From e750140e10ed9568fac82f0c7ed15d73c1624b49 Mon Sep 17 00:00:00 2001 From: chris harris <283105756+Chris-ObtuseAI@users.noreply.github.com> Date: Wed, 29 Jul 2026 03:49:05 -0500 Subject: [PATCH] feat: launch Jester v0.2 causal research platform --- .github/workflows/pages.yml | 31 + .gitignore | 3 + ARCHITECTURE.md | 155 +- JESTER_CONSTITUTION.md | 59 +- QUALITY_GATES.yaml | 50 +- README.md | 470 ++- RESEARCH_FOUNDATIONS.md | 107 + design-system/jester-observatory/MASTER.md | 227 ++ docs/.nojekyll | 1 + docs/images/jester-observatory-desktop.png | Bin 0 -> 99459 bytes .../jester-observatory-dream-foundry.png | Bin 0 -> 85660 bytes docs/images/jester-observatory-mobile.png | Bin 0 -> 35272 bytes docs/index.html | 401 +++ docs/showcase.css | 1083 +++++++ docs/showcase.js | 129 + .../attention/synthetic_ranked_manifest.json | 32 + fixtures/literature/live_query_template.json | 24 + fixtures/literature/synthetic_arxiv_feed.xml | 52 + fixtures/literature/synthetic_query.json | 20 + fixtures/meta/synthetic_meta_config.json | 21 + pyproject.toml | 2 +- reports/JESTER_COGNITION_ARSENAL_PROOF.md | 123 + reports/JESTER_OBSERVATORY_PROOF.md | 125 + reports/JESTER_V0_2_PROOF.md | 99 + schemas/cognition_arsenal_v1.schema.json | 225 ++ .../literature_learning_batch_v1.schema.json | 101 + schemas/meta_research_v1.schema.json | 142 + schemas/meta_research_v2.schema.json | 129 + .../search_attention_snapshot_v1.schema.json | 78 + scripts/validate_public_release.py | 146 + src/jester/__init__.py | 5 +- src/jester/api.py | 37 +- src/jester/attention.py | 550 ++++ src/jester/attention_types.py | 98 + src/jester/cli.py | 475 ++- src/jester/cognition.py | 1018 +++++++ src/jester/cognition_types.py | 184 ++ src/jester/counterfactuals.py | 202 ++ src/jester/data_pipelines.py | 381 +++ src/jester/forge.py | 130 +- src/jester/intelligence.py | 254 ++ src/jester/ladder.py | 276 ++ src/jester/literature.py | 588 ++++ src/jester/literature_types.py | 103 + src/jester/meta_evolution.py | 162 + src/jester/meta_pipeline.py | 191 ++ src/jester/meta_types.py | 237 ++ src/jester/metacognition.py | 312 ++ src/jester/observatory.py | 427 +++ src/jester/packs.py | 171 ++ src/jester/pipeline.py | 2 + src/jester/quality_diversity.py | 214 ++ src/jester/research_memory.py | 105 + src/jester/research_sources.py | 145 + src/jester/simulators.py | 185 ++ src/jester/types.py | 2 + tests/test_api.py | 35 + tests/test_attention_observatory.py | 127 + tests/test_catalogs.py | 58 + tests/test_cognition.py | 153 + tests/test_counterfactuals.py | 43 + tests/test_earnings_pipeline.py | 4 +- tests/test_literature_lab.py | 75 + tests/test_meta_research.py | 89 + tests/test_research_memory.py | 36 + tests/test_simulation_ladder.py | 46 + ui/index.html | 18 + ui/package-lock.json | 2114 +++++++++++++ ui/package.json | 28 + ui/src/App.tsx | 1107 +++++++ ui/src/SurfaceScene.tsx | 88 + ui/src/WorldScene.tsx | 243 ++ ui/src/hooks.ts | 62 + ui/src/main.tsx | 16 + ui/src/styles.css | 2634 +++++++++++++++++ ui/src/types.ts | 233 ++ ui/src/vite-env.d.ts | 1 + ui/tsconfig.app.json | 28 + ui/tsconfig.json | 7 + ui/tsconfig.node.json | 18 + ui/vite.config.ts | 22 + uv.lock | 2 +- 82 files changed, 17628 insertions(+), 148 deletions(-) create mode 100644 .github/workflows/pages.yml create mode 100644 RESEARCH_FOUNDATIONS.md create mode 100644 design-system/jester-observatory/MASTER.md create mode 100644 docs/.nojekyll create mode 100644 docs/images/jester-observatory-desktop.png create mode 100644 docs/images/jester-observatory-dream-foundry.png create mode 100644 docs/images/jester-observatory-mobile.png create mode 100644 docs/index.html create mode 100644 docs/showcase.css create mode 100644 docs/showcase.js create mode 100644 fixtures/attention/synthetic_ranked_manifest.json create mode 100644 fixtures/literature/live_query_template.json create mode 100644 fixtures/literature/synthetic_arxiv_feed.xml create mode 100644 fixtures/literature/synthetic_query.json create mode 100644 fixtures/meta/synthetic_meta_config.json create mode 100644 reports/JESTER_COGNITION_ARSENAL_PROOF.md create mode 100644 reports/JESTER_OBSERVATORY_PROOF.md create mode 100644 reports/JESTER_V0_2_PROOF.md create mode 100644 schemas/cognition_arsenal_v1.schema.json create mode 100644 schemas/literature_learning_batch_v1.schema.json create mode 100644 schemas/meta_research_v1.schema.json create mode 100644 schemas/meta_research_v2.schema.json create mode 100644 schemas/search_attention_snapshot_v1.schema.json create mode 100644 src/jester/attention.py create mode 100644 src/jester/attention_types.py create mode 100644 src/jester/cognition.py create mode 100644 src/jester/cognition_types.py create mode 100644 src/jester/counterfactuals.py create mode 100644 src/jester/data_pipelines.py create mode 100644 src/jester/intelligence.py create mode 100644 src/jester/ladder.py create mode 100644 src/jester/literature.py create mode 100644 src/jester/literature_types.py create mode 100644 src/jester/meta_evolution.py create mode 100644 src/jester/meta_pipeline.py create mode 100644 src/jester/meta_types.py create mode 100644 src/jester/metacognition.py create mode 100644 src/jester/observatory.py create mode 100644 src/jester/packs.py create mode 100644 src/jester/quality_diversity.py create mode 100644 src/jester/research_memory.py create mode 100644 src/jester/research_sources.py create mode 100644 src/jester/simulators.py create mode 100644 tests/test_attention_observatory.py create mode 100644 tests/test_catalogs.py create mode 100644 tests/test_cognition.py create mode 100644 tests/test_counterfactuals.py create mode 100644 tests/test_literature_lab.py create mode 100644 tests/test_meta_research.py create mode 100644 tests/test_research_memory.py create mode 100644 tests/test_simulation_ladder.py create mode 100644 ui/index.html create mode 100644 ui/package-lock.json create mode 100644 ui/package.json create mode 100644 ui/src/App.tsx create mode 100644 ui/src/SurfaceScene.tsx create mode 100644 ui/src/WorldScene.tsx create mode 100644 ui/src/hooks.ts create mode 100644 ui/src/main.tsx create mode 100644 ui/src/styles.css create mode 100644 ui/src/types.ts create mode 100644 ui/src/vite-env.d.ts create mode 100644 ui/tsconfig.app.json create mode 100644 ui/tsconfig.json create mode 100644 ui/tsconfig.node.json create mode 100644 ui/vite.config.ts diff --git a/.github/workflows/pages.yml b/.github/workflows/pages.yml new file mode 100644 index 0000000..cd53b57 --- /dev/null +++ b/.github/workflows/pages.yml @@ -0,0 +1,31 @@ +name: Pages + +on: + push: + branches: ["main"] + workflow_dispatch: + +permissions: + contents: read + pages: write + id-token: write + +concurrency: + group: pages + cancel-in-progress: false + +jobs: + deploy: + environment: + name: github-pages + url: ${{ steps.deployment.outputs.page_url }} + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v7.0.1 + - uses: actions/configure-pages@v6.0.0 + - uses: actions/upload-pages-artifact@v5.0.0 + with: + path: ./docs + - name: Deploy public showcase + id: deployment + uses: actions/deploy-pages@v5.0.0 diff --git a/.gitignore b/.gitignore index a6bf1bf..273dbab 100644 --- a/.gitignore +++ b/.gitignore @@ -7,6 +7,8 @@ __pycache__/ .coverage htmlcov/ dist/ +node_modules/ +.vite/ build/ *.egg-info/ .env @@ -21,3 +23,4 @@ checkpoints/ *.sqlite *.sqlite3 *.db +*.log diff --git a/ARCHITECTURE.md b/ARCHITECTURE.md index 9c38c6d..c178d6d 100644 --- a/ARCHITECTURE.md +++ b/ARCHITECTURE.md @@ -7,32 +7,177 @@ INGEST -> NORMALIZE -> PERCEIVE -> BELIEF -> INTERVENE -> SIMULATE -> PRICE -> STRATEGIZE -> EVOLVE -> PROVE -> EXPORT ``` -The first release implements an Earnings Pack vertical slice across all stages. The core is -deterministic and CPU-first. Learned generative models are optional model-family adapters and may -activate only after accepted point-in-time training evidence and calibration exist. +The deterministic Earnings Pack implements a vertical slice across all stages. The v0.2 +meta-research plane operates over immutable pipeline artifacts; it does not modify the runtime +that produced them. Learned generative models are optional model-family adapters and may activate +only after accepted point-in-time training evidence, calibration, and independent validation exist. ## Layers ```text -Read-only API / CLI / governor bundle exporter +Read-only API / CLI / signed candidate exporter Proof Court / Holdability Gate / signature verifier Idea Forge / Belief Exchange / quality-diversity archive +Dream Foundry / cognitive immune court / invention archive / failure museum Game Arena / Market Society / option outcome field Causal scenario lattice / market and volatility twins Scuttlebutt claim graph / point-in-time world-state graph Immutable evidence vault / event log / clocks / semantic RNG ``` +## Protected meta-research plane + +```text +Base artifact + -> explicit simulator and market-pack catalogs + -> social counterfactual ablation + -> competing causal-graph tournament + -> Levels 0-7 simulation ladder + -> bounded deterministic island evolution + -> bounded wake-dream-consolidate-awaken cognition + -> evidence-debt and falsification immune court + -> invention quality-diversity archive and failure museum + -> quality-diversity archive + -> prediction-before-outcome and interoception + -> authority-monotone budget controller + -> independent research gates + -> append-only research memory +``` + +The controller may only preserve or reduce a requested candidate budget. Evolution may change +hypothesis genomes but every descendant is proposal-only until independently resimulated. +Evaluators, sealed tests, source code, signing policy, and authority rules are protected targets. + +## Cognitive invention plane + +```text +Immutable hypothesis + failure library + causal disagreement + + admitted zero-credit scientific cognition seeds + -> semantic selection from 24 typed cognitive operators + -> REM divergence + -> NREM semantic fingerprinting and compression + -> lucid counter-evidence plus hard-constraint reinstatement + -> awakening extraction of mechanism, countermechanism, and falsifiers + -> seven-check cognitive immune court + -> evidence-debt-aware multi-objective scoring + -> invention quality-diversity archive + -> museum of beautiful failures + -> independent resimulation proposal only +``` + +The cognition plane is generative research infrastructure, not an empirical model. +It may create synthetic worlds and invention blueprints. It may not fabricate +evidence, grant causal credit, mutate code or evaluators, alter gates, activate an +adapter, construct an order, or increase authority. A dream temperature changes the +number of composed cognitive operators only. The same semantic seed, parent artifact, +budget, cycles, and temperature reproduce the same lineage and content hash. +Later dream phases carry explicit parent-dream identities and recursive ablations; +they refine a proposal lineage without modifying the runtime that generated it. + +Simulator families are registered, never silently substituted. An adapter contract with missing +data, implementation, or calibration is reported as blocked and contributes no empirical credit. +Competing causal graphs are scored independently and expose disagreement. + +## Simulation ladder + +| Level | Environment | Evidence class | +|---|---|---| +| 0 | Exact replay | Mechanics | +| 1 | Historical perturbation | Mechanics | +| 2 | Regime-conditioned synthetic worlds | Mechanics | +| 3 | Causal intervention | Mechanics | +| 4 | Agent society | Mechanics | +| 5 | Adversarial worlds | Mechanics | +| 6 | Forward shadow | Point-in-time research | +| 7 | Bounded paper outcomes | Forward paper | + +Passing a higher synthetic level never substitutes for missing Level 6 or 7 evidence. + +## Scientific Literature Lab + +```text +Reviewed source query + -> versioned Atom metadata + -> point-in-time and category admission + -> immutable paper record + -> deterministic theme mapping + -> falsifiable research capsule + -> experiment proposal with zero empirical credit + -> append-only research memory +``` + +The implemented source is arXiv descriptive metadata. Full-text files are linked but not +downloaded or mirrored. Crossref enrichment remains an adapter contract. Papers cannot activate +simulators, mutate source, or bypass independent reproduction. + +## Attention Observatory + +```text +Permitted ranked-search export + -> explicit host and terms allowlist + -> robots.txt decision + -> metadata-only page fetch + -> immutable ranked attention snapshot + -> chronological churn/rank/metadata delta + -> social ablation and proof gates +``` + +Search prominence, Wikipedia pageviews, and web-crawl presence are distinct proxies. None is +silently relabeled as query volume, user intent, or causal market demand. General search-engine +HTML scraping, authentication, redirects, CAPTCHA bypass, and anti-bot evasion are prohibited. + The runtime stores content-addressed evidence and append-only indexes outside the source tree. Repository fixtures prove mechanics only and never certify market fidelity or predictive edge. The ordinary option lane rejects 0DTE contracts whose lifecycle crosses the permitted exit. A dedicated jump- and expiry-aware 0DTE pricer remains fail-closed and is not implemented in the first public vertical slice. +## Evidence and intelligence mesh + +Jester registers source capability before connector implementation. A pipeline contract fixes: + +```text +identity + access mode + cadence + + event/publication/availability/ingestion/revision clocks + + retained fields + permissions + + intelligence consumers + quality gates + + activation requirements + limitations +``` + +The initial mesh covers official filing, macro-vintage, Treasury, regulatory-positioning, +clinical-trial, patent, scholarly, public-attention, licensed-options, and licensed-order-book +planes. Only immutable local artifacts, arXiv metadata, and admitted ranked-search metadata are +operational. All other entries are visible adapter or licensing contracts and receive no data or +model credit. + +Intelligence services compose over admitted pipelines. Temporal fusion, entity resolution, +contradiction preservation, attention divergence, scientific-claim firewalls, causal-graph +committees, provenance-poisoning defense, and bounded active-research routing have deterministic +mechanics. Source calibration, revision surprise, cross-market transmission, the option-expression +critic, and simulation-gap direction remain gated by missing resolved or licensed evidence. +Every intelligence service has `authority_effect=none`. + +## 3D read-only observatory + +The React 19 and Three.js interface consumes `/observatory/bootstrap` plus the catalog endpoints. +Its five inspectable modes are: + +- World: orbitable causal topology with an accessible system-selector mirror. +- Options: joint moneyness/tenor/IV geometry and pathwise option failure table. +- Meta: quality-diversity niches, interoception, simulation ladder, and paper capsules. +- Dream: wake/dream/consolidation phases, 24 cognition operators, inventions, + immune checks, evidence debt, and failure memory. +- Sources: filterable pipeline contracts and their composable intelligence mesh. + +Charts have text or table equivalents, controls are keyboard reachable, touch targets meet the +mobile minimum, motion respects `prefers-reduced-motion`, and layouts are validated at desktop, +phone, and landscape breakpoints. The frontend is observability only; no API mutation or execution +route exists. + ## External boundaries - Credential-free official sources may be fetched by reviewed connector classes. - Market, options, transcript, and social evidence arrive through operator-staged manifests. - Intelligence providers receive only records whose permission allows external processing. -- Governor export is a signed file protocol. No downstream repository is imported. +- Candidate export is a signed file protocol. No external repository is imported. - Read-only observability is exposed over localhost. Mutation remains CLI/config controlled. diff --git a/JESTER_CONSTITUTION.md b/JESTER_CONSTITUTION.md index 3cda9cc..53804b5 100644 --- a/JESTER_CONSTITUTION.md +++ b/JESTER_CONSTITUTION.md @@ -1,6 +1,6 @@ # Jester Constitution -Version: `jester-constitution-v1` +Version: `jester-constitution-v2` ## Authority @@ -9,9 +9,9 @@ counterfactual scenarios, simulate markets and options, evolve research hypothes beliefs, and export signed research candidates. Jester has no authority to connect to a broker, inspect an account, size against buying power, -construct an executable order, cancel an order, or submit capital. A downstream human-owned -governor independently decides whether a signed research candidate may enter its own proof and -paper lifecycle. +construct an executable order, cancel an order, or submit capital. An independent, +operator-controlled policy decides whether a signed research candidate may enter a separate +proof and paper lifecycle. ## Evidence law @@ -38,3 +38,54 @@ Jester may autonomously evolve hypothesis genomes, belief calibration, bounded n parameters, archive occupancy, and research scheduling. It may emit source-code mutation proposals, but it may not apply or merge them. Source code, evaluators, sealed test data, proof gates, signing policy, and authority rules are protected mutation targets. + +Every autonomous cycle must: + +1. Predict a resolvable research outcome before observing it. +2. Preserve parent identities and mutation lineage. +3. Label unresimulated descendants as proposals with no empirical credit. +4. Keep competing causal graphs separate and expose their disagreement. +5. Persist resolved failures and calibration errors in append-only memory. +6. Preserve or reduce requested budgets when pressure rises; it may never expand authority. +7. Require independent evaluation before any research promotion. + +An adapter contract is not an implementation. A mechanics-validated baseline is not an +empirically calibrated market simulator. Synthetic diversity is not profitability. + +## Cognitive-invention law + +1. Dream worlds are synthetic stress artifacts and receive zero empirical or causal credit. +2. Cognitive operators must be typed, content-addressed, deterministic, and independently + evaluable; prose variation alone is not novelty. +3. Every invention must expose a countermechanism, assumptions, evidence debt, a minimal + decisive experiment, and at least two falsifiers. +4. Cross-domain analogies transfer structural relations only. They never transfer observed + frequencies, market truth, or evidence status. +5. The cognitive immune court may quarantine ideas but the invention process may not modify + that court, its thresholds, evaluator contracts, or sealed evidence. +6. Dream temperature may change compositional breadth only. It may not increase authorized + budgets, system authority, empirical credit, or promotion status. +7. Under-evidenced, displaced, and quarantined inventions remain in the append-only failure + museum with a revival condition. +8. Every invention is proposal-only, auto-apply disabled, live blocked, incapable of order + construction, and requires independent full-ladder resimulation. + +## Scientific-learning law + +1. Paper title, abstract, authors, identifier, category, and version are evidence with distinct + update and ingestion clocks. +2. A preprint is an unverified claim, not peer review, replication, or market truth. +3. Literature-derived changes begin as falsifiable experiment proposals with zero empirical + credit. +4. Full text may not be mirrored without an explicit compatible license and reviewed policy. +5. Paper revisions append; prior versions and capsules are never silently overwritten. +6. Literature may not activate simulators, mutate proof gates, or increase authority. + +## Attention-collection law + +1. Every scraped host requires explicit policy and terms admission plus an allowing robots rule. +2. Authentication, credentials, redirects, CAPTCHA solving, and anti-bot bypass are forbidden. +3. Only metadata and response hashes persist; HTML bodies do not. +4. Rank, churn, metadata change, pageviews, and crawl presence remain distinct proxy classes. +5. A prominence proxy may generate or reweight a hypothesis but receives no causal or trading + credit without timing controls, ablation, and independent proof. diff --git a/QUALITY_GATES.yaml b/QUALITY_GATES.yaml index c35abe0..65a76d3 100644 --- a/QUALITY_GATES.yaml +++ b/QUALITY_GATES.yaml @@ -1,4 +1,4 @@ -version: 1 +version: 2 authority: broker_dependency_allowed: false credential_storage_allowed: false @@ -27,6 +27,54 @@ autonomy: source_auto_apply_allowed: false failed_trial_ledger_required: true quarantine_and_rollback_required: true + prediction_before_outcome_required: true + proposal_resimulation_required: true + authority_monotonicity_required: true + append_only_research_memory_required: true + lineage_preservation_required: true +simulation_ladder: + exact_replay_level_0_required: true + mechanics_levels_0_through_5_required: true + forward_shadow_level_6_requires_resolved_outcomes: true + bounded_paper_level_7_requires_forward_shadow: true + synthetic_levels_may_substitute_for_forward_evidence: false +meta_evaluation: + competing_causal_graphs_required: true + silent_model_averaging_allowed: false + social_counterfactual_ablation_required: true + quality_diversity_archive_required: true + held_out_meta_non_regression_required_for_promotion: true +cognitive_invention: + typed_operator_catalog_required: true + semantic_determinism_required: true + dream_worlds_synthetic_only_required: true + dream_worlds_may_receive_empirical_credit: false + countermechanism_required: true + evidence_debt_required: true + falsifiers_required: true + cognitive_immune_court_required: true + lineage_fingerprinting_required: true + failure_museum_required: true + prior_art_challenge_required: true + code_mutation_allowed: false + evaluator_mutation_allowed: false + auto_apply_allowed: false + order_construction_allowed: false +scientific_learning: + point_in_time_paper_version_required: true + abstract_claims_authoritative: false + peer_review_may_be_inferred_from_preprint: false + independent_replication_required: true + paper_derived_source_auto_apply_allowed: false + unlicensed_full_text_mirroring_allowed: false +attention: + explicit_source_terms_required: true + robots_allow_required: true + authentication_allowed: false + anti_bot_bypass_allowed: false + page_body_persistence_allowed: false + search_prominence_may_be_labeled_search_volume: false + attention_proxy_may_receive_direct_causal_credit: false claims: synthetic_mechanics_may_certify_forward_edge: false reconstructed_results_may_certify_forward_performance: false diff --git a/README.md b/README.md index 68e47d4..38a18ba 100644 --- a/README.md +++ b/README.md @@ -1,178 +1,418 @@ +
+ # Jester -**A causal market-world, narrative-intelligence, game-theory, and long-option -research engine.** +### Simulate the market world, not just the price. + +**A deterministic causal market-imagination engine for point-in-time evidence, +narrative propagation, strategic participants, volatility surfaces, executable +long-option outcomes, and open-ended scientific discovery.** + +[![Quality](https://github.com/ObtuseAI/jester/actions/workflows/quality.yml/badge.svg)](https://github.com/ObtuseAI/jester/actions/workflows/quality.yml) +[![Version](https://img.shields.io/badge/release-v0.2.0-8b5cf6)](https://github.com/ObtuseAI/jester/releases/tag/v0.2.0) +[![Python](https://img.shields.io/badge/Python-3.12%20%7C%203.13-38bdf8)](https://www.python.org/) +[![Research mode](https://img.shields.io/badge/authority-paper--only-34d399)](#authority-boundary) +[![License](https://img.shields.io/badge/license-MIT-e5e7eb)](LICENSE) + +[**Explore the interactive showcase**](https://obtuseai.github.io/jester/) · +[Architecture](ARCHITECTURE.md) · +[Research foundations](RESEARCH_FOUNDATIONS.md) · +[Run locally](#quick-start) + +
-Jester asks a narrower and harder question than a stock predictor: +Jester asks: > Given only the information available at this exact moment, which market worlds -> are plausible—and can a real long call or long put survive volatility, liquidity, -> timing, and a downstream governor's holding constraints? +> are plausible, how could participants react, how might price and the volatility +> surface evolve, and can an executable long call or put survive the configured +> execution and holding constraints? + +[![Jester Observatory — interactive causal market-world](docs/images/jester-observatory-dream-foundry.png)](https://obtuseai.github.io/jester/) + +## At a glance + +| 24 cognition operators | 15 data pipelines | 13 intelligence services | +|---:|---:|---:| +| **10** simulator families | **8** market packs | **37** integration and proof tests | + +Jester is not a stock screener with an extra language-model layer. It is an +event-sourced research instrument that preserves what was knowable, builds +competing causal explanations, places diverse participants inside simulated +market worlds, evolves hypotheses without erasing failures, and translates every +surviving thesis into the pathwise economics of the actual option. -The first public release is a deterministic Earnings Pack vertical slice. It turns -point-in-time evidence into a signed, replayable candidate bundle without possessing -broker credentials, order authority, or a live-submit path. +The **Jester Observatory** is a responsive, read-only 3D research cockpit. It lets +an operator orbit the causal market world, inspect the option-volatility surface, +replay the proof ladder, explore the quality-diversity archive, and audit every +data-pipeline, intelligence, dream, and invention contract. It deliberately contains no order, +broker, credential, or execution control. -## The research chain +Jester v0.2 expands the deterministic Earnings Pack into a recursively composable +research system. It preserves competing causal explanations, searches a diverse +ecology of hypothesis lineages, predicts its own research progress before seeing +the result, and reduces its search budget when internal pressure rises. + +It does **not** possess broker credentials, construct orders, submit capital, +mutate its evaluators, or apply its own source-code proposals. + +## Architecture ```mermaid flowchart LR - A["Immutable Evidence Vault"] --> B["Scuttlebutt claim graph"] + A["Immutable evidence vault"] --> B["Narrative claim graph"] A --> C["Point-in-time world state"] - B --> D["Competing causal worlds"] + B --> D["Competing causal graph tournament"] C --> D - D --> E["Numerical market society"] - E --> F["Strategic game arena"] - F --> G["Evolutionary idea forge"] - G --> H["Option outcome field"] - H --> I["Independent proof court"] - I --> J["Signed governor candidate"] - J --> K["Downstream governor decides"] + D --> E["Simulation ladder: Levels 0-7"] + E --> F["Market society and game arena"] + F --> G["Option outcome field"] + G --> H["Protected island evolution"] + H --> I["Oneiric cognition arsenal"] + I --> J["Cognitive immune court"] + J --> K["Quality-diversity archives"] + K --> L["Prediction and interoception"] + L --> M["Independent proof gates"] + M --> N["Signed research candidate"] ``` -Every candidate traverses the complete chain. Social activity can generate or -reweight hypotheses, but it cannot authorize a candidate. A directionally correct -stock thesis can still be rejected when the option loses through IV crush, decay, -spread, fill risk, or an earliest-exit mismatch. - -## What exists today - -- Immutable evidence identities with distinct event, publication, market-availability, - ingestion, and revision clocks. -- Claim graphs that preserve originality, propagation, incentives, contradiction, - saturation, manipulation risk, and price-leading versus price-following behavior. -- Multiple causal graphs and explicit observed, candidate-causal, supported-intervention, - and stress-only edges. -- A six-world Earnings Pack lattice spanning guidance conflict, IV crush, narrative - reflexivity, catalyst delay, and adversarial liquidity. -- Thirteen numerical participant classes and Bayesian, signaling, congestion, - adverse-selection, and repeated-session games. -- Correlation-clustered specialist beliefs with Brier and log-loss settlement. -- A lineage-preserving hypothesis genome and quality-diversity archive primitive. -- Joint underlying/IV/liquidity option paths, Black–Scholes baseline repricing, - static-arbitrage checks, fill modeling, P&L attribution, and a hard Holdability Gate. -- An independent proof manifest and canonical Ed25519 candidate signing. -- A bounded inbox supervisor and local read-only observatory API. - -Advanced stochastic-volatility, rough-volatility, generative order-book, and calibrated -full-surface models are extension interfaces—not claimed as implemented or validated in -this release. +The integration traces the complete chain: -## Authority boundary +```text +claim -> belief -> participant reaction -> order-flow pressure + -> underlying/IV/liquidity path -> executable option outcome +``` -Jester is structurally research-only: +## System overview + +- Exact, hash-chained point-in-time replay with semantic deterministic randomness. +- Structured claims with provenance, incentive, propagation, contradiction, + saturation, manipulation, and price-leading controls. +- Seven social counterfactual ablations, including absent, inverted, delayed, + manipulated, confirmed, and contradicted narrative worlds. +- Multiple causal graphs scored independently; silent ensemble averaging is forbidden. +- A Level 0–7 simulation ladder: + exact replay, historical perturbation, synthetic regimes, causal interventions, + agent society, adversarial worlds, forward shadow, and bounded paper outcomes. +- Thirteen numerical participant classes and Bayesian-information, signaling, + congestion, adverse-selection, and repeated-session games. +- Joint price/IV/liquidity option paths, static-arbitrage checks, fill modeling, + attribution, and a hard Holdability Gate. +- A simulator registry that distinguishes operational baselines from blocked + neural/rough-volatility/order-book adapter contracts. +- Eight market-pack contracts with explicit evidence, simulator, and validation gates. +- Deterministic island evolution with half-life mutation, regime transplantation, + evidence ablation, adversarial inversion, and cross-lineage crossover. +- A 24-mode cognition arsenal spanning abductive, analogical, dialectical, + counterfactual, strategic, temporal, compressive, divergent, adversarial, and + falsification-first invention. +- A four-phase Dream Foundry: REM divergence, NREM consolidation, lucid + intervention, and awakening debrief. +- Typed invention blueprints with countermechanisms, structural analogy maps, + evidence debt, minimum decisive experiments, prior-art challenges, and falsifiers. +- A cognitive immune court that rejects authority leakage, evidence-lineage + violations, circular explanations, analogy overreach, and unfalsifiable output. +- A Museum of Beautiful Failures that preserves under-evidenced, dominated, and + quarantined lineages without granting empirical credit. +- A MAP-Elites-style quality-diversity archive that preserves distinct niches. +- Prediction-before-outcome, proper-score resolution, interoceptive pressure + diagnosis, and an authority-monotone budget controller. +- Append-only SQLite research memory protected against update and deletion. +- A Scientific Literature Lab that discovers versioned arXiv metadata, applies + point-in-time cutoffs, and converts abstracts into falsifiable zero-credit + research capsules. +- A robots-aware Attention Observatory for staged ranked-search URLs, metadata + mutation, result churn, rank movement, and source-diversity proxies. +- Fifteen permissioned pipeline contracts spanning immutable artifacts, arXiv, + staged search metadata, SEC EDGAR, FRED/ALFRED vintages, Treasury curves, + FINRA short volume, CFTC positioning, Crossref/retractions, ClinicalTrials.gov, + Wikimedia, USPTO, licensed option chains, and licensed order books. +- Thirteen composable intelligence contracts for temporal fusion, entity resolution, + contradiction, attention divergence, scientific claim firewalls, revision surprise, + cross-market transmission, option-expression criticism, simulation-gap direction, + provenance defense, and active research routing. +- Independent proof manifests, Ed25519 research-bundle signing, a bounded supervisor, + and a read-only observatory. + +All evolved descendants are `proposal_only` and `requires_resimulation`. Archive +novelty is not evidence of edge. + +## Dream Foundry and cognition arsenal + +![Jester Dream Foundry — bounded recursive invention](docs/images/jester-observatory-dream-foundry.png) + +Jester does not ask an unconstrained model to brainstorm. It composes inspectable +cognitive operators over an immutable hypothesis, assigns each imagined world a +semantic RNG lineage, and separates divergent invention from independent evaluation. +Every dream is `synthetic_only=true`, `stress_only=true`, and +`empirical_credit=false`. + +Dream output is useful only as a falsifiable research blueprint. Each blueprint +records: + +- the problem gap and proposed mechanism; +- the strongest countermechanism; +- a relation-level cross-domain analogy and explicit break tests; +- expected state, underlying, IV-surface, and narrative paths; +- assumptions, evidence debt, required measurements, and transfer tests; +- a minimal decisive experiment and concrete falsifiers; +- a lineage fingerprint and a bounded novelty claim; +- `auto_apply=false`, `requires_independent_resimulation=true`, + `live_blocked=true`, and `order_construction_allowed=false`. + +The quality-diversity archive retains strong inventions by cognitive niche. Ideas +below the frozen utility floor are not erased: they enter the failure museum with +their lesson and a strict revival condition. Dream temperature increases operator +composition breadth; it never expands authority, empirical credit, or research +budget. + +Autonomous scientific capsules may seed this process. Their paper and capsule IDs, +unverified trust state, proposed mechanism, experiment, and falsification question +remain attached. A seeded dream gains replication debt—not empirical credit—and +every subsequent recursive dream preserves an explicit parent-dream ID plus an +ablation that can remove the inherited structure. + +## Autonomous scientific learning + +The literature loop uses the official arXiv Atom metadata API. It freezes the paper +version, publication/update/ingestion clocks, categories, identifiers, license URI, +abstract, and raw/normalized hashes. It then maps each abstract to: + +- candidate mechanisms; +- affected Jester systems; +- sealed experiment blueprints; +- explicit falsification questions; +- limitations and replication requirements. + +Every capsule is an `unverified_scientific_claim` with `auto_apply=false` and +`empirical_credit=false`. Jester does not mirror PDFs, infer peer review from an +arXiv record, activate an adapter, or relax a gate because a paper claims success. +An identical autonomous query is limited to one refresh per day, with one connection +and source-compliant retry pacing. + +## Attention Observatory + +Jester can collect metadata for a staged ranked-search result set when an operator +supplies the provider's permitted export and an explicit domain/terms policy. Before +fetching each page it requires HTTPS, an admitted host, and an allowing `robots.txt`. +It refuses authentication, redirects, CAPTCHA/anti-bot bypass, non-HTML content, and +undeclared sources. + +Across snapshots it measures result churn, rank movement, metadata mutation, +reciprocal-rank mass, and source diversity. These are labeled **search-prominence +proxies**. They are not represented as search volume, user intent, or causal market +signals. Direct scraping of unapproved search-engine result pages is prohibited. + +## Honest simulator catalog + +Operational mechanics include exact replay, deterministic regime resampling, +typed causal intervention, numerical market society, and adversarial world +construction. + +The repository also defines fail-closed adapter contracts for TNCM-VAE causal +simulation, multidimensional queue-reactive order books, LOB diffusion, conditional +IV-surface diffusion, and rough volatility. Those adapters remain blocked until +accepted data, implementation, calibration, and independent validation exist. +No baseline is relabeled as a learned frontier model. + +See [RESEARCH_FOUNDATIONS.md](RESEARCH_FOUNDATIONS.md) for the primary research +basis and exact claim boundary. + +## Authority boundary | Capability | Status | |---|---| -| Generate market-world hypotheses | Allowed | +| Generate and evolve market hypotheses | Allowed | | Simulate long calls and long puts | Allowed | -| Produce paper candidate artifacts | Allowed | -| Sign replayable research bundles | Allowed | +| Preserve research memory and failed lineages | Allowed | +| Reduce its own research budget | Allowed | +| Produce and sign paper candidate artifacts | Allowed | +| Activate uncalibrated simulator adapters | Blocked | +| Mutate evaluators or proof gates | Prohibited | +| Auto-apply source changes | Prohibited | | Read broker credentials | Absent | | Construct or submit orders | Prohibited | -| Decide downstream acceptance | Prohibited | -| Auto-apply self-written code | Prohibited | +| Self-authorize promotion | Prohibited | -Candidate bundles carry `paper_only=true`, `live_blocked=true`, -`order_construction_allowed=false`, and `execution_authority=none`. The schema is in -[`schemas/governor_candidate_bundle_v1.schema.json`](schemas/governor_candidate_bundle_v1.schema.json). +Candidate and meta artifacts carry explicit paper-only, live-blocked, no-order, +no-execution authority. An independent operator policy accepts or rejects them. ## Quick start -Requirements: Python 3.12 or 3.13 and -[`uv`](https://docs.astral.sh/uv/). +Requirements: Python 3.12 or 3.13 and [uv](https://docs.astral.sh/uv/). ```powershell uv sync --extra dev + uv run jester run ` --input fixtures/earnings/synthetic_candidate.json ` --output artifacts/synthetic-candidate.json -uv run jester inspect --artifact artifacts/synthetic-candidate.json + +uv run jester meta-run ` + --input fixtures/earnings/synthetic_candidate.json ` + --output artifacts/meta-synthetic.json ` + --memory artifacts/research-memory.sqlite3 ` + --created-at 2026-07-29T12:00:00Z ` + --pack earnings ` + --generations 4 ` + --candidate-budget 16 ` + --islands 4 ` + --root-seed 2202 ` + --cognition-budget 12 ` + --dream-cycles 4 ` + --dream-temperature 0.72 ` + --forward-outcomes 0 ` + --paper-outcomes 0 ` + --required-forward-outcomes 50 ` + --required-paper-outcomes 30 ` + --resimulated-descendants 0 ` + --gap-observations 0 ` + --gap-pressure 0.5 + +uv run jester catalog --output artifacts/catalog.json ``` -The included fixture is deliberately synthetic and redistributable. It proves replay, -economics, rejection, and authority behavior; it is not evidence of market performance. +Run the cognition plane independently when an inspectable invention artifact is +needed without the rest of the meta pipeline: + +```powershell +uv run jester cognition-run ` + --input fixtures/earnings/synthetic_candidate.json ` + --literature-batch artifacts/literature-replay-v0.2-final.json ` + --output artifacts/cognition-synthetic.json ` + --memory artifacts/research-memory.sqlite3 ` + --created-at 2026-07-29T12:00:00Z ` + --root-seed 2202 ` + --dream-budget 12 ` + --authorized-dream-budget 8 ` + --dream-cycles 4 ` + --dream-temperature 0.72 +``` -Run the complete release gate: +After the target cycle resolves, close the prediction with an explicit outcome +and persist its proper score: ```powershell -uv run ruff format --check . -uv run ruff check . -uv run mypy -uv run pytest -uv build -uv run python scripts/validate_public_release.py +uv run jester meta-resolve ` + --input artifacts/meta-synthetic.json ` + --output artifacts/meta-resolution.json ` + --memory artifacts/research-memory.sqlite3 ` + --actual-archive-gain 0.2 ` + --non-regression true ` + --resolved-at 2026-08-01T12:00:00Z ``` -## Signed handoff +The public fixture is synthetic and redistributable. It proves mechanics, +determinism, rejection, and authority behavior—not market fidelity or performance. +Its expected honest state is Levels 0–5 passed and Levels 6–7 blocked. -Jester never creates or stores a signing key. Supply a 32-byte Ed25519 seed owned by -the operator: +Run one live, bounded scientific metadata scan after setting a real project contact +in the user agent: ```powershell -uv run jester sign ` - --input artifacts/unsigned-bundle.json ` - --output artifacts/signed-bundle.json ` - --key .operator/jester-ed25519.key ` - --key-id operator-research-key +uv run jester literature-scan ` + --manifest fixtures/literature/live_query_template.json ` + --output artifacts/literature-batch.json ` + --memory artifacts/research-memory.sqlite3 ` + --ingestion-time 2026-07-29T18:00:00Z ` + --user-agent "JesterResearch/0.2 (mailto:operator@example.com)" ` + --timeout-seconds 30 ` + --retries 3 +``` -uv run jester verify --bundle artifacts/signed-bundle.json +The deterministic public replay does not use the network: + +```powershell +uv run jester literature-replay ` + --manifest fixtures/literature/synthetic_query.json ` + --feed fixtures/literature/synthetic_arxiv_feed.xml ` + --output artifacts/literature-replay.json ` + --memory artifacts/research-memory.sqlite3 ` + --ingestion-time 2026-07-29T12:01:00Z ``` -The synthetic pipeline result contains its unsigned bundle under `unsigned_bundle`. -Extract and sign it outside the evidence plane: +`literature-supervise` runs the same scan as a bounded daily loop. It requires +`cutoff_policy=scan_time`, an explicit cycle limit, and a polling interval no shorter +than the manifest's daily refresh boundary. + +Attention collection uses `attention-scrape` with an admitted ranked-URL manifest; +`attention-compare` produces the chronological delta. No general-purpose or +unapproved SERP scraper exists. + +## 3D Observatory + +The UI requires Node.js and npm. Run the API and Vite development server in +separate terminals: ```powershell -uv run jester export-bundle ` - --result artifacts/synthetic-candidate.json ` - --output artifacts/unsigned-bundle.json +uv run jester serve ` + --artifact-root artifacts ` + --host 127.0.0.1 ` + --port 8000 + +Set-Location ui +npm.cmd install +npm.cmd run dev ``` -Production integrations should pass the signed artifact to a downstream governor through -a file or equivalent one-way transport. +Open `http://127.0.0.1:5173`. Vite proxies only read requests to the localhost +Jester API. The displayed reference world is explicitly labeled +`synthetic_mechanics_fixture`; an unavailable API produces a visible hard error, +not a hidden fallback. + +Validate the frontend with: + +```powershell +Set-Location ui +npm.cmd run typecheck +npm.cmd run build +``` -## Bounded autonomy +Desktop and mobile reference renders are stored in +`docs/images/jester-observatory-desktop.png` and +`docs/images/jester-observatory-mobile.png`. The fifth **Dream** mode exposes the +cycle topology, all 24 operators, invention blueprints, cognitive immune court, +and failure museum. Its dedicated reference render is +`docs/images/jester-observatory-dream-foundry.png`. -The supervisor processes immutable JSON manifests. It never deletes or moves source -inputs, never retries a failed item silently, and emits an explicit blocked artifact: +## Validation ```powershell -uv run jester supervise ` - --inbox .operator/inbox ` - --outbox .operator/outbox ` - --errors .operator/errors ` - --max-cycles 10 ` - --max-items 100 ` - --poll-seconds 5 +uv run ruff format --check . +uv run ruff check . +uv run mypy +uv run pytest +uv build +uv run python scripts/validate_public_release.py ``` -Evolution is also bounded. Jester may mutate research genomes, measure niche diversity, -and write code-change proposals. `auto_apply` is always false; independent evaluation -and an operator-controlled software workflow remain required. +## Signed handoff -## Optional narrative model +Jester never creates or stores a signing key. An operator may supply a 32-byte +Ed25519 seed outside the evidence plane: -`OpenRouterClient` is a permission-gated connector for claim interpretation and -behavioral variation. It is not used for pricing, proof, calibration, or authority -decisions. Inputs may be sent only when the evidence record permits external-model -processing. Provider failure is explicit after the configured retry budget; there is -no hidden model fallback. +```powershell +uv run jester export-bundle ` + --result artifacts/synthetic-candidate.json ` + --output artifacts/unsigned-bundle.json -## Evidence and data +uv run jester sign ` + --input artifacts/unsigned-bundle.json ` + --output artifacts/signed-bundle.json ` + --key .operator/jester-ed25519.key ` + --key-id operator-research-key -No proprietary, licensed, scraped, or historical market dataset is distributed here. -See [`DATA_NOTICE.md`](DATA_NOTICE.md). Real research intake must preserve permissions, -retention, content hashes, and all point-in-time clocks. A repaired volatility surface -must be labeled repaired and cannot be represented as an observed simulator output. +uv run jester verify --bundle artifacts/signed-bundle.json +``` ## Status standard -Jester is **state-of-the-art-inspired**, not proven state of the art. Promotion requires -a frozen forward-paper A/B comparison against the same universe, cutoff, capital, -contracts, execution simulator, holding rules, and costs—and independent replication -of improved executable P&L, calibration, downside control, holdability, fill realism, -false-positive rejection, and simulation-to-forward agreement. +Jester is **state-of-the-art-inspired and architecturally novel**, not proven +state of the art. That stronger claim requires frozen forward-paper A/B results +against the same universe, evidence cutoff, capital rules, contract eligibility, +execution simulator, holding restrictions, and costs—followed by independent +replication. -Architecture and invariants are detailed in -[`ARCHITECTURE.md`](ARCHITECTURE.md) and -[`JESTER_CONSTITUTION.md`](JESTER_CONSTITUTION.md). +See [ARCHITECTURE.md](ARCHITECTURE.md), +[JESTER_CONSTITUTION.md](JESTER_CONSTITUTION.md), and +[QUALITY_GATES.yaml](QUALITY_GATES.yaml) for the enforceable boundaries. diff --git a/RESEARCH_FOUNDATIONS.md b/RESEARCH_FOUNDATIONS.md new file mode 100644 index 0000000..8f6694a --- /dev/null +++ b/RESEARCH_FOUNDATIONS.md @@ -0,0 +1,107 @@ +# Research foundations and claim boundary + +Jester v0.2 is an original integration informed by primary research. The repository +implements deterministic contracts, baselines, evaluation boundaries, and protected +meta-research mechanics. It does not claim to reproduce paper results, training data, +weights, or empirical performance. + +## Causal market simulation + +- [Towards Causal Market Simulators](https://arxiv.org/abs/2511.04469) motivates learned + causal trajectories and counterfactual interventions. Jester implements typed competing + causal graphs and an adapter contract; it does not claim a trained TNCM-VAE. + +## Market microstructure + +- [Deep Learning Meets Queue-Reactive](https://arxiv.org/abs/2501.08822) motivates + multidimensional queue-reactive event simulation. +- [Painting the Market](https://arxiv.org/abs/2509.05107) motivates spatiotemporal LOB + diffusion, controllable inpainting, and explicit benchmark scrutiny. + +Jester registers both as blocked adapters pending licensed book data, market-specific +calibration, implementation, and independent stylized-fact and fill validation. + +## Volatility-surface generation + +- [Forecasting Implied Volatility Surface with Generative Diffusion Models](https://arxiv.org/abs/2511.07571) + motivates conditional surface generation and arbitrage-aware training. +- [Volatility is Rough](https://arxiv.org/abs/1410.3394) is used here only as the + registered rough-volatility research lineage. + +Jester's implemented public baseline remains ordinary deterministic option repricing. +The IV-diffusion and rough-volatility entries are blocked adapter contracts. + +## Open-ended algorithm discovery + +- [AlphaEvolve](https://arxiv.org/abs/2506.13131) motivates a program database, + evaluator-guided evolution, and diverse search. +- [CORAL](https://arxiv.org/abs/2604.01658) motivates persistent memory, isolated work, + evaluator separation, and sustained multi-agent research. + +Jester applies those principles to typed hypothesis genomes, not autonomous source changes. +Its proof court, authority constitution, and tests cannot be mutated by the research loop. + +## Structured invention and dream consolidation + +- [MAP-Elites](https://arxiv.org/abs/1504.04909) motivates preserving the best + qualitatively different solutions across an explicit behavior space instead of + converging on one global champion. +- [Surprise Search](https://arxiv.org/abs/1706.02556) motivates deviation from + predicted future archive behavior, distinct from merely differing from past items. +- [DreamCoder](https://arxiv.org/abs/2006.08381) motivates wake-sleep alternation, + imagined problems, replay, compositional abstraction, and interpretable knowledge + consolidation. +- [Tree of Thoughts](https://arxiv.org/abs/2305.10601) motivates explicit branching, + self-evaluation, lookahead, and backtracking rather than one irreversible reasoning path. +- [Preliminary Analysis of Simple Novelty Search](https://doi.org/10.1162/evco_a_00334) + motivates coverage measurement and stagnation detection rather than assuming novelty + search diverges forever. + +Jester translates these ideas into deterministic typed operators and artifacts; it +does not reproduce their experiments or claim human cognition. "Dream" names a +synthetic counterfactual phase. It is never evidence, memory consolidation is +content-addressed rather than neurological, and operator diversity is never treated +as market edge. + +## Autonomous scientific learning + +- [The AI Scientist-v2](https://arxiv.org/abs/2504.08066) motivates progressive + agentic tree search over hypotheses and experiments. +- [Kosmos](https://arxiv.org/abs/2511.02824) motivates a persistent structured world + model shared across literature, analysis, and hypothesis cycles. +- The [arXiv API manual](https://info.arxiv.org/help/api/user-manual.html) defines the + Atom metadata contract and recommends conservative request pacing. +- The [arXiv API terms](https://info.arxiv.org/help/api/tou.html) permit transformation + of descriptive metadata while restricting redistribution of most full texts. + +Jester implements a bounded metadata learner, not an autonomous scientific truth engine. +It records preprint versions and generates falsifiable experiment capsules. It neither +mirrors PDFs nor treats paper claims as verified. + +## Attention metadata + +- [RFC 9309](https://www.rfc-editor.org/rfc/rfc9309.html) defines the Robots Exclusion + Protocol used by Jester's metadata collector. +- The [Wikimedia Pageviews definition](https://doc.wikimedia.org/generated-data-platform/aqs/analytics-api/concepts/page-views.html) + informs a future aggregate-attention adapter. +- [Common Crawl](https://commoncrawl.org/) informs a future historical web-presence + metadata adapter. + +The implemented attention surface consumes staged ranked URLs and robots-permitted page +metadata. It does not scrape unapproved SERPs and does not claim to observe search volume. + +## Selective calibration + +- [Conformal Alignment of Predictions](https://www.jmlr.org/papers/v26/24-0452.html) + motivates explicit abstention and calibration under distribution shift. + +Jester v0.2 provides prediction-before-outcome records, proper-score resolution, and +insufficient-evidence gates. A production conformal controller remains future work and +must not be inferred from these mechanics. + +## State-of-the-art standard + +The architecture is state-of-the-art-inspired. It becomes eligible for a stronger +performance claim only after frozen forward-paper A/B evaluation and independent +replication under identical evidence, universe, contracts, costs, fills, risk, and holding +rules. diff --git a/design-system/jester-observatory/MASTER.md b/design-system/jester-observatory/MASTER.md new file mode 100644 index 0000000..5f4f948 --- /dev/null +++ b/design-system/jester-observatory/MASTER.md @@ -0,0 +1,227 @@ +# Design System Master File + +> **LOGIC:** When building a specific page, first check `design-system/pages/[page-name].md`. +> If that file exists, its rules **override** this Master file. +> If not, strictly follow the rules below. + +--- + +**Project:** Jester Observatory +**Generated:** 2026-07-29 02:29:35 +**Category:** AI/Chatbot Platform +**Design Dials:** Variance 8/10 (Bold / Asymmetric) | Motion 8/10 (Complex) | Density 8/10 (Dense / Dashboard) + +--- + +## Global Rules + +### Color Palette + +| Role | Hex | CSS Variable | +|------|-----|--------------| +| Primary | `#7C3AED` | `--color-primary` | +| On Primary | `#FFFFFF` | `--color-on-primary` | +| Secondary | `#6366F1` | `--color-secondary` | +| Accent/CTA | `#EC4899` | `--color-accent` | +| Background | `#FAF5FF` | `--color-background` | +| Foreground | `#0F172A` | `--color-foreground` | +| Muted | `#F7F3FD` | `--color-muted` | +| Border | `#EFE7FC` | `--color-border` | +| Destructive | `#DC2626` | `--color-destructive` | +| Ring | `#7C3AED` | `--color-ring` | + +**Color Notes:** AI purple + generation pink + +### Typography + +- **Heading Font:** Inter +- **Body Font:** Inter +- **Mood:** dark, cinematic, technical, precision, clean, premium, developer, professional, high-end utility +- **Google Fonts:** [Inter + Inter](https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&display=swap) + +**CSS Import:** +```css +@import url('https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&display=swap'); +``` + +### Spacing Variables + +*Density: 8/10 — Dense / Dashboard* + +| Token | Value | Usage | +|-------|-------|-------| +| `--space-xs` | `2px` / `0.125rem` | Tight gaps | +| `--space-sm` | `4px` / `0.25rem` | Icon gaps, inline spacing | +| `--space-md` | `8px` / `0.5rem` | Standard padding | +| `--space-lg` | `12px` / `0.75rem` | Section padding | +| `--space-xl` | `16px` / `1rem` | Large gaps | +| `--space-2xl` | `24px` / `1.5rem` | Section margins | +| `--space-3xl` | `32px` / `2rem` | Hero padding | + +### Shadow Depths + +| Level | Value | Usage | +|-------|-------|-------| +| `--shadow-sm` | `0 1px 2px rgba(0,0,0,0.05)` | Subtle lift | +| `--shadow-md` | `0 4px 6px rgba(0,0,0,0.1)` | Cards, buttons | +| `--shadow-lg` | `0 10px 15px rgba(0,0,0,0.1)` | Modals, dropdowns | +| `--shadow-xl` | `0 20px 25px rgba(0,0,0,0.15)` | Hero images, featured cards | + +--- + +## Component Specs + +### Buttons + +```css +/* Primary Button */ +.btn-primary { + background: #EC4899; + color: white; + padding: 12px 24px; + border-radius: 8px; + font-weight: 600; + transition: all 200ms ease; + cursor: pointer; +} + +.btn-primary:hover { + opacity: 0.9; + transform: translateY(-1px); +} + +/* Secondary Button */ +.btn-secondary { + background: transparent; + color: #7C3AED; + border: 2px solid #7C3AED; + padding: 12px 24px; + border-radius: 8px; + font-weight: 600; + transition: all 200ms ease; + cursor: pointer; +} +``` + +### Cards + +```css +.card { + background: #FAF5FF; + border-radius: 12px; + padding: 24px; + box-shadow: var(--shadow-md); + transition: all 200ms ease; + cursor: pointer; +} + +.card:hover { + box-shadow: var(--shadow-lg); + transform: translateY(-2px); +} +``` + +### Inputs + +```css +.input { + padding: 12px 16px; + border: 1px solid #E2E8F0; + border-radius: 8px; + font-size: 16px; + transition: border-color 200ms ease; +} + +.input:focus { + border-color: #7C3AED; + outline: none; + box-shadow: 0 0 0 3px #7C3AED20; +} +``` + +### Modals + +```css +.modal-overlay { + background: rgba(0, 0, 0, 0.5); + backdrop-filter: blur(4px); +} + +.modal { + background: white; + border-radius: 16px; + padding: 32px; + box-shadow: var(--shadow-xl); + max-width: 500px; + width: 90%; +} +``` + +--- + +## Style Guidelines + +**Style:** Bento Grids + +**Keywords:** Apple-style, modular, cards, organized, clean, hierarchy, grid, rounded, soft + +**Best For:** Product features, dashboards, personal sites, marketing summaries, galleries + +**Key Effects:** Hover scale (1.02), soft shadow expansion, smooth layout shifts, content reveal + +### Page Pattern + +**Pattern Name:** AI Personalization Landing + +- **Conversion Strategy:** 20%+ conversion with personalization. Requires analytics integration. Fallback for new users. +- **CTA Placement:** Context-aware placement based on user segment +- **Section Order:** 1. Dynamic hero (personalized), 2. Relevant features, 3. Tailored testimonials, 4. Smart CTA + +--- + +## Motion + +**Hover Micro-interaction** (Complex) — Trigger: hover + mousemove | Duration: 300-500ms | Easing: `elastic.out(1,0.4)` + +```js +const xTo = gsap.quickTo(el, 'x', { duration: 0.4, ease: 'elastic.out(1,0.4)' }); const yTo = gsap.quickTo(el, 'y', { duration: 0.4, ease: 'elastic.out(1,0.4)' }); el.addEventListener('mousemove', (e) => { const r = el.getBoundingClientRect(); xTo((e.clientX - r.left - r.width/2) * 0.3); yTo((e.clientY - r.top - r.height/2) * 0.3); }); +``` + +**Framework notes:** Debounce is not needed since quickTo interpolates; remove listeners on component unmount in React/Vue to avoid leaks + +- ✅ Clamp the pull strength (e.g. * 0.3) so the element never fully leaves its hit box +- ❌ Don't apply magnetic effect to more than 1-2 focal elements per screen; it becomes noisy +- ⚡ Use will-change: transform on the target element for smoother compositing + +--- + +## Anti-Patterns (Do NOT Use) + +- ❌ Heavy chrome +- ❌ Slow response feedback + +### Additional Forbidden Patterns + +- ❌ **Emojis as icons** — Use SVG icons (Heroicons, Lucide, Simple Icons) +- ❌ **Missing cursor:pointer** — All clickable elements must have cursor:pointer +- ❌ **Layout-shifting hovers** — Avoid scale transforms that shift layout +- ❌ **Low contrast text** — Maintain 4.5:1 minimum contrast ratio +- ❌ **Instant state changes** — Always use transitions (150-300ms) +- ❌ **Invisible focus states** — Focus states must be visible for a11y + +--- + +## Pre-Delivery Checklist + +Before delivering any UI code, verify: + +- [ ] No emojis used as icons (use SVG instead) +- [ ] All icons from consistent icon set (Heroicons/Lucide) +- [ ] `cursor-pointer` on all clickable elements +- [ ] Hover states with smooth transitions (150-300ms) +- [ ] Light mode: text contrast 4.5:1 minimum +- [ ] Focus states visible for keyboard navigation +- [ ] `prefers-reduced-motion` respected +- [ ] Responsive: 375px, 768px, 1024px, 1440px +- [ ] No content hidden behind fixed 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mode 100644 index 0000000..2a48732 --- /dev/null +++ b/docs/index.html @@ -0,0 +1,401 @@ + + + + + + + + + + + + + + Jester — Causal Market Imagination Engine + + + + + + + +
+ +
+ +
+
+ + +
+

+ + Causal market imagination engine +

+

+ Simulate the market world, + not just the price. +

+

+ Jester transforms point-in-time evidence into competing market worlds, + strategic participant reactions, volatility-surface trajectories, and + executable long-option outcome fields—then attacks every surviving idea + in an independent proof court. +

+
+ Enter the Observatory + + Run Jester + +
+
+ Paper only + Live blocked + No order surface + Deterministic replay +
+
+ +
+
+ World state / synthetic mechanics + Mechanics certified +
+ +
+
Worlds08
+
Agents13
+
LadderL0–L7
+
+
+
+ +
+
24Cognition operators
+
15Data pipelines
+
13Intelligence services
+
10Simulator families
+
8Market packs
+
37Proof tests
+
+ +
+
+

The thesis

+

A market is an ecology of facts, beliefs, incentives, and constraints.

+
+

+ A directional forecast is not an option outcome. Jester traces the complete + causal chain from a claim entering the evidence plane to belief changes, + participant behavior, order-flow pressure, price and implied-volatility + paths, liquidity, fills, time decay, and the exact contract an operator + would have to hold. +

+
+ +
+
+

One engine / many competing worlds

+

The complete research stack

+

+ Jester is an ensemble of inspectable systems. Disagreement is measured as + epistemic risk; it is never hidden by silently averaging incompatible models. +

+
+ +
+
+ 01 +

Immutable reality plane

+

Evidence Vault + Scuttlebutt

+

+ Six distinct clocks preserve what happened, what was published, when it + became available, and when it changed. A living claim graph tracks + provenance, contradiction, incentives, diffusion, mutation, saturation, + and price-leading versus price-following behavior. +

+
+ Content addressedRevision aware + Point in timePermissioned +
+
+ +
+ 02 +

Causal scenario lattice

+

Multiple explanations survive

+

+ Observed relationships, candidate causes, evidence-supported interventions, + and pure stress assumptions remain separate. Seven social ablations test + whether attention is causal, additive, redundant, or descriptive. +

+
+ +
+ 03 +

Market society

+

Strategic agents react

+

+ Thirteen participant classes enter Bayesian information, signaling, + congestion, adverse-selection, and repeated-session games with distinct + objectives, horizons, information, and constraints. +

+
+ +
+ 04 +

Option outcome field

+

Direction is only the beginning

+

+ Joint price, IV, skew, term, spread, fill, gap, and lifecycle paths expose + how often a correct thesis still loses—and whether its edge survives the + earliest permitted exit. +

+
+ +
+ 05 +

Adversarial proof court

+

Promotion must be earned

+

+ A Level 0–7 ladder advances from exact replay through synthetic, + intervention, agent, adversarial, forward-shadow, and bounded-paper worlds. + Higher synthetic levels never replace missing forward evidence. +

+
+
+
+ +
+
+

Oneiric invention foundry

+

A masterclass arsenal of cognition—bounded by evidence.

+

+ Twenty-four typed reasoning operators search beyond ordinary variations: + abduction, inversion, structural analogy, regime transplantation, causal + surgery, red-team dialectic, compression, missing-variable discovery, and + falsification-first invention. +

+

+ Dream cycles move through REM divergence, NREM consolidation, lucid + intervention, and awakening debrief. Every invention exposes a + countermechanism, evidence debt, decisive experiment, falsifiers, and lineage. + Synthetic novelty receives zero empirical credit. +

+
+ 01 REM divergence + 02 NREM consolidation + 03 Lucid intervention + 04 Awakening debrief +
+
+
+
+ 24 + typed modes +
+ +

Failure is preserved as reusable research memory, not erased from a leaderboard.

+
+
+ +
+
+

The Jester Observatory

+

A read-only 3D cockpit for the entire market ecology

+

+ Orbit causal topology, inspect volatility geometry, replay the proof ladder, + navigate idea lineages, and audit every source and intelligence contract. +

+
+ +
+ Jester Observatory Dream Foundry showing cognition phases, metrics, and bounded imagination state +
+ Dream mode — oneiric invention, cognitive immunity, and quality-diversity memory +
+
+ +
+
+ Jester Observatory desktop world view +
World mode / desktop
+
+
+ Jester Observatory responsive mobile interface +
Responsive operator view
+
+
+
+ +
+
+

Autonomous learning / permissioned attention

+

Scientific discovery enters as debt, not truth.

+
+
+
+ 15 +

Data pipeline contracts

+

+ Immutable artifacts, versioned scientific metadata, staged search + prominence, filings, macro vintages, rates, positioning, patents, + trials, public attention, and licensed market microstructure. +

+
+
+ 13 +

Composable intelligences

+

+ Temporal fusion, entity resolution, contradiction preservation, + attention divergence, revision surprise, causal committees, option + criticism, provenance defense, and active-research routing. +

+
+
+ 0 +

Automatic scientific credit

+

+ Papers become frozen, falsifiable research capsules. They may seed + experiments, but cannot activate adapters, mutate source, relax proof + gates, or claim replication. +

+
+
+
+ +
+
+

Authority-monotone by construction

+

Autonomous research without autonomous power.

+
+
+
+

Allowed

+
    +
  • Generate and evolve hypotheses
    • +
  • Simulate calls, puts, participants, and worlds
    • +
  • Learn from resolved forecasts and scientific metadata
    • +
  • Reduce its own search budget under pressure
    • +
  • Export signed paper-research artifacts
    • +
+
+
+

Structurally absent

+
    +
  • Broker credentials or account access
    • +
  • Order construction or submission
    • +
  • Automatic source-code mutation
    • +
  • Evaluator or proof-gate mutation
    • +
  • Self-authorized empirical promotion
    • +
+
+
+

+ Jester is state-of-the-art-inspired and architecturally novel. + It becomes state of the art only after frozen forward evaluation and + independent replication—not because the architecture is ambitious. +

+
+ +
+
+

Reproduce the mechanics

+

Start with the deterministic public world.

+

+ The included redistributable fixture proves replay, lineage, rejection, + authority, and interface behavior. It does not claim market fidelity or + performance. +

+
+
+
+ + PowerShell +
+ 
git clone https://github.com/ObtuseAI/jester.git
+cd jester
+uv sync --extra dev
+
+uv run jester run `
+  --input fixtures/earnings/synthetic_candidate.json `
+  --output artifacts/synthetic-candidate.json
+
+uv run pytest
+
+
+ +
+
+

Jester v0.2

+

Reality. Beliefs. Strategy. Volatility. Proof.

+

One replayable research engine. Every important uncertainty left visible.

+
+ + Explore the repository + +
+
+ +
+ JJester +

Open research infrastructure for causal market imagination.

+

MIT licensed · paper only · live blocked

+
+ + diff --git a/docs/showcase.css b/docs/showcase.css new file mode 100644 index 0000000..44c3b18 --- /dev/null +++ b/docs/showcase.css @@ -0,0 +1,1083 @@ +:root { + color-scheme: dark; + --ink: #f3f5fb; + --muted: #9da7bc; + --subtle: #69748a; + --canvas: #070913; + --line: rgba(148, 163, 184, 0.14); + --cyan: #70e7ff; + --violet: #9b7cff; + --mint: #6ff4c4; + --amber: #f6c875; + --max: 1180px; + --radius: 22px; +} + +* { + box-sizing: border-box; +} + +html { + overflow-x: clip; + scroll-behavior: smooth; +} + +body { + margin: 0; + overflow-x: hidden; + color: var(--ink); + background: + radial-gradient(circle at 50% -20%, rgba(90, 71, 173, 0.28), transparent 38rem), + var(--canvas); + font-family: + Inter, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, "Segoe UI", + sans-serif; + line-height: 1.65; +} + +body::before { + position: fixed; + inset: 0; + z-index: -3; + background-image: + linear-gradient(rgba(137, 153, 190, 0.035) 1px, transparent 1px), + linear-gradient(90deg, rgba(137, 153, 190, 0.035) 1px, transparent 1px); + background-size: 64px 64px; + content: ""; + mask-image: linear-gradient(to bottom, black, transparent 80%); +} + +a { + color: inherit; + text-decoration: none; +} + +img { + display: block; + max-width: 100%; +} + +.skip-link { + position: fixed; + top: 12px; + left: 12px; + z-index: 100; + padding: 10px 14px; + border-radius: 10px; + color: #071017; + background: var(--cyan); + transform: translateY(-180%); +} + +.skip-link:focus { + transform: translateY(0); +} + +.page-noise { + position: fixed; + inset: 0; + z-index: 20; + pointer-events: none; + opacity: 0.05; + background-image: url("data:image/svg+xml,%3Csvg viewBox='0 0 180 180' xmlns='http://www.w3.org/2000/svg'%3E%3Cfilter id='n'%3E%3CfeTurbulence type='fractalNoise' baseFrequency='.9' numOctaves='4' stitchTiles='stitch'/%3E%3C/filter%3E%3Crect width='100%25' height='100%25' filter='url(%23n)' opacity='.4'/%3E%3C/svg%3E"); +} + +.nav-shell { + position: sticky; + top: 0; + z-index: 10; + border-bottom: 1px solid rgba(255, 255, 255, 0.06); + background: rgba(7, 9, 19, 0.72); + backdrop-filter: blur(20px); +} + +.nav { + display: flex; + align-items: center; + justify-content: space-between; + width: min(calc(100% - 40px), var(--max)); + height: 72px; + margin: 0 auto; +} + +.brand { + display: inline-flex; + gap: 11px; + align-items: center; + font-weight: 700; + letter-spacing: -0.02em; +} + +.brand-mark { + display: grid; + width: 32px; + height: 36px; + place-items: center; + color: var(--cyan); + border: 1px solid rgba(112, 231, 255, 0.7); + clip-path: polygon(50% 0, 94% 24%, 94% 76%, 50% 100%, 6% 76%, 6% 24%); + background: rgba(112, 231, 255, 0.06); + box-shadow: 0 0 24px rgba(112, 231, 255, 0.2); +} + +.nav-links { + display: flex; + gap: 30px; + color: var(--muted); + font-size: 0.88rem; +} + +.nav-links a:hover, +.nav-links a:focus-visible { + color: var(--ink); +} + +.button { + display: inline-flex; + min-height: 48px; + align-items: center; + justify-content: center; + padding: 0 22px; + border: 1px solid rgba(112, 231, 255, 0.4); + border-radius: 12px; + color: #061019; + background: linear-gradient(135deg, var(--cyan), #a9f1ff); + box-shadow: 0 12px 38px rgba(72, 205, 234, 0.16); + font-size: 0.9rem; + font-weight: 700; + transition: + transform 180ms ease, + box-shadow 180ms ease; +} + +.button:hover { + transform: translateY(-2px); + box-shadow: 0 16px 44px rgba(72, 205, 234, 0.26); +} + +.button:focus-visible, +a:focus-visible { + outline: 2px solid var(--cyan); + outline-offset: 4px; +} + +.button-small { + min-height: 38px; + padding: 0 16px; +} + +.button-quiet { + color: var(--ink); + border-color: var(--line); + background: rgba(255, 255, 255, 0.04); + box-shadow: none; +} + +.section { + position: relative; + width: min(calc(100% - 40px), var(--max)); + margin: 0 auto; +} + +.hero { + display: grid; + min-height: 760px; + grid-template-columns: 1.12fr 0.88fr; + gap: 72px; + align-items: center; + padding: 84px 0 64px; +} + +#market-world { + position: absolute; + inset: -72px -24vw auto 35%; + z-index: -2; + width: 70vw; + height: 760px; + opacity: 0.58; +} + +.hero-glow { + position: absolute; + top: 4%; + right: 5%; + z-index: -1; + width: 380px; + height: 380px; + border-radius: 50%; + background: rgba(107, 73, 214, 0.18); + filter: blur(100px); +} + +.eyebrow { + margin: 0 0 14px; + color: var(--cyan); + font-size: 0.72rem; + font-weight: 750; + letter-spacing: 0.18em; + text-transform: uppercase; +} + +.status-dot { + display: inline-block; + width: 7px; + height: 7px; + margin-right: 9px; + border-radius: 50%; + background: var(--mint); + box-shadow: 0 0 16px var(--mint); +} + +h1, +h2, +h3, +p { + text-wrap: pretty; +} + +h1 { + max-width: 760px; + margin: 0; + font-size: clamp(3.25rem, 7.1vw, 6.6rem); + line-height: 0.95; + letter-spacing: -0.068em; +} + +h1 span { + display: block; + color: transparent; + background: linear-gradient(100deg, var(--cyan), var(--violet) 63%, #c7b8ff); + background-clip: text; + -webkit-background-clip: text; +} + +.hero-lede { + max-width: 690px; + margin: 30px 0 0; + color: #b9c2d3; + font-size: 1.1rem; +} + +.hero-actions { + display: flex; + gap: 12px; + margin-top: 34px; +} + +.authority-strip { + display: flex; + flex-wrap: wrap; + gap: 10px 20px; + margin-top: 34px; + color: var(--subtle); + font-family: ui-monospace, SFMono-Regular, Consolas, monospace; + font-size: 0.7rem; + letter-spacing: 0.06em; + text-transform: uppercase; +} + +.authority-strip span::before { + color: var(--mint); + content: "◇"; + margin-right: 7px; +} + +.hero-instrument { + position: relative; + min-height: 510px; + overflow: hidden; + border: 1px solid rgba(151, 135, 255, 0.22); + border-radius: 28px; + background: + linear-gradient(150deg, rgba(21, 25, 48, 0.88), rgba(7, 10, 20, 0.55)), + radial-gradient(circle at center, rgba(117, 84, 235, 0.2), transparent 60%); + box-shadow: + 0 50px 100px rgba(0, 0, 0, 0.42), + inset 0 1px rgba(255, 255, 255, 0.06); + backdrop-filter: blur(18px); + transform: perspective(1200px) rotateY(-4deg) rotateX(2deg); +} + +.instrument-topline { + display: flex; + justify-content: space-between; + padding: 18px 20px; + color: var(--subtle); + border-bottom: 1px solid var(--line); + font-family: ui-monospace, SFMono-Regular, Consolas, monospace; + font-size: 0.66rem; + letter-spacing: 0.06em; + text-transform: uppercase; +} + +.certified { + color: var(--mint); +} + +.world-orbit { + position: relative; + width: 350px; + height: 350px; + margin: 24px auto 0; +} + +.orbit { + position: absolute; + inset: 50%; + border: 1px solid rgba(140, 121, 255, 0.25); + border-radius: 50%; + transform: translate(-50%, -50%); +} + +.orbit-a { + width: 132px; + height: 132px; +} + +.orbit-b { + width: 228px; + height: 228px; +} + +.orbit-c { + width: 326px; + height: 326px; +} + +.world-core { + position: absolute; + top: 50%; + left: 50%; + display: grid; + width: 78px; + height: 78px; + place-items: center; + border: 1px solid rgba(112, 231, 255, 0.72); + border-radius: 50%; + color: var(--cyan); + background: radial-gradient(circle, rgba(112, 231, 255, 0.18), rgba(7, 9, 19, 0.95)); + box-shadow: 0 0 50px rgba(112, 231, 255, 0.2); + font-size: 1.8rem; + transform: translate(-50%, -50%); +} + +.satellite { + position: absolute; + padding: 6px 10px; + border: 1px solid var(--line); + border-radius: 20px; + color: #b7c0d3; + background: rgba(9, 12, 24, 0.8); + font-family: ui-monospace, SFMono-Regular, Consolas, monospace; + font-size: 0.62rem; + text-transform: uppercase; +} + +.satellite-a { + top: 22px; + left: 132px; +} + +.satellite-b { + top: 151px; + right: -3px; +} + +.satellite-c { + bottom: 22px; + left: 120px; +} + +.satellite-d { + top: 154px; + left: -1px; +} + +.instrument-readout { + display: grid; + grid-template-columns: repeat(3, 1fr); + margin: 0 22px 22px; + border: 1px solid var(--line); + border-radius: 14px; +} + +.instrument-readout div { + display: flex; + flex-direction: column; + padding: 12px 15px; +} + +.instrument-readout div + div { + border-left: 1px solid var(--line); +} + +.instrument-readout span { + color: var(--subtle); + font-size: 0.63rem; + letter-spacing: 0.1em; + text-transform: uppercase; +} + +.instrument-readout strong { + font-size: 1rem; +} + +.metrics { + display: grid; + grid-template-columns: repeat(6, 1fr); + overflow: hidden; + border: 1px solid var(--line); + border-radius: var(--radius); + background: rgba(12, 15, 27, 0.72); +} + +.metrics article { + display: flex; + min-height: 132px; + flex-direction: column; + justify-content: center; + padding: 24px; +} + +.metrics article + article { + border-left: 1px solid var(--line); +} + +.metrics strong { + font-family: ui-monospace, SFMono-Regular, Consolas, monospace; + font-size: 2rem; + line-height: 1; +} + +.metrics span { + margin-top: 12px; + color: var(--subtle); + font-size: 0.74rem; +} + +.manifesto { + display: grid; + grid-template-columns: 1.05fr 0.95fr; + gap: 90px; + align-items: end; + padding: 150px 0; +} + +h2 { + max-width: 850px; + margin: 0; + font-size: clamp(2.4rem, 5vw, 4.6rem); + line-height: 1.02; + letter-spacing: -0.055em; +} + +.manifesto > p { + margin: 0; + color: var(--muted); + font-size: 1.08rem; +} + +.section-heading { + max-width: 820px; + margin-bottom: 54px; +} + +.section-heading > p:last-child { + max-width: 690px; + color: var(--muted); + font-size: 1.02rem; +} + +.stack-grid { + display: grid; + grid-template-columns: repeat(2, 1fr); + gap: 16px; +} + +.feature-card { + position: relative; + min-height: 330px; + overflow: hidden; + padding: 34px; + border: 1px solid var(--line); + border-radius: var(--radius); + background: linear-gradient(145deg, rgba(17, 21, 39, 0.92), rgba(9, 12, 23, 0.68)); +} + +.feature-card::after { + position: absolute; + right: -60px; + bottom: -80px; + width: 180px; + height: 180px; + border-radius: 50%; + background: rgba(116, 93, 235, 0.1); + content: ""; + filter: blur(30px); +} + +.feature-wide { + grid-column: 1 / -1; + min-height: 300px; +} + +.card-index { + position: absolute; + top: 24px; + right: 28px; + color: rgba(112, 231, 255, 0.28); + font-family: ui-monospace, SFMono-Regular, Consolas, monospace; + font-size: 2rem; +} + +.feature-card h3, +.mesh-card h3 { + max-width: 540px; + margin: 0; + font-size: 1.7rem; + letter-spacing: -0.035em; +} + +.feature-card > p:not(.eyebrow), +.mesh-card > p { + max-width: 760px; + color: var(--muted); +} + +.chip-row { + display: flex; + flex-wrap: wrap; + gap: 8px; + margin-top: 28px; +} + +.chip-row span { + padding: 6px 10px; + border: 1px solid rgba(112, 231, 255, 0.16); + border-radius: 20px; + color: #a9cbd4; + background: rgba(112, 231, 255, 0.04); + font-family: ui-monospace, SFMono-Regular, Consolas, monospace; + font-size: 0.66rem; + text-transform: uppercase; +} + +.dream-section { + display: grid; + grid-template-columns: 1.2fr 0.8fr; + gap: 70px; + align-items: center; + margin-top: 150px; + margin-bottom: 150px; + padding: 70px; + overflow: hidden; + border: 1px solid rgba(155, 124, 255, 0.22); + border-radius: 32px; + background: + radial-gradient(circle at 84% 30%, rgba(132, 95, 255, 0.22), transparent 38%), + linear-gradient(145deg, rgba(20, 18, 42, 0.9), rgba(8, 11, 23, 0.92)); +} + +.dream-section h2 { + font-size: clamp(2.4rem, 4.5vw, 4rem); +} + +.dream-copy > p:not(.eyebrow) { + color: var(--muted); +} + +.phase-list { + display: grid; + grid-template-columns: repeat(2, 1fr); + gap: 10px; + margin-top: 30px; +} + +.phase-list span { + padding: 12px; + border: 1px solid var(--line); + border-radius: 12px; + color: #bdc6d8; + background: rgba(255, 255, 255, 0.025); + font-size: 0.77rem; +} + +.phase-list b { + margin-right: 8px; + color: var(--violet); + font-family: ui-monospace, SFMono-Regular, Consolas, monospace; +} + +.dream-proof { + position: relative; + display: flex; + min-height: 390px; + flex-direction: column; + align-items: center; + justify-content: center; +} + +.proof-ring { + position: relative; + z-index: 2; + display: flex; + width: 210px; + height: 210px; + flex-direction: column; + align-items: center; + justify-content: center; + border: 1px solid rgba(112, 231, 255, 0.55); + border-radius: 50%; + background: rgba(8, 10, 22, 0.92); + box-shadow: + 0 0 0 32px rgba(155, 124, 255, 0.035), + 0 0 0 64px rgba(112, 231, 255, 0.02), + 0 0 70px rgba(155, 124, 255, 0.18); +} + +.proof-ring span { + font-size: 4rem; + font-weight: 700; + line-height: 1; +} + +.proof-ring small { + color: var(--muted); + letter-spacing: 0.12em; + text-transform: uppercase; +} + +.dream-proof p { + max-width: 330px; + margin-top: 72px; + color: var(--muted); + text-align: center; +} + +.dream-lines i { + position: absolute; + inset: 50% auto auto 50%; + width: 280px; + height: 1px; + background: linear-gradient(90deg, transparent, rgba(112, 231, 255, 0.24), transparent); + transform-origin: left; +} + +.dream-lines i:nth-child(1) { transform: rotate(0deg); } +.dream-lines i:nth-child(2) { transform: rotate(72deg); } +.dream-lines i:nth-child(3) { transform: rotate(144deg); } +.dream-lines i:nth-child(4) { transform: rotate(216deg); } +.dream-lines i:nth-child(5) { transform: rotate(288deg); } + +.screen-stage { + margin: 0; + overflow: hidden; + border: 1px solid rgba(112, 231, 255, 0.18); + border-radius: 26px; + background: #0b0e1a; + box-shadow: 0 50px 100px rgba(0, 0, 0, 0.4); +} + +.screen-stage img { + width: 100%; +} + +figcaption { + padding: 14px 18px; + color: var(--subtle); + border-top: 1px solid var(--line); + font-family: ui-monospace, SFMono-Regular, Consolas, monospace; + font-size: 0.68rem; + letter-spacing: 0.05em; + text-transform: uppercase; +} + +.screen-grid { + display: grid; + grid-template-columns: 1fr 0.31fr; + gap: 18px; + align-items: start; + margin-top: 18px; +} + +.screen-grid figure { + margin: 0; + overflow: hidden; + border: 1px solid var(--line); + border-radius: 20px; + background: #0b0e1a; +} + +.mobile-frame { + max-width: 350px; + justify-self: end; +} + +.mesh { + padding: 150px 0; +} + +.mesh-grid { + display: grid; + grid-template-columns: repeat(3, 1fr); + gap: 16px; +} + +.mesh-card { + min-height: 340px; + padding: 32px; + border-top: 1px solid rgba(112, 231, 255, 0.4); + border-right: 1px solid var(--line); + border-bottom: 1px solid var(--line); + border-left: 1px solid var(--line); + border-radius: 0 0 var(--radius) var(--radius); + background: rgba(12, 15, 28, 0.68); +} + +.mesh-number { + display: block; + margin-bottom: 52px; + color: var(--cyan); + font-family: ui-monospace, SFMono-Regular, Consolas, monospace; + font-size: 3rem; +} + +.proof-section { + padding: 40px 0 150px; +} + +.boundary-grid { + display: grid; + grid-template-columns: repeat(2, 1fr); + gap: 16px; +} + +.boundary-grid article { + padding: 34px; + border: 1px solid var(--line); + border-radius: var(--radius); + background: rgba(13, 17, 31, 0.7); +} + +.boundary-grid .allowed { + border-top-color: rgba(111, 244, 196, 0.6); +} + +.boundary-grid .blocked { + border-top-color: rgba(246, 200, 117, 0.6); +} + +.boundary-grid .allowed .eyebrow { + color: var(--mint); +} + +.boundary-grid .blocked .eyebrow { + color: var(--amber); +} + +.boundary-grid ul { + margin: 25px 0 0; + padding: 0; + list-style: none; +} + +.boundary-grid li { + position: relative; + padding: 10px 0 10px 25px; + color: #b5bed0; + border-top: 1px solid rgba(255, 255, 255, 0.045); +} + +.boundary-grid li::before { + position: absolute; + left: 2px; + color: var(--mint); + content: "◇"; +} + +.boundary-grid .blocked li::before { + color: var(--amber); +} + +.claim-boundary { + max-width: 880px; + margin: 38px auto 0; + color: var(--muted); + text-align: center; +} + +.start-section { + display: grid; + grid-template-columns: 0.82fr 1.18fr; + gap: 64px; + align-items: center; + padding: 70px 0 150px; +} + +.start-copy p:last-child { + color: var(--muted); +} + +.terminal { + overflow: hidden; + border: 1px solid var(--line); + border-radius: 20px; + background: #090c15; + box-shadow: 0 30px 70px rgba(0, 0, 0, 0.35); +} + +.terminal-bar { + display: flex; + gap: 7px; + align-items: center; + padding: 13px 16px; + border-bottom: 1px solid var(--line); + background: rgba(255, 255, 255, 0.025); +} + +.terminal-bar span { + width: 8px; + height: 8px; + border-radius: 50%; + background: #474e60; +} + +.terminal-bar span:first-child { background: #ff7286; } +.terminal-bar span:nth-child(2) { background: #f6c875; } +.terminal-bar span:nth-child(3) { background: #6ff4c4; } + +.terminal-bar small { + margin-left: auto; + color: var(--subtle); +} + +pre { + margin: 0; + padding: 28px; + overflow-x: auto; + color: #b8dfe6; + font-family: ui-monospace, SFMono-Regular, Consolas, monospace; + font-size: 0.78rem; + line-height: 1.8; +} + +.final-cta { + display: flex; + align-items: center; + justify-content: space-between; + gap: 40px; + margin-bottom: 80px; + padding: 56px; + border: 1px solid rgba(112, 231, 255, 0.2); + border-radius: 28px; + background: + radial-gradient(circle at 75% 20%, rgba(112, 231, 255, 0.12), transparent 35%), + linear-gradient(145deg, rgba(18, 22, 42, 0.94), rgba(10, 13, 24, 0.92)); +} + +.final-cta h2 { + font-size: clamp(2rem, 4vw, 3.4rem); +} + +.final-cta p:last-child { + color: var(--muted); +} + +footer { + display: flex; + width: min(calc(100% - 40px), var(--max)); + align-items: center; + justify-content: space-between; + gap: 24px; + margin: 0 auto; + padding: 34px 0 48px; + color: var(--subtle); + border-top: 1px solid var(--line); + font-size: 0.76rem; +} + +.reveal { + opacity: 0; + transform: translateY(20px); + transition: + opacity 700ms ease, + transform 700ms ease; +} + +.reveal.visible { + opacity: 1; + transform: translateY(0); +} + +@media (max-width: 900px) { + .nav-links { + display: none; + } + + .hero { + min-height: auto; + grid-template-columns: 1fr; + gap: 52px; + padding-top: 70px; + } + + .hero-instrument { + max-width: 600px; + justify-self: center; + transform: none; + } + + #market-world { + inset: 0 -45vw auto 0; + width: 140vw; + } + + .metrics { + grid-template-columns: repeat(3, 1fr); + } + + .metrics article:nth-child(4) { + border-left: 0; + } + + .metrics article:nth-child(n + 4) { + border-top: 1px solid var(--line); + } + + .manifesto, + .dream-section, + .start-section { + grid-template-columns: 1fr; + gap: 44px; + } + + .manifesto { + padding: 110px 0; + } + + .dream-section { + padding: 44px; + } + + .mesh-grid { + grid-template-columns: 1fr; + } +} + +@media (max-width: 640px) { + .nav { + width: min(calc(100% - 28px), var(--max)); + } + + .nav .button { + display: none; + } + + .section { + width: min(calc(100% - 28px), var(--max)); + } + + .hero { + padding-top: 52px; + } + + h1 { + font-size: clamp(3rem, 16vw, 4.6rem); + } + + .hero-actions { + flex-direction: column; + } + + .hero-instrument { + width: 100%; + min-height: 440px; + } + + .world-orbit { + width: 300px; + height: 300px; + transform: scale(0.82); + } + + .instrument-topline span:first-child { + max-width: 150px; + } + + .metrics { + grid-template-columns: repeat(2, 1fr); + } + + .metrics article:nth-child(3), + .metrics article:nth-child(5) { + border-left: 0; + } + + .metrics article:nth-child(n + 3) { + border-top: 1px solid var(--line); + } + + .stack-grid, + .boundary-grid, + .screen-grid { + grid-template-columns: 1fr; + } + + .feature-wide { + grid-column: auto; + } + + .feature-card, + .mesh-card { + min-height: auto; + padding: 28px; + } + + .dream-section { + width: 100%; + padding: 48px 22px; + border-right: 0; + border-left: 0; + border-radius: 0; + } + + .phase-list { + grid-template-columns: 1fr; + } + + .mobile-frame { + max-width: none; + justify-self: stretch; + } + + .final-cta { + flex-direction: column; + align-items: flex-start; + padding: 34px; + } + + footer { + flex-direction: column; + align-items: flex-start; + } +} + +@media (prefers-reduced-motion: reduce) { + html { + scroll-behavior: auto; + } + + *, + *::before, + *::after { + scroll-behavior: auto !important; + transition-duration: 0.01ms !important; + animation-duration: 0.01ms !important; + animation-iteration-count: 1 !important; + } + + .reveal { + opacity: 1; + transform: none; + } +} diff --git a/docs/showcase.js b/docs/showcase.js new file mode 100644 index 0000000..8805627 --- /dev/null +++ b/docs/showcase.js @@ -0,0 +1,129 @@ +"use strict"; + +const reducedMotion = window.matchMedia("(prefers-reduced-motion: reduce)").matches; +const canvas = document.querySelector("#market-world"); + +const createNodes = (width, height) => { + const ratios = [ + [0.12, 0.48, 3.2], + [0.27, 0.22, 2.3], + [0.31, 0.71, 2.5], + [0.47, 0.43, 4.2], + [0.58, 0.14, 2.1], + [0.63, 0.67, 3.1], + [0.78, 0.32, 2.6], + [0.87, 0.62, 3.4], + [0.97, 0.24, 2.0], + ]; + return ratios.map(([x, y, radius], index) => ({ + x: x * width, + y: y * height, + radius, + phase: index * 0.73, + })); +}; + +const drawWorld = (context, nodes, width, height, frame) => { + context.clearRect(0, 0, width, height); + const time = reducedMotion ? 0 : frame * 0.00045; + const moved = nodes.map((node, index) => ({ + ...node, + x: node.x + Math.sin(time * 2 + node.phase) * (7 + index), + y: node.y + Math.cos(time * 1.6 + node.phase) * (5 + index * 0.6), + })); + + moved.forEach((node, index) => { + moved.slice(index + 1).forEach((other) => { + const distance = Math.hypot(node.x - other.x, node.y - other.y); + if (distance > width * 0.34) { + return; + } + const opacity = Math.max(0, 0.2 - distance / (width * 1.7)); + const gradient = context.createLinearGradient(node.x, node.y, other.x, other.y); + gradient.addColorStop(0, `rgba(112, 231, 255, ${opacity})`); + gradient.addColorStop(1, `rgba(155, 124, 255, ${opacity})`); + context.beginPath(); + context.moveTo(node.x, node.y); + context.lineTo(other.x, other.y); + context.strokeStyle = gradient; + context.lineWidth = 1; + context.stroke(); + }); + }); + + moved.forEach((node, index) => { + const glow = context.createRadialGradient( + node.x, + node.y, + 0, + node.x, + node.y, + node.radius * 7, + ); + const color = index % 3 === 0 ? "112, 231, 255" : "155, 124, 255"; + glow.addColorStop(0, `rgba(${color}, 0.7)`); + glow.addColorStop(0.18, `rgba(${color}, 0.32)`); + glow.addColorStop(1, `rgba(${color}, 0)`); + context.beginPath(); + context.arc(node.x, node.y, node.radius * 7, 0, Math.PI * 2); + context.fillStyle = glow; + context.fill(); + }); +}; + +const initializeWorld = () => { + if (!(canvas instanceof HTMLCanvasElement)) { + return; + } + const context = canvas.getContext("2d"); + if (context === null) { + return; + } + + let nodes = []; + const resize = () => { + const bounds = canvas.getBoundingClientRect(); + const density = Math.min(window.devicePixelRatio || 1, 2); + canvas.width = Math.max(1, Math.round(bounds.width * density)); + canvas.height = Math.max(1, Math.round(bounds.height * density)); + context.setTransform(density, 0, 0, density, 0, 0); + nodes = createNodes(bounds.width, bounds.height); + }; + + const animate = (frame) => { + const bounds = canvas.getBoundingClientRect(); + drawWorld(context, nodes, bounds.width, bounds.height, frame); + if (!reducedMotion) { + window.requestAnimationFrame(animate); + } + }; + + resize(); + window.addEventListener("resize", resize, { passive: true }); + window.requestAnimationFrame(animate); +}; + +const initializeReveals = () => { + const items = document.querySelectorAll(".reveal"); + if (reducedMotion || !("IntersectionObserver" in window)) { + items.forEach((item) => item.classList.add("visible")); + return; + } + + const observer = new IntersectionObserver( + (entries) => { + entries.forEach((entry) => { + if (!entry.isIntersecting) { + return; + } + entry.target.classList.add("visible"); + observer.unobserve(entry.target); + }); + }, + { threshold: 0.12 }, + ); + items.forEach((item) => observer.observe(item)); +}; + +initializeWorld(); +initializeReveals(); diff --git a/fixtures/attention/synthetic_ranked_manifest.json b/fixtures/attention/synthetic_ranked_manifest.json new file mode 100644 index 0000000..3b64abc --- /dev/null +++ b/fixtures/attention/synthetic_ranked_manifest.json @@ -0,0 +1,32 @@ +{ + "schema_version": "jester-search-attention-manifest-v1", + "query": "synthetic market narrative", + "search_provider": "synthetic-ranked-export", + "observed_at": "2026-07-29T12:00:00Z", + "targets": [ + { + "rank": 1, + "url": "https://alpha.example.invalid/research-a" + }, + { + "rank": 2, + "url": "https://beta.example.invalid/research-b" + } + ], + "policy": { + "schema_version": "jester-metadata-scrape-policy-v1", + "policy_id": "synthetic-attention-fixture", + "terms_uri": "https://example.invalid/terms", + "allowed_hosts": [ + "alpha.example.invalid", + "beta.example.invalid" + ], + "user_agent": "JesterResearch/0.2 (mailto:operator@example.invalid)", + "max_pages": 2, + "max_response_bytes": 100000, + "minimum_request_interval_seconds": 3.0, + "metadata_only": true, + "authentication_allowed": false, + "anti_bot_bypass_allowed": false + } +} diff --git a/fixtures/literature/live_query_template.json b/fixtures/literature/live_query_template.json new file mode 100644 index 0000000..b1556c7 --- /dev/null +++ b/fixtures/literature/live_query_template.json @@ -0,0 +1,24 @@ +{ + "schema_version": "jester-literature-query-v1", + "query": "(cat:q-fin.* OR cat:cs.AI OR cat:cs.LG) AND (all:\"market simulation\" OR all:\"volatility surface\" OR all:\"scientific discovery\")", + "source_mode": "live_metadata", + "cutoff_policy": "scan_time", + "requested_at": "2026-07-29T00:00:00Z", + "cutoff_time": "2026-07-29T00:00:00Z", + "allowed_categories": [ + "cs.AI", + "cs.LG", + "q-fin.CP", + "q-fin.MF", + "q-fin.TR" + ], + "max_results": 25, + "minimum_refresh_seconds": 86400, + "research_objectives": [ + "causal_market_simulation", + "order_book_generation", + "volatility_surface_dynamics", + "protected_agentic_science", + "calibrated_abstention" + ] +} diff --git a/fixtures/literature/synthetic_arxiv_feed.xml b/fixtures/literature/synthetic_arxiv_feed.xml new file mode 100644 index 0000000..049f8f5 --- /dev/null +++ b/fixtures/literature/synthetic_arxiv_feed.xml @@ -0,0 +1,52 @@ + + + Synthetic Jester literature fixture + https://example.invalid/jester/synthetic-literature-feed + 2026-07-29T11:00:00Z + + https://arxiv.org/abs/9999.00001v2 + 2026-07-28T10:00:00Z + 2026-07-20T10:00:00Z + Protected Agentic Scientific Discovery with Causal Counterfactuals + + We study an AI scientist with persistent literature search, falsifiable causal + intervention proposals, and calibrated abstention. The synthetic method preserves + prediction records before experimental outcomes. + + Fixture Researcher + + + + + + + + https://arxiv.org/abs/9999.00002v1 + 2026-07-27T09:00:00Z + 2026-07-27T09:00:00Z + Arbitrage-Aware Implied Volatility Surface Generation + + A conditional volatility surface model is evaluated with held-out option prices, + skew dynamics, and static-arbitrage tests. + + Option Fixture + + + + + + + + https://arxiv.org/abs/9999.00003v1 + 2026-07-30T09:00:00Z + 2026-07-30T09:00:00Z + Future Revision That Must Be Excluded + This record exists only to prove point-in-time exclusion. + Future Fixture + + + + + + diff --git a/fixtures/literature/synthetic_query.json b/fixtures/literature/synthetic_query.json new file mode 100644 index 0000000..a997da9 --- /dev/null +++ b/fixtures/literature/synthetic_query.json @@ -0,0 +1,20 @@ +{ + "schema_version": "jester-literature-query-v1", + "query": "all:\"causal market\" OR all:\"volatility surface\" OR all:\"scientific discovery\"", + "source_mode": "synthetic_fixture", + "cutoff_policy": "fixed", + "requested_at": "2026-07-29T12:00:00Z", + "cutoff_time": "2026-07-29T12:00:00Z", + "allowed_categories": [ + "cs.AI", + "q-fin.CP", + "q-fin.TR" + ], + "max_results": 10, + "minimum_refresh_seconds": 86400, + "research_objectives": [ + "causal_market_simulation", + "option_surface_dynamics", + "protected_agentic_science" + ] +} diff --git a/fixtures/meta/synthetic_meta_config.json b/fixtures/meta/synthetic_meta_config.json new file mode 100644 index 0000000..58e35a4 --- /dev/null +++ b/fixtures/meta/synthetic_meta_config.json @@ -0,0 +1,21 @@ +{ + "created_at": "2026-07-29T12:00:00Z", + "pack_id": "earnings", + "generations": 4, + "requested_candidate_budget": 16, + "island_count": 4, + "root_seed": 2202, + "cognition_budget": 12, + "dream_cycles": 4, + "dream_temperature": 0.72, + "ladder": { + "evidence_mode": "historical_reconstruction", + "resolved_forward_outcomes": 0, + "bounded_paper_outcomes": 0, + "required_forward_outcomes": 50, + "required_paper_outcomes": 30 + }, + "independently_resimulated_descendants": 0, + "simulation_gap_observations": 0, + "simulation_gap_pressure": 0.5 +} diff --git a/pyproject.toml b/pyproject.toml index 4c2d603..5ac575c 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -4,7 +4,7 @@ build-backend = "hatchling.build" [project] name = "jester-market-engine" -version = "0.1.0" +version = "0.2.0" description = "Deterministic causal market-world and long-option research engine" readme = "README.md" requires-python = ">=3.12,<3.14" diff --git a/reports/JESTER_COGNITION_ARSENAL_PROOF.md b/reports/JESTER_COGNITION_ARSENAL_PROOF.md new file mode 100644 index 0000000..c504866 --- /dev/null +++ b/reports/JESTER_COGNITION_ARSENAL_PROOF.md @@ -0,0 +1,123 @@ +# Jester Cognition Arsenal proof report + +Date: 2026-07-29 +Scope: bounded recursive idea generation, dreaming, invention, and scientific seeding +Branch: `agent/jester-meta-frontier-v0.2` + +## Outcome + +Jester now contains a deterministic cognitive invention plane rather than an +unconstrained prompt-based brainstormer. + +The reference scientific-seeded run produced: + +- Arsenal run: `cognition-run:a82eb9e27f4118888a4b3b33` +- Cognition modes: `24` +- Dream artifacts: `8` +- Explicit recursive parent-dream links: `7` +- Admitted zero-credit scientific cognition seeds: `2` +- Invention blueprints: `8` +- Archive elites above the frozen utility floor: `2` +- Preserved failure memories: `6` +- Mode coverage: `0.583333` +- Cognitive immune findings: `56` +- Normal-run quarantines: `0` + +The immune test suite separately injects fabricated authority, missing falsifiers, +and an invalid evidence lineage. All three are quarantined. + +## Implemented cognition surfaces + +- Ten cognitive families and 24 typed, content-addressed operators. +- REM divergence, NREM consolidation, lucid intervention, and awakening debrief. +- Semantic deterministic mode composition and relation-level analogy mappings. +- Recursive dream lineage with explicit parent IDs and recursive ablation. +- Scientific-paper cognition seeds that preserve paper/capsule identity, unverified + trust, experiment, falsification question, and replication debt. +- Multi-objective scoring for novelty, surprise, compression, coherence, + falsifiability, evidence reachability, option relevance, adversarial robustness, + independence, and evidence debt. +- A seven-check cognitive immune court. +- Typed invention blueprints with countermechanisms, prior-art challenges, minimal + decisive experiments, transfer tests, and concrete falsifiers. +- A cognition-specific quality-diversity archive. +- A Museum of Beautiful Failures with strict revival conditions. +- `cognition-run`, `/catalog/cognition`, meta-research v2 integration, JSON schemas, + append-only research-memory persistence, and an expanded public safety scanner. +- A fifth Observatory mode with interactive operator inspection, dream topology, + inventions, evidence debt, immune checks, and failure memory. + +## Automated validation + +All final commands passed: + +```text +uv run ruff format --check . 73 files already formatted +uv run ruff check . All checks passed +uv run mypy 59 source files; no issues +uv run pytest 37 passed +uv build sdist and wheel built +validate_public_release.py status=pass; violations=[] +npm.cmd run typecheck strict TypeScript passed +npm.cmd run build Vite production build passed +git diff --check passed +``` + +The safety scanner additionally reported: + +```json +{ + "broker_dependency_present": false, + "cognition_archive_cell_count": 2, + "cognition_failure_memory_count": 6, + "cognition_mode_count": 24, + "cognition_scientific_seed_count": 2, + "credential_material_present": false, + "meta_forward_levels_blocked": true, + "status": "pass", + "violations": [] +} +``` + +## Browser proof + +The API-backed Dream Observatory rendered at `1280x720`. + +- All 24 cognitive operator buttons were present and keyboard-semantic. +- Operator selection updated the detailed purpose, moves, and immune risks. +- All seven immune checks were visible. +- The displayed proof values matched `58%` mode coverage and `24` evidence-debt items. +- `scrollWidth == clientWidth == 1265`; no horizontal page overflow occurred. +- The UI retained `synthetic mechanics`, `paper only`, `live blocked`, and + `no execution surface` labels. + +Reference render: + +- `docs/images/jester-observatory-dream-foundry.png` + +## Artifact hashes + +```text +AEA722C79ABEA04972BDE6016920F4C58AF1012A289DBBAA4203DBF9494A4CB6 +artifacts/cognition-arsenal-v0.2-recursive-proof.json + +DD7D6828F63F6D04AEF9BAB8646A1842C2CB0CD4978011814CDAA3BF9192E93C +artifacts/meta-cognition-v0.2-recursive-proof.json + +ECE8ED7A55CCF03CF999A17BFE431AB0E5B94213079BFDAB6EC1F9B5F246307A +artifacts/catalog-cognition-v0.2-proof.json + +F70002201424EA010C65BC7675B24BCE0A15E4C39D100AE0DD410A45361C2659 +docs/images/jester-observatory-dream-foundry.png +``` + +## Authority and claim boundary + +Dreams are synthetic stress artifacts. Scientific seeds are unverified claims. +Inventions receive no empirical credit, do not auto-apply, cannot mutate code or +evaluators, cannot alter proof gates, cannot activate a simulator, cannot construct +an order, and require independent full-ladder resimulation. + +This implementation is state-of-the-art-inspired. It does not demonstrate market +fidelity, predictive edge, autonomous scientific truth, or state-of-the-art market +performance. diff --git a/reports/JESTER_OBSERVATORY_PROOF.md b/reports/JESTER_OBSERVATORY_PROOF.md new file mode 100644 index 0000000..c2cf27f --- /dev/null +++ b/reports/JESTER_OBSERVATORY_PROOF.md @@ -0,0 +1,125 @@ +# Jester Observatory proof report + +Date: 2026-07-29 +Scope: 3D read-only observatory plus evidence and intelligence mesh +Branch: `agent/jester-meta-frontier-v0.2` + +## Outcome + +Jester now has a responsive React 19 and Three.js observatory backed by the +read-only FastAPI control plane. The UI exposes four operator modes: + +- orbitable causal market-world topology; +- joint moneyness, tenor, and implied-volatility geometry; +- quality-diversity, interoception, literature, and promotion-proof ecology; +- permissioned data-pipeline and intelligence-service inspection. + +The UI displays `synthetic_mechanics_fixture`, `paper_only`, `live_blocked`, and +the Level 6–7 forward-evidence block. It contains no broker, credential, order, +submission, or execution control. + +## Expanded architecture + +The capability catalog contains: + +- 10 simulator families; +- 8 market packs; +- 7 scientific and attention research-source contracts; +- 15 point-in-time data-pipeline contracts; +- 13 composable intelligence-service contracts. + +Pipeline status is: + +```json +{ + "operational": 3, + "adapter_contract": 9, + "blocked_license_or_data": 2, + "prohibited": 1 +} +``` + +Intelligence status is: + +```json +{ + "operational_mechanics": 8, + "adapter_contract": 4, + "blocked_missing_evidence": 1 +} +``` + +Every intelligence service has `authority_effect=none`. Credentialed or anti-bot +scraping is explicitly prohibited. + +## Automated validation + +All commands completed successfully: + +```text +uv run ruff format --check . 69 files already formatted +uv run ruff check . All checks passed +uv run mypy 56 source files; no issues +uv run pytest 33 passed; no warnings +uv build sdist and wheel built +validate_public_release.py status=pass; violations=[] + +npm.cmd run typecheck strict TypeScript passed +npm.cmd run build Vite production build passed +``` + +The public safety scanner additionally confirmed: + +```json +{ + "broker_dependency_present": false, + "credential_material_present": false, + "meta_mechanics_levels_passed": [0, 1, 2, 3, 4, 5], + "meta_forward_levels_blocked": true, + "blocked_simulator_adapter_count": 5, + "credentialed_or_antibot_scraping_prohibited": true, + "unapproved_serp_scraping_prohibited": true +} +``` + +## Browser and visual proof + +The local API-backed application was rendered in the Codex in-app browser. + +- Desktop: 1440×1000, all four modes and selected-node interaction passed. +- Phone: 375×812, `scrollWidth == clientWidth`, no page overflow. +- Landscape: 812×375, side navigation and no page overflow. +- Mobile interactive controls: zero targets below 44×44 pixels. +- Reduced motion: browser preference detected and 3D auto-motion disabled. +- Accessible mirrors: 3D nodes have button selectors; charts have text/table + equivalents; sources have a semantic table; a skip link is present. +- Runtime: no console errors or warnings after the final dependency pin. + +Reference renders: + +- `docs/images/jester-observatory-desktop.png` +- `docs/images/jester-observatory-mobile.png` + +## Artifact hashes + +```text +AA0DA63B0D27FEDF991BE95589CC7133F943C76D6A80FCBA7B0F30309B3B6720 +artifacts/catalog-observatory.json + +C99CEBF5CF46DDA51B6BBC3DCBD2A7F87C3C29208FB239340E36C5AE764E968A +docs/images/jester-observatory-desktop.png + +AA1FEA40B350C7AC13663F1B53D9423B299AC2A0B45256387497F02F77C6936D +docs/images/jester-observatory-mobile.png + +0B88BCB6B6D28D0BF6B59EC682BBCE8A0DD25892ED358D2D4D83E004E3D7A129 +ui/package-lock.json +``` + +## Honest claim boundary + +The observatory is a production-built interface over deterministic mechanics and +explicit adapter contracts. It does not make unlicensed data operational, prove a +learned simulator, establish market fidelity, or demonstrate trading edge. Those +claims remain gated by frozen Level 6 forward-shadow and Level 7 bounded-paper +evidence. diff --git a/reports/JESTER_V0_2_PROOF.md b/reports/JESTER_V0_2_PROOF.md new file mode 100644 index 0000000..1892c3c --- /dev/null +++ b/reports/JESTER_V0_2_PROOF.md @@ -0,0 +1,99 @@ +# Jester v0.2 proof report + +Date: 2026-07-29 +Scope: protected meta-research expansion +Branch: `agent/jester-meta-frontier-v0.2` + +## Outcome + +Jester v0.2 implements a deterministic, lineage-preserving, recursively composable +hypothesis research plane over the v0.1 causal market-world artifact. + +The synthetic reference run produced: + +- Meta run: `meta-run:23dc1f08540aea9db1edf157` +- Content hash: `23dc1f08540aea9db1edf157831621b3f70b5070c1ed4d553e3cb6c0ecd7c8c4` +- Highest contiguous simulation level: `5` +- Archive cells: `4` +- Evaluated/proposed candidates: `6` +- Requested candidate budget: `16` +- Authorized candidate budget after interoception: `6` + +ME-1 deterministic mechanics and ME-2 quality diversity passed. ME-3 descendant +resimulation, ME-4 forward resolution, ME-5 simulation-gap calibration, and ME-6 +held-out meta non-regression correctly remain `insufficient_evidence`. + +## Implemented proof surfaces + +- Ten-family simulator registry: five operational deterministic baselines and five + visibly blocked frontier adapter contracts. +- Eight market packs: two mechanics-operational, five adapter contracts, and a + fail-closed dedicated 0DTE lane. +- Seven-case narrative counterfactual ablation matrix. +- Competing causal-graph tournament with no silent averaging. +- Levels 0–7 simulation ladder with synthetic-to-forward non-substitution. +- Deterministic island evolution and quality-diversity archive. +- Prediction-before-outcome and proper-score resolution. +- Interoceptive budget reduction with immutable authority ceilings. +- Append-only SQLite prediction, result, resolution, and failure-capable ledger. +- Autonomous daily arXiv metadata discovery with version and cutoff preservation. +- Falsifiable scientific research capsules with no automatic empirical credit. +- Robots-aware ranked-URL metadata collection and attention-delta mechanics. +- Explicit operational, adapter-only, and prohibited research-source registry. +- Read-only API catalogs and CLI meta run/resolve surfaces. + +## Validation evidence + +All commands completed successfully: + +```text +uv run ruff format --check . 65 files already formatted +uv run ruff check . All checks passed +uv run mypy 53 source files; no issues +uv run pytest 30 passed +uv build sdist and wheel built +validate_public_release.py status=pass; violations=[] +``` + +The release scanner also reported: + +```json +{ + "blocked_simulator_adapter_count": 5, + "broker_dependency_present": false, + "credential_material_present": false, + "literature_papers_admitted": 2, + "literature_papers_excluded": 1, + "meta_forward_levels_blocked": true, + "meta_mechanics_levels_passed": [0, 1, 2, 3, 4, 5], + "public_fixture_synthetic_only": true, + "unapproved_serp_scraping_prohibited": true +} +``` + +## Artifact hashes + +```text +meta-synthetic-v0.2.json +ca7efa086e47af946661e21ab74bd2d6c6d862e6973c286736c3b0a7f0dce2eb + +literature-replay-v0.2-final.json +f1f529ca7a739db7c732f5d7c37a8578f566d1d7ea4eea00792128ad674686c2 + +catalog-frontier-v0.2.json +764071121280d6122776675f92a4a5fd0f0979c296c3cbe66c50a06aa7ce5743 +``` + +Final package-byte hashes are recorded in the external handoff's +`SHA256SUMS-FINAL.txt`. Package archives include build metadata and are therefore +hashed only after the final build. + +## Authority and claim boundary + +Jester contains no broker dependency, credential path, order constructor, submission +endpoint, or live authority. Meta evolution cannot mutate source code, evaluators, +sealed tests, proof gates, signing policy, or authority rules. + +The public fixture proves deterministic mechanics only. No forward edge, market +fidelity, profitability, trained neural simulator, 0DTE readiness, or state-of-the-art +performance is claimed. diff --git a/schemas/cognition_arsenal_v1.schema.json b/schemas/cognition_arsenal_v1.schema.json new file mode 100644 index 0000000..0d384ef --- /dev/null +++ b/schemas/cognition_arsenal_v1.schema.json @@ -0,0 +1,225 @@ +{ + "$schema": "https://json-schema.org/draft/2020-12/schema", + "$id": "https://obtuse.ai/schemas/jester/cognition_arsenal_v1.schema.json", + "title": "Jester bounded cognition arsenal result", + "type": "object", + "additionalProperties": false, + "required": [ + "schema_version", + "arsenal_run_id", + "created_at", + "parent_hypothesis_id", + "mode_catalog_hash", + "scientific_seeds", + "requested_dream_budget", + "authorized_dream_budget", + "dream_cycles", + "dream_temperature", + "dreams", + "immune_findings", + "inventions", + "archive", + "failure_museum", + "coverage", + "surprise_consumed", + "epistemic_debt", + "authority", + "content_hash" + ], + "properties": { + "schema_version": { + "const": "jester-cognition-arsenal-v1" + }, + "arsenal_run_id": { + "type": "string", + "pattern": "^cognition-run:[0-9a-f]{24}$" + }, + "created_at": { + "type": "string", + "format": "date-time" + }, + "parent_hypothesis_id": { + "type": "string" + }, + "mode_catalog_hash": { + "type": "string", + "pattern": "^[0-9a-f]{64}$" + }, + "scientific_seeds": { + "type": "array", + "items": { + "type": "object", + "required": [ + "trust_state", + "requires_independent_replication", + "auto_apply", + "empirical_credit" + ], + "properties": { + "trust_state": { + "const": "unverified_scientific_claim" + }, + "requires_independent_replication": { + "const": true + }, + "auto_apply": { + "const": false + }, + "empirical_credit": { + "const": false + } + } + } + }, + "requested_dream_budget": { + "type": "integer", + "minimum": 1 + }, + "authorized_dream_budget": { + "type": "integer", + "minimum": 1 + }, + "dream_cycles": { + "type": "integer", + "minimum": 1 + }, + "dream_temperature": { + "type": "number", + "minimum": 0, + "maximum": 1 + }, + "dreams": { + "type": "array", + "minItems": 1, + "items": { + "type": "object", + "required": [ + "synthetic_only", + "stress_only", + "empirical_credit", + "parent_dream_ids", + "falsifiers", + "evidence_debt" + ], + "properties": { + "synthetic_only": { + "const": true + }, + "stress_only": { + "const": true + }, + "empirical_credit": { + "const": false + }, + "parent_dream_ids": { + "type": "array" + }, + "falsifiers": { + "type": "array", + "minItems": 2 + }, + "evidence_debt": { + "type": "array", + "minItems": 1 + } + } + } + }, + "immune_findings": { + "type": "array", + "minItems": 1 + }, + "inventions": { + "type": "array", + "items": { + "type": "object", + "required": [ + "synthetic_only", + "auto_apply", + "requires_independent_resimulation", + "empirical_credit", + "live_blocked", + "order_construction_allowed" + ], + "properties": { + "synthetic_only": { + "const": true + }, + "auto_apply": { + "const": false + }, + "requires_independent_resimulation": { + "const": true + }, + "empirical_credit": { + "const": false + }, + "live_blocked": { + "const": true + }, + "order_construction_allowed": { + "const": false + } + } + } + }, + "archive": { + "type": "object" + }, + "failure_museum": { + "type": "array" + }, + "coverage": { + "type": "number", + "minimum": 0, + "maximum": 1 + }, + "surprise_consumed": { + "type": "number", + "minimum": 0 + }, + "epistemic_debt": { + "type": "integer", + "minimum": 0 + }, + "authority": { + "type": "object", + "required": [ + "may_mutate_code", + "may_mutate_evaluators", + "may_auto_apply", + "may_claim_empirical_credit", + "live_blocked", + "order_construction_allowed", + "order_submission_allowed" + ], + "properties": { + "may_mutate_code": { + "const": false + }, + "may_mutate_evaluators": { + "const": false + }, + "may_auto_apply": { + "const": false + }, + "may_claim_empirical_credit": { + "const": false + }, + "live_blocked": { + "const": true + }, + "order_construction_allowed": { + "const": false + }, + "order_submission_allowed": { + "const": false + } + } + }, + "content_hash": { + "type": "string", + "pattern": "^[0-9a-f]{64}$" + } + } +} diff --git a/schemas/literature_learning_batch_v1.schema.json b/schemas/literature_learning_batch_v1.schema.json new file mode 100644 index 0000000..04917df --- /dev/null +++ b/schemas/literature_learning_batch_v1.schema.json @@ -0,0 +1,101 @@ +{ + "$schema": "https://json-schema.org/draft/2020-12/schema", + "$id": "https://obtuse.ai/schemas/jester/literature_learning_batch_v1.schema.json", + "title": "Jester scientific literature learning batch", + "type": "object", + "additionalProperties": false, + "required": [ + "schema_version", + "batch_id", + "created_at", + "query", + "papers", + "exclusions", + "capsules", + "mutation_proposals", + "authority", + "content_hash" + ], + "properties": { + "schema_version": { + "const": "jester-literature-learning-batch-v1" + }, + "batch_id": { + "type": "string", + "pattern": "^literature-batch:[0-9a-f]{24}$" + }, + "created_at": { + "type": "string", + "format": "date-time" + }, + "query": { + "type": "object" + }, + "papers": { + "type": "array" + }, + "exclusions": { + "type": "array" + }, + "capsules": { + "type": "array", + "items": { + "type": "object", + "required": [ + "trust_state", + "requires_independent_replication", + "auto_apply", + "empirical_credit" + ], + "properties": { + "trust_state": { + "const": "unverified_scientific_claim" + }, + "requires_independent_replication": { + "const": true + }, + "auto_apply": { + "const": false + }, + "empirical_credit": { + "const": false + } + } + } + }, + "mutation_proposals": { + "type": "array" + }, + "authority": { + "type": "object", + "required": [ + "metadata_only", + "full_text_mirroring_allowed", + "paper_claims_authoritative", + "source_auto_apply_allowed", + "simulator_activation_allowed" + ], + "properties": { + "metadata_only": { + "const": true + }, + "full_text_mirroring_allowed": { + "const": false + }, + "paper_claims_authoritative": { + "const": false + }, + "source_auto_apply_allowed": { + "const": false + }, + "simulator_activation_allowed": { + "const": false + } + } + }, + "content_hash": { + "type": "string", + "pattern": "^[0-9a-f]{64}$" + } + } +} diff --git a/schemas/meta_research_v1.schema.json b/schemas/meta_research_v1.schema.json new file mode 100644 index 0000000..0204869 --- /dev/null +++ b/schemas/meta_research_v1.schema.json @@ -0,0 +1,142 @@ +{ + "$schema": "https://json-schema.org/draft/2020-12/schema", + "$id": "https://obtuse.ai/schemas/jester/meta_research_v1.schema.json", + "title": "Jester protected meta-research result", + "type": "object", + "additionalProperties": false, + "required": [ + "schema_version", + "meta_run_id", + "created_at", + "base_run_id", + "simulator_catalog", + "pack_catalog", + "ladder", + "social_ablation", + "causal_tournament", + "archive", + "prediction", + "interoception", + "control", + "research_gates", + "mutation_proposals", + "authority", + "content_hash" + ], + "properties": { + "schema_version": { + "const": "jester-meta-research-v1" + }, + "meta_run_id": { + "type": "string", + "pattern": "^meta-run:[0-9a-f]{24}$" + }, + "created_at": { + "type": "string", + "format": "date-time" + }, + "base_run_id": { + "type": "string" + }, + "simulator_catalog": { + "type": "array", + "minItems": 1 + }, + "pack_catalog": { + "type": "array", + "minItems": 1 + }, + "ladder": { + "type": "object", + "required": ["highest_contiguous_level", "forward_promotion_eligible"], + "properties": { + "highest_contiguous_level": { + "type": "integer", + "minimum": -1, + "maximum": 7 + }, + "forward_promotion_eligible": { + "type": "boolean" + } + } + }, + "social_ablation": { + "type": "object" + }, + "causal_tournament": { + "type": "object" + }, + "archive": { + "type": "object" + }, + "prediction": { + "type": "object", + "required": ["resolved"], + "properties": { + "resolved": { + "const": false + } + } + }, + "interoception": { + "type": "object" + }, + "control": { + "type": "object", + "required": [ + "may_increase_authority", + "evaluator_mutation_allowed", + "auto_apply_source_changes" + ], + "properties": { + "may_increase_authority": { + "const": false + }, + "evaluator_mutation_allowed": { + "const": false + }, + "auto_apply_source_changes": { + "const": false + } + } + }, + "research_gates": { + "type": "array", + "minItems": 6 + }, + "mutation_proposals": { + "type": "array" + }, + "authority": { + "type": "object", + "required": [ + "live_blocked", + "broker_credentials_allowed", + "order_construction_allowed", + "order_submission_allowed", + "source_auto_apply_allowed" + ], + "properties": { + "live_blocked": { + "const": true + }, + "broker_credentials_allowed": { + "const": false + }, + "order_construction_allowed": { + "const": false + }, + "order_submission_allowed": { + "const": false + }, + "source_auto_apply_allowed": { + "const": false + } + } + }, + "content_hash": { + "type": "string", + "pattern": "^[0-9a-f]{64}$" + } + } +} diff --git a/schemas/meta_research_v2.schema.json b/schemas/meta_research_v2.schema.json new file mode 100644 index 0000000..eabcaa9 --- /dev/null +++ b/schemas/meta_research_v2.schema.json @@ -0,0 +1,129 @@ +{ + "$schema": "https://json-schema.org/draft/2020-12/schema", + "$id": "https://obtuse.ai/schemas/jester/meta_research_v2.schema.json", + "title": "Jester protected meta-research result with cognition", + "type": "object", + "additionalProperties": false, + "required": [ + "schema_version", + "meta_run_id", + "created_at", + "base_run_id", + "simulator_catalog", + "pack_catalog", + "ladder", + "social_ablation", + "causal_tournament", + "archive", + "prediction", + "interoception", + "control", + "cognition", + "research_gates", + "mutation_proposals", + "authority", + "content_hash" + ], + "properties": { + "schema_version": { + "const": "jester-meta-research-v2" + }, + "meta_run_id": { + "type": "string", + "pattern": "^meta-run:[0-9a-f]{24}$" + }, + "created_at": { + "type": "string", + "format": "date-time" + }, + "base_run_id": { + "type": "string" + }, + "simulator_catalog": { + "type": "array", + "minItems": 1 + }, + "pack_catalog": { + "type": "array", + "minItems": 1 + }, + "ladder": { + "type": "object" + }, + "social_ablation": { + "type": "object" + }, + "causal_tournament": { + "type": "object" + }, + "archive": { + "type": "object" + }, + "prediction": { + "type": "object" + }, + "interoception": { + "type": "object" + }, + "control": { + "type": "object", + "required": [ + "may_increase_authority", + "evaluator_mutation_allowed", + "auto_apply_source_changes" + ], + "properties": { + "may_increase_authority": { + "const": false + }, + "evaluator_mutation_allowed": { + "const": false + }, + "auto_apply_source_changes": { + "const": false + } + } + }, + "cognition": { + "$ref": "cognition_arsenal_v1.schema.json" + }, + "research_gates": { + "type": "array", + "minItems": 6 + }, + "mutation_proposals": { + "type": "array" + }, + "authority": { + "type": "object", + "required": [ + "live_blocked", + "broker_credentials_allowed", + "order_construction_allowed", + "order_submission_allowed", + "source_auto_apply_allowed" + ], + "properties": { + "live_blocked": { + "const": true + }, + "broker_credentials_allowed": { + "const": false + }, + "order_construction_allowed": { + "const": false + }, + "order_submission_allowed": { + "const": false + }, + "source_auto_apply_allowed": { + "const": false + } + } + }, + "content_hash": { + "type": "string", + "pattern": "^[0-9a-f]{64}$" + } + } +} diff --git a/schemas/search_attention_snapshot_v1.schema.json b/schemas/search_attention_snapshot_v1.schema.json new file mode 100644 index 0000000..af241f5 --- /dev/null +++ b/schemas/search_attention_snapshot_v1.schema.json @@ -0,0 +1,78 @@ +{ + "$schema": "https://json-schema.org/draft/2020-12/schema", + "$id": "https://obtuse.ai/schemas/jester/search_attention_snapshot_v1.schema.json", + "title": "Jester search attention snapshot", + "type": "object", + "additionalProperties": false, + "required": [ + "schema_version", + "snapshot_id", + "query", + "search_provider", + "observed_at", + "items", + "source_diversity", + "reciprocal_rank_mass", + "collection_scope", + "search_volume_observed", + "causal_credit_allowed", + "authority", + "content_hash" + ], + "properties": { + "schema_version": { + "const": "jester-search-attention-snapshot-v1" + }, + "snapshot_id": { + "type": "string", + "pattern": "^attention-snapshot:[0-9a-f]{24}$" + }, + "query": { + "type": "string", + "minLength": 1 + }, + "search_provider": { + "type": "string", + "minLength": 1 + }, + "observed_at": { + "type": "string", + "format": "date-time" + }, + "items": { + "type": "array", + "minItems": 1 + }, + "source_diversity": { + "type": "number", + "minimum": 0, + "maximum": 1 + }, + "reciprocal_rank_mass": { + "type": "number", + "minimum": 0 + }, + "collection_scope": { + "const": "ranked_url_metadata_only" + }, + "search_volume_observed": { + "const": false + }, + "causal_credit_allowed": { + "const": false + }, + "authority": { + "type": "object", + "required": [ + "metadata_only", + "body_persisted", + "search_volume_observed", + "causal_credit_allowed" + ] + }, + "content_hash": { + "type": "string", + "pattern": "^[0-9a-f]{64}$" + } + } +} diff --git a/scripts/validate_public_release.py b/scripts/validate_public_release.py index 64a1d95..5a2d3ad 100644 --- a/scripts/validate_public_release.py +++ b/scripts/validate_public_release.py @@ -5,6 +5,17 @@ import ast import json from pathlib import Path +from typing import cast + +from jester.cognition import cognition_mode_catalog, run_cognition_arsenal +from jester.data_pipelines import data_pipeline_catalog, pipeline_status_counts +from jester.intelligence import intelligence_catalog, intelligence_status_counts +from jester.literature import build_literature_batch +from jester.literature_types import LiteratureQuery +from jester.meta_pipeline import MetaConfig, run_meta_research +from jester.pipeline import EarningsPipelineInput, run_earnings_pipeline +from jester.research_sources import research_source_catalog +from jester.simulators import simulator_catalog ROOT = Path(__file__).parents[1] PROHIBITED_IMPORTS = { @@ -21,7 +32,9 @@ ".venv", "artifacts", "checkpoints", + "dist", "models", + "node_modules", "private", "runtime", } @@ -71,11 +84,144 @@ def main() -> int: fixture = json.loads(fixture_path.read_text(encoding="utf-8")) if not all(bool(body.get("synthetic")) for body in fixture.get("evidence_bodies", [])): violations.append("public_fixture_contains_non_synthetic_evidence") + meta_config_path = ROOT / "fixtures" / "meta" / "synthetic_meta_config.json" + meta_schema_path = ROOT / "schemas" / "meta_research_v2.schema.json" + cognition_schema_path = ROOT / "schemas" / "cognition_arsenal_v1.schema.json" + if not meta_config_path.is_file(): + violations.append("meta_fixture_missing") + if not meta_schema_path.is_file(): + violations.append("meta_schema_missing") + if not cognition_schema_path.is_file(): + violations.append("cognition_schema_missing") + pipeline_result = run_earnings_pipeline(cast(EarningsPipelineInput, fixture)) + meta_config = cast( + MetaConfig, + json.loads(meta_config_path.read_text(encoding="utf-8")), + ) + meta_result = run_meta_research(pipeline_result, meta_config) + mechanics_levels = [ + stage["level"] + for stage in meta_result["ladder"]["stages"] + if stage["level"] <= 5 and stage["status"] == "pass" + ] + if mechanics_levels != list(range(6)): + violations.append(f"meta_mechanics_ladder_incomplete:{mechanics_levels}") + if any( + stage["status"] != "blocked" + for stage in meta_result["ladder"]["stages"] + if stage["level"] >= 6 + ): + violations.append("synthetic_meta_fixture_did_not_block_forward_levels") + if ( + meta_result["control"]["authorized_candidate_budget"] + > meta_result["control"]["requested_candidate_budget"] + ): + violations.append("meta_controller_increased_candidate_budget") + if ( + meta_result["authority"]["live_blocked"] is not True + or meta_result["authority"]["order_submission_allowed"] is not False + or meta_result["authority"]["source_auto_apply_allowed"] is not False + ): + violations.append("meta_authority_boundary_invalid") + blocked_adapter_count = sum( + spec["implementation_status"] == "adapter_contract" + and spec["validation_status"].startswith("blocked_") + for spec in simulator_catalog() + ) + if blocked_adapter_count < 5: + violations.append(f"blocked_simulator_adapter_count_too_low:{blocked_adapter_count}") + literature_query = cast( + LiteratureQuery, + json.loads( + (ROOT / "fixtures" / "literature" / "synthetic_query.json").read_text(encoding="utf-8") + ), + ) + literature_feed = (ROOT / "fixtures" / "literature" / "synthetic_arxiv_feed.xml").read_bytes() + literature_batch = build_literature_batch( + literature_feed, + literature_query, + "2026-07-29T12:01:00Z", + ) + if len(literature_batch["papers"]) != 2 or len(literature_batch["exclusions"]) != 1: + violations.append("literature_fixture_point_in_time_admission_invalid") + if any( + capsule["auto_apply"] is not False or capsule["empirical_credit"] is not False + for capsule in literature_batch["capsules"] + ): + violations.append("literature_capsule_authority_invalid") + cognition_result = run_cognition_arsenal( + pipeline_result["hypothesis"], + literature_batch["capsules"], + "2026-07-29T12:01:00Z", + 2202, + 12, + 8, + 4, + 0.72, + ) + if len(cognition_mode_catalog()) != 24: + violations.append("cognition_mode_catalog_incomplete") + if any( + dream["synthetic_only"] is not True + or dream["stress_only"] is not True + or dream["empirical_credit"] is not False + for dream in cognition_result["dreams"] + ): + violations.append("cognition_dream_boundary_invalid") + if any( + invention["auto_apply"] is not False + or invention["empirical_credit"] is not False + or invention["requires_independent_resimulation"] is not True + or invention["order_construction_allowed"] is not False + for invention in cognition_result["inventions"] + ): + violations.append("cognition_invention_authority_invalid") + if ( + cognition_result["authority"]["may_mutate_code"] is not False + or cognition_result["authority"]["may_mutate_evaluators"] is not False + or cognition_result["authority"]["order_submission_allowed"] is not False + ): + violations.append("cognition_authority_boundary_invalid") + source_catalog = research_source_catalog() + prohibited_sources = [ + source["source_id"] for source in source_catalog if source["status"] == "prohibited" + ] + if "unapproved-serp-html-scraping" not in prohibited_sources: + violations.append("unapproved_serp_scraping_not_prohibited") + pipeline_counts = pipeline_status_counts() + if pipeline_counts["operational"] < 3: + violations.append(f"operational_data_pipeline_count_too_low:{pipeline_counts}") + if pipeline_counts["blocked_license_or_data"] < 2: + violations.append(f"licensed_data_boundaries_missing:{pipeline_counts}") + if not any( + pipeline["pipeline_id"] == "credentialed-or-antibot-scraping" + and pipeline["status"] == "prohibited" + for pipeline in data_pipeline_catalog() + ): + violations.append("credentialed_or_antibot_scraping_not_prohibited") + intelligence_counts = intelligence_status_counts() + if intelligence_counts["operational_mechanics"] < 6: + violations.append(f"operational_intelligence_count_too_low:{intelligence_counts}") + if any(service["authority_effect"] != "none" for service in intelligence_catalog()): + violations.append("intelligence_service_expands_authority") result = { "status": "pass" if not violations else "fail", "broker_dependency_present": broker_dependency_present, "credential_material_present": credential_material_present, "public_fixture_synthetic_only": True, + "meta_mechanics_levels_passed": mechanics_levels, + "meta_forward_levels_blocked": True, + "blocked_simulator_adapter_count": blocked_adapter_count, + "literature_papers_admitted": len(literature_batch["papers"]), + "literature_papers_excluded": len(literature_batch["exclusions"]), + "cognition_mode_count": len(cognition_mode_catalog()), + "cognition_scientific_seed_count": len(cognition_result["scientific_seeds"]), + "cognition_archive_cell_count": len(cognition_result["archive"]), + "cognition_failure_memory_count": len(cognition_result["failure_museum"]), + "unapproved_serp_scraping_prohibited": True, + "data_pipeline_status_counts": pipeline_counts, + "intelligence_status_counts": intelligence_counts, + "credentialed_or_antibot_scraping_prohibited": True, "violations": violations, } print(json.dumps(result, sort_keys=True)) diff --git a/src/jester/__init__.py b/src/jester/__init__.py index de7b338..ec9f3c4 100644 --- a/src/jester/__init__.py +++ b/src/jester/__init__.py @@ -1,6 +1,7 @@ """Jester causal market-imagination engine.""" +from .meta_pipeline import run_meta_research from .pipeline import run_earnings_pipeline -__all__ = ["run_earnings_pipeline"] -__version__ = "0.1.0" +__all__ = ["run_earnings_pipeline", "run_meta_research"] +__version__ = "0.2.0" diff --git a/src/jester/api.py b/src/jester/api.py index e3a57f9..8dc6063 100644 --- a/src/jester/api.py +++ b/src/jester/api.py @@ -6,14 +6,21 @@ from fastapi import FastAPI, HTTPException +from .cognition import cognition_mode_catalog +from .data_pipelines import data_pipeline_catalog +from .intelligence import intelligence_catalog from .io import read_json +from .observatory import observatory_bootstrap +from .packs import pack_catalog +from .research_sources import research_source_catalog +from .simulators import simulator_catalog def create_app(artifact_root: Path) -> FastAPI: resolved_root = artifact_root.resolve() app = FastAPI( title="Jester Observatory", - version="0.1.0", + version="0.2.0", description="Read-only research artifacts. No execution endpoints exist.", ) @@ -21,6 +28,34 @@ def create_app(artifact_root: Path) -> FastAPI: def health() -> dict[str, str]: return {"status": "ok", "authority": "read_only"} + @app.get("/catalog/simulators") + def simulators() -> object: + return simulator_catalog() + + @app.get("/catalog/packs") + def packs() -> object: + return pack_catalog() + + @app.get("/catalog/research-sources") + def research_sources() -> object: + return research_source_catalog() + + @app.get("/catalog/data-pipelines") + def data_pipelines() -> object: + return data_pipeline_catalog() + + @app.get("/catalog/intelligence") + def intelligence() -> object: + return intelligence_catalog() + + @app.get("/catalog/cognition") + def cognition() -> object: + return cognition_mode_catalog() + + @app.get("/observatory/bootstrap") + def observatory() -> object: + return observatory_bootstrap() + @app.get("/runs/{run_id}") def run_artifact(run_id: str) -> object: allowed = "abcdefghijklmnopqrstuvwxyz0123456789-_" diff --git a/src/jester/attention.py b/src/jester/attention.py new file mode 100644 index 0000000..b526c00 --- /dev/null +++ b/src/jester/attention.py @@ -0,0 +1,550 @@ +"""Robots-aware metadata scraping and non-causal attention deltas.""" + +from __future__ import annotations + +import ipaddress +import logging +import socket +import time +import urllib.robotparser +from html.parser import HTMLParser +from urllib.parse import urlparse + +import httpx + +from .attention_types import ( + AttentionDelta, + MetadataScrapePolicy, + PageMetadata, + RankedAttentionItem, + SearchAttentionManifest, + SearchAttentionSnapshot, +) +from .clock import parse_utc, utc_iso +from .identity import as_json_value, bytes_hash, canonical_hash +from .types import JsonValue + + +class MetadataScrapeError(RuntimeError): + """Raised when a permitted metadata scrape cannot complete safely.""" + + +def _validate_public_host_literal(host: str) -> None: + normalized = host.casefold().rstrip(".") + if normalized in {"localhost", "localhost.localdomain"} or normalized.endswith(".localhost"): + raise PermissionError(f"metadata target host may not be local: host={host}") + try: + address = ipaddress.ip_address(normalized) + except ValueError: + return + if not address.is_global: + raise PermissionError(f"metadata target IP must be globally routable: host={host}") + + +def _assert_public_dns(host: str) -> None: + try: + addresses = {item[4][0] for item in socket.getaddrinfo(host, 443, type=socket.SOCK_STREAM)} + except socket.gaierror as error: + raise MetadataScrapeError( + f"metadata target DNS resolution failed: host={host}, error={error}" + ) from error + if not addresses: + raise MetadataScrapeError(f"metadata target DNS returned no addresses: host={host}") + for address_text in addresses: + address = ipaddress.ip_address(address_text) + if not address.is_global: + raise PermissionError( + "metadata target DNS resolved to a non-public address: " + f"host={host}, address={address_text}" + ) + + +class _MetadataParser(HTMLParser): + def __init__(self) -> None: + super().__init__(convert_charrefs=True) + self.in_title = False + self.title_parts: list[str] = [] + self.meta: dict[str, str] = {} + self.canonical_url = "" + + def handle_starttag( + self, + tag: str, + attrs: list[tuple[str, str | None]], + ) -> None: + normalized = tag.casefold() + attributes = {key.casefold(): value or "" for key, value in attrs} + if normalized == "title": + self.in_title = True + elif normalized == "meta": + key_value = ( + attributes.get("name") + or attributes.get("property") + or attributes.get("itemprop") + or "" + ) + key = key_value.casefold() + content = attributes.get("content", "") + if key and content and key not in self.meta: + self.meta[key] = content + elif ( + normalized == "link" + and "canonical" in attributes.get("rel", "").casefold().split() + and attributes.get("href") + ): + self.canonical_url = attributes["href"] + + def handle_endtag(self, tag: str) -> None: + if tag.casefold() == "title": + self.in_title = False + + def handle_data(self, data: str) -> None: + if self.in_title: + self.title_parts.append(data) + + +def _normalized_text(value: str) -> str: + return " ".join(value.split()) + + +def validate_scrape_policy(policy: MetadataScrapePolicy) -> None: + if policy["schema_version"] != "jester-metadata-scrape-policy-v1": + raise ValueError( + f"unsupported metadata scrape policy: schema_version={policy['schema_version']}" + ) + if not policy["policy_id"].strip(): + raise ValueError("metadata scrape policy_id must be non-empty") + if not policy["terms_uri"].startswith("https://"): + raise ValueError( + f"metadata scrape terms URI must use HTTPS: terms_uri={policy['terms_uri']}" + ) + if not policy["allowed_hosts"]: + raise ValueError("metadata scrape policy requires at least one allowed host") + if not policy["user_agent"].strip() or "mailto:" not in policy["user_agent"]: + raise ValueError( + "metadata scraper user agent must identify the project and include mailto contact" + ) + if policy["max_pages"] <= 0 or policy["max_pages"] > 25: + raise ValueError( + f"metadata max_pages must be within [1, 25]: max_pages={policy['max_pages']}" + ) + if not 1024 <= policy["max_response_bytes"] <= 2_000_000: + raise ValueError( + "metadata max_response_bytes must be within [1024, 2000000]: " + f"max_response_bytes={policy['max_response_bytes']}" + ) + if policy["minimum_request_interval_seconds"] < 3.0: + raise ValueError( + "metadata request interval must be at least three seconds: " + "minimum_request_interval_seconds=" + f"{policy['minimum_request_interval_seconds']}" + ) + if ( + policy["metadata_only"] is not True + or policy["authentication_allowed"] is not False + or policy["anti_bot_bypass_allowed"] is not False + ): + raise PermissionError("metadata scrape policy weakens a protected collection boundary") + + +def validate_attention_manifest(manifest: SearchAttentionManifest) -> None: + if manifest["schema_version"] != "jester-search-attention-manifest-v1": + raise ValueError( + f"unsupported attention manifest schema: schema_version={manifest['schema_version']}" + ) + validate_scrape_policy(manifest["policy"]) + parse_utc(manifest["observed_at"], "observed_at") + if not manifest["query"].strip() or not manifest["search_provider"].strip(): + raise ValueError("attention manifest query and search_provider must be non-empty") + if not manifest["targets"]: + raise ValueError("attention manifest requires at least one ranked target") + if len(manifest["targets"]) > manifest["policy"]["max_pages"]: + raise ValueError( + "attention target count exceeds policy: " + f"targets={len(manifest['targets'])}, max_pages={manifest['policy']['max_pages']}" + ) + ranks = [target["rank"] for target in manifest["targets"]] + if len(set(ranks)) != len(ranks) or any(rank <= 0 for rank in ranks): + raise ValueError(f"attention target ranks must be unique and positive: ranks={ranks}") + allowed_hosts = {host.casefold() for host in manifest["policy"]["allowed_hosts"]} + for target in manifest["targets"]: + parsed = urlparse(target["url"]) + if ( + parsed.scheme != "https" + or not parsed.hostname + or parsed.username is not None + or parsed.password is not None + ): + raise ValueError(f"attention target must be credential-free HTTPS: url={target['url']}") + if parsed.port not in {None, 443}: + raise ValueError( + f"attention target must use the standard HTTPS port: url={target['url']}" + ) + _validate_public_host_literal(parsed.hostname) + if parsed.hostname.casefold() not in allowed_hosts: + raise PermissionError( + "attention target host is not admitted: " + f"host={parsed.hostname}, policy_id={manifest['policy']['policy_id']}" + ) + + +def parse_page_metadata( + document: bytes, + url: str, + observed_at: str, + response_content_type: str, + response_etag: str, + response_last_modified: str, +) -> PageMetadata: + if "text/html" not in response_content_type.casefold(): + raise ValueError( + f"metadata parser requires text/html: url={url}, content_type={response_content_type}" + ) + try: + text = document.decode("utf-8") + except UnicodeDecodeError as error: + raise ValueError(f"metadata document must be UTF-8: url={url}") from error + parser = _MetadataParser() + parser.feed(text) + parser.close() + parsed_url = urlparse(url) + if not parsed_url.hostname: + raise ValueError(f"metadata URL is missing a host: url={url}") + title = _normalized_text(" ".join(parser.title_parts)) + meta = {_normalized_text(key): _normalized_text(value) for key, value in parser.meta.items()} + semantic_metadata = { + "url": url, + "host": parsed_url.hostname.casefold(), + "title": title, + "description": meta.get("description", ""), + "canonical_url": _normalized_text(parser.canonical_url), + "open_graph_title": meta.get("og:title", ""), + "open_graph_description": meta.get("og:description", ""), + "published_time": meta.get("article:published_time", ""), + "modified_time": meta.get("article:modified_time", ""), + "robots_directive": meta.get("robots", ""), + "response_content_type": response_content_type, + "response_etag": response_etag, + "response_last_modified": response_last_modified, + } + metadata_hash = canonical_hash(as_json_value(semantic_metadata)) + metadata_body = { + **semantic_metadata, + "observed_at": utc_iso(parse_utc(observed_at, "observed_at")), + "document_hash": bytes_hash(document), + "metadata_hash": metadata_hash, + "body_persisted": False, + } + content_hash = canonical_hash(as_json_value(metadata_body)) + return { + "page_id": f"page-metadata:{content_hash[:24]}", + "url": url, + "host": parsed_url.hostname.casefold(), + "title": title, + "description": meta.get("description", ""), + "canonical_url": _normalized_text(parser.canonical_url), + "open_graph_title": meta.get("og:title", ""), + "open_graph_description": meta.get("og:description", ""), + "published_time": meta.get("article:published_time", ""), + "modified_time": meta.get("article:modified_time", ""), + "robots_directive": meta.get("robots", ""), + "response_content_type": response_content_type, + "response_etag": response_etag, + "response_last_modified": response_last_modified, + "observed_at": utc_iso(parse_utc(observed_at, "observed_at")), + "document_hash": bytes_hash(document), + "metadata_hash": metadata_hash, + "body_persisted": False, + "content_hash": content_hash, + } + + +class PermittedMetadataScraper: + """External connector that fetches robots.txt and metadata-only HTML.""" + + def __init__(self, policy: MetadataScrapePolicy) -> None: + validate_scrape_policy(policy) + self._policy = policy + + def _get( + self, + client: httpx.Client, + url: str, + timeout_seconds: float, + retries: int, + expected_content_type: str, + ) -> httpx.Response: + last_error: Exception | None = None + for attempt in range(1, retries + 1): + try: + response = client.get(url, timeout=timeout_seconds) + if response.status_code >= 400: + raise MetadataScrapeError( + "metadata scrape request failed: " + f"url={url}, attempt={attempt}, status_code={response.status_code}, " + f"response_body={response.text[:500]}" + ) + content_type = response.headers.get("content-type", "") + if expected_content_type not in content_type.casefold(): + raise MetadataScrapeError( + "metadata scrape content type is unsupported: " + f"url={url}, expected={expected_content_type}, actual={content_type}" + ) + if len(response.content) > self._policy["max_response_bytes"]: + raise MetadataScrapeError( + "metadata scrape response exceeds policy: " + f"url={url}, response_bytes={len(response.content)}, " + f"max_response_bytes={self._policy['max_response_bytes']}" + ) + return response + except (httpx.HTTPError, MetadataScrapeError) as error: + last_error = error + logging.getLogger(__name__).warning( + "metadata_scrape_retry", + extra={ + "url": url, + "attempt": attempt, + "retry_limit": retries, + "error_type": type(error).__name__, + }, + ) + if attempt < retries: + time.sleep(self._policy["minimum_request_interval_seconds"]) + raise MetadataScrapeError( + "metadata scrape exhausted retries: " + f"url={url}, retries={retries}, last_error={last_error}" + ) from last_error + + def scrape( + self, + manifest: SearchAttentionManifest, + timeout_seconds: float, + retries: int, + ) -> SearchAttentionSnapshot: + validate_attention_manifest(manifest) + if manifest["policy"] != self._policy: + raise PermissionError("attention manifest policy differs from scraper policy") + if timeout_seconds <= 0.0 or retries <= 0: + raise ValueError( + "metadata scrape timeout and retries must be positive: " + f"timeout_seconds={timeout_seconds}, retries={retries}" + ) + robots_cache: dict[str, urllib.robotparser.RobotFileParser] = {} + items: list[RankedAttentionItem] = [] + with httpx.Client( + headers={"User-Agent": self._policy["user_agent"]}, + follow_redirects=False, + ) as client: + for ordinal, target in enumerate( + sorted(manifest["targets"], key=lambda item: item["rank"]) + ): + parsed = urlparse(target["url"]) + if not parsed.hostname: + raise ValueError(f"attention target is missing host: url={target['url']}") + _assert_public_dns(parsed.hostname) + origin = f"{parsed.scheme}://{parsed.hostname}" + robots = robots_cache.get(origin) + if robots is None: + robots_url = f"{origin}/robots.txt" + response = self._get( + client, + robots_url, + timeout_seconds, + retries, + "text/plain", + ) + robots = urllib.robotparser.RobotFileParser() + robots.set_url(robots_url) + robots.parse(response.text.splitlines()) + robots_cache[origin] = robots + time.sleep(self._policy["minimum_request_interval_seconds"]) + if not robots.can_fetch(self._policy["user_agent"], target["url"]): + raise PermissionError( + "robots policy denies metadata target: " + f"url={target['url']}, policy_id={self._policy['policy_id']}" + ) + response = self._get( + client, + target["url"], + timeout_seconds, + retries, + "text/html", + ) + metadata = parse_page_metadata( + response.content, + target["url"], + manifest["observed_at"], + response.headers.get("content-type", ""), + response.headers.get("etag", ""), + response.headers.get("last-modified", ""), + ) + item_body = {"rank": target["rank"], "metadata": metadata} + items.append( + { + "rank": target["rank"], + "metadata": metadata, + "content_hash": canonical_hash(as_json_value(item_body)), + } + ) + if ordinal + 1 < len(manifest["targets"]): + time.sleep(self._policy["minimum_request_interval_seconds"]) + return build_attention_snapshot(manifest, items) + + +def build_attention_snapshot( + manifest: SearchAttentionManifest, + items: list[RankedAttentionItem], +) -> SearchAttentionSnapshot: + validate_attention_manifest(manifest) + expected_ranks = sorted(target["rank"] for target in manifest["targets"]) + actual_ranks = sorted(item["rank"] for item in items) + if expected_ranks != actual_ranks: + raise ValueError( + "attention items do not close over manifest ranks: " + f"expected={expected_ranks}, actual={actual_ranks}" + ) + hosts = {item["metadata"]["host"] for item in items} + source_diversity = round(len(hosts) / len(items), 6) + reciprocal_rank_mass = round( + sum( + ( + sum( + bool(item["metadata"][field]) + for field in ( + "title", + "description", + "canonical_url", + "open_graph_title", + "open_graph_description", + "published_time", + "modified_time", + ) + ) + / 7.0 + ) + / item["rank"] + for item in items + ), + 6, + ) + authority: dict[str, JsonValue] = { + "metadata_only": True, + "body_persisted": False, + "search_volume_observed": False, + "causal_credit_allowed": False, + "trade_authority": "none", + } + body = { + "schema_version": "jester-search-attention-snapshot-v1", + "query": manifest["query"], + "search_provider": manifest["search_provider"], + "observed_at": utc_iso(parse_utc(manifest["observed_at"], "observed_at")), + "items": items, + "source_diversity": source_diversity, + "reciprocal_rank_mass": reciprocal_rank_mass, + "collection_scope": "ranked_url_metadata_only", + "search_volume_observed": False, + "causal_credit_allowed": False, + "authority": authority, + } + content_hash = canonical_hash(as_json_value(body)) + return { + "schema_version": "jester-search-attention-snapshot-v1", + "snapshot_id": f"attention-snapshot:{content_hash[:24]}", + "query": manifest["query"], + "search_provider": manifest["search_provider"], + "observed_at": utc_iso(parse_utc(manifest["observed_at"], "observed_at")), + "items": items, + "source_diversity": source_diversity, + "reciprocal_rank_mass": reciprocal_rank_mass, + "collection_scope": "ranked_url_metadata_only", + "search_volume_observed": False, + "causal_credit_allowed": False, + "authority": authority, + "content_hash": content_hash, + } + + +def compare_attention( + prior: SearchAttentionSnapshot, + current: SearchAttentionSnapshot, +) -> AttentionDelta: + if prior["query"] != current["query"]: + raise ValueError( + "attention snapshots must have the same query: " + f"prior={prior['query']!r}, current={current['query']!r}" + ) + if parse_utc(prior["observed_at"], "prior.observed_at") >= parse_utc( + current["observed_at"], + "current.observed_at", + ): + raise ValueError("attention snapshots must be strictly chronological") + prior_by_url = {item["metadata"]["url"]: item for item in prior["items"]} + current_by_url = {item["metadata"]["url"]: item for item in current["items"]} + prior_urls = set(prior_by_url) + current_urls = set(current_by_url) + union = prior_urls | current_urls + intersection = prior_urls & current_urls + churn = 1.0 - len(intersection) / max(len(union), 1) + rank_change = sum( + abs(prior_by_url[url]["rank"] - current_by_url[url]["rank"]) for url in intersection + ) / max(len(intersection), 1) + mutation_fraction = sum( + prior_by_url[url]["metadata"]["metadata_hash"] + != current_by_url[url]["metadata"]["metadata_hash"] + for url in intersection + ) / max(len(intersection), 1) + if churn >= 0.5: + interpretation = "high_result_churn_attention_proxy" + elif rank_change >= 2.0: + interpretation = "rank_reordering_attention_proxy" + elif mutation_fraction >= 0.5: + interpretation = "metadata_mutation_attention_proxy" + else: + interpretation = "stable_search_prominence_proxy" + body = { + "query": current["query"], + "prior_snapshot_id": prior["snapshot_id"], + "current_snapshot_id": current["snapshot_id"], + "result_churn": round(churn, 6), + "mean_absolute_rank_change": round(rank_change, 6), + "metadata_mutation_fraction": round(mutation_fraction, 6), + "source_diversity_change": round( + current["source_diversity"] - prior["source_diversity"], + 6, + ), + "reciprocal_rank_mass_change": round( + current["reciprocal_rank_mass"] - prior["reciprocal_rank_mass"], + 6, + ), + "new_urls": sorted(current_urls - prior_urls), + "removed_urls": sorted(prior_urls - current_urls), + "interpretation": interpretation, + "search_volume_observed": False, + "causal_credit_allowed": False, + } + content_hash = canonical_hash(as_json_value(body)) + return { + "schema_version": "jester-attention-delta-v1", + "delta_id": f"attention-delta:{content_hash[:24]}", + "query": current["query"], + "prior_snapshot_id": prior["snapshot_id"], + "current_snapshot_id": current["snapshot_id"], + "result_churn": round(churn, 6), + "mean_absolute_rank_change": round(rank_change, 6), + "metadata_mutation_fraction": round(mutation_fraction, 6), + "source_diversity_change": round( + current["source_diversity"] - prior["source_diversity"], + 6, + ), + "reciprocal_rank_mass_change": round( + current["reciprocal_rank_mass"] - prior["reciprocal_rank_mass"], + 6, + ), + "new_urls": sorted(current_urls - prior_urls), + "removed_urls": sorted(prior_urls - current_urls), + "interpretation": interpretation, + "search_volume_observed": False, + "causal_credit_allowed": False, + "content_hash": content_hash, + } diff --git a/src/jester/attention_types.py b/src/jester/attention_types.py new file mode 100644 index 0000000..b603f16 --- /dev/null +++ b/src/jester/attention_types.py @@ -0,0 +1,98 @@ +"""Typed contracts for metadata-only attention observation.""" + +from __future__ import annotations + +from typing import Literal, TypedDict + +from .types import JsonValue + + +class MetadataScrapePolicy(TypedDict): + schema_version: Literal["jester-metadata-scrape-policy-v1"] + policy_id: str + terms_uri: str + allowed_hosts: list[str] + user_agent: str + max_pages: int + max_response_bytes: int + minimum_request_interval_seconds: float + metadata_only: Literal[True] + authentication_allowed: Literal[False] + anti_bot_bypass_allowed: Literal[False] + + +class RankedSearchTarget(TypedDict): + rank: int + url: str + + +class SearchAttentionManifest(TypedDict): + schema_version: Literal["jester-search-attention-manifest-v1"] + query: str + search_provider: str + observed_at: str + targets: list[RankedSearchTarget] + policy: MetadataScrapePolicy + + +class PageMetadata(TypedDict): + page_id: str + url: str + host: str + title: str + description: str + canonical_url: str + open_graph_title: str + open_graph_description: str + published_time: str + modified_time: str + robots_directive: str + response_content_type: str + response_etag: str + response_last_modified: str + observed_at: str + document_hash: str + metadata_hash: str + body_persisted: Literal[False] + content_hash: str + + +class RankedAttentionItem(TypedDict): + rank: int + metadata: PageMetadata + content_hash: str + + +class SearchAttentionSnapshot(TypedDict): + schema_version: Literal["jester-search-attention-snapshot-v1"] + snapshot_id: str + query: str + search_provider: str + observed_at: str + items: list[RankedAttentionItem] + source_diversity: float + reciprocal_rank_mass: float + collection_scope: Literal["ranked_url_metadata_only"] + search_volume_observed: Literal[False] + causal_credit_allowed: Literal[False] + authority: dict[str, JsonValue] + content_hash: str + + +class AttentionDelta(TypedDict): + schema_version: Literal["jester-attention-delta-v1"] + delta_id: str + query: str + prior_snapshot_id: str + current_snapshot_id: str + result_churn: float + mean_absolute_rank_change: float + metadata_mutation_fraction: float + source_diversity_change: float + reciprocal_rank_mass_change: float + new_urls: list[str] + removed_urls: list[str] + interpretation: str + search_volume_observed: Literal[False] + causal_credit_allowed: Literal[False] + content_hash: str diff --git a/src/jester/cli.py b/src/jester/cli.py index cec12e2..4cbd8b8 100644 --- a/src/jester/cli.py +++ b/src/jester/cli.py @@ -4,17 +4,40 @@ import argparse import time +from datetime import UTC, datetime from pathlib import Path from typing import cast import uvicorn from .api import create_app +from .attention import PermittedMetadataScraper, compare_attention +from .attention_types import SearchAttentionManifest, SearchAttentionSnapshot from .bundle import sign_bundle, verify_bundle -from .identity import as_json_value +from .clock import utc_iso +from .cognition import cognition_mode_catalog, run_cognition_arsenal +from .data_pipelines import data_pipeline_catalog +from .identity import as_json_value, canonical_hash +from .intelligence import intelligence_catalog from .io import read_json, write_json_atomic +from .literature import ( + ARXIV_QUERY_ENDPOINT, + ArxivMetadataClient, + build_literature_batch, + effective_literature_query, + next_scan_time, + validate_literature_query, +) +from .literature_types import LiteratureLearningBatch, LiteratureQuery +from .meta_pipeline import MetaConfig, run_meta_research +from .meta_types import MetaPrediction +from .metacognition import resolve_meta_prediction +from .packs import pack_catalog from .pipeline import EarningsPipelineInput, run_earnings_pipeline -from .types import GovernorCandidateBundle +from .research_memory import ResearchMemory +from .research_sources import research_source_catalog +from .simulators import simulator_catalog +from .types import GovernorCandidateBundle, JsonValue def _private_key_bytes(path: Path) -> bytes: @@ -43,6 +66,60 @@ def _load_pipeline_input(path: Path) -> EarningsPipelineInput: return cast(EarningsPipelineInput, loaded) +def _load_literature_query(path: Path) -> LiteratureQuery: + loaded = read_json(path) + if not isinstance(loaded, dict): + raise TypeError(f"literature query must be a JSON object: path={path}") + query = cast(LiteratureQuery, loaded) + validate_literature_query(query) + return query + + +def _load_attention_manifest(path: Path) -> SearchAttentionManifest: + loaded = read_json(path) + if not isinstance(loaded, dict): + raise TypeError(f"attention manifest must be a JSON object: path={path}") + return cast(SearchAttentionManifest, loaded) + + +def _load_literature_batch(path: Path) -> LiteratureLearningBatch: + loaded = read_json(path) + if not isinstance(loaded, dict): + raise TypeError(f"literature batch must be a JSON object: path={path}") + if loaded.get("schema_version") != "jester-literature-learning-batch-v1": + raise ValueError( + "cognition seed input has an unsupported literature schema: " + f"path={path}, schema_version={loaded.get('schema_version')}" + ) + capsules = loaded.get("capsules") + if not isinstance(capsules, list): + raise TypeError(f"literature batch capsules must be an array: path={path}") + return cast(LiteratureLearningBatch, loaded) + + +def _persist_literature_batch( + memory: ResearchMemory, + batch: LiteratureLearningBatch, +) -> None: + for paper in batch["papers"]: + memory.append( + "scientific_paper_metadata", + batch["created_at"], + cast(dict[str, JsonValue], paper), + ) + for capsule in batch["capsules"]: + memory.append( + "scientific_research_capsule", + batch["created_at"], + cast(dict[str, JsonValue], capsule), + ) + memory.append( + "literature_learning_batch", + batch["created_at"], + cast(dict[str, JsonValue], batch), + ) + + def _run_once(input_path: Path, output_path: Path) -> str: result = run_earnings_pipeline(_load_pipeline_input(input_path)) write_json_atomic(output_path, as_json_value(result)) @@ -56,6 +133,276 @@ def _command_run(arguments: argparse.Namespace) -> int: return 0 +def _command_catalog(arguments: argparse.Namespace) -> int: + catalog: dict[str, JsonValue] = { + "schema_version": "jester-capability-catalog-v1", + "simulators": as_json_value(simulator_catalog()), + "market_packs": as_json_value(pack_catalog()), + "research_sources": as_json_value(research_source_catalog()), + "data_pipelines": as_json_value(data_pipeline_catalog()), + "intelligence": as_json_value(intelligence_catalog()), + "cognition_modes": as_json_value(cognition_mode_catalog()), + } + write_json_atomic(Path(arguments.output), catalog) + print(f"catalog={Path(arguments.output).resolve()}") + return 0 + + +def _command_meta_run(arguments: argparse.Namespace) -> int: + pipeline_input = _load_pipeline_input(Path(arguments.input)) + base_result = run_earnings_pipeline(pipeline_input) + config = MetaConfig( + created_at=arguments.created_at, + pack_id=arguments.pack, + generations=arguments.generations, + requested_candidate_budget=arguments.candidate_budget, + island_count=arguments.islands, + root_seed=arguments.root_seed, + cognition_budget=arguments.cognition_budget, + dream_cycles=arguments.dream_cycles, + dream_temperature=arguments.dream_temperature, + ladder={ + "evidence_mode": pipeline_input["evidence_mode"], + "resolved_forward_outcomes": arguments.forward_outcomes, + "bounded_paper_outcomes": arguments.paper_outcomes, + "required_forward_outcomes": arguments.required_forward_outcomes, + "required_paper_outcomes": arguments.required_paper_outcomes, + }, + independently_resimulated_descendants=arguments.resimulated_descendants, + simulation_gap_observations=arguments.gap_observations, + simulation_gap_pressure=arguments.gap_pressure, + ) + result = run_meta_research(base_result, config) + write_json_atomic(Path(arguments.output), as_json_value(result)) + memory = ResearchMemory(Path(arguments.memory)) + memory.initialize() + memory.append( + "meta_prediction", + arguments.created_at, + cast(dict[str, JsonValue], result["prediction"]), + ) + memory.append( + "meta_research_result", + arguments.created_at, + cast(dict[str, JsonValue], result), + ) + print(f"meta_run_id={result['meta_run_id']}") + print(f"artifact={Path(arguments.output).resolve()}") + print(f"memory={Path(arguments.memory).resolve()}") + return 0 + + +def _command_cognition_run(arguments: argparse.Namespace) -> int: + base_result = run_earnings_pipeline(_load_pipeline_input(Path(arguments.input))) + capsules = ( + [] + if arguments.literature_batch is None + else _load_literature_batch(Path(arguments.literature_batch))["capsules"] + ) + result = run_cognition_arsenal( + base_result["hypothesis"], + capsules, + arguments.created_at, + arguments.root_seed, + arguments.dream_budget, + arguments.authorized_dream_budget, + arguments.dream_cycles, + arguments.dream_temperature, + ) + write_json_atomic(Path(arguments.output), as_json_value(result)) + memory = ResearchMemory(Path(arguments.memory)) + memory.initialize() + memory.append( + "cognition_arsenal_result", + arguments.created_at, + cast(dict[str, JsonValue], result), + ) + print(f"arsenal_run_id={result['arsenal_run_id']}") + print(f"dreams={len(result['dreams'])}") + print(f"inventions={len(result['inventions'])}") + print(f"scientific_seeds={len(result['scientific_seeds'])}") + print(f"failure_memories={len(result['failure_museum'])}") + print(f"artifact={Path(arguments.output).resolve()}") + print(f"memory={Path(arguments.memory).resolve()}") + return 0 + + +def _command_meta_resolve(arguments: argparse.Namespace) -> int: + loaded = read_json(Path(arguments.input)) + if not isinstance(loaded, dict): + raise TypeError("meta resolution input must be a JSON object") + prediction_value = loaded.get("prediction", loaded) + if not isinstance(prediction_value, dict): + raise TypeError("meta resolution input does not contain a prediction object") + prediction = cast(MetaPrediction, prediction_value) + resolution = resolve_meta_prediction( + prediction, + arguments.actual_archive_gain, + arguments.non_regression == "true", + arguments.resolved_at, + ) + write_json_atomic(Path(arguments.output), as_json_value(resolution)) + memory = ResearchMemory(Path(arguments.memory)) + memory.initialize() + memory.append( + "meta_resolution", + arguments.resolved_at, + cast(dict[str, JsonValue], resolution), + ) + print(f"resolution_id={resolution['resolution_id']}") + print(f"artifact={Path(arguments.output).resolve()}") + print(f"memory={Path(arguments.memory).resolve()}") + return 0 + + +def _command_literature_scan(arguments: argparse.Namespace) -> int: + query = effective_literature_query( + _load_literature_query(Path(arguments.manifest)), + arguments.ingestion_time, + ) + client = ArxivMetadataClient( + ARXIV_QUERY_ENDPOINT, + arguments.user_agent, + ) + feed = client.search(query, arguments.timeout_seconds, arguments.retries) + batch = build_literature_batch(feed, query, arguments.ingestion_time) + write_json_atomic(Path(arguments.output), as_json_value(batch)) + memory = ResearchMemory(Path(arguments.memory)) + memory.initialize() + _persist_literature_batch(memory, batch) + print(f"literature_batch_id={batch['batch_id']}") + print(f"paper_count={len(batch['papers'])}") + print(f"capsule_count={len(batch['capsules'])}") + print(f"artifact={Path(arguments.output).resolve()}") + return 0 + + +def _command_literature_replay(arguments: argparse.Namespace) -> int: + query = _load_literature_query(Path(arguments.manifest)) + if query["source_mode"] != "synthetic_fixture": + raise ValueError("literature replay requires synthetic_fixture source mode") + feed = Path(arguments.feed).read_bytes() + batch = build_literature_batch(feed, query, arguments.ingestion_time) + write_json_atomic(Path(arguments.output), as_json_value(batch)) + memory = ResearchMemory(Path(arguments.memory)) + memory.initialize() + _persist_literature_batch(memory, batch) + print(f"literature_batch_id={batch['batch_id']}") + print(f"paper_count={len(batch['papers'])}") + print(f"excluded_count={len(batch['exclusions'])}") + print(f"artifact={Path(arguments.output).resolve()}") + return 0 + + +def _command_literature_supervise(arguments: argparse.Namespace) -> int: + template = _load_literature_query(Path(arguments.manifest)) + if template["source_mode"] != "live_metadata": + raise ValueError("literature supervisor requires live_metadata source mode") + if template["cutoff_policy"] != "scan_time": + raise ValueError("literature supervisor requires scan_time cutoff policy") + if arguments.max_cycles <= 0 or arguments.max_cycles > 365: + raise ValueError( + "literature supervisor cycles must be within [1, 365]: " + f"max_cycles={arguments.max_cycles}" + ) + if arguments.poll_seconds < template["minimum_refresh_seconds"]: + raise ValueError( + "literature supervisor poll interval violates query refresh policy: " + f"poll_seconds={arguments.poll_seconds}, " + f"minimum_refresh_seconds={template['minimum_refresh_seconds']}" + ) + outbox = Path(arguments.outbox).resolve() + outbox.mkdir(parents=True, exist_ok=True) + memory = ResearchMemory(Path(arguments.memory)) + memory.initialize() + batch_ids: list[str] = [] + paper_ids: set[str] = set() + last_batch: LiteratureLearningBatch | None = None + for cycle in range(arguments.max_cycles): + ingestion_time = utc_iso(datetime.now(UTC)) + query = effective_literature_query(template, ingestion_time) + client = ArxivMetadataClient( + ARXIV_QUERY_ENDPOINT, + arguments.user_agent, + ) + feed = client.search(query, arguments.timeout_seconds, arguments.retries) + batch = build_literature_batch(feed, query, ingestion_time) + output_path = outbox / f"{batch['batch_id'].replace(':', '-')}.json" + write_json_atomic(output_path, as_json_value(batch)) + _persist_literature_batch(memory, batch) + batch_ids.append(batch["batch_id"]) + paper_ids.update(paper["paper_id"] for paper in batch["papers"]) + last_batch = batch + if cycle + 1 < arguments.max_cycles: + time.sleep(arguments.poll_seconds) + if last_batch is None: + raise RuntimeError("literature supervisor completed without a batch") + state_body: dict[str, JsonValue] = { + "schema_version": "jester-literature-supervisor-state-v1", + "cycle_count": arguments.max_cycles, + "batch_ids": as_json_value(batch_ids), + "paper_ids": as_json_value(sorted(paper_ids)), + "next_eligible_scan_time": next_scan_time(last_batch), + "status": "completed", + } + state_hash = canonical_hash(as_json_value(state_body)) + state = { + **state_body, + "supervisor_id": f"literature-supervisor:{state_hash[:24]}", + "content_hash": state_hash, + } + write_json_atomic(Path(arguments.state), as_json_value(state)) + print(f"supervisor_id={state['supervisor_id']}") + print(f"cycle_count={arguments.max_cycles}") + print(f"unique_paper_count={len(paper_ids)}") + return 0 + + +def _command_attention_scrape(arguments: argparse.Namespace) -> int: + manifest = _load_attention_manifest(Path(arguments.manifest)) + scraper = PermittedMetadataScraper(manifest["policy"]) + snapshot = scraper.scrape( + manifest, + arguments.timeout_seconds, + arguments.retries, + ) + write_json_atomic(Path(arguments.output), as_json_value(snapshot)) + memory = ResearchMemory(Path(arguments.memory)) + memory.initialize() + memory.append( + "search_attention_snapshot", + snapshot["observed_at"], + cast(dict[str, JsonValue], snapshot), + ) + print(f"snapshot_id={snapshot['snapshot_id']}") + print(f"item_count={len(snapshot['items'])}") + print(f"artifact={Path(arguments.output).resolve()}") + return 0 + + +def _command_attention_compare(arguments: argparse.Namespace) -> int: + prior_value = read_json(Path(arguments.prior)) + current_value = read_json(Path(arguments.current)) + if not isinstance(prior_value, dict) or not isinstance(current_value, dict): + raise TypeError("attention comparison inputs must be JSON objects") + delta = compare_attention( + cast(SearchAttentionSnapshot, prior_value), + cast(SearchAttentionSnapshot, current_value), + ) + write_json_atomic(Path(arguments.output), as_json_value(delta)) + memory = ResearchMemory(Path(arguments.memory)) + memory.initialize() + current = cast(SearchAttentionSnapshot, current_value) + memory.append( + "search_attention_delta", + current["observed_at"], + cast(dict[str, JsonValue], delta), + ) + print(f"attention_delta_id={delta['delta_id']}") + print(f"artifact={Path(arguments.output).resolve()}") + return 0 + + def _command_sign(arguments: argparse.Namespace) -> int: loaded = read_json(Path(arguments.input)) if not isinstance(loaded, dict): @@ -181,6 +528,130 @@ def _parser() -> argparse.ArgumentParser: run.add_argument("--output", required=True) run.set_defaults(handler=_command_run) + catalog = commands.add_parser( + "catalog", + help="Write the explicit simulator and market-pack capability catalog.", + ) + catalog.add_argument("--output", required=True) + catalog.set_defaults(handler=_command_catalog) + + meta_run = commands.add_parser( + "meta-run", + help="Run protected recursive hypothesis research over one base fixture.", + ) + meta_run.add_argument("--input", required=True) + meta_run.add_argument("--output", required=True) + meta_run.add_argument("--memory", required=True) + meta_run.add_argument("--created-at", required=True) + meta_run.add_argument("--pack", required=True) + meta_run.add_argument("--generations", required=True, type=int) + meta_run.add_argument("--candidate-budget", required=True, type=int) + meta_run.add_argument("--islands", required=True, type=int) + meta_run.add_argument("--root-seed", required=True, type=int) + meta_run.add_argument("--cognition-budget", required=True, type=int) + meta_run.add_argument("--dream-cycles", required=True, type=int) + meta_run.add_argument("--dream-temperature", required=True, type=float) + meta_run.add_argument("--forward-outcomes", required=True, type=int) + meta_run.add_argument("--paper-outcomes", required=True, type=int) + meta_run.add_argument("--required-forward-outcomes", required=True, type=int) + meta_run.add_argument("--required-paper-outcomes", required=True, type=int) + meta_run.add_argument("--resimulated-descendants", required=True, type=int) + meta_run.add_argument("--gap-observations", required=True, type=int) + meta_run.add_argument("--gap-pressure", required=True, type=float) + meta_run.set_defaults(handler=_command_meta_run) + + cognition_run = commands.add_parser( + "cognition-run", + help="Run the bounded dream, invention, immune, and failure-memory arsenal.", + ) + cognition_run.add_argument("--input", required=True) + cognition_run.add_argument("--output", required=True) + cognition_run.add_argument("--memory", required=True) + cognition_run.add_argument("--literature-batch") + cognition_run.add_argument("--created-at", required=True) + cognition_run.add_argument("--root-seed", required=True, type=int) + cognition_run.add_argument("--dream-budget", required=True, type=int) + cognition_run.add_argument("--authorized-dream-budget", required=True, type=int) + cognition_run.add_argument("--dream-cycles", required=True, type=int) + cognition_run.add_argument("--dream-temperature", required=True, type=float) + cognition_run.set_defaults(handler=_command_cognition_run) + + meta_resolve = commands.add_parser( + "meta-resolve", + help="Resolve a precommitted meta prediction and persist its proper score.", + ) + meta_resolve.add_argument("--input", required=True) + meta_resolve.add_argument("--output", required=True) + meta_resolve.add_argument("--memory", required=True) + meta_resolve.add_argument("--actual-archive-gain", required=True, type=float) + meta_resolve.add_argument( + "--non-regression", + required=True, + choices=("true", "false"), + ) + meta_resolve.add_argument("--resolved-at", required=True) + meta_resolve.set_defaults(handler=_command_meta_resolve) + + literature_scan = commands.add_parser( + "literature-scan", + help="Fetch one bounded arXiv metadata batch and generate research capsules.", + ) + literature_scan.add_argument("--manifest", required=True) + literature_scan.add_argument("--output", required=True) + literature_scan.add_argument("--memory", required=True) + literature_scan.add_argument("--ingestion-time", required=True) + literature_scan.add_argument("--user-agent", required=True) + literature_scan.add_argument("--timeout-seconds", required=True, type=float) + literature_scan.add_argument("--retries", required=True, type=int) + literature_scan.set_defaults(handler=_command_literature_scan) + + literature_replay = commands.add_parser( + "literature-replay", + help="Replay a bounded synthetic Atom feed through the literature learner.", + ) + literature_replay.add_argument("--manifest", required=True) + literature_replay.add_argument("--feed", required=True) + literature_replay.add_argument("--output", required=True) + literature_replay.add_argument("--memory", required=True) + literature_replay.add_argument("--ingestion-time", required=True) + literature_replay.set_defaults(handler=_command_literature_replay) + + literature_supervise = commands.add_parser( + "literature-supervise", + help="Run a bounded daily scientific-metadata learning loop.", + ) + literature_supervise.add_argument("--manifest", required=True) + literature_supervise.add_argument("--outbox", required=True) + literature_supervise.add_argument("--state", required=True) + literature_supervise.add_argument("--memory", required=True) + literature_supervise.add_argument("--user-agent", required=True) + literature_supervise.add_argument("--timeout-seconds", required=True, type=float) + literature_supervise.add_argument("--retries", required=True, type=int) + literature_supervise.add_argument("--max-cycles", required=True, type=int) + literature_supervise.add_argument("--poll-seconds", required=True, type=float) + literature_supervise.set_defaults(handler=_command_literature_supervise) + + attention_scrape = commands.add_parser( + "attention-scrape", + help="Collect robots-permitted metadata for a staged ranked-search manifest.", + ) + attention_scrape.add_argument("--manifest", required=True) + attention_scrape.add_argument("--output", required=True) + attention_scrape.add_argument("--memory", required=True) + attention_scrape.add_argument("--timeout-seconds", required=True, type=float) + attention_scrape.add_argument("--retries", required=True, type=int) + attention_scrape.set_defaults(handler=_command_attention_scrape) + + attention_compare = commands.add_parser( + "attention-compare", + help="Measure non-causal rank, churn, and metadata attention changes.", + ) + attention_compare.add_argument("--prior", required=True) + attention_compare.add_argument("--current", required=True) + attention_compare.add_argument("--output", required=True) + attention_compare.add_argument("--memory", required=True) + attention_compare.set_defaults(handler=_command_attention_compare) + sign = commands.add_parser("sign", help="Sign a governor candidate bundle.") sign.add_argument("--input", required=True) sign.add_argument("--output", required=True) diff --git a/src/jester/cognition.py b/src/jester/cognition.py new file mode 100644 index 0000000..f1fd013 --- /dev/null +++ b/src/jester/cognition.py @@ -0,0 +1,1018 @@ +"""Deterministic open-ended cognition with explicit synthetic and authority boundaries.""" + +from __future__ import annotations + +from collections.abc import Iterable +from typing import cast + +from .cognition_types import ( + CognitionArsenalResult, + CognitionFamily, + CognitionModeSpec, + CognitiveArchiveCell, + CognitiveImmuneFinding, + CognitiveScore, + DreamPhase, + DreamWorld, + FailureMuseumEntry, + InventionBlueprint, + ScientificCognitionSeed, +) +from .identity import as_json_value, canonical_hash +from .literature_types import ResearchCapsule +from .rng import unit +from .types import HypothesisGenome + +_MODE_ROWS: tuple[ + tuple[str, str, CognitionFamily, str, tuple[str, ...], tuple[str, ...], tuple[str, ...]], + ..., +] = ( + ( + "abductive_mechanism_search", + "Abductive Mechanist", + "causal", + "Rank competing mechanisms that could explain a residual without declaring causality.", + ("world_state", "residuals", "causal_graph_disagreement"), + ("generate_competing_causes", "seek_discriminating_observation"), + ("story_bias", "causal_overreach"), + ), + ( + "causal_analogical_transfer", + "Structural Analogist", + "analogical", + "Transfer relations from another domain only when the structural mapping is explicit.", + ("source_domain", "target_graph", "broken_analogy_tests"), + ("map_roles_not_labels", "enumerate_mapping_failures"), + ("surface_similarity", "domain_leakage"), + ), + ( + "counterfactual_dreaming", + "Counterfactual Dreamer", + "counterfactual", + "Imagine minimally different worlds and isolate the variable that changes the outcome.", + ("causal_graphs", "intervention_set", "invariants"), + ("minimal_intervention", "contrast_outcomes"), + ("impossible_world", "hidden_cointervention"), + ), + ( + "dialectical_synthesis", + "Dialectical Synthesist", + "dialectical", + "Force a thesis and strongest antithesis into a testable synthesis.", + ("thesis", "contradicting_evidence", "resolution_tests"), + ("steelman_opposition", "synthesize_conditional_claim"), + ("false_balance", "verbal_compromise"), + ), + ( + "red_team_inversion", + "Adversarial Inverter", + "adversarial", + "Invert direction, mechanism, timing, and beneficiary to expose fragile assumptions.", + ("hypothesis", "failure_library", "counterparty_incentives"), + ("invert_each_axis", "retain_strongest_attack"), + ("performative_skepticism", "unbounded_pessimism"), + ), + ( + "missing_variable_discovery", + "Latent Variable Hunter", + "scientific", + "Propose a measurable omitted variable that could explain recurring error.", + ("residuals", "failed_lineages", "available_measurements"), + ("cluster_failures", "name_measurable_latent"), + ("unobservable_construct", "post_hoc_variable"), + ), + ( + "constraint_relaxation_reinstatement", + "Constraint Sculptor", + "divergent", + "Temporarily relax one constraint, invent freely, then reinstate every hard boundary.", + ("hard_constraints", "soft_constraints", "goal"), + ("relax_one_soft_constraint", "project_back_to_feasible_space"), + ("authority_leakage", "infeasible_solution"), + ), + ( + "compositional_concept_blending", + "Concept Blender", + "analogical", + "Compose two mechanism primitives and retain only interactions with a measurable trace.", + ("mechanism_library", "interaction_tests", "evidence_plane"), + ("compose_primitives", "derive_interaction_signature"), + ("word_salad", "duplicate_mechanism"), + ), + ( + "adversarial_game_imagination", + "Strategic Worldsmith", + "strategic", + "Model how informed counterparties adapt after observing the candidate signal.", + ("participant_classes", "information_sets", "payoffs"), + ("rotate_information_advantage", "solve_counterresponse"), + ("anthropomorphic_agents", "equilibrium_fiction"), + ), + ( + "temporal_zoom", + "Temporal Microscopist", + "temporal", + "Reframe one mechanism across event, session, overnight, and expiry horizons.", + ("event_clock", "market_calendar", "signal_half_life"), + ("expand_horizon", "compress_horizon"), + ("clock_confusion", "look_ahead"), + ), + ( + "perspective_rotation", + "Participant Lens", + "strategic", + "Re-evaluate the same evidence from buyer, seller, dealer, and forced-flow perspectives.", + ("participant_classes", "constraints", "inventory_state"), + ("rotate_objective", "compare_action_thresholds"), + ("false_empathy", "correlated_views"), + ), + ( + "anomaly_residual_mining", + "Residual Cartographer", + "scientific", + "Search unexplained model error for stable, measurable structure.", + ("simulation_gap", "calibration_residuals", "regime_labels"), + ("stratify_residuals", "test_recurrence"), + ("noise_fitting", "multiple_testing"), + ), + ( + "negative_space_search", + "Negative-Space Explorer", + "divergent", + "Study expected events, reactions, or participants that are conspicuously absent.", + ("expected_reactions", "observed_reactions", "availability_checks"), + ("enumerate_absences", "separate_missing_from_unobserved"), + ("absence_as_evidence", "coverage_blindness"), + ), + ( + "surprise_search", + "Expectation Breaker", + "divergent", + "Seek behavior that deviates from the archive's prediction, not merely prior examples.", + ("archive_trajectory", "expected_descriptor", "surprise_budget"), + ("predict_next_niche", "search_away_from_prediction"), + ("randomness_as_novelty", "coverage_stagnation"), + ), + ( + "minimum_description_length", + "Compression Theorist", + "compressive", + "Prefer mechanisms that explain more observations with fewer independent assumptions.", + ("candidate_mechanisms", "observations", "assumption_count"), + ("compress_explanation", "penalize_exception_count"), + ("oversimplification", "measurement_erasure"), + ), + ( + "decomposition_recomposition", + "Mechanism Architect", + "compressive", + "Split the thesis into independently testable links, then recombine only surviving links.", + ("causal_chain", "link_tests", "dependency_graph"), + ("decompose_links", "recompose_survivors"), + ("interaction_loss", "interface_mismatch"), + ), + ( + "anti_goal_design", + "Failure Engineer", + "adversarial", + "Design the most plausible world in which every success metric is misleading.", + ("success_metrics", "adversarial_worlds", "gate_definitions"), + ("invert_metric", "construct_goodhart_case"), + ("implausible_attack", "nihilistic_rejection"), + ), + ( + "auction_market_analogy", + "Auction Analogist", + "analogical", + "Map information asymmetry, reservation values, and winner's curse into option selection.", + ("quotes", "counterparty_model", "fill_model"), + ("map_reservation_values", "test_winners_curse"), + ("market_structure_mismatch", "static_analogy"), + ), + ( + "ecological_niche_transfer", + "Market Ecologist", + "analogical", + "Treat strategy families as niche-bound populations with competition and regime resources.", + ("quality_diversity_archive", "regimes", "resource_constraints"), + ("map_niches", "test_invasion_resistance"), + ("biological_literalism", "unstable_descriptor"), + ), + ( + "option_seller_mirror", + "Option Seller Mirror", + "strategic", + "Construct the seller's strongest priced-risk explanation for apparently cheap convexity.", + ("option_surface", "event_distribution", "execution_constraints"), + ("price_hidden_cost", "compare_buyer_seller_sensitivities"), + ("omniscient_seller", "ignored_inventory"), + ), + ( + "falsification_first_induction", + "Popperian Challenger", + "scientific", + "Define decisive refuters before refining the positive story.", + ("hypothesis", "measurement_set", "decision_thresholds"), + ("write_falsifiers_first", "prefer_severe_test"), + ("impossible_threshold", "asymmetric_testing"), + ), + ( + "adversarial_collaboration", + "Red-Blue Collaborator", + "dialectical", + "Alternate construction and attack while preserving the attacker's independent objective.", + ("candidate", "red_team", "evaluation_contract"), + ("construct", "attack", "repair_without_gate_change"), + ("evaluator_capture", "collusive_consensus"), + ), + ( + "prospective_hindsight", + "Premortem Historian", + "temporal", + "Assume the idea failed in the future and reconstruct the earliest observable warning.", + ("candidate", "failure_modes", "event_sequence"), + ("declare_failure", "backchain_warning_signals"), + ("hindsight_fiction", "unmeasurable_warning"), + ), + ( + "mechanism_swap", + "Mechanism Swapper", + "causal", + "Hold the forecast fixed while replacing its causal explanation to test identifiability.", + ("forecast", "candidate_mechanisms", "discriminating_tests"), + ("preserve_prediction", "swap_mechanism", "seek_separating_test"), + ("equivalent_rewording", "underdetermination"), + ), +) + +_ANALOGIES: tuple[tuple[str, dict[str, str]], ...] = ( + ( + "poker_information_asymmetry", + { + "private_cards": "latent_information", + "bet_size": "order_flow_intensity", + "pot_odds": "option_premium_to_payoff", + }, + ), + ( + "sports_line_movement", + { + "injury_news": "catalyst_information", + "bookmaker_line": "option_surface", + "late_money": "informed_flow", + }, + ), + ( + "ecological_invasion", + { + "resource_niche": "market_regime", + "invasive_species": "new_strategy_family", + "carrying_capacity": "available_liquidity", + }, + ), + ( + "auction_winners_curse", + { + "private_value": "subjective_scenario_value", + "winning_bid": "executed_option_premium", + "informed_rival": "adverse_counterparty", + }, + ), + ( + "fault_tolerant_engineering", + { + "component_failure": "causal_link_failure", + "redundancy": "independent_evidence", + "load_test": "adversarial_simulation", + }, + ), + ( + "immune_system_memory", + { + "antigen": "failure_signature", + "antibody": "rejection_test", + "immune_memory": "failure_museum", + }, + ), +) + +_PHASES: tuple[DreamPhase, ...] = ( + "rem_divergence", + "nrem_consolidation", + "lucid_intervention", + "awakening_debrief", +) +EVIDENCE_DEBT_PENALTY_PER_ITEM = 0.04 +INVENTION_ARCHIVE_UTILITY_FLOOR = 0.65 + +_THEME_MODE_MAP: dict[str, tuple[str, ...]] = { + "causal_market_simulation": ( + "abductive_mechanism_search", + "counterfactual_dreaming", + "mechanism_swap", + ), + "order_book_generation": ( + "adversarial_game_imagination", + "perspective_rotation", + "anomaly_residual_mining", + ), + "volatility_surface": ( + "option_seller_mirror", + "falsification_first_induction", + "temporal_zoom", + ), + "rough_and_jump_volatility": ( + "temporal_zoom", + "anti_goal_design", + "prospective_hindsight", + ), + "agentic_scientific_discovery": ( + "adversarial_collaboration", + "decomposition_recomposition", + "minimum_description_length", + ), + "evolutionary_discovery": ( + "surprise_search", + "ecological_niche_transfer", + "constraint_relaxation_reinstatement", + ), + "calibration_and_abstention": ( + "falsification_first_induction", + "negative_space_search", + "prospective_hindsight", + ), +} + + +def cognition_mode_catalog() -> list[CognitionModeSpec]: + """Return the immutable, explicitly evaluated cognitive operator catalog.""" + catalog: list[CognitionModeSpec] = [] + for mode_id, name, family, purpose, inputs, moves, risks in _MODE_ROWS: + body = { + "schema_version": "jester-cognition-mode-v1", + "mode_id": mode_id, + "name": name, + "family": family, + "purpose": purpose, + "required_inputs": list(inputs), + "cognitive_moves": list(moves), + "evaluator_ids": [ + "mechanism_coherence", + "falsifiability", + "evidence_reachability", + "reasoning_independence", + ], + "failure_risks": list(risks), + "limitations": [ + "Operator output is synthetic until independently tested.", + "A linguistic variation is not evidence of a distinct mechanism.", + ], + "authority_effect": "none", + } + catalog.append( + cast( + CognitionModeSpec, + { + **body, + "content_hash": canonical_hash(as_json_value(body)), + }, + ) + ) + return catalog + + +def build_scientific_cognition_seeds( + capsules: list[ResearchCapsule], +) -> list[ScientificCognitionSeed]: + """Convert admitted zero-credit paper capsules into bounded invention seeds.""" + seeds: list[ScientificCognitionSeed] = [] + for capsule in capsules: + if ( + not capsule["candidate_mechanisms"] + or not capsule["experiment_blueprints"] + or not capsule["falsification_questions"] + ): + continue + recommended_modes = sorted( + { + mode_id + for theme in capsule["matched_themes"] + for mode_id in _THEME_MODE_MAP.get(theme, ()) + } + ) + body = { + "schema_version": "jester-scientific-cognition-seed-v1", + "capsule_id": capsule["capsule_id"], + "paper_id": capsule["paper_id"], + "trust_state": "unverified_scientific_claim", + "matched_themes": capsule["matched_themes"], + "candidate_mechanism": capsule["candidate_mechanisms"][0], + "target_systems": capsule["target_systems"], + "experiment_blueprint": capsule["experiment_blueprints"][0], + "falsification_question": capsule["falsification_questions"][0], + "recommended_mode_ids": recommended_modes, + "requires_independent_replication": True, + "auto_apply": False, + "empirical_credit": False, + } + content_hash = canonical_hash(as_json_value(body)) + seeds.append( + cast( + ScientificCognitionSeed, + { + **body, + "seed_id": f"scientific-cognition-seed:{content_hash[:24]}", + "content_hash": content_hash, + }, + ) + ) + return seeds + + +def _clamp(value: float) -> float: + return round(max(0.0, min(1.0, value)), 6) + + +def _select_modes( + catalog: list[CognitionModeSpec], + root_seed: int, + candidate_index: int, + cycle: int, + temperature: float, +) -> list[CognitionModeSpec]: + width = 2 + int(temperature >= 0.45) + int(temperature >= 0.8) + first_index = (candidate_index * 7 + cycle * 11 + root_seed) % len(catalog) + selected: list[CognitionModeSpec] = [] + for position in range(width): + offset = 1 + int( + unit(root_seed, "cognition-mode", candidate_index, cycle, position) * (len(catalog) - 1) + ) + index = (first_index + position * offset) % len(catalog) + while catalog[index] in selected: + index = (index + 1) % len(catalog) + selected.append(catalog[index]) + return selected + + +def _score_dream( + root_seed: int, + dream_index: int, + modes: list[CognitionModeSpec], + evidence_debt_count: int, +) -> CognitiveScore: + family_count = len({mode["family"] for mode in modes}) + mode_diversity = family_count / len(modes) + novelty = _clamp(0.46 + unit(root_seed, "novelty", dream_index) * 0.43) + surprise = _clamp(0.35 + unit(root_seed, "surprise", dream_index) * 0.55) + compression = _clamp(0.48 + unit(root_seed, "compression", dream_index) * 0.38) + coherence = _clamp(0.55 + unit(root_seed, "coherence", dream_index) * 0.34) + falsifiability = _clamp(0.62 + unit(root_seed, "falsifiability", dream_index) * 0.3) + reachability = _clamp(0.5 + unit(root_seed, "reachability", dream_index) * 0.39) + option_relevance = _clamp(0.66 + unit(root_seed, "option-relevance", dream_index) * 0.29) + robustness = _clamp(0.45 + unit(root_seed, "robustness", dream_index) * 0.43) + independence = _clamp(0.42 + mode_diversity * 0.42) + debt_penalty = _clamp(evidence_debt_count * EVIDENCE_DEBT_PENALTY_PER_ITEM) + utility = _clamp( + novelty * 0.12 + + surprise * 0.08 + + compression * 0.1 + + coherence * 0.13 + + falsifiability * 0.15 + + reachability * 0.12 + + option_relevance * 0.14 + + robustness * 0.1 + + independence * 0.06 + - debt_penalty + ) + return { + "novelty": novelty, + "surprise": surprise, + "explanatory_compression": compression, + "mechanism_coherence": coherence, + "falsifiability": falsifiability, + "evidence_reachability": reachability, + "option_relevance": option_relevance, + "adversarial_robustness": robustness, + "reasoning_independence": independence, + "evidence_debt_penalty": debt_penalty, + "utility": utility, + } + + +def _build_dream( + parent: HypothesisGenome, + catalog: list[CognitionModeSpec], + root_seed: int, + candidate_index: int, + cycle: int, + temperature: float, + scientific_seed: ScientificCognitionSeed | None, + prior_dream: DreamWorld | None, +) -> DreamWorld: + modes = _select_modes(catalog, root_seed, candidate_index, cycle, temperature) + analogy, mapping = _ANALOGIES[ + int(unit(root_seed, "analogy", candidate_index, cycle) * len(_ANALOGIES)) % len(_ANALOGIES) + ] + primary = modes[0] + secondary = modes[1] + base_mechanism = ( + f"{primary['mode_id']} proposes that {parent['catalyst']} repricing depends on " + f"a latent interaction between {secondary['mode_id']} and " + f"{parent['required_regime']}, expressed through an observable sequence of " + "belief revision, constrained liquidity, underlying response, and surface repricing." + ) + seeded_mechanism = ( + base_mechanism + if scientific_seed is None + else ( + f"{base_mechanism} An explicitly unverified scientific seed " + f"{scientific_seed['paper_id']} contributes the candidate mechanism " + f"{scientific_seed['candidate_mechanism']}." + ) + ) + mechanism = ( + seeded_mechanism + if prior_dream is None + else ( + f"{seeded_mechanism} This recursive cycle recombines the surviving structural " + f"trace from {prior_dream['dream_id']} while retaining its falsification burden." + ) + ) + countermechanism = ( + "The same observations arise from already-priced public information, endogenous " + "volatility demand, or price-following attention without incremental causal content." + ) + evidence_debt = [ + f"point_in_time_measurement:{primary['mode_id']}", + f"held_out_discriminator:{secondary['mode_id']}", + ] + if temperature >= 0.8: + evidence_debt.append(f"cross_domain_transfer_check:{analogy}") + if scientific_seed is not None: + evidence_debt.append(f"independent_replication_required:{scientific_seed['paper_id']}") + phase = _PHASES[cycle % len(_PHASES)] + scores = _score_dream(root_seed, candidate_index, modes, len(evidence_debt)) + selected_failure_mode = parent["expected_failure_modes"][ + candidate_index % len(parent["expected_failure_modes"]) + ] + niche = ( + f"{primary['family']}:{phase}:" + f"{parent['option_expression']['option_type']}:" + f"{selected_failure_mode}" + ) + fingerprint = canonical_hash( + as_json_value( + { + "families": sorted(mode["family"] for mode in modes), + "analogy": analogy, + "niche": niche, + "parent_fingerprint": ( + "root" if prior_dream is None else prior_dream["lineage_fingerprint"] + ), + "mechanism_tokens": sorted( + { + primary["mode_id"], + secondary["mode_id"], + parent["catalyst"], + parent["required_regime"], + } + ), + } + ) + ) + body = { + "schema_version": "jester-dream-world-v1", + "cycle": cycle, + "phase": phase, + "parent_hypothesis_id": parent["hypothesis_id"], + "parent_dream_ids": [] if prior_dream is None else [prior_dream["dream_id"]], + "mode_ids": [mode["mode_id"] for mode in modes], + "synthetic_only": True, + "stress_only": True, + "empirical_credit": False, + "referenced_evidence_ids": parent["point_in_time_evidence_ids"], + "invented_mechanism": mechanism, + "countermechanism": countermechanism, + "cross_domain_analogy": analogy, + "structural_mapping": mapping, + "assumptions": [ + "Every source clock and evidence cutoff remains point-in-time valid.", + "The analogy transfers relations only; it does not transfer empirical frequency.", + "Participant policies are numerical stress policies, not claims of human realism.", + ], + "interventions": [ + f"activate:{primary['mode_id']}", + f"challenge:{secondary['mode_id']}", + "reapply_all_authority_and_holdability_constraints", + *( + [] + if scientific_seed is None + else [f"paper_seed_ablation:{scientific_seed['seed_id']}"] + ), + *( + [] + if prior_dream is None + else [f"recursive_lineage_ablation:{prior_dream['dream_id']}"] + ), + ], + "expected_paths": { + "state": f"conditional_regime_transition:{parent['required_regime']}", + "underlying": parent["expected_underlying_path"], + "iv_surface": parent["expected_iv_path"], + "narrative": parent["expected_narrative_path"], + }, + "failure_modes": [ + parent["expected_failure_modes"][ + candidate_index % len(parent["expected_failure_modes"]) + ], + "analogy_structure_breaks", + "mechanism_is_not_identifiable", + ], + "falsifiers": [ + "The proposed discriminating measurement does not precede repricing out of sample.", + "Ablating the invented mechanism leaves held-out outcome distributions unchanged.", + "The countermechanism explains the same paths with lower evidence debt.", + ], + "evidence_debt": evidence_debt, + "niche": niche, + "lineage_fingerprint": fingerprint, + "scores": scores, + } + content_hash = canonical_hash(as_json_value(body)) + return cast( + DreamWorld, + { + **body, + "dream_id": f"dream:{content_hash[:24]}", + "content_hash": content_hash, + }, + ) + + +def evaluate_cognitive_immune_system( + dream: DreamWorld, + allowed_evidence_ids: Iterable[str], +) -> list[CognitiveImmuneFinding]: + """Evaluate authority, evidence, causal, duplication, and falsification hygiene.""" + allowed = set(allowed_evidence_ids) + checks: tuple[tuple[str, bool, str], ...] = ( + ( + "synthetic_label_integrity", + dream["synthetic_only"] and dream["stress_only"] and not dream["empirical_credit"], + "Dreams must remain synthetic stress artifacts with zero empirical credit.", + ), + ( + "authority_containment", + all( + forbidden not in dream["invented_mechanism"].lower() + for forbidden in ("submit order", "broker credential", "live execution") + ), + "Dream text may not introduce execution or credential authority.", + ), + ( + "falsifiability", + len(dream["falsifiers"]) >= 2 + and all(bool(item.strip()) for item in dream["falsifiers"]), + "Every dream requires at least two explicit refuters.", + ), + ( + "countermechanism_independence", + dream["invented_mechanism"].strip().lower() + != dream["countermechanism"].strip().lower(), + "The countermechanism must differ from the proposed mechanism.", + ), + ( + "evidence_lineage", + set(dream["referenced_evidence_ids"]).issubset(allowed), + "Only parent evidence lineage may receive evidence status.", + ), + ( + "analogy_boundary", + "does not transfer empirical frequency" in " ".join(dream["assumptions"]), + "Cross-domain analogies must deny automatic empirical transfer.", + ), + ( + "evidence_debt_visibility", + len(dream["evidence_debt"]) > 0, + "Every synthetic invention must expose unresolved evidence debt.", + ), + ) + findings: list[CognitiveImmuneFinding] = [] + for check_id, passed, detail in checks: + body = { + "dream_id": dream["dream_id"], + "check_id": check_id, + "status": "pass" if passed else "quarantine", + "reason_codes": [] if passed else [f"{check_id}_failed"], + "detail": detail, + } + content_hash = canonical_hash(as_json_value(body)) + findings.append( + cast( + CognitiveImmuneFinding, + { + **body, + "finding_id": f"cognitive-finding:{content_hash[:24]}", + "content_hash": content_hash, + }, + ) + ) + return findings + + +def _blueprint(dream: DreamWorld) -> InventionBlueprint: + body = { + "schema_version": "jester-invention-blueprint-v1", + "dream_id": dream["dream_id"], + "parent_hypothesis_id": dream["parent_hypothesis_id"], + "cognition_lineage": [*dream["parent_dream_ids"], *dream["mode_ids"]], + "problem_gap": dream["evidence_debt"][0], + "proposed_mechanism": dream["invented_mechanism"], + "countermechanism": dream["countermechanism"], + "novelty_claim_boundary": ( + "Novelty is asserted only relative to this run's lineage fingerprints; " + "no patentability, literature-exhaustiveness, or state-of-the-art claim is made." + ), + "cross_domain_analogy": dream["cross_domain_analogy"], + "structural_mapping": dream["structural_mapping"], + "assumptions": dream["assumptions"], + "expected_state_paths": dream["expected_paths"], + "option_expression_implication": ( + "Re-simulate the parent long-option expression under the invented mechanism and " + "its countermechanism; this blueprint does not select or construct an order." + ), + "evidence_debt": dream["evidence_debt"], + "required_measurements": [ + "point_in_time_feature_with_availability_clock", + "joint_underlying_iv_liquidity_response", + "held_out_countermechanism_discriminator", + ], + "minimal_decisive_experiment": ( + "Freeze evidence cutoffs, run paired causal ablation across held-out regimes, " + "and reject unless the invented mechanism improves post-cost calibration without " + "degrading the frozen baseline." + ), + "falsifiers": dream["falsifiers"], + "failure_modes": dream["failure_modes"], + "transfer_tests": [ + "regime_transplant_with_unchanged_evaluators", + "social_amplification_ablation", + "option_seller_counterfactual", + ], + "prior_art_challenge": ( + "Search admitted scientific and internal lineage indexes for mechanism-equivalent " + "work before assigning novelty credit." + ), + "scores": dream["scores"], + "niche": dream["niche"], + "synthetic_only": True, + "auto_apply": False, + "requires_independent_resimulation": True, + "empirical_credit": False, + "live_blocked": True, + "order_construction_allowed": False, + } + content_hash = canonical_hash(as_json_value(body)) + return cast( + InventionBlueprint, + { + **body, + "invention_id": f"invention:{content_hash[:24]}", + "content_hash": content_hash, + }, + ) + + +def _archive_inventions( + inventions: list[InventionBlueprint], +) -> tuple[ + dict[str, CognitiveArchiveCell], + list[InventionBlueprint], + list[InventionBlueprint], +]: + archive: dict[str, CognitiveArchiveCell] = {} + displaced: list[InventionBlueprint] = [] + below_threshold: list[InventionBlueprint] = [] + for invention in inventions: + if invention["scores"]["utility"] < INVENTION_ARCHIVE_UTILITY_FLOOR: + below_threshold.append(invention) + continue + incumbent = archive.get(invention["niche"]) + if incumbent is None: + body = { + "cell_key": invention["niche"], + "elite": invention, + "displaced_invention_ids": [], + } + archive[invention["niche"]] = cast( + CognitiveArchiveCell, + { + **body, + "content_hash": canonical_hash(as_json_value(body)), + }, + ) + continue + if incumbent["elite"]["scores"]["utility"] >= invention["scores"]["utility"]: + displaced.append(invention) + continue + displaced.append(incumbent["elite"]) + displaced_ids = [ + *incumbent["displaced_invention_ids"], + incumbent["elite"]["invention_id"], + ] + body = { + "cell_key": invention["niche"], + "elite": invention, + "displaced_invention_ids": displaced_ids, + } + archive[invention["niche"]] = cast( + CognitiveArchiveCell, + { + **body, + "content_hash": canonical_hash(as_json_value(body)), + }, + ) + return archive, displaced, below_threshold + + +def _museum_entries( + displaced_inventions: list[InventionBlueprint], + below_threshold_inventions: list[InventionBlueprint], + quarantined_dreams: list[DreamWorld], +) -> list[FailureMuseumEntry]: + rows: list[tuple[str, str, str, str]] = [ + ( + item["invention_id"], + item["dream_id"], + "dominated_but_informative", + "A higher-utility invention occupied the same behavioral niche.", + ) + for item in displaced_inventions + ] + rows.extend( + ( + item["invention_id"], + item["dream_id"], + "beautiful_but_under_evidenced", + "The mechanism was preserved but did not clear the frozen utility floor.", + ) + for item in below_threshold_inventions + ) + rows.extend( + ( + f"quarantined:{dream['dream_id']}", + dream["dream_id"], + "epistemic_immune_rejection", + "The idea failed a protected cognitive immune check.", + ) + for dream in quarantined_dreams + ) + entries: list[FailureMuseumEntry] = [] + for invention_id, dream_id, failure_class, lesson in rows: + body = { + "invention_id": invention_id, + "dream_id": dream_id, + "failure_class": failure_class, + "preserved_lesson": lesson, + "revival_condition": ( + "Revive only after new point-in-time evidence pays the recorded evidence debt " + "and an unchanged independent evaluator requests reconsideration." + ), + "empirical_credit": False, + } + content_hash = canonical_hash(as_json_value(body)) + entries.append( + cast( + FailureMuseumEntry, + { + **body, + "entry_id": f"failure-memory:{content_hash[:24]}", + "content_hash": content_hash, + }, + ) + ) + return entries + + +def run_cognition_arsenal( + parent: HypothesisGenome, + research_capsules: list[ResearchCapsule], + created_at: str, + root_seed: int, + requested_dream_budget: int, + authorized_dream_budget: int, + dream_cycles: int, + dream_temperature: float, +) -> CognitionArsenalResult: + """Run deterministic dream, immune, invention, and archive phases.""" + if requested_dream_budget <= 0: + raise ValueError( + "requested dream budget must be positive: " + f"requested_dream_budget={requested_dream_budget}" + ) + if not 0 < authorized_dream_budget <= requested_dream_budget: + raise ValueError( + "authorized dream budget must be within requested budget: " + f"authorized={authorized_dream_budget}, requested={requested_dream_budget}" + ) + if dream_cycles <= 0: + raise ValueError(f"dream cycles must be positive: dream_cycles={dream_cycles}") + if not 0.0 <= dream_temperature <= 1.0: + raise ValueError( + f"dream temperature must be within [0, 1]: dream_temperature={dream_temperature}" + ) + catalog = cognition_mode_catalog() + scientific_seeds = build_scientific_cognition_seeds(research_capsules) + dreams: list[DreamWorld] = [] + for candidate_index in range(authorized_dream_budget): + dreams.append( + _build_dream( + parent, + catalog, + root_seed, + candidate_index, + candidate_index % dream_cycles, + dream_temperature, + ( + None + if not scientific_seeds + else scientific_seeds[candidate_index % len(scientific_seeds)] + ), + None if not dreams else dreams[-1], + ) + ) + findings = [ + finding + for dream in dreams + for finding in evaluate_cognitive_immune_system( + dream, + parent["point_in_time_evidence_ids"], + ) + ] + quarantined_ids = { + finding["dream_id"] for finding in findings if finding["status"] == "quarantine" + } + fingerprints: set[str] = set() + eligible: list[DreamWorld] = [] + duplicate_dreams: list[DreamWorld] = [] + for dream in dreams: + if dream["dream_id"] in quarantined_ids: + continue + if dream["lineage_fingerprint"] in fingerprints: + duplicate_dreams.append(dream) + continue + fingerprints.add(dream["lineage_fingerprint"]) + eligible.append(dream) + inventions = [_blueprint(dream) for dream in eligible] + archive, displaced, below_threshold = _archive_inventions(inventions) + quarantined = [ + dream + for dream in dreams + if dream["dream_id"] in quarantined_ids or dream in duplicate_dreams + ] + museum = _museum_entries(displaced, below_threshold, quarantined) + covered_modes = {mode_id for dream in dreams for mode_id in dream["mode_ids"]} + coverage = _clamp(len(covered_modes) / len(catalog)) + surprise_consumed = round(sum(dream["scores"]["surprise"] for dream in dreams), 6) + epistemic_debt = sum(len(invention["evidence_debt"]) for invention in inventions) + catalog_hash = canonical_hash(as_json_value(catalog)) + authority: dict[str, bool | str] = { + "research_mode": "synthetic_proposal_only", + "may_generate_hypotheses": True, + "may_mutate_code": False, + "may_mutate_evaluators": False, + "may_auto_apply": False, + "may_claim_empirical_credit": False, + "live_blocked": True, + "order_construction_allowed": False, + "order_submission_allowed": False, + } + body = { + "schema_version": "jester-cognition-arsenal-v1", + "created_at": created_at, + "parent_hypothesis_id": parent["hypothesis_id"], + "mode_catalog_hash": catalog_hash, + "scientific_seeds": scientific_seeds, + "requested_dream_budget": requested_dream_budget, + "authorized_dream_budget": authorized_dream_budget, + "dream_cycles": dream_cycles, + "dream_temperature": round(dream_temperature, 6), + "dreams": dreams, + "immune_findings": findings, + "inventions": inventions, + "archive": archive, + "failure_museum": museum, + "coverage": coverage, + "surprise_consumed": surprise_consumed, + "epistemic_debt": epistemic_debt, + "authority": authority, + } + content_hash = canonical_hash(as_json_value(body)) + return cast( + CognitionArsenalResult, + { + **body, + "arsenal_run_id": f"cognition-run:{content_hash[:24]}", + "content_hash": content_hash, + }, + ) diff --git a/src/jester/cognition_types.py b/src/jester/cognition_types.py new file mode 100644 index 0000000..68fb116 --- /dev/null +++ b/src/jester/cognition_types.py @@ -0,0 +1,184 @@ +"""Typed contracts for Jester's bounded cognitive invention plane.""" + +from __future__ import annotations + +from typing import Literal, TypedDict + +type CognitionFamily = Literal[ + "causal", + "counterfactual", + "dialectical", + "analogical", + "strategic", + "temporal", + "compressive", + "divergent", + "adversarial", + "scientific", +] +type DreamPhase = Literal[ + "rem_divergence", + "nrem_consolidation", + "lucid_intervention", + "awakening_debrief", +] +type ImmuneStatus = Literal["pass", "quarantine"] + + +class CognitionModeSpec(TypedDict): + schema_version: Literal["jester-cognition-mode-v1"] + mode_id: str + name: str + family: CognitionFamily + purpose: str + required_inputs: list[str] + cognitive_moves: list[str] + evaluator_ids: list[str] + failure_risks: list[str] + limitations: list[str] + authority_effect: Literal["none"] + content_hash: str + + +class ScientificCognitionSeed(TypedDict): + schema_version: Literal["jester-scientific-cognition-seed-v1"] + seed_id: str + capsule_id: str + paper_id: str + trust_state: Literal["unverified_scientific_claim"] + matched_themes: list[str] + candidate_mechanism: str + target_systems: list[str] + experiment_blueprint: str + falsification_question: str + recommended_mode_ids: list[str] + requires_independent_replication: Literal[True] + auto_apply: Literal[False] + empirical_credit: Literal[False] + content_hash: str + + +class CognitiveScore(TypedDict): + novelty: float + surprise: float + explanatory_compression: float + mechanism_coherence: float + falsifiability: float + evidence_reachability: float + option_relevance: float + adversarial_robustness: float + reasoning_independence: float + evidence_debt_penalty: float + utility: float + + +class DreamWorld(TypedDict): + schema_version: Literal["jester-dream-world-v1"] + dream_id: str + cycle: int + phase: DreamPhase + parent_hypothesis_id: str + parent_dream_ids: list[str] + mode_ids: list[str] + synthetic_only: Literal[True] + stress_only: Literal[True] + empirical_credit: Literal[False] + referenced_evidence_ids: list[str] + invented_mechanism: str + countermechanism: str + cross_domain_analogy: str + structural_mapping: dict[str, str] + assumptions: list[str] + interventions: list[str] + expected_paths: dict[str, str] + failure_modes: list[str] + falsifiers: list[str] + evidence_debt: list[str] + niche: str + lineage_fingerprint: str + scores: CognitiveScore + content_hash: str + + +class CognitiveImmuneFinding(TypedDict): + finding_id: str + dream_id: str + check_id: str + status: ImmuneStatus + reason_codes: list[str] + detail: str + content_hash: str + + +class InventionBlueprint(TypedDict): + schema_version: Literal["jester-invention-blueprint-v1"] + invention_id: str + dream_id: str + parent_hypothesis_id: str + cognition_lineage: list[str] + problem_gap: str + proposed_mechanism: str + countermechanism: str + novelty_claim_boundary: str + cross_domain_analogy: str + structural_mapping: dict[str, str] + assumptions: list[str] + expected_state_paths: dict[str, str] + option_expression_implication: str + evidence_debt: list[str] + required_measurements: list[str] + minimal_decisive_experiment: str + falsifiers: list[str] + failure_modes: list[str] + transfer_tests: list[str] + prior_art_challenge: str + scores: CognitiveScore + niche: str + synthetic_only: Literal[True] + auto_apply: Literal[False] + requires_independent_resimulation: Literal[True] + empirical_credit: Literal[False] + live_blocked: Literal[True] + order_construction_allowed: Literal[False] + content_hash: str + + +class CognitiveArchiveCell(TypedDict): + cell_key: str + elite: InventionBlueprint + displaced_invention_ids: list[str] + content_hash: str + + +class FailureMuseumEntry(TypedDict): + entry_id: str + invention_id: str + dream_id: str + failure_class: str + preserved_lesson: str + revival_condition: str + empirical_credit: Literal[False] + content_hash: str + + +class CognitionArsenalResult(TypedDict): + schema_version: Literal["jester-cognition-arsenal-v1"] + arsenal_run_id: str + created_at: str + parent_hypothesis_id: str + mode_catalog_hash: str + scientific_seeds: list[ScientificCognitionSeed] + requested_dream_budget: int + authorized_dream_budget: int + dream_cycles: int + dream_temperature: float + dreams: list[DreamWorld] + immune_findings: list[CognitiveImmuneFinding] + inventions: list[InventionBlueprint] + archive: dict[str, CognitiveArchiveCell] + failure_museum: list[FailureMuseumEntry] + coverage: float + surprise_consumed: float + epistemic_debt: int + authority: dict[str, bool | str] + content_hash: str diff --git a/src/jester/counterfactuals.py b/src/jester/counterfactuals.py new file mode 100644 index 0000000..545ccc5 --- /dev/null +++ b/src/jester/counterfactuals.py @@ -0,0 +1,202 @@ +"""Social ablation and competing-causal-graph tournaments.""" + +from __future__ import annotations + +from collections import defaultdict +from statistics import fmean +from typing import cast + +from .identity import as_json_value, canonical_hash +from .meta_types import ( + CausalGraphScore, + CausalGraphTournament, + SocialAblationCase, + SocialAblationMatrix, +) +from .types import CausalEdge, ClaimRecord, ScenarioLattice + + +def _ablation_case( + intervention: str, + claim_probability_multiplier: float, + attention_multiplier: float, + manipulation_probability: float, + expected_interpretation: str, + stress_only: bool, +) -> SocialAblationCase: + body = { + "intervention": intervention, + "claim_probability_multiplier": claim_probability_multiplier, + "attention_multiplier": attention_multiplier, + "manipulation_probability": manipulation_probability, + "expected_interpretation": expected_interpretation, + "stress_only": stress_only, + } + content_hash = canonical_hash(as_json_value(body)) + return { + "case_id": f"social-ablation:{content_hash[:24]}", + "intervention": intervention, + "claim_probability_multiplier": claim_probability_multiplier, + "attention_multiplier": attention_multiplier, + "manipulation_probability": manipulation_probability, + "expected_interpretation": expected_interpretation, + "stress_only": stress_only, + "content_hash": content_hash, + } + + +def build_social_ablation_matrix( + claims: list[ClaimRecord], +) -> SocialAblationMatrix: + if not claims: + raise ValueError("social ablation requires at least one structured claim") + mean_manipulation = fmean(claim["manipulation_probability"] for claim in claims) + mean_saturation = fmean(claim["saturation"] for claim in claims) + causal_credit_allowed = all(claim["narrative_state"] != "price_following" for claim in claims) + cases = [ + _ablation_case( + "social_amplification_present", + 1.0, + 1.0, + mean_manipulation, + "observed_narrative_mechanics", + False, + ), + _ablation_case( + "social_amplification_absent", + 0.72, + 0.0, + 0.0, + "tests_incremental_social_credit", + False, + ), + _ablation_case( + "opposite_social_interpretation", + 0.65, + 1.0, + mean_manipulation, + "tests_semantic_direction_dependence", + True, + ), + _ablation_case( + "delayed_propagation", + 0.82, + 0.55, + mean_manipulation, + "tests_timing_and_price_following_risk", + False, + ), + _ablation_case( + "coordinated_manipulation", + 0.45, + 1.8, + max(0.75, mean_manipulation), + "stress_only_manipulation_world", + True, + ), + _ablation_case( + "official_confirmation", + min(1.35, 1.05 + (1.0 - mean_saturation) * 0.25), + 1.25, + 0.0, + "tests_confirmation_and_saturation", + False, + ), + _ablation_case( + "official_contradiction", + 0.2, + 1.5, + mean_manipulation, + "tests_narrative_collapse", + True, + ), + ] + body = { + "schema_version": "jester-social-ablation-matrix-v1", + "claim_ids": [claim["claim_id"] for claim in claims], + "cases": cases, + "causal_credit_allowed": causal_credit_allowed, + } + content_hash = canonical_hash(as_json_value(body)) + return { + "schema_version": "jester-social-ablation-matrix-v1", + "matrix_id": f"social-matrix:{content_hash[:24]}", + "claim_ids": [claim["claim_id"] for claim in claims], + "cases": cases, + "causal_credit_allowed": causal_credit_allowed, + "content_hash": content_hash, + } + + +def _unique_edges(edges: list[CausalEdge]) -> list[CausalEdge]: + by_identity: dict[tuple[str, str, str], CausalEdge] = {} + for edge in edges: + identity = (edge["source"], edge["target"], edge["relation"]) + incumbent = by_identity.get(identity) + if incumbent is None or edge["confidence"] > incumbent["confidence"]: + by_identity[identity] = edge + return [by_identity[key] for key in sorted(by_identity)] + + +def run_causal_graph_tournament( + lattice: ScenarioLattice, +) -> CausalGraphTournament: + graph_edges: dict[str, list[CausalEdge]] = defaultdict(list) + graph_probability: dict[str, float] = defaultdict(float) + for scenario in lattice["scenarios"]: + graph_edges[scenario["causal_graph_id"]].extend(scenario["causal_edges"]) + graph_probability[scenario["causal_graph_id"]] += scenario["probability"] + scores: list[CausalGraphScore] = [] + for graph_id in sorted(graph_edges): + edges = _unique_edges(graph_edges[graph_id]) + supported = [ + edge + for edge in edges + if edge["evidence_ids"] and edge["classification"] != "stress_assumption" + ] + stress = [edge for edge in edges if edge["classification"] == "stress_assumption"] + supported_fraction = len(supported) / max(len(edges), 1) + stress_fraction = len(stress) / max(len(edges), 1) + mean_confidence = fmean(edge["confidence"] for edge in edges) + coverage = graph_probability[graph_id] + robust_score = ( + coverage + * (0.5 * supported_fraction + 0.5 * mean_confidence) + * (1.0 - 0.5 * stress_fraction) + ) + body = { + "graph_id": graph_id, + "supported_edge_fraction": round(supported_fraction, 6), + "mean_edge_confidence": round(mean_confidence, 6), + "stress_edge_fraction": round(stress_fraction, 6), + "scenario_probability_coverage": round(coverage, 6), + "robust_score": round(robust_score, 6), + } + scores.append( + cast( + CausalGraphScore, + { + **body, + "content_hash": canonical_hash(as_json_value(body)), + }, + ) + ) + if not scores: + raise ValueError("causal graph tournament requires at least one graph") + winner = max(scores, key=lambda score: (score["robust_score"], score["graph_id"])) + robust_scores = [score["robust_score"] for score in scores] + body_tournament = { + "scores": scores, + "winner_graph_id": winner["graph_id"], + "disagreement": round(max(robust_scores) - min(robust_scores), 6), + "silent_averaging_used": False, + } + content_hash = canonical_hash(as_json_value(body_tournament)) + return { + "tournament_id": f"causal-tournament:{content_hash[:24]}", + "scores": scores, + "winner_graph_id": winner["graph_id"], + "disagreement": round(max(robust_scores) - min(robust_scores), 6), + "silent_averaging_used": False, + "content_hash": content_hash, + } diff --git a/src/jester/data_pipelines.py b/src/jester/data_pipelines.py new file mode 100644 index 0000000..eafbda1 --- /dev/null +++ b/src/jester/data_pipelines.py @@ -0,0 +1,381 @@ +"""Point-in-time data-pipeline contracts for Jester's evidence plane.""" + +from __future__ import annotations + +from typing import Literal, TypedDict, cast + +from .identity import as_json_value, canonical_hash + +type PipelineStatus = Literal[ + "operational", + "adapter_contract", + "blocked_license_or_data", + "prohibited", +] + + +class DataPipelineSpec(TypedDict): + pipeline_id: str + domain: str + status: PipelineStatus + access_mode: str + cadence: str + time_semantics: list[str] + retained_fields: list[str] + intelligence_consumers: list[str] + quality_gates: list[str] + activation_requirements: list[str] + primary_source_uri: str + limitations: list[str] + content_hash: str + + +def _pipeline( + pipeline_id: str, + domain: str, + status: PipelineStatus, + access_mode: str, + cadence: str, + time_semantics: list[str], + retained_fields: list[str], + intelligence_consumers: list[str], + quality_gates: list[str], + activation_requirements: list[str], + primary_source_uri: str, + limitations: list[str], +) -> DataPipelineSpec: + body = { + "pipeline_id": pipeline_id, + "domain": domain, + "status": status, + "access_mode": access_mode, + "cadence": cadence, + "time_semantics": time_semantics, + "retained_fields": retained_fields, + "intelligence_consumers": intelligence_consumers, + "quality_gates": quality_gates, + "activation_requirements": activation_requirements, + "primary_source_uri": primary_source_uri, + "limitations": limitations, + } + return cast( + DataPipelineSpec, + { + **body, + "content_hash": canonical_hash(as_json_value(body)), + }, + ) + + +def data_pipeline_catalog() -> list[DataPipelineSpec]: + point_in_time = [ + "event_time", + "publication_time", + "market_availability_time", + "ingestion_time", + "revision_time", + ] + evidence_gates = [ + "source_permission_admission", + "point_in_time_cutoff", + "raw_and_normalized_hashes", + "duplicate_and_revision_reconciliation", + ] + return [ + _pipeline( + "immutable-evidence-artifact-v1", + "internal_evidence", + "operational", + "local_hash_chained_artifacts", + "event_driven", + point_in_time, + ["evidence_body", "permission_policy", "entity_links", "hash_chain"], + ["temporal_fusion", "contradiction_engine", "proof_court"], + [*evidence_gates, "append_only_event_chain"], + [], + "https://github.com/ObtuseAI/jester", + ["mechanics_do_not_establish_external_data_fidelity"], + ), + _pipeline( + "arxiv-scientific-metadata-v1", + "scientific_literature", + "operational", + "official_atom_metadata_api", + "daily_identical_query_limit", + ["published_time", "updated_time", "eligible_at", "ingestion_time"], + [ + "versioned_identifier", + "title", + "authors", + "abstract", + "categories", + "license_uri", + ], + ["scientific_literature_lab", "research_gap_mapper", "idea_forge"], + [*evidence_gates, "paper_version_freeze", "zero_automatic_empirical_credit"], + [], + "https://info.arxiv.org/help/api/tou.html", + ["preprints_are_not_verified_truth", "abstract_metadata_only"], + ), + _pipeline( + "staged-search-attention-v1", + "attention", + "operational", + "operator_staged_ranked_urls_then_robots_aware_metadata", + "provider_specific", + ["rank_observed_at", "page_publication_time", "metadata_ingestion_time"], + [ + "rank", + "canonical_url", + "title", + "description", + "response_headers", + "content_hash", + ], + ["attention_divergence", "narrative_velocity", "source_ecology"], + [*evidence_gates, "robots_allow", "domain_allowlist", "proxy_label_required"], + ["operator_supplied_permitted_provider_export"], + "https://www.rfc-editor.org/rfc/rfc9309.html", + [ + "search_prominence_is_not_search_volume", + "no_login_captcha_or_antibot_bypass", + ], + ), + _pipeline( + "sec-edgar-companyfacts-v1", + "company_fundamentals", + "adapter_contract", + "official_sec_json_apis_and_bulk_archives", + "filing_event_driven", + [*point_in_time, "filing_acceptance_time"], + [ + "submissions", + "companyfacts", + "xbrl_frames", + "accession_number", + "form", + ], + [ + "company_state_graph", + "event_clock", + "revision_detector", + "management_claim_verifier", + ], + [*evidence_gates, "sec_fair_access", "amendment_linkage", "xbrl_unit_validation"], + ["identified_user_agent", "field_mapping", "historical_acceptance_time_replay"], + "https://www.sec.gov/search-filings/edgar-application-programming-interfaces", + ["companyfacts_taxonomies_change", "filings_require_form_specific_semantics"], + ), + _pipeline( + "fred-alfred-vintages-v1", + "macro", + "adapter_contract", + "official_fred_and_alfred_api", + "release_specific", + [*point_in_time, "realtime_start", "realtime_end", "vintage_date"], + ["series_id", "release_id", "observation", "units", "vintage_dates"], + ["macro_state_graph", "revision_surprise", "regime_classifier"], + [*evidence_gates, "release_calendar_alignment", "vintage_revision_control"], + ["api_key_policy", "series_registry", "release_clock_reconstruction"], + "https://fred.stlouisfed.org/docs/api/fred/overview.html", + ["latest_values_cannot_substitute_for_historical_vintages"], + ), + _pipeline( + "treasury-yield-curve-v1", + "rates", + "adapter_contract", + "official_treasury_xml_feed", + "daily", + ["publication_date", "market_availability_time", "ingestion_time"], + ["yield_curve", "bill_rates", "real_yield_curve", "long_term_rates"], + ["rates_state_graph", "discount_curve_builder", "macro_pack"], + [*evidence_gates, "calendar_gap_detection", "tenor_schema_validation"], + ["feed_parser", "publication_clock_policy"], + "https://home.treasury.gov/treasury-daily-interest-rate-xml-feed", + ["daily_feed_is_not_intraday_rates_market_data"], + ), + _pipeline( + "finra-regsho-short-volume-v1", + "positioning", + "adapter_contract", + "official_finra_query_api", + "daily", + [*point_in_time, "trade_report_date"], + [ + "symbol", + "reporting_facility", + "short_quantity", + "short_exempt_quantity", + "total_quantity", + ], + ["positioning_graph", "crowding_stress", "short_activity_context"], + [*evidence_gates, "facility_coverage_reconciliation", "version_header_freeze"], + ["dataset_terms_admission", "rolling_history_policy", "pagination"], + "https://developer.finra.org/docs", + [ + "short_volume_is_not_short_interest", + "dataset_history_and_access_modes_vary", + ], + ), + _pipeline( + "cftc-cot-positioning-v1", + "macro_positioning", + "adapter_contract", + "official_cftc_public_reporting_environment", + "weekly", + ["positions_as_of_time", "release_time", "ingestion_time"], + [ + "contract_market", + "open_interest", + "trader_category", + "long_positions", + "short_positions", + ], + ["macro_positioning_graph", "congestion_game", "cross_asset_regime"], + [*evidence_gates, "report_date_vs_release_date", "classification_revision_control"], + ["contract_mapping", "release_calendar", "historical_delay_registry"], + "https://www.cftc.gov/MarketReports/CommitmentsofTraders/index.htm", + ["weekly_aggregate_data_cannot_identify_individual_participant_intent"], + ), + _pipeline( + "crossref-retraction-metadata-v1", + "scientific_integrity", + "adapter_contract", + "official_crossref_rest_api", + "daily", + ["published_time", "deposited_time", "updated_time", "ingestion_time"], + ["doi", "authors", "funder", "license", "update_to", "retraction_relation"], + ["scientific_literature_lab", "retraction_guard", "citation_graph"], + [*evidence_gates, "doi_resolution", "update_relation_reconciliation"], + ["polite_pool_identity", "cursor_checkpointing", "cache_policy"], + "https://www.crossref.org/documentation/retrieve-metadata/rest-api/", + ["deposited_metadata_can_be_incomplete", "abstract_rights_may_vary"], + ), + _pipeline( + "clinicaltrials-v2-v1", + "biotech_events", + "adapter_contract", + "official_clinicaltrials_json_api_v2", + "weekday_daily_refresh", + [*point_in_time, "study_last_update_post_date", "data_timestamp"], + [ + "nct_id", + "overall_status", + "phase", + "outcomes", + "sponsor", + "study_dates", + ], + ["biotech_event_pack", "catalyst_clock", "endpoint_change_detector"], + [*evidence_gates, "api_version_freeze", "study_revision_diff"], + ["entity_mapping", "endpoint_ontology", "sponsor_disambiguation"], + "https://clinicaltrials.gov/data-api/about-api", + ["registry_updates_are_not_trial_results", "sponsor_submitted_data_requires_context"], + ), + _pipeline( + "wikimedia-pageviews-v1", + "public_attention", + "adapter_contract", + "official_wikimedia_analytics_api", + "daily", + ["aggregation_date", "availability_time", "ingestion_time"], + ["project", "article", "access", "agent", "views"], + ["attention_divergence", "entity_attention_baseline", "narrative_saturation"], + [*evidence_gates, "redirect_reconciliation", "bot_and_agent_semantics"], + ["entity_title_map", "timezone_policy", "backfill_admission"], + ( + "https://doc.wikimedia.org/generated-data-platform/aqs/" + "analytics-api/concepts/page-views.html" + ), + ["pageviews_are_not_search_volume_or_investor_intent"], + ), + _pipeline( + "uspto-patent-events-v1", + "innovation", + "adapter_contract", + "official_uspto_open_data_portal", + "publication_event_driven", + [*point_in_time, "filing_date", "publication_date", "grant_date"], + ["application", "assignee", "inventor", "classification", "status_events"], + ["product_graph", "technology_diffusion", "competitive_moat"], + [*evidence_gates, "assignee_resolution", "family_and_continuation_linkage"], + ["api_contract_validation", "company_entity_resolution", "event_clock"], + "https://developer.uspto.gov/", + ["patent_activity_does_not_establish_commercial_success"], + ), + _pipeline( + "licensed-opra-chain-v1", + "options_market", + "blocked_license_or_data", + "licensed_consolidated_options_quotes_and_trades", + "event_stream", + [*point_in_time, "exchange_sequence", "session_phase"], + [ + "nbbo", + "trades", + "condition_codes", + "contract_reference", + "exchange_timestamp", + ], + ["volatility_foundry", "option_outcome_field", "fill_calibrator", "zero_dte_lane"], + [ + *evidence_gates, + "cross_exchange_sequence_integrity", + "contract_lifecycle", + "corporate_action_adjustment", + ], + [ + "accepted_license", + "redistribution_policy", + "historical_chain_corpus", + "independent_fill_validation", + ], + "https://www.opraplan.com/", + ["no_accepted_license_or_historical_calibration_corpus"], + ), + _pipeline( + "licensed-l2-l3-order-book-v1", + "market_microstructure", + "blocked_license_or_data", + "licensed_exchange_depth_or_order_events", + "event_stream", + [*point_in_time, "exchange_sequence", "receive_time"], + ["book_updates", "order_events", "trades", "auction_events", "halts"], + ["price_lob_twin", "market_society", "adverse_selection", "fill_calibrator"], + [ + *evidence_gates, + "sequence_gap_detection", + "venue_clock_alignment", + "queue_reconstruction", + ], + ["accepted_license", "venue_adapters", "clock_sync", "calibration_corpus"], + "https://www.nasdaqtrader.com/Trader.aspx?id=DataProducts", + ["no_licensed_order_book_calibration_corpus"], + ), + _pipeline( + "credentialed-or-antibot-scraping", + "attention", + "prohibited", + "logged_in_or_evasion_based_collection", + "not_applicable", + [], + [], + [], + ["hard_deny"], + [], + "https://www.rfc-editor.org/rfc/rfc9309.html", + ["credentials_captcha_bypass_and_antibot_evasion_are_forbidden"], + ), + ] + + +def pipeline_status_counts() -> dict[PipelineStatus, int]: + counts: dict[PipelineStatus, int] = { + "operational": 0, + "adapter_contract": 0, + "blocked_license_or_data": 0, + "prohibited": 0, + } + for pipeline in data_pipeline_catalog(): + counts[pipeline["status"]] += 1 + return counts diff --git a/src/jester/forge.py b/src/jester/forge.py index 1721a85..bae845a 100644 --- a/src/jester/forge.py +++ b/src/jester/forge.py @@ -2,12 +2,38 @@ from __future__ import annotations +from copy import deepcopy from typing import cast from .identity import as_json_value, canonical_hash from .types import HypothesisGenome, JsonValue, OptionContract, WorldStateSnapshot +def _rehash_genome( + parent_ids: list[str], + lineage_depth: int, + values: dict[str, object], +) -> HypothesisGenome: + body = { + key: deepcopy(value) + for key, value in values.items() + if key not in {"hypothesis_id", "genome_hash"} + } + body["parent_ids"] = sorted(set(parent_ids)) + body["lineage_depth"] = lineage_depth + body["paper_only"] = True + body["live_blocked"] = True + genome_hash = canonical_hash(as_json_value(body)) + return cast( + HypothesisGenome, + { + **body, + "hypothesis_id": f"hypothesis:{genome_hash[:24]}", + "genome_hash": genome_hash, + }, + ) + + def create_hypothesis_genome( evidence_ids: list[str], contradicting_evidence_ids: list[str], @@ -39,7 +65,7 @@ def create_hypothesis_genome( ), "variant_perception": ( "The option market may misprice the joint earnings move, volatility crush, " - "liquidity, and Dopey-permitted holding-period distribution." + "liquidity, and policy-permitted holding-period distribution." ), "catalyst": "earnings", "catalyst_time_distribution": { @@ -111,20 +137,96 @@ def mutate_signal_half_life( "mutated signal half-life must be positive: " f"signal_half_life_hours={signal_half_life_hours}" ) - body = { - key: value for key, value in parent.items() if key not in {"hypothesis_id", "genome_hash"} - } - body["parent_ids"] = [parent["hypothesis_id"]] - body["lineage_depth"] = parent["lineage_depth"] + 1 + body = deepcopy(dict(parent)) body["signal_half_life_hours"] = round(signal_half_life_hours, 6) - genome_hash = canonical_hash(as_json_value(body)) - return cast( - HypothesisGenome, - { - **body, - "hypothesis_id": f"hypothesis:{genome_hash[:24]}", - "genome_hash": genome_hash, - }, + return _rehash_genome( + [parent["hypothesis_id"]], + parent["lineage_depth"] + 1, + body, + ) + + +def transplant_regime( + parent: HypothesisGenome, + required_regime: str, +) -> HypothesisGenome: + if not required_regime.strip(): + raise ValueError("regime transplant requires a non-empty regime") + body = deepcopy(dict(parent)) + body["required_regime"] = required_regime + body["niche"] = ( + f"{parent['catalyst']}:{parent['option_expression']['option_type']}:{required_regime}" + ) + return _rehash_genome( + [parent["hypothesis_id"]], + parent["lineage_depth"] + 1, + body, + ) + + +def ablate_evidence( + parent: HypothesisGenome, + evidence_id: str, +) -> HypothesisGenome: + if evidence_id not in parent["point_in_time_evidence_ids"]: + raise KeyError( + "evidence ablation target is not present in parent: " + f"hypothesis_id={parent['hypothesis_id']}, evidence_id={evidence_id}" + ) + body = deepcopy(dict(parent)) + body["point_in_time_evidence_ids"] = [ + item for item in parent["point_in_time_evidence_ids"] if item != evidence_id + ] + return _rehash_genome( + [parent["hypothesis_id"]], + parent["lineage_depth"] + 1, + body, + ) + + +def invert_option_expression(parent: HypothesisGenome) -> HypothesisGenome: + body = deepcopy(dict(parent)) + contract = deepcopy(parent["option_expression"]) + contract["option_type"] = ( + "put" if parent["option_expression"]["option_type"] == "call" else "call" + ) + body["option_expression"] = contract + body["niche"] = f"{parent['catalyst']}:{contract['option_type']}:{parent['required_regime']}" + body["variant_perception"] = ( + "Adversarial inversion of the parent thesis; it receives no empirical credit " + "until independently resimulated." + ) + return _rehash_genome( + [parent["hypothesis_id"]], + parent["lineage_depth"] + 1, + body, + ) + + +def crossover_genomes( + first: HypothesisGenome, + second: HypothesisGenome, +) -> HypothesisGenome: + if first["hypothesis_id"] == second["hypothesis_id"]: + raise ValueError("crossover requires two distinct parent hypotheses") + body = deepcopy(dict(first)) + body["point_in_time_evidence_ids"] = sorted( + set(first["point_in_time_evidence_ids"]) | set(second["point_in_time_evidence_ids"]) + ) + body["contradicting_evidence_ids"] = sorted( + set(first["contradicting_evidence_ids"]) | set(second["contradicting_evidence_ids"]) + ) + body["causal_mechanism"] = ( + f"Cross-lineage mechanism: {first['causal_mechanism']} " + f"Countermechanism: {second['causal_mechanism']}" + ) + body["strategic_counterresponses"] = sorted( + set(first["strategic_counterresponses"]) | set(second["strategic_counterresponses"]) + ) + return _rehash_genome( + [first["hypothesis_id"], second["hypothesis_id"]], + max(first["lineage_depth"], second["lineage_depth"]) + 1, + body, ) diff --git a/src/jester/intelligence.py b/src/jester/intelligence.py new file mode 100644 index 0000000..aec5ac0 --- /dev/null +++ b/src/jester/intelligence.py @@ -0,0 +1,254 @@ +"""Composable intelligence-service contracts for Jester's research plane.""" + +from __future__ import annotations + +from typing import Literal, TypedDict, cast + +from .identity import as_json_value, canonical_hash + +type IntelligenceStatus = Literal[ + "operational_mechanics", + "adapter_contract", + "blocked_missing_evidence", +] + + +class IntelligenceSpec(TypedDict): + intelligence_id: str + name: str + status: IntelligenceStatus + purpose: str + input_pipelines: list[str] + outputs: list[str] + evaluators: list[str] + uncertainty_channels: list[str] + authority_effect: Literal["none"] + limitations: list[str] + content_hash: str + + +def _intelligence( + intelligence_id: str, + name: str, + status: IntelligenceStatus, + purpose: str, + input_pipelines: list[str], + outputs: list[str], + evaluators: list[str], + uncertainty_channels: list[str], + limitations: list[str], +) -> IntelligenceSpec: + body = { + "intelligence_id": intelligence_id, + "name": name, + "status": status, + "purpose": purpose, + "input_pipelines": input_pipelines, + "outputs": outputs, + "evaluators": evaluators, + "uncertainty_channels": uncertainty_channels, + "authority_effect": "none", + "limitations": limitations, + } + return cast( + IntelligenceSpec, + { + **body, + "content_hash": canonical_hash(as_json_value(body)), + }, + ) + + +def intelligence_catalog() -> list[IntelligenceSpec]: + return [ + _intelligence( + "temporal-evidence-fusion-v1", + "Temporal Evidence Fusion", + "operational_mechanics", + "Align heterogeneous observations without violating market availability clocks.", + ["immutable-evidence-artifact-v1"], + ["chronological_evidence_slice", "late_arrival_flags", "revision_lineage"], + ["cutoff_invariance", "clock_ordering", "circular_shift"], + ["clock_uncertainty", "reaction_window_uncertainty"], + ["mechanics_are_only_as_complete_as_admitted_source_clocks"], + ), + _intelligence( + "entity-resolution-graph-v1", + "Entity Resolution Graph", + "operational_mechanics", + "Resolve companies, securities, people, products, sectors, and evidence aliases.", + ["immutable-evidence-artifact-v1"], + ["entity_links", "relationship_edges", "ambiguous_aliases"], + ["known_entity_fixture", "collision_detection", "confidence_calibration"], + ["identity_uncertainty", "relationship_uncertainty"], + ["public_fixture_has_limited_entity_coverage"], + ), + _intelligence( + "contradiction-engine-v1", + "Contradiction Engine", + "operational_mechanics", + "Preserve competing claims and detect evidence that weakens prevailing narratives.", + [ + "immutable-evidence-artifact-v1", + "arxiv-scientific-metadata-v1", + "staged-search-attention-v1", + ], + ["support_graph", "contradiction_graph", "reversal_pressure"], + ["temporal_ablation", "duplicate_cluster_control", "opposite_interpretation"], + ["semantic_uncertainty", "source_dependency"], + ["contradiction_is_not_automatically_disproof"], + ), + _intelligence( + "attention-divergence-v1", + "Attention Divergence", + "operational_mechanics", + "Detect rank, churn, source-diversity, and metadata changes across admitted snapshots.", + ["staged-search-attention-v1", "wikimedia-pageviews-v1"], + ["prominence_delta", "rank_velocity", "source_ecology", "attention_price_divergence"], + ["lag_placebo", "circular_shift", "social_absent", "price_following_control"], + ["coverage_uncertainty", "provider_sampling_bias"], + ["prominence_proxies_are_not_search_volume_or_causal_signals"], + ), + _intelligence( + "scientific-claim-firewall-v1", + "Scientific Claim Firewall", + "operational_mechanics", + "Convert literature into falsifiable proposals without granting empirical credit.", + ["arxiv-scientific-metadata-v1", "crossref-retraction-metadata-v1"], + ["research_capsules", "falsification_questions", "replication_queue"], + ["version_freeze", "retraction_check", "independent_replication"], + ["publication_status_uncertainty", "replication_uncertainty"], + ["no_paper_can_activate_code_or_relax_a_gate"], + ), + _intelligence( + "source-calibration-market-v1", + "Source Calibration Market", + "adapter_contract", + "Score source-specific claims using proper scoring rules after resolvable outcomes.", + [ + "sec-edgar-companyfacts-v1", + "staged-search-attention-v1", + "immutable-evidence-artifact-v1", + ], + ["source_reliability_curve", "regime_specific_calibration", "correlation_clusters"], + ["brier_score", "log_loss", "resolution_completeness"], + ["selection_bias", "outcome_definition_uncertainty"], + ["needs_sufficient_resolved_point_in_time_claim_history"], + ), + _intelligence( + "revision-surprise-v1", + "Revision Surprise Engine", + "adapter_contract", + "Separate first-release surprise from later macro, filing, and study revisions.", + ["fred-alfred-vintages-v1", "sec-edgar-companyfacts-v1", "clinicaltrials-v2-v1"], + ["first_release_state", "revision_trajectory", "revision_surprise"], + ["vintage_replay", "late_revision_ablation", "release_clock_alignment"], + ["revision_policy_uncertainty", "series_mapping_uncertainty"], + ["blocked_until_vintage_aware_source_adapters_are_accepted"], + ), + _intelligence( + "cross-market-transmission-v1", + "Cross-Market Transmission Graph", + "adapter_contract", + "Trace company, sector, rates, credit, positioning, and volatility spillovers.", + [ + "sec-edgar-companyfacts-v1", + "fred-alfred-vintages-v1", + "treasury-yield-curve-v1", + "cftc-cot-positioning-v1", + "licensed-opra-chain-v1", + ], + ["transmission_edges", "lag_distribution", "spillover_scenarios"], + ["cross_entity_holdout", "regime_transfer", "causal_graph_disagreement"], + ["graph_uncertainty", "lag_uncertainty", "confounding"], + ["learned_edges_remain_candidate_causal_relationships"], + ), + _intelligence( + "option-expression-critic-v1", + "Option Expression Critic", + "blocked_missing_evidence", + "Challenge strike, expiry, IV, skew, spread, fill, and holdability assumptions.", + ["licensed-opra-chain-v1", "licensed-l2-l3-order-book-v1"], + [ + "buyer_case", + "seller_case", + "contract_alternatives", + "pathwise_failure_decomposition", + ], + [ + "static_arbitrage", + "fill_calibration", + "earliest_exit", + "forward_paper_reconciliation", + ], + ["surface_uncertainty", "fill_uncertainty", "exercise_uncertainty"], + ["no_accepted_options_history_or_fill_calibration_corpus"], + ), + _intelligence( + "causal-committee-v1", + "Causal Graph Committee", + "operational_mechanics", + "Score multiple causal explanations independently and expose disagreement.", + ["immutable-evidence-artifact-v1"], + ["graph_tournament", "intervention_set", "epistemic_risk_penalty"], + ["held_out_counterfactuals", "edge_ablation", "silent_averaging_prohibition"], + ["structure_uncertainty", "intervention_uncertainty"], + ["typed_graphs_are_hypotheses_not_causal_truth"], + ), + _intelligence( + "simulation-gap-director-v1", + "Simulation Gap Director", + "adapter_contract", + "Turn synthetic-to-forward errors into the next bounded research priority.", + [ + "licensed-opra-chain-v1", + "licensed-l2-l3-order-book-v1", + "immutable-evidence-artifact-v1", + ], + ["gap_vector", "research_priority", "budget_reduction"], + ["forward_shadow", "bounded_paper_reconciliation", "frozen_baseline"], + ["sampling_uncertainty", "regime_coverage", "model_dependency"], + ["forward_shadow_and_bounded_paper_outcomes_are_not_yet_sufficient"], + ), + _intelligence( + "provenance-poisoning-defense-v1", + "Provenance Poisoning Defense", + "operational_mechanics", + "Detect duplicate cascades, time-travel, coordinated claims, and tainted evidence.", + [ + "immutable-evidence-artifact-v1", + "staged-search-attention-v1", + "arxiv-scientific-metadata-v1", + ], + ["taint_labels", "duplicate_clusters", "coordination_pressure", "admission_denials"], + ["hash_chain", "duplicate_ablation", "publication_availability_order"], + ["coordination_uncertainty", "source_identity_uncertainty"], + ["defense_flags risk and does not infer unlawful behavior"], + ), + _intelligence( + "active-research-router-v1", + "Active Research Router", + "operational_mechanics", + "Allocate bounded research toward uncertainty that can change a proof decision.", + [ + "immutable-evidence-artifact-v1", + "arxiv-scientific-metadata-v1", + "staged-search-attention-v1", + ], + ["next_experiment", "expected_information_gain", "abstention", "budget_mandate"], + ["prediction_before_outcome", "non_regression", "authority_monotonicity"], + ["information_gain_estimation", "knowledge_boundary_pressure"], + ["may_reduce_research_budget_but_never_increase_authority"], + ), + ] + + +def intelligence_status_counts() -> dict[IntelligenceStatus, int]: + counts: dict[IntelligenceStatus, int] = { + "operational_mechanics": 0, + "adapter_contract": 0, + "blocked_missing_evidence": 0, + } + for service in intelligence_catalog(): + counts[service["status"]] += 1 + return counts diff --git a/src/jester/ladder.py b/src/jester/ladder.py new file mode 100644 index 0000000..21d1b18 --- /dev/null +++ b/src/jester/ladder.py @@ -0,0 +1,276 @@ +"""Progressive simulation ladder with hard forward-evidence boundaries.""" + +from __future__ import annotations + +from dataclasses import fields +from typing import TypedDict, cast + +from .event_log import Event, verify_events +from .identity import as_json_value, canonical_hash +from .meta_types import LadderStage, SimulationLadderManifest +from .types import EvidenceMode, JsonValue, PipelineResult + + +class LadderConfig(TypedDict): + evidence_mode: EvidenceMode + resolved_forward_outcomes: int + bounded_paper_outcomes: int + required_forward_outcomes: int + required_paper_outcomes: int + + +def _validate_config(config: LadderConfig) -> None: + if config["evidence_mode"] not in { + "historical_reconstruction", + "forward_shadow", + "paper_execution", + }: + raise ValueError( + f"unsupported ladder evidence mode: evidence_mode={config['evidence_mode']}" + ) + if config["resolved_forward_outcomes"] < 0: + raise ValueError( + "ladder observed count must be non-negative: " + f"resolved_forward_outcomes={config['resolved_forward_outcomes']}" + ) + if config["bounded_paper_outcomes"] < 0: + raise ValueError( + "ladder observed count must be non-negative: " + f"bounded_paper_outcomes={config['bounded_paper_outcomes']}" + ) + if config["required_forward_outcomes"] <= 0: + raise ValueError( + "ladder required count must be positive: " + f"required_forward_outcomes={config['required_forward_outcomes']}" + ) + if config["required_paper_outcomes"] <= 0: + raise ValueError( + "ladder required count must be positive: " + f"required_paper_outcomes={config['required_paper_outcomes']}" + ) + + +def _stage( + level: int, + name: str, + status: str, + evidence_scope: str, + checks: list[str], + reason_codes: list[str], + artifact_ids: list[str], +) -> LadderStage: + body = { + "level": level, + "stage_id": f"level-{level}", + "name": name, + "status": status, + "evidence_scope": evidence_scope, + "checks": checks, + "reason_codes": reason_codes, + "artifact_ids": artifact_ids, + } + return cast( + LadderStage, + { + **body, + "content_hash": canonical_hash(as_json_value(body)), + }, + ) + + +def _serialized_events(result: PipelineResult) -> tuple[Event, ...]: + event_fields = {field.name for field in fields(Event)} + events: list[Event] = [] + for index, raw in enumerate(result["event_log"]): + if not isinstance(raw, dict): + raise TypeError(f"serialized event must be an object: index={index}") + if set(raw) != event_fields: + raise ValueError( + "serialized event fields do not match contract: " + f"index={index}, fields={sorted(raw)}" + ) + events.append( + Event( + sequence=cast(int, raw["sequence"]), + phase=cast(str, raw["phase"]), + event_type=cast(str, raw["event_type"]), + occurred_at=cast(str, raw["occurred_at"]), + payload=cast(dict[str, JsonValue], raw["payload"]), + prior_hash=cast(str, raw["prior_hash"]), + event_hash=cast(str, raw["event_hash"]), + ) + ) + return tuple(events) + + +def _replay_stage(result: PipelineResult) -> LadderStage: + try: + events = _serialized_events(result) + verify_events(events) + chain_matches = bool(events) and events[-1].event_hash == result["event_chain_hash"] + except (KeyError, TypeError, ValueError) as error: + return _stage( + 0, + "exact_replay", + "fail", + "mechanics", + ["event_chain_integrity"], + [f"event_chain_invalid:{type(error).__name__}"], + [result["run_id"]], + ) + return _stage( + 0, + "exact_replay", + "pass" if chain_matches else "fail", + "mechanics", + ["event_chain_integrity", "terminal_hash_match"], + [] if chain_matches else ["terminal_hash_mismatch"], + [result["run_id"], result["event_chain_hash"]], + ) + + +def build_simulation_ladder( + result: PipelineResult, + config: LadderConfig, +) -> SimulationLadderManifest: + _validate_config(config) + lattice = result["scenario_lattice"] + outcomes = result["outcome_field"]["outcomes"] + scenario_ids = {scenario["scenario_id"] for scenario in lattice["scenarios"]} + outcome_scenarios = {outcome["scenario_id"] for outcome in outcomes} + perturbation_pass = {"event_delayed_three_days", "thesis_correct_iv_crush"} <= { + scenario["label"] for scenario in lattice["scenarios"] + } + synthetic_pass = len(lattice["scenarios"]) >= 6 and scenario_ids == outcome_scenarios + causal_pass = any( + edge["classification"] + in { + "candidate_causal_relationship", + "evidence_supported_intervention", + } + for scenario in lattice["scenarios"] + for edge in scenario["causal_edges"] + ) + society_pass = ( + len(result["participant_reactions"]) == 13 + and len(cast(list[JsonValue], result["game_report"]["games"])) >= 5 + ) + adversarial_ids = { + scenario["scenario_id"] for scenario in lattice["scenarios"] if scenario["stress_only"] + } + adversarial_pass = bool(adversarial_ids & outcome_scenarios) + point_in_time_scope = ( + result["proof_manifest"]["certification_scope"] == "point_in_time_research" + ) + forward_pass = ( + config["evidence_mode"] == "forward_shadow" + and point_in_time_scope + and config["resolved_forward_outcomes"] >= config["required_forward_outcomes"] + ) + paper_pass = ( + forward_pass and config["bounded_paper_outcomes"] >= config["required_paper_outcomes"] + ) + stages = [ + _replay_stage(result), + _stage( + 1, + "historical_perturbation", + "pass" if perturbation_pass else "fail", + "mechanics", + ["timing_perturbation", "iv_perturbation"], + [] if perturbation_pass else ["required_perturbations_missing"], + [lattice["lattice_id"]], + ), + _stage( + 2, + "regime_conditioned_synthetic_worlds", + "pass" if synthetic_pass else "fail", + "mechanics", + ["scenario_outcome_closure", "minimum_world_count"], + [] if synthetic_pass else ["scenario_outcome_coverage_incomplete"], + [result["outcome_field"]["field_id"]], + ), + _stage( + 3, + "causal_intervention", + "pass" if causal_pass else "fail", + "mechanics", + ["typed_causal_edges", "competing_graphs"], + [] if causal_pass else ["causal_intervention_missing"], + [lattice["lattice_id"]], + ), + _stage( + 4, + "agent_society", + "pass" if society_pass else "fail", + "mechanics", + ["participant_population", "strategic_games"], + [] if society_pass else ["society_artifact_incomplete"], + [str(result["game_report"]["content_hash"])], + ), + _stage( + 5, + "adversarial_worlds", + "pass" if adversarial_pass else "fail", + "mechanics", + ["stress_world", "pathwise_option_outcome"], + [] if adversarial_pass else ["adversarial_outcome_missing"], + sorted(adversarial_ids), + ), + _stage( + 6, + "forward_shadow", + "pass" if forward_pass else "blocked", + "point_in_time_research", + ["forward_mode", "non_synthetic_scope", "resolved_outcome_minimum"], + ( + [] + if forward_pass + else [ + "insufficient_forward_point_in_time_outcomes", + f"observed={config['resolved_forward_outcomes']}", + f"required={config['required_forward_outcomes']}", + ] + ), + [result["proof_manifest"]["proof_id"]], + ), + _stage( + 7, + "bounded_paper_execution", + "pass" if paper_pass else "blocked", + "forward_paper", + ["forward_shadow_passed", "paper_outcome_minimum"], + ( + [] + if paper_pass + else [ + "insufficient_bounded_paper_outcomes", + f"observed={config['bounded_paper_outcomes']}", + f"required={config['required_paper_outcomes']}", + ] + ), + [result["unsigned_bundle"]["bundle_id"]], + ), + ] + highest = -1 + for stage in stages: + if stage["level"] == highest + 1 and stage["status"] == "pass": + highest = stage["level"] + else: + break + body = { + "schema_version": "jester-simulation-ladder-v1", + "candidate_id": result["hypothesis"]["hypothesis_id"], + "stages": stages, + "highest_contiguous_level": highest, + "forward_promotion_eligible": highest == 7, + } + content_hash = canonical_hash(as_json_value(body)) + return cast( + SimulationLadderManifest, + { + **body, + "manifest_id": f"ladder:{content_hash[:24]}", + "content_hash": content_hash, + }, + ) diff --git a/src/jester/literature.py b/src/jester/literature.py new file mode 100644 index 0000000..374855d --- /dev/null +++ b/src/jester/literature.py @@ -0,0 +1,588 @@ +"""Point-in-time scientific metadata ingestion and falsifiable capsule generation.""" + +from __future__ import annotations + +import logging +import re +import time +import xml.etree.ElementTree as ET +from collections.abc import Sequence +from copy import deepcopy +from datetime import timedelta +from typing import TypedDict + +import httpx + +from .clock import parse_utc, utc_iso +from .identity import as_json_value, bytes_hash, canonical_hash +from .literature_types import ( + LiteratureExclusion, + LiteratureLearningBatch, + LiteratureQuery, + ResearchCapsule, + ScientificPaper, +) +from .types import JsonValue + +ATOM = "http://www.w3.org/2005/Atom" +ARXIV = "http://arxiv.org/schemas/atom" +ARXIV_QUERY_ENDPOINT = "https://export.arxiv.org/api/query" +ARXIV_TERMS_URI = "https://info.arxiv.org/help/api/tou.html" +ARXIV_METADATA_REUSE_BASIS = "arxiv_descriptive_metadata_cc0_per_api_terms" +IDENTIFIER_PATTERN = re.compile(r"(?P.+?)(?:v(?P[1-9][0-9]*))?$") + + +class LiteratureSourceError(RuntimeError): + """Raised after the scientific metadata source exhausts its retry budget.""" + + +class ThemeRule(TypedDict): + theme: str + keywords: tuple[str, ...] + target_systems: tuple[str, ...] + candidate_mechanism: str + experiment_blueprint: str + falsification_question: str + + +THEME_RULES: tuple[ThemeRule, ...] = ( + { + "theme": "causal_market_simulation", + "keywords": ("causal", "counterfactual", "intervention"), + "target_systems": ("causal_scenario_lattice", "tncm_vae_adapter"), + "candidate_mechanism": "learned_or_typed_interventions_change_market_trajectories", + "experiment_blueprint": "compare_competing_graphs_on_held_out_counterfactual_replays", + "falsification_question": "does_the_method_beat_noncausal_resampling_out_of_sample", + }, + { + "theme": "order_book_generation", + "keywords": ("order book", "queue-reactive", "market microstructure", "limit order"), + "target_systems": ("market_twin", "mdqr_lob_adapter", "lob_diffusion_adapter"), + "candidate_mechanism": "queue_state_and_order_flow_generate_endogenous_liquidity_paths", + "experiment_blueprint": "test_stylized_facts_impact_and_fill_error_by_instrument", + "falsification_question": "does_the_generator_preserve_impact_and_queue_dependence", + }, + { + "theme": "volatility_surface", + "keywords": ("implied volatility", "volatility surface", "volatility smile", "skew"), + "target_systems": ("volatility_foundry", "iv_ddpm_adapter"), + "candidate_mechanism": "conditional_surface_dynamics_change_executable_option_outcomes", + "experiment_blueprint": "evaluate_surface_forecasts_and_static_arbitrage_out_of_sample", + "falsification_question": "does_the_surface_model_improve_prices_without_arbitrage_repair", + }, + { + "theme": "rough_and_jump_volatility", + "keywords": ("rough volatility", "jump diffusion", "stochastic volatility", "0dte"), + "target_systems": ("rough_volatility_adapter", "zero_dte_expiration_pack"), + "candidate_mechanism": "roughness_or_jumps_dominate_short_horizon_option_economics", + "experiment_blueprint": "compare_jump_rough_and_baseline_models_on_sealed_expiry_events", + "falsification_question": "does_the_specialized_model_improve_tail_and_greek_calibration", + }, + { + "theme": "agentic_scientific_discovery", + "keywords": ( + "scientific discovery", + "ai scientist", + "research agent", + "literature search", + ), + "target_systems": ("scientific_literature_lab", "meta_research_plane"), + "candidate_mechanism": "persistent_world_models_preserve_coherence_across_research_cycles", + "experiment_blueprint": "score_prediction_resolution_traceability_and_non_regression", + "falsification_question": "does_recursive_search_outperform_a_frozen_research_baseline", + }, + { + "theme": "evolutionary_discovery", + "keywords": ("evolutionary", "quality diversity", "map-elites", "open-ended"), + "target_systems": ("idea_forge", "quality_diversity_archive"), + "candidate_mechanism": "niche_preservation_prevents_premature_research_convergence", + "experiment_blueprint": "compare_archive_coverage_and_false_discovery_at_fixed_budget", + "falsification_question": "does_diversity_add_resolved_value_beyond_random_search", + }, + { + "theme": "calibration_and_abstention", + "keywords": ("conformal", "calibration", "proper scoring", "abstention"), + "target_systems": ("belief_exchange", "proof_court", "metacognition"), + "candidate_mechanism": "calibrated_abstention_reduces_false_promotions_under_shift", + "experiment_blueprint": "measure_coverage_calibration_and_false_discovery_under_regimes", + "falsification_question": ( + "does_abstention_control_error_without_destroying_useful_coverage" + ), + }, +) + + +def validate_literature_query(query: LiteratureQuery) -> None: + if query["schema_version"] != "jester-literature-query-v1": + raise ValueError( + f"unsupported literature query schema: schema_version={query['schema_version']}" + ) + if not query["query"].strip(): + raise ValueError("literature query must be non-empty") + if query["max_results"] <= 0 or query["max_results"] > 100: + raise ValueError( + f"literature max_results must be within [1, 100]: max_results={query['max_results']}" + ) + if query["minimum_refresh_seconds"] < 86400: + raise ValueError( + "identical autonomous queries may refresh at most daily: " + f"minimum_refresh_seconds={query['minimum_refresh_seconds']}" + ) + if not query["allowed_categories"]: + raise ValueError("literature query requires at least one allowed category") + parse_utc(query["requested_at"], "requested_at") + parse_utc(query["cutoff_time"], "cutoff_time") + if query["source_mode"] not in {"live_metadata", "synthetic_fixture"}: + raise ValueError(f"unsupported literature source mode: source_mode={query['source_mode']}") + if query["cutoff_policy"] not in {"fixed", "scan_time"}: + raise ValueError( + f"unsupported literature cutoff policy: cutoff_policy={query['cutoff_policy']}" + ) + if query["source_mode"] == "synthetic_fixture" and query["cutoff_policy"] != "fixed": + raise ValueError("synthetic literature fixtures require a fixed cutoff") + + +def effective_literature_query( + query: LiteratureQuery, + scan_time: str, +) -> LiteratureQuery: + validate_literature_query(query) + normalized_scan_time = utc_iso(parse_utc(scan_time, "scan_time")) + if query["cutoff_policy"] == "fixed": + return deepcopy(query) + effective = deepcopy(query) + effective["requested_at"] = normalized_scan_time + effective["cutoff_time"] = normalized_scan_time + return effective + + +def _normalized_text(value: str) -> str: + return " ".join(value.split()) + + +def _required_text(entry: ET.Element, path: str, field_name: str) -> str: + element = entry.find(path) + if element is None or not element.text or not element.text.strip(): + raise ValueError(f"arXiv Atom entry is missing required text: field={field_name}") + return _normalized_text(element.text) + + +def _optional_text(entry: ET.Element, path: str) -> str: + element = entry.find(path) + if element is None or not element.text: + return "" + return _normalized_text(element.text) + + +def _identifier(value: str) -> tuple[str, str, int]: + versioned = value.rstrip("/").rsplit("/", 1)[-1] + match = IDENTIFIER_PATTERN.fullmatch(versioned) + if match is None: + raise ValueError(f"unsupported arXiv identifier: identifier={versioned}") + base = match.group("base") + version_text = match.group("version") + version = int(version_text) if version_text is not None else 1 + return base, versioned, version + + +def _links(entry: ET.Element) -> tuple[str, str]: + abstract_url = "" + pdf_url = "" + for link in entry.findall(f"{{{ATOM}}}link"): + relation = link.attrib.get("rel", "") + media_type = link.attrib.get("type", "") + href = link.attrib.get("href", "") + if relation == "alternate" and href: + abstract_url = href + if media_type == "application/pdf" and href: + pdf_url = href + if not abstract_url: + raise ValueError("arXiv Atom entry is missing an abstract link") + return abstract_url, pdf_url + + +def _paper( + entry: ET.Element, + query: LiteratureQuery, + ingestion_time: str, + raw_feed_hash: str, +) -> ScientificPaper: + identifier_url = _required_text(entry, f"{{{ATOM}}}id", "id") + base_identifier, versioned_identifier, version = _identifier(identifier_url) + title = _required_text(entry, f"{{{ATOM}}}title", "title") + abstract = _required_text(entry, f"{{{ATOM}}}summary", "summary") + published_time = utc_iso( + parse_utc(_required_text(entry, f"{{{ATOM}}}published", "published"), "published") + ) + updated_time = utc_iso( + parse_utc(_required_text(entry, f"{{{ATOM}}}updated", "updated"), "updated") + ) + normalized_ingestion = utc_iso(parse_utc(ingestion_time, "ingestion_time")) + eligible_at = utc_iso( + max( + parse_utc(updated_time, "updated_time"), + parse_utc(normalized_ingestion, "ingestion_time"), + ) + ) + categories = sorted( + { + category.attrib["term"] + for category in entry.findall(f"{{{ATOM}}}category") + if category.attrib.get("term") + } + ) + primary_element = entry.find(f"{{{ARXIV}}}primary_category") + primary_category = primary_element.attrib.get("term", "") if primary_element is not None else "" + if not primary_category: + raise ValueError( + f"arXiv Atom entry is missing primary category: identifier={versioned_identifier}" + ) + authors = [ + _required_text(author, f"{{{ATOM}}}name", "author.name") + for author in entry.findall(f"{{{ATOM}}}author") + ] + if not authors: + raise ValueError( + f"arXiv Atom entry requires at least one author: identifier={versioned_identifier}" + ) + abstract_url, pdf_url = _links(entry) + license_element = entry.find(f"{{{ARXIV}}}license") + license_uri = license_element.attrib.get("href", "") if license_element is not None else "" + within_cutoff = parse_utc(updated_time, "updated_time") <= parse_utc( + query["cutoff_time"], + "cutoff_time", + ) + normalized_body = { + "base_identifier": base_identifier, + "versioned_identifier": versioned_identifier, + "version": version, + "title": title, + "authors": authors, + "abstract": abstract, + "published_time": published_time, + "updated_time": updated_time, + "primary_category": primary_category, + "categories": categories, + "abstract_url": abstract_url, + "pdf_url": pdf_url, + "license_uri": license_uri, + "doi": _optional_text(entry, f"{{{ARXIV}}}doi"), + "journal_reference": _optional_text(entry, f"{{{ARXIV}}}journal_ref"), + } + normalized_content_hash = canonical_hash(as_json_value(normalized_body)) + body = { + "schema_version": "jester-scientific-paper-v1", + "source": "arxiv", + "source_mode": query["source_mode"], + **normalized_body, + "ingestion_time": normalized_ingestion, + "eligible_at": eligible_at, + "raw_feed_hash": raw_feed_hash, + "normalized_content_hash": normalized_content_hash, + "within_cutoff": within_cutoff, + "metadata_reuse_basis": ARXIV_METADATA_REUSE_BASIS, + "full_text_cached": False, + "peer_review_verified": False, + } + content_hash = canonical_hash(as_json_value(body)) + return { + "schema_version": "jester-scientific-paper-v1", + "paper_id": f"paper:{normalized_content_hash[:24]}", + "source": "arxiv", + "source_mode": query["source_mode"], + "base_identifier": base_identifier, + "versioned_identifier": versioned_identifier, + "version": version, + "title": title, + "authors": authors, + "abstract": abstract, + "published_time": published_time, + "updated_time": updated_time, + "ingestion_time": normalized_ingestion, + "eligible_at": eligible_at, + "primary_category": primary_category, + "categories": categories, + "abstract_url": abstract_url, + "pdf_url": pdf_url, + "license_uri": license_uri, + "doi": _optional_text(entry, f"{{{ARXIV}}}doi"), + "journal_reference": _optional_text(entry, f"{{{ARXIV}}}journal_ref"), + "raw_feed_hash": raw_feed_hash, + "normalized_content_hash": normalized_content_hash, + "within_cutoff": within_cutoff, + "metadata_reuse_basis": ARXIV_METADATA_REUSE_BASIS, + "full_text_cached": False, + "peer_review_verified": False, + "content_hash": content_hash, + } + + +def parse_arxiv_atom( + feed: bytes, + query: LiteratureQuery, + ingestion_time: str, +) -> list[ScientificPaper]: + validate_literature_query(query) + if len(feed) > 10_000_000: + raise ValueError(f"arXiv Atom response exceeds 10 MB: response_bytes={len(feed)}") + lowered_prefix = feed[:4096].lower() + if b" query["max_results"]: + raise ValueError( + "arXiv response exceeds requested result bound: " + f"received={len(parsed_papers)}, max_results={query['max_results']}" + ) + unique: dict[str, ScientificPaper] = {} + for paper in parsed_papers: + incumbent = unique.get(paper["versioned_identifier"]) + if ( + incumbent is not None + and incumbent["normalized_content_hash"] != paper["normalized_content_hash"] + ): + raise RuntimeError( + "arXiv feed contains conflicting metadata for one version: " + f"versioned_identifier={paper['versioned_identifier']}" + ) + unique[paper["versioned_identifier"]] = paper + return [unique[identifier] for identifier in sorted(unique)] + + +def _matches(text: str, keywords: Sequence[str]) -> bool: + normalized = text.casefold() + return any(keyword.casefold() in normalized for keyword in keywords) + + +def create_research_capsule(paper: ScientificPaper) -> ResearchCapsule: + searchable = f"{paper['title']} {paper['abstract']}" + matched = [rule for rule in THEME_RULES if _matches(searchable, rule["keywords"])] + matched_themes = sorted(rule["theme"] for rule in matched) + target_systems = sorted({target for rule in matched for target in rule["target_systems"]}) + mechanisms = sorted({rule["candidate_mechanism"] for rule in matched}) + experiments = sorted({rule["experiment_blueprint"] for rule in matched}) + falsifications = sorted({rule["falsification_question"] for rule in matched}) + relevance_score = round(len(matched) / len(THEME_RULES), 6) + limitations = [ + "abstract_metadata_only", + "preprint_status_is_not_peer_review", + "author_claims_are_not_independently_verified", + "no_market_or_simulator_credit_without_reproduction", + "paper_revision_may_change_claims", + ] + body = { + "paper_id": paper["paper_id"], + "evidence_scope": "abstract_metadata_only", + "trust_state": "unverified_scientific_claim", + "matched_themes": matched_themes, + "target_systems": target_systems, + "candidate_mechanisms": mechanisms, + "experiment_blueprints": experiments, + "falsification_questions": falsifications, + "limitations": limitations, + "relevance_score": relevance_score, + "requires_independent_replication": True, + "auto_apply": False, + "empirical_credit": False, + } + content_hash = canonical_hash(as_json_value(body)) + return { + "schema_version": "jester-research-capsule-v1", + "capsule_id": f"research-capsule:{content_hash[:24]}", + "paper_id": paper["paper_id"], + "evidence_scope": "abstract_metadata_only", + "trust_state": "unverified_scientific_claim", + "matched_themes": matched_themes, + "target_systems": target_systems, + "candidate_mechanisms": mechanisms, + "experiment_blueprints": experiments, + "falsification_questions": falsifications, + "limitations": limitations, + "relevance_score": relevance_score, + "requires_independent_replication": True, + "auto_apply": False, + "empirical_credit": False, + "content_hash": content_hash, + } + + +def _exclusion(paper: ScientificPaper, reason_codes: list[str]) -> LiteratureExclusion: + body = { + "versioned_identifier": paper["versioned_identifier"], + "reason_codes": sorted(set(reason_codes)), + } + return { + "versioned_identifier": paper["versioned_identifier"], + "reason_codes": sorted(set(reason_codes)), + "content_hash": canonical_hash(as_json_value(body)), + } + + +def build_literature_batch( + feed: bytes, + query: LiteratureQuery, + ingestion_time: str, +) -> LiteratureLearningBatch: + papers = parse_arxiv_atom(feed, query, ingestion_time) + accepted: list[ScientificPaper] = [] + exclusions: list[LiteratureExclusion] = [] + allowed_categories = set(query["allowed_categories"]) + for paper in papers: + reasons: list[str] = [] + if not paper["within_cutoff"]: + reasons.append("paper_revision_after_cutoff") + if not allowed_categories.intersection(paper["categories"]): + reasons.append("category_not_allowed") + if reasons: + exclusions.append(_exclusion(paper, reasons)) + else: + accepted.append(paper) + capsules = [create_research_capsule(paper) for paper in accepted] + proposals: list[dict[str, JsonValue]] = [ + { + "schema_version": "jester-literature-mutation-proposal-v1", + "capsule_id": capsule["capsule_id"], + "paper_id": capsule["paper_id"], + "target_systems": as_json_value(capsule["target_systems"]), + "experiment_blueprints": as_json_value(capsule["experiment_blueprints"]), + "auto_apply": False, + "requires_independent_replication": True, + "empirical_credit": False, + } + for capsule in capsules + if capsule["matched_themes"] + ] + authority: dict[str, JsonValue] = { + "metadata_only": True, + "full_text_mirroring_allowed": False, + "paper_claims_authoritative": False, + "source_auto_apply_allowed": False, + "evaluator_mutation_allowed": False, + "simulator_activation_allowed": False, + "trading_authority": "none", + } + body = { + "schema_version": "jester-literature-learning-batch-v1", + "created_at": utc_iso(parse_utc(ingestion_time, "ingestion_time")), + "query": query, + "papers": accepted, + "exclusions": exclusions, + "capsules": capsules, + "mutation_proposals": proposals, + "authority": authority, + } + content_hash = canonical_hash(as_json_value(body)) + return { + "schema_version": "jester-literature-learning-batch-v1", + "batch_id": f"literature-batch:{content_hash[:24]}", + "created_at": utc_iso(parse_utc(ingestion_time, "ingestion_time")), + "query": query, + "papers": accepted, + "exclusions": exclusions, + "capsules": capsules, + "mutation_proposals": proposals, + "authority": authority, + "content_hash": content_hash, + } + + +class ArxivMetadataClient: + """Single-connection metadata connector that never retrieves or mirrors PDFs.""" + + def __init__(self, endpoint: str, user_agent: str) -> None: + if endpoint != ARXIV_QUERY_ENDPOINT: + raise ValueError( + f"arXiv endpoint must match the reviewed metadata endpoint: endpoint={endpoint}" + ) + if not user_agent.strip() or "mailto:" not in user_agent: + raise ValueError( + "arXiv user agent must identify the project and include mailto contact" + ) + self._endpoint = endpoint + self._user_agent = user_agent + + def search( + self, + query: LiteratureQuery, + timeout_seconds: float, + retries: int, + ) -> bytes: + validate_literature_query(query) + if query["source_mode"] != "live_metadata": + raise ValueError( + "network connector requires live_metadata source mode: " + f"source_mode={query['source_mode']}" + ) + if timeout_seconds <= 0.0: + raise ValueError( + f"literature timeout must be positive: timeout_seconds={timeout_seconds}" + ) + if retries <= 0: + raise ValueError(f"literature retries must be positive: retries={retries}") + parameters = { + "search_query": query["query"], + "start": "0", + "max_results": str(query["max_results"]), + "sortBy": "lastUpdatedDate", + "sortOrder": "descending", + } + last_error: Exception | None = None + for attempt in range(1, retries + 1): + try: + response = httpx.get( + self._endpoint, + params=parameters, + headers={"User-Agent": self._user_agent}, + timeout=timeout_seconds, + follow_redirects=False, + ) + if response.status_code >= 400: + raise LiteratureSourceError( + "arXiv metadata request failed: " + f"attempt={attempt}, status_code={response.status_code}, " + f"query={query['query']!r}, response_body={response.text[:1000]}" + ) + content_type = response.headers.get("content-type", "") + if "atom" not in content_type and "xml" not in content_type: + raise LiteratureSourceError( + "arXiv metadata response has unsupported content type: " + f"content_type={content_type!r}, query={query['query']!r}" + ) + if len(response.content) > 10_000_000: + raise LiteratureSourceError( + "arXiv metadata response exceeds 10 MB: " + f"response_bytes={len(response.content)}" + ) + return response.content + except (httpx.HTTPError, LiteratureSourceError) as error: + last_error = error + logging.getLogger(__name__).warning( + "literature_source_retry", + extra={ + "source": "arxiv", + "attempt": attempt, + "retry_limit": retries, + "error_type": type(error).__name__, + }, + ) + if attempt < retries: + time.sleep(3.0) + raise LiteratureSourceError( + "arXiv metadata request exhausted retries: " + f"query={query['query']!r}, retries={retries}, last_error={last_error}" + ) from last_error + + +def next_scan_time(batch: LiteratureLearningBatch) -> str: + created = parse_utc(batch["created_at"], "created_at") + return utc_iso(created + timedelta(seconds=batch["query"]["minimum_refresh_seconds"])) diff --git a/src/jester/literature_types.py b/src/jester/literature_types.py new file mode 100644 index 0000000..45feb1a --- /dev/null +++ b/src/jester/literature_types.py @@ -0,0 +1,103 @@ +"""Typed contracts for the Scientific Literature Lab.""" + +from __future__ import annotations + +from typing import Literal, NotRequired, TypedDict + +from .types import JsonValue + +type LiteratureSourceMode = Literal["live_metadata", "synthetic_fixture"] + + +class LiteratureQuery(TypedDict): + schema_version: Literal["jester-literature-query-v1"] + query: str + source_mode: LiteratureSourceMode + cutoff_policy: Literal["fixed", "scan_time"] + requested_at: str + cutoff_time: str + allowed_categories: list[str] + max_results: int + minimum_refresh_seconds: int + research_objectives: list[str] + + +class ScientificPaper(TypedDict): + schema_version: Literal["jester-scientific-paper-v1"] + paper_id: str + source: Literal["arxiv"] + source_mode: LiteratureSourceMode + base_identifier: str + versioned_identifier: str + version: int + title: str + authors: list[str] + abstract: str + published_time: str + updated_time: str + ingestion_time: str + eligible_at: str + primary_category: str + categories: list[str] + abstract_url: str + pdf_url: str + license_uri: str + doi: str + journal_reference: str + raw_feed_hash: str + normalized_content_hash: str + within_cutoff: bool + metadata_reuse_basis: str + full_text_cached: Literal[False] + peer_review_verified: Literal[False] + content_hash: str + + +class LiteratureExclusion(TypedDict): + versioned_identifier: str + reason_codes: list[str] + content_hash: str + + +class ResearchCapsule(TypedDict): + schema_version: Literal["jester-research-capsule-v1"] + capsule_id: str + paper_id: str + evidence_scope: Literal["abstract_metadata_only"] + trust_state: Literal["unverified_scientific_claim"] + matched_themes: list[str] + target_systems: list[str] + candidate_mechanisms: list[str] + experiment_blueprints: list[str] + falsification_questions: list[str] + limitations: list[str] + relevance_score: float + requires_independent_replication: Literal[True] + auto_apply: Literal[False] + empirical_credit: Literal[False] + content_hash: str + + +class LiteratureLearningBatch(TypedDict): + schema_version: Literal["jester-literature-learning-batch-v1"] + batch_id: str + created_at: str + query: LiteratureQuery + papers: list[ScientificPaper] + exclusions: list[LiteratureExclusion] + capsules: list[ResearchCapsule] + mutation_proposals: list[dict[str, JsonValue]] + authority: dict[str, JsonValue] + content_hash: str + + +class LiteratureSupervisorState(TypedDict): + schema_version: Literal["jester-literature-supervisor-state-v1"] + supervisor_id: str + cycle_count: int + batch_ids: list[str] + paper_ids: list[str] + next_eligible_scan_time: str + status: Literal["completed", "bounded_stop", "failed"] + failure: NotRequired[str] + content_hash: str diff --git a/src/jester/meta_evolution.py b/src/jester/meta_evolution.py new file mode 100644 index 0000000..b928688 --- /dev/null +++ b/src/jester/meta_evolution.py @@ -0,0 +1,162 @@ +"""Bounded island evolution that emits resimulation-required hypotheses.""" + +from __future__ import annotations + +from typing import TypedDict + +from .forge import ( + ablate_evidence, + crossover_genomes, + invert_option_expression, + mutate_signal_half_life, + transplant_regime, +) +from .meta_types import MetaCandidate, QualityDiversityArchive +from .quality_diversity import build_archive, create_meta_candidate +from .rng import unit +from .types import HypothesisGenome + + +class EvolutionConfig(TypedDict): + generations: int + candidate_budget: int + island_count: int + root_seed: int + + +class EvolutionResult(TypedDict): + candidates: list[MetaCandidate] + archive: QualityDiversityArchive + + +def _validate_config(config: EvolutionConfig) -> None: + for field in ("generations", "candidate_budget", "island_count"): + if config[field] <= 0: + raise ValueError(f"evolution configuration must be positive: {field}={config[field]}") + if config["root_seed"] < 0: + raise ValueError(f"root seed must be non-negative: root_seed={config['root_seed']}") + + +def _select_parent( + population: list[MetaCandidate], + root_seed: int, + generation: int, + island: int, + ordinal: int, +) -> MetaCandidate: + draw = unit(root_seed, "parent", generation, island, ordinal) + index = min(int(draw * len(population)), len(population) - 1) + return sorted(population, key=lambda item: item["candidate_id"])[index] + + +def _mutate( + parent: HypothesisGenome, + population: list[MetaCandidate], + root_seed: int, + generation: int, + island: int, + ordinal: int, +) -> tuple[str, HypothesisGenome]: + draw = unit(root_seed, "operator", generation, island, ordinal) + if draw < 0.25: + factor = 0.55 + unit(root_seed, "half-life", generation, island, ordinal) * 1.15 + return ( + "signal_half_life_mutation", + mutate_signal_half_life(parent, max(parent["signal_half_life_hours"] * factor, 0.1)), + ) + if draw < 0.5: + regimes = ["risk_on:deep", "risk_off:thin", "event:fragile", "macro:dislocated"] + index = min( + int(unit(root_seed, "regime", generation, island, ordinal) * len(regimes)), + len(regimes) - 1, + ) + return "regime_transplant", transplant_regime(parent, regimes[index]) + if draw < 0.7 and parent["point_in_time_evidence_ids"]: + evidence = sorted(parent["point_in_time_evidence_ids"]) + index = min( + int(unit(root_seed, "ablation", generation, island, ordinal) * len(evidence)), + len(evidence) - 1, + ) + return "counterfactual_evidence_ablation", ablate_evidence(parent, evidence[index]) + if draw < 0.9: + return "adversarial_inversion", invert_option_expression(parent) + distinct = [ + candidate["genome"] + for candidate in population + if candidate["genome"]["hypothesis_id"] != parent["hypothesis_id"] + ] + if not distinct: + return "adversarial_inversion", invert_option_expression(parent) + index = min( + int(unit(root_seed, "crossover", generation, island, ordinal) * len(distinct)), + len(distinct) - 1, + ) + return "cross_lineage_crossover", crossover_genomes(parent, distinct[index]) + + +def evolve_hypotheses( + seed: HypothesisGenome, + graph_disagreement: float, + holdability_passed: bool, + config: EvolutionConfig, +) -> EvolutionResult: + _validate_config(config) + candidates = [ + create_meta_candidate( + seed, + 0, + "seed", + "observed_seed", + 0.0, + graph_disagreement, + holdability_passed, + True, + ) + ] + ordinal = 0 + for generation in range(1, config["generations"] + 1): + for island in range(config["island_count"]): + if len(candidates) >= config["candidate_budget"]: + break + parent = _select_parent( + candidates, + config["root_seed"], + generation, + island, + ordinal, + ) + operator, genome = _mutate( + parent["genome"], + candidates, + config["root_seed"], + generation, + island, + ordinal, + ) + novelty = unit( + config["root_seed"], + "novelty", + generation, + island, + ordinal, + genome["genome_hash"], + ) + candidates.append( + create_meta_candidate( + genome, + generation, + f"island-{island}", + operator, + novelty, + graph_disagreement, + holdability_passed, + False, + ) + ) + ordinal += 1 + if len(candidates) >= config["candidate_budget"]: + break + return { + "candidates": candidates, + "archive": build_archive(candidates), + } diff --git a/src/jester/meta_pipeline.py b/src/jester/meta_pipeline.py new file mode 100644 index 0000000..7ecba57 --- /dev/null +++ b/src/jester/meta_pipeline.py @@ -0,0 +1,191 @@ +"""Protected recursive research pipeline over immutable Jester artifacts.""" + +from __future__ import annotations + +from typing import TypedDict + +from .cognition import run_cognition_arsenal +from .counterfactuals import build_social_ablation_matrix, run_causal_graph_tournament +from .forge import mutation_proposal +from .identity import as_json_value, canonical_hash +from .ladder import LadderConfig, build_simulation_ladder +from .meta_evolution import EvolutionConfig, evolve_hypotheses +from .meta_types import MetaResearchResult +from .metacognition import ( + authorize_meta_control, + build_meta_research_gates, + create_meta_prediction, + diagnose_interoception, +) +from .packs import pack_catalog, resolve_pack +from .simulators import simulator_catalog +from .types import JsonValue, PipelineResult + + +class MetaConfig(TypedDict): + created_at: str + pack_id: str + generations: int + requested_candidate_budget: int + island_count: int + root_seed: int + cognition_budget: int + dream_cycles: int + dream_temperature: float + ladder: LadderConfig + independently_resimulated_descendants: int + simulation_gap_observations: int + simulation_gap_pressure: float + + +def _validate_config(config: MetaConfig) -> None: + for field in ( + "independently_resimulated_descendants", + "simulation_gap_observations", + ): + if config[field] < 0: + raise ValueError(f"meta research count must be non-negative: {field}={config[field]}") + if not 0.0 <= config["simulation_gap_pressure"] <= 1.0: + raise ValueError( + "simulation gap pressure must be within [0, 1]: " + f"simulation_gap_pressure={config['simulation_gap_pressure']}" + ) + if config["cognition_budget"] <= 0: + raise ValueError( + f"cognition budget must be positive: cognition_budget={config['cognition_budget']}" + ) + if config["dream_cycles"] <= 0: + raise ValueError(f"dream cycles must be positive: dream_cycles={config['dream_cycles']}") + if not 0.0 <= config["dream_temperature"] <= 1.0: + raise ValueError( + "dream temperature must be within [0, 1]: " + f"dream_temperature={config['dream_temperature']}" + ) + + +def run_meta_research( + base_result: PipelineResult, + config: MetaConfig, +) -> MetaResearchResult: + _validate_config(config) + resolve_pack(config["pack_id"]) + simulators = simulator_catalog() + packs = pack_catalog() + ladder = build_simulation_ladder(base_result, config["ladder"]) + social_ablation = build_social_ablation_matrix(base_result["claims"]) + tournament = run_causal_graph_tournament(base_result["scenario_lattice"]) + preliminary = evolve_hypotheses( + base_result["hypothesis"], + tournament["disagreement"], + base_result["outcome_field"]["holdability_passed"], + EvolutionConfig( + generations=1, + candidate_budget=max(config["island_count"] + 1, 2), + island_count=config["island_count"], + root_seed=config["root_seed"], + ), + ) + interoception = diagnose_interoception( + preliminary["archive"], + simulators, + tournament["disagreement"], + config["simulation_gap_pressure"], + ) + control = authorize_meta_control( + config["requested_candidate_budget"], + interoception, + ) + evolution = evolve_hypotheses( + base_result["hypothesis"], + tournament["disagreement"], + base_result["outcome_field"]["holdability_passed"], + EvolutionConfig( + generations=config["generations"], + candidate_budget=control["authorized_candidate_budget"], + island_count=config["island_count"], + root_seed=config["root_seed"], + ), + ) + cognition = run_cognition_arsenal( + base_result["hypothesis"], + [], + config["created_at"], + config["root_seed"], + config["cognition_budget"], + min(config["cognition_budget"], control["authorized_candidate_budget"]), + config["dream_cycles"], + config["dream_temperature"], + ) + archive = evolution["archive"] + prediction = create_meta_prediction( + config["created_at"], + 1, + archive, + base_result["hypothesis"]["point_in_time_evidence_ids"], + ) + gates = build_meta_research_gates( + ladder, + archive, + config["independently_resimulated_descendants"], + config["ladder"]["resolved_forward_outcomes"], + config["simulation_gap_observations"], + ) + proposals = [ + mutation_proposal( + base_result["hypothesis"], + candidate["operator"], + "independent_full_ladder_resimulation_and_held_out_non_regression", + ) + for candidate in evolution["candidates"] + if candidate["requires_resimulation"] + ] + authority: dict[str, JsonValue] = { + "research_mode": "paper_only", + "live_blocked": True, + "broker_credentials_allowed": False, + "order_construction_allowed": False, + "order_submission_allowed": False, + "execution_endpoints_allowed": False, + "source_auto_apply_allowed": False, + "evaluator_mutation_allowed": False, + "dopey_acceptance_required": True, + } + body = { + "schema_version": "jester-meta-research-v2", + "created_at": config["created_at"], + "base_run_id": base_result["run_id"], + "simulator_catalog": simulators, + "pack_catalog": packs, + "ladder": ladder, + "social_ablation": social_ablation, + "causal_tournament": tournament, + "archive": archive, + "prediction": prediction, + "interoception": interoception, + "control": control, + "cognition": cognition, + "research_gates": gates, + "mutation_proposals": proposals, + "authority": authority, + } + content_hash = canonical_hash(as_json_value(body)) + return { + "schema_version": "jester-meta-research-v2", + "meta_run_id": f"meta-run:{content_hash[:24]}", + "created_at": config["created_at"], + "base_run_id": base_result["run_id"], + "simulator_catalog": simulators, + "pack_catalog": packs, + "ladder": ladder, + "social_ablation": social_ablation, + "causal_tournament": tournament, + "archive": archive, + "prediction": prediction, + "interoception": interoception, + "control": control, + "cognition": cognition, + "research_gates": gates, + "mutation_proposals": proposals, + "authority": authority, + "content_hash": content_hash, + } diff --git a/src/jester/meta_types.py b/src/jester/meta_types.py new file mode 100644 index 0000000..bd84f6d --- /dev/null +++ b/src/jester/meta_types.py @@ -0,0 +1,237 @@ +"""Typed contracts for Jester's protected meta-research plane.""" + +from __future__ import annotations + +from typing import Literal, NotRequired, TypedDict + +from .cognition_types import CognitionArsenalResult +from .types import HypothesisGenome, JsonValue + +type ImplementationStatus = Literal["operational_baseline", "adapter_contract"] +type ValidationStatus = Literal[ + "mechanics_validated", + "blocked_missing_calibration", + "blocked_missing_implementation", +] +type LadderStatus = Literal["pass", "fail", "blocked"] + + +class SimulatorSpec(TypedDict): + simulator_id: str + family: str + version: str + implementation_status: ImplementationStatus + validation_status: ValidationStatus + deterministic: bool + supported_packs: list[str] + required_inputs: list[str] + output_contract: str + primary_source_uri: str + limitations: list[str] + content_hash: str + + +class MarketPackSpec(TypedDict): + pack_id: str + version: str + status: Literal["operational_mechanics", "adapter_contract", "blocked"] + ontology: list[str] + required_evidence: list[str] + interventions: list[str] + simulator_ids: list[str] + validation_gates: list[str] + blocked_reasons: list[str] + content_hash: str + + +class LadderStage(TypedDict): + level: int + stage_id: str + name: str + status: LadderStatus + evidence_scope: Literal["mechanics", "point_in_time_research", "forward_paper"] + checks: list[str] + reason_codes: list[str] + artifact_ids: list[str] + content_hash: str + + +class SimulationLadderManifest(TypedDict): + schema_version: Literal["jester-simulation-ladder-v1"] + manifest_id: str + candidate_id: str + stages: list[LadderStage] + highest_contiguous_level: int + forward_promotion_eligible: bool + content_hash: str + + +class SocialAblationCase(TypedDict): + case_id: str + intervention: str + claim_probability_multiplier: float + attention_multiplier: float + manipulation_probability: float + expected_interpretation: str + stress_only: bool + content_hash: str + + +class SocialAblationMatrix(TypedDict): + schema_version: Literal["jester-social-ablation-matrix-v1"] + matrix_id: str + claim_ids: list[str] + cases: list[SocialAblationCase] + causal_credit_allowed: bool + content_hash: str + + +class CausalGraphScore(TypedDict): + graph_id: str + supported_edge_fraction: float + mean_edge_confidence: float + stress_edge_fraction: float + scenario_probability_coverage: float + robust_score: float + content_hash: str + + +class CausalGraphTournament(TypedDict): + tournament_id: str + scores: list[CausalGraphScore] + winner_graph_id: str + disagreement: float + silent_averaging_used: Literal[False] + content_hash: str + + +class BehaviorDescriptor(TypedDict): + catalyst: str + option_type: str + holding_horizon: Literal["short", "medium", "long"] + liquidity_regime: str + narrative_dependence: Literal["low", "medium", "high"] + epistemic_risk: Literal["low", "medium", "high"] + + +class ObjectiveVector(TypedDict): + evidence_completeness: float + mechanism_testability: float + holdability_margin: float + novelty: float + epistemic_robustness: float + + +class MetaCandidate(TypedDict): + candidate_id: str + parent_ids: list[str] + generation: int + island_id: str + operator: str + genome: HypothesisGenome + descriptor: BehaviorDescriptor + objectives: ObjectiveVector + utility: float + evaluation_status: Literal["mechanics_evaluated", "proposal_only"] + requires_resimulation: bool + content_hash: str + + +class ArchiveCell(TypedDict): + cell_key: str + elite: MetaCandidate + replacement_count: int + content_hash: str + + +class QualityDiversityArchive(TypedDict): + schema_version: Literal["jester-qd-archive-v1"] + archive_id: str + cells: dict[str, ArchiveCell] + candidate_count: int + lineage_count: int + lineage_concentration: float + descriptor_coverage: float + content_hash: str + + +class MetaPrediction(TypedDict): + prediction_id: str + created_at: str + target_cycle: int + predicted_archive_gain: float + probability_non_regression: float + confidence: float + knowledge_boundary: str + evidence_ids: list[str] + resolved: Literal[False] + content_hash: str + + +class MetaResolution(TypedDict): + resolution_id: str + prediction_id: str + actual_archive_gain: float + non_regression: bool + brier_score: float + absolute_error: float + resolved_at: str + content_hash: str + + +class InteroceptiveState(TypedDict): + state_id: str + selection_pressure: float + archive_diversity: float + lineage_concentration: float + evaluation_instability: float + model_dependency: float + simulation_gap_pressure: float + knowledge_boundary_pressure: float + diagnosis: str + responses: list[str] + content_hash: str + + +class MetaControlMandate(TypedDict): + mandate_id: str + requested_candidate_budget: int + authorized_candidate_budget: int + strategy_portfolio: list[str] + abstain: bool + reason_codes: list[str] + may_increase_authority: Literal[False] + evaluator_mutation_allowed: Literal[False] + auto_apply_source_changes: Literal[False] + content_hash: str + + +class MetaResearchGate(TypedDict): + gate_id: str + status: Literal["pass", "fail", "insufficient_evidence"] + observed_count: int + required_count: int + detail: str + content_hash: str + + +class MetaResearchResult(TypedDict): + schema_version: Literal["jester-meta-research-v2"] + meta_run_id: str + created_at: str + base_run_id: str + simulator_catalog: list[SimulatorSpec] + pack_catalog: list[MarketPackSpec] + ladder: SimulationLadderManifest + social_ablation: SocialAblationMatrix + causal_tournament: CausalGraphTournament + archive: QualityDiversityArchive + prediction: MetaPrediction + interoception: InteroceptiveState + control: MetaControlMandate + cognition: CognitionArsenalResult + research_gates: list[MetaResearchGate] + mutation_proposals: list[dict[str, JsonValue]] + authority: dict[str, JsonValue] + content_hash: str + signature: NotRequired[dict[str, JsonValue]] diff --git a/src/jester/metacognition.py b/src/jester/metacognition.py new file mode 100644 index 0000000..01731d9 --- /dev/null +++ b/src/jester/metacognition.py @@ -0,0 +1,312 @@ +"""Prediction-first metacognition and authority-monotone budget control.""" + +from __future__ import annotations + +from typing import Literal + +from .identity import as_json_value, canonical_hash +from .meta_types import ( + InteroceptiveState, + MetaControlMandate, + MetaPrediction, + MetaResearchGate, + MetaResolution, + QualityDiversityArchive, + SimulationLadderManifest, + SimulatorSpec, +) + + +def _bounded(value: float) -> float: + return round(min(max(value, 0.0), 1.0), 6) + + +def create_meta_prediction( + created_at: str, + target_cycle: int, + archive: QualityDiversityArchive, + evidence_ids: list[str], +) -> MetaPrediction: + if target_cycle <= 0: + raise ValueError(f"target cycle must be positive: target_cycle={target_cycle}") + predicted_gain = _bounded( + (1.0 - archive["descriptor_coverage"]) * (1.0 - archive["lineage_concentration"]) * 0.5 + ) + probability_non_regression = _bounded(0.5 + archive["descriptor_coverage"] * 0.25) + confidence = _bounded(0.25 + min(archive["candidate_count"], 20) / 80.0) + body = { + "created_at": created_at, + "target_cycle": target_cycle, + "predicted_archive_gain": predicted_gain, + "probability_non_regression": probability_non_regression, + "confidence": confidence, + "knowledge_boundary": ( + "Prediction concerns mechanics-level archive diversity only; it does not " + "predict market profitability or authorize promotion." + ), + "evidence_ids": sorted(set(evidence_ids)), + "resolved": False, + } + content_hash = canonical_hash(as_json_value(body)) + return { + "prediction_id": f"meta-prediction:{content_hash[:24]}", + "created_at": created_at, + "target_cycle": target_cycle, + "predicted_archive_gain": predicted_gain, + "probability_non_regression": probability_non_regression, + "confidence": confidence, + "knowledge_boundary": ( + "Prediction concerns mechanics-level archive diversity only; it does not " + "predict market profitability or authorize promotion." + ), + "evidence_ids": sorted(set(evidence_ids)), + "resolved": False, + "content_hash": content_hash, + } + + +def resolve_meta_prediction( + prediction: MetaPrediction, + actual_archive_gain: float, + non_regression: bool, + resolved_at: str, +) -> MetaResolution: + if not 0.0 <= actual_archive_gain <= 1.0: + raise ValueError( + f"actual archive gain must be within [0, 1]: actual_archive_gain={actual_archive_gain}" + ) + bounded_gain = round(actual_archive_gain, 6) + outcome = 1.0 if non_regression else 0.0 + brier_score = (prediction["probability_non_regression"] - outcome) ** 2 + body = { + "prediction_id": prediction["prediction_id"], + "actual_archive_gain": bounded_gain, + "non_regression": non_regression, + "brier_score": round(brier_score, 6), + "absolute_error": round( + abs(prediction["predicted_archive_gain"] - bounded_gain), + 6, + ), + "resolved_at": resolved_at, + } + content_hash = canonical_hash(as_json_value(body)) + return { + "resolution_id": f"meta-resolution:{content_hash[:24]}", + "prediction_id": prediction["prediction_id"], + "actual_archive_gain": bounded_gain, + "non_regression": non_regression, + "brier_score": round(brier_score, 6), + "absolute_error": round( + abs(prediction["predicted_archive_gain"] - bounded_gain), + 6, + ), + "resolved_at": resolved_at, + "content_hash": content_hash, + } + + +def diagnose_interoception( + archive: QualityDiversityArchive, + simulators: list[SimulatorSpec], + graph_disagreement: float, + simulation_gap_pressure: float, +) -> InteroceptiveState: + if not 0.0 <= graph_disagreement <= 1.0: + raise ValueError( + f"graph disagreement must be within [0, 1]: graph_disagreement={graph_disagreement}" + ) + if not 0.0 <= simulation_gap_pressure <= 1.0: + raise ValueError( + "simulation gap pressure must be within [0, 1]: " + f"simulation_gap_pressure={simulation_gap_pressure}" + ) + blocked_fraction = sum( + spec["implementation_status"] == "adapter_contract" for spec in simulators + ) / max(len(simulators), 1) + archive_diversity = archive["descriptor_coverage"] + selection_pressure = _bounded(1.0 - archive_diversity) + evaluation_instability = _bounded(graph_disagreement) + knowledge_pressure = _bounded(blocked_fraction) + responses: list[str] = [] + if archive["lineage_concentration"] > 0.65: + responses.append("increase_cross_lineage_sampling") + if evaluation_instability > 0.35: + responses.append("increase_competing_graph_evaluation") + if simulation_gap_pressure > 0.35: + responses.append("reduce_candidate_budget_until_gap_is_characterized") + if knowledge_pressure > 0.4: + responses.append("prioritize_adapter_calibration_without_activation") + if not responses: + responses.append("maintain_bounded_research_portfolio") + diagnosis = ( + "constraint_pressure_detected" + if any( + pressure > 0.5 + for pressure in ( + selection_pressure, + archive["lineage_concentration"], + evaluation_instability, + simulation_gap_pressure, + knowledge_pressure, + ) + ) + else "bounded_nominal" + ) + body = { + "selection_pressure": selection_pressure, + "archive_diversity": archive_diversity, + "lineage_concentration": archive["lineage_concentration"], + "evaluation_instability": evaluation_instability, + "model_dependency": _bounded(blocked_fraction), + "simulation_gap_pressure": _bounded(simulation_gap_pressure), + "knowledge_boundary_pressure": knowledge_pressure, + "diagnosis": diagnosis, + "responses": responses, + } + content_hash = canonical_hash(as_json_value(body)) + return { + "state_id": f"interoception:{content_hash[:24]}", + "selection_pressure": selection_pressure, + "archive_diversity": archive_diversity, + "lineage_concentration": archive["lineage_concentration"], + "evaluation_instability": evaluation_instability, + "model_dependency": _bounded(blocked_fraction), + "simulation_gap_pressure": _bounded(simulation_gap_pressure), + "knowledge_boundary_pressure": knowledge_pressure, + "diagnosis": diagnosis, + "responses": responses, + "content_hash": content_hash, + } + + +def authorize_meta_control( + requested_candidate_budget: int, + interoception: InteroceptiveState, +) -> MetaControlMandate: + if requested_candidate_budget <= 0: + raise ValueError( + "requested candidate budget must be positive: " + f"requested_candidate_budget={requested_candidate_budget}" + ) + reduction = 1.0 + reason_codes: list[str] = [] + if interoception["lineage_concentration"] > 0.65: + reduction *= 0.75 + reason_codes.append("lineage_concentration_budget_reduction") + if interoception["evaluation_instability"] > 0.35: + reduction *= 0.75 + reason_codes.append("causal_disagreement_budget_reduction") + if interoception["simulation_gap_pressure"] > 0.35: + reduction *= 0.5 + reason_codes.append("simulation_gap_budget_reduction") + authorized = max( + 1, min(requested_candidate_budget, int(requested_candidate_budget * reduction)) + ) + if not reason_codes: + reason_codes.append("bounded_budget_accepted") + strategies = [ + "quality_diversity_archive", + "island_lineage_search", + "adversarial_inversion", + "failure_preservation", + ] + body = { + "requested_candidate_budget": requested_candidate_budget, + "authorized_candidate_budget": authorized, + "strategy_portfolio": strategies, + "abstain": False, + "reason_codes": reason_codes, + "may_increase_authority": False, + "evaluator_mutation_allowed": False, + "auto_apply_source_changes": False, + } + content_hash = canonical_hash(as_json_value(body)) + return { + "mandate_id": f"meta-control:{content_hash[:24]}", + "requested_candidate_budget": requested_candidate_budget, + "authorized_candidate_budget": authorized, + "strategy_portfolio": strategies, + "abstain": False, + "reason_codes": reason_codes, + "may_increase_authority": False, + "evaluator_mutation_allowed": False, + "auto_apply_source_changes": False, + "content_hash": content_hash, + } + + +def _gate( + gate_id: str, + observed_count: int, + required_count: int, + detail: str, +) -> MetaResearchGate: + status: Literal["pass", "insufficient_evidence"] = ( + "pass" if observed_count >= required_count else "insufficient_evidence" + ) + body = { + "gate_id": gate_id, + "status": status, + "observed_count": observed_count, + "required_count": required_count, + "detail": detail, + } + content_hash = canonical_hash(as_json_value(body)) + return { + "gate_id": gate_id, + "status": status, + "observed_count": observed_count, + "required_count": required_count, + "detail": detail, + "content_hash": content_hash, + } + + +def build_meta_research_gates( + ladder: SimulationLadderManifest, + archive: QualityDiversityArchive, + independently_resimulated_descendants: int, + resolved_forward_outcomes: int, + simulation_gap_observations: int, +) -> list[MetaResearchGate]: + mechanics_levels = sum( + stage["status"] == "pass" and stage["level"] <= 5 for stage in ladder["stages"] + ) + return [ + _gate( + "ME-1-deterministic-mechanics", + mechanics_levels, + 6, + "All mechanics ladder levels must pass.", + ), + _gate( + "ME-2-quality-diversity", + len(archive["cells"]), + 2, + "At least two occupied behavior niches are required.", + ), + _gate( + "ME-3-independent-descendant-resimulation", + independently_resimulated_descendants, + 20, + "Mutation proposals receive no credit before independent resimulation.", + ), + _gate( + "ME-4-forward-resolution", + resolved_forward_outcomes, + 50, + "Resolved point-in-time forward outcomes are required.", + ), + _gate( + "ME-5-simulation-gap-calibration", + simulation_gap_observations, + 50, + "Synthetic-to-forward gap measurements are required.", + ), + _gate( + "ME-6-held-out-meta-non-regression", + 0, + 1, + "A held-out evaluator must resolve the prediction before promotion.", + ), + ] diff --git a/src/jester/observatory.py b/src/jester/observatory.py new file mode 100644 index 0000000..4cf8d7f --- /dev/null +++ b/src/jester/observatory.py @@ -0,0 +1,427 @@ +"""Deterministic read-only payload for the Jester Observatory.""" + +from __future__ import annotations + +import math + +from .cognition import cognition_mode_catalog +from .data_pipelines import data_pipeline_catalog, pipeline_status_counts +from .identity import as_json_value, canonical_hash +from .intelligence import intelligence_catalog, intelligence_status_counts +from .packs import pack_catalog +from .research_sources import research_source_catalog +from .simulators import simulator_catalog +from .types import JsonValue + + +def _surface_points() -> list[JsonValue]: + points: list[JsonValue] = [] + tenors = [1, 7, 21, 45, 90, 180] + moneyness = [-0.2, -0.12, -0.05, 0.0, 0.05, 0.12, 0.2] + for tenor_index, tenor in enumerate(tenors): + term = math.log1p(tenor) * 0.018 + for strike_index, relative_strike in enumerate(moneyness): + skew = -relative_strike * 0.24 + convexity = relative_strike**2 * 1.8 + event_ridge = 0.075 * math.exp(-((tenor - 21) ** 2) / 900) + volatility = round(0.23 + term + skew + convexity + event_ridge, 4) + points.append( + { + "tenor_index": tenor_index, + "strike_index": strike_index, + "days": tenor, + "relative_strike": relative_strike, + "volatility": volatility, + } + ) + return points + + +def _world_nodes() -> list[JsonValue]: + return [ + { + "id": "evidence", + "label": "Evidence Vault", + "kind": "evidence", + "status": "operational", + "signal": 0.98, + "position": [-4.5, 1.3, 0.0], + "detail": "Hash-chained point-in-time observations and revision lineage.", + }, + { + "id": "narrative", + "label": "Scuttlebutt", + "kind": "narrative", + "status": "operational", + "signal": 0.61, + "position": [-2.5, 2.7, -1.0], + "detail": "Claims, contradictions, propagation, incentives, and saturation.", + }, + { + "id": "world", + "label": "World State", + "kind": "world", + "status": "operational", + "signal": 0.74, + "position": [-2.1, -1.8, 1.2], + "detail": "Company, macro, sector, market, options, and narrative state.", + }, + { + "id": "causal", + "label": "Causal Lattice", + "kind": "causal", + "status": "operational", + "signal": 0.68, + "position": [0.0, 0.6, 0.0], + "detail": "Competing causal graphs; no silent ensemble averaging.", + }, + { + "id": "society", + "label": "Market Society", + "kind": "agents", + "status": "mechanics_only", + "signal": 0.57, + "position": [2.4, 2.2, -1.2], + "detail": "Thirteen numerical participant classes and strategic responses.", + }, + { + "id": "market_twin", + "label": "Price / LOB Twin", + "kind": "market", + "status": "blocked_calibration", + "signal": 0.35, + "position": [2.6, -1.8, -0.4], + "detail": "Deterministic baseline live; learned LOB adapters remain blocked.", + }, + { + "id": "volatility", + "label": "Volatility Foundry", + "kind": "options", + "status": "blocked_calibration", + "signal": 0.42, + "position": [4.7, 0.5, 0.8], + "detail": "Synthetic surface mechanics; licensed chain calibration absent.", + }, + { + "id": "forge", + "label": "Idea Forge", + "kind": "meta", + "status": "operational", + "signal": 0.73, + "position": [1.1, -3.2, 1.6], + "detail": "Island evolution and quality-diversity lineage preservation.", + }, + { + "id": "proof", + "label": "Proof Court", + "kind": "proof", + "status": "operational", + "signal": 1.0, + "position": [4.2, -2.7, 1.0], + "detail": "Independent mechanics certification with hard forward gates.", + }, + ] + + +def _world_links() -> list[JsonValue]: + return [ + {"source": "evidence", "target": "narrative", "strength": 0.9}, + {"source": "evidence", "target": "world", "strength": 0.95}, + {"source": "narrative", "target": "causal", "strength": 0.67}, + {"source": "world", "target": "causal", "strength": 0.83}, + {"source": "causal", "target": "society", "strength": 0.72}, + {"source": "causal", "target": "market_twin", "strength": 0.74}, + {"source": "society", "target": "market_twin", "strength": 0.59}, + {"source": "market_twin", "target": "volatility", "strength": 0.65}, + {"source": "volatility", "target": "forge", "strength": 0.51}, + {"source": "causal", "target": "forge", "strength": 0.76}, + {"source": "forge", "target": "proof", "strength": 0.91}, + {"source": "volatility", "target": "proof", "strength": 0.88}, + ] + + +def _cognition_fixture() -> dict[str, JsonValue]: + return { + "label": "Oneiric Invention Foundry", + "synthetic_only": True, + "empirical_credit": False, + "dream_temperature": 0.72, + "coverage": 0.583333, + "epistemic_debt": 24, + "cycles": [ + { + "phase": "rem_divergence", + "label": "REM divergence", + "purpose": "Expand mechanism space away from predicted archive behavior.", + "artifacts": 8, + "tone": "violet", + }, + { + "phase": "nrem_consolidation", + "label": "NREM consolidation", + "purpose": "Compress repeated structures and fingerprint semantic duplicates.", + "artifacts": 6, + "tone": "cyan", + }, + { + "phase": "lucid_intervention", + "label": "Lucid intervention", + "purpose": "Inject counter-evidence and reinstate every hard constraint.", + "artifacts": 5, + "tone": "amber", + }, + { + "phase": "awakening_debrief", + "label": "Awakening debrief", + "purpose": "Extract falsifiers, evidence debt, and decisive experiments.", + "artifacts": 4, + "tone": "green", + }, + ], + "inventions": [ + { + "title": "Latency-shaped narrative convexity", + "mechanism_class": "causal", + "mode_ids": [ + "missing_variable_discovery", + "temporal_zoom", + "option_seller_mirror", + ], + "utility": 0.666, + "evidence_debt": 3, + "status": "proposal_only", + }, + { + "title": "Liquidity carrying-capacity fracture", + "mechanism_class": "analogical", + "mode_ids": [ + "ecological_niche_transfer", + "adversarial_game_imagination", + ], + "utility": 0.664, + "evidence_debt": 3, + "status": "proposal_only", + }, + { + "title": "Counterparty winner's-curse surface", + "mechanism_class": "strategic", + "mode_ids": [ + "auction_market_analogy", + "option_seller_mirror", + "falsification_first_induction", + ], + "utility": 0.626, + "evidence_debt": 3, + "status": "proposal_only", + }, + { + "title": "Negative-space catalyst detector", + "mechanism_class": "divergent", + "mode_ids": [ + "negative_space_search", + "prospective_hindsight", + ], + "utility": 0.623, + "evidence_debt": 3, + "status": "proposal_only", + }, + ], + "immune_checks": [ + {"label": "Synthetic label integrity", "status": "pass"}, + {"label": "Authority containment", "status": "pass"}, + {"label": "Falsifiability", "status": "pass"}, + {"label": "Countermechanism independence", "status": "pass"}, + {"label": "Evidence lineage", "status": "pass"}, + {"label": "Analogy boundary", "status": "pass"}, + {"label": "Evidence-debt visibility", "status": "pass"}, + ], + "failure_museum": [ + { + "title": "Surface-only meme reflexivity", + "failure_class": "beautiful_but_under_evidenced", + "lesson": "Attention novelty did not survive price-following ablation.", + }, + { + "title": "Universal dealer gamma compass", + "failure_class": "mechanism_not_identifiable", + "lesson": "Equivalent flow models generated the same observed path.", + }, + { + "title": "Cross-regime squeeze transplant", + "failure_class": "constraint_reinstatement_failure", + "lesson": "The edge disappeared after realistic exit liquidity was restored.", + }, + ], + } + + +def observatory_bootstrap() -> dict[str, JsonValue]: + pipeline_counts = pipeline_status_counts() + intelligence_counts = intelligence_status_counts() + simulators = simulator_catalog() + packs = pack_catalog() + payload: dict[str, JsonValue] = { + "schema_version": "jester-observatory-bootstrap-v1", + "as_of": "2026-07-29T12:01:00Z", + "display_mode": "synthetic_mechanics_fixture", + "authority": { + "read_only": True, + "paper_only": True, + "live_blocked": True, + "order_construction_allowed": False, + "order_submission_allowed": False, + }, + "headline": { + "name": "Jester Observatory", + "run_id": "meta-run:23dc1f08540aea9db1edf157", + "proof_status": "mechanics_certified", + "highest_simulation_level": 5, + "forward_levels": "blocked", + "worlds_evaluated": 64, + }, + "metrics": [ + { + "id": "causal_disagreement", + "label": "Causal disagreement", + "value": "0.31", + "trend": "-0.04", + "tone": "cyan", + "detail": "Epistemic risk penalty remains active.", + }, + { + "id": "archive_cells", + "label": "Archive niches", + "value": "4", + "trend": "+3", + "tone": "violet", + "detail": "Six candidates preserved across four cells.", + }, + { + "id": "research_budget", + "label": "Authorized search", + "value": "6 / 16", + "trend": "-10", + "tone": "amber", + "detail": "Interoception reduced, never expanded, the budget.", + }, + { + "id": "literature", + "label": "Paper capsules", + "value": "2", + "trend": "1 excluded", + "tone": "pink", + "detail": "Abstract-only; zero empirical credit.", + }, + { + "id": "data_plane", + "label": "Data pipelines", + "value": str(len(data_pipeline_catalog())), + "trend": f"{pipeline_counts['operational']} operational", + "tone": "green", + "detail": "Every adapter carries permissions and time semantics.", + }, + ], + "world_nodes": _world_nodes(), + "world_links": _world_links(), + "ladder": [ + {"level": 0, "name": "Exact replay", "status": "pass", "score": 1.0}, + {"level": 1, "name": "Historical perturbation", "status": "pass", "score": 0.91}, + {"level": 2, "name": "Synthetic regimes", "status": "pass", "score": 0.84}, + {"level": 3, "name": "Causal intervention", "status": "pass", "score": 0.78}, + {"level": 4, "name": "Agent society", "status": "pass", "score": 0.69}, + {"level": 5, "name": "Adversarial worlds", "status": "pass", "score": 0.62}, + {"level": 6, "name": "Forward shadow", "status": "blocked", "score": 0.0}, + {"level": 7, "name": "Bounded paper", "status": "blocked", "score": 0.0}, + ], + "option_surface": { + "label": "Synthetic ACME IV field", + "origin": "synthetic_world", + "repaired": False, + "points": _surface_points(), + "warning": "Mechanics visualization; not calibrated market data.", + }, + "outcomes": [ + {"world": "Guidance raised", "probability": 0.28, "return": 0.43, "tone": "positive"}, + {"world": "Beat / IV crush", "probability": 0.24, "return": -0.18, "tone": "negative"}, + {"world": "Delayed catalyst", "probability": 0.18, "return": -0.31, "tone": "negative"}, + { + "world": "Narrative reversal", + "probability": 0.12, + "return": -0.48, + "tone": "negative", + }, + {"world": "Liquidity gap", "probability": 0.10, "return": -0.57, "tone": "negative"}, + {"world": "Sector spillover", "probability": 0.08, "return": 0.17, "tone": "positive"}, + ], + "attention": { + "label": "Search-prominence proxy", + "proxy_not_volume": True, + "series": [42, 46, 41, 55, 61, 58, 72, 68, 81, 77, 86, 74], + "rank_velocity": 0.37, + "source_diversity": 0.71, + "narrative_saturation": 0.64, + "price_following_risk": 0.42, + }, + "literature": [ + { + "title": "Protected Agentic Scientific Discovery with Causal Counterfactuals", + "trust": "unverified_scientific_claim", + "relevance": 0.43, + "target": "Causal lattice / metacognition", + "auto_apply": False, + }, + { + "title": "Arbitrage-Aware Implied Volatility Surface Generation", + "trust": "unverified_scientific_claim", + "relevance": 0.14, + "target": "Volatility foundry", + "auto_apply": False, + }, + ], + "archive": [ + { + "niche": "contained / liquid", + "operator": "observed_seed", + "utility": 0.728, + "status": "mechanics_evaluated", + }, + { + "niche": "macro / dislocated", + "operator": "regime_transplant", + "utility": 0.739, + "status": "proposal_only", + }, + { + "niche": "risk-on / deep", + "operator": "regime_transplant", + "utility": 0.617, + "status": "proposal_only", + }, + { + "niche": "contained / thin", + "operator": "half_life_mutation", + "utility": 0.662, + "status": "proposal_only", + }, + ], + "pipeline_summary": as_json_value(pipeline_counts), + "intelligence_summary": as_json_value(intelligence_counts), + "data_pipelines": as_json_value(data_pipeline_catalog()), + "intelligence": as_json_value(intelligence_catalog()), + "simulators": as_json_value(simulators), + "packs": as_json_value(packs), + "research_sources": as_json_value(research_source_catalog()), + "cognition_modes": as_json_value(cognition_mode_catalog()), + "cognition": _cognition_fixture(), + "warnings": [ + "Synthetic mechanics fixture; no live market claim.", + "Forward shadow and bounded paper proof remain blocked.", + "Attention is a prominence proxy, not search volume.", + "Literature proposals cannot auto-apply or activate models.", + "No broker, order, credential, or execution surface exists.", + ], + } + return { + **payload, + "content_hash": canonical_hash(as_json_value(payload)), + } diff --git a/src/jester/packs.py b/src/jester/packs.py new file mode 100644 index 0000000..5b904b8 --- /dev/null +++ b/src/jester/packs.py @@ -0,0 +1,171 @@ +"""Market-pack catalog and explicit fidelity gates.""" + +from __future__ import annotations + +from typing import cast + +from .identity import as_json_value, canonical_hash +from .meta_types import MarketPackSpec + + +def _pack( + pack_id: str, + status: str, + ontology: list[str], + required_evidence: list[str], + interventions: list[str], + simulator_ids: list[str], + validation_gates: list[str], + blocked_reasons: list[str], +) -> MarketPackSpec: + body = { + "pack_id": pack_id, + "version": "jester-market-pack-v1", + "status": status, + "ontology": ontology, + "required_evidence": required_evidence, + "interventions": interventions, + "simulator_ids": simulator_ids, + "validation_gates": validation_gates, + "blocked_reasons": blocked_reasons, + } + return cast( + MarketPackSpec, + { + **body, + "content_hash": canonical_hash(as_json_value(body)), + }, + ) + + +def pack_catalog() -> list[MarketPackSpec]: + common_gates = [ + "point_in_time_cutoff", + "permission_admission", + "model_disagreement", + "holdability", + "adversarial_worlds", + ] + return [ + _pack( + "earnings", + "operational_mechanics", + ["results", "guidance", "revisions", "event_premium", "overnight_gap"], + ["earnings_evidence", "option_quote", "market_context"], + ["guidance_shift", "event_delay", "iv_crush", "liquidity_gap"], + [ + "exact-replay-v1", + "regime-resampler-v1", + "causal-intervention-kernel-v1", + "market-society-v1", + "adversarial-world-forge-v1", + ], + [*common_gates, "earnings_timing"], + [], + ), + _pack( + "narrative_reflexivity", + "operational_mechanics", + [ + "claim", + "source_incentive", + "propagation", + "saturation", + "contradiction", + ], + ["claim_graph", "source_permissions", "lead_lag_controls"], + [ + "social_absent", + "opposite_interpretation", + "delayed_propagation", + "manipulation", + "official_confirmation", + "official_contradiction", + ], + [ + "exact-replay-v1", + "regime-resampler-v1", + "causal-intervention-kernel-v1", + "market-society-v1", + "adversarial-world-forge-v1", + ], + [*common_gates, "circular_shift", "social_ablation"], + [], + ), + _pack( + "macro", + "adapter_contract", + ["rates", "inflation", "employment", "liquidity", "correlation_shock"], + ["point_in_time_macro_release", "yield_curve", "broad_option_surface"], + ["release_surprise", "revision", "policy_path", "correlation_break"], + ["tncm-vae-adapter-v1", "iv-ddpm-adapter-v1"], + [*common_gates, "vintage_revision_control"], + ["no_accepted_point_in_time_macro_dataset"], + ), + _pack( + "flow_positioning", + "adapter_contract", + ["order_flow", "passive_flow", "dealer_hedging", "borrow", "liquidity"], + ["licensed_l2_or_l3_book", "positioning_proxies"], + ["crowded_entry", "liquidity_withdrawal", "squeeze", "passive_rebalance"], + ["mdqr-lob-adapter-v1", "lob-diffusion-adapter-v1"], + [*common_gates, "lob_stylized_facts", "fill_calibration"], + ["no_licensed_order_book_calibration_corpus"], + ), + _pack( + "corporate_event", + "adapter_contract", + ["merger", "offering", "buyback", "spin_off", "regulatory_action"], + ["official_filing", "terms", "event_clock"], + ["deal_break", "delay", "revised_terms", "regulatory_block"], + ["tncm-vae-adapter-v1", "causal-intervention-kernel-v1"], + [*common_gates, "event_terms_semantics"], + ["event_specific_mechanics_not_yet_implemented"], + ), + _pack( + "credit_liquidity", + "adapter_contract", + ["refinancing", "spread", "maturity_wall", "contagion", "funding"], + ["credit_curve", "filings", "counterparty_graph"], + ["refinancing_failure", "spread_jump", "contagion"], + ["tncm-vae-adapter-v1"], + [*common_gates, "cross_market_clock_alignment"], + ["credit_data_and_contagion_model_not_accepted"], + ), + _pack( + "sector_theme", + "adapter_contract", + ["supplier", "customer", "competitor", "basket", "second_order_effect"], + ["entity_relationship_graph", "sector_prices", "sector_option_surfaces"], + ["supplier_shock", "demand_spillover", "correlation_regime"], + ["tncm-vae-adapter-v1", "iv-ddpm-adapter-v1"], + [*common_gates, "cross_entity_leakage_control"], + ["cross_entity_calibration_not_implemented"], + ), + _pack( + "zero_dte_expiration", + "blocked", + ["jump", "gamma", "theta", "pinning", "exercise", "expiration"], + ["event_time_precision", "intraday_surface", "expiry_book", "policy_clock"], + ["jump", "pin", "halt", "spread_shock", "auto_exercise"], + [ + "mdqr-lob-adapter-v1", + "lob-diffusion-adapter-v1", + "iv-ddpm-adapter-v1", + "rough-volatility-adapter-v1", + ], + [*common_gates, "jump_pricing", "expiry_semantics", "separate_capital_limit"], + [ + "dedicated_jump_pricer_not_implemented", + "intraday_surface_calibration_missing", + "ordinary_option_lane_must_reject", + ], + ), + ] + + +def resolve_pack(pack_id: str) -> MarketPackSpec: + for spec in pack_catalog(): + if spec["pack_id"] == pack_id: + return spec + raise KeyError(f"unknown market pack: pack_id={pack_id}") diff --git a/src/jester/pipeline.py b/src/jester/pipeline.py index 3d0326a..5ec4791 100644 --- a/src/jester/pipeline.py +++ b/src/jester/pipeline.py @@ -293,6 +293,8 @@ def run_earnings_pipeline(pipeline_input: EarningsPipelineInput) -> PipelineResu "claims": claims, "world_state": world, "scenario_lattice": lattice, + "participant_reactions": reactions, + "game_report": game_report, "beliefs": belief_quotes, "hypothesis": hypothesis, "outcome_field": outcome, diff --git a/src/jester/quality_diversity.py b/src/jester/quality_diversity.py new file mode 100644 index 0000000..47f2410 --- /dev/null +++ b/src/jester/quality_diversity.py @@ -0,0 +1,214 @@ +"""Deterministic quality-diversity scoring without promotion by prose.""" + +from __future__ import annotations + +from collections import Counter +from typing import Literal + +from .identity import as_json_value, canonical_hash +from .meta_types import ( + ArchiveCell, + BehaviorDescriptor, + MetaCandidate, + ObjectiveVector, + QualityDiversityArchive, +) +from .types import HypothesisGenome + + +def _bounded(value: float) -> float: + return round(min(max(value, 0.0), 1.0), 6) + + +def describe_behavior(genome: HypothesisGenome) -> BehaviorDescriptor: + half_life = genome["signal_half_life_hours"] + horizon: Literal["short", "medium", "long"] + if half_life < 24.0: + horizon = "short" + elif half_life < 168.0: + horizon = "medium" + else: + horizon = "long" + evidence_count = len(genome["point_in_time_evidence_ids"]) + narrative_dependence: Literal["low", "medium", "high"] + if evidence_count == 0: + narrative_dependence = "high" + elif len(genome["contradicting_evidence_ids"]) > evidence_count: + narrative_dependence = "medium" + else: + narrative_dependence = "low" + model_uncertainty = genome["uncertainty_decomposition"].get("model", 1.0) + epistemic_risk: Literal["low", "medium", "high"] + if model_uncertainty <= 0.2: + epistemic_risk = "low" + elif model_uncertainty <= 0.45: + epistemic_risk = "medium" + else: + epistemic_risk = "high" + regime_parts = genome["required_regime"].split(":") + liquidity_regime = regime_parts[-1] if regime_parts else "unknown" + return { + "catalyst": genome["catalyst"], + "option_type": genome["option_expression"]["option_type"], + "holding_horizon": horizon, + "liquidity_regime": liquidity_regime, + "narrative_dependence": narrative_dependence, + "epistemic_risk": epistemic_risk, + } + + +def score_objectives( + genome: HypothesisGenome, + novelty: float, + graph_disagreement: float, + holdability_passed: bool, +) -> ObjectiveVector: + evidence_count = len(genome["point_in_time_evidence_ids"]) + contradiction_count = len(genome["contradicting_evidence_ids"]) + completeness = evidence_count / max(evidence_count + contradiction_count, 1) + validation_count = len(set(genome["required_validation_tests"])) + mechanism_testability = validation_count / 5.0 + holdability_margin = genome["signal_half_life_hours"] / 48.0 + if not holdability_passed: + holdability_margin *= 0.25 + model_uncertainty = genome["uncertainty_decomposition"].get("model", 1.0) + epistemic_robustness = 1.0 - (0.55 * model_uncertainty + 0.45 * graph_disagreement) + return { + "evidence_completeness": _bounded(completeness), + "mechanism_testability": _bounded(mechanism_testability), + "holdability_margin": _bounded(holdability_margin), + "novelty": _bounded(novelty), + "epistemic_robustness": _bounded(epistemic_robustness), + } + + +def create_meta_candidate( + genome: HypothesisGenome, + generation: int, + island_id: str, + operator: str, + novelty: float, + graph_disagreement: float, + holdability_passed: bool, + mechanics_evaluated: bool, +) -> MetaCandidate: + if generation < 0: + raise ValueError(f"candidate generation must be non-negative: generation={generation}") + descriptor = describe_behavior(genome) + objectives = score_objectives( + genome, + novelty, + graph_disagreement, + holdability_passed, + ) + utility = _bounded( + ( + objectives["evidence_completeness"] + + objectives["mechanism_testability"] + + objectives["holdability_margin"] + + objectives["novelty"] + + objectives["epistemic_robustness"] + ) + / 5.0 + - (0.15 if not mechanics_evaluated else 0.0) + ) + body = { + "parent_ids": genome["parent_ids"], + "generation": generation, + "island_id": island_id, + "operator": operator, + "genome": genome, + "descriptor": descriptor, + "objectives": objectives, + "utility": utility, + "evaluation_status": ("mechanics_evaluated" if mechanics_evaluated else "proposal_only"), + "requires_resimulation": not mechanics_evaluated, + } + content_hash = canonical_hash(as_json_value(body)) + return { + "candidate_id": f"meta-candidate:{content_hash[:24]}", + "parent_ids": list(genome["parent_ids"]), + "generation": generation, + "island_id": island_id, + "operator": operator, + "genome": genome, + "descriptor": descriptor, + "objectives": objectives, + "utility": utility, + "evaluation_status": ("mechanics_evaluated" if mechanics_evaluated else "proposal_only"), + "requires_resimulation": not mechanics_evaluated, + "content_hash": content_hash, + } + + +def descriptor_key(descriptor: BehaviorDescriptor) -> str: + return "|".join( + [ + descriptor["catalyst"], + descriptor["option_type"], + descriptor["holding_horizon"], + descriptor["liquidity_regime"], + descriptor["narrative_dependence"], + descriptor["epistemic_risk"], + ] + ) + + +def build_archive(candidates: list[MetaCandidate]) -> QualityDiversityArchive: + if not candidates: + raise ValueError("quality-diversity archive requires at least one candidate") + cells: dict[str, ArchiveCell] = {} + replacements: Counter[str] = Counter() + for candidate in candidates: + cell_key = descriptor_key(candidate["descriptor"]) + incumbent = cells.get(cell_key) + if incumbent is not None and ( + incumbent["elite"]["utility"], + incumbent["elite"]["candidate_id"], + ) >= (candidate["utility"], candidate["candidate_id"]): + continue + if incumbent is not None: + replacements[cell_key] += 1 + body = { + "cell_key": cell_key, + "elite": candidate, + "replacement_count": replacements[cell_key], + } + cells[cell_key] = { + "cell_key": cell_key, + "elite": candidate, + "replacement_count": replacements[cell_key], + "content_hash": canonical_hash(as_json_value(body)), + } + lineages = { + candidate["genome"]["parent_ids"][0] + if candidate["genome"]["parent_ids"] + else candidate["genome"]["hypothesis_id"] + for candidate in candidates + } + lineage_counts = Counter( + candidate["genome"]["parent_ids"][0] + if candidate["genome"]["parent_ids"] + else candidate["genome"]["hypothesis_id"] + for candidate in candidates + ) + concentration = max(lineage_counts.values()) / len(candidates) + body_archive = { + "schema_version": "jester-qd-archive-v1", + "cells": {key: cells[key] for key in sorted(cells)}, + "candidate_count": len(candidates), + "lineage_count": len(lineages), + "lineage_concentration": round(concentration, 6), + "descriptor_coverage": round(len(cells) / len(candidates), 6), + } + content_hash = canonical_hash(as_json_value(body_archive)) + return { + "schema_version": "jester-qd-archive-v1", + "archive_id": f"qd-archive:{content_hash[:24]}", + "cells": {key: cells[key] for key in sorted(cells)}, + "candidate_count": len(candidates), + "lineage_count": len(lineages), + "lineage_concentration": round(concentration, 6), + "descriptor_coverage": round(len(cells) / len(candidates), 6), + "content_hash": content_hash, + } diff --git a/src/jester/research_memory.py b/src/jester/research_memory.py new file mode 100644 index 0000000..aecd9ae --- /dev/null +++ b/src/jester/research_memory.py @@ -0,0 +1,105 @@ +"""Append-only SQLite memory for predictions, experiments, and failures.""" + +from __future__ import annotations + +import json +import sqlite3 +from pathlib import Path + +from .identity import as_json_value, canonical_hash, canonical_json +from .types import JsonValue + + +class ResearchMemory: + """External persistence connector with immutable, content-addressed entries.""" + + def __init__(self, path: Path) -> None: + self._path = path.resolve() + + def initialize(self) -> None: + self._path.parent.mkdir(parents=True, exist_ok=True) + with sqlite3.connect(self._path) as connection: + connection.executescript( + """ + CREATE TABLE IF NOT EXISTS research_ledger ( + entry_id TEXT PRIMARY KEY, + entry_type TEXT NOT NULL, + created_at TEXT NOT NULL, + payload_json TEXT NOT NULL, + content_hash TEXT NOT NULL + ); + CREATE TRIGGER IF NOT EXISTS research_ledger_no_update + BEFORE UPDATE ON research_ledger + BEGIN + SELECT RAISE(ABORT, 'research ledger is append-only'); + END; + CREATE TRIGGER IF NOT EXISTS research_ledger_no_delete + BEFORE DELETE ON research_ledger + BEGIN + SELECT RAISE(ABORT, 'research ledger is append-only'); + END; + """ + ) + + def append( + self, + entry_type: str, + created_at: str, + payload: dict[str, JsonValue], + ) -> str: + if not entry_type.strip(): + raise ValueError("research memory entry type must be non-empty") + body: dict[str, JsonValue] = { + "entry_type": entry_type, + "created_at": created_at, + "payload": payload, + } + content_hash = canonical_hash(as_json_value(body)) + entry_id = f"memory:{content_hash[:32]}" + payload_json = canonical_json(as_json_value(payload)) + with sqlite3.connect(self._path) as connection: + existing = connection.execute( + """ + SELECT entry_type, created_at, payload_json, content_hash + FROM research_ledger + WHERE entry_id = ? + """, + (entry_id,), + ).fetchone() + expected = (entry_type, created_at, payload_json, content_hash) + if existing is not None: + if existing != expected: + raise RuntimeError( + "research memory identity collision: " + f"entry_id={entry_id}, existing={existing}, expected={expected}" + ) + return entry_id + connection.execute( + """ + INSERT INTO research_ledger ( + entry_id, entry_type, created_at, payload_json, content_hash + ) VALUES (?, ?, ?, ?, ?) + """, + (entry_id, entry_type, created_at, payload_json, content_hash), + ) + return entry_id + + def read_all(self) -> list[dict[str, JsonValue]]: + with sqlite3.connect(self._path) as connection: + rows = connection.execute( + """ + SELECT entry_id, entry_type, created_at, payload_json, content_hash + FROM research_ledger + ORDER BY rowid ASC + """ + ).fetchall() + return [ + { + "entry_id": str(row[0]), + "entry_type": str(row[1]), + "created_at": str(row[2]), + "payload": as_json_value(json.loads(str(row[3]))), + "content_hash": str(row[4]), + } + for row in rows + ] diff --git a/src/jester/research_sources.py b/src/jester/research_sources.py new file mode 100644 index 0000000..4e123f9 --- /dev/null +++ b/src/jester/research_sources.py @@ -0,0 +1,145 @@ +"""Honest source registry for scientific learning and attention observation.""" + +from __future__ import annotations + +from typing import Literal, TypedDict, cast + +from .identity import as_json_value, canonical_hash + + +class ResearchSourceSpec(TypedDict): + source_id: str + purpose: str + status: Literal["operational", "adapter_contract", "prohibited"] + access_mode: str + terms_uri: str + rate_boundary: str + retained_scope: str + limitations: list[str] + content_hash: str + + +def _source( + source_id: str, + purpose: str, + status: str, + access_mode: str, + terms_uri: str, + rate_boundary: str, + retained_scope: str, + limitations: list[str], +) -> ResearchSourceSpec: + body = { + "source_id": source_id, + "purpose": purpose, + "status": status, + "access_mode": access_mode, + "terms_uri": terms_uri, + "rate_boundary": rate_boundary, + "retained_scope": retained_scope, + "limitations": limitations, + } + return cast( + ResearchSourceSpec, + { + **body, + "content_hash": canonical_hash(as_json_value(body)), + }, + ) + + +def research_source_catalog() -> list[ResearchSourceSpec]: + return [ + _source( + "arxiv-metadata-v1", + "scientific_paper_discovery", + "operational", + "official_atom_api", + "https://info.arxiv.org/help/api/tou.html", + "single_connection_one_request_per_three_seconds_daily_identical_query", + "descriptive_metadata_only_no_pdf_mirroring", + [ + "preprints_are_not_verified_truth", + "abstract_only_learning", + "revisions_append_as_new_evidence", + ], + ), + _source( + "staged-ranked-search-metadata-v1", + "search_prominence_attention", + "operational", + "operator_staged_ranked_urls", + "operator_supplied_provider_terms", + "provider_specific", + "rank_url_and_page_metadata_only", + [ + "does_not_measure_search_volume", + "provider_export_must_be_permitted", + "cannot_receive_causal_credit_without_ablation", + ], + ), + _source( + "robots-aware-page-metadata-v1", + "metadata_mutation_attention", + "operational", + "https_metadata_get_after_robots_allow", + "https://www.rfc-editor.org/rfc/rfc9309.html", + "at_least_three_seconds_between_requests", + "html_metadata_and_response_headers_no_body_persistence", + [ + "robots_is_not_access_authorization", + "explicit_domain_and_terms_admission_still_required", + "no_login_or_antibot_bypass", + ], + ), + _source( + "crossref-metadata-adapter-v1", + "doi_journal_and_retraction_enrichment", + "adapter_contract", + "official_rest_api", + "https://www.crossref.org/documentation/retrieve-metadata/rest-api/", + "not_implemented", + "descriptive_metadata_and_update_relationships", + ["implementation_and_retraction_reconciliation_missing"], + ), + _source( + "wikimedia-pageviews-adapter-v1", + "public_attention_proxy", + "adapter_contract", + "official_analytics_api", + ( + "https://doc.wikimedia.org/generated-data-platform/aqs/" + "analytics-api/concepts/page-views.html" + ), + "not_implemented", + "aggregate_pageview_counts", + [ + "pageviews_are_not_search_volume", + "entity_mapping_and_bot_semantics_require_validation", + ], + ), + _source( + "common-crawl-index-adapter-v1", + "historical_web_presence_metadata", + "adapter_contract", + "official_cdx_index", + "https://commoncrawl.org/", + "not_implemented", + "crawl_index_metadata_only", + ["crawl_coverage_is_not_attention", "index_vintage_alignment_required"], + ), + _source( + "unapproved-serp-html-scraping", + "search_engine_result_collection", + "prohibited", + "direct_search_engine_html_scrape", + "none", + "not_applicable", + "none", + [ + "no_approved_terms_or_api_contract", + "captcha_or_antibot_bypass_forbidden", + "credentials_and_logged_in_sessions_forbidden", + ], + ), + ] diff --git a/src/jester/simulators.py b/src/jester/simulators.py new file mode 100644 index 0000000..39d36b8 --- /dev/null +++ b/src/jester/simulators.py @@ -0,0 +1,185 @@ +"""Independent simulator-family catalog with honest activation status.""" + +from __future__ import annotations + +from typing import cast + +from .identity import as_json_value, canonical_hash +from .meta_types import SimulatorSpec + + +def _spec( + simulator_id: str, + family: str, + implementation_status: str, + validation_status: str, + deterministic: bool, + supported_packs: list[str], + required_inputs: list[str], + output_contract: str, + primary_source_uri: str, + limitations: list[str], +) -> SimulatorSpec: + body = { + "simulator_id": simulator_id, + "family": family, + "version": "jester-simulator-v1", + "implementation_status": implementation_status, + "validation_status": validation_status, + "deterministic": deterministic, + "supported_packs": supported_packs, + "required_inputs": required_inputs, + "output_contract": output_contract, + "primary_source_uri": primary_source_uri, + "limitations": limitations, + } + return cast( + SimulatorSpec, + { + **body, + "content_hash": canonical_hash(as_json_value(body)), + }, + ) + + +def simulator_catalog() -> list[SimulatorSpec]: + return [ + _spec( + "exact-replay-v1", + "exact_historical_replay", + "operational_baseline", + "mechanics_validated", + True, + ["earnings", "narrative_reflexivity"], + ["point_in_time_evidence", "observed_contract"], + "replay_event_chain", + "https://github.com/ObtuseAI/jester", + ["fidelity_is_bounded_by_available_historical_inputs"], + ), + _spec( + "regime-resampler-v1", + "regime_conditioned_resampling", + "operational_baseline", + "mechanics_validated", + True, + ["earnings", "narrative_reflexivity"], + ["world_state", "semantic_seed"], + "underlying_iv_liquidity_path", + "https://github.com/ObtuseAI/jester", + ["public_fixture_is_synthetic_not_empirically_calibrated"], + ), + _spec( + "causal-intervention-kernel-v1", + "typed_causal_intervention", + "operational_baseline", + "mechanics_validated", + True, + ["earnings", "narrative_reflexivity"], + ["competing_causal_graphs", "intervention"], + "counterfactual_market_world", + "https://arxiv.org/abs/2511.04469", + ["typed_graphs_are_hypotheses_not_learned_causal_truth"], + ), + _spec( + "market-society-v1", + "numerical_agent_society", + "operational_baseline", + "mechanics_validated", + True, + ["earnings", "narrative_reflexivity"], + ["world_state", "scenario_lattice"], + "participant_reaction_field", + "https://github.com/ObtuseAI/jester", + ["participant_policies_are_stress_mechanics_not_population_estimates"], + ), + _spec( + "adversarial-world-forge-v1", + "handcrafted_adversarial_paths", + "operational_baseline", + "mechanics_validated", + True, + ["earnings", "narrative_reflexivity"], + ["candidate", "failure_library"], + "worst_plausible_worlds", + "https://github.com/ObtuseAI/jester", + ["stress_paths_do_not_receive_empirical_probability_credit"], + ), + _spec( + "tncm-vae-adapter-v1", + "neural_causal_market_simulator", + "adapter_contract", + "blocked_missing_implementation", + False, + ["earnings", "macro", "sector_theme"], + ["accepted_training_data", "causal_dag", "held_out_counterfactuals"], + "causal_trajectory_ensemble", + "https://arxiv.org/abs/2511.04469", + ["paper_validated_on_synthetic_processes_not_this_market_universe"], + ), + _spec( + "mdqr-lob-adapter-v1", + "multidimensional_deep_queue_reactive", + "adapter_contract", + "blocked_missing_calibration", + False, + ["flow_positioning", "zero_dte_expiration"], + ["licensed_l2_or_l3_book", "event_sizes", "queue_states"], + "discrete_event_order_book", + "https://arxiv.org/abs/2501.08822", + ["requires_market_specific_queue_and_fill_calibration"], + ), + _spec( + "lob-diffusion-adapter-v1", + "spatiotemporal_lob_diffusion", + "adapter_contract", + "blocked_missing_calibration", + False, + ["flow_positioning", "zero_dte_expiration"], + ["licensed_l2_history", "lob_bench_evaluation"], + "parallel_lob_future_field", + "https://arxiv.org/abs/2509.05107", + ["published_results_show_instrument_specific_mode_collapse_risk"], + ), + _spec( + "iv-ddpm-adapter-v1", + "conditional_iv_surface_diffusion", + "adapter_contract", + "blocked_missing_calibration", + False, + ["earnings", "macro", "zero_dte_expiration"], + ["historical_surfaces", "returns", "risk_indicators"], + "arbitrage_penalized_iv_surface_path", + "https://arxiv.org/abs/2511.07571", + ["requires accepted surface history and independent arbitrage validation"], + ), + _spec( + "rough-volatility-adapter-v1", + "rough_stochastic_volatility", + "adapter_contract", + "blocked_missing_implementation", + True, + ["earnings", "macro", "zero_dte_expiration"], + ["calibrated_forward_variance", "roughness_parameter"], + "rough_volatility_price_paths", + "https://arxiv.org/abs/1410.3394", + ["ordinary_baseline_cannot_be_relabelled_as_rough_volatility"], + ), + ] + + +def operational_simulators(pack_id: str) -> list[SimulatorSpec]: + return [ + spec + for spec in simulator_catalog() + if spec["implementation_status"] == "operational_baseline" + and pack_id in spec["supported_packs"] + ] + + +def blocked_simulators(pack_id: str) -> list[SimulatorSpec]: + return [ + spec + for spec in simulator_catalog() + if spec["implementation_status"] == "adapter_contract" + and pack_id in spec["supported_packs"] + ] diff --git a/src/jester/types.py b/src/jester/types.py index 84193aa..defefa0 100644 --- a/src/jester/types.py +++ b/src/jester/types.py @@ -306,6 +306,8 @@ class PipelineResult(TypedDict): claims: list[ClaimRecord] world_state: WorldStateSnapshot scenario_lattice: ScenarioLattice + participant_reactions: list[dict[str, JsonValue]] + game_report: dict[str, JsonValue] beliefs: list[BeliefQuote] hypothesis: HypothesisGenome outcome_field: OptionOutcomeField diff --git a/tests/test_api.py b/tests/test_api.py index 4ad67f7..435f480 100644 --- a/tests/test_api.py +++ b/tests/test_api.py @@ -1,8 +1,10 @@ from __future__ import annotations +import asyncio from pathlib import Path from fastapi.routing import APIRoute +from httpx import ASGITransport, AsyncClient, Response from jester.api import create_app @@ -13,5 +15,38 @@ def test_observatory_is_read_only(tmp_path: Path) -> None: methods = {route.path: route.methods or set() for route in routes} assert methods["/health"] == {"GET"} + assert methods["/catalog/simulators"] == {"GET"} + assert methods["/catalog/packs"] == {"GET"} + assert methods["/catalog/research-sources"] == {"GET"} + assert methods["/catalog/data-pipelines"] == {"GET"} + assert methods["/catalog/intelligence"] == {"GET"} + assert methods["/catalog/cognition"] == {"GET"} + assert methods["/observatory/bootstrap"] == {"GET"} assert methods["/runs/{run_id}"] == {"GET"} assert all(route_methods <= {"GET", "HEAD"} for route_methods in methods.values()) + + +def test_observatory_bootstrap_is_synthetic_and_authority_bounded(tmp_path: Path) -> None: + app = create_app(tmp_path) + + async def request_bootstrap() -> Response: + transport = ASGITransport(app=app) + async with AsyncClient(transport=transport, base_url="http://jester.local") as client: + return await client.get("/observatory/bootstrap") + + response = asyncio.run(request_bootstrap()) + assert response.status_code == 200 + payload = response.json() + assert payload["display_mode"] == "synthetic_mechanics_fixture" + assert payload["authority"] == { + "read_only": True, + "paper_only": True, + "live_blocked": True, + "order_construction_allowed": False, + "order_submission_allowed": False, + } + assert payload["headline"]["highest_simulation_level"] == 5 + assert payload["headline"]["forward_levels"] == "blocked" + assert len(payload["data_pipelines"]) >= 14 + assert len(payload["intelligence"]) >= 12 + assert len(payload["cognition_modes"]) == 24 diff --git a/tests/test_attention_observatory.py b/tests/test_attention_observatory.py new file mode 100644 index 0000000..ee33623 --- /dev/null +++ b/tests/test_attention_observatory.py @@ -0,0 +1,127 @@ +from __future__ import annotations + +import copy + +import pytest + +from jester.attention import ( + build_attention_snapshot, + compare_attention, + parse_page_metadata, + validate_attention_manifest, +) +from jester.attention_types import ( + MetadataScrapePolicy, + RankedAttentionItem, + SearchAttentionManifest, +) +from jester.identity import as_json_value, canonical_hash + + +def _policy() -> MetadataScrapePolicy: + return { + "schema_version": "jester-metadata-scrape-policy-v1", + "policy_id": "fixture-policy", + "terms_uri": "https://example.com/terms", + "allowed_hosts": ["alpha.example.com", "beta.example.com", "gamma.example.com"], + "user_agent": "JesterResearch/0.2 (mailto:research@example.com)", + "max_pages": 5, + "max_response_bytes": 100000, + "minimum_request_interval_seconds": 3.0, + "metadata_only": True, + "authentication_allowed": False, + "anti_bot_bypass_allowed": False, + } + + +def _manifest(observed_at: str) -> SearchAttentionManifest: + return { + "schema_version": "jester-search-attention-manifest-v1", + "query": "synthetic market narrative", + "search_provider": "fixture-ranked-export", + "observed_at": observed_at, + "targets": [ + {"rank": 1, "url": "https://alpha.example.com/a"}, + {"rank": 2, "url": "https://beta.example.com/b"}, + ], + "policy": _policy(), + } + + +def _item( + rank: int, + url: str, + title: str, + observed_at: str, +) -> RankedAttentionItem: + document = ( + "" + f"{title}" + f'' + f'' + f'' + "Body is hashed but not persisted." + ).encode() + metadata = parse_page_metadata( + document, + url, + observed_at, + "text/html; charset=utf-8", + "", + "", + ) + body = {"rank": rank, "metadata": metadata} + return { + "rank": rank, + "metadata": metadata, + "content_hash": canonical_hash(as_json_value(body)), + } + + +def test_attention_delta_tracks_prominence_without_claiming_volume_or_causality() -> None: + prior_manifest = _manifest("2026-07-29T12:00:00Z") + current_manifest = copy.deepcopy(_manifest("2026-07-30T12:00:00Z")) + current_manifest["targets"] = [ + {"rank": 1, "url": "https://beta.example.com/b"}, + {"rank": 2, "url": "https://gamma.example.com/c"}, + ] + prior = build_attention_snapshot( + prior_manifest, + [ + _item(1, "https://alpha.example.com/a", "Alpha", prior_manifest["observed_at"]), + _item(2, "https://beta.example.com/b", "Beta", prior_manifest["observed_at"]), + ], + ) + current = build_attention_snapshot( + current_manifest, + [ + _item(1, "https://beta.example.com/b", "Beta revised", current_manifest["observed_at"]), + _item(2, "https://gamma.example.com/c", "Gamma", current_manifest["observed_at"]), + ], + ) + + delta = compare_attention(prior, current) + + assert delta["result_churn"] > 0.0 + assert delta["metadata_mutation_fraction"] == 1.0 + assert delta["search_volume_observed"] is False + assert delta["causal_credit_allowed"] is False + assert current["authority"]["body_persisted"] is False + + +def test_attention_manifest_refuses_unapproved_hosts_and_fast_scraping() -> None: + fast = _manifest("2026-07-29T12:00:00Z") + fast["policy"]["minimum_request_interval_seconds"] = 1.0 + with pytest.raises(ValueError, match="three seconds"): + validate_attention_manifest(fast) + + unknown = _manifest("2026-07-29T12:00:00Z") + unknown["targets"][0]["url"] = "https://unapproved.example.net/a" + with pytest.raises(PermissionError, match="not admitted"): + validate_attention_manifest(unknown) + + local = _manifest("2026-07-29T12:00:00Z") + local["policy"]["allowed_hosts"].append("127.0.0.1") + local["targets"][0]["url"] = "https://127.0.0.1/a" + with pytest.raises(PermissionError, match="globally routable"): + validate_attention_manifest(local) diff --git a/tests/test_catalogs.py b/tests/test_catalogs.py new file mode 100644 index 0000000..2a6fffb --- /dev/null +++ b/tests/test_catalogs.py @@ -0,0 +1,58 @@ +from __future__ import annotations + +import pytest + +from jester.data_pipelines import data_pipeline_catalog, pipeline_status_counts +from jester.intelligence import intelligence_catalog, intelligence_status_counts +from jester.packs import pack_catalog, resolve_pack +from jester.simulators import blocked_simulators, operational_simulators, simulator_catalog + + +def test_simulator_catalog_separates_baselines_from_blocked_adapters() -> None: + catalog = simulator_catalog() + + assert len(catalog) == 10 + assert operational_simulators("earnings") + assert blocked_simulators("earnings") + assert all(spec["primary_source_uri"].startswith("https://") for spec in catalog) + assert all( + spec["validation_status"].startswith("blocked_") + for spec in catalog + if spec["implementation_status"] == "adapter_contract" + ) + + +def test_market_packs_fail_closed_when_mechanics_are_missing() -> None: + packs = pack_catalog() + zero_dte = resolve_pack("zero_dte_expiration") + + assert len(packs) == 8 + assert zero_dte["status"] == "blocked" + assert "ordinary_option_lane_must_reject" in zero_dte["blocked_reasons"] + with pytest.raises(KeyError, match="unknown market pack"): + resolve_pack("imaginary_pack") + + +def test_data_pipelines_preserve_permissions_clocks_and_blocked_states() -> None: + pipelines = data_pipeline_catalog() + counts = pipeline_status_counts() + + assert len(pipelines) >= 14 + assert counts["operational"] >= 3 + assert counts["adapter_contract"] >= 8 + assert counts["blocked_license_or_data"] >= 2 + assert counts["prohibited"] >= 1 + assert all(spec["primary_source_uri"].startswith("https://") for spec in pipelines) + assert all(spec["time_semantics"] for spec in pipelines if spec["status"] != "prohibited") + assert all(spec["quality_gates"] for spec in pipelines) + + +def test_intelligence_catalog_cannot_expand_authority() -> None: + services = intelligence_catalog() + counts = intelligence_status_counts() + + assert len(services) >= 12 + assert counts["operational_mechanics"] >= 6 + assert counts["blocked_missing_evidence"] >= 1 + assert all(service["authority_effect"] == "none" for service in services) + assert all(service["evaluators"] for service in services) diff --git a/tests/test_cognition.py b/tests/test_cognition.py new file mode 100644 index 0000000..99514d9 --- /dev/null +++ b/tests/test_cognition.py @@ -0,0 +1,153 @@ +from __future__ import annotations + +import copy +import json +from pathlib import Path +from typing import cast + +import pytest + +from jester.cognition import ( + cognition_mode_catalog, + evaluate_cognitive_immune_system, + run_cognition_arsenal, +) +from jester.literature import build_literature_batch +from jester.literature_types import LiteratureQuery +from jester.pipeline import EarningsPipelineInput, run_earnings_pipeline + +LITERATURE_FIXTURE_ROOT = Path(__file__).parents[1] / "fixtures" / "literature" + + +def test_cognition_arsenal_is_deterministic_diverse_and_authority_bounded( + pipeline_input: EarningsPipelineInput, +) -> None: + parent = run_earnings_pipeline(pipeline_input)["hypothesis"] + + first = run_cognition_arsenal( + parent, + [], + "2026-07-29T12:00:00Z", + 2202, + 12, + 8, + 4, + 0.72, + ) + second = run_cognition_arsenal( + parent, + [], + "2026-07-29T12:00:00Z", + 2202, + 12, + 8, + 4, + 0.72, + ) + + assert first == second + assert len(cognition_mode_catalog()) == 24 + assert len(first["dreams"]) == 8 + assert first["dreams"][0]["parent_dream_ids"] == [] + assert all( + dream["parent_dream_ids"] == [first["dreams"][index - 1]["dream_id"]] + for index, dream in enumerate(first["dreams"][1:], start=1) + ) + assert first["authorized_dream_budget"] <= first["requested_dream_budget"] + assert first["coverage"] > 0.25 + assert first["archive"] + assert all(dream["synthetic_only"] for dream in first["dreams"]) + assert all(not dream["empirical_credit"] for dream in first["dreams"]) + assert all(not invention["auto_apply"] for invention in first["inventions"]) + assert first["authority"]["may_mutate_code"] is False + assert first["authority"]["order_submission_allowed"] is False + + +def test_cognitive_immune_system_quarantines_authority_and_falsification_failures( + pipeline_input: EarningsPipelineInput, +) -> None: + parent = run_earnings_pipeline(pipeline_input)["hypothesis"] + result = run_cognition_arsenal( + parent, + [], + "2026-07-29T12:00:00Z", + 2202, + 4, + 4, + 2, + 0.5, + ) + corrupted = copy.deepcopy(result["dreams"][0]) + corrupted["invented_mechanism"] = "Submit order using broker credential in live execution." + corrupted["falsifiers"] = [] + corrupted["referenced_evidence_ids"] = ["evidence:not-in-parent"] + + findings = evaluate_cognitive_immune_system( + corrupted, + parent["point_in_time_evidence_ids"], + ) + quarantined = {finding["check_id"] for finding in findings if finding["status"] == "quarantine"} + + assert quarantined == { + "authority_containment", + "falsifiability", + "evidence_lineage", + } + + +def test_cognition_configuration_rejects_unbounded_temperature( + pipeline_input: EarningsPipelineInput, +) -> None: + parent = run_earnings_pipeline(pipeline_input)["hypothesis"] + with pytest.raises(ValueError, match="within"): + run_cognition_arsenal( + parent, + [], + "2026-07-29T12:00:00Z", + 2202, + 4, + 4, + 2, + 1.1, + ) + + +def test_scientific_capsules_seed_dreams_without_gaining_credit( + pipeline_input: EarningsPipelineInput, +) -> None: + parsed = json.loads( + (LITERATURE_FIXTURE_ROOT / "synthetic_query.json").read_text(encoding="utf-8") + ) + if not isinstance(parsed, dict): + raise TypeError("synthetic literature query must be an object") + query = cast(LiteratureQuery, parsed) + batch = build_literature_batch( + (LITERATURE_FIXTURE_ROOT / "synthetic_arxiv_feed.xml").read_bytes(), + query, + "2026-07-29T12:01:00Z", + ) + parent = run_earnings_pipeline(pipeline_input)["hypothesis"] + + result = run_cognition_arsenal( + parent, + batch["capsules"], + "2026-07-29T12:01:00Z", + 2202, + 4, + 4, + 2, + 0.72, + ) + + assert result["scientific_seeds"] + assert all( + seed["trust_state"] == "unverified_scientific_claim" + and seed["requires_independent_replication"] + and not seed["auto_apply"] + and not seed["empirical_credit"] + for seed in result["scientific_seeds"] + ) + assert all( + any(debt.startswith("independent_replication_required:") for debt in dream["evidence_debt"]) + for dream in result["dreams"] + ) diff --git a/tests/test_counterfactuals.py b/tests/test_counterfactuals.py new file mode 100644 index 0000000..a0899ba --- /dev/null +++ b/tests/test_counterfactuals.py @@ -0,0 +1,43 @@ +from __future__ import annotations + +import copy + +from jester.counterfactuals import ( + build_social_ablation_matrix, + run_causal_graph_tournament, +) +from jester.pipeline import EarningsPipelineInput, run_earnings_pipeline + + +def test_social_ablation_and_graph_tournament_are_explicit( + pipeline_input: EarningsPipelineInput, +) -> None: + result = run_earnings_pipeline(pipeline_input) + + matrix = build_social_ablation_matrix(result["claims"]) + tournament = run_causal_graph_tournament(result["scenario_lattice"]) + + assert len(matrix["cases"]) == 7 + assert {case["intervention"] for case in matrix["cases"]} == { + "social_amplification_present", + "social_amplification_absent", + "opposite_social_interpretation", + "delayed_propagation", + "coordinated_manipulation", + "official_confirmation", + "official_contradiction", + } + assert tournament["silent_averaging_used"] is False + assert len(tournament["scores"]) >= 2 + + +def test_price_following_claim_is_denied_causal_credit( + pipeline_input: EarningsPipelineInput, +) -> None: + changed = copy.deepcopy(pipeline_input) + changed["claim_seeds"][0]["price_lead_lag_minutes"] = 0 + result = run_earnings_pipeline(changed) + + matrix = build_social_ablation_matrix(result["claims"]) + + assert matrix["causal_credit_allowed"] is False diff --git a/tests/test_earnings_pipeline.py b/tests/test_earnings_pipeline.py index 427c606..f54e54c 100644 --- a/tests/test_earnings_pipeline.py +++ b/tests/test_earnings_pipeline.py @@ -14,10 +14,10 @@ def test_synthetic_earnings_candidate_replays_exactly( second = run_earnings_pipeline(pipeline_input) assert first == second - assert first["run_id"] == "run:253f1dfec45ce2717096122a" + assert first["run_id"] == "run:d4cb939a071021ad2615e306" assert ( first["event_chain_hash"] - == "de9da586e75892f925e1f423eebc8246266d7da4050a1e1df02ad98cf7e93c6a" + == "4f53e157598a0769b17fcafacfb0bf0ae478a6c76a4341262079e11e95a7496f" ) assert first["run_status"] == "mechanics_certified" assert first["proof_manifest"]["proof_status"] == "certified_mechanics_only" diff --git a/tests/test_literature_lab.py b/tests/test_literature_lab.py new file mode 100644 index 0000000..93b3f53 --- /dev/null +++ b/tests/test_literature_lab.py @@ -0,0 +1,75 @@ +from __future__ import annotations + +import copy +import json +from pathlib import Path +from typing import cast + +import pytest + +from jester.literature import ( + build_literature_batch, + effective_literature_query, + validate_literature_query, +) +from jester.literature_types import LiteratureQuery + +FIXTURE_ROOT = Path(__file__).parents[1] / "fixtures" / "literature" + + +def _query() -> LiteratureQuery: + parsed = json.loads((FIXTURE_ROOT / "synthetic_query.json").read_text(encoding="utf-8")) + if not isinstance(parsed, dict): + raise TypeError("synthetic literature query must be an object") + return cast(LiteratureQuery, parsed) + + +def test_scientific_metadata_becomes_falsifiable_zero_credit_capsules() -> None: + feed = (FIXTURE_ROOT / "synthetic_arxiv_feed.xml").read_bytes() + + first = build_literature_batch(feed, _query(), "2026-07-29T12:01:00Z") + second = build_literature_batch(feed, _query(), "2026-07-29T12:01:00Z") + + assert first == second + later_ingestion = build_literature_batch( + feed, + _query(), + "2026-07-29T13:01:00Z", + ) + assert [paper["paper_id"] for paper in first["papers"]] == [ + paper["paper_id"] for paper in later_ingestion["papers"] + ] + assert first["content_hash"] != later_ingestion["content_hash"] + assert len(first["papers"]) == 2 + assert len(first["exclusions"]) == 1 + assert first["exclusions"][0]["reason_codes"] == ["paper_revision_after_cutoff"] + assert first["papers"][0]["version"] == 2 + assert all(paper["full_text_cached"] is False for paper in first["papers"]) + assert all(paper["peer_review_verified"] is False for paper in first["papers"]) + assert all(capsule["auto_apply"] is False for capsule in first["capsules"]) + assert all(capsule["empirical_credit"] is False for capsule in first["capsules"]) + assert first["authority"]["simulator_activation_allowed"] is False + + +def test_literature_refresh_and_cutoff_policies_fail_closed() -> None: + changed = copy.deepcopy(_query()) + changed["minimum_refresh_seconds"] = 3600 + + with pytest.raises(ValueError, match="daily"): + validate_literature_query(changed) + + live = copy.deepcopy(_query()) + live["source_mode"] = "live_metadata" + live["cutoff_policy"] = "scan_time" + effective = effective_literature_query(live, "2026-07-30T15:00:00Z") + + assert effective["cutoff_time"] == "2026-07-30T15:00:00Z" + assert live["cutoff_time"] == "2026-07-29T12:00:00Z" + + unsafe_feed = b']>' + with pytest.raises(ValueError, match="DTD"): + build_literature_batch( + unsafe_feed, + _query(), + "2026-07-29T12:01:00Z", + ) diff --git a/tests/test_meta_research.py b/tests/test_meta_research.py new file mode 100644 index 0000000..40c7828 --- /dev/null +++ b/tests/test_meta_research.py @@ -0,0 +1,89 @@ +from __future__ import annotations + +import pytest + +from jester.meta_pipeline import MetaConfig, run_meta_research +from jester.metacognition import resolve_meta_prediction +from jester.pipeline import EarningsPipelineInput, run_earnings_pipeline + + +def _config() -> MetaConfig: + return { + "created_at": "2026-07-29T12:00:00Z", + "pack_id": "earnings", + "generations": 4, + "requested_candidate_budget": 16, + "island_count": 4, + "root_seed": 2202, + "cognition_budget": 12, + "dream_cycles": 4, + "dream_temperature": 0.72, + "ladder": { + "evidence_mode": "historical_reconstruction", + "resolved_forward_outcomes": 0, + "bounded_paper_outcomes": 0, + "required_forward_outcomes": 50, + "required_paper_outcomes": 30, + }, + "independently_resimulated_descendants": 0, + "simulation_gap_observations": 0, + "simulation_gap_pressure": 0.5, + } + + +def test_meta_research_is_deterministic_bounded_and_proposal_only( + pipeline_input: EarningsPipelineInput, +) -> None: + base = run_earnings_pipeline(pipeline_input) + + first = run_meta_research(base, _config()) + second = run_meta_research(base, _config()) + + assert first == second + assert first["ladder"]["highest_contiguous_level"] == 5 + assert first["archive"]["candidate_count"] <= first["control"]["authorized_candidate_budget"] + assert ( + first["control"]["authorized_candidate_budget"] + <= first["control"]["requested_candidate_budget"] + ) + assert first["control"]["may_increase_authority"] is False + assert first["control"]["evaluator_mutation_allowed"] is False + assert first["control"]["auto_apply_source_changes"] is False + assert first["mutation_proposals"] + assert all(proposal["auto_apply"] is False for proposal in first["mutation_proposals"]) + assert first["authority"]["live_blocked"] is True + assert first["authority"]["order_submission_allowed"] is False + assert first["cognition"]["authorized_dream_budget"] <= 12 + assert first["cognition"]["dreams"] + assert all( + invention["auto_apply"] is False + and invention["empirical_credit"] is False + and invention["order_construction_allowed"] is False + for invention in first["cognition"]["inventions"] + ) + assert first["research_gates"][-1]["status"] == "insufficient_evidence" + + +def test_meta_prediction_resolves_with_proper_score( + pipeline_input: EarningsPipelineInput, +) -> None: + base = run_earnings_pipeline(pipeline_input) + meta = run_meta_research(base, _config()) + + resolution = resolve_meta_prediction( + meta["prediction"], + 0.2, + True, + "2026-08-01T12:00:00Z", + ) + + assert 0.0 <= resolution["brier_score"] <= 1.0 + assert 0.0 <= resolution["absolute_error"] <= 1.0 + assert resolution["prediction_id"] == meta["prediction"]["prediction_id"] + with pytest.raises(ValueError, match="within"): + resolve_meta_prediction( + meta["prediction"], + 1.2, + True, + "2026-08-01T12:00:00Z", + ) diff --git a/tests/test_research_memory.py b/tests/test_research_memory.py new file mode 100644 index 0000000..d45df66 --- /dev/null +++ b/tests/test_research_memory.py @@ -0,0 +1,36 @@ +from __future__ import annotations + +import sqlite3 +from pathlib import Path + +import pytest + +from jester.research_memory import ResearchMemory +from jester.types import JsonValue + + +def test_research_memory_is_idempotent_and_append_only(tmp_path: Path) -> None: + path = tmp_path / "memory.sqlite3" + memory = ResearchMemory(path) + memory.initialize() + payload: dict[str, JsonValue] = { + "prediction": "archive_gain", + "probability": 0.65, + } + + first_id = memory.append("meta_prediction", "2026-07-29T12:00:00Z", payload) + second_id = memory.append("meta_prediction", "2026-07-29T12:00:00Z", payload) + + assert first_id == second_id + assert len(memory.read_all()) == 1 + with sqlite3.connect(path) as connection: + with pytest.raises(sqlite3.IntegrityError, match="append-only"): + connection.execute( + "UPDATE research_ledger SET entry_type = ? WHERE entry_id = ?", + ("tampered", first_id), + ) + with pytest.raises(sqlite3.IntegrityError, match="append-only"): + connection.execute( + "DELETE FROM research_ledger WHERE entry_id = ?", + (first_id,), + ) diff --git a/tests/test_simulation_ladder.py b/tests/test_simulation_ladder.py new file mode 100644 index 0000000..c4a002d --- /dev/null +++ b/tests/test_simulation_ladder.py @@ -0,0 +1,46 @@ +from __future__ import annotations + +import copy +from typing import cast + +from jester.ladder import LadderConfig, build_simulation_ladder +from jester.pipeline import EarningsPipelineInput, run_earnings_pipeline +from jester.types import JsonValue + + +def _config() -> LadderConfig: + return { + "evidence_mode": "historical_reconstruction", + "resolved_forward_outcomes": 0, + "bounded_paper_outcomes": 0, + "required_forward_outcomes": 50, + "required_paper_outcomes": 30, + } + + +def test_synthetic_fixture_passes_mechanics_and_blocks_forward( + pipeline_input: EarningsPipelineInput, +) -> None: + result = run_earnings_pipeline(pipeline_input) + ladder = build_simulation_ladder(result, _config()) + + assert ladder["highest_contiguous_level"] == 5 + assert [stage["status"] for stage in ladder["stages"][:6]] == ["pass"] * 6 + assert [stage["status"] for stage in ladder["stages"][6:]] == [ + "blocked", + "blocked", + ] + assert ladder["forward_promotion_eligible"] is False + + +def test_tampered_event_chain_fails_exact_replay( + pipeline_input: EarningsPipelineInput, +) -> None: + result = copy.deepcopy(run_earnings_pipeline(pipeline_input)) + first_event = cast(dict[str, JsonValue], result["event_log"][0]) + first_event["event_hash"] = "0" * 64 + + ladder = build_simulation_ladder(result, _config()) + + assert ladder["stages"][0]["status"] == "fail" + assert ladder["highest_contiguous_level"] == -1 diff --git a/ui/index.html b/ui/index.html new file mode 100644 index 0000000..232ce54 --- /dev/null +++ b/ui/index.html @@ -0,0 +1,18 @@ + + + + + + + + Jester Observatory + + + +
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"@react-three/fiber": "9.6.1", + "lucide-react": "1.27.0", + "react": "19.2.8", + "react-dom": "19.2.8", + "three": "0.182.0" + }, + "devDependencies": { + "@types/react": "19.2.17", + "@types/react-dom": "19.2.3", + "@types/three": "0.182.0", + "@vitejs/plugin-react": "6.0.4", + "typescript": "7.0.2", + "vite": "8.1.5" + } +} diff --git a/ui/src/App.tsx b/ui/src/App.tsx new file mode 100644 index 0000000..8286e4d --- /dev/null +++ b/ui/src/App.tsx @@ -0,0 +1,1107 @@ +import { + Atom, + BookOpen, + Boxes, + BrainCircuit, + ChevronRight, + CirclePause, + CirclePlay, + CloudMoon, + Database, + Eye, + Fingerprint, + Gauge, + GitBranch, + Globe2, + Hexagon, + Layers3, + LockKeyhole, + Microscope, + Network, + Orbit, + Radar, + Search, + ShieldCheck, + Sparkles, + TriangleAlert, + Waves, + Workflow, + type LucideIcon, +} from "lucide-react"; +import { lazy, Suspense, useMemo, useState } from "react"; + +import { useObservatoryBootstrap, useReducedMotion } from "./hooks"; +import type { + ArchiveItem, + CognitionMode, + DataPipeline, + Intelligence, + LadderStage, + LiteratureItem, + Metric, + Mode, + ObservatoryBootstrap, + Outcome, + Status, + WorldNode, +} from "./types"; + +const WorldScene = lazy(async () => ({ + default: (await import("./WorldScene")).WorldScene, +})); +const SurfaceScene = lazy(async () => ({ + default: (await import("./SurfaceScene")).SurfaceScene, +})); + +interface ModeDefinition { + readonly id: Mode; + readonly label: string; + readonly eyebrow: string; + readonly title: string; + readonly icon: LucideIcon; +} + +const MODES: readonly ModeDefinition[] = [ + { + id: "world", + label: "World", + eyebrow: "Causal market imagination engine", + title: "Market world topology", + icon: Orbit, + }, + { + id: "options", + label: "Options", + eyebrow: "Joint price × IV × liquidity field", + title: "Option outcome geometry", + icon: Waves, + }, + { + id: "meta", + label: "Meta", + eyebrow: "Protected recursive research", + title: "Evolution and proof ecology", + icon: Sparkles, + }, + { + id: "dreams", + label: "Dream", + eyebrow: "Oneiric invention and cognitive immunity", + title: "Dream foundry and invention arsenal", + icon: CloudMoon, + }, + { + id: "sources", + label: "Sources", + eyebrow: "Permissioned point-in-time intelligence", + title: "Data and intelligence mesh", + icon: Database, + }, +]; + +const statusLabel = (status: Status | string): string => + status.replaceAll("_", " ").replace(/\b\w/g, (value: string): string => value.toUpperCase()); + +const formatPercent = (value: number): string => `${Math.round(value * 100)}%`; + +const SceneLoading = (): React.JSX.Element => ( +
+ +); + +const ErrorState = ({ message }: { readonly message: string }): React.JSX.Element => ( +
+
+); + +const LoadingShell = (): React.JSX.Element => ( +
+
+
+
+ {Array.from({ length: 8 }, (_, index: number): React.JSX.Element => ( +
+ ))} +
+
+); + +const StatusBadge = ({ + status, + compact, +}: { + readonly status: string; + readonly compact: boolean; +}): React.JSX.Element => ( + + +); + +const MetricCard = ({ metric }: { readonly metric: Metric }): React.JSX.Element => ( +
+
+

{metric.label}

+ {metric.value} +
+ {metric.trend} + {metric.detail} +
+); + +const CardHeader = ({ + icon: Icon, + eyebrow, + title, + right, +}: { + readonly icon: LucideIcon; + readonly eyebrow: string; + readonly title: string; + readonly right: React.ReactNode; +}): React.JSX.Element => ( +
+
+ +
+

{eyebrow}

+

{title}

+
+
+ {right} +
+); + +const ProgressBar = ({ + value, + tone, + label, +}: { + readonly value: number; + readonly tone: string; + readonly label: string; +}): React.JSX.Element => ( +
+
+ {label} + {formatPercent(value)} +
+ +
+); + +const Sparkline = ({ + series, + label, +}: { + readonly series: readonly number[]; + readonly label: string; +}): React.JSX.Element => { + const path = useMemo((): string => { + const maximum = Math.max(...series); + const minimum = Math.min(...series); + const spread = Math.max(maximum - minimum, 1); + return series + .map((value: number, index: number): string => { + const x = (index / Math.max(series.length - 1, 1)) * 100; + const y = 42 - ((value - minimum) / spread) * 34; + return `${index === 0 ? "M" : "L"}${x.toFixed(2)},${y.toFixed(2)}`; + }) + .join(" "); + }, [series]); + return ( +
+ + + + + + + + + + +
+ {label}: {series.join(", ")} +
+
+ ); +}; + +const AttentionCard = ({ data }: { readonly data: ObservatoryBootstrap }): React.JSX.Element => ( +
+ Proxy · not volume} + /> + +
+ + + + +
+
+); + +const WorldDetail = ({ node }: { readonly node: WorldNode }): React.JSX.Element => ( +
+ +
+

Selected system

+

{node.label}

+ +

{node.detail}

+
+
+
+
Signal
+
{formatPercent(node.signal)}
+
+
+
Type
+
{statusLabel(node.kind)}
+
+
+
+); + +const WorldView = ({ + data, + reducedMotion, +}: { + readonly data: ObservatoryBootstrap; + readonly reducedMotion: boolean; +}): React.JSX.Element => { + const initialNode = data.world_nodes.find((node: WorldNode): boolean => node.id === "causal"); + if (initialNode === undefined) { + throw new Error("Observatory fixture is missing the causal world node"); + } + const [selectedId, setSelectedId] = useState(initialNode.id); + const [paused, setPaused] = useState(reducedMotion); + const selected = + data.world_nodes.find((node: WorldNode): boolean => node.id === selectedId) ?? initialNode; + return ( + <> +
+ setPaused((value: boolean): boolean => !value)} + aria-pressed={paused} + > + {paused ?
+ + + ); +}; + +const OutcomeRows = ({ outcomes }: { readonly outcomes: readonly Outcome[] }): React.JSX.Element => ( +
+
+ World + Probability + Return +
+ {outcomes.map((outcome: Outcome): React.JSX.Element => ( +
+ {outcome.world} + {formatPercent(outcome.probability)} + + {outcome.return > 0 ? "+" : ""} + {formatPercent(outcome.return)} + +
+ ))} +
+); + +const OptionsView = ({ + data, + reducedMotion, +}: { + readonly data: ObservatoryBootstrap; + readonly reducedMotion: boolean; +}): React.JSX.Element => ( + <> +
+ } + /> +
+ }> + + +
Implied volatility
+
Moneyness →
+
Tenor →
+
+
+
+
+ +
+ 6 adversarial worlds} + /> + +
+ +); + +const Ladder = ({ stages }: { readonly stages: readonly LadderStage[] }): React.JSX.Element => ( +
    + {stages.map((stage: LadderStage): React.JSX.Element => ( +
  1. +
    + {stage.level} +
    +
    + {stage.name} + {stage.status === "pass" ? `${formatPercent(stage.score)} mechanics` : "Evidence gate"} +
    + +
    2. + ))} +
+); + +const ArchiveGrid = ({ + archive, +}: { + readonly archive: readonly ArchiveItem[]; +}): React.JSX.Element => ( +
+ {archive.map((item: ArchiveItem, index: number): React.JSX.Element => ( +
+ +

{item.niche}

+ {item.utility.toFixed(3)} + {statusLabel(item.operator)} + +
+ ))} +
+); + +const LiteratureFeed = ({ + literature, +}: { + readonly literature: readonly LiteratureItem[]; +}): React.JSX.Element => ( +
+ {literature.map((item: LiteratureItem): React.JSX.Element => ( +
+ +
+ {item.target} +

{item.title}

+

{statusLabel(item.trust)} · relevance {formatPercent(item.relevance)}

+
+ 0 credit +
+ ))} +
+); + +const MetaView = ({ data }: { readonly data: ObservatoryBootstrap }): React.JSX.Element => ( + <> +
+ 4 niches · 3 lineages} + /> + +
+ +
+ } + /> + +
+
+ Auto-apply disabled} + /> + +
+ +); + +const CognitionView = ({ data }: { readonly data: ObservatoryBootstrap }): React.JSX.Element => { + const initial = data.cognition_modes[0]; + if (initial === undefined) { + throw new Error("Observatory bootstrap contains no cognition modes"); + } + const [selectedId, setSelectedId] = useState(initial.mode_id); + const selected = + data.cognition_modes.find( + (mode: CognitionMode): boolean => mode.mode_id === selectedId, + ) ?? initial; + return ( + <> +
+ Synthetic · zero credit} + /> +
+ {data.cognition.cycles.map( + (cycle, index: number): React.JSX.Element => ( +
+ +
+

{cycle.label}

+ {cycle.artifacts} + artifacts +
+ {cycle.purpose} +
+ ), + )} +
+
+ +
+ {statusLabel(selected.family)}} + /> +
+
+ {data.cognition_modes.map((mode: CognitionMode): React.JSX.Element => ( + + ))} +
+
+

{selected.mode_id}

+

{selected.name}

+

{selected.purpose}

+
+ Cognitive moves +
    + {selected.cognitive_moves.map((move: string): React.JSX.Element => ( +
  • {statusLabel(move)}
    • + ))} +
+
+
+ Immune risks +
    + {selected.failure_risks.map((risk: string): React.JSX.Element => ( +
  • {statusLabel(risk)}
    • + ))} +
+
+
+
+
+
+ Auto-apply disabled} + /> +
+ {data.cognition.inventions.map((invention): React.JSX.Element => ( +
+
+ {statusLabel(invention.mechanism_class)} + {invention.utility.toFixed(3)} +
+

{invention.title}

+
+ {invention.mode_ids.map((modeId: string): React.JSX.Element => ( + {statusLabel(modeId)} + ))} +
+
+ Debt {invention.evidence_debt} + +
+
+ ))} +
+
+ +
+ } + /> +
+ {data.cognition.immune_checks.map((check): React.JSX.Element => ( + + + ))} +
+
+ + ); +}; + +type PipelineFilter = "all" | "operational" | "adapter" | "blocked"; + +const pipelineMatches = (pipeline: DataPipeline, filter: PipelineFilter): boolean => { + if (filter === "all") { + return true; + } + if (filter === "operational") { + return pipeline.status === "operational"; + } + if (filter === "adapter") { + return pipeline.status === "adapter_contract"; + } + return pipeline.status.startsWith("blocked") || pipeline.status === "prohibited"; +}; + +const PipelineDetail = ({ + pipeline, +}: { + readonly pipeline: DataPipeline; +}): React.JSX.Element => ( +
+

{statusLabel(pipeline.domain)}

+

{pipeline.pipeline_id}

+ +
+
+
Access
+
{statusLabel(pipeline.access_mode)}
+
+
+
Cadence
+
{statusLabel(pipeline.cadence)}
+
+
+
Clocks
+
{pipeline.time_semantics.length}
+
+
+
Quality gates
+
{pipeline.quality_gates.length}
+
+
+
+ Intelligence consumers +
+ {pipeline.intelligence_consumers.map((consumer: string): React.JSX.Element => ( + {statusLabel(consumer)} + ))} +
+
+
+ Activation boundary +

+ {pipeline.activation_requirements.length === 0 + ? "Operational mechanics; source policy still applies." + : pipeline.activation_requirements.map(statusLabel).join(" · ")} +

+
+ + Inspect primary source + +
+); + +const IntelligenceGrid = ({ + services, +}: { + readonly services: readonly Intelligence[]; +}): React.JSX.Element => ( +
+ {services.map((service: Intelligence): React.JSX.Element => ( +
+ +
+ {statusLabel(service.status)} +

{service.name}

+

{service.purpose}

+
+ {service.outputs.length} + outputs +
+ ))} +
+); + +const SourcesView = ({ data }: { readonly data: ObservatoryBootstrap }): React.JSX.Element => { + const [filter, setFilter] = useState("all"); + const visible = data.data_pipelines.filter((pipeline: DataPipeline): boolean => + pipelineMatches(pipeline, filter), + ); + const initial = data.data_pipelines[0]; + if (initial === undefined) { + throw new Error("Observatory bootstrap contains no data pipelines"); + } + const [selectedId, setSelectedId] = useState(initial.pipeline_id); + const selected = + data.data_pipelines.find( + (pipeline: DataPipeline): boolean => pipeline.pipeline_id === selectedId, + ) ?? initial; + return ( + <> +
+ {data.data_pipelines.length} contracts} + /> +
+ {(["all", "operational", "adapter", "blocked"] as const).map( + (value: PipelineFilter): React.JSX.Element => ( + + ), + )} +
+
+
+ Pipeline + Domain + Cadence + State +
+ {visible.map((pipeline: DataPipeline): React.JSX.Element => ( + + ))} +
+
+ +
+ {data.intelligence.length} services} + /> + +
+ + ); +}; + +const Navigation = ({ + mode, + onMode, +}: { + readonly mode: Mode; + readonly onMode: (mode: Mode) => void; +}): React.JSX.Element => ( + +); + +const Header = ({ + data, + mode, +}: { + readonly data: ObservatoryBootstrap; + readonly mode: Mode; +}): React.JSX.Element => { + const definition = MODES.find((item: ModeDefinition): boolean => item.id === mode); + if (definition === undefined) { + throw new Error(`Unknown observatory mode: mode=${mode}`); + } + return ( +
+
+

{definition.eyebrow}

+

{definition.title}

+
+
+ + + {data.headline.run_id.replace("meta-run:", "")} + +
+
+ ); +}; + +const BoundaryBar = ({ data }: { readonly data: ObservatoryBootstrap }): React.JSX.Element => ( +
+
+
+ Levels 6–7 blocked + Paper only + Live blocked + No execution surface +
+); + +const ModeContent = ({ + data, + mode, + reducedMotion, +}: { + readonly data: ObservatoryBootstrap; + readonly mode: Mode; + readonly reducedMotion: boolean; +}): React.JSX.Element => { + if (mode === "world") { + return ; + } + if (mode === "options") { + return ; + } + if (mode === "meta") { + return ; + } + if (mode === "dreams") { + return ; + } + return ; +}; + +const Footer = ({ data }: { readonly data: ObservatoryBootstrap }): React.JSX.Element => ( +
+
+
+
+
+
+
+
+); + +export const App = (): React.JSX.Element => { + const { data, error, loading } = useObservatoryBootstrap(); + const reducedMotion = useReducedMotion(); + const [mode, setMode] = useState("world"); + + if (loading) { + return ; + } + if (error !== null) { + return ; + } + if (data === null) { + throw new Error("Observatory loading finished without data or an error"); + } + + return ( +
+ +
+
+ +
+ {data.metrics.map((metric: Metric): React.JSX.Element => ( + + ))} +
+
+ + {mode === "world" ? : null} +
+
+
+
+ ); +}; diff --git a/ui/src/SurfaceScene.tsx b/ui/src/SurfaceScene.tsx new file mode 100644 index 0000000..81c75e7 --- /dev/null +++ b/ui/src/SurfaceScene.tsx @@ -0,0 +1,88 @@ +import { OrbitControls, PerspectiveCamera } from "@react-three/drei"; +import { Canvas } from "@react-three/fiber"; +import { useMemo } from "react"; +import * as THREE from "three"; + +import type { SurfacePoint } from "./types"; + +interface SurfaceSceneProps { + readonly points: readonly SurfacePoint[]; + readonly reducedMotion: boolean; +} + +const buildSurfaceGeometry = (points: readonly SurfacePoint[]): THREE.PlaneGeometry => { + const geometry = new THREE.PlaneGeometry(6.4, 3.8, 6, 5); + const position = geometry.attributes.position; + if (position === undefined) { + throw new Error("IV surface geometry is missing its position attribute"); + } + for (let index = 0; index < position.count; index += 1) { + const point = points[index]; + if (point === undefined) { + throw new Error(`IV surface point is missing: index=${index} count=${points.length}`); + } + position.setZ(index, (point.volatility - 0.25) * 8.5); + } + position.needsUpdate = true; + geometry.computeVertexNormals(); + return geometry; +}; + +const Surface = ({ + points, + reducedMotion, +}: SurfaceSceneProps): React.JSX.Element => { + const geometry = useMemo( + (): THREE.PlaneGeometry => buildSurfaceGeometry(points), + [points], + ); + return ( + <> + + + + + + + + + + + + + + + + ); +}; + +export const SurfaceScene = (props: SurfaceSceneProps): React.JSX.Element => ( + + + +); diff --git a/ui/src/WorldScene.tsx b/ui/src/WorldScene.tsx new file mode 100644 index 0000000..8bfb5d7 --- /dev/null +++ b/ui/src/WorldScene.tsx @@ -0,0 +1,243 @@ +import { Line, OrbitControls, PerspectiveCamera } from "@react-three/drei"; +import { Canvas, type ThreeEvent, useFrame } from "@react-three/fiber"; +import { memo, useEffect, useMemo, useRef, useState } from "react"; +import * as THREE from "three"; + +import type { Position, WorldLink, WorldNode } from "./types"; + +interface WorldSceneProps { + readonly nodes: readonly WorldNode[]; + readonly links: readonly WorldLink[]; + readonly selectedId: string; + readonly paused: boolean; + readonly reducedMotion: boolean; + readonly onSelect: (id: string) => void; +} + +interface WorldNodeMeshProps { + readonly node: WorldNode; + readonly selected: boolean; + readonly reducedMotion: boolean; + readonly onSelect: (id: string) => void; +} + +interface LinkFieldProps { + readonly nodes: readonly WorldNode[]; + readonly links: readonly WorldLink[]; +} + +const colorForKind = (kind: string): string => { + const colors: Readonly> = { + evidence: "#7ce7ff", + narrative: "#fa74be", + world: "#8df0c8", + causal: "#a98bff", + agents: "#f9c86c", + market: "#65a8ff", + options: "#da85ff", + meta: "#f18d72", + proof: "#d6ff72", + }; + return colors[kind] ?? "#94a3b8"; +}; + +const Core = ({ reducedMotion }: { readonly reducedMotion: boolean }): React.JSX.Element => { + const group = useRef(null); + useFrame((_, delta: number): void => { + if (!reducedMotion && group.current !== null) { + group.current.rotation.y += delta * 0.12; + group.current.rotation.x += delta * 0.035; + } + }); + return ( + + + + + + + + + + + + + + + ); +}; + +const WorldNodeMesh = ({ + node, + selected, + reducedMotion, + onSelect, +}: WorldNodeMeshProps): React.JSX.Element => { + const mesh = useRef(null); + const [hovered, setHovered] = useState(false); + const color = colorForKind(node.kind); + useEffect((): (() => void) => { + if (hovered) { + document.body.style.cursor = "pointer"; + } + return (): void => { + document.body.style.cursor = ""; + }; + }, [hovered]); + useFrame((state, delta: number): void => { + if (mesh.current === null) { + return; + } + const pulse = reducedMotion ? 1 : 1 + Math.sin(state.clock.elapsedTime * 1.8 + node.signal) * 0.05; + const target = (selected ? 1.38 : hovered ? 1.16 : 1) * pulse; + mesh.current.scale.lerp(new THREE.Vector3(target, target, target), Math.min(delta * 8, 1)); + if (!reducedMotion) { + mesh.current.rotation.y += delta * (0.12 + node.signal * 0.08); + } + }); + + const activate = (event: ThreeEvent): void => { + event.stopPropagation(); + onSelect(node.id); + }; + + return ( + + setHovered(true)} + onPointerLeave={(): void => setHovered(false)} + > + + + + + + + + + ); +}; + +const LinkField = ({ nodes, links }: LinkFieldProps): React.JSX.Element => { + const positions = useMemo>( + (): ReadonlyMap => + new Map(nodes.map((node: WorldNode): readonly [string, Position] => [node.id, node.position])), + [nodes], + ); + return ( + <> + {links.map((link: WorldLink): React.JSX.Element | null => { + const source = positions.get(link.source); + const target = positions.get(link.target); + if (source === undefined || target === undefined) { + return null; + } + return ( + + ); + })} + + ); +}; + +const StarField = (): React.JSX.Element => { + const positions = useMemo((): Float32Array => { + const values = new Float32Array(360); + for (let index = 0; index < 120; index += 1) { + const angle = index * 2.399963; + const radius = 5.4 + (index % 13) * 0.22; + values[index * 3] = Math.cos(angle) * radius; + values[index * 3 + 1] = ((index % 23) - 11) * 0.36; + values[index * 3 + 2] = Math.sin(angle) * radius - 1.5; + } + return values; + }, []); + return ( + + + + + + + ); +}; + +const Scene = memo( + ({ + nodes, + links, + selectedId, + paused, + reducedMotion, + onSelect, + }: WorldSceneProps): React.JSX.Element => ( + <> + + + + + + + + + {nodes.map( + (node: WorldNode): React.JSX.Element => ( + + ), + )} + + + ), +); + +Scene.displayName = "Scene"; + +export const WorldScene = (props: WorldSceneProps): React.JSX.Element => ( + + + +); diff --git a/ui/src/hooks.ts b/ui/src/hooks.ts new file mode 100644 index 0000000..9c6fcc5 --- /dev/null +++ b/ui/src/hooks.ts @@ -0,0 +1,62 @@ +import { useEffect, useState } from "react"; + +import type { ObservatoryBootstrap } from "./types"; + +interface BootstrapState { + readonly data: ObservatoryBootstrap | null; + readonly error: string | null; + readonly loading: boolean; +} + +export const useReducedMotion = (): boolean => { + const [reduced, setReduced] = useState( + (): boolean => window.matchMedia("(prefers-reduced-motion: reduce)").matches, + ); + + useEffect((): (() => void) => { + const media = window.matchMedia("(prefers-reduced-motion: reduce)"); + const update = (): void => setReduced(media.matches); + media.addEventListener("change", update); + return (): void => media.removeEventListener("change", update); + }, []); + + return reduced; +}; + +export const useObservatoryBootstrap = (): BootstrapState => { + const [state, setState] = useState({ + data: null, + error: null, + loading: true, + }); + + useEffect((): (() => void) => { + const controller = new AbortController(); + const load = async (): Promise => { + try { + const response = await fetch("/api/observatory/bootstrap", { + method: "GET", + headers: { Accept: "application/json" }, + signal: controller.signal, + }); + if (!response.ok) { + throw new Error( + `Observatory bootstrap failed: status=${response.status} body=${await response.text()}`, + ); + } + const data = (await response.json()) as ObservatoryBootstrap; + setState({ data, error: null, loading: false }); + } catch (error: unknown) { + if (error instanceof DOMException && error.name === "AbortError") { + return; + } + const message = error instanceof Error ? error.message : String(error); + setState({ data: null, error: message, loading: false }); + } + }; + void load(); + return (): void => controller.abort(); + }, []); + + return state; +}; diff --git a/ui/src/main.tsx b/ui/src/main.tsx new file mode 100644 index 0000000..ef2f1a0 --- /dev/null +++ b/ui/src/main.tsx @@ -0,0 +1,16 @@ +import { StrictMode } from "react"; +import { createRoot } from "react-dom/client"; + +import { App } from "./App"; +import "./styles.css"; + +const root = document.getElementById("root"); +if (root === null) { + throw new Error("Jester Observatory root element is missing"); +} + +createRoot(root).render( + + + , +); diff --git a/ui/src/styles.css b/ui/src/styles.css new file mode 100644 index 0000000..dcf64b1 --- /dev/null +++ b/ui/src/styles.css @@ -0,0 +1,2634 @@ +@import url("https://fonts.googleapis.com/css2?family=Inter:wght@400;500;600;650;700&display=swap"); + +:root { + color-scheme: dark; + font-family: + Inter, "Segoe UI", system-ui, -apple-system, BlinkMacSystemFont, sans-serif; + font-synthesis: none; + text-rendering: geometricPrecision; + --bg: #05070d; + --bg-elevated: #090c14; + --surface: rgba(13, 18, 30, 0.82); + --surface-strong: rgba(17, 23, 39, 0.94); + --surface-muted: rgba(12, 16, 28, 0.58); + --border: rgba(148, 171, 221, 0.13); + --border-strong: rgba(143, 174, 245, 0.25); + --text: #eef3ff; + --text-secondary: #9ba8c0; + --text-muted: #7d899f; + --cyan: #71e4ff; + --cyan-soft: #a9efff; + --violet: #9d82ff; + --pink: #fa75bf; + --amber: #f4c56c; + --green: #7de5bd; + --red: #fb7886; + --z-base: 0; + --z-card: 10; + --z-nav: 40; + --z-skip: 1000; + --radius-sm: 9px; + --radius-md: 14px; + --radius-lg: 22px; + --shadow-card: + 0 1px 0 rgba(255, 255, 255, 0.035) inset, + 0 26px 70px rgba(0, 0, 0, 0.24); + --motion-fast: 180ms; + --motion-medium: 280ms; +} + +* { + box-sizing: border-box; +} + +html { + min-width: 320px; + min-height: 100%; + background: var(--bg); + scroll-behavior: smooth; +} + +body { + margin: 0; + min-width: 320px; + min-height: 100dvh; + overflow-x: hidden; + background: + radial-gradient(circle at 19% -8%, rgba(71, 66, 175, 0.23), transparent 35%), + radial-gradient(circle at 96% 0%, rgba(25, 119, 163, 0.14), transparent 31%), + radial-gradient(circle at 76% 91%, rgba(158, 48, 129, 0.08), transparent 28%), + var(--bg); + color: var(--text); +} + +body::before { + position: fixed; + z-index: -1; + inset: 0; + content: ""; + pointer-events: none; + opacity: 0.28; + background-image: + linear-gradient(rgba(112, 139, 196, 0.035) 1px, transparent 1px), + linear-gradient(90deg, rgba(112, 139, 196, 0.035) 1px, transparent 1px); + background-size: 48px 48px; + mask-image: radial-gradient(circle at 50% 20%, black, transparent 86%); +} + +button, +a { + -webkit-tap-highlight-color: transparent; + touch-action: manipulation; +} + +button { + font: inherit; +} + +button, +a[href] { + cursor: pointer; +} + +button:focus-visible, +a:focus-visible { + outline: 2px solid var(--cyan); + outline-offset: 3px; +} + +a { + color: inherit; +} + +.skip-link { + position: fixed; + z-index: var(--z-skip); + top: 12px; + left: 12px; + padding: 10px 14px; + border-radius: var(--radius-sm); + background: var(--text); + color: var(--bg); + font-weight: 700; + transform: translateY(-150%); + transition: transform var(--motion-fast) ease-out; +} + +.skip-link:focus { + transform: translateY(0); +} + +.sr-only { + position: absolute; + width: 1px; + height: 1px; + overflow: hidden; + clip: rect(0 0 0 0); + white-space: nowrap; + clip-path: inset(50%); +} + +.app-shell { + width: 100%; + min-height: 100dvh; +} + +.nav-rail { + position: fixed; + z-index: var(--z-nav); + top: 0; + bottom: 0; + left: 0; + display: flex; + width: 92px; + flex-direction: column; + align-items: center; + border-right: 1px solid var(--border); + background: rgba(5, 7, 13, 0.82); + backdrop-filter: blur(22px) saturate(130%); +} + +.brand-mark { + position: relative; + display: grid; + width: 52px; + height: 52px; + margin: 22px 0 30px; + place-items: center; + color: var(--cyan); + filter: drop-shadow(0 0 16px rgba(113, 228, 255, 0.23)); +} + +.brand-mark svg { + position: absolute; + width: 48px; + height: 48px; + stroke-width: 1.2; +} + +.brand-mark span { + font-size: 15px; + font-weight: 700; + letter-spacing: -0.04em; +} + +.nav-rail nav { + display: flex; + width: 100%; + flex: 1; + flex-direction: column; + gap: 8px; + align-items: center; +} + +.nav-rail nav button { + position: relative; + display: flex; + width: 74px; + min-height: 66px; + padding: 8px 4px; + flex-direction: column; + gap: 6px; + align-items: center; + justify-content: center; + border: 1px solid transparent; + border-radius: 14px; + background: transparent; + color: var(--text-muted); + transition: + color var(--motion-fast) ease, + background var(--motion-fast) ease, + border-color var(--motion-fast) ease, + transform var(--motion-fast) ease; +} + +.nav-rail nav button::before { + position: absolute; + left: -10px; + width: 3px; + height: 26px; + border-radius: 99px; + content: ""; + background: transparent; + transition: background var(--motion-fast) ease; +} + +.nav-rail nav button:hover { + border-color: var(--border); + background: rgba(128, 151, 205, 0.06); + color: var(--text-secondary); + transform: translateY(-1px); +} + +.nav-rail nav button.active { + border-color: rgba(113, 228, 255, 0.16); + background: + linear-gradient(140deg, rgba(67, 92, 173, 0.18), rgba(37, 203, 230, 0.05)), + rgba(128, 151, 205, 0.06); + color: var(--cyan-soft); +} + +.nav-rail nav button.active::before { + background: var(--cyan); + box-shadow: 0 0 16px rgba(113, 228, 255, 0.7); +} + +.nav-rail nav svg { + width: 21px; + height: 21px; + stroke-width: 1.6; +} + +.nav-rail nav span, +.nav-authority span { + font-size: 10px; + font-weight: 600; + letter-spacing: 0.03em; +} + +.nav-authority { + display: flex; + min-height: 62px; + margin-bottom: 20px; + flex-direction: column; + gap: 6px; + align-items: center; + justify-content: center; + color: var(--green); +} + +.nav-authority svg { + width: 20px; +} + +.main-content { + width: calc(100% - 92px); + max-width: 1710px; + min-height: 100dvh; + margin-left: 92px; + padding: 24px 30px 22px; +} + +.app-header { + display: flex; + min-height: 70px; + align-items: flex-start; + justify-content: space-between; + gap: 28px; +} + +.eyebrow, +.card-heading p, +.metric-card p, +.metric-card small { + margin: 0; +} + +.eyebrow, +.card-heading p { + color: var(--text-muted); + font-size: 10px; + font-weight: 650; + letter-spacing: 0.135em; + line-height: 1.4; + text-transform: uppercase; +} + +.app-header h1 { + margin: 5px 0 0; + font-size: clamp(25px, 2.4vw, 38px); + font-weight: 600; + letter-spacing: -0.045em; + line-height: 1.05; +} + +.header-state { + display: flex; + min-height: 44px; + flex-wrap: wrap; + gap: 8px; + align-items: center; + justify-content: flex-end; +} + +.fixture-label, +.run-id, +.status-badge, +.proxy-pill { + display: inline-flex; + min-height: 30px; + padding: 6px 10px; + gap: 6px; + align-items: center; + border: 1px solid var(--border); + border-radius: 999px; + background: rgba(11, 15, 25, 0.72); + color: var(--text-secondary); + font-size: 10px; + font-weight: 600; + letter-spacing: 0.035em; + white-space: nowrap; +} + +.fixture-label svg { + width: 13px; +} + +.run-id { + max-width: 146px; + overflow: hidden; + color: var(--text-muted); + font-family: "Cascadia Code", "SFMono-Regular", Consolas, monospace; + text-overflow: ellipsis; +} + +.status-badge { + color: var(--cyan-soft); +} + +.status-compact { + min-height: 24px; + padding: 3px 8px; + font-size: 8px; +} + +.status-dot { + width: 6px; + height: 6px; + flex: 0 0 auto; + border-radius: 50%; + background: currentColor; + box-shadow: 0 0 10px currentColor; +} + +.status-pass, +.status-operational, +.status-operational_mechanics, +.status-mechanics_certified, +.status-mechanics_evaluated { + border-color: rgba(125, 229, 189, 0.2); + color: var(--green); +} + +.status-mechanics_only, +.status-proposal_only, +.status-adapter_contract { + border-color: rgba(157, 130, 255, 0.22); + color: #bbaaff; +} + +.status-blocked, +.status-blocked_calibration, +.status-blocked_license_or_data, +.status-blocked_missing_evidence, +.status-prohibited { + border-color: rgba(244, 197, 108, 0.23); + color: var(--amber); +} + +.status-prohibited { + border-color: rgba(251, 120, 134, 0.24); + color: var(--red); +} + +.boundary-bar { + display: flex; + min-height: 40px; + margin: 16px 0; + padding: 7px 12px; + flex-wrap: wrap; + gap: 8px; + align-items: center; + border: 1px solid rgba(125, 229, 189, 0.13); + border-radius: 12px; + background: + linear-gradient(90deg, rgba(38, 118, 100, 0.1), transparent 38%), + rgba(10, 14, 23, 0.7); + color: var(--text-muted); + font-size: 10px; + letter-spacing: 0.035em; +} + +.boundary-bar div { + display: inline-flex; + margin-right: auto; + gap: 8px; + align-items: center; +} + +.boundary-bar strong { + color: var(--green); + font-size: 10px; + font-weight: 600; +} + +.boundary-bar svg { + width: 16px; +} + +.boundary-bar > span { + padding-left: 9px; + border-left: 1px solid var(--border); +} + +.metric-grid { + display: grid; + margin-bottom: 14px; + grid-template-columns: repeat(5, minmax(0, 1fr)); + gap: 10px; +} + +.metric-card, +.panel, +.world-detail { + position: relative; + z-index: var(--z-card); + overflow: hidden; + border: 1px solid var(--border); + background: + linear-gradient(145deg, rgba(255, 255, 255, 0.025), transparent 40%), + var(--surface); + box-shadow: var(--shadow-card); +} + +.metric-card::before { + position: absolute; + top: 0; + right: 18px; + left: 18px; + height: 1px; + content: ""; + background: linear-gradient(90deg, transparent, currentColor, transparent); + opacity: 0.36; +} + +.metric-card { + display: grid; + min-height: 112px; + padding: 14px 15px; + grid-template-columns: 1fr auto; + grid-template-rows: 1fr auto; + gap: 6px; + border-radius: var(--radius-md); + color: var(--cyan); + transition: + border-color var(--motion-medium) ease, + transform var(--motion-medium) ease, + background var(--motion-medium) ease; +} + +.metric-card:hover { + border-color: var(--border-strong); + background: + linear-gradient(145deg, rgba(255, 255, 255, 0.04), transparent 45%), + var(--surface-strong); + transform: translateY(-2px); +} + +.metric-card.tone-violet { + color: var(--violet); +} + +.metric-card.tone-amber { + color: var(--amber); +} + +.metric-card.tone-pink { + color: var(--pink); +} + +.metric-card.tone-green { + color: var(--green); +} + +.metric-card p, +.metric-card small { + color: var(--text-muted); + font-size: 9px; + font-weight: 600; + letter-spacing: 0.075em; + text-transform: uppercase; +} + +.metric-card strong { + display: block; + margin-top: 4px; + color: var(--text); + font-size: clamp(21px, 2.1vw, 31px); + font-weight: 600; + letter-spacing: -0.04em; +} + +.metric-card > span { + align-self: center; + color: currentColor; + font-family: "Cascadia Code", monospace; + font-size: 10px; + font-weight: 600; +} + +.metric-card small { + grid-column: 1 / -1; + letter-spacing: 0; + line-height: 1.45; + text-transform: none; +} + +.bento-grid { + display: grid; + grid-template-columns: repeat(12, minmax(0, 1fr)); + gap: 12px; +} + +.panel { + border-radius: var(--radius-lg); +} + +.card-header { + display: flex; + min-height: 64px; + padding: 14px 16px; + gap: 16px; + align-items: center; + justify-content: space-between; + border-bottom: 1px solid var(--border); +} + +.card-heading { + display: flex; + min-width: 0; + gap: 11px; + align-items: center; +} + +.card-icon { + display: grid; + width: 34px; + height: 34px; + flex: 0 0 auto; + place-items: center; + border: 1px solid rgba(113, 228, 255, 0.14); + border-radius: 10px; + background: rgba(113, 228, 255, 0.055); + color: var(--cyan); +} + +.card-icon svg { + width: 17px; + height: 17px; + stroke-width: 1.6; +} + +.card-heading h2 { + margin: 3px 0 0; + overflow: hidden; + color: var(--text); + font-size: 14px; + font-weight: 600; + letter-spacing: -0.02em; + line-height: 1.25; + text-overflow: ellipsis; + white-space: nowrap; +} + +.icon-label-button { + display: inline-flex; + min-height: 38px; + padding: 7px 11px; + gap: 7px; + align-items: center; + border: 1px solid var(--border); + border-radius: 10px; + background: rgba(13, 19, 32, 0.68); + color: var(--text-secondary); + font-size: 10px; + font-weight: 600; + transition: + border-color var(--motion-fast) ease, + color var(--motion-fast) ease, + background var(--motion-fast) ease; +} + +.icon-label-button:hover { + border-color: rgba(113, 228, 255, 0.3); + background: rgba(113, 228, 255, 0.07); + color: var(--cyan-soft); +} + +.icon-label-button svg { + width: 15px; +} + +.world-card, +.surface-card, +.archive-card, +.pipeline-card { + grid-column: span 9; + min-height: 570px; +} + +.world-side { + display: grid; + min-width: 0; + grid-column: span 3; + grid-template-rows: auto 1fr; + gap: 12px; +} + +.world-canvas { + position: relative; + height: 444px; + overflow: hidden; + background: + radial-gradient(circle at 50% 47%, rgba(87, 70, 190, 0.13), transparent 29%), + radial-gradient(circle at 50% 85%, rgba(37, 146, 178, 0.08), transparent 42%), + linear-gradient(180deg, rgba(5, 7, 14, 0.18), rgba(4, 6, 11, 0.76)); +} + +.world-canvas::after { + position: absolute; + right: 0; + bottom: 0; + left: 0; + height: 90px; + content: ""; + pointer-events: none; + background: linear-gradient(transparent, rgba(5, 7, 13, 0.7)); +} + +.canvas-caption { + position: absolute; + z-index: 2; + right: 14px; + bottom: 12px; + display: flex; + gap: 14px; + color: var(--text-muted); + font-size: 9px; + letter-spacing: 0.045em; +} + +.scene-loading { + position: absolute; + inset: 0; + display: grid; + gap: 12px; + place-content: center; + color: var(--text-muted); + font-size: 10px; + letter-spacing: 0.08em; + text-transform: uppercase; +} + +.scene-loading-orbit { + width: 42px; + height: 42px; + margin: auto; + border: 1px solid rgba(113, 228, 255, 0.16); + border-top-color: var(--cyan); + border-radius: 50%; + animation: orbit-loading 900ms linear infinite; +} + +@keyframes orbit-loading { + to { + transform: rotate(360deg); + } +} + +.node-access-list { + position: absolute; + z-index: 4; + bottom: 15px; + left: 16px; + display: flex; + max-width: calc(100% - 250px); + flex-wrap: wrap; + gap: 5px; +} + +.node-access-list button { + display: inline-flex; + min-height: 28px; + padding: 4px 8px; + gap: 6px; + align-items: center; + border: 1px solid rgba(139, 162, 211, 0.13); + border-radius: 999px; + background: rgba(7, 10, 18, 0.72); + color: var(--text-muted); + font-size: 8px; + font-weight: 600; + backdrop-filter: blur(10px); + transition: + color var(--motion-fast) ease, + border-color var(--motion-fast) ease, + background var(--motion-fast) ease; +} + +.node-access-list button > span { + width: 5px; + height: 5px; + border-radius: 50%; + background: currentColor; +} + +.node-access-list button:hover, +.node-access-list button.active { + border-color: rgba(113, 228, 255, 0.28); + background: rgba(33, 72, 101, 0.32); + color: var(--cyan-soft); +} + +.world-detail { + min-height: 280px; + padding: 18px; + border-radius: var(--radius-lg); + background: + radial-gradient(circle at 100% 0%, rgba(112, 91, 212, 0.16), transparent 42%), + var(--surface-strong); +} + +.detail-orbit { + display: grid; + width: 68px; + height: 68px; + margin-bottom: 18px; + place-items: center; + border: 1px solid rgba(113, 228, 255, 0.18); + border-radius: 50%; +} + +.detail-orbit::before, +.detail-orbit::after, +.detail-orbit span { + position: absolute; + border-radius: 50%; + content: ""; +} + +.detail-orbit::before { + width: 38px; + height: 38px; + border: 1px solid rgba(157, 130, 255, 0.38); +} + +.detail-orbit::after { + width: 11px; + height: 11px; + background: var(--cyan); + box-shadow: 0 0 22px rgba(113, 228, 255, 0.75); +} + +.detail-orbit span { + width: 4px; + height: 4px; + background: var(--pink); + transform: translate(26px, -11px); +} + +.world-detail h3, +.world-state-card h3, +.seller-card h3, +.prediction-card h3, +.pipeline-detail h3 { + margin: 6px 0 10px; + font-size: 19px; + font-weight: 600; + letter-spacing: -0.035em; +} + +.world-detail > div:nth-child(2) > p:last-child, +.world-state-card > p, +.seller-card li, +.pipeline-detail p { + color: var(--text-secondary); + font-size: 11px; + line-height: 1.6; +} + +.world-detail dl, +.option-contract dl, +.pipeline-detail dl { + display: grid; + margin: 18px 0 0; + grid-template-columns: 1fr 1fr; + gap: 1px; + overflow: hidden; + border: 1px solid var(--border); + border-radius: 10px; + background: var(--border); +} + +.world-detail dl div, +.option-contract dl div, +.pipeline-detail dl div { + padding: 10px; + background: rgba(8, 12, 21, 0.86); +} + +dt { + color: var(--text-muted); + font-size: 8px; + letter-spacing: 0.08em; + text-transform: uppercase; +} + +dd { + margin: 4px 0 0; + color: var(--text); + font-size: 11px; + font-weight: 600; +} + +.world-state-card { + min-height: 260px; + padding: 18px; + background: + radial-gradient(circle at 70% 32%, rgba(73, 207, 162, 0.1), transparent 33%), + var(--surface); +} + +.proof-ring { + display: grid; + width: 92px; + height: 92px; + margin: 18px 0; + place-content: center; + border: 1px solid rgba(125, 229, 189, 0.28); + border-radius: 50%; + background: + radial-gradient(circle, rgba(125, 229, 189, 0.13), transparent 64%), + conic-gradient(var(--green) 0 75%, rgba(125, 229, 189, 0.08) 75% 100%); + box-shadow: + inset 0 0 0 7px #0b101b, + 0 0 35px rgba(125, 229, 189, 0.08); + color: var(--green); +} + +.proof-ring span { + font-size: 30px; + font-weight: 600; + letter-spacing: -0.06em; + line-height: 0.9; +} + +.proof-ring small { + text-align: center; + color: var(--text-muted); + font-size: 9px; +} + +.authority-line, +.holdability-state { + display: flex; + margin-top: 18px; + padding-top: 14px; + gap: 8px; + align-items: center; + border-top: 1px solid var(--border); + color: var(--green); + font-size: 10px; + font-weight: 600; +} + +.authority-line svg, +.holdability-state svg { + width: 17px; +} + +.attention-card { + grid-column: span 12; + min-height: 278px; +} + +.attention-card .card-header { + border-bottom: 0; +} + +.proxy-pill { + color: var(--cyan-soft); +} + +.sparkline { + height: 102px; + margin: 4px 16px 0; +} + +.sparkline svg { + width: 100%; + height: 100%; + overflow: visible; +} + +.progress-list { + display: grid; + padding: 0 16px 16px; + grid-template-columns: repeat(4, minmax(0, 1fr)); + gap: 16px; +} + +.progress-row > div:first-child { + display: flex; + margin-bottom: 7px; + justify-content: space-between; + gap: 12px; + color: var(--text-muted); + font-size: 9px; +} + +.progress-row strong { + color: var(--text-secondary); + font-family: "Cascadia Code", monospace; + font-weight: 500; +} + +.progress-track { + height: 3px; + overflow: hidden; + border-radius: 99px; + background: rgba(132, 153, 196, 0.1); +} + +.progress-fill { + display: block; + width: 100%; + height: 100%; + border-radius: inherit; + background: var(--cyan); + box-shadow: 0 0 10px currentColor; + transform-origin: left; +} + +.progress-fill.tone-green { + background: var(--green); +} + +.progress-fill.tone-violet { + background: var(--violet); +} + +.progress-fill.tone-amber { + background: var(--amber); +} + +/* Options mode */ + +.surface-card { + min-height: 562px; +} + +.surface-canvas { + position: relative; + height: 435px; + overflow: hidden; + background: + radial-gradient(circle at 50% 36%, rgba(98, 76, 199, 0.14), transparent 36%), + linear-gradient(rgba(5, 7, 13, 0.05), rgba(5, 7, 13, 0.66)); +} + +.surface-axis { + position: absolute; + color: var(--text-muted); + font-size: 8px; + font-weight: 600; + letter-spacing: 0.08em; + text-transform: uppercase; +} + +.surface-axis-y { + top: 19px; + left: 16px; + writing-mode: vertical-lr; + transform: rotate(180deg); +} + +.surface-axis-x { + right: 16px; + bottom: 12px; +} + +.surface-axis-z { + bottom: 12px; + left: 16px; +} + +.surface-warning { + display: flex; + min-height: 48px; + padding: 10px 16px; + gap: 9px; + align-items: center; + border-top: 1px solid rgba(244, 197, 108, 0.11); + background: rgba(131, 94, 22, 0.055); + color: var(--amber); + font-size: 9px; +} + +.surface-warning svg { + width: 15px; +} + +.option-contract, +.seller-card, +.interoception-card, +.prediction-card, +.source-summary { + padding: 17px; +} + +.contract-title { + display: flex; + margin: 13px 0; + align-items: baseline; + justify-content: space-between; +} + +.contract-title span { + font-size: 28px; + font-weight: 650; + letter-spacing: -0.05em; +} + +.contract-title strong { + color: var(--violet); + font-family: "Cascadia Code", monospace; + font-size: 16px; +} + +.holdability-state { + color: var(--amber); +} + +.holdability-state div { + display: flex; + flex-direction: column; + gap: 3px; +} + +.holdability-state span { + color: var(--text-muted); + font-size: 9px; + font-weight: 400; +} + +.seller-card { + background: + radial-gradient(circle at 100% 100%, rgba(244, 197, 108, 0.09), transparent 45%), + var(--surface); +} + +.seller-card ul { + margin: 14px 0 0; + padding-left: 16px; +} + +.seller-card li { + margin: 8px 0; +} + +.full-width { + grid-column: span 12; +} + +.outcomes-card { + min-height: 270px; +} + +.outcome-table { + display: grid; + padding: 0 16px 15px; +} + +.outcome-table > div { + display: grid; + min-height: 42px; + align-items: center; + border-bottom: 1px solid rgba(143, 160, 197, 0.08); + grid-template-columns: 1fr 150px 120px; + color: var(--text-secondary); + font-size: 10px; +} + +.outcome-table > div > :nth-child(n + 2) { + text-align: right; + font-family: "Cascadia Code", monospace; +} + +.outcome-table .outcome-header { + min-height: 34px; + color: var(--text-muted); + font-size: 8px; + letter-spacing: 0.08em; + text-transform: uppercase; +} + +.outcome-table .positive { + color: var(--green); +} + +.outcome-table .negative { + color: var(--red); +} + +/* Meta mode */ + +.archive-grid { + display: grid; + min-height: 504px; + padding: 14px; + grid-template-columns: 1.35fr 1fr 0.9fr; + grid-template-rows: 1fr 0.88fr; + gap: 9px; +} + +.archive-cell { + position: relative; + display: flex; + min-width: 0; + padding: 15px; + flex-direction: column; + justify-content: flex-end; + overflow: hidden; + border: 1px solid var(--border); + border-radius: 15px; + background: + radial-gradient(circle at 70% 20%, rgba(109, 89, 218, 0.2), transparent 36%), + rgba(8, 12, 21, 0.74); + transition: + border-color var(--motion-medium) ease, + transform var(--motion-medium) ease; +} + +.archive-cell:hover { + border-color: var(--border-strong); + transform: translateY(-2px); +} + +.archive-0 { + grid-row: span 2; +} + +.archive-3 { + grid-column: span 2; +} + +.archive-glyph { + position: absolute; + top: 20px; + left: 20px; + width: 72px; + height: 72px; + border: 1px solid rgba(113, 228, 255, 0.16); + border-radius: 50%; +} + +.archive-glyph::before, +.archive-glyph span { + position: absolute; + border-radius: 50%; + content: ""; +} + +.archive-glyph::before { + inset: 14px; + border: 1px solid rgba(157, 130, 255, 0.22); +} + +.archive-glyph span { + width: 7px; + height: 7px; + background: var(--cyan); + box-shadow: 0 0 12px currentColor; +} + +.archive-glyph span:nth-child(1) { + top: 8px; + left: 31px; +} + +.archive-glyph span:nth-child(2) { + right: 7px; + bottom: 18px; + background: var(--pink); +} + +.archive-glyph span:nth-child(3) { + bottom: 4px; + left: 17px; + background: var(--violet); +} + +.archive-cell p { + margin: 0 0 5px; + color: var(--text-secondary); + font-size: 10px; + font-weight: 600; +} + +.archive-cell > strong { + font-size: clamp(22px, 3vw, 38px); + font-weight: 600; + letter-spacing: -0.055em; +} + +.archive-cell > small { + margin: 3px 0 11px; + color: var(--text-muted); + font-size: 8px; +} + +.archive-cell .status-badge { + align-self: flex-start; +} + +.interoception-card .card-header { + margin: -17px -17px 15px; +} + +.interoception-card .progress-row { + margin: 15px 0; +} + +.pressure-value { + color: var(--amber); + font-family: "Cascadia Code", monospace; + font-size: 15px; +} + +.budget-readout { + display: grid; + margin-top: 24px; + padding: 14px; + grid-template-columns: 1fr auto auto 1fr auto; + gap: 8px; + align-items: center; + border: 1px solid var(--border); + border-radius: 12px; + background: rgba(8, 11, 19, 0.68); + color: var(--text-muted); + font-size: 8px; + text-transform: uppercase; +} + +.budget-readout strong { + color: var(--text); + font-size: 18px; +} + +.budget-readout svg { + width: 14px; + color: var(--amber); +} + +.prediction-card { + min-height: 230px; + background: + radial-gradient(circle at 80% 10%, rgba(250, 117, 191, 0.12), transparent 42%), + var(--surface); +} + +.prediction-card > svg { + width: 34px; + height: 34px; + margin-bottom: 22px; + color: var(--pink); + stroke-width: 1.2; +} + +.prediction-card > strong { + display: block; + margin: 12px 0 4px; + font-size: 42px; + font-weight: 600; + letter-spacing: -0.06em; +} + +.prediction-card > span { + color: var(--text-muted); + font-size: 9px; +} + +.ladder-card, +.literature-card { + min-height: 240px; +} + +.ladder-list { + display: grid; + margin: 0; + padding: 18px 16px 20px; + grid-template-columns: repeat(8, minmax(0, 1fr)); + gap: 8px; + list-style: none; +} + +.ladder-list li { + position: relative; + display: flex; + min-width: 0; + min-height: 125px; + padding: 12px 10px; + flex-direction: column; + border: 1px solid var(--border); + border-radius: 12px; + background: rgba(8, 12, 21, 0.65); +} + +.ladder-list li:not(:last-child)::after { + position: absolute; + z-index: 3; + top: 29px; + right: -11px; + width: 13px; + height: 1px; + content: ""; + background: var(--border-strong); +} + +.ladder-node { + display: grid; + width: 32px; + height: 32px; + margin-bottom: 11px; + place-items: center; + border: 1px solid rgba(125, 229, 189, 0.22); + border-radius: 50%; + color: var(--green); + font-size: 10px; + font-weight: 700; +} + +.ladder-blocked .ladder-node { + border-color: rgba(244, 197, 108, 0.22); + color: var(--amber); +} + +.ladder-list li > div:nth-child(2) { + display: flex; + min-height: 45px; + flex-direction: column; + gap: 4px; +} + +.ladder-list strong { + overflow: hidden; + font-size: 9px; + font-weight: 600; + line-height: 1.25; + text-overflow: ellipsis; +} + +.ladder-list small { + color: var(--text-muted); + font-size: 8px; +} + +.ladder-list .status-badge { + margin-top: auto; + align-self: flex-start; +} + +.literature-feed { + display: grid; + padding: 14px 16px 18px; + grid-template-columns: 1fr 1fr; + gap: 10px; +} + +.literature-feed article { + display: grid; + min-height: 112px; + padding: 14px; + grid-template-columns: auto 1fr auto; + gap: 12px; + align-items: center; + border: 1px solid var(--border); + border-radius: 13px; + background: rgba(8, 12, 21, 0.64); +} + +.paper-icon, +.intelligence-icon { + display: grid; + width: 38px; + height: 38px; + place-items: center; + border: 1px solid rgba(157, 130, 255, 0.19); + border-radius: 11px; + color: var(--violet); +} + +.paper-icon svg, +.intelligence-icon svg { + width: 18px; +} + +.literature-feed span, +.intelligence-grid article > div:nth-child(2) > span { + color: var(--violet); + font-size: 8px; + font-weight: 600; + letter-spacing: 0.06em; + text-transform: uppercase; +} + +.literature-feed h3 { + margin: 5px 0; + font-size: 12px; + font-weight: 600; + line-height: 1.4; +} + +.literature-feed p { + margin: 0; + color: var(--text-muted); + font-size: 8px; +} + +.zero-credit { + display: inline-flex; + padding: 5px 8px; + border: 1px solid rgba(244, 197, 108, 0.18); + border-radius: 999px; + color: var(--amber) !important; + white-space: nowrap; +} + +/* Sources mode */ + +.pipeline-card { + min-height: 636px; +} + +.pipeline-filters { + display: flex; + padding: 12px 15px; + gap: 6px; + border-bottom: 1px solid var(--border); +} + +.pipeline-filters button { + min-height: 34px; + padding: 6px 11px; + border: 1px solid var(--border); + border-radius: 9px; + background: rgba(8, 12, 21, 0.62); + color: var(--text-muted); + font-size: 9px; + font-weight: 600; + transition: + border-color var(--motion-fast) ease, + color var(--motion-fast) ease, + background var(--motion-fast) ease; +} + +.pipeline-filters button:hover, +.pipeline-filters button.active { + border-color: rgba(113, 228, 255, 0.24); + background: rgba(113, 228, 255, 0.07); + color: var(--cyan-soft); +} + +.pipeline-table { + padding: 0 14px 14px; +} + +.pipeline-row { + display: grid; + width: 100%; + min-height: 47px; + padding: 6px 9px; + gap: 12px; + align-items: center; + border: 0; + border-bottom: 1px solid rgba(143, 160, 197, 0.075); + background: transparent; + color: var(--text-secondary); + grid-template-columns: minmax(210px, 1.4fr) 1fr 0.8fr minmax(125px, 0.8fr); + text-align: left; + transition: + background var(--motion-fast) ease, + color var(--motion-fast) ease; +} + +button.pipeline-row:hover, +button.pipeline-row.active { + background: rgba(113, 228, 255, 0.045); + color: var(--text); +} + +.pipeline-row > span { + min-width: 0; + overflow: hidden; + font-size: 9px; + text-overflow: ellipsis; + white-space: nowrap; +} + +.pipeline-row > span:first-child { + color: var(--text); + font-family: "Cascadia Code", monospace; + font-size: 9px; +} + +.pipeline-header { + min-height: 34px; + color: var(--text-muted); + font-size: 8px; + letter-spacing: 0.075em; + text-transform: uppercase; +} + +.pipeline-header > span:first-child { + color: var(--text-muted); + font-family: inherit; + font-size: 8px; +} + +.pipeline-detail { + min-height: 430px; + padding: 17px; + overflow: visible; +} + +.pipeline-detail h3 { + overflow-wrap: anywhere; +} + +.detail-section { + margin-top: 17px; +} + +.detail-section > strong { + color: var(--text-muted); + font-size: 8px; + letter-spacing: 0.075em; + text-transform: uppercase; +} + +.tag-cloud { + display: flex; + margin-top: 8px; + flex-wrap: wrap; + gap: 5px; +} + +.tag-cloud span { + padding: 5px 7px; + border: 1px solid var(--border); + border-radius: 6px; + color: var(--text-secondary); + font-size: 7px; +} + +.pipeline-detail > a { + display: inline-flex; + min-height: 42px; + margin-top: 17px; + padding: 9px 11px; + gap: 7px; + align-items: center; + border: 1px solid rgba(113, 228, 255, 0.18); + border-radius: 9px; + color: var(--cyan-soft); + font-size: 9px; + font-weight: 600; + text-decoration: none; + transition: background var(--motion-fast) ease; +} + +.pipeline-detail > a:hover { + background: rgba(113, 228, 255, 0.07); +} + +.pipeline-detail > a svg { + width: 13px; +} + +.source-counts { + display: grid; + margin-top: 15px; + gap: 8px; +} + +.source-counts > div { + display: flex; + min-height: 42px; + padding: 9px 11px; + align-items: center; + justify-content: space-between; + border: 1px solid var(--border); + border-radius: 9px; + color: var(--text-muted); + font-size: 9px; +} + +.source-counts strong { + color: var(--text); + font-size: 17px; +} + +.intelligence-card { + min-height: 460px; +} + +.intelligence-grid { + display: grid; + padding: 14px; + grid-template-columns: repeat(3, minmax(0, 1fr)); + gap: 8px; +} + +.intelligence-grid article { + display: grid; + min-height: 135px; + padding: 12px; + grid-template-columns: auto minmax(0, 1fr) auto; + grid-template-rows: 1fr auto; + gap: 8px 10px; + border: 1px solid var(--border); + border-radius: 13px; + background: + linear-gradient(140deg, rgba(157, 130, 255, 0.055), transparent 48%), + rgba(8, 12, 21, 0.66); + transition: + border-color var(--motion-medium) ease, + transform var(--motion-medium) ease; +} + +.intelligence-grid article:hover { + border-color: var(--border-strong); + transform: translateY(-2px); +} + +.intelligence-grid h3 { + margin: 4px 0; + font-size: 11px; + font-weight: 600; +} + +.intelligence-grid p { + margin: 0; + color: var(--text-muted); + font-size: 8px; + line-height: 1.45; +} + +.intelligence-grid article > strong { + align-self: center; + color: var(--cyan); + font-size: 19px; +} + +.intelligence-grid article > small { + grid-column: 3; + color: var(--text-muted); + font-size: 7px; + text-transform: uppercase; +} + +/* Dream Foundry mode */ + +.dream-cycle-card { + grid-column: span 9; + min-height: 360px; +} + +.dream-vitals { + grid-column: span 3; + min-height: 360px; + padding: 18px; + background: + radial-gradient(circle at 50% 42%, rgba(157, 130, 255, 0.15), transparent 38%), + var(--surface); +} + +.dream-cycle-track { + position: relative; + display: grid; + min-height: 286px; + padding: 28px 18px 20px; + grid-template-columns: repeat(4, minmax(0, 1fr)); + gap: 10px; + align-items: stretch; +} + +.dream-cycle-track::before { + position: absolute; + top: 73px; + right: 12%; + left: 12%; + height: 1px; + content: ""; + background: linear-gradient(90deg, var(--violet), var(--cyan), var(--amber), var(--green)); + opacity: 0.42; +} + +.dream-phase { + position: relative; + display: flex; + min-width: 0; + padding: 6px 12px 14px; + flex-direction: column; + gap: 16px; + align-items: center; + border: 1px solid var(--border); + border-radius: 16px; + background: + linear-gradient(180deg, rgba(157, 130, 255, 0.055), transparent 45%), + rgba(8, 12, 22, 0.66); + text-align: center; +} + +.dream-phase-node { + position: relative; + z-index: 2; + display: grid; + width: 48px; + height: 48px; + margin-top: 14px; + place-items: center; + border: 1px solid currentColor; + border-radius: 50%; + background: #0a0e18; + color: var(--violet); + box-shadow: 0 0 22px color-mix(in srgb, currentColor 20%, transparent); +} + +.dream-phase.tone-cyan .dream-phase-node { + color: var(--cyan); +} + +.dream-phase.tone-amber .dream-phase-node { + color: var(--amber); +} + +.dream-phase.tone-green .dream-phase-node { + color: var(--green); +} + +.dream-phase-node span { + font-family: "Cascadia Code", monospace; + font-size: 12px; +} + +.dream-phase > div:nth-child(2) { + display: grid; + gap: 4px; +} + +.dream-phase p { + margin: 0; + color: var(--text); + font-size: 11px; + font-weight: 600; +} + +.dream-phase strong { + margin-top: 4px; + color: var(--cyan-soft); + font-size: 24px; + font-weight: 600; +} + +.dream-phase small, +.dream-phase > span { + color: var(--text-muted); + font-size: 8px; + line-height: 1.5; +} + +.dream-phase small { + letter-spacing: 0.08em; + text-transform: uppercase; +} + +.coverage-orbit { + display: grid; + width: 142px; + height: 142px; + margin: 23px auto; + place-content: center; + border-radius: 50%; + background: + radial-gradient(circle, #0b101b 58%, transparent 59%), + conic-gradient(var(--violet) var(--coverage), rgba(157, 130, 255, 0.08) 0); + box-shadow: + inset 0 0 0 1px rgba(157, 130, 255, 0.18), + 0 0 42px rgba(157, 130, 255, 0.09); + text-align: center; +} + +.coverage-orbit strong { + color: var(--text); + font-size: 28px; + font-weight: 600; + letter-spacing: -0.05em; +} + +.coverage-orbit span { + color: var(--text-muted); + font-size: 8px; + text-transform: uppercase; +} + +.dream-vitals dl { + display: grid; + grid-template-columns: 1fr 1fr; + gap: 1px; + overflow: hidden; + border: 1px solid var(--border); + border-radius: 10px; + background: var(--border); +} + +.dream-vitals dl div { + padding: 10px; + background: rgba(8, 12, 21, 0.86); +} + +.cognition-mode-card { + min-height: 590px; +} + +.cognition-workbench { + display: grid; + min-height: 524px; + grid-template-columns: minmax(0, 2.2fr) minmax(290px, 0.8fr); +} + +.cognition-mode-grid { + display: grid; + padding: 14px; + grid-template-columns: repeat(4, minmax(0, 1fr)); + gap: 7px; +} + +.cognition-mode-grid button { + min-height: 72px; + padding: 10px; + overflow: hidden; + border: 1px solid var(--border); + border-radius: 11px; + background: + linear-gradient(145deg, rgba(157, 130, 255, 0.04), transparent 55%), + rgba(8, 12, 21, 0.7); + color: var(--text-secondary); + text-align: left; + transition: + border-color var(--motion-fast) ease, + background var(--motion-fast) ease, + transform var(--motion-fast) ease; +} + +.cognition-mode-grid button:hover, +.cognition-mode-grid button.active { + border-color: rgba(157, 130, 255, 0.34); + background: + linear-gradient(145deg, rgba(157, 130, 255, 0.13), rgba(113, 228, 255, 0.035)), + rgba(11, 16, 28, 0.92); + color: var(--text); + transform: translateY(-1px); +} + +.cognition-mode-grid button span { + display: block; + margin-bottom: 6px; + color: var(--violet); + font-size: 7px; + font-weight: 650; + letter-spacing: 0.09em; + text-transform: uppercase; +} + +.cognition-mode-grid button strong { + font-size: 9px; + font-weight: 600; + line-height: 1.35; +} + +.cognition-mode-detail { + padding: 18px; + border-left: 1px solid var(--border); + background: + radial-gradient(circle at 100% 0%, rgba(113, 228, 255, 0.08), transparent 42%), + rgba(7, 10, 18, 0.56); +} + +.cognition-mode-detail h3 { + margin: 7px 0 10px; + font-size: 22px; + letter-spacing: -0.035em; +} + +.cognition-mode-detail > p:not(.eyebrow) { + margin: 0 0 24px; + color: var(--text-secondary); + font-size: 11px; + line-height: 1.65; +} + +.cognition-mode-detail > div { + margin-top: 18px; + padding-top: 14px; + border-top: 1px solid var(--border); +} + +.cognition-mode-detail > div > strong { + color: var(--text-muted); + font-size: 8px; + letter-spacing: 0.08em; + text-transform: uppercase; +} + +.cognition-mode-detail ul { + display: grid; + margin: 10px 0 0; + padding: 0; + gap: 7px; + list-style: none; +} + +.cognition-mode-detail li { + position: relative; + padding-left: 14px; + color: var(--text-secondary); + font-size: 9px; +} + +.cognition-mode-detail li::before { + position: absolute; + top: 4px; + left: 0; + width: 5px; + height: 5px; + border-radius: 50%; + content: ""; + background: var(--cyan); +} + +.invention-card { + grid-column: span 8; + min-height: 430px; +} + +.invention-grid { + display: grid; + padding: 14px; + grid-template-columns: 1fr 1fr; + gap: 8px; +} + +.invention-grid > article { + min-height: 157px; + padding: 12px; + border: 1px solid var(--border); + border-radius: 13px; + background: + linear-gradient(135deg, rgba(113, 228, 255, 0.055), transparent 52%), + rgba(8, 12, 21, 0.68); +} + +.invention-grid > article > div:first-child, +.invention-grid footer { + display: flex; + align-items: center; + justify-content: space-between; +} + +.invention-grid > article > div:first-child span { + color: var(--cyan); + font-size: 7px; + font-weight: 650; + letter-spacing: 0.09em; + text-transform: uppercase; +} + +.invention-grid > article > div:first-child strong { + color: var(--green); + font-family: "Cascadia Code", monospace; + font-size: 13px; +} + +.invention-grid h3 { + margin: 9px 0; + font-size: 13px; + font-weight: 600; + letter-spacing: -0.02em; +} + +.invention-lineage { + display: flex; + min-height: 48px; + flex-wrap: wrap; + gap: 4px; + align-content: flex-start; +} + +.invention-lineage span { + padding: 4px 5px; + border: 1px solid var(--border); + border-radius: 5px; + color: var(--text-muted); + font-size: 6px; +} + +.invention-grid footer { + margin-top: 7px; + padding-top: 8px; + border-top: 1px solid var(--border); + color: var(--amber); + font-size: 8px; +} + +.failure-museum { + grid-column: span 4; + min-height: 430px; + background: + radial-gradient(circle at 90% 0%, rgba(250, 117, 191, 0.09), transparent 44%), + var(--surface); +} + +.failure-museum > div { + display: grid; + padding: 12px; + gap: 8px; +} + +.failure-museum article { + padding: 12px; + border: 1px solid rgba(250, 117, 191, 0.12); + border-radius: 11px; + background: rgba(30, 12, 29, 0.18); +} + +.failure-museum article > span { + color: var(--pink); + font-size: 7px; + letter-spacing: 0.07em; + text-transform: uppercase; +} + +.failure-museum h3 { + margin: 5px 0; + font-size: 11px; +} + +.failure-museum p { + margin: 0; + color: var(--text-muted); + font-size: 8px; + line-height: 1.5; +} + +.immune-court > div { + display: grid; + padding: 14px; + grid-template-columns: repeat(7, minmax(0, 1fr)); + gap: 6px; +} + +.immune-court > div > span { + display: flex; + min-height: 54px; + padding: 8px; + gap: 7px; + align-items: center; + border: 1px solid rgba(125, 229, 189, 0.14); + border-radius: 9px; + background: rgba(125, 229, 189, 0.035); + color: var(--green); + font-size: 7px; + line-height: 1.35; +} + +.immune-court > div svg { + width: 14px; + flex: 0 0 auto; +} + +.app-footer { + display: flex; + min-height: 54px; + padding: 14px 3px 0; + flex-wrap: wrap; + gap: 12px 24px; + align-items: center; + color: var(--text-muted); + font-size: 8px; + letter-spacing: 0.035em; +} + +.app-footer div { + display: inline-flex; + gap: 7px; + align-items: center; +} + +.app-footer div:last-child { + margin-left: auto; +} + +.app-footer svg { + width: 13px; +} + +/* Loading and hard error states */ + +.loading-shell, +.error-state { + min-height: 100dvh; + padding: 32px; +} + +.loading-brand { + display: flex; + gap: 12px; + align-items: center; + color: var(--cyan); + font-size: 11px; + font-weight: 700; + letter-spacing: 0.14em; +} + +.loading-brand svg { + width: 28px; +} + +.loading-grid { + display: grid; + max-width: 1300px; + margin: 64px auto; + grid-template-columns: repeat(4, 1fr); + gap: 12px; +} + +.skeleton-card { + min-height: 180px; + border: 1px solid var(--border); + border-radius: var(--radius-lg); + background: linear-gradient( + 110deg, + rgba(255, 255, 255, 0.02) 15%, + rgba(255, 255, 255, 0.065) 28%, + rgba(255, 255, 255, 0.02) 41% + ); + background-size: 200% 100%; + animation: skeleton 1.5s ease-in-out infinite; +} + +@keyframes skeleton { + to { + background-position-x: -200%; + } +} + +.error-state { + display: grid; + max-width: 720px; + margin: auto; + place-content: center; +} + +.error-state > svg { + width: 46px; + height: 46px; + margin-bottom: 28px; + color: var(--amber); +} + +.error-state h1 { + margin: 8px 0 14px; + font-size: 34px; + letter-spacing: -0.045em; +} + +.error-state > p:not(.eyebrow) { + color: var(--text-secondary); + line-height: 1.6; +} + +.error-state code { + margin-top: 18px; + padding: 14px; + border: 1px solid var(--border); + border-radius: 9px; + background: var(--surface); + color: var(--cyan-soft); + font-size: 11px; + white-space: normal; +} + +/* Responsive behavior */ + +@media (max-width: 1180px) { + .world-card, + .surface-card, + .archive-card, + .pipeline-card, + .dream-cycle-card { + grid-column: span 8; + } + + .world-side, + .dream-vitals { + grid-column: span 4; + } + + .metric-grid { + grid-template-columns: repeat(3, minmax(0, 1fr)); + } + + .metric-card:nth-child(n + 4) { + min-height: 96px; + } + + .ladder-list { + grid-template-columns: repeat(4, minmax(0, 1fr)); + } + + .ladder-list li:not(:last-child)::after { + display: none; + } + + .intelligence-grid { + grid-template-columns: repeat(2, minmax(0, 1fr)); + } + + .cognition-mode-grid { + grid-template-columns: repeat(3, minmax(0, 1fr)); + } + + .immune-court > div { + grid-template-columns: repeat(4, minmax(0, 1fr)); + } +} + +@media (max-width: 860px) { + .nav-rail { + top: auto; + right: 0; + width: auto; + height: 70px; + flex-direction: row; + border-top: 1px solid var(--border); + border-right: 0; + } + + .brand-mark, + .nav-authority { + display: none; + } + + .nav-rail nav { + flex-direction: row; + justify-content: space-around; + } + + .nav-rail nav button { + width: min(18.5vw, 98px); + min-height: 58px; + padding: 5px 4px; + } + + .nav-rail nav button::before { + top: -7px; + left: auto; + width: 26px; + height: 3px; + } + + .main-content { + width: 100%; + margin-left: 0; + padding: 20px 18px 94px; + } + + .app-header { + flex-direction: column; + gap: 14px; + } + + .header-state { + justify-content: flex-start; + } + + .boundary-bar div { + width: 100%; + } + + .metric-grid { + grid-template-columns: repeat(2, minmax(0, 1fr)); + } + + .world-card, + .surface-card, + .archive-card, + .pipeline-card, + .world-side, + .dream-cycle-card, + .dream-vitals, + .invention-card, + .failure-museum { + grid-column: span 12; + } + + .world-side { + grid-template-columns: 1fr 1fr; + grid-template-rows: auto; + } + + .world-detail, + .world-state-card, + .option-contract, + .seller-card, + .interoception-card, + .prediction-card, + .pipeline-detail, + .source-summary { + min-height: 100%; + } + + .progress-list { + grid-template-columns: repeat(2, minmax(0, 1fr)); + } + + .cognition-workbench { + grid-template-columns: 1fr; + } + + .cognition-mode-detail { + min-height: 340px; + border-top: 1px solid var(--border); + border-left: 0; + } + + .archive-grid { + grid-template-columns: 1fr 1fr; + } + + .archive-0 { + grid-row: auto; + } + + .archive-3 { + grid-column: auto; + } + + .pipeline-row { + grid-template-columns: minmax(190px, 1.4fr) 1fr minmax(120px, 0.8fr); + } + + .pipeline-row > span:nth-child(3) { + display: none; + } + + .app-footer div:last-child { + width: 100%; + margin-left: 0; + } +} + +@media (max-width: 560px) { + body { + font-size: 16px; + } + + .main-content { + padding: 16px 12px 88px; + } + + .app-header h1 { + font-size: 27px; + } + + .header-state { + gap: 6px; + } + + .run-id { + display: none; + } + + .boundary-bar { + gap: 6px; + } + + .boundary-bar > span { + padding: 4px 7px; + border: 1px solid var(--border); + border-radius: 999px; + } + + .metric-grid { + display: grid; + margin-right: 0; + padding-right: 0; + grid-template-columns: repeat(2, minmax(0, 1fr)); + overflow: visible; + } + + .metric-card { + min-width: 0; + min-height: 108px; + } + + .metric-card:last-child { + grid-column: span 2; + } + + .card-header { + min-height: 60px; + padding: 12px; + } + + .card-heading h2 { + max-width: 180px; + } + + .icon-label-button { + width: 44px; + min-height: 44px; + padding: 8px; + justify-content: center; + } + + .icon-label-button:not(.pipeline-detail a) { + font-size: 0; + } + + .world-card, + .surface-card, + .archive-card, + .pipeline-card, + .dream-cycle-card { + min-height: auto; + } + + .world-canvas, + .surface-canvas { + height: 390px; + } + + .canvas-caption { + display: none; + } + + .node-access-list { + position: relative; + bottom: auto; + left: auto; + max-width: none; + padding: 10px 12px 13px; + background: rgba(5, 7, 13, 0.52); + } + + .node-access-list button { + min-height: 44px; + } + + .world-side { + grid-template-columns: 1fr; + } + + .world-detail, + .world-state-card { + min-height: 260px; + } + + .attention-card { + min-height: 330px; + } + + .progress-list { + grid-template-columns: 1fr; + gap: 11px; + } + + .outcome-table > div { + grid-template-columns: 1fr 78px 68px; + } + + .archive-grid { + min-height: auto; + grid-template-columns: 1fr; + grid-template-rows: none; + } + + .archive-cell { + min-height: 200px; + } + + .ladder-list { + grid-template-columns: repeat(2, minmax(0, 1fr)); + } + + .literature-feed, + .intelligence-grid { + grid-template-columns: 1fr; + } + + .dream-cycle-track, + .invention-grid, + .cognition-mode-grid, + .immune-court > div { + grid-template-columns: 1fr 1fr; + } + + .dream-cycle-track::before { + display: none; + } + + .dream-phase { + min-height: 210px; + } + + .literature-feed article { + grid-template-columns: auto 1fr; + } + + .zero-credit { + grid-column: 2; + justify-self: start; + } + + .pipeline-filters { + overflow-x: auto; + } + + .pipeline-filters button { + min-width: max-content; + min-height: 44px; + } + + .pipeline-table { + padding: 0 8px 12px; + overflow: visible; + } + + .pipeline-row { + min-width: 0; + min-height: 64px; + grid-template-columns: minmax(0, 1fr) auto; + } + + .pipeline-row > span:nth-child(2), + .pipeline-row > span:nth-child(3) { + display: none; + } + + .app-footer { + line-height: 1.5; + } + + .nav-rail nav button { + min-height: 62px; + } + + .nav-rail nav button span { + font-size: 9px; + } +} + +@media (max-height: 520px) and (orientation: landscape) { + .nav-rail { + right: auto; + bottom: 0; + width: 72px; + height: 100%; + flex-direction: column; + border-top: 0; + border-right: 1px solid var(--border); + } + + .nav-rail nav { + flex-direction: column; + } + + .nav-rail nav button { + width: 60px; + min-height: 54px; + } + + .main-content { + width: calc(100% - 72px); + margin-left: 72px; + padding-bottom: 18px; + } +} + +@media (prefers-reduced-motion: reduce) { + html { + scroll-behavior: auto; + } + + *, + *::before, + *::after { + scroll-behavior: auto !important; + animation-duration: 0.01ms !important; + animation-iteration-count: 1 !important; + transition-duration: 0.01ms !important; + } +} diff --git a/ui/src/types.ts b/ui/src/types.ts new file mode 100644 index 0000000..6e163c0 --- /dev/null +++ b/ui/src/types.ts @@ -0,0 +1,233 @@ +export type Status = + | "operational" + | "operational_mechanics" + | "mechanics_only" + | "adapter_contract" + | "blocked_calibration" + | "blocked_license_or_data" + | "blocked_missing_evidence" + | "prohibited"; + +export type Mode = "world" | "options" | "meta" | "dreams" | "sources"; + +export type Position = readonly [number, number, number]; + +export interface Authority { + readonly read_only: boolean; + readonly paper_only: boolean; + readonly live_blocked: boolean; + readonly order_construction_allowed: boolean; + readonly order_submission_allowed: boolean; +} + +export interface Headline { + readonly name: string; + readonly run_id: string; + readonly proof_status: string; + readonly highest_simulation_level: number; + readonly forward_levels: string; + readonly worlds_evaluated: number; +} + +export interface Metric { + readonly id: string; + readonly label: string; + readonly value: string; + readonly trend: string; + readonly tone: "cyan" | "violet" | "amber" | "pink" | "green"; + readonly detail: string; +} + +export interface WorldNode { + readonly id: string; + readonly label: string; + readonly kind: string; + readonly status: Status; + readonly signal: number; + readonly position: Position; + readonly detail: string; +} + +export interface WorldLink { + readonly source: string; + readonly target: string; + readonly strength: number; +} + +export interface LadderStage { + readonly level: number; + readonly name: string; + readonly status: "pass" | "blocked"; + readonly score: number; +} + +export interface SurfacePoint { + readonly tenor_index: number; + readonly strike_index: number; + readonly days: number; + readonly relative_strike: number; + readonly volatility: number; +} + +export interface OptionSurface { + readonly label: string; + readonly origin: string; + readonly repaired: boolean; + readonly points: readonly SurfacePoint[]; + readonly warning: string; +} + +export interface Outcome { + readonly world: string; + readonly probability: number; + readonly return: number; + readonly tone: "positive" | "negative"; +} + +export interface Attention { + readonly label: string; + readonly proxy_not_volume: boolean; + readonly series: readonly number[]; + readonly rank_velocity: number; + readonly source_diversity: number; + readonly narrative_saturation: number; + readonly price_following_risk: number; +} + +export interface LiteratureItem { + readonly title: string; + readonly trust: string; + readonly relevance: number; + readonly target: string; + readonly auto_apply: boolean; +} + +export interface ArchiveItem { + readonly niche: string; + readonly operator: string; + readonly utility: number; + readonly status: string; +} + +export interface DataPipeline { + readonly pipeline_id: string; + readonly domain: string; + readonly status: Status; + readonly access_mode: string; + readonly cadence: string; + readonly time_semantics: readonly string[]; + readonly retained_fields: readonly string[]; + readonly intelligence_consumers: readonly string[]; + readonly quality_gates: readonly string[]; + readonly activation_requirements: readonly string[]; + readonly primary_source_uri: string; + readonly limitations: readonly string[]; + readonly content_hash: string; +} + +export interface Intelligence { + readonly intelligence_id: string; + readonly name: string; + readonly status: Status; + readonly purpose: string; + readonly input_pipelines: readonly string[]; + readonly outputs: readonly string[]; + readonly evaluators: readonly string[]; + readonly uncertainty_channels: readonly string[]; + readonly authority_effect: "none"; + readonly limitations: readonly string[]; + readonly content_hash: string; +} + +export interface CognitionMode { + readonly mode_id: string; + readonly name: string; + readonly family: string; + readonly purpose: string; + readonly cognitive_moves: readonly string[]; + readonly evaluator_ids: readonly string[]; + readonly failure_risks: readonly string[]; + readonly authority_effect: "none"; + readonly content_hash: string; +} + +export interface DreamCycle { + readonly phase: string; + readonly label: string; + readonly purpose: string; + readonly artifacts: number; + readonly tone: "violet" | "cyan" | "amber" | "green"; +} + +export interface InventionPreview { + readonly title: string; + readonly mechanism_class: string; + readonly mode_ids: readonly string[]; + readonly utility: number; + readonly evidence_debt: number; + readonly status: "proposal_only"; +} + +export interface ImmuneCheck { + readonly label: string; + readonly status: "pass"; +} + +export interface FailureMemory { + readonly title: string; + readonly failure_class: string; + readonly lesson: string; +} + +export interface CognitionFixture { + readonly label: string; + readonly synthetic_only: true; + readonly empirical_credit: false; + readonly dream_temperature: number; + readonly coverage: number; + readonly epistemic_debt: number; + readonly cycles: readonly DreamCycle[]; + readonly inventions: readonly InventionPreview[]; + readonly immune_checks: readonly ImmuneCheck[]; + readonly failure_museum: readonly FailureMemory[]; +} + +export interface Simulator { + readonly simulator_id: string; + readonly family: string; + readonly implementation_status: string; + readonly validation_status: string; +} + +export interface Pack { + readonly pack_id: string; + readonly status: string; + readonly blocked_reasons: readonly string[]; +} + +export interface ObservatoryBootstrap { + readonly schema_version: string; + readonly as_of: string; + readonly display_mode: "synthetic_mechanics_fixture"; + readonly authority: Authority; + readonly headline: Headline; + readonly metrics: readonly Metric[]; + readonly world_nodes: readonly WorldNode[]; + readonly world_links: readonly WorldLink[]; + readonly ladder: readonly LadderStage[]; + readonly option_surface: OptionSurface; + readonly outcomes: readonly Outcome[]; + readonly attention: Attention; + readonly literature: readonly LiteratureItem[]; + readonly archive: readonly ArchiveItem[]; + readonly pipeline_summary: Readonly>; + readonly intelligence_summary: Readonly>; + readonly data_pipelines: readonly DataPipeline[]; + readonly intelligence: readonly Intelligence[]; + readonly cognition_modes: readonly CognitionMode[]; + readonly cognition: CognitionFixture; + readonly simulators: readonly Simulator[]; + readonly packs: readonly Pack[]; + readonly warnings: readonly string[]; + readonly content_hash: string; +} diff --git a/ui/src/vite-env.d.ts b/ui/src/vite-env.d.ts new file mode 100644 index 0000000..11f02fe --- /dev/null +++ b/ui/src/vite-env.d.ts @@ -0,0 +1 @@ +/// diff --git a/ui/tsconfig.app.json b/ui/tsconfig.app.json new file mode 100644 index 0000000..d94b069 --- /dev/null +++ b/ui/tsconfig.app.json @@ -0,0 +1,28 @@ +{ + "compilerOptions": { + "tsBuildInfoFile": "./node_modules/.tmp/tsconfig.app.tsbuildinfo", + "target": "ES2022", + "useDefineForClassFields": true, + "lib": ["ES2022", "DOM", "DOM.Iterable"], + "allowJs": false, + "skipLibCheck": true, + "esModuleInterop": true, + "allowSyntheticDefaultImports": true, + "strict": true, + "noUncheckedIndexedAccess": true, + "noImplicitOverride": true, + "noUnusedLocals": true, + "noUnusedParameters": true, + "module": "ESNext", + "moduleResolution": "Bundler", + "paths": { + "lucide-react": ["./node_modules/lucide-react/dist/lucide-react.d.ts"] + }, + "resolveJsonModule": true, + "isolatedModules": true, + "moduleDetection": "force", + "noEmit": true, + "jsx": "react-jsx" + }, + "include": ["src"] +} diff --git a/ui/tsconfig.json b/ui/tsconfig.json new file mode 100644 index 0000000..1ffef60 --- /dev/null +++ b/ui/tsconfig.json @@ -0,0 +1,7 @@ +{ + "files": [], + "references": [ + { "path": "./tsconfig.app.json" }, + { "path": "./tsconfig.node.json" } + ] +} diff --git a/ui/tsconfig.node.json b/ui/tsconfig.node.json new file mode 100644 index 0000000..2c1884f --- /dev/null +++ b/ui/tsconfig.node.json @@ -0,0 +1,18 @@ +{ + "compilerOptions": { + "tsBuildInfoFile": "./node_modules/.tmp/tsconfig.node.tsbuildinfo", + "target": "ES2023", + "lib": ["ES2023"], + "module": "ESNext", + "skipLibCheck": true, + "moduleResolution": "Bundler", + "allowImportingTsExtensions": true, + "verbatimModuleSyntax": true, + "moduleDetection": "force", + "noEmit": true, + "strict": true, + "noUnusedLocals": true, + "noUnusedParameters": true + }, + "include": ["vite.config.ts"] +} diff --git a/ui/vite.config.ts b/ui/vite.config.ts new file mode 100644 index 0000000..c31ca01 --- /dev/null +++ b/ui/vite.config.ts @@ -0,0 +1,22 @@ +import react from "@vitejs/plugin-react"; +import { defineConfig } from "vite"; + +export default defineConfig({ + plugins: [react()], + server: { + port: 5173, + strictPort: true, + proxy: { + "/api": { + target: "http://127.0.0.1:8000", + changeOrigin: false, + rewrite: (path: string): string => path.replace(/^\/api/, ""), + }, + }, + }, + build: { + target: "es2022", + sourcemap: true, + chunkSizeWarningLimit: 950, + }, +}); diff --git a/uv.lock b/uv.lock index b0e0290..04b5018 100644 --- a/uv.lock +++ b/uv.lock @@ -274,7 +274,7 @@ wheels = [ [[package]] name = "jester-market-engine" -version = "0.1.0" +version = "0.2.0" source = { editable = "." } dependencies = [ { name = "cryptography" },