `results/generated/path-count-contract-v1.md`. The live timings below are not part of that
+> deterministic result.
+
+> **Correction (2026-09-03) — the default oracle behind the walk has moved.**
+> The runs below pass `bounded_link_oracle(...)` explicitly, so their numbers remain reproducible as
+> written. `path_count_anonymity()` and `analyze()` now default to `ancestry.value_flow_link_oracle`
+> (nominal-value transitions, no dss) instead; the subset-sum walk measured here is opt-in.
+
+`examples/path_count_live.py` exercises the two shipped capabilities on real data: the bounded walk
+(`max_nodes`, makes deep coinjoin `analyze()` tractable) and the §07 path-count object (weighted by
+link probability alone — see "Multiplicity has left the bound", below). Numbers below, with the
+nuances the runs surfaced.
## (a) Bounded walk — deep coinjoin now returns, instead of hanging
@@ -16,39 +27,49 @@ multiplicity/robustness lens). Honest numbers below, with the nuances the runs s
| min-entropy | **3.0 bits** |
| truncated (unexpanded frontier + oracle refusals) | 70 |
-Contrast: the **unbounded** depth-5 walk over the same coinjoin **did not finish in a 25-minute cap**
+Contrast: the unbounded depth-5 walk over the same coinjoin did not finish in a 25-minute cap
(`RESULTS-analyze.md` (b)) — the ancestral fan-out explodes. `max_nodes` caps the expanded coins, so
-the walk returns a **truncated lower bound** (67 origins, 3.0 bits) in bounded time. Two independent
+the walk returns a truncated lower bound (67 origins, 3.0 bits) in bounded time. Two independent
knobs govern this and BOTH matter: `max_nodes` bounds the graph fan-out (number of hops); `budget_ms`
bounds each hop's oracle cost. With `budget_ms=2000` the same call returns in 6 s but the first
coinjoin hop (~2.3 s of exact subset-sum) truncates → a point mass; `budget_ms=5000` resolves the hops
-at the cost of wall time. Bounded, honest, never exact — exact deep-coinjoin resolution is impossible
+at the cost of wall time. Bounded, never exact — exact deep-coinjoin resolution is impossible
(the explosion is the privacy).
-## (b) §07 vs §04 — when they differ, and why usually they don't
+## Multiplicity has left the bound
+
+Earlier revisions reported that the path count up-weights origins reached through
+high-multiplicity transactions, and a test pinned that behaviour. Both are retired.
-On the narrow depth-sweep tx and on the `9-in/17-out` seed coinjoin, the §07 path-count distribution is
-**identical** to the §04 distribution (min-entropy equal; `log_W_paths ≈ 0`). This is not a defect —
-two honest reasons:
+`cost.py` declares the amount channel refuse-only: it may cut a coin from the graph, never weight
+one. Measured on a synthetic two-origin DAG, the multiplicity term moved min-entropy from 1.0 bit
+to 0.137 — note the direction, which is downward; the objection is not that it inflated a number
+but that an ambiguity signal entered the bound as a weight at all. The factor is gone, the origin
+distribution is weighted by link probability alone, and the mass key went with it: after the change
+it is identically zero, because the edge weights are row-stochastic and the absorbed mass is always
+one.
-1. **Structural (depth 1):** a single transaction's `W(E)` is a global constant across all its edges,
- so it **cancels in the normalization** of the origin distribution. §07's *distribution* therefore
- equals §04's at depth 1 for any tx; only the *magnitude* `log_W_paths` (= `log W(E)`) can differ.
-2. **`W(E)=1` on real "coinjoin-shaped" txs:** `12da3dd7…` (9-in/17-out) has `w_total → count=1` — a
- UNIQUE subset-sum mapping. Its specific values pin the assignment, so there is genuinely no
- path-multiplicity to weight. Many real coinjoin-shaped payments are like this.
+The combinatorial sub-transaction literature does read a low mapping count as low privacy, which is
+why the weighting looked principled. But in that literature the count enters as the *denominator* of
+a link probability, never as a multiplier on one, and the column this code weights is already
+renormalized (`ancestry.py:149`). The factor reintroduced as a weight exactly what the matrix had
+just divided out. The refuse-only contract and the literature agree here: there was one defensible
+reading, not two.
-§07 **diverges from §04 only** when *different ancestral origins are reached through
-different-`W(E)` transactions* (depth ≥ 2) **and** those txs carry genuine subset-sum multiplicity
-(`W(E) > 1`) — i.e. true equal-denomination dense coinjoins (Wasabi-style equal outputs). The mechanism
-is proven in `tests/test_path_count.py::test_multiplicity_upweights_higher_w_e_origin` (a controlled
-2-origin fixture where the higher-`W(E)` origin gets strictly more weight: 0.833 vs 0.5). On real data
-the divergence is rare because it requires that specific structure — which is itself the §06/§07 point:
-robustness lives in genuine path multiplicity, and most single payments don't have it.
+**What this costs.** `path_count_anonymity` is the default target of the construction-side cost
+function, and multiplicity was the only thing distinguishing its output from the plain ancestry
+entropy. After this change that instrument measures no structural property of the graph.
+Measuring one would need edge-disjoint paths in a graph whose vertices are transactions and whose
+directed, amount-labelled edges are coins. It must also enforce plausible flow, since a traceable
+path is not necessarily capable of carrying the target value. This repository does not compute that
+k-routes object or its corresponding edge cut. Note
+also that the construction-side cost function never combined its terms in the first place: it
+returns them uncombined and raises on the attempt, so what changes here is what one of the three
+terms means, not a working number.
## (c) The W(E) count gate — a real bug found and fixed (Sasamoto is NOT the bug)
-Investigating why §07 collapsed to §04 on coinjoins surfaced a bug — in **our method selection**, not
+Investigating why §07 collapsed to §04 on coinjoins surfaced a bug — in our method selection, not
in Sasamoto:
- **Sasamoto is correct.** Cross-referenced against the paper (`sasamoto.md`, cond-mat/0106125)
@@ -63,16 +84,17 @@ in Sasamoto:
is architectural: method selection is not decluster's job — it moved into the dss crate as the
`dss.w_count` feasibility-cascade dispatcher (brute → dp → sparse → sasamoto, selecting by REGIME,
beside the density logic, with cross-validated Rust tests). `decluster.counting.w_total` now simply
- delegates to `dss.w_count`. So dense coinjoins — exactly where §07's multiplicity lives — get their
- exact counts (asserted by `tests/test_counting.py::test_w_total_dense_coinjoin_is_exact_not_unknown`).
+ delegates to `dss.w_count`. So dense coinjoins — the case this dispatcher exists for — get their
+ exact counts (asserted by `tests/test_counting.py::test_w_total_dense_coinjoin_is_exact_not_unknown`),
+ independent of whether §07 currently spends that count on anything.
-Radix (dense repeated-denomination recognition) is a **separate, independent** dss path and is not part
+Radix (dense repeated-denomination recognition) is a separate, independent dss path and is not part
of this cascade.
-## Honest limits
+## Limits
- Bounded walk = truncated **lower bound**, never exact; deep-coinjoin exactness is impossible.
-- §07 path-count = weight-of-evidence robustness lens, not a privacy score; it does not extend the
- tractable envelope (same graph fan-out as §04), and it diverges from §04 only on genuine
- path-multiplicity (dense coinjoins at depth ≥ 2).
+- §07 path-count = weighted by link probability alone; identical in distribution to §04's set-size
+ entropy, and adds no structural-property term (see "Multiplicity has left the bound", above). It
+ does not extend the tractable envelope either (same graph fan-out as §04).
- Live non-determinism (real chain + timing-bounded oracle); a small real slice, not a population.
diff --git a/results/RESULTS-path-counting-analysis.md b/results/RESULTS-path-counting-analysis.md
index e4ecf945..8c7dbf45 100644
--- a/results/RESULTS-path-counting-analysis.md
+++ b/results/RESULTS-path-counting-analysis.md
@@ -5,6 +5,10 @@
> the counts into decluster. So statements below like "called nowhere in decluster" and "not a wiring
> task" describe the *starting* state, not the shipped one — see `results/RESULTS-path-count.md` for
> the delivered object and its real numbers.
+>
+> The current executable contract is `catalog/runs/path-count-contract-v1.json`, rendered as
+> `results/generated/path-count-contract-v1.md`. It identifies the delivered object as provenance
+> route accumulation, not edge-disjoint plausible-flow path counting.
# Is dss what's needed for §07 path counting, and is it exposed/integrated in decluster?
@@ -19,16 +23,16 @@ not integrated.**
§07 is a sparse, mostly-TODO sketch. It defines the anonymity set's constituents as *paths*: for a coin
there are *"two apparent paths … one of them is noise"* — Alice's genuine transaction history and
-**counterfactual paths** through unrelated coins. The object to build is *"a sufficiently robust
+counterfactual paths through unrelated coins. The object to build is *"a sufficiently robust
anonymity set whose constituent objects are these paths."* It marks the formalization TODO
-(*"the intersections of the walks create symmetries…"*). It does **not** itself specify subset-sum
+(*"the intersections of the walks create symmetries…"*). It does not itself specify subset-sum
counting — that connection is inferred below, and it is the same combinatorial enumeration §03
attributes to the Boltzmann/Maurer frameworks (*"combinatorial enumeration of possible transaction
partitions with entropy quantification"*).
## What the dss count-paths actually compute (verified)
-Each operates on a **single transaction's** input/output values:
+Each operates on a single transaction's input/output values:
| call | output | meaning |
|---|---|---|
@@ -38,7 +42,7 @@ Each operates on a **single transaction's** input/output values:
| `radix_mappings(ins, outs, max_size)` | radix-structured mapping enumeration | the dense-coinjoin structural count |
| `per_coin_density(ins, outs)` | `{kappa, coins:[{role,index,value,log_w,kappa_c}]}` | per-coin `log_w` magnitude + the κ vs κ_c phase (the privacy-magnitude engine) |
-So the count-paths are exactly a **per-transaction mapping-multiplicity `W(E)`** engine — the local
+So the count-paths are exactly a per-transaction mapping-multiplicity `W(E)` engine — the local
factor a path count is built from — including a *polynomial* approximation (`w_sasamoto`) that is the
tractable alternative to explicit enumeration.
@@ -49,7 +53,7 @@ tractable alternative to explicit enumeration.
The multiplicity of assignments at each transaction is precisely `W(E)`; the count of counterfactual
paths is the graph-level combination of these per-tx multiplicities. dss delivers the **per-tx
`W(E)`** exactly (`w_brute`/`w_sparse`), structurally (`radix_mappings`), and — crucially for
- tractability — **approximately in polynomial time** (`w_sasamoto`). This is the right and necessary
+ tractability — approximately in polynomial time (`w_sasamoto`). This is the right and necessary
counting engine, and its approximation is exactly what could make a deep count tractable where the
§04 walk explodes (see `RESULTS-analyze.md` (b)).
- **What dss does NOT provide:** the graph-level aggregation — walking the ancestry and combining the
@@ -60,10 +64,10 @@ tractable alternative to explicit enumeration.
- **Exposed in dss (Python):** YES — `w_brute`, `w_sparse`, `w_sasamoto`, `radix_mappings`,
`per_coin_density` are all callable and return the shapes above.
-- **Integrated into decluster:** only the **density** path. `decluster/cost.py::dss_oracle` wraps
+- **Integrated into decluster:** only the density path. `decluster/cost.py::dss_oracle` wraps
`dss.per_coin_density` and feeds `amount_cuts` (refuse-only amount-channel cut candidates, using each
- coin's `log_w` as a knee-truncated lower bound). The four `W(E)` **count**-paths
- (`w_brute`/`w_sparse`/`w_sasamoto`/`radix_mappings`) are **called nowhere** in decluster or examples
+ coin's `log_w` as a knee-truncated lower bound). The four `W(E)` count-paths
+ (`w_brute`/`w_sparse`/`w_sasamoto`/`radix_mappings`) are called nowhere in decluster or examples
(`grep` clean). No decluster object aggregates per-tx `W(E)` across the graph.
- **The code already names this gap, twice:**
- `cost.py::amount_cuts`: *"A rigorous cut needs an exact count and is deferred."* (it uses the
diff --git a/results/RESULTS-persistence-curve.md b/results/RESULTS-persistence-curve.md
index 2819c5d2..18dd8bbd 100644
--- a/results/RESULTS-persistence-curve.md
+++ b/results/RESULTS-persistence-curve.md
@@ -1,9 +1,9 @@
# Does widening the view recover the neighbourhoods the matcher needs?
-**Why ask.** `RESULTS-view-match-2026.md` finds the matcher's frontier collapses on its
-first step: of the 400 seeds' 15 324 neighbours, only 14 % were present and linked in the
-other view. The framework's mechanism requires *recurring* relationships, and at a one-day
-view width most relationships do not recur. View width is the obvious untested lever, but
+**Why ask.** Seeded propagation across two views does not ignite
+(`results/generated/graph-rejoin-2016-v1.md`), and the frontier is where it stops: a seed can only
+pass information to a neighbour the other view also holds. The framework's mechanism requires
+*recurring* relationships, and at a one-day view width most relationships do not recur. View width is the obvious untested lever, but
testing it properly means a much larger export and a run near the memory ceiling. This
measures the lever first, cheaply.
@@ -15,7 +15,7 @@ aggregation: 3.7 GB scanned, 23 s. `bigquery/persistence_curve.sql`.
**Metric.** For each address with degree ≥ 3 in view A and any activity in view B, the
share of its A-neighbours that are also active in B. This is address-level (union-find does
not fit in SQL) and uses the looser test *present in B* rather than *present and linked in
-both*, so the absolute level is not comparable with the 14 % above. The **trend** across
+both*, so the absolute level is not comparable with the 14 % above. The trend across
widths is the quantity of interest and is measured consistently.
## Result
@@ -31,7 +31,7 @@ widths is the quantity of interest and is measured consistently.
## Reading
**Widening helps, but it is not a phase change.** Per-vertex persistence rises from 31 % to
-38 % and **saturates by day three**. Past that, a wider window adds relationships without
+38 % and saturates by day three. Past that, a wider window adds relationships without
adding persistent ones. Whatever makes a counterparty a one-off is not fixed by watching
longer.
@@ -56,3 +56,8 @@ constraint is not view width and the negative result is a good deal stronger.
Width three is where the rate saturates; width seven is where the volume is. A run should
use seven and report both.
+
+## Reproducibility / provenance
+
+State 1 in `results/REPRODUCIBILITY.md`: band-pinned on committed data — `tests/fixtures/persistence_curve.json`, compared against the published table by
+`tests/test_state3_promotion.py`.
diff --git a/results/RESULTS-provenance.md b/results/RESULTS-provenance.md
index 5b42c93d..bd569ab2 100644
--- a/results/RESULTS-provenance.md
+++ b/results/RESULTS-provenance.md
@@ -17,7 +17,7 @@ fraction of the child's inputs spending an output of the parent.
| 90th percentile | 0.0508 |
| maximum | 0.2019 |
-The first row is the finding that matters for the premise: coinjoin-to-coinjoin overlap is **common**,
+The first row is the finding that matters for the premise: coinjoin-to-coinjoin overlap is common,
not exceptional. In a remixing population, rounds routinely consume each other's outputs, so a
nonzero share is not by itself evidence of anything.
@@ -46,9 +46,9 @@ first is not one of them.
## Complementary to conservation, not redundant
The two channels peak at opposite ends of the same chain. Conservation forces outputs where a
-participant is large by **value**: it bites on rounds 1–3, where they hold 69%, 75% and 52% of the
+participant is large by value: it bites on rounds 1–3, where they hold 69%, 75% and 52% of the
round's input, and forces nothing at 2.2%, 0.2% and 0.0% (`RESULTS-conservation.md`). Provenance
-overlap ranks by how many **coins** flow forward, and peaks at hops 3→4 and 4→5 — exactly where the
+overlap ranks by how many coins flow forward, and peaks at hops 3→4 and 4→5 — exactly where the
participant's value share has collapsed but their coin count has not.
Neither is a substitute for the other, and the chain is only covered because they fail in different
@@ -67,3 +67,8 @@ the premise, and the reason the negative half of the result is the more trustwor
from decluster.provenance import overlap_share # over cached coinjoins, offline
overlap_share(child_tx, {parent_txid})
```
+
+## Reproducibility / provenance
+
+State 2 in `results/REPRODUCIBILITY.md`: the mechanism is pinned; the reported numbers come from a data-run over the coinjoins in the local cache, which is not committed, and are not asserted.
+The channel is guarded by `tests/test_provenance*.py`.
diff --git a/results/RESULTS-refusing-clusterer.md b/results/RESULTS-refusing-clusterer.md
index 0912e26d..34e9dd43 100644
--- a/results/RESULTS-refusing-clusterer.md
+++ b/results/RESULTS-refusing-clusterer.md
@@ -1,24 +1,85 @@
# Does declining to apply CIOH blindly change the 2026 picture?
-**Why ask.** `RESULTS-contraction-2026.md` flagged its own clustering as a deliberate
+> **The slice-scale numbers below are pending re-execution and must not be cited.** They were
+> measured under a `monitor.is_coinjoin` that no longer exists: at the time it recognised only
+> the many-in/many-out shape, and commit `24caeed` added a second arm that also refuses on a
+> repeated output denomination. They were also measured over `slice_2026.ndjson`, which is
+> **not in this checkout**, so they cannot be re-labelled as "the 20/20 rule" and left standing —
+> the rule that produced them is gone and the data that would settle the difference is gone with
+> it. The rule's effect on the only slice this checkout can still measure is in
+> **[What the current rule actually does](#what-the-current-rule-actually-does)** below, which is
+> re-runnable and is what a reader should use.
+
+**Why ask.** Contracting a 2026 slice flagged its own clustering as a deliberate
simplification: naive common-input union-find is precisely the adversary the framework calls
incompetent, and its largest cluster, 30 304 addresses, was suspected of being a merge
artifact rather than one owner. This measures what a refusing clusterer actually changes.
-**What was implemented.** Two refusal rules, both decidable from the spending transaction
+**What was implemented.** Two refusal channels, both decidable from the spending transaction
alone, so both survive at slice scale:
-- **coinjoin shape** — many inputs against many outputs is where co-spending stops implying
- common ownership, so no input merges with any other. This is the framework's own stated
- rule for the cautious adversary.
-- **de-mix partition** — where `coinjoin_demix` resolves inputs to distinct participants,
+- coinjoin shape — where co-spending stops implying common ownership, no input merges with
+ any other. This is the framework's own stated rule for the cautious adversary. It is now two
+ rules rather than one (`decluster/monitor.py:44-81`): the original many-in/many-out test,
+ twenty inputs *and* twenty outputs, and an equal-output test that fires on three or more
+ inputs against three or more outputs sharing one value. The second is the defining structure
+ of an equal-amount coinjoin — one interchangeable denomination per participant — and the size
+ rule cannot see it: a five-participant Whirlpool round is 5-in/5-out, far under any
+ participant threshold.
+- de-mix partition — where `coinjoin_demix` resolves inputs to distinct participants,
only same-participant inputs merge, and an input it cannot resolve merges with nobody.
Returning the partition rather than per-pair verdicts is what keeps this linear.
-**Data.** `slice_2026.ndjson`, 831 770 transactions, of which **141 452 (17.0 %) have two
-or more distinct input addresses** and so present a CIOH merge decision at all.
+## What the current rule actually does
-## Result
+Measured on `data/amount-channel-812695-812831-v1.json` — 5,491 transactions over blocks
+812,695–812,831, the committed contiguous slice carrying prevout values, and so the only one on
+which the equal-output arm can fire at all. The denominator is the **934 transactions with two or more
+distinct input addresses**, which is the population that presents a CIOH merge decision; the
+1,428 with two or more inputs is the wrong denominator, since a transaction spending two coins
+of one address merges nothing.
+
+| rule | fires on | share of 934 |
+|---|---:|---:|
+| many-in/many-out (≥ 20 in and ≥ 20 out) | 12 | 1.28 % |
+| equal-output group (≥ 3 in and ≥ 3 equal outputs) | 22 | 2.36 % |
+| **either — the rule as it now stands** | **26** | **2.78 %** |
+
+The partition it produces, over the whole sample:
+
+| clustering | addresses | clusters |
+|---|---:|---:|
+| naive CIOH | 9,960 | 889 |
+| refuse, many-in/many-out only | 9,191 | 881 |
+| **refuse, current rule** | **9,109** | **868** |
+
+The equal-output arm newly refuses **16 transactions**, **14** of which have two or more
+distinct input addresses and so actually move the partition: **82 further addresses withheld
+across 13 further clusters**. The refusal is roughly 2.2× wider than the size rule alone on
+this slice — which is the correction the superseded numbers above most need.
+
+**Two things that qualify it.**
+
+*Two of the fourteen are batch shapes, not mixes.* Their in/out/largest-equal-group triples are
+`(3, 196, 3)` and `(11, 238, 7)`: three and seven repeated values among 196 and 238 outputs.
+That is far more likely a batcher paying several recipients the same amount than a mix
+denomination. The error direction is conservative — the rule refuses a merge it should have
+made — so it costs recall, not correctness, but it means the 2.78 % is an upper reading of how
+much genuine mixing is present.
+
+*The arm is structurally inert on address-only exports.* `equal_output_group` reads output
+values, and the graph-scale exports do not carry them. Over `_span6m.ndjson` — 960,717
+transactions, none of them carrying a single output value — it fires zero times, while the
+size rule fires 1,140 times. Any refusal figure measured on an `epoch_2016_*`-shaped export,
+here or in a sibling document, is therefore still the many-in/many-out rule alone and should be
+read as such.
+
+## Superseded slice-scale result
+
+Everything in this section is under the banner: measured under the size rule alone, over
+`slice_2026.ndjson` (831 770 transactions, of which **141 452 (17.0 %) have two or more distinct
+input addresses**), which is not in this checkout. Retained as a record of what was run, not as
+a current figure.
| | fires on | share of multi-input txs |
|---|---:|---:|
@@ -38,37 +99,42 @@ Downstream, matcher margin over a degree-only guess at eccentricity ≥ 5, seed
| naive | 534 652 | 33 033 | 61 | +0.257 |
| refusing | 525 010 | 32 857 | 59 | +0.229 |
-*(This row was re-measured after the edge-attribution fix in `RESULTS-graph-shape.md`. The
+*(This row was re-measured after the edge-attribution fix that `graph_shape` exposed. The
earlier version compared the naive graph against a refusing graph with four times too many
edges — 4.4M against 534k — so it was not a fair comparison even though it happened to give
similar precision. With both graphs corrected to comparable edge counts the two remain
-indistinguishable, so the "inert" conclusion now holds for the right reason.)*
+indistinguishable, so the "inert" conclusion held for the right reason **at the rule strength
+then in force**.)*
## Reading
-**The refusal is inert on this data.** It touches 0.33 % of the merge decisions, and the
-matcher's margin over degree moves from +0.257 to +0.229, which is noise. The distinction between the
-blind adversary and the cautious one, which the framework treats as decisive, does not
-separate them here.
-
-**The mega-clusters are untouched.** 30 304 and 21 912 come out identical under both. They
-are not built from coinjoin-shaped or de-mixable transactions; they are built from ordinary
-multi-input consolidations that neither rule can see. So the earlier suspicion is now
-narrower rather than confirmed: if that cluster is an artifact, it is one only the
-fingerprint channel could expose.
-
-**And that channel is unavailable for a reason worth naming precisely.** The fingerprint
-refusal compares the *funding* transactions of two co-spent coins, not the transaction that
-spends them. For a two-day slice those funders lie almost entirely outside it. The
-limitation is data, not algorithm, and the requirement it implies is bounded and specific:
+**The refusal is narrow, and how narrow is now an open question at slice scale.** On the slice
+this checkout can still measure it touches 2.78 % of merge decisions rather than the 0.33 %
+recorded above, so the "inert" verdict and the matcher-margin comparison that supported it
+(+0.257 → +0.229) both rest on a rule strength that no longer holds. Whether a 2.2×-wider
+refusal moves the margin is exactly what the re-run has to answer; nothing here should be read
+as having answered it.
+
+**The mega-clusters were untouched by the size rule.** 30 304 and 21 912 came out identical
+under both arms of the superseded run. They are not built from many-in/many-out or de-mixable
+transactions; they are built from ordinary multi-input consolidations that neither rule could
+see. Whether the equal-output arm reaches them is untested — it needs output values, and the
+2026 slice would have to be re-exported with them.
+
+**And the fingerprint channel is unavailable for a reason worth naming precisely.** The
+fingerprint refusal compares the *funding* transactions of two co-spent coins, not the
+transaction that spends them. For a two-day slice those funders lie almost entirely outside it.
+The limitation is data, not algorithm, and the requirement it implies is bounded and specific:
one hop back from every in-slice input. That is the concrete change a future export needs
in order to test the cautious adversary properly.
-**One knob is doing a lot of the work and is not calibrated.** The coinjoin-shape rule fires
-at twenty inputs *and* twenty outputs, which is strict; most collaborative transactions,
-payjoins above all, look nothing like that. A looser threshold would refuse more, and
-whether it should is an empirical question this does not answer. Loosening it without
-evidence would trade one unexamined default for another.
+**Neither arm is calibrated, and they fail in opposite directions.** Twenty-in-and-twenty-out
+is strict: most collaborative transactions, payjoins above all, look nothing like that, and on
+the committed slice it reaches 1.28 % of merge decisions. Three equal outputs is loose enough to
+catch batch payments, as the two shapes above show. Both thresholds are defaults rather than
+findings, and where between them the right rule sits is an empirical question this does not
+answer. Neither arm sees a payjoin at all: two inputs against two outputs is the shape a
+payjoin is built to wear, and detecting it needs the amount channel, not a shape rule.
## Scope
@@ -79,8 +145,12 @@ fingerprints. Single slice, single month.
## Reproducibility / provenance
-The refusal **mechanism** (net-bit-balance keep/reject, refuse-edges, coinjoin-shape and
+The refusal mechanism (net-bit-balance keep/reject, refuse-edges, coinjoin-shape and
de-mix rules) is unit-tested and pinned in `tests/test_cluster_refined.py` and
-`tests/test_cluster.py`. The slice-scale **numbers** in this document (refusal shares over the
-2026 window) are a **data-run** over the unversioned `slice_2026.ndjson`, regenerated by the
-analysis scripts, and are **not** pinned as asserted values in a unit test.
+`tests/test_cluster.py`. The table above is a data-run over
+`data/amount-channel-812695-812831-v1.json`, which is committed, recomputable from
+`decluster.monitor.is_coinjoin` and `decluster.views.cluster_addresses`, and not asserted as a
+pinned value. The slice-scale
+figures under the banner are a data-run over `slice_2026.ndjson`, which is absent, under a
+rule that has since changed; they are neither recomputable nor re-labellable and stand only as
+a historical record.
diff --git a/results/RESULTS-reid.md b/results/RESULTS-reid.md
index df77b743..11d3bc62 100644
--- a/results/RESULTS-reid.md
+++ b/results/RESULTS-reid.md
@@ -1,4 +1,12 @@
-# Sparsity predicts de-anonymization: the record-linkage attack, stratified
+# Ancestry-signature record linkage
+
+> This historical narrative is superseded by the result recalculated from
+> `catalog/runs/reid-v1.json`. The structured artifact is
+> `results/artifacts/reid-v1.json`; `results/generated/reid-v1.md` is its canonical rendering.
+> The run demonstrates record linkage under auxiliary origins sampled from each target's stored
+> signature. It does not establish wallet ownership, independently observed auxiliary information,
+> a chain-wide rate, or reproduction of the Netflix experiment. The discussion below is retained
+> as provenance and contains stronger historical interpretations.
**Why this closes the argument.** `RESULTS-ancestry-sparsity.md` measured that the ancestry
feature space is (epsilon, delta)-sparse: the precondition the sparse-dataset attack
@@ -50,7 +58,7 @@ is not.
1.000 in both strata): it never hands back a wrong coin. The strata differ in *how often it
declares*. On sparse coins the target stands clear of the field, the gate fires (declare rate
~0.95), and the pin is exact. On dense coins the near-twin ties the top two scores, the gate
-falls below phi, and it **abstains** (declare rate ~0.20). This is the Definition-3 behaviour:
+falls below phi, and it abstains (declare rate ~0.20). This is the Definition-3 behaviour:
detect when there is no confident match rather than guess. Sharing ancestry is what preserves
a dense coin's pseudonymity, exactly the framework's own defence.
@@ -60,14 +68,14 @@ declare rate falls 0.225 -> 0.200 as the added evidence makes the twin tie tight
## Scope, stated plainly
-- The `.cache/` is an **accreted, non-representative** sample (dominated by two large
+- The `.cache/` is an accreted, non-representative sample (dominated by two large
clusters), not a uniform draw from the chain. This measures the sparsity->de-anon link on
*these* signatures; it does not estimate a chain-wide de-anonymization rate.
-- **Uniform link oracle**, not the real subset-sum dss oracle: conservative, as established
+- Uniform link oracle, not the real subset-sum dss oracle: conservative, as established
in `RESULTS-ancestry-sparsity.md`. The real oracle sharpens signatures and would widen the
gap, not narrow it. `reid.py` takes the scoring through `provenance_link`, so the dss
oracle drops in without changing the harness.
-- The revealed `m` ancestors are drawn at **random** from the target's support. A real
+- The revealed `m` ancestors are drawn at random from the target's support. A real
counterparty (the Eve-Alice-Eve origin) can choose the *rare* ancestors and would do at
least as well: the rates here are a floor for random aux, not a ceiling.
- 200-coin fixture; the required aux grows as log N (Theorem 1), so a chain-scale attack
diff --git a/results/RESULTS-rejoin.md b/results/RESULTS-rejoin.md
deleted file mode 100644
index 82d99eb3..00000000
--- a/results/RESULTS-rejoin.md
+++ /dev/null
@@ -1,72 +0,0 @@
-# Can the matcher join a user's separate pseudonyms?
-
-**Why this is the question.** Every earlier run contracted both views from one clustering,
-so a correct match linked a cluster to *itself*. That recovers the identity map, which the
-adversary already holds; it is not new information and could feed nothing back into its
-heuristics. Worse, precision defined as identity counted as an error the one outcome the
-attack exists to produce. The framework's claim is specific: *"So long as the adversary
-still hasn't collected enough evidence to conclude that several clusters belong to the same
-user, that user still enjoys pseudonymity."* Breaking that means joining two pseudonyms of
-one user, and nothing measured so far had tested it.
-
-**Method.** Clusters are deliberately split before contraction, so the adversary's
-clustering is incomplete in the way the framework assumes, and the answer is known. Three
-outcomes: **identity** (a pseudonym matched to itself, correct but already known),
-**rejoin** (matched to the other half of its own cluster — the discovery, and the only
-outcome worth feeding back), and **error**. The seed is drawn only from *intact* pseudonyms:
-seeding from a split cluster would hand the adversary the answer it is meant to find.
-
-## Result
-
-| split | clusters split | seed | matched | identity | **rejoin** | error | rejoins per split cluster |
-|---|---:|---:|---:|---:|---:|---:|---:|
-| random, half | 27 871 | 400 | 67 | 47 | **2** | 18 | 0.007 % |
-| boundary, all | 2 986 | 361 | 53 | 29 | **4** | 20 | 0.13 % |
-| boundary, half | 1 474 | 400 | 84 | 47 | **1** | 36 | 0.07 % |
-
-At eccentricity ≥ 5 the rejoin counts are 1, 1 and 1 respectively.
-
-## Reading
-
-**The discovery almost never happens.** Across every configuration the matcher produces
-between one and five genuine new links, from thousands of users whose pseudonyms were
-separated. Under the sharpest test, 2 986 users split across the view boundary, it joined
-four. Ninety-nine point nine percent kept their pseudonymity.
-
-*(Re-run on the graph corrected in `RESULTS-graph-shape.md`; the pre-fix figure was five of
-2 986. The correction does not touch the conclusion.)*
-
-**Seed starvation does not explain it.** Splitting consumes exactly the spanning clusters
-that make good seeds, so the boundary runs are seed-poor by construction and that is the
-obvious alternative explanation. Halving the split doubles the intact pool, from 381 to 959,
-and the rejoin rate *falls*. More seed did not buy discoveries.
-
-**The first design was wrong and its number should not be used.** Splitting by drawing
-addresses at random leaves both halves present in both views, so the identity match remains
-available and is structurally easier than the rejoin while being equally correct. That
-design puts the right answer in competition with an easier one. Splitting along the view
-boundary removes the escape, one pseudonym on each side, and the rejoin rate triples, from
-0.007 % to 0.17 % per split cluster. The low rate in the first run was measuring the design,
-not the graph.
-
-**Errors dominate the new information.** In the boundary runs, 34 % and 43 % of matches are
-outright wrong against 9 % and 1 % rejoins. A channel fed from this output would carry far
-more error than discovery unless restricted to the high-confidence band, where the counts
-fall to a single rejoin per run.
-
-## Consequence for feeding a clustering channel
-
-This was the last step of the planned programme, and it is now answerable rather than
-assumed. The channel is buildable and would be nearly empty: on the order of one
-high-confidence new link per two-day slice, against thousands of opportunities. The
-weighting question from `RESULTS-match-confidence.md` is therefore moot at this scale, not
-because the links are unreliable but because there are almost none of them.
-
-## Scope
-
-Two-day views, which `RESULTS-persistence-curve.md` identifies as the weak regime: 31 % of a
-vertex's neighbours recur, rising only to 38 % at a week and saturating there. A single
-slice, a single month, one partition scheme, and a clustering whose refusal channels were
-separately measured to be inert on this data. The result bounds what this method achieves in
-this regime; it does not establish that no regime works, and the persistence curve says
-which lever remains untested.
diff --git a/results/RESULTS-slice-a-channels.md b/results/RESULTS-slice-a-channels.md
new file mode 100644
index 00000000..a6ac10eb
--- /dev/null
+++ b/results/RESULTS-slice-a-channels.md
@@ -0,0 +1,121 @@
+# The entity-attribute and social-graph channels on a real slice
+
+> The reproducible six-block observations are recalculated by
+> `catalog/runs/slice-channels-v1.json`. Its structured artifact is
+> `results/artifacts/slice-channels-v1.json`, with canonical rendering in
+> `results/generated/slice-channels-v1.md`. Measurements below for the 150.000-transaction and
+> 947.000-transaction slices are historical because their exact inputs were not preserved. The
+> canonical run measures attribute density and graph shape; it does not measure graph-matching
+> success or a privacy score.
+
+**What this measures.** The reid capstone (`RESULTS-reid.md`) established that the *ancestry* feature
+space is (epsilon, delta)-sparse and de-anonymizes (~0.94 on sparse coins). This measures the other
+two channels an adversary can read off the graph: each entity's *attribute* distributions, and the
+*cross-view* structure of the pseudonym graph. The attribute space is dense, and the contracted graph
+is transactional rather than social, so on this slice neither channel de-anonymizes by itself.
+
+**Data.** A contiguous 2016 slice, blocks 400000-400600 (601 blocks, ~947k transactions, ~860k
+addresses), exported at address level via `bigquery/graph.sql` and collected with
+`examples/bq_chunk_download.py` (chunked, no billing). Numbers below are the 150k-transaction working
+slice (blocks 400000-~400100) unless the full 947k slice is named. Addresses are clustered with the
+engine's refuse-guarded common-input clustering (`views.cluster_addresses`) and each cluster
+contracted into a pseudonym vertex (`contraction.contract`).
+
+## 1. Entity attributes are dense
+
+`def1_sparsity` measures each entity's nearest-neighbour cosine over its attribute distributions, the
+survival curve `RESULTS-reid.md` uses on ancestry. A sparse space keeps that curve near zero until
+epsilon is high. Here it sits near one throughout.
+
+| epsilon | delta (150k slice) | delta (min_degree=10) |
+|---|---:|---:|
+| 0.5 | 1.000 | 1.000 |
+| 0.9 | 0.994 | 0.993 |
+| 0.95 | 0.982 | — |
+| 0.99 | 0.956 | 0.575 |
+
+Almost every entity has a near-twin at epsilon=0.9, and restricting to well-connected entities
+(min_degree up to 10) leaves it there, so the density is not a singleton artefact: an entity's
+attribute distribution does not single it out. The channel that does is ancestry, measured separately.
+
+**Correction, 2 Sep 2026.** The table above was measured before `feature_vector` normalised its
+neighbour-degree histogram: as a raw count that one component carried almost the whole vector norm at
+high degree, so the cosine read degree similarity. Re-measured on the committed fixture with the
+corrected vector, the density is *higher* than recorded, and it holds across the degree range:
+
+**Superseded by the correction of 6 Sep 2026 below.**
+
+| min_degree | eligible vertices | delta(0.5) | delta(0.9) | delta(0.99) | median top-sim |
+|---:|---:|---:|---:|---:|---:|
+| 2 | 8,398 | 1.000 | 1.000 | 0.990 | 1.000 |
+| 3 | 4,423 | 1.000 | 0.997 | 0.984 | 1.000 |
+| 5 | 641 | 1.000 | 0.986 | 0.949 | 1.000 |
+| 10 | 231 | 1.000 | 0.965 | 0.922 | 0.999 |
+| 20 | 89 | 1.000 | 0.921 | 0.854 | 0.999 |
+
+**Correction, 6 Sep 2026.** This is not an attribute measurement, and the table above was inflated
+by reading absent fields as values. The committed fixture is an address-only export: it carries no
+version, locktime, sequence or fee. `locktime_policy` returned `zero` for a missing locktime and
+`x_version` returned `vNone`, so all 12,000 transactions agreed on two axes for free and every
+feature vector carried the same two components. With the extractors abstaining instead, all four
+axes are skipped on all 12,000 transactions — the canonical artifact now reports those counts — and
+what remains is the structural vector alone: degree, transaction count, self-transfers and the
+neighbour-degree histogram.
+
+| min_degree | eligible vertices | delta(0.5) | delta(0.9) | delta(0.99) | median top-sim |
+|---:|---:|---:|---:|---:|---:|
+| 2 | 8,398 | 1.000 | 0.998 | 0.983 | 1.000 |
+| 3 | 4,423 | 1.000 | 0.995 | 0.977 | 1.000 |
+| 5 | 641 | 1.000 | 0.977 | 0.894 | 1.000 |
+| 10 | 231 | 1.000 | 0.952 | 0.874 | 0.999 |
+| 20 | 89 | 1.000 | 0.876 | 0.798 | 0.999 |
+
+Density still falls with degree and still never approaches sparsity, but the fall is steeper than
+recorded: at min_degree 20 the epsilon-0.99 figure moves 0.854 to 0.798. The conclusion this section
+draws is unchanged and its basis is narrower — structural similarity on a slice that carries no
+attributes, not the entity-attribute channel the heading promises. Pinned as a band in
+`tests/test_slice_a_channels.py`.
+
+## 2. The pseudonym graph is transactional, not social
+
+The cross-view attack (cit 24) re-links a user whose activity a partial clustering left as separate
+pseudonyms across two time views. The form of the test that does not hand over the answer splits each cluster along the view
+boundary (`split_clusters_by_view`): the cluster becomes one pseudonym in view A and another in view
+B, and the matcher must rejoin the two from graph structure alone. Each view is contracted under its
+own lookup — tagging an address by where it is first seen instead leaves an address used in *both*
+windows marked `#a` everywhere, so view B holds `C#a` as well as `C#b` and the identity match, which
+is graded wrong, is the structurally better one. The figures below predate that fix (2 Sep 2026) and
+are superseded; see `results/generated/graph-rejoin-2016-v1.md` for the corrected construction on
+committed data.
+(Contracting a complete clustering against itself instead only lets the matcher recover the identity
+map, which carries no new information.) On this slice, of 5077 split pseudonym pairs a 5-10% seed
+rejoins none (precision 0.000). The graph is disassortative (-0.064, hubs attaching to leaves) with no
+clustering beyond what its degree sequence forces, so there is no neighbourhood regularity to
+propagate along. The limit is the window: 601 blocks is ~10 days, a pseudonym's view-A neighbours and
+view-B neighbours are different one-off counterparties, and a neighbourhood that does not recur cannot
+be rejoined. Stable neighbourhoods need relationships that repeat across many epochs, a multi-year
+span (§10 of `PAPER.md`); this slice measures the short-window regime only.
+
+## Reading
+
+The three channels an adversary reads off the graph carry different amounts of identifying signal.
+Ancestry is sparse and pins a coin to its origin (`RESULTS-reid.md`). Attribute distributions are
+dense: an entity's fee, timing, and value habits do not single it out. Cross-view structure is
+underpowered on a short window, because recurring relationships have not yet accumulated. An attacker
+holding only this slice de-anonymizes through provenance, not through attributes or graph matching.
+
+The full 947k slice holds the same density curve at scale (175293 clusters over 860164 addresses,
+1011271 contracted vertices); the 150k slice is the primary because `graph_shape` transitivity is
+superlinear on a million-vertex graph.
+
+## Scope, stated plainly
+
+- **The density curve is slice-stable and pinned.** It reproduces on the committed 6-block fixture
+ (delta(0.9)=0.996), on the 150k slice, and at full scale. `tests/test_slice_a_channels.py` pins it
+ (delta(0.9) >= 0.95) together with the sign of assortativity.
+- **The cross-view precision and the transitivity ratio are scale-dependent and are not band-pinned.**
+ The transitivity ratio measured 7.0 on 6 blocks, 1.0 on 15 blocks, and 0.0 on 150k, so only the
+ direction (negative assortativity, a dense attribute space) is committed as a test. The precision
+ figures above read one short window, not a chain-wide rate.
+- This is one era (2016), and one time span. The long-run cross-view result is a separate measurement
+ on the multi-year collection.
diff --git a/results/RESULTS-slice-gate-2026.md b/results/RESULTS-slice-gate-2026.md
index 0a96c785..101996cd 100644
--- a/results/RESULTS-slice-gate-2026.md
+++ b/results/RESULTS-slice-gate-2026.md
@@ -83,3 +83,8 @@ contracted graph before the seed strategy is settled.
Address-level proxies for cluster-level quantities, chosen because union-find does not fit
in SQL. Every number here is a bound on the contracted graph, not a measurement of it.
Two single days; seasonal variation across the 158-epoch window is not covered.
+
+## Reproducibility / provenance
+
+State 1 in `results/REPRODUCIBILITY.md`: band-pinned on committed data — `tests/fixtures/slice_gate.json`, compared against the published table by
+`tests/test_state3_promotion.py`.
diff --git a/results/RESULTS-special-change.md b/results/RESULTS-special-change.md
deleted file mode 100644
index b86f690b..00000000
--- a/results/RESULTS-special-change.md
+++ /dev/null
@@ -1,68 +0,0 @@
-# Special-case labels — an independent value-based cross-check of the ordering (Phase 1)
-
-Change identification validated against a **value-based** special-case label (optimal-change / UIH,
-à la Ron & Shamir) that is disjoint from co-spend clustering *and* from the construction fingerprints
-it validates — so this cross-check is not circular. Complements `results/RESULTS-change-id.md`
-(which used a co-spend label). Code: `decluster/change_special.py`, `examples/special_change_validation.py`.
-
-> **Note:** `examples/special_change_validation.py` has since been extended to *triangulate four*
-> near-independent labels (optimal-change, round-number, type-match, address-reuse) and print a
-> label-agreement matrix. The Phase-1 tables below are the single-label (optimal-change) results;
-> the four-label triangulation results are pending a dedicated run.
-
-**Optimal-change label:** in a ≥2-input 2-output tx, the output smaller than the smallest input value
-must be the change (else an input was unnecessary). Reads only values. Disjointness is machine-checked
-(`tests/test_change_special.py::test_optimal_change_reads_only_values`).
-
-**Sample:** the value-carrying BigQuery downloads (`~/Downloads/bquxjob_*.json`), **105,546 txs,
-multi-epoch (blocks 318,531–940,950 ≈ 2014–2025)** → **10,691 optimal-change labels**.
-
-## Within-tx predictors vs the optimal-change label (offline)
-
-| predictor | TPR | FPR | coverage | precision |
-|---|---|---|---|---|
-| round_number (`_change_index`) | 0.524 | 0.092 | 0.617 | 0.85 |
-| address_reuse | 0.121 | 0.012 | 0.132 | 0.92 |
-
-The round-number heuristic agrees with the value label at ~0.85 precision on 62% of labels; address
-reuse is rare here (13%) but the reused output is the change 92% of the time. Two M&N universal
-heuristics cross-confirming, non-circularly.
-
-## Onward-spend fingerprints vs the optimal-change label (live fetch, n=1500)
-
-| axis | TPR | FPR | coverage | precision |
-|---|---|---|---|---|
-| input_order | 0.314 | 0.133 | 0.447 | 0.70 |
-| output_order | 0.286 | 0.191 | 0.477 | 0.60 |
-| nSequence | 0.311 | 0.110 | 0.421 | 0.74 |
-| version | 0.200 | 0.092 | 0.292 | 0.68 |
-
-## The key finding: the co-spend-label numbers are not robust
-
-Against the **co-spend** label (a single 2024-06-01 day; `RESULTS-change-id.md`), `nSequence` reached
-0.99 precision and `version` 1.00, and the ordering axes looked distinctly weaker. Against the
-**value** label, that collapses — **all four onward-spend axes fall to ~0.60–0.74 precision, and
-nSequence/version no longer dominate the ordering axes.** So the "nSequence/version are the strong
-tells, ordering is weak" ranking is **label- and population-dependent, not a robust result.**
-
-The divergence is confounded — four plausible causes, none isolable here:
-
-1. **Epoch.** The value sample spans 2014–2025; construction fingerprints drift heavily over time.
- The co-spend result was a single 2024 day.
-2. **Onward-spend time gap.** Multi-epoch uniform txs can be spent years later (fingerprint drift);
- the one-day slice has short gaps (stable fingerprints).
-3. **Co-spend label selection bias (likely inflates).** The co-spend label selects changes whose
- onward-spender *is* the reveal tx — a same-wallet transaction that shares nSequence/version almost
- always. The value label has no such bias.
-4. **Optimal-change label error.** The "no unnecessary inputs" assumption fails for some txs.
-
-Cause 3 is a real methodological inflation in the co-spend result; causes 1–2 also lower the value
-result. So this does **not** retract the co-spend numbers — it shows they are not robust, and the
-per-axis onward-spend signal is weaker and more label-dependent than the single-day co-spend table
-suggested.
-
-## What closes it (Phase 2)
-
-The clean disentangling — label-inflation vs epoch/time-gap — needs a **contiguous slice with input
-values** (a BigQuery re-query), so **both** labels (co-spend and optimal-change) run on the **same
-transactions**. That, plus the cluster `findNext` against the value label, is Phase 2 (quota-gated).
diff --git a/results/RESULTS-subtx-demix.md b/results/RESULTS-subtx-demix.md
index 166010a9..2bb37a82 100644
--- a/results/RESULTS-subtx-demix.md
+++ b/results/RESULTS-subtx-demix.md
@@ -1,31 +1,23 @@
-# Coinjoin de-mix — amount channel
+# Subtransaction de-mix diagnostic
-The amount channel de-mixes a coinjoin by matching each participant's input to the mix denomination
-plus their change output, minus a bounded maker fee: `input = mix + change − fee`
-(`decluster/coinjoin_demix.py`). Two co-spent inputs assigned to different participants are
-conclusively different owners (`amount_refuse_demix` → refuse), fused with the fingerprint and
-cluster-level topology channels in `cluster_refined`.
+Canonical run: `catalog/runs/subtx-demix-v1.json`.
-## Worked de-anonymisation (real JoinMarket coinjoin)
+The executable summary is `results/generated/subtx-demix-v1.md`; the complete
+measurement is `results/artifacts/subtx-demix-v1.json`. The internal dataset
+preserves 39 cache JSON files containing 36 transactions and three outspend
+responses.
-`0cb4870cf2dfa3877851088c673d163ae3c20ebcd6505c0be964d8fbcc856bbf` — 11 participants, mix denomination
-6357366 sats. The de-mix recovers **8 of the makers** uniquely (`examples/coinjoin_demix_demo.py`),
-with maker fees `[191, 413, 458, 559, 623, 636, 687, 973]` sats. The remaining inputs (the taker and a
-multi-input maker) are left unmatched. This reproduces the reference de-mix (`joinmarket_analyzer`).
+For JoinMarket transaction `0cb4870c…856bbf`, the amount identity recovers eight
+of twelve inputs, with implied fees of 191, 413, 458, 559, 623, 636, 687 and 973
+satoshis. This reproduces the mechanism, not participant identities: there are
+no independent maker labels.
-## Dense coinjoins are amount-private
+The historical specificity claim is not supported. Of 25 width-eligible
+transactions in this CoinJoin-adjacent cache, 22 trigger the heuristic. The
+cache has no labels establishing that those transactions are ordinary payments,
+so this is neither a false-positive rate nor evidence of specificity. The six
+historical Wasabi rounds are not preserved and their zero-recovery claim is not
+reproduced.
-On the labelled Wasabi 2 coinjoins the de-mix recovers **0** participants — Wasabi 2's dense
-denomination tiers defeat the match, so the coinjoins are amount-private and the fingerprint +
-cluster-level topology channels carry the decision. This is the correct, decidability-dependent outcome.
-
-## Specificity
-
-Ordinary and batch payments have no mix+change+maker-fee structure, so the de-mix recovers no
-participants (`examples/subtx_demix_specificity.py`) — it does not false-fire on single-owner txs.
-
-## Fused with fingerprints (one engine)
-
-The de-mix refuse is summed with the fingerprint and topology bits in `cluster_refined`
-(`base[k] = cospend_prior + fp + refuse + topology`) and decided by one fixed-point
-(`tests/test_amount_fingerprint_fusion.py`).
+The fee cap and most-common-output detector are local modeling choices. This is
+attacker-side de-mix evidence, not CoinScore or a privacy certificate.
diff --git a/results/RESULTS-temporal.md b/results/RESULTS-temporal.md
index 013a9761..cef2302f 100644
--- a/results/RESULTS-temporal.md
+++ b/results/RESULTS-temporal.md
@@ -28,7 +28,7 @@ activity schedule.
## Real-data calibration — a NULL under proper controls
-Same owner = address reuse (near-certain): for an input address reused ≥8 times over a **wide**
+Same owner = address reuse (near-certain): for an input address reused ≥8 times over a wide
window, its txs' hour-of-day histogram is its schedule. We ask whether that schedule *identifies
the owner*. Harness: `calibrate_temporal` (`decluster/broadcast.py`), on a **30-day** export
(`bigquery/temporal.sql`, 2024-01) of **20 000** reused input addresses (≥8 txs each, median span
@@ -46,27 +46,28 @@ The headline **0.923 is an artifact of two confounds**, not an owner-identifying
1. **Random split-half tests concentration, not persistence.** The two halves are i.i.d. samples
of one pooled distribution, so the distance is small whenever the address touches a *narrow
- band of hours* — regardless of which hours or whether the band persists. Splitting by **time**
+ band of hours* — regardless of which hours or whether the band persists. Splitting by time
instead (does the schedule hold across the window?) drops the AUC to **0.736**, and to **0.596**
on the ≥7-day subset.
2. **Negatives weren't matched on band width.** Most of the remaining separation is just
"narrow band vs narrow band at different hours." Drawing negatives from addresses with the
- **same active-hours count** removes it: **AUC 0.492 — chance.**
+ same active-hours count removes it: **AUC 0.492 — chance.**
-So on reused-address data the hour-of-day schedule does **not** separate owners once concentration
+So on reused-address data the hour-of-day schedule does not separate owners once concentration
is controlled for. A random-band synthetic (bands placed at arbitrary hours with no owner meaning)
reproduces the ~0.92 baseline, confirming the number measures "addresses have a narrow active-hours
-band," not "the schedule identifies the owner." This joins the ~1-day slice (AUC 0.39, `sample.ndjson`)
-as a **null**: the earlier 0.92 claim was confounded and is retracted.
+band," not "the schedule identifies the owner." This joins the ~1-day slice (AUC 0.39,
+`data/amount-channel-812695-812831-v1.json`)
+as a null: the earlier 0.92 claim was confounded and is retracted.
**A cleaner owner labeling gives the same null.** To rule out "it's only the service-skewed
-reused-address population," we re-ran on **co-spend entities** (`bigquery/temporal-cospend.sql`:
+reused-address population," we re-ran on co-spend entities (`bigquery/temporal-cospend.sql`:
addresses spent together = one owner, tx count capped at 200 to drop mega-services) — closer to
individual, multi-address wallets. Same 30-day window, 20 000 entities: baseline 0.889, persistence
0.760, **matched 0.522** — still chance. The null is *structural* (the metric plus concentration),
not an artifact of picking service addresses.
-## Honest limits
+## Limits
- **Not validated as an owner separator.** The mechanism (`active_hours`/`schedule_distance`) is
well-defined and disjoint active hours are still weak evidence of *different* owners, but this
@@ -75,3 +76,7 @@ not an artifact of picking service addresses.
which may under-show a timezone effect for individuals; isolating that needs a different ground
truth and a persistence-based, band-matched test (as above).
- Uses the broadcast estimate, so it inherits its limit — coarser where the bound is loose.
+
+## Reproducibility / provenance
+
+State 2 in `results/REPRODUCIBILITY.md`: the mechanism is pinned; the reported numbers come from a data-run over the `bigquery/temporal.sql` export, which is not committed, and are not asserted.
diff --git a/results/RESULTS-topology.md b/results/RESULTS-topology.md
index d345b90a..a07e8160 100644
--- a/results/RESULTS-topology.md
+++ b/results/RESULTS-topology.md
@@ -5,7 +5,7 @@ Reproduce: `python3 tests/test_topology.py`. Term in `decluster/cluster.py`
## The gap this addresses
-Fingerprints separate only *different* wallet software. If Alice and Bob use the **same**
+Fingerprints separate only *different* wallet software. If Alice and Bob use the same
wallet (identical fingerprints) and payjoin together, the co-spend heuristic plus the
matching fingerprints threaten to collapse their clusters — a false positive the fingerprint
channel cannot refuse (paper §9). The robust control is graph topology (Narayanan–Shmatikov):
@@ -15,8 +15,8 @@ Alice's counterparties differ from Bob's.
Each counterparty is weighted by its rarity, `−log2(share of nodes touching it)`
(`counterparty_bits`): a hub (an exchange everyone touches) earns ~0 bits, a private address
-many. On a connected real slice (`sample.ndjson`, 5 491 txs, 27 281 addresses, 889 co-spend
-entities) counterparty bits range **4.1 (hub) to 14.7 (unique)**, and a shared-counterparty
+many. On a connected real slice (`data/amount-channel-812695-812831-v1.json`, 5 491 txs,
+27 281 addresses, 889 co-spend entities) counterparty bits range 4.1 (hub) to 14.7 (unique), and a shared-counterparty
score separates held-out same-owner pairs from cross-owner pairs:
| topology score | mean same-owner | mean cross-owner | AUC |
@@ -24,20 +24,20 @@ score separates held-out same-owner pairs from cross-owner pairs:
| raw common-neighbour count | 0.68 | 0.00 | 0.839 |
| **rarity-calibrated bits** | **+3.57 bits** | 0.01 | 0.838 |
-So a shared **rare** counterparty is real, calibrated same-owner evidence, summable with
+So a shared rare counterparty is real, calibrated same-owner evidence, summable with
the fingerprint bits. This is a delivered corroboration signal (it links same-owner coins
the co-spend missed).
## Per-pair mismatch is weak — the refusal needs accumulation
-Calibrating the *disjoint* side **per pair** is the sobering part:
+Calibrating the *disjoint* side per pair is the sobering part:
- **P(disjoint | same owner) = 0.32**, **P(disjoint | different owner) = 1.00** → FS mismatch
weight `log2(0.32/1.00) ≈ −1.65 bits`.
`−1.65` does **not** overcome a same-wallet fingerprint match (`+2.78`): a *single* disjoint
pair is not refused. This is exactly the collaborator's *"if **enough** such distinguishing
-relationships exist"* — the strength has to come from **accumulation**, not one pair.
+relationships exist"* — the strength has to come from accumulation, not one pair.
## Cluster-level accumulation — this is what refuses (proven)
@@ -49,14 +49,14 @@ and calibrate the *aggregate*-disjoint on the same slice:
- FS weight = `log2(0.004 / 0.997) ≈ −8 bits` (Laplace-smoothed)
Same-owner clusters *never* have disjoint aggregate neighbourhoods (they reuse counterparties);
-different owners almost always do. So an aggregate-disjoint is **~−8 calibrated bits** — strong
+different owners almost always do. So an aggregate-disjoint is ~−8 calibrated bits — strong
enough to refuse.
-**End-to-end proof** (`tests/test_topology.py::test_cluster_topology_refuses_same_software_payjoin`):
+End-to-end proof (`tests/test_topology.py::test_cluster_topology_refuses_same_software_payjoin`):
Alice (`A1,A2`) and Bob (`B1,B2`) use the same wallet; each consolidates their own coins, then
a payjoin co-spends `A1,B1`. Merges are evaluated confident-first (Alice and Bob cluster
internally via their shared counterparties), so the payjoin edge is judged against the two
-formed clusters: `fp +2.78 + aggregate-disjoint −8 = −5.2 < −2` → **refused**. Without topology
+formed clusters: `fp +2.78 + aggregate-disjoint −8 = −5.2 < −2` → refused. Without topology
the co-spend collapses them. Alice's cluster is separated from Bob's — the transaction is
interpreted correctly as a payjoin.
@@ -73,16 +73,17 @@ common hub is ~0 bits and a private address is many. `cluster_topology_weight` s
bits of the shared counterparties and compares to a threshold `topo_tau` (bits): an overlap
**below `topo_tau`** — disjoint, or sharing only non-distinctive hubs — is treated as disjoint
(`−8.1`) and the merge is refused; **≥ `topo_tau`** corroborates same owner. Because rarity is
-computed over the *whole graph*, this is **field-independent**: a universal hub (0 bits) is always
+computed over the *whole graph*, this is field-independent: a universal hub (0 bits) is always
below threshold and refused, with no dependence on which clusters sit in any sample. (An earlier
design judged distinctiveness by a windowed N-S *eccentricity* `(max−max2)/σ` over a small
candidate set; that was field-dependent — a genuinely common hub could be missed if the window
held no co-occurring cluster — so it was replaced by this global rarity test, which is N-S's own
quasi-identifier weighting `wt = 1/log|supp|`.)
-**Discriminative calibration on a real slice** (`sample.ndjson`, `calibrate_topo_tau`). Unlike a
+**Discriminative calibration on a real slice** (`data/amount-channel-812695-812831-v1.json`,
+`calibrate_topo_tau`). Unlike a
same-owner-only pass-rate, this measures the actual discrimination: rarity-weighted overlap bits
-for **same-owner** cluster pairs (split-half) vs **different-owner** pairs.
+for same-owner cluster pairs (split-half) vs different-owner pairs.
| overlap bits | value |
|---|---:|
@@ -93,13 +94,13 @@ for **same-owner** cluster pairs (split-half) vs **different-owner** pairs.
The two populations are cleanly separated: same-owner clusters share distinctive counterparties
(11.7 bits), different owners essentially never do. Any `topo_tau` in `[0.5, 3.0]` keeps 100% of
-same-owner and refuses 100% of different-owner on this slice; the default is **`topo_tau = 1.0`**
+same-owner and refuses 100% of different-owner on this slice; the default is `topo_tau = 1.0`
(the shared counterparty must be worth ≥ 1 bit, i.e. touched by ≤ half the nodes), with margin.
End-to-end proof in `test_engine_refuses_hub_only_partial_overlap` (hub-only Alice/Bob
overlap refused; their own distinctive coins kept).
-**Honest limit.** This is the inherent counterparty-quasi-identifier limit, not removed by the
-threshold: two *different* owners who genuinely both transact with the same **rare** counterparty
+**The limit.** This is the inherent counterparty-quasi-identifier limit, not removed by the
+threshold: two *different* owners who genuinely both transact with the same rare counterparty
score above `topo_tau` and would merge — a shared rare quasi-identifier is treated as same-owner
evidence by the FS model itself. The threshold only removes the *hub* false positive; it cannot
tell a shared rare merchant from shared ownership. The AUC ≈1.00 (0.9997) is on one connected slice
@@ -118,3 +119,9 @@ lands near it (overlap is bimodal, 11.7 vs 0.004 bits), but on a denser graph co
overlap treated as disjoint; **discriminatively** validated (same-owner 11.7 vs different-owner
0.004 overlap bits, AUC ≈1.00 on 889 real clusters); field-independent; the residual limit is a
shared *rare* counterparty (inherent FS-QI limit). Validated in `tests/test_topology.py`.
+
+## Reproducibility / provenance
+
+State 2 in `results/REPRODUCIBILITY.md`: the mechanism is pinned; the reported numbers come from a
+data-run over `data/amount-channel-812695-812831-v1.json`, which is committed, and are not asserted
+as pinned values.
diff --git a/results/RESULTS-view-match-2026.md b/results/RESULTS-view-match-2026.md
deleted file mode 100644
index c3f4d181..00000000
--- a/results/RESULTS-view-match-2026.md
+++ /dev/null
@@ -1,89 +0,0 @@
-# Cross-view matching on one-day views of the 2026 graph
-
-**Claim tested.** The cited framework argues that fingerprints need not be sparse on their
-own, because *if the social network structure is recoverable from the transaction graph,
-that will be enough*. That "if" is an untested empirical claim. This tests it: contract two
-views of the pseudonym graph, seed a small correspondence, propagate, and measure what
-comes back.
-
-**Data.** `slice_2026.ndjson`, views A and B of 144 blocks each (≈1 day), one week apart,
-830 770 transactions. Both contracted against one global clustering, then view B relabelled
-with opaque ids and the true correspondence withheld for scoring. 33 033 vertices are
-present and linked in both views; 5 132 hold degree in [3, 100] in both, the population a
-seed can be drawn from and propagation can act on. `examples/view_match_run.py`.
-
-**Seed.** Self-identifying and label-free: mining pools announce themselves in the coinbase
-tag. Only 1 pool payout cluster lands in the seedable band, so the seed is topped up by
-degree within that band.
-
-## Result
-
-| seed | matched | correct | precision | matched per seed |
-|---:|---:|---:|---:|---:|
-| 40 | 5 | 4 | 0.800 | 0.125 |
-| 100 | 34 | 21 | 0.618 | 0.340 |
-| 400 | 146 | 83 | 0.568 | 0.365 |
-| 1 000 | 230 | 111 | 0.483 | 0.230 |
-| 2 500 | 338 | 164 | 0.485 | 0.135 |
-
-At seed 400: **148 matched, 85 correct, precision 0.574**; the shuffle control, scoring the
-same matches against a random bijection, is **0 correct, precision 0.000**; seed-only
-propagation contributes nothing by construction.
-
-## Reading
-
-**A framing correction, added after the fact.** The absence of a cascade is measured
-correctly below, but it was judged against a stricter criterion than the source states.
-The framework explicitly anticipates that "a proliferation of pseudonyms may limit the
-effectiveness of this approach for any particular run of the propagation algorithm" and
-locates the value in the *high confidence* links that feed other clustering heuristics.
-Precision stratified by the matcher's own confidence is the criterion it actually states,
-and it is measured in `RESULTS-match-confidence.md`: 0.727 at eccentricity 5, ten of ten
-above eccentricity 10. Read what follows as a result about coverage, not about the attack.
-
-**The signal is real.** Precision between 0.48 and 0.80 against a shuffle control of
-exactly zero, over a candidate set of 33 033. Structure does carry identity across the
-boundary, and the matcher finds it.
-
-**The cascade does not ignite.** Matches per seed peak at 0.365 and then *fall*. A
-propagation that was self-sustaining would show each seed unlocking more than itself and
-the yield rising with the matched region; instead it saturates below 0.4 and decays.
-Seeding 2 500 of the 5 132 seedable vertices, roughly half the usable population, returns
-338 matches of which 164 are correct.
-
-**Precision degrades as the seed grows**, 0.80 down to 0.485. The later matches are made on
-thinner evidence, so pushing for coverage buys errors rather than reach.
-
-**The mechanism is neighbourhood persistence, and it is the binding constraint.** The 400
-seeds have 15 324 neighbours between them, of which only **2 135 (14 %) exist and are
-linked in both views** and only 1 205 reach degree 3. The frontier collapses on its first
-step: 86 % of a vertex's counterparties in one day are one-offs that never reappear a week
-later. The framework's mechanism requires *recurring* relationships, and at a one-day view
-width most relationships do not recur.
-
-## What this does and does not establish
-
-It establishes that on **one-day views** of the 2026 transaction graph, seeded propagation
-recovers a real but small correspondence and never becomes self-sustaining, and it
-identifies why.
-
-It does not establish that the graph is unrecoverable. View width is the obvious untested
-lever and the measurement points straight at it: the framework's own argument is that the
-more consistent an entity's activity, the more stable its representation, and a one-day
-window samples too little of that activity for a neighbourhood to be distinctive. Wider
-views should raise both persistence and degree. Testing that needs a degree-filtered
-contraction, since the graph structures for a 144-block epoch already run to ~4.3 GB and a
-week-wide view would not fit.
-
-Three further caveats, each removable. The clustering is naive common-input union-find, the
-adversary the framework calls incompetent; `cluster_refined`, which can refuse a co-spend,
-would change the vertex set. Only one partition scheme is exercised here (epoch); the
-ambiguity cut the framework prefers is not. And this is a single pair of views in a single
-month.
-
-## Correction
-
-The first run of this experiment returned one match and was invalid. The seed had been
-topped up with the highest-degree vertices, which are exactly the hubs the matcher refuses
-to route through, so 34 of 40 seeds were invisible to it. The seed selection now draws from
-the non-hub band. The failure was in the experiment, not in the graph.
diff --git a/results/RESULTS-weight-sensitivity.md b/results/RESULTS-weight-sensitivity.md
index bb88beb7..389f3eea 100644
--- a/results/RESULTS-weight-sensitivity.md
+++ b/results/RESULTS-weight-sensitivity.md
@@ -1,55 +1,23 @@
-# Weight sensitivity: is the fingerprint verdict robust to the assumed disagreement weight?
-
-**Claim tested.** The Fellegi-Sunter combiner (`decluster/combiner.py`) scores a mismatch with
-`min(0, log2((1 - c) / (1 - collision)))`, where `c` (`consistency`, default 0.95) is the assumed
-probability that the *same* wallet agrees on an axis. Unlike the agreement weight `-log2(p)`, whose `p`
-is measured from the population, `c` is **assumed, not fitted** — it is not directly measurable without
-same-owner labels (the same labels used to validate), so its exact value is an open, per-axis question.
-Does the separation the model achieves depend on getting `c` right?
-
-**Data.** 165,832 witness-bearing transactions from the local block-tx cache (`.blkcache`) — the same
-corpus as `RESULTS-fingerprint-validation.md`.
-
-**Method.** Sweep `c ∈ {0.60, 0.70, 0.80, 0.90, 0.95, 0.99}`. For each, re-score the **same** seeded
-pairs (`seed=0`, 4,000 same-wallet + 4,000 random, address-reuse label) with
-`LibraryScorer(consistency=c)` over all 23 library axes, and re-measure AUC with a shuffle control. Only
-the weight changes between rows.
-
-## Result
-
-| c | same-wallet mean (bits) | random mean (bits) | AUC | shuffle |
-|---|---:|---:|---:|---:|
-| 0.60 | +20.25 | +5.86 | 0.8970 | 0.4941 |
-| 0.70 | +19.46 | +2.28 | 0.9089 | 0.4937 |
-| 0.80 | +18.25 | −3.07 | 0.9198 | 0.4938 |
-| 0.90 | +16.12 | −12.52 | 0.9281 | 0.4947 |
-| **0.95** | **+13.90** | **−22.39** | **0.9333** | 0.4955 |
-| 0.99 | +8.77 | −45.31 | 0.9365 | 0.4958 |
-
-**The scores are very sensitive to `c`; the verdict is not.** The raw weight of evidence swings
-enormously — the random-pair mean travels ~50 bits (from +5.86 to −45.31, changing sign), the
-same-wallet mean halves. Yet the AUC — the actual same-wallet-vs-random ranking quality — moves only
-from 0.897 to 0.937 across the *entire* range, and by **< 0.01** across the realistic band
-(0.90–0.99). Near the operating point it is flat: 0.928 → 0.933 → 0.937. The shuffle control stays
-pinned at ~0.49 for every `c`, so the signal is real at any weight, not an artifact of the calibration.
-
-The mechanism: the *magnitude* of the verdict depends on the weight, but the *ranking* depends on the
-aggregate of 23 axes, which concentrates and dilutes per-axis weight uncertainty. The `≤0` clamp on the
-mismatch weight bounds it further — a wrong `c` can only soften an axis toward neutral, never manufacture
-a false match. The curve is monotone with no pathological value, and the default `c = 0.95` is mildly
-conservative: it under-reports AUC by ~0.003 relative to `c = 0.99`.
-
-Reproduce: `python3 examples/weight_sensitivity.py`. (The pair sampler is now
-`PYTHONHASHSEED`-independent, so these figures are reproducible for a given `.blkcache`; the third
-decimal shifts only as the cache grows — the flatness, not the exact figures, is the result. The
-`c = 0.95` row's 0.9333 is the headline AUC.)
-
-## Honest limits
-
-- **This moves `c` uniformly across all axes.** It tests robustness to the *global* assumption — the
- value actually hard-coded — not to `c` varying *per axis*, which is the finer epistemic concern. Fitting
- `m` per axis (an EM pass over the pair mixture, Splink-style) would test that; this establishes only
- that the aggregate has wide margin against the global knob.
-- **Same labels and sampling caveats as the base validation.** Address-reuse labels share script type by
- construction; positives are sampled with replacement; a small fraction of random pairs may be truly
- same-wallet. See `RESULTS-fingerprint-validation.md`.
+# Global fingerprint-weight sensitivity
+
+Canonical run: `catalog/runs/weight-sensitivity-v1.json`.
+
+The executable table is `results/generated/weight-sensitivity-v1.md`; exact
+values and source identity are in `results/artifacts/weight-sensitivity-v1.json`.
+Every row uses the same 4,000 positive and 4,000 negative weak-label draws from
+the preserved 22,112-transaction snapshot.
+
+The AUC is locally stable: its range from `c=0.90` through `c=0.99` is 0.00055,
+which is under a fifth of one standard error.
+The evidence magnitudes are not stable. The positive mean falls from +23.06 to
++9.43 bits, while the negative mean falls from +8.85 to -36.95 bits across the
+full sweep.
+
+The historical monotonicity claim is not supported on the preserved data, and it is
+not falsified either. AUC peaks at 0.9245 for `c=0.95` and reads 0.9239 for
+`c=0.99` — a dip of 0.00055 against a standard error of 0.0031 at 4,000 pairs per
+class, which is 0.18 standard errors. Over this band the AUC is flat within what
+the sample resolves. Calling the dip a reversal would read noise as a finding, in the
+direction that happens to suit the argument. So this result supports local ranking
+stability and nothing about the ordering. Address reuse remains a weak label. This attacker-side sensitivity
+analysis is not CoinScore or a privacy certificate.
diff --git a/results/RESULTS-witness-era-drift.md b/results/RESULTS-witness-era-drift.md
deleted file mode 100644
index 204dc93c..00000000
--- a/results/RESULTS-witness-era-drift.md
+++ /dev/null
@@ -1,53 +0,0 @@
-# Witness-axis bits drift by era
-
-The library's witness-axis bits (`low_r`, `sighash`, `pubkey_compression`, `nested_segwit`,
-`multisig`) were calibrated on a single ~3.5k recent mempool snapshot. This re-measures them
-per era on the multi-era `.blkcache` (`examples/witness_bits_by_era.py`), splitting at the
-SegWit (481,824) and Taproot (709,632) activation heights. Bits = `−log2(share)` within each
-era's tx population — the same method as the library, applied per era.
-
-Witness exists only post-SegWit, so pre-segwit is degenerate (all `na`) by protocol and
-omitted below.
-
-**Sample.** Balanced multi-era `.blkcache` (`examples/era_crawler.py`): ~180k txs —
-pre-segwit ~36k, **segwit ~35k, taproot ~110k** witness-bearing (exact counts grow with the
-local cache). The per-era bits are stable across cache sizes (≤ 0.07 variation), so the drift
-is real structure, not a small-sample artifact.
-
-| axis | value | segwit bits | taproot bits | library snapshot |
-|---|---|---:|---:|---:|
-| low_r | `low_r` | 2.33 | **1.01** | 2.30 |
-| | `not_low_r` | 2.72 | 1.82 | 2.68 |
-| pubkey_compression | `compressed` | 2.64 | **0.46** | 1.86 |
-| nested_segwit | `nested_segwit` | 1.88 | **4.01** | 3.15 |
-| sighash | `taproot_default` | — (n/a) | 3.44 | 3.96 |
-| | `all` | 1.51 | 0.38 | 1.49 |
-| multisig | `multisig` | 4.08 | 4.88 | 4.02 |
-
-## Reading — fingerprint bits have a temporal dimension
-
-- **Low-R grinding went mainstream.** `low_r` drops from 2.33 bits (segwit era) to **1.01**
- (taproot era): a once-distinctive wallet tell became a coin-flip as grinding libraries
- spread. The library snapshot (2.30) tracks the *segwit* era, not the current one.
-- **Compressed pubkeys, same story.** `compressed` falls 2.64 → 0.46 as native SegWit
- displaced the older mix.
-- **Nested SegWit inverted.** `nested_segwit` rises 1.88 → **4.01**: P2SH-P2WPKH was a common
- transition wrapper in 2017–2021 and is now rare (native bech32 won), so it *gained*
- distinctiveness.
-- **`taproot_default` is protocol-gated** — zero bits before block 709,632; it cannot appear
- in the segwit column.
-- **`multisig` is stable** (~4 bits both eras): a genuinely rare construction throughout.
-
-## Implication
-
-A single bits number per axis-value is an approximation: for the witness axes it is
-effectively an *era-weighted* snapshot, and the library's happens to sit near the segwit
-era. This does **not** invalidate the validation headline (the pair-AUC is computed on the
-multi-era `.blkcache` directly, not from these per-value bits), but it means the library's
-witness bits should be read as era-representative, and a whole-chain model would carry
-per-era (or time-conditioned) bits for the drifting axes. Adopting new bits into
-`library.py` is deliberately **not** done here — it would shift the locked scored headline
-and is a separate decision.
-
-Reproduce: `python3 -m examples.era_crawler` (balance the cache across eras), then
-`python3 -m examples.witness_bits_by_era`.
diff --git a/results/RESULTS-wp1a.md b/results/RESULTS-wp1a.md
index 8c5f7786..7974aadc 100644
--- a/results/RESULTS-wp1a.md
+++ b/results/RESULTS-wp1a.md
@@ -11,7 +11,7 @@ The nLockTime `zero` share moved with sampling method:
Direction confirmed; the full ~95% (#1676) is **not reachable via the mempool.space
API** at low request cost — it needs uniform-over-ALL-txs sampling, which is exactly
-what the **WP1b dense emitter** provides (every tx, no per-block API cap). Also
+what the WP1b dense emitter provides (every tx, no per-block API cap). Also
`floor=200000` excludes pre-anti-fee-sniping blocks (almost all `locktime=0`), which
holds the number below 95%. This is a documented limitation, not a silent gap.
@@ -32,3 +32,7 @@ the specific, low-frequency fingerprints the combiner should weight most.
Feeds WP2 (per-axis measured bits for the fingerprint library) and is superseded for
the locktime distribution by WP1b once a mainnet dense index exists.
+
+## Reproducibility / provenance
+
+State 2 in `results/REPRODUCIBILITY.md`: the mechanism is pinned; the reported numbers come from a data-run over `.cache`/`.blkcache`, which is not committed, and are not asserted.
diff --git a/results/RESULTS-wp4.md b/results/RESULTS-wp4.md
index c196b276..ab3df7d0 100644
--- a/results/RESULTS-wp4.md
+++ b/results/RESULTS-wp4.md
@@ -15,9 +15,9 @@ it places **{Cake receiver `0a568e3a`, sender `91106666`}** in one cluster — e
the false link the merged transaction is designed to induce.
**2) Fingerprint-aware clustering (real measured bits)**:
-- **REFUSES** the sender↔Cake merge at **−3.1 bits** (`max_ffffffff` vs `seq_0x01`,
- co-spent in `931d6627`) → the merged transaction is **re-partitioned**, sender isolated.
-- **ADDS** the links the co-spend missed:
+- REFUSES the sender↔Cake merge at **−3.1 bits** (`max_ffffffff` vs `seq_0x01`,
+ co-spent in `931d6627`) → the merged transaction is re-partitioned, sender isolated.
+- ADDS the links the co-spend missed:
- Cake lineage `0a568e3a ↔ be2e3620`: **+10.2 bits** (rare `seq_0x01` match).
- sender funding chain (`5b97`, `89300d`, `b8c2bd`, `931d6627`): **+5.4 bits** each.
@@ -25,16 +25,21 @@ Net: the fingerprint-aware clusters are `{sender}`, `{Cake, Cake-lineage}`, and
`{sender funding chain}` — the correct partition. Union-find gave the wrong one.
## Provenance of the bits
-The scores are driven by **real measured mainnet bits** from WP2's `library.py`
+The scores are driven by real measured mainnet bits from WP2's `library.py`
(`Combiner.from_library()`), not a biased top-of-block sample. The biased-sample
version scored the refusal at −3.4 bits; with real unbiased bits it is **−3.1** —
-recorded honestly. The +10.2 Cake-lineage link uses the unseen-value floor
+recorded as such. The +10.2 Cake-lineage link uses the unseen-value floor
(`seq_0x01` was not in the measured `nsequence` table), which only strengthens a rare
match.
-## Honest caveats
+## Caveats
- **Existence demonstration, not a statistical result.** One merged transaction, 7 coins. It
shows the backward-channel breaks merged transaction privacy on a real tx; it does not measure
a rate across the chain (that needs a mainnet dense index — WP1b, deferred).
- Thresholds (`refuse_below=-2.0`, `link_above=4.0`) are the prototype's; the sign of
the refusal (−3.1) and the added links are what the regression test pins.
+
+## Reproducibility / provenance
+
+State 2 in `results/REPRODUCIBILITY.md`: the mechanism is pinned; the reported numbers come from a data-run over `.cache/`, which is not committed, and are not asserted.
+The result is regression-guarded by `tests/test_wp4.py::test_merge_money_shot`.
diff --git a/results/artifacts/amount-channel-survey-v1.json b/results/artifacts/amount-channel-survey-v1.json
new file mode 100644
index 00000000..56ef5dde
--- /dev/null
+++ b/results/artifacts/amount-channel-survey-v1.json
@@ -0,0 +1 @@
+{"experiment":"amount-channel-survey-v1","limitations":["this artifact composes upstream results and performs no mechanism calculation","component SHA-256 identities are mandatory","the five components measure different objects and are not combined into one score","DSS mapping and pairwise claims remain restricted to DSS's mapping family","the Bitcoin snapshot covers one 137-block interval from 2023","this summary is not a privacy certificate or CoinScore"],"report":{"components":{"local":{"experiment":"amount-local-channels-v1","sha256":"210ecb06ecaa32cc4cabe03ced8b85b0882a824dca2c361bd2c5dede7dcccf02"},"mapping_family":{"experiment":"amount-mapping-family-v1","sha256":"11f0ec645ea1be334dc7d24fbfc31047b234f335f48b5a514960472c7444b7d6"},"pairwise_family":{"experiment":"amount-pairwise-family-v1","sha256":"83f6a9e8501ee4df45f08b8c8ffc2f7265d40bf9add6d3f5093d712657c9dbf5"},"per_coin":{"experiment":"amount-per-coin-v1","sha256":"2e75b1330181e7cbf8539a357e2bd3ad650c4d2d207c7a47ec687e991e24a622"},"subset_sum":{"experiment":"amount-subset-sum-v1","sha256":"240592b5af814fbcd28b06221805b934424170119da5e62e9da0281944377a9d"}},"local_channels":{"coinjoin":{"demixed":0,"shape_detected":28},"conservation":{"forced_output_value":1353,"known_participant":"largest input value"},"subtransaction_roundness":{"ranked":483,"top_tied":330},"unnecessary_input":{"classes":{"none":943,"uih2":485},"fires":485}},"mapping_family":{"outcomes":{"answered":223,"failed":0,"refused":26,"timed_out":1},"restricted_family":{"certain_input_output_links":1471,"entropy_bins_bits":{"above_four":0,"above_zero_to_four":2,"zero":221},"non_derived_mappings":227}},"pairwise_family":{"outcomes":{"answered":271,"failed":0,"refused":27,"timed_out":2},"restricted_family_rows":{"full_output_support":898,"nonempty_support":914,"returned":914,"singleton_support":367}},"per_coin_candidates":{"transaction_gated":{"candidate_transactions":62,"candidates":{"input":18,"output":83,"total":101},"candidates_with_exact_transaction_count":{"input":0,"output":61,"total":61},"resolved_transactions":82},"ungated":{"candidate_transactions":95,"candidates":{"input":60,"output":258,"total":318}}},"per_coin_density":{"finite_log_w":{"maximum":19.40651597114107,"median":3.8066624897703196,"minimum":0.0},"reachable":{"input":316,"output":972,"total":1288},"unreachable":22338},"population":{"complete_amounts":5491,"multi_input":1428,"transactions":5491,"two_input_two_output":486},"transaction_subset_sum":{"by_kind":{"exact":1303,"unknown":125},"exact_zero":1223,"guaranteed_nonzero_log_w":80}},"schema_version":1}
diff --git a/results/artifacts/amount-local-channels-v1.json b/results/artifacts/amount-local-channels-v1.json
new file mode 100644
index 00000000..043277b4
--- /dev/null
+++ b/results/artifacts/amount-local-channels-v1.json
@@ -0,0 +1 @@
+{"corpus":"public Bitcoin transaction snapshot","experiment":"amount-local-channels-v1","limitations":["the snapshot covers one 137-block interval from 2023","no recognized JoinMarket round appears in the measured population","roundness ties show abstention, not ownership truth","the conservation probe assumes the largest input is the known participant","this run excludes subset-sum, DSS, mapping entropy, and score composition"],"report":{"coinjoin":{"demixed":0,"shape_detected":28},"conservation":{"forced_output_value":1353,"known_participant":"largest input value"},"population":{"complete_amounts":5491,"multi_input":1428,"transactions":5491,"two_input_two_output":486},"subtransaction_roundness":{"ranked":483,"top_tied":330},"unnecessary_input":{"classes":{"none":943,"uih2":485},"fires":485}},"schema_version":1}
diff --git a/results/artifacts/amount-mapping-family-v1.json b/results/artifacts/amount-mapping-family-v1.json
new file mode 100644
index 00000000..18cf9900
--- /dev/null
+++ b/results/artifacts/amount-mapping-family-v1.json
@@ -0,0 +1 @@
+{"corpus":"public Bitcoin transaction snapshot","experiment":"amount-mapping-family-v1","limitations":["DSS enumerates a strict sub-family of the full balanced mappings","entropy is uniform over that restricted family, not a posterior over ownership","certain links are certain only within the restricted family","refusal, timeout, and execution failure are distinct outcomes","the sample is the first complete multi-input transactions in one 137-block snapshot","this diagnostic is not a privacy score or CoinScore"],"parameters":{"cap":250,"wall_seconds_per_transaction":3.0},"report":{"outcomes":{"answered":223,"failed":0,"refused":26,"timed_out":1},"population":{"selected_transactions":250,"selection":"first complete multi-input transactions in source order"},"restricted_family":{"certain_input_output_links":1471,"entropy_bins_bits":{"above_four":0,"above_zero_to_four":2,"zero":221},"non_derived_mappings":227}},"schema_version":1}
diff --git a/results/artifacts/amount-pairwise-family-v1.json b/results/artifacts/amount-pairwise-family-v1.json
new file mode 100644
index 00000000..f305a72e
--- /dev/null
+++ b/results/artifacts/amount-pairwise-family-v1.json
@@ -0,0 +1 @@
+{"corpus":"public Bitcoin transaction snapshot","experiment":"amount-pairwise-family-v1","limitations":["the matrix is a uniform marginal over DSS's restricted mapping family","a singleton-support row is not a globally deterministic ownership link","the exact-oracle audit found false certainties outside the restricted family","refusal, timeout, and execution failure are distinct outcomes","wall-time outcomes are resource- and platform-dependent","the sample is the first complete multi-input transactions in one 137-block snapshot","this diagnostic is not a privacy score or CoinScore"],"parameters":{"cap":300,"wall_seconds_per_transaction":3.0},"report":{"outcomes":{"answered":271,"failed":0,"refused":27,"timed_out":2},"population":{"selected_input_rows":3384,"selected_outputs":1540,"selected_transactions":300,"selection":"first complete multi-input transactions in source order"},"restricted_family_rows":{"full_output_support":898,"nonempty_support":914,"returned":914,"singleton_support":367}},"schema_version":1}
diff --git a/results/artifacts/amount-per-coin-v1.json b/results/artifacts/amount-per-coin-v1.json
new file mode 100644
index 00000000..7c525c79
--- /dev/null
+++ b/results/artifacts/amount-per-coin-v1.json
@@ -0,0 +1 @@
+{"corpus":"public Bitcoin transaction snapshot","experiment":"amount-per-coin-v1","limitations":["DSS per-coin log_w is a knee-truncated diagnostic, not an exact mapping count","unreachable coins are missing measurements and are never candidates","the transaction gate and per-coin diagnostic measure different objects","an exact transaction count does not make a per-coin candidate exact","input and output indices have separate identities","candidates are refuse signals, not ownership proofs or privacy scores"],"parameters":{"cut_threshold_natural_log":1.0},"report":{"per_coin_density":{"finite_log_w":{"maximum":19.40651597114107,"median":3.8066624897703196,"minimum":0.0},"reachable":{"input":316,"output":972,"total":1288},"unreachable":22338},"population":{"coins":23626,"inputs":17520,"outputs":6106,"transactions":1428},"transaction_gated":{"candidate_transactions":62,"candidates":{"input":18,"output":83,"total":101},"candidates_with_exact_transaction_count":{"input":0,"output":61,"total":61},"resolved_transactions":82},"ungated_diagnostic":{"candidate_transactions":95,"candidates":{"input":60,"output":258,"total":318}}},"schema_version":1}
diff --git a/results/artifacts/amount-subset-sum-v1.json b/results/artifacts/amount-subset-sum-v1.json
new file mode 100644
index 00000000..3bd0d82f
--- /dev/null
+++ b/results/artifacts/amount-subset-sum-v1.json
@@ -0,0 +1 @@
+{"corpus":"public Bitcoin transaction snapshot","experiment":"amount-subset-sum-v1","limitations":["W(E) is a per-target subset count, not a Maurer mapping count","the whole-transaction count is fee-blind","exact zero means no exact subset hit and supplies no ambiguity evidence","transactions above sixteen inputs are reported as unknown","only exact and lower-bound tiers can support a conservative lower bound","this run does not compute pairwise links, entropy, or CoinScore"],"report":{"outcomes":{"by_kind":{"exact":1303,"unknown":125},"exact_zero":1223,"guaranteed_nonzero_log_w":80},"population":{"maximum_inputs":16,"multi_input":1428}},"schema_version":1}
diff --git a/results/artifacts/analyze-contract-v1.json b/results/artifacts/analyze-contract-v1.json
new file mode 100644
index 00000000..eafe01ba
--- /dev/null
+++ b/results/artifacts/analyze-contract-v1.json
@@ -0,0 +1 @@
+{"capabilities":{"depths_one_through_five_return":true,"deterministic_value_flow_default":true,"offline_transaction_input":true,"subjective_fusion_never_widens":true,"truncation_is_reported":true},"change_fusion":{"fused_min_entropy_bits":0.15200309344505,"provenance_min_entropy_bits":1.0,"sharpened":true},"depth_sweep":{"1":{"fused":{"min_entropy_bits":1.0,"shannon_bits":1.0},"provenance":{"min_entropy_bits":1.0,"n_absorbers":2,"shannon_bits":1.0},"truncated":0},"2":{"fused":{"min_entropy_bits":1.0,"shannon_bits":1.0},"provenance":{"min_entropy_bits":1.0,"n_absorbers":2,"shannon_bits":1.0},"truncated":0},"3":{"fused":{"min_entropy_bits":1.0,"shannon_bits":1.0},"provenance":{"min_entropy_bits":1.0,"n_absorbers":2,"shannon_bits":1.0},"truncated":0},"4":{"fused":{"min_entropy_bits":1.0,"shannon_bits":1.0},"provenance":{"min_entropy_bits":1.0,"n_absorbers":2,"shannon_bits":1.0},"truncated":0},"5":{"fused":{"min_entropy_bits":1.0,"shannon_bits":1.0},"provenance":{"min_entropy_bits":1.0,"n_absorbers":2,"shannon_bits":1.0},"truncated":0}},"experiment":"analyze-contract-v1","fixture":{"targets":[0,1],"transactions":5},"limitations":["the fixture is synthetic and validates the public facade contract","the run does not reproduce the historical live Bitcoin measurements","address reuse is a heuristic subjective signal, not verified ownership","provenance entropy is not a privacy certificate or CoinScore","the path_count response key is retained for API compatibility only"],"node_cap":{"cap_is_observable":true,"capped_truncations":2,"uncapped_truncations":0},"route_diagnostic":{"canonical_meaning":"provenance_route_accumulation","present_when_requested":true},"schema_version":1}
diff --git a/results/artifacts/ancestry-contract-v1.json b/results/artifacts/ancestry-contract-v1.json
new file mode 100644
index 00000000..1063bc40
--- /dev/null
+++ b/results/artifacts/ancestry-contract-v1.json
@@ -0,0 +1 @@
+{"bounded":{"is_truncated":true,"state":"TruncatedAncestry","truncation":{"node_capped":2,"oracle_refused":0,"unattributed":0,"zero_link_mass":0}},"capabilities":{"absorbing_distribution":true,"nominal_value_transition":true,"provenance_overlap_diagnostic":true,"truncation_causes_separated":true,"typed_complete_and_truncated_states":true},"complete":{"distribution":{"left:0":0.5,"right:0":0.5},"expected_paper_steps":4.0,"is_complete":true,"shannon_bits":1.0,"state":"CompleteAncestry","truncation":{"node_capped":0,"oracle_refused":0,"unattributed":0,"zero_link_mass":0}},"experiment":"ancestry-contract-v1","fixture":{"depth":2,"target":"target:0","transactions":3},"limitations":["the fixture is synthetic and validates mechanism contracts","the nominal-value model does not infer ownership or payment allocation","provenance overlap is model-relative evidence, not attribution","the run does not reproduce the historical DSS or live Bitcoin measurements","the outputs are not robust connectivity, a privacy certificate or CoinScore"],"oracle_refusal":{"is_truncated":true,"state":"TruncatedAncestry","truncation":{"node_capped":0,"oracle_refused":1,"unattributed":0,"zero_link_mass":0}},"overlap":{"disjoint":0.0,"self":1.0},"schema_version":1}
diff --git a/results/artifacts/ancestry-sparsity-v1.json b/results/artifacts/ancestry-sparsity-v1.json
new file mode 100644
index 00000000..ea5ba68a
--- /dev/null
+++ b/results/artifacts/ancestry-sparsity-v1.json
@@ -0,0 +1 @@
+{"experiment":"ancestry-sparsity-v1","limitations":["the 200-record frozen snapshot is selected and is not a uniform chain sample","the signature-generation recipe and original comparison population are unavailable","the run measures sparsity within this snapshot only","sparsity is an attack precondition, not proof of successful attribution","the run does not establish a chain-wide population rate"],"nearest_neighbour_cosine":{"median":0.02591278115565,"survival":{"0.5":0.175,"0.9":0.175,"0.99":0.145}},"schema_version":1,"snapshot":{"dimensions":{"maximum":11436,"median":5887.0,"minimum":3},"records":200},"stored_top_context":{"different_from_snapshot_recomputation":155,"interpretation":"stored values were computed in an unavailable comparison population"}}
diff --git a/results/artifacts/anchor-axis-families-v1.json b/results/artifacts/anchor-axis-families-v1.json
new file mode 100644
index 00000000..06c2980c
--- /dev/null
+++ b/results/artifacts/anchor-axis-families-v1.json
@@ -0,0 +1 @@
+{"anchor":"931d6627","dataset":"merged-anchor-931d6627-v1","edge":{"cake":"0a568e3ae6fa6bf34ce8925266ac2cdb1668c723980398d9c613d67d72b39729","sender":"91106666451dc43a0e3f78b325764251e205b39d7e9498948885678616ba719a"},"experiment":"anchor-axis-families-v1","limitations":["one named edge, chosen because the design decision was argued on it","the same-owner reading of this edge is the paper's, not an independent label","a score past link_above is what the engine would do, not what it does: the engine scores three axes","the library weights come from a population that was not preserved; library-calibration-v1 measures how far they sit from the one that was","the result is not a privacy score"],"measurement":{"catalogued_axes_abstaining":1,"catalogued_axes_scored":22,"families":{"catalogued":{"axes":23,"bits":11.665618785391802,"past_link_above":true,"verdict":"link"},"construction_only":{"axes":18,"bits":4.905618785391801,"past_link_above":true,"verdict":"link"},"decorrelated":{"axes":14,"bits":3.593522416485701,"past_link_above":false,"verdict":"link"},"engine_three_axis":{"axes":3,"bits":-3.1632198124512647,"past_link_above":false,"verdict":"refuse"}},"link_above":4.0,"negative_axes":3,"negative_bits_total":-10.824381214608199,"per_axis":[{"axis":"nsequence","bits":-3.593219812451265,"cake":"seq_0x01_other","sender":"max_ffffffff"},{"axis":"locktime","bits":0.43,"cake":"zero","sender":"zero"},{"axis":"input_order","bits":-3.237903631093901,"cake":"single","sender":"shuffle"},{"axis":"output_order","bits":1.72,"cake":"single","sender":"single"},{"axis":"change_spk","bits":3.42,"cake":"uniform_v0_p2wpkh","sender":"uniform_v0_p2wpkh"},{"axis":"version","bits":1.42,"cake":"v2","sender":"v2"},{"axis":"io_shape","bits":-3.9932577710630337,"cake":"1in-1out","sender":"3in-1out"},{"axis":"uih","bits":0.12000000000000002,"cake":"none","sender":"none"},{"axis":"fee_rate","bits":0.28,"cake":"precise","sender":"precise"},{"axis":"input_script_type","bits":2.18,"cake":"uniform_v0_p2wpkh","sender":"uniform_v0_p2wpkh"},{"axis":"output_encoding","bits":2.93,"cake":"bech32","sender":"bech32"},{"axis":"input_types_present","bits":2.18,"cake":"v0_p2wpkh","sender":"v0_p2wpkh"},{"axis":"change_index","bits":0.4800000000000001,"cake":"na","sender":"na"},{"axis":"change_type_match","bits":0.4800000000000001,"cake":"na","sender":"na"},{"axis":"change_matches_output","bits":0.4800000000000001,"cake":"na","sender":"na"},{"axis":"change_address_reuse","bits":0.37000000000000005,"cake":"none","sender":"none"},{"axis":"low_r","bits":2.3,"cake":"low_r","sender":"low_r"},{"axis":"sighash","bits":1.49,"cake":"all","sender":"all"},{"axis":"op_return","bits":0.08999999999999997,"cake":"none","sender":"none"},{"axis":"nested_segwit","bits":0.17000000000000004,"cake":"none","sender":"none"},{"axis":"pubkey_compression","bits":1.86,"cake":"compressed","sender":"compressed"},{"axis":"multisig","bits":0.08999999999999997,"cake":"none","sender":"none"},{"axis":"locktime_vs_broadcast","bits":null,"cake":"na","sender":"na"}],"positive_axes":19,"positive_bits_total":22.490000000000002,"redundancy_bits":8.0720963689061},"schema_version":1}
diff --git a/results/artifacts/attribute-drift-v1.json b/results/artifacts/attribute-drift-v1.json
new file mode 100644
index 00000000..36addb3f
--- /dev/null
+++ b/results/artifacts/attribute-drift-v1.json
@@ -0,0 +1 @@
+{"experiment":"attribute-drift-v1","limitations":["only 20 value series across 11 axes are preserved","unpreserved values are combined into one residual category per axis","only projected drift is measured; most full-distribution table rows are historical","volume is reconstructed from redundant template match shares rather than stored directly","population drift does not measure per-cluster attribute distinctiveness","the result is diagnostic and does not produce a clustering score","the dataset publisher authorized redistribution of the aggregate fixture on 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diff --git a/results/artifacts/bayes-vs-fs-v1.json b/results/artifacts/bayes-vs-fs-v1.json
new file mode 100644
index 00000000..0cea789e
--- /dev/null
+++ b/results/artifacts/bayes-vs-fs-v1.json
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+{"dataset":"fs-blkcache-2026-09-04-v1","experiment":"bayes-vs-fs-v1","historical_comparison":{"reason":"the preserved snapshot produces materially different metrics from the historical Markdown","status":"not_reproduced"},"limitations":["address reuse is a weak and partly feature-dependent ownership proxy","the balanced positive-negative sample does not represent population prevalence","parameters are fitted and evaluated on the same selected pair sample","the model assumes conditional independence between correlated axes","u is fixed at the measured collision rate","cluster intervals propagate pair probabilities independently and are not a joint partition posterior","borderline nodes are purpose-selected and neither cluster diagnostic is chain-wide","this is attacker-side record-linkage evidence, not CoinScore or a privacy 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diff --git a/results/artifacts/boltzmann-fee-audit-v1.json b/results/artifacts/boltzmann-fee-audit-v1.json
new file mode 100644
index 00000000..b1db8a69
--- /dev/null
+++ b/results/artifacts/boltzmann-fee-audit-v1.json
@@ -0,0 +1 @@
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new file mode 100644
index 00000000..495fd2c2
--- /dev/null
+++ b/results/artifacts/candidate-set-intersection-v1.json
@@ -0,0 +1 @@
+{"experiment":"candidate-set-intersection-v1","limitations":["synthetic fixture","candidate sets are supplied rather than inferred from chain data","the run validates the abstract intersection mechanism, not JoinMarket detection","no empirical rate is attributed to Goldfeder et al."],"parameters":{"universe_size":64},"scenarios":[{"identified":0,"inconsistent_at":null,"name":"converging","narrowing_bits":6.0,"observations":[[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31],[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15],[0,1,2,3,4,5,6,7],[0,1,2,3],[0,1],[0]],"steps":[{"after":32,"before":64,"index":0,"narrowing_bits":1.0,"observed":32},{"after":16,"before":32,"index":1,"narrowing_bits":1.0,"observed":16},{"after":8,"before":16,"index":2,"narrowing_bits":1.0,"observed":8},{"after":4,"before":8,"index":3,"narrowing_bits":1.0,"observed":4},{"after":2,"before":4,"index":4,"narrowing_bits":1.0,"observed":2},{"after":1,"before":2,"index":5,"narrowing_bits":1.0,"observed":1}],"surviving":[0]},{"identified":null,"inconsistent_at":null,"name":"stalled","narrowing_bits":3.0,"observations":[[0,1,2,3,4,5,6,7],[0,1,2,3,4,5,6,7],[0,1,2,3,4,5,6,7],[0,1,2,3,4,5,6,7]],"steps":[{"after":8,"before":64,"index":0,"narrowing_bits":3.0,"observed":8},{"after":8,"before":8,"index":1,"narrowing_bits":0.0,"observed":8},{"after":8,"before":8,"index":2,"narrowing_bits":0.0,"observed":8},{"after":8,"before":8,"index":3,"narrowing_bits":0.0,"observed":8}],"surviving":[0,1,2,3,4,5,6,7]},{"identified":null,"inconsistent_at":1,"name":"contradictory","narrowing_bits":null,"observations":[[0,1,2,3,4,5,6,7],[8,9,10,11,12,13,14,15],[0,1,2,3]],"steps":[{"after":8,"before":64,"index":0,"narrowing_bits":3.0,"observed":8},{"after":0,"before":8,"index":1,"narrowing_bits":null,"observed":8}],"surviving":[]}],"schema_version":1,"summary":{"contradictory_inconsistent_at":1,"contradictory_survivors":0,"converging_bits":6.0,"converging_identified":0,"converging_survivors":1,"identifications":1,"observations_supplied":13,"refusals":1,"scenarios":3,"stalled_bits":3.0,"stalled_survivors":8,"universe_size":64}}
diff --git a/results/artifacts/change-consolidation-v1.json b/results/artifacts/change-consolidation-v1.json
new file mode 100644
index 00000000..3493951a
--- /dev/null
+++ b/results/artifacts/change-consolidation-v1.json
@@ -0,0 +1 @@
+{"experiment":"change-consolidation-v1","limitations":["synthetic fixture","the report narrows candidate owners and does not identify a real person","a collaborative co-spend invalidates the common-ownership premise"],"result":{"candidates_after":["alice"],"candidates_before":2,"composition":null,"outcome":"candidate_narrowing"},"scenario":{"candidate_owners":[["alice","bob"],["alice","carol"]],"change_outputs":[["payment-1",1],["payment-2",1]],"payments":["payment-1","payment-2"]},"schema_version":1}
diff --git a/results/artifacts/cioh-collaborative-false-merge-v1.json b/results/artifacts/cioh-collaborative-false-merge-v1.json
new file mode 100644
index 00000000..8569bfdd
--- /dev/null
+++ b/results/artifacts/cioh-collaborative-false-merge-v1.json
@@ -0,0 +1 @@
+{"collaborative":{"inputs":2,"predicted_merges":1,"reference_conflicts":1},"composition":null,"conclusion":"CIOH merges both co-spends, but only the labelled collaborative fixture conflicts with its supplied ownership partition","experiment":"cioh-collaborative-false-merge-v1","limitations":["the deterministic fixture supplies participant ownership labels","the result demonstrates one false merge, not its chain-wide prevalence","a co-spend alone does not prove common ownership"],"same_owner_control":{"inputs":2,"predicted_merges":1,"reference_conflicts":0},"schema_version":1}
diff --git a/results/artifacts/cluster-bits-v1.json b/results/artifacts/cluster-bits-v1.json
new file mode 100644
index 00000000..5caac6a5
--- /dev/null
+++ b/results/artifacts/cluster-bits-v1.json
@@ -0,0 +1 @@
+{"bits":{"max":21338.4716,"median":29.0476,"median_topk_floor":29.0476,"p90":68.6917},"dataset":"fs-blkcache-2026-09-04-v1","experiment":"cluster-bits-v1","parameters":{"graph":"payment edges only, co-spend excluded","merge_ambiguity_bits":1.6,"topk":5},"population":{"clusters":2538,"clusters_with_at_most_topk_counterparties":2345,"transactions":22112},"schema_version":1,"share_at_least":{"1.6":1.0,"10":0.978723,"100":0.052403,"50":0.165091},"source":{"bytes":91367352,"digest":"91c444bd4e9715d7739ccc9d68c7d9751762e25b647e3eef647c6ec0b48ba90c","files":904,"pattern":".blkcache/*.json","transactions":22112}}
diff --git a/results/artifacts/coinjoin-sudoku-nominal-v1.json b/results/artifacts/coinjoin-sudoku-nominal-v1.json
new file mode 100644
index 00000000..21a73d15
--- /dev/null
+++ b/results/artifacts/coinjoin-sudoku-nominal-v1.json
@@ -0,0 +1 @@
+{"advisory_group_examples":{"invalid_2_plus_3_to_1_plus_2":0,"valid_2_plus_3_to_1_plus_4":1},"composition":null,"conclusion":"the published mechanism accepts the advisory's balanced example; a separately labelled maximal/uniform adaptation recovers the SX control's one-third symmetry","experiment":"coinjoin-sudoku-nominal-v1","limitations":["this is an independent reconstruction, not parity with the unavailable historical analyzer","largest-group selection, uniform weighting and the fee pseudo-output split are local semantics, not published analyzer rules","the marginals are a function of the local fee unit: at one 30,000-sat unit the same control reads 1.0 instead of one third","digit skipping and the reported 2014 SharedCoin grouping result are not reproduced","amount relations within this family are not ownership attribution"],"local_adaptation":{"fee_model":"the fee split into explicit equal pseudo-outputs of one fee unit","purpose":"reconstruct the SX symmetry argument under explicit semantics","selection":"valid interpretations with the largest group count","weighting":"uniform over indexed selected groupings"},"published_mechanism":{"balance":"exact integer sum per paired group","fee_constraint":"fee is a multiple of a constant, currently 0.0001 BTC","selection":"all valid equal-sum groupings"},"schema_version":1,"sharedcoin_historical_target":{"reason":"the analyzer and complete output-label semantics were not published","reported_grouped_inputs_percent":69,"reported_grouped_outputs_percent":53,"status":"not_reproduced","txid":"0e0337bdf930eba3b082fdfbd30944b18e03f0f810ae531443161f897a4d3db0"},"source":{"advisory":"https://www.coinjoinsudoku.com/advisory/","historical_analyzer_available":false,"repository_revision":"afbd2658b4ac9e0711b12c1bd4ab228ea5dd2499"},"sx_symmetric_control":{"all_equal_sum_groupings":64,"fee_sat":30000,"fee_unit_sat":10000,"inputs_sat":[1010000,1010000,1010000],"local_uniform_real_output_link_probabilities":[[0.3333333333333333,0.3333333333333333,0.3333333333333333],[0.3333333333333333,0.3333333333333333,0.3333333333333333],[0.3333333333333333,0.3333333333333333,0.3333333333333333]],"outputs_sat":[1000000,1000000,1000000],"selected_groupings_collapsing_fee_permutations":6,"selected_indexed_groupings":36,"txid":"9256332b9ca52cbcb06f57296dfd982d8da3f7d4696b4c10cf9bb93dae6edf58"}}
diff --git a/results/artifacts/collaborative-forms-v1.json b/results/artifacts/collaborative-forms-v1.json
new file mode 100644
index 00000000..3fdd4bfc
--- /dev/null
+++ b/results/artifacts/collaborative-forms-v1.json
@@ -0,0 +1 @@
+{"claim":"observable transaction data do not by themselves identify the collaborative form, participant allocation, payment graph or ownership","conclusion":"identical on-chain amounts remain compatible with an ordinary spend and several two-party PayJoin protocols; counterparty knowledge must not be assigned to an external observer","experiment":"collaborative-forms-v1","forms":[{"counterparty_knowledge":[],"external_onchain_knowledge":["input and output amounts","transaction shape and script-visible construction features","participant allocation is not observed","payment amount is not observed"],"form":"ordinary_two_input","limitations":["ownership is latent even when the transaction is actually unilateral"],"observation":{"inputs_sat":[90,60],"outputs_sat":[110,40]},"participants":1,"transport":"ordinary wallet transaction"},{"counterparty_knowledge":["own inputs and outputs","the only other party's remaining inputs and outputs by elimination","negotiated payment amount"],"external_onchain_knowledge":["input and output amounts","transaction shape and script-visible construction features","participant allocation is not observed","payment amount is not observed"],"form":"p2ep","limitations":["the same on-chain observation is compatible with an ordinary single-owner spend","two-party PayJoin does not provide privacy from the counterparty"],"observation":{"inputs_sat":[90,60],"outputs_sat":[110,40]},"participants":2,"transport":"Pay-to-EndPoint negotiation"},{"counterparty_knowledge":["own inputs and outputs","the only other party's remaining inputs and outputs by elimination","negotiated payment amount"],"external_onchain_knowledge":["input and output amounts","transaction shape and script-visible construction features","participant allocation is not observed","payment amount is not observed"],"form":"bip79_bustapay","limitations":["the same on-chain observation is compatible with an ordinary single-owner spend","two-party PayJoin does not provide privacy from the counterparty"],"observation":{"inputs_sat":[90,60],"outputs_sat":[110,40]},"participants":2,"transport":"Bustapay template negotiation"},{"counterparty_knowledge":["own inputs and outputs","the only other party's remaining inputs and outputs by elimination","negotiated payment amount"],"external_onchain_knowledge":["input and output amounts","transaction shape and script-visible construction features","participant allocation is not observed","payment amount is not observed"],"form":"bip78_sync_payjoin","limitations":["the same on-chain observation is compatible with an ordinary single-owner spend","two-party PayJoin does not provide privacy from the counterparty"],"observation":{"inputs_sat":[90,60],"outputs_sat":[110,40]},"participants":2,"transport":"synchronous PayJoin negotiation"},{"counterparty_knowledge":["own inputs and outputs","the only other party's remaining inputs and outputs by elimination","negotiated payment amount"],"external_onchain_knowledge":["input and output amounts","transaction shape and script-visible construction features","participant allocation is not observed","payment amount is not observed"],"form":"bip77_async_payjoin","limitations":["the same on-chain observation is compatible with an ordinary single-owner spend","two-party PayJoin does not provide privacy from the counterparty"],"observation":{"inputs_sat":[90,60],"outputs_sat":[110,40]},"participants":2,"transport":"asynchronous PayJoin negotiation"},{"counterparty_knowledge":["the receiver knows each negotiated payment and sender change","the receiver may not know which input belongs to which sender"],"external_onchain_knowledge":["amounts and transaction shape","sender allocation is not observed"],"form":"many_senders_one_receiver","limitations":["receiver consolidation and sibling-output spending can create fingerprints"],"observation":{"inputs_sat":[40,50,60,90],"outputs_sat":[160,30,30,20]},"participants":4,"transport":"many senders negotiate with one receiver"},{"counterparty_knowledge":["own subtransaction and negotiated payment","other pairings remain latent"],"external_onchain_knowledge":["amounts and transaction shape","payment pairing is not observed"],"form":"many_senders_many_receivers","limitations":["one payment per subtransaction leaves amount-based plausibility signals"],"observation":{"inputs_sat":[90,15,40,30,45,35],"outputs_sat":[85,25,50,40,45,10]},"participants":6,"transport":"paired many-sender many-receiver negotiation"},{"counterparty_knowledge":["own obligations and contributions","other parties' gross obligations remain latent"],"external_onchain_knowledge":["net on-chain balances","participant count may remain latent"],"form":"net_settlement_with_cycles","limitations":["cycles make gross payment amounts and obligation count unidentifiable","community structure and auxiliary information remain observable over time"],"observation":{"inputs_sat":[60,20,50],"outputs_sat":[40,50,40]},"participants":null,"transport":"multiparty settlement of mutual obligations"}],"limitations":["the participant allocations and payment relationships are supplied model possibilities","protocol deployment frequency and empirical indistinguishability are not measured","transport differences are not treated as on-chain observations","the matrix is not ownership attribution or a privacy score"],"schema_version":1,"two_party_onchain_equivalence":{"distinct_transports":5,"forms":["ordinary_two_input","p2ep","bip79_bustapay","bip78_sync_payjoin","bip77_async_payjoin"],"observation":{"inputs_sat":[90,60],"outputs_sat":[110,40]}}}
diff --git a/results/artifacts/conservation-round-three-v1.json b/results/artifacts/conservation-round-three-v1.json
new file mode 100644
index 00000000..219fd192
--- /dev/null
+++ b/results/artifacts/conservation-round-three-v1.json
@@ -0,0 +1 @@
+{"experiment":"conservation-round-three-v1","fixture":{"inputs":454,"known_participant_input":4712126860,"outputs":502,"total_input":9000971062,"txid":"80f11c778a2f486ffc55b4e8665a94971ae9c350ac53969e9efcbf90478cbf90"},"interpretation":"forced provenance of value under conservation","largest_forced_denomination":{"forced":2,"forced_value":1549681956,"margin":360201666,"present":7,"value":774840978},"limitations":["the fixture is a reduced representation of one mainnet transaction","the run does not reproduce the surrounding six-round chain","the known participant input is supplied by an external forward walk","conservation establishes provenance of value, not output ownership","net settlement invalidates an ownership inference from this bound","the result is not a privacy score or 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diff --git a/results/artifacts/counting-fee-sensitivity-v1.json b/results/artifacts/counting-fee-sensitivity-v1.json
new file mode 100644
index 00000000..7f401f7c
--- /dev/null
+++ b/results/artifacts/counting-fee-sensitivity-v1.json
@@ -0,0 +1 @@
+{"experiment":"counting-fee-sensitivity-v1","fixture":{"inputs_sat":[400000,300000,300000],"output_total_before_fee_sat":1000000},"limitations":["this is a deterministic sensitivity fixture, not a population estimate","the transaction-level subset-sum count and fee-aware per-coin count are different objects","an absent per-coin index is a refusal to measure, not zero ambiguity","these diagnostics are not mapping entropy, a privacy score, or CoinScore"],"observations":[{"cascade":{"count":2,"kind":"exact","log_w":0.6931471805599453,"method":"brute"},"fee_sat":0,"measured_input_indices":[0,1,2],"outputs_sat":[600000,400000],"per_coin_log_w":{"0":0.6931471805599453,"1":0.6931471805599453,"2":0.6931471805599453},"router":{"count":2,"kind":"exact","log_w":0.6931471805599453,"method":"sparse"}},{"cascade":{"count":1,"kind":"exact","log_w":0.0,"method":"brute"},"fee_sat":1,"measured_input_indices":[1,2],"outputs_sat":[600000,399999],"per_coin_log_w":{"1":0.0,"2":0.0},"router":{"count":1,"kind":"exact","log_w":0.0,"method":"sparse"}},{"cascade":{"count":1,"kind":"exact","log_w":0.0,"method":"brute"},"fee_sat":10,"measured_input_indices":[],"outputs_sat":[600000,399990],"per_coin_log_w":{},"router":{"count":1,"kind":"exact","log_w":0.0,"method":"sparse"}},{"cascade":{"count":1,"kind":"exact","log_w":0.0,"method":"brute"},"fee_sat":100,"measured_input_indices":[],"outputs_sat":[600000,399900],"per_coin_log_w":{},"router":{"count":1,"kind":"exact","log_w":0.0,"method":"sparse"}},{"cascade":{"count":1,"kind":"exact","log_w":0.0,"method":"brute"},"fee_sat":1000,"measured_input_indices":[],"outputs_sat":[600000,399000],"per_coin_log_w":{},"router":{"count":1,"kind":"exact","log_w":0.0,"method":"sparse"}},{"cascade":{"count":1,"kind":"exact","log_w":0.0,"method":"brute"},"fee_sat":4956,"measured_input_indices":[],"outputs_sat":[600000,395044],"per_coin_log_w":{},"router":{"count":1,"kind":"exact","log_w":0.0,"method":"sparse"}}],"schema_version":1}
diff --git a/results/artifacts/counting-methods-v1.json b/results/artifacts/counting-methods-v1.json
new file mode 100644
index 00000000..dd26c4d0
--- /dev/null
+++ b/results/artifacts/counting-methods-v1.json
@@ -0,0 +1 @@
+{"components":{"fee_sensitivity":{"experiment":"counting-fee-sensitivity-v1","sha256":"0fef54735522470634c696ec026c31c86dff4d4dae23da956b49d8f89eb3a508"},"router":{"experiment":"counting-router-v1","sha256":"7c0764b7f39ad8795b4e349cff0903f68b7edcbc3f0d75cc6691a138813239b7"}},"excluded_historical_measurements":[{"measurement":"wall-clock benchmarks","reason":"no pinned machine and resource protocol supports comparison"},{"measurement":"seven L candidates over 18 synthetic instances","reason":"the instance-generation recipe and seeds were not preserved"},{"measurement":"obsolete router shares","reason":"the current canonical router run supersedes earlier revisions"}],"experiment":"counting-methods-v1","fee_sensitivity":{"fixture":{"inputs_sat":[400000,300000,300000],"output_total_before_fee_sat":1000000},"observations":[{"cascade":{"count":2,"kind":"exact","log_w":0.6931471805599453,"method":"brute"},"fee_sat":0,"measured_input_indices":[0,1,2],"outputs_sat":[600000,400000],"per_coin_log_w":{"0":0.6931471805599453,"1":0.6931471805599453,"2":0.6931471805599453},"router":{"count":2,"kind":"exact","log_w":0.6931471805599453,"method":"sparse"}},{"cascade":{"count":1,"kind":"exact","log_w":0.0,"method":"brute"},"fee_sat":1,"measured_input_indices":[1,2],"outputs_sat":[600000,399999],"per_coin_log_w":{"1":0.0,"2":0.0},"router":{"count":1,"kind":"exact","log_w":0.0,"method":"sparse"}},{"cascade":{"count":1,"kind":"exact","log_w":0.0,"method":"brute"},"fee_sat":10,"measured_input_indices":[],"outputs_sat":[600000,399990],"per_coin_log_w":{},"router":{"count":1,"kind":"exact","log_w":0.0,"method":"sparse"}},{"cascade":{"count":1,"kind":"exact","log_w":0.0,"method":"brute"},"fee_sat":100,"measured_input_indices":[],"outputs_sat":[600000,399900],"per_coin_log_w":{},"router":{"count":1,"kind":"exact","log_w":0.0,"method":"sparse"}},{"cascade":{"count":1,"kind":"exact","log_w":0.0,"method":"brute"},"fee_sat":1000,"measured_input_indices":[],"outputs_sat":[600000,399000],"per_coin_log_w":{},"router":{"count":1,"kind":"exact","log_w":0.0,"method":"sparse"}},{"cascade":{"count":1,"kind":"exact","log_w":0.0,"method":"brute"},"fee_sat":4956,"measured_input_indices":[],"outputs_sat":[600000,395044],"per_coin_log_w":{},"router":{"count":1,"kind":"exact","log_w":0.0,"method":"sparse"}}]},"limitations":["this artifact composes upstream results and performs no counting calculation","component SHA-256 identities are mandatory","runtime benchmarks require a separate benchmark protocol","an L-estimator comparison requires a preserved generator and seeds","the counting objects remain diagnostics and are not a privacy score or CoinScore"],"router":{"current_router":{"by_method_and_kind":{"none:unknown":1333,"radix:diagnostic":13,"sparse:exact":60,"sparse:lower_bound":22},"guaranteed_nonzero_log_w":82},"population":{"multi_input_transactions":1428},"radix_precondition_audit":{"precondition_applies":20,"raw_positive":64,"raw_positive_without_precondition":51}},"schema_version":1}
diff --git a/results/artifacts/counting-router-v1.json b/results/artifacts/counting-router-v1.json
new file mode 100644
index 00000000..0a9c3607
--- /dev/null
+++ b/results/artifacts/counting-router-v1.json
@@ -0,0 +1 @@
+{"corpus":"public Bitcoin transaction snapshot","experiment":"counting-router-v1","limitations":["W(E) is a transaction-level subset-sum diagnostic, not a mapping count","raw radix output is audited only to test its required precondition","zero-count and unknown outcomes provide no ambiguity evidence","Sasamoto remains an explicit estimator and is not a router tier","timings and synthetic L-estimator comparisons are outside this run","this diagnostic is not a privacy score or CoinScore"],"parameters":{"knee":5,"route":["radix","sparse","none"]},"report":{"current_router":{"by_method_and_kind":{"none:unknown":1333,"radix:diagnostic":13,"sparse:exact":60,"sparse:lower_bound":22},"guaranteed_nonzero_log_w":82},"population":{"multi_input_transactions":1428},"radix_precondition_audit":{"precondition_applies":20,"raw_positive":64,"raw_positive_without_precondition":51}},"schema_version":1}
diff --git a/results/artifacts/denomination-preparation-v1.json b/results/artifacts/denomination-preparation-v1.json
new file mode 100644
index 00000000..056d327a
--- /dev/null
+++ b/results/artifacts/denomination-preparation-v1.json
@@ -0,0 +1 @@
+{"arity_matched_controls":{"features":[{"exact_denomination_outputs":0,"exact_denomination_outputs_spent_next":0,"input_count":2,"output_count":2},{"exact_denomination_outputs":0,"exact_denomination_outputs_spent_next":0,"input_count":2,"output_count":2},{"exact_denomination_outputs":0,"exact_denomination_outputs_spent_next":0,"input_count":2,"output_count":2}],"fingerprint_matches":0,"transactions":3},"composition":null,"conclusion":"an exact denomination consumed by the next CoinJoin distinguishes the synthetic preparations from arity-matched controls","experiment":"denomination-preparation-v1","limitations":["deterministic synthetic corpus","controls match transaction arity but not a real-world population distribution","a fingerprint match is not proof of unilateral construction or common ownership","the CTP qualification has no separately cited empirical experiment"],"preparations":{"features":[{"exact_denomination_outputs":1,"exact_denomination_outputs_spent_next":1,"input_count":2,"output_count":2},{"exact_denomination_outputs":1,"exact_denomination_outputs_spent_next":1,"input_count":2,"output_count":2},{"exact_denomination_outputs":1,"exact_denomination_outputs_spent_next":1,"input_count":2,"output_count":2}],"fingerprint_matches":3,"transactions":3},"schema_version":1}
diff --git a/results/artifacts/descent-vs-ascent-v1.json b/results/artifacts/descent-vs-ascent-v1.json
new file mode 100644
index 00000000..15ace718
--- /dev/null
+++ b/results/artifacts/descent-vs-ascent-v1.json
@@ -0,0 +1 @@
+{"experiment":"descent-vs-ascent-v1","limitations":["the snapshot contains six 2016 blocks and is not a chain-wide sample","blocks above max_block get the conservative pass, which cuts only where no pair objects and is not the best cut","signals are counted tells, not calibrated bits, so the thresholds are not likelihoods","the two passes are compared as partitions; neither is checked against same-owner labels","the fingerprint and roundness channels are absent here, as they are in the merge pass","the snapshot recipe is unavailable; the dataset publisher authorized redistribution on 2026-09-04"],"measurement":{"addresses":12341,"addresses_in_exactly_searched_blocks":7748,"ascent_blocks":3282,"ascent_refines_inherited":true,"descent":[{"agreement_with_ascent":{"identical":false,"joined_by_both":597699,"joined_only_by_ascent":0,"joined_only_by_descent":3014},"blocks_approximated":84,"blocks_cut":4,"blocks_left_whole":3178,"cut_below":-1.0,"reaches_ascent":false,"refines_inherited":true,"resulting_blocks":3261,"severed_weight":-823.0},{"agreement_with_ascent":{"identical":false,"joined_by_both":597699,"joined_only_by_ascent":0,"joined_only_by_descent":3837},"blocks_approximated":84,"blocks_cut":0,"blocks_left_whole":3182,"cut_below":-2.0,"reaches_ascent":false,"refines_inherited":true,"resulting_blocks":3182,"severed_weight":0},{"agreement_with_ascent":{"identical":false,"joined_by_both":597699,"joined_only_by_ascent":0,"joined_only_by_descent":3837},"blocks_approximated":84,"blocks_cut":0,"blocks_left_whole":3182,"cut_below":-4.0,"reaches_ascent":false,"refines_inherited":true,"resulting_blocks":3182,"severed_weight":0}],"evidence_reach":{"approximated_blocks_carrying_evidence":5,"joined_pairs":601536,"joined_pairs_in_exactly_searched_blocks":7669,"negative_pairs_in_approximated_blocks":1054,"negative_pairs_in_exactly_searched_blocks":0,"separated_by_ascent_only":3837,"separated_by_ascent_only_in_exactly_searched_blocks":0,"signalled_pairs":1054},"exactly_searched_blocks":3098,"inherited_blocks":3182,"largest_inherited_block":648,"transactions":12000},"parameters":{"channels":["coinjoin shape refuses every pair among the inputs","de-mix partition joins a participant's own inputs and refuses across participants","unnecessary input and mixed input types each refuse once"],"cospend_prior":0.0,"max_block":9,"signal_unit":"one published tell against a pairing","thresholds":[-1.0,-2.0,-4.0]},"schema_version":1}
diff --git a/results/artifacts/deterministic-partition-v1.json b/results/artifacts/deterministic-partition-v1.json
new file mode 100644
index 00000000..308b6a6c
--- /dev/null
+++ b/results/artifacts/deterministic-partition-v1.json
@@ -0,0 +1 @@
+{"balance_model":"exact per-block conservation","composition":null,"conclusion":"the forced example has one non-derived mapping and four unanimous links, while the underdetermined example has three mappings and no unanimous link","experiment":"deterministic-partition-v1","forced_partition":{"inputs":[1,3,20,50],"non_derived_mappings":1,"outputs":[4,70],"unanimous_links":[[0,0],[1,0],[2,1],[3,1]]},"limitations":["certainty applies only within the declared exact non-derived mapping family","the examples are scaled integer renderings of the CTP amount fixtures","fees, contextual priors and net-settlement cycles are not modeled","unanimous mapping links are not ownership attribution"],"mapping_family":"Maurer non-derived mappings","schema_version":1,"underdetermined_control":{"inputs":[1,2,3,4,5],"non_derived_mappings":3,"outputs":[7,8],"unanimous_links":[]}}
diff --git a/results/artifacts/em-m-v1.json b/results/artifacts/em-m-v1.json
new file mode 100644
index 00000000..18677472
--- /dev/null
+++ b/results/artifacts/em-m-v1.json
@@ -0,0 +1 @@
+{"dataset":"fs-blkcache-2026-09-04-v1","experiment":"em-m-v1","historical_comparison":{"reason":"the preserved 22,112-transaction snapshot is not the historical 165,832-transaction cache","status":"not_reproduced"},"limitations":["address reuse is a weak and partly feature-dependent ownership proxy","the balanced selected sample does not represent population prevalence","EM is fitted and evaluated on the same pair sample","the model assumes conditional independence between correlated axes","u is fixed at the measured collision rate","the small AUC difference is measured without a pre-registered separability verdict","this is attacker-side parameter diagnosis, not CoinScore or a privacy certificate"],"measurement":{"em_minus_fixed_auc":0.0009000000000000119,"fit":{"inferred_pair_fraction":0.6639562138569092,"iterations":21,"posterior_auc_against_address_reuse":0.932},"library_scorer_auc":{"address_reuse_agreement":0.9546,"em":0.92535,"fixed_0_95":0.92445},"pairs":{"negative":4000,"positive":4000},"per_axis":[{"address_reuse_agreement":0.98075,"axis":"nsequence","collision":0.3965560341699342,"em":0.8511166236884449,"fixed_assumption":0.95},{"address_reuse_agreement":0.9995,"axis":"locktime","collision":0.6182534217108192,"em":0.9283368897025305,"fixed_assumption":0.95},{"address_reuse_agreement":0.7663456664977192,"axis":"input_order","collision":0.5282888734301753,"em":0.7688637958822665,"fixed_assumption":0.95},{"address_reuse_agreement":0.5979103471520054,"axis":"output_order","collision":0.3372006451199894,"em":0.505342852993097,"fixed_assumption":0.95},{"address_reuse_agreement":0.81225,"axis":"change_spk","collision":0.378434346868815,"em":0.7426082475264336,"fixed_assumption":0.95},{"address_reuse_agreement":0.99675,"axis":"version","collision":0.5346816519684303,"em":0.8923310281944659,"fixed_assumption":0.95},{"address_reuse_agreement":0.6202450032237267,"axis":"io_shape","collision":0.20372996306959482,"em":0.543342181791182,"fixed_assumption":0.95},{"address_reuse_agreement":0.91475,"axis":"uih","collision":0.8536414295210479,"em":0.9224280927609649,"fixed_assumption":0.95},{"address_reuse_agreement":0.962,"axis":"fee_rate","collision":0.7082946392465704,"em":0.958237482619391,"fixed_assumption":0.95},{"address_reuse_agreement":0.9692269201901426,"axis":"input_script_type","collision":0.3775091158197063,"em":0.999999,"fixed_assumption":0.95},{"address_reuse_agreement":0.79975,"axis":"output_encoding","collision":0.6140442327748129,"em":0.759170939844021,"fixed_assumption":0.95},{"address_reuse_agreement":0.968477703741671,"axis":"input_types_present","collision":0.3773038531919157,"em":0.999999,"fixed_assumption":0.95},{"address_reuse_agreement":0.7465,"axis":"change_index","collision":0.5552437345136181,"em":0.748293185217357,"fixed_assumption":0.95},{"address_reuse_agreement":0.7735,"axis":"change_type_match","collision":0.5842472884656746,"em":0.7720901469099556,"fixed_assumption":0.95},{"address_reuse_agreement":0.75875,"axis":"change_matches_output","collision":0.5601290430217203,"em":0.7588628287261019,"fixed_assumption":0.95},{"address_reuse_agreement":0.867,"axis":"change_address_reuse","collision":0.6502137908886876,"em":0.7980906501052633,"fixed_assumption":0.95},{"address_reuse_agreement":0.72075,"axis":"low_r","collision":0.4773790239598302,"em":0.6905656160166325,"fixed_assumption":0.95},{"address_reuse_agreement":0.9844416562107905,"axis":"sighash","collision":0.45625534504142534,"em":0.9939714861741844,"fixed_assumption":0.95},{"address_reuse_agreement":0.9965,"axis":"op_return","collision":0.886609246290655,"em":0.9780339672131558,"fixed_assumption":0.95},{"address_reuse_agreement":0.99125,"axis":"nested_segwit","collision":0.8027327555564433,"em":0.999999,"fixed_assumption":0.95},{"address_reuse_agreement":0.989,"axis":"pubkey_compression","collision":0.6043962005553807,"em":0.999999,"fixed_assumption":0.95},{"address_reuse_agreement":0.99175,"axis":"multisig","collision":0.8865024296789842,"em":0.9946066511297224,"fixed_assumption":0.95},{"address_reuse_agreement":null,"axis":"locktime_vs_broadcast","collision":0.7563732010755665,"em":0.9,"fixed_assumption":0.95}],"transactions":22112},"parameters":{"fixed_u":true,"pair_cap_per_class":4000,"seed":0},"schema_version":1,"source":{"bytes":91367352,"digest":"91c444bd4e9715d7739ccc9d68c7d9751762e25b647e3eef647c6ec0b48ba90c","files":904,"pattern":".blkcache/*.json","transactions":22112}}
diff --git a/results/artifacts/entity-deanon-v1.json b/results/artifacts/entity-deanon-v1.json
new file mode 100644
index 00000000..4f1efe81
--- /dev/null
+++ b/results/artifacts/entity-deanon-v1.json
@@ -0,0 +1 @@
+{"adversary":"payment-graph common-neighbour scorer with independent entity detector","bitmex":{"entity_clusters":1,"payment_auc":0.4974,"positive_mean_shared_neighbours":0.006713271559731377,"positive_pairs":3488761,"transactions":9698},"experiment":"entity-deanon-v1","interpretation":{"bitmex":"null control with hub-and-spoke deposit structure","satoshidice":"positive control with recurring counterparties"},"limitations":["the fixtures are selected entity-centred subgraphs, not uniform chain samples","vanity-prefix detectors label the fixtures and are not general entity attribution","the run evaluates common-neighbour structure, not the full Narayanan-Shmatikov algorithm","the contrast does not establish a universal property of gambling services or exchanges","upstream data licensing is undetermined; the dataset publisher authorized redistribution on 2026-09-04","the result is not a privacy score or CoinScore"],"satoshidice":{"entity_clusters":1,"payment_auc":0.689075,"positive_mean_shared_neighbours":7.948717948717949,"positive_pairs":780,"transactions":15635},"schema_version":1}
diff --git a/results/artifacts/entropy-insufficiency-v1.json b/results/artifacts/entropy-insufficiency-v1.json
new file mode 100644
index 00000000..f19e4170
--- /dev/null
+++ b/results/artifacts/entropy-insufficiency-v1.json
@@ -0,0 +1 @@
+{"brittle_overlap":{"entropy_bits":2.0,"initial_candidates":4,"observations":2,"surviving_candidates":1},"composition":null,"conclusion":"equal point entropy does not determine longitudinal intersection resistance","equal_initial_entropy":true,"experiment":"entropy-insufficiency-v1","limitations":["synthetic counterexample","equal point entropy does not imply equal future candidate-set overlap","the fixture falsifies sufficiency but does not estimate real-world attack frequency","the result is not a privacy score or certificate"],"overlapping_crowd":{"entropy_bits":2.0,"initial_candidates":4,"observations":2,"surviving_candidates":2},"schema_version":1}
diff --git a/results/artifacts/equal-output-consolidation-v1.json b/results/artifacts/equal-output-consolidation-v1.json
new file mode 100644
index 00000000..b1cecf64
--- /dev/null
+++ b/results/artifacts/equal-output-consolidation-v1.json
@@ -0,0 +1 @@
+{"experiment":"equal-output-consolidation-v1","limitations":["synthetic fixture","the report demonstrates conditional narrowing, not an identity","a collaborative consolidation would invalidate the co-ownership premise"],"result":{"candidates_after":["alice"],"candidates_before":3,"composition":null,"outcome":"candidate_narrowing"},"scenario":{"candidate_sets":[["alice","bob","carol"],["alice","dave","erin"]],"outputs":[["coinjoin-1",0],["coinjoin-2",0]]},"schema_version":1}
diff --git a/results/artifacts/eve-alice-eve-v1.json b/results/artifacts/eve-alice-eve-v1.json
new file mode 100644
index 00000000..64a2a299
--- /dev/null
+++ b/results/artifacts/eve-alice-eve-v1.json
@@ -0,0 +1 @@
+{"adversary":"counterparty with private transaction records","consolidated_return":{"candidates_after":["alice"],"composition":null,"observations":2,"outcome":"candidate_narrowing"},"experiment":"eve-alice-eve-v1","isolated_return":{"candidates_after":[],"composition":null,"observations":1,"outcome":"inconclusive"},"limitations":["synthetic fixture","behavioral priors and temporal patterns are not modeled","candidate narrowing is not identity attribution","a collaborative deposit invalidates the common-ownership premise"],"schema_version":1}
diff --git a/results/artifacts/exact-oracle-audit-v1.json b/results/artifacts/exact-oracle-audit-v1.json
new file mode 100644
index 00000000..dc126f38
--- /dev/null
+++ b/results/artifacts/exact-oracle-audit-v1.json
@@ -0,0 +1 @@
+{"dependencies":{"dss_revision":"d8099bc540c9de7d0f3c9f095e8b2aad8f9d8c8d","dss_version":"0.1.0"},"experiment":"exact-oracle-audit-v1","parameters":{"alphabet":[1,2,3,4],"max_coins":8,"min_side":2,"tolerance":1e-09},"report":{"comparisons":{"deterministic_links":{"agreements":179,"bound_direction":"upper","cases":507,"compared":507,"exceeds_exact":[{"inputs":[1,1],"missed_links":[],"outputs":[1,1],"spurious_links":[[0,0],[1,1]]},{"inputs":[2,2],"missed_links":[],"outputs":[2,2],"spurious_links":[[0,0],[1,1]]},{"inputs":[3,3],"missed_links":[],"outputs":[3,3],"spurious_links":[[0,0],[1,1]]},{"inputs":[4,4],"missed_links":[],"outputs":[4,4],"spurious_links":[[0,0],[1,1]]},{"inputs":[1,2],"missed_links":[],"outputs":[1,1,1],"spurious_links":[[0,2],[1,0],[1,1]]},{"inputs":[1,3],"missed_links":[],"outputs":[1,1,2],"spurious_links":[[0,1],[1,0]]},{"inputs":[1,4],"missed_links":[],"outputs":[1,1,3],"spurious_links":[[0,1],[1,0]]},{"inputs":[2,3],"missed_links":[],"outputs":[1,2,2],"spurious_links":[[0,2],[1,1]]},{"inputs":[2,4],"missed_links":[],"outputs":[2,2,2],"spurious_links":[[0,2],[1,0],[1,1]]},{"inputs":[3,4],"missed_links":[],"outputs":[1,3,3],"spurious_links":[[0,2],[1,1]]},{"inputs":[1,3],"missed_links":[],"outputs":[1,1,1,1],"spurious_links":[[0,3],[1,0],[1,1],[1,2]]},{"inputs":[1,4],"missed_links":[],"outputs":[1,1,1,2],"spurious_links":[[0,2],[1,0],[1,1]]}],"exceeds_exact_cases":328,"missed_links":0,"object_note":"the (input, output) pairs every mapping agrees on — over dss's family against the oracle's; counting.link_matrix's docstring reads a unit row to consumers as 'the amounts settle that assignment on their own', which is the claim a spurious link makes","overcounts":328,"refusals":0,"relation":"restriction","spurious_links":945,"undercounts":0},"deterministic_links_finest_only":{"agreements":179,"bound_direction":"upper","cases":507,"compared":507,"exceeds_exact":[{"inputs":[1,1],"missed_links":[],"outputs":[1,1],"spurious_links":[[0,0],[1,1]]},{"inputs":[2,2],"missed_links":[],"outputs":[2,2],"spurious_links":[[0,0],[1,1]]},{"inputs":[3,3],"missed_links":[],"outputs":[3,3],"spurious_links":[[0,0],[1,1]]},{"inputs":[4,4],"missed_links":[],"outputs":[4,4],"spurious_links":[[0,0],[1,1]]},{"inputs":[1,2],"missed_links":[],"outputs":[1,1,1],"spurious_links":[[0,2],[1,0],[1,1]]},{"inputs":[1,3],"missed_links":[],"outputs":[1,1,2],"spurious_links":[[0,1],[1,0]]},{"inputs":[1,4],"missed_links":[],"outputs":[1,1,3],"spurious_links":[[0,1],[1,0]]},{"inputs":[2,3],"missed_links":[],"outputs":[1,2,2],"spurious_links":[[0,2],[1,1]]},{"inputs":[2,4],"missed_links":[],"outputs":[2,2,2],"spurious_links":[[0,2],[1,0],[1,1]]},{"inputs":[3,4],"missed_links":[],"outputs":[1,3,3],"spurious_links":[[0,2],[1,1]]},{"inputs":[1,3],"missed_links":[],"outputs":[1,1,1,1],"spurious_links":[[0,3],[1,0],[1,1],[1,2]]},{"inputs":[1,4],"missed_links":[],"outputs":[1,1,1,2],"spurious_links":[[0,2],[1,0],[1,1]]}],"exceeds_exact_cases":328,"missed_links":0,"object_note":"as deterministic_links, but the oracle side keeps only the maximal-refinement mappings","overcounts":328,"refusals":0,"relation":"restriction","spurious_links":945,"undercounts":0},"link_matrix":{"agreements":16,"bound_direction":"neither","cases":507,"compared":507,"disagreements":491,"entries":6228,"entries_above_exact":1714,"entries_agreeing":410,"entries_below_exact":4104,"exceeds_exact":[{"entries_above_exact":2,"inputs":[1,1],"max_absolute_error":0.6666666666666666,"outputs":[1,1]},{"entries_above_exact":2,"inputs":[2,2],"max_absolute_error":0.6666666666666666,"outputs":[2,2]},{"entries_above_exact":2,"inputs":[3,3],"max_absolute_error":0.6666666666666666,"outputs":[3,3]},{"entries_above_exact":2,"inputs":[4,4],"max_absolute_error":0.6666666666666666,"outputs":[4,4]},{"entries_above_exact":3,"inputs":[1,2],"max_absolute_error":0.75,"outputs":[1,1,1]},{"entries_above_exact":2,"inputs":[1,3],"max_absolute_error":0.6666666666666666,"outputs":[1,1,2]},{"entries_above_exact":2,"inputs":[1,4],"max_absolute_error":0.6666666666666666,"outputs":[1,1,3]},{"entries_above_exact":2,"inputs":[2,3],"max_absolute_error":0.6666666666666666,"outputs":[1,2,2]},{"entries_above_exact":3,"inputs":[2,4],"max_absolute_error":0.75,"outputs":[2,2,2]},{"entries_above_exact":2,"inputs":[3,4],"max_absolute_error":0.6666666666666666,"outputs":[1,3,3]},{"entries_above_exact":4,"inputs":[1,3],"max_absolute_error":0.8,"outputs":[1,1,1,1]},{"entries_above_exact":3,"inputs":[1,4],"max_absolute_error":0.75,"outputs":[1,1,1,2]}],"exceeds_exact_cases":440,"max_absolute_error":0.8333333333333334,"max_relative_error":2.0000000000000004,"object_note":"P(input i and output j share a block): the same quantity both sides, but dss marginalizes over its own restricted family (verify_dss_marginal_family), so it bounds the oracle's marginal in neither direction","refusals":0,"relation":"restriction","shape_mismatches":0,"undefined_relative_errors":0},"link_matrix_finest_only":{"agreements":68,"bound_direction":"neither","cases":507,"compared":507,"disagreements":439,"entries":6228,"entries_above_exact":1811,"entries_agreeing":2314,"entries_below_exact":2103,"exceeds_exact":[{"entries_above_exact":2,"inputs":[1,1],"max_absolute_error":0.5,"outputs":[1,1]},{"entries_above_exact":2,"inputs":[2,2],"max_absolute_error":0.5,"outputs":[2,2]},{"entries_above_exact":2,"inputs":[3,3],"max_absolute_error":0.5,"outputs":[3,3]},{"entries_above_exact":2,"inputs":[4,4],"max_absolute_error":0.5,"outputs":[4,4]},{"entries_above_exact":3,"inputs":[1,2],"max_absolute_error":0.6666666666666667,"outputs":[1,1,1]},{"entries_above_exact":2,"inputs":[1,3],"max_absolute_error":0.5,"outputs":[1,1,2]},{"entries_above_exact":2,"inputs":[1,4],"max_absolute_error":0.5,"outputs":[1,1,3]},{"entries_above_exact":2,"inputs":[2,3],"max_absolute_error":0.5,"outputs":[1,2,2]},{"entries_above_exact":3,"inputs":[2,4],"max_absolute_error":0.6666666666666667,"outputs":[2,2,2]},{"entries_above_exact":2,"inputs":[3,4],"max_absolute_error":0.5,"outputs":[1,3,3]},{"entries_above_exact":4,"inputs":[1,3],"max_absolute_error":0.75,"outputs":[1,1,1,1]},{"entries_above_exact":3,"inputs":[1,4],"max_absolute_error":0.6666666666666667,"outputs":[1,1,1,2]}],"exceeds_exact_cases":439,"max_absolute_error":0.8,"max_relative_error":4.0,"object_note":"as link_matrix, but the oracle side keeps only the maximal-refinement mappings","refusals":0,"relation":"restriction","shape_mismatches":0,"undefined_relative_errors":0},"mapping_count":{"agreements":16,"bound_direction":"lower","cases":507,"compared":507,"exceeds_exact":[],"exceeds_exact_cases":0,"max_absolute_error":107,"max_relative_error":0.92,"object_note":"mappings both sides, over dss's refinement-maximal family against every index-level balanced set partition","overcounts":0,"refusals":0,"relation":"restriction","undefined_relative_errors":0,"undercounts":491},"mapping_entropy_bits":{"agreements":16,"bound_direction":"lower","cases":507,"compared":507,"exceeds_exact":[],"exceeds_exact_cases":0,"max_absolute_error":3.6438561897747244,"max_relative_error":1.0,"object_note":"both are log2 of a mapping count under a uniform prior, over the two families named for mapping_count","overcounts":0,"refusals":0,"relation":"restriction","undefined_relative_errors":0,"undercounts":491}},"family":{"alphabet":[1,2,3,4],"canonical_form":"sorted input multiset, sorted output multiset; scale not folded","fee":0,"max_coins":8,"min_side":2,"size":507},"flags":["link_matrix: 440 case(s) where the approximation exceeds the exact oracle","link_matrix: neither a lower nor an upper bound — errs in both directions","deterministic_links: 328 case(s) where the approximation exceeds the exact oracle","deterministic_links: 945 link(s) reported as certain that the exact oracle leaves ambiguous","link_matrix_finest_only: 439 case(s) where the approximation exceeds the exact oracle","link_matrix_finest_only: neither a lower nor an upper bound — errs in both directions","deterministic_links_finest_only: 328 case(s) where the approximation exceeds the exact oracle","deterministic_links_finest_only: 945 link(s) reported as certain that the exact oracle leaves ambiguous"],"identifications":{"count_w_router_and_saddle_point":{"cases_is_dense_reads_as_dense":443,"kinds":{"exact":486,"lower_bound":5,"unknown":16},"methods":{"none":16,"sparse":491},"note":"The radix tier never wins on this family: it returns zero, `_resolved` reads that as no answer, and the route falls through to sparse. The saddle point declines every case here even though `is_dense` reads most of them as Dense, which is the optimistic-gate discrepancy its own docstring predicts, now measured.","object":"count_w routes to radix or sparse; the sparse answer is the same subset-sum solution count W(E), so the router is a different object from |M| for the same reason w_count is","relation":"different-object","saddle_point_answers":0,"sparse_answers":491,"sparse_answers_matching_the_subset_sum_rederivation":491},"per_coin_density":{"cases_where_exact_input_marginals_vary":339,"coin_invariant_cases":497,"coin_varying_cases":10,"note":"log_w is a natural log while the oracle's entropy is in bits, and the quantity is a subset-sum density rather than a link marginal, so no entrywise comparison is made. The reportable fact is structural: where its value is identical for every coin of a transaction it cannot express per-coin variation that the exact marginals do show.","object":"per-coin log_w (natural log) from the fee-aware density path, not a probability","refusals":0,"relation":"different-object"},"radix_mappings":{"input_independence_witnesses":[],"note":"The call does not take the inputs, so one answer stands for every transaction sharing an output multiset while the exact mapping count differs across them; and the answer moves when every coin is multiplied by a constant, which the exact mapping count cannot. A quantity blind to a parameter the target depends on, and sensitive to one it does not, is not a bound on the target.","object":"permutations of repeated output denominations; a function of the outputs alone","output_multisets_where_radix_applies":25,"output_multisets_where_radix_returned_a_nonzero_count":0,"output_multisets_whose_radix_count_moved_under_scaling":19,"relation":"different-object","scale":1000,"scale_dependence_witnesses":[{"exact_mapping_counts":[7,21,27,29],"outputs":[1,1,2,2,2],"radix_count":0,"scaled_exact_mapping_counts":[7,21,27,29],"scaled_outputs":[1000,1000,2000,2000,2000],"scaled_radix_count":8},{"exact_mapping_counts":[28],"outputs":[1,1,3,3,3],"radix_count":0,"scaled_exact_mapping_counts":[28],"scaled_outputs":[1000,1000,3000,3000,3000],"scaled_radix_count":2},{"exact_mapping_counts":[4,8,11,16,25,30,53],"outputs":[1,2,2,2],"radix_count":0,"scaled_exact_mapping_counts":[4,8,11,16,25,30,53],"scaled_outputs":[1000,2000,2000,2000],"scaled_radix_count":7},{"exact_mapping_counts":[13,20,27],"outputs":[1,2,2,2,2],"radix_count":0,"scaled_exact_mapping_counts":[13,20,27],"scaled_outputs":[1000,2000,2000,2000,2000],"scaled_radix_count":25},{"exact_mapping_counts":[18,19],"outputs":[1,2,2,2,3],"radix_count":0,"scaled_exact_mapping_counts":[18,19],"scaled_outputs":[1000,2000,2000,2000,3000],"scaled_radix_count":9},{"exact_mapping_counts":[18],"outputs":[1,2,2,2,4],"radix_count":0,"scaled_exact_mapping_counts":[18],"scaled_outputs":[1000,2000,2000,2000,4000],"scaled_radix_count":13},{"exact_mapping_counts":[10],"outputs":[1,2,3,3,3],"radix_count":0,"scaled_exact_mapping_counts":[10],"scaled_outputs":[1000,2000,3000,3000,3000],"scaled_radix_count":4},{"exact_mapping_counts":[7,9,13,16,23,53],"outputs":[1,3,3,3],"radix_count":0,"scaled_exact_mapping_counts":[7,9,13,16,23,53],"scaled_outputs":[1000,3000,3000,3000],"scaled_radix_count":1},{"exact_mapping_counts":[4,16,53],"outputs":[1,4,4,4],"radix_count":0,"scaled_exact_mapping_counts":[4,16,53],"scaled_outputs":[1000,4000,4000,4000],"scaled_radix_count":1},{"exact_mapping_counts":[1,4,10,16,40],"outputs":[2,2,2],"radix_count":0,"scaled_exact_mapping_counts":[1,4,10,16,40],"scaled_outputs":[2000,2000,2000],"scaled_radix_count":6},{"exact_mapping_counts":[5,7,17,27,131],"outputs":[2,2,2,2],"radix_count":0,"scaled_exact_mapping_counts":[5,7,17,27,131],"scaled_outputs":[2000,2000,2000,2000],"scaled_radix_count":24},{"exact_mapping_counts":[11,56],"outputs":[2,2,2,2,2],"radix_count":0,"scaled_exact_mapping_counts":[11,56],"scaled_outputs":[2000,2000,2000,2000,2000],"scaled_radix_count":120}]},"subtransaction_ambiguity_bits":{"cases_above_exact_entropy":6,"cases_below_exact_entropy":6,"cases_equal_to_exact_entropy":0,"note":"Its own docstring already calls this a count diagnostic and not a privacy quantity. The count admits partitions that do not conserve value, so it is neither a bound on nor an estimate of the oracle's mapping entropy; the tallies above describe how the two numbers happen to order, not a bound.","object":"log2 of the number of (receiver input, receiver output) pairs with a positive implied payment; no block is required to balance","refusals":4,"relation":"different-object","two_in_two_out_cases":16},"w_count":{"cases_where_w_equals_exact_mapping_count":34,"exact_answers_checked":507,"object":"non-empty proper input subsets whose sum is a proper output-subset sum, W(E)","rederivation":"decluster.baselines.oracle_audit._subset_sum_solutions","rederivation_examples":[],"rederivation_mismatches":0,"relation":"different-object","verdict":"W(E) identified: dss.w_count reproduces the independent subset-sum re-derivation on 507/507 exact answers. It is not the mapping count, so this module reports the object mismatch and refuses to score it as an under/overcount."}},"restriction_evidence":{"dss_marginal_family":{"cases_checked":507,"cases_where_the_dss_family_is_strictly_smaller_than_the_oracle_family":491,"counterexamples":[],"entries_are_multiples_of_one_over_n_non_derived":507,"unit_entries_equal_the_reported_deterministic_links":507,"verdict":"pairwise_link_prob is the uniform marginal over dss's own family: quantized to 1/n_non_derived on 507/507 cases and unit-valued exactly on the deterministic links it reports on 507/507. That family is strictly smaller than the oracle's on 491 cases, so the matrix, the count and the certain-link set are all restrictions and none is a bound in either direction."},"finest_selection_sensitivity":{"cases":507,"cases_where_the_two_selections_differ":106,"max_block_count":{"missed_links":108,"spurious_links":942},"note":"The audit's cross-check uses refinement-maximal selection, the maximal elements of the refinement order, because that is what 'finest' means on a poset of partitions. Under max-block-count selection — a strict subset, so a smaller oracle family and more oracle certainties, a reading strictly more generous to the approximation — the spurious count falls and certain links begin to read as missed. The primary deterministic_links probe uses neither: it runs against the full oracle family.","refinement_maximal":{"missed_links":0,"spurious_links":945}},"mapping_count_mechanism":{"all_equal_value_cases":12,"all_equal_value_cases_answering_one":0,"cases":507,"different_from_the_finest_oracle_mapping_count":0,"equal_to_the_finest_oracle_mapping_count":507,"examples_where_it_differs":[],"note":"The current DSS count equals the independent refinement-maximal mapping count on the entire bounded family. Equal-value permutations do not collapse in general: `[1,3,4,4] -> [3,3,3,3]` answers 4, and those readings differ only in which equal-valued output stands alone. Agreement on this family is measured, not universal."}},"tolerance":1e-09},"schema_version":1,"summary":{"dss_family_strictly_smaller_cases":491,"dss_marginal_cases_checked":507,"dss_marginal_quantized_cases":507,"dss_marginal_unit_entries_match_deterministic_links":507,"dss_rev":"d8099bc540c9de7d0f3c9f095e8b2aad8f9d8c8d","dss_version":"0.1.0","family_size":507,"finest_only_entries_above_exact":1811,"finest_only_entries_below_exact":2103,"finest_only_missed_deterministic_links":0,"finest_only_spurious_deterministic_links":945,"finest_selection_divergent_cases":106,"link_matrix_bound_direction":"neither","link_matrix_entries":6228,"link_matrix_entries_above_exact":1714,"link_matrix_entries_below_exact":4104,"mapping_count_overcounts":0,"mapping_count_undercounts":491,"max_block_count_missed_deterministic_links":108,"max_block_count_spurious_deterministic_links":942,"missed_deterministic_links":0,"spurious_deterministic_links":945,"w_count_rederivation_mismatches":0}}
diff --git a/results/artifacts/fingerprint-catalog-axes-v1.json b/results/artifacts/fingerprint-catalog-axes-v1.json
new file mode 100644
index 00000000..5178135c
--- /dev/null
+++ b/results/artifacts/fingerprint-catalog-axes-v1.json
@@ -0,0 +1 @@
+{"diagnostic_axis_probabilities":{"output_count":{"1":0.15333333333333332,"2":0.695,"3":0.07333333333333333,"4plus":0.07833333333333334},"segwit_serialization":{"non_segwit":0.10666666666666667,"segwit":0.8933333333333333}},"experiment":"fingerprint-catalog-axes-v1","limitations":["address reuse is a weak and partly feature-dependent same-wallet proxy","diagnostic probabilities are fitted and evaluated on the same selected snapshot","the 600-transaction snapshot is not either historical 166k or 180k cache","AUC deltas on one snapshot do not establish conditional dependence","the axes remain excluded from the production fingerprint library","the result measures pair ranking and is not an ownership certificate or privacy score"],"measurement":{"negative_pair_draws":4000,"positive_pair_draws":4000,"rows":[{"added_axes":[],"auc":0.945875,"auc_delta_from_library":0.0,"configuration":"library","negative_mean":-17.41305201591386,"positive_mean":19.646924430510953,"shuffled_auc":0.490825},{"added_axes":["output_count"],"auc":0.94515,"auc_delta_from_library":-0.0007249999999999757,"configuration":"plus_output_count","negative_mean":-18.67177489730795,"positive_mean":20.29629417013815,"shuffled_auc":0.490675},{"added_axes":["segwit_serialization"],"auc":0.948475,"auc_delta_from_library":0.0025999999999999357,"configuration":"plus_segwit_serialization","negative_mean":-17.60219163488365,"positive_mean":20.120861983380525,"shuffled_auc":0.489875},{"added_axes":["output_count","segwit_serialization"],"auc":0.948,"auc_delta_from_library":0.0021249999999999325,"configuration":"plus_both","negative_mean":-18.860914516277745,"positive_mean":20.770231723007722,"shuffled_auc":0.490075}],"transactions":600},"parameters":{"pair_draw_cap":4000,"probability_source":"same frozen snapshot","rarity_floor_count":1000,"same_wallet_consistency":0.95,"seed":0},"schema_version":1}
diff --git a/results/artifacts/fingerprint-regime-v1.json b/results/artifacts/fingerprint-regime-v1.json
new file mode 100644
index 00000000..53662013
--- /dev/null
+++ b/results/artifacts/fingerprint-regime-v1.json
@@ -0,0 +1 @@
+{"experiment":"fingerprint-regime-v1","limitations":["address reuse is a weak and partly feature-dependent owner proxy","candidate identities are reuse-group labels rather than wallet ownership","the 600-transaction snapshot is selected and is not a chain-wide sample","the snapshot-measured weights are fitted and evaluated on the same data","the Lumen weighting and historical growing-cache population are not reproduced","mean eccentricity is descriptive and is not an acceptance rate","the result does not establish that fingerprints are universally sparse or dense"],"measurement":{"rows":[{"candidate_queries":200,"fs_auc":0.93915,"fs_top1":0.73,"ns_auc":0.9207,"ns_top1":0.68,"pair_negative_draws":2000,"pair_positive_draws":2000,"weight_source":"library","within_class_gap_mean":0.39259080055258716,"within_class_queries":163},{"candidate_queries":200,"fs_auc":0.93915,"fs_top1":0.73,"ns_auc":0.95315,"ns_top1":0.785,"pair_negative_draws":2000,"pair_positive_draws":2000,"weight_source":"snapshot_measured","within_class_gap_mean":0.6751155502658324,"within_class_queries":163}],"transactions":600},"parameters":{"candidate_node_cap":200,"conditioning_axes":["input_script_type","nsequence","input_order","output_order","version"],"pair_draw_cap":2000,"seed":0},"schema_version":1}
diff --git a/results/artifacts/fingerprint-sparsity-v1.json b/results/artifacts/fingerprint-sparsity-v1.json
new file mode 100644
index 00000000..a10f1164
--- /dev/null
+++ b/results/artifacts/fingerprint-sparsity-v1.json
@@ -0,0 +1 @@
+{"cross_check":{"consistent_conditional_partitions":["input_order","input_types","low_r","nlocktime","nsequence","op_return","output_order","output_structure","output_types","sighash","uncompressed_pubkey","version"]},"experiment":"fingerprint-sparsity-v1","limitations":["the fixture contains aggregate histograms rather than transaction-level vectors","the 19-axis and per-epoch distributions are not preserved and are not reproduced","exact-vector class size is not nearest-neighbour sparsity in a general feature metric","the result concerns per-transaction vectors, not cluster feature distributions","the scan ends at height 962720 and differs by 6219 records from the cataloged raw export","the dataset publisher authorized redistribution of the aggregate fixture on 2026-09-04"],"measurement":{"class_size_counts":{"1,000-9,999":6135143,"10,000-99,999":15826216,"10-99":344940,"100-999":1636424,"2-9":51250,">=100,000":74176481,"exactly 1":8960},"class_size_shares":{"1,000-9,999":0.062489097765443986,"10,000-99,999":0.16119688797490683,"10-99":0.003513363809647509,"100-999":0.01666768962381462,"2-9":0.0005220035230603434,">=100,000":0.7555196958091439,"exactly 1":9.126149398284247e-05},"scope":"whole_window","share_below_10":0.0006132650170431859,"share_below_100":0.004126628826690695,"vector_width":12},"schema_version":1,"window":{"end_height":962720,"epochs":158,"start_height":939969,"transactions":98179414}}
diff --git a/results/artifacts/fingerprint-validation-v1.json b/results/artifacts/fingerprint-validation-v1.json
new file mode 100644
index 00000000..13ca3fd1
--- /dev/null
+++ b/results/artifacts/fingerprint-validation-v1.json
@@ -0,0 +1 @@
+{"experiment":"fingerprint-validation-v1","labels":{"negative":"random distinct-transaction pairs","positive":"distinct transactions sharing an input address"},"limitations":["address reuse is a weak same-wallet label and shares script information with the features","positive pairs are draws with replacement rather than independent observations","random negative pairs can include transactions controlled by the same wallet","the 600-transaction snapshot is selected and is not a chain-wide sample","the source recipe is unavailable; the dataset publisher authorized redistribution on 2026-09-04","the result measures pair ranking and is not an ownership certificate or privacy score"],"measurement":{"auc":0.945875,"n_neg":4000,"n_pos":4000,"neg_mean":-17.41305201591386,"pos_mean":19.646924430510953,"shuffle_auc":0.490825,"transactions":600},"parameters":{"negative_pair_draw_cap":4000,"positive_pair_draw_cap":4000,"rarity_floor_count":1000,"same_wallet_consistency":0.95,"scorer":"canonical construction-fingerprint library","seed":0},"schema_version":1}
diff --git a/results/artifacts/fs-ablation-v1.json b/results/artifacts/fs-ablation-v1.json
new file mode 100644
index 00000000..422278a5
--- /dev/null
+++ b/results/artifacts/fs-ablation-v1.json
@@ -0,0 +1 @@
+{"dataset":"fs-blkcache-2026-09-04-v1","experiment":"fs-ablation-v1","limitations":["address reuse supplies weak labels rather than independent ownership labels","the block sample is not representative of the whole chain","the association statistic is descriptive rather than causal","input_script_type and input_types_present are duplicates in this sample"],"report":{"association":{"cap":8000,"cluster_threshold":0.6,"clusters":[["input_script_type","input_types_present","nested_segwit","pubkey_compression","sighash"],["change_index","change_matches_output","change_type_match"],["input_order","io_shape","output_order"],["change_spk","output_encoding"]],"min_n":30,"most_dependent_pairs":[{"fields":["input_script_type","input_types_present"],"phi_match":1.0,"phi_non_match":1.0},{"fields":["change_index","change_type_match"],"phi_match":0.9771748425133048,"phi_non_match":0.9504188383893803},{"fields":["change_type_match","change_matches_output"],"phi_match":0.9514396601979146,"phi_non_match":0.9488625417567522},{"fields":["change_index","change_matches_output"],"phi_match":0.948238892195268,"phi_non_match":0.9439472436623789},{"fields":["input_order","io_shape"],"phi_match":0.9240721623848165,"phi_non_match":0.3483023536831875},{"fields":["output_order","io_shape"],"phi_match":0.4464026907612012,"phi_non_match":0.8835975092563046},{"fields":["input_types_present","pubkey_compression"],"phi_match":null,"phi_non_match":0.8710892396271737},{"fields":["input_script_type","pubkey_compression"],"phi_match":0.12234074079149918,"phi_non_match":0.8573879183300378},{"fields":["input_types_present","sighash"],"phi_match":0.6323339722032228,"phi_non_match":0.7796930590904457},{"fields":["input_script_type","nested_segwit"],"phi_match":0.7778619346906303,"phi_non_match":0.4146258686696284},{"fields":["input_script_type","sighash"],"phi_match":0.30093369922075924,"phi_non_match":0.7762328774306926},{"fields":["sighash","pubkey_compression"],"phi_match":0.547409275927606,"phi_non_match":0.7141626042403821},{"fields":["change_spk","output_encoding"],"phi_match":0.6897140359061489,"phi_non_match":0.7112779198528257},{"fields":["input_types_present","nested_segwit"],"phi_match":0.6323359186932145,"phi_non_match":0.40994932138165746},{"fields":["low_r","sighash"],"phi_match":0.050688483327162186,"phi_non_match":0.5353133395600167}],"statistic":"phi coefficient (Pearson correlation of two binary agree/disagree indicators), computed within each class separately"},"cutoff":961653,"group_ablation":[{"baseline_auc":0.9826876875,"cutoff":961653,"group":["input_script_type","input_types_present","nested_segwit","pubkey_compression","sighash"],"keep_representative_auc":0.982762375,"keep_representative_delta":-7.468749999994806e-05,"remove_group_auc":0.97930734375,"remove_group_delta":0.00338034375000007,"representative":"input_script_type"},{"baseline_auc":0.9826876875,"cutoff":961653,"group":["change_index","change_matches_output","change_type_match"],"keep_representative_auc":0.98277978125,"keep_representative_delta":-9.209374999996633e-05,"remove_group_auc":0.9822441875,"remove_group_delta":0.0004435000000000411,"representative":"change_index"},{"baseline_auc":0.9826876875,"cutoff":961653,"group":["input_order","io_shape","output_order"],"keep_representative_auc":0.9815711875,"keep_representative_delta":0.0011164999999999647,"remove_group_auc":0.98113525,"remove_group_delta":0.0015524375000000035,"representative":"io_shape"},{"baseline_auc":0.9826876875,"cutoff":961653,"group":["change_spk","output_encoding"],"keep_representative_auc":0.9836115,"keep_representative_delta":-0.0009238124999999542,"remove_group_auc":0.983839375,"remove_group_delta":-0.0011516874999999427,"representative":"change_spk"}],"leave_one_out":{"baseline":{"auc":0.9826876875,"coverage":0.92525,"false_negative":190,"false_positive":179,"fields":["change_address_reuse","change_index","change_matches_output","change_spk","change_type_match","fee_rate","input_order","input_script_type","input_types_present","io_shape","locktime","low_r","multisig","nested_segwit","nsequence","op_return","output_encoding","output_order","pubkey_compression","sighash","uih","version"],"precision":0.9551265981448984,"recall":0.9525,"selective_accuracy":0.9729802756011888,"true_positive":3810},"cutoff":961653,"leave_one_out":{"change_address_reuse":{"auc":0.97748340625,"auc_delta":0.0052042812500000535,"coverage":0.90275,"selective_accuracy":0.9637219606757131},"change_index":{"auc":0.98282028125,"auc_delta":-0.0001325937499999652,"coverage":0.927625,"selective_accuracy":0.9726451960652204},"change_matches_output":{"auc":0.982797875,"auc_delta":-0.0001101875000000252,"coverage":0.927375,"selective_accuracy":0.9727726108639978},"change_spk":{"auc":0.98372075,"auc_delta":-0.0010330625000000149,"coverage":0.92425,"selective_accuracy":0.9729510413849067},"change_type_match":{"auc":0.98282421875,"auc_delta":-0.00013653125000001598,"coverage":0.926875,"selective_accuracy":0.9728927848954821},"fee_rate":{"auc":0.98232065625,"auc_delta":0.00036703125000003833,"coverage":0.92625,"selective_accuracy":0.9720647773279352},"input_order":{"auc":0.98243978125,"auc_delta":0.00024790625000004063,"coverage":0.931375,"selective_accuracy":0.9714132331230707},"input_script_type":{"auc":0.9831310625,"auc_delta":-0.0004433750000000236,"coverage":0.915625,"selective_accuracy":0.9752901023890785},"input_types_present":{"auc":0.98276846875,"auc_delta":-8.078124999999492e-05,"coverage":0.915875,"selective_accuracy":0.9750238842636823},"io_shape":{"auc":0.98397653125,"auc_delta":-0.001288843749999935,"coverage":0.923625,"selective_accuracy":0.9723913926106375},"locktime":{"auc":0.97993775,"auc_delta":0.002749937499999966,"coverage":0.907,"selective_accuracy":0.9681642778390298},"low_r":{"auc":0.979166125,"auc_delta":0.0035215625000000195,"coverage":0.92975,"selective_accuracy":0.9712288249529444},"multisig":{"auc":0.982671,"auc_delta":1.6687500000056588e-05,"coverage":0.9255,"selective_accuracy":0.9735278227984873},"nested_segwit":{"auc":0.9827461875,"auc_delta":-5.849999999996136e-05,"coverage":0.92125,"selective_accuracy":0.9738127544097693},"nsequence":{"auc":0.97073878125,"auc_delta":0.011948906250000002,"coverage":0.8955,"selective_accuracy":0.9620323841429369},"op_return":{"auc":0.98122940625,"auc_delta":0.0014582812500000264,"coverage":0.921875,"selective_accuracy":0.9713898305084746},"output_encoding":{"auc":0.9836115,"auc_delta":-0.0009238124999999542,"coverage":0.925875,"selective_accuracy":0.9725934926420953},"output_order":{"auc":0.9819833125,"auc_delta":0.0007043749999999793,"coverage":0.9185,"selective_accuracy":0.9708764289602613},"pubkey_compression":{"auc":0.982734125,"auc_delta":-4.6437499999996135e-05,"coverage":0.91875,"selective_accuracy":0.9749659863945578},"sighash":{"auc":0.98288709375,"auc_delta":-0.00019940625000003376,"coverage":0.9215,"selective_accuracy":0.9744981009224091},"uih":{"auc":0.9827365625,"auc_delta":-4.8874999999948265e-05,"coverage":0.92875,"selective_accuracy":0.972678331090175},"version":{"auc":0.9737055625,"auc_delta":0.008982125000000063,"coverage":0.898125,"selective_accuracy":0.9621433542101601}}},"source":{"bytes":91367352,"digest":"91c444bd4e9715d7739ccc9d68c7d9751762e25b647e3eef647c6ec0b48ba90c","files":904,"pattern":".blkcache/*.json","transactions":22112}},"schema_version":1}
diff --git a/results/artifacts/fs-temporal-v1.json b/results/artifacts/fs-temporal-v1.json
new file mode 100644
index 00000000..3cf35dbf
--- /dev/null
+++ b/results/artifacts/fs-temporal-v1.json
@@ -0,0 +1 @@
+{"dataset":"fs-blkcache-2026-09-04-v1","experiment":"fs-temporal-v1","limitations":["address reuse supplies weak labels rather than independent ownership labels","the block sample is not representative of the whole chain","Beta smoothing is a local modeling choice","input_script_type and input_types_present are duplicates in this sample"],"report":{"comparison":{"auc_delta_fs_minus_rarity":0.04820243749999997,"same_test_pairs":true},"fellegi_sunter":{"auc":0.9826876875,"calibration":{"brier":0.042522807237584934,"ece_10":0.04074478440106778,"prior":0.5,"scope":"balanced weak-label case-control sample; not population calibration"},"coverage":0.92525,"false_negative":190,"false_positive":179,"link":3989,"non_link":3413,"precision":0.9551265981448984,"recall":0.9525,"review":598,"selective_accuracy":0.9729802756011888,"true_positive":3810},"fields":{"change_address_reuse":{"agreement_weight":0.8448879604406226,"disagreement_weight":-6.940895773005728,"m":0.9963759060234941,"u":0.5547363159210198},"change_index":{"agreement_weight":0.18977281207184551,"disagreement_weight":-0.37675504681503236,"m":0.7076980754811297,"u":0.6204698825293676},"change_matches_output":{"agreement_weight":0.1896845028611041,"disagreement_weight":-0.3706671683587266,"m":0.7039490127468133,"u":0.6172206948262934},"change_spk":{"agreement_weight":0.713547710609539,"disagreement_weight":-0.7247814865451978,"m":0.6258443832874656,"u":0.38165160642570284},"change_type_match":{"agreement_weight":0.17798556590662828,"disagreement_weight":-0.36860118740568565,"m":0.7149462634341415,"u":0.6319670082479381},"fee_rate":{"agreement_weight":0.14006134752928193,"disagreement_weight":-2.5068738036461737,"m":0.9806298425393651,"u":0.8899025243689078},"input_order":{"agreement_weight":0.3832981302346271,"disagreement_weight":-2.4791910706732754,"m":0.9514848950332822,"u":0.7294871794871794},"input_script_type":{"agreement_weight":0.9875116262398764,"disagreement_weight":-7.308146908572327,"m":0.9968624497991968,"u":0.5027645136969088},"input_types_present":{"agreement_weight":0.957708795210742,"disagreement_weight":-10.326607434034601,"m":0.9996220710506425,"u":0.5146793716198815},"io_shape":{"agreement_weight":1.0655985201111102,"disagreement_weight":-2.8221868491832205,"m":0.9208096590909091,"u":0.43993928881179534},"locktime":{"agreement_weight":0.47436200357597175,"disagreement_weight":-11.131213905086362,"m":0.9998750312421895,"u":0.7196950762309423},"low_r":{"agreement_weight":1.2135314204224876,"disagreement_weight":-1.9823186605894445,"m":0.8384153961509623,"u":0.3615346163459135},"multisig":{"agreement_weight":0.17800012470299587,"disagreement_weight":-7.061467632717484,"m":0.9991252186953262,"u":0.8831542114471382},"nested_segwit":{"agreement_weight":0.2255569214885073,"disagreement_weight":-6.286634764892701,"m":0.9981254686328418,"u":0.853661584603849},"nsequence":{"agreement_weight":1.3046728246726353,"disagreement_weight":-6.203140907207537,"m":0.9918770307423144,"u":0.40152461884528867},"op_return":{"agreement_weight":0.12656396128366987,"disagreement_weight":-9.394462694610407,"m":0.9998750312421895,"u":0.9158960259935016},"output_encoding":{"agreement_weight":0.47621333229506774,"disagreement_weight":-0.4952647123164627,"m":0.5929767558110473,"u":0.42626843289177707},"output_order":{"agreement_weight":1.0333148510366172,"disagreement_weight":-4.3557824560112,"m":0.9744117647058823,"u":0.4760841836734694},"pubkey_compression":{"agreement_weight":0.7999286571848719,"disagreement_weight":-8.566054038171039,"m":0.998875281179705,"u":0.5737315671082229},"sighash":{"agreement_weight":0.7031137358990198,"disagreement_weight":-9.24853346156753,"m":0.9993651599796851,"u":0.6138551165146909},"uih":{"agreement_weight":0.15673463410048158,"disagreement_weight":-2.1580795759520517,"m":0.9711322169457636,"u":0.8711572106973257},"version":{"agreement_weight":0.9933337731517751,"disagreement_weight":-8.261371689946062,"m":0.9983754061484629,"u":0.5014996250937266}},"label_provenance":{"independently_verified":false,"kind":"weak_labels","negative":"sampled pair with no observed shared input address","positive":"distinct transactions sharing an observed input address"},"pairs":{"test_negative":4000,"test_positive":4000,"train_negative":4000,"train_positive":4000},"rarity_baseline":{"auc":0.93448525},"source":{"bytes":91367352,"digest":"91c444bd4e9715d7739ccc9d68c7d9751762e25b647e3eef647c6ec0b48ba90c","files":904,"pattern":".blkcache/*.json","transactions":22112},"split":{"cutoff":961653,"strategy":"strict_block_height","test_height":[961654,965220],"test_transactions":7012,"train_height":[800000,961653],"train_transactions":15100}},"schema_version":1}
diff --git a/results/artifacts/fused-engine-v1.json b/results/artifacts/fused-engine-v1.json
new file mode 100644
index 00000000..36271839
--- /dev/null
+++ b/results/artifacts/fused-engine-v1.json
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diff --git a/results/artifacts/graph-deanon-controls-v1.json b/results/artifacts/graph-deanon-controls-v1.json
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diff --git a/results/artifacts/graph-rejoin-2016-v1.json b/results/artifacts/graph-rejoin-2016-v1.json
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diff --git a/results/artifacts/intersection-cluster-fixture-v1.json b/results/artifacts/intersection-cluster-fixture-v1.json
new file mode 100644
index 00000000..1762a003
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+++ b/results/artifacts/intersection-cluster-fixture-v1.json
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diff --git a/results/artifacts/library-calibration-v1.json b/results/artifacts/library-calibration-v1.json
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index 00000000..d180f831
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diff --git a/results/artifacts/match-controls-v1.json b/results/artifacts/match-controls-v1.json
new file mode 100644
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diff --git a/results/artifacts/ns-bitcoin-v1.json b/results/artifacts/ns-bitcoin-v1.json
new file mode 100644
index 00000000..7b409c49
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+++ b/results/artifacts/ns-bitcoin-v1.json
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diff --git a/results/artifacts/ns-link-prediction-coverage-v2.json b/results/artifacts/ns-link-prediction-coverage-v2.json
new file mode 100644
index 00000000..62252247
--- /dev/null
+++ b/results/artifacts/ns-link-prediction-coverage-v2.json
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+{"components":[{"component":"algorithm1_similarity","limitation":null,"status":"implemented"},{"component":"algorithm2_positive_weights","limitation":null,"status":"implemented"},{"component":"algorithm2_dummy_weights","limitation":"a dummy-incident node term is fixed at zero; the paper states this in prose, not in the printed formula","status":"implemented"},{"component":"annealing","limitation":"iteration budget, RNG and best-state return are explicit local controls","status":"partial"},{"component":"two_stage_mapping","limitation":"the paper picks an arbitrary unmapped node and stage 1 feeds back, so the ``repr`` order this driver imposes is a result-bearing local choice; injectivity comes from Algorithm 3's 1-1 mapping, not from the propagation section","status":"implemented"},{"component":"confidence_pruning","limitation":"an implementation-complete pruning policy is not available","status":"not_reproduced"},{"component":"accepted_mapping_correction","limitation":"schedule and conflict resolution are not specified sufficiently","status":"not_reproduced"},{"component":"algorithm3_cascade","limitation":null,"status":"implemented"},{"component":"learned_25_feature_model","limitation":"the caller supplies an ML score; the published feature producer is absent","status":"not_reproduced"}],"composition":null,"conclusion":"implemented components do not constitute the complete published pipeline","end_to_end_reproduced":false,"experiment":"ns-link-prediction-coverage-v2","implemented_gate":{"accepted":true,"components":["algorithm1_similarity","algorithm2_positive_weights","algorithm2_dummy_weights","two_stage_mapping","algorithm3_cascade"]},"limitations":["the published Flickr/Kaggle corpus and reported metrics are not reproduced","the zero convention for dummy-incident terms is read from the paper's prose, not from its formula","annealing uses explicit local reproducibility controls","confidence pruning, accepted-mapping correction and the learned 25-feature model are not reproduced"],"paper":"Narayanan-Shi-Rubinstein-2011","refusal_controls":{"accepted_mapping_correction":"N-S 2011 pipeline components unavailable: accepted_mapping_correction=not_reproduced","annealing":"N-S 2011 pipeline components unavailable: annealing=partial","confidence_pruning":"N-S 2011 pipeline components unavailable: confidence_pruning=not_reproduced","learned_25_feature_model":"N-S 2011 pipeline components unavailable: learned_25_feature_model=not_reproduced"},"schema_version":1}
diff --git a/results/artifacts/ns1r-change-readoff-v1.json b/results/artifacts/ns1r-change-readoff-v1.json
new file mode 100644
index 00000000..a17bdccf
--- /dev/null
+++ b/results/artifacts/ns1r-change-readoff-v1.json
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+{"claim":"a receiver who knows each negotiated payment can propagate the change constraint across senders, and the propagation can leave one reading of the amounts where no single sender's constraint does","conclusion":"propagation resolved the consolidating receiver to a single reading and left the evenly spaced control with two; the read-off is a receiver capability, not an external-observer one","experiment":"ns1r-change-readoff-v1","limitations":["the scenarios are deterministic fixtures, not sampled transactions","the receiver's knowledge of each negotiated payment is supplied, not inferred","an external observer holds neither the payments nor the receiver's own coins","a unique reading of the amounts is not ownership attribution","the enumeration is bounded at eight senders and does not model fees","the result is not a privacy score"],"measurement":{"locally_ambiguous_senders":5,"scenarios":2,"senders_narrowed_by_propagation":2,"with_a_unique_reading":1},"scenarios":[{"distinct_readings":1,"feasible_candidates":{"Alice":[1,2],"Bob":[1,2],"Carol":[3]},"identifier":"consolidating-receiver","inputs_sat":[40,50,60,90],"local_candidates":{"Alice":[1,2],"Bob":[1,2,3],"Carol":[3]},"matchings":2,"outputs_sat":[160,30,30,20],"payments_sat":[{"payment":20,"sender":"Alice"},{"payment":30,"sender":"Bob"},{"payment":70,"sender":"Carol"}],"reading_is_unique":true,"readings":[[{"change_sat":30,"input_sat":50,"sender":"Alice"},{"change_sat":30,"input_sat":60,"sender":"Bob"},{"change_sat":20,"input_sat":90,"sender":"Carol"}]],"unanimous_links":[[3,3]]},{"distinct_readings":2,"feasible_candidates":{"Alice":[1,3],"Bob":[1,2],"Carol":[2,3]},"identifier":"evenly-spaced-control","inputs_sat":[20,40,50,60],"local_candidates":{"Alice":[1,3],"Bob":[1,2],"Carol":[1,2,3]},"matchings":2,"outputs_sat":[80,30,20,40],"payments_sat":[{"payment":10,"sender":"Alice"},{"payment":30,"sender":"Bob"},{"payment":20,"sender":"Carol"}],"reading_is_unique":false,"readings":[[{"change_sat":30,"input_sat":40,"sender":"Alice"},{"change_sat":20,"input_sat":50,"sender":"Bob"},{"change_sat":40,"input_sat":60,"sender":"Carol"}],[{"change_sat":40,"input_sat":50,"sender":"Alice"},{"change_sat":30,"input_sat":60,"sender":"Bob"},{"change_sat":20,"input_sat":40,"sender":"Carol"}]],"unanimous_links":[]}],"schema_version":1}
diff --git a/results/artifacts/partition-schemes-v1.json b/results/artifacts/partition-schemes-v1.json
new file mode 100644
index 00000000..d5044274
--- /dev/null
+++ b/results/artifacts/partition-schemes-v1.json
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+{"dataset":"slice-a-channels-2016-v1","experiment":"partition-schemes-v1","limitations":["the matcher recovers a handful of pairs at best, so the comparison rests on the straddler subgraph rather than on precision","one slice, one era, one 300,000-transaction prefix","cluster membership is a co-spend label, not wallet ownership","the export carries addresses but no values, so the collapse detector sees only the shape rule and never the de-mix arm","the result is not a privacy score"],"measurement":{"any_scheme_ignited_the_matcher":true,"clusters":71986,"collapse_costs_over_epoch":4989,"schemes":[{"boundary_transactions":0,"edges_among_straddlers":1910,"mean_straddler_degree":1.449648711943794,"scheme":"epoch","seeded":[{"correct":0,"guesses":0,"precision":null,"recall":0.0,"seed_share":0.05,"seeds":128},{"correct":1,"guesses":1,"precision":1.0,"recall":0.0004336513443191674,"seed_share":0.1,"seeds":256}],"straddlers_with_a_straddling_neighbour":1145,"straddling_entities":2562,"view_sizes":[137354,162646]},{"boundary_transactions":117371,"edges_among_straddlers":360,"mean_straddler_degree":0.5486862442040186,"scheme":"decore","seeded":[{"correct":0,"guesses":0,"precision":null,"recall":0.0,"seed_share":0.05,"seeds":64},{"correct":0,"guesses":0,"precision":null,"recall":0.0,"seed_share":0.1,"seeds":129}],"straddlers_with_a_straddling_neighbour":366,"straddling_entities":1294,"view_sizes":[82820,99809]},{"boundary_transactions":4989,"edges_among_straddlers":1941,"mean_straddler_degree":1.4944794952681388,"scheme":"collapse","seeded":[{"correct":0,"guesses":0,"precision":null,"recall":0.0,"seed_share":0.05,"seeds":126},{"correct":1,"guesses":1,"precision":1.0,"recall":0.0004380201489268506,"seed_share":0.1,"seeds":253}],"straddlers_with_a_straddling_neighbour":1129,"straddling_entities":2536,"view_sizes":[135045,159966]}],"transactions":300000,"view_counts":{"collapse":{"2":[135045,159966],"3":[89861,93060,112090],"4":[69302,65459,76829,83421]},"decore":{"2":[82820,99809],"3":[55231,57214,70184],"4":[42474,40182,46904,53069]},"epoch":{"2":[137354,162646],"3":[91349,94577,114074],"4":[70469,66597,78032,84902]}}},"parameters":{"core_fraction":0.01,"min_side":2,"schemes":["epoch","decore","collapse"],"seed":0,"seed_shares":[0.05,0.1],"view_counts":[2,3,4]},"schema_version":1}
diff --git a/results/artifacts/path-count-contract-v1.json b/results/artifacts/path-count-contract-v1.json
new file mode 100644
index 00000000..a1812c7f
--- /dev/null
+++ b/results/artifacts/path-count-contract-v1.json
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+{"ancestry_distribution":{"origin-a:0":0.62,"origin-b:0":0.38},"count_oracle_invariance":{"distributions_equal":true,"high_log_w":4.605170185988092,"low_log_w":0.0},"entropy":{"min_bits":0.6896598793878495,"shannon_bits":0.9580420222262995},"experiment":"path-count-contract-v1","fixture":{"depth":6,"target":"target:0","transactions":4},"implemented_capabilities":{"edge_disjoint_path_enumeration":true,"k_routes":true,"plausible_flow_capacity":true,"provenance_route_accumulation":true},"limitations":["the fixture is synthetic and tests implementation semantics","the function accumulates link-probability mass over ancestry routes","count_oracle is retained for compatibility and does not affect the result","the function does not measure CTP robust connectivity or own-origin robustness","the output is not a privacy score or CoinScore"],"path_count_distribution":{"origin-a:0":0.62,"origin-b:0":0.38},"schema_version":1}
diff --git a/results/artifacts/perfect-matching-disclosure-v1.json b/results/artifacts/perfect-matching-disclosure-v1.json
new file mode 100644
index 00000000..c560881d
--- /dev/null
+++ b/results/artifacts/perfect-matching-disclosure-v1.json
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+{"composition":null,"conclusion":"joint perfect matching resolves a round that independent per-message maxima cannot assign bijectively","experiment":"perfect-matching-disclosure-v1","joint_assignment":{"joint_likelihood":0.19200000000000003,"messages":3,"naive_is_bijective":false,"naive_receiver_positions":[0,0,2],"optimal_assignment_count":1,"optimal_assignments":[[0,1,2]],"unique_assignment_recovered":true},"limitations":["deterministic synthetic threshold-mix fixture","the profile-estimation phase is supplied and tested separately by statistical disclosure","the result links messages within one round and does not attribute Bitcoin ownership","the cited paper's simulations and success rates are not reproduced"],"schema_version":1,"uniform_profile_control":{"joint_likelihood":0.037037037037037035,"messages":3,"naive_is_bijective":false,"naive_receiver_positions":[0,0,0],"optimal_assignment_count":6,"optimal_assignments":[[0,1,2],[0,2,1],[1,0,2],[1,2,0],[2,0,1],[2,1,0]],"unique_assignment_recovered":false}}
diff --git a/results/artifacts/provenance-decay-v1.json b/results/artifacts/provenance-decay-v1.json
new file mode 100644
index 00000000..99b210ba
--- /dev/null
+++ b/results/artifacts/provenance-decay-v1.json
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+{"bridge_removal":{"candidates_after":2,"candidates_before":3,"components_after":2,"components_before":1,"structural_outcome":"graph_fracture"},"composition":null,"experiment":"provenance-decay-v1","limitations":["synthetic fixture","component growth does not by itself establish exponential deanonymization","edge plausibility and private adversary information are not modeled"],"peripheral_removal":{"candidates_after":2,"candidates_before":3,"structural_outcome":"inconclusive"},"schema_version":1}
diff --git a/results/artifacts/pseudonym-graph-contract-v1.json b/results/artifacts/pseudonym-graph-contract-v1.json
new file mode 100644
index 00000000..da1d61e3
--- /dev/null
+++ b/results/artifacts/pseudonym-graph-contract-v1.json
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+{"composition":null,"conclusion":"partial contraction preserves singleton pseudonyms, direction and folded transfer attributes","edges":[{"first_height":100,"last_height":140,"source":"cluster-a","target":"cluster-x","transfers":3,"value":600},{"first_height":120,"last_height":120,"source":"cluster-x","target":"cluster-a","transfers":1,"value":50},{"first_height":150,"last_height":150,"source":"z","target":"cluster-a","transfers":1,"value":10}],"experiment":"pseudonym-graph-contract-v1","limitations":["deterministic synthetic fixture","the partial clustering lookup is supplied","vertices represent pseudonyms unless clustering completeness is independently established","the report validates representation invariants, not deanonymization success"],"schema_version":1,"self_transfers":{"cluster-a":1},"vertices":["cluster-a","cluster-x","z"]}
diff --git a/results/artifacts/reid-v1.json b/results/artifacts/reid-v1.json
new file mode 100644
index 00000000..c14f9869
--- /dev/null
+++ b/results/artifacts/reid-v1.json
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+{"experiment":"reid-v1","limitations":["the 200-record snapshot is selected and is not a uniform chain sample","the signature-generation recipe and original comparison population are unavailable","the target identifier is a record identity, not wallet ownership","the auxiliary origins are sampled from the target signature rather than observed independently","this is an adaptation, not a reproduction of the published Netflix experiment","the result does not establish a chain-wide deanonymization rate or privacy score","upstream data licensing is undetermined; the dataset publisher authorized redistribution on 2026-09-04"],"measurement":{"n":200,"n_dense":35,"n_sparse":165,"rows":[{"attackable":162,"declare_rate":0.9259259259259259,"declared":150,"exact":150,"exact_rate":0.9259259259259259,"m":4,"precision":1.0,"stratum":"sparse"},{"attackable":156,"declare_rate":0.9551282051282052,"declared":149,"exact":149,"exact_rate":0.9551282051282052,"m":8,"precision":1.0,"stratum":"sparse"},{"attackable":35,"declare_rate":0.05714285714285714,"declared":2,"exact":2,"exact_rate":0.05714285714285714,"m":4,"precision":1.0,"stratum":"dense"},{"attackable":35,"declare_rate":0.08571428571428572,"declared":3,"exact":3,"exact_rate":0.08571428571428572,"m":8,"precision":1.0,"stratum":"dense"}]},"method":{"auxiliary_origins":[4,8],"dense_at_least_cosine":0.9,"description":"ancestry-signature record-linkage adaptation","eccentricity_threshold":1.5,"seed":0,"sparse_below_cosine":0.5},"schema_version":1}
diff --git a/results/artifacts/route-capacity-v1.json b/results/artifacts/route-capacity-v1.json
new file mode 100644
index 00000000..fa85ccb0
--- /dev/null
+++ b/results/artifacts/route-capacity-v1.json
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+{"boundary_causes":{"coinbase":0,"depth_capped":48,"fetch_missing":5707},"capacity":{"1":32,"10":4,"11":4,"119":1,"12":2,"129":1,"14":1,"16":1,"17":3,"18":2,"182":1,"19":1,"193":1,"198":1,"2":121,"20":1,"21":1,"22":1,"24":1,"26":1,"27":1,"3":33,"30":1,"32":1,"33":1,"36":1,"4":37,"41":1,"45":1,"5":30,"52":1,"53":1,"55":3,"56":1,"58":1,"59":1,"6":12,"60":5,"61":1,"7":6,"77":1,"8":8,"84":1,"85":1,"89":1,"9":3},"capacity_carrying_the_coin":{"0":128,"1":70,"11":1,"13":1,"16":2,"198":1,"2":57,"21":1,"23":1,"24":1,"29":1,"3":18,"32":1,"33":1,"34":1,"36":1,"4":18,"5":10,"6":10,"69":1,"7":2,"77":1,"8":6,"9":1},"dataset":"fs-blkcache-2026-09-04-v1","experiment":"route-capacity-v1","origin_excess":{"0":134,"1":57,"10":3,"11":2,"12":1,"134":1,"14":1,"17":2,"18":2,"196":1,"2":31,"21":1,"24":2,"25":1,"251":1,"254":1,"259":1,"26":1,"3":19,"33":1,"34":1,"36":1,"4":15,"41":1,"46":1,"5":11,"6":13,"7":12,"8":4,"9":11,"90":1,"92":1},"parameters":{"depth":5,"link_oracle":"value_flow_link_oracle","small_cut_at_most":3},"population":{"measured":335,"targets":335},"readings":{"cut_at_most_small":186,"cut_of_one":32,"cut_of_one_carrying_the_coin":70,"largest_cut":198,"no_route_carries_the_coin":128,"origin_set_overstates":201,"origins_cannot_deliver_the_value":8},"schema_version":1,"source":{"bytes":91367352,"digest":"91c444bd4e9715d7739ccc9d68c7d9751762e25b647e3eef647c6ec0b48ba90c","files":904,"pattern":".blkcache/*.json","transactions":22112}}
diff --git a/results/artifacts/same-block-linkage-v1.json b/results/artifacts/same-block-linkage-v1.json
new file mode 100644
index 00000000..25851453
--- /dev/null
+++ b/results/artifacts/same-block-linkage-v1.json
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+{"dataset":"boltzmann-fee-audit-v1","experiment":"same-block-linkage-v1","limitations":["a block of a value-conserving mapping is an accounting unit, not a wallet","a collaborative transaction can place two owners in one block, so a forced pair is not an ownership finding","only transactions with at most max_coins coins are in scope, which excludes the wide consolidations and coinjoins where the question is hardest","the snapshot is 300 selected transactions and is not a chain-wide sample","fee tolerance admits mappings exact conservation would refuse, which can only widen the family and lower the forced share","the result is not a privacy score"],"measurement":{"input_pairs":864,"input_pairs_forced_apart":0,"input_pairs_forced_together":765,"multi_input_in_scope":300,"output_pairs":318,"output_pairs_forced_together":258,"p_ii_distribution":{"0.2":3,"0.3":14,"0.5":55,"0.7":27,"1.0":765},"transactions":300,"transactions_by_shape":{"every input pair forced":263,"some input pair open":37},"with_a_feasible_mapping":300},"parameters":{"balance_model":"fee_tolerant","fee_tolerance":"the transaction's own observed fee","max_coins":12},"schema_version":1}
diff --git a/results/artifacts/scorer-families-v1.json b/results/artifacts/scorer-families-v1.json
new file mode 100644
index 00000000..bc795cb7
--- /dev/null
+++ b/results/artifacts/scorer-families-v1.json
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+{"dataset":"fs-blkcache-2026-09-04-v1","experiment":"scorer-families-v1","limitations":["address reuse is a weak and partly feature-dependent ownership proxy","the axis clusters and the address-determined set are measured on this same cache","one selected snapshot is reused for all three families and is not chain-wide","a higher AUC on fewer axes does not establish that the dropped axes carry no signal","evidence magnitudes are rarity bits under a fixed consistency, not calibrated likelihoods","this is attacker-side analysis, not a privacy score"],"measurement":{"auc_rises_as_axes_are_dropped":true,"consistency":0.95,"magnitude_fall_from_catalogued_to_decorrelated":6.740208748616556,"pair_cap_per_class":4000,"rows":[{"auc":0.92445,"axes":23,"dropped":[],"family":"catalogued","n_neg":4000,"n_pos":4000,"neg_mean":-16.582117238531236,"pos_mean":15.465038046462228,"shuffle_auc":0.4984},{"auc":0.90035,"axes":18,"dropped":["change_address_reuse","input_script_type","input_types_present","nested_segwit","pubkey_compression"],"family":"construction_only","n_neg":4000,"n_pos":4000,"neg_mean":-13.94129774262037,"pos_mean":8.419667321669898,"shuffle_auc":0.499725},{"auc":0.9432,"axes":14,"dropped":["change_matches_output","change_type_match","input_order","input_types_present","nested_segwit","output_encoding","output_order","pubkey_compression","sighash"],"family":"decorrelated","n_neg":4000,"n_pos":4000,"neg_mean":-10.236736430032744,"pos_mean":8.724829297845671,"shuffle_auc":0.49715}],"seed":0},"schema_version":1,"source":{"bytes":91367352,"digest":"91c444bd4e9715d7739ccc9d68c7d9751762e25b647e3eef647c6ec0b48ba90c","files":904,"pattern":".blkcache/*.json","transactions":22112}}
diff --git a/results/artifacts/slice-channels-v1.json b/results/artifacts/slice-channels-v1.json
new file mode 100644
index 00000000..7e6d660f
--- /dev/null
+++ b/results/artifacts/slice-channels-v1.json
@@ -0,0 +1 @@
+{"experiment":"slice-channels-v1","limitations":["the snapshot carries none of the four axes, so the similarity measured here is structural","the snapshot contains six 2016 blocks and is not a chain-wide sample","the run does not reproduce historical measurements on larger unpreserved slices","dense attributes do not prove resistance to other linkage channels","negative assortativity does not by itself determine graph-matching performance","the snapshot recipe is unavailable; the dataset publisher authorized redistribution on 2026-09-04","the result is not a privacy score or CoinScore"],"measurement":{"axis_transactions_skipped":{"fee_rate":12000,"input_order":12000,"locktime":12000,"version":12000},"axis_values_recorded":{"fee_rate":0,"input_order":0,"locktime":0,"version":0},"clustered_addresses":12238,"clusters":3179,"density_min_degree_2":{"eligible_sample":1200,"survival":{"0.5":1.0,"0.9":0.9975,"0.99":0.9833333333333333}},"density_min_degree_20":{"eligible_sample":89,"survival":{"0.9":0.8764044943820225}},"graph_shape":{"assortativity":-0.043697344509783416,"configuration_transitivity":0.0008655259247667856,"degree_1_share":0.6830943396226415,"edges":23112,"mean_degree":1.7413584905660378,"second_moment":12.746490566037735,"tail_exponent":4.040708964308928,"transitivity":0.006107723283527451,"transitivity_ratio":7.056661283916095,"vertices":26500},"transactions":12000},"parameters":{"background_records":12000,"query_records":1200,"refuse_guard":true,"seed":0,"similarity":"cosine over normalized feature vectors"},"schema_version":1}
diff --git a/results/artifacts/special-change-labels-v1.json b/results/artifacts/special-change-labels-v1.json
new file mode 100644
index 00000000..b5923c16
--- /dev/null
+++ b/results/artifacts/special-change-labels-v1.json
@@ -0,0 +1 @@
+{"agreement":[{"a":"address_reuse","agree":368,"b":"optimal_change","both":410,"disagree":42,"only_a":4570,"only_b":728},{"a":"address_reuse","agree":531,"b":"round_number","both":538,"disagree":7,"only_a":4442,"only_b":807},{"a":"address_reuse","agree":2310,"b":"type_match","both":2310,"disagree":0,"only_a":2670,"only_b":1912},{"a":"optimal_change","agree":239,"b":"round_number","both":246,"disagree":7,"only_a":892,"only_b":1099},{"a":"optimal_change","agree":470,"b":"type_match","both":513,"disagree":43,"only_a":625,"only_b":3709},{"a":"round_number","agree":623,"b":"type_match","both":642,"disagree":19,"only_a":703,"only_b":3580}],"dataset":"fs-blkcache-2026-09-04-v1","experiment":"special-change-labels-v1","onward_spend_reach":{"address_reuse":{"both_outputs_spent":97,"labels":4980,"one_output_spent":1496},"optimal_change":{"both_outputs_spent":12,"labels":1138,"one_output_spent":193},"round_number":{"both_outputs_spent":21,"labels":1345,"one_output_spent":438},"type_match":{"both_outputs_spent":48,"labels":4222,"one_output_spent":1154}},"parameters":{"labels":["address_reuse","optimal_change","round_number","type_match"],"predictors":["address_reuse","round_number"]},"population":{"labels":{"address_reuse":4980,"optimal_change":1138,"round_number":1345,"type_match":4222},"transactions":22112,"two_output_candidates":10850},"schema_version":1,"source":{"bytes":91367352,"digest":"91c444bd4e9715d7739ccc9d68c7d9751762e25b647e3eef647c6ec0b48ba90c","files":904,"pattern":".blkcache/*.json","transactions":22112},"within_tx":[{"coverage":1.0,"fpr":0.0,"label":"address_reuse","precision":1.0,"predictor":"address_reuse","tpr":1.0},{"coverage":0.441767,"fpr":0.067871,"label":"address_reuse","precision":0.846364,"predictor":"round_number","tpr":0.373896},{"coverage":0.360281,"fpr":0.036907,"label":"optimal_change","precision":0.897561,"predictor":"address_reuse","tpr":0.323374},{"coverage":0.533392,"fpr":0.099297,"label":"optimal_change","precision":0.813839,"predictor":"round_number","tpr":0.434095},{"coverage":0.4,"fpr":0.005204,"label":"round_number","precision":0.986989,"predictor":"address_reuse","tpr":0.394796},{"coverage":0.99777,"fpr":0.0,"label":"round_number","precision":1.0,"predictor":"round_number","tpr":0.99777},{"coverage":0.547134,"fpr":0.0,"label":"type_match","precision":1.0,"predictor":"address_reuse","tpr":0.547134},{"coverage":0.465893,"fpr":0.072004,"label":"type_match","precision":0.84545,"predictor":"round_number","tpr":0.393889}]}
diff --git a/results/artifacts/statistical-disclosure-v1.json b/results/artifacts/statistical-disclosure-v1.json
new file mode 100644
index 00000000..45e16414
--- /dev/null
+++ b/results/artifacts/statistical-disclosure-v1.json
@@ -0,0 +1 @@
+{"background_matched_control":{"batch_size":4,"observations":4,"scores":{"alice":0.25,"bob":0.25,"carol":0.25,"dave":0.25},"top_margin":0.0,"unique_top_correspondent":null},"composition":null,"conclusion":"repeated target-active rounds recover a persistent correspondent in the threshold-mix model","experiment":"statistical-disclosure-v1","limitations":["deterministic synthetic threshold-mix fixture","the model assumes one target message and batch_size minus one background messages per round","the result ranks persistent correspondents and does not attribute Bitcoin ownership","the fixture does not estimate real-world attack frequency or confidence","the estimator is implemented; the paper's applicability and efficiency section is not — neither the signal-to-noise condition that decides whether the attack is possible at all, nor the bound on observations needed for a stated confidence. Those are the part of the source that speaks to a rate, so no claim about how fast certainty is gained is covered here"],"persistent_correspondent":{"batch_size":4,"observations":4,"scores":{"alice":1.0,"bob":0.0,"carol":0.0,"dave":0.0},"top_margin":1.0,"unique_top_correspondent":"alice"},"schema_version":1}
diff --git a/results/artifacts/subtx-demix-v1.json b/results/artifacts/subtx-demix-v1.json
new file mode 100644
index 00000000..50f4c27e
--- /dev/null
+++ b/results/artifacts/subtx-demix-v1.json
@@ -0,0 +1 @@
+{"dataset":"subtx-demix-cache-2026-09-04-v1","experiment":"subtx-demix-v1","limitations":["the cache is CoinJoin-adjacent and has no ordinary-transaction labels","22 of 25 eligible cached transactions trigger, so this population cannot support a specificity claim","maker identities are inferred from an amount identity rather than independently labeled","the six historical Wasabi rounds are not preserved as a dataset","the fixed fee cap and most-common-output detector are local modeling choices","this is attacker-side de-mix evidence, not CoinScore or a privacy certificate"],"measurement":{"cache_json_files":37,"cache_transactions":36,"eligible_transactions":25,"eligible_with_any_recovery":22,"joinmarket":{"fees":[458,413,623,559,636,973,687,191],"inputs":12,"outputs":21,"recovered_changes":[113283033,2089662830,765432353,10187122,90781833,8045121,100823618,8125627],"recovered_inputs":[0,1,3,6,7,8,9,11],"txid":"0cb4870cf2dfa3877851088c673d163ae3c20ebcd6505c0be964d8fbcc856bbf"}},"schema_version":1,"verdicts":{"joinmarket_fixture":"reproduced","ordinary_transaction_specificity":"not_tested","wasabi_rounds":"not_reproduced"}}
diff --git a/results/artifacts/unnecessary-input-v2.json b/results/artifacts/unnecessary-input-v2.json
new file mode 100644
index 00000000..37bcc21b
--- /dev/null
+++ b/results/artifacts/unnecessary-input-v2.json
@@ -0,0 +1 @@
+{"boundary_cases":{"equality_boundary":{"blocksci_strict_uih1":false,"change_output_index":null,"fee":1,"gibson_flags":[false,true],"inputs":[10,2],"outputs":[9,2],"reason":null,"removed_input_index":1,"status":"uih2"},"fee_aware_uih1":{"blocksci_strict_uih1":true,"change_output_index":1,"fee":1,"gibson_flags":[true,false],"inputs":[10,2],"outputs":[10,1],"reason":null,"removed_input_index":1,"status":"uih1"},"jointly_funded_uih2":{"blocksci_strict_uih1":false,"change_output_index":null,"fee":1,"gibson_flags":[false,false],"inputs":[6,6,6],"outputs":[10,7],"reason":null,"removed_input_index":0,"status":"uih2"}},"conclusion":"the fee-aware predicate classifies a two-output amount shape; it does not identify PayJoin, ownership, participant roles, or gross obligations","cycle_control":{"cyclic_gross_amount":1400,"cyclic_nonzero_net_balances":{"alice":-500,"bob":500},"simple_gross_amount":500,"simple_nonzero_net_balances":{"alice":-500,"bob":500}},"diagnostic":{"definition":"blockstream_fee_aware","paper_domain":{"amounts":"positive unsigned 64-bit integers","exact_outputs":2,"minimum_inputs":2},"semantic_version":2},"experiment":"unnecessary-input-v2","limitations":["a positive predicate does not identify PayJoin","no participant role, ownership relation, payment output, or gross payment is inferred","ordinary consolidation and collaborative settlement share the same observable","the fixture demonstrates non-identifiability, not real-world prevalence"],"observational_equivalence":{"classification":"uih2","composition":null,"decision":"inconclusive","fee":100,"ownership_inferred":false,"payjoin_identified":false,"payment_output_identified":false,"possible_forms":["ordinary_single_owner_consolidation","two_party_payjoin","net_settlement_with_cycles"]},"schema_version":1,"scope_controls":{"many_senders_many_receivers":{"blocksci_strict_uih1":null,"change_output_index":null,"fee":null,"gibson_flags":null,"inputs":[90,15,40,30,45,35],"outputs":[85,25,50,40,45,10],"reason":"requires at least two inputs and exactly two outputs","removed_input_index":null,"status":"out_of_scope"},"many_senders_one_receiver":{"blocksci_strict_uih1":null,"change_output_index":null,"fee":null,"gibson_flags":null,"inputs":[40,50,60,90],"outputs":[160,30,30,20],"reason":"requires at least two inputs and exactly two outputs","removed_input_index":null,"status":"out_of_scope"}}}
diff --git a/results/artifacts/weight-sensitivity-v1.json b/results/artifacts/weight-sensitivity-v1.json
new file mode 100644
index 00000000..4ca98348
--- /dev/null
+++ b/results/artifacts/weight-sensitivity-v1.json
@@ -0,0 +1 @@
+{"dataset":"fs-blkcache-2026-09-04-v1","experiment":"weight-sensitivity-v1","historical_comparison":{"not_supported":"monotone improvement: the measured sequence dips, but by a fifth of one standard error, which does not establish a reversal either","preserved":"AUC changes little from consistency 0.90 through 0.99","status":"not_supported"},"limitations":["address reuse is a weak and partly feature-dependent ownership proxy","the sweep varies one global value rather than fitting each axis","the same selected sample is reused at every grid point","the snapshot is not representative of the whole chain","AUC stability does not imply calibrated evidence magnitudes","the sweep resolves differences of about one standard error; smaller ones are reported and are not readable as an ordering","this is attacker-side sensitivity analysis, not CoinScore or a privacy certificate"],"measurement":{"auc_is_monotone_non_decreasing":false,"auc_standard_error":0.003100965514608813,"largest_decrement":0.0005499999999999394,"largest_decrement_in_standard_errors":0.17736411366358648,"pair_cap_per_class":4000,"realistic_band_auc_range":0.0005499999999999394,"rows":[{"auc":0.9084,"consistency":0.6,"n_neg":4000,"n_pos":4000,"neg_mean":8.852877813661172,"pos_mean":23.05601893431654,"shuffle_auc":0.4987},{"auc":0.9152,"consistency":0.7,"n_neg":4000,"n_pos":4000,"neg_mean":5.593042362354659,"pos_mean":22.073064493220734,"shuffle_auc":0.4993},{"auc":0.9213,"consistency":0.8,"n_neg":4000,"n_pos":4000,"neg_mean":0.7410834698818792,"pos_mean":20.615851379901276,"shuffle_auc":0.49955},{"auc":0.92405,"consistency":0.9,"n_neg":4000,"n_pos":4000,"neg_mean":-7.809367238531249,"pos_mean":18.064538046462225,"shuffle_auc":0.4983},{"auc":0.92445,"consistency":0.95,"n_neg":4000,"n_pos":4000,"neg_mean":-16.582117238531236,"pos_mean":15.465038046462228,"shuffle_auc":0.4984},{"auc":0.9239,"consistency":0.99,"n_neg":4000,"n_pos":4000,"neg_mean":-36.95181193295434,"pos_mean":9.429185963802531,"shuffle_auc":0.49995}],"seed":0,"transactions":22112},"schema_version":1,"source":{"bytes":91367352,"digest":"91c444bd4e9715d7739ccc9d68c7d9751762e25b647e3eef647c6ec0b48ba90c","files":904,"pattern":".blkcache/*.json","transactions":22112}}
diff --git a/results/boltzmann-fee-audit.json b/results/boltzmann-fee-audit.json
new file mode 100644
index 00000000..bb047a03
--- /dev/null
+++ b/results/boltzmann-fee-audit.json
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+{
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diff --git a/results/fs-ablation.json b/results/fs-ablation.json
new file mode 100644
index 00000000..129c83cf
--- /dev/null
+++ b/results/fs-ablation.json
@@ -0,0 +1,391 @@
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+}
diff --git a/results/fs-temporal.json b/results/fs-temporal.json
new file mode 100644
index 00000000..3f2c2c40
--- /dev/null
+++ b/results/fs-temporal.json
@@ -0,0 +1,195 @@
+{
+ "comparison": {
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+ "same_test_pairs": true
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+ "fellegi_sunter": {
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+ "scope": "balanced weak-label case-control sample; not population calibration"
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+ "kind": "weak_labels",
+ "negative": "sampled pair with no observed shared input address",
+ "positive": "distinct transactions sharing an observed input address"
+ },
+ "pairs": {
+ "test_negative": 4000,
+ "test_positive": 4000,
+ "train_negative": 4000,
+ "train_positive": 4000
+ },
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+ "test_transactions": 7012,
+ "train_height": [
+ 800000,
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+ ],
+ "train_transactions": 15100
+ }
+}
diff --git a/results/generated/amount-channel-survey-v1.md b/results/generated/amount-channel-survey-v1.md
new file mode 100644
index 00000000..c78b167e
--- /dev/null
+++ b/results/generated/amount-channel-survey-v1.md
@@ -0,0 +1,21 @@
+# Amount-channel survey
+
+Generated only from five SHA-256-pinned canonical artifacts. Do not edit manually.
+
+The snapshot contains 5,491 transactions and 1,428 complete multi-input transactions.
+
+## Independent local channels
+
+CoinJoin shape fires 28 times and de-mix resolves 0. Unnecessary-input fires 485 times. Roundness ranks 483 2×2 transactions, with 330 top ties. The conservation probe forces an output value 1,353 times under its declared largest-input assumption.
+
+## Subset-sum diagnostics
+
+Transaction W(E) returns 1,303 exact and 125 unknown outcomes; 80 expose a usable conservative nonzero log W.
+
+Per-coin density yields 318 raw candidates. After the transaction gate, 101 remain in 62 transactions.
+
+## Restricted DSS mapping family
+
+Mapping analysis answers 223 of 250 transactions; 221 answered families have zero-bit entropy. Pairwise analysis returns 914 rows, including 367 singleton-support rows.
+
+Entropy, certainty and support in the final section apply only to DSS's restricted mapping family. The components remain separate and are not a privacy score.
diff --git a/results/generated/amount-local-channels-v1.md b/results/generated/amount-local-channels-v1.md
new file mode 100644
index 00000000..14d50732
--- /dev/null
+++ b/results/generated/amount-local-channels-v1.md
@@ -0,0 +1,11 @@
+# Local amount-channel observations
+
+Generated from the canonical experiment artifact. Do not edit manually.
+
+The snapshot contains 5,491 transactions and 1,428 complete multi-input transactions.
+
+CoinJoin shape fires 28 times; de-mix resolves 0. Unnecessary-input fires 485 times.
+
+The 2×2 roundness model ranks 483 transactions and ties at the top for 330.
+
+This run excludes subset-sum, DSS, mapping entropy, and score composition.
diff --git a/results/generated/amount-mapping-family-v1.md b/results/generated/amount-mapping-family-v1.md
new file mode 100644
index 00000000..0f624e5c
--- /dev/null
+++ b/results/generated/amount-mapping-family-v1.md
@@ -0,0 +1,9 @@
+# DSS restricted mapping-family diagnostic
+
+Generated from the canonical experiment artifact. Do not edit manually.
+
+Of 250 selected transactions, 223 answer, 26 refuse, 1 time out, and 0 fail.
+
+Within the answered restricted families, 221 have zero-bit entropy, 2 have more than zero and at most four bits, and 0 have more than four bits. The families contain 227 non-derived mappings and 1,471 input-output links shared by every mapping.
+
+Certainty and entropy apply only to DSS's restricted family, not to the full mapping space or an ownership posterior.
diff --git a/results/generated/amount-pairwise-family-v1.md b/results/generated/amount-pairwise-family-v1.md
new file mode 100644
index 00000000..aef12ea4
--- /dev/null
+++ b/results/generated/amount-pairwise-family-v1.md
@@ -0,0 +1,9 @@
+# DSS pairwise restricted-family diagnostic
+
+Generated from the canonical experiment artifact. Do not edit manually.
+
+The run selects 300 transactions containing 3,384 input rows. 271 transactions answer, 27 refuse, 2 time out, and 0 fail.
+
+The answered matrices return 914 rows, of which 914 have nonempty support and 367 have singleton support.
+
+Singleton support means only that every mapping in DSS's restricted family selects the same output. It is not a globally deterministic ownership link.
diff --git a/results/generated/amount-per-coin-v1.md b/results/generated/amount-per-coin-v1.md
new file mode 100644
index 00000000..b96474cb
--- /dev/null
+++ b/results/generated/amount-per-coin-v1.md
@@ -0,0 +1,11 @@
+# DSS per-coin amount diagnostic
+
+Generated from the canonical experiment artifact. Do not edit manually.
+
+The run evaluates 23,626 coins in 1,428 complete multi-input transactions. DSS returns a finite per-coin reading for 1,288 coins and no reading for 22,338.
+
+Before the transaction gate, 318 candidates occur in 95 transactions: 60 inputs and 258 outputs.
+
+After requiring a conservative nonzero transaction-level W(E), 101 candidates remain in 62 transactions. An exact transaction count records the gate's provenance; it does not make the per-coin reading exact.
+
+These are refuse-only diagnostics, not ownership proofs, privacy scores, or CoinScore.
diff --git a/results/generated/amount-subset-sum-v1.md b/results/generated/amount-subset-sum-v1.md
new file mode 100644
index 00000000..b68e27db
--- /dev/null
+++ b/results/generated/amount-subset-sum-v1.md
@@ -0,0 +1,9 @@
+# Transaction-level subset-sum W(E)
+
+Generated from the canonical experiment artifact. Do not edit manually.
+
+Of 1,428 multi-input transactions, 1,303 return `exact` and 125 return `unknown`.
+
+Among the exact results, 1,223 find no exact subset hit. Only 80 results expose a usable nonzero conservative log W.
+
+W(E) is not a mapping count, pairwise-link oracle, entropy, or CoinScore.
diff --git a/results/generated/analyze-contract-v1.md b/results/generated/analyze-contract-v1.md
new file mode 100644
index 00000000..ca0913b5
--- /dev/null
+++ b/results/generated/analyze-contract-v1.md
@@ -0,0 +1,13 @@
+# Public analysis facade contract
+
+Generated from the canonical experiment artifact. Do not edit manually.
+
+The deterministic fixture returns at depths 1 through 5 with the default nominal-value flow oracle.
+
+The change target's min-entropy changes from 1.000000 to 0.152003 bits after the address-reuse signal. Sharpening was observed: `True`.
+
+The uncapped walk reports 0 truncations and the one-node cap reports 2. The cap is observable: `True`.
+
+The opt-in `path_count` response field is a compatibility name for `provenance_route_accumulation`; it is not robust connectivity or k-routes.
+
+This run validates an API contract on a synthetic graph. It does not reproduce the historical live Bitcoin measurements and does not certify privacy.
diff --git a/results/generated/ancestry-contract-v1.md b/results/generated/ancestry-contract-v1.md
new file mode 100644
index 00000000..b560913a
--- /dev/null
+++ b/results/generated/ancestry-contract-v1.md
@@ -0,0 +1,11 @@
+# Nominal-value ancestry contract
+
+Generated from the canonical experiment artifact. Do not edit manually.
+
+The complete walk reaches `{'left:0': 0.5, 'right:0': 0.5}` with Shannon entropy 1.000000 bits and 4.0 steps in the uncollapsed paper graph.
+
+A one-node bound returns `TruncatedAncestry` with causes `{'oracle_refused': 0, 'node_capped': 2, 'zero_link_mass': 0, 'unattributed': 0}`. Oracle refusal returns `TruncatedAncestry` with causes `{'oracle_refused': 1, 'node_capped': 0, 'zero_link_mass': 0, 'unattributed': 0}`.
+
+Provenance overlap is 1.0 for the same signature and 0.0 for a disjoint signature.
+
+This measures model-relative provenance. It does not infer ownership, certify privacy, measure robust connectivity or reproduce the historical live Bitcoin observations.
diff --git a/results/generated/ancestry-sparsity-v1.md b/results/generated/ancestry-sparsity-v1.md
new file mode 100644
index 00000000..16d2499b
--- /dev/null
+++ b/results/generated/ancestry-sparsity-v1.md
@@ -0,0 +1,11 @@
+# Ancestry-signature sparsity snapshot
+
+Generated from the canonical experiment artifact. Do not edit manually.
+
+The frozen snapshot contains 200 signatures. Their feature counts have minimum 3, median 5887.0 and maximum 11436.
+
+The median recomputed nearest-neighbour cosine is 0.025913. The fractions with a neighbour above 0.5, 0.9 and 0.99 are 0.175, 0.175 and 0.145.
+
+Stored nearest-neighbour values differ from the self-contained snapshot recomputation for 155 records and are therefore excluded from the result.
+
+This measures an attack precondition within a selected frozen snapshot. It is neither a chain-wide estimate nor proof of successful attribution.
diff --git a/results/generated/anchor-axis-families-v1.md b/results/generated/anchor-axis-families-v1.md
new file mode 100644
index 00000000..cc58131e
--- /dev/null
+++ b/results/generated/anchor-axis-families-v1.md
@@ -0,0 +1,18 @@
+# The merged anchor's false edge under four axis sets
+
+Generated from the canonical experiment artifact. Do not edit manually.
+
+One edge — the sender's funding transaction against the Cake one, on anchor `931d6627`. The engine's refusal threshold is `link_above = 4.0`.
+
+| axis set | axes | bits | verdict | past link_above |
+|---|---:|---:|---|---|
+| engine three axis | 3 | -3.16 | refuse | no |
+| catalogued | 23 | +11.67 | link | yes |
+| construction only | 18 | +4.91 | link | yes |
+| decorrelated | 14 | +3.59 | link | no |
+
+The catalogued model resurrects the edge at +11.67 bits, well past the threshold. Dropping one representative per correlated cluster leaves +3.59 — **8.07 bits of the score were copies of evidence already counted**, and what remains sits below the threshold, so the wide model's failure here is duplication rather than width.
+
+Of the 22 axes that scored, 3 argue against the pairing (-10.82 bits between them) and 19 argue for it (+22.49); 1 abstain. The discriminating axes still refuse — they are outvoted by low-entropy policy axes that two ordinary wallets share, summed under a kernel that assumes they are independent.
+
+This is one edge, chosen because the design decision was argued on it, and its same-owner reading is the paper's rather than an independent label. The library weights come from a population that was not preserved; `library-calibration-v1` measures how far they sit from the one that was.
diff --git a/results/generated/attribute-drift-v1.md b/results/generated/attribute-drift-v1.md
new file mode 100644
index 00000000..94d810e1
--- /dev/null
+++ b/results/generated/attribute-drift-v1.md
@@ -0,0 +1,21 @@
+# Projected attribute drift across epochs
+
+Generated from the canonical experiment artifact. Do not edit manually.
+
+Window: 158 epochs of 144 blocks, covering 98179414 transactions.
+
+| axis projection | TV@1 | TV@7 | TV@30 | TV@120 | weekly gain |
+|---|---:|---:|---:|---:|---:|
+| input_order | 0.008 | 0.008 | 0.011 | 0.013 | 0.83 |
+| input_types | 0.036 | 0.045 | 0.082 | 0.175 | 0.99 |
+| low_r | 0.038 | 0.052 | 0.090 | 0.155 | 1.04 |
+| nlocktime | 0.009 | 0.011 | 0.013 | 0.018 | 0.86 |
+| nsequence | 0.040 | 0.044 | 0.074 | 0.077 | 0.88 |
+| op_return | 0.071 | 0.072 | 0.103 | 0.147 | 0.82 |
+| output_order | 0.031 | 0.035 | 0.057 | 0.127 | 0.90 |
+| output_structure | 0.025 | 0.026 | 0.037 | 0.050 | 0.82 |
+| sighash | 0.024 | 0.034 | 0.064 | 0.089 | 1.07 |
+| uncompressed_pubkey | 0.025 | 0.035 | 0.066 | 0.093 | 1.06 |
+| version | 0.029 | 0.026 | 0.042 | 0.070 | 0.75 |
+
+Each row uses the explicitly preserved values plus one combined residual category. This reproduces every rounded historical cell for nlocktime and low_r. The version projection yields 0.070 rather than the historical 0.071 at gap 120. These projections are not replacements for unavailable full-distribution rows or the historical 70/85 volume-coupling summary.
diff --git a/results/generated/bayes-vs-fs-v1.md b/results/generated/bayes-vs-fs-v1.md
new file mode 100644
index 00000000..efb74c38
--- /dev/null
+++ b/results/generated/bayes-vs-fs-v1.md
@@ -0,0 +1,13 @@
+# Pair-level Bayesian and Fellegi–Sunter comparison
+
+Generated from the canonical experiment artifact. Do not edit manually.
+
+Transactions: 22112; positive pairs: 4000; negative pairs: 4000.
+
+| scorer | AUC | ECE |
+|---|---:|---:|
+| fellegi_sunter_fixed_0_95 | 0.916250 | 0.145541 |
+| fellegi_sunter_em | 0.932000 | 0.174952 |
+| bayesian_integrated_m | 0.932400 | 0.193611 |
+
+The preserved snapshot does not reproduce the metrics in the former handwritten report. These values are the canonical rerun. Address reuse is a weak label, calibration is on a balanced selected sample, and the cluster bands are independent-edge diagnostics rather than a posterior over ownership partitions.
diff --git a/results/generated/boltzmann-fee-audit-v1.md b/results/generated/boltzmann-fee-audit-v1.md
new file mode 100644
index 00000000..bda45d32
--- /dev/null
+++ b/results/generated/boltzmann-fee-audit-v1.md
@@ -0,0 +1,9 @@
+# Exact conservation versus explicit fee allocation
+
+Generated from the canonical experiment artifact. Do not edit manually.
+
+The bounded population contains 300 transactions. Exact zero-fee conservation admits mappings for 0; the local fee-allocation model admits mappings for 300.
+
+Only 37 transactions admit more than the all-coins interpretation. This is the informative comparison.
+
+This run is not a claim of general parity with the external Boltzmann tool. Selected official vectors and modes are tested separately.
diff --git a/results/generated/candidate-set-intersection-v1.md b/results/generated/candidate-set-intersection-v1.md
new file mode 100644
index 00000000..e0b263a2
--- /dev/null
+++ b/results/generated/candidate-set-intersection-v1.md
@@ -0,0 +1,11 @@
+# Candidate-set intersection fixture
+
+Generated from the canonical experiment artifact. Do not edit manually.
+
+| scenario | survivors | narrowing bits | identified | inconsistent at |
+|---|---:|---:|---:|---:|
+| converging | 1 | 6.0 | 0 | None |
+| stalled | 8 | 3.0 | None | None |
+| contradictory | 0 | None | None | 1 |
+
+This synthetic run validates the abstract intersection mechanism. It does not measure JoinMarket or Bitcoin deanonymization rates.
diff --git a/results/generated/change-consolidation-v1.md b/results/generated/change-consolidation-v1.md
new file mode 100644
index 00000000..303ba88c
--- /dev/null
+++ b/results/generated/change-consolidation-v1.md
@@ -0,0 +1,12 @@
+# Change-consolidation failure mode
+
+Generated from the canonical experiment artifact. Do not edit manually.
+
+| quantity | value |
+|---|---:|
+| candidate owners before consolidation | 2 |
+| candidate owners after consolidation | 1 |
+| surviving candidates | `alice` |
+| outcome | `candidate_narrowing` |
+
+The linkage requires correct change identification and a valid common-ownership premise.
diff --git a/results/generated/cioh-collaborative-false-merge-v1.md b/results/generated/cioh-collaborative-false-merge-v1.md
new file mode 100644
index 00000000..8ca11635
--- /dev/null
+++ b/results/generated/cioh-collaborative-false-merge-v1.md
@@ -0,0 +1,12 @@
+# CIOH false merge in a collaborative transaction
+
+Generated from the canonical experiment artifact. Do not edit manually.
+
+| fixture | inputs | predicted merges | reference conflicts |
+|---|---:|---:|---:|
+| collaborative | 2 | 1 | 1 |
+| same-owner control | 2 | 1 | 0 |
+
+CIOH observes only the co-spend and therefore merges both pairs. The collaborative fixture's supplied participant labels expose that merge as false.
+
+The labels are part of the deterministic fixture. This result does not estimate chain-wide prevalence or infer real ownership.
diff --git a/results/generated/cluster-bits-v1.md b/results/generated/cluster-bits-v1.md
new file mode 100644
index 00000000..800fab60
--- /dev/null
+++ b/results/generated/cluster-bits-v1.md
@@ -0,0 +1,29 @@
+# What a cluster carries against what a merge hides
+
+Generated from the canonical experiment artifact. Do not edit manually.
+
+2538 co-spend clusters in 22112 committed transactions, each scored by the rarity of the external counterparties it pays or is paid by. Co-spend edges are excluded, so the identifier is not the thing that built the cluster.
+
+| quantity | bits |
+|---|---:|
+| median cluster | 29.0 |
+| p90 cluster | 68.7 |
+| largest cluster | 21338.5 |
+| median, five rarest counterparties only | 29.0 |
+
+| threshold | share of clusters at or above |
+|---:|---:|
+| 1.6 bits | 100.0% |
+| 10 bits | 97.9% |
+| 50 bits | 16.5% |
+| 100 bits | 5.2% |
+
+The asymmetry the argument needs is the first row against 1.6: a merged transaction contributes about 1.6 bits of ambiguity, and 100.0% of these clusters already carry at least that much, at a median of 29.0.
+
+The five-rarest floor does not test that on this cache: 92% of these clusters have at most 5 counterparties, so the restriction usually keeps the whole set and its median (29.0) repeats the one above it. A slice with richer neighbourhoods is what would make that floor say something about dependence between counterparties; here it says only that the clusters are small.
+
+The cache is block-sampled rather than contiguous, so a cluster's counterparties are undercounted relative to a connected slice and every figure here is a floor. That is the direction the argument can use: a floor above the merge's contribution settles the comparison, and a larger slice can only raise it.
+
+## Reproducibility / provenance
+
+State 1 in `results/REPRODUCIBILITY.md`: band-pinned on committed data — the artifact is recomputed from `data/fs-blkcache-2026-09-04.tar.gz` and compared byte for byte.
diff --git a/results/generated/coinjoin-sudoku-nominal-v1.md b/results/generated/coinjoin-sudoku-nominal-v1.md
new file mode 100644
index 00000000..099fd4ce
--- /dev/null
+++ b/results/generated/coinjoin-sudoku-nominal-v1.md
@@ -0,0 +1,13 @@
+# CoinJoin Sudoku nominal reconstruction
+
+Generated from the canonical experiment artifact. Do not edit manually.
+
+The independently implemented nominal mechanism retains exact equal-sum input/output groupings. The cited repository does not contain the historical analyzer, so this result does not claim code parity.
+
+| primary-source fixture | retained mappings | representative input/output relation |
+|---|---:|---:|
+| SX symmetric control | 36 indexed / 6 fee-collapsed | 0.333333 |
+
+The advisory's larger SharedCoin target `0e0337bdf930eba3b082fdfbd30944b18e03f0f810ae531443161f897a4d3db0` remains **not reproduced**: its analyzer and complete output-label semantics were not published.
+
+The source-backed layer enumerates all exact equal-sum groupings. The displayed marginal comes from a separate local adaptation: explicit 10,000-sat fee pseudo-outputs, largest-group selection, and uniform weighting. Those rules are not attributed to the unavailable analyzer, and the resulting conditional amount relations are not ownership attribution.
diff --git a/results/generated/collaborative-forms-v1.md b/results/generated/collaborative-forms-v1.md
new file mode 100644
index 00000000..e64a821c
--- /dev/null
+++ b/results/generated/collaborative-forms-v1.md
@@ -0,0 +1,20 @@
+# Collaborative transaction observer-knowledge matrix
+
+Generated from the canonical experiment artifact. Do not edit manually.
+
+The same two-input/two-output observation is compatible with an ordinary transaction and four PayJoin protocol forms. This does not identify which protocol form occurred.
+
+| form | participants | external on-chain facts | counterparty facts |
+|---|---:|---|---|
+| ordinary_two_input | 1 | input and output amounts; transaction shape and script-visible construction features; participant allocation is not observed; payment amount is not observed | not applicable |
+| p2ep | 2 | input and output amounts; transaction shape and script-visible construction features; participant allocation is not observed; payment amount is not observed | own inputs and outputs; the only other party's remaining inputs and outputs by elimination; negotiated payment amount |
+| bip79_bustapay | 2 | input and output amounts; transaction shape and script-visible construction features; participant allocation is not observed; payment amount is not observed | own inputs and outputs; the only other party's remaining inputs and outputs by elimination; negotiated payment amount |
+| bip78_sync_payjoin | 2 | input and output amounts; transaction shape and script-visible construction features; participant allocation is not observed; payment amount is not observed | own inputs and outputs; the only other party's remaining inputs and outputs by elimination; negotiated payment amount |
+| bip77_async_payjoin | 2 | input and output amounts; transaction shape and script-visible construction features; participant allocation is not observed; payment amount is not observed | own inputs and outputs; the only other party's remaining inputs and outputs by elimination; negotiated payment amount |
+| many_senders_one_receiver | 4 | amounts and transaction shape; sender allocation is not observed | the receiver knows each negotiated payment and sender change; the receiver may not know which input belongs to which sender |
+| many_senders_many_receivers | 6 | amounts and transaction shape; payment pairing is not observed | own subtransaction and negotiated payment; other pairings remain latent |
+| net_settlement_with_cycles | latent | net on-chain balances; participant count may remain latent | own obligations and contributions; other parties' gross obligations remain latent |
+
+The shared observation is inputs `[90, 60]` and outputs `[110, 40]`. Protocol transport, participant allocation and negotiated payments are not added to the external observer's on-chain knowledge.
+
+All allocations and relationships in this fixture are supplied possible worlds. The result is not ownership attribution, a deployment-frequency measurement or a privacy score.
diff --git a/results/generated/conservation-round-three-v1.md b/results/generated/conservation-round-three-v1.md
new file mode 100644
index 00000000..0439ae1f
--- /dev/null
+++ b/results/generated/conservation-round-three-v1.md
@@ -0,0 +1,9 @@
+# Conservation observation for round three
+
+Generated from the canonical experiment artifact. Do not edit manually.
+
+The reduced mainnet fixture `80f11c778a2f486ffc55b4e8665a94971ae9c350ac53969e9efcbf90478cbf90` contains 502 outputs. Conservation forces 2 of the 7 outputs worth 774840978 sat onto value supplied by the known participant.
+
+The forced value is 1549681956 sat and the margin to the next boundary is 360201666 sat.
+
+This is provenance of value under conservation, not output ownership, identity attribution, a population estimate or CoinScore.
diff --git a/results/generated/counting-fee-sensitivity-v1.md b/results/generated/counting-fee-sensitivity-v1.md
new file mode 100644
index 00000000..6ddbc8c0
--- /dev/null
+++ b/results/generated/counting-fee-sensitivity-v1.md
@@ -0,0 +1,16 @@
+# Counting fee sensitivity
+
+Generated from the canonical experiment artifact. Do not edit manually.
+
+| fee (sat) | transaction count | per-coin inputs measured |
+|---:|---:|---:|
+| 0 | 2 | 3 |
+| 1 | 1 | 2 |
+| 10 | 1 | 0 |
+| 100 | 1 | 0 |
+| 1,000 | 1 | 0 |
+| 4,956 | 1 | 0 |
+
+The transaction-level result remains defined after the per-coin interface stops measuring any input. The two interfaces therefore must not be substituted for one another.
+
+This fixture is a mechanism check, not a population estimate or privacy score.
diff --git a/results/generated/counting-methods-v1.md b/results/generated/counting-methods-v1.md
new file mode 100644
index 00000000..692e0887
--- /dev/null
+++ b/results/generated/counting-methods-v1.md
@@ -0,0 +1,11 @@
+# Counting-method observations
+
+Generated only from two SHA-256-pinned canonical artifacts. Do not edit manually.
+
+The current router evaluates 1,428 complete multi-input transactions. It returns 0 radix exact, 60 sparse exact and 22 sparse lower-bound outcomes. It refuses 1,333 transactions.
+
+In the fixed fee fixture, the per-coin interface first measures no input at 10 sat while the transaction router remains defined. These are different counting objects and cannot substitute for one another.
+
+Historical wall-clock timings and the 18-case L comparison are excluded because their benchmark protocol and synthetic generator were not preserved. Earlier router shares are superseded by the current run.
+
+These observations are diagnostics, not mapping entropy, a privacy score or CoinScore.
diff --git a/results/generated/counting-router-v1.md b/results/generated/counting-router-v1.md
new file mode 100644
index 00000000..d889d3ac
--- /dev/null
+++ b/results/generated/counting-router-v1.md
@@ -0,0 +1,9 @@
+# Counting-router observations
+
+Generated from the canonical experiment artifact. Do not edit manually.
+
+The current router evaluates 1,428 transactions. It resolves 82: 0 radix exact, 60 sparse exact, and 22 sparse lower bound. The remaining 1,333 are unknown.
+
+The radix precondition applies to 20 transactions. Calling radix without that gate returns a positive reading 64 times, including 51 outside its precondition.
+
+Sasamoto is not a router tier. These counts are not mapping entropy or CoinScore.
diff --git a/results/generated/denomination-preparation-v1.md b/results/generated/denomination-preparation-v1.md
new file mode 100644
index 00000000..018d853c
--- /dev/null
+++ b/results/generated/denomination-preparation-v1.md
@@ -0,0 +1,12 @@
+# Fixed-denomination preparation fingerprint
+
+Generated from the canonical experiment artifact. Do not edit manually.
+
+| population | transactions | fingerprint matches |
+|---|---:|---:|
+| synthetic preparations | 3 | 3 |
+| arity-matched controls | 3 | 0 |
+
+The rule observes one exact-denomination predecessor output consumed by the declared next CoinJoin. Controls have the same input and output counts.
+
+This construction-level fixture does not measure prevalence and does not prove that a matched predecessor is unilateral or commonly owned.
diff --git a/results/generated/descent-vs-ascent-v1.md b/results/generated/descent-vs-ascent-v1.md
new file mode 100644
index 00000000..85068854
--- /dev/null
+++ b/results/generated/descent-vs-ascent-v1.md
@@ -0,0 +1,29 @@
+# Ascending and descending over the same slice
+
+Generated from the canonical experiment artifact. Do not edit manually.
+
+Two passes read the same 12000 transactions and 12341 addresses. The ascending pass starts from the discrete partition and declines a merge the spending transaction argues against. The descending pass is handed the common-input-ownership partition and may only cut it, from the same refusal channels with the co-spend prior removed.
+
+| pass | blocks | blocks cut | separations the ascent makes and this does not |
+|---|---:|---:|---:|
+| common-input ownership, inherited | 3182 | n/a | 3837 |
+| refusing merge pass, ascending | 3282 | n/a | 0 |
+| descent at cut_below -1 | 3261 | 4 | 3014 |
+| descent at cut_below -2 | 3182 | 0 | 3837 |
+| descent at cut_below -4 | 3182 | 0 | 3837 |
+
+At the loosest bar the descent recovers **823 of the 3837** separations the ascent makes, cutting 4 blocks and never separating a pair the ascent keeps together. Every signal on this slice is a single tell, so a bar of two or more clears nothing: the threshold is doing real work rather than sitting below the data.
+
+| quantity | pairs |
+|---|---:|
+| joined by the inherited partition | 601536 |
+| carrying evidence against the pairing | 1054 |
+| joined inside an exactly searched block | 7669 |
+| evidence inside an exactly searched block | 0 |
+| evidence inside a block the conservative pass handled | 1054 |
+
+Evidence against a pairing reaches 0.18% of the joined pairs, and all of it lies inside 5 of the 84 blocks that exceed the exact bound of 9 addresses. Not one of the 3098 blocks the exact search reaches contains a pair these channels argue against, so there it correctly did nothing. The heavy tail is where the evidence lives and where an exact search cannot go, which is why the conservative pass exists.
+
+What the two directions do with identical evidence is still not symmetric, and the remaining 3014 pairs are the measure of it. A refusal at merge time separates two addresses without holding any evidence about that pair: the merge is simply not made, and transitivity never carries through it. A cut has to argue about the boundary it names, against blocks reaching 648 addresses. Descending is strictly more expensive than not ascending, and this slice says how much.
+
+These counts describe one six-block slice under two channels, compare the passes as partitions rather than against same-owner labels, and do not establish how either pass behaves where the evidence is denser.
diff --git a/results/generated/deterministic-partition-v1.md b/results/generated/deterministic-partition-v1.md
new file mode 100644
index 00000000..d8743f82
--- /dev/null
+++ b/results/generated/deterministic-partition-v1.md
@@ -0,0 +1,12 @@
+# Amount-constrained deterministic partition
+
+Generated from the canonical experiment artifact. Do not edit manually.
+
+| fixture | non-derived mappings | unanimous input-output links |
+|---|---:|---:|
+| forced partition | 1 | 4 |
+| underdetermined control | 3 | 0 |
+
+The forced fixture is 0.1 + 0.3 = 0.4 and 2 + 5 = 7, scaled by ten. The control is the CTP's three-way underdetermined 0.7/0.8 example, also scaled by ten.
+
+Every statement is conditional on exact per-block conservation and the non-derived mapping family. Fees, contextual priors and net-settlement cycles can change admissibility.
diff --git a/results/generated/em-m-v1.md b/results/generated/em-m-v1.md
new file mode 100644
index 00000000..191c9670
--- /dev/null
+++ b/results/generated/em-m-v1.md
@@ -0,0 +1,15 @@
+# Per-axis Fellegi–Sunter EM diagnostic
+
+Generated from the canonical experiment artifact. Do not edit manually.
+
+Transactions: 22112; EM iterations: 21; inferred selected-pair fraction: 0.663956.
+
+| parameter source | LibraryScorer AUC |
+|---|---:|
+| fixed 0.95 | 0.924450 |
+| unsupervised EM | 0.925350 |
+| address-reuse agreement | 0.954600 |
+
+EM minus fixed AUC: +0.000900. This difference is descriptive; no pre-registered separability verdict was run.
+
+The historical 165,832-transaction table is not reproduced by the preserved 22,112-transaction snapshot. Address reuse is a weak label, and this is an attacker-side parameter diagnostic rather than a privacy score.
diff --git a/results/generated/entity-deanon-v1.md b/results/generated/entity-deanon-v1.md
new file mode 100644
index 00000000..13479356
--- /dev/null
+++ b/results/generated/entity-deanon-v1.md
@@ -0,0 +1,10 @@
+# Entity-labelled graph-structure observations
+
+Generated from the canonical experiment artifact. Do not edit manually.
+
+| snapshot | transactions | positive pairs | payment AUC | mean shared neighbours |
+|---|---:|---:|---:|---:|
+| SatoshiDice | 15635 | 780 | 0.689075 | 7.948718 |
+| BitMEX | 9698 | 3488761 | 0.497400 | 0.006713 |
+
+The selected SatoshiDice subgraph is a positive control and the selected BitMEX subgraph is a null control. This contrast is snapshot-specific and is not general entity attribution or the full Narayanan–Shmatikov attack.
diff --git a/results/generated/entropy-insufficiency-v1.md b/results/generated/entropy-insufficiency-v1.md
new file mode 100644
index 00000000..b6ce7fa0
--- /dev/null
+++ b/results/generated/entropy-insufficiency-v1.md
@@ -0,0 +1,12 @@
+# Point entropy is not a longitudinal privacy certificate
+
+Generated from the canonical experiment artifact. Do not edit manually.
+
+| scenario | initial entropy | initial candidates | observations | survivors |
+|---|---:|---:|---:|---:|
+| brittle overlap | 2.0 bits | 4 | 2 | 1 |
+| overlapping crowd | 2.0 bits | 4 | 2 | 2 |
+
+The initial posteriors are identical. Different overlap between later candidate sets produces different residual ambiguity.
+
+This synthetic counterexample falsifies sufficiency. It does not estimate attack frequency or certify either scenario's privacy.
diff --git a/results/generated/equal-output-consolidation-v1.md b/results/generated/equal-output-consolidation-v1.md
new file mode 100644
index 00000000..6e91744c
--- /dev/null
+++ b/results/generated/equal-output-consolidation-v1.md
@@ -0,0 +1,12 @@
+# Equal-output consolidation failure mode
+
+Generated from the canonical experiment artifact. Do not edit manually.
+
+| quantity | value |
+|---|---:|
+| candidate clusters per output before consolidation | 3 |
+| shared candidate clusters after consolidation | 1 |
+| surviving cluster | `alice` |
+| outcome | `candidate_narrowing` |
+
+The narrowing is conditional on the later co-spend being evidence of common ownership. It is not an attribution to a real person.
diff --git a/results/generated/eve-alice-eve-v1.md b/results/generated/eve-alice-eve-v1.md
new file mode 100644
index 00000000..4bb1641e
--- /dev/null
+++ b/results/generated/eve-alice-eve-v1.md
@@ -0,0 +1,10 @@
+# Eve–Alice–Eve consolidation failure mode
+
+Generated from the canonical experiment artifact. Do not edit manually.
+
+| observation | inputs | outcome | surviving candidates |
+|---|---:|---|---|
+| isolated return | 1 | `inconclusive` | none |
+| consolidated return | 2 | `candidate_narrowing` | `alice` |
+
+The counterparty's records strengthen its observation, but candidate narrowing is not identity attribution.
diff --git a/results/generated/exact-oracle-audit-v1.md b/results/generated/exact-oracle-audit-v1.md
new file mode 100644
index 00000000..82cb1b7c
--- /dev/null
+++ b/results/generated/exact-oracle-audit-v1.md
@@ -0,0 +1,18 @@
+# Exact-oracle audit
+
+Generated from the canonical experiment artifact. Do not edit manually.
+
+| quantity | value |
+|---|---:|
+| transactions | 507 |
+| link-matrix entries | 6228 |
+| entries above exact | 1714 |
+| entries below exact | 4104 |
+| spurious deterministic links | 945 |
+| missed deterministic links | 0 |
+| mapping-count undercounts | 491 |
+| mapping-count overcounts | 0 |
+
+Link-matrix bound direction: `neither`.
+
+DSS: `0.1.0` at revision `d8099bc540c9de7d0f3c9f095e8b2aad8f9d8c8d`.
diff --git a/results/generated/fingerprint-catalog-axes-v1.md b/results/generated/fingerprint-catalog-axes-v1.md
new file mode 100644
index 00000000..5fbe860e
--- /dev/null
+++ b/results/generated/fingerprint-catalog-axes-v1.md
@@ -0,0 +1,14 @@
+# Excluded fingerprint-axis ablation
+
+Generated from the canonical experiment artifact. Do not edit manually.
+
+Transactions: 600; positive draws: 4000; negative draws: 4000.
+
+| configuration | AUC | delta from library | shuffled AUC |
+|---|---:|---:|---:|
+| library | 0.945875 | +0.000000 | 0.490825 |
+| plus_output_count | 0.945150 | -0.000725 | 0.490675 |
+| plus_segwit_serialization | 0.948475 | +0.002600 | 0.489875 |
+| plus_both | 0.948000 | +0.002125 | 0.490075 |
+
+On this frozen snapshot, output count reduces AUC, SegWit serialization increases it, and adding both yields less than SegWit serialization alone. This is consistent with the historical redundancy concern, but one selected snapshot does not prove conditional dependence. Neither diagnostic axis is added to the production library.
diff --git a/results/generated/fingerprint-regime-v1.md b/results/generated/fingerprint-regime-v1.md
new file mode 100644
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+++ b/results/generated/fingerprint-regime-v1.md
@@ -0,0 +1,12 @@
+# Fingerprint ranking regime
+
+Generated from the canonical experiment artifact. Do not edit manually.
+
+Transactions: 600.
+
+| weights | N-S-form AUC | F-S AUC | N-S-form top-1 | F-S top-1 | within-class mean gap |
+|---|---:|---:|---:|---:|---:|
+| library | 0.920700 | 0.939150 | 0.680000 | 0.730000 | 0.392591 |
+| snapshot_measured | 0.953150 | 0.939150 | 0.785000 | 0.730000 | 0.675116 |
+
+The historical claim that the conditioner interpretation is stable across weight sources does not reproduce on this fixture. Snapshot-measured weights outperform the F-S top-1 baseline and have a larger within-class mean gap, but they are fitted and evaluated on the same selected data. This is evidence of sensitivity, not evidence that fingerprints are universal sparse identifiers.
diff --git a/results/generated/fingerprint-sparsity-v1.md b/results/generated/fingerprint-sparsity-v1.md
new file mode 100644
index 00000000..1df40f5a
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+++ b/results/generated/fingerprint-sparsity-v1.md
@@ -0,0 +1,20 @@
+# Fingerprint exact-vector class sizes
+
+Generated from the canonical experiment artifact. Do not edit manually.
+
+Window: blocks 939969 through 962720; 98179414 transactions in 158 epochs.
+
+| class size | transaction share |
+|---|---:|
+| exactly 1 | 0.009% |
+| 2-9 | 0.052% |
+| 10-99 | 0.351% |
+| 100-999 | 1.667% |
+| 1,000-9,999 | 6.249% |
+| 10,000-99,999 | 16.120% |
+| >=100,000 | 75.552% |
+
+Share below 10: 0.061%.
+Share below 100: 0.413%.
+
+All preserved conditional partitions reconstruct the same 12-axis whole-window histogram. The unavailable 19-axis and per-epoch columns are excluded. Exact-vector equivalence classes do not establish general metric sparsity or successful attribution.
diff --git a/results/generated/fingerprint-validation-v1.md b/results/generated/fingerprint-validation-v1.md
new file mode 100644
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+++ b/results/generated/fingerprint-validation-v1.md
@@ -0,0 +1,14 @@
+# Construction-fingerprint pair separation
+
+Generated from the canonical experiment artifact. Do not edit manually.
+
+Transactions: 600; positive draws: 4000; negative draws: 4000.
+
+| metric | value |
+|---|---:|
+| positive mean bits | 19.646924 |
+| negative mean bits | -17.413052 |
+| AUC | 0.945875 |
+| shuffled-label AUC | 0.490825 |
+
+The canonical fingerprint scorer separates address-reuse-labelled pairs from random pairs on this selected snapshot. Address reuse is a weak and partly feature-dependent label, so this is pair-ranking evidence, not wallet attribution or a privacy score.
diff --git a/results/generated/fs-ablation-v1.md b/results/generated/fs-ablation-v1.md
new file mode 100644
index 00000000..16af456f
--- /dev/null
+++ b/results/generated/fs-ablation-v1.md
@@ -0,0 +1,14 @@
+# Fellegi–Sunter conditional-dependence ablation
+
+Generated from the canonical experiment artifact. Do not edit manually.
+
+| quantity | value |
+|---|---:|
+| transactions | 22112 |
+| baseline AUC | 0.982688 |
+| dependent field groups | 4 |
+| most dependent fields | `input_script_type`, `input_types_present` |
+| phi among matches | 1.000000 |
+| phi among non-matches | 1.000000 |
+
+Association is measured conditionally on weak-label class and is not causal.
diff --git a/results/generated/fs-temporal-v1.md b/results/generated/fs-temporal-v1.md
new file mode 100644
index 00000000..d11e32b8
--- /dev/null
+++ b/results/generated/fs-temporal-v1.md
@@ -0,0 +1,14 @@
+# Temporal Fellegi–Sunter evaluation
+
+Generated from the canonical experiment artifact. Do not edit manually.
+
+| quantity | value |
+|---|---:|
+| transactions | 22112 |
+| train transactions | 15100 |
+| test transactions | 7012 |
+| Fellegi–Sunter AUC | 0.982688 |
+| rarity baseline AUC | 0.934485 |
+| AUC difference | 0.048202 |
+
+Labels are based on address reuse and are not independent ownership labels.
diff --git a/results/generated/fused-engine-v1.md b/results/generated/fused-engine-v1.md
new file mode 100644
index 00000000..fc2e2f00
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+++ b/results/generated/fused-engine-v1.md
@@ -0,0 +1,26 @@
+# What each channel adds when the engine fuses them
+
+Generated from the canonical experiment artifact. Do not edit manually.
+
+22112 committed transactions hold 335 co-spends over 753 funding transactions. The engine judges those co-spends; the merge-only baseline takes every one of them.
+
+| arm | groups | refused co-spends | added links | largest group |
+|---|---:|---:|---:|---:|
+| co-spend union-find | 697 | 0 | 0 | 5 |
+| fingerprint | 508 | 19 | 7560 | 87 |
+| fingerprint+amount | 508 | 19 | 7560 | 87 |
+| fingerprint+amount+topology | 511 | 11 | 7560 | 88 |
+| fingerprint+amount+topology+provenance | 514 | 22 | 7560 | 87 |
+| all five channels | 514 | 22 | 7560 | 87 |
+
+The fingerprint channel is what moves the partition: it adds 7560 links the co-spend missed and takes the largest group from 5 to 87. Refusal is the smaller effect and the one the thesis is about — 19 co-spends declined that the baseline merges.
+
+Topology and provenance pull in opposite directions, which is the point of fusing them. Adding the cluster-level topology takes refusals from 19 to 11: it corroborates merges the fingerprint alone objected to. Adding provenance-disjointness takes them to 22, cutting pairs whose ancestry does not overlap.
+
+The amount channel and the subset-sum de-mix move nothing here, and the reason is structural rather than empirical: both are refuse-only and gated behind fingerprint disagreement, so they can only speak on the pairs the fingerprint already objects to. The slice holds 81 two-input two-output co-spends, the only shape the roundness re-partition judges.
+
+The provenance signature here is depth-1 — a node's in-slice funders — because the slice is block-sampled rather than contiguous. A deeper walk needs a contiguous export.
+
+## Reproducibility / provenance
+
+State 1 in `results/REPRODUCIBILITY.md`: band-pinned on committed data — the artifact is recomputed from `data/fs-blkcache-2026-09-04.tar.gz` and compared byte for byte.
diff --git a/results/generated/graph-deanon-controls-v1.md b/results/generated/graph-deanon-controls-v1.md
new file mode 100644
index 00000000..fa080bc3
--- /dev/null
+++ b/results/generated/graph-deanon-controls-v1.md
@@ -0,0 +1,16 @@
+# Graph-structure linkage and the degree-matched control
+
+Generated from the canonical experiment artifact. Do not edit manually.
+
+8927 transactions, 27962 addresses, 2463 clusters of two or more. Positives are same-cluster pairs that do not co-spend; the score is rarity-weighted shared-neighbour structure.
+
+| view | published AUC | degree-matched AUC | fall | degree-only AUC under matching |
+|---|---:|---:|---:|---:|
+| full | 0.9912 | 0.9784 | 0.0128 | 0.5478 |
+| payment | 0.9506 | 0.9154 | 0.0353 | 0.5000 |
+
+The payment-graph headline falls 0.0353 under degree-matched negatives, from 0.9506 to 0.9154. Under that control a score made only of the pair's degree sum reads 0.5000 — chance, by construction — so the remaining separation is not the asymmetry the published sampling leaves in.
+
+The shuffled-label control reads 0.4994, which is what says the score is measuring the labels at all.
+
+Cluster membership here is a co-spend label rather than wallet ownership, the fixture is one selected 2016 slice, and a fall under matching bounds how much of the AUC was degree asymmetry without establishing what the remainder is. Neither number is a re-identification rate or a privacy score.
diff --git a/results/generated/graph-rejoin-2016-v1.md b/results/generated/graph-rejoin-2016-v1.md
new file mode 100644
index 00000000..3771e936
--- /dev/null
+++ b/results/generated/graph-rejoin-2016-v1.md
@@ -0,0 +1,37 @@
+# Rejoining a cluster split across two weekly views
+
+Generated from the canonical experiment artifact. Do not edit manually.
+
+Two consecutive 2016 weekly windows of 982,021 and 1,116,563 transactions. 17,431 clusters straddle the boundary and survive contraction in both views, at a mean internal degree of 2.866.
+
+| statistic | view A | view B |
+|---|---:|---:|
+| vertices | 588,843 | 638,436 |
+| edges | 1,430,236 | 1,561,102 |
+| mean degree | 4.7874 | 4.8199 |
+| degree 1 share | 0.0464 | 0.0600 |
+| transitivity | 0.0062 | 0.0001 |
+| configuration transitivity | 0.6113 | 0.3976 |
+| assortativity | -0.0764 | -0.0782 |
+| tail exponent | 2.8613 | 2.8337 |
+
+The framework's premise is that a contracted view is a social network in the sense its matching algorithm needs. On this measurement it is not, on the two axes that decide it. Clustering sits at 0.0062 and 0.0001 against a configuration-model null of 0.6113 and 0.3976 — the degree distribution alone would produce far more triangles than the graph has. Degree correlation is -0.0764 and -0.0782: hubs attach to leaves, which is what transaction graphs do and the opposite of what a social graph does. A matcher that routes through a vertex's neighbourhood is asking that neighbourhood to be distinctive and stable, and neither statistic says it is.
+
+| seed share | matcher | arm | guesses | correct | precision |
+|---:|---|---|---:|---:|---:|
+| 5% | undirected | seeded | 0 | 0 | — |
+| 5% | undirected | shuffled | 0 | 0 | — |
+| 5% | directed | seeded | 0 | 0 | — |
+| 5% | directed | shuffled | 0 | 0 | — |
+| 10% | undirected | seeded | 0 | 0 | — |
+| 10% | undirected | shuffled | 0 | 0 | — |
+| 10% | directed | seeded | 1 | 1 | 1.000 |
+| 10% | directed | shuffled | 0 | 0 | — |
+
+At the widest seeding the matcher is offered 15,688 non-seed pairs. Across every seeded arm it guesses 1 and gets 1 right; the shuffled-seed arms guess 0. A cascade that carried itself would show each seed unlocking more than itself, and it does not at these view widths. One match is not ignition, and it is a different statement from none.
+
+Cluster membership here is a co-spend relation rather than observed wallet ownership, and the seeds are supplied. The result is what propagation does once a foothold exists, not whether one can be found.
+
+## Reproducibility / provenance
+
+State 1 in `results/REPRODUCIBILITY.md`: band-pinned on committed data — the artifact is recomputed from the committed weekly epoch graphs and compared byte for byte.
diff --git a/results/generated/intersection-cluster-fixture-v1.md b/results/generated/intersection-cluster-fixture-v1.md
new file mode 100644
index 00000000..b7067b77
--- /dev/null
+++ b/results/generated/intersection-cluster-fixture-v1.md
@@ -0,0 +1,14 @@
+# Cluster-level provenance intersection fixture
+
+Generated from the canonical experiment artifact. Do not edit manually.
+
+| quantity | value |
+|---|---:|
+| branches | 2 |
+| candidates before | 2 |
+| candidates after | 1 |
+| candidates removed | 1 |
+| blind | `false` |
+| independently scored | `false` |
+
+This is a synthetic, conditional narrowing. It is not an ownership attribution.
diff --git a/results/generated/library-calibration-v1.md b/results/generated/library-calibration-v1.md
new file mode 100644
index 00000000..cf46049b
--- /dev/null
+++ b/results/generated/library-calibration-v1.md
@@ -0,0 +1,37 @@
+# The fingerprint library against the population that is still here
+
+Generated from the canonical experiment artifact. Do not edit manually.
+
+The shipped bits were measured on a ~105k uniform whole-chain sample that no longer exists. This measures the same quantity on the 22112 transactions of the committed block cache. It is not a reproduction — the populations differ by construction — and the only thing it establishes is how far apart they land.
+
+| axis | values | shown by the cache | mean divergence | max |
+|---|---:|---:|---:|---:|
+| nsequence | 6 | 6 | 2.21 | 4.66 |
+| locktime | 5 | 2 | 0.68 | 1.17 |
+| input_order | 4 | 4 | 2.80 | 6.85 |
+| output_order | 4 | 4 | 1.55 | 4.30 |
+| change_spk | 6 | 6 | 1.62 | 2.99 |
+| version | 3 | 3 | 1.44 | 3.14 |
+| io_shape | 4 | 4 | 0.53 | 1.62 |
+| uih | 2 | 2 | 0.13 | 0.25 |
+| fee_rate | 3 | 2 | 0.82 | 1.45 |
+| input_script_type | 6 | 6 | 1.38 | 2.66 |
+| output_encoding | 5 | 4 | 1.71 | 2.56 |
+| input_types_present | 7 | 7 | 1.76 | 3.85 |
+| change_index | 3 | 2 | 1.74 | 2.05 |
+| change_type_match | 3 | 2 | 2.24 | 2.75 |
+| change_matches_output | 3 | 2 | 1.93 | 2.38 |
+| change_address_reuse | 2 | 2 | 0.36 | 0.52 |
+| low_r | 3 | 2 | 1.44 | 1.62 |
+| sighash | 5 | 4 | 0.83 | 1.59 |
+| op_return | 2 | 2 | 0.23 | 0.44 |
+| nested_segwit | 2 | 2 | 0.24 | 0.43 |
+| pubkey_compression | 2 | 1 | 1.86 | 1.86 |
+| multisig | 2 | 2 | 0.02 | 0.03 |
+| locktime_vs_broadcast | 4 | 0 | n/a | n/a |
+
+Across the 22 axes the cache can compare, the mean divergence is **1.25 bits** and the largest single one is **6.85 bits**, on `input_order`. For scale, the catalogued scorer's whole positive mean on this same cache is about 15 bits.
+
+15 of 86 published values never appear in the cache, so they cannot be checked against it at all.
+
+None of this says the shipped table is wrong. It says the part of it a reader can verify is the part measured here, and that the rest moves by bits rather than by decimals when the population changes. Whether to recalibrate on this cache is a separate decision, and `fingerprint-regime-v1` already reports what changes when the weights are fitted to a snapshot instead of read from the library.
diff --git a/results/generated/link-prediction-v1.md b/results/generated/link-prediction-v1.md
new file mode 100644
index 00000000..055ca351
--- /dev/null
+++ b/results/generated/link-prediction-v1.md
@@ -0,0 +1,13 @@
+# Link prediction on planted views
+
+Generated from the canonical experiment artifact. Do not edit manually.
+
+| seed | de-anonymization AUC | structure-only AUC | verdict |
+|---:|---:|---:|---|
+| 0 | 0.753722 | 0.592923 | `a` |
+| 1 | 0.728741 | 0.594388 | `a` |
+| 2 | 0.722350 | 0.545971 | `a` |
+| 3 | 0.735343 | 0.628809 | `a` |
+| 4 | 0.702266 | 0.546562 | `a` |
+
+This synthetic result transfers links already observed in the auxiliary view. It is not a reproduction of the 2011 paper or its datasets.
diff --git a/results/generated/match-controls-v1.md b/results/generated/match-controls-v1.md
new file mode 100644
index 00000000..9f3e4628
--- /dev/null
+++ b/results/generated/match-controls-v1.md
@@ -0,0 +1,41 @@
+# Does the matcher beat degree alone
+
+Generated from the canonical experiment artifact. Do not edit manually.
+
+Two consecutive 2016 weekly windows of 982,021 and 1,116,563 transactions, contracted into 588,843 and 638,436 pseudonyms. View B's labels are hidden, so 90,145 vertices are known to be present in both views and the correspondence between them is not. Seeds are drawn from the 54,486 of those whose degree sits in [3, 100] on both sides — below that a neighbourhood identifies nobody, and above it propagation refuses to route through the vertex at all, so a seed outside the band is a seed the matcher cannot spend.
+
+At the 10% seeding, precision against the eccentricity each match won by:
+
+| eccentricity | matched | uncontested | correct | precision | degree class alone | margin |
+|---:|---:|---:|---:|---:|---:|---:|
+| ≥ 0 | 623 | 2 | 376 | 0.604 | 0.31078 | +0.293 |
+| ≥ 1 | 577 | 2 | 362 | 0.627 | 0.32650 | +0.301 |
+| ≥ 2 | 515 | 2 | 334 | 0.649 | 0.34301 | +0.306 |
+| ≥ 3 | 426 | 2 | 281 | 0.660 | 0.37982 | +0.280 |
+| ≥ 5 | 283 | 2 | 197 | 0.696 | 0.48597 | +0.210 |
+| ≥ 10 | 129 | 2 | 95 | 0.736 | 0.66658 | +0.070 |
+
+**The matcher beats degree, and least where it is most sure.** Overall it is +0.293 against what degree alone is worth. That margin does not widen as the matcher grows confident; it collapses, to +0.070 at eccentricity 10. Precision does rise with confidence (0.604 to 0.736), which is the property the framework's argument wants — but the degree baseline rises faster (0.311 to 0.667), because the matches the matcher is surest of are the ones whose partner has a rare degree. The high-confidence band is where the matcher is *hardest* to distinguish from reading the degrees off.
+
+The uncontested column is the matches where only one candidate materialised at all. The matcher records those with infinite eccentricity and skips the gate rather than computing it, so they clear every floor: a high band is only evidence about *standing clear of rivals* to the extent it is not made of them.
+
+The degree-class column is what degree is worth with no algorithm attached: one over the number of B-vertices sharing the true partner's degree. It is handed that degree, which a degree-only adversary would have to guess, so it bounds such an adversary from above rather than approximating one. Classes here run to 225,915 vertices with a median of 119542.
+
+Every arm, with the shuffled-seed control beside each seeded one:
+
+| seed share | arm | seeds | guesses | correct | precision |
+|---:|---|---:|---:|---:|---:|
+| 5% | seeded | 2724 | 571 | 309 | 0.541 |
+| 5% | shuffled | 2724 | 15 | 0 | 0.000 |
+| 10% | seeded | 5448 | 623 | 376 | 0.604 |
+| 10% | shuffled | 5448 | 17 | 3 | 0.176 |
+
+The seeded arm returns 623 graded matches. A shuffled seed is the same matcher on the same graphs with the correspondence destroyed, and what it returns is the share of this that survives without the seeds meaning anything.
+
+This is the complete-clustering premise: the matcher is told which vertices appear in both views and asked only which is which. The split-cluster premise, where a straddling entity carries a separate pseudonym per view and the matcher must discover that they are one, is measured beside it (`results/generated/graph-rejoin-2016-v1.md`) and is the harder question.
+
+Cluster membership is a co-spend relation rather than observed wallet ownership, the seeds are supplied, and this is two windows of one era. None of it is a privacy score.
+
+## Reproducibility / provenance
+
+State 1 in `results/REPRODUCIBILITY.md`: band-pinned on committed data — the artifact is recomputed from the committed weekly epoch graphs and compared byte for byte.
diff --git a/results/generated/ns-bitcoin-v1.md b/results/generated/ns-bitcoin-v1.md
new file mode 100644
index 00000000..949732cd
--- /dev/null
+++ b/results/generated/ns-bitcoin-v1.md
@@ -0,0 +1,9 @@
+# N-S propagation on Bitcoin views
+
+Generated from the canonical experiment artifact. Do not edit manually.
+
+The two windows contain 133,576 and 157,724 transactions. No independent gradeable seed was found.
+
+The seed-assisted sweep's highest precision is 1.2837%, with coverage 0.5131%.
+
+The sweep does not establish an independently seeded attack and is not a reproduction of the published social-network corpora.
diff --git a/results/generated/ns-link-prediction-coverage-v2.md b/results/generated/ns-link-prediction-coverage-v2.md
new file mode 100644
index 00000000..52537129
--- /dev/null
+++ b/results/generated/ns-link-prediction-coverage-v2.md
@@ -0,0 +1,19 @@
+# N-S 2011 executable coverage
+
+Generated from the canonical experiment artifact. Do not edit manually.
+
+| component | status | limitation |
+|---|---|---|
+| `algorithm1_similarity` | `implemented` | — |
+| `algorithm2_positive_weights` | `implemented` | — |
+| `algorithm2_dummy_weights` | `implemented` | a dummy-incident node term is fixed at zero; the paper states this in prose, not in the printed formula |
+| `annealing` | `partial` | iteration budget, RNG and best-state return are explicit local controls |
+| `two_stage_mapping` | `implemented` | the paper picks an arbitrary unmapped node and stage 1 feeds back, so the ``repr`` order this driver imposes is a result-bearing local choice; injectivity comes from Algorithm 3's 1-1 mapping, not from the propagation section |
+| `confidence_pruning` | `not_reproduced` | an implementation-complete pruning policy is not available |
+| `accepted_mapping_correction` | `not_reproduced` | schedule and conflict resolution are not specified sufficiently |
+| `algorithm3_cascade` | `implemented` | — |
+| `learned_25_feature_model` | `not_reproduced` | the caller supplies an ML score; the published feature producer is absent |
+
+Every non-implemented component is exercised through the refusal gate. The implemented components pass that gate, but the end-to-end paper pipeline remains unreproduced.
+
+This deterministic contract does not reproduce the Flickr/Kaggle corpus, reported metrics, dummy-node convention, pruning policy, correction schedule, or learned model.
diff --git a/results/generated/ns1r-change-readoff-v1.md b/results/generated/ns1r-change-readoff-v1.md
new file mode 100644
index 00000000..20915b01
--- /dev/null
+++ b/results/generated/ns1r-change-readoff-v1.md
@@ -0,0 +1,16 @@
+# Receiver-side change read-off in a many-senders PayJoin
+
+Generated from the canonical experiment artifact. Do not edit manually.
+
+The receiver knows its own coins and the payment negotiated with each sender, so a candidate change output must satisfy `input - payment == change`. One sender's constraint rarely settles it; the perfect matchings propagate it across senders.
+
+| scenario | senders | matchings | distinct readings | unique reading | unanimous links |
+|---|---:|---:|---:|---|---:|
+| consolidating-receiver | 3 | 2 | 1 | yes | 1 |
+| evenly-spaced-control | 3 | 2 | 2 | no | 0 |
+
+Across 2 scenarios, 5 senders were ambiguous on their own constraint and propagation narrowed 2 of them; 1 scenario reached a single reading of the amounts.
+
+Matchings and readings are counted separately on purpose. Two matchings that assign different output indices but the same amounts are one reading, and the reading is what the receiver learns — the consolidating scenario has two matchings and one reading.
+
+Both scenarios are deterministic fixtures and the negotiated payments are supplied rather than inferred. An external observer holds neither those payments nor the receiver's own coins, so this is a counterparty capability. A unique reading of the amounts is not ownership attribution and is not a privacy score.
diff --git a/results/generated/partition-schemes-v1.md b/results/generated/partition-schemes-v1.md
new file mode 100644
index 00000000..f6186080
--- /dev/null
+++ b/results/generated/partition-schemes-v1.md
@@ -0,0 +1,36 @@
+# Which cut makes two matchable views
+
+Generated from the canonical experiment artifact. Do not edit manually.
+
+300000 transactions, 71986 clusters. Each scheme cuts the slice into two views, a straddling entity gets a distinct pseudonym per view, and the matcher has to rejoin them from structure alone.
+
+| scheme | boundary txs | pairs to rejoin | straddler edges | mean straddler degree | non-isolated | correct @ 10% seed |
+|---|---:|---:|---:|---:|---:|---:|
+| `epoch` | 0 (0.0%) | 2562 | 1910 | 1.45 | 44.7% | 1 |
+| `decore` | 117371 (39.1%) | 1294 | 360 | 0.55 | 28.3% | 0 |
+| `collapse` | 4989 (1.7%) | 2536 | 1941 | 1.49 | 44.5% | 1 |
+
+**`decore` cuts the wrong thing.** Dropping the busiest 1% of addresses removes 39.1% of the transactions, takes the rejoinable population from 2562 to 1294 and the straddler subgraph's mean degree from 1.45 to 0.55. It cuts by degree, and degree is where the recurring relationships live.
+
+**`collapse` is nearly free and does not change the regime.** It costs 1.7% of the transactions and leaves the population and the degree where the temporal baseline leaves them (2536 against 2562 pairs, 1.49 against 1.45). It is the cut the framework asks for and it is not the binding constraint.
+
+| scheme | seed | seeds | guesses | correct | precision |
+|---|---:|---:|---:|---:|---:|
+| `epoch` | 5% | 128 | 0 | 0 | n/a |
+| `epoch` | 10% | 256 | 1 | 1 | 1.000 |
+| `decore` | 5% | 64 | 0 | 0 | n/a |
+| `decore` | 10% | 129 | 0 | 0 | n/a |
+| `collapse` | 5% | 126 | 0 | 0 | n/a |
+| `collapse` | 10% | 253 | 1 | 1 | 1.000 |
+
+The matcher recovers a handful of pairs at best, so what separates the schemes here is the straddler subgraph and not the precision. A cut that halves the mean straddler degree leaves nothing to propagate along, whatever its precision reads on the pairs it does return.
+
+Every scheme generalises to n views:
+
+| scheme | 2 | 3 | 4 |
+|---|---|---|---|
+| `epoch` | 137354/162646 | 91349/94577/114074 | 70469/66597/78032/84902 |
+| `decore` | 82820/99809 | 55231/57214/70184 | 42474/40182/46904/53069 |
+| `collapse` | 135045/159966 | 89861/93060/112090 | 69302/65459/76829/83421 |
+
+Cluster membership here is a co-spend label rather than wallet ownership, the export carries no output values so the collapse detector sees only its shape rule, and this is one slice of one era. None of it is a privacy score.
diff --git a/results/generated/path-count-contract-v1.md b/results/generated/path-count-contract-v1.md
new file mode 100644
index 00000000..1e9f8a7c
--- /dev/null
+++ b/results/generated/path-count-contract-v1.md
@@ -0,0 +1,11 @@
+# Path-count implementation contract
+
+Generated from the canonical experiment artifact. Do not edit manually.
+
+The ancestry and path-count distributions are identical on the fixed two-hop DAG: `{'origin-a:0': 0.62, 'origin-b:0': 0.38}`.
+
+The resulting min-entropy is 0.689660 bits and Shannon entropy is 0.958042 bits. Changing the supplied count oracle from log W = 0 to log W = ln(100) leaves the distribution unchanged: `True`.
+
+Route accumulation is implemented: `True`. Edge-disjoint path enumeration, plausible-flow capacity and k-routes are implemented: `True`, `True`, `True`.
+
+This is a provenance-route diagnostic, not CTP robust connectivity or CoinScore.
diff --git a/results/generated/perfect-matching-disclosure-v1.md b/results/generated/perfect-matching-disclosure-v1.md
new file mode 100644
index 00000000..a0169b15
--- /dev/null
+++ b/results/generated/perfect-matching-disclosure-v1.md
@@ -0,0 +1,12 @@
+# Perfect-matching disclosure
+
+Generated from the canonical experiment artifact. Do not edit manually.
+
+| scenario | naive assignment | naive bijective | optimal matchings | unique optimum |
+|---|---|---:|---:|---:|
+| joint assignment | [0, 0, 2] | false | 1 | true |
+| uniform-profile control | [0, 0, 0] | false | 6 | false |
+
+The joint fixture has the unique sender-to-receiver position assignment [0, 1, 2]. Independent maxima reuse receiver 0 and therefore do not form a matching. The uniform control retains all six perfect matchings.
+
+Profiles are supplied. This fixture tests the defining within-round PMDA objective, not longitudinal profile estimation, Bitcoin value flow, or ownership attribution.
diff --git a/results/generated/provenance-decay-v1.md b/results/generated/provenance-decay-v1.md
new file mode 100644
index 00000000..1cc4aaa1
--- /dev/null
+++ b/results/generated/provenance-decay-v1.md
@@ -0,0 +1,10 @@
+# Provenance decay failure modes
+
+Generated from the canonical experiment artifact. Do not edit manually.
+
+| removal | candidates | structural outcome | components |
+|---|---:|---|---:|
+| bridge | 3 → 2 | `graph_fracture` | 1 → 2 |
+| peripheral | 3 → 2 | `inconclusive` | unchanged |
+
+Equal additive elimination does not imply equal structural decay.
diff --git a/results/generated/pseudonym-graph-contract-v1.md b/results/generated/pseudonym-graph-contract-v1.md
new file mode 100644
index 00000000..3340c8c2
--- /dev/null
+++ b/results/generated/pseudonym-graph-contract-v1.md
@@ -0,0 +1,15 @@
+# Partial-clustering pseudonym graph
+
+Generated from the canonical experiment artifact. Do not edit manually.
+
+Vertices: cluster-a, cluster-x, z.
+
+| source | target | transfers | value | height span |
+|---|---|---:|---:|---|
+| cluster-a | cluster-x | 3 | 600 | 100–140 |
+| cluster-x | cluster-a | 1 | 50 | 120–120 |
+| z | cluster-a | 1 | 10 | 150–150 |
+
+The unknown address `z` remains a singleton pseudonym. Parallel transfers from `cluster-a` to `cluster-x` are folded into one directed edge with count, total value and height span. The reverse direction remains a separate edge; change within `cluster-a` is recorded as a self-transfer rather than a relationship.
+
+The supplied clustering is partial. These vertices are pseudonyms, not verified users.
diff --git a/results/generated/reid-v1.md b/results/generated/reid-v1.md
new file mode 100644
index 00000000..419190f3
--- /dev/null
+++ b/results/generated/reid-v1.md
@@ -0,0 +1,14 @@
+# Ancestry-signature record linkage
+
+Generated from the canonical experiment artifact. Do not edit manually.
+
+Snapshot records: 200 (165 sparse, 35 dense).
+
+| stratum | auxiliary origins | attackable | declared | exact | precision | exact rate |
+|---|---:|---:|---:|---:|---:|---:|
+| sparse | 4 | 162 | 150 | 150 | 1.000000 | 0.925926 |
+| sparse | 8 | 156 | 149 | 149 | 1.000000 | 0.955128 |
+| dense | 4 | 35 | 2 | 2 | 1.000000 | 0.057143 |
+| dense | 8 | 35 | 3 | 3 | 1.000000 | 0.085714 |
+
+The selected snapshot exhibits a large sparse-versus-dense difference under this adapted attack. The auxiliary origins come from each target's own stored signature, so this demonstrates mechanism behavior under supplied auxiliary information, not independent real-world attribution or a chain-wide rate.
diff --git a/results/generated/route-capacity-v1.md b/results/generated/route-capacity-v1.md
new file mode 100644
index 00000000..af5fa2aa
--- /dev/null
+++ b/results/generated/route-capacity-v1.md
@@ -0,0 +1,76 @@
+# How small a cut separates a coin from its origins
+
+Generated from the canonical experiment artifact. Do not edit manually.
+
+335 coins in the committed cache have an ancestry the slice can walk. For each, the number of routes back to its candidate origins that share no coin — the size of the cut that would separate it.
+
+| routes | coins | coins, routes able to carry the coin |
+|---:|---:|---:|
+| 0 | 0 | 128 |
+| 1 | 32 | 70 |
+| 2 | 121 | 57 |
+| 3 | 33 | 18 |
+| 4 | 37 | 18 |
+| 5 | 30 | 10 |
+| 6 | 12 | 10 |
+| 7 | 6 | 2 |
+| 8 | 8 | 6 |
+| 9 | 3 | 1 |
+| 10 | 4 | 0 |
+| 11 | 4 | 1 |
+| 12 | 2 | 0 |
+| 13 | 0 | 1 |
+| 14 | 1 | 0 |
+| 16 | 1 | 2 |
+| 17 | 3 | 0 |
+| 18 | 2 | 0 |
+| 19 | 1 | 0 |
+| 20 | 1 | 0 |
+| 21 | 1 | 1 |
+| 22 | 1 | 0 |
+| 23 | 0 | 1 |
+| 24 | 1 | 1 |
+| 26 | 1 | 0 |
+| 27 | 1 | 0 |
+| 29 | 0 | 1 |
+| 30 | 1 | 0 |
+| 32 | 1 | 1 |
+| 33 | 1 | 1 |
+| 34 | 0 | 1 |
+| 36 | 1 | 1 |
+| 41 | 1 | 0 |
+| 45 | 1 | 0 |
+| 52 | 1 | 0 |
+| 53 | 1 | 0 |
+| 55 | 3 | 0 |
+| 56 | 1 | 0 |
+| 58 | 1 | 0 |
+| 59 | 1 | 0 |
+| 60 | 5 | 0 |
+| 61 | 1 | 0 |
+| 69 | 0 | 1 |
+| 77 | 1 | 1 |
+| 84 | 1 | 0 |
+| 85 | 1 | 0 |
+| 89 | 1 | 0 |
+| 119 | 1 | 0 |
+| 129 | 1 | 0 |
+| 182 | 1 | 0 |
+| 193 | 1 | 0 |
+| 198 | 1 | 1 |
+
+**32 (10%) of these coins are separated from their entire ancestry by removing one coin**, and 186 (56%) by removing at most 3. The framework asks that no small cut do this. At the other end the distribution reaches 198 routes, so redundancy varies by orders of magnitude between coins in one slice.
+
+That count treats every route alike, and the specification does not: a path worth less than the amount in question may have been removed before the question is asked. Pruning each coin's routes to those that could have carried its own value **takes the single-coin cut from 32 (10%) to 70 (21%)**, and leaves 128 (38%) with no route able to carry the coin at all. Counting routes without their value overstates redundancy, in the same direction and for the same reason as counting origins without their overlap.
+
+**Counting origins overstates that redundancy for 201 (60%) of them.** A coin's origin set says how many places its value could have come from; the cut says how many of those a separation would have to defeat, and the two are not the same number because routes share coins. That difference is the reason this is measured rather than counted.
+
+Both readings are lower bounds, and one boundary count says how loose. The walk stopped 5707 times because the slice holds no record of a parent, against 48 times at the depth limit: almost every origin here is the edge of the sample rather than a coinbase or a genuine end of provenance. A cut measured to that edge can only grow on a contiguous export, so these are the cuts the committed data can already show and not a ceiling on redundancy. Read the shape — that a tenth of coins are separated by one coin while others carry 198 routes — rather than the level.
+
+**The framework states its property over the mass of a coin's candidate origins, not only over how many routes reach it**, so the same network is solved a second time with each coin's own value as its capacity. The maximum flow is what the origin set could deliver if every route ran at once, and its minimum cut is the value a separation would have to remove. For 8 (2%) of these coins the origins cannot deliver the coin's own value at all — a shortfall a route count cannot see, because those routes exist and simply are not worth enough together.
+
+That is still one flow and still polynomial. Choosing which routes carry how much — the k-splittable formulation — is strongly NP-hard, is a decision about routing rather than a measurement of capacity, and is not this one.
+
+## Reproducibility / provenance
+
+State 1 in `results/REPRODUCIBILITY.md`: band-pinned on committed data — the artifact is recomputed from `data/fs-blkcache-2026-09-04.tar.gz` and compared byte for byte.
diff --git a/results/generated/same-block-linkage-v1.md b/results/generated/same-block-linkage-v1.md
new file mode 100644
index 00000000..bec01cfd
--- /dev/null
+++ b/results/generated/same-block-linkage-v1.md
@@ -0,0 +1,21 @@
+# What the amounts settle about co-spent inputs
+
+Generated from the canonical experiment artifact. Do not edit manually.
+
+300 of 300 multi-input transactions in scope admit a value-conserving mapping under the transaction's own fee. Across their 864 input pairs, Maurer's `p_II` says how often the two coins share a block in every mapping the amounts admit.
+
+| p_II | input pairs |
+|---:|---:|
+| 0.2 | 3 |
+| 0.3 | 14 |
+| 0.5 | 55 |
+| 0.7 | 27 |
+| 1.0 | 765 |
+
+**765 of 864 pairs (88.5%) are forced together** — no admissible mapping separates them — and 0 are forced apart. The amounts corroborate the co-spend far more often than they contest it here, and on this snapshot they never contest it outright.
+
+On the output side, 258 of 318 pairs are forced together.
+
+**This is not an ownership result, and the gap is the point.** A block of a value-conserving mapping is an accounting unit; a collaborative transaction can put two owners in one block and satisfy every constraint measured here. What the number bounds is what the amounts alone can settle — the part common-input ownership borrows without checking.
+
+Scope is capped at 12 coins, which excludes the wide consolidations and coinjoins where the question is hardest, and the fee-tolerant model admits mappings exact conservation would refuse — which can only widen the family and lower the forced share. The snapshot is 300 selected transactions and is not chain-wide.
diff --git a/results/generated/scorer-families-v1.md b/results/generated/scorer-families-v1.md
new file mode 100644
index 00000000..e9194713
--- /dev/null
+++ b/results/generated/scorer-families-v1.md
@@ -0,0 +1,17 @@
+# The same kernel under three axis sets
+
+Generated from the canonical experiment artifact. Do not edit manually.
+
+One population of 22112 transactions, one pair sample (4000 per class, seed 0), one consistency (0.95). Only the axis set changes.
+
+| family | axes | AUC | positive mean bits | negative mean bits |
+|---|---:|---:|---:|---:|
+| catalogued | 23 | 0.9244 | +15.47 | -16.58 |
+| construction only | 18 | 0.9003 | +8.42 | -13.94 |
+| decorrelated | 14 | 0.9432 | +8.72 | -10.24 |
+
+Dropping the redundant axes costs **6.74 bits** of positive evidence and raises the AUC (0.9244 to 0.9432). The wide model scores several correlated axes as if they were separate facts; the additive rarity kernel has no conditional-independence correction, so the magnitude it reports is inflated while its ranking is not.
+
+The construction-only family drops the axes the shared input address fixes outright, which is the part of the score that is the same-owner label restating itself rather than construction style.
+
+The axis clusters and the address-determined set were measured on this same cache, so the families are not an out-of-sample test. A higher AUC on fewer axes does not show the dropped axes carry no signal, and none of these numbers is a privacy score.
diff --git a/results/generated/slice-channels-v1.md b/results/generated/slice-channels-v1.md
new file mode 100644
index 00000000..f5ebcc80
--- /dev/null
+++ b/results/generated/slice-channels-v1.md
@@ -0,0 +1,14 @@
+# Attribute density and graph shape on a six-block slice
+
+Generated from the canonical experiment artifact. Do not edit manually.
+
+Transactions: 12000; clusters: 3179; contracted vertices: 26500.
+
+| minimum degree | sampled records | delta(0.5) | delta(0.9) | delta(0.99) |
+|---:|---:|---:|---:|---:|
+| 2 | 1200 | 1.000000 | 0.997500 | 0.983333 |
+| 20 | 89 | n/a | 0.876404 | n/a |
+
+Degree assortativity: -0.043697.
+
+This export carries none of the four axes — every one of the 12000 transactions abstains on version, input order, locktime and fee rate — so the vectors compared here are structural: degree, transaction count, self-transfers and the neighbour-degree histogram. On those alone the space is dense, and markedly less so among the higher-degree vertices (0.876 against 0.998 at the same threshold), which is where a matcher would start. The contracted graph is disassortative. These observations do not reproduce the historical larger slices, do not measure attribute density, and do not establish graph-matching performance.
diff --git a/results/generated/special-change-labels-v1.md b/results/generated/special-change-labels-v1.md
new file mode 100644
index 00000000..fde99080
--- /dev/null
+++ b/results/generated/special-change-labels-v1.md
@@ -0,0 +1,59 @@
+# Change labels that do not read co-spend
+
+Generated from the canonical experiment artifact. Do not edit manually.
+
+22112 committed transactions, 10850 of them two-output candidates. Each label picks the change output from values or script types alone, so none of them reads the address relation the fingerprints are meant to extend.
+
+| label | transactions it labels |
+|---|---:|
+| address_reuse | 4980 |
+| optimal_change | 1138 |
+| round_number | 1345 |
+| type_match | 4222 |
+
+## Within-transaction predictors against each label
+
+| label | predictor | TPR | FPR | coverage | precision |
+|---|---|---:|---:|---:|---:|
+| address_reuse | address_reuse | 1.000 | 0.000 | 1.000 | 1.000 |
+| address_reuse | round_number | 0.374 | 0.068 | 0.442 | 0.846 |
+| optimal_change | address_reuse | 0.323 | 0.037 | 0.360 | 0.898 |
+| optimal_change | round_number | 0.434 | 0.099 | 0.533 | 0.814 |
+| round_number | address_reuse | 0.395 | 0.005 | 0.400 | 0.987 |
+| round_number | round_number | 0.998 | 0.000 | 0.998 | 1.000 |
+| type_match | address_reuse | 0.547 | 0.000 | 0.547 | 1.000 |
+| type_match | round_number | 0.394 | 0.072 | 0.466 | 0.845 |
+
+Against the value label, the round-number heuristic is right 0.81 of the time it commits, on 53% of the labelled transactions, and address reuse 0.90 on 36%. Two universal heuristics agreeing with a label neither of them reads.
+
+A predictor scored against its own label is self-agreement and not corroboration: the round-number rows under the `round_number` label, and the address-reuse rows under `address_reuse`, read the same fact twice and are reported only so the table is complete.
+
+## Do the labels agree with each other
+
+| label | label | both label | agree | disagree |
+|---|---|---:|---:|---:|
+| address_reuse | optimal_change | 410 | 368 | 42 |
+| address_reuse | round_number | 538 | 531 | 7 |
+| address_reuse | type_match | 2310 | 2310 | 0 |
+| optimal_change | round_number | 246 | 239 | 7 |
+| optimal_change | type_match | 513 | 470 | 43 |
+| round_number | type_match | 642 | 623 | 19 |
+
+`address_reuse` and `type_match` agree on all 2310 transactions both label, and that is arithmetic rather than evidence: an output reusing an input address carries an input's script type, so the type rule can only select the same output or abstain. They are one label counted twice, which is the number a corroboration between them would double-count.
+
+## The onward-spend arm is not measured here
+
+Voting for the output whose *spender* repeats the transaction's fingerprint needs that spender to be in the data. The cache is block-sampled, so it is usually not:
+
+| label | labelled | one output spent inside | both spent inside |
+|---|---:|---:|---:|
+| optimal_change | 1138 | 193 | 12 |
+| round_number | 1345 | 438 | 21 |
+| type_match | 4222 | 1154 | 48 |
+| address_reuse | 4980 | 1496 | 97 |
+
+At 12 transactions with both outputs spent inside the cache, the per-axis rates that arm would report are not a measurement. Getting it needs a contiguous export deep enough to hold each change output's spender, which is a collection and not a method — state 4 in `results/REPRODUCIBILITY.md`.
+
+## Reproducibility / provenance
+
+State 1 in `results/REPRODUCIBILITY.md`: band-pinned on committed data — the artifact is recomputed from `data/fs-blkcache-2026-09-04.tar.gz` and compared byte for byte.
diff --git a/results/generated/statistical-disclosure-v1.md b/results/generated/statistical-disclosure-v1.md
new file mode 100644
index 00000000..ed04d2cd
--- /dev/null
+++ b/results/generated/statistical-disclosure-v1.md
@@ -0,0 +1,12 @@
+# Longitudinal statistical disclosure
+
+Generated from the canonical experiment artifact. Do not edit manually.
+
+| scenario | rounds | batch size | estimated target distribution | unique top |
+|---|---:|---:|---|---|
+| persistent correspondent | 4 | 4 | alice=1.00, bob=0.00, carol=0.00, dave=0.00 | alice |
+| background-matched control | 4 | 4 | alice=0.25, bob=0.25, carol=0.25, dave=0.25 | none |
+
+The persistent fixture recovers Alice. The control remains tied because the target follows the same distribution as the background.
+
+This is a deterministic reproduction of the threshold-mix estimator. It does not attribute Bitcoin ownership or measure real-world attack frequency.
diff --git a/results/generated/subtx-demix-v1.md b/results/generated/subtx-demix-v1.md
new file mode 100644
index 00000000..8c13a392
--- /dev/null
+++ b/results/generated/subtx-demix-v1.md
@@ -0,0 +1,9 @@
+# Subtransaction de-mix diagnostic
+
+Generated from the canonical experiment artifact. Do not edit manually.
+
+The preserved JoinMarket transaction has 12 inputs and 21 outputs; the amount identity recovers 8 inputs.
+
+The CoinJoin-adjacent cache contains 36 transactions. Among 25 width-eligible transactions, 22 trigger the heuristic.
+
+This cache has no ordinary-transaction labels and cannot establish specificity. The historical Wasabi result is not reproduced. This is attacker-side evidence, not a privacy score.
diff --git a/results/generated/unnecessary-input-v2.md b/results/generated/unnecessary-input-v2.md
new file mode 100644
index 00000000..8a20ee81
--- /dev/null
+++ b/results/generated/unnecessary-input-v2.md
@@ -0,0 +1,20 @@
+# Fee-aware unnecessary-input diagnostic
+
+Generated from the canonical experiment artifact. Do not edit manually.
+
+Definition: `blockstream_fee_aware` v2.
+Its paper domain is transactions with at least two inputs and exactly two outputs.
+
+| case | classification | fee | Gibson flags |
+|---|---|---:|---|
+| jointly funded uih2 | uih2 | 1 | `[False, False]` |
+| fee aware uih1 | uih1 | 1 | `[True, False]` |
+| equality boundary | uih2 | 1 | `[False, True]` |
+
+The observational-equivalence fixture is classified `uih2`, yet remains `inconclusive` across 3 declared latent forms. It infers no ownership, PayJoin identity, payment output, or composition.
+
+NS1R and NSNR controls are outside the two-output definition: `out_of_scope` and `out_of_scope`.
+
+The cycle control preserves the nonzero net balances while changing gross obligations from 500 to 1400. Therefore the gross payment graph is not identified by the on-chain net amounts.
+
+This is a deterministic diagnostic reproduction, not a PayJoin detector, ownership classifier, prevalence estimate, or privacy score.
diff --git a/results/generated/weight-sensitivity-v1.md b/results/generated/weight-sensitivity-v1.md
new file mode 100644
index 00000000..e513c681
--- /dev/null
+++ b/results/generated/weight-sensitivity-v1.md
@@ -0,0 +1,18 @@
+# Global fingerprint-weight sensitivity
+
+Generated from the canonical experiment artifact. Do not edit manually.
+
+| consistency | positive mean bits | negative mean bits | AUC | shuffled AUC |
+|---:|---:|---:|---:|---:|
+| 0.60 | +23.056019 | +8.852878 | 0.908400 | 0.498700 |
+| 0.70 | +22.073064 | +5.593042 | 0.915200 | 0.499300 |
+| 0.80 | +20.615851 | +0.741083 | 0.921300 | 0.499550 |
+| 0.90 | +18.064538 | -7.809367 | 0.924050 | 0.498300 |
+| 0.95 | +15.465038 | -16.582117 | 0.924450 | 0.498400 |
+| 0.99 | +9.429186 | -36.951812 | 0.923900 | 0.499950 |
+
+AUC range from 0.90 through 0.99: 0.000550. Monotone non-decreasing: false.
+
+That flag reads the measured sequence, and the sequence does dip. The largest dip is 0.000550, against a standard error of 0.003101 at 4000 pairs per class — 0.18 standard errors. So the sweep does not support monotone improvement, and it does not establish a reversal either: over this band the AUC is flat within what the sample resolves.
+
+Evidence magnitudes are the part that moves. Address reuse is a weak label; this is not a privacy score.
diff --git a/results/link-prediction.json b/results/link-prediction.json
new file mode 100644
index 00000000..9ca12d43
--- /dev/null
+++ b/results/link-prediction.json
@@ -0,0 +1,1009 @@
+{
+ "configuration": {
+ "cutoff": 2,
+ "fixture_seeds": [
+ 0,
+ 1,
+ 2,
+ 3,
+ 4
+ ],
+ "groups": 8,
+ "holdout": 0.25,
+ "keep": 0.9,
+ "negatives_per_positive": 4,
+ "p_in": 0.18,
+ "p_out": 0.012,
+ "scorer": "closed",
+ "seed_fraction": 0.25,
+ "theta": 1.5,
+ "vertices": 80
+ },
+ "fixtures": [
+ {
+ "fixture_seed": 0,
+ "held_out_links": 46,
+ "held_out_links_transferred_directly": 22,
+ "negative_pairs": 184,
+ "runs": [
+ {
+ "arm": "attack",
+ "cutoff": 2,
+ "deanonymization": {
+ "auc": 0.7537216446124764,
+ "correct": 24,
+ "declared": 30,
+ "name": "deanonymization",
+ "precision": 0.8,
+ "recall": 0.5217391304347826
+ },
+ "held_out": 46,
+ "mapping_against_withheld_correspondence": {
+ "correct": 44,
+ "coverage": 0.7333333333333333,
+ "declared": 44,
+ "eligible": 60,
+ "precision": 1.0
+ },
+ "mapping_size": 64,
+ "negatives": 184,
+ "rounds": 4,
+ "seed_provenance": "withheld-correspondence-sample",
+ "seed_size": 20,
+ "separability": {
+ "beats_structure_only": "a",
+ "deanonymization_only": 21,
+ "min_correct_gain": 1,
+ "p": 0.002493917942047119,
+ "structure_only": 5
+ },
+ "structure_only": {
+ "auc": 0.5929229678638941,
+ "correct": 3,
+ "declared": 4,
+ "name": "structure_only",
+ "precision": 0.75,
+ "recall": 0.06521739130434782
+ },
+ "theta": 1.5,
+ "transferred_pairs": 27
+ },
+ {
+ "arm": "independent-auxiliary-control",
+ "cutoff": 2,
+ "deanonymization": {
+ "auc": 0.5935137051039697,
+ "correct": 4,
+ "declared": 7,
+ "name": "deanonymization",
+ "precision": 0.5714285714285714,
+ "recall": 0.08695652173913043
+ },
+ "held_out": 46,
+ "mapping_size": 25,
+ "negatives": 184,
+ "rounds": 1,
+ "seed_provenance": "unrelated-auxiliary-control",
+ "seed_size": 20,
+ "separability": {
+ "beats_structure_only": null,
+ "deanonymization_only": 1,
+ "min_correct_gain": 1,
+ "p": 1.0,
+ "structure_only": 2
+ },
+ "structure_only": {
+ "auc": 0.5929229678638941,
+ "correct": 3,
+ "declared": 4,
+ "name": "structure_only",
+ "precision": 0.75,
+ "recall": 0.06521739130434782
+ },
+ "theta": 1.5,
+ "transferred_pairs": 14
+ },
+ {
+ "arm": "deranged-seed-control",
+ "cutoff": 2,
+ "deanonymization": {
+ "auc": 0.592804820415879,
+ "correct": 3,
+ "declared": 5,
+ "name": "deanonymization",
+ "precision": 0.6,
+ "recall": 0.06521739130434782
+ },
+ "held_out": 46,
+ "mapping_size": 22,
+ "negatives": 184,
+ "rounds": 1,
+ "seed_provenance": "deranged-correspondence-control",
+ "seed_size": 20,
+ "separability": {
+ "beats_structure_only": null,
+ "deanonymization_only": 0,
+ "min_correct_gain": 1,
+ "p": 1.0,
+ "structure_only": 1
+ },
+ "structure_only": {
+ "auc": 0.5929229678638941,
+ "correct": 3,
+ "declared": 4,
+ "name": "structure_only",
+ "precision": 0.75,
+ "recall": 0.06521739130434782
+ },
+ "theta": 1.5,
+ "transferred_pairs": 12
+ },
+ {
+ "arm": "stripped-auxiliary-control",
+ "cutoff": 2,
+ "deanonymization": {
+ "auc": 0.5831758034026465,
+ "correct": 5,
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+ [774840978, 7], [387420489, 2], [100000000, 8], [50853104, 1],
+ [50000000, 4], [43046721, 6], [40000000, 1], [33554432, 11],
+ [28697814, 9], [20000000, 13], [16777216, 5], [14348907, 1],
+ [14149484, 1], [10000000, 16], [5000000, 15], [3188646, 24],
+ [2097152, 17], [1594323, 27], [1062882, 23], [531441, 22],
+ [354294, 14], [262144, 29], [200000, 14], [131072, 15],
+ [118098, 15], [100000, 24], [99267, 1], [65536, 20],
+ [59049, 4], [50000, 21], [39366, 24], [32768, 16],
+ [20000, 18], [16384, 11], [15403, 1], [13122, 30], [10000, 32]
+ ]
+}
diff --git a/tests/fixtures/merged_anchor_931d6627.json b/tests/fixtures/merged_anchor_931d6627.json
new file mode 100644
index 00000000..ad1b7adc
--- /dev/null
+++ b/tests/fixtures/merged_anchor_931d6627.json
@@ -0,0 +1,134 @@
+{
+ "anchor": "931d6627",
+ "description": "the two funding transactions of the merged anchor's false Cake<->sender edge",
+ "transactions": {
+ "cake": {
+ "fee": 220,
+ "locktime": 0,
+ "sigops": 1,
+ "size": 191,
+ "status": {
+ "block_hash": "000000000000000000016a5b353443db3179d515d081621c402ae37055c294c5",
+ "block_height": 956926,
+ "block_time": 1783348132,
+ "confirmed": true
+ },
+ "txid": "0a568e3ae6fa6bf34ce8925266ac2cdb1668c723980398d9c613d67d72b39729",
+ "version": 2,
+ "vin": [
+ {
+ "is_coinbase": false,
+ "prevout": {
+ "scriptpubkey": "00141306bbe37c68f426779dff2854901861ad01f99e",
+ "scriptpubkey_address": "bc1qzvrthcmudr6zvaualu59fyqcvxksr7v7lsldyn",
+ "scriptpubkey_asm": "OP_0 OP_PUSHBYTES_20 1306bbe37c68f426779dff2854901861ad01f99e",
+ "scriptpubkey_type": "v0_p2wpkh",
+ "value": 5970
+ },
+ "scriptsig": "",
+ "scriptsig_asm": "",
+ "sequence": 1,
+ "txid": "be2e362095f78c0e2761e0a04a1116d5ea9f119d2a55a5f1d1ee8edc3db9640a",
+ "vout": 0,
+ "witness": [
+ "304402207dd1e423ddff3064c4af0d4d0966f4785fc322e9ed8e1a97578c019c85db593b0220689609d7468df2ef99ab64d01447db89d256949d8fde3842310e845eaed0b2fc01",
+ "02f2f5bcf89d6d469eb9fdefe388f5b3492511c8cbadf826d9f81f377292935b6d"
+ ]
+ }
+ ],
+ "vout": [
+ {
+ "scriptpubkey": "001433a4fc41c991e1b3b9a9b75584ba9456eb722b54",
+ "scriptpubkey_address": "bc1qxwj0cswfj8sm8wdfka2cfw552m4hy265e52mk7",
+ "scriptpubkey_asm": "OP_0 OP_PUSHBYTES_20 33a4fc41c991e1b3b9a9b75584ba9456eb722b54",
+ "scriptpubkey_type": "v0_p2wpkh",
+ "value": 5750
+ }
+ ],
+ "weight": 437
+ },
+ "sender": {
+ "fee": 1000,
+ "locktime": 0,
+ "sigops": 3,
+ "size": 487,
+ "status": {
+ "block_hash": "00000000000000000000de1da2589c16d06a4bbadd8014cec42172880b612696",
+ "block_height": 956298,
+ "block_time": 1782961565,
+ "confirmed": true
+ },
+ "txid": "91106666451dc43a0e3f78b325764251e205b39d7e9498948885678616ba719a",
+ "version": 2,
+ "vin": [
+ {
+ "is_coinbase": false,
+ "prevout": {
+ "scriptpubkey": "0014fb31eb03eed8e7a7f69ccb41fc31d0e8922fa65d",
+ "scriptpubkey_address": "bc1qlvc7kqlwmrn60a5uedqlcvwsazfzlfja0slrf5",
+ "scriptpubkey_asm": "OP_0 OP_PUSHBYTES_20 fb31eb03eed8e7a7f69ccb41fc31d0e8922fa65d",
+ "scriptpubkey_type": "v0_p2wpkh",
+ "value": 1000
+ },
+ "scriptsig": "",
+ "scriptsig_asm": "",
+ "sequence": 4294967295,
+ "txid": "5b97102cbed69f796756ba4bd9a268c95d8ca53f9151602d561ff58686d7be98",
+ "vout": 1,
+ "witness": [
+ "304402203b3b0fb15ecd23e1b44a0eb761301555f5ce0cb6267f9b11a40140b2b6b0daff02207cf32f84046f6e581db4dde7c344dc1ea0ff8a503fe23bb24329574741c60e6601",
+ "0281486ad6f4f820131f5a28af49ace57d0b3dfd922b9f991361f3b7a2ed654399"
+ ]
+ },
+ {
+ "is_coinbase": false,
+ "prevout": {
+ "scriptpubkey": "00147a06420787862c6b3cc620d1ce44ba06b00dd4a6",
+ "scriptpubkey_address": "bc1q0gryypu8sckxk0xxyrguu396q6cqm49xc2uxcp",
+ "scriptpubkey_asm": "OP_0 OP_PUSHBYTES_20 7a06420787862c6b3cc620d1ce44ba06b00dd4a6",
+ "scriptpubkey_type": "v0_p2wpkh",
+ "value": 1000
+ },
+ "scriptsig": "",
+ "scriptsig_asm": "",
+ "sequence": 4294967295,
+ "txid": "89300d3b2cc8e6ee8238a482c5b9aa696d9b09e1862c5c4153f8a0019fbae2c8",
+ "vout": 0,
+ "witness": [
+ "3044022017340a704b2f1c6cc0555a5cce4caedfc6c8b53b6940d4487db238624ff65c760220727b9e13aaa0a62c722f71709996e3bf0efabd56a9a0a8bca1772b4bf1005b6101",
+ "02d70eb70310ab37bf889fc48ea17b6704f68875dc8edf62af8ec7859f23c45d34"
+ ]
+ },
+ {
+ "is_coinbase": false,
+ "prevout": {
+ "scriptpubkey": "0014a23dae2ae9e270305a85bff8497ba8ef7abec890",
+ "scriptpubkey_address": "bc1q5g76u2hfufcrqk59hluyj7agaaatajysscqp2e",
+ "scriptpubkey_asm": "OP_0 OP_PUSHBYTES_20 a23dae2ae9e270305a85bff8497ba8ef7abec890",
+ "scriptpubkey_type": "v0_p2wpkh",
+ "value": 1000
+ },
+ "scriptsig": "",
+ "scriptsig_asm": "",
+ "sequence": 4294967295,
+ "txid": "b8c2bd603566490fd6d0418136b1302321b1e7f5b20a818be34e55ee823f0519",
+ "vout": 0,
+ "witness": [
+ "3044022025dba6ad73cd1a24ed52cdb2922b26808f9a72db0096f3421d972d815123a2560220733550d87fd64c36858704d31d53d1d9356d447c397a9b4f99fae07ea6c252c301",
+ "02dcadae98b40e036b8827239019f91d38652496badc08617d3cdae91fc3ffdbc5"
+ ]
+ }
+ ],
+ "vout": [
+ {
+ "scriptpubkey": "0014c92e76fa901418c59e3752584a306fbbdd8d39e6",
+ "scriptpubkey_address": "bc1qeyh8d75szsvvt83h2fvy5vr0h0wc6w0xw63qx8",
+ "scriptpubkey_asm": "OP_0 OP_PUSHBYTES_20 c92e76fa901418c59e3752584a306fbbdd8d39e6",
+ "scriptpubkey_type": "v0_p2wpkh",
+ "value": 2000
+ }
+ ],
+ "weight": 979
+ }
+ }
+}
diff --git a/tests/fixtures/persistence_curve.json b/tests/fixtures/persistence_curve.json
new file mode 100644
index 00000000..d273b60d
--- /dev/null
+++ b/tests/fixtures/persistence_curve.json
@@ -0,0 +1,42 @@
+[
+ {
+ "core_vertices": 30335,
+ "mean_degree_a": 15.71,
+ "mean_per_vertex_persistence": 0.3124,
+ "neighbour_persistence": 0.2004,
+ "width": 1,
+ "with_3plus_surviving": 5929
+ },
+ {
+ "core_vertices": 68896,
+ "mean_degree_a": 14.83,
+ "mean_per_vertex_persistence": 0.3468,
+ "neighbour_persistence": 0.2331,
+ "width": 2,
+ "with_3plus_surviving": 16312
+ },
+ {
+ "core_vertices": 116471,
+ "mean_degree_a": 15.21,
+ "mean_per_vertex_persistence": 0.3796,
+ "neighbour_persistence": 0.2584,
+ "width": 3,
+ "with_3plus_surviving": 34359
+ },
+ {
+ "core_vertices": 212460,
+ "mean_degree_a": 17.23,
+ "mean_per_vertex_persistence": 0.3834,
+ "neighbour_persistence": 0.2477,
+ "width": 5,
+ "with_3plus_surviving": 70372
+ },
+ {
+ "core_vertices": 305402,
+ "mean_degree_a": 22.33,
+ "mean_per_vertex_persistence": 0.3833,
+ "neighbour_persistence": 0.2049,
+ "width": 7,
+ "with_3plus_surviving": 110017
+ }
+]
diff --git a/tests/fixtures/slice_a_channels_2016.ndjson.gz b/tests/fixtures/slice_a_channels_2016.ndjson.gz
new file mode 100644
index 00000000..6a420b3f
Binary files /dev/null and b/tests/fixtures/slice_a_channels_2016.ndjson.gz differ
diff --git a/tests/test_amount_channel_snapshot.py b/tests/test_amount_channel_snapshot.py
new file mode 100644
index 00000000..c21bac5a
--- /dev/null
+++ b/tests/test_amount_channel_snapshot.py
@@ -0,0 +1,32 @@
+import json
+
+import pytest
+
+from decluster.amount_channel_snapshot import build_snapshot
+
+
+def test_snapshot_normalizes_numbers_and_is_deterministic(tmp_path):
+ source = tmp_path / "source.ndjson"
+ first = tmp_path / "first.json"
+ second = tmp_path / "second.json"
+ source.write_text(
+ "\n".join([
+ json.dumps({"txid": "a", "height": "2", "vin": [], "vout": []}),
+ json.dumps({"txid": "b", "height": "1", "vin": [], "vout": []}),
+ ])
+ )
+
+ assert build_snapshot(source, first, expected_records=2) == 2
+ assert build_snapshot(source, second, expected_records=2) == 2
+ assert first.read_bytes() == second.read_bytes()
+ rows = json.loads(first.read_text())
+ assert [row["txid"] for row in rows] == ["a", "b"]
+ assert [row["height"] for row in rows] == [2, 1]
+
+
+def test_snapshot_rejects_an_unexpected_population(tmp_path):
+ source = tmp_path / "source.json"
+ source.write_text("[]")
+
+ with pytest.raises(ValueError, match="expected 1 transactions, found 0"):
+ build_snapshot(source, tmp_path / "output.json", expected_records=1)
diff --git a/tests/test_amount_channel_summary_experiment.py b/tests/test_amount_channel_summary_experiment.py
new file mode 100644
index 00000000..6f82a433
--- /dev/null
+++ b/tests/test_amount_channel_summary_experiment.py
@@ -0,0 +1,30 @@
+import json
+
+import pytest
+
+from decluster.experiments import amount_channel_summary as experiment
+
+
+def test_summary_is_derived_from_all_pinned_components():
+ artifact = experiment.build_artifact()
+
+ assert set(artifact["report"]["components"]) == set(experiment.COMPONENTS)
+ assert artifact["report"]["per_coin_candidates"]["transaction_gated"]["candidates"]["total"] == 101
+ assert artifact["report"]["mapping_family"]["outcomes"]["answered"] == 223
+ assert artifact["report"]["pairwise_family"]["restricted_family_rows"]["singleton_support"] == 367
+
+
+def test_component_digest_drift_is_rejected(tmp_path):
+ path = tmp_path / "changed.json"
+ path.write_text(json.dumps({"experiment": "amount-local-channels-v1"}), encoding="utf-8")
+ with pytest.raises(experiment.VerificationError, match="SHA-256"):
+ experiment._load_component(
+ path, "amount-local-channels-v1", experiment.COMPONENTS["local"][2]
+ )
+
+
+def test_verifier_rejects_changed_composition(monkeypatch):
+ artifact = {"schema_version": 1, "experiment": experiment.EXPERIMENT_ID, "value": 2}
+ monkeypatch.setattr(experiment, "build_artifact", lambda _paths: {**artifact, "value": 1})
+ with pytest.raises(experiment.VerificationError, match="component composition"):
+ experiment.verify_artifact(artifact)
diff --git a/tests/test_amount_fingerprint_fusion.py b/tests/test_amount_fingerprint_fusion.py
index c5ae4313..7f8d0dd3 100644
--- a/tests/test_amount_fingerprint_fusion.py
+++ b/tests/test_amount_fingerprint_fusion.py
@@ -29,7 +29,7 @@ def _run(subsetsum):
import decluster.cluster as cl
orig = cl._cospent_pairs
cl.fetch_tx = _fetch
- cl._cospent_pairs = lambda nodes: [("A", "B", "T"), ("A", "C", "T"), ("B", "C", "T")]
+ cl._cospent_pairs = lambda nodes, fetch=None: [("A", "B", "T"), ("A", "C", "T"), ("B", "C", "T")]
try:
groups = cl.cluster_refined(["A", "B", "C"], _FingerprintLinkAB(),
amount=False, link_above=99, subsetsum=subsetsum)[0]
diff --git a/tests/test_amount_local_channels_experiment.py b/tests/test_amount_local_channels_experiment.py
new file mode 100644
index 00000000..4dd0fc07
--- /dev/null
+++ b/tests/test_amount_local_channels_experiment.py
@@ -0,0 +1,30 @@
+import json
+
+import pytest
+
+from decluster.experiments import amount_local_channels as experiment
+
+
+def test_report_preserves_published_local_channel_counts():
+ report = experiment.build_report()
+
+ assert report["population"]["transactions"] == 5491
+ assert report["population"]["multi_input"] == 1428
+ assert report["coinjoin"] == {"shape_detected": 28, "demixed": 0}
+ assert report["unnecessary_input"]["fires"] == 485
+ assert report["subtransaction_roundness"] == {"ranked": 483, "top_tied": 330}
+
+
+def test_artifact_excludes_dss_and_composition_claims():
+ limitations = experiment.build_artifact()["limitations"]
+
+ assert "largest input" in limitations[3]
+ assert "excludes subset-sum, DSS" in limitations[4]
+
+
+def test_verifier_rejects_changed_measurement():
+ changed = json.loads(json.dumps(experiment.build_artifact()))
+ changed["report"]["coinjoin"]["demixed"] = 1
+
+ with pytest.raises(experiment.VerificationError, match="fresh experiment"):
+ experiment.verify_artifact(changed)
diff --git a/tests/test_amount_mapping_family_experiment.py b/tests/test_amount_mapping_family_experiment.py
new file mode 100644
index 00000000..2585e6c7
--- /dev/null
+++ b/tests/test_amount_mapping_family_experiment.py
@@ -0,0 +1,85 @@
+import pytest
+
+from decluster.experiments import amount_mapping_family as experiment
+
+
+def test_report_preserves_published_mapping_family_outcomes():
+ report = experiment.build_report()
+
+ assert report["outcomes"] == {
+ "answered": 223,
+ "refused": 26,
+ "timed_out": 1,
+ "failed": 0,
+ }
+ assert report["restricted_family"] == {
+ "entropy_bins_bits": {
+ "zero": 221,
+ "above_zero_to_four": 2,
+ "above_four": 0,
+ },
+ "non_derived_mappings": 227,
+ "certain_input_output_links": 1471,
+ }
+
+
+def _completed_context(result):
+ class Process:
+ def start(self):
+ pass
+
+ def join(self, _timeout=None):
+ pass
+
+ def is_alive(self):
+ return False
+
+ class Queue:
+ def get(self, timeout):
+ return "answered", result
+
+ def close(self):
+ pass
+
+ class FakeContext:
+ def Queue(self, maxsize):
+ return Queue()
+
+ def Process(self, target, args):
+ return Process()
+
+ return FakeContext()
+
+
+def test_bounded_analysis_distinguishes_legacy_refusal(monkeypatch):
+ monkeypatch.setattr(
+ experiment.multiprocessing, "get_context", lambda _method: _completed_context(None)
+ )
+ assert experiment._bounded_analysis([1], [1], 1.0) == ("refused", None)
+
+
+def test_bounded_analysis_reads_structured_dss_refusal(monkeypatch):
+ refusal = {"status": "refused", "reason": "size_guard"}
+ monkeypatch.setattr(
+ experiment.multiprocessing, "get_context", lambda _method: _completed_context(refusal)
+ )
+ assert experiment._bounded_analysis([1], [1], 1.0) == ("refused", None)
+
+
+def test_artifact_limits_claims_to_restricted_family(monkeypatch):
+ monkeypatch.setattr(experiment, "build_report", lambda _dataset, _cap, _wall: {})
+ limitations = experiment.build_artifact("unused", 1, 1.0)["limitations"]
+
+ assert "strict sub-family" in limitations[0]
+ assert "not a posterior" in limitations[1]
+ assert "only within" in limitations[2]
+
+
+def test_verifier_rejects_changed_measurement(monkeypatch):
+ artifact = {"schema_version": 1, "experiment": experiment.EXPERIMENT_ID, "value": 2}
+ monkeypatch.setattr(
+ experiment, "build_artifact",
+ lambda _dataset, _cap, _wall: {**artifact, "value": 1},
+ )
+ with pytest.raises(experiment.VerificationError, match="fresh experiment"):
+ experiment.verify_artifact(artifact, dataset="unused", cap=1, wall_seconds=1.0)
diff --git a/tests/test_amount_pairwise_family_experiment.py b/tests/test_amount_pairwise_family_experiment.py
new file mode 100644
index 00000000..c385daa6
--- /dev/null
+++ b/tests/test_amount_pairwise_family_experiment.py
@@ -0,0 +1,80 @@
+import pytest
+
+from decluster.experiments import amount_pairwise_family as experiment
+
+
+def test_report_preserves_published_pairwise_family_outcomes():
+ report = experiment.build_report()
+
+ assert report["population"] == {
+ "selected_transactions": 300,
+ "selected_input_rows": 3384,
+ "selected_outputs": 1540,
+ "selection": "first complete multi-input transactions in source order",
+ }
+ assert report["outcomes"] == {
+ "answered": 271,
+ "refused": 27,
+ "timed_out": 2,
+ "failed": 0,
+ }
+ assert report["restricted_family_rows"] == {
+ "returned": 914,
+ "nonempty_support": 914,
+ "singleton_support": 367,
+ "full_output_support": 898,
+ }
+
+
+def _completed_context(result):
+ class Process:
+ def start(self):
+ pass
+
+ def join(self, _timeout=None):
+ pass
+
+ def is_alive(self):
+ return False
+
+ class Queue:
+ def get(self, timeout):
+ return "answered", result
+
+ def close(self):
+ pass
+
+ class FakeContext:
+ def Queue(self, maxsize):
+ return Queue()
+
+ def Process(self, target, args):
+ return Process()
+
+ return FakeContext()
+
+
+def test_bounded_matrix_distinguishes_refusal(monkeypatch):
+ monkeypatch.setattr(
+ experiment.multiprocessing, "get_context", lambda _method: _completed_context(None)
+ )
+ assert experiment._bounded_matrix([1], [1], 1.0) == ("refused", None)
+
+
+def test_artifact_limits_claims_to_restricted_family(monkeypatch):
+ monkeypatch.setattr(experiment, "build_report", lambda _dataset, _cap, _wall: {})
+ limitations = experiment.build_artifact("unused", 1, 1.0)["limitations"]
+
+ assert "restricted mapping family" in limitations[0]
+ assert "not a globally deterministic" in limitations[1]
+ assert "false certainties" in limitations[2]
+
+
+def test_verifier_rejects_changed_measurement(monkeypatch):
+ artifact = {"schema_version": 1, "experiment": experiment.EXPERIMENT_ID, "value": 2}
+ monkeypatch.setattr(
+ experiment, "build_artifact",
+ lambda _dataset, _cap, _wall: {**artifact, "value": 1},
+ )
+ with pytest.raises(experiment.VerificationError, match="fresh experiment"):
+ experiment.verify_artifact(artifact, dataset="unused", cap=1, wall_seconds=1.0)
diff --git a/tests/test_amount_per_coin_experiment.py b/tests/test_amount_per_coin_experiment.py
new file mode 100644
index 00000000..a830a935
--- /dev/null
+++ b/tests/test_amount_per_coin_experiment.py
@@ -0,0 +1,69 @@
+import pytest
+
+from decluster.experiments import amount_per_coin as experiment
+
+
+def test_report_preserves_published_per_coin_outcomes():
+ pytest.importorskip("dss")
+ report = experiment.build_report()
+
+ assert report["population"] == {
+ "transactions": 1428,
+ "inputs": 17520,
+ "outputs": 6106,
+ "coins": 23626,
+ }
+ assert report["per_coin_density"]["reachable"] == {
+ "input": 316,
+ "output": 972,
+ "total": 1288,
+ }
+ assert report["ungated_diagnostic"] == {
+ "candidate_transactions": 95,
+ "candidates": {"input": 60, "output": 258, "total": 318},
+ }
+ assert report["transaction_gated"] == {
+ "resolved_transactions": 82,
+ "candidate_transactions": 62,
+ "candidates": {"input": 18, "output": 83, "total": 101},
+ "candidates_with_exact_transaction_count": {
+ "input": 0,
+ "output": 61,
+ "total": 61,
+ },
+ }
+ # The gate resolves fewer transactions than the diagnostic enumerates, and no
+ # candidate carries an exact transaction count: the radix tier is a diagnostic.
+ assert report["transaction_gated"]["resolved_transactions"] < (
+ report["ungated_diagnostic"]["candidate_transactions"]
+ )
+
+
+def test_artifact_keeps_diagnostic_and_gate_semantics_distinct(monkeypatch):
+ monkeypatch.setattr(experiment, "build_report", lambda _dataset, _threshold: {})
+ artifact = experiment.build_artifact("unused", 1.0)
+
+ assert "different objects" in artifact["limitations"][2]
+ assert "does not make" in artifact["limitations"][3]
+ assert "separate identities" in artifact["limitations"][4]
+
+
+def test_verifier_rejects_changed_measurement(monkeypatch):
+ artifact = {
+ "schema_version": 1,
+ "experiment": experiment.EXPERIMENT_ID,
+ "value": 2,
+ }
+ monkeypatch.setattr(
+ experiment,
+ "build_artifact",
+ lambda _dataset, _threshold: {**artifact, "value": 1},
+ )
+
+ with pytest.raises(experiment.VerificationError, match="fresh experiment"):
+ experiment.verify_artifact(artifact, dataset="unused")
+
+
+def test_non_finite_threshold_is_rejected():
+ with pytest.raises(SystemExit, match="must be finite"):
+ experiment.main(["--cut-threshold", "nan", "verify", "--artifact", "unused"])
diff --git a/tests/test_amount_subset_sum_experiment.py b/tests/test_amount_subset_sum_experiment.py
new file mode 100644
index 00000000..6e80e276
--- /dev/null
+++ b/tests/test_amount_subset_sum_experiment.py
@@ -0,0 +1,37 @@
+import json
+
+import pytest
+
+from decluster.experiments import amount_subset_sum as experiment
+
+
+def test_report_preserves_published_counting_outcomes():
+ pytest.importorskip("dss")
+ report = experiment.build_report()
+
+ assert report["population"] == {"multi_input": 1428, "maximum_inputs": 16}
+ assert report["outcomes"] == {
+ "by_kind": {"exact": 1303, "unknown": 125},
+ "guaranteed_nonzero_log_w": 80,
+ "exact_zero": 1223,
+ }
+
+
+def test_artifact_keeps_counting_objects_distinct(monkeypatch):
+ monkeypatch.setattr(experiment, "build_report", lambda _dataset: {})
+ limitations = experiment.build_artifact("unused")["limitations"]
+
+ assert "not a Maurer mapping count" in limitations[0]
+ assert "does not compute pairwise links" in limitations[5]
+
+
+def test_verifier_rejects_changed_measurement(monkeypatch):
+ artifact = {
+ "schema_version": 1,
+ "experiment": experiment.EXPERIMENT_ID,
+ "value": 2,
+ }
+ monkeypatch.setattr(experiment, "build_artifact", lambda _dataset: {**artifact, "value": 1})
+
+ with pytest.raises(experiment.VerificationError, match="fresh experiment"):
+ experiment.verify_artifact(artifact, dataset="unused")
diff --git a/tests/test_analyze.py b/tests/test_analyze.py
index 54f66d56..f68024dd 100644
--- a/tests/test_analyze.py
+++ b/tests/test_analyze.py
@@ -75,7 +75,7 @@ def test_path_count_true_adds_path_count_block():
out = analyze(txs["t1"], targets=[0], depth=1, fetch=fetch, link_oracle=_uniform,
path_count=True)
pc = out[0]["path_count"]
- assert set(pc) == {"log_W_paths", "min_entropy", "shannon", "origins_weighted"}
+ assert set(pc) == {"min_entropy", "shannon", "origins_weighted"}
def test_path_count_false_by_default_omits_the_key():
diff --git a/tests/test_analyze_contract_experiment.py b/tests/test_analyze_contract_experiment.py
new file mode 100644
index 00000000..64f6d259
--- /dev/null
+++ b/tests/test_analyze_contract_experiment.py
@@ -0,0 +1,37 @@
+import json
+
+import pytest
+
+from decluster.experiments import analyze_contract as experiment
+
+
+def test_contract_covers_depth_fusion_truncation_and_route_naming():
+ artifact = experiment.build_artifact()
+ assert set(artifact["depth_sweep"]) == {"1", "2", "3", "4", "5"}
+ assert artifact["change_fusion"]["sharpened"] is True
+ assert artifact["node_cap"]["cap_is_observable"] is True
+ assert artifact["route_diagnostic"] == {
+ "present_when_requested": True,
+ "canonical_meaning": "provenance_route_accumulation",
+ }
+ for result in artifact["depth_sweep"].values():
+ assert result["fused"]["min_entropy_bits"] <= result["provenance"]["min_entropy_bits"]
+
+
+def test_reproduce_then_verify_round_trip(tmp_path):
+ artifact = tmp_path / "artifact.json"
+ markdown = tmp_path / "result.md"
+ assert experiment.main([
+ "reproduce", "--artifact", str(artifact), "--markdown", str(markdown),
+ ]) == 0
+ assert experiment.main([
+ "verify", "--artifact", str(artifact), "--markdown", str(markdown),
+ ]) == 0
+ assert json.loads(artifact.read_text())["experiment"] == experiment.EXPERIMENT_ID
+
+
+def test_verify_rejects_modified_artifact():
+ artifact = experiment.build_artifact()
+ artifact["change_fusion"]["sharpened"] = False
+ with pytest.raises(experiment.VerificationError, match="differs"):
+ experiment.verify_artifact(artifact)
diff --git a/tests/test_ancestry.py b/tests/test_ancestry.py
index f4e384d9..1ca2823e 100644
--- a/tests/test_ancestry.py
+++ b/tests/test_ancestry.py
@@ -3,6 +3,11 @@
from decluster import ancestry
+def test_truncation_support_rejects_negative_counts():
+ with pytest.raises(ValueError, match="non-negative"):
+ ancestry.TruncationSupport(oracle_refused=-1, node_capped=0)
+
+
def _g(transient, absorbers, edges, truncated=0):
g = ancestry.Graph()
g.transient, g.absorbers, g.edges, g.truncated = transient, absorbers, edges, truncated
@@ -154,6 +159,37 @@ def test_max_nodes_none_reproduces_current_behavior():
assert dist_default == dist_none
+def test_kelen_seres_figure_two_nominal_value_transition():
+ """Equation 1 / Figure 2: reverse transition mass follows 5:3:2 inputs."""
+ matrix = ancestry.value_flow_link_oracle([5, 3, 2], [6, 3, 1])
+ assert matrix == [[0.5, 0.5, 0.5], [0.3, 0.3, 0.3], [0.2, 0.2, 0.2]]
+
+
+def test_kelen_seres_expected_steps_expand_collapsed_utxo_transitions_exactly():
+ fetch = _two_parent_fetch()
+ graph = ancestry.build_value_flow_graph(("t1", 0), depth=2, fetch=fetch)
+ # One collapsed transition reaches either coinbase parent. In the paper's
+ # graph: output -> t1 -> parent coin -> coinbase tx -> auxiliary source.
+ assert ancestry.collapsed_expected_steps(graph, ("t1", 0)) == pytest.approx(1.0)
+ assert ancestry.kelen_seres_expected_steps(graph, ("t1", 0)) == pytest.approx(4.0)
+ result = ancestry.value_flow_untraceability(("t1", 0), depth=2, fetch=fetch)
+ assert result["expected_steps"] == pytest.approx(4.0)
+
+
+def test_kelen_seres_expected_steps_refuse_depth_and_node_cap_boundaries():
+ fetch = _binary_tree_fetch(max_len=4)
+ depth_cut = ancestry.build_value_flow_graph(("", 0), depth=1, fetch=fetch)
+ with pytest.raises(ValueError, match="real sources"):
+ ancestry.kelen_seres_expected_steps(depth_cut, ("", 0))
+ assert ancestry.value_flow_untraceability(("", 0), depth=1, fetch=fetch)[
+ "expected_steps"
+ ] is None
+
+ node_cut = ancestry.build_value_flow_graph(("", 0), depth=8, fetch=fetch, max_nodes=2)
+ with pytest.raises(ValueError, match="real sources"):
+ ancestry.kelen_seres_expected_steps(node_cut, ("", 0))
+
+
def test_max_nodes_only_truncates_never_invents():
# max_len=3, cap=6: cap lands mid-tree (levels 0-2 alone total 7 coins), so some non-coinbase
# interior coins get truncated in the capped run instead of being expanded to their origins.
@@ -164,3 +200,21 @@ def test_max_nodes_only_truncates_never_invents():
uncapped_coins = set(uncapped.transient) | set(uncapped.absorbers)
assert set(capped.absorbers) <= uncapped_coins
assert capped.truncated > 0
+
+
+def test_truncation_is_counted_over_the_mass_carrying_boundary():
+ """`blind` compares the truncation count against the signature size, so the two must count
+ the same objects. Counting every refused coin — including atoms the target never reaches —
+ reports a branch as blind while it is still resolving a full-mass origin."""
+ from decluster.ancestry import NODE_CAPPED, ORACLE_REFUSED, Graph, truncated_support
+ g = Graph()
+ g.truncated_coins = {("refused", 0): ORACLE_REFUSED, ("never_reached", 0): NODE_CAPPED}
+ g.truncated = 2
+ sig = {("origin", 0): 1.0} # one genuine origin holds all the mass
+ assert truncated_support(sig, g).total == 0 # not blind: the walk did see an origin
+ assert truncated_support(sig, g).total < len(sig)
+
+ blind_sig = {("refused", 0): 1.0} # the whole boundary is the oracle refusing
+ support = truncated_support(blind_sig, g)
+ assert support.total == len(blind_sig)
+ assert (support.oracle_refused, support.node_capped) == (1, 0)
diff --git a/tests/test_ancestry_adapter.py b/tests/test_ancestry_adapter.py
new file mode 100644
index 00000000..402e3c69
--- /dev/null
+++ b/tests/test_ancestry_adapter.py
@@ -0,0 +1,57 @@
+from decluster.adaptations.ancestry import (
+ CompleteAncestry,
+ TruncatedAncestry,
+ ancestry_signature_report,
+)
+
+
+def coinbase_fetch(txid):
+ return {"vin": [{"is_coinbase": True}], "vout": [{"value": 10}]}
+
+
+def binary_fetch(txid):
+ if txid in {"left", "right"}:
+ return coinbase_fetch(txid)
+ return {
+ "vin": [
+ {"txid": "left", "vout": 0, "prevout": {"value": 5}},
+ {"txid": "right", "vout": 0, "prevout": {"value": 5}},
+ ],
+ "vout": [{"value": 10}],
+ }
+
+
+def test_complete_signature_has_typed_distribution_and_round_trip():
+ report = ancestry_signature_report(("target", 0), depth=2, fetch=binary_fetch)
+
+ assert isinstance(report.state, CompleteAncestry)
+ distribution, support = report.as_legacy()
+ assert distribution == {("left", 0): 0.5, ("right", 0): 0.5}
+ assert support.total == 0
+ attack_report = report.as_attack_report("complete-ancestry")
+ assert attack_report.channels[0].identifier == "absorbing_ancestry_walk"
+ assert type(attack_report.outcomes[0]).__name__ == "CompleteProvenanceMeasured"
+
+
+def test_node_cap_is_not_conflated_with_oracle_refusal():
+ report = ancestry_signature_report(
+ ("target", 0), depth=2, fetch=binary_fetch, max_nodes=1
+ )
+
+ assert isinstance(report.state, TruncatedAncestry)
+ assert report.truncation.node_capped == 2
+ assert report.truncation.oracle_refused == 0
+ assert report.truncation.total == 2
+ attack_report = report.as_attack_report("truncated-ancestry")
+ assert type(attack_report.outcomes[0]).__name__ == "TruncatedProvenanceMeasured"
+
+
+def test_oracle_refusal_is_preserved_as_its_own_cause():
+ report = ancestry_signature_report(
+ ("target", 0), depth=2, fetch=binary_fetch, link_oracle=lambda ins, outs: None
+ )
+
+ assert isinstance(report.state, TruncatedAncestry)
+ assert report.truncation.oracle_refused == 1
+ assert report.truncation.node_capped == 0
+ assert "does not identify them individually" in report.state.evidence.context.limitations[1]
diff --git a/tests/test_ancestry_contract_experiment.py b/tests/test_ancestry_contract_experiment.py
new file mode 100644
index 00000000..82665be3
--- /dev/null
+++ b/tests/test_ancestry_contract_experiment.py
@@ -0,0 +1,33 @@
+import pytest
+
+from decluster.experiments import ancestry_contract as experiment
+
+
+def test_contract_separates_provenance_states_and_truncation_causes():
+ artifact = experiment.build_artifact()
+ assert artifact["complete"]["is_complete"] is True
+ assert artifact["complete"]["distribution"] == {"left:0": 0.5, "right:0": 0.5}
+ assert artifact["complete"]["expected_paper_steps"] == pytest.approx(4.0)
+ assert artifact["bounded"]["truncation"]["node_capped"] == 2
+ assert artifact["bounded"]["truncation"]["oracle_refused"] == 0
+ assert artifact["oracle_refusal"]["truncation"]["oracle_refused"] == 1
+ assert artifact["oracle_refusal"]["truncation"]["node_capped"] == 0
+ assert artifact["overlap"] == {"self": 1.0, "disjoint": 0.0}
+
+
+def test_reproduce_then_verify_round_trip(tmp_path):
+ artifact = tmp_path / "artifact.json"
+ markdown = tmp_path / "result.md"
+ assert experiment.main([
+ "reproduce", "--artifact", str(artifact), "--markdown", str(markdown),
+ ]) == 0
+ assert experiment.main([
+ "verify", "--artifact", str(artifact), "--markdown", str(markdown),
+ ]) == 0
+
+
+def test_verify_rejects_modified_artifact():
+ artifact = experiment.build_artifact()
+ artifact["overlap"]["disjoint"] = 0.5
+ with pytest.raises(experiment.VerificationError, match="differs"):
+ experiment.verify_artifact(artifact)
diff --git a/tests/test_ancestry_seam.py b/tests/test_ancestry_seam.py
index c4c5ff05..deb42d08 100644
--- a/tests/test_ancestry_seam.py
+++ b/tests/test_ancestry_seam.py
@@ -2,7 +2,9 @@
multi-output tx, to pin down that a coin's vout indexes the matrix COLUMN it occupies (not the
row, not some other coin's vout). Single-output fakes (vout=0 everywhere) can't catch a
matrix[vout][i]-vs-matrix[i][vout] transpose or an off-by-one fee-column drop; this can."""
-import dss
+import pytest
+
+dss = pytest.importorskip("dss", reason="requires the optional dense-subset-sum extension")
import pytest
from decluster import ancestry
diff --git a/tests/test_ancestry_sparsity_experiment.py b/tests/test_ancestry_sparsity_experiment.py
new file mode 100644
index 00000000..2f34b918
--- /dev/null
+++ b/tests/test_ancestry_sparsity_experiment.py
@@ -0,0 +1,46 @@
+import gzip
+import json
+
+import pytest
+
+from decluster.def1_sparsity import cosine
+from decluster.experiments import ancestry_sparsity as experiment
+
+
+def test_sparse_inverted_index_matches_pairwise_cosine():
+ signatures = {
+ "a": {"x": 1.0, "y": 2.0},
+ "b": {"x": 3.0},
+ "c": {"z": 4.0},
+ }
+ actual = experiment._nearest_cosines(signatures)
+ assert actual["a"] == pytest.approx(cosine(signatures["a"], signatures["b"]))
+ assert actual["b"] == pytest.approx(cosine(signatures["b"], signatures["a"]))
+ assert actual["c"] == 0.0
+
+
+def test_stored_comparison_values_are_not_reused(tmp_path):
+ dataset = tmp_path / "signatures.json.gz"
+ records = {
+ "a": {"sig": {"x": 1.0}, "top": 0.75},
+ "b": {"sig": {"y": 1.0}, "top": 0.75},
+ }
+ with gzip.open(dataset, "wt", encoding="utf-8") as target:
+ json.dump(records, target)
+ artifact = experiment.build_artifact(dataset)
+ assert artifact["nearest_neighbour_cosine"]["median"] == 0.0
+ assert artifact["stored_top_context"]["different_from_snapshot_recomputation"] == 2
+
+
+def test_reproduce_then_verify_real_snapshot(tmp_path):
+ dataset = "tests/fixtures/reid_sigs.json.gz"
+ artifact = tmp_path / "artifact.json"
+ markdown = tmp_path / "result.md"
+ assert experiment.main([
+ "reproduce", "--dataset", dataset, "--artifact", str(artifact),
+ "--markdown", str(markdown),
+ ]) == 0
+ assert experiment.main([
+ "verify", "--dataset", dataset, "--artifact", str(artifact),
+ "--markdown", str(markdown),
+ ]) == 0
diff --git a/tests/test_ancestry_walk.py b/tests/test_ancestry_walk.py
index 58c3d690..cc13152a 100644
--- a/tests/test_ancestry_walk.py
+++ b/tests/test_ancestry_walk.py
@@ -82,7 +82,7 @@ def test_signature_and_truncation_come_from_one_walk():
("C", 0), depth=6, fetch=make_fetch(txs), link_oracle=lambda i, o: [[0.5], [0.5]]
)
assert set(sig) == {("P", 0), ("R", 0)}
- assert truncated == 0, "a coinbase boundary is an origin, not a refusal"
+ assert truncated.total == 0, "a coinbase boundary is an origin, not a refusal"
def test_an_oracle_refusal_is_counted_as_truncation():
@@ -95,4 +95,92 @@ def test_an_oracle_refusal_is_counted_as_truncation():
("C", 0), depth=6, fetch=make_fetch(txs), link_oracle=lambda i, o: None
)
assert sig == {("C", 0): 1.0}, "the coin becomes its own boundary"
- assert truncated == 1
+ assert truncated == ancestry.TruncationSupport(oracle_refused=1, node_capped=0)
+ assert truncated.total == 1
+
+
+def test_a_zero_link_column_is_a_named_boundary_not_an_origin():
+ txs = {
+ "C": {"vin": [vin("P", 0, 7)], "vout": [{"value": 7}]},
+ "P": {"vin": cb_vin(), "vout": [{"value": 7}]},
+ }
+ sig, support = ancestry.ancestry_signature_and_truncation(
+ ("C", 0), depth=6, fetch=make_fetch(txs), link_oracle=lambda i, o: [[0.0]]
+ )
+ assert sig == {("C", 0): 1.0}
+ assert support.zero_link_mass == support.total == 1
+ assert support.oracle_refused == support.node_capped == 0
+
+
+def test_the_two_truncation_causes_are_reported_apart():
+ """A capped walk and a refusing oracle are different limits. Collapsed into one count, a
+ consumer reading "no view" cannot tell which one it hit — and under the walk's default oracle,
+ which refuses only on zero input value, only the cap can fire at all."""
+ txs = {
+ "C": {"vin": [vin("P", 0, 5)], "vout": [{"value": 5}]},
+ "P": {"vin": [vin("Q", 0, 5)], "vout": [{"value": 5}]},
+ "Q": {"vin": cb_vin(), "vout": [{"value": 5}]},
+ }
+ flat = lambda i, o: [[1.0] for _ in i]
+
+ capped = ancestry.build_extended_graph(
+ ("C", 0), depth=6, fetch=make_fetch(txs), link_oracle=flat, max_nodes=1
+ )
+ assert (capped.node_capped, capped.oracle_refused) == (capped.truncated, 0)
+ assert capped.truncated > 0
+
+ refused = ancestry.build_extended_graph(
+ ("C", 0), depth=6, fetch=make_fetch(txs), link_oracle=lambda i, o: None
+ )
+ assert (refused.oracle_refused, refused.node_capped) == (refused.truncated, 0)
+ assert refused.truncated > 0
+
+ sig, support = ancestry.ancestry_signature_and_truncation(
+ ("C", 0), depth=6, fetch=make_fetch(txs), link_oracle=lambda i, o: None
+ )
+ assert support == ancestry.TruncationSupport(oracle_refused=1, node_capped=0)
+
+
+def test_a_capped_walk_reports_its_blindness_as_the_cap_not_a_refusal():
+ """The default oracle never refuses on width, so a blind default walk is `max_nodes`. The
+ support must say so rather than leave the cause to be inferred from the oracle in use."""
+ txs = {
+ "C": {"vin": [vin("P", 0, 5)], "vout": [{"value": 5}]},
+ "P": {"vin": [vin("Q", 0, 5)], "vout": [{"value": 5}]},
+ "Q": {"vin": cb_vin(), "vout": [{"value": 5}]},
+ }
+ g = ancestry.build_extended_graph(
+ ("C", 0), depth=6, fetch=make_fetch(txs),
+ link_oracle=ancestry.value_flow_link_oracle, max_nodes=1,
+ )
+ sig = ancestry.absorber_distribution(g, ("C", 0))
+ support = ancestry.truncated_support(sig, g)
+ assert support.oracle_refused == 0
+ assert support.node_capped == support.total == len(sig)
+
+
+def test_an_unnamed_cause_stays_in_the_total_without_taking_a_name():
+ """Keeping an unrecognised cause in the total is right -- under-reporting truncation lets an
+ empty intersection read as a refusal. Naming it is not: a consumer branching on the cause would
+ act on a walk limit that never fired."""
+ from decluster.ancestry import Graph, TruncationSupport, truncated_support
+
+ g = Graph()
+ g.truncated_coins = {("odd", 0): "some_future_cause"}
+ g.truncated = 1
+ support = truncated_support({("odd", 0): 1.0}, g)
+ assert support == TruncationSupport(oracle_refused=0, node_capped=0, unattributed=1)
+ assert support.total == 1
+ assert support.node_capped == 0, "an unknown cause is not the node cap"
+
+
+def test_an_all_zero_support_is_falsey_like_the_bare_count_it_replaced():
+ """`>=` against an int raises, but `if truncated:` would not -- it would silently flip from
+ False to True for a caller written against the old bare count."""
+ from decluster.ancestry import TruncationSupport
+
+ assert not TruncationSupport(0, 0)
+ assert TruncationSupport(0, 1)
+ assert TruncationSupport(1, 0)
+ assert TruncationSupport(0, 0, unattributed=1)
+ assert TruncationSupport(0, 0, zero_link_mass=1)
diff --git a/tests/test_anchor_axis_families.py b/tests/test_anchor_axis_families.py
new file mode 100644
index 00000000..cf3b85b3
--- /dev/null
+++ b/tests/test_anchor_axis_families.py
@@ -0,0 +1,50 @@
+"""The one edge PAPER §4 argues from, and the properties that argument needs."""
+import json
+
+import pytest
+
+from decluster.experiments import anchor_axis_families as experiment
+
+
+@pytest.fixture(scope="module")
+def artifact():
+ return experiment.build_artifact()
+
+
+def test_reproduce_then_verify(tmp_path):
+ art, markdown = tmp_path / "a.json", tmp_path / "a.md"
+ assert experiment.main(["reproduce", "--artifact", str(art), "--markdown", str(markdown)]) == 0
+ assert experiment.main(["verify", "--artifact", str(art), "--markdown", str(markdown)]) == 0
+
+
+def test_the_engine_refuses_and_the_wide_library_links(artifact):
+ families = artifact["measurement"]["families"]
+ assert families["engine_three_axis"]["verdict"] == "refuse"
+ assert families["catalogued"]["verdict"] == "link"
+ assert families["catalogued"]["past_link_above"]
+
+
+def test_dropping_the_redundant_axes_brings_it_back_under_the_threshold(artifact):
+ families = artifact["measurement"]["families"]
+ assert not families["decorrelated"]["past_link_above"]
+ assert artifact["measurement"]["redundancy_bits"] > 0
+
+
+def test_the_discriminating_axes_still_refuse_and_are_outvoted(artifact):
+ measured = artifact["measurement"]
+ assert measured["negative_bits_total"] < 0
+ assert measured["positive_bits_total"] > -measured["negative_bits_total"]
+
+
+def test_an_abstaining_axis_carries_no_bits(artifact):
+ rows = artifact["measurement"]["per_axis"]
+ assert any(row["bits"] is None for row in rows)
+ scored = [row for row in rows if row["bits"] is not None]
+ assert len(scored) == artifact["measurement"]["catalogued_axes_scored"]
+
+
+def test_a_fixture_missing_a_txid_is_refused(tmp_path):
+ broken = tmp_path / "broken.json"
+ broken.write_text(json.dumps({"anchor": "x", "transactions": {"cake": {}, "sender": {}}}))
+ with pytest.raises(experiment.VerificationError, match="txid"):
+ experiment.build_artifact(str(broken))
diff --git a/tests/test_attack_report.py b/tests/test_attack_report.py
new file mode 100644
index 00000000..1b751592
--- /dev/null
+++ b/tests/test_attack_report.py
@@ -0,0 +1,81 @@
+import pytest
+
+from decluster.domain import (
+ AttackReport,
+ ClusterMerge,
+ Direction,
+ EvidenceChannel,
+ EvidenceContext,
+ ExperimentalComposition,
+ OwnershipLikelihoodEvidence,
+ Subject,
+ SubjectKind,
+)
+
+
+def fixture():
+ left = Subject(SubjectKind.COIN, "left")
+ right = Subject(SubjectKind.COIN, "right")
+ context = EvidenceContext(
+ adversary="external observer",
+ observables=("fingerprint",),
+ hypothesis="common ownership",
+ algorithm="fingerprint-v1",
+ )
+ evidence = OwnershipLikelihoodEvidence(
+ left, right, 3.0, context, Direction.SUPPORTS_COMMON_OWNERSHIP
+ )
+ outcome = ClusterMerge(left, right, (evidence,))
+ channel = EvidenceChannel("fingerprint", (evidence,))
+ return left, right, outcome, channel
+
+
+def test_report_preserves_channels_when_composition_is_present():
+ left, right, outcome, channel = fixture()
+ composition = ExperimentalComposition(
+ method="sum for comparison only",
+ channel_ids=("fingerprint",),
+ value=3.0,
+ unit="bits",
+ interpretation="positive values favor the tested ownership hypothesis",
+ )
+
+ report = AttackReport(
+ "fixture-report",
+ "fingerprint matching",
+ (left, right),
+ (channel,),
+ (outcome,),
+ ("synthetic fixture",),
+ composition,
+ )
+
+ assert report.channels == (channel,)
+ assert report.evidence() == channel.evidence
+
+
+def test_composition_cannot_reference_an_absent_channel():
+ left, right, outcome, channel = fixture()
+ composition = ExperimentalComposition(
+ "sum", ("amount",), 1.0, "bits", "diagnostic only"
+ )
+
+ with pytest.raises(ValueError, match="unknown channels"):
+ AttackReport(
+ "fixture-report",
+ "fingerprint matching",
+ (left, right),
+ (channel,),
+ (outcome,),
+ (),
+ composition,
+ )
+
+
+def test_report_rejects_duplicate_channels_and_subjects():
+ left, right, outcome, channel = fixture()
+
+ with pytest.raises(ValueError, match="unique subjects"):
+ AttackReport("id", "attack", (left, left), (channel,), (outcome,), ())
+ with pytest.raises(ValueError, match="channel identifiers"):
+ AttackReport("id", "attack", (left, right), (channel, channel), (outcome,), ())
diff --git a/tests/test_attribute_drift.py b/tests/test_attribute_drift.py
index 76ef0c48..44ae7a1f 100644
--- a/tests/test_attribute_drift.py
+++ b/tests/test_attribute_drift.py
@@ -1,7 +1,13 @@
-import sys, os
-sys.path.insert(0, os.path.dirname(os.path.dirname(__file__)))
-from examples.attribute_drift import (normalise, total_variation, pearson, drift,
- cycle_gain, CYCLE)
+import pytest
+
+from decluster.attribute_drift import (
+ CYCLE,
+ cycle_gain,
+ drift,
+ normalise,
+ pearson,
+ total_variation,
+)
def test_total_variation_bounds():
@@ -25,6 +31,13 @@ def test_normalise_and_pearson():
assert pearson([1, 1, 1], [1, 2, 3]) == 0.0
+def test_pearson_rejects_empty_or_misaligned_inputs():
+ with pytest.raises(ValueError):
+ pearson([], [])
+ with pytest.raises(ValueError):
+ pearson([1], [1, 2])
+
+
def test_drift_is_zero_on_a_constant_series():
series = [{"a": .5, "b": .5}] * 20
assert set(drift(series, (1, 3, 7)).values()) == {0.0}
diff --git a/tests/test_attribute_drift_experiment.py b/tests/test_attribute_drift_experiment.py
new file mode 100644
index 00000000..2f023018
--- /dev/null
+++ b/tests/test_attribute_drift_experiment.py
@@ -0,0 +1,58 @@
+import copy
+import json
+
+import pytest
+
+from decluster.experiments import attribute_drift as experiment
+
+
+def _row(artifact, axis):
+ return next(row for row in artifact["measurement"]["axes"] if row["axis"] == axis)
+
+
+@pytest.mark.parametrize("axis, expected", [
+ ("nlocktime", (0.009, 0.011, 0.013, 0.018, 0.86)),
+ ("low_r", (0.038, 0.052, 0.090, 0.155, 1.04)),
+ ("version", (0.029, 0.026, 0.042, 0.070, 0.75)),
+])
+def test_preserved_projection_reproduces_selected_published_rows(axis, expected):
+ row = _row(experiment.build_artifact(), axis)
+ observed = tuple(round(row["drift"][str(gap)], 3) for gap in (1, 7, 30, 120))
+ observed += (round(row["weekly_gain"], 2),)
+ assert observed == expected
+
+
+def test_volume_is_reconstructed_consistently_from_all_templates():
+ artifact = experiment.build_artifact()
+ volume = artifact["measurement"]["volume_range"]
+ assert volume["minimum"] > 0
+ assert volume["maximum"] >= volume["minimum"]
+ assert len(artifact["measurement"]["volume_coupling"]) > 1
+ assert artifact["window"]["transactions"] == 98_179_414
+
+
+def test_inconsistent_template_volume_is_refused(tmp_path):
+ with open(experiment.DEFAULT_DATASET, encoding="utf-8") as dataset:
+ source = json.load(dataset)
+ corrupted = copy.deepcopy(source)
+ template = sorted(corrupted["template_series"])[0]
+ corrupted["template_series"][template][0]["matches"] += 1
+ path = tmp_path / "corrupted.json"
+ path.write_text(json.dumps(corrupted), encoding="utf-8")
+ with pytest.raises(experiment.VerificationError, match="reconstruct volume|inconsistent volume"):
+ experiment.build_artifact(path)
+
+
+def test_the_stored_artifact_still_equals_a_fresh_execution():
+ """The named rows above pin what the numbers mean; this pins that nothing else moved.
+
+ Without it, a change to any field these tests do not name reaches the offline suite green and
+ is only caught by the reproduction gate twenty minutes later.
+ """
+ from pathlib import Path
+
+ root = Path(__file__).resolve().parents[1]
+ dataset = str(root / "tests" / "fixtures" / "lumen_explorer_data.json")
+ assert experiment.main(["--dataset", dataset, "verify",
+ "--artifact", str(root / "results/artifacts/attribute-drift-v1.json"),
+ "--markdown", str(root / "results/generated/attribute-drift-v1.md")]) == 0
diff --git a/tests/test_audit_fixes.py b/tests/test_audit_fixes.py
index 8e0cb8b9..f6e7e3f1 100644
--- a/tests/test_audit_fixes.py
+++ b/tests/test_audit_fixes.py
@@ -7,19 +7,19 @@ def test_uih_reads_prevout_value():
# prevout-only shape: input value ONLY under prevout.value (no top-level vin.value)
tx = {"vin": [{"prevout": {"value": 1000}}, {"prevout": {"value": 50}}],
"vout": [{"value": 900}, {"value": 100}]}
- assert x_uih(tx) == "uih1" # max input 1000 >= max output 900
+ assert x_uih(tx) == "uih2" # max input 1000 >= max output 900
def test_uih_bigquery_shape_still_works():
from decluster.extractors import x_uih
tx = {"vin": [{"value": 1000, "prevout": {"value": 1000}}, {"value": 50, "prevout": {"value": 50}}],
"vout": [{"value": 900}, {"value": 100}]}
- assert x_uih(tx) == "uih1"
+ assert x_uih(tx) == "uih2"
-def test_fs_score_mismatch_clamped():
- from decluster.combiner import fs_score
+def test_rarity_score_mismatch_clamped():
+ from decluster.combiner import rarity_score
# one degenerate axis: collision (0.999) >= consistency (0.95) -> a mismatch must clamp to <= 0
axes = [("probe", lambda tx: tx["probe"], {"a": 1.0, "b": 1.0}, 0.999, lambda va, vb: False)]
- total = fs_score(axes, {"probe": "a"}, {"probe": "b"}, c=0.95, floor_n=1000)
+ total = rarity_score(axes, {"probe": "a"}, {"probe": "b"}, c=0.95, floor_n=1000)
assert total <= 0 # without the clamp this would be +5.6 bits (a false same-owner vote)
def test_extractors_tolerate_prevout_none():
diff --git a/tests/test_baselines_cite_their_source.py b/tests/test_baselines_cite_their_source.py
new file mode 100644
index 00000000..2be89d9f
--- /dev/null
+++ b/tests/test_baselines_cite_their_source.py
@@ -0,0 +1,49 @@
+"""A reimplementation that does not name what it reimplements is not checkable.
+
+Half the modules under `decluster/baselines/` were reachable from no claim that named a source, and
+five of those six cited nothing in the file either — no identifier, no year, no note. A reader
+opening `kelen_seres_graphs.py` could not tell which paper, or which edition of it, the code was
+supposed to agree with, and neither could a reviewer.
+
+The rule is the weakest one that fixes that: the module says which catalogue entry it implements,
+and the entry exists. Fidelity to the source is a separate question this cannot answer.
+"""
+
+import json
+import re
+from pathlib import Path
+
+import pytest
+
+ROOT = Path(__file__).resolve().parent.parent
+BASELINES = sorted(p for p in (ROOT / "decluster" / "baselines").glob("*.py")
+ if p.stem != "__init__")
+REFERENCE = re.compile(r"`([a-z0-9-]+)` in `catalog/ctp-sources\.json`")
+
+
+def _docstring(path):
+ text = path.read_text()
+ if not text.startswith('"""'):
+ return ""
+ return text[3:text.index('"""', 3)]
+
+
+@pytest.fixture(scope="module")
+def registered():
+ catalogue = json.loads((ROOT / "catalog" / "ctp-sources.json").read_text())
+ return {source["id"] for source in catalogue["sources"]}
+
+
+def test_every_baseline_names_the_work_it_reimplements(registered):
+ assert BASELINES, "no baseline module was found; this gate is measuring nothing"
+ silent = [p.name for p in BASELINES if not REFERENCE.search(_docstring(p))]
+ assert not silent, (
+ "these reimplement a published algorithm without naming which:\n " + "\n ".join(silent)
+ + "\nAdd `Source: `` in `catalog/ctp-sources.json`` to the module docstring.")
+
+
+def test_the_source_a_baseline_names_is_registered(registered):
+ dangling = sorted({identifier for path in BASELINES
+ for identifier in REFERENCE.findall(_docstring(path))
+ if identifier not in registered})
+ assert not dangling, f"baselines cite catalogue entries that do not exist: {dangling}"
diff --git a/tests/test_bayes_vs_fs_experiment.py b/tests/test_bayes_vs_fs_experiment.py
new file mode 100644
index 00000000..7132d236
--- /dev/null
+++ b/tests/test_bayes_vs_fs_experiment.py
@@ -0,0 +1,42 @@
+import json
+from pathlib import Path
+
+import pytest
+
+from decluster.experiments import bayes_vs_fs as experiment
+
+ROOT = Path(__file__).resolve().parents[1]
+SNAPSHOT = ROOT / "data" / "fs-blkcache-2026-09-04.tar.gz"
+
+
+@pytest.fixture(scope="module")
+def artifact():
+ return experiment.build_artifact(SNAPSHOT)
+
+
+def test_snapshot_reproduces_the_canonical_artifact(artifact):
+ stored = json.loads(
+ (ROOT / "results" / "artifacts" / "bayes-vs-fs-v1.json").read_text()
+ )
+ assert artifact == stored
+ assert artifact["dataset"] == "fs-blkcache-2026-09-04-v1"
+ assert artifact["measurement"]["pairs"] == {"positive": 4000, "negative": 4000}
+
+
+def test_experiment_preserves_attack_and_defense_boundary(artifact):
+ assert any("not CoinScore" in item for item in artifact["limitations"])
+ assert artifact["historical_comparison"]["status"] == "not_reproduced"
+
+
+def test_axis_rows_name_weak_label_estimate_without_calling_it_an_oracle(artifact):
+ rows = artifact["measurement"]["per_axis"]
+ assert rows
+ assert all("address_reuse_agreement" in row for row in rows)
+ assert "oracle" not in experiment.render_markdown(artifact).lower()
+
+
+def test_verifier_recomputes_instead_of_trusting_stored_values(monkeypatch, artifact):
+ monkeypatch.setattr(experiment, "build_artifact", lambda snapshot: artifact)
+ changed = {**artifact, "dataset": "wrong"}
+ with pytest.raises(experiment.VerificationError, match="fresh experiment"):
+ experiment.verify_artifact(changed, SNAPSHOT)
diff --git a/tests/test_blob_locations.py b/tests/test_blob_locations.py
new file mode 100644
index 00000000..7358b23f
--- /dev/null
+++ b/tests/test_blob_locations.py
@@ -0,0 +1,60 @@
+"""A declared canonical URL has to serve the bytes the bundle pins, or it is worse than none.
+
+One did not. `slice_a_channels_2016.ndjson.gz` named revision `43d3faaa`, which is not on the
+published branch and, after the trailer rewrite, is not on this branch either — a link that 404s
+for every reader, sitting in the field that is supposed to make the bundle fetchable.
+
+The check is content-addressed: the file at the named revision must hash to the pinned digest, and
+the revision must be an ancestor of what is published. A URL that merely looks plausible fails.
+"""
+import json
+import subprocess
+from pathlib import Path
+
+import pytest
+
+from decluster import blob_locations
+
+ROOT = Path(__file__).resolve().parents[1]
+PUBLISHED = "origin/master"
+
+
+def _has_published_branch():
+ return subprocess.run(["git", "rev-parse", "--verify", PUBLISHED],
+ cwd=ROOT, capture_output=True).returncode == 0
+
+
+requires_published = pytest.mark.skipif(
+ not _has_published_branch(),
+ reason=f"no {PUBLISHED} to resolve against; declared locations went unchecked",
+)
+
+
+@requires_published
+def test_no_declared_location_is_dangling():
+ dangling = [row for row in blob_locations.audit(ROOT, PUBLISHED) if row["state"] == "dangling"]
+ assert not dangling, (
+ "these name a revision that does not serve the pinned bytes:\n "
+ + "\n ".join(f"{row['bundle']} {row['name']}" for row in dangling)
+ + "\nRun `python -m decluster.blob_locations` to clear or repoint them."
+ )
+
+
+@requires_published
+def test_a_blob_the_published_branch_serves_is_declared():
+ """Leaving one undeclared is a bundle that bootstraps from the checkout for no reason."""
+ available = [row for row in blob_locations.audit(ROOT, PUBLISHED) if row["state"] == "available"]
+ assert not available, f"could be declared and are not: {[r['name'] for r in available]}"
+
+
+@requires_published
+def test_the_declared_share_is_reported():
+ """Most blobs are absent because the branch is not pushed; that is a state, not a decision."""
+ rows = blob_locations.audit(ROOT, PUBLISHED)
+ resolves = sum(1 for row in rows if row["state"] == "resolves")
+ assert resolves >= 10, f"{resolves} of {len(rows)} resolve; this used to be 10"
+
+
+def test_a_url_whose_bytes_differ_is_not_resolvable(tmp_path):
+ blob = {"name": "README.md", "sha256": "0" * 64}
+ assert not blob_locations.resolvable(blob, "HEAD", "HEAD", root=str(ROOT))
diff --git a/tests/test_block_cache_split.py b/tests/test_block_cache_split.py
new file mode 100644
index 00000000..3cfe0720
--- /dev/null
+++ b/tests/test_block_cache_split.py
@@ -0,0 +1,43 @@
+"""The published block slice is read, never written.
+
+Two result manifests fingerprint `.blkcache` file-for-file, so a live fetch landing there moves a
+source that published numbers were measured on. It happened once with the suite green.
+"""
+import json
+
+import pytest
+
+from decluster import fetch
+
+
+@pytest.fixture
+def caches(tmp_path, monkeypatch):
+ published, live = tmp_path / "blk", tmp_path / "blk-live"
+ published.mkdir()
+ live.mkdir()
+ monkeypatch.setattr(fetch, "BLK", str(published))
+ monkeypatch.setattr(fetch, "BLK_LIVE", str(live))
+ return published, live
+
+
+def test_a_published_block_is_served_without_a_request(caches, monkeypatch):
+ published, live = caches
+ (published / "abc_0.json").write_text(json.dumps([{"txid": "t"}]))
+ monkeypatch.setattr(fetch, "_get", lambda url: pytest.fail(f"requested {url}"))
+ assert fetch.fetch_block_txs("abc", 0) == [{"txid": "t"}]
+ assert not list(live.iterdir())
+
+
+def test_a_fetched_block_lands_beside_the_published_slice_not_in_it(caches, monkeypatch):
+ published, live = caches
+ monkeypatch.setattr(fetch, "_get", lambda url: [{"txid": "fresh"}])
+ assert fetch.fetch_block_txs("def", 25) == [{"txid": "fresh"}]
+ assert not list(published.iterdir())
+ assert [path.name for path in live.iterdir()] == ["def_25.json"]
+
+
+def test_a_block_already_fetched_is_not_requested_twice(caches, monkeypatch):
+ _published, live = caches
+ (live / "ghi_0.json").write_text(json.dumps([{"txid": "cached"}]))
+ monkeypatch.setattr(fetch, "_get", lambda url: pytest.fail(f"requested {url}"))
+ assert fetch.fetch_block_txs("ghi", 0) == [{"txid": "cached"}]
diff --git a/tests/test_boltzmann_fee_audit.py b/tests/test_boltzmann_fee_audit.py
new file mode 100644
index 00000000..e300c445
--- /dev/null
+++ b/tests/test_boltzmann_fee_audit.py
@@ -0,0 +1,32 @@
+import json
+import os
+
+import pytest
+
+from decluster import reproducibility
+from examples.boltzmann_fee_audit import DOC, audit, invariants, values
+
+ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+SOURCE = "data/amount-channel-812695-812831-v1.json"
+
+
+def test_values_requires_complete_multi_input_amounts():
+ assert values({"vin": [], "vout": []}) is None
+ assert values({"vin": [{"prevout": {"value": 2}}, {"prevout": {"value": 3}}],
+ "vout": [{"value": 4}]}) == ((2, 3), (4,))
+
+
+def test_committed_report_and_manifest_recompute_from_sample():
+ """The snapshot is committed, so this is a check a reader runs rather than one that skips."""
+ report = audit(os.path.join(ROOT, SOURCE))
+ with open(os.path.join(ROOT, "results", "boltzmann-fee-audit.json")) as handle:
+ assert report == json.load(handle)
+ status, message = reproducibility.check_manifest(DOC, invariants(report), root=ROOT)
+ assert status == "ok", message
+
+
+def test_roundness_is_recorded_but_does_not_select_mappings():
+ report = audit(os.path.join(ROOT, SOURCE), cap=20)
+ assert all("fee_roundness" in row for row in report["rows"])
+ assert all(set(row) >= {"exact_mappings", "fee_tolerant_mappings"}
+ for row in report["rows"])
diff --git a/tests/test_boltzmann_fee_experiment.py b/tests/test_boltzmann_fee_experiment.py
new file mode 100644
index 00000000..395d534b
--- /dev/null
+++ b/tests/test_boltzmann_fee_experiment.py
@@ -0,0 +1,43 @@
+import json
+from pathlib import Path
+
+import pytest
+
+from decluster.experiments import boltzmann_fee_audit as experiment
+
+
+ROOT = Path(__file__).resolve().parents[1]
+
+
+def test_canonical_report_preserves_all_historical_measurements():
+ historical = json.loads(
+ (ROOT / "results" / "boltzmann-fee-audit.json").read_text()
+ )
+
+ assert experiment.build_artifact()["report"] == historical
+
+
+def test_artifact_keeps_reference_parity_claim_separate():
+ limitations = experiment.build_artifact()["limitations"]
+
+ assert "not general parity" in limitations[3]
+ assert "official Boltzmann vectors" in limitations[4]
+
+
+def test_verifier_recomputes_the_full_report():
+ changed = json.loads(json.dumps(experiment.build_artifact()))
+ changed["report"]["outcomes"]["fee_tolerant_with_nontrivial_split"] = 0
+
+ with pytest.raises(experiment.VerificationError, match="fresh experiment"):
+ experiment.verify_artifact(changed)
+
+
+def test_cli_reproduces_and_verifies_json_and_markdown(tmp_path):
+ artifact = tmp_path / "artifact.json"
+ markdown = tmp_path / "result.md"
+ assert experiment.main(
+ ["reproduce", "--artifact", str(artifact), "--markdown", str(markdown)]
+ ) == 0
+ assert experiment.main(
+ ["verify", "--artifact", str(artifact), "--markdown", str(markdown)]
+ ) == 0
diff --git a/tests/test_boltzmann_fee_snapshot.py b/tests/test_boltzmann_fee_snapshot.py
new file mode 100644
index 00000000..c9494a12
--- /dev/null
+++ b/tests/test_boltzmann_fee_snapshot.py
@@ -0,0 +1,42 @@
+import json
+
+import pytest
+
+from decluster.boltzmann_fee_snapshot import build_snapshot
+
+
+def _transaction(txid, inputs, outputs):
+ return {
+ "txid": txid,
+ "vin": [{"prevout": {"value": value}} for value in inputs],
+ "vout": [{"value": value} for value in outputs],
+ }
+
+
+def test_snapshot_selects_exact_population_and_is_deterministic(tmp_path):
+ source = tmp_path / "source.json"
+ first = tmp_path / "first.json"
+ second = tmp_path / "second.json"
+ rows = [
+ _transaction("single", [3], [2]),
+ _transaction("kept-1", [3, 2], [4]),
+ _transaction("large", [1, 2, 3], [1, 2, 3]),
+ _transaction("kept-2", [5, 4], [8]),
+ ]
+ source.write_text(json.dumps(rows), encoding="utf-8")
+
+ assert build_snapshot(source, first, cap=2, max_coins=4) == 2
+ assert build_snapshot(source, second, cap=2, max_coins=4) == 2
+ assert first.read_bytes() == second.read_bytes()
+ assert [row["txid"] for row in json.loads(first.read_text())] == [
+ "kept-1",
+ "kept-2",
+ ]
+
+
+def test_snapshot_refuses_incomplete_population(tmp_path):
+ source = tmp_path / "source.json"
+ source.write_text(json.dumps([_transaction("one", [2, 1], [2])]))
+
+ with pytest.raises(ValueError, match="only 1 eligible"):
+ build_snapshot(source, tmp_path / "output.json", cap=2)
diff --git a/tests/test_boltzmann_reference_parity.py b/tests/test_boltzmann_reference_parity.py
new file mode 100644
index 00000000..cb01ce62
--- /dev/null
+++ b/tests/test_boltzmann_reference_parity.py
@@ -0,0 +1,177 @@
+"""Parity probes transcribed from Samourai-Wallet/boltzmann's official tests.
+
+Expected values were recomputed with commit ed0b649c6ca4abf0cecb69467de6c4e97e847904 and
+``options=[LINKABILITY]``. The reference matrix is transposed here to local input-by-output order.
+"""
+
+import pytest
+
+from decluster.baselines.boltzmann import fee_tolerant_link_analysis
+from decluster.baselines.boltzmann_reference import (
+ boltzmann_reference_analysis,
+ boltzmann_reference_with_linked_inputs,
+)
+
+
+def _local_counts(inputs, outputs):
+ fee = sum(inputs) - sum(outputs)
+ analysis = fee_tolerant_link_analysis(inputs, outputs, fee_tolerance=fee)
+ total = len(analysis.mappings)
+ return total, tuple(tuple(round(probability * total) for probability in row)
+ for row in analysis.matrix)
+
+
+def test_no_fee_official_vectors_match_boltzmann_count_and_matrix():
+ vectors = [
+ ((10, 10), (8, 2, 3, 7), 3, ((2, 2, 2, 2), (2, 2, 2, 2))),
+ ((10, 10), (8, 2, 2, 8), 5, ((3, 3, 3, 3), (3, 3, 3, 3))),
+ ((10, 10), (5, 5, 5, 5), 7, ((4, 4, 4, 4), (4, 4, 4, 4))),
+ ((5, 5), (5, 5), 3, ((2, 2), (2, 2))),
+ ((5, 5, 5), (5, 5, 5), 16, ((8, 8, 8), (8, 8, 8), (8, 8, 8))),
+ ]
+ for inputs, outputs, expected_count, expected_matrix in vectors:
+ assert _local_counts(inputs, outputs) == (expected_count, expected_matrix)
+
+
+def test_fee_vector_reproduces_boltzmann_multiplicity_separately():
+ # Official TEST P3 with fees reports 28 combinations and each input linked
+ # 14/13/13 times to outputs 5/3/2. The local unique-mapping family has 19;
+ # Boltzmann's traversal assigns multiplicity to fee-compatible readings.
+ count, matrix = _local_counts((5, 5, 5), (5, 3, 2))
+ assert (count, matrix) == (19, ((10, 9, 9), (10, 9, 9), (10, 9, 9)))
+ assert count != 28
+ assert matrix != ((14, 13, 13),) * 3
+ reference = boltzmann_reference_analysis((5, 5, 5), (5, 3, 2))
+ assert reference.combination_count == 28
+ assert reference.link_counts == ((14, 13, 13),) * 3
+
+
+def test_reference_backend_also_preserves_no_fee_parity():
+ vectors = [
+ ((10, 10), (8, 2, 3, 7), 3, ((2, 2, 2, 2), (2, 2, 2, 2))),
+ ((10, 10), (8, 2, 2, 8), 5, ((3, 3, 3, 3), (3, 3, 3, 3))),
+ ((10, 10), (5, 5, 5, 5), 7, ((4, 4, 4, 4), (4, 4, 4, 4))),
+ ((5, 5), (5, 5), 3, ((2, 2), (2, 2))),
+ ((5, 5, 5), (5, 5, 5), 16, ((8, 8, 8),) * 3),
+ (
+ (10, 10, 2),
+ (8, 2, 2, 8, 2),
+ 28,
+ ((16, 16, 13, 13, 13), (16, 16, 13, 13, 13), (7, 7, 14, 14, 14)),
+ ),
+ ((5, 5, 10), (5, 5, 10), 9, ((8, 4, 4), (4, 5, 5), (4, 5, 5))),
+ ]
+ for inputs, outputs, count, matrix in vectors:
+ result = boltzmann_reference_analysis(inputs, outputs)
+ assert (result.combination_count, result.link_counts) == (count, matrix)
+
+
+def test_merge_fees_reproduces_the_official_synthetic_output_semantics():
+ result = boltzmann_reference_analysis((5, 5, 5), (5, 3, 2), merge_fees=True)
+ assert result.observed_fee == 5
+ assert result.outputs == (5, 5, 3, 2)
+ assert result.fee_output_index == 1
+ assert result.combination_count == 16
+ assert result.link_counts == ((8, 8, 8, 8),) * 3
+
+
+def test_merge_fees_can_make_every_link_deterministic_like_the_reference():
+ result = boltzmann_reference_analysis((10, 7), (8, 6), merge_fees=True)
+ assert result.outputs == (8, 6, 3)
+ assert result.fee_output_index == 2
+ assert result.combination_count == 1
+ assert result.link_counts == ((1, 1, 1), (1, 1, 1))
+
+
+def test_linked_inputs_reproduce_official_pack_and_expansion():
+ result = boltzmann_reference_with_linked_inputs(
+ (5, 5, 5), (5, 5, 5), [{0, 1}]
+ )
+ assert result.combination_count == 4
+ assert result.inputs == (5, 5, 5)
+ assert result.link_counts == ((3, 3, 3), (3, 3, 3), (2, 2, 2))
+ assert result.linked_input_groups == ((0, 1),)
+
+
+def test_linked_inputs_preserve_reference_unpack_order_for_asymmetric_values():
+ result = boltzmann_reference_with_linked_inputs(
+ (8, 5, 3), (7, 5, 3), [{0, 2}]
+ )
+ assert result.combination_count == 2
+ assert result.inputs == (8, 3, 5)
+ assert result.link_counts == ((2, 1, 2), (2, 1, 2), (1, 2, 1))
+
+
+def test_overlapping_owner_groups_are_transitively_merged():
+ result = boltzmann_reference_with_linked_inputs(
+ (4, 4, 4), (6, 5), [{0, 1}, {1, 2}]
+ )
+ assert result.linked_input_groups == ((0, 1, 2),)
+ assert result.combination_count == 1
+ assert result.link_counts == ((1, 1),) * 3
+
+
+def test_null_value_txos_are_filtered_like_the_reference():
+ # The reference drops null-value txos (OP_RETURN and the like) before any
+ # aggregate is formed, so an added zero output must change nothing.
+ plain = boltzmann_reference_analysis((5, 5), (5, 5))
+ padded = boltzmann_reference_analysis((5, 5), (5, 5, 0))
+
+ assert padded.combination_count == plain.combination_count == 3
+ assert padded.link_counts == plain.link_counts == ((2, 2), (2, 2))
+ assert padded.outputs == (5, 5)
+ assert boltzmann_reference_analysis((5, 5, 0), (5, 5)).inputs == (5, 5)
+
+
+def test_null_value_inputs_are_filtered_before_known_owners_are_packed():
+ result = boltzmann_reference_with_linked_inputs((5, 0, 5), (5, 5), [{0, 2}])
+
+ assert result.inputs == (5, 5)
+ assert result.combination_count == 1
+ assert result.link_counts == ((1, 1), (1, 1))
+ assert result.linked_input_groups == ((0, 2),)
+
+
+def test_precheck_exposes_deterministic_links_without_changing_linkability():
+ ordinary = boltzmann_reference_analysis((1, 2), (1, 2))
+ checked = boltzmann_reference_analysis((1, 2), (1, 2), precheck=True)
+
+ assert checked.combination_count == ordinary.combination_count == 2
+ assert checked.link_counts == ordinary.link_counts == ((2, 1), (1, 2))
+ assert checked.precheck_deterministic_links == ((0, 0), (1, 1))
+
+
+def test_precheck_does_not_reorder_inputs_the_way_the_reference_does():
+ # The reference packs its deterministic links before the traversal and
+ # reinserts them where the pack sat, which can leave the returned inputs off
+ # descending-value order. This port never repacks, so its order is stable.
+ result = boltzmann_reference_analysis((1, 2, 3), (2, 4), precheck=True)
+ assert result.inputs == (3, 2, 1)
+
+
+def test_precheck_links_expand_across_packed_inputs():
+ result = boltzmann_reference_with_linked_inputs(
+ (1, 1, 1), (1, 2), [{0, 1}], precheck=True
+ )
+
+ assert result.inputs == (1, 1, 1)
+ assert result.precheck_deterministic_links == ((0, 0), (1, 0), (2, 1))
+
+
+def test_joinmarket_intrafees_widen_the_reference_matching_interval():
+ ordinary = boltzmann_reference_analysis((10, 10), (8, 2, 3, 7))
+ bounded = boltzmann_reference_analysis(
+ (10, 10), (8, 2, 3, 7), intrafees=(1, 2), precheck=True
+ )
+
+ assert ordinary.combination_count == 3
+ assert bounded.combination_count == 5
+ assert bounded.link_counts == ((3, 3, 3, 3),) * 2
+ assert bounded.precheck_deterministic_links == ()
+ assert bounded.intrafees == (1.0, 2.0)
+
+
+@pytest.mark.parametrize("intrafees", [(-1, 0), (0, float("inf")), (True, 0), (1,)])
+def test_intrafees_reject_invalid_bounds(intrafees):
+ with pytest.raises(ValueError, match="intrafee"):
+ boltzmann_reference_analysis((2, 1), (2, 1), intrafees=intrafees)
diff --git a/tests/test_bundle_cli.py b/tests/test_bundle_cli.py
new file mode 100644
index 00000000..01407db1
--- /dev/null
+++ b/tests/test_bundle_cli.py
@@ -0,0 +1,77 @@
+import json
+from pathlib import Path
+
+from decluster import bundle_cli
+
+
+ROOT = Path(__file__).resolve().parents[1]
+INDEX = ROOT / "releases" / "exact-oracle-evidence-v1.bundle.json"
+
+
+def test_verify_committed_bundle(capsys):
+ assert bundle_cli.main([
+ "--index", str(INDEX), "--store", str(ROOT / "artifacts"),
+ "--json", "verify",
+ ]) == 0
+ records = json.loads(capsys.readouterr().out)
+ assert len(records) == 7
+ assert {record["status"] for record in records} == {"verified"}
+
+
+def test_verify_missing_store_returns_operational_failure(tmp_path, capsys):
+ assert bundle_cli.main([
+ "--index", str(INDEX), "--store", str(tmp_path / "missing"), "verify",
+ ]) == 1
+ assert "missing" in capsys.readouterr().out
+
+
+def test_bootstrap_cli_delegates_to_api(monkeypatch, tmp_path, capsys):
+ called = {}
+
+ def fake_bootstrap(bundle, store, root, *, replace):
+ called.update(store=store, root=root, replace=replace, bundle=bundle.id)
+ return ()
+
+ monkeypatch.setattr(bundle_cli, "bootstrap_bundle", fake_bootstrap)
+ assert bundle_cli.main([
+ "--index", str(INDEX), "--store", str(tmp_path / "store"),
+ "--root", str(tmp_path / "checkout"), "--replace", "bootstrap",
+ ]) == 0
+ assert called == {
+ "store": tmp_path / "store",
+ "root": tmp_path / "checkout",
+ "replace": True,
+ "bundle": "exact-oracle-evidence-v1",
+ }
+ assert not capsys.readouterr().out
+
+
+def test_readiness_cli_is_machine_readable_and_blocked(capsys):
+ assert bundle_cli.main([
+ "--index", str(INDEX), "--store", str(ROOT / "artifacts"),
+ "--root", str(ROOT), "--json", "readiness",
+ ]) == 1
+ record = json.loads(capsys.readouterr().out)[0]
+ assert record["status"] == "blocked"
+ assert {issue["kind"] for issue in record["issues"]} == {
+ "canonical_location_missing", "mirror_missing",
+ }
+
+
+def test_reproduce_cli_delegates_to_api(monkeypatch, tmp_path, capsys):
+ output = tmp_path / "artifact.json"
+
+ def fake_reproduce(bundle, root, work):
+ assert bundle.id == "exact-oracle-evidence-v1"
+ assert root == tmp_path / "root"
+ assert work == tmp_path / "work"
+ return (output,)
+
+ monkeypatch.setattr(bundle_cli, "reproduce_bundle", fake_reproduce)
+ assert bundle_cli.main([
+ "--index", str(INDEX), "--root", str(tmp_path / "root"),
+ "--work", str(tmp_path / "work"), "--json", "reproduce",
+ ]) == 0
+ assert json.loads(capsys.readouterr().out) == [{
+ "name": str(output), "status": "reproduced",
+ }]
diff --git a/tests/test_bundle_sync.py b/tests/test_bundle_sync.py
new file mode 100644
index 00000000..fd824bbd
--- /dev/null
+++ b/tests/test_bundle_sync.py
@@ -0,0 +1,180 @@
+"""The synchroniser has to move index, store and tree together, or it is worse than doing it by hand.
+
+Each case here is a way the manual procedure went wrong during the audit: a digest updated without
+its byte count, a store-only blob treated as missing, an orphan removed before the indexes were
+rewritten.
+"""
+import hashlib
+import json
+from pathlib import Path
+
+import pytest
+
+from decluster.bundle_sync import Change, synchronise, unresolved
+
+ROOT = Path(__file__).resolve().parents[1]
+
+
+def digest(data):
+ return hashlib.sha256(data).hexdigest()
+
+
+def store_blob(root, data):
+ d = digest(data)
+ target = root / "artifacts" / "sha256" / d[:2] / d[2:]
+ target.parent.mkdir(parents=True, exist_ok=True)
+ target.write_bytes(data)
+ return d
+
+
+@pytest.fixture
+def repo(tmp_path):
+ (tmp_path / "releases").mkdir()
+ (tmp_path / "results").mkdir()
+ (tmp_path / "artifacts" / "sha256").mkdir(parents=True)
+ return tmp_path
+
+
+def write_bundle(root, name, blobs):
+ index = root / "releases" / f"{name}.bundle.json"
+ index.write_text(json.dumps(
+ {"schema_version": 1, "id": name, "runs": [], "blobs": blobs}, indent=2) + "\n")
+ return index
+
+
+def blob_for(root, relative):
+ data = (root / relative).read_bytes()
+ return {"name": relative, "role": "result", "bytes": len(data), "sha256": digest(data)}
+
+
+def write_run(root, name, outputs):
+ (root / "catalog" / "runs").mkdir(parents=True, exist_ok=True)
+ path = root / "catalog" / "runs" / f"{name}.json"
+ path.write_text(json.dumps({"schema_version": 1, "id": name, "outputs": outputs}, indent=2) + "\n")
+ return path
+
+
+def output_for(root, relative):
+ data = (root / relative).read_bytes()
+ return {"path": relative, "bytes": len(data), "sha256": digest(data)}
+
+
+def test_a_run_manifest_that_pins_stale_outputs_is_brought_forward(repo):
+ target = repo / "results" / "a.json"
+ target.write_text("{}")
+ write_run(repo, "r1", [output_for(repo, "results/a.json")])
+ target.write_text('{"longer": true}')
+
+ changes = synchronise(repo)
+ assert [c.kind for c in changes] == ["output"]
+ pinned = json.loads((repo / "catalog" / "runs" / "r1.json").read_text())["outputs"][0]
+ assert (pinned["bytes"], pinned["sha256"]) == (16, digest(b'{"longer": true}'))
+
+
+def test_the_manifest_is_rewritten_before_the_bundle_that_pins_it(repo):
+ """Otherwise the bundle pins bytes that are stale again the moment it is written."""
+ target = repo / "results" / "a.json"
+ target.write_text("{}")
+ manifest = write_run(repo, "r1", [output_for(repo, "results/a.json")])
+ write_bundle(repo, "b1", [blob_for(repo, "catalog/runs/r1.json")])
+ store_blob(repo, manifest.read_bytes())
+ target.write_text('{"longer": true}')
+
+ synchronise(repo)
+ pinned = json.loads((repo / "releases" / "b1.bundle.json").read_text())["blobs"][0]
+ assert pinned["sha256"] == digest(manifest.read_bytes())
+
+
+def test_a_declared_output_that_is_absent_is_reported_not_invented(repo):
+ write_run(repo, "r1", [{"path": "results/gone.json", "bytes": 2, "sha256": digest(b"{}")}])
+ changes = synchronise(repo, write=False)
+ assert [c.kind for c in changes] == ["unavailable"]
+
+
+def test_a_tree_in_step_needs_no_change(repo):
+ (repo / "results" / "a.json").write_text("{}")
+ store_blob(repo, b"{}")
+ write_bundle(repo, "b1", [blob_for(repo, "results/a.json")])
+ assert synchronise(repo, write=False) == ()
+
+
+def test_an_edited_file_moves_its_byte_count_as_well_as_its_digest(repo):
+ target = repo / "results" / "a.json"
+ target.write_text("{}")
+ write_bundle(repo, "b1", [blob_for(repo, "results/a.json")])
+ store_blob(repo, b"{}")
+ target.write_text('{"longer": true}')
+
+ changes = synchronise(repo)
+ assert Change("identity", "results/a.json", changes[0].detail) in changes
+ pinned = json.loads((repo / "releases" / "b1.bundle.json").read_text())["blobs"][0]
+ assert (pinned["bytes"], pinned["sha256"]) == (16, digest(b'{"longer": true}'))
+
+
+def test_the_new_content_lands_in_the_store_and_the_old_leaves_it(repo):
+ target = repo / "results" / "a.json"
+ target.write_text("{}")
+ write_bundle(repo, "b1", [blob_for(repo, "results/a.json")])
+ old = store_blob(repo, b"{}")
+ target.write_text('{"longer": true}')
+
+ synchronise(repo)
+ store = {p.parent.name + p.name for p in (repo / "artifacts" / "sha256").glob("*/*")}
+ assert store == {digest(b'{"longer": true}')}
+ assert old not in store
+
+
+def test_a_blob_that_was_never_a_tracked_file_keeps_its_identity(repo):
+ wheel = b"not-in-the-tree"
+ stored = store_blob(repo, wheel)
+ write_bundle(repo, "b1", [{"name": "dependencies/x.whl", "role": "dependency",
+ "bytes": len(wheel), "sha256": stored}])
+ assert synchronise(repo, write=False) == ()
+
+
+def test_a_referenced_blob_with_neither_store_nor_tree_is_reported_not_invented(repo):
+ write_bundle(repo, "b1", [{"name": "dependencies/x.whl", "role": "dependency",
+ "bytes": 3, "sha256": digest(b"gone")}])
+ changes = synchronise(repo, write=False)
+ assert [c.kind for c in changes] == ["unavailable"]
+ assert unresolved(changes) == changes
+
+
+def test_orphans_are_judged_after_every_index_is_rewritten(repo):
+ shared = repo / "results" / "shared.json"
+ shared.write_text("{}")
+ write_bundle(repo, "b1", [blob_for(repo, "results/shared.json")])
+ write_bundle(repo, "b2", [blob_for(repo, "results/shared.json")])
+ store_blob(repo, b"{}")
+ shared.write_text('{"x": 1}')
+
+ synchronise(repo)
+ store = {p.parent.name + p.name for p in (repo / "artifacts" / "sha256").glob("*/*")}
+ assert store == {digest(b'{"x": 1}')}
+ for name in ("b1", "b2"):
+ pinned = json.loads((repo / "releases" / f"{name}.bundle.json").read_text())["blobs"][0]
+ assert pinned["sha256"] == digest(b'{"x": 1}')
+
+
+def test_check_mode_reports_without_touching_anything(repo):
+ target = repo / "results" / "a.json"
+ target.write_text("{}")
+ write_bundle(repo, "b1", [blob_for(repo, "results/a.json")])
+ store_blob(repo, b"{}")
+ target.write_text('{"longer": true}')
+ before = (repo / "releases" / "b1.bundle.json").read_text()
+
+ assert synchronise(repo, write=False)
+ assert (repo / "releases" / "b1.bundle.json").read_text() == before
+ assert {p.parent.name + p.name for p in (repo / "artifacts" / "sha256").glob("*/*")} == {
+ digest(b"{}")
+ }
+
+
+def test_the_committed_tree_is_already_in_step():
+ drift = synchronise(ROOT, write=False)
+ assert not drift, (
+ "releases/, artifacts/sha256/ and the tree disagree:\n "
+ + "\n ".join(f"{c.kind}\t{c.name}\t{c.detail}" for c in drift)
+ + "\nRun `decluster-bundle --root . sync` and commit the result."
+ )
diff --git a/tests/test_candidate_set_intersection.py b/tests/test_candidate_set_intersection.py
new file mode 100644
index 00000000..f19f1bb5
--- /dev/null
+++ b/tests/test_candidate_set_intersection.py
@@ -0,0 +1,192 @@
+"""The candidate-set intersection baseline, and the three answers it is allowed to give.
+
+Narrowing to one, narrowing and stopping short, and refusing. The third is the one worth testing
+hardest: an empty intersection means the observations contradict each other, and a baseline that
+reported it as a maximal narrowing would answer loudest exactly where its premise broke.
+"""
+import os
+
+import pytest
+
+from decluster import reproducibility as rp
+from decluster.baselines import candidate_set_intersection as csi
+from decluster.baselines import goldfeder_cluster_intersection, intersect_candidate_sets
+
+ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+DOC = "RESULTS-candidate-set-intersection.md"
+
+
+def test_successive_observations_narrow_a_longitudinal_set_to_one():
+ """The attack's whole shape: no single observation names the origin, and together they do."""
+ result = intersect_candidate_sets(
+ [{"a", "b", "c", "d"}, {"b", "c", "d"}, {"c", "d", "e"}, {"a", "c"}]
+ )
+ assert result.surviving == frozenset({"c"})
+ assert result.identified == "c"
+ assert result.inconsistent_at is None
+ assert result.narrowing_bits == pytest.approx(2.0) # 4 -> 1, relative to the first set
+ assert [step.after for step in result.steps] == [4, 3, 2, 1]
+
+
+def test_a_known_universe_credits_the_first_observation_with_its_own_narrowing():
+ """A first set of 8 out of 64 has already said 3 bits. Without the universe there is nothing
+ to measure it against, and the run says so by carrying no bits for that step."""
+ observations = [set(range(8)), {0, 1, 2, 3}]
+ known = intersect_candidate_sets(observations, universe_size=64)
+ unknown = intersect_candidate_sets(observations)
+ assert known.narrowing_bits == pytest.approx(4.0)
+ assert unknown.narrowing_bits == pytest.approx(1.0)
+ assert known.steps[0].narrowing_bits == pytest.approx(3.0)
+ assert unknown.steps[0].narrowing_bits == pytest.approx(0.0)
+ assert known.surviving == unknown.surviving
+
+
+def test_observations_that_do_not_converge_leave_the_set_wide():
+ """Repeating what is already known narrows nothing. The result is a set, not a name."""
+ result = intersect_candidate_sets([set(range(8))] * 4, universe_size=64)
+ assert len(result.surviving) == 8
+ assert result.identified is None, "eight survivors identify nobody"
+ assert result.narrowing_bits == pytest.approx(3.0), \
+ "all of it from the universe, none from repetition"
+ assert [step.narrowing_bits for step in result.steps[1:]] == [0.0, 0.0, 0.0]
+
+
+def test_a_partial_narrowing_is_reported_as_a_set_and_not_rounded_to_a_name():
+ result = intersect_candidate_sets([{"a", "b", "c", "d"}, {"a", "b", "c"}, {"a", "b"}])
+ assert result.surviving == frozenset({"a", "b"})
+ assert result.identified is None
+ assert result.narrowing_bits == pytest.approx(1.0)
+
+
+def test_inconsistent_observations_are_refused_rather_than_guessed():
+ """Disjoint observations contradict each other: either a candidate set is wrong or the coins
+ were not co-held. The baseline has no answer and must not manufacture one."""
+ result = intersect_candidate_sets([{"a", "b"}, {"c", "d"}], universe_size=64)
+ assert result.surviving == frozenset()
+ assert result.identified is None
+ assert result.inconsistent_at == 1
+ assert result.narrowing_bits is None, "an empty intersection is a refusal, not infinite bits"
+ assert result.steps[-1].narrowing_bits is None
+
+
+def test_an_inconsistent_run_stops_rather_than_absorbing_later_observations():
+ """Nothing after the contradiction can narrow an empty set, and continuing would let the run
+ look like it kept working."""
+ result = intersect_candidate_sets([{"a"}, {"b"}, {"a"}, {"a"}])
+ assert result.inconsistent_at == 1
+ assert len(result.steps) == 2
+ assert result.identified is None
+
+
+def test_an_observation_with_no_candidates_is_a_refusal_not_an_identification():
+ result = intersect_candidate_sets([set(), {"a"}])
+ assert result.surviving == frozenset() and result.inconsistent_at == 0
+ assert result.identified is None and result.narrowing_bits is None
+
+
+def test_no_observations_is_not_a_refusal():
+ """Never asked and asked-and-contradicted are different states."""
+ result = intersect_candidate_sets([])
+ assert result.surviving == frozenset()
+ assert result.steps == ()
+ assert result.inconsistent_at is None
+ assert result.identified is None
+
+
+def test_order_does_not_change_what_survives():
+ observations = [{"a", "b", "c"}, {"b", "c", "d"}, {"c", "d", "e"}]
+ forward = intersect_candidate_sets(observations)
+ backward = intersect_candidate_sets(list(reversed(observations)))
+ assert forward.surviving == backward.surviving == frozenset({"c"})
+
+
+def test_the_baseline_takes_candidate_sets_and_computes_none_of_its_own():
+ """Independence from this repository's ancestry walk is the separation the module claims;
+ a hidden import of it would make the baseline a second opinion on the same walk."""
+ source = open(os.path.join(ROOT, "decluster", "baselines",
+ "candidate_set_intersection.py")).read()
+ imports = [line.strip() for line in source.splitlines()
+ if line.strip().startswith(("import ", "from "))]
+ assert imports
+ for line in imports:
+ assert not line.startswith("from ."), line
+ assert "decluster" not in line, line
+
+
+def test_the_module_scopes_what_is_and_is_not_reproduced():
+ """The honesty constraint is part of the deliverable, so it is checked like any other."""
+ doc = " ".join(csi.__doc__.split())
+ assert "Algorithm 2 is implemented" in doc
+ assert "empirical rates are NOT reproduced" in doc
+ assert "no shrink law" in doc and "not Goldfeder's" in doc
+
+
+def test_goldfeder_algorithm_two_uses_only_join_paths_and_identifies_unique_cluster():
+ parents = {
+ "late-a": ("mid-a", "decoy-a"),
+ "mid-a": ("alice-a", "bob-a"),
+ "late-b": ("mid-b", "decoy-b"),
+ "mid-b": ("alice-b", "carol-b"),
+ # These are deliberately absent: non-join ancestors stop traversal.
+ }
+ clusters = {
+ "late-a": "spent-a", "mid-a": "mixed-a", "decoy-a": "dave",
+ "alice-a": "alice", "bob-a": "bob",
+ "late-b": "spent-b", "mid-b": "mixed-b", "decoy-b": "erin",
+ "alice-b": "alice", "carol-b": "carol",
+ }
+
+ result = goldfeder_cluster_intersection(
+ ["late-a", "late-b"], 2, lambda coin: parents.get(coin, ()), clusters.get
+ )
+ assert result.identified == "alice"
+ assert result.surviving == frozenset({"alice"})
+ assert not result.incorrect_assumptions
+
+
+def test_goldfeder_algorithm_two_refuses_zero_or_multiple_clusters():
+ clusters = {"a": "alice", "b": "bob"}
+ none = goldfeder_cluster_intersection(
+ ["a", "b"], 0, lambda _coin: (), clusters.get
+ )
+ many = goldfeder_cluster_intersection(
+ ["a"], 0, lambda _coin: (), lambda _coin: "alice"
+ )
+ assert none.surviving == frozenset() and none.incorrect_assumptions
+ # A one-coin/one-cluster observation is mechanically unique; test the true
+ # multiple-survivor path with one join layer.
+ many = goldfeder_cluster_intersection(
+ ["a"], 1, lambda _coin: ("b",), clusters.get
+ )
+ assert many.surviving == frozenset({"alice", "bob"})
+ assert many.identified is None and many.incorrect_assumptions
+
+
+def test_scenarios_are_deterministic():
+ assert csi.scenarios() == csi.scenarios()
+
+
+def test_manifest_invariants_are_recomputed_and_match_results_candidate_set_intersection():
+ """`check_manifest` only compares invariants a caller recomputes and passes in, so this is
+ that caller. The source is this repository's own module text, so an `absent` status means the
+ file the document was measured from is gone -- reported, not passed over."""
+ measured = csi.manifest_invariants()
+ status, message = rp.check_manifest(DOC, measured, root=ROOT)
+ if status == "absent":
+ pytest.skip(message)
+ assert status == "ok", message
+
+
+def test_every_number_in_the_manifest_is_stated_in_the_document():
+ recorded = rp.read_manifest(DOC, root=ROOT)
+ assert recorded is not None, f"{DOC} has no manifest"
+ text = open(os.path.join(ROOT, "results", DOC)).read()
+ for key, value in recorded["invariants"].items():
+ if value is None:
+ continue
+ assert str(value) in text, f"{key}={value} is not stated in {DOC}"
+
+
+def test_the_document_is_indexed_in_the_reproducibility_policy():
+ policy = open(os.path.join(ROOT, "results", "REPRODUCIBILITY.md")).read()
+ assert DOC[:-3] in policy
diff --git a/tests/test_candidate_set_intersection_experiment.py b/tests/test_candidate_set_intersection_experiment.py
new file mode 100644
index 00000000..6525b844
--- /dev/null
+++ b/tests/test_candidate_set_intersection_experiment.py
@@ -0,0 +1,43 @@
+import json
+
+import pytest
+
+from decluster.experiments import candidate_set_intersection as experiment
+from decluster.result_artifacts import canonical_json_bytes
+
+
+def test_artifact_is_deterministic_and_preserves_refusal():
+ artifact = experiment.build_artifact()
+
+ assert canonical_json_bytes(artifact) == canonical_json_bytes(experiment.build_artifact())
+ scenarios = {scenario["name"]: scenario for scenario in artifact["scenarios"]}
+ assert scenarios["converging"]["identified"] == 0
+ assert scenarios["stalled"]["identified"] is None
+ assert scenarios["contradictory"]["surviving"] == []
+ assert scenarios["contradictory"]["narrowing_bits"] is None
+ assert scenarios["contradictory"]["inconsistent_at"] == 1
+
+
+def test_verifier_recomputes_instead_of_trusting_summary():
+ changed = json.loads(json.dumps(experiment.build_artifact()))
+ changed["summary"]["identifications"] = 2
+
+ with pytest.raises(experiment.VerificationError, match="fresh fixture"):
+ experiment.verify_artifact(changed)
+
+
+def test_cli_reproduces_and_verifies_both_outputs(tmp_path):
+ artifact = tmp_path / "artifact.json"
+ markdown = tmp_path / "result.md"
+
+ assert experiment.main([
+ "reproduce", "--artifact", str(artifact), "--markdown", str(markdown)
+ ]) == 0
+ assert experiment.main([
+ "verify", "--artifact", str(artifact), "--markdown", str(markdown)
+ ]) == 0
+ markdown.write_text(markdown.read_text() + "changed\n")
+ with pytest.raises(experiment.VerificationError, match="Markdown differs"):
+ experiment.main([
+ "verify", "--artifact", str(artifact), "--markdown", str(markdown)
+ ])
diff --git a/tests/test_canonical_namespaces.py b/tests/test_canonical_namespaces.py
new file mode 100644
index 00000000..8aee27b1
--- /dev/null
+++ b/tests/test_canonical_namespaces.py
@@ -0,0 +1,32 @@
+"""Canonical names expose the existing objects without changing numerical behavior."""
+
+
+def test_rarity_weight_baseline_is_the_existing_implementation():
+ from decluster import combiner, rarity_weight_baseline
+ assert rarity_weight_baseline.Combiner is combiner.Combiner
+ assert rarity_weight_baseline.rarity_score is combiner.rarity_score
+
+
+def test_single_view_propagation_is_the_existing_implementation():
+ from decluster import propagate, single_view_propagation
+ assert single_view_propagation.NSPropagator is propagate.NSPropagator
+ assert single_view_propagation.propagate_merge is propagate.propagate_merge
+
+
+def test_ns_social_attack_is_the_reference_baseline():
+ from decluster.baselines import narayanan_shmatikov, ns_social_attack
+ assert ns_social_attack.propagate is narayanan_shmatikov.propagate
+
+
+def test_the_two_ancestry_namespaces_select_different_models():
+ from decluster import kelen_seres_value_flow, subset_sum_weighted_ancestry
+ assert kelen_seres_value_flow.value_flow_link_oracle is not \
+ subset_sum_weighted_ancestry.dss_link_oracle
+
+
+def test_provenance_route_accumulation_is_the_canonical_name():
+ from decluster import path_count, weighted_path_count
+ from decluster.provenance_route_accumulation import provenance_route_accumulation
+ assert provenance_route_accumulation is path_count.provenance_route_accumulation
+ assert weighted_path_count.path_count_anonymity is path_count.path_count_anonymity
+ assert path_count.path_count_anonymity is path_count.provenance_route_accumulation
diff --git a/tests/test_change_consolidation.py b/tests/test_change_consolidation.py
new file mode 100644
index 00000000..33f02e9d
--- /dev/null
+++ b/tests/test_change_consolidation.py
@@ -0,0 +1,65 @@
+import pytest
+
+from decluster.domain import CandidateNarrowing, NoSharedCandidates, Subject, SubjectKind
+from decluster.failure_modes.change_consolidation import (
+ ChangeConsolidationScenario,
+ ctp_example,
+ evaluate,
+)
+
+
+def test_change_consolidation_links_the_prior_payments_conditionally():
+ scenario = ctp_example()
+ report = evaluate(scenario)
+ intersection = report.channels[1].evidence[0]
+ assert intersection.observations == scenario.payments
+ assert {candidate.identifier for candidate in intersection.candidates_after} == {
+ "alice"
+ }
+ assert report.outcomes == (CandidateNarrowing(intersection),)
+ assert report.composition is None
+
+
+def test_change_consolidation_preserves_both_required_assumptions():
+ report = evaluate(ctp_example())
+ assert (
+ "change identification"
+ in report.channels[0].evidence[0].context.limitations[0]
+ )
+ assert (
+ "both change identifications"
+ in report.channels[1].evidence[0].context.limitations[0]
+ )
+ assert "collaborative co-spend" in report.limitations[2]
+
+
+def test_disjoint_change_candidates_do_not_create_an_owner():
+ scenario = ctp_example()
+ report = evaluate(ChangeConsolidationScenario(
+ scenario.payments,
+ scenario.change_outputs,
+ (
+ scenario.candidate_owners[0],
+ frozenset(
+ {
+ Subject(SubjectKind.CLUSTER, "carol"),
+ Subject(SubjectKind.CLUSTER, "dave"),
+ }
+ ),
+ ),
+ ))
+ assert isinstance(report.outcomes[0], NoSharedCandidates)
+
+
+@pytest.mark.parametrize("field", ["payments", "change_outputs"])
+def test_scenario_rejects_duplicate_observations(field):
+ scenario = ctp_example()
+ values = getattr(scenario, field)
+ arguments = {
+ "payments": scenario.payments,
+ "change_outputs": scenario.change_outputs,
+ "candidate_owners": scenario.candidate_owners,
+ }
+ arguments[field] = (values[0], values[0])
+ with pytest.raises(ValueError, match="distinct"):
+ ChangeConsolidationScenario(**arguments)
diff --git a/tests/test_change_consolidation_experiment.py b/tests/test_change_consolidation_experiment.py
new file mode 100644
index 00000000..eac5526a
--- /dev/null
+++ b/tests/test_change_consolidation_experiment.py
@@ -0,0 +1,34 @@
+import json
+
+import pytest
+
+from decluster.experiments import change_consolidation as experiment
+
+
+def test_artifact_preserves_retroactive_linkage_without_composition():
+ artifact = experiment.build_artifact()
+ assert artifact["scenario"]["payments"] == ["payment-1", "payment-2"]
+ assert artifact["result"] == {
+ "candidates_before": 2,
+ "candidates_after": ["alice"],
+ "outcome": "candidate_narrowing",
+ "composition": None,
+ }
+
+
+def test_verifier_recomputes_the_result():
+ changed = json.loads(json.dumps(experiment.build_artifact()))
+ changed["result"]["candidates_after"] = ["bob"]
+ with pytest.raises(experiment.VerificationError, match="fresh failure-mode"):
+ experiment.verify_artifact(changed)
+
+
+def test_cli_reproduces_and_verifies_json_and_markdown(tmp_path):
+ artifact = tmp_path / "artifact.json"
+ markdown = tmp_path / "result.md"
+ assert experiment.main(
+ ["reproduce", "--artifact", str(artifact), "--markdown", str(markdown)]
+ ) == 0
+ assert experiment.main(
+ ["verify", "--artifact", str(artifact), "--markdown", str(markdown)]
+ ) == 0
diff --git a/tests/test_change_link.py b/tests/test_change_link.py
new file mode 100644
index 00000000..6518819f
--- /dev/null
+++ b/tests/test_change_link.py
@@ -0,0 +1,37 @@
+"""Optimal-change (UIH, cit-15/16) wiring into cluster_addresses, collapse-safe (fresh change only)."""
+from decluster.views import cluster_addresses
+
+
+def _tx(ins_val, outs, height=0):
+ # ins_val: [(addr,value)], outs: [(addr,value)]
+ return ({"height": height,
+ "vin": [{"prevout": {"scriptpubkey_address": a, "value": v}} for a, v in ins_val],
+ "vout": [{"scriptpubkey_address": a, "value": v} for a, v in outs]}, 0)
+
+
+def test_change_link_merges_fresh_change_only_when_enabled():
+ prior = _tx([("in1", 60), ("in2", 60)], [("prior_out", 110)])
+ tx = _tx([("in1", 100), ("in2", 100)], [("change", 50), ("pay", 140)]) # change=out0 (<100)
+ no = cluster_addresses([prior, tx], change_link=False)
+ yes = cluster_addresses([prior, tx], change_link=True)
+ assert "change" not in no # off: change stays an unlinked output
+ assert yes["change"] == yes["in1"] # on: fresh change joins the input cluster
+
+
+def test_change_link_skips_non_fresh_change_collapse_guard():
+ # 'shared' is used as an input elsewhere -> not fresh -> linking it could collapse clusters
+ tx1 = _tx([("in1", 100), ("in2", 100)], [("shared", 50), ("pay", 140)])
+ tx2 = _tx([("shared", 30), ("other", 30)], [("z", 55)]) # 'shared' appears as an input
+ yes = cluster_addresses([tx1, tx2], change_link=True)
+ # 'shared' clusters with tx2's inputs (co-spend), NOT pulled into tx1's input cluster
+ assert yes.get("shared") != yes.get("in1")
+
+
+def test_change_link_abstains_without_prior_same_owner_evidence():
+ # A two-party interpretation is possible. Input order must not assign the small output to
+ # whichever input appears first when no independent evidence links the inputs.
+ tx = _tx([("alice", 10), ("bob", 100)], [("alice_out", 9), ("bob_out", 90)])
+ forward = cluster_addresses([tx], change_link=True)
+ reverse_tx = _tx([("bob", 100), ("alice", 10)], [("alice_out", 9), ("bob_out", 90)])
+ reverse = cluster_addresses([reverse_tx], change_link=True)
+ assert "alice_out" not in forward and "alice_out" not in reverse
diff --git a/tests/test_change_special.py b/tests/test_change_special.py
index a373639c..855d6ebb 100644
--- a/tests/test_change_special.py
+++ b/tests/test_change_special.py
@@ -41,6 +41,15 @@ def test_optimal_change_reads_only_values():
# change a value so nothing is < min input -> label drops (value-sensitive)
assert label_optimal_change(_tx(in_vals=(100, 200), out_vals=(150, 500))) is None
+
+def test_optimal_change_does_not_require_addresses():
+ tx = _tx(in_vals=(100, 200), out_vals=(50, 500))
+ for item in tx["vin"]:
+ item["prevout"].pop("scriptpubkey_address")
+ for item in tx["vout"]:
+ item.pop("scriptpubkey_address")
+ assert label_optimal_change(tx) == 0
+
def test_address_reuse():
assert label_address_reuse(_tx(in_addrs=("A", "B"), out_addrs=("A", "d"))) == 0 # output0 == input A
assert label_address_reuse(_tx(in_addrs=("A", "B"), out_addrs=("c", "d"))) is None
diff --git a/tests/test_cioh_collaborative.py b/tests/test_cioh_collaborative.py
new file mode 100644
index 00000000..3ffbe2c3
--- /dev/null
+++ b/tests/test_cioh_collaborative.py
@@ -0,0 +1,30 @@
+import pytest
+
+from decluster.cluster import cluster_cospends
+from decluster.domain import ClusterMerge, CoSpendEvidence, ReferenceOwnershipConflict
+from decluster.failure_modes.cioh_collaborative import evaluate
+
+
+def test_explicit_cospends_apply_transitive_common_input_ownership():
+ groups = cluster_cospends(("a", "b", "c", "d"), (("a", "b"), ("b", "c")))
+ assert {frozenset(group) for group in groups} == {frozenset(("a", "b", "c")), frozenset(("d",))}
+
+
+def test_collaborative_labels_expose_the_cioh_merge_conflict():
+ report = evaluate("tx", {"alice-coin": "alice", "bob-coin": "bob"})
+ assert isinstance(report.channels[0].evidence[0], CoSpendEvidence)
+ assert [type(item) for item in report.outcomes] == [
+ ClusterMerge,
+ ReferenceOwnershipConflict,
+ ]
+ assert report.composition is None
+
+
+def test_same_owner_control_has_the_merge_without_a_reference_conflict():
+ report = evaluate("tx", {"coin-1": "alice", "coin-2": "alice"})
+ assert [type(item) for item in report.outcomes] == [ClusterMerge]
+
+
+def test_fixture_requires_two_labelled_coins():
+ with pytest.raises(ValueError, match="at least two"):
+ evaluate("tx", {"coin": "alice"})
diff --git a/tests/test_cioh_collaborative_experiment.py b/tests/test_cioh_collaborative_experiment.py
new file mode 100644
index 00000000..72ce776e
--- /dev/null
+++ b/tests/test_cioh_collaborative_experiment.py
@@ -0,0 +1,25 @@
+from decluster.experiments.cioh_collaborative import (
+ build_artifact,
+ render_markdown,
+ verify_artifact,
+)
+
+
+def test_artifact_separates_collaborative_fixture_from_control():
+ artifact = build_artifact()
+ assert artifact["collaborative"] == {
+ "inputs": 2,
+ "predicted_merges": 1,
+ "reference_conflicts": 1,
+ }
+ assert artifact["same_owner_control"]["reference_conflicts"] == 0
+
+
+def test_artifact_verifies_against_fresh_execution():
+ assert verify_artifact(build_artifact()) == build_artifact()
+
+
+def test_markdown_is_derived_from_artifact():
+ markdown = render_markdown(build_artifact())
+ assert "| collaborative | 2 | 1 | 1 |" in markdown
+ assert "infer real ownership" in markdown
diff --git a/tests/test_cited_paths.py b/tests/test_cited_paths.py
new file mode 100644
index 00000000..850441bd
--- /dev/null
+++ b/tests/test_cited_paths.py
@@ -0,0 +1,89 @@
+"""Every file a published document names in backticks either exists or is declared unavailable.
+
+Nothing linked `PAPER.md` or the result documents to the tree, so a path could rot silently: three
+line references had already drifted by +148, +148 and +270 lines. This is the cheap half of that
+gate — it does not check that a number came from an artifact, only that every path a reader is sent
+to is a path a reader has.
+
+A path the repository deliberately does not ship is declared here with the reason, the way
+`NOT_YET_MIGRATED` declares the results documents that carry no manifest. The list is checked in
+both directions: an entry that starts resolving is removed rather than left to grow stale.
+"""
+import re
+from pathlib import Path
+
+import pytest
+
+ROOT = Path(__file__).resolve().parents[1]
+
+DOCUMENTS = ["PAPER.md", "README.md"] + sorted(
+ str(p.relative_to(ROOT)) for p in (ROOT / "results").glob("*.md")
+)
+
+# A backticked path, optionally followed by :line or :line-line.
+CITATION = re.compile(
+ r"`([A-Za-z0-9_][A-Za-z0-9_./-]*"
+ r"\.(?:py|md|json|gz|ndjson|tar|sql|toml|yml|txt|whl|lock))(?::\d+(?:-\d+)?)?`"
+)
+
+# Documents name modules and fixtures by basename, so a citation resolves against the directories
+# a reader would look in.
+ROOTS = (
+ "", "decluster/", "decluster/experiments/", "decluster/baselines/", "decluster/failure_modes/",
+ "results/", "results/generated/", "results/artifacts/", "catalog/", "tests/",
+ "tests/fixtures/", "examples/",
+)
+
+UNAVAILABLE = {
+ "slice_2026.ndjson": "1.1 GB and never committed; the one input this repository cannot restore",
+ "catalog/entities.ndjson": "a curated address list the reader supplies; only the example is shipped",
+ "epoch_2016_01_391104-392111.ndjson.gz": "a gitignored epoch export from that collection",
+ "epoch_2016_01_392112-393119.ndjson.gz": "a gitignored epoch export from that collection",
+ "fingerprints.md": "a private reference corpus held outside this repository",
+ "sasamoto.md": "a private copy of cond-mat/0106125, cited by its arXiv identifier in the same line",
+ "task-2-report.md": "a private working note that predates this repository",
+}
+
+
+def cited_paths():
+ for document in DOCUMENTS:
+ for path in sorted(set(CITATION.findall((ROOT / document).read_text()))):
+ yield document, path
+
+
+def resolves(path):
+ return any((ROOT / prefix / path).exists() for prefix in ROOTS)
+
+
+def test_documents_are_actually_being_scanned():
+ assert len(DOCUMENTS) > 50
+ assert sum(1 for _ in cited_paths()) > 400
+
+
+@pytest.mark.parametrize("document", DOCUMENTS)
+def test_every_cited_path_resolves_or_is_declared_unavailable(document):
+ dangling = [
+ path
+ for doc, path in cited_paths()
+ if doc == document and not resolves(path) and path not in UNAVAILABLE
+ ]
+ assert not dangling, (
+ f"{document} sends a reader to paths that are not here: {dangling}. Either ship them, cite "
+ f"something that exists, or declare them in UNAVAILABLE with the reason."
+ )
+
+
+def test_the_unavailable_list_carries_no_entry_that_now_resolves():
+ stale = sorted(path for path in UNAVAILABLE if resolves(path))
+ assert not stale, f"UNAVAILABLE still excuses paths that exist: {stale}"
+
+
+def test_every_unavailable_entry_is_actually_cited():
+ cited = {path for _, path in cited_paths()}
+ unused = sorted(set(UNAVAILABLE) - cited)
+ assert not unused, f"UNAVAILABLE names paths no document cites: {unused}"
+
+
+def test_every_unavailable_entry_states_a_reason():
+ for path, reason in UNAVAILABLE.items():
+ assert len(reason.split()) >= 5, f"{path}: the reason is too thin to audit"
diff --git a/tests/test_claim_status_is_earned.py b/tests/test_claim_status_is_earned.py
new file mode 100644
index 00000000..7de4c647
--- /dev/null
+++ b/tests/test_claim_status_is_earned.py
@@ -0,0 +1,84 @@
+"""A claim's status has to be earned by the evidence the catalogue names for it.
+
+Every claim carried `partially_supported`, which made the field carry no information: nineteen of
+nineteen identical. The schema distinguishes that from `supported_at_evidence_level`, and the
+distinction is the useful one — a claim worded existentially and demonstrated on a deterministic
+fixture is not partly proven, it is proven at the level it declares, and its limitations say what it
+does not reach rather than what is missing from it.
+
+So the two statuses are held to different rules. A supported claim must name code and be exercised
+by a run. A partial one must say what is incomplete, in words a reader can check against the code.
+"""
+
+import json
+import re
+from pathlib import Path
+
+import pytest
+
+ROOT = Path(__file__).resolve().parent.parent
+# What this looks for is an unfinished mechanism *here*, which is not the same sentence as "the
+# paper's dataset is not reproduced" — that one is scope, and a supported claim may carry it. The
+# distinction is the whole point of the two statuses, so the pattern names our own gaps only —
+# including a measurement that does not test the claim, such as an auxiliary drawn from the target
+# it is supposed to be matched against.
+INCOMPLETE = re.compile(
+ r"not implemented|not computed|(? 1, f"every claim carries {statuses}, so the field says nothing"
+
+
+def test_a_supported_claim_does_not_also_declare_itself_incomplete(claims):
+ """Scope limits belong on a supported claim; an unfinished mechanism does not."""
+ contradictory = []
+ for claim in claims:
+ if claim["status"] != "supported_at_evidence_level":
+ continue
+ limitations = claim["limitations"]
+ text = " ".join(limitations) if isinstance(limitations, list) else limitations
+ if INCOMPLETE.search(text):
+ contradictory.append((claim["id"], INCOMPLETE.search(text).group(0)))
+ assert not contradictory, (
+ "supported, yet naming something unfinished:\n "
+ + "\n ".join(f"{i}: {w}" for i, w in contradictory))
diff --git a/tests/test_cluster_bits_experiment.py b/tests/test_cluster_bits_experiment.py
new file mode 100644
index 00000000..628d5d3e
--- /dev/null
+++ b/tests/test_cluster_bits_experiment.py
@@ -0,0 +1,63 @@
+"""The asymmetry §2 rests on, taken again on data a reader has.
+
+A merged transaction contributes about 1.6 bits of ambiguity and an established cluster carries far
+more, so the partition is decidable without the merge. That figure came from a slice that no longer
+exists, which left the argument's load-bearing number unverifiable; on the committed cache it lands
+in the same place and the comparison it is needed for does not move at all.
+
+The five-rarest floor is checked rather than asserted. Ninety-two per cent of these clusters have at
+most five counterparties, so the restriction usually keeps the whole set — a floor that repeats the
+number above it tests nothing, and the document has to say so instead of borrowing the rigour.
+"""
+
+import json
+from pathlib import Path
+
+import pytest
+
+from decluster.experiments import cluster_bits as cb
+
+ROOT = Path(__file__).resolve().parent.parent
+ARTIFACT = ROOT / "results" / "artifacts" / "cluster-bits-v1.json"
+SNAPSHOT = ROOT / "data" / "fs-blkcache-2026-09-04.tar.gz"
+
+
+@pytest.fixture(scope="module")
+def artifact():
+ if not ARTIFACT.is_file():
+ pytest.skip("the canonical cluster-bits artifact is not committed")
+ return json.loads(ARTIFACT.read_text())
+
+
+def test_every_cluster_clears_the_merge_it_would_have_to_hide_behind(artifact):
+ """The comparison the paper makes, asserted where a future slice can break it."""
+ merge = str(artifact["parameters"]["merge_ambiguity_bits"])
+ assert artifact["share_at_least"][merge] == 1.0, (
+ "a cluster now carries less identifying structure than the merge's ambiguity, which is the "
+ "premise of section 2")
+
+
+def test_the_median_is_an_order_of_magnitude_above_the_merge(artifact):
+ merge = artifact["parameters"]["merge_ambiguity_bits"]
+ assert artifact["bits"]["median"] > 10 * merge
+
+
+def test_the_topk_floor_is_reported_with_the_reason_it_says_little(artifact):
+ """It equals the median because the clusters are small; claiming independence would be false."""
+ population = artifact["population"]
+ small = population["clusters_with_at_most_topk_counterparties"] / population["clusters"]
+ assert small > 0.5, "most clusters no longer fit inside the top-k, so the floor now means more"
+ rendered = cb.render_markdown(artifact)
+ assert f"{small:.0%}" in rendered and "says only that the clusters are small" in rendered
+
+
+def test_the_identifier_excludes_the_edges_that_built_the_cluster(artifact):
+ """A co-spend edge cannot be the quasi-identifier of the cluster co-spending defined."""
+ assert "co-spend excluded" in artifact["parameters"]["graph"]
+
+
+@pytest.mark.reproduction
+def test_the_committed_artifact_is_what_a_fresh_run_produces(artifact):
+ if not SNAPSHOT.is_file():
+ pytest.skip("the block-cache snapshot is not committed")
+ cb.verify_artifact(artifact, str(SNAPSHOT))
diff --git a/tests/test_cluster_decisions.py b/tests/test_cluster_decisions.py
new file mode 100644
index 00000000..fe014b8b
--- /dev/null
+++ b/tests/test_cluster_decisions.py
@@ -0,0 +1,68 @@
+import decluster.cluster as cluster
+
+
+class Scorer:
+ def __init__(self, value):
+ self.value = value
+
+ def score(self, left, right, explain=False):
+ return self.value
+
+
+def test_decision_log_separates_direct_transitive_and_refused(monkeypatch):
+ monkeypatch.setattr(
+ cluster,
+ "_cospent_pairs",
+ lambda nodes, fetch=None: [("a", "b", "t1"), ("a", "c", "t2"), ("b", "c", "t3")],
+ )
+ monkeypatch.setattr(cluster, "fetch_tx", lambda txid: {"txid": txid})
+ scores = {
+ frozenset(("a", "b")): 1.0,
+ frozenset(("a", "c")): -5.0,
+ frozenset(("b", "c")): 1.0,
+ }
+
+ class PairScorer:
+ def score(self, left, right, explain=False):
+ return scores.get(frozenset((left["txid"], right["txid"])), 0.0)
+
+ groups, refused, linked, decisions = cluster.cluster_refined_decisions(
+ ["a", "b", "c"], PairScorer(), amount=False, link_above=6.0
+ )
+
+ assert refused == []
+ assert linked == []
+ assert len(groups) == 1
+ by_pair = {(decision.left, decision.right): decision for decision in decisions}
+ assert by_pair[("a", "b")].status is cluster.PairDecisionStatus.DIRECT_MERGE
+ assert by_pair[("a", "c")].status is cluster.PairDecisionStatus.TRANSITIVE_MEMBERSHIP
+ assert by_pair[("b", "c")].status is cluster.PairDecisionStatus.DIRECT_MERGE
+
+
+def test_decision_log_preserves_each_numeric_channel(monkeypatch):
+ monkeypatch.setattr(cluster, "_cospent_pairs", lambda nodes, fetch=None: [("a", "b", "spend")])
+ monkeypatch.setattr(cluster, "fetch_tx", lambda txid: {"txid": txid})
+ monkeypatch.setattr(cluster, "amount_refuse_weight", lambda *args: -1.5)
+ signatures = {"a": {"x": 1.0}, "b": {"y": 1.0}}
+
+ _, refused, _, decisions = cluster.cluster_refined_decisions(
+ ["a", "b"],
+ Scorer(-2.0),
+ provenance=True,
+ signatures=signatures,
+ rarity={"x": 1.0, "y": 1.0},
+ prov_refuse_bits=-3.0,
+ subsetsum=True,
+ _ss_fn=lambda tx, left, right: -4.0,
+ )
+
+ assert len(refused) == 1
+ decision = decisions[0]
+ assert decision.status is cluster.PairDecisionStatus.REFUSED
+ assert decision.cospend_prior_bits == 2.0
+ assert decision.fingerprint_bits == -2.0
+ assert decision.amount_bits == -1.5
+ assert decision.provenance_bits == -3.0
+ assert decision.subset_sum_bits == -4.0
+ assert decision.topology_bits == 0.0
+ assert decision.total_bits == -8.5
diff --git a/tests/test_cluster_refined.py b/tests/test_cluster_refined.py
index baa48cfc..de14454d 100644
--- a/tests/test_cluster_refined.py
+++ b/tests/test_cluster_refined.py
@@ -7,7 +7,7 @@
def _stub(cl, pairs):
cl.fetch_tx = lambda t: {"txid": t, "vin": [{"txid": "seed_" + t, "prevout": {"value": 1000}}],
"vout": [{"value": 900}]}
- cl._cospent_pairs = lambda nodes: pairs
+ cl._cospent_pairs = lambda nodes, fetch=None: pairs
class _Neutral:
diff --git a/tests/test_cluster_refined_adapter.py b/tests/test_cluster_refined_adapter.py
new file mode 100644
index 00000000..035ac18b
--- /dev/null
+++ b/tests/test_cluster_refined_adapter.py
@@ -0,0 +1,76 @@
+import decluster.adaptations.cluster_refined as adapter
+from decluster.cluster import ClusterPairDecision, PairDecisionStatus
+from decluster.domain import ClusterMerge, MergeRefused
+
+
+class Scorer:
+ def __init__(self, score):
+ self.value = score
+
+ def score(self, left, right, explain=False):
+ return self.value
+
+
+def test_adapter_preserves_legacy_result_and_types_refusal(monkeypatch):
+ legacy = ([['a'], ['b']], [('a', 'b', 'spend', -5.0, -1.0, -8.0)], [])
+ decision = ClusterPairDecision(
+ "a", "b", "spend", PairDecisionStatus.REFUSED, 2.0, -5.0, -1.0, -4.0, 0.0, 0.0
+ )
+ monkeypatch.setattr(
+ adapter,
+ "cluster_refined_decisions",
+ lambda nodes, combiner, **options: legacy + ([decision],),
+ )
+
+ report = adapter.cluster_refined_report(['a', 'b'], Scorer(-5.0), amount=False)
+
+ assert report.as_legacy() == legacy
+ assert len(report.merge_refusals) == 1
+ assert type(report.merge_refusals[0]) is MergeRefused
+ assert report.added_links == ()
+
+ attack_report = report.as_attack_report("refusal-fixture")
+ assert attack_report.composition is None
+ assert [channel.identifier for channel in attack_report.channels] == [
+ "cluster_refined",
+ "cluster_refined.amount",
+ "cluster_refined.cospend_prior",
+ "cluster_refined.fingerprint",
+ "cluster_refined.topology",
+ ]
+ assert attack_report.outcomes == report.pair_outcomes
+
+
+def test_adapter_preserves_legacy_result_and_types_added_link(monkeypatch):
+ legacy = ([['a', 'b']], [], [('a', 'b', 6.0)])
+ monkeypatch.setattr(
+ adapter,
+ "cluster_refined_decisions",
+ lambda nodes, combiner, **options: legacy + ([],),
+ )
+
+ report = adapter.cluster_refined_report(['a', 'b'], Scorer(6.0), link_above=4.0)
+
+ assert report.as_legacy() == legacy
+ assert report.merge_refusals == ()
+ assert len(report.added_links) == 1
+ assert type(report.added_links[0]) is ClusterMerge
+
+
+def test_adapter_materializes_a_single_pass_node_iterable(monkeypatch):
+ observed = []
+
+ def fake(nodes, combiner, **options):
+ observed.append(nodes)
+ return ([nodes], [], [], [])
+
+ monkeypatch.setattr(adapter, "cluster_refined_decisions", fake)
+ nodes = (node for node in ['a', 'b'])
+
+ report = adapter.cluster_refined_report(nodes, Scorer(0.0))
+
+ assert observed == [['a', 'b']]
+ assert report.groups == (('a', 'b'),)
+ attack_report = report.as_attack_report("no-reported-decisions")
+ assert attack_report.channels == ()
+ assert attack_report.outcomes[0].observable == "merge refusal or additional fingerprint link"
diff --git a/tests/test_coinjoin_sudoku.py b/tests/test_coinjoin_sudoku.py
new file mode 100644
index 00000000..0a290fc0
--- /dev/null
+++ b/tests/test_coinjoin_sudoku.py
@@ -0,0 +1,71 @@
+import pytest
+
+from decluster.baselines.coinjoin_sudoku import (
+ collapse_fee_permutations,
+ equal_sum_groupings,
+ maximally_separated_groupings,
+ uniform_link_probabilities,
+)
+
+
+def test_advisory_valid_and_invalid_group_examples():
+ assert len(equal_sum_groupings((2, 3), (1, 4))) == 1
+ assert equal_sum_groupings((2, 3), (1, 2)) == ()
+
+
+def test_sx_control_reconstructs_one_third_relations():
+ inputs = (1_010_000,) * 3
+ outputs = (1_000_000,) * 3
+ mappings = maximally_separated_groupings(inputs, outputs, fee_unit=10_000)
+ assert len(mappings) == 36
+ assert len(collapse_fee_permutations(mappings, len(outputs))) == 6
+ expected = tuple(tuple(pytest.approx(1 / 3) for _ in range(3)) for _ in range(3))
+ assert uniform_link_probabilities(inputs, outputs, fee_unit=10_000) == expected
+
+
+def test_duplicate_values_remain_distinct_indexed_coins():
+ assert len(equal_sum_groupings((2, 2), (2, 2))) == 3
+ assert len(maximally_separated_groupings((2, 2), (2, 2))) == 2
+ assert uniform_link_probabilities((2, 2), (2, 2)) == ((0.5, 0.5), (0.5, 0.5))
+
+
+def test_fee_requires_an_explicit_exact_unit_convention():
+ assert equal_sum_groupings((101,), (100,)) == ()
+ assert len(equal_sum_groupings((101,), (100,), fee_unit=1)) == 1
+ assert equal_sum_groupings((102,), (100,), fee_unit=3) == ()
+
+
+@pytest.mark.parametrize("values", [((0,), (0,)), ((-1,), (1,)), ((True,), (1,))])
+def test_amount_domain_is_strict(values):
+ with pytest.raises(ValueError, match="positive integers"):
+ equal_sum_groupings(*values)
+
+
+def test_bound_is_explicit():
+ with pytest.raises(ValueError, match="max_coins"):
+ equal_sum_groupings((1,) * 7, (1,) * 7)
+
+
+def test_fee_expansion_is_bounded_before_materialization():
+ with pytest.raises(ValueError, match="max_coins"):
+ equal_sum_groupings((10**18,), (1,), fee_unit=1)
+
+
+@pytest.mark.parametrize("max_coins", [True, 1, 2.5])
+def test_invalid_bound_is_rejected(max_coins):
+ with pytest.raises(ValueError, match="max_coins"):
+ equal_sum_groupings((1,), (1,), max_coins=max_coins)
+
+
+def test_grouping_count_is_scale_invariant():
+ original = equal_sum_groupings((1, 2, 3), (2, 4))
+ scaled = equal_sum_groupings((10, 20, 30), (20, 40))
+ assert len(original) == len(scaled)
+
+
+def test_permuting_values_only_relabels_the_uniform_matrix():
+ inputs = (2, 3, 5)
+ outputs = (3, 7)
+ original = uniform_link_probabilities(inputs, outputs)
+ permuted = uniform_link_probabilities(inputs[::-1], outputs[::-1])
+ assert permuted == tuple(tuple(reversed(row)) for row in reversed(original))
diff --git a/tests/test_coinjoin_sudoku_experiment.py b/tests/test_coinjoin_sudoku_experiment.py
new file mode 100644
index 00000000..50b17416
--- /dev/null
+++ b/tests/test_coinjoin_sudoku_experiment.py
@@ -0,0 +1,29 @@
+from decluster.experiments.coinjoin_sudoku import (
+ build_artifact,
+ render_markdown,
+ verify_artifact,
+)
+
+
+def test_artifact_reproduces_only_the_nominal_primary_source_controls():
+ artifact = build_artifact()
+ assert artifact["advisory_group_examples"] == {
+ "valid_2_plus_3_to_1_plus_4": 1,
+ "invalid_2_plus_3_to_1_plus_2": 0,
+ }
+ matrix = artifact["sx_symmetric_control"]["local_uniform_real_output_link_probabilities"]
+ assert all(abs(value - 1 / 3) < 1e-12 for row in matrix for value in row)
+ assert artifact["sx_symmetric_control"]["selected_indexed_groupings"] == 36
+ assert artifact["sx_symmetric_control"]["selected_groupings_collapsing_fee_permutations"] == 6
+ assert artifact["sharedcoin_historical_target"]["status"] == "not_reproduced"
+
+
+def test_artifact_verifies_against_fresh_execution():
+ assert verify_artifact(build_artifact()) == build_artifact()
+
+
+def test_markdown_refuses_historical_parity_and_ownership_claims():
+ markdown = render_markdown(build_artifact())
+ assert "does not claim code parity" in markdown
+ assert "**not reproduced**" in markdown
+ assert "not ownership attribution" in markdown
diff --git a/tests/test_collaborative_forms.py b/tests/test_collaborative_forms.py
new file mode 100644
index 00000000..d7d04a54
--- /dev/null
+++ b/tests/test_collaborative_forms.py
@@ -0,0 +1,102 @@
+"""Executable observer-knowledge matrix for CTP collaborative forms."""
+
+from decluster.failure_modes.collaborative_forms import (
+ TWO_PARTY_OBSERVATION,
+ CollaborativeForm,
+ Observer,
+ form_matrix,
+ knowledge,
+)
+from decluster.failure_modes.ns1r_change_readoff import ReadOffScenario, analyze
+
+TWO_PARTY_FORMS = (
+ CollaborativeForm.ORDINARY_TWO_INPUT,
+ CollaborativeForm.P2EP,
+ CollaborativeForm.BIP79,
+ CollaborativeForm.BIP78,
+ CollaborativeForm.BIP77,
+)
+
+
+def _by_form():
+ return {row.form: row for row in form_matrix()}
+
+
+def _read_off(form, receiver_input, receiver_output, payments):
+ row = _by_form()[form]
+ return analyze(
+ ReadOffScenario.from_payments(
+ form.value,
+ row.observation.inputs,
+ row.observation.outputs,
+ receiver_input,
+ receiver_output,
+ payments,
+ )
+ )
+
+
+def test_all_declared_ctp_forms_are_present_once():
+ rows = form_matrix()
+
+ assert len(rows) == len(CollaborativeForm)
+ assert {row.form for row in rows} == set(CollaborativeForm)
+
+
+def test_two_party_protocols_can_share_one_onchain_observation_with_ordinary_spend():
+ rows = _by_form()
+
+ assert {rows[form].observation for form in TWO_PARTY_FORMS} == {TWO_PARTY_OBSERVATION}
+ assert len({rows[form].transport for form in TWO_PARTY_FORMS}) == len(TWO_PARTY_FORMS)
+
+
+def test_external_observer_sees_the_same_thing_in_every_two_party_form():
+ rows = _by_form()
+
+ external = {knowledge(rows[form], Observer.EXTERNAL) for form in TWO_PARTY_FORMS}
+ counterparty = {knowledge(rows[form], Observer.COUNTERPARTY) for form in TWO_PARTY_FORMS}
+
+ assert len(external) == 1
+ assert len(counterparty) == 2
+ assert knowledge(rows[CollaborativeForm.ORDINARY_TWO_INPUT], Observer.COUNTERPARTY) == ()
+
+
+def test_two_party_counterparty_can_eliminate_own_coins():
+ analysis = _read_off(CollaborativeForm.BIP78, 1, 0, {"sender": 50})
+
+ assert analysis.readings == ((("sender", 90, 40),),)
+ assert analysis.unanimous_links == ((0, 1),)
+
+
+def test_ns1r_receiver_knowledge_does_not_become_input_attribution():
+ analysis = _read_off(
+ CollaborativeForm.NS1R, 0, 0, {"Alice": 20, "Bob": 30, "Carol": 70}
+ )
+ local = dict(analysis.local_candidates)
+
+ assert len(local["Alice"]) > 1 and len(local["Bob"]) > 1
+ assert len(analysis.matchings) > 1
+ assert analysis.readings == (
+ (("Alice", 50, 30), ("Bob", 60, 30), ("Carol", 90, 20)),
+ )
+
+
+def test_nsnr_row_keeps_six_parties_with_one_coin_per_party_on_each_side():
+ row = _by_form()[CollaborativeForm.NSNR]
+
+ assert row.participants == 6
+ assert len(row.observation.inputs) == len(row.observation.outputs) == row.participants
+ assert sum(row.observation.inputs) == sum(row.observation.outputs)
+
+
+def test_net_settlement_row_leaves_the_participant_count_latent():
+ row = _by_form()[CollaborativeForm.NET_SETTLEMENT]
+
+ assert row.participants is None
+ assert len(row.observation.inputs) == len(row.observation.outputs)
+ assert sum(row.observation.inputs) == sum(row.observation.outputs)
+
+
+def test_matrix_never_places_transport_in_external_onchain_knowledge():
+ for row in form_matrix():
+ assert row.transport not in knowledge(row, Observer.EXTERNAL)
diff --git a/tests/test_collaborative_forms_experiment.py b/tests/test_collaborative_forms_experiment.py
new file mode 100644
index 00000000..6e193d63
--- /dev/null
+++ b/tests/test_collaborative_forms_experiment.py
@@ -0,0 +1,44 @@
+from decluster.experiments.collaborative_forms import (
+ build_artifact,
+ render_markdown,
+ verify_artifact,
+)
+
+
+def test_artifact_preserves_observer_boundaries_and_onchain_equivalence():
+ artifact = build_artifact()
+ rows = {row["form"]: row for row in artifact["forms"]}
+ equivalent = artifact["two_party_onchain_equivalence"]
+
+ assert equivalent["forms"] == [
+ "ordinary_two_input",
+ "p2ep",
+ "bip79_bustapay",
+ "bip78_sync_payjoin",
+ "bip77_async_payjoin",
+ ]
+ assert equivalent["observation"] == {"inputs_sat": [90, 60], "outputs_sat": [110, 40]}
+ assert all(
+ "participant allocation is not observed" in rows[form]["external_onchain_knowledge"]
+ for form in equivalent["forms"][1:]
+ )
+ assert any(
+ "only other party" in item
+ for item in rows["bip78_sync_payjoin"]["counterparty_knowledge"]
+ )
+
+
+def test_artifact_keeps_multiparty_limits_explicit():
+ rows = {row["form"]: row for row in build_artifact()["forms"]}
+
+ assert any("may not know which input" in item for item in rows["many_senders_one_receiver"]["counterparty_knowledge"])
+ assert any("amount-based" in item for item in rows["many_senders_many_receivers"]["limitations"])
+ assert any("cycles" in item for item in rows["net_settlement_with_cycles"]["limitations"])
+
+
+def test_artifact_and_markdown_are_exactly_recomputable():
+ artifact = build_artifact()
+ assert verify_artifact(artifact) == artifact
+ markdown = render_markdown(artifact)
+ assert "does not identify which protocol form occurred" in markdown
+ assert "not ownership attribution" in markdown
diff --git a/tests/test_committed_bundles.py b/tests/test_committed_bundles.py
new file mode 100644
index 00000000..7186b06c
--- /dev/null
+++ b/tests/test_committed_bundles.py
@@ -0,0 +1,79 @@
+import platform
+from pathlib import Path
+import sys
+
+import pytest
+
+from decluster.evidence_bundle import (
+ BlobStatus,
+ bootstrap_bundle,
+ load_bundle,
+ reproduce_bundle,
+ verify_bundle,
+)
+from decluster.data_catalog import load_dataset_catalog
+from decluster.data_manifest import load_run_manifest
+from decluster.reference_registry import load_claims, load_sources
+from decluster.result_artifacts import content_identity
+
+
+ROOT = Path(__file__).resolve().parents[1]
+
+
+def expected_output_names(bundle):
+ sources = load_sources(ROOT / "catalog" / "ctp-sources.json")
+ claims = load_claims(
+ ROOT / "catalog" / "ctp-claims.json", {source.id for source in sources}
+ )
+ datasets = {dataset.id: dataset for dataset in load_dataset_catalog(ROOT)}
+ names = set()
+ for run_id in bundle.runs:
+ manifest = load_run_manifest(
+ ROOT / "catalog" / "runs" / f"{run_id}.json",
+ claim_ids={claim.id for claim in claims},
+ datasets=datasets,
+ )
+ names.update(Path(output.path).name for output in manifest.outputs)
+ return names
+
+
+def test_every_committed_bundle_is_complete_and_content_verified():
+ indexes = sorted((ROOT / "releases").glob("*.bundle.json"))
+ assert indexes, "no evidence bundle is committed"
+ referenced = set()
+ for index in indexes:
+ bundle = load_bundle(index)
+ checks = verify_bundle(bundle, ROOT / "artifacts")
+ failures = [check for check in checks if check.status is not BlobStatus.VERIFIED]
+ assert not failures, f"{index}: {failures}"
+ for blob in bundle.blobs:
+ referenced.add(blob.content.sha256)
+ named = ROOT / blob.name
+ if named.is_file():
+ assert content_identity(named) == blob.content, (
+ f"{index}: {blob.name} drifted from its bundled identity"
+ )
+ stored = {
+ path.parent.name + path.name
+ for path in (ROOT / "artifacts" / "sha256").glob("*/*")
+ if path.is_file()
+ }
+ assert stored == referenced, (
+ f"artifact store has missing={sorted(referenced - stored)} "
+ f"or orphaned={sorted(stored - referenced)} blobs"
+ )
+
+
+@pytest.mark.skipif(
+ sys.version_info[:2] != (3, 13) or platform.system() != "Darwin"
+ or platform.machine() != "arm64",
+ reason="the first reproduction environment targets CPython 3.13 on macOS arm64",
+)
+@pytest.mark.parametrize("index", sorted((ROOT / "releases").glob("*.bundle.json")))
+@pytest.mark.reproduction
+def test_committed_bundle_reproduces_from_materialized_contents(tmp_path, index):
+ bundle = load_bundle(index)
+ materialized = tmp_path / "bundle"
+ bootstrap_bundle(bundle, ROOT / "artifacts", materialized)
+ outputs = reproduce_bundle(bundle, materialized, tmp_path / "work")
+ assert {path.name for path in outputs} == expected_output_names(bundle)
diff --git a/tests/test_conservation_experiment.py b/tests/test_conservation_experiment.py
new file mode 100644
index 00000000..20918b78
--- /dev/null
+++ b/tests/test_conservation_experiment.py
@@ -0,0 +1,50 @@
+import json
+
+import pytest
+
+from decluster.experiments import conservation_round_three as experiment
+
+
+def test_artifact_preserves_the_reduced_round_observation():
+ artifact = experiment.build_artifact()
+
+ assert artifact["fixture"]["outputs"] == 502
+ assert artifact["largest_forced_denomination"] == {
+ "value": 774840978,
+ "forced": 2,
+ "present": 7,
+ "forced_value": 1549681956,
+ "margin": 360201666,
+ }
+ assert artifact["interpretation"] == "forced provenance of value under conservation"
+
+
+def test_fixture_validation_rejects_duplicate_denominations(tmp_path):
+ fixture = experiment.load_fixture()
+ fixture["outputs"].append(fixture["outputs"][0])
+ path = tmp_path / "invalid.json"
+ path.write_text(json.dumps(fixture), encoding="utf-8")
+
+ with pytest.raises(experiment.VerificationError, match="must be unique"):
+ experiment.load_fixture(path)
+
+
+def test_verifier_recomputes_the_observation():
+ changed = json.loads(json.dumps(experiment.build_artifact()))
+ changed["largest_forced_denomination"]["forced"] = 3
+
+ with pytest.raises(experiment.VerificationError, match="fresh experiment"):
+ experiment.verify_artifact(changed)
+
+
+def test_cli_reproduces_and_verifies_json_and_markdown(tmp_path):
+ artifact = tmp_path / "artifact.json"
+ markdown = tmp_path / "result.md"
+ assert experiment.main([
+ "--dataset", experiment.DATASET,
+ "reproduce", "--artifact", str(artifact), "--markdown", str(markdown),
+ ]) == 0
+ assert experiment.main([
+ "--dataset", experiment.DATASET,
+ "verify", "--artifact", str(artifact), "--markdown", str(markdown),
+ ]) == 0
diff --git a/tests/test_conspicuous_order_slice.py b/tests/test_conspicuous_order_slice.py
new file mode 100644
index 00000000..d7a11400
--- /dev/null
+++ b/tests/test_conspicuous_order_slice.py
@@ -0,0 +1,70 @@
+"""The conspicuous-order tables, pinned on the committed slice that produces them.
+
+These numbers were published from an export storing amounts as JSON strings, where `"330" > "1000"`
+compares true and every multi-input transaction therefore appeared to argue against itself. The
+committed snapshot for the same blocks holds integers, and the corrected figures are asserted here
+rather than left as prose a later change could contradict silently.
+
+The doubt gate is the row that moved most: under the string reading it looked like a substantial
+intervention, and on the amounts as numbers it moves the partition by one cluster.
+"""
+
+import json
+from collections import Counter
+from pathlib import Path
+
+import pytest
+
+from decluster.tx_addrs import in_addrs
+from decluster.views import cluster_addresses, merge_objections
+
+ROOT = Path(__file__).resolve().parent.parent
+SOURCE = ROOT / "data" / "amount-channel-812695-812831-v1.json"
+
+
+@pytest.fixture(scope="module")
+def sample():
+ if not SOURCE.is_file():
+ pytest.skip("the committed amount-channel slice is not present")
+ return [(tx, 0) for tx in json.loads(SOURCE.read_text())]
+
+
+def _partition(sample, **options):
+ sizes = Counter(cluster_addresses(sample, **options).values())
+ clusters = {root: n for root, n in sizes.items() if n >= 2}
+ return len(clusters), max(clusters.values()), sum(clusters.values())
+
+
+def test_a_majority_of_multi_input_transactions_raises_no_objection(sample):
+ multi = [tx for tx, _ in sample if len(tx.get("vin", [])) >= 2]
+ counts = Counter(merge_objections(tx) for tx in multi)
+ assert len(multi) == 1428
+ assert dict(counts) == {0: 855, 1: 512, 2: 61}
+ assert counts[0] / len(multi) > 0.5, (
+ "the conspicuous tier is no longer a majority, which is the premise the merge order rests on")
+
+
+def test_the_slice_is_the_one_the_documents_describe(sample):
+ assert len(sample) == 5491
+ assert len({a for tx, _ in sample for a in in_addrs(tx)}) == 14113
+ assert _partition(sample, refuse=False)[0] == 889
+
+
+def test_refusal_moves_the_partition_and_the_doubt_gate_barely_does(sample):
+ naive = _partition(sample, refuse=False)
+ refusing = _partition(sample, refuse=True)
+ gated = _partition(sample, refuse=True, doubt_min_side=2)
+ assert naive == (889, 996, 9960)
+ assert refusing == (868, 996, 9109)
+ assert gated == (869, 996, 9009)
+ assert gated[0] - refusing[0] == 1, (
+ "the doubt gate has stopped being nearly inert on this slice; the document says it moves the "
+ "partition by one cluster")
+
+
+def test_the_order_the_merges_are_taken_in_changes_nothing(sample):
+ """Union-find is order-independent, and the one state-dependent decision is taken in sample
+ order under both settings, so staging must not move either partition."""
+ assert _partition(sample, refuse=True) == _partition(sample, refuse=True, staged=True)
+ assert (_partition(sample, refuse=True, doubt_min_side=2)
+ == _partition(sample, refuse=True, staged=True, doubt_min_side=2))
diff --git a/tests/test_cost.py b/tests/test_cost.py
index e622a5ba..f09a10d5 100644
--- a/tests/test_cost.py
+++ b/tests/test_cost.py
@@ -17,17 +17,25 @@ def _fake_oracle(coins, kappa=0.5):
return lambda inputs, outputs: {"kappa": kappa, "coins": coins}
+def _resolved(kind="lower_bound", count=3, log_w=1.58):
+ """A whole-transaction reading that resolved, which step three is gated on."""
+ return lambda inputs, outputs: {"kind": kind, "count": count, "log_w": log_w}
+
+
def test_amount_cuts_fires_only_on_low_w():
coins = [
{"role": "maker", "index": 0, "value": 100, "log_w": 0.0, "kappa_c": 0.9}, # low W -> cut
{"role": "maker", "index": 1, "value": 200, "log_w": 8.0, "kappa_c": 0.9}, # high W -> ambiguous, no cut
]
- cuts = cost.amount_cuts([100, 200], [150, 150], _fake_oracle(coins), cut_threshold=1.0)
+ cuts = cost.amount_cuts([100, 200], [150, 150], _fake_oracle(coins), cut_threshold=1.0,
+ count_oracle=_resolved())
assert [c.index for c in cuts] == [0]
+ assert cuts[0].role == "maker"
assert cuts[0].value == 100
def test_amount_cuts_dense_returns_none():
+ pytest.importorskip("dss")
coins = [{"role": "mix", "index": i, "value": 100, "log_w": 12.0, "kappa_c": 0.9} for i in range(3)]
assert cost.amount_cuts([1, 2, 3], [1, 1, 1], _fake_oracle(coins)) == []
@@ -38,30 +46,36 @@ def test_amount_cuts_skips_unreachable_none_log_w():
{"role": "in", "index": 0, "value": 100, "log_w": None, "kappa_c": 0.9}, # unreachable -> skip
{"role": "in", "index": 1, "value": 200, "log_w": 0.0, "kappa_c": 0.9}, # low -> cut
]
- cuts = cost.amount_cuts([100, 200], [150, 150], _fake_oracle(coins))
+ cuts = cost.amount_cuts([100, 200], [150, 150], _fake_oracle(coins),
+ count_oracle=_resolved())
assert [c.index for c in cuts] == [1]
-def test_amount_cuts_default_no_count_oracle_is_candidate_only():
+def test_amount_cuts_on_a_lower_bound_reading_stays_a_candidate():
+ """The transaction resolved, so the coins can be read — but a floor is not an exact count and
+ the cut it supports is a candidate, not a rigorous one."""
coins = [{"role": "in", "index": 0, "value": 100, "log_w": 0.0, "kappa_c": 0.9}]
- cuts = cost.amount_cuts([100, 200], [150, 150], _fake_oracle(coins))
- assert cuts[0].exact is False
+ cuts = cost.amount_cuts([100, 200], [150, 150], _fake_oracle(coins),
+ count_oracle=_resolved(kind="lower_bound"))
+ assert cuts[0].transaction_count_exact is False
-def test_amount_cuts_exact_count_oracle_corroborates_rigorous_cut():
+def test_amount_cuts_preserves_exact_transaction_gate_provenance():
coins = [{"role": "in", "index": 0, "value": 100, "log_w": 0.0, "kappa_c": 0.9}]
exact_count_oracle = lambda inputs, outputs: {"kind": "exact", "count": 3, "log_w": 1.58}
cuts = cost.amount_cuts([100, 200], [150, 150], _fake_oracle(coins),
count_oracle=exact_count_oracle)
- assert cuts[0].exact is True
+ assert cuts[0].transaction_count_exact is True
-def test_amount_cuts_approx_count_oracle_stays_candidate():
+def test_no_cuts_where_the_transaction_as_a_whole_did_not_resolve():
+ """Apportioning ambiguity across the coins of a transaction whose ambiguity did not resolve is
+ apportioning nothing. Measured on a real slice, a third of the transactions the per-coin oracle
+ spoke for had no transaction-level reading behind them."""
coins = [{"role": "in", "index": 0, "value": 100, "log_w": 0.0, "kappa_c": 0.9}]
- approx_count_oracle = lambda inputs, outputs: {"kind": "unknown", "count": None, "log_w": None}
- cuts = cost.amount_cuts([100, 200], [150, 150], _fake_oracle(coins),
- count_oracle=approx_count_oracle)
- assert cuts[0].exact is False
+ unresolved = lambda inputs, outputs: {"kind": "unknown", "count": None, "log_w": None}
+ assert cost.amount_cuts([100, 200], [150, 150], _fake_oracle(coins),
+ count_oracle=unresolved) == []
def test_topology_bits_disjoint_penalises():
@@ -89,10 +103,10 @@ def test_construction_cost_returns_structured_terms_with_path_count_target():
assert terms == {"leak": 1.0, "topology": 0.0, "target": path_count_anonymity}
-def test_construction_cost_default_target_is_path_count_anonymity():
- from decluster.path_count import path_count_anonymity
+def test_construction_cost_default_target_is_provenance_route_accumulation():
+ from decluster.provenance_route_accumulation import provenance_route_accumulation
terms = cost.construction_cost(leak=1.0, topology=0.0)
- assert terms["target"] is path_count_anonymity
+ assert terms["target"] is provenance_route_accumulation
def test_construction_cost_combine_still_raises_citing_only_combination():
@@ -119,3 +133,48 @@ def test_density_gate_kappa_vs_kappa_c_orients_dense_and_sparse():
assert dense["kappa"] < dense["coins"][0]["kappa_c"] # kappa < kappa_c -> dense regime
sparse = dss.per_coin_density([3, 7, 19, 41], [10, 60])
assert sparse["kappa"] > sparse["coins"][0]["kappa_c"] # kappa > kappa_c -> sparse/decidable
+
+
+def test_an_unreachable_coin_is_skipped_rather_than_cut():
+ """"No balancing subset was found" is the absence of a measurement, not a measurement of zero
+ ambiguity. The per-coin oracle spells it as negative infinity, and reading that as a low value
+ cuts nearly every coin on the chain: 98.2% of input coins over a real 1,428-transaction slice."""
+ from decluster.cost import amount_cuts
+ def oracle(inputs, outputs):
+ return {"coins": [{"role": "in", "index": 0, "value": 10, "log_w": float("-inf")},
+ {"role": "in", "index": 1, "value": 20, "log_w": None},
+ {"role": "in", "index": 2, "value": 30, "log_w": float("nan")},
+ {"role": "in", "index": 3, "value": 40, "log_w": 0.5}]}
+ cuts = amount_cuts([10, 20, 30, 40], [95], oracle, count_oracle=_resolved())
+ assert [c.index for c in cuts] == [3] # only the coin that was actually measured
+
+
+def test_the_per_coin_step_is_gated_on_the_whole_transaction_step():
+ """The sequence is: classify the amounts, evaluate the transaction as a whole, then apportion
+ across its coins. The third step running independently of the second is what let the per-coin
+ oracle speak for transactions the transaction-level reading had found nothing in."""
+ coins = [{"role": "in", "index": 0, "value": 100, "log_w": 0.0, "kappa_c": 0.9}]
+ oracle = _fake_oracle(coins)
+ calls = []
+
+ def counting(inputs, outputs):
+ calls.append((tuple(inputs), tuple(outputs)))
+ return {"kind": "unknown", "count": None, "log_w": None}
+
+ assert cost.amount_cuts([100, 200], [150, 150], oracle, count_oracle=counting) == []
+ assert calls == [((100, 200), (150, 150))] # the second step ran, and its verdict held
+
+
+def test_the_link_matrix_oracle_reads_a_fee_paying_transaction():
+ """The per-coin density oracle asks for an exact subset hit and goes quiet the moment a fee is
+ paid. The link matrix reads the transaction's actual balance, so it still answers — and a row
+ with one plausible output is a deterministic link, the amounts settling the assignment."""
+ pytest.importorskip("dss")
+ ins = [500_000, 300_000, 200_000]
+ with_fee = [600_000, 395_000] # 5,000 sats of fee
+ coins = cost.boltzmann_oracle(ins, with_fee)["coins"]
+ assert [c["index"] for c in coins] == [0, 1, 2]
+ assert all(c["log_w"] is not None for c in coins)
+ assert cost.dss_oracle(ins, with_fee)["coins"] # the other oracle runs, but goes quiet:
+ assert all(c["log_w"] == float("-inf")
+ for c in cost.dss_oracle(ins, with_fee)["coins"] if c["role"] == "in")
diff --git a/tests/test_counting.py b/tests/test_counting.py
index efa7aa4a..943bafd5 100644
--- a/tests/test_counting.py
+++ b/tests/test_counting.py
@@ -27,6 +27,7 @@ def test_w_total_dense_coinjoin_is_exact_not_unknown():
def test_w_total_swallows_panic_from_dss(monkeypatch):
# w_total delegates to dss.w_count; if that ever raises (e.g. a Rust panic surfaced as a
# BaseException), w_total degrades to unknown rather than crashing the caller.
+ pytest.importorskip("dss")
import dss
class _Panic(BaseException):
@@ -34,3 +35,155 @@ class _Panic(BaseException):
monkeypatch.setattr(dss, "w_count", lambda i, o: (_ for _ in ()).throw(_Panic()))
assert counting.w_total([100, 200], [100, 200]) == {"kind": "unknown", "count": None, "log_w": None}
+
+
+def test_a_wide_transaction_is_refused_rather_than_attempted():
+ """The counting cascade takes no budget and does not bound itself: past twenty inputs it stops
+ returning, and a hang inside Rust is caught by neither the panic guard nor a Python alarm. The
+ only bound is declining to make the call, which reads as `unknown` — multiplicity 1, the safe
+ direction for a refuse-only channel."""
+ pytest.importorskip("dss")
+ from decluster import counting
+ wide = counting.w_total(list(range(1, 40)), [100, 200])
+ assert wide == {"kind": "unknown", "count": None, "log_w": None}
+ assert counting.guaranteed_log_w(wide) is None
+ # the cap is a parameter, not a hard-coded refusal
+ assert counting.w_total([1, 2, 3], [3, 3], max_size=3)["kind"] == "exact"
+
+
+def test_an_exact_zero_does_not_corroborate_a_cut():
+ """A fee-paying transaction has no exactly-balancing input subset, so the fee-blind count
+ completes at zero. That is the counter finding nothing, not finding certainty."""
+ from decluster.cost import amount_cuts
+ oracle = lambda i, o: {"coins": [{"role": "in", "index": 0, "value": 10, "log_w": 0.1}]}
+ zero = lambda i, o: {"kind": "exact", "count": 0, "log_w": None}
+ found = lambda i, o: {"kind": "exact", "count": 3, "log_w": 1.58}
+ # an exact zero resolved nothing, so there is nothing to apportion at all
+ assert amount_cuts([10, 20], [25], oracle, count_oracle=zero) == []
+ assert amount_cuts([10, 20], [25], oracle, count_oracle=found)[0].transaction_count_exact is True
+
+
+def test_the_per_coin_path_returns_where_the_whole_tx_count_will_not():
+ """The whole-transaction count is width-bounded; the per-coin path is not. Neither invents a
+ measurement for a coin it could not reach — unreachable coins are simply absent."""
+ pytest.importorskip("dss")
+ from decluster import counting
+ ins = [680849, 390740, 434988, 225790, 300000, 120000, 90000]
+ outs = [104781, 2500000]
+ assert counting.w_total(ins, outs)["count"] == 0 # fee-blind: nothing balances exactly
+ assert counting.per_coin_log_w(ins, outs) == {} # and no coin was reachable either
+
+ wide = list(range(1_000_000, 1_000_000 + 60)) # past the whole-tx count's width limit
+ assert counting.w_total(wide, [100, 200])["kind"] == "unknown"
+ counting.per_coin_log_w(wide, [100, 200]) # returns rather than hangs
+
+
+def test_the_router_skips_brute_and_bounds_the_convolution():
+ """Brute force is the convolution below its crossover, so the order is radix then the
+ convolution bounded by the knee. The crate's own cascade instead tries brute first and hands the
+ convolution an unbounded subset size, which is why it does not return past twenty inputs."""
+ pytest.importorskip("dss")
+ from decluster import counting
+ wide = [1_000_000 + i for i in range(60)]
+ r = counting.count_w(wide, [30_000_000, 100])
+ assert r["method"] in ("radix", "sparse", "none")
+ assert counting.w_total(wide, [30_000_000, 100])["kind"] == "unknown" # the cascade declines
+
+
+def test_a_tier_that_counted_nothing_does_not_short_circuit_the_next():
+ """An exact zero is the absence of an answer, not an answer of no ambiguity. The radix path
+ returns one on 63% of real multi-input transactions; accepting it would stop the routing."""
+ from decluster.counting import _resolved
+ assert _resolved(lambda: {"kind": "exact", "count": 0, "log_w": None}) is None
+ assert _resolved(lambda: {"kind": "unknown", "count": None, "log_w": None}) is None
+ assert _resolved(lambda: {"kind": "exact", "count": 4, "log_w": 2.0})["count"] == 4
+
+
+def test_the_saddle_point_is_available_but_never_a_tier():
+ """It estimates a magnitude where the exact tiers return a truncated floor, and those are
+ different readings — chaining it as an automatic upgrade would let an estimate stand where a
+ guarantee was asked for. So it is reachable on purpose, not through the routing."""
+ pytest.importorskip("dss")
+ from decluster import counting
+ dense = [1000 + i for i in range(200)] # deep in the regime the estimator wants
+ assert counting.is_dense(dense, [50_000, 50_000]) is True
+ assert counting.count_w(dense, [50_000, 50_000])["method"] != "sasamoto"
+ assert hasattr(counting, "saddle_point_log_w")
+ assert counting.saddle_point_log_w([1, 2, 3], [3]) is None # declines, never invents
+
+
+def test_the_denominational_diagnostic_needs_a_repeated_denomination():
+ """It counts the ways a repeated denomination permutes among participants, so it says nothing
+ unless one repeats. The crate returns a number either way and leaves the precondition to the
+ caller: unguarded it answers on 64 of 1,428 real multi-input transactions, 51 of which carry no
+ repeated value."""
+ pytest.importorskip("dss")
+ from decluster import counting
+ assert counting.radix_applies([5_000, 5_000, 5_000, 1_234]) is True
+ assert counting.radix_applies([600_000, 400_000]) is False
+ assert counting.radix_applies([5_000, 5_000, 1_234]) is False # twice is not a series
+ assert counting.radix_applies([]) is False
+ ordinary = counting.count_w([500_000, 300_000, 200_000], [600_000, 400_000])
+ assert ordinary["method"] != "radix"
+
+
+def test_the_denominational_count_never_passes_the_guarantee_gate():
+ """It is a function of the outputs alone and moves under a rescaling that leaves the mapping
+ count fixed, so it bounds the transaction's ambiguity in neither direction. The crate tags it
+ `diagnostic`; the gate refuses that kind, and `cost.amount_cuts` cannot read it as exact."""
+ pytest.importorskip("dss")
+ import dss
+ from decluster import counting
+ denominated = [5_000, 5_000, 5_000]
+ reading = dss.radix_mappings(denominated, counting.KNEE)
+ assert reading["kind"] == "diagnostic"
+ # One value repeated three times contributes 3! once, not once per coin, and a decomposition
+ # naming a denomination no output carries has no subset to exchange.
+ assert reading["count"] == 6
+ assert dss.radix_mappings(denominated + [5_512], counting.KNEE)["count"] == 6
+ assert counting.guaranteed_log_w(reading) is None
+
+ routed = counting.count_w([15_000], denominated)
+ assert routed["method"] == "radix"
+ assert counting.guaranteed_log_w(routed) is None
+
+
+def test_a_repeated_value_is_not_automatically_a_denomination():
+ """The classification separates by kind of value first: a denomination is a single digit times
+ a power of one of the bases. A value that repeats but is not one of those is not a series, and
+ counting its repeats towards the bound counts the wrong thing."""
+ from decluster import counting
+ assert counting.radix_series(5_000_000) == (10, 6, 5)
+ assert counting.radix_series(1 << 20) == (2, 20, 1)
+ assert counting.radix_series(2_500_000) is None # 25 x 10^5: two digits, not one
+ assert counting.radix_series(104_781) is None
+ assert counting.radix_applies([5_000_000] * 3 + [123]) is True
+ assert counting.radix_applies([2_500_000] * 3 + [123]) is False # repeats, not a denomination
+
+
+def test_mapping_entropy_answers_on_a_fee_paying_transaction():
+ """Entropy over DSS's mapping family is distinct from a mapping count. It balances the fee in
+ as an extra output, so unlike the counts it answers when a fee is paid. Zero means one reading
+ survives in this restricted family, not that ownership is globally proven."""
+ pytest.importorskip("dss")
+ from decluster import counting
+ ins = [500_000, 300_000, 200_000]
+ for fee in (0, 5_000, 50_000):
+ r = counting.mapping_entropy(ins, [600_000, 400_000 - fee])
+ assert r is not None
+ assert r["entropy"] == 0.0 and r["n_non_derived"] == 1
+ assert r["deterministic_links"] # every mapping in this family shares links
+ assert all(o < 2 for _, o in r["deterministic_links"]) # the fee column is dropped
+
+
+def test_mapping_entropy_turns_structured_refusal_into_no_measurement(monkeypatch):
+ import sys
+ from decluster import counting
+
+ class RefusingDss:
+ @staticmethod
+ def mapping_analysis(inputs, outputs, budget_ms):
+ return {"status": "refused", "reason": "size_guard"}
+
+ monkeypatch.setitem(sys.modules, "dss", RefusingDss)
+ assert counting.mapping_entropy([1], [1]) is None
diff --git a/tests/test_counting_fee_sensitivity_experiment.py b/tests/test_counting_fee_sensitivity_experiment.py
new file mode 100644
index 00000000..e79b1d60
--- /dev/null
+++ b/tests/test_counting_fee_sensitivity_experiment.py
@@ -0,0 +1,28 @@
+import pytest
+
+from decluster.experiments import counting_fee_sensitivity as experiment
+
+
+def test_fee_sensitivity_preserves_distinct_counting_objects():
+ pytest.importorskip("dss")
+ artifact = experiment.build_artifact()
+ observations = {row["fee_sat"]: row for row in artifact["observations"]}
+
+ assert observations[0]["router"]["count"] == 2
+ assert observations[0]["measured_input_indices"] == [0, 1, 2]
+ assert observations[1]["router"]["count"] == 1
+ assert observations[1]["measured_input_indices"] == [1, 2]
+ assert observations[10]["router"]["count"] == 1
+ assert observations[10]["measured_input_indices"] == []
+
+
+def test_artifact_round_trip(tmp_path):
+ pytest.importorskip("dss")
+ artifact = tmp_path / "result.json"
+ markdown = tmp_path / "result.md"
+ assert experiment.main([
+ "reproduce", "--artifact", str(artifact), "--markdown", str(markdown)
+ ]) == 0
+ assert experiment.main([
+ "verify", "--artifact", str(artifact), "--markdown", str(markdown)
+ ]) == 0
diff --git a/tests/test_counting_methods_summary_experiment.py b/tests/test_counting_methods_summary_experiment.py
new file mode 100644
index 00000000..68623f4f
--- /dev/null
+++ b/tests/test_counting_methods_summary_experiment.py
@@ -0,0 +1,39 @@
+import json
+
+import pytest
+
+from decluster.experiments import counting_methods_summary as experiment
+
+
+def test_summary_is_composition_and_records_exclusions():
+ artifact = experiment.build_artifact()
+ excluded = {row["measurement"] for row in artifact["excluded_historical_measurements"]}
+ assert excluded == {
+ "wall-clock benchmarks",
+ "seven L candidates over 18 synthetic instances",
+ "obsolete router shares",
+ }
+ assert artifact["components"]["router"]["sha256"]
+ assert artifact["components"]["fee_sensitivity"]["sha256"]
+
+
+def test_component_digest_is_enforced(tmp_path):
+ paths = {name: specification[0] for name, specification in experiment.COMPONENTS.items()}
+ changed = json.loads(open(paths["router"], encoding="utf-8").read())
+ changed["report"]["population"]["multi_input_transactions"] += 1
+ replacement = tmp_path / "changed.json"
+ replacement.write_text(json.dumps(changed), encoding="utf-8")
+ paths["router"] = replacement
+ with pytest.raises(experiment.VerificationError, match="SHA-256"):
+ experiment.build_artifact(paths)
+
+
+def test_artifact_round_trip(tmp_path):
+ artifact = tmp_path / "result.json"
+ markdown = tmp_path / "result.md"
+ assert experiment.main([
+ "reproduce", "--artifact", str(artifact), "--markdown", str(markdown)
+ ]) == 0
+ assert experiment.main([
+ "verify", "--artifact", str(artifact), "--markdown", str(markdown)
+ ]) == 0
diff --git a/tests/test_counting_router_experiment.py b/tests/test_counting_router_experiment.py
new file mode 100644
index 00000000..fdaec425
--- /dev/null
+++ b/tests/test_counting_router_experiment.py
@@ -0,0 +1,43 @@
+import pytest
+
+from decluster.experiments import counting_router as experiment
+
+
+def test_report_preserves_current_router_outcomes():
+ pytest.importorskip("dss")
+ report = experiment.build_report()
+
+ assert report["population"] == {"multi_input_transactions": 1428}
+ # The denominational path answers `diagnostic`, which the guarantee gate refuses: none of its
+ # readings count towards `guaranteed_nonzero_log_w`, and six of the nineteen it used to answer
+ # fall through to the convolution instead.
+ assert report["current_router"] == {
+ "by_method_and_kind": {
+ "none:unknown": 1333,
+ "radix:diagnostic": 13,
+ "sparse:exact": 60,
+ "sparse:lower_bound": 22,
+ },
+ "guaranteed_nonzero_log_w": 82,
+ }
+ assert report["radix_precondition_audit"] == {
+ "precondition_applies": 20,
+ "raw_positive": 64,
+ "raw_positive_without_precondition": 51,
+ }
+
+
+def test_artifact_keeps_objects_and_scope_distinct(monkeypatch):
+ monkeypatch.setattr(experiment, "build_report", lambda _dataset: {})
+ limitations = experiment.build_artifact("unused")["limitations"]
+
+ assert "not a mapping count" in limitations[0]
+ assert "outside this run" in limitations[4]
+ assert "not a privacy score" in limitations[5]
+
+
+def test_verifier_rejects_changed_measurement(monkeypatch):
+ artifact = {"schema_version": 1, "experiment": experiment.EXPERIMENT_ID, "value": 2}
+ monkeypatch.setattr(experiment, "build_artifact", lambda _dataset: {**artifact, "value": 1})
+ with pytest.raises(experiment.VerificationError, match="fresh experiment"):
+ experiment.verify_artifact(artifact, dataset="unused")
diff --git a/tests/test_data_catalog.py b/tests/test_data_catalog.py
new file mode 100644
index 00000000..8199335e
--- /dev/null
+++ b/tests/test_data_catalog.py
@@ -0,0 +1,235 @@
+import json
+from io import BytesIO
+from pathlib import Path
+
+import pytest
+
+from decluster.data_catalog import (
+ DatasetStatus,
+ DatasetFetchError,
+ FetchAttemptStatus,
+ FetchStatus,
+ DatasetPrepareError,
+ PrepareFailure,
+ PrepareStatus,
+ catalog_status,
+ dataset_path,
+ dataset_status,
+ load_dataset_catalog,
+ require_verified_datasets,
+ fetch_dataset,
+ prepare_dataset,
+)
+from decluster.evidence_bundle import blob_path
+from decluster.data_manifest import ManifestError, load_dataset_manifest
+
+
+ROOT = Path(__file__).resolve().parents[1]
+
+
+def manifest_document(dataset_id, local_path, payload):
+ import hashlib
+ return {
+ "schema_version": 1,
+ "id": dataset_id,
+ "title": dataset_id,
+ "kind": "git_fixture",
+ "schema": "bytes-v1",
+ "format": "bin",
+ "content": {"bytes": len(payload), "sha256": hashlib.sha256(payload).hexdigest()},
+ "locations": {"local_path": local_path, "canonical": None, "mirrors": []},
+ "source": {"provider": "test", "snapshot_time": None, "recipe": None, "recipe_sha256": None},
+ "license": {"data": "MIT", "recipe": None},
+ "redistribution": "allowed",
+ "sensitivity": "synthetic",
+ "normalization": {"code_revision": None, "parameters": {}, "parents": []},
+ "determinism": {"ordering": "stored bytes", "rng_algorithm": None, "rng_seed": None},
+ }
+
+
+def write_manifest(path, document):
+ path.parent.mkdir(parents=True, exist_ok=True)
+ path.write_text(json.dumps(document))
+ return load_dataset_manifest(path)
+
+
+def test_dataset_status_has_distinct_verified_missing_and_mismatch_states(tmp_path):
+ payload = b"dataset"
+ manifest = write_manifest(
+ tmp_path / "manifest.json", manifest_document("sample", "data/sample.bin", payload)
+ )
+ assert dataset_status(manifest, tmp_path).status is DatasetStatus.MISSING
+ target = tmp_path / "data" / "sample.bin"
+ target.parent.mkdir()
+ target.write_bytes(b"wrong")
+ assert dataset_status(manifest, tmp_path).status is DatasetStatus.IDENTITY_MISMATCH
+ target.write_bytes(payload)
+ assert dataset_status(manifest, tmp_path).status is DatasetStatus.VERIFIED
+
+
+def test_dataset_paths_cannot_escape_the_project_root(tmp_path):
+ manifest = write_manifest(
+ tmp_path / "manifest.json", manifest_document("escape", "../outside.bin", b"x")
+ )
+ with pytest.raises(ManifestError, match="escapes project root"):
+ dataset_path(manifest, tmp_path)
+
+
+def test_catalog_is_sorted_and_duplicate_ids_are_rejected(tmp_path):
+ catalog = tmp_path / "catalog" / "datasets"
+ write_manifest(catalog / "b.json", manifest_document("b", "b.bin", b"b"))
+ write_manifest(catalog / "a.json", manifest_document("a", "a.bin", b"a"))
+ assert [dataset.id for dataset in load_dataset_catalog(tmp_path)] == ["a", "b"]
+ write_manifest(catalog / "duplicate.json", manifest_document("a", "c.bin", b"c"))
+ with pytest.raises(ManifestError, match="duplicate dataset id"):
+ load_dataset_catalog(tmp_path)
+
+
+def test_empty_catalog_is_not_vacuously_verified(tmp_path):
+ with pytest.raises(ManifestError, match="catalog is empty"):
+ require_verified_datasets(tmp_path)
+
+
+def test_require_verified_refuses_partial_catalog(tmp_path):
+ catalog = tmp_path / "catalog" / "datasets"
+ write_manifest(catalog / "a.json", manifest_document("a", "a.bin", b"a"))
+ with pytest.raises(ManifestError, match="a=missing"):
+ require_verified_datasets(tmp_path)
+
+
+def test_committed_catalog_is_fully_verified():
+ catalogued = len(list((ROOT / "catalog" / "datasets").glob("*.json")))
+ checks = catalog_status(ROOT)
+ assert len(checks) == catalogued
+ assert all(check.status is DatasetStatus.VERIFIED for check in checks)
+ assert len(require_verified_datasets(ROOT)) == catalogued
+
+
+def test_fetch_uses_mirror_after_identity_mismatch_and_is_then_cached(tmp_path):
+ payload = b"dataset"
+ document = manifest_document("sample", "data/sample.bin", payload)
+ document["locations"] = {
+ "local_path": "data/sample.bin",
+ "canonical": "https://primary.invalid/sample",
+ "mirrors": ["https://mirror.invalid/sample"],
+ }
+ manifest = write_manifest(tmp_path / "manifest.json", document)
+ opened = []
+
+ def opener(url):
+ opened.append(url)
+ return BytesIO(b"wrong" if "primary" in url else payload)
+
+ result = fetch_dataset(manifest, tmp_path / "store", opener=opener)
+ assert result.status is FetchStatus.DOWNLOADED
+ assert result.source_url == "https://mirror.invalid/sample"
+ assert result.path.read_bytes() == payload
+ assert opened == ["https://primary.invalid/sample", "https://mirror.invalid/sample"]
+
+ cached = fetch_dataset(
+ manifest, tmp_path / "store", opener=lambda _: pytest.fail("network used")
+ )
+ assert cached.status is FetchStatus.ALREADY_AVAILABLE
+ assert cached.source_url is None
+
+
+def test_fetch_failure_is_structured_and_leaves_no_blob(tmp_path):
+ payload = b"expected"
+ document = manifest_document("sample", "data/sample.bin", payload)
+ document["locations"] = {
+ "local_path": "data/sample.bin",
+ "canonical": "https://primary.invalid/sample",
+ "mirrors": ["https://mirror.invalid/sample"],
+ }
+ manifest = write_manifest(tmp_path / "manifest.json", document)
+
+ def opener(url):
+ if "primary" in url:
+ raise OSError("offline")
+ return BytesIO(b"tampered")
+
+ with pytest.raises(DatasetFetchError) as raised:
+ fetch_dataset(manifest, tmp_path / "store", opener=opener)
+ assert [attempt.status for attempt in raised.value.attempts] == [
+ FetchAttemptStatus.TRANSPORT_ERROR,
+ FetchAttemptStatus.IDENTITY_MISMATCH,
+ ]
+ assert not list((tmp_path / "store").rglob("*.*"))
+
+
+def test_fetch_rejects_oversized_response_without_reading_it_unbounded(tmp_path):
+ payload = b"small"
+ document = manifest_document("sample", "data/sample.bin", payload)
+ document["locations"]["canonical"] = "https://primary.invalid/sample"
+ manifest = write_manifest(tmp_path / "manifest.json", document)
+ with pytest.raises(DatasetFetchError) as raised:
+ fetch_dataset(manifest, tmp_path / "store", opener=lambda _: BytesIO(b"x" * 100))
+ assert raised.value.attempts[0].status is FetchAttemptStatus.IDENTITY_MISMATCH
+
+
+def test_prepare_materializes_verified_blob_and_is_idempotent(tmp_path):
+ payload = b"dataset"
+ manifest = write_manifest(
+ tmp_path / "manifest.json", manifest_document("sample", "data/sample.bin", payload)
+ )
+ store = tmp_path / "store"
+ source = blob_path(store, manifest.content.sha256)
+ source.parent.mkdir(parents=True)
+ source.write_bytes(payload)
+
+ result = prepare_dataset(manifest, tmp_path, store)
+ assert result.status is PrepareStatus.MATERIALIZED
+ assert result.path.read_bytes() == payload
+ assert prepare_dataset(manifest, tmp_path, store).status is PrepareStatus.ALREADY_PREPARED
+
+
+def test_prepare_requires_verified_store_blob(tmp_path):
+ payload = b"dataset"
+ manifest = write_manifest(
+ tmp_path / "manifest.json", manifest_document("sample", "data/sample.bin", payload)
+ )
+ store = tmp_path / "store"
+ with pytest.raises(DatasetPrepareError) as missing:
+ prepare_dataset(manifest, tmp_path, store)
+ assert missing.value.failure is PrepareFailure.STORE_MISSING
+
+ source = blob_path(store, manifest.content.sha256)
+ source.parent.mkdir(parents=True)
+ source.write_bytes(b"tampered")
+ with pytest.raises(DatasetPrepareError) as mismatch:
+ prepare_dataset(manifest, tmp_path, store)
+ assert mismatch.value.failure is PrepareFailure.STORE_IDENTITY_MISMATCH
+
+
+def test_prepare_reuses_verified_destination_without_requiring_store_copy(tmp_path):
+ payload = b"dataset"
+ manifest = write_manifest(
+ tmp_path / "manifest.json", manifest_document("sample", "data/sample.bin", payload)
+ )
+ destination = tmp_path / "data" / "sample.bin"
+ destination.parent.mkdir()
+ destination.write_bytes(payload)
+ result = prepare_dataset(manifest, tmp_path, tmp_path / "absent-store")
+ assert result.status is PrepareStatus.ALREADY_PREPARED
+ assert result.path == destination
+
+
+def test_prepare_refuses_to_replace_divergent_destination_by_default(tmp_path):
+ payload = b"dataset"
+ manifest = write_manifest(
+ tmp_path / "manifest.json", manifest_document("sample", "data/sample.bin", payload)
+ )
+ store = tmp_path / "store"
+ source = blob_path(store, manifest.content.sha256)
+ source.parent.mkdir(parents=True)
+ source.write_bytes(payload)
+ destination = tmp_path / "data" / "sample.bin"
+ destination.parent.mkdir()
+ destination.write_bytes(b"local work")
+
+ with pytest.raises(DatasetPrepareError) as mismatch:
+ prepare_dataset(manifest, tmp_path, store)
+ assert mismatch.value.failure is PrepareFailure.DESTINATION_IDENTITY_MISMATCH
+ assert destination.read_bytes() == b"local work"
+ assert prepare_dataset(manifest, tmp_path, store, replace=True).status is PrepareStatus.MATERIALIZED
+ assert destination.read_bytes() == payload
diff --git a/tests/test_data_cli.py b/tests/test_data_cli.py
new file mode 100644
index 00000000..6b49b189
--- /dev/null
+++ b/tests/test_data_cli.py
@@ -0,0 +1,92 @@
+import json
+from pathlib import Path
+
+from decluster import data_cli
+from decluster.data_catalog import (
+ FetchResult,
+ FetchStatus,
+ PrepareResult,
+ PrepareStatus,
+)
+
+
+ROOT = Path(__file__).resolve().parents[1]
+
+
+def test_list_is_machine_readable_and_deterministic(capsys):
+ assert data_cli.main(["--root", str(ROOT), "--json", "list"]) == 0
+ records = json.loads(capsys.readouterr().out)
+ expected_ids = sorted(path.stem for path in (ROOT / "catalog" / "datasets").glob("*.json"))
+ assert [item["id"] for item in records] == expected_ids
+ assert set(records[0]) == {"id", "kind", "bytes", "sha256"}
+
+
+def test_status_reports_without_turning_missing_data_into_command_failure(tmp_path, capsys):
+ catalog = tmp_path / "catalog" / "datasets"
+ catalog.mkdir(parents=True)
+ source = ROOT / "catalog" / "datasets" / "lumen-explorer-data-v1.json"
+ (catalog / source.name).write_bytes(source.read_bytes())
+ assert data_cli.main(["--root", str(tmp_path), "status"]) == 0
+ assert "lumen-explorer-data-v1\tmissing" in capsys.readouterr().out
+
+
+def test_verify_returns_failure_for_missing_data(tmp_path, capsys):
+ catalog = tmp_path / "catalog" / "datasets"
+ catalog.mkdir(parents=True)
+ source = ROOT / "catalog" / "datasets" / "lumen-explorer-data-v1.json"
+ (catalog / source.name).write_bytes(source.read_bytes())
+ assert data_cli.main(["--root", str(tmp_path), "verify"]) == 1
+ assert "missing" in capsys.readouterr().out
+
+
+def test_catalog_error_has_distinct_exit_code(tmp_path, capsys):
+ assert data_cli.main(["--root", str(tmp_path), "verify"]) == 2
+ captured = capsys.readouterr()
+ assert not captured.out
+ assert "catalog is empty" in captured.err
+
+
+def test_fetch_delegates_to_api_and_reports_materialized_path(monkeypatch, capsys):
+ destination = ROOT / "artifacts" / "sha256" / "example"
+
+ def fake_fetch(dataset, store):
+ assert store == ROOT / "custom-store"
+ return FetchResult(dataset, FetchStatus.DOWNLOADED, destination, "https://source.invalid")
+
+ monkeypatch.setattr(data_cli, "fetch_dataset", fake_fetch)
+ assert data_cli.main([
+ "--root", str(ROOT), "--store", "custom-store", "--json",
+ "fetch", "lumen-explorer-data-v1",
+ ]) == 0
+ record = json.loads(capsys.readouterr().out)[0]
+ assert record == {
+ "id": "lumen-explorer-data-v1",
+ "path": str(destination),
+ "source_url": "https://source.invalid",
+ "status": "downloaded",
+ }
+
+
+def test_fetch_requires_a_known_dataset_id(capsys):
+ assert data_cli.main(["--root", str(ROOT), "fetch"]) == 2
+ assert "dataset id is required" in capsys.readouterr().err
+ assert data_cli.main(["--root", str(ROOT), "fetch", "unknown"]) == 2
+ assert "unknown dataset id" in capsys.readouterr().err
+
+
+def test_prepare_delegates_to_api_without_duplicating_materialization(monkeypatch, capsys):
+ destination = ROOT / "tests" / "fixtures" / "lumen_explorer_data.json"
+
+ def fake_prepare(dataset, root, store, *, replace):
+ assert root == ROOT
+ assert store == ROOT / "artifacts"
+ assert replace is True
+ return PrepareResult(dataset, PrepareStatus.ALREADY_PREPARED, destination)
+
+ monkeypatch.setattr(data_cli, "prepare_dataset", fake_prepare)
+ assert data_cli.main([
+ "--root", str(ROOT), "--replace", "prepare", "lumen-explorer-data-v1",
+ ]) == 0
+ assert capsys.readouterr().out.strip().endswith(
+ f"already_prepared\t{destination}"
+ )
diff --git a/tests/test_data_manifest.py b/tests/test_data_manifest.py
new file mode 100644
index 00000000..536f351f
--- /dev/null
+++ b/tests/test_data_manifest.py
@@ -0,0 +1,414 @@
+import json
+import hashlib
+from pathlib import Path
+
+import pytest
+
+from decluster.data_manifest import ManifestError, load_dataset_manifest, load_run_manifest
+from decluster.reference_registry import load_claims, load_sources
+from decluster.result_artifacts import OutputStatus, verify_run_outputs
+
+
+DIGEST = "a" * 64
+ROOT = Path(__file__).resolve().parent.parent
+
+
+def dataset_document(**overrides):
+ document = {
+ "schema_version": 1,
+ "id": "fixture-v1",
+ "title": "Small deterministic fixture",
+ "kind": "git_fixture",
+ "schema": "tx-ndjson-v1",
+ "format": "ndjson",
+ "content": {"bytes": 12, "sha256": DIGEST},
+ "locations": {"local_path": "tests/fixtures/example.ndjson", "canonical": None, "mirrors": []},
+ "source": {"provider": "decluster", "snapshot_time": None, "recipe": None, "recipe_sha256": None},
+ "license": {"data": "MIT", "recipe": None},
+ "redistribution": "allowed",
+ "sensitivity": "synthetic",
+ "normalization": {"code_revision": None, "parameters": {}, "parents": []},
+ "determinism": {"ordering": "txid ascending", "rng_algorithm": None, "rng_seed": None},
+ }
+ document.update(overrides)
+ return document
+
+
+def run_document(**overrides):
+ document = {
+ "schema_version": 1,
+ "id": "demo-run-v1",
+ "claim_ids": ["ctp.demo"],
+ "code": {"revision": "abc123", "dirty": False},
+ "command": {"argv": ["python", "-m", "decluster.demo"]},
+ "environment": {"python": "3.13", "lock_digest": None, "platform": "any", "dependencies": []},
+ "datasets": [{"id": "fixture-v1", "sha256": DIGEST}],
+ "parameters": {"cutoff": 2},
+ "rng": {"algorithm": "MT19937", "seeds": [7]},
+ "outputs": [{"path": "results/demo.json", "bytes": 4, "sha256": "b" * 64}],
+ "reproducibility": {"level": "bitwise_reproducible", "availability": "complete"},
+ "verification": {
+ "mode": "exact",
+ "argv": ["python", "-m", "pytest", "tests/test_demo.py"],
+ "tests": ["tests/test_demo.py::test_result"],
+ "properties": ["output matches canonical artifact"],
+ "tolerance": None,
+ },
+ "limitations": ["synthetic fixture"],
+ }
+ document.update(overrides)
+ return document
+
+
+def write_json(path, document):
+ path.write_text(json.dumps(document))
+ return path
+
+
+def test_dataset_manifest_round_trip(tmp_path):
+ manifest = load_dataset_manifest(write_json(tmp_path / "dataset.json", dataset_document()))
+ assert manifest.id == "fixture-v1"
+ assert manifest.content.sha256 == DIGEST
+ assert manifest.local_path == "tests/fixtures/example.ndjson"
+ assert manifest.parameters == {}
+ with pytest.raises(TypeError):
+ manifest.parameters["new"] = 1
+
+
+def test_dataset_manifest_rejects_unknown_fields(tmp_path):
+ document = dataset_document(surprise=True)
+ with pytest.raises(ManifestError, match="unknown=.*surprise"):
+ load_dataset_manifest(write_json(tmp_path / "dataset.json", document))
+
+
+def test_derived_dataset_requires_parent(tmp_path):
+ document = dataset_document(kind="derived")
+ with pytest.raises(ManifestError, match="must name at least one parent"):
+ load_dataset_manifest(write_json(tmp_path / "dataset.json", document))
+
+
+def test_rng_seed_requires_named_algorithm(tmp_path):
+ document = dataset_document()
+ document["determinism"] = {"ordering": "stable", "rng_algorithm": None, "rng_seed": 7}
+ with pytest.raises(ManifestError, match="rng_seed requires rng_algorithm"):
+ load_dataset_manifest(write_json(tmp_path / "dataset.json", document))
+
+
+def test_run_manifest_resolves_claim_and_dataset(tmp_path):
+ dataset = load_dataset_manifest(write_json(tmp_path / "dataset.json", dataset_document()))
+ run = load_run_manifest(
+ write_json(tmp_path / "run.json", run_document()),
+ claim_ids={"ctp.demo"}, datasets={dataset.id: dataset},
+ )
+ assert run.claim_ids == ("ctp.demo",)
+ assert run.datasets[0].id == dataset.id
+ assert run.rng_seeds == (7,)
+ assert run.reproducibility_level.value == "bitwise_reproducible"
+ assert run.verification.mode.value == "exact"
+ assert run.verification.tests == ("tests/test_demo.py::test_result",)
+
+
+def test_run_manifest_preserves_dependency_provenance(tmp_path):
+ dataset = load_dataset_manifest(write_json(tmp_path / "dataset.json", dataset_document()))
+ document = run_document()
+ document["environment"]["dependencies"] = [{
+ "name": "dss", "version": "0.1.0", "source": "https://example.invalid/dss",
+ "revision": "abc123", "lock_sha256": DIGEST, "license": "unknown",
+ "redistribution": "unknown", "editable": True,
+ }]
+ run = load_run_manifest(
+ write_json(tmp_path / "run.json", document),
+ claim_ids={"ctp.demo"}, datasets={dataset.id: dataset},
+ )
+ assert run.dependencies[0].name == "dss"
+ assert run.dependencies[0].editable is True
+ assert run.dependencies[0].redistribution.value == "unknown"
+
+
+def test_run_manifest_rejects_unknown_claim(tmp_path):
+ dataset = load_dataset_manifest(write_json(tmp_path / "dataset.json", dataset_document()))
+ with pytest.raises(ManifestError, match="unknown claim ids"):
+ load_run_manifest(
+ write_json(tmp_path / "run.json", run_document()),
+ claim_ids=set(), datasets={dataset.id: dataset},
+ )
+
+
+def test_run_manifest_rejects_dataset_digest_mismatch(tmp_path):
+ dataset = load_dataset_manifest(write_json(tmp_path / "dataset.json", dataset_document()))
+ document = run_document()
+ document["datasets"] = [{"id": dataset.id, "sha256": "c" * 64}]
+ with pytest.raises(ManifestError, match="does not match dataset manifest"):
+ load_run_manifest(
+ write_json(tmp_path / "run.json", document),
+ claim_ids={"ctp.demo"}, datasets={dataset.id: dataset},
+ )
+
+
+def test_run_manifest_requires_output(tmp_path):
+ dataset = load_dataset_manifest(write_json(tmp_path / "dataset.json", dataset_document()))
+ with pytest.raises(ManifestError, match="outputs: expected non-empty"):
+ load_run_manifest(
+ write_json(tmp_path / "run.json", run_document(outputs=[])),
+ claim_ids={"ctp.demo"}, datasets={dataset.id: dataset},
+ )
+
+
+@pytest.mark.parametrize("path", ["../outside.json", "/tmp/out.json", "."])
+def test_run_manifest_rejects_unsafe_output_paths(tmp_path, path):
+ dataset = load_dataset_manifest(write_json(tmp_path / "dataset.json", dataset_document()))
+ document = run_document()
+ document["outputs"] = [{"path": path, "bytes": 4, "sha256": "b" * 64}]
+ with pytest.raises(ManifestError, match="safe relative path"):
+ load_run_manifest(
+ write_json(tmp_path / "run.json", document),
+ claim_ids={"ctp.demo"}, datasets={dataset.id: dataset},
+ )
+
+
+def test_run_manifest_rejects_duplicate_output_paths(tmp_path):
+ dataset = load_dataset_manifest(write_json(tmp_path / "dataset.json", dataset_document()))
+ document = run_document()
+ document["outputs"] = document["outputs"] * 2
+ with pytest.raises(ManifestError, match="duplicate"):
+ load_run_manifest(
+ write_json(tmp_path / "run.json", document),
+ claim_ids={"ctp.demo"}, datasets={dataset.id: dataset},
+ )
+
+
+def test_run_manifest_reports_non_array_datasets(tmp_path):
+ with pytest.raises(ManifestError, match="datasets: expected array"):
+ load_run_manifest(
+ write_json(tmp_path / "run.json", run_document(datasets={})),
+ claim_ids={"ctp.demo"}, datasets={},
+ )
+
+
+def test_run_manifest_requires_an_executable_check(tmp_path):
+ dataset = load_dataset_manifest(write_json(tmp_path / "dataset.json", dataset_document()))
+ document = run_document()
+ document["verification"] = {
+ "mode": "exact", "argv": ["verify"], "tests": [], "properties": [], "tolerance": None,
+ }
+ with pytest.raises(ManifestError, match="at least one test or property"):
+ load_run_manifest(
+ write_json(tmp_path / "run.json", document),
+ claim_ids={"ctp.demo"}, datasets={dataset.id: dataset},
+ )
+
+
+@pytest.mark.parametrize("mode,tolerance", [("exact", "1e-9"), ("tolerance", None)])
+def test_run_manifest_rejects_incoherent_tolerance(tmp_path, mode, tolerance):
+ dataset = load_dataset_manifest(write_json(tmp_path / "dataset.json", dataset_document()))
+ document = run_document()
+ document["verification"] = {
+ **document["verification"], "mode": mode, "tolerance": tolerance,
+ }
+ document["reproducibility"] = {"level": "verified", "availability": "complete"}
+ with pytest.raises(ManifestError, match="tolerance"):
+ load_run_manifest(
+ write_json(tmp_path / "run.json", document),
+ claim_ids={"ctp.demo"}, datasets={dataset.id: dataset},
+ )
+
+
+def test_bitwise_run_requires_exact_verification(tmp_path):
+ dataset = load_dataset_manifest(write_json(tmp_path / "dataset.json", dataset_document()))
+ document = run_document()
+ document["verification"] = {
+ **document["verification"], "mode": "tolerance", "tolerance": "absolute <= 1e-9",
+ }
+ with pytest.raises(ManifestError, match="bitwise reproducibility requires exact"):
+ load_run_manifest(
+ write_json(tmp_path / "run.json", document),
+ claim_ids={"ctp.demo"}, datasets={dataset.id: dataset},
+ )
+
+
+def test_all_committed_dataset_manifests_match_their_files():
+ manifests = sorted((ROOT / "catalog" / "datasets").glob("*.json"))
+ expected_ids = {
+ "amount-channel-812695-812831-v1",
+ "epoch-graph-2016-391104-392111-v1",
+ "epoch-graph-2016-392112-393119-v1",
+ "epoch-graph-2016-393120-394127-v1",
+ "epoch-graph-2016-394128-395135-v1",
+ "epoch-graph-2016-395136-396143-v1",
+
+ "boltzmann-fee-audit-v1",
+ "conservation-round-three-v1",
+ "entity-bitmex-2019-v1",
+ "entity-satoshidice-2013-v1",
+ "fingerprint-blkcache-sample-v1",
+ "fs-blkcache-2026-09-04-v1",
+ "graph-deanon-2016-v1",
+ "lumen-explorer-data-v1",
+ "merged-anchor-931d6627-v1",
+ "ns-bitcoin-left-2016-v1",
+ "partition-cuts-300k-2016-v1",
+ "ns-bitcoin-right-2016-v1",
+ "reid-signatures-v1",
+ "slice-a-channels-2016-v1",
+ "subtx-demix-cache-2026-09-04-v1",
+ }
+ seen = set()
+ for path in manifests:
+ manifest = load_dataset_manifest(path)
+ assert manifest.id not in seen
+ seen.add(manifest.id)
+ fixture = ROOT / manifest.local_path
+ assert fixture.is_file(), manifest.id
+ assert fixture.stat().st_size == manifest.content.bytes, manifest.id
+ digest = hashlib.sha256(fixture.read_bytes()).hexdigest()
+ assert digest == manifest.content.sha256, manifest.id
+ assert seen == expected_ids
+
+
+def test_unknown_fixture_licensing_is_not_presented_as_redistributable():
+ for path in (ROOT / "catalog" / "datasets").glob("*.json"):
+ manifest = load_dataset_manifest(path)
+ if manifest.data_license == "unknown":
+ assert manifest.redistribution.value == "unknown"
+
+
+def test_all_committed_run_manifests_resolve_and_their_outputs_match():
+ sources = load_sources(ROOT / "catalog" / "ctp-sources.json")
+ claims = load_claims(
+ ROOT / "catalog" / "ctp-claims.json", {source.id for source in sources}
+ )
+ datasets = {
+ manifest.id: manifest
+ for manifest in (
+ load_dataset_manifest(path)
+ for path in (ROOT / "catalog" / "datasets").glob("*.json")
+ )
+ }
+ paths = sorted((ROOT / "catalog" / "runs").glob("*.json"))
+ assert paths
+ for path in paths:
+ manifest = load_run_manifest(
+ path, claim_ids={claim.id for claim in claims}, datasets=datasets,
+ )
+ checks = verify_run_outputs(manifest, ROOT)
+ assert checks
+ assert all(check.status is OutputStatus.VERIFIED for check in checks)
+
+
+def test_every_canonical_artifact_is_owned_by_a_run_manifest():
+ sources = load_sources(ROOT / "catalog" / "ctp-sources.json")
+ claims = load_claims(
+ ROOT / "catalog" / "ctp-claims.json", {source.id for source in sources}
+ )
+ datasets = {
+ manifest.id: manifest
+ for manifest in (
+ load_dataset_manifest(path)
+ for path in (ROOT / "catalog" / "datasets").glob("*.json")
+ )
+ }
+ declared = set()
+ for path in sorted((ROOT / "catalog" / "runs").glob("*.json")):
+ manifest = load_run_manifest(
+ path, claim_ids={claim.id for claim in claims}, datasets=datasets,
+ )
+ declared.update(output.path for output in manifest.outputs)
+ canonical = {
+ str(path.relative_to(ROOT))
+ for directory in (ROOT / "results" / "artifacts", ROOT / "results" / "generated")
+ for path in directory.iterdir()
+ if path.is_file()
+ }
+
+ assert canonical == declared, (
+ f"canonical outputs without manifests={sorted(canonical - declared)}; "
+ f"manifest outputs without canonical files={sorted(declared - canonical)}"
+ )
+
+
+# The five `results/*.json` beside the canonical artifacts are not leftovers: each is the frozen
+# report a canonical run is held against, and deleting one silently removes that guard. It was
+# nearly deleted as redundant during the audit, on a reference sweep that searched the documents
+# and the examples but not the tests.
+HISTORICAL_BASELINES = {
+ "results/boltzmann-fee-audit.json": "tests/test_boltzmann_fee_experiment.py",
+ "results/fs-ablation.json": "tests/test_fs_ablation_experiment.py",
+ "results/fs-temporal.json": "tests/test_fs_temporal_experiment.py",
+ "results/link-prediction.json": "tests/test_link_prediction_experiment.py",
+ "results/ns-bitcoin.json": "tests/test_ns_bitcoin_experiment.py",
+}
+
+
+def test_every_historical_baseline_is_present_and_still_guarded():
+ for baseline, guard in HISTORICAL_BASELINES.items():
+ assert (ROOT / baseline).is_file(), f"{baseline} is the frozen report {guard} compares against"
+ assert baseline.split("/", 1)[1] in (ROOT / guard).read_text(), (
+ f"{guard} no longer reads {baseline}; if the guard moved, this list has to move with it"
+ )
+
+
+def test_no_other_result_json_sits_outside_the_canonical_directories():
+ loose = {
+ str(path.relative_to(ROOT))
+ for path in (ROOT / "results").glob("*.json")
+ }
+ assert loose - set(HISTORICAL_BASELINES) == {"results/scale_output.json"}
+
+
+def test_every_run_manifest_is_carried_by_a_bundle():
+ """A run nothing bundles is a result the reproduction gate never executes.
+
+ `descent-vs-ascent-v1` — the run this repository's central comparison rests on — sat outside
+ every bundle for a day, verifying its own outputs and never being reproduced from a pinned
+ runtime by anyone.
+ """
+ runs = {path.stem for path in (ROOT / "catalog" / "runs").glob("*.json")}
+ carried = set()
+ for index in (ROOT / "releases").glob("*.bundle.json"):
+ carried.update(json.loads(index.read_text())["runs"])
+ assert runs - carried == set(), f"runs no bundle carries: {sorted(runs - carried)}"
+ assert carried - runs == set(), f"bundles naming absent runs: {sorted(carried - runs)}"
+
+
+def test_every_experiment_test_compares_against_a_fresh_execution():
+ """A test that only asserts named fields lets every other field drift past the offline suite.
+
+ Two files asserted published literals and never compared the whole artifact, so a change
+ anywhere else in them reached green here and was caught twenty minutes later by the
+ reproduction gate, if at all.
+ """
+ import re
+
+ fresh = re.compile(r"verify_artifact|\"verify\"|'verify'|artifact\s*==\s*stored"
+ r"|stored\s*==\s*artifact|== *json\.loads|canonical_json_bytes")
+ builds = re.compile(r"build_artifact|verify_artifact")
+ without = [
+ path.name
+ for path in sorted((ROOT / "tests").glob("test_*.py"))
+ if builds.search(path.read_text()) and not fresh.search(path.read_text())
+ ]
+ assert not without, (
+ f"these build an artifact and never compare it to the stored one: {without}"
+ )
+
+
+def test_every_run_passes_its_datasets_on_the_command_line():
+ """A dataset the argv never names cannot be redirected at the materialized bundle copy.
+
+ `_resolve_dataset_arguments` rewrites an argument that exactly equals a declared dataset's
+ local path, and nothing else. A run that hardcodes its inputs instead therefore verifies fine
+ in the tree — where those relative paths resolve — and fails the moment a third party
+ reproduces it from the bundle, which is the only run of it that proves anything.
+ `graph-rejoin-2016-v1` shipped that way and its cold start had never once succeeded.
+ """
+ catalog = {manifest["id"]: manifest["locations"]["local_path"]
+ for manifest in (json.loads(path.read_text())
+ for path in (ROOT / "catalog" / "datasets").glob("*.json"))}
+ missing = {}
+ for path in sorted((ROOT / "catalog" / "runs").glob("*.json")):
+ run = json.loads(path.read_text())
+ absent = [catalog[entry["id"]] for entry in run.get("datasets", [])
+ if entry["id"] in catalog and catalog[entry["id"]] not in run["command"]["argv"]]
+ if absent:
+ missing[path.stem] = absent
+ assert not missing, f"runs whose argv does not name their datasets: {missing}"
diff --git a/tests/test_declustering.py b/tests/test_declustering.py
new file mode 100644
index 00000000..c4a56acf
--- /dev/null
+++ b/tests/test_declustering.py
@@ -0,0 +1,116 @@
+import random
+
+import pytest
+
+from decluster.declustering import decluster
+from decluster.partition import Partition
+
+
+def table_weight(pairs, default=0.0):
+ """A weight function reading a dict keyed by unordered pairs."""
+ lookup = {frozenset(pair): value for pair, value in pairs.items()}
+ return lambda a, b: lookup.get(frozenset((a, b)), default)
+
+
+def test_a_positive_threshold_is_refused():
+ with pytest.raises(ValueError):
+ decluster(Partition([[1, 2]]), lambda a, b: 0.0, cut_below=0.5)
+
+
+def test_evidence_for_a_pairing_never_cuts_it():
+ inherited = Partition([[1, 2, 3], [4, 5]])
+ result = decluster(inherited, lambda a, b: 4.0, cut_below=-1.0)
+ assert result.partition == inherited
+ assert result.cut == ()
+
+
+def test_two_groups_with_evidence_against_each_other_are_separated():
+ weight = table_weight(
+ {(1, 2): 5.0, (3, 4): 5.0} | {pair: -4.0 for pair in ((1, 3), (1, 4), (2, 3), (2, 4))}
+ )
+ result = decluster(Partition([[1, 2, 3, 4]]), weight, cut_below=-1.0)
+ assert result.partition == Partition([[1, 2], [3, 4]])
+ outcome, = result.cut
+ assert (outcome.interior_weight, outcome.severed_weight) == (10.0, -16.0)
+
+
+def test_one_negative_pair_does_not_carry_a_cut_the_block_argues_against():
+ # Admissibility is between groups: 1 owes -3 to 2 but +5 to 3, so no split clears the bar.
+ weight = table_weight({(1, 2): -3.0, (1, 3): 5.0, (2, 3): 5.0})
+ result = decluster(Partition([[1, 2, 3]]), weight, cut_below=-1.0)
+ assert result.partition == Partition([[1, 2, 3]])
+
+
+def test_evidence_against_every_pairing_shatters_the_block():
+ result = decluster(Partition([[1, 2, 3]]), lambda a, b: -5.0, cut_below=-1.0)
+ assert result.partition == Partition.discrete([1, 2, 3])
+
+
+def test_two_equally_supported_cuts_leave_the_block_whole():
+ # 1|23 and 2|13 both sever -2 and keep 8; naming either would place a boundary evidence does not.
+ weight = table_weight({(1, 2): -10.0, (1, 3): 8.0, (2, 3): 8.0})
+ result = decluster(Partition([[1, 2, 3]]), weight, cut_below=-1.0)
+ assert result.partition == Partition([[1, 2, 3]])
+ assert result.cut == ()
+
+
+def test_a_block_too_large_to_search_gets_the_conservative_pass_and_is_counted():
+ block = list(range(6))
+ result = decluster(Partition([block]), lambda a, b: -5.0, cut_below=-1.0, max_block=5)
+ assert result.partition == Partition.discrete(block)
+ assert result.approximated == (frozenset(block),)
+ assert result.summary()["approximated"] == 1
+
+
+def test_the_conservative_pass_keeps_together_anything_a_pair_holds():
+ # 1-2 is above the bar, so no boundary may separate them however the rest argues.
+ weight = table_weight({(1, 2): 0.0}, default=-9.0)
+ result = decluster(Partition([[1, 2, 3, 4]]), weight, cut_below=-1.0, max_block=3)
+ assert result.approximated == (frozenset({1, 2, 3, 4}),)
+ assert result.partition.same_block(1, 2)
+ assert result.partition == Partition([[1, 2], [3], [4]])
+
+
+def test_the_conservative_pass_is_admissible_at_any_size():
+ rng = random.Random(5)
+ for _ in range(30):
+ members = list(range(14))
+ pairs = {(a, b): rng.choice([0.0, -2.0, -8.0, 3.0])
+ for a in members for b in members if a < b}
+ weight = table_weight(pairs)
+ result = decluster(Partition([members]), weight, cut_below=-1.0, max_block=9)
+ groups = sorted(result.partition.blocks(), key=lambda g: sorted(g))
+ for index, left in enumerate(groups):
+ for right in groups[index + 1:]:
+ crossing = sum(weight(a, b) for a in left for b in right)
+ assert crossing <= -1.0
+
+
+def test_singletons_are_neither_cut_nor_counted():
+ result = decluster(Partition.discrete([1, 2, 3]), lambda a, b: -5.0, cut_below=-1.0)
+ assert result.partition == Partition.discrete([1, 2, 3])
+ assert result.outcomes == () and result.approximated == ()
+
+
+def test_a_stricter_threshold_never_cuts_more():
+ weight = table_weight({(1, 2): -2.0, (1, 3): -6.0, (2, 3): -6.0})
+ lenient = decluster(Partition([[1, 2, 3]]), weight, cut_below=-1.0).partition
+ strict = decluster(Partition([[1, 2, 3]]), weight, cut_below=-5.0).partition
+ assert lenient < strict
+
+
+def test_the_result_only_ever_refines_the_partition_it_was_given():
+ rng = random.Random(17)
+ for _ in range(40):
+ labels = {element: rng.randrange(1, 4) for element in range(8)}
+ inherited = Partition.from_lookup(labels)
+ pairs = {
+ (a, b): rng.uniform(-6.0, 6.0) for a in range(8) for b in range(a + 1, 8)
+ }
+ result = decluster(inherited, table_weight(pairs), cut_below=-1.0)
+ assert result.partition.refines(inherited)
+
+
+def test_the_threshold_travels_with_the_result():
+ result = decluster(Partition([[1, 2]]), lambda a, b: -9.0, cut_below=-2.5)
+ assert result.cut_below == -2.5 and result.summary()["cut_below"] == -2.5
diff --git a/tests/test_def1_sparsity.py b/tests/test_def1_sparsity.py
index 47890bbf..1dca42a8 100644
--- a/tests/test_def1_sparsity.py
+++ b/tests/test_def1_sparsity.py
@@ -2,7 +2,7 @@
sys.path.insert(0, os.path.dirname(os.path.dirname(__file__)))
from decluster.def1_sparsity import (cosine, feature_vector, nearest_similarities,
survival)
-from decluster.views import PseudonymGraph
+from decluster.contraction import PseudonymGraph
def test_cosine_basic():
diff --git a/tests/test_denomination_preparation.py b/tests/test_denomination_preparation.py
new file mode 100644
index 00000000..5306f671
--- /dev/null
+++ b/tests/test_denomination_preparation.py
@@ -0,0 +1,67 @@
+import pytest
+
+from decluster.domain import Inconclusive, TransactionFingerprintObserved
+from decluster.failure_modes.denomination_preparation import (
+ DenominationPreparationObservation,
+ evaluate,
+ fingerprint,
+ paired_corpus,
+)
+
+
+def test_preparations_match_the_declared_predecessor_fingerprint():
+ preparations, _ = paired_corpus()
+
+ assert all(
+ isinstance(evaluate(observation).outcomes[0], TransactionFingerprintObserved)
+ for observation in preparations
+ )
+ assert all(
+ dict(fingerprint(observation).features)[
+ "exact_denomination_outputs_spent_next"
+ ] == 1
+ for observation in preparations
+ )
+
+
+def test_arity_matched_controls_do_not_match():
+ preparations, controls = paired_corpus()
+
+ assert {(item.input_count, len(item.output_amounts)) for item in preparations} == {
+ (item.input_count, len(item.output_amounts)) for item in controls
+ }
+ assert all(isinstance(evaluate(item).outcomes[0], Inconclusive) for item in controls)
+
+
+def test_report_does_not_turn_shape_into_ownership():
+ report = evaluate(paired_corpus()[0][0])
+
+ assert report.channels[0].identifier == "transaction_shape"
+ assert report.composition is None
+ assert "not proof" in report.limitations[2]
+
+
+def test_next_coinjoin_indices_must_reference_an_output():
+ observation = paired_corpus()[0][0]
+
+ with pytest.raises(ValueError, match="outside"):
+ DenominationPreparationObservation(
+ observation.transaction,
+ observation.input_count,
+ observation.output_amounts,
+ frozenset({len(observation.output_amounts)}),
+ observation.denomination,
+ )
+
+
+def test_denomination_must_be_positive():
+ observation = paired_corpus()[0][0]
+
+ with pytest.raises(ValueError, match="positive"):
+ DenominationPreparationObservation(
+ observation.transaction,
+ observation.input_count,
+ observation.output_amounts,
+ observation.next_coinjoin_inputs,
+ 0,
+ )
diff --git a/tests/test_denomination_preparation_experiment.py b/tests/test_denomination_preparation_experiment.py
new file mode 100644
index 00000000..fabd5b3a
--- /dev/null
+++ b/tests/test_denomination_preparation_experiment.py
@@ -0,0 +1,33 @@
+import json
+
+import pytest
+
+from decluster.experiments import denomination_preparation as experiment
+
+
+def test_artifact_separates_the_paired_synthetic_populations():
+ artifact = experiment.build_artifact()
+
+ assert artifact["preparations"]["fingerprint_matches"] == 3
+ assert artifact["arity_matched_controls"]["fingerprint_matches"] == 0
+ assert artifact["composition"] is None
+
+
+def test_verifier_recomputes_the_fingerprint():
+ changed = json.loads(json.dumps(experiment.build_artifact()))
+ changed["arity_matched_controls"]["fingerprint_matches"] = 1
+
+ with pytest.raises(experiment.VerificationError, match="fresh failure-mode"):
+ experiment.verify_artifact(changed)
+
+
+def test_cli_reproduces_and_verifies_json_and_markdown(tmp_path):
+ artifact = tmp_path / "artifact.json"
+ markdown = tmp_path / "result.md"
+
+ assert experiment.main(
+ ["reproduce", "--artifact", str(artifact), "--markdown", str(markdown)]
+ ) == 0
+ assert experiment.main(
+ ["verify", "--artifact", str(artifact), "--markdown", str(markdown)]
+ ) == 0
diff --git a/tests/test_descent_vs_ascent.py b/tests/test_descent_vs_ascent.py
new file mode 100644
index 00000000..f3866276
--- /dev/null
+++ b/tests/test_descent_vs_ascent.py
@@ -0,0 +1,102 @@
+import gzip
+
+import pytest
+
+from decluster.experiments import descent_vs_ascent as experiment
+from decluster.partition import Partition
+
+DATASET = "tests/fixtures/slice_a_channels_2016.ndjson.gz"
+
+
+def test_reproduce_then_verify_snapshot(tmp_path):
+ artifact = tmp_path / "artifact.json"
+ markdown = tmp_path / "result.md"
+ argv = ["--dataset", DATASET]
+ assert experiment.main(argv + [
+ "reproduce", "--artifact", str(artifact), "--markdown", str(markdown),
+ ]) == 0
+ assert experiment.main(argv + [
+ "verify", "--artifact", str(artifact), "--markdown", str(markdown),
+ ]) == 0
+
+
+def test_empty_snapshot_is_rejected(tmp_path):
+ dataset = tmp_path / "empty.ndjson.gz"
+ with gzip.open(dataset, "wt", encoding="utf-8"):
+ pass
+ with pytest.raises(experiment.VerificationError, match="snapshot is empty"):
+ experiment.build_artifact(dataset)
+
+
+def test_a_plain_cospend_contributes_no_evidence():
+ # Two inputs of one type funding one output: nothing here argues either way, and counting the
+ # co-spend itself would let the inherited partition vouch for the claim under test.
+ transaction = {
+ "txid": "t",
+ "vin": [
+ {"prevout": {"scriptpubkey_address": a, "scriptpubkey_type": "v0_p2wpkh",
+ "value": 5000}}
+ for a in ("a", "b")
+ ],
+ "vout": [{"scriptpubkey_address": "c", "value": 9000}],
+ }
+ assert experiment.pair_evidence([(transaction, 0)]) == {}
+
+
+def test_disagreeing_input_types_argue_against_the_pairing():
+ transaction = {
+ "txid": "t",
+ "vin": [
+ {"prevout": {"scriptpubkey_address": "a", "scriptpubkey_type": "v0_p2wpkh",
+ "value": 5000}},
+ {"prevout": {"scriptpubkey_address": "b", "scriptpubkey_type": "p2pkh",
+ "value": 5000}},
+ ],
+ "vout": [{"scriptpubkey_address": "c", "value": 9000}],
+ }
+ assert experiment.pair_evidence([(transaction, 0)])[("a", "b")] < 0
+
+
+def test_the_ascent_covering_an_unknown_address_is_refused():
+ with pytest.raises(experiment.VerificationError, match="addresses the inherited"):
+ experiment._shared_ground({"a": 1}, {"a": 1, "b": 1})
+
+
+def test_an_address_the_ascent_never_merged_becomes_a_singleton():
+ partition = experiment._shared_ground({"a": 1, "b": 1, "c": 1}, {"a": 9, "b": 9})
+ assert partition == Partition([["a", "b"], ["c"]])
+
+
+def test_the_descent_never_coarsens_the_partition_it_inherits():
+ artifact = experiment.build_artifact(DATASET)
+ runs = artifact["measurement"]["descent"]
+ assert runs and all(run["refines_inherited"] for run in runs)
+
+
+def test_the_ascent_is_finer_than_the_partition_it_replaces():
+ artifact = experiment.build_artifact(DATASET)
+ measured = artifact["measurement"]
+ assert measured["ascent_refines_inherited"]
+ assert measured["ascent_blocks"] >= measured["inherited_blocks"]
+
+
+def test_the_descent_never_separates_a_pair_the_ascent_keeps():
+ """The descent may recover less than the ascent; it must never claim more.
+
+ A cut the ascent did not make is a boundary the same evidence did not justify at merge time,
+ and inventing one is the error that reads downstream as privacy.
+ """
+ measured = experiment.build_artifact(DATASET)["measurement"]
+ for run in measured["descent"]:
+ assert run["agreement_with_ascent"]["joined_only_by_ascent"] == 0
+
+
+def test_the_conservative_pass_reaches_the_blocks_the_evidence_is_in():
+ measured = experiment.build_artifact(DATASET)["measurement"]
+ reach = measured["evidence_reach"]
+ # Every signalled pair sits above the exact bound, so a run that only searched exactly would
+ # cut nothing at all on this slice.
+ assert reach["negative_pairs_in_exactly_searched_blocks"] == 0
+ assert reach["negative_pairs_in_approximated_blocks"] == reach["signalled_pairs"]
+ loosest = max(measured["descent"], key=lambda run: run["cut_below"])
+ assert loosest["blocks_cut"] > 0 and loosest["blocks_approximated"] > 0
diff --git a/tests/test_deterministic_partition.py b/tests/test_deterministic_partition.py
new file mode 100644
index 00000000..9c8477b2
--- /dev/null
+++ b/tests/test_deterministic_partition.py
@@ -0,0 +1,36 @@
+import pytest
+
+from decluster.domain import ConditionalLinksMeasured, UnanimousMappingLinksEvidence
+from decluster.failure_modes.deterministic_partition import evaluate
+
+
+def test_forced_ctp_example_has_one_mapping_and_four_unanimous_links():
+ report = evaluate((1, 3, 20, 50), (4, 70))
+ evidence = report.channels[0].evidence[0]
+ assert isinstance(evidence, UnanimousMappingLinksEvidence)
+ assert evidence.mapping_count == 1
+ assert {(left.identifier, right.identifier) for left, right in evidence.links} == {
+ (("input", 0), ("output", 0)),
+ (("input", 1), ("output", 0)),
+ (("input", 2), ("output", 1)),
+ (("input", 3), ("output", 1)),
+ }
+ assert isinstance(report.outcomes[0], ConditionalLinksMeasured)
+ assert report.composition is None
+
+
+def test_underdetermined_ctp_control_has_three_mappings_and_no_unanimous_link():
+ evidence = evaluate((1, 2, 3, 4, 5), (7, 8)).channels[0].evidence[0]
+ assert evidence.mapping_count == 3
+ assert evidence.links == ()
+
+
+def test_absent_exact_mapping_is_refused():
+ with pytest.raises(ValueError, match="no exact"):
+ evaluate((1, 2), (4,))
+
+
+def test_result_declares_model_limitations():
+ report = evaluate((1, 3, 20, 50), (4, 70))
+ assert any("only within" in limitation for limitation in report.limitations)
+ assert any("net-settlement" in limitation for limitation in report.limitations)
diff --git a/tests/test_deterministic_partition_experiment.py b/tests/test_deterministic_partition_experiment.py
new file mode 100644
index 00000000..0c1f25a3
--- /dev/null
+++ b/tests/test_deterministic_partition_experiment.py
@@ -0,0 +1,23 @@
+from decluster.experiments.deterministic_partition import (
+ build_artifact,
+ render_markdown,
+ verify_artifact,
+)
+
+
+def test_artifact_contrasts_forced_and_underdetermined_examples():
+ artifact = build_artifact()
+ assert artifact["forced_partition"]["non_derived_mappings"] == 1
+ assert len(artifact["forced_partition"]["unanimous_links"]) == 4
+ assert artifact["underdetermined_control"]["non_derived_mappings"] == 3
+ assert artifact["underdetermined_control"]["unanimous_links"] == []
+
+
+def test_artifact_verifies_against_fresh_execution():
+ assert verify_artifact(build_artifact()) == build_artifact()
+
+
+def test_markdown_states_the_model_condition():
+ markdown = render_markdown(build_artifact())
+ assert "| forced partition | 1 | 4 |" in markdown
+ assert "conditional on exact per-block conservation" in markdown
diff --git a/tests/test_disjoint_routes.py b/tests/test_disjoint_routes.py
new file mode 100644
index 00000000..ce1fa64e
--- /dev/null
+++ b/tests/test_disjoint_routes.py
@@ -0,0 +1,195 @@
+"""The cut the framework states its positive properties over, measured rather than asserted.
+
+Robust connectivity and own-origin robustness are both the size of a minimum cut: how many coins a
+separation would have to take before an output stops reaching its candidate origins. `path_count`
+answers a different question — it weights routes by link probability, and many routes may run
+through the same coin — so a large count there is consistent with a cut of one.
+
+Three modelling decisions carry the answer, and each is pinned below because getting any of them
+wrong returns a plausible number rather than an error: a coin is an edge and not a vertex, the
+target is the query point rather than a resource routes compete for, and flow decomposition has to
+spend a saturated arc once per unit it carries.
+"""
+
+import pytest
+
+from decluster.ancestry import Graph
+from decluster.disjoint_routes import cut_size, route_capacity
+
+
+def graph(edges, absorbers):
+ built = Graph()
+ built.edges = {coin: [(nxt, 1.0 / len(nexts)) for nxt in nexts]
+ for coin, nexts in edges.items()}
+ built.absorbers = list(absorbers)
+ return built
+
+
+TARGET = ("T", 0)
+
+
+def test_two_independent_routes_are_two():
+ g = graph({TARGET: [("a", 0), ("b", 0)], ("a", 0): [("A", 0)], ("b", 0): [("B", 0)]},
+ [("A", 0), ("B", 0)])
+ assert cut_size(g, TARGET) == 2
+
+
+def test_a_shared_coin_is_a_cut_of_one():
+ """Two origins, but every route runs through one coin: taking it separates the target."""
+ g = graph({TARGET: [("x", 0)], ("x", 0): [("A", 0), ("B", 0)]}, [("A", 0), ("B", 0)])
+ assert cut_size(g, TARGET) == 1
+
+
+def test_routes_ending_at_one_origin_do_not_both_count():
+ """Three ways out, two of them landing on the same origin coin, is a capacity of two."""
+ g = graph({TARGET: [("a", 0), ("b", 0), ("c", 0)],
+ ("a", 0): [("A", 0)], ("b", 0): [("A", 0)], ("c", 0): [("B", 0)]},
+ [("A", 0), ("B", 0)])
+ assert cut_size(g, TARGET) == 2
+
+
+def test_an_origin_the_target_cannot_reach_is_not_an_origin():
+ g = graph({TARGET: []}, [("A", 0)])
+ assert cut_size(g, TARGET) == 0
+
+
+def test_the_target_is_not_a_resource_its_own_routes_compete_for():
+ """Every route leaves the target; charging its coin would cap every graph at one."""
+ g = graph({TARGET: [("a", 0), ("b", 0), ("c", 0)],
+ ("a", 0): [("A", 0)], ("b", 0): [("B", 0)], ("c", 0): [("C", 0)]},
+ [("A", 0), ("B", 0), ("C", 0)])
+ assert cut_size(g, TARGET) == 3
+
+
+def test_the_reported_routes_share_no_coin_beyond_the_target():
+ g = graph({TARGET: [("a", 0), ("b", 0)], ("a", 0): [("A", 0)], ("b", 0): [("B", 0)]},
+ [("A", 0), ("B", 0)])
+ count, routes = route_capacity(g, TARGET)
+ assert len(routes) == count
+ beyond = [coin for route in routes for coin in route[1:]]
+ assert len(beyond) == len(set(beyond)), "a reported route reuses a coin another one took"
+
+
+def test_a_route_is_reported_for_every_unit_of_capacity():
+ """Decomposition spends a saturated arc once per unit; marking it used truncates the walk."""
+ g = graph({TARGET: [("a", 0), ("b", 0), ("c", 0)],
+ ("a", 0): [("A", 0)], ("b", 0): [("B", 0)], ("c", 0): [("C", 0)]},
+ [("A", 0), ("B", 0), ("C", 0)])
+ count, routes = route_capacity(g, TARGET)
+ assert count == 3 and len(routes) == 3
+
+
+def test_a_deeper_graph_counts_the_narrowest_layer():
+ """A cut is as small as the tightest layer, however wide the graph is elsewhere."""
+ g = graph({TARGET: [("a", 0), ("b", 0)],
+ ("a", 0): [("m", 0)], ("b", 0): [("m", 0)], # both funnel through m
+ ("m", 0): [("A", 0), ("B", 0), ("C", 0)]},
+ [("A", 0), ("B", 0), ("C", 0)])
+ assert cut_size(g, TARGET) == 1
+
+
+def test_explicit_origins_override_the_absorber_boundary():
+ g = graph({TARGET: [("a", 0), ("b", 0)], ("a", 0): [("A", 0)], ("b", 0): [("B", 0)]},
+ [("A", 0), ("B", 0)])
+ assert cut_size(g, TARGET, origins=[("A", 0)]) == 1
+
+
+def test_routes_may_share_a_transaction_but_not_a_coin():
+ """The distinction the specification settles, and the reason it is edges and not vertices.
+
+ Two routes leaving the target descend from the same transaction through different outputs. Coins
+ are what a seizure or a label removes, so that is two routes; requiring the transactions to
+ differ as well would report one, and on a real graph almost everything shares a transaction with
+ almost everything.
+ """
+ g = graph({TARGET: [("p", 0), ("q", 0)],
+ ("p", 0): [("M", 0)], # one output of M
+ ("q", 0): [("M", 1)], # a different output of the same transaction
+ ("M", 0): [("A", 0)], ("M", 1): [("B", 0)]},
+ [("A", 0), ("B", 0)])
+ count, routes = route_capacity(g, TARGET)
+ assert count == 2
+ assert {coin for route in routes for coin in route} >= {("M", 0), ("M", 1)}
+
+
+def test_two_routes_through_one_output_of_that_transaction_are_one():
+ """The contrast: same transaction and same output is a shared coin, so the cut is one."""
+ g = graph({TARGET: [("p", 0), ("q", 0)],
+ ("p", 0): [("M", 0)], ("q", 0): [("M", 0)],
+ ("M", 0): [("A", 0), ("B", 0)]},
+ [("A", 0), ("B", 0)])
+ assert cut_size(g, TARGET) == 1
+
+
+def valued(edges, absorbers, values):
+ g = graph(edges, absorbers)
+ g.values = values
+ return g
+
+
+def test_a_route_too_small_to_carry_the_coin_is_not_a_route():
+ """The specification prunes this way: a path worth less than the amount may have been removed.
+
+ Counting it makes redundancy look larger than it is, which is the wrong direction for a measure
+ of fragility — the same overstatement this module exists to correct in the origin count.
+ """
+ g = valued({TARGET: [("big", 0), ("dust", 0)],
+ ("big", 0): [("A", 0)], ("dust", 0): [("B", 0)]},
+ [("A", 0), ("B", 0)],
+ {TARGET: 1_000_000, ("big", 0): 5_000_000, ("dust", 0): 1_000,
+ ("A", 0): 9_000_000, ("B", 0): 1_000})
+ from decluster.disjoint_routes import TARGET_VALUE
+ assert cut_size(g, TARGET) == 2
+ assert cut_size(g, TARGET, carries=TARGET_VALUE) == 1
+
+
+def test_a_threshold_below_every_coin_prunes_nothing():
+ g = valued({TARGET: [("a", 0), ("b", 0)], ("a", 0): [("A", 0)], ("b", 0): [("B", 0)]},
+ [("A", 0), ("B", 0)],
+ {TARGET: 500, ("a", 0): 900, ("b", 0): 900, ("A", 0): 900, ("B", 0): 900})
+ assert cut_size(g, TARGET, carries=100) == 2
+
+
+def test_pruning_recomputes_reachability_rather_than_only_dropping_coins():
+ """A coin that survives the threshold but whose only way onward did not is no longer a route."""
+ g = valued({TARGET: [("a", 0)], ("a", 0): [("thin", 0)], ("thin", 0): [("A", 0)]},
+ [("A", 0)],
+ {TARGET: 1_000, ("a", 0): 9_000, ("thin", 0): 10, ("A", 0): 9_000})
+ assert cut_size(g, TARGET, carries=1_000) == 0
+
+
+def test_flow_capacity_is_the_value_the_origins_could_deliver():
+ """The mass the framework states its property over, which counting routes cannot see.
+
+ Two routes both pass a per-route threshold and are both disjoint, so counting says two and
+ pruning says two. Together they carry less than the coin is worth, which only capacity says.
+ """
+ from decluster.disjoint_routes import flow_capacity
+ g = valued({TARGET: [("a", 0), ("b", 0)], ("a", 0): [("A", 0)], ("b", 0): [("B", 0)]},
+ [("A", 0), ("B", 0)],
+ {TARGET: 1_000_000, ("a", 0): 700_000, ("b", 0): 250_000,
+ ("A", 0): 9_000_000, ("B", 0): 9_000_000})
+ assert cut_size(g, TARGET) == 2
+ carried, routes = flow_capacity(g, TARGET)
+ assert carried == 950_000 < g.values[TARGET]
+ assert sum(amount for amount, _route in routes) == carried
+ assert len(routes) == 2, "valued flow must be decomposed by path, not once per satoshi"
+
+
+def test_a_narrow_coin_bounds_the_flow_through_it():
+ """Capacity is a min cut in satoshis: the tightest coin on the path is what the path delivers."""
+ from decluster.disjoint_routes import flow_capacity
+ g = valued({TARGET: [("wide", 0)], ("wide", 0): [("narrow", 0)], ("narrow", 0): [("A", 0)]},
+ [("A", 0)],
+ {TARGET: 500_000, ("wide", 0): 9_000_000, ("narrow", 0): 40_000,
+ ("A", 0): 9_000_000})
+ assert flow_capacity(g, TARGET)[0] == 40_000
+
+
+def test_a_coin_with_no_recorded_value_does_not_invent_a_cut():
+ """Treating an unknown value as zero would report a separation the graph does not have."""
+ from decluster.disjoint_routes import flow_capacity
+ g = valued({TARGET: [("unknown", 0)], ("unknown", 0): [("A", 0)]},
+ [("A", 0)],
+ {TARGET: 100, ("A", 0): 900}) # ("unknown", 0) has no recorded value
+ assert flow_capacity(g, TARGET)[0] > 0
diff --git a/tests/test_domain_evidence.py b/tests/test_domain_evidence.py
new file mode 100644
index 00000000..9b94c097
--- /dev/null
+++ b/tests/test_domain_evidence.py
@@ -0,0 +1,86 @@
+from dataclasses import FrozenInstanceError
+
+import pytest
+
+from decluster.domain import (
+ Attribution,
+ ClusterMerge,
+ DeclusterSplit,
+ Direction,
+ EvidenceContext,
+ NodeCapped,
+ NotObserved,
+ OwnershipLikelihoodEvidence,
+ ProvenanceDistributionEvidence,
+ MergeRefused,
+ Subject,
+ SubjectKind,
+)
+
+
+def context():
+ return EvidenceContext(
+ adversary="external observer",
+ observables=("co-spend", "wallet fingerprint"),
+ hypothesis="the coins share an owner",
+ algorithm="fingerprint-v1",
+ dataset="fixture-v1",
+ )
+
+
+def coin(identifier):
+ return Subject(SubjectKind.COIN, identifier)
+
+
+def test_ownership_evidence_is_immutable_and_directional():
+ evidence = OwnershipLikelihoodEvidence(
+ coin("a"), coin("b"), -2.5, context(), Direction.OPPOSES_COMMON_OWNERSHIP
+ )
+
+ with pytest.raises(FrozenInstanceError):
+ evidence.log2_odds = 1.0
+ with pytest.raises(ValueError, match="ownership direction"):
+ OwnershipLikelihoodEvidence(
+ coin("a"), coin("b"), 0.0, context(), Direction.DESCRIPTIVE
+ )
+
+
+def test_provenance_distribution_rejects_invalid_probabilities():
+ with pytest.raises(ValueError, match="sum to 1"):
+ ProvenanceDistributionEvidence(
+ coin("target"), ((coin("a"), 0.7), (coin("b"), 0.2)), context()
+ )
+
+
+def test_outcomes_do_not_encode_refusal_as_a_boolean():
+ evidence = OwnershipLikelihoodEvidence(
+ coin("a"), coin("b"), -2.5, context(), Direction.OPPOSES_COMMON_OWNERSHIP
+ )
+
+ clustered = ClusterMerge(coin("a"), coin("b"), (evidence,))
+ refused = MergeRefused(coin("a"), coin("b"), (evidence,), "net evidence opposes merge")
+ attributed = Attribution(coin("a"), coin("b"), (evidence,))
+ split = DeclusterSplit(
+ Subject(SubjectKind.CLUSTER, "wallet"),
+ (frozenset({coin("a")}), frozenset({coin("b")})),
+ (evidence,),
+ )
+
+ assert type(clustered) is ClusterMerge
+ assert type(refused) is MergeRefused
+ assert type(attributed) is Attribution
+ assert type(split) is DeclusterSplit
+
+
+@pytest.mark.parametrize(
+ ("factory", "message"),
+ [
+ (lambda: Subject(SubjectKind.COIN, ""), "identifier"),
+ (lambda: Subject(SubjectKind.COIN, []), "hashable"),
+ (lambda: NodeCapped(observed=10, limit=10), "observed"),
+ (lambda: NotObserved(""), "observable"),
+ ],
+)
+def test_invalid_domain_states_are_rejected(factory, message):
+ with pytest.raises(ValueError, match=message):
+ factory()
diff --git a/tests/test_e2e.py b/tests/test_e2e.py
index c7f89dcf..96b48acf 100644
--- a/tests/test_e2e.py
+++ b/tests/test_e2e.py
@@ -42,6 +42,7 @@ def _fixture():
def test_e2e_offline_pipeline_holds_invariants():
+ pytest.importorskip("dss")
txs = _fixture()
fetch = lambda txid: txs[txid]
uniform = lambda ins, outs: [[1.0] * len(outs) for _ in ins]
diff --git a/tests/test_em_m_experiment.py b/tests/test_em_m_experiment.py
new file mode 100644
index 00000000..63a7232f
--- /dev/null
+++ b/tests/test_em_m_experiment.py
@@ -0,0 +1,34 @@
+import json
+from pathlib import Path
+
+import pytest
+
+from decluster.experiments import em_m as experiment
+
+ROOT = Path(__file__).resolve().parents[1]
+SNAPSHOT = ROOT / "data" / "fs-blkcache-2026-09-04.tar.gz"
+
+
+@pytest.fixture(scope="module")
+def artifact():
+ return experiment.build_artifact(SNAPSHOT)
+
+
+def test_snapshot_reproduces_the_canonical_artifact(artifact):
+ stored = json.loads((ROOT / "results" / "artifacts" / "em-m-v1.json").read_text())
+ assert artifact == stored
+ assert artifact["measurement"]["transactions"] == 22112
+
+
+def test_result_reports_difference_without_promoting_it_to_a_verdict(artifact):
+ measurement = artifact["measurement"]
+ assert measurement["em_minus_fixed_auc"] == pytest.approx(0.0009)
+ assert any("without a pre-registered" in item for item in artifact["limitations"])
+ assert artifact["historical_comparison"]["status"] == "not_reproduced"
+
+
+def test_output_names_the_weak_label_and_attack_boundary(artifact):
+ markdown = experiment.render_markdown(artifact).lower()
+ assert "address-reuse agreement" in markdown
+ assert "oracle" not in markdown
+ assert any("not CoinScore" in item for item in artifact["limitations"])
diff --git a/tests/test_entity_deanon.py b/tests/test_entity_deanon.py
index fb211638..48c996c1 100644
--- a/tests/test_entity_deanon.py
+++ b/tests/test_entity_deanon.py
@@ -32,6 +32,17 @@ def test_structure_relinks_what_cospend_leaves_separate():
assert r["pos_mean"] >= 1.0 # the held-out same-entity pair shares >=1 neighbor
assert r["auc_payment"] is not None and r["auc_payment"] > 0.5
+def test_entity_evaluation_is_independent_of_sample_order():
+ sample = [(_sweep("3BMEXaaa", "hotwallet"), None),
+ (_sweep("3BMEXbbb", "hotwallet"), None),
+ ({"txid": "ctl", "vin": [{"txid": "fc", "vout": 0,
+ "prevout": {"scriptpubkey_address": "1control"}}],
+ "vout": [{"value": 100, "scriptpubkey_type": "p2pkh",
+ "scriptpubkey_address": "1elsewhere"}]}, None)]
+ assert evaluate_entity(sample, detect_bitmex, seed=7) == evaluate_entity(
+ list(reversed(sample)), detect_bitmex, seed=7
+ )
+
if __name__ == "__main__":
fns = [v for k, v in sorted(globals().items()) if k.startswith("test_") and callable(v)]
for fn in fns: fn(); print(f"ok {fn.__name__}")
diff --git a/tests/test_entity_deanon_experiment.py b/tests/test_entity_deanon_experiment.py
new file mode 100644
index 00000000..188dc997
--- /dev/null
+++ b/tests/test_entity_deanon_experiment.py
@@ -0,0 +1,33 @@
+import json
+
+import pytest
+
+from decluster.experiments import entity_deanon as experiment
+
+
+def test_artifact_preserves_positive_and_null_controls():
+ artifact = experiment.build_artifact()
+
+ assert artifact["satoshidice"]["payment_auc"] >= 0.65
+ assert 7.0 <= artifact["satoshidice"]["positive_mean_shared_neighbours"] <= 9.0
+ assert 0.45 <= artifact["bitmex"]["payment_auc"] <= 0.55
+ assert artifact["bitmex"]["positive_mean_shared_neighbours"] < 1.0
+
+
+def test_verifier_recomputes_both_snapshots():
+ changed = json.loads(json.dumps(experiment.build_artifact()))
+ changed["bitmex"]["payment_auc"] = 0.75
+
+ with pytest.raises(experiment.VerificationError, match="fresh experiment"):
+ experiment.verify_artifact(changed)
+
+
+def test_cli_reproduces_and_verifies_json_and_markdown(tmp_path):
+ artifact = tmp_path / "artifact.json"
+ markdown = tmp_path / "result.md"
+ assert experiment.main([
+ "reproduce", "--artifact", str(artifact), "--markdown", str(markdown),
+ ]) == 0
+ assert experiment.main([
+ "verify", "--artifact", str(artifact), "--markdown", str(markdown),
+ ]) == 0
diff --git a/tests/test_entropy_insufficiency.py b/tests/test_entropy_insufficiency.py
new file mode 100644
index 00000000..16dda8a9
--- /dev/null
+++ b/tests/test_entropy_insufficiency.py
@@ -0,0 +1,55 @@
+import pytest
+
+from decluster.domain import CandidateNarrowing
+from decluster.failure_modes.entropy_insufficiency import EntropyScenario, evaluate, paired_examples
+
+
+def test_equal_point_entropy_has_different_longitudinal_survivors():
+ brittle, overlapping = paired_examples()
+ brittle_report = evaluate(brittle)
+ overlapping_report = evaluate(overlapping)
+
+ assert brittle.entropy_bits == pytest.approx(2.0)
+ assert overlapping.entropy_bits == pytest.approx(2.0)
+ assert len(brittle_report.outcomes[0].evidence.candidates_after) == 1
+ assert len(overlapping_report.outcomes[0].evidence.candidates_after) == 2
+ assert all(isinstance(report.outcomes[0], CandidateNarrowing) for report in (
+ brittle_report, overlapping_report,
+ ))
+
+
+def test_counterexample_preserves_posterior_and_intersection_as_separate_channels():
+ report = evaluate(paired_examples()[0])
+
+ assert tuple(channel.identifier for channel in report.channels) == (
+ "initial_posterior",
+ "longitudinal_intersection",
+ )
+ assert report.composition is None
+ assert "does not imply" in report.limitations[1]
+ assert "not a privacy score" in report.limitations[3]
+
+
+def test_observations_must_be_subsets_of_the_declared_posterior():
+ scenario, _ = paired_examples()
+ unknown = scenario.target
+
+ with pytest.raises(ValueError, match="subsets of the posterior"):
+ EntropyScenario(
+ scenario.identifier,
+ scenario.target,
+ scenario.probabilities,
+ ((scenario.observations[0][0], frozenset({unknown})), scenario.observations[1]),
+ )
+
+
+def test_posterior_must_be_normalized():
+ scenario, _ = paired_examples()
+
+ with pytest.raises(ValueError, match="sum to one"):
+ EntropyScenario(
+ scenario.identifier,
+ scenario.target,
+ tuple((candidate, 0.2) for candidate, _ in scenario.probabilities),
+ scenario.observations,
+ )
diff --git a/tests/test_entropy_insufficiency_experiment.py b/tests/test_entropy_insufficiency_experiment.py
new file mode 100644
index 00000000..32e20b80
--- /dev/null
+++ b/tests/test_entropy_insufficiency_experiment.py
@@ -0,0 +1,36 @@
+import json
+
+import pytest
+
+from decluster.experiments import entropy_insufficiency as experiment
+
+
+def test_artifact_preserves_equal_entropy_and_different_residual_ambiguity():
+ artifact = experiment.build_artifact()
+
+ assert artifact["equal_initial_entropy"] is True
+ assert artifact["brittle_overlap"]["entropy_bits"] == pytest.approx(2.0)
+ assert artifact["overlapping_crowd"]["entropy_bits"] == pytest.approx(2.0)
+ assert artifact["brittle_overlap"]["surviving_candidates"] == 1
+ assert artifact["overlapping_crowd"]["surviving_candidates"] == 2
+ assert artifact["composition"] is None
+
+
+def test_verifier_recomputes_the_counterexample():
+ changed = json.loads(json.dumps(experiment.build_artifact()))
+ changed["overlapping_crowd"]["surviving_candidates"] = 1
+
+ with pytest.raises(experiment.VerificationError, match="fresh failure-mode"):
+ experiment.verify_artifact(changed)
+
+
+def test_cli_reproduces_and_verifies_json_and_markdown(tmp_path):
+ artifact = tmp_path / "artifact.json"
+ markdown = tmp_path / "result.md"
+
+ assert experiment.main(
+ ["reproduce", "--artifact", str(artifact), "--markdown", str(markdown)]
+ ) == 0
+ assert experiment.main(
+ ["verify", "--artifact", str(artifact), "--markdown", str(markdown)]
+ ) == 0
diff --git a/tests/test_epoch_graph.py b/tests/test_epoch_graph.py
new file mode 100644
index 00000000..bb837a76
--- /dev/null
+++ b/tests/test_epoch_graph.py
@@ -0,0 +1,154 @@
+"""The derived format is only worth having if the pipeline cannot tell the difference.
+
+It drops the transaction id, which nothing reads, and replaces each address with an integer, which
+is safe only because clustering asks which addresses coincide rather than what they are called. That
+is a claim about the pipeline, not about the format, so it is tested against the pipeline: the same
+engine over the committed block cache, once with the real addresses and once with a renaming, has to
+return the same partition. A renaming that reversed the sort order is included because two places in
+the engine sort addresses, and a tie-break that leaked into the result would show up there.
+"""
+
+import gzip
+import json
+import tempfile
+from pathlib import Path
+
+import pytest
+
+from decluster import cluster as cl
+from decluster import epoch_graph as eg
+from decluster.archive_snapshot import extract_tar_gz
+from decluster.combiner import Combiner
+from decluster.fingerprint_validate import load_blkcache
+
+ROOT = Path(__file__).resolve().parent.parent
+SNAPSHOT = ROOT / "data" / "fs-blkcache-2026-09-04.tar.gz"
+DERIVED = ROOT / "data" / "epochs-2016-weekly-v1"
+
+
+def _records():
+ return [
+ {"height": 100, "vin": [{"prevout": {"scriptpubkey_address": "1dice7fUkz5h4z2wPc"}}],
+ "vout": [{"scriptpubkey_address": "bc1qexample0"}]},
+ {"height": 100, "vin": [{"prevout": {"scriptpubkey_address": "bc1qexample0"}},
+ {"prevout": {"scriptpubkey_address": "3BMEXaaaaaa"}}],
+ "vout": [{"scriptpubkey_address": "1other"}, {"scriptpubkey_address": "bc1qexample0"}]},
+ {"height": 103, "vin": [], "vout": [{"scriptpubkey_address": "1other"}]},
+ ]
+
+
+def _shape(rows):
+ """The coincidence graph: which addresses repeat where, independent of their names."""
+ seen = {}
+ return [(r["height"],
+ [seen.setdefault(a, len(seen)) for a in eg.in_addresses(r)],
+ [seen.setdefault(a, len(seen)) for a in eg.out_addresses(r)]) for r in rows]
+
+
+def test_a_round_trip_preserves_heights_and_the_coincidence_graph(tmp_path):
+ rows = _records()
+ target = tmp_path / "probe.epochgraph.xz"
+ eg.encode(rows, target)
+ back = list(eg.decode(target))
+ assert [r["height"] for r in back] == [r["height"] for r in rows]
+ assert _shape(back) == _shape(rows)
+
+
+def test_the_detectors_that_read_a_prefix_still_see_it(tmp_path):
+ """An address an entity names itself in keeps its text; every other address does not need to."""
+ target = tmp_path / "probe.epochgraph.xz"
+ eg.encode(_records(), target)
+ addresses = {a for r in eg.decode(target) for a in eg.in_addresses(r) + eg.out_addresses(r)}
+ assert any(a.startswith("1dice") for a in addresses)
+ assert any(a.startswith("3BMEX") for a in addresses)
+
+
+def test_an_unknown_container_is_refused(tmp_path):
+ import lzma
+ target = tmp_path / "bad.epochgraph.xz"
+ target.write_bytes(lzma.compress(b"not an epoch graph"))
+ with pytest.raises(ValueError, match="not an epoch-graph file"):
+ list(eg.decode(target))
+
+
+def _ladder(index):
+ fetch = index.__getitem__
+ nodes = sorted({v["txid"] for t in index.values() for v in t["vin"]
+ if v.get("txid") in index
+ if len([x for x in t["vin"] if x.get("txid") in index]) >= 2})
+ groups, refused, linked = cl.cluster_refined(nodes, Combiner.from_library(), fetch=fetch)
+ return len(groups), len(refused), len(linked), max(len(g) for g in groups)
+
+
+@pytest.mark.reproduction
+def test_the_engine_cannot_tell_a_renamed_address_from_a_real_one():
+ """The claim the format rests on, measured on committed transactions rather than argued."""
+ if not SNAPSHOT.is_file():
+ pytest.skip("the block-cache snapshot is not committed")
+ with tempfile.TemporaryDirectory() as temporary:
+ transactions = load_blkcache(str(extract_tar_gz(str(SNAPSHOT), temporary) / ".blkcache"))
+
+ universe = sorted({a for t in transactions
+ for a in eg.in_addresses(t) + eg.out_addresses(t)})
+ forward = {a: f"a{i:09d}" for i, a in enumerate(universe)}
+ backward = {a: f"a{len(universe) - 1 - i:09d}" for i, a in enumerate(universe)}
+
+ def renamed(mapping):
+ out = {}
+ for transaction in transactions:
+ copy = json.loads(json.dumps(transaction))
+ for vin in copy.get("vin", ()):
+ prevout = vin.get("prevout") or {}
+ if prevout.get("scriptpubkey_address"):
+ prevout["scriptpubkey_address"] = mapping[prevout["scriptpubkey_address"]]
+ for vout in copy.get("vout", ()):
+ if vout.get("scriptpubkey_address"):
+ vout["scriptpubkey_address"] = mapping[vout["scriptpubkey_address"]]
+ out[copy["txid"]] = copy
+ return out
+
+ real = _ladder({t["txid"]: t for t in transactions})
+ assert _ladder(renamed(forward)) == real, "an order-preserving renaming moved the partition"
+ assert _ladder(renamed(backward)) == real, "an order-reversing renaming moved the partition"
+
+
+@pytest.mark.skipif(not DERIVED.is_dir(), reason="the derived epochs are not committed")
+def test_every_committed_epoch_decodes():
+ files = sorted(DERIVED.glob("*.epochgraph.xz"))
+ assert files, "the derived directory carries no epoch"
+ for path in files:
+ first = next(eg.decode(path))
+ assert first["height"] > 0 and ("vin" in first and "vout" in first)
+
+def test_windows_encoded_together_share_one_address_namespace(tmp_path):
+ """The failure this catches did not lose links, it invented them.
+
+ Numbering each file from zero passed every single-file check — counts, heights, coincidence
+ graph — and still corrupted the pair, because `a000000000` then stood for a different address in
+ each window and the clusterer read that as the same owner. On the committed epochs it turned
+ 17,431 rejoinable pairs into 4,972, and the control that caught it was running the same pipeline
+ over the original export.
+ """
+ shared = {"scriptpubkey_address": "bc1qshared"}
+ left = [{"height": 1, "vin": [{"prevout": dict(shared)}],
+ "vout": [{"scriptpubkey_address": "bc1qleft"}]}]
+ right = [{"height": 2, "vin": [{"prevout": {"scriptpubkey_address": "bc1qright"}}],
+ "vout": [dict(shared)]}]
+ targets = [tmp_path / "left.epochgraph.xz", tmp_path / "right.epochgraph.xz"]
+ eg.encode_many([lambda: iter(left), lambda: iter(right)], targets)
+
+ names = [{a for r in eg.decode(t) for a in eg.in_addresses(r) + eg.out_addresses(r)}
+ for t in targets]
+ assert names[0] & names[1], "the address in both windows did not survive as one name"
+ assert len(names[0] & names[1]) == 1, "windows collided on a name they do not share"
+
+
+def test_encoding_windows_apart_is_refused_by_its_own_namespace_tag(tmp_path):
+ """Two files numbered alone are not comparable, and each says which namespace it belongs to."""
+ import json, lzma
+ rows = [{"height": 1, "vin": [], "vout": [{"scriptpubkey_address": "bc1qone"}]}]
+ target = tmp_path / "alone.epochgraph.xz"
+ eg.encode(rows, target)
+ raw = lzma.decompress(target.read_bytes())[len(eg.MAGIC):]
+ header = json.loads(eg._split(raw)[0])
+ assert header["namespace"] == "single"
diff --git a/tests/test_equal_output_consolidation.py b/tests/test_equal_output_consolidation.py
new file mode 100644
index 00000000..44da3bc0
--- /dev/null
+++ b/tests/test_equal_output_consolidation.py
@@ -0,0 +1,71 @@
+import pytest
+
+from decluster.domain import (
+ CandidateNarrowing,
+ CandidateSetEvidence,
+ IntersectionEvidence,
+ NoSharedCandidates,
+ Subject,
+ SubjectKind,
+)
+from decluster.failure_modes.equal_output_consolidation import (
+ ConsolidationScenario,
+ ctp_example,
+ evaluate,
+)
+
+
+def test_consolidation_retroactively_narrows_two_ambiguous_outputs():
+ scenario = ctp_example()
+ report = evaluate(scenario)
+
+ assert tuple(len(candidates) for candidates in scenario.candidate_sets) == (3, 3)
+ assert tuple(channel.identifier for channel in report.channels) == (
+ "candidate_origins",
+ "consolidation_intersection",
+ )
+ assert all(
+ isinstance(item, CandidateSetEvidence) for item in report.channels[0].evidence
+ )
+ intersection = report.channels[1].evidence[0]
+ assert isinstance(intersection, IntersectionEvidence)
+ assert {candidate.identifier for candidate in intersection.candidates_after} == {"alice"}
+ assert report.outcomes == (CandidateNarrowing(intersection),)
+ assert report.composition is None
+
+
+def test_scenario_rejects_an_output_that_was_not_ambiguous():
+ scenario = ctp_example()
+
+ with pytest.raises(ValueError, match="ambiguous"):
+ ConsolidationScenario(
+ scenario.outputs,
+ (scenario.candidate_sets[0], frozenset({next(iter(scenario.candidate_sets[1]))})),
+ )
+
+
+def test_result_declares_that_the_consolidation_link_is_conditional():
+ report = evaluate(ctp_example())
+ intersection = report.channels[1].evidence[0]
+
+ assert "conditional" in intersection.context.limitations[0]
+ assert "collaborative consolidation" in report.limitations[2]
+
+
+def test_disjoint_candidate_sets_refuse_instead_of_claiming_narrowing():
+ scenario = ctp_example()
+ disjoint = ConsolidationScenario(
+ scenario.outputs,
+ (
+ scenario.candidate_sets[0],
+ frozenset({
+ Subject(SubjectKind.CLUSTER, "dave"),
+ Subject(SubjectKind.CLUSTER, "erin"),
+ }),
+ ),
+ )
+
+ report = evaluate(disjoint)
+
+ assert report.channels[1].evidence[0].candidates_after == frozenset()
+ assert isinstance(report.outcomes[0], NoSharedCandidates)
diff --git a/tests/test_equal_output_consolidation_experiment.py b/tests/test_equal_output_consolidation_experiment.py
new file mode 100644
index 00000000..097f8ae4
--- /dev/null
+++ b/tests/test_equal_output_consolidation_experiment.py
@@ -0,0 +1,48 @@
+import json
+
+import pytest
+
+from decluster.experiments import equal_output_consolidation as experiment
+from decluster.result_artifacts import canonical_json_bytes
+
+
+def test_artifact_preserves_conditional_narrowing_without_composition():
+ artifact = experiment.build_artifact()
+
+ assert artifact["scenario"]["candidate_sets"] == [
+ ["alice", "bob", "carol"],
+ ["alice", "dave", "erin"],
+ ]
+ assert artifact["result"] == {
+ "candidates_before": 3,
+ "candidates_after": ["alice"],
+ "outcome": "candidate_narrowing",
+ "composition": None,
+ }
+ assert "conditional narrowing" in artifact["limitations"][1]
+ assert canonical_json_bytes(artifact) == canonical_json_bytes(experiment.build_artifact())
+
+
+def test_verifier_recomputes_instead_of_trusting_the_stored_result():
+ changed = json.loads(json.dumps(experiment.build_artifact()))
+ changed["result"]["candidates_after"] = ["bob"]
+
+ with pytest.raises(experiment.VerificationError, match="fresh failure-mode"):
+ experiment.verify_artifact(changed)
+
+
+def test_cli_reproduces_and_verifies_artifact_and_markdown(tmp_path):
+ artifact = tmp_path / "artifact.json"
+ markdown = tmp_path / "result.md"
+
+ assert experiment.main([
+ "reproduce", "--artifact", str(artifact), "--markdown", str(markdown)
+ ]) == 0
+ assert experiment.main([
+ "verify", "--artifact", str(artifact), "--markdown", str(markdown)
+ ]) == 0
+ markdown.write_text(markdown.read_text() + "changed\n")
+ with pytest.raises(experiment.VerificationError, match="Markdown differs"):
+ experiment.main([
+ "verify", "--artifact", str(artifact), "--markdown", str(markdown)
+ ])
diff --git a/tests/test_eve_alice_eve.py b/tests/test_eve_alice_eve.py
new file mode 100644
index 00000000..6c49e261
--- /dev/null
+++ b/tests/test_eve_alice_eve.py
@@ -0,0 +1,84 @@
+import pytest
+
+from decluster.domain import CandidateNarrowing, Inconclusive, Subject, SubjectKind
+from decluster.failure_modes.eve_alice_eve import (
+ CounterpartyReturnScenario,
+ accumulate,
+ ctp_compounding_example,
+ ctp_consolidation_example,
+ evaluate,
+)
+
+
+def test_two_returned_descendants_narrow_to_the_one_shared_customer():
+ scenario = ctp_consolidation_example()
+ report = evaluate(scenario)
+ intersection = report.channels[1].evidence[0]
+
+ assert {candidate.identifier for candidate in intersection.candidates_after} == {
+ "alice"
+ }
+ assert report.outcomes == (CandidateNarrowing(intersection),)
+
+
+def test_the_accumulator_and_the_baseline_agree_on_what_survives():
+ """Two independent intersections back the same claim; a disagreement is a defect in one."""
+ scenario = ctp_compounding_example()
+ survivors, _bits = accumulate(scenario)
+
+ assert survivors == evaluate(scenario).channels[1].evidence[0].candidates_after
+
+
+def test_the_loss_compounds_rather_than_adding_up():
+ """The writeup's claim is about a rate, so the report carries the additive baseline too."""
+ composition = evaluate(ctp_compounding_example()).composition
+
+ assert composition.unit == "bits"
+ assert composition.channel_ids == ("return_intersection",)
+ # log2(8/1) accumulated over three intersections, against log2(8/5) for one strike per return.
+ assert composition.value == pytest.approx(3.0)
+ assert "0.68" in composition.interpretation
+ assert "not a measured rate" in composition.interpretation
+
+
+def test_no_composition_without_a_stated_candidate_universe():
+ """The published two-return fixture states no universe, so it claims no accumulation."""
+ assert evaluate(ctp_consolidation_example()).composition is None
+
+
+def test_one_return_remains_inconclusive_without_behavioral_prior():
+ scenario = ctp_consolidation_example()
+ report = evaluate(
+ CounterpartyReturnScenario(
+ scenario.counterparty,
+ scenario.deposited_inputs[:1],
+ scenario.candidate_customers[:1],
+ )
+ )
+
+ assert isinstance(report.outcomes[0], Inconclusive)
+ assert len(report.channels) == 1
+
+
+def test_report_does_not_turn_privileged_knowledge_into_attribution():
+ report = evaluate(ctp_consolidation_example())
+
+ assert (
+ "private transaction records"
+ in report.channels[0].evidence[0].context.adversary
+ )
+ assert "not identity attribution" in report.limitations[2]
+ assert "collaborative deposit" in report.limitations[3]
+
+
+def test_scenario_requires_ambiguous_candidate_sets():
+ scenario = ctp_consolidation_example()
+ with pytest.raises(ValueError, match="ambiguous"):
+ CounterpartyReturnScenario(
+ scenario.counterparty,
+ scenario.deposited_inputs,
+ (
+ scenario.candidate_customers[0],
+ frozenset({Subject(SubjectKind.CLUSTER, "alice")}),
+ ),
+ )
diff --git a/tests/test_eve_alice_eve_experiment.py b/tests/test_eve_alice_eve_experiment.py
new file mode 100644
index 00000000..e59c688d
--- /dev/null
+++ b/tests/test_eve_alice_eve_experiment.py
@@ -0,0 +1,41 @@
+import json
+
+import pytest
+
+from decluster.experiments import eve_alice_eve as experiment
+
+
+def test_artifact_contrasts_isolated_and_consolidated_returns():
+ artifact = experiment.build_artifact()
+
+ assert artifact["isolated_return"] == {
+ "observations": 1,
+ "outcome": "inconclusive",
+ "candidates_after": [],
+ "composition": None,
+ }
+ assert artifact["consolidated_return"] == {
+ "observations": 2,
+ "outcome": "candidate_narrowing",
+ "candidates_after": ["alice"],
+ "composition": None,
+ }
+
+
+def test_verifier_recomputes_both_scenarios():
+ changed = json.loads(json.dumps(experiment.build_artifact()))
+ changed["consolidated_return"]["candidates_after"] = ["bob"]
+
+ with pytest.raises(experiment.VerificationError, match="fresh failure-mode"):
+ experiment.verify_artifact(changed)
+
+
+def test_cli_reproduces_and_verifies_json_and_markdown(tmp_path):
+ artifact = tmp_path / "artifact.json"
+ markdown = tmp_path / "result.md"
+ assert experiment.main(
+ ["reproduce", "--artifact", str(artifact), "--markdown", str(markdown)]
+ ) == 0
+ assert experiment.main(
+ ["verify", "--artifact", str(artifact), "--markdown", str(markdown)]
+ ) == 0
diff --git a/tests/test_evidence_bundle.py b/tests/test_evidence_bundle.py
new file mode 100644
index 00000000..1f1c888d
--- /dev/null
+++ b/tests/test_evidence_bundle.py
@@ -0,0 +1,170 @@
+import hashlib
+from io import BytesIO
+import json
+from pathlib import Path
+
+import pytest
+
+from decluster.data_manifest import ManifestError
+from decluster.evidence_bundle import (
+ BlobStatus,
+ AcquisitionStatus,
+ BundleBootstrapError,
+ MaterializationStatus,
+ ReadinessIssueKind,
+ ReproductionStatus,
+ assess_reproduction_readiness,
+ blob_path,
+ bootstrap_bundle,
+ ingest_blob,
+ load_bundle,
+ verify_bundle,
+)
+
+
+def index_for(payload):
+ return {
+ "schema_version": 1,
+ "id": "evidence-v1",
+ "capabilities": ["verify_outputs"],
+ "runs": ["run-v1"],
+ "blobs": [{
+ "name": "catalog/runs/run-v1.json",
+ "role": "run_manifest",
+ "bytes": len(payload),
+ "sha256": hashlib.sha256(payload).hexdigest(),
+ "locations": {
+ "canonical": "https://primary.invalid/run",
+ "mirrors": ["https://mirror.invalid/run"],
+ },
+ }],
+ "limitations": ["environment is not bundled"],
+ }
+
+
+def write_index(tmp_path, raw):
+ path = tmp_path / "bundle.json"
+ path.write_text(json.dumps(raw))
+ return path
+
+
+def test_ingest_and_verify_are_content_addressed_and_idempotent(tmp_path):
+ payload = b'{"run":1}\n'
+ source = tmp_path / "run.json"
+ source.write_bytes(payload)
+ store = tmp_path / "store"
+ identity = ingest_blob(source, store)
+ assert blob_path(store, identity.sha256).read_bytes() == payload
+ assert ingest_blob(source, store) == identity
+
+ bundle = load_bundle(write_index(tmp_path, index_for(payload)))
+ assert verify_bundle(bundle, store)[0].status is BlobStatus.VERIFIED
+
+
+def test_verification_distinguishes_missing_and_corrupt_blobs(tmp_path):
+ payload = b"expected"
+ bundle = load_bundle(write_index(tmp_path, index_for(payload)))
+ store = tmp_path / "store"
+ check = verify_bundle(bundle, store)[0]
+ assert check.status is BlobStatus.MISSING
+ assert check.actual is None
+
+ destination = blob_path(store, hashlib.sha256(payload).hexdigest())
+ destination.parent.mkdir(parents=True)
+ destination.write_bytes(b"tampered")
+ assert verify_bundle(bundle, store)[0].status is BlobStatus.IDENTITY_MISMATCH
+
+
+def test_reproduction_capability_requires_complete_environment_roles(tmp_path):
+ raw = index_for(b"manifest")
+ raw["capabilities"].append("reproduce_runs")
+ with pytest.raises(ManifestError, match="missing roles"):
+ load_bundle(write_index(tmp_path, raw))
+
+
+def test_reproduction_capability_allows_a_dependency_free_environment(tmp_path):
+ raw = index_for(b"manifest")
+ raw["capabilities"].append("reproduce_runs")
+ for role, name, payload in (
+ ("source", "sources/source.tar", b"source"),
+ ("lockfile", "reproduction/run/requirements.lock", b"# No third-party dependencies.\n"),
+ ("environment", "reproduction/run/environment.json", b"environment"),
+ ):
+ raw["blobs"].append({
+ "name": name,
+ "role": role,
+ "bytes": len(payload),
+ "sha256": hashlib.sha256(payload).hexdigest(),
+ "locations": {
+ "canonical": "https://primary.invalid/blob",
+ "mirrors": ["https://mirror.invalid/blob"],
+ },
+ })
+
+ bundle = load_bundle(write_index(tmp_path, raw))
+
+ assert all(blob.role.value != "dependency" for blob in bundle.blobs)
+
+
+@pytest.mark.parametrize("mutation", [
+ lambda raw: raw["blobs"].append(dict(raw["blobs"][0])),
+ lambda raw: raw["capabilities"].append("verify_outputs"),
+ lambda raw: raw["blobs"][0].update(sha256="ABC"),
+])
+def test_bundle_rejects_ambiguous_or_invalid_identity(tmp_path, mutation):
+ raw = index_for(b"manifest")
+ mutation(raw)
+ with pytest.raises(ManifestError):
+ load_bundle(write_index(tmp_path, raw))
+
+
+def test_bootstrap_falls_back_verifies_and_materializes_without_future_network(tmp_path):
+ payload = b"manifest"
+ bundle = load_bundle(write_index(tmp_path, index_for(payload)))
+ calls = []
+
+ def opener(url):
+ calls.append(url)
+ return BytesIO(b"wrong" if "primary" in url else payload)
+
+ result = bootstrap_bundle(bundle, tmp_path / "store", tmp_path / "checkout", opener=opener)
+ assert result[0].acquisition is AcquisitionStatus.DOWNLOADED
+ assert result[0].materialization is MaterializationStatus.MATERIALIZED
+ assert result[0].path.read_bytes() == payload
+ assert calls == ["https://primary.invalid/run", "https://mirror.invalid/run"]
+
+ cached = bootstrap_bundle(
+ bundle, tmp_path / "store", tmp_path / "checkout",
+ opener=lambda _: pytest.fail("network used"),
+ )
+ assert cached[0].acquisition is AcquisitionStatus.ALREADY_AVAILABLE
+ assert cached[0].materialization is MaterializationStatus.ALREADY_MATERIALIZED
+
+
+def test_bootstrap_preserves_divergent_destination(tmp_path):
+ payload = b"manifest"
+ bundle = load_bundle(write_index(tmp_path, index_for(payload)))
+ target = tmp_path / "checkout" / "catalog" / "runs" / "run-v1.json"
+ target.parent.mkdir(parents=True)
+ target.write_bytes(b"local work")
+ with pytest.raises(BundleBootstrapError, match="destination_identity_mismatch"):
+ bootstrap_bundle(
+ bundle, tmp_path / "store", tmp_path / "checkout",
+ opener=lambda _: BytesIO(payload),
+ )
+ assert target.read_bytes() == b"local work"
+
+
+@pytest.mark.parametrize(
+ "index", sorted((Path(__file__).resolve().parents[1] / "releases").glob("*.bundle.json"))
+)
+def test_committed_bundle_reports_only_public_distribution_blockers(index):
+ root = Path(__file__).resolve().parents[1]
+ bundle = load_bundle(index)
+ readiness = assess_reproduction_readiness(bundle, root / "artifacts", root)
+ assert readiness.status is ReproductionStatus.BLOCKED
+ kinds = {issue.kind for issue in readiness.issues}
+ assert kinds == {
+ ReadinessIssueKind.CANONICAL_LOCATION_MISSING,
+ ReadinessIssueKind.MIRROR_MISSING,
+ }
diff --git a/tests/test_exact_oracle_experiment.py b/tests/test_exact_oracle_experiment.py
new file mode 100644
index 00000000..06e6f398
--- /dev/null
+++ b/tests/test_exact_oracle_experiment.py
@@ -0,0 +1,80 @@
+import json
+
+import pytest
+
+from decluster.experiments import exact_oracle_audit as experiment
+from decluster.result_artifacts import canonical_json_bytes
+
+
+pytest.importorskip("dss")
+
+
+@pytest.fixture(scope="module")
+def artifact():
+ return experiment.build_artifact(max_coins=6)
+
+
+def test_artifact_is_deterministic_and_self_describing(artifact):
+ assert canonical_json_bytes(artifact) == canonical_json_bytes(
+ experiment.build_artifact(max_coins=6)
+ )
+ assert artifact["experiment"] == experiment.EXPERIMENT_ID
+ assert artifact["summary"]["family_size"] == artifact["report"]["family"]["size"]
+ assert artifact["parameters"]["max_coins"] == 6
+ assert len(artifact["dependencies"]["dss_revision"]) == 40
+
+
+def test_publishable_artifact_requires_an_identified_dss_build(monkeypatch):
+ import dss
+
+ def unidentified():
+ raise RuntimeError("missing revision")
+
+ monkeypatch.setattr(dss, "require_build_revision", unidentified)
+ with pytest.raises(experiment.VerificationError, match="embedded Git revision"):
+ experiment.build_artifact(max_coins=6)
+
+
+def test_verifier_recomputes_the_experiment(artifact):
+ assert experiment.verify_artifact(artifact) == artifact
+ changed = json.loads(json.dumps(artifact))
+ changed["summary"]["family_size"] += 1
+ with pytest.raises(experiment.VerificationError, match="differs from a fresh"):
+ experiment.verify_artifact(changed)
+
+
+def test_verifier_rejects_parameter_drift_before_computation(artifact):
+ changed = json.loads(json.dumps(artifact))
+ changed["parameters"]["alphabet"] = [1, 2]
+ with pytest.raises(experiment.VerificationError, match="unsupported parameter alphabet"):
+ experiment.verify_artifact(changed)
+
+
+def test_cli_round_trip_and_renderer(tmp_path):
+ artifact_path = tmp_path / "audit.json"
+ markdown_path = tmp_path / "audit.md"
+ assert experiment.main(["run", "--output", str(artifact_path), "--max-coins", "6"]) == 0
+ assert experiment.main(["verify", "--artifact", str(artifact_path)]) == 0
+ assert experiment.main([
+ "render", "--artifact", str(artifact_path), "--output", str(markdown_path),
+ ]) == 0
+ markdown = markdown_path.read_text()
+ assert "Generated from the canonical experiment artifact" in markdown
+ assert f"| transactions | {json.loads(artifact_path.read_text())['summary']['family_size']} |" in markdown
+
+
+def test_reproduce_and_verify_cover_artifact_and_markdown(tmp_path):
+ artifact_path = tmp_path / "results" / "audit.json"
+ markdown_path = tmp_path / "results" / "audit.md"
+ assert experiment.main([
+ "reproduce", "--artifact", str(artifact_path), "--markdown", str(markdown_path),
+ "--max-coins", "6",
+ ]) == 0
+ assert experiment.main([
+ "verify", "--artifact", str(artifact_path), "--markdown", str(markdown_path),
+ ]) == 0
+ markdown_path.write_text(markdown_path.read_text() + "changed\n")
+ with pytest.raises(experiment.VerificationError, match="Markdown differs"):
+ experiment.main([
+ "verify", "--artifact", str(artifact_path), "--markdown", str(markdown_path),
+ ])
diff --git a/tests/test_facade_agreement.py b/tests/test_facade_agreement.py
new file mode 100644
index 00000000..fc2faa47
--- /dev/null
+++ b/tests/test_facade_agreement.py
@@ -0,0 +1,73 @@
+"""`analyze()` and `report()` are documented as one walk read two ways. This pins that: on one
+fixture, with no oracle passed, both facades must produce the same provenance distribution.
+
+It would have failed while `report`/`ancestry` defaulted to the value-flow oracle and `analyze`
+still defaulted to the subset-sum one."""
+import pytest
+
+from decluster import ancestry, path_count, report
+from decluster.analyze import analyze
+
+
+def _coinbase(value):
+ return {"vin": [{"is_coinbase": True}], "vout": [{"value": value}]}
+
+
+def _vin(txid, value):
+ return {"is_coinbase": False, "txid": txid, "vout": 0, "prevout": {"value": value}}
+
+
+def _txs():
+ return {
+ "C": {"txid": "C", "vin": [_vin("P", 3), _vin("R", 5)],
+ "vout": [{"value": 3, "scriptpubkey_type": "v0_p2wpkh"},
+ {"value": 5, "scriptpubkey_type": "v0_p2wpkh"}]},
+ "P": _coinbase(3),
+ "R": _coinbase(5),
+ }
+
+
+# The amount channel is not under test here and its default count oracle needs the compiled `dss`
+# extension; refusing at the transaction level keeps this a pure provenance-walk comparison.
+def _no_counts(inputs, outputs):
+ return {}
+
+
+def test_analyze_and_report_agree_on_the_default_walk():
+ txs = _txs()
+ fetch = lambda t: txs[t]
+
+ a = analyze(txs["C"], depth=6, fetch=fetch, subjective=False)
+ r = report.report(txs["C"], depth=6, fetch=fetch, subjective=False,
+ count_oracle=_no_counts)
+
+ assert set(a) == set(r["targets"])
+ for vout in a:
+ prov, target = a[vout]["provenance"], r["targets"][vout]
+ assert prov["shannon"] == pytest.approx(target["shannon"])
+ assert prov["min_entropy"] == pytest.approx(target["min_entropy"])
+ assert prov["n_absorbers"] == target["n_absorbers"]
+
+
+def test_the_agreed_walk_is_the_value_flow_one():
+ txs = _txs()
+ fetch = lambda t: txs[t]
+ expected = ancestry.value_flow_signature(("C", 0), depth=6, fetch=fetch)
+
+ assert expected == pytest.approx({("P", 0): 0.375, ("R", 0): 0.625})
+ a = analyze(txs["C"], targets=[0], depth=6, fetch=fetch, subjective=False)
+ assert a[0]["provenance"]["origins"] == pytest.approx(expected)
+
+
+def test_no_facade_reaches_the_subset_sum_oracle_by_default(monkeypatch):
+ txs = _txs()
+ fetch = lambda t: txs[t]
+
+ def fail_if_called(*args, **kwargs):
+ raise AssertionError("the subset-sum oracle is no longer any facade's default")
+
+ monkeypatch.setattr(ancestry, "dss_link_oracle", fail_if_called)
+
+ analyze(txs["C"], depth=6, fetch=fetch, subjective=False)
+ report.report(txs["C"], depth=6, fetch=fetch, subjective=False, count_oracle=_no_counts)
+ path_count.path_count_anonymity(("C", 0), depth=6, fetch=fetch)
diff --git a/tests/test_fellegi_sunter.py b/tests/test_fellegi_sunter.py
new file mode 100644
index 00000000..bfcaf374
--- /dev/null
+++ b/tests/test_fellegi_sunter.py
@@ -0,0 +1,132 @@
+import math
+
+import pytest
+
+from decluster.fellegi_sunter import (
+ ComparisonField,
+ FellegiSunterModel,
+ FieldParameters,
+ comparison_vector,
+ comparison_vectors,
+ evaluate,
+ fit_model,
+ fit_model_for_error_rates,
+ fit_supervised,
+ thresholds_from_error_rates,
+)
+
+
+def test_classical_agreement_and_disagreement_weights():
+ p = FieldParameters(m=0.9, u=0.3)
+ assert p.agreement_weight == pytest.approx(math.log2(0.9 / 0.3))
+ assert p.disagreement_weight == pytest.approx(math.log2(0.1 / 0.7))
+
+
+def test_comparison_vectors_are_named_and_can_abstain():
+ fields = [
+ ComparisonField("name", lambda a, b: a["name"] == b["name"]),
+ ComparisonField("age", lambda a, b: None if "age" not in a or "age" not in b
+ else a["age"] == b["age"]),
+ ]
+ pair = ({"name": "Ada"}, {"name": "Ada", "age": 30})
+ assert comparison_vector(*pair, fields) == {"name": True, "age": None}
+ assert comparison_vectors([pair], fields) == [{"name": True, "age": None}]
+
+
+def test_supervised_fit_estimates_m_and_u_from_separate_classes():
+ vectors = [
+ {"a": True}, {"a": True}, {"a": False}, # matches: 2 / 3
+ {"a": True}, {"a": False}, {"a": False}, # non-matches: 1 / 3
+ ]
+ params = fit_supervised(vectors, [1, 1, 1, 0, 0, 0], alpha=1)
+ assert params["a"].m == pytest.approx(3 / 5)
+ assert params["a"].u == pytest.approx(2 / 5)
+
+
+def test_abstentions_do_not_enter_denominators():
+ vectors = [{"a": True}, {"a": None}, {"a": False}, {"a": None}]
+ p = fit_supervised(vectors, [1, 1, 0, 0], alpha=1)["a"]
+ assert p.m == pytest.approx(2 / 3)
+ assert p.u == pytest.approx(1 / 3)
+
+
+def test_score_is_sum_of_log_likelihood_ratios():
+ model = FellegiSunterModel(
+ {"a": FieldParameters(0.9, 0.3), "b": FieldParameters(0.8, 0.4)},
+ non_link_below=-1,
+ link_at_or_above=1,
+ )
+ want = math.log2(0.9 / 0.3) + math.log2((1 - 0.8) / (1 - 0.4))
+ assert model.score({"a": True, "b": False}) == pytest.approx(want)
+ assert model.contributions({"a": None, "b": True}) == {
+ "b": pytest.approx(math.log2(0.8 / 0.4))
+ }
+
+
+def test_thresholds_produce_three_class_decision():
+ model = FellegiSunterModel(
+ {"a": FieldParameters(0.8, 0.2)},
+ non_link_below=-1.5,
+ link_at_or_above=1.5,
+ )
+ assert model.classify({"a": True}) == "link"
+ assert model.classify({"a": False}) == "non-link"
+ assert model.classify({"a": None}) == "review"
+
+
+def test_classical_thresholds_are_derived_from_error_rates():
+ lower, upper = thresholds_from_error_rates(0.05, 0.10)
+ assert lower == pytest.approx(math.log2(0.10 / 0.95))
+ assert upper == pytest.approx(math.log2(0.90 / 0.05))
+
+ vectors = ([{"a": True}] * 9 + [{"a": False}] +
+ [{"a": True}] + [{"a": False}] * 9)
+ model = fit_model_for_error_rates(
+ vectors,
+ [1] * 10 + [0] * 10,
+ false_match_rate=0.05,
+ false_non_match_rate=0.10,
+ )
+ assert model.non_link_below == pytest.approx(lower)
+ assert model.link_at_or_above == pytest.approx(upper)
+
+
+def test_error_rates_must_define_a_review_region():
+ with pytest.raises(ValueError, match="review region"):
+ thresholds_from_error_rates(0.9, 0.9)
+
+
+def test_out_of_sample_evaluation_does_not_refit():
+ train = [{"a": True}] * 9 + [{"a": False}] + [{"a": True}] + [{"a": False}] * 9
+ labels = [1] * 10 + [0] * 10
+ model = fit_model(train, labels, non_link_below=-1, link_at_or_above=1, alpha=0.5)
+ fitted = model.parameters["a"]
+
+ held_out = [{"a": True}, {"a": False}, {"a": None}]
+ result = evaluate(model, held_out, [1, 0, 1])
+ assert model.parameters["a"] == fitted
+ assert result["decisions"] == ["link", "non-link", "review"]
+ assert result["coverage"] == pytest.approx(2 / 3)
+ assert result["selective_accuracy"] == 1.0
+
+
+def test_fit_rejects_a_field_without_both_label_classes():
+ with pytest.raises(ValueError, match="active match and non-match"):
+ fit_supervised([{"a": True}, {"a": None}], [1, 0])
+
+
+def test_unknown_field_is_not_silently_ignored():
+ model = FellegiSunterModel(
+ {"a": FieldParameters(0.8, 0.2)}, -1, 1
+ )
+ with pytest.raises(ValueError, match="unknown comparison fields"):
+ model.score({"a": True, "typo": False})
+
+
+def test_the_misleading_alias_is_gone():
+ """`fs_score` named a rarity kernel after a model it never fitted. Nothing imported it."""
+ import decluster.combiner as combiner
+ import decluster.rarity_weight_baseline as baseline
+
+ assert not hasattr(combiner, "fs_score")
+ assert not hasattr(baseline, "fs_score")
diff --git a/tests/test_fingerprint_catalog_axes_experiment.py b/tests/test_fingerprint_catalog_axes_experiment.py
new file mode 100644
index 00000000..7fff20a8
--- /dev/null
+++ b/tests/test_fingerprint_catalog_axes_experiment.py
@@ -0,0 +1,46 @@
+import json
+
+import pytest
+
+from decluster.experiments import fingerprint_catalog_axes as experiment
+
+
+def _rows(artifact):
+ return {row["configuration"]: row for row in artifact["measurement"]["rows"]}
+
+
+def test_axis_ablation_uses_one_pair_population_and_preserves_non_additivity():
+ artifact = experiment.build_artifact()
+ rows = _rows(artifact)
+ assert artifact["measurement"]["positive_pair_draws"] == 4000
+ assert artifact["measurement"]["negative_pair_draws"] == 4000
+ assert rows["plus_output_count"]["auc_delta_from_library"] < 0
+ assert rows["plus_segwit_serialization"]["auc_delta_from_library"] > 0
+ assert rows["plus_both"]["auc"] < rows["plus_segwit_serialization"]["auc"]
+
+
+def test_observed_deltas_are_pinned_without_claiming_historical_cache_reproduction():
+ rows = _rows(experiment.build_artifact())
+ assert rows["plus_output_count"]["auc_delta_from_library"] == pytest.approx(-0.000725)
+ assert rows["plus_segwit_serialization"]["auc_delta_from_library"] == pytest.approx(0.0026)
+ assert rows["plus_both"]["auc_delta_from_library"] == pytest.approx(0.002125)
+
+
+def test_duplicate_transaction_ids_are_refused(tmp_path):
+ path = tmp_path / "duplicate.json"
+ transaction = {"txid": "same", "vin": [], "vout": []}
+ path.write_text(json.dumps([transaction, transaction]), encoding="utf-8")
+ with pytest.raises(experiment.VerificationError, match="unique"):
+ experiment.build_artifact(path)
+
+
+def test_the_stored_artifact_still_equals_a_fresh_execution():
+ """The pinned deltas above say what the numbers mean; this says nothing else moved."""
+ from pathlib import Path
+
+ root = Path(__file__).resolve().parents[1]
+ dataset = str(root / "tests" / "fixtures" / "fingerprint_blkcache_sample.json")
+ artifact = root / "results/artifacts/fingerprint-catalog-axes-v1.json"
+ markdown = root / "results/generated/fingerprint-catalog-axes-v1.md"
+ assert experiment.main(["--dataset", dataset, "verify",
+ "--artifact", str(artifact), "--markdown", str(markdown)]) == 0
diff --git a/tests/test_fingerprint_ns.py b/tests/test_fingerprint_ns.py
index b3e84389..fc8152d3 100644
--- a/tests/test_fingerprint_ns.py
+++ b/tests/test_fingerprint_ns.py
@@ -69,20 +69,20 @@ def test_equivalence_key_groups_matching_conditioners():
assert k1 == k2 and k1 != k3
def test_build_labeled_nodes_assigns_one_owner_per_reuse_group():
- import examples.fingerprint_ns as ex
+ from decluster import fingerprint_regime as regime
txs = [
{"txid": "t1", "vin": [{"prevout": {"scriptpubkey_address": "A"}}]},
{"txid": "t2", "vin": [{"prevout": {"scriptpubkey_address": "A"}}]},
{"txid": "t3", "vin": [{"prevout": {"scriptpubkey_address": "B"}}]},
{"txid": "t4", "vin": [{"prevout": {"scriptpubkey_address": "B"}}]},
]
- nodes = ex.build_labeled_nodes(txs, cap=10)
+ nodes = regime.build_labeled_nodes(txs, cap=10)
owners = {tx["txid"]: owner for tx, owner in nodes}
assert owners["t1"] == owners["t2"] and owners["t3"] == owners["t4"]
assert owners["t1"] != owners["t3"]
def test_build_labeled_nodes_dedups_tx_that_reuses_two_addresses():
- import examples.fingerprint_ns as ex
+ from decluster import fingerprint_regime as regime
txs = [
# t1 reuses both address A (with t2) and address C (with t3) -> must land in one owner only
{"txid": "t1", "vin": [{"prevout": {"scriptpubkey_address": "A"}},
@@ -92,7 +92,7 @@ def test_build_labeled_nodes_dedups_tx_that_reuses_two_addresses():
{"txid": "t4", "vin": [{"prevout": {"scriptpubkey_address": "B"}}]},
{"txid": "t5", "vin": [{"prevout": {"scriptpubkey_address": "B"}}]},
]
- nodes = ex.build_labeled_nodes(txs, cap=10)
+ nodes = regime.build_labeled_nodes(txs, cap=10)
txids = [tx["txid"] for tx, _ in nodes]
assert len(txids) == len(set(txids)) # no txid appears twice, under any owner
owners = {tx["txid"]: owner for tx, owner in nodes}
diff --git a/tests/test_fingerprint_regime_experiment.py b/tests/test_fingerprint_regime_experiment.py
new file mode 100644
index 00000000..961d0a9b
--- /dev/null
+++ b/tests/test_fingerprint_regime_experiment.py
@@ -0,0 +1,32 @@
+import json
+
+import pytest
+
+from decluster.experiments import fingerprint_regime as experiment
+
+
+def test_reproduce_then_verify_snapshot(tmp_path):
+ artifact = tmp_path / "artifact.json"
+ markdown = tmp_path / "result.md"
+ argv = ["--dataset", "tests/fixtures/fingerprint_blkcache_sample.json"]
+ assert experiment.main(argv + [
+ "reproduce", "--artifact", str(artifact), "--markdown", str(markdown),
+ ]) == 0
+ assert experiment.main(argv + [
+ "verify", "--artifact", str(artifact), "--markdown", str(markdown),
+ ]) == 0
+
+
+def test_snapshot_measured_result_does_not_support_stable_conditioner_claim():
+ artifact = experiment.build_artifact()
+ rows = {row["weight_source"]: row for row in artifact["measurement"]["rows"]}
+ measured = rows["snapshot_measured"]
+ assert measured["ns_top1"] > measured["fs_top1"]
+ assert measured["within_class_gap_mean"] > 0.5
+
+
+def test_duplicate_transaction_ids_are_rejected(tmp_path):
+ dataset = tmp_path / "duplicate.json"
+ dataset.write_text(json.dumps([{"txid": "x"}, {"txid": "x"}]), encoding="utf-8")
+ with pytest.raises(experiment.VerificationError, match="must be unique"):
+ experiment.build_artifact(dataset)
diff --git a/tests/test_fingerprint_sparsity.py b/tests/test_fingerprint_sparsity.py
index 6256278b..fafa2e19 100644
--- a/tests/test_fingerprint_sparsity.py
+++ b/tests/test_fingerprint_sparsity.py
@@ -1,6 +1,6 @@
-import sys, os
-sys.path.insert(0, os.path.dirname(os.path.dirname(__file__)))
-from examples.fingerprint_sparsity import bucket, distribution, LABELS
+import pytest
+
+from decluster.fingerprint_sparsity import LABELS, bucket, distribution, reconstruct_histogram
def test_bucket_boundaries():
@@ -30,3 +30,20 @@ def test_distribution_is_complete_and_normalised():
assert total == sum(counts.values())
assert set(dist) == set(LABELS)
assert abs(sum(dist.values()) - 1.0) < 1e-12
+
+
+@pytest.mark.parametrize("value", [0, -1, True, 1.5])
+def test_distribution_rejects_invalid_counts(value):
+ with pytest.raises(ValueError):
+ distribution({"invalid": value})
+
+
+def test_reconstruct_histogram_sums_a_conditional_partition():
+ histogram, total = reconstruct_histogram({
+ "a": {"buckets": {"exactly 1": 2, "2-9": 3}},
+ "b": {"buckets": {"2-9": 4, ">=100,000": 5}},
+ })
+ assert total == 14
+ assert histogram["exactly 1"] == 2
+ assert histogram["2-9"] == 7
+ assert histogram[">=100,000"] == 5
diff --git a/tests/test_fingerprint_sparsity_experiment.py b/tests/test_fingerprint_sparsity_experiment.py
new file mode 100644
index 00000000..f311b6c8
--- /dev/null
+++ b/tests/test_fingerprint_sparsity_experiment.py
@@ -0,0 +1,44 @@
+import copy
+import json
+
+import pytest
+
+from decluster.experiments import fingerprint_sparsity as experiment
+
+
+def test_reconstructs_published_whole_window_column():
+ artifact = experiment.build_artifact()
+ measurement = artifact["measurement"]
+ assert artifact["window"]["transactions"] == 98_179_414
+ assert round(measurement["class_size_shares"]["exactly 1"] * 100, 3) == 0.009
+ assert round(measurement["class_size_shares"][">=100,000"] * 100, 3) == 75.552
+ assert round(measurement["share_below_10"] * 100, 3) == 0.061
+ assert round(measurement["share_below_100"] * 100, 3) == 0.413
+ assert len(artifact["cross_check"]["consistent_conditional_partitions"]) >= 2
+
+
+def test_disagreeing_conditional_partition_is_refused(tmp_path):
+ with open(experiment.DEFAULT_DATASET, encoding="utf-8") as dataset:
+ source = json.load(dataset)
+ corrupted = copy.deepcopy(source)
+ axis = sorted(corrupted["cond"])[0]
+ value = sorted(corrupted["cond"][axis])[0]
+ corrupted["cond"][axis][value]["buckets"]["exactly 1"] += 1
+ path = tmp_path / "corrupted.json"
+ path.write_text(json.dumps(corrupted), encoding="utf-8")
+ with pytest.raises(experiment.VerificationError, match="covers"):
+ experiment.build_artifact(path)
+
+
+def test_markdown_is_canonical(tmp_path):
+ artifact = experiment.build_artifact()
+ artifact_path = tmp_path / "artifact.json"
+ markdown_path = tmp_path / "result.md"
+ from decluster.result_artifacts import write_canonical_json
+
+ write_canonical_json(artifact_path, artifact)
+ markdown_path.write_text(experiment.render_markdown(artifact), encoding="utf-8")
+ assert experiment.main([
+ "--dataset", experiment.DEFAULT_DATASET, "verify",
+ "--artifact", str(artifact_path), "--markdown", str(markdown_path),
+ ]) == 0
diff --git a/tests/test_fingerprint_validate.py b/tests/test_fingerprint_validate.py
index e1f8b7f4..3ae51875 100644
--- a/tests/test_fingerprint_validate.py
+++ b/tests/test_fingerprint_validate.py
@@ -88,11 +88,11 @@ def test_library_scorer_locktime_not_inert():
# height_* sub-classes folded into the "height" bucket the extractor emits -> axis is not inert
assert "height" in lp and "zero" in lp and "timestamp" in lp
-def test_library_scorer_delegates_to_fs_score():
+def test_library_scorer_delegates_to_rarity_score():
from decluster.fingerprint_validate import LibraryScorer
from decluster import combiner
s = LibraryScorer()
- # each axis tuple is (name, fn, p, collision, abstain) — the fs_score contract
+ # each axis tuple is (name, fn, p, collision, abstain) — the rarity-score contract
assert all(len(a) == 5 and callable(a[4]) for a in s.axes)
tx = {"version": 2, "locktime": 0, "fee": 300, "weight": 800,
"vin": [{"sequence": 0xfffffffd, "txid": "aa" * 32, "vout": 0,
@@ -100,7 +100,42 @@ def test_library_scorer_delegates_to_fs_score():
"witness": ["30450221" + "11" * 33, "02" + "11" * 32]}],
"vout": [{"value": 900, "scriptpubkey_type": "v0_p2wpkh", "scriptpubkey_address": "bc1qc"},
{"value": 2000, "scriptpubkey_type": "v0_p2wpkh", "scriptpubkey_address": "bc1qd"}]}
- assert s.score(tx, tx) == combiner.fs_score(s.axes, tx, tx, s.c, s.floor_n) # delegation is exact
+ assert s.score(tx, tx) == combiner.rarity_score(s.axes, tx, tx, s.c, s.floor_n)
+
+def test_named_axis_variants_are_strict_subsets_of_the_default():
+ from decluster.fingerprint_validate import (ADDRESS_DETERMINED_AXES, LibraryScorer,
+ REDUNDANT_AXES, construction_only_scorer,
+ decorrelated_scorer)
+ full = {a[0] for a in LibraryScorer().axes}
+ decorrelated = {a[0] for a in decorrelated_scorer().axes}
+ construction = {a[0] for a in construction_only_scorer().axes}
+ assert decorrelated == full - REDUNDANT_AXES and len(decorrelated) == 14
+ assert construction == full - ADDRESS_DETERMINED_AXES and len(construction) == 18
+ assert REDUNDANT_AXES and ADDRESS_DETERMINED_AXES <= full
+
+def test_decorrelated_variant_keeps_one_axis_per_cluster():
+ from decluster.fingerprint_validate import AXIS_CLUSTERS, decorrelated_scorer
+ kept = {a[0] for a in decorrelated_scorer().axes}
+ for cluster, representative in AXIS_CLUSTERS:
+ assert kept & set(cluster) == {representative}
+
+def test_redundant_pair_is_the_measured_phi_one_pair():
+ from decluster import library
+ from decluster.fingerprint_validate import REDUNDANT_AXES
+ for a, b in library.REDUNDANT_AXIS_PAIRS:
+ assert (a in REDUNDANT_AXES) != (b in REDUNDANT_AXES) # exactly one survives
+
+def test_annotation_only_axis_abstains_on_stored_records():
+ from decluster import library
+ from decluster.extractors import x_locktime_vs_broadcast
+ from decluster.fingerprint_validate import LibraryScorer
+ tx = {"txid": "T", "locktime": 0, "status": {"block_height": 800000},
+ "vin": [{"sequence": 0xfffffffd, "txid": "aa" * 32, "vout": 0, "prevout": {}}],
+ "vout": [{"value": 1, "scriptpubkey_type": "v0_p2wpkh"}]}
+ assert x_locktime_vs_broadcast(tx) == "na" # no tx["_bc"] on a stored record
+ scored = {name for name, _, p, *_ in LibraryScorer().axes if "na" in p}
+ for axis in library.ANNOTATION_ONLY_AXES:
+ assert axis not in scored # "na" unmeasured -> the axis abstains
if __name__ == "__main__":
fns = [v for k, v in sorted(globals().items()) if k.startswith("test_") and callable(v)]
diff --git a/tests/test_fingerprint_validation_experiment.py b/tests/test_fingerprint_validation_experiment.py
new file mode 100644
index 00000000..36b0db33
--- /dev/null
+++ b/tests/test_fingerprint_validation_experiment.py
@@ -0,0 +1,24 @@
+import json
+
+import pytest
+
+from decluster.experiments import fingerprint_validation as experiment
+
+
+def test_reproduce_then_verify_snapshot(tmp_path):
+ artifact = tmp_path / "artifact.json"
+ markdown = tmp_path / "result.md"
+ argv = ["--dataset", "tests/fixtures/fingerprint_blkcache_sample.json"]
+ assert experiment.main(argv + [
+ "reproduce", "--artifact", str(artifact), "--markdown", str(markdown),
+ ]) == 0
+ assert experiment.main(argv + [
+ "verify", "--artifact", str(artifact), "--markdown", str(markdown),
+ ]) == 0
+
+
+def test_duplicate_transaction_id_is_rejected(tmp_path):
+ dataset = tmp_path / "duplicate.json"
+ dataset.write_text(json.dumps([{"txid": "x"}, {"txid": "x"}]), encoding="utf-8")
+ with pytest.raises(experiment.VerificationError, match="duplicate transaction id"):
+ experiment.build_artifact(dataset)
diff --git a/tests/test_fs_ablation.py b/tests/test_fs_ablation.py
new file mode 100644
index 00000000..3b063af7
--- /dev/null
+++ b/tests/test_fs_ablation.py
@@ -0,0 +1,169 @@
+import glob
+import json
+import statistics
+from pathlib import Path
+
+import pytest
+
+from decluster import reproducibility as rp
+from decluster.fingerprint_validate import load_blkcache
+from decluster.fs_ablation import (
+ class_conditional_association,
+ dependent_clusters,
+ group_ablation,
+ leave_one_out,
+ most_dependent_pairs,
+ phi_coefficient,
+ run_ablation_study,
+)
+from decluster.fs_temporal import temporal_split
+
+FIXTURE = Path(__file__).parent / "fixtures" / "fingerprint_blkcache_sample.json"
+ROOT = Path(__file__).resolve().parent.parent
+BLKCACHE = ROOT / ".blkcache"
+
+# The cluster of mutually correlated script/signature-encoding axes this fixture reliably
+# reproduces at the module's default cluster_threshold=0.6 (also found on the full .blkcache
+# population, there joined by a fifth field, `nested_segwit`, that this smaller sample does not
+# have enough active observations to connect).
+SCRIPT_CLUSTER = {"input_script_type", "input_types_present", "pubkey_compression", "sighash"}
+
+
+def test_phi_coefficient_perfect_agreement_and_disagreement():
+ assert phi_coefficient([True, True, False, False], [True, True, False, False]) == 1.0
+ assert phi_coefficient([True, True, False, False], [False, False, True, True]) == -1.0
+
+
+def test_phi_coefficient_none_on_degenerate_margin():
+ # every "a" value is True: the a-margin is degenerate, phi is undefined
+ assert phi_coefficient([True, True, True], [True, False, True]) is None
+
+
+def test_phi_coefficient_zero_on_independence():
+ a = [True, True, False, False] * 5
+ b = [True, False, True, False] * 5
+ assert phi_coefficient(a, b) == 0.0
+
+
+def test_class_conditional_association_is_class_specific_not_pooled():
+ """Two fields that each track the label perfectly but in OPPOSITE directions per class:
+ pooling classes would show them moving together on some slice of the data by construction of
+ the labels, but within either single class they are constant (one value only) and so must
+ score as no observation at all -- not as agreement, and not as the strong pooled correlation
+ unconditional statistics would report."""
+ class Field:
+ def __init__(self, name):
+ self.name = name
+
+ fields = [Field("a"), Field("b")]
+ # within match: a and b always agree; within non-match: a and b always disagree.
+ vectors = (
+ [{"a": True, "b": True}] * 20 + [{"a": False, "b": False}] * 20 +
+ [{"a": True, "b": False}] * 20 + [{"a": False, "b": True}] * 20
+ )
+ labels = [True] * 40 + [False] * 40
+ assoc = class_conditional_association(vectors, labels, fields, min_n=10)
+ assert assoc["match"][("a", "b")]["phi"] == 1.0
+ assert assoc["non_match"][("a", "b")]["phi"] == -1.0
+
+
+def test_most_dependent_pairs_ranks_by_larger_magnitude():
+ assoc = {
+ "match": {("a", "b"): {"phi": 0.9, "n": 50}, ("a", "c"): {"phi": 0.1, "n": 50}},
+ "non_match": {("a", "b"): {"phi": 0.2, "n": 50}},
+ }
+ ranked = most_dependent_pairs(assoc, top_n=2)
+ assert ranked[0][0] == ("a", "b")
+ assert ranked[1][0] == ("a", "c")
+
+
+def test_dependent_clusters_groups_transitively_and_drops_singletons():
+ assoc = {
+ "match": {
+ ("a", "b"): {"phi": 0.9, "n": 50},
+ ("b", "c"): {"phi": 0.7, "n": 50},
+ ("d", "e"): {"phi": 0.1, "n": 50},
+ },
+ "non_match": {},
+ }
+ clusters = dependent_clusters(assoc, threshold=0.6)
+ assert clusters == [{"a", "b", "c"}]
+
+
+def test_leave_one_out_reports_a_delta_per_baseline_field():
+ with FIXTURE.open() as stream:
+ txs = json.load(stream)
+ result = leave_one_out(txs, cap=200, seed=0)
+ assert set(result["leave_one_out"]) == set(result["baseline"]["fields"])
+ for entry in result["leave_one_out"].values():
+ assert 0 <= entry["auc"] <= 1
+
+
+def test_group_ablation_needs_at_least_two_fields():
+ with FIXTURE.open() as stream:
+ txs = json.load(stream)
+ with pytest.raises(ValueError, match="at least two"):
+ group_ablation(txs, {"version"}, cap=200, seed=0)
+
+
+def test_run_ablation_study_shape():
+ with FIXTURE.open() as stream:
+ txs = json.load(stream)
+ result = run_ablation_study(txs, ablation_cap=200, association_cap=200, seed=0)
+ assert result["association"]["statistic"].startswith("phi coefficient")
+ assert isinstance(result["association"]["clusters"], list)
+ assert len(result["group_ablation"]) == len(result["association"]["clusters"])
+ for group in result["group_ablation"]:
+ assert group["representative"] in group["group"]
+
+
+def test_removing_the_whole_correlated_cluster_hurts_more_than_keeping_one_representative():
+ """The pinned direction, in the style of
+ `test_fs_temporal.py::test_fellegi_sunter_beats_the_rarity_baseline_out_of_period`: if a
+ cluster of fields is genuinely redundant (each a near-copy of the others' information), then
+ dropping every member should cost held-out AUC more than dropping every member but one. A
+ verdict here does not depend on which field the model happens to keep, only on group-vs-keep
+ outperforming by a pre-registered margin, gated through `reproducibility.separable` on the
+ paired per-seed discordant wins rather than read off one run.
+ """
+ with FIXTURE.open() as stream:
+ txs = json.load(stream)
+
+ diffs = []
+ for seed in range(6):
+ result = group_ablation(txs, SCRIPT_CLUSTER, cap=300, seed=seed)
+ diffs.append(result["remove_group_delta"] - result["keep_representative_delta"])
+
+ wins_remove = sum(1 for d in diffs if d > 0)
+ wins_keep = sum(1 for d in diffs if d < 0)
+ verdict, p = rp.separable(wins_remove, wins_keep, statistics.median(diffs), min_effect=0.001)
+ assert verdict == "a", f"diffs={diffs} wins={wins_remove}/{wins_keep} p={p}"
+
+
+def test_manifest_invariants_are_recomputed_and_match_results_fs_ablation():
+ """Mirrors `test_fs_temporal.py`'s manifest test: recompute the population invariants
+ `results/RESULTS-fs-ablation.md` depends on directly from `.blkcache`, and assert
+ `check_manifest` calls them `ok` against that live recomputation, not just `identity-only`
+ against an unchanged byte digest."""
+ if not glob.glob(str(BLKCACHE / "*.json")):
+ pytest.skip(".blkcache not present in this checkout; population invariants not recomputed")
+
+ txs = load_blkcache(str(BLKCACHE))
+ train, test, cutoff = temporal_split(txs, 0.7)
+ result = run_ablation_study(txs, ablation_cap=4000, association_cap=8000, seed=0)
+
+ invariants = {
+ "transactions": len(txs),
+ "split_cutoff": cutoff,
+ "train_transactions": len(train),
+ "test_transactions": len(test),
+ "cluster_threshold": result["association"]["cluster_threshold"],
+ "clusters": result["association"]["clusters"],
+ "most_dependent_pair": result["association"]["most_dependent_pairs"][0]["fields"],
+ "most_dependent_pair_phi_match": result["association"]["most_dependent_pairs"][0]["phi_match"],
+ "most_dependent_pair_phi_non_match":
+ result["association"]["most_dependent_pairs"][0]["phi_non_match"],
+ "baseline_auc": result["leave_one_out"]["baseline"]["auc"],
+ }
+ status, message = rp.check_manifest("RESULTS-fs-ablation.md", invariants, root=str(ROOT))
+ assert status == "ok", message
diff --git a/tests/test_fs_ablation_experiment.py b/tests/test_fs_ablation_experiment.py
new file mode 100644
index 00000000..cf28e699
--- /dev/null
+++ b/tests/test_fs_ablation_experiment.py
@@ -0,0 +1,39 @@
+import json
+from pathlib import Path
+
+import pytest
+
+from decluster.experiments import fs_ablation as experiment
+
+ROOT = Path(__file__).resolve().parents[1]
+SNAPSHOT = ROOT / "data" / "fs-blkcache-2026-09-04.tar.gz"
+
+
+def test_snapshot_reproduces_the_historical_ablation_report():
+ historical = json.loads((ROOT / "results" / "fs-ablation.json").read_text())
+ artifact = experiment.build_artifact(SNAPSHOT)
+
+ assert artifact["report"] == historical
+ assert artifact["dataset"] == "fs-blkcache-2026-09-04-v1"
+ assert "duplicates" in artifact["limitations"][3]
+
+
+def test_verifier_recomputes_the_population_ablation():
+ artifact = experiment.build_artifact(SNAPSHOT)
+ artifact["report"]["association"]["cluster_threshold"] = 0.9
+
+ with pytest.raises(experiment.VerificationError, match="fresh experiment"):
+ experiment.verify_artifact(artifact, SNAPSHOT)
+
+
+def test_cli_reproduces_and_verifies_json_and_markdown(tmp_path):
+ artifact = tmp_path / "artifact.json"
+ markdown = tmp_path / "result.md"
+ assert experiment.main([
+ "--snapshot", str(SNAPSHOT), "reproduce",
+ "--artifact", str(artifact), "--markdown", str(markdown),
+ ]) == 0
+ assert experiment.main([
+ "--snapshot", str(SNAPSHOT), "verify",
+ "--artifact", str(artifact), "--markdown", str(markdown),
+ ]) == 0
diff --git a/tests/test_fs_em.py b/tests/test_fs_em.py
index af448277..62ec6aaa 100644
--- a/tests/test_fs_em.py
+++ b/tests/test_fs_em.py
@@ -1,4 +1,4 @@
-"""EM per-axis m: fs_score dict extension + fs_em engine (agree_matrix / em_fit / oracle_m)."""
+"""EM per-axis m plus the legacy rarity-score parameter extension."""
import sys, os, math, random
sys.path.insert(0, os.path.dirname(os.path.dirname(__file__)))
@@ -9,24 +9,24 @@ def _one_axis(collision=0.5):
return [("ax", fn, {"a": 0.5}, collision, lambda va, vb: False)]
-def test_fs_score_scalar_path_byte_identical():
- from decluster.combiner import fs_score
+def test_rarity_score_scalar_path_byte_identical():
+ from decluster.combiner import rarity_score
axes = _one_axis(collision=0.5)
- agree = fs_score(axes, {"k": "a"}, {"k": "a"}, 0.95, 1000)
- disagree = fs_score(axes, {"k": "a"}, {"k": "b"}, 0.95, 1000)
+ agree = rarity_score(axes, {"k": "a"}, {"k": "a"}, 0.95, 1000)
+ disagree = rarity_score(axes, {"k": "a"}, {"k": "b"}, 0.95, 1000)
assert agree == -math.log2(0.5) # +1.0 bit
assert disagree == min(0.0, math.log2((1 - 0.95) / (1 - 0.5)))
-def test_fs_score_dict_applies_per_axis_c():
- from decluster.combiner import fs_score
+def test_rarity_score_dict_applies_per_axis_c():
+ from decluster.combiner import rarity_score
axes = _one_axis(collision=0.5)
# c as dict: m=0.5 makes the disagreement weight log2((1-0.5)/(1-0.5)) = 0
- w = fs_score(axes, {"k": "a"}, {"k": "b"}, {"ax": 0.5}, 1000)
+ w = rarity_score(axes, {"k": "a"}, {"k": "b"}, {"ax": 0.5}, 1000)
assert w == 0.0
# and c dict with 0.95 matches the scalar path exactly
- w95 = fs_score(axes, {"k": "a"}, {"k": "b"}, {"ax": 0.95}, 1000)
- assert w95 == fs_score(axes, {"k": "a"}, {"k": "b"}, 0.95, 1000)
+ w95 = rarity_score(axes, {"k": "a"}, {"k": "b"}, {"ax": 0.95}, 1000)
+ assert w95 == rarity_score(axes, {"k": "a"}, {"k": "b"}, 0.95, 1000)
def _synth(n, m_true, u_true, lam, seed):
diff --git a/tests/test_fs_temporal.py b/tests/test_fs_temporal.py
new file mode 100644
index 00000000..05706415
--- /dev/null
+++ b/tests/test_fs_temporal.py
@@ -0,0 +1,101 @@
+import glob
+import json
+import os
+import statistics
+from pathlib import Path
+
+import pytest
+
+from decluster import reproducibility as rp
+from decluster.fingerprint_validate import load_blkcache
+from decluster.fs_temporal import (
+ _height,
+ _score_metrics,
+ evaluate_temporal,
+ temporal_split,
+ weak_label_pairs,
+)
+
+
+FIXTURE = Path(__file__).parent / "fixtures" / "fingerprint_blkcache_sample.json"
+ROOT = Path(__file__).resolve().parent.parent
+BLKCACHE = ROOT / ".blkcache"
+
+
+def test_temporal_evaluation_uses_late_weak_labels_and_same_baseline_pairs():
+ with FIXTURE.open() as stream:
+ txs = json.load(stream)
+ result = evaluate_temporal(txs, cap=200, seed=7)
+ assert result["label_provenance"]["independently_verified"] is False
+ assert result["label_provenance"]["kind"] == "weak_labels"
+ assert result["split"]["train_height"][1] < result["split"]["test_height"][0]
+ assert result["pairs"]["test_positive"] == result["pairs"]["test_negative"]
+ assert result["comparison"]["same_test_pairs"] is True
+ assert 0 <= result["fellegi_sunter"]["auc"] <= 1
+ assert 0 <= result["rarity_baseline"]["auc"] <= 1
+ assert "brier" in result["fellegi_sunter"]["calibration"]
+
+
+def test_fellegi_sunter_beats_the_rarity_baseline_out_of_period():
+ """The direction RESULTS-fs-temporal.md publishes, pinned on committed data.
+
+ `test_temporal_evaluation_uses_late_weak_labels_and_same_baseline_pairs` asserts only that both
+ AUCs are in [0, 1], which a constant scorer satisfies. This asserts the sign: on the identical
+ held-out pairs, the fitted Fellegi-Sunter model must beat the fixed-rarity `LibraryScorer`.
+
+ The fixture is a real block-cache sample, not one constructed to display this direction — per
+ `results/REPRODUCIBILITY.md`, a direction is only pinned by data that could have shown the
+ opposite. The verdict goes through `reproducibility.separable` on the paired per-seed discordant
+ wins, with a pre-registered minimum effect, rather than being read off one run.
+ """
+ with FIXTURE.open() as stream:
+ txs = json.load(stream)
+
+ deltas = []
+ for seed in range(6):
+ result = evaluate_temporal(txs, cap=300, seed=seed)
+ assert result["comparison"]["same_test_pairs"] is True
+ deltas.append(result["fellegi_sunter"]["auc"] - result["rarity_baseline"]["auc"])
+
+ wins_fs = sum(1 for d in deltas if d > 0)
+ wins_rarity = sum(1 for d in deltas if d < 0)
+ verdict, p = rp.separable(wins_fs, wins_rarity, statistics.median(deltas), min_effect=0.01)
+ assert verdict == "a", f"deltas={deltas} wins={wins_fs}/{wins_rarity} p={p}"
+
+
+def test_temporal_split_rejects_missing_height():
+ with pytest.raises(ValueError, match="block_height"):
+ temporal_split([{"txid": "a"}, {"txid": "b"}])
+
+
+def test_auc_is_exact_and_ties_get_half_credit():
+ assert _score_metrics([3, 2, 2, 1], [True, True, False, False])["auc"] == 0.875
+
+
+def test_manifest_invariants_are_recomputed_and_match_results_fs_temporal():
+ """results/manifests/RESULTS-fs-temporal.json records population invariants that a byte digest
+ of .blkcache/ cannot see; check_manifest only compares them when a caller recomputes and passes
+ them in, so this is that caller. Recomputing the uncapped pair counts needs no rng result
+ (positive pairs are never sampled at this cap), so a fixed large cap is deterministic."""
+ if not glob.glob(str(BLKCACHE / "*.json")):
+ pytest.skip(".blkcache not present in this checkout; population invariants not recomputed")
+
+ txs = load_blkcache(str(BLKCACHE))
+ heights = sorted({_height(tx) for tx in txs})
+ train, test, cutoff = temporal_split(txs, 0.7)
+ _, train_labels = weak_label_pairs(train, cap=10**7, seed=0)
+ _, test_labels = weak_label_pairs(test, cap=10**7, seed=0)
+
+ invariants = {
+ "transactions": len(txs),
+ "height_min": heights[0],
+ "height_max": heights[-1],
+ "distinct_heights": len(heights),
+ "split_cutoff": cutoff,
+ "train_transactions": len(train),
+ "test_transactions": len(test),
+ "train_positive_pairs_available": sum(train_labels),
+ "test_positive_pairs_available": sum(test_labels),
+ }
+ status, message = rp.check_manifest("RESULTS-fs-temporal.md", invariants, root=str(ROOT))
+ assert status == "ok", message
diff --git a/tests/test_fs_temporal_experiment.py b/tests/test_fs_temporal_experiment.py
new file mode 100644
index 00000000..bce5ea2b
--- /dev/null
+++ b/tests/test_fs_temporal_experiment.py
@@ -0,0 +1,39 @@
+import io
+import json
+from pathlib import Path
+import tarfile
+
+import pytest
+
+from decluster.archive_snapshot import UnsafeArchiveError, extract_tar_gz
+from decluster.experiments import fs_temporal as experiment
+
+ROOT = Path(__file__).resolve().parents[1]
+SNAPSHOT = ROOT / "data" / "fs-blkcache-2026-09-04.tar.gz"
+
+
+def test_snapshot_reproduces_the_historical_report():
+ historical = json.loads((ROOT / "results" / "fs-temporal.json").read_text())
+ artifact = experiment.build_artifact(SNAPSHOT)
+
+ assert artifact["report"] == historical
+ assert artifact["dataset"] == "fs-blkcache-2026-09-04-v1"
+
+
+def test_verifier_recomputes_the_population_result():
+ artifact = experiment.build_artifact(SNAPSHOT)
+ artifact["report"]["fellegi_sunter"]["auc"] = 1.0
+
+ with pytest.raises(experiment.VerificationError, match="fresh experiment"):
+ experiment.verify_artifact(artifact, SNAPSHOT)
+
+
+def test_archive_extraction_rejects_path_traversal(tmp_path):
+ archive = tmp_path / "unsafe.tar.gz"
+ with tarfile.open(archive, "w:gz") as target:
+ member = tarfile.TarInfo("../escape")
+ member.size = 1
+ target.addfile(member, io.BytesIO(b"x"))
+
+ with pytest.raises(UnsafeArchiveError, match="unsafe archive path"):
+ extract_tar_gz(archive, tmp_path / "output")
diff --git a/tests/test_fused_engine_experiment.py b/tests/test_fused_engine_experiment.py
new file mode 100644
index 00000000..0dfb8d00
--- /dev/null
+++ b/tests/test_fused_engine_experiment.py
@@ -0,0 +1,59 @@
+"""The fusion is the object the argument rests on, and it was the one nothing published executed.
+
+Every channel had a canonical run; `cluster_refined`, which fuses them, had none. It ran in unit
+tests on stubs and in scripts over data this repository does not ship, so no committed artifact ever
+exercised the engine itself. These pin the run that closes that.
+"""
+
+import json
+from pathlib import Path
+
+import pytest
+
+from decluster.experiments import fused_engine as fe
+
+ROOT = Path(__file__).resolve().parent.parent
+ARTIFACT = ROOT / "results" / "artifacts" / "fused-engine-v1.json"
+SNAPSHOT = ROOT / "data" / "fs-blkcache-2026-09-04.tar.gz"
+
+
+@pytest.fixture(scope="module")
+def artifact():
+ if not ARTIFACT.is_file():
+ pytest.skip("the canonical fused-engine artifact is not committed")
+ return json.loads(ARTIFACT.read_text())
+
+
+def test_the_ladder_switches_on_every_channel_the_engine_fuses(artifact):
+ """A ladder that skips a channel would report a fusion the engine never performed."""
+ arms = [row["arm"] for row in artifact["ladder"]]
+ assert arms[0] == "fingerprint"
+ assert arms[-1] == "all five channels"
+ for channel in ("amount", "topology", "provenance"):
+ assert any(channel in arm for arm in arms), f"{channel} is never switched on"
+
+
+def test_the_engine_refuses_cospends_the_merge_only_baseline_takes(artifact):
+ """The thesis in one assertion: the baseline merges what the engine declines."""
+ baseline = artifact["cospend_baseline"]
+ assert baseline["refused_cospends"] == 0
+ assert all(row["refused_cospends"] > 0 for row in artifact["ladder"])
+ assert all(row["groups"] < baseline["groups"] for row in artifact["ladder"]), (
+ "the fingerprint link channel should still collapse more than co-spend alone")
+
+
+def test_topology_and_provenance_move_refusals_in_opposite_directions(artifact):
+ """Fusing is only worth doing where the channels disagree; this is where they do."""
+ by_arm = {row["arm"]: row for row in artifact["ladder"]}
+ fingerprint = by_arm["fingerprint+amount"]["refused_cospends"]
+ topology = by_arm["fingerprint+amount+topology"]["refused_cospends"]
+ provenance = by_arm["fingerprint+amount+topology+provenance"]["refused_cospends"]
+ assert topology < fingerprint, "topology should corroborate merges the fingerprint objected to"
+ assert provenance > topology, "provenance-disjointness should cut pairs topology kept"
+
+
+@pytest.mark.reproduction
+def test_the_committed_artifact_is_what_a_fresh_run_produces(artifact):
+ if not SNAPSHOT.is_file():
+ pytest.skip("the block-cache snapshot is not committed")
+ fe.verify_artifact(artifact, str(SNAPSHOT))
diff --git a/tests/test_graph_deanon_controls.py b/tests/test_graph_deanon_controls.py
new file mode 100644
index 00000000..6d6ef62c
--- /dev/null
+++ b/tests/test_graph_deanon_controls.py
@@ -0,0 +1,60 @@
+"""The graph-structure headline, its control, and the determinism both depend on."""
+import json
+import subprocess
+import sys
+
+import pytest
+
+from decluster.experiments import graph_deanon_controls as experiment
+
+
+@pytest.fixture(scope="module")
+def artifact():
+ return experiment.build_artifact()
+
+
+def test_reproduce_then_verify(tmp_path):
+ art, markdown = tmp_path / "a.json", tmp_path / "a.md"
+ assert experiment.main(["reproduce", "--artifact", str(art), "--markdown", str(markdown)]) == 0
+ assert experiment.main(["verify", "--artifact", str(art), "--markdown", str(markdown)]) == 0
+
+
+def test_the_run_is_the_same_in_a_second_process():
+ """A seeded run whose population order came out of a set answered differently every time.
+
+ Three processes gave 0.9905, 0.9908 and 0.9910 for the same fixture and the same seed, which is
+ why the AUC this fixture carries had no canonical run behind it.
+ """
+ script = (
+ "import json;"
+ "from decluster.experiments import graph_deanon_controls as e;"
+ "a=e.build_artifact();"
+ "print(json.dumps(a['measurement']['views'], sort_keys=True))"
+ )
+ first, second = (
+ subprocess.run([sys.executable, "-c", script], capture_output=True, text=True, check=True)
+ for _ in range(2)
+ )
+ assert first.stdout == second.stdout
+
+
+def test_the_degree_only_score_is_at_chance_under_matching(artifact):
+ payment = artifact["measurement"]["views"]["payment"]
+ assert payment["degree_only_auc_under_matching"] == pytest.approx(0.5, abs=0.01)
+
+
+def test_the_shuffled_label_control_is_at_chance(artifact):
+ assert artifact["measurement"]["shuffled_label_auc"] == pytest.approx(0.5, abs=0.01)
+
+
+def test_the_headline_falls_under_the_control_and_the_fall_is_reported(artifact):
+ for view in artifact["measurement"]["views"].values():
+ assert view["fall_under_matching"] > 0
+ assert view["degree_matched_auc"] < view["published_auc"]
+
+
+def test_a_tampered_artifact_is_refused(artifact):
+ stored = json.loads(json.dumps(artifact))
+ stored["measurement"]["views"]["payment"]["published_auc"] += 0.01
+ with pytest.raises(experiment.VerificationError):
+ experiment.verify_artifact(stored)
diff --git a/tests/test_graph_deanon_real.py b/tests/test_graph_deanon_real.py
index 5ee8b829..bfdc86aa 100644
--- a/tests/test_graph_deanon_real.py
+++ b/tests/test_graph_deanon_real.py
@@ -8,7 +8,7 @@
import json
import os
-from decluster.graph_deanon import evaluate
+from decluster.graph_deanon import degree_matched_auc, evaluate
FIX = os.path.join(os.path.dirname(__file__), "fixtures", "graph_deanon_2016.ndjson.gz")
@@ -31,3 +31,16 @@ def test_structural_score_deanonymises_the_real_slice():
assert r["auc_payment"] >= 0.85 # honest (no co-spend) separation; doc ~0.950
assert r["auc_full"] >= 0.90 # co-spend + payment; doc ~0.992
assert 0.40 <= r["auc_shuffle"] <= 0.60 # shuffled-label control at chance
+
+
+def test_degree_matched_negatives_leave_most_of_the_separation_standing():
+ """The published negatives are drawn over cluster roots while the positives are every
+ intra-cluster pair, so the classes differ in degree and a common-neighbour score reads
+ degree. Under negatives matched to each positive's degrees a degree-only score sits at
+ chance, and the payment arm still separates."""
+ r = degree_matched_auc(_sample(), seed=0)
+
+ assert 0.49 <= r["degree_only_payment"] <= 0.51
+ assert r["auc_payment"] >= 0.88 # published 0.950 on root-sampled negatives
+ assert r["auc_full"] >= 0.95 # published 0.992
+ assert r["neg_pairs_payment"] > 100000
diff --git a/tests/test_graph_rejoin_experiment.py b/tests/test_graph_rejoin_experiment.py
new file mode 100644
index 00000000..fae65ab9
--- /dev/null
+++ b/tests/test_graph_rejoin_experiment.py
@@ -0,0 +1,61 @@
+"""The rejoin measurement, taken again on data that exists.
+
+The numbers this run carries were first measured on a 1.1 GB slice that is gone, so they could
+neither be checked nor taken again. The committed weekly graph holds the same object, and the run
+lands on the same population: 17,431 clusters straddling the boundary, 15,688 of them offered to the
+matcher once a tenth are seeded. The published figure was one correct rejoin out of that 15,688.
+"""
+
+import json
+from pathlib import Path
+
+import pytest
+
+from decluster.experiments import graph_rejoin_2016 as gr
+
+ROOT = Path(__file__).resolve().parent.parent
+ARTIFACT = ROOT / "results" / "artifacts" / "graph-rejoin-2016-v1.json"
+
+
+@pytest.fixture(scope="module")
+def artifact():
+ if not ARTIFACT.is_file():
+ pytest.skip("the canonical graph-rejoin artifact is not committed")
+ return json.loads(ARTIFACT.read_text())
+
+
+def test_every_seeded_arm_runs_beside_a_shuffled_one(artifact):
+ """A seeded arm without its control cannot say whether the graph answered or the matcher did."""
+ seen = {(row["seed_share"], row["matcher"], row["arm"]) for row in artifact["matching"]}
+ for share, matcher, _ in list(seen):
+ assert (share, matcher, "seeded") in seen and (share, matcher, "shuffled") in seen
+
+
+def test_the_shuffled_controls_recover_nothing(artifact):
+ """The claim the seeded arms rest on: what little they find is not the matcher answering itself."""
+ shuffled = [row for row in artifact["matching"] if row["arm"] == "shuffled"]
+ assert shuffled, "no control arm ran"
+ assert all(row["correct"] == 0 for row in shuffled)
+
+
+def test_the_cascade_does_not_ignite(artifact):
+ """Stated as a bound, so a future run that does ignite fails here rather than passing quietly."""
+ seeded = [row for row in artifact["matching"] if row["arm"] == "seeded"]
+ correct = sum(row["correct"] for row in seeded)
+ offered = artifact["population"]["pairs_to_rejoin"]
+ assert correct <= 2, f"{correct} rejoins of {offered}: this is no longer a null result"
+
+
+def test_the_population_is_the_one_the_published_measurement_names(artifact):
+ population = artifact["population"]
+ assert population["pairs_to_rejoin"] == 17431
+ assert abs(population["mean_internal_degree"] - 2.866) < 5e-4
+ widest = max(row["seeds"] for row in artifact["matching"])
+ assert population["pairs_to_rejoin"] - widest == 15688
+
+
+@pytest.mark.reproduction
+def test_the_committed_artifact_is_what_a_fresh_run_produces(artifact):
+ if not (ROOT / gr.DEFAULT_EPOCHS[0]).is_file():
+ pytest.skip("the committed epoch graphs are not present")
+ gr.verify_artifact(artifact)
diff --git a/tests/test_graph_shape.py b/tests/test_graph_shape.py
index 5020e1e8..5ee9eb6c 100644
--- a/tests/test_graph_shape.py
+++ b/tests/test_graph_shape.py
@@ -2,7 +2,7 @@
sys.path.insert(0, os.path.dirname(os.path.dirname(__file__)))
from decluster.graph_shape import (assortativity, configuration_transitivity, degrees,
moments, summary, tail_exponent, transitivity)
-from decluster.views import PseudonymGraph
+from decluster.contraction import PseudonymGraph
def graph(edges):
@@ -69,3 +69,20 @@ def test_sampling_bounds_the_cost_without_changing_a_uniform_graph():
full = transitivity(ring)
part = transitivity(ring, sample=20, rng=random.Random(0))
assert full == part == 0.0
+
+
+def test_assortativity_walks_a_graph_whose_vertices_are_not_mutually_comparable():
+ """A contracted view mixes integer cluster roots with tagged strings, and `<` has no meaning."""
+ from decluster.graph_shape import assortativity
+
+ class Mixed:
+ vertices = [1, "2#a", 3, "4#b"]
+ _edges = {1: ["2#a"], "2#a": [1, 3], 3: ["2#a", "4#b"], "4#b": [3]}
+
+ def neighbours(self, u):
+ return self._edges[u]
+
+ def degree(self, u):
+ return len(self._edges[u])
+
+ assert assortativity(Mixed()) is not None
diff --git a/tests/test_intersect.py b/tests/test_intersect.py
index 45870dce..b0e15751 100644
--- a/tests/test_intersect.py
+++ b/tests/test_intersect.py
@@ -214,3 +214,130 @@ def test_an_empty_intersection_with_real_boundaries_is_not_blind():
def test_truncation_is_absent_rather_than_assumed_when_not_supplied():
out = evaluate({"txid": "t", "outpoints": [("a", 0)]}, lambda op: {"x": 1.0})
assert out["truncated"] is None and out["blind"] is False
+ assert out["truncated_causes"] is None and out["blind_cause"] is None
+
+
+def test_a_blind_branch_names_which_walk_limit_blinded_it():
+ """`blind` says the branch saw nothing; it does not say why. An oracle that declined to link
+ and a walk cut off at `max_nodes` are different limits with different remedies."""
+ from decluster.ancestry import TruncationSupport
+
+ cand = {"txid": "t", "outpoints": [("a", 0), ("b", 0)]}
+ sigs = {("a", 0): {"x": 0.5, "y": 0.5}, ("b", 0): {"p": 1.0}}
+
+ refused = evaluate(cand, sigs.__getitem__, truncation_of={
+ ("a", 0): TruncationSupport(oracle_refused=2, node_capped=0),
+ ("b", 0): TruncationSupport(oracle_refused=0, node_capped=0),
+ }.__getitem__)
+ assert refused["truncated"] == [2, 0]
+ assert refused["truncated_causes"][0] == {"oracle_refused": 2, "node_capped": 0,
+ "zero_link_mass": 0,
+ "unattributed": 0}
+ assert refused["blind"] is True and refused["blind_cause"] == "oracle_refused"
+
+ capped = evaluate(cand, sigs.__getitem__, truncation_of={
+ ("a", 0): TruncationSupport(oracle_refused=0, node_capped=2),
+ ("b", 0): TruncationSupport(oracle_refused=0, node_capped=0),
+ }.__getitem__)
+ assert capped["blind"] is True and capped["blind_cause"] == "node_capped"
+
+ both = evaluate(cand, sigs.__getitem__, truncation_of={
+ ("a", 0): TruncationSupport(oracle_refused=1, node_capped=1),
+ ("b", 0): TruncationSupport(oracle_refused=0, node_capped=0),
+ }.__getitem__)
+ assert both["blind"] is True and both["blind_cause"] == "mixed"
+
+
+def test_a_bare_total_is_reported_as_an_unnamed_cause_rather_than_guessed():
+ """A caller that reports only a total measured the blindness without its cause. Assigning it
+ to one of the two would be the invention this field exists to prevent."""
+ cand = {"txid": "t", "outpoints": [("a", 0), ("b", 0)]}
+ sigs = {("a", 0): {"x": 0.5, "y": 0.5}, ("b", 0): {"p": 1.0}}
+ out = evaluate(cand, sigs.__getitem__, truncation_of={("a", 0): 2, ("b", 0): 0}.__getitem__)
+ assert out["truncated"] == [2, 0] and out["truncated_causes"] == [None, None]
+ assert out["blind"] is True and out["blind_cause"] == "unknown"
+
+
+def test_a_branch_that_sees_is_not_credited_with_a_cause():
+ """Only the blind branches carry a cause; a walk that resolved origins was not blinded by
+ anything, however much of its boundary was truncated."""
+ from decluster.ancestry import TruncationSupport
+
+ cand = {"txid": "t", "outpoints": [("a", 0), ("b", 0)]}
+ sigs = {("a", 0): {"x": 0.5, "y": 0.5}, ("b", 0): {"x": 1.0}}
+ out = evaluate(cand, sigs.__getitem__, truncation_of={
+ ("a", 0): TruncationSupport(oracle_refused=1, node_capped=0),
+ ("b", 0): TruncationSupport(oracle_refused=0, node_capped=0),
+ }.__getitem__)
+ assert out["shared"] and out["blind"] is False and out["blind_cause"] is None
+
+
+def test_an_unmeasured_cause_dominates_rather_than_mixing_into_a_named_one():
+ """One blind branch reporting a bare total and another reporting a real cause must not come
+ back as `"mixed"`: `"mixed"` asserts both named walk limits fired, and one of them was never
+ observed. That is the unnamed-becomes-named laundering this field exists to refuse."""
+ from decluster.ancestry import TruncationSupport
+
+ cand = {"txid": "t", "outpoints": [("a", 0), ("b", 0)]}
+ sigs = {("a", 0): {"x": 0.5, "y": 0.5}, ("b", 0): {"p": 1.0, "q": 1.0, "r": 1.0}}
+ out = evaluate(cand, sigs.__getitem__, truncation_of={
+ ("a", 0): 2, # blind, cause not measured
+ ("b", 0): TruncationSupport(oracle_refused=0, node_capped=3), # blind, cause measured
+ }.__getitem__)
+ assert out["blind"] is True
+ assert out["truncated_causes"] == [None, {"oracle_refused": 0, "node_capped": 3,
+ "zero_link_mass": 0,
+ "unattributed": 0}]
+ assert out["blind_cause"] == "unknown"
+
+
+def test_a_cause_this_tree_does_not_name_is_reported_unknown_not_capped():
+ """`TruncationSupport.unattributed` keeps an unrecognised cause inside the total without
+ giving it one of the two names; `blind_cause` must carry that through."""
+ from decluster.ancestry import TruncationSupport
+
+ cand = {"txid": "t", "outpoints": [("a", 0)]}
+ sigs = {("a", 0): {"x": 0.5, "y": 0.5}}
+ out = evaluate(cand, sigs.__getitem__, truncation_of={
+ ("a", 0): TruncationSupport(oracle_refused=0, node_capped=0, unattributed=2),
+ }.__getitem__)
+ assert out["truncated"] == [2], "an unnamed cause still counts toward blindness"
+ assert out["blind"] is True and out["blind_cause"] == "unknown"
+
+
+def test_a_zero_link_column_is_reported_as_its_own_blind_cause():
+ from decluster.ancestry import TruncationSupport
+
+ cand = {"txid": "t", "outpoints": [("a", 0)]}
+ out = evaluate(cand, lambda op: {"x": 1.0}, truncation_of=lambda op:
+ TruncationSupport(oracle_refused=0, node_capped=0, zero_link_mass=1))
+ assert out["blind"] is True
+ assert out["blind_cause"] == "zero_link_mass"
+ assert out["truncated_causes"][0]["zero_link_mass"] == 1
+
+
+def test_a_branch_blind_with_no_truncation_is_blind_without_a_cause():
+ """Blind because it observed nothing at all, not because a walk limit cut it. `blind_cause` is
+ None here and None again when `truncation_of` was never supplied; `blind` is what tells the two
+ states apart."""
+ from decluster.ancestry import TruncationSupport
+
+ cand = {"txid": "t", "outpoints": [("a", 0), ("b", 0)]}
+ sigs = {("a", 0): {}, ("b", 0): {"p": 1.0}}
+ out = evaluate(cand, sigs.__getitem__, truncation_of={
+ ("a", 0): TruncationSupport(oracle_refused=0, node_capped=0),
+ ("b", 0): TruncationSupport(oracle_refused=0, node_capped=0),
+ }.__getitem__)
+ assert out["sizes"] == [0, 1]
+ assert out["blind"] is True and out["blind_cause"] is None
+
+ unasked = evaluate(cand, sigs.__getitem__)
+ assert unasked["blind"] is False and unasked["blind_cause"] is None
+
+
+def test_a_truncation_count_that_is_neither_an_int_nor_a_support_is_refused():
+ """Coercing a float would round a caller's measurement without telling them."""
+ cand = {"txid": "t", "outpoints": [("a", 0)]}
+ sigs = {("a", 0): {"x": 1.0}}
+ with pytest.raises(TypeError):
+ evaluate(cand, sigs.__getitem__, truncation_of=lambda op: 2.7)
diff --git a/tests/test_intersect_extras.py b/tests/test_intersect_extras.py
new file mode 100644
index 00000000..a9d379e1
--- /dev/null
+++ b/tests/test_intersect_extras.py
@@ -0,0 +1,86 @@
+"""Cluster-lift (intersect wallets, not coins; cit-42) and cross-event accumulation."""
+import math
+import pytest
+
+from decluster.intersect import shared_origins, accumulate_intersections
+
+
+def test_cluster_lift_intersects_wallets_not_coins():
+ # two branches sharing no COIN-origin, but whose coins belong to the same wallet W
+ s1, s2 = {"coinA": 1.0}, {"coinB": 1.0}
+ assert shared_origins([s1, s2]) == [] # coin-level: no overlap
+ lifted = shared_origins([s1, s2], cluster_of={"coinA": "W", "coinB": "W"})
+ assert [a for a, _, _ in lifted] == ["W"] # wallet-level: they meet at W
+
+
+def test_cluster_lift_requires_population_rarity_in_the_cluster_space():
+ sigs = [{"a1": 1.0, "hub1": 1.0}, {"a2": 1.0, "hub2": 1.0}]
+ clusters = {"a1": "rare", "a2": "rare", "hub1": "hub", "hub2": "hub"}
+ with pytest.raises(ValueError, match="cluster_rarity"):
+ shared_origins(sigs, rarity={"a1": 10}, cluster_of=clusters)
+ got = shared_origins(sigs, rarity={"a1": 10}, cluster_of=clusters,
+ cluster_rarity={"rare": 2, "hub": 1023})
+ weights = {a: w for a, _m, w in got}
+ assert weights["rare"] == pytest.approx(1.0 / math.log2(3))
+ assert weights["hub"] < 0.2
+
+
+def test_accumulation_narrows_across_observations():
+ # each observation's shared origins; the surviving set shrinks as observations accumulate
+ obs = [[{"x": 1.0, "y": 1.0, "z": 1.0}, {"x": 1.0, "y": 1.0, "z": 1.0}], # {x,y,z}
+ [{"x": 1.0, "y": 1.0}, {"x": 1.0, "y": 1.0}], # {x,y}
+ [{"x": 1.0}, {"x": 1.0}]] # {x}
+ surviving, bits = accumulate_intersections(obs)
+ assert surviving == {"x"} # converged to one origin
+ assert bits > 0 # narrowing recorded in bits
+
+
+def test_accumulation_can_include_the_first_observation_from_a_known_universe():
+ obs = [[{"x": 1.0, "y": 1.0}, {"x": 1.0, "y": 1.0}]]
+ _surviving, relative = accumulate_intersections(obs)
+ _surviving, absolute = accumulate_intersections(obs, universe_size=8)
+ assert relative == 0.0
+ assert absolute == 2.0
+
+
+def test_an_unknown_coin_never_collapses_into_a_shared_pseudo_cluster():
+ """A callable cluster map answering None for a coin it does not know must fall back to the
+ coin, exactly as the dict form does. Collapsing every unclustered origin onto one key makes
+ branches with nothing in common intersect at full mass — a fabricated attribution."""
+ s1, s2 = {"coinA": 1.0}, {"coinB": 1.0}
+ assert shared_origins([s1, s2], cluster_of={}) == []
+ assert shared_origins([s1, s2], cluster_of=lambda a: None) == []
+
+
+def test_evaluate_intersects_wallets_when_given_a_cluster_map():
+ """The writeup's core move: candidate origins are clusters, so two branches overlap without
+ their origin coins being connected on the transaction graph."""
+ from decluster.intersect import evaluate
+ sigs = {("t", 0): {"c1": 1.0}, ("t", 1): {"c2": 1.0}}
+ cand = {"txid": "j", "outpoints": [("t", 0), ("t", 1)]}
+
+ coin_level = evaluate(cand, sigs.__getitem__)
+ assert coin_level["shared"] == [] # different coins: no overlap
+
+ lifted = evaluate(cand, sigs.__getitem__, cluster_of={"c1": "W", "c2": "W"})
+ assert [a for a, _, _ in lifted["shared"]] == ["W"] # one wallet held both
+ assert lifted["sizes"] == [1, 1] # sizes are wallets, not coins
+
+
+def test_evaluate_refuses_to_weight_a_lifted_intersection_on_coin_support():
+ from decluster.intersect import evaluate
+ sigs = {("t", 0): {"c1": 1.0}, ("t", 1): {"c1": 1.0}}
+ cand = {"txid": "j", "outpoints": [("t", 0), ("t", 1)]}
+ with pytest.raises(ValueError, match="cluster_rarity"):
+ evaluate(cand, sigs.__getitem__, rarity={"c1": 10}, cluster_of={"c1": "W"})
+
+
+def test_cluster_rarity_is_recounted_over_the_population_after_lifting():
+ """Coin support does not survive coarsening: a wallet holding many coins would inherit the
+ support of the rarest of them."""
+ from decluster.propagate import build_cluster_rarity
+ corpus = [{"c1": 1.0}, {"c2": 1.0}, {"c3": 1.0}]
+ cluster_of = {"c1": "W", "c2": "W", "c3": "V"}
+ assert build_cluster_rarity(corpus, cluster_of) == {"W": 2, "V": 1}
+ # an unclustered coin keeps its own identity rather than joining a None bucket
+ assert build_cluster_rarity([{"c1": 1.0}, {"zz": 1.0}], lambda a: None) == {"c1": 1, "zz": 1}
diff --git a/tests/test_intersection_adapter.py b/tests/test_intersection_adapter.py
new file mode 100644
index 00000000..bee4a46b
--- /dev/null
+++ b/tests/test_intersection_adapter.py
@@ -0,0 +1,112 @@
+import pytest
+
+from decluster.adaptations.intersection import (
+ BlindCause,
+ BlindIntersection,
+ CompleteIntersection,
+ evaluate_ancestry_reports,
+ evaluate_report,
+)
+from decluster.adaptations.ancestry import ancestry_signature_report
+from decluster.ancestry import TruncationSupport
+from decluster.domain import SubjectKind
+
+
+def candidate():
+ return {"txid": "spend", "outpoints": [("a", 0), ("b", 1)]}
+
+
+def test_surviving_origins_are_coins_unless_they_were_lifted_to_wallets():
+ """The lift is what makes an origin a cluster, so the kind has to follow `cluster_of`."""
+ signatures = {("a", 0): {"x": 1.0, "y": 1.0}, ("b", 1): {"x": 1.0, "z": 1.0}}
+
+ unlifted = evaluate_report(candidate(), signatures.__getitem__)
+ assert {s.kind for s in unlifted.state.evidence.candidates_after} == {SubjectKind.COIN}
+ assert {s.identifier for s in unlifted.state.evidence.candidates_after} == {"x"}
+
+ lifted = evaluate_report(
+ candidate(), signatures.__getitem__, cluster_of={"x": "wallet", "y": "y", "z": "z"}
+ )
+ assert {s.kind for s in lifted.state.evidence.candidates_after} == {SubjectKind.CLUSTER}
+ assert {s.identifier for s in lifted.state.evidence.candidates_after} == {"wallet"}
+
+
+def test_complete_empty_intersection_is_not_blind():
+ signatures = {("a", 0): {"x": 1.0}, ("b", 1): {"y": 1.0}}
+
+ report = evaluate_report(candidate(), signatures.__getitem__)
+
+ assert isinstance(report.state, CompleteIntersection)
+ assert report.state.evidence.candidates_after == frozenset()
+ assert report.as_legacy()["blind"] is False
+ attack_report = report.as_attack_report("complete-empty")
+ assert attack_report.channels[0].identifier == "provenance_intersection"
+ assert attack_report.outcomes[0].evidence == report.state.evidence
+ assert type(attack_report.outcomes[0]).__name__ == "NoSharedCandidates"
+
+
+def test_oracle_refusal_is_a_typed_blind_state_with_branch_details():
+ signatures = {("a", 0): {"x": 1.0}, ("b", 1): {"y": 1.0}}
+ truncation = {
+ ("a", 0): TruncationSupport(oracle_refused=1, node_capped=0),
+ ("b", 1): TruncationSupport(oracle_refused=0, node_capped=0),
+ }
+
+ report = evaluate_report(
+ candidate(), signatures.__getitem__, truncation_of=truncation.__getitem__
+ )
+
+ assert report.state == BlindIntersection(BlindCause.ORACLE_REFUSED, report.branches)
+ assert report.truncation[0].oracle_refused == 1
+ assert report.truncation[0].node_capped == 0
+ assert report.as_legacy()["blind_cause"] == "oracle_refused"
+ attack_report = report.as_attack_report("blind-oracle")
+ assert attack_report.channels == ()
+ assert attack_report.outcomes[0].reason == "intersection is blind: oracle_refused"
+
+
+def test_empty_untruncated_branch_has_an_explicit_blind_cause():
+ signatures = {("a", 0): {}, ("b", 1): {"y": 1.0}}
+ zeros = {
+ ("a", 0): TruncationSupport(oracle_refused=0, node_capped=0),
+ ("b", 1): TruncationSupport(oracle_refused=0, node_capped=0),
+ }
+
+ report = evaluate_report(candidate(), signatures.__getitem__, truncation_of=zeros.__getitem__)
+
+ assert isinstance(report.state, BlindIntersection)
+ assert report.state.cause is BlindCause.NO_OBSERVED_ORIGIN
+
+
+def test_legacy_copy_cannot_mutate_the_typed_report():
+ signatures = {("a", 0): {"x": 1.0}, ("b", 1): {"x": 1.0}}
+ report = evaluate_report(candidate(), signatures.__getitem__)
+
+ legacy = report.as_legacy()
+ legacy["sizes"].append(999)
+
+ assert report.sizes == (1, 1)
+ assert report.as_legacy()["sizes"] == [1, 1]
+
+
+def test_atomic_ancestry_observations_drive_intersection():
+ reports = {
+ ("a", 0): ancestry_signature_report(("a", 0), fetch=coinbase),
+ ("b", 1): ancestry_signature_report(("b", 1), fetch=coinbase),
+ }
+
+ report = evaluate_ancestry_reports(candidate(), reports.__getitem__)
+
+ assert isinstance(report.state, CompleteIntersection)
+ assert report.state.evidence.candidates_after == frozenset()
+
+
+def coinbase(txid):
+ return {"vin": [{"is_coinbase": True}], "vout": [{"value": 1}, {"value": 1}]}
+
+
+def test_atomic_intersection_rejects_a_mismatched_report():
+ wrong = ancestry_signature_report(("wrong", 0), fetch=coinbase)
+
+ with pytest.raises(ValueError, match="does not match"):
+ evaluate_ancestry_reports(candidate(), lambda outpoint: wrong)
diff --git a/tests/test_intersection_experiment.py b/tests/test_intersection_experiment.py
new file mode 100644
index 00000000..c85e1b07
--- /dev/null
+++ b/tests/test_intersection_experiment.py
@@ -0,0 +1,49 @@
+import json
+
+import pytest
+
+from decluster.experiments import intersection_fixture as experiment
+from decluster.result_artifacts import canonical_json_bytes
+
+
+def test_artifact_is_deterministic_and_keeps_the_claim_conditional():
+ artifact = experiment.build_artifact()
+
+ assert canonical_json_bytes(artifact) == canonical_json_bytes(experiment.build_artifact())
+ assert artifact["summary"] == {
+ "blind": False,
+ "branches": 2,
+ "candidates_before": 2,
+ "candidates_after": 1,
+ "collapsed": 1,
+ "scored": False,
+ }
+ assert any(
+ "does not run the clustering verdict" in limitation
+ for limitation in artifact["limitations"]
+ )
+
+
+def test_verifier_recomputes_instead_of_trusting_stored_numbers():
+ changed = json.loads(json.dumps(experiment.build_artifact()))
+ changed["summary"]["candidates_after"] = 0
+
+ with pytest.raises(experiment.VerificationError, match="fresh fixture"):
+ experiment.verify_artifact(changed)
+
+
+def test_cli_reproduces_and_verifies_artifact_and_markdown(tmp_path):
+ artifact = tmp_path / "artifact.json"
+ markdown = tmp_path / "result.md"
+
+ assert experiment.main([
+ "reproduce", "--artifact", str(artifact), "--markdown", str(markdown)
+ ]) == 0
+ assert experiment.main([
+ "verify", "--artifact", str(artifact), "--markdown", str(markdown)
+ ]) == 0
+ markdown.write_text(markdown.read_text() + "changed\n")
+ with pytest.raises(experiment.VerificationError, match="Markdown differs"):
+ experiment.main([
+ "verify", "--artifact", str(artifact), "--markdown", str(markdown)
+ ])
diff --git a/tests/test_intersection_pipeline.py b/tests/test_intersection_pipeline.py
index b60c168b..2d1fc8dd 100644
--- a/tests/test_intersection_pipeline.py
+++ b/tests/test_intersection_pipeline.py
@@ -1,4 +1,7 @@
+import pytest
+
from examples.intersection_pipeline import run
+from decluster.adaptations.ancestry import ancestry_signature_report
def _fixture():
@@ -33,6 +36,29 @@ def test_the_four_channels_chain_end_to_end():
assert entry["verdict"]["believed"] is True
+def test_pipeline_accepts_atomic_ancestry_reports():
+ get_tx, get_outspends = _fixture()
+
+ def coinbase(txid):
+ return {"vin": [{"is_coinbase": True}], "vout": [{"value": 1}, {"value": 1}]}
+
+ out = run(
+ seeds=[("seed", 0), ("seed", 1)],
+ get_tx=get_tx,
+ get_outspends=get_outspends,
+ ancestry_report_of=lambda outpoint: ancestry_signature_report(
+ outpoint, fetch=coinbase
+ ),
+ )
+
+ assert out["results"][0]["narrowing"]["blind"] is False
+
+
+def test_pipeline_rejects_two_ancestry_sources():
+ with pytest.raises(ValueError, match="not both"):
+ run(seeds=[], signature_of=lambda op: None, ancestry_report_of=lambda op: None)
+
+
def test_the_engine_can_refuse_a_cospend_the_walk_found():
"""The step that separates this from the common-input heuristic."""
get_tx, get_outspends = _fixture()
@@ -166,19 +192,22 @@ def test_default_seeds_reads_the_round_it_is_pointed_at():
def test_default_signature_of_asks_for_both_halves(monkeypatch):
- """Production must request the truncation count, not only the signature."""
+ """Production must request the truncation count, not only the signature, and must name the
+ link oracle rather than inherit a default that has moved."""
import decluster.ancestry as ancestry
from examples.intersection_pipeline import SIGNATURE_DEPTH, default_signature_of
seen = {}
- def fake(target, depth=6):
+ def fake(target, depth=6, link_oracle=None):
seen["depth"] = depth
+ seen["link_oracle"] = link_oracle
return ({"origin": 1.0}, 3)
monkeypatch.setattr(ancestry, "ancestry_signature_and_truncation", fake)
sig, truncated = default_signature_of()(("a", 0))
assert seen["depth"] == SIGNATURE_DEPTH
+ assert seen["link_oracle"] is ancestry.dss_link_oracle
assert sig == {"origin": 1.0} and truncated == 3
diff --git a/tests/test_intersection_real.py b/tests/test_intersection_real.py
index 10de37e1..ddd96e62 100644
--- a/tests/test_intersection_real.py
+++ b/tests/test_intersection_real.py
@@ -72,6 +72,10 @@ def test_four_branches_resolve_and_their_intersection_is_empty_but_not_blind():
assert out["sizes"] == SIZES
assert out["truncated"] == TRUNCATED
assert out["blind"] is False, "no branch is pure truncation"
+ assert out["blind_cause"] is None
+ assert out["truncated_causes"] == [None] * len(FUNDERS), (
+ "the pinned counts are totals recorded before the cause split existed; naming one would "
+ "attribute a cause that was never measured")
assert out["shared"] == []
assert out["collapsed_bits"] is None, "an empty intersection is not unbounded narrowing"
diff --git a/tests/test_kelen_seres_graphs.py b/tests/test_kelen_seres_graphs.py
new file mode 100644
index 00000000..dd85fe7a
--- /dev/null
+++ b/tests/test_kelen_seres_graphs.py
@@ -0,0 +1,67 @@
+"""Section 3 stationary and temporal account-graph contracts."""
+
+import pytest
+
+from decluster.baselines.kelen_seres_graphs import (
+ AuxiliarySource,
+ Transfer,
+ absorbing_transaction_chain,
+ stationary_account_graph,
+ temporal_account_graph,
+)
+
+
+def test_stationary_graph_aggregates_repeated_account_edges_and_ignores_time():
+ transfers = [
+ Transfer("later", "a", "b", 3, 2),
+ Transfer("earlier", "a", "b", 2, 1),
+ ]
+ graph = stationary_account_graph(transfers, opening_balances={"a": 5})
+ assert graph.edges == {("a", "b"): 5}
+ assert graph.balances == {"a": 0, "b": 5}
+
+
+def test_temporal_graph_splits_on_receipt_and_carries_previous_balance():
+ transfers = [
+ Transfer("receive-1", "source", "a", 10, 1),
+ Transfer("spend", "a", "merchant", 4, 2),
+ Transfer("receive-2", "other", "a", 3, 3),
+ ]
+ graph = temporal_account_graph(
+ transfers, opening_balances={"source": 10, "other": 3, "a": 0, "merchant": 0}
+ )
+ assert graph.edges[("source", 0), ("a", 1)] == 10
+ assert graph.edges[("a", 1), ("merchant", 1)] == 4
+ assert graph.edges[("a", 1), ("a", 2)] == 6
+ assert graph.edges[("other", 0), ("a", 2)] == 3
+ # The old spend originates at a1. There is no forward edge from a2 back
+ # into a1, so reversing the graph cannot trace it to receive-2.
+ assert (("a", 2), ("a", 1)) not in graph.edges
+ assert graph.balances["a"] == 9
+
+
+def test_temporal_graph_refuses_to_invent_pre_window_balance():
+ with pytest.raises(ValueError, match="insufficient"):
+ temporal_account_graph(
+ [Transfer("tx", "a", "b", 1, 0)], opening_balances={"a": 0, "b": 0}
+ )
+
+
+def test_temporal_self_transfer_conserves_instead_of_double_counting_balance():
+ graph = temporal_account_graph(
+ [Transfer("self", "a", "a", 4, 1)], opening_balances={"a": 10}
+ )
+ assert graph.edges == {(('a', 0), ('a', 1)): 10}
+ assert graph.balances == {"a": 10}
+
+
+def test_auxiliary_sources_and_reversed_equation_one_transitions_are_materialized():
+ graph = stationary_account_graph([
+ Transfer("left", "a", "sink", 2, 1),
+ Transfer("right", "c", "sink", 3, 1),
+ ])
+ chain = absorbing_transaction_chain(graph)
+ assert set(chain.absorbers) == {AuxiliarySource("a"), AuxiliarySource("c")}
+ assert dict(chain.transitions["sink"]) == {"a": 0.4, "c": 0.6}
+ assert chain.transitions["a"] == ((AuxiliarySource("a"), 1.0),)
+ assert chain.transitions[AuxiliarySource("a")] == ((AuxiliarySource("a"), 1.0),)
diff --git a/tests/test_legacy_manifest_bridge.py b/tests/test_legacy_manifest_bridge.py
new file mode 100644
index 00000000..c184a130
--- /dev/null
+++ b/tests/test_legacy_manifest_bridge.py
@@ -0,0 +1,82 @@
+"""The legacy `results/manifests/` invariants, checked against the canonical run artifacts.
+
+`check_manifest` compares a recorded invariant only when a caller recomputes it, and no caller
+does, so all six manifests report `identity-only`: the digest matches and not one number is
+compared. Every one of the six now has a canonical run whose artifact carries the same
+measurements, which makes the comparison free wherever the two record a value the same way.
+
+Two of the six record every invariant exactly as the artifact does and are bridged here. The other
+four aggregate or round — a list of per-fixture values against the per-fixture records, `0.467344`
+against `0.46734397677793904` — so bridging them needs an extractor written per document. They are
+named in `NEEDS_AN_EXTRACTOR` rather than left to look bridged, and the test fails if that set
+grows.
+"""
+import json
+from pathlib import Path
+
+import pytest
+
+ROOT = Path(__file__).resolve().parents[1]
+
+# document stem -> the canonical run whose artifact measures the same thing
+BRIDGED = {
+ "RESULTS-boltzmann-fee-audit": "boltzmann-fee-audit-v1",
+ "RESULTS-candidate-set-intersection": "candidate-set-intersection-v1",
+}
+
+NEEDS_AN_EXTRACTOR = {
+ "RESULTS-fs-ablation": "six invariants are not carried by the artifact at all",
+ "RESULTS-fs-temporal": "six invariants are not carried by the artifact at all",
+ "RESULTS-link-prediction": "per-fixture lists against per-fixture records, and a count against a list",
+ "RESULTS-ns-bitcoin": "a rounded edge overlap, and a seed count against the object that holds it",
+}
+
+
+def values_by_key(value, found=None):
+ """Every value in the artifact, indexed by the key that holds it."""
+ found = {} if found is None else found
+ if isinstance(value, dict):
+ for key, item in value.items():
+ found.setdefault(key, []).append(item)
+ values_by_key(item, found)
+ elif isinstance(value, list):
+ for item in value:
+ values_by_key(item, found)
+ return found
+
+
+def invariants(document):
+ return json.loads((ROOT / "results" / "manifests" / f"{document}.json").read_text())["invariants"]
+
+
+def artifact(run):
+ return json.loads((ROOT / "results" / "artifacts" / f"{run}.json").read_text())
+
+
+@pytest.mark.parametrize("document,run", sorted(BRIDGED.items()))
+def test_every_recorded_invariant_matches_the_canonical_artifact(document, run):
+ carried = values_by_key(artifact(run))
+ disagreeing = []
+ for name, recorded in invariants(document).items():
+ assert name in carried, f"{document}: {run} no longer carries {name}"
+ if recorded not in carried[name]:
+ disagreeing.append(f"{name}: manifest {recorded!r}, artifact {carried[name]!r}")
+ assert not disagreeing, f"{document} disagrees with {run}:\n " + "\n ".join(disagreeing)
+
+
+def test_no_further_document_has_quietly_stopped_being_bridgeable():
+ manifests = {p.stem for p in (ROOT / "results" / "manifests").glob("*.json")}
+ assert manifests == set(BRIDGED) | set(NEEDS_AN_EXTRACTOR), (
+ f"unaccounted legacy manifests: {sorted(manifests - set(BRIDGED) - set(NEEDS_AN_EXTRACTOR))}"
+ )
+
+
+@pytest.mark.parametrize("document", sorted(NEEDS_AN_EXTRACTOR))
+def test_a_document_that_needs_an_extractor_still_needs_one(document):
+ """If a document becomes exactly comparable, move it to BRIDGED rather than leaving it excused."""
+ run = {"RESULTS-fs-ablation": "fs-ablation-v1", "RESULTS-fs-temporal": "fs-temporal-v1",
+ "RESULTS-link-prediction": "link-prediction-v1", "RESULTS-ns-bitcoin": "ns-bitcoin-v1"}[document]
+ carried = values_by_key(artifact(run))
+ exact = all(name in carried and recorded in carried[name]
+ for name, recorded in invariants(document).items())
+ assert not exact, f"{document} now matches {run} exactly; move it to BRIDGED"
diff --git a/tests/test_library_calibration.py b/tests/test_library_calibration.py
new file mode 100644
index 00000000..b83ff087
--- /dev/null
+++ b/tests/test_library_calibration.py
@@ -0,0 +1,62 @@
+"""What the run may and may not claim about a calibration whose population is gone."""
+import json
+from pathlib import Path
+
+import pytest
+
+from decluster import library
+from decluster.experiments import library_calibration as experiment
+
+ROOT = Path(__file__).resolve().parents[1]
+SNAPSHOT = str(ROOT / "data" / "fs-blkcache-2026-09-04.tar.gz")
+
+
+@pytest.fixture(scope="module")
+def artifact():
+ return experiment.build_artifact(SNAPSHOT)
+
+
+def test_reproduce_then_verify(tmp_path):
+ art, markdown = tmp_path / "a.json", tmp_path / "a.md"
+ argv = ["--snapshot", SNAPSHOT]
+ assert experiment.main(argv + ["reproduce", "--artifact", str(art), "--markdown", str(markdown)]) == 0
+ assert experiment.main(argv + ["verify", "--artifact", str(art), "--markdown", str(markdown)]) == 0
+
+
+def test_every_catalogued_axis_is_walked(artifact):
+ assert {row["axis"] for row in artifact["measurement"]["axes"]} == {
+ entry["axis"] for entry in library.AXES
+ }
+
+
+def test_a_value_the_cache_never_shows_is_counted_not_estimated(artifact):
+ absent = [row for axis in artifact["measurement"]["axes"] for row in axis["values"]
+ if row["observations"] == 0]
+ assert absent, "the comparison is only interesting while something is uncomparable"
+ for row in absent:
+ assert row["measured_bits"] is None and "divergence" not in row
+
+
+def test_an_axis_with_nothing_to_compare_reports_no_divergence(artifact):
+ empty = [axis for axis in artifact["measurement"]["axes"]
+ if axis["values_the_cache_shows"] == 0]
+ for axis in empty:
+ assert axis["mean_divergence_bits"] is None and axis["max_divergence_bits"] is None
+
+
+def test_abstentions_are_kept_out_of_the_scored_population(artifact):
+ for axis in artifact["measurement"]["axes"]:
+ observed = sum(row["observations"] for row in axis["values"])
+ assert observed <= axis["scored_transactions"]
+
+
+def test_the_divergence_is_reported_in_bits_not_hidden_in_an_average(artifact):
+ measured = artifact["measurement"]
+ assert measured["largest_divergence"][1] > measured["mean_divergence_bits"]
+
+
+def test_a_tampered_artifact_is_refused(artifact):
+ stored = json.loads(json.dumps(artifact))
+ stored["measurement"]["axes"][0]["values"][0]["published_bits"] += 1.0
+ with pytest.raises(experiment.VerificationError):
+ experiment.verify_artifact(stored, SNAPSHOT)
diff --git a/tests/test_link_prediction.py b/tests/test_link_prediction.py
new file mode 100644
index 00000000..cc740d98
--- /dev/null
+++ b/tests/test_link_prediction.py
@@ -0,0 +1,197 @@
+"""De-anonymization-driven link prediction against the same predictor without the mapping.
+
+Every fixture here is synthetic and controlled. The direction is asserted, never a recorded
+number, so a change in the mechanism that moves the numbers without inverting the finding does
+not fail; the published numbers are pinned by the manifest test at the bottom instead.
+"""
+import os
+import random
+
+import pytest
+
+from decluster import reproducibility as rp
+from decluster.baselines import link_prediction as lp
+from decluster.graph_deanon import structural_score
+from examples.link_prediction_run import (MANIFEST_DOC, MANIFEST_SOURCE, build_parser,
+ build_report, directed_graph, manifest_invariants,
+ planted_views, run_fixture, sampled_seeds)
+
+ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+
+
+def args(**overrides):
+ parsed = build_parser().parse_args([])
+ for key, value in overrides.items():
+ setattr(parsed, key, value)
+ return parsed
+
+
+def fixture(seed=0, **overrides):
+ return planted_views(args(**overrides), random.Random(seed))
+
+
+def arm(report, name):
+ return next(run for run in report["runs"] if run["arm"] == name)
+
+
+def test_transfer_pulls_back_only_edges_whose_endpoints_are_both_mapped():
+ target = directed_graph(["a0", "a1", "a2"], [("a0", "a1")])
+ auxiliary = directed_graph(["b0", "b1", "b2", "b3"],
+ [("b1", "b2"), ("b2", "b3"), ("b0", "b3")])
+ # b3 is not an image, so neither of its edges can say anything about the target view.
+ neighbours, new_pairs = lp.transferred_neighbourhoods(
+ target, auxiliary, {"a0": "b0", "a1": "b1", "a2": "b2"})
+ assert new_pairs == 1
+ assert neighbours["a1"] == {"a0", "a2"}
+ assert neighbours["a2"] == {"a1"}
+
+
+def test_score_is_common_neighbours_on_a_pair_the_graph_does_not_join():
+ target = directed_graph(["a0", "a1", "a2", "a3"],
+ [("a0", "a2"), ("a1", "a2"), ("a0", "a3"), ("a1", "a3")])
+ neighbours = lp.neighbourhoods(target)
+ assert lp.score(neighbours, "a0", "a1") == structural_score("a0", "a1", neighbours) == 2
+
+
+def test_a_directly_transferred_edge_scores_above_the_pairs_common_neighbours():
+ """The auxiliary view's plainest statement is that the two transacted; the score has to
+ carry it, and it is the closed neighbourhood that does."""
+ target = directed_graph(["a0", "a1"], [])
+ auxiliary = directed_graph(["b0", "b1"], [("b0", "b1")])
+ neighbours, _ = lp.transferred_neighbourhoods(target, auxiliary, {"a0": "b0", "a1": "b1"})
+ assert structural_score("a0", "a1", neighbours) == 0
+ assert lp.score(neighbours, "a0", "a1") == 2
+
+
+def test_candidate_pairs_are_the_pairs_the_view_does_not_join():
+ target = directed_graph(["a0", "a1", "a2"], [("a0", "a1")])
+ assert lp.candidate_pairs(target) == [("a0", "a2"), ("a1", "a2")]
+
+
+def test_candidates_already_joined_by_the_target_view_are_refused():
+ target = directed_graph(["a0", "a1"], [("a0", "a1")])
+ auxiliary = directed_graph(["b0", "b1"], [("b0", "b1")])
+ with pytest.raises(ValueError, match="already an edge"):
+ lp.compare(target, auxiliary, {"a0": "b0"}, [("a0", "a1")], [],
+ seed_provenance="test")
+
+
+def test_predictions_are_ranked_by_score():
+ views = fixture()
+ seeds = sampled_seeds(views["correspondence"], 0.25, random.Random(1))
+ prediction = lp.predict(views["target"], views["auxiliary"], seeds,
+ views["held_out"] + views["negatives"], seed_provenance="test")
+ ranked = prediction.ranked()
+ assert [score for _, score in ranked] == sorted((s for s in prediction.scores), reverse=True)
+ assert set(prediction.declared(cutoff=2)) == {pair for pair, s in ranked if s >= 2}
+
+
+@pytest.mark.parametrize("fixture_seed", [0, 1, 2, 3, 4])
+def test_deanonymization_beats_structure_only_on_planted_views(fixture_seed):
+ """The published claim's direction, on views built so the structure-only arm has real
+ signal to find: the communities are what a common-neighbour predictor lives on."""
+ report = run_fixture(args(), fixture_seed)
+ attack = arm(report, "attack")
+ ceiling = arm(report, "perfect-mapping-ceiling")
+ assert attack["separability"]["beats_structure_only"] == "a"
+ assert attack["deanonymization"]["auc"] > attack["structure_only"]["auc"]
+ # The recall comparison cannot invert (see the dominance test below), so it is a level and
+ # not a finding; the AUC and the paired verdict are what carry the direction.
+ assert attack["deanonymization"]["recall"] > attack["structure_only"]["recall"]
+ # Propagation coverage, not the transfer, is what the attack arm is short of.
+ assert ceiling["deanonymization"]["recall"] > attack["deanonymization"]["recall"]
+
+
+def test_the_deanonymization_score_dominates_the_structure_only_score_on_every_pair():
+ """A property of the construction, published so it is not read as a finding: the transfer
+ only ever adds neighbours, so the de-anonymization arm's declaration set is a superset at
+ any cutoff and its recall can never come out lower."""
+ views = fixture()
+ seeds = sampled_seeds(views["correspondence"], 0.25, random.Random(1))
+ pairs = views["held_out"] + views["negatives"]
+ prediction = lp.predict(views["target"], views["auxiliary"], seeds, pairs,
+ seed_provenance="test")
+ structure = lp.neighbourhoods(views["target"])
+ assert all(transferred >= lp.score(structure, *pair)
+ for pair, transferred in zip(prediction.candidates, prediction.scores))
+
+
+@pytest.mark.parametrize("fixture_seed", [0, 1, 2, 3, 4])
+def test_controls_carrying_no_transferable_evidence_are_not_certified(fixture_seed):
+ """An auxiliary view that corresponds to nothing, a deranged mapping of one that does, and
+ a real one with the held-out links deleted from it. None may reach a verdict: the last is
+ the sharp one, since it keeps everything except the evidence the transfer carries."""
+ controls = [run for run in run_fixture(args(), fixture_seed)["runs"]
+ if run["arm"].endswith("-control")]
+ assert len(controls) == 3
+ for control in controls:
+ assert control["separability"]["beats_structure_only"] != "a"
+
+
+@pytest.mark.parametrize("cutoff", [1, 2, 3])
+def test_ablating_the_closed_term_removes_the_result_rather_than_shrinking_it(cutoff):
+ """The load-bearing design decision, measured instead of argued. Scored with
+ `open_score` — `graph_deanon.structural_score`, both arms — the de-anonymization arm keeps
+ the transferred adjacency but may no longer read a transferred edge as evidence for the
+ pair it joins, and nothing is left: no fixture seed is certified at any cutoff, and at
+ cutoff 1 two of them are certified the other way."""
+ for fixture_seed in range(5):
+ attack = arm(run_fixture(args(cutoff=cutoff, scorer="open"), fixture_seed), "attack")
+ assert attack["separability"]["beats_structure_only"] != "a"
+
+
+@pytest.mark.parametrize("fixture_seed", [0, 1, 2, 3, 4])
+def test_the_advantage_is_not_certified_at_the_looser_cutoff(fixture_seed):
+ """The published nuance, asserted rather than left in prose: the ranking is better at every
+ threshold, but declaring on any evidence at all buys the extra recall with enough false
+ positives that the paired test reaches no verdict on a fixture this size."""
+ attack = arm(run_fixture(args(cutoff=1), fixture_seed), "attack")
+ assert attack["deanonymization"]["auc"] > attack["structure_only"]["auc"]
+ assert attack["separability"]["beats_structure_only"] is None
+
+
+def test_the_structure_only_arm_is_unchanged_by_the_auxiliary_view():
+ """Both arms are scored on identical pairs and the control differs from the attack in the
+ auxiliary view alone, so the structure-only column must be bit-identical across the rows.
+ A control that quietly moved it would be comparing two different experiments."""
+ runs = run_fixture(args(), 0)["runs"]
+ assert len({tuple(sorted(run["structure_only"].items())) for run in runs}) == 1
+
+
+def test_seed_provenance_is_required_and_recorded():
+ views = fixture()
+ seeds = sampled_seeds(views["correspondence"], 0.1, random.Random(2))
+ with pytest.raises(TypeError):
+ lp.predict(views["target"], views["auxiliary"], seeds, views["held_out"])
+ result = lp.compare(views["target"], views["auxiliary"], seeds, views["held_out"],
+ views["negatives"], seed_provenance="withheld-correspondence-sample")
+ assert result.seed_provenance == "withheld-correspondence-sample"
+
+
+def test_results_document_disclaims_the_paper_reproduction_and_avoids_the_banned_phrase():
+ text = open(os.path.join(ROOT, "results", MANIFEST_DOC)).read()
+ assert "ground truth" not in text.lower()
+ assert "not a reproduction" in text.lower()
+ assert "open cell" in text.lower()
+
+
+def test_the_documents_headline_table_is_the_one_the_run_produces():
+ """The manifest pins the numbers; this pins the prose to them, which is the direction the
+ drift actually goes."""
+ text = open(os.path.join(ROOT, "results", MANIFEST_DOC)).read()
+ for fixture in build_report(build_parser().parse_args([]))["fixtures"]:
+ for column in ("deanonymization", "structure_only"):
+ row = arm(fixture, "attack")[column]
+ assert f"{row['auc']:.4f}" in text, (fixture["fixture_seed"], column, row["auc"])
+ assert f"{row['recall']:.3f}" in text, (fixture["fixture_seed"], column, row["recall"])
+
+
+def test_manifest_invariants_are_recomputed_and_match_results_link_prediction():
+ """`check_manifest` compares invariants only when a caller recomputes and supplies them.
+ The corpus is generated by the source the manifest names, so this recomputation is the
+ whole check: a mechanism change that moves a published number lands here."""
+ recorded = rp.read_manifest(MANIFEST_DOC, root=ROOT)
+ assert recorded is not None and recorded["source"]["pattern"] == MANIFEST_SOURCE
+ measured = manifest_invariants(build_report(build_parser().parse_args([])))
+ status, message = rp.check_manifest(MANIFEST_DOC, measured, root=ROOT)
+ assert status == "ok", message
diff --git a/tests/test_link_prediction_experiment.py b/tests/test_link_prediction_experiment.py
new file mode 100644
index 00000000..ff7bfbfa
--- /dev/null
+++ b/tests/test_link_prediction_experiment.py
@@ -0,0 +1,43 @@
+import json
+from pathlib import Path
+
+import pytest
+
+from decluster.experiments import link_prediction as experiment
+
+
+ROOT = Path(__file__).resolve().parents[1]
+
+
+def test_artifact_preserves_configuration_controls_and_limitations():
+ artifact = experiment.build_artifact()
+
+ assert artifact["report"]["configuration"]["fixture_seeds"] == [0, 1, 2, 3, 4]
+ assert len(artifact["report"]["fixtures"]) == 5
+ assert all(len(fixture["runs"]) == 5 for fixture in artifact["report"]["fixtures"])
+ assert "not a reproduction" in artifact["limitations"][2]
+
+
+def test_canonical_report_preserves_the_legacy_measurements():
+ legacy = json.loads((ROOT / "results" / "link-prediction.json").read_text())
+
+ assert experiment.build_artifact()["report"] == legacy
+
+
+def test_verifier_recomputes_the_full_report():
+ changed = json.loads(json.dumps(experiment.build_artifact()))
+ changed["report"]["fixtures"][0]["runs"][0]["deanonymization"]["auc"] = 1.0
+
+ with pytest.raises(experiment.VerificationError, match="fresh experiment"):
+ experiment.verify_artifact(changed)
+
+
+def test_cli_reproduces_and_verifies_json_and_markdown(tmp_path):
+ artifact = tmp_path / "artifact.json"
+ markdown = tmp_path / "result.md"
+ assert experiment.main(
+ ["reproduce", "--artifact", str(artifact), "--markdown", str(markdown)]
+ ) == 0
+ assert experiment.main(
+ ["verify", "--artifact", str(artifact), "--markdown", str(markdown)]
+ ) == 0
diff --git a/tests/test_lockfile_binding.py b/tests/test_lockfile_binding.py
new file mode 100644
index 00000000..cc9a2b55
--- /dev/null
+++ b/tests/test_lockfile_binding.py
@@ -0,0 +1,57 @@
+"""A pinned wheel has four copies of one identity, and nothing was checking they agreed.
+
+Rebuilding `dss` moves its digest. The bundle blob, the lockfile that `--require-hashes` reads and
+the manifest's `environment.lock_digest` each carry a copy of that same fact, and the synchroniser
+only refreshes the first: it re-identifies a bundle blob from the tree, but a lockfile pinning a
+wheel it no longer ships, or a `lock_digest` naming bytes that exist nowhere, both survive it. The
+reproduction gate catches the first of those only by spending four minutes on a pip install that is
+going to fail, and caught the second not at all.
+"""
+
+import hashlib
+import json
+from pathlib import Path
+
+ROOT = Path(__file__).resolve().parent.parent
+LOCK_HASH = __import__("re").compile(r"--hash=sha256:([0-9a-f]{64})")
+
+
+def _digest(path):
+ return hashlib.sha256(path.read_bytes()).hexdigest()
+
+
+def _bundles():
+ return [json.loads(p.read_text()) for p in sorted(Path(ROOT, "releases").glob("*.bundle.json"))]
+
+
+def test_every_pinned_wheel_is_the_wheel_the_bundle_ships():
+ checked = 0
+ for bundle in _bundles():
+ wheels = {Path(b["name"]).name: b["sha256"] for b in bundle["blobs"] if b["name"].endswith(".whl")}
+ for blob in bundle["blobs"]:
+ if blob.get("role") != "lockfile":
+ continue
+ for pinned in LOCK_HASH.findall(Path(ROOT, blob["name"]).read_text()):
+ assert pinned in wheels.values(), (
+ f"{bundle['id']} pins {pinned[:12]} in {blob['name']} but ships {sorted(wheels)}"
+ )
+ checked += 1
+ assert checked, "no bundle pins a wheel; the binding this guards has disappeared"
+
+
+def test_a_lockfile_blob_still_matches_the_file_it_names():
+ for bundle in _bundles():
+ for blob in bundle["blobs"]:
+ source = Path(ROOT, blob["name"])
+ if blob.get("role") != "lockfile" or not source.is_file():
+ continue
+ assert _digest(source) == blob["sha256"], f"{bundle['id']} pins stale bytes for {blob['name']}"
+
+
+def test_each_run_locks_the_environment_of_a_bundle_that_ships_it():
+ locks = {_digest(p) for p in Path(ROOT, "reproduction").glob("*/requirements.lock")}
+ for manifest in sorted(Path(ROOT, "catalog/runs").glob("*.json")):
+ declared = json.loads(manifest.read_text()).get("environment", {}).get("lock_digest")
+ if declared is None:
+ continue
+ assert declared in locks, f"{manifest.name} locks {declared[:12]}, which is no lockfile here"
diff --git a/tests/test_lumen_catalog.py b/tests/test_lumen_catalog.py
new file mode 100644
index 00000000..06983567
--- /dev/null
+++ b/tests/test_lumen_catalog.py
@@ -0,0 +1,75 @@
+"""`catalog/lumen-vectors-2026.json` is the declared identity of the Lumen vector artifact, which
+this checkout does not hold (4 GiB, zstd CSV). These tests wire the catalog to
+`decluster.lumen_data`'s schema contract, so a drift in either — a renamed manifest field, a
+changed column requirement, an edited catalog — fails loudly instead of being discovered the next
+time someone has the artifact.
+
+Nothing here fabricates artifact content. The row count is read from the catalog as a *declaration*;
+the only bytes actually hashed are the committed explorer fixture.
+"""
+import hashlib
+import json
+from pathlib import Path
+
+import pytest
+
+from decluster.lumen_data import (
+ TXID_COLUMN,
+ VectorManifest,
+ VectorSummary,
+ load_explorer_aggregate,
+ reconcile_counts,
+ validate_filename_range,
+)
+
+ROOT = Path(__file__).resolve().parent.parent
+CATALOG = ROOT / "catalog" / "lumen-vectors-2026.json"
+EXPLORER_FIXTURE = ROOT / "tests" / "fixtures" / "lumen_explorer_data.json"
+
+
+@pytest.fixture(scope="module")
+def catalog():
+ with CATALOG.open(encoding="utf-8") as stream:
+ return json.load(stream)
+
+
+def test_catalog_entry_parses_as_a_vector_manifest(catalog):
+ manifest = VectorManifest.from_mapping(catalog)
+ assert manifest.columns[0] == TXID_COLUMN, "summarize_vector_stream requires a leading txid"
+ assert len(set(manifest.columns)) == len(manifest.columns)
+ assert manifest.row_count == catalog["row_count"]
+ assert manifest.sha256 == catalog["sha256"]
+
+
+def test_catalog_filename_agrees_with_its_declared_block_range(catalog):
+ """The vector rows carry no height column, so the filename is the only provenance the loader
+ can check the declared range against."""
+ validate_filename_range(catalog["artifact"], VectorManifest.from_mapping(catalog))
+
+
+def test_catalog_comparison_aggregate_matches_the_committed_fixture(catalog):
+ comparison = catalog["comparison_aggregate"]
+ digest = hashlib.sha256(EXPLORER_FIXTURE.read_bytes()).hexdigest()
+ assert digest == comparison["fixture_sha256"]
+
+ aggregate = load_explorer_aggregate(str(EXPLORER_FIXTURE))
+ assert aggregate.transaction_count == comparison["transaction_count"]
+ assert aggregate.start_height == comparison["start_height"]
+ assert aggregate.end_height == comparison["end_height"]
+
+
+def test_the_recorded_delta_is_still_unreconciled(catalog):
+ """`reconcile_counts` describes the discrepancy and refuses to attribute it. The catalog says
+ the same in words; this pins that the two agree, including that adjacent end heights are
+ reported as a coincidence to be explained, not as the explanation."""
+ manifest = VectorManifest.from_mapping(catalog)
+ aggregate = load_explorer_aggregate(str(EXPLORER_FIXTURE))
+ declared = VectorSummary(manifest.row_count, manifest.columns, {})
+
+ result = reconcile_counts(declared, aggregate, manifest)
+
+ assert result["delta"] == catalog["comparison_aggregate"]["delta"] == 6219
+ assert result["same_start_height"] is True
+ assert result["adjacent_end_height"] is True
+ assert result["requires_txid_or_block_evidence"] is True
+ assert catalog["comparison_aggregate"]["status"].startswith("unreconciled")
diff --git a/tests/test_lumen_data.py b/tests/test_lumen_data.py
new file mode 100644
index 00000000..483cec13
--- /dev/null
+++ b/tests/test_lumen_data.py
@@ -0,0 +1,99 @@
+import io
+import json
+import hashlib
+
+import pytest
+
+from decluster.lumen_data import (
+ LumenValidationError,
+ VectorManifest,
+ load_explorer_aggregate,
+ reconcile_counts,
+ summarize_vector_file,
+ summarize_vector_stream,
+ validate_file_digest,
+)
+
+
+COLUMNS = ("txid", "version", "nsequence")
+CSV = "txid,version,nsequence\na,2,Rbf\nb,1,Final\nc,2,Rbf\n"
+
+
+def test_vector_stream_validates_and_aggregates_axes():
+ summary = summarize_vector_stream(io.StringIO(CSV), VectorManifest(COLUMNS, row_count=3))
+ assert summary.row_count == 3
+ assert summary.axis_counts["version"] == {"2": 2, "1": 1}
+ assert summary.axis_counts["nsequence"] == {"Rbf": 2, "Final": 1}
+ assert "txid" not in summary.axis_counts
+
+
+def test_manifest_can_be_loaded_from_json(tmp_path):
+ path = tmp_path / "vectors.manifest.json"
+ path.write_text(json.dumps({
+ "columns": list(COLUMNS), "row_count": 3,
+ "start_height": 939969, "end_height": 962721,
+ "sha256": "a" * 64,
+ }))
+ assert VectorManifest.from_json(str(path)) == VectorManifest(
+ COLUMNS, 3, 939969, 962721, "a" * 64
+ )
+
+
+def test_file_digest_is_verified(tmp_path):
+ path = tmp_path / "vectors.csv"
+ path.write_text(CSV)
+ digest = hashlib.sha256(path.read_bytes()).hexdigest()
+ validate_file_digest(str(path), digest)
+ with pytest.raises(LumenValidationError, match="SHA-256 differs"):
+ validate_file_digest(str(path), "0" * 64)
+
+
+def test_vector_stream_rejects_schema_and_count_mismatches():
+ with pytest.raises(LumenValidationError, match="columns differ"):
+ summarize_vector_stream(io.StringIO(CSV), VectorManifest(("txid", "nsequence")))
+ with pytest.raises(LumenValidationError, match="row count differs"):
+ summarize_vector_stream(io.StringIO(CSV), VectorManifest(COLUMNS, row_count=4))
+
+
+def test_file_range_is_manifest_provenance_not_a_row_field(tmp_path):
+ path = tmp_path / "939969-962721.csv"
+ path.write_text(CSV)
+ manifest = VectorManifest(COLUMNS, 3, 939969, 962721)
+ assert summarize_vector_file(str(path), manifest).row_count == 3
+ with pytest.raises(LumenValidationError, match="filename range differs"):
+ summarize_vector_file(str(path), VectorManifest(COLUMNS, 3, 939969, 962720))
+
+
+def test_explorer_loader_returns_aggregate_not_transaction_rows(tmp_path):
+ path = tmp_path / "explorer-data.json"
+ path.write_text(json.dumps({
+ "window": {"start_height": 10, "end_height": 20, "epochs": 1},
+ "totals": {"txs": 3, "defects": 0},
+ "axis_summaries": {
+ "version": {"distinct_values": 2, "values": [
+ {"value": "1", "count": 1}, {"value": "2", "count": 2}
+ ]}
+ },
+ }))
+ aggregate = load_explorer_aggregate(str(path))
+ assert aggregate.transaction_count == 3
+ assert not hasattr(aggregate, "rows")
+
+
+def test_reconciliation_reports_delta_without_claiming_its_cause(tmp_path):
+ vector = summarize_vector_stream(io.StringIO(CSV), VectorManifest(COLUMNS))
+ path = tmp_path / "aggregate.json"
+ path.write_text(json.dumps({
+ "window": {"start_height": 10, "end_height": 19, "epochs": 1},
+ "totals": {"txs": 2, "defects": 0},
+ "axis_summaries": {"version": {"distinct_values": 1, "values": [
+ {"value": "2", "count": 2}
+ ]}},
+ }))
+ report = reconcile_counts(
+ vector, load_explorer_aggregate(str(path)), VectorManifest(COLUMNS, 3, 10, 20)
+ )
+ assert report["delta"] == 1
+ assert report["same_start_height"] is True
+ assert report["adjacent_end_height"] is True
+ assert report["requires_txid_or_block_evidence"] is True
diff --git a/tests/test_match_controls_experiment.py b/tests/test_match_controls_experiment.py
new file mode 100644
index 00000000..d8a6767b
--- /dev/null
+++ b/tests/test_match_controls_experiment.py
@@ -0,0 +1,114 @@
+"""Whether the matcher beats degree alone, and whether it knows which answers are good.
+
+The two documents this replaces were measured on a slice that no longer exists. What is asserted
+here is what they were read for, rather than the numbers they happened to land on: that the
+comparison is made against the exact degree-class expectation and not only against chance, that a
+shuffled-seed arm runs beside every seeded one, and that precision is reported against the
+eccentricity each match won by so the framework's own criterion — high-confidence links — can be
+read off it.
+
+The seedable band is asserted too. Propagation refuses to route through a matched neighbour above
+the hub cap, so seeding by raw degree hands the matcher a budget of vertices it discards; that
+mistake is cheap to make and invisible in the output, which is why the band is a property here and
+not a comment.
+"""
+
+import json
+from pathlib import Path
+
+import pytest
+
+from decluster.experiments import match_controls as mc
+
+ROOT = Path(__file__).resolve().parent.parent
+ARTIFACT = ROOT / "results" / "artifacts" / "match-controls-v1.json"
+EPOCHS = tuple(ROOT / path for path in mc.DEFAULT_EPOCHS)
+
+
+@pytest.fixture(scope="module")
+def artifact():
+ if not ARTIFACT.is_file():
+ pytest.skip("the canonical match-controls artifact is not committed")
+ return json.loads(ARTIFACT.read_text())
+
+
+def _widest(artifact):
+ return artifact["confidence"][max(artifact["confidence"], key=float)]
+
+
+def test_seeds_come_from_the_band_the_matcher_can_spend_them_in(artifact):
+ population = artifact["population"]
+ assert 0 < population["seedable"] < population["matchable_in_both_views"], (
+ "seeds are no longer drawn from a band; a hub handed to the matcher is evidence it refuses "
+ "to route through")
+ assert mc.SEEDABLE[1] == 100, "the band's ceiling has to track the matcher's hub cap"
+
+
+def test_every_seeded_arm_has_a_shuffled_arm_beside_it(artifact):
+ arms = artifact["matching"]
+ seeded = {row["seed_share"] for row in arms if row["arm"] == "seeded"}
+ shuffled = {row["seed_share"] for row in arms if row["arm"] == "shuffled"}
+ assert seeded and seeded == shuffled
+
+
+def test_the_shuffle_control_does_not_keep_up_with_the_seeds(artifact):
+ """A correspondence destroyed and still recovered would mean the seeds carried nothing."""
+ by_key = {(row["seed_share"], row["arm"]): row for row in artifact["matching"]}
+ for share in {row["seed_share"] for row in artifact["matching"]}:
+ seeded, shuffled = by_key[(share, "seeded")], by_key[(share, "shuffled")]
+ assert shuffled["correct"] <= seeded["correct"]
+
+
+def test_the_bands_are_scored_against_degree_and_not_only_against_chance(artifact):
+ for row in _widest(artifact):
+ if row["matched"]:
+ assert row["degree_class_expectation"] is not None, (
+ "a band without its degree-class column is a precision nobody can read")
+
+
+def test_raising_the_confidence_floor_never_admits_more_matches(artifact):
+ bands = _widest(artifact)
+ counts = [row["matched"] for row in bands]
+ assert counts == sorted(counts, reverse=True)
+ assert [row["eccentricity_at_least"] for row in bands] == sorted(
+ row["eccentricity_at_least"] for row in bands)
+
+
+def test_the_margin_over_degree_collapses_where_the_matcher_is_most_confident(artifact):
+ """The reading the replaced document got backwards, asserted so it cannot drift back.
+
+ It reported the margin over a degree-only guess *growing* into the high-confidence band. On
+ committed data it falls: precision rises with the matcher's confidence, but the degree baseline
+ rises faster, because the matches it is surest of are the ones whose partner has a rare degree.
+ """
+ for bands in artifact["confidence"].values():
+ overall, narrowest = bands[0], bands[-1]
+ assert overall["precision"] > overall["degree_class_expectation"], (
+ "the matcher no longer beats degree at all, which is a different result again")
+ assert narrowest["precision"] > overall["precision"], (
+ "precision is no longer monotone in the matcher's own confidence")
+ wide = overall["precision"] - overall["degree_class_expectation"]
+ tight = narrowest["precision"] - narrowest["degree_class_expectation"]
+ assert tight < wide / 2, (
+ "the margin no longer collapses into the high-confidence band; the document's central "
+ "reading has changed and its prose has to change with it")
+
+
+def test_the_bands_are_gate_decisions_and_not_lone_candidates(artifact):
+ """An uncontested match skips the gate, so a band made of them would say nothing about it."""
+ for bands in artifact["confidence"].values():
+ assert bands[-1]["uncontested"] < 0.1 * bands[-1]["matched"]
+
+
+def test_the_seeds_carry_information_the_shuffle_destroys(artifact):
+ by_key = {(row["seed_share"], row["arm"]): row for row in artifact["matching"]}
+ for share in {row["seed_share"] for row in artifact["matching"]}:
+ seeded, shuffled = by_key[(share, "seeded")], by_key[(share, "shuffled")]
+ assert shuffled["guesses"] < 0.05 * seeded["guesses"]
+
+
+@pytest.mark.reproduction
+def test_the_committed_artifact_is_what_a_fresh_run_produces(artifact):
+ if not all(path.is_file() for path in EPOCHS):
+ pytest.skip("the committed weekly epoch graphs are not present")
+ mc.verify_artifact(artifact, tuple(str(path) for path in EPOCHS))
diff --git a/tests/test_ns1r_change_readoff.py b/tests/test_ns1r_change_readoff.py
new file mode 100644
index 00000000..d1fe8da0
--- /dev/null
+++ b/tests/test_ns1r_change_readoff.py
@@ -0,0 +1,121 @@
+"""Receiver-side change read-off over the CTP's many-senders/one-receiver amounts."""
+
+import pytest
+
+from decluster.domain import ConditionalLinksMeasured, Inconclusive
+from decluster.failure_modes.ns1r_change_readoff import (
+ ReadOffScenario,
+ analyze,
+ evaluate,
+ ns1r_examples,
+)
+
+
+def _amounts(scenario, candidates):
+ return {name: sorted(scenario.outputs[index] for index in group) for name, group in candidates}
+
+
+def _ns1r():
+ return ns1r_examples()[0]
+
+
+def _control():
+ return ns1r_examples()[1]
+
+
+def test_per_sender_readoff_is_locally_ambiguous():
+ scenario = _ns1r()
+
+ local = _amounts(scenario, analyze(scenario).local_candidates)
+
+ assert local == {"Alice": [30, 30], "Bob": [20, 30, 30], "Carol": [20]}
+
+
+def test_propagation_leaves_one_reading_of_every_sender_change():
+ scenario = _ns1r()
+ analysis = analyze(scenario)
+
+ assert _amounts(scenario, analysis.feasible_candidates) == {
+ "Alice": [30, 30],
+ "Bob": [30, 30],
+ "Carol": [20],
+ }
+ assert analysis.readings == (
+ (("Alice", 50, 30), ("Bob", 60, 30), ("Carol", 90, 20)),
+ )
+
+
+def test_the_residual_ambiguity_is_coin_identity_not_amount():
+ analysis = analyze(_ns1r())
+
+ assert len(analysis.matchings) == 2
+ assert len(analysis.readings) == 1
+ assert analysis.unanimous_links == ((3, 3),)
+
+
+def test_a_single_sender_readoff_reduces_to_two_party_elimination():
+ scenario = ReadOffScenario.from_payments(
+ "two-party", (90, 60), (110, 40), 1, 0, {"Alice": 50}
+ )
+
+ assert analyze(scenario).readings == ((("Alice", 90, 40),),)
+
+
+def test_evenly_spaced_amounts_keep_several_readings():
+ scenario = _control()
+ analysis = analyze(scenario)
+
+ assert len(analysis.readings) == 2
+ assert analysis.unanimous_links == ()
+ assert {
+ change for reading in analysis.readings for name, _, change in reading if name == "Bob"
+ } == {20, 30}
+
+
+def test_report_records_propagation_and_abstains_when_readings_disagree():
+ decided = evaluate(_ns1r())
+ undecided = evaluate(_control())
+
+ assert [channel.identifier for channel in decided.channels] == [
+ "per_sender_remainder",
+ "matching_propagation",
+ ]
+ assert [type(outcome) for outcome in decided.outcomes] == [ConditionalLinksMeasured]
+ assert Inconclusive in {type(outcome) for outcome in undecided.outcomes}
+
+
+def test_report_does_not_claim_ownership():
+ report = evaluate(_ns1r())
+
+ assert any("does not attribute ownership" in item for item in report.limitations)
+ assert any("declared knowledge" in item for item in report.limitations)
+
+
+def test_receiver_block_never_conserves_value_so_exact_block_mappings_do_not_apply():
+ scenario = _ns1r()
+
+ assert scenario.outputs[scenario.receiver_output] != scenario.inputs[scenario.receiver_input]
+
+
+@pytest.mark.parametrize(
+ "kwargs",
+ [
+ {"outputs": (160, 30, 30, 30)},
+ {"receiver_output": 1},
+ {"payments": (("Alice", 20), ("Alice", 30), ("Carol", 70))},
+ {"payments": (("Alice", 20), ("Bob", 30))},
+ ],
+)
+def test_scenario_rejects_models_the_readoff_cannot_state(kwargs):
+ fields = {
+ "identifier": "invalid",
+ "inputs": (40, 50, 60, 90),
+ "outputs": (160, 30, 30, 20),
+ "receiver_input": 0,
+ "receiver_output": 0,
+ "payments": (("Alice", 20), ("Bob", 30), ("Carol", 70)),
+ }
+ fields.update(kwargs)
+
+ with pytest.raises(ValueError):
+ ReadOffScenario(**fields)
diff --git a/tests/test_ns1r_change_readoff_experiment.py b/tests/test_ns1r_change_readoff_experiment.py
new file mode 100644
index 00000000..69f0c29c
--- /dev/null
+++ b/tests/test_ns1r_change_readoff_experiment.py
@@ -0,0 +1,40 @@
+import pytest
+
+from decluster.experiments import ns1r_change_readoff as experiment
+
+
+def test_reproduce_then_verify(tmp_path):
+ artifact, markdown = tmp_path / "a.json", tmp_path / "a.md"
+ assert experiment.main(["reproduce", "--artifact", str(artifact), "--markdown", str(markdown)]) == 0
+ assert experiment.main(["verify", "--artifact", str(artifact), "--markdown", str(markdown)]) == 0
+
+
+def test_a_tampered_artifact_is_refused(tmp_path):
+ artifact = tmp_path / "a.json"
+ experiment.main(["reproduce", "--artifact", str(artifact), "--markdown", str(tmp_path / "a.md")])
+ stored = experiment.load_artifact(artifact)
+ stored["scenarios"][0]["distinct_readings"] += 1
+ with pytest.raises(experiment.VerificationError):
+ experiment.verify_artifact(stored)
+
+
+def test_a_reading_is_amounts_not_an_index_matching():
+ # The consolidating receiver admits two matchings that differ only in which index each sender's
+ # change sits at; the receiver reads one set of amounts, and that is what the leak is.
+ rows = {row["identifier"]: row for row in experiment.build_artifact()["scenarios"]}
+ consolidating = rows["consolidating-receiver"]
+ assert consolidating["matchings"] == 2
+ assert consolidating["distinct_readings"] == 1 and consolidating["reading_is_unique"]
+
+
+def test_the_control_stays_ambiguous():
+ rows = {row["identifier"]: row for row in experiment.build_artifact()["scenarios"]}
+ control = rows["evenly-spaced-control"]
+ assert control["distinct_readings"] > 1
+ assert not control["reading_is_unique"] and control["unanimous_links"] == []
+
+
+def test_propagation_only_ever_narrows_a_sender():
+ for row in experiment.build_artifact()["scenarios"]:
+ for sender, local in row["local_candidates"].items():
+ assert set(row["feasible_candidates"][sender]) <= set(local)
diff --git a/tests/test_ns_bitcoin.py b/tests/test_ns_bitcoin.py
new file mode 100644
index 00000000..50c66e6b
--- /dev/null
+++ b/tests/test_ns_bitcoin.py
@@ -0,0 +1,264 @@
+import glob
+import os
+import random
+from collections import Counter
+
+import pytest
+
+from decluster import reproducibility as rp
+from decluster.ns_bitcoin import (SEED_INDEPENDENT, SEED_SAMPLED, eligible_correspondence,
+ graph_overlap, run_bitcoin_views, sampled_seeds,
+ sweep_bitcoin_views, unique_entity_seeds)
+from decluster.contraction import PseudonymGraph
+from examples.ns_bitcoin_views import build_parser, build_views, independent_entity_labels, run_configuration, window_size
+
+ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+
+
+def graph(vertices, edges):
+ g = PseudonymGraph(axes=False)
+ for vertex in vertices:
+ g._vertex(vertex)
+ for source, target in edges:
+ g.edges[(source, target)] = {"transfers": 1, "value": 1}
+ g._out[source].add(target)
+ g._in[target].add(source)
+ return g
+
+
+def chain_views():
+ left = graph(range(6), [(0, 2), (1, 2), (0, 3), (2, 4), (3, 5)])
+ right = graph([f"r{i}" for i in range(6)],
+ [("r0", "r2"), ("r1", "r2"), ("r0", "r3"),
+ ("r2", "r4"), ("r3", "r5")])
+ return left, right, {i: f"r{i}" for i in range(6)}
+
+
+def path_views(n):
+ """A directed path, whose neighbourhoods are asymmetric enough that misplacing the seeds
+ really destroys the propagation."""
+ left = graph(range(n), [(i, i + 1) for i in range(n - 1)])
+ right = graph([f"r{i}" for i in range(n)],
+ [(f"r{i}", f"r{i + 1}") for i in range(n - 1)])
+ return left, right, {i: f"r{i}" for i in range(n)}
+
+
+def tx(height, inputs, outputs):
+ return {"height": height, "txid": f"{height}-{inputs[0]}",
+ "vin": [{"prevout": {"scriptpubkey_address": a}} for a in inputs],
+ "vout": [{"scriptpubkey_address": a, "value": 1000} for a in outputs]}
+
+
+def test_entity_seeds_require_unique_independent_labels():
+ left = [("pool-a", "a"), ("pool-b", "b1"), ("pool-b", "b2")]
+ right = [("pool-a", "x"), ("pool-b", "y")]
+ assert unique_entity_seeds(left, right) == {"a": "x"}
+
+
+def test_entity_seeds_refuse_a_right_vertex_two_entities_both_claim():
+ """Two labels landing on one right vertex would return a non-injective map, which
+ `propagate` rejects outright. Both are refused rather than one being kept by iteration
+ order."""
+ left = [("pool-a", "La"), ("pool-b", "Lb")]
+ right = [("pool-a", "R1"), ("pool-b", "R1")]
+ assert unique_entity_seeds(left, right) == {}
+
+
+def test_entity_seeds_refuse_a_left_vertex_two_entities_both_claim():
+ """The mirror case, which a dict comprehension resolves silently: one contracted cluster
+ holding both a pool address and an exchange deposit address carries two entity labels, and
+ keeping whichever came last would drop a label without saying so."""
+ left = [("pool-a", "L1"), ("exchange", "L1")]
+ right = [("pool-a", "R1"), ("exchange", "R2")]
+ assert unique_entity_seeds(left, right) == {}
+
+
+def test_entity_seeds_keep_the_unambiguous_labels_beside_a_refused_collision():
+ left = [("pool-a", "La"), ("pool-b", "Lb"), ("exchange", "Lc")]
+ right = [("pool-a", "R1"), ("pool-b", "R1"), ("exchange", "R3")]
+ assert unique_entity_seeds(left, right) == {"Lc": "R3"}
+
+
+def test_bitcoin_seam_reports_overlap_and_controls_without_counting_seeds():
+ left, right, truth = chain_views()
+ result = run_bitcoin_views(left, right, truth, {0: "r0", 1: "r1"}, theta=0.1)
+ assert result.seed_size == 2
+ assert result.seed_provenance == SEED_INDEPENDENT
+ assert result.seed_fraction == 2 / 6
+ assert result.theta == 0.1
+ assert result.overlap == {"vertices": 6, "left_internal_edges": 5,
+ "recurring_edges": 5, "edge_overlap": 1.0}
+ assert result.attack["declared"] > 0
+ assert result.attack["precision"] == 1.0
+ assert result.seed_only_control["declared"] == 0
+
+
+def test_declarations_are_split_by_whether_an_image_exists_at_all():
+ left = graph(range(5), [(0, 1), (1, 2), (0, 3)])
+ right = graph([f"r{i}" for i in range(5)],
+ [("r0", "r1"), ("r1", "r2"), ("r0", "r3")])
+ # Vertices 3 and 4 have no image at all: the right view is a different observation, not a
+ # relabelling of the left one. Declaring `r3` for vertex 3 is a failure the algorithm has
+ # no abstention for, and lumping it in with a wrong image hides which one happened.
+ result = run_bitcoin_views(left, right, {0: "r0", 1: "r1", 2: "r2"},
+ {0: "r0", 2: "r2"}, theta=0.1)
+ assert result.attack["declared"] == 2
+ assert result.attack["declared_with_an_image"] == 1
+ assert result.attack["precision"] == 0.5
+ assert result.attack["precision_where_an_image_exists"] == 1.0
+
+
+def test_seed_may_not_be_selected_from_withheld_truth_incorrectly():
+ left = graph([0], [])
+ right = graph(["r0"], [])
+ try:
+ run_bitcoin_views(left, right, {0: "r0"}, {0: "wrong"})
+ except ValueError as error:
+ assert "conflicts" in str(error)
+ else:
+ raise AssertionError("truth-conflicting seed accepted")
+
+
+def test_separability_credits_the_attack_only_over_the_shuffled_control():
+ left, right, truth = path_views(9)
+ result = run_bitcoin_views(left, right, truth, {0: "r0", 8: "r8"})
+ # Six of the seven held-out vertices: the last one is the only unclaimed candidate left,
+ # and a population of one has no eccentricity, so the algorithm declines it.
+ assert result.attack["correct"] == 6
+ assert result.shuffled_seed_control["correct"] == 0
+ assert result.shuffled_seed_control["fixed_points"] == 0
+ assert result.separability["attack_only"] == 6
+ assert result.separability["shuffled_only"] == 0
+ assert result.separability["p"] < 0.05
+ assert result.separability["beats_shuffled"] == "a"
+
+
+def test_a_real_advantage_too_small_to_separate_is_not_reported_as_a_verdict():
+ left, right, truth = path_views(6)
+ result = run_bitcoin_views(left, right, truth, {0: "r0", 5: "r5"})
+ assert (result.separability["attack_only"], result.separability["shuffled_only"]) == (3, 0)
+ # Three discordant wins cannot clear a two-sided sign test at 0.05: measured, not separable.
+ assert result.separability["p"] == 0.25
+ assert result.separability["beats_shuffled"] is None
+
+
+def test_a_symmetric_neighbourhood_leaves_the_shuffled_control_mostly_intact():
+ """The control falsifies a seed-position artifact, not the algorithm. Where two seeds feed
+ the same vertex, swapping them changes no vote there, so the control keeps most of the
+ attack's output and the paired advantage is one vertex — no verdict either way."""
+ left, right, truth = chain_views()
+ result = run_bitcoin_views(left, right, truth, {0: "r0", 1: "r1"}, theta=0.1)
+ assert result.attack["correct"] == 3
+ assert result.shuffled_seed_control["correct"] == 2
+ assert result.separability["attack_only"] == 1
+ assert result.separability["beats_shuffled"] is None
+
+
+def test_isolated_views_report_a_negative_result_rather_than_no_result():
+ left = graph(range(4), [])
+ right = graph([f"r{i}" for i in range(4)], [])
+ truth = {i: f"r{i}" for i in range(4)}
+ result = run_bitcoin_views(left, right, truth, {0: "r0", 1: "r1"})
+ assert result.attack["declared"] == 0
+ assert result.attack["precision"] is None
+ assert result.attack["coverage"] == 0.0
+ assert result.separability == {"attack_only": 0, "shuffled_only": 0,
+ "min_correct_gain": 1, "p": 1.0,
+ "beats_shuffled": None}
+
+
+def test_sampled_seeds_come_from_the_correspondence_and_are_reproducible():
+ truth = {i: f"r{i}" for i in range(20)}
+ first = sampled_seeds(truth, 0.25, random.Random(3))
+ assert first == sampled_seeds(truth, 0.25, random.Random(3))
+ assert len(first) == 5
+ assert all(truth[u] == v for u, v in first.items())
+ # Never fewer than two: one seed cannot establish an eccentricity at all.
+ assert len(sampled_seeds(truth, 0.01, random.Random(3))) == 2
+
+
+def test_sampled_seeds_are_drawn_uniformly_and_not_in_key_order():
+ """Nothing but the signature stops a degree- or order-biased draw, and a biased one would
+ seed the vertices propagation finds easiest and report the spread as the attack's reach."""
+ truth = {i: f"r{i}" for i in range(10)}
+ counts = Counter()
+ for seed in range(400):
+ counts.update(sampled_seeds(truth, 0.2, random.Random(seed)).keys())
+ assert set(counts) == set(truth)
+ # 800 draws over 10 keys: 80 expected each, measured 70-88. An order-biased draw would
+ # put 400 on the first two keys and nothing on the rest.
+ assert 60 <= min(counts.values()) and max(counts.values()) <= 100
+
+
+def test_manifest_invariants_are_recomputed_and_match_results_ns_bitcoin():
+ """`check_manifest` compares invariants only when a caller recomputes and supplies them,
+ so this is that caller. It recomputes the two kinds that are cheap: the run configuration,
+ from the driver's own defaults (the failure this catches is a changed `--min-degree` or
+ `--blocks`, which leaves the epoch bytes and so the digest untouched), and the window
+ transaction counts, straight off the compressed epochs. The contracted populations are
+ identity-only by design: rebuilding two 100k-vertex views takes minutes, not seconds."""
+ args = build_parser().parse_args([])
+ if not glob.glob(os.path.join(ROOT, "data", "epochs_2016_weekly", "*.ndjson.gz")):
+ pytest.skip("data/epochs_2016_weekly not present; manifest invariants not recomputed")
+
+ invariants = dict(run_configuration(args))
+ invariants["left_transactions"] = window_size(
+ os.path.join(ROOT, args.left), args.boundary - args.blocks, args.boundary - 1)
+ invariants["right_transactions"] = window_size(
+ os.path.join(ROOT, args.right), args.boundary, args.boundary + args.blocks - 1)
+ status, message = rp.check_manifest("RESULTS-ns-bitcoin.md", invariants, root=ROOT)
+ assert status == "partial", message
+ assert "not recomputed" in message
+
+
+def test_eligible_correspondence_drops_pairs_no_view_can_be_graded_on():
+ left, right, truth = chain_views()
+ truth = {**truth, 99: "r99", 5: "absent"}
+ assert eligible_correspondence(left, right, truth) == {i: f"r{i}" for i in range(5)}
+
+
+def test_sweep_covers_the_grid_and_stamps_every_row_as_seed_assisted():
+ left, right, truth = chain_views()
+ rows = sweep_bitcoin_views(left, right, truth, [0.3, 0.5], [0.1, 1.5])
+ assert [(row.seed_size, row.theta) for row in rows] == [
+ (2, 0.1), (2, 1.5), (3, 0.1), (3, 1.5)]
+ assert {row.seed_provenance for row in rows} == {SEED_SAMPLED}
+ assert [round(row.seed_fraction, 4) for row in rows] == [0.3333, 0.3333, 0.5, 0.5]
+
+
+def straddling_windows():
+ """One cluster active on both sides of the boundary, plus per-window counterparties."""
+ before = [tx(499, ["e1", "e2"], ["x1", "x2"]), tx(499, ["x1"], ["x3"]),
+ tx(498, ["x2"], ["x3", "1dice8EMZmqKvrGE4Qc9bUFf9PX3xaYDp"])]
+ # `e3` is co-spent with `e2` only after the boundary, which is what makes the cluster
+ # straddle it: a cluster all of whose addresses were already seen before the boundary
+ # is wholly inside view A and there is nothing to rejoin.
+ after = [tx(500, ["e2", "e3"], ["y1", "y2"]), tx(500, ["y1"], ["y3"]),
+ tx(501, ["y2"], ["y3", "3BMEXvanityaddress"])]
+ return before, after
+
+
+def test_view_construction_gives_a_straddling_cluster_one_pseudonym_per_view():
+ before, after = straddling_windows()
+ left, right, correspondence, lookups, addresses, split = build_views(
+ before, after, min_degree=1, split_frac=1.0, rng=random.Random(0))
+ assert split == 1 and len(correspondence) == 1
+ (u, v), = correspondence.items()
+ assert u.endswith("#a") and v.endswith("#b")
+ assert u[:-2] == v[:-2]
+ # The trivial escape: no pseudonym string may be shared by the two views.
+ assert not set(left.vertices) & set(right.vertices)
+ assert addresses >= len({"e1", "e2", "e3", "x1", "x2", "x3", "y1", "y2", "y3"})
+ assert graph_overlap(left, right, correspondence)["vertices"] == 1
+
+
+def test_independent_entity_labels_name_entities_from_the_chain_alone():
+ before, after = straddling_windows()
+ _, _, _, lookups, _, _ = build_views(before, after, min_degree=1, split_frac=1.0,
+ rng=random.Random(0))
+ left_labels = independent_entity_labels(before, lookups[0])
+ right_labels = independent_entity_labels(after, lookups[1])
+ assert {entity for entity, _ in left_labels} == {"satoshidice"}
+ assert {entity for entity, _ in right_labels} == {"bitmex"}
+ # No entity is unambiguous on both sides, so no seed is manufactured.
+ assert unique_entity_seeds(left_labels, right_labels) == {}
diff --git a/tests/test_ns_bitcoin_experiment.py b/tests/test_ns_bitcoin_experiment.py
new file mode 100644
index 00000000..1f6a352c
--- /dev/null
+++ b/tests/test_ns_bitcoin_experiment.py
@@ -0,0 +1,63 @@
+import json
+from pathlib import Path
+
+import pytest
+
+from decluster.experiments import ns_bitcoin as experiment
+
+
+ROOT = Path(__file__).resolve().parents[1]
+
+
+def test_artifact_preserves_corpus_and_attack_limitations(monkeypatch):
+ monkeypatch.setattr(
+ experiment.legacy,
+ "build_report",
+ lambda _args: {"views": {"left": {}, "right": {}}, "runs": []},
+ )
+ artifact = experiment.build_artifact("left.gz", "right.gz")
+
+ assert artifact["experiment"] == "ns-bitcoin-v1"
+ assert "independent gradeable seeds" in artifact["limitations"][0]
+ assert "Twitter, Flickr, or LiveJournal" in artifact["limitations"][4]
+
+
+def test_verifier_rejects_a_changed_measurement(monkeypatch):
+ expected = {
+ "schema_version": 1,
+ "experiment": experiment.EXPERIMENT_ID,
+ "corpus": "adjacent public Bitcoin block windows",
+ "report": {"value": 1},
+ "limitations": [
+ "no independent gradeable seeds were found in these windows",
+ "the measured sweep uses seeds sampled from withheld correspondence",
+ "operational precision is approximately one percent",
+ "the observation windows are not representative of the whole chain",
+ "this does not reproduce the Twitter, Flickr, or LiveJournal experiments",
+ ],
+ }
+ monkeypatch.setattr(experiment, "build_artifact", lambda *_args: expected)
+ changed = json.loads(json.dumps(expected))
+ changed["report"]["value"] = 2
+
+ with pytest.raises(experiment.VerificationError, match="fresh experiment"):
+ experiment.verify_artifact(changed)
+
+
+def test_canonical_report_preserves_historical_observation_population(monkeypatch):
+ historical = json.loads((ROOT / "results" / "ns-bitcoin.json").read_text())
+ report = {
+ "views": historical["views"],
+ "withheld_correspondence": historical["withheld_correspondence"],
+ "independent_entity_seeds": historical["independent_entity_seeds"],
+ "runs": [],
+ }
+ report["views"]["left"]["path"] = experiment.LEFT
+ report["views"]["right"]["path"] = experiment.RIGHT
+ monkeypatch.setattr(experiment.legacy, "build_report", lambda _args: report)
+
+ measured = experiment.build_artifact()["report"]
+ assert measured["views"]["left"]["transactions"] == 133576
+ assert measured["views"]["right"]["transactions"] == 157724
+ assert measured["withheld_correspondence"]["vertices"] == 2599
+ assert measured["independent_entity_seeds"]["found"] == 0
diff --git a/tests/test_ns_bitcoin_snapshot.py b/tests/test_ns_bitcoin_snapshot.py
new file mode 100644
index 00000000..c74632ff
--- /dev/null
+++ b/tests/test_ns_bitcoin_snapshot.py
@@ -0,0 +1,35 @@
+import gzip
+import json
+
+import pytest
+
+from decluster.ns_bitcoin_snapshot import build_window
+
+
+def _write_records(path):
+ with gzip.open(path, "wt", encoding="utf-8") as target:
+ for height in (9, 10, 11, 12, 13):
+ target.write(json.dumps({"height": height, "txid": str(height)}) + "\n")
+
+
+def test_builder_filters_inclusively_and_is_byte_deterministic(tmp_path):
+ source = tmp_path / "source.ndjson.gz"
+ first = tmp_path / "first.ndjson.gz"
+ second = tmp_path / "second.ndjson.gz"
+ _write_records(source)
+
+ assert build_window(source, first, minimum_height=10, maximum_height=12) == 3
+ assert build_window(source, second, minimum_height=10, maximum_height=12) == 3
+ assert first.read_bytes() == second.read_bytes()
+ with gzip.open(first, "rt", encoding="utf-8") as records:
+ assert [json.loads(line)["height"] for line in records] == [10, 11, 12]
+
+
+def test_builder_rejects_an_inverted_range(tmp_path):
+ with pytest.raises(ValueError, match="must not exceed"):
+ build_window(
+ tmp_path / "unused.gz",
+ tmp_path / "output.gz",
+ minimum_height=2,
+ maximum_height=1,
+ )
diff --git a/tests/test_ns_link_prediction_2011.py b/tests/test_ns_link_prediction_2011.py
new file mode 100644
index 00000000..be7463f5
--- /dev/null
+++ b/tests/test_ns_link_prediction_2011.py
@@ -0,0 +1,259 @@
+"""Executable contract for Algorithm 3 of Narayanan–Shi–Rubinstein (2011)."""
+
+from decluster.baselines.ns_link_prediction_2011 import (
+ ComponentStatus,
+ IncompletePipelineError,
+ PipelineComponent,
+ SimilarityEvidence,
+ UndefinedAlgorithm2Weight,
+ algorithm2_node_distance,
+ algorithm2_pair_distance,
+ algorithm2_potential,
+ anneal_seed_mapping,
+ combine_predictions,
+ pipeline_coverage,
+ require_components,
+ similarity_evidence,
+ stage1_match,
+ stage2_candidates,
+ two_stage_mapping,
+)
+import random
+import pytest
+from examples.link_prediction_run import directed_graph
+
+
+def test_pipeline_coverage_separates_components_from_end_to_end_reproduction():
+ coverage = {row.component: row for row in pipeline_coverage()}
+
+ assert set(coverage) == set(PipelineComponent)
+ assert coverage[PipelineComponent.ALGORITHM1_SIMILARITY].status is ComponentStatus.IMPLEMENTED
+ assert coverage[PipelineComponent.ANNEALING].status is ComponentStatus.PARTIAL
+ assert (
+ coverage[PipelineComponent.ALGORITHM2_DUMMY_WEIGHTS].status
+ is ComponentStatus.IMPLEMENTED
+ )
+ assert coverage[PipelineComponent.TWO_STAGE_MAPPING].limitation
+ assert (
+ coverage[PipelineComponent.CONFIDENCE_PRUNING].status
+ is ComponentStatus.NOT_REPRODUCED
+ )
+ assert (
+ coverage[PipelineComponent.LEARNED_25_FEATURE_MODEL].status
+ is ComponentStatus.NOT_REPRODUCED
+ )
+
+
+def test_component_gate_accepts_only_fully_implemented_parts():
+ require_components(
+ PipelineComponent.ALGORITHM1_SIMILARITY,
+ PipelineComponent.ALGORITHM3_CASCADE,
+ )
+ require_components(PipelineComponent.ALGORITHM2_DUMMY_WEIGHTS)
+ with pytest.raises(IncompletePipelineError, match="annealing=partial"):
+ require_components(PipelineComponent.ANNEALING)
+
+
+def test_algorithm1_uses_in_neighbours_and_conditionally_uses_out_neighbours():
+ target = directed_graph(["k", "ki", "ko"], [("ki", "k"), ("k", "ko")])
+ auxiliary = directed_graph(["f", "fi", "fo"], [("fi", "f"), ("f", "fo")])
+ mapping = {"ki": "fi", "ko": "fo"}
+
+ uncrawled = similarity_evidence(
+ target, auxiliary, "k", "f", mapping,
+ crawled_target={"ki", "ko"}, crawled_auxiliary={"fi", "fo"},
+ )
+ assert (uncrawled.score, uncrawled.common_mapped_neighbours) == (1.0, 1)
+
+ crawled = similarity_evidence(
+ target, auxiliary, "k", "f", mapping,
+ crawled_target={"k", "ki", "ko"}, crawled_auxiliary={"f", "fi", "fo"},
+ )
+ assert (crawled.score, crawled.common_mapped_neighbours) == (1.0, 2)
+
+
+def test_published_stage1_thresholds_require_support_score_and_margin():
+ rows = [
+ SimilarityEvidence("k", "best", 0.7, 4),
+ SimilarityEvidence("k", "second", 0.49, 4),
+ ]
+ assert stage1_match(rows) == "best"
+ assert stage1_match([rows[0], SimilarityEvidence("k", "close", 0.51, 4)]) is None
+ assert stage1_match([SimilarityEvidence("k", "weak", 0.9, 3)]) is None
+
+
+def test_stage2_drops_margin_and_returns_at_most_three_eligible_candidates():
+ rows = [SimilarityEvidence("k", f"f{i}", score, support) for i, (score, support) in enumerate([
+ (0.9, 3), (0.8, 4), (0.7, 3), (0.6, 3), (0.99, 2), (0.49, 9)
+ ])]
+ assert stage2_candidates(rows) == ("f0", "f1", "f2")
+
+
+def test_algorithm2_pair_distance_refuses_only_the_ratio_the_paper_leaves_open():
+ assert algorithm2_pair_distance(1, 4) == algorithm2_pair_distance(4, 1) == 3 ** 0.5
+ # A zero against a positive weight is an infinite ratio, which the formula does define.
+ assert algorithm2_pair_distance(0, 1) == algorithm2_pair_distance(1, 0) == float("inf")
+ with pytest.raises(UndefinedAlgorithm2Weight, match="two zero weights"):
+ algorithm2_pair_distance(0, 0)
+
+
+def test_algorithm2_potential_prefers_the_weight_preserving_bijection():
+ nodes = ("a", "b", "c")
+ images = ("A", "B", "C")
+ target = {("a", "b"): 1, ("a", "c"): 4, ("b", "c"): 2}
+ auxiliary = {("A", "B"): 1, ("A", "C"): 4, ("B", "C"): 2}
+
+ def weight(table):
+ return lambda left, right: table.get((left, right), table.get((right, left)))
+
+ exact = algorithm2_potential(nodes, images, (), (), weight(target), weight(auxiliary))
+ swapped = algorithm2_potential(nodes, ("B", "A", "C"), (), (),
+ weight(target), weight(auxiliary))
+ assert exact == 0.0
+ assert swapped > exact
+
+
+def test_seed_annealing_is_reproducible_and_returns_best_visited_mapping():
+ nodes = ("a", "b", "c")
+ images = ("A", "B", "C")
+ target = {("a", "b"): 1, ("a", "c"): 4, ("b", "c"): 2}
+ auxiliary = {("A", "B"): 1, ("A", "C"): 4, ("B", "C"): 2}
+
+ def weight(table):
+ return lambda left, right: table.get((left, right), table.get((right, left)))
+
+ first = anneal_seed_mapping(nodes, images, (), (), weight(target), weight(auxiliary),
+ iterations=200, rng=random.Random(3))
+ second = anneal_seed_mapping(nodes, images, (), (), weight(target), weight(auxiliary),
+ iterations=200, rng=random.Random(3))
+ assert first == second == ({"a": "A", "b": "B", "c": "C"}, 0.0)
+
+
+def test_a_dummy_incident_node_term_costs_nothing():
+ """The paper's own claims about dummies — a mapping of size n-k, and dummy-to-dummy being
+ improvable in one step — hold only if a dummy-incident term is zero."""
+ nodes = ("a", "b", "dk")
+ weights = {("a", "b"): 1.0, ("b", "a"): 1.0}
+ weight = lambda left, right: weights.get((left, right), 0.0)
+
+ assert algorithm2_node_distance(nodes, ("A", "B", "DF"), {"dk"}, {"DF"},
+ weight, weight, 2) == 0.0
+ # A real node whose image is a dummy is equally free, which is what buys the partial map.
+ assert algorithm2_node_distance(nodes, ("A", "DF", "B"), {"dk"}, {"DF"},
+ weight, weight, 1) == 0.0
+
+
+def test_unequal_dummy_counts_leave_the_paper_with_no_common_index():
+ """sigmaK drops the positions holding a Kaggle dummy and sigmaF those holding a Flickr
+ dummy, but PairDist reads both at one j. With unequal dummy counts the vectors are not
+ even the same length, and the paper says nothing about how to pair them."""
+ nodes = ("a", "b", "dk")
+ weight = lambda left, right: 1.0
+
+ with pytest.raises(ValueError, match="no common index"):
+ algorithm2_node_distance(nodes, ("A", "B", "C"), {"dk"}, (), weight, weight, 0)
+
+
+def test_annealing_uses_dummies_to_return_a_partial_mapping():
+ """With k dummies a side the annealer must leave k real nodes matched to dummies, and
+ prefer that over pairing the two dummies with each other."""
+ nodes = ("a", "b", "c", "dk")
+ images = ("A", "B", "C", "DF")
+ target = {("a", "b"): 1.0, ("a", "c"): 4.0, ("b", "c"): 2.0}
+ auxiliary = {("A", "B"): 1.0, ("A", "C"): 4.0, ("B", "C"): 2.0}
+
+ def weight(table):
+ return lambda left, right: table.get((left, right), table.get((right, left), 0.0))
+
+ mapping, potential = anneal_seed_mapping(
+ nodes, images, {"dk"}, {"DF"}, weight(target), weight(auxiliary),
+ iterations=400, rng=random.Random(0),
+ )
+
+ assert potential == 0.0
+ assert mapping["dk"] != "DF"
+ assert sum(1 for node, image in mapping.items()
+ if node != "dk" and image != "DF") == 2
+
+
+def test_two_stage_driver_feeds_stage1_back_but_not_stage2_candidates():
+ # Seeds s/t expose x; accepting x immediately supplies the second mapped
+ # neighbour needed to accept y later in the same deterministic pass.
+ target = directed_graph(
+ ["s", "t", "x", "y"],
+ [("s", "x"), ("t", "x"), ("s", "y"), ("x", "y")],
+ )
+ auxiliary = directed_graph(
+ ["S", "T", "X", "Y", "noise"],
+ [("S", "X"), ("T", "X"), ("S", "Y"), ("X", "Y"), ("S", "noise")],
+ )
+ result = two_stage_mapping(
+ target, auxiliary, {"s": "S", "t": "T"}, ["s", "t", "x", "y"],
+ ["S", "T", "X", "Y", "noise"],
+ crawled_target=target.vertices, crawled_auxiliary=auxiliary.vertices,
+ stage1_k=2, stage1_theta=0.5, stage1_delta=0.2,
+ stage2_k=1, stage2_theta=0.5,
+ )
+ assert result.deterministic == {"s": "S", "t": "T", "x": "X", "y": "Y"}
+ assert result.candidates == {}
+ assert result.stage1_rounds == 1
+
+
+def test_two_stage_driver_keeps_relaxed_candidates_out_of_deterministic_mapping():
+ target = directed_graph(["s", "a"], [("s", "a")])
+ auxiliary = directed_graph(["S", "A", "B"], [("S", "A"), ("S", "B")])
+ result = two_stage_mapping(
+ target, auxiliary, {"s": "S"}, ["s", "a"], ["S", "A", "B"],
+ crawled_target=target.vertices, crawled_auxiliary=auxiliary.vertices,
+ stage1_k=1, stage1_theta=0.5, stage1_delta=0.2,
+ stage2_k=1, stage2_theta=0.5,
+ )
+ assert result.deterministic == {"s": "S"}
+ assert result.candidates == {"a": ("A", "B")}
+
+
+def test_deterministic_mapping_has_first_precedence():
+ result = combine_predictions(
+ [("a", "b")], {("A", "B")}, {"a": "A", "b": "B"},
+ {"a": {"wrong"}, "b": {"wrong"}}, lambda pair: 0.25,
+ )
+ assert (result[0].score, result[0].source) == (1.0, "deanonymized")
+
+
+def test_candidate_cartesian_product_can_vote_unanimously_for_edge_or_non_edge():
+ results = combine_predictions(
+ [("yes-a", "yes-b"), ("no-a", "no-b")],
+ {("A1", "B1"), ("A1", "B2"), ("A2", "B1"), ("A2", "B2")},
+ {},
+ {
+ "yes-a": {"A1", "A2"}, "yes-b": {"B1", "B2"},
+ "no-a": {"X1", "X2"}, "no-b": {"Y1", "Y2"},
+ },
+ lambda pair: 0.25,
+ )
+ assert [(row.score, row.source, row.votes) for row in results] == [
+ (1.0, "unanimous_vote", 4),
+ (0.0, "unanimous_vote", 4),
+ ]
+
+
+def test_split_vote_and_missing_candidates_fall_back_to_ml():
+ scores = {("split-a", "split-b"): 0.75, ("missing", "split-b"): 0.2}
+ results = combine_predictions(
+ scores, {("A1", "B1")}, {},
+ {"split-a": {"A1", "A2"}, "split-b": {"B1"}}, scores.__getitem__,
+ )
+ assert [(row.score, row.source, row.votes) for row in results] == [
+ (0.75, "machine_learning", None),
+ (0.2, "machine_learning", None),
+ ]
+
+
+def test_auxiliary_edges_are_directional_and_duplicate_candidates_have_no_weight():
+ result = combine_predictions(
+ [("a", "b")], {("B", "A")}, {},
+ {"a": ["A", "A"], "b": ["B", "B"]}, lambda pair: 0.4,
+ )
+ assert (result[0].score, result[0].source, result[0].votes) == (
+ 0.0, "unanimous_vote", 1
+ )
diff --git a/tests/test_ns_link_prediction_coverage_experiment.py b/tests/test_ns_link_prediction_coverage_experiment.py
new file mode 100644
index 00000000..ad2ec3c0
--- /dev/null
+++ b/tests/test_ns_link_prediction_coverage_experiment.py
@@ -0,0 +1,51 @@
+"""Canonical executable-coverage contract for N-S 2011."""
+
+import json
+
+import pytest
+
+from decluster.baselines.ns_link_prediction_2011 import PipelineComponent
+from decluster.experiments import ns_link_prediction_coverage as experiment
+
+
+def test_artifact_covers_every_declared_component_once():
+ artifact = experiment.build_artifact()
+ rows = artifact["components"]
+
+ assert len(rows) == len(PipelineComponent)
+ assert {row["component"] for row in rows} == {
+ component.value for component in PipelineComponent
+ }
+ assert artifact["end_to_end_reproduced"] is False
+ assert artifact["composition"] is None
+
+
+def test_every_unavailable_component_has_an_exercised_refusal():
+ artifact = experiment.build_artifact()
+ unavailable = {
+ row["component"] for row in artifact["components"] if row["status"] != "implemented"
+ }
+
+ assert set(artifact["refusal_controls"]) == unavailable
+ assert all("unavailable" in message for message in artifact["refusal_controls"].values())
+ assert artifact["implemented_gate"]["accepted"] is True
+
+
+def test_verifier_recomputes_coverage_and_refusals():
+ changed = json.loads(json.dumps(experiment.build_artifact()))
+ changed["end_to_end_reproduced"] = True
+
+ with pytest.raises(experiment.VerificationError, match="fresh coverage"):
+ experiment.verify_artifact(changed)
+
+
+def test_cli_reproduces_and_verifies_json_and_markdown(tmp_path):
+ artifact = tmp_path / "artifact.json"
+ markdown = tmp_path / "result.md"
+
+ assert experiment.main(
+ ["reproduce", "--artifact", str(artifact), "--markdown", str(markdown)]
+ ) == 0
+ assert experiment.main(
+ ["verify", "--artifact", str(artifact), "--markdown", str(markdown)]
+ ) == 0
diff --git a/tests/test_ns_social_baseline.py b/tests/test_ns_social_baseline.py
new file mode 100644
index 00000000..6ac28798
--- /dev/null
+++ b/tests/test_ns_social_baseline.py
@@ -0,0 +1,196 @@
+import random
+
+import pytest
+
+from decluster.baselines.narayanan_shmatikov import (
+ _sparse_match_scores,
+ _sparse_winner,
+ _winner,
+ conservative_revisit,
+ eccentricity,
+ evaluate,
+ match_scores,
+ propagate,
+)
+from decluster.contraction import PseudonymGraph
+
+
+def graph(vertices, edges):
+ result = PseudonymGraph()
+ for vertex in vertices:
+ result._vertex(vertex)
+ for source, target in edges:
+ result.edges[(source, target)] = {"transfers": 1, "value": 1}
+ result._out[source].add(target)
+ result._in[target].add(source)
+ return result
+
+
+def two_views(*, n=80, edge_probability=0.12, keep_probability=0.86, seed=7):
+ """Independently perturb two relabelled views of one planted directed graph."""
+ rng = random.Random(seed)
+ vertices = list(range(n))
+ underlying = [(u, v) for u in vertices for v in vertices
+ if u != v and rng.random() < edge_probability]
+ left_edges = [edge for edge in underlying if rng.random() < keep_probability]
+ right_edges = [(f"v{u}", f"v{v}") for u, v in underlying
+ if rng.random() < keep_probability]
+ # Independent false edges make the views noisy rather than mere edge-deleted copies.
+ for u in vertices:
+ if rng.random() < 0.18:
+ v = rng.choice([x for x in vertices if x != u])
+ left_edges.append((u, v))
+ if rng.random() < 0.18:
+ v = rng.choice([x for x in vertices if x != u])
+ right_edges.append((f"v{u}", f"v{v}"))
+ return (graph(vertices, left_edges),
+ graph([f"v{v}" for v in vertices], right_edges),
+ {v: f"v{v}" for v in vertices})
+
+
+def test_eccentricity_uses_the_full_score_population():
+ assert eccentricity({"a": 4.0, "b": 1.0, "c": 0.0}) == pytest.approx(
+ 3.0 / 1.699673171197595
+ )
+ assert eccentricity({"a": 1.0, "b": 1.0}) == 0.0
+ assert eccentricity({"a": 1.0}) == 0.0
+
+
+def test_match_scores_preserves_direction_and_normalizes_candidate_degree():
+ left = graph(["u", "incoming", "outgoing"],
+ [("incoming", "u"), ("u", "outgoing")])
+ right = graph(["x", "i", "o", "busy"],
+ [("i", "x"), ("x", "o"), ("busy", "x")])
+ scores = match_scores(left, right, {"incoming": "i", "outgoing": "o"}, "u")
+ assert scores["x"] == pytest.approx(1 / (2 ** 0.5) + 1.0)
+
+
+def test_two_distinct_views_propagate_from_independent_seeds_under_noise():
+ left, right, truth = two_views()
+ seed_nodes = list(range(16))
+ seeds = {node: truth[node] for node in seed_nodes}
+ result = propagate(left, right, seeds, theta=1.5)
+ metrics = evaluate(result.mapping, truth, seeds)
+ assert result.rounds >= 1
+ assert metrics["declared"] >= 45
+ assert metrics["precision"] >= 0.95
+ assert metrics["coverage"] >= 0.65
+
+
+def test_lower_overlap_reduces_coverage_without_forcing_guesses():
+ clean_left, clean_right, truth = two_views(keep_probability=0.90, seed=19)
+ noisy_left, noisy_right, _ = two_views(keep_probability=0.55, seed=19)
+ seeds = {node: truth[node] for node in range(18)}
+ clean = evaluate(propagate(clean_left, clean_right, seeds).mapping, truth, seeds)
+ noisy = evaluate(propagate(noisy_left, noisy_right, seeds).mapping, truth, seeds)
+ assert clean["coverage"] > noisy["coverage"]
+ assert clean["precision"] >= 0.95
+ assert noisy["precision"] is None or noisy["precision"] >= 0.90
+
+
+def test_bad_or_nonexistent_seed_mapping_is_rejected():
+ left, right, truth = two_views(n=10)
+ with pytest.raises(ValueError, match="one-to-one"):
+ propagate(left, right, {0: truth[0], 1: truth[0]})
+ with pytest.raises(ValueError, match="left view"):
+ propagate(left, right, {999: truth[0]})
+ with pytest.raises(ValueError, match="right view"):
+ propagate(left, right, {0: "absent"})
+
+
+def test_conservative_revisit_preserves_seeds_bijection_and_coverage():
+ left, right, truth = two_views(seed=23)
+ seeds = {node: truth[node] for node in range(16)}
+ base = propagate(left, right, seeds)
+ revisited = propagate(left, right, seeds, conservative_revisit_rounds=5)
+
+ assert revisited.revisit_policy == "conservative_leave_one_out"
+ assert all(revisited.mapping[node] == image for node, image in seeds.items())
+ assert len(revisited.mapping) == len(set(revisited.mapping.values()))
+ assert set(revisited.mapping) == set(base.mapping)
+
+
+def test_conservative_revisit_corrects_an_unclaimed_wrong_image():
+ left = graph(["s", "t", "x", "z"], [("s", "x"), ("t", "x")])
+ right = graph(["S", "T", "X", "Y"], [("S", "X"), ("T", "X")])
+ seeds = {"s": "S", "t": "T"}
+
+ result = conservative_revisit(
+ left,
+ right,
+ {**seeds, "x": "Y"},
+ seeds,
+ theta=0.5,
+ max_rounds=2,
+ )
+
+ assert result.mapping == {**seeds, "x": "X"}
+ assert result.remapped_per_round == (1,)
+
+
+def test_revisit_is_disabled_by_default_and_rejects_negative_budget():
+ left, right, truth = two_views(n=10)
+ result = propagate(left, right, {0: truth[0], 1: truth[1]})
+
+ assert result.revisit_policy == "disabled"
+ assert result.remapped_per_round == ()
+ with pytest.raises(ValueError, match="non-negative"):
+ propagate(left, right, {0: truth[0]}, conservative_revisit_rounds=-1)
+
+
+def test_revisit_rejects_mapping_vertices_outside_either_view():
+ left, right, truth = two_views(n=10)
+ seeds = {0: truth[0]}
+
+ with pytest.raises(ValueError, match="left view"):
+ conservative_revisit(left, right, {**seeds, 999: truth[1]}, seeds)
+ with pytest.raises(ValueError, match="right view"):
+ conservative_revisit(left, right, {**seeds, 1: "absent"}, seeds)
+
+
+def test_the_sparse_gate_propagate_runs_agrees_with_the_dense_reference():
+ """`propagate` scores with `_sparse_match_scores`/`_sparse_winner`; only `match_scores`
+ and `eccentricity` are checked against the paper. Pin that the two families decide the
+ same way on every call a real propagation makes."""
+ decisions = 0
+ for fixture_seed in (7, 19, 23):
+ left, right, truth = two_views(seed=fixture_seed)
+ mapping = {node: truth[node] for node in range(16)}
+ while True:
+ accepted = 0
+ for node in sorted((v for v in left.vertices if v not in mapping), key=repr):
+ dense = _winner(match_scores(left, right, mapping, node), 1.5)
+ sparse = _sparse_winner(*_sparse_match_scores(left, right, mapping, node), 1.5)
+ assert dense == sparse
+ decisions += 1
+ if sparse is None:
+ continue
+ reverse = {image: v for v, image in mapping.items()}
+ back = _sparse_winner(
+ *_sparse_match_scores(right, left, reverse, sparse), 1.5
+ )
+ if back != node:
+ continue
+ mapping[node] = sparse
+ accepted += 1
+ if not accepted:
+ break
+ assert decisions > 200
+
+
+def test_the_two_readings_of_the_eccentricity_population_are_both_available():
+ """The prose scores against the unmapped right vertices, the pseudocode against all of
+ them with the claimed ones left at zero. On this fixture the wider population declares
+ one vertex more; the default is the prose, which every published run used."""
+ left, right, truth = two_views()
+ seeds = {node: truth[node] for node in range(16)}
+
+ prose = evaluate(propagate(left, right, seeds).mapping, truth, seeds)
+ pseudocode = evaluate(
+ propagate(left, right, seeds, population="all").mapping, truth, seeds
+ )
+
+ assert (prose["declared"], prose["correct"]) == (63, 63)
+ assert (pseudocode["declared"], pseudocode["correct"]) == (64, 64)
+ with pytest.raises(ValueError, match="population"):
+ propagate(left, right, seeds, population="every")
diff --git a/tests/test_oracle.py b/tests/test_oracle.py
index dd78d497..26b22d61 100644
--- a/tests/test_oracle.py
+++ b/tests/test_oracle.py
@@ -1,5 +1,6 @@
"""Robust oracle layer: dss_link_oracle panic-safety, and the in-process default /
opt-in subprocess oracles built on top of it."""
+import pytest
import decluster.oracle as oracle
import examples.anonymity_set as anonymity_set_example
from decluster import ancestry
@@ -22,6 +23,7 @@ def pairwise_link_prob(inputs, outputs, budget_ms):
def test_bounded_link_oracle_is_callable_and_tolerant():
+ pytest.importorskip("dss")
for budget_ms in (500, 6000):
link = oracle.bounded_link_oracle(budget_ms)
assert callable(link)
diff --git a/tests/test_oracle_audit.py b/tests/test_oracle_audit.py
new file mode 100644
index 00000000..21332032
--- /dev/null
+++ b/tests/test_oracle_audit.py
@@ -0,0 +1,359 @@
+"""The audit of the approximations against the exact oracle, and the audit's own falsifiability.
+
+An audit that cannot fail is decoration. Half of what follows feeds `audit` deliberately wrong
+counters — one that overcounts by exactly one, one that undercounts, one that errs in both
+directions, a matrix of ones, a link set naming every pair as certain — and asserts the report flags
+each. The other half is the measurement itself, on a reduced family so the suite stays fast.
+"""
+import json
+import os
+from math import log2
+
+import pytest
+
+from decluster.baselines import exact_link_analysis, exact_subtransaction_mappings
+from decluster.baselines import oracle_audit as oa
+from decluster.experiments import exact_oracle_audit as experiment
+
+ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+DOC = "RESULTS-exact-oracle-audit.md"
+
+REDUCED_MAX_COINS = 6
+
+
+@pytest.fixture(scope="module")
+def reduced():
+ return oa.enumerate_family(max_coins=REDUCED_MAX_COINS)
+
+
+@pytest.fixture(scope="module")
+def report(reduced):
+ pytest.importorskip("dss")
+ return oa.audit(reduced, include_cases=False)
+
+
+def test_family_is_deterministic_exhaustive_and_balanced(reduced):
+ assert reduced == oa.enumerate_family(max_coins=REDUCED_MAX_COINS)
+ assert len(reduced) == len(set(reduced))
+ assert reduced
+ for inputs, outputs in reduced:
+ assert sum(inputs) == sum(outputs)
+ assert len(inputs) >= 2 and len(outputs) >= 2
+ assert len(inputs) + len(outputs) <= REDUCED_MAX_COINS
+ assert list(inputs) == sorted(inputs) and list(outputs) == sorted(outputs)
+
+
+def test_equal_value_closed_form_matches_the_oracle():
+ # The self-derived form in the module docstring, checked against the enumeration rather than
+ # trusted: 3, 16, 131, 1496 for n = 2..5.
+ assert [oa.equal_value_mapping_count(n) for n in range(2, 6)] == [3, 16, 131, 1496]
+ for n in range(2, 6):
+ assert oa.equal_value_mapping_count(n) == len(
+ exact_subtransaction_mappings((1,) * n, (1,) * n))
+
+
+def test_canonical_equal_value_join_has_three_mappings_and_log2_three_bits():
+ analysis = exact_link_analysis((5, 5), (5, 5))
+ assert len(analysis.mappings) == oa.equal_value_mapping_count(2) == 3
+ assert analysis.entropy_bits == pytest.approx(log2(3))
+ assert analysis.deterministic_links == () # equal values pin nothing
+
+
+def test_w_count_is_identified_as_subset_sum_solutions_not_mappings(reduced):
+ pytest.importorskip("dss")
+ identification = oa.identify_objects(reduced)
+ assert identification["exact_answers_checked"] > 0
+ assert identification["rederivation_mismatches"] == 0
+ assert identification["verdict"].startswith("W(E) identified")
+ # The re-derivation is of a different object, so it must not coincide with |M| everywhere.
+ assert identification["cases_where_w_equals_exact_mapping_count"] < len(reduced)
+
+
+def test_subset_sum_solutions_rederivation_is_not_the_mapping_count():
+ # (1,1,1)/(1,1,1): two proper input subsets sizes {1,2} hitting proper output sums {1,2} gives
+ # 3 + 3 = 6 subset-sum solutions, against 16 balanced mappings.
+ assert oa._subset_sum_solutions((1, 1, 1), (1, 1, 1)) == 6
+ assert len(exact_subtransaction_mappings((1, 1, 1), (1, 1, 1))) == 16
+
+
+def test_link_matrix_is_neither_a_lower_nor_an_upper_bound(report):
+ stats = report["comparisons"]["link_matrix"]
+ assert stats["relation"] == oa.RESTRICTION
+ assert stats["entries_above_exact"] > 0 and stats["entries_below_exact"] > 0
+ assert stats["bound_direction"] == "neither"
+ assert stats["exceeds_exact_cases"] > 0
+ assert any("neither a lower nor an upper bound" in flag for flag in report["flags"])
+
+
+def test_the_finding_survives_the_finest_only_reading_of_the_oracle(report):
+ """The oracle includes coarser readings up to the single all-coins block. Dropping them —
+ refinement-maximal selection — is a robustness reading, NOT the strongest one available to an
+ approximation that reports few mappings: max-block-count selection keeps a strict subset, so it
+ is smaller, has more oracle certainties and is more generous still (see
+ `test_the_finest_only_reading_depends_on_which_notion_of_finest`). Under this reading the
+ verdict does not move: still both directions, still the same certain-link overclaim."""
+ stats = report["comparisons"]["link_matrix_finest_only"]
+ assert stats["entries_above_exact"] > 0 and stats["entries_below_exact"] > 0
+ assert stats["bound_direction"] == "neither"
+ links = report["comparisons"]["deterministic_links_finest_only"]
+ assert links["missed_links"] == 0
+ assert links["spurious_links"] == report["comparisons"]["deterministic_links"]["spurious_links"]
+
+
+def test_the_finest_only_reading_depends_on_which_notion_of_finest():
+ """The cross-check's "no certain link missed" holds under refinement-maximal selection and NOT
+ under max-block-count selection, which keeps a strict subset. The audit measures both rather
+ than describing the choice once, because a claim that turns on a definition must carry it.
+
+ The 132-case reduced family is too small to separate them — the two selections already differ
+ there, but not yet on a case that costs a certain link — so this one runs at max_coins=7 (270
+ cases, ~0.2 s), the smallest family where the divergence reaches the tally.
+ """
+ pytest.importorskip("dss")
+ sensitivity = oa.finest_selection_sensitivity(oa.enumerate_family(max_coins=7))
+ assert sensitivity["cases_where_the_two_selections_differ"] == 32 # oracle-only, no dss
+ assert sensitivity["refinement_maximal"]["missed_links"] == 0
+ assert sensitivity["max_block_count"]["missed_links"] > 0
+ # Strictly more generous to the approximation: fewer spurious, because more oracle certainties.
+ assert (sensitivity["max_block_count"]["spurious_links"]
+ < sensitivity["refinement_maximal"]["spurious_links"])
+
+
+def test_max_block_count_selection_is_a_strict_subset_of_the_refinement_maximal_one(reduced):
+ """A strict refinement always has strictly more blocks, so argmax-by-block-count is always
+ refinement-maximal — the containment that makes it the smaller, more generous oracle family."""
+ strictly_smaller = 0
+ for inputs, outputs in reduced:
+ mappings = exact_subtransaction_mappings(inputs, outputs)
+ maximal = {m.blocks for m in oa.finest_mappings(mappings)}
+ by_blocks = {m.blocks for m in oa.max_block_count_mappings(mappings)}
+ assert by_blocks <= maximal
+ strictly_smaller += by_blocks < maximal
+ assert strictly_smaller > 0
+
+
+def test_finest_mappings_keeps_only_the_maximal_refinements():
+ mappings = exact_subtransaction_mappings((2, 2), (1, 1, 2))
+ finest = oa.finest_mappings(mappings)
+ assert len(mappings) == 3 and len(finest) == 2
+ assert all(len(m.blocks) == 2 for m in finest) # the single-block reading is dropped
+ # An unambiguous transaction keeps its one mapping rather than losing it.
+ assert len(oa.finest_mappings(exact_subtransaction_mappings((1, 3), (2, 2)))) == 1
+
+
+def test_dss_marginal_is_verified_to_be_over_dss_own_family(reduced):
+ """The one-line check that demotes the matrix from "same object" to "restriction": if it were
+ the oracle's marginal it would not be quantized to dss's own mapping count."""
+ pytest.importorskip("dss")
+ evidence = oa.verify_dss_marginal_family(reduced)
+ checked = evidence["cases_checked"]
+ assert checked == len(reduced)
+ assert evidence["entries_are_multiples_of_one_over_n_non_derived"] == checked
+ assert evidence["unit_entries_equal_the_reported_deterministic_links"] == checked
+ assert evidence["counterexamples"] == []
+ assert 0 < evidence[
+ "cases_where_the_dss_family_is_strictly_smaller_than_the_oracle_family"] <= checked
+ assert evidence["verdict"].startswith("pairwise_link_prob is the uniform marginal")
+
+
+def test_mapping_analysis_pairwise_matrix_and_exact_oracle_are_cross_checked(reduced):
+ """Exercise the public DSS diagnostics and the independent oracle in one differential test."""
+ pytest.importorskip("dss")
+ import dss
+
+ saw_above = saw_below = False
+ for inputs, outputs in reduced:
+ diagnostics = dss.mapping_analysis(list(inputs), list(outputs), None)
+ assert diagnostics["status"] == "complete"
+ matrix = dss.pairwise_link_prob(list(inputs), list(outputs), None)
+ claimed = {(i, o) for i, row in enumerate(matrix)
+ for o, probability in enumerate(row) if probability == 1.0}
+ assert claimed == {tuple(link) for link in diagnostics["deterministic_links"]}
+
+ exact = exact_link_analysis(inputs, outputs).matrix
+ for approximate_row, exact_row in zip(matrix, exact):
+ for approximate, reference in zip(approximate_row, exact_row):
+ saw_above |= approximate > reference + oa.TOLERANCE
+ saw_below |= approximate < reference - oa.TOLERANCE
+
+ assert saw_above and saw_below
+
+
+def test_mapping_count_matches_refinement_maximal_family_without_collapsing_indices(reduced):
+ pytest.importorskip("dss")
+ import dss
+
+ mechanism = oa.mapping_count_mechanism(reduced)
+ assert mechanism["equal_to_the_finest_oracle_mapping_count"] == len(reduced)
+ assert mechanism["different_from_the_finest_oracle_mapping_count"] == 0
+ assert mechanism["all_equal_value_cases"] > 0
+ assert mechanism["all_equal_value_cases_answering_one"] == 0
+ # Two refinement-maximal mappings survive.
+ assert dss.mapping_analysis([2, 2], [1, 1, 2], None)["n_non_derived"] == 2
+ assert len(oa.finest_mappings(exact_subtransaction_mappings((2, 2), (1, 1, 2)))) == 2
+ # Equal-value permutations do not collapse in general.
+ assert dss.mapping_analysis([1, 3, 4, 4], [3, 3, 3, 3], None)["n_non_derived"] == 4
+
+
+def test_the_router_and_the_saddle_point_are_probed(report):
+ identification = report["identifications"]["count_w_router_and_saddle_point"]
+ assert identification["relation"] == oa.DIFFERENT_OBJECT
+ assert identification["sparse_answers"] > 0
+ assert (identification["sparse_answers_matching_the_subset_sum_rederivation"]
+ == identification["sparse_answers"])
+ assert identification["methods"].get("radix") is None # the radix tier never wins here
+ assert identification["saddle_point_answers"] == 0
+ assert identification["cases_is_dense_reads_as_dense"] > 0
+
+
+def test_deterministic_links_are_overclaimed_and_never_missed(report):
+ stats = report["comparisons"]["deterministic_links"]
+ assert stats["spurious_links"] > 0
+ assert stats["missed_links"] == 0
+ assert stats["bound_direction"] == "upper"
+
+
+def test_mapping_count_undercounts_across_this_family_and_never_exceeds(report):
+ stats = report["comparisons"]["mapping_count"]
+ assert stats["relation"] == oa.RESTRICTION
+ assert stats["undercounts"] > 0
+ assert stats["overcounts"] == 0 and stats["exceeds_exact_cases"] == 0
+ assert stats["bound_direction"] == "lower"
+
+
+def test_radix_is_identified_as_input_blind_and_scale_dependent(report):
+ identification = report["identifications"]["radix_mappings"]
+ assert identification["relation"] == oa.DIFFERENT_OBJECT
+ assert identification["output_multisets_where_radix_applies"] > 0
+ assert identification["output_multisets_whose_radix_count_moved_under_scaling"] > 0
+ for witness in identification["scale_dependence_witnesses"]:
+ assert witness["exact_mapping_counts"] == witness["scaled_exact_mapping_counts"]
+ assert witness["radix_count"] != witness["scaled_radix_count"]
+
+
+def test_per_coin_density_is_coin_invariant_where_the_exact_marginals_vary(report):
+ identification = report["identifications"]["per_coin_density"]
+ assert identification["coin_invariant_cases"] > 0
+ assert identification["cases_where_exact_input_marginals_vary"] > 0
+
+
+def test_report_is_strictly_json_serializable_with_cases(reduced):
+ pytest.importorskip("dss")
+ full = oa.audit(reduced[:20])
+ assert len(full["cases"]) == 20
+ # allow_nan=False: an undefined relative error must be counted, never emitted as Infinity.
+ assert json.loads(json.dumps(full, allow_nan=False))["family"]["fee"] == 0
+ assert oa._relative_error(0, 1) is None and oa._relative_error(0, 0) == 0.0
+
+
+def _flags(report, name):
+ return [flag for flag in report["flags"] if flag.startswith(name + ":")]
+
+
+def test_audit_detects_a_counter_that_overcounts_by_one(reduced):
+ """The detection test the deliverable turns on: an approximation off by +1 must be flagged as
+ exceeding the oracle, on every case, with the offending cases named."""
+ pytest.importorskip("dss")
+ report = oa.audit(
+ reduced, include_cases=False,
+ approximations={"mapping_count": lambda i, o: oa.exact_mapping_count(i, o) + 1})
+ stats = report["comparisons"]["mapping_count"]
+ assert stats["overcounts"] == len(reduced)
+ assert stats["undercounts"] == 0 and stats["agreements"] == 0
+ assert stats["exceeds_exact_cases"] == len(reduced)
+ assert stats["max_absolute_error"] == 1
+ assert stats["bound_direction"] == "upper"
+ assert stats["exceeds_exact"] and stats["exceeds_exact"][0]["approx"] == (
+ stats["exceeds_exact"][0]["exact"] + 1)
+ assert _flags(report, "mapping_count")
+
+
+def test_audit_detects_a_counter_that_undercounts(reduced):
+ pytest.importorskip("dss")
+ report = oa.audit(reduced, include_cases=False,
+ approximations={"mapping_count": lambda i, o: 0})
+ stats = report["comparisons"]["mapping_count"]
+ assert stats["undercounts"] == len(reduced)
+ assert stats["overcounts"] == 0 and stats["exceeds_exact_cases"] == 0
+ assert stats["bound_direction"] == "lower"
+ assert stats["max_relative_error"] == pytest.approx(1.0)
+
+
+def test_audit_detects_a_counter_that_errs_in_both_directions(reduced):
+ pytest.importorskip("dss")
+ def two_faced(inputs, outputs):
+ exact = oa.exact_mapping_count(inputs, outputs)
+ return exact + 1 if sum(inputs) % 2 else max(exact - 1, 0)
+
+ report = oa.audit(reduced, include_cases=False,
+ approximations={"mapping_count": two_faced})
+ stats = report["comparisons"]["mapping_count"]
+ assert stats["overcounts"] > 0 and stats["undercounts"] > 0
+ assert stats["bound_direction"] == "neither"
+ assert any("neither a lower nor an upper bound" in flag
+ for flag in _flags(report, "mapping_count"))
+
+
+def test_audit_detects_a_matrix_of_ones_and_a_shape_mismatch(reduced):
+ pytest.importorskip("dss")
+ report = oa.audit(
+ reduced, include_cases=False,
+ approximations={"link_matrix": lambda i, o: [[1.0] * len(o) for _ in i]})
+ stats = report["comparisons"]["link_matrix"]
+ assert stats["entries_above_exact"] > 0
+ assert stats["entries_below_exact"] == 0
+ assert stats["exceeds_exact_cases"] > 0
+ assert _flags(report, "link_matrix")
+
+ misshapen = oa.audit(reduced, include_cases=False,
+ approximations={"link_matrix": lambda i, o: [[1.0]]})
+ assert misshapen["comparisons"]["link_matrix"]["shape_mismatches"] > 0
+ assert any("shape mismatch" in flag for flag in _flags(misshapen, "link_matrix"))
+
+
+def test_audit_detects_link_sets_that_claim_certainty_everywhere(reduced):
+ pytest.importorskip("dss")
+ report = oa.audit(
+ reduced, include_cases=False,
+ approximations={"deterministic_links":
+ lambda i, o: {(a, b) for a in range(len(i)) for b in range(len(o))}})
+ stats = report["comparisons"]["deterministic_links"]
+ assert stats["spurious_links"] > 0
+ assert stats["exceeds_exact_cases"] > 0
+ assert any("reported as certain" in flag for flag in _flags(report, "deterministic_links"))
+
+
+def test_audit_records_a_refusal_rather_than_scoring_it(reduced):
+ pytest.importorskip("dss")
+ report = oa.audit(reduced, include_cases=False,
+ approximations={"mapping_count": lambda i, o: None})
+ stats = report["comparisons"]["mapping_count"]
+ assert stats["refusals"] == len(reduced)
+ assert stats["compared"] == 0
+ assert stats["bound_direction"] == "unmeasured"
+
+
+def test_an_agreeing_approximation_raises_no_flag(reduced):
+ pytest.importorskip("dss")
+ report = oa.audit(reduced, include_cases=False,
+ approximations={"mapping_count": oa.exact_mapping_count})
+ stats = report["comparisons"]["mapping_count"]
+ assert stats["agreements"] == len(reduced)
+ assert stats["bound_direction"] == "exact"
+ assert not _flags(report, "mapping_count")
+
+
+def test_published_numbers_are_reproducible_and_the_manifest_is_current():
+ """Recompute the canonical artifact and its generated Markdown, without parsing prose."""
+ pytest.importorskip("dss")
+ artifact_path = os.path.join(ROOT, "results", "artifacts", "exact-oracle-audit-v1.json")
+ markdown_path = os.path.join(ROOT, "results", "generated", "exact-oracle-audit-v1.md")
+ artifact = experiment.verify_artifact(experiment.load_artifact(artifact_path))
+ with open(markdown_path) as generated:
+ assert generated.read() == experiment.render_markdown(artifact)
+
+ text = open(os.path.join(ROOT, "results", DOC)).read()
+ assert "ground truth" not in text.lower()
+ assert "catalog/runs/exact-oracle-audit-v1.json" in text
+ policy = open(os.path.join(ROOT, "results", "REPRODUCIBILITY.md")).read()
+ assert DOC[:-3] in policy, f"{DOC} is not indexed in REPRODUCIBILITY.md"
diff --git a/tests/test_paper_numbers.py b/tests/test_paper_numbers.py
new file mode 100644
index 00000000..c1dfeb08
--- /dev/null
+++ b/tests/test_paper_numbers.py
@@ -0,0 +1,149 @@
+"""Every measurement `PAPER.md` states either resolves to a canonical artifact or is declared.
+
+Nothing linked the paper's numbers to the evidence, so a figure could survive the run that produced
+it. `test_cited_paths.py` is the cheap half — it checks that every path a reader is sent to exists.
+This is the other half: it takes each measurement-shaped token in the paper and looks for it in the
+artifacts.
+
+Matching is by value with a tolerance set by the printed precision, not by provenance: a number that
+resolves has *some* artifact carrying it, which is weaker than knowing it came from that one. It is
+enough to catch the failure this exists for — a published figure whose run no longer produces it —
+and it costs no annotation in the prose.
+
+A number the artifacts cannot carry is declared below with the reason, the way `UNAVAILABLE` and
+`NOT_YET_MIGRATED` declare theirs. The list is checked in both directions, so an entry that becomes
+backed has to leave it.
+"""
+import json
+import re
+from functools import cache
+from pathlib import Path
+
+import pytest
+
+ROOT = Path(__file__).resolve().parents[1]
+
+# A measurement, not a section number: a thousands-separated count or a decimal with two or more
+# places. The paper writes a negative bits value with U+2212.
+MEASUREMENT = re.compile(r"(?**Fellegi & Sunter**")[0]
+ body = re.sub(r"\[(?:doi|arXiv):[^\]]*\]", " ", body)
+ return re.sub(r"https?://\S+", " ", body)
+
+
+def _values(node, found=None):
+ found = set() if found is None else found
+ if isinstance(node, dict):
+ for item in node.values():
+ _values(item, found)
+ elif isinstance(node, list):
+ for item in node:
+ _values(item, found)
+ elif isinstance(node, (int, float)) and not isinstance(node, bool):
+ found.add(float(node))
+ return found
+
+
+def artifact_values():
+ pool = set()
+ for path in sorted((ROOT / "results" / "artifacts").glob("*.json")):
+ pool |= _values(json.loads(path.read_text()))
+ return pool
+
+
+def cited():
+ return sorted(set(MEASUREMENT.findall(_paper_body())))
+
+
+@cache
+def percent_tokens():
+ """The tokens the paper prints as a percentage.
+
+ An artifact stores a rate as a fraction, so the paper's `0.081%` is `0.00081` there and the
+ literal token never matches. Scaling only the tokens that carry a `%` keeps the tolerance tied
+ to the printed precision; scaling every token would let any of them match a value two orders of
+ magnitude away, which is the failure this gate exists to catch.
+ """
+ return {match.group(1) for match in re.finditer(MEASUREMENT.pattern + r"\s*%", _paper_body())}
+
+
+def resolves(token, pool):
+ target = float(token.replace(",", "").replace("−", "-"))
+ places = len(token.split(".")[1]) if "." in token else 0
+ tolerance = 0.5 * 10 ** (-places) if places else 0.5
+ scales = [(target, tolerance)]
+ if token in percent_tokens():
+ scales.append((target / 100, tolerance / 100))
+ return any(abs(value - scaled) < allowed or abs(value + scaled) < allowed
+ for scaled, allowed in scales for value in pool)
+
+
+@pytest.fixture(scope="module")
+def pool():
+ return artifact_values()
+
+
+def test_the_paper_is_actually_being_read():
+ assert len(cited()) > 100
+
+
+def test_every_measurement_resolves_or_is_declared(pool):
+ declared = set(DERIVED) | set(UNBACKED)
+ stray = [token for token in cited() if token not in declared and not resolves(token, pool)]
+ assert not stray, (
+ "these are stated as measurements and no artifact carries them:\n "
+ + "\n ".join(stray)
+ + "\nProduce the run, declare the derivation, or say why the data is not here."
+ )
+
+
+def test_no_declaration_covers_a_number_that_now_resolves(pool):
+ stale = [token for token in UNBACKED if resolves(token, pool)]
+ assert not stale, f"UNBACKED still excuses numbers an artifact now carries: {stale}"
+
+
+def test_every_declaration_is_cited_by_the_paper():
+ tokens = set(cited())
+ unused = sorted((set(DERIVED) | set(UNBACKED)) - tokens)
+ assert not unused, f"declared and not cited: {unused}"
+
+
+def test_every_declaration_states_a_reason():
+ for token, reason in {**DERIVED, **UNBACKED}.items():
+ assert len(reason.split()) >= 8, f"{token}: the reason is too thin to audit"
+
+
+def test_the_backed_share_is_reported_and_does_not_fall(pool):
+ """A floor, not a target: the point is that it can only be raised deliberately."""
+ tokens = cited()
+ backed = sum(1 for token in tokens if resolves(token, pool))
+ assert backed >= 126, f"{backed}/{len(tokens)} resolve; this used to be 126"
diff --git a/tests/test_partition.py b/tests/test_partition.py
new file mode 100644
index 00000000..076e2ab1
--- /dev/null
+++ b/tests/test_partition.py
@@ -0,0 +1,119 @@
+import random
+
+import pytest
+
+from decluster.partition import GroundSetMismatch, Partition
+from decluster.unionfind import UF
+
+
+GROUND = tuple(range(6))
+
+
+def random_partition(rng, elements=GROUND):
+ labels = [rng.randrange(1, len(elements) + 1) for _ in elements]
+ return Partition.from_lookup(dict(zip(elements, labels)))
+
+
+def sample(seed, count=40):
+ rng = random.Random(seed)
+ return [random_partition(rng) for _ in range(count)]
+
+
+def test_blocks_partition_the_ground_set():
+ p = Partition([[1, 2], [3]])
+ assert p.elements() == {1, 2, 3}
+ assert p.block(1) == {1, 2}
+ assert p.same_block(1, 2) and not p.same_block(1, 3)
+
+
+def test_an_element_in_two_blocks_is_refused():
+ with pytest.raises(ValueError):
+ Partition([[1, 2], [2, 3]])
+
+
+def test_empty_blocks_are_dropped_rather_than_kept():
+ assert Partition([[1], [], [2]]) == Partition([[1], [2]])
+
+
+def test_lookup_and_union_find_agree():
+ uf = UF([1, 2, 3])
+ uf.union(1, 2)
+ assert Partition.from_union_find(uf) == Partition.from_lookup({1: "a", 2: "a", 3: "b"})
+
+
+def test_discrete_is_the_bottom_and_trivial_the_top():
+ bottom, top = Partition.discrete(GROUND), Partition.trivial(GROUND)
+ for p in sample(0):
+ assert bottom.refines(p) and p.refines(top)
+ assert len(bottom) == len(GROUND) and len(top) == 1
+
+
+def test_join_divides_only_where_both_divide():
+ # Yuval's whiteboard case: A cuts 1|2|34, B cuts 12|34. Only the 2|3 cut is in both.
+ a = Partition([[1], [2], [3, 4]])
+ b = Partition([[1, 2], [3, 4]])
+ assert a.join(b) == Partition([[1, 2], [3, 4]])
+ assert a.meet(b) == Partition([[1], [2], [3, 4]])
+
+
+def test_meet_is_finer_and_join_is_coarser():
+ for p, q in zip(sample(1), sample(2)):
+ assert (p & q).refines(p) and (p & q).refines(q)
+ assert p.refines(p | q) and q.refines(p | q)
+
+
+def test_meet_is_the_greatest_lower_bound():
+ for p, q, r in zip(sample(3), sample(4), sample(5)):
+ if r.refines(p) and r.refines(q):
+ assert r.refines(p & q)
+
+
+def test_join_is_the_least_upper_bound():
+ for p, q, r in zip(sample(6), sample(7), sample(8)):
+ if p.refines(r) and q.refines(r):
+ assert (p | q).refines(r)
+
+
+def test_the_operations_are_commutative_idempotent_and_absorbing():
+ for p, q in zip(sample(9), sample(10)):
+ assert p & q == q & p and p | q == q | p
+ assert p & p == p and p | p == p
+ assert p & (p | q) == p and p | (p & q) == p
+
+
+def test_the_operations_are_associative():
+ for p, q, r in zip(sample(11), sample(12), sample(13)):
+ assert (p & q) & r == p & (q & r)
+ assert (p | q) | r == p | (q | r)
+
+
+def test_refinement_is_a_partial_order():
+ partitions = sample(14)
+ for p in partitions:
+ assert p.refines(p) and not p < p
+ for p, q in zip(partitions, sample(15)):
+ if p.refines(q) and q.refines(p):
+ assert p == q
+
+
+def test_a_different_ground_set_is_refused_rather_than_resolved():
+ p, q = Partition([[1, 2]]), Partition([[1, 2, 3]])
+ for operation in (Partition.meet, Partition.join, Partition.refines):
+ with pytest.raises(GroundSetMismatch):
+ operation(p, q)
+
+
+def test_clustering_only_ever_moves_up():
+ # A merge pass can reach any coarsening of where it started and nothing below it.
+ start = Partition([[1], [2], [3], [4]])
+ uf = UF(start.elements())
+ uf.union(1, 2)
+ merged = Partition.from_union_find(uf)
+ assert start < merged and not merged.refines(start)
+
+
+def test_declustering_only_ever_moves_down():
+ inherited = Partition([[1, 2, 3], [4]])
+ cut = inherited & Partition([[1, 2], [3, 4]])
+ assert cut < inherited
+ assert cut == Partition([[1, 2], [3], [4]])
diff --git a/tests/test_partition_schemes.py b/tests/test_partition_schemes.py
new file mode 100644
index 00000000..be2c55a1
--- /dev/null
+++ b/tests/test_partition_schemes.py
@@ -0,0 +1,69 @@
+"""The cut comparison the framework asked for, reproducing the published table.
+
+It used to rest on a 977 MB export nobody else held. The run consumes one prefix of it, that prefix
+recompresses to 50 MB, and it is now committed — so every figure in `RESULTS-partition-cuts.md` is
+checkable rather than asserted.
+"""
+import json
+
+import pytest
+
+from decluster.experiments import partition_schemes as experiment
+
+
+@pytest.fixture(scope="module")
+def artifact():
+ return experiment.build_artifact()
+
+
+def test_reproduce_then_verify(tmp_path):
+ art, markdown = tmp_path / "a.json", tmp_path / "a.md"
+ assert experiment.main(["reproduce", "--artifact", str(art), "--markdown", str(markdown)]) == 0
+ assert experiment.main(["verify", "--artifact", str(art), "--markdown", str(markdown)]) == 0
+
+
+def test_the_collapse_cut_tracks_the_temporal_one(artifact):
+ rows = {row["scheme"]: row for row in artifact["measurement"]["schemes"]}
+ assert abs(rows["collapse"]["straddling_entities"] - rows["epoch"]["straddling_entities"]) <= 40
+ assert rows["collapse"]["mean_straddler_degree"] >= rows["epoch"]["mean_straddler_degree"]
+ assert rows["collapse"]["boundary_transactions"] < 0.05 * artifact["measurement"]["transactions"]
+
+
+def test_decore_is_the_scheme_that_destroys_the_signal(artifact):
+ rows = {row["scheme"]: row for row in artifact["measurement"]["schemes"]}
+ assert rows["decore"]["mean_straddler_degree"] < rows["epoch"]["mean_straddler_degree"] / 2
+ assert rows["decore"]["straddling_entities"] < rows["epoch"]["straddling_entities"] / 1.5
+ assert rows["decore"]["boundary_transactions"] > 0.3 * artifact["measurement"]["transactions"]
+
+
+def test_a_seed_share_that_yields_no_guess_reports_no_precision(artifact):
+ """Zero guesses is not zero precision; printing the second reads as a measured tie."""
+ for row in artifact["measurement"]["schemes"]:
+ for seed in row["seeded"]:
+ if seed["guesses"] == 0:
+ assert seed["precision"] is None
+ else:
+ assert seed["precision"] is not None
+
+
+def test_the_published_table_is_reproduced(artifact):
+ """The figures RESULTS-partition-cuts.md reports, which had no reproducible source."""
+ rows = {row["scheme"]: row for row in artifact["measurement"]["schemes"]}
+ assert artifact["measurement"]["transactions"] == 300000
+ assert rows["epoch"]["straddling_entities"] == 2562
+ assert rows["decore"]["straddling_entities"] == 1294
+ assert rows["collapse"]["straddling_entities"] == 2536
+ assert rows["collapse"]["edges_among_straddlers"] == 1941
+
+
+def test_every_scheme_reports_the_view_counts_it_was_asked_for(artifact):
+ for scheme, counts in artifact["measurement"]["view_counts"].items():
+ for asked, sizes in counts.items():
+ assert len(sizes) == int(asked), f"{scheme} at n_views={asked}"
+
+
+def test_a_tampered_artifact_is_refused(artifact):
+ stored = json.loads(json.dumps(artifact))
+ stored["measurement"]["schemes"][0]["straddling_entities"] += 1
+ with pytest.raises(experiment.VerificationError):
+ experiment.verify_artifact(stored)
diff --git a/tests/test_path_count.py b/tests/test_path_count.py
index 18e2f18d..7def38f9 100644
--- a/tests/test_path_count.py
+++ b/tests/test_path_count.py
@@ -2,7 +2,7 @@
import pytest
-from decluster import ancestry, path_count
+from decluster import path_count
def make_fetch(txs):
@@ -75,81 +75,29 @@ def test_known_two_hop_dag_matches_hand_computation():
assert res["origins_weighted"].keys() == {("Q", 0), ("R", 0)}
assert res["origins_weighted"][("Q", 0)] == pytest.approx(0.62)
assert res["origins_weighted"][("R", 0)] == pytest.approx(0.38)
- assert res["log_W_paths"] == pytest.approx(math.log(15.0))
assert res["min_entropy"] == pytest.approx(-math.log2(0.62))
assert res["shannon"] == pytest.approx(
-(0.62 * math.log2(0.62) + 0.38 * math.log2(0.38)))
assert res["truncated"] == 0
-def test_multiplicity_upweights_higher_w_e_origin():
- # T:0 splits 0.5/0.5 (equal under plain link-prob absorption) into P:0 and
- # Q:0, each a single-input pass-through to a coinbase origin. Under §04
- # (link-probability only) the two origins are exactly tied at 0.5/0.5.
- # W(E): tx P = 10 (highly ambiguous), tx Q = 2 (much less ambiguous) ->
- # path-count must break the tie in O_P's favor.
- txs = {
- "T": {"vin": [vin("P", 0, 1), vin("Q", 0, 1)], "vout": [{"value": 2}]},
- "P": {"vin": [vin("OP", 0, 1)], "vout": [{"value": 1}]},
- "Q": {"vin": [vin("OQ", 0, 1)], "vout": [{"value": 1}]},
- "OP": {"vin": cb_vin(), "vout": [{"value": 1}]},
- "OQ": {"vin": cb_vin(), "vout": [{"value": 1}]},
- }
-
- def link_oracle(in_vals, out_vals):
- if len(in_vals) == 2:
- return [[0.5], [0.5]]
- return [[1.0]]
-
- # values alone can't tell tx P and tx Q apart (both single-input
- # pass-throughs of value 1), so dispatch W(E) by the txid the walk is
- # currently fetching: a stateful fetch records it for count_oracle to read.
- log_w_by_txid = {"T": math.log(5), "P": math.log(10), "Q": math.log(2)}
- last_txid = {}
-
- def fetch(txid):
- last_txid["v"] = txid
- return txs[txid]
-
- def count_oracle(in_vals, out_vals):
- return count_report(log_w_by_txid.get(last_txid["v"]))
+def test_count_oracle_is_accepted_but_has_no_effect_on_the_result():
+ # `count_oracle` is kept only for backward compatibility with callers that still pass one
+ # (see the module docstring): whether it returns a real count or refuses, the weighting is
+ # link probability alone, so the result on this fixture is identical either way.
+ txs, link_oracle, real_count_oracle = _two_hop_dag_fixture()
- g = ancestry.build_extended_graph(
- ("T", 0), depth=6, fetch=fetch, link_oracle=link_oracle)
- dist04 = ancestry.absorber_distribution(g, ("T", 0))
- assert dist04[("OP", 0)] == pytest.approx(0.5)
- assert dist04[("OQ", 0)] == pytest.approx(0.5)
-
- res = path_count.path_count_anonymity(
- ("T", 0), depth=6, fetch=fetch,
- link_oracle=link_oracle, count_oracle=count_oracle)
-
- assert res["origins_weighted"][("OP", 0)] > dist04[("OP", 0)]
- assert res["origins_weighted"][("OQ", 0)] < dist04[("OQ", 0)]
- assert res["origins_weighted"][("OP", 0)] == pytest.approx(25 / 30)
- assert res["origins_weighted"][("OQ", 0)] == pytest.approx(5 / 30)
-
-
-def test_w_e_none_falls_back_to_link_prob_only():
- # tx P's count_oracle refuses (log_w=None) -> that hop's multiplicity
- # factor is 1 (link-prob weight alone), never a fabricated count.
- txs, link_oracle, _ = _two_hop_dag_fixture()
-
- def count_oracle(in_vals, out_vals):
- if in_vals == [3, 4]:
- return count_report(math.log(5), count=5)
- return count_report(None) # tx P: off-regime, no count
+ def refusing_count_oracle(in_vals, out_vals):
+ return count_report(None)
- res = path_count.path_count_anonymity(
+ with_counts = path_count.path_count_anonymity(
("C", 0), depth=6, fetch=make_fetch(txs),
- link_oracle=link_oracle, count_oracle=count_oracle)
+ link_oracle=link_oracle, count_oracle=real_count_oracle)
+ without_counts = path_count.path_count_anonymity(
+ ("C", 0), depth=6, fetch=make_fetch(txs),
+ link_oracle=link_oracle, count_oracle=refusing_count_oracle)
- # mult(P:0)=3.0, mult(P:1)=2.0 as before (tx C's W(E) still applies);
- # tx P falls back to factor 1: mult(Q:0)=3.0*0.7 + 2.0*0.5=3.1,
- # mult(R:0)=3.0*0.3 + 2.0*0.5=1.9, W_paths=5.0
- assert res["origins_weighted"][("Q", 0)] == pytest.approx(3.1 / 5.0)
- assert res["origins_weighted"][("R", 0)] == pytest.approx(1.9 / 5.0)
- assert res["truncated"] == 0
+ assert with_counts["origins_weighted"] == without_counts["origins_weighted"]
def _binary_tree_fetch(max_len):
@@ -185,3 +133,105 @@ def test_max_nodes_bounds_walk_without_error():
assert len(res_capped["origins_weighted"]) < len(res_full["origins_weighted"])
total = sum(res_capped["origins_weighted"].values())
assert total == pytest.approx(1.0)
+
+
+def test_the_saddle_point_estimate_is_refused_rather_than_multiplied_in():
+ """The path count is a lower bound only if every W(E) in it is one. The Sasamoto tier is an
+ approximation in both directions, so an over-estimate would credit a coin with counterfactual
+ paths the transaction may not admit — privacy the holder does not have."""
+ from decluster.counting import guaranteed_log_w
+ assert guaranteed_log_w({"kind": "exact", "log_w": 2.0}) == 2.0
+ assert guaranteed_log_w({"kind": "LowerBound", "log_w": 2.0}) == 2.0
+ assert guaranteed_log_w({"kind": "lower_bound", "log_w": 2.0}) == 2.0
+ assert guaranteed_log_w({"kind": "LogApprox", "log_w": 2.0}) is None
+ assert guaranteed_log_w({"kind": "unknown", "log_w": None}) is None
+ assert guaranteed_log_w({"log_w": 2.0}) is None # no tier declared: refuse
+
+
+# P and Q differ in VALUE, so the count oracle can tell them apart. Without that the factor is
+# symmetric and cancels in the normalisation, and the test passes against the unfixed module.
+_AB_TXS = {
+ "T": {"vin": [vin("P", 0, 1), vin("Q", 0, 3)], "vout": [{"value": 4}]},
+ "P": {"vin": [vin("OP", 0, 1)], "vout": [{"value": 1}]},
+ "Q": {"vin": [vin("OQ", 0, 3)], "vout": [{"value": 3}]},
+ "OP": {"vin": cb_vin(), "vout": [{"value": 1}]},
+ "OQ": {"vin": cb_vin(), "vout": [{"value": 3}]},
+}
+
+
+def _ab_link(in_vals, out_vals):
+ return [[0.5], [0.5]] if len(in_vals) == 2 else [[1.0]]
+
+
+def test_multiplicity_does_not_move_the_bound():
+ """The amount channel is refuse-only: it may cut a coin, never weight one.
+
+ Same DAG twice; the only difference is W(E) on one branch. Under the contract the reported
+ anonymity must be identical.
+ """
+ flat = path_count.path_count_anonymity(
+ ("T", 0), depth=6, fetch=make_fetch(_AB_TXS), link_oracle=_ab_link,
+ count_oracle=lambda i, o: count_report(0.0))
+ skew = path_count.path_count_anonymity(
+ ("T", 0), depth=6, fetch=make_fetch(_AB_TXS), link_oracle=_ab_link,
+ count_oracle=lambda i, o: count_report(math.log(10.0) if i == [1] else 0.0))
+
+ assert flat["min_entropy"] == pytest.approx(skew["min_entropy"])
+ assert flat["shannon"] == pytest.approx(skew["shannon"])
+ assert flat["origins_weighted"] == pytest.approx(skew["origins_weighted"])
+
+
+def test_the_dead_mass_key_is_gone():
+ res = path_count.path_count_anonymity(
+ ("T", 0), depth=6, fetch=make_fetch(_AB_TXS), link_oracle=_ab_link,
+ count_oracle=lambda i, o: count_report(0.0))
+ assert "log_W_paths" not in res
+ assert set(res) == {"origins_weighted", "min_entropy", "shannon", "truncated"}
+
+
+def test_the_results_doc_states_the_contract_and_its_cost():
+ import os
+ root = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+ doc = open(os.path.join(root, "results", "RESULTS-path-count.md")).read()
+ assert "refuse-only" in doc
+ assert "no structural property" in doc
+ assert "edge-disjoint" in doc
+ assert "minimum *vertex* cut" not in doc
+
+
+def test_no_document_says_the_walk_weights_by_multiplicity():
+ """The claim, not three phrasings of it.
+
+ An earlier guard pinned three literal strings in two files and declared rewording a review
+ question rather than a test question. Four sites then restated the retracted claim in other
+ words and stayed green, one of them the paper's own abstract. This sweeps every tracked
+ document and module for a weighting applied to the subset-sum count under any of its names.
+
+ Adjacency, not sentence scope: the abstract's restatement shared its sentence with "never a
+ privacy score", so any sentence-level negation test cleared it. And the object is the subset-sum
+ COUNT -- the subset-sum link matrix is the walk's correct and current weighting, so naming
+ "subset-sum" alone would flag every honest site.
+ """
+ import os, re, glob
+ root = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+ obj = r"(?:multiplicity|W\(E\)|subset-sum (?:path )?count)"
+ claim = re.compile(r"weight(?:s|ed|ing)?\b[^.\n]{0,50}?\b" + obj, re.I)
+ withdrawing = re.compile(r"\b(?:not|never|no longer|without|withdrawn|retired|removed|out of"
+ r"|earlier revisions?|once|gone|has left|nothing)\b", re.I)
+ # superpowers/ records what was planned; counting.py's results doc is about the object that
+ # legitimately counts multiplicity; this file must state the claim in order to ban it.
+ exempt = re.compile(r"docs/superpowers/|\.venv/|results/RESULTS-counting-methods\.md"
+ r"|tests/test_path_count\.py")
+
+ offenders = []
+ for pattern in ("**/*.md", "**/*.py"):
+ for path in glob.glob(os.path.join(root, pattern), recursive=True):
+ rel = os.path.relpath(path, root)
+ if exempt.search(rel):
+ continue
+ text = open(path, encoding="utf-8", errors="replace").read()
+ for m in claim.finditer(text):
+ if withdrawing.search(text[max(0, m.start() - 90):m.start()]):
+ continue
+ offenders.append(f"{rel}: " + " ".join(text[m.start():m.end() + 20].split()))
+ assert not offenders, "the retracted claim is restated in:\n" + "\n".join(offenders)
diff --git a/tests/test_path_count_contract_experiment.py b/tests/test_path_count_contract_experiment.py
new file mode 100644
index 00000000..3e2b03b4
--- /dev/null
+++ b/tests/test_path_count_contract_experiment.py
@@ -0,0 +1,27 @@
+from decluster.experiments import path_count_contract as experiment
+
+
+def test_contract_distinguishes_route_accumulation_from_robust_connectivity():
+ artifact = experiment.build_artifact()
+ assert artifact["ancestry_distribution"] == artifact["path_count_distribution"]
+ assert artifact["count_oracle_invariance"]["distributions_equal"] is True
+ # Pinned rather than derived, so a capability cannot start or stop being claimed without
+ # someone saying so here. `path_count` still accumulates route mass and is still not the
+ # connectivity object; what changed is that `disjoint_routes` now measures that object beside it.
+ assert artifact["implemented_capabilities"] == {
+ "provenance_route_accumulation": True,
+ "edge_disjoint_path_enumeration": True,
+ "plausible_flow_capacity": True,
+ "k_routes": True,
+ }
+
+
+def test_artifact_round_trip(tmp_path):
+ artifact = tmp_path / "result.json"
+ markdown = tmp_path / "result.md"
+ assert experiment.main([
+ "reproduce", "--artifact", str(artifact), "--markdown", str(markdown)
+ ]) == 0
+ assert experiment.main([
+ "verify", "--artifact", str(artifact), "--markdown", str(markdown)
+ ]) == 0
diff --git a/tests/test_perfect_matching_disclosure.py b/tests/test_perfect_matching_disclosure.py
new file mode 100644
index 00000000..3113e52b
--- /dev/null
+++ b/tests/test_perfect_matching_disclosure.py
@@ -0,0 +1,68 @@
+import pytest
+
+from decluster.domain import Inconclusive, MessageAssignmentRecovered
+from decluster.failure_modes.perfect_matching_disclosure import (
+ PerfectMatchingScenario,
+ analyze,
+ evaluate,
+ paired_examples,
+)
+
+
+def test_joint_assignment_is_unique_and_bijective():
+ joint, _ = paired_examples()
+ analysis = analyze(joint)
+ outcome = evaluate(joint).outcomes[0]
+
+ assert isinstance(outcome, MessageAssignmentRecovered)
+ assert len(analysis.assignments) == 1
+ assert analysis.assignments[0] == tuple(zip(joint.senders, joint.receivers))
+
+
+def test_independent_maxima_do_not_form_the_joint_assignment():
+ joint, _ = paired_examples()
+ naive = tuple(max(range(3), key=row.__getitem__) for row in joint.profile_weights)
+
+ assert naive == (0, 0, 2)
+ assert len(set(naive)) < len(naive)
+
+
+def test_uniform_profiles_preserve_all_perfect_matchings():
+ _, control = paired_examples()
+ analysis = analyze(control)
+ report = evaluate(control)
+
+ assert len(analysis.assignments) == 6
+ assert isinstance(report.outcomes[0], Inconclusive)
+
+
+def test_zero_probability_edges_are_excluded():
+ joint, _ = paired_examples()
+ scenario = PerfectMatchingScenario(
+ "zero-edge",
+ joint.senders,
+ joint.receivers,
+ ((1.0, 0.0, 0.0), (0.0, 1.0, 0.0), (0.0, 0.0, 1.0)),
+ )
+
+ assert analyze(scenario).assignments == (tuple(zip(scenario.senders, scenario.receivers)),)
+
+
+def test_profile_matrix_must_match_the_round():
+ joint, _ = paired_examples()
+
+ with pytest.raises(ValueError, match="square"):
+ PerfectMatchingScenario(
+ joint.identifier,
+ joint.senders,
+ joint.receivers,
+ joint.profile_weights[:-1],
+ )
+
+
+def test_report_does_not_claim_bitcoin_ownership():
+ report = evaluate(paired_examples()[0])
+
+ assert report.channels[0].identifier == "joint_round_assignment"
+ assert report.composition is None
+ assert "does not attribute Bitcoin ownership" in report.limitations[2]
diff --git a/tests/test_perfect_matching_disclosure_experiment.py b/tests/test_perfect_matching_disclosure_experiment.py
new file mode 100644
index 00000000..b53e1041
--- /dev/null
+++ b/tests/test_perfect_matching_disclosure_experiment.py
@@ -0,0 +1,38 @@
+import json
+
+import pytest
+
+from decluster.experiments import perfect_matching_disclosure as experiment
+
+
+def test_artifact_distinguishes_joint_assignment_from_tied_control():
+ artifact = experiment.build_artifact()
+ joint = artifact["joint_assignment"]
+ control = artifact["uniform_profile_control"]
+
+ assert joint["naive_is_bijective"] is False
+ assert joint["optimal_assignments"] == [[0, 1, 2]]
+ assert joint["joint_likelihood"] == pytest.approx(0.192)
+ assert joint["unique_assignment_recovered"] is True
+ assert control["optimal_assignment_count"] == 6
+ assert control["unique_assignment_recovered"] is False
+
+
+def test_verifier_recomputes_the_joint_objective():
+ changed = json.loads(json.dumps(experiment.build_artifact()))
+ changed["joint_assignment"]["optimal_assignment_count"] = 2
+
+ with pytest.raises(experiment.VerificationError, match="fresh failure-mode"):
+ experiment.verify_artifact(changed)
+
+
+def test_cli_reproduces_and_verifies_json_and_markdown(tmp_path):
+ artifact = tmp_path / "artifact.json"
+ markdown = tmp_path / "result.md"
+
+ assert experiment.main(
+ ["reproduce", "--artifact", str(artifact), "--markdown", str(markdown)]
+ ) == 0
+ assert experiment.main(
+ ["verify", "--artifact", str(artifact), "--markdown", str(markdown)]
+ ) == 0
diff --git a/tests/test_pipeline.py b/tests/test_pipeline.py
index 5290ac59..0d2c3f77 100644
--- a/tests/test_pipeline.py
+++ b/tests/test_pipeline.py
@@ -1,29 +1,41 @@
-"""Pipeline sanity tests."""
-import sys, os
-sys.path.insert(0, os.path.dirname(os.path.dirname(__file__)))
-from decluster import fetch_tx
+"""Offline pipeline sanity tests over the committed merged-anchor fixture."""
+import json
+from pathlib import Path
+
from decluster.extractors import x_nsequence, x_input_order
from decluster.combiner import Combiner
-MERGE = "931d6627f7b63491cbc2e6d860dc630537385fd9ee3171f2013b64e6a143a4e4"
-CAKE ="0a568e3ae6fa6bf34ce8925266ac2cdb1668c723980398d9c613d67d72b39729"
-SENDER ="91106666451dc43a0e3f78b325764251e205b39d7e9498948885678616ba719a"
+FIXTURE = Path(__file__).parent / "fixtures" / "merged_anchor_931d6627.json"
+
+
+def transactions():
+ return json.loads(FIXTURE.read_text())["transactions"]
def test_extractor_cake_signature():
+ txs = transactions()
# Cake coin: 1-in seq=0x01 -> lone 0x01 (ambiguous, not the strict group-C pattern)
- assert x_nsequence(fetch_tx(CAKE)) == "seq_0x01_other"
+ assert x_nsequence(txs["cake"]) == "seq_0x01_other"
# sender coin: 3-in all MAX
- assert x_nsequence(fetch_tx(SENDER)) == "max_ffffffff"
+ assert x_nsequence(txs["sender"]) == "max_ffffffff"
def test_merge_is_intra_uniform():
# the merged transaction tx itself is clean: both inputs rbf_fffffffd
- assert x_nsequence(fetch_tx(MERGE)) == "rbf_fffffffd"
+ merged = {"vin": [{"sequence": 0xFFFFFFFD}, {"sequence": 0xFFFFFFFD}]}
+ assert x_nsequence(merged) == "rbf_fffffffd"
def test_combiner_separates_and_links():
- cmb = Combiner()
- diff = cmb.score(fetch_tx(CAKE), fetch_tx(SENDER)) # merged transaction: donos diferentes
- be2e = fetch_tx(CAKE)["vin"][0]["txid"]
- same = cmb.score(fetch_tx(CAKE), fetch_tx(be2e)) # Cake lineage: same owner
+ txs = transactions()
+ cmb = Combiner.from_library()
+ diff = cmb.score(txs["cake"], txs["sender"])
+ cake_lineage = {
+ "locktime": 0,
+ "vin": [{
+ "sequence": 1,
+ "txid": "92a2c97f398cd7672e6a9f5d15e0f3523ba47483c916f6c84d397d9159e6f8eb",
+ "vout": 0,
+ }],
+ }
+ same = cmb.score(txs["cake"], cake_lineage)
assert diff < -2, f"esperado <-2 bits, got {diff:.2f}"
assert same > +3, f"esperado >+3 bits, got {same:.2f}"
diff --git a/tests/test_provenance_consistency.py b/tests/test_provenance_consistency.py
new file mode 100644
index 00000000..b01d0fd2
--- /dev/null
+++ b/tests/test_provenance_consistency.py
@@ -0,0 +1,175 @@
+"""What a run manifest says about its code has to match what its bundle actually executes.
+
+`reproduce` extracts the bundle's source archive and runs *that* code, so the archive is the
+runtime, not the working tree. A manifest naming a different revision is not caught by anything
+else here: the outputs still verify, the reproduction still passes, and the declaration is still
+wrong. That combination is how three manifests went a whole correction round claiming a clean tree
+at a revision their own experiment module predates.
+
+Every check needs git history. A shallow checkout skips rather than passes, and says what went
+unchecked.
+"""
+import json
+import subprocess
+from pathlib import Path
+
+import pytest
+
+ROOT = Path(__file__).resolve().parents[1]
+BUNDLES = sorted((ROOT / "releases").glob("*.bundle.json"))
+
+
+def _git(*args):
+ return subprocess.run(("git", *args), cwd=ROOT, capture_output=True)
+
+
+def _has_history():
+ return not (ROOT / ".git" / "shallow").exists() and _git("rev-parse", "HEAD").returncode == 0
+
+
+requires_history = pytest.mark.skipif(
+ not _has_history(), reason="shallow or absent git history; revision consistency went unchecked"
+)
+
+
+def _bundle_runs():
+ for index in BUNDLES:
+ bundle = json.loads(index.read_text())
+ archive = next((b["name"] for b in bundle["blobs"] if b["role"] == "source"), None)
+ for run in bundle["runs"]:
+ yield index.name, run, archive
+
+
+def _manifest(run):
+ return json.loads((ROOT / "catalog" / "runs" / f"{run}.json").read_text())
+
+
+def _module_path(manifest):
+ module = next((a for a in manifest["command"]["argv"] if a.startswith("decluster.")), None)
+ if module is None:
+ return None
+ return "decluster/" + module.split("decluster.", 1)[1].replace(".", "/") + ".py"
+
+
+def _archive_revision(archive):
+ # runtime_snapshot names the archive decluster--runtime.tar
+ return archive.split("-")[1]
+
+
+def test_bundles_exist_to_check():
+ assert BUNDLES, "no evidence bundle is committed"
+
+
+@requires_history
+def test_every_declared_revision_is_a_real_commit():
+ for path in sorted((ROOT / "catalog" / "runs").glob("*.json")):
+ revision = json.loads(path.read_text())["code"]["revision"]
+ assert _git("cat-file", "-e", f"{revision}^{{commit}}").returncode == 0, (
+ f"{path.name} declares revision {revision}, which is not a commit here"
+ )
+
+
+@requires_history
+def test_no_run_predates_the_archive_its_bundle_ships():
+ for index, run, archive in _bundle_runs():
+ assert archive, f"{index} ships no source archive"
+ revision = _manifest(run)["code"]["revision"]
+ assert _git("merge-base", "--is-ancestor", revision,
+ _archive_revision(archive)).returncode == 0, (
+ f"{index}: {run} declares {revision[:7]}, which is not an ancestor of the "
+ f"archive's {_archive_revision(archive)}"
+ )
+
+
+@requires_history
+def test_the_experiment_a_bundle_runs_is_the_one_its_manifest_names():
+ """The declared revision and the archive's revision must hold the same experiment module.
+
+ When they differ the bundle reproduces with code the manifest does not describe. The outputs
+ can still match — they did — so nothing else in the suite notices.
+ """
+ drifted = []
+ for index, run, archive in _bundle_runs():
+ manifest = _manifest(run)
+ path = _module_path(manifest)
+ if path is None:
+ continue
+ declared = _git("show", f"{manifest['code']['revision']}:{path}")
+ shipped = _git("show", f"{_archive_revision(archive)}:{path}")
+ if declared.returncode or shipped.returncode or declared.stdout != shipped.stdout:
+ drifted.append(f"{run} ({manifest['code']['revision'][:7]} vs "
+ f"{_archive_revision(archive)}): {path}")
+ assert not drifted, "manifest revision and shipped archive disagree on:\n " + "\n ".join(drifted)
+
+
+def test_a_declared_dependency_revision_matches_what_its_artifact_recorded():
+ """A run that pins a dependency revision and an output that records one must agree."""
+ for path in sorted((ROOT / "catalog" / "runs").glob("*.json")):
+ manifest = json.loads(path.read_text())
+ declared = {d["revision"] for d in manifest["environment"].get("dependencies", ())
+ if d.get("revision")}
+ if not declared:
+ continue
+ for output in manifest["outputs"]:
+ target = ROOT / output["path"]
+ if target.suffix != ".json" or not target.is_file():
+ continue
+ recorded = _recorded_revisions(json.loads(target.read_text()))
+ unmatched = recorded - declared
+ assert not unmatched, (
+ f"{path.name} declares {sorted(declared)} but {output['path']} records "
+ f"{sorted(unmatched)}"
+ )
+
+
+def _recorded_revisions(value):
+ """Every value under a key ending in `_rev`/`revision` anywhere in the artifact."""
+ found = set()
+ if isinstance(value, dict):
+ for key, item in value.items():
+ if isinstance(item, str) and (key.endswith("_rev") or key.endswith("revision")):
+ found.add(item)
+ else:
+ found |= _recorded_revisions(item)
+ elif isinstance(value, list):
+ for item in value:
+ found |= _recorded_revisions(item)
+ return found
+
+
+def test_every_declared_dependency_revision_is_the_same_one():
+ """Seven manifests pinned a dependency revision and a wheel; moving one moved none of the rest.
+
+ Amending the dependency's commit renamed it, and the pins sat in nine places across the CI, the
+ packaging metadata, seven run manifests and an artifact. `test_a_declared_dependency_revision_
+ matches_what_its_artifact_recorded` did not catch it, because a manifest and its artifact can
+ agree on a revision that no longer exists.
+ """
+ declared = set()
+ for path in sorted((ROOT / "catalog" / "runs").glob("*.json")):
+ manifest = json.loads(path.read_text())
+ for dependency in manifest["environment"].get("dependencies", ()):
+ if dependency.get("revision"):
+ declared.add((dependency.get("name", "?"), dependency["revision"]))
+ by_name = {}
+ for name, revision in declared:
+ by_name.setdefault(name, set()).add(revision)
+ split = {name: sorted(revs) for name, revs in by_name.items() if len(revs) > 1}
+ assert not split, f"one dependency, more than one declared revision: {split}"
+
+
+@requires_history
+def test_the_packaging_and_the_ci_pin_what_the_manifests_declare():
+ """The extra, the CI checkout, the CI assertion and the manifests are four copies of one fact."""
+ declared = {
+ dependency["revision"]
+ for path in (ROOT / "catalog" / "runs").glob("*.json")
+ for dependency in json.loads(path.read_text())["environment"].get("dependencies", ())
+ if dependency.get("revision")
+ }
+ if not declared:
+ pytest.skip("no run manifest declares a dependency revision")
+ for relative in ("pyproject.toml", ".github/workflows/ci.yml"):
+ text = (ROOT / relative).read_text()
+ for revision in declared:
+ assert revision in text, f"{relative} does not pin {revision[:12]}"
diff --git a/tests/test_provenance_decay.py b/tests/test_provenance_decay.py
new file mode 100644
index 00000000..e4088863
--- /dev/null
+++ b/tests/test_provenance_decay.py
@@ -0,0 +1,48 @@
+from decluster.domain import AdditiveDecayMeasured, GraphFractureMeasured, Inconclusive
+from decluster.failure_modes.provenance_decay import (
+ ProvenanceDecayScenario,
+ ctp_fracture_example,
+ evaluate,
+)
+
+
+def test_known_auxiliary_causes_additive_candidate_decay():
+ report = evaluate(ctp_fracture_example())
+ evidence = report.channels[0].evidence[0]
+
+ assert len(evidence.candidates_before) == 3
+ assert len(evidence.candidates_after) == 2
+ assert isinstance(report.outcomes[0], AdditiveDecayMeasured)
+
+
+def test_bridge_removal_is_measured_as_a_separate_graph_fracture():
+ report = evaluate(ctp_fracture_example())
+ fracture = report.channels[1].evidence[0]
+
+ assert (fracture.components_before, fracture.components_after) == (1, 2)
+ assert isinstance(report.outcomes[1], GraphFractureMeasured)
+ assert report.composition is None
+
+
+def test_component_growth_does_not_claim_an_exponential_rate():
+ report = evaluate(ctp_fracture_example())
+
+ assert "does not by itself" in report.limitations[1]
+ assert "edge plausibility" in report.limitations[2]
+
+
+def test_non_bridge_removal_remains_structurally_inconclusive():
+ scenario = ctp_fracture_example()
+ left = next(
+ node for node in scenario.graph_nodes if node.identifier == "left-origin"
+ )
+ report = evaluate(ProvenanceDecayScenario(
+ scenario.target,
+ scenario.candidates,
+ frozenset({left}),
+ scenario.graph_nodes,
+ scenario.graph_edges,
+ ))
+
+ assert len(report.channels) == 1
+ assert isinstance(report.outcomes[1], Inconclusive)
diff --git a/tests/test_provenance_decay_experiment.py b/tests/test_provenance_decay_experiment.py
new file mode 100644
index 00000000..a9eb66f3
--- /dev/null
+++ b/tests/test_provenance_decay_experiment.py
@@ -0,0 +1,42 @@
+import json
+
+import pytest
+
+from decluster.experiments import provenance_decay as experiment
+
+
+def test_artifact_separates_additive_decay_from_graph_fracture():
+ artifact = experiment.build_artifact()
+
+ assert artifact["bridge_removal"] == {
+ "candidates_before": 3,
+ "candidates_after": 2,
+ "structural_outcome": "graph_fracture",
+ "components_before": 1,
+ "components_after": 2,
+ }
+ assert artifact["peripheral_removal"] == {
+ "candidates_before": 3,
+ "candidates_after": 2,
+ "structural_outcome": "inconclusive",
+ }
+ assert artifact["composition"] is None
+
+
+def test_verifier_recomputes_the_structural_measurement():
+ changed = json.loads(json.dumps(experiment.build_artifact()))
+ changed["bridge_removal"]["components_after"] = 3
+
+ with pytest.raises(experiment.VerificationError, match="fresh failure-mode"):
+ experiment.verify_artifact(changed)
+
+
+def test_cli_reproduces_and_verifies_json_and_markdown(tmp_path):
+ artifact = tmp_path / "artifact.json"
+ markdown = tmp_path / "result.md"
+ assert experiment.main(
+ ["reproduce", "--artifact", str(artifact), "--markdown", str(markdown)]
+ ) == 0
+ assert experiment.main(
+ ["verify", "--artifact", str(artifact), "--markdown", str(markdown)]
+ ) == 0
diff --git a/tests/test_provenance_link.py b/tests/test_provenance_link.py
index 3d0286df..427527a1 100644
--- a/tests/test_provenance_link.py
+++ b/tests/test_provenance_link.py
@@ -1,6 +1,7 @@
"""Deep-feature matching: two coins are linked by the OVERLAP of their provenance signatures (the
sparse ancestral-boundary distribution), a Narayanan-Shmatikov quasi-identifier. A shared *rare*
ancestor is strong same-origin evidence; a shared hub (a coinbase everyone descends from) is weak."""
+import pytest
import sys, os, math
sys.path.insert(0, os.path.dirname(os.path.dirname(__file__)))
from decluster.ancestry import (ancestry_signature, provenance_link,
@@ -29,6 +30,7 @@ def _fetch(txs):
def test_ancestry_signature_is_absorber_distribution():
# C.vout0 = 3 sats comes from input P:0 (also 3); its provenance signature is the atom {P:0: 1.0}
+ pytest.importorskip("dss")
txs = {
"C": {"vin": [{"is_coinbase": False, "txid": "P", "vout": 0, "prevout": {"value": 3}},
{"is_coinbase": False, "txid": "R", "vout": 0, "prevout": {"value": 5}}],
diff --git a/tests/test_pseudonym_graph_contract.py b/tests/test_pseudonym_graph_contract.py
new file mode 100644
index 00000000..62220b99
--- /dev/null
+++ b/tests/test_pseudonym_graph_contract.py
@@ -0,0 +1,55 @@
+import pytest
+
+from decluster.adaptations.pseudonym_graph import contract_evidence, contract_report
+from decluster.domain import ContractedTransfer, PseudonymGraphConstructed
+from decluster.experiments.pseudonym_graph_contract import fixture
+
+
+def test_partial_clustering_preserves_unknown_singleton():
+ sample, lookup = fixture()
+ evidence = contract_evidence(sample, lookup)
+
+ assert {vertex.identifier for vertex in evidence.vertices} == {
+ "cluster-a",
+ "cluster-x",
+ "z",
+ }
+
+
+def test_direction_and_folded_parallel_transfer_attributes_are_preserved():
+ sample, lookup = fixture()
+ evidence = contract_evidence(sample, lookup)
+ edges = {(edge.source.identifier, edge.target.identifier): edge for edge in evidence.edges}
+
+ forward = edges[("cluster-a", "cluster-x")]
+ assert (forward.transfers, forward.value) == (3, 600)
+ assert (forward.first_height, forward.last_height) == (100, 140)
+ assert ("cluster-x", "cluster-a") in edges
+ assert ("cluster-a", "cluster-x") != ("cluster-x", "cluster-a")
+
+
+def test_self_transfer_does_not_become_a_relational_edge():
+ sample, lookup = fixture()
+ evidence = contract_evidence(sample, lookup)
+
+ assert {(vertex.identifier, count) for vertex, count in evidence.self_transfers} == {
+ ("cluster-a", 1)
+ }
+ assert all(edge.source != edge.target for edge in evidence.edges)
+
+
+def test_report_calls_the_vertices_pseudonyms_not_users():
+ sample, lookup = fixture()
+ report = contract_report(sample, lookup)
+
+ assert isinstance(report.outcomes[0], PseudonymGraphConstructed)
+ assert report.composition is None
+ assert "pseudonyms" in report.limitations[2]
+
+
+def test_typed_edge_rejects_an_invalid_height_span():
+ sample, lookup = fixture()
+ vertex = contract_evidence(sample, lookup).vertices
+
+ with pytest.raises(ValueError, match="height span"):
+ ContractedTransfer(vertex[0], vertex[1], 1, 10, 20, 19)
diff --git a/tests/test_pseudonym_graph_contract_experiment.py b/tests/test_pseudonym_graph_contract_experiment.py
new file mode 100644
index 00000000..8919ba4a
--- /dev/null
+++ b/tests/test_pseudonym_graph_contract_experiment.py
@@ -0,0 +1,37 @@
+import json
+
+import pytest
+
+from decluster.experiments import pseudonym_graph_contract as experiment
+
+
+def test_artifact_preserves_the_representation_invariants():
+ artifact = experiment.build_artifact()
+ edges = {(edge["source"], edge["target"]): edge for edge in artifact["edges"]}
+
+ assert artifact["vertices"] == ["cluster-a", "cluster-x", "z"]
+ assert edges[("cluster-a", "cluster-x")]["transfers"] == 3
+ assert edges[("cluster-a", "cluster-x")]["value"] == 600
+ assert edges[("cluster-x", "cluster-a")]["transfers"] == 1
+ assert artifact["self_transfers"] == {"cluster-a": 1}
+ assert artifact["composition"] is None
+
+
+def test_verifier_recomputes_contraction():
+ changed = json.loads(json.dumps(experiment.build_artifact()))
+ changed["edges"][0]["transfers"] += 1
+
+ with pytest.raises(experiment.VerificationError, match="fresh contraction"):
+ experiment.verify_artifact(changed)
+
+
+def test_cli_reproduces_and_verifies_json_and_markdown(tmp_path):
+ artifact = tmp_path / "artifact.json"
+ markdown = tmp_path / "result.md"
+
+ assert experiment.main(
+ ["reproduce", "--artifact", str(artifact), "--markdown", str(markdown)]
+ ) == 0
+ assert experiment.main(
+ ["verify", "--artifact", str(artifact), "--markdown", str(markdown)]
+ ) == 0
diff --git a/tests/test_public_api.py b/tests/test_public_api.py
index 4d286264..0ad13f57 100644
--- a/tests/test_public_api.py
+++ b/tests/test_public_api.py
@@ -12,6 +12,7 @@ def test_public_api_names_are_importable_and_callable():
"bounded_link_oracle",
"subprocess_link_oracle",
"path_count_anonymity",
+ "provenance_route_accumulation",
):
assert hasattr(decluster, name)
assert callable(getattr(decluster, name))
diff --git a/tests/test_reference_registry.py b/tests/test_reference_registry.py
new file mode 100644
index 00000000..d2541279
--- /dev/null
+++ b/tests/test_reference_registry.py
@@ -0,0 +1,128 @@
+import json
+from pathlib import Path
+
+import pytest
+
+from decluster.reference_registry import RegistryError, load_claims, load_sources
+
+
+ROOT = Path(__file__).resolve().parent.parent
+SOURCES = ROOT / "catalog" / "ctp-sources.json"
+CLAIMS = ROOT / "catalog" / "ctp-claims.json"
+EXPECTED_CTP_SOURCE_IDS = {
+ "androulaki", "bip77", "bip78", "bip79", "boltzmann", "cioh-scroll",
+ "cioh-wp", "danezis", "diaz", "flow", "forms", "goldfeder",
+ "harrigan-fretter", "kappos", "kelen-seres", "maurer", "maxwell",
+ "meiklejohn", "minor-nitpick", "moser-narayanan", "multigraph-nitpick",
+ "nick", "ns-linkpred", "ns-netflix", "ns-retro", "ns-social", "ns1r",
+ "p2ep", "payjoin-entropy", "proximity", "radix", "reid-harrigan",
+ "ron-shamir", "sabouri", "scam-exchanges", "scroll-history",
+ "scroll-intersection", "serjantov-danezis", "sudoku", "syverson", "troncoso",
+ "tx0", "uih-adamisz", "uih-ghesmati",
+}
+EXPECTED_CTP_CLAIM_IDS = {
+ "ctp.amount.denomination_preparation_fingerprint",
+ "ctp.amount.deterministic_partition",
+ "ctp.amount.subtransaction_ambiguity",
+ "ctp.cioh.collaborative_false_merge",
+ "ctp.disclosure.perfect_matching",
+ "ctp.disclosure.statistical_longitudinal",
+ "ctp.entropy.not_sufficient",
+ "ctp.fingerprint.statistical_linkage",
+ "ctp.forms.observer_knowledge",
+ "ctp.graph.pseudonym_representation",
+ "ctp.intersection.longitudinal",
+ "ctp.payjoin.receiver_change_readoff",
+ "ctp.payjoin.unnecessary_input",
+ "ctp.provenance.traceability",
+ "ctp.robustness.counterfactual_paths",
+ "ctp.social_graph.link_prediction",
+ "ctp.social_graph.propagation",
+ "ctp.social_graph.structural_linkage",
+ "ctp.sparse_dataset.feature_uniqueness",
+}
+
+
+def test_ctp_registry_pins_every_footnote_in_the_reviewed_revision():
+ sources = load_sources(SOURCES)
+ assert {source.id for source in sources} == EXPECTED_CTP_SOURCE_IDS
+ raw = json.loads(SOURCES.read_text())
+ assert raw["ctp_revision"] == "51c2ed8f2c2fa817ce44fd662e6f4b5e83577042"
+ assert sum(source.url is not None for source in sources) == 37
+
+
+def test_every_claim_resolves_to_registered_sources():
+ sources = load_sources(SOURCES)
+ claims = load_claims(CLAIMS, {source.id for source in sources})
+ assert {claim.id for claim in claims} == EXPECTED_CTP_CLAIM_IDS
+
+
+def test_context_only_sources_are_not_silently_dropped():
+ sources = load_sources(SOURCES)
+ obligations = {source.id: source.obligation.value for source in sources}
+ assert obligations["scam-exchanges"] == "no_code_required"
+ assert obligations["scroll-history"] == "context"
+ assert obligations["flow"] == "qualification"
+
+
+def test_registry_rejects_unknown_fields(tmp_path):
+ raw = json.loads(SOURCES.read_text())
+ raw["sources"][0]["surprise"] = True
+ path = tmp_path / "sources.json"
+ path.write_text(json.dumps(raw))
+ with pytest.raises(RegistryError, match="unknown=.*surprise"):
+ load_sources(path)
+
+
+def test_claim_registry_rejects_dangling_sources(tmp_path):
+ raw = json.loads(CLAIMS.read_text())
+ raw["claims"][0]["sources"].append("missing-source")
+ path = tmp_path / "claims.json"
+ path.write_text(json.dumps(raw))
+ sources = load_sources(SOURCES)
+ with pytest.raises(RegistryError, match="unknown source ids"):
+ load_claims(path, {source.id for source in sources})
+
+
+def test_registry_rejects_duplicate_source_ids(tmp_path):
+ raw = json.loads(SOURCES.read_text())
+ raw["sources"].append(dict(raw["sources"][0]))
+ path = tmp_path / "sources.json"
+ path.write_text(json.dumps(raw))
+ with pytest.raises(RegistryError, match="duplicate"):
+ load_sources(path)
+
+
+def test_every_claim_names_code_that_exists_and_a_run_that_exercises_it():
+ """A claim's `implementation` and its runs are the two ends of its evidence chain.
+
+ The catalogue went twenty-four commits without being touched while forty modules moved, and
+ three claims drifted: one named a comparator instead of the scorer every published AUC comes
+ from, one declared no implementation while a run already cited it, and one carried a limitation
+ the code had settled. Nothing compared the two ends.
+ """
+ import re
+
+ root = Path(__file__).resolve().parents[1]
+ claims = json.loads((root / "catalog" / "ctp-claims.json").read_text())["claims"]
+ exercised = {}
+ for path in sorted((root / "catalog" / "runs").glob("*.json")):
+ for identifier in json.loads(path.read_text())["claim_ids"]:
+ exercised.setdefault(identifier, []).append(path.stem)
+
+ dotted = re.compile(r"\bdecluster\.[A-Za-z_][A-Za-z0-9_.]*")
+ problems = []
+ for claim in claims:
+ implementation = claim.get("implementation") or ""
+ named = dotted.findall(implementation)
+ if not named:
+ problems.append(f"{claim['id']}: names no module")
+ for reference in named:
+ parts = reference.split(".")
+ if not any((root / "/".join(parts[:cut])).with_suffix(".py").exists()
+ or (root / "/".join(parts[:cut])).is_dir()
+ for cut in range(len(parts), 1, -1)):
+ problems.append(f"{claim['id']}: {reference} does not resolve")
+ if not exercised.get(claim["id"]):
+ problems.append(f"{claim['id']}: no run declares it")
+ assert not problems, "\n ".join(problems)
diff --git a/tests/test_reference_subtransactions.py b/tests/test_reference_subtransactions.py
new file mode 100644
index 00000000..1a245c4d
--- /dev/null
+++ b/tests/test_reference_subtransactions.py
@@ -0,0 +1,213 @@
+import math
+
+import pytest
+
+from decluster.baselines.boltzmann import (
+ exact_link_analysis,
+ exact_per_coin_link_evidence,
+ fee_tolerant_link_analysis,
+ fee_tolerant_subtransaction_mappings,
+ link_analysis,
+ same_block_probabilities,
+)
+from decluster.baselines.maurer import (
+ exact_non_derived_mappings,
+ exact_subtransaction_mappings,
+)
+
+
+def test_maurer_exact_unique_batch_and_single_owner_readings():
+ # Besides the all-coins reading, the only balanced split is
+ # (100 -> 100) and (200 -> 200).
+ mappings = exact_subtransaction_mappings([100, 200], [100, 200])
+ assert len(mappings) == 2
+ assert any(len(mapping.blocks) == 1 for mapping in mappings)
+ assert any(mapping.blocks == (((0,), (0,)), ((1,), (1,))) for mapping in mappings)
+
+
+def test_maurer_exact_underdetermined_values_have_three_splits():
+ # The 700 output can be funded by 300+400, 200+500, or 100+200+400;
+ # the remaining inputs fund 800. Add the single-owner interpretation.
+ mappings = exact_subtransaction_mappings(
+ [100, 200, 300, 400, 500], [700, 800]
+ )
+ assert len(mappings) == 4
+ assert sorted(len(mapping.blocks) for mapping in mappings) == [1, 2, 2, 2]
+
+
+def test_maurer_exact_refuses_fees_instead_of_silently_balancing_them():
+ assert exact_subtransaction_mappings([500, 500], [600, 390]) == ()
+
+
+def test_maurer_paper_figure_2_distinguishes_derived_mapping():
+ """Maurer et al. Fig. 2: the two-party split is non-derived; the merged
+ all-coins interpretation is derived from it and excluded in their evaluation.
+ """
+
+ inputs = [21, 12, 36, 28]
+ outputs = [25, 8, 50, 14]
+ all_mappings = exact_subtransaction_mappings(inputs, outputs)
+ non_derived = exact_non_derived_mappings(inputs, outputs)
+
+ assert len(all_mappings) == 2
+ assert len(non_derived) == 1
+ assert non_derived[0].blocks == (
+ ((0, 1), (0, 1)),
+ ((2, 3), (2, 3)),
+ )
+
+
+def test_maurer_paper_figure_6_keeps_both_knapsack_mixed_mappings():
+ """Maurer et al. Fig. 6: splitting output 3 of Fig. 2 into 31 and 19 turns the
+ single non-derived mapping into two, neither of which can be preferred.
+ """
+
+ inputs = [21, 12, 36, 28]
+ outputs = [25, 8, 31, 19, 14]
+ all_mappings = exact_subtransaction_mappings(inputs, outputs)
+ non_derived = exact_non_derived_mappings(inputs, outputs)
+
+ assert len(all_mappings) == 3
+ assert {mapping.blocks for mapping in non_derived} == {
+ (((0, 1), (0, 1)), ((2, 3), (2, 3, 4))),
+ (((0, 1), (3, 4)), ((2, 3), (0, 1, 2))),
+ }
+
+
+def test_maurer_values_are_satoshis_not_floats():
+ # The paper's coin domain is [0..2**64-1]. 0.1 + 0.2 + 0.4 != 0.7 in binary
+ # floating point, so accepting floats would drop a mapping in silence.
+ with pytest.raises(ValueError, match="non-negative integers"):
+ exact_subtransaction_mappings([0.1, 0.2, 0.3, 0.4, 0.5], [0.7, 0.8])
+ with pytest.raises(ValueError, match="non-negative integers"):
+ exact_subtransaction_mappings([True, True], [True, True])
+ assert len(exact_non_derived_mappings([10, 20, 30, 40, 50], [70, 80])) == 3
+
+
+def test_same_block_probabilities_expose_the_input_input_metric():
+ # Fig. 2: both mappings keep 21 and 12 together, only the merged one puts
+ # 21 with 36. p_II is the quantity the input-ownership heuristic asserts.
+ analysis = same_block_probabilities([21, 12, 36, 28], [25, 8, 50, 14])
+
+ assert analysis.mapping_count == 2
+ assert analysis.input_matrix == (
+ (1.0, 1.0, 0.5, 0.5),
+ (1.0, 1.0, 0.5, 0.5),
+ (0.5, 0.5, 1.0, 1.0),
+ (0.5, 0.5, 1.0, 1.0),
+ )
+ assert analysis.output_matrix == analysis.input_matrix
+
+
+def test_same_block_probabilities_report_no_family_instead_of_zero():
+ analysis = same_block_probabilities([500, 500], [600, 390])
+ assert analysis.mapping_count == 0
+ assert analysis.input_matrix == () and analysis.output_matrix == ()
+
+
+def test_fee_tolerant_balance_is_separate_and_bounded():
+ assert fee_tolerant_subtransaction_mappings(
+ [500, 500], [600, 390], fee_tolerance=9
+ ) == ()
+ mappings = fee_tolerant_subtransaction_mappings(
+ [500, 500], [600, 390], fee_tolerance=10
+ )
+ assert len(mappings) == 1
+ assert len(mappings[0].blocks) == 1
+
+
+def test_fee_can_be_allocated_across_separate_blocks():
+ mappings = fee_tolerant_subtransaction_mappings(
+ [500, 500], [490, 490], fee_tolerance=20
+ )
+ assert len(mappings) == 3 # all-coins plus the two indexed pairings
+
+
+def test_balance_model_is_explicit_in_the_link_result():
+ exact = exact_link_analysis([500, 500], [600, 390])
+ tolerant = fee_tolerant_link_analysis([500, 500], [600, 390], fee_tolerance=10)
+ assert exact.balance_model == "exact" and exact.mappings == ()
+ assert tolerant.balance_model == "fee_tolerant" and tolerant.observed_fee == 10
+ assert tolerant.fee_tolerance == 10 and len(tolerant.mappings) == 1
+
+
+def test_roundness_is_not_silently_used_as_a_balance_prior():
+ a = fee_tolerant_link_analysis([500, 500], [600, 390], fee_tolerance=10)
+ b = fee_tolerant_link_analysis([500, 500], [601, 389], fee_tolerance=10)
+ assert a.matrix == b.matrix
+
+
+def test_fee_tolerant_model_cannot_express_a_maker_receiving_block():
+ # {10} -> {12} needs a block deficit of -2. Every block here is a payer, so
+ # no tolerance recovers the split and only the all-coins reading survives.
+ for tolerance in range(1, 6):
+ mappings = fee_tolerant_subtransaction_mappings(
+ [10, 20], [12, 17], fee_tolerance=tolerance
+ )
+ assert [len(mapping.blocks) for mapping in mappings] == [1]
+
+
+@pytest.mark.parametrize("model,tolerance", [("unknown", 0), ("exact", 1)])
+def test_invalid_balance_contracts_are_refused(model, tolerance):
+ with pytest.raises(ValueError):
+ link_analysis([1], [1], balance_model=model, fee_tolerance=tolerance)
+
+
+def test_boltzmann_exact_matrix_is_derived_from_uniform_mappings():
+ analysis = exact_link_analysis([100, 200], [100, 200])
+ assert analysis.entropy_bits == pytest.approx(1.0)
+ assert analysis.matrix == ((1.0, 0.5), (0.5, 1.0))
+ assert analysis.deterministic_links == ((0, 0), (1, 1))
+
+
+def test_boltzmann_exact_entropy_is_log_of_distinct_readings():
+ analysis = exact_link_analysis([100, 200, 300, 400, 500], [700, 800])
+ assert analysis.entropy_bits == pytest.approx(math.log2(4))
+ assert len(analysis.mappings) == 4
+ assert all(0.0 <= probability <= 1.0
+ for row in analysis.matrix for probability in row)
+
+
+def test_reference_baselines_have_an_explicit_exactness_bound():
+ with pytest.raises(ValueError, match="max_coins"):
+ exact_subtransaction_mappings([1] * 7, [1] * 6)
+
+
+def test_per_coin_evidence_preserves_exact_mapping_support():
+ evidence = exact_per_coin_link_evidence([100, 200], [100, 200])
+
+ assert evidence.mapping_count == 2
+ first_input = evidence.inputs[0]
+ assert first_input.role == "input"
+ assert first_input.candidate_count == 2
+ assert first_input.max_link_probability == 1.0
+ assert [
+ (candidate.counterpart_index, candidate.mapping_count, candidate.probability)
+ for candidate in first_input.candidates
+ ] == [(0, 2, 1.0), (1, 1, 0.5)]
+
+ first_output = evidence.outputs[0]
+ assert first_output.role == "output"
+ assert [
+ (candidate.counterpart_index, candidate.mapping_count, candidate.probability)
+ for candidate in first_output.candidates
+ ] == [(0, 2, 1.0), (1, 1, 0.5)]
+
+
+def test_per_coin_evidence_does_not_hide_an_absent_exact_mapping():
+ evidence = exact_per_coin_link_evidence([500, 500], [600, 390])
+
+ assert evidence.mapping_count == 0
+ assert all(coin.candidate_count == 0 for coin in evidence.inputs + evidence.outputs)
+ assert all(coin.max_link_probability is None for coin in evidence.inputs + evidence.outputs)
+
+
+def test_per_coin_candidate_counts_are_matrix_support_not_a_privacy_score():
+ evidence = exact_per_coin_link_evidence(
+ [100, 200, 300, 400, 500], [700, 800]
+ )
+
+ assert evidence.mapping_count == 4
+ assert [coin.candidate_count for coin in evidence.inputs] == [2, 2, 2, 2, 2]
+ assert [coin.candidate_count for coin in evidence.outputs] == [5, 5]
+ assert all(coin.max_link_probability == 0.75 for coin in evidence.inputs)
diff --git a/tests/test_register.py b/tests/test_register.py
new file mode 100644
index 00000000..659df565
--- /dev/null
+++ b/tests/test_register.py
@@ -0,0 +1,102 @@
+"""Register rules the corpus sets, checked across the tree rather than at two render sites.
+
+`decluster.md`'s own review text — the ~1,500 lines this repository writes in the register of —
+uses "ground truth" **zero** times in 1,500 lines. All 98 occurrences in that file are inside the
+third-party papers appended to it, and six of those are in scare quotes, used where the labels come
+from a simulator and really are known by construction.
+
+This repository's labels are an address-reuse heuristic. It used to say so by negation — "same-owner
+labels, not ground truth" — which spends a clause denying a frame the corpus never raises and leaked
+the phrase into `results/artifacts/*.json` and the claim catalogue. Two render-time assertions
+already banned it in two documents; this bans it everywhere, so the fix cannot be undone one file at
+a time.
+"""
+import re
+import subprocess
+from pathlib import Path
+
+import pytest
+
+ROOT = Path(__file__).resolve().parents[1]
+PHRASE = re.compile(r"ground[\s-]truth", re.IGNORECASE)
+
+# The corpus uses "honest" only for a protocol's honest party — honest nodes, honest users, an
+# honest chain — and its own review text uses the word zero times in 1,500 lines. This repository
+# had 33 of the other kind: "the honest ceiling", "the honest answer", "Reading it honestly", a
+# section titled "Limitations (honest)". A writer telling the reader that the writer is being
+# honest is the one register move the corpus never makes, and it says nothing a limit does not.
+SELF_ASSESSMENT = re.compile(
+ r"\bhonest(ly)?\b(?!\s+(node|nodes|user|users|party|parties|participant|participants|"
+ r"chain|sender|senders|majority|jondo|jondos|people))",
+ re.IGNORECASE,
+)
+
+# The store keeps byte-identical copies of tracked files and the archive is a tar of them; both
+# follow whatever the text says.
+SKIP = ("artifacts/sha256/", "sources/")
+
+# A file that bans the phrase has to name it.
+EXEMPT = {
+ "tests/test_register.py",
+ "tests/test_link_prediction.py",
+ "tests/test_oracle_audit.py",
+}
+
+SUFFIXES = {".py", ".md", ".json", ".toml", ".yml"}
+
+
+def tracked_text():
+ """Only what git tracks: the ban is about what gets published, not what sits in the checkout."""
+ listing = subprocess.run(["git", "ls-files"], cwd=ROOT, capture_output=True, text=True,
+ check=True).stdout.splitlines()
+ for relative in listing:
+ if relative in EXEMPT or any(relative.startswith(part) for part in SKIP):
+ continue
+ if Path(relative).suffix not in SUFFIXES:
+ continue
+ path = ROOT / relative
+ if path.is_file():
+ yield relative, path
+
+
+def _offenders(pattern, suffixes=None):
+ found = []
+ for relative, path in tracked_text():
+ if suffixes and Path(relative).suffix not in suffixes:
+ continue
+ try:
+ text = path.read_text(encoding="utf-8")
+ except (OSError, UnicodeDecodeError):
+ continue
+ for number, line in enumerate(text.splitlines(), start=1):
+ if pattern.search(line):
+ found.append(f"{relative}:{number}: {line.strip()[:90]}")
+ return found
+
+
+def test_the_phrase_the_corpus_never_uses_appears_nowhere():
+ offenders = []
+ for relative, path in tracked_text():
+ try:
+ text = path.read_text(encoding="utf-8")
+ except (OSError, UnicodeDecodeError):
+ continue
+ for number, line in enumerate(text.splitlines(), start=1):
+ if PHRASE.search(line):
+ offenders.append(f"{relative}:{number}: {line.strip()[:90]}")
+ assert not offenders, (
+ "say what the labels are — same-owner labels, an address-reuse heuristic — rather than "
+ "what they are not:\n " + "\n ".join(offenders)
+ )
+
+
+def test_the_ban_is_checked_over_the_tree_and_not_a_handful_of_files():
+ assert sum(1 for _ in tracked_text()) > 300
+
+
+def test_the_prose_does_not_assess_its_own_honesty():
+ offenders = _offenders(SELF_ASSESSMENT, suffixes={".md"})
+ assert not offenders, (
+ "state the limit; the reader decides whether it was honestly stated:\n "
+ + "\n ".join(offenders)
+ )
diff --git a/tests/test_reid_experiment.py b/tests/test_reid_experiment.py
new file mode 100644
index 00000000..50ba3f00
--- /dev/null
+++ b/tests/test_reid_experiment.py
@@ -0,0 +1,45 @@
+import gzip
+import json
+
+import pytest
+
+from decluster.experiments import reid as experiment
+
+
+def test_reproduce_then_verify_snapshot(tmp_path):
+ artifact = tmp_path / "artifact.json"
+ markdown = tmp_path / "result.md"
+ assert experiment.main([
+ "--dataset", "tests/fixtures/reid_sigs.json.gz", "reproduce",
+ "--artifact", str(artifact), "--markdown", str(markdown),
+ ]) == 0
+ assert experiment.main([
+ "--dataset", "tests/fixtures/reid_sigs.json.gz", "verify",
+ "--artifact", str(artifact), "--markdown", str(markdown),
+ ]) == 0
+
+
+def test_seeded_sampling_is_independent_of_serialized_object_order(tmp_path):
+ first = {
+ "a": {"sig": {"x": 1.0, "y": 2.0, "z": 3.0, "w": 4.0}},
+ "b": {"sig": {"q": 1.0, "r": 2.0, "s": 3.0, "t": 4.0}},
+ }
+ second = {
+ "b": {"sig": {"t": 4.0, "s": 3.0, "r": 2.0, "q": 1.0}},
+ "a": {"sig": {"w": 4.0, "z": 3.0, "y": 2.0, "x": 1.0}},
+ }
+ paths = []
+ for index, value in enumerate((first, second)):
+ path = tmp_path / f"{index}.json.gz"
+ with gzip.open(path, "wt", encoding="utf-8") as target:
+ json.dump(value, target)
+ paths.append(path)
+ assert experiment.build_artifact(paths[0]) == experiment.build_artifact(paths[1])
+
+
+def test_invalid_signature_weight_is_rejected(tmp_path):
+ dataset = tmp_path / "bad.json.gz"
+ with gzip.open(dataset, "wt", encoding="utf-8") as target:
+ json.dump({"a": {"sig": {"x": -1}}, "b": {"sig": {"y": 1}}}, target)
+ with pytest.raises(experiment.VerificationError, match="invalid signature entry"):
+ experiment.build_artifact(dataset)
diff --git a/tests/test_report.py b/tests/test_report.py
index b604839b..de1e4ff4 100644
--- a/tests/test_report.py
+++ b/tests/test_report.py
@@ -18,9 +18,11 @@ def test_report_computes_amount_and_targets_without_pair():
}
fake_oracle = lambda i, o: {"coins": [{"role": "o", "index": 0, "value": 5,
"log_w": 0.0, "kappa_c": 0.9}]}
- rep = report.report(txs["C"], fetch=lambda t: txs[t],
- oracle=fake_oracle, link_oracle=lambda i, o: [[1.0]])
+ resolved = lambda i, o: {"kind": "exact", "count": 3, "log_w": 1.58}
+ rep = report.report(txs["C"], fetch=lambda t: txs[t], oracle=fake_oracle,
+ link_oracle=lambda i, o: [[1.0]], count_oracle=resolved)
assert [c.index for c in rep["amount"]] == [0] # refuse-only cut fired (log_w 0 <= 1.0)
+ assert [c.role for c in rep["amount"]] == ["o"] # input/output indices are distinct
assert set(rep["targets"]) == {0} # one spendable output
assert rep["targets"][0]["min_entropy"] == pytest.approx(0.0)
assert rep["leak"] is None and rep["topology"] is None # no pair/graph context -> not fabricated
@@ -28,6 +30,7 @@ def test_report_computes_amount_and_targets_without_pair():
def test_report_skips_op_return_outputs():
+ pytest.importorskip("dss")
txs = {
"C": {"txid": "C", "vin": [vin("P", 0, 5)],
"vout": [{"value": 5, "scriptpubkey_type": "v0_p2wpkh"},
@@ -42,6 +45,7 @@ def test_report_skips_op_return_outputs():
def test_report_pairwise_terms_when_context_given():
+ pytest.importorskip("dss")
txs = {"C": {"txid": "C", "vin": [vin("P", 0, 5)],
"vout": [{"value": 5, "scriptpubkey_type": "v0_p2wpkh"}]},
"P": {"vin": cb_vin(), "vout": [{"value": 5}]}}
@@ -59,6 +63,7 @@ def score(self, a, b):
def test_report_targets_arg_restricts_walk():
+ pytest.importorskip("dss")
txs = {"C": {"txid": "C", "vin": [vin("P", 0, 5)],
"vout": [{"value": 5, "scriptpubkey_type": "v0_p2wpkh"},
{"value": 3, "scriptpubkey_type": "v0_p2wpkh"}]},
@@ -79,6 +84,7 @@ def _round(total_in, outs):
def test_the_forced_term_is_absent_rather_than_assumed():
"""Conservation needs one participant's input, which the transaction alone
does not give — so without it the term is None, like leak and topology."""
+ pytest.importorskip("dss")
tx = _round(100, [20, 20, 20])
rep = report.report(tx, fetch=lambda t: tx, oracle=lambda i, o: {"coins": []},
link_oracle=lambda i, o: [[1.0, 0.0, 0.0]])
@@ -87,6 +93,7 @@ def test_the_forced_term_is_absent_rather_than_assumed():
def test_the_forced_term_reports_what_the_others_could_not_have_funded():
"""others hold 50, so they afford two of the three 20s; the third has no source."""
+ pytest.importorskip("dss")
tx = _round(90, [20, 20, 20])
rep = report.report(tx, fetch=lambda t: tx, oracle=lambda i, o: {"coins": []},
link_oracle=lambda i, o: [[1.0, 0.0, 0.0]], known_input=40)
@@ -95,6 +102,7 @@ def test_the_forced_term_reports_what_the_others_could_not_have_funded():
def test_the_forced_term_is_empty_when_the_inequality_does_not_bite():
"""The common answer: a participant small relative to the round forces nothing."""
+ pytest.importorskip("dss")
tx = _round(100, [20, 20, 20])
rep = report.report(tx, fetch=lambda t: tx, oracle=lambda i, o: {"coins": []},
link_oracle=lambda i, o: [[1.0, 0.0, 0.0]], known_input=10)
@@ -119,6 +127,7 @@ def _uniform_link_oracle(ins, outs):
def test_report_adds_fused_headline_and_is_conservative():
+ pytest.importorskip("dss")
fetch = _subjective_fetch_factory()
tx = fetch("t1")
rep = report.report(tx, oracle=lambda i, o: {"coins": []}, link_oracle=_uniform_link_oracle,
@@ -133,6 +142,7 @@ def test_fused_headline_is_clamped_when_subjective_pin_favors_graph_minority():
"""Reproduces the widening bug: a same-owner pin (via address reuse) on the graph-MINORITY
input boosts it enough that the raw fused distribution is more spread than graph-only — which
would violate the 'never widen' claim. The fused headline must be clamped to graph-only."""
+ pytest.importorskip("dss")
txs = {
"t1": {"txid": "t1",
"vin": [{"txid": "p0", "vout": 0, "prevout": {"value": 500}},
@@ -158,6 +168,7 @@ def skewed_link_oracle(ins, outs):
def test_report_subjective_false_is_graph_only_backward_compatible():
+ pytest.importorskip("dss")
fetch = _subjective_fetch_factory()
tx = fetch("t1")
rep = report.report(tx, oracle=lambda i, o: {"coins": []}, link_oracle=_uniform_link_oracle,
@@ -167,6 +178,7 @@ def test_report_subjective_false_is_graph_only_backward_compatible():
def test_report_cluster_of_feeds_subjective_source():
+ pytest.importorskip("dss")
fetch = _subjective_fetch_factory()
tx = fetch("t1")
# cluster that links input-0 addr "a" with output-0 addr "z" (from _subjective_fetch_factory's t1)
diff --git a/tests/test_report_seam.py b/tests/test_report_seam.py
index b1ce9c47..93464ad1 100644
--- a/tests/test_report_seam.py
+++ b/tests/test_report_seam.py
@@ -1,13 +1,12 @@
-"""End-to-end: report() through the REAL dss oracle on a multi-output tx — verifies the orchestrator
-reaches the compiled dss link matrix and populates one target per spendable output with a
-deterministic-provenance lower bound. The column-vs-row (vout) indexing itself is discriminated by
-tests/test_ancestry_seam.py, which asserts the resolved absorber identity on the same fixture; here
-the matrix is the symmetric identity, so this test pins report()'s plumbing and the deterministic
-outcome, not the transpose."""
+"""End-to-end coverage for report()'s default walk and its explicit dss extension.
+
+The default test must stay runnable in a checkout WITHOUT the compiled `dss` extension — that the
+default takes no dss at all is half of what it asserts — so it stubs the amount channel's
+transaction-level count oracle (refusing there short-circuits `amount_cuts` before the per-coin
+`dss_oracle` is ever called). The dss arm below guards with `importorskip` on its own."""
import pytest
-dss = pytest.importorskip("dss")
-from decluster import report
+from decluster import ancestry, report
def cb_vin():
@@ -18,7 +17,29 @@ def vin(txid, vout, value):
return {"is_coinbase": False, "txid": txid, "vout": vout, "prevout": {"value": value}}
-def test_report_targets_through_real_dss_oracle():
+def no_counts(inputs, outputs):
+ """A refusing count oracle: the amount channel is not what these tests measure, and its default
+ routes through `dss`."""
+ return {}
+
+
+def test_report_uses_value_flow_by_default():
+ txs = {
+ "C": {"txid": "C", "vin": [vin("P", 0, 3), vin("R", 0, 5)],
+ "vout": [{"value": 3, "scriptpubkey_type": "v0_p2wpkh"},
+ {"value": 5, "scriptpubkey_type": "v0_p2wpkh"}]},
+ "P": {"vin": cb_vin(), "vout": [{"value": 3}]},
+ "R": {"vin": cb_vin(), "vout": [{"value": 5}]},
+ }
+ rep = report.report(txs["C"], fetch=lambda t: txs[t], subjective=False,
+ count_oracle=no_counts)
+ for target in rep["targets"].values():
+ assert target["shannon"] == pytest.approx(0.954434002924965)
+ assert target["n_absorbers"] == 2
+
+
+def test_report_accepts_dss_as_an_explicit_extension():
+ dss = pytest.importorskip("dss")
# tx C: inputs (coinbase P:0=3, coinbase R:0=5), outputs [3, 5], no fee.
# real dss.pairwise_link_prob([3,5],[3,5]) == [[1,0],[0,1]] -> vout 0 <- P, vout 1 <- R.
assert dss.pairwise_link_prob([3, 5], [3, 5]) == [[1.0, 0.0], [0.0, 1.0]]
@@ -29,7 +50,10 @@ def test_report_targets_through_real_dss_oracle():
"P": {"vin": cb_vin(), "vout": [{"value": 3}]},
"R": {"vin": cb_vin(), "vout": [{"value": 5}]},
}
- rep = report.report(txs["C"], fetch=lambda t: txs[t]) # real dss oracles (defaults)
+ rep = report.report(
+ txs["C"], fetch=lambda t: txs[t],
+ link_oracle=ancestry.dss_link_oracle, subjective=False,
+ )
assert set(rep["targets"]) == {0, 1}
for vout in (0, 1):
t = rep["targets"][vout]
diff --git a/tests/test_reproducibility.py b/tests/test_reproducibility.py
new file mode 100644
index 00000000..ff464de8
--- /dev/null
+++ b/tests/test_reproducibility.py
@@ -0,0 +1,212 @@
+from decluster import reproducibility as rp
+
+
+def test_fingerprint_counts_files_and_bytes(tmp_path):
+ (tmp_path / "a.txt").write_bytes(b"x" * 10)
+ (tmp_path / "b.txt").write_bytes(b"y" * 20)
+ fp = rp.fingerprint_source(str(tmp_path / "*.txt"))
+ assert fp["files"] == 2
+ assert fp["bytes"] == 30
+ assert len(fp["digest"]) == 64
+
+
+def test_fingerprint_digest_changes_when_a_file_grows(tmp_path):
+ (tmp_path / "a.txt").write_bytes(b"x" * 10)
+ before = rp.fingerprint_source(str(tmp_path / "*.txt"))["digest"]
+ (tmp_path / "a.txt").write_bytes(b"x" * 11)
+ assert rp.fingerprint_source(str(tmp_path / "*.txt"))["digest"] != before
+
+
+def test_fingerprint_digest_changes_on_a_rename_at_equal_size(tmp_path):
+ (tmp_path / "a.txt").write_bytes(b"x" * 10)
+ before = rp.fingerprint_source(str(tmp_path / "*.txt"))["digest"]
+ (tmp_path / "a.txt").rename(tmp_path / "b.txt")
+ assert rp.fingerprint_source(str(tmp_path / "*.txt"))["digest"] != before
+
+
+def test_fingerprint_reports_missing_rather_than_raising(tmp_path):
+ fp = rp.fingerprint_source(str(tmp_path / "nothing" / "*.gz"))
+ assert fp["missing"] is True
+ assert "files" not in fp
+
+
+def _seeded(tmp_path, n=5):
+ (tmp_path / "src.txt").write_bytes(b"z" * n)
+ rp.write_manifest("RESULTS-demo.md", str(tmp_path / "*.txt"),
+ {"n_owners": 57}, root=str(tmp_path))
+
+
+def test_manifest_round_trips(tmp_path):
+ _seeded(tmp_path)
+ m = rp.read_manifest("RESULTS-demo.md", root=str(tmp_path))
+ assert m["invariants"]["n_owners"] == 57
+ assert m["source"]["files"] == 1
+
+
+def test_check_is_ok_when_invariants_are_supplied_and_match(tmp_path):
+ _seeded(tmp_path)
+ status, msg = rp.check_manifest("RESULTS-demo.md", {"n_owners": 57}, root=str(tmp_path))
+ assert status == "ok", msg
+
+
+def test_check_refuses_a_partial_invariant_recomputation(tmp_path):
+ (tmp_path / "src.txt").write_bytes(b"z" * 5)
+ rp.write_manifest("RESULTS-demo.md", str(tmp_path / "*.txt"),
+ {"n_owners": 57, "n_pairs": 100}, root=str(tmp_path))
+ status, msg = rp.check_manifest("RESULTS-demo.md", {"n_owners": 57}, root=str(tmp_path))
+ assert status == "partial"
+ assert "not recomputed: n_pairs" in msg
+
+
+def test_check_refuses_an_unrecorded_invariant(tmp_path):
+ _seeded(tmp_path)
+ status, msg = rp.check_manifest(
+ "RESULTS-demo.md", {"n_owners": 57, "new_metric": 1}, root=str(tmp_path)
+ )
+ assert status == "stale"
+ assert "not recorded: new_metric" in msg
+
+
+def test_check_reports_identity_only_when_no_invariants_are_supplied(tmp_path):
+ _seeded(tmp_path)
+ status, msg = rp.check_manifest("RESULTS-demo.md", root=str(tmp_path))
+ assert status == "identity-only"
+ assert "NOT checked" in msg
+
+
+def test_check_is_stale_when_the_source_changed(tmp_path):
+ _seeded(tmp_path)
+ (tmp_path / "src.txt").write_bytes(b"z" * 500)
+ status, msg = rp.check_manifest("RESULTS-demo.md", root=str(tmp_path))
+ assert status == "stale"
+ assert "source moved" in msg
+
+
+def test_check_is_stale_when_an_invariant_moved(tmp_path):
+ _seeded(tmp_path)
+ status, msg = rp.check_manifest("RESULTS-demo.md", {"n_owners": 32}, root=str(tmp_path))
+ assert status == "stale"
+ assert "n_owners" in msg
+
+
+def test_check_reports_absent_source_distinctly(tmp_path):
+ _seeded(tmp_path)
+ (tmp_path / "src.txt").unlink()
+ assert rp.check_manifest("RESULTS-demo.md", root=str(tmp_path))[0] == "absent"
+
+
+def test_check_reports_unrecorded_for_a_doc_with_no_manifest(tmp_path):
+ assert rp.check_manifest("RESULTS-nope.md", root=str(tmp_path))[0] == "unrecorded"
+
+
+def test_check_manifest_verdict_does_not_depend_on_the_callers_cwd(tmp_path, monkeypatch):
+ # A relative source pattern must resolve against `root`, never against os.getcwd() -- that bug
+ # made the identical manifest read "stale" from one working directory and "absent" from
+ # another, and the walker's "absent" skip is indistinguishable from unversioned data legitimately
+ # not being there.
+ (tmp_path / "src.txt").write_bytes(b"z" * 5)
+ rp.write_manifest("RESULTS-demo.md", "src.txt", {"n_owners": 57}, root=str(tmp_path))
+
+ monkeypatch.chdir(tmp_path)
+ from_root = rp.check_manifest("RESULTS-demo.md", {"n_owners": 57}, root=str(tmp_path))
+
+ elsewhere = tmp_path.parent
+ assert elsewhere != tmp_path
+ monkeypatch.chdir(elsewhere)
+ from_elsewhere = rp.check_manifest("RESULTS-demo.md", {"n_owners": 57}, root=str(tmp_path))
+
+ assert from_root == from_elsewhere == ("ok", from_root[1])
+
+
+def test_manifest_path_strips_the_suffix_rather_than_the_substring(tmp_path):
+ import os
+ def base(doc):
+ return os.path.basename(rp.manifest_path(doc, root=str(tmp_path)))
+ assert base("RESULTS-demo.md") == "RESULTS-demo.json"
+ assert base("RESULTS-demo") == "RESULTS-demo.json"
+ assert base("a.mdx.md") == "a.mdx.json"
+ assert base("x.md.notes.md") == "x.md.notes.json"
+
+
+import pytest
+
+
+def test_exact_mcnemar_matches_known_values():
+ assert rp.separable(3, 0, effect=1.0, min_effect=0.0)[1] == pytest.approx(0.25)
+ assert rp.separable(3, 1, effect=1.0, min_effect=0.0)[1] == pytest.approx(0.625)
+ assert rp.separable(6, 0, effect=1.0, min_effect=0.0)[1] == pytest.approx(0.03125)
+
+
+def test_three_against_zero_does_not_separate():
+ verdict, p = rp.separable(3, 0, effect=1.0, min_effect=0.0)
+ assert verdict is None and p > 0.05
+
+
+def test_six_against_zero_separates():
+ assert rp.separable(6, 0, effect=1.0, min_effect=0.0)[0] == "a"
+
+
+def test_it_does_not_say_a_won_when_b_won():
+ """The failure this whole instrument exists to prevent, in one assertion: B won the discordant
+ pairs, but `effect` was passed positive (as if favoring A) -- the direction mismatch must not
+ be read as an A win."""
+ verdict, p = rp.separable(0, 20, effect=1.0, min_effect=0.0)
+ assert p < 0.05
+ assert verdict is None
+
+
+def test_a_decisive_inversion_is_reported_as_b_not_as_noise():
+ """A false verdict and a decisive inversion must not look alike: this is the historical failure
+ (a published sign inverted with the suite green), and `separable` must name it "b", distinctly
+ from the true-noise case below, even though neither is a verdict for A."""
+ verdict, p = rp.separable(0, 60, effect=-1.0, min_effect=0.01)
+ assert verdict == "b"
+ assert p < 1e-15
+
+
+def test_a_negative_effect_is_not_a_win_for_a():
+ assert rp.separable(20, 0, effect=-1.0, min_effect=0.0)[0] is None
+
+
+def test_a_significant_but_tiny_effect_does_not_separate():
+ verdict, p = rp.separable(20, 0, effect=0.001, min_effect=0.02)
+ assert p < 0.05 and verdict is None
+
+
+def test_true_noise_is_not_separable_and_is_distinct_from_an_inversion():
+ verdict, p = rp.separable(5, 5, effect=0.0, min_effect=0.01)
+ assert verdict is None
+ assert p == pytest.approx(1.0)
+
+
+def test_no_discordant_pairs_is_not_separable():
+ assert rp.separable(0, 0, effect=1.0, min_effect=0.0) == (None, 1.0)
+
+
+def test_a_near_tie_at_scale_falls_back_and_is_fast():
+ """A near-even split is the only shape the exact sum is expensive for, and it is also the shape
+ that returns no verdict either way, so the approximation's error cannot change the answer."""
+ import time
+ start = time.time()
+ verdict, _ = rp.separable(16000, 16000, effect=1.0, min_effect=0.0)
+ assert time.time() - start < 1.0
+ assert verdict is None
+
+
+def test_a_lopsided_discordance_at_scale_stays_exact_and_is_fast():
+ """The exact sum costs min(a, b) terms. The decisive shape a direction claim actually takes is
+ cheap to compute exactly no matter how large the sample, so it must not reach the fallback."""
+ import time
+ start = time.time()
+ verdict, p = rp.separable(100_000, 200, effect=1.0, min_effect=0.0)
+ assert time.time() - start < 1.0
+ assert verdict == "a" and p < 1e-15
+
+
+@pytest.mark.parametrize("a,b", [(533, 470), (549, 485)])
+def test_borderline_pairs_are_not_separated_by_the_approximation(a, b):
+ """These sat just above alpha exactly and just below it under an uncorrected normal
+ approximation, so the instrument reported a direction the exact test refuses."""
+ verdict, p = rp.separable(a, b, effect=1.0, min_effect=0.0)
+ assert p >= 0.05
+ assert verdict is None
diff --git a/tests/test_result_artifacts.py b/tests/test_result_artifacts.py
new file mode 100644
index 00000000..27c0b73f
--- /dev/null
+++ b/tests/test_result_artifacts.py
@@ -0,0 +1,71 @@
+import hashlib
+import json
+
+import pytest
+
+from decluster.data_manifest import (
+ ContentIdentity,
+ RunManifest,
+ RunOutput,
+ RunVerification,
+ ReproducibilityLevel,
+ RunAvailability,
+ VerificationMode,
+)
+from decluster.result_artifacts import (
+ OutputStatus,
+ canonical_json_bytes,
+ verify_run_outputs,
+ write_canonical_json,
+)
+
+
+def manifest_for(path, payload):
+ return RunManifest(
+ id="run", claim_ids=("claim",), code_revision="abc", dirty=False,
+ argv=("run",), python="3.13", lock_digest=None, platform="any", dependencies=(),
+ datasets=(), parameters={}, rng_algorithm=None, rng_seeds=(),
+ outputs=(RunOutput(
+ path=path,
+ bytes=len(payload),
+ sha256=hashlib.sha256(payload).hexdigest(),
+ ),),
+ reproducibility_level=ReproducibilityLevel.BITWISE,
+ availability=RunAvailability.COMPLETE,
+ verification=RunVerification(
+ mode=VerificationMode.EXACT, argv=("verify",), tests=(),
+ properties=("content identity",), tolerance=None,
+ ),
+ limitations=(),
+ )
+
+
+def test_canonical_json_is_stable_and_rejects_non_finite_numbers():
+ assert canonical_json_bytes({"b": 2, "a": [1]}) == b'{"a":[1],"b":2}\n'
+ with pytest.raises(ValueError):
+ canonical_json_bytes({"value": float("nan")})
+
+
+def test_atomic_writer_returns_the_written_identity(tmp_path):
+ path = tmp_path / "nested" / "result.json"
+ identity = write_canonical_json(path, {"answer": 42})
+ assert path.read_bytes() == b'{"answer":42}\n'
+ assert identity == ContentIdentity(
+ bytes=14,
+ sha256=hashlib.sha256(path.read_bytes()).hexdigest(),
+ )
+
+
+def test_output_verification_distinguishes_verified_missing_and_mismatch(tmp_path):
+ payload = canonical_json_bytes({"answer": 42})
+ path = tmp_path / "result.json"
+ path.write_bytes(payload)
+ manifest = manifest_for("result.json", payload)
+
+ assert verify_run_outputs(manifest, tmp_path)[0].status is OutputStatus.VERIFIED
+ path.write_text(json.dumps({"answer": 41}))
+ assert verify_run_outputs(manifest, tmp_path)[0].status is OutputStatus.IDENTITY_MISMATCH
+ path.unlink()
+ check = verify_run_outputs(manifest, tmp_path)[0]
+ assert check.status is OutputStatus.MISSING
+ assert check.actual is None
diff --git a/tests/test_result_inventory.py b/tests/test_result_inventory.py
new file mode 100644
index 00000000..d9c02761
--- /dev/null
+++ b/tests/test_result_inventory.py
@@ -0,0 +1,58 @@
+from pathlib import Path
+
+from decluster.result_inventory import inventory_results, summarize
+
+
+ROOT = Path(__file__).resolve().parents[1]
+
+
+def test_every_legacy_result_receives_an_explicit_status():
+ entries = inventory_results(ROOT)
+
+ assert entries
+ assert all(entry.document.endswith(".md") for entry in entries)
+ assert len({entry.document for entry in entries}) == len(entries)
+ assert sum(summarize(entries).values()) == len(entries)
+
+
+def test_canonical_counterparts_are_not_treated_as_unregistered_history():
+ entries = {entry.document: entry for entry in inventory_results(ROOT)}
+
+ candidate = entries["results/RESULTS-candidate-set-intersection.md"]
+ oracle = entries["results/RESULTS-exact-oracle-audit.md"]
+ assert candidate.canonical_run == "candidate-set-intersection-v1"
+ assert oracle.canonical_run == "exact-oracle-audit-v1"
+ assert candidate.status == oracle.status == "superseded_by_canonical_run"
+
+
+def test_migrated_temporal_result_is_bound_to_its_canonical_run():
+ entries = {entry.document: entry for entry in inventory_results(ROOT)}
+ temporal = entries["results/RESULTS-fs-temporal.md"]
+
+ assert temporal.status == "superseded_by_canonical_run"
+ assert temporal.legacy_manifest == "results/manifests/RESULTS-fs-temporal.json"
+ assert temporal.canonical_run == "fs-temporal-v1"
+
+
+def test_unregistered_markdown_is_not_presented_as_reproducible():
+ entries = {entry.document: entry for entry in inventory_results(ROOT)}
+
+ conservation = entries["results/RESULTS-conservation.md"]
+ assert conservation.status == "superseded_by_canonical_run"
+ assert conservation.canonical_run == "conservation-round-three-v1"
+
+
+def test_slice_channel_document_uses_its_explicit_run_alias():
+ entries = {entry.document: entry for entry in inventory_results(ROOT)}
+ channels = entries["results/RESULTS-slice-a-channels.md"]
+
+ assert channels.status == "superseded_by_canonical_run"
+ assert channels.canonical_run == "slice-channels-v1"
+
+
+def test_catalog_axes_document_uses_the_run_it_names():
+ entries = {entry.document: entry for entry in inventory_results(ROOT)}
+ axes = entries["results/RESULTS-catalog-axes.md"]
+
+ assert axes.status == "superseded_by_canonical_run"
+ assert axes.canonical_run == "fingerprint-catalog-axes-v1"
diff --git a/tests/test_results_index.py b/tests/test_results_index.py
new file mode 100644
index 00000000..58b17920
--- /dev/null
+++ b/tests/test_results_index.py
@@ -0,0 +1,39 @@
+"""The index of `results/` is generated, so it cannot drift from the catalogue.
+
+Sixty-six written documents and fifty-seven generated ones sat in one directory with no entry
+point, and the distinction that matters — which are reproduced by a run and which are prose over
+data the repository does not ship — could only be found by opening each.
+"""
+import subprocess
+import sys
+from pathlib import Path
+
+from decluster import results_index
+
+ROOT = Path(__file__).resolve().parents[1]
+
+
+def test_the_index_matches_the_catalogue():
+ assert results_index.main(["--root", str(ROOT), "--check"]) == 0, (
+ "run `python -m decluster.results_index` and commit the result"
+ )
+
+
+def test_every_generated_document_is_listed_with_its_run():
+ index = (ROOT / "results" / "INDEX.md").read_text()
+ for path in (ROOT / "results" / "generated").glob("*.md"):
+ assert path.name in index, f"{path.name} is not in the index"
+
+
+def test_every_written_document_is_listed():
+ index = (ROOT / "results" / "INDEX.md").read_text()
+ for path in (ROOT / "results").glob("RESULTS-*.md"):
+ assert path.name in index, f"{path.name} is not in the index"
+
+
+def test_a_generated_document_is_never_filed_as_written():
+ """The two sections are the point; mixing them would make the index worse than none."""
+ index = (ROOT / "results" / "INDEX.md").read_text()
+ written = index.split("## Written, with a legacy manifest")[1]
+ for path in (ROOT / "results" / "generated").glob("*.md"):
+ assert f"({path.name})" not in written, f"{path.name} is listed as written"
diff --git a/tests/test_results_manifests.py b/tests/test_results_manifests.py
new file mode 100644
index 00000000..cce49543
--- /dev/null
+++ b/tests/test_results_manifests.py
@@ -0,0 +1,122 @@
+"""Every manifest in results/manifests/ is checked against its live source.
+
+An absent source SKIPS with a message naming what went unchecked; it never passes silently. So does
+a manifest whose invariants nothing recomputed. A published number went stale once with the suite
+green, because nothing recorded what it had been measured on.
+"""
+import glob
+import os
+
+import pytest
+
+from decluster import reproducibility as rp
+
+ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+MANIFESTS = sorted(glob.glob(os.path.join(ROOT, "results", "manifests", "*.json")))
+MIGRATED_RUN_DOCS = {
+ "RESULTS-analyze.md": "catalog/runs/analyze-contract-v1.json",
+ "RESULTS-ancestry.md": "catalog/runs/ancestry-contract-v1.json",
+ "RESULTS-ancestry-sparsity.md": "catalog/runs/ancestry-sparsity-v1.json",
+ "RESULTS-attribute-drift.md": "catalog/runs/attribute-drift-v1.json",
+ "RESULTS-amount-channel-survey.md": "catalog/runs/amount-channel-survey-v1.json",
+ "RESULTS-bayes-vs-fs.md": "catalog/runs/bayes-vs-fs-v1.json",
+ "RESULTS-catalog-axes.md": "catalog/runs/fingerprint-catalog-axes-v1.json",
+ "RESULTS-counting-methods.md": "catalog/runs/counting-methods-v1.json",
+ "RESULTS-conservation.md": "catalog/runs/conservation-round-three-v1.json",
+ "RESULTS-entity-deanon.md": "catalog/runs/entity-deanon-v1.json",
+ "RESULTS-em-m.md": "catalog/runs/em-m-v1.json",
+ "RESULTS-fingerprint-validation.md": "catalog/runs/fingerprint-validation-v1.json",
+ "RESULTS-fingerprint-regime.md": "catalog/runs/fingerprint-regime-v1.json",
+ "RESULTS-fingerprint-sparsity.md": "catalog/runs/fingerprint-sparsity-v1.json",
+ "RESULTS-exact-oracle-audit.md": "catalog/runs/exact-oracle-audit-v1.json",
+ "RESULTS-path-count.md": "catalog/runs/path-count-contract-v1.json",
+ "RESULTS-path-counting-analysis.md": "catalog/runs/path-count-contract-v1.json",
+ "RESULTS-reid.md": "catalog/runs/reid-v1.json",
+ "RESULTS-slice-a-channels.md": "catalog/runs/slice-channels-v1.json",
+ "RESULTS-subtx-demix.md": "catalog/runs/subtx-demix-v1.json",
+ "RESULTS-weight-sensitivity.md": "catalog/runs/weight-sensitivity-v1.json",
+}
+
+
+def _doc(path):
+ return os.path.basename(path)[:-len(".json")] + ".md"
+
+
+@pytest.mark.skipif(not MANIFESTS, reason="no manifests recorded yet")
+@pytest.mark.parametrize("manifest", MANIFESTS, ids=_doc)
+def test_manifest_is_not_stale(manifest):
+ status, message = rp.check_manifest(_doc(manifest), root=ROOT)
+ if status == "absent":
+ pytest.skip(message)
+ if status == "identity-only":
+ pytest.skip(message) # loud: names how many invariants went uncompared
+ assert status == "ok", message
+
+
+def test_every_manifest_names_a_document_that_exists():
+ for manifest in MANIFESTS:
+ doc = os.path.join(ROOT, "results", _doc(manifest))
+ assert os.path.exists(doc), f"{manifest} describes {doc}, which is not in the tree"
+
+
+# The walker above only checks manifest -> doc: a manifest naming a document that vanished. It has
+# no doc -> manifest direction, so a results document with no manifest is invisible to it, silently
+# — indistinguishable from one that was never meant to carry one. Migration to canonical run
+# manifests is incremental, so requiring every document to carry one now would be dishonest about
+# where this phase actually left the instrument. Until later phases file real run manifests, every
+# `RESULTS-*.md` is either mapped above, backed by a legacy manifest, or listed here explicitly. A
+# new results document that lands in none of those sets fails loudly rather than joining the pile
+# invisibly.
+NOT_YET_MIGRATED = {
+ "RESULTS-3v23-engine.md",
+ "RESULTS-anonymity-set-scale.md",
+ "RESULTS-anonymity-set.md",
+ "RESULTS-broadcast.md",
+ "RESULTS-change-id.md",
+ "RESULTS-cluster-robustness.md",
+ "RESULTS-conspicuous-order.md",
+ "RESULTS-def1-sparsity.md",
+ "RESULTS-e2e.md",
+ "RESULTS-entropy.md",
+ "RESULTS-era-sweep.md",
+ "RESULTS-gap1.md",
+ "RESULTS-graph-deanon.md",
+ "RESULTS-intersection.md",
+ "RESULTS-ns-propagation.md",
+ "RESULTS-partition-cuts.md",
+ "RESULTS-partition-posterior.md",
+ "RESULTS-persistence-curve.md",
+ "RESULTS-provenance.md",
+ "RESULTS-refusing-clusterer.md",
+ "RESULTS-slice-gate-2026.md",
+ "RESULTS-temporal.md",
+ "RESULTS-topology.md",
+ "RESULTS-wp1a.md",
+ "RESULTS-wp4.md",
+}
+
+
+def test_every_results_doc_has_a_manifest_or_is_explicitly_not_yet_migrated():
+ docs = {os.path.basename(p) for p in glob.glob(os.path.join(ROOT, "results", "RESULTS-*.md"))}
+ backed = {_doc(m) for m in MANIFESTS} | set(MIGRATED_RUN_DOCS)
+ unaccounted = docs - backed - NOT_YET_MIGRATED
+ assert not unaccounted, (
+ f"no manifest and not listed in NOT_YET_MIGRATED: {sorted(unaccounted)}")
+ stale = (NOT_YET_MIGRATED - docs) - backed
+ assert not stale, f"NOT_YET_MIGRATED names a document that no longer exists: {sorted(stale)}"
+
+
+def test_migrated_documents_name_their_canonical_run():
+ for doc, manifest in MIGRATED_RUN_DOCS.items():
+ assert os.path.isfile(os.path.join(ROOT, manifest))
+ with open(os.path.join(ROOT, "results", doc)) as source:
+ assert manifest in source.read()
+
+
+def test_policy_documents_state_five_and_the_direction_rule():
+ """The rule state 5 carries is about falsifiability, not about which table a claim sits in: the
+ failure it was written for was a green test whose fixture was built to display the claim."""
+ policy = open(os.path.join(ROOT, "results", "REPRODUCIBILITY.md")).read()
+ assert "## 5. Measured, not separable" in policy
+ assert "Being asserted is not the same as being tested" in policy
+ assert "could have shown the opposite" in policy
diff --git a/tests/test_results_provenance_footer.py b/tests/test_results_provenance_footer.py
new file mode 100644
index 00000000..1ab36d77
--- /dev/null
+++ b/tests/test_results_provenance_footer.py
@@ -0,0 +1,66 @@
+"""A reader opening a results document has to be told what backs it, in the document.
+
+`REPRODUCIBILITY.md` says the five states are indexed centrally and that "per-doc `Reproducibility /
+provenance` footers point back here". Thirty-three documents said nothing at all, and nothing noticed:
+the central index is not reachable from the page a reader is on, so a number band-pinned on committed
+data and one from an uncommitted data-run read exactly alike.
+
+Three answers count, because the repository already keeps provenance in three places: a footer naming
+the state, a canonical run manifest the document points at, or a legacy manifest recorded for it.
+`UNFILED` is the honest fourth — better than a guessed state, and unlike silence it is visible to the
+reader and counted here, so the backlog shrinks only when someone reads a document and files it.
+"""
+
+import re
+from pathlib import Path
+
+ROOT = Path(__file__).resolve().parent.parent
+DOCS = sorted((ROOT / "results").glob("RESULTS-*.md"))
+FOOTER = re.compile(r"^#+ *(?:reproduc\w*|data and reproducibility)(?: */ *provenance)? *$", re.M | re.I)
+RUN = re.compile(r"catalog/runs/[\w.-]+\.json")
+STATE = re.compile(r"\bstate [1-5]\b", re.I)
+UNFILED = "Not yet filed against `results/REPRODUCIBILITY.md`"
+# A footer states the level in substance when it points at what pins the number.
+EVIDENCE = re.compile(r"tests?/[\w./-]+|test_[\w]+\.py|data/[\w./-]+|bigquery/[\w./-]+\.sql")
+LEGACY = {p.stem + ".md" for p in (ROOT / "results" / "manifests").glob("*.json")}
+
+# Documents nobody has assigned an evidence state yet. Filing one is deliberate work; so is adding to
+# the pile, which is what the ceiling stops.
+UNFILED_CEILING = 2
+
+
+def _footer(doc):
+ text = doc.read_text()
+ match = FOOTER.search(text)
+ return text[match.end():] if match else None
+
+
+def test_every_results_document_says_what_backs_it():
+ assert DOCS, "no results document was found; this gate is measuring nothing"
+ silent = [d.name for d in DOCS
+ if _footer(d) is None and not RUN.search(d.read_text()) and d.name not in LEGACY]
+ assert not silent, (
+ "these send a reader numbers with no statement of what backs them:\n "
+ + "\n ".join(silent)
+ + "\nAdd a `## Reproducibility / provenance` section, or point at the run manifest."
+ )
+
+
+def test_every_footer_names_a_state_or_declares_none_assigned():
+ """Naming the state is one way; pointing at the fixture, test or query that pins it is another."""
+ vague = [d.name for d in DOCS
+ if (body := _footer(d)) is not None
+ and not STATE.search(body) and UNFILED not in body
+ and not EVIDENCE.search(body)
+ and not RUN.search(d.read_text()) and d.name not in LEGACY]
+ assert not vague, (
+ "these carry a footer that names neither a state nor a manifest, and does not declare the\n"
+ "state unassigned:\n " + "\n ".join(vague))
+
+
+def test_the_unfiled_backlog_does_not_grow():
+ """A ceiling, not a target: it falls when a document is read and filed, never quietly."""
+ unfiled = [d.name for d in DOCS if (body := _footer(d)) is not None and UNFILED in body]
+ assert len(unfiled) <= UNFILED_CEILING, (
+ f"{len(unfiled)} documents are unfiled; the ceiling is {UNFILED_CEILING}. "
+ "File the new one against REPRODUCIBILITY.md rather than raising this.")
diff --git a/tests/test_route_capacity_experiment.py b/tests/test_route_capacity_experiment.py
new file mode 100644
index 00000000..756a1103
--- /dev/null
+++ b/tests/test_route_capacity_experiment.py
@@ -0,0 +1,102 @@
+"""The cut the framework states robust connectivity over, measured for the first time.
+
+`path_count` accumulates route mass and says in its own docstring that this is a different number:
+many routes may run through one coin, so a large count is consistent with a cut of one. On the
+committed cache the two disagree for three coins in every five, which is what this run reports.
+
+The boundary counts are part of the result rather than diagnostics beside it. Almost every origin
+the walk reaches is the edge of the slice rather than a coinbase, so every cut here is a lower bound
+and the document has to say by how much — a reader given the fragility without the sample limit
+would take a floor for a ceiling.
+"""
+
+import json
+from pathlib import Path
+
+import pytest
+
+from decluster.experiments import route_capacity as rc
+
+ROOT = Path(__file__).resolve().parent.parent
+ARTIFACT = ROOT / "results" / "artifacts" / "route-capacity-v1.json"
+SNAPSHOT = ROOT / "data" / "fs-blkcache-2026-09-04.tar.gz"
+
+
+@pytest.fixture(scope="module")
+def artifact():
+ if not ARTIFACT.is_file():
+ pytest.skip("the canonical route-capacity artifact is not committed")
+ return json.loads(ARTIFACT.read_text())
+
+
+def test_every_measured_coin_lands_in_the_distribution(artifact):
+ assert sum(artifact["capacity"].values()) == artifact["population"]["measured"]
+ assert sum(artifact["origin_excess"].values()) == artifact["population"]["measured"]
+
+
+def test_no_coin_is_reported_with_more_routes_than_it_has_origins(artifact):
+ """`origins - k` is a count of routes an origin set promises and the graph does not deliver."""
+ assert min(int(gap) for gap in artifact["origin_excess"]) >= 0
+
+
+def test_the_origin_set_overstates_redundancy_for_most_coins(artifact):
+ """The finding the module exists for, asserted rather than left in a table."""
+ measured = artifact["population"]["measured"]
+ overstated = artifact["readings"]["origin_set_overstates"]
+ assert overstated / measured > 0.5, (
+ f"{overstated}/{measured}: counting origins no longer overstates the cut, which would "
+ "make this measurement redundant with path_count")
+
+
+def test_a_cut_of_one_is_reported_and_bounded(artifact):
+ """Stated as a band: a run that finds none, or finds it everywhere, has changed the object."""
+ measured = artifact["population"]["measured"]
+ fragile = artifact["readings"]["cut_of_one"]
+ assert 0 < fragile < measured * 0.5, f"{fragile}/{measured} coins fall to a single-coin cut"
+
+
+def test_the_boundary_the_walk_stopped_at_is_recorded(artifact):
+ """Without this the fragility reads as a property of the chain rather than of the sample."""
+ causes = artifact["boundary_causes"]
+ assert set(causes) >= {"fetch_missing", "depth_capped", "coinbase"}
+ assert causes["fetch_missing"] > causes["depth_capped"], (
+ "the slice now bounds the walk less than the depth limit does; the document's reading of "
+ "these cuts as sample-bounded lower bounds needs rewriting")
+
+
+def test_the_document_states_the_sample_limit_beside_the_fragility(artifact):
+ """The number and its caveat travel together or the number is misread."""
+ rendered = rc.render_markdown(artifact)
+ assert str(artifact["readings"]["cut_of_one"]) in rendered
+ assert str(artifact["boundary_causes"]["fetch_missing"]) in rendered
+ assert "lower bound" in rendered
+
+
+@pytest.mark.reproduction
+def test_the_committed_artifact_is_what_a_fresh_run_produces(artifact):
+ if not SNAPSHOT.is_file():
+ pytest.skip("the block-cache snapshot is not committed")
+ rc.verify_artifact(artifact, str(SNAPSHOT))
+
+
+def test_pruning_by_value_does_not_lower_the_cut(artifact):
+ """Removing routes can only remove routes: the pruned distribution cannot exceed the raw one."""
+ raw = artifact["capacity"]
+ carrying = artifact["capacity_carrying_the_coin"]
+ assert sum(carrying.values()) == sum(raw.values())
+ assert max((int(k) for k in carrying), default=0) <= max(int(k) for k in raw)
+
+
+def test_the_value_threshold_changes_the_answer(artifact):
+ """If it never did, the specification's pruning step would be measuring nothing here."""
+ readings = artifact["readings"]
+ assert readings["cut_of_one_carrying_the_coin"] > readings["cut_of_one"], (
+ "pruning to routes that could carry the coin no longer moves the single-coin cut; either "
+ "the slice changed or the threshold stopped being applied")
+
+
+def test_both_readings_reach_the_document(artifact):
+ """The unpruned number alone overstates redundancy, so it must not travel on its own."""
+ rendered = rc.render_markdown(artifact)
+ for key in ("cut_of_one", "cut_of_one_carrying_the_coin", "no_route_carries_the_coin"):
+ assert str(artifact["readings"][key]) in rendered, key
diff --git a/tests/test_runtime_snapshot_repoint.py b/tests/test_runtime_snapshot_repoint.py
new file mode 100644
index 00000000..57057604
--- /dev/null
+++ b/tests/test_runtime_snapshot_repoint.py
@@ -0,0 +1,67 @@
+"""Moving the runtime archive has to move every record that names it, in one step.
+
+Done by hand it is four edits in a required order — environments, run manifests, bundle source
+blobs, then deleting the superseded archive — and skipping any one of them leaves a record
+describing a runtime the repository does not ship. That happened three times during the audit
+before this existed.
+"""
+import json
+
+import pytest
+
+from decluster.runtime_snapshot import repoint
+
+
+@pytest.fixture
+def tree(tmp_path):
+ (tmp_path / "sources").mkdir()
+ old = tmp_path / "sources" / "decluster-aaaaaaa-runtime.tar"
+ new = tmp_path / "sources" / "decluster-bbbbbbb-runtime.tar"
+ old.write_bytes(b"old")
+ new.write_bytes(b"new")
+
+ environment = tmp_path / "reproduction" / "one" / "environment.json"
+ environment.parent.mkdir(parents=True)
+ environment.write_text(json.dumps(
+ {"schema_version": 1, "source_archive": "sources/decluster-aaaaaaa-runtime.tar"}, indent=2) + "\n")
+
+ manifest = tmp_path / "catalog" / "runs" / "one.json"
+ manifest.parent.mkdir(parents=True)
+ manifest.write_text(json.dumps({"id": "one", "code": {"revision": "a" * 40, "dirty": False}},
+ indent=2) + "\n")
+
+ index = tmp_path / "releases" / "one.bundle.json"
+ index.parent.mkdir(parents=True)
+ index.write_text(json.dumps({"id": "one", "blobs": [
+ {"name": "sources/decluster-aaaaaaa-runtime.tar", "role": "source"},
+ {"name": "results/artifacts/one.json", "role": "result"},
+ ]}, indent=2) + "\n")
+ return tmp_path, new
+
+
+def test_every_record_follows_the_new_archive(tree):
+ root, archive = tree
+ moved, superseded = repoint(root, archive, "b" * 40)
+ assert len(moved) == 3 and superseded == ["decluster-aaaaaaa-runtime.tar"]
+
+ environment = json.loads((root / "reproduction" / "one" / "environment.json").read_text())
+ assert environment["source_archive"] == "sources/decluster-bbbbbbb-runtime.tar"
+ manifest = json.loads((root / "catalog" / "runs" / "one.json").read_text())
+ assert manifest["code"]["revision"] == "b" * 40
+ blobs = json.loads((root / "releases" / "one.bundle.json").read_text())["blobs"]
+ assert blobs[0]["name"] == "sources/decluster-bbbbbbb-runtime.tar"
+ assert blobs[1]["name"] == "results/artifacts/one.json"
+
+
+def test_the_superseded_archive_is_removed_and_the_new_one_kept(tree):
+ root, archive = tree
+ repoint(root, archive, "b" * 40)
+ remaining = sorted(p.name for p in (root / "sources").glob("*.tar"))
+ assert remaining == ["decluster-bbbbbbb-runtime.tar"]
+
+
+def test_repointing_a_tree_already_in_step_changes_nothing(tree):
+ root, archive = tree
+ repoint(root, archive, "b" * 40)
+ moved, superseded = repoint(root, archive, "b" * 40)
+ assert moved == [] and superseded == []
diff --git a/tests/test_same_block_linkage.py b/tests/test_same_block_linkage.py
new file mode 100644
index 00000000..837a92b9
--- /dev/null
+++ b/tests/test_same_block_linkage.py
@@ -0,0 +1,54 @@
+"""p_II is what the co-spend heuristic asserts; these pin what it does and does not mean."""
+import json
+
+import pytest
+
+from decluster.baselines import boltzmann
+from decluster.experiments import same_block_linkage as experiment
+
+
+@pytest.fixture(scope="module")
+def artifact():
+ return experiment.build_artifact()
+
+
+def test_reproduce_then_verify(tmp_path):
+ art, markdown = tmp_path / "a.json", tmp_path / "a.md"
+ assert experiment.main(["reproduce", "--artifact", str(art), "--markdown", str(markdown)]) == 0
+ assert experiment.main(["verify", "--artifact", str(art), "--markdown", str(markdown)]) == 0
+
+
+def test_the_diagonal_is_not_counted_as_a_pair(artifact):
+ # Every coin shares its own block, so counting the diagonal would report p_II = 1 for free.
+ measured = artifact["measurement"]
+ assert measured["input_pairs"] < sum(
+ count for count in measured["p_ii_distribution"].values()
+ ) + 1
+ assert measured["input_pairs"] == sum(measured["p_ii_distribution"].values())
+
+
+def test_p_ii_is_the_same_side_quantity():
+ """Two inputs of a transaction whose amounts admit two readings are not forced together."""
+ analysis = boltzmann.same_block_probabilities([4, 6], [4, 6], max_coins=12)
+ assert analysis.mapping_count
+ assert analysis.input_matrix[0][1] < 1.0
+
+
+def test_a_transaction_the_amounts_cannot_split_forces_its_inputs():
+ analysis = boltzmann.same_block_probabilities([3, 5], [8], max_coins=12)
+ assert analysis.mapping_count
+ assert analysis.input_matrix[0][1] == 1.0
+
+
+def test_the_forced_share_is_reported_with_both_directions(artifact):
+ measured = artifact["measurement"]
+ assert measured["input_pairs_forced_together"] > 0
+ assert "input_pairs_forced_apart" in measured
+ assert measured["input_pairs_forced_together"] <= measured["input_pairs"]
+
+
+def test_a_tampered_artifact_is_refused(artifact):
+ stored = json.loads(json.dumps(artifact))
+ stored["measurement"]["input_pairs_forced_together"] += 1
+ with pytest.raises(experiment.VerificationError):
+ experiment.verify_artifact(stored)
diff --git a/tests/test_scale_cluster.py b/tests/test_scale_cluster.py
new file mode 100644
index 00000000..20c0af82
--- /dev/null
+++ b/tests/test_scale_cluster.py
@@ -0,0 +1,79 @@
+"""The dense-integer (numpy) union-find must produce the identical partition to the dict-based
+streaming clustering; it is only a more compact representation."""
+import json
+import os
+import tempfile
+import gzip
+from collections import defaultdict
+
+from decluster.scale_cluster import cluster_scale, cluster_scale_np
+
+
+def _tx(ins, outs):
+ return {"vin": [{"prevout": {"scriptpubkey_address": a}} for a in ins],
+ "vout": [{"scriptpubkey_address": a} for a in outs]}
+
+
+def _write(rows):
+ fd, path = tempfile.mkstemp(suffix=".ndjson")
+ with os.fdopen(fd, "w") as f:
+ for r in rows:
+ f.write(json.dumps(r) + "\n")
+ return path
+
+
+def _partition(cl, addrs):
+ g = defaultdict(set)
+ for a in addrs:
+ g[cl.get(a, a)].add(a)
+ return {frozenset(v) for v in g.values() if len(v) >= 2}
+
+
+def test_np_union_find_reproduces_the_dict_partition():
+ rows = [_tx(["a1", "a2"], ["a3"]), _tx(["a2", "a4"], ["x"]), # cluster {a1,a2,a4}
+ _tx(["b1", "b2"], ["y"]), # cluster {b1,b2}
+ _tx(["c1"], ["z"]), # singleton (single input)
+ _tx([f"m{i}" for i in range(20)], [f"o{i}" for i in range(20)])] # coinjoin -> skipped
+ addrs = {"a1", "a2", "a4", "b1", "b2", "c1"}
+ path = _write(rows)
+ try:
+ p_dict = _partition(cluster_scale([path]), addrs)
+ p_np = _partition(cluster_scale_np([path]), addrs)
+ assert p_dict == p_np
+ assert frozenset({"a1", "a2", "a4"}) in p_np and frozenset({"b1", "b2"}) in p_np
+ finally:
+ os.remove(path)
+
+
+def test_np_union_find_reads_gzip_directly():
+ rows = [_tx(["a", "b"], ["x"]), _tx(["b", "c"], ["y"])]
+ fd, path = tempfile.mkstemp(suffix=".ndjson.gz")
+ os.close(fd)
+ try:
+ with gzip.open(path, "wt") as f:
+ for row in rows:
+ f.write(json.dumps(row) + "\n")
+ got = cluster_scale_np([path])
+ assert got.get("a") == got.get("b") == got.get("c")
+ mapped = got.map_many(["a", "b", "c", "singleton"])
+ assert mapped["a"] == mapped["b"] == mapped["c"]
+ assert mapped["singleton"] == "singleton"
+ finally:
+ os.remove(path)
+
+
+def test_the_scalable_clusterer_refuses_the_same_shapes_as_the_reference_detector():
+ """`cluster_scale*` duplicated the many-in/many-out rule inline, so it could not see the
+ equal-amount coinjoin the reference detector was taught to catch. One detector, both paths."""
+ from decluster.monitor import is_coinjoin
+ from decluster.scale_cluster import cluster_scale_stream, cluster_scale_np_stream
+
+ def mix(ins, denom, n_equal):
+ return {"vin": [{"prevout": {"scriptpubkey_address": a, "value": 6_000_000}} for a in ins],
+ "vout": [{"scriptpubkey_address": f"o{i}", "value": denom} for i in range(n_equal)]}
+
+ whirlpool = mix(["w1", "w2", "w3", "w4", "w5"], 5_000_000, 5)
+ assert is_coinjoin(whirlpool) # equal denomination, five participants
+ for backend in (cluster_scale_stream, cluster_scale_np_stream):
+ c = backend([whirlpool])
+ assert c.get("w1", "w1") != c.get("w2", "w2") # refused: no common-input merge
diff --git a/tests/test_scorer_families.py b/tests/test_scorer_families.py
new file mode 100644
index 00000000..72ede829
--- /dev/null
+++ b/tests/test_scorer_families.py
@@ -0,0 +1,52 @@
+"""The three axis families, and the one property that makes their magnitudes comparable."""
+import json
+from pathlib import Path
+
+import pytest
+
+from decluster.experiments import scorer_families as experiment
+
+ROOT = Path(__file__).resolve().parents[1]
+SNAPSHOT = str(ROOT / "data" / "fs-blkcache-2026-09-04.tar.gz")
+
+
+@pytest.fixture(scope="module")
+def artifact():
+ return experiment.build_artifact(SNAPSHOT)
+
+
+def test_reproduce_then_verify(tmp_path):
+ art, markdown = tmp_path / "a.json", tmp_path / "a.md"
+ argv = ["--snapshot", SNAPSHOT]
+ assert experiment.main(argv + ["reproduce", "--artifact", str(art), "--markdown", str(markdown)]) == 0
+ assert experiment.main(argv + ["verify", "--artifact", str(art), "--markdown", str(markdown)]) == 0
+
+
+def test_only_the_axis_set_differs_between_families(artifact):
+ rows = artifact["measurement"]["rows"]
+ assert {row["family"] for row in rows} == {"catalogued", "construction_only", "decorrelated"}
+ assert len({row["n_pos"] for row in rows}) == 1
+ assert len({row["n_neg"] for row in rows}) == 1
+ assert [row["axes"] for row in rows] == [23, 18, 14]
+
+
+def test_the_catalogued_family_agrees_with_the_weight_sweep_at_the_same_consistency(artifact):
+ """A second run of the same kernel at 0.95 has to land on the same row, or one of them is wrong."""
+ sweep = json.loads((ROOT / "results" / "artifacts" / "weight-sensitivity-v1.json").read_text())
+ row = next(r for r in sweep["measurement"]["rows"] if r["consistency"] == 0.95)
+ catalogued = next(r for r in artifact["measurement"]["rows"] if r["family"] == "catalogued")
+ for key in ("auc", "pos_mean", "neg_mean", "n_pos", "n_neg"):
+ assert catalogued[key] == pytest.approx(row[key]), key
+
+
+def test_dropping_redundant_axes_costs_magnitude(artifact):
+ measured = artifact["measurement"]
+ assert measured["magnitude_fall_from_catalogued_to_decorrelated"] > 0
+ assert measured["auc_rises_as_axes_are_dropped"]
+
+
+def test_a_tampered_artifact_is_refused(artifact):
+ stored = json.loads(json.dumps(artifact))
+ stored["measurement"]["rows"][0]["auc"] += 0.01
+ with pytest.raises(experiment.VerificationError):
+ experiment.verify_artifact(stored, SNAPSHOT)
diff --git a/tests/test_slice_a_channels.py b/tests/test_slice_a_channels.py
new file mode 100644
index 00000000..64ea9e6d
--- /dev/null
+++ b/tests/test_slice_a_channels.py
@@ -0,0 +1,61 @@
+"""Pin what Collection A settles about the *entity-attribute* and *social-graph* channels, on a
+committed real slice (2016 mainnet blocks 400000-400005, addresses only, from `bigquery/graph.sql`,
+frozen at `tests/fixtures/slice_a_channels_2016.ndjson.gz`).
+
+The load-bearing, reproducible finding is a NEGATIVE that sharpens the thesis: the entity-attribute
+feature space is *dense*, not (epsilon,delta)-sparse. Almost every entity has a near-twin even at
+epsilon=0.9, so the Netflix sparse-record precondition (Def 1 / Theorem 2) does NOT hold on this
+channel. The de-anonymizing sparsity lives in the *ancestry* channel (see `test_reid.py`,
+`RESULTS-reid.md`), not here. The contracted graph is also disassortative (a transactional, not a
+social, network), the structural reason the cross-view matcher stays underpowered.
+
+Only the two SLICE-STABLE facts are pinned: the density curve and the sign of assortativity. The
+cross-view match precision and the transitivity ratio are strongly scale-dependent (measured
+transitivity_ratio ~7 on 6 blocks, ~0 on 150k txs), so they are reported in
+`results/RESULTS-slice-a-channels.md` from the full-slice run rather than band-pinned here."""
+import gzip
+import json
+import os
+
+from decluster import contraction, def1_sparsity, graph_shape, views
+
+FIX = os.path.join(os.path.dirname(__file__), "fixtures", "slice_a_channels_2016.ndjson.gz")
+
+
+def _sample():
+ with gzip.open(FIX, "rt") as f:
+ return [(json.loads(l), 0) for l in f]
+
+
+def _contracted(sample):
+ lookup = views.cluster_addresses(sample, refuse=True)
+ return lookup, contraction.contract(sample, lookup=lookup, axes=True)
+
+
+def test_entity_attribute_space_is_dense_not_sparse():
+ """Def 1 measured on entity attribute vectors: the survival curve stays high, so the space is
+ dense. This is the opposite of the ancestry channel and is why the attribute channel does not
+ de-anonymize by itself."""
+ sample = _sample()
+ assert len(sample) > 5000
+ lookup, g = _contracted(sample)
+ assert len(set(lookup.values())) > 1000 # clustering actually ran
+ tops = def1_sparsity.nearest_similarities(g, query_n=1200, background_n=12000, min_degree=2, seed=0)
+ surv = def1_sparsity.survival(tops, epsilons=(0.5, 0.9, 0.99))
+ assert surv[0.5] >= 0.99 # doc 1.000
+ assert surv[0.9] >= 0.95 # doc 0.998 — dense: near-twin at eps 0.9
+ assert surv[0.99] >= 0.90 # doc 0.983
+
+ # Not an artefact of the one-transaction clusters that dominate the population: the
+ # density holds, weakening only mildly, when the query set is restricted to the hubs.
+ hubs = def1_sparsity.nearest_similarities(g, query_n=1200, background_n=12000,
+ min_degree=20, seed=0)
+ assert def1_sparsity.survival(hubs, epsilons=(0.9,))[0.9] >= 0.85 # doc 0.876
+
+
+def test_contracted_graph_is_disassortative_not_social():
+ """The stable structural fact behind the underpowered cross-view match: the pseudonym graph is
+ disassortative (hubs attach to leaves), a transactional rather than a social network."""
+ _, g = _contracted(_sample())
+ s = graph_shape.summary(g)
+ assert s["assortativity"] < 0 # doc -0.044; social graphs are positive
diff --git a/tests/test_slice_channels_experiment.py b/tests/test_slice_channels_experiment.py
new file mode 100644
index 00000000..eaf4d8be
--- /dev/null
+++ b/tests/test_slice_channels_experiment.py
@@ -0,0 +1,25 @@
+import gzip
+
+import pytest
+
+from decluster.experiments import slice_channels as experiment
+
+
+def test_reproduce_then_verify_snapshot(tmp_path):
+ artifact = tmp_path / "artifact.json"
+ markdown = tmp_path / "result.md"
+ argv = ["--dataset", "tests/fixtures/slice_a_channels_2016.ndjson.gz"]
+ assert experiment.main(argv + [
+ "reproduce", "--artifact", str(artifact), "--markdown", str(markdown),
+ ]) == 0
+ assert experiment.main(argv + [
+ "verify", "--artifact", str(artifact), "--markdown", str(markdown),
+ ]) == 0
+
+
+def test_empty_snapshot_is_rejected(tmp_path):
+ dataset = tmp_path / "empty.ndjson.gz"
+ with gzip.open(dataset, "wt", encoding="utf-8"):
+ pass
+ with pytest.raises(experiment.VerificationError, match="snapshot is empty"):
+ experiment.build_artifact(dataset)
diff --git a/tests/test_source_obligations.py b/tests/test_source_obligations.py
new file mode 100644
index 00000000..73365f23
--- /dev/null
+++ b/tests/test_source_obligations.py
@@ -0,0 +1,71 @@
+"""Every source's `obligation` has to account for whether a claim cites it.
+
+The catalogue already carries the vocabulary — `context` and `no_code_required` are sources that
+inform the writing without asserting anything checkable, while `baseline`, `fixture`, `metric` and
+`qualification` are sources some claim is supposed to discharge. Nothing checked that the two
+agreed, so three clustering baselines sat uncited beside sixteen carrying the same obligation, and
+the difference was invisible.
+
+An unclaimed source with a discharging obligation is either a gap or a decision. This forces it to
+be written down as one.
+"""
+import json
+from pathlib import Path
+
+import pytest
+
+ROOT = Path(__file__).resolve().parents[1]
+
+# A source of this kind informs the prose; no claim asserts anything on its behalf.
+NO_CLAIM_EXPECTED = {"context", "no_code_required"}
+
+# A source whose obligation would normally be discharged by a claim, and is deliberately not.
+EXEMPT = {
+ "radix": "a constructive decomposition notebook the counting router reads as method, not an "
+ "attack oracle or a mapping-count bound; its own note says so and redistribution "
+ "awaits a licence",
+}
+
+
+def sources():
+ return json.loads((ROOT / "catalog" / "ctp-sources.json").read_text())["sources"]
+
+
+def cited():
+ claims = json.loads((ROOT / "catalog" / "ctp-claims.json").read_text())["claims"]
+ return {source for claim in claims for source in claim["sources"]}
+
+
+def test_every_source_carries_an_obligation():
+ for source in sources():
+ assert source.get("obligation"), f"{source['id']} declares no obligation"
+
+
+def test_a_source_whose_obligation_a_claim_discharges_is_cited_by_one():
+ uncited = [
+ source["id"]
+ for source in sources()
+ if source["obligation"] not in NO_CLAIM_EXPECTED
+ and source["id"] not in EXEMPT
+ and source["id"] not in cited()
+ ]
+ assert not uncited, (
+ f"these carry an obligation a claim is meant to discharge and no claim cites them: "
+ f"{uncited}. Cite them from the claim that already covers the material, or write the "
+ f"exemption down with its reason."
+ )
+
+
+def test_the_exemptions_are_real_and_reasoned():
+ known = {source["id"] for source in sources()}
+ for identifier, reason in EXEMPT.items():
+ assert identifier in known, f"{identifier} is exempted and is not a source"
+ assert identifier not in cited(), f"{identifier} is exempted and is cited; drop the exemption"
+ assert len(reason.split()) >= 8, f"{identifier}: the reason is too thin to audit"
+
+
+def test_the_censorship_aside_is_declared_rather_than_missing():
+ """The one gist section with no claim, and the catalogue says why rather than staying silent."""
+ aside = next(s for s in sources() if s["id"] == "scam-exchanges")
+ assert aside["obligation"] == "no_code_required"
+ assert aside["id"] not in cited()
diff --git a/tests/test_source_snapshot.py b/tests/test_source_snapshot.py
new file mode 100644
index 00000000..60266164
--- /dev/null
+++ b/tests/test_source_snapshot.py
@@ -0,0 +1,212 @@
+import hashlib
+from io import BytesIO
+import json
+from pathlib import Path
+
+import pytest
+
+from decluster.source_snapshot import (
+ SourceSnapshotError,
+ fetch_source,
+ load_source_snapshot,
+ verify_normalized_source,
+ verify_source,
+)
+
+
+ROOT_KEYS = {
+ "schema_version", "id", "title", "canonical_url", "retrieval_url",
+ "retrieved_at", "format", "content", "structure", "normalization",
+ "license", "redistribution", "availability", "interpretation",
+}
+
+
+def document(payload):
+ return {
+ "schema_version": 1,
+ "id": "source",
+ "title": "Source",
+ "canonical_url": "https://example.invalid/source",
+ "retrieval_url": "https://example.invalid/export",
+ "retrieved_at": "2026-09-05T00:00:00Z",
+ "format": "application/json",
+ "content": {
+ "bytes": len(payload),
+ "sha256": hashlib.sha256(payload).hexdigest(),
+ },
+ "structure": {},
+ "normalization": {},
+ "license": "unknown",
+ "redistribution": "unknown",
+ "availability": "remote",
+ "interpretation": "fixture",
+ }
+
+
+def notebook_document(payload):
+ value = document(payload)
+ value["structure"] = {
+ "cells": 1,
+ "code_cells": 1,
+ "markdown_cells": 0,
+ "display_data_outputs": 0,
+ "execute_result_outputs": 0,
+ }
+ value["normalization"] = {
+ "algorithm": "jq-cS-strip-runtime-v1",
+ "tool": "jq 1.8.1",
+ "removed": [
+ ".cells[].outputs",
+ ".cells[].execution_count",
+ ".metadata.colab.authorship_tag",
+ ".metadata.colab.provenance",
+ ],
+ "serialization": "jq -cS with one trailing LF; not RFC 8785 JCS",
+ "source_only_sha256": "539b2989c3df9ae53831a9d23599aa219ff77def7cb6aae22512580b25dd7ef3",
+ }
+ return value
+
+
+def write_manifest(path, value):
+ assert set(value) == ROOT_KEYS
+ path.write_text(json.dumps(value), encoding="utf-8")
+ return path
+
+
+def test_source_snapshot_verifies_exact_bytes(tmp_path):
+ payload = b'{"notebook":true}\n'
+ manifest = load_source_snapshot(
+ write_manifest(tmp_path / "source.json", document(payload))
+ )
+ source = tmp_path / "source.ipynb"
+ source.write_bytes(payload)
+ assert verify_source(manifest, source) == manifest
+
+
+def test_source_snapshot_rejects_modified_bytes(tmp_path):
+ payload = b"original"
+ manifest = load_source_snapshot(
+ write_manifest(tmp_path / "source.json", document(payload))
+ )
+ source = tmp_path / "source.ipynb"
+ source.write_bytes(b"modified")
+ with pytest.raises(SourceSnapshotError, match="identity mismatch"):
+ verify_source(manifest, source)
+
+
+def test_source_snapshot_fetch_is_identity_checked_and_atomic(tmp_path):
+ payload = b"locked notebook"
+ manifest = load_source_snapshot(
+ write_manifest(tmp_path / "source.json", document(payload))
+ )
+ destination = tmp_path / "source.ipynb"
+ opener = lambda _, **__: BytesIO(payload)
+ assert fetch_source(manifest, destination, opener=opener) == manifest
+ assert destination.read_bytes() == payload
+
+ with pytest.raises(SourceSnapshotError, match="identity mismatch"):
+ fetch_source(manifest, destination, opener=lambda _, **__: BytesIO(b"changed"))
+ assert destination.read_bytes() == payload
+
+
+def test_source_snapshot_fetch_rejects_oversized_response(tmp_path):
+ payload = b"small"
+ manifest = load_source_snapshot(
+ write_manifest(tmp_path / "source.json", document(payload))
+ )
+ with pytest.raises(SourceSnapshotError, match="exceeds locked size"):
+ fetch_source(
+ manifest, tmp_path / "source.ipynb", opener=lambda _, **__: BytesIO(b"too large")
+ )
+
+
+def test_source_snapshot_wraps_transport_failure(tmp_path):
+ payload = b"source"
+ manifest = load_source_snapshot(
+ write_manifest(tmp_path / "source.json", document(payload))
+ )
+
+ def unavailable(_, **__):
+ raise OSError("offline")
+
+ with pytest.raises(SourceSnapshotError, match="download failed"):
+ fetch_source(manifest, tmp_path / "source.ipynb", opener=unavailable)
+
+
+def test_normalization_pipeline_has_a_committed_golden_fixture(tmp_path):
+ notebook = {
+ "nbformat": 4,
+ "nbformat_minor": 0,
+ "metadata": {"colab": {"authorship_tag": "remove", "provenance": []}},
+ "cells": [{
+ "cell_type": "code",
+ "metadata": {},
+ "source": ["x = 1\n"],
+ "execution_count": 1,
+ "outputs": [],
+ }],
+ }
+ payload = json.dumps(notebook, separators=(",", ":")).encode()
+ manifest = load_source_snapshot(
+ write_manifest(tmp_path / "source.json", notebook_document(payload))
+ )
+ source = tmp_path / "source.ipynb"
+ source.write_bytes(payload)
+ assert verify_normalized_source(manifest, source) == manifest
+
+
+def test_fetch_validates_normalization_before_replacing_destination(tmp_path):
+ payload = json.dumps({"cells": []}, separators=(",", ":")).encode()
+ value = notebook_document(payload)
+ manifest = load_source_snapshot(write_manifest(tmp_path / "source.json", value))
+ destination = tmp_path / "source.ipynb"
+ destination.write_bytes(b"preserve")
+ with pytest.raises(SourceSnapshotError, match="structure mismatch"):
+ fetch_source(manifest, destination, opener=lambda _, **__: BytesIO(payload))
+ assert destination.read_bytes() == b"preserve"
+
+
+def test_radix_lock_records_raw_and_source_only_identities():
+ root = Path(__file__).resolve().parents[1]
+ path = root / "catalog" / "source-snapshots" / "radix-notebook-v1.json"
+ snapshot = load_source_snapshot(path)
+ raw = json.loads(path.read_text())
+ assert snapshot.sha256 == "afaee22d223b442774eeaa50f3da00c97b1fba5c7c300b5d3cde56ddc57ff48f"
+ assert raw["normalization"]["source_only_sha256"] == "a8a9c3d6f70f42c928490ba5974569d6846ff0906cb87481993a3dc6ed2d83f9"
+ assert snapshot.redistribution == "unknown"
+
+
+def test_radix_export_recomputes_normalized_identity_when_available():
+ source = Path("/tmp/radix-source")
+ if not source.is_file():
+ pytest.skip("audited external export not present")
+ root = Path(__file__).resolve().parents[1]
+ manifest = load_source_snapshot(
+ root / "catalog" / "source-snapshots" / "radix-notebook-v1.json"
+ )
+ assert verify_normalized_source(manifest, source) == manifest
+
+
+def test_content_size_rejects_boolean(tmp_path):
+ value = document(b"x")
+ value["content"]["bytes"] = True
+ with pytest.raises(SourceSnapshotError, match="content identity is invalid"):
+ load_source_snapshot(write_manifest(tmp_path / "source.json", value))
+
+
+def test_manifest_text_fields_are_strict(tmp_path):
+ value = document(b"x")
+ value["title"] = {"not": "text"}
+ with pytest.raises(SourceSnapshotError, match="title is invalid"):
+ load_source_snapshot(write_manifest(tmp_path / "source.json", value))
+
+
+def test_malformed_notebook_is_a_structured_error(tmp_path):
+ payload = b'{"cells":[null]}'
+ manifest = load_source_snapshot(
+ write_manifest(tmp_path / "source.json", notebook_document(payload))
+ )
+ source = tmp_path / "source.ipynb"
+ source.write_bytes(payload)
+ with pytest.raises(SourceSnapshotError, match="cells are missing"):
+ verify_normalized_source(manifest, source)
diff --git a/tests/test_special_change_labels_experiment.py b/tests/test_special_change_labels_experiment.py
new file mode 100644
index 00000000..469df472
--- /dev/null
+++ b/tests/test_special_change_labels_experiment.py
@@ -0,0 +1,86 @@
+"""Change labels that do not read co-spend, taken again on data a reader has.
+
+The figures this replaces came from downloads outside the repository. What the committed cache
+supports is the offline half, and the half it does not support is asserted to stay absent: the
+onward-spend arm needs a labelled transaction's outputs to be spent inside the same data, and the
+count of those is small enough that a rate computed from them would be a number, not a measurement.
+
+The agreement between address reuse and type match is checked as arithmetic rather than trusted as a
+result. An output reusing an input address carries an input's script type, so the type rule can only
+choose that same output or abstain — the two labels are one label, and a document that read their
+agreement as corroboration would be counting one fact twice.
+"""
+
+import json
+from pathlib import Path
+
+import pytest
+
+from decluster.change_special import label_address_reuse, label_type_match
+from decluster.experiments import special_change_labels as scl
+
+ROOT = Path(__file__).resolve().parent.parent
+ARTIFACT = ROOT / "results" / "artifacts" / "special-change-labels-v1.json"
+SNAPSHOT = ROOT / "data" / "fs-blkcache-2026-09-04.tar.gz"
+
+
+@pytest.fixture(scope="module")
+def artifact():
+ if not ARTIFACT.is_file():
+ pytest.skip("the canonical special-change artifact is not committed")
+ return json.loads(ARTIFACT.read_text())
+
+
+def _pair(artifact, a, b):
+ return next(row for row in artifact["agreement"] if {row["a"], row["b"]} == {a, b})
+
+
+def _row(artifact, label, predictor):
+ return next(row for row in artifact["within_tx"]
+ if row["label"] == label and row["predictor"] == predictor)
+
+
+def test_universal_heuristics_agree_with_a_label_neither_of_them_reads(artifact):
+ """The non-circular cross-check: values-only label, address- and roundness-only predictors."""
+ for predictor in ("round_number", "address_reuse"):
+ row = _row(artifact, "optimal_change", predictor)
+ assert row["precision"] > 0.75 and row["coverage"] > 0.3
+
+
+def test_address_reuse_and_type_match_are_one_label(artifact):
+ row = _pair(artifact, "address_reuse", "type_match")
+ assert row["disagree"] == 0 and row["both"] > 100
+ assert "arithmetic rather than evidence" in scl.render_markdown(artifact)
+
+
+def test_the_type_rule_cannot_contradict_a_reused_address():
+ """Why that column is zero, decided on transactions rather than read off the run."""
+ reused = {"scriptpubkey_address": "1AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA"}
+ payment = {"scriptpubkey_address": "bc1qaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa"}
+ tx = {"txid": "t", "vin": [{"prevout": reused}, {"prevout": reused}],
+ "vout": [dict(reused), dict(payment)]}
+ assert label_address_reuse(tx) == 0
+ assert label_type_match(tx) == 0
+
+ # The type rule abstains rather than dissenting when the payment shares the reused type.
+ same_type = {"scriptpubkey_address": "1BBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBB"}
+ tx = {"txid": "t", "vin": [{"prevout": reused}, {"prevout": reused}],
+ "vout": [dict(reused), dict(same_type)]}
+ assert label_address_reuse(tx) == 0
+ assert label_type_match(tx) is None
+
+
+def test_the_onward_spend_arm_is_absent_because_the_data_cannot_carry_it(artifact):
+ reach = artifact["onward_spend_reach"]["optimal_change"]
+ assert reach["both_outputs_spent"] < 0.05 * reach["labels"], (
+ "the cache now holds enough spenders to measure the per-axis arm, so it should be measured "
+ "rather than declared out of reach")
+ rendered = scl.render_markdown(artifact)
+ assert "not measured here" in rendered and "state 4" in rendered
+
+
+@pytest.mark.reproduction
+def test_the_committed_artifact_is_what_a_fresh_run_produces(artifact):
+ if not SNAPSHOT.is_file():
+ pytest.skip("the block-cache snapshot is not committed")
+ scl.verify_artifact(artifact, str(SNAPSHOT))
diff --git a/tests/test_split_epochs.py b/tests/test_split_epochs.py
new file mode 100644
index 00000000..3164347f
--- /dev/null
+++ b/tests/test_split_epochs.py
@@ -0,0 +1,32 @@
+import gzip
+import importlib.util
+import json
+from pathlib import Path
+
+
+SCRIPT = Path(__file__).parents[1] / "examples" / "split_epochs.py"
+SPEC = importlib.util.spec_from_file_location("split_epochs", SCRIPT)
+MOD = importlib.util.module_from_spec(SPEC)
+SPEC.loader.exec_module(MOD)
+
+
+def test_split_file_streams_sorted_chunks_and_hashes_them(tmp_path):
+ source = tmp_path / "month.ndjson.gz"
+ with gzip.open(source, "wt") as f:
+ for height in (10, 11, 12, 13, 14):
+ f.write(json.dumps({"height": height, "vin": [], "vout": []}) + "\n")
+ records = MOD.split_file(source, tmp_path, blocks=2)
+ assert [r["transactions"] for r in records] == [2, 2, 1]
+ assert all(len(r["sha256"]) == 64 for r in records)
+ heights = []
+ for record in records:
+ with gzip.open(record["path"], "rt") as f:
+ heights.extend(json.loads(line)["height"] for line in f)
+ assert heights == [10, 11, 12, 13, 14]
+
+
+def test_split_file_accepts_unsorted_paginated_input(tmp_path):
+ source = tmp_path / "bad.ndjson"
+ source.write_text('{"height": 2}\n{"height": 1}\n')
+ records = MOD.split_file(source, tmp_path, blocks=2)
+ assert sum(record["transactions"] for record in records) == 2
diff --git a/tests/test_state3_promotion.py b/tests/test_state3_promotion.py
new file mode 100644
index 00000000..e050c2fe
--- /dev/null
+++ b/tests/test_state3_promotion.py
@@ -0,0 +1,119 @@
+"""State 3 says a claim becomes reproducible once a small query output is committed. This is the wiring.
+
+`REPRODUCIBILITY.md` state 3 listed four claims as "run the query, commit the small output as a
+fixture, and the number becomes band-pinnable". Nothing connected that sentence to anything: the
+fixture had no agreed path, so committing one would have pinned nothing until somebody also wrote the
+test. Here the test exists first and skips, naming the query to run and the file to write, so the
+promotion is a single commit rather than a small project.
+
+A skip is loud on purpose — it names what went unchecked, the way `test_results_manifests.py` does.
+"""
+
+import json
+import re
+from pathlib import Path
+
+import pytest
+
+ROOT = Path(__file__).resolve().parent.parent
+FIXTURES = ROOT / "tests" / "fixtures"
+
+SLOTS = {
+ "persistence-curve": ("bigquery/persistence_curve.sql", "persistence_curve.json",
+ "RESULTS-persistence-curve.md", "5-row aggregate"),
+ "slice-gate": ("bigquery/slice_gate.sql", "slice_gate.json",
+ "RESULTS-slice-gate-2026.md", "three aggregates, one per section"),
+ # The shipped library bits came from a ~105k uniform sample that no longer exists; PAPER.md §5
+ # states them and `test_paper_numbers.py` carries "105,000" as unbacked. A fresh small sample
+ # recalibrates them on data a reader has. `library-calibration-v1` does not close this: it
+ # measures divergence against a different population and says so.
+ "fingerprint-calibration": ("bigquery/sample_small.sql", "fingerprint_calibration.ndjson.gz",
+ "PAPER.md", "15k sampled transactions"),
+}
+
+COLUMNS = ("width", "core_vertices", "mean_degree_a",
+ "neighbour_persistence", "mean_per_vertex_persistence", "with_3plus_surviving")
+
+
+def _published_rows(doc, columns):
+ """The document's own table, as the query's columns name them."""
+ body = (ROOT / "results" / doc).read_text()
+ rows = []
+ for line in body.splitlines():
+ cells = [c.strip() for c in line.strip().strip("|").split("|")] if line.startswith("|") else []
+ if len(cells) != len(columns) or not re.match(r"^[\d ]+ day", cells[0]):
+ continue
+ values = [int(re.match(r"(\d+)", cells[0]).group(1))]
+ values += [float(c.replace(" ", "")) for c in cells[1:]]
+ rows.append(dict(zip(columns, values)))
+ return rows
+
+
+@pytest.mark.parametrize("claim", sorted(SLOTS))
+def test_the_state_three_fixture_is_committed(claim):
+ query, fixture, doc, shape = SLOTS[claim]
+ if not (FIXTURES / fixture).is_file():
+ pytest.skip(f"{claim}: NOT pinned — run `{query}` ({shape}) and commit its output as "
+ f"tests/fixtures/{fixture}; {doc} is a data-run until then")
+
+
+def test_the_persistence_curve_table_is_the_committed_query_output():
+ """The worked example: once the fixture lands, the published table has to equal it."""
+ fixture = FIXTURES / SLOTS["persistence-curve"][1]
+ if not fixture.is_file():
+ pytest.skip("persistence-curve: fixture absent, so the published table is unchecked")
+ recorded = json.loads(fixture.read_text())
+ published = _published_rows(SLOTS["persistence-curve"][2], COLUMNS)
+ assert published, "the document's table did not parse; the gate would pass on nothing"
+ by_width = {row["width"]: row for row in recorded}
+ for row in published:
+ expected = by_width.get(row["width"])
+ assert expected is not None, f"width {row['width']} is published but not in the query output"
+ for column in COLUMNS:
+ assert abs(float(expected[column]) - row[column]) < 0.5 * 10 ** -3, (
+ f"width {row['width']}: {column} is {row[column]} in the document, "
+ f"{expected[column]} in the committed query output")
+
+SEED_COLUMNS = ("k", "in_top_k_both", "precision_at_k")
+
+
+def _numeric_rows(doc, width):
+ """Every table row in `doc` with `width` numeric cells, as floats."""
+ rows = []
+ for line in (ROOT / "results" / doc).read_text().splitlines():
+ if not line.startswith("|"):
+ continue
+ cells = [c.strip().replace("\u202f", "").replace(" ", "") for c in line.strip().strip("|").split("|")]
+ if len(cells) != width or not all(re.fullmatch(r"-?[\d.]+", c) for c in cells):
+ continue
+ rows.append([float(c) for c in cells])
+ return rows
+
+
+def test_the_slice_gate_seed_supply_table_is_the_committed_query_output():
+ """The go/no-go table a reader is asked to trust, against the query that produced it."""
+ fixture = FIXTURES / SLOTS["slice-gate"][1]
+ if not fixture.is_file():
+ pytest.skip("slice-gate: fixture absent, so the published tables are unchecked")
+ recorded = {row["k"]: row for row in json.loads(fixture.read_text())["seed_supply"]}
+ published = _numeric_rows(SLOTS["slice-gate"][2], 3)
+ assert len(published) == len(recorded), (
+ f"{len(published)} seed-supply rows are published, {len(recorded)} were measured")
+ for k, in_both, precision in published:
+ row = recorded.get(int(k))
+ assert row is not None, f"top-{int(k)} is published but not in the query output"
+ assert row["in_top_k_both"] == int(in_both), (
+ f"top-{int(k)}: {int(in_both)} published, {row['in_top_k_both']} measured")
+ assert abs(row["precision_at_k"] - precision) < 0.5e-3, (
+ f"top-{int(k)}: precision {precision} published, {row['precision_at_k']} measured")
+
+
+def test_the_slice_gate_spanning_counts_are_the_committed_query_output():
+ fixture = FIXTURES / SLOTS["slice-gate"][1]
+ if not fixture.is_file():
+ pytest.skip("slice-gate: fixture absent, so the spanning counts are unchecked")
+ spanning = json.loads(fixture.read_text())["spanning"]
+ body = (ROOT / "results" / SLOTS["slice-gate"][2]).read_text().replace("\u202f", "").replace(" ", "")
+ for column in ("txs_a", "txs_b", "in_addrs_a", "in_addrs_b", "reused_addrs_a"):
+ assert str(spanning[column]) in body, (
+ f"{column} is {spanning[column]} in the query output and appears nowhere in the document")
diff --git a/tests/test_statistical_disclosure.py b/tests/test_statistical_disclosure.py
new file mode 100644
index 00000000..4b244e08
--- /dev/null
+++ b/tests/test_statistical_disclosure.py
@@ -0,0 +1,66 @@
+import pytest
+
+from decluster.domain import Inconclusive, PersistentCorrespondentRanked
+from decluster.failure_modes.statistical_disclosure import (
+ StatisticalDisclosureScenario,
+ estimate,
+ evaluate,
+ paired_examples,
+)
+
+
+def test_repeated_rounds_recover_the_persistent_correspondent():
+ persistent, _ = paired_examples()
+ report = evaluate(persistent)
+ outcome = report.outcomes[0]
+
+ assert isinstance(outcome, PersistentCorrespondentRanked)
+ assert outcome.correspondent.identifier == "alice"
+ assert outcome.margin == pytest.approx(1.0)
+ assert dict(outcome.evidence.scores) == pytest.approx(
+ dict(zip(persistent.correspondents, (1.0, 0.0, 0.0, 0.0)))
+ )
+
+
+def test_background_matched_control_does_not_invent_a_correspondent():
+ _, control = paired_examples()
+ report = evaluate(control)
+
+ assert isinstance(report.outcomes[0], Inconclusive)
+ assert dict(estimate(control).scores) == pytest.approx(
+ dict(zip(control.correspondents, control.background_distribution))
+ )
+
+
+def test_report_keeps_model_scope_and_has_no_composition():
+ report = evaluate(paired_examples()[0])
+
+ assert report.channels[0].identifier == "correspondent_distribution"
+ assert report.composition is None
+ assert "does not attribute Bitcoin ownership" in report.limitations[2]
+
+
+def test_rounds_must_have_a_consistent_batch_size():
+ scenario, _ = paired_examples()
+
+ with pytest.raises(ValueError, match="one batch size"):
+ StatisticalDisclosureScenario(
+ scenario.identifier,
+ scenario.target,
+ scenario.correspondents,
+ scenario.background_distribution,
+ scenario.rounds + ((scenario.correspondents[0],),),
+ )
+
+
+def test_background_distribution_must_be_normalized():
+ scenario, _ = paired_examples()
+
+ with pytest.raises(ValueError, match="sum to one"):
+ StatisticalDisclosureScenario(
+ scenario.identifier,
+ scenario.target,
+ scenario.correspondents,
+ (0.2, 0.2, 0.2, 0.2),
+ scenario.rounds,
+ )
diff --git a/tests/test_statistical_disclosure_experiment.py b/tests/test_statistical_disclosure_experiment.py
new file mode 100644
index 00000000..a13c55b5
--- /dev/null
+++ b/tests/test_statistical_disclosure_experiment.py
@@ -0,0 +1,34 @@
+import json
+
+import pytest
+
+from decluster.experiments import statistical_disclosure as experiment
+
+
+def test_artifact_contains_recovery_and_null_control():
+ artifact = experiment.build_artifact()
+
+ assert artifact["persistent_correspondent"]["unique_top_correspondent"] == "alice"
+ assert artifact["persistent_correspondent"]["top_margin"] == pytest.approx(1.0)
+ assert artifact["background_matched_control"]["unique_top_correspondent"] is None
+ assert artifact["composition"] is None
+
+
+def test_verifier_recomputes_the_estimator():
+ changed = json.loads(json.dumps(experiment.build_artifact()))
+ changed["persistent_correspondent"]["top_margin"] = 0.5
+
+ with pytest.raises(experiment.VerificationError, match="fresh failure-mode"):
+ experiment.verify_artifact(changed)
+
+
+def test_cli_reproduces_and_verifies_json_and_markdown(tmp_path):
+ artifact = tmp_path / "artifact.json"
+ markdown = tmp_path / "result.md"
+
+ assert experiment.main(
+ ["reproduce", "--artifact", str(artifact), "--markdown", str(markdown)]
+ ) == 0
+ assert experiment.main(
+ ["verify", "--artifact", str(artifact), "--markdown", str(markdown)]
+ ) == 0
diff --git a/tests/test_subsetsum_demix_wiring.py b/tests/test_subsetsum_demix_wiring.py
index 050f2252..c2a6ee87 100644
--- a/tests/test_subsetsum_demix_wiring.py
+++ b/tests/test_subsetsum_demix_wiring.py
@@ -10,7 +10,7 @@ def score(self, a, b, explain=False):
def _setup(cl):
cl.fetch_tx = lambda t: {"txid": t, "vin": [{"txid": "s_" + t, "prevout": {"value": 100}}],
"vout": [{"value": 90}]}
- cl._cospent_pairs = lambda nodes: [("A", "B", "T")]
+ cl._cospent_pairs = lambda nodes, fetch=None: [("A", "B", "T")]
def test_subsetsum_off_matches_no_flag():
@@ -48,7 +48,7 @@ def test_default_resolve_routes_to_demix(monkeypatch):
calls = []
cl.fetch_tx = lambda t: {"txid": t, "vin": [{"txid": "s_" + t, "prevout": {"value": 100}}],
"vout": [{"value": 90}]}
- cl._cospent_pairs = lambda nodes: [("A", "B", "T")]
+ cl._cospent_pairs = lambda nodes, fetch=None: [("A", "B", "T")]
monkeypatch.setattr(ss, "amount_refuse_demix", lambda tx, a, b, *_: calls.append((a, b)) or 0.0)
try:
cl.cluster_refined(["A", "B"], _Neutral(), link_above=99, subsetsum=True)
diff --git a/tests/test_subtransaction_model.py b/tests/test_subtransaction_model.py
new file mode 100644
index 00000000..e40bffff
--- /dev/null
+++ b/tests/test_subtransaction_model.py
@@ -0,0 +1,89 @@
+"""Model gates for amount-interpretation ranking."""
+
+import pytest
+
+from decluster.subtransaction import (
+ AmountDecisionStatus,
+ TransactionModel,
+ enumerate_amount_interpretations,
+ evaluate_amount_model,
+ partition_signal,
+ rank_amount_interpretations,
+)
+
+
+def _transaction(inputs=(913, 407), outputs=(907, 413)):
+ return {
+ "txid": "ambiguous-roundness",
+ "vin": [
+ {"txid": f"input-{index}", "prevout": {"value": value}}
+ for index, value in enumerate(inputs)
+ ],
+ "vout": [{"value": value} for value in outputs],
+ }
+
+
+def test_enumeration_and_ranking_are_separate_operations():
+ candidates = enumerate_amount_interpretations(_transaction())
+ ranked = rank_amount_interpretations(candidates)
+
+ assert {item.implied_net_transfer for item in candidates} == {6, 500}
+ assert ranked[0].implied_net_transfer == 500
+ assert set(ranked) == set(candidates)
+
+
+def test_unknown_model_preserves_candidates_without_directional_output():
+ decision = evaluate_amount_model(_transaction(), TransactionModel.UNKNOWN)
+
+ assert decision.status is AmountDecisionStatus.INCONCLUSIVE
+ assert decision.ranked
+ assert set(decision.ranked) == set(decision.interpretations)
+ assert decision.refuse == ()
+ assert decision.links == ()
+
+
+def test_net_settlement_model_cannot_turn_roundness_into_ownership():
+ decision = evaluate_amount_model(
+ _transaction(), TransactionModel.NET_SETTLEMENT_ALLOWED
+ )
+
+ assert decision.status is AmountDecisionStatus.INCONCLUSIVE
+ assert decision.refuse == ()
+ assert decision.links == ()
+ assert "not restricted" in decision.reason
+
+
+def test_restricted_model_exposes_only_a_directional_hypothesis():
+ decision = evaluate_amount_model(
+ _transaction(), TransactionModel.RESTRICTED_TWO_PARTY_PAYMENT
+ )
+
+ assert decision.status is AmountDecisionStatus.DIRECTIONAL_HYPOTHESIS
+ assert decision.ranked[0].implied_net_transfer == 500
+ assert decision.refuse
+ assert decision.links
+
+
+def test_legacy_partition_signal_is_an_explicit_restricted_model_adapter():
+ signal = partition_signal(_transaction())
+ decision = evaluate_amount_model(
+ _transaction(), TransactionModel.RESTRICTED_TWO_PARTY_PAYMENT
+ )
+
+ assert signal["payment"] == decision.ranked[0].implied_net_transfer
+ assert signal["refuse"] == list(decision.refuse)
+ assert signal["link"] == list(decision.links)
+
+
+def test_out_of_scope_shape_has_no_directional_output():
+ tx = _transaction(inputs=(10,), outputs=(9,))
+ decision = evaluate_amount_model(tx, TransactionModel.RESTRICTED_TWO_PARTY_PAYMENT)
+
+ assert decision.status is AmountDecisionStatus.OUT_OF_SCOPE
+ assert decision.refuse == ()
+ assert decision.links == ()
+
+
+def test_model_cannot_be_an_unchecked_string():
+ with pytest.raises(TypeError, match="TransactionModel"):
+ evaluate_amount_model(_transaction(), "restricted_two_party_payment")
diff --git a/tests/test_subtx_demix_experiment.py b/tests/test_subtx_demix_experiment.py
new file mode 100644
index 00000000..decd997d
--- /dev/null
+++ b/tests/test_subtx_demix_experiment.py
@@ -0,0 +1,32 @@
+import json
+from pathlib import Path
+
+from decluster.experiments import subtx_demix as experiment
+
+ROOT = Path(__file__).resolve().parents[1]
+SNAPSHOT = ROOT / "data" / "subtx-demix-cache-2026-09-04.tar.gz"
+
+
+def test_snapshot_reproduces_the_canonical_artifact():
+ artifact = experiment.build_artifact(SNAPSHOT)
+ stored = json.loads(
+ (ROOT / "results" / "artifacts" / "subtx-demix-v1.json").read_text()
+ )
+ assert artifact == stored
+
+
+def test_joinmarket_mechanism_reproduces_without_claiming_identity_labels():
+ artifact = experiment.build_artifact(SNAPSHOT)
+ measurement = artifact["measurement"]
+ assert len(measurement["joinmarket"]["recovered_inputs"]) == 8
+ assert sorted(measurement["joinmarket"]["fees"]) == [191, 413, 458, 559, 623, 636, 687, 973]
+ assert artifact["verdicts"]["joinmarket_fixture"] == "reproduced"
+
+
+def test_unlabelled_cache_cannot_support_the_historical_specificity_claim():
+ artifact = experiment.build_artifact(SNAPSHOT)
+ measurement = artifact["measurement"]
+ assert measurement["eligible_transactions"] == 25
+ assert measurement["eligible_with_any_recovery"] == 22
+ assert artifact["verdicts"]["ordinary_transaction_specificity"] == "not_tested"
+ assert artifact["verdicts"]["wasabi_rounds"] == "not_reproduced"
diff --git a/tests/test_unnecessary_input_baseline.py b/tests/test_unnecessary_input_baseline.py
new file mode 100644
index 00000000..aeb74b5f
--- /dev/null
+++ b/tests/test_unnecessary_input_baseline.py
@@ -0,0 +1,154 @@
+"""Paper-faithful and differential tests for unnecessary-input analysis."""
+
+from itertools import combinations, permutations
+
+from decluster.baselines.unnecessary_input import (
+ UIHStatus,
+ analyze_blockstream,
+ analyze_transaction,
+ blocksci_uih1,
+ gibson_flags,
+ optimal_change_candidate,
+)
+
+
+def _proper_subset_can_fund(inputs, target):
+ """Independent exhaustive oracle used only for small differential tests."""
+
+ return any(
+ sum(inputs[index] for index in selected) >= target
+ for size in range(1, len(inputs))
+ for selected in combinations(range(len(inputs)), size)
+ )
+
+
+def test_multiple_retained_inputs_can_trigger_fee_aware_uih2():
+ result = analyze_blockstream([6, 6, 6], [10, 7])
+ assert result.status is UIHStatus.UIH2
+ assert result.fee == 1
+ assert gibson_flags([6, 6, 6], [10, 7]) == (False, False)
+
+
+def test_fee_and_equality_put_boundary_case_in_uih1():
+ result = analyze_blockstream([10, 2], [10, 1])
+ assert result.status is UIHStatus.UIH1
+ assert result.fee == 1
+ assert gibson_flags([10, 2], [10, 1]) == (True, False)
+
+
+def test_equality_of_smallest_input_and_output_is_uih2():
+ result = analyze_blockstream([10, 2], [9, 2])
+ assert result.status is UIHStatus.UIH2
+ assert blocksci_uih1([10, 2], [9, 2]) is False
+
+
+def test_tied_minimum_input_has_a_deterministic_witness():
+ result = analyze_blockstream([3, 3, 8], [8, 5])
+ assert result.status is UIHStatus.UIH2
+ assert result.removed_input_index == 0
+
+
+def test_equal_outputs_fall_outside_both_algorithm_2_branches():
+ result = analyze_blockstream([7, 7], [6, 6])
+ assert result.status is UIHStatus.UNCATEGORIZED
+ assert result.change_output_index is None
+
+
+def test_algorithm_2_leaves_transactions_uncategorized():
+ result = analyze_blockstream([20, 22], [9, 16])
+ assert result.status is UIHStatus.UNCATEGORIZED
+ assert result.fee == 17
+ assert result.change_output_index is None
+
+
+def test_scope_and_invalid_amounts_are_explicit():
+ assert analyze_blockstream([10], [5, 4]).status is UIHStatus.OUT_OF_SCOPE
+ assert analyze_blockstream([10, 2], [4, 3, 2]).status is UIHStatus.OUT_OF_SCOPE
+ assert analyze_blockstream([10, 0], [5, 4]).status is UIHStatus.INVALID
+ assert analyze_blockstream([10, 2], [20, 1]).status is UIHStatus.INVALID
+
+
+def test_classification_is_invariant_under_input_and_output_permutations():
+ expected = UIHStatus.UIH2
+ for inputs in permutations([8, 3, 3]):
+ for outputs in permutations([7, 6]):
+ assert analyze_blockstream(inputs, outputs).status is expected
+
+
+def test_closed_form_agrees_with_independent_subset_enumeration():
+ cases = [
+ ([2, 3], [4, 1]),
+ ([6, 6, 6], [10, 7]),
+ ([10, 2], [10, 1]),
+ ([2, 4, 9], [8, 6]),
+ ([3, 3, 8], [8, 5]),
+ ([20, 22], [9, 16]),
+ ([7, 7], [6, 6]),
+ ]
+ for inputs, outputs in cases:
+ fee = sum(inputs) - sum(outputs)
+ status = analyze_blockstream(inputs, outputs).status
+ assert (status is UIHStatus.UIH2) is _proper_subset_can_fund(
+ inputs, max(outputs) + fee
+ )
+ assert (status is not UIHStatus.UNCATEGORIZED) is _proper_subset_can_fund(
+ inputs, min(outputs) + fee
+ )
+
+
+def test_transaction_adapter_does_not_fall_back_when_amounts_are_missing():
+ transaction = {
+ "vin": [{"prevout": {"value": 6}}, {"value": 6}, {"prevout": {"value": 6}}],
+ "vout": [{"value": 10}, {"value": 7}],
+ }
+ assert analyze_transaction(transaction).status is UIHStatus.UIH2
+ transaction["vin"][0]["prevout"].pop("value")
+ assert analyze_transaction(transaction).status is UIHStatus.INVALID
+
+
+def test_legacy_extractor_and_paper_baseline_are_named_separately():
+ from decluster.extractors import x_uih, x_uih_fee_aware
+
+ transaction = {
+ "vin": [{"value": 6}, {"value": 6}, {"value": 6}],
+ "vout": [{"value": 10}, {"value": 7}],
+ }
+ assert x_uih(transaction) == "none"
+ assert x_uih_fee_aware(transaction) == "uih2"
+
+
+def test_optimal_change_candidate_is_values_only_and_output_equivariant():
+ assert optimal_change_candidate([100, 200], [50, 500]) == 0
+ assert optimal_change_candidate([100, 200], [500, 50]) == 1
+ assert optimal_change_candidate([1_000, 2_000], [50, 60]) is None
+
+
+def test_the_three_branches_reduce_to_the_position_of_the_smallest_input():
+ for first in range(1, 8):
+ for second in range(1, 8):
+ for larger in range(1, 8):
+ for smaller in range(1, 8):
+ inputs, outputs = [first, second], [larger, smaller]
+ status = analyze_blockstream(inputs, outputs).status
+ if status is UIHStatus.INVALID:
+ continue
+ if min(inputs) <= min(outputs):
+ assert status is UIHStatus.UIH2
+ elif min(inputs) <= max(outputs):
+ assert status is UIHStatus.UIH1
+ else:
+ assert status is UIHStatus.UNCATEGORIZED
+
+
+def test_change_attribution_never_contradicts_the_optimal_change_candidate():
+ for first in range(1, 8):
+ for second in range(1, 8):
+ for larger in range(1, 8):
+ for smaller in range(1, 8):
+ inputs, outputs = [first, second], [larger, smaller]
+ result = analyze_blockstream(inputs, outputs)
+ if result.status is UIHStatus.INVALID:
+ continue
+ assert result.change_output_index == optimal_change_candidate(
+ inputs, outputs
+ )
diff --git a/tests/test_unnecessary_input_experiment.py b/tests/test_unnecessary_input_experiment.py
new file mode 100644
index 00000000..fb75430b
--- /dev/null
+++ b/tests/test_unnecessary_input_experiment.py
@@ -0,0 +1,67 @@
+"""Canonical fee-aware unnecessary-input reproduction."""
+
+import json
+
+import pytest
+
+from decluster.experiments import unnecessary_input as experiment
+
+
+def test_artifact_separates_diagnostic_from_latent_interpretation():
+ artifact = experiment.build_artifact()
+ equivalence = artifact["observational_equivalence"]
+
+ assert artifact["diagnostic"] == {
+ "definition": "blockstream_fee_aware",
+ "semantic_version": 2,
+ "paper_domain": {
+ "minimum_inputs": 2,
+ "exact_outputs": 2,
+ "amounts": "positive unsigned 64-bit integers",
+ },
+ }
+ assert equivalence["classification"] == "uih2"
+ assert equivalence["decision"] == "inconclusive"
+ assert equivalence["ownership_inferred"] is False
+ assert equivalence["payjoin_identified"] is False
+ assert equivalence["payment_output_identified"] is False
+ assert equivalence["composition"] is None
+
+
+def test_artifact_covers_definition_boundaries_and_scope_controls():
+ artifact = experiment.build_artifact()
+
+ assert artifact["boundary_cases"]["jointly_funded_uih2"]["status"] == "uih2"
+ assert artifact["boundary_cases"]["jointly_funded_uih2"]["gibson_flags"] == [False, False]
+ assert artifact["boundary_cases"]["fee_aware_uih1"]["status"] == "uih1"
+ assert artifact["boundary_cases"]["equality_boundary"]["status"] == "uih2"
+ assert {case["status"] for case in artifact["scope_controls"].values()} == {
+ "out_of_scope"
+ }
+
+
+def test_cycle_control_preserves_net_but_not_gross_obligations():
+ cycle = experiment.build_artifact()["cycle_control"]
+
+ assert cycle["simple_nonzero_net_balances"] == cycle["cyclic_nonzero_net_balances"]
+ assert cycle["simple_gross_amount"] != cycle["cyclic_gross_amount"]
+
+
+def test_verifier_recomputes_the_diagnostic():
+ changed = json.loads(json.dumps(experiment.build_artifact()))
+ changed["observational_equivalence"]["payjoin_identified"] = True
+
+ with pytest.raises(experiment.VerificationError, match="fresh diagnostic"):
+ experiment.verify_artifact(changed)
+
+
+def test_cli_reproduces_and_verifies_json_and_markdown(tmp_path):
+ artifact = tmp_path / "artifact.json"
+ markdown = tmp_path / "result.md"
+
+ assert experiment.main(
+ ["reproduce", "--artifact", str(artifact), "--markdown", str(markdown)]
+ ) == 0
+ assert experiment.main(
+ ["verify", "--artifact", str(artifact), "--markdown", str(markdown)]
+ ) == 0
diff --git a/tests/test_unnecessary_input_failure_mode.py b/tests/test_unnecessary_input_failure_mode.py
new file mode 100644
index 00000000..2f866bd1
--- /dev/null
+++ b/tests/test_unnecessary_input_failure_mode.py
@@ -0,0 +1,96 @@
+"""Negative interpretation gates for unnecessary-input evidence."""
+
+import pytest
+
+from decluster.domain import Inconclusive, UnnecessaryInputEvidence
+from decluster.failure_modes.unnecessary_input import (
+ LatentTransactionForm,
+ UnnecessaryInputScenario,
+ collaborative_form_examples,
+ cycle_equivalent_obligations,
+ evaluate,
+ observationally_equivalent_example,
+ net_balances,
+)
+
+
+def test_positive_uih_remains_inconclusive_across_equivalent_worlds():
+ scenario = observationally_equivalent_example()
+ report = evaluate(scenario)
+
+ evidence = report.channels[0].evidence[0]
+ assert isinstance(evidence, UnnecessaryInputEvidence)
+ assert evidence.classification == "uih2"
+ assert evidence.fee == 100
+ assert isinstance(report.outcomes[0], Inconclusive)
+ assert report.composition is None
+
+
+def test_report_emits_no_ownership_or_payjoin_decision():
+ report = evaluate(observationally_equivalent_example())
+
+ assert len(report.outcomes) == 1
+ assert type(report.outcomes[0]) is Inconclusive
+ assert "does not identify PayJoin" in report.limitations[0]
+ assert "ownership" in report.limitations[1]
+
+
+def test_net_settlement_and_unilateral_consolidation_are_both_preserved():
+ forms = set(observationally_equivalent_example().possible_forms)
+
+ assert LatentTransactionForm.ORDINARY_CONSOLIDATION in forms
+ assert LatentTransactionForm.NET_SETTLEMENT in forms
+ assert LatentTransactionForm.TWO_PARTY_PAYJOIN in forms
+
+
+def test_a_single_asserted_world_cannot_claim_observational_equivalence():
+ base = observationally_equivalent_example()
+
+ with pytest.raises(ValueError, match="at least two distinct"):
+ UnnecessaryInputScenario(
+ base.transaction,
+ base.inputs,
+ base.outputs,
+ (LatentTransactionForm.TWO_PARTY_PAYJOIN,),
+ )
+
+
+def test_duplicate_worlds_do_not_satisfy_the_gate():
+ base = observationally_equivalent_example()
+
+ with pytest.raises(ValueError, match="at least two distinct"):
+ UnnecessaryInputScenario(
+ base.transaction,
+ base.inputs,
+ base.outputs,
+ (
+ LatentTransactionForm.NET_SETTLEMENT,
+ LatentTransactionForm.NET_SETTLEMENT,
+ ),
+ )
+
+
+def test_ns1r_and_nsnr_are_explicitly_outside_two_output_uih_baseline():
+ from decluster.baselines.unnecessary_input import UIHStatus, analyze_blockstream
+
+ ns1r, nsnr = collaborative_form_examples()
+ assert {ns1r.form, nsnr.form} == {
+ LatentTransactionForm.NS1R,
+ LatentTransactionForm.NSNR,
+ }
+ assert analyze_blockstream(ns1r.inputs, ns1r.outputs).status is UIHStatus.OUT_OF_SCOPE
+ assert analyze_blockstream(nsnr.inputs, nsnr.outputs).status is UIHStatus.OUT_OF_SCOPE
+
+
+def test_adding_a_gross_cycle_preserves_every_net_balance():
+ simple, with_cycle = cycle_equivalent_obligations()
+
+ assert net_balances(simple) == {"alice": -500, "bob": 500}
+ assert net_balances(with_cycle) == {"alice": -500, "bob": 500, "carol": 0}
+ nonzero_cycle_balances = {
+ party: balance
+ for party, balance in net_balances(with_cycle).items()
+ if balance
+ }
+ assert nonzero_cycle_balances == net_balances(simple)
+ assert sum(item.amount for item in with_cycle) != sum(item.amount for item in simple)
diff --git a/tests/test_value_flow_ancestry.py b/tests/test_value_flow_ancestry.py
new file mode 100644
index 00000000..32f4055c
--- /dev/null
+++ b/tests/test_value_flow_ancestry.py
@@ -0,0 +1,103 @@
+import pytest
+
+from decluster import ancestry
+
+
+def _coinbase(value):
+ return {"vin": [{"is_coinbase": True}], "vout": [{"value": value}]}
+
+
+def _input(txid, value):
+ return {"txid": txid, "vout": 0, "prevout": {"value": value}}
+
+
+def _fetch(transactions):
+ return transactions.__getitem__
+
+
+def test_value_flow_matrix_is_input_value_over_total_for_every_output():
+ matrix = ancestry.value_flow_link_oracle([5, 3, 2], [3, 3, 2, 1])
+
+ assert matrix == [
+ pytest.approx([0.5, 0.5, 0.5, 0.5]),
+ pytest.approx([0.3, 0.3, 0.3, 0.3]),
+ pytest.approx([0.2, 0.2, 0.2, 0.2]),
+ ]
+ assert [sum(column) for column in zip(*matrix)] == pytest.approx([1.0] * 4)
+
+
+def test_value_flow_refuses_zero_total_instead_of_inventing_a_transition():
+ assert ancestry.value_flow_link_oracle([0, 0], [0]) is None
+
+
+def test_value_flow_graph_does_not_call_subset_sum_oracle(monkeypatch):
+ transactions = {
+ "target": {
+ "vin": [_input("small", 1), _input("large", 9)],
+ "vout": [{"value": 10}],
+ },
+ "small": _coinbase(1),
+ "large": _coinbase(9),
+ }
+
+ def fail_if_called(*args, **kwargs):
+ raise AssertionError("the subset-sum oracle is not part of nominal-value flow")
+
+ monkeypatch.setattr(ancestry, "dss_link_oracle", fail_if_called)
+ graph = ancestry.build_value_flow_graph(
+ ("target", 0), depth=2, fetch=_fetch(transactions)
+ )
+
+ assert ancestry.absorber_distribution(graph, ("target", 0)) == pytest.approx(
+ {("small", 0): 0.1, ("large", 0): 0.9}
+ )
+
+
+def test_value_flow_absorption_matches_hand_calculated_two_hop_distribution():
+ # target -> {A: 2/8, middle: 6/8}; middle -> {B: 2/6, D: 4/6}
+ # Therefore the source distribution is A=1/4, B=1/4, D=1/2.
+ transactions = {
+ "target": {
+ "vin": [_input("A", 2), _input("middle", 6)],
+ "vout": [{"value": 8}],
+ },
+ "middle": {
+ "vin": [_input("B", 2), _input("D", 4)],
+ "vout": [{"value": 6}],
+ },
+ "A": _coinbase(2),
+ "B": _coinbase(2),
+ "D": _coinbase(4),
+ }
+
+ result = ancestry.value_flow_untraceability(
+ ("target", 0), depth=3, fetch=_fetch(transactions)
+ )
+
+ assert result["distribution"] == pytest.approx(
+ {("A", 0): 0.25, ("B", 0): 0.25, ("D", 0): 0.5}
+ )
+ assert result["untraceability"] == pytest.approx(1.5)
+ assert result["n_absorbers"] == 3
+ assert result["truncated"] == 0
+
+
+def test_value_flow_is_output_independent_within_one_transaction():
+ transactions = {
+ "target": {
+ "vin": [_input("A", 4), _input("B", 6)],
+ "vout": [{"value": 1}, {"value": 9}],
+ },
+ "A": _coinbase(4),
+ "B": _coinbase(6),
+ }
+
+ left = ancestry.value_flow_signature(
+ ("target", 0), depth=2, fetch=_fetch(transactions)
+ )
+ right = ancestry.value_flow_signature(
+ ("target", 1), depth=2, fetch=_fetch(transactions)
+ )
+
+ assert left == pytest.approx({("A", 0): 0.4, ("B", 0): 0.6})
+ assert right == pytest.approx(left)
diff --git a/tests/test_view_match.py b/tests/test_view_match.py
index 8794acfb..9a392449 100644
--- a/tests/test_view_match.py
+++ b/tests/test_view_match.py
@@ -2,7 +2,7 @@
from collections import Counter
sys.path.insert(0, os.path.dirname(os.path.dirname(__file__)))
from decluster.view_match import ViewMatcher, candidate_scores
-from decluster.views import PseudonymGraph
+from decluster.contraction import PseudonymGraph
def graph(edges):
@@ -94,6 +94,14 @@ def test_an_unmatched_neighbourhood_scores_nothing():
assert candidate_scores("a", ga, gb, {}) == Counter()
+def test_min_common_requires_candidate_specific_mapped_neighbours():
+ ga = graph([("u", "s1"), ("u", "s2"), ("u", "s3"), ("u", "s4")])
+ gb = graph([("u'", "s1'"), ("u'", "s2'"), ("u'", "s3'"), ("u'", "s4'")])
+ seed = {f"s{i}": f"s{i}'" for i in range(1, 5)}
+ assert "u" not in ViewMatcher(theta=0.0, min_common=4).match(ga, gb, dict(list(seed.items())[:3]))
+ assert ViewMatcher(theta=0.0, min_common=4).match(ga, gb, seed)["u"] == "u'"
+
+
def test_stat_overrides_the_graph_degree_for_damping_and_the_hub_cap():
"""Both guards read connectedness, so they must be able to read it from the *unfiltered*
graph. Dropping leaves to fit a wide view in memory otherwise lowers the degree of
@@ -152,7 +160,7 @@ def test_the_conditioner_is_bounded_and_neutral_at_half_agreement():
def test_edge_signature_agreement_reorders_candidates():
"""Two candidates equally supported by structure; the one whose edge to the matched
neighbour looks like the reference edge must win."""
- from decluster.views import contract
+ from decluster.contraction import contract
def tx(a, b, version=2, txid="t"):
return {"txid": txid, "height": 1, "version": version, "locktime": 0,
@@ -177,3 +185,141 @@ def test_attributes_are_off_by_default():
"""The conditioners change scores, so they must be opt-in rather than silently on."""
m = ViewMatcher()
assert m.edge_alpha == 0.0 and m.vertex_alpha == 0.0
+
+
+def sigged(edges, sigs):
+ g = graph(edges)
+ g.edge_sig.update(sigs)
+ return g
+
+
+def test_the_conditioner_cannot_manufacture_a_score_but_does_manufacture_eccentricity():
+ """Why both conditioners default to off, decided rather than measured.
+
+ The design argument was that a bounded multiplicative factor cannot create a match where
+ structure found none, since zero times anything is zero. That holds for the score. The gate
+ does not read the score; it reads how far the leader stands from the runner-up, and scaling
+ tied candidates by different factors is exactly how that separation is made. A refusal becomes
+ an accepted match without one new structural fact.
+ """
+ ga = sigged([("u", "n1"), ("u", "n2")],
+ {("u", "n1"): ("x", "y"), ("u", "n2"): ("x", "y")})
+ gb = sigged([("v1", "m1"), ("v1", "m2"), ("v2", "m1"), ("v2", "m2")],
+ {("v1", "m1"): ("x", "y"), ("v1", "m2"): ("x", "y"),
+ ("v2", "m1"): ("p", "q"), ("v2", "m2"): ("p", "q")})
+ seed = {"n1": "m1", "n2": "m2"}
+
+ flat = candidate_scores("u", ga, gb, seed, edge_alpha=0.0)
+ assert flat["v1"] == flat["v2"] > 0 # a tie, and neither candidate is at zero
+ assert ViewMatcher(edge_alpha=0.0)._best("u", ga, gb, dict(seed)) is None
+
+ for alpha in (0.05, 0.5):
+ cond = candidate_scores("u", ga, gb, seed, edge_alpha=alpha)
+ assert cond["v1"] > cond["v2"] > 0 # no score was created from nothing
+ assert ViewMatcher(edge_alpha=alpha)._best("u", ga, gb, dict(seed)) == "v1"
+
+
+def test_the_smallest_perturbation_costs_as_much_as_the_largest():
+ """The gate normalises by the spread of the scores, so its verdict is scale-free in alpha.
+
+ That is why a sweep finds most of the loss between zero and the first nonzero setting: what
+ the gate reads is the ordering the conditioner imposes, not how hard it pushed.
+ """
+ ga = sigged([("u", "n1"), ("u", "n2")],
+ {("u", "n1"): ("x", "y"), ("u", "n2"): ("x", "y")})
+ gb = sigged([("v1", "m1"), ("v1", "m2"), ("v2", "m1"), ("v2", "m2")],
+ {("v1", "m1"): ("x", "y"), ("v1", "m2"): ("x", "y"),
+ ("v2", "m1"): ("p", "q"), ("v2", "m2"): ("p", "q")})
+ seed = {"n1": "m1", "n2": "m2"}
+ eccentricities = {alpha: ViewMatcher(edge_alpha=alpha)._best(
+ "u", ga, gb, dict(seed), detail=True)[1] for alpha in (0.05, 0.25, 0.5)}
+ assert len(set(eccentricities.values())) == 1
+
+
+def test_revisit_converges_and_reproduces_the_planted_mapping():
+ """The opt-in NS'09 self-reinforcing pass must not thrash: on the ring it still lands on
+ the planted correspondence, and every match points at its true image."""
+ ga, gb = graph(RING), graph(relabel(RING))
+ base = ViewMatcher(theta=0.0).match(ga, gb, {"a": "a'", "b": "b'"})
+ ga2, gb2 = graph(RING), graph(relabel(RING))
+ rev = ViewMatcher(theta=0.0, revisit=True).match(ga2, gb2, {"a": "a'", "b": "b'"})
+ assert all(v == k + "'" for k, v in rev.items()), rev
+ assert set(rev) >= set(base) # revisiting never drops coverage
+ assert len(set(rev.values())) == len(rev) # still a one-to-one map
+
+
+def test_revisit_is_off_by_default():
+ assert ViewMatcher().revisit is False
+
+
+def test_predict_link_da_path_reads_edges_off_the_mapping():
+ """cit-25 DA path: when both endpoints are uniquely mapped, an edge is read straight
+ off the target view."""
+ from decluster.view_match import predict_link
+ ga, gb = graph(RING), graph(relabel(RING))
+ m = ViewMatcher().match(ga, gb, {v: v + "'" for v in ga.vertices})
+ assert predict_link("a", "b", gb, m, {}) == 1 # a-b is a ring edge
+ assert predict_link("a", "e", gb, m, {}) == 0 # a-e is not
+ assert predict_link("a", "zzz", gb, m, {}) is None # unknown endpoint -> abstain
+
+
+def test_predict_link_unanimous_vote_over_candidate_sets():
+ """cit-25 voting path: with candidate sets rather than unique matches, a link is
+ predicted only on a unanimous vote."""
+ from decluster.view_match import predict_link
+ gb = graph(relabel(RING))
+ cand = {"a": ["a'"], "b": ["b'"], "e": ["e'"]}
+ assert predict_link("a", "b", gb, {}, cand) == 1 # ring edge, unanimous
+ assert predict_link("a", "e", gb, {}, cand) == 0 # not a ring edge
+
+
+def test_match_candidates_returns_bounded_sets_disjoint_from_accepted():
+ ga, gb = graph(RING), graph(relabel(RING))
+ m, cand = ViewMatcher(theta=5.0).match_candidates(ga, gb, {"a": "a'", "b": "b'"}, top_k=3)
+ assert isinstance(cand, dict)
+ assert all(len(vs) <= 3 for vs in cand.values())
+ assert all(v not in set(m.values()) for vs in cand.values() for v in vs)
+
+
+def test_directional_is_off_by_default_and_runs_when_on():
+ assert ViewMatcher().directional is False
+ ga, gb = graph(RING), graph(relabel(RING))
+ out = ViewMatcher(theta=0.0, directional=True).match(ga, gb, {"a": "a'", "b": "b'"})
+ assert out["a"] == "a'" and out["b"] == "b'" # seeds preserved, runs without error
+ assert len(set(out.values())) == len(out) # still one-to-one
+
+
+def test_directional_scoring_separates_in_from_out():
+ """A candidate reachable only by an out-edge must not be scored from an in-neighbour."""
+ from decluster.view_match import candidate_scores
+ # u -> m, so the true candidate must also point to m'.
+ ga = graph([("u", "m")]) # u -> m (m is an out-neighbour of u)
+ gb = graph([("m'", "bad"), ("good", "m'")]) # m' -> bad ; good -> m'
+ mapping = {"m": "m'"}
+ # undirected: both get scored; direction-preserving scoring keeps only good -> m'.
+ flat = candidate_scores("u", ga, gb, mapping, directional=False)
+ dir_ = candidate_scores("u", ga, gb, mapping, directional=True)
+ assert "bad" in flat and "good" in flat
+ assert "good" in dir_ and "bad" not in dir_
+
+
+def test_find_seeds_matches_unique_degree_signatures():
+ from decluster.view_match import find_seeds
+ edges = ([("hub", f"n{i}") for i in range(5)] # hub: unique high degree
+ + [("t1", "t2"), ("t2", "t3"), ("t3", "t1")]) # a triangle
+ ga, gb = graph(edges), graph(relabel(edges))
+ seeds = find_seeds(ga, gb)
+ assert seeds.get("hub") == "hub'" # unique signature -> seeded
+ assert all(v == k + "'" for k, v in seeds.items()) # every seed is correct
+
+
+def test_predict_link_ml_fallback_only_on_abstain():
+ from decluster.view_match import predict_link
+ gb = graph([("a'", "b1'")]) # a'->b1' exists, a'->b2' does not
+ cand = {"a": ["a'"], "b": ["b1'", "b2'"]}
+ # mixed vote (one edge present, one absent) -> abstain without ml, ml score with it
+ assert predict_link("a", "b", gb, {}, cand) is None
+ assert predict_link("a", "b", gb, {}, cand, ml=lambda a, b: 0.7) == 0.7
+ # unanimous edge -> DA/vote wins, ml is NOT consulted
+ assert predict_link("a", "b", graph([("a'", "b1'")]), {}, {"a": ["a'"], "b": ["b1'"]},
+ ml=lambda a, b: 0.7) == 1
diff --git a/tests/test_views.py b/tests/test_views.py
index a3264609..9022ab99 100644
--- a/tests/test_views.py
+++ b/tests/test_views.py
@@ -1,8 +1,10 @@
import sys, os
sys.path.insert(0, os.path.dirname(os.path.dirname(__file__)))
import pytest
-from decluster.views import (AXES, PseudonymGraph, cluster_addresses, contract,
- partition_coins)
+from decluster.contraction import AXES, PseudonymGraph, contract
+from decluster.view_partition import height_bands, partition_coins
+from decluster.views import cluster_addresses
+from decluster.contraction import contract_degrees as views_contract_degrees
def tx(ins, outs, height=100, version=2, locktime=0, seq=0xFFFFFFFF):
@@ -54,7 +56,7 @@ def test_a_cut_removes_the_boundary_and_keeps_the_rest():
def test_ambiguity_cut_removes_the_dense_core_and_returns_the_components_it_leaves():
"""Two otherwise separate neighbourhoods joined only through a busy hub. Cutting the hub
is what lets them come out as two views; keeping it would leave one component."""
- from decluster.views import ambiguity_partition
+ from decluster.view_partition import ambiguity_partition
left = [tx(["l1"], [("l2", 1)]), tx(["l2"], [("l3", 1)])]
right = [tx(["r1"], [("r2", 1)]), tx(["r2"], [("r3", 1)])]
via_hub = [tx(["l3"], [("hub", 1)]), tx(["hub"], [("r1", 1)]),
@@ -68,7 +70,7 @@ def test_ambiguity_cut_removes_the_dense_core_and_returns_the_components_it_leav
def test_ambiguity_cut_on_an_empty_sample_returns_empty_views():
- from decluster.views import ambiguity_partition
+ from decluster.view_partition import ambiguity_partition
assert ambiguity_partition(iter([]), iter([])) == [[], []]
@@ -94,7 +96,8 @@ def test_parallel_transfers_fold_into_one_attributed_edge():
lk = {"a": "A", "x": "X", "y": "X"}
g = contract(s, [0, 1], lk)
assert list(g.edges) == [("A", "X")]
- assert g.edges[("A", "X")] == {"transfers": 3, "value": 600}
+ assert g.edges[("A", "X")]["transfers"] == 3
+ assert g.edges[("A", "X")]["value"] == 600
def test_direction_is_kept():
@@ -112,6 +115,15 @@ def test_self_transfer_is_a_vertex_attribute_not_an_edge():
assert g.degree("A") == 0
+def test_self_transfer_keeps_its_value_and_span():
+ """The loop stays out of the structure, but it is still a labelled edge of the user
+ network, so contracting it must not drop the value and time it carries."""
+ s = S(tx(["a"], [("b", 100)], height=10), tx(["b"], [("c", 250)], height=40))
+ g = contract(s, [0, 1], {"a": "A", "b": "A", "c": "A"})
+ assert g.edges == {}
+ assert g.self_edges == {"A": {"transfers": 2, "value": 350, "first": 10, "last": 40}}
+
+
def test_unknown_address_is_its_own_pseudonym():
"""A partial clustering is the premise, so an address outside the lookup is a singleton
pseudonym rather than an error."""
@@ -183,7 +195,7 @@ def test_an_axis_that_cannot_be_read_is_counted_not_swallowed():
def test_transfer_counts_bound_the_degree():
"""Distinct degree is at most the transfer count, which is what makes the cheap count
safe as a pre-filter: it never drops a vertex that degree would have kept."""
- from decluster.views import transfer_counts
+ from decluster.contraction import transfer_counts
s = S(tx(["a"], [("x", 100), ("x", 200), ("y", 300)]))
counts = transfer_counts(s)
g = contract(s, [0], {})
@@ -202,7 +214,7 @@ def test_filtering_leaves_does_not_change_what_the_matcher_finds():
"""The pre-filter is only legitimate if it is invisible to the result: a vertex below
degree two can neither be matched (no neighbourhood to discriminate on) nor bridge two
others, and contributes nothing to scoring while unmatched."""
- from decluster.views import transfer_counts
+ from decluster.contraction import transfer_counts
from decluster.view_match import ViewMatcher
core = [("c1", "c2"), ("c2", "c3"), ("c3", "c4"), ("c4", "c1"), ("c1", "c3")]
leaves = [("c1", "l1"), ("c2", "l2"), ("c3", "l3")]
@@ -302,23 +314,44 @@ def test_splitting_along_the_view_boundary_puts_one_pseudonym_on_each_side():
identity match and never attempt the rejoin. Splitting on the boundary removes that
escape: the discovery becomes the only correspondence available."""
import random
- from decluster.views import split_clusters_by_view
+ from decluster.views import split_clusters_by_view, view_lookup
lookup = {"a1": "C", "a2": "C", "b1": "C", "b2": "C", "solo": "S"}
- out, origin = split_clusters_by_view(lookup, {"a1", "a2", "solo"}, frac=1.0,
- rng=random.Random(0))
- assert out["a1"] == out["a2"] == "C#a"
- assert out["b1"] == out["b2"] == "C#b"
+ split_cids, origin = split_clusters_by_view(lookup, {"a1", "a2", "solo"}, frac=1.0,
+ rng=random.Random(0))
+ assert split_cids == {"C"}
+ la = view_lookup(lookup, split_cids, "#a")
+ lb = view_lookup(lookup, split_cids, "#b")
+ assert la.get("a1") == la.get("b1") == "C#a"
+ assert lb.get("a1") == lb.get("b1") == "C#b"
assert origin["C#a"] == origin["C#b"] == "C"
- assert out["solo"] == "S" # wholly on one side: nothing to split
+ assert la.get("solo") == lb.get("solo") == "S" # wholly on one side: nothing to split
+ assert la.get("unknown", "unknown") == "unknown" # absent addresses stay singletons
+
+
+def test_an_address_used_in_both_windows_does_not_carry_its_tag_into_the_other_view():
+ """The rejoin is the only correspondence on offer only if each view holds one side of
+ the split. Tagging by where an address is first seen leaves a both-windows address
+ marked #a everywhere, so view B holds C#a as well as C#b and the identity match — the
+ structurally better one — is available and graded wrong."""
+ import random
+ from decluster.views import split_clusters_by_view, view_lookup
+ lookup = {"shared": "C", "b_only": "C", "q": "Q"}
+ split_cids, _ = split_clusters_by_view(lookup, {"shared"}, frac=1.0,
+ rng=random.Random(0))
+ window_b = S(tx(["shared"], [("q", 5000)]), tx(["b_only"], [("q", 5000)]))
+ gb = contract(window_b, lookup=view_lookup(lookup, split_cids, "#b"), axes=False)
+ assert "C#a" not in set(gb.vertices)
+ assert "C#b" in set(gb.vertices)
def test_a_cluster_confined_to_one_view_is_left_whole():
import random
- from decluster.views import split_clusters_by_view
+ from decluster.views import split_clusters_by_view, view_lookup
lookup = {"a1": "C", "a2": "C"}
- out, origin = split_clusters_by_view(lookup, {"a1", "a2"}, frac=1.0,
- rng=random.Random(0))
- assert out == lookup and origin == {"C": "C"}
+ split_cids, origin = split_clusters_by_view(lookup, {"a1", "a2"}, frac=1.0,
+ rng=random.Random(0))
+ assert split_cids == set() and origin == {"C": "C"}
+ assert view_lookup(lookup, split_cids, "#a").get("a1") == "C"
def test_a_transaction_funded_by_several_pseudonyms_asserts_no_edges():
@@ -336,7 +369,287 @@ def test_a_transaction_funded_by_several_pseudonyms_asserts_no_edges():
assert merged.edges and merged.unattributed == 0
+def test_keep_drops_edges_and_never_invents_one():
+ """`keep` is a view restriction, not a re-attribution. Applying it before the
+ max_sources bound would let a transaction the contraction refused emit an edge as soon
+ as the filter left it a single source — and the survivor is systematically the hub."""
+ s = S(tx(["a", "b", "c"], [("x", 5000)]))
+ lookup = {"a": "A", "b": "B", "c": "C", "x": "X"}
+ assert contract(s, lookup=lookup, axes=False).edges == {}
+ filtered = contract(s, lookup=lookup, axes=False, keep={"A", "X"})
+ assert filtered.edges == {}
+ assert filtered.unattributed == 1
+
+
def test_max_sources_can_be_raised_deliberately():
s = S(vtx([10_000, 20_000], [15_000, 14_000], ["a", "b"]))
g = contract(s, [0], {"a": "A", "b": "B"}, max_sources=2)
assert len(g.edges) == 4 and g.unattributed == 0
+
+
+def _tx(ins, outs, height=0):
+ return ({"height": height,
+ "vin": [{"prevout": {"scriptpubkey_address": a}} for a in ins],
+ "vout": [{"scriptpubkey_address": a} for a in outs]}, 0)
+
+
+def test_decore_partition_yields_overlapping_views_unlike_ambiguity_cut():
+ """The reformulated ambiguity-cut: de-core then split on an orthogonal axis (height), so an
+ entity active on both sides appears in BOTH views. Contrast with ambiguity_partition, whose
+ connected components are vertex-disjoint."""
+ from decluster.tx_addrs import in_addrs, out_addrs
+ from decluster.view_partition import ambiguity_partition, decore_partition
+ # HUB is the busiest address -> the dense core; a1 is active in both height windows
+ s = ([_tx(["HUB"], [f"h{i}"], height=i) for i in range(20)]
+ + [_tx(["a1"], ["x1"], height=1), _tx(["a1"], ["x2"], height=100),
+ _tx(["b1"], ["y1"], height=1), _tx(["c1"], ["z1"], height=100),
+ _tx(["a1", "HUB"], ["core_out"], height=50)]) # core-touching -> dropped from both
+
+ def addrs(idxs):
+ out = set()
+ for i in idxs:
+ out |= set(in_addrs(s[i][0])) | {a for a, _ in out_addrs(s[i][0])}
+ return out
+
+ views = decore_partition(s, core_frac=0.02, scheme="epoch")
+ assert len(views) == 2 and all(views)
+ va, vb = addrs(views[0]), addrs(views[1])
+ assert "a1" in (va & vb) # the recurring entity appears in both views
+ assert "HUB" not in (va | vb) # the core is removed from both
+ # ambiguity_partition on the same sample: its top-2 components share no address
+ comps = ambiguity_partition(iter(s), iter(s), core_frac=0.02)
+ if len(comps) >= 2 and all(comps):
+ assert not (addrs(comps[0]) & addrs(comps[1]))
+
+
+def test_transfer_counts_skips_multi_source_without_raising():
+ """Regression: a multi-source tx must not raise NameError (the old `g.unattributed` bug)."""
+ from decluster.contraction import transfer_counts
+ s = [_tx(["p", "q"], ["out"])] # 2 distinct source pseudonyms, max_sources=1
+ counts = transfer_counts(s, lookup={}, max_sources=1) # must not raise
+ assert counts == {} or isinstance(counts, dict)
+
+
+def test_contract_degrees_matches_the_graph_it_avoids_building():
+ """The degree-only pass exists to skip the throwaway first contraction, so it has to agree
+ with it everywhere — isolated vertices and unattributable transactions included."""
+ s = S(tx(["a"], [("x", 5000)]),
+ tx(["x"], [("y", 4000)]),
+ tx(["b", "c"], [("z", 3000)]), # two sources: unattributable, no edges
+ tx(["y"], [("y", 3000)])) # self-transfer: not a degree
+ lookup = {"a": "A", "x": "X", "y": "Y", "b": "B", "c": "C", "z": "Z"}
+ full = contract(s, lookup=lookup, axes=False)
+ expected = {v: full.degree(v) for v in full.vertices}
+ assert views_contract_degrees(s, lookup=lookup) == expected
+
+
+def test_structure_only_contraction_allocates_no_axis_counters():
+ """The per-axis Counters are the vertex record's bulk; a matcher run that uses structure
+ alone must not pay for them."""
+ s = S(tx(["a"], [("x", 5000)]))
+ assert "axes" not in contract(s, axes=False).vertices["a"]
+ assert "axes" in contract(s, axes=True).vertices["a"]
+
+
+def test_asking_for_a_degree_does_not_create_the_vertex():
+ """A full degree sweep asks about every vertex; indexing the adjacency defaultdict would
+ leave an empty set behind for each one."""
+ g = contract(S(tx(["a"], [("x", 5000)])), axes=False)
+ assert g.degree("never_seen") == 0
+ assert "never_seen" not in g._out and "never_seen" not in g._in
+
+
+# --- collapse cut -----------------------------------------------------------
+
+def test_collapse_boundary_cuts_the_merge_of_two_substantial_clusters():
+ """The cut the framework asks for is the cluster-collapse event, not the busiest address. A
+ merge that grows one cluster by a fresh address is not a collapse; a merge that joins two
+ established clusters is."""
+ from decluster.view_partition import collapse_boundary
+ s = S(tx(["a1", "a2"], [("x", 1)]), # builds cluster A (not a collapse: both fresh)
+ tx(["b1", "b2"], [("y", 1)]), # builds cluster B (not a collapse)
+ tx(["a1", "a3"], [("z", 1)]), # extends A by a fresh address -> not a collapse
+ tx(["a1", "b1"], [("w", 1)])) # joins A and B -> THE collapse
+ boundary, uf = collapse_boundary(s, min_side=2)
+ assert boundary == [3]
+ assert uf.find("a1") == uf.find("a3") # A stayed whole
+ assert uf.find("a1") != uf.find("b1") # the declined merge really was declined
+
+
+def test_a_fresh_address_joining_one_cluster_is_never_a_collapse():
+ from decluster.view_partition import collapse_boundary
+ s = S(tx(["a1", "a2"], [("x", 1)]), tx(["a1", "a2", "a3", "a4"], [("y", 1)]))
+ assert collapse_boundary(s, min_side=2)[0] == []
+
+
+def test_collapse_partition_removes_the_boundary_and_splits_the_rest_by_height():
+ """Cut for ambiguity, split for overlap: taking the components the cut leaves would give
+ views that share no entity, so the survivors are split on an orthogonal axis."""
+ from decluster.view_partition import collapse_partition
+ s = S(tx(["a1", "a2"], [("x", 1)], height=10),
+ tx(["b1", "b2"], [("y", 1)], height=20),
+ tx(["a1", "b1"], [("w", 1)], height=30), # collapse -> in no view
+ tx(["a1"], [("z", 1)], height=90))
+ parts = collapse_partition(s, min_side=2)
+ assert len(parts) == 2
+ flat = sorted(sum(parts, []))
+ assert 2 not in flat # the boundary is in no view
+ assert flat == [0, 1, 3]
+
+
+def test_collapse_partition_generalises_beyond_two_views():
+ from decluster.view_partition import collapse_partition
+ s = S(*[tx([f"u{i}"], [(f"o{i}", 1)], height=10 * i) for i in range(1, 10)])
+ parts = collapse_partition(s, n_views=3)
+ assert len(parts) == 3
+ assert sorted(sum(parts, [])) == list(range(9)) # nothing lost, nothing duplicated
+
+
+def test_an_edge_carries_the_height_span_of_the_transfers_it_folds():
+ """Reid and Harrigan label every user-network edge with value and time. Folding parallel
+ transfers into one edge throws the time away unless the span is kept, and the span is what
+ separates a relationship that recurs from one that fired once."""
+ s = S(tx(["a"], [("x", 100)], height=100),
+ tx(["a"], [("x", 200)], height=140),
+ tx(["a"], [("x", 300)], height=120))
+ e = contract(s, lookup={"a": "A", "x": "X"}, axes=False).edges[("A", "X")]
+ assert e["transfers"] == 3 and e["value"] == 600
+ assert (e["first"], e["last"]) == (100, 140)
+
+
+def test_a_single_transfer_edge_has_a_zero_width_span():
+ s = S(tx(["a"], [("x", 100)], height=77))
+ e = contract(s, lookup={"a": "A", "x": "X"}, axes=False).edges[("A", "X")]
+ assert e["first"] == e["last"] == 77
+
+
+# --- conspicuousness ordering ------------------------------------------------
+
+def vtx_typed(in_vals, out_vals, addrs, types=None, height=100):
+ """A transaction carrying the input values and script types a merge ranking needs."""
+ types = types or ["v0_p2wpkh"] * len(in_vals)
+ return {"txid": f"t{addrs}", "height": height, "version": 2, "locktime": 0,
+ "fee": 100, "weight": 400,
+ "vin": [{"txid": f"p{a}", "vout": 0, "sequence": 0xFFFFFFFF,
+ "prevout": {"value": v, "scriptpubkey_type": t,
+ "scriptpubkey_address": a}}
+ for a, v, t in zip(addrs, in_vals, types)],
+ "vout": [{"value": v, "scriptpubkey_type": "v0_p2wpkh",
+ "scriptpubkey_address": f"o{i}"} for i, v in enumerate(out_vals)]}
+
+
+def test_a_transaction_that_argues_against_itself_is_less_conspicuous():
+ from decluster.views import merge_objections
+ # neither tell fires: one owner is the plain reading
+ assert merge_objections(vtx_typed([40_000, 50_000], [70_000, 19_000], ["a", "b"])) == 0
+ # an input alone already covers the largest output -> the others were unnecessary
+ assert merge_objections(vtx_typed([90_000, 10_000], [60_000, 39_000], ["a", "b"])) == 1
+ # inputs disagree on script type -> more than one plausible contributor
+ assert merge_objections(
+ vtx_typed([40_000, 50_000], [70_000, 19_000], ["a", "b"],
+ types=["v0_p2wpkh", "p2pkh"])) == 1
+ # both
+ assert merge_objections(
+ vtx_typed([90_000, 10_000], [60_000, 39_000], ["a", "b"],
+ types=["v0_p2wpkh", "p2pkh"])) == 2
+
+
+def test_a_single_input_transaction_has_nothing_to_argue_about():
+ from decluster.views import merge_objections
+ assert merge_objections(vtx_typed([90_000], [89_000], ["a"])) == 0
+
+
+def test_an_address_only_transaction_cannot_be_ranked():
+ """Absence of evidence is not absence of objection: reading it as zero would put every
+ transaction of an address-only export into the conspicuous tier for free."""
+ from decluster.views import merge_objections, merge_order
+
+ def addr_only(ins, outs): # what the graph exports actually carry
+ return {"txid": f"t{ins}", "height": 100,
+ "vin": [{"prevout": {"scriptpubkey_address": a}} for a in ins],
+ "vout": [{"scriptpubkey_address": a} for a in outs]}
+
+ assert merge_objections(addr_only(["a", "b"], ["x"])) is None
+ with pytest.raises(ValueError, match="address-only"):
+ merge_order(S(addr_only(["a", "b"], ["x"]), addr_only(["c", "d"], ["y"])))
+
+ # single-input transactions score zero for free, so they must not make the export look
+ # rankable — an unrankable sample stays unrankable however many of them it carries
+ with pytest.raises(ValueError, match="address-only"):
+ merge_order(S(addr_only(["a"], ["x"]), addr_only(["b", "c"], ["y"]),
+ addr_only(["d"], ["z"])))
+
+
+def test_staging_clusters_the_conspicuous_transactions_first():
+ from decluster.views import merge_order
+ doubtful = vtx_typed([90_000, 10_000], [60_000, 39_000], ["a", "b"], height=10)
+ clean = vtx_typed([40_000, 50_000], [70_000, 19_000], ["c", "d"], height=20)
+ order = merge_order(S(doubtful, clean))
+ assert order == [1, 0] # block order was doubtful-first; staging inverts
+
+
+def test_staging_leaves_the_partition_reachable_by_either_order_alone():
+ """Ordering changes which merges are judged against which context, not what a merge means."""
+ from decluster.views import cluster_addresses
+ s = S(vtx_typed([40_000, 50_000], [70_000, 19_000], ["a", "b"], height=10),
+ vtx_typed([40_000, 50_000], [70_000, 19_000], ["b", "c"], height=20))
+ plain = cluster_addresses(s, refuse=False)
+ ordered = cluster_addresses(s, refuse=False, staged=True)
+ assert {frozenset(g) for g in _groups(plain)} == {frozenset(g) for g in _groups(ordered)}
+
+
+def test_staging_leaves_the_partition_alone_under_change_linking_too():
+ """The change link is the one decision that reads the partition built so far, so it is where
+ the order could leak into the result. A doubtful merge of a,b followed by a clean spend of
+ a,b with fresh optimal change: in sample order the spend sees a,b already joined and the
+ change attaches, and staging must not take that away by running the clean spend first."""
+ from decluster.views import cluster_addresses
+
+ def vtx_out(in_vals, out_pairs, addrs, height=100):
+ t = vtx_typed(in_vals, [v for _, v in out_pairs], addrs, height=height)
+ for o, (addr, _) in zip(t["vout"], out_pairs):
+ o["scriptpubkey_address"] = addr
+ return t
+
+ doubtful = vtx_out([90_000, 10_000], [("pay0", 60_000), ("out0", 39_000)], ["a", "b"], height=10)
+ clean = vtx_out([40_000, 50_000], [("pay1", 70_000), ("chg", 19_000)], ["a", "b"], height=20)
+ s = S(doubtful, clean)
+ plain = cluster_addresses(s, change_link=True)
+ ordered = cluster_addresses(s, change_link=True, staged=True)
+ assert {frozenset(g) for g in _groups(plain)} == {frozenset(g) for g in _groups(ordered)}
+ assert plain["chg"] == plain["a"] == plain["b"]
+
+
+def _groups(lookup):
+ out = {}
+ for addr, cid in lookup.items():
+ out.setdefault(cid, []).append(addr)
+ return out.values()
+
+
+def test_every_scheme_honours_the_view_count():
+ """The framework asks for the n > 2 generalisation of every cut, not of two of them.
+
+ `epoch` and `decore` used to halve the height range whatever `n_views` said, so a caller
+ asking for four views got two and was told nothing.
+ """
+ sample = [({"txid": f"t{i}", "height": 800000 + i,
+ "vin": [{"prevout": {"scriptpubkey_address": f"a{i}"}}],
+ "vout": [{"scriptpubkey_address": f"b{i}", "value": 1000}]}, 0)
+ for i in range(40)]
+ for scheme in ("epoch", "decore", "collapse"):
+ for n in (2, 3, 4):
+ parts = partition_coins(sample, scheme=scheme, n_views=n)
+ assert len(parts) == n, f"{scheme} returned {len(parts)} views for n_views={n}"
+
+
+def test_the_bands_cover_the_range_without_overlap():
+ bounds = height_bands([800000, 800001, 800009], 3)
+ assert bounds[0][0] == 800000 and bounds[-1][1] == 800009
+ for (_, top), (bottom, _) in zip(bounds, bounds[1:]):
+ assert bottom == top + 1
+
+
+def test_a_view_count_below_one_is_refused():
+ with pytest.raises(ValueError, match="at least 1"):
+ height_bands([1, 2, 3], 0)
diff --git a/tests/test_weight_sensitivity_experiment.py b/tests/test_weight_sensitivity_experiment.py
new file mode 100644
index 00000000..82fd188d
--- /dev/null
+++ b/tests/test_weight_sensitivity_experiment.py
@@ -0,0 +1,57 @@
+import json
+from pathlib import Path
+
+import pytest
+
+from decluster.experiments import weight_sensitivity as experiment
+
+ROOT = Path(__file__).resolve().parents[1]
+SNAPSHOT = ROOT / "data" / "fs-blkcache-2026-09-04.tar.gz"
+
+
+@pytest.fixture(scope="module")
+def artifact():
+ return experiment.build_artifact(SNAPSHOT)
+
+
+def test_snapshot_reproduces_the_canonical_artifact(artifact):
+ stored = json.loads(
+ (ROOT / "results" / "artifacts" / "weight-sensitivity-v1.json").read_text()
+ )
+ assert artifact == stored
+
+
+def test_preserved_snapshot_falsifies_monotonicity_but_retains_local_stability(artifact):
+ measurement = artifact["measurement"]
+ assert measurement["auc_is_monotone_non_decreasing"] is False
+ assert measurement["realistic_band_auc_range"] < 0.001
+ rows = {row["consistency"]: row for row in measurement["rows"]}
+ assert rows[0.99]["auc"] < rows[0.95]["auc"]
+
+
+def test_result_keeps_attack_and_privacy_score_distinct(artifact):
+ assert any("not CoinScore" in limit for limit in artifact["limitations"])
+
+
+def test_a_dip_is_reported_against_what_the_sample_resolves(artifact):
+ """The flag reads the measured sequence; the standard error says whether it means anything.
+
+ The document used to call the historical monotonicity claim *false* on a dip of 0.00055, which
+ is 0.18 standard errors at 4,000 pairs per class — reading noise as a finding, in the direction
+ that suited the argument.
+ """
+ measurement = artifact["measurement"]
+ assert measurement["auc_standard_error"] > 0
+ assert measurement["largest_decrement"] < measurement["auc_standard_error"]
+ assert measurement["largest_decrement_in_standard_errors"] < 1.0
+ assert artifact["historical_comparison"]["status"] == "not_supported"
+
+
+def test_the_standard_error_is_the_hanley_mcneil_one():
+ from decluster.weight_sensitivity import auc_standard_error
+
+ # A perfect separator has no sampling variance left to report.
+ assert auc_standard_error(1.0, 100, 100) == pytest.approx(0.0, abs=1e-12)
+ # More pairs, tighter resolution.
+ assert auc_standard_error(0.92, 8000, 8000) < auc_standard_error(0.92, 1000, 1000)
+ assert auc_standard_error(0.92, 0, 100) is None
diff --git a/tests/test_wp1a.py b/tests/test_wp1a.py
index 7852334c..319573d0 100644
--- a/tests/test_wp1a.py
+++ b/tests/test_wp1a.py
@@ -79,7 +79,7 @@ def test_x_uih():
from decluster.extractors import x_uih
assert x_uih(_tx([{"value": 100, "scriptpubkey_type": "a"}],
vin=[{"txid": "p", "vout": 0, "sequence": 1, "value": 4000},
- {"txid": "q", "vout": 0, "sequence": 1, "value": 4000}])) == "uih1"
+ {"txid": "q", "vout": 0, "sequence": 1, "value": 4000}])) == "uih2"
assert x_uih(_tx([{"value": 5000, "scriptpubkey_type": "a"}],
vin=[{"txid": "p", "vout": 0, "sequence": 1, "value": 6000}])) == "none"
diff --git a/tests/test_wp2.py b/tests/test_wp2.py
index b9bf1477..0df6f6aa 100644
--- a/tests/test_wp2.py
+++ b/tests/test_wp2.py
@@ -37,7 +37,9 @@ def test_locktime_policy_shared():
from decluster.extractors import locktime_policy
assert locktime_policy({"locktime": 0}) == "zero"
assert locktime_policy({"locktime": 800000}) == "height"
- assert locktime_policy({}) == "zero" # .get default (missing key -> zero)
+ # An export that carries no locktime is not an export that carries locktime 0: reading the
+ # absence as "zero" made every transaction in an address-only slice agree on this axis.
+ assert locktime_policy({}) == "na"
def test_norm_shared():
from decluster.subtransaction import norm