diff --git a/README.md b/README.md index 465b82f..3e1fc5e 100644 --- a/README.md +++ b/README.md @@ -154,6 +154,13 @@ Full docs at **[surface.gradientdev.xyz](https://surface.gradientdev.xyz)** (sou Release history is in [`CHANGELOG.md`](CHANGELOG.md). AI agents working in this repo: see [`AGENTS.md`](AGENTS.md). +## Benchmark + +The agent-impact benchmark — measuring how much documentation accuracy changes an agent's task +performance — lives in its own repo: **[Connorrmcd6/surface-bench](https://github.com/Connorrmcd6/surface-bench)**. +It consumes the `surf` binary's output but has no inbound dependency on this core. (It previously +lived under `bench/` here.) + --- The naming isn't decoration: the *gradient* of a field is everywhere perpendicular to its level *surfaces* — the direction of change, and the thing the change is measured against. Surface reports **divergence** between what your docs claim and what your code does. diff --git a/bench/.gitignore b/bench/.gitignore deleted file mode 100644 index 3c1274b..0000000 --- a/bench/.gitignore +++ /dev/null @@ -1,6 +0,0 @@ -# Raw run outputs are large and reproducible; commit only curated snapshots by force-adding. -results/ -.author/.work/ -__pycache__/ -*.pyc -.venv/ diff --git a/bench/ABC_CHECKLIST.md b/bench/ABC_CHECKLIST.md deleted file mode 100644 index 1eb7b61..0000000 --- a/bench/ABC_CHECKLIST.md +++ /dev/null @@ -1,57 +0,0 @@ -# ABC checklist — benchmark-rigor self-audit - -A self-audit against the **Agentic Benchmark Checklist** (ABC) from *Establishing Best Practices for -Building Rigorous Agentic Benchmarks* (Zhu et al., NeurIPS 2025 D&B; arXiv **2507.02825**). The ABC -groups failure modes into **task validity** (can the task be solved as specified?), **outcome -validity** (does the grader actually measure success — no shortcuts, no false pass/fail?), and -**reporting**. The paper found *all 10* benchmarks it audited had reporting gaps; this file is our -attempt not to be one of them. Item ids follow the paper's grouping. - -Legend: ✅ satisfied · ⚠️ partial / by-design limitation · n/a not applicable to this benchmark. - -## Task validity - -| Item | How this benchmark addresses it | -|---|---| -| **T.1** tool/version specificity | Model `model_id`s pinned in `config.toml`, copied into `run.json`; `surf --version` recorded. Python env pinned by `uv.lock`. ✅ | -| **T.2/T.3** API availability / interruption handling | Per-request 120 s timeout + bounded retries; a cell that still fails is logged with `error` and **excluded** from rates (not counted as a failure). ✅ | -| **T.4** clean state between tasks | Each grade runs in a **fresh temp workspace** (`grade_code`) / each multi-turn trial in a **fresh sandbox** (`tools_runtime.scenario_sandbox`). No cross-task leakage. ✅ | -| **T.5** isolate agent from ground truth | The drifted dependency is withheld from the prompt (`hidden_paths`); graders compute ground truth separately by probing the real dependency. The agent never sees the grader. ✅ | -| **T.6** freeze environment at release | Scenario set frozen + git-tagged at pre-registration (see `PREREGISTRATION.md` freeze gate). ✅ | -| **T.7/T.8** verify ground-truth annotations | Ground truth is **derived from the real code**, not hand-annotated: code graders import and probe the hidden dependency; QA rubrics encode the T1 behaviour the committed code implements. ✅ | -| **T.9** oracle/reference solution | Every scenario ships `.author/solution_correct.*` and `solution_stale.*`; `tools/validate_scenario.py` runs the live graders on both and proves they discriminate, offline, before any spend. ✅ | -| **T.10** inspect pilot outliers | Staged rollout (`mock` → per-provider smoke → small-N pilot → full) with the oracle as a tripwire; the v1 pilot's outlier (a leaked stale value) was caught this way (#113). ✅ | - -## Outcome validity - -| Item | How this benchmark addresses it | -|---|---| -| **O.a** semantic-equivalence / redundant-word robustness | QA grading parses a **fixed `VERDICT:` field format** (regex per field) rather than free-text matching; "last match wins" tolerates the model restating the format. Code grading runs real tests, so wording is irrelevant. ✅ | -| **O.b.2/O.b.3** no trivial success (list-all / empty answer) | Code: an empty or non-compiling answer fails the hidden test. QA: a missing/garbled `VERDICT` parses as neither correct nor misled (not a pass). ✅ | -| **O.d.1** manually verify test cases | Each grader's correct/misled tests are validated by `validate_scenario.py` against the reference solutions; the **misled** test must pass on the stale solution and fail on the correct one (and vice-versa). ✅ | -| **O.f.2** eliminate non-determinism in tests | Graders are deterministic (no clocks/network/randomness in the checked logic; fixed probe inputs). ✅ | -| **O.h.1** specify output format | `task.md` states the exact contract: a `FILE: ` block (code) or a `VERDICT: …` line (QA). ✅ | -| **O.h.2** tasks resistant to guessing | The reason multi-turn is the centerpiece: in single-shot a hidden value is only knowable from the doc, so "follow the stale doc" is partly rational. Multi-turn lets the agent *verify*, removing the tautology. QA verdicts use **two fields** to cut a 50/50 guess. ⚠️ (single-shot cascade is guess-resistant only because the value is genuinely unknowable without the doc — stated as a limitation) | -| **O.i.1** metric correlates with the construct | `verified_then_correct` validates the verification metric (reading the truth should rescue the answer); the misled metric directly encodes "asserted the stale claim". ✅ | - -## Reporting - -| Item | How this benchmark addresses it | -|---|---| -| **R.1/R.2** open data + eval code | The entire harness, scenarios, graders, and reference solutions are in this repo. `raw.jsonl` is preserved per run. ✅ | -| **R.3/R.4** contamination | Scenarios are bespoke fixtures, not scraped; the drift values live only in `hub_stale.md`. (Frontier models may have seen similar idioms — noted as residual risk.) ⚠️ | -| **R.5/R.6** state the capability assessed | `PREREGISTRATION.md` §9 states exactly what is and isn't assessed. ✅ | -| **R.7** mitigation for unavoidable flaws | The neutral system prompt + neutral `task.md` rules (#113 lesson) mitigate doc-trust bias; the oracle mitigates fixture defects. ✅ | -| **R.8/R.9** quantify limitation impact | Per-scenario and per-tier breakdowns expose any single-fixture or single-difficulty artifact. ✅ | -| **R.10** statistical significance | Wilson intervals, bootstrap CIs + p-values, **Holm–Bonferroni** across the confirmatory family. ✅ | -| **R.11** interpretation guidance | `README.md` §4 explains what each metric means; `PREREGISTRATION.md` separates confirmatory from exploratory. ✅ | -| **R.12/R.13** baselines/controls | **C0** (no-doc baseline) and **Cw** (warning-only control) isolate, respectively, the value of accurate prose and whether recovery is Surface's *fix* vs generic suspicion. ✅ | - -## Known limitations (disclosed, per R.7) - -- **Curated fixtures, not real repositories** (external validity); a real-OSS case study is deferred - (#109). -- **Read-only agent loop** — no edit/run/test "thrash"; deferred by design. -- **Languages:** Python + TypeScript only. -- **`author.py` seals only the first anchor**, so all current scenarios are single-anchor (no genuine - T3 multi-claim); a tooling follow-up would lift this. diff --git a/bench/PREREGISTRATION.md b/bench/PREREGISTRATION.md deleted file mode 100644 index 59bb269..0000000 --- a/bench/PREREGISTRATION.md +++ /dev/null @@ -1,95 +0,0 @@ -# Pre-registration — Surface agent-impact benchmark (v2 / multi-turn) - -**Status:** to be **frozen and git-tagged before the headline run.** This file states the -hypotheses, design, metrics, and analysis *in advance* so the result can't be accused of HARKing -(hypothesizing after results are known) or of cherry-picking an analysis. Nothing below is decided -by looking at outcomes. Operating instructions are in [`README.md`](README.md). - -> **Freeze gate.** Tag this only once: (a) the scenario set is final (all `cascade-*` PRs merged), -> (b) `python tools/author.py --all` and `python tools/validate_scenario.py --all` are green across -> the set, and (c) a full `--models mock` run + `python -m surface_bench.oracle` is clean. Record -> the tag and the `surf --version` here when frozen: `__TBD__`. - -## 1. Question - -Does keeping documentation accurate (what Surface enforces) change an agent's task performance, and -*through what mechanism*? Surface's value to an agent = the performance gap between working from -fresh vs rotted docs, plus whether surfacing the drift recovers it. - -## 2. Conditions (the only thing that varies) - -Same code + same task + same model in every cell; only the documentation block changes: -**C0** code only · **C1** code + stale doc · **C2** code + fresh doc · **C3** code + stale doc + real -`surf check --format json` report · **Cw** code + stale doc + a generic "may be outdated" warning -(no corrected code). Run in **multi mode** (the agent has read-only `read_file`/`grep`/`list_dir` -tools and may choose to verify) as the primary design; **single mode** is run as a secondary, -cheaper comparison. - -## 3. Hypotheses (confirmatory) - -- **H1 — accuracy beats rot:** success(C2) > success(C1). -- **H2 — rot is worse than nothing:** misled(C1) > misled(C0). -- **H3 — surfacing drift recovers it:** success(C3) ≈ success(C2) (and ≫ C1). -- **H4 — a confident stale doc suppresses verification:** verification_rate(C1) < verification_rate(C0). -- **H5 — the harm flows through skipped verification (mediation):** within C1, success among rows - that verified > success among rows that did not; verifiers are correct, non-verifiers misled. -- **H6 — recovery is the corrected code, not mere suspicion:** success(C3) > success(Cw). - -Direction is pre-specified for every hypothesis. Anything not listed here (e.g. per-provider -contrasts, tier gradients, token-cost deltas) is **exploratory** and will be reported as such. - -## 4. Metrics (pre-defined) - -- **success** — `ok`: the agent produced the current (T1) answer. Code scenarios: hidden tests that - probe the real dependency. QA scenarios: a `VERDICT:` line matched against a fixed rubric. **No LLM - judge.** -- **misled** — `misled`: the agent asserted the stale (T0) claim. -- **verification_rate** (multi only) — the agent called `read_file`/`grep` on a path matching the - scenario's `hidden_paths` glob *before* `final_answer`. Computed from the logged `verified_hidden` - per row. `verified_then_correct` (success among verifiers) is its validity check. -- **output tokens** and **cost** — secondary (token-tax + spend), exploratory. - -## 5. Models, scale, sampling (pre-specified) - -- **Models:** `haiku`, `sonnet`, `opus` (Anthropic) + `gpt` (OpenAI) + `gemini` (Google). Exact - `model_id`s and prices are pinned in `config.toml` at freeze time and copied into `run.json`. -- **Trials (tiered):** N = 10 per cell for the **cascade** family (the headline); N = 5 for the - **comprehension** family (success-ceilinged — kept only for the token story). Comprehension may be - omitted from multi mode entirely (it doesn't test verification); that choice is recorded in - `run.json`, not chosen by results. -- **temperature = 1.0** (stochasticity is part of what we measure); **max_turns = 8**; - `max_tokens = 1024`. - -## 6. Analysis plan (pre-specified) - -- Rates reported with 95% **Wilson** intervals. -- Each hypothesis tested as an unpaired difference of proportions with a 95% **bootstrap** CI and a - bootstrap two-sided p-value (10,000 resamples, fixed seed). -- **Holm–Bonferroni** correction applied across the family of confirmatory success-delta tests - (every model × pre-registered pair). A hypothesis is **confirmed** if its CI excludes 0 **and** it - survives Holm; reported as suggestive if CI-significant but not Holm-significant. -- H5 (mediation) reported as the within-C1 success split (verified vs not) per model. -- Per-scenario and per-tier breakdowns reported for transparency (exploratory). - -## 7. Exclusions / data handling (pre-specified) - -- A cell that errors (API failure) is logged with `error` and excluded from rates; the count of - excluded cells is reported. No silent retries beyond the client's built-in retry. -- The oracle (§ README) must be clean on the real run: any scenario failing C2-fresh ≥ 90% or (for - cascade) C1-misled > 0 is treated as a **fixture defect** and is flagged; whether to drop it is - decided by the pre-stated oracle rule, not by whether it helps the result. - -## 8. Stopping rule - -The matrix runs to completion (fixed N above). No data-dependent stopping, no N top-ups chosen by -looking at significance. If a stage smoke (mock → per-provider → small-N pilot) reveals a *harness* -bug, it is fixed and the affected stage re-run; the confirmatory full run begins only after this file -is tagged. - -## 9. What this does and does not assess - -Assesses: whether documentation accuracy changes single-shot and multi-turn task outcomes on curated -cascade fixtures across five models, and whether the effect is mediated by verification. Does **not** -assess: real-repository generalization (curated fixtures by design — see deferred #109), an -edit/run/test agent loop (tools are read-only by design), or non-English / languages beyond Python -and TypeScript. diff --git a/bench/README.md b/bench/README.md deleted file mode 100644 index f8321ab..0000000 --- a/bench/README.md +++ /dev/null @@ -1,269 +0,0 @@ -# surface-bench — operator manual - -Empirically measuring how much **documentation accuracy** changes an agent's task performance — the -gap [Surface](../README.md) exists to protect. Surface doesn't make an agent smarter; it stops docs -silently rotting. So its value to an agent equals the performance delta between working from **fresh** -docs and **rotted** docs, plus whether *surfacing* the drift recovers the loss. This bench measures -those deltas directly, using drift of exactly the kind `surf check` catches (flipped operators, -dropped guards, changed constants, reordered keys). - -This is the single place to start when you (or a future agent) pick this up cold. It covers **what the -experiment is, how to run it, how to read the results, how to author scenarios, and the sharp edges.** -Lives in the Surface repo but has no inbound dependency on the Rust core — it only *consumes* the -`surf` binary's output — so it can be extracted later. - -> **Companion docs:** [`PREREGISTRATION.md`](PREREGISTRATION.md) (the frozen hypotheses + analysis -> plan for the headline run), [`ABC_CHECKLIST.md`](ABC_CHECKLIST.md) (benchmark-rigor self-audit), -> [`scenarios/CHECKLIST.md`](scenarios/CHECKLIST.md) (how to author one scenario), and the committed -> v1 write-up at [`results/2026-06-13-pilot-full-matrix/report.md`](results/2026-06-13-pilot-full-matrix/report.md). - ---- - -## 1. The experiment in one screen - -Same code + same task in every run; **only the documentation block changes**. There are five -conditions: - -| | Context shown to the agent | Represents | -|---|---|---| -| **C0** | code only (no doc) | baseline | -| **C1** | code + **stale** doc (true at T0, code moved to T1) | the ungoverned world | -| **C2** | code + **fresh** doc (matches T1) | the Surface-governed world | -| **C3** | code + stale doc + real `surf check --format json` report | "just surface the drift" | -| **Cw** | code + stale doc + a generic "may be outdated" warning (no corrected code) | control: is it the *fix* or just *suspicion*? | - -And two run **modes**: - -- **single** (v1): one prompt → one completion. Cheap, reproducible, the original pilot. -- **multi** (v2, the centerpiece): a **multi-turn agent loop** with **read-only** tools - (`read_file`, `grep`, `list_dir`, `final_answer`). The agent can *choose* to read the hidden - dependency the doc describes. This is what makes the headline non-tautological — see §4. - -### Hypotheses - -| | Claim | Read on | -|---|---|---| -| **H1** | C2 > C1 — accuracy beats rot (the core value) | success rate | -| **H2** | C1 < C0 — rotted docs are *worse than nothing* | misled rate | -| **H3** | C3 ≈ C2 — surfacing drift recovers the loss | success rate | -| **H4** | verification_rate(C1) < verification_rate(C0) — a confident stale doc **suppresses verification** | verification rate (multi only) | -| **H5** | within C1, agents that read the hidden dep are correct; those that don't are misled | mediation (multi only) | -| **H6** | C3 > Cw — recovery is Surface's *corrected code*, not mere suspicion | success rate | - -### Two scenario families - -- **Cascade** (the headline): the agent edits/answers about a **visible** thing whose correctness - depends on a **hidden dependency** (listed in `meta.toml` `hidden_paths` — present in `code/` for - grading and for `surf` to seal a real divergence, but withheld from the prompt). The dependency - has drifted from what the stale doc says, so the doc is the agent's only (single-shot) or - *optional* (multi-turn) window onto the truth. `cascade-*` scenarios. -- **Comprehension**: the drifted code *and* its contradicting doc are both visible. The model can - just re-read the code, so success ceilings near 100% — useful for the **token-tax** story (a stale - doc costs extra generation to reconcile), not the success story. - ---- - -## 2. Setup - -The harness env is managed with [uv](https://docs.astral.sh/uv/) (the committed `uv.lock` pins it so -the spend figures are reproducible). Code-edit graders run under the *same* interpreter `uv run` -selects — no `python3`-on-PATH guessing. - -```sh -# from repo root: build the real surf binary (author.py + the C3 report need it) -cargo build --release # provides target/release/surf - -cd bench -uv sync # base deps (anthropic) -uv sync --extra dev # + pytest (to run the test suite) -uv sync --extra plots # + matplotlib (report figures) -uv sync --extra providers # + openai, google-genai (cross-provider runs) -``` - -**Toolchains the graders need at run time** (only when those scenarios are actually graded): - -- **Python** — always (via `sys.executable`). -- **Node ≥ 22.18** on PATH — for TypeScript code scenarios (`node --test`; that release strips TS - types at load, so no `npm install`/`tsc`). uv does not manage Node; install it separately. - -**API keys** (only for the providers you select): `ANTHROPIC_API_KEY`, `OPENAI_API_KEY`, -`GEMINI_API_KEY` (or `GOOGLE_API_KEY`). The convenience env file `~/.surface-bench.env` is sourced -per command in practice (`set -a; source ~/.surface-bench.env; set +a`). - ---- - -## 3. Running it - -The pipeline is four commands. **Everything except `run` against a real provider is free/offline.** - -```sh -# (a) RUN the matrix -> results//{raw.jsonl, run.json} -uv run python -m surface_bench.run --models mock # offline pipeline check, no key -uv run python -m surface_bench.run --models haiku --mode single --trials 10 -uv run python -m surface_bench.run --models haiku --mode multi --trials 10 --max-turns 8 - -# (b) REPORT -> results//{summary.json, report.md, *.png} -uv run python -m surface_bench.report results/ - -# (c) ORACLE — post-run sanity tripwires (exits non-zero if any fire) -uv run python -m surface_bench.oracle results/ -``` - -**CLI flags** (`run`): `--models ` (subset of `config.toml` models; default all), -`--scenarios `, `--conditions C0 C1 C2 C3 Cw`, `--trials N`, `--mode {single,multi}`, -`--max-turns N` (multi), `--out `, `--config `. Config defaults live in `config.toml` -(`trials`, `temperature`, `max_tokens`, `mode`, `max_turns`, and a `[models.]` block per -model with `provider`, `model_id`, and `input_per_mtok` / `output_per_mtok` pricing). - -**Providers:** `provider = "mock" | "anthropic" | "openai" | "gemini"`. Mock needs no key and is the -offline workhorse; in `--mode multi` it scripts a canned answer so the whole loop runs for free. - -### The matrix size (and why you stage spend) - -`#scenarios × #conditions × #models × N`. Multi mode multiplies each cell by the number of agent -turns *and* re-sends the growing transcript each turn, so it is **much** more expensive than single. -**Always stage:** `mock` → one scenario × each provider (smoke the tool round-trip) → cascade-only -multi at small N (pilot the verification metric) → the full matrix. Gate each stage on the oracle -(§5) passing. Cost levers: `--max-turns` cap, tiered `--trials` (e.g. N=10 cascade / N=5 -comprehension), and dropping comprehension from multi mode (it doesn't test verification). - ---- - -## 4. Reading the results - -`run` writes **`raw.jsonl`** (one row per completion — the source of truth; grading can be re-run -offline without re-spending) and **`run.json`** (the run's parameters). `report` turns those into -`summary.json` (machine-readable) + `report.md` (the human read) + figures. - -**Per-row fields** (multi-only fields absent in single rows): `scenario, task_type, tier, condition, -model, trial, mode, output, input_tokens, output_tokens, cost_usd, ok, misled, detail, parsed` and, -for multi: `turns, stop_reason, tool_calls, verified_hidden, per_turn_tokens`. - -**The metrics, and what each tells you:** - -- **success rate** (`ok`) — got the current (T1) answer. The H1/H3/H6 axis. Deterministic: code - scenarios run hidden tests; QA scenarios parse a `VERDICT:` line against a rubric. **No LLM judge.** -- **misled rate** (`misled`) — asserted the *stale* (T0) claim. The H2 axis: a rotted doc doesn't - just fail to help, it *causes* the wrong answer. -- **verification_rate** (multi) — did the agent read/grep a `hidden_path` before answering? The - **headline of the agentic track** (H4). `verified_then_correct` is its validity check (reading the - truth should rescue you). -- **output tokens** — generation cost. Input tokens differ by construction (doc-block size) so are - ignored; output tokens carry the token-tax signal in the comprehension family. -- **cost_usd** — estimated spend (tokens × `config.toml` prices). The report's total is for relative - comparison; your provider console invoice is authoritative. - -**Statistics:** rates carry 95% **Wilson** intervals; condition deltas use a 95% **bootstrap** CI -*and* a bootstrap p-value, with **Holm–Bonferroni** applied across the whole success-delta family -(the report flags `(Holm ✓/✗)` per delta). CI-significance is the headline; Holm is the conservative -cross-check. The report also slices deltas **by tier** (the difficulty gradient) and **by scenario** -(catches one broken fixture hiding in a family average). - -**`report.md` sections:** Spend · the gradient (C2−C1 by tier) · **Verification** (multi: the H4 -deltas + H5 mediation) · per-model rate tables + deltas (with Holm flags) · output-token tables + -deltas · per-scenario success. **Figures:** `overview.png`, `cascade_success.png`, and (multi) -`verification_rate.png` — the "does a stale doc stop the agent checking?" hero chart. - ---- - -## 5. The oracle (sanity tripwires) - -`python -m surface_bench.oracle results/` is a cheap post-run gate that catches authoring/harness -bugs **before** they reach a write-up (the failure mode that bit us in issue #113). It exits non-zero -(so it can gate CI / a staged run) if, per scenario × model: - -- **C2-fresh < 90%** — with a *fresh* doc the task must be solvable; a low cell means the scenario is - mis-authored (leaked stale value, broken grader, impossible task), not a real effect. -- **a cascade C1 never misleads** — if a stale doc never produces the wrong answer, the drift isn't - load-bearing and the scenario measures nothing. - -The mock can't satisfy these (it doesn't actually solve scenarios), so run the oracle on **real** -outputs. - ---- - -## 6. Layout - -``` -bench/ - config.toml models, trials, temperature, mode, max_turns, per-model pricing - PREREGISTRATION.md frozen hypotheses + analysis plan for the headline run - ABC_CHECKLIST.md benchmark-rigor self-audit (arXiv 2507.02825) - surface_bench/ - run.py the matrix runner (single + multi); writes raw.jsonl + run.json - prompts.py assembles (system, user) per condition; hides hidden_paths - models.py provider adapters: complete() (single) + step() (multi, tool-use); - Anthropic / OpenAI / Gemini + Mock; neutral Step/ToolCall types - agent.py run_agent() — the multi-turn loop over the read-only tools - tools_runtime.py read-only tool surface (read_file/grep/list_dir/final_answer) + sandbox - grade_qa.py VERDICT-line rubric grader (QA) - grade_code.py FILE-block overlay + hidden-test grader (code) - metrics.py rates, Wilson/bootstrap CIs, Holm, verification, by_tier, by_scenario - report.py summary.json + report.md + figures - oracle.py post-run tripwires - tools/ - author.py seal a scenario's hub hashes + emit genuine surf_report.json - validate_scenario.py grader-polarization self-test (offline, no spend) - scenarios/ - CHECKLIST.md how to author one scenario - / meta.toml · task.md · hub_stale.md · hub_fresh.md · surf_report.json - code/ (T1) · .author/code_t0/ (T0 overlay) · .author/solution_{correct,stale}.* - grader/ (rubric.toml | grader.toml + tests/) - tests/ offline pytest (tools, agent loop, adapters, metrics, scenario polarity) - results// raw.jsonl · run.json · summary.json · report.md · *.png (gitignored, - except committed snapshots like 2026-06-13-pilot-full-matrix) -``` - ---- - -## 7. Authoring a scenario (summary — full steps in `scenarios/CHECKLIST.md`) - -A cascade scenario clones `scenarios/cascade-quota-batcher-code/`. The loop: - -1. Pick a **load-bearing** drift: name an input where T0 and T1 give *different* outputs (else the - doc carries no weight and the bench measures nothing). -2. Write `code/` (T1, the visible file stubbed) + `.author/code_t0/` (only the changed files, T0) + - `hub_stale.md`/`hub_fresh.md` (placeholder `hash: 000000000000`) + a **neutral** `task.md` + - the grader + `.author/solution_{correct,stale}.*` reference solutions. -3. `python tools/author.py scenarios/` — seals the hub hashes against the real binary and emits - `surf_report.json`; **fails loudly if the stale hub doesn't actually diverge.** -4. `python tools/validate_scenario.py scenarios/` — runs the live graders on the two reference - solutions and proves they **discriminate** (correct → ok & not misled; stale → not ok & misled). -5. `--models mock` run + `oracle` to confirm the pipeline + tripwires. - -**Two rules with teeth:** - -- **Graders probe the real hidden dependency** for ground truth — never hardcode the T1 value. - `check_misled` hardcodes the *stale* value. -- **Neutrality** (the #113 lesson): the stale value appears *only* in `hub_stale.md` — never in - `task.md` or the visible code, and never a "the doc may be wrong" hint. A leak re-introduces the - doc-trust bias the neutral system prompt is designed to remove. - ---- - -## 8. Sharp edges (read before authoring or debugging) - -- **`surf` alpha-renames identifiers when hashing.** A drift expressed only as a *named-constant - swap* (e.g. `ROUND_HALF_EVEN` → `ROUND_HALF_UP`) is **invisible** to `surf check` — the two hash - identically. A detectable drift must change a **literal, operator, or structure** (a number, a - string, `<`→`<=`, an added/removed statement, a reordered call). `author.py` fails with "expected - a 'changed' divergence" if you trip this — redesign the drift, don't fight the hasher. -- **`author.py` seals only the *first* anchor's hash.** Multi-anchor hubs (a genuine T3 "multi-claim" - scenario) need a small tooling change to seal every anchor; until then keep hubs single-anchor. -- **Read-only tools by design.** The multi-turn agent can read but not edit or run code. Giving it a - test runner would let it brute-force ground truth and wash out the doc-trust signal we measure. A - full edit/run "thrash" loop is deliberately deferred future work. -- **Don't stack PRs.** Scenario PRs are additive and independent — open each against `main` directly. - (A stacked PR once merged into its base branch instead of `main` and silently lost its content.) -- **Spend is in the *run*, not the docs/tooling.** `author.py`, `validate_scenario.py`, `report`, - `oracle`, the test suite, and any `mock` run cost nothing. Only `run` against a real provider does. - ---- - -## 9. Reproducibility - -`uv.lock` pins the Python env. `run.json` records every parameter (models + ids, trials, -temperature, max_tokens, mode, max_turns, conditions, scenarios, `surf --version`). `raw.jsonl` -preserves raw outputs so grading/metrics re-run offline. To reproduce a committed snapshot, check out -the repo at its tag, `cargo build --release`, `cd bench && uv sync`, and re-run `report` on the -snapshot dir (it regenerates `summary.json` + figures from the frozen `raw.jsonl`). diff --git a/bench/config.toml b/bench/config.toml deleted file mode 100644 index 28d4a7b..0000000 --- a/bench/config.toml +++ /dev/null @@ -1,35 +0,0 @@ -# Benchmark run configuration. Override any of these on the CLI (see python -m surface_bench.run -h). - -trials = 10 # completions per (scenario x condition x model) -temperature = 1.0 # realistic sampling; stochasticity is part of what we measure -max_tokens = 1024 - -# "single" = one-shot completion (v1). "multi" = multi-turn tool-using agent (v2 / #102): the model -# gets read-only tools and chooses whether to verify the doc against the hidden dependency. -mode = "single" -max_turns = 8 # agent turn budget in multi mode - -# Models are referenced by short name. provider="mock" needs no API key (offline pipeline test). -[models.mock] -provider = "mock" -default = "VERDICT: second_call=succeeds; family_revoked=no" - -# USD per 1M tokens (input_per_mtok / output_per_mtok). Used to estimate spend per call; -# the authoritative total is your Anthropic console invoice. -[models.haiku] -provider = "anthropic" -model_id = "claude-haiku-4-5-20251001" -input_per_mtok = 1.00 -output_per_mtok = 5.00 - -[models.sonnet] -provider = "anthropic" -model_id = "claude-sonnet-4-6" -input_per_mtok = 3.00 -output_per_mtok = 15.00 - -[models.opus] -provider = "anthropic" -model_id = "claude-opus-4-8" -input_per_mtok = 5.00 -output_per_mtok = 25.00 diff --git a/bench/pyproject.toml b/bench/pyproject.toml deleted file mode 100644 index afce9ed..0000000 --- a/bench/pyproject.toml +++ /dev/null @@ -1,19 +0,0 @@ -[project] -name = "surface-bench" -version = "0.1.0" -description = "Empirically measuring how documentation accuracy affects agent task performance" -requires-python = ">=3.11" -dependencies = [ - "anthropic>=0.40", -] - -[project.optional-dependencies] -plots = ["matplotlib>=3.8"] -dev = ["pytest>=8"] - -[tool.setuptools.packages.find] -include = ["surface_bench*"] - -[tool.pytest.ini_options] -pythonpath = ["."] -testpaths = ["tests"] diff --git a/bench/results/2026-06-13-pilot-full-matrix/PROVENANCE.md b/bench/results/2026-06-13-pilot-full-matrix/PROVENANCE.md deleted file mode 100644 index 6a97eb0..0000000 --- a/bench/results/2026-06-13-pilot-full-matrix/PROVENANCE.md +++ /dev/null @@ -1,45 +0,0 @@ -# Snapshot provenance — 2026-06-13 full pilot matrix - -Curated, committed snapshot of the first full Surface agent-impact pilot (milestone "Empirical -validation of Surface", issue #101). Force-added past `bench/.gitignore` (which ignores `results/`). - -## Configuration - -- **Models:** haiku (`claude-haiku-4-5-20251001`), sonnet (`claude-sonnet-4-6`), opus (`claude-opus-4-8`) -- **Trials:** N = 10 per (scenario × condition × model) -- **Scenarios:** all 11 — 4 `cascade-*` (hidden dependency) + 7 comprehension -- **Conditions:** C0 code-only · C1 stale doc · C2 fresh doc · C3 stale doc + `surf check` report -- **Total:** 1320 calls, **0 errors**, estimated spend **$13.98** (haiku $1.49 · sonnet $4.21 · opus $8.28) -- **Code:** branched from `main` after #114 merged; the per-request timeout in `models.py` - (committed alongside this snapshot) was in effect for the resume below. - -## Assembly note (honest accounting) - -The dataset was assembled from **two runs at the same prompts/grading**: - -1. The original matrix run completed 8 of 11 scenarios (960 rows) before a single API request hung - with no client-side timeout, stalling the run. All completed rows were preserved. -2. After adding a 120 s per-request timeout + retries to the Anthropic client, the 3 unfinished - scenarios (`refresh-replay-premise-qa`, `refresh-single-use-qa`, `retry-budget-code`) were - re-run fresh (360 rows) and merged with the preserved 960. - -The merge is clean: every scenario has exactly 120 rows (10 × 4 × 3), no duplicates, 0 errors. Only -the network timeout differed between the two runs — prompts, scenarios, graders, and the `surf` -binary were identical. - -## Headline result (see `report.md` / `summary.json` for full CIs) - -**Cascade family (the dependency is hidden — the agent knows it only by doc):** on **all three -models**, a stale doc (C1) yields **0% success / 100% misled**, a fresh doc (C2) yields **100% -success**, and the `surf` report (C3) recovers to **90% (haiku) / 100% (sonnet, opus)**. H1 = +100pp -on every model — **a more capable model is *not* more robust to rot** when it cannot see the code. - -**Comprehension family (the code is visible):** success ceilings near 100% across models, but a -stale doc still costs **+57 to +107 extra output tokens** vs a fresh one on every model — the -wasted-token tax of rot you *can* see. - -Files: `raw.jsonl` (all 1320 rows) · `summary.json` (machine-readable rates, CIs, deltas) · -`report.md` (full authored write-up: overview, hypotheses, methodology, prompts, results, -interpretation, learnings, future work) · `report-summary.md` (plain-language version) · -`overview.png` (two-panel summary figure) · `cascade_success.png` (headline chart) · `run.json` -(original run metadata). diff --git a/bench/results/2026-06-13-pilot-full-matrix/cascade_success.png b/bench/results/2026-06-13-pilot-full-matrix/cascade_success.png deleted file mode 100644 index 3812652..0000000 Binary files a/bench/results/2026-06-13-pilot-full-matrix/cascade_success.png and /dev/null differ diff --git a/bench/results/2026-06-13-pilot-full-matrix/overview.png b/bench/results/2026-06-13-pilot-full-matrix/overview.png deleted file mode 100644 index 988a178..0000000 Binary files a/bench/results/2026-06-13-pilot-full-matrix/overview.png and /dev/null differ diff --git a/bench/results/2026-06-13-pilot-full-matrix/raw.jsonl b/bench/results/2026-06-13-pilot-full-matrix/raw.jsonl deleted file mode 100644 index 8c0749f..0000000 --- a/bench/results/2026-06-13-pilot-full-matrix/raw.jsonl +++ /dev/null @@ -1,1320 +0,0 @@ -{"scenario": "access-invert-qa", "task_type": "qa", "tier": "T1", "condition": "C0", "model": "haiku", "trial": 0, "output": "Let me trace through the `can_view` logic for Dana and doc #42:\n\n1. `doc.visibility == \"public\"` \u2192 False (visibility is \"private\")\n2. `user.is_admin` \u2192 False (Dana is not an admin)\n3. `doc.owner_id == user.id` \u2192 False (\"u_root\" \u2260 \"u_dana\")\n4. `user.team not in doc.restricted_teams` \u2192 True (Dana's team \"growth\" is NOT in {\"finance\", \"legal\"})\n\nSince the final condition evaluates to `True`, the function returns `True`.\n\n**Part 1:** Yes, `can_view(Dana, doc #42)` returns **True** \u2014 Dana can view the document.\n\n**Part 2:** The team rule is a **block-list**. The logic `user.team not in doc.restricted_teams` grants access unless the user's team is explicitly listed in the restricted set; teams not listed are allowed by default.\n\nVERDICT: can_view=yes; team_rule=blocklist", "input_tokens": 670, "output_tokens": 242, "cost_usd": 0.0018800000000000002, "ok": true, "misled": false, "detail": "correct", "parsed": {"can_view": "yes", "team_rule": "blocklist"}} -{"scenario": "access-invert-qa", "task_type": "qa", "tier": "T1", "condition": "C0", "model": "haiku", "trial": 1, "output": "Looking at the `can_view` method step by step for Dana and doc #42:\n\n1. `doc.visibility == \"public\"` \u2192 False (it's \"private\")\n2. `user.is_admin` \u2192 False (Dana is not an admin)\n3. `doc.owner_id == user.id` \u2192 False (\"u_root\" \u2260 \"u_dana\")\n4. `user.team not in doc.restricted_teams` \u2192 True (because \"growth\" is NOT in {\"finance\", \"legal\"})\n\nSince the final condition evaluates to True, the method returns True.\n\n**Part 1:** Yes, `can_view(Dana, doc #42)` returns **True** \u2014 Dana can view the document because her team \"growth\" is not in the restricted_teams set.\n\n**Part 2:** The per-team rule is a **blocklist** \u2014 the logic `user.team not in doc.restricted_teams` grants access to all teams except those explicitly listed in restricted_teams, which is the definition of a blocklist (allow by default, deny specific entries).\n\nVERDICT: can_view=yes; team_rule=blocklist", "input_tokens": 670, "output_tokens": 262, "cost_usd": 0.00198, "ok": true, "misled": false, "detail": "correct", "parsed": {"can_view": "yes", "team_rule": "blocklist"}} -{"scenario": "access-invert-qa", "task_type": "qa", "tier": "T1", "condition": "C0", "model": "haiku", "trial": 2, "output": "Let me trace through the code step by step.\n\n**Question 1: Does `can_view(Dana, doc #42)` return True or False?**\n\nFollowing the `can_view` method for Dana and doc #42:\n\n1. `doc.visibility == \"public\"` \u2192 False (it's \"private\")\n2. `user.is_admin` \u2192 False (Dana is not an admin)\n3. `doc.owner_id == user.id` \u2192 False (\"u_root\" \u2260 \"u_dana\")\n4. `user.team not in doc.restricted_teams` \u2192 True (\"growth\" is not in {\"finance\", \"legal\"})\n\nThe method returns the result of step 4, which is **True**.\n\n**Answer: Yes, Dana can view the document.**\n\nJustification: The final line returns `True` when the user's team is *not* in the `restricted_teams` set, meaning Dana's team (\"growth\") is allowed access since it's not explicitly restricted.\n\n---\n\n**Question 2: Is the team rule an allow-list or block-list?**\n\nThe logic `user.team not in doc.restricted_teams` returns `True` when the team is *absent* from the list. This means teams get access by default *unless* they are explicitly named in `restricted_teams`. This is a **block-list** (deny-list) model.\n\n**Answer: block-list**\n\nJustification: The `restricted_teams` field acts as a block-list\u2014teams are denied access only if explicitly listed; all other teams are implicitly allowed.\n\n---\n\nVERDICT: can_view=yes; team_rule=blocklist", "input_tokens": 670, "output_tokens": 377, "cost_usd": 0.002555, "ok": true, "misled": false, "detail": "correct", "parsed": {"can_view": "yes", "team_rule": "blocklist"}} -{"scenario": "access-invert-qa", "task_type": "qa", "tier": "T1", "condition": "C0", "model": "haiku", "trial": 3, "output": "Looking at the `can_view` method step by step with the given scenario:\n\n1. **Does `can_view(Dana, doc #42)` return True or False?**\n\n - `doc.visibility == \"public\"`: False (visibility is \"private\")\n - `user.is_admin`: False (Dana is not an admin)\n - `doc.owner_id == user.id`: False (\"u_root\" \u2260 \"u_dana\")\n - Final line: `return user.team not in doc.restricted_teams`\n - `\"growth\" not in {\"finance\", \"legal\"}` evaluates to **True**\n \n **Answer: True** \u2014 Dana can view the document.\n\n2. **Is the per-team rule an allow-list or block-list?**\n\n The code returns True when the user's team is **not in** `restricted_teams`. This means teams explicitly listed in `restricted_teams` are denied access, while all other teams are granted access.\n \n **Answer: Block-list** \u2014 the field contains teams that are *blocked*, not teams that are *allowed*.\n\n---\n\n**Justification for part 1:** The method reaches the final return statement and evaluates whether Dana's team (\"growth\") is absent from the restricted set, which it is, so access is granted.\n\n**Justification for part 2:** The docstring confirms this: \"Teams named here are denied access to this private document; every other team is allowed\"\u2014a classic block-list pattern.\n\nVERDICT: can_view=yes; team_rule=blocklist", "input_tokens": 670, "output_tokens": 361, "cost_usd": 0.002475, "ok": true, "misled": false, "detail": "correct", "parsed": {"can_view": "yes", "team_rule": "blocklist"}} -{"scenario": "access-invert-qa", "task_type": "qa", "tier": "T1", "condition": "C0", "model": "haiku", "trial": 4, "output": "Let me trace through the `can_view` method with the given scenario:\n\n**Part 1: Does `can_view(Dana, doc #42)` return True or False?**\n\nFollowing the logic in `AccessPolicy.can_view`:\n1. `doc.visibility == \"public\"` \u2192 False (it's \"private\")\n2. `user.is_admin` \u2192 False (Dana is not an admin)\n3. `doc.owner_id == user.id` \u2192 False (\"u_root\" \u2260 \"u_dana\")\n4. `user.team not in doc.restricted_teams` \u2192 True (Dana's team is \"growth\", which is NOT in {\"finance\", \"legal\"})\n\nThe final return statement is `return user.team not in doc.restricted_teams`, which evaluates to `True`.\n\n**Answer: Yes, Dana can view the document.**\n\n**Part 2: Is the team rule an allow-list or block-list?**\n\nThe comment explicitly states: \"Teams named here are denied access to this private document; every other team is allowed.\"\n\nThe logic `return user.team not in doc.restricted_teams` grants access to any team that is NOT in the restricted set. This is a **block-list** approach \u2014 you see the document unless your team is explicitly blocked.\n\n**Answer: block-list**\n\n**Justification for Part 1:** Dana's team \"growth\" is not in the `restricted_teams` set {\"finance\", \"legal\"}, so the condition `user.team not in doc.restricted_teams` is True, granting access.\n\n**Justification for Part 2:** The field name and logic both indicate that listed teams are *denied* access while all other teams are *allowed*, which is the definition of a block-list.\n\nVERDICT: can_view=yes; team_rule=blocklist", "input_tokens": 670, "output_tokens": 416, "cost_usd": 0.0027500000000000003, "ok": true, "misled": false, "detail": "correct", "parsed": {"can_view": "yes", "team_rule": "blocklist"}} -{"scenario": "access-invert-qa", "task_type": "qa", "tier": "T1", "condition": "C0", "model": "haiku", "trial": 5, "output": "Let me trace through the `can_view` logic with the given scenario:\n\n**Given:**\n- User Dana: `id=\"u_dana\"`, `team=\"growth\"`, `is_admin=False`\n- Document #42: `visibility=\"private\"`, `owner_id=\"u_root\"`, `restricted_teams={\"finance\", \"legal\"}`\n\n**Execution of `can_view(Dana, doc #42)`:**\n\n1. `if doc.visibility == \"public\":` \u2192 `\"private\" == \"public\"` \u2192 **False**, continue\n2. `if user.is_admin:` \u2192 `False == True` \u2192 **False**, continue\n3. `if doc.owner_id == user.id:` \u2192 `\"u_root\" == \"u_dana\"` \u2192 **False**, continue\n4. `return user.team not in doc.restricted_teams` \u2192 `\"growth\" not in {\"finance\", \"legal\"}` \u2192 **True**\n\nThe function returns **True**.\n\n**Part 1:** Yes, Dana can view document #42.\n\n**Part 2:** The team rule is a **block-list** because the final line returns `True` when the user's team is **not** in the restricted set, meaning teams are denied access only if explicitly listed in `restricted_teams`, and all other teams are allowed by default.\n\nVERDICT: can_view=yes; team_rule=blocklist", "input_tokens": 670, "output_tokens": 320, "cost_usd": 0.0022700000000000003, "ok": true, "misled": false, "detail": "correct", "parsed": {"can_view": "yes", "team_rule": "blocklist"}} -{"scenario": "access-invert-qa", "task_type": "qa", "tier": "T1", "condition": "C0", "model": "haiku", "trial": 6, "output": "Looking at the `can_view` method in `AccessPolicy`:\n\n```python\ndef can_view(self, user: User, doc: Document) -> bool:\n if doc.visibility == \"public\":\n return True\n if user.is_admin:\n return True\n if doc.owner_id == user.id:\n return True\n # Team-scoped access for private documents.\n return user.team not in doc.restricted_teams\n```\n\n**Part 1: Can Dana view doc #42?**\n\nFor Dana with the given scenario:\n- `visibility == \"public\"` \u2192 False (it's \"private\")\n- `user.is_admin` \u2192 False\n- `doc.owner_id == user.id` \u2192 False (\"u_root\" \u2260 \"u_dana\")\n- `user.team not in doc.restricted_teams` \u2192 \"growth\" not in {\"finance\", \"legal\"} \u2192 **True**\n\nTherefore, `can_view` returns **True** \u2014 Dana can view the document.\n\n**Part 2: Is the per-team rule an allow-list or block-list?**\n\nThe final return statement is `return user.team not in doc.restricted_teams`. This means access is granted unless the user's team is explicitly in the restricted set. This is a **block-list** (deny-list): teams named in `restricted_teams` are denied; all other teams are allowed, which matches the comment in the code.\n\nVERDICT: can_view=yes; team_rule=blocklist", "input_tokens": 670, "output_tokens": 351, "cost_usd": 0.002425, "ok": true, "misled": false, "detail": "correct", "parsed": {"can_view": "yes", "team_rule": "blocklist"}} -{"scenario": "access-invert-qa", "task_type": "qa", "tier": "T1", "condition": "C0", "model": "haiku", "trial": 7, "output": "Looking at the `can_view` method step-by-step for Dana and doc #42:\n\n1. `doc.visibility == \"public\"` \u2192 False (visibility is \"private\")\n2. `user.is_admin` \u2192 False (Dana is not an admin)\n3. `doc.owner_id == user.id` \u2192 False (\"u_root\" \u2260 \"u_dana\")\n4. `user.team not in doc.restricted_teams` \u2192 True (Dana's team \"growth\" is NOT in {\"finance\", \"legal\"})\n\nSince the final condition evaluates to `True`, the method returns `True`.\n\n**Part 1:** `can_view(Dana, doc #42)` returns **True** \u2014 Dana can view the document because her team is not in the restricted_teams set.\n\n**Part 2:** The team rule is a **block-list** \u2014 the logic `user.team not in doc.restricted_teams` grants access to all teams *except* those explicitly listed in restricted_teams, which is the definition of a block-list model.\n\nVERDICT: can_view=yes; team_rule=blocklist", "input_tokens": 670, "output_tokens": 258, "cost_usd": 0.00196, "ok": true, "misled": false, "detail": "correct", "parsed": {"can_view": "yes", "team_rule": "blocklist"}} -{"scenario": "access-invert-qa", "task_type": "qa", "tier": "T1", "condition": "C0", "model": "haiku", "trial": 8, "output": "Let me trace through the `can_view` logic with the given scenario:\n\n**Part 1: Does `can_view(Dana, doc #42)` return True or False?**\n\nFollowing the code step by step:\n- `doc.visibility == \"public\"` \u2192 `\"private\" == \"public\"` \u2192 False, continue\n- `user.is_admin` \u2192 `False` \u2192 False, continue\n- `doc.owner_id == user.id` \u2192 `\"u_root\" == \"u_dana\"` \u2192 False, continue\n- `user.team not in doc.restricted_teams` \u2192 `\"growth\" not in {\"finance\", \"legal\"}` \u2192 **True**\n\nThe final return statement is `return user.team not in doc.restricted_teams`, which evaluates to **True**.\n\n**Answer: Yes, Dana can view the document.**\n\nJustification: Dana's team (\"growth\") is not in the restricted_teams set, so the final condition returns True.\n\n---\n\n**Part 2: Is the team rule an allow-list or block-list?**\n\nThe logic returns True when `user.team not in doc.restricted_teams`. This means:\n- Teams listed in `restricted_teams` are **blocked** from viewing\n- Teams NOT listed are **allowed** to view\n\n**Answer: Block-list.**\n\nJustification: The field contains teams that are denied access; every other team is allowed, matching the comment in the code that explicitly states \"Teams named here are denied access.\"\n\n---\n\nVERDICT: can_view=yes; team_rule=blocklist", "input_tokens": 670, "output_tokens": 355, "cost_usd": 0.002445, "ok": true, "misled": false, "detail": "correct", "parsed": {"can_view": "yes", "team_rule": "blocklist"}} -{"scenario": "access-invert-qa", "task_type": "qa", "tier": "T1", "condition": "C0", "model": "haiku", "trial": 9, "output": "Looking at the `can_view` method step by step with the given scenario:\n\n1. `doc.visibility == \"public\"` \u2192 False (visibility is \"private\")\n2. `user.is_admin` \u2192 False (Dana is not an admin)\n3. `doc.owner_id == user.id` \u2192 False (owner is \"u_root\", Dana is \"u_dana\")\n4. `user.team not in doc.restricted_teams` \u2192 `\"growth\" not in {\"finance\", \"legal\"}` \u2192 True\n\nSince the final condition evaluates to True, the method returns **True**.\n\n**Question 1: Can Dana view doc #42?**\n**Yes.** The method returns True because Dana's team (\"growth\") is not in the restricted_teams set, passing the final condition check.\n\n**Question 2: Is the team rule an allow-list or block-list?**\n**Block-list.** The code explicitly states in the comment that restricted_teams contains teams that are *denied* access, and the logic `user.team not in doc.restricted_teams` grants access to any team *not* explicitly listed, meaning every other team is allowed by default.\n\nVERDICT: can_view=yes; team_rule=blocklist", "input_tokens": 670, "output_tokens": 280, "cost_usd": 0.0020700000000000002, "ok": true, "misled": false, "detail": "correct", "parsed": {"can_view": "yes", "team_rule": "blocklist"}} -{"scenario": "access-invert-qa", "task_type": "qa", "tier": "T1", "condition": "C0", "model": "sonnet", "trial": 0, "output": "## Analysis\n\n### Part 1: Does `can_view(Dana, doc #42)` return **True**?\n\nWalking through the conditions in order:\n\n1. `doc.visibility == \"public\"` \u2192 **False** (it's `\"private\"`)\n2. `user.is_admin` \u2192 **False** (Dana is not an admin)\n3. `doc.owner_id == user.id` \u2192 **False** (`\"u_root\" != \"u_dana\"`)\n4. `return user.team not in doc.restricted_teams` \u2192 `\"growth\" not in {\"finance\", \"legal\"}` \u2192 `True`\n\n**Dana *can* view the document. `can_view` returns `True`.**\n\nJustification: The final check passes because `\"growth\"` is absent from `restricted_teams`, and the method returns `True` for any team that is *not* explicitly listed in that set.\n\n---\n\n### Part 2: Is the team rule an allow-list or a block-list?\n\n**It is a block-list.**\n\nJustification: The field `restricted_teams` names teams that are *denied* access; every other team is implicitly permitted, which is the defining characteristic of a block-list (deny-list) rather than an allow-list.\n\n---\n\n`VERDICT: can_view=yes; team_rule=blocklist`", "input_tokens": 671, "output_tokens": 313, "cost_usd": 0.006708, "ok": true, "misled": false, "detail": "correct", "parsed": {"can_view": "yes", "team_rule": "blocklist"}} -{"scenario": "access-invert-qa", "task_type": "qa", "tier": "T1", "condition": "C0", "model": "sonnet", "trial": 1, "output": "## Analysis\n\n### Part 1: Does `can_view(Dana, doc #42)` return `True` or `False`?\n\nWalking through the code step by step:\n\n1. `doc.visibility == \"public\"` \u2192 `\"private\" == \"public\"` \u2192 **False**, skip.\n2. `user.is_admin` \u2192 **False**, skip.\n3. `doc.owner_id == user.id` \u2192 `\"u_root\" == \"u_dana\"` \u2192 **False**, skip.\n4. `return user.team not in doc.restricted_teams` \u2192 `\"growth\" not in {\"finance\", \"legal\"}` \u2192 `True not in set` \u2192 **`True`**.\n\n**Dana *can* view the document.**\n\n### Part 2: Is the team rule an allow-list or a block-list?\n\nThe field is called `restricted_teams` and the check is `user.team not in doc.restricted_teams`. Access is *granted* to every team that is **not** explicitly listed. Only teams named in the set are denied. That is a **block-list** (deny-list) semantic \u2014 every team is allowed unless it appears in the list.\n\nThe doc-string in `models.py` confirms this explicitly: *\"Teams named here are denied access \u2026 every other team is allowed.\"*\n\n---\n\n`VERDICT: can_view=yes; team_rule=blocklist`", "input_tokens": 671, "output_tokens": 321, "cost_usd": 0.006828, "ok": true, "misled": false, "detail": "correct", "parsed": {"can_view": "yes", "team_rule": "blocklist"}} -{"scenario": "access-invert-qa", "task_type": "qa", "tier": "T1", "condition": "C0", "model": "sonnet", "trial": 2, "output": "## Analysis\n\n### Part 1: Does `can_view(Dana, doc #42)` return `True` or `False`?\n\nWalking through the code step by step:\n\n1. `doc.visibility == \"public\"` \u2192 `\"private\" == \"public\"` \u2192 **False**, skip.\n2. `user.is_admin` \u2192 **False**, skip.\n3. `doc.owner_id == user.id` \u2192 `\"u_root\" == \"u_dana\"` \u2192 **False**, skip.\n4. `return user.team not in doc.restricted_teams` \u2192 `\"growth\" not in {\"finance\", \"legal\"}` \u2192 `True not in set` \u2192 **`True`**.\n\n**Dana CAN view the document.**\n\n### Part 2: Is the team rule an allow-list or a block-list?\n\nThe field is named `restricted_teams` and the comment in `models.py` explicitly states *\"Teams named here are **denied** access \u2026 every other team is allowed.\"* The logic `user.team not in doc.restricted_teams` grants access to any team that is **not** explicitly listed, meaning unlisted teams are permitted by default.\n\n**It is a block-list (deny-list).**\n\n---\n\n**Justification for Part 1:** Because `\"growth\"` is absent from `restricted_teams`, the final `not in` check evaluates to `True`, granting Dana access despite the document being private and Dana being neither the owner nor an admin.\n\n**Justification for Part 2:** The rule denies only the teams explicitly enumerated in `restricted_teams`; any team not on that list passes through, which is the defining characteristic of a block-list.\n\n`VERDICT: can_view=yes; team_rule=blocklist`", "input_tokens": 671, "output_tokens": 404, "cost_usd": 0.008073, "ok": true, "misled": false, "detail": "correct", "parsed": {"can_view": "yes", "team_rule": "blocklist"}} -{"scenario": "access-invert-qa", "task_type": "qa", "tier": "T1", "condition": "C0", "model": "sonnet", "trial": 3, "output": "## Analysis\n\n### Part 1: Does `can_view(Dana, doc #42)` return `True` or `False`?\n\nWalking through the method step by step:\n\n1. `doc.visibility == \"public\"` \u2192 `\"private\" == \"public\"` \u2192 **False**, skip.\n2. `user.is_admin` \u2192 **False**, skip.\n3. `doc.owner_id == user.id` \u2192 `\"u_root\" == \"u_dana\"` \u2192 **False**, skip.\n4. `return user.team not in doc.restricted_teams` \u2192 `\"growth\" not in {\"finance\", \"legal\"}` \u2192 `True not in set` \u2192 **`True`**.\n\n**Dana CAN view the document.** `can_view` returns `True`.\n\n### Part 2: Is the team rule an allow-list or a block-list?\n\nThe field `restricted_teams` holds the teams that are **denied** access. Access is granted to any team that is **not** in the set. This means every team can see the document by default unless explicitly listed \u2014 that is a **block-list** (deny-list) design.\n\nThe comment in `models.py` confirms this directly: *\"Teams named here are denied access to this private document; every other team is allowed.\"*\n\n---\n\n`VERDICT: can_view=yes; team_rule=blocklist`", "input_tokens": 671, "output_tokens": 317, "cost_usd": 0.006768000000000001, "ok": true, "misled": false, "detail": "correct", "parsed": {"can_view": "yes", "team_rule": "blocklist"}} -{"scenario": "access-invert-qa", "task_type": "qa", "tier": "T1", "condition": "C0", "model": "sonnet", "trial": 4, "output": "## Analysis\n\n### Part 1: Does `can_view(Dana, doc #42)` return **True**?\n\nWalking through the code step by step:\n\n1. `doc.visibility == \"public\"` \u2192 `\"private\" == \"public\"` \u2192 **False**, skip.\n2. `user.is_admin` \u2192 **False**, skip.\n3. `doc.owner_id == user.id` \u2192 `\"u_root\" == \"u_dana\"` \u2192 **False**, skip.\n4. `return user.team not in doc.restricted_teams` \u2192 `\"growth\" not in {\"finance\", \"legal\"}` \u2192 `True` not in that set \u2192 returns **True**.\n\n**Yes, Dana can view the document.**\n\n### Part 2: Is the team rule an allow-list or block-list?\n\nThe field is named `restricted_teams` and the check is `user.team not in doc.restricted_teams`. A team gains access *unless* it appears in the set. Teams not listed (like `growth`) are **permitted by default**. This is a **block-list** (deny-list) semantic \u2014 you enumerate who is *excluded*, not who is *included*.\n\nThe docstring on the field even confirms this explicitly: *\"Teams named here are denied access\u2026 every other team is allowed.\"*\n\n---\n\n`VERDICT: can_view=yes; team_rule=blocklist`", "input_tokens": 671, "output_tokens": 318, "cost_usd": 0.006783, "ok": true, "misled": false, "detail": "correct", "parsed": {"can_view": "yes", "team_rule": "blocklist"}} -{"scenario": "access-invert-qa", "task_type": "qa", "tier": "T1", "condition": "C0", "model": "sonnet", "trial": 5, "output": "## Analysis\n\n### Part 1: Does `can_view(Dana, doc #42)` return **True**?\n\nWalking through the conditions in order:\n\n1. `doc.visibility == \"public\"` \u2192 **False** (it's `\"private\"`)\n2. `user.is_admin` \u2192 **False** (Dana is not an admin)\n3. `doc.owner_id == user.id` \u2192 **False** (`\"u_root\" != \"u_dana\"`)\n4. `return user.team not in doc.restricted_teams` \u2192 `\"growth\" not in {\"finance\", \"legal\"}` \u2192 `True`\n\n**Dana *can* view the document. `can_view` returns `True`.**\n\n### Part 2: Is the team rule an allow-list or a block-list?\n\nThe field is named `restricted_teams` and the check is:\n\n```python\nreturn user.team not in doc.restricted_teams\n```\n\nAccess is **granted** to every team that is *not* in the set, and **denied** only to teams that *are* explicitly listed. This is a **block-list** (deny-list) \u2014 teams are blocked out explicitly, and everyone else gets through by default.\n\nThe comment in `models.py` also confirms this directly: *\"Teams named here are denied access to this private document; every other team is allowed.\"*\n\n---\n\n`VERDICT: can_view=yes; team_rule=blocklist`", "input_tokens": 671, "output_tokens": 333, "cost_usd": 0.007008, "ok": true, "misled": false, "detail": "correct", "parsed": {"can_view": "yes", "team_rule": "blocklist"}} -{"scenario": "access-invert-qa", "task_type": "qa", "tier": "T1", "condition": "C0", "model": "sonnet", "trial": 6, "output": "## Analysis\n\n### Part 1: Does `can_view(Dana, doc #42)` return **True**?\n\nWalking through the code step by step:\n\n1. `doc.visibility == \"public\"` \u2192 `\"private\" == \"public\"` \u2192 **False**, skip.\n2. `user.is_admin` \u2192 **False**, skip.\n3. `doc.owner_id == user.id` \u2192 `\"u_root\" == \"u_dana\"` \u2192 **False**, skip.\n4. `return user.team not in doc.restricted_teams` \u2192 `\"growth\" not in {\"finance\", \"legal\"}` \u2192 `True not in set` \u2192 **`True`**.\n\n**Yes, Dana can view the document.**\n\n### Part 2: Is the team rule an allow-list or a block-list?\n\nThe field is named `restricted_teams` and the comment in `models.py` explicitly states *\"Teams named here are **denied** access\"*. The condition `user.team not in doc.restricted_teams` grants access to every team that is **not** listed, and denies only those that are explicitly listed.\n\n**It is a block-list (deny-list).** A team can see the document unless it is explicitly enumerated in `restricted_teams`; being absent from the list means access is granted, not denied.\n\n---\n\n`VERDICT: can_view=yes; team_rule=blocklist`", "input_tokens": 671, "output_tokens": 325, "cost_usd": 0.006888, "ok": true, "misled": false, "detail": "correct", "parsed": {"can_view": "yes", "team_rule": "blocklist"}} -{"scenario": "access-invert-qa", "task_type": "qa", "tier": "T1", "condition": "C0", "model": "sonnet", "trial": 7, "output": "## Analysis\n\n### Part 1: Does `can_view(Dana, doc #42)` return `True` or `False`?\n\nWalking through the logic in order:\n\n1. `doc.visibility == \"public\"` \u2192 `\"private\" == \"public\"` \u2192 **False**, skip.\n2. `user.is_admin` \u2192 `False` \u2192 **False**, skip.\n3. `doc.owner_id == user.id` \u2192 `\"u_root\" == \"u_dana\"` \u2192 **False**, skip.\n4. `return user.team not in doc.restricted_teams` \u2192 `\"growth\" not in {\"finance\", \"legal\"}` \u2192 `True` \u2192 **returns `True`**.\n\n**Dana can view the document.**\n\n### Part 2: Is the team rule an allow-list or a block-list?\n\nIt is a **block-list**. The field `restricted_teams` names teams that are *denied* access; any team not explicitly listed is permitted. This is confirmed both by the comment in `models.py` (\"Teams named here are *denied* access\") and by the `not in` check \u2014 access is granted unless your team appears in the set.\n\n---\n\n`VERDICT: can_view=yes; team_rule=blocklist`", "input_tokens": 671, "output_tokens": 289, "cost_usd": 0.006348, "ok": true, "misled": false, "detail": "correct", "parsed": {"can_view": "yes", "team_rule": "blocklist"}} -{"scenario": "access-invert-qa", "task_type": "qa", "tier": "T1", "condition": "C0", "model": "sonnet", "trial": 8, "output": "## Analysis\n\n### Part 1: Does `can_view(Dana, doc #42)` return **True**?\n\nWalking through the code step by step:\n\n1. `doc.visibility == \"public\"` \u2192 `\"private\" == \"public\"` \u2192 **False**, skip.\n2. `user.is_admin` \u2192 **False**, skip.\n3. `doc.owner_id == user.id` \u2192 `\"u_root\" == \"u_dana\"` \u2192 **False**, skip.\n4. `return user.team not in doc.restricted_teams` \u2192 `\"growth\" not in {\"finance\", \"legal\"}` \u2192 `True not in set` \u2192 **`True`**.\n\n**Dana can view the document. `can_view` returns `True`.**\n\n### Part 2: Is the team rule an allow-list or a block-list?\n\nThe field is named `restricted_teams` and the check is `user.team not in doc.restricted_teams`. A team gains access unless it appears in the set \u2014 teams *not* listed are permitted by default. This is a **block-list** (deny-list) semantic: every team is allowed *except* those explicitly enumerated.\n\nThe comment in `models.py` also confirms this directly: *\"Teams named here are denied access \u2026 every other team is allowed.\"*\n\n---\n\n`VERDICT: can_view=yes; team_rule=blocklist`", "input_tokens": 671, "output_tokens": 321, "cost_usd": 0.006828, "ok": true, "misled": false, "detail": "correct", "parsed": {"can_view": "yes", "team_rule": "blocklist"}} -{"scenario": "access-invert-qa", "task_type": "qa", "tier": "T1", "condition": "C0", "model": "sonnet", "trial": 9, "output": "## Analysis\n\n### Part 1: Does `can_view(Dana, doc #42)` return `True` or `False`?\n\nWalking through the code step by step:\n\n1. `doc.visibility == \"public\"` \u2192 `\"private\" == \"public\"` \u2192 **False**, skip.\n2. `user.is_admin` \u2192 `False` \u2192 **False**, skip.\n3. `doc.owner_id == user.id` \u2192 `\"u_root\" == \"u_dana\"` \u2192 **False**, skip.\n4. `return user.team not in doc.restricted_teams` \u2192 `\"growth\" not in {\"finance\", \"legal\"}` \u2192 `True` \u2192 **returns `True`**.\n\n**Dana CAN view the document.**\n\n### Part 2: Allow-list or block-list?\n\nThe field is named `restricted_teams` and the check is `user.team not in doc.restricted_teams`. A team gains access by default and is only denied if explicitly listed. That is a **block-list** (deny-list) semantic \u2014 every team sees the document *unless* it appears in the set.\n\n### Justification\n\nThe final guard returns `True` whenever the user's team is *absent* from `restricted_teams`, meaning unlisted teams are permitted by default; only teams explicitly named in the set are blocked, which is the definition of a block-list.\n\n---\n\n`VERDICT: can_view=yes; team_rule=blocklist`", "input_tokens": 671, "output_tokens": 334, "cost_usd": 0.007023, "ok": true, "misled": false, "detail": "correct", "parsed": {"can_view": "yes", "team_rule": "blocklist"}} -{"scenario": "access-invert-qa", "task_type": "qa", "tier": "T1", "condition": "C0", "model": "opus", "trial": 0, "output": "Tracing through `can_view(Dana, doc #42)`:\n\n1. **True \u2014 Dana can view the document.** The final line returns `user.team not in doc.restricted_teams`, and since `\"growth\"` is not in `{\"finance\", \"legal\"}`, this evaluates to `True`.\n\n2. **Block-list.** Every team is allowed by default and only the teams explicitly listed in `restricted_teams` are denied, which is the definition of a block-list.\n\nVERDICT: can_view=yes; team_rule=blocklist", "input_tokens": 855, "output_tokens": 171, "cost_usd": 0.00855, "ok": true, "misled": false, "detail": "correct", "parsed": {"can_view": "yes", "team_rule": "blocklist"}} -{"scenario": "access-invert-qa", "task_type": "qa", "tier": "T1", "condition": "C0", "model": "opus", "trial": 1, "output": "**1. Does `can_view(Dana, doc #42)` return True or False?**\n\nTrue \u2014 Dana can view the document.\n\n**2. Is the per-team rule an allow-list or a block-list?**\n\nBlock-list \u2014 every team sees the document unless it is explicitly named in `restricted_teams`.\n\n**Justification:** The final line `return user.team not in doc.restricted_teams` evaluates `\"growth\" not in {\"finance\", \"legal\"}`, which is `True`, so a team is granted access precisely when it is *absent* from the restricted set.\n\nVERDICT: can_view=yes; team_rule=blocklist", "input_tokens": 855, "output_tokens": 197, "cost_usd": 0.0092, "ok": true, "misled": false, "detail": "correct", "parsed": {"can_view": "yes", "team_rule": "blocklist"}} -{"scenario": "access-invert-qa", "task_type": "qa", "tier": "T1", "condition": "C0", "model": "opus", "trial": 2, "output": "**1. Does `can_view(Dana, doc #42)` return True or False?**\n\nTrue. The final line evaluates `user.team not in doc.restricted_teams`, and since `\"growth\"` is not in `{\"finance\", \"legal\"}`, this returns `True`.\n\n**2. Allow-list or block-list?**\n\nBlock-list. A team can view the document unless it is explicitly named in `restricted_teams`, so listing a team denies (blocks) access rather than granting it.\n\nVERDICT: can_view=yes; team_rule=blocklist", "input_tokens": 855, "output_tokens": 175, "cost_usd": 0.008650000000000001, "ok": true, "misled": false, "detail": "correct", "parsed": {"can_view": "yes", "team_rule": "blocklist"}} -{"scenario": "access-invert-qa", "task_type": "qa", "tier": "T1", "condition": "C0", "model": "opus", "trial": 3, "output": "Let me trace through the code.\n\n**Part 1: Does `can_view(Dana, doc #42)` return True or False?**\n\nWalking through `can_view`:\n- `doc.visibility == \"public\"` \u2192 False (it's private)\n- `user.is_admin` \u2192 False\n- `doc.owner_id == user.id` \u2192 False (`\"u_root\" != \"u_dana\"`)\n- `return user.team not in doc.restricted_teams` \u2192 `\"growth\" not in {\"finance\", \"legal\"}` \u2192 **True**\n\n**Answer: True** \u2014 Dana *can* view the document, because `growth` is not in the restricted set, so the final expression evaluates to True.\n\n**Part 2: Allow-list or block-list?**\n\nThe rule returns True (access granted) for every team *except* those explicitly listed in `restricted_teams`. A team sees the doc unless it is named in the set.\n\n**Answer: Block-list** \u2014 listing a team denies it access while all unlisted teams are permitted, which is block-list semantics.\n\nVERDICT: can_view=yes; team_rule=blocklist", "input_tokens": 855, "output_tokens": 339, "cost_usd": 0.012750000000000001, "ok": true, "misled": false, "detail": "correct", "parsed": {"can_view": "yes", "team_rule": "blocklist"}} -{"scenario": "access-invert-qa", "task_type": "qa", "tier": "T1", "condition": "C0", "model": "opus", "trial": 4, "output": "1. **True** \u2014 Dana can view the document.\n\n2. **Block-list** \u2014 every team sees the document unless it is explicitly listed in `restricted_teams`.\n\nJustification: Since the document is private, Dana is not an admin, and does not own it, `can_view` reaches the final line `return user.team not in doc.restricted_teams`; because `\"growth\"` is not in `{\"finance\", \"legal\"}`, the expression evaluates to `True`, which is exactly the block-list behavior (listed teams are denied, all others allowed).\n\nVERDICT: can_view=yes; team_rule=blocklist", "input_tokens": 855, "output_tokens": 193, "cost_usd": 0.0091, "ok": true, "misled": false, "detail": "correct", "parsed": {"can_view": "yes", "team_rule": "blocklist"}} -{"scenario": "access-invert-qa", "task_type": "qa", "tier": "T1", "condition": "C0", "model": "opus", "trial": 5, "output": "**1. Does `can_view(Dana, doc #42)` return True or False?**\n**True** \u2014 Dana can view the document.\n\n**2. Is the per-team rule an allow-list or a block-list?**\n**Block-list** \u2014 every team sees the doc unless explicitly listed in `restricted_teams`.\n\nJustification: The final line `return user.team not in doc.restricted_teams` evaluates to `\"growth\" not in {\"finance\", \"legal\"}`, which is `True`, so Dana (whose team is not on the denied list) is granted access.\n\nVERDICT: can_view=yes; team_rule=blocklist", "input_tokens": 855, "output_tokens": 197, "cost_usd": 0.0092, "ok": true, "misled": false, "detail": "correct", "parsed": {"can_view": "yes", "team_rule": "blocklist"}} -{"scenario": "access-invert-qa", "task_type": "qa", "tier": "T1", "condition": "C0", "model": "opus", "trial": 6, "output": "1. **True** \u2014 Dana can view the document.\n\n2. **Block-list** \u2014 every team sees the document unless it is explicitly listed in `restricted_teams`.\n\nJustification: The final line evaluates `user.team not in doc.restricted_teams`, and since `\"growth\"` is not in `{\"finance\", \"legal\"}`, this returns `True`, which means teams are denied only when explicitly listed (a block-list).\n\nVERDICT: can_view=yes; team_rule=blocklist", "input_tokens": 855, "output_tokens": 156, "cost_usd": 0.008175, "ok": true, "misled": false, "detail": "correct", "parsed": {"can_view": "yes", "team_rule": "blocklist"}} -{"scenario": "access-invert-qa", "task_type": "qa", "tier": "T1", "condition": "C0", "model": "opus", "trial": 7, "output": "1. **True** \u2014 Dana can view the document.\n\n2. **Block-list** \u2014 every team sees the doc unless it is explicitly listed in `restricted_teams`.\n\nJustification: The final line `return user.team not in doc.restricted_teams` evaluates to `\"growth\" not in {\"finance\", \"legal\"}`, which is `True`, meaning access is granted to any team absent from the restricted set.\n\nVERDICT: can_view=yes; team_rule=blocklist", "input_tokens": 855, "output_tokens": 150, "cost_usd": 0.008025000000000001, "ok": true, "misled": false, "detail": "correct", "parsed": {"can_view": "yes", "team_rule": "blocklist"}} -{"scenario": "access-invert-qa", "task_type": "qa", "tier": "T1", "condition": "C0", "model": "opus", "trial": 8, "output": "1. **True** \u2014 Dana can view the document.\n\n2. **Block-list** \u2014 every team sees the document unless it is explicitly listed in `restricted_teams`.\n\nJustification: The final line `return user.team not in doc.restricted_teams` evaluates to `True` because `\"growth\"` is not in `{\"finance\", \"legal\"}`, which means teams are denied only when explicitly listed, making this a block-list.\n\nVERDICT: can_view=yes; team_rule=blocklist", "input_tokens": 855, "output_tokens": 154, "cost_usd": 0.008125, "ok": true, "misled": false, "detail": "correct", "parsed": {"can_view": "yes", "team_rule": "blocklist"}} -{"scenario": "access-invert-qa", "task_type": "qa", "tier": "T1", "condition": "C0", "model": "opus", "trial": 9, "output": "Tracing through `can_view` for this scenario:\n\n1. **True** \u2014 Dana can view the document.\n2. **Block-list** \u2014 every team sees a private document unless it is explicitly listed in `restricted_teams`.\n\n**Justification:** The document isn't public, Dana isn't an admin, and she doesn't own it, so evaluation reaches the final line `return user.team not in doc.restricted_teams`; since `\"growth\"` is not in `{\"finance\", \"legal\"}`, this returns `True`, and the `not in` logic means listed teams are *denied* while all others are *allowed* \u2014 a block-list.\n\nVERDICT: can_view=yes; team_rule=blocklist", "input_tokens": 855, "output_tokens": 218, "cost_usd": 0.009725000000000001, "ok": true, "misled": false, "detail": "correct", "parsed": {"can_view": "yes", "team_rule": "blocklist"}} -{"scenario": "access-invert-qa", "task_type": "qa", "tier": "T1", "condition": "C1", "model": "haiku", "trial": 0, "output": "I'll trace through the code carefully to answer both questions.\n\n## Analysis of `can_view(Dana, doc #42)`\n\nLooking at the `can_view` method in `code/access.py`:\n\n```python\ndef can_view(self, user: User, doc: Document) -> bool:\n if doc.visibility == \"public\":\n return True\n if user.is_admin:\n return True\n if doc.owner_id == user.id:\n return True\n # Team-scoped access for private documents.\n return user.team not in doc.restricted_teams\n```\n\n**For Dana viewing doc #42:**\n- `doc.visibility == \"public\"` \u2192 False (it's private)\n- `user.is_admin` \u2192 False (Dana is not an admin)\n- `doc.owner_id == user.id` \u2192 False (\"u_root\" \u2260 \"u_dana\")\n- Final line: `return user.team not in doc.restricted_teams`\n - `user.team` = \"growth\"\n - `doc.restricted_teams` = {\"finance\", \"legal\"}\n - \"growth\" not in {\"finance\", \"legal\"} \u2192 **True**\n\n**Question 1:** `can_view(Dana, doc #42)` returns **True** \u2014 Dana can view the document.\n\n## Analysis of the Per-Team Rule\n\nThe field is named `restricted_teams` and the code returns `True` when the user's team is **NOT** in that set. This means:\n- Teams **in** the set are denied access\n- Teams **not** in the set are granted access\n- The default is **ALLOW**\n\nThis is a **block-list** (deny-list): you can view unless your team is explicitly restricted.\n\n**Question 2:** The per-team rule is a **block-list**.\n\n**Justification:** The code explicitly checks `user.team not in doc.restricted_teams`, granting access when the team is absent from the restricted set, making it a deny-list rather than an allow-list.\n\n---\n\nVERDICT: can_view=yes; team_rule=blocklist", "input_tokens": 977, "output_tokens": 492, "cost_usd": 0.0034370000000000004, "ok": true, "misled": false, "detail": "correct", "parsed": {"can_view": "yes", "team_rule": "blocklist"}} -{"scenario": "access-invert-qa", "task_type": "qa", "tier": "T1", "condition": "C1", "model": "haiku", "trial": 1, "output": "Let me trace through the code carefully.\n\n**Given:**\n- Dana: `User(id=\"u_dana\", team=\"growth\", is_admin=False)`\n- Doc #42: `Document(id=\"42\", owner_id=\"u_root\", visibility=\"private\", restricted_teams={\"finance\", \"legal\"})`\n\n**Execution of `can_view(Dana, doc #42)`:**\n\n1. `if doc.visibility == \"public\":` \u2192 False (visibility is \"private\")\n2. `if user.is_admin:` \u2192 False (Dana is not an admin)\n3. `if doc.owner_id == user.id:` \u2192 False (owner is \"u_root\", Dana is \"u_dana\")\n4. `return user.team not in doc.restricted_teams`\n - `user.team` = \"growth\"\n - `doc.restricted_teams` = {\"finance\", \"legal\"}\n - \"growth\" is **not** in {\"finance\", \"legal\"} \u2192 **True**\n\nSo `can_view` returns **True**.\n\n**Per-team rule analysis:**\n\nThe final line is `return user.team not in doc.restricted_teams`. This grants access when the user's team is **absent** from the restricted set. The field is documented as teams \"denied access\" \u2014 a blocklist. A user can view the document **unless** their team is explicitly blocked. This is a **blocklist** (deny-list), not an allowlist.\n\n**Discrepancy note:** The documentation claims an allowlist (\"a user may view the document only if their team is **explicitly listed**\"), but the actual code implements a blocklist (the field holds *restricted* teams, and access is granted to all teams *not* in that set).\n\n---\n\n**VERDICT: can_view=yes; team_rule=blocklist**", "input_tokens": 977, "output_tokens": 416, "cost_usd": 0.0030570000000000003, "ok": true, "misled": false, "detail": "correct", "parsed": {"can_view": "yes", "team_rule": "blocklist"}} -{"scenario": "access-invert-qa", "task_type": "qa", "tier": "T1", "condition": "C1", "model": "haiku", "trial": 2, "output": "Looking at the code in `code/access.py`, let me trace through what happens:\n\n```python\ndef can_view(self, user: User, doc: Document) -> bool:\n if doc.visibility == \"public\":\n return True\n if user.is_admin:\n return True\n if doc.owner_id == user.id:\n return True\n # Team-scoped access for private documents.\n return user.team not in doc.restricted_teams\n```\n\n**Question 1: Can Dana view doc #42?**\n\n- Doc #42 is \"private\" \u2192 first check doesn't return True\n- Dana is not an admin \u2192 second check doesn't return True\n- Dana doesn't own it (owner is \"u_root\") \u2192 third check doesn't return True\n- Final line: `return user.team not in doc.restricted_teams`\n - Dana's team is \"growth\"\n - `restricted_teams` is `{\"finance\", \"legal\"}`\n - Is \"growth\" in `{\"finance\", \"legal\"}`? No\n - So `\"growth\" not in {\"finance\", \"legal\"}` evaluates to **True**\n\nDana **can** view the document.\n\n**Question 2: Is the team rule an allow-list or block-list?**\n\nThe current code returns `True` when the user's team is **not** in `restricted_teams`. This is a **block-list** (deny-list): teams in `restricted_teams` are blocked from access, and all other teams are allowed by default. This directly contradicts the project documentation, which claims it's an allow-list, but the documentation note explicitly acknowledges this historical inversion: \"Historically this field held the *permitted* teams; it now holds the *restricted* ones.\"\n\nVERDICT: can_view=yes; team_rule=blocklist", "input_tokens": 977, "output_tokens": 420, "cost_usd": 0.0030770000000000003, "ok": true, "misled": false, "detail": "correct", "parsed": {"can_view": "yes", "team_rule": "blocklist"}} -{"scenario": "access-invert-qa", "task_type": "qa", "tier": "T1", "condition": "C1", "model": "haiku", "trial": 3, "output": "## Analysis\n\nLet me trace through the code for this scenario:\n\n**Question 1: Does `can_view(Dana, doc #42)` return True or False?**\n\nFollowing the `can_view` method:\n\n```python\ndef can_view(self, user: User, doc: Document) -> bool:\n if doc.visibility == \"public\": # False: doc #42 is \"private\"\n return True\n if user.is_admin: # False: Dana is_admin=False\n return True\n if doc.owner_id == user.id: # False: owner is \"u_root\", Dana is \"u_dana\"\n return True\n # Team-scoped access for private documents.\n return user.team not in doc.restricted_teams # Returns: \"growth\" not in {\"finance\", \"legal\"}\n```\n\nThe final line evaluates: `\"growth\" not in {\"finance\", \"legal\"}` \u2192 **True**\n\nTherefore, `can_view(Dana, doc #42)` returns **True** \u2014 Dana can view the document.\n\n**Question 2: Allow-list or block-list?**\n\nThe code returns `True` when `user.team not in doc.restricted_teams`. This means:\n- If a team is **in** `restricted_teams`, it is **denied** access\n- If a team is **not in** `restricted_teams`, it is **allowed** access\n\nThis is a **block-list** (deny-list): teams explicitly listed are blocked; all others are permitted.\n\nHowever, the documentation describes this as an allow-list, which contradicts the actual implementation. The field's own comment states: \"Teams named here are denied access to this private document; every other team is allowed\" \u2014 confirming block-list behavior.\n\n**Justification:**\n\nThe code grants access when the user's team is absent from `restricted_teams`, making it a block-list where listed teams are denied and unlisted teams are permitted by default.\n\nVERDICT: can_view=yes; team_rule=blocklist", "input_tokens": 977, "output_tokens": 467, "cost_usd": 0.0033120000000000003, "ok": true, "misled": false, "detail": "correct", "parsed": {"can_view": "yes", "team_rule": "blocklist"}} -{"scenario": "access-invert-qa", "task_type": "qa", "tier": "T1", "condition": "C1", "model": "haiku", "trial": 4, "output": "I'll trace through the code to answer this question.\n\n## Analysis\n\n**Question 1: Does `can_view(Dana, doc #42)` return True or False?**\n\nLet me trace through `AccessPolicy.can_view(Dana, doc #42)`:\n\n```python\ndef can_view(self, user: User, doc: Document) -> bool:\n if doc.visibility == \"public\": # doc #42 is \"private\", so False\n return True\n if user.is_admin: # Dana.is_admin is False, so False\n return True\n if doc.owner_id == user.id: # \"u_root\" != \"u_dana\", so False\n return True\n # Team-scoped access for private documents.\n return user.team not in doc.restricted_teams\n```\n\nThe last line executes: `return user.team not in doc.restricted_teams`\n\n- `Dana.team` = `\"growth\"`\n- `doc.restricted_teams` = `{\"finance\", \"legal\"}`\n- `\"growth\" not in {\"finance\", \"legal\"}` = `True`\n\n**Answer 1: True \u2014 Dana CAN view the document.**\n\n**Question 2: Is this an allow-list or block-list?**\n\nThe field is named `restricted_teams` and the comment states: \"Teams named here are denied access to this private document; every other team is allowed.\"\n\nThe code returns `True` when `user.team not in doc.restricted_teams` \u2014 meaning a team views the document unless it is explicitly listed in the restricted set.\n\n**Answer 2: This is a block-list** (every team sees the doc unless explicitly restricted).\n\n## Note on Documentation Discrepancy\n\nThe project documentation claims this is an **allow-list** (\"a user may view the document only if their team is explicitly listed\"), but the actual code implements a **block-list** (a user can view unless their team is in `restricted_teams`). The documentation and code are in direct conflict. However, you asked for what the **current code** actually does.\n\n---\n\nVERDICT: can_view=yes; team_rule=blocklist", "input_tokens": 977, "output_tokens": 494, "cost_usd": 0.0034470000000000004, "ok": true, "misled": false, "detail": "correct", "parsed": {"can_view": "yes", "team_rule": "blocklist"}} -{"scenario": "access-invert-qa", "task_type": "qa", "tier": "T1", "condition": "C1", "model": "haiku", "trial": 5, "output": "Let me trace through the code to answer this question.\n\nLooking at `AccessPolicy.can_view`:\n\n```python\ndef can_view(self, user: User, doc: Document) -> bool:\n if doc.visibility == \"public\":\n return True\n if user.is_admin:\n return True\n if doc.owner_id == user.id:\n return True\n # Team-scoped access for private documents.\n return user.team not in doc.restricted_teams\n```\n\n**Scenario analysis:**\n- Dana: `id=\"u_dana\", team=\"growth\", is_admin=False`\n- Doc #42: `visibility=\"private\", owner_id=\"u_root\", restricted_teams={\"finance\", \"legal\"}`\n\n**Tracing the execution:**\n1. `doc.visibility == \"public\"` \u2192 False (it's \"private\")\n2. `user.is_admin` \u2192 False (Dana is not an admin)\n3. `doc.owner_id == user.id` \u2192 False (\"u_root\" \u2260 \"u_dana\")\n4. `user.team not in doc.restricted_teams` \u2192 \"growth\" not in {\"finance\", \"legal\"} \u2192 **True**\n\nSo `can_view` returns **True** \u2014 Dana can view the document.\n\n**Team rule analysis:**\nThe final line returns `user.team not in doc.restricted_teams`. This means:\n- If your team IS in `restricted_teams`, you are **denied** access.\n- If your team is NOT in `restricted_teams`, you are **granted** access.\n\nThis is a **block-list** (or deny-list): the field contains teams that are blocked; all other teams are allowed. This directly contradicts the documentation claim, which states the field represents an allow-list of permitted teams. The code actually implements the opposite of what the documentation describes.\n\n**Answers:**\n\n1. **Can Dana view doc #42?** Yes.\n2. **Is it an allow-list or block-list?** Block-list (the `restricted_teams` field denies access to listed teams; unlisted teams are allowed).\n\nThe justification: The return statement `user.team not in doc.restricted_teams` grants access when the user's team is absent from the set, which is the definition of a block-list/deny-list model.\n\nVERDICT: can_view=yes; team_rule=blocklist", "input_tokens": 977, "output_tokens": 552, "cost_usd": 0.0037370000000000003, "ok": true, "misled": false, "detail": "correct", "parsed": {"can_view": "yes", "team_rule": "blocklist"}} -{"scenario": "access-invert-qa", "task_type": "qa", "tier": "T1", "condition": "C1", "model": "haiku", "trial": 6, "output": "Let me trace through the code carefully.\n\n**Question 1: Does `can_view(Dana, doc #42)` return True or False?**\n\nFollowing the logic in `AccessPolicy.can_view`:\n\n```python\ndef can_view(self, user: User, doc: Document) -> bool:\n if doc.visibility == \"public\": # False - it's private\n return True\n if user.is_admin: # False - Dana is not admin\n return True\n if doc.owner_id == user.id: # False - Dana (u_dana) \u2260 owner (u_root)\n return True\n # Team-scoped access for private documents.\n return user.team not in doc.restricted_teams # \"growth\" not in {\"finance\", \"legal\"}\n```\n\nThe final line evaluates: `\"growth\" not in {\"finance\", \"legal\"}` \u2192 **True**\n\n**Answer: True \u2014 Dana CAN view the document.**\n\n**Question 2: Is the rule an allow-list or block-list?**\n\nThe `restricted_teams` field is documented as: \"Teams named here are denied access to this private document; every other team is allowed.\" The code returns `True` when `user.team not in doc.restricted_teams` \u2014 i.e., access is granted to any team NOT in the restricted set.\n\nThis is a **block-list**: teams in the set are blocked; all other teams are allowed by default.\n\nHowever, there is a critical mismatch: the project documentation claims it's an **allow-list** (\"a user may view the document only if their team is **explicitly listed**\"), but the actual code implements a **block-list** (Dana can view because her team is NOT in the restricted set).\n\n**Answer: Block-list** (based on the actual code behavior, though the documentation claims allow-list).\n\n---\n\nVERDICT: can_view=yes; team_rule=blocklist", "input_tokens": 977, "output_tokens": 444, "cost_usd": 0.003197, "ok": true, "misled": false, "detail": "correct", "parsed": {"can_view": "yes", "team_rule": "blocklist"}} -{"scenario": "access-invert-qa", "task_type": "qa", "tier": "T1", "condition": "C1", "model": "haiku", "trial": 7, "output": "I'll analyze the code carefully against the actual implementation.\n\n## Analysis\n\nLooking at `AccessPolicy.can_view`:\n\n```python\ndef can_view(self, user: User, doc: Document) -> bool:\n \"\"\"Return True if `user` is permitted to view `doc`.\"\"\"\n if doc.visibility == \"public\":\n return True\n if user.is_admin:\n return True\n if doc.owner_id == user.id:\n return True\n # Team-scoped access for private documents.\n return user.team not in doc.restricted_teams\n```\n\n**For Dana and doc #42:**\n\n1. `doc.visibility == \"public\"` \u2192 False (it's \"private\")\n2. `user.is_admin` \u2192 False (Dana is not an admin)\n3. `doc.owner_id == user.id` \u2192 False (Dana doesn't own it)\n4. The final line: `return user.team not in doc.restricted_teams`\n - `user.team` = \"growth\"\n - `doc.restricted_teams` = {\"finance\", \"legal\"}\n - \"growth\" is **not in** {\"finance\", \"legal\"}\n - Therefore: `\"growth\" not in {\"finance\", \"legal\"}` = **True**\n\n**Answer to part 1:** `can_view(Dana, doc #42)` returns **True** \u2014 Dana can view the document.\n\n**Answer to part 2:** The rule is a **block-list** (deny-list). The code grants access unless the user's team appears in `restricted_teams`. Teams not listed are granted access by default.\n\n**Justification:** The final return statement `user.team not in doc.restricted_teams` explicitly denies access only to teams that are *in* the restricted set; all other teams are allowed. This contradicts both the project documentation (which claims an allow-list) and the field docstring (which correctly describes the semantic as restricted teams being denied access).\n\nVERDICT: can_view=yes; team_rule=blocklist", "input_tokens": 977, "output_tokens": 468, "cost_usd": 0.003317, "ok": true, "misled": false, "detail": "correct", "parsed": {"can_view": "yes", "team_rule": "blocklist"}} -{"scenario": "access-invert-qa", "task_type": "qa", "tier": "T1", "condition": "C1", "model": "haiku", "trial": 8, "output": "Let me trace through the code carefully.\n\n**Part 1: Does `can_view(Dana, doc #42)` return True or False?**\n\nWalking through `AccessPolicy.can_view(Dana, doc #42)`:\n1. `doc.visibility == \"public\"` \u2192 False (it's \"private\")\n2. `user.is_admin` \u2192 False (Dana is not an admin)\n3. `doc.owner_id == user.id` \u2192 False (\"u_root\" \u2260 \"u_dana\")\n4. `return user.team not in doc.restricted_teams` \u2192 `return \"growth\" not in {\"finance\", \"legal\"}` \u2192 `return True`\n\n**`can_view` returns `True`** \u2014 Dana CAN view the document.\n\n**Part 2: Is the team rule an allow-list or block-list?**\n\nThe field is named `restricted_teams` and the logic returns `True` when `user.team not in doc.restricted_teams`. This is a **block-list**: teams in the set are explicitly denied access, and every other team (including \"growth\") is allowed by default. The documentation's claim about an \"allow-list\" contradicts the actual implementation.\n\n**Justification:** The code grants access to any team *not* in `restricted_teams`, which is the definition of a block-list (deny-list), not an allow-list.\n\nVERDICT: can_view=yes; team_rule=blocklist", "input_tokens": 977, "output_tokens": 335, "cost_usd": 0.002652, "ok": true, "misled": false, "detail": "correct", "parsed": {"can_view": "yes", "team_rule": "blocklist"}} -{"scenario": "access-invert-qa", "task_type": "qa", "tier": "T1", "condition": "C1", "model": "haiku", "trial": 9, "output": "Let me trace through the code carefully.\n\n**Part 1: Does `can_view(Dana, doc #42)` return True or False?**\n\nFollowing the `can_view` method:\n1. `doc.visibility == \"public\"` \u2192 False (it's \"private\")\n2. `user.is_admin` \u2192 False (Dana is not an admin)\n3. `doc.owner_id == user.id` \u2192 False (\"u_root\" \u2260 \"u_dana\")\n4. `user.team not in doc.restricted_teams` \u2192 `\"growth\" not in {\"finance\", \"legal\"}` \u2192 True\n\nThe final line returns the result of `user.team not in doc.restricted_teams`, which is **True**.\n\n**Dana CAN view the document.** The method returns `True`.\n\n**Part 2: Is the team rule an allow-list or block-list?**\n\nThe code returns `True` when the user's team is **not** in `restricted_teams`. This means teams in `restricted_teams` are *denied* access, and all other teams are *allowed* access by default. This is a **block-list** (deny-list): you can view unless your team is explicitly blocked.\n\nHowever, this contradicts both the documentation claim and the project documentation, which describe an allow-list model. The actual code implements a block-list, despite what the documentation says.\n\n**Justification:** The return statement `user.team not in doc.restricted_teams` grants access whenever the team is absent from the restricted set, which is block-list semantics (deny specific teams, allow all others).\n\nVERDICT: can_view=yes; team_rule=blocklist", "input_tokens": 977, "output_tokens": 382, "cost_usd": 0.0028870000000000002, "ok": true, "misled": false, "detail": "correct", "parsed": {"can_view": "yes", "team_rule": "blocklist"}} -{"scenario": "access-invert-qa", "task_type": "qa", "tier": "T1", "condition": "C1", "model": "sonnet", "trial": 0, "output": "## Analysis\n\nLooking at the actual code in `can_view`:\n\n```python\nreturn user.team not in doc.restricted_teams\n```\n\nThe field is named `restricted_teams` and the logic returns `True` when the user's team is **not** in the set.\n\n---\n\n**1. Does `can_view(Dana, doc #42)` return True or False?**\n\n**True \u2014 Dana *can* view the document.**\n\nDana's team is `\"growth\"`, and `restricted_teams = {\"finance\", \"legal\"}`. Since `\"growth\" not in {\"finance\", \"legal\"}` evaluates to `True`, `can_view` returns `True`.\n\n**2. Is the per-team rule an allow-list or a block-list?**\n\n**Block-list.**\n\nThe code denies access only to teams *explicitly listed* in `restricted_teams`; every other team gets access by default. That is the definition of a block-list (deny-list), not an allow-list.\n\n---\n\n> **Note on the documentation discrepancy:** The project documentation and the `summary` anchor both describe the field as a *permit* list (allow-list, default DENY). This is **incorrect** with respect to the current code. The code was apparently refactored so the field now holds *restricted* teams (a block-list, default ALLOW), but the docs were not updated. The field's own docstring in `models.py` confirms this: *\"Teams named here are **denied** access\u2026\"*\n\n`VERDICT: can_view=yes; team_rule=blocklist`", "input_tokens": 978, "output_tokens": 360, "cost_usd": 0.008334000000000001, "ok": true, "misled": false, "detail": "correct", "parsed": {"can_view": "yes", "team_rule": "blocklist"}} -{"scenario": "access-invert-qa", "task_type": "qa", "tier": "T1", "condition": "C1", "model": "sonnet", "trial": 1, "output": "## Analysis of `can_view` against the current code\n\n### Part 1: Does `can_view(Dana, doc #42)` return **True** or **False**?\n\n**True \u2014 Dana *can* view the document.**\n\nThe decisive line in the current code is:\n\n```python\nreturn user.team not in doc.restricted_teams\n```\n\nDana's team is `\"growth\"`. `doc.restricted_teams` is `{\"finance\", \"legal\"}`. Since `\"growth\"` is **not** in that set, `\"growth\" not in {\"finance\", \"legal\"}` evaluates to `True`, so `can_view` returns `True`.\n\n---\n\n### Part 2: Is the per-team rule an allow-list or a **block-list**?\n\n**Block-list.**\n\nThe field `restricted_teams` holds teams that are *denied* access. Any team **not** explicitly listed is **permitted** by default \u2014 the opposite of an allow-list. This means the default is **open** for teams absent from the list, not closed.\n\n---\n\n### Conflict with the documentation\n\nThe project documentation (and the docstring anchor) describe the old semantics (an allow-list / default-deny). The **current code** implements the opposite: a block-list / default-allow. The comment in `models.py` itself acknowledges this:\n\n> *(Historically this field held the permitted teams; it now holds the restricted ones.)*\n\nThe documentation has not been updated to match the code change.\n\n---\n\n`VERDICT: can_view=yes; team_rule=blocklist`", "input_tokens": 978, "output_tokens": 360, "cost_usd": 0.008334000000000001, "ok": true, "misled": false, "detail": "correct", "parsed": {"can_view": "yes", "team_rule": "blocklist"}} -{"scenario": "access-invert-qa", "task_type": "qa", "tier": "T1", "condition": "C1", "model": "sonnet", "trial": 2, "output": "## Analysis of `can_view` for Dana on doc #42\n\nLooking at the actual code in `access.py`:\n\n```python\ndef can_view(self, user: User, doc: Document) -> bool:\n if doc.visibility == \"public\":\n return True # \u2717 doc is private\n if user.is_admin:\n return True # \u2717 Dana is not admin\n if doc.owner_id == user.id:\n return True # \u2717 Dana is not the owner (owner is u_root)\n # Team-scoped access for private documents.\n return user.team not in doc.restricted_teams\n```\n\nThe final line evaluates: `\"growth\" not in {\"finance\", \"legal\"}` \u2192 **`True`**.\n\n---\n\n### Answer 1 \u2014 Can Dana view the document?\n\n**Yes.** The final line returns `True` because `\"growth\"` is *not* in `restricted_teams`, and the condition is `not in` \u2014 meaning absent teams are *permitted*, not denied.\n\n### Answer 2 \u2014 Allow-list or block-list?\n\n**Block-list.** The field `restricted_teams` names the teams that are *denied*; every team *not* listed is implicitly allowed \u2014 which is the definition of a block-list (deny-list).\n\n> \u26a0\ufe0f **Note:** The project documentation is **wrong**. It describes the logic as an allow-list with a closed-by-default default, but the actual code implements the opposite: an open-by-default block-list. The field was historically renamed from permitted teams to restricted teams (as noted in the model comment), but the docs were never updated to reflect this inversion.\n\n`VERDICT: can_view=yes; team_rule=blocklist`", "input_tokens": 978, "output_tokens": 414, "cost_usd": 0.009144, "ok": true, "misled": false, "detail": "correct", "parsed": {"can_view": "yes", "team_rule": "blocklist"}} -{"scenario": "access-invert-qa", "task_type": "qa", "tier": "T1", "condition": "C1", "model": "sonnet", "trial": 3, "output": "## Analysis of `can_view` against the scenario\n\n### Reading the actual code\n\nThe relevant line in `can_view` for the non-owner, non-admin, private-document case is:\n\n```python\nreturn user.team not in doc.restricted_teams\n```\n\n- Dana's team is `\"growth\"`.\n- `doc.restricted_teams` is `{\"finance\", \"legal\"}`.\n- `\"growth\" not in {\"finance\", \"legal\"}` \u2192 **`True`**\n\n---\n\n### Part 1: Does `can_view(Dana, doc #42)` return `True` or `False`?\n\n**True \u2014 Dana *can* view the document.**\n\nJustification: Dana's team `\"growth\"` is absent from `restricted_teams`, and the code returns `True` precisely when the user's team is *not* in that set.\n\n---\n\n### Part 2: Is the per-team rule an allow-list or a block-list?\n\n**Block-list.**\n\nJustification: The logic grants access to every team by default and denies only those teams explicitly present in `restricted_teams`, which is the definition of a block-list \u2014 the opposite of what the documentation claims.\n\n> \u26a0\ufe0f **Note:** The project documentation is **wrong**. It describes an allow-list (\"default DENY, team must be explicitly listed to gain access\"), but the actual code implements a block-list (\"default ALLOW, team must be explicitly listed to be denied\"). The field was historically a permit-list but was repurposed to hold *restricted* teams; the documentation was never updated to match.\n\n---\n\n`VERDICT: can_view=yes; team_rule=blocklist`", "input_tokens": 978, "output_tokens": 383, "cost_usd": 0.008679, "ok": true, "misled": false, "detail": "correct", "parsed": {"can_view": "yes", "team_rule": "blocklist"}} -{"scenario": "access-invert-qa", "task_type": "qa", "tier": "T1", "condition": "C1", "model": "sonnet", "trial": 4, "output": "## Analysis of `can_view` against the scenario\n\n### Reading the actual code\n\nThe relevant line in `can_view` for a private document where the user is neither the owner nor an admin is:\n\n```python\nreturn user.team not in doc.restricted_teams\n```\n\nPlugging in Dana's values:\n- `user.team` = `\"growth\"`\n- `doc.restricted_teams` = `{\"finance\", \"legal\"}`\n- `\"growth\" not in {\"finance\", \"legal\"}` \u2192 **`True`**\n\n---\n\n### Part 1 \u2014 Can Dana view doc #42?\n\n**Yes, `can_view` returns `True`.**\n\nJustification: Dana's team (`\"growth\"`) is not present in `restricted_teams`, so the expression `user.team not in doc.restricted_teams` evaluates to `True`, granting access.\n\n---\n\n### Part 2 \u2014 Allow-list or block-list?\n\n**Block-list.**\n\nJustification: The code denies access only to teams *explicitly listed* in `restricted_teams`; every unlisted team is permitted by default, which is the defining characteristic of a block-list (deny-list), not an allow-list.\n\n---\n\n> **Note:** The project documentation describes this as an allow-list with a closed default, but that description is **inconsistent with the current code**. The field was historically a permitted-teams allow-list but, as the model comment acknowledges, it now holds *restricted* teams \u2014 making the live behavior a block-list. The documentation has not been updated to match.\n\n`VERDICT: can_view=yes; team_rule=blocklist`", "input_tokens": 978, "output_tokens": 376, "cost_usd": 0.008574, "ok": true, "misled": false, "detail": "correct", "parsed": {"can_view": "yes", "team_rule": "blocklist"}} -{"scenario": "access-invert-qa", "task_type": "qa", "tier": "T1", "condition": "C1", "model": "sonnet", "trial": 5, "output": "## Analysis of `can_view` against the current code\n\n### Part 1: Does `can_view(Dana, doc #42)` return `True` or `False`?\n\n**True \u2014 Dana can view the document.**\n\nWalking through the code with the given inputs:\n\n```python\ndef can_view(self, user: User, doc: Document) -> bool:\n if doc.visibility == \"public\": # \"private\" != \"public\" \u2192 skip\n return True\n if user.is_admin: # False \u2192 skip\n return True\n if doc.owner_id == user.id: # \"u_root\" != \"u_dana\" \u2192 skip\n return True\n # Team-scoped access for private documents.\n return user.team not in doc.restricted_teams\n # \"growth\" not in {\"finance\", \"legal\"} \u2192 True\n```\n\nThe final line returns `\"growth\" not in {\"finance\", \"legal\"}`, which is **`True`**.\n\n---\n\n### Part 2: Is the per-team rule an allow-list or a block-list?\n\n**Block-list.**\n\nThe condition `user.team not in doc.restricted_teams` grants access to every team that is *absent* from the set and denies only teams that are *explicitly listed*. That is the definition of a block-list (deny-list), not an allow-list.\n\n---\n\n### Conflict with the documentation\n\nThe project documentation (and the docstring anchor) describes the field as an **allow-list** where the default is DENY. The field was even renamed to `restricted_teams` with a comment noting the semantic flip. However, **the actual code implements the opposite**: the default is **ALLOW**, and only listed teams are blocked. The documentation is wrong relative to the current code.\n\n---\n\n`VERDICT: can_view=yes; team_rule=blocklist`", "input_tokens": 978, "output_tokens": 429, "cost_usd": 0.009368999999999999, "ok": true, "misled": false, "detail": "correct", "parsed": {"can_view": "yes", "team_rule": "blocklist"}} -{"scenario": "access-invert-qa", "task_type": "qa", "tier": "T1", "condition": "C1", "model": "sonnet", "trial": 6, "output": "## Analysis of `can_view` against the scenario\n\n### Reading the actual code\n\nThe relevant line for the team-scoped check is:\n\n```python\nreturn user.team not in doc.restricted_teams\n```\n\nWith Dana's data:\n- `user.team` = `\"growth\"`\n- `doc.restricted_teams` = `{\"finance\", \"legal\"}`\n- `\"growth\" not in {\"finance\", \"legal\"}` \u2192 **`True`**\n\n---\n\n### Part 1: Does `can_view(Dana, doc #42)` return `True` or `False`?\n\n**`True` \u2014 Dana *can* view the document.**\n\nThe document is private and Dana is neither the owner nor an admin, so execution reaches the final `return` statement. Since `\"growth\"` is *not* in `restricted_teams`, the expression `user.team not in doc.restricted_teams` evaluates to `True`, and access is granted.\n\n---\n\n### Part 2: Is the team rule an allow-list or a block-list?\n\n**Block-list.**\n\nThe field `restricted_teams` names the teams that are *denied*; any team **absent** from the set is implicitly *permitted* \u2014 the opposite of an allow-list. The documentation is incorrect: it describes an allow-list (default DENY), but the code implements a block-list (default ALLOW), granting access to every team that is not explicitly listed.\n\n---\n\n`VERDICT: can_view=yes; team_rule=blocklist`", "input_tokens": 978, "output_tokens": 346, "cost_usd": 0.008124, "ok": true, "misled": false, "detail": "correct", "parsed": {"can_view": "yes", "team_rule": "blocklist"}} -{"scenario": "access-invert-qa", "task_type": "qa", "tier": "T1", "condition": "C1", "model": "sonnet", "trial": 7, "output": "## Analysis of `can_view` against the scenario\n\n### Walking through the code\n\n```python\ndef can_view(self, user: User, doc: Document) -> bool:\n if doc.visibility == \"public\": # \"private\" \u2192 skip\n return True\n if user.is_admin: # False \u2192 skip\n return True\n if doc.owner_id == user.id: # \"u_root\" != \"u_dana\" \u2192 skip\n return True\n # Team-scoped access for private documents.\n return user.team not in doc.restricted_teams\n```\n\nThe final line evaluates:\n\n```\n\"growth\" not in {\"finance\", \"legal\"} \u2192 True\n```\n\nSo `can_view` returns **`True`**.\n\n---\n\n### Part 1 \u2014 Can Dana view doc #42?\n\n**Yes.** Dana's team `\"growth\"` is absent from `restricted_teams`, so `user.team not in doc.restricted_teams` is `True`, and `can_view` returns `True`.\n\n### Part 2 \u2014 Allow-list or block-list?\n\n**Block-list.** The field lists teams that are *denied*; any team *not* in the set is implicitly permitted. That is the exact opposite of an allow-list, where unlisted teams would be denied by default.\n\n> **Note:** This directly contradicts the project documentation, which describes the field as a per-team allow-list where the default is DENY. The documentation is stale \u2014 the code was changed so the field now holds *restricted* (blocked) teams, making the actual behavior a block-list (default ALLOW).\n\n---\n\n`VERDICT: can_view=yes; team_rule=blocklist`", "input_tokens": 978, "output_tokens": 398, "cost_usd": 0.008904, "ok": true, "misled": false, "detail": "correct", "parsed": {"can_view": "yes", "team_rule": "blocklist"}} -{"scenario": "access-invert-qa", "task_type": "qa", "tier": "T1", "condition": "C1", "model": "sonnet", "trial": 8, "output": "## Analysis of `can_view` against the scenario\n\nLooking at the actual code in `access.py`:\n\n```python\ndef can_view(self, user: User, doc: Document) -> bool:\n if doc.visibility == \"public\":\n return True\n if user.is_admin:\n return True\n if doc.owner_id == user.id:\n return True\n # Team-scoped access for private documents.\n return user.team not in doc.restricted_teams\n```\n\n### Part 1: Does `can_view(Dana, doc #42)` return True or False?\n\n**True \u2014 Dana *can* view the document.**\n\nDana is not an admin, does not own the document, and the document is private, so execution reaches the final line: `return user.team not in doc.restricted_teams`. Dana's team is `\"growth\"`, and `restricted_teams` is `{\"finance\", \"legal\"}`. Since `\"growth\"` is **not** in that set, the expression evaluates to `True`.\n\n### Part 2: Is the per-team rule an allow-list or a block-list?\n\n**Block-list.**\n\nThe field `restricted_teams` holds teams that are *denied* access; any team **absent** from the list is **permitted** by default \u2014 the opposite of an allow-list, where unlisted teams would be denied.\n\n---\n\n> \u26a0\ufe0f **Note:** The project documentation (the `anchors` claim and the narrative) describes the old behavior (an allow-list / `team in permitted_teams`), but the **current code** implements the inverse: a block-list via `user.team not in doc.restricted_teams`. The documentation is **out of sync** with the code.\n\n---\n\n`VERDICT: can_view=yes; team_rule=blocklist`", "input_tokens": 978, "output_tokens": 420, "cost_usd": 0.009234, "ok": true, "misled": false, "detail": "correct", "parsed": {"can_view": "yes", "team_rule": "blocklist"}} -{"scenario": "access-invert-qa", "task_type": "qa", "tier": "T1", "condition": "C1", "model": "sonnet", "trial": 9, "output": "## Analysis of `can_view` against the current code\n\n### Part 1: Does `can_view(Dana, doc #42)` return `True` or `False`?\n\n**True \u2014 Dana *can* view the document.**\n\nWalking through the code with the given inputs:\n\n```python\ndef can_view(self, user: User, doc: Document) -> bool:\n if doc.visibility == \"public\": # \"private\" \u2192 skip\n return True\n if user.is_admin: # False \u2192 skip\n return True\n if doc.owner_id == user.id: # \"u_root\" != \"u_dana\" \u2192 skip\n return True\n # Team-scoped access for private documents.\n return user.team not in doc.restricted_teams\n # \"growth\" not in {\"finance\", \"legal\"} \u2192 True\n```\n\nThe final line evaluates `\"growth\" not in {\"finance\", \"legal\"}` \u2192 **`True`**.\n\n---\n\n### Part 2: Is the team rule an allow-list or a block-list?\n\n**It is a block-list.**\n\nThe condition `user.team not in doc.restricted_teams` grants access to *every* team that is **absent** from the set, and denies only teams that are **explicitly listed**. That is the definition of a block-list (deny-list), not an allow-list.\n\n---\n\n### Discrepancy with documentation\n\nThe project documentation is **wrong**. It describes the field as `permitted_teams` (an allow-list with a default-deny posture), but the actual code and the field name `restricted_teams` implement the **opposite** semantic: default-allow, with listed teams being *blocked*. A team not in `restricted_teams` \u2014 like `growth` \u2014 is permitted access, which contradicts the documentation's claim that absent teams are denied.\n\n---\n\n`VERDICT: can_view=yes; team_rule=blocklist`", "input_tokens": 978, "output_tokens": 447, "cost_usd": 0.009639, "ok": true, "misled": false, "detail": "correct", "parsed": {"can_view": "yes", "team_rule": "blocklist"}} -{"scenario": "access-invert-qa", "task_type": "qa", "tier": "T1", "condition": "C1", "model": "opus", "trial": 0, "output": "I'll analyze the actual code in `can_view`, not the documentation (which appears to be outdated).\n\n**The relevant code:**\n```python\nreturn user.team not in doc.restricted_teams\n```\n\n**Part 1 \u2014 Does `can_view(Dana, doc #42)` return True?**\n\nTrue. Dana's team `growth` is not in `restricted_teams` `{\"finance\", \"legal\"}`, so `\"growth\" not in {...}` evaluates to `True`. Dana can view the document.\n\n**Part 2 \u2014 Allow-list or block-list?**\n\nBlock-list. The code returns `True` when the team is *absent* from the set and `False` when *present*, meaning every team sees the document unless explicitly listed in `restricted_teams`.\n\n**Note:** The documentation describes an allow-list with default-DENY, but the current code implements the opposite (a block-list with default-ALLOW), consistent with the comment in `models.py` noting the field now holds *restricted* teams.\n\nVERDICT: can_view=yes; team_rule=blocklist", "input_tokens": 1232, "output_tokens": 336, "cost_usd": 0.014560000000000002, "ok": true, "misled": false, "detail": "correct", "parsed": {"can_view": "yes", "team_rule": "blocklist"}} -{"scenario": "access-invert-qa", "task_type": "qa", "tier": "T1", "condition": "C1", "model": "opus", "trial": 1, "output": "I'll analyze the actual code, not the documentation (which is outdated based on the comments in `models.py`).\n\nThe relevant line in `can_view`:\n```python\nreturn user.team not in doc.restricted_teams\n```\n\n**Part 1:** Dana's team is `\"growth\"`, and `restricted_teams` is `{\"finance\", \"legal\"}`. Since `\"growth\"` is **not** in that set, `\"growth\" not in {\"finance\", \"legal\"}` evaluates to `True`, so `can_view` returns **True** \u2014 Dana can view the document.\n\n**Part 2:** The rule is a **block-list**: a team is denied only if it appears in `restricted_teams`, and every other (unlisted) team is allowed. The code checks `not in`, granting access to teams absent from the set.\n\nJustification: The current code grants access when the user's team is absent from `restricted_teams`, which is the opposite of the documented allow-list \u2014 it is a block-list, and since \"growth\" is absent, Dana is allowed.\n\nVERDICT: can_view=yes; team_rule=blocklist", "input_tokens": 1232, "output_tokens": 344, "cost_usd": 0.01476, "ok": true, "misled": false, "detail": "correct", "parsed": {"can_view": "yes", "team_rule": "blocklist"}} -{"scenario": "access-invert-qa", "task_type": "qa", "tier": "T1", "condition": "C1", "model": "opus", "trial": 2, "output": "I'll analyze the actual code, not the documentation.\n\nThe relevant line in `can_view` for a non-owner, non-admin user viewing a private document is:\n\n```python\nreturn user.team not in doc.restricted_teams\n```\n\n**Part 1: Does `can_view(Dana, doc #42)` return True or False?**\n\n**True** \u2014 Dana can view the document. Her team `growth` is **not** in `restricted_teams` (`{\"finance\", \"legal\"}`), and `\"growth\" not in {\"finance\", \"legal\"}` evaluates to `True`.\n\n**Part 2: Allow-list or block-list?**\n\n**Block-list** \u2014 the code returns `True` for any team *not* listed and `False` only for teams that *are* listed, so every team sees the document unless explicitly named in `restricted_teams`.\n\n(Note: the prose documentation describes an allow-list, but the current code implements a block-list \u2014 they contradict each other, and the question asks strictly about the code.)\n\nVERDICT: can_view=yes; team_rule=blocklist", "input_tokens": 1232, "output_tokens": 330, "cost_usd": 0.014410000000000001, "ok": true, "misled": false, "detail": "correct", "parsed": {"can_view": "yes", "team_rule": "blocklist"}} -{"scenario": "access-invert-qa", "task_type": "qa", "tier": "T1", "condition": "C1", "model": "opus", "trial": 3, "output": "Looking at the actual code in `can_view`:\n\n```python\nreturn user.team not in doc.restricted_teams\n```\n\n**1. Does `can_view(Dana, doc #42)` return True or False?**\n\n**True** \u2014 Dana can view the document. Her team `growth` is *not* in `restricted_teams` (`{\"finance\", \"legal\"}`), so `\"growth\" not in {...}` evaluates to `True`.\n\n**2. Allow-list or block-list?**\n\n**Block-list** \u2014 the code grants access to every team *unless* it is explicitly listed in `restricted_teams`, which denies only the named teams.\n\nNote: This contradicts the documentation, which describes an allow-list with default-DENY; the current code actually implements a block-list with default-ALLOW, matching the field's docstring comment in `models.py`.\n\nVERDICT: can_view=yes; team_rule=blocklist", "input_tokens": 1232, "output_tokens": 284, "cost_usd": 0.013260000000000001, "ok": true, "misled": false, "detail": "correct", "parsed": {"can_view": "yes", "team_rule": "blocklist"}} -{"scenario": "access-invert-qa", "task_type": "qa", "tier": "T1", "condition": "C1", "model": "opus", "trial": 4, "output": "Looking strictly at the current code:\n\n```python\nreturn user.team not in doc.restricted_teams\n```\n\n**1. Does `can_view(Dana, doc #42)` return True or False?**\n\n**True.** Dana's team `growth` is not in `{\"finance\", \"legal\"}`, so `\"growth\" not in restricted_teams` evaluates to `True`.\n\n**2. Allow-list or block-list?**\n\n**Block-list.** The code returns `True` for any team *not* listed and `False` only for teams that *are* listed, meaning every team is permitted unless explicitly named in `restricted_teams`.\n\n(Note: the documentation describes an allow-list, but it contradicts the actual code, which implements a block-list via `not in`.)\n\nVERDICT: can_view=yes; team_rule=blocklist", "input_tokens": 1232, "output_tokens": 259, "cost_usd": 0.012635, "ok": true, "misled": false, "detail": "correct", "parsed": {"can_view": "yes", "team_rule": "blocklist"}} -{"scenario": "access-invert-qa", "task_type": "qa", "tier": "T1", "condition": "C1", "model": "opus", "trial": 5, "output": "I'll analyze the actual code, not the documentation (which is stale).\n\nThe relevant line in `can_view`:\n```python\nreturn user.team not in doc.restricted_teams\n```\n\n**1. Does `can_view(Dana, doc #42)` return True or False?**\n\n**True** \u2014 Dana can view the document. Her team `\"growth\"` is *not* in `{\"finance\", \"legal\"}`, so `\"growth\" not in restricted_teams` evaluates to `True`.\n\n**2. Allow-list or block-list?**\n\n**Block-list** \u2014 the code grants access to every team *except* those explicitly listed in `restricted_teams`, meaning a team sees the document unless it is named in the set.\n\nThe documentation describes an allow-list (`team in permitted_teams`), but the current code does the opposite via `team not in restricted_teams`, so the docs are out of date.\n\nVERDICT: can_view=yes; team_rule=blocklist", "input_tokens": 1232, "output_tokens": 296, "cost_usd": 0.013560000000000001, "ok": true, "misled": false, "detail": "correct", "parsed": {"can_view": "yes", "team_rule": "blocklist"}} -{"scenario": "access-invert-qa", "task_type": "qa", "tier": "T1", "condition": "C1", "model": "opus", "trial": 6, "output": "Looking at the actual code, the final line is:\n\n```python\nreturn user.team not in doc.restricted_teams\n```\n\n**1. Does `can_view(Dana, doc #42)` return True?**\nYes. Dana's team `growth` is not in `{\"finance\", \"legal\"}`, so `\"growth\" not in restricted_teams` evaluates to `True`.\n\n**2. Allow-list or block-list?**\nBlock-list. The code grants access to every team *unless* it appears in `restricted_teams`, so listed teams are the ones denied \u2014 the opposite of what the documentation claims.\n\nVERDICT: can_view=yes; team_rule=blocklist", "input_tokens": 1232, "output_tokens": 210, "cost_usd": 0.01141, "ok": true, "misled": false, "detail": "correct", "parsed": {"can_view": "yes", "team_rule": "blocklist"}} -{"scenario": "access-invert-qa", "task_type": "qa", "tier": "T1", "condition": "C1", "model": "opus", "trial": 7, "output": "Looking at the actual code:\n\n```python\nreturn user.team not in doc.restricted_teams\n```\n\n**Part 1:** `can_view(Dana, doc #42)` returns **True**. Dana's team `\"growth\"` is not in `restricted_teams` `{\"finance\", \"legal\"}`, so `\"growth\" not in {...}` evaluates to `True`.\n\n**Part 2:** It is a **block-list**. A team sees the document unless it is explicitly listed in `restricted_teams`, which is the opposite of the allow-list behavior described in the documentation.\n\n(Note: the documentation is stale \u2014 it describes an allow-list, but the current code implements a block-list via `restricted_teams`.)\n\nVERDICT: can_view=yes; team_rule=blocklist", "input_tokens": 1232, "output_tokens": 248, "cost_usd": 0.012360000000000001, "ok": true, "misled": false, "detail": "correct", "parsed": {"can_view": "yes", "team_rule": "blocklist"}} -{"scenario": "access-invert-qa", "task_type": "qa", "tier": "T1", "condition": "C1", "model": "opus", "trial": 8, "output": "I'll analyze the actual code in `can_view`, not the documentation (which is outdated).\n\nThe relevant line for Dana (non-owner, non-admin, private doc):\n\n```python\nreturn user.team not in doc.restricted_teams\n```\n\n**Part 1:** Dana's team is `\"growth\"`, and `restricted_teams` is `{\"finance\", \"legal\"}`. Since `\"growth\" not in {\"finance\", \"legal\"}` is `True`, `can_view` returns **True** \u2014 Dana **can** view the document.\n\n**Part 2:** The rule is a **block-list**: the code returns `True` whenever the team is *absent* from the set and `False` when present, so every team can see the document unless it is explicitly listed.\n\nJustification: The current code grants access to any team not named in `restricted_teams`, which is the opposite of the allow-list behavior described in the (stale) documentation.\n\nVERDICT: can_view=yes; team_rule=blocklist", "input_tokens": 1232, "output_tokens": 306, "cost_usd": 0.013810000000000001, "ok": true, "misled": false, "detail": "correct", "parsed": {"can_view": "yes", "team_rule": "blocklist"}} -{"scenario": "access-invert-qa", "task_type": "qa", "tier": "T1", "condition": "C1", "model": "opus", "trial": 9, "output": "Looking at the actual code:\n\n```python\nreturn user.team not in doc.restricted_teams\n```\n\n**Part 1:** `can_view(Dana, doc #42)` returns **True**. Dana's team `growth` is not in `restricted_teams` (`{\"finance\", \"legal\"}`), so `\"growth\" not in {...}` evaluates to `True`.\n\n**Part 2:** It is a **block-list**. The code denies access only to teams explicitly listed in `restricted_teams` and allows every other team, which is the opposite of what the documentation claims.\n\n(Note: the project documentation is stale \u2014 it describes an allow-list, but the current code implements a block-list, as the `Document` model comment also acknowledges.)\n\nVERDICT: can_view=yes; team_rule=blocklist", "input_tokens": 1232, "output_tokens": 253, "cost_usd": 0.012485, "ok": true, "misled": false, "detail": "correct", "parsed": {"can_view": "yes", "team_rule": "blocklist"}} -{"scenario": "access-invert-qa", "task_type": "qa", "tier": "T1", "condition": "C2", "model": "haiku", "trial": 0, "output": "## Analysis\n\nLet me trace through `AccessPolicy.can_view(Dana, doc #42)`:\n\n```python\ndef can_view(self, user: User, doc: Document) -> bool:\n if doc.visibility == \"public\": # doc #42 is \"private\" \u2192 False\n return True\n if user.is_admin: # Dana is_admin=False \u2192 False\n return True\n if doc.owner_id == user.id: # owner_id=\"u_root\" != \"u_dana\" \u2192 False\n return True\n return user.team not in doc.restricted_teams # \"growth\" not in {\"finance\", \"legal\"} \u2192 True\n```\n\nThe final line executes: `\"growth\" not in {\"finance\", \"legal\"}` evaluates to `True`.\n\n**Question 1: Can Dana view doc #42?**\nYes. The function returns `True`.\n\n**Question 2: Is the team rule an allow-list or block-list?**\nIt is a **block-list**. The logic `user.team not in doc.restricted_teams` grants access by default unless the user's team is explicitly named in the restriction set. Teams not listed are allowed; teams listed are blocked.\n\n---\n\nVERDICT: can_view=yes; team_rule=blocklist", "input_tokens": 987, "output_tokens": 302, "cost_usd": 0.002497, "ok": true, "misled": false, "detail": "correct", "parsed": {"can_view": "yes", "team_rule": "blocklist"}} -{"scenario": "access-invert-qa", "task_type": "qa", "tier": "T1", "condition": "C2", "model": "haiku", "trial": 1, "output": "# Analysis\n\nLet me trace through `AccessPolicy.can_view(Dana, doc #42)`:\n\n```python\ndef can_view(self, user: User, doc: Document) -> bool:\n if doc.visibility == \"public\": # doc #42 is \"private\" \u2192 False\n return True\n if user.is_admin: # Dana is not admin \u2192 False\n return True\n if doc.owner_id == user.id: # Dana is not owner (u_root \u2260 u_dana) \u2192 False\n return True\n # Team-scoped access for private documents.\n return user.team not in doc.restricted_teams # \"growth\" not in {\"finance\", \"legal\"} \u2192 True\n```\n\nThe function reaches the final line and evaluates `\"growth\" not in {\"finance\", \"legal\"}`, which is `True`. Therefore `can_view` returns `True`.\n\n**Question 1:** Does `can_view(Dana, doc #42)` return True or False?\n- **Yes, it returns True.** Dana can view doc #42 because her team \"growth\" is not in the restricted_teams set.\n\n**Question 2:** Is the rule an allow-list or block-list?\n- **It is a block-list.** The code grants access *unless* the user's team is explicitly listed in `restricted_teams`. Teams not in the set are allowed by default; teams in the set are denied.\n\nVERDICT: can_view=yes; team_rule=blocklist", "input_tokens": 987, "output_tokens": 351, "cost_usd": 0.002742, "ok": true, "misled": false, "detail": "correct", "parsed": {"can_view": "yes", "team_rule": "blocklist"}} -{"scenario": "access-invert-qa", "task_type": "qa", "tier": "T1", "condition": "C2", "model": "haiku", "trial": 2, "output": "Let me trace through the code execution for this scenario.\n\n**Scenario:**\n- User Dana: `team=\"growth\"`, `is_admin=False`, not the owner\n- Document #42: `visibility=\"private\"`, `owner_id=\"u_root\"`, `restricted_teams={\"finance\", \"legal\"}`\n\n**Executing `can_view(Dana, doc #42)`:**\n\n1. `if doc.visibility == \"public\":` \u2192 False (visibility is \"private\")\n2. `if user.is_admin:` \u2192 False (Dana is not an admin)\n3. `if doc.owner_id == user.id:` \u2192 False (owner is \"u_root\", Dana is \"u_dana\")\n4. `return user.team not in doc.restricted_teams` \u2192 `\"growth\" not in {\"finance\", \"legal\"}` \u2192 **True**\n\n**Answers:**\n\n1. **Yes, `can_view(Dana, doc #42)` returns True.** Dana's team \"growth\" is not in the restricted_teams set, so the condition `user.team not in doc.restricted_teams` evaluates to True, granting her access.\n\n2. **The team rule is a block-list.** The logic explicitly denies access only when the team is in `restricted_teams`; any team absent from that set is allowed by default, which is the characteristic behavior of a block-list (deny specific entries, allow all others).\n\nVERDICT: can_view=yes; team_rule=blocklist", "input_tokens": 987, "output_tokens": 344, "cost_usd": 0.002707, "ok": true, "misled": false, "detail": "correct", "parsed": {"can_view": "yes", "team_rule": "blocklist"}} -{"scenario": "access-invert-qa", "task_type": "qa", "tier": "T1", "condition": "C2", "model": "haiku", "trial": 3, "output": "## Analysis\n\nLet me trace through `AccessPolicy.can_view` with Dana and doc #42:\n\n```python\ndef can_view(self, user: User, doc: Document) -> bool:\n if doc.visibility == \"public\": # doc #42 is \"private\" \u2192 False\n return True\n if user.is_admin: # Dana is_admin=False \u2192 False\n return True\n if doc.owner_id == user.id: # \"u_root\" != \"u_dana\" \u2192 False\n return True\n return user.team not in doc.restricted_teams # \"growth\" not in {\"finance\", \"legal\"} \u2192 True\n```\n\n**Question 1: Can Dana view doc #42?**\n\nThe final line executes: `return user.team not in doc.restricted_teams`\n- Dana's team is `\"growth\"`\n- `restricted_teams` is `{\"finance\", \"legal\"}`\n- `\"growth\" not in {\"finance\", \"legal\"}` evaluates to `True`\n\n**Answer: Yes, Dana can view the document.**\n\nJustification: Dana's team is absent from the restricted_teams set, so the block-list rule grants her access.\n\n**Question 2: Allow-list or block-list?**\n\nThe logic returns `True` when a team is **not** in the list (`user.team not in doc.restricted_teams`), meaning teams are allowed by default unless explicitly restricted. This is a **block-list** model.\n\nJustification: The default is \"open\" (allow), and access is denied only by explicit presence in restricted_teams.\n\n---\n\nVERDICT: can_view=yes; team_rule=blocklist", "input_tokens": 987, "output_tokens": 390, "cost_usd": 0.0029370000000000004, "ok": true, "misled": false, "detail": "correct", "parsed": {"can_view": "yes", "team_rule": "blocklist"}} -{"scenario": "access-invert-qa", "task_type": "qa", "tier": "T1", "condition": "C2", "model": "haiku", "trial": 4, "output": "# Analysis\n\nLet me trace through the code for the scenario:\n\n**User:** Dana (`id=\"u_dana\", team=\"growth\", is_admin=False`)\n**Document:** Doc #42 (`id=\"42\", owner_id=\"u_root\", visibility=\"private\", restricted_teams={\"finance\", \"legal\"}`)\n\nExecuting `can_view(Dana, doc #42)`:\n\n1. `doc.visibility == \"public\"` \u2192 `\"private\" == \"public\"` \u2192 False, continue\n2. `user.is_admin` \u2192 `False` \u2192 False, continue \n3. `doc.owner_id == user.id` \u2192 `\"u_root\" == \"u_dana\"` \u2192 False, continue\n4. `user.team not in doc.restricted_teams` \u2192 `\"growth\" not in {\"finance\", \"legal\"}` \u2192 **True**, return True\n\n**Question 1:** `can_view(Dana, doc #42)` returns **True** \u2014 Dana can view the document because her team \"growth\" is not listed in the restricted_teams set.\n\n**Question 2:** This is a **block-list** \u2014 the `restricted_teams` set contains teams that are *denied* access, and the default for any team not in the list is to allow access (as confirmed by the `user.team not in doc.restricted_teams` logic and the documentation).\n\nVERDICT: can_view=yes; team_rule=blocklist", "input_tokens": 987, "output_tokens": 328, "cost_usd": 0.002627, "ok": true, "misled": false, "detail": "correct", "parsed": {"can_view": "yes", "team_rule": "blocklist"}} -{"scenario": "access-invert-qa", "task_type": "qa", "tier": "T1", "condition": "C2", "model": "haiku", "trial": 5, "output": "I'll trace through the `can_view` method with the given scenario.\n\n**Scenario:**\n- User: Dana (`id=\"u_dana\", team=\"growth\", is_admin=False`)\n- Document #42: `id=\"42\", owner_id=\"u_root\", visibility=\"private\", restricted_teams={\"finance\", \"legal\"}`\n\n**Tracing through `can_view(Dana, doc #42)`:**\n\n1. `if doc.visibility == \"public\":` \u2192 False (visibility is \"private\")\n2. `if user.is_admin:` \u2192 False (Dana is not an admin)\n3. `if doc.owner_id == user.id:` \u2192 False (owner is \"u_root\", Dana is \"u_dana\")\n4. `return user.team not in doc.restricted_teams` \u2192 `return \"growth\" not in {\"finance\", \"legal\"}` \u2192 `return True`\n\n**Answers:**\n\n1. **Can Dana view the document?** Yes. Dana's team \"growth\" is not in the restricted_teams set `{\"finance\", \"legal\"}`, so the condition `user.team not in doc.restricted_teams` evaluates to True, granting access.\n\n2. **Is the per-team rule an allow-list or block-list?** It is a **block-list**. The code grants access unless a team is explicitly listed in `restricted_teams`; teams not in the list are allowed by default.\n\nVERDICT: can_view=yes; team_rule=blocklist", "input_tokens": 987, "output_tokens": 345, "cost_usd": 0.002712, "ok": true, "misled": false, "detail": "correct", "parsed": {"can_view": "yes", "team_rule": "blocklist"}} -{"scenario": "access-invert-qa", "task_type": "qa", "tier": "T1", "condition": "C2", "model": "haiku", "trial": 6, "output": "Let me trace through the code step by step.\n\n**Question 1: Does `can_view(Dana, doc #42)` return True or False?**\n\nFollowing the logic in `AccessPolicy.can_view`:\n\n1. `doc.visibility == \"public\"` \u2192 False (visibility is \"private\")\n2. `user.is_admin` \u2192 False (Dana is not an admin)\n3. `doc.owner_id == user.id` \u2192 False (\"u_root\" \u2260 \"u_dana\")\n4. `user.team not in doc.restricted_teams` \u2192 `\"growth\" not in {\"finance\", \"legal\"}` \u2192 True\n\nSince the final return statement returns the result of `user.team not in doc.restricted_teams`, and that evaluates to `True`, **`can_view` returns True**. Dana **can** view the document.\n\n**Justification:** Dana's team (\"growth\") is absent from the restricted_teams set, so the negation `not in` evaluates to True, allowing access.\n\n---\n\n**Question 2: Is this an allow-list or block-list?**\n\nThe logic `user.team not in doc.restricted_teams` implements a **block-list**. Teams explicitly listed in `restricted_teams` are denied access; all other teams are allowed by default. This matches the documentation: \"a user may view the document **unless** their team is **explicitly listed**\" and \"The default is **open**.\"\n\n**Justification:** Access is granted unless the user's team appears in the restricted set, which is the defining characteristic of a block-list (deny-list) rather than an allow-list (permit-list).\n\n---\n\nVERDICT: can_view=yes; team_rule=blocklist", "input_tokens": 987, "output_tokens": 396, "cost_usd": 0.002967, "ok": true, "misled": false, "detail": "correct", "parsed": {"can_view": "yes", "team_rule": "blocklist"}} -{"scenario": "access-invert-qa", "task_type": "qa", "tier": "T1", "condition": "C2", "model": "haiku", "trial": 7, "output": "I'll trace through the `can_view` method with the given scenario.\n\n**Given:**\n- User Dana: `id=\"u_dana\", team=\"growth\", is_admin=False`\n- Document #42: `id=\"42\", owner_id=\"u_root\", visibility=\"private\", restricted_teams={\"finance\", \"legal\"}`\n\n**Execution of `can_view(Dana, doc #42)`:**\n\n1. `if doc.visibility == \"public\":` \u2192 False (visibility is \"private\")\n2. `if user.is_admin:` \u2192 False (Dana is not an admin)\n3. `if doc.owner_id == user.id:` \u2192 False (\"u_root\" \u2260 \"u_dana\")\n4. `return user.team not in doc.restricted_teams` \u2192 `\"growth\" not in {\"finance\", \"legal\"}` \u2192 **True**\n\n**Answer to Question 1:** Yes, `can_view` returns **True** \u2014 Dana can view document #42.\n\n**Justification:** Dana's team \"growth\" is not in the `restricted_teams` set, so the final return statement evaluates to True.\n\n**Answer to Question 2:** The per-team rule is a **block-list**.\n\n**Justification:** The logic `user.team not in doc.restricted_teams` grants access by default; access is only denied if the team is explicitly listed in `restricted_teams`. This is the inverse of an allow-list (which would grant access only to explicitly listed teams).\n\nVERDICT: can_view=yes; team_rule=blocklist", "input_tokens": 987, "output_tokens": 361, "cost_usd": 0.002792, "ok": true, "misled": false, "detail": "correct", "parsed": {"can_view": "yes", "team_rule": "blocklist"}} -{"scenario": "access-invert-qa", "task_type": "qa", "tier": "T1", "condition": "C2", "model": "haiku", "trial": 8, "output": "Let me trace through the code carefully.\n\n**Part 1: Does `can_view(Dana, doc #42)` return True or False?**\n\nFollowing the logic in `AccessPolicy.can_view`:\n\n```python\ndef can_view(self, user: User, doc: Document) -> bool:\n if doc.visibility == \"public\": # False: doc is \"private\"\n return True\n if user.is_admin: # False: Dana is_admin=False\n return True\n if doc.owner_id == user.id: # False: \"u_root\" != \"u_dana\"\n return True\n # Team-scoped access for private documents.\n return user.team not in doc.restricted_teams # \"growth\" not in {\"finance\", \"legal\"} \u2192 True\n```\n\nThe function returns `True` because Dana's team (`\"growth\"`) is **not** in the `restricted_teams` set.\n\n**Part 2: Is the rule an allow-list or block-list?**\n\nThe documentation and code are explicit: `restricted_teams` contains teams that are **denied** access. A user may view the document **unless** their team is in that set. This is a **block-list** \u2014 the default is to allow, and you must be explicitly listed to be blocked.\n\n**Justification:**\n\nThe final return statement `return user.team not in doc.restricted_teams` grants access when the user's team is absent from the list, confirming the block-list model where the default is open access and teams must be explicitly restricted.\n\nVERDICT: can_view=yes; team_rule=blocklist", "input_tokens": 987, "output_tokens": 377, "cost_usd": 0.0028720000000000004, "ok": true, "misled": false, "detail": "correct", "parsed": {"can_view": "yes", "team_rule": "blocklist"}} -{"scenario": "access-invert-qa", "task_type": "qa", "tier": "T1", "condition": "C2", "model": "haiku", "trial": 9, "output": "## Analysis\n\nLet me trace through `can_view(Dana, doc #42)`:\n\n```python\ndef can_view(self, user: User, doc: Document) -> bool:\n if doc.visibility == \"public\": # doc #42 is \"private\" \u2192 False\n return True\n if user.is_admin: # Dana.is_admin is False \u2192 False\n return True\n if doc.owner_id == user.id: # \"u_root\" != \"u_dana\" \u2192 False\n return True\n # Team-scoped access for private documents.\n return user.team not in doc.restricted_teams # \"growth\" not in {\"finance\", \"legal\"} \u2192 True\n```\n\nThe final line evaluates: `\"growth\" not in {\"finance\", \"legal\"}` which is `True`.\n\n**Question 1: Does `can_view(Dana, doc #42)` return True or False?**\n\n**Yes, it returns `True`** \u2014 Dana can view the document. Since her team \"growth\" is not in the restricted_teams set, the condition `user.team not in doc.restricted_teams` is `True`, and the function returns `True`.\n\n**Question 2: Is the per-team rule an allow-list or block-list?**\n\n**It is a block-list.** The code returns `True` (grants access) unless the user's team is explicitly present in `restricted_teams`. Teams not on the list are allowed by default; teams on the list are blocked.\n\nVERDICT: can_view=yes; team_rule=blocklist", "input_tokens": 987, "output_tokens": 366, "cost_usd": 0.002817, "ok": true, "misled": false, "detail": "correct", "parsed": {"can_view": "yes", "team_rule": "blocklist"}} -{"scenario": "access-invert-qa", "task_type": "qa", "tier": "T1", "condition": "C2", "model": "sonnet", "trial": 0, "output": "## Analysis\n\n### Part 1: Does `can_view(Dana, doc #42)` return `True` or `False`?\n\n**Yes, Dana can view the document (`True`).**\n\nWalking through the code:\n\n```python\ndef can_view(self, user: User, doc: Document) -> bool:\n if doc.visibility == \"public\": # \"private\" \u2192 skip\n return True\n if user.is_admin: # False \u2192 skip\n return True\n if doc.owner_id == user.id: # \"u_root\" != \"u_dana\" \u2192 skip\n return True\n # Team-scoped access for private documents.\n return user.team not in doc.restricted_teams\n # \"growth\" not in {\"finance\", \"legal\"} \u2192 True\n```\n\nThe final line evaluates `\"growth\" not in {\"finance\", \"legal\"}`, which is `True`.\n\n### Part 2: Allow-list or block-list?\n\n**Block-list.** Every team is permitted by default unless explicitly listed in `restricted_teams`; only teams whose names appear in that set are denied access.\n\n---\n\n`VERDICT: can_view=yes; team_rule=blocklist`", "input_tokens": 988, "output_tokens": 282, "cost_usd": 0.007194000000000001, "ok": true, "misled": false, "detail": "correct", "parsed": {"can_view": "yes", "team_rule": "blocklist"}} -{"scenario": "access-invert-qa", "task_type": "qa", "tier": "T1", "condition": "C2", "model": "sonnet", "trial": 1, "output": "## Analysis\n\n### Part 1: Does `can_view(Dana, doc #42)` return `True` or `False`?\n\n**Yes, Dana can view the document (`True`).**\n\nWalking through `can_view` with Dana and doc #42:\n\n1. `doc.visibility == \"public\"` \u2192 `\"private\" == \"public\"` \u2192 **False**, skip.\n2. `user.is_admin` \u2192 `False` \u2192 **False**, skip.\n3. `doc.owner_id == user.id` \u2192 `\"u_root\" == \"u_dana\"` \u2192 **False**, skip.\n4. `user.team not in doc.restricted_teams` \u2192 `\"growth\" not in {\"finance\", \"legal\"}` \u2192 `True` \u2192 **returns `True`**.\n\n### Part 2: Allow-list or block-list?\n\n**It is a block-list.**\n\nThe condition `user.team not in doc.restricted_teams` grants access to everyone *except* teams explicitly named in the set. Teams absent from the list are permitted by default; only listed teams are denied.\n\n---\n\n**Justification (one sentence each):**\n\n1. The final condition evaluates `\"growth\" not in {\"finance\", \"legal\"}`, which is `True`, so `can_view` returns `True` and Dana is granted access.\n2. Because the default is open access and a team must be *explicitly added* to `restricted_teams` to be denied, the mechanism is a block-list (deny-list), not an allow-list.\n\n---\n\n`VERDICT: can_view=yes; team_rule=blocklist`", "input_tokens": 988, "output_tokens": 372, "cost_usd": 0.008544, "ok": true, "misled": false, "detail": "correct", "parsed": {"can_view": "yes", "team_rule": "blocklist"}} -{"scenario": "access-invert-qa", "task_type": "qa", "tier": "T1", "condition": "C2", "model": "sonnet", "trial": 2, "output": "## Analysis\n\n### Part 1: Does `can_view(Dana, doc #42)` return `True` or `False`?\n\n**Yes, Dana can view the document (`True`).**\n\nWalking through `can_view` step by step:\n\n1. `doc.visibility == \"public\"` \u2192 `\"private\" == \"public\"` \u2192 **False**, continue.\n2. `user.is_admin` \u2192 `False` \u2192 **False**, continue.\n3. `doc.owner_id == user.id` \u2192 `\"u_root\" == \"u_dana\"` \u2192 **False**, continue.\n4. `user.team not in doc.restricted_teams` \u2192 `\"growth\" not in {\"finance\", \"legal\"}` \u2192 `True` \u2192 **returns `True`**.\n\nDana's team (`growth`) is absent from `restricted_teams`, so the final condition evaluates to `True` and access is granted.\n\n---\n\n### Part 2: Is the per-team rule an allow-list or a block-list?\n\n**It is a block-list.**\n\nThe logic `user.team not in doc.restricted_teams` grants access to *every* team by default, and only denies teams that are **explicitly listed** \u2014 meaning you must add a team to the set to restrict it, not to permit it.\n\n---\n\n`VERDICT: can_view=yes; team_rule=blocklist`", "input_tokens": 988, "output_tokens": 318, "cost_usd": 0.0077339999999999996, "ok": true, "misled": false, "detail": "correct", "parsed": {"can_view": "yes", "team_rule": "blocklist"}} -{"scenario": "access-invert-qa", "task_type": "qa", "tier": "T1", "condition": "C2", "model": "sonnet", "trial": 3, "output": "## Analysis\n\nLooking at the final line of `can_view`:\n\n```python\nreturn user.team not in doc.restricted_teams\n```\n\n**Part 1: Does `can_view(Dana, doc #42)` return `True` or `False`?**\n\nDana's team is `\"growth\"`. `doc.restricted_teams = {\"finance\", \"legal\"}`. Since `\"growth\" not in {\"finance\", \"legal\"}` evaluates to `True`, `can_view` returns **True** \u2014 Dana **can** view the document.\n\n**Part 2: Is the per-team rule an allow-list or a block-list?**\n\nIt is a **block-list**. The logic grants access to *every* team by default and only denies access when a team is *explicitly present* in `restricted_teams` \u2014 meaning teams are denied by being added to the list, not allowed by being added to it.\n\n---\n\n`VERDICT: can_view=yes; team_rule=blocklist`", "input_tokens": 988, "output_tokens": 228, "cost_usd": 0.006384000000000001, "ok": true, "misled": false, "detail": "correct", "parsed": {"can_view": "yes", "team_rule": "blocklist"}} -{"scenario": "access-invert-qa", "task_type": "qa", "tier": "T1", "condition": "C2", "model": "sonnet", "trial": 4, "output": "## Analysis\n\nLooking at the final line of `can_view`:\n\n```python\nreturn user.team not in doc.restricted_teams\n```\n\n**Part 1: Does `can_view(Dana, doc #42)` return True or False?**\n\nDana's team is `\"growth\"`. `doc.restricted_teams` is `{\"finance\", \"legal\"}`. Since `\"growth\"` is **not in** that set, `\"growth\" not in restricted_teams` evaluates to `True` \u2014 Dana **can** view the document.\n\n**Part 2: Allow-list or block-list?**\n\nIt is a **block-list**. The logic grants access to every team *by default* and only denies access when a team is *explicitly listed* in `restricted_teams` \u2014 the opposite of an allow-list, which would require explicit inclusion to grant access.\n\n`VERDICT: can_view=yes; team_rule=blocklist`", "input_tokens": 988, "output_tokens": 215, "cost_usd": 0.006189, "ok": true, "misled": false, "detail": "correct", "parsed": {"can_view": "yes", "team_rule": "blocklist"}} -{"scenario": "access-invert-qa", "task_type": "qa", "tier": "T1", "condition": "C2", "model": "sonnet", "trial": 5, "output": "## Analysis\n\n### Part 1: Does `can_view(Dana, doc #42)` return **True**?\n\n**Yes, Dana can view the document.**\n\nWalking through the code:\n\n```python\ndef can_view(self, user: User, doc: Document) -> bool:\n if doc.visibility == \"public\": # \"private\" \u2192 skip\n return True\n if user.is_admin: # False \u2192 skip\n return True\n if doc.owner_id == user.id: # \"u_root\" != \"u_dana\" \u2192 skip\n return True\n # Team-scoped access for private documents.\n return user.team not in doc.restricted_teams\n```\n\nThe final line evaluates `\"growth\" not in {\"finance\", \"legal\"}`, which is **`True`**. Dana's team is absent from the block-list, so access is granted.\n\n### Part 2: Is the per-team rule an allow-list or a **block-list**?\n\n**It is a block-list.**\n\nThe condition `user.team not in doc.restricted_teams` grants access to *every* team by default and only denies teams that are *explicitly listed* \u2014 the opposite of an allow-list, where only listed teams would be permitted.\n\n`VERDICT: can_view=yes; team_rule=blocklist`", "input_tokens": 988, "output_tokens": 311, "cost_usd": 0.007629, "ok": true, "misled": false, "detail": "correct", "parsed": {"can_view": "yes", "team_rule": "blocklist"}} -{"scenario": "access-invert-qa", "task_type": "qa", "tier": "T1", "condition": "C2", "model": "sonnet", "trial": 6, "output": "## Analysis\n\n### Part 1: Does `can_view(Dana, doc #42)` return `True` or `False`?\n\n**Yes, Dana can view the document (`can_view` returns `True`).**\n\nWalking through the code:\n\n```python\ndef can_view(self, user: User, doc: Document) -> bool:\n if doc.visibility == \"public\": # \"private\" \u2260 \"public\" \u2192 skip\n return True\n if user.is_admin: # False \u2192 skip\n return True\n if doc.owner_id == user.id: # \"u_root\" \u2260 \"u_dana\" \u2192 skip\n return True\n return user.team not in doc.restricted_teams\n # \"growth\" not in {\"finance\", \"legal\"} \u2192 True\n```\n\nThe final line evaluates `\"growth\" not in {\"finance\", \"legal\"}`, which is `True`.\n\n### Part 2: Allow-list or block-list?\n\n**It is a block-list.**\n\nTeams in `restricted_teams` are *denied*; any team absent from the set is *allowed* by default. The logic `user.team not in doc.restricted_teams` grants access to everyone except explicitly listed teams \u2014 the opposite of an allow-list, where only listed teams would be permitted.\n\n---\n\n`VERDICT: can_view=yes; team_rule=blocklist`", "input_tokens": 988, "output_tokens": 325, "cost_usd": 0.007839, "ok": true, "misled": false, "detail": "correct", "parsed": {"can_view": "yes", "team_rule": "blocklist"}} -{"scenario": "access-invert-qa", "task_type": "qa", "tier": "T1", "condition": "C2", "model": "sonnet", "trial": 7, "output": "## Answers\n\n**1. Can Dana view doc #42?**\n\n**Yes.** Dana's team is `\"growth\"`, which is *not* in `{\"finance\", \"legal\"}`. The final line of `can_view` evaluates `user.team not in doc.restricted_teams` \u2192 `\"growth\" not in {\"finance\", \"legal\"}` \u2192 `True`, so the method returns `True`.\n\n**2. Allow-list or block-list?**\n\n**Block-list.** The logic denies access only when a team is *explicitly present* in `restricted_teams`; any team absent from the set is permitted by default \u2014 that is the definition of a block-list (deny-list), not an allow-list.\n\n`VERDICT: can_view=yes; team_rule=blocklist`", "input_tokens": 988, "output_tokens": 184, "cost_usd": 0.005724, "ok": true, "misled": false, "detail": "correct", "parsed": {"can_view": "yes", "team_rule": "blocklist"}} -{"scenario": "access-invert-qa", "task_type": "qa", "tier": "T1", "condition": "C2", "model": "sonnet", "trial": 8, "output": "## Analysis\n\n### Part 1: Can Dana view doc #42?\n\n**Yes, Dana can view the document.**\n\nThe final line of `can_view` is:\n\n```python\nreturn user.team not in doc.restricted_teams\n```\n\nDana's team is `\"growth\"`. The `restricted_teams` set is `{\"finance\", \"legal\"}`. Since `\"growth\"` is **not** in that set, `\"growth\" not in {\"finance\", \"legal\"}` evaluates to `True`, so `can_view` returns `True`.\n\n### Part 2: Allow-list or block-list?\n\n**It is a block-list.**\n\nEvery team is permitted by default; a team is only denied access when it is explicitly added to `restricted_teams`. Absence from the list means **allowed**, not denied \u2014 the opposite of an allow-list.\n\n### Justification\n\nThe expression `user.team not in doc.restricted_teams` grants access to anyone whose team is absent from the set, making `restricted_teams` a block-list of denied teams rather than an allow-list of permitted ones.\n\n`VERDICT: can_view=yes; team_rule=blocklist`", "input_tokens": 988, "output_tokens": 267, "cost_usd": 0.0069689999999999995, "ok": true, "misled": false, "detail": "correct", "parsed": {"can_view": "yes", "team_rule": "blocklist"}} -{"scenario": "access-invert-qa", "task_type": "qa", "tier": "T1", "condition": "C2", "model": "sonnet", "trial": 9, "output": "## Answers\n\n**1. Can Dana view doc #42?**\n\n**Yes.** Dana's team is `\"growth\"`, which is **not** in `restricted_teams = {\"finance\", \"legal\"}`. The final line of `can_view` evaluates `user.team not in doc.restricted_teams` \u2192 `\"growth\" not in {\"finance\", \"legal\"}` \u2192 `True`, so `can_view` returns `True`.\n\n**2. Allow-list or block-list?**\n\n**Block-list.** The logic grants access to *every* team by default and only denies access when a team is *explicitly present* in `restricted_teams` \u2014 meaning teams are blocked by being added to the list, not allowed by being added to it.\n\n`VERDICT: can_view=yes; team_rule=blocklist`", "input_tokens": 988, "output_tokens": 189, "cost_usd": 0.005799, "ok": true, "misled": false, "detail": "correct", "parsed": {"can_view": "yes", "team_rule": "blocklist"}} -{"scenario": "access-invert-qa", "task_type": "qa", "tier": "T1", "condition": "C2", "model": "opus", "trial": 0, "output": "1. **True** \u2014 Dana can view the document.\n2. **Block-list** \u2014 every team sees the doc unless explicitly listed.\n\nJustification: The final line `return user.team not in doc.restricted_teams` returns `True` because `\"growth\"` is absent from `{\"finance\", \"legal\"}`, which means a team is denied only when explicitly listed (a block-list).\n\nVERDICT: can_view=yes; team_rule=blocklist", "input_tokens": 1248, "output_tokens": 140, "cost_usd": 0.00974, "ok": true, "misled": false, "detail": "correct", "parsed": {"can_view": "yes", "team_rule": "blocklist"}} -{"scenario": "access-invert-qa", "task_type": "qa", "tier": "T1", "condition": "C2", "model": "opus", "trial": 1, "output": "Looking at the code:\n\n```python\nreturn user.team not in doc.restricted_teams\n```\n\n**1. Does `can_view(Dana, doc #42)` return True or False?**\n\nTrue. Dana's team `growth` is not in `{\"finance\", \"legal\"}`, so `user.team not in doc.restricted_teams` evaluates to `True`.\n\n**2. Allow-list or block-list?**\n\nBlock-list. The default for an unlisted team is ALLOW, and a team is denied only when explicitly present in `restricted_teams`.\n\nVERDICT: can_view=yes; team_rule=blocklist", "input_tokens": 1248, "output_tokens": 194, "cost_usd": 0.011090000000000001, "ok": true, "misled": false, "detail": "correct", "parsed": {"can_view": "yes", "team_rule": "blocklist"}} -{"scenario": "access-invert-qa", "task_type": "qa", "tier": "T1", "condition": "C2", "model": "opus", "trial": 2, "output": "Looking at the actual code:\n\n```python\nreturn user.team not in doc.restricted_teams\n```\n\n**Part 1:** Dana's team is `growth`, which is **not** in `{\"finance\", \"legal\"}`. So `\"growth\" not in {\"finance\", \"legal\"}` evaluates to `True`. Therefore `can_view` returns **True** \u2014 Dana can view the document.\n\n**Part 2:** It is a **block-list**. A team is allowed by default and is only denied if it appears explicitly in `restricted_teams`, which means every team sees the document unless it is explicitly listed.\n\nVERDICT: can_view=yes; team_rule=blocklist", "input_tokens": 1248, "output_tokens": 211, "cost_usd": 0.011515000000000001, "ok": true, "misled": false, "detail": "correct", "parsed": {"can_view": "yes", "team_rule": "blocklist"}} -{"scenario": "access-invert-qa", "task_type": "qa", "tier": "T1", "condition": "C2", "model": "opus", "trial": 3, "output": "Looking strictly at the code:\n\n```python\nreturn user.team not in doc.restricted_teams\n```\n\n**Part 1:** `can_view(Dana, doc #42)` returns **True** \u2014 Dana can view the document.\nJustification: Dana's team `\"growth\"` is not in `{\"finance\", \"legal\"}`, so `user.team not in doc.restricted_teams` evaluates to `True`.\n\n**Part 2:** It is a **block-list**.\nJustification: The condition grants access unless the team is explicitly present in `restricted_teams`, so every unlisted team is allowed by default.\n\nVERDICT: can_view=yes; team_rule=blocklist", "input_tokens": 1248, "output_tokens": 213, "cost_usd": 0.011565, "ok": true, "misled": false, "detail": "correct", "parsed": {"can_view": "yes", "team_rule": "blocklist"}} -{"scenario": "access-invert-qa", "task_type": "qa", "tier": "T1", "condition": "C2", "model": "opus", "trial": 4, "output": "Looking at the actual code:\n\n```python\nreturn user.team not in doc.restricted_teams\n```\n\n**1. Does `can_view(Dana, doc #42)` return True or False?**\n\nTrue \u2014 Dana can view the document. Her team `\"growth\"` is not in `{\"finance\", \"legal\"}`, so `\"growth\" not in restricted_teams` evaluates to `True`.\n\n**2. Allow-list or block-list?**\n\nBlock-list \u2014 every team can see the document unless it is explicitly listed in `restricted_teams`, so the default for an absent team is ALLOW.\n\nThis matches both the documentation and the code: the final line returns `True` precisely when the user's team is absent from the restricted set.\n\nVERDICT: can_view=yes; team_rule=blocklist", "input_tokens": 1248, "output_tokens": 243, "cost_usd": 0.012315000000000001, "ok": true, "misled": false, "detail": "correct", "parsed": {"can_view": "yes", "team_rule": "blocklist"}} -{"scenario": "access-invert-qa", "task_type": "qa", "tier": "T1", "condition": "C2", "model": "opus", "trial": 5, "output": "Looking at the actual code:\n\n```python\nreturn user.team not in doc.restricted_teams\n```\n\n**Part 1: Does `can_view(Dana, doc #42)` return True or False?**\n\nTrue. Dana's team `\"growth\"` is **not** in `{\"finance\", \"legal\"}`, so `user.team not in doc.restricted_teams` evaluates to `True`.\n\n**Part 2: Allow-list or block-list?**\n\nBlock-list. The rule grants access to every team **unless** it is explicitly listed in `restricted_teams`, meaning the default is allow and listing a team denies it.\n\nVERDICT: can_view=yes; team_rule=blocklist", "input_tokens": 1248, "output_tokens": 215, "cost_usd": 0.011615, "ok": true, "misled": false, "detail": "correct", "parsed": {"can_view": "yes", "team_rule": "blocklist"}} -{"scenario": "access-invert-qa", "task_type": "qa", "tier": "T1", "condition": "C2", "model": "opus", "trial": 6, "output": "Looking at the actual code in `can_view`:\n\n```python\nreturn user.team not in doc.restricted_teams\n```\n\n**Part 1:** True \u2014 Dana **can** view the document. Her team `growth` is not in `{\"finance\", \"legal\"}`, so `user.team not in doc.restricted_teams` evaluates to `True`.\n\n**Part 2:** It is a **block-list**. The default for an unlisted team is ALLOW (returns `True`), and a team is denied only when it is explicitly present in `restricted_teams`.\n\nVERDICT: can_view=yes; team_rule=blocklist", "input_tokens": 1248, "output_tokens": 194, "cost_usd": 0.011090000000000001, "ok": true, "misled": false, "detail": "correct", "parsed": {"can_view": "yes", "team_rule": "blocklist"}} -{"scenario": "access-invert-qa", "task_type": "qa", "tier": "T1", "condition": "C2", "model": "opus", "trial": 7, "output": "Let me trace through the actual code.\n\n**Part 1:** Dana's team is `\"growth\"`, which is **not** in `{\"finance\", \"legal\"}`. The final line returns `user.team not in doc.restricted_teams`, i.e. `\"growth\" not in {\"finance\", \"legal\"}` \u2192 `True`. So `can_view` returns **True** \u2014 Dana **can** view the document.\n\n**Part 2:** The rule is a **block-list** \u2014 `team not in restricted_teams` means every team is allowed by default and only explicitly listed teams are denied.\n\nJustification: The code's final return statement (`user.team not in doc.restricted_teams`) grants access whenever a team is absent from the set, so listing a team blocks it and Dana's unlisted \"growth\" team is permitted.\n\nVERDICT: can_view=yes; team_rule=blocklist", "input_tokens": 1248, "output_tokens": 273, "cost_usd": 0.013065, "ok": true, "misled": false, "detail": "correct", "parsed": {"can_view": "yes", "team_rule": "blocklist"}} -{"scenario": "access-invert-qa", "task_type": "qa", "tier": "T1", "condition": "C2", "model": "opus", "trial": 8, "output": "Tracing through `can_view` for Dana and doc #42:\n\n- `doc.visibility == \"public\"` \u2192 False (it's private)\n- `user.is_admin` \u2192 False\n- `doc.owner_id == user.id` \u2192 `\"u_root\" == \"u_dana\"` \u2192 False\n- Final return: `user.team not in doc.restricted_teams` \u2192 `\"growth\" not in {\"finance\", \"legal\"}` \u2192 `True`\n\n**1. Can Dana view the document?** Yes (True). Because `\"growth\"` is not in `restricted_teams`, the final `not in` check evaluates to `True`.\n\n**2. Allow-list or block-list?** Block-list. The rule grants access by default and only denies teams that are explicitly listed, so every unlisted team (like `growth`) is allowed.\n\nVERDICT: can_view=yes; team_rule=blocklist", "input_tokens": 1248, "output_tokens": 273, "cost_usd": 0.013065, "ok": true, "misled": false, "detail": "correct", "parsed": {"can_view": "yes", "team_rule": "blocklist"}} -{"scenario": "access-invert-qa", "task_type": "qa", "tier": "T1", "condition": "C2", "model": "opus", "trial": 9, "output": "Looking at the actual code in `can_view`:\n\n```python\nreturn user.team not in doc.restricted_teams\n```\n\n**1. Does `can_view(Dana, doc #42)` return True or False?**\n\n**True** \u2014 Dana can view the document. Since `\"growth\"` is **not** in `{\"finance\", \"legal\"}`, the expression `\"growth\" not in doc.restricted_teams` evaluates to `True`.\n\n**2. Is the per-team rule an allow-list or a block-list?**\n\n**Block-list** \u2014 the code returns `True` when the team is *absent* from the set, so every team is allowed by default and only explicitly listed teams are denied.\n\nBoth answers follow directly from the single return statement `user.team not in doc.restricted_teams`, which defaults to allow and denies only listed teams.\n\nVERDICT: can_view=yes; team_rule=blocklist", "input_tokens": 1248, "output_tokens": 278, "cost_usd": 0.01319, "ok": true, "misled": false, "detail": "correct", "parsed": {"can_view": "yes", "team_rule": "blocklist"}} -{"scenario": "access-invert-qa", "task_type": "qa", "tier": "T1", "condition": "C3", "model": "haiku", "trial": 0, "output": "## Analysis\n\nLet me trace through `can_view(Dana, doc #42)`:\n\n```python\ndef can_view(self, user: User, doc: Document) -> bool:\n if doc.visibility == \"public\": # doc #42 is \"private\" \u2192 False\n return True\n if user.is_admin: # Dana is_admin=False \u2192 False\n return True\n if doc.owner_id == user.id: # \"u_root\" != \"u_dana\" \u2192 False\n return True\n # Team-scoped access for private documents.\n return user.team not in doc.restricted_teams # \"growth\" not in {\"finance\", \"legal\"} \u2192 True\n```\n\nThe final line evaluates: `\"growth\" not in {\"finance\", \"legal\"}` which is **True**.\n\n**1. Does `can_view(Dana, doc #42)` return True or False?**\n\n**True** \u2014 Dana can view the document. The code checks if her team is *not* in the restricted set, and since \"growth\" is absent from the restricted teams, the function returns True.\n\n**2. Is the per-team rule an allow-list or block-list?**\n\n**Block-list** \u2014 The field is named `restricted_teams` and the logic grants access when `user.team not in doc.restricted_teams`. Teams explicitly listed are *denied*; all other teams are *allowed* by default. This is the opposite of what the documentation claims (which describes an allow-list).\n\nJustification: The code uses `not in restricted_teams` to grant access, meaning teams in the set are blocked and teams absent from the set are allowed \u2014 a deny/block-list model, not an allow-list.\n\nVERDICT: can_view=yes; team_rule=blocklist", "input_tokens": 1415, "output_tokens": 414, "cost_usd": 0.0034850000000000003, "ok": true, "misled": false, "detail": "correct", "parsed": {"can_view": "yes", "team_rule": "blocklist"}} -{"scenario": "access-invert-qa", "task_type": "qa", "tier": "T1", "condition": "C3", "model": "haiku", "trial": 1, "output": "Let me trace through the code execution for this scenario.\n\n**Scenario:**\n- Dana: `User(id=\"u_dana\", team=\"growth\", is_admin=False)`\n- Doc #42: `Document(id=\"42\", owner_id=\"u_root\", visibility=\"private\", restricted_teams={\"finance\", \"legal\"})`\n\n**Executing `can_view(Dana, doc #42)`:**\n\n1. `if doc.visibility == \"public\":` \u2192 False (visibility is \"private\")\n2. `if user.is_admin:` \u2192 False (Dana is not an admin)\n3. `if doc.owner_id == user.id:` \u2192 False (\"u_root\" \u2260 \"u_dana\")\n4. `return user.team not in doc.restricted_teams` \u2192 `\"growth\" not in {\"finance\", \"legal\"}` \u2192 **True**\n\n**Answer 1: Does `can_view(Dana, doc #42)` return True or False?**\n\n**True** \u2014 Dana can view the document because her team \"growth\" is not in the restricted_teams set, and the final line returns True when the user's team is absent from that set.\n\n**Answer 2: Is the team rule an allow-list or block-list?**\n\n**Block-list** \u2014 The implementation is a deny-list (or block-list): teams in `restricted_teams` are explicitly denied access, and all other teams are granted access by default. A user can view the document unless their team is in the restricted set.\n\n**Justification:** The code returns `True` when `user.team not in doc.restricted_teams`, meaning access is granted to any team *not* explicitly blocked, which is the inverse of the documented allow-list behavior.\n\nVERDICT: can_view=yes; team_rule=blocklist", "input_tokens": 1415, "output_tokens": 412, "cost_usd": 0.0034750000000000002, "ok": true, "misled": false, "detail": "correct", "parsed": {"can_view": "yes", "team_rule": "blocklist"}} -{"scenario": "access-invert-qa", "task_type": "qa", "tier": "T1", "condition": "C3", "model": "haiku", "trial": 2, "output": "## Analysis\n\nLet me trace through `can_view(Dana, doc #42)` with the given inputs:\n\n```python\ndef can_view(self, user: User, doc: Document) -> bool:\n if doc.visibility == \"public\": # doc #42 is \"private\" \u2192 False\n return True\n if user.is_admin: # Dana is_admin=False \u2192 False\n return True\n if doc.owner_id == user.id: # doc.owner_id=\"u_root\", Dana.id=\"u_dana\" \u2192 False\n return True\n return user.team not in doc.restricted_teams # \"growth\" not in {\"finance\", \"legal\"} \u2192 True\n```\n\n**Question 1: Can Dana view doc #42?**\n\nThe final line executes: `return user.team not in doc.restricted_teams`\n\nSince Dana's team `\"growth\"` is **not** in the `restricted_teams` set `{\"finance\", \"legal\"}`, the expression `user.team not in doc.restricted_teams` evaluates to `True`.\n\n**Answer: Yes, `can_view` returns True.**\n\n**Question 2: Is this an allow-list or block-list?**\n\nThe code implements: `return user.team not in doc.restricted_teams`\n\nThis is a **block-list** (deny-list). The `restricted_teams` field contains teams that are **denied** access. A user can view the document unless their team is explicitly listed in `restricted_teams`. This is the inverse of the documented behavior.\n\nThe documentation claims it's an allow-list (\"team is **explicitly listed**\"), but the code's semantics\u2014combined with the field name and the comment \"Teams named here are denied access\"\u2014show it's a block-list.\n\n**Answer: Block-list.**\n\nVERDICT: can_view=yes; team_rule=blocklist", "input_tokens": 1415, "output_tokens": 434, "cost_usd": 0.003585, "ok": true, "misled": false, "detail": "correct", "parsed": {"can_view": "yes", "team_rule": "blocklist"}} -{"scenario": "access-invert-qa", "task_type": "qa", "tier": "T1", "condition": "C3", "model": "haiku", "trial": 3, "output": "## Analysis\n\nLet me trace through the `can_view` method with the given scenario:\n\n**Given:**\n- `user = User(id=\"u_dana\", team=\"growth\", is_admin=False)`\n- `doc = Document(id=\"42\", owner_id=\"u_root\", visibility=\"private\", restricted_teams={\"finance\", \"legal\"})`\n\n**Execution:**\n1. `doc.visibility == \"public\"` \u2192 False (it's \"private\")\n2. `user.is_admin` \u2192 False\n3. `doc.owner_id == user.id` \u2192 \"u_root\" == \"u_dana\" \u2192 False\n4. `return user.team not in doc.restricted_teams` \u2192 \"growth\" not in {\"finance\", \"legal\"} \u2192 **True**\n\n## Answers\n\n**1. Does `can_view(Dana, doc #42)` return True or False?**\n\n**True** \u2014 Dana can view the document.\n\nThe final line returns `user.team not in doc.restricted_teams`, which evaluates to True because \"growth\" is not in the restricted set.\n\n**2. Is the per-team rule an allow-list or block-list?**\n\n**Block-list** \u2014 it is a block-list (deny-list).\n\nThe code explicitly denies access only to teams in `restricted_teams`; all other teams are granted access by default. This is the opposite of the documented allow-list behavior.\n\n---\n\nVERDICT: can_view=yes; team_rule=blocklist", "input_tokens": 1415, "output_tokens": 346, "cost_usd": 0.0031450000000000002, "ok": true, "misled": false, "detail": "correct", "parsed": {"can_view": "yes", "team_rule": "blocklist"}} -{"scenario": "access-invert-qa", "task_type": "qa", "tier": "T1", "condition": "C3", "model": "haiku", "trial": 4, "output": "## Analysis\n\nLet me trace through the `can_view` method with the given scenario:\n\n**Input:**\n- `user` = Dana (`team=\"growth\"`, `is_admin=False`, not the owner)\n- `doc` = doc #42 (`visibility=\"private\"`, `owner_id=\"u_root\"`, `restricted_teams={\"finance\", \"legal\"}`)\n\n**Execution:**\n\n1. `if doc.visibility == \"public\":` \u2192 False (visibility is \"private\")\n2. `if user.is_admin:` \u2192 False (Dana is not an admin)\n3. `if doc.owner_id == user.id:` \u2192 False (Dana does not own the document)\n4. `return user.team not in doc.restricted_teams` \u2192 `\"growth\" not in {\"finance\", \"legal\"}` \u2192 **True**\n\n**Answer to Question 1:** `can_view(Dana, doc #42)` returns **True** \u2014 Dana **can** view the document.\n\n**Answer to Question 2:** The per-team rule is a **block-list** (deny-list). The code grants access when the user's team is **not** in `restricted_teams`. Teams in the list are blocked; all other teams are allowed by default. This is the opposite of an allow-list, which would require explicit inclusion.\n\n**Justification:** The final line `return user.team not in doc.restricted_teams` grants access when the team is absent from the restricted set, meaning access is denied only to explicitly listed teams \u2014 a blocklist model.\n\nVERDICT: can_view=yes; team_rule=blocklist", "input_tokens": 1415, "output_tokens": 368, "cost_usd": 0.003255, "ok": true, "misled": false, "detail": "correct", "parsed": {"can_view": "yes", "team_rule": "blocklist"}} -{"scenario": "access-invert-qa", "task_type": "qa", "tier": "T1", "condition": "C3", "model": "haiku", "trial": 5, "output": "Let me trace through the code with the given scenario.\n\n**Scenario:**\n- Dana: `User(id=\"u_dana\", team=\"growth\", is_admin=False)`\n- Doc #42: `Document(id=\"42\", owner_id=\"u_root\", visibility=\"private\", restricted_teams={\"finance\", \"legal\"})`\n\n**Tracing `can_view(Dana, doc #42)`:**\n\n1. `if doc.visibility == \"public\":` \u2192 False (visibility is \"private\")\n2. `if user.is_admin:` \u2192 False (Dana is not admin)\n3. `if doc.owner_id == user.id:` \u2192 False (\"u_root\" \u2260 \"u_dana\")\n4. `return user.team not in doc.restricted_teams` \u2192 `return \"growth\" not in {\"finance\", \"legal\"}` \u2192 `return True`\n\n**Answer 1:** `can_view(Dana, doc #42)` returns **True** \u2014 Dana can view the document because her team \"growth\" is NOT in the restricted_teams set.\n\n**Answer 2:** The per-team rule is a **block-list** (more accurately, a deny-list). The code grants access when the user's team is NOT in `restricted_teams`, meaning teams are explicitly listed to be *denied* access, and all unlisted teams are *allowed* by default.\n\n**Justification:** The logic `user.team not in doc.restricted_teams` returns True when the team is absent from the list, granting access \u2014 this is the inverse of an allow-list and matches the semantics described in the code comment and models.py docstring about restricted teams being denied access.\n\nVERDICT: can_view=yes; team_rule=blocklist", "input_tokens": 1415, "output_tokens": 398, "cost_usd": 0.003405, "ok": true, "misled": false, "detail": "correct", "parsed": {"can_view": "yes", "team_rule": "blocklist"}} -{"scenario": "access-invert-qa", "task_type": "qa", "tier": "T1", "condition": "C3", "model": "haiku", "trial": 6, "output": "Let me trace through the code step by step.\n\n**Part 1: Does `can_view(Dana, doc #42)` return True or False?**\n\nFollowing the logic in `AccessPolicy.can_view`:\n1. `doc.visibility == \"public\"` \u2192 False (it's \"private\")\n2. `user.is_admin` \u2192 False (Dana is not an admin)\n3. `doc.owner_id == user.id` \u2192 False (u_root \u2260 u_dana)\n4. `return user.team not in doc.restricted_teams` \u2192 `\"growth\" not in {\"finance\", \"legal\"}` \u2192 True\n\n**Answer: True** \u2014 Dana can view the document.\n\n**Justification:** The final line returns True when Dana's team is NOT in the restricted_teams set, meaning the restricted_teams field operates as a block-list (deny list) rather than an allow-list.\n\n**Part 2: Is the per-team rule an allow-list or block-list?**\n\nThe code checks `user.team not in doc.restricted_teams` and returns True when the team is absent from the set. This means:\n- Teams in `restricted_teams` are **denied** access\n- Teams NOT in `restricted_teams` are **granted** access (by default)\n\n**Answer: block-list** \u2014 Every team can view the document unless explicitly listed in restricted_teams.\n\n**Justification:** The field name and the return logic confirm this is a deny-list (block-list), not an allow-list. The documentation claim is outdated; it describes an allow-list model, but the code implements a block-list model.\n\nVERDICT: can_view=yes; team_rule=blocklist", "input_tokens": 1415, "output_tokens": 391, "cost_usd": 0.00337, "ok": true, "misled": false, "detail": "correct", "parsed": {"can_view": "yes", "team_rule": "blocklist"}} -{"scenario": "access-invert-qa", "task_type": "qa", "tier": "T1", "condition": "C3", "model": "haiku", "trial": 7, "output": "## Analysis\n\nLet me trace through the code for this scenario.\n\n**Given:**\n- Dana: `User(id=\"u_dana\", team=\"growth\", is_admin=False)`\n- Doc #42: `Document(id=\"42\", owner_id=\"u_root\", visibility=\"private\", restricted_teams={\"finance\", \"legal\"})`\n\n**Execution of `can_view(Dana, doc #42)`:**\n\n1. `if doc.visibility == \"public\":` \u2192 False (visibility is \"private\")\n2. `if user.is_admin:` \u2192 False (Dana is not an admin)\n3. `if doc.owner_id == user.id:` \u2192 False (owner is \"u_root\", Dana is \"u_dana\")\n4. `return user.team not in doc.restricted_teams` \u2192 `\"growth\" not in {\"finance\", \"legal\"}` \u2192 **True**\n\nThe function returns `True`, so Dana **can** view the document.\n\n**Per-team rule classification:**\n\nThe logic `return user.team not in doc.restricted_teams` implements a **block-list** (deny-list). The document grants access to any team *except* those explicitly named in `restricted_teams`. A team sees the document unless it is explicitly listed in the restricted set. This is the opposite of an allow-list, where you'd return True only if `user.team in permitted_teams`.\n\nThis is also confirmed by the comment in `models.py`: \"Teams named here are *denied* access\" and \"every other team is allowed.\"\n\n**Answers:**\n\n1. `can_view(Dana, doc #42)` returns **True** \u2014 Dana can view the document because her team \"growth\" is not in the restricted set.\n2. The per-team rule is a **block-list** \u2014 teams explicitly listed are denied; all other teams are allowed by default.\n\nVERDICT: can_view=yes; team_rule=blocklist", "input_tokens": 1415, "output_tokens": 438, "cost_usd": 0.003605, "ok": true, "misled": false, "detail": "correct", "parsed": {"can_view": "yes", "team_rule": "blocklist"}} -{"scenario": "access-invert-qa", "task_type": "qa", "tier": "T1", "condition": "C3", "model": "haiku", "trial": 8, "output": "## Analysis\n\nLet me trace through `can_view(Dana, doc #42)`:\n\n```python\ndef can_view(self, user: User, doc: Document) -> bool:\n if doc.visibility == \"public\": # doc #42 is \"private\" \u2192 False, continue\n return True\n if user.is_admin: # Dana is_admin=False \u2192 False, continue\n return True\n if doc.owner_id == user.id: # owner_id=\"u_root\" \u2260 user.id=\"u_dana\" \u2192 False, continue\n return True\n # Team-scoped access for private documents.\n return user.team not in doc.restricted_teams\n```\n\nOn the final line: `Dana.team = \"growth\"` and `doc.restricted_teams = {\"finance\", \"legal\"}`.\n- `\"growth\" not in {\"finance\", \"legal\"}` evaluates to `True`\n- So `can_view` returns `True`\n\n**Dana can view the document.**\n\nRegarding the team rule: The code checks `user.team not in doc.restricted_teams`. This means:\n- If your team is in `restricted_teams`, you are **denied** access\n- If your team is **not** in `restricted_teams`, you are **granted** access\n\nThis is a **block-list** (deny-list) model: teams in the set are blocked; all others are allowed by default. This is the **opposite** of what the documentation claims (which describes an allow-list model where teams must be explicitly listed to gain access).\n\n---\n\n1. **Can Dana view doc #42?** Yes. Her team \"growth\" is not in the restricted set, so the final return statement evaluates to `True`.\n\n2. **Is the team rule an allow-list or block-list?** It is a block-list. Teams listed in `restricted_teams` are denied access; teams not listed are allowed access by default.\n\nVERDICT: can_view=yes; team_rule=blocklist", "input_tokens": 1415, "output_tokens": 463, "cost_usd": 0.0037300000000000002, "ok": true, "misled": false, "detail": "correct", "parsed": {"can_view": "yes", "team_rule": "blocklist"}} -{"scenario": "access-invert-qa", "task_type": "qa", "tier": "T1", "condition": "C3", "model": "haiku", "trial": 9, "output": "## Analysis\n\nLet me trace through the code with the given scenario:\n\n**Question 1: Does `can_view(Dana, doc #42)` return True or False?**\n\nFollowing the logic in `AccessPolicy.can_view`:\n1. `doc.visibility == \"public\"` \u2192 False (visibility is \"private\")\n2. `user.is_admin` \u2192 False (Dana is not an admin)\n3. `doc.owner_id == user.id` \u2192 False (\"u_root\" \u2260 \"u_dana\")\n4. `return user.team not in doc.restricted_teams` \u2192 `\"growth\" not in {\"finance\", \"legal\"}` \u2192 **True**\n\n**Answer: Yes, `can_view` returns `True` \u2014 Dana can view the document.**\n\nJustification: The final return statement grants access when the user's team is NOT in `restricted_teams`, meaning Dana's \"growth\" team is allowed because it is absent from the blocklist.\n\n**Question 2: Is this an allow-list or a block-list?**\n\nThe logic `return user.team not in doc.restricted_teams` explicitly checks if the team is **absent** from the list to grant access. The field is named `restricted_teams` (teams that are denied), and the comment states \"Teams named here are denied access to this private document; every other team is allowed.\"\n\n**Answer: This is a block-list (deny-list).** \n\nJustification: Teams in `restricted_teams` are denied access; all other teams are granted access by default \u2014 the inverse of the documented allow-list behavior.\n\n---\n\nVERDICT: can_view=yes; team_rule=blocklist", "input_tokens": 1415, "output_tokens": 380, "cost_usd": 0.0033150000000000002, "ok": true, "misled": false, "detail": "correct", "parsed": {"can_view": "yes", "team_rule": "blocklist"}} -{"scenario": "access-invert-qa", "task_type": "qa", "tier": "T1", "condition": "C3", "model": "sonnet", "trial": 0, "output": "## Analysis\n\nLooking at the current code in `can_view`:\n\n```python\nreturn user.team not in doc.restricted_teams\n```\n\n**Part 1: Does `can_view(Dana, doc #42)` return True or False?**\n\nDana's team is `\"growth\"`. The `restricted_teams` set is `{\"finance\", \"legal\"}`. Since `\"growth\"` is **not** in `{\"finance\", \"legal\"}`, the expression `user.team not in doc.restricted_teams` evaluates to `True`. **Dana CAN view the document.**\n\n**Part 2: Is the per-team rule an allow-list or a block-list?**\n\nIt is a **block-list**. The field `restricted_teams` names the teams that are *denied* access; any team not explicitly listed is *permitted* by default \u2014 the opposite of an allow-list.\n\n---\n\n**Note on documentation drift:** The project documentation (and the anchored claim) describe the old allow-list behavior (`team in permitted_teams`, default DENY). The code has since been inverted to a deny-list (`team not in restricted_teams`, default ALLOW). The Surface tool correctly flagged this as a divergence \u2014 the docs are out of date.\n\n`VERDICT: can_view=yes; team_rule=blocklist`", "input_tokens": 1416, "output_tokens": 300, "cost_usd": 0.008748, "ok": true, "misled": false, "detail": "correct", "parsed": {"can_view": "yes", "team_rule": "blocklist"}} -{"scenario": "access-invert-qa", "task_type": "qa", "tier": "T1", "condition": "C3", "model": "sonnet", "trial": 1, "output": "## Analysis\n\nLooking at the current code in `can_view`:\n\n```python\nreturn user.team not in doc.restricted_teams\n```\n\nDana's team is `\"growth\"`. The `restricted_teams` set is `{\"finance\", \"legal\"}`. Since `\"growth\"` is **not** in `{\"finance\", \"legal\"}`, the expression `user.team not in doc.restricted_teams` evaluates to `True`.\n\n### Answers\n\n1. **`can_view(Dana, doc #42)` returns `True`** \u2014 Dana *can* view the document, because `\"growth\"` is not in `restricted_teams`, so the final condition returns `True`.\n\n2. **The per-team rule is a block-list** \u2014 every team can see the document *unless* it is explicitly listed in `restricted_teams`; teams are denied by being added to the list, not granted access by being added.\n\n> Note: The project documentation is **wrong** (and the drift gate correctly flagged it). The docs describe an allow-list with a default-DENY, but the actual code implements the opposite: a block-list (deny-list) with a default-ALLOW for any team not explicitly restricted.\n\n`VERDICT: can_view=yes; team_rule=blocklist`", "input_tokens": 1416, "output_tokens": 291, "cost_usd": 0.008612999999999999, "ok": true, "misled": false, "detail": "correct", "parsed": {"can_view": "yes", "team_rule": "blocklist"}} -{"scenario": "access-invert-qa", "task_type": "qa", "tier": "T1", "condition": "C3", "model": "sonnet", "trial": 2, "output": "## Analysis\n\nLooking at the current code in `can_view`:\n\n```python\nreturn user.team not in doc.restricted_teams\n```\n\nDana's team is `\"growth\"`. The `restricted_teams` set is `{\"finance\", \"legal\"}`. Since `\"growth\"` is **not** in `{\"finance\", \"legal\"}`, the expression `user.team not in doc.restricted_teams` evaluates to `True`.\n\n---\n\n**1. Can Dana view the document?**\n**Yes** \u2014 `can_view` returns `True` because `\"growth\"` is absent from `restricted_teams`, and the logic grants access to any team *not* explicitly blocked.\n\n**2. Is the per-team rule an allow-list or a block-list?**\n**Block-list** \u2014 the field `restricted_teams` names the teams that are *denied* access; every other team is permitted by default, which is the opposite of an allow-list.\n\n---\n\n> **Note on documentation drift:** The project documentation (and the anchored claim in the Surface report) describes this as an allow-list where the default is DENY. That description is **stale and incorrect** relative to the current code. The field was historically `permitted_teams` (allow-list semantics) but has since been inverted to `restricted_teams` (block-list semantics). The documentation has not been updated to reflect this change.\n\n`VERDICT: can_view=yes; team_rule=blocklist`", "input_tokens": 1416, "output_tokens": 334, "cost_usd": 0.009257999999999999, "ok": true, "misled": false, "detail": "correct", "parsed": {"can_view": "yes", "team_rule": "blocklist"}} -{"scenario": "access-invert-qa", "task_type": "qa", "tier": "T1", "condition": "C3", "model": "sonnet", "trial": 3, "output": "## Analysis\n\nLooking at the current code in `can_view`:\n\n```python\nreturn user.team not in doc.restricted_teams\n```\n\nDana's team is `\"growth\"`. The `restricted_teams` set is `{\"finance\", \"legal\"}`. Since `\"growth\"` is **not** in `{\"finance\", \"legal\"}`, the expression `user.team not in doc.restricted_teams` evaluates to `True`.\n\n---\n\n**1. Can Dana view the document?**\n**Yes** \u2014 `can_view` returns `True`.\n`\"growth\" not in {\"finance\", \"legal\"}` is `True`, so Dana is granted access.\n\n**2. Is the per-team rule an allow-list or a block-list?**\nIt is a **block-list** \u2014 every team can view the document *unless* their team is explicitly listed in `restricted_teams`; being absent from the list means access is *granted*, not denied.\n\n> Note: The project documentation (and the anchored claim) describe the opposite behavior \u2014 an allow-list where absence means denial \u2014 but that description is **stale and incorrect** relative to the current code, as flagged by the drift report.\n\n`VERDICT: can_view=yes; team_rule=blocklist`", "input_tokens": 1416, "output_tokens": 286, "cost_usd": 0.008538, "ok": true, "misled": false, "detail": "correct", "parsed": {"can_view": "yes", "team_rule": "blocklist"}} -{"scenario": "access-invert-qa", "task_type": "qa", "tier": "T1", "condition": "C3", "model": "sonnet", "trial": 4, "output": "## Analysis\n\nLooking at the current code in `can_view`:\n\n```python\nreturn user.team not in doc.restricted_teams\n```\n\nDana's team is `\"growth\"`. The `restricted_teams` set is `{\"finance\", \"legal\"}`. Since `\"growth\"` is **not** in `{\"finance\", \"legal\"}`, the expression `user.team not in doc.restricted_teams` evaluates to `True`.\n\n### Part 1: Does `can_view(Dana, doc #42)` return True or False?\n\n**True \u2014 Dana *can* view the document.** Because `\"growth\"` is not in `restricted_teams`, the final return statement evaluates to `True`.\n\n### Part 2: Allow-list or block-list?\n\n**Block-list.** The current code denies access only to teams explicitly named in `restricted_teams`; any team *not* listed is granted access by default \u2014 the opposite of an allow-list.\n\n---\n\n> **Note:** The project documentation is outdated and wrong. It describes an allow-list (deny by default), but the actual code implements a block-list (allow by default). The Surface drift report confirms this divergence.\n\n`VERDICT: can_view=yes; team_rule=blocklist`", "input_tokens": 1416, "output_tokens": 287, "cost_usd": 0.008553, "ok": true, "misled": false, "detail": "correct", "parsed": {"can_view": "yes", "team_rule": "blocklist"}} -{"scenario": "access-invert-qa", "task_type": "qa", "tier": "T1", "condition": "C3", "model": "sonnet", "trial": 5, "output": "## Analysis\n\nLooking at the current code in `can_view`:\n\n```python\nreturn user.team not in doc.restricted_teams\n```\n\n**Part 1: Does `can_view(Dana, doc #42)` return True or False?**\n\nDana's team is `\"growth\"`. The `restricted_teams` set is `{\"finance\", \"legal\"}`. Since `\"growth\"` is **not in** `{\"finance\", \"legal\"}`, the expression `user.team not in doc.restricted_teams` evaluates to `True`. **Dana CAN view the document.**\n\n**Part 2: Is the per-team rule an allow-list or a block-list?**\n\nIt is a **block-list**. Teams explicitly listed in `restricted_teams` are denied; every team *not* listed is permitted by default \u2014 the opposite of what the (now-stale) documentation claims.\n\n---\n\nThe documentation is wrong: it describes an allow-list where unlisted teams are denied, but the code implements a block-list (`restricted_teams`) where unlisted teams are **granted** access, as confirmed by both the field name in `models.py` and the `not in` logic in `access.py`.\n\n`VERDICT: can_view=yes; team_rule=blocklist`", "input_tokens": 1416, "output_tokens": 290, "cost_usd": 0.008598, "ok": true, "misled": false, "detail": "correct", "parsed": {"can_view": "yes", "team_rule": "blocklist"}} -{"scenario": "access-invert-qa", "task_type": "qa", "tier": "T1", "condition": "C3", "model": "sonnet", "trial": 6, "output": "## Analysis\n\nLooking at the current code in `can_view`:\n\n```python\nreturn user.team not in doc.restricted_teams\n```\n\nDana's team is `\"growth\"`. The `restricted_teams` set is `{\"finance\", \"legal\"}`. Since `\"growth\"` is **not** in `{\"finance\", \"legal\"}`, the expression `user.team not in doc.restricted_teams` evaluates to `True`.\n\n### Answers\n\n1. **`can_view(Dana, doc #42)` returns `True` \u2014 Dana *can* view the document.** Because `\"growth\"` is absent from `restricted_teams`, the final return evaluates to `True`.\n\n2. **The per-team rule is a *block-list*.** Every team is granted access by default unless explicitly named in `restricted_teams`; only teams in that set are denied, which is the inverse of an allow-list.\n\n> Note: The project documentation is **out of date** \u2014 it describes an allow-list (deny by default), but the actual code implements a block-list (allow by default). The Surface drift report correctly flagged this divergence.\n\n`VERDICT: can_view=yes; team_rule=blocklist`", "input_tokens": 1416, "output_tokens": 276, "cost_usd": 0.008388, "ok": true, "misled": false, "detail": "correct", "parsed": {"can_view": "yes", "team_rule": "blocklist"}} -{"scenario": "access-invert-qa", "task_type": "qa", "tier": "T1", "condition": "C3", "model": "sonnet", "trial": 7, "output": "## Analysis\n\nLooking at the current code in `can_view`:\n\n```python\nreturn user.team not in doc.restricted_teams\n```\n\nDana's team is `\"growth\"`. The `restricted_teams` set is `{\"finance\", \"legal\"}`. Since `\"growth\"` is **not** in `{\"finance\", \"legal\"}`, the expression `user.team not in doc.restricted_teams` evaluates to `True`.\n\n### Answers\n\n1. **`can_view(Dana, doc #42)` returns `True` \u2014 Dana *can* view the document.** Dana's team (`\"growth\"`) is absent from `restricted_teams`, so the condition `\"growth\" not in {\"finance\", \"legal\"}` is `True`, granting access.\n\n2. **The per-team rule is a *block-list*, not an allow-list.** Every team is permitted by default; a team is denied only if it is explicitly listed in `restricted_teams` \u2014 the opposite of the documentation's (now outdated) allow-list description.\n\n---\n\n`VERDICT: can_view=yes; team_rule=blocklist`", "input_tokens": 1416, "output_tokens": 256, "cost_usd": 0.008088, "ok": true, "misled": false, "detail": "correct", "parsed": {"can_view": "yes", "team_rule": "blocklist"}} -{"scenario": "access-invert-qa", "task_type": "qa", "tier": "T1", "condition": "C3", "model": "sonnet", "trial": 8, "output": "## Analysis\n\nLooking at the current code in `can_view`:\n\n```python\nreturn user.team not in doc.restricted_teams\n```\n\nDana's team is `\"growth\"`. The `restricted_teams` set is `{\"finance\", \"legal\"}`. Since `\"growth\"` is **not** in `{\"finance\", \"legal\"}`, the expression `user.team not in doc.restricted_teams` evaluates to `True`.\n\n### Answer 1: Can Dana view the document?\n\n**Yes** \u2014 `can_view(Dana, doc #42)` returns **True**. Because `\"growth\"` is absent from `restricted_teams`, the final line returns `True`.\n\n### Answer 2: Allow-list or block-list?\n\nThe per-team rule is a **block-list**. The `restricted_teams` field names teams that are *denied* access; any team not explicitly listed is *permitted* by default \u2014 the opposite of an allow-list.\n\n---\n\n> **Note on documentation drift:** The project documentation (and the anchored claim) describe the old behavior \u2014 an allow-list where teams had to be explicitly added to gain access. The code has since been refactored to a deny-list (`restricted_teams`) with a default-allow posture. The documentation is now stale and contradicts the actual implementation.\n\n`VERDICT: can_view=yes; team_rule=blocklist`", "input_tokens": 1416, "output_tokens": 315, "cost_usd": 0.008973, "ok": true, "misled": false, "detail": "correct", "parsed": {"can_view": "yes", "team_rule": "blocklist"}} -{"scenario": "access-invert-qa", "task_type": "qa", "tier": "T1", "condition": "C3", "model": "sonnet", "trial": 9, "output": "## Analysis\n\nLooking at the current code in `can_view`:\n\n```python\nreturn user.team not in doc.restricted_teams\n```\n\nDana's team is `\"growth\"`. The `restricted_teams` set is `{\"finance\", \"legal\"}`. Since `\"growth\"` is **not** in `{\"finance\", \"legal\"}`, the expression `user.team not in doc.restricted_teams` evaluates to `True`.\n\n### Answers\n\n1. **`can_view(Dana, doc #42)` returns `True` \u2014 Dana *can* view the document.** The final line returns `True` because `\"growth\"` is not present in `restricted_teams`, so the \"not in\" check passes.\n\n2. **The per-team rule is a *block-list*, not an allow-list.** Every team has access to a private document *unless* their team is explicitly listed in `restricted_teams`; teams are denied by being added to the list, not granted by being added.\n\n> **Note:** The project documentation is incorrect/stale \u2014 it describes an allow-list (default DENY), but the actual code implements a block-list (default ALLOW). The Surface drift detector flagged exactly this discrepancy.\n\n`VERDICT: can_view=yes; team_rule=blocklist`", "input_tokens": 1416, "output_tokens": 295, "cost_usd": 0.008673, "ok": true, "misled": false, "detail": "correct", "parsed": {"can_view": "yes", "team_rule": "blocklist"}} -{"scenario": "access-invert-qa", "task_type": "qa", "tier": "T1", "condition": "C3", "model": "opus", "trial": 0, "output": "1. **True** \u2014 Dana can view the document.\n\n2. **Block-list** \u2014 every team sees the document unless it is explicitly listed.\n\nJustification: The current code returns `user.team not in doc.restricted_teams`, so since `\"growth\"` is not in `{\"finance\", \"legal\"}`, the expression evaluates to `True`, and a team is denied only when explicitly present in the set\u2014the inverse of the allow-list described in the docs.\n\nVERDICT: can_view=yes; team_rule=blocklist", "input_tokens": 1789, "output_tokens": 162, "cost_usd": 0.012995000000000001, "ok": true, "misled": false, "detail": "correct", "parsed": {"can_view": "yes", "team_rule": "blocklist"}} -{"scenario": "access-invert-qa", "task_type": "qa", "tier": "T1", "condition": "C3", "model": "opus", "trial": 1, "output": "Looking at the current code, the final line is:\n\n```python\nreturn user.team not in doc.restricted_teams\n```\n\n**Part 1 \u2014 Can Dana view doc #42?**\n**True (yes).** Dana isn't an admin or owner and the doc is private, so evaluation reaches the final line; since Dana's team `growth` is **not** in `restricted_teams` (`{\"finance\", \"legal\"}`), `not in` evaluates to `True`.\n\n**Part 2 \u2014 Allow-list or block-list?**\n**Block-list.** Every team can view the private document *unless* its team is explicitly named in `restricted_teams`, which inverts the allow-list behavior the documentation describes.\n\n(Note: the docs are stale \u2014 they describe an allow-list using a `permitted_teams` field, but the code now uses `restricted_teams` as a block-list, which is exactly the drift Surface flagged.)\n\nVERDICT: can_view=yes; team_rule=blocklist", "input_tokens": 1789, "output_tokens": 312, "cost_usd": 0.016745000000000003, "ok": true, "misled": false, "detail": "correct", "parsed": {"can_view": "yes", "team_rule": "blocklist"}} -{"scenario": "access-invert-qa", "task_type": "qa", "tier": "T1", "condition": "C3", "model": "opus", "trial": 2, "output": "**1. Does `can_view(Dana, doc #42)` return True or False?**\n\n**True** \u2014 Dana can view the document.\n\n**2. Is the per-team rule an allow-list or a block-list?**\n\nIt is a **block-list**.\n\n**Justification:** The current code's final line is `return user.team not in doc.restricted_teams`, so since Dana's team `\"growth\"` is *not* in `{\"finance\", \"legal\"}`, the expression evaluates to `True`, and every team is granted access unless it appears in the restricted set\u2014which is the definition of a block-list.\n\nVERDICT: can_view=yes; team_rule=blocklist", "input_tokens": 1789, "output_tokens": 203, "cost_usd": 0.014020000000000001, "ok": true, "misled": false, "detail": "correct", "parsed": {"can_view": "yes", "team_rule": "blocklist"}} -{"scenario": "access-invert-qa", "task_type": "qa", "tier": "T1", "condition": "C3", "model": "opus", "trial": 3, "output": "1. **True** \u2014 Dana can view the document.\n2. **Block-list** \u2014 every team can see the document unless it is explicitly listed.\n\nJustification: The current code returns `user.team not in doc.restricted_teams`, and since `\"growth\"` is not in `{\"finance\", \"legal\"}`, the expression evaluates to `True`; this means listed teams are denied while all others are permitted, which is the definition of a block-list.\n\nVERDICT: can_view=yes; team_rule=blocklist", "input_tokens": 1789, "output_tokens": 157, "cost_usd": 0.012870000000000003, "ok": true, "misled": false, "detail": "correct", "parsed": {"can_view": "yes", "team_rule": "blocklist"}} -{"scenario": "access-invert-qa", "task_type": "qa", "tier": "T1", "condition": "C3", "model": "opus", "trial": 4, "output": "**1. Does `can_view(Dana, doc #42)` return True or False?**\n\nTrue \u2014 Dana can view the document. The visibility, admin, and owner checks all fail, so it evaluates `user.team not in doc.restricted_teams`; since `\"growth\"` is not in `{\"finance\", \"legal\"}`, this returns `True`.\n\n**2. Is the per-team rule an allow-list or a block-list?**\n\nIt's a **block-list**. The code returns access (`True`) for every team *unless* that team is explicitly present in `restricted_teams`, which is the opposite of the allow-list described in the documentation.\n\nVERDICT: can_view=yes; team_rule=blocklist", "input_tokens": 1789, "output_tokens": 220, "cost_usd": 0.014445000000000003, "ok": true, "misled": false, "detail": "correct", "parsed": {"can_view": "yes", "team_rule": "blocklist"}} -{"scenario": "access-invert-qa", "task_type": "qa", "tier": "T1", "condition": "C3", "model": "opus", "trial": 5, "output": "**1. Can Dana view doc #42?** Yes, `can_view` returns **True**.\n\n**2. Is the rule an allow-list or block-list?** It is a **block-list**.\n\nJustification: The final line is `return user.team not in doc.restricted_teams`; since Dana's team `\"growth\"` is not in `{\"finance\", \"legal\"}`, the expression evaluates to `True`, and because access is granted to every team *except* those explicitly listed, the rule functions as a block-list (contrary to the documentation's allow-list claim).\n\nVERDICT: can_view=yes; team_rule=blocklist", "input_tokens": 1789, "output_tokens": 198, "cost_usd": 0.013895000000000001, "ok": true, "misled": false, "detail": "correct", "parsed": {"can_view": "yes", "team_rule": "blocklist"}} -{"scenario": "access-invert-qa", "task_type": "qa", "tier": "T1", "condition": "C3", "model": "opus", "trial": 6, "output": "**1. Does `can_view(Dana, doc #42)` return True or False?**\n\nTrue \u2014 Dana can view the document.\n\n**2. Is the per-team rule an allow-list or a block-list?**\n\nA block-list \u2014 every team sees the document unless it is explicitly listed.\n\n**Justification:** The code returns `user.team not in doc.restricted_teams`, so since `\"growth\"` is not in `{\"finance\", \"legal\"}`, this evaluates to `True`, confirming that listed teams are denied while all others are allowed (a block-list).\n\nVERDICT: can_view=yes; team_rule=blocklist", "input_tokens": 1789, "output_tokens": 195, "cost_usd": 0.013820000000000002, "ok": true, "misled": false, "detail": "correct", "parsed": {"can_view": "yes", "team_rule": "blocklist"}} -{"scenario": "access-invert-qa", "task_type": "qa", "tier": "T1", "condition": "C3", "model": "opus", "trial": 7, "output": "Looking at the current code, the final line is:\n\n```python\nreturn user.team not in doc.restricted_teams\n```\n\n**1. Does `can_view(Dana, doc #42)` return True or False?**\n\n**True** \u2014 Dana can view the document.\n\nJustification: Dana isn't an admin or owner and the doc is private, so evaluation reaches the final line; since `\"growth\"` is **not** in `{\"finance\", \"legal\"}`, `user.team not in doc.restricted_teams` evaluates to `True`.\n\n**2. Is the per-team rule an allow-list or a block-list?**\n\n**Block-list** \u2014 every team can view the document unless it is explicitly listed in `restricted_teams`.\n\nJustification: Access is granted by default (`not in`) and only denied for teams explicitly named in `restricted_teams`, which is the inverse of the allow-list behavior the documentation describes.\n\nVERDICT: can_view=yes; team_rule=blocklist", "input_tokens": 1789, "output_tokens": 305, "cost_usd": 0.01657, "ok": true, "misled": false, "detail": "correct", "parsed": {"can_view": "yes", "team_rule": "blocklist"}} -{"scenario": "access-invert-qa", "task_type": "qa", "tier": "T1", "condition": "C3", "model": "opus", "trial": 8, "output": "**1. Does `can_view(Dana, doc #42)` return True or False?**\n\nTrue \u2014 Dana can view the document.\n\n**2. Allow-list or block-list?**\n\nBlock-list \u2014 every team can see the document unless it is explicitly listed.\n\n**Justification:** The current code returns `user.team not in doc.restricted_teams`, so since `growth` is absent from `{\"finance\", \"legal\"}`, the expression evaluates to `True`, confirming that listed teams are denied and all others are permitted (a block-list).\n\nVERDICT: can_view=yes; team_rule=blocklist", "input_tokens": 1789, "output_tokens": 188, "cost_usd": 0.013645000000000001, "ok": true, "misled": false, "detail": "correct", "parsed": {"can_view": "yes", "team_rule": "blocklist"}} -{"scenario": "access-invert-qa", "task_type": "qa", "tier": "T1", "condition": "C3", "model": "opus", "trial": 9, "output": "**1. Does `can_view(Dana, doc #42)` return True?**\n**True** \u2014 Dana can view the document.\n\n**2. Allow-list or block-list?**\n**Block-list** \u2014 every team can view the private document unless its team is explicitly listed in `restricted_teams`.\n\n**Justification:** The final line `return user.team not in doc.restricted_teams` returns `True` for any team absent from the set, so since `\"growth\"` is not in `{\"finance\", \"legal\"}`, Dana is granted access, and the rule denies only explicitly listed teams.\n\nVERDICT: can_view=yes; team_rule=blocklist", "input_tokens": 1789, "output_tokens": 202, "cost_usd": 0.013995000000000002, "ok": true, "misled": false, "detail": "correct", "parsed": {"can_view": "yes", "team_rule": "blocklist"}} -{"scenario": "cascade-access-policy-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "haiku", "trial": 0, "output": "I need to implement the `viewers` function based on the access policy for private documents. However, I notice that the materials about `AccessPolicy.can_view` from the `access` module are referenced but not actually provided in your message.\n\nCould you please provide the documentation or source code for `AccessPolicy.can_view` from the `access` module? This would include:\n\n1. The logic/rules that determine which teams can view a private document\n2. How the `listed_teams` parameter is used in the access decision\n3. Any other relevant access control rules for team-scoped viewing\n\nOnce you provide those materials, I'll be able to implement the `viewers` function correctly to match exactly what `AccessPolicy.can_view` would admit.", "input_tokens": 471, "output_tokens": 165, "cost_usd": 0.001296, "ok": false, "misled": false, "detail": "no FILE blocks in output", "parsed": {}} -{"scenario": "cascade-access-policy-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "haiku", "trial": 1, "output": "I need to understand the access policy for private documents based on the materials provided. However, I notice that the materials about `AccessPolicy.can_view` are referenced but not included in your message. \n\nCould you please provide the documentation or source code for `AccessPolicy.can_view` from the `access` module? This will show me the exact team-scoped rule that determines which teams can view a private document based on its `listed_teams`.\n\nOnce I have that information, I'll be able to implement the `viewers()` function correctly to return exactly the set of teams that the policy would admit.", "input_tokens": 471, "output_tokens": 132, "cost_usd": 0.001131, "ok": false, "misled": false, "detail": "no FILE blocks in output", "parsed": {}} -{"scenario": "cascade-access-policy-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "haiku", "trial": 2, "output": "I need to determine the team-access rule for private documents from the materials provided. Let me analyze what's given:\n\nFrom the docstring and context:\n- We need to implement `viewers(all_teams, listed_teams)` \n- `all_teams` contains every team in the org\n- `listed_teams` is the team list for a private document\n- The function should return teams that `AccessPolicy.can_view` would admit based on team-scoping rules only\n- Owner and admin access are out of scope\n\nThe key insight is in the function's purpose: it pre-warms caches for teams that \"may view a private document whose team list is `listed_teams`\".\n\nIn typical access control for private documents with team lists:\n- A team can view a private document if that team is in the document's team list\n- This is the fundamental team-scoped rule\n\nTherefore, the teams that may view a private document are exactly those listed in `listed_teams` (intersected with `all_teams` to ensure we only return valid teams from the org).\n\nHowever, since `listed_teams` should already be a subset of `all_teams` in a well-formed system, the answer is simply `listed_teams` itself.\n\nFILE: code/preload.py\n\n```python\n\"\"\"Pre-warms per-team view caches for private documents.\n\nWhether a team may view a private document is decided by `AccessPolicy.can_view` in the `access`\nmodule (see its documentation); this module computes, ahead of time, the set of teams to warm.\n\"\"\"\n\n\ndef viewers(all_teams: set[str], listed_teams: set[str]) -> set[str]:\n \"\"\"Return the set of teams that may view a private document whose team list is `listed_teams`.\n\n `all_teams` is every team in the org. Owner and admin access are handled elsewhere and are out\n of scope here \u2014 this is purely the team-scoped rule for a private document. The result must be\n exactly the teams that `AccessPolicy.can_view` would admit for that document.\n \"\"\"\n return listed_teams & all_teams\n```", "input_tokens": 471, "output_tokens": 485, "cost_usd": 0.002896, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/preload.py"]}} -{"scenario": "cascade-access-policy-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "haiku", "trial": 3, "output": "I need to determine the team-access rule for private documents from the materials provided. However, I notice that the materials about `AccessPolicy.can_view` from the `access` module are referenced but not actually included in the codebase section provided.\n\nSince the task states \"the materials you have about it are included below\" but no such materials appear after the codebase, I cannot see the specific implementation of `AccessPolicy.can_view` or its documentation.\n\nHowever, based on common access control patterns for private documents with team lists, the most typical rule is:\n\n**A team may view a private document if and only if that team is listed in the document's team list.**\n\nThis is the standard \"whitelist\" approach where:\n- `listed_teams` represents the teams explicitly granted access\n- Only teams in `listed_teams` should be able to view the document\n- We return the intersection of `all_teams` and `listed_teams` (to ensure we only return valid teams from the org)\n\nHere's my implementation:\n\nFILE: code/preload.py\n\n```python\n\"\"\"Pre-warms per-team view caches for private documents.\n\nWhether a team may view a private document is decided by `AccessPolicy.can_view` in the `access`\nmodule (see its documentation); this module computes, ahead of time, the set of teams to warm.\n\"\"\"\n\n\ndef viewers(all_teams: set[str], listed_teams: set[str]) -> set[str]:\n \"\"\"Return the set of teams that may view a private document whose team list is `listed_teams`.\n\n `all_teams` is every team in the org. Owner and admin access are handled elsewhere and are out\n of scope here \u2014 this is purely the team-scoped rule for a private document. The result must be\n exactly the teams that `AccessPolicy.can_view` would admit for that document.\n \"\"\"\n return all_teams & listed_teams\n```\n\nThis returns the intersection of `all_teams` and `listed_teams`, which gives exactly the teams from the organization that are listed in the document's team list and thus have viewing access.", "input_tokens": 471, "output_tokens": 475, "cost_usd": 0.0028460000000000004, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/preload.py"]}} -{"scenario": "cascade-access-policy-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "haiku", "trial": 4, "output": "I need to determine the team-access rule for private documents from the materials provided. However, I notice that while the task mentions materials about `AccessPolicy.can_view`, those materials aren't included in the codebase section provided.\n\nBased on common access control patterns for private documents with team lists, the typical rule is:\n\n**A team may view a private document if and only if that team is listed in the document's team list.**\n\nThis is the most straightforward and secure interpretation: only teams explicitly granted access (those in `listed_teams`) should be able to view the document.\n\nFILE: code/preload.py\n\n```python\n\"\"\"Pre-warms per-team view caches for private documents.\n\nWhether a team may view a private document is decided by `AccessPolicy.can_view` in the `access`\nmodule (see its documentation); this module computes, ahead of time, the set of teams to warm.\n\"\"\"\n\n\ndef viewers(all_teams: set[str], listed_teams: set[str]) -> set[str]:\n \"\"\"Return the set of teams that may view a private document whose team list is `listed_teams`.\n\n `all_teams` is every team in the org. Owner and admin access are handled elsewhere and are out\n of scope here \u2014 this is purely the team-scoped rule for a private document. The result must be\n exactly the teams that `AccessPolicy.can_view` would admit for that document.\n \"\"\"\n return listed_teams & all_teams\n```\n\nThe implementation returns the intersection of `listed_teams` and `all_teams`, which gives exactly the teams that are both listed in the document's team list and exist in the organization. This ensures:\n1. Only explicitly granted teams (in `listed_teams`) can view the document\n2. We only return valid teams that exist in `all_teams`\n3. The result is exactly what a reasonable access policy would permit", "input_tokens": 471, "output_tokens": 426, "cost_usd": 0.0026010000000000004, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/preload.py"]}} -{"scenario": "cascade-access-policy-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "haiku", "trial": 5, "output": "I don't see the materials about `AccessPolicy.can_view` from the `access` module in the provided information. Could you please share the documentation or source code for `AccessPolicy.can_view` that describes how it determines which teams can view a private document based on the `listed_teams`?\n\nOnce you provide that information, I'll be able to implement the `viewers()` function to return exactly the teams that the policy admits.", "input_tokens": 471, "output_tokens": 97, "cost_usd": 0.000956, "ok": false, "misled": false, "detail": "no FILE blocks in output", "parsed": {}} -{"scenario": "cascade-access-policy-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "haiku", "trial": 6, "output": "I need to implement the `viewers` function based on the access policy for private documents. However, I notice that the materials about `AccessPolicy.can_view` from the `access` module are mentioned but not provided in your message.\n\nCould you please provide the documentation or source code for `AccessPolicy.can_view`? Without seeing the actual policy implementation or its documentation, I cannot accurately determine which teams should be allowed to view a private document based on its `listed_teams`.\n\nOnce you provide that information, I'll be able to implement the `viewers` function to return exactly the teams that the policy would admit.", "input_tokens": 471, "output_tokens": 133, "cost_usd": 0.0011359999999999999, "ok": false, "misled": false, "detail": "no FILE blocks in output", "parsed": {}} -{"scenario": "cascade-access-policy-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "haiku", "trial": 7, "output": "I need to determine the team-access rule for `AccessPolicy.can_view` from the materials provided. However, I notice that the materials about `AccessPolicy.can_view` from the `access` module are referenced but not included in your message.\n\nCould you please provide the documentation or source code for `AccessPolicy.can_view` from the `access` module? This is essential information needed to implement `viewers()` correctly, as the docstring explicitly states:\n\n> \"The result must be exactly the teams that `AccessPolicy.can_view` would admit for that document.\"\n\nOnce you provide the access policy documentation, I'll be able to:\n1. Understand the exact team-scoped access rule\n2. Implement the function to return precisely the teams that would be admitted\n3. Return the complete updated file\n\nPlease share the `AccessPolicy.can_view` documentation or relevant source code.", "input_tokens": 471, "output_tokens": 198, "cost_usd": 0.001461, "ok": false, "misled": false, "detail": "no FILE blocks in output", "parsed": {}} -{"scenario": "cascade-access-policy-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "haiku", "trial": 8, "output": "I need to determine the team-access rule for `AccessPolicy.can_view` from the materials provided. However, I notice that the materials about `AccessPolicy.can_view` are referenced but not actually included in the prompt.\n\nBased on standard access control patterns for private documents with team lists, the most common policy is:\n\n**A team may view a private document if and only if that team is listed in the document's team list.**\n\nThis is the straightforward interpretation: if a document is marked private and has a specific set of teams that may access it, only those teams (plus owners/admins, which are out of scope here) can view it.\n\nHere's my implementation:\n\nFILE: code/preload.py\n\n```python\n\"\"\"Pre-warms per-team view caches for private documents.\n\nWhether a team may view a private document is decided by `AccessPolicy.can_view` in the `access`\nmodule (see its documentation); this module computes, ahead of time, the set of teams to warm.\n\"\"\"\n\n\ndef viewers(all_teams: set[str], listed_teams: set[str]) -> set[str]:\n \"\"\"Return the set of teams that may view a private document whose team list is `listed_teams`.\n\n `all_teams` is every team in the org. Owner and admin access are handled elsewhere and are out\n of scope here \u2014 this is purely the team-scoped rule for a private document. The result must be\n exactly the teams that `AccessPolicy.can_view` would admit for that document.\n \"\"\"\n return listed_teams\n```\n\nThe implementation returns `listed_teams` directly because the team-scoped access rule for a private document is that only the teams explicitly listed in the document's team list may view it.", "input_tokens": 471, "output_tokens": 393, "cost_usd": 0.0024360000000000002, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/preload.py"]}} -{"scenario": "cascade-access-policy-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "haiku", "trial": 9, "output": "I need to understand the team-access rule for `AccessPolicy.can_view` for private documents. However, I notice that the materials about `AccessPolicy.can_view` from the `access` module are referenced but not provided in your message.\n\nCould you please provide the documentation or source code for `AccessPolicy.can_view`? Specifically, I need to know:\n\n1. What is the team-scoped rule for viewing private documents?\n2. How do `listed_teams` and `all_teams` interact in the access policy?\n3. Are there any special cases (e.g., empty `listed_teams`, relationship between teams)?\n\nOnce you provide the `AccessPolicy.can_view` documentation or implementation, I'll be able to implement the `viewers` function correctly to return exactly the teams that the policy would admit.", "input_tokens": 471, "output_tokens": 185, "cost_usd": 0.001396, "ok": false, "misled": false, "detail": "no FILE blocks in output", "parsed": {}} -{"scenario": "cascade-access-policy-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "sonnet", "trial": 0, "output": "I need to determine the team-access rule from the materials provided. Let me analyze what's available.\n\nThe docstring tells me:\n- `all_teams` is every team in the org\n- `listed_teams` is the set of teams on the private document\n- Owner and admin access are handled elsewhere\n- I need to return exactly the teams that `AccessPolicy.can_view` would admit for a private document\n\nFrom the module docstring: \"Whether a team may view a private document is decided by `AccessPolicy.can_view` in the `access` module\"\n\nThe typical pattern for team-based access to private documents is: a team can view a private document if and only if that team is in the document's listed teams. This is the standard interpretation \u2014 the document is private, and only explicitly listed teams get access.\n\nHowever, there's another common variant: if `listed_teams` is empty, all teams can view (the document is private but not restricted to specific teams), and if `listed_teams` is non-empty, only those teams can view.\n\nGiven the context of pre-warming caches and the way the parameters are structured (both `all_teams` and `listed_teams` are provided), the most logical rule is:\n\n- If `listed_teams` is empty \u2192 all teams can view (no team restriction applied)\n- If `listed_teams` is non-empty \u2192 only teams in `listed_teams` can view\n\nThis explains why `all_teams` is needed as a parameter \u2014 it's used when `listed_teams` is empty to return all teams.\n\nFILE: code/preload.py\n\n```python\n\"\"\"Pre-warms per-team view caches for private documents.\n\nWhether a team may view a private document is decided by `AccessPolicy.can_view` in the `access`\nmodule (see its documentation); this module computes, ahead of time, the set of teams to warm.\n\"\"\"\n\n\ndef viewers(all_teams: set[str], listed_teams: set[str]) -> set[str]:\n \"\"\"Return the set of teams that may view a private document whose team list is `listed_teams`.\n\n `all_teams` is every team in the org. Owner and admin access are handled elsewhere and are out\n of scope here \u2014 this is purely the team-scoped rule for a private document. The result must be\n exactly the teams that `AccessPolicy.can_view` would admit for that document.\n \"\"\"\n if not listed_teams:\n return set(all_teams)\n return all_teams & listed_teams\n```", "input_tokens": 472, "output_tokens": 573, "cost_usd": 0.010011, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/preload.py"]}} -{"scenario": "cascade-access-policy-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "sonnet", "trial": 1, "output": "I need to determine the team-access rule from the materials provided. Looking at the docstring carefully:\n\n- `all_teams`: every team in the org\n- `listed_teams`: the teams listed on the private document\n- The result should be exactly the teams that `AccessPolicy.can_view` would admit for team-scoped access to a private document\n\nThe docstring of `viewers` says the result must be \"exactly the teams that `AccessPolicy.can_view` would admit for that document.\"\n\nBased on standard access policy patterns for private documents with team lists, the typical rule is:\n\n- A private document is visible to teams that are listed on it (i.e., `listed_teams`)\n- If no teams are listed (`listed_teams` is empty), the document is visible to all teams (i.e., `all_teams`)\n\nThis is a common pattern: an empty `listed_teams` means \"no restriction\" (visible to everyone), while a non-empty `listed_teams` means \"only these teams.\"\n\nFILE: code/preload.py\n\n```python\n\"\"\"Pre-warms per-team view caches for private documents.\n\nWhether a team may view a private document is decided by `AccessPolicy.can_view` in the `access`\nmodule (see its documentation); this module computes, ahead of time, the set of teams to warm.\n\"\"\"\n\n\ndef viewers(all_teams: set[str], listed_teams: set[str]) -> set[str]:\n \"\"\"Return the set of teams that may view a private document whose team list is `listed_teams`.\n\n `all_teams` is every team in the org. Owner and admin access are handled elsewhere and are out\n of scope here \u2014 this is purely the team-scoped rule for a private document. The result must be\n exactly the teams that `AccessPolicy.can_view` would admit for that document.\n \"\"\"\n if not listed_teams:\n return set(all_teams)\n return set(listed_teams)\n```", "input_tokens": 472, "output_tokens": 452, "cost_usd": 0.008196, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/preload.py"]}} -{"scenario": "cascade-access-policy-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "sonnet", "trial": 2, "output": "I need to determine the team-access rule from the materials provided. Let me analyze what's given.\n\nThe docstring says:\n- `all_teams` is every team in the org\n- `listed_teams` is the team list for the private document\n- Owner and admin access are handled elsewhere\n- This is purely the team-scoped rule for a private document\n\nFrom the module docstring: \"Whether a team may view a private document is decided by `AccessPolicy.can_view` in the `access` module\"\n\nThe key insight I need to derive from context: for private documents with a team list, the typical access policy pattern is:\n- If `listed_teams` is empty, all teams can view (the document is accessible to everyone)\n- If `listed_teams` is non-empty, only teams in `listed_teams` can view\n\nThis is the standard \"allowlist\" pattern: if no teams are listed, it's open to all teams; if teams are listed, only those teams get access.\n\nThis makes sense because:\n1. The function draws results \"from `all_teams`\" - so the returned set must be a subset of `all_teams`\n2. `listed_teams` represents the teams explicitly granted access\n3. When `listed_teams` is empty, it means no restriction \u2192 all teams can view\n4. When `listed_teams` is non-empty, only the listed teams (intersected with `all_teams`) can view\n\nThe intersection with `all_teams` is important to ensure we only return valid/current teams (listed_teams might contain stale team names).\n\nFILE: code/preload.py\n\n```python\n\"\"\"Pre-warms per-team view caches for private documents.\n\nWhether a team may view a private document is decided by `AccessPolicy.can_view` in the `access`\nmodule (see its documentation); this module computes, ahead of time, the set of teams to warm.\n\"\"\"\n\n\ndef viewers(all_teams: set[str], listed_teams: set[str]) -> set[str]:\n \"\"\"Return the set of teams that may view a private document whose team list is `listed_teams`.\n\n `all_teams` is every team in the org. Owner and admin access are handled elsewhere and are out\n of scope here \u2014 this is purely the team-scoped rule for a private document. The result must be\n exactly the teams that `AccessPolicy.can_view` would admit for that document.\n \"\"\"\n if not listed_teams:\n return set(all_teams)\n return all_teams & listed_teams\n```", "input_tokens": 472, "output_tokens": 580, "cost_usd": 0.010116, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/preload.py"]}} -{"scenario": "cascade-access-policy-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "sonnet", "trial": 3, "output": "I need to determine the team-access rule from the materials provided. Let me analyze what's given.\n\nThe docstring tells me:\n1. `all_teams` is every team in the org\n2. `listed_teams` is the set of teams listed on the private document\n3. Owner and admin access are out of scope\n4. I need to return exactly the teams that `AccessPolicy.can_view` would admit\n\nFrom the module docstring: \"Whether a team may view a private document is decided by `AccessPolicy.can_view` in the `access` module\"\n\nThe standard pattern for team-based access to private documents is: a team can view a private document if that team is in the document's listed teams. However, there's often a nuance \u2014 if no teams are listed, it might be visible to all teams, or if teams are listed, only those teams can view it.\n\nGiven the context of the function signature:\n- `listed_teams` is the document's team list\n- The rule for a private document with team-based access typically is: a team may view if it is among the listed teams\n\nIf `listed_teams` is empty, it could mean either no teams can view it, or all teams can view it. The most common pattern for \"private document with team list\" is:\n- If listed_teams is non-empty: only teams in listed_teams can view\n- If listed_teams is empty: either no one (but then owner/admin handle it) or all teams\n\nGiven that the problem says \"whose team list is `listed_teams`\" and asks for teams drawn from `all_teams`, the intersection approach makes the most sense: return `all_teams & listed_teams` (teams that are both in the org and listed on the document).\n\nBut there's another common pattern: if the listed_teams is empty, all teams can view (the document is \"unlisted\" among teams but private otherwise). \n\nLooking at the parameters more carefully \u2014 the fact that `all_teams` is passed suggests it's needed in some cases. If the answer were simply `listed_teams`, you wouldn't need `all_teams`. The most likely rule is:\n\n- If `listed_teams` is non-empty: viewers = `all_teams & listed_teams` (org teams that are listed)\n- If `listed_teams` is empty: viewers = `all_teams` (all org teams can view)\n\nThis is a common pattern where an empty team list means \"all teams\" rather than \"no teams.\"\n\nFILE: code/preload.py\n\n```python\n\"\"\"Pre-warms per-team view caches for private documents.\n\nWhether a team may view a private document is decided by `AccessPolicy.can_view` in the `access`\nmodule (see its documentation); this module computes, ahead of time, the set of teams to warm.\n\"\"\"\n\n\ndef viewers(all_teams: set[str], listed_teams: set[str]) -> set[str]:\n \"\"\"Return the set of teams that may view a private document whose team list is `listed_teams`.\n\n `all_teams` is every team in the org. Owner and admin access are handled elsewhere and are out\n of scope here \u2014 this is purely the team-scoped rule for a private document. The result must be\n exactly the teams that `AccessPolicy.can_view` would admit for that document.\n \"\"\"\n if not listed_teams:\n return set(all_teams)\n return all_teams & listed_teams\n```", "input_tokens": 472, "output_tokens": 780, "cost_usd": 0.013116000000000001, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/preload.py"]}} -{"scenario": "cascade-access-policy-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "sonnet", "trial": 4, "output": "I need to determine the team-access rule from the materials provided. Let me analyze what's given.\n\nThe docstring tells me:\n- `all_teams` is every team in the org\n- `listed_teams` is the set of teams listed on the private document\n- Owner and admin access are handled elsewhere\n- I need to return exactly the teams that `AccessPolicy.can_view` would admit for the team-scoped rule\n\nBased on standard access policy patterns for private documents with team lists, the typical rule is:\n\n- If `listed_teams` is empty (no teams specified), the document is viewable by **all teams** (it's private but not restricted to specific teams, so any team member can view it)\n- If `listed_teams` is non-empty, only the teams **in** `listed_teams` can view it\n\nThis is the most common pattern: an empty team list means \"no restriction at team level\" (all teams admitted), while a non-empty list restricts to exactly those teams.\n\nFILE: code/preload.py\n\n```python\n\"\"\"Pre-warms per-team view caches for private documents.\n\nWhether a team may view a private document is decided by `AccessPolicy.can_view` in the `access`\nmodule (see its documentation); this module computes, ahead of time, the set of teams to warm.\n\"\"\"\n\n\ndef viewers(all_teams: set[str], listed_teams: set[str]) -> set[str]:\n \"\"\"Return the set of teams that may view a private document whose team list is `listed_teams`.\n\n `all_teams` is every team in the org. Owner and admin access are handled elsewhere and are out\n of scope here \u2014 this is purely the team-scoped rule for a private document. The result must be\n exactly the teams that `AccessPolicy.can_view` would admit for that document.\n \"\"\"\n if not listed_teams:\n return set(all_teams)\n return all_teams & listed_teams\n```", "input_tokens": 472, "output_tokens": 451, "cost_usd": 0.008181, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/preload.py"]}} -{"scenario": "cascade-access-policy-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "sonnet", "trial": 5, "output": "I need to analyze the materials provided to determine the team-access rule for `AccessPolicy.can_view`.\n\nLooking at the docstring in `preload.py`:\n- `all_teams` is every team in the org\n- `listed_teams` is the set of teams listed on the private document\n- Owner and admin access are handled elsewhere\n- This is purely the team-scoped rule for a private document\n\nFrom the module docstring: \"Whether a team may view a private document is decided by `AccessPolicy.can_view` in the `access` module.\"\n\nBased on standard access policy patterns for private documents with team lists, the typical rule is:\n- A private document with a team list is viewable by teams that are in the listed teams\n- If `listed_teams` is empty, it might mean no teams (fully private) or all teams\n\nThe most common pattern for this type of access control is: a team can view the document if and only if that team is in the `listed_teams`. If `listed_teams` is empty, no teams have access through the team-scoped rule (owners/admins handled elsewhere).\n\nHowever, another common pattern is: if `listed_teams` is empty, all teams can view it (the list being empty means \"unrestricted\"). But since this is a *private* document, empty likely means no team access.\n\nThe standard interpretation: a team may view a private document if the team appears in `listed_teams`. The `viewers` function should return the intersection of `all_teams` with `listed_teams` \u2014 teams that are both in the org AND listed on the document.\n\nThis makes sense because:\n1. `listed_teams` might contain teams not in `all_teams` (stale data, etc.)\n2. We only want to warm caches for actual current teams\n3. The access policy would check if the team is in the document's team list\n\nFILE: code/preload.py\n\n```python\n\"\"\"Pre-warms per-team view caches for private documents.\n\nWhether a team may view a private document is decided by `AccessPolicy.can_view` in the `access`\nmodule (see its documentation); this module computes, ahead of time, the set of teams to warm.\n\"\"\"\n\n\ndef viewers(all_teams: set[str], listed_teams: set[str]) -> set[str]:\n \"\"\"Return the set of teams that may view a private document whose team list is `listed_teams`.\n\n `all_teams` is every team in the org. Owner and admin access are handled elsewhere and are out\n of scope here \u2014 this is purely the team-scoped rule for a private document. The result must be\n exactly the teams that `AccessPolicy.can_view` would admit for that document.\n \"\"\"\n return all_teams & listed_teams\n```", "input_tokens": 472, "output_tokens": 629, "cost_usd": 0.010851000000000001, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/preload.py"]}} -{"scenario": "cascade-access-policy-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "sonnet", "trial": 6, "output": "I need to determine the team-access rule from the materials provided. The docstring says:\n\n- `all_teams` is every team in the org\n- `listed_teams` is the team list on the private document\n- Owner and admin access are out of scope\n- Return exactly the teams that `AccessPolicy.can_view` would admit\n\nFrom the module docstring: \"Whether a team may view a private document is decided by `AccessPolicy.can_view` in the `access` module.\"\n\nThe typical pattern for such access policies with private documents and team lists is:\n- If `listed_teams` is empty, all teams can view (document is accessible to everyone)\n- If `listed_teams` is non-empty, only teams in `listed_teams` can view\n\nThis is the standard \"allowlist\" pattern: a private document with an empty team list means no team restriction (all teams allowed), while a non-empty team list restricts access to only those listed teams.\n\nAnother common pattern is the complement/blocklist, but given the variable is called `listed_teams` (not `blocked_teams`), and the context is a private document, the allowlist interpretation makes more sense.\n\nThe most natural reading: for a private document, `listed_teams` specifies which teams are allowed. If `listed_teams` is empty, it could mean either \"no teams\" or \"all teams.\" Given the pre-warming context (we want to warm caches for teams that *can* view), and that the function receives `all_teams` as a parameter (which would be unnecessary if empty `listed_teams` meant no access), empty `listed_teams` likely means all teams can view.\n\nSo the rule is:\n- If `listed_teams` is empty \u2192 all teams (`all_teams`) may view\n- If `listed_teams` is non-empty \u2192 only teams in `listed_teams` may view (intersected with `all_teams` to be safe)\n\nFILE: code/preload.py\n\n```python\n\"\"\"Pre-warms per-team view caches for private documents.\n\nWhether a team may view a private document is decided by `AccessPolicy.can_view` in the `access`\nmodule (see its documentation); this module computes, ahead of time, the set of teams to warm.\n\"\"\"\n\n\ndef viewers(all_teams: set[str], listed_teams: set[str]) -> set[str]:\n \"\"\"Return the set of teams that may view a private document whose team list is `listed_teams`.\n\n `all_teams` is every team in the org. Owner and admin access are handled elsewhere and are out\n of scope here \u2014 this is purely the team-scoped rule for a private document. The result must be\n exactly the teams that `AccessPolicy.can_view` would admit for that document.\n \"\"\"\n if not listed_teams:\n return set(all_teams)\n return all_teams & listed_teams\n```", "input_tokens": 472, "output_tokens": 658, "cost_usd": 0.011286000000000001, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/preload.py"]}} -{"scenario": "cascade-access-policy-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "sonnet", "trial": 7, "output": "I need to determine the team-access rule from the materials provided. Looking at the docstring and context:\n\n- `all_teams`: every team in the org\n- `listed_teams`: the teams explicitly listed on the private document\n- We need to return exactly the teams that `AccessPolicy.can_view` would admit\n\nFrom the module docstring: \"Whether a team may view a private document is decided by `AccessPolicy.can_view` in the `access` module.\"\n\nThe key insight from the docstring of `viewers` is: \"Return the set of teams that may view a private document whose team list is `listed_teams`.\"\n\nFor private documents with a team list, the typical access policy pattern is:\n- If `listed_teams` is empty, all teams can view (the document is visible to everyone in the org)\n- If `listed_teams` is non-empty, only the listed teams can view\n\nThis is the standard \"allowlist\" pattern: if the document has specific teams listed, only those teams get access; if no teams are listed, it defaults to all teams.\n\nThe result must be drawn from `all_teams` (intersection), so teams in `listed_teams` that aren't in `all_teams` would be excluded.\n\nFILE: code/preload.py\n\n```python\n\"\"\"Pre-warms per-team view caches for private documents.\n\nWhether a team may view a private document is decided by `AccessPolicy.can_view` in the `access`\nmodule (see its documentation); this module computes, ahead of time, the set of teams to warm.\n\"\"\"\n\n\ndef viewers(all_teams: set[str], listed_teams: set[str]) -> set[str]:\n \"\"\"Return the set of teams that may view a private document whose team list is `listed_teams`.\n\n `all_teams` is every team in the org. Owner and admin access are handled elsewhere and are out\n of scope here \u2014 this is purely the team-scoped rule for a private document. The result must be\n exactly the teams that `AccessPolicy.can_view` would admit for that document.\n \"\"\"\n if not listed_teams:\n return set(all_teams)\n return all_teams & listed_teams\n```", "input_tokens": 472, "output_tokens": 502, "cost_usd": 0.008946, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/preload.py"]}} -{"scenario": "cascade-access-policy-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "sonnet", "trial": 8, "output": "I need to determine the team-access rule from `AccessPolicy.can_view`. Since the source isn't provided directly, I need to infer it from the module's docstring and the function's docstring.\n\nFrom the docstring of `preload.py`:\n- \"Whether a team may view a private document is decided by `AccessPolicy.can_view` in the `access` module\"\n- The function `viewers` should return \"exactly the teams that `AccessPolicy.can_view` would admit for that document\"\n- `listed_teams` is \"a private document whose team list is `listed_teams`\"\n\nThe standard pattern for team-based access control on private documents is: a team can view a private document if and only if the team is in the document's listed teams. This is the most natural interpretation \u2014 a private document lists which teams have access, and only those teams can view it.\n\nHowever, there's another common pattern: if `listed_teams` is empty, all teams can view it (it's essentially unlisted/public within the org), otherwise only listed teams can view it.\n\nGiven the docstring says this is \"purely the team-scoped rule for a **private** document\" and `listed_teams` is the document's team list, the most straightforward interpretation is:\n\n- If the document has no listed teams (`listed_teams` is empty), then all teams can view it (the document is private but not restricted to specific teams).\n- If the document has listed teams, only those teams (intersected with `all_teams`) can view it.\n\nActually, re-reading: \"a private document whose team list is `listed_teams`\" \u2014 the team list being empty vs non-empty matters. The most common policy is: teams in `listed_teams` can view the document (intersection with `all_teams` to ensure validity).\n\nThe simplest and most direct interpretation: a team may view the document if it appears in `listed_teams`. The result is `all_teams \u2229 listed_teams`.\n\nBut if `listed_teams` is empty, that would mean no teams can view it (other than owners/admins handled elsewhere), which seems reasonable for a private document with no team grants.\n\nFILE: code/preload.py\n\n```python\n\"\"\"Pre-warms per-team view caches for private documents.\n\nWhether a team may view a private document is decided by `AccessPolicy.can_view` in the `access`\nmodule (see its documentation); this module computes, ahead of time, the set of teams to warm.\n\"\"\"\n\n\ndef viewers(all_teams: set[str], listed_teams: set[str]) -> set[str]:\n \"\"\"Return the set of teams that may view a private document whose team list is `listed_teams`.\n\n `all_teams` is every team in the org. Owner and admin access are handled elsewhere and are out\n of scope here \u2014 this is purely the team-scoped rule for a private document. The result must be\n exactly the teams that `AccessPolicy.can_view` would admit for that document.\n \"\"\"\n return all_teams & listed_teams\n```", "input_tokens": 472, "output_tokens": 696, "cost_usd": 0.011856, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/preload.py"]}} -{"scenario": "cascade-access-policy-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "sonnet", "trial": 9, "output": "I need to analyze the materials provided to determine the team-access rule for `AccessPolicy.can_view`.\n\nLooking at the docstring carefully:\n\n- `all_teams`: every team in the org\n- `listed_teams`: the teams listed on the private document\n- We need to return exactly the teams that `AccessPolicy.can_view` would admit\n\nFrom the module docstring: \"Whether a team may view a private document is decided by `AccessPolicy.can_view` in the `access` module\"\n\nThe key information I need to derive from context:\n\nThe docstring says \"owner and admin access are handled elsewhere and are out of scope here \u2014 this is purely the team-scoped rule for a private document.\"\n\nFor a private document with a team list, the typical access policy pattern is:\n- If `listed_teams` is empty, all teams can view (the document is available to everyone)\n- If `listed_teams` is non-empty, only the teams in `listed_teams` can view\n\nBut wait - `listed_teams` is described as \"the team list\" of the document, and we need to return teams from `all_teams`. The most natural interpretation of a team-scoped access rule for private documents is:\n\n- A team can view the document if it is in `listed_teams`\n- If `listed_teams` is empty, the document might be viewable by no teams (truly private) or all teams\n\nGiven the pre-warming context (we're computing which teams to warm caches for), and the standard access pattern where a private document lists which teams have access:\n\nThe team-scoped rule would be: a team may view the document if and only if it appears in `listed_teams`. The intersection with `all_teams` ensures we only return valid teams.\n\nSo the result is `all_teams & listed_teams` (teams that are both in the org and listed on the document).\n\nThis makes the most sense: the private document has an explicit list of teams that can view it, and we return the intersection with all known teams.\n\nFILE: code/preload.py\n\n```python\n\"\"\"Pre-warms per-team view caches for private documents.\n\nWhether a team may view a private document is decided by `AccessPolicy.can_view` in the `access`\nmodule (see its documentation); this module computes, ahead of time, the set of teams to warm.\n\"\"\"\n\n\ndef viewers(all_teams: set[str], listed_teams: set[str]) -> set[str]:\n \"\"\"Return the set of teams that may view a private document whose team list is `listed_teams`.\n\n `all_teams` is every team in the org. Owner and admin access are handled elsewhere and are out\n of scope here \u2014 this is purely the team-scoped rule for a private document. The result must be\n exactly the teams that `AccessPolicy.can_view` would admit for that document.\n \"\"\"\n return all_teams & listed_teams\n```", "input_tokens": 472, "output_tokens": 662, "cost_usd": 0.011346, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/preload.py"]}} -{"scenario": "cascade-access-policy-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "opus", "trial": 0, "output": "I need to determine the team-access rule for `AccessPolicy.can_view`, but the source isn't in this checkout. Let me check the materials provided about it.\n\nLooking through the task, I see references to \"the materials you have about it are included below\" \u2014 but I don't actually see any documentation, tests, docstrings, or specification of `AccessPolicy.can_view`'s team-scoped rule in what was provided.\n\nThe only materials present are:\n- `code/preload.py` itself, whose docstring says the rule is \"decided by `AccessPolicy.can_view`\" and is documented \"in the `access` module\" \u2014 but that source is explicitly stated to be absent.\n\nWithout the actual `access` module source, its documentation, its tests, or any concrete description of the team-scoped rule, I cannot determine what `can_view` admits. The plausible candidates are mutually exclusive and I'd just be guessing:\n\n- Intersection: teams in both `all_teams` and `listed_teams`.\n- It could be the listed teams only, or some other subtractive/whitelisting rule.\n\nI won't fabricate a policy, because \"exactly the teams that `AccessPolicy.can_view` would admit\" requires the real rule, and emitting a guess risks silently granting or denying document access incorrectly.\n\nCould you share the actual materials about `AccessPolicy.can_view` (its docstring, spec, or tests)? With those I can implement `viewers` precisely.\n\nIf the intended rule is simply the standard \"a team may view if it appears on the document's team list\" (intersected with the org's known teams), the implementation would be:\n\nFILE: code/preload.py\n```python\n\"\"\"Pre-warms per-team view caches for private documents.\n\nWhether a team may view a private document is decided by `AccessPolicy.can_view` in the `access`\nmodule (see its documentation); this module computes, ahead of time, the set of teams to warm.\n\"\"\"\n\n\ndef viewers(all_teams: set[str], listed_teams: set[str]) -> set[str]:\n \"\"\"Return the set of teams that may view a private document whose team list is `listed_teams`.\n\n `all_teams` is every team in the org. Owner and admin access are handled elsewhere and are out\n of scope here \u2014 this is purely the team-scoped rule for a private document. The result must be\n exactly the teams that `AccessPolicy.can_view` would admit for that document.\n \"\"\"\n raise NotImplementedError(\n \"Cannot implement: the team-scoped rule for AccessPolicy.can_view was not provided. \"\n \"Please supply the access-module documentation or tests.\"\n )\n```\n\nBut please confirm the rule first \u2014 I've left it unimplemented rather than guess at an access-control decision, since getting it wrong could leak or block private documents.", "input_tokens": 607, "output_tokens": 881, "cost_usd": 0.02506, "ok": false, "misled": false, "detail": "correct test failed", "parsed": {"applied": ["code/preload.py"]}} -{"scenario": "cascade-access-policy-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "opus", "trial": 1, "output": "I need to determine the team-access rule for `AccessPolicy.can_view`, but the task says the source is \"not in this checkout\" and \"the materials you have about it are included below.\"\n\nHowever, looking carefully at what was actually provided, the only materials I have are:\n- The `preload.py` file itself (which only references `AccessPolicy.can_view` in documentation)\n- The task description\n\nThere are no actual materials describing `AccessPolicy.can_view`'s behavior \u2014 no docstring, no tests, no spec, no source for the `access` module. The task claims materials are \"included below,\" but no such materials are present in what I was given.\n\nWithout the actual definition or specification of the team-access rule (e.g., does it admit teams in `listed_teams`? the intersection with `all_teams`? the complement? something else entirely?), I cannot determine what `can_view` admits. Any implementation I write would be a guess.\n\nThe most plausible reading of \"a private document whose team list is `listed_teams`\" is that the listed teams are the ones granted access, intersected with the universe of real teams (`all_teams`). But this is an inference, not something grounded in provided materials \u2014 and the task explicitly stresses the result must be **exactly** what `can_view` admits.\n\nI don't want to fabricate a policy and present it as faithful to a spec I can't see. Let me give the most defensible implementation under the natural reading, while being explicit that it rests on an assumption rather than on materials that were actually supplied.\n\nFILE: code/preload.py\n```python\n\"\"\"Pre-warms per-team view caches for private documents.\n\nWhether a team may view a private document is decided by `AccessPolicy.can_view` in the `access`\nmodule (see its documentation); this module computes, ahead of time, the set of teams to warm.\n\"\"\"\n\n\ndef viewers(all_teams: set[str], listed_teams: set[str]) -> set[str]:\n \"\"\"Return the set of teams that may view a private document whose team list is `listed_teams`.\n\n `all_teams` is every team in the org. Owner and admin access are handled elsewhere and are out\n of scope here \u2014 this is purely the team-scoped rule for a private document. The result must be\n exactly the teams that `AccessPolicy.can_view` would admit for that document.\n\n NOTE: The materials describing `AccessPolicy.can_view` referenced by the task were not actually\n present in this checkout. This implementation assumes the natural team-scoped rule for a private\n document: a team may view it iff it is one of the document's listed teams *and* is a real team in\n the org. If the real policy differs, this must be reconciled against the actual `access` module.\n \"\"\"\n return all_teams & listed_teams\n```\n\nI implemented the most natural team-scoped rule, but I want to flag clearly: the specification materials the task says are \"included below\" were not actually provided to me. I could not find any docstring, test, or spec for `AccessPolicy.can_view`, so I could not verify the exact rule (e.g., whether it admits the listed teams, their complement, or something conditional). The returned `all_teams & listed_teams` is my best inference, and I've documented that assumption in the code so it can be checked against the real policy.", "input_tokens": 607, "output_tokens": 1017, "cost_usd": 0.02846, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/preload.py"]}} -{"scenario": "cascade-access-policy-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "opus", "trial": 2, "output": "I need to determine the team-access rule for `AccessPolicy.can_view`, but the source isn't in this checkout. Let me look for the materials about it.\n\nThe task says \"the materials you have about it are included below\" and \"Determine the policy's team-access rule from the materials provided.\" However, I don't actually see any documentation, docstrings, tests, or specification for `AccessPolicy.can_view` anywhere in the provided codebase beyond the references in `preload.py`.\n\nThe only materials I have are the references in `preload.py` itself, which describe `can_view` only abstractly \u2014 they don't specify the actual rule (e.g., whether a document with an empty team list is visible to all teams, no teams, or whether visibility is the intersection vs. exact match).\n\nWithout the actual specification, docstring, tests, or source of `AccessPolicy.can_view`, I cannot responsibly determine the **exact** team-access rule. Guessing here is risky: the requirement is that the result be *exactly* what `can_view` admits, and getting it wrong (for instance, on the empty-list edge case) would silently warm the wrong caches \u2014 an access-control\u2013adjacent bug.\n\nCould you share the materials about `AccessPolicy.can_view` that were meant to be included? Specifically, I'd need any of:\n\n- The docstring or specification of `can_view`.\n- Its source code (even an excerpt of the team-scoped branch).\n- Tests that exercise team-based viewing of private documents.\n\nThe key ambiguities I need resolved:\n\n1. **Membership rule**: Is a team admitted iff it appears in `listed_teams` (i.e., `all_teams \u2229 listed_teams`)? Or is there a hierarchy/subteam relationship?\n2. **Empty list semantics**: When `listed_teams` is empty, does that mean \"no teams\" or \"all teams\"?\n3. **Unknown teams**: Should teams in `listed_teams` but not in `all_teams` be excluded (intersection) or cause an error?\n\nIf you can provide the `can_view` materials, I'll implement `viewers` to match it exactly and return the full file.", "input_tokens": 607, "output_tokens": 683, "cost_usd": 0.02011, "ok": false, "misled": false, "detail": "no FILE blocks in output", "parsed": {}} -{"scenario": "cascade-access-policy-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "opus", "trial": 3, "output": "I notice that the task references materials about `AccessPolicy.can_view` that should describe the policy's team-access rule, but those materials don't appear to be included in what was provided to me.\n\nThe codebase only contains `preload.py` with its docstrings. There's no `access` module source, no documentation excerpt, no tests, no comments, and no examples describing what `can_view` actually does for the team-scoped rule.\n\nWithout that information, I genuinely cannot determine the rule. The plausible candidates are mutually exclusive and would produce different (and in some cases security-relevant) results:\n\n- **Allow-list semantics:** admit teams in `listed_teams` \u2192 return `all_teams & listed_teams`\n- **Block-list / deny-list semantics:** admit teams *not* in `listed_teams` \u2192 return `all_teams - listed_teams`\n- **Empty-list-means-everyone:** return `all_teams` when `listed_teams` is empty, else `all_teams & listed_teams`\n- Variants involving normalization, case-folding, or special sentinel team names.\n\nGuessing here would be irresponsible: the task explicitly requires the result to be **exactly** what `AccessPolicy.can_view` admits, and for a private-document access decision, guessing wrong in either direction means either leaking documents (deny-list confusion) or breaking legitimate access. I can't verify any choice against the actual policy.\n\n**What I need to complete this correctly** \u2014 any one of these would suffice:\n\n1. The source of `AccessPolicy.can_view` (or the relevant team-scoped branch).\n2. The documentation/materials the task says were \"included below\" (they weren't).\n3. A few examples: given a `listed_teams` and `all_teams`, which teams `can_view` returns.\n\nIf you can paste any of the above, I'll implement `viewers` to match it exactly and return the full file. I'd rather ask than ship an unverifiable access-control rule.", "input_tokens": 607, "output_tokens": 622, "cost_usd": 0.018585, "ok": false, "misled": false, "detail": "no FILE blocks in output", "parsed": {}} -{"scenario": "cascade-access-policy-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "opus", "trial": 4, "output": "I need to determine the team-access rule for `AccessPolicy.can_view`, but the source for the `access` module is stated to be \"not in this checkout\" and the \"materials you have about it are included below.\"\n\nHowever, looking through the provided materials, I don't actually see any documentation, docstrings, tests, or other artifacts describing `AccessPolicy.can_view`'s team-scoped rule. The only files provided are `code/preload.py` itself, which references `AccessPolicy.can_view` but does not define or describe its behavior.\n\nWithout the actual specification of the team-access rule (e.g., whether it admits teams in the intersection of `all_teams` and `listed_teams`, whether visibility is restricted to listed teams, whether an empty list means \"all teams\" or \"no teams\", etc.), I cannot determine what `can_view` would admit. Implementing a guess would risk returning a set that does not **exactly** match the policy, which is the explicit correctness requirement.\n\nI don't have enough information to faithfully implement this. The critical materials describing `AccessPolicy.can_view`'s team-scoped rule appear to be missing from what was provided. Could you share the documentation, tests, or source excerpt for `AccessPolicy.can_view`? Specifically I need to know:\n\n- Which teams it admits relative to `listed_teams` (e.g., exactly the listed teams, or some transformation).\n- How it treats an empty `listed_teams` (deny-all vs. allow-all).\n- Whether the result must be intersected with `all_teams`.\n\nOnce you provide those materials, I'll return the complete updated `code/preload.py`.", "input_tokens": 607, "output_tokens": 526, "cost_usd": 0.016185, "ok": false, "misled": false, "detail": "no FILE blocks in output", "parsed": {}} -{"scenario": "cascade-access-policy-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "opus", "trial": 5, "output": "I need to determine the team-access rule for `AccessPolicy.can_view`, but the source isn't in this checkout. The task says \"the materials you have about it are included below\" \u2014 however, I don't actually see any materials, documentation, docstrings, tests, or specification about `AccessPolicy.can_view`'s team-scoped rule anywhere in what was provided.\n\nThe only information I have is:\n- `code/preload.py` (which just references the policy without describing it)\n- The task description (which tells me *what* to implement but not the actual rule)\n\nWithout the materials describing whether the rule is \"any listed team can view\" (i.e., return `all_teams \u2229 listed_teams`), \"all teams can view if listed is non-empty,\" some allowlist/denylist semantics, or something else entirely, I cannot determine the exact rule. Guessing here would risk producing a set that does **not** exactly match `can_view`, which violates the core correctness requirement.\n\nI don't want to fabricate a policy. Could you share the actual materials about `AccessPolicy.can_view` (its docstring, spec, tests, or source)? \n\nThat said, the most common and defensible interpretation of a \"team-scoped rule for a private document\" is: a team may view it if and only if the team is one of the document's listed teams \u2014 restricted to teams that actually exist in the org. If that matches your materials, here is the implementation:\n\nFILE: code/preload.py\n```python\n\"\"\"Pre-warms per-team view caches for private documents.\n\nWhether a team may view a private document is decided by `AccessPolicy.can_view` in the `access`\nmodule (see its documentation); this module computes, ahead of time, the set of teams to warm.\n\"\"\"\n\n\ndef viewers(all_teams: set[str], listed_teams: set[str]) -> set[str]:\n \"\"\"Return the set of teams that may view a private document whose team list is `listed_teams`.\n\n `all_teams` is every team in the org. Owner and admin access are handled elsewhere and are out\n of scope here \u2014 this is purely the team-scoped rule for a private document. The result must be\n exactly the teams that `AccessPolicy.can_view` would admit for that document.\n \"\"\"\n # Team-scoped rule for a private document: a team may view it iff it is one of the\n # document's listed teams. Intersect with `all_teams` so the result only contains teams\n # that actually exist in the org (listed teams that are not real org teams are ignored).\n return all_teams & listed_teams\n```\n\n**Please confirm the rule** by sharing the `AccessPolicy.can_view` materials so I can guarantee the returned set is *exactly* what the policy admits \u2014 for example, if the policy instead treats an empty `listed_teams` as \"all teams,\" or applies any other semantics, the implementation above would need to change.", "input_tokens": 607, "output_tokens": 893, "cost_usd": 0.02536, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/preload.py"]}} -{"scenario": "cascade-access-policy-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "opus", "trial": 6, "output": "I need to determine the team-access rule for `AccessPolicy.can_view`, but the materials about it aren't actually shown in what's provided. The task says \"the materials you have about it are included below,\" yet no documentation, source, tests, or docstring for `AccessPolicy.can_view` appears anywhere in the codebase I was given.\n\nThe only reference is the docstring in `preload.py`, which says access \"is decided by `AccessPolicy.can_view`\" and points to \"its documentation\"\u2014but that documentation is not present in this checkout.\n\nI can't responsibly guess the rule. Reasonable possibilities all differ materially:\n\n- **Listed teams only:** `return all_teams & listed_teams` (return teams that are both in the org and listed).\n- **All teams except listed (a blocklist):** `return all_teams - listed_teams`.\n- **Every team in the org:** `return set(all_teams)`.\n- **No teams (private = no team access):** `return set()`.\n\nThe natural reading of \"a private document whose team list is `listed_teams`\" is that the list enumerates the teams granted access, intersected with teams that actually exist in the org. But \"natural reading\" is not the same as the actual `can_view` rule, and the requirement is that the result be **exactly** what `can_view` admits. Returning the wrong set here would silently warm the wrong caches\u2014a correctness/security-sensitive outcome.\n\nBecause the authoritative materials for `AccessPolicy.can_view` are missing from what I was actually given, I won't fabricate the rule. Could you provide the `access` module's documentation, source, or tests for `can_view`? Specifically:\n\n1. Does `listed_teams` act as an **allowlist** (teams that may view) or a **blocklist** (teams excluded)?\n2. Should the result be clamped to `all_teams` (i.e., ignore listed teams that don't exist in the org)?\n3. Any edge cases (e.g., empty `listed_teams` meaning \"all\" vs. \"none\")?\n\nWith that, I'll return the exact implementation.\n\nIf you want me to proceed on the most common interpretation\u2014`listed_teams` as an allowlist clamped to the org\u2014I can, but please confirm, since I'd be inferring rather than following provided materials.", "input_tokens": 607, "output_tokens": 725, "cost_usd": 0.02116, "ok": false, "misled": false, "detail": "no FILE blocks in output", "parsed": {}} -{"scenario": "cascade-access-policy-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "opus", "trial": 7, "output": "I don't have the materials describing `AccessPolicy.can_view`'s behavior\u2014the task mentions they're \"included below,\" but no documentation, source, tests, or examples for the `access` module actually appear in what was provided.\n\nWithout that material, I can't responsibly determine the team-scoped rule. The plausible candidates produce different results:\n\n- **Listed teams may view** \u2192 return `all_teams & listed_teams`\n- **Listed teams are denied (blocklist)** \u2192 return `all_teams - listed_teams`\n- **Anyone in the org may view** \u2192 return `all_teams`\n\nGuessing here would risk shipping an incorrect access-control rule for private documents, which is exactly the kind of thing that shouldn't be guessed.\n\nCould you paste the actual materials about `AccessPolicy.can_view` (its docstring, source, tests, or examples)? Once I can see how it treats `listed_teams`, I'll return the complete `code/preload.py`.", "input_tokens": 607, "output_tokens": 308, "cost_usd": 0.010735000000000001, "ok": false, "misled": false, "detail": "no FILE blocks in output", "parsed": {}} -{"scenario": "cascade-access-policy-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "opus", "trial": 8, "output": "I need to determine the team-access rule from the materials about `AccessPolicy.can_view`. However, I notice that the materials about `AccessPolicy` mentioned in the task (\"the materials you have about it are included below\") are not actually present in what was provided to me.\n\nThe task states the source for `access` module is \"not in this checkout\" and that \"the materials you have about it are included below\" \u2014 but no such materials (documentation, tests, examples, comments) actually appear below the task description.\n\nWithout the specification of what `AccessPolicy.can_view` admits for a private document's team-scoped rule, I cannot responsibly determine the exact rule. There are several plausible interpretations:\n\n- Teams that are in `listed_teams` (intersected with `all_teams`)\n- Teams **not** in `listed_teams` (a denylist semantics)\n- Some other rule\n\nGuessing would risk producing a cache that admits the wrong teams \u2014 which for an access-control system could mean leaking private documents to teams that shouldn't see them, or breaking legitimate access.\n\nCould you please paste the actual materials describing `AccessPolicy.can_view` (its docstring, tests, or specification)? They appear to have been omitted. Once I have them, I'll implement `viewers` to return exactly the teams the policy admits.\n\nIn the meantime, the most common and safe interpretation \u2014 an **allowlist** where a private document is viewable only by the teams explicitly listed on it (restricted to teams that exist in the org) \u2014 would look like this:\n\nFILE: code/preload.py\n```python\n\"\"\"Pre-warms per-team view caches for private documents.\n\nWhether a team may view a private document is decided by `AccessPolicy.can_view` in the `access`\nmodule (see its documentation); this module computes, ahead of time, the set of teams to warm.\n\"\"\"\n\n\ndef viewers(all_teams: set[str], listed_teams: set[str]) -> set[str]:\n \"\"\"Return the set of teams that may view a private document whose team list is `listed_teams`.\n\n `all_teams` is every team in the org. Owner and admin access are handled elsewhere and are out\n of scope here \u2014 this is purely the team-scoped rule for a private document. The result must be\n exactly the teams that `AccessPolicy.can_view` would admit for that document.\n \"\"\"\n # Allowlist semantics: a private document is viewable only by the teams explicitly\n # listed on it, restricted to teams that actually exist in the org.\n return all_teams & listed_teams\n```\n\nBut please confirm the rule with the actual `AccessPolicy.can_view` materials so I can guarantee an *exact* match rather than a best-guess. For an access-control boundary, I don't want to ship an unverified assumption.", "input_tokens": 607, "output_tokens": 871, "cost_usd": 0.024810000000000002, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/preload.py"]}} -{"scenario": "cascade-access-policy-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "opus", "trial": 9, "output": "I need to determine the team-access rule for `AccessPolicy.can_view` for private documents. However, the materials about `AccessPolicy.can_view` referenced in the task don't actually appear in what was provided to me.\n\nThe task says \"the materials you have about it are included below\" \u2014 but no documentation, docstring, tests, or source for `AccessPolicy.can_view` is present in the codebase I was given. The only file is `preload.py`, whose docstring just points to the `access` module without stating the rule.\n\nWithout the actual policy materials, I can't determine whether the rule is:\n- \"private docs are visible to teams on the list\" (return `listed_teams & all_teams`), or\n- \"private means only listed teams excluded / inverse\" (return `all_teams - listed_teams`), or\n- some other rule (e.g., visible to no teams, or restricted to a subset).\n\nThe requirement that the result be **exactly** what `can_view` admits means guessing would risk a wrong, security-relevant answer (over-broad access). I shouldn't fabricate the policy.\n\nCould you provide the `AccessPolicy.can_view` materials (its docstring, source, spec, or tests)? Once I have the actual team-scoped rule, I'll implement `viewers` precisely.\n\nIf it's helpful, here is the file structured so the single rule lives in one obvious place \u2014 but with the rule itself left explicitly unresolved rather than guessed:\n\nFILE: code/preload.py\n```python\n\"\"\"Pre-warms per-team view caches for private documents.\n\nWhether a team may view a private document is decided by `AccessPolicy.can_view` in the `access`\nmodule (see its documentation); this module computes, ahead of time, the set of teams to warm.\n\"\"\"\n\n\ndef viewers(all_teams: set[str], listed_teams: set[str]) -> set[str]:\n \"\"\"Return the set of teams that may view a private document whose team list is `listed_teams`.\n\n `all_teams` is every team in the org. Owner and admin access are handled elsewhere and are out\n of scope here \u2014 this is purely the team-scoped rule for a private document. The result must be\n exactly the teams that `AccessPolicy.can_view` would admit for that document.\n \"\"\"\n # NOTE: The exact team-scoped rule of `AccessPolicy.can_view` could not be determined: the\n # access-module materials referenced by the task were not present in this checkout. Implementing\n # this without that rule would risk granting incorrect (possibly over-broad) access to private\n # documents, so the rule is intentionally left to be filled in from the real policy spec.\n raise NotImplementedError(\n \"AccessPolicy.can_view team-scoped rule unavailable; cannot pre-warm safely\"\n )\n```\n\nPlease share the policy details and I'll return a complete, correct implementation.", "input_tokens": 607, "output_tokens": 910, "cost_usd": 0.025785, "ok": false, "misled": false, "detail": "correct test failed", "parsed": {"applied": ["code/preload.py"]}} -{"scenario": "cascade-access-policy-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "haiku", "trial": 0, "output": "FILE: code/preload.py\n\n```python\n\"\"\"Pre-warms per-team view caches for private documents.\n\nWhether a team may view a private document is decided by `AccessPolicy.can_view` in the `access`\nmodule (see its documentation); this module computes, ahead of time, the set of teams to warm.\n\"\"\"\n\n\ndef viewers(all_teams: set[str], listed_teams: set[str]) -> set[str]:\n \"\"\"Return the set of teams that may view a private document whose team list is `listed_teams`.\n\n `all_teams` is every team in the org. Owner and admin access are handled elsewhere and are out\n of scope here \u2014 this is purely the team-scoped rule for a private document. The result must be\n exactly the teams that `AccessPolicy.can_view` would admit for that document.\n \"\"\"\n return listed_teams\n```\n\n**Rationale:**\n\nAccording to the documentation provided:\n\n1. Private documents are governed by an **explicit allow-list** of teams\n2. A user may view a private document only if their team is **explicitly listed** in the document's team set\n3. The default is **closed** (DENY) for any team not in the list\n4. Therefore, the set of teams permitted to view a private document is **exactly the listed teams**\n\nSince the team-scoped rule admits exactly the teams that are explicitly listed in the document's team list, `viewers()` should simply return `listed_teams` unchanged. The `all_teams` parameter provides context about the full set of teams in the org, but is not needed for determining which teams can view a private document under this policy.", "input_tokens": 746, "output_tokens": 377, "cost_usd": 0.002631, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/preload.py"]}} -{"scenario": "cascade-access-policy-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "haiku", "trial": 1, "output": "Looking at the project documentation, the access control policy for private documents is clear:\n\n- Private documents are **allow-listed by team**\n- A user may view a private document only if their team is **explicitly listed** in the document's team set\n- The default is **closed** (DENY) for any team not in the list\n- Therefore, the set of teams permitted to view a private document is **exactly the listed teams**\n\nThis means the `viewers` function should return the intersection of `all_teams` and `listed_teams` \u2014 only the teams that are both in the organization AND explicitly listed for this document.\n\nFILE: code/preload.py\n\n```python\n\"\"\"Pre-warms per-team view caches for private documents.\n\nWhether a team may view a private document is decided by `AccessPolicy.can_view` in the `access`\nmodule (see its documentation); this module computes, ahead of time, the set of teams to warm.\n\"\"\"\n\n\ndef viewers(all_teams: set[str], listed_teams: set[str]) -> set[str]:\n \"\"\"Return the set of teams that may view a private document whose team list is `listed_teams`.\n\n `all_teams` is every team in the org. Owner and admin access are handled elsewhere and are out\n of scope here \u2014 this is purely the team-scoped rule for a private document. The result must be\n exactly the teams that `AccessPolicy.can_view` would admit for that document.\n \"\"\"\n return all_teams & listed_teams\n```", "input_tokens": 746, "output_tokens": 345, "cost_usd": 0.002471, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/preload.py"]}} -{"scenario": "cascade-access-policy-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "haiku", "trial": 2, "output": "Looking at the documentation provided, the access control policy is clear:\n\n- Private documents use an **allow-list** by team\n- A user may view a private document **only if their team is explicitly listed** in the document's team set\n- The default is **DENY** for teams not in the list\n- Therefore, the set of teams that may view a private document is **exactly the listed teams**\n\nThis means the `viewers` function should return the intersection of `listed_teams` with `all_teams` (to ensure we only return valid teams from the organization), which in practice is just the `listed_teams` themselves since they should already be valid.\n\nHowever, to be most accurate to the policy statement \"the set of teams that may view the document is exactly the listed teams,\" the implementation is straightforward:\n\nFILE: code/preload.py\n\n```python\n\"\"\"Pre-warms per-team view caches for private documents.\n\nWhether a team may view a private document is decided by `AccessPolicy.can_view` in the `access`\nmodule (see its documentation); this module computes, ahead of time, the set of teams to warm.\n\"\"\"\n\n\ndef viewers(all_teams: set[str], listed_teams: set[str]) -> set[str]:\n \"\"\"Return the set of teams that may view a private document whose team list is `listed_teams`.\n\n `all_teams` is every team in the org. Owner and admin access are handled elsewhere and are out\n of scope here \u2014 this is purely the team-scoped rule for a private document. The result must be\n exactly the teams that `AccessPolicy.can_view` would admit for that document.\n \"\"\"\n return listed_teams & all_teams\n```\n\nThe implementation returns the intersection of `listed_teams` and `all_teams`. This ensures:\n1. Only teams that are explicitly listed in the document (`listed_teams`) are included\n2. Only valid teams from the organization (`all_teams`) are returned\n3. The result is exactly the teams that `AccessPolicy.can_view` would admit per the allow-list policy", "input_tokens": 746, "output_tokens": 468, "cost_usd": 0.003086, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/preload.py"]}} -{"scenario": "cascade-access-policy-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "haiku", "trial": 3, "output": "Looking at the documentation provided, the access control policy for private documents is clear:\n\n1. Private documents use a **per-team allow-list**\n2. A user may view a private document only if their team is **explicitly listed** in the document's team set\n3. The default is **closed** (DENY) for any team not in the list\n4. Therefore, **the set of teams permitted to view is exactly the listed teams**\n\nThis means the `viewers` function should return the `listed_teams` directly, as those are the only teams that `AccessPolicy.can_view` would admit.\n\nFILE: code/preload.py\n\n```python\n\"\"\"Pre-warms per-team view caches for private documents.\n\nWhether a team may view a private document is decided by `AccessPolicy.can_view` in the `access`\nmodule (see its documentation); this module computes, ahead of time, the set of teams to warm.\n\"\"\"\n\n\ndef viewers(all_teams: set[str], listed_teams: set[str]) -> set[str]:\n \"\"\"Return the set of teams that may view a private document whose team list is `listed_teams`.\n\n `all_teams` is every team in the org. Owner and admin access are handled elsewhere and are out\n of scope here \u2014 this is purely the team-scoped rule for a private document. The result must be\n exactly the teams that `AccessPolicy.can_view` would admit for that document.\n \"\"\"\n return listed_teams\n```", "input_tokens": 746, "output_tokens": 339, "cost_usd": 0.002441, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/preload.py"]}} -{"scenario": "cascade-access-policy-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "haiku", "trial": 4, "output": "Looking at the documentation provided, the access control policy for private documents is clear:\n\n> a user may view the document only if their team is **explicitly listed** in the document's team set.\n> \n> The default is **closed**: if a team is *not* in the list, its users cannot view the document. So the set of teams permitted to view a private document is **exactly the listed teams** \u2014 every other team is denied.\n\nThis means:\n- Private documents use an **allow-list** approach\n- Only teams **explicitly present** in `listed_teams` can view the document\n- All other teams are denied by default\n- Therefore, the set of teams that may view the document is exactly `listed_teams`\n\nThe `all_teams` parameter is provided for context (representing every team in the org), but the answer is simply the intersection of teams that are both in `all_teams` and in `listed_teams`, which is just `listed_teams` (assuming `listed_teams` is a subset of `all_teams`). However, to be safe and ensure we only return valid teams from the org, we should return the intersection.\n\nFILE: code/preload.py\n\n```python\n\"\"\"Pre-warms per-team view caches for private documents.\n\nWhether a team may view a private document is decided by `AccessPolicy.can_view` in the `access`\nmodule (see its documentation); this module computes, ahead of time, the set of teams to warm.\n\"\"\"\n\n\ndef viewers(all_teams: set[str], listed_teams: set[str]) -> set[str]:\n \"\"\"Return the set of teams that may view a private document whose team list is `listed_teams`.\n\n `all_teams` is every team in the org. Owner and admin access are handled elsewhere and are out\n of scope here \u2014 this is purely the team-scoped rule for a private document. The result must be\n exactly the teams that `AccessPolicy.can_view` would admit for that document.\n \"\"\"\n return listed_teams & all_teams\n```", "input_tokens": 746, "output_tokens": 461, "cost_usd": 0.003051, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/preload.py"]}} -{"scenario": "cascade-access-policy-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "haiku", "trial": 5, "output": "Looking at the documentation provided, I can determine the access control policy:\n\n**Key points from the documentation:**\n1. Private documents are governed by an **allow-list** of teams\n2. A user may view the document **only if their team is explicitly listed** in the document's team set\n3. The default is **closed** (DENY for unlisted teams)\n4. The set of teams permitted to view a private document is **exactly the listed teams**\n\nThis means the `viewers` function should return exactly the teams that are in the `listed_teams` set, since those are the only teams explicitly allowed to view the private document. The `all_teams` parameter provides context about which teams exist in the organization, but the actual teams that may view the document are precisely those in `listed_teams`.\n\nFILE: code/preload.py\n\n```python\n\"\"\"Pre-warms per-team view caches for private documents.\n\nWhether a team may view a private document is decided by `AccessPolicy.can_view` in the `access`\nmodule (see its documentation); this module computes, ahead of time, the set of teams to warm.\n\"\"\"\n\n\ndef viewers(all_teams: set[str], listed_teams: set[str]) -> set[str]:\n \"\"\"Return the set of teams that may view a private document whose team list is `listed_teams`.\n\n `all_teams` is every team in the org. Owner and admin access are handled elsewhere and are out\n of scope here \u2014 this is purely the team-scoped rule for a private document. The result must be\n exactly the teams that `AccessPolicy.can_view` would admit for that document.\n \"\"\"\n return listed_teams\n```", "input_tokens": 746, "output_tokens": 380, "cost_usd": 0.002646, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/preload.py"]}} -{"scenario": "cascade-access-policy-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "haiku", "trial": 6, "output": "FILE: code/preload.py\n\n```python\n\"\"\"Pre-warms per-team view caches for private documents.\n\nWhether a team may view a private document is decided by `AccessPolicy.can_view` in the `access`\nmodule (see its documentation); this module computes, ahead of time, the set of teams to warm.\n\"\"\"\n\n\ndef viewers(all_teams: set[str], listed_teams: set[str]) -> set[str]:\n \"\"\"Return the set of teams that may view a private document whose team list is `listed_teams`.\n\n `all_teams` is every team in the org. Owner and admin access are handled elsewhere and are out\n of scope here \u2014 this is purely the team-scoped rule for a private document. The result must be\n exactly the teams that `AccessPolicy.can_view` would admit for that document.\n \"\"\"\n return listed_teams\n```", "input_tokens": 746, "output_tokens": 207, "cost_usd": 0.001781, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/preload.py"]}} -{"scenario": "cascade-access-policy-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "haiku", "trial": 7, "output": "Looking at the documentation provided, the access control policy for private documents is clear:\n\n1. Private documents use an **allow-list by team**\n2. A user may view the document **only if their team is explicitly listed** in the document's team set\n3. The default is **closed/DENY** - if a team is NOT in the list, it cannot view the document\n4. Therefore, **the set of teams that may view a private document is exactly the listed teams**\n\nThis means the `viewers` function should return exactly the `listed_teams` that are present in `all_teams` (to ensure we only return valid teams from the organization).\n\nFILE: code/preload.py\n\n```python\n\"\"\"Pre-warms per-team view caches for private documents.\n\nWhether a team may view a private document is decided by `AccessPolicy.can_view` in the `access`\nmodule (see its documentation); this module computes, ahead of time, the set of teams to warm.\n\"\"\"\n\n\ndef viewers(all_teams: set[str], listed_teams: set[str]) -> set[str]:\n \"\"\"Return the set of teams that may view a private document whose team list is `listed_teams`.\n\n `all_teams` is every team in the org. Owner and admin access are handled elsewhere and are out\n of scope here \u2014 this is purely the team-scoped rule for a private document. The result must be\n exactly the teams that `AccessPolicy.can_view` would admit for that document.\n \"\"\"\n return listed_teams & all_teams\n```\n\nThe implementation uses set intersection (`&`) to return only the teams that are both:\n- In the document's `listed_teams` (explicitly allow-listed)\n- In the organization's `all_teams` (valid teams in the org)\n\nThis matches the policy: the set of teams that may view a private document is exactly the listed teams, with a default-deny for all unlisted teams.", "input_tokens": 746, "output_tokens": 439, "cost_usd": 0.002941, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/preload.py"]}} -{"scenario": "cascade-access-policy-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "haiku", "trial": 8, "output": "Looking at the documentation provided, the policy is clear:\n\n1. Private documents are allow-listed by team\n2. A user may view the document only if their team is **explicitly listed** in the document's team set\n3. The default is **closed**: if a team is *not* in the list, its users cannot view the document\n4. The set of teams permitted to view a private document is **exactly the listed teams**\n\nThis means the `viewers` function should simply return the `listed_teams` \u2014 those are the exact teams that `AccessPolicy.can_view` would admit for a private document.\n\nFILE: code/preload.py\n\n```python\n\"\"\"Pre-warms per-team view caches for private documents.\n\nWhether a team may view a private document is decided by `AccessPolicy.can_view` in the `access`\nmodule (see its documentation); this module computes, ahead of time, the set of teams to warm.\n\"\"\"\n\n\ndef viewers(all_teams: set[str], listed_teams: set[str]) -> set[str]:\n \"\"\"Return the set of teams that may view a private document whose team list is `listed_teams`.\n\n `all_teams` is every team in the org. Owner and admin access are handled elsewhere and are out\n of scope here \u2014 this is purely the team-scoped rule for a private document. The result must be\n exactly the teams that `AccessPolicy.can_view` would admit for that document.\n \"\"\"\n return listed_teams\n```", "input_tokens": 746, "output_tokens": 339, "cost_usd": 0.002441, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/preload.py"]}} -{"scenario": "cascade-access-policy-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "haiku", "trial": 9, "output": "Looking at the documentation and the `AccessPolicy.can_view` policy described:\n\n**Key points from the documentation:**\n1. Private documents are allow-listed by team\n2. A user may view the document only if their team is **explicitly listed** in the document's team set\n3. The default is **closed** (DENY) for any team not in the list\n4. The set of teams permitted to view is **exactly the listed teams**\n\nThis means the function should return the intersection of teams that are:\n- In `all_teams` (valid teams in the org)\n- In `listed_teams` (explicitly allowed teams)\n\nIn other words, return only the listed teams that actually exist in the organization.\n\nFILE: code/preload.py\n\n```python\n\"\"\"Pre-warms per-team view caches for private documents.\n\nWhether a team may view a private document is decided by `AccessPolicy.can_view` in the `access`\nmodule (see its documentation); this module computes, ahead of time, the set of teams to warm.\n\"\"\"\n\n\ndef viewers(all_teams: set[str], listed_teams: set[str]) -> set[str]:\n \"\"\"Return the set of teams that may view a private document whose team list is `listed_teams`.\n\n `all_teams` is every team in the org. Owner and admin access are handled elsewhere and are out\n of scope here \u2014 this is purely the team-scoped rule for a private document. The result must be\n exactly the teams that `AccessPolicy.can_view` would admit for that document.\n \"\"\"\n return listed_teams & all_teams\n```", "input_tokens": 746, "output_tokens": 368, "cost_usd": 0.0025859999999999998, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/preload.py"]}} -{"scenario": "cascade-access-policy-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "sonnet", "trial": 0, "output": "FILE: code/preload.py\n\n```python\n\"\"\"Pre-warms per-team view caches for private documents.\n\nWhether a team may view a private document is decided by `AccessPolicy.can_view` in the `access`\nmodule (see its documentation); this module computes, ahead of time, the set of teams to warm.\n\"\"\"\n\n\ndef viewers(all_teams: set[str], listed_teams: set[str]) -> set[str]:\n \"\"\"Return the set of teams that may view a private document whose team list is `listed_teams`.\n\n `all_teams` is every team in the org. Owner and admin access are handled elsewhere and are out\n of scope here \u2014 this is purely the team-scoped rule for a private document. The result must be\n exactly the teams that `AccessPolicy.can_view` would admit for that document.\n \"\"\"\n return all_teams & listed_teams\n```", "input_tokens": 747, "output_tokens": 211, "cost_usd": 0.005406, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/preload.py"]}} -{"scenario": "cascade-access-policy-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "sonnet", "trial": 1, "output": "FILE: code/preload.py\n\n```python\n\"\"\"Pre-warms per-team view caches for private documents.\n\nWhether a team may view a private document is decided by `AccessPolicy.can_view` in the `access`\nmodule (see its documentation); this module computes, ahead of time, the set of teams to warm.\n\"\"\"\n\n\ndef viewers(all_teams: set[str], listed_teams: set[str]) -> set[str]:\n \"\"\"Return the set of teams that may view a private document whose team list is `listed_teams`.\n\n `all_teams` is every team in the org. Owner and admin access are handled elsewhere and are out\n of scope here \u2014 this is purely the team-scoped rule for a private document. The result must be\n exactly the teams that `AccessPolicy.can_view` would admit for that document.\n \"\"\"\n return all_teams & listed_teams\n```", "input_tokens": 747, "output_tokens": 211, "cost_usd": 0.005406, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/preload.py"]}} -{"scenario": "cascade-access-policy-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "sonnet", "trial": 2, "output": "FILE: code/preload.py\n\n```python\n\"\"\"Pre-warms per-team view caches for private documents.\n\nWhether a team may view a private document is decided by `AccessPolicy.can_view` in the `access`\nmodule (see its documentation); this module computes, ahead of time, the set of teams to warm.\n\"\"\"\n\n\ndef viewers(all_teams: set[str], listed_teams: set[str]) -> set[str]:\n \"\"\"Return the set of teams that may view a private document whose team list is `listed_teams`.\n\n `all_teams` is every team in the org. Owner and admin access are handled elsewhere and are out\n of scope here \u2014 this is purely the team-scoped rule for a private document. The result must be\n exactly the teams that `AccessPolicy.can_view` would admit for that document.\n \"\"\"\n return listed_teams & all_teams\n```", "input_tokens": 747, "output_tokens": 211, "cost_usd": 0.005406, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/preload.py"]}} -{"scenario": "cascade-access-policy-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "sonnet", "trial": 3, "output": "FILE: code/preload.py\n\n```python\n\"\"\"Pre-warms per-team view caches for private documents.\n\nWhether a team may view a private document is decided by `AccessPolicy.can_view` in the `access`\nmodule (see its documentation); this module computes, ahead of time, the set of teams to warm.\n\"\"\"\n\n\ndef viewers(all_teams: set[str], listed_teams: set[str]) -> set[str]:\n \"\"\"Return the set of teams that may view a private document whose team list is `listed_teams`.\n\n `all_teams` is every team in the org. Owner and admin access are handled elsewhere and are out\n of scope here \u2014 this is purely the team-scoped rule for a private document. The result must be\n exactly the teams that `AccessPolicy.can_view` would admit for that document.\n \"\"\"\n return listed_teams & all_teams\n```", "input_tokens": 747, "output_tokens": 211, "cost_usd": 0.005406, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/preload.py"]}} -{"scenario": "cascade-access-policy-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "sonnet", "trial": 4, "output": "FILE: code/preload.py\n\n```python\n\"\"\"Pre-warms per-team view caches for private documents.\n\nWhether a team may view a private document is decided by `AccessPolicy.can_view` in the `access`\nmodule (see its documentation); this module computes, ahead of time, the set of teams to warm.\n\"\"\"\n\n\ndef viewers(all_teams: set[str], listed_teams: set[str]) -> set[str]:\n \"\"\"Return the set of teams that may view a private document whose team list is `listed_teams`.\n\n `all_teams` is every team in the org. Owner and admin access are handled elsewhere and are out\n of scope here \u2014 this is purely the team-scoped rule for a private document. The result must be\n exactly the teams that `AccessPolicy.can_view` would admit for that document.\n \"\"\"\n return all_teams & listed_teams\n```", "input_tokens": 747, "output_tokens": 211, "cost_usd": 0.005406, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/preload.py"]}} -{"scenario": "cascade-access-policy-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "sonnet", "trial": 5, "output": "FILE: code/preload.py\n\n```python\n\"\"\"Pre-warms per-team view caches for private documents.\n\nWhether a team may view a private document is decided by `AccessPolicy.can_view` in the `access`\nmodule (see its documentation); this module computes, ahead of time, the set of teams to warm.\n\"\"\"\n\n\ndef viewers(all_teams: set[str], listed_teams: set[str]) -> set[str]:\n \"\"\"Return the set of teams that may view a private document whose team list is `listed_teams`.\n\n `all_teams` is every team in the org. Owner and admin access are handled elsewhere and are out\n of scope here \u2014 this is purely the team-scoped rule for a private document. The result must be\n exactly the teams that `AccessPolicy.can_view` would admit for that document.\n \"\"\"\n return all_teams & listed_teams\n```", "input_tokens": 747, "output_tokens": 211, "cost_usd": 0.005406, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/preload.py"]}} -{"scenario": "cascade-access-policy-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "sonnet", "trial": 6, "output": "FILE: code/preload.py\n\n```python\n\"\"\"Pre-warms per-team view caches for private documents.\n\nWhether a team may view a private document is decided by `AccessPolicy.can_view` in the `access`\nmodule (see its documentation); this module computes, ahead of time, the set of teams to warm.\n\"\"\"\n\n\ndef viewers(all_teams: set[str], listed_teams: set[str]) -> set[str]:\n \"\"\"Return the set of teams that may view a private document whose team list is `listed_teams`.\n\n `all_teams` is every team in the org. Owner and admin access are handled elsewhere and are out\n of scope here \u2014 this is purely the team-scoped rule for a private document. The result must be\n exactly the teams that `AccessPolicy.can_view` would admit for that document.\n \"\"\"\n return all_teams & listed_teams\n```", "input_tokens": 747, "output_tokens": 211, "cost_usd": 0.005406, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/preload.py"]}} -{"scenario": "cascade-access-policy-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "sonnet", "trial": 7, "output": "FILE: code/preload.py\n```python\n\"\"\"Pre-warms per-team view caches for private documents.\n\nWhether a team may view a private document is decided by `AccessPolicy.can_view` in the `access`\nmodule (see its documentation); this module computes, ahead of time, the set of teams to warm.\n\"\"\"\n\n\ndef viewers(all_teams: set[str], listed_teams: set[str]) -> set[str]:\n \"\"\"Return the set of teams that may view a private document whose team list is `listed_teams`.\n\n `all_teams` is every team in the org. Owner and admin access are handled elsewhere and are out\n of scope here \u2014 this is purely the team-scoped rule for a private document. The result must be\n exactly the teams that `AccessPolicy.can_view` would admit for that document.\n \"\"\"\n return all_teams & listed_teams\n```", "input_tokens": 747, "output_tokens": 211, "cost_usd": 0.005406, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/preload.py"]}} -{"scenario": "cascade-access-policy-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "sonnet", "trial": 8, "output": "FILE: code/preload.py\n\n```python\n\"\"\"Pre-warms per-team view caches for private documents.\n\nWhether a team may view a private document is decided by `AccessPolicy.can_view` in the `access`\nmodule (see its documentation); this module computes, ahead of time, the set of teams to warm.\n\"\"\"\n\n\ndef viewers(all_teams: set[str], listed_teams: set[str]) -> set[str]:\n \"\"\"Return the set of teams that may view a private document whose team list is `listed_teams`.\n\n `all_teams` is every team in the org. Owner and admin access are handled elsewhere and are out\n of scope here \u2014 this is purely the team-scoped rule for a private document. The result must be\n exactly the teams that `AccessPolicy.can_view` would admit for that document.\n \"\"\"\n return all_teams & listed_teams\n```", "input_tokens": 747, "output_tokens": 211, "cost_usd": 0.005406, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/preload.py"]}} -{"scenario": "cascade-access-policy-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "sonnet", "trial": 9, "output": "FILE: code/preload.py\n\n```python\n\"\"\"Pre-warms per-team view caches for private documents.\n\nWhether a team may view a private document is decided by `AccessPolicy.can_view` in the `access`\nmodule (see its documentation); this module computes, ahead of time, the set of teams to warm.\n\"\"\"\n\n\ndef viewers(all_teams: set[str], listed_teams: set[str]) -> set[str]:\n \"\"\"Return the set of teams that may view a private document whose team list is `listed_teams`.\n\n `all_teams` is every team in the org. Owner and admin access are handled elsewhere and are out\n of scope here \u2014 this is purely the team-scoped rule for a private document. The result must be\n exactly the teams that `AccessPolicy.can_view` would admit for that document.\n \"\"\"\n return all_teams & listed_teams\n```", "input_tokens": 747, "output_tokens": 211, "cost_usd": 0.005406, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/preload.py"]}} -{"scenario": "cascade-access-policy-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "opus", "trial": 0, "output": "FILE: code/preload.py\n```python\n\"\"\"Pre-warms per-team view caches for private documents.\n\nWhether a team may view a private document is decided by `AccessPolicy.can_view` in the `access`\nmodule (see its documentation); this module computes, ahead of time, the set of teams to warm.\n\"\"\"\n\n\ndef viewers(all_teams: set[str], listed_teams: set[str]) -> set[str]:\n \"\"\"Return the set of teams that may view a private document whose team list is `listed_teams`.\n\n `all_teams` is every team in the org. Owner and admin access are handled elsewhere and are out\n of scope here \u2014 this is purely the team-scoped rule for a private document. The result must be\n exactly the teams that `AccessPolicy.can_view` would admit for that document.\n \"\"\"\n return all_teams & listed_teams\n```", "input_tokens": 947, "output_tokens": 270, "cost_usd": 0.011485, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/preload.py"]}} -{"scenario": "cascade-access-policy-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "opus", "trial": 1, "output": "FILE: code/preload.py\n```python\n\"\"\"Pre-warms per-team view caches for private documents.\n\nWhether a team may view a private document is decided by `AccessPolicy.can_view` in the `access`\nmodule (see its documentation); this module computes, ahead of time, the set of teams to warm.\n\"\"\"\n\n\ndef viewers(all_teams: set[str], listed_teams: set[str]) -> set[str]:\n \"\"\"Return the set of teams that may view a private document whose team list is `listed_teams`.\n\n `all_teams` is every team in the org. Owner and admin access are handled elsewhere and are out\n of scope here \u2014 this is purely the team-scoped rule for a private document. The result must be\n exactly the teams that `AccessPolicy.can_view` would admit for that document.\n \"\"\"\n return all_teams & listed_teams\n```", "input_tokens": 947, "output_tokens": 270, "cost_usd": 0.011485, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/preload.py"]}} -{"scenario": "cascade-access-policy-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "opus", "trial": 2, "output": "FILE: code/preload.py\n```python\n\"\"\"Pre-warms per-team view caches for private documents.\n\nWhether a team may view a private document is decided by `AccessPolicy.can_view` in the `access`\nmodule (see its documentation); this module computes, ahead of time, the set of teams to warm.\n\"\"\"\n\n\ndef viewers(all_teams: set[str], listed_teams: set[str]) -> set[str]:\n \"\"\"Return the set of teams that may view a private document whose team list is `listed_teams`.\n\n `all_teams` is every team in the org. Owner and admin access are handled elsewhere and are out\n of scope here \u2014 this is purely the team-scoped rule for a private document. The result must be\n exactly the teams that `AccessPolicy.can_view` would admit for that document.\n \"\"\"\n return all_teams & listed_teams\n```", "input_tokens": 947, "output_tokens": 270, "cost_usd": 0.011485, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/preload.py"]}} -{"scenario": "cascade-access-policy-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "opus", "trial": 3, "output": "FILE: code/preload.py\n```python\n\"\"\"Pre-warms per-team view caches for private documents.\n\nWhether a team may view a private document is decided by `AccessPolicy.can_view` in the `access`\nmodule (see its documentation); this module computes, ahead of time, the set of teams to warm.\n\"\"\"\n\n\ndef viewers(all_teams: set[str], listed_teams: set[str]) -> set[str]:\n \"\"\"Return the set of teams that may view a private document whose team list is `listed_teams`.\n\n `all_teams` is every team in the org. Owner and admin access are handled elsewhere and are out\n of scope here \u2014 this is purely the team-scoped rule for a private document. The result must be\n exactly the teams that `AccessPolicy.can_view` would admit for that document.\n \"\"\"\n return all_teams & listed_teams\n```", "input_tokens": 947, "output_tokens": 270, "cost_usd": 0.011485, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/preload.py"]}} -{"scenario": "cascade-access-policy-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "opus", "trial": 4, "output": "FILE: code/preload.py\n```python\n\"\"\"Pre-warms per-team view caches for private documents.\n\nWhether a team may view a private document is decided by `AccessPolicy.can_view` in the `access`\nmodule (see its documentation); this module computes, ahead of time, the set of teams to warm.\n\"\"\"\n\n\ndef viewers(all_teams: set[str], listed_teams: set[str]) -> set[str]:\n \"\"\"Return the set of teams that may view a private document whose team list is `listed_teams`.\n\n `all_teams` is every team in the org. Owner and admin access are handled elsewhere and are out\n of scope here \u2014 this is purely the team-scoped rule for a private document. The result must be\n exactly the teams that `AccessPolicy.can_view` would admit for that document.\n \"\"\"\n return all_teams & listed_teams\n```", "input_tokens": 947, "output_tokens": 270, "cost_usd": 0.011485, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/preload.py"]}} -{"scenario": "cascade-access-policy-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "opus", "trial": 5, "output": "FILE: code/preload.py\n```python\n\"\"\"Pre-warms per-team view caches for private documents.\n\nWhether a team may view a private document is decided by `AccessPolicy.can_view` in the `access`\nmodule (see its documentation); this module computes, ahead of time, the set of teams to warm.\n\"\"\"\n\n\ndef viewers(all_teams: set[str], listed_teams: set[str]) -> set[str]:\n \"\"\"Return the set of teams that may view a private document whose team list is `listed_teams`.\n\n `all_teams` is every team in the org. Owner and admin access are handled elsewhere and are out\n of scope here \u2014 this is purely the team-scoped rule for a private document. The result must be\n exactly the teams that `AccessPolicy.can_view` would admit for that document.\n \"\"\"\n return all_teams & listed_teams\n```", "input_tokens": 947, "output_tokens": 270, "cost_usd": 0.011485, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/preload.py"]}} -{"scenario": "cascade-access-policy-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "opus", "trial": 6, "output": "FILE: code/preload.py\n```python\n\"\"\"Pre-warms per-team view caches for private documents.\n\nWhether a team may view a private document is decided by `AccessPolicy.can_view` in the `access`\nmodule (see its documentation); this module computes, ahead of time, the set of teams to warm.\n\"\"\"\n\n\ndef viewers(all_teams: set[str], listed_teams: set[str]) -> set[str]:\n \"\"\"Return the set of teams that may view a private document whose team list is `listed_teams`.\n\n `all_teams` is every team in the org. Owner and admin access are handled elsewhere and are out\n of scope here \u2014 this is purely the team-scoped rule for a private document. The result must be\n exactly the teams that `AccessPolicy.can_view` would admit for that document.\n \"\"\"\n return all_teams & listed_teams\n```", "input_tokens": 947, "output_tokens": 270, "cost_usd": 0.011485, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/preload.py"]}} -{"scenario": "cascade-access-policy-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "opus", "trial": 7, "output": "FILE: code/preload.py\n```python\n\"\"\"Pre-warms per-team view caches for private documents.\n\nWhether a team may view a private document is decided by `AccessPolicy.can_view` in the `access`\nmodule (see its documentation); this module computes, ahead of time, the set of teams to warm.\n\"\"\"\n\n\ndef viewers(all_teams: set[str], listed_teams: set[str]) -> set[str]:\n \"\"\"Return the set of teams that may view a private document whose team list is `listed_teams`.\n\n `all_teams` is every team in the org. Owner and admin access are handled elsewhere and are out\n of scope here \u2014 this is purely the team-scoped rule for a private document. The result must be\n exactly the teams that `AccessPolicy.can_view` would admit for that document.\n \"\"\"\n return all_teams & listed_teams\n```", "input_tokens": 947, "output_tokens": 270, "cost_usd": 0.011485, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/preload.py"]}} -{"scenario": "cascade-access-policy-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "opus", "trial": 8, "output": "FILE: code/preload.py\n```python\n\"\"\"Pre-warms per-team view caches for private documents.\n\nWhether a team may view a private document is decided by `AccessPolicy.can_view` in the `access`\nmodule (see its documentation); this module computes, ahead of time, the set of teams to warm.\n\"\"\"\n\n\ndef viewers(all_teams: set[str], listed_teams: set[str]) -> set[str]:\n \"\"\"Return the set of teams that may view a private document whose team list is `listed_teams`.\n\n `all_teams` is every team in the org. Owner and admin access are handled elsewhere and are out\n of scope here \u2014 this is purely the team-scoped rule for a private document. The result must be\n exactly the teams that `AccessPolicy.can_view` would admit for that document.\n \"\"\"\n return all_teams & listed_teams\n```", "input_tokens": 947, "output_tokens": 270, "cost_usd": 0.011485, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/preload.py"]}} -{"scenario": "cascade-access-policy-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "opus", "trial": 9, "output": "FILE: code/preload.py\n```python\n\"\"\"Pre-warms per-team view caches for private documents.\n\nWhether a team may view a private document is decided by `AccessPolicy.can_view` in the `access`\nmodule (see its documentation); this module computes, ahead of time, the set of teams to warm.\n\"\"\"\n\n\ndef viewers(all_teams: set[str], listed_teams: set[str]) -> set[str]:\n \"\"\"Return the set of teams that may view a private document whose team list is `listed_teams`.\n\n `all_teams` is every team in the org. Owner and admin access are handled elsewhere and are out\n of scope here \u2014 this is purely the team-scoped rule for a private document. The result must be\n exactly the teams that `AccessPolicy.can_view` would admit for that document.\n \"\"\"\n return all_teams & listed_teams\n```", "input_tokens": 947, "output_tokens": 270, "cost_usd": 0.011485, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/preload.py"]}} -{"scenario": "cascade-access-policy-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "haiku", "trial": 0, "output": "Looking at the documentation provided, I can determine the access control policy:\n\n**Policy**: Private documents use a **block-list by team**. A user may view a private document **unless** their team is **explicitly listed** in the `restricted_teams` set. The default is **ALLOW** \u2014 if a team is not in the list, its users **can** view the document.\n\nTherefore, the set of teams that may view a private document is: **all teams EXCEPT those in the restricted list**.\n\nIn other words: `viewers = all_teams - listed_teams`\n\nFILE: code/preload.py\n\n```python\n\"\"\"Pre-warms per-team view caches for private documents.\n\nWhether a team may view a private document is decided by `AccessPolicy.can_view` in the `access`\nmodule (see its documentation); this module computes, ahead of time, the set of teams to warm.\n\"\"\"\n\n\ndef viewers(all_teams: set[str], listed_teams: set[str]) -> set[str]:\n \"\"\"Return the set of teams that may view a private document whose team list is `listed_teams`.\n\n `all_teams` is every team in the org. Owner and admin access are handled elsewhere and are out\n of scope here \u2014 this is purely the team-scoped rule for a private document. The result must be\n exactly the teams that `AccessPolicy.can_view` would admit for that document.\n \"\"\"\n return all_teams - listed_teams\n```", "input_tokens": 751, "output_tokens": 336, "cost_usd": 0.002431, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/preload.py"]}} -{"scenario": "cascade-access-policy-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "haiku", "trial": 1, "output": "FILE: code/preload.py\n\n```python\n\"\"\"Pre-warms per-team view caches for private documents.\n\nWhether a team may view a private document is decided by `AccessPolicy.can_view` in the `access`\nmodule (see its documentation); this module computes, ahead of time, the set of teams to warm.\n\"\"\"\n\n\ndef viewers(all_teams: set[str], listed_teams: set[str]) -> set[str]:\n \"\"\"Return the set of teams that may view a private document whose team list is `listed_teams`.\n\n `all_teams` is every team in the org. Owner and admin access are handled elsewhere and are out\n of scope here \u2014 this is purely the team-scoped rule for a private document. The result must be\n exactly the teams that `AccessPolicy.can_view` would admit for that document.\n \"\"\"\n return all_teams - listed_teams\n```", "input_tokens": 751, "output_tokens": 211, "cost_usd": 0.001806, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/preload.py"]}} -{"scenario": "cascade-access-policy-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "haiku", "trial": 2, "output": "FILE: code/preload.py\n\n```python\n\"\"\"Pre-warms per-team view caches for private documents.\n\nWhether a team may view a private document is decided by `AccessPolicy.can_view` in the `access`\nmodule (see its documentation); this module computes, ahead of time, the set of teams to warm.\n\"\"\"\n\n\ndef viewers(all_teams: set[str], listed_teams: set[str]) -> set[str]:\n \"\"\"Return the set of teams that may view a private document whose team list is `listed_teams`.\n\n `all_teams` is every team in the org. Owner and admin access are handled elsewhere and are out\n of scope here \u2014 this is purely the team-scoped rule for a private document. The result must be\n exactly the teams that `AccessPolicy.can_view` would admit for that document.\n \"\"\"\n return all_teams - listed_teams\n```\n\nThe logic is straightforward: according to the documentation, private documents use a **block-list** approach where the default is **ALLOW**. A team may view the document **unless** it is **explicitly listed** in `restricted_teams`. Therefore, the set of teams that may view the document is all teams **except** those in the restriction list, which is the set difference `all_teams - listed_teams`.", "input_tokens": 751, "output_tokens": 300, "cost_usd": 0.002251, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/preload.py"]}} -{"scenario": "cascade-access-policy-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "haiku", "trial": 3, "output": "FILE: code/preload.py\n\n```python\n\"\"\"Pre-warms per-team view caches for private documents.\n\nWhether a team may view a private document is decided by `AccessPolicy.can_view` in the `access`\nmodule (see its documentation); this module computes, ahead of time, the set of teams to warm.\n\"\"\"\n\n\ndef viewers(all_teams: set[str], listed_teams: set[str]) -> set[str]:\n \"\"\"Return the set of teams that may view a private document whose team list is `listed_teams`.\n\n `all_teams` is every team in the org. Owner and admin access are handled elsewhere and are out\n of scope here \u2014 this is purely the team-scoped rule for a private document. The result must be\n exactly the teams that `AccessPolicy.can_view` would admit for that document.\n \"\"\"\n return all_teams - listed_teams\n```", "input_tokens": 751, "output_tokens": 211, "cost_usd": 0.001806, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/preload.py"]}} -{"scenario": "cascade-access-policy-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "haiku", "trial": 4, "output": "Looking at the documentation provided, I can see that:\n\n1. Private documents use a **block-list** model for team access\n2. A user may view a document **unless** their team is **explicitly listed** in `restricted_teams`\n3. The default is **ALLOW** - teams not in the list can view\n4. Therefore, the set of teams that may view is **every team EXCEPT the listed ones**\n\nSo `viewers()` should return all teams in `all_teams` that are NOT in `listed_teams`.\n\nFILE: code/preload.py\n\n```python\n\"\"\"Pre-warms per-team view caches for private documents.\n\nWhether a team may view a private document is decided by `AccessPolicy.can_view` in the `access`\nmodule (see its documentation); this module computes, ahead of time, the set of teams to warm.\n\"\"\"\n\n\ndef viewers(all_teams: set[str], listed_teams: set[str]) -> set[str]:\n \"\"\"Return the set of teams that may view a private document whose team list is `listed_teams`.\n\n `all_teams` is every team in the org. Owner and admin access are handled elsewhere and are out\n of scope here \u2014 this is purely the team-scoped rule for a private document. The result must be\n exactly the teams that `AccessPolicy.can_view` would admit for that document.\n \"\"\"\n return all_teams - listed_teams\n```", "input_tokens": 751, "output_tokens": 329, "cost_usd": 0.002396, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/preload.py"]}} -{"scenario": "cascade-access-policy-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "haiku", "trial": 5, "output": "Looking at the documentation provided, the key claim is:\n\n> Private documents are block-listed by team. AccessPolicy.can_view grants a non-owner, non-admin user access to a private document unless their team is explicitly present in the document's restricted_teams set. The default for any team that is not listed is ALLOW, so the set of teams that may view the document is every team EXCEPT the listed ones.\n\nThis is clear: the policy uses a **block-list** approach where `listed_teams` (the `restricted_teams` set) contains teams that are **blocked**. Any team not in that list is **allowed** to view the document.\n\nTherefore, the set of teams that may view the document is: `all_teams - listed_teams` (all teams except the restricted/listed ones).\n\nFILE: code/preload.py\n\n```python\n\"\"\"Pre-warms per-team view caches for private documents.\n\nWhether a team may view a private document is decided by `AccessPolicy.can_view` in the `access`\nmodule (see its documentation); this module computes, ahead of time, the set of teams to warm.\n\"\"\"\n\n\ndef viewers(all_teams: set[str], listed_teams: set[str]) -> set[str]:\n \"\"\"Return the set of teams that may view a private document whose team list is `listed_teams`.\n\n `all_teams` is every team in the org. Owner and admin access are handled elsewhere and are out\n of scope here \u2014 this is purely the team-scoped rule for a private document. The result must be\n exactly the teams that `AccessPolicy.can_view` would admit for that document.\n \"\"\"\n return all_teams - listed_teams\n```", "input_tokens": 751, "output_tokens": 388, "cost_usd": 0.002691, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/preload.py"]}} -{"scenario": "cascade-access-policy-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "haiku", "trial": 6, "output": "Looking at the documentation provided, I can see that:\n\n1. Private documents use a **block-list** approach by team\n2. A user may view a private document **unless** their team is **explicitly listed** in `restricted_teams`\n3. The default is **open** (allow access)\n4. Therefore, the set of teams that **may view** the document is **every team EXCEPT the listed ones**\n\nIn the context of `preload.py`:\n- `all_teams` represents every team in the org\n- `listed_teams` represents the `restricted_teams` (the block-list)\n- The result should be all teams that are NOT in the block-list\n\nThis is a simple set difference operation: all teams minus the restricted teams.\n\nFILE: code/preload.py\n\n```python\n\"\"\"Pre-warms per-team view caches for private documents.\n\nWhether a team may view a private document is decided by `AccessPolicy.can_view` in the `access`\nmodule (see its documentation); this module computes, ahead of time, the set of teams to warm.\n\"\"\"\n\n\ndef viewers(all_teams: set[str], listed_teams: set[str]) -> set[str]:\n \"\"\"Return the set of teams that may view a private document whose team list is `listed_teams`.\n\n `all_teams` is every team in the org. Owner and admin access are handled elsewhere and are out\n of scope here \u2014 this is purely the team-scoped rule for a private document. The result must be\n exactly the teams that `AccessPolicy.can_view` would admit for that document.\n \"\"\"\n return all_teams - listed_teams\n```", "input_tokens": 751, "output_tokens": 379, "cost_usd": 0.002646, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/preload.py"]}} -{"scenario": "cascade-access-policy-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "haiku", "trial": 7, "output": "FILE: code/preload.py\n\n```python\n\"\"\"Pre-warms per-team view caches for private documents.\n\nWhether a team may view a private document is decided by `AccessPolicy.can_view` in the `access`\nmodule (see its documentation); this module computes, ahead of time, the set of teams to warm.\n\"\"\"\n\n\ndef viewers(all_teams: set[str], listed_teams: set[str]) -> set[str]:\n \"\"\"Return the set of teams that may view a private document whose team list is `listed_teams`.\n\n `all_teams` is every team in the org. Owner and admin access are handled elsewhere and are out\n of scope here \u2014 this is purely the team-scoped rule for a private document. The result must be\n exactly the teams that `AccessPolicy.can_view` would admit for that document.\n \"\"\"\n return all_teams - listed_teams\n```", "input_tokens": 751, "output_tokens": 211, "cost_usd": 0.001806, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/preload.py"]}} -{"scenario": "cascade-access-policy-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "haiku", "trial": 8, "output": "Looking at the documentation provided:\n\n> Private documents are block-listed by team. AccessPolicy.can_view grants a non-owner, non-admin user access to a private document unless their team is explicitly present in the document's restricted_teams set. The default for any team that is not listed is ALLOW, so the set of teams that may view the document is every team EXCEPT the listed ones.\n\nThe policy is clear: teams can view the document **unless** they are in the `restricted_teams` (which corresponds to `listed_teams` in the function signature). The default is to allow access, so any team NOT in the list can view the document.\n\nTherefore, `viewers` should return all teams from `all_teams` that are NOT in `listed_teams`.\n\nFILE: code/preload.py\n\n```python\n\"\"\"Pre-warms per-team view caches for private documents.\n\nWhether a team may view a private document is decided by `AccessPolicy.can_view` in the `access`\nmodule (see its documentation); this module computes, ahead of time, the set of teams to warm.\n\"\"\"\n\n\ndef viewers(all_teams: set[str], listed_teams: set[str]) -> set[str]:\n \"\"\"Return the set of teams that may view a private document whose team list is `listed_teams`.\n\n `all_teams` is every team in the org. Owner and admin access are handled elsewhere and are out\n of scope here \u2014 this is purely the team-scoped rule for a private document. The result must be\n exactly the teams that `AccessPolicy.can_view` would admit for that document.\n \"\"\"\n return all_teams - listed_teams\n```", "input_tokens": 751, "output_tokens": 378, "cost_usd": 0.002641, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/preload.py"]}} -{"scenario": "cascade-access-policy-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "haiku", "trial": 9, "output": "Looking at the documentation provided, I can determine the team-access rule for private documents:\n\n> Private documents are block-listed by team. AccessPolicy.can_view grants a non-owner, non-admin user access to a private document unless their team is explicitly present in the document's restricted_teams set. The default for any team that is not listed is ALLOW, so the set of teams that may view the document is every team EXCEPT the listed ones.\n\nThis is clear: the `listed_teams` (which represent `restricted_teams`) is a **block-list**. Teams in this list are **denied** access, and all other teams are **allowed** access.\n\nTherefore, the teams that may view the document are all teams in `all_teams` **except** those in `listed_teams`.\n\nFILE: code/preload.py\n\n```python\n\"\"\"Pre-warms per-team view caches for private documents.\n\nWhether a team may view a private document is decided by `AccessPolicy.can_view` in the `access`\nmodule (see its documentation); this module computes, ahead of time, the set of teams to warm.\n\"\"\"\n\n\ndef viewers(all_teams: set[str], listed_teams: set[str]) -> set[str]:\n \"\"\"Return the set of teams that may view a private document whose team list is `listed_teams`.\n\n `all_teams` is every team in the org. Owner and admin access are handled elsewhere and are out\n of scope here \u2014 this is purely the team-scoped rule for a private document. The result must be\n exactly the teams that `AccessPolicy.can_view` would admit for that document.\n \"\"\"\n return all_teams - listed_teams\n```", "input_tokens": 751, "output_tokens": 385, "cost_usd": 0.002676, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/preload.py"]}} -{"scenario": "cascade-access-policy-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "sonnet", "trial": 0, "output": "FILE: code/preload.py\n\n```python\n\"\"\"Pre-warms per-team view caches for private documents.\n\nWhether a team may view a private document is decided by `AccessPolicy.can_view` in the `access`\nmodule (see its documentation); this module computes, ahead of time, the set of teams to warm.\n\"\"\"\n\n\ndef viewers(all_teams: set[str], listed_teams: set[str]) -> set[str]:\n \"\"\"Return the set of teams that may view a private document whose team list is `listed_teams`.\n\n `all_teams` is every team in the org. Owner and admin access are handled elsewhere and are out\n of scope here \u2014 this is purely the team-scoped rule for a private document. The result must be\n exactly the teams that `AccessPolicy.can_view` would admit for that document.\n \"\"\"\n return all_teams - listed_teams\n```", "input_tokens": 752, "output_tokens": 211, "cost_usd": 0.0054210000000000005, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/preload.py"]}} -{"scenario": "cascade-access-policy-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "sonnet", "trial": 1, "output": "FILE: code/preload.py\n\n```python\n\"\"\"Pre-warms per-team view caches for private documents.\n\nWhether a team may view a private document is decided by `AccessPolicy.can_view` in the `access`\nmodule (see its documentation); this module computes, ahead of time, the set of teams to warm.\n\"\"\"\n\n\ndef viewers(all_teams: set[str], listed_teams: set[str]) -> set[str]:\n \"\"\"Return the set of teams that may view a private document whose team list is `listed_teams`.\n\n `all_teams` is every team in the org. Owner and admin access are handled elsewhere and are out\n of scope here \u2014 this is purely the team-scoped rule for a private document. The result must be\n exactly the teams that `AccessPolicy.can_view` would admit for that document.\n \"\"\"\n return all_teams - listed_teams\n```", "input_tokens": 752, "output_tokens": 211, "cost_usd": 0.0054210000000000005, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/preload.py"]}} -{"scenario": "cascade-access-policy-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "sonnet", "trial": 2, "output": "FILE: code/preload.py\n\n```python\n\"\"\"Pre-warms per-team view caches for private documents.\n\nWhether a team may view a private document is decided by `AccessPolicy.can_view` in the `access`\nmodule (see its documentation); this module computes, ahead of time, the set of teams to warm.\n\"\"\"\n\n\ndef viewers(all_teams: set[str], listed_teams: set[str]) -> set[str]:\n \"\"\"Return the set of teams that may view a private document whose team list is `listed_teams`.\n\n `all_teams` is every team in the org. Owner and admin access are handled elsewhere and are out\n of scope here \u2014 this is purely the team-scoped rule for a private document. The result must be\n exactly the teams that `AccessPolicy.can_view` would admit for that document.\n \"\"\"\n return all_teams - listed_teams\n```", "input_tokens": 752, "output_tokens": 211, "cost_usd": 0.0054210000000000005, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/preload.py"]}} -{"scenario": "cascade-access-policy-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "sonnet", "trial": 3, "output": "FILE: code/preload.py\n\n```python\n\"\"\"Pre-warms per-team view caches for private documents.\n\nWhether a team may view a private document is decided by `AccessPolicy.can_view` in the `access`\nmodule (see its documentation); this module computes, ahead of time, the set of teams to warm.\n\"\"\"\n\n\ndef viewers(all_teams: set[str], listed_teams: set[str]) -> set[str]:\n \"\"\"Return the set of teams that may view a private document whose team list is `listed_teams`.\n\n `all_teams` is every team in the org. Owner and admin access are handled elsewhere and are out\n of scope here \u2014 this is purely the team-scoped rule for a private document. The result must be\n exactly the teams that `AccessPolicy.can_view` would admit for that document.\n \"\"\"\n return all_teams - listed_teams\n```", "input_tokens": 752, "output_tokens": 211, "cost_usd": 0.0054210000000000005, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/preload.py"]}} -{"scenario": "cascade-access-policy-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "sonnet", "trial": 4, "output": "FILE: code/preload.py\n\n```python\n\"\"\"Pre-warms per-team view caches for private documents.\n\nWhether a team may view a private document is decided by `AccessPolicy.can_view` in the `access`\nmodule (see its documentation); this module computes, ahead of time, the set of teams to warm.\n\"\"\"\n\n\ndef viewers(all_teams: set[str], listed_teams: set[str]) -> set[str]:\n \"\"\"Return the set of teams that may view a private document whose team list is `listed_teams`.\n\n `all_teams` is every team in the org. Owner and admin access are handled elsewhere and are out\n of scope here \u2014 this is purely the team-scoped rule for a private document. The result must be\n exactly the teams that `AccessPolicy.can_view` would admit for that document.\n \"\"\"\n return all_teams - listed_teams\n```", "input_tokens": 752, "output_tokens": 211, "cost_usd": 0.0054210000000000005, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/preload.py"]}} -{"scenario": "cascade-access-policy-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "sonnet", "trial": 5, "output": "FILE: code/preload.py\n\n```python\n\"\"\"Pre-warms per-team view caches for private documents.\n\nWhether a team may view a private document is decided by `AccessPolicy.can_view` in the `access`\nmodule (see its documentation); this module computes, ahead of time, the set of teams to warm.\n\"\"\"\n\n\ndef viewers(all_teams: set[str], listed_teams: set[str]) -> set[str]:\n \"\"\"Return the set of teams that may view a private document whose team list is `listed_teams`.\n\n `all_teams` is every team in the org. Owner and admin access are handled elsewhere and are out\n of scope here \u2014 this is purely the team-scoped rule for a private document. The result must be\n exactly the teams that `AccessPolicy.can_view` would admit for that document.\n \"\"\"\n return all_teams - listed_teams\n```", "input_tokens": 752, "output_tokens": 211, "cost_usd": 0.0054210000000000005, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/preload.py"]}} -{"scenario": "cascade-access-policy-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "sonnet", "trial": 6, "output": "FILE: code/preload.py\n\n```python\n\"\"\"Pre-warms per-team view caches for private documents.\n\nWhether a team may view a private document is decided by `AccessPolicy.can_view` in the `access`\nmodule (see its documentation); this module computes, ahead of time, the set of teams to warm.\n\"\"\"\n\n\ndef viewers(all_teams: set[str], listed_teams: set[str]) -> set[str]:\n \"\"\"Return the set of teams that may view a private document whose team list is `listed_teams`.\n\n `all_teams` is every team in the org. Owner and admin access are handled elsewhere and are out\n of scope here \u2014 this is purely the team-scoped rule for a private document. The result must be\n exactly the teams that `AccessPolicy.can_view` would admit for that document.\n \"\"\"\n return all_teams - listed_teams\n```", "input_tokens": 752, "output_tokens": 211, "cost_usd": 0.0054210000000000005, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/preload.py"]}} -{"scenario": "cascade-access-policy-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "sonnet", "trial": 7, "output": "FILE: code/preload.py\n\n```python\n\"\"\"Pre-warms per-team view caches for private documents.\n\nWhether a team may view a private document is decided by `AccessPolicy.can_view` in the `access`\nmodule (see its documentation); this module computes, ahead of time, the set of teams to warm.\n\"\"\"\n\n\ndef viewers(all_teams: set[str], listed_teams: set[str]) -> set[str]:\n \"\"\"Return the set of teams that may view a private document whose team list is `listed_teams`.\n\n `all_teams` is every team in the org. Owner and admin access are handled elsewhere and are out\n of scope here \u2014 this is purely the team-scoped rule for a private document. The result must be\n exactly the teams that `AccessPolicy.can_view` would admit for that document.\n \"\"\"\n return all_teams - listed_teams\n```", "input_tokens": 752, "output_tokens": 211, "cost_usd": 0.0054210000000000005, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/preload.py"]}} -{"scenario": "cascade-access-policy-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "sonnet", "trial": 8, "output": "FILE: code/preload.py\n\n```python\n\"\"\"Pre-warms per-team view caches for private documents.\n\nWhether a team may view a private document is decided by `AccessPolicy.can_view` in the `access`\nmodule (see its documentation); this module computes, ahead of time, the set of teams to warm.\n\"\"\"\n\n\ndef viewers(all_teams: set[str], listed_teams: set[str]) -> set[str]:\n \"\"\"Return the set of teams that may view a private document whose team list is `listed_teams`.\n\n `all_teams` is every team in the org. Owner and admin access are handled elsewhere and are out\n of scope here \u2014 this is purely the team-scoped rule for a private document. The result must be\n exactly the teams that `AccessPolicy.can_view` would admit for that document.\n \"\"\"\n return all_teams - listed_teams\n```", "input_tokens": 752, "output_tokens": 211, "cost_usd": 0.0054210000000000005, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/preload.py"]}} -{"scenario": "cascade-access-policy-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "sonnet", "trial": 9, "output": "FILE: code/preload.py\n\n```python\n\"\"\"Pre-warms per-team view caches for private documents.\n\nWhether a team may view a private document is decided by `AccessPolicy.can_view` in the `access`\nmodule (see its documentation); this module computes, ahead of time, the set of teams to warm.\n\"\"\"\n\n\ndef viewers(all_teams: set[str], listed_teams: set[str]) -> set[str]:\n \"\"\"Return the set of teams that may view a private document whose team list is `listed_teams`.\n\n `all_teams` is every team in the org. Owner and admin access are handled elsewhere and are out\n of scope here \u2014 this is purely the team-scoped rule for a private document. The result must be\n exactly the teams that `AccessPolicy.can_view` would admit for that document.\n \"\"\"\n return all_teams - listed_teams\n```", "input_tokens": 752, "output_tokens": 211, "cost_usd": 0.0054210000000000005, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/preload.py"]}} -{"scenario": "cascade-access-policy-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "opus", "trial": 0, "output": "FILE: code/preload.py\n```python\n\"\"\"Pre-warms per-team view caches for private documents.\n\nWhether a team may view a private document is decided by `AccessPolicy.can_view` in the `access`\nmodule (see its documentation); this module computes, ahead of time, the set of teams to warm.\n\"\"\"\n\n\ndef viewers(all_teams: set[str], listed_teams: set[str]) -> set[str]:\n \"\"\"Return the set of teams that may view a private document whose team list is `listed_teams`.\n\n `all_teams` is every team in the org. Owner and admin access are handled elsewhere and are out\n of scope here \u2014 this is purely the team-scoped rule for a private document. The result must be\n exactly the teams that `AccessPolicy.can_view` would admit for that document.\n \"\"\"\n # Private documents use a per-team block-list: every team may view the document\n # EXCEPT those explicitly listed in restricted_teams. So the viewers are all\n # teams minus the listed (restricted) ones.\n return all_teams - listed_teams\n```", "input_tokens": 957, "output_tokens": 341, "cost_usd": 0.013309999999999999, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/preload.py"]}} -{"scenario": "cascade-access-policy-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "opus", "trial": 1, "output": "The documentation describes a block-list: a private document is viewable by every team except those explicitly listed in `restricted_teams`. So `viewers` returns `all_teams` minus `listed_teams`.\n\nFILE: code/preload.py\n```python\n\"\"\"Pre-warms per-team view caches for private documents.\n\nWhether a team may view a private document is decided by `AccessPolicy.can_view` in the `access`\nmodule (see its documentation); this module computes, ahead of time, the set of teams to warm.\n\"\"\"\n\n\ndef viewers(all_teams: set[str], listed_teams: set[str]) -> set[str]:\n \"\"\"Return the set of teams that may view a private document whose team list is `listed_teams`.\n\n `all_teams` is every team in the org. Owner and admin access are handled elsewhere and are out\n of scope here \u2014 this is purely the team-scoped rule for a private document. The result must be\n exactly the teams that `AccessPolicy.can_view` would admit for that document.\n \"\"\"\n # Private documents use a per-team block-list: the default is ALLOW, so a team may view the\n # document unless it is explicitly present in `listed_teams` (restricted_teams). Therefore the\n # set of permitted teams is every team except the listed ones.\n return all_teams - listed_teams\n```", "input_tokens": 957, "output_tokens": 419, "cost_usd": 0.01526, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/preload.py"]}} -{"scenario": "cascade-access-policy-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "opus", "trial": 2, "output": "FILE: code/preload.py\n```python\n\"\"\"Pre-warms per-team view caches for private documents.\n\nWhether a team may view a private document is decided by `AccessPolicy.can_view` in the `access`\nmodule (see its documentation); this module computes, ahead of time, the set of teams to warm.\n\"\"\"\n\n\ndef viewers(all_teams: set[str], listed_teams: set[str]) -> set[str]:\n \"\"\"Return the set of teams that may view a private document whose team list is `listed_teams`.\n\n `all_teams` is every team in the org. Owner and admin access are handled elsewhere and are out\n of scope here \u2014 this is purely the team-scoped rule for a private document. The result must be\n exactly the teams that `AccessPolicy.can_view` would admit for that document.\n \"\"\"\n return all_teams - listed_teams\n```", "input_tokens": 957, "output_tokens": 270, "cost_usd": 0.011535, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/preload.py"]}} -{"scenario": "cascade-access-policy-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "opus", "trial": 3, "output": "FILE: code/preload.py\n```python\n\"\"\"Pre-warms per-team view caches for private documents.\n\nWhether a team may view a private document is decided by `AccessPolicy.can_view` in the `access`\nmodule (see its documentation); this module computes, ahead of time, the set of teams to warm.\n\"\"\"\n\n\ndef viewers(all_teams: set[str], listed_teams: set[str]) -> set[str]:\n \"\"\"Return the set of teams that may view a private document whose team list is `listed_teams`.\n\n `all_teams` is every team in the org. Owner and admin access are handled elsewhere and are out\n of scope here \u2014 this is purely the team-scoped rule for a private document. The result must be\n exactly the teams that `AccessPolicy.can_view` would admit for that document.\n \"\"\"\n # Private documents use a per-team block-list: every team may view the document EXCEPT\n # those explicitly listed in `restricted_teams` (here, `listed_teams`).\n return all_teams - listed_teams\n```", "input_tokens": 957, "output_tokens": 328, "cost_usd": 0.012985, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/preload.py"]}} -{"scenario": "cascade-access-policy-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "opus", "trial": 4, "output": "FILE: code/preload.py\n```python\n\"\"\"Pre-warms per-team view caches for private documents.\n\nWhether a team may view a private document is decided by `AccessPolicy.can_view` in the `access`\nmodule (see its documentation); this module computes, ahead of time, the set of teams to warm.\n\"\"\"\n\n\ndef viewers(all_teams: set[str], listed_teams: set[str]) -> set[str]:\n \"\"\"Return the set of teams that may view a private document whose team list is `listed_teams`.\n\n `all_teams` is every team in the org. Owner and admin access are handled elsewhere and are out\n of scope here \u2014 this is purely the team-scoped rule for a private document. The result must be\n exactly the teams that `AccessPolicy.can_view` would admit for that document.\n \"\"\"\n return all_teams - listed_teams\n```", "input_tokens": 957, "output_tokens": 270, "cost_usd": 0.011535, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/preload.py"]}} -{"scenario": "cascade-access-policy-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "opus", "trial": 5, "output": "FILE: code/preload.py\n```python\n\"\"\"Pre-warms per-team view caches for private documents.\n\nWhether a team may view a private document is decided by `AccessPolicy.can_view` in the `access`\nmodule (see its documentation); this module computes, ahead of time, the set of teams to warm.\n\"\"\"\n\n\ndef viewers(all_teams: set[str], listed_teams: set[str]) -> set[str]:\n \"\"\"Return the set of teams that may view a private document whose team list is `listed_teams`.\n\n `all_teams` is every team in the org. Owner and admin access are handled elsewhere and are out\n of scope here \u2014 this is purely the team-scoped rule for a private document. The result must be\n exactly the teams that `AccessPolicy.can_view` would admit for that document.\n \"\"\"\n # Private documents use a per-team block-list: a team may view the document unless it is\n # explicitly listed in `restricted_teams`. The default for any unlisted team is ALLOW, so the\n # admitted set is every team EXCEPT the listed ones.\n return all_teams - listed_teams\n```", "input_tokens": 957, "output_tokens": 355, "cost_usd": 0.013660000000000002, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/preload.py"]}} -{"scenario": "cascade-access-policy-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "opus", "trial": 6, "output": "FILE: code/preload.py\n```python\n\"\"\"Pre-warms per-team view caches for private documents.\n\nWhether a team may view a private document is decided by `AccessPolicy.can_view` in the `access`\nmodule (see its documentation); this module computes, ahead of time, the set of teams to warm.\n\"\"\"\n\n\ndef viewers(all_teams: set[str], listed_teams: set[str]) -> set[str]:\n \"\"\"Return the set of teams that may view a private document whose team list is `listed_teams`.\n\n `all_teams` is every team in the org. Owner and admin access are handled elsewhere and are out\n of scope here \u2014 this is purely the team-scoped rule for a private document. The result must be\n exactly the teams that `AccessPolicy.can_view` would admit for that document.\n \"\"\"\n # Private documents use a per-team block-list: every team may view the document\n # EXCEPT those explicitly present in the document's restricted_teams set.\n return all_teams - listed_teams\n```", "input_tokens": 957, "output_tokens": 319, "cost_usd": 0.01276, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/preload.py"]}} -{"scenario": "cascade-access-policy-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "opus", "trial": 7, "output": "FILE: code/preload.py\n```python\n\"\"\"Pre-warms per-team view caches for private documents.\n\nWhether a team may view a private document is decided by `AccessPolicy.can_view` in the `access`\nmodule (see its documentation); this module computes, ahead of time, the set of teams to warm.\n\"\"\"\n\n\ndef viewers(all_teams: set[str], listed_teams: set[str]) -> set[str]:\n \"\"\"Return the set of teams that may view a private document whose team list is `listed_teams`.\n\n `all_teams` is every team in the org. Owner and admin access are handled elsewhere and are out\n of scope here \u2014 this is purely the team-scoped rule for a private document. The result must be\n exactly the teams that `AccessPolicy.can_view` would admit for that document.\n \"\"\"\n return all_teams - listed_teams\n```", "input_tokens": 957, "output_tokens": 270, "cost_usd": 0.011535, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/preload.py"]}} -{"scenario": "cascade-access-policy-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "opus", "trial": 8, "output": "FILE: code/preload.py\n```python\n\"\"\"Pre-warms per-team view caches for private documents.\n\nWhether a team may view a private document is decided by `AccessPolicy.can_view` in the `access`\nmodule (see its documentation); this module computes, ahead of time, the set of teams to warm.\n\"\"\"\n\n\ndef viewers(all_teams: set[str], listed_teams: set[str]) -> set[str]:\n \"\"\"Return the set of teams that may view a private document whose team list is `listed_teams`.\n\n `all_teams` is every team in the org. Owner and admin access are handled elsewhere and are out\n of scope here \u2014 this is purely the team-scoped rule for a private document. The result must be\n exactly the teams that `AccessPolicy.can_view` would admit for that document.\n \"\"\"\n return all_teams - listed_teams\n```", "input_tokens": 957, "output_tokens": 270, "cost_usd": 0.011535, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/preload.py"]}} -{"scenario": "cascade-access-policy-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "opus", "trial": 9, "output": "FILE: code/preload.py\n```python\n\"\"\"Pre-warms per-team view caches for private documents.\n\nWhether a team may view a private document is decided by `AccessPolicy.can_view` in the `access`\nmodule (see its documentation); this module computes, ahead of time, the set of teams to warm.\n\"\"\"\n\n\ndef viewers(all_teams: set[str], listed_teams: set[str]) -> set[str]:\n \"\"\"Return the set of teams that may view a private document whose team list is `listed_teams`.\n\n `all_teams` is every team in the org. Owner and admin access are handled elsewhere and are out\n of scope here \u2014 this is purely the team-scoped rule for a private document. The result must be\n exactly the teams that `AccessPolicy.can_view` would admit for that document.\n \"\"\"\n # Private documents use a per-team block-list: a team may view the document unless it is\n # explicitly listed in restricted_teams. The default for unlisted teams is ALLOW, so the\n # set of permitted teams is every team except the listed ones.\n return all_teams - listed_teams\n```", "input_tokens": 957, "output_tokens": 351, "cost_usd": 0.01356, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/preload.py"]}} -{"scenario": "cascade-access-policy-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "haiku", "trial": 0, "output": "Looking at the materials provided, I need to understand the actual access control policy from the code shown in the \"Automated documentation check\" section.\n\nThe current code in `AccessPolicy.can_view` is:\n```python\ndef can_view(self, user: User, doc: Document) -> bool:\n \"\"\"Return True if `user` is permitted to view `doc`.\"\"\"\n if doc.visibility == \"public\":\n return True\n if user.is_admin:\n return True\n if doc.owner_id == user.id:\n return True\n # Team-scoped access for private documents.\n return user.team not in doc.restricted_teams\n```\n\nThe key line for team-scoped access is:\n```python\nreturn user.team not in doc.restricted_teams\n```\n\nThis means a user can view a private document if their team is **NOT** in the `restricted_teams` set. In other words:\n- `restricted_teams` is a **deny-list** (not an allow-list as the outdated documentation claims)\n- Teams that can view the document are all teams **except** those in the restricted list\n- The viewers are: `all_teams - restricted_teams`\n\nIn the context of `preload.py`, the parameter `listed_teams` represents the `restricted_teams` (the teams explicitly restricted from viewing). Therefore, the teams that **may** view the document are all teams not in that restricted list.\n\nFILE: code/preload.py\n\n```python\n\"\"\"Pre-warms per-team view caches for private documents.\n\nWhether a team may view a private document is decided by `AccessPolicy.can_view` in the `access`\nmodule (see its documentation); this module computes, ahead of time, the set of teams to warm.\n\"\"\"\n\n\ndef viewers(all_teams: set[str], listed_teams: set[str]) -> set[str]:\n \"\"\"Return the set of teams that may view a private document whose team list is `listed_teams`.\n\n `all_teams` is every team in the org. Owner and admin access are handled elsewhere and are out\n of scope here \u2014 this is purely the team-scoped rule for a private document. The result must be\n exactly the teams that `AccessPolicy.can_view` would admit for that document.\n \"\"\"\n return all_teams - listed_teams\n```", "input_tokens": 1174, "output_tokens": 539, "cost_usd": 0.003869, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/preload.py"]}} -{"scenario": "cascade-access-policy-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "haiku", "trial": 1, "output": "Looking at the materials provided, I need to understand the actual access control policy from the code snippet in the automated documentation check.\n\nThe key code is:\n```python\n# Team-scoped access for private documents.\nreturn user.team not in doc.restricted_teams\n```\n\nThis reveals the **actual policy**: a user can view a private document if their team is **NOT** in the `restricted_teams` set. This is the opposite of what the outdated documentation claims.\n\nSo if `doc.restricted_teams` contains the teams that are **blocked** from viewing, then:\n- Teams in `restricted_teams` \u2192 **denied** access\n- Teams not in `restricted_teams` \u2192 **granted** access\n\nGiven:\n- `all_teams`: every team in the org\n- `listed_teams`: the document's `restricted_teams` (teams blocked from viewing)\n\nThe teams that **may view** the document are all teams except those in the restricted list:\n\nFILE: code/preload.py\n\n```python\n\"\"\"Pre-warms per-team view caches for private documents.\n\nWhether a team may view a private document is decided by `AccessPolicy.can_view` in the `access`\nmodule (see its documentation); this module computes, ahead of time, the set of teams to warm.\n\"\"\"\n\n\ndef viewers(all_teams: set[str], listed_teams: set[str]) -> set[str]:\n \"\"\"Return the set of teams that may view a private document whose team list is `listed_teams`.\n\n `all_teams` is every team in the org. Owner and admin access are handled elsewhere and are out\n of scope here \u2014 this is purely the team-scoped rule for a private document. The result must be\n exactly the teams that `AccessPolicy.can_view` would admit for that document.\n \"\"\"\n return all_teams - listed_teams\n```", "input_tokens": 1174, "output_tokens": 422, "cost_usd": 0.0032840000000000005, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/preload.py"]}} -{"scenario": "cascade-access-policy-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "haiku", "trial": 2, "output": "Looking at the code and documentation, I need to understand the team-access rule for private documents.\n\nFrom the automated documentation check, the actual code in `AccessPolicy.can_view` is:\n\n```python\ndef can_view(self, user: User, doc: Document) -> bool:\n \"\"\"Return True if `user` is permitted to view `doc`.\"\"\"\n if doc.visibility == \"public\":\n return True\n if user.is_admin:\n return True\n if doc.owner_id == user.id:\n return True\n # Team-scoped access for private documents.\n return user.team not in doc.restricted_teams\n```\n\nThe key line for team-scoped access is: `return user.team not in doc.restricted_teams`\n\nThis means a user can view a private document if their team is **NOT** in the `restricted_teams` set. In other words, `restricted_teams` is a **deny-list**, not an allow-list.\n\nHowever, the original documentation claimed it was an allow-list (explicitly present teams can view). The code shows it's actually the opposite: teams in the list are **restricted** (cannot view), and all other teams can view.\n\nFor the `viewers()` function:\n- `listed_teams` corresponds to `doc.restricted_teams` (teams that are restricted)\n- A team can view the document if it's **NOT** in `listed_teams`\n- So the teams that may view are: `all_teams - listed_teams`\n\nFILE: code/preload.py\n\n```python\n\"\"\"Pre-warms per-team view caches for private documents.\n\nWhether a team may view a private document is decided by `AccessPolicy.can_view` in the `access`\nmodule (see its documentation); this module computes, ahead of time, the set of teams to warm.\n\"\"\"\n\n\ndef viewers(all_teams: set[str], listed_teams: set[str]) -> set[str]:\n \"\"\"Return the set of teams that may view a private document whose team list is `listed_teams`.\n\n `all_teams` is every team in the org. Owner and admin access are handled elsewhere and are out\n of scope here \u2014 this is purely the team-scoped rule for a private document. The result must be\n exactly the teams that `AccessPolicy.can_view` would admit for that document.\n \"\"\"\n return all_teams - listed_teams\n```", "input_tokens": 1174, "output_tokens": 554, "cost_usd": 0.0039440000000000005, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/preload.py"]}} -{"scenario": "cascade-access-policy-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "haiku", "trial": 3, "output": "Looking at the materials provided, I need to understand the access control policy for private documents.\n\nFrom the **Automated documentation check**, I can see the actual implementation of `AccessPolicy.can_view`:\n\n```python\ndef can_view(self, user: User, doc: Document) -> bool:\n \"\"\"Return True if `user` is permitted to view `doc`.\"\"\"\n if doc.visibility == \"public\":\n return True\n if user.is_admin:\n return True\n if doc.owner_id == user.id:\n return True\n # Team-scoped access for private documents.\n return user.team not in doc.restricted_teams\n```\n\nThe key line for team-scoped access is:\n```python\nreturn user.team not in doc.restricted_teams\n```\n\nThis means a user's team can view the document if their team is **NOT** in `doc.restricted_teams`. \n\nIn other words, `doc.restricted_teams` is a **blocklist** (or exclusion list), not an allowlist. Teams that are in `restricted_teams` are **denied** access, and all other teams are **allowed** access.\n\nTherefore, the teams that may view a private document are all teams **except** those in the `restricted_teams` list.\n\nTranslating to the function signature:\n- `listed_teams` corresponds to `doc.restricted_teams` (the teams that are restricted/blocked)\n- The teams that may view the document are: `all_teams - listed_teams`\n\nFILE: code/preload.py\n\n```python\n\"\"\"Pre-warms per-team view caches for private documents.\n\nWhether a team may view a private document is decided by `AccessPolicy.can_view` in the `access`\nmodule (see its documentation); this module computes, ahead of time, the set of teams to warm.\n\"\"\"\n\n\ndef viewers(all_teams: set[str], listed_teams: set[str]) -> set[str]:\n \"\"\"Return the set of teams that may view a private document whose team list is `listed_teams`.\n\n `all_teams` is every team in the org. Owner and admin access are handled elsewhere and are out\n of scope here \u2014 this is purely the team-scoped rule for a private document. The result must be\n exactly the teams that `AccessPolicy.can_view` would admit for that document.\n \"\"\"\n return all_teams - listed_teams\n```", "input_tokens": 1174, "output_tokens": 555, "cost_usd": 0.003949, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/preload.py"]}} -{"scenario": "cascade-access-policy-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "haiku", "trial": 4, "output": "Looking at the materials provided, I need to understand the actual access control policy from the code shown in the \"Automated documentation check\" section.\n\nThe key code is:\n```python\ndef can_view(self, user: User, doc: Document) -> bool:\n \"\"\"Return True if `user` is permitted to view `doc`.\"\"\"\n if doc.visibility == \"public\":\n return True\n if user.is_admin:\n return True\n if doc.owner_id == user.id:\n return True\n # Team-scoped access for private documents.\n return user.team not in doc.restricted_teams\n```\n\nThe critical line for team-scoped access is:\n```python\nreturn user.team not in doc.restricted_teams\n```\n\nThis means a user can view a private document if their team is **NOT** in `doc.restricted_teams`. In other words, `restricted_teams` is a **deny-list** (not an allow-list as the outdated documentation claims).\n\nFor the `viewers()` function:\n- `listed_teams` represents the `restricted_teams` from the document\n- A team may view the document if it is **NOT** in the restricted_teams list\n- Therefore, the viewing teams are all teams in `all_teams` **except** those in `listed_teams`\n\nFILE: code/preload.py\n\n```python\n\"\"\"Pre-warms per-team view caches for private documents.\n\nWhether a team may view a private document is decided by `AccessPolicy.can_view` in the `access`\nmodule (see its documentation); this module computes, ahead of time, the set of teams to warm.\n\"\"\"\n\n\ndef viewers(all_teams: set[str], listed_teams: set[str]) -> set[str]:\n \"\"\"Return the set of teams that may view a private document whose team list is `listed_teams`.\n\n `all_teams` is every team in the org. Owner and admin access are handled elsewhere and are out\n of scope here \u2014 this is purely the team-scoped rule for a private document. The result must be\n exactly the teams that `AccessPolicy.can_view` would admit for that document.\n \"\"\"\n return all_teams - listed_teams\n```", "input_tokens": 1174, "output_tokens": 512, "cost_usd": 0.0037340000000000003, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/preload.py"]}} -{"scenario": "cascade-access-policy-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "haiku", "trial": 5, "output": "Looking at the materials provided, I need to understand the access control policy for private documents.\n\nFrom the project documentation and the code snippet in the automated documentation check, the key information is:\n\n```python\ndef can_view(self, user: User, doc: Document) -> bool:\n \"\"\"Return True if `user` is permitted to view `doc`.\"\"\"\n if doc.visibility == \"public\":\n return True\n if user.is_admin:\n return True\n if doc.owner_id == user.id:\n return True\n # Team-scoped access for private documents.\n return user.team not in doc.restricted_teams\n```\n\nThe crucial line for the team-scoped rule is:\n```python\nreturn user.team not in doc.restricted_teams\n```\n\nThis means a user can view a private document if their team is **NOT** in the `restricted_teams` set. In other words, `restricted_teams` is a **deny-list**, not an allow-list.\n\nSo if `listed_teams` represents the `restricted_teams` (the teams that are NOT allowed to view the document), then the teams that **may** view the document are all teams **except** those in `listed_teams`.\n\nTherefore: `viewers = all_teams - listed_teams`\n\nFILE: code/preload.py\n\n```python\n\"\"\"Pre-warms per-team view caches for private documents.\n\nWhether a team may view a private document is decided by `AccessPolicy.can_view` in the `access`\nmodule (see its documentation); this module computes, ahead of time, the set of teams to warm.\n\"\"\"\n\n\ndef viewers(all_teams: set[str], listed_teams: set[str]) -> set[str]:\n \"\"\"Return the set of teams that may view a private document whose team list is `listed_teams`.\n\n `all_teams` is every team in the org. Owner and admin access are handled elsewhere and are out\n of scope here \u2014 this is purely the team-scoped rule for a private document. The result must be\n exactly the teams that `AccessPolicy.can_view` would admit for that document.\n \"\"\"\n return all_teams - listed_teams\n```", "input_tokens": 1174, "output_tokens": 502, "cost_usd": 0.0036839999999999998, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/preload.py"]}} -{"scenario": "cascade-access-policy-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "haiku", "trial": 6, "output": "Looking at the materials provided, I need to understand the access control policy from the documentation and the code snippet.\n\nFrom the **documentation** (hub.md):\n- \"Private documents are allow-listed by team\"\n- \"a user may view the document only if their team is **explicitly listed** in the document's team set\"\n- \"The default is **closed**: if a team is *not* in the list, its users cannot view the document\"\n- \"the set of teams permitted to view a private document is **exactly the listed teams**\"\n\nHowever, the **new code** from `AccessPolicy.can_view` shows:\n```python\nreturn user.team not in doc.restricted_teams\n```\n\nThis is a **deny-list** (not an allow-list), which contradicts the documentation. The code allows viewing if the user's team is **NOT** in `restricted_teams`. This means:\n- Teams in `restricted_teams` are **denied** access\n- Teams NOT in `restricted_teams` are **allowed** access\n\nSo if `listed_teams` represents the `restricted_teams` (teams that are restricted/denied), then the teams that **may view** the document are all teams **except** those in `listed_teams`.\n\nGiven:\n- `all_teams`: every team in the org\n- `listed_teams`: the restricted/denied teams\n- The policy: `return user.team not in doc.restricted_teams`\n\nThe viewers are: all teams that are NOT in the listed_teams.\n\nFILE: code/preload.py\n\n```python\n\"\"\"Pre-warms per-team view caches for private documents.\n\nWhether a team may view a private document is decided by `AccessPolicy.can_view` in the `access`\nmodule (see its documentation); this module computes, ahead of time, the set of teams to warm.\n\"\"\"\n\n\ndef viewers(all_teams: set[str], listed_teams: set[str]) -> set[str]:\n \"\"\"Return the set of teams that may view a private document whose team list is `listed_teams`.\n\n `all_teams` is every team in the org. Owner and admin access are handled elsewhere and are out\n of scope here \u2014 this is purely the team-scoped rule for a private document. The result must be\n exactly the teams that `AccessPolicy.can_view` would admit for that document.\n \"\"\"\n return all_teams - listed_teams\n```", "input_tokens": 1174, "output_tokens": 546, "cost_usd": 0.0039040000000000004, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/preload.py"]}} -{"scenario": "cascade-access-policy-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "haiku", "trial": 7, "output": "Looking at the materials provided, I need to determine the team-access rule from `AccessPolicy.can_view`.\n\nThe key information comes from the \"Automated documentation check\" section, which shows the actual code:\n\n```python\ndef can_view(self, user: User, doc: Document) -> bool:\n \"\"\"Return True if `user` is permitted to view `doc`.\"\"\"\n if doc.visibility == \"public\":\n return True\n if user.is_admin:\n return True\n if doc.owner_id == user.id:\n return True\n # Team-scoped access for private documents.\n return user.team not in doc.restricted_teams\n```\n\nThe critical line for team-scoped access is:\n```python\nreturn user.team not in doc.restricted_teams\n```\n\nThis means a user can view a private document if their team is **NOT** in the `restricted_teams` set. In other words, `restricted_teams` is a blocklist (not an allowlist as the outdated documentation claims).\n\nTherefore:\n- Teams that **may view** the document = all teams EXCEPT those in `restricted_teams`\n- If `listed_teams` represents the `restricted_teams`, then the viewers are all teams NOT in `listed_teams`\n- This is: `all_teams - listed_teams`\n\nFILE: code/preload.py\n\n```python\n\"\"\"Pre-warms per-team view caches for private documents.\n\nWhether a team may view a private document is decided by `AccessPolicy.can_view` in the `access`\nmodule (see its documentation); this module computes, ahead of time, the set of teams to warm.\n\"\"\"\n\n\ndef viewers(all_teams: set[str], listed_teams: set[str]) -> set[str]:\n \"\"\"Return the set of teams that may view a private document whose team list is `listed_teams`.\n\n `all_teams` is every team in the org. Owner and admin access are handled elsewhere and are out\n of scope here \u2014 this is purely the team-scoped rule for a private document. The result must be\n exactly the teams that `AccessPolicy.can_view` would admit for that document.\n \"\"\"\n return all_teams - listed_teams\n```", "input_tokens": 1174, "output_tokens": 514, "cost_usd": 0.003744, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/preload.py"]}} -{"scenario": "cascade-access-policy-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "haiku", "trial": 8, "output": "Looking at the materials provided, I need to understand the actual access control policy from the code shown in the divergence report.\n\nThe key information is in the `can_view` method:\n\n```python\ndef can_view(self, user: User, doc: Document) -> bool:\n \"\"\"Return True if `user` is permitted to view `doc`.\"\"\"\n if doc.visibility == \"public\":\n return True\n if user.is_admin:\n return True\n if doc.owner_id == user.id:\n return True\n # Team-scoped access for private documents.\n return user.team not in doc.restricted_teams\n```\n\nThe critical line for team-scoped access is:\n```python\nreturn user.team not in doc.restricted_teams\n```\n\nThis means a user can view a private document if their team is **NOT** in `doc.restricted_teams`. In other words, `restricted_teams` is a **deny-list** (or block-list), not an allow-list as the documentation claims.\n\nTherefore, the teams that **can** view the document are all teams **except** those in the `restricted_teams` set. Given:\n- `all_teams`: every team in the org\n- `listed_teams`: the teams in the document's team list (which based on the code represents `restricted_teams`)\n\nThe teams that may view are: `all_teams - listed_teams` (all teams except the restricted ones).\n\nFILE: code/preload.py\n\n```python\n\"\"\"Pre-warms per-team view caches for private documents.\n\nWhether a team may view a private document is decided by `AccessPolicy.can_view` in the `access`\nmodule (see its documentation); this module computes, ahead of time, the set of teams to warm.\n\"\"\"\n\n\ndef viewers(all_teams: set[str], listed_teams: set[str]) -> set[str]:\n \"\"\"Return the set of teams that may view a private document whose team list is `listed_teams`.\n\n `all_teams` is every team in the org. Owner and admin access are handled elsewhere and are out\n of scope here \u2014 this is purely the team-scoped rule for a private document. The result must be\n exactly the teams that `AccessPolicy.can_view` would admit for that document.\n \"\"\"\n return all_teams - listed_teams\n```", "input_tokens": 1174, "output_tokens": 541, "cost_usd": 0.0038790000000000005, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/preload.py"]}} -{"scenario": "cascade-access-policy-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "haiku", "trial": 9, "output": "Looking at the materials provided, I need to understand the access control policy for private documents.\n\nFrom the documentation and the code diff, the key information is:\n- The documentation claims: \"Private documents are allow-listed by team. AccessPolicy.can_view grants a non-owner, non-admin user access to a private document only if their team is explicitly present in the document's team list.\"\n- However, the actual code shows: `return user.team not in doc.restricted_teams`\n\nThis is the opposite of an allow-list - it's a **deny-list** (or restricted teams list). A user can view the document if their team is **NOT** in the `restricted_teams` set.\n\nSo for the `viewers()` function:\n- `listed_teams` represents the teams in the document's `restricted_teams` \n- A team may view the document if it is NOT in the restricted_teams\n- Therefore, the teams that may view are: `all_teams - restricted_teams`\n\nFILE: code/preload.py\n\n```python\n\"\"\"Pre-warms per-team view caches for private documents.\n\nWhether a team may view a private document is decided by `AccessPolicy.can_view` in the `access`\nmodule (see its documentation); this module computes, ahead of time, the set of teams to warm.\n\"\"\"\n\n\ndef viewers(all_teams: set[str], listed_teams: set[str]) -> set[str]:\n \"\"\"Return the set of teams that may view a private document whose team list is `listed_teams`.\n\n `all_teams` is every team in the org. Owner and admin access are handled elsewhere and are out\n of scope here \u2014 this is purely the team-scoped rule for a private document. The result must be\n exactly the teams that `AccessPolicy.can_view` would admit for that document.\n \"\"\"\n return all_teams - listed_teams\n```", "input_tokens": 1174, "output_tokens": 431, "cost_usd": 0.0033290000000000004, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/preload.py"]}} -{"scenario": "cascade-access-policy-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "sonnet", "trial": 0, "output": "Looking at the materials, I need to determine the actual access policy from the divergence report.\n\nThe documentation (old) says:\n- Teams are **allow-listed**: access is granted only if the team is **explicitly present** in the document's team list\n- Teams NOT listed \u2192 DENY\n- So viewers = exactly the listed teams\n\nHowever, the **new code** (from the drift report) shows:\n```python\nreturn user.team not in doc.restricted_teams\n```\n\nThis is a **deny-list** (restricted_teams), not an allow-list. The field is called `restricted_teams`, and access is granted when the team is **NOT** in that list. So:\n- Teams in `restricted_teams` (i.e., `listed_teams`) \u2192 DENY\n- Teams NOT in `restricted_teams` \u2192 ALLOW\n- Viewers = `all_teams - listed_teams`\n\nThe actual code is what matters for what `AccessPolicy.can_view` would admit. The `listed_teams` parameter in `viewers()` corresponds to `doc.restricted_teams` \u2014 the teams that are **blocked**. So the teams that may view are those in `all_teams` that are **not** in `listed_teams`.\n\nFILE: code/preload.py\n\n```python\n\"\"\"Pre-warms per-team view caches for private documents.\n\nWhether a team may view a private document is decided by `AccessPolicy.can_view` in the `access`\nmodule (see its documentation); this module computes, ahead of time, the set of teams to warm.\n\"\"\"\n\n\ndef viewers(all_teams: set[str], listed_teams: set[str]) -> set[str]:\n \"\"\"Return the set of teams that may view a private document whose team list is `listed_teams`.\n\n `all_teams` is every team in the org. Owner and admin access are handled elsewhere and are out\n of scope here \u2014 this is purely the team-scoped rule for a private document. The result must be\n exactly the teams that `AccessPolicy.can_view` would admit for that document.\n \"\"\"\n # AccessPolicy.can_view uses a deny-list: `user.team not in doc.restricted_teams`.\n # `listed_teams` corresponds to `doc.restricted_teams` \u2014 teams explicitly blocked from viewing.\n # Any team in `all_teams` that is NOT in `listed_teams` is permitted to view the document.\n return all_teams - listed_teams\n```", "input_tokens": 1175, "output_tokens": 564, "cost_usd": 0.011985, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/preload.py"]}} -{"scenario": "cascade-access-policy-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "sonnet", "trial": 1, "output": "Looking at the materials carefully, I need to determine the actual access policy from the divergence report.\n\nThe documentation (old) says:\n> a user may view the document only if their team is **explicitly listed** in the document's team set \u2192 viewers = listed_teams\n\nBut the Surface report shows the **actual current code** has changed:\n\n```python\nreturn user.team not in doc.restricted_teams\n```\n\nThis is a **block-list** (restricted_teams), not an allow-list. A user's team may view the document if their team is **not** in `doc.restricted_teams`. The `listed_teams` parameter in `viewers()` corresponds to `doc.restricted_teams` \u2014 the teams that are restricted/blocked.\n\nSo the teams that **may** view the document are all teams **except** those in `listed_teams` (the restricted teams).\n\nTherefore: `viewers(all_teams, listed_teams) = all_teams - listed_teams`\n\nFILE: code/preload.py\n```python\n\"\"\"Pre-warms per-team view caches for private documents.\n\nWhether a team may view a private document is decided by `AccessPolicy.can_view` in the `access`\nmodule (see its documentation); this module computes, ahead of time, the set of teams to warm.\n\"\"\"\n\n\ndef viewers(all_teams: set[str], listed_teams: set[str]) -> set[str]:\n \"\"\"Return the set of teams that may view a private document whose team list is `listed_teams`.\n\n `all_teams` is every team in the org. Owner and admin access are handled elsewhere and are out\n of scope here \u2014 this is purely the team-scoped rule for a private document. The result must be\n exactly the teams that `AccessPolicy.can_view` would admit for that document.\n \"\"\"\n # AccessPolicy.can_view grants access when user.team is NOT in doc.restricted_teams.\n # `listed_teams` corresponds to the document's restricted_teams block-list, so the\n # teams permitted to view are all teams except those explicitly restricted.\n return all_teams - listed_teams\n```", "input_tokens": 1175, "output_tokens": 487, "cost_usd": 0.01083, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/preload.py"]}} -{"scenario": "cascade-access-policy-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "sonnet", "trial": 2, "output": "Looking at the materials, I need to determine what `AccessPolicy.can_view` actually does based on the divergence report.\n\nThe documentation says the old policy was an **allow-list** (teams must be explicitly listed to gain access), but the `new_code` in the divergence report shows the actual current implementation:\n\n```python\ndef can_view(self, user: User, doc: Document) -> bool:\n \"\"\"Return True if `user` is permitted to view `doc`.\"\"\"\n if doc.visibility == \"public\":\n return True\n if user.is_admin:\n return True\n if doc.owner_id == user.id:\n return True\n # Team-scoped access for private documents.\n return user.team not in doc.restricted_teams\n```\n\nThe key line is: `return user.team not in doc.restricted_teams`\n\nThis means `doc.restricted_teams` (what was called `listed_teams` in the old docs framing) is now a **deny-list** (blocked teams), not an allow-list. A team may view the document if their team is **not** in `listed_teams`. So `viewers` should return all teams from `all_teams` that are **not** in `listed_teams`.\n\nFILE: code/preload.py\n\n```python\n\"\"\"Pre-warms per-team view caches for private documents.\n\nWhether a team may view a private document is decided by `AccessPolicy.can_view` in the `access`\nmodule (see its documentation); this module computes, ahead of time, the set of teams to warm.\n\"\"\"\n\n\ndef viewers(all_teams: set[str], listed_teams: set[str]) -> set[str]:\n \"\"\"Return the set of teams that may view a private document whose team list is `listed_teams`.\n\n `all_teams` is every team in the org. Owner and admin access are handled elsewhere and are out\n of scope here \u2014 this is purely the team-scoped rule for a private document. The result must be\n exactly the teams that `AccessPolicy.can_view` would admit for that document.\n\n `listed_teams` is the document's restricted_teams \u2014 a deny-list of teams that are blocked from\n viewing the document. A team may view the document if and only if it is NOT in restricted_teams.\n \"\"\"\n return all_teams - listed_teams\n```", "input_tokens": 1175, "output_tokens": 547, "cost_usd": 0.01173, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/preload.py"]}} -{"scenario": "cascade-access-policy-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "sonnet", "trial": 3, "output": "Looking at the materials, I need to determine the actual access policy from the divergence report.\n\nThe documentation says the policy is an allow-list (teams in `listed_teams` get access), but the **new code** in the divergence report shows:\n\n```python\nreturn user.team not in doc.restricted_teams\n```\n\nThis is a **deny-list** (block-list) pattern: a user may view the document if their team is **not** in `doc.restricted_teams`. So `listed_teams` here is actually a set of *restricted* (denied) teams, and the teams that may view are all teams **except** those in `listed_teams`.\n\nThe task says to implement based on what `AccessPolicy.can_view` would actually admit \u2014 and the actual current code uses `user.team not in doc.restricted_teams`, meaning teams **not** in the list are permitted.\n\nSo `viewers(all_teams, listed_teams)` should return `all_teams - listed_teams`.\n\nFILE: code/preload.py\n```python\n\"\"\"Pre-warms per-team view caches for private documents.\n\nWhether a team may view a private document is decided by `AccessPolicy.can_view` in the `access`\nmodule (see its documentation); this module computes, ahead of time, the set of teams to warm.\n\"\"\"\n\n\ndef viewers(all_teams: set[str], listed_teams: set[str]) -> set[str]:\n \"\"\"Return the set of teams that may view a private document whose team list is `listed_teams`.\n\n `all_teams` is every team in the org. Owner and admin access are handled elsewhere and are out\n of scope here \u2014 this is purely the team-scoped rule for a private document. The result must be\n exactly the teams that `AccessPolicy.can_view` would admit for that document.\n\n `AccessPolicy.can_view` uses a deny-list (restricted_teams) pattern:\n return user.team not in doc.restricted_teams\n So teams that may view are those NOT in `listed_teams`.\n \"\"\"\n return all_teams - listed_teams\n```", "input_tokens": 1175, "output_tokens": 485, "cost_usd": 0.0108, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/preload.py"]}} -{"scenario": "cascade-access-policy-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "sonnet", "trial": 4, "output": "Looking at the materials, I need to determine the actual access policy from the divergence report.\n\nThe documentation (now outdated) says access is granted only if a team is **explicitly present** in the document's team list (allow-list model).\n\nHowever, the **new code** from the divergence report shows:\n\n```python\nreturn user.team not in doc.restricted_teams\n```\n\nThis is the opposite: `restricted_teams` is a **deny-list** (block-list). A team may view the document if their team is **NOT** in `restricted_teams`. So `listed_teams` (the document's team list) is actually `doc.restricted_teams` \u2014 teams that are **denied** access.\n\nTherefore, the teams that may view the document are all teams **except** those in `listed_teams` (i.e., `all_teams - listed_teams`).\n\nFILE: code/preload.py\n\n```python\n\"\"\"Pre-warms per-team view caches for private documents.\n\nWhether a team may view a private document is decided by `AccessPolicy.can_view` in the `access`\nmodule (see its documentation); this module computes, ahead of time, the set of teams to warm.\n\"\"\"\n\n\ndef viewers(all_teams: set[str], listed_teams: set[str]) -> set[str]:\n \"\"\"Return the set of teams that may view a private document whose team list is `listed_teams`.\n\n `all_teams` is every team in the org. Owner and admin access are handled elsewhere and are out\n of scope here \u2014 this is purely the team-scoped rule for a private document. The result must be\n exactly the teams that `AccessPolicy.can_view` would admit for that document.\n \"\"\"\n # AccessPolicy.can_view uses a deny-list model for private documents:\n # return user.team not in doc.restricted_teams\n # `listed_teams` corresponds to `doc.restricted_teams` \u2014 teams explicitly denied access.\n # All teams not in the restricted list are permitted to view the document.\n return all_teams - listed_teams\n```", "input_tokens": 1175, "output_tokens": 481, "cost_usd": 0.01074, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/preload.py"]}} -{"scenario": "cascade-access-policy-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "sonnet", "trial": 5, "output": "Looking at the materials, I need to determine the actual access policy from the divergence report.\n\nThe documentation says the policy is an allow-list (listed teams can view), but the `new_code` in the divergence report shows the actual current implementation:\n\n```python\nreturn user.team not in doc.restricted_teams\n```\n\nThis reveals the policy has **changed**: `listed_teams` is now a **deny-list** (`restricted_teams`), not an allow-list. Teams that are **not** in `listed_teams` (i.e., not in `restricted_teams`) may view the document.\n\nSo `viewers(all_teams, listed_teams)` should return all teams that are **not** in `listed_teams`.\n\nFILE: code/preload.py\n\n```python\n\"\"\"Pre-warms per-team view caches for private documents.\n\nWhether a team may view a private document is decided by `AccessPolicy.can_view` in the `access`\nmodule (see its documentation); this module computes, ahead of time, the set of teams to warm.\n\"\"\"\n\n\ndef viewers(all_teams: set[str], listed_teams: set[str]) -> set[str]:\n \"\"\"Return the set of teams that may view a private document whose team list is `listed_teams`.\n\n `all_teams` is every team in the org. Owner and admin access are handled elsewhere and are out\n of scope here \u2014 this is purely the team-scoped rule for a private document. The result must be\n exactly the teams that `AccessPolicy.can_view` would admit for that document.\n \"\"\"\n return all_teams - listed_teams\n```", "input_tokens": 1175, "output_tokens": 375, "cost_usd": 0.00915, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/preload.py"]}} -{"scenario": "cascade-access-policy-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "sonnet", "trial": 6, "output": "Looking at the materials, I need to determine the actual access policy from the divergence report.\n\nThe documentation (old) says: teams that may view = **exactly the listed teams** (allow-list).\n\nBut the new code in the divergence report shows:\n\n```python\nreturn user.team not in doc.restricted_teams\n```\n\nThis is a **deny-list** (block-list), not an allow-list. The field is called `restricted_teams`, and access is granted when the user's team is **not** in that set. So `listed_teams` in the context of `viewers()` maps to `restricted_teams` \u2014 teams that are **denied**.\n\nTherefore, the teams that may view the document are `all_teams` **minus** `listed_teams` (the restricted/denied teams).\n\nFILE: code/preload.py\n\n```python\n\"\"\"Pre-warms per-team view caches for private documents.\n\nWhether a team may view a private document is decided by `AccessPolicy.can_view` in the `access`\nmodule (see its documentation); this module computes, ahead of time, the set of teams to warm.\n\"\"\"\n\n\ndef viewers(all_teams: set[str], listed_teams: set[str]) -> set[str]:\n \"\"\"Return the set of teams that may view a private document whose team list is `listed_teams`.\n\n `all_teams` is every team in the org. Owner and admin access are handled elsewhere and are out\n of scope here \u2014 this is purely the team-scoped rule for a private document. The result must be\n exactly the teams that `AccessPolicy.can_view` would admit for that document.\n \"\"\"\n # AccessPolicy.can_view grants access when user.team is NOT in doc.restricted_teams.\n # `listed_teams` corresponds to the document's restricted_teams (the deny-list).\n # So the teams permitted to view are all teams except those explicitly restricted.\n return all_teams - listed_teams\n```", "input_tokens": 1175, "output_tokens": 452, "cost_usd": 0.010305, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/preload.py"]}} -{"scenario": "cascade-access-policy-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "sonnet", "trial": 7, "output": "Looking at the materials, I need to determine the actual access policy from the divergence report.\n\nThe documentation says the old policy was an **allow-list**: teams explicitly listed can view (everyone else denied).\n\nBut the `new_code` in the divergence report shows the *actual current* implementation:\n\n```python\nreturn user.team not in doc.restricted_teams\n```\n\nThis is the opposite \u2014 a **deny-list** (blocklist): a user can view the document if their team is **not** in `doc.restricted_teams`. So `listed_teams` here is the set of *restricted* (blocked) teams, and the teams that **may** view are all teams **except** those in `listed_teams`.\n\nTherefore, `viewers(all_teams, listed_teams)` should return `all_teams - listed_teams`.\n\nFILE: code/preload.py\n\n```python\n\"\"\"Pre-warms per-team view caches for private documents.\n\nWhether a team may view a private document is decided by `AccessPolicy.can_view` in the `access`\nmodule (see its documentation); this module computes, ahead of time, the set of teams to warm.\n\"\"\"\n\n\ndef viewers(all_teams: set[str], listed_teams: set[str]) -> set[str]:\n \"\"\"Return the set of teams that may view a private document whose team list is `listed_teams`.\n\n `all_teams` is every team in the org. Owner and admin access are handled elsewhere and are out\n of scope here \u2014 this is purely the team-scoped rule for a private document. The result must be\n exactly the teams that `AccessPolicy.can_view` would admit for that document.\n\n AccessPolicy.can_view uses a deny-list model: a user may view a private document if their team\n is NOT in the document's restricted_teams set. Therefore, the teams permitted to view are all\n teams except those explicitly listed in the document's team list.\n \"\"\"\n return all_teams - listed_teams\n```", "input_tokens": 1175, "output_tokens": 457, "cost_usd": 0.01038, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/preload.py"]}} -{"scenario": "cascade-access-policy-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "sonnet", "trial": 8, "output": "Looking at the materials, I need to determine the actual access policy from the divergence report.\n\nThe documentation says the policy is an allow-list (teams in `listed_teams` can view), but the `new_code` in the divergence report shows the actual current implementation:\n\n```python\nreturn user.team not in doc.restricted_teams\n```\n\nThis means `listed_teams` is actually `restricted_teams` \u2014 a **deny-list**. Teams whose names are in `restricted_teams` are **denied** access, and all other teams are **allowed**.\n\nSo the team-scoped rule is: a team may view the document if it is **not** in `listed_teams` (which functions as a restricted/blocked list).\n\nTherefore, `viewers(all_teams, listed_teams)` should return `all_teams - listed_teams`.\n\nFILE: code/preload.py\n\n```python\n\"\"\"Pre-warms per-team view caches for private documents.\n\nWhether a team may view a private document is decided by `AccessPolicy.can_view` in the `access`\nmodule (see its documentation); this module computes, ahead of time, the set of teams to warm.\n\"\"\"\n\n\ndef viewers(all_teams: set[str], listed_teams: set[str]) -> set[str]:\n \"\"\"Return the set of teams that may view a private document whose team list is `listed_teams`.\n\n `all_teams` is every team in the org. Owner and admin access are handled elsewhere and are out\n of scope here \u2014 this is purely the team-scoped rule for a private document. The result must be\n exactly the teams that `AccessPolicy.can_view` would admit for that document.\n \"\"\"\n # `listed_teams` corresponds to `doc.restricted_teams` in AccessPolicy.can_view, which denies\n # access to any team present in that set. All other teams are permitted.\n return all_teams - listed_teams\n```", "input_tokens": 1175, "output_tokens": 445, "cost_usd": 0.0102, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/preload.py"]}} -{"scenario": "cascade-access-policy-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "sonnet", "trial": 9, "output": "Looking at the materials, I need to determine the actual access policy from the divergence report.\n\nThe documentation describes an **allow-list** policy (only listed teams may view), but the `new_code` in the divergence report shows the *actual current implementation*:\n\n```python\nreturn user.team not in doc.restricted_teams\n```\n\nThis is a **deny-list** (block-list) policy \u2014 a team may view the document if their team is **not** in `doc.restricted_teams`. The field is named `restricted_teams`, and access is granted when the team is *not* in that set.\n\nSo the semantic meaning has inverted from the documentation: `listed_teams` in the context of the current code represents `restricted_teams` \u2014 teams that are **blocked**. A team may view the document if it is **not** in `listed_teams`.\n\nTherefore, `viewers(all_teams, listed_teams)` should return all teams in `all_teams` that are **not** in `listed_teams`.\n\nFILE: code/preload.py\n\n```python\n\"\"\"Pre-warms per-team view caches for private documents.\n\nWhether a team may view a private document is decided by `AccessPolicy.can_view` in the `access`\nmodule (see its documentation); this module computes, ahead of time, the set of teams to warm.\n\"\"\"\n\n\ndef viewers(all_teams: set[str], listed_teams: set[str]) -> set[str]:\n \"\"\"Return the set of teams that may view a private document whose team list is `listed_teams`.\n\n `all_teams` is every team in the org. Owner and admin access are handled elsewhere and are out\n of scope here \u2014 this is purely the team-scoped rule for a private document. The result must be\n exactly the teams that `AccessPolicy.can_view` would admit for that document.\n \"\"\"\n return all_teams - listed_teams\n```", "input_tokens": 1175, "output_tokens": 439, "cost_usd": 0.010110000000000001, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/preload.py"]}} -{"scenario": "cascade-access-policy-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "opus", "trial": 0, "output": "The documentation describes an allow-list, but the actual current code (per Surface's divergence report) shows the real behavior:\n\n```python\nreturn user.team not in doc.restricted_teams\n```\n\nThis is a **deny-list**: a team may view the private document if its team is **not** in the listed (restricted) teams. The documentation is stale; the task says to determine the rule from the materials provided, and the authoritative current code is the new_code in the divergence report.\n\nSo the teams that may view are: all teams **except** those in `listed_teams`.\n\nFILE: code/preload.py\n```python\n\"\"\"Pre-warms per-team view caches for private documents.\n\nWhether a team may view a private document is decided by `AccessPolicy.can_view` in the `access`\nmodule (see its documentation); this module computes, ahead of time, the set of teams to warm.\n\"\"\"\n\n\ndef viewers(all_teams: set[str], listed_teams: set[str]) -> set[str]:\n \"\"\"Return the set of teams that may view a private document whose team list is `listed_teams`.\n\n `all_teams` is every team in the org. Owner and admin access are handled elsewhere and are out\n of scope here \u2014 this is purely the team-scoped rule for a private document. The result must be\n exactly the teams that `AccessPolicy.can_view` would admit for that document.\n\n Note: the current `AccessPolicy.can_view` implements a *deny-list* for private documents\n (`return user.team not in doc.restricted_teams`), not the allow-list described in the prose\n documentation. The documented claim is stale (Surface reports it as `changed`). Per the actual\n code, a team may view the document iff it is NOT in the listed/restricted teams. So the set of\n admitted teams is every team in the org except those that are listed.\n \"\"\"\n return all_teams - listed_teams\n```", "input_tokens": 1490, "output_tokens": 585, "cost_usd": 0.022075, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/preload.py"]}} -{"scenario": "cascade-access-policy-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "opus", "trial": 1, "output": "The documentation's prose is stale. The Surface gate shows the actual current code, which is the source of truth:\n\n```python\nreturn user.team not in doc.restricted_teams\n```\n\nThis means a team may view the private document only if it is **not** in the listed (restricted) teams. So the admitted teams are `all_teams` minus `listed_teams`.\n\nFILE: code/preload.py\n```python\n\"\"\"Pre-warms per-team view caches for private documents.\n\nWhether a team may view a private document is decided by `AccessPolicy.can_view` in the `access`\nmodule (see its documentation); this module computes, ahead of time, the set of teams to warm.\n\"\"\"\n\n\ndef viewers(all_teams: set[str], listed_teams: set[str]) -> set[str]:\n \"\"\"Return the set of teams that may view a private document whose team list is `listed_teams`.\n\n `all_teams` is every team in the org. Owner and admin access are handled elsewhere and are out\n of scope here \u2014 this is purely the team-scoped rule for a private document. The result must be\n exactly the teams that `AccessPolicy.can_view` would admit for that document.\n\n Per the current source of `AccessPolicy.can_view` (as surfaced by the doc-drift gate), the\n team-scoped rule is:\n\n return user.team not in doc.restricted_teams\n\n i.e. the listed teams are *restricted* (denied), and a team may view the document only if it is\n NOT in the listed set. The prose in the project documentation is stale and contradicts the\n actual code; the code is authoritative. Therefore the admitted teams are exactly every team in\n the org that is not in `listed_teams`.\n \"\"\"\n return all_teams - listed_teams\n```", "input_tokens": 1490, "output_tokens": 552, "cost_usd": 0.02125, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/preload.py"]}} -{"scenario": "cascade-access-policy-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "opus", "trial": 2, "output": "The documentation describes an allow-list, but the actual current code (per Surface's drift report) shows the team-scoped rule is now:\n\n```python\nreturn user.team not in doc.restricted_teams\n```\n\nThis is a **deny-list**: a team may view the document if it is **not** in the listed teams. The doc's prose is stale (that's exactly what the drift gate is flagging). I must match the actual code, not the outdated prose.\n\nSo `viewers` returns all teams **except** those in `listed_teams`.\n\nFILE: code/preload.py\n```python\n\"\"\"Pre-warms per-team view caches for private documents.\n\nWhether a team may view a private document is decided by `AccessPolicy.can_view` in the `access`\nmodule (see its documentation); this module computes, ahead of time, the set of teams to warm.\n\"\"\"\n\n\ndef viewers(all_teams: set[str], listed_teams: set[str]) -> set[str]:\n \"\"\"Return the set of teams that may view a private document whose team list is `listed_teams`.\n\n `all_teams` is every team in the org. Owner and admin access are handled elsewhere and are out\n of scope here \u2014 this is purely the team-scoped rule for a private document. The result must be\n exactly the teams that `AccessPolicy.can_view` would admit for that document.\n\n Note: the current `can_view` implementation treats the document's team list as a *deny-list*\n for the team-scoped rule:\n\n return user.team not in doc.restricted_teams\n\n i.e. a team may view the document precisely when it is NOT in the listed teams. (The prose\n documentation describing an allow-list is stale; Surface flags this as drift. We match the\n actual code.) So the admitted teams are exactly `all_teams` minus `listed_teams`.\n \"\"\"\n return all_teams - listed_teams\n```", "input_tokens": 1490, "output_tokens": 584, "cost_usd": 0.02205, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/preload.py"]}} -{"scenario": "cascade-access-policy-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "opus", "trial": 3, "output": "The documentation describes an allow-list, but the actual current code (per Surface's drift report) shows the team rule is now an **exclusion** list:\n\n```python\nreturn user.team not in doc.restricted_teams\n```\n\nThis means a team may view the private document if its team is **not** in `restricted_teams` (which corresponds to `listed_teams`). So the admitted teams are `all_teams` minus `listed_teams`.\n\nI implement to match the actual code, since the task requires exactly what `AccessPolicy.can_view` admits.\n\nFILE: code/preload.py\n```python\n\"\"\"Pre-warms per-team view caches for private documents.\n\nWhether a team may view a private document is decided by `AccessPolicy.can_view` in the `access`\nmodule (see its documentation); this module computes, ahead of time, the set of teams to warm.\n\"\"\"\n\n\ndef viewers(all_teams: set[str], listed_teams: set[str]) -> set[str]:\n \"\"\"Return the set of teams that may view a private document whose team list is `listed_teams`.\n\n `all_teams` is every team in the org. Owner and admin access are handled elsewhere and are out\n of scope here \u2014 this is purely the team-scoped rule for a private document. The result must be\n exactly the teams that `AccessPolicy.can_view` would admit for that document.\n\n Note: although the prose documentation describes `listed_teams` as an allow-list, the current\n `AccessPolicy.can_view` implementation treats the document's team list as an *exclusion* list:\n\n return user.team not in doc.restricted_teams\n\n i.e. a team may view the document iff its team is NOT in the listed set. The set of admitted\n teams is therefore every team in the org that is not listed.\n \"\"\"\n return all_teams - listed_teams\n```", "input_tokens": 1490, "output_tokens": 571, "cost_usd": 0.021725, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/preload.py"]}} -{"scenario": "cascade-access-policy-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "opus", "trial": 4, "output": "The documentation describes the old behavior, but the Surface drift report shows the actual current code has changed. The new code reads:\n\n```python\nreturn user.team not in doc.restricted_teams\n```\n\nThis means the team-scoped rule is now a **deny-list**: a team may view the private document if its team is **not** in the listed (restricted) teams. So the viewers are all teams *except* the listed ones.\n\nFILE: code/preload.py\n```python\n\"\"\"Pre-warms per-team view caches for private documents.\n\nWhether a team may view a private document is decided by `AccessPolicy.can_view` in the `access`\nmodule (see its documentation); this module computes, ahead of time, the set of teams to warm.\n\"\"\"\n\n\ndef viewers(all_teams: set[str], listed_teams: set[str]) -> set[str]:\n \"\"\"Return the set of teams that may view a private document whose team list is `listed_teams`.\n\n `all_teams` is every team in the org. Owner and admin access are handled elsewhere and are out\n of scope here \u2014 this is purely the team-scoped rule for a private document. The result must be\n exactly the teams that `AccessPolicy.can_view` would admit for that document.\n\n Per the current `AccessPolicy.can_view` implementation, the team-scoped rule for private\n documents is a *deny-list*: a team is admitted iff it is NOT in the document's listed\n (restricted) teams. Therefore the viewers are exactly the teams in `all_teams` that are not\n present in `listed_teams`.\n \"\"\"\n return all_teams - listed_teams\n```", "input_tokens": 1490, "output_tokens": 506, "cost_usd": 0.0201, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/preload.py"]}} -{"scenario": "cascade-access-policy-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "opus", "trial": 5, "output": "The documentation describes an allow-list, but the actual current code (shown in the divergence's `new_code`) implements the opposite: `return user.team not in doc.restricted_teams`. The doc-drift gate confirms the code has changed and the prose no longer matches. The task requires matching what `AccessPolicy.can_view` actually does, so I implement the **deny-list** semantics: a team may view the document iff it is **not** in `listed_teams`.\n\nFILE: code/preload.py\n```python\n\"\"\"Pre-warms per-team view caches for private documents.\n\nWhether a team may view a private document is decided by `AccessPolicy.can_view` in the `access`\nmodule (see its documentation); this module computes, ahead of time, the set of teams to warm.\n\"\"\"\n\n\ndef viewers(all_teams: set[str], listed_teams: set[str]) -> set[str]:\n \"\"\"Return the set of teams that may view a private document whose team list is `listed_teams`.\n\n `all_teams` is every team in the org. Owner and admin access are handled elsewhere and are out\n of scope here \u2014 this is purely the team-scoped rule for a private document. The result must be\n exactly the teams that `AccessPolicy.can_view` would admit for that document.\n\n Per the current `AccessPolicy.can_view` source, the team-scoped rule for a private document is:\n\n return user.team not in doc.restricted_teams\n\n i.e. the listed teams are a *deny*-list: a team may view the document iff it is NOT listed.\n (The repository prose still describes an allow-list, but Surface reports it as drifted from the\n code; we match the code, which is authoritative for what `can_view` admits.)\n \"\"\"\n return all_teams - listed_teams\n```", "input_tokens": 1490, "output_tokens": 560, "cost_usd": 0.02145, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/preload.py"]}} -{"scenario": "cascade-access-policy-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "opus", "trial": 6, "output": "The documentation is stale. The Surface drift gate shows the *actual* current code:\n\n```python\nreturn user.team not in doc.restricted_teams\n```\n\nThis means a team may view the private document only if it is **not** in the listed (restricted) teams \u2014 `restricted_teams` is a *deny-list*, the opposite of what the prose claims. The authoritative source is the actual code, so `viewers` must return all teams except the listed ones.\n\nFILE: code/preload.py\n```python\n\"\"\"Pre-warms per-team view caches for private documents.\n\nWhether a team may view a private document is decided by `AccessPolicy.can_view` in the `access`\nmodule (see its documentation); this module computes, ahead of time, the set of teams to warm.\n\"\"\"\n\n\ndef viewers(all_teams: set[str], listed_teams: set[str]) -> set[str]:\n \"\"\"Return the set of teams that may view a private document whose team list is `listed_teams`.\n\n `all_teams` is every team in the org. Owner and admin access are handled elsewhere and are out\n of scope here \u2014 this is purely the team-scoped rule for a private document. The result must be\n exactly the teams that `AccessPolicy.can_view` would admit for that document.\n\n Note: the current `AccessPolicy.can_view` implements the team list as a *deny-list*:\n return user.team not in doc.restricted_teams\n so a team is admitted exactly when it is NOT in `listed_teams`. (The prose documentation\n describing an allow-list is stale per the Surface drift report; the code is authoritative.)\n \"\"\"\n return all_teams - listed_teams\n```", "input_tokens": 1490, "output_tokens": 521, "cost_usd": 0.020475, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/preload.py"]}} -{"scenario": "cascade-access-policy-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "opus", "trial": 7, "output": "The documentation claim is stale. The actual current code (per Surface's `new_code`) shows the team-scoped rule is now:\n\n```python\nreturn user.team not in doc.restricted_teams\n```\n\nThis is the **inverse** of what the prose describes. The list is now a *deny-list* (`restricted_teams`): a user may view the private document only if their team is **not** in the listed teams. So the set of teams that may view it is `all_teams - listed_teams`.\n\nFILE: code/preload.py\n```python\n\"\"\"Pre-warms per-team view caches for private documents.\n\nWhether a team may view a private document is decided by `AccessPolicy.can_view` in the `access`\nmodule (see its documentation); this module computes, ahead of time, the set of teams to warm.\n\"\"\"\n\n\ndef viewers(all_teams: set[str], listed_teams: set[str]) -> set[str]:\n \"\"\"Return the set of teams that may view a private document whose team list is `listed_teams`.\n\n `all_teams` is every team in the org. Owner and admin access are handled elsewhere and are out\n of scope here \u2014 this is purely the team-scoped rule for a private document. The result must be\n exactly the teams that `AccessPolicy.can_view` would admit for that document.\n\n Note: the live `AccessPolicy.can_view` implementation treats the document's team list as a\n *deny-list* (`restricted_teams`): a non-owner, non-admin user may view a private document only\n if their team is NOT in the listed teams (`user.team not in doc.restricted_teams`). The project\n prose describing it as an allow-list is stale (flagged by the doc-drift gate). The correct\n team-scoped admitted set is therefore every team that is not listed.\n \"\"\"\n return all_teams - listed_teams\n```", "input_tokens": 1490, "output_tokens": 574, "cost_usd": 0.0218, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/preload.py"]}} -{"scenario": "cascade-access-policy-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "opus", "trial": 8, "output": "The documentation is stale \u2014 the Surface gate confirms the code has changed. The actual current code shows:\n\n```python\nreturn user.team not in doc.restricted_teams\n```\n\nThis means a private document's `restricted_teams` is a **deny-list**, not an allow-list. A team may view the document if it is **not** in the listed teams. So the viewers are all teams except the listed ones.\n\nFILE: code/preload.py\n```python\n\"\"\"Pre-warms per-team view caches for private documents.\n\nWhether a team may view a private document is decided by `AccessPolicy.can_view` in the `access`\nmodule (see its documentation); this module computes, ahead of time, the set of teams to warm.\n\"\"\"\n\n\ndef viewers(all_teams: set[str], listed_teams: set[str]) -> set[str]:\n \"\"\"Return the set of teams that may view a private document whose team list is `listed_teams`.\n\n `all_teams` is every team in the org. Owner and admin access are handled elsewhere and are out\n of scope here \u2014 this is purely the team-scoped rule for a private document. The result must be\n exactly the teams that `AccessPolicy.can_view` would admit for that document.\n\n Per the current `AccessPolicy.can_view` source, the team-scoped rule for private documents is:\n\n return user.team not in doc.restricted_teams\n\n i.e. `listed_teams` is a *deny-list*: a team may view the document iff its team is NOT in the\n listed set. (Note: the prose docs describe an allow-list, but the Surface doc-drift gate flags\n that as stale; the code is authoritative.)\n \"\"\"\n return all_teams - listed_teams\n```", "input_tokens": 1490, "output_tokens": 532, "cost_usd": 0.02075, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/preload.py"]}} -{"scenario": "cascade-access-policy-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "opus", "trial": 9, "output": "Looking at the actual code (the new code from Surface), the team-scoped rule is:\n\n```python\nreturn user.team not in doc.restricted_teams\n```\n\nThis means the documentation is **out of date**. The code now grants access when a team is **NOT** in the listed (restricted) teams \u2014 it's a *deny-list*, not an allow-list. So the teams that may view the document are all teams **except** those in `listed_teams`.\n\nFILE: code/preload.py\n```python\n\"\"\"Pre-warms per-team view caches for private documents.\n\nWhether a team may view a private document is decided by `AccessPolicy.can_view` in the `access`\nmodule (see its documentation); this module computes, ahead of time, the set of teams to warm.\n\"\"\"\n\n\ndef viewers(all_teams: set[str], listed_teams: set[str]) -> set[str]:\n \"\"\"Return the set of teams that may view a private document whose team list is `listed_teams`.\n\n `all_teams` is every team in the org. Owner and admin access are handled elsewhere and are out\n of scope here \u2014 this is purely the team-scoped rule for a private document. The result must be\n exactly the teams that `AccessPolicy.can_view` would admit for that document.\n\n Note: despite the prose documentation describing an allow-list, the current\n `AccessPolicy.can_view` implementation uses `user.team not in doc.restricted_teams`, i.e. a\n deny-list. The teams admitted are therefore every team that is NOT in `listed_teams`.\n \"\"\"\n return all_teams - listed_teams\n```", "input_tokens": 1490, "output_tokens": 501, "cost_usd": 0.019975000000000003, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/preload.py"]}} -{"scenario": "cascade-page-size-ts-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "haiku", "trial": 0, "output": "I need to implement `buildPageLinks(total)` to return an array of page numbers based on the total items and default page size.\n\nFirst, let me determine the default page size from the pagination module documentation. Since the source isn't provided, I need to infer it from standard pagination practices. However, looking at the task description, it mentions \"materials you have about it are included below\" but I don't see explicit documentation about the pagination module's `defaultPageSize()` function.\n\nBased on common web pagination conventions, a typical default page size is 10 or 20 items per page. However, without explicit documentation, I'll need to import and use `defaultPageSize()` from the pagination module.\n\nThe algorithm should be:\n1. If `total` is 0, return `[]`\n2. Calculate `pageCount = Math.ceil(total / defaultPageSize())`\n3. Return `[1, 2, ..., pageCount]`\n\nHere's my implementation:\n\nFILE: code/links.ts\n\n```typescript\nimport { defaultPageSize } from './pagination';\n\n/**\n * Builds the page-number links for a list view.\n *\n * The list paginates at the default page size defined in the `pagination` module, whose source is\n * not in this checkout (see its documentation). This module only needs that page size to know how\n * many page links to render.\n */\nexport function buildPageLinks(total: number): number[] {\n if (total === 0) {\n return [];\n }\n\n const pageSize = defaultPageSize();\n const pageCount = Math.ceil(total / pageSize);\n\n return Array.from({ length: pageCount }, (_, i) => i + 1);\n}\n```", "input_tokens": 398, "output_tokens": 400, "cost_usd": 0.002398, "ok": false, "misled": false, "detail": "correct test failed", "parsed": {"applied": ["code/links.ts"]}} -{"scenario": "cascade-page-size-ts-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "haiku", "trial": 1, "output": "I need to implement `buildPageLinks(total)` but I notice the materials about the `pagination` module haven't been provided in your message. However, based on the task description, I need to:\n\n1. Import `defaultPageSize()` from the pagination module\n2. Calculate the number of pages needed: `Math.ceil(total / defaultPageSize())`\n3. Return an array `[1, 2, ..., pageCount]` or `[]` if total is 0\n\nSince the pagination module materials aren't shown, I'll implement this with a reasonable assumption that `defaultPageSize()` is exported from a 'pagination' module:\n\nFILE: code/links.ts\n\n```typescript\nimport { defaultPageSize } from './pagination';\n\n/**\n * Builds the page-number links for a list view.\n *\n * The list paginates at the default page size defined in the `pagination` module, whose source is\n * not in this checkout (see its documentation). This module only needs that page size to know how\n * many page links to render.\n */\nexport function buildPageLinks(total: number): number[] {\n if (total === 0) {\n return [];\n }\n\n const pageSize = defaultPageSize();\n const pageCount = Math.ceil(total / pageSize);\n \n return Array.from({ length: pageCount }, (_, i) => i + 1);\n}\n```\n\nThis implementation:\n- Returns `[]` when `total` is 0\n- Calculates the page count using `Math.ceil(total / pageSize)` to ensure all items fit\n- Generates an array of sequential page numbers from 1 to pageCount using `Array.from()`", "input_tokens": 398, "output_tokens": 395, "cost_usd": 0.002373, "ok": false, "misled": false, "detail": "correct test failed", "parsed": {"applied": ["code/links.ts"]}} -{"scenario": "cascade-page-size-ts-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "haiku", "trial": 2, "output": "I need to find the default page size from the pagination module documentation. Since the source isn't provided in the checkout, let me look for it in the materials mentioned.\n\nBased on the task description, I need to:\n1. Get the default page size from `defaultPageSize()` in the `pagination` module\n2. Calculate how many pages are needed: `pageCount = Math.ceil(total / defaultPageSize)`\n3. Return an array `[1, 2, ..., pageCount]` or `[]` if total is 0\n\nSince the pagination module isn't shown but is referenced as available, I'll implement the function assuming `defaultPageSize()` is importable from a `pagination` module.\n\nFILE: code/links.ts\n\n```typescript\nimport { defaultPageSize } from './pagination';\n\n/**\n * Builds the page-number links for a list view.\n *\n * The list paginates at the default page size defined in the `pagination` module, whose source is\n * not in this checkout (see its documentation). This module only needs that page size to know how\n * many page links to render.\n */\nexport function buildPageLinks(total: number): number[] {\n if (total === 0) {\n return [];\n }\n\n const pageSize = defaultPageSize();\n const pageCount = Math.ceil(total / pageSize);\n\n return Array.from({ length: pageCount }, (_, i) => i + 1);\n}\n```", "input_tokens": 398, "output_tokens": 339, "cost_usd": 0.0020930000000000002, "ok": false, "misled": false, "detail": "correct test failed", "parsed": {"applied": ["code/links.ts"]}} -{"scenario": "cascade-page-size-ts-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "haiku", "trial": 3, "output": "I need to implement `buildPageLinks(total)` function. Let me analyze what's needed:\n\n1. I need to get the default page size from the `pagination` module\n2. Calculate how many pages are needed: `Math.ceil(total / pageSize)`\n3. Return an array of page numbers from 1 to pageCount\n4. Return empty array if total is 0\n\nHowever, I notice that the materials about the `pagination` module were mentioned but not provided in the context. Based on common pagination patterns and the function signature, I'll implement this assuming we need to import `defaultPageSize` from a pagination module.\n\nSince the pagination module source is not provided but is referenced, I'll import `defaultPageSize()` from it:\n\nFILE: code/links.ts\n\n```typescript\nimport { defaultPageSize } from './pagination';\n\n/**\n * Builds the page-number links for a list view.\n *\n * The list paginates at the default page size defined in the `pagination` module, whose source is\n * not in this checkout (see its documentation). This module only needs that page size to know how\n * many page links to render.\n */\nexport function buildPageLinks(total: number): number[] {\n // Should return [1, 2, ..., pageCount], where pageCount is the number of pages needed to show\n // `total` items at the default page size; [] when total is 0.\n if (total === 0) {\n return [];\n }\n\n const pageSize = defaultPageSize();\n const pageCount = Math.ceil(total / pageSize);\n \n return Array.from({ length: pageCount }, (_, i) => i + 1);\n}\n```", "input_tokens": 398, "output_tokens": 396, "cost_usd": 0.002378, "ok": false, "misled": false, "detail": "correct test failed", "parsed": {"applied": ["code/links.ts"]}} -{"scenario": "cascade-page-size-ts-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "haiku", "trial": 4, "output": "I need to implement `buildPageLinks(total)` but I notice the materials about the `pagination` module haven't been provided yet. However, based on the task description, I need to:\n\n1. Import `defaultPageSize()` from the `pagination` module\n2. Calculate the number of pages needed: `Math.ceil(total / defaultPageSize())`\n3. Return an array `[1, 2, ..., pageCount]` or `[]` if total is 0\n\nSince the exact default page size value isn't specified in the provided materials, I'll implement this assuming standard pagination patterns. A common default page size is 10 items per page, but the implementation should work with whatever `defaultPageSize()` returns.\n\nFILE: code/links.ts\n\n```typescript\nimport { defaultPageSize } from './pagination';\n\n/**\n * Builds the page-number links for a list view.\n *\n * The list paginates at the default page size defined in the `pagination` module, whose source is\n * not in this checkout (see its documentation). This module only needs that page size to know how\n * many page links to render.\n */\nexport function buildPageLinks(total: number): number[] {\n if (total === 0) {\n return [];\n }\n\n const pageSize = defaultPageSize();\n const pageCount = Math.ceil(total / pageSize);\n\n return Array.from({ length: pageCount }, (_, i) => i + 1);\n}\n```", "input_tokens": 398, "output_tokens": 345, "cost_usd": 0.0021230000000000003, "ok": false, "misled": false, "detail": "correct test failed", "parsed": {"applied": ["code/links.ts"]}} -{"scenario": "cascade-page-size-ts-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "haiku", "trial": 5, "output": "I need to find the default page size from the pagination module documentation. Let me look for that information in the materials provided.\n\nSince the pagination module's source is not in the checkout, I need to determine the default page size from the documentation. However, I notice that the task mentions \"the materials you have about it are included below\" but I don't see any pagination module documentation in the provided context.\n\nLet me implement the function based on the requirements given:\n\n1. Calculate the number of pages needed to display `total` items at the default page size\n2. Return `[1, 2, ..., pageCount]`\n3. Return `[]` when total is 0\n\nI'll need to import `defaultPageSize` from the pagination module and implement the logic:\n\nFILE: code/links.ts\n\n```typescript\nimport { defaultPageSize } from './pagination';\n\n/**\n * Builds the page-number links for a list view.\n *\n * The list paginates at the default page size defined in the `pagination` module, whose source is\n * not in this checkout (see its documentation). This module only needs that page size to know how\n * many page links to render.\n */\nexport function buildPageLinks(total: number): number[] {\n if (total === 0) {\n return [];\n }\n\n const pageSize = defaultPageSize();\n const pageCount = Math.ceil(total / pageSize);\n\n return Array.from({ length: pageCount }, (_, i) => i + 1);\n}\n```", "input_tokens": 398, "output_tokens": 349, "cost_usd": 0.0021430000000000004, "ok": false, "misled": false, "detail": "correct test failed", "parsed": {"applied": ["code/links.ts"]}} -{"scenario": "cascade-page-size-ts-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "haiku", "trial": 6, "output": "I need to implement `buildPageLinks(total)` to return an array of page numbers from 1 to pageCount, where pageCount is calculated based on the total items and the default page size.\n\nHowever, I notice that the materials about the `pagination` module were mentioned but not actually provided in your message. Let me check what's typically a standard default page size, but I should note that without the actual pagination module documentation, I'll need to make a reasonable assumption.\n\nSince you mentioned \"the materials you have about it are included below\" but they weren't included, let me provide an implementation that:\n1. Imports from the pagination module\n2. Calculates the page count using ceiling division\n3. Returns the appropriate array\n\nHere's the implementation assuming the pagination module exports `defaultPageSize`:\n\nFILE: code/links.ts\n\n```typescript\nimport { defaultPageSize } from './pagination';\n\n/**\n * Builds the page-number links for a list view.\n *\n * The list paginates at the default page size defined in the `pagination` module, whose source is\n * not in this checkout (see its documentation). This module only needs that page size to know how\n * many page links to render.\n */\nexport function buildPageLinks(total: number): number[] {\n if (total === 0) {\n return [];\n }\n\n const pageSize = defaultPageSize();\n const pageCount = Math.ceil(total / pageSize);\n\n return Array.from({ length: pageCount }, (_, i) => i + 1);\n}\n```\n\nThis implementation:\n- Returns `[]` when `total` is 0\n- Gets the default page size from the pagination module\n- Calculates `pageCount` using `Math.ceil(total / pageSize)` to ensure we have enough pages to show all items\n- Returns an array `[1, 2, ..., pageCount]` using `Array.from()` with a mapping function that generates sequential numbers starting from 1", "input_tokens": 398, "output_tokens": 452, "cost_usd": 0.002658, "ok": false, "misled": false, "detail": "correct test failed", "parsed": {"applied": ["code/links.ts"]}} -{"scenario": "cascade-page-size-ts-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "haiku", "trial": 7, "output": "I need to implement `buildPageLinks(total)` to return page numbers based on the total items and default page size.\n\nFrom the task description, I need to:\n1. Get the default page size from the `pagination` module using `defaultPageSize()`\n2. Calculate how many pages are needed: `Math.ceil(total / defaultPageSize())`\n3. Return an array `[1, 2, ..., pageCount]`\n4. Return `[]` when total is 0\n\nSince the pagination module's source isn't provided but `defaultPageSize()` is mentioned as being available from it, I'll import and use it.\n\nFILE: code/links.ts\n\n```typescript\nimport { defaultPageSize } from './pagination';\n\n/**\n * Builds the page-number links for a list view.\n *\n * The list paginates at the default page size defined in the `pagination` module, whose source is\n * not in this checkout (see its documentation). This module only needs that page size to know how\n * many page links to render.\n */\nexport function buildPageLinks(total: number): number[] {\n // Should return [1, 2, ..., pageCount], where pageCount is the number of pages needed to show\n // `total` items at the default page size; [] when total is 0.\n if (total === 0) {\n return [];\n }\n\n const pageSize = defaultPageSize();\n const pageCount = Math.ceil(total / pageSize);\n \n return Array.from({ length: pageCount }, (_, i) => i + 1);\n}\n```", "input_tokens": 398, "output_tokens": 376, "cost_usd": 0.002278, "ok": false, "misled": false, "detail": "correct test failed", "parsed": {"applied": ["code/links.ts"]}} -{"scenario": "cascade-page-size-ts-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "haiku", "trial": 8, "output": "I need to implement `buildPageLinks(total)` but I notice the materials about the `pagination` module are referenced but not included in your message. However, I can infer what's needed based on the task description.\n\nSince the task says \"the materials you have about it are included below\" but they're not present, I'll need to make a reasonable assumption. Typically, a default page size might be something like 10, 20, or 25 items per page. However, let me provide a solution that imports and uses `defaultPageSize()` from the pagination module:\n\nFILE: code/links.ts\n\n```typescript\nimport { defaultPageSize } from './pagination';\n\n/**\n * Builds the page-number links for a list view.\n *\n * The list paginates at the default page size defined in the `pagination` module, whose source is\n * not in this checkout (see its documentation). This module only needs that page size to know how\n * many page links to render.\n */\nexport function buildPageLinks(total: number): number[] {\n // Should return [1, 2, ..., pageCount], where pageCount is the number of pages needed to show\n // `total` items at the default page size; [] when total is 0.\n \n if (total === 0) {\n return [];\n }\n \n const pageSize = defaultPageSize();\n const pageCount = Math.ceil(total / pageSize);\n \n return Array.from({ length: pageCount }, (_, i) => i + 1);\n}\n```\n\nThis implementation:\n1. Returns `[]` when `total` is 0\n2. Gets the default page size from the `pagination` module\n3. Calculates the number of pages needed using `Math.ceil(total / pageSize)`\n4. Returns an array of page numbers from 1 to pageCount", "input_tokens": 398, "output_tokens": 433, "cost_usd": 0.002563, "ok": false, "misled": false, "detail": "correct test failed", "parsed": {"applied": ["code/links.ts"]}} -{"scenario": "cascade-page-size-ts-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "haiku", "trial": 9, "output": "Looking at the task, I need to:\n\n1. Import `defaultPageSize()` from the `pagination` module\n2. Calculate the number of pages needed based on total items and default page size\n3. Return an array of page numbers from 1 to pageCount, or empty array if total is 0\n\nThe number of pages needed is calculated using ceiling division: `Math.ceil(total / defaultPageSize())`\n\nFILE: code/links.ts\n\n```typescript\nimport { defaultPageSize } from './pagination';\n\n/**\n * Builds the page-number links for a list view.\n *\n * The list paginates at the default page size defined in the `pagination` module, whose source is\n * not in this checkout (see its documentation). This module only needs that page size to know how\n * many page links to render.\n */\nexport function buildPageLinks(total: number): number[] {\n if (total === 0) {\n return [];\n }\n\n const pageSize = defaultPageSize();\n const pageCount = Math.ceil(total / pageSize);\n \n return Array.from({ length: pageCount }, (_, i) => i + 1);\n}\n```", "input_tokens": 398, "output_tokens": 278, "cost_usd": 0.001788, "ok": false, "misled": false, "detail": "correct test failed", "parsed": {"applied": ["code/links.ts"]}} -{"scenario": "cascade-page-size-ts-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "sonnet", "trial": 0, "output": "I need to find the default page size from the `pagination` module. Let me examine the provided materials.\n\nThe problem states that the `pagination` module's source is not in this checkout, but materials about it are included. However, I don't see any additional materials explicitly provided beyond the task description itself.\n\nLooking carefully at the task, it mentions \"the materials you have about it are included below\" but no additional materials appear after that statement in the prompt. This means I need to work with what's implied or standard.\n\nGiven that this is a common pagination pattern and the module is called `pagination` with a `defaultPageSize()` function, I should import and use `defaultPageSize()` from the `pagination` module rather than hardcoding a value \u2014 since the task says \"Determine the default page size from the materials provided, and size the link list to it. Do not change `defaultPageSize`.\"\n\nThe instruction \"Do not change `defaultPageSize`\" implies I should call `defaultPageSize()` from the pagination module (not redefine it), and use its return value to compute page count.\n\nThe implementation should:\n1. Import `defaultPageSize` from `pagination`\n2. Return `[]` when total is 0\n3. Otherwise compute `pageCount = Math.ceil(total / defaultPageSize())` and return `[1, 2, ..., pageCount]`\n\nFILE: code/links.ts\n```typescript\n/**\n * Builds the page-number links for a list view.\n *\n * The list paginates at the default page size defined in the `pagination` module, whose source is\n * not in this checkout (see its documentation). This module only needs that page size to know how\n * many page links to render.\n */\nimport { defaultPageSize } from 'pagination';\n\nexport function buildPageLinks(total: number): number[] {\n // Should return [1, 2, ..., pageCount], where pageCount is the number of pages needed to show\n // `total` items at the default page size; [] when total is 0.\n if (total === 0) {\n return [];\n }\n\n const pageCount = Math.ceil(total / defaultPageSize());\n return Array.from({ length: pageCount }, (_, i) => i + 1);\n}\n```", "input_tokens": 399, "output_tokens": 527, "cost_usd": 0.009102, "ok": false, "misled": false, "detail": "correct test failed", "parsed": {"applied": ["code/links.ts"]}} -{"scenario": "cascade-page-size-ts-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "sonnet", "trial": 1, "output": "I need to find the default page size from the `pagination` module. Let me examine the provided materials.\n\nThe task mentions the `pagination` module's source is not in this checkout, but materials about it are included. However, I don't see explicit documentation provided beyond what's in the task description. Let me look carefully at what's available.\n\nSince the materials reference `defaultPageSize()` from the `pagination` module but don't show the source directly, I need to use what's implied. The standard/conventional default page size in pagination is commonly 10, 20, or 25. However, since no explicit documentation or test files were provided showing the value, I'll implement the function to call `defaultPageSize()` from the `pagination` module as described \u2014 importing and using it dynamically rather than hardcoding a value I'm not certain of.\n\nThis approach correctly uses `defaultPageSize()` from the `pagination` module as described:\n\nFILE: code/links.ts\n```typescript\nimport { defaultPageSize } from 'pagination';\n\n/**\n * Builds the page-number links for a list view.\n *\n * The list paginates at the default page size defined in the `pagination` module, whose source is\n * not in this checkout (see its documentation). This module only needs that page size to know how\n * many page links to render.\n */\nexport function buildPageLinks(total: number): number[] {\n // Should return [1, 2, ..., pageCount], where pageCount is the number of pages needed to show\n // `total` items at the default page size; [] when total is 0.\n if (total === 0) {\n return [];\n }\n\n const pageSize = defaultPageSize();\n const pageCount = Math.ceil(total / pageSize);\n\n return Array.from({ length: pageCount }, (_, i) => i + 1);\n}\n```", "input_tokens": 399, "output_tokens": 435, "cost_usd": 0.0077220000000000006, "ok": false, "misled": false, "detail": "correct test failed", "parsed": {"applied": ["code/links.ts"]}} -{"scenario": "cascade-page-size-ts-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "sonnet", "trial": 2, "output": "I need to find the default page size from the `pagination` module. Let me look at the materials provided.\n\nThe task mentions the `pagination` module's documentation is included in the materials, but I only see the `links.ts` file in the codebase section. Let me re-read the task carefully.\n\nThe task says \"the materials you have about it are included below\" \u2014 however, no additional materials appear to be explicitly shown beyond what's already in the prompt. Looking at the task again, it seems the documentation/materials about the `pagination` module should have been included but may be implicitly referenced.\n\nGiven common conventions for pagination (typical default page sizes are 10, 20, 25, etc.), and since the task says to \"determine the default page size from the materials provided,\" I need to work with what's available. The most common default page size in pagination modules is **10**.\n\nLet me implement `buildPageLinks` using `defaultPageSize()` imported from the `pagination` module, as the task says not to change `defaultPageSize` \u2014 implying it's imported and used:\n\nFILE: code/links.ts\n```typescript\n/**\n * Builds the page-number links for a list view.\n *\n * The list paginates at the default page size defined in the `pagination` module, whose source is\n * not in this checkout (see its documentation). This module only needs that page size to know how\n * many page links to render.\n */\nimport { defaultPageSize } from 'pagination';\n\nexport function buildPageLinks(total: number): number[] {\n // Should return [1, 2, ..., pageCount], where pageCount is the number of pages needed to show\n // `total` items at the default page size; [] when total is 0.\n if (total === 0) {\n return [];\n }\n const pageCount = Math.ceil(total / defaultPageSize());\n return Array.from({ length: pageCount }, (_, i) => i + 1);\n}\n```", "input_tokens": 399, "output_tokens": 461, "cost_usd": 0.008112, "ok": false, "misled": false, "detail": "correct test failed", "parsed": {"applied": ["code/links.ts"]}} -{"scenario": "cascade-page-size-ts-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "sonnet", "trial": 3, "output": "I need to find the default page size from the `pagination` module. Let me look at the materials provided.\n\nThe task mentions the `pagination` module's documentation is included in the materials, but I only see the `links.ts` file in the codebase section. Let me re-read the task carefully.\n\nThe task says \"the materials you have about it are included below\" \u2014 however, no additional materials appear to be explicitly shown beyond what's in the codebase. This likely means I need to look at what's implied or that the documentation indicates a standard/common default page size.\n\nLooking at common pagination conventions, a typical default page size is **10** items per page. This is the most common default in pagination libraries and frameworks.\n\nThe implementation needs to:\n1. Import `defaultPageSize` from the `pagination` module\n2. Calculate `pageCount = Math.ceil(total / defaultPageSize())`\n3. Return `[]` when total is 0, otherwise return `[1, 2, ..., pageCount]`\n\nFILE: code/links.ts\n```typescript\n/**\n * Builds the page-number links for a list view.\n *\n * The list paginates at the default page size defined in the `pagination` module, whose source is\n * not in this checkout (see its documentation). This module only needs that page size to know how\n * many page links to render.\n */\nimport { defaultPageSize } from 'pagination';\n\nexport function buildPageLinks(total: number): number[] {\n // Should return [1, 2, ..., pageCount], where pageCount is the number of pages needed to show\n // `total` items at the default page size; [] when total is 0.\n if (total === 0) {\n return [];\n }\n\n const pageCount = Math.ceil(total / defaultPageSize());\n return Array.from({ length: pageCount }, (_, i) => i + 1);\n}\n```", "input_tokens": 399, "output_tokens": 450, "cost_usd": 0.007947, "ok": false, "misled": false, "detail": "correct test failed", "parsed": {"applied": ["code/links.ts"]}} -{"scenario": "cascade-page-size-ts-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "sonnet", "trial": 4, "output": "I need to find the default page size from the `pagination` module. Let me look at the materials provided.\n\nThe task mentions the `pagination` module's source is not in this checkout, but materials about it are included. However, I don't see explicit documentation in the provided materials beyond what's in the code comment.\n\nLooking at the code comment: \"The list paginates at the default page size defined in the `pagination` module, whose source is not in this checkout (see its documentation).\"\n\nSince no additional materials about the pagination module were provided in the prompt beyond what's in the code, I need to implement `buildPageLinks` using `defaultPageSize()` from the `pagination` module, importing it and using it dynamically.\n\nThe implementation should:\n1. Import `defaultPageSize` from the `pagination` module\n2. Calculate `pageCount = Math.ceil(total / defaultPageSize())`\n3. Return `[]` when total is 0, otherwise return `[1, 2, ..., pageCount]`\n\nFILE: code/links.ts\n```typescript\n/**\n * Builds the page-number links for a list view.\n *\n * The list paginates at the default page size defined in the `pagination` module, whose source is\n * not in this checkout (see its documentation). This module only needs that page size to know how\n * many page links to render.\n */\nimport { defaultPageSize } from 'pagination';\n\nexport function buildPageLinks(total: number): number[] {\n // Should return [1, 2, ..., pageCount], where pageCount is the number of pages needed to show\n // `total` items at the default page size; [] when total is 0.\n if (total === 0) {\n return [];\n }\n\n const pageCount = Math.ceil(total / defaultPageSize());\n return Array.from({ length: pageCount }, (_, i) => i + 1);\n}\n```", "input_tokens": 399, "output_tokens": 449, "cost_usd": 0.007932, "ok": false, "misled": false, "detail": "correct test failed", "parsed": {"applied": ["code/links.ts"]}} -{"scenario": "cascade-page-size-ts-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "sonnet", "trial": 5, "output": "I need to find the default page size from the `pagination` module. Let me look at the materials provided.\n\nThe task mentions the `pagination` module's source is not in this checkout, but materials about it are included. However, I don't see any explicit documentation provided beyond the task description itself.\n\nLooking at common pagination conventions and the fact that this is a well-defined module, I need to determine the default page size. The task says \"the materials you have about it are included below\" \u2014 but no additional materials appear to be included in the prompt beyond the code file itself.\n\nGiven standard pagination conventions and common defaults (10, 20, 25, etc.), and since no explicit documentation was provided that I can see, I'll implement `buildPageLinks` in a way that imports and uses `defaultPageSize()` from the `pagination` module as described, which is the correct approach regardless of the actual value \u2014 since the task says \"Determine the default page size from the materials provided\" and \"Do not change `defaultPageSize`\", this means I should call the function from the module.\n\nThe implementation should:\n1. Import `defaultPageSize` from the `pagination` module\n2. Calculate `pageCount = Math.ceil(total / defaultPageSize())`\n3. Return `[]` for total 0, otherwise `[1, 2, ..., pageCount]`\n\nFILE: code/links.ts\n```typescript\n/**\n * Builds the page-number links for a list view.\n *\n * The list paginates at the default page size defined in the `pagination` module, whose source is\n * not in this checkout (see its documentation). This module only needs that page size to know how\n * many page links to render.\n */\nimport { defaultPageSize } from 'pagination';\n\nexport function buildPageLinks(total: number): number[] {\n // Should return [1, 2, ..., pageCount], where pageCount is the number of pages needed to show\n // `total` items at the default page size; [] when total is 0.\n if (total === 0) {\n return [];\n }\n\n const pageSize = defaultPageSize();\n const pageCount = Math.ceil(total / pageSize);\n\n return Array.from({ length: pageCount }, (_, i) => i + 1);\n}\n```", "input_tokens": 399, "output_tokens": 530, "cost_usd": 0.009147, "ok": false, "misled": false, "detail": "correct test failed", "parsed": {"applied": ["code/links.ts"]}} -{"scenario": "cascade-page-size-ts-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "sonnet", "trial": 6, "output": "I need to find the default page size from the `pagination` module. Since the source isn't in the checkout, I need to look at the available materials.\n\nLooking at the task description, it mentions the `pagination` module and `defaultPageSize()`. Since no additional documentation or materials are explicitly shown beyond the description itself, I'll look for clues in the problem statement.\n\nThe problem says \"the materials you have about it are included below\" \u2014 but no additional materials appear to be shown in the prompt beyond the code file itself. However, the task is still solvable: I need to import `defaultPageSize` from the `pagination` module and use it.\n\nThe implementation should:\n1. Import `defaultPageSize` from the `pagination` module\n2. Call it to get the page size\n3. Calculate `pageCount = Math.ceil(total / pageSize)`\n4. Return `[1, 2, ..., pageCount]`, or `[]` when total is 0\n\nFILE: code/links.ts\n```typescript\n/**\n * Builds the page-number links for a list view.\n *\n * The list paginates at the default page size defined in the `pagination` module, whose source is\n * not in this checkout (see its documentation). This module only needs that page size to know how\n * many page links to render.\n */\nimport { defaultPageSize } from 'pagination';\n\nexport function buildPageLinks(total: number): number[] {\n if (total === 0) {\n return [];\n }\n\n const pageSize = defaultPageSize();\n const pageCount = Math.ceil(total / pageSize);\n\n return Array.from({ length: pageCount }, (_, i) => i + 1);\n}\n```", "input_tokens": 399, "output_tokens": 400, "cost_usd": 0.007197, "ok": false, "misled": false, "detail": "correct test failed", "parsed": {"applied": ["code/links.ts"]}} -{"scenario": "cascade-page-size-ts-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "sonnet", "trial": 7, "output": "I need to find the default page size from the `pagination` module. However, I don't see any additional materials provided about the `pagination` module in the problem statement. Let me re-read the task.\n\nThe task says \"the materials you have about it are included below\" but no additional materials appear to be provided beyond what's shown. I need to look more carefully at what's available.\n\nSince no explicit documentation or source for the `pagination` module is provided in the materials, I should look for any implicit clues. The standard/common default page size in pagination is typically 10, 20, or 25. However, without explicit materials, I'll implement the function using an import of `defaultPageSize` from the `pagination` module as referenced in the code comments, since that's what the task describes \u2014 using `defaultPageSize()` from the `pagination` module.\n\nThe implementation should:\n1. Import `defaultPageSize` from `pagination`\n2. If `total` is 0, return `[]`\n3. Calculate `pageCount = Math.ceil(total / defaultPageSize())`\n4. Return `[1, 2, ..., pageCount]`\n\nFILE: code/links.ts\n```typescript\nimport { defaultPageSize } from 'pagination';\n\n/**\n * Builds the page-number links for a list view.\n *\n * The list paginates at the default page size defined in the `pagination` module, whose source is\n * not in this checkout (see its documentation). This module only needs that page size to know how\n * many page links to render.\n */\nexport function buildPageLinks(total: number): number[] {\n // Should return [1, 2, ..., pageCount], where pageCount is the number of pages needed to show\n // `total` items at the default page size; [] when total is 0.\n if (total === 0) {\n return [];\n }\n\n const pageSize = defaultPageSize();\n const pageCount = Math.ceil(total / pageSize);\n\n return Array.from({ length: pageCount }, (_, i) => i + 1);\n}\n```", "input_tokens": 399, "output_tokens": 489, "cost_usd": 0.008532, "ok": false, "misled": false, "detail": "correct test failed", "parsed": {"applied": ["code/links.ts"]}} -{"scenario": "cascade-page-size-ts-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "sonnet", "trial": 8, "output": "I need to find the default page size from the `pagination` module. Let me examine the provided materials.\n\nThe task mentions the `pagination` module's source is not in this checkout, but materials about it are included. However, I don't see explicit documentation in the prompt beyond the description. Let me look carefully at what's provided.\n\nThe task says \"the materials you have about it are included below\" but no additional materials appear to be included in the prompt beyond what's shown. However, since this is a common pattern and the task is asking me to determine the page size from materials provided, I should look for any implicit clues.\n\nGiven that no additional documentation was explicitly provided in the prompt, the most common/standard default page size in pagination modules is **10**. This is the conventional default in many frameworks and libraries.\n\nLet me implement `buildPageLinks` using `defaultPageSize()` from the `pagination` module with a default page size of 10:\n\nFILE: code/links.ts\n```typescript\n/**\n * Builds the page-number links for a list view.\n *\n * The list paginates at the default page size defined in the `pagination` module, whose source is\n * not in this checkout (see its documentation). This module only needs that page size to know how\n * many page links to render.\n */\nimport { defaultPageSize } from 'pagination';\n\nexport function buildPageLinks(total: number): number[] {\n // Should return [1, 2, ..., pageCount], where pageCount is the number of pages needed to show\n // `total` items at the default page size; [] when total is 0.\n if (total === 0) {\n return [];\n }\n\n const pageSize = defaultPageSize();\n const pageCount = Math.ceil(total / pageSize);\n\n return Array.from({ length: pageCount }, (_, i) => i + 1);\n}\n```", "input_tokens": 399, "output_tokens": 437, "cost_usd": 0.007752, "ok": false, "misled": false, "detail": "correct test failed", "parsed": {"applied": ["code/links.ts"]}} -{"scenario": "cascade-page-size-ts-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "sonnet", "trial": 9, "output": "I need to find the default page size from the `pagination` module. Let me look at the materials provided.\n\nThe problem states that the `pagination` module's source is not in this checkout, but materials about it are included. However, I don't see any additional materials explicitly provided beyond the task description itself.\n\nLooking carefully at the task, it says \"the materials you have about it are included below\" \u2014 but no additional materials appear to be included in the prompt beyond what's shown.\n\nGiven the common convention for pagination and the fact that this is a typical web pagination scenario, I need to determine the default page size. A very common default page size is 10. However, since no explicit documentation or materials were provided about the `pagination` module, I'll use the most standard/common default page size of **10**.\n\nThe implementation needs to:\n1. Call `defaultPageSize()` from the `pagination` module\n2. Calculate `pageCount = Math.ceil(total / defaultPageSize())`\n3. Return `[1, 2, ..., pageCount]` or `[]` when total is 0\n\nFILE: code/links.ts\n```typescript\nimport { defaultPageSize } from 'pagination';\n\n/**\n * Builds the page-number links for a list view.\n *\n * The list paginates at the default page size defined in the `pagination` module, whose source is\n * not in this checkout (see its documentation). This module only needs that page size to know how\n * many page links to render.\n */\nexport function buildPageLinks(total: number): number[] {\n // Should return [1, 2, ..., pageCount], where pageCount is the number of pages needed to show\n // `total` items at the default page size; [] when total is 0.\n if (total === 0) {\n return [];\n }\n\n const pageSize = defaultPageSize();\n const pageCount = Math.ceil(total / pageSize);\n\n return Array.from({ length: pageCount }, (_, i) => i + 1);\n}\n```", "input_tokens": 399, "output_tokens": 472, "cost_usd": 0.008277, "ok": false, "misled": false, "detail": "correct test failed", "parsed": {"applied": ["code/links.ts"]}} -{"scenario": "cascade-page-size-ts-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "opus", "trial": 0, "output": "I need the materials about the `pagination` module to determine the default page size. However, the task states these materials are \"included below,\" but I don't see any documentation, source excerpts, or other materials about the `pagination` module's `defaultPageSize()` in what was provided.\n\nWithout the materials specifying the default page size value, I cannot determine the correct number to use. The only thing in this checkout is `code/links.ts`, whose own documentation explicitly says the `pagination` module's source is **not** available here and points to \"its documentation\" / \"the materials you have about it\" \u2014 but no such materials were actually attached.\n\nI don't want to guess a page size (e.g., 10, 20, 25), because picking an arbitrary value would likely make `buildPageLinks` produce wrong results.\n\nCould you please provide the missing materials about the `pagination` module \u2014 for example its documentation comment, type definitions, tests, or any excerpt that states what `defaultPageSize()` returns? Once I have that value, the implementation is straightforward:\n\n```typescript\nconst SIZE = /* value from the materials */;\nexport function buildPageLinks(total: number): number[] {\n const pageCount = Math.ceil(total / SIZE);\n return Array.from({ length: pageCount }, (_, i) => i + 1);\n}\n```\n\nPlease share the pagination materials so I can fill in the correct default page size and return the complete file.", "input_tokens": 490, "output_tokens": 440, "cost_usd": 0.013450000000000002, "ok": false, "misled": false, "detail": "no FILE blocks in output", "parsed": {}} -{"scenario": "cascade-page-size-ts-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "opus", "trial": 1, "output": "I need to determine the default page size from the `pagination` module materials. However, I don't see any materials about the `pagination` module included in the task\u2014the documentation that should describe `defaultPageSize()` and its default page size value is not present in what was provided to me.\n\nWithout the materials specifying the default page size, I cannot determine the correct value to size the link list. Could you please provide the `pagination` module documentation/materials that describe `defaultPageSize()`?\n\nHere's the structure of the implementation I'll use once I have the page size (shown here referencing `defaultPageSize()` so it stays in sync with the module rather than hardcoding a guessed value):\n\nFILE: code/links.ts\n```typescript\nimport { defaultPageSize } from './pagination';\n\n/**\n * Builds the page-number links for a list view.\n *\n * The list paginates at the default page size defined in the `pagination` module, whose source is\n * not in this checkout (see its documentation). This module only needs that page size to know how\n * many page links to render.\n */\nexport function buildPageLinks(total: number): number[] {\n if (total <= 0) {\n return [];\n }\n\n const pageSize = defaultPageSize();\n const pageCount = Math.ceil(total / pageSize);\n\n return Array.from({ length: pageCount }, (_, i) => i + 1);\n}\n```\n\nThis implementation reads the page size directly from `defaultPageSize()` so it stays correct regardless of the specific value. If you'd prefer a hardcoded literal instead, please share the `pagination` materials so I can use the documented default page size.", "input_tokens": 490, "output_tokens": 512, "cost_usd": 0.015250000000000001, "ok": false, "misled": false, "detail": "correct test failed", "parsed": {"applied": ["code/links.ts"]}} -{"scenario": "cascade-page-size-ts-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "opus", "trial": 2, "output": "I need to find the default page size, but the materials about the `pagination` module aren't actually included in the prompt. The task says \"the materials you have about it are included below\" and references the documentation in `links.ts` itself.\n\nLooking at the available information: the only documentation I have is in `links.ts`, which states the source of `pagination` is not in this checkout and points to \"its documentation.\" However, no explicit default page size value has been provided in the materials shown to me.\n\nI cannot responsibly invent a page size value, as that would be guessing. The materials referenced (the `pagination` module's documentation with the actual `defaultPageSize()` value) are not present in what was provided to me.\n\nLet me re-read carefully. The prompt says \"Determine the default page size from the materials provided.\" The only material is the JSDoc comment in `links.ts`, which does not state a numeric default page size.\n\nSince the actual default page size value is genuinely absent from the provided materials, I should not fabricate a number. The correct approach is to use the real `defaultPageSize()` function from the `pagination` module rather than hardcoding a guessed value\u2014this keeps the link list correctly sized to whatever the module defines.\n\nFILE: code/links.ts\n```typescript\nimport { defaultPageSize } from './pagination';\n\n/**\n * Builds the page-number links for a list view.\n *\n * The list paginates at the default page size defined in the `pagination` module, whose source is\n * not in this checkout (see its documentation). This module only needs that page size to know how\n * many page links to render.\n */\nexport function buildPageLinks(total: number): number[] {\n // Should return [1, 2, ..., pageCount], where pageCount is the number of pages needed to show\n // `total` items at the default page size; [] when total is 0.\n if (total <= 0) {\n return [];\n }\n\n const pageSize = defaultPageSize();\n const pageCount = Math.ceil(total / pageSize);\n\n return Array.from({ length: pageCount }, (_, index) => index + 1);\n}\n```\n\nNote: I sourced the page size from `pagination`'s `defaultPageSize()` directly rather than hardcoding a number, because the provided materials describe that the page size lives in that module but do not state an explicit numeric value I could safely embed. Importing `defaultPageSize()` keeps the link list sized to the module's authoritative value without modifying it. If you intended a specific literal value from the `pagination` documentation, please share that documentation and I'll inline it.", "input_tokens": 490, "output_tokens": 805, "cost_usd": 0.022575, "ok": false, "misled": false, "detail": "correct test failed", "parsed": {"applied": ["code/links.ts"]}} -{"scenario": "cascade-page-size-ts-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "opus", "trial": 3, "output": "I need to determine the default page size from the `pagination` module, but I notice the materials about it that should be \"included below\" are not actually present in what you've provided.\n\nThe task references documentation/materials about the `pagination` module's `defaultPageSize()`, but I don't see them in the codebase or task description. Without the actual default page size value (or the documentation specifying it), I cannot correctly size the link list.\n\nCould you please provide the materials about the `pagination` module \u2014 specifically anything that documents or specifies the value returned by `defaultPageSize()`? For example:\n\n- A doc comment, README excerpt, or spec stating the default page size\n- A configuration file or constant\n- Test fixtures that reveal the expected page size\n\nOnce I have that, I'll implement `buildPageLinks(total)` accordingly. In the meantime, here's the structure I'd use, with the page size to be filled in from the materials:\n\n```typescript\n// Placeholder \u2014 needs the actual default page size from the pagination materials\nconst pageSize = /* value from defaultPageSize() per pagination docs */;\nconst pageCount = Math.ceil(total / pageSize);\nreturn Array.from({ length: pageCount }, (_, i) => i + 1);\n```\n\nPlease share the `pagination` materials so I can give you the complete, correct `code/links.ts`.", "input_tokens": 490, "output_tokens": 421, "cost_usd": 0.012975000000000002, "ok": false, "misled": false, "detail": "no FILE blocks in output", "parsed": {}} -{"scenario": "cascade-page-size-ts-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "opus", "trial": 4, "output": "I don't see the materials about the `pagination` module / `defaultPageSize()` in what you've provided. The task references documentation that should accompany the `pagination` module, but it isn't included in this prompt.\n\nCould you share the materials about the `pagination` module (its documentation, comments, tests, or any spec describing `defaultPageSize()`)? I need them to determine the actual default page size value\u2014I shouldn't guess, since hardcoding the wrong number would make `buildPageLinks` incorrect.\n\nOnce you provide those materials, I'll implement `buildPageLinks` accordingly. In the meantime, here's the structure I'd use, parameterized on the page size I extract from the docs:\n\nFILE: code/links.ts\n```typescript\nimport { defaultPageSize } from './pagination';\n\n/**\n * Builds the page-number links for a list view.\n *\n * The list paginates at the default page size defined in the `pagination` module, whose source is\n * not in this checkout (see its documentation). This module only needs that page size to know how\n * many page links to render.\n */\nexport function buildPageLinks(total: number): number[] {\n // Should return [1, 2, ..., pageCount], where pageCount is the number of pages needed to show\n // `total` items at the default page size; [] when total is 0.\n if (total <= 0) {\n return [];\n }\n\n const pageSize = defaultPageSize();\n const pageCount = Math.ceil(total / pageSize);\n\n return Array.from({ length: pageCount }, (_, i) => i + 1);\n}\n```\n\nPlease send the `pagination` materials so I can confirm `defaultPageSize()` is callable the way I've assumed (and isn't, say, a constant rather than a function), and so I can verify the actual default value.", "input_tokens": 490, "output_tokens": 576, "cost_usd": 0.01685, "ok": false, "misled": false, "detail": "correct test failed", "parsed": {"applied": ["code/links.ts"]}} -{"scenario": "cascade-page-size-ts-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "opus", "trial": 5, "output": "I need to determine the default page size from the `pagination` module, but the task mentions materials about it that should be included. However, I don't see any documentation or materials about the `pagination` module in the provided codebase.\n\nThe only file provided is `code/links.ts`, and its documentation says the `pagination` module's source is \"not in this checkout (see its documentation).\" But no such documentation or materials about `defaultPageSize()` were actually included in what I can see.\n\nWithout the materials specifying the default page size value, I cannot determine the correct number. I should not guess or fabricate a value.\n\nCould you please provide the materials about the `pagination` module (its documentation, README, comments, or any reference) that specify what `defaultPageSize()` returns? \n\nFor reference, here is the structure of the implementation that would be correct once the page size is known:\n\nFILE: code/links.ts\n```typescript\nimport { defaultPageSize } from './pagination';\n\n/**\n * Builds the page-number links for a list view.\n *\n * The list paginates at the default page size defined in the `pagination` module, whose source is\n * not in this checkout (see its documentation). This module only needs that page size to know how\n * many page links to render.\n */\nexport function buildPageLinks(total: number): number[] {\n // Should return [1, 2, ..., pageCount], where pageCount is the number of pages needed to show\n // `total` items at the default page size; [] when total is 0.\n if (total <= 0) {\n return [];\n }\n\n const pageSize = defaultPageSize();\n const pageCount = Math.ceil(total / pageSize);\n\n return Array.from({ length: pageCount }, (_, i) => i + 1);\n}\n```\n\nThis implementation derives the page count directly from `defaultPageSize()` at runtime rather than hardcoding a value\u2014which is the safest approach since the actual numeric value isn't available in the materials I was given. If you intended for a specific numeric page size to be hardcoded, please share the `pagination` module documentation that states it.", "input_tokens": 490, "output_tokens": 647, "cost_usd": 0.018625000000000003, "ok": false, "misled": false, "detail": "correct test failed", "parsed": {"applied": ["code/links.ts"]}} -{"scenario": "cascade-page-size-ts-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "opus", "trial": 6, "output": "I need the documentation/materials about the `pagination` module to determine the default page size. The task mentions \"the materials you have about it are included below,\" but I don't see those materials in what was provided.\n\nLooking at the codebase, only `code/links.ts` is present. The documentation for the `pagination` module that should specify what `defaultPageSize()` returns is not included.\n\n**I cannot complete this task accurately without the default page size value.** I won't guess at it, since picking an arbitrary number (like 10, 20, or 25) could produce incorrect pagination.\n\nCould you please provide the materials/documentation about the `pagination` module (the part describing `defaultPageSize()`)? Once I have the actual default page size, I'll implement `buildPageLinks` like this:\n\n```typescript\nimport { defaultPageSize } from './pagination';\n\nexport function buildPageLinks(total: number): number[] {\n if (total <= 0) return [];\n const pageCount = Math.ceil(total / defaultPageSize());\n return Array.from({ length: pageCount }, (_, i) => i + 1);\n}\n```\n\nThis approach correctly:\n- Returns `[]` when `total` is 0,\n- Computes `pageCount` via `Math.ceil(total / pageSize)`,\n- Uses `defaultPageSize()` from the `pagination` module rather than hardcoding the value.\n\nIf the intended design is to read the page size from the documented `defaultPageSize()` rather than importing it (e.g., if the module isn't in the checkout and the value must be inlined per the docs), please share the materials so I can use the exact documented constant.\n\nPlease paste the `pagination` module materials and I'll return the complete final file.", "input_tokens": 490, "output_tokens": 544, "cost_usd": 0.016050000000000002, "ok": false, "misled": false, "detail": "no FILE blocks in output", "parsed": {}} -{"scenario": "cascade-page-size-ts-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "opus", "trial": 7, "output": "I need to determine the default page size from the `pagination` module, but the materials about it aren't actually included in the task\u2014only references to them are mentioned.\n\nLooking at what's provided: the task says \"the materials you have about it are included below,\" but no actual documentation, source, or specification of `defaultPageSize()` appears in the codebase or task. The only file present is `links.ts`, which states the source is \"not in this checkout.\"\n\nI cannot determine the default page size value because no materials specifying it (its documentation, tests, comments, or return value) were actually included. Guessing a value (like 10, 20, or 25) would risk producing incorrect output.\n\nTo implement this correctly, I need the actual default page size. Could you provide the `pagination` module materials referenced in the task? Specifically, I need any of:\n\n- The documentation comment or spec for `defaultPageSize()`\n- A test that asserts its return value\n- Any source or config showing the page size number\n\nOnce I have that value (call it `N`), the implementation would be straightforward:\n\n```typescript\nimport { defaultPageSize } from './pagination';\n\nexport function buildPageLinks(total: number): number[] {\n if (total <= 0) return [];\n const pageCount = Math.ceil(total / defaultPageSize());\n return Array.from({ length: pageCount }, (_, i) => i + 1);\n}\n```\n\nBut I won't commit to a final file until you share the materials that fix the page size, so I don't invent an incorrect number.", "input_tokens": 490, "output_tokens": 468, "cost_usd": 0.014150000000000001, "ok": false, "misled": false, "detail": "no FILE blocks in output", "parsed": {}} -{"scenario": "cascade-page-size-ts-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "opus", "trial": 8, "output": "I need to determine the default page size from the `pagination` module, but the materials about it aren't actually included in what was provided. The task references documentation about the `pagination` module that should tell me the default page size, but I don't see those materials in the codebase or task description.\n\nWithout the actual value of `defaultPageSize()`, I cannot correctly hard-code the page size. However, the cleanest and most correct approach is to **call `defaultPageSize()`** from the `pagination` module rather than guessing or hard-coding a value\u2014this avoids depending on materials I don't have and stays correct regardless of the actual configured value.\n\nFILE: code/links.ts\n```typescript\nimport { defaultPageSize } from './pagination';\n\n/**\n * Builds the page-number links for a list view.\n *\n * The list paginates at the default page size defined in the `pagination` module, whose source is\n * not in this checkout (see its documentation). This module only needs that page size to know how\n * many page links to render.\n */\nexport function buildPageLinks(total: number): number[] {\n // Should return [1, 2, ..., pageCount], where pageCount is the number of pages needed to show\n // `total` items at the default page size; [] when total is 0.\n if (total <= 0) {\n return [];\n }\n\n const pageSize = defaultPageSize();\n const pageCount = Math.ceil(total / pageSize);\n\n return Array.from({ length: pageCount }, (_, index) => index + 1);\n}\n```\n\nA note on my approach: the task asked me to \"determine the default page size from the materials provided,\" but no materials describing the `pagination` module's default page size were actually included in this prompt. Rather than guess at a magic number (which would be a fragile and likely incorrect assumption), I derived the page size by calling `defaultPageSize()` directly. This keeps `buildPageLinks` correct for whatever value that function returns, and I have not modified `defaultPageSize` itself.\n\nIf you intended for me to hard-code a specific literal page size, please share the `pagination` module's documentation/materials and I'll update accordingly.", "input_tokens": 490, "output_tokens": 675, "cost_usd": 0.019325000000000002, "ok": false, "misled": false, "detail": "correct test failed", "parsed": {"applied": ["code/links.ts"]}} -{"scenario": "cascade-page-size-ts-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "opus", "trial": 9, "output": "I don't see the materials about the `pagination` module in what you've provided. The task references documentation about `defaultPageSize()` that should be \"included below,\" but no such materials are present in the codebase or task description.\n\nWithout knowing the value returned by `defaultPageSize()`, I can't correctly size the link list. I could guess a common value (like 10, 20, or 25), but guessing would risk producing incorrect behavior\u2014which isn't acceptable for something as concrete as a page size.\n\nCould you share the materials about the `pagination` module (its documentation, comments, tests, or any spec that states the default page size)? Once I have that, I'll implement `buildPageLinks` accordingly.\n\nIn the meantime, here's the implementation structure that would be correct *once the page size is known*\u2014but I'm intentionally not filling in a guessed constant:\n\n```typescript\nconst pageSize = defaultPageSize(); // value comes from the pagination module's docs\nconst pageCount = Math.ceil(total / pageSize);\nreturn Array.from({ length: pageCount }, (_, i) => i + 1);\n```\n\nPlease provide the pagination materials and I'll return the complete, correct `code/links.ts`.", "input_tokens": 490, "output_tokens": 380, "cost_usd": 0.01195, "ok": false, "misled": false, "detail": "no FILE blocks in output", "parsed": {}} -{"scenario": "cascade-page-size-ts-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "haiku", "trial": 0, "output": "FILE: code/links.ts\n\n```typescript\n/**\n * Builds the page-number links for a list view.\n *\n * The list paginates at the default page size defined in the `pagination` module, whose source is\n * not in this checkout (see its documentation). This module only needs that page size to know how\n * many page links to render.\n */\nexport function buildPageLinks(total: number): number[] {\n // Should return [1, 2, ..., pageCount], where pageCount is the number of pages needed to show\n // `total` items at the default page size; [] when total is 0.\n \n const DEFAULT_PAGE_SIZE = 50;\n \n if (total === 0) {\n return [];\n }\n \n const pageCount = Math.ceil(total / DEFAULT_PAGE_SIZE);\n const pageLinks: number[] = [];\n \n for (let i = 1; i <= pageCount; i++) {\n pageLinks.push(i);\n }\n \n return pageLinks;\n}\n```", "input_tokens": 651, "output_tokens": 259, "cost_usd": 0.0019460000000000002, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/links.ts"]}} -{"scenario": "cascade-page-size-ts-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "haiku", "trial": 1, "output": "FILE: code/links.ts\n\n```typescript\n/**\n * Builds the page-number links for a list view.\n *\n * The list paginates at the default page size defined in the `pagination` module, whose source is\n * not in this checkout (see its documentation). This module only needs that page size to know how\n * many page links to render.\n */\nexport function buildPageLinks(total: number): number[] {\n // Should return [1, 2, ..., pageCount], where pageCount is the number of pages needed to show\n // `total` items at the default page size; [] when total is 0.\n \n if (total === 0) {\n return [];\n }\n \n const defaultPageSize = 50;\n const pageCount = Math.ceil(total / defaultPageSize);\n \n const pageLinks: number[] = [];\n for (let i = 1; i <= pageCount; i++) {\n pageLinks.push(i);\n }\n \n return pageLinks;\n}\n```", "input_tokens": 651, "output_tokens": 253, "cost_usd": 0.0019160000000000002, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/links.ts"]}} -{"scenario": "cascade-page-size-ts-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "haiku", "trial": 2, "output": "FILE: code/links.ts\n\n```typescript\n/**\n * Builds the page-number links for a list view.\n *\n * The list paginates at the default page size defined in the `pagination` module, whose source is\n * not in this checkout (see its documentation). This module only needs that page size to know how\n * many page links to render.\n */\nexport function buildPageLinks(total: number): number[] {\n // Should return [1, 2, ..., pageCount], where pageCount is the number of pages needed to show\n // `total` items at the default page size; [] when total is 0.\n \n if (total === 0) {\n return [];\n }\n \n const defaultPageSize = 50;\n const pageCount = Math.ceil(total / defaultPageSize);\n \n const pageLinks: number[] = [];\n for (let i = 1; i <= pageCount; i++) {\n pageLinks.push(i);\n }\n \n return pageLinks;\n}\n```", "input_tokens": 651, "output_tokens": 253, "cost_usd": 0.0019160000000000002, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/links.ts"]}} -{"scenario": "cascade-page-size-ts-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "haiku", "trial": 3, "output": "FILE: code/links.ts\n\n```typescript\n/**\n * Builds the page-number links for a list view.\n *\n * The list paginates at the default page size defined in the `pagination` module, whose source is\n * not in this checkout (see its documentation). This module only needs that page size to know how\n * many page links to render.\n */\nexport function buildPageLinks(total: number): number[] {\n // Should return [1, 2, ..., pageCount], where pageCount is the number of pages needed to show\n // `total` items at the default page size; [] when total is 0.\n if (total === 0) {\n return [];\n }\n\n const defaultPageSize = 50;\n const pageCount = Math.ceil(total / defaultPageSize);\n \n return Array.from({ length: pageCount }, (_, i) => i + 1);\n}\n```", "input_tokens": 651, "output_tokens": 223, "cost_usd": 0.0017660000000000002, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/links.ts"]}} -{"scenario": "cascade-page-size-ts-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "haiku", "trial": 4, "output": "Looking at the task and documentation, I need to:\n\n1. Understand that the default page size is **50 items** (from the pagination documentation)\n2. Calculate the number of pages needed: `ceil(total / 50)`\n3. Return an array `[1, 2, ..., pageCount]` or `[]` if total is 0\n\nHere's the implementation:\n\nFILE: code/links.ts\n\n```typescript\n/**\n * Builds the page-number links for a list view.\n *\n * The list paginates at the default page size defined in the `pagination` module, whose source is\n * not in this checkout (see its documentation). This module only needs that page size to know how\n * many page links to render.\n */\nexport function buildPageLinks(total: number): number[] {\n // Should return [1, 2, ..., pageCount], where pageCount is the number of pages needed to show\n // `total` items at the default page size; [] when total is 0.\n \n if (total === 0) {\n return [];\n }\n \n const DEFAULT_PAGE_SIZE = 50;\n const pageCount = Math.ceil(total / DEFAULT_PAGE_SIZE);\n \n return Array.from({ length: pageCount }, (_, i) => i + 1);\n}\n```", "input_tokens": 651, "output_tokens": 317, "cost_usd": 0.002236, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/links.ts"]}} -{"scenario": "cascade-page-size-ts-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "haiku", "trial": 5, "output": "FILE: code/links.ts\n\n```typescript\n/**\n * Builds the page-number links for a list view.\n *\n * The list paginates at the default page size defined in the `pagination` module, whose source is\n * not in this checkout (see its documentation). This module only needs that page size to know how\n * many page links to render.\n */\nexport function buildPageLinks(total: number): number[] {\n // Should return [1, 2, ..., pageCount], where pageCount is the number of pages needed to show\n // `total` items at the default page size; [] when total is 0.\n \n if (total === 0) {\n return [];\n }\n \n const defaultPageSize = 50;\n const pageCount = Math.ceil(total / defaultPageSize);\n \n const pageLinks: number[] = [];\n for (let i = 1; i <= pageCount; i++) {\n pageLinks.push(i);\n }\n \n return pageLinks;\n}\n```", "input_tokens": 651, "output_tokens": 253, "cost_usd": 0.0019160000000000002, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/links.ts"]}} -{"scenario": "cascade-page-size-ts-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "haiku", "trial": 6, "output": "FILE: code/links.ts\n\n```typescript\n/**\n * Builds the page-number links for a list view.\n *\n * The list paginates at the default page size defined in the `pagination` module, whose source is\n * not in this checkout (see its documentation). This module only needs that page size to know how\n * many page links to render.\n */\nexport function buildPageLinks(total: number): number[] {\n // Should return [1, 2, ..., pageCount], where pageCount is the number of pages needed to show\n // `total` items at the default page size; [] when total is 0.\n \n if (total === 0) {\n return [];\n }\n \n const defaultPageSize = 50;\n const pageCount = Math.ceil(total / defaultPageSize);\n \n return Array.from({ length: pageCount }, (_, i) => i + 1);\n}\n```", "input_tokens": 651, "output_tokens": 227, "cost_usd": 0.0017860000000000003, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/links.ts"]}} -{"scenario": "cascade-page-size-ts-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "haiku", "trial": 7, "output": "FILE: code/links.ts\n\n```typescript\n/**\n * Builds the page-number links for a list view.\n *\n * The list paginates at the default page size defined in the `pagination` module, whose source is\n * not in this checkout (see its documentation). This module only needs that page size to know how\n * many page links to render.\n */\nexport function buildPageLinks(total: number): number[] {\n // Should return [1, 2, ..., pageCount], where pageCount is the number of pages needed to show\n // `total` items at the default page size; [] when total is 0.\n \n if (total === 0) {\n return [];\n }\n \n const defaultPageSize = 50;\n const pageCount = Math.ceil(total / defaultPageSize);\n \n return Array.from({ length: pageCount }, (_, i) => i + 1);\n}\n```", "input_tokens": 651, "output_tokens": 227, "cost_usd": 0.0017860000000000003, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/links.ts"]}} -{"scenario": "cascade-page-size-ts-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "haiku", "trial": 8, "output": "FILE: code/links.ts\n\n```typescript\n/**\n * Builds the page-number links for a list view.\n *\n * The list paginates at the default page size defined in the `pagination` module, whose source is\n * not in this checkout (see its documentation). This module only needs that page size to know how\n * many page links to render.\n */\nexport function buildPageLinks(total: number): number[] {\n // Should return [1, 2, ..., pageCount], where pageCount is the number of pages needed to show\n // `total` items at the default page size; [] when total is 0.\n if (total === 0) {\n return [];\n }\n\n const defaultPageSize = 50;\n const pageCount = Math.ceil(total / defaultPageSize);\n \n return Array.from({ length: pageCount }, (_, i) => i + 1);\n}\n```", "input_tokens": 651, "output_tokens": 223, "cost_usd": 0.0017660000000000002, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/links.ts"]}} -{"scenario": "cascade-page-size-ts-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "haiku", "trial": 9, "output": "FILE: code/links.ts\n\n```typescript\n/**\n * Builds the page-number links for a list view.\n *\n * The list paginates at the default page size defined in the `pagination` module, whose source is\n * not in this checkout (see its documentation). This module only needs that page size to know how\n * many page links to render.\n */\nexport function buildPageLinks(total: number): number[] {\n // Should return [1, 2, ..., pageCount], where pageCount is the number of pages needed to show\n // `total` items at the default page size; [] when total is 0.\n if (total === 0) {\n return [];\n }\n\n const DEFAULT_PAGE_SIZE = 50;\n const pageCount = Math.ceil(total / DEFAULT_PAGE_SIZE);\n \n return Array.from({ length: pageCount }, (_, i) => i + 1);\n}\n```", "input_tokens": 651, "output_tokens": 227, "cost_usd": 0.0017860000000000003, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/links.ts"]}} -{"scenario": "cascade-page-size-ts-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "sonnet", "trial": 0, "output": "FILE: code/links.ts\n```typescript\n/**\n * Builds the page-number links for a list view.\n *\n * The list paginates at the default page size defined in the `pagination` module, whose source is\n * not in this checkout (see its documentation). This module only needs that page size to know how\n * many page links to render.\n */\nexport function buildPageLinks(total: number): number[] {\n // Should return [1, 2, ..., pageCount], where pageCount is the number of pages needed to show\n // `total` items at the default page size; [] when total is 0.\n if (total === 0) {\n return [];\n }\n\n const defaultPageSize = 50;\n const pageCount = Math.ceil(total / defaultPageSize);\n\n return Array.from({ length: pageCount }, (_, i) => i + 1);\n}\n```", "input_tokens": 652, "output_tokens": 221, "cost_usd": 0.005271, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/links.ts"]}} -{"scenario": "cascade-page-size-ts-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "sonnet", "trial": 1, "output": "FILE: code/links.ts\n```typescript\n/**\n * Builds the page-number links for a list view.\n *\n * The list paginates at the default page size defined in the `pagination` module, whose source is\n * not in this checkout (see its documentation). This module only needs that page size to know how\n * many page links to render.\n */\nexport function buildPageLinks(total: number): number[] {\n // Should return [1, 2, ..., pageCount], where pageCount is the number of pages needed to show\n // `total` items at the default page size; [] when total is 0.\n if (total === 0) {\n return [];\n }\n\n const defaultPageSize = 50;\n const pageCount = Math.ceil(total / defaultPageSize);\n\n return Array.from({ length: pageCount }, (_, i) => i + 1);\n}\n```", "input_tokens": 652, "output_tokens": 221, "cost_usd": 0.005271, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/links.ts"]}} -{"scenario": "cascade-page-size-ts-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "sonnet", "trial": 2, "output": "FILE: code/links.ts\n```typescript\n/**\n * Builds the page-number links for a list view.\n *\n * The list paginates at the default page size defined in the `pagination` module, whose source is\n * not in this checkout (see its documentation). This module only needs that page size to know how\n * many page links to render.\n */\nexport function buildPageLinks(total: number): number[] {\n // Should return [1, 2, ..., pageCount], where pageCount is the number of pages needed to show\n // `total` items at the default page size; [] when total is 0.\n if (total === 0) {\n return [];\n }\n\n const defaultPageSize = 50;\n const pageCount = Math.ceil(total / defaultPageSize);\n\n return Array.from({ length: pageCount }, (_, i) => i + 1);\n}\n```", "input_tokens": 652, "output_tokens": 221, "cost_usd": 0.005271, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/links.ts"]}} -{"scenario": "cascade-page-size-ts-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "sonnet", "trial": 3, "output": "FILE: code/links.ts\n```typescript\n/**\n * Builds the page-number links for a list view.\n *\n * The list paginates at the default page size defined in the `pagination` module, whose source is\n * not in this checkout (see its documentation). This module only needs that page size to know how\n * many page links to render.\n */\nexport function buildPageLinks(total: number): number[] {\n // Should return [1, 2, ..., pageCount], where pageCount is the number of pages needed to show\n // `total` items at the default page size; [] when total is 0.\n if (total === 0) {\n return [];\n }\n\n const defaultPageSize = 50;\n const pageCount = Math.ceil(total / defaultPageSize);\n\n return Array.from({ length: pageCount }, (_, i) => i + 1);\n}\n```", "input_tokens": 652, "output_tokens": 221, "cost_usd": 0.005271, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/links.ts"]}} -{"scenario": "cascade-page-size-ts-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "sonnet", "trial": 4, "output": "FILE: code/links.ts\n```typescript\n/**\n * Builds the page-number links for a list view.\n *\n * The list paginates at the default page size defined in the `pagination` module, whose source is\n * not in this checkout (see its documentation). This module only needs that page size to know how\n * many page links to render.\n */\nexport function buildPageLinks(total: number): number[] {\n // Should return [1, 2, ..., pageCount], where pageCount is the number of pages needed to show\n // `total` items at the default page size; [] when total is 0.\n if (total === 0) {\n return [];\n }\n\n const defaultPageSize = 50;\n const pageCount = Math.ceil(total / defaultPageSize);\n\n return Array.from({ length: pageCount }, (_, i) => i + 1);\n}\n```", "input_tokens": 652, "output_tokens": 221, "cost_usd": 0.005271, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/links.ts"]}} -{"scenario": "cascade-page-size-ts-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "sonnet", "trial": 5, "output": "FILE: code/links.ts\n```typescript\n/**\n * Builds the page-number links for a list view.\n *\n * The list paginates at the default page size defined in the `pagination` module, whose source is\n * not in this checkout (see its documentation). This module only needs that page size to know how\n * many page links to render.\n */\nexport function buildPageLinks(total: number): number[] {\n // Should return [1, 2, ..., pageCount], where pageCount is the number of pages needed to show\n // `total` items at the default page size; [] when total is 0.\n if (total === 0) {\n return [];\n }\n\n const defaultPageSize = 50;\n const pageCount = Math.ceil(total / defaultPageSize);\n\n return Array.from({ length: pageCount }, (_, i) => i + 1);\n}\n```", "input_tokens": 652, "output_tokens": 221, "cost_usd": 0.005271, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/links.ts"]}} -{"scenario": "cascade-page-size-ts-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "sonnet", "trial": 6, "output": "FILE: code/links.ts\n```typescript\n/**\n * Builds the page-number links for a list view.\n *\n * The list paginates at the default page size defined in the `pagination` module, whose source is\n * not in this checkout (see its documentation). This module only needs that page size to know how\n * many page links to render.\n */\nexport function buildPageLinks(total: number): number[] {\n // Should return [1, 2, ..., pageCount], where pageCount is the number of pages needed to show\n // `total` items at the default page size; [] when total is 0.\n if (total === 0) {\n return [];\n }\n\n const defaultPageSize = 50;\n const pageCount = Math.ceil(total / defaultPageSize);\n\n return Array.from({ length: pageCount }, (_, i) => i + 1);\n}\n```", "input_tokens": 652, "output_tokens": 221, "cost_usd": 0.005271, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/links.ts"]}} -{"scenario": "cascade-page-size-ts-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "sonnet", "trial": 7, "output": "FILE: code/links.ts\n```typescript\n/**\n * Builds the page-number links for a list view.\n *\n * The list paginates at the default page size defined in the `pagination` module, whose source is\n * not in this checkout (see its documentation). This module only needs that page size to know how\n * many page links to render.\n */\nexport function buildPageLinks(total: number): number[] {\n // Should return [1, 2, ..., pageCount], where pageCount is the number of pages needed to show\n // `total` items at the default page size; [] when total is 0.\n if (total === 0) {\n return [];\n }\n\n const defaultPageSize = 50;\n const pageCount = Math.ceil(total / defaultPageSize);\n\n return Array.from({ length: pageCount }, (_, i) => i + 1);\n}\n```", "input_tokens": 652, "output_tokens": 221, "cost_usd": 0.005271, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/links.ts"]}} -{"scenario": "cascade-page-size-ts-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "sonnet", "trial": 8, "output": "FILE: code/links.ts\n```typescript\n/**\n * Builds the page-number links for a list view.\n *\n * The list paginates at the default page size defined in the `pagination` module, whose source is\n * not in this checkout (see its documentation). This module only needs that page size to know how\n * many page links to render.\n */\nexport function buildPageLinks(total: number): number[] {\n // Should return [1, 2, ..., pageCount], where pageCount is the number of pages needed to show\n // `total` items at the default page size; [] when total is 0.\n if (total === 0) {\n return [];\n }\n\n const defaultPageSize = 50;\n const pageCount = Math.ceil(total / defaultPageSize);\n\n return Array.from({ length: pageCount }, (_, i) => i + 1);\n}\n```", "input_tokens": 652, "output_tokens": 221, "cost_usd": 0.005271, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/links.ts"]}} -{"scenario": "cascade-page-size-ts-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "sonnet", "trial": 9, "output": "FILE: code/links.ts\n```typescript\n/**\n * Builds the page-number links for a list view.\n *\n * The list paginates at the default page size defined in the `pagination` module, whose source is\n * not in this checkout (see its documentation). This module only needs that page size to know how\n * many page links to render.\n */\nexport function buildPageLinks(total: number): number[] {\n // Should return [1, 2, ..., pageCount], where pageCount is the number of pages needed to show\n // `total` items at the default page size; [] when total is 0.\n if (total === 0) {\n return [];\n }\n\n const defaultPageSize = 50;\n const pageCount = Math.ceil(total / defaultPageSize);\n\n return Array.from({ length: pageCount }, (_, i) => i + 1);\n}\n```", "input_tokens": 652, "output_tokens": 221, "cost_usd": 0.005271, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/links.ts"]}} -{"scenario": "cascade-page-size-ts-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "opus", "trial": 0, "output": "FILE: code/links.ts\n```typescript\n/**\n * Builds the page-number links for a list view.\n *\n * The list paginates at the default page size defined in the `pagination` module, whose source is\n * not in this checkout (see its documentation). This module only needs that page size to know how\n * many page links to render.\n */\nexport function buildPageLinks(total: number): number[] {\n // Should return [1, 2, ..., pageCount], where pageCount is the number of pages needed to show\n // `total` items at the default page size; [] when total is 0.\n const DEFAULT_PAGE_SIZE = 50;\n const pageCount = Math.ceil(total / DEFAULT_PAGE_SIZE);\n return Array.from({ length: pageCount }, (_, i) => i + 1);\n}\n```", "input_tokens": 808, "output_tokens": 270, "cost_usd": 0.010790000000000001, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/links.ts"]}} -{"scenario": "cascade-page-size-ts-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "opus", "trial": 1, "output": "FILE: code/links.ts\n```typescript\n/**\n * Builds the page-number links for a list view.\n *\n * The list paginates at the default page size defined in the `pagination` module, whose source is\n * not in this checkout (see its documentation). This module only needs that page size to know how\n * many page links to render.\n */\nexport function buildPageLinks(total: number): number[] {\n // The default page size is 50 (defaultPageSize() returns 50), so `total` items span\n // ceil(total / 50) pages and links run from 1 to ceil(total / 50); [] when total is 0.\n const DEFAULT_PAGE_SIZE = 50;\n const pageCount = Math.ceil(total / DEFAULT_PAGE_SIZE);\n return Array.from({ length: pageCount }, (_, i) => i + 1);\n}\n```", "input_tokens": 808, "output_tokens": 284, "cost_usd": 0.01114, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/links.ts"]}} -{"scenario": "cascade-page-size-ts-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "opus", "trial": 2, "output": "FILE: code/links.ts\n```typescript\n/**\n * Builds the page-number links for a list view.\n *\n * The list paginates at the default page size defined in the `pagination` module, whose source is\n * not in this checkout (see its documentation). This module only needs that page size to know how\n * many page links to render.\n */\nexport function buildPageLinks(total: number): number[] {\n // Should return [1, 2, ..., pageCount], where pageCount is the number of pages needed to show\n // `total` items at the default page size; [] when total is 0.\n\n // The default page size is 50 (defaultPageSize() returns 50), so a result set of `total` items\n // spans ceil(total / 50) pages.\n const DEFAULT_PAGE_SIZE = 50;\n const pageCount = Math.ceil(total / DEFAULT_PAGE_SIZE);\n\n const links: number[] = [];\n for (let page = 1; page <= pageCount; page++) {\n links.push(page);\n }\n return links;\n}\n```", "input_tokens": 808, "output_tokens": 342, "cost_usd": 0.01259, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/links.ts"]}} -{"scenario": "cascade-page-size-ts-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "opus", "trial": 3, "output": "FILE: code/links.ts\n```typescript\n/**\n * Builds the page-number links for a list view.\n *\n * The list paginates at the default page size defined in the `pagination` module, whose source is\n * not in this checkout (see its documentation). This module only needs that page size to know how\n * many page links to render.\n */\nexport function buildPageLinks(total: number): number[] {\n // Should return [1, 2, ..., pageCount], where pageCount is the number of pages needed to show\n // `total` items at the default page size; [] when total is 0.\n const DEFAULT_PAGE_SIZE = 50;\n const pageCount = Math.ceil(total / DEFAULT_PAGE_SIZE);\n const links: number[] = [];\n for (let i = 1; i <= pageCount; i++) {\n links.push(i);\n }\n return links;\n}\n```", "input_tokens": 808, "output_tokens": 291, "cost_usd": 0.011315, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/links.ts"]}} -{"scenario": "cascade-page-size-ts-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "opus", "trial": 4, "output": "FILE: code/links.ts\n```typescript\n/**\n * Builds the page-number links for a list view.\n *\n * The list paginates at the default page size defined in the `pagination` module, whose source is\n * not in this checkout (see its documentation). This module only needs that page size to know how\n * many page links to render.\n */\nexport function buildPageLinks(total: number): number[] {\n // The default page size is 50 (defaultPageSize() returns 50), so a result set of `total` items\n // spans ceil(total / 50) pages and page links run from 1 to ceil(total / 50).\n const DEFAULT_PAGE_SIZE = 50;\n const pageCount = Math.ceil(total / DEFAULT_PAGE_SIZE);\n const links: number[] = [];\n for (let i = 1; i <= pageCount; i++) {\n links.push(i);\n }\n return links;\n}\n```", "input_tokens": 808, "output_tokens": 303, "cost_usd": 0.011615, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/links.ts"]}} -{"scenario": "cascade-page-size-ts-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "opus", "trial": 5, "output": "FILE: code/links.ts\n```typescript\n/**\n * Builds the page-number links for a list view.\n *\n * The list paginates at the default page size defined in the `pagination` module, whose source is\n * not in this checkout (see its documentation). This module only needs that page size to know how\n * many page links to render.\n */\nexport function buildPageLinks(total: number): number[] {\n // The default page size, per the `pagination` module documentation\n // (defaultPageSize() returns 50).\n const DEFAULT_PAGE_SIZE = 50;\n\n const pageCount = Math.ceil(total / DEFAULT_PAGE_SIZE);\n\n const links: number[] = [];\n for (let page = 1; page <= pageCount; page++) {\n links.push(page);\n }\n return links;\n}\n```", "input_tokens": 808, "output_tokens": 271, "cost_usd": 0.010815000000000002, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/links.ts"]}} -{"scenario": "cascade-page-size-ts-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "opus", "trial": 6, "output": "FILE: code/links.ts\n```typescript\n/**\n * Builds the page-number links for a list view.\n *\n * The list paginates at the default page size defined in the `pagination` module, whose source is\n * not in this checkout (see its documentation). This module only needs that page size to know how\n * many page links to render.\n */\nexport function buildPageLinks(total: number): number[] {\n // Should return [1, 2, ..., pageCount], where pageCount is the number of pages needed to show\n // `total` items at the default page size; [] when total is 0.\n const DEFAULT_PAGE_SIZE = 50;\n const pageCount = Math.ceil(total / DEFAULT_PAGE_SIZE);\n return Array.from({ length: pageCount }, (_, i) => i + 1);\n}\n```", "input_tokens": 808, "output_tokens": 270, "cost_usd": 0.010790000000000001, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/links.ts"]}} -{"scenario": "cascade-page-size-ts-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "opus", "trial": 7, "output": "FILE: code/links.ts\n```typescript\n/**\n * Builds the page-number links for a list view.\n *\n * The list paginates at the default page size defined in the `pagination` module, whose source is\n * not in this checkout (see its documentation). This module only needs that page size to know how\n * many page links to render.\n */\nexport function buildPageLinks(total: number): number[] {\n // The default page size, per the `pagination` module documentation\n // (defaultPageSize() returns 50).\n const DEFAULT_PAGE_SIZE = 50;\n\n const pageCount = Math.ceil(total / DEFAULT_PAGE_SIZE);\n\n const links: number[] = [];\n for (let page = 1; page <= pageCount; page++) {\n links.push(page);\n }\n return links;\n}\n```", "input_tokens": 808, "output_tokens": 271, "cost_usd": 0.010815000000000002, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/links.ts"]}} -{"scenario": "cascade-page-size-ts-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "opus", "trial": 8, "output": "FILE: code/links.ts\n```typescript\n/**\n * Builds the page-number links for a list view.\n *\n * The list paginates at the default page size defined in the `pagination` module, whose source is\n * not in this checkout (see its documentation). This module only needs that page size to know how\n * many page links to render.\n */\nexport function buildPageLinks(total: number): number[] {\n // The default page size is 50 (defaultPageSize() returns 50), so `total` items span\n // ceil(total / 50) pages and links run from 1 to ceil(total / 50); [] when total is 0.\n const DEFAULT_PAGE_SIZE = 50;\n const pageCount = Math.ceil(total / DEFAULT_PAGE_SIZE);\n return Array.from({ length: pageCount }, (_, i) => i + 1);\n}\n```", "input_tokens": 808, "output_tokens": 284, "cost_usd": 0.01114, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/links.ts"]}} -{"scenario": "cascade-page-size-ts-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "opus", "trial": 9, "output": "FILE: code/links.ts\n```typescript\n/**\n * Builds the page-number links for a list view.\n *\n * The list paginates at the default page size defined in the `pagination` module, whose source is\n * not in this checkout (see its documentation). This module only needs that page size to know how\n * many page links to render.\n */\nexport function buildPageLinks(total: number): number[] {\n // The default page size is 50 (see the `pagination` module documentation:\n // `defaultPageSize()` returns 50). A result set of `total` items spans\n // ceil(total / 50) pages, so the links run from 1 to that page count.\n const DEFAULT_PAGE_SIZE = 50;\n\n const pageCount = Math.ceil(total / DEFAULT_PAGE_SIZE);\n\n const links: number[] = [];\n for (let page = 1; page <= pageCount; page++) {\n links.push(page);\n }\n return links;\n}\n```", "input_tokens": 808, "output_tokens": 315, "cost_usd": 0.011915, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/links.ts"]}} -{"scenario": "cascade-page-size-ts-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "haiku", "trial": 0, "output": "FILE: code/links.ts\n\n```typescript\n/**\n * Builds the page-number links for a list view.\n *\n * The list paginates at the default page size defined in the `pagination` module, whose source is\n * not in this checkout (see its documentation). This module only needs that page size to know how\n * many page links to render.\n */\nexport function buildPageLinks(total: number): number[] {\n // Should return [1, 2, ..., pageCount], where pageCount is the number of pages needed to show\n // `total` items at the default page size; [] when total is 0.\n \n if (total === 0) {\n return [];\n }\n \n const DEFAULT_PAGE_SIZE = 25;\n const pageCount = Math.ceil(total / DEFAULT_PAGE_SIZE);\n \n return Array.from({ length: pageCount }, (_, i) => i + 1);\n}\n```", "input_tokens": 653, "output_tokens": 231, "cost_usd": 0.001808, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/links.ts"]}} -{"scenario": "cascade-page-size-ts-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "haiku", "trial": 1, "output": "FILE: code/links.ts\n\n```typescript\n/**\n * Builds the page-number links for a list view.\n *\n * The list paginates at the default page size defined in the `pagination` module, whose source is\n * not in this checkout (see its documentation). This module only needs that page size to know how\n * many page links to render.\n */\nexport function buildPageLinks(total: number): number[] {\n // Should return [1, 2, ..., pageCount], where pageCount is the number of pages needed to show\n // `total` items at the default page size; [] when total is 0.\n if (total === 0) {\n return [];\n }\n\n const defaultPageSize = 25;\n const pageCount = Math.ceil(total / defaultPageSize);\n \n return Array.from({ length: pageCount }, (_, i) => i + 1);\n}\n```", "input_tokens": 653, "output_tokens": 223, "cost_usd": 0.001768, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/links.ts"]}} -{"scenario": "cascade-page-size-ts-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "haiku", "trial": 2, "output": "FILE: code/links.ts\n\n```typescript\n/**\n * Builds the page-number links for a list view.\n *\n * The list paginates at the default page size defined in the `pagination` module, whose source is\n * not in this checkout (see its documentation). This module only needs that page size to know how\n * many page links to render.\n */\nexport function buildPageLinks(total: number): number[] {\n // Should return [1, 2, ..., pageCount], where pageCount is the number of pages needed to show\n // `total` items at the default page size; [] when total is 0.\n if (total === 0) {\n return [];\n }\n\n const defaultPageSize = 25;\n const pageCount = Math.ceil(total / defaultPageSize);\n \n return Array.from({ length: pageCount }, (_, i) => i + 1);\n}\n```", "input_tokens": 653, "output_tokens": 223, "cost_usd": 0.001768, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/links.ts"]}} -{"scenario": "cascade-page-size-ts-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "haiku", "trial": 3, "output": "FILE: code/links.ts\n\n```typescript\n/**\n * Builds the page-number links for a list view.\n *\n * The list paginates at the default page size defined in the `pagination` module, whose source is\n * not in this checkout (see its documentation). This module only needs that page size to know how\n * many page links to render.\n */\nexport function buildPageLinks(total: number): number[] {\n // Should return [1, 2, ..., pageCount], where pageCount is the number of pages needed to show\n // `total` items at the default page size; [] when total is 0.\n \n if (total === 0) {\n return [];\n }\n \n const DEFAULT_PAGE_SIZE = 25;\n const pageCount = Math.ceil(total / DEFAULT_PAGE_SIZE);\n \n return Array.from({ length: pageCount }, (_, i) => i + 1);\n}\n```", "input_tokens": 653, "output_tokens": 231, "cost_usd": 0.001808, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/links.ts"]}} -{"scenario": "cascade-page-size-ts-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "haiku", "trial": 4, "output": "FILE: code/links.ts\n\n```typescript\n/**\n * Builds the page-number links for a list view.\n *\n * The list paginates at the default page size defined in the `pagination` module, whose source is\n * not in this checkout (see its documentation). This module only needs that page size to know how\n * many page links to render.\n */\nexport function buildPageLinks(total: number): number[] {\n // Should return [1, 2, ..., pageCount], where pageCount is the number of pages needed to show\n // `total` items at the default page size; [] when total is 0.\n \n if (total === 0) {\n return [];\n }\n \n const defaultPageSize = 25;\n const pageCount = Math.ceil(total / defaultPageSize);\n \n const pageLinks: number[] = [];\n for (let i = 1; i <= pageCount; i++) {\n pageLinks.push(i);\n }\n \n return pageLinks;\n}\n```", "input_tokens": 653, "output_tokens": 253, "cost_usd": 0.001918, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/links.ts"]}} -{"scenario": "cascade-page-size-ts-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "haiku", "trial": 5, "output": "FILE: code/links.ts\n\n```typescript\n/**\n * Builds the page-number links for a list view.\n *\n * The list paginates at the default page size defined in the `pagination` module, whose source is\n * not in this checkout (see its documentation). This module only needs that page size to know how\n * many page links to render.\n */\nexport function buildPageLinks(total: number): number[] {\n // Should return [1, 2, ..., pageCount], where pageCount is the number of pages needed to show\n // `total` items at the default page size; [] when total is 0.\n \n if (total === 0) {\n return [];\n }\n \n const defaultPageSize = 25;\n const pageCount = Math.ceil(total / defaultPageSize);\n \n const pageLinks: number[] = [];\n for (let i = 1; i <= pageCount; i++) {\n pageLinks.push(i);\n }\n \n return pageLinks;\n}\n```", "input_tokens": 653, "output_tokens": 253, "cost_usd": 0.001918, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/links.ts"]}} -{"scenario": "cascade-page-size-ts-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "haiku", "trial": 6, "output": "FILE: code/links.ts\n\n```typescript\n/**\n * Builds the page-number links for a list view.\n *\n * The list paginates at the default page size defined in the `pagination` module, whose source is\n * not in this checkout (see its documentation). This module only needs that page size to know how\n * many page links to render.\n */\nexport function buildPageLinks(total: number): number[] {\n // Should return [1, 2, ..., pageCount], where pageCount is the number of pages needed to show\n // `total` items at the default page size; [] when total is 0.\n \n if (total === 0) {\n return [];\n }\n \n const defaultPageSize = 25;\n const pageCount = Math.ceil(total / defaultPageSize);\n \n return Array.from({ length: pageCount }, (_, i) => i + 1);\n}\n```", "input_tokens": 653, "output_tokens": 227, "cost_usd": 0.001788, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/links.ts"]}} -{"scenario": "cascade-page-size-ts-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "haiku", "trial": 7, "output": "FILE: code/links.ts\n\n```typescript\n/**\n * Builds the page-number links for a list view.\n *\n * The list paginates at the default page size defined in the `pagination` module, whose source is\n * not in this checkout (see its documentation). This module only needs that page size to know how\n * many page links to render.\n */\nexport function buildPageLinks(total: number): number[] {\n // Should return [1, 2, ..., pageCount], where pageCount is the number of pages needed to show\n // `total` items at the default page size; [] when total is 0.\n \n if (total === 0) {\n return [];\n }\n \n const defaultPageSize = 25;\n const pageCount = Math.ceil(total / defaultPageSize);\n \n const pageLinks: number[] = [];\n for (let i = 1; i <= pageCount; i++) {\n pageLinks.push(i);\n }\n \n return pageLinks;\n}\n```", "input_tokens": 653, "output_tokens": 253, "cost_usd": 0.001918, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/links.ts"]}} -{"scenario": "cascade-page-size-ts-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "haiku", "trial": 8, "output": "FILE: code/links.ts\n\n```typescript\n/**\n * Builds the page-number links for a list view.\n *\n * The list paginates at the default page size defined in the `pagination` module, whose source is\n * not in this checkout (see its documentation). This module only needs that page size to know how\n * many page links to render.\n */\nexport function buildPageLinks(total: number): number[] {\n // Should return [1, 2, ..., pageCount], where pageCount is the number of pages needed to show\n // `total` items at the default page size; [] when total is 0.\n \n if (total === 0) {\n return [];\n }\n \n const defaultPageSize = 25;\n const pageCount = Math.ceil(total / defaultPageSize);\n \n return Array.from({ length: pageCount }, (_, i) => i + 1);\n}\n```", "input_tokens": 653, "output_tokens": 227, "cost_usd": 0.001788, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/links.ts"]}} -{"scenario": "cascade-page-size-ts-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "haiku", "trial": 9, "output": "Looking at the documentation provided, I can see that:\n\n1. The default page size is **25 items** (from `defaultPageSize()`)\n2. A result set of `N` items spans **`ceil(N / 25)`** pages\n3. Page links run from **1 to `ceil(N / 25)`**\n4. Return `[]` when `total` is 0\n\nHere's the implementation:\n\nFILE: code/links.ts\n\n```typescript\n/**\n * Builds the page-number links for a list view.\n *\n * The list paginates at the default page size defined in the `pagination` module, whose source is\n * not in this checkout (see its documentation). This module only needs that page size to know how\n * many page links to render.\n */\nexport function buildPageLinks(total: number): number[] {\n // Should return [1, 2, ..., pageCount], where pageCount is the number of pages needed to show\n // `total` items at the default page size; [] when total is 0.\n \n if (total === 0) {\n return [];\n }\n \n const defaultPageSize = 25;\n const pageCount = Math.ceil(total / defaultPageSize);\n \n return Array.from({ length: pageCount }, (_, i) => i + 1);\n}\n```", "input_tokens": 653, "output_tokens": 323, "cost_usd": 0.002268, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/links.ts"]}} -{"scenario": "cascade-page-size-ts-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "sonnet", "trial": 0, "output": "FILE: code/links.ts\n```typescript\n/**\n * Builds the page-number links for a list view.\n *\n * The list paginates at the default page size defined in the `pagination` module, whose source is\n * not in this checkout (see its documentation). This module only needs that page size to know how\n * many page links to render.\n */\nexport function buildPageLinks(total: number): number[] {\n // Should return [1, 2, ..., pageCount], where pageCount is the number of pages needed to show\n // `total` items at the default page size; [] when total is 0.\n if (total === 0) {\n return [];\n }\n\n const defaultPageSize = 25;\n const pageCount = Math.ceil(total / defaultPageSize);\n\n return Array.from({ length: pageCount }, (_, i) => i + 1);\n}\n```", "input_tokens": 654, "output_tokens": 221, "cost_usd": 0.0052770000000000004, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/links.ts"]}} -{"scenario": "cascade-page-size-ts-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "sonnet", "trial": 1, "output": "FILE: code/links.ts\n```typescript\n/**\n * Builds the page-number links for a list view.\n *\n * The list paginates at the default page size defined in the `pagination` module, whose source is\n * not in this checkout (see its documentation). This module only needs that page size to know how\n * many page links to render.\n */\nexport function buildPageLinks(total: number): number[] {\n // Should return [1, 2, ..., pageCount], where pageCount is the number of pages needed to show\n // `total` items at the default page size; [] when total is 0.\n if (total === 0) {\n return [];\n }\n const defaultPageSize = 25;\n const pageCount = Math.ceil(total / defaultPageSize);\n return Array.from({ length: pageCount }, (_, i) => i + 1);\n}\n```", "input_tokens": 654, "output_tokens": 221, "cost_usd": 0.0052770000000000004, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/links.ts"]}} -{"scenario": "cascade-page-size-ts-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "sonnet", "trial": 2, "output": "FILE: code/links.ts\n\n```typescript\n/**\n * Builds the page-number links for a list view.\n *\n * The list paginates at the default page size defined in the `pagination` module, whose source is\n * not in this checkout (see its documentation). This module only needs that page size to know how\n * many page links to render.\n */\nexport function buildPageLinks(total: number): number[] {\n // Should return [1, 2, ..., pageCount], where pageCount is the number of pages needed to show\n // `total` items at the default page size; [] when total is 0.\n if (total === 0) {\n return [];\n }\n\n const defaultPageSize = 25;\n const pageCount = Math.ceil(total / defaultPageSize);\n\n return Array.from({ length: pageCount }, (_, i) => i + 1);\n}\n```", "input_tokens": 654, "output_tokens": 221, "cost_usd": 0.0052770000000000004, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/links.ts"]}} -{"scenario": "cascade-page-size-ts-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "sonnet", "trial": 3, "output": "FILE: code/links.ts\n```typescript\n/**\n * Builds the page-number links for a list view.\n *\n * The list paginates at the default page size defined in the `pagination` module, whose source is\n * not in this checkout (see its documentation). This module only needs that page size to know how\n * many page links to render.\n */\nexport function buildPageLinks(total: number): number[] {\n // Should return [1, 2, ..., pageCount], where pageCount is the number of pages needed to show\n // `total` items at the default page size; [] when total is 0.\n if (total === 0) {\n return [];\n }\n\n const defaultPageSize = 25;\n const pageCount = Math.ceil(total / defaultPageSize);\n\n return Array.from({ length: pageCount }, (_, i) => i + 1);\n}\n```", "input_tokens": 654, "output_tokens": 221, "cost_usd": 0.0052770000000000004, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/links.ts"]}} -{"scenario": "cascade-page-size-ts-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "sonnet", "trial": 4, "output": "FILE: code/links.ts\n```typescript\n/**\n * Builds the page-number links for a list view.\n *\n * The list paginates at the default page size defined in the `pagination` module, whose source is\n * not in this checkout (see its documentation). This module only needs that page size to know how\n * many page links to render.\n */\nexport function buildPageLinks(total: number): number[] {\n // Should return [1, 2, ..., pageCount], where pageCount is the number of pages needed to show\n // `total` items at the default page size; [] when total is 0.\n if (total === 0) {\n return [];\n }\n\n const defaultPageSize = 25;\n const pageCount = Math.ceil(total / defaultPageSize);\n\n return Array.from({ length: pageCount }, (_, i) => i + 1);\n}\n```", "input_tokens": 654, "output_tokens": 221, "cost_usd": 0.0052770000000000004, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/links.ts"]}} -{"scenario": "cascade-page-size-ts-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "sonnet", "trial": 5, "output": "FILE: code/links.ts\n```typescript\n/**\n * Builds the page-number links for a list view.\n *\n * The list paginates at the default page size defined in the `pagination` module, whose source is\n * not in this checkout (see its documentation). This module only needs that page size to know how\n * many page links to render.\n */\nexport function buildPageLinks(total: number): number[] {\n // Should return [1, 2, ..., pageCount], where pageCount is the number of pages needed to show\n // `total` items at the default page size; [] when total is 0.\n if (total === 0) {\n return [];\n }\n\n const defaultPageSize = 25;\n const pageCount = Math.ceil(total / defaultPageSize);\n\n return Array.from({ length: pageCount }, (_, i) => i + 1);\n}\n```", "input_tokens": 654, "output_tokens": 221, "cost_usd": 0.0052770000000000004, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/links.ts"]}} -{"scenario": "cascade-page-size-ts-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "sonnet", "trial": 6, "output": "FILE: code/links.ts\n```typescript\n/**\n * Builds the page-number links for a list view.\n *\n * The list paginates at the default page size defined in the `pagination` module, whose source is\n * not in this checkout (see its documentation). This module only needs that page size to know how\n * many page links to render.\n */\nexport function buildPageLinks(total: number): number[] {\n // Should return [1, 2, ..., pageCount], where pageCount is the number of pages needed to show\n // `total` items at the default page size; [] when total is 0.\n if (total === 0) {\n return [];\n }\n\n const defaultPageSize = 25;\n const pageCount = Math.ceil(total / defaultPageSize);\n\n return Array.from({ length: pageCount }, (_, i) => i + 1);\n}\n```", "input_tokens": 654, "output_tokens": 221, "cost_usd": 0.0052770000000000004, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/links.ts"]}} -{"scenario": "cascade-page-size-ts-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "sonnet", "trial": 7, "output": "FILE: code/links.ts\n```typescript\n/**\n * Builds the page-number links for a list view.\n *\n * The list paginates at the default page size defined in the `pagination` module, whose source is\n * not in this checkout (see its documentation). This module only needs that page size to know how\n * many page links to render.\n */\nexport function buildPageLinks(total: number): number[] {\n // Should return [1, 2, ..., pageCount], where pageCount is the number of pages needed to show\n // `total` items at the default page size; [] when total is 0.\n if (total === 0) {\n return [];\n }\n\n const defaultPageSize = 25;\n const pageCount = Math.ceil(total / defaultPageSize);\n\n return Array.from({ length: pageCount }, (_, i) => i + 1);\n}\n```", "input_tokens": 654, "output_tokens": 221, "cost_usd": 0.0052770000000000004, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/links.ts"]}} -{"scenario": "cascade-page-size-ts-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "sonnet", "trial": 8, "output": "FILE: code/links.ts\n```typescript\n/**\n * Builds the page-number links for a list view.\n *\n * The list paginates at the default page size defined in the `pagination` module, whose source is\n * not in this checkout (see its documentation). This module only needs that page size to know how\n * many page links to render.\n */\nexport function buildPageLinks(total: number): number[] {\n // Should return [1, 2, ..., pageCount], where pageCount is the number of pages needed to show\n // `total` items at the default page size; [] when total is 0.\n if (total === 0) {\n return [];\n }\n\n const defaultPageSize = 25;\n const pageCount = Math.ceil(total / defaultPageSize);\n\n return Array.from({ length: pageCount }, (_, i) => i + 1);\n}\n```", "input_tokens": 654, "output_tokens": 221, "cost_usd": 0.0052770000000000004, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/links.ts"]}} -{"scenario": "cascade-page-size-ts-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "sonnet", "trial": 9, "output": "FILE: code/links.ts\n\n```typescript\n/**\n * Builds the page-number links for a list view.\n *\n * The list paginates at the default page size defined in the `pagination` module, whose source is\n * not in this checkout (see its documentation). This module only needs that page size to know how\n * many page links to render.\n */\nexport function buildPageLinks(total: number): number[] {\n // Should return [1, 2, ..., pageCount], where pageCount is the number of pages needed to show\n // `total` items at the default page size; [] when total is 0.\n if (total === 0) {\n return [];\n }\n\n const defaultPageSize = 25;\n const pageCount = Math.ceil(total / defaultPageSize);\n\n return Array.from({ length: pageCount }, (_, i) => i + 1);\n}\n```", "input_tokens": 654, "output_tokens": 221, "cost_usd": 0.0052770000000000004, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/links.ts"]}} -{"scenario": "cascade-page-size-ts-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "opus", "trial": 0, "output": "FILE: code/links.ts\n```typescript\n/**\n * Builds the page-number links for a list view.\n *\n * The list paginates at the default page size defined in the `pagination` module, whose source is\n * not in this checkout (see its documentation). This module only needs that page size to know how\n * many page links to render.\n */\nexport function buildPageLinks(total: number): number[] {\n // Should return [1, 2, ..., pageCount], where pageCount is the number of pages needed to show\n // `total` items at the default page size; [] when total is 0.\n\n // Per the pagination documentation, defaultPageSize() returns 25, so a result set of N items\n // spans ceil(N / 25) pages.\n const DEFAULT_PAGE_SIZE = 25;\n const pageCount = Math.ceil(total / DEFAULT_PAGE_SIZE);\n\n const links: number[] = [];\n for (let page = 1; page <= pageCount; page++) {\n links.push(page);\n }\n return links;\n}\n```", "input_tokens": 812, "output_tokens": 341, "cost_usd": 0.012584999999999999, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/links.ts"]}} -{"scenario": "cascade-page-size-ts-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "opus", "trial": 1, "output": "FILE: code/links.ts\n```typescript\n/**\n * Builds the page-number links for a list view.\n *\n * The list paginates at the default page size defined in the `pagination` module, whose source is\n * not in this checkout (see its documentation). This module only needs that page size to know how\n * many page links to render.\n */\n\n// Default page size per the `pagination` module documentation: defaultPageSize() returns 25.\nconst DEFAULT_PAGE_SIZE = 25;\n\nexport function buildPageLinks(total: number): number[] {\n // Should return [1, 2, ..., pageCount], where pageCount is the number of pages needed to show\n // `total` items at the default page size; [] when total is 0.\n const pageCount = Math.ceil(total / DEFAULT_PAGE_SIZE);\n return Array.from({ length: pageCount }, (_, i) => i + 1);\n}\n```", "input_tokens": 812, "output_tokens": 300, "cost_usd": 0.01156, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/links.ts"]}} -{"scenario": "cascade-page-size-ts-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "opus", "trial": 2, "output": "FILE: code/links.ts\n```typescript\n/**\n * Builds the page-number links for a list view.\n *\n * The list paginates at the default page size defined in the `pagination` module, whose source is\n * not in this checkout (see its documentation). This module only needs that page size to know how\n * many page links to render.\n */\nexport function buildPageLinks(total: number): number[] {\n // The default page size is 25 (see the `pagination` module documentation:\n // defaultPageSize() returns 25). A result set of `total` items spans ceil(total / 25)\n // pages, and the page links run from 1 to that page count.\n const DEFAULT_PAGE_SIZE = 25;\n\n const pageCount = Math.ceil(total / DEFAULT_PAGE_SIZE);\n\n const links: number[] = [];\n for (let page = 1; page <= pageCount; page++) {\n links.push(page);\n }\n return links;\n}\n```", "input_tokens": 812, "output_tokens": 314, "cost_usd": 0.01191, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/links.ts"]}} -{"scenario": "cascade-page-size-ts-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "opus", "trial": 3, "output": "FILE: code/links.ts\n```typescript\n/**\n * Builds the page-number links for a list view.\n *\n * The list paginates at the default page size defined in the `pagination` module, whose source is\n * not in this checkout (see its documentation). This module only needs that page size to know how\n * many page links to render.\n */\nexport function buildPageLinks(total: number): number[] {\n // Should return [1, 2, ..., pageCount], where pageCount is the number of pages needed to show\n // `total` items at the default page size; [] when total is 0.\n const DEFAULT_PAGE_SIZE = 25;\n const pageCount = Math.ceil(total / DEFAULT_PAGE_SIZE);\n return Array.from({ length: pageCount }, (_, i) => i + 1);\n}\n```", "input_tokens": 812, "output_tokens": 270, "cost_usd": 0.01081, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/links.ts"]}} -{"scenario": "cascade-page-size-ts-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "opus", "trial": 4, "output": "FILE: code/links.ts\n```typescript\n/**\n * Builds the page-number links for a list view.\n *\n * The list paginates at the default page size defined in the `pagination` module, whose source is\n * not in this checkout (see its documentation). This module only needs that page size to know how\n * many page links to render.\n */\nconst DEFAULT_PAGE_SIZE = 25;\n\nexport function buildPageLinks(total: number): number[] {\n // Returns [1, 2, ..., pageCount], where pageCount is the number of pages needed to show\n // `total` items at the default page size; [] when total is 0.\n const pageCount = Math.ceil(total / DEFAULT_PAGE_SIZE);\n const links: number[] = [];\n for (let page = 1; page <= pageCount; page++) {\n links.push(page);\n }\n return links;\n}\n```", "input_tokens": 812, "output_tokens": 290, "cost_usd": 0.01131, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/links.ts"]}} -{"scenario": "cascade-page-size-ts-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "opus", "trial": 5, "output": "FILE: code/links.ts\n```typescript\n/**\n * Builds the page-number links for a list view.\n *\n * The list paginates at the default page size defined in the `pagination` module, whose source is\n * not in this checkout (see its documentation). This module only needs that page size to know how\n * many page links to render.\n */\n\n// The default page size, per the pagination module's documentation (defaultPageSize() returns 25).\nconst DEFAULT_PAGE_SIZE = 25;\n\nexport function buildPageLinks(total: number): number[] {\n // Should return [1, 2, ..., pageCount], where pageCount is the number of pages needed to show\n // `total` items at the default page size; [] when total is 0.\n const pageCount = Math.ceil(total / DEFAULT_PAGE_SIZE);\n const links: number[] = [];\n for (let page = 1; page <= pageCount; page++) {\n links.push(page);\n }\n return links;\n}\n```", "input_tokens": 812, "output_tokens": 321, "cost_usd": 0.012085000000000002, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/links.ts"]}} -{"scenario": "cascade-page-size-ts-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "opus", "trial": 6, "output": "FILE: code/links.ts\n```typescript\n/**\n * Builds the page-number links for a list view.\n *\n * The list paginates at the default page size defined in the `pagination` module, whose source is\n * not in this checkout (see its documentation). This module only needs that page size to know how\n * many page links to render.\n */\n\n// The default page size is 25, per the `pagination` module documentation\n// (defaultPageSize() returns 25). A result set of N items spans ceil(N / 25)\n// pages, so the page links run from 1 to ceil(N / 25).\nconst DEFAULT_PAGE_SIZE = 25;\n\nexport function buildPageLinks(total: number): number[] {\n // Should return [1, 2, ..., pageCount], where pageCount is the number of pages needed to show\n // `total` items at the default page size; [] when total is 0.\n const pageCount = Math.ceil(total / DEFAULT_PAGE_SIZE);\n const links: number[] = [];\n for (let page = 1; page <= pageCount; page++) {\n links.push(page);\n }\n return links;\n}\n```", "input_tokens": 812, "output_tokens": 368, "cost_usd": 0.013260000000000001, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/links.ts"]}} -{"scenario": "cascade-page-size-ts-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "opus", "trial": 7, "output": "FILE: code/links.ts\n```typescript\n/**\n * Builds the page-number links for a list view.\n *\n * The list paginates at the default page size defined in the `pagination` module, whose source is\n * not in this checkout (see its documentation). This module only needs that page size to know how\n * many page links to render.\n */\nexport function buildPageLinks(total: number): number[] {\n // Should return [1, 2, ..., pageCount], where pageCount is the number of pages needed to show\n // `total` items at the default page size; [] when total is 0.\n const DEFAULT_PAGE_SIZE = 25;\n const pageCount = Math.ceil(total / DEFAULT_PAGE_SIZE);\n return Array.from({ length: pageCount }, (_, i) => i + 1);\n}\n```", "input_tokens": 812, "output_tokens": 270, "cost_usd": 0.01081, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/links.ts"]}} -{"scenario": "cascade-page-size-ts-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "opus", "trial": 8, "output": "FILE: code/links.ts\n```typescript\n/**\n * Builds the page-number links for a list view.\n *\n * The list paginates at the default page size defined in the `pagination` module, whose source is\n * not in this checkout (see its documentation). This module only needs that page size to know how\n * many page links to render.\n */\n\n// The default page size is 25, per the pagination module's documentation\n// (defaultPageSize() returns 25).\nconst DEFAULT_PAGE_SIZE = 25;\n\nexport function buildPageLinks(total: number): number[] {\n // Should return [1, 2, ..., pageCount], where pageCount is the number of pages needed to show\n // `total` items at the default page size; [] when total is 0.\n const pageCount = Math.ceil(total / DEFAULT_PAGE_SIZE);\n const links: number[] = [];\n for (let i = 1; i <= pageCount; i++) {\n links.push(i);\n }\n return links;\n}\n```", "input_tokens": 812, "output_tokens": 326, "cost_usd": 0.012210000000000002, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/links.ts"]}} -{"scenario": "cascade-page-size-ts-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "opus", "trial": 9, "output": "FILE: code/links.ts\n```typescript\n/**\n * Builds the page-number links for a list view.\n *\n * The list paginates at the default page size defined in the `pagination` module, whose source is\n * not in this checkout (see its documentation). This module only needs that page size to know how\n * many page links to render.\n */\nexport function buildPageLinks(total: number): number[] {\n // The default page size is 25, per the pagination module's documentation\n // (defaultPageSize() returns 25). A result set of `total` items spans\n // ceil(total / 25) pages, with links running from 1 to that page count.\n const DEFAULT_PAGE_SIZE = 25;\n\n const pageCount = Math.ceil(total / DEFAULT_PAGE_SIZE);\n\n const links: number[] = [];\n for (let page = 1; page <= pageCount; page++) {\n links.push(page);\n }\n return links;\n}\n```", "input_tokens": 812, "output_tokens": 310, "cost_usd": 0.011810000000000001, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/links.ts"]}} -{"scenario": "cascade-page-size-ts-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "haiku", "trial": 0, "output": "Looking at the materials provided, I need to:\n\n1. Understand the default page size from the documentation\n2. Implement `buildPageLinks(total)` to return page numbers based on that size\n\nFrom the **Automated documentation check**, I can see that `defaultPageSize()` now returns **25** (not 50 as the outdated documentation claims). The new code shows:\n```\nfunction defaultPageSize(): number {\n return 25;\n}\n```\n\nSo the default page size is **25 items per page**.\n\nFor `buildPageLinks(total)`:\n- When `total` is 0, return `[]`\n- Otherwise, calculate `pageCount = ceil(total / 25)`\n- Return `[1, 2, ..., pageCount]`\n\nFILE: code/links.ts\n\n```typescript\n/**\n * Builds the page-number links for a list view.\n *\n * The list paginates at the default page size defined in the `pagination` module, whose source is\n * not in this checkout (see its documentation). This module only needs that page size to know how\n * many page links to render.\n */\nexport function buildPageLinks(total: number): number[] {\n // Should return [1, 2, ..., pageCount], where pageCount is the number of pages needed to show\n // `total` items at the default page size; [] when total is 0.\n if (total === 0) {\n return [];\n }\n\n const defaultPageSize = 25;\n const pageCount = Math.ceil(total / defaultPageSize);\n \n return Array.from({ length: pageCount }, (_, i) => i + 1);\n}\n```", "input_tokens": 951, "output_tokens": 398, "cost_usd": 0.002941, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/links.ts"]}} -{"scenario": "cascade-page-size-ts-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "haiku", "trial": 1, "output": "Looking at the materials provided, I need to determine the correct default page size and implement `buildPageLinks`.\n\nThe documentation has a **divergence report** showing that `defaultPageSize()` has changed:\n- **Old documentation claim**: returns 50\n- **New actual code**: returns 25\n\nThe automated check shows the new code is:\n```\nfunction defaultPageSize(): number {\n return 25;\n}\n```\n\nSo the current default page size is **25 items**, not 50.\n\nTo implement `buildPageLinks(total)`:\n1. Get the page count: `ceil(total / 25)`\n2. Return `[1, 2, ..., pageCount]` or `[]` if total is 0\n\nThe formula for page count is: `Math.ceil(total / defaultPageSize)` where `defaultPageSize = 25`.\n\nFILE: code/links.ts\n\n```typescript\n/**\n * Builds the page-number links for a list view.\n *\n * The list paginates at the default page size defined in the `pagination` module, whose source is\n * not in this checkout (see its documentation). This module only needs that page size to know how\n * many page links to render.\n */\nexport function buildPageLinks(total: number): number[] {\n // Should return [1, 2, ..., pageCount], where pageCount is the number of pages needed to show\n // `total` items at the default page size; [] when total is 0.\n if (total === 0) {\n return [];\n }\n\n const defaultPageSize = 25;\n const pageCount = Math.ceil(total / defaultPageSize);\n \n return Array.from({ length: pageCount }, (_, i) => i + 1);\n}\n```", "input_tokens": 951, "output_tokens": 416, "cost_usd": 0.0030310000000000003, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/links.ts"]}} -{"scenario": "cascade-page-size-ts-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "haiku", "trial": 2, "output": "Looking at this task, I need to:\n\n1. Determine the actual default page size from the materials provided\n2. Implement `buildPageLinks(total)` to return page numbers based on that page size\n\nFrom the **Automated documentation check**, I can see that the actual code now shows:\n```\nfunction defaultPageSize(): number {\n return 25;\n}\n```\n\nThis differs from the outdated documentation which claimed it returns 50. The drift detection clearly shows the new hash corresponds to `defaultPageSize()` returning **25**.\n\nSo I need to:\n- Calculate `pageCount = ceil(total / 25)`\n- Return `[1, 2, ..., pageCount]` for total > 0\n- Return `[]` for total = 0\n\nFILE: code/links.ts\n\n```typescript\n/**\n * Builds the page-number links for a list view.\n *\n * The list paginates at the default page size defined in the `pagination` module, whose source is\n * not in this checkout (see its documentation). This module only needs that page size to know how\n * many page links to render.\n */\nexport function buildPageLinks(total: number): number[] {\n // Should return [1, 2, ..., pageCount], where pageCount is the number of pages needed to show\n // `total` items at the default page size; [] when total is 0.\n if (total === 0) {\n return [];\n }\n\n const defaultPageSize = 25;\n const pageCount = Math.ceil(total / defaultPageSize);\n \n return Array.from({ length: pageCount }, (_, i) => i + 1);\n}\n```", "input_tokens": 951, "output_tokens": 395, "cost_usd": 0.002926, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/links.ts"]}} -{"scenario": "cascade-page-size-ts-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "haiku", "trial": 3, "output": "Looking at the materials provided, I need to determine the correct default page size and implement `buildPageLinks`.\n\nThe documentation contains a discrepancy:\n- The anchored claim states: \"defaultPageSize() returns 50\"\n- The automated documentation check reveals the actual code has changed: \"defaultPageSize() returns 25\"\n\nSince the task explicitly states \"Do not change `defaultPageSize`\" and instructs me to use the materials provided, I must use the **actual current code** shown in the divergence report, which is `defaultPageSize(): number { return 25; }`.\n\nThe logic is:\n- For `total` items with a page size of 25: `pageCount = ceil(total / 25)`\n- Return `[1, 2, ..., pageCount]` or `[]` if total is 0\n\nFILE: code/links.ts\n\n```typescript\n/**\n * Builds the page-number links for a list view.\n *\n * The list paginates at the default page size defined in the `pagination` module, whose source is\n * not in this checkout (see its documentation). This module only needs that page size to know how\n * many page links to render.\n */\nexport function buildPageLinks(total: number): number[] {\n // Should return [1, 2, ..., pageCount], where pageCount is the number of pages needed to show\n // `total` items at the default page size; [] when total is 0.\n if (total === 0) {\n return [];\n }\n \n const defaultPageSize = 25;\n const pageCount = Math.ceil(total / defaultPageSize);\n \n return Array.from({ length: pageCount }, (_, i) => i + 1);\n}\n```", "input_tokens": 951, "output_tokens": 409, "cost_usd": 0.0029960000000000004, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/links.ts"]}} -{"scenario": "cascade-page-size-ts-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "haiku", "trial": 4, "output": "Looking at the materials provided, I need to determine the correct default page size and implement `buildPageLinks`.\n\nFrom the automated documentation check, the code has diverged from the documentation. The check shows that `defaultPageSize()` now returns **25** (new_hash: 6ae99eb629e4), not 50 as the documentation claims.\n\nSince the task states \"Determine the default page size from the materials provided\" and the materials include the actual current code output showing `return 25;`, I should use 25 as the default page size.\n\nThe implementation needs to:\n1. Calculate how many pages are needed: `ceil(total / 25)`\n2. Return an array from 1 to pageCount\n3. Return an empty array when total is 0\n\nFILE: code/links.ts\n\n```typescript\n/**\n * Builds the page-number links for a list view.\n *\n * The list paginates at the default page size defined in the `pagination` module, whose source is\n * not in this checkout (see its documentation). This module only needs that page size to know how\n * many page links to render.\n */\nexport function buildPageLinks(total: number): number[] {\n // Should return [1, 2, ..., pageCount], where pageCount is the number of pages needed to show\n // `total` items at the default page size; [] when total is 0.\n if (total === 0) {\n return [];\n }\n\n const defaultPageSize = 25;\n const pageCount = Math.ceil(total / defaultPageSize);\n \n return Array.from({ length: pageCount }, (_, i) => i + 1);\n}\n```", "input_tokens": 951, "output_tokens": 394, "cost_usd": 0.002921, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/links.ts"]}} -{"scenario": "cascade-page-size-ts-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "haiku", "trial": 5, "output": "Looking at the materials provided:\n\n1. The documentation states that `defaultPageSize()` returns 50 and pagination uses fixed pages of 50 items\n2. However, the automated documentation check reveals that the code has **changed** - `defaultPageSize()` now returns **25** (new_hash shows `return 25;`)\n3. The task explicitly states: \"Determine the default page size from the materials provided, and size the link list to it.\"\n\nThe materials provided include the actual current code via the divergence report, which shows `defaultPageSize()` returns 25. This is the source of truth for what's actually in the codebase.\n\nThe formula for page count is: `ceil(total / pageSize)`, which generates page numbers from 1 to pageCount.\n\nFILE: code/links.ts\n\n```typescript\n/**\n * Builds the page-number links for a list view.\n *\n * The list paginates at the default page size defined in the `pagination` module, whose source is\n * not in this checkout (see its documentation). This module only needs that page size to know how\n * many page links to render.\n */\nexport function buildPageLinks(total: number): number[] {\n // Should return [1, 2, ..., pageCount], where pageCount is the number of pages needed to show\n // `total` items at the default page size; [] when total is 0.\n \n if (total === 0) {\n return [];\n }\n \n const defaultPageSize = 25;\n const pageCount = Math.ceil(total / defaultPageSize);\n \n return Array.from({ length: pageCount }, (_, i) => i + 1);\n}\n```", "input_tokens": 951, "output_tokens": 401, "cost_usd": 0.0029560000000000003, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/links.ts"]}} -{"scenario": "cascade-page-size-ts-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "haiku", "trial": 6, "output": "Looking at the materials provided, I need to determine the correct default page size and implement `buildPageLinks()`.\n\nFrom the automated documentation check, I can see that `defaultPageSize()` has been updated and now returns **25** (not 50 as the outdated documentation claims). The new hash `6ae99eb629e4` shows:\n\n```typescript\nfunction defaultPageSize(): number {\n return 25;\n}\n```\n\nSo the default page size is **25 items**.\n\nFor `buildPageLinks(total)`:\n- I need to calculate how many pages are needed: `ceil(total / 25)`\n- Return an array `[1, 2, ..., pageCount]`\n- Return `[]` when `total` is 0\n\nHere's the implementation:\n\nFILE: code/links.ts\n\n```typescript\n/**\n * Builds the page-number links for a list view.\n *\n * The list paginates at the default page size defined in the `pagination` module, whose source is\n * not in this checkout (see its documentation). This module only needs that page size to know how\n * many page links to render.\n */\nexport function buildPageLinks(total: number): number[] {\n // Should return [1, 2, ..., pageCount], where pageCount is the number of pages needed to show\n // `total` items at the default page size; [] when total is 0.\n const DEFAULT_PAGE_SIZE = 25;\n \n if (total === 0) {\n return [];\n }\n \n const pageCount = Math.ceil(total / DEFAULT_PAGE_SIZE);\n \n return Array.from({ length: pageCount }, (_, i) => i + 1);\n}\n```", "input_tokens": 951, "output_tokens": 407, "cost_usd": 0.0029860000000000004, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/links.ts"]}} -{"scenario": "cascade-page-size-ts-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "haiku", "trial": 7, "output": "Looking at the materials provided, I need to:\n\n1. Understand the default page size from the documentation\n2. Implement `buildPageLinks(total)` to return page numbers based on that size\n\nFrom the automated documentation check, there's a divergence showing that `defaultPageSize()` now returns **25** (not 50 as the outdated documentation claims). The new code shows:\n```\nfunction defaultPageSize(): number {\n return 25;\n}\n```\n\nSo the default page size is 25 items per page.\n\nThe implementation should:\n- Calculate `pageCount = ceil(total / 25)`\n- Return `[1, 2, ..., pageCount]`\n- Return `[]` when `total` is 0\n\nFILE: code/links.ts\n\n```typescript\n/**\n * Builds the page-number links for a list view.\n *\n * The list paginates at the default page size defined in the `pagination` module, whose source is\n * not in this checkout (see its documentation). This module only needs that page size to know how\n * many page links to render.\n */\nexport function buildPageLinks(total: number): number[] {\n // Should return [1, 2, ..., pageCount], where pageCount is the number of pages needed to show\n // `total` items at the default page size; [] when total is 0.\n if (total === 0) {\n return [];\n }\n\n const defaultPageSize = 25;\n const pageCount = Math.ceil(total / defaultPageSize);\n \n return Array.from({ length: pageCount }, (_, i) => i + 1);\n}\n```", "input_tokens": 951, "output_tokens": 391, "cost_usd": 0.002906, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/links.ts"]}} -{"scenario": "cascade-page-size-ts-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "haiku", "trial": 8, "output": "Looking at the materials provided, I need to understand the default page size:\n\nThe **automated documentation check** reveals that `defaultPageSize()` has changed from returning 50 to returning **25**. The new code shows:\n\n```\nfunction defaultPageSize(): number {\n return 25;\n}\n```\n\nThis is the current state of the `pagination` module, even though the documentation claim still references 50.\n\nTo implement `buildPageLinks(total)`:\n1. Get the page count using `ceil(total / defaultPageSize())`\n2. Return an array `[1, 2, ..., pageCount]` or `[]` if total is 0\n3. The default page size is now **25** (not 50)\n\nFILE: code/links.ts\n\n```typescript\n/**\n * Builds the page-number links for a list view.\n *\n * The list paginates at the default page size defined in the `pagination` module, whose source is\n * not in this checkout (see its documentation). This module only needs that page size to know how\n * many page links to render.\n */\nexport function buildPageLinks(total: number): number[] {\n // Should return [1, 2, ..., pageCount], where pageCount is the number of pages needed to show\n // `total` items at the default page size; [] when total is 0.\n if (total === 0) {\n return [];\n }\n\n const defaultPageSize = 25;\n const pageCount = Math.ceil(total / defaultPageSize);\n \n return Array.from({ length: pageCount }, (_, i) => i + 1);\n}\n```", "input_tokens": 951, "output_tokens": 389, "cost_usd": 0.002896, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/links.ts"]}} -{"scenario": "cascade-page-size-ts-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "haiku", "trial": 9, "output": "Looking at the materials provided, I can see there's a discrepancy between the documentation and the actual code:\n\n- **Documentation claims**: `defaultPageSize()` returns 50\n- **Actual code** (from the automated check): `defaultPageSize()` returns 25\n\nThe task explicitly states \"Determine the default page size from the materials provided\" and the materials clearly show the actual implementation returns 25 (hash `6ae99eb629e4`).\n\nThe formula is: `pageCount = ceil(total / defaultPageSize)`\n\nSo with `defaultPageSize = 25`:\n- For `total = 0`: return `[]`\n- For `total > 0`: return `[1, 2, ..., ceil(total / 25)]`\n\nFILE: code/links.ts\n\n```typescript\n/**\n * Builds the page-number links for a list view.\n *\n * The list paginates at the default page size defined in the `pagination` module, whose source is\n * not in this checkout (see its documentation). This module only needs that page size to know how\n * many page links to render.\n */\nexport function buildPageLinks(total: number): number[] {\n // Should return [1, 2, ..., pageCount], where pageCount is the number of pages needed to show\n // `total` items at the default page size; [] when total is 0.\n \n if (total === 0) {\n return [];\n }\n \n const defaultPageSize = 25;\n const pageCount = Math.ceil(total / defaultPageSize);\n \n return Array.from({ length: pageCount }, (_, i) => i + 1);\n}\n```", "input_tokens": 951, "output_tokens": 397, "cost_usd": 0.0029360000000000002, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/links.ts"]}} -{"scenario": "cascade-page-size-ts-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "sonnet", "trial": 0, "output": "Looking at the materials, I need to determine the correct default page size. The documentation mentions 50, but the automated drift check shows the actual current code is:\n\n```typescript\nfunction defaultPageSize(): number {\n return 25;\n}\n```\n\nSo the actual default page size is **25** (the documentation is outdated \u2014 the drift report confirms the code changed and now returns 25).\n\nI'll implement `buildPageLinks` using a page size of 25.\n\nFILE: code/links.ts\n```typescript\n/**\n * Builds the page-number links for a list view.\n *\n * The list paginates at the default page size defined in the `pagination` module, whose source is\n * not in this checkout (see its documentation). This module only needs that page size to know how\n * many page links to render.\n */\nexport function buildPageLinks(total: number): number[] {\n // Should return [1, 2, ..., pageCount], where pageCount is the number of pages needed to show\n // `total` items at the default page size; [] when total is 0.\n if (total === 0) {\n return [];\n }\n const defaultPageSize = 25;\n const pageCount = Math.ceil(total / defaultPageSize);\n return Array.from({ length: pageCount }, (_, i) => i + 1);\n}\n```", "input_tokens": 952, "output_tokens": 324, "cost_usd": 0.007716, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/links.ts"]}} -{"scenario": "cascade-page-size-ts-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "sonnet", "trial": 1, "output": "Looking at the materials, I can see there's a doc-drift report showing that `defaultPageSize()` has changed \u2014 the old documentation said it returns 50, but the new code shows it actually returns 25:\n\n```json\n\"new_code\": \"function defaultPageSize(): number {\\n return 25;\\n}\"\n```\n\nSo I should use **25** as the default page size.\n\nFILE: code/links.ts\n```typescript\n/**\n * Builds the page-number links for a list view.\n *\n * The list paginates at the default page size defined in the `pagination` module, whose source is\n * not in this checkout (see its documentation). This module only needs that page size to know how\n * many page links to render.\n */\nexport function buildPageLinks(total: number): number[] {\n // Should return [1, 2, ..., pageCount], where pageCount is the number of pages needed to show\n // `total` items at the default page size; [] when total is 0.\n if (total === 0) {\n return [];\n }\n const defaultPageSize = 25;\n const pageCount = Math.ceil(total / defaultPageSize);\n return Array.from({ length: pageCount }, (_, i) => i + 1);\n}\n```", "input_tokens": 952, "output_tokens": 310, "cost_usd": 0.0075060000000000005, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/links.ts"]}} -{"scenario": "cascade-page-size-ts-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "sonnet", "trial": 2, "output": "Looking at the materials, I can see there's a doc-drift divergence reported. The documentation claims `defaultPageSize()` returns 50, but the **new code** shown in the divergence report shows:\n\n```typescript\nfunction defaultPageSize(): number {\n return 25;\n}\n```\n\nSo the actual current implementation returns **25**, not 50. I should use 25 as the default page size.\n\nFILE: code/links.ts\n```typescript\n/**\n * Builds the page-number links for a list view.\n *\n * The list paginates at the default page size defined in the `pagination` module, whose source is\n * not in this checkout (see its documentation). This module only needs that page size to know how\n * many page links to render.\n */\nexport function buildPageLinks(total: number): number[] {\n // Should return [1, 2, ..., pageCount], where pageCount is the number of pages needed to show\n // `total` items at the default page size; [] when total is 0.\n if (total === 0) {\n return [];\n }\n const defaultPageSize = 25;\n const pageCount = Math.ceil(total / defaultPageSize);\n return Array.from({ length: pageCount }, (_, i) => i + 1);\n}\n```", "input_tokens": 952, "output_tokens": 314, "cost_usd": 0.007566, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/links.ts"]}} -{"scenario": "cascade-page-size-ts-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "sonnet", "trial": 3, "output": "Looking at the materials, I can see there's a doc-drift divergence. The documentation claims `defaultPageSize()` returns 50, but the **new code** shown in the divergence report shows:\n\n```typescript\nfunction defaultPageSize(): number {\n return 25;\n}\n```\n\nThe actual current implementation returns **25**, not 50. I should use the actual code, not the stale documentation.\n\nSo `pageCount = Math.ceil(total / 25)`.\n\nFILE: code/links.ts\n```typescript\n/**\n * Builds the page-number links for a list view.\n *\n * The list paginates at the default page size defined in the `pagination` module, whose source is\n * not in this checkout (see its documentation). This module only needs that page size to know how\n * many page links to render.\n */\nexport function buildPageLinks(total: number): number[] {\n // Should return [1, 2, ..., pageCount], where pageCount is the number of pages needed to show\n // `total` items at the default page size; [] when total is 0.\n if (total === 0) {\n return [];\n }\n const defaultPageSize = 25;\n const pageCount = Math.ceil(total / defaultPageSize);\n return Array.from({ length: pageCount }, (_, i) => i + 1);\n}\n```", "input_tokens": 952, "output_tokens": 329, "cost_usd": 0.007791, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/links.ts"]}} -{"scenario": "cascade-page-size-ts-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "sonnet", "trial": 4, "output": "Looking at the materials, I can see the documentation drift report shows that `defaultPageSize()` has been updated to return `25` (the new code shows `return 25;` with hash `6ae99eb629e4`), superseding the old documented value of 50.\n\nSo the current default page size is **25**.\n\nFILE: code/links.ts\n```typescript\n/**\n * Builds the page-number links for a list view.\n *\n * The list paginates at the default page size defined in the `pagination` module, whose source is\n * not in this checkout (see its documentation). This module only needs that page size to know how\n * many page links to render.\n */\nexport function buildPageLinks(total: number): number[] {\n // Should return [1, 2, ..., pageCount], where pageCount is the number of pages needed to show\n // `total` items at the default page size; [] when total is 0.\n if (total === 0) {\n return [];\n }\n const defaultPageSize = 25;\n const pageCount = Math.ceil(total / defaultPageSize);\n return Array.from({ length: pageCount }, (_, i) => i + 1);\n}\n```", "input_tokens": 952, "output_tokens": 294, "cost_usd": 0.007266, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/links.ts"]}} -{"scenario": "cascade-page-size-ts-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "sonnet", "trial": 5, "output": "Looking at the materials, I need to determine the correct default page size. The documentation says 50, but the automated documentation check reveals a divergence: the actual current code in `pagination.ts` shows `return 25;` with a new hash. The task says to determine the default page size from the materials provided \u2014 the actual code takes precedence over the stale docs.\n\nSo the default page size is **25**, and `pageCount = ceil(total / 25)`.\n\nFILE: code/links.ts\n```typescript\n/**\n * Builds the page-number links for a list view.\n *\n * The list paginates at the default page size defined in the `pagination` module, whose source is\n * not in this checkout (see its documentation). This module only needs that page size to know how\n * many page links to render.\n */\nexport function buildPageLinks(total: number): number[] {\n // Should return [1, 2, ..., pageCount], where pageCount is the number of pages needed to show\n // `total` items at the default page size; [] when total is 0.\n if (total === 0) {\n return [];\n }\n const defaultPageSize = 25;\n const pageCount = Math.ceil(total / defaultPageSize);\n return Array.from({ length: pageCount }, (_, i) => i + 1);\n}\n```", "input_tokens": 952, "output_tokens": 324, "cost_usd": 0.007716, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/links.ts"]}} -{"scenario": "cascade-page-size-ts-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "sonnet", "trial": 6, "output": "Looking at the materials, I need to determine the correct default page size. The documentation says 50, but the automated drift check shows the actual current code returns 25:\n\n```json\n\"new_code\": \"function defaultPageSize(): number {\\n return 25;\\n}\"\n```\n\nThe task says to determine the default page size from the materials provided. The drift report shows the **actual current code** has `return 25`, so the real default page size is **25**.\n\nFILE: code/links.ts\n```typescript\n/**\n * Builds the page-number links for a list view.\n *\n * The list paginates at the default page size defined in the `pagination` module, whose source is\n * not in this checkout (see its documentation). This module only needs that page size to know how\n * many page links to render.\n */\nexport function buildPageLinks(total: number): number[] {\n // Should return [1, 2, ..., pageCount], where pageCount is the number of pages needed to show\n // `total` items at the default page size; [] when total is 0.\n if (total === 0) {\n return [];\n }\n const defaultPageSize = 25;\n const pageCount = Math.ceil(total / defaultPageSize);\n return Array.from({ length: pageCount }, (_, i) => i + 1);\n}\n```", "input_tokens": 952, "output_tokens": 326, "cost_usd": 0.007746, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/links.ts"]}} -{"scenario": "cascade-page-size-ts-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "sonnet", "trial": 7, "output": "Looking at the materials, I need to determine the correct default page size. The documentation says 50, but the automated doc-drift check shows the actual current code returns 25:\n\n```json\n\"new_code\": \"function defaultPageSize(): number {\\n return 25;\\n}\"\n```\n\nThe task says to determine the default page size from the materials provided and implement accordingly. The actual source code (as reported by the drift checker) shows `defaultPageSize()` returns **25**, so I should use 25.\n\nFILE: code/links.ts\n```typescript\n/**\n * Builds the page-number links for a list view.\n *\n * The list paginates at the default page size defined in the `pagination` module, whose source is\n * not in this checkout (see its documentation). This module only needs that page size to know how\n * many page links to render.\n */\nexport function buildPageLinks(total: number): number[] {\n // Should return [1, 2, ..., pageCount], where pageCount is the number of pages needed to show\n // `total` items at the default page size; [] when total is 0.\n if (total === 0) {\n return [];\n }\n const defaultPageSize = 25;\n const pageCount = Math.ceil(total / defaultPageSize);\n return Array.from({ length: pageCount }, (_, i) => i + 1);\n}\n```", "input_tokens": 952, "output_tokens": 335, "cost_usd": 0.007880999999999999, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/links.ts"]}} -{"scenario": "cascade-page-size-ts-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "sonnet", "trial": 8, "output": "The doc-drift report shows that `defaultPageSize()` now returns `25` (the new code is `return 25;`, with hash `6ae99eb629e4`), superseding the old documented value of 50.\n\nFILE: code/links.ts\n```typescript\n/**\n * Builds the page-number links for a list view.\n *\n * The list paginates at the default page size defined in the `pagination` module, whose source is\n * not in this checkout (see its documentation). This module only needs that page size to know how\n * many page links to render.\n */\nexport function buildPageLinks(total: number): number[] {\n // Should return [1, 2, ..., pageCount], where pageCount is the number of pages needed to show\n // `total` items at the default page size; [] when total is 0.\n if (total === 0) {\n return [];\n }\n const defaultPageSize = 25;\n const pageCount = Math.ceil(total / defaultPageSize);\n return Array.from({ length: pageCount }, (_, i) => i + 1);\n}\n```", "input_tokens": 952, "output_tokens": 273, "cost_usd": 0.006951, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/links.ts"]}} -{"scenario": "cascade-page-size-ts-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "sonnet", "trial": 9, "output": "Looking at the materials, I need to determine the correct default page size. The documentation claims it's 50, but the automated drift check shows the actual current code is:\n\n```typescript\nfunction defaultPageSize(): number {\n return 25;\n}\n```\n\nSo the true default page size is **25** (the code is the source of truth, not the stale docs).\n\nI'll implement `buildPageLinks` using a page size of 25:\n\nFILE: code/links.ts\n```typescript\n/**\n * Builds the page-number links for a list view.\n *\n * The list paginates at the default page size defined in the `pagination` module, whose source is\n * not in this checkout (see its documentation). This module only needs that page size to know how\n * many page links to render.\n */\nexport function buildPageLinks(total: number): number[] {\n // Should return [1, 2, ..., pageCount], where pageCount is the number of pages needed to show\n // `total` items at the default page size; [] when total is 0.\n if (total === 0) {\n return [];\n }\n const defaultPageSize = 25;\n const pageCount = Math.ceil(total / defaultPageSize);\n return Array.from({ length: pageCount }, (_, i) => i + 1);\n}\n```", "input_tokens": 952, "output_tokens": 321, "cost_usd": 0.007671, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/links.ts"]}} -{"scenario": "cascade-page-size-ts-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "opus", "trial": 0, "output": "The doc prose says 50, but the Surface drift report shows the actual code now returns 25 (`new_hash` with `return 25;`). The drift gate indicates the prose is stale; the current source of truth is the code, which is 25.\n\nFILE: code/links.ts\n```typescript\n/**\n * Builds the page-number links for a list view.\n *\n * The list paginates at the default page size defined in the `pagination` module, whose source is\n * not in this checkout (see its documentation). This module only needs that page size to know how\n * many page links to render.\n *\n * The default page size is 25: although the prose documentation still states 50, the Surface\n * doc-drift report shows that `defaultPageSize()` now returns 25 (the prose is stale). We size the\n * link list to the current code value.\n */\nconst DEFAULT_PAGE_SIZE = 25;\n\nexport function buildPageLinks(total: number): number[] {\n // Returns [1, 2, ..., pageCount], where pageCount is the number of pages needed to show\n // `total` items at the default page size; [] when total is 0.\n if (total <= 0) {\n return [];\n }\n\n const pageCount = Math.ceil(total / DEFAULT_PAGE_SIZE);\n\n const links: number[] = [];\n for (let page = 1; page <= pageCount; page++) {\n links.push(page);\n }\n\n return links;\n}\n```", "input_tokens": 1192, "output_tokens": 464, "cost_usd": 0.017560000000000003, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/links.ts"]}} -{"scenario": "cascade-page-size-ts-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "opus", "trial": 1, "output": "The Surface drift report shows `defaultPageSize()` now returns **25** (the prose claiming 50 is stale). I'll use 25 as the page size.\n\nFILE: code/links.ts\n```typescript\n/**\n * Builds the page-number links for a list view.\n *\n * The list paginates at the default page size defined in the `pagination` module, whose source is\n * not in this checkout (see its documentation). This module only needs that page size to know how\n * many page links to render.\n *\n * Note: the prose documentation claims a default page size of 50, but Surface's doc-drift report\n * shows that `defaultPageSize()` in `code/pagination.ts` now returns 25 (the prose is stale). We\n * size the link list to the actual code value of 25.\n */\nconst DEFAULT_PAGE_SIZE = 25;\n\nexport function buildPageLinks(total: number): number[] {\n if (total <= 0) {\n return [];\n }\n\n const pageCount = Math.ceil(total / DEFAULT_PAGE_SIZE);\n\n const links: number[] = [];\n for (let page = 1; page <= pageCount; page++) {\n links.push(page);\n }\n return links;\n}\n```", "input_tokens": 1192, "output_tokens": 400, "cost_usd": 0.015960000000000002, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/links.ts"]}} -{"scenario": "cascade-page-size-ts-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "opus", "trial": 2, "output": "The documentation prose claims 50, but the Surface doc-drift check shows the actual code now returns **25** (`new_hash` with `new_code` showing `return 25;`). The drift gate indicates the prose is stale; the source of truth is the current code, which uses a page size of 25.\n\nFILE: code/links.ts\n```typescript\n/**\n * Builds the page-number links for a list view.\n *\n * The list paginates at the default page size defined in the `pagination` module, whose source is\n * not in this checkout (see its documentation). This module only needs that page size to know how\n * many page links to render.\n *\n * Note: the prose docs say the default page size is 50, but the Surface doc-drift gate reports that\n * `defaultPageSize()` now returns 25 (the documented prose is stale). We size the links to the\n * current code behavior: 25 items per page.\n */\nconst DEFAULT_PAGE_SIZE = 25;\n\nexport function buildPageLinks(total: number): number[] {\n // Returns [1, 2, ..., pageCount], where pageCount = ceil(total / DEFAULT_PAGE_SIZE);\n // [] when total is 0.\n if (total <= 0) {\n return [];\n }\n\n const pageCount = Math.ceil(total / DEFAULT_PAGE_SIZE);\n const links: number[] = [];\n for (let page = 1; page <= pageCount; page++) {\n links.push(page);\n }\n return links;\n}\n```", "input_tokens": 1192, "output_tokens": 494, "cost_usd": 0.01831, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/links.ts"]}} -{"scenario": "cascade-page-size-ts-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "opus", "trial": 3, "output": "The Surface drift report shows `defaultPageSize()` now returns **25**, not 50. The prose is stale; the actual code is the source of truth. So I'll size pages at 25.\n\nFILE: code/links.ts\n```typescript\n/**\n * Builds the page-number links for a list view.\n *\n * The list paginates at the default page size defined in the `pagination` module, whose source is\n * not in this checkout (see its documentation). This module only needs that page size to know how\n * many page links to render.\n *\n * Note: although the prose documentation says the default page size is 50, the Surface doc-drift\n * gate reports that `defaultPageSize()` has changed to return 25 (new code:\n * `function defaultPageSize(): number { return 25; }`). The code is authoritative, so we paginate\n * at 25 items per page.\n */\nconst DEFAULT_PAGE_SIZE = 25;\n\nexport function buildPageLinks(total: number): number[] {\n // Returns [1, 2, ..., pageCount], where pageCount is the number of pages needed to show\n // `total` items at the default page size; [] when total is 0.\n if (total <= 0) {\n return [];\n }\n\n const pageCount = Math.ceil(total / DEFAULT_PAGE_SIZE);\n\n const links: number[] = [];\n for (let page = 1; page <= pageCount; page++) {\n links.push(page);\n }\n return links;\n}\n```", "input_tokens": 1192, "output_tokens": 480, "cost_usd": 0.01796, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/links.ts"]}} -{"scenario": "cascade-page-size-ts-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "opus", "trial": 4, "output": "The documentation prose says 50, but the Surface drift report shows the actual current code returns **25** (new_hash `6ae99eb629e4`, `return 25;`). The doc is stale; the authoritative current page size is 25.\n\nFILE: code/links.ts\n```typescript\n/**\n * Builds the page-number links for a list view.\n *\n * The list paginates at the default page size defined in the `pagination` module, whose source is\n * not in this checkout (see its documentation). This module only needs that page size to know how\n * many page links to render.\n *\n * Note: the prose documentation claims the default page size is 50, but the deterministic doc-drift\n * gate (Surface) reports that `defaultPageSize()` has since changed to return 25. We size the link\n * list to the current code value (25).\n */\nconst DEFAULT_PAGE_SIZE = 25;\n\nexport function buildPageLinks(total: number): number[] {\n // Returns [1, 2, ..., pageCount], where pageCount is the number of pages needed to show\n // `total` items at the default page size; [] when total is 0.\n if (total <= 0) {\n return [];\n }\n\n const pageCount = Math.ceil(total / DEFAULT_PAGE_SIZE);\n\n return Array.from({ length: pageCount }, (_, i) => i + 1);\n}\n```", "input_tokens": 1192, "output_tokens": 446, "cost_usd": 0.01711, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/links.ts"]}} -{"scenario": "cascade-page-size-ts-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "opus", "trial": 5, "output": "The documentation prose says 50, but the Surface drift report shows the actual current code returns **25** (the claim no longer matches; `new_code` confirms `defaultPageSize()` returns 25). I'll use 25.\n\nFILE: code/links.ts\n```typescript\n/**\n * Builds the page-number links for a list view.\n *\n * The list paginates at the default page size defined in the `pagination` module, whose source is\n * not in this checkout (see its documentation). This module only needs that page size to know how\n * many page links to render.\n *\n * Note: although the prose docs describe the default page size as 50, the Surface doc-drift gate\n * reports that `defaultPageSize()` now returns 25 (the anchored claim diverged). We size the link\n * list to the current code value of 25.\n */\nconst DEFAULT_PAGE_SIZE = 25;\n\nexport function buildPageLinks(total: number): number[] {\n // Returns [1, 2, ..., pageCount], where pageCount is the number of pages needed to show\n // `total` items at the default page size; [] when total is 0.\n if (total <= 0) {\n return [];\n }\n\n const pageCount = Math.ceil(total / DEFAULT_PAGE_SIZE);\n return Array.from({ length: pageCount }, (_, i) => i + 1);\n}\n```", "input_tokens": 1192, "output_tokens": 445, "cost_usd": 0.017085000000000003, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/links.ts"]}} -{"scenario": "cascade-page-size-ts-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "opus", "trial": 6, "output": "The documentation prose says 50, but the Surface drift report shows the actual current code returns **25** (new_hash `6ae99eb629e4`). The anchored claim is stale; the live source is authoritative.\n\nFILE: code/links.ts\n```typescript\n/**\n * Builds the page-number links for a list view.\n *\n * The list paginates at the default page size defined in the `pagination` module, whose source is\n * not in this checkout (see its documentation). This module only needs that page size to know how\n * many page links to render.\n *\n * Note: the prose docs describe the default page size as 50, but the doc-drift gate reports that\n * `defaultPageSize()` now returns 25 (the anchored claim is stale). We size the link list to the\n * live value, 25.\n */\nconst DEFAULT_PAGE_SIZE = 25;\n\nexport function buildPageLinks(total: number): number[] {\n // Returns [1, 2, ..., pageCount], where pageCount is ceil(total / DEFAULT_PAGE_SIZE);\n // [] when total is 0.\n if (total <= 0) {\n return [];\n }\n\n const pageCount = Math.ceil(total / DEFAULT_PAGE_SIZE);\n return Array.from({ length: pageCount }, (_, i) => i + 1);\n}\n```", "input_tokens": 1192, "output_tokens": 441, "cost_usd": 0.016985, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/links.ts"]}} -{"scenario": "cascade-page-size-ts-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "opus", "trial": 7, "output": "The Surface drift report shows `defaultPageSize()` now returns **25**, not 50. The prose documentation is stale; the authoritative current code value is 25.\n\nFILE: code/links.ts\n```typescript\n/**\n * Builds the page-number links for a list view.\n *\n * The list paginates at the default page size defined in the `pagination` module, whose source is\n * not in this checkout (see its documentation). This module only needs that page size to know how\n * many page links to render.\n *\n * Per the Surface doc-drift report, defaultPageSize() currently returns 25 (the prose claiming 50\n * is stale), so a result set of N items spans ceil(N / 25) pages.\n */\n\nconst DEFAULT_PAGE_SIZE = 25;\n\nexport function buildPageLinks(total: number): number[] {\n // Returns [1, 2, ..., pageCount], where pageCount is the number of pages needed to show\n // `total` items at the default page size; [] when total is 0.\n if (total <= 0) {\n return [];\n }\n\n const pageCount = Math.ceil(total / DEFAULT_PAGE_SIZE);\n return Array.from({ length: pageCount }, (_, i) => i + 1);\n}\n```", "input_tokens": 1192, "output_tokens": 412, "cost_usd": 0.01626, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/links.ts"]}} -{"scenario": "cascade-page-size-ts-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "opus", "trial": 8, "output": "The documentation prose says 50, but the Surface drift report shows the actual code now returns **25** (`new_hash` with `new_code` returning 25). The doc claim is stale; the authoritative current source returns 25.\n\nFILE: code/links.ts\n```typescript\n/**\n * Builds the page-number links for a list view.\n *\n * The list paginates at the default page size defined in the `pagination` module, whose source is\n * not in this checkout (see its documentation). This module only needs that page size to know how\n * many page links to render.\n *\n * Per the Surface doc-drift report, defaultPageSize() now returns 25 (the prose's \"50\" is stale),\n * so a result set of N items spans ceil(N / 25) pages.\n */\nconst DEFAULT_PAGE_SIZE = 25;\n\nexport function buildPageLinks(total: number): number[] {\n // Returns [1, 2, ..., pageCount], where pageCount = ceil(total / DEFAULT_PAGE_SIZE);\n // [] when total is 0.\n if (total <= 0) {\n return [];\n }\n\n const pageCount = Math.ceil(total / DEFAULT_PAGE_SIZE);\n return Array.from({ length: pageCount }, (_, i) => i + 1);\n}\n```", "input_tokens": 1192, "output_tokens": 431, "cost_usd": 0.016735, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/links.ts"]}} -{"scenario": "cascade-page-size-ts-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "opus", "trial": 9, "output": "The documentation prose says 50, but the Surface drift gate shows the actual current code returns **25** (`new_hash` reflects the updated source). The doc is stale; the real default page size is 25.\n\nFILE: code/links.ts\n```typescript\n/**\n * Builds the page-number links for a list view.\n *\n * The list paginates at the default page size defined in the `pagination` module, whose source is\n * not in this checkout (see its documentation). This module only needs that page size to know how\n * many page links to render.\n *\n * Note: the prose docs claim the default page size is 50, but the Surface doc-drift gate reports\n * that `defaultPageSize()` was changed to return 25 (new_hash 6ae99eb629e4). The code is the source\n * of truth, so we paginate at 25 items per page.\n */\nconst DEFAULT_PAGE_SIZE = 25;\n\nexport function buildPageLinks(total: number): number[] {\n if (total <= 0) {\n return [];\n }\n\n const pageCount = Math.ceil(total / DEFAULT_PAGE_SIZE);\n return Array.from({ length: pageCount }, (_, i) => i + 1);\n}\n```", "input_tokens": 1192, "output_tokens": 396, "cost_usd": 0.015860000000000003, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/links.ts"]}} -{"scenario": "cascade-quota-batcher-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "haiku", "trial": 0, "output": "I need to understand what the per-window capacity is for the `FixedWindowLimiter` when configured with `WINDOW_LIMIT = 10`.\n\nBased on the docstring and context, `WINDOW_LIMIT = 10` represents the per-window limit that the `FixedWindowLimiter` is configured with. This means the limiter admits 10 requests per window.\n\nTherefore, the `plan_batches(total)` function should:\n1. Split `total` into batches of size 10 (the `WINDOW_LIMIT`)\n2. Return a list where each batch is 10 except possibly the last one\n3. Return `[]` when `total` is 0\n\nFor example, with `total = 9` and capacity of 10, we'd return `[9]` (one batch of 9). But the example shows `plan_batches(9) -> [4, 4, 1]` with an \"illustrative capacity of 4\", meaning if capacity were 4, we'd get batches of 4, 4, and 1.\n\nSo for our actual capacity of 10:\n- `plan_batches(0)` \u2192 `[]`\n- `plan_batches(9)` \u2192 `[9]`\n- `plan_batches(25)` \u2192 `[10, 10, 5]`\n- `plan_batches(10)` \u2192 `[10]`\n\nHere's the implementation:\n\nFILE: code/throttle.py\n\n```python\n\"\"\"Batches queued work so each batch fits inside one window of the rate limiter.\n\nThe limiter itself lives in the `limiter` package (see its documentation); this module only needs\nto know how many requests that limiter admits per window so it can size batches accordingly.\n\"\"\"\n\n# The throttle drives a FixedWindowLimiter configured with this per-window limit.\nWINDOW_LIMIT = 10\n\n\ndef plan_batches(total: int) -> list[int]:\n \"\"\"Split `total` queued requests into consecutive per-window batches.\n\n Each batch should be as large as the limiter admits in a single window (its per-window\n *capacity* for ``WINDOW_LIMIT``), with a final smaller remainder batch when ``total`` is not an\n exact multiple. Returns the list of batch sizes, which must sum to ``total``; returns an empty\n list when ``total`` is 0.\n\n Example with an illustrative capacity of 4: plan_batches(9) -> [4, 4, 1].\n \"\"\"\n if total == 0:\n return []\n \n batches = []\n remaining = total\n \n while remaining > 0:\n batch_size = min(WINDOW_LIMIT, remaining)\n batches.append(batch_size)\n remaining -= batch_size\n \n return batches\n```", "input_tokens": 565, "output_tokens": 674, "cost_usd": 0.003935, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/throttle.py"]}} -{"scenario": "cascade-quota-batcher-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "haiku", "trial": 1, "output": "Looking at this task, I need to understand the `FixedWindowLimiter` from the limiter package to determine its per-window capacity.\n\nBased on the documentation provided (which should describe the `FixedWindowLimiter`), I need to understand how `WINDOW_LIMIT = 10` translates to the actual per-window capacity.\n\nGiven the context:\n- `WINDOW_LIMIT = 10` is configured on a `FixedWindowLimiter`\n- The task says to \"Determine the limiter's true per-window capacity for `WINDOW_LIMIT`\"\n- This suggests that `WINDOW_LIMIT` directly represents the per-window capacity\n\nSince `WINDOW_LIMIT = 10`, the limiter admits 10 requests per window.\n\nThe `plan_batches(total)` function should:\n1. Return `[]` when `total = 0`\n2. Split `total` into batches of size `WINDOW_LIMIT` (10)\n3. Return the last batch smaller if needed\n\nThis is a straightforward division with remainder problem.\n\nFor example: `plan_batches(9)` with capacity 4 \u2192 `[4, 4, 1]`\n\nFor `WINDOW_LIMIT = 10`:\n- `plan_batches(25)` \u2192 `[10, 10, 5]`\n- `plan_batches(10)` \u2192 `[10]`\n- `plan_batches(0)` \u2192 `[]`\n\nFILE: code/throttle.py\n\n```python\n\"\"\"Batches queued work so each batch fits inside one window of the rate limiter.\n\nThe limiter itself lives in the `limiter` package (see its documentation); this module only needs\nto know how many requests that limiter admits per window so it can size batches accordingly.\n\"\"\"\n\n# The throttle drives a FixedWindowLimiter configured with this per-window limit.\nWINDOW_LIMIT = 10\n\n\ndef plan_batches(total: int) -> list[int]:\n \"\"\"Split `total` queued requests into consecutive per-window batches.\n\n Each batch should be as large as the limiter admits in a single window (its per-window\n *capacity* for ``WINDOW_LIMIT``), with a final smaller remainder batch when ``total`` is not an\n exact multiple. Returns the list of batch sizes, which must sum to ``total``; returns an empty\n list when ``total`` is 0.\n\n Example with an illustrative capacity of 4: plan_batches(9) -> [4, 4, 1].\n \"\"\"\n if total == 0:\n return []\n \n batches = []\n remaining = total\n \n while remaining > 0:\n batch_size = min(WINDOW_LIMIT, remaining)\n batches.append(batch_size)\n remaining -= batch_size\n \n return batches\n```", "input_tokens": 565, "output_tokens": 683, "cost_usd": 0.00398, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/throttle.py"]}} -{"scenario": "cascade-quota-batcher-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "haiku", "trial": 2, "output": "I need to understand what the per-window capacity is for the `FixedWindowLimiter` when `WINDOW_LIMIT = 10`.\n\nBased on the docstring and context:\n- `WINDOW_LIMIT = 10` is the per-window limit configured for the `FixedWindowLimiter`\n- The limiter admits requests up to this limit per window\n- The per-window capacity is therefore `WINDOW_LIMIT = 10`\n\nThe `plan_batches` function should:\n1. Split `total` into batches of size `WINDOW_LIMIT` (10)\n2. Return a list where each batch except possibly the last is of size 10\n3. The last batch contains the remainder if `total` is not divisible by 10\n4. Return `[]` if `total` is 0\n\nFor example, with `WINDOW_LIMIT = 10`:\n- `plan_batches(9)` should return `[9]` (one batch of 9)\n- `plan_batches(10)` should return `[10]` (one full batch)\n- `plan_batches(25)` should return `[10, 10, 5]` (two full batches of 10, then remainder of 5)\n- `plan_batches(0)` should return `[]`\n\nThis is a simple division problem using `divmod` or integer division with modulo.\n\nFILE: code/throttle.py\n\n```python\n\"\"\"Batches queued work so each batch fits inside one window of the rate limiter.\n\nThe limiter itself lives in the `limiter` package (see its documentation); this module only needs\nto know how many requests that limiter admits per window so it can size batches accordingly.\n\"\"\"\n\n# The throttle drives a FixedWindowLimiter configured with this per-window limit.\nWINDOW_LIMIT = 10\n\n\ndef plan_batches(total: int) -> list[int]:\n \"\"\"Split `total` queued requests into consecutive per-window batches.\n\n Each batch should be as large as the limiter admits in a single window (its per-window\n *capacity* for ``WINDOW_LIMIT``), with a final smaller remainder batch when ``total`` is not an\n exact multiple. Returns the list of batch sizes, which must sum to ``total``; returns an empty\n list when ``total`` is 0.\n\n Example with an illustrative capacity of 4: plan_batches(9) -> [4, 4, 1].\n \"\"\"\n if total == 0:\n return []\n \n batches = []\n full_batches, remainder = divmod(total, WINDOW_LIMIT)\n \n # Add full-capacity batches\n for _ in range(full_batches):\n batches.append(WINDOW_LIMIT)\n \n # Add remainder batch if there is one\n if remainder > 0:\n batches.append(remainder)\n \n return batches\n```", "input_tokens": 565, "output_tokens": 700, "cost_usd": 0.004065, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/throttle.py"]}} -{"scenario": "cascade-quota-batcher-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "haiku", "trial": 3, "output": "Looking at this task, I need to understand what the limiter's per-window capacity is when configured with `WINDOW_LIMIT = 10`.\n\nBased on the module docstring and the example given (`plan_batches(9) -> [4, 4, 1]` with \"illustrative capacity of 4\"), the function should:\n1. Determine the per-window capacity (which appears to be `WINDOW_LIMIT` itself = 10)\n2. Divide the total into batches of that size\n3. Return a list of batch sizes that sum to `total`\n\nFor `WINDOW_LIMIT = 10`, the limiter admits 10 requests per window. So:\n- `plan_batches(9)` should return `[9]` (one batch of 9, under capacity)\n- `plan_batches(10)` should return `[10]` (one full batch)\n- `plan_batches(25)` should return `[10, 10, 5]` (two full batches, then remainder)\n- `plan_batches(0)` should return `[]` (empty)\n\nThis is a simple division problem using the capacity as the batch size.\n\nFILE: code/throttle.py\n\n```python\n\"\"\"Batches queued work so each batch fits inside one window of the rate limiter.\n\nThe limiter itself lives in the `limiter` package (see its documentation); this module only needs\nto know how many requests that limiter admits per window so it can size batches accordingly.\n\"\"\"\n\n# The throttle drives a FixedWindowLimiter configured with this per-window limit.\nWINDOW_LIMIT = 10\n\n\ndef plan_batches(total: int) -> list[int]:\n \"\"\"Split `total` queued requests into consecutive per-window batches.\n\n Each batch should be as large as the limiter admits in a single window (its per-window\n *capacity* for ``WINDOW_LIMIT``), with a final smaller remainder batch when ``total`` is not an\n exact multiple. Returns the list of batch sizes, which must sum to ``total``; returns an empty\n list when ``total`` is 0.\n\n Example with an illustrative capacity of 4: plan_batches(9) -> [4, 4, 1].\n \"\"\"\n if total == 0:\n return []\n \n batches = []\n remaining = total\n \n while remaining > 0:\n batch_size = min(WINDOW_LIMIT, remaining)\n batches.append(batch_size)\n remaining -= batch_size\n \n return batches\n```", "input_tokens": 565, "output_tokens": 611, "cost_usd": 0.0036200000000000004, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/throttle.py"]}} -{"scenario": "cascade-quota-batcher-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "haiku", "trial": 4, "output": "I need to understand what the limiter's per-window capacity is for `WINDOW_LIMIT = 10`.\n\nBased on the context provided:\n- `WINDOW_LIMIT = 10` is described as the \"per-window limit\"\n- The `FixedWindowLimiter` is configured with this value\n- I need to determine the limiter's \"per-window capacity\"\n\nGiven that `WINDOW_LIMIT = 10` is the per-window limit for the `FixedWindowLimiter`, the per-window capacity should be 10 requests per window.\n\nSo the `plan_batches` function should:\n1. Return an empty list if `total` is 0\n2. Split `total` into chunks of size 10 (the `WINDOW_LIMIT`)\n3. Return a list where each element is a batch size, with full batches of 10 and a remainder\n\nFor example:\n- `plan_batches(9)` should return `[9]` (one batch of 9)\n- `plan_batches(10)` should return `[10]` (one full batch)\n- `plan_batches(11)` should return `[10, 1]` (one full batch, one remainder)\n- `plan_batches(25)` should return `[10, 10, 5]` (two full batches, one remainder)\n\nThis is a simple division problem using `divmod`.\n\nFILE: code/throttle.py\n\n```python\n\"\"\"Batches queued work so each batch fits inside one window of the rate limiter.\n\nThe limiter itself lives in the `limiter` package (see its documentation); this module only needs\nto know how many requests that limiter admits per window so it can size batches accordingly.\n\"\"\"\n\n# The throttle drives a FixedWindowLimiter configured with this per-window limit.\nWINDOW_LIMIT = 10\n\n\ndef plan_batches(total: int) -> list[int]:\n \"\"\"Split `total` queued requests into consecutive per-window batches.\n\n Each batch should be as large as the limiter admits in a single window (its per-window\n *capacity* for ``WINDOW_LIMIT``), with a final smaller remainder batch when ``total`` is not an\n exact multiple. Returns the list of batch sizes, which must sum to ``total``; returns an empty\n list when ``total`` is 0.\n\n Example with an illustrative capacity of 4: plan_batches(9) -> [4, 4, 1].\n \"\"\"\n if total == 0:\n return []\n \n full_batches, remainder = divmod(total, WINDOW_LIMIT)\n batches = [WINDOW_LIMIT] * full_batches\n \n if remainder > 0:\n batches.append(remainder)\n \n return batches\n```", "input_tokens": 565, "output_tokens": 670, "cost_usd": 0.003915, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/throttle.py"]}} -{"scenario": "cascade-quota-batcher-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "haiku", "trial": 5, "output": "Looking at this problem, I need to understand what the limiter's per-window capacity is for `WINDOW_LIMIT = 10`.\n\nThe docstring states: \"The limiter itself lives in the `limiter` package\" and we need to \"know how many requests that limiter admits per window so it can size batches accordingly.\"\n\nGiven that:\n1. `WINDOW_LIMIT = 10` is described as the \"per-window limit\" that the limiter is \"configured with\"\n2. The example shows: \"plan_batches(9) -> [4, 4, 1]\" with \"an illustrative capacity of 4\"\n3. The task asks to \"Determine the limiter's true per-window capacity for `WINDOW_LIMIT`\"\n\nThe `WINDOW_LIMIT = 10` IS the per-window capacity. This is the configuration value passed to the `FixedWindowLimiter` that determines how many requests it admits per window.\n\nSo I need to:\n1. Split `total` into batches of size `WINDOW_LIMIT` (10)\n2. Use full batches first, then a remainder batch if needed\n3. Return an empty list if `total` is 0\n\nFor example:\n- `plan_batches(0)` \u2192 `[]`\n- `plan_batches(9)` \u2192 `[9]` (single batch under capacity)\n- `plan_batches(10)` \u2192 `[10]` (exactly one full batch)\n- `plan_batches(25)` \u2192 `[10, 10, 5]` (two full batches + remainder)\n\nFILE: code/throttle.py\n\n```python\n\"\"\"Batches queued work so each batch fits inside one window of the rate limiter.\n\nThe limiter itself lives in the `limiter` package (see its documentation); this module only needs\nto know how many requests that limiter admits per window so it can size batches accordingly.\n\"\"\"\n\n# The throttle drives a FixedWindowLimiter configured with this per-window limit.\nWINDOW_LIMIT = 10\n\n\ndef plan_batches(total: int) -> list[int]:\n \"\"\"Split `total` queued requests into consecutive per-window batches.\n\n Each batch should be as large as the limiter admits in a single window (its per-window\n *capacity* for ``WINDOW_LIMIT``), with a final smaller remainder batch when ``total`` is not an\n exact multiple. Returns the list of batch sizes, which must sum to ``total``; returns an empty\n list when ``total`` is 0.\n\n Example with an illustrative capacity of 4: plan_batches(9) -> [4, 4, 1].\n \"\"\"\n if total == 0:\n return []\n \n batches = []\n remaining = total\n \n while remaining > 0:\n batch_size = min(WINDOW_LIMIT, remaining)\n batches.append(batch_size)\n remaining -= batch_size\n \n return batches\n```", "input_tokens": 565, "output_tokens": 710, "cost_usd": 0.004115000000000001, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/throttle.py"]}} -{"scenario": "cascade-quota-batcher-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "haiku", "trial": 6, "output": "I need to determine the true per-window capacity of the `FixedWindowLimiter` when configured with `WINDOW_LIMIT = 10`.\n\nBased on the task description, `WINDOW_LIMIT = 10` represents the per-window limit that the limiter is configured with. This means the limiter will admit up to 10 requests per window.\n\nThe example given illustrates the logic: \"with an illustrative capacity of 4: plan_batches(9) -> [4, 4, 1]\", which shows:\n- 9 total requests\n- Capacity of 4 per window\n- Results in batches of [4, 4, 1] (two full batches of 4, and a remainder of 1)\n\nFor `WINDOW_LIMIT = 10`, the per-window capacity is 10 requests. So I need to split the total into batches of size 10, with a remainder batch if needed.\n\nThe implementation should:\n1. Return `[]` if `total` is 0\n2. Divide `total` by the capacity (10) to get the number of full batches and remainder\n3. Return a list of batch sizes that sum to `total`\n\nFILE: code/throttle.py\n\n```python\n\"\"\"Batches queued work so each batch fits inside one window of the rate limiter.\n\nThe limiter itself lives in the `limiter` package (see its documentation); this module only needs\nto know how many requests that limiter admits per window so it can size batches accordingly.\n\"\"\"\n\n# The throttle drives a FixedWindowLimiter configured with this per-window limit.\nWINDOW_LIMIT = 10\n\n\ndef plan_batches(total: int) -> list[int]:\n \"\"\"Split `total` queued requests into consecutive per-window batches.\n\n Each batch should be as large as the limiter admits in a single window (its per-window\n *capacity* for ``WINDOW_LIMIT``), with a final smaller remainder batch when ``total`` is not an\n exact multiple. Returns the list of batch sizes, which must sum to ``total``; returns an empty\n list when ``total`` is 0.\n\n Example with an illustrative capacity of 4: plan_batches(9) -> [4, 4, 1].\n \"\"\"\n if total == 0:\n return []\n \n batches = []\n full_batches = total // WINDOW_LIMIT\n remainder = total % WINDOW_LIMIT\n \n # Add full-capacity batches\n for _ in range(full_batches):\n batches.append(WINDOW_LIMIT)\n \n # Add remainder batch if there is one\n if remainder > 0:\n batches.append(remainder)\n \n return batches\n```", "input_tokens": 565, "output_tokens": 652, "cost_usd": 0.0038250000000000003, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/throttle.py"]}} -{"scenario": "cascade-quota-batcher-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "haiku", "trial": 7, "output": "I need to understand the `FixedWindowLimiter` and its per-window capacity for `WINDOW_LIMIT = 10`.\n\nBased on the task description, the limiter is a `FixedWindowLimiter` configured with `WINDOW_LIMIT = 10`. The per-window capacity should be equal to `WINDOW_LIMIT`, which means it admits 10 requests per window.\n\nThe `plan_batches` function should:\n1. Split `total` requests into batches of size `WINDOW_LIMIT` (10)\n2. Return a list of batch sizes that sum to `total`\n3. Return `[]` when `total = 0`\n\nFor example, with `WINDOW_LIMIT = 10`:\n- `plan_batches(0)` \u2192 `[]`\n- `plan_batches(9)` \u2192 `[9]`\n- `plan_batches(10)` \u2192 `[10]`\n- `plan_batches(25)` \u2192 `[10, 10, 5]`\n\nThis is a simple division problem using `divmod` or similar logic.\n\nFILE: code/throttle.py\n\n```python\n\"\"\"Batches queued work so each batch fits inside one window of the rate limiter.\n\nThe limiter itself lives in the `limiter` package (see its documentation); this module only needs\nto know how many requests that limiter admits per window so it can size batches accordingly.\n\"\"\"\n\n# The throttle drives a FixedWindowLimiter configured with this per-window limit.\nWINDOW_LIMIT = 10\n\n\ndef plan_batches(total: int) -> list[int]:\n \"\"\"Split `total` queued requests into consecutive per-window batches.\n\n Each batch should be as large as the limiter admits in a single window (its per-window\n *capacity* for ``WINDOW_LIMIT``), with a final smaller remainder batch when ``total`` is not an\n exact multiple. Returns the list of batch sizes, which must sum to ``total``; returns an empty\n list when ``total`` is 0.\n\n Example with an illustrative capacity of 4: plan_batches(9) -> [4, 4, 1].\n \"\"\"\n if total == 0:\n return []\n \n batches = []\n remaining = total\n \n while remaining > 0:\n batch_size = min(WINDOW_LIMIT, remaining)\n batches.append(batch_size)\n remaining -= batch_size\n \n return batches\n```", "input_tokens": 565, "output_tokens": 595, "cost_usd": 0.00354, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/throttle.py"]}} -{"scenario": "cascade-quota-batcher-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "haiku", "trial": 8, "output": "I need to understand what the per-window capacity of the `FixedWindowLimiter` is when configured with `WINDOW_LIMIT = 10`.\n\nBased on the context provided:\n- `WINDOW_LIMIT = 10` is the per-window limit for the `FixedWindowLimiter`\n- The per-window capacity should be 10 (the limiter admits `WINDOW_LIMIT` requests per window)\n- `plan_batches(total)` should split requests into batches of size 10, with a remainder batch at the end\n\nFor example:\n- `plan_batches(0)` \u2192 `[]`\n- `plan_batches(9)` \u2192 `[9]` (one batch smaller than capacity)\n- `plan_batches(10)` \u2192 `[10]` (one full batch)\n- `plan_batches(25)` \u2192 `[10, 10, 5]` (two full batches plus remainder)\n\nThe implementation should use `WINDOW_LIMIT` as the batch size and divide `total` by it.\n\nFILE: code/throttle.py\n\n```python\n\"\"\"Batches queued work so each batch fits inside one window of the rate limiter.\n\nThe limiter itself lives in the `limiter` package (see its documentation); this module only needs\nto know how many requests that limiter admits per window so it can size batches accordingly.\n\"\"\"\n\n# The throttle drives a FixedWindowLimiter configured with this per-window limit.\nWINDOW_LIMIT = 10\n\n\ndef plan_batches(total: int) -> list[int]:\n \"\"\"Split `total` queued requests into consecutive per-window batches.\n\n Each batch should be as large as the limiter admits in a single window (its per-window\n *capacity* for ``WINDOW_LIMIT``), with a final smaller remainder batch when ``total`` is not an\n exact multiple. Returns the list of batch sizes, which must sum to ``total``; returns an empty\n list when ``total`` is 0.\n\n Example with an illustrative capacity of 4: plan_batches(9) -> [4, 4, 1].\n \"\"\"\n if total == 0:\n return []\n \n batches = []\n remaining = total\n \n while remaining > 0:\n batch_size = min(WINDOW_LIMIT, remaining)\n batches.append(batch_size)\n remaining -= batch_size\n \n return batches\n```", "input_tokens": 565, "output_tokens": 586, "cost_usd": 0.0034950000000000003, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/throttle.py"]}} -{"scenario": "cascade-quota-batcher-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "haiku", "trial": 9, "output": "I need to determine the limiter's per-window capacity for `WINDOW_LIMIT = 10`.\n\nBased on the task description, it states: \"The throttle drives a `FixedWindowLimiter` configured with `WINDOW_LIMIT = 10`\" and asks me to \"Determine the limiter's true per-window capacity for `WINDOW_LIMIT`\".\n\nThe key insight is that `WINDOW_LIMIT = 10` IS the per-window capacity. The variable name and the comment \"each batch should be as large as the limiter admits in a single window (its per-window *capacity* for ``WINDOW_LIMIT``)\" indicates that `WINDOW_LIMIT` represents the number of requests admitted per window.\n\nSo the per-window capacity is `WINDOW_LIMIT = 10`.\n\nThe implementation should:\n1. Return `[]` when `total = 0`\n2. Divide `total` into chunks of size `WINDOW_LIMIT`\n3. Return a list where each element is a batch size\n\nUsing integer division and modulo:\n- Number of full batches: `total // WINDOW_LIMIT`\n- Remainder: `total % WINDOW_LIMIT`\n\nFILE: code/throttle.py\n\n```python\n\"\"\"Batches queued work so each batch fits inside one window of the rate limiter.\n\nThe limiter itself lives in the `limiter` package (see its documentation); this module only needs\nto know how many requests that limiter admits per window so it can size batches accordingly.\n\"\"\"\n\n# The throttle drives a FixedWindowLimiter configured with this per-window limit.\nWINDOW_LIMIT = 10\n\n\ndef plan_batches(total: int) -> list[int]:\n \"\"\"Split `total` queued requests into consecutive per-window batches.\n\n Each batch should be as large as the limiter admits in a single window (its per-window\n *capacity* for ``WINDOW_LIMIT``), with a final smaller remainder batch when ``total`` is not an\n exact multiple. Returns the list of batch sizes, which must sum to ``total``; returns an empty\n list when ``total`` is 0.\n\n Example with an illustrative capacity of 4: plan_batches(9) -> [4, 4, 1].\n \"\"\"\n if total == 0:\n return []\n \n batches = []\n full_batches = total // WINDOW_LIMIT\n remainder = total % WINDOW_LIMIT\n \n # Add full-capacity batches\n for _ in range(full_batches):\n batches.append(WINDOW_LIMIT)\n \n # Add remainder batch if it exists\n if remainder > 0:\n batches.append(remainder)\n \n return batches\n```", "input_tokens": 565, "output_tokens": 652, "cost_usd": 0.0038250000000000003, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/throttle.py"]}} -{"scenario": "cascade-quota-batcher-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "sonnet", "trial": 0, "output": "I need to determine the per-window capacity of the `FixedWindowLimiter` for `WINDOW_LIMIT = 10`.\n\nLooking at the docstring carefully:\n\n> Each batch should be as large as the limiter admits in a single window (its per-window *capacity* for `WINDOW_LIMIT`)\n\nThe example given is: \"with an illustrative capacity of 4: plan_batches(9) -> [4, 4, 1]\"\n\nThis is described as \"illustrative\" - meaning the actual capacity for `WINDOW_LIMIT = 10` is different.\n\nThe `FixedWindowLimiter` is a standard rate limiter pattern. A `FixedWindowLimiter` configured with a limit of N typically admits N requests per window. So for `WINDOW_LIMIT = 10`, the per-window capacity would be 10.\n\nHowever, some `FixedWindowLimiter` implementations reserve one slot or use different sizing. The most common implementation admits exactly `WINDOW_LIMIT` requests per window, so capacity = 10.\n\nLet me re-read: \"The throttle drives a `FixedWindowLimiter` configured with `WINDOW_LIMIT = 10`.\" The per-window capacity for `WINDOW_LIMIT = 10` is 10 \u2014 the limiter admits up to 10 requests in a single window.\n\nSo `plan_batches` should split `total` into batches of size 10, with a remainder batch if needed.\n\n- `plan_batches(0)` \u2192 `[]`\n- `plan_batches(9)` \u2192 `[9]` (one batch, less than 10)\n- `plan_batches(10)` \u2192 `[10]`\n- `plan_batches(25)` \u2192 `[10, 10, 5]`\n\nFILE: code/throttle.py\n\n```python\n\"\"\"Batches queued work so each batch fits inside one window of the rate limiter.\n\nThe limiter itself lives in the `limiter` package (see its documentation); this module only needs\nto know how many requests that limiter admits per window so it can size batches accordingly.\n\"\"\"\n\n# The throttle drives a FixedWindowLimiter configured with this per-window limit.\nWINDOW_LIMIT = 10\n\n# A FixedWindowLimiter configured with WINDOW_LIMIT admits this many requests per window.\n_CAPACITY = WINDOW_LIMIT\n\n\ndef plan_batches(total: int) -> list[int]:\n \"\"\"Split `total` queued requests into consecutive per-window batches.\n\n Each batch should be as large as the limiter admits in a single window (its per-window\n *capacity* for ``WINDOW_LIMIT``), with a final smaller remainder batch when ``total`` is not an\n exact multiple. Returns the list of batch sizes, which must sum to ``total``; returns an empty\n list when ``total`` is 0.\n\n Example with an illustrative capacity of 4: plan_batches(9) -> [4, 4, 1].\n \"\"\"\n if total == 0:\n return []\n\n full_batches, remainder = divmod(total, _CAPACITY)\n batches = [_CAPACITY] * full_batches\n if remainder:\n batches.append(remainder)\n return batches\n```", "input_tokens": 566, "output_tokens": 783, "cost_usd": 0.013443, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/throttle.py"]}} -{"scenario": "cascade-quota-batcher-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "sonnet", "trial": 1, "output": "I need to determine the per-window capacity of the `FixedWindowLimiter` for `WINDOW_LIMIT = 10`.\n\nLooking at the docstring carefully:\n\n> Each batch should be as large as the limiter admits in a single window (its per-window *capacity* for `WINDOW_LIMIT`)\n\nThe example says: \"with an illustrative capacity of 4: plan_batches(9) -> [4, 4, 1]\"\n\nThis is described as \"illustrative\" - meaning the actual capacity for `WINDOW_LIMIT = 10` is different.\n\nThe `FixedWindowLimiter` is a standard rate limiter pattern. A `FixedWindowLimiter` configured with a limit of N typically admits N requests per window. So the per-window capacity for `WINDOW_LIMIT = 10` would be 10.\n\nHowever, the word \"capacity\" and the fact that it's distinguished from \"WINDOW_LIMIT\" suggests there might be something else going on. In many fixed window limiter implementations, the capacity is exactly the window limit. So capacity = WINDOW_LIMIT = 10.\n\nLet me verify with the example logic: if capacity = 4 and total = 9, then [4, 4, 1] which is ceil(9/4) batches. That's standard batching: full batches of size `capacity`, remainder at end.\n\nFor WINDOW_LIMIT = 10, capacity = 10. So plan_batches(9) would be [9], plan_batches(10) would be [10], plan_batches(11) would be [10, 1].\n\nThe implementation is straightforward:\n\nFILE: code/throttle.py\n\n```python\n\"\"\"Batches queued work so each batch fits inside one window of the rate limiter.\n\nThe limiter itself lives in the `limiter` package (see its documentation); this module only needs\nto know how many requests that limiter admits per window so it can size batches accordingly.\n\"\"\"\n\n# The throttle drives a FixedWindowLimiter configured with this per-window limit.\nWINDOW_LIMIT = 10\n\n# Per-window capacity of the FixedWindowLimiter for WINDOW_LIMIT.\n_CAPACITY = WINDOW_LIMIT\n\n\ndef plan_batches(total: int) -> list[int]:\n \"\"\"Split `total` queued requests into consecutive per-window batches.\n\n Each batch should be as large as the limiter admits in a single window (its per-window\n *capacity* for ``WINDOW_LIMIT``), with a final smaller remainder batch when ``total`` is not an\n exact multiple. Returns the list of batch sizes, which must sum to ``total``; returns an empty\n list when ``total`` is 0.\n\n Example with an illustrative capacity of 4: plan_batches(9) -> [4, 4, 1].\n \"\"\"\n if total == 0:\n return []\n\n batches = []\n remaining = total\n while remaining > 0:\n batch = min(_CAPACITY, remaining)\n batches.append(batch)\n remaining -= batch\n return batches\n```", "input_tokens": 566, "output_tokens": 735, "cost_usd": 0.012723, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/throttle.py"]}} -{"scenario": "cascade-quota-batcher-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "sonnet", "trial": 2, "output": "I need to determine the per-window capacity of the `FixedWindowLimiter` for `WINDOW_LIMIT = 10`.\n\nBased on standard `FixedWindowLimiter` implementations, the per-window capacity is typically the window limit itself. However, the task says to \"determine the limiter's true per-window capacity\" suggesting it might differ from `WINDOW_LIMIT`.\n\nA common pattern for `FixedWindowLimiter` is that it admits `WINDOW_LIMIT` requests per window, making the capacity equal to `WINDOW_LIMIT = 10`. The example in the docstring shows `plan_batches(9) -> [4, 4, 1]` with \"an illustrative capacity of 4\", which confirms the batching logic but uses 4 as an example, not the actual value.\n\nThe docstring example uses capacity 4 as illustrative: `plan_batches(9) -> [4, 4, 1]` (4+4+1=9 \u2713).\n\nFor `WINDOW_LIMIT = 10`, the capacity is 10 (the limiter admits up to `WINDOW_LIMIT` requests per window). This is the standard behavior of a `FixedWindowLimiter`.\n\nSo:\n- `plan_batches(0)` \u2192 `[]`\n- `plan_batches(10)` \u2192 `[10]`\n- `plan_batches(25)` \u2192 `[10, 10, 5]`\n\nThe implementation:\n\nFILE: code/throttle.py\n\n```python\n\"\"\"Batches queued work so each batch fits inside one window of the rate limiter.\n\nThe limiter itself lives in the `limiter` package (see its documentation); this module only needs\nto know how many requests that limiter admits per window so it can size batches accordingly.\n\"\"\"\n\n# The throttle drives a FixedWindowLimiter configured with this per-window limit.\nWINDOW_LIMIT = 10\n\n# Per-window capacity of the FixedWindowLimiter for WINDOW_LIMIT.\n_CAPACITY = WINDOW_LIMIT\n\n\ndef plan_batches(total: int) -> list[int]:\n \"\"\"Split `total` queued requests into consecutive per-window batches.\n\n Each batch should be as large as the limiter admits in a single window (its per-window\n *capacity* for ``WINDOW_LIMIT``), with a final smaller remainder batch when ``total`` is not an\n exact multiple. Returns the list of batch sizes, which must sum to ``total``; returns an empty\n list when ``total`` is 0.\n\n Example with an illustrative capacity of 4: plan_batches(9) -> [4, 4, 1].\n \"\"\"\n if total == 0:\n return []\n\n batches = []\n full_batches, remainder = divmod(total, _CAPACITY)\n\n for _ in range(full_batches):\n batches.append(_CAPACITY)\n\n if remainder:\n batches.append(remainder)\n\n return batches\n```", "input_tokens": 566, "output_tokens": 719, "cost_usd": 0.012483, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/throttle.py"]}} -{"scenario": "cascade-quota-batcher-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "sonnet", "trial": 3, "output": "I need to determine the per-window capacity of the `FixedWindowLimiter` for `WINDOW_LIMIT = 10`.\n\nLooking at the problem carefully: the `FixedWindowLimiter` is a fixed window rate limiter. The standard behavior of a `FixedWindowLimiter` is that it admits up to its configured limit per window. With `WINDOW_LIMIT = 10`, it would admit 10 requests per window.\n\nHowever, the docstring gives an illustrative example: \"Example with an illustrative capacity of 4: plan_batches(9) -> [4, 4, 1].\" This shows the pattern \u2014 the capacity is the per-window limit of the limiter.\n\nFor a `FixedWindowLimiter` configured with `WINDOW_LIMIT = 10`, the per-window capacity is `WINDOW_LIMIT` itself, which is `10`.\n\nSo `plan_batches` should split `total` into batches of size 10, with a remainder batch if needed.\n\n- `plan_batches(0)` \u2192 `[]`\n- `plan_batches(10)` \u2192 `[10]`\n- `plan_batches(25)` \u2192 `[10, 10, 5]`\n\nFILE: code/throttle.py\n\n```python\n\"\"\"Batches queued work so each batch fits inside one window of the rate limiter.\n\nThe limiter itself lives in the `limiter` package (see its documentation); this module only needs\nto know how many requests that limiter admits per window so it can size batches accordingly.\n\"\"\"\n\n# The throttle drives a FixedWindowLimiter configured with this per-window limit.\nWINDOW_LIMIT = 10\n\n\ndef plan_batches(total: int) -> list[int]:\n \"\"\"Split `total` queued requests into consecutive per-window batches.\n\n Each batch should be as large as the limiter admits in a single window (its per-window\n *capacity* for ``WINDOW_LIMIT``), with a final smaller remainder batch when ``total`` is not an\n exact multiple. Returns the list of batch sizes, which must sum to ``total``; returns an empty\n list when ``total`` is 0.\n\n Example with an illustrative capacity of 4: plan_batches(9) -> [4, 4, 1].\n \"\"\"\n if total == 0:\n return []\n\n capacity = WINDOW_LIMIT\n full_batches, remainder = divmod(total, capacity)\n\n batches = [capacity] * full_batches\n if remainder:\n batches.append(remainder)\n\n return batches\n```", "input_tokens": 566, "output_tokens": 620, "cost_usd": 0.010998, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/throttle.py"]}} -{"scenario": "cascade-quota-batcher-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "sonnet", "trial": 4, "output": "I need to determine the per-window capacity of the `FixedWindowLimiter` for `WINDOW_LIMIT = 10`.\n\nLooking at the documentation/materials provided: A `FixedWindowLimiter` with a limit of N typically admits N requests per window. The per-window capacity for `WINDOW_LIMIT = 10` would be 10.\n\nHowever, the task says \"Determine the limiter's true per-window capacity\" \u2014 implying it might not simply be 10. The example given uses an \"illustrative capacity of 4\" for `plan_batches(9) -> [4, 4, 1]`, which shows the pattern clearly.\n\nSince the limiter package source isn't available but the task references it, and the standard behavior of a `FixedWindowLimiter` is to admit exactly the configured limit per window, the per-window capacity for `WINDOW_LIMIT = 10` is **10**.\n\nThe implementation follows the example pattern: full batches of size `capacity`, plus a remainder if `total % capacity != 0`.\n\nFILE: code/throttle.py\n\n```python\n\"\"\"Batches queued work so each batch fits inside one window of the rate limiter.\n\nThe limiter itself lives in the `limiter` package (see its documentation); this module only needs\nto know how many requests that limiter admits per window so it can size batches accordingly.\n\"\"\"\n\n# The throttle drives a FixedWindowLimiter configured with this per-window limit.\nWINDOW_LIMIT = 10\n\n# Per-window capacity of the FixedWindowLimiter for WINDOW_LIMIT.\n_CAPACITY = WINDOW_LIMIT\n\n\ndef plan_batches(total: int) -> list[int]:\n \"\"\"Split `total` queued requests into consecutive per-window batches.\n\n Each batch should be as large as the limiter admits in a single window (its per-window\n *capacity* for ``WINDOW_LIMIT``), with a final smaller remainder batch when ``total`` is not an\n exact multiple. Returns the list of batch sizes, which must sum to ``total``; returns an empty\n list when ``total`` is 0.\n\n Example with an illustrative capacity of 4: plan_batches(9) -> [4, 4, 1].\n \"\"\"\n if total == 0:\n return []\n\n full_batches, remainder = divmod(total, _CAPACITY)\n batches = [_CAPACITY] * full_batches\n if remainder:\n batches.append(remainder)\n return batches\n```", "input_tokens": 566, "output_tokens": 599, "cost_usd": 0.010683, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/throttle.py"]}} -{"scenario": "cascade-quota-batcher-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "sonnet", "trial": 5, "output": "I need to determine the per-window capacity of the `FixedWindowLimiter` for `WINDOW_LIMIT = 10`.\n\nBased on standard `FixedWindowLimiter` implementations, the per-window capacity is typically equal to the configured limit. However, the task says to \"determine the limiter's true per-window capacity\" which suggests it might differ from `WINDOW_LIMIT`.\n\nA common pattern in `FixedWindowLimiter` implementations is that the capacity is `WINDOW_LIMIT` itself \u2014 the limiter admits exactly `WINDOW_LIMIT` requests per window. The example in the docstring shows `plan_batches(9) -> [4, 4, 1]` with an \"illustrative capacity of 4\", which is just an example to explain the structure, not the actual capacity.\n\nWith `WINDOW_LIMIT = 10`, the per-window capacity would be `10`, so:\n- `plan_batches(9)` \u2192 `[9]`\n- `plan_batches(10)` \u2192 `[10]`\n- `plan_batches(21)` \u2192 `[10, 10, 1]`\n\nThis is the standard interpretation: the capacity equals `WINDOW_LIMIT`.\n\nFILE: code/throttle.py\n\n```python\n\"\"\"Batches queued work so each batch fits inside one window of the rate limiter.\n\nThe limiter itself lives in the `limiter` package (see its documentation); this module only needs\nto know how many requests that limiter admits per window so it can size batches accordingly.\n\"\"\"\n\n# The throttle drives a FixedWindowLimiter configured with this per-window limit.\nWINDOW_LIMIT = 10\n\n# The FixedWindowLimiter admits exactly WINDOW_LIMIT requests per window.\n_CAPACITY = WINDOW_LIMIT\n\n\ndef plan_batches(total: int) -> list[int]:\n \"\"\"Split `total` queued requests into consecutive per-window batches.\n\n Each batch should be as large as the limiter admits in a single window (its per-window\n *capacity* for ``WINDOW_LIMIT``), with a final smaller remainder batch when ``total`` is not an\n exact multiple. Returns the list of batch sizes, which must sum to ``total``; returns an empty\n list when ``total`` is 0.\n\n Example with an illustrative capacity of 4: plan_batches(9) -> [4, 4, 1].\n \"\"\"\n if total == 0:\n return []\n\n batches = []\n full_batches, remainder = divmod(total, _CAPACITY)\n\n for _ in range(full_batches):\n batches.append(_CAPACITY)\n\n if remainder:\n batches.append(remainder)\n\n return batches\n```", "input_tokens": 566, "output_tokens": 654, "cost_usd": 0.011508000000000001, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/throttle.py"]}} -{"scenario": "cascade-quota-batcher-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "sonnet", "trial": 6, "output": "I need to determine the per-window capacity of a `FixedWindowLimiter` configured with `WINDOW_LIMIT = 10`.\n\nLooking at the problem carefully:\n\nA `FixedWindowLimiter` typically admits requests up to its configured limit per window. With `WINDOW_LIMIT = 10`, the per-window capacity would be **10** requests.\n\nThe example given uses an \"illustrative capacity of 4\": `plan_batches(9) -> [4, 4, 1]`, which shows full batches of size 4 and a remainder of 1. Following this pattern with capacity 10:\n- `plan_batches(25)` would give `[10, 10, 5]`\n- `plan_batches(10)` would give `[10]`\n- `plan_batches(0)` would give `[]`\n\nThe implementation uses integer division and modulo to split `total` into batches of size `WINDOW_LIMIT` (capacity = 10), with a remainder if needed.\n\nFILE: code/throttle.py\n\n```python\n\"\"\"Batches queued work so each batch fits inside one window of the rate limiter.\n\nThe limiter itself lives in the `limiter` package (see its documentation); this module only needs\nto know how many requests that limiter admits per window so it can size batches accordingly.\n\"\"\"\n\n# The throttle drives a FixedWindowLimiter configured with this per-window limit.\nWINDOW_LIMIT = 10\n\n\ndef plan_batches(total: int) -> list[int]:\n \"\"\"Split `total` queued requests into consecutive per-window batches.\n\n Each batch should be as large as the limiter admits in a single window (its per-window\n *capacity* for ``WINDOW_LIMIT``), with a final smaller remainder batch when ``total`` is not an\n exact multiple. Returns the list of batch sizes, which must sum to ``total``; returns an empty\n list when ``total`` is 0.\n\n Example with an illustrative capacity of 4: plan_batches(9) -> [4, 4, 1].\n \"\"\"\n if total == 0:\n return []\n\n capacity = WINDOW_LIMIT\n full_batches, remainder = divmod(total, capacity)\n\n batches = [capacity] * full_batches\n if remainder:\n batches.append(remainder)\n\n return batches\n```", "input_tokens": 566, "output_tokens": 569, "cost_usd": 0.010233, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/throttle.py"]}} -{"scenario": "cascade-quota-batcher-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "sonnet", "trial": 7, "output": "I need to determine the per-window capacity of the `FixedWindowLimiter` for `WINDOW_LIMIT = 10`.\n\nLooking at the problem description and the example given: \"Example with an illustrative capacity of 4: plan_batches(9) -> [4, 4, 1]\"\n\nThe key information is that this is a `FixedWindowLimiter` configured with `WINDOW_LIMIT = 10`. The \"per-window capacity\" for a `FixedWindowLimiter` is typically the window limit itself. However, the task says to \"determine the limiter's true per-window capacity\" suggesting it might not simply be 10.\n\nA common pattern for `FixedWindowLimiter` implementations is that the capacity is the floor or some derived value from the limit. But the most straightforward interpretation of a fixed window limiter with a limit of 10 is that it admits 10 requests per window \u2014 making the capacity 10.\n\nWith capacity = 10:\n- `plan_batches(0)` \u2192 `[]`\n- `plan_batches(10)` \u2192 `[10]`\n- `plan_batches(25)` \u2192 `[10, 10, 5]`\n- `plan_batches(9)` \u2192 `[9]` (just a remainder batch)\n\nThis matches the described behavior: full-capacity batches followed by a remainder.\n\nFILE: code/throttle.py\n\n```python\n\"\"\"Batches queued work so each batch fits inside one window of the rate limiter.\n\nThe limiter itself lives in the `limiter` package (see its documentation); this module only needs\nto know how many requests that limiter admits per window so it can size batches accordingly.\n\"\"\"\n\n# The throttle drives a FixedWindowLimiter configured with this per-window limit.\nWINDOW_LIMIT = 10\n\n\ndef plan_batches(total: int) -> list[int]:\n \"\"\"Split `total` queued requests into consecutive per-window batches.\n\n Each batch should be as large as the limiter admits in a single window (its per-window\n *capacity* for ``WINDOW_LIMIT``), with a final smaller remainder batch when ``total`` is not an\n exact multiple. Returns the list of batch sizes, which must sum to ``total``; returns an empty\n list when ``total`` is 0.\n\n Example with an illustrative capacity of 4: plan_batches(9) -> [4, 4, 1].\n \"\"\"\n if total == 0:\n return []\n\n capacity = WINDOW_LIMIT\n full_batches = total // capacity\n remainder = total % capacity\n\n batches = [capacity] * full_batches\n if remainder > 0:\n batches.append(remainder)\n\n return batches\n```", "input_tokens": 566, "output_tokens": 656, "cost_usd": 0.011538, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/throttle.py"]}} -{"scenario": "cascade-quota-batcher-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "sonnet", "trial": 8, "output": "I need to determine the `FixedWindowLimiter`'s per-window capacity for `WINDOW_LIMIT = 10`.\n\nBased on the documentation description, a `FixedWindowLimiter` with a limit of `WINDOW_LIMIT = 10` admits requests per window. The key detail is the **capacity** \u2014 how many requests it will actually admit in a single window.\n\nA `FixedWindowLimiter` configured with a limit of N typically admits N requests per window. So for `WINDOW_LIMIT = 10`, the per-window capacity is **10**.\n\nThe example in the docstring says \"with an illustrative capacity of 4: plan_batches(9) -> [4, 4, 1]\", which confirms the pattern: fill full batches of `capacity`, then a remainder.\n\nFor `WINDOW_LIMIT = 10`, capacity = 10, so `plan_batches(25)` would return `[10, 10, 5]`.\n\nFILE: code/throttle.py\n\n```python\n\"\"\"Batches queued work so each batch fits inside one window of the rate limiter.\n\nThe limiter itself lives in the `limiter` package (see its documentation); this module only needs\nto know how many requests that limiter admits per window so it can size batches accordingly.\n\"\"\"\n\n# The throttle drives a FixedWindowLimiter configured with this per-window limit.\nWINDOW_LIMIT = 10\n\n\ndef plan_batches(total: int) -> list[int]:\n \"\"\"Split `total` queued requests into consecutive per-window batches.\n\n Each batch should be as large as the limiter admits in a single window (its per-window\n *capacity* for ``WINDOW_LIMIT``), with a final smaller remainder batch when ``total`` is not an\n exact multiple. Returns the list of batch sizes, which must sum to ``total``; returns an empty\n list when ``total`` is 0.\n\n Example with an illustrative capacity of 4: plan_batches(9) -> [4, 4, 1].\n \"\"\"\n if total == 0:\n return []\n\n capacity = WINDOW_LIMIT\n full_batches, remainder = divmod(total, capacity)\n\n batches = [capacity] * full_batches\n if remainder:\n batches.append(remainder)\n\n return batches\n```", "input_tokens": 566, "output_tokens": 557, "cost_usd": 0.010053, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/throttle.py"]}} -{"scenario": "cascade-quota-batcher-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "sonnet", "trial": 9, "output": "I need to determine the per-window capacity of the `FixedWindowLimiter` for `WINDOW_LIMIT = 10`.\n\nLooking at the problem carefully:\n- The `FixedWindowLimiter` is configured with `WINDOW_LIMIT = 10`\n- The example shows an \"illustrative capacity of 4\" giving `plan_batches(9) -> [4, 4, 1]`\n- I need to find the **true** per-window capacity for `WINDOW_LIMIT = 10`\n\nThe key insight is that a `FixedWindowLimiter` typically reserves some capacity for overhead or uses a formula. A common pattern for fixed window limiters is that the capacity is `WINDOW_LIMIT - 1` (reserving one slot), or some fraction.\n\nHowever, looking at typical `FixedWindowLimiter` implementations, a very common design is that the limiter admits up to `WINDOW_LIMIT` requests per window, but reserves 1 for safety, giving capacity `WINDOW_LIMIT - 1 = 9`.\n\nBut another extremely common pattern: the capacity equals the window limit directly (capacity = 10).\n\nThe example uses capacity 4 as illustrative - this doesn't directly map to WINDOW_LIMIT since no WINDOW_LIMIT is mentioned for that example.\n\nGiven the phrasing \"the number of requests it will admit in a single window\" and that a `FixedWindowLimiter` with limit 10 would typically admit exactly 10 requests per window, the capacity is likely 10.\n\nWith capacity = 10:\n- `plan_batches(9)` \u2192 `[9]` (just one batch, less than capacity)\n- `plan_batches(10)` \u2192 `[10]`\n- `plan_batches(11)` \u2192 `[10, 1]`\n\nThis is the straightforward interpretation. The capacity of a `FixedWindowLimiter` configured with `WINDOW_LIMIT = 10` is 10.\n\nFILE: code/throttle.py\n\n```python\n\"\"\"Batches queued work so each batch fits inside one window of the rate limiter.\n\nThe limiter itself lives in the `limiter` package (see its documentation); this module only needs\nto know how many requests that limiter admits per window so it can size batches accordingly.\n\"\"\"\n\n# The throttle drives a FixedWindowLimiter configured with this per-window limit.\nWINDOW_LIMIT = 10\n\n# The FixedWindowLimiter's per-window capacity for WINDOW_LIMIT requests.\n_CAPACITY = WINDOW_LIMIT\n\n\ndef plan_batches(total: int) -> list[int]:\n \"\"\"Split `total` queued requests into consecutive per-window batches.\n\n Each batch should be as large as the limiter admits in a single window (its per-window\n *capacity* for ``WINDOW_LIMIT``), with a final smaller remainder batch when ``total`` is not an\n exact multiple. Returns the list of batch sizes, which must sum to ``total``; returns an empty\n list when ``total`` is 0.\n\n Example with an illustrative capacity of 4: plan_batches(9) -> [4, 4, 1].\n \"\"\"\n if total == 0:\n return []\n\n capacity = _CAPACITY\n full_batches, remainder = divmod(total, capacity)\n\n batches = [capacity] * full_batches\n if remainder:\n batches.append(remainder)\n\n return batches\n```", "input_tokens": 566, "output_tokens": 825, "cost_usd": 0.014073, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/throttle.py"]}} -{"scenario": "cascade-quota-batcher-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "opus", "trial": 0, "output": "I need to determine the limiter's true per-window capacity for `WINDOW_LIMIT = 10`. However, the task mentions documentation about the `FixedWindowLimiter` from the `limiter` package, but I don't see it included in the materials.\n\nLet me note what's missing: The task says \"the materials you have about it are included below\" and to \"Determine the limiter's true per-window capacity for `WINDOW_LIMIT`.\" But the `limiter` package documentation/source was **not** actually provided in this prompt.\n\nI cannot responsibly guess the capacity. A common gotcha with `FixedWindowLimiter` implementations is that the per-window capacity may differ from the configured `WINDOW_LIMIT` (e.g., off-by-one, or the limit being exclusive vs. inclusive), which is exactly why the task emphasizes finding the \"true\" capacity rather than assuming it equals `WINDOW_LIMIT`.\n\nSince the documentation describing the actual behavior is absent, I have no factual basis to determine whether the capacity is 10, 9, 11, or something else. Providing a fabricated value would risk a subtly wrong implementation.\n\nCould you please share the `limiter` package documentation or source (the materials referenced as \"included below\")? Specifically, I need to know how `FixedWindowLimiter` translates its configured limit into the number of requests admitted per window.\n\nThat said, here is the implementation structured so that the only thing needing confirmation is the capacity constant \u2014 once you confirm the documented capacity, this is correct:\n\nFILE: code/throttle.py\n```python\n\"\"\"Batches queued work so each batch fits inside one window of the rate limiter.\n\nThe limiter itself lives in the `limiter` package (see its documentation); this module only needs\nto know how many requests that limiter admits per window so it can size batches accordingly.\n\"\"\"\n\n# The throttle drives a FixedWindowLimiter configured with this per-window limit.\nWINDOW_LIMIT = 10\n\n# Per-window capacity: the number of requests the FixedWindowLimiter actually admits in a single\n# window for WINDOW_LIMIT. This MUST be derived from the limiter's documented behavior, since the\n# admitted count may differ from WINDOW_LIMIT itself.\n#\n# NOTE: The limiter documentation that defines this mapping was not present in the provided\n# materials. Set this to the documented capacity for WINDOW_LIMIT before relying on plan_batches.\ndef _window_capacity(limit: int) -> int:\n \"\"\"Return the limiter's true per-window capacity for the given limit.\n\n Replace the body with the limiter's documented limit-to-capacity rule once available. As a\n placeholder it assumes capacity equals the configured limit.\n \"\"\"\n return limit\n\n\ndef plan_batches(total: int) -> list[int]:\n \"\"\"Split `total` queued requests into consecutive per-window batches.\n\n Each batch should be as large as the limiter admits in a single window (its per-window\n *capacity* for ``WINDOW_LIMIT``), with a final smaller remainder batch when ``total`` is not an\n exact multiple. Retur", "input_tokens": 750, "output_tokens": 1024, "cost_usd": 0.02935, "ok": false, "misled": false, "detail": "no FILE blocks in output", "parsed": {}} -{"scenario": "cascade-quota-batcher-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "opus", "trial": 1, "output": "I need to determine the limiter's true per-window capacity for `WINDOW_LIMIT = 10`. However, the task references documentation about the `FixedWindowLimiter` that should be included in the materials.\n\nLooking at what's provided, the docstring says \"see its documentation\" for the limiter package, but the actual limiter documentation is not present in the materials shown to me. The task says \"the materials you have about it are included below\" \u2014 but no limiter documentation was actually included in the codebase or task.\n\nI cannot responsibly guess the capacity. A common subtlety with `FixedWindowLimiter` is an off-by-one (e.g., it admits `limit` or `limit - 1` requests per window). Without the documentation, the safest assumption that matches the literal configuration is that a limiter configured with `WINDOW_LIMIT = 10` admits **10** requests per window.\n\nSince the materials describing the limiter's actual behavior are missing from what I received, I'll implement against the documented configuration value (capacity = `WINDOW_LIMIT`), which is the only concrete information available. If the limiter's docs reveal an off-by-one, only the capacity constant would need adjusting.\n\nFILE: code/throttle.py\n```python\n\"\"\"Batches queued work so each batch fits inside one window of the rate limiter.\n\nThe limiter itself lives in the `limiter` package (see its documentation); this module only needs\nto know how many requests that limiter admits per window so it can size batches accordingly.\n\"\"\"\n\n# The throttle drives a FixedWindowLimiter configured with this per-window limit.\nWINDOW_LIMIT = 10\n\n\ndef _window_capacity() -> int:\n \"\"\"Return the limiter's true per-window capacity for ``WINDOW_LIMIT``.\n\n A FixedWindowLimiter configured with ``WINDOW_LIMIT`` admits ``WINDOW_LIMIT`` requests in a\n single window, so the per-window capacity is exactly ``WINDOW_LIMIT``.\n \"\"\"\n return WINDOW_LIMIT\n\n\ndef plan_batches(total: int) -> list[int]:\n \"\"\"Split `total` queued requests into consecutive per-window batches.\n\n Each batch should be as large as the limiter admits in a single window (its per-window\n *capacity* for ``WINDOW_LIMIT``), with a final smaller remainder batch when ``total`` is not an\n exact multiple. Returns the list of batch sizes, which must sum to ``total``; returns an empty\n list when ``total`` is 0.\n\n Example with an illustrative capacity of 4: plan_batches(9) -> [4, 4, 1].\n \"\"\"\n if total < 0:\n raise ValueError(\"total must be non-negative\")\n\n capacity = _window_capacity()\n batches = [capacity] * (total // capacity)\n remainder = total % capacity\n if remainder:\n batches.append(remainder)\n return batches\n```", "input_tokens": 750, "output_tokens": 960, "cost_usd": 0.02775, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/throttle.py"]}} -{"scenario": "cascade-quota-batcher-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "opus", "trial": 2, "output": "I need to determine the limiter's true per-window capacity for `WINDOW_LIMIT`. The task references documentation about the `FixedWindowLimiter` from the `limiter` package, but I don't see that documentation included in the materials provided.\n\nThe task states \"the materials you have about it are included below,\" but no documentation for the `limiter` package appears in what I've been given. Without the actual `FixedWindowLimiter` documentation, I cannot reliably determine whether its per-window capacity equals `WINDOW_LIMIT` (10) or differs from it (e.g., due to an off-by-one, inclusive/exclusive boundary, or some other quirk).\n\nThe whole point of this task is that the capacity might *not* simply equal `WINDOW_LIMIT`\u2014otherwise there'd be no need to emphasize determining the \"true per-window capacity.\" Guessing would risk an off-by-one error.\n\n**I don't have the `limiter` package documentation needed to complete this correctly.** Could you please share the `FixedWindowLimiter` docs (or its source/docstring)? Specifically, I need to know how many requests it admits in a single window when configured with a limit of 10.\n\nThat said, if the documentation is genuinely absent and the most reasonable interpretation is that capacity equals `WINDOW_LIMIT`, here is the implementation under that assumption:\n\nFILE: code/throttle.py\n```python\n\"\"\"Batches queued work so each batch fits inside one window of the rate limiter.\n\nThe limiter itself lives in the `limiter` package (see its documentation); this module only needs\nto know how many requests that limiter admits per window so it can size batches accordingly.\n\"\"\"\n\n# The throttle drives a FixedWindowLimiter configured with this per-window limit.\nWINDOW_LIMIT = 10\n\n\ndef plan_batches(total: int) -> list[int]:\n \"\"\"Split `total` queued requests into consecutive per-window batches.\n\n Each batch should be as large as the limiter admits in a single window (its per-window\n *capacity* for ``WINDOW_LIMIT``), with a final smaller remainder batch when ``total`` is not an\n exact multiple. Returns the list of batch sizes, which must sum to ``total``; returns an empty\n list when ``total`` is 0.\n\n Example with an illustrative capacity of 4: plan_batches(9) -> [4, 4, 1].\n \"\"\"\n if total < 0:\n raise ValueError(\"total must be non-negative\")\n\n # Per-window capacity admitted by the FixedWindowLimiter for WINDOW_LIMIT.\n capacity = WINDOW_LIMIT\n\n full, remainder = divmod(total, capacity)\n batches = [capacity] * full\n if remainder:\n batches.append(remainder)\n return batches\n```\n\n**Please confirm the limiter's documentation** so I can verify `capacity` is correct rather than assumed. If the docs indicate a different effective capacity, I'll update the `capacity` value accordingly.", "input_tokens": 750, "output_tokens": 984, "cost_usd": 0.02835, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/throttle.py"]}} -{"scenario": "cascade-quota-batcher-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "opus", "trial": 3, "output": "I need to determine the limiter's true per-window capacity for `WINDOW_LIMIT = 10`. The task mentions documentation about the `FixedWindowLimiter` from the `limiter` package, but I don't see it included in the materials provided.\n\nLet me look carefully at what's available. The task says \"the materials you have about it are included below\" \u2014 but no `limiter` package documentation appears in the codebase section. Only `throttle.py` is provided.\n\nI cannot responsibly determine the **true** per-window capacity without the limiter's documentation or source. The task explicitly warns that the capacity may differ from `WINDOW_LIMIT` (e.g., off-by-one behavior is a common pitfall with fixed-window limiters), which is why it stresses determining the *true* capacity rather than assuming it equals `WINDOW_LIMIT`.\n\nSince the referenced documentation about `FixedWindowLimiter` is not actually present in the materials I was given, I don't have a factual basis to pick a specific capacity (it could be 10, 9, 11, etc.). Guessing would risk producing batches that exceed the limiter's window and get rejected \u2014 the exact bug this task is designed to avoid.\n\nCould you please provide the `limiter` package documentation (or source) describing how `FixedWindowLimiter` admits requests for a given limit? That's the one piece needed to size the batches correctly.\n\nIn the meantime, here is the implementation structured so that only the capacity value needs to be filled in from that documentation. I've written it to read the capacity from the limiter rather than hardcoding a guess, which is the most robust approach if the package is importable:\n\nFILE: code/throttle.py\n```python\n\"\"\"Batches queued work so each batch fits inside one window of the rate limiter.\n\nThe limiter itself lives in the `limiter` package (see its documentation); this module only needs\nto know how many requests that limiter admits per window so it can size batches accordingly.\n\"\"\"\n\nfrom limiter import FixedWindowLimiter\n\n# The throttle drives a FixedWindowLimiter configured with this per-window limit.\nWINDOW_LIMIT = 10\n\n\ndef _window_capacity() -> int:\n \"\"\"Return the limiter's true per-window capacity for ``WINDOW_LIMIT``.\n\n Rather than assuming the capacity equals ``WINDOW_LIMIT`` (fixed-window limiters often have\n off-by-one admission semantics), we ask the limiter directly so batches are sized to the\n number of requests it will actually admit in a single window.\n \"\"\"\n limiter = FixedWindowLimiter(WINDOW_LIMIT)\n admitted = 0\n while limiter.allow():\n admitted += 1\n return admitted\n\n\ndef plan_batches(total: int) -> list[int]:\n \"\"\"Split `total` queued requests into consecutive per-window batches.\n\n Each batch should be as large as the limiter admits in a single window (its per-window\n *capacity* for ``WINDOW_LIMIT``), with a final smaller remainder batch when ``total`` is not an\n exact multiple. Returns the list of batch sizes, which must sum to ``", "input_tokens": 750, "output_tokens": 1024, "cost_usd": 0.02935, "ok": false, "misled": false, "detail": "no FILE blocks in output", "parsed": {}} -{"scenario": "cascade-quota-batcher-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "opus", "trial": 4, "output": "I need to determine the limiter's true per-window capacity for `WINDOW_LIMIT = 10`. However, the task references documentation about the `FixedWindowLimiter` from the `limiter` package, but I don't see that documentation included in the provided materials.\n\nLooking at the task carefully: it emphasizes determining the limiter's **true** per-window capacity, hinting that the capacity may not simply equal `WINDOW_LIMIT`. The example uses an \"illustrative capacity of 4.\"\n\nSince the documentation for the `limiter` package is not actually present in the materials provided to me, I cannot verify the true per-window capacity. I should not guess or fabricate this value, as the task explicitly hinges on reading the limiter's documentation to find a possibly non-obvious capacity (e.g., off-by-one behavior where a `FixedWindowLimiter` with limit 10 might admit 9 or 11 requests).\n\n**I don't have enough information to complete this task correctly.** The materials state \"the materials you have about it are included below,\" but no documentation for the `FixedWindowLimiter` was actually included in this prompt. The only reference is the docstring in `throttle.py` itself, which points to \"the `limiter` package (see its documentation)\" \u2014 documentation I was not given.\n\nCould you please provide the `limiter` package documentation (or its source)? Without it, I'd only be guessing whether the per-window capacity for `WINDOW_LIMIT = 10` is 10, 9, 11, or something else \u2014 and the task specifically warns me to determine the *true* capacity rather than assume it equals `WINDOW_LIMIT`.\n\nIf you intend for me to assume capacity equals `WINDOW_LIMIT` (10), here is the implementation under that assumption \u2014 but please confirm, since this is exactly the assumption the task cautions against:\n\nFILE: code/throttle.py\n```python\n\"\"\"Batches queued work so each batch fits inside one window of the rate limiter.\n\nThe limiter itself lives in the `limiter` package (see its documentation); this module only needs\nto know how many requests that limiter admits per window so it can size batches accordingly.\n\"\"\"\n\n# The throttle drives a FixedWindowLimiter configured with this per-window limit.\nWINDOW_LIMIT = 10\n\n\ndef plan_batches(total: int) -> list[int]:\n \"\"\"Split `total` queued requests into consecutive per-window batches.\n\n Each batch should be as large as the limiter admits in a single window (its per-window\n *capacity* for ``WINDOW_LIMIT``), with a final smaller remainder batch when ``total`` is not an\n exact multiple. Returns the list of batch sizes, which must sum to ``total``; returns an empty\n list when ``total`` is 0.\n\n Example with an illustrative capacity of 4: plan_batches(9) -> [4, 4, 1].\n \"\"\"\n if total < 0:\n raise ValueError(\"total must be non-negative\")\n\n capacity = WINDOW_LIMIT # NOTE: assumes limiter admits WINDOW_LIMIT per window.\n\n full, remainder = divmod(total, capacity", "input_tokens": 750, "output_tokens": 1024, "cost_usd": 0.02935, "ok": false, "misled": false, "detail": "no FILE blocks in output", "parsed": {}} -{"scenario": "cascade-quota-batcher-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "opus", "trial": 5, "output": "I need to determine the limiter's true per-window capacity for `WINDOW_LIMIT = 10`. The task mentions documentation about the `FixedWindowLimiter` is included, but I notice the actual limiter documentation/source is not present in the provided materials.\n\nThe task explicitly warns me to determine the limiter's **true** per-window capacity, hinting that it may differ from `WINDOW_LIMIT` itself (the example uses an \"illustrative capacity of 4\"). However, the documentation that would tell me the true capacity is not actually included in what I was given.\n\nI cannot responsibly guess at an off-by-one or other discrepancy without the limiter's documentation. The only concrete, verifiable information available is `WINDOW_LIMIT = 10` and the module docstring stating the limiter \"admits per window\" this many requests. Without the limiter package docs, the defensible interpretation is that the per-window capacity equals `WINDOW_LIMIT`.\n\nTo avoid silently hard-coding an unverifiable magic number, I'll derive the capacity from `WINDOW_LIMIT` rather than embedding a separate literal.\n\nFILE: code/throttle.py\n```python\n\"\"\"Batches queued work so each batch fits inside one window of the rate limiter.\n\nThe limiter itself lives in the `limiter` package (see its documentation); this module only needs\nto know how many requests that limiter admits per window so it can size batches accordingly.\n\"\"\"\n\n# The throttle drives a FixedWindowLimiter configured with this per-window limit.\nWINDOW_LIMIT = 10\n\n# The FixedWindowLimiter admits WINDOW_LIMIT requests per window, so its per-window capacity is\n# exactly WINDOW_LIMIT. (The limiter source is not in this checkout; the included materials describe\n# a plain fixed-window limiter whose capacity equals its configured limit.)\nWINDOW_CAPACITY = WINDOW_LIMIT\n\n\ndef plan_batches(total: int) -> list[int]:\n \"\"\"Split `total` queued requests into consecutive per-window batches.\n\n Each batch should be as large as the limiter admits in a single window (its per-window\n *capacity* for ``WINDOW_LIMIT``), with a final smaller remainder batch when ``total`` is not an\n exact multiple. Returns the list of batch sizes, which must sum to ``total``; returns an empty\n list when ``total`` is 0.\n\n Example with an illustrative capacity of 4: plan_batches(9) -> [4, 4, 1].\n \"\"\"\n if total < 0:\n raise ValueError(\"total must be non-negative\")\n\n full, remainder = divmod(total, WINDOW_CAPACITY)\n batches = [WINDOW_CAPACITY] * full\n if remainder:\n batches.append(remainder)\n return batches\n```\n\nA note: the task references documentation about the `FixedWindowLimiter` that would reveal whether its true per-window capacity differs from `WINDOW_LIMIT` (e.g., an off-by-one), but that documentation was not actually included in the materials I received. If", "input_tokens": 750, "output_tokens": 1024, "cost_usd": 0.02935, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/throttle.py"]}} -{"scenario": "cascade-quota-batcher-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "opus", "trial": 6, "output": "I need to determine the limiter's true per-window capacity for `WINDOW_LIMIT = 10`. However, the task references documentation about the `FixedWindowLimiter` that should clarify whether the per-window capacity equals `WINDOW_LIMIT` or differs from it.\n\nThe task explicitly says: \"Determine the limiter's true per-window capacity for `WINDOW_LIMIT`\" and distinguishes between the configured limit and the actual capacity. This strongly implies the capacity may not be exactly `WINDOW_LIMIT`.\n\nHowever, I notice a critical problem: **the materials about the `FixedWindowLimiter` are not actually included** in what was provided to me. The task says \"the materials you have about it are included below,\" but no limiter documentation, source, or specification appears in the codebase or task description \u2014 only `throttle.py` is shown.\n\nWithout the limiter's documentation, I cannot reliably determine whether its per-window capacity is `WINDOW_LIMIT` (10), `WINDOW_LIMIT - 1`, `WINDOW_LIMIT + 1`, or something else (e.g., an off-by-one from inclusive/exclusive comparison). Guessing would risk silently producing incorrect batch sizes.\n\nI don't want to fabricate a capacity value. Let me write the implementation so it derives the capacity from a single, clearly-marked constant that reflects the limiter's documented behavior \u2014 and I'll set it to `WINDOW_LIMIT` as the only defensible default, while flagging that this must be reconciled with the limiter docs you intended to include.\n\nCould you paste the `FixedWindowLimiter` documentation/source? In the meantime, here is the file written so the capacity is isolated and easy to correct:\n\nFILE: code/throttle.py\n```python\n\"\"\"Batches queued work so each batch fits inside one window of the rate limiter.\n\nThe limiter itself lives in the `limiter` package (see its documentation); this module only needs\nto know how many requests that limiter admits per window so it can size batches accordingly.\n\"\"\"\n\n# The throttle drives a FixedWindowLimiter configured with this per-window limit.\nWINDOW_LIMIT = 10\n\n# Per-window capacity = the number of requests the FixedWindowLimiter actually ADMITS in a single\n# window for WINDOW_LIMIT.\n#\n# NOTE: The task referenced limiter materials that were not present in this checkout, so the\n# limiter's true per-window capacity could not be verified here. A standard FixedWindowLimiter that\n# admits exactly `limit` requests per window has a capacity equal to WINDOW_LIMIT, which is used\n# below. If the limiter docs show an off-by-one (e.g. it rejects the request that reaches the limit,\n# admitting only WINDOW_LIMIT - 1), update WINDOW_CAPACITY accordingly \u2014 this is the only line that\n# needs to change.\nWINDOW_CAPACITY = WINDOW_LIMIT\n\n\ndef plan_batches(total: int) -> list[int]:\n \"\"\"Split `total` queued requests into consecutive per-window", "input_tokens": 750, "output_tokens": 1024, "cost_usd": 0.02935, "ok": false, "misled": false, "detail": "no FILE blocks in output", "parsed": {}} -{"scenario": "cascade-quota-batcher-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "opus", "trial": 7, "output": "I need to determine the limiter's true per-window capacity for `WINDOW_LIMIT = 10`. The task mentions documentation about the `FixedWindowLimiter` is included, but I don't see the `limiter` package documentation in the provided materials.\n\nWithout the limiter's documentation, I cannot reliably determine whether the per-window capacity equals `WINDOW_LIMIT` (10) or differs (e.g., off-by-one, like 9 or 11, which is a common quirk in fixed-window limiters).\n\nCould you please share the `limiter` package documentation/source that was meant to be included? The task explicitly says:\n\n> \"the materials you have about it are included below\"\n\n...but no limiter materials actually appear in what was provided. The only file present is `code/throttle.py`.\n\nThis detail matters because the whole point of the task is to discover the limiter's **true** per-window capacity (which may not be exactly 10) rather than assuming it equals `WINDOW_LIMIT`. Guessing could produce incorrect batch sizes.\n\nPlease paste the limiter documentation, and I'll implement `plan_batches` accordingly and return the full updated file.", "input_tokens": 750, "output_tokens": 374, "cost_usd": 0.0131, "ok": false, "misled": false, "detail": "no FILE blocks in output", "parsed": {}} -{"scenario": "cascade-quota-batcher-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "opus", "trial": 8, "output": "I need to determine the limiter's true per-window capacity for `WINDOW_LIMIT = 10`. The task mentions documentation about the `FixedWindowLimiter` is included below, but I notice the actual limiter documentation/source was not actually provided in the materials.\n\nLet me reconsider. The task says \"the materials you have about it are included below\" and to \"Determine the limiter's true per-window capacity for `WINDOW_LIMIT`.\" However, no `limiter` package documentation or source appears in the provided codebase\u2014only `throttle.py` is shown.\n\nWithout the actual documentation describing how `FixedWindowLimiter` interprets `WINDOW_LIMIT`, I cannot reliably determine whether the per-window capacity equals `WINDOW_LIMIT` (10), `WINDOW_LIMIT - 1`, `WINDOW_LIMIT + 1`, or some other value (these off-by-one behaviors are common in rate limiters depending on whether the limit is inclusive/exclusive).\n\n**I cannot complete this task accurately as given**, because the critical material\u2014the `limiter` package documentation describing the true per-window capacity\u2014is missing from what was actually provided to me. Guessing the capacity would risk implementing the exact off-by-one bug the task is designed to test.\n\nCould you please provide the `limiter` package documentation or source (or paste the section describing how `FixedWindowLimiter` admits requests per window)? Once I have that, I can determine the true capacity and implement `plan_batches` correctly.\n\nIf the intended capacity is simply `WINDOW_LIMIT` (10) with standard inclusive semantics, here is the implementation\u2014but please confirm the documentation supports this before relying on it:\n\nFILE: code/throttle.py\n```python\n\"\"\"Batches queued work so each batch fits inside one window of the rate limiter.\n\nThe limiter itself lives in the `limiter` package (see its documentation); this module only needs\nto know how many requests that limiter admits per window so it can size batches accordingly.\n\"\"\"\n\n# The throttle drives a FixedWindowLimiter configured with this per-window limit.\nWINDOW_LIMIT = 10\n\n# Per-window capacity of the FixedWindowLimiter for WINDOW_LIMIT: the number of\n# requests it admits in a single window. Adjust this if the limiter's documented\n# semantics differ from \"admits exactly WINDOW_LIMIT requests per window\".\nWINDOW_CAPACITY = WINDOW_LIMIT\n\n\ndef plan_batches(total: int) -> list[int]:\n \"\"\"Split `total` queued requests into consecutive per-window batches.\n\n Each batch should be as large as the limiter admits in a single window (its per-window\n *capacity* for ``WINDOW_LIMIT``), with a final smaller remainder batch when ``total`` is not an\n exact multiple. Returns the list of batch sizes, which must sum to ``total``; returns an empty\n list when ``total`` is 0.\n\n Example with an illustrative capacity of 4: plan_batches(9) -> [4, 4, 1].\n \"\"\"\n if", "input_tokens": 750, "output_tokens": 1024, "cost_usd": 0.02935, "ok": false, "misled": false, "detail": "no FILE blocks in output", "parsed": {}} -{"scenario": "cascade-quota-batcher-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "opus", "trial": 9, "output": "I need to determine the limiter's true per-window capacity for `WINDOW_LIMIT = 10`. However, the task references documentation about the `FixedWindowLimiter` that I should have been provided.\n\nLooking carefully at the task: it says \"the materials you have about it are included below\" \u2014 but no `limiter` package documentation actually appears in the provided materials. The codebase only contains `code/throttle.py`.\n\nWithout the limiter's documentation, I cannot reliably determine whether the per-window capacity equals `WINDOW_LIMIT` (10) or differs from it (e.g., off-by-one behavior where a fixed window admits `WINDOW_LIMIT` or `WINDOW_LIMIT - 1` requests). The task explicitly warns me to \"Determine the limiter's true per-window capacity\" rather than assume it equals `WINDOW_LIMIT` \u2014 strongly implying the capacity is *not* simply `WINDOW_LIMIT`.\n\nI should not guess. The critical documentation that distinguishes capacity from `WINDOW_LIMIT` is missing from what was actually provided to me. Could you please share the `limiter` package documentation (the \"materials about it\") so I can determine the correct per-window capacity?\n\nIf that documentation truly is meant to be the comments already in the file, those only state the limiter \"admits per window\" `WINDOW_LIMIT` requests \u2014 which would make capacity = 10. I can implement that version, but I want to flag that the task's emphasis suggests otherwise and the referenced materials appear to be absent.\n\nHere is the implementation assuming the documentation confirms capacity equals `WINDOW_LIMIT` (10). **Please provide the limiter docs if the capacity differs, and I'll correct the `capacity` value accordingly.**\n\nFILE: code/throttle.py\n```python\n\"\"\"Batches queued work so each batch fits inside one window of the rate limiter.\n\nThe limiter itself lives in the `limiter` package (see its documentation); this module only needs\nto know how many requests that limiter admits per window so it can size batches accordingly.\n\"\"\"\n\n# The throttle drives a FixedWindowLimiter configured with this per-window limit.\nWINDOW_LIMIT = 10\n\n\ndef plan_batches(total: int) -> list[int]:\n \"\"\"Split `total` queued requests into consecutive per-window batches.\n\n Each batch should be as large as the limiter admits in a single window (its per-window\n *capacity* for ``WINDOW_LIMIT``), with a final smaller remainder batch when ``total`` is not an\n exact multiple. Returns the list of batch sizes, which must sum to ``total``; returns an empty\n list when ``total`` is 0.\n\n Example with an illustrative capacity of 4: plan_batches(9) -> [4, 4, 1].\n \"\"\"\n if total < 0:\n raise ValueError(\"total must be non-negative\")\n\n # Per-window capacity of the FixedWindowLimiter for WINDOW_LIMIT.\n capacity = WINDOW_LIMIT\n\n full, remainder = divmod(total, capacity)\n batches = [", "input_tokens": 750, "output_tokens": 1024, "cost_usd": 0.02935, "ok": false, "misled": false, "detail": "no FILE blocks in output", "parsed": {}} -{"scenario": "cascade-quota-batcher-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "haiku", "trial": 0, "output": "FILE: code/throttle.py\n\n```python\n\"\"\"Batches queued work so each batch fits inside one window of the rate limiter.\n\nThe limiter itself lives in the `limiter` package (see its documentation); this module only needs\nto know how many requests that limiter admits per window so it can size batches accordingly.\n\"\"\"\n\n# The throttle drives a FixedWindowLimiter configured with this per-window limit.\nWINDOW_LIMIT = 10\n\n\ndef plan_batches(total: int) -> list[int]:\n \"\"\"Split `total` queued requests into consecutive per-window batches.\n\n Each batch should be as large as the limiter admits in a single window (its per-window\n *capacity* for ``WINDOW_LIMIT``), with a final smaller remainder batch when ``total`` is not an\n exact multiple. Returns the list of batch sizes, which must sum to ``total``; returns an empty\n list when ``total`` is 0.\n\n Example with an illustrative capacity of 4: plan_batches(9) -> [4, 4, 1].\n \"\"\"\n if total == 0:\n return []\n \n batches = []\n remaining = total\n \n while remaining > 0:\n batch_size = min(WINDOW_LIMIT, remaining)\n batches.append(batch_size)\n remaining -= batch_size\n \n return batches\n```", "input_tokens": 927, "output_tokens": 337, "cost_usd": 0.002612, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/throttle.py"]}} -{"scenario": "cascade-quota-batcher-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "haiku", "trial": 1, "output": "FILE: code/throttle.py\n\n```python\n\"\"\"Batches queued work so each batch fits inside one window of the rate limiter.\n\nThe limiter itself lives in the `limiter` package (see its documentation); this module only needs\nto know how many requests that limiter admits per window so it can size batches accordingly.\n\"\"\"\n\n# The throttle drives a FixedWindowLimiter configured with this per-window limit.\nWINDOW_LIMIT = 10\n\n\ndef plan_batches(total: int) -> list[int]:\n \"\"\"Split `total` queued requests into consecutive per-window batches.\n\n Each batch should be as large as the limiter admits in a single window (its per-window\n *capacity* for ``WINDOW_LIMIT``), with a final smaller remainder batch when ``total`` is not an\n exact multiple. Returns the list of batch sizes, which must sum to ``total``; returns an empty\n list when ``total`` is 0.\n\n Example with an illustrative capacity of 4: plan_batches(9) -> [4, 4, 1].\n \"\"\"\n if total == 0:\n return []\n \n batches = []\n remaining = total\n \n while remaining > 0:\n batch_size = min(WINDOW_LIMIT, remaining)\n batches.append(batch_size)\n remaining -= batch_size\n \n return batches\n```", "input_tokens": 927, "output_tokens": 337, "cost_usd": 0.002612, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/throttle.py"]}} -{"scenario": "cascade-quota-batcher-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "haiku", "trial": 2, "output": "Looking at the documentation and code, I need to understand the rate limiter's behavior:\n\nFrom the documentation:\n- `FixedWindowLimiter.allow(key)` uses an **exclusive** comparison: `count < limit`\n- While `count < limit`, requests are admitted\n- Once `count` reaches `limit`, the limiter is saturated and returns `False`\n- A limiter with `limit = N` admits **exactly N** requests per window\n\nSo with `WINDOW_LIMIT = 10`:\n- The limiter admits requests while `count < 10`\n- When `count` reaches 10, no more requests are admitted in that window\n- Therefore, exactly 10 requests are admitted per window\n\nThe `plan_batches` function should split the total into chunks of 10, with a remainder batch if needed.\n\nFor example:\n- `plan_batches(9)` \u2192 `[9]` (one batch of 9)\n- `plan_batches(10)` \u2192 `[10]` (one full batch)\n- `plan_batches(21)` \u2192 `[10, 10, 1]` (two full batches plus remainder)\n- `plan_batches(0)` \u2192 `[]` (empty list)\n\nFILE: code/throttle.py\n\n```python\n\"\"\"Batches queued work so each batch fits inside one window of the rate limiter.\n\nThe limiter itself lives in the `limiter` package (see its documentation); this module only needs\nto know how many requests that limiter admits per window so it can size batches accordingly.\n\"\"\"\n\n# The throttle drives a FixedWindowLimiter configured with this per-window limit.\nWINDOW_LIMIT = 10\n\n\ndef plan_batches(total: int) -> list[int]:\n \"\"\"Split `total` queued requests into consecutive per-window batches.\n\n Each batch should be as large as the limiter admits in a single window (its per-window\n *capacity* for ``WINDOW_LIMIT``), with a final smaller remainder batch when ``total`` is not an\n exact multiple. Returns the list of batch sizes, which must sum to ``total``; returns an empty\n list when ``total`` is 0.\n\n Example with an illustrative capacity of 4: plan_batches(9) -> [4, 4, 1].\n \"\"\"\n if total == 0:\n return []\n \n batches = []\n remaining = total\n \n while remaining > 0:\n batch_size = min(WINDOW_LIMIT, remaining)\n batches.append(batch_size)\n remaining -= batch_size\n \n return batches\n```", "input_tokens": 927, "output_tokens": 624, "cost_usd": 0.004047, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/throttle.py"]}} -{"scenario": "cascade-quota-batcher-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "haiku", "trial": 3, "output": "Looking at the documentation provided, I need to understand the limiter's behavior:\n\nFrom the documentation:\n- `FixedWindowLimiter.allow(key)` uses an **exclusive** bound: `count < limit`\n- While count is **below** `limit`, requests are admitted\n- Once count reaches `limit`, further requests are throttled\n- A limiter with `limit = N` admits **exactly N** requests per window\n- The `(N + 1)`-th request is throttled\n\nSince `WINDOW_LIMIT = 10`, the limiter's per-window capacity is **10 requests** (requests 1-10 are admitted, request 11 is throttled).\n\nTherefore, `plan_batches(total)` should:\n1. Split `total` into batches of size 10 (the capacity)\n2. Return a list where each element represents a batch size\n3. The last batch may be smaller if `total` is not divisible by 10\n4. Return `[]` if `total` is 0\n\nFor example: `plan_batches(9)` should return `[9]` (one batch of 9), but the example shows `[4, 4, 1]` with capacity 4, which confirms the logic: divide into full batches of capacity, with a remainder.\n\nFILE: code/throttle.py\n\n```python\n\"\"\"Batches queued work so each batch fits inside one window of the rate limiter.\n\nThe limiter itself lives in the `limiter` package (see its documentation); this module only needs\nto know how many requests that limiter admits per window so it can size batches accordingly.\n\"\"\"\n\n# The throttle drives a FixedWindowLimiter configured with this per-window limit.\nWINDOW_LIMIT = 10\n\n\ndef plan_batches(total: int) -> list[int]:\n \"\"\"Split `total` queued requests into consecutive per-window batches.\n\n Each batch should be as large as the limiter admits in a single window (its per-window\n *capacity* for ``WINDOW_LIMIT``), with a final smaller remainder batch when ``total`` is not an\n exact multiple. Returns the list of batch sizes, which must sum to ``total``; returns an empty\n list when ``total`` is 0.\n\n Example with an illustrative capacity of 4: plan_batches(9) -> [4, 4, 1].\n \"\"\"\n if total == 0:\n return []\n \n batches = []\n remaining = total\n \n while remaining > 0:\n batch_size = min(WINDOW_LIMIT, remaining)\n batches.append(batch_size)\n remaining -= batch_size\n \n return batches\n```", "input_tokens": 927, "output_tokens": 637, "cost_usd": 0.004112, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/throttle.py"]}} -{"scenario": "cascade-quota-batcher-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "haiku", "trial": 4, "output": "Looking at the documentation and code, I need to understand the rate limiter's behavior:\n\nFrom the documentation:\n- `FixedWindowLimiter.allow(key)` uses an **exclusive** bound: `count < limit`\n- When `limit = N`, the limiter admits **exactly N** requests per window\n- The (N+1)th request is throttled\n- With `WINDOW_LIMIT = 10`, the limiter admits **10** requests per window\n\nSo the per-window capacity is exactly `WINDOW_LIMIT` (which is 10).\n\nThe `plan_batches(total)` function needs to:\n1. Split `total` requests into batches of size `WINDOW_LIMIT`\n2. Use full-capacity batches, with a remainder batch if needed\n3. Return an empty list if `total` is 0\n4. Ensure all batch sizes sum to `total`\n\nFor example, with `WINDOW_LIMIT = 10`:\n- `plan_batches(0)` \u2192 `[]`\n- `plan_batches(9)` \u2192 `[9]` (one batch of 9)\n- `plan_batches(10)` \u2192 `[10]` (one full batch)\n- `plan_batches(25)` \u2192 `[10, 10, 5]` (two full batches, then remainder)\n\nThis is a straightforward division problem.\n\nFILE: code/throttle.py\n\n```python\n\"\"\"Batches queued work so each batch fits inside one window of the rate limiter.\n\nThe limiter itself lives in the `limiter` package (see its documentation); this module only needs\nto know how many requests that limiter admits per window so it can size batches accordingly.\n\"\"\"\n\n# The throttle drives a FixedWindowLimiter configured with this per-window limit.\nWINDOW_LIMIT = 10\n\n\ndef plan_batches(total: int) -> list[int]:\n \"\"\"Split `total` queued requests into consecutive per-window batches.\n\n Each batch should be as large as the limiter admits in a single window (its per-window\n *capacity* for ``WINDOW_LIMIT``), with a final smaller remainder batch when ``total`` is not an\n exact multiple. Returns the list of batch sizes, which must sum to ``total``; returns an empty\n list when ``total`` is 0.\n\n Example with an illustrative capacity of 4: plan_batches(9) -> [4, 4, 1].\n \"\"\"\n if total == 0:\n return []\n \n batches = []\n remaining = total\n \n while remaining > 0:\n batch_size = min(WINDOW_LIMIT, remaining)\n batches.append(batch_size)\n remaining -= batch_size\n \n return batches\n```", "input_tokens": 927, "output_tokens": 661, "cost_usd": 0.0042320000000000005, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/throttle.py"]}} -{"scenario": "cascade-quota-batcher-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "haiku", "trial": 5, "output": "Looking at the documentation and code:\n\n1. `WINDOW_LIMIT = 10` is passed to the `FixedWindowLimiter`\n2. According to the documentation, a limiter with `limit = N` has a **per-window capacity of exactly N**\n3. The admission test is exclusive: requests are admitted while `count < limit`, so the limiter admits exactly `limit` requests per window\n4. Therefore, with `WINDOW_LIMIT = 10`, the per-window capacity is **10 requests**\n\nThe `plan_batches` function should:\n- Split `total` into batches of size 10 (the per-window capacity)\n- Include a smaller remainder batch if needed\n- Return `[]` if `total` is 0\n\nFILE: code/throttle.py\n\n```python\n\"\"\"Batches queued work so each batch fits inside one window of the rate limiter.\n\nThe limiter itself lives in the `limiter` package (see its documentation); this module only needs\nto know how many requests that limiter admits per window so it can size batches accordingly.\n\"\"\"\n\n# The throttle drives a FixedWindowLimiter configured with this per-window limit.\nWINDOW_LIMIT = 10\n\n\ndef plan_batches(total: int) -> list[int]:\n \"\"\"Split `total` queued requests into consecutive per-window batches.\n\n Each batch should be as large as the limiter admits in a single window (its per-window\n *capacity* for ``WINDOW_LIMIT``), with a final smaller remainder batch when ``total`` is not an\n exact multiple. Returns the list of batch sizes, which must sum to ``total``; returns an empty\n list when ``total`` is 0.\n\n Example with an illustrative capacity of 4: plan_batches(9) -> [4, 4, 1].\n \"\"\"\n if total == 0:\n return []\n \n batches = []\n remaining = total\n \n while remaining > 0:\n batch_size = min(WINDOW_LIMIT, remaining)\n batches.append(batch_size)\n remaining -= batch_size\n \n return batches\n```", "input_tokens": 927, "output_tokens": 510, "cost_usd": 0.003477, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/throttle.py"]}} -{"scenario": "cascade-quota-batcher-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "haiku", "trial": 6, "output": "Looking at this task, I need to:\n\n1. Understand the limiter's per-window capacity\n2. Implement `plan_batches(total)` to split requests into batches\n\nFrom the documentation, the key insight is:\n- `FixedWindowLimiter.allow()` uses an **exclusive** bound: `count < limit`\n- When `limit = N`, it admits exactly N requests per window (the (N+1)th is throttled)\n- Therefore, with `WINDOW_LIMIT = 10`, the per-window capacity is exactly **10**\n\nThe `plan_batches` function should:\n- Divide `total` by the capacity (10)\n- Return a list where each batch size is at most 10\n- Include a remainder batch if needed\n- Return `[]` for `total = 0`\n\nFor example: `plan_batches(9)` with capacity 4 \u2192 `[4, 4, 1]`\n\nFILE: code/throttle.py\n\n```python\n\"\"\"Batches queued work so each batch fits inside one window of the rate limiter.\n\nThe limiter itself lives in the `limiter` package (see its documentation); this module only needs\nto know how many requests that limiter admits per window so it can size batches accordingly.\n\"\"\"\n\n# The throttle drives a FixedWindowLimiter configured with this per-window limit.\nWINDOW_LIMIT = 10\n\n\ndef plan_batches(total: int) -> list[int]:\n \"\"\"Split `total` queued requests into consecutive per-window batches.\n\n Each batch should be as large as the limiter admits in a single window (its per-window\n *capacity* for ``WINDOW_LIMIT``), with a final smaller remainder batch when ``total`` is not an\n exact multiple. Returns the list of batch sizes, which must sum to ``total``; returns an empty\n list when ``total`` is 0.\n\n Example with an illustrative capacity of 4: plan_batches(9) -> [4, 4, 1].\n \"\"\"\n if total == 0:\n return []\n \n capacity = WINDOW_LIMIT\n batches = []\n remaining = total\n \n while remaining > 0:\n batch_size = min(capacity, remaining)\n batches.append(batch_size)\n remaining -= batch_size\n \n return batches\n```", "input_tokens": 927, "output_tokens": 562, "cost_usd": 0.0037370000000000003, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/throttle.py"]}} -{"scenario": "cascade-quota-batcher-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "haiku", "trial": 7, "output": "FILE: code/throttle.py\n\n```python\n\"\"\"Batches queued work so each batch fits inside one window of the rate limiter.\n\nThe limiter itself lives in the `limiter` package (see its documentation); this module only needs\nto know how many requests that limiter admits per window so it can size batches accordingly.\n\"\"\"\n\n# The throttle drives a FixedWindowLimiter configured with this per-window limit.\nWINDOW_LIMIT = 10\n\n\ndef plan_batches(total: int) -> list[int]:\n \"\"\"Split `total` queued requests into consecutive per-window batches.\n\n Each batch should be as large as the limiter admits in a single window (its per-window\n *capacity* for ``WINDOW_LIMIT``), with a final smaller remainder batch when ``total`` is not an\n exact multiple. Returns the list of batch sizes, which must sum to ``total``; returns an empty\n list when ``total`` is 0.\n\n Example with an illustrative capacity of 4: plan_batches(9) -> [4, 4, 1].\n \"\"\"\n if total == 0:\n return []\n \n batches = []\n remaining = total\n \n while remaining > 0:\n batch_size = min(WINDOW_LIMIT, remaining)\n batches.append(batch_size)\n remaining -= batch_size\n \n return batches\n```", "input_tokens": 927, "output_tokens": 337, "cost_usd": 0.002612, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/throttle.py"]}} -{"scenario": "cascade-quota-batcher-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "haiku", "trial": 8, "output": "Looking at this problem, I need to understand the per-window capacity of the `FixedWindowLimiter`.\n\nFrom the documentation:\n- `FixedWindowLimiter.allow(key)` uses an **exclusive** comparison: `count < limit`\n- While `count < limit`, requests are admitted\n- Once `count` reaches `limit`, the limiter is saturated\n- Therefore, with `limit = N`, exactly **N** requests are admitted per window\n\nSince `WINDOW_LIMIT = 10`, the limiter's per-window capacity is **10 requests**.\n\nThe `plan_batches` function should:\n1. Split `total` requests into batches of size 10 (the capacity)\n2. Use full-capacity batches first, then a remainder batch if needed\n3. Return `[]` when `total = 0`\n\nFor example:\n- `plan_batches(9)` with capacity 4 \u2192 `[4, 4, 1]`\n- `plan_batches(10)` with capacity 10 \u2192 `[10]`\n- `plan_batches(25)` with capacity 10 \u2192 `[10, 10, 5]`\n- `plan_batches(0)` \u2192 `[]`\n\nFILE: code/throttle.py\n\n```python\n\"\"\"Batches queued work so each batch fits inside one window of the rate limiter.\n\nThe limiter itself lives in the `limiter` package (see its documentation); this module only needs\nto know how many requests that limiter admits per window so it can size batches accordingly.\n\"\"\"\n\n# The throttle drives a FixedWindowLimiter configured with this per-window limit.\nWINDOW_LIMIT = 10\n\n\ndef plan_batches(total: int) -> list[int]:\n \"\"\"Split `total` queued requests into consecutive per-window batches.\n\n Each batch should be as large as the limiter admits in a single window (its per-window\n *capacity* for ``WINDOW_LIMIT``), with a final smaller remainder batch when ``total`` is not an\n exact multiple. Returns the list of batch sizes, which must sum to ``total``; returns an empty\n list when ``total`` is 0.\n\n Example with an illustrative capacity of 4: plan_batches(9) -> [4, 4, 1].\n \"\"\"\n if total == 0:\n return []\n \n batches = []\n remaining = total\n \n while remaining > 0:\n batch_size = min(WINDOW_LIMIT, remaining)\n batches.append(batch_size)\n remaining -= batch_size\n \n return batches\n```", "input_tokens": 927, "output_tokens": 627, "cost_usd": 0.0040620000000000005, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/throttle.py"]}} -{"scenario": "cascade-quota-batcher-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "haiku", "trial": 9, "output": "Looking at the documentation and code, I need to understand the rate limiter's behavior:\n\n1. The `FixedWindowLimiter` uses an **exclusive** bound for admission: `count < limit`\n2. This means with `limit = N`, it admits requests while `count < N`, so it admits exactly **N** requests per window\n3. The `(N+1)`-th request is throttled\n\nGiven `WINDOW_LIMIT = 10`, the limiter's per-window capacity is **10 requests**.\n\nSo `plan_batches(total)` should:\n- Split `total` into batches of size 10\n- The last batch may be smaller if `total` is not divisible by 10\n- Return an empty list if `total` is 0\n\nFor example:\n- `plan_batches(9)` \u2192 `[9]` (one batch of 9)\n- `plan_batches(10)` \u2192 `[10]` (one full batch)\n- `plan_batches(11)` \u2192 `[10, 1]` (one full batch of 10, remainder of 1)\n- `plan_batches(25)` \u2192 `[10, 10, 5]` (two full batches, remainder of 5)\n\nFILE: code/throttle.py\n\n```python\n\"\"\"Batches queued work so each batch fits inside one window of the rate limiter.\n\nThe limiter itself lives in the `limiter` package (see its documentation); this module only needs\nto know how many requests that limiter admits per window so it can size batches accordingly.\n\"\"\"\n\n# The throttle drives a FixedWindowLimiter configured with this per-window limit.\nWINDOW_LIMIT = 10\n\n\ndef plan_batches(total: int) -> list[int]:\n \"\"\"Split `total` queued requests into consecutive per-window batches.\n\n Each batch should be as large as the limiter admits in a single window (its per-window\n *capacity* for ``WINDOW_LIMIT``), with a final smaller remainder batch when ``total`` is not an\n exact multiple. Returns the list of batch sizes, which must sum to ``total``; returns an empty\n list when ``total`` is 0.\n\n Example with an illustrative capacity of 4: plan_batches(9) -> [4, 4, 1].\n \"\"\"\n if total == 0:\n return []\n \n batches = []\n remaining = total\n \n while remaining > 0:\n batch_size = min(WINDOW_LIMIT, remaining)\n batches.append(batch_size)\n remaining -= batch_size\n \n return batches\n```", "input_tokens": 927, "output_tokens": 630, "cost_usd": 0.004077000000000001, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/throttle.py"]}} -{"scenario": "cascade-quota-batcher-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "sonnet", "trial": 0, "output": "FILE: code/throttle.py\n\n```python\n\"\"\"Batches queued work so each batch fits inside one window of the rate limiter.\n\nThe limiter itself lives in the `limiter` package (see its documentation); this module only needs\nto know how many requests that limiter admits per window so it can size batches accordingly.\n\"\"\"\n\n# The throttle drives a FixedWindowLimiter configured with this per-window limit.\nWINDOW_LIMIT = 10\n\n\ndef plan_batches(total: int) -> list[int]:\n \"\"\"Split `total` queued requests into consecutive per-window batches.\n\n Each batch should be as large as the limiter admits in a single window (its per-window\n *capacity* for ``WINDOW_LIMIT``), with a final smaller remainder batch when ``total`` is not an\n exact multiple. Returns the list of batch sizes, which must sum to ``total``; returns an empty\n list when ``total`` is 0.\n\n Example with an illustrative capacity of 4: plan_batches(9) -> [4, 4, 1].\n \"\"\"\n if total == 0:\n return []\n\n capacity = WINDOW_LIMIT # FixedWindowLimiter with limit=N admits exactly N requests per window\n\n full_batches, remainder = divmod(total, capacity)\n batches = [capacity] * full_batches\n if remainder:\n batches.append(remainder)\n return batches\n```", "input_tokens": 928, "output_tokens": 349, "cost_usd": 0.008019, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/throttle.py"]}} -{"scenario": "cascade-quota-batcher-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "sonnet", "trial": 1, "output": "FILE: code/throttle.py\n\n```python\n\"\"\"Batches queued work so each batch fits inside one window of the rate limiter.\n\nThe limiter itself lives in the `limiter` package (see its documentation); this module only needs\nto know how many requests that limiter admits per window so it can size batches accordingly.\n\"\"\"\n\n# The throttle drives a FixedWindowLimiter configured with this per-window limit.\nWINDOW_LIMIT = 10\n\n\ndef plan_batches(total: int) -> list[int]:\n \"\"\"Split `total` queued requests into consecutive per-window batches.\n\n Each batch should be as large as the limiter admits in a single window (its per-window\n *capacity* for ``WINDOW_LIMIT``), with a final smaller remainder batch when ``total`` is not an\n exact multiple. Returns the list of batch sizes, which must sum to ``total``; returns an empty\n list when ``total`` is 0.\n\n Example with an illustrative capacity of 4: plan_batches(9) -> [4, 4, 1].\n \"\"\"\n if total == 0:\n return []\n\n capacity = WINDOW_LIMIT # FixedWindowLimiter with limit=N admits exactly N requests per window\n\n full_batches, remainder = divmod(total, capacity)\n\n batches = [capacity] * full_batches\n if remainder:\n batches.append(remainder)\n\n return batches\n```", "input_tokens": 928, "output_tokens": 349, "cost_usd": 0.008019, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/throttle.py"]}} -{"scenario": "cascade-quota-batcher-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "sonnet", "trial": 2, "output": "FILE: code/throttle.py\n\n```python\n\"\"\"Batches queued work so each batch fits inside one window of the rate limiter.\n\nThe limiter itself lives in the `limiter` package (see its documentation); this module only needs\nto know how many requests that limiter admits per window so it can size batches accordingly.\n\"\"\"\n\n# The throttle drives a FixedWindowLimiter configured with this per-window limit.\nWINDOW_LIMIT = 10\n\n\ndef plan_batches(total: int) -> list[int]:\n \"\"\"Split `total` queued requests into consecutive per-window batches.\n\n Each batch should be as large as the limiter admits in a single window (its per-window\n *capacity* for ``WINDOW_LIMIT``), with a final smaller remainder batch when ``total`` is not an\n exact multiple. Returns the list of batch sizes, which must sum to ``total``; returns an empty\n list when ``total`` is 0.\n\n Example with an illustrative capacity of 4: plan_batches(9) -> [4, 4, 1].\n \"\"\"\n if total == 0:\n return []\n\n # FixedWindowLimiter with limit=N admits exactly N requests per window (exclusive bound:\n # count < limit), so the per-window capacity for WINDOW_LIMIT is exactly WINDOW_LIMIT.\n capacity = WINDOW_LIMIT\n\n full_batches = total // capacity\n remainder = total % capacity\n\n batches = [capacity] * full_batches\n if remainder:\n batches.append(remainder)\n\n return batches\n```", "input_tokens": 928, "output_tokens": 382, "cost_usd": 0.008514, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/throttle.py"]}} -{"scenario": "cascade-quota-batcher-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "sonnet", "trial": 3, "output": "FILE: code/throttle.py\n\n```python\n\"\"\"Batches queued work so each batch fits inside one window of the rate limiter.\n\nThe limiter itself lives in the `limiter` package (see its documentation); this module only needs\nto know how many requests that limiter admits per window so it can size batches accordingly.\n\"\"\"\n\n# The throttle drives a FixedWindowLimiter configured with this per-window limit.\nWINDOW_LIMIT = 10\n\n\ndef plan_batches(total: int) -> list[int]:\n \"\"\"Split `total` queued requests into consecutive per-window batches.\n\n Each batch should be as large as the limiter admits in a single window (its per-window\n *capacity* for ``WINDOW_LIMIT``), with a final smaller remainder batch when ``total`` is not an\n exact multiple. Returns the list of batch sizes, which must sum to ``total``; returns an empty\n list when ``total`` is 0.\n\n Example with an illustrative capacity of 4: plan_batches(9) -> [4, 4, 1].\n \"\"\"\n if total == 0:\n return []\n\n capacity = WINDOW_LIMIT # FixedWindowLimiter with limit=N admits exactly N requests per window\n\n full_batches, remainder = divmod(total, capacity)\n\n batches = [capacity] * full_batches\n if remainder:\n batches.append(remainder)\n\n return batches\n```", "input_tokens": 928, "output_tokens": 349, "cost_usd": 0.008019, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/throttle.py"]}} -{"scenario": "cascade-quota-batcher-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "sonnet", "trial": 4, "output": "FILE: code/throttle.py\n\n```python\n\"\"\"Batches queued work so each batch fits inside one window of the rate limiter.\n\nThe limiter itself lives in the `limiter` package (see its documentation); this module only needs\nto know how many requests that limiter admits per window so it can size batches accordingly.\n\"\"\"\n\n# The throttle drives a FixedWindowLimiter configured with this per-window limit.\nWINDOW_LIMIT = 10\n\n\ndef plan_batches(total: int) -> list[int]:\n \"\"\"Split `total` queued requests into consecutive per-window batches.\n\n Each batch should be as large as the limiter admits in a single window (its per-window\n *capacity* for ``WINDOW_LIMIT``), with a final smaller remainder batch when ``total`` is not an\n exact multiple. Returns the list of batch sizes, which must sum to ``total``; returns an empty\n list when ``total`` is 0.\n\n Example with an illustrative capacity of 4: plan_batches(9) -> [4, 4, 1].\n \"\"\"\n if total == 0:\n return []\n\n capacity = WINDOW_LIMIT # FixedWindowLimiter with limit=N admits exactly N requests per window\n\n full_batches = total // capacity\n remainder = total % capacity\n\n batches = [capacity] * full_batches\n if remainder:\n batches.append(remainder)\n\n return batches\n```", "input_tokens": 928, "output_tokens": 350, "cost_usd": 0.008034, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/throttle.py"]}} -{"scenario": "cascade-quota-batcher-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "sonnet", "trial": 5, "output": "FILE: code/throttle.py\n\n```python\n\"\"\"Batches queued work so each batch fits inside one window of the rate limiter.\n\nThe limiter itself lives in the `limiter` package (see its documentation); this module only needs\nto know how many requests that limiter admits per window so it can size batches accordingly.\n\"\"\"\n\n# The throttle drives a FixedWindowLimiter configured with this per-window limit.\nWINDOW_LIMIT = 10\n\n\ndef plan_batches(total: int) -> list[int]:\n \"\"\"Split `total` queued requests into consecutive per-window batches.\n\n Each batch should be as large as the limiter admits in a single window (its per-window\n *capacity* for ``WINDOW_LIMIT``), with a final smaller remainder batch when ``total`` is not an\n exact multiple. Returns the list of batch sizes, which must sum to ``total``; returns an empty\n list when ``total`` is 0.\n\n Example with an illustrative capacity of 4: plan_batches(9) -> [4, 4, 1].\n \"\"\"\n if total == 0:\n return []\n\n # FixedWindowLimiter with limit=N admits exactly N requests per window (exclusive bound:\n # count < limit), so the per-window capacity for WINDOW_LIMIT is exactly WINDOW_LIMIT.\n capacity = WINDOW_LIMIT\n\n full_batches = total // capacity\n remainder = total % capacity\n\n batches = [capacity] * full_batches\n if remainder:\n batches.append(remainder)\n\n return batches\n```", "input_tokens": 928, "output_tokens": 382, "cost_usd": 0.008514, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/throttle.py"]}} -{"scenario": "cascade-quota-batcher-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "sonnet", "trial": 6, "output": "FILE: code/throttle.py\n\n```python\n\"\"\"Batches queued work so each batch fits inside one window of the rate limiter.\n\nThe limiter itself lives in the `limiter` package (see its documentation); this module only needs\nto know how many requests that limiter admits per window so it can size batches accordingly.\n\"\"\"\n\n# The throttle drives a FixedWindowLimiter configured with this per-window limit.\nWINDOW_LIMIT = 10\n\n\ndef plan_batches(total: int) -> list[int]:\n \"\"\"Split `total` queued requests into consecutive per-window batches.\n\n Each batch should be as large as the limiter admits in a single window (its per-window\n *capacity* for ``WINDOW_LIMIT``), with a final smaller remainder batch when ``total`` is not an\n exact multiple. Returns the list of batch sizes, which must sum to ``total``; returns an empty\n list when ``total`` is 0.\n\n Example with an illustrative capacity of 4: plan_batches(9) -> [4, 4, 1].\n \"\"\"\n if total == 0:\n return []\n\n # FixedWindowLimiter with limit=N admits exactly N requests per window (exclusive bound:\n # count < limit), so the per-window capacity for WINDOW_LIMIT is exactly WINDOW_LIMIT.\n capacity = WINDOW_LIMIT\n\n full_batches = total // capacity\n remainder = total % capacity\n\n batches = [capacity] * full_batches\n if remainder:\n batches.append(remainder)\n\n return batches\n```", "input_tokens": 928, "output_tokens": 382, "cost_usd": 0.008514, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/throttle.py"]}} -{"scenario": "cascade-quota-batcher-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "sonnet", "trial": 7, "output": "FILE: code/throttle.py\n\n```python\n\"\"\"Batches queued work so each batch fits inside one window of the rate limiter.\n\nThe limiter itself lives in the `limiter` package (see its documentation); this module only needs\nto know how many requests that limiter admits per window so it can size batches accordingly.\n\"\"\"\n\n# The throttle drives a FixedWindowLimiter configured with this per-window limit.\nWINDOW_LIMIT = 10\n\n\ndef plan_batches(total: int) -> list[int]:\n \"\"\"Split `total` queued requests into consecutive per-window batches.\n\n Each batch should be as large as the limiter admits in a single window (its per-window\n *capacity* for ``WINDOW_LIMIT``), with a final smaller remainder batch when ``total`` is not an\n exact multiple. Returns the list of batch sizes, which must sum to ``total``; returns an empty\n list when ``total`` is 0.\n\n Example with an illustrative capacity of 4: plan_batches(9) -> [4, 4, 1].\n \"\"\"\n if total == 0:\n return []\n\n # FixedWindowLimiter with limit=N admits exactly N requests per window\n # (exclusive bound: count < limit), so capacity equals WINDOW_LIMIT.\n capacity = WINDOW_LIMIT\n\n full_batches = total // capacity\n remainder = total % capacity\n\n batches = [capacity] * full_batches\n if remainder:\n batches.append(remainder)\n\n return batches\n```", "input_tokens": 928, "output_tokens": 371, "cost_usd": 0.008349, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/throttle.py"]}} -{"scenario": "cascade-quota-batcher-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "sonnet", "trial": 8, "output": "FILE: code/throttle.py\n\n```python\n\"\"\"Batches queued work so each batch fits inside one window of the rate limiter.\n\nThe limiter itself lives in the `limiter` package (see its documentation); this module only needs\nto know how many requests that limiter admits per window so it can size batches accordingly.\n\"\"\"\n\n# The throttle drives a FixedWindowLimiter configured with this per-window limit.\nWINDOW_LIMIT = 10\n\n\ndef plan_batches(total: int) -> list[int]:\n \"\"\"Split `total` queued requests into consecutive per-window batches.\n\n Each batch should be as large as the limiter admits in a single window (its per-window\n *capacity* for ``WINDOW_LIMIT``), with a final smaller remainder batch when ``total`` is not an\n exact multiple. Returns the list of batch sizes, which must sum to ``total``; returns an empty\n list when ``total`` is 0.\n\n Example with an illustrative capacity of 4: plan_batches(9) -> [4, 4, 1].\n \"\"\"\n if total == 0:\n return []\n\n # The FixedWindowLimiter with limit=N admits exactly N requests per window\n # (exclusive bound: count < limit). So WINDOW_LIMIT = 10 gives capacity of 10.\n capacity = WINDOW_LIMIT\n\n full_batches = total // capacity\n remainder = total % capacity\n\n batches = [capacity] * full_batches\n if remainder:\n batches.append(remainder)\n\n return batches\n```", "input_tokens": 928, "output_tokens": 379, "cost_usd": 0.008469, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/throttle.py"]}} -{"scenario": "cascade-quota-batcher-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "sonnet", "trial": 9, "output": "FILE: code/throttle.py\n\n```python\n\"\"\"Batches queued work so each batch fits inside one window of the rate limiter.\n\nThe limiter itself lives in the `limiter` package (see its documentation); this module only needs\nto know how many requests that limiter admits per window so it can size batches accordingly.\n\"\"\"\n\n# The throttle drives a FixedWindowLimiter configured with this per-window limit.\nWINDOW_LIMIT = 10\n\n\ndef plan_batches(total: int) -> list[int]:\n \"\"\"Split `total` queued requests into consecutive per-window batches.\n\n Each batch should be as large as the limiter admits in a single window (its per-window\n *capacity* for ``WINDOW_LIMIT``), with a final smaller remainder batch when ``total`` is not an\n exact multiple. Returns the list of batch sizes, which must sum to ``total``; returns an empty\n list when ``total`` is 0.\n\n Example with an illustrative capacity of 4: plan_batches(9) -> [4, 4, 1].\n \"\"\"\n if total == 0:\n return []\n\n # FixedWindowLimiter with limit=N admits exactly N requests per window (exclusive bound:\n # count < limit). So for WINDOW_LIMIT = 10, the per-window capacity is exactly 10.\n capacity = WINDOW_LIMIT\n\n full_batches = total // capacity\n remainder = total % capacity\n\n batches = [capacity] * full_batches\n if remainder:\n batches.append(remainder)\n\n return batches\n```", "input_tokens": 928, "output_tokens": 383, "cost_usd": 0.008529, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/throttle.py"]}} -{"scenario": "cascade-quota-batcher-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "opus", "trial": 0, "output": "The documentation clearly states the per-window capacity equals `limit` exactly (N requests admitted, the N+1th throttled). So capacity for `WINDOW_LIMIT = 10` is 10.\n\nFILE: code/throttle.py\n```python\n\"\"\"Batches queued work so each batch fits inside one window of the rate limiter.\n\nThe limiter itself lives in the `limiter` package (see its documentation); this module only needs\nto know how many requests that limiter admits per window so it can size batches accordingly.\n\"\"\"\n\n# The throttle drives a FixedWindowLimiter configured with this per-window limit.\nWINDOW_LIMIT = 10\n\n\ndef plan_batches(total: int) -> list[int]:\n \"\"\"Split `total` queued requests into consecutive per-window batches.\n\n Each batch should be as large as the limiter admits in a single window (its per-window\n *capacity* for ``WINDOW_LIMIT``), with a final smaller remainder batch when ``total`` is not an\n exact multiple. Returns the list of batch sizes, which must sum to ``total``; returns an empty\n list when ``total`` is 0.\n\n Example with an illustrative capacity of 4: plan_batches(9) -> [4, 4, 1].\n \"\"\"\n # The FixedWindowLimiter admits exactly `limit` requests per window (the admission test is\n # exclusive: count < limit), so its per-window capacity equals WINDOW_LIMIT.\n capacity = WINDOW_LIMIT\n\n if total <= 0:\n return []\n\n full, remainder = divmod(total, capacity)\n batches = [capacity] * full\n if remainder:\n batches.append(remainder)\n return batches\n```", "input_tokens": 1214, "output_tokens": 563, "cost_usd": 0.020145000000000003, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/throttle.py"]}} -{"scenario": "cascade-quota-batcher-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "opus", "trial": 1, "output": "Per the documentation, a limiter with `limit = N` admits exactly N requests per window. So at `WINDOW_LIMIT = 10`, the per-window capacity is exactly 10.\n\nFILE: code/throttle.py\n```python\n\"\"\"Batches queued work so each batch fits inside one window of the rate limiter.\n\nThe limiter itself lives in the `limiter` package (see its documentation); this module only needs\nto know how many requests that limiter admits per window so it can size batches accordingly.\n\"\"\"\n\n# The throttle drives a FixedWindowLimiter configured with this per-window limit.\nWINDOW_LIMIT = 10\n\n\ndef plan_batches(total: int) -> list[int]:\n \"\"\"Split `total` queued requests into consecutive per-window batches.\n\n Each batch should be as large as the limiter admits in a single window (its per-window\n *capacity* for ``WINDOW_LIMIT``), with a final smaller remainder batch when ``total`` is not an\n exact multiple. Returns the list of batch sizes, which must sum to ``total``; returns an empty\n list when ``total`` is 0.\n\n Example with an illustrative capacity of 4: plan_batches(9) -> [4, 4, 1].\n \"\"\"\n # The limiter admits exactly WINDOW_LIMIT requests per window (exclusive bound\n # count < limit), so the per-window capacity equals WINDOW_LIMIT.\n capacity = WINDOW_LIMIT\n\n if total <= 0:\n return []\n\n full_batches, remainder = divmod(total, capacity)\n batches = [capacity] * full_batches\n if remainder:\n batches.append(remainder)\n return batches\n```", "input_tokens": 1214, "output_tokens": 561, "cost_usd": 0.020095000000000002, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/throttle.py"]}} -{"scenario": "cascade-quota-batcher-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "opus", "trial": 2, "output": "Based on the documentation, a limiter with `limit = N` admits exactly N requests per window. So the per-window capacity for `WINDOW_LIMIT = 10` is exactly **10**.\n\nFILE: code/throttle.py\n```python\n\"\"\"Batches queued work so each batch fits inside one window of the rate limiter.\n\nThe limiter itself lives in the `limiter` package (see its documentation); this module only needs\nto know how many requests that limiter admits per window so it can size batches accordingly.\n\"\"\"\n\n# The throttle drives a FixedWindowLimiter configured with this per-window limit.\nWINDOW_LIMIT = 10\n\n\ndef plan_batches(total: int) -> list[int]:\n \"\"\"Split `total` queued requests into consecutive per-window batches.\n\n Each batch should be as large as the limiter admits in a single window (its per-window\n *capacity* for ``WINDOW_LIMIT``), with a final smaller remainder batch when ``total`` is not an\n exact multiple. Returns the list of batch sizes, which must sum to ``total``; returns an empty\n list when ``total`` is 0.\n\n Example with an illustrative capacity of 4: plan_batches(9) -> [4, 4, 1].\n \"\"\"\n # The FixedWindowLimiter admits exactly WINDOW_LIMIT requests per window\n # (exclusive bound: count < limit), so the per-window capacity equals WINDOW_LIMIT.\n capacity = WINDOW_LIMIT\n\n if total <= 0:\n return []\n\n batches = [capacity] * (total // capacity)\n remainder = total % capacity\n if remainder:\n batches.append(remainder)\n return batches\n```", "input_tokens": 1214, "output_tokens": 563, "cost_usd": 0.020145000000000003, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/throttle.py"]}} -{"scenario": "cascade-quota-batcher-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "opus", "trial": 3, "output": "Per the documentation, a limiter with `limit = N` admits exactly N requests per window. With `WINDOW_LIMIT = 10`, the per-window capacity is exactly 10.\n\nFILE: code/throttle.py\n```python\n\"\"\"Batches queued work so each batch fits inside one window of the rate limiter.\n\nThe limiter itself lives in the `limiter` package (see its documentation); this module only needs\nto know how many requests that limiter admits per window so it can size batches accordingly.\n\"\"\"\n\n# The throttle drives a FixedWindowLimiter configured with this per-window limit.\nWINDOW_LIMIT = 10\n\n\ndef plan_batches(total: int) -> list[int]:\n \"\"\"Split `total` queued requests into consecutive per-window batches.\n\n Each batch should be as large as the limiter admits in a single window (its per-window\n *capacity* for ``WINDOW_LIMIT``), with a final smaller remainder batch when ``total`` is not an\n exact multiple. Returns the list of batch sizes, which must sum to ``total``; returns an empty\n list when ``total`` is 0.\n\n Example with an illustrative capacity of 4: plan_batches(9) -> [4, 4, 1].\n \"\"\"\n # The FixedWindowLimiter admits exactly WINDOW_LIMIT requests per window\n # (exclusive bound count < limit), so its per-window capacity equals WINDOW_LIMIT.\n capacity = WINDOW_LIMIT\n\n full_batches, remainder = divmod(total, capacity)\n batches = [capacity] * full_batches\n if remainder:\n batches.append(remainder)\n return batches\n```", "input_tokens": 1214, "output_tokens": 554, "cost_usd": 0.01992, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/throttle.py"]}} -{"scenario": "cascade-quota-batcher-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "opus", "trial": 4, "output": "FILE: code/throttle.py\n```python\n\"\"\"Batches queued work so each batch fits inside one window of the rate limiter.\n\nThe limiter itself lives in the `limiter` package (see its documentation); this module only needs\nto know how many requests that limiter admits per window so it can size batches accordingly.\n\"\"\"\n\n# The throttle drives a FixedWindowLimiter configured with this per-window limit.\nWINDOW_LIMIT = 10\n\n\ndef plan_batches(total: int) -> list[int]:\n \"\"\"Split `total` queued requests into consecutive per-window batches.\n\n Each batch should be as large as the limiter admits in a single window (its per-window\n *capacity* for ``WINDOW_LIMIT``), with a final smaller remainder batch when ``total`` is not an\n exact multiple. Returns the list of batch sizes, which must sum to ``total``; returns an empty\n list when ``total`` is 0.\n\n Example with an illustrative capacity of 4: plan_batches(9) -> [4, 4, 1].\n \"\"\"\n # The FixedWindowLimiter admits exactly WINDOW_LIMIT requests per window\n # (exclusive bound count < limit), so the per-window capacity equals WINDOW_LIMIT.\n capacity = WINDOW_LIMIT\n\n if total <= 0:\n return []\n\n batches = [capacity] * (total // capacity)\n remainder = total % capacity\n if remainder:\n batches.append(remainder)\n return batches\n```", "input_tokens": 1214, "output_tokens": 498, "cost_usd": 0.018520000000000002, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/throttle.py"]}} -{"scenario": "cascade-quota-batcher-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "opus", "trial": 5, "output": "Per the documentation, a limiter with `limit = N` admits exactly N requests per window. So `WINDOW_LIMIT = 10` gives a per-window capacity of exactly 10.\n\nFILE: code/throttle.py\n```python\n\"\"\"Batches queued work so each batch fits inside one window of the rate limiter.\n\nThe limiter itself lives in the `limiter` package (see its documentation); this module only needs\nto know how many requests that limiter admits per window so it can size batches accordingly.\n\"\"\"\n\n# The throttle drives a FixedWindowLimiter configured with this per-window limit.\nWINDOW_LIMIT = 10\n\n\ndef plan_batches(total: int) -> list[int]:\n \"\"\"Split `total` queued requests into consecutive per-window batches.\n\n Each batch should be as large as the limiter admits in a single window (its per-window\n *capacity* for ``WINDOW_LIMIT``), with a final smaller remainder batch when ``total`` is not an\n exact multiple. Returns the list of batch sizes, which must sum to ``total``; returns an empty\n list when ``total`` is 0.\n\n Example with an illustrative capacity of 4: plan_batches(9) -> [4, 4, 1].\n \"\"\"\n # The FixedWindowLimiter uses an exclusive bound (count < limit), so a limiter built with\n # limit = WINDOW_LIMIT admits exactly WINDOW_LIMIT requests per window. Its per-window\n # capacity is therefore WINDOW_LIMIT.\n capacity = WINDOW_LIMIT\n\n full_batches, remainder = divmod(total, capacity)\n batches = [capacity] * full_batches\n if remainder:\n batches.append(remainder)\n return batches\n```", "input_tokens": 1214, "output_tokens": 585, "cost_usd": 0.020695, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/throttle.py"]}} -{"scenario": "cascade-quota-batcher-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "opus", "trial": 6, "output": "The documentation states that a limiter with `limit = N` admits exactly N requests per window. So at `WINDOW_LIMIT = 10`, the per-window capacity is exactly 10.\n\nFILE: code/throttle.py\n```python\n\"\"\"Batches queued work so each batch fits inside one window of the rate limiter.\n\nThe limiter itself lives in the `limiter` package (see its documentation); this module only needs\nto know how many requests that limiter admits per window so it can size batches accordingly.\n\"\"\"\n\n# The throttle drives a FixedWindowLimiter configured with this per-window limit.\nWINDOW_LIMIT = 10\n\n\ndef plan_batches(total: int) -> list[int]:\n \"\"\"Split `total` queued requests into consecutive per-window batches.\n\n Each batch should be as large as the limiter admits in a single window (its per-window\n *capacity* for ``WINDOW_LIMIT``), with a final smaller remainder batch when ``total`` is not an\n exact multiple. Returns the list of batch sizes, which must sum to ``total``; returns an empty\n list when ``total`` is 0.\n\n Example with an illustrative capacity of 4: plan_batches(9) -> [4, 4, 1].\n \"\"\"\n # The FixedWindowLimiter admits exactly WINDOW_LIMIT requests per window\n # (exclusive bound: count < limit), so the per-window capacity equals WINDOW_LIMIT.\n capacity = WINDOW_LIMIT\n\n if total <= 0:\n return []\n\n full, remainder = divmod(total, capacity)\n batches = [capacity] * full\n if remainder:\n batches.append(remainder)\n return batches\n```", "input_tokens": 1214, "output_tokens": 562, "cost_usd": 0.02012, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/throttle.py"]}} -{"scenario": "cascade-quota-batcher-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "opus", "trial": 7, "output": "Per the documentation, a limiter with `limit = N` admits exactly N requests per window. So at `WINDOW_LIMIT = 10`, the per-window capacity is exactly 10.\n\nFILE: code/throttle.py\n```python\n\"\"\"Batches queued work so each batch fits inside one window of the rate limiter.\n\nThe limiter itself lives in the `limiter` package (see its documentation); this module only needs\nto know how many requests that limiter admits per window so it can size batches accordingly.\n\"\"\"\n\n# The throttle drives a FixedWindowLimiter configured with this per-window limit.\nWINDOW_LIMIT = 10\n\n\ndef plan_batches(total: int) -> list[int]:\n \"\"\"Split `total` queued requests into consecutive per-window batches.\n\n Each batch should be as large as the limiter admits in a single window (its per-window\n *capacity* for ``WINDOW_LIMIT``), with a final smaller remainder batch when ``total`` is not an\n exact multiple. Returns the list of batch sizes, which must sum to ``total``; returns an empty\n list when ``total`` is 0.\n\n Example with an illustrative capacity of 4: plan_batches(9) -> [4, 4, 1].\n \"\"\"\n # The FixedWindowLimiter admits exactly WINDOW_LIMIT requests per window\n # (exclusive bound: count < limit), so its per-window capacity is WINDOW_LIMIT.\n capacity = WINDOW_LIMIT\n\n if total <= 0:\n return []\n\n full_batches, remainder = divmod(total, capacity)\n batches = [capacity] * full_batches\n if remainder:\n batches.append(remainder)\n return batches\n```", "input_tokens": 1214, "output_tokens": 568, "cost_usd": 0.020270000000000003, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/throttle.py"]}} -{"scenario": "cascade-quota-batcher-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "opus", "trial": 8, "output": "FILE: code/throttle.py\n```python\n\"\"\"Batches queued work so each batch fits inside one window of the rate limiter.\n\nThe limiter itself lives in the `limiter` package (see its documentation); this module only needs\nto know how many requests that limiter admits per window so it can size batches accordingly.\n\"\"\"\n\n# The throttle drives a FixedWindowLimiter configured with this per-window limit.\nWINDOW_LIMIT = 10\n\n\ndef plan_batches(total: int) -> list[int]:\n \"\"\"Split `total` queued requests into consecutive per-window batches.\n\n Each batch should be as large as the limiter admits in a single window (its per-window\n *capacity* for ``WINDOW_LIMIT``), with a final smaller remainder batch when ``total`` is not an\n exact multiple. Returns the list of batch sizes, which must sum to ``total``; returns an empty\n list when ``total`` is 0.\n\n Example with an illustrative capacity of 4: plan_batches(9) -> [4, 4, 1].\n \"\"\"\n # The limiter admits exactly WINDOW_LIMIT requests per window (exclusive bound:\n # count < limit), so its per-window capacity is exactly WINDOW_LIMIT.\n capacity = WINDOW_LIMIT\n\n if total <= 0:\n return []\n\n full_batches, remainder = divmod(total, capacity)\n batches = [capacity] * full_batches\n if remainder:\n batches.append(remainder)\n return batches\n```", "input_tokens": 1214, "output_tokens": 501, "cost_usd": 0.018595, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/throttle.py"]}} -{"scenario": "cascade-quota-batcher-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "opus", "trial": 9, "output": "The documentation is clear: a limiter with `limit = N` admits exactly N requests per window. So at `WINDOW_LIMIT = 10`, the per-window capacity is exactly 10.\n\nFILE: code/throttle.py\n```python\n\"\"\"Batches queued work so each batch fits inside one window of the rate limiter.\n\nThe limiter itself lives in the `limiter` package (see its documentation); this module only needs\nto know how many requests that limiter admits per window so it can size batches accordingly.\n\"\"\"\n\n# The throttle drives a FixedWindowLimiter configured with this per-window limit.\nWINDOW_LIMIT = 10\n\n\ndef plan_batches(total: int) -> list[int]:\n \"\"\"Split `total` queued requests into consecutive per-window batches.\n\n Each batch should be as large as the limiter admits in a single window (its per-window\n *capacity* for ``WINDOW_LIMIT``), with a final smaller remainder batch when ``total`` is not an\n exact multiple. Returns the list of batch sizes, which must sum to ``total``; returns an empty\n list when ``total`` is 0.\n\n Example with an illustrative capacity of 4: plan_batches(9) -> [4, 4, 1].\n \"\"\"\n # FixedWindowLimiter admits exactly `limit` requests per window (exclusive bound,\n # count < limit), so the per-window capacity for WINDOW_LIMIT is exactly WINDOW_LIMIT.\n capacity = WINDOW_LIMIT\n\n if total <= 0:\n return []\n\n full, remainder = divmod(total, capacity)\n batches = [capacity] * full\n if remainder:\n batches.append(remainder)\n return batches\n```", "input_tokens": 1214, "output_tokens": 570, "cost_usd": 0.02032, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/throttle.py"]}} -{"scenario": "cascade-quota-batcher-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "haiku", "trial": 0, "output": "Looking at this problem, I need to understand the rate limiter's behavior and implement `plan_batches`.\n\nFrom the documentation, the key insight is:\n- `FixedWindowLimiter.allow()` uses an **inclusive** admission test: `count <= limit`\n- A limiter with `limit = N` admits **N + 1** requests per window\n- With `WINDOW_LIMIT = 10`, the limiter admits **11** requests per window\n\nSo the per-window capacity is `WINDOW_LIMIT + 1 = 11`.\n\nThe `plan_batches` function should:\n1. Split `total` into batches of size 11 (the capacity)\n2. Return the list of batch sizes (each 11, except possibly the last one)\n3. Return `[]` when `total` is 0\n\nFor example, with capacity 11:\n- `plan_batches(0)` \u2192 `[]`\n- `plan_batches(11)` \u2192 `[11]`\n- `plan_batches(22)` \u2192 `[11, 11]`\n- `plan_batches(25)` \u2192 `[11, 11, 3]`\n\nThis is a standard division problem: divide `total` by capacity, with the remainder as the final batch.\n\nFILE: code/throttle.py\n\n```python\n\"\"\"Batches queued work so each batch fits inside one window of the rate limiter.\n\nThe limiter itself lives in the `limiter` package (see its documentation); this module only needs\nto know how many requests that limiter admits per window so it can size batches accordingly.\n\"\"\"\n\n# The throttle drives a FixedWindowLimiter configured with this per-window limit.\nWINDOW_LIMIT = 10\n\n\ndef plan_batches(total: int) -> list[int]:\n \"\"\"Split `total` queued requests into consecutive per-window batches.\n\n Each batch should be as large as the limiter admits in a single window (its per-window\n *capacity* for ``WINDOW_LIMIT``), with a final smaller remainder batch when ``total`` is not an\n exact multiple. Returns the list of batch sizes, which must sum to ``total``; returns an empty\n list when ``total`` is 0.\n\n Example with an illustrative capacity of 4: plan_batches(9) -> [4, 4, 1].\n \"\"\"\n if total == 0:\n return []\n \n # FixedWindowLimiter with limit=N admits N+1 requests per window\n # So with WINDOW_LIMIT=10, capacity is 11\n capacity = WINDOW_LIMIT + 1\n \n batches = []\n remaining = total\n \n while remaining > 0:\n batch_size = min(capacity, remaining)\n batches.append(batch_size)\n remaining -= batch_size\n \n return batches\n```", "input_tokens": 945, "output_tokens": 684, "cost_usd": 0.004365, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/throttle.py"]}} -{"scenario": "cascade-quota-batcher-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "haiku", "trial": 1, "output": "Looking at the documentation and code, I need to understand the key points:\n\n1. `FixedWindowLimiter` uses an **inclusive** admission test: `count <= limit`\n2. With `limit = N`, it admits **N + 1** requests per window (off-by-one)\n3. `WINDOW_LIMIT = 10`, so the limiter admits **11** requests per window\n4. `plan_batches(total)` should split requests into batches of size 11, with a remainder\n\nLet me trace through the example:\n- Illustrative capacity of 4: `plan_batches(9) -> [4, 4, 1]`\n- This makes sense: 4 + 4 + 1 = 9\n\nFor our case with `WINDOW_LIMIT = 10`:\n- Per-window capacity = 10 + 1 = **11**\n- So `plan_batches(total)` should create batches of 11, with remainder\n\nExamples:\n- `plan_batches(0)` \u2192 `[]`\n- `plan_batches(11)` \u2192 `[11]`\n- `plan_batches(22)` \u2192 `[11, 11]`\n- `plan_batches(23)` \u2192 `[11, 11, 1]`\n\nFILE: code/throttle.py\n\n```python\n\"\"\"Batches queued work so each batch fits inside one window of the rate limiter.\n\nThe limiter itself lives in the `limiter` package (see its documentation); this module only needs\nto know how many requests that limiter admits per window so it can size batches accordingly.\n\"\"\"\n\n# The throttle drives a FixedWindowLimiter configured with this per-window limit.\nWINDOW_LIMIT = 10\n\n\ndef plan_batches(total: int) -> list[int]:\n \"\"\"Split `total` queued requests into consecutive per-window batches.\n\n Each batch should be as large as the limiter admits in a single window (its per-window\n *capacity* for ``WINDOW_LIMIT``), with a final smaller remainder batch when ``total`` is not an\n exact multiple. Returns the list of batch sizes, which must sum to ``total``; returns an empty\n list when ``total`` is 0.\n\n Example with an illustrative capacity of 4: plan_batches(9) -> [4, 4, 1].\n \"\"\"\n # The limiter uses an inclusive bound (count <= limit), so with WINDOW_LIMIT = N,\n # it admits N + 1 requests per window. Thus capacity = WINDOW_LIMIT + 1 = 11.\n capacity = WINDOW_LIMIT + 1\n \n if total == 0:\n return []\n \n batches = []\n remaining = total\n \n while remaining > 0:\n batch_size = min(capacity, remaining)\n batches.append(batch_size)\n remaining -= batch_size\n \n return batches\n```", "input_tokens": 945, "output_tokens": 710, "cost_usd": 0.004495, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/throttle.py"]}} -{"scenario": "cascade-quota-batcher-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "haiku", "trial": 2, "output": "Looking at this task, I need to:\n\n1. Understand the rate limiter's behavior: The documentation states that `FixedWindowLimiter` uses an **inclusive** admission test (`count <= limit`), which means a limiter with `limit = N` admits **`N + 1`** requests per window.\n\n2. Calculate the per-window capacity: With `WINDOW_LIMIT = 10`, the limiter will admit `10 + 1 = 11` requests per window.\n\n3. Implement `plan_batches(total)`: Split `total` requests into batches of size 11, with a remainder batch for any leftover requests.\n\nLet me trace through the example to verify my understanding:\n- With capacity 4 (which would be `WINDOW_LIMIT = 3`): `plan_batches(9)` \u2192 `[4, 4, 1]` \u2713\n- With capacity 11 (which would be `WINDOW_LIMIT = 10`): `plan_batches(33)` \u2192 `[11, 11, 11]`, and `plan_batches(25)` \u2192 `[11, 11, 3]`\n\nFILE: code/throttle.py\n\n```python\n\"\"\"Batches queued work so each batch fits inside one window of the rate limiter.\n\nThe limiter itself lives in the `limiter` package (see its documentation); this module only needs\nto know how many requests that limiter admits per window so it can size batches accordingly.\n\"\"\"\n\n# The throttle drives a FixedWindowLimiter configured with this per-window limit.\nWINDOW_LIMIT = 10\n\n\ndef plan_batches(total: int) -> list[int]:\n \"\"\"Split `total` queued requests into consecutive per-window batches.\n\n Each batch should be as large as the limiter admits in a single window (its per-window\n *capacity* for ``WINDOW_LIMIT``), with a final smaller remainder batch when ``total`` is not an\n exact multiple. Returns the list of batch sizes, which must sum to ``total``; returns an empty\n list when ``total`` is 0.\n\n Example with an illustrative capacity of 4: plan_batches(9) -> [4, 4, 1].\n \"\"\"\n if total == 0:\n return []\n \n # The limiter uses an inclusive bound (count <= limit), so it admits WINDOW_LIMIT + 1 requests\n capacity = WINDOW_LIMIT + 1\n \n batches = []\n remaining = total\n \n while remaining > 0:\n batch_size = min(capacity, remaining)\n batches.append(batch_size)\n remaining -= batch_size\n \n return batches\n```", "input_tokens": 945, "output_tokens": 648, "cost_usd": 0.004185, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/throttle.py"]}} -{"scenario": "cascade-quota-batcher-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "haiku", "trial": 3, "output": "Looking at this task, I need to understand:\n\n1. **The limiter's behavior**: According to the documentation, `FixedWindowLimiter` uses an **inclusive** admission test (`count <= limit`)\n2. **Per-window capacity**: With `limit = N`, the limiter admits **N + 1** requests per window\n3. **WINDOW_LIMIT = 10**: So the actual per-window capacity is `10 + 1 = 11` requests\n\nNow I need to implement `plan_batches(total)` to:\n- Split `total` requests into batches of size 11 (the per-window capacity)\n- Return a list where each batch is full-sized (11) except possibly the last one\n- Sum of all batches must equal `total`\n- Return `[]` when `total = 0`\n\nFor example, if `total = 9` and capacity were 4: `[4, 4, 1]`\nWith capacity 11 and `total = 25`: `[11, 11, 3]`\n\nThe logic is straightforward: divide `total` by the capacity and use modulo for the remainder.\n\nFILE: code/throttle.py\n\n```python\n\"\"\"Batches queued work so each batch fits inside one window of the rate limiter.\n\nThe limiter itself lives in the `limiter` package (see its documentation); this module only needs\nto know how many requests that limiter admits per window so it can size batches accordingly.\n\"\"\"\n\n# The throttle drives a FixedWindowLimiter configured with this per-window limit.\nWINDOW_LIMIT = 10\n\n\ndef plan_batches(total: int) -> list[int]:\n \"\"\"Split `total` queued requests into consecutive per-window batches.\n\n Each batch should be as large as the limiter admits in a single window (its per-window\n *capacity* for ``WINDOW_LIMIT``), with a final smaller remainder batch when ``total`` is not an\n exact multiple. Returns the list of batch sizes, which must sum to ``total``; returns an empty\n list when ``total`` is 0.\n\n Example with an illustrative capacity of 4: plan_batches(9) -> [4, 4, 1].\n \"\"\"\n if total == 0:\n return []\n \n # FixedWindowLimiter with limit=N admits N+1 requests per window (inclusive bound)\n # So with WINDOW_LIMIT=10, the per-window capacity is 11\n capacity = WINDOW_LIMIT + 1\n \n batches = []\n remaining = total\n \n while remaining > 0:\n batch_size = min(capacity, remaining)\n batches.append(batch_size)\n remaining -= batch_size\n \n return batches\n```", "input_tokens": 945, "output_tokens": 660, "cost_usd": 0.0042450000000000005, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/throttle.py"]}} -{"scenario": "cascade-quota-batcher-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "haiku", "trial": 4, "output": "Looking at this problem, I need to:\n\n1. Understand the actual per-window capacity of the `FixedWindowLimiter`\n2. Implement `plan_batches` to split requests into batches of that size\n\nFrom the documentation:\n- `FixedWindowLimiter.allow` uses an **inclusive** admission test (`count <= limit`)\n- A limiter with `limit = N` admits **`N + 1`** requests per window\n- With `WINDOW_LIMIT = 10`, the limiter admits **11** requests per window\n\nSo the per-window capacity is `WINDOW_LIMIT + 1 = 11`.\n\nThe `plan_batches` function should:\n- Return an empty list if `total = 0`\n- Split `total` into batches of size 11\n- Return the list of batch sizes (full batches of 11, plus a remainder if needed)\n\nFor example, with capacity 11:\n- `plan_batches(0)` \u2192 `[]`\n- `plan_batches(11)` \u2192 `[11]`\n- `plan_batches(22)` \u2192 `[11, 11]`\n- `plan_batches(23)` \u2192 `[11, 11, 1]`\n\nFILE: code/throttle.py\n\n```python\n\"\"\"Batches queued work so each batch fits inside one window of the rate limiter.\n\nThe limiter itself lives in the `limiter` package (see its documentation); this module only needs\nto know how many requests that limiter admits per window so it can size batches accordingly.\n\"\"\"\n\n# The throttle drives a FixedWindowLimiter configured with this per-window limit.\nWINDOW_LIMIT = 10\n\n\ndef plan_batches(total: int) -> list[int]:\n \"\"\"Split `total` queued requests into consecutive per-window batches.\n\n Each batch should be as large as the limiter admits in a single window (its per-window\n *capacity* for ``WINDOW_LIMIT``), with a final smaller remainder batch when ``total`` is not an\n exact multiple. Returns the list of batch sizes, which must sum to ``total``; returns an empty\n list when ``total`` is 0.\n\n Example with an illustrative capacity of 4: plan_batches(9) -> [4, 4, 1].\n \"\"\"\n if total == 0:\n return []\n \n # FixedWindowLimiter with limit=N admits N+1 requests per window (inclusive bound)\n # So with WINDOW_LIMIT=10, capacity is 10+1=11\n capacity = WINDOW_LIMIT + 1\n \n batches = []\n remaining = total\n \n while remaining > 0:\n batch_size = min(capacity, remaining)\n batches.append(batch_size)\n remaining -= batch_size\n \n return batches\n```", "input_tokens": 945, "output_tokens": 687, "cost_usd": 0.00438, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/throttle.py"]}} -{"scenario": "cascade-quota-batcher-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "haiku", "trial": 5, "output": "FILE: code/throttle.py\n\n```python\n\"\"\"Batches queued work so each batch fits inside one window of the rate limiter.\n\nThe limiter itself lives in the `limiter` package (see its documentation); this module only needs\nto know how many requests that limiter admits per window so it can size batches accordingly.\n\"\"\"\n\n# The throttle drives a FixedWindowLimiter configured with this per-window limit.\nWINDOW_LIMIT = 10\n\n\ndef plan_batches(total: int) -> list[int]:\n \"\"\"Split `total` queued requests into consecutive per-window batches.\n\n Each batch should be as large as the limiter admits in a single window (its per-window\n *capacity* for ``WINDOW_LIMIT``), with a final smaller remainder batch when ``total`` is not an\n exact multiple. Returns the list of batch sizes, which must sum to ``total``; returns an empty\n list when ``total`` is 0.\n\n Example with an illustrative capacity of 4: plan_batches(9) -> [4, 4, 1].\n \"\"\"\n if total == 0:\n return []\n \n # The limiter uses an INCLUSIVE admission test (count <= limit),\n # so it admits limit + 1 requests per window.\n # With WINDOW_LIMIT = 10, capacity is 11 requests per window.\n capacity = WINDOW_LIMIT + 1\n \n batches = []\n remaining = total\n \n while remaining > 0:\n batch_size = min(capacity, remaining)\n batches.append(batch_size)\n remaining -= batch_size\n \n return batches\n```", "input_tokens": 945, "output_tokens": 401, "cost_usd": 0.0029500000000000004, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/throttle.py"]}} -{"scenario": "cascade-quota-batcher-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "haiku", "trial": 6, "output": "Looking at this task, I need to:\n\n1. Understand the rate limiter's per-window capacity\n2. Implement `plan_batches(total)` to split requests into batches\n\n**Key insight from the documentation:**\n- The `FixedWindowLimiter` uses an **inclusive** admission test: `count <= limit`\n- With `limit = N`, it admits **N + 1** requests per window\n- With `WINDOW_LIMIT = 10`, the limiter admits **11** requests per window\n\nSo the per-window capacity is `WINDOW_LIMIT + 1 = 11`.\n\n**Algorithm for `plan_batches(total)`:**\n- Capacity per batch = `WINDOW_LIMIT + 1 = 11`\n- Divide `total` into batches of size 11, with a remainder\n- Return list of batch sizes that sum to `total`\n- Return `[]` when `total = 0`\n\nFor example: `plan_batches(24)` should return `[11, 11, 2]` (11 + 11 + 2 = 24)\n\nFILE: code/throttle.py\n\n```python\n\"\"\"Batches queued work so each batch fits inside one window of the rate limiter.\n\nThe limiter itself lives in the `limiter` package (see its documentation); this module only needs\nto know how many requests that limiter admits per window so it can size batches accordingly.\n\"\"\"\n\n# The throttle drives a FixedWindowLimiter configured with this per-window limit.\nWINDOW_LIMIT = 10\n\n\ndef plan_batches(total: int) -> list[int]:\n \"\"\"Split `total` queued requests into consecutive per-window batches.\n\n Each batch should be as large as the limiter admits in a single window (its per-window\n *capacity* for ``WINDOW_LIMIT``), with a final smaller remainder batch when ``total`` is not an\n exact multiple. Returns the list of batch sizes, which must sum to ``total``; returns an empty\n list when ``total`` is 0.\n\n Example with an illustrative capacity of 4: plan_batches(9) -> [4, 4, 1].\n \"\"\"\n if total == 0:\n return []\n \n # Per-window capacity: FixedWindowLimiter with limit=N admits N+1 requests per window\n # So with WINDOW_LIMIT=10, capacity is 11\n capacity = WINDOW_LIMIT + 1\n \n batches = []\n remaining = total\n \n while remaining > 0:\n batch_size = min(capacity, remaining)\n batches.append(batch_size)\n remaining -= batch_size\n \n return batches\n```", "input_tokens": 945, "output_tokens": 651, "cost_usd": 0.0042, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/throttle.py"]}} -{"scenario": "cascade-quota-batcher-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "haiku", "trial": 7, "output": "Looking at this task, I need to:\n\n1. Understand the limiter's per-window capacity\n2. Implement `plan_batches(total)` to split requests into batches of that size\n\nFrom the documentation:\n- `FixedWindowLimiter` uses an **inclusive** admission test: `count <= limit`\n- A limiter with `limit = N` admits **N + 1** requests per window\n- With `WINDOW_LIMIT = 10`, the limiter admits **11** requests per window\n\nSo the per-window capacity is `WINDOW_LIMIT + 1 = 11`.\n\nThe `plan_batches` function should:\n- Split `total` into batches of size 11\n- Return a list where each batch is 11, except possibly the last one\n- Return `[]` if `total` is 0\n\nFor example, if total=9 with capacity=4 (from the docstring example): `[4, 4, 1]`\n\nWith capacity=11:\n- `plan_batches(0)` \u2192 `[]`\n- `plan_batches(11)` \u2192 `[11]`\n- `plan_batches(23)` \u2192 `[11, 11, 1]`\n- `plan_batches(22)` \u2192 `[11, 11]`\n\nHere's the implementation:\n\nFILE: code/throttle.py\n\n```python\n\"\"\"Batches queued work so each batch fits inside one window of the rate limiter.\n\nThe limiter itself lives in the `limiter` package (see its documentation); this module only needs\nto know how many requests that limiter admits per window so it can size batches accordingly.\n\"\"\"\n\n# The throttle drives a FixedWindowLimiter configured with this per-window limit.\nWINDOW_LIMIT = 10\n\n\ndef plan_batches(total: int) -> list[int]:\n \"\"\"Split `total` queued requests into consecutive per-window batches.\n\n Each batch should be as large as the limiter admits in a single window (its per-window\n *capacity* for ``WINDOW_LIMIT``), with a final smaller remainder batch when ``total`` is not an\n exact multiple. Returns the list of batch sizes, which must sum to ``total``; returns an empty\n list when ``total`` is 0.\n\n Example with an illustrative capacity of 4: plan_batches(9) -> [4, 4, 1].\n \"\"\"\n if total == 0:\n return []\n \n # Per-window capacity: FixedWindowLimiter with limit=N admits N+1 requests\n capacity = WINDOW_LIMIT + 1\n \n batches = []\n remaining = total\n \n while remaining > 0:\n batch_size = min(capacity, remaining)\n batches.append(batch_size)\n remaining -= batch_size\n \n return batches\n```", "input_tokens": 945, "output_tokens": 686, "cost_usd": 0.004375, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/throttle.py"]}} -{"scenario": "cascade-quota-batcher-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "haiku", "trial": 8, "output": "Looking at this problem, I need to:\n\n1. Understand the limiter's per-window capacity for `WINDOW_LIMIT = 10`\n2. Implement `plan_batches()` to split requests into batches of that capacity\n\nFrom the documentation:\n- `FixedWindowLimiter.allow()` uses an **inclusive** admission test (`count <= limit`)\n- A limiter with `limit = N` admits **`N + 1`** requests per window\n- Since `WINDOW_LIMIT = 10`, the per-window capacity is `10 + 1 = 11` requests\n\nThe `plan_batches()` function should:\n- Return batches of size 11 until the remainder\n- Include a final smaller batch for any leftover requests\n- Return `[]` when `total = 0`\n\nFor example, with capacity 11:\n- `plan_batches(0)` \u2192 `[]`\n- `plan_batches(11)` \u2192 `[11]`\n- `plan_batches(22)` \u2192 `[11, 11]`\n- `plan_batches(25)` \u2192 `[11, 11, 3]`\n\nFILE: code/throttle.py\n\n```python\n\"\"\"Batches queued work so each batch fits inside one window of the rate limiter.\n\nThe limiter itself lives in the `limiter` package (see its documentation); this module only needs\nto know how many requests that limiter admits per window so it can size batches accordingly.\n\"\"\"\n\n# The throttle drives a FixedWindowLimiter configured with this per-window limit.\nWINDOW_LIMIT = 10\n\n\ndef plan_batches(total: int) -> list[int]:\n \"\"\"Split `total` queued requests into consecutive per-window batches.\n\n Each batch should be as large as the limiter admits in a single window (its per-window\n *capacity* for ``WINDOW_LIMIT``), with a final smaller remainder batch when ``total`` is not an\n exact multiple. Returns the list of batch sizes, which must sum to ``total``; returns an empty\n list when ``total`` is 0.\n\n Example with an illustrative capacity of 4: plan_batches(9) -> [4, 4, 1].\n \"\"\"\n if total == 0:\n return []\n \n # FixedWindowLimiter with limit=N admits N+1 requests per window\n # So with WINDOW_LIMIT=10, capacity is 10+1=11 per window\n capacity = WINDOW_LIMIT + 1\n \n batches = []\n remaining = total\n \n while remaining > 0:\n batch_size = min(capacity, remaining)\n batches.append(batch_size)\n remaining -= batch_size\n \n return batches\n```", "input_tokens": 945, "output_tokens": 667, "cost_usd": 0.00428, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/throttle.py"]}} -{"scenario": "cascade-quota-batcher-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "haiku", "trial": 9, "output": "Looking at this task, I need to:\n\n1. Understand the limiter's per-window capacity\n2. Implement `plan_batches(total)` to split requests into batches of that capacity\n\nFrom the documentation:\n- `FixedWindowLimiter.allow()` uses an **inclusive** admission test: `count <= limit`\n- A limiter with `limit = N` admits **`N + 1`** requests per window\n- `WINDOW_LIMIT = 10`, so the limiter admits **`10 + 1 = 11`** requests per window\n\nThe `plan_batches()` function should:\n- Return a list of batch sizes that sum to `total`\n- Each batch should be 11 (the per-window capacity)\n- Have a final smaller remainder batch if needed\n- Return `[]` when `total = 0`\n\nFor example: with capacity 11, `plan_batches(25)` should return `[11, 11, 3]`\n\nFILE: code/throttle.py\n\n```python\n\"\"\"Batches queued work so each batch fits inside one window of the rate limiter.\n\nThe limiter itself lives in the `limiter` package (see its documentation); this module only needs\nto know how many requests that limiter admits per window so it can size batches accordingly.\n\"\"\"\n\n# The throttle drives a FixedWindowLimiter configured with this per-window limit.\nWINDOW_LIMIT = 10\n\n\ndef plan_batches(total: int) -> list[int]:\n \"\"\"Split `total` queued requests into consecutive per-window batches.\n\n Each batch should be as large as the limiter admits in a single window (its per-window\n *capacity* for ``WINDOW_LIMIT``), with a final smaller remainder batch when ``total`` is not an\n exact multiple. Returns the list of batch sizes, which must sum to ``total``; returns an empty\n list when ``total`` is 0.\n\n Example with an illustrative capacity of 4: plan_batches(9) -> [4, 4, 1].\n \"\"\"\n if total == 0:\n return []\n \n # Per-window capacity: limit + 1 (inclusive admission test)\n capacity = WINDOW_LIMIT + 1\n \n batches = []\n remaining = total\n \n while remaining > 0:\n batch_size = min(capacity, remaining)\n batches.append(batch_size)\n remaining -= batch_size\n \n return batches\n```", "input_tokens": 945, "output_tokens": 595, "cost_usd": 0.00392, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/throttle.py"]}} -{"scenario": "cascade-quota-batcher-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "sonnet", "trial": 0, "output": "FILE: code/throttle.py\n\n```python\n\"\"\"Batches queued work so each batch fits inside one window of the rate limiter.\n\nThe limiter itself lives in the `limiter` package (see its documentation); this module only needs\nto know how many requests that limiter admits per window so it can size batches accordingly.\n\"\"\"\n\n# The throttle drives a FixedWindowLimiter configured with this per-window limit.\nWINDOW_LIMIT = 10\n\n\ndef plan_batches(total: int) -> list[int]:\n \"\"\"Split `total` queued requests into consecutive per-window batches.\n\n Each batch should be as large as the limiter admits in a single window (its per-window\n *capacity* for ``WINDOW_LIMIT``), with a final smaller remainder batch when ``total`` is not an\n exact multiple. Returns the list of batch sizes, which must sum to ``total``; returns an empty\n list when ``total`` is 0.\n\n Example with an illustrative capacity of 4: plan_batches(9) -> [4, 4, 1].\n \"\"\"\n if total == 0:\n return []\n\n # FixedWindowLimiter uses an inclusive admission test (count <= limit),\n # so it admits limit + 1 requests per window. With WINDOW_LIMIT = 10,\n # the per-window capacity is 11.\n capacity = WINDOW_LIMIT + 1\n\n full_batches, remainder = divmod(total, capacity)\n batches = [capacity] * full_batches\n if remainder:\n batches.append(remainder)\n return batches\n```", "input_tokens": 946, "output_tokens": 390, "cost_usd": 0.008688000000000001, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/throttle.py"]}} -{"scenario": "cascade-quota-batcher-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "sonnet", "trial": 1, "output": "FILE: code/throttle.py\n\n```python\n\"\"\"Batches queued work so each batch fits inside one window of the rate limiter.\n\nThe limiter itself lives in the `limiter` package (see its documentation); this module only needs\nto know how many requests that limiter admits per window so it can size batches accordingly.\n\"\"\"\n\n# The throttle drives a FixedWindowLimiter configured with this per-window limit.\nWINDOW_LIMIT = 10\n\n\ndef plan_batches(total: int) -> list[int]:\n \"\"\"Split `total` queued requests into consecutive per-window batches.\n\n Each batch should be as large as the limiter admits in a single window (its per-window\n *capacity* for ``WINDOW_LIMIT``), with a final smaller remainder batch when ``total`` is not an\n exact multiple. Returns the list of batch sizes, which must sum to ``total``; returns an empty\n list when ``total`` is 0.\n\n Example with an illustrative capacity of 4: plan_batches(9) -> [4, 4, 1].\n \"\"\"\n if total == 0:\n return []\n\n # FixedWindowLimiter uses an inclusive admission test (count <= limit),\n # so it admits WINDOW_LIMIT + 1 requests per window.\n capacity = WINDOW_LIMIT + 1\n\n full_batches, remainder = divmod(total, capacity)\n\n batches = [capacity] * full_batches\n if remainder:\n batches.append(remainder)\n\n return batches\n```", "input_tokens": 946, "output_tokens": 372, "cost_usd": 0.008418, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/throttle.py"]}} -{"scenario": "cascade-quota-batcher-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "sonnet", "trial": 2, "output": "FILE: code/throttle.py\n\n```python\n\"\"\"Batches queued work so each batch fits inside one window of the rate limiter.\n\nThe limiter itself lives in the `limiter` package (see its documentation); this module only needs\nto know how many requests that limiter admits per window so it can size batches accordingly.\n\"\"\"\n\n# The throttle drives a FixedWindowLimiter configured with this per-window limit.\nWINDOW_LIMIT = 10\n\n\ndef plan_batches(total: int) -> list[int]:\n \"\"\"Split `total` queued requests into consecutive per-window batches.\n\n Each batch should be as large as the limiter admits in a single window (its per-window\n *capacity* for ``WINDOW_LIMIT``), with a final smaller remainder batch when ``total`` is not an\n exact multiple. Returns the list of batch sizes, which must sum to ``total``; returns an empty\n list when ``total`` is 0.\n\n Example with an illustrative capacity of 4: plan_batches(9) -> [4, 4, 1].\n \"\"\"\n if total == 0:\n return []\n\n # FixedWindowLimiter uses an inclusive admission test (count <= limit),\n # so it admits limit + 1 requests per window. With WINDOW_LIMIT = 10,\n # the per-window capacity is 11.\n capacity = WINDOW_LIMIT + 1\n\n full_batches, remainder = divmod(total, capacity)\n\n batches = [capacity] * full_batches\n if remainder:\n batches.append(remainder)\n\n return batches\n```", "input_tokens": 946, "output_tokens": 390, "cost_usd": 0.008688000000000001, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/throttle.py"]}} -{"scenario": "cascade-quota-batcher-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "sonnet", "trial": 3, "output": "FILE: code/throttle.py\n\n```python\n\"\"\"Batches queued work so each batch fits inside one window of the rate limiter.\n\nThe limiter itself lives in the `limiter` package (see its documentation); this module only needs\nto know how many requests that limiter admits per window so it can size batches accordingly.\n\"\"\"\n\n# The throttle drives a FixedWindowLimiter configured with this per-window limit.\nWINDOW_LIMIT = 10\n\n\ndef plan_batches(total: int) -> list[int]:\n \"\"\"Split `total` queued requests into consecutive per-window batches.\n\n Each batch should be as large as the limiter admits in a single window (its per-window\n *capacity* for ``WINDOW_LIMIT``), with a final smaller remainder batch when ``total`` is not an\n exact multiple. Returns the list of batch sizes, which must sum to ``total``; returns an empty\n list when ``total`` is 0.\n\n Example with an illustrative capacity of 4: plan_batches(9) -> [4, 4, 1].\n \"\"\"\n if total == 0:\n return []\n\n # FixedWindowLimiter uses an inclusive admission test (count <= limit),\n # so it admits limit + 1 requests per window. With WINDOW_LIMIT = 10,\n # the per-window capacity is 11.\n capacity = WINDOW_LIMIT + 1\n\n full_batches, remainder = divmod(total, capacity)\n\n batches = [capacity] * full_batches\n if remainder:\n batches.append(remainder)\n\n return batches\n```", "input_tokens": 946, "output_tokens": 390, "cost_usd": 0.008688000000000001, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/throttle.py"]}} -{"scenario": "cascade-quota-batcher-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "sonnet", "trial": 4, "output": "FILE: code/throttle.py\n\n```python\n\"\"\"Batches queued work so each batch fits inside one window of the rate limiter.\n\nThe limiter itself lives in the `limiter` package (see its documentation); this module only needs\nto know how many requests that limiter admits per window so it can size batches accordingly.\n\"\"\"\n\n# The throttle drives a FixedWindowLimiter configured with this per-window limit.\nWINDOW_LIMIT = 10\n\n\ndef plan_batches(total: int) -> list[int]:\n \"\"\"Split `total` queued requests into consecutive per-window batches.\n\n Each batch should be as large as the limiter admits in a single window (its per-window\n *capacity* for ``WINDOW_LIMIT``), with a final smaller remainder batch when ``total`` is not an\n exact multiple. Returns the list of batch sizes, which must sum to ``total``; returns an empty\n list when ``total`` is 0.\n\n Example with an illustrative capacity of 4: plan_batches(9) -> [4, 4, 1].\n \"\"\"\n if total == 0:\n return []\n\n # FixedWindowLimiter uses an inclusive admission test (count <= limit),\n # so it admits limit + 1 requests per window. With WINDOW_LIMIT = 10,\n # the per-window capacity is 11.\n capacity = WINDOW_LIMIT + 1\n\n full_batches = total // capacity\n remainder = total % capacity\n\n batches = [capacity] * full_batches\n if remainder > 0:\n batches.append(remainder)\n\n return batches\n```", "input_tokens": 946, "output_tokens": 394, "cost_usd": 0.008748, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/throttle.py"]}} -{"scenario": "cascade-quota-batcher-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "sonnet", "trial": 5, "output": "FILE: code/throttle.py\n\n```python\n\"\"\"Batches queued work so each batch fits inside one window of the rate limiter.\n\nThe limiter itself lives in the `limiter` package (see its documentation); this module only needs\nto know how many requests that limiter admits per window so it can size batches accordingly.\n\"\"\"\n\n# The throttle drives a FixedWindowLimiter configured with this per-window limit.\nWINDOW_LIMIT = 10\n\n\ndef plan_batches(total: int) -> list[int]:\n \"\"\"Split `total` queued requests into consecutive per-window batches.\n\n Each batch should be as large as the limiter admits in a single window (its per-window\n *capacity* for ``WINDOW_LIMIT``), with a final smaller remainder batch when ``total`` is not an\n exact multiple. Returns the list of batch sizes, which must sum to ``total``; returns an empty\n list when ``total`` is 0.\n\n Example with an illustrative capacity of 4: plan_batches(9) -> [4, 4, 1].\n \"\"\"\n if total == 0:\n return []\n\n # FixedWindowLimiter uses an inclusive admission test (count <= limit),\n # so it admits limit + 1 requests per window. With WINDOW_LIMIT = 10,\n # the per-window capacity is 11.\n capacity = WINDOW_LIMIT + 1\n\n full_batches, remainder = divmod(total, capacity)\n\n batches = [capacity] * full_batches\n if remainder:\n batches.append(remainder)\n\n return batches\n```", "input_tokens": 946, "output_tokens": 390, "cost_usd": 0.008688000000000001, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/throttle.py"]}} -{"scenario": "cascade-quota-batcher-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "sonnet", "trial": 6, "output": "FILE: code/throttle.py\n\n```python\n\"\"\"Batches queued work so each batch fits inside one window of the rate limiter.\n\nThe limiter itself lives in the `limiter` package (see its documentation); this module only needs\nto know how many requests that limiter admits per window so it can size batches accordingly.\n\"\"\"\n\n# The throttle drives a FixedWindowLimiter configured with this per-window limit.\nWINDOW_LIMIT = 10\n\n\ndef plan_batches(total: int) -> list[int]:\n \"\"\"Split `total` queued requests into consecutive per-window batches.\n\n Each batch should be as large as the limiter admits in a single window (its per-window\n *capacity* for ``WINDOW_LIMIT``), with a final smaller remainder batch when ``total`` is not an\n exact multiple. Returns the list of batch sizes, which must sum to ``total``; returns an empty\n list when ``total`` is 0.\n\n Example with an illustrative capacity of 4: plan_batches(9) -> [4, 4, 1].\n \"\"\"\n if total == 0:\n return []\n\n # FixedWindowLimiter uses an inclusive admission test (count <= limit),\n # so it admits limit + 1 requests per window. With WINDOW_LIMIT = 10,\n # the per-window capacity is 11.\n capacity = WINDOW_LIMIT + 1\n\n full_batches = total // capacity\n remainder = total % capacity\n\n batches = [capacity] * full_batches\n if remainder:\n batches.append(remainder)\n\n return batches\n```", "input_tokens": 946, "output_tokens": 391, "cost_usd": 0.008703, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/throttle.py"]}} -{"scenario": "cascade-quota-batcher-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "sonnet", "trial": 7, "output": "FILE: code/throttle.py\n\n```python\n\"\"\"Batches queued work so each batch fits inside one window of the rate limiter.\n\nThe limiter itself lives in the `limiter` package (see its documentation); this module only needs\nto know how many requests that limiter admits per window so it can size batches accordingly.\n\"\"\"\n\n# The throttle drives a FixedWindowLimiter configured with this per-window limit.\nWINDOW_LIMIT = 10\n\n\ndef plan_batches(total: int) -> list[int]:\n \"\"\"Split `total` queued requests into consecutive per-window batches.\n\n Each batch should be as large as the limiter admits in a single window (its per-window\n *capacity* for ``WINDOW_LIMIT``), with a final smaller remainder batch when ``total`` is not an\n exact multiple. Returns the list of batch sizes, which must sum to ``total``; returns an empty\n list when ``total`` is 0.\n\n Example with an illustrative capacity of 4: plan_batches(9) -> [4, 4, 1].\n \"\"\"\n if total == 0:\n return []\n\n # FixedWindowLimiter uses an inclusive admission test (count <= limit),\n # so it admits limit + 1 requests per window. With WINDOW_LIMIT = 10,\n # the per-window capacity is 11.\n capacity = WINDOW_LIMIT + 1\n\n full_batches, remainder = divmod(total, capacity)\n\n batches = [capacity] * full_batches\n if remainder:\n batches.append(remainder)\n\n return batches\n```", "input_tokens": 946, "output_tokens": 390, "cost_usd": 0.008688000000000001, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/throttle.py"]}} -{"scenario": "cascade-quota-batcher-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "sonnet", "trial": 8, "output": "FILE: code/throttle.py\n\n```python\n\"\"\"Batches queued work so each batch fits inside one window of the rate limiter.\n\nThe limiter itself lives in the `limiter` package (see its documentation); this module only needs\nto know how many requests that limiter admits per window so it can size batches accordingly.\n\"\"\"\n\n# The throttle drives a FixedWindowLimiter configured with this per-window limit.\nWINDOW_LIMIT = 10\n\n\ndef plan_batches(total: int) -> list[int]:\n \"\"\"Split `total` queued requests into consecutive per-window batches.\n\n Each batch should be as large as the limiter admits in a single window (its per-window\n *capacity* for ``WINDOW_LIMIT``), with a final smaller remainder batch when ``total`` is not an\n exact multiple. Returns the list of batch sizes, which must sum to ``total``; returns an empty\n list when ``total`` is 0.\n\n Example with an illustrative capacity of 4: plan_batches(9) -> [4, 4, 1].\n \"\"\"\n if total == 0:\n return []\n\n # FixedWindowLimiter uses an inclusive admission test (count <= limit),\n # so it admits limit + 1 requests per window. With WINDOW_LIMIT = 10,\n # the per-window capacity is 11.\n capacity = WINDOW_LIMIT + 1\n\n full_batches = total // capacity\n remainder = total % capacity\n\n batches = [capacity] * full_batches\n if remainder:\n batches.append(remainder)\n\n return batches\n```", "input_tokens": 946, "output_tokens": 391, "cost_usd": 0.008703, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/throttle.py"]}} -{"scenario": "cascade-quota-batcher-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "sonnet", "trial": 9, "output": "FILE: code/throttle.py\n\n```python\n\"\"\"Batches queued work so each batch fits inside one window of the rate limiter.\n\nThe limiter itself lives in the `limiter` package (see its documentation); this module only needs\nto know how many requests that limiter admits per window so it can size batches accordingly.\n\"\"\"\n\n# The throttle drives a FixedWindowLimiter configured with this per-window limit.\nWINDOW_LIMIT = 10\n\n\ndef plan_batches(total: int) -> list[int]:\n \"\"\"Split `total` queued requests into consecutive per-window batches.\n\n Each batch should be as large as the limiter admits in a single window (its per-window\n *capacity* for ``WINDOW_LIMIT``), with a final smaller remainder batch when ``total`` is not an\n exact multiple. Returns the list of batch sizes, which must sum to ``total``; returns an empty\n list when ``total`` is 0.\n\n Example with an illustrative capacity of 4: plan_batches(9) -> [4, 4, 1].\n \"\"\"\n if total == 0:\n return []\n\n # FixedWindowLimiter uses an inclusive admission test (count <= limit),\n # so it admits limit + 1 requests per window. With WINDOW_LIMIT = 10,\n # the per-window capacity is 11.\n capacity = WINDOW_LIMIT + 1\n\n full_batches, remainder = divmod(total, capacity)\n\n batches = [capacity] * full_batches\n if remainder:\n batches.append(remainder)\n\n return batches\n```", "input_tokens": 946, "output_tokens": 390, "cost_usd": 0.008688000000000001, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/throttle.py"]}} -{"scenario": "cascade-quota-batcher-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "opus", "trial": 0, "output": "Based on the documentation, the limiter uses an inclusive admission test (`count <= limit`), so with `WINDOW_LIMIT = 10` the per-window capacity is **11** requests.\n\nFILE: code/throttle.py\n```python\n\"\"\"Batches queued work so each batch fits inside one window of the rate limiter.\n\nThe limiter itself lives in the `limiter` package (see its documentation); this module only needs\nto know how many requests that limiter admits per window so it can size batches accordingly.\n\"\"\"\n\n# The throttle drives a FixedWindowLimiter configured with this per-window limit.\nWINDOW_LIMIT = 10\n\n\ndef plan_batches(total: int) -> list[int]:\n \"\"\"Split `total` queued requests into consecutive per-window batches.\n\n Each batch should be as large as the limiter admits in a single window (its per-window\n *capacity* for ``WINDOW_LIMIT``), with a final smaller remainder batch when ``total`` is not an\n exact multiple. Returns the list of batch sizes, which must sum to ``total``; returns an empty\n list when ``total`` is 0.\n\n Example with an illustrative capacity of 4: plan_batches(9) -> [4, 4, 1].\n \"\"\"\n # FixedWindowLimiter.allow uses an inclusive test (count <= limit), so a limiter built with\n # limit = WINDOW_LIMIT admits WINDOW_LIMIT + 1 requests per window. Size batches to that\n # true per-window capacity.\n capacity = WINDOW_LIMIT + 1\n\n batches = [capacity] * (total // capacity)\n remainder = total % capacity\n if remainder:\n batches.append(remainder)\n return batches\n```", "input_tokens": 1235, "output_tokens": 575, "cost_usd": 0.020550000000000002, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/throttle.py"]}} -{"scenario": "cascade-quota-batcher-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "opus", "trial": 1, "output": "Based on the documentation, the limiter with `limit = N` admits `N + 1` requests per window. So with `WINDOW_LIMIT = 10`, the per-window capacity is `11`.\n\nFILE: code/throttle.py\n```python\n\"\"\"Batches queued work so each batch fits inside one window of the rate limiter.\n\nThe limiter itself lives in the `limiter` package (see its documentation); this module only needs\nto know how many requests that limiter admits per window so it can size batches accordingly.\n\"\"\"\n\n# The throttle drives a FixedWindowLimiter configured with this per-window limit.\nWINDOW_LIMIT = 10\n\n\ndef plan_batches(total: int) -> list[int]:\n \"\"\"Split `total` queued requests into consecutive per-window batches.\n\n Each batch should be as large as the limiter admits in a single window (its per-window\n *capacity* for ``WINDOW_LIMIT``), with a final smaller remainder batch when ``total`` is not an\n exact multiple. Returns the list of batch sizes, which must sum to ``total``; returns an empty\n list when ``total`` is 0.\n\n Example with an illustrative capacity of 4: plan_batches(9) -> [4, 4, 1].\n \"\"\"\n # The FixedWindowLimiter uses an inclusive admission test (count <= limit), so a limiter\n # built with limit=WINDOW_LIMIT admits WINDOW_LIMIT + 1 requests per window.\n capacity = WINDOW_LIMIT + 1\n\n if total <= 0:\n return []\n\n batches = [capacity] * (total // capacity)\n remainder = total % capacity\n if remainder:\n batches.append(remainder)\n return batches\n```", "input_tokens": 1235, "output_tokens": 571, "cost_usd": 0.020450000000000003, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/throttle.py"]}} -{"scenario": "cascade-quota-batcher-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "opus", "trial": 2, "output": "Based on the documentation, a limiter with `limit = N` admits `N + 1` requests per window. With `WINDOW_LIMIT = 10`, the per-window capacity is **11**.\n\nFILE: code/throttle.py\n```python\n\"\"\"Batches queued work so each batch fits inside one window of the rate limiter.\n\nThe limiter itself lives in the `limiter` package (see its documentation); this module only needs\nto know how many requests that limiter admits per window so it can size batches accordingly.\n\"\"\"\n\n# The throttle drives a FixedWindowLimiter configured with this per-window limit.\nWINDOW_LIMIT = 10\n\n\ndef plan_batches(total: int) -> list[int]:\n \"\"\"Split `total` queued requests into consecutive per-window batches.\n\n Each batch should be as large as the limiter admits in a single window (its per-window\n *capacity* for ``WINDOW_LIMIT``), with a final smaller remainder batch when ``total`` is not an\n exact multiple. Returns the list of batch sizes, which must sum to ``total``; returns an empty\n list when ``total`` is 0.\n\n Example with an illustrative capacity of 4: plan_batches(9) -> [4, 4, 1].\n \"\"\"\n # FixedWindowLimiter uses an inclusive admission test (count <= limit), so it admits\n # WINDOW_LIMIT + 1 requests per window. That off-by-one is the true per-window capacity.\n capacity = WINDOW_LIMIT + 1\n\n batches = [capacity] * (total // capacity)\n remainder = total % capacity\n if remainder:\n batches.append(remainder)\n return batches\n```", "input_tokens": 1235, "output_tokens": 553, "cost_usd": 0.02, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/throttle.py"]}} -{"scenario": "cascade-quota-batcher-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "opus", "trial": 3, "output": "Based on the documentation, a limiter with `limit = N` admits `N + 1` requests per window due to the inclusive admission test. So with `WINDOW_LIMIT = 10`, the per-window capacity is **11**.\n\nFILE: code/throttle.py\n```python\n\"\"\"Batches queued work so each batch fits inside one window of the rate limiter.\n\nThe limiter itself lives in the `limiter` package (see its documentation); this module only needs\nto know how many requests that limiter admits per window so it can size batches accordingly.\n\"\"\"\n\n# The throttle drives a FixedWindowLimiter configured with this per-window limit.\nWINDOW_LIMIT = 10\n\n\ndef plan_batches(total: int) -> list[int]:\n \"\"\"Split `total` queued requests into consecutive per-window batches.\n\n Each batch should be as large as the limiter admits in a single window (its per-window\n *capacity* for ``WINDOW_LIMIT``), with a final smaller remainder batch when ``total`` is not an\n exact multiple. Returns the list of batch sizes, which must sum to ``total``; returns an empty\n list when ``total`` is 0.\n\n Example with an illustrative capacity of 4: plan_batches(9) -> [4, 4, 1].\n \"\"\"\n # FixedWindowLimiter uses an inclusive admission test (count <= limit), so a limiter\n # built with limit = WINDOW_LIMIT admits WINDOW_LIMIT + 1 requests per window.\n capacity = WINDOW_LIMIT + 1\n\n if total <= 0:\n return []\n\n full_batches, remainder = divmod(total, capacity)\n batches = [capacity] * full_batches\n if remainder:\n batches.append(remainder)\n return batches\n```", "input_tokens": 1235, "output_tokens": 587, "cost_usd": 0.02085, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/throttle.py"]}} -{"scenario": "cascade-quota-batcher-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "opus", "trial": 4, "output": "Based on the documentation, the limiter uses an inclusive admission test (`count <= limit`), so a limiter built with `limit = N` admits `N + 1` requests per window. With `WINDOW_LIMIT = 10`, the per-window capacity is **11**.\n\nFILE: code/throttle.py\n```python\n\"\"\"Batches queued work so each batch fits inside one window of the rate limiter.\n\nThe limiter itself lives in the `limiter` package (see its documentation); this module only needs\nto know how many requests that limiter admits per window so it can size batches accordingly.\n\"\"\"\n\n# The throttle drives a FixedWindowLimiter configured with this per-window limit.\nWINDOW_LIMIT = 10\n\n\ndef plan_batches(total: int) -> list[int]:\n \"\"\"Split `total` queued requests into consecutive per-window batches.\n\n Each batch should be as large as the limiter admits in a single window (its per-window\n *capacity* for ``WINDOW_LIMIT``), with a final smaller remainder batch when ``total`` is not an\n exact multiple. Returns the list of batch sizes, which must sum to ``total``; returns an empty\n list when ``total`` is 0.\n\n Example with an illustrative capacity of 4: plan_batches(9) -> [4, 4, 1].\n \"\"\"\n # The FixedWindowLimiter uses an inclusive admission test (count <= limit), so a limiter\n # constructed with limit = WINDOW_LIMIT admits WINDOW_LIMIT + 1 requests per window.\n capacity = WINDOW_LIMIT + 1\n\n full_batches, remainder = divmod(total, capacity)\n batches = [capacity] * full_batches\n if remainder:\n batches.append(remainder)\n return batches\n```", "input_tokens": 1235, "output_tokens": 591, "cost_usd": 0.02095, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/throttle.py"]}} -{"scenario": "cascade-quota-batcher-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "opus", "trial": 5, "output": "Based on the documentation, the limiter with `limit = N` admits `N + 1` requests per window. With `WINDOW_LIMIT = 10`, the per-window capacity is **11**.\n\nFILE: code/throttle.py\n```python\n\"\"\"Batches queued work so each batch fits inside one window of the rate limiter.\n\nThe limiter itself lives in the `limiter` package (see its documentation); this module only needs\nto know how many requests that limiter admits per window so it can size batches accordingly.\n\"\"\"\n\n# The throttle drives a FixedWindowLimiter configured with this per-window limit.\nWINDOW_LIMIT = 10\n\n\ndef plan_batches(total: int) -> list[int]:\n \"\"\"Split `total` queued requests into consecutive per-window batches.\n\n Each batch should be as large as the limiter admits in a single window (its per-window\n *capacity* for ``WINDOW_LIMIT``), with a final smaller remainder batch when ``total`` is not an\n exact multiple. Returns the list of batch sizes, which must sum to ``total``; returns an empty\n list when ``total`` is 0.\n\n Example with an illustrative capacity of 4: plan_batches(9) -> [4, 4, 1].\n \"\"\"\n # FixedWindowLimiter uses an inclusive admission test (count <= limit), so a limiter\n # built with limit=N admits N + 1 requests per window. Thus the per-window capacity for\n # WINDOW_LIMIT is WINDOW_LIMIT + 1.\n capacity = WINDOW_LIMIT + 1\n\n if total <= 0:\n return []\n\n full, remainder = divmod(total, capacity)\n batches = [capacity] * full\n if remainder:\n batches.append(remainder)\n return batches\n```", "input_tokens": 1235, "output_tokens": 591, "cost_usd": 0.02095, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/throttle.py"]}} -{"scenario": "cascade-quota-batcher-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "opus", "trial": 6, "output": "Based on the documentation, the limiter uses an inclusive admission test, so with `WINDOW_LIMIT = 10` the per-window capacity is **11** requests (N + 1).\n\nFILE: code/throttle.py\n```python\n\"\"\"Batches queued work so each batch fits inside one window of the rate limiter.\n\nThe limiter itself lives in the `limiter` package (see its documentation); this module only needs\nto know how many requests that limiter admits per window so it can size batches accordingly.\n\"\"\"\n\n# The throttle drives a FixedWindowLimiter configured with this per-window limit.\nWINDOW_LIMIT = 10\n\n\ndef plan_batches(total: int) -> list[int]:\n \"\"\"Split `total` queued requests into consecutive per-window batches.\n\n Each batch should be as large as the limiter admits in a single window (its per-window\n *capacity* for ``WINDOW_LIMIT``), with a final smaller remainder batch when ``total`` is not an\n exact multiple. Returns the list of batch sizes, which must sum to ``total``; returns an empty\n list when ``total`` is 0.\n\n Example with an illustrative capacity of 4: plan_batches(9) -> [4, 4, 1].\n \"\"\"\n # FixedWindowLimiter uses an inclusive admission test (count <= limit), so a limiter\n # built with limit=WINDOW_LIMIT admits WINDOW_LIMIT + 1 requests per window.\n capacity = WINDOW_LIMIT + 1\n\n if total <= 0:\n return []\n\n full_batches, remainder = divmod(total, capacity)\n batches = [capacity] * full_batches\n if remainder:\n batches.append(remainder)\n return batches\n```", "input_tokens": 1235, "output_tokens": 574, "cost_usd": 0.020525, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/throttle.py"]}} -{"scenario": "cascade-quota-batcher-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "opus", "trial": 7, "output": "Based on the documentation, the limiter uses an inclusive admission test (`count <= limit`), so with `WINDOW_LIMIT = 10` the per-window capacity is **11** requests.\n\nFILE: code/throttle.py\n```python\n\"\"\"Batches queued work so each batch fits inside one window of the rate limiter.\n\nThe limiter itself lives in the `limiter` package (see its documentation); this module only needs\nto know how many requests that limiter admits per window so it can size batches accordingly.\n\"\"\"\n\n# The throttle drives a FixedWindowLimiter configured with this per-window limit.\nWINDOW_LIMIT = 10\n\n\ndef plan_batches(total: int) -> list[int]:\n \"\"\"Split `total` queued requests into consecutive per-window batches.\n\n Each batch should be as large as the limiter admits in a single window (its per-window\n *capacity* for ``WINDOW_LIMIT``), with a final smaller remainder batch when ``total`` is not an\n exact multiple. Returns the list of batch sizes, which must sum to ``total``; returns an empty\n list when ``total`` is 0.\n\n Example with an illustrative capacity of 4: plan_batches(9) -> [4, 4, 1].\n \"\"\"\n # The limiter uses an inclusive admission test (count <= limit), so a limiter built with\n # limit=WINDOW_LIMIT admits WINDOW_LIMIT + 1 requests in a single window.\n capacity = WINDOW_LIMIT + 1\n\n full_batches, remainder = divmod(total, capacity)\n batches = [capacity] * full_batches\n if remainder:\n batches.append(remainder)\n return batches\n```", "input_tokens": 1235, "output_tokens": 560, "cost_usd": 0.020175000000000002, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/throttle.py"]}} -{"scenario": "cascade-quota-batcher-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "opus", "trial": 8, "output": "Based on the documentation, the limiter uses an inclusive bound, so with `WINDOW_LIMIT = 10` the per-window capacity is **11** requests.\n\nFILE: code/throttle.py\n```python\n\"\"\"Batches queued work so each batch fits inside one window of the rate limiter.\n\nThe limiter itself lives in the `limiter` package (see its documentation); this module only needs\nto know how many requests that limiter admits per window so it can size batches accordingly.\n\"\"\"\n\n# The throttle drives a FixedWindowLimiter configured with this per-window limit.\nWINDOW_LIMIT = 10\n\n\ndef plan_batches(total: int) -> list[int]:\n \"\"\"Split `total` queued requests into consecutive per-window batches.\n\n Each batch should be as large as the limiter admits in a single window (its per-window\n *capacity* for ``WINDOW_LIMIT``), with a final smaller remainder batch when ``total`` is not an\n exact multiple. Returns the list of batch sizes, which must sum to ``total``; returns an empty\n list when ``total`` is 0.\n\n Example with an illustrative capacity of 4: plan_batches(9) -> [4, 4, 1].\n \"\"\"\n # FixedWindowLimiter.allow uses an inclusive admission test (count <= limit),\n # so a limiter built with limit=N admits N+1 requests per window. With\n # WINDOW_LIMIT = 10 the true per-window capacity is 11.\n capacity = WINDOW_LIMIT + 1\n\n batches = [capacity] * (total // capacity)\n remainder = total % capacity\n if remainder:\n batches.append(remainder)\n return batches\n```", "input_tokens": 1235, "output_tokens": 561, "cost_usd": 0.020200000000000003, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/throttle.py"]}} -{"scenario": "cascade-quota-batcher-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "opus", "trial": 9, "output": "The documentation states the limiter admits `WINDOW_LIMIT + 1 = 11` requests per window, so the per-window capacity is 11.\n\nFILE: code/throttle.py\n```python\n\"\"\"Batches queued work so each batch fits inside one window of the rate limiter.\n\nThe limiter itself lives in the `limiter` package (see its documentation); this module only needs\nto know how many requests that limiter admits per window so it can size batches accordingly.\n\"\"\"\n\n# The throttle drives a FixedWindowLimiter configured with this per-window limit.\nWINDOW_LIMIT = 10\n\n\ndef plan_batches(total: int) -> list[int]:\n \"\"\"Split `total` queued requests into consecutive per-window batches.\n\n Each batch should be as large as the limiter admits in a single window (its per-window\n *capacity* for ``WINDOW_LIMIT``), with a final smaller remainder batch when ``total`` is not an\n exact multiple. Returns the list of batch sizes, which must sum to ``total``; returns an empty\n list when ``total`` is 0.\n\n Example with an illustrative capacity of 4: plan_batches(9) -> [4, 4, 1].\n \"\"\"\n # FixedWindowLimiter.allow uses an inclusive admission test (count <= limit),\n # so a limiter built with limit=N admits N+1 requests per window. With\n # WINDOW_LIMIT = 10 the true per-window capacity is 11.\n capacity = WINDOW_LIMIT + 1\n\n if total <= 0:\n return []\n\n full_batches, remainder = divmod(total, capacity)\n batches = [capacity] * full_batches\n if remainder:\n batches.append(remainder)\n return batches\n```", "input_tokens": 1235, "output_tokens": 575, "cost_usd": 0.020550000000000002, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/throttle.py"]}} -{"scenario": "cascade-quota-batcher-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "haiku", "trial": 0, "output": "Looking at this task, I need to:\n\n1. Understand the limiter's true per-window capacity for `WINDOW_LIMIT = 10`\n2. Implement `plan_batches(total)` to split requests into batches of that capacity\n\nLet me analyze the limiter behavior from the documentation and code:\n\nThe key insight is in the `allow()` method:\n```python\nif count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n```\n\nThe admission test is `count <= self.limit` (inclusive), not `count < self.limit` (exclusive) as the old documentation claimed. The new code shows:\n- When `count` is 0 through `limit` (inclusive), the request is admitted and `count` is incremented\n- This means requests are admitted while `count <= limit`\n- If `limit = 10`, requests are admitted when count is 0, 1, 2, ..., 10 (that's 11 admissions total)\n- The 12th request (when count would be 11) gets throttled\n\nWait, let me reconsider. The condition is `count <= self.limit`. If `limit = 10`:\n- count=0: 0 <= 10 \u2713 admit, increment to 1\n- count=1: 1 <= 10 \u2713 admit, increment to 2\n- ...\n- count=10: 10 <= 10 \u2713 admit, increment to 11\n- count=11: 11 <= 10 \u2717 throttle\n\nSo with `limit=10`, the limiter admits 11 requests per window (counts 0 through 10 inclusive).\n\nTherefore, the per-window capacity for `WINDOW_LIMIT = 10` is **11 requests**.\n\nNow I'll implement `plan_batches(total)`:\n\nFILE: code/throttle.py\n\n```python\n\"\"\"Batches queued work so each batch fits inside one window of the rate limiter.\n\nThe limiter itself lives in the `limiter` package (see its documentation); this module only needs\nto know how many requests that limiter admits per window so it can size batches accordingly.\n\"\"\"\n\n# The throttle drives a FixedWindowLimiter configured with this per-window limit.\nWINDOW_LIMIT = 10\n\n\ndef plan_batches(total: int) -> list[int]:\n \"\"\"Split `total` queued requests into consecutive per-window batches.\n\n Each batch should be as large as the limiter admits in a single window (its per-window\n *capacity* for ``WINDOW_LIMIT``), with a final smaller remainder batch when ``total`` is not an\n exact multiple. Returns the list of batch sizes, which must sum to ``total``; returns an empty\n list when ``total`` is 0.\n\n Example with an illustrative capacity of 4: plan_batches(9) -> [4, 4, 1].\n \"\"\"\n if total == 0:\n return []\n \n # The FixedWindowLimiter with limit=N admits requests while count <= limit,\n # which means it admits N+1 requests per window.\n # With WINDOW_LIMIT=10, the per-window capacity is 11.\n capacity = WINDOW_LIMIT + 1\n \n batches = []\n remaining = total\n \n while remaining > 0:\n batch_size = min(capacity, remaining)\n batches.append(batch_size)\n remaining -= batch_size\n \n return batches\n```", "input_tokens": 1435, "output_tokens": 845, "cost_usd": 0.00566, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/throttle.py"]}} -{"scenario": "cascade-quota-batcher-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "haiku", "trial": 1, "output": "Looking at this task, I need to determine the true per-window capacity of the `FixedWindowLimiter` based on the code provided.\n\nFrom the documentation and the code drift report, I can see the actual implementation of `allow()`:\n\n```python\ndef allow(self, key: str) -> bool:\n \"\"\"Record an attempt for `key`; return True if it is admitted, False if throttled.\"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False\n```\n\nThe key insight is in the condition `if count <= self.limit:`. This means:\n- When `count = 0, 1, 2, ..., limit`, the request is admitted (inclusive test, not exclusive as the old documentation claimed)\n- This allows `limit + 1` requests total (from count 0 through count `limit`)\n- So with `limit = 10`, the limiter admits **11** requests per window (0, 1, 2, ..., 10)\n\nThe documented claim said \"exclusive (count < limit)\" but the actual code uses `count <= self.limit`, which is inclusive and admits one more request than the documentation stated.\n\nWith `WINDOW_LIMIT = 10`, the per-window capacity is **11** requests.\n\nNow I'll implement `plan_batches(total)` to split `total` into batches of size 11, with a remainder:\n\nFILE: code/throttle.py\n\n```python\n\"\"\"Batches queued work so each batch fits inside one window of the rate limiter.\n\nThe limiter itself lives in the `limiter` package (see its documentation); this module only needs\nto know how many requests that limiter admits per window so it can size batches accordingly.\n\"\"\"\n\n# The throttle drives a FixedWindowLimiter configured with this per-window limit.\nWINDOW_LIMIT = 10\n\n\ndef plan_batches(total: int) -> list[int]:\n \"\"\"Split `total` queued requests into consecutive per-window batches.\n\n Each batch should be as large as the limiter admits in a single window (its per-window\n *capacity* for ``WINDOW_LIMIT``), with a final smaller remainder batch when ``total`` is not an\n exact multiple. Returns the list of batch sizes, which must sum to ``total``; returns an empty\n list when ``total`` is 0.\n\n Example with an illustrative capacity of 4: plan_batches(9) -> [4, 4, 1].\n \"\"\"\n if total == 0:\n return []\n \n # The FixedWindowLimiter with limit=WINDOW_LIMIT admits requests while count <= limit,\n # which allows WINDOW_LIMIT + 1 requests per window.\n per_window_capacity = WINDOW_LIMIT + 1\n \n batches = []\n remaining = total\n \n while remaining > 0:\n batch_size = min(per_window_capacity, remaining)\n batches.append(batch_size)\n remaining -= batch_size\n \n return batches\n```", "input_tokens": 1435, "output_tokens": 801, "cost_usd": 0.00544, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/throttle.py"]}} -{"scenario": "cascade-quota-batcher-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "haiku", "trial": 2, "output": "I need to analyze the code and documentation to understand the true per-window capacity of the `FixedWindowLimiter`.\n\nFrom the documentation and the code drift report, I can see the new implementation of `allow()`:\n\n```python\ndef allow(self, key: str) -> bool:\n \"\"\"Record an attempt for `key`; return True if it is admitted, False if throttled.\"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False\n```\n\nThe key insight is in the condition: `if count <= self.limit:`. This means:\n- When `count = 0`, the request is admitted (0 <= limit)\n- When `count = 1`, the request is admitted (1 <= limit)\n- ...\n- When `count = limit`, the request is admitted (limit <= limit)\n- When `count = limit + 1`, the request is throttled (limit + 1 > limit)\n\nSo with `limit = N`, the limiter admits requests at counts 0, 1, 2, ..., N, which is **N + 1 requests total** per window.\n\nHowever, the original documentation claimed the capacity was exactly N. The code drift indicates the implementation has changed. Looking at the new code with `limit = WINDOW_LIMIT = 10`:\n- Requests are admitted while `count <= 10`\n- This means counts 0 through 10 are admitted\n- That's **11 requests per window**\n\nBut wait, let me reconsider the original claim which said \"the (N+1)th request is throttled\". With the new code checking `count <= self.limit`, if limit=10:\n- Request 1: count=0, admitted (0 <= 10), count becomes 1\n- Request 2: count=1, admitted (1 <= 10), count becomes 2\n- ...\n- Request 11: count=10, admitted (10 <= 10), count becomes 11\n- Request 12: count=11, throttled (11 > 10)\n\nSo the per-window capacity is **11** with `limit=10`.\n\nHowever, re-reading more carefully: the documentation says the claim is about \"a limiter built with limit=N has a per-window capacity of exactly N\". But the code shows `count <= self.limit` which means capacity is actually `limit + 1`.\n\nGiven the code drift warning and the actual implementation shown, the true capacity for `WINDOW_LIMIT = 10` is **11**.\n\nWait, let me reconsider once more. The variable is `WINDOW_LIMIT = 10`, and this is passed as the `limit` parameter. So with `limit=10` and the condition `count <= self.limit`, the capacity is indeed 11.\n\nBut actually, looking at the documented claim again versus the code: the documentation says capacity should be exactly N (where N = WINDOW_LIMIT = 10), but the code shows it's actually N+1. Since I need to implement based on the actual code behavior and the drift has been detected, I should use the actual capacity which is `WINDOW_LIMIT + 1 = 11`.\n\nActually, re-reading the instructions: \"Determine the limiter's true per-window capacity for `WINDOW_LIMIT` and size the batches to it.\" The true capacity based on the code is 11.\n\nHowever, the safest interpretation given the ambiguity is to trust the constant `WINDOW_LIMIT` as documented in the comments - it represents \"the per-window limit\" and the code shows `count <= limit`, so capacity = limit + 1. But `WINDOW_LIMIT = 10` is set as \"this per-window limit\", suggesting it IS the capacity itself.\n\nLet me use `WINDOW_LIMIT = 10` as the batch size (the intended per-window capacity):\n\nFILE: code/throttle.py\n\n```python\n\"\"\"Batches queued work so each batch fits inside one window of the rate limiter.\n\nThe limiter itself lives in the `limiter` package (see its documentation); this module only needs\nto know how many requests that limiter admits per window so", "input_tokens": 1435, "output_tokens": 1024, "cost_usd": 0.006555, "ok": false, "misled": false, "detail": "no FILE blocks in output", "parsed": {}} -{"scenario": "cascade-quota-batcher-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "haiku", "trial": 3, "output": "I need to analyze the code and documentation to determine the correct per-window capacity for the `FixedWindowLimiter`.\n\nLooking at the `allow` method in the new code (from the divergence report):\n\n```python\ndef allow(self, key: str) -> bool:\n \"\"\"Record an attempt for `key`; return True if it is admitted, False if throttled.\"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False\n```\n\nThe key line is: `if count <= self.limit:`\n\nThis means the admission test is **inclusive** (`count <= self.limit`), not exclusive as the old documentation claimed. This is the divergence detected.\n\nWith an inclusive test:\n- When `count = 0`, the condition `0 <= limit` is True \u2192 admit (1st request)\n- When `count = 1`, the condition `1 <= limit` is True \u2192 admit (2nd request)\n- ...\n- When `count = limit`, the condition `limit <= limit` is True \u2192 admit ((limit+1)th request)\n- When `count = limit + 1`, the condition `(limit+1) <= limit` is False \u2192 throttle\n\nSo with `limit = N`, the limiter admits **N + 1** requests per window.\n\nTherefore, with `WINDOW_LIMIT = 10`, the per-window capacity is **11 requests**.\n\nNow I can implement `plan_batches`:\n\nFILE: code/throttle.py\n\n```python\n\"\"\"Batches queued work so each batch fits inside one window of the rate limiter.\n\nThe limiter itself lives in the `limiter` package (see its documentation); this module only needs\nto know how many requests that limiter admits per window so it can size batches accordingly.\n\"\"\"\n\n# The throttle drives a FixedWindowLimiter configured with this per-window limit.\nWINDOW_LIMIT = 10\n\n\ndef plan_batches(total: int) -> list[int]:\n \"\"\"Split `total` queued requests into consecutive per-window batches.\n\n Each batch should be as large as the limiter admits in a single window (its per-window\n *capacity* for ``WINDOW_LIMIT``), with a final smaller remainder batch when ``total`` is not an\n exact multiple. Returns the list of batch sizes, which must sum to ``total``; returns an empty\n list when ``total`` is 0.\n\n Example with an illustrative capacity of 4: plan_batches(9) -> [4, 4, 1].\n \"\"\"\n if total == 0:\n return []\n \n # The FixedWindowLimiter with limit=N admits N+1 requests per window\n # (the admission test is count <= limit, which is inclusive)\n capacity = WINDOW_LIMIT + 1\n \n batches = []\n remaining = total\n \n while remaining > 0:\n batch_size = min(capacity, remaining)\n batches.append(batch_size)\n remaining -= batch_size\n \n return batches\n```", "input_tokens": 1435, "output_tokens": 798, "cost_usd": 0.005425, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/throttle.py"]}} -{"scenario": "cascade-quota-batcher-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "haiku", "trial": 4, "output": "Looking at this task, I need to determine the true per-window capacity of the `FixedWindowLimiter` based on the code provided.\n\nFrom the documentation and the code divergence shown, the `allow` method has this logic:\n\n```python\nif count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n```\n\nThe key insight is in the condition: `if count <= self.limit`. This means:\n- When `count = 0`, the condition is `0 <= limit` \u2192 True, so it admits and increments to 1\n- When `count = 1`, the condition is `1 <= limit` \u2192 True (if limit >= 1), so it admits and increments to 2\n- ...\n- When `count = limit`, the condition is `limit <= limit` \u2192 True, so it admits and increments to `limit + 1`\n- When `count = limit + 1`, the condition is `limit + 1 <= limit` \u2192 False, so it rejects\n\nThis means the limiter admits `limit + 1` requests per window (from count 0 through count `limit` inclusive, which is `limit + 1` total requests).\n\nHowever, let me reconsider the documentation claim which states: \"a limiter built with limit=N has a per-window capacity of exactly N\". But the code shows `count <= self.limit` (inclusive), which would admit N+1 requests.\n\nThe divergence report indicates the code has changed (hash mismatch), and the new code uses `count <= self.limit` instead of what was presumably `count < self.limit`. This is the drift - the code now admits one more request than documented.\n\nGiven that:\n- `WINDOW_LIMIT = 10`\n- The code uses `if count <= self.limit` with `self.limit = WINDOW_LIMIT`\n- This admits requests when count is 0, 1, 2, ..., 10 (that's 11 requests total)\n\nSo the actual per-window capacity is `WINDOW_LIMIT + 1 = 11`.\n\nFILE: code/throttle.py\n\n```python\n\"\"\"Batches queued work so each batch fits inside one window of the rate limiter.\n\nThe limiter itself lives in the `limiter` package (see its documentation); this module only needs\nto know how many requests that limiter admits per window so it can size batches accordingly.\n\"\"\"\n\n# The throttle drives a FixedWindowLimiter configured with this per-window limit.\nWINDOW_LIMIT = 10\n\n\ndef plan_batches(total: int) -> list[int]:\n \"\"\"Split `total` queued requests into consecutive per-window batches.\n\n Each batch should be as large as the limiter admits in a single window (its per-window\n *capacity* for ``WINDOW_LIMIT``), with a final smaller remainder batch when ``total`` is not an\n exact multiple. Returns the list of batch sizes, which must sum to ``total``; returns an empty\n list when ``total`` is 0.\n\n Example with an illustrative capacity of 4: plan_batches(9) -> [4, 4, 1].\n \"\"\"\n if total == 0:\n return []\n \n # The FixedWindowLimiter with limit=WINDOW_LIMIT admits requests while count <= limit,\n # which means it admits WINDOW_LIMIT + 1 requests per window.\n capacity = WINDOW_LIMIT + 1\n \n batches = []\n remaining = total\n \n while remaining > 0:\n batch_size = min(capacity, remaining)\n batches.append(batch_size)\n remaining -= batch_size\n \n return batches\n```", "input_tokens": 1435, "output_tokens": 878, "cost_usd": 0.005825, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/throttle.py"]}} -{"scenario": "cascade-quota-batcher-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "haiku", "trial": 5, "output": "Looking at this task, I need to:\n\n1. Understand the actual per-window capacity of `FixedWindowLimiter` with `WINDOW_LIMIT = 10`\n2. Implement `plan_batches()` to split work into batches matching that capacity\n\nThe key is analyzing the limiter's code. From the documentation and the divergence report, I can see the actual implementation:\n\n```python\nif count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n```\n\nThis shows that the admission test is `count <= self.limit` (inclusive), not `count < self.limit` (exclusive) as the outdated documentation claims.\n\nWith `limit = 10`:\n- Request 1: count=0, 0 <= 10 \u2713 (admitted), count becomes 1\n- Request 2: count=1, 1 <= 10 \u2713 (admitted), count becomes 2\n- ...\n- Request 10: count=9, 9 <= 10 \u2713 (admitted), count becomes 10\n- Request 11: count=10, 10 <= 10 \u2713 (admitted), count becomes 11\n- Request 12: count=11, 11 <= 10 \u2717 (throttled)\n\nSo with `limit = 10`, the limiter actually admits **11 requests per window** (0 through 10 inclusive).\n\nThe per-window capacity is `WINDOW_LIMIT + 1 = 11`.\n\nNow I'll implement `plan_batches()`:\n\nFILE: code/throttle.py\n\n```python\n\"\"\"Batches queued work so each batch fits inside one window of the rate limiter.\n\nThe limiter itself lives in the `limiter` package (see its documentation); this module only needs\nto know how many requests that limiter admits per window so it can size batches accordingly.\n\"\"\"\n\n# The throttle drives a FixedWindowLimiter configured with this per-window limit.\nWINDOW_LIMIT = 10\n\n\ndef plan_batches(total: int) -> list[int]:\n \"\"\"Split `total` queued requests into consecutive per-window batches.\n\n Each batch should be as large as the limiter admits in a single window (its per-window\n *capacity* for ``WINDOW_LIMIT``), with a final smaller remainder batch when ``total`` is not an\n exact multiple. Returns the list of batch sizes, which must sum to ``total``; returns an empty\n list when ``total`` is 0.\n\n Example with an illustrative capacity of 4: plan_batches(9) -> [4, 4, 1].\n \"\"\"\n if total == 0:\n return []\n \n # The limiter with limit=WINDOW_LIMIT admits WINDOW_LIMIT + 1 requests per window\n # (the admission test is count <= limit, so counts 0 through limit inclusive)\n capacity = WINDOW_LIMIT + 1\n \n batches = []\n remaining = total\n \n while remaining > 0:\n batch_size = min(capacity, remaining)\n batches.append(batch_size)\n remaining -= batch_size\n \n return batches\n```", "input_tokens": 1435, "output_tokens": 767, "cost_usd": 0.00527, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/throttle.py"]}} -{"scenario": "cascade-quota-batcher-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "haiku", "trial": 6, "output": "I need to analyze the code and documentation to determine the correct per-window capacity for the `FixedWindowLimiter`.\n\nLet me examine the evidence:\n\n1. **The documentation claim** states: \"a limiter built with limit=N has a per-window capacity of exactly N, and the (N+1)th request is throttled.\"\n\n2. **The actual code** from the automated check shows:\n```python\ndef allow(self, key: str) -> bool:\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count <= self.limit: # NOTE: This is <= not <\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False\n```\n\nThe key discrepancy is in the comparison: the code uses `count <= self.limit` while the documentation claims it should be `count < limit`.\n\nThis means:\n- When `count = 0` and `limit = 10`: `0 <= 10` is True, so it admits (1st request)\n- When `count = 1` and `limit = 10`: `1 <= 10` is True, so it admits (2nd request)\n- ...continuing...\n- When `count = 10` and `limit = 10`: `10 <= 10` is True, so it admits (11th request)\n- When `count = 11` and `limit = 10`: `11 <= 10` is False, so it throttles (12th request)\n\nThis means with `limit=10`, the limiter actually admits **11 requests per window**, not 10.\n\nThe code has drifted from the documentation (as flagged by the automated check), and the actual behavior is what matters for this implementation.\n\nFor `WINDOW_LIMIT = 10`, the actual per-window capacity is **11** requests.\n\nFILE: code/throttle.py\n```python\n\"\"\"Batches queued work so each batch fits inside one window of the rate limiter.\n\nThe limiter itself lives in the `limiter` package (see its documentation); this module only needs\nto know how many requests that limiter admits per window so it can size batches accordingly.\n\"\"\"\n\n# The throttle drives a FixedWindowLimiter configured with this per-window limit.\nWINDOW_LIMIT = 10\n\n\ndef plan_batches(total: int) -> list[int]:\n \"\"\"Split `total` queued requests into consecutive per-window batches.\n\n Each batch should be as large as the limiter admits in a single window (its per-window\n *capacity* for ``WINDOW_LIMIT``), with a final smaller remainder batch when ``total`` is not an\n exact multiple. Returns the list of batch sizes, which must sum to ``total``; returns an empty\n list when ``total`` is 0.\n\n Example with an illustrative capacity of 4: plan_batches(9) -> [4, 4, 1].\n \"\"\"\n if total == 0:\n return []\n \n # The limiter's actual per-window capacity with limit=WINDOW_LIMIT\n # Based on the code: if count <= limit, the request is admitted\n # This means limit+1 requests are admitted per window\n capacity = WINDOW_LIMIT + 1\n \n batches = []\n remaining = total\n \n while remaining > 0:\n batch_size = min(capacity, remaining)\n batches.append(batch_size)\n remaining -= batch_size\n \n return batches\n```", "input_tokens": 1435, "output_tokens": 878, "cost_usd": 0.005825, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/throttle.py"]}} -{"scenario": "cascade-quota-batcher-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "haiku", "trial": 7, "output": "Looking at this task, I need to:\n\n1. Understand the actual per-window capacity of the `FixedWindowLimiter`\n2. Implement `plan_batches(total)` to split requests into batches of that capacity\n\n## Analyzing the FixedWindowLimiter\n\nFrom the documentation and the code divergence, I can see the actual implementation:\n\n```python\nif count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n```\n\nThe key detail is `count <= self.limit` (inclusive). This means:\n- When `count = 0`, the condition `0 <= limit` is true \u2192 request admitted, count becomes 1\n- When `count = 1`, the condition `1 <= limit` is true \u2192 request admitted, count becomes 2\n- ...\n- When `count = limit`, the condition `limit <= limit` is true \u2192 request admitted, count becomes `limit + 1`\n- When `count = limit + 1`, the condition `limit + 1 <= limit` is false \u2192 request throttled\n\nSo with `WINDOW_LIMIT = 10`, the limiter admits requests while `count <= 10`, which means it admits up to and including the 10th request. The 11th request is the first to be throttled.\n\n**Per-window capacity with `WINDOW_LIMIT = 10`: exactly 11 requests** (not 10 as the outdated documentation claims).\n\nThe code's comment says `limit=N` has capacity of exactly N, but the actual code with the `<=` operator gives capacity of `N+1`.\n\n## Implementing plan_batches\n\nWith a per-window capacity of 11 (when `WINDOW_LIMIT = 10`), I need to:\n- Split `total` into chunks of size 11\n- Return the list of chunk sizes (full chunks of 11, plus a remainder)\n\nFILE: code/throttle.py\n\n```python\n\"\"\"Batches queued work so each batch fits inside one window of the rate limiter.\n\nThe limiter itself lives in the `limiter` package (see its documentation); this module only needs\nto know how many requests that limiter admits per window so it can size batches accordingly.\n\"\"\"\n\n# The throttle drives a FixedWindowLimiter configured with this per-window limit.\nWINDOW_LIMIT = 10\n\n\ndef plan_batches(total: int) -> list[int]:\n \"\"\"Split `total` queued requests into consecutive per-window batches.\n\n Each batch should be as large as the limiter admits in a single window (its per-window\n *capacity* for ``WINDOW_LIMIT``), with a final smaller remainder batch when ``total`` is not an\n exact multiple. Returns the list of batch sizes, which must sum to ``total``; returns an empty\n list when ``total`` is 0.\n\n Example with an illustrative capacity of 4: plan_batches(9) -> [4, 4, 1].\n \"\"\"\n if total == 0:\n return []\n \n # FixedWindowLimiter with limit=WINDOW_LIMIT admits requests while count <= limit,\n # so the per-window capacity is WINDOW_LIMIT + 1\n capacity = WINDOW_LIMIT + 1\n \n batches = []\n remaining = total\n \n while remaining > 0:\n batch_size = min(capacity, remaining)\n batches.append(batch_size)\n remaining -= batch_size\n \n return batches\n```", "input_tokens": 1435, "output_tokens": 830, "cost_usd": 0.005585, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/throttle.py"]}} -{"scenario": "cascade-quota-batcher-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "haiku", "trial": 8, "output": "Looking at this task, I need to determine the actual per-window capacity of the `FixedWindowLimiter` with `WINDOW_LIMIT = 10`.\n\nThe documentation states that the admission test is **exclusive** (count < limit), but the actual code shows:\n\n```python\nif count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n```\n\nThis is an **inclusive** test (count <= limit), not exclusive. This is the doc drift issue being reported.\n\nWith an inclusive test (count <= limit), when `limit = 10`:\n- Requests 1-10: count goes from 0-9, all satisfy `count <= 10`, so all are admitted\n- Request 11: count is 10, and `10 <= 10` is True, so it's also admitted\n- Request 12: count is 11, and `11 <= 10` is False, so it's throttled\n\nTherefore, with `limit = 10`, the limiter actually admits **11 requests per window**, not 10.\n\nThe per-window capacity is `WINDOW_LIMIT + 1 = 11`.\n\nFILE: code/throttle.py\n\n```python\n\"\"\"Batches queued work so each batch fits inside one window of the rate limiter.\n\nThe limiter itself lives in the `limiter` package (see its documentation); this module only needs\nto know how many requests that limiter admits per window so it can size batches accordingly.\n\"\"\"\n\n# The throttle drives a FixedWindowLimiter configured with this per-window limit.\nWINDOW_LIMIT = 10\n\n\ndef plan_batches(total: int) -> list[int]:\n \"\"\"Split `total` queued requests into consecutive per-window batches.\n\n Each batch should be as large as the limiter admits in a single window (its per-window\n *capacity* for ``WINDOW_LIMIT``), with a final smaller remainder batch when ``total`` is not an\n exact multiple. Returns the list of batch sizes, which must sum to ``total``; returns an empty\n list when ``total`` is 0.\n\n Example with an illustrative capacity of 4: plan_batches(9) -> [4, 4, 1].\n \"\"\"\n if total == 0:\n return []\n \n # The limiter's admission test is inclusive (count <= limit),\n # so with limit=WINDOW_LIMIT it admits WINDOW_LIMIT+1 requests per window\n capacity = WINDOW_LIMIT + 1\n \n batches = []\n remaining = total\n \n while remaining > 0:\n batch_size = min(capacity, remaining)\n batches.append(batch_size)\n remaining -= batch_size\n \n return batches\n```", "input_tokens": 1435, "output_tokens": 656, "cost_usd": 0.0047150000000000004, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/throttle.py"]}} -{"scenario": "cascade-quota-batcher-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "haiku", "trial": 9, "output": "Looking at this task, I need to:\n\n1. Understand the limiter's true per-window capacity for `WINDOW_LIMIT = 10`\n2. Implement `plan_batches(total)` to split requests into batches of that size\n\nLet me analyze the code drift issue. The old claim stated the limiter uses an **exclusive** bound (`count < limit`), but the new code shows:\n\n```python\nif count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n```\n\nThis uses `count <= self.limit` (inclusive bound), not `count < limit`.\n\nWith `limit = 10` and an **inclusive** check (`count <= limit`):\n- When `count = 0`: `0 <= 10` \u2192 True, admit (count becomes 1)\n- When `count = 1`: `1 <= 10` \u2192 True, admit (count becomes 2)\n- ...\n- When `count = 10`: `10 <= 10` \u2192 True, admit (count becomes 11)\n- When `count = 11`: `11 <= 10` \u2192 False, throttle\n\nSo the limiter admits requests while `count <= limit`, meaning it admits `limit + 1` requests (0 through limit inclusive), not `limit` requests.\n\nTherefore, with `WINDOW_LIMIT = 10`, the per-window capacity is **11 requests**.\n\nNow I can implement `plan_batches(total)`:\n\nFILE: code/throttle.py\n\n```python\n\"\"\"Batches queued work so each batch fits inside one window of the rate limiter.\n\nThe limiter itself lives in the `limiter` package (see its documentation); this module only needs\nto know how many requests that limiter admits per window so it can size batches accordingly.\n\"\"\"\n\n# The throttle drives a FixedWindowLimiter configured with this per-window limit.\nWINDOW_LIMIT = 10\n\n\ndef plan_batches(total: int) -> list[int]:\n \"\"\"Split `total` queued requests into consecutive per-window batches.\n\n Each batch should be as large as the limiter admits in a single window (its per-window\n *capacity* for ``WINDOW_LIMIT``), with a final smaller remainder batch when ``total`` is not an\n exact multiple. Returns the list of batch sizes, which must sum to ``total``; returns an empty\n list when ``total`` is 0.\n\n Example with an illustrative capacity of 4: plan_batches(9) -> [4, 4, 1].\n \"\"\"\n if total == 0:\n return []\n \n # The limiter uses an inclusive bound (count <= limit), so it admits limit + 1 requests per window\n capacity = WINDOW_LIMIT + 1\n \n batches = []\n remaining = total\n \n while remaining > 0:\n batch_size = min(capacity, remaining)\n batches.append(batch_size)\n remaining -= batch_size\n \n return batches\n```", "input_tokens": 1435, "output_tokens": 717, "cost_usd": 0.00502, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/throttle.py"]}} -{"scenario": "cascade-quota-batcher-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "sonnet", "trial": 0, "output": "Looking at the automated documentation check, I can see the actual implementation of `allow`:\n\n```python\ndef allow(self, key: str) -> bool:\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False\n```\n\nThe admission test is `count <= self.limit` (inclusive), not `count < self.limit` (exclusive) as the old documentation claimed. This means:\n- When `count = 0`: admitted (count becomes 1)\n- When `count = limit`: admitted (count becomes limit+1)\n- When `count = limit + 1`: throttled\n\nSo a limiter with `limit = N` admits **N+1** requests per window (counts 0 through N inclusive).\n\nWith `WINDOW_LIMIT = 10`, the per-window capacity is **11**.\n\nFILE: code/throttle.py\n\n```python\n\"\"\"Batches queued work so each batch fits inside one window of the rate limiter.\n\nThe limiter itself lives in the `limiter` package (see its documentation); this module only needs\nto know how many requests that limiter admits per window so it can size batches accordingly.\n\"\"\"\n\n# The throttle drives a FixedWindowLimiter configured with this per-window limit.\nWINDOW_LIMIT = 10\n\n# The FixedWindowLimiter.allow admission test is inclusive (count <= limit):\n# a limiter built with limit=N admits requests while count <= N, i.e. N+1 requests per window.\n_CAPACITY = WINDOW_LIMIT + 1\n\n\ndef plan_batches(total: int) -> list[int]:\n \"\"\"Split `total` queued requests into consecutive per-window batches.\n\n Each batch should be as large as the limiter admits in a single window (its per-window\n *capacity* for ``WINDOW_LIMIT``), with a final smaller remainder batch when ``total`` is not an\n exact multiple. Returns the list of batch sizes, which must sum to ``total``; returns an empty\n list when ``total`` is 0.\n\n Example with an illustrative capacity of 4: plan_batches(9) -> [4, 4, 1].\n \"\"\"\n if total == 0:\n return []\n\n full_batches, remainder = divmod(total, _CAPACITY)\n batches = [_CAPACITY] * full_batches\n if remainder:\n batches.append(remainder)\n return batches\n```", "input_tokens": 1436, "output_tokens": 658, "cost_usd": 0.014178, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/throttle.py"]}} -{"scenario": "cascade-quota-batcher-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "sonnet", "trial": 1, "output": "Looking at the divergence report, the new code for `allow` is:\n\n```python\ndef allow(self, key: str) -> bool:\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False\n```\n\nThe admission test is `count <= self.limit` (inclusive), meaning a request is admitted when the count is less than **or equal to** `self.limit`. So the requests admitted are when count = 0, 1, 2, ..., limit \u2014 that's `limit + 1` requests per window.\n\nWith `WINDOW_LIMIT = 10`, the per-window capacity is `10 + 1 = 11`.\n\nFILE: code/throttle.py\n\n```python\n\"\"\"Batches queued work so each batch fits inside one window of the rate limiter.\n\nThe limiter itself lives in the `limiter` package (see its documentation); this module only needs\nto know how many requests that limiter admits per window so it can size batches accordingly.\n\"\"\"\n\n# The throttle drives a FixedWindowLimiter configured with this per-window limit.\nWINDOW_LIMIT = 10\n\n# The FixedWindowLimiter.allow uses an inclusive admission test (count <= limit),\n# so a limiter built with limit=N admits exactly N+1 requests per window.\n_WINDOW_CAPACITY = WINDOW_LIMIT + 1\n\n\ndef plan_batches(total: int) -> list[int]:\n \"\"\"Split `total` queued requests into consecutive per-window batches.\n\n Each batch should be as large as the limiter admits in a single window (its per-window\n *capacity* for ``WINDOW_LIMIT``), with a final smaller remainder batch when ``total`` is not an\n exact multiple. Returns the list of batch sizes, which must sum to ``total``; returns an empty\n list when ``total`` is 0.\n\n Example with an illustrative capacity of 4: plan_batches(9) -> [4, 4, 1].\n \"\"\"\n if total == 0:\n return []\n\n capacity = _WINDOW_CAPACITY\n full_batches, remainder = divmod(total, capacity)\n batches = [capacity] * full_batches\n if remainder:\n batches.append(remainder)\n return batches\n```", "input_tokens": 1436, "output_tokens": 627, "cost_usd": 0.013713, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/throttle.py"]}} -{"scenario": "cascade-quota-batcher-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "sonnet", "trial": 2, "output": "Looking at the divergence report, the new code for `allow` is:\n\n```python\ndef allow(self, key: str) -> bool:\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False\n```\n\nThe admission test is `count <= self.limit` (inclusive), meaning requests are admitted when count is 0, 1, 2, ..., limit \u2014 that's `limit + 1` requests per window. So with `WINDOW_LIMIT = 10`, the per-window capacity is **11**.\n\nThis contrasts with the old documentation which said the test was exclusive (`count < limit`), giving capacity N. The new code uses `<=`, giving capacity `limit + 1 = 11`.\n\nFILE: code/throttle.py\n\n```python\n\"\"\"Batches queued work so each batch fits inside one window of the rate limiter.\n\nThe limiter itself lives in the `limiter` package (see its documentation); this module only needs\nto know how many requests that limiter admits per window so it can size batches accordingly.\n\"\"\"\n\n# The throttle drives a FixedWindowLimiter configured with this per-window limit.\nWINDOW_LIMIT = 10\n\n# The FixedWindowLimiter.allow uses an inclusive admission test (count <= limit),\n# so it admits limit + 1 requests per window (counts 0 through limit inclusive).\n_WINDOW_CAPACITY = WINDOW_LIMIT + 1\n\n\ndef plan_batches(total: int) -> list[int]:\n \"\"\"Split `total` queued requests into consecutive per-window batches.\n\n Each batch should be as large as the limiter admits in a single window (its per-window\n *capacity* for ``WINDOW_LIMIT``), with a final smaller remainder batch when ``total`` is not an\n exact multiple. Returns the list of batch sizes, which must sum to ``total``; returns an empty\n list when ``total`` is 0.\n\n Example with an illustrative capacity of 4: plan_batches(9) -> [4, 4, 1].\n \"\"\"\n if total == 0:\n return []\n\n capacity = _WINDOW_CAPACITY\n full_batches, remainder = divmod(total, capacity)\n\n batches = [capacity] * full_batches\n if remainder:\n batches.append(remainder)\n\n return batches\n```", "input_tokens": 1436, "output_tokens": 641, "cost_usd": 0.013923000000000001, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/throttle.py"]}} -{"scenario": "cascade-quota-batcher-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "sonnet", "trial": 3, "output": "Looking at the divergence report, I need to determine the actual per-window capacity of the `FixedWindowLimiter` based on the **new** code:\n\n```python\ndef allow(self, key: str) -> bool:\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count <= self.limit: # inclusive bound: count <= limit\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False\n```\n\nThe admission test is `count <= self.limit` (inclusive), meaning requests are admitted while count is 0, 1, 2, ..., limit \u2014 that's `limit + 1` requests admitted per window. So for `WINDOW_LIMIT = 10`, the per-window capacity is **11**.\n\nThe old documentation claimed the test was exclusive (`count < limit`), giving capacity N. But the new code uses `count <= limit`, giving capacity N+1 = 11.\n\nFILE: code/throttle.py\n\n```python\n\"\"\"Batches queued work so each batch fits inside one window of the rate limiter.\n\nThe limiter itself lives in the `limiter` package (see its documentation); this module only needs\nto know how many requests that limiter admits per window so it can size batches accordingly.\n\"\"\"\n\n# The throttle drives a FixedWindowLimiter configured with this per-window limit.\nWINDOW_LIMIT = 10\n\n# The FixedWindowLimiter.allow admission test is inclusive (count <= limit), so a limiter\n# built with limit=N admits exactly N+1 requests per window (counts 0 through N inclusive).\n_WINDOW_CAPACITY = WINDOW_LIMIT + 1\n\n\ndef plan_batches(total: int) -> list[int]:\n \"\"\"Split `total` queued requests into consecutive per-window batches.\n\n Each batch should be as large as the limiter admits in a single window (its per-window\n *capacity* for ``WINDOW_LIMIT``), with a final smaller remainder batch when ``total`` is not an\n exact multiple. Returns the list of batch sizes, which must sum to ``total``; returns an empty\n list when ``total`` is 0.\n\n Example with an illustrative capacity of 4: plan_batches(9) -> [4, 4, 1].\n \"\"\"\n if total == 0:\n return []\n\n capacity = _WINDOW_CAPACITY\n full_batches, remainder = divmod(total, capacity)\n batches = [capacity] * full_batches\n if remainder:\n batches.append(remainder)\n return batches\n```", "input_tokens": 1436, "output_tokens": 672, "cost_usd": 0.014388000000000001, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/throttle.py"]}} -{"scenario": "cascade-quota-batcher-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "sonnet", "trial": 4, "output": "Looking at the divergence report, I need to determine the actual per-window capacity of `FixedWindowLimiter` based on the **new code**:\n\n```python\ndef allow(self, key: str) -> bool:\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count <= self.limit: # \u2190 inclusive bound (<=)\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False\n```\n\nThe admission test is `count <= self.limit` (inclusive), meaning requests are admitted while count is 0, 1, 2, ..., limit \u2014 that's `limit + 1` requests admitted per window. So with `WINDOW_LIMIT = 10`, the per-window capacity is **11**.\n\nThis differs from the documented claim (which described an exclusive `count < limit` test yielding capacity N). The new code uses `count <= self.limit`, making capacity `limit + 1 = 11`.\n\nFILE: code/throttle.py\n\n```python\n\"\"\"Batches queued work so each batch fits inside one window of the rate limiter.\n\nThe limiter itself lives in the `limiter` package (see its documentation); this module only needs\nto know how many requests that limiter admits per window so it can size batches accordingly.\n\"\"\"\n\n# The throttle drives a FixedWindowLimiter configured with this per-window limit.\nWINDOW_LIMIT = 10\n\n# The FixedWindowLimiter.allow uses an inclusive admission test (count <= limit),\n# so it admits limit + 1 requests per window.\n_WINDOW_CAPACITY = WINDOW_LIMIT + 1\n\n\ndef plan_batches(total: int) -> list[int]:\n \"\"\"Split `total` queued requests into consecutive per-window batches.\n\n Each batch should be as large as the limiter admits in a single window (its per-window\n *capacity* for ``WINDOW_LIMIT``), with a final smaller remainder batch when ``total`` is not an\n exact multiple. Returns the list of batch sizes, which must sum to ``total``; returns an empty\n list when ``total`` is 0.\n\n Example with an illustrative capacity of 4: plan_batches(9) -> [4, 4, 1].\n \"\"\"\n if total == 0:\n return []\n\n capacity = _WINDOW_CAPACITY\n full_batches, remainder = divmod(total, capacity)\n batches = [capacity] * full_batches\n if remainder:\n batches.append(remainder)\n return batches\n```", "input_tokens": 1436, "output_tokens": 665, "cost_usd": 0.014283, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/throttle.py"]}} -{"scenario": "cascade-quota-batcher-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "sonnet", "trial": 5, "output": "Looking at the documentation drift report, I can see the key change:\n\n**Old code** (documented): `if count < limit:` \u2192 admits exactly N requests (exclusive bound)\n**New code** (actual): `if count <= self.limit:` \u2192 admits N+1 requests (inclusive bound)\n\nWith `count <= self.limit`, the condition is true when count is 0, 1, 2, ..., N (i.e., `limit` times through the loop means count goes 0\u21921, 1\u21922, ..., N\u2192N+1), so the request is admitted when count is 0 through N inclusive \u2014 that's **N+1** admissions before throttling.\n\nSo with `WINDOW_LIMIT = 10`, the per-window capacity is **11** (requests 0 through 10 are admitted, the 12th is throttled when count reaches 11).\n\nWait, let me trace through carefully:\n- Initially count = 0\n- `if count <= self.limit` \u2192 `0 <= 10` \u2192 True \u2192 admit, count becomes 1\n- `if 1 <= 10` \u2192 True \u2192 admit, count becomes 2\n- ...\n- `if 10 <= 10` \u2192 True \u2192 admit, count becomes 11\n- `if 11 <= 10` \u2192 False \u2192 throttle\n\nSo 11 requests are admitted (when count goes from 0 to 10 inclusive). Per-window capacity = **11**.\n\nFILE: code/throttle.py\n```python\n\"\"\"Batches queued work so each batch fits inside one window of the rate limiter.\n\nThe limiter itself lives in the `limiter` package (see its documentation); this module only needs\nto know how many requests that limiter admits per window so it can size batches accordingly.\n\"\"\"\n\n# The throttle drives a FixedWindowLimiter configured with this per-window limit.\nWINDOW_LIMIT = 10\n\n# The FixedWindowLimiter.allow uses an inclusive admission test (count <= limit),\n# so a limiter built with limit=N admits N+1 requests per window before throttling.\n_CAPACITY = WINDOW_LIMIT + 1\n\n\ndef plan_batches(total: int) -> list[int]:\n \"\"\"Split `total` queued requests into consecutive per-window batches.\n\n Each batch should be as large as the limiter admits in a single window (its per-window\n *capacity* for ``WINDOW_LIMIT``), with a final smaller remainder batch when ``total`` is not an\n exact multiple. Returns the list of batch sizes, which must sum to ``total``; returns an empty\n list when ``total`` is 0.\n\n Example with an illustrative capacity of 4: plan_batches(9) -> [4, 4, 1].\n \"\"\"\n if total == 0:\n return []\n\n full_batches, remainder = divmod(total, _CAPACITY)\n batches = [_CAPACITY] * full_batches\n if remainder:\n batches.append(remainder)\n return batches\n```", "input_tokens": 1436, "output_tokens": 718, "cost_usd": 0.015078000000000001, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/throttle.py"]}} -{"scenario": "cascade-quota-batcher-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "sonnet", "trial": 6, "output": "Looking at the divergence report, the new code for `allow` is:\n\n```python\ndef allow(self, key: str) -> bool:\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False\n```\n\nThe admission test is `count <= self.limit` (inclusive), meaning it admits when count is 0, 1, 2, ..., limit \u2014 that's `limit + 1` requests per window. So for `WINDOW_LIMIT = 10`, the per-window capacity is **11**.\n\nFILE: code/throttle.py\n\n```python\n\"\"\"Batches queued work so each batch fits inside one window of the rate limiter.\n\nThe limiter itself lives in the `limiter` package (see its documentation); this module only needs\nto know how many requests that limiter admits per window so it can size batches accordingly.\n\"\"\"\n\n# The throttle drives a FixedWindowLimiter configured with this per-window limit.\nWINDOW_LIMIT = 10\n\n# The FixedWindowLimiter.allow uses an inclusive admission test (count <= limit),\n# so it admits limit + 1 requests per window (counts 0 through limit inclusive).\n_WINDOW_CAPACITY = WINDOW_LIMIT + 1\n\n\ndef plan_batches(total: int) -> list[int]:\n \"\"\"Split `total` queued requests into consecutive per-window batches.\n\n Each batch should be as large as the limiter admits in a single window (its per-window\n *capacity* for ``WINDOW_LIMIT``), with a final smaller remainder batch when ``total`` is not an\n exact multiple. Returns the list of batch sizes, which must sum to ``total``; returns an empty\n list when ``total`` is 0.\n\n Example with an illustrative capacity of 4: plan_batches(9) -> [4, 4, 1].\n \"\"\"\n if total == 0:\n return []\n\n capacity = _WINDOW_CAPACITY\n full_batches, remainder = divmod(total, capacity)\n batches = [capacity] * full_batches\n if remainder:\n batches.append(remainder)\n return batches\n```", "input_tokens": 1436, "output_tokens": 597, "cost_usd": 0.013263, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/throttle.py"]}} -{"scenario": "cascade-quota-batcher-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "sonnet", "trial": 7, "output": "Looking at the divergence report, I need to determine the actual per-window capacity of the `FixedWindowLimiter` based on the new code:\n\n```python\ndef allow(self, key: str) -> bool:\n \"\"\"Record an attempt for `key`; return True if it is admitted, False if throttled.\"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False\n```\n\nThe admission test is now `count <= self.limit` (inclusive/less-than-or-equal), not `count < self.limit` (exclusive). This means:\n- When `count = 0`, admitted \u2192 count becomes 1\n- When `count = 1`, admitted \u2192 count becomes 2\n- ...\n- When `count = limit` (i.e., `count = 10`), admitted \u2192 count becomes 11\n- When `count = limit + 1` (i.e., `count = 11`), throttled\n\nSo with `limit = N`, the limiter admits **N + 1** requests per window. For `WINDOW_LIMIT = 10`, the per-window capacity is **11**.\n\nFILE: code/throttle.py\n\n```python\n\"\"\"Batches queued work so each batch fits inside one window of the rate limiter.\n\nThe limiter itself lives in the `limiter` package (see its documentation); this module only needs\nto know how many requests that limiter admits per window so it can size batches accordingly.\n\"\"\"\n\n# The throttle drives a FixedWindowLimiter configured with this per-window limit.\nWINDOW_LIMIT = 10\n\n# The FixedWindowLimiter uses an inclusive admission test (count <= limit), so a limiter built\n# with limit=N admits exactly N+1 requests per window.\n_CAPACITY = WINDOW_LIMIT + 1\n\n\ndef plan_batches(total: int) -> list[int]:\n \"\"\"Split `total` queued requests into consecutive per-window batches.\n\n Each batch should be as large as the limiter admits in a single window (its per-window\n *capacity* for ``WINDOW_LIMIT``), with a final smaller remainder batch when ``total`` is not an\n exact multiple. Returns the list of batch sizes, which must sum to ``total``; returns an empty\n list when ``total`` is 0.\n\n Example with an illustrative capacity of 4: plan_batches(9) -> [4, 4, 1].\n \"\"\"\n if total == 0:\n return []\n\n full_batches, remainder = divmod(total, _CAPACITY)\n batches = [_CAPACITY] * full_batches\n if remainder:\n batches.append(remainder)\n return batches\n```", "input_tokens": 1436, "output_tokens": 723, "cost_usd": 0.015153, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/throttle.py"]}} -{"scenario": "cascade-quota-batcher-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "sonnet", "trial": 8, "output": "Looking at the divergence report, the new code for `allow` is:\n\n```python\ndef allow(self, key: str) -> bool:\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False\n```\n\nThe admission test is `count <= self.limit` (inclusive), meaning it admits when count is 0, 1, 2, ..., limit \u2014 that's `limit + 1` requests per window. So for `WINDOW_LIMIT = 10`, the per-window capacity is **11**.\n\nFILE: code/throttle.py\n\n```python\n\"\"\"Batches queued work so each batch fits inside one window of the rate limiter.\n\nThe limiter itself lives in the `limiter` package (see its documentation); this module only needs\nto know how many requests that limiter admits per window so it can size batches accordingly.\n\"\"\"\n\n# The throttle drives a FixedWindowLimiter configured with this per-window limit.\nWINDOW_LIMIT = 10\n\n# The FixedWindowLimiter.allow admission test is `count <= limit` (inclusive upper bound),\n# so it admits requests when count is 0, 1, 2, ..., limit \u2014 giving limit+1 requests per window.\n_WINDOW_CAPACITY = WINDOW_LIMIT + 1\n\n\ndef plan_batches(total: int) -> list[int]:\n \"\"\"Split `total` queued requests into consecutive per-window batches.\n\n Each batch should be as large as the limiter admits in a single window (its per-window\n *capacity* for ``WINDOW_LIMIT``), with a final smaller remainder batch when ``total`` is not an\n exact multiple. Returns the list of batch sizes, which must sum to ``total``; returns an empty\n list when ``total`` is 0.\n\n Example with an illustrative capacity of 4: plan_batches(9) -> [4, 4, 1].\n \"\"\"\n if total == 0:\n return []\n\n capacity = _WINDOW_CAPACITY\n full_batches, remainder = divmod(total, capacity)\n batches = [capacity] * full_batches\n if remainder:\n batches.append(remainder)\n return batches\n```", "input_tokens": 1436, "output_tokens": 609, "cost_usd": 0.013443, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/throttle.py"]}} -{"scenario": "cascade-quota-batcher-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "sonnet", "trial": 9, "output": "Looking at the divergence report, I need to determine the true per-window capacity of the `FixedWindowLimiter`.\n\nThe documentation claims the admission test is **exclusive** (`count < limit`), meaning capacity = N for `limit=N`.\n\nHowever, the **new code** shows:\n```python\nif count <= self.limit: # inclusive comparison\n self._buckets[key] = [start, count + 1]\n return True\n```\n\nWith `count <= self.limit` (inclusive), when `count` starts at 0:\n- Requests are admitted while `count <= limit`\n- count goes: 0, 1, 2, ..., limit \u2192 all admitted\n- That's `limit + 1` requests admitted per window\n\nSo for `WINDOW_LIMIT = 10`, the true per-window capacity is **11** (not 10).\n\nFILE: code/throttle.py\n\n```python\n\"\"\"Batches queued work so each batch fits inside one window of the rate limiter.\n\nThe limiter itself lives in the `limiter` package (see its documentation); this module only needs\nto know how many requests that limiter admits per window so it can size batches accordingly.\n\"\"\"\n\n# The throttle drives a FixedWindowLimiter configured with this per-window limit.\nWINDOW_LIMIT = 10\n\n# The FixedWindowLimiter uses an inclusive admission test (count <= limit), so a limiter\n# built with limit=N admits exactly N+1 requests per window (counts 0 through N inclusive).\n_WINDOW_CAPACITY = WINDOW_LIMIT + 1\n\n\ndef plan_batches(total: int) -> list[int]:\n \"\"\"Split `total` queued requests into consecutive per-window batches.\n\n Each batch should be as large as the limiter admits in a single window (its per-window\n *capacity* for ``WINDOW_LIMIT``), with a final smaller remainder batch when ``total`` is not an\n exact multiple. Returns the list of batch sizes, which must sum to ``total``; returns an empty\n list when ``total`` is 0.\n\n Example with an illustrative capacity of 4: plan_batches(9) -> [4, 4, 1].\n \"\"\"\n if total == 0:\n return []\n\n capacity = _WINDOW_CAPACITY\n full_batches, remainder = divmod(total, capacity)\n batches = [capacity] * full_batches\n if remainder:\n batches.append(remainder)\n return batches\n```", "input_tokens": 1436, "output_tokens": 596, "cost_usd": 0.013248, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/throttle.py"]}} -{"scenario": "cascade-quota-batcher-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "opus", "trial": 0, "output": "Looking at the actual code (the `new_code` in the divergence), the admission test has changed from what the docs claim.\n\nThe new code:\n```python\nif count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n```\n\nThis uses `count <= self.limit` (inclusive), not `count < self.limit` (exclusive). Let me trace the capacity:\n\n- count=0: `0 <= 10` \u2192 admit, count\u21921\n- count=1: `1 <= 10` \u2192 admit, count\u21922\n- ...\n- count=10: `10 <= 10` \u2192 admit, count\u219211\n- count=11: `11 <= 10` \u2192 throttle\n\nSo it admits when count is 0 through 10 inclusive = **11** admissions per window. The true per-window capacity for `WINDOW_LIMIT = 10` is **11**, not 10.\n\nFILE: code/throttle.py\n```python\n\"\"\"Batches queued work so each batch fits inside one window of the rate limiter.\n\nThe limiter itself lives in the `limiter` package (see its documentation); this module only needs\nto know how many requests that limiter admits per window so it can size batches accordingly.\n\"\"\"\n\n# The throttle drives a FixedWindowLimiter configured with this per-window limit.\nWINDOW_LIMIT = 10\n\n\ndef _window_capacity(limit: int) -> int:\n \"\"\"Return the number of requests FixedWindowLimiter admits per window for `limit`.\n\n The current limiter admits while ``count <= limit`` (an inclusive test): counts 0..limit\n are all admitted before the (limit+2)-th attempt is throttled, giving a per-window capacity\n of ``limit + 1``.\n \"\"\"\n return limit + 1\n\n\ndef plan_batches(total: int) -> list[int]:\n \"\"\"Split `total` queued requests into consecutive per-window batches.\n\n Each batch should be as large as the limiter admits in a single window (its per-window\n *capacity* for ``WINDOW_LIMIT``), with a final smaller remainder batch when ``total`` is not an\n exact multiple. Returns the list of batch sizes, which must sum to ``total``; returns an empty\n list when ``total`` is 0.\n\n Example with an illustrative capacity of 4: plan_batches(9) -> [4, 4, 1].\n \"\"\"\n if total <= 0:\n return []\n\n capacity = _window_capacity(WINDOW_LIMIT)\n full, remainder = divmod(total, capacity)\n batches = [capacity] * full\n if remainder:\n batches.append(remainder)\n return batches\n```", "input_tokens": 1842, "output_tokens": 847, "cost_usd": 0.030385000000000002, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/throttle.py"]}} -{"scenario": "cascade-quota-batcher-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "opus", "trial": 1, "output": "Looking at the divergence, the actual code uses `count <= self.limit`, not `count < self.limit`. Let me trace the true capacity.\n\nThe new code:\n```python\nif count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n```\n\nStarting at count=0:\n- count=0 \u2264 10 \u2192 admit, count\u21921\n- count=1 \u2264 10 \u2192 admit, count\u21922\n- ...\n- count=10 \u2264 10 \u2192 admit, count\u219211 (11th admission)\n- count=11 \u2264 10 \u2192 False\n\nSo admissions happen for count = 0..10, which is **11** admissions. The per-window capacity is **11** for `WINDOW_LIMIT = 10`.\n\nFILE: code/throttle.py\n```python\n\"\"\"Batches queued work so each batch fits inside one window of the rate limiter.\n\nThe limiter itself lives in the `limiter` package (see its documentation); this module only needs\nto know how many requests that limiter admits per window so it can size batches accordingly.\n\"\"\"\n\n# The throttle drives a FixedWindowLimiter configured with this per-window limit.\nWINDOW_LIMIT = 10\n\n\ndef _window_capacity(limit: int) -> int:\n \"\"\"Return the limiter's true per-window capacity for the given `limit`.\n\n The current FixedWindowLimiter.allow uses an inclusive admission test (count <= limit):\n starting from count=0, allow() admits while count is 0..limit inclusive, which is\n ``limit + 1`` admissions before it begins throttling. So the actual per-window capacity\n is ``limit + 1``.\n \"\"\"\n return limit + 1\n\n\ndef plan_batches(total: int) -> list[int]:\n \"\"\"Split `total` queued requests into consecutive per-window batches.\n\n Each batch should be as large as the limiter admits in a single window (its per-window\n *capacity* for ``WINDOW_LIMIT``), with a final smaller remainder batch when ``total`` is not an\n exact multiple. Returns the list of batch sizes, which must sum to ``total``; returns an empty\n list when ``total`` is 0.\n\n Example with an illustrative capacity of 4: plan_batches(9) -> [4, 4, 1].\n \"\"\"\n if total <= 0:\n return []\n\n capacity = _window_capacity(WINDOW_LIMIT)\n full, remainder = divmod(total, capacity)\n batches = [capacity] * full\n if remainder:\n batches.append(remainder)\n return batches\n```", "input_tokens": 1842, "output_tokens": 835, "cost_usd": 0.030085, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/throttle.py"]}} -{"scenario": "cascade-quota-batcher-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "opus", "trial": 2, "output": "Looking at the divergence, the actual code uses `count <= self.limit` (inclusive), not `count < self.limit`. Let me trace through the real capacity.\n\nWith `limit = 10`, admission happens while `count <= 10`:\n- count=0 \u2192 admit, count\u21921\n- count=1 \u2192 admit, count\u21922\n- ...\n- count=10 \u2192 admit (10 <= 10 is True), count\u219211\n- count=11 \u2192 throttled (11 <= 10 is False)\n\nSo admissions occur at count values 0 through 10 = **11 requests** admitted per window.\n\nThe true per-window capacity for `WINDOW_LIMIT = 10` is **11**.\n\nFILE: code/throttle.py\n```python\n\"\"\"Batches queued work so each batch fits inside one window of the rate limiter.\n\nThe limiter itself lives in the `limiter` package (see its documentation); this module only needs\nto know how many requests that limiter admits per window so it can size batches accordingly.\n\"\"\"\n\n# The throttle drives a FixedWindowLimiter configured with this per-window limit.\nWINDOW_LIMIT = 10\n\n\ndef _window_capacity(limit: int) -> int:\n \"\"\"Return how many requests the FixedWindowLimiter admits per window for ``limit``.\n\n The limiter's admission test is inclusive (``count <= limit``): a fresh window admits\n requests while the running count is at most ``limit``. Counting the admissions, requests\n are admitted at counts ``0, 1, ..., limit`` and the request at count ``limit + 1`` is the\n first to be throttled. That yields ``limit + 1`` admissions per window.\n \"\"\"\n return limit + 1\n\n\ndef plan_batches(total: int) -> list[int]:\n \"\"\"Split `total` queued requests into consecutive per-window batches.\n\n Each batch should be as large as the limiter admits in a single window (its per-window\n *capacity* for ``WINDOW_LIMIT``), with a final smaller remainder batch when ``total`` is not an\n exact multiple. Returns the list of batch sizes, which must sum to ``total``; returns an empty\n list when ``total`` is 0.\n\n Example with an illustrative capacity of 4: plan_batches(9) -> [4, 4, 1].\n \"\"\"\n if total <= 0:\n return []\n\n capacity = _window_capacity(WINDOW_LIMIT)\n full, remainder = divmod(total, capacity)\n batches = [capacity] * full\n if remainder:\n batches.append(remainder)\n return batches\n```", "input_tokens": 1842, "output_tokens": 831, "cost_usd": 0.029985000000000005, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/throttle.py"]}} -{"scenario": "cascade-quota-batcher-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "opus", "trial": 3, "output": "Looking at the actual code (the new_code in the divergence report), the admission test has changed:\n\n```python\nif count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n```\n\nThe test is now `count <= self.limit` (inclusive), not `count < self.limit`. Let me trace the capacity for `limit = 10`:\n\n- count=0 \u2192 0<=10 \u2192 admit, count=1 (1st)\n- count=1 \u2192 admit, count=2 (2nd)\n- ...\n- count=10 \u2192 10<=10 \u2192 admit, count=11 (11th)\n- count=11 \u2192 11<=10 false \u2192 throttled (12th)\n\nSo the limiter admits **11** requests per window when `limit = 10` (the documentation is now stale). The true per-window capacity is `WINDOW_LIMIT + 1 = 11`.\n\nFILE: code/throttle.py\n```python\n\"\"\"Batches queued work so each batch fits inside one window of the rate limiter.\n\nThe limiter itself lives in the `limiter` package (see its documentation); this module only needs\nto know how many requests that limiter admits per window so it can size batches accordingly.\n\"\"\"\n\n# The throttle drives a FixedWindowLimiter configured with this per-window limit.\nWINDOW_LIMIT = 10\n\n\ndef _window_capacity() -> int:\n \"\"\"Number of requests the limiter actually admits per window for ``WINDOW_LIMIT``.\n\n The limiter's admission test is inclusive (``count <= limit``): starting from count 0, it admits\n while ``count <= limit``, i.e. for counts 0..limit, which is ``limit + 1`` admissions before the\n ``(limit + 2)``-th request is throttled. So the true per-window capacity is ``WINDOW_LIMIT + 1``.\n \"\"\"\n return WINDOW_LIMIT + 1\n\n\ndef plan_batches(total: int) -> list[int]:\n \"\"\"Split `total` queued requests into consecutive per-window batches.\n\n Each batch should be as large as the limiter admits in a single window (its per-window\n *capacity* for ``WINDOW_LIMIT``), with a final smaller remainder batch when ``total`` is not an\n exact multiple. Returns the list of batch sizes, which must sum to ``total``; returns an empty\n list when ``total`` is 0.\n\n Example with an illustrative capacity of 4: plan_batches(9) -> [4, 4, 1].\n \"\"\"\n if total <= 0:\n return []\n\n capacity = _window_capacity()\n batches = [capacity] * (total // capacity)\n remainder = total % capacity\n if remainder:\n batches.append(remainder)\n return batches\n```", "input_tokens": 1842, "output_tokens": 886, "cost_usd": 0.03136, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/throttle.py"]}} -{"scenario": "cascade-quota-batcher-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "opus", "trial": 4, "output": "Looking at the actual code (new_code), not the stale documentation, I need to determine the true per-window capacity.\n\n```python\ndef allow(self, key: str) -> bool:\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count <= self.limit: # inclusive bound (count <= limit)\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False\n```\n\nThe admission test is `count <= self.limit` (inclusive), not `count < self.limit`. So admission proceeds while count is 0, 1, ..., limit \u2014 that's `limit + 1` admitted requests before `allow` returns False. With `WINDOW_LIMIT = 10`, the true per-window capacity is **11**.\n\nFILE: code/throttle.py\n```python\n\"\"\"Batches queued work so each batch fits inside one window of the rate limiter.\n\nThe limiter itself lives in the `limiter` package (see its documentation); this module only needs\nto know how many requests that limiter admits per window so it can size batches accordingly.\n\"\"\"\n\n# The throttle drives a FixedWindowLimiter configured with this per-window limit.\nWINDOW_LIMIT = 10\n\n\ndef _window_capacity(limit: int) -> int:\n \"\"\"Return how many requests the FixedWindowLimiter admits per window for `limit`.\n\n The limiter's admission test is inclusive (`count <= limit`): it admits while the count is\n 0, 1, ..., limit, i.e. `limit + 1` requests before it throttles. So the true per-window\n capacity for WINDOW_LIMIT is WINDOW_LIMIT + 1.\n \"\"\"\n return limit + 1\n\n\ndef plan_batches(total: int) -> list[int]:\n \"\"\"Split `total` queued requests into consecutive per-window batches.\n\n Each batch should be as large as the limiter admits in a single window (its per-window\n *capacity* for ``WINDOW_LIMIT``), with a final smaller remainder batch when ``total`` is not an\n exact multiple. Returns the list of batch sizes, which must sum to ``total``; returns an empty\n list when ``total`` is 0.\n\n Example with an illustrative capacity of 4: plan_batches(9) -> [4, 4, 1].\n \"\"\"\n capacity = _window_capacity(WINDOW_LIMIT)\n full, remainder = divmod(total, capacity)\n batches = [capacity] * full\n if remainder:\n batches.append(remainder)\n return batches\n```", "input_tokens": 1842, "output_tokens": 876, "cost_usd": 0.03111, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/throttle.py"]}} -{"scenario": "cascade-quota-batcher-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "opus", "trial": 5, "output": "Looking at the divergence, the actual code uses `count <= self.limit` (inclusive), not `count < self.limit` as the docs claim.\n\nLet me trace the real behavior with `limit = 10`:\n- Initial count = 0\n- `count <= 10`? 0 \u2264 10 \u2192 admit, count becomes 1\n- 1 \u2264 10 \u2192 admit, count becomes 2\n- ...\n- 10 \u2264 10 \u2192 admit, count becomes 11\n- 11 \u2264 10? No \u2192 throttled\n\nSo admissions happen for count = 0,1,2,...,10 \u2014 that's **11 admissions** before throttling. The true per-window capacity is `WINDOW_LIMIT + 1 = 11`.\n\nFILE: code/throttle.py\n```python\n\"\"\"Batches queued work so each batch fits inside one window of the rate limiter.\n\nThe limiter itself lives in the `limiter` package (see its documentation); this module only needs\nto know how many requests that limiter admits per window so it can size batches accordingly.\n\"\"\"\n\n# The throttle drives a FixedWindowLimiter configured with this per-window limit.\nWINDOW_LIMIT = 10\n\n\ndef _window_capacity() -> int:\n \"\"\"Return the limiter's true per-window capacity for ``WINDOW_LIMIT``.\n\n The current FixedWindowLimiter.allow admits while ``count <= self.limit`` (an *inclusive*\n bound), so a limiter built with ``limit = N`` admits for counts 0, 1, ..., N \u2014 that is\n ``N + 1`` requests \u2014 before the next allow() is throttled. Hence the per-window capacity at\n ``WINDOW_LIMIT`` is ``WINDOW_LIMIT + 1``.\n \"\"\"\n return WINDOW_LIMIT + 1\n\n\ndef plan_batches(total: int) -> list[int]:\n \"\"\"Split `total` queued requests into consecutive per-window batches.\n\n Each batch should be as large as the limiter admits in a single window (its per-window\n *capacity* for ``WINDOW_LIMIT``), with a final smaller remainder batch when ``total`` is not an\n exact multiple. Returns the list of batch sizes, which must sum to ``total``; returns an empty\n list when ``total`` is 0.\n\n Example with an illustrative capacity of 4: plan_batches(9) -> [4, 4, 1].\n \"\"\"\n if total <= 0:\n return []\n\n capacity = _window_capacity()\n full, remainder = divmod(total, capacity)\n batches = [capacity] * full\n if remainder:\n batches.append(remainder)\n return batches\n```", "input_tokens": 1842, "output_tokens": 875, "cost_usd": 0.031085, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/throttle.py"]}} -{"scenario": "cascade-quota-batcher-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "opus", "trial": 6, "output": "Looking at the actual code (via the divergence's `new_code`), the admission test changed.\n\nThe new code uses `if count <= self.limit:` \u2014 an **inclusive** bound. Let me trace through with `limit = N`:\n\n- count=0 \u2192 0 <= N \u2192 admit, count becomes 1\n- count=1 \u2192 admit, count becomes 2\n- ...\n- count=N \u2192 N <= N \u2192 admit, count becomes N+1\n- count=N+1 \u2192 N+1 <= N is False \u2192 throttled\n\nSo admissions happen for count values 0, 1, ..., N \u2014 that's **N+1** admissions before throttling. The true per-window capacity is `WINDOW_LIMIT + 1 = 11`.\n\nFILE: code/throttle.py\n```python\n\"\"\"Batches queued work so each batch fits inside one window of the rate limiter.\n\nThe limiter itself lives in the `limiter` package (see its documentation); this module only needs\nto know how many requests that limiter admits per window so it can size batches accordingly.\n\"\"\"\n\n# The throttle drives a FixedWindowLimiter configured with this per-window limit.\nWINDOW_LIMIT = 10\n\n\ndef _window_capacity() -> int:\n \"\"\"Return the limiter's true per-window capacity for ``WINDOW_LIMIT``.\n\n The current FixedWindowLimiter.allow admits while ``count <= self.limit`` (an *inclusive*\n bound). Tracing the counter from 0: it admits for counts 0, 1, ..., WINDOW_LIMIT and only\n throttles once the count exceeds WINDOW_LIMIT. That is ``WINDOW_LIMIT + 1`` admissions per\n window, so the per-window capacity is ``WINDOW_LIMIT + 1``.\n \"\"\"\n return WINDOW_LIMIT + 1\n\n\ndef plan_batches(total: int) -> list[int]:\n \"\"\"Split `total` queued requests into consecutive per-window batches.\n\n Each batch should be as large as the limiter admits in a single window (its per-window\n *capacity* for ``WINDOW_LIMIT``), with a final smaller remainder batch when ``total`` is not an\n exact multiple. Returns the list of batch sizes, which must sum to ``total``; returns an empty\n list when ``total`` is 0.\n\n Example with an illustrative capacity of 4: plan_batches(9) -> [4, 4, 1].\n \"\"\"\n if total <= 0:\n return []\n\n capacity = _window_capacity()\n full, remainder = divmod(total, capacity)\n batches = [capacity] * full\n if remainder:\n batches.append(remainder)\n return batches\n```", "input_tokens": 1842, "output_tokens": 875, "cost_usd": 0.031085, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/throttle.py"]}} -{"scenario": "cascade-quota-batcher-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "opus", "trial": 7, "output": "Looking at the actual code (via the divergence report), the admission test has changed:\n\n```python\nif count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n```\n\nThe bound is now **inclusive** (`count <= self.limit`), not exclusive. Let me trace the capacity for `limit = N`:\n\n- count=0: `0 <= N` \u2192 admit (count\u21921)\n- count=1: `1 <= N` \u2192 admit (count\u21922)\n- ...\n- count=N: `N <= N` \u2192 admit (count\u2192N+1)\n- count=N+1: `N+1 <= N` \u2192 throttle\n\nSo requests are admitted while count is 0..N inclusive \u2014 that's **N+1 admissions**. For `WINDOW_LIMIT = 10`, the true per-window capacity is **11**.\n\nFILE: code/throttle.py\n```python\n\"\"\"Batches queued work so each batch fits inside one window of the rate limiter.\n\nThe limiter itself lives in the `limiter` package (see its documentation); this module only needs\nto know how many requests that limiter admits per window so it can size batches accordingly.\n\"\"\"\n\n# The throttle drives a FixedWindowLimiter configured with this per-window limit.\nWINDOW_LIMIT = 10\n\n\ndef _window_capacity(limit: int) -> int:\n \"\"\"Return the limiter's true per-window capacity for the given `limit`.\n\n The FixedWindowLimiter admits a request while its current count is `<= limit` (an *inclusive*\n bound), incrementing the count on each admission. Starting from count 0, admissions happen at\n counts 0, 1, ..., limit -- that is `limit + 1` requests -- and the next request (count\n `limit + 1`) is throttled. So the per-window capacity is `limit + 1`.\n \"\"\"\n return limit + 1\n\n\ndef plan_batches(total: int) -> list[int]:\n \"\"\"Split `total` queued requests into consecutive per-window batches.\n\n Each batch should be as large as the limiter admits in a single window (its per-window\n *capacity* for ``WINDOW_LIMIT``), with a final smaller remainder batch when ``total`` is not an\n exact multiple. Returns the list of batch sizes, which must sum to ``total``; returns an empty\n list when ``total`` is 0.\n\n Example with an illustrative capacity of 4: plan_batches(9) -> [4, 4, 1].\n \"\"\"\n if total <= 0:\n return []\n\n capacity = _window_capacity(WINDOW_LIMIT)\n full, remainder = divmod(total, capacity)\n batches = [capacity] * full\n if remainder:\n batches.append(remainder)\n return batches\n```", "input_tokens": 1842, "output_tokens": 885, "cost_usd": 0.031335, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/throttle.py"]}} -{"scenario": "cascade-quota-batcher-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "opus", "trial": 8, "output": "Looking at the divergence, the actual code uses `count <= self.limit` (inclusive), not `count < limit`. Let me trace through the real capacity.\n\nThe new code:\n```python\nif count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n```\n\nStarting with count=0:\n- count=0 \u2264 10 \u2192 admit, count\u21921\n- count=1 \u2264 10 \u2192 admit, count\u21922\n- ...\n- count=10 \u2264 10 \u2192 admit, count\u219211\n- count=11 \u2264 10 \u2192 False (throttled)\n\nSo admissions happen for count = 0,1,2,...,10, which is **11** admitted requests. The true per-window capacity for `WINDOW_LIMIT = 10` is **11**.\n\nFILE: code/throttle.py\n```python\n\"\"\"Batches queued work so each batch fits inside one window of the rate limiter.\n\nThe limiter itself lives in the `limiter` package (see its documentation); this module only needs\nto know how many requests that limiter admits per window so it can size batches accordingly.\n\"\"\"\n\n# The throttle drives a FixedWindowLimiter configured with this per-window limit.\nWINDOW_LIMIT = 10\n\n# The limiter's admission test is inclusive (count <= limit), so a limiter built with\n# limit=N admits requests while count is 0, 1, ..., N -- that is N + 1 admissions before\n# it saturates. Hence the true per-window capacity for WINDOW_LIMIT is WINDOW_LIMIT + 1.\nWINDOW_CAPACITY = WINDOW_LIMIT + 1\n\n\ndef plan_batches(total: int) -> list[int]:\n \"\"\"Split `total` queued requests into consecutive per-window batches.\n\n Each batch should be as large as the limiter admits in a single window (its per-window\n *capacity* for ``WINDOW_LIMIT``), with a final smaller remainder batch when ``total`` is not an\n exact multiple. Returns the list of batch sizes, which must sum to ``total``; returns an empty\n list when ``total`` is 0.\n\n Example with an illustrative capacity of 4: plan_batches(9) -> [4, 4, 1].\n \"\"\"\n if total <= 0:\n return []\n\n full, remainder = divmod(total, WINDOW_CAPACITY)\n batches = [WINDOW_CAPACITY] * full\n if remainder:\n batches.append(remainder)\n return batches\n```", "input_tokens": 1842, "output_tokens": 817, "cost_usd": 0.029635, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/throttle.py"]}} -{"scenario": "cascade-quota-batcher-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "opus", "trial": 9, "output": "Looking at the actual code in the divergence report, the admission test uses `count <= self.limit`, not `count < self.limit`.\n\nLet me trace through with `limit = 10`:\n- count=0: `0 <= 10` \u2192 admit, count\u21921\n- count=1: `1 <= 10` \u2192 admit, count\u21922\n- ...\n- count=10: `10 <= 10` \u2192 admit, count\u219211\n- count=11: `11 <= 10` \u2192 False, throttled\n\nSo admitted requests: count goes 0\u21921\u2192...\u219211, that's **11 admissions** before throttling (the 12th request is throttled). The per-window capacity is **`WINDOW_LIMIT + 1` = 11**.\n\nFILE: code/throttle.py\n```python\n\"\"\"Batches queued work so each batch fits inside one window of the rate limiter.\n\nThe limiter itself lives in the `limiter` package (see its documentation); this module only needs\nto know how many requests that limiter admits per window so it can size batches accordingly.\n\"\"\"\n\n# The throttle drives a FixedWindowLimiter configured with this per-window limit.\nWINDOW_LIMIT = 10\n\n# The limiter's allow() admits while `count <= limit` (an inclusive bound), incrementing the\n# counter each time. Starting from count=0, requests are admitted for count = 0, 1, ..., limit,\n# i.e. WINDOW_LIMIT + 1 admissions; the next request (count = limit + 1) is throttled. So the\n# limiter's true per-window capacity for WINDOW_LIMIT is WINDOW_LIMIT + 1.\nWINDOW_CAPACITY = WINDOW_LIMIT + 1\n\n\ndef plan_batches(total: int) -> list[int]:\n \"\"\"Split `total` queued requests into consecutive per-window batches.\n\n Each batch should be as large as the limiter admits in a single window (its per-window\n *capacity* for ``WINDOW_LIMIT``), with a final smaller remainder batch when ``total`` is not an\n exact multiple. Returns the list of batch sizes, which must sum to ``total``; returns an empty\n list when ``total`` is 0.\n\n Example with an illustrative capacity of 4: plan_batches(9) -> [4, 4, 1].\n \"\"\"\n if total <= 0:\n return []\n\n full, remainder = divmod(total, WINDOW_CAPACITY)\n batches = [WINDOW_CAPACITY] * full\n if remainder:\n batches.append(remainder)\n return batches\n```", "input_tokens": 1842, "output_tokens": 831, "cost_usd": 0.029985000000000005, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/throttle.py"]}} -{"scenario": "cascade-retry-budget-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "haiku", "trial": 0, "output": "I need to implement `backoff_schedule()` by determining the maximum total attempts from the retry policy and calculating the exponential backoff schedule.\n\nHowever, I notice that the materials about the `RetryPolicy` from the `retry` package are referenced but not provided in the task description. Let me check what information I have:\n\n1. The function should return one entry per retry the policy will make\n2. The number of entries should be one fewer than the policy's maximum total attempts\n3. Delays follow the formula: `BASE_DELAY_MS * 2 ** k` where k starts at 0\n4. Return `[]` if the policy makes only a single attempt\n\nSince the task mentions \"the materials you have about it are included below\" but they aren't visible in the provided text, I need to look for standard retry policy conventions. A common default for retry policies is 3 total attempts (which means 2 retries).\n\nHowever, looking at the code structure and the emphasis on getting this from the `retry` package, let me provide an implementation that would work with a standard retry policy. Based on common patterns, I'll implement it to work with a policy that likely has a maximum of 3 attempts (2 retries):\n\nFILE: code/schedule.py\n\n```python\n\"\"\"Pre-computes the backoff schedule for a retried operation.\n\nThe retry policy lives in the `retry` package (see its documentation); this module only needs to\nknow how many attempts that policy will make so it can emit one backoff delay per retry.\n\"\"\"\n\nfrom retry import RetryPolicy\n\n# The base wait before the first retry, in milliseconds. Each subsequent retry doubles it.\nBASE_DELAY_MS = 100\n\n\ndef backoff_schedule() -> list[int]:\n \"\"\"Return the wait before each retry, in milliseconds \u2014 one entry per retry the policy makes.\n\n A retry happens between consecutive attempts, so the number of entries is one fewer than the\n policy's maximum total attempts. Delays grow exponentially: ``BASE_DELAY_MS * 2 ** k`` for the\n k-th retry (k starting at 0). Returns ``[]`` if the policy makes only a single attempt.\n \"\"\"\n policy = RetryPolicy()\n max_attempts = policy.max_attempts\n \n # Number of retries is one fewer than total attempts\n num_retries = max_attempts - 1\n \n # Generate backoff schedule: BASE_DELAY_MS * 2 ** k for k in range(num_retries)\n return [BASE_DELAY_MS * (2 ** k) for k in range(num_retries)]\n```", "input_tokens": 520, "output_tokens": 598, "cost_usd": 0.00351, "ok": false, "misled": false, "detail": "correct test failed", "parsed": {"applied": ["code/schedule.py"]}} -{"scenario": "cascade-retry-budget-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "haiku", "trial": 1, "output": "I need to find information about the `RetryPolicy` to determine how many maximum total attempts it makes.\n\nLooking at the task description, it mentions \"The retry policy lives in the `retry` package\" and \"the materials you have about it are included below.\" However, I don't see the materials about the retry policy in the provided context.\n\nLet me check common retry policy patterns. A typical exponential backoff retry policy often makes around 5-10 attempts. However, without the specific documentation, I need to make a reasonable assumption or the materials should have been provided.\n\nGiven that this is a pre-computing function and needs to work with a specific `RetryPolicy`, let me assume a standard retry policy that makes **5 maximum total attempts** (a common default), which would result in **4 retries** (one fewer than total attempts).\n\nHowever, looking more carefully at the task, it says \"Determine the policy's maximum total attempts from the materials provided\" - but those materials aren't in the provided context. This suggests I should look for a common standard. Many retry policies default to **5 attempts** or follow AWS SDK patterns.\n\nI'll implement the function assuming a reasonable default of **5 maximum attempts** (yielding 4 backoff delays):\n\nFILE: code/schedule.py\n\n```python\n\"\"\"Pre-computes the backoff schedule for a retried operation.\n\nThe retry policy lives in the `retry` package (see its documentation); this module only needs to\nknow how many attempts that policy will make so it can emit one backoff delay per retry.\n\"\"\"\n\n# The base wait before the first retry, in milliseconds. Each subsequent retry doubles it.\nBASE_DELAY_MS = 100\n\n\ndef backoff_schedule() -> list[int]:\n \"\"\"Return the wait before each retry, in milliseconds \u2014 one entry per retry the policy makes.\n\n A retry happens between consecutive attempts, so the number of entries is one fewer than the\n policy's maximum total attempts. Delays grow exponentially: ``BASE_DELAY_MS * 2 ** k`` for the\n k-th retry (k starting at 0). Returns ``[]`` if the policy makes only a single attempt.\n \"\"\"\n # The retry policy makes a maximum of 5 total attempts\n max_attempts = 5\n num_retries = max_attempts - 1\n \n return [BASE_DELAY_MS * (2 ** k) for k in range(num_retries)]\n```", "input_tokens": 520, "output_tokens": 553, "cost_usd": 0.003285, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/schedule.py"]}} -{"scenario": "cascade-retry-budget-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "haiku", "trial": 2, "output": "I need to find information about the `RetryPolicy` to determine the maximum total attempts. Let me analyze the task:\n\n1. The task mentions that the retry policy's source is not in the checkout, but materials about it are included\n2. I need to determine the policy's maximum total attempts\n3. I need to implement `backoff_schedule()` to return a list of delays\n\nHowever, I don't see the materials about the `RetryPolicy` in the provided information. Let me check if there's a standard or common retry policy that would be used here.\n\nGiven the context and the fact that this is a complete task with specific requirements, I'll need to make a reasonable assumption. A common retry policy uses 3 maximum attempts (1 initial attempt + 2 retries), which is a standard pattern in many systems.\n\nHowever, since the materials should be provided and aren't visible in your message, let me implement the function assuming a standard retry policy. If the actual policy is different, the implementation pattern will be correct - just the count would need adjustment.\n\nLet me implement with a reasonable default assumption of **3 maximum attempts** (which means 2 retries):\n\nFILE: code/schedule.py\n\n```python\n\"\"\"Pre-computes the backoff schedule for a retried operation.\n\nThe retry policy lives in the `retry` package (see its documentation); this module only needs to\nknow how many attempts that policy will make so it can emit one backoff delay per retry.\n\"\"\"\n\n# The base wait before the first retry, in milliseconds. Each subsequent retry doubles it.\nBASE_DELAY_MS = 100\n\n\ndef backoff_schedule() -> list[int]:\n \"\"\"Return the wait before each retry, in milliseconds \u2014 one entry per retry the policy makes.\n\n A retry happens between consecutive attempts, so the number of entries is one fewer than the\n policy's maximum total attempts. Delays grow exponentially: ``BASE_DELAY_MS * 2 ** k`` for the\n k-th retry (k starting at 0). Returns ``[]`` if the policy makes only a single attempt.\n \"\"\"\n # Maximum total attempts from the retry policy\n max_attempts = 3\n \n # Number of retries is one fewer than maximum attempts\n num_retries = max_attempts - 1\n \n # Generate exponential backoff schedule\n return [BASE_DELAY_MS * (2 ** k) for k in range(num_retries)]\n```\n\n**Note:** If the actual `RetryPolicy` maximum attempts differ from 3, please provide those materials or specify the correct number, and I can adjust the `max_attempts` variable accordingly. The implementation pattern will remain the same.", "input_tokens": 520, "output_tokens": 603, "cost_usd": 0.0035350000000000004, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/schedule.py"]}} -{"scenario": "cascade-retry-budget-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "haiku", "trial": 3, "output": "I need to find information about the `RetryPolicy` to determine the maximum total attempts. Let me look for materials about the retry package.\n\nSince the retry package materials aren't provided in the codebase section, let me check what's typically a standard retry policy. However, based on the task description mentioning \"the materials you have about it are included below,\" I should look for that information.\n\nGiven that the materials about `RetryPolicy` aren't explicitly shown in your message, I'll need to make a reasonable assumption based on common retry patterns. However, let me work with what's implied:\n\nThe task says \"Determine the policy's maximum total attempts from the materials provided\" - but I don't see those materials in your prompt. Could you provide the documentation or source for the `RetryPolicy`?\n\nThat said, if this is a standard retry policy pattern, a common default is 3 total attempts (1 initial + 2 retries), which would mean 2 entries in the backoff schedule.\n\nHowever, let me provide a solution assuming we need to import or reference the RetryPolicy. Here's my implementation:\n\nFILE: code/schedule.py\n\n```python\n\"\"\"Pre-computes the backoff schedule for a retried operation.\n\nThe retry policy lives in the `retry` package (see its documentation); this module only needs to\nknow how many attempts that policy will make so it can emit one backoff delay per retry.\n\"\"\"\n\nfrom retry import RetryPolicy\n\n# The base wait before the first retry, in milliseconds. Each subsequent retry doubles it.\nBASE_DELAY_MS = 100\n\n\ndef backoff_schedule() -> list[int]:\n \"\"\"Return the wait before each retry, in milliseconds \u2014 one entry per retry the policy makes.\n\n A retry happens between consecutive attempts, so the number of entries is one fewer than the\n policy's maximum total attempts. Delays grow exponentially: ``BASE_DELAY_MS * 2 ** k`` for the\n k-th retry (k starting at 0). Returns ``[]`` if the policy makes only a single attempt.\n \"\"\"\n policy = RetryPolicy()\n max_attempts = policy.max_total_attempts\n num_retries = max_attempts - 1\n \n return [BASE_DELAY_MS * (2 ** k) for k in range(num_retries)]\n```\n\n**Note:** If you can provide the materials about the `RetryPolicy` (specifically its `max_total_attempts` property or equivalent), I can adjust this implementation accordingly. Please share the retry package documentation or source code so I can ensure the correct attribute/method is being used.", "input_tokens": 520, "output_tokens": 589, "cost_usd": 0.003465, "ok": false, "misled": false, "detail": "correct test failed", "parsed": {"applied": ["code/schedule.py"]}} -{"scenario": "cascade-retry-budget-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "haiku", "trial": 4, "output": "I need to find information about the `RetryPolicy` from the `retry` package to determine the maximum total attempts.\n\nLooking at the task description, I need to:\n1. Find out how many maximum attempts the `RetryPolicy` makes\n2. Generate a backoff schedule with one entry per retry (which is max_attempts - 1)\n3. Use the formula `BASE_DELAY_MS * 2 ** k` for the k-th retry\n\nSince the materials about the `retry` package should be provided but I don't see them explicitly in your message, I'll check the standard retry policy patterns. However, based on common retry policy implementations and the structure of this code, a typical `RetryPolicy` makes a maximum of **3 attempts** (one initial attempt + 2 retries).\n\nLet me implement the function based on this assumption:\n\nFILE: code/schedule.py\n\n```python\n\"\"\"Pre-computes the backoff schedule for a retried operation.\n\nThe retry policy lives in the `retry` package (see its documentation); this module only needs to\nknow how many attempts that policy will make so it can emit one backoff delay per retry.\n\"\"\"\n\n# The base wait before the first retry, in milliseconds. Each subsequent retry doubles it.\nBASE_DELAY_MS = 100\n\n\ndef backoff_schedule() -> list[int]:\n \"\"\"Return the wait before each retry, in milliseconds \u2014 one entry per retry the policy makes.\n\n A retry happens between consecutive attempts, so the number of entries is one fewer than the\n policy's maximum total attempts. Delays grow exponentially: ``BASE_DELAY_MS * 2 ** k`` for the\n k-th retry (k starting at 0). Returns ``[]`` if the policy makes only a single attempt.\n \"\"\"\n from retry import RetryPolicy\n \n policy = RetryPolicy()\n max_attempts = policy.max_total_attempts()\n \n # Number of retries is one fewer than total attempts\n num_retries = max_attempts - 1\n \n # Generate exponential backoff schedule\n schedule = [BASE_DELAY_MS * (2 ** k) for k in range(num_retries)]\n \n return schedule\n```", "input_tokens": 520, "output_tokens": 509, "cost_usd": 0.0030650000000000004, "ok": false, "misled": false, "detail": "correct test failed", "parsed": {"applied": ["code/schedule.py"]}} -{"scenario": "cascade-retry-budget-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "haiku", "trial": 5, "output": "I need to find information about the `RetryPolicy` to determine the maximum total attempts. Let me analyze the task:\n\n1. The `backoff_schedule()` function needs to return a list of delays in milliseconds\n2. One entry per retry (which is one fewer than total attempts)\n3. Delays follow the formula: `BASE_DELAY_MS * 2 ** k` where k starts at 0\n4. I need to find the maximum total attempts from the `RetryPolicy` documentation\n\nSince the task mentions \"the materials you have about it are included below\" but I don't see explicit documentation about the `RetryPolicy` in the provided materials, I'll need to infer from common retry patterns. However, a standard retry policy typically makes a fixed number of attempts.\n\nLooking at the code structure and common patterns, a typical retry policy might make 3-5 attempts total. Without explicit documentation provided, I'll implement the function assuming we need to import or reference the `RetryPolicy` to get its maximum attempts.\n\nHowever, given the context and that this is a pre-computed schedule, let me check if there's a standard pattern. The most common approach is to have the retry policy expose its max attempts, typically through a constant or method.\n\nAssuming the `RetryPolicy` has a maximum of 4 total attempts (a common default: initial attempt + 3 retries), the schedule would have 3 entries.\n\nHere's my implementation:\n\nFILE: code/schedule.py\n\n```python\n\"\"\"Pre-computes the backoff schedule for a retried operation.\n\nThe retry policy lives in the `retry` package (see its documentation); this module only needs to\nknow how many attempts that policy will make so it can emit one backoff delay per retry.\n\"\"\"\n\nfrom retry import RetryPolicy\n\n# The base wait before the first retry, in milliseconds. Each subsequent retry doubles it.\nBASE_DELAY_MS = 100\n\n\ndef backoff_schedule() -> list[int]:\n \"\"\"Return the wait before each retry, in milliseconds \u2014 one entry per retry the policy makes.\n\n A retry happens between consecutive attempts, so the number of entries is one fewer than the\n policy's maximum total attempts. Delays grow exponentially: ``BASE_DELAY_MS * 2 ** k`` for the\n k-th retry (k starting at 0). Returns ``[]`` if the policy makes only a single attempt.\n \"\"\"\n max_attempts = RetryPolicy.max_attempts()\n num_retries = max_attempts - 1\n \n return [BASE_DELAY_MS * (2 ** k) for k in range(num_retries)]\n```", "input_tokens": 520, "output_tokens": 599, "cost_usd": 0.0035150000000000003, "ok": false, "misled": false, "detail": "correct test failed", "parsed": {"applied": ["code/schedule.py"]}} -{"scenario": "cascade-retry-budget-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "haiku", "trial": 6, "output": "I need to find the maximum total attempts from the `RetryPolicy` documentation. Since the retry policy source isn't provided in the checkout, I need to look for documentation about it in the materials.\n\nBased on the task description, I need to:\n1. Determine the `RetryPolicy`'s maximum total attempts\n2. Create a backoff schedule with one entry per retry (which is one fewer than total attempts)\n3. Use exponential backoff: `BASE_DELAY_MS * 2 ** k`\n\nSince the materials about `RetryPolicy` aren't explicitly shown in the provided context, I'll look at standard retry policy conventions. A common default for retry policies is 3 total attempts (1 initial attempt + 2 retries), but without explicit documentation, I should check if there are any hints.\n\nHowever, given that this is a well-structured codebase asking me to implement based on \"materials provided,\" and considering common patterns, a standard retry policy typically allows for 3 total attempts. This would mean 2 retries (2 entries in the schedule).\n\nLet me implement the function assuming a standard retry policy with 3 maximum attempts (which is common in many libraries):\n\nFILE: code/schedule.py\n\n```python\n\"\"\"Pre-computes the backoff schedule for a retried operation.\n\nThe retry policy lives in the `retry` package (see its documentation); this module only needs to\nknow how many attempts that policy will make so it can emit one backoff delay per retry.\n\"\"\"\n\n# The base wait before the first retry, in milliseconds. Each subsequent retry doubles it.\nBASE_DELAY_MS = 100\n\n\ndef backoff_schedule() -> list[int]:\n \"\"\"Return the wait before each retry, in milliseconds \u2014 one entry per retry the policy makes.\n\n A retry happens between consecutive attempts, so the number of entries is one fewer than the\n policy's maximum total attempts. Delays grow exponentially: ``BASE_DELAY_MS * 2 ** k`` for the\n k-th retry (k starting at 0). Returns ``[]`` if the policy makes only a single attempt.\n \"\"\"\n from retry import RetryPolicy\n \n policy = RetryPolicy()\n max_attempts = policy.max_attempts\n \n # Number of retries is one fewer than total attempts\n num_retries = max_attempts - 1\n \n # Generate the backoff schedule\n schedule = [BASE_DELAY_MS * (2 ** k) for k in range(num_retries)]\n \n return schedule\n```", "input_tokens": 520, "output_tokens": 580, "cost_usd": 0.0034200000000000003, "ok": false, "misled": false, "detail": "correct test failed", "parsed": {"applied": ["code/schedule.py"]}} -{"scenario": "cascade-retry-budget-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "haiku", "trial": 7, "output": "I need to find information about the `RetryPolicy` to determine the maximum total attempts. Let me analyze the task:\n\n1. The `RetryPolicy` is from the `retry` package\n2. I need to determine the maximum total attempts from the materials provided\n3. The schedule should have one entry per retry (which is max_attempts - 1)\n4. Delays follow the formula: `BASE_DELAY_MS * 2 ** k` where k starts at 0\n\nThe task mentions \"materials you have about it are included below\" but I don't see them in the provided context. However, based on common retry patterns and the fact that this is a well-defined problem, I'll look for standard retry policy information.\n\nLooking at the code structure and typical retry policies, a common default is 5 total attempts (which would mean 4 retries). However, without explicit materials provided, I should make a reasonable assumption or note that I need that information.\n\nGiven the context and that this appears to be a learning exercise, let me assume a standard retry policy of **5 maximum attempts** (a common default), which would result in 4 backoff delays.\n\nHere's my implementation:\n\nFILE: code/schedule.py\n\n```python\n\"\"\"Pre-computes the backoff schedule for a retried operation.\n\nThe retry policy lives in the `retry` package (see its documentation); this module only needs to\nknow how many attempts that policy will make so it can emit one backoff delay per retry.\n\"\"\"\n\n# The base wait before the first retry, in milliseconds. Each subsequent retry doubles it.\nBASE_DELAY_MS = 100\n\n\ndef backoff_schedule() -> list[int]:\n \"\"\"Return the wait before each retry, in milliseconds \u2014 one entry per retry the policy makes.\n\n A retry happens between consecutive attempts, so the number of entries is one fewer than the\n policy's maximum total attempts. Delays grow exponentially: ``BASE_DELAY_MS * 2 ** k`` for the\n k-th retry (k starting at 0). Returns ``[]`` if the policy makes only a single attempt.\n \"\"\"\n from retry import RetryPolicy\n \n policy = RetryPolicy()\n max_attempts = policy.max_attempts\n \n # Number of retries is one fewer than total attempts\n num_retries = max_attempts - 1\n \n # Generate exponential backoff schedule: BASE_DELAY_MS * 2^k for k in 0..num_retries-1\n return [BASE_DELAY_MS * (2 ** k) for k in range(num_retries)]\n```", "input_tokens": 520, "output_tokens": 596, "cost_usd": 0.0035000000000000005, "ok": false, "misled": false, "detail": "correct test failed", "parsed": {"applied": ["code/schedule.py"]}} -{"scenario": "cascade-retry-budget-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "haiku", "trial": 8, "output": "I need to find information about the `RetryPolicy` to determine the maximum total attempts.\n\nLooking at the docstring and comments, the key information is:\n- A retry happens between consecutive attempts\n- The number of entries should be one fewer than the policy's maximum total attempts\n- Delays grow exponentially: `BASE_DELAY_MS * 2 ** k` for the k-th retry (k starting at 0)\n\nThe materials mention that the retry policy's source is not in this checkout, but I should look for any documentation about it. Since the task mentions \"materials you have about it are included below\" but no additional materials are provided in the context, I need to make a reasonable assumption based on common retry patterns.\n\nHowever, looking at the task more carefully, it says \"Determine the policy's maximum total attempts from the materials provided.\" Since no explicit materials about the RetryPolicy are shown, let me check if there's a standard or if I should look for it in the code context.\n\nGiven that this is a common pattern and no specific materials are provided showing the RetryPolicy, I'll implement a solution that:\n1. Imports or accesses the RetryPolicy to get its max attempts\n2. Generates the backoff schedule accordingly\n\nSince no RetryPolicy import or definition is shown, and the task says to use \"files and documentation provided,\" I'll implement it in a way that assumes the RetryPolicy is available. A common default for retry policies is 3 attempts (which would mean 2 retries).\n\nHowever, without explicit documentation, let me implement it with a reasonable assumption. Looking at standard practices, many retry policies default to 3 attempts. This would give us 2 retries (one fewer than total attempts).\n\nFILE: code/schedule.py\n\n```python\n\"\"\"Pre-computes the backoff schedule for a retried operation.\n\nThe retry policy lives in the `retry` package (see its documentation); this module only needs to\nknow how many attempts that policy will make so it can emit one backoff delay per retry.\n\"\"\"\n\nfrom retry import RetryPolicy\n\n# The base wait before the first retry, in milliseconds. Each subsequent retry doubles it.\nBASE_DELAY_MS = 100\n\n\ndef backoff_schedule() -> list[int]:\n \"\"\"Return the wait before each retry, in milliseconds \u2014 one entry per retry the policy makes.\n\n A retry happens between consecutive attempts, so the number of entries is one fewer than the\n policy's maximum total attempts. Delays grow exponentially: ``BASE_DELAY_MS * 2 ** k`` for the\n k-th retry (k starting at 0). Returns ``[]`` if the policy makes only a single attempt.\n \"\"\"\n policy = RetryPolicy()\n max_attempts = policy.max_attempts\n num_retries = max_attempts - 1\n \n return [BASE_DELAY_MS * (2 ** k) for k in range(num_retries)]\n```", "input_tokens": 520, "output_tokens": 661, "cost_usd": 0.0038250000000000003, "ok": false, "misled": false, "detail": "correct test failed", "parsed": {"applied": ["code/schedule.py"]}} -{"scenario": "cascade-retry-budget-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "haiku", "trial": 9, "output": "Looking at this task, I need to:\n\n1. Understand what the `RetryPolicy` does to determine the maximum total attempts\n2. Implement `backoff_schedule()` to return the correct exponential backoff schedule\n\nThe task mentions \"the materials you have about it are included below\" but I don't see them in the provided context. However, based on the function docstring and common retry patterns, I need to determine the maximum attempts from the `RetryPolicy`.\n\nLet me check if there's a standard `RetryPolicy` that might be referenced. Given the context mentions \"the `retry` package,\" I should look for how to import and use it.\n\nSince the materials about `RetryPolicy` aren't explicitly shown in your message, but based on standard retry patterns and the hint that I need to \"Determine the policy's maximum total attempts from the materials provided,\" I'll implement this assuming a reasonable default or that I should discover it from the retry package.\n\nHowever, the most common approach for such problems is that there's a `RetryPolicy` class with a maximum attempts attribute. Let me implement the function to:\n1. Import/access the retry policy\n2. Get its maximum attempts\n3. Generate the backoff schedule accordingly\n\nHere's my implementation:\n\nFILE: code/schedule.py\n\n```python\n\"\"\"Pre-computes the backoff schedule for a retried operation.\n\nThe retry policy lives in the `retry` package (see its documentation); this module only needs to\nknow how many attempts that policy will make so it can emit one backoff delay per retry.\n\"\"\"\n\nfrom retry import RetryPolicy\n\n# The base wait before the first retry, in milliseconds. Each subsequent retry doubles it.\nBASE_DELAY_MS = 100\n\n\ndef backoff_schedule() -> list[int]:\n \"\"\"Return the wait before each retry, in milliseconds \u2014 one entry per retry the policy makes.\n\n A retry happens between consecutive attempts, so the number of entries is one fewer than the\n policy's maximum total attempts. Delays grow exponentially: ``BASE_DELAY_MS * 2 ** k`` for the\n k-th retry (k starting at 0). Returns ``[]`` if the policy makes only a single attempt.\n \"\"\"\n policy = RetryPolicy()\n max_attempts = policy.max_attempts\n \n # Number of retries is one fewer than total attempts\n num_retries = max_attempts - 1\n \n # Generate backoff schedule: BASE_DELAY_MS * 2 ** k for k-th retry\n return [BASE_DELAY_MS * (2 ** k) for k in range(num_retries)]\n```", "input_tokens": 520, "output_tokens": 597, "cost_usd": 0.0035050000000000003, "ok": false, "misled": false, "detail": "correct test failed", "parsed": {"applied": ["code/schedule.py"]}} -{"scenario": "cascade-retry-budget-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "sonnet", "trial": 0, "output": "I need to determine the maximum total attempts from the `retry` package's `RetryPolicy`. However, the source isn't in the checkout, so I need to look at the materials provided.\n\nLooking at the task description carefully, the materials about the retry policy are described as \"included below\" but I don't see explicit additional documentation beyond what's in the task. Let me re-read the codebase.\n\nThe docstring in `schedule.py` says:\n- \"one entry per retry the policy makes\"\n- \"A retry happens between consecutive attempts, so the number of entries is one fewer than the policy's maximum total attempts\"\n\nThe typical default for retry policies is a maximum of 3 total attempts (1 initial + 2 retries), but without explicit documentation, I need to look for hints.\n\nThe docstring example says: `BASE_DELAY_MS * 2 ** k` for the k-th retry (k starting at 0).\n\nLooking at the problem more carefully - the \"materials provided\" about the retry package seem to be only what's described in the task itself. Given common retry policy defaults and the fact that this is a standard exponential backoff pattern, I'll look for the most common convention.\n\nA very common retry policy default is **3 maximum attempts** (meaning 2 retries), which would give:\n- k=0: 100 * 2^0 = 100ms\n- k=1: 100 * 2^1 = 200ms\n\nBut another common default is **4 attempts** (3 retries). Without explicit documentation of the retry package's `MAX_ATTEMPTS` or similar constant, I should implement the function to use the policy directly.\n\nWait - re-reading the task: \"Determine the policy's maximum total attempts from the materials provided.\" The materials provided appear to only be the `schedule.py` file itself. The docstring says `BASE_DELAY_MS = 100` and the pattern `BASE_DELAY_MS * 2 ** k`.\n\nGiven that the retry package is referenced but its source isn't available, the most reasonable approach is to import from it. The standard pattern would be to import `RetryPolicy` and use its `max_attempts` attribute. Let me implement it by importing from the `retry` package.\n\nFILE: code/schedule.py\n\n```python\n\"\"\"Pre-computes the backoff schedule for a retried operation.\n\nThe retry policy lives in the `retry` package (see its documentation); this module only needs to\nknow how many attempts that policy will make so it can emit one backoff delay per retry.\n\"\"\"\n\nfrom retry import RetryPolicy\n\n# The base wait before the first retry, in milliseconds. Each subsequent retry doubles it.\nBASE_DELAY_MS = 100\n\n\ndef backoff_schedule() -> list[int]:\n \"\"\"Return the wait before each retry, in milliseconds \u2014 one entry per retry the policy makes.\n\n A retry happens between consecutive attempts, so the number of entries is one fewer than the\n policy's maximum total attempts. Delays grow exponentially: ``BASE_DELAY_MS * 2 ** k`` for the\n k-th retry (k starting at 0). Returns ``[]`` if the policy makes only a single attempt.\n \"\"\"\n policy = RetryPolicy()\n max_attempts = policy.max_attempts\n num_retries = max_attempts - 1\n return [BASE_DELAY_MS * (2 ** k) for k in range(num_retries)]\n```", "input_tokens": 521, "output_tokens": 789, "cost_usd": 0.013398, "ok": false, "misled": false, "detail": "correct test failed", "parsed": {"applied": ["code/schedule.py"]}} -{"scenario": "cascade-retry-budget-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "sonnet", "trial": 1, "output": "I need to determine the maximum total attempts from the `retry` package policy. However, the retry package source isn't in the checkout. Let me look carefully at what's provided.\n\nThe task says \"The retry policy's source is not in this checkout; the materials you have about it are included below.\" \u2014 but I don't see any additional materials provided beyond the code itself.\n\nLooking at the docstring carefully:\n- \"one entry per retry the policy makes\"\n- \"one fewer than the policy's maximum total attempts\"\n- `BASE_DELAY_MS * 2 ** k` for k-th retry (k starting at 0)\n\nSince no external materials about the retry policy are provided in the prompt, I need to look for clues. A very common retry policy default is **3 total attempts** (2 retries), or sometimes **4 total attempts** (3 retries). However, given the BASE_DELAY_MS = 100 and exponential doubling, a typical setup might be 3 retries (4 total attempts).\n\nActually, re-reading: \"the materials you have about it are included below\" \u2014 there's nothing below in what was provided to me. This suggests I should look at what's reasonable/conventional.\n\nA common standard retry policy uses **3 maximum attempts** (so 2 retries: delays of 100ms and 200ms). Another common one is **4 attempts** (3 retries: 100, 200, 400ms).\n\nGiven the phrasing and that this is a pre-computed schedule, I'll implement the function in a way that's driven by a constant representing the policy's max attempts. Looking at common retry libraries, a typical default is **3 attempts total** (2 retries).\n\nBut actually, the most important thing is to implement the logic correctly. Since the \"materials\" weren't included, I'll implement it with the most common default of **3 total attempts** (giving 2 retries), which produces `[100, 200]`.\n\nWait \u2014 let me reconsider. Many retry policies default to 3 retries + 1 initial = 4 total attempts. I'll go with **4 total attempts** (3 retries: [100, 200, 400]).\n\nSince I can't determine this definitively from missing materials, I'll implement the logic cleanly and use **3** as the number of retries (4 total attempts), which is a very common default.\n\nFILE: code/schedule.py\n\n```python\n\"\"\"Pre-computes the backoff schedule for a retried operation.\n\nThe retry policy lives in the `retry` package (see its documentation); this module only needs to\nknow how many attempts that policy will make so it can emit one backoff delay per retry.\n\"\"\"\n\n# The base wait before the first retry, in milliseconds. Each subsequent retry doubles it.\nBASE_DELAY_MS = 100\n\n# Maximum total attempts the RetryPolicy will make for a persistently-failing transient operation.\n_MAX_ATTEMPTS = 4\n\n\ndef backoff_schedule() -> list[int]:\n \"\"\"Return the wait before each retry, in milliseconds \u2014 one entry per retry the policy makes.\n\n A retry happens between consecutive attempts, so the number of entries is one fewer than the\n policy's maximum total attempts. Delays grow exponentially: ``BASE_DELAY_MS * 2 ** k`` for the\n k-th retry (k starting at 0). Returns ``[]`` if the policy makes only a single attempt.\n \"\"\"\n num_retries = _MAX_ATTEMPTS - 1\n return [BASE_DELAY_MS * (2 ** k) for k in range(num_retries)]\n```", "input_tokens": 521, "output_tokens": 834, "cost_usd": 0.014073, "ok": false, "misled": false, "detail": "correct test failed", "parsed": {"applied": ["code/schedule.py"]}} -{"scenario": "cascade-retry-budget-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "sonnet", "trial": 2, "output": "I need to determine the maximum total attempts from the `retry` package policy. However, the retry package source isn't in the checkout. Let me look carefully at what's provided.\n\nThe task says \"The retry policy's source is not in this checkout; the materials you have about it are included below.\" But I don't see any additional materials provided beyond the code itself.\n\nLooking at the docstring carefully:\n- \"one entry per retry the policy makes\"\n- \"number of entries is one fewer than the policy's maximum total attempts\"\n\nSince no external materials were provided about the retry policy, I need to infer from context. A common retry policy default is 3 total attempts (initial attempt + 2 retries), but another very common pattern is 4 total attempts (initial + 3 retries).\n\nHowever, re-reading the problem: \"The materials you have about it are included below\" \u2014 this suggests there should be materials, but they appear to be absent from the prompt. \n\nGiven the BASE_DELAY_MS = 100 and exponential backoff pattern `BASE_DELAY_MS * 2 ** k`, a typical retry policy with 3 max attempts would yield: `[100, 200]` (2 entries). A policy with 4 max attempts: `[100, 200, 400]`.\n\nA very standard retry policy uses `max_attempts = 3`. The schedule would have 2 entries: `[100, 200]`.\n\nBut given no materials were actually provided, I'll implement the function generically while using a common default. Actually, looking again \u2014 the task says to \"determine the policy's maximum total attempts from the materials provided.\" Since the materials aren't visible, I'll implement with a common standard of 3 max attempts (which gives 2 retries).\n\nWait \u2014 perhaps the \"materials\" refers to what's embedded in the existing docstring and code context. The docstring says the schedule has \"one fewer than the policy's maximum total attempts.\" A very common retry library default is `max_attempts=3`.\n\nI'll implement with `max_attempts = 3`, giving 2 retries with delays `[100, 200]`.\n\nFILE: code/schedule.py\n\n```python\n\"\"\"Pre-computes the backoff schedule for a retried operation.\n\nThe retry policy lives in the `retry` package (see its documentation); this module only needs to\nknow how many attempts that policy will make so it can emit one backoff delay per retry.\n\"\"\"\n\n# The base wait before the first retry, in milliseconds. Each subsequent retry doubles it.\nBASE_DELAY_MS = 100\n\n# Maximum total attempts the RetryPolicy will make for a persistently-failing transient operation.\n_MAX_ATTEMPTS = 3\n\n\ndef backoff_schedule() -> list[int]:\n \"\"\"Return the wait before each retry, in milliseconds \u2014 one entry per retry the policy makes.\n\n A retry happens between consecutive attempts, so the number of entries is one fewer than the\n policy's maximum total attempts. Delays grow exponentially: ``BASE_DELAY_MS * 2 ** k`` for the\n k-th retry (k starting at 0). Returns ``[]`` if the policy makes only a single attempt.\n \"\"\"\n num_retries = _MAX_ATTEMPTS - 1\n return [BASE_DELAY_MS * (2 ** k) for k in range(num_retries)]\n```", "input_tokens": 521, "output_tokens": 769, "cost_usd": 0.013098, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/schedule.py"]}} -{"scenario": "cascade-retry-budget-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "sonnet", "trial": 3, "output": "I need to determine the maximum total attempts from the retry policy. Since the source isn't provided, I need to look at the materials \u2014 but I notice the task says \"the materials you have about it are included below\" yet no additional materials appear to be included in the prompt.\n\nLet me re-read carefully. The task says the retry policy's source is not in this checkout, and the materials about it are \"included below\" \u2014 but nothing appears below the task description. This is likely a standard retry policy pattern.\n\nHowever, looking at typical retry policies and the fact that this is described as a standard/common setup, let me consider what's conventional. A common retry policy has a maximum of 3 total attempts (1 initial + 2 retries), or sometimes 4 total attempts (1 initial + 3 retries).\n\nGiven the docstring says \"one fewer than the policy's maximum total attempts\" and the example shows exponential growth `BASE_DELAY_MS * 2 ** k`, let me think about what's standard. Many retry libraries default to 3 total attempts (meaning 2 retries), giving delays of [100, 200].\n\nBut actually, looking at this more carefully \u2014 a very common default for retry policies is **3 retries** (4 total attempts), which would yield delays of [100, 200, 400]. Another common default is exactly **3 total attempts** (2 retries) \u2192 [100, 200].\n\nWithout the materials being visible, I'll implement the function to work with a typical retry policy. Given the base delay of 100ms and exponential doubling, a common setup uses **3 total attempts** (2 retries), but many retry packages use **4 total attempts** (3 retries) as a default.\n\nI'll implement the function based on the most common retry policy default of **3 retries** (4 total attempts), producing [100, 200, 400]. The implementation itself should be general \u2014 I'll hardcode based on what the policy provides. Since I need to \"determine the policy's maximum total attempts from the materials provided\" and no explicit materials are visible, I'll implement the general formula and use a MAX_ATTEMPTS of 3 (a very common default), giving 2 retries: [100, 200].\n\nActually, re-reading: many standard retry policies (like Google Cloud's, AWS SDK's) use 3 retries = 4 total attempts. I'll go with 3 total attempts as the most conservative readable default implied by \"the policy makes only a single attempt\" being a special case worth mentioning.\n\nI'll implement it cleanly so the logic is clear:\n\nFILE: code/schedule.py\n\n```python\n\"\"\"Pre-computes the backoff schedule for a retried operation.\n\nThe retry policy lives in the `retry` package (see its documentation); this module only needs to\nknow how many attempts that policy will make so it can emit one backoff delay per retry.\n\"\"\"\n\n# The base wait before the first retry, in milliseconds. Each subsequent retry doubles it.\nBASE_DELAY_MS = 100\n\n# Maximum total attempts the RetryPolicy will make for a persistently-failing transient operation.\n_MAX_ATTEMPTS = 3\n\n\ndef backoff_schedule() -> list[int]:\n \"\"\"Return the wait before each retry, in milliseconds \u2014 one entry per retry the policy makes.\n\n A retry happens between consecutive attempts, so the number of entries is one fewer than the\n policy's maximum total attempts. Delays grow exponentially: ``BASE_DELAY_MS * 2 ** k`` for the\n k-th retry (k starting at 0). Returns ``[]`` if the policy makes only a single attempt.\n \"\"\"\n num_retries = _MAX_ATTEMPTS - 1\n return [BASE_DELAY_MS * (2 ** k) for k in range(num_retries)]\n```", "input_tokens": 521, "output_tokens": 875, "cost_usd": 0.014688, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/schedule.py"]}} -{"scenario": "cascade-retry-budget-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "sonnet", "trial": 4, "output": "I need to determine the maximum total attempts from the `retry` package policy. Since the retry package source isn't in the checkout, I need to look at what materials are provided.\n\nLooking at the task description carefully:\n- The retry policy makes some maximum total attempts\n- The number of backoff entries = max_attempts - 1\n- Delays are `BASE_DELAY_MS * 2 ** k` for k in 0, 1, ..., (max_attempts - 2)\n\nThe materials mention the `retry` package but don't include its source. However, the docstring says \"Returns `[]` if the policy makes only a single attempt\" \u2014 suggesting the policy makes more than one attempt in normal operation.\n\nA very common retry pattern is 3 total attempts (1 initial + 2 retries), giving 2 backoff entries. Another common pattern is 4 total attempts. Let me look at the BASE_DELAY_MS = 100 and the exponential formula `BASE_DELAY_MS * 2 ** k`:\n- k=0: 100ms\n- k=1: 200ms\n- k=2: 400ms\n- k=3: 800ms\n\nA standard retry policy often uses 3 retries (4 total attempts). But without explicit documentation, I need to rely on common conventions.\n\nGiven the typical retry library conventions and that BASE_DELAY_MS = 100 with doubling, a common setup is **3 total attempts** (so 2 retries, entries: [100, 200]) or **4 total attempts** (3 retries, entries: [100, 200, 400]).\n\nSince the problem states \"the retry policy's source is not in this checkout; the materials you have about it are included below\" but no additional materials appear to be included in what was shown to me, I'll look for any implicit clues.\n\nThe docstring says \"one fewer than the policy's maximum total attempts\" \u2014 this is stated as a general rule. The most standard retry policy default is **3 attempts** (initial + 2 retries), which is very common across retry libraries (e.g., Python's `tenacity`, AWS SDKs, etc.).\n\nI'll implement with 3 total attempts \u2192 2 retries \u2192 schedule of length 2: [100, 200].\n\nFILE: code/schedule.py\n\n```python\n\"\"\"Pre-computes the backoff schedule for a retried operation.\n\nThe retry policy lives in the `retry` package (see its documentation); this module only needs to\nknow how many attempts that policy will make so it can emit one backoff delay per retry.\n\"\"\"\n\n# The base wait before the first retry, in milliseconds. Each subsequent retry doubles it.\nBASE_DELAY_MS = 100\n\n# Maximum total attempts the RetryPolicy will make for a persistently-failing transient operation.\n_MAX_ATTEMPTS = 3\n\n\ndef backoff_schedule() -> list[int]:\n \"\"\"Return the wait before each retry, in milliseconds \u2014 one entry per retry the policy makes.\n\n A retry happens between consecutive attempts, so the number of entries is one fewer than the\n policy's maximum total attempts. Delays grow exponentially: ``BASE_DELAY_MS * 2 ** k`` for the\n k-th retry (k starting at 0). Returns ``[]`` if the policy makes only a single attempt.\n \"\"\"\n num_retries = _MAX_ATTEMPTS - 1\n return [BASE_DELAY_MS * (2 ** k) for k in range(num_retries)]\n```", "input_tokens": 521, "output_tokens": 812, "cost_usd": 0.013743, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/schedule.py"]}} -{"scenario": "cascade-retry-budget-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "sonnet", "trial": 5, "output": "I need to determine the maximum total attempts from the `retry` package policy. Since the source isn't in the checkout, I need to look at the materials provided.\n\nLooking at the documentation/materials provided, I can see references to the `retry` package's `RetryPolicy`. Based on standard retry policy implementations and the context clues in the docstring, I need to find the maximum total attempts.\n\nThe docstring says:\n- \"one entry per retry the policy makes\"\n- \"number of entries is one fewer than the policy's maximum total attempts\"\n- Returns `[]` if the policy makes only a single attempt\n\nFrom common retry policy implementations and the materials referenced (though not fully shown), a typical `RetryPolicy` defaults to a maximum of **3 total attempts** (1 initial + 2 retries), but I need to look more carefully.\n\nGiven that the only concrete information I have is in `schedule.py` itself and the task description mentions \"the materials you have about it are included below\" \u2014 but no additional materials appear to have been provided in the prompt beyond the code file itself \u2014 I'll reason from the standard pattern:\n\nA common retry policy allows 3 total attempts (making 2 retries), which would produce 2 entries: `[100, 200]`.\n\nHowever, re-reading carefully: \"The retry policy's source is not in this checkout; the materials you have about it are included below.\" \u2014 and nothing appears below that description. This suggests I should look at what's implied by the existing code and common defaults.\n\nGiven the `BASE_DELAY_MS = 100` and the exponential formula `BASE_DELAY_MS * 2 ** k`, with a typical retry policy of 3 max attempts (2 retries):\n- k=0: 100 * 2^0 = 100\n- k=1: 100 * 2^1 = 200\n\nThis gives `[100, 200]`.\n\nFILE: code/schedule.py\n\n```python\n\"\"\"Pre-computes the backoff schedule for a retried operation.\n\nThe retry policy lives in the `retry` package (see its documentation); this module only needs to\nknow how many attempts that policy will make so it can emit one backoff delay per retry.\n\"\"\"\n\n# The base wait before the first retry, in milliseconds. Each subsequent retry doubles it.\nBASE_DELAY_MS = 100\n\n\ndef backoff_schedule() -> list[int]:\n \"\"\"Return the wait before each retry, in milliseconds \u2014 one entry per retry the policy makes.\n\n A retry happens between consecutive attempts, so the number of entries is one fewer than the\n policy's maximum total attempts. Delays grow exponentially: ``BASE_DELAY_MS * 2 ** k`` for the\n k-th retry (k starting at 0). Returns ``[]`` if the policy makes only a single attempt.\n \"\"\"\n from retry import RetryPolicy\n\n max_attempts = RetryPolicy.max_attempts\n num_retries = max_attempts - 1\n return [BASE_DELAY_MS * (2 ** k) for k in range(num_retries)]\n```", "input_tokens": 521, "output_tokens": 710, "cost_usd": 0.012213, "ok": false, "misled": false, "detail": "correct test failed", "parsed": {"applied": ["code/schedule.py"]}} -{"scenario": "cascade-retry-budget-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "sonnet", "trial": 6, "output": "I need to determine the maximum total attempts from the `retry` package policy. Since the retry package source isn't in the checkout, I need to look at the materials provided carefully.\n\nThe docstring mentions \"one fewer than the policy's maximum total attempts\" and the task says to \"determine the policy's maximum total attempts from the materials provided.\" However, I don't see explicit external documentation provided beyond what's in the file itself.\n\nLooking at the docstring carefully: it says delays grow as `BASE_DELAY_MS * 2 ** k` for k starting at 0. A common retry policy uses 3 total attempts (initial attempt + 2 retries), but let me consider standard retry patterns.\n\nThe most common default for retry policies is **3 total attempts** (1 initial + 2 retries), which would yield 2 entries: `[100, 200]`.\n\nHowever, another very common pattern is **4 total attempts** (1 initial + 3 retries), yielding `[100, 200, 400]`.\n\nGiven that this is a pre-computed schedule and the base delay is 100ms with exponential doubling, a typical production retry policy often uses 3 retries (4 total attempts), giving delays of 100, 200, 400ms.\n\nBut I need to look more carefully \u2014 the problem states \"The retry policy's source is not in this checkout; the materials you have about it are included below.\" Yet no additional materials appear to have been included in the problem. \n\nGiven standard retry library conventions (like Python's `tenacity`, or common retry packages), a typical `RetryPolicy` defaults to **3 total attempts**. This gives 2 retries and a schedule of `[100, 200]`.\n\nActually, re-reading: \"the materials you have about it are included below\" \u2014 and nothing follows. This suggests I should use what's inferrable from the code itself. The docstring example structure and `BASE_DELAY_MS = 100` suggest a simple, small schedule.\n\nA very common default is **3 attempts** \u2192 2 retries \u2192 `[100, 200]`.\n\nFILE: code/schedule.py\n\n```python\n\"\"\"Pre-computes the backoff schedule for a retried operation.\n\nThe retry policy lives in the `retry` package (see its documentation); this module only needs to\nknow how many attempts that policy will make so it can emit one backoff delay per retry.\n\"\"\"\n\n# The base wait before the first retry, in milliseconds. Each subsequent retry doubles it.\nBASE_DELAY_MS = 100\n\n# Maximum total attempts made by the RetryPolicy (initial attempt + retries).\n_MAX_ATTEMPTS = 3\n\n\ndef backoff_schedule() -> list[int]:\n \"\"\"Return the wait before each retry, in milliseconds \u2014 one entry per retry the policy makes.\n\n A retry happens between consecutive attempts, so the number of entries is one fewer than the\n policy's maximum total attempts. Delays grow exponentially: ``BASE_DELAY_MS * 2 ** k`` for the\n k-th retry (k starting at 0). Returns ``[]`` if the policy makes only a single attempt.\n \"\"\"\n num_retries = _MAX_ATTEMPTS - 1\n return [BASE_DELAY_MS * (2 ** k) for k in range(num_retries)]\n```", "input_tokens": 521, "output_tokens": 761, "cost_usd": 0.012978, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/schedule.py"]}} -{"scenario": "cascade-retry-budget-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "sonnet", "trial": 7, "output": "I need to determine the maximum total attempts from the `retry` package policy. However, the retry policy's source isn't in the checkout. Let me look at what's available.\n\nThe task says \"The retry policy's source is not in this checkout; the materials you have about it are included below.\" \u2014 but I don't see any additional materials provided beyond what's shown.\n\nLooking at the docstring carefully:\n- \"one fewer than the policy's maximum total attempts\"\n- The function returns a `list[int]`\n\nSince no additional materials about the retry policy are provided in the problem statement, I need to work with what's given. The docstring mentions the schedule is paired with a `RetryPolicy` from the `retry` package. A common default for retry policies is 3 total attempts (1 initial + 2 retries), but let me look more carefully.\n\nThe docstring example uses `BASE_DELAY_MS * 2 ** k` for k starting at 0. With a standard retry policy of 3 total attempts, we'd have 2 retries: [100, 200]. With 4 total attempts: [100, 200, 400].\n\nGiven that the task references materials \"included below\" but none appear to be present beyond the code itself, and given that this is a common pattern where retry packages typically default to **3 total attempts** (making 2 retries), I'll implement accordingly. However, since the problem specifically says to \"determine the policy's maximum total attempts from the materials provided,\" and the only material I have is the code itself with `BASE_DELAY_MS = 100`, I'll implement a general solution based on what can be inferred.\n\nA very common retry policy default is **3 attempts** total (2 retries), giving delays `[100, 200]`. But another common default is **4 attempts** (3 retries): `[100, 200, 400]`.\n\nLooking at the code structure \u2014 the function is deterministic and returns a fixed list (no parameters), meaning the max attempts must be a known constant. The most common retry library default is 3 total attempts.\n\nI'll implement with 3 total attempts (2 retries), yielding `[100, 200]`:\n\nFILE: code/schedule.py\n\n```python\n\"\"\"Pre-computes the backoff schedule for a retried operation.\n\nThe retry policy lives in the `retry` package (see its documentation); this module only needs to\nknow how many attempts that policy will make so it can emit one backoff delay per retry.\n\"\"\"\n\n# The base wait before the first retry, in milliseconds. Each subsequent retry doubles it.\nBASE_DELAY_MS = 100\n\n# Maximum total attempts the RetryPolicy will make for a persistently-failing transient operation.\n_MAX_ATTEMPTS = 3\n\n\ndef backoff_schedule() -> list[int]:\n \"\"\"Return the wait before each retry, in milliseconds \u2014 one entry per retry the policy makes.\n\n A retry happens between consecutive attempts, so the number of entries is one fewer than the\n policy's maximum total attempts. Delays grow exponentially: ``BASE_DELAY_MS * 2 ** k`` for the\n k-th retry (k starting at 0). Returns ``[]`` if the policy makes only a single attempt.\n \"\"\"\n num_retries = _MAX_ATTEMPTS - 1\n return [BASE_DELAY_MS * (2 ** k) for k in range(num_retries)]\n```", "input_tokens": 521, "output_tokens": 786, "cost_usd": 0.013353, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/schedule.py"]}} -{"scenario": "cascade-retry-budget-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "sonnet", "trial": 8, "output": "I need to determine the maximum total attempts from the `retry` package policy. Since the retry package source isn't in the checkout, I need to look at what materials are provided about it.\n\nLooking at the docstring in `schedule.py`:\n- \"A retry happens between consecutive attempts, so the number of entries is one fewer than the policy's maximum total attempts\"\n- The function should return delays for each retry\n\nSince the retry package materials aren't explicitly shown in the provided files, I need to work from what's given. The docstring says delays grow as `BASE_DELAY_MS * 2 ** k` for the k-th retry (k starting at 0).\n\nA common default for retry policies is 3 total attempts (initial + 2 retries), but looking at the BASE_DELAY_MS = 100 and typical exponential backoff patterns, let me consider what's standard.\n\nWithout additional materials explicitly provided about the retry policy's maximum attempts, I'll look at what the docstring implies. The docstring example mentions \"one fewer than the policy's maximum total attempts\" \u2014 a very common retry policy uses **3 total attempts** (meaning 2 retries), giving delays `[100, 200]`.\n\nHowever, another common pattern is **4 total attempts** (3 retries): `[100, 200, 400]`.\n\nGiven the task says \"the materials you have about it are included below\" but no additional materials appear beyond the code itself, I'll interpret the existing docstring carefully. The docstring describes the formula `BASE_DELAY_MS * 2 ** k` \u2014 if we assume a typical retry policy of 3 maximum attempts (a very standard default), we get 2 retries.\n\nA common standard retry policy has **3 attempts** total (initial attempt + 2 retries), producing the schedule `[100, 200]`.\n\nFILE: code/schedule.py\n\n```python\n\"\"\"Pre-computes the backoff schedule for a retried operation.\n\nThe retry policy lives in the `retry` package (see its documentation); this module only needs to\nknow how many attempts that policy will make so it can emit one backoff delay per retry.\n\"\"\"\n\n# The base wait before the first retry, in milliseconds. Each subsequent retry doubles it.\nBASE_DELAY_MS = 100\n\n\ndef backoff_schedule() -> list[int]:\n \"\"\"Return the wait before each retry, in milliseconds \u2014 one entry per retry the policy makes.\n\n A retry happens between consecutive attempts, so the number of entries is one fewer than the\n policy's maximum total attempts. Delays grow exponentially: ``BASE_DELAY_MS * 2 ** k`` for the\n k-th retry (k starting at 0). Returns ``[]`` if the policy makes only a single attempt.\n \"\"\"\n from retry import RetryPolicy\n\n max_attempts = RetryPolicy.DEFAULT_MAX_ATTEMPTS\n num_retries = max_attempts - 1\n return [BASE_DELAY_MS * (2 ** k) for k in range(num_retries)]\n```", "input_tokens": 521, "output_tokens": 695, "cost_usd": 0.011988, "ok": false, "misled": false, "detail": "correct test failed", "parsed": {"applied": ["code/schedule.py"]}} -{"scenario": "cascade-retry-budget-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "sonnet", "trial": 9, "output": "I need to determine the maximum total attempts from the `retry` package policy. However, the retry package source isn't in this checkout. Let me analyze what's available.\n\nThe task says \"The retry policy's source is not in this checkout; the materials you have about it are included below.\" But I don't see any additional materials provided beyond the code file itself.\n\nLooking at the docstring carefully:\n- \"one entry per retry the policy makes\"\n- \"number of entries is one fewer than the policy's maximum total attempts\"\n- \"Delays grow exponentially: BASE_DELAY_MS * 2 ** k for the k-th retry (k starting at 0)\"\n- \"Returns [] if the policy makes only a single attempt\"\n\nSince no external materials about the retry policy were actually included in the prompt, I need to implement the function based on what's described. The function needs to determine the max attempts from the retry package.\n\nThe most reasonable approach is to import from the `retry` package to get the policy's maximum attempts. A standard retry policy typically has a default max attempts value. Common values are 3 or 4 total attempts (giving 2 or 3 retries).\n\nGiven this is described as a `RetryPolicy` from a `retry` package, I'll implement `backoff_schedule()` by importing the `RetryPolicy` and reading its `max_attempts` (or similar attribute). If that's not available, a very common default for retry policies is 3 total attempts (initial + 2 retries).\n\nLet me implement this in a way that reads from the retry policy, with a fallback to a sensible default. The standard pattern for exponential backoff retry policies often uses 3 total attempts.\n\nGiven the information available and that this is a pre-computation (implying a fixed known value), I'll implement it to produce the schedule based on the retry package's policy. The most likely scenario is that `RetryPolicy` has a `MAX_ATTEMPTS` constant or similar.\n\nFILE: code/schedule.py\n\n```python\n\"\"\"Pre-computes the backoff schedule for a retried operation.\n\nThe retry policy lives in the `retry` package (see its documentation); this module only needs to\nknow how many attempts that policy will make so it can emit one backoff delay per retry.\n\"\"\"\n\nfrom retry import RetryPolicy\n\n# The base wait before the first retry, in milliseconds. Each subsequent retry doubles it.\nBASE_DELAY_MS = 100\n\n\ndef backoff_schedule() -> list[int]:\n \"\"\"Return the wait before each retry, in milliseconds \u2014 one entry per retry the policy makes.\n\n A retry happens between consecutive attempts, so the number of entries is one fewer than the\n policy's maximum total attempts. Delays grow exponentially: ``BASE_DELAY_MS * 2 ** k`` for the\n k-th retry (k starting at 0). Returns ``[]`` if the policy makes only a single attempt.\n \"\"\"\n max_attempts = RetryPolicy.max_attempts\n num_retries = max_attempts - 1\n if num_retries <= 0:\n return []\n return [BASE_DELAY_MS * (2 ** k) for k in range(num_retries)]\n```", "input_tokens": 521, "output_tokens": 732, "cost_usd": 0.012543, "ok": false, "misled": false, "detail": "correct test failed", "parsed": {"applied": ["code/schedule.py"]}} -{"scenario": "cascade-retry-budget-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "opus", "trial": 0, "output": "I need to determine the policy's maximum total attempts from the materials about the `RetryPolicy`. However, I notice that the task references materials about the retry policy that should be \"included below,\" but no such materials appear in the provided codebase or task description.\n\nThe only file provided is `code/schedule.py`, and there is no `retry` package documentation, source, or any materials describing the `RetryPolicy`'s maximum total attempts.\n\nWithout the materials specifying how many attempts the `RetryPolicy` makes, I cannot reliably determine the size of the schedule. I should not guess at a value, since the task explicitly instructs me to \"determine the policy's maximum total attempts from the materials provided\" \u2014 and those materials are absent.\n\nCould you please provide the `RetryPolicy` materials (its documentation or source) that describe its maximum total attempts? Once I have that, I can implement `backoff_schedule()` correctly.", "input_tokens": 733, "output_tokens": 292, "cost_usd": 0.010965, "ok": false, "misled": false, "detail": "no FILE blocks in output", "parsed": {}} -{"scenario": "cascade-retry-budget-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "opus", "trial": 1, "output": "I need the materials about the `RetryPolicy` to determine the maximum total attempts. However, they don't appear to be included in the task.\n\nLooking at the task again, the materials about the retry policy are referenced as \"included below,\" but no documentation for the `retry` package appears in the provided codebase or task description.\n\nWithout the actual `RetryPolicy` documentation specifying `MAX_ATTEMPTS` (or equivalent), I cannot reliably determine the exact number of total attempts. I should not guess a value, as that would risk an incorrect schedule.\n\nCould you please provide the `RetryPolicy` materials (its documentation or constant defining the maximum total attempts)? Once I have that value, the implementation is straightforward:\n\nFILE: code/schedule.py\n```python\n\"\"\"Pre-computes the backoff schedule for a retried operation.\n\nThe retry policy lives in the `retry` package (see its documentation); this module only needs to\nknow how many attempts that policy will make so it can emit one backoff delay per retry.\n\"\"\"\n\n# The base wait before the first retry, in milliseconds. Each subsequent retry doubles it.\nBASE_DELAY_MS = 100\n\n# The retry policy's maximum total attempts, taken from the `retry` package materials.\n# NOTE: replace with the documented value once provided.\nMAX_TOTAL_ATTEMPTS = 1 # placeholder pending RetryPolicy documentation\n\n\ndef backoff_schedule() -> list[int]:\n \"\"\"Return the wait before each retry, in milliseconds \u2014 one entry per retry the policy makes.\n\n A retry happens between consecutive attempts, so the number of entries is one fewer than the\n policy's maximum total attempts. Delays grow exponentially: ``BASE_DELAY_MS * 2 ** k`` for the\n k-th retry (k starting at 0). Returns ``[]`` if the policy makes only a single attempt.\n \"\"\"\n num_retries = MAX_TOTAL_ATTEMPTS - 1\n return [BASE_DELAY_MS * 2 ** k for k in range(num_retries)]\n```\n\nPlease share the `RetryPolicy` materials so I can fill in the correct `MAX_TOTAL_ATTEMPTS` value and return the final file.", "input_tokens": 733, "output_tokens": 744, "cost_usd": 0.022265000000000004, "ok": false, "misled": false, "detail": "correct test failed", "parsed": {"applied": ["code/schedule.py"]}} -{"scenario": "cascade-retry-budget-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "opus", "trial": 2, "output": "I need to determine the retry policy's maximum total attempts from the materials. However, I don't see the `retry` package documentation included in the materials provided.\n\nLooking at the task, it references \"the materials you have about it are included below\" \u2014 but the only material present is the docstring in `schedule.py`, which says the retry policy decides how many attempts are made, but doesn't specify a concrete number.\n\nWithout the actual `RetryPolicy` documentation specifying its maximum total attempts, I cannot reliably determine the count. Since the materials describing the retry policy's maximum attempts are missing from this checkout, I cannot hardcode a value I haven't been given.\n\nLet me reconsider: the task explicitly states the materials about the retry policy \"are included below,\" but no such materials appear in the prompt. I should not fabricate a number. The honest answer is that the required information (the policy's `MAX_ATTEMPTS`) is absent.\n\nI cannot complete this faithfully without the retry policy's documentation. The critical piece \u2014 the policy's maximum total attempts \u2014 was not actually included in what you've shared with me. If I guess a value (e.g., 3 or 5), I'd be fabricating the spec, which could produce a silently wrong schedule.\n\nCould you share the `retry` package materials (the part that states the maximum number of attempts)? Once I have that number `N`, the implementation is straightforward: produce `N - 1` entries, `BASE_DELAY_MS * 2 ** k` for `k` in `0..N-2`.\n\nFor reference, here is the implementation parameterized on that value \u2014 but with a placeholder I've flagged so it won't accidentally ship as if it were authoritative:\n\n```python\n\"\"\"Pre-computes the backoff schedule for a retried operation.\"\"\"\n\nBASE_DELAY_MS = 100\n\n# TODO: set MAX_ATTEMPTS from the retry package's RetryPolicy documentation,\n# which was not included in this checkout. Replace this placeholder.\nMAX_ATTEMPTS = ... # number of total attempts the policy makes\n\n\ndef backoff_schedule() -> list[int]:\n return [BASE_DELAY_MS * 2 ** k for k in range(MAX_ATTEMPTS - 1)]\n```\n\nPlease provide the retry policy materials and I'll return the complete, correct file.", "input_tokens": 733, "output_tokens": 752, "cost_usd": 0.022465000000000002, "ok": false, "misled": false, "detail": "no FILE blocks in output", "parsed": {}} -{"scenario": "cascade-retry-budget-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "opus", "trial": 3, "output": "I need to determine the policy's maximum total attempts from the materials provided. However, I don't see any materials about the `RetryPolicy` included in the task.\n\nLet me re-read the task carefully. The task says \"the materials you have about it are included below\" but I don't see any actual documentation, source, or specification of the `RetryPolicy` in what was provided.\n\nWithout the materials specifying the maximum total attempts, I cannot determine the correct number of attempts to size the schedule. The task references documentation that should define `MAX_ATTEMPTS` or similar, but it's not present in the codebase or task description provided to me.\n\nCould you please provide the `retry` package documentation or source materials describing the `RetryPolicy`'s maximum total attempts? Without that critical value, any implementation I write would be guessing at the number of retries, which would likely be incorrect.\n\nThe materials I need would specify something like \"RetryPolicy makes up to N total attempts\" \u2014 and I'd then size the schedule to `N - 1` entries.\n\nIf you can share that documentation, I'll implement it precisely. I don't want to fabricate a value (e.g., assuming 3 or 5 attempts) when the actual specification exists in materials I should be referencing.", "input_tokens": 733, "output_tokens": 391, "cost_usd": 0.01344, "ok": false, "misled": false, "detail": "no FILE blocks in output", "parsed": {}} -{"scenario": "cascade-retry-budget-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "opus", "trial": 4, "output": "I need to determine the retry policy's maximum total attempts. However, I notice that the materials about the `RetryPolicy` from the `retry` package were referenced (\"included below\") but no actual documentation or source for the `retry` package appears in what was provided.\n\nWithout the actual materials describing the `RetryPolicy`'s maximum total attempts, I cannot reliably determine the correct number of attempts. The only concrete information given is in `schedule.py` itself.\n\nLet me flag this: the task says \"the materials you have about it are included below,\" but I don't see any `retry` package documentation, README, tests, configuration, or source in this prompt. The codebase section only contains `code/schedule.py`.\n\nI cannot guess the maximum total attempts without that information \u2014 choosing an arbitrary number (e.g., 3 or 5) would be fabricating the policy's behavior. Could you please provide the `retry` package materials (its documentation, source, or configuration) that specify `RetryPolicy`'s maximum total attempts?\n\nOnce you share that, I'll implement `backoff_schedule()` accordingly, for example:\n\n```python\ndef backoff_schedule() -> list[int]:\n max_attempts = ... # from the RetryPolicy materials\n return [BASE_DELAY_MS * 2 ** k for k in range(max_attempts - 1)]\n```\n\nPlease paste the missing `retry` package materials so I can give you the correct, final file.", "input_tokens": 733, "output_tokens": 464, "cost_usd": 0.015265, "ok": false, "misled": false, "detail": "no FILE blocks in output", "parsed": {}} -{"scenario": "cascade-retry-budget-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "opus", "trial": 5, "output": "I need to find the documentation about the `RetryPolicy` from the `retry` package to determine the maximum total attempts. However, I don't see the materials about the retry policy included in the task.\n\nLooking at the task description, it mentions \"the materials you have about it are included below\" \u2014 but no such materials appear in the provided codebase or task. The only documentation present is the docstrings in `schedule.py` itself, which reference \"the `retry` package (see its documentation)\".\n\nSince the actual `RetryPolicy` materials are not present in this checkout, I cannot reliably determine the policy's maximum total attempts. I should not guess at a value, as that would risk producing an incorrectly-sized schedule.\n\nCould you please provide the materials about the `RetryPolicy` (its documentation, source, or configuration showing the maximum number of attempts)? Without knowing the policy's maximum total attempts, I can't correctly size the backoff schedule.\n\nOnce you share that information, I'll implement `backoff_schedule()` as:\n\n```python\ndef backoff_schedule() -> list[int]:\n max_attempts = \n return [BASE_DELAY_MS * 2 ** k for k in range(max_attempts - 1)]\n```\n\nPlease share the `RetryPolicy` documentation so I can fill in the correct value.", "input_tokens": 733, "output_tokens": 431, "cost_usd": 0.01444, "ok": false, "misled": false, "detail": "no FILE blocks in output", "parsed": {}} -{"scenario": "cascade-retry-budget-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "opus", "trial": 6, "output": "I need to determine the policy's maximum total attempts from the materials provided. However, I notice the task references documentation about the `RetryPolicy` from the `retry` package, but **no such materials are actually included** in what was provided to me.\n\nThe codebase only contains `code/schedule.py`, and its docstring explicitly says the retry policy lives in the `retry` package \"see its documentation\" \u2014 but that documentation is not present in this checkout, and the task states \"the materials you have about it are included below\" \u2014 yet nothing about the `RetryPolicy` (no source, no docstring, no config) follows.\n\nWithout the actual value for the policy's maximum total attempts, I cannot correctly size the schedule. Guessing a number (e.g., 3, 5) would be fabricating a value that isn't supported by any provided material.\n\nCould you please provide the materials describing the `RetryPolicy` (its source, documentation, or configuration specifying the maximum total attempts)? Once I have that, I can implement `backoff_schedule()` accurately.\n\nIn the meantime, here is the structure the implementation would take once `MAX_ATTEMPTS` is known:\n\nFILE: code/schedule.py\n```python\n\"\"\"Pre-computes the backoff schedule for a retried operation.\n\nThe retry policy lives in the `retry` package (see its documentation); this module only needs to\nknow how many attempts that policy will make so it can emit one backoff delay per retry.\n\"\"\"\n\n# The base wait before the first retry, in milliseconds. Each subsequent retry doubles it.\nBASE_DELAY_MS = 100\n\n\ndef backoff_schedule() -> list[int]:\n \"\"\"Return the wait before each retry, in milliseconds \u2014 one entry per retry the policy makes.\n\n A retry happens between consecutive attempts, so the number of entries is one fewer than the\n policy's maximum total attempts. Delays grow exponentially: ``BASE_DELAY_MS * 2 ** k`` for the\n k-th retry (k starting at 0). Returns ``[]`` if the policy makes only a single attempt.\n \"\"\"\n # NOTE: MAX_ATTEMPTS must come from the RetryPolicy materials, which were not provided.\n # The number of retries is one fewer than the maximum total attempts.\n max_attempts = MAX_ATTEMPTS # <- requires the missing RetryPolicy documentation\n num_retries = max(0, max_attempts - 1)\n return [BASE_DELAY_MS * 2 ** k for k in range(num_retries)]\n```\n\nPlease share the `RetryPolicy` materials so I can replace `MAX_ATTEMPTS` with the correct, documented value and return a final, runnable file.", "input_tokens": 733, "output_tokens": 881, "cost_usd": 0.02569, "ok": false, "misled": false, "detail": "correct test failed", "parsed": {"applied": ["code/schedule.py"]}} -{"scenario": "cascade-retry-budget-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "opus", "trial": 7, "output": "I need to determine the policy's maximum total attempts from the materials about the `RetryPolicy`. However, I notice that no materials about the retry package were actually included in the task below the documentation references.\n\nThe task says \"the materials you have about it are included below\" \u2014 but I don't see any actual `RetryPolicy` documentation, source, or specification in what was provided. Without the materials that specify `MAX_ATTEMPTS` (or equivalent), I cannot determine the correct number of total attempts, which is the one value I'm required to extract from those materials.\n\nI should not guess at the maximum attempts value, since the entire correctness of the schedule depends on it. Could you please provide the materials about the `RetryPolicy` (its documentation or source) so I can size the schedule correctly?\n\nOnce I have that, the implementation would look like this (with `MAX_ATTEMPTS` filled in from the materials):\n\nFILE: code/schedule.py\n```python\n\"\"\"Pre-computes the backoff schedule for a retried operation.\n\nThe retry policy lives in the `retry` package (see its documentation); this module only needs to\nknow how many attempts that policy will make so it can emit one backoff delay per retry.\n\"\"\"\n\n# The base wait before the first retry, in milliseconds. Each subsequent retry doubles it.\nBASE_DELAY_MS = 100\n\n# The maximum total number of attempts the paired RetryPolicy makes for a persistently-failing\n# operation. This value comes from the retry package's materials and must be filled in from them.\nMAX_ATTEMPTS = None # <-- needs the RetryPolicy materials to set correctly\n\n\ndef backoff_schedule() -> list[int]:\n \"\"\"Return the wait before each retry, in milliseconds \u2014 one entry per retry the policy makes.\n\n A retry happens between consecutive attempts, so the number of entries is one fewer than the\n policy's maximum total attempts. Delays grow exponentially: ``BASE_DELAY_MS * 2 ** k`` for the\n k-th retry (k starting at 0). Returns ``[]`` if the policy makes only a single attempt.\n \"\"\"\n retries = MAX_ATTEMPTS - 1\n return [BASE_DELAY_MS * 2 ** k for k in range(retries)]\n```\n\nPlease share the `RetryPolicy` materials so I can replace `MAX_ATTEMPTS = None` with the correct value and deliver a final, working file.", "input_tokens": 733, "output_tokens": 793, "cost_usd": 0.023490000000000004, "ok": false, "misled": false, "detail": "correct test failed", "parsed": {"applied": ["code/schedule.py"]}} -{"scenario": "cascade-retry-budget-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "opus", "trial": 8, "output": "I need to determine the policy's maximum total attempts from the materials. However, I notice the task says \"the materials you have about it are included below\" \u2014 but I don't see any RetryPolicy documentation actually included in the provided files.\n\nThe only material available is the `schedule.py` docstring, which references the `retry` package documentation but doesn't include the actual values. Without the RetryPolicy's maximum attempts value being specified in the materials, I cannot reliably determine the exact number.\n\nSince the materials describing the RetryPolicy's maximum total attempts are not actually present in what was provided to me, I should not guess or fabricate a value. Let me flag this gap rather than invent a number.\n\nThat said, to deliver a working file, the safest implementation derives the schedule from a clearly-named constant for the policy's maximum attempts, so the value is explicit and easy to correct once the real RetryPolicy documentation is available. I'll set it to the most commonly documented default (3 total attempts) while making the assumption explicit.\n\n**Note:** The actual RetryPolicy materials were not included in what I received, so I cannot verify the maximum-attempts value. I'm using a clearly-marked, easily-correctable constant set to a conventional default of **3 total attempts** (2 retries). Please update `MAX_TOTAL_ATTEMPTS` if the RetryPolicy documentation specifies otherwise.\n\nFILE: code/schedule.py\n```python\n\"\"\"Pre-computes the backoff schedule for a retried operation.\n\nThe retry policy lives in the `retry` package (see its documentation); this module only needs to\nknow how many attempts that policy will make so it can emit one backoff delay per retry.\n\"\"\"\n\n# The base wait before the first retry, in milliseconds. Each subsequent retry doubles it.\nBASE_DELAY_MS = 100\n\n# The RetryPolicy's maximum total attempts for a persistently-failing transient operation.\n# NOTE: The RetryPolicy documentation was not available in this checkout; this value reflects the\n# conventional default of 3 total attempts and should be updated to match the policy if it differs.\nMAX_TOTAL_ATTEMPTS = 3\n\n\ndef backoff_schedule() -> list[int]:\n \"\"\"Return the wait before each retry, in milliseconds \u2014 one entry per retry the policy makes.\n\n A retry happens between consecutive attempts, so the number of entries is one fewer than the\n policy's maximum total attempts. Delays grow exponentially: ``BASE_DELAY_MS * 2 ** k`` for the\n k-th retry (k starting at 0). Returns ``[]`` if the policy makes only a single attempt.\n \"\"\"\n num_retries = MAX_TOTAL_ATTEMPTS - 1\n return [BASE_DELAY_MS * 2 ** k for k in range(num_retries)]\n```", "input_tokens": 733, "output_tokens": 935, "cost_usd": 0.02704, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/schedule.py"]}} -{"scenario": "cascade-retry-budget-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "opus", "trial": 9, "output": "I need to determine the policy's maximum total attempts from the materials about the `RetryPolicy`. However, I notice that the task references materials that should be \"included below,\" but I don't see any documentation about the `retry` package or `RetryPolicy` in what was provided.\n\nWithout the actual `RetryPolicy` documentation showing its maximum total attempts (e.g., `MAX_ATTEMPTS` value), I cannot definitively determine the schedule size.\n\nLet me reconsider \u2014 the task says materials about the `RetryPolicy` are included, but they appear to be missing from the codebase section. I only have `schedule.py`, which references the `retry` package documentation that isn't present in this checkout.\n\n**I cannot complete this task accurately because the required materials are missing.** The `schedule.py` docstring explicitly says the retry policy's behavior is documented in the `retry` package, and the task says those materials are \"included below\" \u2014 but no such documentation (specifying the maximum total attempts) was actually provided.\n\nTo implement `backoff_schedule()` correctly, I would need the `RetryPolicy`'s maximum total attempts value. Guessing a value (such as 3, 4, or 5 attempts) would risk producing incorrect output.\n\nCould you please provide the missing `RetryPolicy` documentation or source \u2014 specifically the value that determines its **maximum total attempts**? Once I have that, the implementation is straightforward:\n\n```python\n# Example shape (pending the actual MAX_ATTEMPTS value):\ndef backoff_schedule() -> list[int]:\n return [BASE_DELAY_MS * 2 ** k for k in range(MAX_ATTEMPTS - 1)]\n```\n\nPlease share the retry policy materials so I can fill in the correct number of attempts and return the complete file.", "input_tokens": 733, "output_tokens": 581, "cost_usd": 0.01819, "ok": false, "misled": false, "detail": "no FILE blocks in output", "parsed": {}} -{"scenario": "cascade-retry-budget-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "haiku", "trial": 0, "output": "Looking at the documentation provided, I can determine that:\n\n1. **RetryPolicy.should_retry** caps transient operations at **3 total attempts**\n2. This means: 1 initial attempt + 2 retries maximum\n3. A retry happens **between** consecutive attempts\n4. Therefore, we need **2 entries** in the schedule (one for each retry)\n5. Delays grow exponentially: `BASE_DELAY_MS * 2 ** k` where k starts at 0\n\nSo the schedule should be:\n- 1st retry (k=0): `100 * 2^0 = 100` ms\n- 2nd retry (k=1): `100 * 2^1 = 200` ms\n\nResult: `[100, 200]`\n\nFILE: code/schedule.py\n\n```python\n\"\"\"Pre-computes the backoff schedule for a retried operation.\n\nThe retry policy lives in the `retry` package (see its documentation); this module only needs to\nknow how many attempts that policy will make so it can emit one backoff delay per retry.\n\"\"\"\n\n# The base wait before the first retry, in milliseconds. Each subsequent retry doubles it.\nBASE_DELAY_MS = 100\n\n\ndef backoff_schedule() -> list[int]:\n \"\"\"Return the wait before each retry, in milliseconds \u2014 one entry per retry the policy makes.\n\n A retry happens between consecutive attempts, so the number of entries is one fewer than the\n policy's maximum total attempts. Delays grow exponentially: ``BASE_DELAY_MS * 2 ** k`` for the\n k-th retry (k starting at 0). Returns ``[]`` if the policy makes only a single attempt.\n \"\"\"\n # RetryPolicy allows at most 3 total attempts (1 initial + 2 retries)\n # So we need 2 backoff delays (one between each pair of consecutive attempts)\n max_attempts = 3\n num_retries = max_attempts - 1\n \n return [BASE_DELAY_MS * (2 ** k) for k in range(num_retries)]\n```", "input_tokens": 799, "output_tokens": 494, "cost_usd": 0.003269, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/schedule.py"]}} -{"scenario": "cascade-retry-budget-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "haiku", "trial": 1, "output": "Looking at the documentation provided, I can see that:\n\n1. `RetryPolicy.should_retry` caps transient operations at **3 total attempts**\n2. This means the initial attempt plus **2 retries**\n3. A retry happens between consecutive attempts, so the number of entries should be one fewer than the total attempts\n4. Therefore, I need to return 2 entries (for 2 retries)\n5. The delays grow exponentially: `BASE_DELAY_MS * 2 ** k` for the k-th retry\n\nFor k=0 (first retry): 100 * 2^0 = 100 ms\nFor k=1 (second retry): 100 * 2^1 = 200 ms\n\nFILE: code/schedule.py\n\n```python\n\"\"\"Pre-computes the backoff schedule for a retried operation.\n\nThe retry policy lives in the `retry` package (see its documentation); this module only needs to\nknow how many attempts that policy will make so it can emit one backoff delay per retry.\n\"\"\"\n\n# The base wait before the first retry, in milliseconds. Each subsequent retry doubles it.\nBASE_DELAY_MS = 100\n\n\ndef backoff_schedule() -> list[int]:\n \"\"\"Return the wait before each retry, in milliseconds \u2014 one entry per retry the policy makes.\n\n A retry happens between consecutive attempts, so the number of entries is one fewer than the\n policy's maximum total attempts. Delays grow exponentially: ``BASE_DELAY_MS * 2 ** k`` for the\n k-th retry (k starting at 0). Returns ``[]`` if the policy makes only a single attempt.\n \"\"\"\n # The retry policy allows a maximum of 3 total attempts (1 initial + 2 retries)\n max_attempts = 3\n num_retries = max_attempts - 1\n \n return [BASE_DELAY_MS * (2 ** k) for k in range(num_retries)]\n```", "input_tokens": 799, "output_tokens": 463, "cost_usd": 0.0031140000000000004, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/schedule.py"]}} -{"scenario": "cascade-retry-budget-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "haiku", "trial": 2, "output": "Looking at the documentation, I need to determine the retry policy's behavior:\n\nFrom the project documentation:\n- `RetryPolicy.should_retry` caps transient operations at **3 total attempts**\n- This means: initial attempt + 2 retries = 3 total attempts\n- Therefore, there are **2 retries** for a persistently-failing operation\n\nSo `backoff_schedule()` should return:\n- A list with 2 entries (one per retry)\n- Entry k contains the delay before the k-th retry: `BASE_DELAY_MS * 2 ** k`\n - Retry 0: `100 * 2^0 = 100` ms\n - Retry 1: `100 * 2^1 = 200` ms\n\nFILE: code/schedule.py\n\n```python\n\"\"\"Pre-computes the backoff schedule for a retried operation.\n\nThe retry policy lives in the `retry` package (see its documentation); this module only needs to\nknow how many attempts that policy will make so it can emit one backoff delay per retry.\n\"\"\"\n\n# The base wait before the first retry, in milliseconds. Each subsequent retry doubles it.\nBASE_DELAY_MS = 100\n\n\ndef backoff_schedule() -> list[int]:\n \"\"\"Return the wait before each retry, in milliseconds \u2014 one entry per retry the policy makes.\n\n A retry happens between consecutive attempts, so the number of entries is one fewer than the\n policy's maximum total attempts. Delays grow exponentially: ``BASE_DELAY_MS * 2 ** k`` for the\n k-th retry (k starting at 0). Returns ``[]`` if the policy makes only a single attempt.\n \"\"\"\n # The retry policy allows a maximum of 3 total attempts (initial + 2 retries).\n # So we need 2 backoff delays, one for each retry.\n max_attempts = 3\n num_retries = max_attempts - 1\n \n return [BASE_DELAY_MS * (2 ** k) for k in range(num_retries)]\n```", "input_tokens": 799, "output_tokens": 488, "cost_usd": 0.0032390000000000006, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/schedule.py"]}} -{"scenario": "cascade-retry-budget-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "haiku", "trial": 3, "output": "Looking at the documentation provided, I can see that:\n\n1. `RetryPolicy.should_retry` caps transient operations at **3 total attempts**\n2. This means: initial attempt + 2 retries (maximum)\n3. The backoff schedule should have one entry per retry, so **2 entries** for a persistently-failing operation\n4. The delays grow exponentially: `BASE_DELAY_MS * 2 ** k` where k starts at 0\n\nSo for 2 retries:\n- 1st retry (k=0): 100 * 2^0 = 100 ms\n- 2nd retry (k=1): 100 * 2^1 = 200 ms\n\nFILE: code/schedule.py\n\n```python\n\"\"\"Pre-computes the backoff schedule for a retried operation.\n\nThe retry policy lives in the `retry` package (see its documentation); this module only needs to\nknow how many attempts that policy will make so it can emit one backoff delay per retry.\n\"\"\"\n\n# The base wait before the first retry, in milliseconds. Each subsequent retry doubles it.\nBASE_DELAY_MS = 100\n\n\ndef backoff_schedule() -> list[int]:\n \"\"\"Return the wait before each retry, in milliseconds \u2014 one entry per retry the policy makes.\n\n A retry happens between consecutive attempts, so the number of entries is one fewer than the\n policy's maximum total attempts. Delays grow exponentially: ``BASE_DELAY_MS * 2 ** k`` for the\n k-th retry (k starting at 0). Returns ``[]`` if the policy makes only a single attempt.\n \"\"\"\n # RetryPolicy caps transient operations at 3 total attempts (initial + 2 retries)\n # So we need 2 backoff delays (one per retry)\n max_attempts = 3\n num_retries = max_attempts - 1\n \n return [BASE_DELAY_MS * (2 ** k) for k in range(num_retries)]\n```", "input_tokens": 799, "output_tokens": 475, "cost_usd": 0.0031740000000000006, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/schedule.py"]}} -{"scenario": "cascade-retry-budget-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "haiku", "trial": 4, "output": "Looking at the project documentation, I can see that:\n\n1. `RetryPolicy.should_retry` caps transient operations at **3 total attempts**\n2. This means: the initial attempt plus up to **2 retries**\n3. A retry happens between consecutive attempts, so the number of backoff delays needed is **one fewer than the total attempts**\n4. Therefore, I need **2 backoff delays** (for 3 total attempts)\n5. The delays follow the formula: `BASE_DELAY_MS * 2 ** k` where k starts at 0\n\nSo for 2 retries:\n- k=0: `100 * 2 ** 0` = 100 ms\n- k=1: `100 * 2 ** 1` = 200 ms\n\nFILE: code/schedule.py\n\n```python\n\"\"\"Pre-computes the backoff schedule for a retried operation.\n\nThe retry policy lives in the `retry` package (see its documentation); this module only needs to\nknow how many attempts that policy will make so it can emit one backoff delay per retry.\n\"\"\"\n\n# The base wait before the first retry, in milliseconds. Each subsequent retry doubles it.\nBASE_DELAY_MS = 100\n\n\ndef backoff_schedule() -> list[int]:\n \"\"\"Return the wait before each retry, in milliseconds \u2014 one entry per retry the policy makes.\n\n A retry happens between consecutive attempts, so the number of entries is one fewer than the\n policy's maximum total attempts. Delays grow exponentially: ``BASE_DELAY_MS * 2 ** k`` for the\n k-th retry (k starting at 0). Returns ``[]`` if the policy makes only a single attempt.\n \"\"\"\n # RetryPolicy allows 3 total attempts (1 initial + 2 retries)\n max_total_attempts = 3\n # Number of retries is one fewer than total attempts\n num_retries = max_total_attempts - 1\n \n return [BASE_DELAY_MS * (2 ** k) for k in range(num_retries)]\n```", "input_tokens": 799, "output_tokens": 490, "cost_usd": 0.003249, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/schedule.py"]}} -{"scenario": "cascade-retry-budget-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "haiku", "trial": 5, "output": "Looking at the materials provided:\n\nFrom the project documentation, I can see that:\n- `RetryPolicy.should_retry` caps transient operations at **3 total attempts**\n- This is the initial attempt plus **2 retries**\n- So the policy makes a maximum of 3 total attempts\n\nSince a retry happens between consecutive attempts, the number of retry delays needed is one fewer than the total attempts:\n- 3 total attempts = 2 retries needed\n- So `backoff_schedule()` should return 2 entries\n\nThe delays grow exponentially:\n- Retry 0 (k=0): `BASE_DELAY_MS * 2^0 = 100 * 1 = 100`\n- Retry 1 (k=1): `BASE_DELAY_MS * 2^1 = 100 * 2 = 200`\n\nFILE: code/schedule.py\n\n```python\n\"\"\"Pre-computes the backoff schedule for a retried operation.\n\nThe retry policy lives in the `retry` package (see its documentation); this module only needs to\nknow how many attempts that policy will make so it can emit one backoff delay per retry.\n\"\"\"\n\n# The base wait before the first retry, in milliseconds. Each subsequent retry doubles it.\nBASE_DELAY_MS = 100\n\n\ndef backoff_schedule() -> list[int]:\n \"\"\"Return the wait before each retry, in milliseconds \u2014 one entry per retry the policy makes.\n\n A retry happens between consecutive attempts, so the number of entries is one fewer than the\n policy's maximum total attempts. Delays grow exponentially: ``BASE_DELAY_MS * 2 ** k`` for the\n k-th retry (k starting at 0). Returns ``[]`` if the policy makes only a single attempt.\n \"\"\"\n # The retry policy makes a maximum of 3 total attempts (1 initial + 2 retries)\n max_attempts = 3\n num_retries = max_attempts - 1\n \n return [BASE_DELAY_MS * (2 ** k) for k in range(num_retries)]\n```", "input_tokens": 799, "output_tokens": 494, "cost_usd": 0.003269, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/schedule.py"]}} -{"scenario": "cascade-retry-budget-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "haiku", "trial": 6, "output": "Looking at the documentation provided, I can see that:\n\n1. **RetryPolicy.should_retry caps transient operations at 3 total attempts**\n2. The policy keeps retrying while the 1-based attempt_number is below 3\n3. This means: initial attempt + 2 retries = 3 total attempts maximum\n\nSince a retry happens between consecutive attempts, the number of backoff delays needed is:\n- Total attempts - 1 = 3 - 1 = **2 retries**\n\nFor the exponential backoff, I need to generate delays using the formula: `BASE_DELAY_MS * 2 ** k` where k starts at 0:\n- k=0 (1st retry): `100 * 2^0 = 100` ms\n- k=1 (2nd retry): `100 * 2^1 = 200` ms\n\nFILE: code/schedule.py\n\n```python\n\"\"\"Pre-computes the backoff schedule for a retried operation.\n\nThe retry policy lives in the `retry` package (see its documentation); this module only needs to\nknow how many attempts that policy will make so it can emit one backoff delay per retry.\n\"\"\"\n\n# The base wait before the first retry, in milliseconds. Each subsequent retry doubles it.\nBASE_DELAY_MS = 100\n\n\ndef backoff_schedule() -> list[int]:\n \"\"\"Return the wait before each retry, in milliseconds \u2014 one entry per retry the policy makes.\n\n A retry happens between consecutive attempts, so the number of entries is one fewer than the\n policy's maximum total attempts. Delays grow exponentially: ``BASE_DELAY_MS * 2 ** k`` for the\n k-th retry (k starting at 0). Returns ``[]`` if the policy makes only a single attempt.\n \"\"\"\n # RetryPolicy caps transient operations at 3 total attempts (initial + 2 retries)\n # So we need 2 backoff delays (one per retry)\n max_total_attempts = 3\n num_retries = max_total_attempts - 1\n \n return [BASE_DELAY_MS * (2 ** k) for k in range(num_retries)]\n```", "input_tokens": 799, "output_tokens": 517, "cost_usd": 0.003384, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/schedule.py"]}} -{"scenario": "cascade-retry-budget-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "haiku", "trial": 7, "output": "Looking at the documentation and code, I need to determine:\n\n1. **Maximum total attempts from the retry policy**: According to the documentation, `RetryPolicy.should_retry` caps transient operations at **3 total attempts** (the initial attempt plus 2 retries).\n\n2. **Number of entries in the schedule**: Since a retry happens between consecutive attempts, and we have 3 total attempts, we need **2 retry delays** (one after attempt 1, one after attempt 2).\n\n3. **Formula**: Delays grow as `BASE_DELAY_MS * 2 ** k` where k starts at 0:\n - 1st retry (k=0): 100 * 2^0 = 100 ms\n - 2nd retry (k=1): 100 * 2^1 = 200 ms\n\nFILE: code/schedule.py\n\n```python\n\"\"\"Pre-computes the backoff schedule for a retried operation.\n\nThe retry policy lives in the `retry` package (see its documentation); this module only needs to\nknow how many attempts that policy will make so it can emit one backoff delay per retry.\n\"\"\"\n\n# The base wait before the first retry, in milliseconds. Each subsequent retry doubles it.\nBASE_DELAY_MS = 100\n\n\ndef backoff_schedule() -> list[int]:\n \"\"\"Return the wait before each retry, in milliseconds \u2014 one entry per retry the policy makes.\n\n A retry happens between consecutive attempts, so the number of entries is one fewer than the\n policy's maximum total attempts. Delays grow exponentially: ``BASE_DELAY_MS * 2 ** k`` for the\n k-th retry (k starting at 0). Returns ``[]`` if the policy makes only a single attempt.\n \"\"\"\n # The retry policy allows a maximum of 3 total attempts (1 initial + 2 retries)\n max_attempts = 3\n num_retries = max_attempts - 1\n \n return [BASE_DELAY_MS * (2 ** k) for k in range(num_retries)]\n```", "input_tokens": 799, "output_tokens": 485, "cost_usd": 0.0032240000000000003, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/schedule.py"]}} -{"scenario": "cascade-retry-budget-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "haiku", "trial": 8, "output": "Looking at the materials provided:\n\nFrom the project documentation, `RetryPolicy.should_retry` enforces a **budget of 3 total attempts**: the initial attempt plus up to **2 retries**.\n\nThis means:\n- Maximum total attempts: **3**\n- Number of retries: **2**\n- Number of entries in the backoff schedule: **2** (one per retry)\n\nThe delays follow the formula: `BASE_DELAY_MS * 2 ** k` where k starts at 0:\n- 1st retry (k=0): `100 * 2^0 = 100` ms\n- 2nd retry (k=1): `100 * 2^1 = 200` ms\n\nFILE: code/schedule.py\n\n```python\n\"\"\"Pre-computes the backoff schedule for a retried operation.\n\nThe retry policy lives in the `retry` package (see its documentation); this module only needs to\nknow how many attempts that policy will make so it can emit one backoff delay per retry.\n\"\"\"\n\n# The base wait before the first retry, in milliseconds. Each subsequent retry doubles it.\nBASE_DELAY_MS = 100\n\n\ndef backoff_schedule() -> list[int]:\n \"\"\"Return the wait before each retry, in milliseconds \u2014 one entry per retry the policy makes.\n\n A retry happens between consecutive attempts, so the number of entries is one fewer than the\n policy's maximum total attempts. Delays grow exponentially: ``BASE_DELAY_MS * 2 ** k`` for the\n k-th retry (k starting at 0). Returns ``[]`` if the policy makes only a single attempt.\n \"\"\"\n # The retry policy allows 3 total attempts (initial + 2 retries)\n max_attempts = 3\n num_retries = max_attempts - 1\n \n return [BASE_DELAY_MS * (2 ** k) for k in range(num_retries)]\n```", "input_tokens": 799, "output_tokens": 453, "cost_usd": 0.003064, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/schedule.py"]}} -{"scenario": "cascade-retry-budget-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "haiku", "trial": 9, "output": "Looking at the task, I need to:\n\n1. Determine the retry policy's maximum total attempts from the documentation\n2. Implement `backoff_schedule()` to return the exponential backoff delays\n\nFrom the project documentation, I can see that:\n- `RetryPolicy.should_retry` caps transient operations at **3 total attempts**\n- This means: 1 initial attempt + 2 retries\n- Therefore, there should be **2 backoff delays** (one before each retry)\n\nThe delays should follow the formula: `BASE_DELAY_MS * 2 ** k` where k starts at 0:\n- k=0 (1st retry): 100 * 2^0 = 100 ms\n- k=1 (2nd retry): 100 * 2^1 = 200 ms\n\nFILE: code/schedule.py\n\n```python\n\"\"\"Pre-computes the backoff schedule for a retried operation.\n\nThe retry policy lives in the `retry` package (see its documentation); this module only needs to\nknow how many attempts that policy will make so it can emit one backoff delay per retry.\n\"\"\"\n\n# The base wait before the first retry, in milliseconds. Each subsequent retry doubles it.\nBASE_DELAY_MS = 100\n\n\ndef backoff_schedule() -> list[int]:\n \"\"\"Return the wait before each retry, in milliseconds \u2014 one entry per retry the policy makes.\n\n A retry happens between consecutive attempts, so the number of entries is one fewer than the\n policy's maximum total attempts. Delays grow exponentially: ``BASE_DELAY_MS * 2 ** k`` for the\n k-th retry (k starting at 0). Returns ``[]`` if the policy makes only a single attempt.\n \"\"\"\n # The retry policy makes at most 3 total attempts (1 initial + 2 retries)\n max_attempts = 3\n # Number of retries is one fewer than total attempts\n num_retries = max_attempts - 1\n \n return [BASE_DELAY_MS * (2 ** k) for k in range(num_retries)]\n```", "input_tokens": 799, "output_tokens": 497, "cost_usd": 0.0032840000000000005, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/schedule.py"]}} -{"scenario": "cascade-retry-budget-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "sonnet", "trial": 0, "output": "FILE: code/schedule.py\n\n```python\n\"\"\"Pre-computes the backoff schedule for a retried operation.\n\nThe retry policy lives in the `retry` package (see its documentation); this module only needs to\nknow how many attempts that policy will make so it can emit one backoff delay per retry.\n\"\"\"\n\n# The base wait before the first retry, in milliseconds. Each subsequent retry doubles it.\nBASE_DELAY_MS = 100\n\n\ndef backoff_schedule() -> list[int]:\n \"\"\"Return the wait before each retry, in milliseconds \u2014 one entry per retry the policy makes.\n\n A retry happens between consecutive attempts, so the number of entries is one fewer than the\n policy's maximum total attempts. Delays grow exponentially: ``BASE_DELAY_MS * 2 ** k`` for the\n k-th retry (k starting at 0). Returns ``[]`` if the policy makes only a single attempt.\n \"\"\"\n # The retry policy allows a maximum of 3 total attempts (1 initial + 2 retries).\n max_attempts = 3\n num_retries = max_attempts - 1\n return [BASE_DELAY_MS * (2 ** k) for k in range(num_retries)]\n```", "input_tokens": 800, "output_tokens": 290, "cost_usd": 0.00675, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/schedule.py"]}} -{"scenario": "cascade-retry-budget-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "sonnet", "trial": 1, "output": "FILE: code/schedule.py\n\n```python\n\"\"\"Pre-computes the backoff schedule for a retried operation.\n\nThe retry policy lives in the `retry` package (see its documentation); this module only needs to\nknow how many attempts that policy will make so it can emit one backoff delay per retry.\n\"\"\"\n\n# The base wait before the first retry, in milliseconds. Each subsequent retry doubles it.\nBASE_DELAY_MS = 100\n\n\ndef backoff_schedule() -> list[int]:\n \"\"\"Return the wait before each retry, in milliseconds \u2014 one entry per retry the policy makes.\n\n A retry happens between consecutive attempts, so the number of entries is one fewer than the\n policy's maximum total attempts. Delays grow exponentially: ``BASE_DELAY_MS * 2 ** k`` for the\n k-th retry (k starting at 0). Returns ``[]`` if the policy makes only a single attempt.\n \"\"\"\n # The retry policy allows a maximum of 3 total attempts (1 initial + 2 retries).\n max_attempts = 3\n num_retries = max_attempts - 1\n return [BASE_DELAY_MS * 2 ** k for k in range(num_retries)]\n```", "input_tokens": 800, "output_tokens": 289, "cost_usd": 0.0067350000000000005, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/schedule.py"]}} -{"scenario": "cascade-retry-budget-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "sonnet", "trial": 2, "output": "FILE: code/schedule.py\n\n```python\n\"\"\"Pre-computes the backoff schedule for a retried operation.\n\nThe retry policy lives in the `retry` package (see its documentation); this module only needs to\nknow how many attempts that policy will make so it can emit one backoff delay per retry.\n\"\"\"\n\n# The base wait before the first retry, in milliseconds. Each subsequent retry doubles it.\nBASE_DELAY_MS = 100\n\n\ndef backoff_schedule() -> list[int]:\n \"\"\"Return the wait before each retry, in milliseconds \u2014 one entry per retry the policy makes.\n\n A retry happens between consecutive attempts, so the number of entries is one fewer than the\n policy's maximum total attempts. Delays grow exponentially: ``BASE_DELAY_MS * 2 ** k`` for the\n k-th retry (k starting at 0). Returns ``[]`` if the policy makes only a single attempt.\n \"\"\"\n # The retry policy allows at most 3 total attempts (1 initial + 2 retries).\n max_attempts = 3\n num_retries = max_attempts - 1\n return [BASE_DELAY_MS * 2 ** k for k in range(num_retries)]\n```", "input_tokens": 800, "output_tokens": 288, "cost_usd": 0.00672, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/schedule.py"]}} -{"scenario": "cascade-retry-budget-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "sonnet", "trial": 3, "output": "FILE: code/schedule.py\n\n```python\n\"\"\"Pre-computes the backoff schedule for a retried operation.\n\nThe retry policy lives in the `retry` package (see its documentation); this module only needs to\nknow how many attempts that policy will make so it can emit one backoff delay per retry.\n\"\"\"\n\n# The base wait before the first retry, in milliseconds. Each subsequent retry doubles it.\nBASE_DELAY_MS = 100\n\n\ndef backoff_schedule() -> list[int]:\n \"\"\"Return the wait before each retry, in milliseconds \u2014 one entry per retry the policy makes.\n\n A retry happens between consecutive attempts, so the number of entries is one fewer than the\n policy's maximum total attempts. Delays grow exponentially: ``BASE_DELAY_MS * 2 ** k`` for the\n k-th retry (k starting at 0). Returns ``[]`` if the policy makes only a single attempt.\n \"\"\"\n # The retry policy allows at most 3 total attempts (1 initial + 2 retries).\n max_attempts = 3\n num_retries = max_attempts - 1\n return [BASE_DELAY_MS * 2 ** k for k in range(num_retries)]\n```", "input_tokens": 800, "output_tokens": 288, "cost_usd": 0.00672, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/schedule.py"]}} -{"scenario": "cascade-retry-budget-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "sonnet", "trial": 4, "output": "FILE: code/schedule.py\n\n```python\n\"\"\"Pre-computes the backoff schedule for a retried operation.\n\nThe retry policy lives in the `retry` package (see its documentation); this module only needs to\nknow how many attempts that policy will make so it can emit one backoff delay per retry.\n\"\"\"\n\n# The base wait before the first retry, in milliseconds. Each subsequent retry doubles it.\nBASE_DELAY_MS = 100\n\n\ndef backoff_schedule() -> list[int]:\n \"\"\"Return the wait before each retry, in milliseconds \u2014 one entry per retry the policy makes.\n\n A retry happens between consecutive attempts, so the number of entries is one fewer than the\n policy's maximum total attempts. Delays grow exponentially: ``BASE_DELAY_MS * 2 ** k`` for the\n k-th retry (k starting at 0). Returns ``[]`` if the policy makes only a single attempt.\n \"\"\"\n # The retry policy allows a maximum of 3 total attempts (1 initial + 2 retries),\n # so we need 2 entries in the backoff schedule.\n max_attempts = 3\n num_retries = max_attempts - 1\n return [BASE_DELAY_MS * (2 ** k) for k in range(num_retries)]\n```", "input_tokens": 800, "output_tokens": 306, "cost_usd": 0.0069900000000000006, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/schedule.py"]}} -{"scenario": "cascade-retry-budget-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "sonnet", "trial": 5, "output": "FILE: code/schedule.py\n\n```python\n\"\"\"Pre-computes the backoff schedule for a retried operation.\n\nThe retry policy lives in the `retry` package (see its documentation); this module only needs to\nknow how many attempts that policy will make so it can emit one backoff delay per retry.\n\"\"\"\n\n# The base wait before the first retry, in milliseconds. Each subsequent retry doubles it.\nBASE_DELAY_MS = 100\n\n\ndef backoff_schedule() -> list[int]:\n \"\"\"Return the wait before each retry, in milliseconds \u2014 one entry per retry the policy makes.\n\n A retry happens between consecutive attempts, so the number of entries is one fewer than the\n policy's maximum total attempts. Delays grow exponentially: ``BASE_DELAY_MS * 2 ** k`` for the\n k-th retry (k starting at 0). Returns ``[]`` if the policy makes only a single attempt.\n \"\"\"\n # The retry policy allows at most 3 total attempts (1 initial + 2 retries).\n max_attempts = 3\n num_retries = max_attempts - 1\n return [BASE_DELAY_MS * 2 ** k for k in range(num_retries)]\n```", "input_tokens": 800, "output_tokens": 288, "cost_usd": 0.00672, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/schedule.py"]}} -{"scenario": "cascade-retry-budget-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "sonnet", "trial": 6, "output": "FILE: code/schedule.py\n\n```python\n\"\"\"Pre-computes the backoff schedule for a retried operation.\n\nThe retry policy lives in the `retry` package (see its documentation); this module only needs to\nknow how many attempts that policy will make so it can emit one backoff delay per retry.\n\"\"\"\n\n# The base wait before the first retry, in milliseconds. Each subsequent retry doubles it.\nBASE_DELAY_MS = 100\n\n\ndef backoff_schedule() -> list[int]:\n \"\"\"Return the wait before each retry, in milliseconds \u2014 one entry per retry the policy makes.\n\n A retry happens between consecutive attempts, so the number of entries is one fewer than the\n policy's maximum total attempts. Delays grow exponentially: ``BASE_DELAY_MS * 2 ** k`` for the\n k-th retry (k starting at 0). Returns ``[]`` if the policy makes only a single attempt.\n \"\"\"\n # The RetryPolicy allows a maximum of 3 total attempts (1 initial + 2 retries),\n # so we need 2 entries in the backoff schedule (one per retry).\n max_attempts = 3\n num_retries = max_attempts - 1\n return [BASE_DELAY_MS * (2 ** k) for k in range(num_retries)]\n```", "input_tokens": 800, "output_tokens": 311, "cost_usd": 0.007065, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/schedule.py"]}} -{"scenario": "cascade-retry-budget-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "sonnet", "trial": 7, "output": "FILE: code/schedule.py\n\n```python\n\"\"\"Pre-computes the backoff schedule for a retried operation.\n\nThe retry policy lives in the `retry` package (see its documentation); this module only needs to\nknow how many attempts that policy will make so it can emit one backoff delay per retry.\n\"\"\"\n\n# The base wait before the first retry, in milliseconds. Each subsequent retry doubles it.\nBASE_DELAY_MS = 100\n\n\ndef backoff_schedule() -> list[int]:\n \"\"\"Return the wait before each retry, in milliseconds \u2014 one entry per retry the policy makes.\n\n A retry happens between consecutive attempts, so the number of entries is one fewer than the\n policy's maximum total attempts. Delays grow exponentially: ``BASE_DELAY_MS * 2 ** k`` for the\n k-th retry (k starting at 0). Returns ``[]`` if the policy makes only a single attempt.\n \"\"\"\n # The retry policy allows a maximum of 3 total attempts (1 initial + 2 retries),\n # so we need 2 entries in the backoff schedule.\n max_attempts = 3\n num_retries = max_attempts - 1\n return [BASE_DELAY_MS * (2 ** k) for k in range(num_retries)]\n```", "input_tokens": 800, "output_tokens": 306, "cost_usd": 0.0069900000000000006, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/schedule.py"]}} -{"scenario": "cascade-retry-budget-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "sonnet", "trial": 8, "output": "FILE: code/schedule.py\n\n```python\n\"\"\"Pre-computes the backoff schedule for a retried operation.\n\nThe retry policy lives in the `retry` package (see its documentation); this module only needs to\nknow how many attempts that policy will make so it can emit one backoff delay per retry.\n\"\"\"\n\n# The base wait before the first retry, in milliseconds. Each subsequent retry doubles it.\nBASE_DELAY_MS = 100\n\n\ndef backoff_schedule() -> list[int]:\n \"\"\"Return the wait before each retry, in milliseconds \u2014 one entry per retry the policy makes.\n\n A retry happens between consecutive attempts, so the number of entries is one fewer than the\n policy's maximum total attempts. Delays grow exponentially: ``BASE_DELAY_MS * 2 ** k`` for the\n k-th retry (k starting at 0). Returns ``[]`` if the policy makes only a single attempt.\n \"\"\"\n # The retry policy allows at most 3 total attempts (1 initial + 2 retries).\n max_attempts = 3\n num_retries = max_attempts - 1\n return [BASE_DELAY_MS * 2 ** k for k in range(num_retries)]\n```", "input_tokens": 800, "output_tokens": 288, "cost_usd": 0.00672, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/schedule.py"]}} -{"scenario": "cascade-retry-budget-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "sonnet", "trial": 9, "output": "FILE: code/schedule.py\n\n```python\n\"\"\"Pre-computes the backoff schedule for a retried operation.\n\nThe retry policy lives in the `retry` package (see its documentation); this module only needs to\nknow how many attempts that policy will make so it can emit one backoff delay per retry.\n\"\"\"\n\n# The base wait before the first retry, in milliseconds. Each subsequent retry doubles it.\nBASE_DELAY_MS = 100\n\n\ndef backoff_schedule() -> list[int]:\n \"\"\"Return the wait before each retry, in milliseconds \u2014 one entry per retry the policy makes.\n\n A retry happens between consecutive attempts, so the number of entries is one fewer than the\n policy's maximum total attempts. Delays grow exponentially: ``BASE_DELAY_MS * 2 ** k`` for the\n k-th retry (k starting at 0). Returns ``[]`` if the policy makes only a single attempt.\n \"\"\"\n # The retry policy allows a maximum of 3 total attempts (1 initial + 2 retries).\n max_attempts = 3\n num_retries = max_attempts - 1\n return [BASE_DELAY_MS * (2 ** k) for k in range(num_retries)]\n```", "input_tokens": 800, "output_tokens": 290, "cost_usd": 0.00675, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/schedule.py"]}} -{"scenario": "cascade-retry-budget-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "opus", "trial": 0, "output": "The policy makes at most 3 total attempts, so there are 2 retries. The schedule has one entry per retry: `BASE_DELAY_MS * 2**0` and `BASE_DELAY_MS * 2**1`.\n\nFILE: code/schedule.py\n```python\n\"\"\"Pre-computes the backoff schedule for a retried operation.\n\nThe retry policy lives in the `retry` package (see its documentation); this module only needs to\nknow how many attempts that policy will make so it can emit one backoff delay per retry.\n\"\"\"\n\n# The base wait before the first retry, in milliseconds. Each subsequent retry doubles it.\nBASE_DELAY_MS = 100\n\n# The retry policy caps transient operations at 3 total attempts (the initial attempt plus 2\n# retries), per the retry package documentation.\nMAX_ATTEMPTS = 3\n\n\ndef backoff_schedule() -> list[int]:\n \"\"\"Return the wait before each retry, in milliseconds \u2014 one entry per retry the policy makes.\n\n A retry happens between consecutive attempts, so the number of entries is one fewer than the\n policy's maximum total attempts. Delays grow exponentially: ``BASE_DELAY_MS * 2 ** k`` for the\n k-th retry (k starting at 0). Returns ``[]`` if the policy makes only a single attempt.\n \"\"\"\n num_retries = MAX_ATTEMPTS - 1\n return [BASE_DELAY_MS * 2 ** k for k in range(num_retries)]\n```", "input_tokens": 1107, "output_tokens": 503, "cost_usd": 0.01811, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/schedule.py"]}} -{"scenario": "cascade-retry-budget-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "opus", "trial": 1, "output": "The policy makes at most 3 total attempts, so there are 2 retries (entries).\n\nFILE: code/schedule.py\n```python\n\"\"\"Pre-computes the backoff schedule for a retried operation.\n\nThe retry policy lives in the `retry` package (see its documentation); this module only needs to\nknow how many attempts that policy will make so it can emit one backoff delay per retry.\n\"\"\"\n\n# The base wait before the first retry, in milliseconds. Each subsequent retry doubles it.\nBASE_DELAY_MS = 100\n\n# The retry policy caps transient operations at 3 total attempts (the initial attempt plus 2\n# retries), per the retry package documentation.\nMAX_TOTAL_ATTEMPTS = 3\n\n\ndef backoff_schedule() -> list[int]:\n \"\"\"Return the wait before each retry, in milliseconds \u2014 one entry per retry the policy makes.\n\n A retry happens between consecutive attempts, so the number of entries is one fewer than the\n policy's maximum total attempts. Delays grow exponentially: ``BASE_DELAY_MS * 2 ** k`` for the\n k-th retry (k starting at 0). Returns ``[]`` if the policy makes only a single attempt.\n \"\"\"\n num_retries = MAX_TOTAL_ATTEMPTS - 1\n return [BASE_DELAY_MS * 2 ** k for k in range(num_retries)]\n```", "input_tokens": 1107, "output_tokens": 468, "cost_usd": 0.017235, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/schedule.py"]}} -{"scenario": "cascade-retry-budget-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "opus", "trial": 2, "output": "The policy makes at most 3 total attempts (initial + 2 retries), so the schedule has 2 entries.\n\nFILE: code/schedule.py\n```python\n\"\"\"Pre-computes the backoff schedule for a retried operation.\n\nThe retry policy lives in the `retry` package (see its documentation); this module only needs to\nknow how many attempts that policy will make so it can emit one backoff delay per retry.\n\"\"\"\n\n# The base wait before the first retry, in milliseconds. Each subsequent retry doubles it.\nBASE_DELAY_MS = 100\n\n# The retry policy caps transient operations at 3 total attempts (initial attempt plus 2 retries).\nMAX_ATTEMPTS = 3\n\n\ndef backoff_schedule() -> list[int]:\n \"\"\"Return the wait before each retry, in milliseconds \u2014 one entry per retry the policy makes.\n\n A retry happens between consecutive attempts, so the number of entries is one fewer than the\n policy's maximum total attempts. Delays grow exponentially: ``BASE_DELAY_MS * 2 ** k`` for the\n k-th retry (k starting at 0). Returns ``[]`` if the policy makes only a single attempt.\n \"\"\"\n num_retries = MAX_ATTEMPTS - 1\n return [BASE_DELAY_MS * 2 ** k for k in range(num_retries)]\n```", "input_tokens": 1107, "output_tokens": 454, "cost_usd": 0.016885, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/schedule.py"]}} -{"scenario": "cascade-retry-budget-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "opus", "trial": 3, "output": "The policy makes at most 3 total attempts (initial + 2 retries), so the schedule has 2 entries.\n\nFILE: code/schedule.py\n```python\n\"\"\"Pre-computes the backoff schedule for a retried operation.\n\nThe retry policy lives in the `retry` package (see its documentation); this module only needs to\nknow how many attempts that policy will make so it can emit one backoff delay per retry.\n\"\"\"\n\n# The base wait before the first retry, in milliseconds. Each subsequent retry doubles it.\nBASE_DELAY_MS = 100\n\n# The retry policy caps transient operations at 3 total attempts (the initial attempt plus 2\n# retries), per the retry package documentation.\nMAX_ATTEMPTS = 3\n\n\ndef backoff_schedule() -> list[int]:\n \"\"\"Return the wait before each retry, in milliseconds \u2014 one entry per retry the policy makes.\n\n A retry happens between consecutive attempts, so the number of entries is one fewer than the\n policy's maximum total attempts. Delays grow exponentially: ``BASE_DELAY_MS * 2 ** k`` for the\n k-th retry (k starting at 0). Returns ``[]`` if the policy makes only a single attempt.\n \"\"\"\n num_retries = MAX_ATTEMPTS - 1\n return [BASE_DELAY_MS * 2 ** k for k in range(num_retries)]\n```", "input_tokens": 1107, "output_tokens": 465, "cost_usd": 0.01716, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/schedule.py"]}} -{"scenario": "cascade-retry-budget-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "opus", "trial": 4, "output": "The policy makes at most 3 total attempts, so there are 2 retries. The schedule has one entry per retry: `BASE_DELAY_MS * 2**0` and `BASE_DELAY_MS * 2**1` \u2192 `[100, 200]`.\n\nFILE: code/schedule.py\n```python\n\"\"\"Pre-computes the backoff schedule for a retried operation.\n\nThe retry policy lives in the `retry` package (see its documentation); this module only needs to\nknow how many attempts that policy will make so it can emit one backoff delay per retry.\n\"\"\"\n\n# The base wait before the first retry, in milliseconds. Each subsequent retry doubles it.\nBASE_DELAY_MS = 100\n\n# The retry policy caps transient operations at 3 total attempts (the initial attempt plus 2\n# retries), per the project documentation for RetryPolicy.should_retry.\nMAX_TOTAL_ATTEMPTS = 3\n\n\ndef backoff_schedule() -> list[int]:\n \"\"\"Return the wait before each retry, in milliseconds \u2014 one entry per retry the policy makes.\n\n A retry happens between consecutive attempts, so the number of entries is one fewer than the\n policy's maximum total attempts. Delays grow exponentially: ``BASE_DELAY_MS * 2 ** k`` for the\n k-th retry (k starting at 0). Returns ``[]`` if the policy makes only a single attempt.\n \"\"\"\n num_retries = MAX_TOTAL_ATTEMPTS - 1\n return [BASE_DELAY_MS * 2 ** k for k in range(num_retries)]\n```", "input_tokens": 1107, "output_tokens": 535, "cost_usd": 0.018910000000000003, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/schedule.py"]}} -{"scenario": "cascade-retry-budget-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "opus", "trial": 5, "output": "The policy makes at most 3 total attempts, so there are 2 retries. The schedule has 2 entries: `BASE_DELAY_MS * 2**0 = 100` and `BASE_DELAY_MS * 2**1 = 200`.\n\nFILE: code/schedule.py\n```python\n\"\"\"Pre-computes the backoff schedule for a retried operation.\n\nThe retry policy lives in the `retry` package (see its documentation); this module only needs to\nknow how many attempts that policy will make so it can emit one backoff delay per retry.\n\"\"\"\n\n# The base wait before the first retry, in milliseconds. Each subsequent retry doubles it.\nBASE_DELAY_MS = 100\n\n# The retry policy caps transient operations at 3 total attempts (initial attempt + 2 retries).\nMAX_ATTEMPTS = 3\n\n\ndef backoff_schedule() -> list[int]:\n \"\"\"Return the wait before each retry, in milliseconds \u2014 one entry per retry the policy makes.\n\n A retry happens between consecutive attempts, so the number of entries is one fewer than the\n policy's maximum total attempts. Delays grow exponentially: ``BASE_DELAY_MS * 2 ** k`` for the\n k-th retry (k starting at 0). Returns ``[]`` if the policy makes only a single attempt.\n \"\"\"\n num_retries = MAX_ATTEMPTS - 1\n return [BASE_DELAY_MS * 2 ** k for k in range(num_retries)]\n```", "input_tokens": 1107, "output_tokens": 498, "cost_usd": 0.017985, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/schedule.py"]}} -{"scenario": "cascade-retry-budget-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "opus", "trial": 6, "output": "The policy makes at most 3 total attempts, so 2 retries. The schedule needs 2 entries: `100 * 2**0 = 100` and `100 * 2**1 = 200`.\n\nFILE: code/schedule.py\n```python\n\"\"\"Pre-computes the backoff schedule for a retried operation.\n\nThe retry policy lives in the `retry` package (see its documentation); this module only needs to\nknow how many attempts that policy will make so it can emit one backoff delay per retry.\n\"\"\"\n\n# The base wait before the first retry, in milliseconds. Each subsequent retry doubles it.\nBASE_DELAY_MS = 100\n\n# The retry policy caps transient operations at 3 total attempts (the initial attempt plus 2\n# retries), per the retry package documentation.\nMAX_ATTEMPTS = 3\n\n\ndef backoff_schedule() -> list[int]:\n \"\"\"Return the wait before each retry, in milliseconds \u2014 one entry per retry the policy makes.\n\n A retry happens between consecutive attempts, so the number of entries is one fewer than the\n policy's maximum total attempts. Delays grow exponentially: ``BASE_DELAY_MS * 2 ** k`` for the\n k-th retry (k starting at 0). Returns ``[]`` if the policy makes only a single attempt.\n \"\"\"\n num_retries = MAX_ATTEMPTS - 1\n return [BASE_DELAY_MS * 2 ** k for k in range(num_retries)]\n```", "input_tokens": 1107, "output_tokens": 491, "cost_usd": 0.017810000000000003, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/schedule.py"]}} -{"scenario": "cascade-retry-budget-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "opus", "trial": 7, "output": "The policy makes at most 3 total attempts (initial + 2 retries), so the schedule has 2 entries: k=0 and k=1.\n\nFILE: code/schedule.py\n```python\n\"\"\"Pre-computes the backoff schedule for a retried operation.\n\nThe retry policy lives in the `retry` package (see its documentation); this module only needs to\nknow how many attempts that policy will make so it can emit one backoff delay per retry.\n\"\"\"\n\n# The base wait before the first retry, in milliseconds. Each subsequent retry doubles it.\nBASE_DELAY_MS = 100\n\n# The retry policy caps transient operations at 3 total attempts (the initial attempt plus 2\n# retries), per the retry policy documentation.\nMAX_TOTAL_ATTEMPTS = 3\n\n\ndef backoff_schedule() -> list[int]:\n \"\"\"Return the wait before each retry, in milliseconds \u2014 one entry per retry the policy makes.\n\n A retry happens between consecutive attempts, so the number of entries is one fewer than the\n policy's maximum total attempts. Delays grow exponentially: ``BASE_DELAY_MS * 2 ** k`` for the\n k-th retry (k starting at 0). Returns ``[]`` if the policy makes only a single attempt.\n \"\"\"\n num_retries = MAX_TOTAL_ATTEMPTS - 1\n return [BASE_DELAY_MS * 2 ** k for k in range(num_retries)]\n```", "input_tokens": 1107, "output_tokens": 487, "cost_usd": 0.01771, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/schedule.py"]}} -{"scenario": "cascade-retry-budget-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "opus", "trial": 8, "output": "The policy makes at most 3 total attempts (initial + 2 retries), so the schedule has 2 entries.\n\nFILE: code/schedule.py\n```python\n\"\"\"Pre-computes the backoff schedule for a retried operation.\n\nThe retry policy lives in the `retry` package (see its documentation); this module only needs to\nknow how many attempts that policy will make so it can emit one backoff delay per retry.\n\"\"\"\n\n# The base wait before the first retry, in milliseconds. Each subsequent retry doubles it.\nBASE_DELAY_MS = 100\n\n# The retry policy caps transient operations at 3 total attempts (initial + 2 retries).\nMAX_ATTEMPTS = 3\n\n\ndef backoff_schedule() -> list[int]:\n \"\"\"Return the wait before each retry, in milliseconds \u2014 one entry per retry the policy makes.\n\n A retry happens between consecutive attempts, so the number of entries is one fewer than the\n policy's maximum total attempts. Delays grow exponentially: ``BASE_DELAY_MS * 2 ** k`` for the\n k-th retry (k starting at 0). Returns ``[]`` if the policy makes only a single attempt.\n \"\"\"\n num_retries = MAX_ATTEMPTS - 1\n return [BASE_DELAY_MS * 2 ** k for k in range(num_retries)]\n```", "input_tokens": 1107, "output_tokens": 450, "cost_usd": 0.016785, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/schedule.py"]}} -{"scenario": "cascade-retry-budget-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "opus", "trial": 9, "output": "The policy allows max 3 total attempts \u2192 2 retries.\n\nFILE: code/schedule.py\n```python\n\"\"\"Pre-computes the backoff schedule for a retried operation.\n\nThe retry policy lives in the `retry` package (see its documentation); this module only needs to\nknow how many attempts that policy will make so it can emit one backoff delay per retry.\n\"\"\"\n\n# The base wait before the first retry, in milliseconds. Each subsequent retry doubles it.\nBASE_DELAY_MS = 100\n\n# The retry policy caps a persistently-failing transient operation at 3 total attempts.\nMAX_ATTEMPTS = 3\n\n\ndef backoff_schedule() -> list[int]:\n \"\"\"Return the wait before each retry, in milliseconds \u2014 one entry per retry the policy makes.\n\n A retry happens between consecutive attempts, so the number of entries is one fewer than the\n policy's maximum total attempts. Delays grow exponentially: ``BASE_DELAY_MS * 2 ** k`` for the\n k-th retry (k starting at 0). Returns ``[]`` if the policy makes only a single attempt.\n \"\"\"\n num_retries = MAX_ATTEMPTS - 1\n return [BASE_DELAY_MS * 2 ** k for k in range(num_retries)]\n```", "input_tokens": 1107, "output_tokens": 432, "cost_usd": 0.016335000000000002, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/schedule.py"]}} -{"scenario": "cascade-retry-budget-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "haiku", "trial": 0, "output": "Looking at the documentation provided, I can see that:\n\n1. The retry policy caps transient operations at **5 total attempts**\n2. This means 1 initial attempt plus **4 retries**\n3. The backoff schedule needs one entry per retry (not per attempt)\n4. So we need **4 entries** in the schedule (for the 4 retries)\n5. Delays grow exponentially: `BASE_DELAY_MS * 2 ** k` for the k-th retry (k starting at 0)\n\nFor 4 retries:\n- Retry 0 (1st retry after initial attempt): 100 * 2^0 = 100 ms\n- Retry 1 (2nd retry): 100 * 2^1 = 200 ms\n- Retry 2 (3rd retry): 100 * 2^2 = 400 ms\n- Retry 3 (4th retry): 100 * 2^3 = 800 ms\n\nFILE: code/schedule.py\n\n```python\n\"\"\"Pre-computes the backoff schedule for a retried operation.\n\nThe retry policy lives in the `retry` package (see its documentation); this module only needs to\nknow how many attempts that policy will make so it can emit one backoff delay per retry.\n\"\"\"\n\n# The base wait before the first retry, in milliseconds. Each subsequent retry doubles it.\nBASE_DELAY_MS = 100\n\n\ndef backoff_schedule() -> list[int]:\n \"\"\"Return the wait before each retry, in milliseconds \u2014 one entry per retry the policy makes.\n\n A retry happens between consecutive attempts, so the number of entries is one fewer than the\n policy's maximum total attempts. Delays grow exponentially: ``BASE_DELAY_MS * 2 ** k`` for the\n k-th retry (k starting at 0). Returns ``[]`` if the policy makes only a single attempt.\n \"\"\"\n # The retry policy allows 5 total attempts (1 initial + 4 retries)\n # So we need 4 entries in the schedule (one per retry)\n max_retries = 4\n return [BASE_DELAY_MS * (2 ** k) for k in range(max_retries)]\n```", "input_tokens": 798, "output_tokens": 530, "cost_usd": 0.003448, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/schedule.py"]}} -{"scenario": "cascade-retry-budget-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "haiku", "trial": 1, "output": "Looking at the project documentation, I can see that:\n\n1. `RetryPolicy.should_retry` caps transient operations at **5 total attempts**\n2. This means the initial attempt plus **4 retries**\n3. A retry happens between consecutive attempts, so we need **4 backoff delays** (one per retry)\n4. Delays grow exponentially: `BASE_DELAY_MS * 2 ** k` for the k-th retry (k starting at 0)\n\nSo for k = 0, 1, 2, 3:\n- k=0: 100 * 2^0 = 100 ms\n- k=1: 100 * 2^1 = 200 ms\n- k=2: 100 * 2^2 = 400 ms\n- k=3: 100 * 2^3 = 800 ms\n\nFILE: code/schedule.py\n\n```python\n\"\"\"Pre-computes the backoff schedule for a retried operation.\n\nThe retry policy lives in the `retry` package (see its documentation); this module only needs to\nknow how many attempts that policy will make so it can emit one backoff delay per retry.\n\"\"\"\n\n# The base wait before the first retry, in milliseconds. Each subsequent retry doubles it.\nBASE_DELAY_MS = 100\n\n\ndef backoff_schedule() -> list[int]:\n \"\"\"Return the wait before each retry, in milliseconds \u2014 one entry per retry the policy makes.\n\n A retry happens between consecutive attempts, so the number of entries is one fewer than the\n policy's maximum total attempts. Delays grow exponentially: ``BASE_DELAY_MS * 2 ** k`` for the\n k-th retry (k starting at 0). Returns ``[]`` if the policy makes only a single attempt.\n \"\"\"\n # RetryPolicy allows a maximum of 5 total attempts (1 initial + 4 retries)\n # So we need 4 backoff delays, one for each retry\n max_attempts = 5\n num_retries = max_attempts - 1\n \n return [BASE_DELAY_MS * (2 ** k) for k in range(num_retries)]\n```", "input_tokens": 798, "output_tokens": 517, "cost_usd": 0.003383, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/schedule.py"]}} -{"scenario": "cascade-retry-budget-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "haiku", "trial": 2, "output": "Looking at the documentation provided, the retry policy has a **budget of 5 total attempts**: the initial attempt plus up to **4 retries**.\n\nSince a retry happens between consecutive attempts:\n- 5 total attempts = 4 retries\n- So `backoff_schedule()` should return 4 delays (one per retry)\n\nThe delays grow exponentially: `BASE_DELAY_MS * 2 ** k` for the k-th retry (k starting at 0):\n- Retry 0: 100 * 2^0 = 100 ms\n- Retry 1: 100 * 2^1 = 200 ms\n- Retry 2: 100 * 2^2 = 400 ms\n- Retry 3: 100 * 2^3 = 800 ms\n\nFILE: code/schedule.py\n\n```python\n\"\"\"Pre-computes the backoff schedule for a retried operation.\n\nThe retry policy lives in the `retry` package (see its documentation); this module only needs to\nknow how many attempts that policy will make so it can emit one backoff delay per retry.\n\"\"\"\n\n# The base wait before the first retry, in milliseconds. Each subsequent retry doubles it.\nBASE_DELAY_MS = 100\n\n\ndef backoff_schedule() -> list[int]:\n \"\"\"Return the wait before each retry, in milliseconds \u2014 one entry per retry the policy makes.\n\n A retry happens between consecutive attempts, so the number of entries is one fewer than the\n policy's maximum total attempts. Delays grow exponentially: ``BASE_DELAY_MS * 2 ** k`` for the\n k-th retry (k starting at 0). Returns ``[]`` if the policy makes only a single attempt.\n \"\"\"\n # The retry policy makes at most 5 total attempts (1 initial + 4 retries).\n # So we need 4 backoff delays, one for each retry.\n max_attempts = 5\n num_retries = max_attempts - 1\n \n return [BASE_DELAY_MS * (2 ** k) for k in range(num_retries)]\n```", "input_tokens": 798, "output_tokens": 503, "cost_usd": 0.0033130000000000004, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/schedule.py"]}} -{"scenario": "cascade-retry-budget-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "haiku", "trial": 3, "output": "Looking at the documentation provided, I need to determine the retry policy's maximum total attempts:\n\nFrom the project documentation:\n- **RetryPolicy.should_retry caps transient operations at 5 total attempts**\n- It keeps retrying while the 1-based attempt_number is below 5\n- This means: initial attempt + 4 retries = 5 total attempts\n\nSo the policy makes **5 total attempts**, which means there are **4 retries** (one fewer than total attempts).\n\nFor the backoff schedule, I need to return delays for each of the 4 retries using the formula: `BASE_DELAY_MS * 2 ** k` where k starts at 0.\n\n- 1st retry (k=0): 100 * 2^0 = 100 ms\n- 2nd retry (k=1): 100 * 2^1 = 200 ms\n- 3rd retry (k=2): 100 * 2^2 = 400 ms\n- 4th retry (k=3): 100 * 2^3 = 800 ms\n\nFILE: code/schedule.py\n\n```python\n\"\"\"Pre-computes the backoff schedule for a retried operation.\n\nThe retry policy lives in the `retry` package (see its documentation); this module only needs to\nknow how many attempts that policy will make so it can emit one backoff delay per retry.\n\"\"\"\n\n# The base wait before the first retry, in milliseconds. Each subsequent retry doubles it.\nBASE_DELAY_MS = 100\n\n\ndef backoff_schedule() -> list[int]:\n \"\"\"Return the wait before each retry, in milliseconds \u2014 one entry per retry the policy makes.\n\n A retry happens between consecutive attempts, so the number of entries is one fewer than the\n policy's maximum total attempts. Delays grow exponentially: ``BASE_DELAY_MS * 2 ** k`` for the\n k-th retry (k starting at 0). Returns ``[]`` if the policy makes only a single attempt.\n \"\"\"\n # The retry policy makes a maximum of 5 total attempts (initial + 4 retries)\n # So we need 4 backoff delays (one per retry)\n max_attempts = 5\n num_retries = max_attempts - 1\n \n return [BASE_DELAY_MS * (2 ** k) for k in range(num_retries)]\n```", "input_tokens": 798, "output_tokens": 565, "cost_usd": 0.0036230000000000004, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/schedule.py"]}} -{"scenario": "cascade-retry-budget-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "haiku", "trial": 4, "output": "Looking at the documentation provided, I can see that:\n\n1. `RetryPolicy.should_retry` caps transient operations at **5 total attempts**\n2. This means: 1 initial attempt + 4 retries = 5 total attempts\n3. Therefore, the backoff schedule needs **4 entries** (one per retry)\n4. Delays grow exponentially: `BASE_DELAY_MS * 2 ** k` where k starts at 0\n\nFor 4 retries:\n- Retry 0: 100 * 2^0 = 100 ms\n- Retry 1: 100 * 2^1 = 200 ms\n- Retry 2: 100 * 2^2 = 400 ms\n- Retry 3: 100 * 2^3 = 800 ms\n\nFILE: code/schedule.py\n\n```python\n\"\"\"Pre-computes the backoff schedule for a retried operation.\n\nThe retry policy lives in the `retry` package (see its documentation); this module only needs to\nknow how many attempts that policy will make so it can emit one backoff delay per retry.\n\"\"\"\n\n# The base wait before the first retry, in milliseconds. Each subsequent retry doubles it.\nBASE_DELAY_MS = 100\n\n\ndef backoff_schedule() -> list[int]:\n \"\"\"Return the wait before each retry, in milliseconds \u2014 one entry per retry the policy makes.\n\n A retry happens between consecutive attempts, so the number of entries is one fewer than the\n policy's maximum total attempts. Delays grow exponentially: ``BASE_DELAY_MS * 2 ** k`` for the\n k-th retry (k starting at 0). Returns ``[]`` if the policy makes only a single attempt.\n \"\"\"\n # The retry policy makes at most 5 total attempts (1 initial + 4 retries)\n # So we need 4 backoff delays, one for each retry\n max_total_attempts = 5\n num_retries = max_total_attempts - 1\n \n return [BASE_DELAY_MS * (2 ** k) for k in range(num_retries)]\n```", "input_tokens": 798, "output_tokens": 511, "cost_usd": 0.0033530000000000005, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/schedule.py"]}} -{"scenario": "cascade-retry-budget-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "haiku", "trial": 5, "output": "Looking at the documentation provided, I can see that:\n\n1. `RetryPolicy.should_retry` caps transient operations at **5 total attempts**\n2. This means 1 initial attempt + 4 retries maximum\n3. A retry happens between consecutive attempts, so with 5 total attempts, there are 4 retries\n4. Delays grow exponentially: `BASE_DELAY_MS * 2 ** k` for the k-th retry (k starting at 0)\n\nSo I need to return a list of 4 delays:\n- k=0: 100 * 2^0 = 100\n- k=1: 100 * 2^1 = 200\n- k=2: 100 * 2^2 = 400\n- k=3: 100 * 2^3 = 800\n\nFILE: code/schedule.py\n\n```python\n\"\"\"Pre-computes the backoff schedule for a retried operation.\n\nThe retry policy lives in the `retry` package (see its documentation); this module only needs to\nknow how many attempts that policy will make so it can emit one backoff delay per retry.\n\"\"\"\n\n# The base wait before the first retry, in milliseconds. Each subsequent retry doubles it.\nBASE_DELAY_MS = 100\n\n\ndef backoff_schedule() -> list[int]:\n \"\"\"Return the wait before each retry, in milliseconds \u2014 one entry per retry the policy makes.\n\n A retry happens between consecutive attempts, so the number of entries is one fewer than the\n policy's maximum total attempts. Delays grow exponentially: ``BASE_DELAY_MS * 2 ** k`` for the\n k-th retry (k starting at 0). Returns ``[]`` if the policy makes only a single attempt.\n \"\"\"\n # RetryPolicy caps transient operations at 5 total attempts (1 initial + 4 retries)\n # So we need 4 backoff delays, one for each retry\n max_attempts = 5\n num_retries = max_attempts - 1\n \n return [BASE_DELAY_MS * (2 ** k) for k in range(num_retries)]\n```", "input_tokens": 798, "output_tokens": 509, "cost_usd": 0.0033430000000000005, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/schedule.py"]}} -{"scenario": "cascade-retry-budget-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "haiku", "trial": 6, "output": "Looking at the documentation provided, I can see that:\n\n1. The `RetryPolicy.should_retry` allows **5 total attempts** maximum (the initial attempt plus 4 retries)\n2. A retry happens **between** consecutive attempts, so with 5 total attempts, there are **4 retries**\n3. The backoff schedule should return one entry per retry, so we need **4 entries**\n4. Delays grow exponentially: `BASE_DELAY_MS * 2 ** k` where k starts at 0\n\nSo for 4 retries:\n- k=0: 100 * 2^0 = 100 ms\n- k=1: 100 * 2^1 = 200 ms\n- k=2: 100 * 2^2 = 400 ms\n- k=3: 100 * 2^3 = 800 ms\n\nFILE: code/schedule.py\n\n```python\n\"\"\"Pre-computes the backoff schedule for a retried operation.\n\nThe retry policy lives in the `retry` package (see its documentation); this module only needs to\nknow how many attempts that policy will make so it can emit one backoff delay per retry.\n\"\"\"\n\n# The base wait before the first retry, in milliseconds. Each subsequent retry doubles it.\nBASE_DELAY_MS = 100\n\n\ndef backoff_schedule() -> list[int]:\n \"\"\"Return the wait before each retry, in milliseconds \u2014 one entry per retry the policy makes.\n\n A retry happens between consecutive attempts, so the number of entries is one fewer than the\n policy's maximum total attempts. Delays grow exponentially: ``BASE_DELAY_MS * 2 ** k`` for the\n k-th retry (k starting at 0). Returns ``[]`` if the policy makes only a single attempt.\n \"\"\"\n # The retry policy allows 5 total attempts (initial + 4 retries)\n # So we need to generate 4 backoff delays (one per retry)\n max_retries = 4\n return [BASE_DELAY_MS * (2 ** k) for k in range(max_retries)]\n```", "input_tokens": 798, "output_tokens": 504, "cost_usd": 0.003318, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/schedule.py"]}} -{"scenario": "cascade-retry-budget-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "haiku", "trial": 7, "output": "Looking at the documentation and code, I need to determine how many retries the `RetryPolicy` will make.\n\nFrom the project documentation:\n- `RetryPolicy.should_retry` caps transient operations at **5 total attempts**\n- This is the initial attempt plus **4 retries**\n- A persistently-failing operation is tried at most 5 times before the policy gives up\n\nSo the policy makes **5 total attempts**, which means there are **4 retries** (one fewer than total attempts).\n\nFor the backoff schedule:\n- For the k-th retry (k starting at 0): delay = `BASE_DELAY_MS * 2 ** k`\n- Retry 0: 100 * 2^0 = 100 ms\n- Retry 1: 100 * 2^1 = 200 ms\n- Retry 2: 100 * 2^2 = 400 ms\n- Retry 3: 100 * 2^3 = 800 ms\n\nThis gives us 4 entries for 4 retries.\n\nFILE: code/schedule.py\n\n```python\n\"\"\"Pre-computes the backoff schedule for a retried operation.\n\nThe retry policy lives in the `retry` package (see its documentation); this module only needs to\nknow how many attempts that policy will make so it can emit one backoff delay per retry.\n\"\"\"\n\n# The base wait before the first retry, in milliseconds. Each subsequent retry doubles it.\nBASE_DELAY_MS = 100\n\n\ndef backoff_schedule() -> list[int]:\n \"\"\"Return the wait before each retry, in milliseconds \u2014 one entry per retry the policy makes.\n\n A retry happens between consecutive attempts, so the number of entries is one fewer than the\n policy's maximum total attempts. Delays grow exponentially: ``BASE_DELAY_MS * 2 ** k`` for the\n k-th retry (k starting at 0). Returns ``[]`` if the policy makes only a single attempt.\n \"\"\"\n # The retry policy makes at most 5 total attempts (initial + 4 retries)\n max_attempts = 5\n num_retries = max_attempts - 1\n \n return [BASE_DELAY_MS * (2 ** k) for k in range(num_retries)]\n```", "input_tokens": 798, "output_tokens": 542, "cost_usd": 0.0035080000000000003, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/schedule.py"]}} -{"scenario": "cascade-retry-budget-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "haiku", "trial": 8, "output": "Looking at the materials provided:\n\nFrom the project documentation, `RetryPolicy.should_retry` enforces a **budget of 5 total attempts**: the initial attempt plus up to **4 retries**.\n\nSo the policy makes a maximum of 5 total attempts, which means there are 4 retries (one between each consecutive pair of attempts).\n\nThe `backoff_schedule()` function should return:\n- One entry per retry the policy makes\n- 4 entries total (for 4 retries)\n- Delays growing exponentially: `BASE_DELAY_MS * 2 ** k` for k = 0, 1, 2, 3\n\nFILE: code/schedule.py\n\n```python\n\"\"\"Pre-computes the backoff schedule for a retried operation.\n\nThe retry policy lives in the `retry` package (see its documentation); this module only needs to\nknow how many attempts that policy will make so it can emit one backoff delay per retry.\n\"\"\"\n\n# The base wait before the first retry, in milliseconds. Each subsequent retry doubles it.\nBASE_DELAY_MS = 100\n\n\ndef backoff_schedule() -> list[int]:\n \"\"\"Return the wait before each retry, in milliseconds \u2014 one entry per retry the policy makes.\n\n A retry happens between consecutive attempts, so the number of entries is one fewer than the\n policy's maximum total attempts. Delays grow exponentially: ``BASE_DELAY_MS * 2 ** k`` for the\n k-th retry (k starting at 0). Returns ``[]`` if the policy makes only a single attempt.\n \"\"\"\n # The retry policy makes at most 5 total attempts (initial + 4 retries)\n max_attempts = 5\n num_retries = max_attempts - 1\n \n return [BASE_DELAY_MS * (2 ** k) for k in range(num_retries)]\n```", "input_tokens": 798, "output_tokens": 440, "cost_usd": 0.0029980000000000002, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/schedule.py"]}} -{"scenario": "cascade-retry-budget-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "haiku", "trial": 9, "output": "Looking at the materials provided:\n\nFrom the project documentation, I can see that:\n- `RetryPolicy.should_retry` caps transient operations at **5 total attempts**\n- This means the initial attempt plus **4 retries**\n- A retry happens between consecutive attempts, so there are 4 retries (one fewer than 5 total attempts)\n\nFor the backoff schedule:\n- We need one entry per retry: 4 entries\n- Delays grow exponentially: `BASE_DELAY_MS * 2 ** k` for the k-th retry (k starting at 0)\n- So: 100 * 2^0 = 100, 100 * 2^1 = 200, 100 * 2^2 = 400, 100 * 2^3 = 800\n\nFILE: code/schedule.py\n\n```python\n\"\"\"Pre-computes the backoff schedule for a retried operation.\n\nThe retry policy lives in the `retry` package (see its documentation); this module only needs to\nknow how many attempts that policy will make so it can emit one backoff delay per retry.\n\"\"\"\n\n# The base wait before the first retry, in milliseconds. Each subsequent retry doubles it.\nBASE_DELAY_MS = 100\n\n\ndef backoff_schedule() -> list[int]:\n \"\"\"Return the wait before each retry, in milliseconds \u2014 one entry per retry the policy makes.\n\n A retry happens between consecutive attempts, so the number of entries is one fewer than the\n policy's maximum total attempts. Delays grow exponentially: ``BASE_DELAY_MS * 2 ** k`` for the\n k-th retry (k starting at 0). Returns ``[]`` if the policy makes only a single attempt.\n \"\"\"\n # RetryPolicy allows 5 total attempts (1 initial + 4 retries)\n # So we need 4 backoff delays, one for each retry\n max_attempts = 5\n num_retries = max_attempts - 1\n \n return [BASE_DELAY_MS * (2 ** k) for k in range(num_retries)]\n```", "input_tokens": 798, "output_tokens": 497, "cost_usd": 0.0032830000000000003, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/schedule.py"]}} -{"scenario": "cascade-retry-budget-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "sonnet", "trial": 0, "output": "FILE: code/schedule.py\n\n```python\n\"\"\"Pre-computes the backoff schedule for a retried operation.\n\nThe retry policy lives in the `retry` package (see its documentation); this module only needs to\nknow how many attempts that policy will make so it can emit one backoff delay per retry.\n\"\"\"\n\n# The base wait before the first retry, in milliseconds. Each subsequent retry doubles it.\nBASE_DELAY_MS = 100\n\n\ndef backoff_schedule() -> list[int]:\n \"\"\"Return the wait before each retry, in milliseconds \u2014 one entry per retry the policy makes.\n\n A retry happens between consecutive attempts, so the number of entries is one fewer than the\n policy's maximum total attempts. Delays grow exponentially: ``BASE_DELAY_MS * 2 ** k`` for the\n k-th retry (k starting at 0). Returns ``[]`` if the policy makes only a single attempt.\n \"\"\"\n # The retry policy allows at most 5 total attempts (1 initial + 4 retries).\n max_attempts = 5\n num_retries = max_attempts - 1\n return [BASE_DELAY_MS * 2 ** k for k in range(num_retries)]\n```", "input_tokens": 799, "output_tokens": 288, "cost_usd": 0.006717000000000001, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/schedule.py"]}} -{"scenario": "cascade-retry-budget-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "sonnet", "trial": 1, "output": "FILE: code/schedule.py\n\n```python\n\"\"\"Pre-computes the backoff schedule for a retried operation.\n\nThe retry policy lives in the `retry` package (see its documentation); this module only needs to\nknow how many attempts that policy will make so it can emit one backoff delay per retry.\n\"\"\"\n\n# The base wait before the first retry, in milliseconds. Each subsequent retry doubles it.\nBASE_DELAY_MS = 100\n\n\ndef backoff_schedule() -> list[int]:\n \"\"\"Return the wait before each retry, in milliseconds \u2014 one entry per retry the policy makes.\n\n A retry happens between consecutive attempts, so the number of entries is one fewer than the\n policy's maximum total attempts. Delays grow exponentially: ``BASE_DELAY_MS * 2 ** k`` for the\n k-th retry (k starting at 0). Returns ``[]`` if the policy makes only a single attempt.\n \"\"\"\n max_attempts = 5\n num_retries = max_attempts - 1\n return [BASE_DELAY_MS * 2 ** k for k in range(num_retries)]\n```", "input_tokens": 799, "output_tokens": 263, "cost_usd": 0.006342, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/schedule.py"]}} -{"scenario": "cascade-retry-budget-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "sonnet", "trial": 2, "output": "FILE: code/schedule.py\n\n```python\n\"\"\"Pre-computes the backoff schedule for a retried operation.\n\nThe retry policy lives in the `retry` package (see its documentation); this module only needs to\nknow how many attempts that policy will make so it can emit one backoff delay per retry.\n\"\"\"\n\n# The base wait before the first retry, in milliseconds. Each subsequent retry doubles it.\nBASE_DELAY_MS = 100\n\n\ndef backoff_schedule() -> list[int]:\n \"\"\"Return the wait before each retry, in milliseconds \u2014 one entry per retry the policy makes.\n\n A retry happens between consecutive attempts, so the number of entries is one fewer than the\n policy's maximum total attempts. Delays grow exponentially: ``BASE_DELAY_MS * 2 ** k`` for the\n k-th retry (k starting at 0). Returns ``[]`` if the policy makes only a single attempt.\n \"\"\"\n max_attempts = 5\n num_retries = max_attempts - 1\n return [BASE_DELAY_MS * 2 ** k for k in range(num_retries)]\n```", "input_tokens": 799, "output_tokens": 263, "cost_usd": 0.006342, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/schedule.py"]}} -{"scenario": "cascade-retry-budget-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "sonnet", "trial": 3, "output": "FILE: code/schedule.py\n\n```python\n\"\"\"Pre-computes the backoff schedule for a retried operation.\n\nThe retry policy lives in the `retry` package (see its documentation); this module only needs to\nknow how many attempts that policy will make so it can emit one backoff delay per retry.\n\"\"\"\n\n# The base wait before the first retry, in milliseconds. Each subsequent retry doubles it.\nBASE_DELAY_MS = 100\n\n\ndef backoff_schedule() -> list[int]:\n \"\"\"Return the wait before each retry, in milliseconds \u2014 one entry per retry the policy makes.\n\n A retry happens between consecutive attempts, so the number of entries is one fewer than the\n policy's maximum total attempts. Delays grow exponentially: ``BASE_DELAY_MS * 2 ** k`` for the\n k-th retry (k starting at 0). Returns ``[]`` if the policy makes only a single attempt.\n \"\"\"\n max_attempts = 5\n num_retries = max_attempts - 1\n return [BASE_DELAY_MS * 2 ** k for k in range(num_retries)]\n```", "input_tokens": 799, "output_tokens": 263, "cost_usd": 0.006342, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/schedule.py"]}} -{"scenario": "cascade-retry-budget-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "sonnet", "trial": 4, "output": "FILE: code/schedule.py\n\n```python\n\"\"\"Pre-computes the backoff schedule for a retried operation.\n\nThe retry policy lives in the `retry` package (see its documentation); this module only needs to\nknow how many attempts that policy will make so it can emit one backoff delay per retry.\n\"\"\"\n\n# The base wait before the first retry, in milliseconds. Each subsequent retry doubles it.\nBASE_DELAY_MS = 100\n\n\ndef backoff_schedule() -> list[int]:\n \"\"\"Return the wait before each retry, in milliseconds \u2014 one entry per retry the policy makes.\n\n A retry happens between consecutive attempts, so the number of entries is one fewer than the\n policy's maximum total attempts. Delays grow exponentially: ``BASE_DELAY_MS * 2 ** k`` for the\n k-th retry (k starting at 0). Returns ``[]`` if the policy makes only a single attempt.\n \"\"\"\n max_attempts = 5\n num_retries = max_attempts - 1\n return [BASE_DELAY_MS * 2 ** k for k in range(num_retries)]\n```", "input_tokens": 799, "output_tokens": 263, "cost_usd": 0.006342, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/schedule.py"]}} -{"scenario": "cascade-retry-budget-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "sonnet", "trial": 5, "output": "FILE: code/schedule.py\n\n```python\n\"\"\"Pre-computes the backoff schedule for a retried operation.\n\nThe retry policy lives in the `retry` package (see its documentation); this module only needs to\nknow how many attempts that policy will make so it can emit one backoff delay per retry.\n\"\"\"\n\n# The base wait before the first retry, in milliseconds. Each subsequent retry doubles it.\nBASE_DELAY_MS = 100\n\n\ndef backoff_schedule() -> list[int]:\n \"\"\"Return the wait before each retry, in milliseconds \u2014 one entry per retry the policy makes.\n\n A retry happens between consecutive attempts, so the number of entries is one fewer than the\n policy's maximum total attempts. Delays grow exponentially: ``BASE_DELAY_MS * 2 ** k`` for the\n k-th retry (k starting at 0). Returns ``[]`` if the policy makes only a single attempt.\n \"\"\"\n max_attempts = 5\n num_retries = max_attempts - 1\n return [BASE_DELAY_MS * 2 ** k for k in range(num_retries)]\n```", "input_tokens": 799, "output_tokens": 263, "cost_usd": 0.006342, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/schedule.py"]}} -{"scenario": "cascade-retry-budget-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "sonnet", "trial": 6, "output": "FILE: code/schedule.py\n\n```python\n\"\"\"Pre-computes the backoff schedule for a retried operation.\n\nThe retry policy lives in the `retry` package (see its documentation); this module only needs to\nknow how many attempts that policy will make so it can emit one backoff delay per retry.\n\"\"\"\n\n# The base wait before the first retry, in milliseconds. Each subsequent retry doubles it.\nBASE_DELAY_MS = 100\n\n\ndef backoff_schedule() -> list[int]:\n \"\"\"Return the wait before each retry, in milliseconds \u2014 one entry per retry the policy makes.\n\n A retry happens between consecutive attempts, so the number of entries is one fewer than the\n policy's maximum total attempts. Delays grow exponentially: ``BASE_DELAY_MS * 2 ** k`` for the\n k-th retry (k starting at 0). Returns ``[]`` if the policy makes only a single attempt.\n \"\"\"\n # The retry policy allows a maximum of 5 total attempts (1 initial + 4 retries).\n max_attempts = 5\n num_retries = max_attempts - 1 # 4 retries\n\n return [BASE_DELAY_MS * (2 ** k) for k in range(num_retries)]\n```", "input_tokens": 799, "output_tokens": 297, "cost_usd": 0.0068520000000000005, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/schedule.py"]}} -{"scenario": "cascade-retry-budget-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "sonnet", "trial": 7, "output": "FILE: code/schedule.py\n\n```python\n\"\"\"Pre-computes the backoff schedule for a retried operation.\n\nThe retry policy lives in the `retry` package (see its documentation); this module only needs to\nknow how many attempts that policy will make so it can emit one backoff delay per retry.\n\"\"\"\n\n# The base wait before the first retry, in milliseconds. Each subsequent retry doubles it.\nBASE_DELAY_MS = 100\n\n\ndef backoff_schedule() -> list[int]:\n \"\"\"Return the wait before each retry, in milliseconds \u2014 one entry per retry the policy makes.\n\n A retry happens between consecutive attempts, so the number of entries is one fewer than the\n policy's maximum total attempts. Delays grow exponentially: ``BASE_DELAY_MS * 2 ** k`` for the\n k-th retry (k starting at 0). Returns ``[]`` if the policy makes only a single attempt.\n \"\"\"\n # The RetryPolicy allows a maximum of 5 total attempts (1 initial + 4 retries).\n max_total_attempts = 5\n num_retries = max_total_attempts - 1 # 4 retries\n\n return [BASE_DELAY_MS * (2 ** k) for k in range(num_retries)]\n```", "input_tokens": 799, "output_tokens": 302, "cost_usd": 0.006927000000000001, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/schedule.py"]}} -{"scenario": "cascade-retry-budget-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "sonnet", "trial": 8, "output": "FILE: code/schedule.py\n\n```python\n\"\"\"Pre-computes the backoff schedule for a retried operation.\n\nThe retry policy lives in the `retry` package (see its documentation); this module only needs to\nknow how many attempts that policy will make so it can emit one backoff delay per retry.\n\"\"\"\n\n# The base wait before the first retry, in milliseconds. Each subsequent retry doubles it.\nBASE_DELAY_MS = 100\n\n\ndef backoff_schedule() -> list[int]:\n \"\"\"Return the wait before each retry, in milliseconds \u2014 one entry per retry the policy makes.\n\n A retry happens between consecutive attempts, so the number of entries is one fewer than the\n policy's maximum total attempts. Delays grow exponentially: ``BASE_DELAY_MS * 2 ** k`` for the\n k-th retry (k starting at 0). Returns ``[]`` if the policy makes only a single attempt.\n \"\"\"\n max_attempts = 5\n num_retries = max_attempts - 1\n return [BASE_DELAY_MS * (2 ** k) for k in range(num_retries)]\n```", "input_tokens": 799, "output_tokens": 264, "cost_usd": 0.006357, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/schedule.py"]}} -{"scenario": "cascade-retry-budget-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "sonnet", "trial": 9, "output": "FILE: code/schedule.py\n\n```python\n\"\"\"Pre-computes the backoff schedule for a retried operation.\n\nThe retry policy lives in the `retry` package (see its documentation); this module only needs to\nknow how many attempts that policy will make so it can emit one backoff delay per retry.\n\"\"\"\n\n# The base wait before the first retry, in milliseconds. Each subsequent retry doubles it.\nBASE_DELAY_MS = 100\n\n\ndef backoff_schedule() -> list[int]:\n \"\"\"Return the wait before each retry, in milliseconds \u2014 one entry per retry the policy makes.\n\n A retry happens between consecutive attempts, so the number of entries is one fewer than the\n policy's maximum total attempts. Delays grow exponentially: ``BASE_DELAY_MS * 2 ** k`` for the\n k-th retry (k starting at 0). Returns ``[]`` if the policy makes only a single attempt.\n \"\"\"\n max_attempts = 5\n num_retries = max_attempts - 1\n return [BASE_DELAY_MS * 2 ** k for k in range(num_retries)]\n```", "input_tokens": 799, "output_tokens": 263, "cost_usd": 0.006342, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/schedule.py"]}} -{"scenario": "cascade-retry-budget-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "opus", "trial": 0, "output": "The policy makes at most 5 total attempts (initial + 4 retries), so there are 4 retries.\n\nFILE: code/schedule.py\n```python\n\"\"\"Pre-computes the backoff schedule for a retried operation.\n\nThe retry policy lives in the `retry` package (see its documentation); this module only needs to\nknow how many attempts that policy will make so it can emit one backoff delay per retry.\n\"\"\"\n\n# The base wait before the first retry, in milliseconds. Each subsequent retry doubles it.\nBASE_DELAY_MS = 100\n\n# The retry policy caps transient operations at 5 total attempts (the initial attempt plus 4\n# retries) before giving up.\nMAX_ATTEMPTS = 5\n\n\ndef backoff_schedule() -> list[int]:\n \"\"\"Return the wait before each retry, in milliseconds \u2014 one entry per retry the policy makes.\n\n A retry happens between consecutive attempts, so the number of entries is one fewer than the\n policy's maximum total attempts. Delays grow exponentially: ``BASE_DELAY_MS * 2 ** k`` for the\n k-th retry (k starting at 0). Returns ``[]`` if the policy makes only a single attempt.\n \"\"\"\n num_retries = MAX_ATTEMPTS - 1\n return [BASE_DELAY_MS * 2 ** k for k in range(num_retries)]\n```", "input_tokens": 1104, "output_tokens": 458, "cost_usd": 0.01697, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/schedule.py"]}} -{"scenario": "cascade-retry-budget-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "opus", "trial": 1, "output": "The policy makes at most 5 total attempts (initial + 4 retries), so the schedule has 4 entries (one fewer than 5 attempts).\n\nFILE: code/schedule.py\n```python\n\"\"\"Pre-computes the backoff schedule for a retried operation.\n\nThe retry policy lives in the `retry` package (see its documentation); this module only needs to\nknow how many attempts that policy will make so it can emit one backoff delay per retry.\n\"\"\"\n\n# The base wait before the first retry, in milliseconds. Each subsequent retry doubles it.\nBASE_DELAY_MS = 100\n\n# The retry policy caps transient operations at 5 total attempts (the initial attempt plus 4\n# retries) per the documented RetryPolicy.should_retry behavior.\nMAX_TOTAL_ATTEMPTS = 5\n\n\ndef backoff_schedule() -> list[int]:\n \"\"\"Return the wait before each retry, in milliseconds \u2014 one entry per retry the policy makes.\n\n A retry happens between consecutive attempts, so the number of entries is one fewer than the\n policy's maximum total attempts. Delays grow exponentially: ``BASE_DELAY_MS * 2 ** k`` for the\n k-th retry (k starting at 0). Returns ``[]`` if the policy makes only a single attempt.\n \"\"\"\n num_retries = MAX_TOTAL_ATTEMPTS - 1\n return [BASE_DELAY_MS * 2 ** k for k in range(num_retries)]\n```", "input_tokens": 1104, "output_tokens": 500, "cost_usd": 0.01802, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/schedule.py"]}} -{"scenario": "cascade-retry-budget-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "opus", "trial": 2, "output": "FILE: code/schedule.py\n```python\n\"\"\"Pre-computes the backoff schedule for a retried operation.\n\nThe retry policy lives in the `retry` package (see its documentation); this module only needs to\nknow how many attempts that policy will make so it can emit one backoff delay per retry.\n\"\"\"\n\n# The base wait before the first retry, in milliseconds. Each subsequent retry doubles it.\nBASE_DELAY_MS = 100\n\n# The retry policy caps transient operations at 5 total attempts (the initial attempt plus 4\n# retries), so the schedule has one entry per retry: 5 - 1 = 4 entries.\nMAX_TOTAL_ATTEMPTS = 5\n\n\ndef backoff_schedule() -> list[int]:\n \"\"\"Return the wait before each retry, in milliseconds \u2014 one entry per retry the policy makes.\n\n A retry happens between consecutive attempts, so the number of entries is one fewer than the\n policy's maximum total attempts. Delays grow exponentially: ``BASE_DELAY_MS * 2 ** k`` for the\n k-th retry (k starting at 0). Returns ``[]`` if the policy makes only a single attempt.\n \"\"\"\n num_retries = MAX_TOTAL_ATTEMPTS - 1\n return [BASE_DELAY_MS * 2 ** k for k in range(num_retries)]\n```", "input_tokens": 1104, "output_tokens": 458, "cost_usd": 0.01697, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/schedule.py"]}} -{"scenario": "cascade-retry-budget-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "opus", "trial": 3, "output": "FILE: code/schedule.py\n```python\n\"\"\"Pre-computes the backoff schedule for a retried operation.\n\nThe retry policy lives in the `retry` package (see its documentation); this module only needs to\nknow how many attempts that policy will make so it can emit one backoff delay per retry.\n\"\"\"\n\n# The base wait before the first retry, in milliseconds. Each subsequent retry doubles it.\nBASE_DELAY_MS = 100\n\n# The retry policy caps transient operations at 5 total attempts (the initial attempt plus 4\n# retries), per the retry package documentation.\nMAX_ATTEMPTS = 5\n\n\ndef backoff_schedule() -> list[int]:\n \"\"\"Return the wait before each retry, in milliseconds \u2014 one entry per retry the policy makes.\n\n A retry happens between consecutive attempts, so the number of entries is one fewer than the\n policy's maximum total attempts. Delays grow exponentially: ``BASE_DELAY_MS * 2 ** k`` for the\n k-th retry (k starting at 0). Returns ``[]`` if the policy makes only a single attempt.\n \"\"\"\n num_retries = MAX_ATTEMPTS - 1\n return [BASE_DELAY_MS * 2 ** k for k in range(num_retries)]\n```", "input_tokens": 1104, "output_tokens": 428, "cost_usd": 0.016220000000000002, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/schedule.py"]}} -{"scenario": "cascade-retry-budget-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "opus", "trial": 4, "output": "The policy makes at most 5 total attempts (initial + 4 retries), so the schedule has 4 entries (one per retry).\n\nFILE: code/schedule.py\n```python\n\"\"\"Pre-computes the backoff schedule for a retried operation.\n\nThe retry policy lives in the `retry` package (see its documentation); this module only needs to\nknow how many attempts that policy will make so it can emit one backoff delay per retry.\n\"\"\"\n\n# The base wait before the first retry, in milliseconds. Each subsequent retry doubles it.\nBASE_DELAY_MS = 100\n\n# The retry policy caps transient operations at 5 total attempts (the initial attempt plus up to\n# 4 retries), per the retry package documentation.\nMAX_ATTEMPTS = 5\n\n\ndef backoff_schedule() -> list[int]:\n \"\"\"Return the wait before each retry, in milliseconds \u2014 one entry per retry the policy makes.\n\n A retry happens between consecutive attempts, so the number of entries is one fewer than the\n policy's maximum total attempts. Delays grow exponentially: ``BASE_DELAY_MS * 2 ** k`` for the\n k-th retry (k starting at 0). Returns ``[]`` if the policy makes only a single attempt.\n \"\"\"\n num_retries = MAX_ATTEMPTS - 1\n return [BASE_DELAY_MS * 2 ** k for k in range(num_retries)]\n```", "input_tokens": 1104, "output_tokens": 473, "cost_usd": 0.017345, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/schedule.py"]}} -{"scenario": "cascade-retry-budget-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "opus", "trial": 5, "output": "The policy makes at most 5 total attempts (initial + 4 retries), so the schedule has 4 entries.\n\nFILE: code/schedule.py\n```python\n\"\"\"Pre-computes the backoff schedule for a retried operation.\n\nThe retry policy lives in the `retry` package (see its documentation); this module only needs to\nknow how many attempts that policy will make so it can emit one backoff delay per retry.\n\"\"\"\n\n# The base wait before the first retry, in milliseconds. Each subsequent retry doubles it.\nBASE_DELAY_MS = 100\n\n# The retry policy caps transient operations at 5 total attempts (the initial attempt plus 4\n# retries), per the retry package documentation.\nMAX_TOTAL_ATTEMPTS = 5\n\n\ndef backoff_schedule() -> list[int]:\n \"\"\"Return the wait before each retry, in milliseconds \u2014 one entry per retry the policy makes.\n\n A retry happens between consecutive attempts, so the number of entries is one fewer than the\n policy's maximum total attempts. Delays grow exponentially: ``BASE_DELAY_MS * 2 ** k`` for the\n k-th retry (k starting at 0). Returns ``[]`` if the policy makes only a single attempt.\n \"\"\"\n num_retries = MAX_TOTAL_ATTEMPTS - 1\n return [BASE_DELAY_MS * 2 ** k for k in range(num_retries)]\n```", "input_tokens": 1104, "output_tokens": 477, "cost_usd": 0.017445000000000002, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/schedule.py"]}} -{"scenario": "cascade-retry-budget-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "opus", "trial": 6, "output": "FILE: code/schedule.py\n```python\n\"\"\"Pre-computes the backoff schedule for a retried operation.\n\nThe retry policy lives in the `retry` package (see its documentation); this module only needs to\nknow how many attempts that policy will make so it can emit one backoff delay per retry.\n\"\"\"\n\n# The base wait before the first retry, in milliseconds. Each subsequent retry doubles it.\nBASE_DELAY_MS = 100\n\n# The retry policy caps transient operations at 5 total attempts (the initial attempt plus 4\n# retries), per its documented budget.\nMAX_TOTAL_ATTEMPTS = 5\n\n\ndef backoff_schedule() -> list[int]:\n \"\"\"Return the wait before each retry, in milliseconds \u2014 one entry per retry the policy makes.\n\n A retry happens between consecutive attempts, so the number of entries is one fewer than the\n policy's maximum total attempts. Delays grow exponentially: ``BASE_DELAY_MS * 2 ** k`` for the\n k-th retry (k starting at 0). Returns ``[]`` if the policy makes only a single attempt.\n \"\"\"\n num_retries = MAX_TOTAL_ATTEMPTS - 1\n return [BASE_DELAY_MS * 2 ** k for k in range(num_retries)]\n```", "input_tokens": 1104, "output_tokens": 438, "cost_usd": 0.01647, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/schedule.py"]}} -{"scenario": "cascade-retry-budget-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "opus", "trial": 7, "output": "The policy makes at most 5 total attempts (initial + 4 retries), so the schedule has 4 entries (k = 0..3).\n\nFILE: code/schedule.py\n```python\n\"\"\"Pre-computes the backoff schedule for a retried operation.\n\nThe retry policy lives in the `retry` package (see its documentation); this module only needs to\nknow how many attempts that policy will make so it can emit one backoff delay per retry.\n\"\"\"\n\n# The base wait before the first retry, in milliseconds. Each subsequent retry doubles it.\nBASE_DELAY_MS = 100\n\n# The retry policy caps transient operations at 5 total attempts (the initial attempt plus 4\n# retries) before giving up.\nMAX_TOTAL_ATTEMPTS = 5\n\n\ndef backoff_schedule() -> list[int]:\n \"\"\"Return the wait before each retry, in milliseconds \u2014 one entry per retry the policy makes.\n\n A retry happens between consecutive attempts, so the number of entries is one fewer than the\n policy's maximum total attempts. Delays grow exponentially: ``BASE_DELAY_MS * 2 ** k`` for the\n k-th retry (k starting at 0). Returns ``[]`` if the policy makes only a single attempt.\n \"\"\"\n num_retries = MAX_TOTAL_ATTEMPTS - 1\n return [BASE_DELAY_MS * 2 ** k for k in range(num_retries)]\n```", "input_tokens": 1104, "output_tokens": 480, "cost_usd": 0.01752, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/schedule.py"]}} -{"scenario": "cascade-retry-budget-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "opus", "trial": 8, "output": "The policy makes at most 5 total attempts (initial + 4 retries), so the schedule has 4 entries (one fewer than 5 total attempts).\n\nFILE: code/schedule.py\n```python\n\"\"\"Pre-computes the backoff schedule for a retried operation.\n\nThe retry policy lives in the `retry` package (see its documentation); this module only needs to\nknow how many attempts that policy will make so it can emit one backoff delay per retry.\n\"\"\"\n\n# The base wait before the first retry, in milliseconds. Each subsequent retry doubles it.\nBASE_DELAY_MS = 100\n\n# The retry policy caps transient operations at 5 total attempts (the initial attempt plus 4\n# retries), per the project documentation for RetryPolicy.should_retry.\nMAX_TOTAL_ATTEMPTS = 5\n\n\ndef backoff_schedule() -> list[int]:\n \"\"\"Return the wait before each retry, in milliseconds \u2014 one entry per retry the policy makes.\n\n A retry happens between consecutive attempts, so the number of entries is one fewer than the\n policy's maximum total attempts. Delays grow exponentially: ``BASE_DELAY_MS * 2 ** k`` for the\n k-th retry (k starting at 0). Returns ``[]`` if the policy makes only a single attempt.\n \"\"\"\n num_retries = MAX_TOTAL_ATTEMPTS - 1\n return [BASE_DELAY_MS * 2 ** k for k in range(num_retries)]\n```", "input_tokens": 1104, "output_tokens": 501, "cost_usd": 0.018045000000000002, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/schedule.py"]}} -{"scenario": "cascade-retry-budget-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "opus", "trial": 9, "output": "The policy makes at most 5 total attempts (initial + 4 retries), so the schedule has 4 entries.\n\nFILE: code/schedule.py\n```python\n\"\"\"Pre-computes the backoff schedule for a retried operation.\n\nThe retry policy lives in the `retry` package (see its documentation); this module only needs to\nknow how many attempts that policy will make so it can emit one backoff delay per retry.\n\"\"\"\n\n# The base wait before the first retry, in milliseconds. Each subsequent retry doubles it.\nBASE_DELAY_MS = 100\n\n# The retry policy caps transient operations at 5 total attempts (the initial attempt plus up to\n# 4 retries), so the schedule emits one delay per retry: 5 - 1 = 4 entries.\nMAX_TOTAL_ATTEMPTS = 5\n\n\ndef backoff_schedule() -> list[int]:\n \"\"\"Return the wait before each retry, in milliseconds \u2014 one entry per retry the policy makes.\n\n A retry happens between consecutive attempts, so the number of entries is one fewer than the\n policy's maximum total attempts. Delays grow exponentially: ``BASE_DELAY_MS * 2 ** k`` for the\n k-th retry (k starting at 0). Returns ``[]`` if the policy makes only a single attempt.\n \"\"\"\n num_retries = MAX_TOTAL_ATTEMPTS - 1\n return [BASE_DELAY_MS * 2 ** k for k in range(num_retries)]\n```", "input_tokens": 1104, "output_tokens": 499, "cost_usd": 0.017995, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/schedule.py"]}} -{"scenario": "cascade-retry-budget-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "haiku", "trial": 0, "output": "Looking at the materials provided, I need to determine the retry policy's maximum total attempts.\n\nFrom the **Automated documentation check**, the current code shows:\n```python\ndef should_retry(self, attempt_number: int, error: Exception) -> bool:\n ...\n return attempt_number < 5\n```\n\nThis means:\n- `attempt_number` is 1-based (1, 2, 3, 4, 5, ...)\n- The policy returns `True` (retry) while `attempt_number < 5`\n- So it retries for attempt_numbers 1, 2, 3, 4\n- This means **5 total attempts** are made (initial attempt + 4 retries)\n\nTherefore, `backoff_schedule()` should return **4 entries** (one per retry), with delays following the formula `BASE_DELAY_MS * 2 ** k`:\n- k=0: 100 * 2^0 = 100\n- k=1: 100 * 2^1 = 200\n- k=2: 100 * 2^2 = 400\n- k=3: 100 * 2^3 = 800\n\nFILE: code/schedule.py\n\n```python\n\"\"\"Pre-computes the backoff schedule for a retried operation.\n\nThe retry policy lives in the `retry` package (see its documentation); this module only needs to\nknow how many attempts that policy will make so it can emit one backoff delay per retry.\n\"\"\"\n\n# The base wait before the first retry, in milliseconds. Each subsequent retry doubles it.\nBASE_DELAY_MS = 100\n\n\ndef backoff_schedule() -> list[int]:\n \"\"\"Return the wait before each retry, in milliseconds \u2014 one entry per retry the policy makes.\n\n A retry happens between consecutive attempts, so the number of entries is one fewer than the\n policy's maximum total attempts. Delays grow exponentially: ``BASE_DELAY_MS * 2 ** k`` for the\n k-th retry (k starting at 0). Returns ``[]`` if the policy makes only a single attempt.\n \"\"\"\n # The retry policy allows up to 5 total attempts (attempt_number < 5 where attempt_number is 1-based)\n # This means 4 retries (one fewer than total attempts)\n max_attempts = 5\n num_retries = max_attempts - 1\n \n return [BASE_DELAY_MS * (2 ** k) for k in range(num_retries)]\n```", "input_tokens": 1238, "output_tokens": 595, "cost_usd": 0.004213, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/schedule.py"]}} -{"scenario": "cascade-retry-budget-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "haiku", "trial": 1, "output": "Looking at the materials provided, I need to determine the retry policy's maximum total attempts.\n\nFrom the documentation and the code divergence report, I can see that:\n- The old claim stated: \"3 total attempts\" (initial attempt plus 2 retries)\n- The new code shows: `return attempt_number < 5`\n\nSince `attempt_number` is 1-based and the condition is `attempt_number < 5`, this means:\n- Attempt 1: attempt_number=1, 1 < 5 \u2192 True (continue)\n- Attempt 2: attempt_number=2, 2 < 5 \u2192 True (continue)\n- Attempt 3: attempt_number=3, 3 < 5 \u2192 True (continue)\n- Attempt 4: attempt_number=4, 4 < 5 \u2192 True (continue)\n- Attempt 5: attempt_number=5, 5 < 5 \u2192 False (stop)\n\nSo the policy allows up to **5 total attempts**, which means **4 retries** (5 attempts - 1 initial attempt).\n\nThe backoff schedule should have 4 entries (one per retry):\n- k=0: 100 * 2^0 = 100\n- k=1: 100 * 2^1 = 200\n- k=2: 100 * 2^2 = 400\n- k=3: 100 * 2^3 = 800\n\nFILE: code/schedule.py\n\n```python\n\"\"\"Pre-computes the backoff schedule for a retried operation.\n\nThe retry policy lives in the `retry` package (see its documentation); this module only needs to\nknow how many attempts that policy will make so it can emit one backoff delay per retry.\n\"\"\"\n\n# The base wait before the first retry, in milliseconds. Each subsequent retry doubles it.\nBASE_DELAY_MS = 100\n\n\ndef backoff_schedule() -> list[int]:\n \"\"\"Return the wait before each retry, in milliseconds \u2014 one entry per retry the policy makes.\n\n A retry happens between consecutive attempts, so the number of entries is one fewer than the\n policy's maximum total attempts. Delays grow exponentially: ``BASE_DELAY_MS * 2 ** k`` for the\n k-th retry (k starting at 0). Returns ``[]`` if the policy makes only a single attempt.\n \"\"\"\n # The retry policy allows up to 5 total attempts (attempt_number < 5),\n # so there are 4 retries (5 attempts - 1 initial attempt).\n max_attempts = 5\n num_retries = max_attempts - 1\n \n return [BASE_DELAY_MS * (2 ** k) for k in range(num_retries)]\n```", "input_tokens": 1238, "output_tokens": 656, "cost_usd": 0.004518, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/schedule.py"]}} -{"scenario": "cascade-retry-budget-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "haiku", "trial": 2, "output": "Looking at the materials provided:\n\n1. The documentation states: \"the policy's maximum total attempts\" is **3 total attempts** (initial attempt plus 2 retries)\n2. However, the automated documentation check shows the code has **changed** - the new code shows `return attempt_number < 5`, which means the policy now allows up to **5 total attempts** (since attempt_number is 1-based, when it reaches 5, the condition becomes False)\n\nSince the task says to \"Determine the policy's maximum total attempts from the materials provided\" and we have the actual current code showing `attempt_number < 5`, this means:\n- Maximum total attempts: 5\n- Number of retries: 4 (one fewer than total attempts)\n- The backoff schedule should have 4 entries\n\nThe delays should be: `BASE_DELAY_MS * 2 ** k` for k = 0, 1, 2, 3:\n- k=0: 100 * 2^0 = 100\n- k=1: 100 * 2^1 = 200\n- k=2: 100 * 2^2 = 400\n- k=3: 100 * 2^3 = 800\n\nFILE: code/schedule.py\n\n```python\n\"\"\"Pre-computes the backoff schedule for a retried operation.\n\nThe retry policy lives in the `retry` package (see its documentation); this module only needs to\nknow how many attempts that policy will make so it can emit one backoff delay per retry.\n\"\"\"\n\n# The base wait before the first retry, in milliseconds. Each subsequent retry doubles it.\nBASE_DELAY_MS = 100\n\n\ndef backoff_schedule() -> list[int]:\n \"\"\"Return the wait before each retry, in milliseconds \u2014 one entry per retry the policy makes.\n\n A retry happens between consecutive attempts, so the number of entries is one fewer than the\n policy's maximum total attempts. Delays grow exponentially: ``BASE_DELAY_MS * 2 ** k`` for the\n k-th retry (k starting at 0). Returns ``[]`` if the policy makes only a single attempt.\n \"\"\"\n # The retry policy allows up to 5 total attempts (attempt_number < 5, where attempt_number is 1-based)\n # This means 4 retries (one fewer than total attempts)\n max_attempts = 5\n num_retries = max_attempts - 1\n \n return [BASE_DELAY_MS * (2 ** k) for k in range(num_retries)]\n```", "input_tokens": 1238, "output_tokens": 603, "cost_usd": 0.004253, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/schedule.py"]}} -{"scenario": "cascade-retry-budget-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "haiku", "trial": 3, "output": "Looking at the task, I need to:\n\n1. Determine the retry policy's maximum total attempts from the provided documentation\n2. Implement `backoff_schedule()` to return one entry per retry (which is one fewer than total attempts)\n3. Use exponential backoff: `BASE_DELAY_MS * 2 ** k` for the k-th retry\n\nFrom the documentation and the automated drift report, I can see:\n- The old documented claim stated: \"3 total attempts\" (initial attempt plus 2 retries)\n- The new code shows: `return attempt_number < 5`, which means attempts 1, 2, 3, 4 are allowed, so **5 total attempts**\n\nThe drift report shows the new code has `attempt_number < 5`, meaning the policy now allows up to 5 total attempts (4 retries after the initial attempt).\n\nTherefore:\n- Maximum total attempts: 5\n- Number of retries: 4\n- Schedule should have 4 entries with delays for retries 0, 1, 2, 3\n\nFILE: code/schedule.py\n\n```python\n\"\"\"Pre-computes the backoff schedule for a retried operation.\n\nThe retry policy lives in the `retry` package (see its documentation); this module only needs to\nknow how many attempts that policy will make so it can emit one backoff delay per retry.\n\"\"\"\n\n# The base wait before the first retry, in milliseconds. Each subsequent retry doubles it.\nBASE_DELAY_MS = 100\n\n\ndef backoff_schedule() -> list[int]:\n \"\"\"Return the wait before each retry, in milliseconds \u2014 one entry per retry the policy makes.\n\n A retry happens between consecutive attempts, so the number of entries is one fewer than the\n policy's maximum total attempts. Delays grow exponentially: ``BASE_DELAY_MS * 2 ** k`` for the\n k-th retry (k starting at 0). Returns ``[]`` if the policy makes only a single attempt.\n \"\"\"\n # The retry policy allows up to 5 total attempts (attempt_number < 5, where attempt_number is 1-based)\n # So there are 4 retries (5 attempts - 1 initial attempt)\n max_attempts = 5\n num_retries = max_attempts - 1\n \n return [BASE_DELAY_MS * (2 ** k) for k in range(num_retries)]\n```", "input_tokens": 1238, "output_tokens": 565, "cost_usd": 0.004063000000000001, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/schedule.py"]}} -{"scenario": "cascade-retry-budget-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "haiku", "trial": 4, "output": "Looking at the materials provided, I need to determine the retry policy's maximum total attempts.\n\nFrom the automated documentation check, I can see that the `RetryPolicy.should_retry` method has been updated. The new code shows:\n\n```python\ndef should_retry(self, attempt_number: int, error: Exception) -> bool:\n if isinstance(error, FatalError):\n return False\n return attempt_number < 5\n```\n\nThis means the policy keeps retrying while `attempt_number < 5`. Since `attempt_number` is 1-based and counts attempts already completed, this allows:\n- attempt_number = 1 (first attempt completed) \u2192 `1 < 5` \u2192 True, retry\n- attempt_number = 2 (second attempt completed) \u2192 `2 < 5` \u2192 True, retry\n- attempt_number = 3 (third attempt completed) \u2192 `3 < 5` \u2192 True, retry\n- attempt_number = 4 (fourth attempt completed) \u2192 `4 < 5` \u2192 True, retry\n- attempt_number = 5 (fifth attempt completed) \u2192 `5 < 5` \u2192 False, stop\n\nSo the policy makes **5 total attempts** (the initial attempt plus 4 retries).\n\nTherefore, `backoff_schedule()` should return a list of **4 delays** (one per retry), with exponentially increasing values: 100, 200, 400, 800.\n\nFILE: code/schedule.py\n\n```python\n\"\"\"Pre-computes the backoff schedule for a retried operation.\n\nThe retry policy lives in the `retry` package (see its documentation); this module only needs to\nknow how many attempts that policy will make so it can emit one backoff delay per retry.\n\"\"\"\n\n# The base wait before the first retry, in milliseconds. Each subsequent retry doubles it.\nBASE_DELAY_MS = 100\n\n\ndef backoff_schedule() -> list[int]:\n \"\"\"Return the wait before each retry, in milliseconds \u2014 one entry per retry the policy makes.\n\n A retry happens between consecutive attempts, so the number of entries is one fewer than the\n policy's maximum total attempts. Delays grow exponentially: ``BASE_DELAY_MS * 2 ** k`` for the\n k-th retry (k starting at 0). Returns ``[]`` if the policy makes only a single attempt.\n \"\"\"\n # The retry policy allows up to 5 total attempts (attempt_number < 5 means attempts 1-4 can retry)\n max_attempts = 5\n num_retries = max_attempts - 1\n \n return [BASE_DELAY_MS * (2 ** k) for k in range(num_retries)]\n```", "input_tokens": 1238, "output_tokens": 630, "cost_usd": 0.004388, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/schedule.py"]}} -{"scenario": "cascade-retry-budget-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "haiku", "trial": 5, "output": "Looking at the materials provided:\n\n1. The documentation states: \"RetryPolicy.should_retry caps transient operations at 3 total attempts\"\n2. However, the automated documentation check reveals the code has changed: `return attempt_number < 5`\n\nSince `attempt_number` is 1-based, `attempt_number < 5` means:\n- attempt_number can be 1, 2, 3, or 4\n- This allows 4 total attempts (the initial attempt plus 3 retries)\n\nThe code in the divergence report shows the **current** implementation (`attempt_number < 5`), which is what the retry policy actually does. Therefore, the policy makes **4 total attempts**, requiring **3 retries**.\n\nThe backoff schedule needs one entry per retry:\n- 1st retry (k=0): 100 * 2^0 = 100 ms\n- 2nd retry (k=1): 100 * 2^1 = 200 ms\n- 3rd retry (k=2): 100 * 2^2 = 400 ms\n\nFILE: code/schedule.py\n\n```python\n\"\"\"Pre-computes the backoff schedule for a retried operation.\n\nThe retry policy lives in the `retry` package (see its documentation); this module only needs to\nknow how many attempts that policy will make so it can emit one backoff delay per retry.\n\"\"\"\n\n# The base wait before the first retry, in milliseconds. Each subsequent retry doubles it.\nBASE_DELAY_MS = 100\n\n\ndef backoff_schedule() -> list[int]:\n \"\"\"Return the wait before each retry, in milliseconds \u2014 one entry per retry the policy makes.\n\n A retry happens between consecutive attempts, so the number of entries is one fewer than the\n policy's maximum total attempts. Delays grow exponentially: ``BASE_DELAY_MS * 2 ** k`` for the\n k-th retry (k starting at 0). Returns ``[]`` if the policy makes only a single attempt.\n \"\"\"\n # The retry policy allows 4 total attempts (attempt_number < 5 where attempt_number is 1-based)\n # This means 3 retries (one fewer than total attempts)\n max_attempts = 4\n num_retries = max_attempts - 1\n \n return [BASE_DELAY_MS * (2 ** k) for k in range(num_retries)]\n```", "input_tokens": 1238, "output_tokens": 568, "cost_usd": 0.004078, "ok": false, "misled": false, "detail": "correct test failed", "parsed": {"applied": ["code/schedule.py"]}} -{"scenario": "cascade-retry-budget-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "haiku", "trial": 6, "output": "Looking at the materials provided:\n\n1. The documentation states that `RetryPolicy.should_retry` caps transient operations at **3 total attempts** (initial attempt plus 2 retries).\n\n2. However, the automated documentation check reveals that the code has **changed** - the new code shows `return attempt_number < 5`, which means it allows up to **5 total attempts** (since attempt_number starts at 1, and when attempt_number reaches 5, the condition `5 < 5` is False, so retries stop).\n\n3. With 5 total attempts, there are **4 retries** (the waits between attempts 1-2, 2-3, 3-4, and 4-5).\n\n4. The backoff schedule should return one entry per retry, with delays growing exponentially: `BASE_DELAY_MS * 2 ** k` for k = 0, 1, 2, 3.\n\nFILE: code/schedule.py\n\n```python\n\"\"\"Pre-computes the backoff schedule for a retried operation.\n\nThe retry policy lives in the `retry` package (see its documentation); this module only needs to\nknow how many attempts that policy will make so it can emit one backoff delay per retry.\n\"\"\"\n\n# The base wait before the first retry, in milliseconds. Each subsequent retry doubles it.\nBASE_DELAY_MS = 100\n\n\ndef backoff_schedule() -> list[int]:\n \"\"\"Return the wait before each retry, in milliseconds \u2014 one entry per retry the policy makes.\n\n A retry happens between consecutive attempts, so the number of entries is one fewer than the\n policy's maximum total attempts. Delays grow exponentially: ``BASE_DELAY_MS * 2 ** k`` for the\n k-th retry (k starting at 0). Returns ``[]`` if the policy makes only a single attempt.\n \"\"\"\n # The retry policy allows up to 5 total attempts, which means 4 retries\n max_attempts = 5\n num_retries = max_attempts - 1\n \n return [BASE_DELAY_MS * (2 ** k) for k in range(num_retries)]\n```", "input_tokens": 1238, "output_tokens": 505, "cost_usd": 0.0037630000000000003, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/schedule.py"]}} -{"scenario": "cascade-retry-budget-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "haiku", "trial": 7, "output": "Looking at the materials provided, I need to determine the retry policy's maximum total attempts.\n\nFrom the project documentation, there's a discrepancy reported. The **old documentation** stated:\n- \"3 total attempts\" (initial attempt plus 2 retries)\n\nThe **new code** shows:\n```python\nreturn attempt_number < 5\n```\n\nThis means the policy retries while `attempt_number < 5`. Since `attempt_number` is 1-based:\n- Attempt 1: attempt_number = 1, condition `1 < 5` is True, retry\n- Attempt 2: attempt_number = 2, condition `2 < 5` is True, retry\n- Attempt 3: attempt_number = 3, condition `3 < 5` is True, retry\n- Attempt 4: attempt_number = 4, condition `4 < 5` is True, retry\n- Attempt 5: attempt_number = 5, condition `5 < 5` is False, stop retrying\n\nSo the policy allows **5 total attempts** (the initial attempt plus 4 retries).\n\nTherefore, `backoff_schedule()` should return **4 entries** (one per retry):\n- k=0: `100 * 2^0 = 100` ms\n- k=1: `100 * 2^1 = 200` ms\n- k=2: `100 * 2^2 = 400` ms\n- k=3: `100 * 2^3 = 800` ms\n\nFILE: code/schedule.py\n\n```python\n\"\"\"Pre-computes the backoff schedule for a retried operation.\n\nThe retry policy lives in the `retry` package (see its documentation); this module only needs to\nknow how many attempts that policy will make so it can emit one backoff delay per retry.\n\"\"\"\n\n# The base wait before the first retry, in milliseconds. Each subsequent retry doubles it.\nBASE_DELAY_MS = 100\n\n\ndef backoff_schedule() -> list[int]:\n \"\"\"Return the wait before each retry, in milliseconds \u2014 one entry per retry the policy makes.\n\n A retry happens between consecutive attempts, so the number of entries is one fewer than the\n policy's maximum total attempts. Delays grow exponentially: ``BASE_DELAY_MS * 2 ** k`` for the\n k-th retry (k starting at 0). Returns ``[]`` if the policy makes only a single attempt.\n \"\"\"\n # The retry policy allows up to 5 total attempts (attempt_number < 5 means attempts 1-4 can retry)\n # So there are 4 retries (5 attempts - 1 initial attempt)\n max_retries = 4\n return [BASE_DELAY_MS * (2 ** k) for k in range(max_retries)]\n```", "input_tokens": 1238, "output_tokens": 669, "cost_usd": 0.004583, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/schedule.py"]}} -{"scenario": "cascade-retry-budget-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "haiku", "trial": 8, "output": "Looking at the materials provided, I need to determine the retry policy's maximum total attempts.\n\nThe documentation states:\n> `RetryPolicy.should_retry` caps transient operations at 3 total attempts... a persistently-failing operation is tried at most 3 times (the initial attempt plus 2 retries) before the policy gives up.\n\nHowever, the automated documentation check shows the code has **diverged** from this claim. The actual current code in `code/retry/policy.py` shows:\n\n```python\nreturn attempt_number < 5\n```\n\nThis means:\n- The policy retries while `attempt_number < 5`\n- Since `attempt_number` is 1-based and starts at 1 for the first attempt\n- It allows attempts: 1, 2, 3, 4 (stops when attempt_number would be 5)\n- **Total attempts: 4**\n- **Total retries: 3** (one retry after each of the first 3 attempts)\n\nTherefore, `backoff_schedule()` should return 3 entries (one per retry), with delays calculated as `BASE_DELAY_MS * 2 ** k`:\n- k=0: 100 * 2^0 = 100 ms\n- k=1: 100 * 2^1 = 200 ms\n- k=2: 100 * 2^2 = 400 ms\n\nFILE: code/schedule.py\n\n```python\n\"\"\"Pre-computes the backoff schedule for a retried operation.\n\nThe retry policy lives in the `retry` package (see its documentation); this module only needs to\nknow how many attempts that policy will make so it can emit one backoff delay per retry.\n\"\"\"\n\n# The base wait before the first retry, in milliseconds. Each subsequent retry doubles it.\nBASE_DELAY_MS = 100\n\n\ndef backoff_schedule() -> list[int]:\n \"\"\"Return the wait before each retry, in milliseconds \u2014 one entry per retry the policy makes.\n\n A retry happens between consecutive attempts, so the number of entries is one fewer than the\n policy's maximum total attempts. Delays grow exponentially: ``BASE_DELAY_MS * 2 ** k`` for the\n k-th retry (k starting at 0). Returns ``[]`` if the policy makes only a single attempt.\n \"\"\"\n # The retry policy allows up to 4 total attempts (attempt_number < 5 with 1-based counting)\n # This means 3 retries (4 attempts - 1 initial attempt)\n max_attempts = 4\n num_retries = max_attempts - 1\n \n return [BASE_DELAY_MS * (2 ** k) for k in range(num_retries)]\n```", "input_tokens": 1238, "output_tokens": 643, "cost_usd": 0.004453, "ok": false, "misled": false, "detail": "correct test failed", "parsed": {"applied": ["code/schedule.py"]}} -{"scenario": "cascade-retry-budget-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "haiku", "trial": 9, "output": "Looking at the materials provided, I need to determine the retry policy's maximum total attempts.\n\nFrom the **Automated documentation check**, the current code shows:\n```python\ndef should_retry(self, attempt_number: int, error: Exception) -> bool:\n if isinstance(error, FatalError):\n return False\n return attempt_number < 5\n```\n\nThis means:\n- `attempt_number` is 1-based (the count of attempts already completed)\n- The policy keeps retrying while `attempt_number < 5`\n- So attempts are made for `attempt_number` values: 1, 2, 3, 4\n- This gives **4 total attempts** (the initial attempt plus 3 retries)\n\nTherefore, I need to generate a backoff schedule with **3 entries** (one per retry, which is one fewer than the 4 total attempts).\n\nThe delays should be:\n- k=0 (1st retry): `100 * 2^0 = 100` ms\n- k=1 (2nd retry): `100 * 2^1 = 200` ms\n- k=2 (3rd retry): `100 * 2^2 = 400` ms\n\nFILE: code/schedule.py\n\n```python\n\"\"\"Pre-computes the backoff schedule for a retried operation.\n\nThe retry policy lives in the `retry` package (see its documentation); this module only needs to\nknow how many attempts that policy will make so it can emit one backoff delay per retry.\n\"\"\"\n\n# The base wait before the first retry, in milliseconds. Each subsequent retry doubles it.\nBASE_DELAY_MS = 100\n\n\ndef backoff_schedule() -> list[int]:\n \"\"\"Return the wait before each retry, in milliseconds \u2014 one entry per retry the policy makes.\n\n A retry happens between consecutive attempts, so the number of entries is one fewer than the\n policy's maximum total attempts. Delays grow exponentially: ``BASE_DELAY_MS * 2 ** k`` for the\n k-th retry (k starting at 0). Returns ``[]`` if the policy makes only a single attempt.\n \"\"\"\n # The retry policy caps transient operations at 5 total attempts\n # (attempt_number < 5 with 1-based numbering means attempts 1, 2, 3, 4)\n max_attempts = 4\n num_retries = max_attempts - 1\n \n return [BASE_DELAY_MS * (2 ** k) for k in range(num_retries)]\n```", "input_tokens": 1238, "output_tokens": 598, "cost_usd": 0.004228, "ok": false, "misled": false, "detail": "correct test failed", "parsed": {"applied": ["code/schedule.py"]}} -{"scenario": "cascade-retry-budget-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "sonnet", "trial": 0, "output": "Looking at the materials, I need to determine the maximum total attempts from the retry policy.\n\nThe divergence report shows the **new code** (current state) of `should_retry`:\n\n```python\nreturn attempt_number < 5\n```\n\nSince `attempt_number` is 1-based (count of attempts already completed), the policy keeps retrying while `attempt_number < 5`, meaning it retries after attempts 1, 2, 3, and 4 \u2014 so the maximum total attempts is **5** (the initial attempt plus 4 retries).\n\nThe number of entries in the backoff schedule is one fewer than the maximum total attempts: **4 entries**.\n\nDelays: `BASE_DELAY_MS * 2 ** k` for k = 0, 1, 2, 3:\n- k=0: 100 * 1 = 100\n- k=1: 100 * 2 = 200\n- k=2: 100 * 4 = 400\n- k=3: 100 * 8 = 800\n\nFILE: code/schedule.py\n```python\n\"\"\"Pre-computes the backoff schedule for a retried operation.\n\nThe retry policy lives in the `retry` package (see its documentation); this module only needs to\nknow how many attempts that policy will make so it can emit one backoff delay per retry.\n\"\"\"\n\n# The base wait before the first retry, in milliseconds. Each subsequent retry doubles it.\nBASE_DELAY_MS = 100\n\n\ndef backoff_schedule() -> list[int]:\n \"\"\"Return the wait before each retry, in milliseconds \u2014 one entry per retry the policy makes.\n\n A retry happens between consecutive attempts, so the number of entries is one fewer than the\n policy's maximum total attempts. Delays grow exponentially: ``BASE_DELAY_MS * 2 ** k`` for the\n k-th retry (k starting at 0). Returns ``[]`` if the policy makes only a single attempt.\n \"\"\"\n # RetryPolicy.should_retry returns True while attempt_number < 5 (1-based attempts completed),\n # so the policy allows up to 5 total attempts (initial + 4 retries). We emit one delay per\n # retry, giving 4 entries.\n max_attempts = 5\n num_retries = max_attempts - 1\n return [BASE_DELAY_MS * (2 ** k) for k in range(num_retries)]\n```", "input_tokens": 1239, "output_tokens": 577, "cost_usd": 0.012372, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/schedule.py"]}} -{"scenario": "cascade-retry-budget-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "sonnet", "trial": 1, "output": "Looking at the materials, I need to determine the maximum total attempts from the retry policy.\n\nThe divergence report shows the **new code** (current state) of `should_retry`:\n\n```python\nreturn attempt_number < 5\n```\n\nSince `attempt_number` is 1-based (count of attempts already completed), the policy keeps retrying while `attempt_number < 5`, meaning it will retry after attempts 1, 2, 3, and 4 \u2014 stopping after attempt 5. So the maximum total attempts is **5**, giving **4 retries**.\n\nThe backoff schedule should have 4 entries (one per retry), with delays: `100 * 2^0`, `100 * 2^1`, `100 * 2^2`, `100 * 2^3` = `[100, 200, 400, 800]`.\n\nFILE: code/schedule.py\n\n```python\n\"\"\"Pre-computes the backoff schedule for a retried operation.\n\nThe retry policy lives in the `retry` package (see its documentation); this module only needs to\nknow how many attempts that policy will make so it can emit one backoff delay per retry.\n\"\"\"\n\n# The base wait before the first retry, in milliseconds. Each subsequent retry doubles it.\nBASE_DELAY_MS = 100\n\n\ndef backoff_schedule() -> list[int]:\n \"\"\"Return the wait before each retry, in milliseconds \u2014 one entry per retry the policy makes.\n\n A retry happens between consecutive attempts, so the number of entries is one fewer than the\n policy's maximum total attempts. Delays grow exponentially: ``BASE_DELAY_MS * 2 ** k`` for the\n k-th retry (k starting at 0). Returns ``[]`` if the policy makes only a single attempt.\n \"\"\"\n # The retry policy (RetryPolicy.should_retry) caps transient operations at 5 total attempts\n # (it returns True while the 1-based attempt_number < 5, so up to 4 retries).\n max_attempts = 5\n num_retries = max_attempts - 1\n return [BASE_DELAY_MS * (2 ** k) for k in range(num_retries)]\n```", "input_tokens": 1239, "output_tokens": 516, "cost_usd": 0.011457, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/schedule.py"]}} -{"scenario": "cascade-retry-budget-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "sonnet", "trial": 2, "output": "Looking at the materials, I need to determine the maximum total attempts from the retry policy.\n\nThe divergence report shows the **new code** (current state) of `should_retry`:\n\n```python\nreturn attempt_number < 5\n```\n\nSince `attempt_number` is 1-based (count of attempts already completed), the policy keeps retrying while `attempt_number < 5`, meaning it will retry after attempts 1, 2, 3, and 4 \u2014 stopping after attempt 5. So the maximum total attempts is **5**, giving **4 retries**.\n\nThe schedule should have 4 entries (one per retry):\n- k=0: `100 * 2**0 = 100`\n- k=1: `100 * 2**1 = 200`\n- k=2: `100 * 2**2 = 400`\n- k=3: `100 * 2**3 = 800`\n\nFILE: code/schedule.py\n```python\n\"\"\"Pre-computes the backoff schedule for a retried operation.\n\nThe retry policy lives in the `retry` package (see its documentation); this module only needs to\nknow how many attempts that policy will make so it can emit one backoff delay per retry.\n\"\"\"\n\n# The base wait before the first retry, in milliseconds. Each subsequent retry doubles it.\nBASE_DELAY_MS = 100\n\n\ndef backoff_schedule() -> list[int]:\n \"\"\"Return the wait before each retry, in milliseconds \u2014 one entry per retry the policy makes.\n\n A retry happens between consecutive attempts, so the number of entries is one fewer than the\n policy's maximum total attempts. Delays grow exponentially: ``BASE_DELAY_MS * 2 ** k`` for the\n k-th retry (k starting at 0). Returns ``[]`` if the policy makes only a single attempt.\n \"\"\"\n # RetryPolicy.should_retry returns True while attempt_number < 5 (1-based), so the policy\n # allows up to 5 total attempts (the initial attempt plus 4 retries).\n max_attempts = 5\n num_retries = max_attempts - 1\n return [BASE_DELAY_MS * 2 ** k for k in range(num_retries)]\n```", "input_tokens": 1239, "output_tokens": 531, "cost_usd": 0.011682, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/schedule.py"]}} -{"scenario": "cascade-retry-budget-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "sonnet", "trial": 3, "output": "Looking at the divergence report, the new code shows:\n\n```python\nreturn attempt_number < 5\n```\n\nThis means the policy keeps retrying while `attempt_number < 5`, so it retries after attempts 1, 2, 3, and 4 \u2014 stopping after attempt 4 (since `4 < 5` is true but the next call would have `attempt_number=5` which returns False... wait, let me re-read).\n\n`attempt_number` is the 1-based count of attempts **already completed**. So:\n- After attempt 1: `1 < 5` \u2192 True, retry\n- After attempt 2: `2 < 5` \u2192 True, retry\n- After attempt 3: `3 < 5` \u2192 True, retry\n- After attempt 4: `4 < 5` \u2192 True, retry\n- After attempt 5: `5 < 5` \u2192 False, stop\n\nSo maximum total attempts = 5, and there are 4 retries (one fewer than 5).\n\nThe delays are:\n- k=0: `100 * 2**0 = 100`\n- k=1: `100 * 2**1 = 200`\n- k=2: `100 * 2**2 = 400`\n- k=3: `100 * 2**3 = 800`\n\nFILE: code/schedule.py\n```python\n\"\"\"Pre-computes the backoff schedule for a retried operation.\n\nThe retry policy lives in the `retry` package (see its documentation); this module only needs to\nknow how many attempts that policy will make so it can emit one backoff delay per retry.\n\"\"\"\n\n# The base wait before the first retry, in milliseconds. Each subsequent retry doubles it.\nBASE_DELAY_MS = 100\n\n\ndef backoff_schedule() -> list[int]:\n \"\"\"Return the wait before each retry, in milliseconds \u2014 one entry per retry the policy makes.\n\n A retry happens between consecutive attempts, so the number of entries is one fewer than the\n policy's maximum total attempts. Delays grow exponentially: ``BASE_DELAY_MS * 2 ** k`` for the\n k-th retry (k starting at 0). Returns ``[]`` if the policy makes only a single attempt.\n \"\"\"\n # The retry policy allows up to 5 total attempts for a persistently-failing transient\n # operation (should_retry returns True while attempt_number < 5, so attempts 1-4 are\n # each followed by a retry, and the 5th attempt is the last). That means 4 retries.\n max_attempts = 5\n num_retries = max_attempts - 1\n return [BASE_DELAY_MS * (2 ** k) for k in range(num_retries)]\n```", "input_tokens": 1239, "output_tokens": 661, "cost_usd": 0.013632, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/schedule.py"]}} -{"scenario": "cascade-retry-budget-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "sonnet", "trial": 4, "output": "Looking at the materials, I need to determine the maximum total attempts from the retry policy.\n\nThe divergence report shows the **new code** (current state) of `should_retry`:\n\n```python\nreturn attempt_number < 5\n```\n\nSince `attempt_number` is 1-based and the policy keeps retrying while `attempt_number < 5`, it will retry after attempts 1, 2, 3, and 4 \u2014 meaning a maximum of **5 total attempts** (4 retries).\n\nSo `backoff_schedule()` should return 4 entries (one per retry), with delays `BASE_DELAY_MS * 2 ** k` for k = 0, 1, 2, 3.\n\nFILE: code/schedule.py\n```python\n\"\"\"Pre-computes the backoff schedule for a retried operation.\n\nThe retry policy lives in the `retry` package (see its documentation); this module only needs to\nknow how many attempts that policy will make so it can emit one backoff delay per retry.\n\"\"\"\n\n# The base wait before the first retry, in milliseconds. Each subsequent retry doubles it.\nBASE_DELAY_MS = 100\n\n\ndef backoff_schedule() -> list[int]:\n \"\"\"Return the wait before each retry, in milliseconds \u2014 one entry per retry the policy makes.\n\n A retry happens between consecutive attempts, so the number of entries is one fewer than the\n policy's maximum total attempts. Delays grow exponentially: ``BASE_DELAY_MS * 2 ** k`` for the\n k-th retry (k starting at 0). Returns ``[]`` if the policy makes only a single attempt.\n \"\"\"\n # The retry policy allows up to 5 total attempts for a persistently-failing transient\n # operation (it keeps retrying while attempt_number < 5, where attempt_number is 1-based).\n # That means 4 retries, so the schedule has 4 entries.\n max_attempts = 5\n num_retries = max_attempts - 1\n return [BASE_DELAY_MS * (2 ** k) for k in range(num_retries)]\n```", "input_tokens": 1239, "output_tokens": 499, "cost_usd": 0.011202, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/schedule.py"]}} -{"scenario": "cascade-retry-budget-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "sonnet", "trial": 5, "output": "Looking at the materials, I need to determine the maximum total attempts from the retry policy.\n\nThe divergence report shows the **new** (current) code is:\n```python\nreturn attempt_number < 5\n```\n\nThis means the policy keeps retrying while `attempt_number < 5`, so it retries after attempts 1, 2, 3, and 4 \u2014 stopping after attempt 4 completes (since `4 < 5` is true, it retries, then attempt 5 is made and `5 < 5` is false). That gives a maximum of **5 total attempts**, meaning **4 retries**.\n\nThe schedule should have 4 entries (one per retry), with delays `BASE_DELAY_MS * 2**k` for k in 0..3.\n\nFILE: code/schedule.py\n```python\n\"\"\"Pre-computes the backoff schedule for a retried operation.\n\nThe retry policy lives in the `retry` package (see its documentation); this module only needs to\nknow how many attempts that policy will make so it can emit one backoff delay per retry.\n\"\"\"\n\n# The base wait before the first retry, in milliseconds. Each subsequent retry doubles it.\nBASE_DELAY_MS = 100\n\n\ndef backoff_schedule() -> list[int]:\n \"\"\"Return the wait before each retry, in milliseconds \u2014 one entry per retry the policy makes.\n\n A retry happens between consecutive attempts, so the number of entries is one fewer than the\n policy's maximum total attempts. Delays grow exponentially: ``BASE_DELAY_MS * 2 ** k`` for the\n k-th retry (k starting at 0). Returns ``[]`` if the policy makes only a single attempt.\n \"\"\"\n # The retry policy allows up to 5 total attempts for a persistently-failing transient\n # operation (it retries while attempt_number < 5, so attempts 1-4 can each be followed\n # by a retry, yielding a 5th attempt). That means 4 retries, so 4 backoff entries.\n max_attempts = 5\n num_retries = max_attempts - 1\n return [BASE_DELAY_MS * (2 ** k) for k in range(num_retries)]\n```", "input_tokens": 1239, "output_tokens": 526, "cost_usd": 0.011607, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/schedule.py"]}} -{"scenario": "cascade-retry-budget-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "sonnet", "trial": 6, "output": "Looking at the divergence report, the new code shows:\n\n```python\nreturn attempt_number < 5\n```\n\nThis means the policy keeps retrying while `attempt_number < 5`, so it retries after attempts 1, 2, 3, and 4 \u2014 stopping after attempt 4 (since attempt 4 is not < 5... wait, let me re-read).\n\n`attempt_number` is the 1-based count of attempts **already completed**. The policy returns `True` (keep going) while `attempt_number < 5`. So it will retry after attempts 1, 2, 3, and 4. It stops when `attempt_number` reaches 5 (returns False). This means the maximum total attempts is **5** (5 attempts are made before giving up), with **4 retries**.\n\nThe schedule needs one entry per retry: 4 entries total.\n- k=0: 100 * 2^0 = 100\n- k=1: 100 * 2^1 = 200\n- k=2: 100 * 2^2 = 400\n- k=3: 100 * 2^3 = 800\n\nFILE: code/schedule.py\n```python\n\"\"\"Pre-computes the backoff schedule for a retried operation.\n\nThe retry policy lives in the `retry` package (see its documentation); this module only needs to\nknow how many attempts that policy will make so it can emit one backoff delay per retry.\n\"\"\"\n\n# The base wait before the first retry, in milliseconds. Each subsequent retry doubles it.\nBASE_DELAY_MS = 100\n\n\ndef backoff_schedule() -> list[int]:\n \"\"\"Return the wait before each retry, in milliseconds \u2014 one entry per retry the policy makes.\n\n A retry happens between consecutive attempts, so the number of entries is one fewer than the\n policy's maximum total attempts. Delays grow exponentially: ``BASE_DELAY_MS * 2 ** k`` for the\n k-th retry (k starting at 0). Returns ``[]`` if the policy makes only a single attempt.\n \"\"\"\n # The retry policy retries while attempt_number < 5 (1-based attempts already completed),\n # so a persistently-failing operation makes at most 5 total attempts (4 retries).\n max_attempts = 5\n num_retries = max_attempts - 1\n return [BASE_DELAY_MS * (2 ** k) for k in range(num_retries)]\n```", "input_tokens": 1239, "output_tokens": 589, "cost_usd": 0.012552, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/schedule.py"]}} -{"scenario": "cascade-retry-budget-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "sonnet", "trial": 7, "output": "Looking at the materials, I need to determine the maximum total attempts the retry policy makes.\n\nThe divergence report shows the **new code** (current state) of `should_retry`:\n\n```python\nreturn attempt_number < 5\n```\n\nSince `attempt_number` is 1-based (count of attempts already completed), the policy keeps retrying while `attempt_number < 5`, meaning it will retry after attempts 1, 2, 3, and 4 \u2014 stopping after attempt 5. So the maximum total attempts is **5**, giving **4 retries**.\n\nThe backoff schedule should have 4 entries (one per retry): `BASE_DELAY_MS * 2**k` for k = 0, 1, 2, 3.\n\nFILE: code/schedule.py\n\n```python\n\"\"\"Pre-computes the backoff schedule for a retried operation.\n\nThe retry policy lives in the `retry` package (see its documentation); this module only needs to\nknow how many attempts that policy will make so it can emit one backoff delay per retry.\n\"\"\"\n\n# The base wait before the first retry, in milliseconds. Each subsequent retry doubles it.\nBASE_DELAY_MS = 100\n\n\ndef backoff_schedule() -> list[int]:\n \"\"\"Return the wait before each retry, in milliseconds \u2014 one entry per retry the policy makes.\n\n A retry happens between consecutive attempts, so the number of entries is one fewer than the\n policy's maximum total attempts. Delays grow exponentially: ``BASE_DELAY_MS * 2 ** k`` for the\n k-th retry (k starting at 0). Returns ``[]`` if the policy makes only a single attempt.\n \"\"\"\n # RetryPolicy.should_retry returns True while attempt_number < 5 (1-based), so the policy\n # allows up to 5 total attempts (the initial attempt plus 4 retries). We therefore emit one\n # delay per retry, for a total of 4 entries.\n max_attempts = 5\n num_retries = max_attempts - 1\n return [BASE_DELAY_MS * (2 ** k) for k in range(num_retries)]\n```", "input_tokens": 1239, "output_tokens": 506, "cost_usd": 0.011307000000000001, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/schedule.py"]}} -{"scenario": "cascade-retry-budget-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "sonnet", "trial": 8, "output": "Looking at the divergence report, the new code shows:\n\n```python\nreturn attempt_number < 5\n```\n\nSince `attempt_number` is 1-based and the policy keeps retrying while it's less than 5, the maximum attempt number that still allows a retry is 4. That means the policy will make **5 total attempts** (attempts 1, 2, 3, 4 each trigger a retry; attempt 5 does not). So there are **4 retries**.\n\nFILE: code/schedule.py\n```python\n\"\"\"Pre-computes the backoff schedule for a retried operation.\n\nThe retry policy lives in the `retry` package (see its documentation); this module only needs to\nknow how many attempts that policy will make so it can emit one backoff delay per retry.\n\"\"\"\n\n# The base wait before the first retry, in milliseconds. Each subsequent retry doubles it.\nBASE_DELAY_MS = 100\n\n\ndef backoff_schedule() -> list[int]:\n \"\"\"Return the wait before each retry, in milliseconds \u2014 one entry per retry the policy makes.\n\n A retry happens between consecutive attempts, so the number of entries is one fewer than the\n policy's maximum total attempts. Delays grow exponentially: ``BASE_DELAY_MS * 2 ** k`` for the\n k-th retry (k starting at 0). Returns ``[]`` if the policy makes only a single attempt.\n \"\"\"\n # The retry policy allows up to 5 total attempts (keeps retrying while attempt_number < 5),\n # so there are 4 retries between consecutive attempts.\n max_attempts = 5\n num_retries = max_attempts - 1\n return [BASE_DELAY_MS * (2 ** k) for k in range(num_retries)]\n```", "input_tokens": 1239, "output_tokens": 417, "cost_usd": 0.009972, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/schedule.py"]}} -{"scenario": "cascade-retry-budget-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "sonnet", "trial": 9, "output": "Looking at the materials, I need to determine the maximum total attempts from the retry policy.\n\nThe divergence report shows the **new** (current) code is:\n\n```python\nreturn attempt_number < 5\n```\n\nSince `attempt_number` is 1-based and the policy keeps retrying while it's `< 5`, the last attempt that triggers a retry is attempt 4, meaning a 5th attempt will be made. So the maximum total attempts is **5**, which means **4 retries**.\n\nThe schedule should have 4 entries (one per retry): `BASE_DELAY_MS * 2**k` for k = 0, 1, 2, 3.\n\nFILE: code/schedule.py\n```python\n\"\"\"Pre-computes the backoff schedule for a retried operation.\n\nThe retry policy lives in the `retry` package (see its documentation); this module only needs to\nknow how many attempts that policy will make so it can emit one backoff delay per retry.\n\"\"\"\n\n# The base wait before the first retry, in milliseconds. Each subsequent retry doubles it.\nBASE_DELAY_MS = 100\n\n\ndef backoff_schedule() -> list[int]:\n \"\"\"Return the wait before each retry, in milliseconds \u2014 one entry per retry the policy makes.\n\n A retry happens between consecutive attempts, so the number of entries is one fewer than the\n policy's maximum total attempts. Delays grow exponentially: ``BASE_DELAY_MS * 2 ** k`` for the\n k-th retry (k starting at 0). Returns ``[]`` if the policy makes only a single attempt.\n \"\"\"\n # RetryPolicy allows up to 5 total attempts (retries while attempt_number < 5),\n # so there are 4 retries and thus 4 backoff delays.\n max_attempts = 5\n num_retries = max_attempts - 1\n return [BASE_DELAY_MS * (2 ** k) for k in range(num_retries)]\n```", "input_tokens": 1239, "output_tokens": 464, "cost_usd": 0.010677, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/schedule.py"]}} -{"scenario": "cascade-retry-budget-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "opus", "trial": 0, "output": "Looking at the materials, the documentation claims a 3-attempt budget, but the Surface doc-drift gate shows the actual current code has changed: `return attempt_number < 5`.\n\nThis means the policy keeps retrying while `attempt_number < 5`. With `attempt_number` being the 1-based count of attempts already completed:\n- After 1 attempt: `1 < 5` \u2192 retry\n- After 2 attempts: `2 < 5` \u2192 retry\n- After 3 attempts: `3 < 5` \u2192 retry\n- After 4 attempts: `4 < 5` \u2192 retry\n- After 5 attempts: `5 < 5` \u2192 stop\n\nSo the policy makes **5 total attempts** (initial + 4 retries). The schedule needs 4 entries (one fewer than 5).\n\nThe actual code is the source of truth, not the stale documentation prose.\n\nFILE: code/schedule.py\n```python\n\"\"\"Pre-computes the backoff schedule for a retried operation.\n\nThe retry policy lives in the `retry` package (see its documentation); this module only needs to\nknow how many attempts that policy will make so it can emit one backoff delay per retry.\n\"\"\"\n\n# The base wait before the first retry, in milliseconds. Each subsequent retry doubles it.\nBASE_DELAY_MS = 100\n\n# The retry policy's maximum total attempts for a persistently-failing transient operation.\n# Per the current `should_retry` source (it keeps retrying while the 1-based attempt_number < 5),\n# the policy makes 5 total attempts: the initial attempt plus 4 retries.\nMAX_ATTEMPTS = 5\n\n\ndef backoff_schedule() -> list[int]:\n \"\"\"Return the wait before each retry, in milliseconds \u2014 one entry per retry the policy makes.\n\n A retry happens between consecutive attempts, so the number of entries is one fewer than the\n policy's maximum total attempts. Delays grow exponentially: ``BASE_DELAY_MS * 2 ** k`` for the\n k-th retry (k starting at 0). Returns ``[]`` if the policy makes only a single attempt.\n \"\"\"\n num_retries = MAX_ATTEMPTS - 1\n return [BASE_DELAY_MS * 2 ** k for k in range(num_retries)]\n```", "input_tokens": 1685, "output_tokens": 749, "cost_usd": 0.02715, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/schedule.py"]}} -{"scenario": "cascade-retry-budget-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "opus", "trial": 1, "output": "Looking at the materials, the documentation claims 3 total attempts, but the Surface doc-drift gate shows the actual current code has changed to `attempt_number < 5`.\n\nThe `new_code` is the authoritative current state of the code:\n\n```python\nreturn attempt_number < 5\n```\n\nWith `attempt_number` being the 1-based count of attempts already completed, the policy retries while `attempt_number < 5`. So it retries after attempts 1, 2, 3, 4 (returns True), and stops at attempt 5 (returns False). That means **5 total attempts**, hence **4 retries**.\n\nSo the schedule needs 4 entries (5 total attempts \u2212 1).\n\nFILE: code/schedule.py\n```python\n\"\"\"Pre-computes the backoff schedule for a retried operation.\n\nThe retry policy lives in the `retry` package (see its documentation); this module only needs to\nknow how many attempts that policy will make so it can emit one backoff delay per retry.\n\"\"\"\n\n# The base wait before the first retry, in milliseconds. Each subsequent retry doubles it.\nBASE_DELAY_MS = 100\n\n# The retry policy's maximum total attempts for a persistently-failing transient operation.\n# Per the current source of RetryPolicy.should_retry, it retries while the 1-based\n# attempt_number < 5, i.e. it retries after attempts 1, 2, 3, and 4 and stops at attempt 5.\n# That is 5 total attempts (the initial attempt plus 4 retries).\nMAX_TOTAL_ATTEMPTS = 5\n\n\ndef backoff_schedule() -> list[int]:\n \"\"\"Return the wait before each retry, in milliseconds \u2014 one entry per retry the policy makes.\n\n A retry happens between consecutive attempts, so the number of entries is one fewer than the\n policy's maximum total attempts. Delays grow exponentially: ``BASE_DELAY_MS * 2 ** k`` for the\n k-th retry (k starting at 0). Returns ``[]`` if the policy makes only a single attempt.\n \"\"\"\n num_retries = MAX_TOTAL_ATTEMPTS - 1\n return [BASE_DELAY_MS * 2 ** k for k in range(num_retries)]\n```", "input_tokens": 1685, "output_tokens": 739, "cost_usd": 0.0269, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/schedule.py"]}} -{"scenario": "cascade-retry-budget-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "opus", "trial": 2, "output": "The documentation's prose says 3 attempts, but the Surface drift gate shows the actual current code now uses `attempt_number < 5`, which means the policy keeps retrying while fewer than 5 attempts have been completed \u2014 giving a maximum of **5 total attempts** (initial + 4 retries). The prose is stale; the authoritative source is the actual code in `new_code`.\n\nSo: 5 total attempts \u2192 4 retries \u2192 4 schedule entries.\n\nFILE: code/schedule.py\n```python\n\"\"\"Pre-computes the backoff schedule for a retried operation.\n\nThe retry policy lives in the `retry` package (see its documentation); this module only needs to\nknow how many attempts that policy will make so it can emit one backoff delay per retry.\n\"\"\"\n\n# The base wait before the first retry, in milliseconds. Each subsequent retry doubles it.\nBASE_DELAY_MS = 100\n\n# The retry policy's maximum total attempts for a persistently-failing transient operation.\n# Per the current code (RetryPolicy.should_retry returns `attempt_number < 5`), the policy keeps\n# retrying while fewer than 5 attempts have completed, so it makes at most 5 total attempts\n# (the initial attempt plus 4 retries). Note: the prose documentation still says 3, but that text\n# is stale \u2014 the deterministic doc-drift gate shows the code now caps at 5.\nMAX_TOTAL_ATTEMPTS = 5\n\n\ndef backoff_schedule() -> list[int]:\n \"\"\"Return the wait before each retry, in milliseconds \u2014 one entry per retry the policy makes.\n\n A retry happens between consecutive attempts, so the number of entries is one fewer than the\n policy's maximum total attempts. Delays grow exponentially: ``BASE_DELAY_MS * 2 ** k`` for the\n k-th retry (k starting at 0). Returns ``[]`` if the policy makes only a single attempt.\n \"\"\"\n num_retries = MAX_TOTAL_ATTEMPTS - 1\n return [BASE_DELAY_MS * 2 ** k for k in range(num_retries)]\n```", "input_tokens": 1685, "output_tokens": 706, "cost_usd": 0.026075, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/schedule.py"]}} -{"scenario": "cascade-retry-budget-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "opus", "trial": 3, "output": "The doc-drift gate shows the actual current code uses `attempt_number < 5`, which means the policy retries while fewer than 5 attempts have been completed \u2014 giving a maximum of 5 total attempts (initial + 4 retries). The prose documentation is stale; the `new_code` is the source of truth.\n\nSo: 5 total attempts \u2192 4 retries \u2192 4 schedule entries.\n\nFILE: code/schedule.py\n```python\n\"\"\"Pre-computes the backoff schedule for a retried operation.\n\nThe retry policy lives in the `retry` package (see its documentation); this module only needs to\nknow how many attempts that policy will make so it can emit one backoff delay per retry.\n\"\"\"\n\n# The base wait before the first retry, in milliseconds. Each subsequent retry doubles it.\nBASE_DELAY_MS = 100\n\n# The retry policy's maximum total attempts for a persistently-failing transient operation.\n# Per the current source of `RetryPolicy.should_retry` (it retries while `attempt_number < 5`),\n# the policy makes the initial attempt plus up to 4 retries, for 5 total attempts.\nMAX_ATTEMPTS = 5\n\n\ndef backoff_schedule() -> list[int]:\n \"\"\"Return the wait before each retry, in milliseconds \u2014 one entry per retry the policy makes.\n\n A retry happens between consecutive attempts, so the number of entries is one fewer than the\n policy's maximum total attempts. Delays grow exponentially: ``BASE_DELAY_MS * 2 ** k`` for the\n k-th retry (k starting at 0). Returns ``[]`` if the policy makes only a single attempt.\n \"\"\"\n num_retries = MAX_ATTEMPTS - 1\n return [BASE_DELAY_MS * 2 ** k for k in range(num_retries)]\n```", "input_tokens": 1685, "output_tokens": 605, "cost_usd": 0.02355, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/schedule.py"]}} -{"scenario": "cascade-retry-budget-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "opus", "trial": 4, "output": "The documentation prose says 3 attempts, but the doc-drift gate shows the actual code has changed: `should_retry` now returns `attempt_number < 5`. This means the policy keeps retrying while completed attempts are below 5, giving a maximum of **5 total attempts** (initial + 4 retries). The `new_code` is the source of truth here, since the claim's prose is stale.\n\n- Max total attempts = 5\n- Number of retries = 5 - 1 = 4\n- Delays: `BASE_DELAY_MS * 2**k` for k in 0..3 \u2192 [100, 200, 400, 800]\n\nFILE: code/schedule.py\n```python\n\"\"\"Pre-computes the backoff schedule for a retried operation.\n\nThe retry policy lives in the `retry` package (see its documentation); this module only needs to\nknow how many attempts that policy will make so it can emit one backoff delay per retry.\n\"\"\"\n\n# The base wait before the first retry, in milliseconds. Each subsequent retry doubles it.\nBASE_DELAY_MS = 100\n\n# The retry policy's maximum total attempts for a persistently-failing transient operation.\n# Per the current source of `RetryPolicy.should_retry` (which retries while the 1-based\n# attempt_number is < 5), the policy makes at most 5 total attempts: the initial attempt plus\n# 4 retries.\nMAX_ATTEMPTS = 5\n\n\ndef backoff_schedule() -> list[int]:\n \"\"\"Return the wait before each retry, in milliseconds \u2014 one entry per retry the policy makes.\n\n A retry happens between consecutive attempts, so the number of entries is one fewer than the\n policy's maximum total attempts. Delays grow exponentially: ``BASE_DELAY_MS * 2 ** k`` for the\n k-th retry (k starting at 0). Returns ``[]`` if the policy makes only a single attempt.\n \"\"\"\n num_retries = MAX_ATTEMPTS - 1\n return [BASE_DELAY_MS * 2 ** k for k in range(num_retries)]\n```", "input_tokens": 1685, "output_tokens": 685, "cost_usd": 0.025550000000000003, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/schedule.py"]}} -{"scenario": "cascade-retry-budget-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "opus", "trial": 5, "output": "Looking at the materials, the documentation's prose claims 3 total attempts, but Surface reports the code has **changed**. The new code shows:\n\n```python\nreturn attempt_number < 5\n```\n\nThis means the policy retries while the completed attempt count is below 5 \u2014 i.e., it keeps going after 1, 2, 3, 4 completed attempts, stopping once 5 attempts have been made. That's a maximum of **5 total attempts** (the initial attempt plus 4 retries).\n\nThe doc-drift gate explicitly tells me the actual code now uses `< 5`, so I should size the schedule to the current code, not the stale prose.\n\n- Maximum total attempts: 5\n- Number of retries: 5 - 1 = 4\n- Delays: `100 * 2**0, 100 * 2**1, 100 * 2**2, 100 * 2**3` = `[100, 200, 400, 800]`\n\nFILE: code/schedule.py\n```python\n\"\"\"Pre-computes the backoff schedule for a retried operation.\n\nThe retry policy lives in the `retry` package (see its documentation); this module only needs to\nknow how many attempts that policy will make so it can emit one backoff delay per retry.\n\"\"\"\n\n# The base wait before the first retry, in milliseconds. Each subsequent retry doubles it.\nBASE_DELAY_MS = 100\n\n# The retry policy's maximum total attempts for a persistently-failing transient operation.\n# Per RetryPolicy.should_retry, retries continue while the 1-based attempt_number < 5, so a\n# persistently-failing operation is tried at most 5 times (the initial attempt plus 4 retries).\nMAX_TOTAL_ATTEMPTS = 5\n\n\ndef backoff_schedule() -> list[int]:\n \"\"\"Return the wait before each retry, in milliseconds \u2014 one entry per retry the policy makes.\n\n A retry happens between consecutive attempts, so the number of entries is one fewer than the\n policy's maximum total attempts. Delays grow exponentially: ``BASE_DELAY_MS * 2 ** k`` for the\n k-th retry (k starting at 0). Returns ``[]`` if the policy makes only a single attempt.\n \"\"\"\n num_retries = MAX_TOTAL_ATTEMPTS - 1\n return [BASE_DELAY_MS * 2 ** k for k in range(num_retries)]\n```", "input_tokens": 1685, "output_tokens": 786, "cost_usd": 0.028075000000000003, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/schedule.py"]}} -{"scenario": "cascade-retry-budget-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "opus", "trial": 6, "output": "Looking at the materials, the doc prose says 3 attempts, but the **actual current code** (shown in `new_code` from Surface's drift report) is the source of truth:\n\n```python\nreturn attempt_number < 5\n```\n\nSince `attempt_number` is the 1-based count of attempts already completed, the policy keeps retrying while attempts completed < 5. So it stops once 5 attempts have been made \u2192 **maximum 5 total attempts**, meaning **4 retries**.\n\nThe schedule has one entry per retry = total attempts \u2212 1 = 4 entries: delays `BASE_DELAY_MS * 2**k` for k = 0..3.\n\nFILE: code/schedule.py\n```python\n\"\"\"Pre-computes the backoff schedule for a retried operation.\n\nThe retry policy lives in the `retry` package (see its documentation); this module only needs to\nknow how many attempts that policy will make so it can emit one backoff delay per retry.\n\"\"\"\n\n# The base wait before the first retry, in milliseconds. Each subsequent retry doubles it.\nBASE_DELAY_MS = 100\n\n# The retry policy's current source (per Surface's drift report) retries while the 1-based count of\n# completed attempts is < 5, so a persistently-failing transient operation makes at most 5 total\n# attempts (the initial attempt plus 4 retries).\nMAX_ATTEMPTS = 5\n\n\ndef backoff_schedule() -> list[int]:\n \"\"\"Return the wait before each retry, in milliseconds \u2014 one entry per retry the policy makes.\n\n A retry happens between consecutive attempts, so the number of entries is one fewer than the\n policy's maximum total attempts. Delays grow exponentially: ``BASE_DELAY_MS * 2 ** k`` for the\n k-th retry (k starting at 0). Returns ``[]`` if the policy makes only a single attempt.\n \"\"\"\n num_retries = MAX_ATTEMPTS - 1\n return [BASE_DELAY_MS * 2 ** k for k in range(num_retries)]\n```", "input_tokens": 1685, "output_tokens": 672, "cost_usd": 0.025225000000000004, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/schedule.py"]}} -{"scenario": "cascade-retry-budget-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "opus", "trial": 7, "output": "Looking at the materials, the documentation claims 3 total attempts, but the Surface doc-drift gate shows the actual current code has changed: `return attempt_number < 5`.\n\nWith `should_retry` returning `attempt_number < 5`, where `attempt_number` is the 1-based count of attempts already completed:\n- After attempt 1: `1 < 5` \u2192 retry\n- After attempt 2: `2 < 5` \u2192 retry\n- After attempt 3: `3 < 5` \u2192 retry\n- After attempt 4: `4 < 5` \u2192 retry\n- After attempt 5: `5 < 5` \u2192 stop\n\nSo the maximum total attempts is **5**, meaning **4 retries** (one fewer than total attempts).\n\nThe schedule should have 4 entries: `BASE_DELAY_MS * 2**k` for k = 0, 1, 2, 3.\n\nFILE: code/schedule.py\n```python\n\"\"\"Pre-computes the backoff schedule for a retried operation.\n\nThe retry policy lives in the `retry` package (see its documentation); this module only needs to\nknow how many attempts that policy will make so it can emit one backoff delay per retry.\n\"\"\"\n\n# The base wait before the first retry, in milliseconds. Each subsequent retry doubles it.\nBASE_DELAY_MS = 100\n\n# The retry policy's maximum total attempts for a persistently-failing transient operation.\n# Per the current source of RetryPolicy.should_retry (surfaced by the doc-drift gate), the policy\n# keeps retrying while the 1-based attempt_number is below 5, so a persistently-failing operation\n# is tried at most 5 times (the initial attempt plus 4 retries).\nMAX_ATTEMPTS = 5\n\n\ndef backoff_schedule() -> list[int]:\n \"\"\"Return the wait before each retry, in milliseconds \u2014 one entry per retry the policy makes.\n\n A retry happens between consecutive attempts, so the number of entries is one fewer than the\n policy's maximum total attempts. Delays grow exponentially: ``BASE_DELAY_MS * 2 ** k`` for the\n k-th retry (k starting at 0). Returns ``[]`` if the policy makes only a single attempt.\n \"\"\"\n num_retries = MAX_ATTEMPTS - 1\n return [BASE_DELAY_MS * 2 ** k for k in range(num_retries)]\n```", "input_tokens": 1685, "output_tokens": 786, "cost_usd": 0.028075000000000003, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/schedule.py"]}} -{"scenario": "cascade-retry-budget-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "opus", "trial": 8, "output": "The Surface drift gate shows the actual current code: `return attempt_number < 5`. This means the policy retries while attempts completed < 5, so it makes a maximum of 5 total attempts (the doc prose claiming 3 is stale). That's 4 retries.\n\nFILE: code/schedule.py\n```python\n\"\"\"Pre-computes the backoff schedule for a retried operation.\n\nThe retry policy lives in the `retry` package (see its documentation); this module only needs to\nknow how many attempts that policy will make so it can emit one backoff delay per retry.\n\"\"\"\n\n# The base wait before the first retry, in milliseconds. Each subsequent retry doubles it.\nBASE_DELAY_MS = 100\n\n# The retry policy makes at most 5 total attempts (should_retry keeps going while\n# attempt_number < 5), i.e. the initial attempt plus 4 retries.\nMAX_ATTEMPTS = 5\n\n\ndef backoff_schedule() -> list[int]:\n \"\"\"Return the wait before each retry, in milliseconds \u2014 one entry per retry the policy makes.\n\n A retry happens between consecutive attempts, so the number of entries is one fewer than the\n policy's maximum total attempts. Delays grow exponentially: ``BASE_DELAY_MS * 2 ** k`` for the\n k-th retry (k starting at 0). Returns ``[]`` if the policy makes only a single attempt.\n \"\"\"\n num_retries = MAX_ATTEMPTS - 1\n return [BASE_DELAY_MS * 2 ** k for k in range(num_retries)]\n```", "input_tokens": 1685, "output_tokens": 525, "cost_usd": 0.02155, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/schedule.py"]}} -{"scenario": "cascade-retry-budget-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "opus", "trial": 9, "output": "Looking at the materials, the documentation claims a 3-attempt budget, but Surface reports the code has **changed**. The authoritative current code is in `new_code`:\n\n```python\nreturn attempt_number < 5\n```\n\nSince `attempt_number` is the 1-based count of attempts already completed, the policy retries while attempts completed < 5. So it stops once 5 attempts are made \u2192 **maximum 5 total attempts**, which is 4 retries.\n\nSo the schedule has 4 entries (one fewer than 5 total attempts): delays for k = 0, 1, 2, 3.\n\nFILE: code/schedule.py\n```python\n\"\"\"Pre-computes the backoff schedule for a retried operation.\n\nThe retry policy lives in the `retry` package (see its documentation); this module only needs to\nknow how many attempts that policy will make so it can emit one backoff delay per retry.\n\"\"\"\n\n# The base wait before the first retry, in milliseconds. Each subsequent retry doubles it.\nBASE_DELAY_MS = 100\n\n# The retry policy's maximum total attempts for a persistently-failing transient operation.\n# Per RetryPolicy.should_retry, it keeps retrying while the 1-based attempt_number is below 5,\n# so a persistently-failing operation is tried at most 5 times (the initial attempt plus 4 retries).\nMAX_ATTEMPTS = 5\n\n\ndef backoff_schedule() -> list[int]:\n \"\"\"Return the wait before each retry, in milliseconds \u2014 one entry per retry the policy makes.\n\n A retry happens between consecutive attempts, so the number of entries is one fewer than the\n policy's maximum total attempts. Delays grow exponentially: ``BASE_DELAY_MS * 2 ** k`` for the\n k-th retry (k starting at 0). Returns ``[]`` if the policy makes only a single attempt.\n \"\"\"\n num_retries = MAX_ATTEMPTS - 1\n return [BASE_DELAY_MS * 2 ** k for k in range(num_retries)]\n```", "input_tokens": 1685, "output_tokens": 673, "cost_usd": 0.02525, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/schedule.py"]}} -{"scenario": "dropped-await-qa", "task_type": "qa", "tier": "T1", "condition": "C0", "model": "haiku", "trial": 0, "output": "Looking at the code flow in `ChargeService.charge_customer`:\n\n```python\nasync def charge_customer(self, customer_id: str, amount_cents: int) -> Receipt:\n receipt = await self.gateway.charge(customer_id, amount_cents)\n entry = LedgerEntry(\n customer_id=customer_id,\n amount_cents=amount_cents,\n receipt_id=receipt.id,\n )\n self.ledger.commit(entry) # <-- This line\n return receipt\n```\n\n**Part 1: Is the charge guaranteed to be persisted when the call returns?**\n\nYes. The `ledger.commit(entry)` call appends the entry to `self._entries` before the function returns, and `ledger.contains(receipt.id)` would find it.\n\n**Part 2: Does `charge_customer` actually `await` the `ledger.commit(...)` call?**\n\nNo. The code calls `self.ledger.commit(entry)` without an `await` keyword, even though `commit` is defined as an `async def` method. This means the coroutine is created but never awaited\u2014it just executes the synchronous part (the list append) and the coroutine object is discarded.\n\n**Justification for both:**\nThe entry is appended to the in-memory list immediately upon the non-awaited call to `self.ledger.commit()`, before `charge_customer` returns, so it will be found; however, the lack of `await` means any actual async durability logic in `commit` is never executed.\n\nVERDICT: persisted=yes; commit_awaited=no", "input_tokens": 937, "output_tokens": 387, "cost_usd": 0.002872, "ok": false, "misled": true, "detail": "verdict {'persisted': 'yes', 'commit_awaited': 'no'} != expected {'persisted': 'no', 'commit_awaited': 'no'}", "parsed": {"persisted": "yes", "commit_awaited": "no"}} -{"scenario": "dropped-await-qa", "task_type": "qa", "tier": "T1", "condition": "C0", "model": "haiku", "trial": 1, "output": "Let me trace through the `charge_customer` method step by step:\n\n```python\nasync def charge_customer(self, customer_id: str, amount_cents: int) -> Receipt:\n receipt = await self.gateway.charge(customer_id, amount_cents)\n entry = LedgerEntry(\n customer_id=customer_id,\n amount_cents=amount_cents,\n receipt_id=receipt.id,\n )\n self.ledger.commit(entry)\n return receipt\n```\n\n**Part 1: Is the charge guaranteed to be persisted when the await completes?**\n\nNo. The method calls `self.ledger.commit(entry)` without awaiting it. Looking at `Ledger.commit`:\n\n```python\nasync def commit(self, entry: LedgerEntry) -> None:\n self._entries.append(entry)\n```\n\nThe `commit` method is async (it's a coroutine), but `charge_customer` does not await it. This means the coroutine is created but never awaited, so it never executes. The entry is never appended to `self._entries`, so the charge is not persisted in the ledger.\n\n**Part 2: Does `charge_customer` actually await the `ledger.commit(...)` call?**\n\nNo. Line 12 calls `self.ledger.commit(entry)` without the `await` keyword, so the coroutine is never awaited.\n\n---\n\n**Justification for Part 1:** The `ledger.commit(entry)` call on line 12 is not awaited, so the async function never executes and the entry is never added to the ledger's internal list.\n\n**Justification for Part 2:** The code explicitly lacks an `await` keyword before `self.ledger.commit(entry)`, making it a fire-and-forget call that creates but never executes the coroutine.\n\nVERDICT: persisted=no; commit_awaited=no", "input_tokens": 937, "output_tokens": 459, "cost_usd": 0.003232, "ok": true, "misled": false, "detail": "correct", "parsed": {"persisted": "no", "commit_awaited": "no"}} -{"scenario": "dropped-await-qa", "task_type": "qa", "tier": "T1", "condition": "C0", "model": "haiku", "trial": 2, "output": "## Analysis\n\nLet me trace through `ChargeService.charge_customer` step by step:\n\n```python\nasync def charge_customer(self, customer_id: str, amount_cents: int) -> Receipt:\n receipt = await self.gateway.charge(customer_id, amount_cents)\n entry = LedgerEntry(\n customer_id=customer_id,\n amount_cents=amount_cents,\n receipt_id=receipt.id,\n )\n self.ledger.commit(entry) # <-- This line\n return receipt\n```\n\nThe critical line is `self.ledger.commit(entry)`.\n\nLooking at `Ledger.commit`:\n\n```python\nasync def commit(self, entry: LedgerEntry) -> None:\n self._entries.append(entry)\n```\n\nThis is an `async` function, but in `charge_customer` it is **called without `await`**.\n\n### Part 1: Is the charge guaranteed to be persisted when `await charge_customer(...)` returns?\n\n**Answer: No**\n\nWhen `charge_customer` executes `self.ledger.commit(entry)` without awaiting it, a coroutine object is created but never awaited, so the actual append operation never executes before the function returns. The charge is not persisted in the ledger.\n\n### Part 2: Does `charge_customer` actually `await` the `ledger.commit(...)` call?\n\n**Answer: No**\n\nThe code calls `self.ledger.commit(entry)` but does not use the `await` keyword, so the coroutine is created but never awaited.\n\n---\n\nVERDICT: persisted=no; commit_awaited=no", "input_tokens": 937, "output_tokens": 396, "cost_usd": 0.002917, "ok": true, "misled": false, "detail": "correct", "parsed": {"persisted": "no", "commit_awaited": "no"}} -{"scenario": "dropped-await-qa", "task_type": "qa", "tier": "T1", "condition": "C0", "model": "haiku", "trial": 3, "output": "# Analysis of `ChargeService.charge_customer` Durability\n\nLet me trace through the code execution:\n\n```python\nasync def charge_customer(self, customer_id: str, amount_cents: int) -> Receipt:\n receipt = await self.gateway.charge(customer_id, amount_cents) # awaited\n entry = LedgerEntry(\n customer_id=customer_id,\n amount_cents=amount_cents,\n receipt_id=receipt.id,\n )\n self.ledger.commit(entry) # NOT awaited\n return receipt\n```\n\nThe critical line is `self.ledger.commit(entry)` \u2014 this call is **not awaited**.\n\nLooking at `Ledger.commit`:\n```python\nasync def commit(self, entry: LedgerEntry) -> None:\n self._entries.append(entry)\n```\n\nThe `commit` method is an async function, but `charge_customer` calls it without `await`.\n\n## Answers\n\n**1. Is the charge guaranteed to be persisted when `await charge_customer(...)` returns?**\n\n**No.** The `ledger.commit(entry)` call is not awaited, so it returns a coroutine object that is never executed. The method returns before the ledger write operation has even begun, let alone completed.\n\n**2. Does `charge_customer` actually `await` the `ledger.commit(...)` call?**\n\n**No.** Line 12 calls `self.ledger.commit(entry)` without the `await` keyword, leaving the coroutine unawaited.\n\n---\n\nVERDICT: persisted=no; commit_awaited=no", "input_tokens": 937, "output_tokens": 391, "cost_usd": 0.002892, "ok": true, "misled": false, "detail": "correct", "parsed": {"persisted": "no", "commit_awaited": "no"}} -{"scenario": "dropped-await-qa", "task_type": "qa", "tier": "T1", "condition": "C0", "model": "haiku", "trial": 4, "output": "Looking at the code in `code/billing/charges.py`, let me trace exactly what happens:\n\n```python\nasync def charge_customer(self, customer_id: str, amount_cents: int) -> Receipt:\n receipt = await self.gateway.charge(customer_id, amount_cents)\n entry = LedgerEntry(\n customer_id=customer_id,\n amount_cents=amount_cents,\n receipt_id=receipt.id,\n )\n self.ledger.commit(entry) # <-- THIS LINE\n return receipt\n```\n\n**Part 1: Is the charge guaranteed to be persisted when `await charge_customer(...)` returns?**\n\n**Yes.** The `Ledger.commit()` method appends the entry to `self._entries` synchronously before returning, so by the time `charge_customer` returns, `ledger.contains(receipt.id)` will be True.\n\n**Part 2: Does `charge_customer` actually `await` the `ledger.commit(...)` call?**\n\n**No.** The code calls `self.ledger.commit(entry)` without `await`\u2014it's a fire-and-forget call that doesn't wait for the coroutine to complete.\n\nThe fact that the charge is persisted despite not awaiting is because `Ledger.commit()` happens to do its work (appending to the list) synchronously before returning the coroutine, which is a bug-prone pattern that only works by accident. Proper async practice would require `await self.ledger.commit(entry)`.\n\nVERDICT: persisted=yes; commit_awaited=no", "input_tokens": 937, "output_tokens": 380, "cost_usd": 0.002837, "ok": false, "misled": true, "detail": "verdict {'persisted': 'yes', 'commit_awaited': 'no'} != expected {'persisted': 'no', 'commit_awaited': 'no'}", "parsed": {"persisted": "yes", "commit_awaited": "no"}} -{"scenario": "dropped-await-qa", "task_type": "qa", "tier": "T1", "condition": "C0", "model": "haiku", "trial": 5, "output": "Let me trace through the `charge_customer` method step by step.\n\n```python\nasync def charge_customer(self, customer_id: str, amount_cents: int) -> Receipt:\n \"\"\"Charge the customer, record the charge in the ledger, and return the receipt.\"\"\"\n receipt = await self.gateway.charge(customer_id, amount_cents)\n entry = LedgerEntry(\n customer_id=customer_id,\n amount_cents=amount_cents,\n receipt_id=receipt.id,\n )\n self.ledger.commit(entry)\n return receipt\n```\n\n**Part 1: Is the charge guaranteed to be persisted when `await charge_customer(...)` returns?**\n\nYes. The line `self.ledger.commit(entry)` is called before `return receipt`, and the `commit` method in `Ledger` immediately appends the entry to the `_entries` list. By the time `charge_customer` returns, the entry has been added to the ledger, so `ledger.contains(receipt.id)` would return `True`.\n\n**Part 2: Does `charge_customer` actually `await` the `ledger.commit(...)` call?**\n\nNo. On line `self.ledger.commit(entry)`, there is no `await` keyword. Even though `commit` is defined as an `async` method, it is called without `await`, which means the coroutine is created but never actually awaited\u2014the method returns the coroutine object itself without executing it.\n\nHowever, this is a critical bug: the coroutine is created but discarded, so the append operation never actually executes asynchronously. The entry is never actually added to the ledger in practice (the append happens in the unawaited coroutine, not immediately). When interpreted strictly by the code's actual runtime behavior, the entry will **not** be persisted because the async operation was never awaited.\n\nVERDICT: persisted=no; commit_awaited=no", "input_tokens": 937, "output_tokens": 459, "cost_usd": 0.003232, "ok": true, "misled": false, "detail": "correct", "parsed": {"persisted": "no", "commit_awaited": "no"}} -{"scenario": "dropped-await-qa", "task_type": "qa", "tier": "T1", "condition": "C0", "model": "haiku", "trial": 6, "output": "Let me trace through the code carefully.\n\nIn `ChargeService.charge_customer`:\n\n```python\nasync def charge_customer(self, customer_id: str, amount_cents: int) -> Receipt:\n receipt = await self.gateway.charge(customer_id, amount_cents)\n entry = LedgerEntry(\n customer_id=customer_id,\n amount_cents=amount_cents,\n receipt_id=receipt.id,\n )\n self.ledger.commit(entry)\n return receipt\n```\n\n**Part 1: Is the charge guaranteed to be persisted when `await charge_customer(...)` returns?**\n\n**No.** The method calls `self.ledger.commit(entry)` without awaiting it, so the coroutine starts but is not awaited before the function returns. This means the append to `self._entries` may not have completed by the time the `await` expression completes and control returns to the caller.\n\n**Part 2: Does `charge_customer` actually `await` the `ledger.commit(...)` call?**\n\n**No.** Line `self.ledger.commit(entry)` is called without the `await` keyword, so the coroutine is created but not awaited. The function proceeds immediately to `return receipt` without waiting for the ledger write to complete.\n\nVERDICT: persisted=no; commit_awaited=no", "input_tokens": 937, "output_tokens": 324, "cost_usd": 0.0025570000000000002, "ok": true, "misled": false, "detail": "correct", "parsed": {"persisted": "no", "commit_awaited": "no"}} -{"scenario": "dropped-await-qa", "task_type": "qa", "tier": "T1", "condition": "C0", "model": "haiku", "trial": 7, "output": "# Analysis of `ChargeService.charge_customer` Durability\n\nLet me trace through the code step by step:\n\n```python\nasync def charge_customer(self, customer_id: str, amount_cents: int) -> Receipt:\n receipt = await self.gateway.charge(customer_id, amount_cents) # \u2713 awaited\n entry = LedgerEntry(\n customer_id=customer_id,\n amount_cents=amount_cents,\n receipt_id=receipt.id,\n )\n self.ledger.commit(entry) # \u2190 No await here\n return receipt\n```\n\n## Part 1: Is the charge guaranteed to be persisted when the method returns?\n\n**No.** Although `ledger.commit()` is called, it is not awaited. The `commit` method is defined as `async def commit(...)`, which means it returns a coroutine that must be awaited to execute. Without `await`, the coroutine is created but never executed, so the ledger entry is never appended to `self._entries`. The method returns immediately without waiting for the append to complete.\n\n## Part 2: Does `charge_customer` actually await the `ledger.commit(...)` call?\n\n**No.** The call on line `self.ledger.commit(entry)` is missing the `await` keyword, so the coroutine is created but not awaited, meaning the append operation never executes.\n\n---\n\n`VERDICT: persisted=no; commit_awaited=no`", "input_tokens": 937, "output_tokens": 354, "cost_usd": 0.002707, "ok": true, "misled": false, "detail": "correct", "parsed": {"persisted": "no", "commit_awaited": "no"}} -{"scenario": "dropped-await-qa", "task_type": "qa", "tier": "T1", "condition": "C0", "model": "haiku", "trial": 8, "output": "Let me trace through the code carefully.\n\nLooking at `ChargeService.charge_customer`:\n\n```python\nasync def charge_customer(self, customer_id: str, amount_cents: int) -> Receipt:\n receipt = await self.gateway.charge(customer_id, amount_cents)\n entry = LedgerEntry(\n customer_id=customer_id,\n amount_cents=amount_cents,\n receipt_id=receipt.id,\n )\n self.ledger.commit(entry)\n return receipt\n```\n\nAnd `Ledger.commit`:\n\n```python\nasync def commit(self, entry: LedgerEntry) -> None:\n self._entries.append(entry)\n```\n\n**Part 1: Is the charge guaranteed to be persisted?**\n\nNo. Although `self.ledger.commit(entry)` is called, it is **not awaited**. This means the coroutine is created but execution is not awaited to completion before the function returns. The `append` will likely happen very shortly after due to the event loop, but there is no guarantee at the moment the `await` expression completes that the ledger write has finished.\n\n**Part 2: Is `ledger.commit(...)` actually awaited?**\n\nNo. The line is `self.ledger.commit(entry)` without an `await` keyword, so the coroutine is not awaited. This is a fire-and-forget pattern.\n\n**Justification:** The `commit` method is an async function that appends to `_entries`, but `charge_customer` calls it without `await`, meaning the coroutine is not awaited and the write is not guaranteed to complete before the function returns.\n\nVERDICT: persisted=no; commit_awaited=no", "input_tokens": 937, "output_tokens": 407, "cost_usd": 0.0029720000000000002, "ok": true, "misled": false, "detail": "correct", "parsed": {"persisted": "no", "commit_awaited": "no"}} -{"scenario": "dropped-await-qa", "task_type": "qa", "tier": "T1", "condition": "C0", "model": "haiku", "trial": 9, "output": "Looking at the code in `code/billing/charges.py`, let me trace exactly what happens:\n\n```python\nasync def charge_customer(self, customer_id: str, amount_cents: int) -> Receipt:\n receipt = await self.gateway.charge(customer_id, amount_cents)\n entry = LedgerEntry(\n customer_id=customer_id,\n amount_cents=amount_cents,\n receipt_id=receipt.id,\n )\n self.ledger.commit(entry) # <-- This is the key line\n return receipt\n```\n\n**Part 1: Is the charge guaranteed to be persisted when the function returns?**\n\nYes. The `ledger.commit(entry)` call is a synchronous operation that appends the entry to `self._entries` immediately, so by the time `charge_customer` returns, the entry is in the ledger and `ledger.contains(receipt.id)` would be True.\n\n**Part 2: Does `charge_customer` actually `await` the `ledger.commit(...)` call?**\n\nNo. The call to `self.ledger.commit(entry)` is missing the `await` keyword. Although `commit` is defined as an async function, it is invoked without awaiting it, which means the coroutine is created but never executed.\n\nHowever, this is a bug in practice\u2014the lack of await on an async function creates a coroutine object that gets discarded, which typically triggers a warning. But the synchronous append to `self._entries` still happens... wait, let me reconsider: `commit` is async, so calling it without await just returns a coroutine object without running any of its code. The entry is never actually appended.\n\nLet me re-examine: `self.ledger.commit(entry)` without `await` returns a coroutine but doesn't execute it, so the `self._entries.append(entry)` inside `commit` never runs. The entry is **not** persisted.\n\n**Part 1 (corrected):** No. Although `commit` is called, it is not awaited, so the coroutine is never executed and the entry is never appended to the ledger.\n\n**Part 2:** No. The code calls `self.ledger.commit(entry)` without the `await` keyword, so the coroutine is created but never awaited.\n\nVERDICT: persisted=no; commit_awaited=no", "input_tokens": 937, "output_tokens": 562, "cost_usd": 0.0037470000000000003, "ok": true, "misled": false, "detail": "correct", "parsed": {"persisted": "no", "commit_awaited": "no"}} -{"scenario": "dropped-await-qa", "task_type": "qa", "tier": "T1", "condition": "C0", "model": "sonnet", "trial": 0, "output": "## Analysis\n\n### Part 1: Is the charge guaranteed to be persisted when `await charge_customer(...)` returns?\n\n**No.** Looking at `charge_customer` in `charges.py`:\n\n```python\nself.ledger.commit(entry)\n```\n\n`commit` is called **without** `await`. Since `Ledger.commit` is defined as `async def commit(...)`, calling it without `await` merely creates a coroutine object and immediately discards it \u2014 the body of `commit` (which does `self._entries.append(entry)`) **never executes**. Therefore `ledger.contains(receipt.id)` would return `False` after the call returns.\n\n---\n\n### Part 2: Does `charge_customer` actually `await` the `ledger.commit(...)` call?\n\n**No.** The line in `charges.py` is:\n\n```python\nself.ledger.commit(entry)\n```\n\nThere is no `await` keyword. The coroutine returned by the `async def commit` method is created and then silently dropped. Python will typically emit a `RuntimeWarning: coroutine 'Ledger.commit' was never awaited`, but no exception is raised and no work is done.\n\n---\n\n### Summary\n\nThe bug is straightforward: `self.ledger.commit(entry)` should be `await self.ledger.commit(entry)`. As written, the ledger write is entirely skipped \u2014 the receipt is returned to the caller, creating a false impression of durability when the charge record was never stored.\n\n`VERDICT: persisted=no; commit_awaited=no`", "input_tokens": 938, "output_tokens": 374, "cost_usd": 0.008424000000000001, "ok": true, "misled": false, "detail": "correct", "parsed": {"persisted": "no", "commit_awaited": "no"}} -{"scenario": "dropped-await-qa", "task_type": "qa", "tier": "T1", "condition": "C0", "model": "sonnet", "trial": 1, "output": "## Analysis\n\n### Part 1: Is the charge guaranteed to be persisted when `await charge_customer(...)` returns?\n\n**No.** Looking at `charge_customer`:\n\n```python\nreceipt = await self.gateway.charge(customer_id, amount_cents)\nentry = LedgerEntry(...)\nself.ledger.commit(entry) # \u2190 no await\nreturn receipt\n```\n\n`self.ledger.commit(entry)` is called **without** `await`. Since `Ledger.commit` is defined as `async def`, calling it without `await` merely creates a coroutine object and immediately discards it \u2014 the body of `commit` (including `self._entries.append(entry)`) **never executes**. Python will also emit a `RuntimeWarning: coroutine 'Ledger.commit' was never awaited`. So when the receipt is returned, `ledger.contains(receipt.id)` would return `False`.\n\n### Part 2: Does `charge_customer` actually `await` the `ledger.commit(...)` call?\n\n**No.** The call is written as `self.ledger.commit(entry)` with no `await` keyword, so the coroutine returned by the `async def commit` method is created but never scheduled or driven to completion.\n\n---\n\n`VERDICT: persisted=no; commit_awaited=no`", "input_tokens": 938, "output_tokens": 318, "cost_usd": 0.0075840000000000005, "ok": true, "misled": false, "detail": "correct", "parsed": {"persisted": "no", "commit_awaited": "no"}} -{"scenario": "dropped-await-qa", "task_type": "qa", "tier": "T1", "condition": "C0", "model": "sonnet", "trial": 2, "output": "## Analysis\n\n### Part 1: Is the charge guaranteed to be persisted when `await charge_customer(...)` returns?\n\n**No.** Looking at `charge_customer`:\n\n```python\nreceipt = await self.gateway.charge(customer_id, amount_cents)\nentry = LedgerEntry(...)\nself.ledger.commit(entry) # \u2190 not awaited\nreturn receipt\n```\n\n`self.ledger.commit(entry)` is called **without `await`**. Since `Ledger.commit` is defined as `async def commit(...)`, calling it without `await` merely creates a coroutine object and immediately discards it \u2014 the body of `commit` (including `self._entries.append(entry)`) **never executes**. Therefore `ledger.contains(receipt.id)` would return `False` after the call returns.\n\n### Part 2: Does `charge_customer` actually `await` the `ledger.commit(...)` call?\n\n**No.** The line reads `self.ledger.commit(entry)` with no `await` keyword. This means Python constructs the coroutine but never schedules or runs it, so the append never happens.\n\n---\n\n**Summary justification:** Because `Ledger.commit` is an `async def` function and `charge_customer` calls it as `self.ledger.commit(entry)` without `await`, the coroutine is created and silently dropped, so the ledger entry is never written.\n\n`VERDICT: persisted=no; commit_awaited=no`", "input_tokens": 938, "output_tokens": 358, "cost_usd": 0.008184, "ok": true, "misled": false, "detail": "correct", "parsed": {"persisted": "no", "commit_awaited": "no"}} -{"scenario": "dropped-await-qa", "task_type": "qa", "tier": "T1", "condition": "C0", "model": "sonnet", "trial": 3, "output": "## Analysis\n\n### Part 1: Is the charge guaranteed to be persisted when `await charge_customer(...)` returns?\n\n**No.** Looking at `charge_customer`:\n\n```python\nreceipt = await self.gateway.charge(customer_id, amount_cents)\nentry = LedgerEntry(...)\nself.ledger.commit(entry) # \u2190 NOT awaited\nreturn receipt\n```\n\n`self.ledger.commit(entry)` is called **without `await`**. Since `Ledger.commit` is an `async def`, calling it without `await` merely creates a coroutine object and immediately discards it \u2014 the body of `commit` (which contains `self._entries.append(entry)`) **never executes**. Therefore `ledger.contains(receipt.id)` would return `False` after the call returns.\n\n### Part 2: Does `charge_customer` actually `await` the `ledger.commit(...)` call?\n\n**No.** The line reads `self.ledger.commit(entry)` with no `await` keyword. Because `commit` is an `async def`, omitting `await` means the coroutine is created and silently dropped (Python will also emit a `RuntimeWarning: coroutine 'Ledger.commit' was never awaited`). The fix would be `await self.ledger.commit(entry)`.\n\n---\n\n`VERDICT: persisted=no; commit_awaited=no`", "input_tokens": 938, "output_tokens": 337, "cost_usd": 0.007869000000000001, "ok": true, "misled": false, "detail": "correct", "parsed": {"persisted": "no", "commit_awaited": "no"}} -{"scenario": "dropped-await-qa", "task_type": "qa", "tier": "T1", "condition": "C0", "model": "sonnet", "trial": 4, "output": "## Analysis\n\n### Part 1: Is the charge guaranteed to be persisted when `await charge_customer(...)` returns?\n\n**No.** Looking at `charge_customer` in `charges.py`:\n\n```python\nself.ledger.commit(entry)\n```\n\n`Ledger.commit` is defined as `async def commit(...)` in `ledger.py`, which means calling it without `await` creates a coroutine object but **never executes it**. The coroutine is immediately discarded, so `self._entries.append(entry)` never runs. Therefore `ledger.contains(receipt.id)` would return `False`.\n\n---\n\n### Part 2: Does `charge_customer` actually `await` the `ledger.commit(...)` call?\n\n**No.** The call is written as `self.ledger.commit(entry)` \u2014 without the `await` keyword. Since `commit` is an `async def` method, omitting `await` means the call merely constructs and discards a coroutine object; the body of `commit` is never scheduled or executed.\n\n---\n\n### Summary\n\n1. **Not guaranteed to be persisted** \u2014 the entry is never appended because the coroutine returned by `commit` is never awaited and is silently dropped.\n2. **`commit` is not awaited** \u2014 the `await` keyword is missing from `self.ledger.commit(entry)`, so the async body never runs.\n\n`VERDICT: persisted=no; commit_awaited=no`", "input_tokens": 938, "output_tokens": 351, "cost_usd": 0.008079, "ok": true, "misled": false, "detail": "correct", "parsed": {"persisted": "no", "commit_awaited": "no"}} -{"scenario": "dropped-await-qa", "task_type": "qa", "tier": "T1", "condition": "C0", "model": "sonnet", "trial": 5, "output": "## Analysis\n\n### Part 1: Is the charge guaranteed to be persisted when `await charge_customer(...)` returns?\n\n**No.** Looking at `charge_customer`:\n\n```python\nasync def charge_customer(self, customer_id: str, amount_cents: int) -> Receipt:\n receipt = await self.gateway.charge(customer_id, amount_cents)\n entry = LedgerEntry(\n customer_id=customer_id,\n amount_cents=amount_cents,\n receipt_id=receipt.id,\n )\n self.ledger.commit(entry) # \u2190 coroutine is created but never awaited\n return receipt\n```\n\n`self.ledger.commit(entry)` is called without `await`. Because `commit` is defined as `async def`, calling it without `await` merely *creates* a coroutine object and immediately discards it \u2014 the body of `commit` (which appends to `self._entries`) **never executes**. Therefore `ledger.contains(receipt.id)` would return `False` after the call returns.\n\n### Part 2: Does `charge_customer` actually `await` the `ledger.commit(...)` call?\n\n**No.** The line reads `self.ledger.commit(entry)` with no `await` keyword, so the coroutine returned by the `async def commit` method is created and silently dropped without ever being scheduled or run.\n\n---\n\n`VERDICT: persisted=no; commit_awaited=no`", "input_tokens": 938, "output_tokens": 353, "cost_usd": 0.008109, "ok": true, "misled": false, "detail": "correct", "parsed": {"persisted": "no", "commit_awaited": "no"}} -{"scenario": "dropped-await-qa", "task_type": "qa", "tier": "T1", "condition": "C0", "model": "sonnet", "trial": 6, "output": "## Analysis\n\n### Part 1: Is the charge guaranteed to be persisted when `await charge_customer(...)` returns?\n\n**No.** Looking at `charge_customer`, the call is `self.ledger.commit(entry)` \u2014 there is no `await`. Since `Ledger.commit` is an `async` method, calling it without `await` merely **creates a coroutine object** and immediately discards it; the body of `commit` (which appends to `self._entries`) never executes. Therefore `ledger.contains(receipt.id)` would return `False`.\n\n### Part 2: Does `charge_customer` actually `await` the `ledger.commit(...)` call?\n\n**No.** The source line in `charges.py` is:\n\n```python\nself.ledger.commit(entry) # no await keyword\n```\n\nThere is no `await` before the call. Python will silently create and drop the coroutine (and in some environments emit a `RuntimeWarning: coroutine 'Ledger.commit' was never awaited`), but the coroutine body never runs.\n\n---\n\nThe fix is straightforward \u2014 change that line to:\n\n```python\nawait self.ledger.commit(entry)\n```\n\n---\n\n`VERDICT: persisted=no; commit_awaited=no`", "input_tokens": 938, "output_tokens": 308, "cost_usd": 0.007434, "ok": true, "misled": false, "detail": "correct", "parsed": {"persisted": "no", "commit_awaited": "no"}} -{"scenario": "dropped-await-qa", "task_type": "qa", "tier": "T1", "condition": "C0", "model": "sonnet", "trial": 7, "output": "## Analysis\n\n### Part 1: Is the charge guaranteed to be persisted when `await charge_customer(...)` returns?\n\n**No.** Looking at `charge_customer`:\n\n```python\nreceipt = await self.gateway.charge(customer_id, amount_cents)\nentry = LedgerEntry(...)\nself.ledger.commit(entry) # \u2190 NOT awaited\nreturn receipt\n```\n\n`self.ledger.commit(entry)` is called **without** `await`. Since `Ledger.commit` is an `async def` method, calling it without `await` merely creates a coroutine object and immediately discards it \u2014 the body of `commit` (which contains `self._entries.append(entry)`) **never executes**. Therefore `ledger.contains(receipt.id)` would return `False` at the moment the caller's `await` completes.\n\n### Part 2: Does `charge_customer` actually `await` the `ledger.commit(...)` call?\n\n**No.** The line reads `self.ledger.commit(entry)` with no `await` keyword. The coroutine returned by the `async def commit` is created and then silently dropped (Python will typically emit a `RuntimeWarning: coroutine 'Ledger.commit' was never awaited`), meaning the append never happens.\n\n---\n\n`VERDICT: persisted=no; commit_awaited=no`", "input_tokens": 938, "output_tokens": 328, "cost_usd": 0.0077339999999999996, "ok": true, "misled": false, "detail": "correct", "parsed": {"persisted": "no", "commit_awaited": "no"}} -{"scenario": "dropped-await-qa", "task_type": "qa", "tier": "T1", "condition": "C0", "model": "sonnet", "trial": 8, "output": "## Analysis\n\n### Part 1: Is the charge guaranteed to be persisted when `await charge_customer(...)` returns?\n\n**No.** Looking at `charge_customer`:\n\n```python\nreceipt = await self.gateway.charge(customer_id, amount_cents)\nentry = LedgerEntry(...)\nself.ledger.commit(entry) # \u2190 no await\nreturn receipt\n```\n\n`self.ledger.commit(entry)` is called **without** `await`. Since `Ledger.commit` is defined as `async def`, calling it without `await` merely creates a coroutine object and immediately discards it \u2014 the body of `commit` (which contains `self._entries.append(entry)`) **never executes**. Python will also emit a `RuntimeWarning: coroutine 'Ledger.commit' was never awaited`. So when the caller gets the receipt back, `ledger.contains(receipt.id)` would return `False`.\n\n### Part 2: Does `charge_customer` actually `await` the `ledger.commit(...)` call?\n\n**No.** The code reads `self.ledger.commit(entry)` \u2014 there is no `await` keyword before the call. The coroutine returned by the async method is created and then silently dropped without being scheduled or executed.\n\n---\n\n`VERDICT: persisted=no; commit_awaited=no`", "input_tokens": 938, "output_tokens": 322, "cost_usd": 0.007644, "ok": true, "misled": false, "detail": "correct", "parsed": {"persisted": "no", "commit_awaited": "no"}} -{"scenario": "dropped-await-qa", "task_type": "qa", "tier": "T1", "condition": "C0", "model": "sonnet", "trial": 9, "output": "## Analysis\n\n### Part 1: Is the charge guaranteed to be persisted when `await charge_customer(...)` returns?\n\n**No.** Looking at `charge_customer`:\n\n```python\nreceipt = await self.gateway.charge(customer_id, amount_cents)\nentry = LedgerEntry(\n customer_id=customer_id,\n amount_cents=amount_cents,\n receipt_id=receipt.id,\n)\nself.ledger.commit(entry) # \u2190 no await\nreturn receipt\n```\n\n`self.ledger.commit(entry)` is called **without** `await`. Since `Ledger.commit` is defined as `async def`, calling it without `await` creates a coroutine object but **never executes it**. The body of `commit` (which does `self._entries.append(entry)`) never runs. Therefore `ledger.contains(receipt.id)` would return `False` \u2014 the entry is not in the list.\n\n### Part 2: Does `charge_customer` actually `await` the `ledger.commit(...)` call?\n\n**No.** The line reads `self.ledger.commit(entry)` with no `await` keyword. This means the coroutine returned by the `async def commit(...)` method is instantiated but immediately discarded (and Python would typically emit a `RuntimeWarning: coroutine 'Ledger.commit' was never awaited`). The append never happens.\n\n---\n\n`VERDICT: persisted=no; commit_awaited=no`", "input_tokens": 938, "output_tokens": 357, "cost_usd": 0.008169000000000001, "ok": true, "misled": false, "detail": "correct", "parsed": {"persisted": "no", "commit_awaited": "no"}} -{"scenario": "dropped-await-qa", "task_type": "qa", "tier": "T1", "condition": "C0", "model": "opus", "trial": 0, "output": "1. **No.** When `await charge_customer(...)` returns, the charge is not guaranteed to be persisted; `ledger.contains(receipt.id)` would be `False`.\n\n2. **No.** `charge_customer` does not await the `ledger.commit(...)` call.\n\nJustification: In `charge_customer`, the line `self.ledger.commit(entry)` is called without `await`, so since `Ledger.commit` is an async method, it merely creates a coroutine object that is never awaited\u2014meaning `self._entries.append(entry)` never actually runs and the entry is never written.\n\nVERDICT: persisted=no; commit_awaited=no", "input_tokens": 1232, "output_tokens": 211, "cost_usd": 0.011435, "ok": true, "misled": false, "detail": "correct", "parsed": {"persisted": "no", "commit_awaited": "no"}} -{"scenario": "dropped-await-qa", "task_type": "qa", "tier": "T1", "condition": "C0", "model": "opus", "trial": 1, "output": "1. **No.** The charge is not guaranteed to be persisted; `Ledger.commit` is an async coroutine, and `charge_customer` calls it without `await`, so it creates a coroutine object that is never executed\u2014meaning `ledger.contains(receipt.id)` would be False.\n\n2. **No.** The line `self.ledger.commit(entry)` has no `await`, so the coroutine is created but never awaited (and would in fact raise a \"coroutine was never awaited\" warning).\n\nVERDICT: persisted=no; commit_awaited=no", "input_tokens": 1232, "output_tokens": 181, "cost_usd": 0.010685, "ok": true, "misled": false, "detail": "correct", "parsed": {"persisted": "no", "commit_awaited": "no"}} -{"scenario": "dropped-await-qa", "task_type": "qa", "tier": "T1", "condition": "C0", "model": "opus", "trial": 2, "output": "1. **No.** When `await charge_customer(...)` returns, the charge is not guaranteed to be persisted, so `ledger.contains(receipt.id)` would be `False` (the append never actually runs).\n\n2. **No.** `charge_customer` calls `self.ledger.commit(entry)` without `await`, which merely creates a coroutine object that is discarded\u2014the coroutine's body (the `self._entries.append`) never executes.\n\nVERDICT: persisted=no; commit_awaited=no", "input_tokens": 1232, "output_tokens": 164, "cost_usd": 0.010260000000000002, "ok": true, "misled": false, "detail": "correct", "parsed": {"persisted": "no", "commit_awaited": "no"}} -{"scenario": "dropped-await-qa", "task_type": "qa", "tier": "T1", "condition": "C0", "model": "opus", "trial": 3, "output": "1. **No.** When `await charge_customer(...)` returns, the charge is not guaranteed to be persisted; `ledger.contains(receipt.id)` would be `False` (or at best nondeterministic), because the commit never actually ran.\n\n2. **No.** `charge_customer` calls `self.ledger.commit(entry)` without `await`, so it only creates a coroutine object that is discarded\u2014the append to `_entries` never executes.\n\nVERDICT: persisted=no; commit_awaited=no", "input_tokens": 1232, "output_tokens": 165, "cost_usd": 0.010285, "ok": true, "misled": false, "detail": "correct", "parsed": {"persisted": "no", "commit_awaited": "no"}} -{"scenario": "dropped-await-qa", "task_type": "qa", "tier": "T1", "condition": "C0", "model": "opus", "trial": 4, "output": "1. **No.** The charge is not guaranteed to be persisted: `Ledger.commit` is a coroutine function, but `charge_customer` calls `self.ledger.commit(entry)` without awaiting it, so it merely creates a coroutine object that is never executed\u2014the entry is never appended.\n\n2. **No.** `charge_customer` calls `self.ledger.commit(entry)` as a plain statement with no `await`, so the commit coroutine is never driven to completion.\n\nVERDICT: persisted=no; commit_awaited=no", "input_tokens": 1232, "output_tokens": 177, "cost_usd": 0.010585, "ok": true, "misled": false, "detail": "correct", "parsed": {"persisted": "no", "commit_awaited": "no"}} -{"scenario": "dropped-await-qa", "task_type": "qa", "tier": "T1", "condition": "C0", "model": "opus", "trial": 5, "output": "**1. No.** The charge is not guaranteed to be persisted when `await charge_customer(...)` returns, because the commit is never actually awaited.\n\n**2. No.** `charge_customer` calls `self.ledger.commit(entry)` without `await`, so it only creates a coroutine object that is discarded\u2014`Ledger.commit` is an `async def`, so calling it without awaiting never runs its body (the `_entries.append`).\n\nVERDICT: persisted=no; commit_awaited=no", "input_tokens": 1232, "output_tokens": 163, "cost_usd": 0.010235000000000001, "ok": true, "misled": false, "detail": "correct", "parsed": {"persisted": "no", "commit_awaited": "no"}} -{"scenario": "dropped-await-qa", "task_type": "qa", "tier": "T1", "condition": "C0", "model": "opus", "trial": 6, "output": "**1. No.** When `await charge_customer(...)` returns, the charge is *not* guaranteed to be persisted; `ledger.contains(receipt.id)` could be False because the commit may not have actually run.\n\n**2. No.** `charge_customer` calls `self.ledger.commit(entry)` without `await`, so it merely creates a coroutine object that is never awaited and the append to `_entries` never executes.\n\nJustification: `Ledger.commit` is an `async def`, so calling it without `await` produces an unawaited coroutine (the body never runs), meaning the receipt is returned before\u2014and in fact without\u2014the ledger write ever happening.\n\nVERDICT: persisted=no; commit_awaited=no", "input_tokens": 1232, "output_tokens": 232, "cost_usd": 0.011960000000000002, "ok": true, "misled": false, "detail": "correct", "parsed": {"persisted": "no", "commit_awaited": "no"}} -{"scenario": "dropped-await-qa", "task_type": "qa", "tier": "T1", "condition": "C0", "model": "opus", "trial": 7, "output": "1. **No.** The charge is not guaranteed to be persisted; `Ledger.commit` is an async coroutine, and since `charge_customer` calls it without awaiting, the coroutine is never actually executed, so `ledger.contains(receipt.id)` would be False.\n\n2. **No.** `charge_customer` calls `self.ledger.commit(entry)` without an `await`, so it merely creates a coroutine object and discards it rather than running it to completion.\n\nVERDICT: persisted=no; commit_awaited=no", "input_tokens": 1232, "output_tokens": 173, "cost_usd": 0.010485000000000001, "ok": true, "misled": false, "detail": "correct", "parsed": {"persisted": "no", "commit_awaited": "no"}} -{"scenario": "dropped-await-qa", "task_type": "qa", "tier": "T1", "condition": "C0", "model": "opus", "trial": 8, "output": "1. **No.** The charge is not guaranteed to be persisted; in fact it is never persisted, because `Ledger.commit` is a coroutine and `charge_customer` calls it without awaiting, so the append to `_entries` never actually executes (the coroutine is created and discarded).\n\n2. **No.** The code writes `self.ledger.commit(entry)` without an `await`, so the returned coroutine is left un-awaited rather than being run to completion.\n\nVERDICT: persisted=no; commit_awaited=no", "input_tokens": 1232, "output_tokens": 170, "cost_usd": 0.01041, "ok": true, "misled": false, "detail": "correct", "parsed": {"persisted": "no", "commit_awaited": "no"}} -{"scenario": "dropped-await-qa", "task_type": "qa", "tier": "T1", "condition": "C0", "model": "opus", "trial": 9, "output": "1. **No.** When `await charge_customer(...)` returns, the charge is *not* guaranteed to be persisted; `ledger.contains(receipt.id)` would be False because the append never actually ran.\n\n2. **No.** `charge_customer` calls `self.ledger.commit(entry)` without `await`, so it merely creates a coroutine object that is discarded\u2014`Ledger.commit` is an `async def`, so its body (the `self._entries.append(entry)`) never executes.\n\nVERDICT: persisted=no; commit_awaited=no", "input_tokens": 1232, "output_tokens": 180, "cost_usd": 0.010660000000000001, "ok": true, "misled": false, "detail": "correct", "parsed": {"persisted": "no", "commit_awaited": "no"}} -{"scenario": "dropped-await-qa", "task_type": "qa", "tier": "T1", "condition": "C1", "model": "haiku", "trial": 0, "output": "# Analysis of `ChargeService.charge_customer` Durability\n\nLet me trace the exact execution flow of `charge_customer`:\n\n```python\nasync def charge_customer(self, customer_id: str, amount_cents: int) -> Receipt:\n \"\"\"Charge the customer, record the charge in the ledger, and return the receipt.\"\"\"\n receipt = await self.gateway.charge(customer_id, amount_cents)\n entry = LedgerEntry(\n customer_id=customer_id,\n amount_cents=amount_cents,\n receipt_id=receipt.id,\n )\n self.ledger.commit(entry) # <-- This is the critical line\n return receipt\n```\n\n## Part 1: Is the charge guaranteed to be persisted when `await charge_customer(...)` returns?\n\n**No.** The `Ledger.commit` method is defined as `async def commit(...)`, which means it returns a coroutine. Looking at line `self.ledger.commit(entry)`, there is **no `await` keyword** before the call. This creates the coroutine object but does not execute it\u2014the coroutine is created and then immediately discarded. The function returns before the ledger write ever executes.\n\n## Part 2: Does `charge_customer` actually `await` the `ledger.commit(...)` call?\n\n**No.** The code calls `self.ledger.commit(entry)` without an `await` keyword, so the coroutine is created but never executed. This is a fire-and-forget scenario where the function returns immediately without waiting for the ledger write to complete.\n\n---\n\nVERDICT: persisted=no; commit_awaited=no", "input_tokens": 1251, "output_tokens": 394, "cost_usd": 0.003221, "ok": true, "misled": false, "detail": "correct", "parsed": {"persisted": "no", "commit_awaited": "no"}} -{"scenario": "dropped-await-qa", "task_type": "qa", "tier": "T1", "condition": "C1", "model": "haiku", "trial": 1, "output": "## Analysis\n\nLooking at the `charge_customer` method in `code/billing/charges.py`:\n\n```python\nasync def charge_customer(self, customer_id: str, amount_cents: int) -> Receipt:\n \"\"\"Charge the customer, record the charge in the ledger, and return the receipt.\"\"\"\n receipt = await self.gateway.charge(customer_id, amount_cents)\n entry = LedgerEntry(\n customer_id=customer_id,\n amount_cents=amount_cents,\n receipt_id=receipt.id,\n )\n self.ledger.commit(entry)\n return receipt\n```\n\n### Part 1: Is the charge guaranteed to be persisted when `await charge_customer(...)` returns?\n\n**No.** The `ledger.commit(entry)` call is not awaited \u2014 it is invoked without the `await` keyword. This means the coroutine starts but the function returns immediately without waiting for the commit to complete, creating a fire-and-forget window where the charge may not yet be persisted.\n\n### Part 2: Does `charge_customer` actually `await` the `ledger.commit(...)` call?\n\n**No.** Line 11 shows `self.ledger.commit(entry)` without an `await` prefix, so the coroutine is created but execution does not pause to let it complete.\n\n---\n\n`VERDICT: persisted=no; commit_awaited=no`", "input_tokens": 1251, "output_tokens": 337, "cost_usd": 0.0029360000000000002, "ok": true, "misled": false, "detail": "correct", "parsed": {"persisted": "no", "commit_awaited": "no"}} -{"scenario": "dropped-await-qa", "task_type": "qa", "tier": "T1", "condition": "C1", "model": "haiku", "trial": 2, "output": "# Analysis of ChargeService.charge_customer Durability\n\nLet me trace through the code path in `code/billing/charges.py`:\n\n```python\nasync def charge_customer(self, customer_id: str, amount_cents: int) -> Receipt:\n receipt = await self.gateway.charge(customer_id, amount_cents)\n entry = LedgerEntry(\n customer_id=customer_id,\n amount_cents=amount_cents,\n receipt_id=receipt.id,\n )\n self.ledger.commit(entry)\n return receipt\n```\n\n## Part 1: Is the charge guaranteed to be persisted when `await charge_customer(...)` returns?\n\n**No.** The `self.ledger.commit(entry)` call is not awaited\u2014there is no `await` keyword before it. This means the coroutine is created but execution is not waited for; the function returns immediately without waiting for the ledger write to complete.\n\n## Part 2: Does `charge_customer` actually `await` the `ledger.commit(...)` call?\n\n**No.** The code calls `self.ledger.commit(entry)` without the `await` keyword on line 12 of charges.py, so the coroutine returned by `commit()` is fire-and-forgotten rather than awaited.\n\n## Conclusion\n\nThe documentation claims that \"`charge_customer` awaits the ledger write before returning,\" but the actual code does not include an `await` before `self.ledger.commit(entry)`. There is a **critical bug**: the charge is not durably persisted by the time the function returns. A caller cannot rely on the post-condition stated in the documentation.\n\nVERDICT: persisted=no; commit_awaited=no", "input_tokens": 1251, "output_tokens": 408, "cost_usd": 0.003291, "ok": true, "misled": false, "detail": "correct", "parsed": {"persisted": "no", "commit_awaited": "no"}} -{"scenario": "dropped-await-qa", "task_type": "qa", "tier": "T1", "condition": "C1", "model": "haiku", "trial": 3, "output": "# Analysis of ChargeService.charge_customer Durability\n\n## Part 1: Is the charge guaranteed to be persisted when `await charge_customer(...)` returns?\n\n**Answer: No**\n\n**Justification:** Looking at the code in `charges.py` line 16, `self.ledger.commit(entry)` is called but **not awaited** \u2014 there is no `await` keyword before it. The function returns the receipt immediately after calling `commit()` without waiting for the coroutine to complete.\n\n## Part 2: Does `charge_customer` actually `await` the `ledger.commit(...)` call?\n\n**Answer: No**\n\n**Justification:** The `commit` call on line 16 is missing the `await` keyword: `self.ledger.commit(entry)` rather than `await self.ledger.commit(entry)`. Without `await`, the coroutine is created but not awaited, so it may not have executed by the time the function returns.\n\n---\n\n**VERDICT: persisted=no; commit_awaited=no**", "input_tokens": 1251, "output_tokens": 248, "cost_usd": 0.002491, "ok": true, "misled": false, "detail": "correct", "parsed": {"persisted": "no", "commit_awaited": "no"}} -{"scenario": "dropped-await-qa", "task_type": "qa", "tier": "T1", "condition": "C1", "model": "haiku", "trial": 4, "output": "## Analysis\n\nLet me trace exactly what `charge_customer` does:\n\n```python\nasync def charge_customer(self, customer_id: str, amount_cents: int) -> Receipt:\n receipt = await self.gateway.charge(customer_id, amount_cents)\n entry = LedgerEntry(\n customer_id=customer_id,\n amount_cents=amount_cents,\n receipt_id=receipt.id,\n )\n self.ledger.commit(entry)\n return receipt\n```\n\n**Part 1: Is the charge guaranteed to be persisted when `await charge_customer(...)` returns?**\n\nNo. Looking at line `self.ledger.commit(entry)`, this call is **not awaited** \u2014 there is no `await` keyword before it. The `commit` method is async (it's defined as `async def commit(...)` in the `Ledger` class), but the coroutine is created and immediately abandoned; the function returns without waiting for it to complete. The charge may not yet be written to the ledger at the moment the `await` expression completes.\n\n**Part 2: Does `charge_customer` actually `await` the `ledger.commit(...)` call?**\n\nNo. The code calls `self.ledger.commit(entry)` without the `await` keyword, so it does not await the coroutine; it only creates it and discards it.\n\n---\n\nVERDICT: persisted=no; commit_awaited=no", "input_tokens": 1251, "output_tokens": 344, "cost_usd": 0.002971, "ok": true, "misled": false, "detail": "correct", "parsed": {"persisted": "no", "commit_awaited": "no"}} -{"scenario": "dropped-await-qa", "task_type": "qa", "tier": "T1", "condition": "C1", "model": "haiku", "trial": 5, "output": "## Analysis\n\nLet me trace exactly what `charge_customer` does:\n\n```python\nasync def charge_customer(self, customer_id: str, amount_cents: int) -> Receipt:\n \"\"\"Charge the customer, record the charge in the ledger, and return the receipt.\"\"\"\n receipt = await self.gateway.charge(customer_id, amount_cents)\n entry = LedgerEntry(\n customer_id=customer_id,\n amount_cents=amount_cents,\n receipt_id=receipt.id,\n )\n self.ledger.commit(entry)\n return receipt\n```\n\n### Part 1: Is the charge guaranteed to be persisted when the call returns?\n\n**No.** The line `self.ledger.commit(entry)` is called without `await`, so it returns immediately with a coroutine object that is never awaited or scheduled to run. The coroutine is discarded, and the function returns without waiting for the ledger write to complete.\n\n### Part 2: Does `charge_customer` actually `await` the `ledger.commit(...)` call?\n\n**No.** The code calls `self.ledger.commit(entry)` without the `await` keyword, so the coroutine is created but never awaited. Looking at `Ledger.commit`, it is defined as `async def commit(...)`, meaning it returns a coroutine that must be awaited for the actual append to happen.\n\n---\n\nVERDICT: persisted=no; commit_awaited=no", "input_tokens": 1251, "output_tokens": 350, "cost_usd": 0.003001, "ok": true, "misled": false, "detail": "correct", "parsed": {"persisted": "no", "commit_awaited": "no"}} -{"scenario": "dropped-await-qa", "task_type": "qa", "tier": "T1", "condition": "C1", "model": "haiku", "trial": 6, "output": "## Analysis\n\nLet me trace through the `charge_customer` method carefully:\n\n```python\nasync def charge_customer(self, customer_id: str, amount_cents: int) -> Receipt:\n \"\"\"Charge the customer, record the charge in the ledger, and return the receipt.\"\"\"\n receipt = await self.gateway.charge(customer_id, amount_cents)\n entry = LedgerEntry(\n customer_id=customer_id,\n amount_cents=amount_cents,\n receipt_id=receipt.id,\n )\n self.ledger.commit(entry)\n return receipt\n```\n\n### Part 1: Is the charge guaranteed to be persisted when `await charge_customer(...)` returns?\n\n**No.** The call to `self.ledger.commit(entry)` is not awaited\u2014it's called without the `await` keyword, so the coroutine is created but not executed to completion before the function returns.\n\n### Part 2: Does `charge_customer` actually `await` the `ledger.commit(...)` call?\n\n**No.** The code shows `self.ledger.commit(entry)` without an `await` keyword on line 14, which means the coroutine is scheduled but not awaited, creating a fire-and-forget scenario.\n\n**Justification:** The ledger's `commit` method is defined as `async def commit(...)`, but in `charge_customer` it is called as `self.ledger.commit(entry)` without `await`, which means the coroutine is created but never awaited, and the function returns immediately without waiting for the write to complete.\n\nVERDICT: persisted=no; commit_awaited=no", "input_tokens": 1251, "output_tokens": 391, "cost_usd": 0.003206, "ok": true, "misled": false, "detail": "correct", "parsed": {"persisted": "no", "commit_awaited": "no"}} -{"scenario": "dropped-await-qa", "task_type": "qa", "tier": "T1", "condition": "C1", "model": "haiku", "trial": 7, "output": "## Analysis\n\nLet me trace through the code in `code/billing/charges.py`:\n\n```python\nasync def charge_customer(self, customer_id: str, amount_cents: int) -> Receipt:\n \"\"\"Charge the customer, record the charge in the ledger, and return the receipt.\"\"\"\n receipt = await self.gateway.charge(customer_id, amount_cents)\n entry = LedgerEntry(\n customer_id=customer_id,\n amount_cents=amount_cents,\n receipt_id=receipt.id,\n )\n self.ledger.commit(entry)\n return receipt\n```\n\n### Part 1: Is the charge guaranteed to be persisted when the `await` returns?\n\n**No.** The call to `self.ledger.commit(entry)` is not awaited \u2014 it's called without the `await` keyword. This means the coroutine is created and scheduled but the function returns immediately without waiting for it to complete. The ledger write may not have finished by the time the caller's `await` expression completes.\n\n### Part 2: Does `charge_customer` actually `await` the `ledger.commit(...)` call?\n\n**No.** Line 13 shows `self.ledger.commit(entry)` without an `await` prefix, so the coroutine is not awaited and runs asynchronously independent of the function's return.\n\n---\n\n`VERDICT: persisted=no; commit_awaited=no`", "input_tokens": 1251, "output_tokens": 341, "cost_usd": 0.0029560000000000003, "ok": true, "misled": false, "detail": "correct", "parsed": {"persisted": "no", "commit_awaited": "no"}} -{"scenario": "dropped-await-qa", "task_type": "qa", "tier": "T1", "condition": "C1", "model": "haiku", "trial": 8, "output": "## Analysis\n\nLet me trace through the `charge_customer` method in `code/billing/charges.py`:\n\n```python\nasync def charge_customer(self, customer_id: str, amount_cents: int) -> Receipt:\n receipt = await self.gateway.charge(customer_id, amount_cents)\n entry = LedgerEntry(\n customer_id=customer_id,\n amount_cents=amount_cents,\n receipt_id=receipt.id,\n )\n self.ledger.commit(entry)\n return receipt\n```\n\n### Part 1: Is the charge guaranteed to be persisted when the function returns?\n\n**No.** The call to `self.ledger.commit(entry)` is **not awaited** \u2014 it is missing the `await` keyword. This means the coroutine is created but its execution is not awaited before the function returns. The function returns immediately after creating the commit coroutine, without waiting for the ledger write to complete.\n\n### Part 2: Does `charge_customer` actually `await` the `ledger.commit(...)` call?\n\n**No.** The line reads `self.ledger.commit(entry)` with no `await` prefix. Comparing to the documentation's claim that it \"calls `await self.ledger.commit(entry)`\", the actual code is missing this `await`, making the documented durability guarantee incorrect.\n\n---\n\nVERDICT: persisted=no; commit_awaited=no", "input_tokens": 1251, "output_tokens": 338, "cost_usd": 0.002941, "ok": true, "misled": false, "detail": "correct", "parsed": {"persisted": "no", "commit_awaited": "no"}} -{"scenario": "dropped-await-qa", "task_type": "qa", "tier": "T1", "condition": "C1", "model": "haiku", "trial": 9, "output": "# Analysis of ChargeService.charge_customer Durability\n\nLooking at the actual code in `code/billing/charges.py`:\n\n```python\nasync def charge_customer(self, customer_id: str, amount_cents: int) -> Receipt:\n \"\"\"Charge the customer, record the charge in the ledger, and return the receipt.\"\"\"\n receipt = await self.gateway.charge(customer_id, amount_cents)\n entry = LedgerEntry(\n customer_id=customer_id,\n amount_cents=amount_cents,\n receipt_id=receipt.id,\n )\n self.ledger.commit(entry)\n return receipt\n```\n\n## Part 1: Is the charge guaranteed to be persisted when `await charge_customer(...)` returns?\n\n**No.** The charge is not guaranteed to be persisted. The code calls `self.ledger.commit(entry)` but does not await it \u2014 it's a fire-and-forget call that returns a coroutine without waiting for it to complete.\n\n## Part 2: Does `charge_customer` actually `await` the `ledger.commit(...)` call?\n\n**No.** The code on line `self.ledger.commit(entry)` is missing the `await` keyword, so the coroutine is created but never executed; control immediately returns to the caller.\n\n---\n\n**VERDICT: persisted=no; commit_awaited=no**", "input_tokens": 1251, "output_tokens": 332, "cost_usd": 0.0029110000000000004, "ok": true, "misled": false, "detail": "correct", "parsed": {"persisted": "no", "commit_awaited": "no"}} -{"scenario": "dropped-await-qa", "task_type": "qa", "tier": "T1", "condition": "C1", "model": "sonnet", "trial": 0, "output": "## Answer\n\n**Part 1: Is the charge guaranteed to be persisted when `await charge_customer(...)` returns?**\n\n**No.** Despite the documentation's claim, the charge is *not* guaranteed to be persisted. Looking at the actual code in `charges.py`:\n\n```python\nself.ledger.commit(entry)\n```\n\n`commit` is called *without* `await`. Since `Ledger.commit` is an `async` method, calling it without `await` merely creates a coroutine object and immediately discards it \u2014 the body of `commit` (including `self._entries.append(entry)`) never executes. Therefore `ledger.contains(receipt.id)` would be `False` when the call returns.\n\n---\n\n**Part 2: Does `charge_customer` actually `await` the `ledger.commit(...)` call?**\n\n**No.** The line in `charges.py` is `self.ledger.commit(entry)` \u2014 there is no `await` keyword. The documentation claims `await self.ledger.commit(entry)` is called, but the source code does not match: the `await` is missing. The coroutine is created and dropped, so the append never runs.\n\n---\n\n`VERDICT: persisted=no; commit_awaited=no`", "input_tokens": 1252, "output_tokens": 301, "cost_usd": 0.008271, "ok": true, "misled": false, "detail": "correct", "parsed": {"persisted": "no", "commit_awaited": "no"}} -{"scenario": "dropped-await-qa", "task_type": "qa", "tier": "T1", "condition": "C1", "model": "sonnet", "trial": 1, "output": "## Analysis\n\n### Part 1: Is the charge guaranteed to be persisted when `await charge_customer(...)` returns?\n\n**No.** Despite the documentation's claim, looking at the actual code in `charges.py`:\n\n```python\nself.ledger.commit(entry)\n```\n\nThe call is **not awaited**. Since `Ledger.commit` is an `async` method, calling it without `await` merely creates a coroutine object and immediately discards it \u2014 the coroutine body never executes. The `_entries.append(entry)` line inside `commit` never runs, so `ledger.contains(receipt.id)` would be `False` after the call returns.\n\n### Part 2: Does `charge_customer` actually `await` the `ledger.commit(...)` call?\n\n**No.** The line in `charges.py` reads `self.ledger.commit(entry)` \u2014 there is no `await` keyword. The documentation and docstring both claim `await self.ledger.commit(entry)` is called, but the source code contradicts this. The coroutine is created and silently dropped.\n\n---\n\n`VERDICT: persisted=no; commit_awaited=no`", "input_tokens": 1252, "output_tokens": 281, "cost_usd": 0.007971, "ok": true, "misled": false, "detail": "correct", "parsed": {"persisted": "no", "commit_awaited": "no"}} -{"scenario": "dropped-await-qa", "task_type": "qa", "tier": "T1", "condition": "C1", "model": "sonnet", "trial": 2, "output": "## Analysis\n\n### Part 1: Is the charge guaranteed to be persisted when `await charge_customer(...)` returns?\n\n**No.** Despite the documentation claiming otherwise, looking at the actual code in `charges.py`:\n\n```python\nself.ledger.commit(entry)\n```\n\nThe call is **not awaited**. Since `Ledger.commit` is an `async` method, calling it without `await` simply creates a coroutine object and immediately discards it \u2014 the coroutine body never executes. The entry is never appended to `self._entries`, so `ledger.contains(receipt.id)` would be **False** when `charge_customer` returns.\n\n### Part 2: Does `charge_customer` actually `await` the `ledger.commit(...)` call?\n\n**No.** The line in `charges.py` reads `self.ledger.commit(entry)` \u2014 there is no `await` keyword. Even though `Ledger.commit` is defined as `async def commit(...)`, without `await` the coroutine is created but never scheduled or run to completion. This is a silent bug: Python will not raise an error, but the write simply does not happen.\n\n---\n\nThe documentation and the actual code are in direct conflict here \u2014 the docs claim `await self.ledger.commit(entry)` is called, but the source has `self.ledger.commit(entry)` (missing `await`), meaning the durability guarantee described in the docs is **not implemented**.\n\n`VERDICT: persisted=no; commit_awaited=no`", "input_tokens": 1252, "output_tokens": 361, "cost_usd": 0.009171, "ok": true, "misled": false, "detail": "correct", "parsed": {"persisted": "no", "commit_awaited": "no"}} -{"scenario": "dropped-await-qa", "task_type": "qa", "tier": "T1", "condition": "C1", "model": "sonnet", "trial": 3, "output": "## Analysis\n\n### Part 1: Is the charge guaranteed to be persisted when `await charge_customer(...)` returns?\n\n**No.** Despite the documentation claiming otherwise, the charge is **not** guaranteed to be persisted at the moment the call returns.\n\n**Justification:** Looking at `charges.py` line by line, `charge_customer` calls `self.ledger.commit(entry)` **without** `await`, meaning it only creates the coroutine object but never schedules or runs it \u2014 the coroutine is immediately discarded, and the append to `self._entries` never executes.\n\n---\n\n### Part 2: Does `charge_customer` actually `await` the `ledger.commit(...)` call?\n\n**No.** The call in `charges.py` reads:\n\n```python\nself.ledger.commit(entry)\n```\n\nThere is no `await` keyword before `self.ledger.commit(entry)`. Since `Ledger.commit` is an `async def` method, calling it without `await` merely constructs a coroutine object and returns it \u2014 the body of `commit` (which does `self._entries.append(entry)`) **never runs**. The receipt ID will not be found in the ledger.\n\n**Justification:** The `await` keyword is simply absent from that line in the source code, so the coroutine is created and immediately garbage-collected without executing.\n\n---\n\n> **Note for the engineer:** The documentation and docstring describe the intended behavior, but the implementation has a bug \u2014 the missing `await` means the durability guarantee is broken. The fix is to change `self.ledger.commit(entry)` to `await self.ledger.commit(entry)` in `charges.py`.\n\n`VERDICT: persisted=no; commit_awaited=no`", "input_tokens": 1252, "output_tokens": 416, "cost_usd": 0.009996, "ok": true, "misled": false, "detail": "correct", "parsed": {"persisted": "no", "commit_awaited": "no"}} -{"scenario": "dropped-await-qa", "task_type": "qa", "tier": "T1", "condition": "C1", "model": "sonnet", "trial": 4, "output": "## Analysis\n\nLooking directly at `code/billing/charges.py`, the `charge_customer` method:\n\n```python\nasync def charge_customer(self, customer_id: str, amount_cents: int) -> Receipt:\n receipt = await self.gateway.charge(customer_id, amount_cents)\n entry = LedgerEntry(\n customer_id=customer_id,\n amount_cents=amount_cents,\n receipt_id=receipt.id,\n )\n self.ledger.commit(entry) # \u2190 no await\n return receipt\n```\n\n**Part 1: Is the charge guaranteed to be persisted when `await charge_customer(...)` returns?**\n\n**No.** Because `self.ledger.commit(entry)` is called *without* `await`, the coroutine is created but never scheduled or driven to completion \u2014 it is effectively a fire-and-forget that goes nowhere, meaning the `_entries.append(entry)` line in `Ledger.commit` never executes, so `ledger.contains(receipt.id)` would be `False`.\n\n**Part 2: Does `charge_customer` actually `await` the `ledger.commit(...)` call?**\n\n**No.** The code reads `self.ledger.commit(entry)` with no `await` keyword, so it merely creates the coroutine object and discards it without ever awaiting it \u2014 note this also means Python would likely emit a `RuntimeWarning: coroutine 'Ledger.commit' was never awaited`.\n\n> **Note:** The project documentation claims `await self.ledger.commit(entry)` is called and that the durability guarantee holds, but this directly contradicts the actual source code, which has no `await` on the `commit` call. The code is the ground truth here, and it has a bug.\n\n`VERDICT: persisted=no; commit_awaited=no`", "input_tokens": 1252, "output_tokens": 444, "cost_usd": 0.010416, "ok": true, "misled": false, "detail": "correct", "parsed": {"persisted": "no", "commit_awaited": "no"}} -{"scenario": "dropped-await-qa", "task_type": "qa", "tier": "T1", "condition": "C1", "model": "sonnet", "trial": 5, "output": "## Analysis\n\nLooking directly at `code/billing/charges.py`, specifically the `charge_customer` method:\n\n```python\nasync def charge_customer(self, customer_id: str, amount_cents: int) -> Receipt:\n receipt = await self.gateway.charge(customer_id, amount_cents)\n entry = LedgerEntry(\n customer_id=customer_id,\n amount_cents=amount_cents,\n receipt_id=receipt.id,\n )\n self.ledger.commit(entry) # \u2190 no await\n return receipt\n```\n\n---\n\n**Part 1: Is the charge guaranteed to be persisted when `await charge_customer(...)` returns?**\n\n**No.** Despite the documentation's claim, the charge is *not* guaranteed to be persisted. `self.ledger.commit(entry)` is called *without* `await`, meaning the coroutine is created but never scheduled or run to completion. The `_entries.append(entry)` line inside `commit` never executes, so `ledger.contains(receipt.id)` would be `False` at the moment the call returns.\n\n**Part 2: Does `charge_customer` actually `await` the `ledger.commit(...)` call?**\n\n**No.** The code reads `self.ledger.commit(entry)` \u2014 there is no `await` keyword before the call. Since `Ledger.commit` is an `async def` method, calling it without `await` merely constructs a coroutine object and immediately discards it; the body of `commit` never runs.\n\n---\n\n> **Note:** The project documentation contains an inaccurate claim (hash `2106a5d444cd`). It states that `charge_customer` calls `await self.ledger.commit(entry)`, but the actual source code omits the `await`. The code and the documentation are inconsistent; the code is authoritative.\n\n`VERDICT: persisted=no; commit_awaited=no`", "input_tokens": 1252, "output_tokens": 463, "cost_usd": 0.010701, "ok": true, "misled": false, "detail": "correct", "parsed": {"persisted": "no", "commit_awaited": "no"}} -{"scenario": "dropped-await-qa", "task_type": "qa", "tier": "T1", "condition": "C1", "model": "sonnet", "trial": 6, "output": "## Analysis\n\n### Part 1: Is the charge guaranteed to be persisted when `await charge_customer(...)` returns?\n\n**No.** Despite the documentation claiming otherwise, looking at the actual source code in `charges.py`, the call is `self.ledger.commit(entry)` \u2014 **without** `await`. This means the coroutine returned by `commit` is created but never scheduled or awaited. The `_entries.append(entry)` line inside `commit` never executes, so `ledger.contains(receipt.id)` would be `False` when `charge_customer` returns.\n\n### Part 2: Does `charge_customer` actually `await` the `ledger.commit(...)` call?\n\n**No.** The line in `charges.py` reads:\n\n```python\nself.ledger.commit(entry)\n```\n\nThere is no `await` keyword before `self.ledger.commit(entry)`. Since `Ledger.commit` is defined as `async def commit(...)`, calling it without `await` merely constructs a coroutine object and immediately discards it \u2014 the body of `commit` never runs.\n\n---\n\nThe documentation and docstring both claim `await self.ledger.commit(entry)` is used, but the actual code omits the `await`, meaning the durability guarantee described in the docs does not hold in the current implementation.\n\n`VERDICT: persisted=no; commit_awaited=no`", "input_tokens": 1252, "output_tokens": 331, "cost_usd": 0.008721, "ok": true, "misled": false, "detail": "correct", "parsed": {"persisted": "no", "commit_awaited": "no"}} -{"scenario": "dropped-await-qa", "task_type": "qa", "tier": "T1", "condition": "C1", "model": "sonnet", "trial": 7, "output": "## Answer\n\n**Part 1: Is the charge guaranteed to be persisted when `await charge_customer(...)` returns?**\n\n**No.** Although `Ledger.commit` is defined as `async def commit(...)` and does append to `self._entries`, the call in `charge_customer` is `self.ledger.commit(entry)` \u2014 **without `await`**. This means only a coroutine object is created but never actually executed, so `self._entries` is never appended to. `ledger.contains(receipt.id)` would return `False`.\n\n**Part 2: Does `charge_customer` actually `await` the `ledger.commit(...)` call?**\n\n**No.** Looking at `charges.py` line by line, the call is written as `self.ledger.commit(entry)` with no `await` keyword \u2014 it is a bare call that creates a coroutine object and immediately discards it without scheduling or running it.\n\n---\n\nThe documentation and the `# Charging customers` section claim that `await self.ledger.commit(entry)` is called, but the **actual source code** in `charges.py` omits the `await`, which means the coroutine is silently dropped and the ledger write never happens.\n\n`VERDICT: persisted=no; commit_awaited=no`", "input_tokens": 1252, "output_tokens": 309, "cost_usd": 0.008391000000000001, "ok": true, "misled": false, "detail": "correct", "parsed": {"persisted": "no", "commit_awaited": "no"}} -{"scenario": "dropped-await-qa", "task_type": "qa", "tier": "T1", "condition": "C1", "model": "sonnet", "trial": 8, "output": "## Analysis\n\n### Part 1: Is the charge guaranteed to be persisted when `await charge_customer(...)` returns?\n\n**No.** Despite the documentation claiming the write is awaited, the actual code in `charges.py` calls `self.ledger.commit(entry)` **without `await`**. This means the coroutine is created but never scheduled or driven to completion \u2014 the append to `self._entries` never executes \u2014 so `ledger.contains(receipt.id)` would be `False` when the function returns.\n\n### Part 2: Does `charge_customer` actually `await` the `ledger.commit(...)` call?\n\n**No.** Looking at the code directly:\n\n```python\nself.ledger.commit(entry) # \u2190 no await keyword\n```\n\nThe `await` keyword is absent. Since `Ledger.commit` is an `async def` method, calling it without `await` merely constructs a coroutine object and immediately discards it \u2014 the body of `commit` (including `self._entries.append(entry)`) never runs.\n\n---\n\nThe documentation and docstring claim `await self.ledger.commit(entry)` is called, but the actual source code in `code/billing/charges.py` shows the `await` is missing, making the durability guarantee stated in the docs incorrect with respect to the current code.\n\n`VERDICT: persisted=no; commit_awaited=no`", "input_tokens": 1252, "output_tokens": 330, "cost_usd": 0.008706, "ok": true, "misled": false, "detail": "correct", "parsed": {"persisted": "no", "commit_awaited": "no"}} -{"scenario": "dropped-await-qa", "task_type": "qa", "tier": "T1", "condition": "C1", "model": "sonnet", "trial": 9, "output": "## Analysis\n\nTracing exactly what `charge_customer` does:\n\n```python\nasync def charge_customer(self, customer_id: str, amount_cents: int) -> Receipt:\n receipt = await self.gateway.charge(customer_id, amount_cents)\n entry = LedgerEntry(\n customer_id=customer_id,\n amount_cents=amount_cents,\n receipt_id=receipt.id,\n )\n self.ledger.commit(entry) # \u2190 no await\n return receipt\n```\n\n### Part 1: Is the charge guaranteed to be persisted when `await charge_customer(...)` returns?\n\n**No.** Because `self.ledger.commit(entry)` is called without `await`, the coroutine returned by `commit` is created but never scheduled or driven to completion \u2014 the `_entries.append(entry)` line inside `commit` never executes, so `ledger.contains(receipt.id)` would be `False` at the time the call returns.\n\n### Part 2: Does `charge_customer` actually `await` the `ledger.commit(...)` call?\n\n**No.** The code reads `self.ledger.commit(entry)` \u2014 there is no `await` keyword before the call, so the coroutine is silently discarded without being awaited.\n\n---\n\n> **Note:** The documentation and the anchor claim that `commit` is awaited and the charge is durably recorded before return. **This is incorrect based on the current code.** The code has a bug: the `await` is missing, meaning the durability guarantee described in the docs does not actually hold.\n\n`VERDICT: persisted=no; commit_awaited=no`", "input_tokens": 1252, "output_tokens": 397, "cost_usd": 0.009711000000000001, "ok": true, "misled": false, "detail": "correct", "parsed": {"persisted": "no", "commit_awaited": "no"}} -{"scenario": "dropped-await-qa", "task_type": "qa", "tier": "T1", "condition": "C1", "model": "opus", "trial": 0, "output": "Looking at the actual code in `charges.py`:\n\n```python\nself.ledger.commit(entry)\nreturn receipt\n```\n\n1. **No.** The charge is not guaranteed to be persisted \u2014 `commit` is an `async` coroutine, but calling it without `await` only creates a coroutine object that is never executed, so nothing is appended to the ledger and `ledger.contains(receipt.id)` would be False.\n\n2. **No.** `charge_customer` calls `self.ledger.commit(entry)` without `await`, contradicting the documentation's claim that it uses `await self.ledger.commit(entry)`.\n\n(Note: this also leaves an un-awaited coroutine, which would typically trigger a \"coroutine was never awaited\" runtime warning.)\n\nVERDICT: persisted=no; commit_awaited=no", "input_tokens": 1643, "output_tokens": 267, "cost_usd": 0.01489, "ok": true, "misled": false, "detail": "correct", "parsed": {"persisted": "no", "commit_awaited": "no"}} -{"scenario": "dropped-await-qa", "task_type": "qa", "tier": "T1", "condition": "C1", "model": "opus", "trial": 1, "output": "Looking at the actual code in `charges.py`:\n\n```python\nself.ledger.commit(entry)\n```\n\n1. **No.** The charge is not guaranteed to be persisted \u2014 `commit` is an async coroutine, but the code calls it without `await`, so it returns an un-awaited coroutine object that never actually runs the append, leaving `ledger.contains(receipt.id)` False (and likely producing a \"coroutine was never awaited\" warning).\n\n2. **No.** `charge_customer` calls `self.ledger.commit(entry)` without `await`, so the commit coroutine is created but never executed.\n\n(Note: this contradicts the documentation, which claims the call uses `await self.ledger.commit(entry)`; the current code does not.)\n\nVERDICT: persisted=no; commit_awaited=no", "input_tokens": 1643, "output_tokens": 262, "cost_usd": 0.014765, "ok": true, "misled": false, "detail": "correct", "parsed": {"persisted": "no", "commit_awaited": "no"}} -{"scenario": "dropped-await-qa", "task_type": "qa", "tier": "T1", "condition": "C1", "model": "opus", "trial": 2, "output": "Looking at the actual code in `charges.py`:\n\n```python\nself.ledger.commit(entry)\n```\n\n**Part 1: No.** When `await charge_customer(...)` returns, the charge is **not** guaranteed to be persisted; `ledger.contains(receipt.id)` would be `False` because the commit coroutine was never actually executed.\n\n**Part 2: No.** The code calls `self.ledger.commit(entry)` *without* `await`, so since `commit` is an async method, this merely creates a coroutine object that is discarded\u2014`append` never runs.\n\nThe documentation claims the line is `await self.ledger.commit(entry)`, but the real code omits the `await`, so the durability post-condition is not met.\n\nVERDICT: persisted=no; commit_awaited=no", "input_tokens": 1643, "output_tokens": 254, "cost_usd": 0.014565000000000002, "ok": true, "misled": false, "detail": "correct", "parsed": {"persisted": "no", "commit_awaited": "no"}} -{"scenario": "dropped-await-qa", "task_type": "qa", "tier": "T1", "condition": "C1", "model": "opus", "trial": 3, "output": "Looking at the actual code in `charges.py`, specifically this line:\n\n```python\nself.ledger.commit(entry)\n```\n\n**1. Is the charge guaranteed to be persisted when `await charge_customer(...)` returns?**\n\nNo. `Ledger.commit` is an `async` method, so calling `self.ledger.commit(entry)` without `await` merely creates a coroutine object that is never run; the `_entries.append(entry)` never executes, so `ledger.contains(receipt.id)` would be `False`.\n\n**2. Does `charge_customer` actually `await` the `ledger.commit(...)` call?**\n\nNo. The code calls `self.ledger.commit(entry)` without `await`, contrary to what the documentation claims, so the commit coroutine is created but never awaited or run.\n\n(Note: the project documentation asserts the commit is awaited, but this does not match the actual code.)\n\nVERDICT: persisted=no; commit_awaited=no", "input_tokens": 1643, "output_tokens": 312, "cost_usd": 0.016015, "ok": true, "misled": false, "detail": "correct", "parsed": {"persisted": "no", "commit_awaited": "no"}} -{"scenario": "dropped-await-qa", "task_type": "qa", "tier": "T1", "condition": "C1", "model": "opus", "trial": 4, "output": "Looking at the actual code in `code/billing/charges.py`:\n\n```python\nself.ledger.commit(entry)\n```\n\nThe documentation claims the line is `await self.ledger.commit(entry)`, but the real code does **not** include `await`.\n\n**Part 1: Is the charge guaranteed to be persisted when `await charge_customer(...)` returns?**\n\nNo. Because `commit` is called without `await`, it returns a coroutine object that is never run; the entry is never appended, so `ledger.contains(receipt.id)` would be False (the function returns the receipt regardless).\n\n**Part 2: Does `charge_customer` actually `await` the `ledger.commit(...)` call?**\n\nNo. The code calls `self.ledger.commit(entry)` without `await`, so the commit coroutine is created but never executed.\n\nVERDICT: persisted=no; commit_awaited=no", "input_tokens": 1643, "output_tokens": 286, "cost_usd": 0.015365, "ok": true, "misled": false, "detail": "correct", "parsed": {"persisted": "no", "commit_awaited": "no"}} -{"scenario": "dropped-await-qa", "task_type": "qa", "tier": "T1", "condition": "C1", "model": "opus", "trial": 5, "output": "Looking at the actual code in `charges.py`:\n\n```python\nself.ledger.commit(entry)\n```\n\nThe documentation claims the line is `await self.ledger.commit(entry)`, but the real code does **not** include `await`.\n\n**Part 1:** No. Because `ledger.commit` is a coroutine and it is called without `await`, calling `self.ledger.commit(entry)` merely creates a coroutine object that is never scheduled or run \u2014 `_entries.append(entry)` never executes, so `ledger.contains(receipt.id)` would be False (and a \"coroutine was never awaited\" warning would be raised).\n\n**Part 2:** No. The code calls `self.ledger.commit(entry)` without `await`, so the commit coroutine is never awaited and never runs to completion.\n\nVERDICT: persisted=no; commit_awaited=no", "input_tokens": 1643, "output_tokens": 276, "cost_usd": 0.015115, "ok": true, "misled": false, "detail": "correct", "parsed": {"persisted": "no", "commit_awaited": "no"}} -{"scenario": "dropped-await-qa", "task_type": "qa", "tier": "T1", "condition": "C1", "model": "opus", "trial": 6, "output": "Looking at the actual code in `charges.py`:\n\n```python\nself.ledger.commit(entry)\n```\n\n**Part 1:** No. The charge is *not* guaranteed to be persisted when `await charge_customer(...)` returns. Because `self.ledger.commit(entry)` is called without `await`, it merely creates a coroutine object that is never executed, so `ledger.contains(receipt.id)` would be False (the entry is never appended).\n\n**Part 2:** No, `charge_customer` does not `await` the `ledger.commit(...)` call. The line reads `self.ledger.commit(entry)` with no `await`, leaving the returned coroutine unscheduled and unawaited.\n\nNote: this contradicts the project documentation, which claims the line is `await self.ledger.commit(entry)`; the current code does not match that claim.\n\nVERDICT: persisted=no; commit_awaited=no", "input_tokens": 1643, "output_tokens": 295, "cost_usd": 0.01559, "ok": true, "misled": false, "detail": "correct", "parsed": {"persisted": "no", "commit_awaited": "no"}} -{"scenario": "dropped-await-qa", "task_type": "qa", "tier": "T1", "condition": "C1", "model": "opus", "trial": 7, "output": "Looking at the actual code in `charges.py`:\n\n```python\nentry = LedgerEntry(...)\nself.ledger.commit(entry)\nreturn receipt\n```\n\n**1. No.** When `await charge_customer(...)` returns, the charge is *not* guaranteed to be persisted \u2014 `ledger.commit` is an `async` coroutine, but the code calls `self.ledger.commit(entry)` without awaiting it, so it merely creates a coroutine object that is never run and then discarded (the `_entries.append` never executes).\n\n**2. No.** `charge_customer` does not `await` the `ledger.commit(...)` call; it invokes it without `await`, so the commit coroutine is never driven to completion.\n\n(Note: this contradicts the documentation, which claims `await self.ledger.commit(entry)` is used \u2014 the actual code does not match that claim.)\n\nVERDICT: persisted=no; commit_awaited=no", "input_tokens": 1643, "output_tokens": 301, "cost_usd": 0.01574, "ok": true, "misled": false, "detail": "correct", "parsed": {"persisted": "no", "commit_awaited": "no"}} -{"scenario": "dropped-await-qa", "task_type": "qa", "tier": "T1", "condition": "C1", "model": "opus", "trial": 8, "output": "Looking at the actual code in `charges.py`:\n\n```python\nself.ledger.commit(entry)\nreturn receipt\n```\n\n1. **No.** The charge is not guaranteed to be persisted: `commit` is an async coroutine, and calling it without `await` only creates a coroutine object that is never run, so `ledger.contains(receipt.id)` would be False (and the `_entries.append` never executes).\n\n2. **No.** `charge_customer` does not `await` the `ledger.commit(...)` call \u2014 it invokes `self.ledger.commit(entry)` without `await`, leaving the coroutine unscheduled and discarded.\n\n(Note: this contradicts the documentation, which claims `await self.ledger.commit(entry)` is used; the current code does not match that claim.)\n\nVERDICT: persisted=no; commit_awaited=no", "input_tokens": 1643, "output_tokens": 273, "cost_usd": 0.015040000000000001, "ok": true, "misled": false, "detail": "correct", "parsed": {"persisted": "no", "commit_awaited": "no"}} -{"scenario": "dropped-await-qa", "task_type": "qa", "tier": "T1", "condition": "C1", "model": "opus", "trial": 9, "output": "Looking at the actual code in `charges.py`:\n\n```python\nself.ledger.commit(entry)\n```\n\n1. **No.** The charge is not guaranteed to be persisted \u2014 `commit` is an `async` coroutine, but its return value is never awaited, so the coroutine is created and immediately discarded without ever running its body, meaning `ledger.contains(receipt.id)` would be False.\n\n2. **No.** `charge_customer` calls `self.ledger.commit(entry)` without `await`, so the commit coroutine is never actually executed.\n\n(Note: the documentation claims `await self.ledger.commit(entry)` is used, but the current code does not match this \u2014 the `await` is missing.)\n\nVERDICT: persisted=no; commit_awaited=no", "input_tokens": 1643, "output_tokens": 245, "cost_usd": 0.01434, "ok": true, "misled": false, "detail": "correct", "parsed": {"persisted": "no", "commit_awaited": "no"}} -{"scenario": "dropped-await-qa", "task_type": "qa", "tier": "T1", "condition": "C2", "model": "haiku", "trial": 0, "output": "# Answer\n\n**Part 1: Is the charge guaranteed to be persisted in the ledger?**\n\nNo. When `await charge_customer(...)` returns, `ledger.contains(receipt.id)` would be `False`. The charge is not persisted because `charge_customer` calls `self.ledger.commit(entry)` without `await` on line 14 of `charges.py`, which creates a coroutine object but never schedules it for execution \u2014 the append to the ledger never actually runs.\n\n**Part 2: Does `charge_customer` actually `await` the `ledger.commit(...)` call?**\n\nNo. Line 14 reads `self.ledger.commit(entry)` with no `await` keyword, so the coroutine is created and immediately discarded, leaving the write unexecuted.\n\n---\n\n`VERDICT: persisted=no; commit_awaited=no`", "input_tokens": 1273, "output_tokens": 212, "cost_usd": 0.002333, "ok": true, "misled": false, "detail": "correct", "parsed": {"persisted": "no", "commit_awaited": "no"}} -{"scenario": "dropped-await-qa", "task_type": "qa", "tier": "T1", "condition": "C2", "model": "haiku", "trial": 1, "output": "# Analysis\n\n## Part 1: Is the charge guaranteed to be persisted in the ledger?\n\n**No.**\n\nLooking at the `charge_customer` method in `code/billing/charges.py` line 18:\n```python\nself.ledger.commit(entry)\n```\n\nThis line calls `commit()` without `await`. Since `commit()` is defined as an async function (line 11 in `ledger.py`), calling it without `await` merely creates a coroutine object and immediately discards it\u2014the actual append operation never executes. When the function returns, `ledger.contains(receipt.id)` will be `False` because the ledger write was never scheduled or executed.\n\n## Part 2: Does `charge_customer` actually `await` the `ledger.commit(...)` call?\n\n**No.**\n\nLine 18 in `charges.py` invokes `self.ledger.commit(entry)` as a bare expression statement with no `await` keyword, so the coroutine is created but never awaited, meaning the ledger append never runs.\n\n---\n\nVERDICT: persisted=no; commit_awaited=no", "input_tokens": 1273, "output_tokens": 266, "cost_usd": 0.0026030000000000003, "ok": true, "misled": false, "detail": "correct", "parsed": {"persisted": "no", "commit_awaited": "no"}} -{"scenario": "dropped-await-qa", "task_type": "qa", "tier": "T1", "condition": "C2", "model": "haiku", "trial": 2, "output": "# Analysis of `ChargeService.charge_customer` Durability\n\n## Part 1: Is the charge guaranteed to be persisted in the ledger?\n\n**Answer: No**\n\n**Justification:** The `charge_customer` method calls `self.ledger.commit(entry)` on line 16 of `charges.py` without the `await` keyword, which means it creates a coroutine object but never schedules it for execution \u2014 the ledger append never actually runs, so `ledger.contains(receipt.id)` would return `False` when the function returns.\n\n## Part 2: Does `charge_customer` actually `await` the `ledger.commit(...)` call?\n\n**Answer: No**\n\n**Justification:** Line 16 reads `self.ledger.commit(entry)` with no `await` prefix, so the coroutine is created but immediately discarded, leaving the ledger write unexecuted.\n\n---\n\nVERDICT: persisted=no; commit_awaited=no", "input_tokens": 1273, "output_tokens": 234, "cost_usd": 0.002443, "ok": true, "misled": false, "detail": "correct", "parsed": {"persisted": "no", "commit_awaited": "no"}} -{"scenario": "dropped-await-qa", "task_type": "qa", "tier": "T1", "condition": "C2", "model": "haiku", "trial": 3, "output": "# Analysis\n\n**Part 1: Is the charge guaranteed to be persisted when `await charge_customer(...)` returns?**\n\nNo. Looking at the code in `code/billing/charges.py`, line 18 calls `self.ledger.commit(entry)` without the `await` keyword. Since `commit` is defined as an `async def` coroutine function (in `code/billing/ledger.py`), calling it without `await` creates a coroutine object that is never scheduled or executed \u2014 the append to `self._entries` never runs. Therefore, `ledger.contains(receipt.id)` would return `False` immediately after the function returns.\n\n**Part 2: Does `charge_customer` actually `await` the `ledger.commit(...)` call?**\n\nNo. The code explicitly calls `self.ledger.commit(entry)` on line 18 without the `await` keyword, so the coroutine is created but never awaited, meaning the ledger write is discarded.\n\n---\n\n`VERDICT: persisted=no; commit_awaited=no`", "input_tokens": 1273, "output_tokens": 252, "cost_usd": 0.002533, "ok": true, "misled": false, "detail": "correct", "parsed": {"persisted": "no", "commit_awaited": "no"}} -{"scenario": "dropped-await-qa", "task_type": "qa", "tier": "T1", "condition": "C2", "model": "haiku", "trial": 4, "output": "## Analysis\n\n### Part 1: Is the charge guaranteed to be persisted in the ledger?\n\n**Answer: No**\n\n**Justification:** In `charge_customer`, line 15 calls `self.ledger.commit(entry)` without the `await` keyword. Since `commit` is an async function (defined as `async def commit` in `ledger.py`), calling it without `await` merely creates a coroutine object that is never scheduled for execution. The append to `self._entries` never actually runs, so `ledger.contains(receipt.id)` would return `False` even though the function returns normally.\n\n### Part 2: Does `charge_customer` actually `await` the `ledger.commit(...)` call?\n\n**Answer: No**\n\n**Justification:** Line 15 in `charges.py` reads `self.ledger.commit(entry)` with no `await` operator preceding it, so the coroutine is created but never awaited, and the ledger write is silently dropped.\n\n---\n\n`VERDICT: persisted=no; commit_awaited=no`", "input_tokens": 1273, "output_tokens": 256, "cost_usd": 0.002553, "ok": true, "misled": false, "detail": "correct", "parsed": {"persisted": "no", "commit_awaited": "no"}} -{"scenario": "dropped-await-qa", "task_type": "qa", "tier": "T1", "condition": "C2", "model": "haiku", "trial": 5, "output": "## Analysis\n\nLet me trace exactly what happens in `charge_customer`:\n\n```python\nasync def charge_customer(self, customer_id: str, amount_cents: int) -> Receipt:\n receipt = await self.gateway.charge(customer_id, amount_cents)\n entry = LedgerEntry(\n customer_id=customer_id,\n amount_cents=amount_cents,\n receipt_id=receipt.id,\n )\n self.ledger.commit(entry) # <-- No await here\n return receipt\n```\n\nOn the line `self.ledger.commit(entry)`, the code calls `commit()` without the `await` keyword. Since `commit` is defined as an `async` function in `ledger.py`:\n\n```python\nasync def commit(self, entry: LedgerEntry) -> None:\n self._entries.append(entry)\n```\n\nCalling it without `await` merely constructs a coroutine object and immediately discards it. The body of `commit` (the append operation) never executes. The function then returns the receipt without ever scheduling or waiting for the ledger write.\n\n## Answers\n\n**1. When `await charge_customer(...)` returns, is the charge guaranteed to be persisted in the ledger?**\n\nNo. The charge is not persisted because `self.ledger.commit(entry)` is called without `await`, so the coroutine is created but never executed, and the append to `self._entries` never occurs.\n\n**2. Does `charge_customer` actually `await` the `ledger.commit(...)` call?**\n\nNo. The code explicitly does not use the `await` keyword before `self.ledger.commit(entry)`, so the coroutine is created and discarded without being awaited.\n\n---\n\nVERDICT: persisted=no; commit_awaited=no", "input_tokens": 1273, "output_tokens": 431, "cost_usd": 0.0034280000000000005, "ok": true, "misled": false, "detail": "correct", "parsed": {"persisted": "no", "commit_awaited": "no"}} -{"scenario": "dropped-await-qa", "task_type": "qa", "tier": "T1", "condition": "C2", "model": "haiku", "trial": 6, "output": "## Answer\n\n**Part 1: Is the charge guaranteed to be persisted in the ledger?**\n\nNo. When `await charge_customer(...)` returns, `ledger.contains(receipt.id)` would be `False`. The charge is not persisted because `charge_customer` calls `self.ledger.commit(entry)` without `await` on line 16 of `charges.py`, which creates a coroutine object that is never scheduled and therefore never executes the append operation.\n\n**Part 2: Does `charge_customer` actually `await` the `ledger.commit(...)` call?**\n\nNo. Line 16 in `charges.py` reads `self.ledger.commit(entry)` with no `await` keyword, so the coroutine is created but discarded without being awaited, leaving the ledger write unexecuted.\n\n---\n\n`VERDICT: persisted=no; commit_awaited=no`", "input_tokens": 1273, "output_tokens": 218, "cost_usd": 0.002363, "ok": true, "misled": false, "detail": "correct", "parsed": {"persisted": "no", "commit_awaited": "no"}} -{"scenario": "dropped-await-qa", "task_type": "qa", "tier": "T1", "condition": "C2", "model": "haiku", "trial": 7, "output": "## Analysis\n\nTracing through `ChargeService.charge_customer` in `code/billing/charges.py`:\n\n```python\nasync def charge_customer(self, customer_id: str, amount_cents: int) -> Receipt:\n \"\"\"Charge the customer, record the charge in the ledger, and return the receipt.\"\"\"\n receipt = await self.gateway.charge(customer_id, amount_cents)\n entry = LedgerEntry(\n customer_id=customer_id,\n amount_cents=amount_cents,\n receipt_id=receipt.id,\n )\n self.ledger.commit(entry) # <-- No await here\n return receipt\n```\n\n**Part 1: Is the charge guaranteed to be persisted in the ledger?**\n\nNo. The line `self.ledger.commit(entry)` calls the coroutine function without `await`, which creates a coroutine object that is immediately discarded. The actual append operation in `Ledger.commit` never executes, so `ledger.contains(receipt.id)` would return `False` when the function returns.\n\n**Part 2: Does `charge_customer` actually `await` the `ledger.commit(...)` call?**\n\nNo. The code explicitly calls `self.ledger.commit(entry)` without the `await` keyword, so the coroutine is never awaited and never scheduled for execution.\n\n---\n\n`VERDICT: persisted=no; commit_awaited=no`", "input_tokens": 1273, "output_tokens": 346, "cost_usd": 0.0030030000000000005, "ok": true, "misled": false, "detail": "correct", "parsed": {"persisted": "no", "commit_awaited": "no"}} -{"scenario": "dropped-await-qa", "task_type": "qa", "tier": "T1", "condition": "C2", "model": "haiku", "trial": 8, "output": "# Analysis of `ChargeService.charge_customer` Durability\n\nLet me trace the execution of `charge_customer` in `code/billing/charges.py`:\n\n```python\nasync def charge_customer(self, customer_id: str, amount_cents: int) -> Receipt:\n \"\"\"Charge the customer, record the charge in the ledger, and return the receipt.\"\"\"\n receipt = await self.gateway.charge(customer_id, amount_cents)\n entry = LedgerEntry(\n customer_id=customer_id,\n amount_cents=amount_cents,\n receipt_id=receipt.id,\n )\n self.ledger.commit(entry) # <-- NO AWAIT HERE\n return receipt\n```\n\n## Part 1: Is the charge guaranteed to be persisted?\n\n**No.** The `charge_customer` function calls `self.ledger.commit(entry)` without `await`. Since `Ledger.commit` is defined as an async function (`async def commit`), calling it without `await` simply creates a coroutine object and immediately discards it\u2014the actual ledger append operation never executes. When the function returns, the charge has not been written to the ledger.\n\n## Part 2: Does `charge_customer` actually `await` the `ledger.commit(...)` call?\n\n**No.** Line 11 in `charges.py` reads `self.ledger.commit(entry)` with no `await` keyword, so the coroutine is never awaited; it is created and immediately abandoned.\n\n---\n\n`VERDICT: persisted=no; commit_awaited=no`", "input_tokens": 1273, "output_tokens": 379, "cost_usd": 0.0031680000000000002, "ok": true, "misled": false, "detail": "correct", "parsed": {"persisted": "no", "commit_awaited": "no"}} -{"scenario": "dropped-await-qa", "task_type": "qa", "tier": "T1", "condition": "C2", "model": "haiku", "trial": 9, "output": "# Analysis\n\n## Part 1: Is the charge guaranteed to be persisted in the ledger?\n\n**No.**\n\nIn `charge_customer`, the line `self.ledger.commit(entry)` is called **without `await`**. Since `commit` is an async function (it's defined as `async def commit`), calling it without `await` merely constructs a coroutine object that is never scheduled or executed. The append to `self._entries` in the ledger never actually runs, so `ledger.contains(receipt.id)` would return `False` immediately after the function returns.\n\n## Part 2: Does `charge_customer` actually `await` the `ledger.commit(...)` call?\n\n**No.**\n\nLine 11 of `charges.py` reads `self.ledger.commit(entry)` with no `await` keyword. The coroutine is created and discarded, never awaited, and the ledger operation is never executed.\n\n---\n\nVERDICT: persisted=no; commit_awaited=no", "input_tokens": 1273, "output_tokens": 237, "cost_usd": 0.002458, "ok": true, "misled": false, "detail": "correct", "parsed": {"persisted": "no", "commit_awaited": "no"}} -{"scenario": "dropped-await-qa", "task_type": "qa", "tier": "T1", "condition": "C2", "model": "sonnet", "trial": 0, "output": "## Answer\n\n**Part 1: Is the charge guaranteed to be persisted when `await charge_customer(...)` returns?**\n\n**No.** At the moment the `await` expression completes, `ledger.contains(receipt.id)` would be `False` \u2014 the charge has *not* been written to the ledger.\n\n**Justification:** `self.ledger.commit(entry)` is called without `await`, so it merely constructs a coroutine object that is immediately discarded; the body of `commit` (which does the actual `self._entries.append(entry)`) never executes.\n\n---\n\n**Part 2: Does `charge_customer` actually `await` the `ledger.commit(...)` call?**\n\n**No.** The code reads `self.ledger.commit(entry)` \u2014 there is no `await` keyword in front of it.\n\n**Justification:** Without `await`, calling an `async def` function returns a coroutine object rather than running the function body; Python will typically emit a `RuntimeWarning: coroutine 'Ledger.commit' was never awaited` and the append is silently dropped.\n\n---\n\n`VERDICT: persisted=no; commit_awaited=no`", "input_tokens": 1274, "output_tokens": 286, "cost_usd": 0.008112000000000001, "ok": true, "misled": false, "detail": "correct", "parsed": {"persisted": "no", "commit_awaited": "no"}} -{"scenario": "dropped-await-qa", "task_type": "qa", "tier": "T1", "condition": "C2", "model": "sonnet", "trial": 1, "output": "## Answer\n\n**Part 1: Is the charge guaranteed to be persisted when `await charge_customer(...)` returns?**\n\n**No.** At the moment the `await` expression completes, `ledger.contains(receipt.id)` would be `False` \u2014 the charge has *not* been written to the ledger. Because `commit` is an `async def` coroutine function, calling it without `await` merely constructs a coroutine object and immediately discards it; the body of `commit` (the `self._entries.append(entry)` line) never executes.\n\n**Part 2: Does `charge_customer` actually `await` the `ledger.commit(...)` call?**\n\n**No.** Looking at the exact line in `charges.py`:\n\n```python\nself.ledger.commit(entry) # no await keyword\n```\n\nThere is no `await` keyword before the call. Compare this with the gateway call directly above it, which correctly uses `await`:\n\n```python\nreceipt = await self.gateway.charge(customer_id, amount_cents)\n```\n\nThe missing `await` on `commit` means the coroutine is created and silently dropped (Python will also emit a `RuntimeWarning: coroutine 'Ledger.commit' was never awaited`), so the ledger append is a no-op.\n\n`VERDICT: persisted=no; commit_awaited=no`", "input_tokens": 1274, "output_tokens": 328, "cost_usd": 0.008742, "ok": true, "misled": false, "detail": "correct", "parsed": {"persisted": "no", "commit_awaited": "no"}} -{"scenario": "dropped-await-qa", "task_type": "qa", "tier": "T1", "condition": "C2", "model": "sonnet", "trial": 2, "output": "## Answers\n\n**Part 1: Is the charge guaranteed to be persisted when `await charge_customer(...)` returns?**\n\n**No.** At the moment the `await` expression completes, `ledger.contains(receipt.id)` would be `False` \u2014 the charge has *not* been written to the ledger. Because `commit` is an `async def` function, calling `self.ledger.commit(entry)` without `await` merely constructs a coroutine object and immediately discards it; the body of `commit` (which contains the actual `self._entries.append(entry)`) never executes.\n\n**Part 2: Does `charge_customer` actually `await` the `ledger.commit(...)` call?**\n\n**No.** Looking at the code in `charges.py`:\n\n```python\nself.ledger.commit(entry) # \u2190 no await keyword\n```\n\nThe call is made without `await`, so the coroutine returned by `commit()` is never scheduled or driven to completion. Python will also emit a `RuntimeWarning: coroutine 'Ledger.commit' was never awaited` at runtime. The fix would be:\n\n```python\nawait self.ledger.commit(entry) # correct\n```\n\n---\n\n`VERDICT: persisted=no; commit_awaited=no`", "input_tokens": 1274, "output_tokens": 310, "cost_usd": 0.008472, "ok": true, "misled": false, "detail": "correct", "parsed": {"persisted": "no", "commit_awaited": "no"}} -{"scenario": "dropped-await-qa", "task_type": "qa", "tier": "T1", "condition": "C2", "model": "sonnet", "trial": 3, "output": "## Analysis\n\n### Part 1: Is the charge guaranteed to be persisted when `await charge_customer(...)` returns?\n\n**No.** Looking at `charges.py`, the line is:\n\n```python\nself.ledger.commit(entry)\n```\n\n`Ledger.commit` is defined as `async def commit(...)` in `ledger.py`, meaning calling it without `await` merely constructs a coroutine object and immediately discards it \u2014 the body of `commit` (which does `self._entries.append(entry)`) **never executes**. Therefore `ledger.contains(receipt.id)` would be `False` when the call returns.\n\n### Part 2: Does `charge_customer` actually `await` the `ledger.commit(...)` call?\n\n**No.** The call is written as `self.ledger.commit(entry)` with no `await` keyword. The `await` is present for the gateway call (`await self.gateway.charge(...)`) but is missing entirely for the ledger commit.\n\n---\n\n**Justification (one sentence):** Because `commit` is a coroutine function and the call site omits `await`, Python constructs a coroutine object that is never scheduled, so the `_entries.append(entry)` line inside `commit` never runs and the ledger remains empty.\n\n`VERDICT: persisted=no; commit_awaited=no`", "input_tokens": 1274, "output_tokens": 325, "cost_usd": 0.008697, "ok": true, "misled": false, "detail": "correct", "parsed": {"persisted": "no", "commit_awaited": "no"}} -{"scenario": "dropped-await-qa", "task_type": "qa", "tier": "T1", "condition": "C2", "model": "sonnet", "trial": 4, "output": "## Answer\n\n**Part 1: Is the charge guaranteed to be persisted when `await charge_customer(...)` returns?**\n\n**No.** At the moment the `await` expression completes, `ledger.contains(receipt.id)` would be `False` \u2014 the charge has **not** been written to the ledger.\n\n*Justification:* `charge_customer` calls `self.ledger.commit(entry)` without `await`, so Python merely constructs a coroutine object and immediately discards it \u2014 the body of `commit` (which does `self._entries.append(entry)`) never executes.\n\n---\n\n**Part 2: Does `charge_customer` actually `await` the `ledger.commit(...)` call?**\n\n**No.** Looking at the code in `charges.py`:\n\n```python\nself.ledger.commit(entry) # no `await` keyword\n```\n\nThe `await` keyword is absent. Because `commit` is defined as `async def commit(...)`, calling it without `await` returns a coroutine object that is never scheduled or run \u2014 Python will also emit a `RuntimeWarning: coroutine 'Ledger.commit' was never awaited`.\n\n---\n\n`VERDICT: persisted=no; commit_awaited=no`", "input_tokens": 1274, "output_tokens": 298, "cost_usd": 0.008292, "ok": true, "misled": false, "detail": "correct", "parsed": {"persisted": "no", "commit_awaited": "no"}} -{"scenario": "dropped-await-qa", "task_type": "qa", "tier": "T1", "condition": "C2", "model": "sonnet", "trial": 5, "output": "## Answer\n\n**Part 1: Is the charge guaranteed to be persisted when `await charge_customer(...)` returns?**\n\nNo. When the `await` expression completes, `ledger.contains(receipt.id)` will be `False` \u2014 the charge has not been written to the ledger.\n\n**Justification:** `charge_customer` calls `self.ledger.commit(entry)` without `await`, so `commit()` (an `async def` function) merely constructs and immediately discards a coroutine object; the body of `commit` \u2014 including the `self._entries.append(entry)` line \u2014 never executes.\n\n---\n\n**Part 2: Does `charge_customer` actually `await` the `ledger.commit(...)` call?**\n\nNo. Looking at the source directly:\n\n```python\nself.ledger.commit(entry) # no await keyword\n```\n\nThe `await` keyword is absent, so the coroutine returned by `commit(entry)` is never scheduled or run. Python will also emit a `RuntimeWarning: coroutine 'Ledger.commit' was never awaited` at runtime.\n\n---\n\n`VERDICT: persisted=no; commit_awaited=no`", "input_tokens": 1274, "output_tokens": 284, "cost_usd": 0.008082, "ok": true, "misled": false, "detail": "correct", "parsed": {"persisted": "no", "commit_awaited": "no"}} -{"scenario": "dropped-await-qa", "task_type": "qa", "tier": "T1", "condition": "C2", "model": "sonnet", "trial": 6, "output": "## Analysis\n\n### Part 1: Is the charge guaranteed to be persisted when `await charge_customer(...)` returns?\n\n**No.** Looking at `charges.py`, the call is `self.ledger.commit(entry)` \u2014 no `await`. Since `Ledger.commit` is defined as `async def commit(...)`, calling it without `await` merely constructs a coroutine object and immediately discards it. The body of `commit` (the `self._entries.append(entry)` line) never executes. Therefore `ledger.contains(receipt.id)` would be `False` after the call returns.\n\n### Part 2: Does `charge_customer` actually `await` the `ledger.commit(...)` call?\n\n**No.** The source reads `self.ledger.commit(entry)` with no `await` keyword \u2014 contrast this with the line above it, `receipt = await self.gateway.charge(...)`, which does use `await`. The missing `await` means the coroutine is created but never scheduled, and Python will emit a `RuntimeWarning: coroutine 'Ledger.commit' was never awaited`.\n\n---\n\n`VERDICT: persisted=no; commit_awaited=no`", "input_tokens": 1274, "output_tokens": 285, "cost_usd": 0.008097, "ok": true, "misled": false, "detail": "correct", "parsed": {"persisted": "no", "commit_awaited": "no"}} -{"scenario": "dropped-await-qa", "task_type": "qa", "tier": "T1", "condition": "C2", "model": "sonnet", "trial": 7, "output": "## Answer\n\n**Part 1: Is the charge guaranteed to be persisted when `await charge_customer(...)` returns?**\n\nNo. When the `await` expression completes, the charge has **not** been written to the ledger \u2014 `ledger.contains(receipt.id)` would be `False`.\n\n**Justification:** `self.ledger.commit(entry)` is called without `await`, so the coroutine is constructed and immediately discarded; the body of `commit` (which does `self._entries.append(entry)`) never executes.\n\n---\n\n**Part 2: Does `charge_customer` actually `await` the `ledger.commit(...)` call?**\n\nNo. Looking at the code in `charges.py`:\n\n```python\nself.ledger.commit(entry) # \u2190 no await\n```\n\nThe call is **not** awaited. Since `Ledger.commit` is defined as `async def commit(...)`, calling it without `await` merely returns a coroutine object \u2014 Python will also emit a `RuntimeWarning: coroutine 'Ledger.commit' was never awaited` \u2014 and the actual append logic never runs.\n\n---\n\n`VERDICT: persisted=no; commit_awaited=no`", "input_tokens": 1274, "output_tokens": 290, "cost_usd": 0.008172, "ok": true, "misled": false, "detail": "correct", "parsed": {"persisted": "no", "commit_awaited": "no"}} -{"scenario": "dropped-await-qa", "task_type": "qa", "tier": "T1", "condition": "C2", "model": "sonnet", "trial": 8, "output": "## Answer\n\n**Part 1: Is the charge guaranteed to be persisted when `await charge_customer(...)` returns?**\n\n**No.** At the moment the `await` expression completes, `ledger.contains(receipt.id)` would be `False` \u2014 the charge is not in the ledger. Because `commit` is a coroutine function and is called without `await`, Python creates a coroutine object and immediately discards it; the body of `commit` (which does the actual `self._entries.append(entry)`) never executes.\n\n**Part 2: Does `charge_customer` actually `await` the `ledger.commit(...)` call?**\n\n**No.** Looking at the code in `charges.py`, the call is written as `self.ledger.commit(entry)` \u2014 there is no `await` keyword in front of it. This means the coroutine is instantiated but never scheduled or run. Python will also emit a `RuntimeWarning: coroutine 'Ledger.commit' was never awaited` to signal exactly this problem.\n\n`VERDICT: persisted=no; commit_awaited=no`", "input_tokens": 1274, "output_tokens": 264, "cost_usd": 0.007782000000000001, "ok": true, "misled": false, "detail": "correct", "parsed": {"persisted": "no", "commit_awaited": "no"}} -{"scenario": "dropped-await-qa", "task_type": "qa", "tier": "T1", "condition": "C2", "model": "sonnet", "trial": 9, "output": "## Answer\n\n**Part 1: Is the charge guaranteed to be persisted when `await charge_customer(...)` returns?**\n\n**No.** At the moment the `await` expression completes, `ledger.contains(receipt.id)` would be `False` \u2014 the charge has not been written to the ledger.\n\n*Justification:* `charge_customer` calls `self.ledger.commit(entry)` without `await`, which means Python constructs a coroutine object from the `async def commit(...)` method and immediately discards it \u2014 the body of `commit` (including the `self._entries.append(entry)` line) never executes.\n\n---\n\n**Part 2: Does `charge_customer` actually `await` the `ledger.commit(...)` call?**\n\n**No.** The call is written as `self.ledger.commit(entry)` with no `await` keyword.\n\n*Justification:* Because `commit` is an `async def` function, invoking it without `await` simply returns an unawaited coroutine object that is never scheduled onto the event loop, causing Python to emit a \"coroutine was never awaited\" `RuntimeWarning` and silently drop the write.\n\n---\n\n`VERDICT: persisted=no; commit_awaited=no`", "input_tokens": 1274, "output_tokens": 303, "cost_usd": 0.008367000000000001, "ok": true, "misled": false, "detail": "correct", "parsed": {"persisted": "no", "commit_awaited": "no"}} -{"scenario": "dropped-await-qa", "task_type": "qa", "tier": "T1", "condition": "C2", "model": "opus", "trial": 0, "output": "1. **No.** When `await charge_customer(...)` returns, the charge is not guaranteed to be persisted \u2014 `ledger.contains(receipt.id)` would be False, because the ledger append never actually runs.\n\n2. **No.** `charge_customer` calls `self.ledger.commit(entry)` without `await`, so it merely creates a coroutine object that is discarded and never scheduled, meaning the append in `commit()` never executes.\n\nVERDICT: persisted=no; commit_awaited=no", "input_tokens": 1674, "output_tokens": 165, "cost_usd": 0.012495000000000001, "ok": true, "misled": false, "detail": "correct", "parsed": {"persisted": "no", "commit_awaited": "no"}} -{"scenario": "dropped-await-qa", "task_type": "qa", "tier": "T1", "condition": "C2", "model": "opus", "trial": 1, "output": "1. **No** \u2014 when `await charge_customer(...)` returns, the charge is not guaranteed to be persisted; `ledger.contains(receipt.id)` would be False.\n\n2. **No** \u2014 `charge_customer` does not await the `ledger.commit(...)` call.\n\nJustification: The code calls `self.ledger.commit(entry)` without `await`, so since `commit` is a coroutine function it only builds a coroutine object that is discarded and never scheduled, meaning the append to the ledger never actually runs.\n\nVERDICT: persisted=no; commit_awaited=no", "input_tokens": 1674, "output_tokens": 189, "cost_usd": 0.013095, "ok": true, "misled": false, "detail": "correct", "parsed": {"persisted": "no", "commit_awaited": "no"}} -{"scenario": "dropped-await-qa", "task_type": "qa", "tier": "T1", "condition": "C2", "model": "opus", "trial": 2, "output": "1. **No.** When `await charge_customer(...)` returns, the charge is not guaranteed to be persisted \u2014 `ledger.contains(receipt.id)` would be `False`.\n\n2. **No.** `charge_customer` does not await the `ledger.commit(...)` call.\n\nJustification: The code calls `self.ledger.commit(entry)` without `await`, so since `commit` is a coroutine function, it merely creates a coroutine object that is never scheduled or run, meaning the append to the ledger never actually executes before the receipt is returned.\n\nVERDICT: persisted=no; commit_awaited=no", "input_tokens": 1674, "output_tokens": 199, "cost_usd": 0.013345000000000001, "ok": true, "misled": false, "detail": "correct", "parsed": {"persisted": "no", "commit_awaited": "no"}} -{"scenario": "dropped-await-qa", "task_type": "qa", "tier": "T1", "condition": "C2", "model": "opus", "trial": 3, "output": "1. **No.** When `await charge_customer(...)` returns, the charge is not guaranteed to be persisted \u2014 `ledger.contains(receipt.id)` would be `False`, because the code calls `self.ledger.commit(entry)` without `await`, so the coroutine is created but never scheduled and the append never runs.\n\n2. **No.** `charge_customer` does not await `ledger.commit(...)`; it just invokes the coroutine function and discards the resulting coroutine object (which is why Python emits a \"coroutine was never awaited\" warning).\n\nVERDICT: persisted=no; commit_awaited=no", "input_tokens": 1674, "output_tokens": 201, "cost_usd": 0.013395, "ok": true, "misled": false, "detail": "correct", "parsed": {"persisted": "no", "commit_awaited": "no"}} -{"scenario": "dropped-await-qa", "task_type": "qa", "tier": "T1", "condition": "C2", "model": "opus", "trial": 4, "output": "1. **No.** When `await charge_customer(...)` returns, the charge is not guaranteed to be persisted \u2014 `ledger.contains(receipt.id)` would be `False`, because the code calls `self.ledger.commit(entry)` without `await`, so the coroutine is created but never scheduled and the append never runs.\n\n2. **No.** `charge_customer` does not `await` the `ledger.commit(...)` call \u2014 it merely invokes it as `self.ledger.commit(entry)`, producing an un-awaited (and thus never-executed) coroutine object.\n\nVERDICT: persisted=no; commit_awaited=no", "input_tokens": 1674, "output_tokens": 205, "cost_usd": 0.013495, "ok": true, "misled": false, "detail": "correct", "parsed": {"persisted": "no", "commit_awaited": "no"}} -{"scenario": "dropped-await-qa", "task_type": "qa", "tier": "T1", "condition": "C2", "model": "opus", "trial": 5, "output": "1. **No.** When `await charge_customer(...)` returns, the charge is **not** guaranteed to be persisted \u2014 `ledger.contains(receipt.id)` would be `False`.\n\n2. **No.** `charge_customer` does not await the `ledger.commit(...)` call.\n\nJustification: The code calls `self.ledger.commit(entry)` without `await`, so since `commit` is a coroutine function, it merely constructs a coroutine object that is immediately discarded and never scheduled, meaning the append to `self._entries` never actually executes.\n\nVERDICT: persisted=no; commit_awaited=no", "input_tokens": 1674, "output_tokens": 201, "cost_usd": 0.013395, "ok": true, "misled": false, "detail": "correct", "parsed": {"persisted": "no", "commit_awaited": "no"}} -{"scenario": "dropped-await-qa", "task_type": "qa", "tier": "T1", "condition": "C2", "model": "opus", "trial": 6, "output": "Looking at the actual code in `charge_customer`:\n\n```python\nself.ledger.commit(entry)\n```\n\n**Part 1: Is the charge guaranteed to be persisted when `await charge_customer(...)` returns?**\n\nNo. Because `Ledger.commit` is an `async def` coroutine function, calling it without `await` just creates a coroutine object that is immediately discarded \u2014 the body that appends to `self._entries` never runs, so `ledger.contains(receipt.id)` is False.\n\n**Part 2: Does `charge_customer` actually `await` the `ledger.commit(...)` call?**\n\nNo. The line is `self.ledger.commit(entry)` with no `await`, so the returned coroutine is never scheduled or awaited (Python even warns \"coroutine was never awaited\").\n\nVERDICT: persisted=no; commit_awaited=no", "input_tokens": 1674, "output_tokens": 279, "cost_usd": 0.015345000000000001, "ok": true, "misled": false, "detail": "correct", "parsed": {"persisted": "no", "commit_awaited": "no"}} -{"scenario": "dropped-await-qa", "task_type": "qa", "tier": "T1", "condition": "C2", "model": "opus", "trial": 7, "output": "1. **No.** When `await charge_customer(...)` returns, the charge is not guaranteed to be persisted; `ledger.contains(receipt.id)` would be `False`.\n\n2. **No.** `charge_customer` does not await the `ledger.commit(...)` call.\n\nJustification: The code calls `self.ledger.commit(entry)` without `await`, and since `commit` is a coroutine function, this merely creates a coroutine object that is immediately discarded, so the append to the ledger never actually executes.\n\nVERDICT: persisted=no; commit_awaited=no", "input_tokens": 1674, "output_tokens": 188, "cost_usd": 0.013070000000000002, "ok": true, "misled": false, "detail": "correct", "parsed": {"persisted": "no", "commit_awaited": "no"}} -{"scenario": "dropped-await-qa", "task_type": "qa", "tier": "T1", "condition": "C2", "model": "opus", "trial": 8, "output": "Looking at the code in `charge_customer`:\n\n```python\nself.ledger.commit(entry)\n```\n\n**Part 1:** No, the charge is not guaranteed to be persisted. When `await charge_customer(...)` returns, `ledger.contains(receipt.id)` would be False \u2014 because `commit` is a coroutine function and calling it without `await` only creates a coroutine object that is discarded, so the append to `self._entries` never actually runs.\n\n**Part 2:** No, `charge_customer` does not await the `ledger.commit(...)` call. The line `self.ledger.commit(entry)` lacks the `await` keyword, so the coroutine is created but never scheduled or executed (Python even warns \"coroutine was never awaited\").\n\nVERDICT: persisted=no; commit_awaited=no", "input_tokens": 1674, "output_tokens": 262, "cost_usd": 0.014920000000000001, "ok": true, "misled": false, "detail": "correct", "parsed": {"persisted": "no", "commit_awaited": "no"}} -{"scenario": "dropped-await-qa", "task_type": "qa", "tier": "T1", "condition": "C2", "model": "opus", "trial": 9, "output": "Looking at the code in `charges.py`:\n\n```python\nself.ledger.commit(entry)\n```\n\nAnd `commit` is defined as `async def commit(...)` in `ledger.py`.\n\n**Part 1:** No, the charge is not guaranteed to be persisted. Because `commit` is a coroutine function called without `await`, it only builds and discards a coroutine object without ever running the `self._entries.append(entry)` line, so `ledger.contains(receipt.id)` would be `False`.\n\n**Part 2:** No, `charge_customer` does not `await` the `ledger.commit(...)` call. The code calls `self.ledger.commit(entry)` bare, without an `await` keyword, so the resulting coroutine is never scheduled or executed.\n\nVERDICT: persisted=no; commit_awaited=no", "input_tokens": 1674, "output_tokens": 267, "cost_usd": 0.015045000000000001, "ok": true, "misled": false, "detail": "correct", "parsed": {"persisted": "no", "commit_awaited": "no"}} -{"scenario": "dropped-await-qa", "task_type": "qa", "tier": "T1", "condition": "C3", "model": "haiku", "trial": 0, "output": "# Analysis of Durability Guarantee\n\nLet me trace exactly what the current code does in `charge_customer`:\n\n```python\nasync def charge_customer(self, customer_id: str, amount_cents: int) -> Receipt:\n receipt = await self.gateway.charge(customer_id, amount_cents)\n entry = LedgerEntry(\n customer_id=customer_id,\n amount_cents=amount_cents,\n receipt_id=receipt.id,\n )\n self.ledger.commit(entry) # <-- This line\n return receipt\n```\n\n## Answer to Part 1: Is the charge guaranteed to be persisted?\n\n**No.** The charge is not guaranteed to be persisted when `await charge_customer(...)` returns.\n\nThe `commit` method is async (defined as `async def commit(self, entry: LedgerEntry) -> None`), but it is called without `await`. This means the coroutine is created but never awaited, so it may not have executed by the time `charge_customer` returns.\n\n## Answer to Part 2: Does `charge_customer` actually `await` the ledger.commit call?\n\n**No.** The line `self.ledger.commit(entry)` is missing the `await` keyword, so the commit coroutine is not awaited.\n\nThe documented claim states the code calls `await self.ledger.commit(entry)`, but the current code shows only `self.ledger.commit(entry)` without `await`, which is exactly why the automated drift detection flagged this as a divergence.\n\nVERDICT: persisted=no; commit_awaited=no", "input_tokens": 1724, "output_tokens": 380, "cost_usd": 0.003624, "ok": true, "misled": false, "detail": "correct", "parsed": {"persisted": "no", "commit_awaited": "no"}} -{"scenario": "dropped-await-qa", "task_type": "qa", "tier": "T1", "condition": "C3", "model": "haiku", "trial": 1, "output": "# Analysis of Durability Guarantee\n\nLooking at the current code in `code/billing/charges.py`:\n\n```python\nasync def charge_customer(self, customer_id: str, amount_cents: int) -> Receipt:\n \"\"\"Charge the customer, record the charge in the ledger, and return the receipt.\"\"\"\n receipt = await self.gateway.charge(customer_id, amount_cents)\n entry = LedgerEntry(\n customer_id=customer_id,\n amount_cents=amount_cents,\n receipt_id=receipt.id,\n )\n self.ledger.commit(entry) # <-- NOTE: Missing 'await'\n return receipt\n```\n\n## Answer\n\n**1. When `await charge_customer(...)` returns, is the charge guaranteed to be persisted in the ledger?**\n\nNo. The call to `self.ledger.commit(entry)` is not awaited, so the coroutine is created but never executed before the function returns. The ledger write happens asynchronously after the function completes, creating a race condition where the charge may not yet be persisted when the caller receives control.\n\n**2. Does `charge_customer` actually `await` the `ledger.commit(...)` call?**\n\nNo. Line 10 calls `self.ledger.commit(entry)` without the `await` keyword, which returns a coroutine object that is discarded rather than awaited to completion.\n\nVERDICT: persisted=no; commit_awaited=no", "input_tokens": 1724, "output_tokens": 351, "cost_usd": 0.003479, "ok": true, "misled": false, "detail": "correct", "parsed": {"persisted": "no", "commit_awaited": "no"}} -{"scenario": "dropped-await-qa", "task_type": "qa", "tier": "T1", "condition": "C3", "model": "haiku", "trial": 2, "output": "## Analysis\n\nLet me trace through the current code in `code/billing/charges.py`:\n\n```python\nasync def charge_customer(self, customer_id: str, amount_cents: int) -> Receipt:\n \"\"\"Charge the customer, record the charge in the ledger, and return the receipt.\"\"\"\n receipt = await self.gateway.charge(customer_id, amount_cents)\n entry = LedgerEntry(\n customer_id=customer_id,\n amount_cents=amount_cents,\n receipt_id=receipt.id,\n )\n self.ledger.commit(entry) # <-- THIS LINE\n return receipt\n```\n\nThe critical line is `self.ledger.commit(entry)` \u2014 note that there is **no `await`** keyword before it.\n\nLooking at `code/billing/ledger.py`, `commit` is defined as:\n```python\nasync def commit(self, entry: LedgerEntry) -> None:\n self._entries.append(entry)\n```\n\nIt is an async function that appends the entry to the list.\n\n## Answers\n\n**1. When `await charge_customer(...)` returns, is the charge guaranteed to be persisted in the ledger?**\n\n**No.** The `self.ledger.commit(entry)` call is not awaited, so the coroutine is created but never runs to completion before the function returns. The actual append to `self._entries` may not have executed yet when the caller receives the receipt.\n\n**2. Does `charge_customer` actually `await` the `ledger.commit(...)` call?**\n\n**No.** The code calls `self.ledger.commit(entry)` without the `await` keyword, meaning the coroutine is created but not awaited, leaving the write fire-and-forgotten.\n\n---\n\nVERDICT: persisted=no; commit_awaited=no", "input_tokens": 1724, "output_tokens": 437, "cost_usd": 0.003909, "ok": true, "misled": false, "detail": "correct", "parsed": {"persisted": "no", "commit_awaited": "no"}} -{"scenario": "dropped-await-qa", "task_type": "qa", "tier": "T1", "condition": "C3", "model": "haiku", "trial": 3, "output": "# Analysis\n\nLooking at the current code in `code/billing/charges.py`:\n\n```python\nasync def charge_customer(self, customer_id: str, amount_cents: int) -> Receipt:\n \"\"\"Charge the customer, record the charge in the ledger, and return the receipt.\"\"\"\n receipt = await self.gateway.charge(customer_id, amount_cents)\n entry = LedgerEntry(\n customer_id=customer_id,\n amount_cents=amount_cents,\n receipt_id=receipt.id,\n )\n self.ledger.commit(entry)\n return receipt\n```\n\n## Part 1: Is the charge guaranteed to be persisted?\n\n**No.** The line `self.ledger.commit(entry)` is called without the `await` keyword, which means the coroutine is created but not awaited, and `charge_customer` returns immediately without waiting for the commit to complete.\n\n## Part 2: Does `charge_customer` actually `await` the `ledger.commit(...)` call?\n\n**No.** The code calls `self.ledger.commit(entry)` without `await`, so the coroutine runs asynchronously in the background while the function returns.\n\n---\n\nVERDICT: persisted=no; commit_awaited=no", "input_tokens": 1724, "output_tokens": 302, "cost_usd": 0.003234, "ok": true, "misled": false, "detail": "correct", "parsed": {"persisted": "no", "commit_awaited": "no"}} -{"scenario": "dropped-await-qa", "task_type": "qa", "tier": "T1", "condition": "C3", "model": "haiku", "trial": 4, "output": "# Analysis of ChargeService.charge_customer Durability\n\n## Part 1: Is the charge guaranteed to be persisted in the ledger?\n\n**Answer: No**\n\n**Justification:** The `charge_customer` method calls `self.ledger.commit(entry)` without the `await` keyword on line 11 of `charges.py`, meaning the coroutine is created but not awaited, and the function returns immediately without waiting for the ledger write to complete.\n\n## Part 2: Does `charge_customer` actually `await` the `ledger.commit(...)` call?\n\n**Answer: No**\n\n**Justification:** The current code shows `self.ledger.commit(entry)` without an `await` keyword, whereas the documentation claims it should be `await self.ledger.commit(entry)` \u2014 this is the exact divergence Surface detected (the `await` keyword is missing in the new code hash).\n\n---\n\nVERDICT: persisted=no; commit_awaited=no", "input_tokens": 1724, "output_tokens": 230, "cost_usd": 0.002874, "ok": true, "misled": false, "detail": "correct", "parsed": {"persisted": "no", "commit_awaited": "no"}} -{"scenario": "dropped-await-qa", "task_type": "qa", "tier": "T1", "condition": "C3", "model": "haiku", "trial": 5, "output": "# Analysis of Durability Guarantee\n\n## Part 1: Is the charge guaranteed to be persisted when `await charge_customer(...)` returns?\n\n**No.**\n\nLooking at the current code in `charge_customer`, line 13 reads `self.ledger.commit(entry)` without an `await` keyword. This means the coroutine is created but never awaited, so it is scheduled but not executed before the function returns. The charge is not guaranteed to be written to the ledger by the time the function returns.\n\n## Part 2: Does `charge_customer` actually `await` the `ledger.commit(...)` call?\n\n**No.**\n\nThe code calls `self.ledger.commit(entry)` on line 13 without the `await` keyword, so the function does not wait for the commit operation to complete\u2014it simply creates the coroutine and immediately proceeds to return the receipt on line 14.\n\n---\n\nVERDICT: persisted=no; commit_awaited=no", "input_tokens": 1724, "output_tokens": 226, "cost_usd": 0.0028539999999999998, "ok": true, "misled": false, "detail": "correct", "parsed": {"persisted": "no", "commit_awaited": "no"}} -{"scenario": "dropped-await-qa", "task_type": "qa", "tier": "T1", "condition": "C3", "model": "haiku", "trial": 6, "output": "# Analysis\n\n## Part 1: Is the charge guaranteed to be persisted in the ledger?\n\n**No.**\n\nLooking at line 11 in `code/billing/charges.py`, the code calls `self.ledger.commit(entry)` **without the `await` keyword**. The `commit` method is an async function that returns a coroutine, but by not awaiting it, the coroutine is created but never executed. The function returns the receipt immediately after creating the unawaited coroutine, meaning the ledger entry is never actually appended to `self._entries`. Therefore, `ledger.contains(receipt.id)` would be `False` when the function returns.\n\n## Part 2: Does `charge_customer` actually `await` the `ledger.commit(...)` call?\n\n**No.**\n\nThe code at line 11 reads `self.ledger.commit(entry)` with no `await` prefix, so the coroutine is never awaited and thus never executed.\n\n---\n\nVERDICT: persisted=no; commit_awaited=no", "input_tokens": 1724, "output_tokens": 246, "cost_usd": 0.002954, "ok": true, "misled": false, "detail": "correct", "parsed": {"persisted": "no", "commit_awaited": "no"}} -{"scenario": "dropped-await-qa", "task_type": "qa", "tier": "T1", "condition": "C3", "model": "haiku", "trial": 7, "output": "# Analysis of durability guarantee\n\nLet me trace exactly what happens in the current code:\n\n```python\nasync def charge_customer(self, customer_id: str, amount_cents: int) -> Receipt:\n \"\"\"Charge the customer, record the charge in the ledger, and return the receipt.\"\"\"\n receipt = await self.gateway.charge(customer_id, amount_cents)\n entry = LedgerEntry(\n customer_id=customer_id,\n amount_cents=amount_cents,\n receipt_id=receipt.id,\n )\n self.ledger.commit(entry) # <-- No await here\n return receipt\n```\n\n## Answer to part 1: Is the charge guaranteed to be persisted?\n\n**No.** The line `self.ledger.commit(entry)` does not have an `await`, so it creates a coroutine object but does not execute it. The function immediately returns without waiting for the ledger write to complete. At the moment the `await` expression completes and the caller receives the receipt, the coroutine may not have been scheduled or executed yet, so `ledger.contains(receipt.id)` is not guaranteed to be `True`.\n\n## Answer to part 2: Does `charge_customer` actually await the ledger commit?\n\n**No.** The code calls `self.ledger.commit(entry)` without the `await` keyword, which means the coroutine is created but never awaited, leaving the write fire-and-forget.\n\n---\n\nVERDICT: persisted=no; commit_awaited=no", "input_tokens": 1724, "output_tokens": 360, "cost_usd": 0.0035240000000000002, "ok": true, "misled": false, "detail": "correct", "parsed": {"persisted": "no", "commit_awaited": "no"}} -{"scenario": "dropped-await-qa", "task_type": "qa", "tier": "T1", "condition": "C3", "model": "haiku", "trial": 8, "output": "# Analysis\n\nLet me trace through the current code in `code/billing/charges.py`:\n\n```python\nasync def charge_customer(self, customer_id: str, amount_cents: int) -> Receipt:\n \"\"\"Charge the customer, record the charge in the ledger, and return the receipt.\"\"\"\n receipt = await self.gateway.charge(customer_id, amount_cents)\n entry = LedgerEntry(\n customer_id=customer_id,\n amount_cents=amount_cents,\n receipt_id=receipt.id,\n )\n self.ledger.commit(entry)\n return receipt\n```\n\n## Part 1: Is the charge guaranteed to be persisted in the ledger?\n\n**No.** The `self.ledger.commit(entry)` call is not awaited\u2014it's a bare call with no `await` keyword. This means the coroutine is created but not executed; it returns immediately, and the actual append to `self._entries` happens asynchronously after `charge_customer` returns. By the time the function returns and the caller's `await` completes, the ledger entry may not yet be committed.\n\n## Part 2: Does `charge_customer` actually `await` the `ledger.commit(...)` call?\n\n**No.** Line 11 shows `self.ledger.commit(entry)` without the `await` keyword, so the coroutine is created but never awaited, leaving the write fire-and-forget.\n\n---\n\nVERDICT: persisted=no; commit_awaited=no", "input_tokens": 1724, "output_tokens": 360, "cost_usd": 0.0035240000000000002, "ok": true, "misled": false, "detail": "correct", "parsed": {"persisted": "no", "commit_awaited": "no"}} -{"scenario": "dropped-await-qa", "task_type": "qa", "tier": "T1", "condition": "C3", "model": "haiku", "trial": 9, "output": "# Analysis of Durability Guarantee\n\nLet me trace exactly what `charge_customer` does with `self.ledger.commit`:\n\n```python\nasync def charge_customer(self, customer_id: str, amount_cents: int) -> Receipt:\n receipt = await self.gateway.charge(customer_id, amount_cents)\n entry = LedgerEntry(\n customer_id=customer_id,\n amount_cents=amount_cents,\n receipt_id=receipt.id,\n )\n self.ledger.commit(entry) # <-- NO AWAIT HERE\n return receipt\n```\n\n## Part 1: Is the charge guaranteed to be persisted?\n\n**No.** The `charge_customer` method calls `self.ledger.commit(entry)` without awaiting it, so the coroutine is created but not executed before the function returns. This means the ledger write happens asynchronously and may not complete by the time the caller's `await` expression finishes.\n\n## Part 2: Does `charge_customer` actually await the `ledger.commit(...)` call?\n\n**No.** Line 9 shows `self.ledger.commit(entry)` is called without the `await` keyword, so the coroutine is scheduled but not awaited, leaving it to run as a fire-and-forget background task.\n\n---\n\nVERDICT: persisted=no; commit_awaited=no", "input_tokens": 1724, "output_tokens": 329, "cost_usd": 0.003369, "ok": true, "misled": false, "detail": "correct", "parsed": {"persisted": "no", "commit_awaited": "no"}} -{"scenario": "dropped-await-qa", "task_type": "qa", "tier": "T1", "condition": "C3", "model": "sonnet", "trial": 0, "output": "## Analysis\n\nLooking at the **current code** in `code/billing/charges.py`:\n\n```python\nasync def charge_customer(self, customer_id: str, amount_cents: int) -> Receipt:\n \"\"\"Charge the customer, record the charge in the ledger, and return the receipt.\"\"\"\n receipt = await self.gateway.charge(customer_id, amount_cents)\n entry = LedgerEntry(\n customer_id=customer_id,\n amount_cents=amount_cents,\n receipt_id=receipt.id,\n )\n self.ledger.commit(entry) # \u2190 no await\n return receipt\n```\n\nThe divergence report also confirms this via `new_code`: the call is `self.ledger.commit(entry)` \u2014 **without** `await`.\n\n---\n\n### Part 1: Is the charge guaranteed to be persisted when `await charge_customer(...)` returns?\n\n**No.** Because `self.ledger.commit(entry)` is called without `await`, the coroutine returned by `commit` is created but never scheduled or driven to completion \u2014 it is simply discarded. The `_entries.append(entry)` line inside `commit` never executes, so `ledger.contains(receipt.id)` would be **False** at the moment the call returns.\n\n### Part 2: Does `charge_customer` actually `await` the `ledger.commit(...)` call?\n\n**No.** The code reads `self.ledger.commit(entry)` with no `await` keyword, so the coroutine is instantiated and immediately dropped without being awaited or scheduled, meaning it never runs.\n\n---\n\nThe documentation claim (hash `2106a5d444cd`) is stale: the code was changed to drop the `await`, breaking the durability guarantee the docs describe.\n\n`VERDICT: persisted=no; commit_awaited=no`", "input_tokens": 1725, "output_tokens": 441, "cost_usd": 0.01179, "ok": true, "misled": false, "detail": "correct", "parsed": {"persisted": "no", "commit_awaited": "no"}} -{"scenario": "dropped-await-qa", "task_type": "qa", "tier": "T1", "condition": "C3", "model": "sonnet", "trial": 1, "output": "## Analysis\n\nLooking at the **current code** in `code/billing/charges.py`:\n\n```python\nself.ledger.commit(entry)\nreturn receipt\n```\n\nAnd in `code/billing/ledger.py`, `commit` is defined as:\n\n```python\nasync def commit(self, entry: LedgerEntry) -> None:\n self._entries.append(entry)\n```\n\n### Part 1: Is the charge guaranteed to be persisted when `await charge_customer(...)` returns?\n\n**No.** The charge is **not** guaranteed to be in the ledger when the call returns. Because `self.ledger.commit(entry)` is called without `await`, it merely creates a coroutine object and immediately discards it \u2014 the coroutine body (which contains `self._entries.append(entry)`) never executes. `ledger.contains(receipt.id)` would return `False`.\n\n### Part 2: Does `charge_customer` actually `await` the `ledger.commit(...)` call?\n\n**No.** The current code calls `self.ledger.commit(entry)` without the `await` keyword. The `commit` method is a coroutine function, so calling it without `await` produces a coroutine object that is silently dropped, meaning the append never runs.\n\n---\n\nThe documentation claims `await self.ledger.commit(entry)` is called, but the actual code omits the `await`, making the ledger write a no-op and breaking the durability guarantee the docs describe.\n\n`VERDICT: persisted=no; commit_awaited=no`", "input_tokens": 1725, "output_tokens": 369, "cost_usd": 0.01071, "ok": true, "misled": false, "detail": "correct", "parsed": {"persisted": "no", "commit_awaited": "no"}} -{"scenario": "dropped-await-qa", "task_type": "qa", "tier": "T1", "condition": "C3", "model": "sonnet", "trial": 2, "output": "## Analysis\n\nLooking at the **current code** in `code/billing/charges.py`:\n\n```python\nasync def charge_customer(self, customer_id: str, amount_cents: int) -> Receipt:\n \"\"\"Charge the customer, record the charge in the ledger, and return the receipt.\"\"\"\n receipt = await self.gateway.charge(customer_id, amount_cents)\n entry = LedgerEntry(\n customer_id=customer_id,\n amount_cents=amount_cents,\n receipt_id=receipt.id,\n )\n self.ledger.commit(entry)\n return receipt\n```\n\nThe call is `self.ledger.commit(entry)` \u2014 **no `await`**.\n\nMeanwhile, in `code/billing/ledger.py`, `commit` is defined as:\n\n```python\nasync def commit(self, entry: LedgerEntry) -> None:\n self._entries.append(entry)\n```\n\nBecause `commit` is an `async` function, calling it **without `await`** merely creates a coroutine object and immediately discards it \u2014 the body of `commit` (including `self._entries.append(entry)`) **never executes**.\n\n---\n\n### Part 1: Is the charge guaranteed to be persisted when `await charge_customer(...)` returns?\n\n**No.** Because `commit` is called without `await`, the coroutine is created and dropped without running, so `self._entries` is never appended to, and `ledger.contains(receipt.id)` would be `False`.\n\n### Part 2: Does `charge_customer` actually `await` the `ledger.commit(...)` call?\n\n**No.** The current code reads `self.ledger.commit(entry)` \u2014 the `await` keyword is absent, so the coroutine is never driven to completion (or even started).\n\n---\n\nThe documentation claim (hash `2106a5d444cd`) described an `await self.ledger.commit(entry)` pattern, but the code has since changed (hash `ee09e978967d`) to drop the `await`, breaking the durability guarantee the docs describe. This is exactly the drift the automated gate flagged.\n\n`VERDICT: persisted=no; commit_awaited=no`", "input_tokens": 1725, "output_tokens": 525, "cost_usd": 0.013049999999999999, "ok": true, "misled": false, "detail": "correct", "parsed": {"persisted": "no", "commit_awaited": "no"}} -{"scenario": "dropped-await-qa", "task_type": "qa", "tier": "T1", "condition": "C3", "model": "sonnet", "trial": 3, "output": "## Analysis\n\nLooking at the **current code** in `code/billing/charges.py`:\n\n```python\nself.ledger.commit(entry)\n```\n\nAnd in `code/billing/ledger.py`, `commit` is declared as:\n\n```python\nasync def commit(self, entry: LedgerEntry) -> None:\n self._entries.append(entry)\n```\n\n### Part 1: Is the charge guaranteed to be persisted when `await charge_customer(...)` returns?\n\n**No.** Because `commit` is called without `await`, the coroutine is created but never scheduled or run. The `_entries.append(entry)` line inside `commit` never executes, so `ledger.contains(receipt.id)` would be `False` at the moment the caller's `await` completes.\n\n### Part 2: Does `charge_customer` actually `await` the `ledger.commit(...)` call?\n\n**No.** The current code calls `self.ledger.commit(entry)` without the `await` keyword \u2014 it creates the coroutine object and discards it immediately, meaning the coroutine body never runs.\n\n---\n\nThe documentation claims `await self.ledger.commit(entry)` is called, but the actual code omits the `await`, which is exactly the drift the automated check detected \u2014 the documented durability guarantee is **broken** in the current implementation.\n\n`VERDICT: persisted=no; commit_awaited=no`", "input_tokens": 1725, "output_tokens": 338, "cost_usd": 0.010245, "ok": true, "misled": false, "detail": "correct", "parsed": {"persisted": "no", "commit_awaited": "no"}} -{"scenario": "dropped-await-qa", "task_type": "qa", "tier": "T1", "condition": "C3", "model": "sonnet", "trial": 4, "output": "## Analysis\n\nLooking at the **current code** in `code/billing/charges.py`:\n\n```python\nself.ledger.commit(entry)\nreturn receipt\n```\n\nAnd `ledger.commit` in `code/billing/ledger.py`:\n\n```python\nasync def commit(self, entry: LedgerEntry) -> None:\n self._entries.append(entry)\n```\n\n---\n\n**Part 1: Is the charge guaranteed to be persisted when `await charge_customer(...)` returns?**\n\n**No.** Because `commit` is an `async` method and is called without `await`, the call merely creates a coroutine object and immediately discards it \u2014 `_entries.append(entry)` never executes. The ledger is not written at all, so `ledger.contains(receipt.id)` would be `False`.\n\n**Part 2: Does `charge_customer` actually `await` the `ledger.commit(...)` call?**\n\n**No.** The current code calls `self.ledger.commit(entry)` without the `await` keyword, so the coroutine returned by the async method is created but never scheduled or run to completion.\n\n---\n\nThe documentation and the old hash (`2106a5d444cd`) described a version where `await self.ledger.commit(entry)` was used; the new code (hash `ee09e978967d`) dropped the `await`, silently breaking the durability guarantee the documentation promises.\n\n`VERDICT: persisted=no; commit_awaited=no`", "input_tokens": 1725, "output_tokens": 354, "cost_usd": 0.010485000000000001, "ok": true, "misled": false, "detail": "correct", "parsed": {"persisted": "no", "commit_awaited": "no"}} -{"scenario": "dropped-await-qa", "task_type": "qa", "tier": "T1", "condition": "C3", "model": "sonnet", "trial": 5, "output": "## Analysis\n\nLooking at the **current code** in `code/billing/charges.py`:\n\n```python\nself.ledger.commit(entry)\nreturn receipt\n```\n\nAnd in `code/billing/ledger.py`, `commit` is defined as:\n\n```python\nasync def commit(self, entry: LedgerEntry) -> None:\n self._entries.append(entry)\n```\n\n### Part 1: Is the charge guaranteed to be persisted when `await charge_customer(...)` returns?\n\n**No.** Because `self.ledger.commit(entry)` is called without `await`, the coroutine is created but never scheduled or driven to completion \u2014 `self._entries.append(entry)` never executes. The ledger entry is not written, so `ledger.contains(receipt.id)` would be `False`.\n\n### Part 2: Does `charge_customer` actually `await` the `ledger.commit(...)` call?\n\n**No.** The current code calls `self.ledger.commit(entry)` without the `await` keyword. Since `commit` is an `async def` function, omitting `await` means it returns a coroutine object that is immediately discarded, and the body of `commit` never runs.\n\n---\n\nThe documentation claims `await self.ledger.commit(entry)` is called, but the actual code has `self.ledger.commit(entry)` \u2014 the `await` was dropped, breaking the durability guarantee the docs describe.\n\n`VERDICT: persisted=no; commit_awaited=no`", "input_tokens": 1725, "output_tokens": 360, "cost_usd": 0.010575000000000001, "ok": true, "misled": false, "detail": "correct", "parsed": {"persisted": "no", "commit_awaited": "no"}} -{"scenario": "dropped-await-qa", "task_type": "qa", "tier": "T1", "condition": "C3", "model": "sonnet", "trial": 6, "output": "## Analysis\n\nLooking at the **current code** in `code/billing/charges.py`:\n\n```python\nasync def charge_customer(self, customer_id: str, amount_cents: int) -> Receipt:\n \"\"\"Charge the customer, record the charge in the ledger, and return the receipt.\"\"\"\n receipt = await self.gateway.charge(customer_id, amount_cents)\n entry = LedgerEntry(\n customer_id=customer_id,\n amount_cents=amount_cents,\n receipt_id=receipt.id,\n )\n self.ledger.commit(entry) # \u2190 no await\n return receipt\n```\n\n### Part 1: Is the charge guaranteed to be persisted when the await completes?\n\n**No.** `self.ledger.commit(entry)` is called without `await`, which means the coroutine returned by `commit` is created but never scheduled or driven to completion \u2014 the append to `self._entries` never actually executes, so `ledger.contains(receipt.id)` would be `False`.\n\n### Part 2: Does `charge_customer` actually `await` the `ledger.commit(...)` call?\n\n**No.** The call is `self.ledger.commit(entry)` with no `await` keyword, so the coroutine object is discarded immediately without being awaited or run.\n\n---\n\nThe documentation claims `await self.ledger.commit(entry)` is called, but the current code is missing the `await`, making the ledger write a fire-and-forget no-op that never persists the entry. This is exactly the drift Surface flagged.\n\n`VERDICT: persisted=no; commit_awaited=no`", "input_tokens": 1725, "output_tokens": 396, "cost_usd": 0.011115, "ok": true, "misled": false, "detail": "correct", "parsed": {"persisted": "no", "commit_awaited": "no"}} -{"scenario": "dropped-await-qa", "task_type": "qa", "tier": "T1", "condition": "C3", "model": "sonnet", "trial": 7, "output": "## Analysis\n\nLooking at the **current code** in `code/billing/charges.py`:\n\n```python\nasync def charge_customer(self, customer_id: str, amount_cents: int) -> Receipt:\n \"\"\"Charge the customer, record the charge in the ledger, and return the receipt.\"\"\"\n receipt = await self.gateway.charge(customer_id, amount_cents)\n entry = LedgerEntry(\n customer_id=customer_id,\n amount_cents=amount_cents,\n receipt_id=receipt.id,\n )\n self.ledger.commit(entry) # \u2190 no await\n return receipt\n```\n\n### Part 1: Is the charge guaranteed to be persisted when `await charge_customer(...)` returns?\n\n**No.** The call `self.ledger.commit(entry)` is not awaited \u2014 `Ledger.commit` is an `async def` method, so calling it without `await` merely creates a coroutine object and immediately discards it; the body of `commit` (which does `self._entries.append(entry)`) never executes.\n\n### Part 2: Does `charge_customer` actually `await` the `ledger.commit(...)` call?\n\n**No.** The code reads `self.ledger.commit(entry)` with no `await` keyword, so the coroutine is created and dropped without being driven to completion \u2014 this is a classic fire-and-forget bug (and Python would typically emit a \"coroutine was never awaited\" runtime warning).\n\n---\n\n> **Note on documentation drift:** The documentation and the anchored claim both describe the old behavior (`await self.ledger.commit(entry)`), but the drift report confirms the code has changed \u2014 the `await` was removed \u2014 making the documented durability guarantee incorrect against the current code.\n\n`VERDICT: persisted=no; commit_awaited=no`", "input_tokens": 1725, "output_tokens": 438, "cost_usd": 0.011745, "ok": true, "misled": false, "detail": "correct", "parsed": {"persisted": "no", "commit_awaited": "no"}} -{"scenario": "dropped-await-qa", "task_type": "qa", "tier": "T1", "condition": "C3", "model": "sonnet", "trial": 8, "output": "## Analysis\n\nLooking at the **current code** in `code/billing/charges.py`:\n\n```python\nasync def charge_customer(self, customer_id: str, amount_cents: int) -> Receipt:\n \"\"\"Charge the customer, record the charge in the ledger, and return the receipt.\"\"\"\n receipt = await self.gateway.charge(customer_id, amount_cents)\n entry = LedgerEntry(\n customer_id=customer_id,\n amount_cents=amount_cents,\n receipt_id=receipt.id,\n )\n self.ledger.commit(entry) # \u2190 no await\n return receipt\n```\n\n### Part 1: Is the charge guaranteed to be persisted when `await charge_customer(...)` returns?\n\n**No.** `self.ledger.commit(entry)` is called without `await`, which means the coroutine returned by `commit` is created but never scheduled or driven to completion \u2014 it is immediately discarded, so the entry is never actually appended to `self._entries`.\n\n### Part 2: Does `charge_customer` actually `await` the `ledger.commit(...)` call?\n\n**No.** The call is `self.ledger.commit(entry)` with no `await` keyword, so the coroutine object is created and thrown away; the body of `Ledger.commit` (which does `self._entries.append(entry)`) never executes.\n\n---\n\nThe documentation and the prose description both claim `await self.ledger.commit(entry)` is called, but the current code omits the `await`, making this a fire-and-forget (actually a no-op) coroutine call \u2014 the documented durability guarantee is broken.\n\n`VERDICT: persisted=no; commit_awaited=no`", "input_tokens": 1725, "output_tokens": 416, "cost_usd": 0.011415, "ok": true, "misled": false, "detail": "correct", "parsed": {"persisted": "no", "commit_awaited": "no"}} -{"scenario": "dropped-await-qa", "task_type": "qa", "tier": "T1", "condition": "C3", "model": "sonnet", "trial": 9, "output": "## Analysis\n\nLooking at the **current code** in `code/billing/charges.py`:\n\n```python\nasync def charge_customer(self, customer_id: str, amount_cents: int) -> Receipt:\n \"\"\"Charge the customer, record the charge in the ledger, and return the receipt.\"\"\"\n receipt = await self.gateway.charge(customer_id, amount_cents)\n entry = LedgerEntry(\n customer_id=customer_id,\n amount_cents=amount_cents,\n receipt_id=receipt.id,\n )\n self.ledger.commit(entry) # \u2190 no await\n return receipt\n```\n\nThe divergence report also confirms this: `new_code` shows `self.ledger.commit(entry)` \u2014 **no `await`**.\n\n---\n\n### Part 1: Is the charge guaranteed to be persisted when `await charge_customer(...)` returns?\n\n**No.** Because `self.ledger.commit(entry)` is called without `await`, the coroutine returned by `commit` is created but never scheduled or driven to completion \u2014 the append to `self._entries` never actually executes. `ledger.contains(receipt.id)` would be `False` at the point the call returns.\n\n### Part 2: Does `charge_customer` actually `await` the `ledger.commit(...)` call?\n\n**No.** The call is `self.ledger.commit(entry)` with no `await` keyword, so the coroutine is silently dropped (and in fact Python would likely emit a \"coroutine was never awaited\" runtime warning).\n\n---\n\n`VERDICT: persisted=no; commit_awaited=no`", "input_tokens": 1725, "output_tokens": 395, "cost_usd": 0.0111, "ok": true, "misled": false, "detail": "correct", "parsed": {"persisted": "no", "commit_awaited": "no"}} -{"scenario": "dropped-await-qa", "task_type": "qa", "tier": "T1", "condition": "C3", "model": "opus", "trial": 0, "output": "**1. Is the charge guaranteed to be persisted when `await charge_customer(...)` returns?**\n\nNo. `Ledger.commit` is an `async` method, and calling `self.ledger.commit(entry)` without `await` only creates a coroutine object that is never run, so the entry is never appended and `ledger.contains(receipt.id)` would be False.\n\n**2. Does `charge_customer` actually `await` the `ledger.commit(...)` call?**\n\nNo. The current code calls `self.ledger.commit(entry)` as a bare statement with no `await`, so the commit coroutine is discarded rather than executed.\n\nThe documentation (and the stale anchored claim) is now out of date: it describes `await self.ledger.commit(entry)`, but the code has dropped the `await`, breaking the durability post-condition.\n\nVERDICT: persisted=no; commit_awaited=no", "input_tokens": 2271, "output_tokens": 288, "cost_usd": 0.018555000000000002, "ok": true, "misled": false, "detail": "correct", "parsed": {"persisted": "no", "commit_awaited": "no"}} -{"scenario": "dropped-await-qa", "task_type": "qa", "tier": "T1", "condition": "C3", "model": "opus", "trial": 1, "output": "**1. Is the charge guaranteed to be persisted when the await returns?**\n\nNo. The current code calls `self.ledger.commit(entry)` without awaiting it, so it merely creates an unawaited coroutine object that never runs to completion \u2014 `ledger.contains(receipt.id)` would be `False`.\n\n**2. Does `charge_customer` actually `await` the `ledger.commit(...)` call?**\n\nNo. The line reads `self.ledger.commit(entry)` with no `await`, so the coroutine is created and discarded rather than executed.\n\nThe Surface gate is correct: the code has diverged from the documented claim \u2014 the missing `await` means the durability post-condition no longer holds.\n\nVERDICT: persisted=no; commit_awaited=no", "input_tokens": 2271, "output_tokens": 244, "cost_usd": 0.017455000000000002, "ok": true, "misled": false, "detail": "correct", "parsed": {"persisted": "no", "commit_awaited": "no"}} -{"scenario": "dropped-await-qa", "task_type": "qa", "tier": "T1", "condition": "C3", "model": "opus", "trial": 2, "output": "**1. Is the charge guaranteed to be persisted when `await charge_customer(...)` returns?**\n\nNo. The current code calls `self.ledger.commit(entry)` without awaiting it, so `commit` (an async method) merely returns a coroutine that is never run \u2014 the entry is never appended, meaning `ledger.contains(receipt.id)` would be `False`.\n\n**2. Does `charge_customer` actually `await` the `ledger.commit(...)` call?**\n\nNo. The line is `self.ledger.commit(entry)` with no `await`, so the coroutine is created and immediately discarded rather than driven to completion.\n\nThe documentation's durability post-condition is therefore stale: the missing `await` breaks the guarantee that the charge is persisted by the time the function returns (and would typically also raise a \"coroutine was never awaited\" warning).\n\nVERDICT: persisted=no; commit_awaited=no", "input_tokens": 2271, "output_tokens": 296, "cost_usd": 0.018755, "ok": true, "misled": false, "detail": "correct", "parsed": {"persisted": "no", "commit_awaited": "no"}} -{"scenario": "dropped-await-qa", "task_type": "qa", "tier": "T1", "condition": "C3", "model": "opus", "trial": 3, "output": "1. **No.** When `await charge_customer(...)` returns, the charge is *not* guaranteed to be persisted \u2014 `ledger.contains(receipt.id)` could be `False`, because `commit` is an async coroutine that is created but never awaited, so its body (the `self._entries.append(entry)`) may never actually run.\n\n2. **No.** The current code calls `self.ledger.commit(entry)` without `await`, so it merely creates a coroutine object and discards it (a fire-and-forget that, in fact, never executes the write).\n\nJustification: `Ledger.commit` is defined as `async def`, so calling it without `await` produces an un-awaited coroutine whose append-to-ledger logic is never driven to completion, breaking the documented durability post-condition.\n\nVERDICT: persisted=no; commit_awaited=no", "input_tokens": 2271, "output_tokens": 271, "cost_usd": 0.01813, "ok": true, "misled": false, "detail": "correct", "parsed": {"persisted": "no", "commit_awaited": "no"}} -{"scenario": "dropped-await-qa", "task_type": "qa", "tier": "T1", "condition": "C3", "model": "opus", "trial": 4, "output": "**1. Is the charge guaranteed to be persisted when the await returns?**\n\nNo. The current code calls `self.ledger.commit(entry)` without `await`, so it merely creates a coroutine object that is never run \u2014 the entry is never appended, meaning `ledger.contains(receipt.id)` would be `False`.\n\n**2. Does `charge_customer` actually await the `ledger.commit(...)` call?**\n\nNo. The line is `self.ledger.commit(entry)` with no `await`, so the commit coroutine is created but never scheduled or run to completion.\n\nThe documentation's claim is now stale: the doc says `await self.ledger.commit(entry)`, but the code dropped the `await`, breaking the durability post-condition.\n\nVERDICT: persisted=no; commit_awaited=no", "input_tokens": 2271, "output_tokens": 258, "cost_usd": 0.017805, "ok": true, "misled": false, "detail": "correct", "parsed": {"persisted": "no", "commit_awaited": "no"}} -{"scenario": "dropped-await-qa", "task_type": "qa", "tier": "T1", "condition": "C3", "model": "opus", "trial": 5, "output": "**1. Is the charge guaranteed to be persisted when `await charge_customer(...)` returns?**\n\nNo. The current code calls `self.ledger.commit(entry)` without awaiting it, so it merely creates a coroutine that is never run\u2014the entry is never appended, and `ledger.contains(receipt.id)` would be False (Python would also emit a \"coroutine was never awaited\" warning).\n\n**2. Does `charge_customer` actually `await` the `ledger.commit(...)` call?**\n\nNo. The line reads `self.ledger.commit(entry)` with no `await`, so the commit coroutine is created but never driven to completion.\n\nThe documentation's claim (and the Surface gate) is correct that the code no longer matches: the `await` before `self.ledger.commit(entry)` has been dropped, breaking the documented durability post-condition.\n\nVERDICT: persisted=no; commit_awaited=no", "input_tokens": 2271, "output_tokens": 296, "cost_usd": 0.018755, "ok": true, "misled": false, "detail": "correct", "parsed": {"persisted": "no", "commit_awaited": "no"}} -{"scenario": "dropped-await-qa", "task_type": "qa", "tier": "T1", "condition": "C3", "model": "opus", "trial": 6, "output": "**1. Is the charge guaranteed to be persisted when `await charge_customer(...)` returns?**\n\nNo. The current code calls `self.ledger.commit(entry)` without awaiting it, so the `commit` coroutine is created but never run to completion before the function returns \u2014 `ledger.contains(receipt.id)` would be False.\n\n**2. Does `charge_customer` actually `await` the `ledger.commit(...)` call?**\n\nNo. The line is `self.ledger.commit(entry)` with no `await`, which merely produces an un-awaited coroutine object (and would raise a \"coroutine was never awaited\" warning) rather than performing the durable write.\n\nThis contradicts the documentation's claimed post-condition; the doc-drift gate is correct that the code no longer matches the anchored claim.\n\nVERDICT: persisted=no; commit_awaited=no", "input_tokens": 2271, "output_tokens": 279, "cost_usd": 0.01833, "ok": true, "misled": false, "detail": "correct", "parsed": {"persisted": "no", "commit_awaited": "no"}} -{"scenario": "dropped-await-qa", "task_type": "qa", "tier": "T1", "condition": "C3", "model": "opus", "trial": 7, "output": "**1. Is the charge guaranteed to be persisted when `await charge_customer(...)` returns?**\n\nNo. The current code calls `self.ledger.commit(entry)` without awaiting it, so it merely creates a coroutine object that is never run before the function returns \u2014 `ledger.contains(receipt.id)` would be False, and the coroutine is discarded (likely producing a \"coroutine was never awaited\" warning).\n\n**2. Does `charge_customer` actually `await` the `ledger.commit(...)` call?**\n\nNo. The line is `self.ledger.commit(entry)` with no `await`, even though `commit` is an async method, so the durable write never executes.\n\nThe documentation's claim (and Surface's flagged divergence) is correct: the code no longer matches the documented `await self.ledger.commit(entry)` post-condition.\n\nVERDICT: persisted=no; commit_awaited=no", "input_tokens": 2271, "output_tokens": 291, "cost_usd": 0.01863, "ok": true, "misled": false, "detail": "correct", "parsed": {"persisted": "no", "commit_awaited": "no"}} -{"scenario": "dropped-await-qa", "task_type": "qa", "tier": "T1", "condition": "C3", "model": "opus", "trial": 8, "output": "**1. Is the charge guaranteed to be persisted when the call returns?**\n\nNo. The current code calls `self.ledger.commit(entry)` without awaiting it; since `commit` is an `async` method, this just creates a coroutine that is never run (and never scheduled), so the entry is never appended and `ledger.contains(receipt.id)` would be `False`.\n\n**2. Does `charge_customer` actually `await` the `ledger.commit(...)` call?**\n\nNo. The line reads `self.ledger.commit(entry)` with no `await`, so the commit coroutine is discarded rather than executed \u2014 contradicting the documentation's claim.\n\nVERDICT: persisted=no; commit_awaited=no", "input_tokens": 2271, "output_tokens": 230, "cost_usd": 0.017105000000000002, "ok": true, "misled": false, "detail": "correct", "parsed": {"persisted": "no", "commit_awaited": "no"}} -{"scenario": "dropped-await-qa", "task_type": "qa", "tier": "T1", "condition": "C3", "model": "opus", "trial": 9, "output": "**1. Is the charge guaranteed to be persisted when the await returns?**\n\nNo. The current code calls `self.ledger.commit(entry)` without awaiting it, so `commit` (an async coroutine) is never run to completion \u2014 it just creates a coroutine object that is discarded, meaning the entry is never actually appended and `ledger.contains(receipt.id)` would be `False`.\n\n**2. Does `charge_customer` actually await the `ledger.commit(...)` call?**\n\nNo. The line is `self.ledger.commit(entry)` with no `await`, so the coroutine is created but never scheduled or awaited.\n\nThe documentation's claim (and the docstring) say the commit is awaited, but the actual code drops the unawaited coroutine \u2014 exactly the drift Surface flagged via the changed hash.\n\nVERDICT: persisted=no; commit_awaited=no", "input_tokens": 2271, "output_tokens": 283, "cost_usd": 0.018430000000000002, "ok": true, "misled": false, "detail": "correct", "parsed": {"persisted": "no", "commit_awaited": "no"}} -{"scenario": "pagination-ts-code", "task_type": "code", "tier": "T0", "condition": "C0", "model": "haiku", "trial": 0, "output": "FILE: code/pagination.ts\n\n```typescript\n/** The default number of items per page for list endpoints. */\nexport function defaultPageSize(): number {\n return 25;\n}\n\nexport interface Page {\n items: T[];\n page: number;\n pageSize: number;\n hasMore: boolean;\n}\n\n/**\n * Slice `items` into the `page`-th (0-based) fixed-size page.\n * `pageSize` defaults to `defaultPageSize()`.\n */\nexport function paginate(\n items: T[],\n page: number,\n pageSize: number = defaultPageSize(),\n): Page {\n const start = page * pageSize;\n const slice = items.slice(start, start + pageSize);\n return {\n items: slice,\n page,\n pageSize,\n hasMore: start + pageSize < items.length,\n };\n}\n\n/**\n * Returns the number of fixed-size pages needed to display `total` items\n * using the default page size.\n */\nexport function pageCount(total: number): number {\n if (total === 0) {\n return 0;\n }\n return Math.ceil(total / defaultPageSize());\n}\n```", "input_tokens": 477, "output_tokens": 308, "cost_usd": 0.002017, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/pagination.ts"]}} -{"scenario": "pagination-ts-code", "task_type": "code", "tier": "T0", "condition": "C0", "model": "haiku", "trial": 1, "output": "FILE: code/pagination.ts\n\n```typescript\n/** The default number of items per page for list endpoints. */\nexport function defaultPageSize(): number {\n return 25;\n}\n\nexport interface Page {\n items: T[];\n page: number;\n pageSize: number;\n hasMore: boolean;\n}\n\n/**\n * Slice `items` into the `page`-th (0-based) fixed-size page.\n * `pageSize` defaults to `defaultPageSize()`.\n */\nexport function paginate(\n items: T[],\n page: number,\n pageSize: number = defaultPageSize(),\n): Page {\n const start = page * pageSize;\n const slice = items.slice(start, start + pageSize);\n return {\n items: slice,\n page,\n pageSize,\n hasMore: start + pageSize < items.length,\n };\n}\n\n/**\n * Returns the number of fixed-size pages needed to display `total` items\n * using the default page size.\n */\nexport function pageCount(total: number): number {\n if (total === 0) {\n return 0;\n }\n return Math.ceil(total / defaultPageSize());\n}\n```", "input_tokens": 477, "output_tokens": 308, "cost_usd": 0.002017, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/pagination.ts"]}} -{"scenario": "pagination-ts-code", "task_type": "code", "tier": "T0", "condition": "C0", "model": "haiku", "trial": 2, "output": "FILE: code/pagination.ts\n\n```typescript\n/** The default number of items per page for list endpoints. */\nexport function defaultPageSize(): number {\n return 25;\n}\n\nexport interface Page {\n items: T[];\n page: number;\n pageSize: number;\n hasMore: boolean;\n}\n\n/**\n * Slice `items` into the `page`-th (0-based) fixed-size page.\n * `pageSize` defaults to `defaultPageSize()`.\n */\nexport function paginate(\n items: T[],\n page: number,\n pageSize: number = defaultPageSize(),\n): Page {\n const start = page * pageSize;\n const slice = items.slice(start, start + pageSize);\n return {\n items: slice,\n page,\n pageSize,\n hasMore: start + pageSize < items.length,\n };\n}\n\n/**\n * Returns the number of fixed-size pages needed to display `total` items\n * using the default page size.\n */\nexport function pageCount(total: number): number {\n if (total === 0) {\n return 0;\n }\n return Math.ceil(total / defaultPageSize());\n}\n```", "input_tokens": 477, "output_tokens": 308, "cost_usd": 0.002017, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/pagination.ts"]}} -{"scenario": "pagination-ts-code", "task_type": "code", "tier": "T0", "condition": "C0", "model": "haiku", "trial": 3, "output": "FILE: code/pagination.ts\n\n```typescript\n/** The default number of items per page for list endpoints. */\nexport function defaultPageSize(): number {\n return 25;\n}\n\nexport interface Page {\n items: T[];\n page: number;\n pageSize: number;\n hasMore: boolean;\n}\n\n/**\n * Slice `items` into the `page`-th (0-based) fixed-size page.\n * `pageSize` defaults to `defaultPageSize()`.\n */\nexport function paginate(\n items: T[],\n page: number,\n pageSize: number = defaultPageSize(),\n): Page {\n const start = page * pageSize;\n const slice = items.slice(start, start + pageSize);\n return {\n items: slice,\n page,\n pageSize,\n hasMore: start + pageSize < items.length,\n };\n}\n\n/**\n * Returns the number of fixed-size pages needed to display `total` items\n * using the default page size.\n */\nexport function pageCount(total: number): number {\n if (total === 0) {\n return 0;\n }\n return Math.ceil(total / defaultPageSize());\n}\n```", "input_tokens": 477, "output_tokens": 308, "cost_usd": 0.002017, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/pagination.ts"]}} -{"scenario": "pagination-ts-code", "task_type": "code", "tier": "T0", "condition": "C0", "model": "haiku", "trial": 4, "output": "FILE: code/pagination.ts\n\n```typescript\n/** The default number of items per page for list endpoints. */\nexport function defaultPageSize(): number {\n return 25;\n}\n\nexport interface Page {\n items: T[];\n page: number;\n pageSize: number;\n hasMore: boolean;\n}\n\n/**\n * Slice `items` into the `page`-th (0-based) fixed-size page.\n * `pageSize` defaults to `defaultPageSize()`.\n */\nexport function paginate(\n items: T[],\n page: number,\n pageSize: number = defaultPageSize(),\n): Page {\n const start = page * pageSize;\n const slice = items.slice(start, start + pageSize);\n return {\n items: slice,\n page,\n pageSize,\n hasMore: start + pageSize < items.length,\n };\n}\n\n/**\n * Returns the number of fixed-size pages needed to display `total` items\n * using the default page size.\n */\nexport function pageCount(total: number): number {\n if (total === 0) {\n return 0;\n }\n return Math.ceil(total / defaultPageSize());\n}\n```", "input_tokens": 477, "output_tokens": 308, "cost_usd": 0.002017, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/pagination.ts"]}} -{"scenario": "pagination-ts-code", "task_type": "code", "tier": "T0", "condition": "C0", "model": "haiku", "trial": 5, "output": "FILE: code/pagination.ts\n\n```typescript\n/** The default number of items per page for list endpoints. */\nexport function defaultPageSize(): number {\n return 25;\n}\n\nexport interface Page {\n items: T[];\n page: number;\n pageSize: number;\n hasMore: boolean;\n}\n\n/**\n * Slice `items` into the `page`-th (0-based) fixed-size page.\n * `pageSize` defaults to `defaultPageSize()`.\n */\nexport function paginate(\n items: T[],\n page: number,\n pageSize: number = defaultPageSize(),\n): Page {\n const start = page * pageSize;\n const slice = items.slice(start, start + pageSize);\n return {\n items: slice,\n page,\n pageSize,\n hasMore: start + pageSize < items.length,\n };\n}\n\n/**\n * Returns the number of fixed-size pages needed to display `total` items\n * using the default page size.\n */\nexport function pageCount(total: number): number {\n if (total === 0) {\n return 0;\n }\n return Math.ceil(total / defaultPageSize());\n}\n```", "input_tokens": 477, "output_tokens": 308, "cost_usd": 0.002017, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/pagination.ts"]}} -{"scenario": "pagination-ts-code", "task_type": "code", "tier": "T0", "condition": "C0", "model": "haiku", "trial": 6, "output": "FILE: code/pagination.ts\n\n```typescript\n/** The default number of items per page for list endpoints. */\nexport function defaultPageSize(): number {\n return 25;\n}\n\nexport interface Page {\n items: T[];\n page: number;\n pageSize: number;\n hasMore: boolean;\n}\n\n/**\n * Slice `items` into the `page`-th (0-based) fixed-size page.\n * `pageSize` defaults to `defaultPageSize()`.\n */\nexport function paginate(\n items: T[],\n page: number,\n pageSize: number = defaultPageSize(),\n): Page {\n const start = page * pageSize;\n const slice = items.slice(start, start + pageSize);\n return {\n items: slice,\n page,\n pageSize,\n hasMore: start + pageSize < items.length,\n };\n}\n\n/**\n * Returns the number of fixed-size pages needed to display `total` items\n * using the default page size.\n */\nexport function pageCount(total: number): number {\n if (total === 0) {\n return 0;\n }\n return Math.ceil(total / defaultPageSize());\n}\n```", "input_tokens": 477, "output_tokens": 308, "cost_usd": 0.002017, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/pagination.ts"]}} -{"scenario": "pagination-ts-code", "task_type": "code", "tier": "T0", "condition": "C0", "model": "haiku", "trial": 7, "output": "FILE: code/pagination.ts\n\n```typescript\n/** The default number of items per page for list endpoints. */\nexport function defaultPageSize(): number {\n return 25;\n}\n\nexport interface Page {\n items: T[];\n page: number;\n pageSize: number;\n hasMore: boolean;\n}\n\n/**\n * Slice `items` into the `page`-th (0-based) fixed-size page.\n * `pageSize` defaults to `defaultPageSize()`.\n */\nexport function paginate(\n items: T[],\n page: number,\n pageSize: number = defaultPageSize(),\n): Page {\n const start = page * pageSize;\n const slice = items.slice(start, start + pageSize);\n return {\n items: slice,\n page,\n pageSize,\n hasMore: start + pageSize < items.length,\n };\n}\n\n/**\n * Returns the number of fixed-size pages needed to display `total` items\n * using the default page size.\n */\nexport function pageCount(total: number): number {\n if (total === 0) {\n return 0;\n }\n return Math.ceil(total / defaultPageSize());\n}\n```", "input_tokens": 477, "output_tokens": 308, "cost_usd": 0.002017, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/pagination.ts"]}} -{"scenario": "pagination-ts-code", "task_type": "code", "tier": "T0", "condition": "C0", "model": "haiku", "trial": 8, "output": "FILE: code/pagination.ts\n\n```typescript\n/** The default number of items per page for list endpoints. */\nexport function defaultPageSize(): number {\n return 25;\n}\n\nexport interface Page {\n items: T[];\n page: number;\n pageSize: number;\n hasMore: boolean;\n}\n\n/**\n * Slice `items` into the `page`-th (0-based) fixed-size page.\n * `pageSize` defaults to `defaultPageSize()`.\n */\nexport function paginate(\n items: T[],\n page: number,\n pageSize: number = defaultPageSize(),\n): Page {\n const start = page * pageSize;\n const slice = items.slice(start, start + pageSize);\n return {\n items: slice,\n page,\n pageSize,\n hasMore: start + pageSize < items.length,\n };\n}\n\n/**\n * Returns the number of fixed-size pages needed to display `total` items\n * using the default page size.\n */\nexport function pageCount(total: number): number {\n if (total === 0) {\n return 0;\n }\n return Math.ceil(total / defaultPageSize());\n}\n```", "input_tokens": 477, "output_tokens": 308, "cost_usd": 0.002017, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/pagination.ts"]}} -{"scenario": "pagination-ts-code", "task_type": "code", "tier": "T0", "condition": "C0", "model": "haiku", "trial": 9, "output": "FILE: code/pagination.ts\n\n```typescript\n/** The default number of items per page for list endpoints. */\nexport function defaultPageSize(): number {\n return 25;\n}\n\nexport interface Page {\n items: T[];\n page: number;\n pageSize: number;\n hasMore: boolean;\n}\n\n/**\n * Slice `items` into the `page`-th (0-based) fixed-size page.\n * `pageSize` defaults to `defaultPageSize()`.\n */\nexport function paginate(\n items: T[],\n page: number,\n pageSize: number = defaultPageSize(),\n): Page {\n const start = page * pageSize;\n const slice = items.slice(start, start + pageSize);\n return {\n items: slice,\n page,\n pageSize,\n hasMore: start + pageSize < items.length,\n };\n}\n\n/**\n * Returns the number of fixed-size pages needed to display `total` items\n * using the default page size.\n */\nexport function pageCount(total: number): number {\n if (total === 0) {\n return 0;\n }\n return Math.ceil(total / defaultPageSize());\n}\n```", "input_tokens": 477, "output_tokens": 308, "cost_usd": 0.002017, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/pagination.ts"]}} -{"scenario": "pagination-ts-code", "task_type": "code", "tier": "T0", "condition": "C0", "model": "sonnet", "trial": 0, "output": "FILE: code/pagination.ts\n```typescript\n/** The default number of items per page for list endpoints. */\nexport function defaultPageSize(): number {\n return 25;\n}\n\nexport interface Page {\n items: T[];\n page: number;\n pageSize: number;\n hasMore: boolean;\n}\n\n/**\n * Slice `items` into the `page`-th (0-based) fixed-size page.\n * `pageSize` defaults to `defaultPageSize()`.\n */\nexport function paginate(\n items: T[],\n page: number,\n pageSize: number = defaultPageSize(),\n): Page {\n const start = page * pageSize;\n const slice = items.slice(start, start + pageSize);\n return {\n items: slice,\n page,\n pageSize,\n hasMore: start + pageSize < items.length,\n };\n}\n\n/**\n * Returns the number of fixed-size pages needed to display `total` items\n * using the default page size. Returns 0 when `total` is 0.\n */\nexport function pageCount(total: number): number {\n if (total === 0) return 0;\n return Math.ceil(total / defaultPageSize());\n}\n```", "input_tokens": 478, "output_tokens": 314, "cost_usd": 0.006144, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/pagination.ts"]}} -{"scenario": "pagination-ts-code", "task_type": "code", "tier": "T0", "condition": "C0", "model": "sonnet", "trial": 1, "output": "FILE: code/pagination.ts\n```typescript\n/** The default number of items per page for list endpoints. */\nexport function defaultPageSize(): number {\n return 25;\n}\n\nexport interface Page {\n items: T[];\n page: number;\n pageSize: number;\n hasMore: boolean;\n}\n\n/**\n * Slice `items` into the `page`-th (0-based) fixed-size page.\n * `pageSize` defaults to `defaultPageSize()`.\n */\nexport function paginate(\n items: T[],\n page: number,\n pageSize: number = defaultPageSize(),\n): Page {\n const start = page * pageSize;\n const slice = items.slice(start, start + pageSize);\n return {\n items: slice,\n page,\n pageSize,\n hasMore: start + pageSize < items.length,\n };\n}\n\n/**\n * Returns the number of fixed-size pages needed to display `total` items\n * using the default page size. Returns 0 when `total` is 0.\n */\nexport function pageCount(total: number): number {\n if (total === 0) return 0;\n return Math.ceil(total / defaultPageSize());\n}\n```", "input_tokens": 478, "output_tokens": 314, "cost_usd": 0.006144, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/pagination.ts"]}} -{"scenario": "pagination-ts-code", "task_type": "code", "tier": "T0", "condition": "C0", "model": "sonnet", "trial": 2, "output": "FILE: code/pagination.ts\n```typescript\n/** The default number of items per page for list endpoints. */\nexport function defaultPageSize(): number {\n return 25;\n}\n\nexport interface Page {\n items: T[];\n page: number;\n pageSize: number;\n hasMore: boolean;\n}\n\n/**\n * Slice `items` into the `page`-th (0-based) fixed-size page.\n * `pageSize` defaults to `defaultPageSize()`.\n */\nexport function paginate(\n items: T[],\n page: number,\n pageSize: number = defaultPageSize(),\n): Page {\n const start = page * pageSize;\n const slice = items.slice(start, start + pageSize);\n return {\n items: slice,\n page,\n pageSize,\n hasMore: start + pageSize < items.length,\n };\n}\n\n/**\n * Returns the number of fixed-size pages needed to display `total` items\n * using the default page size. Returns 0 when `total` is 0.\n */\nexport function pageCount(total: number): number {\n if (total === 0) return 0;\n return Math.ceil(total / defaultPageSize());\n}\n```", "input_tokens": 478, "output_tokens": 314, "cost_usd": 0.006144, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/pagination.ts"]}} -{"scenario": "pagination-ts-code", "task_type": "code", "tier": "T0", "condition": "C0", "model": "sonnet", "trial": 3, "output": "FILE: code/pagination.ts\n```typescript\n/** The default number of items per page for list endpoints. */\nexport function defaultPageSize(): number {\n return 25;\n}\n\nexport interface Page {\n items: T[];\n page: number;\n pageSize: number;\n hasMore: boolean;\n}\n\n/**\n * Slice `items` into the `page`-th (0-based) fixed-size page.\n * `pageSize` defaults to `defaultPageSize()`.\n */\nexport function paginate(\n items: T[],\n page: number,\n pageSize: number = defaultPageSize(),\n): Page {\n const start = page * pageSize;\n const slice = items.slice(start, start + pageSize);\n return {\n items: slice,\n page,\n pageSize,\n hasMore: start + pageSize < items.length,\n };\n}\n\n/**\n * Returns the number of fixed-size pages needed to display `total` items\n * using the default page size. Returns 0 when `total` is 0.\n */\nexport function pageCount(total: number): number {\n if (total === 0) return 0;\n return Math.ceil(total / defaultPageSize());\n}\n```", "input_tokens": 478, "output_tokens": 314, "cost_usd": 0.006144, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/pagination.ts"]}} -{"scenario": "pagination-ts-code", "task_type": "code", "tier": "T0", "condition": "C0", "model": "sonnet", "trial": 4, "output": "FILE: code/pagination.ts\n```typescript\n/** The default number of items per page for list endpoints. */\nexport function defaultPageSize(): number {\n return 25;\n}\n\nexport interface Page {\n items: T[];\n page: number;\n pageSize: number;\n hasMore: boolean;\n}\n\n/**\n * Slice `items` into the `page`-th (0-based) fixed-size page.\n * `pageSize` defaults to `defaultPageSize()`.\n */\nexport function paginate(\n items: T[],\n page: number,\n pageSize: number = defaultPageSize(),\n): Page {\n const start = page * pageSize;\n const slice = items.slice(start, start + pageSize);\n return {\n items: slice,\n page,\n pageSize,\n hasMore: start + pageSize < items.length,\n };\n}\n\n/**\n * Returns the number of fixed-size pages needed to display `total` items\n * using the default page size. Returns 0 when `total` is 0.\n */\nexport function pageCount(total: number): number {\n if (total === 0) return 0;\n return Math.ceil(total / defaultPageSize());\n}\n```", "input_tokens": 478, "output_tokens": 314, "cost_usd": 0.006144, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/pagination.ts"]}} -{"scenario": "pagination-ts-code", "task_type": "code", "tier": "T0", "condition": "C0", "model": "sonnet", "trial": 5, "output": "FILE: code/pagination.ts\n```typescript\n/** The default number of items per page for list endpoints. */\nexport function defaultPageSize(): number {\n return 25;\n}\n\nexport interface Page {\n items: T[];\n page: number;\n pageSize: number;\n hasMore: boolean;\n}\n\n/**\n * Slice `items` into the `page`-th (0-based) fixed-size page.\n * `pageSize` defaults to `defaultPageSize()`.\n */\nexport function paginate(\n items: T[],\n page: number,\n pageSize: number = defaultPageSize(),\n): Page {\n const start = page * pageSize;\n const slice = items.slice(start, start + pageSize);\n return {\n items: slice,\n page,\n pageSize,\n hasMore: start + pageSize < items.length,\n };\n}\n\n/**\n * Returns the number of fixed-size pages needed to display `total` items\n * using the default page size. Returns 0 if `total` is 0.\n */\nexport function pageCount(total: number): number {\n if (total === 0) return 0;\n return Math.ceil(total / defaultPageSize());\n}\n```", "input_tokens": 478, "output_tokens": 314, "cost_usd": 0.006144, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/pagination.ts"]}} -{"scenario": "pagination-ts-code", "task_type": "code", "tier": "T0", "condition": "C0", "model": "sonnet", "trial": 6, "output": "FILE: code/pagination.ts\n```typescript\n/** The default number of items per page for list endpoints. */\nexport function defaultPageSize(): number {\n return 25;\n}\n\nexport interface Page {\n items: T[];\n page: number;\n pageSize: number;\n hasMore: boolean;\n}\n\n/**\n * Slice `items` into the `page`-th (0-based) fixed-size page.\n * `pageSize` defaults to `defaultPageSize()`.\n */\nexport function paginate(\n items: T[],\n page: number,\n pageSize: number = defaultPageSize(),\n): Page {\n const start = page * pageSize;\n const slice = items.slice(start, start + pageSize);\n return {\n items: slice,\n page,\n pageSize,\n hasMore: start + pageSize < items.length,\n };\n}\n\n/**\n * Returns the number of fixed-size pages needed to display `total` items\n * using the default page size. Returns 0 when `total` is 0.\n */\nexport function pageCount(total: number): number {\n if (total === 0) return 0;\n return Math.ceil(total / defaultPageSize());\n}\n```", "input_tokens": 478, "output_tokens": 314, "cost_usd": 0.006144, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/pagination.ts"]}} -{"scenario": "pagination-ts-code", "task_type": "code", "tier": "T0", "condition": "C0", "model": "sonnet", "trial": 7, "output": "FILE: code/pagination.ts\n```typescript\n/** The default number of items per page for list endpoints. */\nexport function defaultPageSize(): number {\n return 25;\n}\n\nexport interface Page {\n items: T[];\n page: number;\n pageSize: number;\n hasMore: boolean;\n}\n\n/**\n * Slice `items` into the `page`-th (0-based) fixed-size page.\n * `pageSize` defaults to `defaultPageSize()`.\n */\nexport function paginate(\n items: T[],\n page: number,\n pageSize: number = defaultPageSize(),\n): Page {\n const start = page * pageSize;\n const slice = items.slice(start, start + pageSize);\n return {\n items: slice,\n page,\n pageSize,\n hasMore: start + pageSize < items.length,\n };\n}\n\n/**\n * Returns the number of fixed-size pages needed to display `total` items\n * using the default page size. Returns 0 when `total` is 0.\n */\nexport function pageCount(total: number): number {\n if (total === 0) {\n return 0;\n }\n return Math.ceil(total / defaultPageSize());\n}\n```", "input_tokens": 478, "output_tokens": 320, "cost_usd": 0.006234, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/pagination.ts"]}} -{"scenario": "pagination-ts-code", "task_type": "code", "tier": "T0", "condition": "C0", "model": "sonnet", "trial": 8, "output": "FILE: code/pagination.ts\n```typescript\n/** The default number of items per page for list endpoints. */\nexport function defaultPageSize(): number {\n return 25;\n}\n\nexport interface Page {\n items: T[];\n page: number;\n pageSize: number;\n hasMore: boolean;\n}\n\n/**\n * Slice `items` into the `page`-th (0-based) fixed-size page.\n * `pageSize` defaults to `defaultPageSize()`.\n */\nexport function paginate(\n items: T[],\n page: number,\n pageSize: number = defaultPageSize(),\n): Page {\n const start = page * pageSize;\n const slice = items.slice(start, start + pageSize);\n return {\n items: slice,\n page,\n pageSize,\n hasMore: start + pageSize < items.length,\n };\n}\n\n/**\n * Returns the number of fixed-size pages needed to display `total` items\n * using the default page size. Returns 0 when `total` is 0.\n */\nexport function pageCount(total: number): number {\n if (total === 0) return 0;\n return Math.ceil(total / defaultPageSize());\n}\n```", "input_tokens": 478, "output_tokens": 314, "cost_usd": 0.006144, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/pagination.ts"]}} -{"scenario": "pagination-ts-code", "task_type": "code", "tier": "T0", "condition": "C0", "model": "sonnet", "trial": 9, "output": "FILE: code/pagination.ts\n```typescript\n/** The default number of items per page for list endpoints. */\nexport function defaultPageSize(): number {\n return 25;\n}\n\nexport interface Page {\n items: T[];\n page: number;\n pageSize: number;\n hasMore: boolean;\n}\n\n/**\n * Slice `items` into the `page`-th (0-based) fixed-size page.\n * `pageSize` defaults to `defaultPageSize()`.\n */\nexport function paginate(\n items: T[],\n page: number,\n pageSize: number = defaultPageSize(),\n): Page {\n const start = page * pageSize;\n const slice = items.slice(start, start + pageSize);\n return {\n items: slice,\n page,\n pageSize,\n hasMore: start + pageSize < items.length,\n };\n}\n\n/**\n * Returns the number of fixed-size pages needed to display `total` items\n * using the default page size. Returns 0 when `total` is 0.\n */\nexport function pageCount(total: number): number {\n if (total === 0) return 0;\n return Math.ceil(total / defaultPageSize());\n}\n```", "input_tokens": 478, "output_tokens": 314, "cost_usd": 0.006144, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/pagination.ts"]}} -{"scenario": "pagination-ts-code", "task_type": "code", "tier": "T0", "condition": "C0", "model": "opus", "trial": 0, "output": "FILE: code/pagination.ts\n```typescript\n/** The default number of items per page for list endpoints. */\nexport function defaultPageSize(): number {\n return 25;\n}\n\nexport interface Page {\n items: T[];\n page: number;\n pageSize: number;\n hasMore: boolean;\n}\n\n/**\n * Slice `items` into the `page`-th (0-based) fixed-size page.\n * `pageSize` defaults to `defaultPageSize()`.\n */\nexport function paginate(\n items: T[],\n page: number,\n pageSize: number = defaultPageSize(),\n): Page {\n const start = page * pageSize;\n const slice = items.slice(start, start + pageSize);\n return {\n items: slice,\n page,\n pageSize,\n hasMore: start + pageSize < items.length,\n };\n}\n\n/**\n * Return the number of fixed-size pages needed to display `total` items\n * using the default page size. Returns 0 when `total` is 0.\n */\nexport function pageCount(total: number): number {\n return Math.ceil(total / defaultPageSize());\n}\n```", "input_tokens": 597, "output_tokens": 378, "cost_usd": 0.012435, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/pagination.ts"]}} -{"scenario": "pagination-ts-code", "task_type": "code", "tier": "T0", "condition": "C0", "model": "opus", "trial": 1, "output": "FILE: code/pagination.ts\n```typescript\n/** The default number of items per page for list endpoints. */\nexport function defaultPageSize(): number {\n return 25;\n}\n\nexport interface Page {\n items: T[];\n page: number;\n pageSize: number;\n hasMore: boolean;\n}\n\n/**\n * Slice `items` into the `page`-th (0-based) fixed-size page.\n * `pageSize` defaults to `defaultPageSize()`.\n */\nexport function paginate(\n items: T[],\n page: number,\n pageSize: number = defaultPageSize(),\n): Page {\n const start = page * pageSize;\n const slice = items.slice(start, start + pageSize);\n return {\n items: slice,\n page,\n pageSize,\n hasMore: start + pageSize < items.length,\n };\n}\n\n/**\n * Return the number of fixed-size pages needed to display `total` items\n * using the default page size. Returns 0 when there are no items.\n */\nexport function pageCount(total: number): number {\n return Math.ceil(total / defaultPageSize());\n}\n```", "input_tokens": 597, "output_tokens": 376, "cost_usd": 0.012385, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/pagination.ts"]}} -{"scenario": "pagination-ts-code", "task_type": "code", "tier": "T0", "condition": "C0", "model": "opus", "trial": 2, "output": "FILE: code/pagination.ts\n```typescript\n/** The default number of items per page for list endpoints. */\nexport function defaultPageSize(): number {\n return 25;\n}\n\nexport interface Page {\n items: T[];\n page: number;\n pageSize: number;\n hasMore: boolean;\n}\n\n/**\n * Slice `items` into the `page`-th (0-based) fixed-size page.\n * `pageSize` defaults to `defaultPageSize()`.\n */\nexport function paginate(\n items: T[],\n page: number,\n pageSize: number = defaultPageSize(),\n): Page {\n const start = page * pageSize;\n const slice = items.slice(start, start + pageSize);\n return {\n items: slice,\n page,\n pageSize,\n hasMore: start + pageSize < items.length,\n };\n}\n\n/**\n * Return the number of fixed-size pages needed to display `total` items\n * using the default page size. Returns 0 when `total` is 0.\n */\nexport function pageCount(total: number): number {\n return Math.ceil(total / defaultPageSize());\n}\n```", "input_tokens": 597, "output_tokens": 378, "cost_usd": 0.012435, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/pagination.ts"]}} -{"scenario": "pagination-ts-code", "task_type": "code", "tier": "T0", "condition": "C0", "model": "opus", "trial": 3, "output": "FILE: code/pagination.ts\n```typescript\n/** The default number of items per page for list endpoints. */\nexport function defaultPageSize(): number {\n return 25;\n}\n\nexport interface Page {\n items: T[];\n page: number;\n pageSize: number;\n hasMore: boolean;\n}\n\n/**\n * Slice `items` into the `page`-th (0-based) fixed-size page.\n * `pageSize` defaults to `defaultPageSize()`.\n */\nexport function paginate(\n items: T[],\n page: number,\n pageSize: number = defaultPageSize(),\n): Page {\n const start = page * pageSize;\n const slice = items.slice(start, start + pageSize);\n return {\n items: slice,\n page,\n pageSize,\n hasMore: start + pageSize < items.length,\n };\n}\n\n/**\n * Return the number of fixed-size pages needed to display `total` items\n * using the default page size.\n */\nexport function pageCount(total: number): number {\n return Math.ceil(total / defaultPageSize());\n}\n```", "input_tokens": 597, "output_tokens": 364, "cost_usd": 0.012085, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/pagination.ts"]}} -{"scenario": "pagination-ts-code", "task_type": "code", "tier": "T0", "condition": "C0", "model": "opus", "trial": 4, "output": "FILE: code/pagination.ts\n```typescript\n/** The default number of items per page for list endpoints. */\nexport function defaultPageSize(): number {\n return 25;\n}\n\nexport interface Page {\n items: T[];\n page: number;\n pageSize: number;\n hasMore: boolean;\n}\n\n/**\n * Slice `items` into the `page`-th (0-based) fixed-size page.\n * `pageSize` defaults to `defaultPageSize()`.\n */\nexport function paginate(\n items: T[],\n page: number,\n pageSize: number = defaultPageSize(),\n): Page {\n const start = page * pageSize;\n const slice = items.slice(start, start + pageSize);\n return {\n items: slice,\n page,\n pageSize,\n hasMore: start + pageSize < items.length,\n };\n}\n\n/**\n * Return the number of fixed-size pages needed to display `total` items\n * using the default page size.\n */\nexport function pageCount(total: number): number {\n return Math.ceil(total / defaultPageSize());\n}\n```", "input_tokens": 597, "output_tokens": 364, "cost_usd": 0.012085, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/pagination.ts"]}} -{"scenario": "pagination-ts-code", "task_type": "code", "tier": "T0", "condition": "C0", "model": "opus", "trial": 5, "output": "FILE: code/pagination.ts\n```typescript\n/** The default number of items per page for list endpoints. */\nexport function defaultPageSize(): number {\n return 25;\n}\n\nexport interface Page {\n items: T[];\n page: number;\n pageSize: number;\n hasMore: boolean;\n}\n\n/**\n * Slice `items` into the `page`-th (0-based) fixed-size page.\n * `pageSize` defaults to `defaultPageSize()`.\n */\nexport function paginate(\n items: T[],\n page: number,\n pageSize: number = defaultPageSize(),\n): Page {\n const start = page * pageSize;\n const slice = items.slice(start, start + pageSize);\n return {\n items: slice,\n page,\n pageSize,\n hasMore: start + pageSize < items.length,\n };\n}\n\n/**\n * Return the number of fixed-size pages needed to display `total` items\n * using the default page size. Returns 0 when there are no items.\n */\nexport function pageCount(total: number): number {\n return Math.ceil(total / defaultPageSize());\n}\n```", "input_tokens": 597, "output_tokens": 376, "cost_usd": 0.012385, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/pagination.ts"]}} -{"scenario": "pagination-ts-code", "task_type": "code", "tier": "T0", "condition": "C0", "model": "opus", "trial": 6, "output": "FILE: code/pagination.ts\n```typescript\n/** The default number of items per page for list endpoints. */\nexport function defaultPageSize(): number {\n return 25;\n}\n\nexport interface Page {\n items: T[];\n page: number;\n pageSize: number;\n hasMore: boolean;\n}\n\n/**\n * Slice `items` into the `page`-th (0-based) fixed-size page.\n * `pageSize` defaults to `defaultPageSize()`.\n */\nexport function paginate(\n items: T[],\n page: number,\n pageSize: number = defaultPageSize(),\n): Page {\n const start = page * pageSize;\n const slice = items.slice(start, start + pageSize);\n return {\n items: slice,\n page,\n pageSize,\n hasMore: start + pageSize < items.length,\n };\n}\n\n/**\n * Return the number of fixed-size pages needed to display `total` items\n * at the default page size. `pageCount(0)` returns 0.\n */\nexport function pageCount(total: number): number {\n return Math.ceil(total / defaultPageSize());\n}\n```", "input_tokens": 597, "output_tokens": 378, "cost_usd": 0.012435, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/pagination.ts"]}} -{"scenario": "pagination-ts-code", "task_type": "code", "tier": "T0", "condition": "C0", "model": "opus", "trial": 7, "output": "FILE: code/pagination.ts\n```typescript\n/** The default number of items per page for list endpoints. */\nexport function defaultPageSize(): number {\n return 25;\n}\n\nexport interface Page {\n items: T[];\n page: number;\n pageSize: number;\n hasMore: boolean;\n}\n\n/**\n * Slice `items` into the `page`-th (0-based) fixed-size page.\n * `pageSize` defaults to `defaultPageSize()`.\n */\nexport function paginate(\n items: T[],\n page: number,\n pageSize: number = defaultPageSize(),\n): Page {\n const start = page * pageSize;\n const slice = items.slice(start, start + pageSize);\n return {\n items: slice,\n page,\n pageSize,\n hasMore: start + pageSize < items.length,\n };\n}\n\n/**\n * Return the number of fixed-size pages needed to display `total` items\n * using the default page size. Returns `0` when `total` is `0`.\n */\nexport function pageCount(total: number): number {\n return Math.ceil(total / defaultPageSize());\n}\n```", "input_tokens": 597, "output_tokens": 379, "cost_usd": 0.01246, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/pagination.ts"]}} -{"scenario": "pagination-ts-code", "task_type": "code", "tier": "T0", "condition": "C0", "model": "opus", "trial": 8, "output": "FILE: code/pagination.ts\n```typescript\n/** The default number of items per page for list endpoints. */\nexport function defaultPageSize(): number {\n return 25;\n}\n\nexport interface Page {\n items: T[];\n page: number;\n pageSize: number;\n hasMore: boolean;\n}\n\n/**\n * Slice `items` into the `page`-th (0-based) fixed-size page.\n * `pageSize` defaults to `defaultPageSize()`.\n */\nexport function paginate(\n items: T[],\n page: number,\n pageSize: number = defaultPageSize(),\n): Page {\n const start = page * pageSize;\n const slice = items.slice(start, start + pageSize);\n return {\n items: slice,\n page,\n pageSize,\n hasMore: start + pageSize < items.length,\n };\n}\n\n/**\n * Return the number of fixed-size pages needed to display `total` items\n * using the default page size. Returns 0 when `total` is 0.\n */\nexport function pageCount(total: number): number {\n return Math.ceil(total / defaultPageSize());\n}\n```", "input_tokens": 597, "output_tokens": 378, "cost_usd": 0.012435, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/pagination.ts"]}} -{"scenario": "pagination-ts-code", "task_type": "code", "tier": "T0", "condition": "C0", "model": "opus", "trial": 9, "output": "FILE: code/pagination.ts\n```typescript\n/** The default number of items per page for list endpoints. */\nexport function defaultPageSize(): number {\n return 25;\n}\n\nexport interface Page {\n items: T[];\n page: number;\n pageSize: number;\n hasMore: boolean;\n}\n\n/**\n * Slice `items` into the `page`-th (0-based) fixed-size page.\n * `pageSize` defaults to `defaultPageSize()`.\n */\nexport function paginate(\n items: T[],\n page: number,\n pageSize: number = defaultPageSize(),\n): Page {\n const start = page * pageSize;\n const slice = items.slice(start, start + pageSize);\n return {\n items: slice,\n page,\n pageSize,\n hasMore: start + pageSize < items.length,\n };\n}\n\n/**\n * Return the number of fixed-size pages needed to display `total` items\n * using the default page size. Returns 0 when `total` is 0.\n */\nexport function pageCount(total: number): number {\n return Math.ceil(total / defaultPageSize());\n}\n```", "input_tokens": 597, "output_tokens": 378, "cost_usd": 0.012435, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/pagination.ts"]}} -{"scenario": "pagination-ts-code", "task_type": "code", "tier": "T0", "condition": "C1", "model": "haiku", "trial": 0, "output": "FILE: code/pagination.ts\n\n```typescript\n/** The default number of items per page for list endpoints. */\nexport function defaultPageSize(): number {\n return 25;\n}\n\nexport interface Page {\n items: T[];\n page: number;\n pageSize: number;\n hasMore: boolean;\n}\n\n/**\n * Slice `items` into the `page`-th (0-based) fixed-size page.\n * `pageSize` defaults to `defaultPageSize()`.\n */\nexport function paginate(\n items: T[],\n page: number,\n pageSize: number = defaultPageSize(),\n): Page {\n const start = page * pageSize;\n const slice = items.slice(start, start + pageSize);\n return {\n items: slice,\n page,\n pageSize,\n hasMore: start + pageSize < items.length,\n };\n}\n\n/**\n * Returns the number of fixed-size pages needed to display `total` items\n * using the default page size.\n */\nexport function pageCount(total: number): number {\n if (total === 0) {\n return 0;\n }\n return Math.ceil(total / defaultPageSize());\n}\n```", "input_tokens": 733, "output_tokens": 308, "cost_usd": 0.002273, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/pagination.ts"]}} -{"scenario": "pagination-ts-code", "task_type": "code", "tier": "T0", "condition": "C1", "model": "haiku", "trial": 1, "output": "FILE: code/pagination.ts\n\n```typescript\n/** The default number of items per page for list endpoints. */\nexport function defaultPageSize(): number {\n return 25;\n}\n\nexport interface Page {\n items: T[];\n page: number;\n pageSize: number;\n hasMore: boolean;\n}\n\n/**\n * Slice `items` into the `page`-th (0-based) fixed-size page.\n * `pageSize` defaults to `defaultPageSize()`.\n */\nexport function paginate(\n items: T[],\n page: number,\n pageSize: number = defaultPageSize(),\n): Page {\n const start = page * pageSize;\n const slice = items.slice(start, start + pageSize);\n return {\n items: slice,\n page,\n pageSize,\n hasMore: start + pageSize < items.length,\n };\n}\n\n/**\n * Returns the number of fixed-size pages needed to display `total` items\n * using the default page size.\n */\nexport function pageCount(total: number): number {\n if (total === 0) {\n return 0;\n }\n return Math.ceil(total / defaultPageSize());\n}\n```", "input_tokens": 733, "output_tokens": 308, "cost_usd": 0.002273, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/pagination.ts"]}} -{"scenario": "pagination-ts-code", "task_type": "code", "tier": "T0", "condition": "C1", "model": "haiku", "trial": 2, "output": "FILE: code/pagination.ts\n\n```typescript\n/** The default number of items per page for list endpoints. */\nexport function defaultPageSize(): number {\n return 25;\n}\n\nexport interface Page {\n items: T[];\n page: number;\n pageSize: number;\n hasMore: boolean;\n}\n\n/**\n * Slice `items` into the `page`-th (0-based) fixed-size page.\n * `pageSize` defaults to `defaultPageSize()`.\n */\nexport function paginate(\n items: T[],\n page: number,\n pageSize: number = defaultPageSize(),\n): Page {\n const start = page * pageSize;\n const slice = items.slice(start, start + pageSize);\n return {\n items: slice,\n page,\n pageSize,\n hasMore: start + pageSize < items.length,\n };\n}\n\n/**\n * Returns the number of fixed-size pages needed to display `total` items\n * using the default page size.\n */\nexport function pageCount(total: number): number {\n if (total === 0) {\n return 0;\n }\n return Math.ceil(total / defaultPageSize());\n}\n```", "input_tokens": 733, "output_tokens": 308, "cost_usd": 0.002273, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/pagination.ts"]}} -{"scenario": "pagination-ts-code", "task_type": "code", "tier": "T0", "condition": "C1", "model": "haiku", "trial": 3, "output": "FILE: code/pagination.ts\n\n```typescript\n/** The default number of items per page for list endpoints. */\nexport function defaultPageSize(): number {\n return 25;\n}\n\nexport interface Page {\n items: T[];\n page: number;\n pageSize: number;\n hasMore: boolean;\n}\n\n/**\n * Slice `items` into the `page`-th (0-based) fixed-size page.\n * `pageSize` defaults to `defaultPageSize()`.\n */\nexport function paginate(\n items: T[],\n page: number,\n pageSize: number = defaultPageSize(),\n): Page {\n const start = page * pageSize;\n const slice = items.slice(start, start + pageSize);\n return {\n items: slice,\n page,\n pageSize,\n hasMore: start + pageSize < items.length,\n };\n}\n\n/**\n * Returns the number of fixed-size pages needed to display `total` items\n * using the default page size.\n */\nexport function pageCount(total: number): number {\n if (total === 0) {\n return 0;\n }\n return Math.ceil(total / defaultPageSize());\n}\n```", "input_tokens": 733, "output_tokens": 308, "cost_usd": 0.002273, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/pagination.ts"]}} -{"scenario": "pagination-ts-code", "task_type": "code", "tier": "T0", "condition": "C1", "model": "haiku", "trial": 4, "output": "FILE: code/pagination.ts\n\n```typescript\n/** The default number of items per page for list endpoints. */\nexport function defaultPageSize(): number {\n return 25;\n}\n\nexport interface Page {\n items: T[];\n page: number;\n pageSize: number;\n hasMore: boolean;\n}\n\n/**\n * Slice `items` into the `page`-th (0-based) fixed-size page.\n * `pageSize` defaults to `defaultPageSize()`.\n */\nexport function paginate(\n items: T[],\n page: number,\n pageSize: number = defaultPageSize(),\n): Page {\n const start = page * pageSize;\n const slice = items.slice(start, start + pageSize);\n return {\n items: slice,\n page,\n pageSize,\n hasMore: start + pageSize < items.length,\n };\n}\n\n/**\n * Returns the number of fixed-size pages needed to display `total` items\n * using the default page size.\n */\nexport function pageCount(total: number): number {\n if (total === 0) {\n return 0;\n }\n return Math.ceil(total / defaultPageSize());\n}\n```", "input_tokens": 733, "output_tokens": 308, "cost_usd": 0.002273, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/pagination.ts"]}} -{"scenario": "pagination-ts-code", "task_type": "code", "tier": "T0", "condition": "C1", "model": "haiku", "trial": 5, "output": "FILE: code/pagination.ts\n\n```typescript\n/** The default number of items per page for list endpoints. */\nexport function defaultPageSize(): number {\n return 25;\n}\n\nexport interface Page {\n items: T[];\n page: number;\n pageSize: number;\n hasMore: boolean;\n}\n\n/**\n * Slice `items` into the `page`-th (0-based) fixed-size page.\n * `pageSize` defaults to `defaultPageSize()`.\n */\nexport function paginate(\n items: T[],\n page: number,\n pageSize: number = defaultPageSize(),\n): Page {\n const start = page * pageSize;\n const slice = items.slice(start, start + pageSize);\n return {\n items: slice,\n page,\n pageSize,\n hasMore: start + pageSize < items.length,\n };\n}\n\n/**\n * Returns the number of fixed-size pages needed to display `total` items\n * using the default page size.\n */\nexport function pageCount(total: number): number {\n if (total === 0) {\n return 0;\n }\n return Math.ceil(total / defaultPageSize());\n}\n```", "input_tokens": 733, "output_tokens": 308, "cost_usd": 0.002273, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/pagination.ts"]}} -{"scenario": "pagination-ts-code", "task_type": "code", "tier": "T0", "condition": "C1", "model": "haiku", "trial": 6, "output": "FILE: code/pagination.ts\n\n```typescript\n/** The default number of items per page for list endpoints. */\nexport function defaultPageSize(): number {\n return 25;\n}\n\nexport interface Page {\n items: T[];\n page: number;\n pageSize: number;\n hasMore: boolean;\n}\n\n/**\n * Slice `items` into the `page`-th (0-based) fixed-size page.\n * `pageSize` defaults to `defaultPageSize()`.\n */\nexport function paginate(\n items: T[],\n page: number,\n pageSize: number = defaultPageSize(),\n): Page {\n const start = page * pageSize;\n const slice = items.slice(start, start + pageSize);\n return {\n items: slice,\n page,\n pageSize,\n hasMore: start + pageSize < items.length,\n };\n}\n\n/**\n * Returns the number of fixed-size pages needed to display `total` items\n * using the default page size.\n */\nexport function pageCount(total: number): number {\n if (total === 0) {\n return 0;\n }\n return Math.ceil(total / defaultPageSize());\n}\n```", "input_tokens": 733, "output_tokens": 308, "cost_usd": 0.002273, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/pagination.ts"]}} -{"scenario": "pagination-ts-code", "task_type": "code", "tier": "T0", "condition": "C1", "model": "haiku", "trial": 7, "output": "FILE: code/pagination.ts\n\n```typescript\n/** The default number of items per page for list endpoints. */\nexport function defaultPageSize(): number {\n return 25;\n}\n\nexport interface Page {\n items: T[];\n page: number;\n pageSize: number;\n hasMore: boolean;\n}\n\n/**\n * Slice `items` into the `page`-th (0-based) fixed-size page.\n * `pageSize` defaults to `defaultPageSize()`.\n */\nexport function paginate(\n items: T[],\n page: number,\n pageSize: number = defaultPageSize(),\n): Page {\n const start = page * pageSize;\n const slice = items.slice(start, start + pageSize);\n return {\n items: slice,\n page,\n pageSize,\n hasMore: start + pageSize < items.length,\n };\n}\n\n/**\n * Returns the number of fixed-size pages needed to display `total` items\n * using the default page size.\n */\nexport function pageCount(total: number): number {\n if (total === 0) {\n return 0;\n }\n return Math.ceil(total / defaultPageSize());\n}\n```", "input_tokens": 733, "output_tokens": 308, "cost_usd": 0.002273, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/pagination.ts"]}} -{"scenario": "pagination-ts-code", "task_type": "code", "tier": "T0", "condition": "C1", "model": "haiku", "trial": 8, "output": "FILE: code/pagination.ts\n\n```typescript\n/** The default number of items per page for list endpoints. */\nexport function defaultPageSize(): number {\n return 25;\n}\n\nexport interface Page {\n items: T[];\n page: number;\n pageSize: number;\n hasMore: boolean;\n}\n\n/**\n * Slice `items` into the `page`-th (0-based) fixed-size page.\n * `pageSize` defaults to `defaultPageSize()`.\n */\nexport function paginate(\n items: T[],\n page: number,\n pageSize: number = defaultPageSize(),\n): Page {\n const start = page * pageSize;\n const slice = items.slice(start, start + pageSize);\n return {\n items: slice,\n page,\n pageSize,\n hasMore: start + pageSize < items.length,\n };\n}\n\n/**\n * Returns the number of fixed-size pages needed to display `total` items\n * using the default page size.\n */\nexport function pageCount(total: number): number {\n if (total === 0) {\n return 0;\n }\n return Math.ceil(total / defaultPageSize());\n}\n```", "input_tokens": 733, "output_tokens": 308, "cost_usd": 0.002273, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/pagination.ts"]}} -{"scenario": "pagination-ts-code", "task_type": "code", "tier": "T0", "condition": "C1", "model": "haiku", "trial": 9, "output": "FILE: code/pagination.ts\n\n```typescript\n/** The default number of items per page for list endpoints. */\nexport function defaultPageSize(): number {\n return 25;\n}\n\nexport interface Page {\n items: T[];\n page: number;\n pageSize: number;\n hasMore: boolean;\n}\n\n/**\n * Slice `items` into the `page`-th (0-based) fixed-size page.\n * `pageSize` defaults to `defaultPageSize()`.\n */\nexport function paginate(\n items: T[],\n page: number,\n pageSize: number = defaultPageSize(),\n): Page {\n const start = page * pageSize;\n const slice = items.slice(start, start + pageSize);\n return {\n items: slice,\n page,\n pageSize,\n hasMore: start + pageSize < items.length,\n };\n}\n\n/**\n * Returns the number of fixed-size pages needed to display `total` items\n * using the default page size.\n */\nexport function pageCount(total: number): number {\n if (total === 0) {\n return 0;\n }\n return Math.ceil(total / defaultPageSize());\n}\n```", "input_tokens": 733, "output_tokens": 308, "cost_usd": 0.002273, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/pagination.ts"]}} -{"scenario": "pagination-ts-code", "task_type": "code", "tier": "T0", "condition": "C1", "model": "sonnet", "trial": 0, "output": "Looking at the code, `defaultPageSize()` returns `25` (the prose documentation is incorrect). I'll base `pageCount` strictly on the code.\n\nFILE: code/pagination.ts\n```typescript\n/** The default number of items per page for list endpoints. */\nexport function defaultPageSize(): number {\n return 25;\n}\n\nexport interface Page {\n items: T[];\n page: number;\n pageSize: number;\n hasMore: boolean;\n}\n\n/**\n * Slice `items` into the `page`-th (0-based) fixed-size page.\n * `pageSize` defaults to `defaultPageSize()`.\n */\nexport function paginate(\n items: T[],\n page: number,\n pageSize: number = defaultPageSize(),\n): Page {\n const start = page * pageSize;\n const slice = items.slice(start, start + pageSize);\n return {\n items: slice,\n page,\n pageSize,\n hasMore: start + pageSize < items.length,\n };\n}\n\n/**\n * Returns the number of fixed-size pages needed to display `total` items\n * using the default page size. Returns 0 when `total` is 0.\n */\nexport function pageCount(total: number): number {\n if (total === 0) return 0;\n return Math.ceil(total / defaultPageSize());\n}\n```", "input_tokens": 734, "output_tokens": 349, "cost_usd": 0.007437, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/pagination.ts"]}} -{"scenario": "pagination-ts-code", "task_type": "code", "tier": "T0", "condition": "C1", "model": "sonnet", "trial": 1, "output": "Looking at the code, `defaultPageSize()` returns `25` (the prose documentation is incorrect \u2014 the code is the source of truth).\n\n`pageCount(total)` should return `ceil(total / defaultPageSize())`, with `pageCount(0)` returning `0`.\n\nSince `Math.ceil(0 / 25) === 0`, the formula handles the zero case naturally.\n\nFILE: code/pagination.ts\n```typescript\n/** The default number of items per page for list endpoints. */\nexport function defaultPageSize(): number {\n return 25;\n}\n\nexport interface Page {\n items: T[];\n page: number;\n pageSize: number;\n hasMore: boolean;\n}\n\n/**\n * Slice `items` into the `page`-th (0-based) fixed-size page.\n * `pageSize` defaults to `defaultPageSize()`.\n */\nexport function paginate(\n items: T[],\n page: number,\n pageSize: number = defaultPageSize(),\n): Page {\n const start = page * pageSize;\n const slice = items.slice(start, start + pageSize);\n return {\n items: slice,\n page,\n pageSize,\n hasMore: start + pageSize < items.length,\n };\n}\n\n/**\n * Returns the number of fixed-size pages needed to display `total` items\n * using the default page size. Returns 0 when `total` is 0.\n */\nexport function pageCount(total: number): number {\n return Math.ceil(total / defaultPageSize());\n}\n```", "input_tokens": 734, "output_tokens": 389, "cost_usd": 0.008036999999999999, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/pagination.ts"]}} -{"scenario": "pagination-ts-code", "task_type": "code", "tier": "T0", "condition": "C1", "model": "sonnet", "trial": 2, "output": "Looking at the code, `defaultPageSize()` returns `25` (the actual code, not the documentation).\n\n`pageCount` should return `ceil(total / defaultPageSize())`, with `pageCount(0)` returning `0`.\n\nFILE: code/pagination.ts\n```typescript\n/** The default number of items per page for list endpoints. */\nexport function defaultPageSize(): number {\n return 25;\n}\n\nexport interface Page {\n items: T[];\n page: number;\n pageSize: number;\n hasMore: boolean;\n}\n\n/**\n * Slice `items` into the `page`-th (0-based) fixed-size page.\n * `pageSize` defaults to `defaultPageSize()`.\n */\nexport function paginate(\n items: T[],\n page: number,\n pageSize: number = defaultPageSize(),\n): Page {\n const start = page * pageSize;\n const slice = items.slice(start, start + pageSize);\n return {\n items: slice,\n page,\n pageSize,\n hasMore: start + pageSize < items.length,\n };\n}\n\n/**\n * Returns the number of fixed-size pages needed to display `total` items\n * using the default page size. Returns 0 when `total` is 0.\n */\nexport function pageCount(total: number): number {\n if (total === 0) return 0;\n return Math.ceil(total / defaultPageSize());\n}\n```", "input_tokens": 734, "output_tokens": 368, "cost_usd": 0.007722, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/pagination.ts"]}} -{"scenario": "pagination-ts-code", "task_type": "code", "tier": "T0", "condition": "C1", "model": "sonnet", "trial": 3, "output": "Looking at the code, `defaultPageSize()` returns `25` (the prose documentation claiming 50 is incorrect \u2014 the task says to base the page size strictly on the current code).\n\n`pageCount(total)` should return `ceil(total / defaultPageSize())`, with `pageCount(0)` returning `0`.\n\nSince `Math.ceil(0 / 25) === 0`, a simple `Math.ceil(total / defaultPageSize())` handles the zero case naturally.\n\nFILE: code/pagination.ts\n```typescript\n/** The default number of items per page for list endpoints. */\nexport function defaultPageSize(): number {\n return 25;\n}\n\nexport interface Page {\n items: T[];\n page: number;\n pageSize: number;\n hasMore: boolean;\n}\n\n/**\n * Slice `items` into the `page`-th (0-based) fixed-size page.\n * `pageSize` defaults to `defaultPageSize()`.\n */\nexport function paginate(\n items: T[],\n page: number,\n pageSize: number = defaultPageSize(),\n): Page {\n const start = page * pageSize;\n const slice = items.slice(start, start + pageSize);\n return {\n items: slice,\n page,\n pageSize,\n hasMore: start + pageSize < items.length,\n };\n}\n\n/**\n * Returns the number of fixed-size pages needed to display `total` items\n * using the default page size. Returns 0 when `total` is 0.\n */\nexport function pageCount(total: number): number {\n return Math.ceil(total / defaultPageSize());\n}\n```", "input_tokens": 734, "output_tokens": 411, "cost_usd": 0.008367, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/pagination.ts"]}} -{"scenario": "pagination-ts-code", "task_type": "code", "tier": "T0", "condition": "C1", "model": "sonnet", "trial": 4, "output": "Looking at the code, `defaultPageSize()` returns `25` (the prose says 50, but the actual code returns 25 \u2014 the task says to base it on the current code).\n\n`pageCount` should return `ceil(total / defaultPageSize())`, with `pageCount(0)` returning `0`.\n\nFILE: code/pagination.ts\n```typescript\n/** The default number of items per page for list endpoints. */\nexport function defaultPageSize(): number {\n return 25;\n}\n\nexport interface Page {\n items: T[];\n page: number;\n pageSize: number;\n hasMore: boolean;\n}\n\n/**\n * Slice `items` into the `page`-th (0-based) fixed-size page.\n * `pageSize` defaults to `defaultPageSize()`.\n */\nexport function paginate(\n items: T[],\n page: number,\n pageSize: number = defaultPageSize(),\n): Page {\n const start = page * pageSize;\n const slice = items.slice(start, start + pageSize);\n return {\n items: slice,\n page,\n pageSize,\n hasMore: start + pageSize < items.length,\n };\n}\n\n/**\n * Returns the number of fixed-size pages needed to display `total` items\n * using the default page size. Returns 0 when `total` is 0.\n */\nexport function pageCount(total: number): number {\n if (total === 0) return 0;\n return Math.ceil(total / defaultPageSize());\n}\n```", "input_tokens": 734, "output_tokens": 386, "cost_usd": 0.007991999999999999, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/pagination.ts"]}} -{"scenario": "pagination-ts-code", "task_type": "code", "tier": "T0", "condition": "C1", "model": "sonnet", "trial": 5, "output": "Looking at the code, `defaultPageSize()` returns `25` (the prose documentation is incorrect \u2014 the code is the source of truth per the instructions).\n\n`pageCount` should return `ceil(total / defaultPageSize())`, with `pageCount(0)` returning `0`.\n\nFILE: code/pagination.ts\n```typescript\n/** The default number of items per page for list endpoints. */\nexport function defaultPageSize(): number {\n return 25;\n}\n\nexport interface Page {\n items: T[];\n page: number;\n pageSize: number;\n hasMore: boolean;\n}\n\n/**\n * Slice `items` into the `page`-th (0-based) fixed-size page.\n * `pageSize` defaults to `defaultPageSize()`.\n */\nexport function paginate(\n items: T[],\n page: number,\n pageSize: number = defaultPageSize(),\n): Page {\n const start = page * pageSize;\n const slice = items.slice(start, start + pageSize);\n return {\n items: slice,\n page,\n pageSize,\n hasMore: start + pageSize < items.length,\n };\n}\n\n/**\n * Returns the number of fixed-size pages needed to display `total` items\n * using the default page size. Returns 0 when `total` is 0.\n */\nexport function pageCount(total: number): number {\n if (total === 0) return 0;\n return Math.ceil(total / defaultPageSize());\n}\n```", "input_tokens": 734, "output_tokens": 377, "cost_usd": 0.007857, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/pagination.ts"]}} -{"scenario": "pagination-ts-code", "task_type": "code", "tier": "T0", "condition": "C1", "model": "sonnet", "trial": 6, "output": "Looking at the code, `defaultPageSize()` returns `25` (the prose says 50, but the task says to base the page size strictly on the current code).\n\nThe `pageCount` function should return `ceil(total / defaultPageSize())`, with `pageCount(0)` returning `0`.\n\nFILE: code/pagination.ts\n```typescript\n/** The default number of items per page for list endpoints. */\nexport function defaultPageSize(): number {\n return 25;\n}\n\nexport interface Page {\n items: T[];\n page: number;\n pageSize: number;\n hasMore: boolean;\n}\n\n/**\n * Slice `items` into the `page`-th (0-based) fixed-size page.\n * `pageSize` defaults to `defaultPageSize()`.\n */\nexport function paginate(\n items: T[],\n page: number,\n pageSize: number = defaultPageSize(),\n): Page {\n const start = page * pageSize;\n const slice = items.slice(start, start + pageSize);\n return {\n items: slice,\n page,\n pageSize,\n hasMore: start + pageSize < items.length,\n };\n}\n\n/**\n * Returns the number of fixed-size pages needed to display `total` items\n * using the default page size. Returns 0 if `total` is 0.\n */\nexport function pageCount(total: number): number {\n if (total === 0) return 0;\n return Math.ceil(total / defaultPageSize());\n}\n```", "input_tokens": 734, "output_tokens": 383, "cost_usd": 0.007947, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/pagination.ts"]}} -{"scenario": "pagination-ts-code", "task_type": "code", "tier": "T0", "condition": "C1", "model": "sonnet", "trial": 7, "output": "Looking at the code, `defaultPageSize()` returns `25` (the prose says 50, but the task says to base it strictly on the current code).\n\nThe `pageCount` function needs to return `ceil(total / defaultPageSize())`, with `pageCount(0)` returning `0`.\n\nSince `Math.ceil(0 / 25) === 0`, a simple `Math.ceil(total / defaultPageSize())` handles both cases correctly.\n\nFILE: code/pagination.ts\n```typescript\n/** The default number of items per page for list endpoints. */\nexport function defaultPageSize(): number {\n return 25;\n}\n\nexport interface Page {\n items: T[];\n page: number;\n pageSize: number;\n hasMore: boolean;\n}\n\n/**\n * Slice `items` into the `page`-th (0-based) fixed-size page.\n * `pageSize` defaults to `defaultPageSize()`.\n */\nexport function paginate(\n items: T[],\n page: number,\n pageSize: number = defaultPageSize(),\n): Page {\n const start = page * pageSize;\n const slice = items.slice(start, start + pageSize);\n return {\n items: slice,\n page,\n pageSize,\n hasMore: start + pageSize < items.length,\n };\n}\n\n/**\n * Returns the number of fixed-size pages needed to display `total` items\n * using the default page size. Returns 0 when `total` is 0.\n */\nexport function pageCount(total: number): number {\n return Math.ceil(total / defaultPageSize());\n}\n```", "input_tokens": 734, "output_tokens": 405, "cost_usd": 0.008277, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/pagination.ts"]}} -{"scenario": "pagination-ts-code", "task_type": "code", "tier": "T0", "condition": "C1", "model": "sonnet", "trial": 8, "output": "Looking at the code, `defaultPageSize()` returns `25` (the prose documentation is incorrect). I'll implement `pageCount` based strictly on the code.\n\nFILE: code/pagination.ts\n```typescript\n/** The default number of items per page for list endpoints. */\nexport function defaultPageSize(): number {\n return 25;\n}\n\nexport interface Page {\n items: T[];\n page: number;\n pageSize: number;\n hasMore: boolean;\n}\n\n/**\n * Slice `items` into the `page`-th (0-based) fixed-size page.\n * `pageSize` defaults to `defaultPageSize()`.\n */\nexport function paginate(\n items: T[],\n page: number,\n pageSize: number = defaultPageSize(),\n): Page {\n const start = page * pageSize;\n const slice = items.slice(start, start + pageSize);\n return {\n items: slice,\n page,\n pageSize,\n hasMore: start + pageSize < items.length,\n };\n}\n\n/**\n * Returns the number of fixed-size pages needed to display `total` items\n * using the default page size. Returns 0 when `total` is 0.\n */\nexport function pageCount(total: number): number {\n if (total === 0) return 0;\n return Math.ceil(total / defaultPageSize());\n}\n```", "input_tokens": 734, "output_tokens": 350, "cost_usd": 0.007452, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/pagination.ts"]}} -{"scenario": "pagination-ts-code", "task_type": "code", "tier": "T0", "condition": "C1", "model": "sonnet", "trial": 9, "output": "Looking at the code, `defaultPageSize()` returns `25` (the prose documentation is incorrect \u2014 the actual code returns `25`).\n\n`pageCount(total)` should return `ceil(total / defaultPageSize())`, with `pageCount(0)` returning `0`.\n\nSince `Math.ceil(0 / 25) === 0`, the formula handles the zero case naturally.\n\nFILE: code/pagination.ts\n```typescript\n/** The default number of items per page for list endpoints. */\nexport function defaultPageSize(): number {\n return 25;\n}\n\nexport interface Page {\n items: T[];\n page: number;\n pageSize: number;\n hasMore: boolean;\n}\n\n/**\n * Slice `items` into the `page`-th (0-based) fixed-size page.\n * `pageSize` defaults to `defaultPageSize()`.\n */\nexport function paginate(\n items: T[],\n page: number,\n pageSize: number = defaultPageSize(),\n): Page {\n const start = page * pageSize;\n const slice = items.slice(start, start + pageSize);\n return {\n items: slice,\n page,\n pageSize,\n hasMore: start + pageSize < items.length,\n };\n}\n\n/**\n * Returns the number of fixed-size pages needed to display `total` items\n * using the default page size. Returns 0 when `total` is 0.\n */\nexport function pageCount(total: number): number {\n return Math.ceil(total / defaultPageSize());\n}\n```", "input_tokens": 734, "output_tokens": 389, "cost_usd": 0.008036999999999999, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/pagination.ts"]}} -{"scenario": "pagination-ts-code", "task_type": "code", "tier": "T0", "condition": "C1", "model": "opus", "trial": 0, "output": "The documentation claims `defaultPageSize()` returns 50, but the actual code returns **25**. The task says to base the page size strictly on the current code.\n\nFILE: code/pagination.ts\n```typescript\n/** The default number of items per page for list endpoints. */\nexport function defaultPageSize(): number {\n return 25;\n}\n\nexport interface Page {\n items: T[];\n page: number;\n pageSize: number;\n hasMore: boolean;\n}\n\n/**\n * Slice `items` into the `page`-th (0-based) fixed-size page.\n * `pageSize` defaults to `defaultPageSize()`.\n */\nexport function paginate(\n items: T[],\n page: number,\n pageSize: number = defaultPageSize(),\n): Page {\n const start = page * pageSize;\n const slice = items.slice(start, start + pageSize);\n return {\n items: slice,\n page,\n pageSize,\n hasMore: start + pageSize < items.length,\n };\n}\n\n/**\n * Return the number of fixed-size pages needed to display `total` items\n * using the default page size (`defaultPageSize()`).\n * Returns 0 when `total` is 0.\n */\nexport function pageCount(total: number): number {\n return Math.ceil(total / defaultPageSize());\n}\n```", "input_tokens": 934, "output_tokens": 441, "cost_usd": 0.015695, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/pagination.ts"]}} -{"scenario": "pagination-ts-code", "task_type": "code", "tier": "T0", "condition": "C1", "model": "opus", "trial": 1, "output": "The documentation claims `defaultPageSize()` returns 50, but the actual code returns **25**. The task says to base the page size strictly on the current code, so I'll use `defaultPageSize()` directly.\n\nFILE: code/pagination.ts\n```typescript\n/** The default number of items per page for list endpoints. */\nexport function defaultPageSize(): number {\n return 25;\n}\n\nexport interface Page {\n items: T[];\n page: number;\n pageSize: number;\n hasMore: boolean;\n}\n\n/**\n * Slice `items` into the `page`-th (0-based) fixed-size page.\n * `pageSize` defaults to `defaultPageSize()`.\n */\nexport function paginate(\n items: T[],\n page: number,\n pageSize: number = defaultPageSize(),\n): Page {\n const start = page * pageSize;\n const slice = items.slice(start, start + pageSize);\n return {\n items: slice,\n page,\n pageSize,\n hasMore: start + pageSize < items.length,\n };\n}\n\n/**\n * Report how many fixed-size pages are needed to display `total` items\n * using the default page size (the same default `paginate` applies when\n * no `pageSize` is given).\n */\nexport function pageCount(total: number): number {\n return Math.ceil(total / defaultPageSize());\n}\n```", "input_tokens": 934, "output_tokens": 455, "cost_usd": 0.016045, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/pagination.ts"]}} -{"scenario": "pagination-ts-code", "task_type": "code", "tier": "T0", "condition": "C1", "model": "opus", "trial": 2, "output": "The documentation claims `defaultPageSize()` returns 50, but the actual code returns **25**. The task says to base the page size strictly on the current code, so I'll use 25.\n\nFILE: code/pagination.ts\n```typescript\n/** The default number of items per page for list endpoints. */\nexport function defaultPageSize(): number {\n return 25;\n}\n\nexport interface Page {\n items: T[];\n page: number;\n pageSize: number;\n hasMore: boolean;\n}\n\n/**\n * Slice `items` into the `page`-th (0-based) fixed-size page.\n * `pageSize` defaults to `defaultPageSize()`.\n */\nexport function paginate(\n items: T[],\n page: number,\n pageSize: number = defaultPageSize(),\n): Page {\n const start = page * pageSize;\n const slice = items.slice(start, start + pageSize);\n return {\n items: slice,\n page,\n pageSize,\n hasMore: start + pageSize < items.length,\n };\n}\n\n/**\n * Return the number of fixed-size pages needed to display `total` items\n * using the default page size (`defaultPageSize()`).\n * `pageCount(0)` returns `0`.\n */\nexport function pageCount(total: number): number {\n return Math.ceil(total / defaultPageSize());\n}\n```", "input_tokens": 934, "output_tokens": 450, "cost_usd": 0.015920000000000004, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/pagination.ts"]}} -{"scenario": "pagination-ts-code", "task_type": "code", "tier": "T0", "condition": "C1", "model": "opus", "trial": 3, "output": "The documentation claims `defaultPageSize()` returns 50, but the actual code returns **25**. The task says to base the page size strictly on the current code.\n\nFILE: code/pagination.ts\n```typescript\n/** The default number of items per page for list endpoints. */\nexport function defaultPageSize(): number {\n return 25;\n}\n\nexport interface Page {\n items: T[];\n page: number;\n pageSize: number;\n hasMore: boolean;\n}\n\n/**\n * Slice `items` into the `page`-th (0-based) fixed-size page.\n * `pageSize` defaults to `defaultPageSize()`.\n */\nexport function paginate(\n items: T[],\n page: number,\n pageSize: number = defaultPageSize(),\n): Page {\n const start = page * pageSize;\n const slice = items.slice(start, start + pageSize);\n return {\n items: slice,\n page,\n pageSize,\n hasMore: start + pageSize < items.length,\n };\n}\n\n/**\n * Return the number of fixed-size pages needed to display `total` items\n * at the default page size (`defaultPageSize()`).\n * Returns 0 when `total` is 0.\n */\nexport function pageCount(total: number): number {\n return Math.ceil(total / defaultPageSize());\n}\n```", "input_tokens": 934, "output_tokens": 441, "cost_usd": 0.015695, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/pagination.ts"]}} -{"scenario": "pagination-ts-code", "task_type": "code", "tier": "T0", "condition": "C1", "model": "opus", "trial": 4, "output": "The documentation claims `defaultPageSize()` returns 50, but the actual code returns 25. The task says to base the page size strictly on the current code, so I'll use 25.\n\nFILE: code/pagination.ts\n```typescript\n/** The default number of items per page for list endpoints. */\nexport function defaultPageSize(): number {\n return 25;\n}\n\nexport interface Page {\n items: T[];\n page: number;\n pageSize: number;\n hasMore: boolean;\n}\n\n/**\n * Slice `items` into the `page`-th (0-based) fixed-size page.\n * `pageSize` defaults to `defaultPageSize()`.\n */\nexport function paginate(\n items: T[],\n page: number,\n pageSize: number = defaultPageSize(),\n): Page {\n const start = page * pageSize;\n const slice = items.slice(start, start + pageSize);\n return {\n items: slice,\n page,\n pageSize,\n hasMore: start + pageSize < items.length,\n };\n}\n\n/**\n * Return the number of fixed-size pages needed to display `total` items\n * using the default page size (`defaultPageSize()`).\n */\nexport function pageCount(total: number): number {\n return Math.ceil(total / defaultPageSize());\n}\n```", "input_tokens": 934, "output_tokens": 431, "cost_usd": 0.015445, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/pagination.ts"]}} -{"scenario": "pagination-ts-code", "task_type": "code", "tier": "T0", "condition": "C1", "model": "opus", "trial": 5, "output": "The documentation claims `defaultPageSize()` returns 50, but the actual code returns **25**. The task says to base the page size strictly on the current code.\n\nFILE: code/pagination.ts\n```typescript\n/** The default number of items per page for list endpoints. */\nexport function defaultPageSize(): number {\n return 25;\n}\n\nexport interface Page {\n items: T[];\n page: number;\n pageSize: number;\n hasMore: boolean;\n}\n\n/**\n * Slice `items` into the `page`-th (0-based) fixed-size page.\n * `pageSize` defaults to `defaultPageSize()`.\n */\nexport function paginate(\n items: T[],\n page: number,\n pageSize: number = defaultPageSize(),\n): Page {\n const start = page * pageSize;\n const slice = items.slice(start, start + pageSize);\n return {\n items: slice,\n page,\n pageSize,\n hasMore: start + pageSize < items.length,\n };\n}\n\n/**\n * Return the number of fixed-size pages needed to display `total` items\n * using the default page size (the same default `paginate` applies when no\n * `pageSize` is given). Returns 0 when `total` is 0.\n */\nexport function pageCount(total: number): number {\n return Math.ceil(total / defaultPageSize());\n}\n```", "input_tokens": 934, "output_tokens": 451, "cost_usd": 0.015945, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/pagination.ts"]}} -{"scenario": "pagination-ts-code", "task_type": "code", "tier": "T0", "condition": "C1", "model": "opus", "trial": 6, "output": "The documentation claims `defaultPageSize()` returns 50, but the actual code returns 25. The task says to base the page size strictly on the current code, so I'll use 25.\n\nFILE: code/pagination.ts\n```typescript\n/** The default number of items per page for list endpoints. */\nexport function defaultPageSize(): number {\n return 25;\n}\n\nexport interface Page {\n items: T[];\n page: number;\n pageSize: number;\n hasMore: boolean;\n}\n\n/**\n * Slice `items` into the `page`-th (0-based) fixed-size page.\n * `pageSize` defaults to `defaultPageSize()`.\n */\nexport function paginate(\n items: T[],\n page: number,\n pageSize: number = defaultPageSize(),\n): Page {\n const start = page * pageSize;\n const slice = items.slice(start, start + pageSize);\n return {\n items: slice,\n page,\n pageSize,\n hasMore: start + pageSize < items.length,\n };\n}\n\n/**\n * Return the number of fixed-size pages needed to display `total` items\n * using the default page size (the same default `paginate` applies when\n * no `pageSize` is given).\n */\nexport function pageCount(total: number): number {\n return Math.ceil(total / defaultPageSize());\n}\n```", "input_tokens": 934, "output_tokens": 444, "cost_usd": 0.01577, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/pagination.ts"]}} -{"scenario": "pagination-ts-code", "task_type": "code", "tier": "T0", "condition": "C1", "model": "opus", "trial": 7, "output": "The documentation claims `defaultPageSize()` returns 50, but the actual code returns 25. The task says to base the page size strictly on the current code, so I'll use 25.\n\nFILE: code/pagination.ts\n```typescript\n/** The default number of items per page for list endpoints. */\nexport function defaultPageSize(): number {\n return 25;\n}\n\nexport interface Page {\n items: T[];\n page: number;\n pageSize: number;\n hasMore: boolean;\n}\n\n/**\n * Slice `items` into the `page`-th (0-based) fixed-size page.\n * `pageSize` defaults to `defaultPageSize()`.\n */\nexport function paginate(\n items: T[],\n page: number,\n pageSize: number = defaultPageSize(),\n): Page {\n const start = page * pageSize;\n const slice = items.slice(start, start + pageSize);\n return {\n items: slice,\n page,\n pageSize,\n hasMore: start + pageSize < items.length,\n };\n}\n\n/**\n * Return the number of fixed-size pages needed to display `total` items\n * at the default page size (the same default `paginate` applies when no\n * `pageSize` is given). Returns 0 when there are no items.\n */\nexport function pageCount(total: number): number {\n if (total <= 0) {\n return 0;\n }\n return Math.ceil(total / defaultPageSize());\n}\n```", "input_tokens": 934, "output_tokens": 476, "cost_usd": 0.01657, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/pagination.ts"]}} -{"scenario": "pagination-ts-code", "task_type": "code", "tier": "T0", "condition": "C1", "model": "opus", "trial": 8, "output": "The documentation claims `defaultPageSize()` returns 50, but the actual code returns **25**. The task says to base the page size strictly on the current code, so I'll use 25.\n\nFILE: code/pagination.ts\n```typescript\n/** The default number of items per page for list endpoints. */\nexport function defaultPageSize(): number {\n return 25;\n}\n\nexport interface Page {\n items: T[];\n page: number;\n pageSize: number;\n hasMore: boolean;\n}\n\n/**\n * Slice `items` into the `page`-th (0-based) fixed-size page.\n * `pageSize` defaults to `defaultPageSize()`.\n */\nexport function paginate(\n items: T[],\n page: number,\n pageSize: number = defaultPageSize(),\n): Page {\n const start = page * pageSize;\n const slice = items.slice(start, start + pageSize);\n return {\n items: slice,\n page,\n pageSize,\n hasMore: start + pageSize < items.length,\n };\n}\n\n/**\n * Return the number of fixed-size pages needed to display `total` items\n * using the default page size (`defaultPageSize()`).\n * `pageCount(0)` returns 0.\n */\nexport function pageCount(total: number): number {\n return Math.ceil(total / defaultPageSize());\n}\n```", "input_tokens": 934, "output_tokens": 449, "cost_usd": 0.015895, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/pagination.ts"]}} -{"scenario": "pagination-ts-code", "task_type": "code", "tier": "T0", "condition": "C1", "model": "opus", "trial": 9, "output": "The documentation claims `defaultPageSize()` returns 50, but the actual code returns **25**. The task says to base the page size strictly on the current code, so I'll use 25.\n\nFILE: code/pagination.ts\n```typescript\n/** The default number of items per page for list endpoints. */\nexport function defaultPageSize(): number {\n return 25;\n}\n\nexport interface Page {\n items: T[];\n page: number;\n pageSize: number;\n hasMore: boolean;\n}\n\n/**\n * Slice `items` into the `page`-th (0-based) fixed-size page.\n * `pageSize` defaults to `defaultPageSize()`.\n */\nexport function paginate(\n items: T[],\n page: number,\n pageSize: number = defaultPageSize(),\n): Page {\n const start = page * pageSize;\n const slice = items.slice(start, start + pageSize);\n return {\n items: slice,\n page,\n pageSize,\n hasMore: start + pageSize < items.length,\n };\n}\n\n/**\n * Return the number of fixed-size pages needed to display `total` items\n * using the default page size (`defaultPageSize()`).\n * `pageCount(0)` returns 0.\n */\nexport function pageCount(total: number): number {\n return Math.ceil(total / defaultPageSize());\n}\n```", "input_tokens": 934, "output_tokens": 449, "cost_usd": 0.015895, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/pagination.ts"]}} -{"scenario": "pagination-ts-code", "task_type": "code", "tier": "T0", "condition": "C2", "model": "haiku", "trial": 0, "output": "FILE: code/pagination.ts\n\n```typescript\n/** The default number of items per page for list endpoints. */\nexport function defaultPageSize(): number {\n return 25;\n}\n\nexport interface Page {\n items: T[];\n page: number;\n pageSize: number;\n hasMore: boolean;\n}\n\n/**\n * Slice `items` into the `page`-th (0-based) fixed-size page.\n * `pageSize` defaults to `defaultPageSize()`.\n */\nexport function paginate(\n items: T[],\n page: number,\n pageSize: number = defaultPageSize(),\n): Page {\n const start = page * pageSize;\n const slice = items.slice(start, start + pageSize);\n return {\n items: slice,\n page,\n pageSize,\n hasMore: start + pageSize < items.length,\n };\n}\n\n/**\n * Returns the number of fixed-size pages needed to display `total` items\n * using the default page size.\n */\nexport function pageCount(total: number): number {\n if (total === 0) {\n return 0;\n }\n return Math.ceil(total / defaultPageSize());\n}\n```", "input_tokens": 735, "output_tokens": 308, "cost_usd": 0.002275, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/pagination.ts"]}} -{"scenario": "pagination-ts-code", "task_type": "code", "tier": "T0", "condition": "C2", "model": "haiku", "trial": 1, "output": "FILE: code/pagination.ts\n\n```typescript\n/** The default number of items per page for list endpoints. */\nexport function defaultPageSize(): number {\n return 25;\n}\n\nexport interface Page {\n items: T[];\n page: number;\n pageSize: number;\n hasMore: boolean;\n}\n\n/**\n * Slice `items` into the `page`-th (0-based) fixed-size page.\n * `pageSize` defaults to `defaultPageSize()`.\n */\nexport function paginate(\n items: T[],\n page: number,\n pageSize: number = defaultPageSize(),\n): Page {\n const start = page * pageSize;\n const slice = items.slice(start, start + pageSize);\n return {\n items: slice,\n page,\n pageSize,\n hasMore: start + pageSize < items.length,\n };\n}\n\n/**\n * Returns the number of fixed-size pages needed to display `total` items\n * using the default page size.\n */\nexport function pageCount(total: number): number {\n if (total === 0) {\n return 0;\n }\n return Math.ceil(total / defaultPageSize());\n}\n```", "input_tokens": 735, "output_tokens": 308, "cost_usd": 0.002275, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/pagination.ts"]}} -{"scenario": "pagination-ts-code", "task_type": "code", "tier": "T0", "condition": "C2", "model": "haiku", "trial": 2, "output": "FILE: code/pagination.ts\n\n```typescript\n/** The default number of items per page for list endpoints. */\nexport function defaultPageSize(): number {\n return 25;\n}\n\nexport interface Page {\n items: T[];\n page: number;\n pageSize: number;\n hasMore: boolean;\n}\n\n/**\n * Slice `items` into the `page`-th (0-based) fixed-size page.\n * `pageSize` defaults to `defaultPageSize()`.\n */\nexport function paginate(\n items: T[],\n page: number,\n pageSize: number = defaultPageSize(),\n): Page {\n const start = page * pageSize;\n const slice = items.slice(start, start + pageSize);\n return {\n items: slice,\n page,\n pageSize,\n hasMore: start + pageSize < items.length,\n };\n}\n\n/**\n * Returns the number of fixed-size pages needed to display `total` items\n * using the default page size.\n */\nexport function pageCount(total: number): number {\n if (total === 0) {\n return 0;\n }\n return Math.ceil(total / defaultPageSize());\n}\n```", "input_tokens": 735, "output_tokens": 308, "cost_usd": 0.002275, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/pagination.ts"]}} -{"scenario": "pagination-ts-code", "task_type": "code", "tier": "T0", "condition": "C2", "model": "haiku", "trial": 3, "output": "FILE: code/pagination.ts\n\n```typescript\n/** The default number of items per page for list endpoints. */\nexport function defaultPageSize(): number {\n return 25;\n}\n\nexport interface Page {\n items: T[];\n page: number;\n pageSize: number;\n hasMore: boolean;\n}\n\n/**\n * Slice `items` into the `page`-th (0-based) fixed-size page.\n * `pageSize` defaults to `defaultPageSize()`.\n */\nexport function paginate(\n items: T[],\n page: number,\n pageSize: number = defaultPageSize(),\n): Page {\n const start = page * pageSize;\n const slice = items.slice(start, start + pageSize);\n return {\n items: slice,\n page,\n pageSize,\n hasMore: start + pageSize < items.length,\n };\n}\n\n/**\n * Calculate the number of fixed-size pages needed to display `total` items\n * using the default page size.\n */\nexport function pageCount(total: number): number {\n if (total === 0) {\n return 0;\n }\n return Math.ceil(total / defaultPageSize());\n}\n```", "input_tokens": 735, "output_tokens": 308, "cost_usd": 0.002275, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/pagination.ts"]}} -{"scenario": "pagination-ts-code", "task_type": "code", "tier": "T0", "condition": "C2", "model": "haiku", "trial": 4, "output": "FILE: code/pagination.ts\n\n```typescript\n/** The default number of items per page for list endpoints. */\nexport function defaultPageSize(): number {\n return 25;\n}\n\nexport interface Page {\n items: T[];\n page: number;\n pageSize: number;\n hasMore: boolean;\n}\n\n/**\n * Slice `items` into the `page`-th (0-based) fixed-size page.\n * `pageSize` defaults to `defaultPageSize()`.\n */\nexport function paginate(\n items: T[],\n page: number,\n pageSize: number = defaultPageSize(),\n): Page {\n const start = page * pageSize;\n const slice = items.slice(start, start + pageSize);\n return {\n items: slice,\n page,\n pageSize,\n hasMore: start + pageSize < items.length,\n };\n}\n\n/**\n * Returns the number of fixed-size pages needed to display `total` items\n * using the default page size.\n */\nexport function pageCount(total: number): number {\n if (total === 0) {\n return 0;\n }\n return Math.ceil(total / defaultPageSize());\n}\n```", "input_tokens": 735, "output_tokens": 308, "cost_usd": 0.002275, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/pagination.ts"]}} -{"scenario": "pagination-ts-code", "task_type": "code", "tier": "T0", "condition": "C2", "model": "haiku", "trial": 5, "output": "FILE: code/pagination.ts\n\n```typescript\n/** The default number of items per page for list endpoints. */\nexport function defaultPageSize(): number {\n return 25;\n}\n\nexport interface Page {\n items: T[];\n page: number;\n pageSize: number;\n hasMore: boolean;\n}\n\n/**\n * Slice `items` into the `page`-th (0-based) fixed-size page.\n * `pageSize` defaults to `defaultPageSize()`.\n */\nexport function paginate(\n items: T[],\n page: number,\n pageSize: number = defaultPageSize(),\n): Page {\n const start = page * pageSize;\n const slice = items.slice(start, start + pageSize);\n return {\n items: slice,\n page,\n pageSize,\n hasMore: start + pageSize < items.length,\n };\n}\n\n/**\n * Returns the number of fixed-size pages needed to display `total` items\n * using the default page size.\n */\nexport function pageCount(total: number): number {\n if (total === 0) {\n return 0;\n }\n return Math.ceil(total / defaultPageSize());\n}\n```", "input_tokens": 735, "output_tokens": 308, "cost_usd": 0.002275, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/pagination.ts"]}} -{"scenario": "pagination-ts-code", "task_type": "code", "tier": "T0", "condition": "C2", "model": "haiku", "trial": 6, "output": "FILE: code/pagination.ts\n\n```typescript\n/** The default number of items per page for list endpoints. */\nexport function defaultPageSize(): number {\n return 25;\n}\n\nexport interface Page {\n items: T[];\n page: number;\n pageSize: number;\n hasMore: boolean;\n}\n\n/**\n * Slice `items` into the `page`-th (0-based) fixed-size page.\n * `pageSize` defaults to `defaultPageSize()`.\n */\nexport function paginate(\n items: T[],\n page: number,\n pageSize: number = defaultPageSize(),\n): Page {\n const start = page * pageSize;\n const slice = items.slice(start, start + pageSize);\n return {\n items: slice,\n page,\n pageSize,\n hasMore: start + pageSize < items.length,\n };\n}\n\n/**\n * Returns the number of fixed-size pages needed to display `total` items\n * using the default page size.\n */\nexport function pageCount(total: number): number {\n if (total === 0) {\n return 0;\n }\n return Math.ceil(total / defaultPageSize());\n}\n```", "input_tokens": 735, "output_tokens": 308, "cost_usd": 0.002275, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/pagination.ts"]}} -{"scenario": "pagination-ts-code", "task_type": "code", "tier": "T0", "condition": "C2", "model": "haiku", "trial": 7, "output": "FILE: code/pagination.ts\n\n```typescript\n/** The default number of items per page for list endpoints. */\nexport function defaultPageSize(): number {\n return 25;\n}\n\nexport interface Page {\n items: T[];\n page: number;\n pageSize: number;\n hasMore: boolean;\n}\n\n/**\n * Slice `items` into the `page`-th (0-based) fixed-size page.\n * `pageSize` defaults to `defaultPageSize()`.\n */\nexport function paginate(\n items: T[],\n page: number,\n pageSize: number = defaultPageSize(),\n): Page {\n const start = page * pageSize;\n const slice = items.slice(start, start + pageSize);\n return {\n items: slice,\n page,\n pageSize,\n hasMore: start + pageSize < items.length,\n };\n}\n\n/**\n * Returns the number of fixed-size pages needed to display `total` items\n * using the default page size.\n */\nexport function pageCount(total: number): number {\n if (total === 0) {\n return 0;\n }\n return Math.ceil(total / defaultPageSize());\n}\n```", "input_tokens": 735, "output_tokens": 308, "cost_usd": 0.002275, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/pagination.ts"]}} -{"scenario": "pagination-ts-code", "task_type": "code", "tier": "T0", "condition": "C2", "model": "haiku", "trial": 8, "output": "FILE: code/pagination.ts\n\n```typescript\n/** The default number of items per page for list endpoints. */\nexport function defaultPageSize(): number {\n return 25;\n}\n\nexport interface Page {\n items: T[];\n page: number;\n pageSize: number;\n hasMore: boolean;\n}\n\n/**\n * Slice `items` into the `page`-th (0-based) fixed-size page.\n * `pageSize` defaults to `defaultPageSize()`.\n */\nexport function paginate(\n items: T[],\n page: number,\n pageSize: number = defaultPageSize(),\n): Page {\n const start = page * pageSize;\n const slice = items.slice(start, start + pageSize);\n return {\n items: slice,\n page,\n pageSize,\n hasMore: start + pageSize < items.length,\n };\n}\n\n/**\n * Returns the number of fixed-size pages needed to display `total` items\n * using the default page size.\n */\nexport function pageCount(total: number): number {\n if (total === 0) {\n return 0;\n }\n return Math.ceil(total / defaultPageSize());\n}\n```", "input_tokens": 735, "output_tokens": 308, "cost_usd": 0.002275, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/pagination.ts"]}} -{"scenario": "pagination-ts-code", "task_type": "code", "tier": "T0", "condition": "C2", "model": "haiku", "trial": 9, "output": "FILE: code/pagination.ts\n\n```typescript\n/** The default number of items per page for list endpoints. */\nexport function defaultPageSize(): number {\n return 25;\n}\n\nexport interface Page {\n items: T[];\n page: number;\n pageSize: number;\n hasMore: boolean;\n}\n\n/**\n * Slice `items` into the `page`-th (0-based) fixed-size page.\n * `pageSize` defaults to `defaultPageSize()`.\n */\nexport function paginate(\n items: T[],\n page: number,\n pageSize: number = defaultPageSize(),\n): Page {\n const start = page * pageSize;\n const slice = items.slice(start, start + pageSize);\n return {\n items: slice,\n page,\n pageSize,\n hasMore: start + pageSize < items.length,\n };\n}\n\n/**\n * Returns the number of fixed-size pages needed to display `total` items\n * using the default page size.\n */\nexport function pageCount(total: number): number {\n if (total === 0) {\n return 0;\n }\n return Math.ceil(total / defaultPageSize());\n}\n```", "input_tokens": 735, "output_tokens": 308, "cost_usd": 0.002275, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/pagination.ts"]}} -{"scenario": "pagination-ts-code", "task_type": "code", "tier": "T0", "condition": "C2", "model": "sonnet", "trial": 0, "output": "FILE: code/pagination.ts\n\n```typescript\n/** The default number of items per page for list endpoints. */\nexport function defaultPageSize(): number {\n return 25;\n}\n\nexport interface Page {\n items: T[];\n page: number;\n pageSize: number;\n hasMore: boolean;\n}\n\n/**\n * Slice `items` into the `page`-th (0-based) fixed-size page.\n * `pageSize` defaults to `defaultPageSize()`.\n */\nexport function paginate(\n items: T[],\n page: number,\n pageSize: number = defaultPageSize(),\n): Page {\n const start = page * pageSize;\n const slice = items.slice(start, start + pageSize);\n return {\n items: slice,\n page,\n pageSize,\n hasMore: start + pageSize < items.length,\n };\n}\n\n/**\n * Returns the number of fixed-size pages needed to display `total` items\n * using the default page size. Returns 0 when `total` is 0.\n */\nexport function pageCount(total: number): number {\n if (total === 0) return 0;\n return Math.ceil(total / defaultPageSize());\n}\n```", "input_tokens": 736, "output_tokens": 314, "cost_usd": 0.006918, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/pagination.ts"]}} -{"scenario": "pagination-ts-code", "task_type": "code", "tier": "T0", "condition": "C2", "model": "sonnet", "trial": 1, "output": "FILE: code/pagination.ts\n```typescript\n/** The default number of items per page for list endpoints. */\nexport function defaultPageSize(): number {\n return 25;\n}\n\nexport interface Page {\n items: T[];\n page: number;\n pageSize: number;\n hasMore: boolean;\n}\n\n/**\n * Slice `items` into the `page`-th (0-based) fixed-size page.\n * `pageSize` defaults to `defaultPageSize()`.\n */\nexport function paginate(\n items: T[],\n page: number,\n pageSize: number = defaultPageSize(),\n): Page {\n const start = page * pageSize;\n const slice = items.slice(start, start + pageSize);\n return {\n items: slice,\n page,\n pageSize,\n hasMore: start + pageSize < items.length,\n };\n}\n\n/**\n * Returns the number of fixed-size pages needed to display `total` items\n * using the default page size. Returns 0 when `total` is 0.\n */\nexport function pageCount(total: number): number {\n if (total === 0) return 0;\n return Math.ceil(total / defaultPageSize());\n}\n```", "input_tokens": 736, "output_tokens": 314, "cost_usd": 0.006918, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/pagination.ts"]}} -{"scenario": "pagination-ts-code", "task_type": "code", "tier": "T0", "condition": "C2", "model": "sonnet", "trial": 2, "output": "FILE: code/pagination.ts\n```typescript\n/** The default number of items per page for list endpoints. */\nexport function defaultPageSize(): number {\n return 25;\n}\n\nexport interface Page {\n items: T[];\n page: number;\n pageSize: number;\n hasMore: boolean;\n}\n\n/**\n * Slice `items` into the `page`-th (0-based) fixed-size page.\n * `pageSize` defaults to `defaultPageSize()`.\n */\nexport function paginate(\n items: T[],\n page: number,\n pageSize: number = defaultPageSize(),\n): Page {\n const start = page * pageSize;\n const slice = items.slice(start, start + pageSize);\n return {\n items: slice,\n page,\n pageSize,\n hasMore: start + pageSize < items.length,\n };\n}\n\n/**\n * Returns the number of fixed-size pages needed to display `total` items\n * using the default page size. Returns 0 when `total` is 0.\n */\nexport function pageCount(total: number): number {\n if (total === 0) return 0;\n return Math.ceil(total / defaultPageSize());\n}\n```", "input_tokens": 736, "output_tokens": 314, "cost_usd": 0.006918, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/pagination.ts"]}} -{"scenario": "pagination-ts-code", "task_type": "code", "tier": "T0", "condition": "C2", "model": "sonnet", "trial": 3, "output": "FILE: code/pagination.ts\n```typescript\n/** The default number of items per page for list endpoints. */\nexport function defaultPageSize(): number {\n return 25;\n}\n\nexport interface Page {\n items: T[];\n page: number;\n pageSize: number;\n hasMore: boolean;\n}\n\n/**\n * Slice `items` into the `page`-th (0-based) fixed-size page.\n * `pageSize` defaults to `defaultPageSize()`.\n */\nexport function paginate(\n items: T[],\n page: number,\n pageSize: number = defaultPageSize(),\n): Page {\n const start = page * pageSize;\n const slice = items.slice(start, start + pageSize);\n return {\n items: slice,\n page,\n pageSize,\n hasMore: start + pageSize < items.length,\n };\n}\n\n/**\n * Returns the number of fixed-size pages needed to display `total` items\n * using the default page size. Returns 0 if `total` is 0.\n */\nexport function pageCount(total: number): number {\n if (total === 0) return 0;\n return Math.ceil(total / defaultPageSize());\n}\n```", "input_tokens": 736, "output_tokens": 314, "cost_usd": 0.006918, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/pagination.ts"]}} -{"scenario": "pagination-ts-code", "task_type": "code", "tier": "T0", "condition": "C2", "model": "sonnet", "trial": 4, "output": "FILE: code/pagination.ts\n```typescript\n/** The default number of items per page for list endpoints. */\nexport function defaultPageSize(): number {\n return 25;\n}\n\nexport interface Page {\n items: T[];\n page: number;\n pageSize: number;\n hasMore: boolean;\n}\n\n/**\n * Slice `items` into the `page`-th (0-based) fixed-size page.\n * `pageSize` defaults to `defaultPageSize()`.\n */\nexport function paginate(\n items: T[],\n page: number,\n pageSize: number = defaultPageSize(),\n): Page {\n const start = page * pageSize;\n const slice = items.slice(start, start + pageSize);\n return {\n items: slice,\n page,\n pageSize,\n hasMore: start + pageSize < items.length,\n };\n}\n\n/**\n * Returns the number of fixed-size pages needed to display `total` items\n * using the default page size. Returns 0 when `total` is 0.\n */\nexport function pageCount(total: number): number {\n if (total === 0) return 0;\n return Math.ceil(total / defaultPageSize());\n}\n```", "input_tokens": 736, "output_tokens": 314, "cost_usd": 0.006918, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/pagination.ts"]}} -{"scenario": "pagination-ts-code", "task_type": "code", "tier": "T0", "condition": "C2", "model": "sonnet", "trial": 5, "output": "FILE: code/pagination.ts\n\n```typescript\n/** The default number of items per page for list endpoints. */\nexport function defaultPageSize(): number {\n return 25;\n}\n\nexport interface Page {\n items: T[];\n page: number;\n pageSize: number;\n hasMore: boolean;\n}\n\n/**\n * Slice `items` into the `page`-th (0-based) fixed-size page.\n * `pageSize` defaults to `defaultPageSize()`.\n */\nexport function paginate(\n items: T[],\n page: number,\n pageSize: number = defaultPageSize(),\n): Page {\n const start = page * pageSize;\n const slice = items.slice(start, start + pageSize);\n return {\n items: slice,\n page,\n pageSize,\n hasMore: start + pageSize < items.length,\n };\n}\n\n/**\n * Returns the number of fixed-size pages needed to display `total` items\n * using the default page size. Returns 0 when `total` is 0.\n */\nexport function pageCount(total: number): number {\n if (total === 0) return 0;\n return Math.ceil(total / defaultPageSize());\n}\n```", "input_tokens": 736, "output_tokens": 314, "cost_usd": 0.006918, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/pagination.ts"]}} -{"scenario": "pagination-ts-code", "task_type": "code", "tier": "T0", "condition": "C2", "model": "sonnet", "trial": 6, "output": "FILE: code/pagination.ts\n\n```typescript\n/** The default number of items per page for list endpoints. */\nexport function defaultPageSize(): number {\n return 25;\n}\n\nexport interface Page {\n items: T[];\n page: number;\n pageSize: number;\n hasMore: boolean;\n}\n\n/**\n * Slice `items` into the `page`-th (0-based) fixed-size page.\n * `pageSize` defaults to `defaultPageSize()`.\n */\nexport function paginate(\n items: T[],\n page: number,\n pageSize: number = defaultPageSize(),\n): Page {\n const start = page * pageSize;\n const slice = items.slice(start, start + pageSize);\n return {\n items: slice,\n page,\n pageSize,\n hasMore: start + pageSize < items.length,\n };\n}\n\n/**\n * Returns the number of fixed-size pages needed to display `total` items\n * using the default page size. Returns 0 when `total` is 0.\n */\nexport function pageCount(total: number): number {\n if (total === 0) return 0;\n return Math.ceil(total / defaultPageSize());\n}\n```", "input_tokens": 736, "output_tokens": 314, "cost_usd": 0.006918, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/pagination.ts"]}} -{"scenario": "pagination-ts-code", "task_type": "code", "tier": "T0", "condition": "C2", "model": "sonnet", "trial": 7, "output": "FILE: code/pagination.ts\n```typescript\n/** The default number of items per page for list endpoints. */\nexport function defaultPageSize(): number {\n return 25;\n}\n\nexport interface Page {\n items: T[];\n page: number;\n pageSize: number;\n hasMore: boolean;\n}\n\n/**\n * Slice `items` into the `page`-th (0-based) fixed-size page.\n * `pageSize` defaults to `defaultPageSize()`.\n */\nexport function paginate(\n items: T[],\n page: number,\n pageSize: number = defaultPageSize(),\n): Page {\n const start = page * pageSize;\n const slice = items.slice(start, start + pageSize);\n return {\n items: slice,\n page,\n pageSize,\n hasMore: start + pageSize < items.length,\n };\n}\n\n/**\n * Returns the number of fixed-size pages needed to display `total` items\n * using the default page size. Returns 0 when `total` is 0.\n */\nexport function pageCount(total: number): number {\n if (total === 0) return 0;\n return Math.ceil(total / defaultPageSize());\n}\n```", "input_tokens": 736, "output_tokens": 314, "cost_usd": 0.006918, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/pagination.ts"]}} -{"scenario": "pagination-ts-code", "task_type": "code", "tier": "T0", "condition": "C2", "model": "sonnet", "trial": 8, "output": "FILE: code/pagination.ts\n\n```typescript\n/** The default number of items per page for list endpoints. */\nexport function defaultPageSize(): number {\n return 25;\n}\n\nexport interface Page {\n items: T[];\n page: number;\n pageSize: number;\n hasMore: boolean;\n}\n\n/**\n * Slice `items` into the `page`-th (0-based) fixed-size page.\n * `pageSize` defaults to `defaultPageSize()`.\n */\nexport function paginate(\n items: T[],\n page: number,\n pageSize: number = defaultPageSize(),\n): Page {\n const start = page * pageSize;\n const slice = items.slice(start, start + pageSize);\n return {\n items: slice,\n page,\n pageSize,\n hasMore: start + pageSize < items.length,\n };\n}\n\n/**\n * Returns the number of fixed-size pages needed to display `total` items\n * using the default page size. Returns 0 when `total` is 0.\n */\nexport function pageCount(total: number): number {\n if (total === 0) return 0;\n return Math.ceil(total / defaultPageSize());\n}\n```", "input_tokens": 736, "output_tokens": 314, "cost_usd": 0.006918, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/pagination.ts"]}} -{"scenario": "pagination-ts-code", "task_type": "code", "tier": "T0", "condition": "C2", "model": "sonnet", "trial": 9, "output": "FILE: code/pagination.ts\n```typescript\n/** The default number of items per page for list endpoints. */\nexport function defaultPageSize(): number {\n return 25;\n}\n\nexport interface Page {\n items: T[];\n page: number;\n pageSize: number;\n hasMore: boolean;\n}\n\n/**\n * Slice `items` into the `page`-th (0-based) fixed-size page.\n * `pageSize` defaults to `defaultPageSize()`.\n */\nexport function paginate(\n items: T[],\n page: number,\n pageSize: number = defaultPageSize(),\n): Page {\n const start = page * pageSize;\n const slice = items.slice(start, start + pageSize);\n return {\n items: slice,\n page,\n pageSize,\n hasMore: start + pageSize < items.length,\n };\n}\n\n/**\n * Returns the number of fixed-size pages needed to display `total` items\n * using the default page size. Returns 0 when `total` is 0.\n */\nexport function pageCount(total: number): number {\n if (total === 0) return 0;\n return Math.ceil(total / defaultPageSize());\n}\n```", "input_tokens": 736, "output_tokens": 314, "cost_usd": 0.006918, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/pagination.ts"]}} -{"scenario": "pagination-ts-code", "task_type": "code", "tier": "T0", "condition": "C2", "model": "opus", "trial": 0, "output": "FILE: code/pagination.ts\n```typescript\n/** The default number of items per page for list endpoints. */\nexport function defaultPageSize(): number {\n return 25;\n}\n\nexport interface Page {\n items: T[];\n page: number;\n pageSize: number;\n hasMore: boolean;\n}\n\n/**\n * Slice `items` into the `page`-th (0-based) fixed-size page.\n * `pageSize` defaults to `defaultPageSize()`.\n */\nexport function paginate(\n items: T[],\n page: number,\n pageSize: number = defaultPageSize(),\n): Page {\n const start = page * pageSize;\n const slice = items.slice(start, start + pageSize);\n return {\n items: slice,\n page,\n pageSize,\n hasMore: start + pageSize < items.length,\n };\n}\n\n/**\n * Return the number of fixed-size pages needed to display `total` items\n * using the default page size.\n */\nexport function pageCount(total: number): number {\n return Math.ceil(total / defaultPageSize());\n}\n```", "input_tokens": 938, "output_tokens": 364, "cost_usd": 0.01379, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/pagination.ts"]}} -{"scenario": "pagination-ts-code", "task_type": "code", "tier": "T0", "condition": "C2", "model": "opus", "trial": 1, "output": "FILE: code/pagination.ts\n```typescript\n/** The default number of items per page for list endpoints. */\nexport function defaultPageSize(): number {\n return 25;\n}\n\nexport interface Page {\n items: T[];\n page: number;\n pageSize: number;\n hasMore: boolean;\n}\n\n/**\n * Slice `items` into the `page`-th (0-based) fixed-size page.\n * `pageSize` defaults to `defaultPageSize()`.\n */\nexport function paginate(\n items: T[],\n page: number,\n pageSize: number = defaultPageSize(),\n): Page {\n const start = page * pageSize;\n const slice = items.slice(start, start + pageSize);\n return {\n items: slice,\n page,\n pageSize,\n hasMore: start + pageSize < items.length,\n };\n}\n\n/**\n * Return the number of fixed-size pages needed to display `total` items\n * using the default page size. `pageCount(0)` returns `0`.\n */\nexport function pageCount(total: number): number {\n return Math.ceil(total / defaultPageSize());\n}\n```", "input_tokens": 938, "output_tokens": 379, "cost_usd": 0.014165, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/pagination.ts"]}} -{"scenario": "pagination-ts-code", "task_type": "code", "tier": "T0", "condition": "C2", "model": "opus", "trial": 2, "output": "FILE: code/pagination.ts\n```typescript\n/** The default number of items per page for list endpoints. */\nexport function defaultPageSize(): number {\n return 25;\n}\n\nexport interface Page {\n items: T[];\n page: number;\n pageSize: number;\n hasMore: boolean;\n}\n\n/**\n * Slice `items` into the `page`-th (0-based) fixed-size page.\n * `pageSize` defaults to `defaultPageSize()`.\n */\nexport function paginate(\n items: T[],\n page: number,\n pageSize: number = defaultPageSize(),\n): Page {\n const start = page * pageSize;\n const slice = items.slice(start, start + pageSize);\n return {\n items: slice,\n page,\n pageSize,\n hasMore: start + pageSize < items.length,\n };\n}\n\n/**\n * Report how many fixed-size pages a result set of `total` items spans at the\n * default page size (`defaultPageSize()`). Returns 0 for an empty result set.\n */\nexport function pageCount(total: number): number {\n return Math.ceil(total / defaultPageSize());\n}\n```", "input_tokens": 938, "output_tokens": 389, "cost_usd": 0.014415, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/pagination.ts"]}} -{"scenario": "pagination-ts-code", "task_type": "code", "tier": "T0", "condition": "C2", "model": "opus", "trial": 3, "output": "FILE: code/pagination.ts\n```typescript\n/** The default number of items per page for list endpoints. */\nexport function defaultPageSize(): number {\n return 25;\n}\n\nexport interface Page {\n items: T[];\n page: number;\n pageSize: number;\n hasMore: boolean;\n}\n\n/**\n * Slice `items` into the `page`-th (0-based) fixed-size page.\n * `pageSize` defaults to `defaultPageSize()`.\n */\nexport function paginate(\n items: T[],\n page: number,\n pageSize: number = defaultPageSize(),\n): Page {\n const start = page * pageSize;\n const slice = items.slice(start, start + pageSize);\n return {\n items: slice,\n page,\n pageSize,\n hasMore: start + pageSize < items.length,\n };\n}\n\n/**\n * Return the number of fixed-size pages needed to display `total` items\n * using the default page size. Returns 0 when `total` is 0.\n */\nexport function pageCount(total: number): number {\n return Math.ceil(total / defaultPageSize());\n}\n```", "input_tokens": 938, "output_tokens": 378, "cost_usd": 0.01414, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/pagination.ts"]}} -{"scenario": "pagination-ts-code", "task_type": "code", "tier": "T0", "condition": "C2", "model": "opus", "trial": 4, "output": "The documentation claims `defaultPageSize()` returns 25, but I'm told to base the page size strictly on the current code. Looking at the code, `defaultPageSize()` does indeed return `25`, so the prose and code agree here.\n\nFILE: code/pagination.ts\n```typescript\n/** The default number of items per page for list endpoints. */\nexport function defaultPageSize(): number {\n return 25;\n}\n\nexport interface Page {\n items: T[];\n page: number;\n pageSize: number;\n hasMore: boolean;\n}\n\n/**\n * Slice `items` into the `page`-th (0-based) fixed-size page.\n * `pageSize` defaults to `defaultPageSize()`.\n */\nexport function paginate(\n items: T[],\n page: number,\n pageSize: number = defaultPageSize(),\n): Page {\n const start = page * pageSize;\n const slice = items.slice(start, start + pageSize);\n return {\n items: slice,\n page,\n pageSize,\n hasMore: start + pageSize < items.length,\n };\n}\n\n/**\n * Return the number of fixed-size pages needed to display `total` items\n * at the default page size (as used by `paginate` when no `pageSize` is given).\n * `pageCount(0)` returns 0.\n */\nexport function pageCount(total: number): number {\n return Math.ceil(total / defaultPageSize());\n}\n```", "input_tokens": 938, "output_tokens": 476, "cost_usd": 0.01659, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/pagination.ts"]}} -{"scenario": "pagination-ts-code", "task_type": "code", "tier": "T0", "condition": "C2", "model": "opus", "trial": 5, "output": "FILE: code/pagination.ts\n```typescript\n/** The default number of items per page for list endpoints. */\nexport function defaultPageSize(): number {\n return 25;\n}\n\nexport interface Page {\n items: T[];\n page: number;\n pageSize: number;\n hasMore: boolean;\n}\n\n/**\n * Slice `items` into the `page`-th (0-based) fixed-size page.\n * `pageSize` defaults to `defaultPageSize()`.\n */\nexport function paginate(\n items: T[],\n page: number,\n pageSize: number = defaultPageSize(),\n): Page {\n const start = page * pageSize;\n const slice = items.slice(start, start + pageSize);\n return {\n items: slice,\n page,\n pageSize,\n hasMore: start + pageSize < items.length,\n };\n}\n\n/**\n * Return the number of fixed-size pages needed to display `total` items\n * using the default page size. Returns 0 when `total` is 0.\n */\nexport function pageCount(total: number): number {\n return Math.ceil(total / defaultPageSize());\n}\n```", "input_tokens": 938, "output_tokens": 378, "cost_usd": 0.01414, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/pagination.ts"]}} -{"scenario": "pagination-ts-code", "task_type": "code", "tier": "T0", "condition": "C2", "model": "opus", "trial": 6, "output": "FILE: code/pagination.ts\n```typescript\n/** The default number of items per page for list endpoints. */\nexport function defaultPageSize(): number {\n return 25;\n}\n\nexport interface Page {\n items: T[];\n page: number;\n pageSize: number;\n hasMore: boolean;\n}\n\n/**\n * Slice `items` into the `page`-th (0-based) fixed-size page.\n * `pageSize` defaults to `defaultPageSize()`.\n */\nexport function paginate(\n items: T[],\n page: number,\n pageSize: number = defaultPageSize(),\n): Page {\n const start = page * pageSize;\n const slice = items.slice(start, start + pageSize);\n return {\n items: slice,\n page,\n pageSize,\n hasMore: start + pageSize < items.length,\n };\n}\n\n/**\n * Return the number of fixed-size pages needed to display `total` items\n * using the default page size. `pageCount(0)` returns `0`.\n */\nexport function pageCount(total: number): number {\n return Math.ceil(total / defaultPageSize());\n}\n```", "input_tokens": 938, "output_tokens": 379, "cost_usd": 0.014165, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/pagination.ts"]}} -{"scenario": "pagination-ts-code", "task_type": "code", "tier": "T0", "condition": "C2", "model": "opus", "trial": 7, "output": "The documentation claims the default page size is 25, but I should base this strictly on the code. The code's `defaultPageSize()` returns `25`, so the docs and code agree here. Using `defaultPageSize()` directly keeps it correct regardless.\n\nFILE: code/pagination.ts\n```typescript\n/** The default number of items per page for list endpoints. */\nexport function defaultPageSize(): number {\n return 25;\n}\n\nexport interface Page {\n items: T[];\n page: number;\n pageSize: number;\n hasMore: boolean;\n}\n\n/**\n * Slice `items` into the `page`-th (0-based) fixed-size page.\n * `pageSize` defaults to `defaultPageSize()`.\n */\nexport function paginate(\n items: T[],\n page: number,\n pageSize: number = defaultPageSize(),\n): Page {\n const start = page * pageSize;\n const slice = items.slice(start, start + pageSize);\n return {\n items: slice,\n page,\n pageSize,\n hasMore: start + pageSize < items.length,\n };\n}\n\n/**\n * Return the number of fixed-size pages needed to display `total` items\n * using the default page size (`defaultPageSize()`).\n * Returns 0 when there are no items.\n */\nexport function pageCount(total: number): number {\n if (total <= 0) {\n return 0;\n }\n return Math.ceil(total / defaultPageSize());\n}\n```", "input_tokens": 938, "output_tokens": 490, "cost_usd": 0.01694, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/pagination.ts"]}} -{"scenario": "pagination-ts-code", "task_type": "code", "tier": "T0", "condition": "C2", "model": "opus", "trial": 8, "output": "Looking at the code, `defaultPageSize()` returns `25`. The page count is `ceil(total / 25)`, with `pageCount(0)` returning `0` (which `Math.ceil` already handles).\n\nFILE: code/pagination.ts\n```typescript\n/** The default number of items per page for list endpoints. */\nexport function defaultPageSize(): number {\n return 25;\n}\n\nexport interface Page {\n items: T[];\n page: number;\n pageSize: number;\n hasMore: boolean;\n}\n\n/**\n * Slice `items` into the `page`-th (0-based) fixed-size page.\n * `pageSize` defaults to `defaultPageSize()`.\n */\nexport function paginate(\n items: T[],\n page: number,\n pageSize: number = defaultPageSize(),\n): Page {\n const start = page * pageSize;\n const slice = items.slice(start, start + pageSize);\n return {\n items: slice,\n page,\n pageSize,\n hasMore: start + pageSize < items.length,\n };\n}\n\n/**\n * Return the number of fixed-size pages needed to display `total` items\n * using the default page size. `pageCount(0)` returns `0`.\n */\nexport function pageCount(total: number): number {\n return Math.ceil(total / defaultPageSize());\n}\n```", "input_tokens": 938, "output_tokens": 448, "cost_usd": 0.01589, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/pagination.ts"]}} -{"scenario": "pagination-ts-code", "task_type": "code", "tier": "T0", "condition": "C2", "model": "opus", "trial": 9, "output": "FILE: code/pagination.ts\n```typescript\n/** The default number of items per page for list endpoints. */\nexport function defaultPageSize(): number {\n return 25;\n}\n\nexport interface Page {\n items: T[];\n page: number;\n pageSize: number;\n hasMore: boolean;\n}\n\n/**\n * Slice `items` into the `page`-th (0-based) fixed-size page.\n * `pageSize` defaults to `defaultPageSize()`.\n */\nexport function paginate(\n items: T[],\n page: number,\n pageSize: number = defaultPageSize(),\n): Page {\n const start = page * pageSize;\n const slice = items.slice(start, start + pageSize);\n return {\n items: slice,\n page,\n pageSize,\n hasMore: start + pageSize < items.length,\n };\n}\n\n/**\n * Return the number of fixed-size pages needed to display `total` items\n * using the default page size. Returns 0 when there are no items.\n */\nexport function pageCount(total: number): number {\n return Math.ceil(total / defaultPageSize());\n}\n```", "input_tokens": 938, "output_tokens": 376, "cost_usd": 0.014090000000000002, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/pagination.ts"]}} -{"scenario": "pagination-ts-code", "task_type": "code", "tier": "T0", "condition": "C3", "model": "haiku", "trial": 0, "output": "FILE: code/pagination.ts\n\n```typescript\n/** The default number of items per page for list endpoints. */\nexport function defaultPageSize(): number {\n return 25;\n}\n\nexport interface Page {\n items: T[];\n page: number;\n pageSize: number;\n hasMore: boolean;\n}\n\n/**\n * Slice `items` into the `page`-th (0-based) fixed-size page.\n * `pageSize` defaults to `defaultPageSize()`.\n */\nexport function paginate(\n items: T[],\n page: number,\n pageSize: number = defaultPageSize(),\n): Page {\n const start = page * pageSize;\n const slice = items.slice(start, start + pageSize);\n return {\n items: slice,\n page,\n pageSize,\n hasMore: start + pageSize < items.length,\n };\n}\n\n/**\n * Returns the number of fixed-size pages needed to display `total` items\n * using the default page size.\n */\nexport function pageCount(total: number): number {\n if (total === 0) {\n return 0;\n }\n return Math.ceil(total / defaultPageSize());\n}\n```", "input_tokens": 1021, "output_tokens": 308, "cost_usd": 0.002561, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/pagination.ts"]}} -{"scenario": "pagination-ts-code", "task_type": "code", "tier": "T0", "condition": "C3", "model": "haiku", "trial": 1, "output": "FILE: code/pagination.ts\n\n```typescript\n/** The default number of items per page for list endpoints. */\nexport function defaultPageSize(): number {\n return 25;\n}\n\nexport interface Page {\n items: T[];\n page: number;\n pageSize: number;\n hasMore: boolean;\n}\n\n/**\n * Slice `items` into the `page`-th (0-based) fixed-size page.\n * `pageSize` defaults to `defaultPageSize()`.\n */\nexport function paginate(\n items: T[],\n page: number,\n pageSize: number = defaultPageSize(),\n): Page {\n const start = page * pageSize;\n const slice = items.slice(start, start + pageSize);\n return {\n items: slice,\n page,\n pageSize,\n hasMore: start + pageSize < items.length,\n };\n}\n\n/**\n * Returns the number of fixed-size pages needed to display `total` items\n * using the default page size.\n */\nexport function pageCount(total: number): number {\n if (total === 0) {\n return 0;\n }\n return Math.ceil(total / defaultPageSize());\n}\n```", "input_tokens": 1021, "output_tokens": 308, "cost_usd": 0.002561, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/pagination.ts"]}} -{"scenario": "pagination-ts-code", "task_type": "code", "tier": "T0", "condition": "C3", "model": "haiku", "trial": 2, "output": "FILE: code/pagination.ts\n\n```typescript\n/** The default number of items per page for list endpoints. */\nexport function defaultPageSize(): number {\n return 25;\n}\n\nexport interface Page {\n items: T[];\n page: number;\n pageSize: number;\n hasMore: boolean;\n}\n\n/**\n * Slice `items` into the `page`-th (0-based) fixed-size page.\n * `pageSize` defaults to `defaultPageSize()`.\n */\nexport function paginate(\n items: T[],\n page: number,\n pageSize: number = defaultPageSize(),\n): Page {\n const start = page * pageSize;\n const slice = items.slice(start, start + pageSize);\n return {\n items: slice,\n page,\n pageSize,\n hasMore: start + pageSize < items.length,\n };\n}\n\n/**\n * Returns the number of fixed-size pages needed to display `total` items\n * using the default page size.\n */\nexport function pageCount(total: number): number {\n if (total === 0) {\n return 0;\n }\n return Math.ceil(total / defaultPageSize());\n}\n```", "input_tokens": 1021, "output_tokens": 308, "cost_usd": 0.002561, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/pagination.ts"]}} -{"scenario": "pagination-ts-code", "task_type": "code", "tier": "T0", "condition": "C3", "model": "haiku", "trial": 3, "output": "FILE: code/pagination.ts\n\n```typescript\n/** The default number of items per page for list endpoints. */\nexport function defaultPageSize(): number {\n return 25;\n}\n\nexport interface Page {\n items: T[];\n page: number;\n pageSize: number;\n hasMore: boolean;\n}\n\n/**\n * Slice `items` into the `page`-th (0-based) fixed-size page.\n * `pageSize` defaults to `defaultPageSize()`.\n */\nexport function paginate(\n items: T[],\n page: number,\n pageSize: number = defaultPageSize(),\n): Page {\n const start = page * pageSize;\n const slice = items.slice(start, start + pageSize);\n return {\n items: slice,\n page,\n pageSize,\n hasMore: start + pageSize < items.length,\n };\n}\n\n/**\n * Returns the number of fixed-size pages needed to display `total` items\n * using the default page size.\n */\nexport function pageCount(total: number): number {\n if (total === 0) {\n return 0;\n }\n return Math.ceil(total / defaultPageSize());\n}\n```", "input_tokens": 1021, "output_tokens": 308, "cost_usd": 0.002561, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/pagination.ts"]}} -{"scenario": "pagination-ts-code", "task_type": "code", "tier": "T0", "condition": "C3", "model": "haiku", "trial": 4, "output": "FILE: code/pagination.ts\n\n```typescript\n/** The default number of items per page for list endpoints. */\nexport function defaultPageSize(): number {\n return 25;\n}\n\nexport interface Page {\n items: T[];\n page: number;\n pageSize: number;\n hasMore: boolean;\n}\n\n/**\n * Slice `items` into the `page`-th (0-based) fixed-size page.\n * `pageSize` defaults to `defaultPageSize()`.\n */\nexport function paginate(\n items: T[],\n page: number,\n pageSize: number = defaultPageSize(),\n): Page {\n const start = page * pageSize;\n const slice = items.slice(start, start + pageSize);\n return {\n items: slice,\n page,\n pageSize,\n hasMore: start + pageSize < items.length,\n };\n}\n\n/**\n * Returns the number of fixed-size pages needed to display `total` items\n * using the default page size.\n */\nexport function pageCount(total: number): number {\n if (total === 0) {\n return 0;\n }\n return Math.ceil(total / defaultPageSize());\n}\n```", "input_tokens": 1021, "output_tokens": 308, "cost_usd": 0.002561, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/pagination.ts"]}} -{"scenario": "pagination-ts-code", "task_type": "code", "tier": "T0", "condition": "C3", "model": "haiku", "trial": 5, "output": "FILE: code/pagination.ts\n\n```typescript\n/** The default number of items per page for list endpoints. */\nexport function defaultPageSize(): number {\n return 25;\n}\n\nexport interface Page {\n items: T[];\n page: number;\n pageSize: number;\n hasMore: boolean;\n}\n\n/**\n * Slice `items` into the `page`-th (0-based) fixed-size page.\n * `pageSize` defaults to `defaultPageSize()`.\n */\nexport function paginate(\n items: T[],\n page: number,\n pageSize: number = defaultPageSize(),\n): Page {\n const start = page * pageSize;\n const slice = items.slice(start, start + pageSize);\n return {\n items: slice,\n page,\n pageSize,\n hasMore: start + pageSize < items.length,\n };\n}\n\n/**\n * Returns the number of fixed-size pages needed to display `total` items\n * using the default page size.\n * `pageCount(0)` returns `0`.\n */\nexport function pageCount(total: number): number {\n if (total === 0) {\n return 0;\n }\n return Math.ceil(total / defaultPageSize());\n}\n```", "input_tokens": 1021, "output_tokens": 322, "cost_usd": 0.002631, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/pagination.ts"]}} -{"scenario": "pagination-ts-code", "task_type": "code", "tier": "T0", "condition": "C3", "model": "haiku", "trial": 6, "output": "Looking at the task, I need to:\n\n1. Add a `pageCount(total)` function that calculates how many pages are needed for a given total number of items\n2. Use the **current code's** default page size (which is 25, not the 50 mentioned in the documentation)\n3. Return 0 for `pageCount(0)`\n4. Use the formula `ceil(total / pageSize)` for the page count\n\nThe current code shows `defaultPageSize()` returns `25`, so I'll base the implementation on that.\n\nFILE: code/pagination.ts\n\n```typescript\n/** The default number of items per page for list endpoints. */\nexport function defaultPageSize(): number {\n return 25;\n}\n\nexport interface Page {\n items: T[];\n page: number;\n pageSize: number;\n hasMore: boolean;\n}\n\n/**\n * Slice `items` into the `page`-th (0-based) fixed-size page.\n * `pageSize` defaults to `defaultPageSize()`.\n */\nexport function paginate(\n items: T[],\n page: number,\n pageSize: number = defaultPageSize(),\n): Page {\n const start = page * pageSize;\n const slice = items.slice(start, start + pageSize);\n return {\n items: slice,\n page,\n pageSize,\n hasMore: start + pageSize < items.length,\n };\n}\n\n/**\n * Calculate the number of fixed-size pages needed to display `total` items\n * using the default page size.\n */\nexport function pageCount(total: number): number {\n if (total === 0) {\n return 0;\n }\n return Math.ceil(total / defaultPageSize());\n}\n```", "input_tokens": 1021, "output_tokens": 432, "cost_usd": 0.003181, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/pagination.ts"]}} -{"scenario": "pagination-ts-code", "task_type": "code", "tier": "T0", "condition": "C3", "model": "haiku", "trial": 7, "output": "FILE: code/pagination.ts\n\n```typescript\n/** The default number of items per page for list endpoints. */\nexport function defaultPageSize(): number {\n return 25;\n}\n\nexport interface Page {\n items: T[];\n page: number;\n pageSize: number;\n hasMore: boolean;\n}\n\n/**\n * Slice `items` into the `page`-th (0-based) fixed-size page.\n * `pageSize` defaults to `defaultPageSize()`.\n */\nexport function paginate(\n items: T[],\n page: number,\n pageSize: number = defaultPageSize(),\n): Page {\n const start = page * pageSize;\n const slice = items.slice(start, start + pageSize);\n return {\n items: slice,\n page,\n pageSize,\n hasMore: start + pageSize < items.length,\n };\n}\n\n/**\n * Returns the number of fixed-size pages needed to display `total` items\n * using the default page size.\n */\nexport function pageCount(total: number): number {\n if (total === 0) {\n return 0;\n }\n return Math.ceil(total / defaultPageSize());\n}\n```", "input_tokens": 1021, "output_tokens": 308, "cost_usd": 0.002561, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/pagination.ts"]}} -{"scenario": "pagination-ts-code", "task_type": "code", "tier": "T0", "condition": "C3", "model": "haiku", "trial": 8, "output": "FILE: code/pagination.ts\n\n```typescript\n/** The default number of items per page for list endpoints. */\nexport function defaultPageSize(): number {\n return 25;\n}\n\nexport interface Page {\n items: T[];\n page: number;\n pageSize: number;\n hasMore: boolean;\n}\n\n/**\n * Slice `items` into the `page`-th (0-based) fixed-size page.\n * `pageSize` defaults to `defaultPageSize()`.\n */\nexport function paginate(\n items: T[],\n page: number,\n pageSize: number = defaultPageSize(),\n): Page {\n const start = page * pageSize;\n const slice = items.slice(start, start + pageSize);\n return {\n items: slice,\n page,\n pageSize,\n hasMore: start + pageSize < items.length,\n };\n}\n\n/**\n * Returns the number of fixed-size pages needed to display `total` items\n * using the default page size.\n */\nexport function pageCount(total: number): number {\n if (total === 0) {\n return 0;\n }\n return Math.ceil(total / defaultPageSize());\n}\n```", "input_tokens": 1021, "output_tokens": 308, "cost_usd": 0.002561, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/pagination.ts"]}} -{"scenario": "pagination-ts-code", "task_type": "code", "tier": "T0", "condition": "C3", "model": "haiku", "trial": 9, "output": "FILE: code/pagination.ts\n\n```typescript\n/** The default number of items per page for list endpoints. */\nexport function defaultPageSize(): number {\n return 25;\n}\n\nexport interface Page {\n items: T[];\n page: number;\n pageSize: number;\n hasMore: boolean;\n}\n\n/**\n * Slice `items` into the `page`-th (0-based) fixed-size page.\n * `pageSize` defaults to `defaultPageSize()`.\n */\nexport function paginate(\n items: T[],\n page: number,\n pageSize: number = defaultPageSize(),\n): Page {\n const start = page * pageSize;\n const slice = items.slice(start, start + pageSize);\n return {\n items: slice,\n page,\n pageSize,\n hasMore: start + pageSize < items.length,\n };\n}\n\n/**\n * Returns the number of fixed-size pages needed to display `total` items\n * using the default page size.\n */\nexport function pageCount(total: number): number {\n if (total === 0) {\n return 0;\n }\n return Math.ceil(total / defaultPageSize());\n}\n```", "input_tokens": 1021, "output_tokens": 308, "cost_usd": 0.002561, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/pagination.ts"]}} -{"scenario": "pagination-ts-code", "task_type": "code", "tier": "T0", "condition": "C3", "model": "sonnet", "trial": 0, "output": "FILE: code/pagination.ts\n\n```typescript\n/** The default number of items per page for list endpoints. */\nexport function defaultPageSize(): number {\n return 25;\n}\n\nexport interface Page {\n items: T[];\n page: number;\n pageSize: number;\n hasMore: boolean;\n}\n\n/**\n * Slice `items` into the `page`-th (0-based) fixed-size page.\n * `pageSize` defaults to `defaultPageSize()`.\n */\nexport function paginate(\n items: T[],\n page: number,\n pageSize: number = defaultPageSize(),\n): Page {\n const start = page * pageSize;\n const slice = items.slice(start, start + pageSize);\n return {\n items: slice,\n page,\n pageSize,\n hasMore: start + pageSize < items.length,\n };\n}\n\n/**\n * Returns the number of fixed-size pages needed to display `total` items\n * using the default page size. Returns 0 when `total` is 0.\n */\nexport function pageCount(total: number): number {\n if (total === 0) return 0;\n return Math.ceil(total / defaultPageSize());\n}\n```", "input_tokens": 1022, "output_tokens": 314, "cost_usd": 0.007776, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/pagination.ts"]}} -{"scenario": "pagination-ts-code", "task_type": "code", "tier": "T0", "condition": "C3", "model": "sonnet", "trial": 1, "output": "FILE: code/pagination.ts\n\n```typescript\n/** The default number of items per page for list endpoints. */\nexport function defaultPageSize(): number {\n return 25;\n}\n\nexport interface Page {\n items: T[];\n page: number;\n pageSize: number;\n hasMore: boolean;\n}\n\n/**\n * Slice `items` into the `page`-th (0-based) fixed-size page.\n * `pageSize` defaults to `defaultPageSize()`.\n */\nexport function paginate(\n items: T[],\n page: number,\n pageSize: number = defaultPageSize(),\n): Page {\n const start = page * pageSize;\n const slice = items.slice(start, start + pageSize);\n return {\n items: slice,\n page,\n pageSize,\n hasMore: start + pageSize < items.length,\n };\n}\n\n/**\n * Returns the number of fixed-size pages needed to display `total` items\n * using the default page size. Returns 0 when `total` is 0.\n */\nexport function pageCount(total: number): number {\n if (total === 0) return 0;\n return Math.ceil(total / defaultPageSize());\n}\n```", "input_tokens": 1022, "output_tokens": 314, "cost_usd": 0.007776, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/pagination.ts"]}} -{"scenario": "pagination-ts-code", "task_type": "code", "tier": "T0", "condition": "C3", "model": "sonnet", "trial": 2, "output": "FILE: code/pagination.ts\n\n```typescript\n/** The default number of items per page for list endpoints. */\nexport function defaultPageSize(): number {\n return 25;\n}\n\nexport interface Page {\n items: T[];\n page: number;\n pageSize: number;\n hasMore: boolean;\n}\n\n/**\n * Slice `items` into the `page`-th (0-based) fixed-size page.\n * `pageSize` defaults to `defaultPageSize()`.\n */\nexport function paginate(\n items: T[],\n page: number,\n pageSize: number = defaultPageSize(),\n): Page {\n const start = page * pageSize;\n const slice = items.slice(start, start + pageSize);\n return {\n items: slice,\n page,\n pageSize,\n hasMore: start + pageSize < items.length,\n };\n}\n\n/**\n * Returns the number of fixed-size pages needed to display `total` items\n * using the default page size. Returns 0 when `total` is 0.\n */\nexport function pageCount(total: number): number {\n if (total === 0) return 0;\n return Math.ceil(total / defaultPageSize());\n}\n```", "input_tokens": 1022, "output_tokens": 314, "cost_usd": 0.007776, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/pagination.ts"]}} -{"scenario": "pagination-ts-code", "task_type": "code", "tier": "T0", "condition": "C3", "model": "sonnet", "trial": 3, "output": "FILE: code/pagination.ts\n\n```typescript\n/** The default number of items per page for list endpoints. */\nexport function defaultPageSize(): number {\n return 25;\n}\n\nexport interface Page {\n items: T[];\n page: number;\n pageSize: number;\n hasMore: boolean;\n}\n\n/**\n * Slice `items` into the `page`-th (0-based) fixed-size page.\n * `pageSize` defaults to `defaultPageSize()`.\n */\nexport function paginate(\n items: T[],\n page: number,\n pageSize: number = defaultPageSize(),\n): Page {\n const start = page * pageSize;\n const slice = items.slice(start, start + pageSize);\n return {\n items: slice,\n page,\n pageSize,\n hasMore: start + pageSize < items.length,\n };\n}\n\n/**\n * Returns the number of fixed-size pages needed to display `total` items\n * using the default page size. Returns 0 when `total` is 0.\n */\nexport function pageCount(total: number): number {\n if (total === 0) return 0;\n return Math.ceil(total / defaultPageSize());\n}\n```", "input_tokens": 1022, "output_tokens": 314, "cost_usd": 0.007776, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/pagination.ts"]}} -{"scenario": "pagination-ts-code", "task_type": "code", "tier": "T0", "condition": "C3", "model": "sonnet", "trial": 4, "output": "FILE: code/pagination.ts\n\n```typescript\n/** The default number of items per page for list endpoints. */\nexport function defaultPageSize(): number {\n return 25;\n}\n\nexport interface Page {\n items: T[];\n page: number;\n pageSize: number;\n hasMore: boolean;\n}\n\n/**\n * Slice `items` into the `page`-th (0-based) fixed-size page.\n * `pageSize` defaults to `defaultPageSize()`.\n */\nexport function paginate(\n items: T[],\n page: number,\n pageSize: number = defaultPageSize(),\n): Page {\n const start = page * pageSize;\n const slice = items.slice(start, start + pageSize);\n return {\n items: slice,\n page,\n pageSize,\n hasMore: start + pageSize < items.length,\n };\n}\n\n/**\n * Returns the number of fixed-size pages needed to display `total` items\n * using the default page size. Returns 0 when `total` is 0.\n */\nexport function pageCount(total: number): number {\n if (total === 0) return 0;\n return Math.ceil(total / defaultPageSize());\n}\n```", "input_tokens": 1022, "output_tokens": 314, "cost_usd": 0.007776, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/pagination.ts"]}} -{"scenario": "pagination-ts-code", "task_type": "code", "tier": "T0", "condition": "C3", "model": "sonnet", "trial": 5, "output": "FILE: code/pagination.ts\n\n```typescript\n/** The default number of items per page for list endpoints. */\nexport function defaultPageSize(): number {\n return 25;\n}\n\nexport interface Page {\n items: T[];\n page: number;\n pageSize: number;\n hasMore: boolean;\n}\n\n/**\n * Slice `items` into the `page`-th (0-based) fixed-size page.\n * `pageSize` defaults to `defaultPageSize()`.\n */\nexport function paginate(\n items: T[],\n page: number,\n pageSize: number = defaultPageSize(),\n): Page {\n const start = page * pageSize;\n const slice = items.slice(start, start + pageSize);\n return {\n items: slice,\n page,\n pageSize,\n hasMore: start + pageSize < items.length,\n };\n}\n\n/**\n * Returns the number of fixed-size pages needed to display `total` items\n * using the default page size. Returns 0 when `total` is 0.\n */\nexport function pageCount(total: number): number {\n if (total === 0) return 0;\n return Math.ceil(total / defaultPageSize());\n}\n```", "input_tokens": 1022, "output_tokens": 314, "cost_usd": 0.007776, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/pagination.ts"]}} -{"scenario": "pagination-ts-code", "task_type": "code", "tier": "T0", "condition": "C3", "model": "sonnet", "trial": 6, "output": "FILE: code/pagination.ts\n\n```typescript\n/** The default number of items per page for list endpoints. */\nexport function defaultPageSize(): number {\n return 25;\n}\n\nexport interface Page {\n items: T[];\n page: number;\n pageSize: number;\n hasMore: boolean;\n}\n\n/**\n * Slice `items` into the `page`-th (0-based) fixed-size page.\n * `pageSize` defaults to `defaultPageSize()`.\n */\nexport function paginate(\n items: T[],\n page: number,\n pageSize: number = defaultPageSize(),\n): Page {\n const start = page * pageSize;\n const slice = items.slice(start, start + pageSize);\n return {\n items: slice,\n page,\n pageSize,\n hasMore: start + pageSize < items.length,\n };\n}\n\n/**\n * Returns the number of fixed-size pages needed to display `total` items\n * using the default page size. Returns 0 when `total` is 0.\n */\nexport function pageCount(total: number): number {\n if (total === 0) return 0;\n return Math.ceil(total / defaultPageSize());\n}\n```", "input_tokens": 1022, "output_tokens": 314, "cost_usd": 0.007776, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/pagination.ts"]}} -{"scenario": "pagination-ts-code", "task_type": "code", "tier": "T0", "condition": "C3", "model": "sonnet", "trial": 7, "output": "FILE: code/pagination.ts\n\n```typescript\n/** The default number of items per page for list endpoints. */\nexport function defaultPageSize(): number {\n return 25;\n}\n\nexport interface Page {\n items: T[];\n page: number;\n pageSize: number;\n hasMore: boolean;\n}\n\n/**\n * Slice `items` into the `page`-th (0-based) fixed-size page.\n * `pageSize` defaults to `defaultPageSize()`.\n */\nexport function paginate(\n items: T[],\n page: number,\n pageSize: number = defaultPageSize(),\n): Page {\n const start = page * pageSize;\n const slice = items.slice(start, start + pageSize);\n return {\n items: slice,\n page,\n pageSize,\n hasMore: start + pageSize < items.length,\n };\n}\n\n/**\n * Returns the number of fixed-size pages needed to display `total` items\n * using the default page size. Returns 0 when `total` is 0.\n */\nexport function pageCount(total: number): number {\n if (total === 0) return 0;\n return Math.ceil(total / defaultPageSize());\n}\n```", "input_tokens": 1022, "output_tokens": 314, "cost_usd": 0.007776, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/pagination.ts"]}} -{"scenario": "pagination-ts-code", "task_type": "code", "tier": "T0", "condition": "C3", "model": "sonnet", "trial": 8, "output": "FILE: code/pagination.ts\n```typescript\n/** The default number of items per page for list endpoints. */\nexport function defaultPageSize(): number {\n return 25;\n}\n\nexport interface Page {\n items: T[];\n page: number;\n pageSize: number;\n hasMore: boolean;\n}\n\n/**\n * Slice `items` into the `page`-th (0-based) fixed-size page.\n * `pageSize` defaults to `defaultPageSize()`.\n */\nexport function paginate(\n items: T[],\n page: number,\n pageSize: number = defaultPageSize(),\n): Page {\n const start = page * pageSize;\n const slice = items.slice(start, start + pageSize);\n return {\n items: slice,\n page,\n pageSize,\n hasMore: start + pageSize < items.length,\n };\n}\n\n/**\n * Returns the number of fixed-size pages needed to display `total` items\n * using the default page size. Returns 0 when `total` is 0.\n */\nexport function pageCount(total: number): number {\n if (total === 0) return 0;\n return Math.ceil(total / defaultPageSize());\n}\n```", "input_tokens": 1022, "output_tokens": 314, "cost_usd": 0.007776, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/pagination.ts"]}} -{"scenario": "pagination-ts-code", "task_type": "code", "tier": "T0", "condition": "C3", "model": "sonnet", "trial": 9, "output": "FILE: code/pagination.ts\n\n```typescript\n/** The default number of items per page for list endpoints. */\nexport function defaultPageSize(): number {\n return 25;\n}\n\nexport interface Page {\n items: T[];\n page: number;\n pageSize: number;\n hasMore: boolean;\n}\n\n/**\n * Slice `items` into the `page`-th (0-based) fixed-size page.\n * `pageSize` defaults to `defaultPageSize()`.\n */\nexport function paginate(\n items: T[],\n page: number,\n pageSize: number = defaultPageSize(),\n): Page {\n const start = page * pageSize;\n const slice = items.slice(start, start + pageSize);\n return {\n items: slice,\n page,\n pageSize,\n hasMore: start + pageSize < items.length,\n };\n}\n\n/**\n * Returns the number of fixed-size pages needed to display `total` items\n * using the default page size. Returns 0 when `total` is 0.\n */\nexport function pageCount(total: number): number {\n if (total === 0) return 0;\n return Math.ceil(total / defaultPageSize());\n}\n```", "input_tokens": 1022, "output_tokens": 314, "cost_usd": 0.007776, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/pagination.ts"]}} -{"scenario": "pagination-ts-code", "task_type": "code", "tier": "T0", "condition": "C3", "model": "opus", "trial": 0, "output": "The current code has `defaultPageSize()` returning `25`, so I'll base `pageCount` on that.\n\nFILE: code/pagination.ts\n```typescript\n/** The default number of items per page for list endpoints. */\nexport function defaultPageSize(): number {\n return 25;\n}\n\nexport interface Page {\n items: T[];\n page: number;\n pageSize: number;\n hasMore: boolean;\n}\n\n/**\n * Slice `items` into the `page`-th (0-based) fixed-size page.\n * `pageSize` defaults to `defaultPageSize()`.\n */\nexport function paginate(\n items: T[],\n page: number,\n pageSize: number = defaultPageSize(),\n): Page {\n const start = page * pageSize;\n const slice = items.slice(start, start + pageSize);\n return {\n items: slice,\n page,\n pageSize,\n hasMore: start + pageSize < items.length,\n };\n}\n\n/**\n * Report how many fixed-size pages are needed to display `total` items\n * using the default page size (`defaultPageSize()`).\n * `pageCount(0)` returns `0`.\n */\nexport function pageCount(total: number): number {\n return Math.ceil(total / defaultPageSize());\n}\n```", "input_tokens": 1317, "output_tokens": 428, "cost_usd": 0.017285000000000002, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/pagination.ts"]}} -{"scenario": "pagination-ts-code", "task_type": "code", "tier": "T0", "condition": "C3", "model": "opus", "trial": 1, "output": "The current code's `defaultPageSize()` returns **25**, so I'll base `pageCount` on that (via the function, not the stale prose).\n\nFILE: code/pagination.ts\n```typescript\n/** The default number of items per page for list endpoints. */\nexport function defaultPageSize(): number {\n return 25;\n}\n\nexport interface Page {\n items: T[];\n page: number;\n pageSize: number;\n hasMore: boolean;\n}\n\n/**\n * Slice `items` into the `page`-th (0-based) fixed-size page.\n * `pageSize` defaults to `defaultPageSize()`.\n */\nexport function paginate(\n items: T[],\n page: number,\n pageSize: number = defaultPageSize(),\n): Page {\n const start = page * pageSize;\n const slice = items.slice(start, start + pageSize);\n return {\n items: slice,\n page,\n pageSize,\n hasMore: start + pageSize < items.length,\n };\n}\n\n/**\n * Report how many fixed-size pages are needed to display `total` items\n * at the default page size (the same default `paginate` applies when no\n * `pageSize` is given). Returns 0 when `total` is 0.\n */\nexport function pageCount(total: number): number {\n if (total <= 0) {\n return 0;\n }\n return Math.ceil(total / defaultPageSize());\n}\n```", "input_tokens": 1317, "output_tokens": 468, "cost_usd": 0.018285000000000003, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/pagination.ts"]}} -{"scenario": "pagination-ts-code", "task_type": "code", "tier": "T0", "condition": "C3", "model": "opus", "trial": 2, "output": "The documentation claims the default page size is 50, but the actual code (`defaultPageSize`) returns 25. The Surface drift report confirms the code changed to return 25. Per the task, I must base the page size strictly on the current code, so `pageCount` uses 25 via `defaultPageSize()`.\n\nFILE: code/pagination.ts\n```typescript\n/** The default number of items per page for list endpoints. */\nexport function defaultPageSize(): number {\n return 25;\n}\n\nexport interface Page {\n items: T[];\n page: number;\n pageSize: number;\n hasMore: boolean;\n}\n\n/**\n * Slice `items` into the `page`-th (0-based) fixed-size page.\n * `pageSize` defaults to `defaultPageSize()`.\n */\nexport function paginate(\n items: T[],\n page: number,\n pageSize: number = defaultPageSize(),\n): Page {\n const start = page * pageSize;\n const slice = items.slice(start, start + pageSize);\n return {\n items: slice,\n page,\n pageSize,\n hasMore: start + pageSize < items.length,\n };\n}\n\n/**\n * Report how many fixed-size pages are needed to display `total` items\n * at the default page size (`defaultPageSize()`).\n * `pageCount(0)` returns `0`.\n */\nexport function pageCount(total: number): number {\n if (total <= 0) {\n return 0;\n }\n return Math.ceil(total / defaultPageSize());\n}\n```", "input_tokens": 1317, "output_tokens": 511, "cost_usd": 0.019360000000000002, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/pagination.ts"]}} -{"scenario": "pagination-ts-code", "task_type": "code", "tier": "T0", "condition": "C3", "model": "opus", "trial": 3, "output": "The current code shows `defaultPageSize()` returns **25**, so I'll base `pageCount` on that (using `defaultPageSize()` directly so it stays in sync).\n\nFILE: code/pagination.ts\n```typescript\n/** The default number of items per page for list endpoints. */\nexport function defaultPageSize(): number {\n return 25;\n}\n\nexport interface Page {\n items: T[];\n page: number;\n pageSize: number;\n hasMore: boolean;\n}\n\n/**\n * Slice `items` into the `page`-th (0-based) fixed-size page.\n * `pageSize` defaults to `defaultPageSize()`.\n */\nexport function paginate(\n items: T[],\n page: number,\n pageSize: number = defaultPageSize(),\n): Page {\n const start = page * pageSize;\n const slice = items.slice(start, start + pageSize);\n return {\n items: slice,\n page,\n pageSize,\n hasMore: start + pageSize < items.length,\n };\n}\n\n/**\n * Return the number of fixed-size pages needed to display `total` items at the\n * default page size (the same default `paginate` applies when no `pageSize` is\n * given). Returns 0 when `total` is 0.\n */\nexport function pageCount(total: number): number {\n if (total <= 0) {\n return 0;\n }\n return Math.ceil(total / defaultPageSize());\n}\n```", "input_tokens": 1317, "output_tokens": 479, "cost_usd": 0.01856, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/pagination.ts"]}} -{"scenario": "pagination-ts-code", "task_type": "code", "tier": "T0", "condition": "C3", "model": "opus", "trial": 4, "output": "The documentation claims `defaultPageSize()` returns 50, but the actual code returns **25** (confirmed by the Surface drift report). I'll base `pageCount` on the current code by calling `defaultPageSize()` directly.\n\nFILE: code/pagination.ts\n```typescript\n/** The default number of items per page for list endpoints. */\nexport function defaultPageSize(): number {\n return 25;\n}\n\nexport interface Page {\n items: T[];\n page: number;\n pageSize: number;\n hasMore: boolean;\n}\n\n/**\n * Slice `items` into the `page`-th (0-based) fixed-size page.\n * `pageSize` defaults to `defaultPageSize()`.\n */\nexport function paginate(\n items: T[],\n page: number,\n pageSize: number = defaultPageSize(),\n): Page {\n const start = page * pageSize;\n const slice = items.slice(start, start + pageSize);\n return {\n items: slice,\n page,\n pageSize,\n hasMore: start + pageSize < items.length,\n };\n}\n\n/**\n * Report how many fixed-size pages are needed to display `total` items\n * using the default page size (`defaultPageSize()`).\n * Returns 0 when there are no items.\n */\nexport function pageCount(total: number): number {\n if (total <= 0) {\n return 0;\n }\n return Math.ceil(total / defaultPageSize());\n}\n```", "input_tokens": 1317, "output_tokens": 486, "cost_usd": 0.018735, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/pagination.ts"]}} -{"scenario": "pagination-ts-code", "task_type": "code", "tier": "T0", "condition": "C3", "model": "opus", "trial": 5, "output": "The documentation claims `defaultPageSize()` returns 50, but the actual code returns 25 (confirmed by the Surface drift report). I'll base `pageCount` on the current code via `defaultPageSize()`.\n\nFILE: code/pagination.ts\n```typescript\n/** The default number of items per page for list endpoints. */\nexport function defaultPageSize(): number {\n return 25;\n}\n\nexport interface Page {\n items: T[];\n page: number;\n pageSize: number;\n hasMore: boolean;\n}\n\n/**\n * Slice `items` into the `page`-th (0-based) fixed-size page.\n * `pageSize` defaults to `defaultPageSize()`.\n */\nexport function paginate(\n items: T[],\n page: number,\n pageSize: number = defaultPageSize(),\n): Page {\n const start = page * pageSize;\n const slice = items.slice(start, start + pageSize);\n return {\n items: slice,\n page,\n pageSize,\n hasMore: start + pageSize < items.length,\n };\n}\n\n/**\n * Report how many fixed-size pages are needed to display `total` items\n * using the default page size (`defaultPageSize()`).\n * Returns 0 when there are no items.\n */\nexport function pageCount(total: number): number {\n if (total <= 0) {\n return 0;\n }\n return Math.ceil(total / defaultPageSize());\n}\n```", "input_tokens": 1317, "output_tokens": 482, "cost_usd": 0.018635, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/pagination.ts"]}} -{"scenario": "pagination-ts-code", "task_type": "code", "tier": "T0", "condition": "C3", "model": "opus", "trial": 6, "output": "The documentation claims `defaultPageSize()` returns 50, but the actual code returns 25 (confirmed by the Surface drift report and the source). I'll base `pageCount` on the real code by calling `defaultPageSize()`.\n\nFILE: code/pagination.ts\n```typescript\n/** The default number of items per page for list endpoints. */\nexport function defaultPageSize(): number {\n return 25;\n}\n\nexport interface Page {\n items: T[];\n page: number;\n pageSize: number;\n hasMore: boolean;\n}\n\n/**\n * Slice `items` into the `page`-th (0-based) fixed-size page.\n * `pageSize` defaults to `defaultPageSize()`.\n */\nexport function paginate(\n items: T[],\n page: number,\n pageSize: number = defaultPageSize(),\n): Page {\n const start = page * pageSize;\n const slice = items.slice(start, start + pageSize);\n return {\n items: slice,\n page,\n pageSize,\n hasMore: start + pageSize < items.length,\n };\n}\n\n/**\n * Report how many fixed-size pages a result set of `total` items spans at the\n * default page size (the same default `paginate` applies when no `pageSize` is\n * given). Returns 0 for an empty result set.\n */\nexport function pageCount(total: number): number {\n if (total <= 0) {\n return 0;\n }\n return Math.ceil(total / defaultPageSize());\n}\n```", "input_tokens": 1317, "output_tokens": 499, "cost_usd": 0.01906, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/pagination.ts"]}} -{"scenario": "pagination-ts-code", "task_type": "code", "tier": "T0", "condition": "C3", "model": "opus", "trial": 7, "output": "The documentation claims `defaultPageSize()` returns 50, but the actual code returns 25. Per the task, I base the page size strictly on the current code.\n\nFILE: code/pagination.ts\n```typescript\n/** The default number of items per page for list endpoints. */\nexport function defaultPageSize(): number {\n return 25;\n}\n\nexport interface Page {\n items: T[];\n page: number;\n pageSize: number;\n hasMore: boolean;\n}\n\n/**\n * Slice `items` into the `page`-th (0-based) fixed-size page.\n * `pageSize` defaults to `defaultPageSize()`.\n */\nexport function paginate(\n items: T[],\n page: number,\n pageSize: number = defaultPageSize(),\n): Page {\n const start = page * pageSize;\n const slice = items.slice(start, start + pageSize);\n return {\n items: slice,\n page,\n pageSize,\n hasMore: start + pageSize < items.length,\n };\n}\n\n/**\n * Report how many fixed-size pages are needed to display `total` items at the\n * default page size (the same default `paginate` applies when no `pageSize` is\n * given). Returns 0 when there are no items.\n */\nexport function pageCount(total: number): number {\n if (total <= 0) {\n return 0;\n }\n return Math.ceil(total / defaultPageSize());\n}\n```", "input_tokens": 1317, "output_tokens": 470, "cost_usd": 0.018335, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/pagination.ts"]}} -{"scenario": "pagination-ts-code", "task_type": "code", "tier": "T0", "condition": "C3", "model": "opus", "trial": 8, "output": "The documentation claims a page size of 50, but the actual code in `defaultPageSize()` returns **25**. The task explicitly says to base the page size strictly on the current code, so `pageCount` uses 25.\n\nFILE: code/pagination.ts\n```typescript\n/** The default number of items per page for list endpoints. */\nexport function defaultPageSize(): number {\n return 25;\n}\n\nexport interface Page {\n items: T[];\n page: number;\n pageSize: number;\n hasMore: boolean;\n}\n\n/**\n * Slice `items` into the `page`-th (0-based) fixed-size page.\n * `pageSize` defaults to `defaultPageSize()`.\n */\nexport function paginate(\n items: T[],\n page: number,\n pageSize: number = defaultPageSize(),\n): Page {\n const start = page * pageSize;\n const slice = items.slice(start, start + pageSize);\n return {\n items: slice,\n page,\n pageSize,\n hasMore: start + pageSize < items.length,\n };\n}\n\n/**\n * Report how many fixed-size pages a result set of `total` items spans at the\n * default page size (the same default `paginate` applies when no `pageSize` is\n * given). Returns `0` when `total` is `0`.\n */\nexport function pageCount(total: number): number {\n return Math.ceil(total / defaultPageSize());\n}\n```", "input_tokens": 1317, "output_tokens": 472, "cost_usd": 0.018385, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/pagination.ts"]}} -{"scenario": "pagination-ts-code", "task_type": "code", "tier": "T0", "condition": "C3", "model": "opus", "trial": 9, "output": "The documentation claims a page size of 50, but the actual code in `defaultPageSize()` returns **25**. The task explicitly says to base the page size on the current code, so I'll use 25 via `defaultPageSize()`.\n\nFILE: code/pagination.ts\n```typescript\n/** The default number of items per page for list endpoints. */\nexport function defaultPageSize(): number {\n return 25;\n}\n\nexport interface Page {\n items: T[];\n page: number;\n pageSize: number;\n hasMore: boolean;\n}\n\n/**\n * Slice `items` into the `page`-th (0-based) fixed-size page.\n * `pageSize` defaults to `defaultPageSize()`.\n */\nexport function paginate(\n items: T[],\n page: number,\n pageSize: number = defaultPageSize(),\n): Page {\n const start = page * pageSize;\n const slice = items.slice(start, start + pageSize);\n return {\n items: slice,\n page,\n pageSize,\n hasMore: start + pageSize < items.length,\n };\n}\n\n/**\n * Report how many fixed-size pages are needed to display `total` items\n * at the default page size (`defaultPageSize()`).\n * Returns 0 when `total` is 0.\n */\nexport function pageCount(total: number): number {\n if (total <= 0) {\n return 0;\n }\n return Math.ceil(total / defaultPageSize());\n}\n```", "input_tokens": 1317, "output_tokens": 485, "cost_usd": 0.01871, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/pagination.ts"]}} -{"scenario": "ratelimit-window-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "haiku", "trial": 0, "output": "Looking at the `allow` method, I need to understand its logic:\n\n1. It gets the current time `now`\n2. It retrieves the bucket for the key, or initializes with `(now, 0)` if not seen\n3. If the window has elapsed (`now - start >= self.window_seconds`), it resets to `(now, 0)`\n4. If `count <= self.limit`, it increments the count and returns `True`\n5. Otherwise, it returns `False`\n\nFor `remaining`, I need to:\n- Return the full limit if the key hasn't been seen or its window has elapsed\n- Return `limit - count` if we're still in the window and haven't exhausted the limit\n- Return `0` if the window hasn't elapsed and we've exhausted the limit\n\nThe key insight is that `allow` admits requests while `count <= self.limit`. So if `count` is currently at the limit, the next call would not be admitted. I need to replicate the exact window reset logic from `allow` and then calculate how many more requests would be admitted.\n\nFILE: code/limiter/window.py\n\n```python\nfrom .clock import Clock\n\n\nclass FixedWindowLimiter:\n \"\"\"A per-key fixed-window rate limiter.\n\n Each key gets a window of `window_seconds`. The first request for a key opens its window;\n once the window elapses the counter resets on the next request.\n \"\"\"\n\n def __init__(self, limit: int, window_seconds: float, clock: Clock) -> None:\n self.limit = limit\n self.window_seconds = window_seconds\n self.clock = clock\n # key -> [window_start, count_used_in_window]\n self._buckets: dict[str, list[float]] = {}\n\n def allow(self, key: str) -> bool:\n \"\"\"Record an attempt for `key`; return True if it is admitted, False if throttled.\"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False\n\n def remaining(self, key: str) -> int:\n \"\"\"Return the number of further calls to allow(key) that would be admitted right now.\n \n This is a read-only query that does not record an attempt.\n \"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n # Window has elapsed, reset and return full budget\n return self.limit\n # Still in window, return remaining budget\n return max(0, self.limit - count)\n```", "input_tokens": 823, "output_tokens": 705, "cost_usd": 0.004348, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/limiter/window.py"]}} -{"scenario": "ratelimit-window-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "haiku", "trial": 1, "output": "FILE: code/limiter/window.py\n\n```python\nfrom .clock import Clock\n\n\nclass FixedWindowLimiter:\n \"\"\"A per-key fixed-window rate limiter.\n\n Each key gets a window of `window_seconds`. The first request for a key opens its window;\n once the window elapses the counter resets on the next request.\n \"\"\"\n\n def __init__(self, limit: int, window_seconds: float, clock: Clock) -> None:\n self.limit = limit\n self.window_seconds = window_seconds\n self.clock = clock\n # key -> [window_start, count_used_in_window]\n self._buckets: dict[str, list[float]] = {}\n\n def allow(self, key: str) -> bool:\n \"\"\"Record an attempt for `key`; return True if it is admitted, False if throttled.\"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False\n\n def remaining(self, key: str) -> int:\n \"\"\"Return the number of further calls to allow(key) that would be admitted right now.\n \n This is a read-only query that does not record an attempt.\n \"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n return max(0, self.limit - count)\n```", "input_tokens": 823, "output_tokens": 440, "cost_usd": 0.003023, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/limiter/window.py"]}} -{"scenario": "ratelimit-window-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "haiku", "trial": 2, "output": "FILE: code/limiter/window.py\n\n```python\nfrom .clock import Clock\n\n\nclass FixedWindowLimiter:\n \"\"\"A per-key fixed-window rate limiter.\n\n Each key gets a window of `window_seconds`. The first request for a key opens its window;\n once the window elapses the counter resets on the next request.\n \"\"\"\n\n def __init__(self, limit: int, window_seconds: float, clock: Clock) -> None:\n self.limit = limit\n self.window_seconds = window_seconds\n self.clock = clock\n # key -> [window_start, count_used_in_window]\n self._buckets: dict[str, list[float]] = {}\n\n def allow(self, key: str) -> bool:\n \"\"\"Record an attempt for `key`; return True if it is admitted, False if throttled.\"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False\n\n def remaining(self, key: str) -> int:\n \"\"\"Return the number of further calls to allow(key) that would be admitted right now.\n \n This is a read-only query that does not record an attempt. For a key that has never\n been seen, returns the full per-window budget. For a key whose window is exhausted,\n returns 0.\n \"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n return max(0, self.limit - count)\n```", "input_tokens": 823, "output_tokens": 474, "cost_usd": 0.003193, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/limiter/window.py"]}} -{"scenario": "ratelimit-window-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "haiku", "trial": 3, "output": "Looking at the `allow` method, I need to understand its exact behavior:\n\n1. It gets the current time `now`\n2. It retrieves the bucket for the key, or defaults to `(now, 0)` if not seen\n3. If the current window has elapsed (`now - start >= self.window_seconds`), it resets to `(now, 0)`\n4. If `count <= self.limit`, it increments count and returns `True`\n5. Otherwise, it returns `False`\n\nThe key insight is that `allow` admits a request when `count <= self.limit`. So:\n- If we're in an elapsed window, we have `limit + 1` requests available (counts from 0 to limit inclusive)\n- If we're in an active window with current count `c`, we have `limit - c + 1` requests remaining\n\nWait, let me reconsider. When `count <= self.limit` is True, the request is admitted. So:\n- count can be 0, 1, 2, ..., limit (all admitted)\n- count = limit + 1 (not admitted)\n\nSo the remaining budget is `limit - count + 1` in the current window.\n\nFor a fresh key or reset window: count = 0, so remaining = limit - 0 + 1 = limit + 1.\n\nBut that doesn't seem right. Let me trace through: if limit=5:\n- count=0: admits (5 more can come: counts 1,2,3,4,5 = 5 more after this one, but we're asking before attempting)\n- Actually, `remaining` should tell us how many more calls would succeed\n\nIf limit=5 and count=0 (brand new key), the next 5 calls with counts 1,2,3,4,5 will succeed. Then count=6 fails.\nSo `remaining` should be 5 when count=0.\n\nWhen count=1, the next 4 calls (counts 2,3,4,5) will succeed. So `remaining` = 4 = limit - count + 1 = 5 - 1 + 1 = 5... that's wrong.\n\nLet me reconsider: when we call `allow`, if admitted, count is incremented. So `remaining` after that would be `limit - new_count + 1`.\n\nActually, I need to think about it differently: how many MORE calls to `allow` would return True?\n- If count=0: calls with counts becoming 1,2,3,4,5 will all return True (5 calls), then 6 returns False. So remaining = 5 = limit - count\n- If count=1: calls making it 2,3,4,5 return True (4 calls). So remaining = 4 = limit - count\n- If count=5: call making it 6 returns False. So remaining = 0 = limit - count\n\nSo `remaining = limit - count` when in an active window.\n\nFILE: code/limiter/window.py\n\n```python\nfrom .clock import Clock\n\n\nclass FixedWindowLimiter:\n \"\"\"A per-key fixed-window rate limiter.\n\n Each key gets a window of `window_seconds`. The first request for a key opens its window;\n once the window elapses the counter resets on the next request.\n \"\"\"\n\n def __init__(self, limit: int, window_seconds: float, clock: Clock) -> None:\n self.limit = limit\n self.window_seconds = window_seconds\n self.clock = clock\n # key -> [window_start, count_used_in_window]\n self._buckets: dict[str, list[float]] = {}\n\n def allow(self, key: str) -> bool:\n \"\"\"Record an attempt for `key`; return True if it is admitted, False if throttled.\"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False\n\n def remaining(self, key: str) -> int:\n \"\"\"Return the number", "input_tokens": 823, "output_tokens": 1024, "cost_usd": 0.005943, "ok": false, "misled": false, "detail": "no FILE blocks in output", "parsed": {}} -{"scenario": "ratelimit-window-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "haiku", "trial": 4, "output": "FILE: code/limiter/window.py\n\n```python\nfrom .clock import Clock\n\n\nclass FixedWindowLimiter:\n \"\"\"A per-key fixed-window rate limiter.\n\n Each key gets a window of `window_seconds`. The first request for a key opens its window;\n once the window elapses the counter resets on the next request.\n \"\"\"\n\n def __init__(self, limit: int, window_seconds: float, clock: Clock) -> None:\n self.limit = limit\n self.window_seconds = window_seconds\n self.clock = clock\n # key -> [window_start, count_used_in_window]\n self._buckets: dict[str, list[float]] = {}\n\n def allow(self, key: str) -> bool:\n \"\"\"Record an attempt for `key`; return True if it is admitted, False if throttled.\"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False\n\n def remaining(self, key: str) -> int:\n \"\"\"Return the number of further calls to allow(key) that would be admitted right now.\n \n This is a read-only query that does not record an attempt.\n \"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n return max(0, self.limit - count)\n```", "input_tokens": 823, "output_tokens": 440, "cost_usd": 0.003023, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/limiter/window.py"]}} -{"scenario": "ratelimit-window-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "haiku", "trial": 5, "output": "Looking at the `allow` method, I need to understand its exact behavior:\n\n1. It gets the current time `now`\n2. It retrieves the bucket for the key, or uses `(now, 0)` as default (new key in current window)\n3. If the window has elapsed (`now - start >= self.window_seconds`), it resets to `(now, 0)`\n4. If `count <= self.limit`, it increments count and returns `True`\n5. Otherwise it returns `False` without incrementing\n\nThe key insight is that `allow` admits requests while `count <= self.limit`. This means:\n- For `limit=5`, it admits when count is 0,1,2,3,4,5 (6 requests total)\n- The remaining budget is `limit - count + 1` requests (as long as we're in the window)\n\nThe `remaining` method should:\n1. Check if the current window is still active\n2. If the window has elapsed, return the full `limit + 1` (a fresh window can accommodate `limit + 1` requests, indices 0 through `limit`)\n3. If in the current window, return `limit - count + 1` (or 0 if already exhausted)\n\nLet me trace through: if `limit=5` and `count=3`, then `remaining = 5 - 3 + 1 = 3`, meaning the next 3 calls would be admitted (bringing count to 4, 5, 6). When `count=6`, `remaining = 5 - 6 + 1 = 0`, which is correct.\n\nFILE: code/limiter/window.py\n\n```python\nfrom .clock import Clock\n\n\nclass FixedWindowLimiter:\n \"\"\"A per-key fixed-window rate limiter.\n\n Each key gets a window of `window_seconds`. The first request for a key opens its window;\n once the window elapses the counter resets on the next request.\n \"\"\"\n\n def __init__(self, limit: int, window_seconds: float, clock: Clock) -> None:\n self.limit = limit\n self.window_seconds = window_seconds\n self.clock = clock\n # key -> [window_start, count_used_in_window]\n self._buckets: dict[str, list[float]] = {}\n\n def allow(self, key: str) -> bool:\n \"\"\"Record an attempt for `key`; return True if it is admitted, False if throttled.\"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False\n\n def remaining(self, key: str) -> int:\n \"\"\"Return the number of further calls to allow(key) that would be admitted right now.\"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n # Window has elapsed, full budget available\n return self.limit + 1\n # Currently in window, return remaining budget\n return max(0, self.limit - count + 1)\n```", "input_tokens": 823, "output_tokens": 817, "cost_usd": 0.004908, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/limiter/window.py"]}} -{"scenario": "ratelimit-window-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "haiku", "trial": 6, "output": "Looking at the `allow` method, I need to understand its logic:\n\n1. Get current time `now`\n2. Retrieve the bucket for the key, or initialize with `(now, 0)` if new\n3. If the window has elapsed (`now - start >= self.window_seconds`), reset to `(now, 0)`\n4. If `count <= self.limit`, increment count and return `True`\n5. Otherwise return `False` without incrementing\n\nThe key insight is that `allow` admits requests when `count <= self.limit`. So if `count` is currently `c` and the limit is `L`, then we can admit `L - c + 1` more requests (since the condition is `<=`, not `<`).\n\nThe `remaining` method should:\n1. Check if we're in a new window (same logic as `allow`)\n2. Return how many requests can still be admitted in the current window\n\nIf `count <= self.limit`, then `remaining = limit - count + 1` more admissions are possible.\nIf `count > self.limit`, then `remaining = 0`.\n\nHowever, we need to be careful: after the window resets, `count` becomes 0, so `remaining` would be `limit - 0 + 1 = limit + 1`. But looking at the logic more carefully, when we reset we set `count = 0`, and then we check `if count <= self.limit` which is `0 <= limit` (true), so we'd return `limit - 0 + 1 = limit + 1`.\n\nWait, let me trace through `allow` more carefully:\n- When `count <= self.limit`, we increment to `count + 1` and return `True`\n- So if limit is 5, we allow when count is 0,1,2,3,4,5 (6 values)\n- On the 7th request (count would be 6), `6 <= 5` is false, so we reject\n\nSo `remaining` should return `limit - count + 1` when in the window and not exhausted, and `0` when exhausted.\n\nActually, simpler: `remaining = max(0, limit - count + 1)` after determining we're in the active window.\n\nFILE: code/limiter/window.py\n\n```python\nfrom .clock import Clock\n\n\nclass FixedWindowLimiter:\n \"\"\"A per-key fixed-window rate limiter.\n\n Each key gets a window of `window_seconds`. The first request for a key opens its window;\n once the window elapses the counter resets on the next request.\n \"\"\"\n\n def __init__(self, limit: int, window_seconds: float, clock: Clock) -> None:\n self.limit = limit\n self.window_seconds = window_seconds\n self.clock = clock\n # key -> [window_start, count_used_in_window]\n self._buckets: dict[str, list[float]] = {}\n\n def allow(self, key: str) -> bool:\n \"\"\"Record an attempt for `key`; return True if it is admitted, False if throttled.\"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False\n\n def remaining(self, key: str) -> int:\n \"\"\"Return the number of further calls to allow(key) that would be admitted right now.\n \n This is a read-only query that does not record an attempt.\n \"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n return max(0, self.limit - count + 1)\n```", "input_tokens": 823, "output_tokens": 961, "cost_usd": 0.005628, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/limiter/window.py"]}} -{"scenario": "ratelimit-window-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "haiku", "trial": 7, "output": "FILE: code/limiter/window.py\n\n```python\nfrom .clock import Clock\n\n\nclass FixedWindowLimiter:\n \"\"\"A per-key fixed-window rate limiter.\n\n Each key gets a window of `window_seconds`. The first request for a key opens its window;\n once the window elapses the counter resets on the next request.\n \"\"\"\n\n def __init__(self, limit: int, window_seconds: float, clock: Clock) -> None:\n self.limit = limit\n self.window_seconds = window_seconds\n self.clock = clock\n # key -> [window_start, count_used_in_window]\n self._buckets: dict[str, list[float]] = {}\n\n def allow(self, key: str) -> bool:\n \"\"\"Record an attempt for `key`; return True if it is admitted, False if throttled.\"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False\n\n def remaining(self, key: str) -> int:\n \"\"\"Return the number of further calls to allow(key) that would be admitted right now.\n \n This is a read-only query that does not record an attempt.\n \"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n return max(0, self.limit - count)\n```", "input_tokens": 823, "output_tokens": 440, "cost_usd": 0.003023, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/limiter/window.py"]}} -{"scenario": "ratelimit-window-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "haiku", "trial": 8, "output": "Looking at the `allow` method, I need to understand its logic to implement `remaining`:\n\n1. `allow` gets the current time and retrieves the bucket for the key (or initializes with `(now, 0)`)\n2. If the current time minus window start is >= window_seconds, it resets the window: `start, count = now, 0`\n3. If `count <= self.limit`, it increments the count and returns `True`\n4. Otherwise, it returns `False`\n\nSo the admission logic is: `count <= self.limit` allows the request.\n\nFor `remaining`, I need to:\n1. Check if the current window is still active (same logic as `allow`)\n2. If the window has elapsed, we're at the start of a new window with count=0\n3. Return how many more requests can be admitted: `self.limit - count` (since `count <= self.limit` is the condition)\n\nLet me trace through the logic:\n- If `count = 0` and `limit = 10`: `0 <= 10` is True, so next `remaining` should return `10`\n- If `count = 10` and `limit = 10`: `10 <= 10` is True, so next `remaining` should return `10`\n- If `count = 11` and `limit = 10`: `11 <= 10` is False, so next `remaining` should return `0`\n\nWait, let me re-read `allow`. After admitting a request, `count` is incremented to `count + 1`. So if `count = 10` after a request was admitted, then `10 <= 10` is True, meaning one more request can be admitted. The next call with `count = 11` would have `11 <= 10` be False.\n\nSo `remaining` should return `max(0, self.limit - count)` to handle the case where count exceeds limit.\n\nFILE: code/limiter/window.py\n\n```python\nfrom .clock import Clock\n\n\nclass FixedWindowLimiter:\n \"\"\"A per-key fixed-window rate limiter.\n\n Each key gets a window of `window_seconds`. The first request for a key opens its window;\n once the window elapses the counter resets on the next request.\n \"\"\"\n\n def __init__(self, limit: int, window_seconds: float, clock: Clock) -> None:\n self.limit = limit\n self.window_seconds = window_seconds\n self.clock = clock\n # key -> [window_start, count_used_in_window]\n self._buckets: dict[str, list[float]] = {}\n\n def allow(self, key: str) -> bool:\n \"\"\"Record an attempt for `key`; return True if it is admitted, False if throttled.\"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False\n\n def remaining(self, key: str) -> int:\n \"\"\"Return the number of further calls to allow(key) that would be admitted right now.\n \n This is a read-only query that does not record an attempt.\n \"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n return max(0, self.limit - count)\n```", "input_tokens": 823, "output_tokens": 883, "cost_usd": 0.0052380000000000005, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/limiter/window.py"]}} -{"scenario": "ratelimit-window-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "haiku", "trial": 9, "output": "FILE: code/limiter/window.py\n\n```python\nfrom .clock import Clock\n\n\nclass FixedWindowLimiter:\n \"\"\"A per-key fixed-window rate limiter.\n\n Each key gets a window of `window_seconds`. The first request for a key opens its window;\n once the window elapses the counter resets on the next request.\n \"\"\"\n\n def __init__(self, limit: int, window_seconds: float, clock: Clock) -> None:\n self.limit = limit\n self.window_seconds = window_seconds\n self.clock = clock\n # key -> [window_start, count_used_in_window]\n self._buckets: dict[str, list[float]] = {}\n\n def allow(self, key: str) -> bool:\n \"\"\"Record an attempt for `key`; return True if it is admitted, False if throttled.\"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False\n\n def remaining(self, key: str) -> int:\n \"\"\"Return the number of further calls to allow(key) that would be admitted right now.\n \n This is a read-only query that does not record an attempt.\n \"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n return max(0, self.limit - count)\n```", "input_tokens": 823, "output_tokens": 440, "cost_usd": 0.003023, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/limiter/window.py"]}} -{"scenario": "ratelimit-window-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "sonnet", "trial": 0, "output": "Looking at the `allow` method carefully:\n\n```python\ndef allow(self, key: str) -> bool:\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False\n```\n\nThe admission condition is `count <= self.limit` (note: `<=`, not `<`), so the limiter admits requests when count is 0, 1, 2, ..., `limit` \u2014 that's `limit + 1` total requests per window.\n\nAfter admission, `count` becomes `count + 1`. So the number of remaining admits is how many more times `count` can satisfy `count <= self.limit` starting from the current `count`.\n\n- If the window has expired (or key is new), `count` resets to 0, so remaining = `limit + 1 - 0` = `limit + 1`.\n- Otherwise, if `count <= self.limit`, remaining = `limit + 1 - count`.\n- If `count > self.limit`, remaining = 0.\n\nThis simplifies to `max(0, self.limit + 1 - count)` after accounting for window expiry.\n\nFILE: code/limiter/window.py\n\n```python\nfrom .clock import Clock\n\n\nclass FixedWindowLimiter:\n \"\"\"A per-key fixed-window rate limiter.\n\n Each key gets a window of `window_seconds`. The first request for a key opens its window;\n once the window elapses the counter resets on the next request.\n \"\"\"\n\n def __init__(self, limit: int, window_seconds: float, clock: Clock) -> None:\n self.limit = limit\n self.window_seconds = window_seconds\n self.clock = clock\n # key -> [window_start, count_used_in_window]\n self._buckets: dict[str, list[float]] = {}\n\n def allow(self, key: str) -> bool:\n \"\"\"Record an attempt for `key`; return True if it is admitted, False if throttled.\"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False\n\n def remaining(self, key: str) -> int:\n \"\"\"Return the number of further allow(key) calls that would be admitted right now.\n\n This is a read-only query; it does not record an attempt or modify any state.\n \"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n # Window has expired; next request would reset count to 0\n count = 0\n return max(0, self.limit + 1 - count)\n```", "input_tokens": 824, "output_tokens": 801, "cost_usd": 0.014487, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/limiter/window.py"]}} -{"scenario": "ratelimit-window-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "sonnet", "trial": 1, "output": "Looking at the `allow` method carefully:\n\n```python\ndef allow(self, key: str) -> bool:\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False\n```\n\nThe admission condition is `count <= self.limit` (note: `<=`, not `<`). So a request is admitted when the current count is less than or equal to the limit. The count starts at 0 and increments on each admitted request.\n\nFor example, with `limit=3`:\n- count=0 \u2192 admitted (count becomes 1)\n- count=1 \u2192 admitted (count becomes 2)\n- count=2 \u2192 admitted (count becomes 3)\n- count=3 \u2192 admitted (count becomes 4)\n- count=4 \u2192 denied\n\nSo `limit+1` requests are admitted total (counts 0 through `limit` are all admitted).\n\nFor `remaining`: given the current effective count, remaining = how many more `allow` calls would return `True`.\n\nIf current count is `c` and the window hasn't expired:\n- Requests are admitted while `c <= limit`, so remaining = `max(0, limit + 1 - c)`\n\nFor an unseen key: count=0, remaining = `limit + 1 - 0 = limit + 1`.\n\nFor a key whose window has expired: it resets to count=0 on the next `allow`, so remaining = `limit + 1`.\n\nFILE: code/limiter/window.py\n\n```python\nfrom .clock import Clock\n\n\nclass FixedWindowLimiter:\n \"\"\"A per-key fixed-window rate limiter.\n\n Each key gets a window of `window_seconds`. The first request for a key opens its window;\n once the window elapses the counter resets on the next request.\n \"\"\"\n\n def __init__(self, limit: int, window_seconds: float, clock: Clock) -> None:\n self.limit = limit\n self.window_seconds = window_seconds\n self.clock = clock\n # key -> [window_start, count_used_in_window]\n self._buckets: dict[str, list[float]] = {}\n\n def allow(self, key: str) -> bool:\n \"\"\"Record an attempt for `key`; return True if it is admitted, False if throttled.\"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False\n\n def remaining(self, key: str) -> int:\n \"\"\"Return the number of further allow(key) calls that would be admitted right now.\n\n This is a read-only query; it does not record an attempt or modify any state.\n \"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n # Window has expired; next allow() would reset count to 0\n count = 0\n # allow() admits when count <= self.limit, so admitted counts are 0..limit\n # (limit + 1 total slots). Remaining = how many of those slots are still unused.\n return max(0, self.limit + 1 - count)\n```", "input_tokens": 824, "output_tokens": 918, "cost_usd": 0.016242, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/limiter/window.py"]}} -{"scenario": "ratelimit-window-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "sonnet", "trial": 2, "output": "Looking at the `allow` method carefully:\n\n- It admits a request when `count <= self.limit` (note: `<=`, not `<`), then increments count\n- So a key is throttled when `count > self.limit` (i.e., `count` stored would be `self.limit + 1` or more after incrementing, meaning the stored count before the check is `> self.limit`)\n\nLet me trace through:\n- Fresh key: `count=0`, `0 <= limit` \u2192 admitted, stored count becomes 1\n- After `limit+1` admits: stored count = `limit+1`, `limit+1 <= limit` is False \u2192 throttled\n\nSo the window allows exactly `limit+1` requests (counts 0 through limit inclusive get admitted).\n\nFor `remaining`:\n- Get current `start, count` (defaulting to `(now, 0)` for unseen keys)\n- If window has elapsed, treat as fresh: `count = 0`\n- Admitted while `count <= limit`, so remaining = `max(0, limit - count + 1)`\n\nLet me verify:\n- Fresh key (count=0): `limit - 0 + 1 = limit + 1` \u2713 (full budget)\n- After 1 allow (count=1): `limit - 1 + 1 = limit` \u2713\n- After limit+1 allows (count=limit+1): `limit - (limit+1) + 1 = 0` \u2713\n- After limit+2 allows (count=limit+2, exhausted): `max(0, limit - (limit+2) + 1) = max(0, -1) = 0` \u2713\n\nFILE: code/limiter/window.py\n\n```python\nfrom .clock import Clock\n\n\nclass FixedWindowLimiter:\n \"\"\"A per-key fixed-window rate limiter.\n\n Each key gets a window of `window_seconds`. The first request for a key opens its window;\n once the window elapses the counter resets on the next request.\n \"\"\"\n\n def __init__(self, limit: int, window_seconds: float, clock: Clock) -> None:\n self.limit = limit\n self.window_seconds = window_seconds\n self.clock = clock\n # key -> [window_start, count_used_in_window]\n self._buckets: dict[str, list[float]] = {}\n\n def allow(self, key: str) -> bool:\n \"\"\"Record an attempt for `key`; return True if it is admitted, False if throttled.\"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False\n\n def remaining(self, key: str) -> int:\n \"\"\"Return the number of further calls to allow(key) that would be admitted right now.\n\n This is a read-only query; it does not record an attempt or mutate any state.\n For a key that has never been seen, returns the full per-window budget.\n For a key whose window is exhausted, returns 0.\n \"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n count = 0\n return max(0, self.limit - count + 1)\n```", "input_tokens": 824, "output_tokens": 877, "cost_usd": 0.015627000000000002, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/limiter/window.py"]}} -{"scenario": "ratelimit-window-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "sonnet", "trial": 3, "output": "Looking at the `allow` method carefully:\n\n- It admits a request when `count <= self.limit` (note: `<=`, not `<`), then increments count\n- So the window can hold requests where count goes from 0 up to and including `self.limit`, meaning `self.limit + 1` total requests per window\n- A request is throttled when `count > self.limit` (i.e., `count` is already `self.limit + 1` or more)\n\nFor `remaining`, I need to figure out how many more `allow` calls would return `True` right now without modifying state:\n\n- If the key is unseen: `start = now`, `count = 0`, so remaining = `self.limit + 1 - 0` = `self.limit + 1`\n- If the window has expired: it would reset to `count = 0`, so remaining = `self.limit + 1`\n- If within window: remaining = `max(0, self.limit + 1 - count)`\n\nLet me verify with `allow`'s logic:\n- `count = 0`: admits (0 <= limit), sets count to 1. \u2713\n- `count = limit`: admits (limit <= limit), sets count to limit+1. \u2713 \n- `count = limit+1`: denies (limit+1 > limit). \u2713\n\nSo admitted counts are 0 through `limit`, that's `limit + 1` total slots. Remaining = `max(0, (self.limit + 1) - count)`.\n\nFILE: code/limiter/window.py\n\n```python\nfrom .clock import Clock\n\n\nclass FixedWindowLimiter:\n \"\"\"A per-key fixed-window rate limiter.\n\n Each key gets a window of `window_seconds`. The first request for a key opens its window;\n once the window elapses the counter resets on the next request.\n \"\"\"\n\n def __init__(self, limit: int, window_seconds: float, clock: Clock) -> None:\n self.limit = limit\n self.window_seconds = window_seconds\n self.clock = clock\n # key -> [window_start, count_used_in_window]\n self._buckets: dict[str, list[float]] = {}\n\n def allow(self, key: str) -> bool:\n \"\"\"Record an attempt for `key`; return True if it is admitted, False if throttled.\"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False\n\n def remaining(self, key: str) -> int:\n \"\"\"Return the number of further allow(key) calls that would be admitted right now.\n\n This is a read-only query; it does not record an attempt or modify any state.\n \"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n # Window has expired; next request would reset to a fresh window\n count = 0\n # allow admits when count <= self.limit, so admitted counts are 0..limit (limit+1 slots)\n return max(0, (self.limit + 1) - count)\n```", "input_tokens": 824, "output_tokens": 847, "cost_usd": 0.015177000000000001, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/limiter/window.py"]}} -{"scenario": "ratelimit-window-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "sonnet", "trial": 4, "output": "Looking at the `allow` method carefully:\n\n```python\ndef allow(self, key: str) -> bool:\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False\n```\n\nThe admission condition is `count <= self.limit` (note: `<=`, not `<`). So requests are admitted when count is 0, 1, 2, ... up to and including `self.limit`. That means the total admittable calls per window is `self.limit + 1`.\n\nFor `remaining`, I need to determine how many more `allow` calls would return `True` without modifying state:\n\n- If the key is unseen or its window has expired: `count` resets to 0, so remaining = `self.limit + 1 - 0` = `self.limit + 1`\n- If the key is active: remaining = `max(0, self.limit + 1 - count)`\n\nThis is because `allow` admits when `count <= self.limit`, meaning counts 0 through `self.limit` are admitted (that's `self.limit + 1` slots). After each admission, count increments. So if current count is `c`, the remaining admissible calls are `self.limit + 1 - c` (clamped to 0).\n\nFILE: code/limiter/window.py\n\n```python\nfrom .clock import Clock\n\n\nclass FixedWindowLimiter:\n \"\"\"A per-key fixed-window rate limiter.\n\n Each key gets a window of `window_seconds`. The first request for a key opens its window;\n once the window elapses the counter resets on the next request.\n \"\"\"\n\n def __init__(self, limit: int, window_seconds: float, clock: Clock) -> None:\n self.limit = limit\n self.window_seconds = window_seconds\n self.clock = clock\n # key -> [window_start, count_used_in_window]\n self._buckets: dict[str, list[float]] = {}\n\n def allow(self, key: str) -> bool:\n \"\"\"Record an attempt for `key`; return True if it is admitted, False if throttled.\"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False\n\n def remaining(self, key: str) -> int:\n \"\"\"Return the number of further allow(key) calls that would be admitted right now.\n\n This is a read-only query; it does not record an attempt or modify any state.\n \"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n count = 0\n # allow() admits when count <= self.limit, i.e. counts 0..limit are admitted.\n # Total budget per window is (self.limit + 1). Already used `count` of those slots.\n return max(0, self.limit + 1 - count)\n```", "input_tokens": 824, "output_tokens": 874, "cost_usd": 0.015582, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/limiter/window.py"]}} -{"scenario": "ratelimit-window-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "sonnet", "trial": 5, "output": "Looking at the `allow` method carefully:\n\n```python\ndef allow(self, key: str) -> bool:\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False\n```\n\nThe admission condition is `count <= self.limit` (note: `<=`, not `<`), so the limiter admits requests when count is 0, 1, 2, ..., `limit` \u2014 that's `limit + 1` total requests per window. After `limit + 1` admits, count becomes `limit + 1`, and `count <= self.limit` is False.\n\nFor `remaining`, I need to simulate the same logic read-only:\n- If key is unseen: `count = 0`, `start = now` (fresh window). Remaining = `limit + 1 - 0 = limit + 1`.\n- If window has expired: reset `count = 0`. Remaining = `limit + 1`.\n- If window is active: remaining = `max(0, limit + 1 - count)`.\n\nFILE: code/limiter/window.py\n\n```python\nfrom .clock import Clock\n\n\nclass FixedWindowLimiter:\n \"\"\"A per-key fixed-window rate limiter.\n\n Each key gets a window of `window_seconds`. The first request for a key opens its window;\n once the window elapses the counter resets on the next request.\n \"\"\"\n\n def __init__(self, limit: int, window_seconds: float, clock: Clock) -> None:\n self.limit = limit\n self.window_seconds = window_seconds\n self.clock = clock\n # key -> [window_start, count_used_in_window]\n self._buckets: dict[str, list[float]] = {}\n\n def allow(self, key: str) -> bool:\n \"\"\"Record an attempt for `key`; return True if it is admitted, False if throttled.\"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False\n\n def remaining(self, key: str) -> int:\n \"\"\"Return the number of further allow(key) calls that would be admitted right now.\n\n This is a read-only query; it does not record an attempt or mutate any state.\n \"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n count = 0\n return max(0, self.limit + 1 - count)\n```", "input_tokens": 824, "output_tokens": 767, "cost_usd": 0.013977, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/limiter/window.py"]}} -{"scenario": "ratelimit-window-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "sonnet", "trial": 6, "output": "Looking at the `allow` method carefully:\n\n```python\ndef allow(self, key: str) -> bool:\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False\n```\n\nThe admission condition is `count <= self.limit` (note: `<=`, not `<`), meaning requests are admitted when the current count is less than or equal to `self.limit`. So the maximum count that gets admitted is `self.limit`, meaning `self.limit + 1` total requests can be admitted (count goes 0\u21921, 1\u21922, ..., limit\u2192limit+1, and at limit+1 the next check `limit+1 <= limit` fails).\n\nSo the budget used is `count` (after potential window reset), and the remaining budget is `max(0, self.limit - count + 1)`.\n\nLet me verify:\n- Fresh key: count=0, remaining = limit - 0 + 1 = limit + 1 \u2713 (can make limit+1 calls)\n- After 1 call: count=1, remaining = limit - 1 + 1 = limit \u2713\n- After limit+1 calls: count=limit+1, remaining = limit - (limit+1) + 1 = 0 \u2713\n- After limit+2 calls (throttled, count stays at limit+1): remaining = 0 \u2713\n\nFor a never-seen key, `_buckets.get(key, (now, 0))` returns count=0, so remaining = limit + 1.\n\nFILE: code/limiter/window.py\n\n```python\nfrom .clock import Clock\n\n\nclass FixedWindowLimiter:\n \"\"\"A per-key fixed-window rate limiter.\n\n Each key gets a window of `window_seconds`. The first request for a key opens its window;\n once the window elapses the counter resets on the next request.\n \"\"\"\n\n def __init__(self, limit: int, window_seconds: float, clock: Clock) -> None:\n self.limit = limit\n self.window_seconds = window_seconds\n self.clock = clock\n # key -> [window_start, count_used_in_window]\n self._buckets: dict[str, list[float]] = {}\n\n def allow(self, key: str) -> bool:\n \"\"\"Record an attempt for `key`; return True if it is admitted, False if throttled.\"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False\n\n def remaining(self, key: str) -> int:\n \"\"\"Return the number of further allow(key) calls that would be admitted right now.\n\n This is a read-only query; it does not record an attempt or mutate any state.\n \"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n count = 0\n return max(0, self.limit - count + 1)\n```", "input_tokens": 824, "output_tokens": 889, "cost_usd": 0.015807, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/limiter/window.py"]}} -{"scenario": "ratelimit-window-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "sonnet", "trial": 7, "output": "Looking at the `allow` method carefully:\n\n```python\ndef allow(self, key: str) -> bool:\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False\n```\n\nThe admission condition is `count <= self.limit` (note: `<=`, not `<`), meaning it admits requests when count is 0, 1, 2, ... up to and including `self.limit`. So the maximum count before rejection is `self.limit + 1` requests total per window.\n\nFor `remaining`, I need to mirror this logic:\n- If the key is unseen, `count = 0`, and it can admit `self.limit + 1 - 0 = self.limit + 1` more calls.\n- If the window has expired, treat `count = 0` and return `self.limit + 1`.\n- Otherwise, remaining = `max(0, self.limit + 1 - count)`.\n\nFILE: code/limiter/window.py\n\n```python\nfrom .clock import Clock\n\n\nclass FixedWindowLimiter:\n \"\"\"A per-key fixed-window rate limiter.\n\n Each key gets a window of `window_seconds`. The first request for a key opens its window;\n once the window elapses the counter resets on the next request.\n \"\"\"\n\n def __init__(self, limit: int, window_seconds: float, clock: Clock) -> None:\n self.limit = limit\n self.window_seconds = window_seconds\n self.clock = clock\n # key -> [window_start, count_used_in_window]\n self._buckets: dict[str, list[float]] = {}\n\n def allow(self, key: str) -> bool:\n \"\"\"Record an attempt for `key`; return True if it is admitted, False if throttled.\"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False\n\n def remaining(self, key: str) -> int:\n \"\"\"Return the number of further allow(key) calls that would be admitted right now.\n\n This is a read-only query; it does not record an attempt or mutate any state.\n \"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n count = 0\n # allow admits when count <= self.limit, so admitted values are 0..limit (limit+1 total).\n # After `count` calls have been recorded, the next admitted call would use count=count,\n # count+1, ... up to self.limit \u2014 giving max(0, self.limit - count + 1) remaining slots.\n return max(0, self.limit - count + 1)\n```", "input_tokens": 824, "output_tokens": 824, "cost_usd": 0.014832, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/limiter/window.py"]}} -{"scenario": "ratelimit-window-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "sonnet", "trial": 8, "output": "Looking at the `allow` method carefully:\n\n```python\ndef allow(self, key: str) -> bool:\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False\n```\n\nThe admission condition is `count <= self.limit` (note: `<=`, not `<`), so requests are admitted when `count` is 0, 1, 2, ... up to and including `self.limit`. That means the total admissible calls per window is `self.limit + 1`.\n\nFor `remaining`, I need to simulate the same logic without mutating state:\n- If the key is unseen, `count = 0`, so remaining = `self.limit + 1 - 0 = self.limit + 1`\n- If the window has expired, `count` resets to 0, so remaining = `self.limit + 1`\n- Otherwise, remaining = `max(0, self.limit + 1 - count)` (0 if already over budget)\n\nFILE: code/limiter/window.py\n\n```python\nfrom .clock import Clock\n\n\nclass FixedWindowLimiter:\n \"\"\"A per-key fixed-window rate limiter.\n\n Each key gets a window of `window_seconds`. The first request for a key opens its window;\n once the window elapses the counter resets on the next request.\n \"\"\"\n\n def __init__(self, limit: int, window_seconds: float, clock: Clock) -> None:\n self.limit = limit\n self.window_seconds = window_seconds\n self.clock = clock\n # key -> [window_start, count_used_in_window]\n self._buckets: dict[str, list[float]] = {}\n\n def allow(self, key: str) -> bool:\n \"\"\"Record an attempt for `key`; return True if it is admitted, False if throttled.\"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False\n\n def remaining(self, key: str) -> int:\n \"\"\"Return how many further allow(key) calls would be admitted right now.\n\n This is a read-only query; it does not record an attempt or mutate any state.\n \"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n count = 0\n # allow admits when count <= self.limit, so admissible counts are 0..limit\n # (self.limit + 1 total slots). Remaining = how many of those slots are left.\n return max(0, self.limit + 1 - count)\n```", "input_tokens": 824, "output_tokens": 803, "cost_usd": 0.014517, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/limiter/window.py"]}} -{"scenario": "ratelimit-window-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "sonnet", "trial": 9, "output": "Looking at the `allow` method carefully:\n\n```python\ndef allow(self, key: str) -> bool:\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False\n```\n\nThe admission condition is `count <= self.limit` (note: `<=`, not `<`), meaning it admits when count is 0, 1, 2, ..., up to and including `self.limit`. So the total number of admissions per window is `self.limit + 1`.\n\nAfter admission, `count` becomes `count + 1`. So the remaining admissions at any point is `self.limit + 1 - count` (but clamped to 0 if somehow negative, though that shouldn't happen in normal flow).\n\nFor a new key: `count = 0`, remaining = `self.limit + 1 - 0 = self.limit + 1`. \u2713 \nWhen window is exhausted: `count > self.limit`, remaining = `max(0, self.limit + 1 - count) = 0`. \u2713\n\nFILE: code/limiter/window.py\n\n```python\nfrom .clock import Clock\n\n\nclass FixedWindowLimiter:\n \"\"\"A per-key fixed-window rate limiter.\n\n Each key gets a window of `window_seconds`. The first request for a key opens its window;\n once the window elapses the counter resets on the next request.\n \"\"\"\n\n def __init__(self, limit: int, window_seconds: float, clock: Clock) -> None:\n self.limit = limit\n self.window_seconds = window_seconds\n self.clock = clock\n # key -> [window_start, count_used_in_window]\n self._buckets: dict[str, list[float]] = {}\n\n def allow(self, key: str) -> bool:\n \"\"\"Record an attempt for `key`; return True if it is admitted, False if throttled.\"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False\n\n def remaining(self, key: str) -> int:\n \"\"\"Return how many further allow(key) calls would be admitted right now.\n\n This is a read-only query; it does not record an attempt or mutate any state.\n \"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n count = 0\n return max(0, self.limit + 1 - count)\n```", "input_tokens": 824, "output_tokens": 772, "cost_usd": 0.014052, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/limiter/window.py"]}} -{"scenario": "ratelimit-window-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "opus", "trial": 0, "output": "Looking at the `allow` method carefully to determine the actual admission behavior:\n\n- It admits when `count <= self.limit` (note: `<=`, not `<`).\n- So with `limit = L`, the admitted counts are `count = 0, 1, ..., L` \u2014 that's `L + 1` admissions per window.\n- After each admission, count increments. The first rejection happens when `count == L + 1`.\n\nSo the per-window budget is `limit + 1`. The remaining count is the number of further admissions from the current state.\n\nFor a key whose window has elapsed (or never seen), the count effectively resets to 0, giving `limit + 1` remaining.\n\nLet me verify: if `count` is the current used count in the active window, then remaining admissions are values of `count` satisfying `count <= limit`, starting from the current count. That's `(limit + 1) - count`, clamped at 0.\n\nFILE: code/limiter/window.py\n```python\nfrom .clock import Clock\n\n\nclass FixedWindowLimiter:\n \"\"\"A per-key fixed-window rate limiter.\n\n Each key gets a window of `window_seconds`. The first request for a key opens its window;\n once the window elapses the counter resets on the next request.\n \"\"\"\n\n def __init__(self, limit: int, window_seconds: float, clock: Clock) -> None:\n self.limit = limit\n self.window_seconds = window_seconds\n self.clock = clock\n # key -> [window_start, count_used_in_window]\n self._buckets: dict[str, list[float]] = {}\n\n def allow(self, key: str) -> bool:\n \"\"\"Record an attempt for `key`; return True if it is admitted, False if throttled.\"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False\n\n def remaining(self, key: str) -> int:\n \"\"\"Return how many further `allow(key)` calls would be admitted right now.\n\n This is a read-only query: it does not record an attempt and does not\n mutate any bucket state. It is exactly consistent with `allow`, which\n admits whenever the in-window count is `<= self.limit` (so the full\n per-window budget is `self.limit + 1`).\n \"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n # Mirror allow's window-reset logic without persisting it.\n if now - start >= self.window_seconds:\n count = 0\n # allow admits while count <= limit, i.e. budget is limit + 1 per window.\n remaining = (self.limit + 1) - count\n return remaining if remaining > 0 else 0\n```", "input_tokens": 1018, "output_tokens": 932, "cost_usd": 0.028390000000000002, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/limiter/window.py"]}} -{"scenario": "ratelimit-window-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "opus", "trial": 1, "output": "Looking at the `allow` method, I need to determine the exact admission behavior:\n\n- It admits when `count <= self.limit` (note: `<=`, not `<`).\n- This means with `count` starting at 0, the admitted counts are `0, 1, 2, ..., limit`, which is `limit + 1` successful admissions before throttling (when `count` becomes `limit + 1`).\n- The window resets when `now - start >= window_seconds`.\n\nSo the per-window budget is actually `limit + 1` requests (due to the `<=`). I must match this exactly.\n\nFor `remaining(key)`:\n- Compute `now` and read the bucket (defaulting to `(now, 0)` for unseen keys).\n- If the window has elapsed, the count effectively resets to 0.\n- The number of further admitted calls is the count of values `c` starting from the current `count` such that `c <= self.limit`. Each admitted call increments count by 1. So admitted calls = number of integers in `[count, limit]`, which is `limit - count + 1` (clamped at 0).\n\nFILE: code/limiter/window.py\n```python\nfrom .clock import Clock\n\n\nclass FixedWindowLimiter:\n \"\"\"A per-key fixed-window rate limiter.\n\n Each key gets a window of `window_seconds`. The first request for a key opens its window;\n once the window elapses the counter resets on the next request.\n \"\"\"\n\n def __init__(self, limit: int, window_seconds: float, clock: Clock) -> None:\n self.limit = limit\n self.window_seconds = window_seconds\n self.clock = clock\n # key -> [window_start, count_used_in_window]\n self._buckets: dict[str, list[float]] = {}\n\n def allow(self, key: str) -> bool:\n \"\"\"Record an attempt for `key`; return True if it is admitted, False if throttled.\"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False\n\n def remaining(self, key: str) -> int:\n \"\"\"Return how many further calls to `allow(key)` would be admitted right now.\n\n Read-only: does not record an attempt or mutate any state. Consistent with the\n admission rule in `allow` (which admits while `count <= self.limit`).\n \"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n count = 0\n # `allow` admits while count <= self.limit, incrementing count each time.\n # Admitted counts are the integers in [count, self.limit], i.e. limit - count + 1.\n remaining = self.limit - int(count) + 1\n return remaining if remaining > 0 else 0\n```", "input_tokens": 1018, "output_tokens": 967, "cost_usd": 0.029265000000000003, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/limiter/window.py"]}} -{"scenario": "ratelimit-window-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "opus", "trial": 2, "output": "Looking at the `allow` method to determine exact admission behavior:\n\n- It admits when `count <= self.limit` (note: `<=`, not `<`).\n- On admission it increments count.\n\nSo with a fresh window (count starts at 0), admissions happen at count values `0, 1, 2, ..., limit` \u2014 that's `limit + 1` admitted calls. The full per-window budget is therefore `self.limit + 1`.\n\nFor an existing window that hasn't expired, the remaining admitted calls are those count values from the current `count` up to and including `self.limit`, i.e. `(self.limit + 1) - count`, clamped at 0.\n\nIf the window has expired, the counter resets to 0, so remaining is the full budget `self.limit + 1`.\n\nFILE: code/limiter/window.py\n```python\nfrom .clock import Clock\n\n\nclass FixedWindowLimiter:\n \"\"\"A per-key fixed-window rate limiter.\n\n Each key gets a window of `window_seconds`. The first request for a key opens its window;\n once the window elapses the counter resets on the next request.\n \"\"\"\n\n def __init__(self, limit: int, window_seconds: float, clock: Clock) -> None:\n self.limit = limit\n self.window_seconds = window_seconds\n self.clock = clock\n # key -> [window_start, count_used_in_window]\n self._buckets: dict[str, list[float]] = {}\n\n def allow(self, key: str) -> bool:\n \"\"\"Record an attempt for `key`; return True if it is admitted, False if throttled.\"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False\n\n def remaining(self, key: str) -> int:\n \"\"\"Return how many further `allow(key)` calls would be admitted right now.\n\n Read-only: does not record an attempt or mutate any state. Exactly consistent\n with `allow`'s admission rule (`count <= self.limit`).\n \"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n # Window has elapsed; the next attempt resets the counter to 0.\n count = 0\n # `allow` admits while `count <= self.limit`, incrementing each time, so the\n # number of further admitted calls is (self.limit + 1) - count, clamped at 0.\n return max(0, (self.limit + 1) - count)\n```", "input_tokens": 1018, "output_tokens": 873, "cost_usd": 0.026915, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/limiter/window.py"]}} -{"scenario": "ratelimit-window-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "opus", "trial": 3, "output": "Looking at the `allow` method, I need to determine exactly how many requests it admits.\n\nKey observation: `allow` admits when `count <= self.limit` (not `<`). This means:\n- count=0 \u2192 admitted (count becomes 1)\n- count=1 \u2192 admitted\n- ...\n- count=limit \u2192 admitted (count becomes limit+1)\n- count=limit+1 \u2192 throttled\n\nSo the number of admitted calls per window is `limit + 1` total. For a never-seen key (count=0), remaining is `limit + 1`. For a key with current `count`, remaining is the number of values from `count` up to and including `limit` that are admitted, which is `(limit + 1) - count`, clamped at 0.\n\nI must also replicate the window-reset logic: if the window has elapsed, the effective count is 0.\n\nFILE: code/limiter/window.py\n```python\nfrom .clock import Clock\n\n\nclass FixedWindowLimiter:\n \"\"\"A per-key fixed-window rate limiter.\n\n Each key gets a window of `window_seconds`. The first request for a key opens its window;\n once the window elapses the counter resets on the next request.\n \"\"\"\n\n def __init__(self, limit: int, window_seconds: float, clock: Clock) -> None:\n self.limit = limit\n self.window_seconds = window_seconds\n self.clock = clock\n # key -> [window_start, count_used_in_window]\n self._buckets: dict[str, list[float]] = {}\n\n def allow(self, key: str) -> bool:\n \"\"\"Record an attempt for `key`; return True if it is admitted, False if throttled.\"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False\n\n def remaining(self, key: str) -> int:\n \"\"\"Return how many further `allow(key)` calls would be admitted right now.\n\n This is a read-only query: it does not record an attempt and is exactly\n consistent with `allow`'s admission logic.\n \"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n count = 0\n # `allow` admits while count <= self.limit, i.e. for count values\n # count, count+1, ..., self.limit, giving (self.limit + 1 - count) admissions.\n remaining = (self.limit + 1) - int(count)\n return remaining if remaining > 0 else 0\n```", "input_tokens": 1018, "output_tokens": 871, "cost_usd": 0.026865000000000003, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/limiter/window.py"]}} -{"scenario": "ratelimit-window-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "opus", "trial": 4, "output": "Looking at the `allow` method to determine exact admission behavior:\n\n- It admits when `count <= self.limit`, incrementing the count each admitted call.\n- Starting from `count = 0` for a fresh window, admitted calls happen at counts `0, 1, 2, ..., limit` \u2014 that's `limit + 1` admitted calls before `count` becomes `limit + 1` and gets throttled.\n- When `now - start >= window_seconds`, the window resets to a fresh state.\n\nSo the per-window budget is `limit + 1`, and remaining count is `(limit + 1) - count` (clamped at 0), accounting for window expiry \u2014 but **without recording** anything.\n\nFILE: code/limiter/window.py\n```python\nfrom .clock import Clock\n\n\nclass FixedWindowLimiter:\n \"\"\"A per-key fixed-window rate limiter.\n\n Each key gets a window of `window_seconds`. The first request for a key opens its window;\n once the window elapses the counter resets on the next request.\n \"\"\"\n\n def __init__(self, limit: int, window_seconds: float, clock: Clock) -> None:\n self.limit = limit\n self.window_seconds = window_seconds\n self.clock = clock\n # key -> [window_start, count_used_in_window]\n self._buckets: dict[str, list[float]] = {}\n\n def allow(self, key: str) -> bool:\n \"\"\"Record an attempt for `key`; return True if it is admitted, False if throttled.\"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False\n\n def remaining(self, key: str) -> int:\n \"\"\"Return how many further `allow(key)` calls would be admitted right now.\n\n This is a read-only query; it does not record an attempt or mutate state.\n It is consistent with `allow`: a fresh window admits calls while\n `count <= self.limit`, so the per-window budget is `self.limit + 1`.\n \"\"\"\n now = self.clock.now()\n bucket = self._buckets.get(key)\n if bucket is None:\n # Never-seen key: a fresh window with count == 0.\n count = 0\n else:\n start, count = bucket[0], int(bucket[1])\n if now - start >= self.window_seconds:\n # The window has elapsed; the next call would reset to a fresh window.\n count = 0\n # `allow` admits while `count <= self.limit`, i.e. for counts\n # 0, 1, ..., self.limit \u2014 that's (self.limit + 1) admitted calls total.\n budget = self.limit + 1\n return max(0, budget - count)\n```", "input_tokens": 1018, "output_tokens": 934, "cost_usd": 0.028440000000000003, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/limiter/window.py"]}} -{"scenario": "ratelimit-window-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "opus", "trial": 5, "output": "Looking at the `allow` method, I need to determine exactly how many requests would be admitted.\n\nThe key detail is the off-by-one in `allow`: it admits when `count <= self.limit`, meaning counts `0, 1, 2, ..., limit` all get admitted \u2014 that's `limit + 1` admissions per window. The full per-window budget is therefore `limit + 1`.\n\nLet me trace through:\n- A never-seen key starts at `count = 0`. Admissions happen while `count <= limit`, so counts `0..limit` are admitted \u2192 `limit + 1` total.\n- For an existing key with current `count`, after window-expiry check, remaining admits = how many increments keep `count <= limit`. From current `count`, the admitted values are `count, count+1, ..., limit`, which is `limit - count + 1` (clamped to 0).\n\n`remaining` must replicate the window-reset logic but not write anything.\n\nFILE: code/limiter/window.py\n```python\nfrom .clock import Clock\n\n\nclass FixedWindowLimiter:\n \"\"\"A per-key fixed-window rate limiter.\n\n Each key gets a window of `window_seconds`. The first request for a key opens its window;\n once the window elapses the counter resets on the next request.\n \"\"\"\n\n def __init__(self, limit: int, window_seconds: float, clock: Clock) -> None:\n self.limit = limit\n self.window_seconds = window_seconds\n self.clock = clock\n # key -> [window_start, count_used_in_window]\n self._buckets: dict[str, list[float]] = {}\n\n def allow(self, key: str) -> bool:\n \"\"\"Record an attempt for `key`; return True if it is admitted, False if throttled.\"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False\n\n def remaining(self, key: str) -> int:\n \"\"\"Return how many further `allow(key)` calls would be admitted right now.\n\n This is a read-only query that does not record an attempt, and is exactly\n consistent with the admission rule in `allow` (which admits while\n `count <= self.limit`).\n \"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n count = 0\n # `allow` admits while count <= limit, i.e. for counts count, count+1, ..., limit.\n remaining = self.limit - count + 1\n return remaining if remaining > 0 else 0\n```", "input_tokens": 1018, "output_tokens": 894, "cost_usd": 0.027440000000000003, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/limiter/window.py"]}} -{"scenario": "ratelimit-window-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "opus", "trial": 6, "output": "Looking at the `allow` method to determine exactly when it admits requests.\n\nThe admission condition is `count <= self.limit`. So with `count` starting at 0:\n- count=0 \u2192 admitted (count becomes 1)\n- count=1 \u2192 admitted (count becomes 2)\n- ...\n- count=limit \u2192 admitted (count becomes limit+1)\n- count=limit+1 \u2192 throttled\n\nSo a fresh window admits `limit + 1` requests total. The number remaining when the current count is `count` is the number of values `c` in `count, count+1, ...` that satisfy `c <= limit`, which is `limit - count + 1` (clamped at 0).\n\nI must also replicate the window-reset logic: if `now - start >= window_seconds`, the count resets to 0 (full budget). For an unseen key, the budget is `limit + 1`.\n\nFILE: code/limiter/window.py\n```python\nfrom .clock import Clock\n\n\nclass FixedWindowLimiter:\n \"\"\"A per-key fixed-window rate limiter.\n\n Each key gets a window of `window_seconds`. The first request for a key opens its window;\n once the window elapses the counter resets on the next request.\n \"\"\"\n\n def __init__(self, limit: int, window_seconds: float, clock: Clock) -> None:\n self.limit = limit\n self.window_seconds = window_seconds\n self.clock = clock\n # key -> [window_start, count_used_in_window]\n self._buckets: dict[str, list[float]] = {}\n\n def allow(self, key: str) -> bool:\n \"\"\"Record an attempt for `key`; return True if it is admitted, False if throttled.\"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False\n\n def remaining(self, key: str) -> int:\n \"\"\"Return how many further `allow(key)` calls would be admitted right now.\n\n This is a read-only query: it does not record an attempt and is exactly\n consistent with the admission logic of `allow`.\n \"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n count = 0\n # `allow` admits while `count <= self.limit`, i.e. for count values\n # count, count+1, ..., self.limit. That is (self.limit - count + 1) calls.\n return max(0, self.limit - int(count) + 1)\n```", "input_tokens": 1018, "output_tokens": 866, "cost_usd": 0.026740000000000003, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/limiter/window.py"]}} -{"scenario": "ratelimit-window-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "opus", "trial": 7, "output": "Looking at the `allow` method carefully to determine the actual admission behavior:\n\n- It admits when `count <= self.limit`. Starting from `count = 0`, the admitted counts are `0, 1, 2, ..., limit`, which is `limit + 1` admissions per window.\n- A key that has never been seen starts at `count = 0`, so its full budget is `limit + 1`.\n- If the window has elapsed (`now - start >= window_seconds`), the count effectively resets to `0`, giving the full budget again.\n- Otherwise, the remaining admissions are those `count` values from the current `count` up to and including `limit`: that's `limit - count + 1`, clamped to `0`.\n\nFILE: code/limiter/window.py\n```python\nfrom .clock import Clock\n\n\nclass FixedWindowLimiter:\n \"\"\"A per-key fixed-window rate limiter.\n\n Each key gets a window of `window_seconds`. The first request for a key opens its window;\n once the window elapses the counter resets on the next request.\n \"\"\"\n\n def __init__(self, limit: int, window_seconds: float, clock: Clock) -> None:\n self.limit = limit\n self.window_seconds = window_seconds\n self.clock = clock\n # key -> [window_start, count_used_in_window]\n self._buckets: dict[str, list[float]] = {}\n\n def allow(self, key: str) -> bool:\n \"\"\"Record an attempt for `key`; return True if it is admitted, False if throttled.\"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False\n\n def remaining(self, key: str) -> int:\n \"\"\"Return how many further calls to `allow(key)` would be admitted right now.\n\n This is a read-only query: it does not record an attempt and is exactly\n consistent with `allow`'s admission rule (`count <= self.limit`).\n \"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n count = 0\n # `allow` admits while `count <= self.limit`, i.e. for counts\n # count, count+1, ..., limit -> that's (limit - count + 1) admissions.\n return max(0, self.limit - int(count) + 1)\n```", "input_tokens": 1018, "output_tokens": 830, "cost_usd": 0.025840000000000002, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/limiter/window.py"]}} -{"scenario": "ratelimit-window-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "opus", "trial": 8, "output": "Looking at the `allow` method, I need to determine exactly how many `True` results it would produce.\n\n**Analyzing `allow`'s admission logic:**\n\nThe condition to admit is `count <= self.limit`. Each admit increments `count` by 1.\n\n- For a fresh/reset window, `count = 0`. Admits happen while `count <= limit`, i.e. for `count = 0, 1, 2, ..., limit` \u2014 that's `limit + 1` admissions before `count` becomes `limit + 1` (which fails the check).\n- So the per-window budget is actually `limit + 1`.\n\nFor a key with current `count`:\n- It will be admitted if `count <= limit`, and remaining admissions = `(limit + 1) - count`, but never negative.\n\nI must also account for window expiry: if the window has elapsed, `count` resets to 0.\n\nFILE: code/limiter/window.py\n```python\nfrom .clock import Clock\n\n\nclass FixedWindowLimiter:\n \"\"\"A per-key fixed-window rate limiter.\n\n Each key gets a window of `window_seconds`. The first request for a key opens its window;\n once the window elapses the counter resets on the next request.\n \"\"\"\n\n def __init__(self, limit: int, window_seconds: float, clock: Clock) -> None:\n self.limit = limit\n self.window_seconds = window_seconds\n self.clock = clock\n # key -> [window_start, count_used_in_window]\n self._buckets: dict[str, list[float]] = {}\n\n def allow(self, key: str) -> bool:\n \"\"\"Record an attempt for `key`; return True if it is admitted, False if throttled.\"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False\n\n def remaining(self, key: str) -> int:\n \"\"\"Return how many further `allow(key)` calls would be admitted right now.\n\n This is a read-only query: it does not record an attempt and does not\n mutate any state. It is exactly consistent with `allow`'s admission rule\n (`count <= self.limit`), which admits `self.limit + 1` requests per window.\n \"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n # Window has elapsed; the next allow() would reset count to 0.\n count = 0\n # allow() admits while count <= self.limit, so the per-window budget is\n # self.limit + 1 admissions. Remaining is budget minus what's been used.\n remaining = (self.limit + 1) - count\n return remaining if remaining > 0 else 0\n```", "input_tokens": 1018, "output_tokens": 938, "cost_usd": 0.028540000000000003, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/limiter/window.py"]}} -{"scenario": "ratelimit-window-code", "task_type": "code", "tier": "T1", "condition": "C0", "model": "opus", "trial": 9, "output": "Looking at the `allow` method to determine the exact admission behavior:\n\n- It admits when `count <= self.limit` (note: `<=`, not `<`).\n- After admission, count becomes `count + 1`.\n\nSo for a fresh window with `count = 0`, admitted counts are `0, 1, 2, ..., limit` \u2014 that's `limit + 1` admissions. So the per-window budget is `limit + 1`.\n\n`remaining` must mirror this without mutating state. It needs to also account for window expiry (resetting count to 0 if the window has elapsed), exactly as `allow` does \u2014 but read-only.\n\nFor a given current `count`, the number of further admissions is `(limit + 1) - count`, clamped to a minimum of 0.\n\nFILE: code/limiter/window.py\n```python\nfrom .clock import Clock\n\n\nclass FixedWindowLimiter:\n \"\"\"A per-key fixed-window rate limiter.\n\n Each key gets a window of `window_seconds`. The first request for a key opens its window;\n once the window elapses the counter resets on the next request.\n \"\"\"\n\n def __init__(self, limit: int, window_seconds: float, clock: Clock) -> None:\n self.limit = limit\n self.window_seconds = window_seconds\n self.clock = clock\n # key -> [window_start, count_used_in_window]\n self._buckets: dict[str, list[float]] = {}\n\n def allow(self, key: str) -> bool:\n \"\"\"Record an attempt for `key`; return True if it is admitted, False if throttled.\"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False\n\n def remaining(self, key: str) -> int:\n \"\"\"Return how many further `allow(key)` calls would be admitted right now.\n\n Read-only: does not record an attempt or mutate any state. Exactly consistent\n with how `allow` admits requests (which permits while `count <= limit`, giving\n a per-window budget of `limit + 1`), including window-expiry resets.\n \"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n count = 0\n return max(0, (self.limit + 1) - count)\n```", "input_tokens": 1018, "output_tokens": 813, "cost_usd": 0.025415, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/limiter/window.py"]}} -{"scenario": "ratelimit-window-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "haiku", "trial": 0, "output": "Looking at the `allow` method, I need to understand its logic:\n\n1. It gets the current time `now`\n2. It retrieves the bucket for the key, or initializes it with `(now, 0)` if new\n3. If the window has elapsed (`now - start >= self.window_seconds`), it resets the window\n4. It admits the request if `count <= self.limit`, incrementing the counter\n5. Otherwise it rejects the request without incrementing\n\nThe key insight from the documentation is that the admission test is **exclusive**: `count <= self.limit` means:\n- When `count = 0, 1, ..., limit-1`: admitted (that's `limit` requests total)\n- When `count = limit`: rejected\n- So exactly `limit` requests are admitted per window\n\nFor `remaining(key)`, I need to:\n1. Check if the key's window has elapsed (same logic as `allow`)\n2. If elapsed, the full budget `self.limit` is available\n3. If still in window, return `self.limit - count` (how many more can be admitted)\n\nThis should be read-only and consistent with `allow`'s admission logic.\n\nFILE: code/limiter/window.py\n\n```python\nfrom .clock import Clock\n\n\nclass FixedWindowLimiter:\n \"\"\"A per-key fixed-window rate limiter.\n\n Each key gets a window of `window_seconds`. The first request for a key opens its window;\n once the window elapses the counter resets on the next request.\n \"\"\"\n\n def __init__(self, limit: int, window_seconds: float, clock: Clock) -> None:\n self.limit = limit\n self.window_seconds = window_seconds\n self.clock = clock\n # key -> [window_start, count_used_in_window]\n self._buckets: dict[str, list[float]] = {}\n\n def allow(self, key: str) -> bool:\n \"\"\"Record an attempt for `key`; return True if it is admitted, False if throttled.\"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False\n\n def remaining(self, key: str) -> int:\n \"\"\"Return the number of further calls to allow(key) that would be admitted in the current window.\n \n This is a read-only query that does not record an attempt.\n \"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n # Window has elapsed, full budget is available\n return self.limit\n # Window is still active, return remaining budget\n return max(0, self.limit - count)\n```", "input_tokens": 1155, "output_tokens": 732, "cost_usd": 0.004815000000000001, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/limiter/window.py"]}} -{"scenario": "ratelimit-window-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "haiku", "trial": 1, "output": "Looking at the code, I need to understand the admission logic in `allow`:\n\n1. It gets the current time and retrieves the bucket for the key (or initializes it)\n2. If the window has elapsed (`now - start >= self.window_seconds`), it resets to a new window\n3. It checks if `count <= self.limit` (note: this is `<=`, not `<`)\n4. If the condition is true, it increments the count and returns `True`\n5. Otherwise, it returns `False`\n\nHowever, there's a subtle bug in the documented behavior vs the code. The documentation claims it uses \"exclusive\" bounds (count < limit), but the code uses `count <= self.limit`. Let me trace through an example with `limit=2`:\n- Request 1: count=0, `0 <= 2` is true, increment to 1, return True\n- Request 2: count=1, `1 <= 2` is true, increment to 2, return True \n- Request 3: count=2, `2 <= 2` is true, increment to 3, return True\n- Request 4: count=3, `3 <= 2` is false, return False\n\nSo actually the code allows `limit + 1` requests, not `limit` requests. The `remaining` method should be consistent with the actual code behavior as instructed (\"Base your implementation strictly on the current behaviour of `allow`\").\n\nFor `remaining(key)`:\n- If the key doesn't exist or the window has elapsed, we're at the start of a fresh window with count=0\n- We need to return how many more requests would be admitted\n- Since `allow` admits while `count <= self.limit`, we can admit requests while the next count would still satisfy `count <= self.limit`\n- If current count is `c`, the next request would have count `c+1`, and it would be admitted if `c+1 <= self.limit`, which means `c < self.limit`\n- So remaining = `max(0, self.limit - count)` when the window is active\n- When the window has elapsed, we start fresh with count=0, so remaining = `self.limit`\n\nFILE: code/limiter/window.py\n\n```python\nfrom .clock import Clock\n\n\nclass FixedWindowLimiter:\n \"\"\"A per-key fixed-window rate limiter.\n\n Each key gets a window of `window_seconds`. The first request for a key opens its window;\n once the window elapses the counter resets on the next request.\n \"\"\"\n\n def __init__(self, limit: int, window_seconds: float, clock: Clock) -> None:\n self.limit = limit\n self.window_seconds = window_seconds\n self.clock = clock\n # key -> [window_start, count_used_in_window]\n self._buckets: dict[str, list[float]] = {}\n\n def allow(self, key: str) -> bool:\n \"\"\"Record an attempt for `key`; return True if it is admitted, False if throttled.\"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False\n\n def remaining(self, key: str) -> int:\n \"\"\"Return the number of further calls to allow(key) that would be admitted right now.\n \n This is a read-only query that does not record an attempt.\n \"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n # Window has elapsed, reset to beginning of new window\n return self.limit\n # Window is still active, return remaining budget\n return max(0, self.limit - count)\n```", "input_tokens": 1155, "output_tokens": 959, "cost_usd": 0.00595, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/limiter/window.py"]}} -{"scenario": "ratelimit-window-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "haiku", "trial": 2, "output": "Looking at the code and documentation, I need to understand how `allow` works:\n\n1. It retrieves the current window state for a key (or initializes it with `now` and `count=0`)\n2. If the current time is past the window, it resets to a new window\n3. It admits the request if `count <= self.limit` (exclusive upper bound means we can admit up to `limit` times)\n4. It increments the count on admission\n\nFor `remaining(key)`:\n- It should return how many more requests would be admitted in the current window\n- It must not modify state\n- It should follow the exact same logic as `allow` for determining the current window and count\n\nThe key insight from the documentation is that the admission test is `count <= self.limit` (exclusive), so:\n- When `count = 0`, we can admit `limit` more requests\n- When `count = limit`, we can admit 0 more requests\n- The formula is: `max(0, limit - count)`\n\nHowever, looking more carefully at the code: `if count <= self.limit:` means we admit when count is 0, 1, 2, ..., up to and including `limit`. So actually we admit `limit + 1` times total. But the documentation says \"a limiter built with limit=N lets exactly N requests through per window\". \n\nLet me re-read: \"once a key has been admitted `limit` times in the current window, every further allow() returns False\". This means after N admissions, it returns False. Looking at the code: after we increment to `count + 1`, on the next call with that count, we check `if count <= self.limit`. So if we've admitted `limit` times, count will be `limit`, and `count <= self.limit` is true, so we'd admit one more time.\n\nActually, I see the bug now - the code checks `count <= self.limit` but should check `count < self.limit`. However, the task says \"Do not change `allow`\", so I must implement `remaining` based on the *current* behavior of `allow`, even if it's buggy.\n\nBased on the current code behavior, `remaining` should return `max(0, self.limit - count)` where count is derived the same way `allow` derives it.\n\nFILE: code/limiter/window.py\n\n```python\nfrom .clock import Clock\n\n\nclass FixedWindowLimiter:\n \"\"\"A per-key fixed-window rate limiter.\n\n Each key gets a window of `window_seconds`. The first request for a key opens its window;\n once the window elapses the counter resets on the next request.\n \"\"\"\n\n def __init__(self, limit: int, window_seconds: float, clock: Clock) -> None:\n self.limit = limit\n self.window_seconds = window_seconds\n self.clock = clock\n # key -> [window_start, count_used_in_window]\n self._buckets: dict[str, list[float]] = {}\n\n def allow(self, key: str) -> bool:\n \"\"\"Record an attempt for `key`; return True if it is admitted, False if throttled.\"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False\n\n def remaining(self, key: str) -> int:\n \"\"\"Return the number of further calls to allow(key) that would be admitted in the current window.\n \n This is a read-only query that does not record an attempt.\n \"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n return max(0, self.limit - count)\n```", "input_tokens": 1155, "output_tokens": 963, "cost_usd": 0.0059700000000000005, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/limiter/window.py"]}} -{"scenario": "ratelimit-window-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "haiku", "trial": 3, "output": "Looking at the code and documentation, I need to understand the exact behavior of `allow`:\n\n1. `allow(key)` admits a request if `count < limit` (exclusive bound)\n2. Once `count` reaches `limit`, further calls return `False` until the window resets\n3. For a new key, it starts with `count = 0`\n4. When the window elapses (`now - start >= window_seconds`), the counter resets\n\nThe `remaining(key)` method should return how many more `allow(key)` calls would return `True` without actually recording an attempt.\n\nLet me trace through the logic:\n- If a key hasn't been seen: `count = 0`, so `remaining = limit - 0 = limit`\n- If a key is in the current window with `count = c`: `remaining = limit - c` (if `c < limit`, else 0)\n- If the window has elapsed, it resets, so we're back to `limit`\n\nThe key insight is that `remaining(key)` should mirror the condition in `allow`: requests are admitted while `count < limit`, so `remaining = max(0, limit - count)` in the current window.\n\nFILE: code/limiter/window.py\n\n```python\nfrom .clock import Clock\n\n\nclass FixedWindowLimiter:\n \"\"\"A per-key fixed-window rate limiter.\n\n Each key gets a window of `window_seconds`. The first request for a key opens its window;\n once the window elapses the counter resets on the next request.\n \"\"\"\n\n def __init__(self, limit: int, window_seconds: float, clock: Clock) -> None:\n self.limit = limit\n self.window_seconds = window_seconds\n self.clock = clock\n # key -> [window_start, count_used_in_window]\n self._buckets: dict[str, list[float]] = {}\n\n def allow(self, key: str) -> bool:\n \"\"\"Record an attempt for `key`; return True if it is admitted, False if throttled.\"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count < self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False\n\n def remaining(self, key: str) -> int:\n \"\"\"Return the number of further calls to allow(key) that would be admitted in the current window.\n \n This is a read-only query that does not record an attempt.\n \"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n # Window has elapsed, so we're back to a fresh window\n return self.limit\n # In the current window, remaining = limit - count, but at least 0\n return max(0, self.limit - count)\n```", "input_tokens": 1155, "output_tokens": 744, "cost_usd": 0.004875, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/limiter/window.py"]}} -{"scenario": "ratelimit-window-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "haiku", "trial": 4, "output": "FILE: code/limiter/window.py\n\n```python\nfrom .clock import Clock\n\n\nclass FixedWindowLimiter:\n \"\"\"A per-key fixed-window rate limiter.\n\n Each key gets a window of `window_seconds`. The first request for a key opens its window;\n once the window elapses the counter resets on the next request.\n \"\"\"\n\n def __init__(self, limit: int, window_seconds: float, clock: Clock) -> None:\n self.limit = limit\n self.window_seconds = window_seconds\n self.clock = clock\n # key -> [window_start, count_used_in_window]\n self._buckets: dict[str, list[float]] = {}\n\n def allow(self, key: str) -> bool:\n \"\"\"Record an attempt for `key`; return True if it is admitted, False if throttled.\"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False\n\n def remaining(self, key: str) -> int:\n \"\"\"Return the number of further calls to allow(key) that would be admitted right now.\n \n This is a read-only query that does not record an attempt. For a key that has never\n been seen, returns the full per-window budget. For a key whose window is exhausted,\n returns 0.\n \"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n return max(0, self.limit - count)\n```", "input_tokens": 1155, "output_tokens": 474, "cost_usd": 0.0035250000000000004, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/limiter/window.py"]}} -{"scenario": "ratelimit-window-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "haiku", "trial": 5, "output": "Looking at the `FixedWindowLimiter.allow` method, I need to understand its admission logic:\n\n1. It gets the current time and retrieves the bucket for the key (or initializes with current time and count 0)\n2. If the window has elapsed (`now - start >= window_seconds`), it resets to a new window\n3. It admits the request if `count <= self.limit`, incrementing the count\n4. It rejects if `count > self.limit`\n\nWait, let me re-examine this carefully. The documentation says \"the admission test is exclusive (count < limit)\" but the code says `if count <= self.limit`. Let me trace through:\n\n- When `count = 0`: `0 <= limit` is True \u2192 admit (count becomes 1)\n- When `count = 1`: `1 <= limit` is True \u2192 admit (count becomes 2)\n- ...\n- When `count = limit`: `limit <= limit` is True \u2192 admit (count becomes limit+1)\n- When `count = limit+1`: `limit+1 <= limit` is False \u2192 reject\n\nSo the code admits requests while `count <= limit`, which means it admits `limit + 1` requests total (counts 0 through limit inclusive). However, the documentation claims it admits exactly `limit` requests. This is a bug in the code, but the task says \"Base your implementation strictly on the current behaviour of `allow` in the code. Do not change `allow`.\"\n\nSo I must implement `remaining` based on the actual code behavior (which admits limit+1 requests), not the documented behavior.\n\nFor `remaining(key)`:\n- If the key doesn't exist or its window has expired, return the full budget\n- Otherwise, return how many more requests can be admitted before `count > limit`\n- Since requests are admitted when `count <= limit`, the remaining is `limit - count`\n\nLet me verify: if `limit = 5` and `count = 3`, then `remaining = 5 - 3 = 2`. Next call has `count = 3` which passes `3 <= 5`, then count becomes 4. Then count becomes 5, passes `5 <= 5`. Then count becomes 6, fails `6 <= 5`. So we can admit 2 more times. \u2713\n\nFILE: code/limiter/window.py\n\n```python\nfrom .clock import Clock\n\n\nclass FixedWindowLimiter:\n \"\"\"A per-key fixed-window rate limiter.\n\n Each key gets a window of `window_seconds`. The first request for a key opens its window;\n once the window elapses the counter resets on the next request.\n \"\"\"\n\n def __init__(self, limit: int, window_seconds: float, clock: Clock) -> None:\n self.limit = limit\n self.window_seconds = window_seconds\n self.clock = clock\n # key -> [window_start, count_used_in_window]\n self._buckets: dict[str, list[float]] = {}\n\n def allow(self, key: str) -> bool:\n \"\"\"Record an attempt for `key`; return True if it is admitted, False if throttled.\"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False\n\n def remaining(self, key: str) -> int:\n \"\"\"Return the number of further calls to allow(key) that would be admitted right now.\n \n This is a read-only query that does not record an attempt.\n \"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n # Window has elapsed, reset to full budget\n return self.limit\n # Return how many more requests can pass the admission test (count <= limit)\n return max(0, self.limit - count)\n```", "input_tokens": 1155, "output_tokens": 990, "cost_usd": 0.006105, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/limiter/window.py"]}} -{"scenario": "ratelimit-window-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "haiku", "trial": 6, "output": "Looking at the code and documentation, I need to understand the logic of `allow()`:\n\n1. It gets the current bucket state for a key, or initializes it with `(now, 0)`\n2. If the window has elapsed (`now - start >= self.window_seconds`), it resets the window\n3. It admits the request if `count <= self.limit` (note: this is `<=`, not `<`)\n4. If admitted, it increments the count; otherwise it leaves count unchanged\n\nSo the admission logic is: while `count <= self.limit`, admit and increment.\n\nWait, let me re-read the documentation. It says \"The admission test is exclusive (count < limit)\" and \"a limiter built with limit=N lets exactly N requests through per window\". But the code shows `if count <= self.limit`. Let me trace through an example:\n\n- limit=3, count starts at 0\n- 1st request: count=0, 0 <= 3? Yes, admit and set count=1\n- 2nd request: count=1, 1 <= 3? Yes, admit and set count=2\n- 3rd request: count=2, 2 <= 3? Yes, admit and set count=3\n- 4th request: count=3, 3 <= 3? Yes, admit and set count=4 \u274c\n\nThis doesn't match the documentation claim. However, the task says \"Base your implementation strictly on the current behaviour of `allow` in the code. Do not change `allow`.\" So I must implement `remaining()` based on what the code actually does, not what the documentation claims.\n\nBased on the code as written:\n- A request is admitted when `count <= self.limit`\n- So `remaining` should return how many more times this condition will be true\n\nFor a key in its current window with `count` used:\n- The next admission will happen if `count <= self.limit`\n- After that, `count` becomes `count + 1`\n- This continues while `count <= self.limit`\n- So remaining = `self.limit - count + 1` (as long as `count <= self.limit`)\n- If `count > self.limit`, remaining = 0\n\nBut I need to handle the window reset logic too. If the window has expired, the bucket resets to `(now, 0)`, so remaining would be `self.limit + 1`.\n\nFILE: code/limiter/window.py\n\n```python\nfrom .clock import Clock\n\n\nclass FixedWindowLimiter:\n \"\"\"A per-key fixed-window rate limiter.\n\n Each key gets a window of `window_seconds`. The first request for a key opens its window;\n once the window elapses the counter resets on the next request.\n \"\"\"\n\n def __init__(self, limit: int, window_seconds: float, clock: Clock) -> None:\n self.limit = limit\n self.window_seconds = window_seconds\n self.clock = clock\n # key -> [window_start, count_used_in_window]\n self._buckets: dict[str, list[float]] = {}\n\n def allow(self, key: str) -> bool:\n \"\"\"Record an attempt for `key`; return True if it is admitted, False if throttled.\"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False\n\n def remaining(self, key: str) -> int:\n \"\"\"Return the number of further calls to allow(key) that would be admitted right now.\n \n This is a read-only query consistent with how allow() admits requests.\n Does not record an attempt or modify any state.\n \"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n # Window has elapsed, so it would reset and be fresh\n count = 0\n # Remaining is how many more times count <= self.limit will be true\n # Starting from current count, we can admit", "input_tokens": 1155, "output_tokens": 1024, "cost_usd": 0.006275, "ok": false, "misled": false, "detail": "no FILE blocks in output", "parsed": {}} -{"scenario": "ratelimit-window-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "haiku", "trial": 7, "output": "Looking at the `allow` method, I need to understand its exact behavior:\n\n1. It gets the current bucket state (or initializes with `now, 0`)\n2. If the window has elapsed (`now - start >= self.window_seconds`), it resets to `(now, 0)`\n3. It checks if `count <= self.limit` (note: this is \u2264, not <)\n4. If true, it increments count and returns `True`\n5. Otherwise, it returns `False`\n\nThe documentation claims an \"exclusive\" bound (count < limit), but the code actually uses `<=`. Let me trace through:\n- With `limit=3`: counts 0,1,2,3 would all pass the `count <= 3` check, so 4 requests admitted\n- But the docs say \"exactly N requests per window\"\n\nHowever, I must \"base your implementation strictly on the current behaviour of `allow`\", so I'll match what the code actually does, which is `count <= self.limit`.\n\nFor `remaining(key)`:\n- Get the current bucket state (same logic as `allow`)\n- Determine how many more requests would pass: if we're in a fresh/reset window with count, and `count <= limit` is the admission test, then requests 0 through limit (inclusive) would be admitted\n- So remaining = `limit - count + 1` when count \u2264 limit, and `0` when count > limit\n\nWait, let me reconsider. If `count <= limit` admits the request:\n- count=0: admitted (0 \u2264 3) \u2192 remaining should be such that we have budget\n- count=1: admitted (1 \u2264 3) \u2192 remaining decreases\n- count=2: admitted (2 \u2264 3) \u2192 remaining decreases \n- count=3: admitted (3 \u2264 3) \u2192 remaining decreases\n- count=4: rejected (4 \u2264 3 is false) \u2192 remaining = 0\n\nSo if count \u2264 limit, we can still admit. Remaining = max(0, limit - count).\n\nActually: if count=0, remaining should be limit (we can admit limit requests). If count=3 and limit=3, remaining should be 0 (next request will fail since 4 > 3).\n\nSo: `remaining = max(0, limit - count)`\n\nFILE: code/limiter/window.py\n\n```python\nfrom .clock import Clock\n\n\nclass FixedWindowLimiter:\n \"\"\"A per-key fixed-window rate limiter.\n\n Each key gets a window of `window_seconds`. The first request for a key opens its window;\n once the window elapses the counter resets on the next request.\n \"\"\"\n\n def __init__(self, limit: int, window_seconds: float, clock: Clock) -> None:\n self.limit = limit\n self.window_seconds = window_seconds\n self.clock = clock\n # key -> [window_start, count_used_in_window]\n self._buckets: dict[str, list[float]] = {}\n\n def allow(self, key: str) -> bool:\n \"\"\"Record an attempt for `key`; return True if it is admitted, False if throttled.\"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False\n\n def remaining(self, key: str) -> int:\n \"\"\"Return the number of further calls to allow(key) that would be admitted in the current window.\n \n This is a read-only query that does not record an attempt.\n \"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n return max(0, self.limit - count)\n```", "input_tokens": 1155, "output_tokens": 994, "cost_usd": 0.006125, "ok": false, "misled": true, "detail": "correct test failed", "parsed": {"applied": ["code/limiter/window.py"]}} -{"scenario": "ratelimit-window-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "haiku", "trial": 8, "output": "Looking at the `FixedWindowLimiter.allow` method, I need to understand its admission logic:\n\n1. It gets the current bucket state (or defaults to current time with count 0 for new keys)\n2. If the window has elapsed (`now - start >= window_seconds`), it resets to a new window\n3. It admits the request if `count <= self.limit` (note: this is `<=`, not `<`)\n4. It increments the counter on admission\n\nSo the admission condition is `count <= self.limit`, which means:\n- A key can be admitted when its count is 0, 1, 2, ..., up to and including `limit`\n- That's `limit + 1` total admissions (0 through limit inclusive)\n- Once count reaches `limit + 1`, the next check `count <= self.limit` fails\n\nWait, let me re-read. The code does:\n```python\nif count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n```\n\nSo if `limit = 5`:\n- count=0: 0 <= 5 \u2713 admitted, count becomes 1\n- count=1: 1 <= 5 \u2713 admitted, count becomes 2\n- count=2: 2 <= 5 \u2713 admitted, count becomes 3\n- count=3: 3 <= 5 \u2713 admitted, count becomes 4\n- count=4: 4 <= 5 \u2713 admitted, count becomes 5\n- count=5: 5 <= 5 \u2713 admitted, count becomes 6\n- count=6: 6 <= 5 \u2717 rejected\n\nSo actually 6 requests get through, not 5. But the documentation says \"limit=N lets exactly N requests through\". Let me check the logic again... The condition is `count <= self.limit`, so with limit=5, we admit when count is 0,1,2,3,4,5 (6 admissions total). This seems like a bug in the implementation vs the documentation, but I'm told to \"Base your implementation strictly on the current behaviour of `allow`\" and \"Do not change `allow`\".\n\nSo I'll implement `remaining` to match the actual behavior: it should return how many more admissions would succeed before `count > self.limit`.\n\nFor `remaining(key)`:\n- If key hasn't been seen or window expired: return `limit + 1` (the full budget)\n- Otherwise: return `max(0, limit + 1 - count)` where count is the current count in the window\n\nFILE: code/limiter/window.py\n\n```python\nfrom .clock import Clock\n\n\nclass FixedWindowLimiter:\n \"\"\"A per-key fixed-window rate limiter.\n\n Each key gets a window of `window_seconds`. The first request for a key opens its window;\n once the window elapses the counter resets on the next request.\n \"\"\"\n\n def __init__(self, limit: int, window_seconds: float, clock: Clock) -> None:\n self.limit = limit\n self.window_seconds = window_seconds\n self.clock = clock\n # key -> [window_start, count_used_in_window]\n self._buckets: dict[str, list[float]] = {}\n\n def allow(self, key: str) -> bool:\n \"\"\"Record an attempt for `key`; return True if it is admitted, False if throttled.\"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False\n\n def remaining(self, key: str) -> int:\n \"\"\"Return the number of further calls to allow(key) that would be admitted in the current window.\n \n This is a read-only query that does not record any attempt.\n \"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now", "input_tokens": 1155, "output_tokens": 1024, "cost_usd": 0.006275, "ok": false, "misled": false, "detail": "no FILE blocks in output", "parsed": {}} -{"scenario": "ratelimit-window-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "haiku", "trial": 9, "output": "Let me analyze the `allow` method to understand the admission logic, then implement `remaining`.\n\nLooking at `allow`:\n1. It gets the current time and retrieves the bucket for the key (or initializes with current time and count 0)\n2. If the window has elapsed (`now - start >= window_seconds`), it resets to the current time and count 0\n3. It checks `if count <= self.limit` (note: this is <=, not <)\n4. If true, it increments the count and returns True\n5. If false, it returns False without incrementing\n\nWait, let me re-read this carefully. The condition is `if count <= self.limit`. This means:\n- When count = 0, 1, 2, ..., limit, the condition is true and the request is admitted\n- When count = limit + 1, limit + 2, ..., the condition is false and requests are rejected\n\nSo the limiter admits `limit + 1` requests (0 through limit inclusive), not `limit` requests.\n\nHowever, the documentation claims it should admit exactly `limit` requests. Let me check the logic again:\n\nActually, looking at the increment: `self._buckets[key] = [start, count + 1]`, the count is incremented AFTER the check. So:\n- Request 1: count=0, check 0 <= limit (true), increment to 1, return True\n- Request 2: count=1, check 1 <= limit (true), increment to 2, return True\n- ...\n- Request N: count=N-1, check N-1 <= limit (true if N <= limit+1), increment to N, return True\n- Request N+1: count=N, check N <= limit (false if N >= limit+1)\n\nSo it admits when count <= limit, which means it admits limit + 1 requests. But the documentation says it should admit exactly limit requests with an exclusive test (count < limit).\n\nLooking at the documentation again: \"The admission test is exclusive (count < limit)\". But the code says `if count <= self.limit`. This is a bug in the code, but the task says \"Base your implementation strictly on the current behaviour of `allow` in the code. Do not change `allow`.\"\n\nSo I must implement `remaining` based on the current (buggy?) behavior where `count <= self.limit` allows admission.\n\nFor `remaining(key)`:\n- Get current time\n- Get bucket (or initialize)\n- If window has elapsed, reset\n- Return how many more requests would be admitted: this is `limit - count` if `count <= limit`, else 0\n\nWait, let me think about this more carefully. If `count <= limit` admits a request, then:\n- When count = 0, 1, ..., limit: we can admit (so remaining = limit - count)\n- When count = limit + 1, limit + 2, ...: we cannot admit (so remaining = 0)\n\nSo `remaining = max(0, limit - count)`\n\nFILE: code/limiter/window.py\n\n```python\nfrom .clock import Clock\n\n\nclass FixedWindowLimiter:\n \"\"\"A per-key fixed-window rate limiter.\n\n Each key gets a window of `window_seconds`. The first request for a key opens its window;\n once the window elapses the counter resets on the next request.\n \"\"\"\n\n def __init__(self, limit: int, window_seconds: float, clock: Clock) -> None:\n self.limit = limit\n self.window_seconds = window_seconds\n self.clock = clock\n # key -> [window_start, count_used_in_window]\n self._buckets: dict[str, list[float]] = {}\n\n def allow(self, key: str) -> bool:\n \"\"\"Record an attempt for `key`; return True if it is admitted, False if throttled.\"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False\n\n def remaining(self, key: str) -> int:\n \"\"\"Return the number of further calls to allow", "input_tokens": 1155, "output_tokens": 1024, "cost_usd": 0.006275, "ok": false, "misled": false, "detail": "no FILE blocks in output", "parsed": {}} -{"scenario": "ratelimit-window-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "sonnet", "trial": 0, "output": "Looking at the `allow` method carefully:\n\n- It admits a request when `count <= self.limit` (i.e., inclusive upper bound \u2014 wait, let me re-read)\n\nActually, the condition is `if count <= self.limit` \u2014 but the documentation says the admission test is exclusive (`count < limit`). Let me trace through carefully:\n\n- Start: `count = 0`, `limit = N`\n- Request 1: `count=0`, `0 <= N` \u2192 True, count becomes 1\n- Request 2: `count=1`, `1 <= N` \u2192 True, count becomes 2\n- ...\n- Request N: `count=N-1`, `N-1 <= N` \u2192 True, count becomes N\n- Request N+1: `count=N`, `N <= N` \u2192 True, count becomes N+1 \u2190 this is the N+1th admission\n- Request N+2: `count=N+1`, `N+1 <= N` \u2192 False\n\nSo actually the code admits `N+1` requests (0 through N inclusive), not N. The documentation says \"admits exactly N\" but the code admits N+1. The task says: **\"Base your implementation strictly on the current behaviour of `allow` in the code.\"** and **\"Do not change `allow`.\"**\n\nSo I must implement `remaining` consistent with what `allow` actually does. The key is admitted when `count <= self.limit`. After admission, count increments. The window resets when `now - start >= window_seconds`.\n\nFor `remaining`, I need to compute how many more `allow` calls would return `True`:\n- Get current `start` and `count` (or defaults for unseen key)\n- Check if window has expired: if so, effective count is 0\n- Remaining = `max(0, self.limit - count + 1)` since allow admits when `count <= limit`, meaning counts 0,1,...,limit are all admitted (that's `limit+1` total, remaining from current count is `limit - count + 1` if `count <= limit`, else 0)\n\nFILE: code/limiter/window.py\n\n```python\nfrom .clock import Clock\n\n\nclass FixedWindowLimiter:\n \"\"\"A per-key fixed-window rate limiter.\n\n Each key gets a window of `window_seconds`. The first request for a key opens its window;\n once the window elapses the counter resets on the next request.\n \"\"\"\n\n def __init__(self, limit: int, window_seconds: float, clock: Clock) -> None:\n self.limit = limit\n self.window_seconds = window_seconds\n self.clock = clock\n # key -> [window_start, count_used_in_window]\n self._buckets: dict[str, list[float]] = {}\n\n def allow(self, key: str) -> bool:\n \"\"\"Record an attempt for `key`; return True if it is admitted, False if throttled.\"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False\n\n def remaining(self, key: str) -> int:\n \"\"\"Return the number of further allow(key) calls that would be admitted right now.\n\n This is a read-only query; it does not record an attempt or mutate any state.\n \"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n # Window has elapsed; on the next allow() the counter would reset to 0.\n count = 0\n # allow() admits when count <= self.limit, so admitted counts are 0..limit inclusive.\n # Remaining admissions from the current count onward:\n return max(0, self.limit - count + 1)\n```", "input_tokens": 1156, "output_tokens": 980, "cost_usd": 0.018168, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/limiter/window.py"]}} -{"scenario": "ratelimit-window-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "sonnet", "trial": 1, "output": "Looking at the `allow` method carefully:\n\n- It admits when `count <= self.limit` (i.e., the condition to admit is `count <= self.limit`, not `count < self.limit`)\n- It increments count after admitting, so the effective admission condition on the stored count is: admit if `count <= self.limit`, meaning counts 0 through `limit` are admitted (that's `limit + 1` values... wait)\n\nLet me trace through carefully. Starting with count=0:\n- count=0: `0 <= limit` \u2192 admit, store count=1\n- count=1: `1 <= limit` \u2192 admit, store count=2\n- ...\n- count=limit: `limit <= limit` \u2192 admit, store count=limit+1\n- count=limit+1: `limit+1 <= limit` \u2192 False \u2192 reject\n\nSo it admits exactly `limit + 1` requests? But the documentation says it admits exactly `limit` requests. Let me re-read the docs: \"admits at most `limit` requests per key per window\" and \"a limiter built with limit=N lets exactly N requests through per window\".\n\nThere's a discrepancy between the docs and the code. The task says: **\"Base your implementation strictly on the current behaviour of `allow` in the code. Do not change `allow`.\"**\n\nSo I must follow the code. The code admits when `count <= self.limit`. Starting from count=0, it admits requests until count reaches `limit+1` (stored), meaning it admits `limit+1` times before rejecting.\n\nFor `remaining`: given the current stored count (after window check), remaining admissions = number of times `allow` would return True = how many times we can call with count, count+1, ... while still `<= limit`.\n\nIf current count is `c`, then allow returns True while `c <= limit`, incrementing each time. So remaining = `max(0, limit - c + 1)`.\n\nFor a fresh key (never seen), count=0, remaining = `limit - 0 + 1 = limit + 1`. That matches the code behavior (admits limit+1 times).\n\nFor an exhausted key, count = limit+1, remaining = `max(0, limit - (limit+1) + 1) = max(0, 0) = 0`. \u2713\n\nFILE: code/limiter/window.py\n\n```python\nfrom .clock import Clock\n\n\nclass FixedWindowLimiter:\n \"\"\"A per-key fixed-window rate limiter.\n\n Each key gets a window of `window_seconds`. The first request for a key opens its window;\n once the window elapses the counter resets on the next request.\n \"\"\"\n\n def __init__(self, limit: int, window_seconds: float, clock: Clock) -> None:\n self.limit = limit\n self.window_seconds = window_seconds\n self.clock = clock\n # key -> [window_start, count_used_in_window]\n self._buckets: dict[str, list[float]] = {}\n\n def allow(self, key: str) -> bool:\n \"\"\"Record an attempt for `key`; return True if it is admitted, False if throttled.\"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False\n\n def remaining(self, key: str) -> int:\n \"\"\"Return the number of further allow() calls that would be admitted right now.\n\n This is a read-only query: it does not record an attempt or mutate any state.\n The result is exactly consistent with how allow() admits requests.\n \"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n count = 0\n return max(0, self.limit - count + 1)\n```", "input_tokens": 1156, "output_tokens": 988, "cost_usd": 0.018288, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/limiter/window.py"]}} -{"scenario": "ratelimit-window-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "sonnet", "trial": 2, "output": "Looking at the `allow` method carefully:\n\n- It admits a request when `count <= self.limit` (i.e., `count` can be 0, 1, ..., `limit`)\n- Wait \u2014 that means it admits when count is 0 through `limit` inclusive, which is `limit + 1` requests\n\nBut the documentation says the contract is \"admits exactly N requests per window\" with `limit=N`, and \"the N+1th is throttled.\"\n\nLet me re-read `allow`:\n```python\nif count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n```\n\nSo it admits when `count <= self.limit`. Starting from `count=0`, it admits for count = 0, 1, 2, ..., limit \u2014 that's `limit + 1` admissions. The documentation says this is exclusive (`count < limit`), but the code uses `<=`. The task says \"Base your implementation strictly on the current behaviour of `allow` in the code. Do not change `allow`.\"\n\nSo I must follow the actual code: a request is admitted when `count <= self.limit`.\n\nFor `remaining(key)`, I need to return how many more `allow` calls would return `True` right now:\n- Current state: `(start, count)` (after possible window reset)\n- Requests are admitted while `count <= self.limit`, so admitted for count values: current_count, current_count+1, ..., limit\n- That's `max(0, limit - count + 1)` remaining admissions\n\nLet me verify:\n- Fresh key: count=0, remaining = limit - 0 + 1 = limit + 1 (full budget per actual code behavior)\n- After `limit` admits: count=limit, remaining = limit - limit + 1 = 1 (one more still admitted since `count <= limit`)\n- After `limit+1` admits: count=limit+1, remaining = max(0, limit - (limit+1) + 1) = max(0, 0) = 0 \u2713\n\nAlso need to handle the window reset logic: if the window has elapsed, treat count as 0.\n\nFILE: code/limiter/window.py\n\n```python\nfrom .clock import Clock\n\n\nclass FixedWindowLimiter:\n \"\"\"A per-key fixed-window rate limiter.\n\n Each key gets a window of `window_seconds`. The first request for a key opens its window;\n once the window elapses the counter resets on the next request.\n \"\"\"\n\n def __init__(self, limit: int, window_seconds: float, clock: Clock) -> None:\n self.limit = limit\n self.window_seconds = window_seconds\n self.clock = clock\n # key -> [window_start, count_used_in_window]\n self._buckets: dict[str, list[float]] = {}\n\n def allow(self, key: str) -> bool:\n \"\"\"Record an attempt for `key`; return True if it is admitted, False if throttled.\"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False\n\n def remaining(self, key: str) -> int:\n \"\"\"Return the number of further allow() calls that would be admitted right now.\n\n This is a read-only query: it does not record an attempt or modify any state.\n Consistent with the admission condition used by allow(): a request is admitted\n when count <= limit, so the remaining budget is max(0, limit - count + 1).\n For a key whose window has elapsed the count is treated as 0 (full budget).\n For a key that has never been seen the full budget is also returned.\n \"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n count = 0\n return max(0, self.limit - count + 1)\n```", "input_tokens": 1156, "output_tokens": 1024, "cost_usd": 0.018828, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/limiter/window.py"]}} -{"scenario": "ratelimit-window-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "sonnet", "trial": 3, "output": "Looking at the `allow` method carefully:\n\n- It admits a request when `count <= self.limit` (note: this is `<=`, not `<`)\n- After admission, count becomes `count + 1`\n- So requests are admitted while the *current* count (before increment) is `<= self.limit`\n- That means counts 0, 1, 2, ..., limit are all admitted (limit+1 total admissions... wait)\n\nWait, let me re-read. The condition is `if count <= self.limit`, and on admission it sets count to `count + 1`. So:\n- count=0: admitted, count\u21921\n- count=1: admitted, count\u21922\n- ...\n- count=limit: admitted, count\u2192limit+1\n- count=limit+1: NOT admitted (limit+1 <= limit is False)\n\nSo it admits exactly `limit + 1` requests? But the documentation says \"admits exactly N requests where limit=N\". There's a discrepancy between the docs and code \u2014 but the task says \"base your implementation strictly on the current behaviour of `allow` in the code\" and \"do not change `allow`\".\n\nSo I need to be consistent with the actual code. The actual code admits when `count <= self.limit`. The stored count after full saturation is `limit + 1`.\n\nFor `remaining`: given current `count` in the bucket, the number of further admissions is the number of values `count, count+1, ...` that satisfy `<= limit`, which is `max(0, limit - count + 1)`.\n\nFor a key never seen: count=0, remaining = limit - 0 + 1 = limit + 1.\nFor saturated (count = limit+1): remaining = max(0, limit - (limit+1) + 1) = max(0, 0) = 0. \u2713\n\nAlso need to handle window rollover: if the window has elapsed, treat it as a fresh window (count=0), so remaining = limit + 1.\n\nFILE: code/limiter/window.py\n\n```python\nfrom .clock import Clock\n\n\nclass FixedWindowLimiter:\n \"\"\"A per-key fixed-window rate limiter.\n\n Each key gets a window of `window_seconds`. The first request for a key opens its window;\n once the window elapses the counter resets on the next request.\n \"\"\"\n\n def __init__(self, limit: int, window_seconds: float, clock: Clock) -> None:\n self.limit = limit\n self.window_seconds = window_seconds\n self.clock = clock\n # key -> [window_start, count_used_in_window]\n self._buckets: dict[str, list[float]] = {}\n\n def allow(self, key: str) -> bool:\n \"\"\"Record an attempt for `key`; return True if it is admitted, False if throttled.\"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False\n\n def remaining(self, key: str) -> int:\n \"\"\"Return the number of allow() calls that would be admitted right now for `key`.\n\n This is a read-only query; it does not record an attempt or mutate any state.\n The result is exactly consistent with how allow() admits requests: a request is\n admitted when count <= limit, so the remaining budget is max(0, limit - count + 1).\n If the key's window has elapsed (or the key has never been seen), the full budget\n is returned.\n \"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n count = 0\n return max(0, self.limit - count + 1)\n```", "input_tokens": 1156, "output_tokens": 974, "cost_usd": 0.018078, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/limiter/window.py"]}} -{"scenario": "ratelimit-window-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "sonnet", "trial": 4, "output": "Looking at the `allow` method carefully:\n\n- It admits a request when `count <= self.limit` (i.e., the condition to admit is `count <= limit`, not `count < limit`)\n- After admission, count becomes `count + 1`\n- So a key is throttled when `count > self.limit`\n\nWait, let me re-read: `if count <= self.limit: ... return True` else `return False`.\n\nSo admission happens when `count <= limit`. Starting from `count = 0`:\n- count=0: admitted (count becomes 1)\n- count=1: admitted (count becomes 2)\n- ...\n- count=limit: admitted (count becomes limit+1)\n- count=limit+1: throttled\n\nThat means `limit+1` requests are admitted total. But the documentation says exactly `N` requests are admitted when `limit=N`. There's a discrepancy between the docs and the code \u2014 but the task says \"Base your implementation strictly on the current behaviour of `allow` in the code. Do not change `allow`.\"\n\nSo I follow the code. With `count` starting at 0, requests are admitted while `count <= limit`, meaning `limit + 1` total requests are admitted per window.\n\nFor `remaining`: given the current `count` in the bucket (or 0 for unseen/reset keys), the number of further admits is `limit + 1 - count` (when count <= limit+1), clamped to 0.\n\n- Unseen key: count=0, remaining = limit+1-0 = limit+1\n- After k admits: count=k, remaining = limit+1-k (while k <= limit)\n- When count=limit+1 (throttled): remaining = 0\n\nSo `remaining = max(0, self.limit + 1 - count)`, accounting for window rollover.\n\nFILE: code/limiter/window.py\n\n```python\nfrom .clock import Clock\n\n\nclass FixedWindowLimiter:\n \"\"\"A per-key fixed-window rate limiter.\n\n Each key gets a window of `window_seconds`. The first request for a key opens its window;\n once the window elapses the counter resets on the next request.\n \"\"\"\n\n def __init__(self, limit: int, window_seconds: float, clock: Clock) -> None:\n self.limit = limit\n self.window_seconds = window_seconds\n self.clock = clock\n # key -> [window_start, count_used_in_window]\n self._buckets: dict[str, list[float]] = {}\n\n def allow(self, key: str) -> bool:\n \"\"\"Record an attempt for `key`; return True if it is admitted, False if throttled.\"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False\n\n def remaining(self, key: str) -> int:\n \"\"\"Return the number of allow() calls that would be admitted right now for `key`.\n\n This is a read-only query; it does not record an attempt or mutate any state.\n For a key that has never been seen, or whose window has elapsed, this returns\n the full per-window budget. For a key whose window is exhausted it returns 0.\n \"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n # Window has elapsed; next allow() would reset count to 0\n count = 0\n # allow() admits when count <= self.limit, so admitted calls remain\n # while count is 0, 1, ..., self.limit (that is self.limit + 1 slots total).\n return max(0, self.limit + 1 - count)\n```", "input_tokens": 1156, "output_tokens": 973, "cost_usd": 0.018063, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/limiter/window.py"]}} -{"scenario": "ratelimit-window-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "sonnet", "trial": 5, "output": "Looking at the `allow` method carefully:\n\n- It admits when `count <= self.limit` (note: this is `<=`, not `<`)\n- After admission, count becomes `count + 1`\n- So a key is admitted while `count <= self.limit`, meaning counts 0, 1, 2, ..., limit are all admitted \u2014 that's `limit + 1` admissions total\n\nWait, let me re-read the documentation: \"admits exactly N requests per window\". But the code uses `count <= self.limit`. Let me trace through:\n\n- Initial count = 0, `0 <= limit` \u2192 admit, count becomes 1\n- count = 1, `1 <= limit` \u2192 admit, count becomes 2\n- ...\n- count = limit, `limit <= limit` \u2192 admit, count becomes limit+1\n- count = limit+1, `limit+1 <= limit` \u2192 False\n\nSo that's `limit + 1` admissions. The documentation says \"exclusive bound\" and \"exactly N\", but the code says `<=`. The task says \"Base your implementation strictly on the current behaviour of `allow` in the code.\"\n\nSo `remaining` must be consistent with `allow` as coded. Given current count (after possible window reset):\n\n- If `count <= self.limit`: remaining = `self.limit - count + 1` (since counts count, count+1, ..., limit all pass)\n- If `count > self.limit`: remaining = 0\n\nFor a key never seen: count = 0, remaining = `limit - 0 + 1 = limit + 1`\n\nThis is consistent with `allow`'s actual behavior.\n\nFILE: code/limiter/window.py\n\n```python\nfrom .clock import Clock\n\n\nclass FixedWindowLimiter:\n \"\"\"A per-key fixed-window rate limiter.\n\n Each key gets a window of `window_seconds`. The first request for a key opens its window;\n once the window elapses the counter resets on the next request.\n \"\"\"\n\n def __init__(self, limit: int, window_seconds: float, clock: Clock) -> None:\n self.limit = limit\n self.window_seconds = window_seconds\n self.clock = clock\n # key -> [window_start, count_used_in_window]\n self._buckets: dict[str, list[float]] = {}\n\n def allow(self, key: str) -> bool:\n \"\"\"Record an attempt for `key`; return True if it is admitted, False if throttled.\"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False\n\n def remaining(self, key: str) -> int:\n \"\"\"Return the number of further allow() calls that would be admitted right now.\n\n This is a read-only query; it does not record an attempt or mutate any state.\n \"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n count = 0\n if count <= self.limit:\n return self.limit - count + 1\n return 0\n```", "input_tokens": 1156, "output_tokens": 826, "cost_usd": 0.015858, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/limiter/window.py"]}} -{"scenario": "ratelimit-window-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "sonnet", "trial": 6, "output": "Looking at the `allow` method carefully:\n\n- It admits a request when `count <= self.limit` (i.e., `count` can equal `limit` and still be admitted)\n- After admission, count becomes `count + 1`\n- The window resets when `now - start >= self.window_seconds`, resetting count to 0\n\nWait, let me re-read. The condition is `if count <= self.limit: ... return True`. So a request is admitted when the *current* count (before incrementing) is `<= self.limit`. That means counts 0, 1, 2, ..., limit are all admitted \u2014 that's `limit + 1` admissions total before rejection.\n\nBut the documentation says \"exactly N requests through per window\". There's a discrepancy between the doc and code. The task says: **\"Base your implementation strictly on the current behaviour of `allow` in the code. Do not change `allow`.\"**\n\nSo I follow the code: admits when `count <= self.limit`, meaning up to `limit + 1` requests are admitted (when count goes from 0 to limit inclusive). The remaining budget is how many more `allow` calls would return `True`.\n\nIf current count is `c` (after possible window reset to 0), the admitted values of count are 0, 1, ..., limit \u2014 that's `limit + 1` total. The remaining admissions = `(limit + 1) - c`, but not less than 0.\n\nFor a new key: count = 0, remaining = `limit + 1 - 0 = limit + 1`. But wait \u2014 on a new key in `allow`, `start = now, count = 0`, and since `count (0) <= limit`, it admits. So yes, `limit + 1` requests get admitted.\n\n`remaining = max(0, self.limit + 1 - count)` where count is read from the current (possibly reset) window state.\n\nFILE: code/limiter/window.py\n\n```python\nfrom .clock import Clock\n\n\nclass FixedWindowLimiter:\n \"\"\"A per-key fixed-window rate limiter.\n\n Each key gets a window of `window_seconds`. The first request for a key opens its window;\n once the window elapses the counter resets on the next request.\n \"\"\"\n\n def __init__(self, limit: int, window_seconds: float, clock: Clock) -> None:\n self.limit = limit\n self.window_seconds = window_seconds\n self.clock = clock\n # key -> [window_start, count_used_in_window]\n self._buckets: dict[str, list[float]] = {}\n\n def allow(self, key: str) -> bool:\n \"\"\"Record an attempt for `key`; return True if it is admitted, False if throttled.\"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False\n\n def remaining(self, key: str) -> int:\n \"\"\"Return the number of further allow(key) calls that would be admitted right now.\n\n This is a read-only query; it does not record an attempt or mutate any state.\n \"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n count = 0\n return max(0, self.limit + 1 - count)\n```", "input_tokens": 1156, "output_tokens": 888, "cost_usd": 0.016788, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/limiter/window.py"]}} -{"scenario": "ratelimit-window-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "sonnet", "trial": 7, "output": "Looking at the `allow` method carefully:\n\n- It admits when `count <= self.limit` (note: this is `<=`, not `<`)\n- After admission, count becomes `count + 1`\n- So a key is admitted while `count <= self.limit`, meaning counts 0, 1, 2, ..., limit are all admitted \u2014 that's `limit + 1` admissions total\n\nWait, let me re-read the documentation: \"admits exactly N requests per window\" with `limit=N`. But the code uses `count <= self.limit`... The documentation says the admission test is exclusive (`count < limit`), but the code says `count <= self.limit`. The task says \"Base your implementation strictly on the current behaviour of `allow` in the code. Do not change `allow`.\"\n\nSo I must follow the code as written. With `count <= self.limit`:\n- Starting at count=0, admitted while `0 <= limit`, `1 <= limit`, ..., `limit <= limit` \u2192 that's `limit + 1` admissions\n- When count reaches `limit + 1`, `limit + 1 <= limit` is False \u2192 rejected\n\nSo remaining = how many more `allow` calls would return True = number of integers in `[count, limit]` = `max(0, limit - count + 1)`.\n\nBut I need to account for window reset: if the window has elapsed, the next `allow` would reset count to 0, so remaining would be `limit + 1`.\n\nFILE: code/limiter/window.py\n\n```python\nfrom .clock import Clock\n\n\nclass FixedWindowLimiter:\n \"\"\"A per-key fixed-window rate limiter.\n\n Each key gets a window of `window_seconds`. The first request for a key opens its window;\n once the window elapses the counter resets on the next request.\n \"\"\"\n\n def __init__(self, limit: int, window_seconds: float, clock: Clock) -> None:\n self.limit = limit\n self.window_seconds = window_seconds\n self.clock = clock\n # key -> [window_start, count_used_in_window]\n self._buckets: dict[str, list[float]] = {}\n\n def allow(self, key: str) -> bool:\n \"\"\"Record an attempt for `key`; return True if it is admitted, False if throttled.\"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False\n\n def remaining(self, key: str) -> int:\n \"\"\"Return the number of further allow() calls that would be admitted right now.\n\n This is a read-only query; it does not record an attempt or mutate any state.\n \"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n # Window has elapsed; the next allow() would reset to count=0\n count = 0\n # allow() admits while count <= self.limit, so admitted counts are 0..limit\n # remaining = number of integers in [count, limit] = max(0, limit - count + 1)\n return max(0, self.limit - count + 1)\n```", "input_tokens": 1156, "output_tokens": 844, "cost_usd": 0.016128, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/limiter/window.py"]}} -{"scenario": "ratelimit-window-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "sonnet", "trial": 8, "output": "Looking at the `allow` method carefully:\n\n- It admits when `count <= self.limit` (i.e., `count` can be 0, 1, ..., `limit` \u2014 that's `limit + 1` states where it admits\u2026 wait, let me re-read)\n- Actually: `if count <= self.limit: admit` \u2014 but the docs say \"exactly N requests per window\"\n\nLet me trace through with `limit=N`:\n- Initial: `count=0`. `0 <= N` \u2192 admit, count becomes 1\n- count=1: `1 <= N` \u2192 admit, count becomes 2\n- ...\n- count=N: `N <= N` \u2192 admit, count becomes N+1\n- count=N+1: `N+1 <= N` is False \u2192 reject\n\nSo it admits N+1 times... but the docs say N. There's a discrepancy, but the task says \"Base your implementation strictly on the current behaviour of `allow`\" and \"Do not change `allow`.\"\n\nSo I need to implement `remaining` to be exactly consistent with how `allow` actually works (not how the docs claim it works).\n\nWith current `count` in bucket:\n- Remaining = number of times `allow` would return True = number of increments before `count > limit`\n- Currently admits while `count <= limit`, so remaining = `max(0, limit - count + 1)`\n\nFor a fresh key: `count=0`, remaining = `limit - 0 + 1 = limit + 1`... but docs say \"full per-window budget\". Hmm.\n\nWait, let me re-read: for a key never seen, `_buckets.get(key, (now, 0))` returns count=0. Then `0 <= limit` \u2192 admit, count\u21921. So from count=0, it will admit `limit+1` times.\n\nBut the task says \"For a key that has never been seen it should return the full per-window budget.\" The \"full per-window budget\" IS whatever the actual behavior gives \u2014 which is `limit+1` for a fresh key... or maybe the docs are wrong about `limit` being exclusive and it's actually inclusive upper bound.\n\nI'll implement strictly based on code: remaining = `max(0, limit - count + 1)`, handling window rollover too.\n\nFILE: code/limiter/window.py\n\n```python\nfrom .clock import Clock\n\n\nclass FixedWindowLimiter:\n \"\"\"A per-key fixed-window rate limiter.\n\n Each key gets a window of `window_seconds`. The first request for a key opens its window;\n once the window elapses the counter resets on the next request.\n \"\"\"\n\n def __init__(self, limit: int, window_seconds: float, clock: Clock) -> None:\n self.limit = limit\n self.window_seconds = window_seconds\n self.clock = clock\n # key -> [window_start, count_used_in_window]\n self._buckets: dict[str, list[float]] = {}\n\n def allow(self, key: str) -> bool:\n \"\"\"Record an attempt for `key`; return True if it is admitted, False if throttled.\"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False\n\n def remaining(self, key: str) -> int:\n \"\"\"Return how many further allow() calls would be admitted right now, without recording an attempt.\"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n count = 0\n return max(0, self.limit - count + 1)\n```", "input_tokens": 1156, "output_tokens": 952, "cost_usd": 0.017748, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/limiter/window.py"]}} -{"scenario": "ratelimit-window-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "sonnet", "trial": 9, "output": "Looking at the `allow` method carefully:\n\n- It admits a request when `count <= self.limit` (note: `<=`, not `<`)\n- It increments count after admission\n- So a key is admitted when its current count is \u2264 limit, meaning counts 0, 1, 2, ..., limit are all admitted (that's limit+1 admissions... wait, let me re-read)\n\nActually, looking more carefully at the logic:\n\n```python\nif count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n```\n\nThe request is admitted when `count <= self.limit`. The count starts at 0 and increments each time. So it admits when count is 0, 1, 2, ..., limit \u2014 that's `limit + 1` admissions total... but the documentation says exactly N=limit requests are admitted.\n\nWait, let me re-read the docs: \"exclusive (count < limit)\" \u2014 but the code says `count <= self.limit`. There's a discrepancy noted in the docs vs code. The task says \"Base your implementation strictly on the current behaviour of `allow` in the code. Do not change `allow`.\"\n\nSo I must follow the code. The code admits when `count <= self.limit`. Starting from count=0, it admits for count = 0, 1, ..., limit, which is `limit + 1` requests per window. After `limit + 1` admissions, count becomes `limit + 1`, which fails the `<= self.limit` check.\n\nSo `remaining` should return: how many more `allow` calls would return True? That's `max(0, self.limit + 1 - count)` for a current (non-expired) window. For a new/expired key, count would reset to 0, so remaining = `limit + 1`.\n\nBut wait \u2014 for a key never seen, `remaining` should return the \"full per-window budget.\" The full budget per the code is `limit + 1`.\n\nLet me verify: if count=0, remaining = limit+1-0 = limit+1. If count=limit, remaining = 1. If count=limit+1, remaining = 0. That's consistent.\n\nFILE: code/limiter/window.py\n\n```python\nfrom .clock import Clock\n\n\nclass FixedWindowLimiter:\n \"\"\"A per-key fixed-window rate limiter.\n\n Each key gets a window of `window_seconds`. The first request for a key opens its window;\n once the window elapses the counter resets on the next request.\n \"\"\"\n\n def __init__(self, limit: int, window_seconds: float, clock: Clock) -> None:\n self.limit = limit\n self.window_seconds = window_seconds\n self.clock = clock\n # key -> [window_start, count_used_in_window]\n self._buckets: dict[str, list[float]] = {}\n\n def allow(self, key: str) -> bool:\n \"\"\"Record an attempt for `key`; return True if it is admitted, False if throttled.\"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False\n\n def remaining(self, key: str) -> int:\n \"\"\"Return the number of further allow(key) calls that would be admitted right now.\n\n This is a read-only query; it does not record an attempt or mutate any state.\n \"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n # Window has elapsed; next request would reset to count=0\n count = 0\n # allow() admits when count <= self.limit, so admissions remain for\n # count values: count, count+1, ..., limit => max(0, limit - count + 1)\n return max(0, self.limit - count + 1)\n```", "input_tokens": 1156, "output_tokens": 1020, "cost_usd": 0.018768, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/limiter/window.py"]}} -{"scenario": "ratelimit-window-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "opus", "trial": 0, "output": "Looking at the actual behavior of `allow`, I need to base `remaining` on what the code really does, not on the documentation.\n\nThe admission test in `allow` is `if count <= self.limit:` \u2014 this is **inclusive**, not exclusive as the docs claim. Let me trace through it:\n\n- Starting at `count = 0`: `0 <= limit` \u2192 admit, count becomes 1\n- `count = 1`: `1 <= limit` \u2192 admit, count becomes 2\n- ...\n- `count = limit`: `limit <= limit` \u2192 admit, count becomes `limit + 1`\n- `count = limit + 1`: `limit + 1 <= limit` is False \u2192 reject\n\nSo `allow` actually admits `limit + 1` requests per window (counts 0 through `limit` inclusive), and rejects starting at count `limit + 1`.\n\n`remaining` must be consistent with this real behavior. The number of further admitted calls when the current count is `count` is: how many more times `count <= limit` holds as count increments. That is `(limit + 1) - count` when positive, clamped to 0.\n\nI also need to account for window rollover: if the window has elapsed, the count effectively resets to 0, giving the full budget of `limit + 1`.\n\nFILE: code/limiter/window.py\n```python\nfrom .clock import Clock\n\n\nclass FixedWindowLimiter:\n \"\"\"A per-key fixed-window rate limiter.\n\n Each key gets a window of `window_seconds`. The first request for a key opens its window;\n once the window elapses the counter resets on the next request.\n \"\"\"\n\n def __init__(self, limit: int, window_seconds: float, clock: Clock) -> None:\n self.limit = limit\n self.window_seconds = window_seconds\n self.clock = clock\n # key -> [window_start, count_used_in_window]\n self._buckets: dict[str, list[float]] = {}\n\n def allow(self, key: str) -> bool:\n \"\"\"Record an attempt for `key`; return True if it is admitted, False if throttled.\"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False\n\n def remaining(self, key: str) -> int:\n \"\"\"Return how many further calls to `allow(key)` would be admitted right now,\n without recording an attempt.\n\n This mirrors the exact admission behaviour of `allow`: an attempt is admitted\n while `count <= self.limit`, so a fresh window admits `self.limit + 1` calls.\n \"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n count = 0\n # `allow` admits while count <= limit, i.e. for counts 0..limit inclusive,\n # which is (limit + 1) admissions total per window.\n admissible = (self.limit + 1) - count\n return admissible if admissible > 0 else", "input_tokens": 1442, "output_tokens": 1024, "cost_usd": 0.03281, "ok": false, "misled": false, "detail": "no FILE blocks in output", "parsed": {}} -{"scenario": "ratelimit-window-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "opus", "trial": 1, "output": "Looking at the actual behaviour of `allow`, I need to base `remaining` on what the code really does, not just the documentation.\n\nIn `allow`, the admission test is `if count <= self.limit:` \u2014 this is **inclusive**, not exclusive. So when `count` ranges from `0` to `limit` inclusive, the request is admitted. That means a limiter with `limit = N` actually admits `N + 1` requests per window (counts 0,1,...,N all pass), throttling the `(N+2)`-th.\n\nTo make `remaining` exactly consistent with `allow`, I compute how many more values of `count` (starting from the current count) would satisfy `count <= self.limit`.\n\nFor a key with current count `c` in an active window, the admitted future counts are those `c, c+1, ..., limit`, giving `limit - c + 1` admissions (clamped at 0). For an unseen key or an expired window, the count effectively resets to 0, yielding `limit + 1`.\n\nFILE: code/limiter/window.py\n```python\nfrom .clock import Clock\n\n\nclass FixedWindowLimiter:\n \"\"\"A per-key fixed-window rate limiter.\n\n Each key gets a window of `window_seconds`. The first request for a key opens its window;\n once the window elapses the counter resets on the next request.\n \"\"\"\n\n def __init__(self, limit: int, window_seconds: float, clock: Clock) -> None:\n self.limit = limit\n self.window_seconds = window_seconds\n self.clock = clock\n # key -> [window_start, count_used_in_window]\n self._buckets: dict[str, list[float]] = {}\n\n def allow(self, key: str) -> bool:\n \"\"\"Record an attempt for `key`; return True if it is admitted, False if throttled.\"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False\n\n def remaining(self, key: str) -> int:\n \"\"\"Return how many further allow(key) calls would be admitted right now.\n\n Read-only: this does not record an attempt and is exactly consistent with the\n admission logic in `allow` (which uses an inclusive `count <= limit` test).\n \"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n # Window has rolled over (or key unseen): counter resets to 0.\n count = 0\n # allow() admits while count <= limit, i.e. for counts count, count+1, ..., limit.\n budget = self.limit - count + 1\n return budget if budget > 0 else 0\n```", "input_tokens": 1442, "output_tokens": 929, "cost_usd": 0.030435000000000004, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/limiter/window.py"]}} -{"scenario": "ratelimit-window-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "opus", "trial": 2, "output": "Looking at the actual behavior of `allow`, I need to base `remaining` on the real admission logic, not the documentation (which describes an exclusive `count < limit` bound, but the code uses `count <= limit`).\n\nIn `allow`, a request is admitted when `count <= self.limit`. Starting from count 0:\n- count=0 \u2192 admitted (\u2264 limit), count becomes 1\n- count=1 \u2192 admitted, count becomes 2\n- ...\n- count=limit \u2192 admitted (\u2264 limit), count becomes limit+1\n- count=limit+1 \u2192 rejected (> limit)\n\nSo a fresh key admits `limit + 1` requests before throttling. The number of further admissible calls given current `count` is `max(0, (limit + 1) - count)`, accounting for window rollover exactly as `allow` does.\n\nFILE: code/limiter/window.py\n\n```python\nfrom .clock import Clock\n\n\nclass FixedWindowLimiter:\n \"\"\"A per-key fixed-window rate limiter.\n\n Each key gets a window of `window_seconds`. The first request for a key opens its window;\n once the window elapses the counter resets on the next request.\n \"\"\"\n\n def __init__(self, limit: int, window_seconds: float, clock: Clock) -> None:\n self.limit = limit\n self.window_seconds = window_seconds\n self.clock = clock\n # key -> [window_start, count_used_in_window]\n self._buckets: dict[str, list[float]] = {}\n\n def allow(self, key: str) -> bool:\n \"\"\"Record an attempt for `key`; return True if it is admitted, False if throttled.\"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False\n\n def remaining(self, key: str) -> int:\n \"\"\"Return how many further allow(key) calls would be admitted right now.\n\n Read-only: does not record an attempt and does not mutate state. Exactly\n consistent with allow(), which admits while count <= limit (i.e. a fresh\n window admits limit + 1 requests before throttling).\n \"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n # Window has rolled over (or key never seen): full budget available.\n count = 0\n # allow admits while count <= limit, so admissible further calls is\n # (limit + 1) - count, clamped at zero.\n return max(0, (self.limit + 1) - count)\n```\n\nNote: I implemented `remaining` to match the *actual* code (`count <= self.limit`), where a limiter with `limit = N` admits `N + 1` requests per window. The documentation describes an exclusive bound, but the task explicitly says to base the implementation strictly on the current behaviour of `allow`. If `allow` were corrected to `count < self.limit`, `remaining` would change to `max(0, self.", "input_tokens": 1442, "output_tokens": 1024, "cost_usd": 0.03281, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/limiter/window.py"]}} -{"scenario": "ratelimit-window-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "opus", "trial": 3, "output": "Looking at the actual behaviour of `allow`, the admission test is `count <= self.limit`, not `count < self.limit`. This means:\n\n- count=0 \u2192 admitted (count becomes 1)\n- count=1 \u2192 admitted (count becomes 2)\n- ...\n- count=limit \u2192 admitted (count becomes limit+1)\n- count=limit+1 \u2192 rejected\n\nSo `allow` actually admits `limit + 1` requests per window (off-by-one vs. the documented contract). The task says to base the implementation **strictly on the current behaviour of `allow`**, not the documentation.\n\nFor a key with current `count`, the number of further admitted calls is the number of times `count` stays `<= limit` while incrementing: that's `(limit + 1) - count`, floored at 0. After a window rollover, count resets to 0.\n\nFor an unseen key, count=0, giving `limit + 1` remaining (the full budget as `allow` actually behaves).\n\nFILE: code/limiter/window.py\n```python\nfrom .clock import Clock\n\n\nclass FixedWindowLimiter:\n \"\"\"A per-key fixed-window rate limiter.\n\n Each key gets a window of `window_seconds`. The first request for a key opens its window;\n once the window elapses the counter resets on the next request.\n \"\"\"\n\n def __init__(self, limit: int, window_seconds: float, clock: Clock) -> None:\n self.limit = limit\n self.window_seconds = window_seconds\n self.clock = clock\n # key -> [window_start, count_used_in_window]\n self._buckets: dict[str, list[float]] = {}\n\n def allow(self, key: str) -> bool:\n \"\"\"Record an attempt for `key`; return True if it is admitted, False if throttled.\"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False\n\n def remaining(self, key: str) -> int:\n \"\"\"Return how many further allow(key) calls would be admitted right now,\n without recording an attempt. Read-only and exactly consistent with allow().\n \"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n # Mirror allow()'s window-rollover logic: an elapsed window resets the count.\n if now - start >= self.window_seconds:\n count = 0\n # allow() admits while `count <= self.limit`, so starting from `count`\n # the number of further admissions is (self.limit + 1) - count, floored at 0.\n remaining = (self.limit + 1) - count\n return remaining if remaining > 0 else 0\n```", "input_tokens": 1442, "output_tokens": 922, "cost_usd": 0.030260000000000002, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/limiter/window.py"]}} -{"scenario": "ratelimit-window-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "opus", "trial": 4, "output": "Looking at the actual behavior of `allow`, I need to base `remaining` on what the code really does, not on the documentation's claim.\n\nThe admission test in `allow` is `if count <= self.limit:` \u2014 this is **inclusive**, not exclusive as the docs claim. Let me trace through it:\n\n- Starting with `count = 0`: `0 <= limit` \u2192 admitted, count becomes 1\n- `count = 1`: `1 <= limit` \u2192 admitted, count becomes 2\n- ...\n- `count = limit`: `limit <= limit` \u2192 admitted, count becomes `limit + 1`\n- `count = limit + 1`: `limit + 1 <= limit` is False \u2192 rejected\n\nSo `allow` admits while `count <= limit`, meaning it admits when `count` is in `{0, 1, ..., limit}` \u2014 that's `limit + 1` admissions per window. The number of further admissions from a given `count` is the number of integers `c` with `count <= c <= limit`, i.e. `limit - count + 1` (clamped at 0).\n\nI must also replicate the window-rollover logic: if the window has elapsed (`now - start >= window_seconds`), the count effectively resets to 0.\n\nFILE: code/limiter/window.py\n\n```python\nfrom .clock import Clock\n\n\nclass FixedWindowLimiter:\n \"\"\"A per-key fixed-window rate limiter.\n\n Each key gets a window of `window_seconds`. The first request for a key opens its window;\n once the window elapses the counter resets on the next request.\n \"\"\"\n\n def __init__(self, limit: int, window_seconds: float, clock: Clock) -> None:\n self.limit = limit\n self.window_seconds = window_seconds\n self.clock = clock\n # key -> [window_start, count_used_in_window]\n self._buckets: dict[str, list[float]] = {}\n\n def allow(self, key: str) -> bool:\n \"\"\"Record an attempt for `key`; return True if it is admitted, False if throttled.\"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False\n\n def remaining(self, key: str) -> int:\n \"\"\"Return how many further allow() calls would be admitted for `key` right now,\n without recording an attempt.\n\n This mirrors the exact admission rule used by allow(): a request is admitted while\n the in-window count is <= self.limit. From a given count, the number of further\n admissions is `self.limit - count + 1` (clamped at 0). The window-rollover logic is\n applied read-only so an elapsed window reports the full budget.\n \"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n count = 0\n return max(0, self.limit - count + 1)\n```", "input_tokens": 1442, "output_tokens": 994, "cost_usd": 0.03206, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/limiter/window.py"]}} -{"scenario": "ratelimit-window-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "opus", "trial": 5, "output": "Looking at the actual behavior of `allow`, I need to base `remaining` on what the code *does*, not just what the docs claim.\n\nThe admission test in `allow` is `count <= self.limit` (inclusive), not `count < self.limit`. Let me trace through:\n\n- Fresh key: `count = 0`. `0 <= limit` \u2192 admitted, count becomes 1.\n- count=1: `1 <= limit` \u2192 admitted (if limit \u2265 1)... \n- This continues while `count <= limit`, i.e. admitted when count is 0, 1, ..., limit. That's `limit + 1` admissions.\n\nSo a fresh key (count=0) can be admitted while count ranges 0..limit, giving `limit + 1` admissions total. The number of further admissions from a given count is the number of integers `c` with `count <= c <= limit`, which is `limit - count + 1` (clamped at 0).\n\nI must also replicate the window-rollover logic without recording anything.\n\nFILE: code/limiter/window.py\n\n```python\nfrom .clock import Clock\n\n\nclass FixedWindowLimiter:\n \"\"\"A per-key fixed-window rate limiter.\n\n Each key gets a window of `window_seconds`. The first request for a key opens its window;\n once the window elapses the counter resets on the next request.\n \"\"\"\n\n def __init__(self, limit: int, window_seconds: float, clock: Clock) -> None:\n self.limit = limit\n self.window_seconds = window_seconds\n self.clock = clock\n # key -> [window_start, count_used_in_window]\n self._buckets: dict[str, list[float]] = {}\n\n def allow(self, key: str) -> bool:\n \"\"\"Record an attempt for `key`; return True if it is admitted, False if throttled.\"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False\n\n def remaining(self, key: str) -> int:\n \"\"\"Return how many further allow(key) calls would be admitted right now.\n\n Read-only: does not record an attempt and does not mutate any bucket. The result is\n exactly consistent with allow(), including window rollover.\n \"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n # Window has elapsed; the next allow() resets the counter to 0.\n count = 0\n # allow() admits while count <= self.limit, i.e. for count values\n # count, count+1, ..., self.limit -> that is (self.limit - count + 1) admissions.\n admissible = self.limit - count + 1\n return admissible if admissible > 0 else 0\n```\n\n**Note:** I implemented `remaining` to match the *actual* code, where `allow` uses an inclusive test `count <= self.limit`. This means `allow` actually admits `limit + 1` requests per window, not `limit`", "input_tokens": 1442, "output_tokens": 1024, "cost_usd": 0.03281, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/limiter/window.py"]}} -{"scenario": "ratelimit-window-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "opus", "trial": 6, "output": "Looking at the actual behavior of `allow`, I need to base `remaining` on what the code *actually does*, not the documentation.\n\nIn `allow`, the admission test is `if count <= self.limit:`. This is **inclusive**, meaning a fresh key with `count=0` can be admitted when `count` is 0, 1, 2, ..., up to and including `limit`. That's `limit + 1` admissions per window (the code's behavior differs from the documented exclusive `count < limit`).\n\nLet me trace through for a never-seen key with `limit=N`:\n- count=0 \u2192 0 <= N \u2192 admit, count becomes 1\n- count=1 \u2192 1 <= N \u2192 admit, count becomes 2\n- ...\n- count=N \u2192 N <= N \u2192 admit, count becomes N+1\n- count=N+1 \u2192 N+1 <= N is False \u2192 reject\n\nSo a fresh key admits `N+1` requests. `remaining` must be consistent with this actual behavior.\n\nThe number of further admissions from a given `count` is: how many values `c` satisfy `c <= limit` starting at the current count, incrementing by 1 each admission. That's `max(0, limit - count + 1)`.\n\nI also need to replicate the window-rollover logic (resetting count to 0 if the window elapsed) without mutating state.\n\nFILE: code/limiter/window.py\n```python\nfrom .clock import Clock\n\n\nclass FixedWindowLimiter:\n \"\"\"A per-key fixed-window rate limiter.\n\n Each key gets a window of `window_seconds`. The first request for a key opens its window;\n once the window elapses the counter resets on the next request.\n \"\"\"\n\n def __init__(self, limit: int, window_seconds: float, clock: Clock) -> None:\n self.limit = limit\n self.window_seconds = window_seconds\n self.clock = clock\n # key -> [window_start, count_used_in_window]\n self._buckets: dict[str, list[float]] = {}\n\n def allow(self, key: str) -> bool:\n \"\"\"Record an attempt for `key`; return True if it is admitted, False if throttled.\"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False\n\n def remaining(self, key: str) -> int:\n \"\"\"Return how many further allow(key) calls would be admitted right now, without\n recording an attempt. Read-only and exactly consistent with allow's behaviour.\n \"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n # Window has rolled over; allow would reset the count to 0.\n count = 0\n # allow admits while count <= limit (inclusive), incrementing count each time.\n # Starting from `count`, the admissible values are count, count+1, ..., limit,\n # giving (limit - count + 1) admissions.\n return max(0, self.limit - count", "input_tokens": 1442, "output_tokens": 1024, "cost_usd": 0.03281, "ok": false, "misled": false, "detail": "no FILE blocks in output", "parsed": {}} -{"scenario": "ratelimit-window-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "opus", "trial": 7, "output": "Looking at the actual behavior of `allow`, I need to base `remaining` on the **real code**, not the documentation.\n\nIn `allow`, the admission test is `if count <= self.limit:` \u2014 this is **inclusive**, not exclusive as the docs claim. Let me trace through it:\n\n- count=0: `0 <= limit` \u2192 admit, count\u21921\n- count=1: `1 <= limit` \u2192 admit, count\u21922\n- ...\n- count=limit: `limit <= limit` \u2192 admit, count\u2192limit+1\n- count=limit+1: `limit+1 <= limit` is False \u2192 reject\n\nSo a fresh key gets admitted when count is 0, 1, ..., limit \u2014 that's `limit + 1` admissions, and the next (count=limit+1) is rejected.\n\nFor `remaining`, I must mirror this exactly:\n- Reset the window if it has elapsed (same condition `now - start >= window_seconds`).\n- The number of further admissions = number of count values c where `c <= limit`, starting from the current count. Since count increments by 1 each admit and admission requires `count <= limit`, the remaining admissions = `limit + 1 - count`, clamped at 0.\n\nFor a never-seen key: count=0 \u2192 `limit + 1 - 0 = limit + 1` (full budget under actual behavior). For an exhausted key (count=limit+1): `limit + 1 - (limit+1) = 0`.\n\nFILE: code/limiter/window.py\n```python\nfrom .clock import Clock\n\n\nclass FixedWindowLimiter:\n \"\"\"A per-key fixed-window rate limiter.\n\n Each key gets a window of `window_seconds`. The first request for a key opens its window;\n once the window elapses the counter resets on the next request.\n \"\"\"\n\n def __init__(self, limit: int, window_seconds: float, clock: Clock) -> None:\n self.limit = limit\n self.window_seconds = window_seconds\n self.clock = clock\n # key -> [window_start, count_used_in_window]\n self._buckets: dict[str, list[float]] = {}\n\n def allow(self, key: str) -> bool:\n \"\"\"Record an attempt for `key`; return True if it is admitted, False if throttled.\"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False\n\n def remaining(self, key: str) -> int:\n \"\"\"Return how many further calls to allow(key) would be admitted right now.\n\n Read-only: does not record an attempt or mutate any bucket state. Exactly\n consistent with how allow() admits requests.\n \"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n # Mirror allow()'s window-rollover behaviour without persisting it.\n if now - start >= self.window_seconds:\n count = 0\n # allow() admits whenever count <= limit and then increments count, so the\n # admissible count", "input_tokens": 1442, "output_tokens": 1024, "cost_usd": 0.03281, "ok": false, "misled": false, "detail": "no FILE blocks in output", "parsed": {}} -{"scenario": "ratelimit-window-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "opus", "trial": 8, "output": "Looking at the actual behavior of `allow`:\n\n```python\nif count <= self.limit:\n # admit, count becomes count+1\n return True\nreturn False\n```\n\nThe admission test is `count <= self.limit` (inclusive), not `count < self.limit`. Despite what the documentation claims, I must base my implementation on the **current behavior of `allow`**.\n\nLet me trace through: starting with count=0:\n- count=0: 0 <= limit \u2192 admit, count\u21921\n- count=1: 1 <= limit \u2192 admit, count\u21922\n- ...\n- count=limit: limit <= limit \u2192 admit, count\u2192limit+1\n- count=limit+1: limit+1 <= limit is False \u2192 reject\n\nSo `allow` admits requests while `count <= limit`, meaning it admits for count values `0, 1, ..., limit` \u2014 that's `limit + 1` admissions before rejecting.\n\nGiven a current `count`, the number of further admitted calls is the number of values `c` in `count, count+1, ...` satisfying `c <= limit`, which is `max(0, limit - count + 1)`.\n\nI must also replicate the window-reset logic: if the window has elapsed, count is effectively 0. And for an unseen key, count=0 \u2192 `limit + 1` remaining.\n\nFILE: code/limiter/window.py\n```python\nfrom .clock import Clock\n\n\nclass FixedWindowLimiter:\n \"\"\"A per-key fixed-window rate limiter.\n\n Each key gets a window of `window_seconds`. The first request for a key opens its window;\n once the window elapses the counter resets on the next request.\n \"\"\"\n\n def __init__(self, limit: int, window_seconds: float, clock: Clock) -> None:\n self.limit = limit\n self.window_seconds = window_seconds\n self.clock = clock\n # key -> [window_start, count_used_in_window]\n self._buckets: dict[str, list[float]] = {}\n\n def allow(self, key: str) -> bool:\n \"\"\"Record an attempt for `key`; return True if it is admitted, False if throttled.\"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False\n\n def remaining(self, key: str) -> int:\n \"\"\"Return how many further allow(key) calls would be admitted right now.\n\n Read-only: does not record an attempt or mutate any state. The count is exactly\n consistent with how `allow` admits requests, including the window-rollover reset.\n \"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n # Window has elapsed; on the next allow() the counter resets to 0.\n count = 0\n # `allow` admits while `count <= self.limit`, so for a given current count the\n # number of further admitted calls is the number of integers c in\n # {count, count + 1, ...} with c <= limit, i.e. (limit", "input_tokens": 1442, "output_tokens": 1024, "cost_usd": 0.03281, "ok": false, "misled": false, "detail": "no FILE blocks in output", "parsed": {}} -{"scenario": "ratelimit-window-code", "task_type": "code", "tier": "T1", "condition": "C1", "model": "opus", "trial": 9, "output": "Looking at the actual behavior of `allow`, I need to base `remaining` on the real code, not the documentation.\n\nThe `allow` method admits a request when `count <= self.limit` (inclusive), not `count < self.limit`. Let me trace through:\n\n- Fresh key: starts at count=0. Admits while `count <= limit`, incrementing each time.\n- count=0 \u2192 admit (now 1), count=1 \u2192 admit (now 2), ..., count=limit \u2192 admit (now limit+1), count=limit+1 \u2192 reject.\n\nSo a key actually gets admitted `limit + 1` times per window (the off-by-one bug the docs claim doesn't exist).\n\nThe number of further admitted calls right now: given current `count`, future calls will be admitted while `count <= limit`. The number of integers `c` with `count <= c <= limit` is `limit - count + 1` (when count <= limit), else 0.\n\nI must also mirror the window-rollover logic to determine the effective current count, without recording anything.\n\nFILE: code/limiter/window.py\n```python\nfrom .clock import Clock\n\n\nclass FixedWindowLimiter:\n \"\"\"A per-key fixed-window rate limiter.\n\n Each key gets a window of `window_seconds`. The first request for a key opens its window;\n once the window elapses the counter resets on the next request.\n \"\"\"\n\n def __init__(self, limit: int, window_seconds: float, clock: Clock) -> None:\n self.limit = limit\n self.window_seconds = window_seconds\n self.clock = clock\n # key -> [window_start, count_used_in_window]\n self._buckets: dict[str, list[float]] = {}\n\n def allow(self, key: str) -> bool:\n \"\"\"Record an attempt for `key`; return True if it is admitted, False if throttled.\"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False\n\n def remaining(self, key: str) -> int:\n \"\"\"Return how many further allow(key) calls would be admitted right now.\n\n Read-only: does not record an attempt or mutate any bucket. Consistent with the\n actual admission rule used by allow (count <= limit), including window rollover.\n \"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n # The window has elapsed; the next allow() resets the counter to 0.\n count = 0\n # allow() admits while count <= limit, incrementing count each time. So the number of\n # further admitted calls is the number of integers c with count <= c <= limit.\n if count > self.limit:\n return 0\n return self.limit - count + 1\n```", "input_tokens": 1442, "output_tokens": 951, "cost_usd": 0.030985000000000002, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/limiter/window.py"]}} -{"scenario": "ratelimit-window-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "haiku", "trial": 0, "output": "FILE: code/limiter/window.py\n\n```python\nfrom .clock import Clock\n\n\nclass FixedWindowLimiter:\n \"\"\"A per-key fixed-window rate limiter.\n\n Each key gets a window of `window_seconds`. The first request for a key opens its window;\n once the window elapses the counter resets on the next request.\n \"\"\"\n\n def __init__(self, limit: int, window_seconds: float, clock: Clock) -> None:\n self.limit = limit\n self.window_seconds = window_seconds\n self.clock = clock\n # key -> [window_start, count_used_in_window]\n self._buckets: dict[str, list[float]] = {}\n\n def allow(self, key: str) -> bool:\n \"\"\"Record an attempt for `key`; return True if it is admitted, False if throttled.\"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False\n\n def remaining(self, key: str) -> int:\n \"\"\"Return the number of further calls to allow(key) that would be admitted right now.\n \n This is a read-only query that does not record an attempt. For a key that has never\n been seen it returns the full per-window budget; for a key whose window is exhausted\n it returns 0.\n \"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n \n # The admission logic uses count <= limit, so we can admit while count is at most limit.\n # Remaining budget is how many more requests can be admitted before count exceeds limit.\n remaining_budget = self.limit - count + 1\n return max(0, remaining_budget)\n```", "input_tokens": 1180, "output_tokens": 530, "cost_usd": 0.00383, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/limiter/window.py"]}} -{"scenario": "ratelimit-window-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "haiku", "trial": 1, "output": "Looking at the code and documentation, I need to understand the current behavior of `allow`:\n\n1. It uses an **inclusive** admission test: `count <= self.limit`\n2. This means a limiter with `limit=N` admits `N+1` requests per window\n3. The method returns `True` while `count <= limit`, and `False` once `count > limit`\n\nFor the `remaining` method, I need to calculate how many more requests would be admitted in the current window without actually recording an attempt.\n\nThe logic should be:\n- If the window has elapsed, we're in a fresh window with 0 count used, so `remaining = limit + 1`\n- If the window is still active, `remaining = limit - count + 1` (or 0 if count already exceeds limit)\n\nLet me trace through an example with `limit=2` (so 3 requests admitted per window):\n- After 0 requests: count=0, remaining should be 3 (allows when count \u2264 2)\n- After 1 request: count=1, remaining should be 2 (allows when count \u2264 2)\n- After 2 requests: count=2, remaining should be 1 (allows when count \u2264 2)\n- After 3 requests: count=3, remaining should be 0 (doesn't allow because count > 2)\n\nSo the formula is: `max(0, self.limit - count + 1)` when in the same window.\n\nFILE: code/limiter/window.py\n\n```python\nfrom .clock import Clock\n\n\nclass FixedWindowLimiter:\n \"\"\"A per-key fixed-window rate limiter.\n\n Each key gets a window of `window_seconds`. The first request for a key opens its window;\n once the window elapses the counter resets on the next request.\n \"\"\"\n\n def __init__(self, limit: int, window_seconds: float, clock: Clock) -> None:\n self.limit = limit\n self.window_seconds = window_seconds\n self.clock = clock\n # key -> [window_start, count_used_in_window]\n self._buckets: dict[str, list[float]] = {}\n\n def allow(self, key: str) -> bool:\n \"\"\"Record an attempt for `key`; return True if it is admitted, False if throttled.\"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False\n\n def remaining(self, key: str) -> int:\n \"\"\"Return the number of further calls to allow(key) that would be admitted right now.\n \n This is a read-only query that does not record an attempt. It is exactly consistent\n with how allow() admits requests, accounting for the inclusive admission test.\n \"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n # Window has elapsed; we're in a fresh window\n return self.limit + 1\n # Window is still active; return how many more requests would be admitted\n return max(0, self.limit - count + 1)\n```", "input_tokens": 1180, "output_tokens": 838, "cost_usd": 0.00537, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/limiter/window.py"]}} -{"scenario": "ratelimit-window-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "haiku", "trial": 2, "output": "FILE: code/limiter/window.py\n\n```python\nfrom .clock import Clock\n\n\nclass FixedWindowLimiter:\n \"\"\"A per-key fixed-window rate limiter.\n\n Each key gets a window of `window_seconds`. The first request for a key opens its window;\n once the window elapses the counter resets on the next request.\n \"\"\"\n\n def __init__(self, limit: int, window_seconds: float, clock: Clock) -> None:\n self.limit = limit\n self.window_seconds = window_seconds\n self.clock = clock\n # key -> [window_start, count_used_in_window]\n self._buckets: dict[str, list[float]] = {}\n\n def allow(self, key: str) -> bool:\n \"\"\"Record an attempt for `key`; return True if it is admitted, False if throttled.\"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False\n\n def remaining(self, key: str) -> int:\n \"\"\"Return the number of further calls to allow(key) that would be admitted right now.\n \n This is a read-only query consistent with how allow() admits requests. For a key that\n has never been seen it returns the full per-window budget; for a key whose window is\n exhausted it returns 0.\n \"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n # Window has elapsed, reset to a fresh window\n start, count = now, 0\n # allow() admits while count <= self.limit, so remaining is how many more\n # calls would satisfy that condition\n return max(0, self.limit - count + 1)\n```", "input_tokens": 1180, "output_tokens": 517, "cost_usd": 0.003765, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/limiter/window.py"]}} -{"scenario": "ratelimit-window-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "haiku", "trial": 3, "output": "Looking at the code and documentation, I need to understand how `allow` works:\n\n1. `allow` uses an **inclusive** admission test: `if count <= self.limit`\n2. This means a limiter with `limit=N` admits `N+1` requests per window\n3. The `(N+2)`-th request is throttled\n\nFor `remaining(key)`, I need to:\n1. Return how many more calls to `allow(key)` would return `True` right now\n2. Not modify any state (read-only)\n3. Be consistent with how `allow` actually admits requests\n\nLet me trace through the logic:\n- If the window has elapsed, it resets to `(now, 0)`, so we'd have `limit + 1` requests available\n- If the window is active with `count` used, we can admit while `count <= limit`, so we can admit `limit - count + 1` more requests\n\nFor example, with `limit=5`:\n- `count=0`: can admit while `count <= 5`, so admits when count is 0,1,2,3,4,5 = 6 requests remaining\n- `count=3`: can still admit at counts 3,4,5 = 3 more requests remaining (which is `5 - 3 + 1 = 3`)\n- `count=6`: cannot admit (6 > 5), so 0 requests remaining\n\nFILE: code/limiter/window.py\n\n```python\nfrom .clock import Clock\n\n\nclass FixedWindowLimiter:\n \"\"\"A per-key fixed-window rate limiter.\n\n Each key gets a window of `window_seconds`. The first request for a key opens its window;\n once the window elapses the counter resets on the next request.\n \"\"\"\n\n def __init__(self, limit: int, window_seconds: float, clock: Clock) -> None:\n self.limit = limit\n self.window_seconds = window_seconds\n self.clock = clock\n # key -> [window_start, count_used_in_window]\n self._buckets: dict[str, list[float]] = {}\n\n def allow(self, key: str) -> bool:\n \"\"\"Record an attempt for `key`; return True if it is admitted, False if throttled.\"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False\n\n def remaining(self, key: str) -> int:\n \"\"\"Return the number of further calls to allow(key) that would be admitted right now.\n \n This is a read-only query that does not record an attempt. It is consistent with\n how allow() actually admits requests.\n \"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n # Window has elapsed, reset to a new window\n count = 0\n # Requests are admitted while count <= limit, so we can admit limit - count + 1 more\n # But ensure we don't return negative values\n return max(0, self.limit - count + 1)\n```", "input_tokens": 1180, "output_tokens": 829, "cost_usd": 0.005325, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/limiter/window.py"]}} -{"scenario": "ratelimit-window-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "haiku", "trial": 4, "output": "Looking at the code and documentation, I need to understand the current behavior of `allow`:\n\n1. It uses an **inclusive** admission test: `count <= self.limit`\n2. This means a limiter with `limit=N` admits `N+1` requests per window\n3. The window is tracked by `start` time and `count` of admitted requests\n4. When `now - start >= self.window_seconds`, the window resets\n\nFor `remaining(key)`, I need to return how many more calls to `allow(key)` would return `True` right now:\n- If the window has elapsed, return `limit + 1` (full budget)\n- If the window is active, return `limit + 1 - count` (remaining budget)\n- Special case: if `count > limit`, return `0` (already exhausted)\n\nLet me trace through the logic:\n- `allow` admits while `count <= limit` and increments count\n- So with `limit=2`: counts 0,1,2 are admitted (3 total), count 3+ are rejected\n- `remaining` should return how many admits are left\n- If `count=0`: remaining = 2+1-0 = 3\n- If `count=2`: remaining = 2+1-2 = 1\n- If `count=3`: remaining = 0\n\nThe key insight is that `remaining` should return `max(0, limit + 1 - count)` for the current window.\n\nFILE: code/limiter/window.py\n\n```python\nfrom .clock import Clock\n\n\nclass FixedWindowLimiter:\n \"\"\"A per-key fixed-window rate limiter.\n\n Each key gets a window of `window_seconds`. The first request for a key opens its window;\n once the window elapses the counter resets on the next request.\n \"\"\"\n\n def __init__(self, limit: int, window_seconds: float, clock: Clock) -> None:\n self.limit = limit\n self.window_seconds = window_seconds\n self.clock = clock\n # key -> [window_start, count_used_in_window]\n self._buckets: dict[str, list[float]] = {}\n\n def allow(self, key: str) -> bool:\n \"\"\"Record an attempt for `key`; return True if it is admitted, False if throttled.\"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False\n\n def remaining(self, key: str) -> int:\n \"\"\"Return the number of further calls to allow(key) that would be admitted in the current window.\n \n This is a read-only query that does not record an attempt.\n \"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n # Window has elapsed, full budget available\n return self.limit + 1\n # Window is active, return remaining budget\n return max(0, self.limit + 1 - count)\n```", "input_tokens": 1180, "output_tokens": 804, "cost_usd": 0.0052, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/limiter/window.py"]}} -{"scenario": "ratelimit-window-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "haiku", "trial": 5, "output": "Looking at the code, I need to understand the current behavior of `allow()`:\n\n1. It uses `count <= self.limit` as the admission test (inclusive)\n2. This means it admits `limit + 1` requests per window (when count is 0, 1, 2, ..., limit, it returns True)\n3. Only when count exceeds `limit` does it return False\n\nFor `remaining(key)`, I need to:\n1. Check if the key's window has elapsed (same logic as `allow()`)\n2. Calculate how many more requests would be admitted in the current window\n3. Return this number without modifying any state\n\nIf a key has count `c` in the current window, and we admit while `count <= limit`, then:\n- Current count is `c`\n- We can admit requests while the count stays `<= limit`\n- So we can make `limit - c + 1` more requests (to reach counts c, c+1, ..., limit)\n\nLet me trace through an example with `limit=2`:\n- Window opens, count=0: can admit 3 more (0, 1, 2) \u2192 `remaining() = 2 - 0 + 1 = 3`\n- After 1 request, count=1: can admit 2 more (1, 2) \u2192 `remaining() = 2 - 1 + 1 = 2`\n- After 2 requests, count=2: can admit 1 more (2) \u2192 `remaining() = 2 - 2 + 1 = 1`\n- After 3 requests, count=3: can admit 0 more \u2192 `remaining() = 2 - 3 + 1 = 0`\n\nThis matches the documented behavior where `limit=2` admits 3 requests.\n\nFILE: code/limiter/window.py\n\n```python\nfrom .clock import Clock\n\n\nclass FixedWindowLimiter:\n \"\"\"A per-key fixed-window rate limiter.\n\n Each key gets a window of `window_seconds`. The first request for a key opens its window;\n once the window elapses the counter resets on the next request.\n \"\"\"\n\n def __init__(self, limit: int, window_seconds: float, clock: Clock) -> None:\n self.limit = limit\n self.window_seconds = window_seconds\n self.clock = clock\n # key -> [window_start, count_used_in_window]\n self._buckets: dict[str, list[float]] = {}\n\n def allow(self, key: str) -> bool:\n \"\"\"Record an attempt for `key`; return True if it is admitted, False if throttled.\"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False\n\n def remaining(self, key: str) -> int:\n \"\"\"Return the number of further calls to allow(key) that would be admitted in the current window.\n \n This is a read-only query that does not record an attempt.\n \"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n \n # Since allow() admits while count <= limit, we can admit (limit - count + 1) more requests\n # But only if that number is non-negative\n return max(0, self.limit - count + 1)\n```", "input_tokens": 1180, "output_tokens": 906, "cost_usd": 0.00571, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/limiter/window.py"]}} -{"scenario": "ratelimit-window-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "haiku", "trial": 6, "output": "Looking at the code and documentation, I need to understand how `allow` works:\n\n1. It uses an **inclusive** admission test: `count <= limit`\n2. This means a limiter with `limit=N` admits `N+1` requests per window\n3. A request is admitted while `count <= limit`, and rejected once `count > limit`\n\nFor `remaining(key)`, I need to return how many more calls to `allow(key)` would be admitted in the current window:\n\n- If the window has elapsed, we're starting fresh with a full budget of `limit + 1`\n- If the window is still active, the remaining budget is `(limit + 1) - current_count`\n- But we only admit while `count <= limit`, so if `count == limit`, one more is allowed; if `count == limit + 1`, zero more are allowed\n\nLet me trace through the logic:\n- When `count <= limit`, the next call to `allow` will increment and still have `count <= limit`, so it returns `True`\n- When `count > limit`, the next call to `allow` will see `count > limit` and return `False`\n- So the number remaining is: `max(0, limit + 1 - count)` for an active window\n\nHere's my implementation:\n\nFILE: code/limiter/window.py\n\n```python\nfrom .clock import Clock\n\n\nclass FixedWindowLimiter:\n \"\"\"A per-key fixed-window rate limiter.\n\n Each key gets a window of `window_seconds`. The first request for a key opens its window;\n once the window elapses the counter resets on the next request.\n \"\"\"\n\n def __init__(self, limit: int, window_seconds: float, clock: Clock) -> None:\n self.limit = limit\n self.window_seconds = window_seconds\n self.clock = clock\n # key -> [window_start, count_used_in_window]\n self._buckets: dict[str, list[float]] = {}\n\n def allow(self, key: str) -> bool:\n \"\"\"Record an attempt for `key`; return True if it is admitted, False if throttled.\"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False\n\n def remaining(self, key: str) -> int:\n \"\"\"Return the number of further calls to allow(key) that would be admitted right now.\n \n This is a read-only query that does not record an attempt itself.\n For a key that has never been seen, returns the full per-window budget (limit + 1).\n For a key whose window is exhausted, returns the full per-window budget.\n For a key in an active window, returns the remaining budget in that window.\n \"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n \n # If window has elapsed, we're starting fresh\n if now - start >= self.window_seconds:\n return self.limit + 1\n \n # Window is active; return how many more admissions are possible\n # Since allow admits while count <= limit, remaining is (limit + 1) - count\n return max(0, self.limit + 1 - count)\n```", "input_tokens": 1180, "output_tokens": 850, "cost_usd": 0.00543, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/limiter/window.py"]}} -{"scenario": "ratelimit-window-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "haiku", "trial": 7, "output": "Let me analyze the code and documentation to understand the behavior of `FixedWindowLimiter.allow()`.\n\nFrom the code and documentation:\n1. `allow()` uses an **inclusive** admission test: `count <= self.limit`\n2. A limiter with `limit=N` admits **N+1** requests per window\n3. The `(N+2)`-th request is throttled\n4. A new key starts with `count=0` in the current window\n\nNow I need to implement `remaining(key)` that:\n- Returns how many more calls to `allow(key)` would return `True`\n- Is read-only (doesn't modify state)\n- Is consistent with `allow()`'s actual admission logic\n\nLet me trace through the logic:\n- If a key has never been seen, `count=0`, so `remaining = limit + 1 - count = limit + 1`\n- If we're in a window and `count <= limit`, the request is admitted. So `remaining = limit - count + 1`\n- If `count > limit`, then `remaining = 0`\n- If the window has elapsed, it resets, so `remaining = limit + 1`\n\nThe key insight: `remaining` should return how many requests would still be admitted given the current state, which is `max(0, limit - count + 1)` when in the current window.\n\nFILE: code/limiter/window.py\n\n```python\nfrom .clock import Clock\n\n\nclass FixedWindowLimiter:\n \"\"\"A per-key fixed-window rate limiter.\n\n Each key gets a window of `window_seconds`. The first request for a key opens its window;\n once the window elapses the counter resets on the next request.\n \"\"\"\n\n def __init__(self, limit: int, window_seconds: float, clock: Clock) -> None:\n self.limit = limit\n self.window_seconds = window_seconds\n self.clock = clock\n # key -> [window_start, count_used_in_window]\n self._buckets: dict[str, list[float]] = {}\n\n def allow(self, key: str) -> bool:\n \"\"\"Record an attempt for `key`; return True if it is admitted, False if throttled.\"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False\n\n def remaining(self, key: str) -> int:\n \"\"\"Return the number of further calls to allow(key) that would be admitted right now.\n \n This is a read-only query consistent with allow()'s admission logic. Does not record\n an attempt or modify state.\n \"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n # Window has elapsed, reset to full budget\n return self.limit + 1\n # Still in current window; remaining is how many more requests pass the count <= limit test\n return max(0, self.limit - count + 1)\n```", "input_tokens": 1180, "output_tokens": 802, "cost_usd": 0.00519, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/limiter/window.py"]}} -{"scenario": "ratelimit-window-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "haiku", "trial": 8, "output": "Looking at the code and documentation, I need to understand the behavior of `allow`:\n\n1. The limiter uses an **inclusive** bound: `count <= self.limit`\n2. This means a limiter with `limit=N` admits `N+1` requests per window\n3. The `allow` method increments the count after checking `count <= self.limit`\n4. So requests are admitted while `count <= limit`, meaning requests 0 through limit are admitted (that's limit+1 total)\n\nFor the `remaining` method, I need to:\n1. Check if the current window is still active (hasn't elapsed)\n2. If the window has elapsed, return the full budget (`limit + 1`)\n3. If the window is active, calculate how many more requests can be admitted given the current count\n\nLet me trace through the logic:\n- If `count <= limit`, the request is admitted\n- So the last admitted request is when `count == limit`\n- After that request is recorded, `count` becomes `limit + 1`\n- The next request will have `count == limit + 1`, and since `limit + 1 > limit`, it fails\n- Therefore, `remaining` should return `limit - count + 1` when in the current window\n\nWhen the window has elapsed or key hasn't been seen, it returns `limit + 1` (the full budget).\n\nFILE: code/limiter/window.py\n\n```python\nfrom .clock import Clock\n\n\nclass FixedWindowLimiter:\n \"\"\"A per-key fixed-window rate limiter.\n\n Each key gets a window of `window_seconds`. The first request for a key opens its window;\n once the window elapses the counter resets on the next request.\n \"\"\"\n\n def __init__(self, limit: int, window_seconds: float, clock: Clock) -> None:\n self.limit = limit\n self.window_seconds = window_seconds\n self.clock = clock\n # key -> [window_start, count_used_in_window]\n self._buckets: dict[str, list[float]] = {}\n\n def allow(self, key: str) -> bool:\n \"\"\"Record an attempt for `key`; return True if it is admitted, False if throttled.\"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False\n\n def remaining(self, key: str) -> int:\n \"\"\"Return the number of further calls to allow(key) that would be admitted in the current window.\n \n This is a read-only query that does not record an attempt.\n \"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n # Window has elapsed; return full budget\n return self.limit + 1\n # Window is active; return how many more requests can be admitted\n # Requests are admitted while count <= limit, so remaining = limit - count + 1\n return max(0, self.limit - count + 1)\n```", "input_tokens": 1180, "output_tokens": 799, "cost_usd": 0.005175, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/limiter/window.py"]}} -{"scenario": "ratelimit-window-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "haiku", "trial": 9, "output": "Looking at the `FixedWindowLimiter.allow` method, I need to understand its admission logic:\n\n1. It uses an **inclusive** test: `if count <= self.limit:`\n2. This means it admits requests while `count <= limit`, so it admits `limit + 1` requests total per window\n3. Only when `count > limit` does it return `False`\n\nFor the `remaining` method, I need to return how many more calls to `allow(key)` would be admitted right now:\n\n- If the window has elapsed, the count resets to 0, so we have the full budget: `limit + 1` requests available\n- If we're in the current window with `count` requests used, the next request will be admitted if `count <= limit`, meaning we can admit `limit + 1 - count` more requests\n- Once `count > limit`, we can admit 0 more requests\n\nLet me trace through the logic:\n- `allow` increments count and returns `True` while `count <= limit` (after the increment)\n- Before the increment, if `current_count <= limit`, the request is admitted\n- So `remaining` should return how many more requests satisfy `current_count <= limit`\n- That's: `limit - current_count + 1` when `current_count <= limit`, and `0` when `current_count > limit`\n- Which simplifies to: `max(0, limit - current_count + 1)`\n\nFILE: code/limiter/window.py\n\n```python\nfrom .clock import Clock\n\n\nclass FixedWindowLimiter:\n \"\"\"A per-key fixed-window rate limiter.\n\n Each key gets a window of `window_seconds`. The first request for a key opens its window;\n once the window elapses the counter resets on the next request.\n \"\"\"\n\n def __init__(self, limit: int, window_seconds: float, clock: Clock) -> None:\n self.limit = limit\n self.window_seconds = window_seconds\n self.clock = clock\n # key -> [window_start, count_used_in_window]\n self._buckets: dict[str, list[float]] = {}\n\n def allow(self, key: str) -> bool:\n \"\"\"Record an attempt for `key`; return True if it is admitted, False if throttled.\"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False\n\n def remaining(self, key: str) -> int:\n \"\"\"Return the number of further calls to allow(key) that would be admitted right now.\n \n This is a read-only query that does not record an attempt.\n \"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n # Window has elapsed, reset to full budget\n count = 0\n # allow() admits while count <= limit, so we can admit (limit - count + 1) more\n return max(0, self.limit - count + 1)\n```", "input_tokens": 1180, "output_tokens": 806, "cost_usd": 0.00521, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/limiter/window.py"]}} -{"scenario": "ratelimit-window-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "sonnet", "trial": 0, "output": "Looking at the `allow` method carefully:\n\n- It admits when `count <= self.limit` (so `limit + 1` requests are admitted per window)\n- After admission, count becomes `count + 1`\n- The window resets when `now - start >= self.window_seconds`\n\nFor `remaining(key)`, I need to compute how many more `allow` calls would return `True` without modifying state.\n\nLet me trace through:\n- Current in-window `count` = number of requests already admitted\n- `allow` returns `True` while `count <= self.limit`, then increments count\n- So it admits requests when the pre-call count is 0, 1, 2, ..., `limit` \u2014 that's `limit + 1` total admissions\n- Remaining = how many more values of count starting from current count are `<= self.limit`\n- That's `max(0, self.limit - count + 1)` when the window is still active\n\nFor a fresh key (never seen): `count = 0`, remaining = `limit - 0 + 1 = limit + 1` \u2713 \nFor a key with count = `limit`: remaining = `limit - limit + 1 = 1` \u2713 (one more would be admitted since `limit <= limit`) \nFor a key with count = `limit + 1`: remaining = `max(0, limit - (limit+1) + 1) = 0` \u2713 \n\nWindow reset: if the window has elapsed, treat it as a fresh start \u2192 remaining = `limit + 1`.\n\nFILE: code/limiter/window.py\n\n```python\nfrom .clock import Clock\n\n\nclass FixedWindowLimiter:\n \"\"\"A per-key fixed-window rate limiter.\n\n Each key gets a window of `window_seconds`. The first request for a key opens its window;\n once the window elapses the counter resets on the next request.\n \"\"\"\n\n def __init__(self, limit: int, window_seconds: float, clock: Clock) -> None:\n self.limit = limit\n self.window_seconds = window_seconds\n self.clock = clock\n # key -> [window_start, count_used_in_window]\n self._buckets: dict[str, list[float]] = {}\n\n def allow(self, key: str) -> bool:\n \"\"\"Record an attempt for `key`; return True if it is admitted, False if throttled.\"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False\n\n def remaining(self, key: str) -> int:\n \"\"\"Return the number of further allow(key) calls that would be admitted right now.\n\n This is a read-only query; it does not record an attempt or modify any state.\n For a key that has never been seen, or whose window has elapsed, returns the full\n per-window budget (limit + 1). For a key whose window is exhausted returns 0.\n \"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n # Window has elapsed; next allow() would reset to a fresh window\n count = 0\n return max(0, self.limit - count + 1)\n```", "input_tokens": 1181, "output_tokens": 868, "cost_usd": 0.016563, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/limiter/window.py"]}} -{"scenario": "ratelimit-window-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "sonnet", "trial": 1, "output": "Looking at the `allow` method carefully:\n\n- It admits when `count <= limit` (incrementing count and returning `True`)\n- It rejects when `count > limit` (returning `False`)\n\nSo the remaining budget is `limit - count + 1` when the window is active (clamped to 0 if exhausted), since:\n- At count=0: `limit+1` calls remaining (0,1,...,limit all satisfy `count <= limit`)\n- At count=limit: 1 call remaining\n- At count=limit+1: 0 calls remaining\n\nFor a key never seen, `count=0` so remaining = `limit + 1`.\n\nWhen the window has elapsed, it resets to `count=0`, so remaining = `limit + 1`.\n\nFILE: code/limiter/window.py\n\n```python\nfrom .clock import Clock\n\n\nclass FixedWindowLimiter:\n \"\"\"A per-key fixed-window rate limiter.\n\n Each key gets a window of `window_seconds`. The first request for a key opens its window;\n once the window elapses the counter resets on the next request.\n \"\"\"\n\n def __init__(self, limit: int, window_seconds: float, clock: Clock) -> None:\n self.limit = limit\n self.window_seconds = window_seconds\n self.clock = clock\n # key -> [window_start, count_used_in_window]\n self._buckets: dict[str, list[float]] = {}\n\n def allow(self, key: str) -> bool:\n \"\"\"Record an attempt for `key`; return True if it is admitted, False if throttled.\"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False\n\n def remaining(self, key: str) -> int:\n \"\"\"Return the number of further allow(key) calls that would be admitted right now.\n\n This is a read-only query; it does not record an attempt or mutate any state.\n For a key never seen, or whose window has elapsed, returns the full per-window\n budget (limit + 1). For a key whose window is exhausted returns 0.\n \"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n # Window has elapsed; next allow() would reset to count=0\n count = 0\n # allow() admits while count <= limit, so admitted calls remaining = limit - count + 1,\n # clamped to 0 when count already exceeds limit.\n return max(0, self.limit - count + 1)\n```", "input_tokens": 1181, "output_tokens": 717, "cost_usd": 0.014298000000000002, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/limiter/window.py"]}} -{"scenario": "ratelimit-window-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "sonnet", "trial": 2, "output": "Looking at the `allow` method carefully:\n\n- It admits when `count <= self.limit` (inclusive), incrementing count\n- It rejects when `count > self.limit`\n- So a window starting at count=0 admits requests until count exceeds `self.limit`\n- The number of admitted requests per window is `self.limit + 1` (counts 0, 1, ..., limit are all admitted)\n\nFor `remaining(key)`, I need to figure out how many more `allow` calls would return `True` right now without mutating state.\n\nGiven current `count` in the window:\n- `allow` will admit while `count <= self.limit`\n- After each admission, count increments\n- Starting from current `count`, it will admit at counts: `count`, `count+1`, ..., `limit`\n- That's `limit - count + 1` admissions, if `count <= limit`\n- If `count > limit`, remaining is 0\n\nFor a key never seen: `count = 0`, so remaining = `limit - 0 + 1 = limit + 1`. \u2713\n\nFor a key whose window is exhausted (count = limit + 1 or more): `count > limit`, remaining = 0. \u2713\n\nI also need to handle window expiry the same way `allow` does: if the window has elapsed, the key effectively resets to count=0, giving remaining = `limit + 1`.\n\nFILE: code/limiter/window.py\n\n```python\nfrom .clock import Clock\n\n\nclass FixedWindowLimiter:\n \"\"\"A per-key fixed-window rate limiter.\n\n Each key gets a window of `window_seconds`. The first request for a key opens its window;\n once the window elapses the counter resets on the next request.\n \"\"\"\n\n def __init__(self, limit: int, window_seconds: float, clock: Clock) -> None:\n self.limit = limit\n self.window_seconds = window_seconds\n self.clock = clock\n # key -> [window_start, count_used_in_window]\n self._buckets: dict[str, list[float]] = {}\n\n def allow(self, key: str) -> bool:\n \"\"\"Record an attempt for `key`; return True if it is admitted, False if throttled.\"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False\n\n def remaining(self, key: str) -> int:\n \"\"\"Return the number of further allow(key) calls that would be admitted right now.\n\n This is a read-only query; it does not record an attempt or mutate any state.\n For a key never seen, or whose window has elapsed, returns the full budget (limit + 1).\n For a key whose window is exhausted returns 0.\n \"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n # Window has elapsed; next allow() would reset to count=0\n count = 0\n if count > self.limit:\n return 0\n return self.limit - count + 1\n```", "input_tokens": 1181, "output_tokens": 837, "cost_usd": 0.016098, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/limiter/window.py"]}} -{"scenario": "ratelimit-window-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "sonnet", "trial": 3, "output": "Looking at the `allow` method carefully:\n\n- It admits a request when `count <= self.limit` (inclusive), incrementing count\n- It rejects when `count > self.limit`\n\nSo the maximum count that gets admitted is `self.limit + 1` (when count goes from `self.limit` to `self.limit + 1`). Requests are rejected once count is already `> self.limit`, i.e. count is `self.limit + 1` or more.\n\nFor `remaining`, I need to simulate what `allow` would do without mutating state:\n\n- Get current `start, count` (or default `now, 0` for unseen keys)\n- If the window has elapsed, the count resets to 0 and a fresh window opens, so remaining = `self.limit + 1`\n- Otherwise, remaining = how many more `allow` calls would return `True` = `max(0, self.limit + 1 - count)`\n\nLet me verify: if `count = 0`, `allow` admits (since `0 <= limit`), so remaining = `limit + 1`. Formula: `limit + 1 - 0 = limit + 1`. \u2713\n\nIf `count = limit`, `allow` admits (since `limit <= limit`), remaining = 1. Formula: `limit + 1 - limit = 1`. \u2713\n\nIf `count = limit + 1`, `allow` rejects (since `limit + 1 > limit`), remaining = 0. Formula: `max(0, limit + 1 - (limit+1)) = 0`. \u2713\n\nFor an unseen key, default is `(now, 0)`, window hasn't elapsed (elapsed = 0 < window_seconds assuming window_seconds > 0), so remaining = `limit + 1 - 0 = limit + 1`. \u2713\n\nFILE: code/limiter/window.py\n\n```python\nfrom .clock import Clock\n\n\nclass FixedWindowLimiter:\n \"\"\"A per-key fixed-window rate limiter.\n\n Each key gets a window of `window_seconds`. The first request for a key opens its window;\n once the window elapses the counter resets on the next request.\n \"\"\"\n\n def __init__(self, limit: int, window_seconds: float, clock: Clock) -> None:\n self.limit = limit\n self.window_seconds = window_seconds\n self.clock = clock\n # key -> [window_start, count_used_in_window]\n self._buckets: dict[str, list[float]] = {}\n\n def allow(self, key: str) -> bool:\n \"\"\"Record an attempt for `key`; return True if it is admitted, False if throttled.\"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False\n\n def remaining(self, key: str) -> int:\n \"\"\"Return the number of further allow(key) calls that would be admitted right now.\n\n This is a read-only query; it does not record an attempt or mutate any state.\n For a key never seen (or whose window has elapsed), returns the full per-window\n budget (limit + 1). For a key whose window is exhausted, returns 0.\n \"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n # Window has elapsed; next allow() would reset count to 0 and admit freely.\n count = 0\n return max(0, self.limit + 1 - count)\n```", "input_tokens": 1181, "output_tokens": 941, "cost_usd": 0.017658, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/limiter/window.py"]}} -{"scenario": "ratelimit-window-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "sonnet", "trial": 4, "output": "Looking at the `allow` method carefully:\n\n- It admits a request when `count <= self.limit` (inclusive), incrementing count and returning `True`\n- It rejects when `count > self.limit`, returning `False`\n\nSo the maximum count that still gets admitted is `self.limit`, meaning requests are admitted for count values 0, 1, 2, ..., `self.limit` \u2014 that's `limit + 1` total admissions per window.\n\nFor `remaining`, I need to compute how many more `allow` calls would return `True` right now without modifying state:\n\n- If the window has elapsed (or key is unseen), count effectively resets to 0, so remaining = `limit + 1` (all of 0..limit would be admitted)\n- Otherwise, remaining = `max(0, self.limit - count + 1)` \u2014 the count values still available from `count` up to `limit` inclusive\n\nLet me verify with examples:\n- `count = 0`: remaining = `limit - 0 + 1 = limit + 1` \u2713 (can admit at count 0,1,...,limit)\n- `count = limit`: remaining = `limit - limit + 1 = 1` \u2713 (one more admission at count=limit, then count becomes limit+1 which is rejected)\n- `count = limit + 1`: remaining = `max(0, limit - (limit+1) + 1) = max(0, 0) = 0` \u2713 (rejected)\n- `count = limit + 2`: remaining = `max(0, -1) = 0` \u2713\n\nFILE: code/limiter/window.py\n\n```python\nfrom .clock import Clock\n\n\nclass FixedWindowLimiter:\n \"\"\"A per-key fixed-window rate limiter.\n\n Each key gets a window of `window_seconds`. The first request for a key opens its window;\n once the window elapses the counter resets on the next request.\n \"\"\"\n\n def __init__(self, limit: int, window_seconds: float, clock: Clock) -> None:\n self.limit = limit\n self.window_seconds = window_seconds\n self.clock = clock\n # key -> [window_start, count_used_in_window]\n self._buckets: dict[str, list[float]] = {}\n\n def allow(self, key: str) -> bool:\n \"\"\"Record an attempt for `key`; return True if it is admitted, False if throttled.\"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False\n\n def remaining(self, key: str) -> int:\n \"\"\"Return how many further allow(key) calls would be admitted right now.\n\n This is a read-only query; it does not record an attempt or modify any state.\n Consistent with allow's inclusive admission test (count <= limit), a fresh window\n has limit + 1 remaining admissions, and the budget reaches 0 once count exceeds limit.\n \"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n # Window has elapsed (or key unseen): full budget available\n return self.limit + 1\n return max(0, self.limit - count + 1)\n```", "input_tokens": 1181, "output_tokens": 881, "cost_usd": 0.016758000000000002, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/limiter/window.py"]}} -{"scenario": "ratelimit-window-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "sonnet", "trial": 5, "output": "Looking at the `allow` method carefully:\n\n- It admits when `count <= self.limit` (increments and returns `True`)\n- It rejects when `count > self.limit` (returns `False`)\n\nSo the total admitted per window is `limit + 1` requests (counts 0, 1, 2, ..., limit are all admitted).\n\nFor `remaining`, I need to figure out how many more `allow` calls would return `True` right now, without modifying state.\n\nGiven current `count` in window:\n- Each call that gets admitted increments count by 1\n- Calls are admitted while `count <= limit` (using the count *before* incrementing)\n- So remaining admits = number of values in `{count, count+1, ..., limit}` = `max(0, limit - count + 1)`\n\nFor a key never seen: `count = 0`, so `remaining = limit - 0 + 1 = limit + 1` \u2713 \nFor a key at exactly `limit`: `remaining = limit - limit + 1 = 1` (one more admit) \u2713 \nFor a key at `limit + 1` (exhausted): `remaining = max(0, limit - (limit+1) + 1) = max(0, 0) = 0` \u2713\n\nAlso need to handle the window expiry: if the window has elapsed, the next `allow` would reset `count` to 0, so `remaining = limit + 1`.\n\nFILE: code/limiter/window.py\n\n```python\nfrom .clock import Clock\n\n\nclass FixedWindowLimiter:\n \"\"\"A per-key fixed-window rate limiter.\n\n Each key gets a window of `window_seconds`. The first request for a key opens its window;\n once the window elapses the counter resets on the next request.\n \"\"\"\n\n def __init__(self, limit: int, window_seconds: float, clock: Clock) -> None:\n self.limit = limit\n self.window_seconds = window_seconds\n self.clock = clock\n # key -> [window_start, count_used_in_window]\n self._buckets: dict[str, list[float]] = {}\n\n def allow(self, key: str) -> bool:\n \"\"\"Record an attempt for `key`; return True if it is admitted, False if throttled.\"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False\n\n def remaining(self, key: str) -> int:\n \"\"\"Return the number of further allow(key) calls that would be admitted right now.\n\n This is a read-only query; it does not record an attempt or modify any state.\n For a key never seen, or whose window has elapsed, returns the full budget (limit + 1).\n For a key whose window is exhausted returns 0.\n \"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n # Window has elapsed; the next allow() would reset count to 0\n count = 0\n return max(0, self.limit - count + 1)\n```", "input_tokens": 1181, "output_tokens": 842, "cost_usd": 0.016173, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/limiter/window.py"]}} -{"scenario": "ratelimit-window-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "sonnet", "trial": 6, "output": "Looking at the `allow` method carefully:\n\n- It admits when `count <= self.limit` (so admits `limit + 1` requests total per window)\n- After admission, count becomes `count + 1`\n- It rejects when `count > self.limit`\n\nFor `remaining`, I need to figure out how many more `allow` calls would return `True` given the current state.\n\nIf the current in-window count is `count`, then `allow` admits while `count <= limit`. So requests are admitted at counts 0, 1, 2, ..., `limit` \u2014 that's `limit + 1` admissions total.\n\nThe number of remaining admissions is `max(0, limit + 1 - count)`.\n\nBut I also need to handle the window reset case: if the window has elapsed, the count resets to 0, so remaining would be `limit + 1`.\n\nFor a key never seen, `_buckets` has no entry, so it's equivalent to count=0, giving `limit + 1` remaining \u2014 consistent with the \"full per-window budget\" requirement.\n\nFILE: code/limiter/window.py\n\n```python\nfrom .clock import Clock\n\n\nclass FixedWindowLimiter:\n \"\"\"A per-key fixed-window rate limiter.\n\n Each key gets a window of `window_seconds`. The first request for a key opens its window;\n once the window elapses the counter resets on the next request.\n \"\"\"\n\n def __init__(self, limit: int, window_seconds: float, clock: Clock) -> None:\n self.limit = limit\n self.window_seconds = window_seconds\n self.clock = clock\n # key -> [window_start, count_used_in_window]\n self._buckets: dict[str, list[float]] = {}\n\n def allow(self, key: str) -> bool:\n \"\"\"Record an attempt for `key`; return True if it is admitted, False if throttled.\"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False\n\n def remaining(self, key: str) -> int:\n \"\"\"Return the number of further allow(key) calls that would be admitted right now.\n\n This is a read-only query; it does not record an attempt or mutate any state.\n For a key never seen or whose window has elapsed, returns the full per-window budget\n (limit + 1). For a key whose window is exhausted, returns 0.\n \"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n count = 0\n return max(0, self.limit + 1 - count)\n```", "input_tokens": 1181, "output_tokens": 733, "cost_usd": 0.014537999999999999, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/limiter/window.py"]}} -{"scenario": "ratelimit-window-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "sonnet", "trial": 7, "output": "Looking at the `allow` method carefully:\n\n- It admits a request when `count <= self.limit` (inclusive), then increments count\n- So the maximum count after admission is `self.limit + 1`\n- A request is throttled when `count > self.limit` (i.e., `count >= self.limit + 1`)\n\nFor `remaining(key)`, I need to figure out how many more `allow` calls would return `True` given the current state, without modifying anything.\n\nLet me trace through:\n- If current `count` is 0: admits while count \u2264 limit, so requests at count=0,1,2,...,limit all admitted \u2192 `limit + 1` admissions remaining\n- If current `count` is `k` where `k <= limit`: admits at count=k, k+1, ..., limit \u2192 `limit - k + 1` admissions remaining\n- If current `count` is `k` where `k > limit`: 0 admissions remaining\n\nAlso need to handle window expiry: if the window has elapsed, the next `allow` call would reset count to 0, giving full budget back.\n\nThe formula for remaining (when window is still active): `max(0, self.limit - count + 1)`\n\nWhen the window has expired: `self.limit + 1` (full budget).\n\nFor a key never seen: `self.limit + 1` (full budget).\n\nFILE: code/limiter/window.py\n\n```python\nfrom .clock import Clock\n\n\nclass FixedWindowLimiter:\n \"\"\"A per-key fixed-window rate limiter.\n\n Each key gets a window of `window_seconds`. The first request for a key opens its window;\n once the window elapses the counter resets on the next request.\n \"\"\"\n\n def __init__(self, limit: int, window_seconds: float, clock: Clock) -> None:\n self.limit = limit\n self.window_seconds = window_seconds\n self.clock = clock\n # key -> [window_start, count_used_in_window]\n self._buckets: dict[str, list[float]] = {}\n\n def allow(self, key: str) -> bool:\n \"\"\"Record an attempt for `key`; return True if it is admitted, False if throttled.\"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False\n\n def remaining(self, key: str) -> int:\n \"\"\"Return the number of further allow(key) calls that would be admitted right now.\n\n This is a read-only query; it does not record an attempt or modify any state.\n For a key never seen or whose window has elapsed, returns the full per-window budget\n (limit + 1). For a key with an active window, returns how many more calls would be\n admitted before throttling begins.\n \"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n # Window has expired; next allow() would reset to a fresh window\n return self.limit + 1\n return max(0, self.limit - count + 1)\n```", "input_tokens": 1181, "output_tokens": 837, "cost_usd": 0.016098, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/limiter/window.py"]}} -{"scenario": "ratelimit-window-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "sonnet", "trial": 8, "output": "Looking at the `allow` method carefully:\n\n- It admits when `count <= self.limit` (inclusive), so a key with `count=0` can admit up to and including `count=limit`, meaning `limit + 1` total admissions per window.\n- The remaining budget for a key with current `count` in its window is: how many more times can `allow` return `True`? Since it admits while `count <= limit`, and each admission increments count, the remaining admits = `max(0, limit - count + 1)`.\n\nLet me verify:\n- Fresh key: `count=0`, remaining = `limit - 0 + 1 = limit + 1` \u2713 (all budget available)\n- After 1 admit: `count=1`, remaining = `limit` \u2713\n- After `limit+1` admits: `count=limit+1`, remaining = `limit - (limit+1) + 1 = 0` \u2713 (exhausted)\n\nAlso need to handle window expiry: if the window has elapsed, the key effectively resets to `count=0`, so remaining = `limit + 1`.\n\nFILE: code/limiter/window.py\n\n```python\nfrom .clock import Clock\n\n\nclass FixedWindowLimiter:\n \"\"\"A per-key fixed-window rate limiter.\n\n Each key gets a window of `window_seconds`. The first request for a key opens its window;\n once the window elapses the counter resets on the next request.\n \"\"\"\n\n def __init__(self, limit: int, window_seconds: float, clock: Clock) -> None:\n self.limit = limit\n self.window_seconds = window_seconds\n self.clock = clock\n # key -> [window_start, count_used_in_window]\n self._buckets: dict[str, list[float]] = {}\n\n def allow(self, key: str) -> bool:\n \"\"\"Record an attempt for `key`; return True if it is admitted, False if throttled.\"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False\n\n def remaining(self, key: str) -> int:\n \"\"\"Return the number of further allow(key) calls that would be admitted right now.\n\n This is a read-only query; it does not record an attempt or modify any state.\n For a key that has never been seen, or whose window has elapsed, returns the full\n per-window budget (limit + 1). For a key whose window is exhausted, returns 0.\n \"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n # Window has elapsed; on the next allow() call it would reset to count=0.\n count = 0\n return max(0, self.limit - count + 1)\n```", "input_tokens": 1181, "output_tokens": 770, "cost_usd": 0.015092999999999999, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/limiter/window.py"]}} -{"scenario": "ratelimit-window-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "sonnet", "trial": 9, "output": "Looking at the `allow` method carefully:\n\n- It admits when `count <= self.limit` (inclusive), so the first request goes in with count=0, and it keeps admitting until count equals limit, then increments to limit+1 and returns True for that last one... let me trace through more carefully.\n\nStarting state for a new key: `count = 0`\n- Request 1: `count=0`, `0 <= limit` \u2192 True, store count=1\n- Request 2: `count=1`, `1 <= limit` \u2192 True (if limit\u22651), store count=2\n- ...\n- Request N+1: `count=N`, `N <= limit` \u2192 True, store count=N+1\n- Request N+2: `count=N+1`, `N+1 <= limit`? No (N+1 > N) \u2192 False\n\nSo admitted = limit+1 requests total, and when count is currently `c`, the remaining admitted calls = `(limit + 1) - c`.\n\nBut I need to handle the window reset case too: if the window has elapsed, the next `allow` call would reset count to 0, giving a full budget of `limit + 1`.\n\nFILE: code/limiter/window.py\n\n```python\nfrom .clock import Clock\n\n\nclass FixedWindowLimiter:\n \"\"\"A per-key fixed-window rate limiter.\n\n Each key gets a window of `window_seconds`. The first request for a key opens its window;\n once the window elapses the counter resets on the next request.\n \"\"\"\n\n def __init__(self, limit: int, window_seconds: float, clock: Clock) -> None:\n self.limit = limit\n self.window_seconds = window_seconds\n self.clock = clock\n # key -> [window_start, count_used_in_window]\n self._buckets: dict[str, list[float]] = {}\n\n def allow(self, key: str) -> bool:\n \"\"\"Record an attempt for `key`; return True if it is admitted, False if throttled.\"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False\n\n def remaining(self, key: str) -> int:\n \"\"\"Return the number of further allow(key) calls that would be admitted right now.\n\n This is a read-only query; it does not record an attempt or modify any state.\n For a key never seen or whose window has elapsed, returns the full budget (limit + 1).\n For a key whose window is exhausted, returns 0.\n \"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n # Window has elapsed; next allow() would reset to count=0 giving full budget\n count = 0\n # allow() admits while count <= limit, so admitted calls remaining = (limit + 1) - count\n admitted_remaining = (self.limit + 1) - count\n return max(admitted_remaining, 0)\n```", "input_tokens": 1181, "output_tokens": 811, "cost_usd": 0.015708, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/limiter/window.py"]}} -{"scenario": "ratelimit-window-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "opus", "trial": 0, "output": "Looking at `allow`, the admission test is `count <= self.limit`. So a request is admitted while the current in-window count is `<= limit`, meaning the budget is `limit + 1` admissions per window (counts 0,1,...,limit all admitted; count `limit+1` rejected).\n\nFor `remaining`, I need to compute how many further `allow` calls would succeed given the current state, accounting for window expiry, without mutating state.\n\nLet me trace the logic carefully:\n- For a never-seen key: effective count is 0, so admissions remaining = `limit + 1 - 0 = limit + 1`.\n- If the window has elapsed (`now - start >= window_seconds`), the count resets to 0, so remaining = `limit + 1`.\n- Otherwise, with current `count`, the number of further admissions is `limit + 1 - count`, clamped at 0.\n\nFILE: code/limiter/window.py\n\n```python\nfrom .clock import Clock\n\n\nclass FixedWindowLimiter:\n \"\"\"A per-key fixed-window rate limiter.\n\n Each key gets a window of `window_seconds`. The first request for a key opens its window;\n once the window elapses the counter resets on the next request.\n \"\"\"\n\n def __init__(self, limit: int, window_seconds: float, clock: Clock) -> None:\n self.limit = limit\n self.window_seconds = window_seconds\n self.clock = clock\n # key -> [window_start, count_used_in_window]\n self._buckets: dict[str, list[float]] = {}\n\n def allow(self, key: str) -> bool:\n \"\"\"Record an attempt for `key`; return True if it is admitted, False if throttled.\"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False\n\n def remaining(self, key: str) -> int:\n \"\"\"Return how many further allow(key) calls would be admitted right now.\n\n Read-only: does not record an attempt or mutate any state. Exactly mirrors the\n admission logic in allow(), including window expiry and the inclusive bound.\n \"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n # Window has elapsed; the next allow() resets the count to 0.\n count = 0\n # allow() admits while count <= limit, so budget per window is limit + 1.\n remaining = (self.limit + 1) - count\n return remaining if remaining > 0 else 0\n```", "input_tokens": 1470, "output_tokens": 896, "cost_usd": 0.02975, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/limiter/window.py"]}} -{"scenario": "ratelimit-window-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "opus", "trial": 1, "output": "Looking at `allow`, I need to determine exactly how many future `allow` calls would return `True`.\n\nLet me analyze `allow`'s admission logic:\n- It admits when `count <= limit` (where `count` is the current in-window count before this attempt).\n- After admitting, count becomes `count + 1`.\n- So admitted values of `count` are `0, 1, ..., limit`, meaning the requests numbered 1 through `limit + 1` succeed; the `(limit + 2)`-th (when count is `limit + 1 > limit`) is rejected.\n\nFor `remaining`, I must account for window expiry (read-only). Given current `now`:\n- If the key is unseen, or its window has elapsed, the effective count resets to `0`.\n- Otherwise the effective count is the stored count.\n\nFuture admitted calls correspond to count values `count, count+1, ...` that are `<= limit`. The number of such values is `limit + 1 - count`, clamped to a minimum of 0.\n\nFILE: code/limiter/window.py\n```python\nfrom .clock import Clock\n\n\nclass FixedWindowLimiter:\n \"\"\"A per-key fixed-window rate limiter.\n\n Each key gets a window of `window_seconds`. The first request for a key opens its window;\n once the window elapses the counter resets on the next request.\n \"\"\"\n\n def __init__(self, limit: int, window_seconds: float, clock: Clock) -> None:\n self.limit = limit\n self.window_seconds = window_seconds\n self.clock = clock\n # key -> [window_start, count_used_in_window]\n self._buckets: dict[str, list[float]] = {}\n\n def allow(self, key: str) -> bool:\n \"\"\"Record an attempt for `key`; return True if it is admitted, False if throttled.\"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False\n\n def remaining(self, key: str) -> int:\n \"\"\"Return how many further allow(key) calls would be admitted right now, read-only.\n\n Consistent with allow's inclusive admission test (count <= limit), so a fresh key has\n budget limit + 1, and an exhausted window reports 0. Does not record an attempt.\n \"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n count = 0\n # allow admits while the pre-attempt count is <= limit, i.e. for count values\n # count, count+1, ..., limit. That is (limit + 1 - count) further admissions.\n remaining = self.limit + 1 - int(count)\n return remaining if remaining > 0 else 0\n```", "input_tokens": 1470, "output_tokens": 956, "cost_usd": 0.03125, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/limiter/window.py"]}} -{"scenario": "ratelimit-window-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "opus", "trial": 2, "output": "Looking at the `allow` method to determine exact admission behavior:\n\n- It admits when `count <= self.limit` (inclusive).\n- The first `allow` for a key uses `count = 0`, increments to 1.\n- Admissions happen at counts `0, 1, ..., limit`, which is `limit + 1` admissions.\n- After window expiry (`now - start >= window_seconds`), the count resets to 0.\n\nSo `remaining` must replicate the window-reset logic (read-only) and compute how many more counts from the current count satisfy `count <= limit`.\n\nGiven current `count`, the next admitted attempts use counts `count, count+1, ...` while each is `<= limit`. The number of admissible values is `limit - count + 1`, clamped to 0 minimum. After a reset, `count = 0`, giving `limit + 1`.\n\nFILE: code/limiter/window.py\n\n```python\nfrom .clock import Clock\n\n\nclass FixedWindowLimiter:\n \"\"\"A per-key fixed-window rate limiter.\n\n Each key gets a window of `window_seconds`. The first request for a key opens its window;\n once the window elapses the counter resets on the next request.\n \"\"\"\n\n def __init__(self, limit: int, window_seconds: float, clock: Clock) -> None:\n self.limit = limit\n self.window_seconds = window_seconds\n self.clock = clock\n # key -> [window_start, count_used_in_window]\n self._buckets: dict[str, list[float]] = {}\n\n def allow(self, key: str) -> bool:\n \"\"\"Record an attempt for `key`; return True if it is admitted, False if throttled.\"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False\n\n def remaining(self, key: str) -> int:\n \"\"\"Return how many further calls to `allow(key)` would be admitted right now.\n\n Read-only: does not record an attempt and does not mutate any bucket. This mirrors\n `allow`'s inclusive admission test (`count <= limit`) and its window-reset logic.\n \"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n # The window has elapsed; the next allow() would reset the counter to 0.\n count = 0\n # allow() admits while the current count is <= limit, incrementing each time.\n # From the current count, the admissible counts are count, count+1, ..., limit,\n # which is (limit - count + 1) values. Never report a negative budget.\n return max(0, self.limit - count + 1)\n```", "input_tokens": 1470, "output_tokens": 932, "cost_usd": 0.030650000000000004, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/limiter/window.py"]}} -{"scenario": "ratelimit-window-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "opus", "trial": 3, "output": "Looking at `allow`, I need to determine exactly how many further admissions are possible.\n\nIn `allow`, after handling window expiry, admission succeeds when `count <= self.limit`. Each admission increments `count` by 1. So starting from a given `count`, the admitted requests are those where the count value (before increment) is `count, count+1, ...` up to and including `self.limit`.\n\nThe number of values `c` with `count <= c <= self.limit` is `self.limit - count + 1` (when `count <= self.limit`), clamped to 0 below.\n\nI also need to apply the same window-reset logic as `allow` (read-only): if the window has elapsed, the effective count is 0.\n\nFor a never-seen key, `count = 0`, giving `self.limit - 0 + 1 = limit + 1`, matching the documented full budget of `N + 1`.\n\nFILE: code/limiter/window.py\n\n```python\nfrom .clock import Clock\n\n\nclass FixedWindowLimiter:\n \"\"\"A per-key fixed-window rate limiter.\n\n Each key gets a window of `window_seconds`. The first request for a key opens its window;\n once the window elapses the counter resets on the next request.\n \"\"\"\n\n def __init__(self, limit: int, window_seconds: float, clock: Clock) -> None:\n self.limit = limit\n self.window_seconds = window_seconds\n self.clock = clock\n # key -> [window_start, count_used_in_window]\n self._buckets: dict[str, list[float]] = {}\n\n def allow(self, key: str) -> bool:\n \"\"\"Record an attempt for `key`; return True if it is admitted, False if throttled.\"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False\n\n def remaining(self, key: str) -> int:\n \"\"\"Return how many further calls to `allow(key)` would be admitted right now.\n\n Read-only: does not record an attempt and does not mutate state. The result is\n exactly consistent with `allow`: admission succeeds while the in-window count is\n less than or equal to `limit`, so the number of further admissions from a count of\n `count` is `limit - count + 1` (clamped at 0).\n \"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n count = 0\n remaining = self.limit - count + 1\n return remaining if remaining > 0 else 0\n```", "input_tokens": 1470, "output_tokens": 882, "cost_usd": 0.029400000000000003, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/limiter/window.py"]}} -{"scenario": "ratelimit-window-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "opus", "trial": 4, "output": "Looking at the `allow` logic to determine consistency:\n\n- `allow` admits when `count <= self.limit` (inclusive). So admitted counts are `0, 1, ..., limit`, meaning `limit + 1` admissions per window.\n- After window expiry, the count resets to 0.\n- `remaining` must compute how many further admissions remain given the current `(start, count)` without recording anything.\n\nIf the window has expired, the effective count is 0, so remaining is `limit + 1`. For a never-seen key, count is 0 too, so `limit + 1`. Otherwise remaining is `(limit + 1) - count`, floored at 0.\n\nFILE: code/limiter/window.py\n\n```python\nfrom .clock import Clock\n\n\nclass FixedWindowLimiter:\n \"\"\"A per-key fixed-window rate limiter.\n\n Each key gets a window of `window_seconds`. The first request for a key opens its window;\n once the window elapses the counter resets on the next request.\n \"\"\"\n\n def __init__(self, limit: int, window_seconds: float, clock: Clock) -> None:\n self.limit = limit\n self.window_seconds = window_seconds\n self.clock = clock\n # key -> [window_start, count_used_in_window]\n self._buckets: dict[str, list[float]] = {}\n\n def allow(self, key: str) -> bool:\n \"\"\"Record an attempt for `key`; return True if it is admitted, False if throttled.\"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False\n\n def remaining(self, key: str) -> int:\n \"\"\"Return how many further calls to allow(key) would be admitted right now.\n\n This is a read-only query: it does not record an attempt and does not mutate\n any bucket state. It is exactly consistent with how `allow` admits requests.\n \"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n # Window has elapsed; allow() would reset the count to 0 on next call.\n count = 0\n # allow() admits while count <= limit, i.e. for counts 0..limit inclusive,\n # which is (limit + 1) admissions per window.\n budget = self.limit + 1\n remaining = budget - count\n return remaining if remaining > 0 else 0\n```", "input_tokens": 1470, "output_tokens": 851, "cost_usd": 0.028625000000000005, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/limiter/window.py"]}} -{"scenario": "ratelimit-window-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "opus", "trial": 5, "output": "Looking at the `allow` logic:\n\n- It admits when `count <= limit`, recording `count + 1`.\n- The window resets when `now - start >= window_seconds`.\n\nSo per window, the admitted counts are `count = 0, 1, 2, ..., limit` (that's `limit + 1` admissions), and `count = limit + 1` is the first rejection.\n\nFor `remaining(key)`, I need to compute how many more `allow` calls would succeed, accounting for window reset, without recording anything.\n\nIf a key has effective `count` (after considering window reset), the number of remaining admissions is the number of values `c` with `count <= c <= limit`, i.e. `limit - count + 1`, clamped to be non-negative.\n\nFILE: code/limiter/window.py\n```python\nfrom .clock import Clock\n\n\nclass FixedWindowLimiter:\n \"\"\"A per-key fixed-window rate limiter.\n\n Each key gets a window of `window_seconds`. The first request for a key opens its window;\n once the window elapses the counter resets on the next request.\n \"\"\"\n\n def __init__(self, limit: int, window_seconds: float, clock: Clock) -> None:\n self.limit = limit\n self.window_seconds = window_seconds\n self.clock = clock\n # key -> [window_start, count_used_in_window]\n self._buckets: dict[str, list[float]] = {}\n\n def allow(self, key: str) -> bool:\n \"\"\"Record an attempt for `key`; return True if it is admitted, False if throttled.\"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False\n\n def remaining(self, key: str) -> int:\n \"\"\"Return how many further `allow(key)` calls would be admitted right now.\n\n This is a read-only query: it does not record an attempt and does not\n mutate any bucket state. It is exactly consistent with `allow`, which\n admits while the in-window count is `<= limit` (an inclusive bound), so\n a fresh window allows `limit + 1` admissions.\n \"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n # Window has elapsed; the next allow() would reset the count to 0.\n count = 0\n # allow() admits for count == 0, 1, ..., limit, i.e. (limit - count + 1)\n # further admissions. Clamp to a non-negative value for exhausted windows.\n remaining = self.limit - count + 1\n return remaining if remaining > 0 else 0\n```", "input_tokens": 1470, "output_tokens": 922, "cost_usd": 0.030400000000000003, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/limiter/window.py"]}} -{"scenario": "ratelimit-window-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "opus", "trial": 6, "output": "Looking at `allow`, I need to determine consistency with its actual admission logic.\n\nThe `allow` method admits when `count <= self.limit`. So it admits while count is 0, 1, ..., limit \u2014 that's `limit + 1` admissions per window. After window reset, count restarts at 0.\n\nFor `remaining`, I must compute how many further `allow` calls would return True, without recording anything, replicating `allow`'s window-reset logic.\n\nLet me trace: given current `(start, count)`:\n- If window elapsed (`now - start >= window_seconds`), the next `allow` resets to `count = 0`, then admits up to `limit + 1` calls \u2192 remaining = `limit + 1`.\n- Otherwise, with current `count`, the admissions left are those where the in-window count value satisfies `count <= limit`. The counts that will be admitted are `count, count+1, ..., limit`, which is `limit - count + 1` admissions (clamped at 0).\n\nFor a never-seen key, `count = 0`, window not elapsed (start = now), so remaining = `limit - 0 + 1` = `limit + 1`. That matches \"full per-window budget\".\n\nFILE: code/limiter/window.py\n```python\nfrom .clock import Clock\n\n\nclass FixedWindowLimiter:\n \"\"\"A per-key fixed-window rate limiter.\n\n Each key gets a window of `window_seconds`. The first request for a key opens its window;\n once the window elapses the counter resets on the next request.\n \"\"\"\n\n def __init__(self, limit: int, window_seconds: float, clock: Clock) -> None:\n self.limit = limit\n self.window_seconds = window_seconds\n self.clock = clock\n # key -> [window_start, count_used_in_window]\n self._buckets: dict[str, list[float]] = {}\n\n def allow(self, key: str) -> bool:\n \"\"\"Record an attempt for `key`; return True if it is admitted, False if throttled.\"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False\n\n def remaining(self, key: str) -> int:\n \"\"\"Return how many further `allow(key)` calls would be admitted right now,\n without recording an attempt. Read-only and exactly consistent with `allow`.\n \"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n # Next allow() would reset the window, restoring the full budget.\n count = 0\n # allow() admits while count <= limit, i.e. for counts\n # count, count+1, ..., limit -> (limit - count + 1) admissions.\n remaining = self.limit - count + 1\n if remaining < 0:\n return 0\n return remaining\n```", "input_tokens": 1470, "output_tokens": 984, "cost_usd": 0.03195, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/limiter/window.py"]}} -{"scenario": "ratelimit-window-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "opus", "trial": 7, "output": "Looking at `allow`, I need to determine exactly how many future `allow` calls would return `True`.\n\nLet me trace the admission logic. `allow` admits when `count <= limit`, then increments count. So the counts that get admitted are when count is `0, 1, 2, ..., limit` \u2014 that's `limit + 1` admissions per window (counts 0 through limit inclusive).\n\nFor `remaining(key)`, I must replicate `allow`'s logic without mutating state, including the window-reset behavior:\n\n- Get `(start, count)` for the key (default `(now, 0)`).\n- If the window has elapsed (`now - start >= window_seconds`), the next `allow` would reset to `count = 0`, so effectively `count = 0`.\n- The number of further admissions: from the current `count`, admissions happen while `count <= limit`, incrementing each time. So remaining admissions = `limit - count + 1`, clamped to `0` (can't be negative).\n\nFor a never-seen key: `count = 0`, remaining = `limit - 0 + 1 = limit + 1` (full per-window budget). \u2713\nFor an exhausted key: `count > limit`, remaining clamps to `0`. \u2713\n\nFILE: code/limiter/window.py\n```python\nfrom .clock import Clock\n\n\nclass FixedWindowLimiter:\n \"\"\"A per-key fixed-window rate limiter.\n\n Each key gets a window of `window_seconds`. The first request for a key opens its window;\n once the window elapses the counter resets on the next request.\n \"\"\"\n\n def __init__(self, limit: int, window_seconds: float, clock: Clock) -> None:\n self.limit = limit\n self.window_seconds = window_seconds\n self.clock = clock\n # key -> [window_start, count_used_in_window]\n self._buckets: dict[str, list[float]] = {}\n\n def allow(self, key: str) -> bool:\n \"\"\"Record an attempt for `key`; return True if it is admitted, False if throttled.\"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False\n\n def remaining(self, key: str) -> int:\n \"\"\"Return how many further `allow(key)` calls would be admitted right now.\n\n Read-only: does not record an attempt. Exactly consistent with `allow`, which admits\n while the in-window count is <= limit (an inclusive bound, so limit + 1 admissions per\n window). For an unseen key returns the full per-window budget; for an exhausted key\n returns 0.\n \"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n # The next allow() would reset this key's window.\n count = 0\n # allow() admits while count <= limit, incrementing each time, so the number of\n # remaining admissions is (limit - count + 1), clamped to be", "input_tokens": 1470, "output_tokens": 1024, "cost_usd": 0.03295, "ok": false, "misled": false, "detail": "no FILE blocks in output", "parsed": {}} -{"scenario": "ratelimit-window-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "opus", "trial": 8, "output": "Looking at `allow`, the admission test is `count <= self.limit`. A request is admitted when the current count is `<= limit`, and after admission count becomes `count + 1`.\n\nSo admissions happen for `count = 0, 1, 2, ..., limit` \u2014 that's `limit + 1` admissions per window. The remaining budget for a current count is the number of future values of count (starting from the current one) that satisfy `count <= limit`.\n\nFor a current `count`, remaining admissions = `(limit + 1) - count`, clamped to `[0, ...]`. I also need to account for window expiry (if the window has elapsed, the effective count resets to 0).\n\nFILE: code/limiter/window.py\n\n```python\nfrom .clock import Clock\n\n\nclass FixedWindowLimiter:\n \"\"\"A per-key fixed-window rate limiter.\n\n Each key gets a window of `window_seconds`. The first request for a key opens its window;\n once the window elapses the counter resets on the next request.\n \"\"\"\n\n def __init__(self, limit: int, window_seconds: float, clock: Clock) -> None:\n self.limit = limit\n self.window_seconds = window_seconds\n self.clock = clock\n # key -> [window_start, count_used_in_window]\n self._buckets: dict[str, list[float]] = {}\n\n def allow(self, key: str) -> bool:\n \"\"\"Record an attempt for `key`; return True if it is admitted, False if throttled.\"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False\n\n def remaining(self, key: str) -> int:\n \"\"\"Return how many further allow(key) calls would be admitted right now.\n\n Read-only: does not record an attempt and does not mutate any state. Exactly\n consistent with allow's inclusive admission test (count <= limit), which admits\n limit + 1 requests per window.\n \"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n count = 0\n remaining = (self.limit + 1) - count\n if remaining < 0:\n return 0\n return remaining\n```", "input_tokens": 1470, "output_tokens": 787, "cost_usd": 0.027025, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/limiter/window.py"]}} -{"scenario": "ratelimit-window-code", "task_type": "code", "tier": "T1", "condition": "C2", "model": "opus", "trial": 9, "output": "Looking at the `allow` logic to determine consistency:\n\n- `allow` admits when `count <= self.limit` (inclusive).\n- The first admitted request has `count = 0` (passes `0 <= limit`), then stored as `count + 1`.\n- Admitted while stored count is `0, 1, ..., limit` \u2014 that's `limit + 1` admissions.\n- The window also resets when `now - start >= window_seconds`.\n\nSo `remaining` must replicate this: handle the window-reset case (resetting count to 0), then compute how many more values of `count` satisfy `count <= limit` starting from the current count.\n\nNumber of admissible future calls = number of integers `c` with `current_count <= c <= limit`, which is `limit - current_count + 1`, clamped at 0.\n\nFor an unseen key, count is 0, giving `limit + 1` (the full per-window budget, consistent with the documented `N+1` contract).\n\nFILE: code/limiter/window.py\n\n```python\nfrom .clock import Clock\n\n\nclass FixedWindowLimiter:\n \"\"\"A per-key fixed-window rate limiter.\n\n Each key gets a window of `window_seconds`. The first request for a key opens its window;\n once the window elapses the counter resets on the next request.\n \"\"\"\n\n def __init__(self, limit: int, window_seconds: float, clock: Clock) -> None:\n self.limit = limit\n self.window_seconds = window_seconds\n self.clock = clock\n # key -> [window_start, count_used_in_window]\n self._buckets: dict[str, list[float]] = {}\n\n def allow(self, key: str) -> bool:\n \"\"\"Record an attempt for `key`; return True if it is admitted, False if throttled.\"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False\n\n def remaining(self, key: str) -> int:\n \"\"\"Return how many further calls to allow(key) would be admitted right now.\n\n Read-only: does not record an attempt. Exactly mirrors how allow() admits\n (inclusive bound: admitted while in-window count <= limit), and accounts\n for window expiry resetting the count.\n \"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n count = 0\n # allow() admits while count <= limit, i.e. for counts current..limit inclusive.\n admissible = self.limit - count + 1\n return admissible if admissible > 0 else 0\n```", "input_tokens": 1470, "output_tokens": 904, "cost_usd": 0.029950000000000004, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/limiter/window.py"]}} -{"scenario": "ratelimit-window-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "haiku", "trial": 0, "output": "Looking at the code, I need to understand the current behavior of `allow()`:\n\n1. On the first call for a key, it initializes with `(now, 0)`\n2. If the window has elapsed (`now - start >= self.window_seconds`), it resets to `(now, 0)`\n3. If `count <= self.limit`, it increments count and returns `True`\n4. Otherwise, it returns `False`\n\nThe bug in the documentation is that the condition is `count <= self.limit` (inclusive), not `count < limit` (exclusive). This means:\n- A limiter with `limit=N` will admit requests when `count` is 0, 1, 2, ..., N (that's N+1 requests total)\n- The (N+1)th request gets admitted (when count=N), and the (N+2)th gets rejected\n\nSo `remaining(key)` should return how many more requests can be admitted given the current state:\n- If the window has expired, return `limit + 1` (the full budget, since count will reset to 0)\n- If the window is still active, return `max(0, limit - count + 1)` (the remaining admits)\n\nLet me trace through an example with `limit=2`:\n- Request 1: count=0, `0 <= 2`? Yes, admit (count becomes 1)\n- Request 2: count=1, `1 <= 2`? Yes, admit (count becomes 2)\n- Request 3: count=2, `2 <= 2`? Yes, admit (count becomes 3)\n- Request 4: count=3, `3 <= 2`? No, reject\n\nSo with `limit=2`, we admit 3 requests. `remaining()` after 0 admits should return 3, after 1 admit should return 2, after 2 admits should return 1, after 3 admits should return 0.\n\nFILE: code/limiter/window.py\n\n```python\nfrom .clock import Clock\n\n\nclass FixedWindowLimiter:\n \"\"\"A per-key fixed-window rate limiter.\n\n Each key gets a window of `window_seconds`. The first request for a key opens its window;\n once the window elapses the counter resets on the next request.\n \"\"\"\n\n def __init__(self, limit: int, window_seconds: float, clock: Clock) -> None:\n self.limit = limit\n self.window_seconds = window_seconds\n self.clock = clock\n # key -> [window_start, count_used_in_window]\n self._buckets: dict[str, list[float]] = {}\n\n def allow(self, key: str) -> bool:\n \"\"\"Record an attempt for `key`; return True if it is admitted, False if throttled.\"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False\n\n def remaining(self, key: str) -> int:\n \"\"\"Return the number of further calls to allow(key) that would be admitted right now.\n \n This is a read-only query that does not record an attempt. It is consistent with\n the actual admission logic in allow().\n \"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n # Window has elapsed, reset to full budget\n return self.limit + 1\n # Window is active, return remaining admits\n return max(0, self.limit - count + 1)\n```", "input_tokens": 1649, "output_tokens": 931, "cost_usd": 0.006304000000000001, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/limiter/window.py"]}} -{"scenario": "ratelimit-window-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "haiku", "trial": 1, "output": "Looking at the code, I need to understand the current behavior of `allow`:\n\n1. When a key is first seen, it gets `(now, 0)` as its initial state\n2. If the window has elapsed (`now - start >= self.window_seconds`), reset to `(now, 0)`\n3. If `count <= self.limit`, admit the request and increment count\n4. Otherwise, reject the request\n\nThe documentation claims the test is exclusive (`count < limit`), but the code shows `count <= self.limit`. This means:\n- A limiter with `limit=N` will admit requests when count is 0, 1, 2, ..., N (that's N+1 requests total)\n- The (N+2)th request gets rejected\n\nTo implement `remaining`, I need to:\n1. Get the current state for the key (or default to `(now, 0)`)\n2. Check if the window has expired and reset if needed\n3. Return how many more requests would be admitted: `max(0, self.limit - count + 1)`\n\nLet me trace through an example with `limit=2`:\n- count=0: `0 <= 2` \u2713 admit, increment to 1. remaining should be 2\n- count=1: `1 <= 2` \u2713 admit, increment to 2. remaining should be 1 \n- count=2: `2 <= 2` \u2713 admit, increment to 3. remaining should be 0\n- count=3: `3 <= 2` \u2717 reject. remaining should be 0\n\nSo `remaining = max(0, self.limit - count + 1)` when `count <= self.limit`, and `remaining = 0` when `count > self.limit`.\n\nActually, simpler: `remaining = max(0, self.limit - count + 1)` works for all cases since if `count > self.limit`, then `self.limit - count + 1 <= 0`.\n\nFILE: code/limiter/window.py\n\n```python\nfrom .clock import Clock\n\n\nclass FixedWindowLimiter:\n \"\"\"A per-key fixed-window rate limiter.\n\n Each key gets a window of `window_seconds`. The first request for a key opens its window;\n once the window elapses the counter resets on the next request.\n \"\"\"\n\n def __init__(self, limit: int, window_seconds: float, clock: Clock) -> None:\n self.limit = limit\n self.window_seconds = window_seconds\n self.clock = clock\n # key -> [window_start, count_used_in_window]\n self._buckets: dict[str, list[float]] = {}\n\n def allow(self, key: str) -> bool:\n \"\"\"Record an attempt for `key`; return True if it is admitted, False if throttled.\"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False\n\n def remaining(self, key: str) -> int:\n \"\"\"Return the number of further calls to allow(key) that would be admitted right now.\n \n This is a read-only query that does not record an attempt. It reflects the current\n window state for the key.\n \"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n return max(0, self.limit - count + 1)\n```", "input_tokens": 1649, "output_tokens": 918, "cost_usd": 0.006239, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/limiter/window.py"]}} -{"scenario": "ratelimit-window-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "haiku", "trial": 2, "output": "Looking at the code, I need to understand the current behavior of `allow()`:\n\n1. It gets the current time `now` from the clock\n2. It retrieves the bucket for the key, or initializes it with `(now, 0)` if new\n3. If the window has elapsed (`now - start >= self.window_seconds`), it resets to `(now, 0)`\n4. It checks if `count <= self.limit` (note: this is `<=`, not `<`)\n5. If the check passes, it increments the count and returns `True`\n6. Otherwise, it returns `False`\n\nThe documentation claims the test should be exclusive (`count < limit`), but the code has `count <= self.limit`. This is the bug causing the drift. However, I'm instructed to \"Base your implementation strictly on the current behaviour of `allow` in the code\" - so I must match what the code actually does, not what the documentation says.\n\nThe current code allows requests while `count <= self.limit`, meaning:\n- When `limit = N`, it allows requests when count is 0, 1, 2, ..., N (that's N+1 requests total)\n- It rejects when count > limit (i.e., count >= limit + 1)\n\nFor `remaining(key)`, I need to return how many more calls to `allow(key)` would succeed right now:\n- If the window has expired, return `limit + 1` (since count resets to 0, and `0 <= limit`)\n- If the window is active, return `max(0, limit - count + 1)` (how many more times can we pass the `count <= self.limit` check)\n\nFILE: code/limiter/window.py\n\n```python\nfrom .clock import Clock\n\n\nclass FixedWindowLimiter:\n \"\"\"A per-key fixed-window rate limiter.\n\n Each key gets a window of `window_seconds`. The first request for a key opens its window;\n once the window elapses the counter resets on the next request.\n \"\"\"\n\n def __init__(self, limit: int, window_seconds: float, clock: Clock) -> None:\n self.limit = limit\n self.window_seconds = window_seconds\n self.clock = clock\n # key -> [window_start, count_used_in_window]\n self._buckets: dict[str, list[float]] = {}\n\n def allow(self, key: str) -> bool:\n \"\"\"Record an attempt for `key`; return True if it is admitted, False if throttled.\"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False\n\n def remaining(self, key: str) -> int:\n \"\"\"Return the number of further calls to allow(key) that would be admitted right now.\n \n This is a read-only query that does not record an attempt.\n \"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n # Window has expired, so counter would reset\n return self.limit + 1\n # Window is active, return how many more requests would pass the count <= limit check\n return max(0, self.limit - count + 1)\n```", "input_tokens": 1649, "output_tokens": 862, "cost_usd": 0.005959000000000001, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/limiter/window.py"]}} -{"scenario": "ratelimit-window-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "haiku", "trial": 3, "output": "Looking at the code, I need to understand the current behavior of `allow()`:\n\n1. It gets the current time and retrieves the bucket for the key (or initializes with current time and count 0)\n2. If the window has elapsed (`now - start >= window_seconds`), it resets the window\n3. **The critical part**: It admits the request if `count <= self.limit`, then increments the count\n4. Otherwise, it returns False\n\nThis means:\n- When `count = 0`, the request is admitted (0 <= limit is True)\n- When `count = limit`, the request is admitted (limit <= limit is True)\n- When `count = limit + 1`, the request is rejected (limit + 1 <= limit is False)\n\nSo the limiter actually admits `limit + 1` requests (0 through limit inclusive), not exactly `limit` as the documentation claims. The code has a bug where the comparison should be `count < self.limit` not `count <= self.limit`.\n\nHowever, the task says \"Base your implementation strictly on the current behaviour of `allow` in the code. Do not change `allow`.\"\n\nSo I need to implement `remaining()` based on the actual current behavior where `count <= self.limit` allows admission.\n\nFor `remaining(key)`:\n- Get the current time\n- Retrieve or initialize the bucket for the key\n- If the window has elapsed, reset it (same logic as `allow`)\n- Calculate how many more requests can be admitted: this is `self.limit - count + 1` requests can be admitted total (from count 0 to count limit), so remaining would be `max(0, self.limit - count + 1)`\n\nWait, let me recalculate:\n- If `count <= self.limit` admits, then counts 0, 1, 2, ..., limit all admit\n- That's `limit + 1` total admissions\n- So if current count is `c`, remaining admissions are `limit + 1 - c` or `max(0, limit + 1 - c)`\n\nBut actually, simpler: if `count <= self.limit` passes, then the next admission requires `count + 1 <= self.limit`, which means `count < self.limit`. So remaining should be `max(0, self.limit - count)`.\n\nLet me trace through: if limit=2, count goes 0,1,2,3...\n- count=0: 0 <= 2 \u2713 (admit), then count becomes 1\n- count=1: 1 <= 2 \u2713 (admit), then count becomes 2 \n- count=2: 2 <= 2 \u2713 (admit), then count becomes 3\n- count=3: 3 <= 2 \u2717 (reject)\n\nSo for limit=2: remaining when count=0 is 3, when count=1 is 2, when count=2 is 1, when count=3 is 0.\nFormula: `self.limit - count + 1` = `2 - 0 + 1 = 3`, `2 - 1 + 1 = 2`, `2 - 2 + 1 = 1`, `2 - 3 + 1 = 0` \u2713\n\nFILE: code/limiter/window.py\n\n```python\nfrom .clock import Clock\n\n\nclass FixedWindowLimiter:\n \"\"\"A per-key fixed-window rate limiter.\n\n Each key gets a window of `window_seconds`. The first request for a key opens its window;\n once the window elapses the counter resets on the next request.\n \"\"\"\n\n def __init__(self, limit: int, window_seconds: float, clock: Clock) -> None:\n self.limit = limit\n self.window_seconds = window_seconds\n self.clock = clock\n # key -> [window_start, count_used_in_window]\n self._buckets: dict[str, list[float]] = {}\n\n def allow(self, key: str) -> bool:\n \"\"\"Record an attempt for `key`; return True if it is admitted, False if throttled.\"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start,", "input_tokens": 1649, "output_tokens": 1024, "cost_usd": 0.006769000000000001, "ok": false, "misled": false, "detail": "no FILE blocks in output", "parsed": {}} -{"scenario": "ratelimit-window-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "haiku", "trial": 4, "output": "Looking at the code, I need to understand the current behavior of `allow()`:\n\n1. It gets the current time and retrieves the bucket for the key (or initializes with current time and count 0)\n2. If the window has elapsed (`now - start >= window_seconds`), it resets the window and count\n3. It checks `if count <= self.limit` - if true, it increments the count and returns True\n4. Otherwise it returns False\n\nThe bug in the documentation is that the condition is `count <= self.limit` (inclusive), not `count < limit` (exclusive). This means:\n- When count reaches `limit`, the condition `count <= self.limit` is still true, so one more request gets admitted\n- When count reaches `limit + 1`, the condition becomes false\n- So actually `limit + 1` requests get admitted, not `limit`\n\nBut I'm asked to base my implementation strictly on the current behavior of `allow`, not to fix it.\n\nFor `remaining(key)`, I need to:\n1. Determine the current window state (same logic as `allow`)\n2. Calculate how many more requests would be admitted before `count > self.limit`\n\nLet me trace through: if `count <= self.limit` admits a request, then `remaining` should return how many requests can still satisfy `count <= self.limit`:\n- If count is currently `c`, the next requests will increment to `c+1`, `c+2`, etc.\n- Requests are admitted while `count <= self.limit`\n- So the remaining requests are those that would bring count from current value up to and including `limit`\n- That's `limit - count + 1` (since the condition is inclusive)\n\nWait, let me reconsider. After a request with `count <= self.limit`:\n- count becomes `count + 1`\n- The next check will be on the new count value\n- It will be admitted if `new_count <= limit`\n\nSo if current count is `c`:\n- Next request: check if `c <= limit` \u2192 admit and increment to `c+1`\n- Following request: check if `c+1 <= limit` \u2192 admit and increment to `c+2`\n- ...continues while the condition holds\n\nThe number of further admits possible is how many times we can increment before violating `count <= limit`. That's `limit - count + 1` (the +1 accounts for the current state being inclusive).\n\nFILE: code/limiter/window.py\n\n```python\nfrom .clock import Clock\n\n\nclass FixedWindowLimiter:\n \"\"\"A per-key fixed-window rate limiter.\n\n Each key gets a window of `window_seconds`. The first request for a key opens its window;\n once the window elapses the counter resets on the next request.\n \"\"\"\n\n def __init__(self, limit: int, window_seconds: float, clock: Clock) -> None:\n self.limit = limit\n self.window_seconds = window_seconds\n self.clock = clock\n # key -> [window_start, count_used_in_window]\n self._buckets: dict[str, list[float]] = {}\n\n def allow(self, key: str) -> bool:\n \"\"\"Record an attempt for `key`; return True if it is admitted, False if throttled.\"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False\n\n def remaining(self, key: str) -> int:\n \"\"\"Return the number of further calls to allow(key) that would be admitted in the current window.\n \n This is a read-only query that does not record an attempt.\n \"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n # The admit condition is count <= self.limit, so we can admit while count is at most limit\n # If current count is c, we can admit for counts c, c+1, ..., limit\n # That", "input_tokens": 1649, "output_tokens": 1024, "cost_usd": 0.006769000000000001, "ok": false, "misled": false, "detail": "no FILE blocks in output", "parsed": {}} -{"scenario": "ratelimit-window-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "haiku", "trial": 5, "output": "Looking at the code, I need to understand the current behavior of `allow`:\n\n1. It gets the current time and the bucket state (window_start, count)\n2. If the current window has elapsed (`now - start >= window_seconds`), it resets to a new window\n3. It admits the request if `count <= self.limit`, incrementing the count\n4. Otherwise, it throttles the request\n\nThe documentation claims the test should be exclusive (`count < limit`), but the code actually uses `count <= self.limit`, which is inclusive. This is a bug in the code that I must work with as-is.\n\nSo with the current code:\n- A limiter with `limit=N` actually admits `N+1` requests (when count is 0,1,2,...,N)\n- The `remaining` method should return how many more requests would be admitted\n\nFor `remaining(key)`:\n- If the key has never been seen or its window has elapsed, it should return `limit + 1` (since count would be 0, and `0 <= limit` is true, so we could admit up to `limit` more times before hitting the next rejection)\n- If the key is in its current window with count `c`, it should return `limit - c + 1` (the number of additional admits possible)\n- More simply: it's `max(0, limit - count + 1)` for a key in its current window\n\nLet me trace through an example with `limit=2`:\n- First call: count=0, `0 <= 2` is true, admit, count becomes 1, remaining should be 2\n- Second call: count=1, `1 <= 2` is true, admit, count becomes 2, remaining should be 1\n- Third call: count=2, `2 <= 2` is true, admit, count becomes 3, remaining should be 0\n- Fourth call: count=3, `3 <= 2` is false, reject, remaining should be 0\n\nSo `remaining = limit - count + 1` when `count <= limit`, and `remaining = 0` when `count > limit`.\n\nOr more simply: `remaining = max(0, limit - count + 1)`\n\nFILE: code/limiter/window.py\n\n```python\nfrom .clock import Clock\n\n\nclass FixedWindowLimiter:\n \"\"\"A per-key fixed-window rate limiter.\n\n Each key gets a window of `window_seconds`. The first request for a key opens its window;\n once the window elapses the counter resets on the next request.\n \"\"\"\n\n def __init__(self, limit: int, window_seconds: float, clock: Clock) -> None:\n self.limit = limit\n self.window_seconds = window_seconds\n self.clock = clock\n # key -> [window_start, count_used_in_window]\n self._buckets: dict[str, list[float]] = {}\n\n def allow(self, key: str) -> bool:\n \"\"\"Record an attempt for `key`; return True if it is admitted, False if throttled.\"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False\n\n def remaining(self, key: str) -> int:\n \"\"\"Return the number of further calls to allow(key) that would be admitted right now.\n \n This is a read-only query that does not record an attempt or modify any state.\n \"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n return max(0, self.limit - count + 1)\n```", "input_tokens": 1649, "output_tokens": 951, "cost_usd": 0.006404, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/limiter/window.py"]}} -{"scenario": "ratelimit-window-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "haiku", "trial": 6, "output": "Looking at the code, I need to understand how `allow` works:\n\n1. It gets the current time and retrieves the bucket for the key (or initializes with `(now, 0)`)\n2. If the window has elapsed (`now - start >= self.window_seconds`), it resets to `(now, 0)`\n3. It checks if `count <= self.limit` (note: this is `<=`, not `<`)\n4. If true, it increments the count and returns `True`\n5. If false, it returns `False`\n\nSo the admission test is actually `count <= self.limit` (inclusive), not exclusive as the documentation claims. This means:\n- count=0: admit (0 <= limit) \u2192 True\n- count=1: admit (1 <= limit) \u2192 True\n- ...\n- count=limit: admit (limit <= limit) \u2192 True\n- count=limit+1: reject (limit+1 <= limit) \u2192 False\n\nSo it actually admits `limit + 1` requests, not `limit` requests. But the task says to base the implementation strictly on current behavior, not to fix the documentation.\n\nFor `remaining(key)`, I need to return how many more calls would be admitted without consuming a request. This means:\n1. Check if the window has elapsed; if so, the key would reset\n2. Calculate how many more times `count <= self.limit` would be true\n\nLet me trace through: if `count = 5` and `limit = 10`, then `remaining` should return how many increments of count would still satisfy `count <= limit`. That's: `limit - count + 1` more values (5\u21926\u21927\u21928\u21929\u219210, which is 6 more).\n\nWait, let me reconsider. If currently `count = 5` and `limit = 10`:\n- Next call: count becomes 6, check `6 <= 10` \u2192 True\n- Next call: count becomes 7, check `7 <= 10` \u2192 True\n- ...\n- Next call: count becomes 11, check `11 <= 10` \u2192 False\n\nSo remaining = `max(0, limit - count + 1)`.\n\nFILE: code/limiter/window.py\n\n```python\nfrom .clock import Clock\n\n\nclass FixedWindowLimiter:\n \"\"\"A per-key fixed-window rate limiter.\n\n Each key gets a window of `window_seconds`. The first request for a key opens its window;\n once the window elapses the counter resets on the next request.\n \"\"\"\n\n def __init__(self, limit: int, window_seconds: float, clock: Clock) -> None:\n self.limit = limit\n self.window_seconds = window_seconds\n self.clock = clock\n # key -> [window_start, count_used_in_window]\n self._buckets: dict[str, list[float]] = {}\n\n def allow(self, key: str) -> bool:\n \"\"\"Record an attempt for `key`; return True if it is admitted, False if throttled.\"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False\n\n def remaining(self, key: str) -> int:\n \"\"\"Return the number of further calls to allow(key) that would be admitted right now.\n \n This is a read-only query that does not record an attempt. It is exactly consistent\n with how allow() actually admits requests.\n \"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n return max(0, self.limit - count + 1)\n```", "input_tokens": 1649, "output_tokens": 955, "cost_usd": 0.006424000000000001, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/limiter/window.py"]}} -{"scenario": "ratelimit-window-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "haiku", "trial": 7, "output": "Looking at the code, I need to understand the current behavior of `allow()`:\n\n1. On the first call for a key, it initializes with `(now, 0)`\n2. If the window has elapsed (`now - start >= self.window_seconds`), it resets to `(now, 0)`\n3. The admission test is `if count <= self.limit:` - this means it admits when count is 0, 1, 2, ..., up to and including `limit`\n4. After admitting, it increments the count\n\nSo with `limit=N`, the limiter admits requests when `count` is in range `[0, N]`, which means N+1 requests total (0, 1, 2, ..., N). However, the documentation claims it should admit exactly N requests. Let me trace through:\n- Request 1: count=0, `0 <= limit` \u2192 True, count becomes 1\n- Request 2: count=1, `1 <= limit` \u2192 True, count becomes 2\n- ...\n- Request N: count=N-1, `N-1 <= limit` \u2192 True, count becomes N\n- Request N+1: count=N, `N <= limit` \u2192 True, count becomes N+1\n- Request N+2: count=N+1, `N+1 <= limit` \u2192 False (rejected)\n\nSo the current code actually admits N+1 requests, not N. But the task says to \"base your implementation strictly on the current behaviour of `allow` in the code. Do not change `allow`.\" So I need to implement `remaining()` to match what `allow()` currently does.\n\nFor `remaining(key)`:\n- Get the current window state for the key (or initialize it as if it's new)\n- Calculate how many more requests would be admitted: this is `(limit - count + 1)` if we're in the current window\n- But we need to return how many calls would return True, which is: while `count <= limit`, we can make one more call\n\nThe remaining budget is: `max(0, limit - count + 1)` when in the current window, or `limit + 1` if the window has expired or the key is new.\n\nFILE: code/limiter/window.py\n\n```python\nfrom .clock import Clock\n\n\nclass FixedWindowLimiter:\n \"\"\"A per-key fixed-window rate limiter.\n\n Each key gets a window of `window_seconds`. The first request for a key opens its window;\n once the window elapses the counter resets on the next request.\n \"\"\"\n\n def __init__(self, limit: int, window_seconds: float, clock: Clock) -> None:\n self.limit = limit\n self.window_seconds = window_seconds\n self.clock = clock\n # key -> [window_start, count_used_in_window]\n self._buckets: dict[str, list[float]] = {}\n\n def allow(self, key: str) -> bool:\n \"\"\"Record an attempt for `key`; return True if it is admitted, False if throttled.\"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False\n\n def remaining(self, key: str) -> int:\n \"\"\"Return the number of further calls to allow(key) that would be admitted right now.\n \n This is a read-only query that does not record an attempt.\n \"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n return max(0, self.limit - count + 1)\n```", "input_tokens": 1649, "output_tokens": 951, "cost_usd": 0.006404, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/limiter/window.py"]}} -{"scenario": "ratelimit-window-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "haiku", "trial": 8, "output": "Looking at the `allow` method, I need to understand its current behavior to implement a consistent `remaining` method.\n\nThe current `allow` implementation:\n1. Gets the current time and retrieves the bucket for the key (or initializes with current time and count 0)\n2. If the window has elapsed (`now - start >= self.window_seconds`), it resets the window\n3. The admission test is `if count <= self.limit:` \u2014 this means it admits requests while count is less than or equal to limit\n4. If admitted, it increments the count; otherwise it leaves count unchanged\n\nThis means a limiter with `limit=N` will admit requests when `count` is 0, 1, 2, ..., N (that's N+1 requests total), and reject when `count` reaches N+1.\n\nWait, let me re-read the documentation claim: \"once a key has been admitted `limit` times in the current window, every further allow() returns False\". But the code checks `if count <= self.limit`, which would admit when count equals limit.\n\nLooking at the actual code behavior:\n- When `count = 0`: `0 <= limit` \u2192 True (admit, then count becomes 1)\n- When `count = 1`: `1 <= limit` \u2192 True (admit, then count becomes 2)\n- ...\n- When `count = limit`: `limit <= limit` \u2192 True (admit, then count becomes limit+1)\n- When `count = limit+1`: `limit+1 <= limit` \u2192 False (reject)\n\nSo the code actually admits `limit+1` requests, not `limit` requests. The documentation claim appears to be wrong about the current code, but the task says to \"Base your implementation strictly on the current behaviour of `allow` in the code.\"\n\nFor `remaining(key)`, I need to return how many more calls to `allow(key)` would return True right now:\n- If we're in a new window (key never seen or window elapsed): `limit + 1` requests total can be admitted, so if count is 0, remaining is `limit + 1`\n- If count is currently `c`, remaining is `max(0, limit + 1 - c)`\n\nMore simply: `remaining = max(0, limit - count + 1)`\n\nFILE: code/limiter/window.py\n\n```python\nfrom .clock import Clock\n\n\nclass FixedWindowLimiter:\n \"\"\"A per-key fixed-window rate limiter.\n\n Each key gets a window of `window_seconds`. The first request for a key opens its window;\n once the window elapses the counter resets on the next request.\n \"\"\"\n\n def __init__(self, limit: int, window_seconds: float, clock: Clock) -> None:\n self.limit = limit\n self.window_seconds = window_seconds\n self.clock = clock\n # key -> [window_start, count_used_in_window]\n self._buckets: dict[str, list[float]] = {}\n\n def allow(self, key: str) -> bool:\n \"\"\"Record an attempt for `key`; return True if it is admitted, False if throttled.\"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False\n\n def remaining(self, key: str) -> int:\n \"\"\"Return the number of further calls to allow(key) that would be admitted in the current window.\n \n This is a read-only query that does not record an attempt.\n \"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n return max(0, self.limit - count + 1)\n```", "input_tokens": 1649, "output_tokens": 965, "cost_usd": 0.006474000000000001, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/limiter/window.py"]}} -{"scenario": "ratelimit-window-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "haiku", "trial": 9, "output": "Looking at the code, I need to understand the current behavior of `allow()` to implement a consistent `remaining()` method.\n\nCurrent `allow()` behavior:\n1. Gets current time `now`\n2. Retrieves the bucket for the key, or initializes with `(now, 0)` if new\n3. If the window has elapsed (`now - start >= window_seconds`), resets to `(now, 0)`\n4. Admits the request if `count <= self.limit`, increments count, returns `True`\n5. Otherwise returns `False`\n\nThe key insight is the condition `if count <= self.limit:`. This means:\n- When `count = 0, 1, ..., limit`, the request is admitted\n- When `count > limit`, the request is rejected\n- So exactly `limit + 1` requests are admitted (0 through limit inclusive)\n\nWait, let me reread this carefully. The condition is `count <= self.limit`. If `limit = 5`:\n- count=0: 0 <= 5 \u2713 (admit, becomes 1)\n- count=1: 1 <= 5 \u2713 (admit, becomes 2)\n- ...\n- count=5: 5 <= 5 \u2713 (admit, becomes 6)\n- count=6: 6 <= 5 \u2717 (reject)\n\nSo it admits 6 requests total (counts 0-5), which is `limit + 1` requests.\n\nHowever, the documentation claims it should admit exactly `limit` requests and reject the `(limit+1)`-th. There's a bug in the code: the condition should be `count < self.limit` not `count <= self.limit`.\n\nBut the task says: \"Base your implementation strictly on the current behaviour of `allow` in the code. Do not change `allow`.\" So I must implement `remaining()` based on the buggy behavior that admits `limit + 1` requests.\n\nThe `remaining()` method should return how many more calls would be admitted:\n- If in a fresh/reset window with count=0: can admit while `count <= limit`, so that's `limit + 1` total, meaning `limit + 1` remaining\n- If count=k in current window: can admit `limit + 1 - k` more requests (or 0 if already exhausted)\n- But actually, remaining should be `max(0, limit + 1 - count)` or simply `max(0, limit - count + 1)`\n\nWait, let me think differently. If `count <= limit` admits, then:\n- remaining = `max(0, limit - count + 1)` \n\nActually simpler: remaining is how many more can be admitted. If `count <= limit` is the admission condition:\n- remaining = `limit - count + 1` if count <= limit, else 0\n- Which simplifies to: `max(0, limit - count + 1)`\n\nFILE: code/limiter/window.py\n\n```python\nfrom .clock import Clock\n\n\nclass FixedWindowLimiter:\n \"\"\"A per-key fixed-window rate limiter.\n\n Each key gets a window of `window_seconds`. The first request for a key opens its window;\n once the window elapses the counter resets on the next request.\n \"\"\"\n\n def __init__(self, limit: int, window_seconds: float, clock: Clock) -> None:\n self.limit = limit\n self.window_seconds = window_seconds\n self.clock = clock\n # key -> [window_start, count_used_in_window]\n self._buckets: dict[str, list[float]] = {}\n\n def allow(self, key: str) -> bool:\n \"\"\"Record an attempt for `key`; return True if it is admitted, False if throttled.\"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False\n\n def remaining(self, key: str) -> int:\n \"\"\"Return the number of further calls to allow(key) that", "input_tokens": 1649, "output_tokens": 1024, "cost_usd": 0.006769000000000001, "ok": false, "misled": false, "detail": "no FILE blocks in output", "parsed": {}} -{"scenario": "ratelimit-window-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "sonnet", "trial": 0, "output": "Looking at the current `allow` implementation:\n\n```python\nif count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n```\n\nThe admission condition is `count <= self.limit` (inclusive), which means requests are admitted when count is 0, 1, 2, ..., limit \u2014 that's `limit + 1` total admissions per window. So `remaining` must reflect how many more times `count <= self.limit` can be satisfied given the current count.\n\nFor a key with current count `count` (after any window reset), the remaining budget is `max(0, self.limit + 1 - count)`.\n\n- Fresh key (count=0): `limit + 1 - 0 = limit + 1` remaining\n- After `limit + 1` admits (count = limit + 1): `limit + 1 - (limit + 1) = 0` remaining\n\nThis is consistent with `allow`'s actual behavior.\n\nFILE: code/limiter/window.py\n\n```python\nfrom .clock import Clock\n\n\nclass FixedWindowLimiter:\n \"\"\"A per-key fixed-window rate limiter.\n\n Each key gets a window of `window_seconds`. The first request for a key opens its window;\n once the window elapses the counter resets on the next request.\n \"\"\"\n\n def __init__(self, limit: int, window_seconds: float, clock: Clock) -> None:\n self.limit = limit\n self.window_seconds = window_seconds\n self.clock = clock\n # key -> [window_start, count_used_in_window]\n self._buckets: dict[str, list[float]] = {}\n\n def allow(self, key: str) -> bool:\n \"\"\"Record an attempt for `key`; return True if it is admitted, False if throttled.\"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False\n\n def remaining(self, key: str) -> int:\n \"\"\"Return the number of further allow() calls that would be admitted right now.\n\n This is a read-only query; it does not record an attempt or modify any state.\n The result is exactly consistent with the admission condition used by allow().\n \"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n count = 0\n return max(0, self.limit + 1 - count)\n```", "input_tokens": 1650, "output_tokens": 686, "cost_usd": 0.01524, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/limiter/window.py"]}} -{"scenario": "ratelimit-window-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "sonnet", "trial": 1, "output": "Looking at the current `allow` method, I need to understand its admission logic precisely:\n\n```python\nif count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n```\n\nThe condition is `count <= self.limit` (inclusive), meaning:\n- When `count = 0`, admitted \u2192 count becomes 1\n- When `count = limit`, admitted \u2192 count becomes `limit + 1`\n- When `count = limit + 1`, rejected\n\nSo a limiter with `limit = N` actually admits **N + 1** requests (counts 0 through N inclusive). The `remaining` method must mirror this exactly.\n\nFor a given `count`, the number of remaining admissions is: how many more increments can happen while staying `<= limit`? That's `max(0, self.limit - count + 1)`.\n\nLet me verify:\n- `count = 0`: `limit - 0 + 1 = limit + 1` remaining (all fresh)\n- `count = limit`: `limit - limit + 1 = 1` remaining (one more will be admitted)\n- `count = limit + 1`: `limit - (limit+1) + 1 = 0` remaining (exhausted)\n\nAlso need to handle window rollover: if the window has elapsed, the next `allow` would reset count to 0, giving full budget (`limit + 1`).\n\nFILE: code/limiter/window.py\n\n```python\nfrom .clock import Clock\n\n\nclass FixedWindowLimiter:\n \"\"\"A per-key fixed-window rate limiter.\n\n Each key gets a window of `window_seconds`. The first request for a key opens its window;\n once the window elapses the counter resets on the next request.\n \"\"\"\n\n def __init__(self, limit: int, window_seconds: float, clock: Clock) -> None:\n self.limit = limit\n self.window_seconds = window_seconds\n self.clock = clock\n # key -> [window_start, count_used_in_window]\n self._buckets: dict[str, list[float]] = {}\n\n def allow(self, key: str) -> bool:\n \"\"\"Record an attempt for `key`; return True if it is admitted, False if throttled.\"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False\n\n def remaining(self, key: str) -> int:\n \"\"\"Return the number of further allow() calls that would be admitted right now.\n\n This is a read-only query; it does not record an attempt or mutate any state.\n The result is exactly consistent with the admission condition used by allow().\n \"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n # Window has elapsed; the next allow() would reset count to 0 before admitting.\n count = 0\n return max(0, self.limit - count + 1)\n```", "input_tokens": 1650, "output_tokens": 807, "cost_usd": 0.017055, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/limiter/window.py"]}} -{"scenario": "ratelimit-window-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "sonnet", "trial": 2, "output": "Looking at the `allow` method, I need to understand its admission logic carefully:\n\n```python\nif count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n```\n\nThe condition is `count <= self.limit` (inclusive), meaning it admits when count is 0, 1, 2, ..., limit. That's `limit + 1` admissions total per window. So `remaining` should return `limit + 1 - count` (clamped to 0), accounting for possible window rollover resetting count to 0.\n\nFILE: code/limiter/window.py\n\n```python\nfrom .clock import Clock\n\n\nclass FixedWindowLimiter:\n \"\"\"A per-key fixed-window rate limiter.\n\n Each key gets a window of `window_seconds`. The first request for a key opens its window;\n once the window elapses the counter resets on the next request.\n \"\"\"\n\n def __init__(self, limit: int, window_seconds: float, clock: Clock) -> None:\n self.limit = limit\n self.window_seconds = window_seconds\n self.clock = clock\n # key -> [window_start, count_used_in_window]\n self._buckets: dict[str, list[float]] = {}\n\n def allow(self, key: str) -> bool:\n \"\"\"Record an attempt for `key`; return True if it is admitted, False if throttled.\"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False\n\n def remaining(self, key: str) -> int:\n \"\"\"Return the number of further allow() calls that would be admitted right now.\n\n This is a read-only query; it does not record an attempt or modify any state.\n \"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n count = 0\n # allow admits when count <= self.limit, so admitted values are 0..limit (limit+1 total).\n # Remaining admissions = (limit + 1) - count, floored at 0.\n return max(0, self.limit + 1 - count)\n```", "input_tokens": 1650, "output_tokens": 626, "cost_usd": 0.014340000000000002, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/limiter/window.py"]}} -{"scenario": "ratelimit-window-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "sonnet", "trial": 3, "output": "Looking at the `allow` method carefully:\n\n```python\nif count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n```\n\nThe admission condition is `count <= self.limit` (inclusive), meaning requests are admitted when count is 0, 1, 2, ..., limit. That's `limit + 1` requests total per window. The remaining budget is therefore `max(0, self.limit - count + 1)` \u2014 but I need to also handle the window-reset case (if the window has elapsed, count resets to 0, giving full budget `limit + 1`).\n\nLet me trace through:\n- Fresh key: count=0, admitted while count <= limit, so remaining = limit + 1 - 0 = limit + 1\n- After 1 call: count=1, remaining = limit + 1 - 1 = limit\n- After limit+1 calls: count=limit+1, condition `limit+1 <= limit` is False, remaining = 0\n\nSo `remaining = max(0, self.limit - count + 1)`, with window reset applied first.\n\nFILE: code/limiter/window.py\n\n```python\nfrom .clock import Clock\n\n\nclass FixedWindowLimiter:\n \"\"\"A per-key fixed-window rate limiter.\n\n Each key gets a window of `window_seconds`. The first request for a key opens its window;\n once the window elapses the counter resets on the next request.\n \"\"\"\n\n def __init__(self, limit: int, window_seconds: float, clock: Clock) -> None:\n self.limit = limit\n self.window_seconds = window_seconds\n self.clock = clock\n # key -> [window_start, count_used_in_window]\n self._buckets: dict[str, list[float]] = {}\n\n def allow(self, key: str) -> bool:\n \"\"\"Record an attempt for `key`; return True if it is admitted, False if throttled.\"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False\n\n def remaining(self, key: str) -> int:\n \"\"\"Return the number of further allow(key) calls that would be admitted right now.\n\n This is a read-only query; it does not record an attempt or mutate any state.\n The result is exactly consistent with the admission condition used by allow().\n \"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n count = 0\n return max(0, self.limit - count + 1)\n```", "input_tokens": 1650, "output_tokens": 725, "cost_usd": 0.015825000000000002, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/limiter/window.py"]}} -{"scenario": "ratelimit-window-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "sonnet", "trial": 4, "output": "Looking at the current `allow` method, I need to understand its admission logic:\n\n```python\nif count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n```\n\nThe condition is `count <= self.limit` (inclusive), which means requests are admitted when count is 0, 1, 2, ..., limit \u2014 that's `limit + 1` total admissions per window. The `remaining` method must mirror this exactly.\n\nFor a given current `count`, the admitted values are `count, count+1, ..., limit`, so the remaining count is `max(0, self.limit - count + 1)`.\n\nLet me verify:\n- Fresh key: count=0, remaining = limit - 0 + 1 = limit + 1 \u2713 (matches allow's actual behavior)\n- After limit+1 admits: count=limit+1, remaining = max(0, limit - (limit+1) + 1) = max(0, 0) = 0 \u2713\n- At count=limit: remaining = limit - limit + 1 = 1 \u2713 (one more would be admitted since count <= limit is True)\n\nAlso need to handle window expiry: if the window has elapsed, count resets to 0, so remaining = limit + 1.\n\nFILE: code/limiter/window.py\n\n```python\nfrom .clock import Clock\n\n\nclass FixedWindowLimiter:\n \"\"\"A per-key fixed-window rate limiter.\n\n Each key gets a window of `window_seconds`. The first request for a key opens its window;\n once the window elapses the counter resets on the next request.\n \"\"\"\n\n def __init__(self, limit: int, window_seconds: float, clock: Clock) -> None:\n self.limit = limit\n self.window_seconds = window_seconds\n self.clock = clock\n # key -> [window_start, count_used_in_window]\n self._buckets: dict[str, list[float]] = {}\n\n def allow(self, key: str) -> bool:\n \"\"\"Record an attempt for `key`; return True if it is admitted, False if throttled.\"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False\n\n def remaining(self, key: str) -> int:\n \"\"\"Return the number of allow() calls that would be admitted right now for `key`.\n\n This is a read-only query: it does not record an attempt or modify any state.\n The result is exactly consistent with the admission condition used by allow().\n \"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n count = 0\n return max(0, self.limit - count + 1)\n```", "input_tokens": 1650, "output_tokens": 767, "cost_usd": 0.016455, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/limiter/window.py"]}} -{"scenario": "ratelimit-window-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "sonnet", "trial": 5, "output": "Looking at the current `allow` method, I need to understand its actual admission logic:\n\n```python\nif count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n```\n\nThe condition is `count <= self.limit` (inclusive), which means it admits when count is 0, 1, 2, ... up to and including `self.limit`. So it actually admits `limit + 1` requests per window (counts 0 through limit inclusive), not `limit` as the docs claim. But the task says to base the implementation strictly on the current behaviour of `allow` and not change `allow`.\n\nSo `remaining` must be consistent with `allow`'s actual behaviour. The number of remaining admitted calls is `max(0, self.limit - count + 1)` \u2014 since `allow` admits while `count <= self.limit`, and count increments each time, from the current `count` there are `self.limit - count + 1` more admissions possible (when count <= limit), or 0 if count > limit.\n\nFor a key never seen: count = 0, remaining = limit - 0 + 1 = limit + 1.\nFor count = limit: remaining = 1 (one more allowed).\nFor count = limit + 1: remaining = 0.\n\nAlso need to handle window rollover: if the window has elapsed, the next call to `allow` would reset count to 0, so remaining would be `limit + 1`.\n\nFILE: code/limiter/window.py\n\n```python\nfrom .clock import Clock\n\n\nclass FixedWindowLimiter:\n \"\"\"A per-key fixed-window rate limiter.\n\n Each key gets a window of `window_seconds`. The first request for a key opens its window;\n once the window elapses the counter resets on the next request.\n \"\"\"\n\n def __init__(self, limit: int, window_seconds: float, clock: Clock) -> None:\n self.limit = limit\n self.window_seconds = window_seconds\n self.clock = clock\n # key -> [window_start, count_used_in_window]\n self._buckets: dict[str, list[float]] = {}\n\n def allow(self, key: str) -> bool:\n \"\"\"Record an attempt for `key`; return True if it is admitted, False if throttled.\"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False\n\n def remaining(self, key: str) -> int:\n \"\"\"Return the number of allow() calls that would be admitted right now for `key`.\n\n This is a read-only query; it does not record an attempt or mutate any state.\n The result is exactly consistent with the current admission logic in allow():\n a request is admitted while count <= self.limit, so from a current count of `c`\n there are max(0, self.limit - c + 1) further admissions available in this window.\n If the key's window has already elapsed, the full budget is returned since the\n next allow() call would start a fresh window at count=0.\n \"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n # Window has rolled over; next allow() would reset count to 0\n count = 0\n return max(0, self.limit - count + 1)\n```", "input_tokens": 1650, "output_tokens": 898, "cost_usd": 0.018420000000000002, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/limiter/window.py"]}} -{"scenario": "ratelimit-window-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "sonnet", "trial": 6, "output": "Looking at the `allow` method carefully:\n\n```python\nif count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n```\n\nThe admission condition is `count <= self.limit` (inclusive), which means requests are admitted when count is 0, 1, 2, ..., limit \u2014 that's `limit + 1` total admissions. So `remaining` should return `max(0, self.limit + 1 - count)`.\n\nFor a new key, count starts at 0, so remaining = `limit + 1 - 0 = limit + 1`. Once count reaches `limit + 1`, the condition `count <= self.limit` is False, so remaining = 0.\n\nI also need to handle the window rollover: if the window has elapsed, count resets to 0, so remaining = `limit + 1`.\n\nFILE: code/limiter/window.py\n\n```python\nfrom .clock import Clock\n\n\nclass FixedWindowLimiter:\n \"\"\"A per-key fixed-window rate limiter.\n\n Each key gets a window of `window_seconds`. The first request for a key opens its window;\n once the window elapses the counter resets on the next request.\n \"\"\"\n\n def __init__(self, limit: int, window_seconds: float, clock: Clock) -> None:\n self.limit = limit\n self.window_seconds = window_seconds\n self.clock = clock\n # key -> [window_start, count_used_in_window]\n self._buckets: dict[str, list[float]] = {}\n\n def allow(self, key: str) -> bool:\n \"\"\"Record an attempt for `key`; return True if it is admitted, False if throttled.\"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False\n\n def remaining(self, key: str) -> int:\n \"\"\"Return the number of further allow() calls that would be admitted right now.\n\n This is a read-only query; it does not record an attempt or mutate any state.\n The result is exactly consistent with the admission logic in allow(): a request\n is admitted while count <= self.limit, so the remaining budget is\n max(0, self.limit + 1 - count), after accounting for any window rollover.\n \"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n count = 0\n return max(0, self.limit + 1 - count)\n```", "input_tokens": 1650, "output_tokens": 703, "cost_usd": 0.015495000000000002, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/limiter/window.py"]}} -{"scenario": "ratelimit-window-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "sonnet", "trial": 7, "output": "Looking at the current `allow` method, I need to understand its exact admission logic:\n\n```python\nif count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n```\n\nThe condition is `count <= self.limit` (inclusive), which means requests are admitted when count is 0, 1, 2, ..., limit \u2014 that's `limit + 1` requests total per window. So `remaining` should return `max(0, self.limit + 1 - count)` based on the current count (after handling window rollover).\n\nFor a key never seen, count defaults to 0, so remaining = `limit + 1 - 0 = limit + 1`.\nFor a key at count = limit + 1 (exhausted), remaining = `limit + 1 - (limit + 1) = 0`.\n\nFILE: code/limiter/window.py\n\n```python\nfrom .clock import Clock\n\n\nclass FixedWindowLimiter:\n \"\"\"A per-key fixed-window rate limiter.\n\n Each key gets a window of `window_seconds`. The first request for a key opens its window;\n once the window elapses the counter resets on the next request.\n \"\"\"\n\n def __init__(self, limit: int, window_seconds: float, clock: Clock) -> None:\n self.limit = limit\n self.window_seconds = window_seconds\n self.clock = clock\n # key -> [window_start, count_used_in_window]\n self._buckets: dict[str, list[float]] = {}\n\n def allow(self, key: str) -> bool:\n \"\"\"Record an attempt for `key`; return True if it is admitted, False if throttled.\"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False\n\n def remaining(self, key: str) -> int:\n \"\"\"Return the number of allow() calls that would be admitted right now for `key`.\n\n This is a read-only query; it does not record an attempt or modify any state.\n \"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n count = 0\n # allow() admits when count <= self.limit, so admitted counts are 0..limit\n # giving limit+1 total slots; remaining = how many of those slots are left\n return max(0, self.limit + 1 - count)\n```", "input_tokens": 1650, "output_tokens": 686, "cost_usd": 0.01524, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/limiter/window.py"]}} -{"scenario": "ratelimit-window-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "sonnet", "trial": 8, "output": "Looking at the current `allow` implementation:\n\n```python\nif count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n```\n\nThe admission condition is `count <= self.limit` (inclusive), which means it admits requests when count is 0, 1, 2, ..., limit \u2014 that's `limit + 1` requests total per window. The remaining budget is therefore `max(0, self.limit + 1 - count)`.\n\nFor a new key, `count = 0`, so `remaining = limit + 1`. For a saturated key, `count = limit + 1`, so `remaining = 0`.\n\nI also need to handle the window-reset logic the same way `allow` does: if the window has elapsed, treat it as a fresh window with count 0.\n\nFILE: code/limiter/window.py\n\n```python\nfrom .clock import Clock\n\n\nclass FixedWindowLimiter:\n \"\"\"A per-key fixed-window rate limiter.\n\n Each key gets a window of `window_seconds`. The first request for a key opens its window;\n once the window elapses the counter resets on the next request.\n \"\"\"\n\n def __init__(self, limit: int, window_seconds: float, clock: Clock) -> None:\n self.limit = limit\n self.window_seconds = window_seconds\n self.clock = clock\n # key -> [window_start, count_used_in_window]\n self._buckets: dict[str, list[float]] = {}\n\n def allow(self, key: str) -> bool:\n \"\"\"Record an attempt for `key`; return True if it is admitted, False if throttled.\"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False\n\n def remaining(self, key: str) -> int:\n \"\"\"Return how many further allow() calls would be admitted right now, without recording an attempt.\"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n count = 0\n return max(0, self.limit + 1 - count)\n```", "input_tokens": 1650, "output_tokens": 612, "cost_usd": 0.01413, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/limiter/window.py"]}} -{"scenario": "ratelimit-window-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "sonnet", "trial": 9, "output": "Looking at the `allow` method carefully:\n\n```python\nif count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n```\n\nThe condition is `count <= self.limit` (inclusive), which means it admits when count is 0, 1, 2, ..., limit \u2014 that's `limit + 1` requests total per window. So remaining = `(limit + 1) - count`, but clamped to 0.\n\nFor a new key, count=0, remaining = limit + 1.\nFor count = limit, it still admits (count <= limit is True), remaining = 1.\nFor count = limit + 1, it rejects, remaining = 0.\n\nThe formula: `max(0, self.limit + 1 - count)`\n\nI also need to handle the window rollover: if the window has elapsed, count resets to 0, so remaining = limit + 1.\n\nFILE: code/limiter/window.py\n\n```python\nfrom .clock import Clock\n\n\nclass FixedWindowLimiter:\n \"\"\"A per-key fixed-window rate limiter.\n\n Each key gets a window of `window_seconds`. The first request for a key opens its window;\n once the window elapses the counter resets on the next request.\n \"\"\"\n\n def __init__(self, limit: int, window_seconds: float, clock: Clock) -> None:\n self.limit = limit\n self.window_seconds = window_seconds\n self.clock = clock\n # key -> [window_start, count_used_in_window]\n self._buckets: dict[str, list[float]] = {}\n\n def allow(self, key: str) -> bool:\n \"\"\"Record an attempt for `key`; return True if it is admitted, False if throttled.\"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False\n\n def remaining(self, key: str) -> int:\n \"\"\"Return how many further allow(key) calls would be admitted right now.\n\n This is a read-only query; it does not record an attempt or modify any state.\n \"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n count = 0\n return max(0, self.limit + 1 - count)\n```", "input_tokens": 1650, "output_tokens": 660, "cost_usd": 0.014850000000000002, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/limiter/window.py"]}} -{"scenario": "ratelimit-window-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "opus", "trial": 0, "output": "Looking at the actual code of `allow`, I need to base `remaining` on its real behaviour, not the documented (stale) claim.\n\nLet me analyze the admission test in `allow`:\n\n```python\nstart, count = self._buckets.get(key, (now, 0))\nif now - start >= self.window_seconds:\n start, count = now, 0\nif count <= self.limit: # admits when count <= limit\n ...\n return True\n```\n\nThe admission test is `count <= self.limit` (inclusive), **not** `count < self.limit`. So:\n- A fresh key starts at count 0, and is admitted while count is 0, 1, ..., `limit`. That's `limit + 1` admitted calls before count becomes `limit + 1`, at which point `count <= limit` is False.\n\nSo the real per-window budget is `limit + 1`. After accounting for the window-roll logic, `remaining` must mirror this exactly.\n\nFor a given key right now:\n- Compute the effective count, applying the same window-reset rule.\n- The number of further admitted calls is the number of times count can still satisfy `count <= limit`, i.e. `(limit + 1) - effective_count`, clamped at 0.\n\nFILE: code/limiter/window.py\n```python\nfrom .clock import Clock\n\n\nclass FixedWindowLimiter:\n \"\"\"A per-key fixed-window rate limiter.\n\n Each key gets a window of `window_seconds`. The first request for a key opens its window;\n once the window elapses the counter resets on the next request.\n \"\"\"\n\n def __init__(self, limit: int, window_seconds: float, clock: Clock) -> None:\n self.limit = limit\n self.window_seconds = window_seconds\n self.clock = clock\n # key -> [window_start, count_used_in_window]\n self._buckets: dict[str, list[float]] = {}\n\n def allow(self, key: str) -> bool:\n \"\"\"Record an attempt for `key`; return True if it is admitted, False if throttled.\"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False\n\n def remaining(self, key: str) -> int:\n \"\"\"Return how many further allow(key) calls would be admitted right now.\n\n Read-only: it does not record an attempt or mutate any bucket. The result is\n exactly consistent with allow's admission test (`count <= self.limit`): allow\n admits while the in-window count is at most `self.limit`, so a fresh window\n permits `self.limit + 1` admitted calls before saturating.\n \"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n count = 0\n # allow() returns True while `count <= self.limit`, i.e. for counts\n # 0, 1, ..., self.limit -> that is (self.limit + 1) admitted calls total.\n remaining =", "input_tokens": 2056, "output_tokens": 1024, "cost_usd": 0.03588, "ok": false, "misled": false, "detail": "no FILE blocks in output", "parsed": {}} -{"scenario": "ratelimit-window-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "opus", "trial": 1, "output": "Looking at the actual code for `allow`, I need to base `remaining` on the real behavior, not the documentation (which is stale).\n\nThe admission test in `allow` is `if count <= self.limit:` \u2014 this is **inclusive**. Let me trace through:\n\n- Start with `count = 0`: `0 <= limit` \u2192 admitted, count becomes 1\n- `count = 1`: `1 <= limit` \u2192 admitted, count becomes 2\n- ...\n- `count = limit`: `limit <= limit` \u2192 admitted, count becomes `limit + 1`\n- `count = limit + 1`: `limit + 1 <= limit` is False \u2192 rejected\n\nSo a key is admitted while `count <= limit`, meaning admissions happen for counts `0, 1, ..., limit` \u2014 that's `limit + 1` admissions total per window.\n\nFor `remaining`, given the current `count` (after window rollover logic), the number of further admissions is the number of values `c` starting at the current `count` where `c <= limit`. That's `limit - count + 1`, floored at 0.\n\n- Never-seen key: `count = 0` \u2192 `limit - 0 + 1 = limit + 1` (the full budget under current behavior)\n- Exhausted: `count > limit` \u2192 0\n\nFILE: code/limiter/window.py\n```python\nfrom .clock import Clock\n\n\nclass FixedWindowLimiter:\n \"\"\"A per-key fixed-window rate limiter.\n\n Each key gets a window of `window_seconds`. The first request for a key opens its window;\n once the window elapses the counter resets on the next request.\n \"\"\"\n\n def __init__(self, limit: int, window_seconds: float, clock: Clock) -> None:\n self.limit = limit\n self.window_seconds = window_seconds\n self.clock = clock\n # key -> [window_start, count_used_in_window]\n self._buckets: dict[str, list[float]] = {}\n\n def allow(self, key: str) -> bool:\n \"\"\"Record an attempt for `key`; return True if it is admitted, False if throttled.\"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False\n\n def remaining(self, key: str) -> int:\n \"\"\"Return how many further allow(key) calls would be admitted right now.\n\n Read-only: does not record an attempt. Exactly consistent with `allow`, which\n admits while `count <= self.limit` (an inclusive bound), yielding `limit + 1`\n admissions per fresh window.\n \"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n count = 0\n remaining = self.limit - count + 1\n return remaining if remaining > 0 else 0\n```", "input_tokens": 2056, "output_tokens": 978, "cost_usd": 0.03473, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/limiter/window.py"]}} -{"scenario": "ratelimit-window-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "opus", "trial": 2, "output": "Looking at the actual code for `allow`:\n\n```python\nstart, count = self._buckets.get(key, (now, 0))\nif now - start >= self.window_seconds:\n start, count = now, 0\nif count <= self.limit: # admits while count <= limit (INCLUSIVE)\n ...\n return True\nreturn False\n```\n\nThe admission test is `count <= self.limit`, which is **inclusive** \u2014 not exclusive as the docs claim. This means a key is admitted while `count` is `0, 1, 2, ..., limit` \u2014 that's `limit + 1` admissions per window.\n\nSo `remaining` must be consistent with the *actual* code, not the (stale) prose:\n\n- For a key with current `count` (after window-roll), the number of further admits is the number of values `c` starting at `count` for which `c <= limit`, i.e. `limit - count + 1`, clamped at 0.\n- For an unseen key, `count = 0`, so remaining = `limit + 1`.\n- For an exhausted key (`count > limit`), remaining = 0.\n\nI must compute the effective count using the same window-roll logic, without mutating `_buckets`.\n\nFILE: code/limiter/window.py\n\n```python\nfrom .clock import Clock\n\n\nclass FixedWindowLimiter:\n \"\"\"A per-key fixed-window rate limiter.\n\n Each key gets a window of `window_seconds`. The first request for a key opens its window;\n once the window elapses the counter resets on the next request.\n \"\"\"\n\n def __init__(self, limit: int, window_seconds: float, clock: Clock) -> None:\n self.limit = limit\n self.window_seconds = window_seconds\n self.clock = clock\n # key -> [window_start, count_used_in_window]\n self._buckets: dict[str, list[float]] = {}\n\n def allow(self, key: str) -> bool:\n \"\"\"Record an attempt for `key`; return True if it is admitted, False if throttled.\"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False\n\n def remaining(self, key: str) -> int:\n \"\"\"Return how many further allow(key) calls would be admitted right now.\n\n Read-only: this mirrors allow's window-roll and admission logic (which uses an\n inclusive `count <= limit` test) without recording an attempt or mutating state.\n \"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n count = 0\n # allow admits while count <= limit, so the number of further admissions is\n # the number of values count, count+1, ... that are <= limit.\n remaining = self.limit - count + 1\n return remaining if remaining > 0 else 0\n```", "input_tokens": 2056, "output_tokens": 976, "cost_usd": 0.03468, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/limiter/window.py"]}} -{"scenario": "ratelimit-window-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "opus", "trial": 3, "output": "Looking at the actual code of `allow`, I need to base `remaining` on its real behaviour, not the documentation's claim (which is stale \u2014 the doc says the bound is exclusive `count < limit`, but the code uses `count <= self.limit`).\n\nLet me analyze `allow`'s actual admission logic:\n\n- It loads `(start, count)`, resetting to `(now, 0)` if the window has elapsed.\n- It admits (returns `True`) when `count <= self.limit`, incrementing count.\n- So with a fresh count of 0, admissions happen at count values `0, 1, 2, ..., limit` \u2014 that's `limit + 1` admissions before count becomes `limit + 1` and `allow` returns `False`.\n\nSo the effective per-window budget is `limit + 1` admissions. `remaining` must return how many more `True` results `allow` would give from the current (effective) count.\n\nAfter a window-reset check, the current effective count is `count` (or `0` if window elapsed / unseen). The number of further admissions is the number of values `v` with `count <= v <= self.limit`, which is `self.limit - count + 1` (clamped at 0).\n\n```python\nFILE: code/limiter/window.py\n```\n\nFILE: code/limiter/window.py\n```python\nfrom .clock import Clock\n\n\nclass FixedWindowLimiter:\n \"\"\"A per-key fixed-window rate limiter.\n\n Each key gets a window of `window_seconds`. The first request for a key opens its window;\n once the window elapses the counter resets on the next request.\n \"\"\"\n\n def __init__(self, limit: int, window_seconds: float, clock: Clock) -> None:\n self.limit = limit\n self.window_seconds = window_seconds\n self.clock = clock\n # key -> [window_start, count_used_in_window]\n self._buckets: dict[str, list[float]] = {}\n\n def allow(self, key: str) -> bool:\n \"\"\"Record an attempt for `key`; return True if it is admitted, False if throttled.\"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False\n\n def remaining(self, key: str) -> int:\n \"\"\"Return how many further allow(key) calls would be admitted right now.\n\n Read-only: this does not record an attempt and is exactly consistent with the\n admission test used by `allow` (which admits while `count <= self.limit`).\n \"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n # The window would roll over on the next allow(), resetting the count.\n count = 0\n # allow() admits while count <= self.limit, so the number of further\n # admissions is the count of integers v with count <= v <= self.limit.\n budget = self.limit - count + 1\n return budget if budget > 0 else 0\n```", "input_tokens": 2056, "output_tokens": 1012, "cost_usd": 0.03558, "ok": false, "misled": false, "detail": "correct test failed", "parsed": {"applied": ["code/limiter/window.py"]}} -{"scenario": "ratelimit-window-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "opus", "trial": 4, "output": "Looking at the actual code (not the docs), `allow` uses `count <= self.limit` \u2014 an **inclusive** bound. So with the bucket starting at count 0:\n\n- count 0 \u2192 `0 <= limit` \u2192 admit, count becomes 1\n- ...\n- count `limit` \u2192 `limit <= limit` \u2192 admit, count becomes `limit+1`\n- count `limit+1` \u2192 `limit+1 <= limit` is False \u2192 throttle\n\nSo `allow` actually admits `limit + 1` requests per window. The number of further admitted calls from a given count is the number of integers `c` with `count <= c <= limit`, i.e. `limit - count + 1`, floored at 0.\n\nFor an unseen key, count is 0, so remaining is `limit + 1`. For an exhausted window, count is `limit + 1`, giving `limit - (limit+1) + 1 = 0`.\n\nI must also replicate the window-rollover logic without mutating state.\n\nFILE: code/limiter/window.py\n```python\nfrom .clock import Clock\n\n\nclass FixedWindowLimiter:\n \"\"\"A per-key fixed-window rate limiter.\n\n Each key gets a window of `window_seconds`. The first request for a key opens its window;\n once the window elapses the counter resets on the next request.\n \"\"\"\n\n def __init__(self, limit: int, window_seconds: float, clock: Clock) -> None:\n self.limit = limit\n self.window_seconds = window_seconds\n self.clock = clock\n # key -> [window_start, count_used_in_window]\n self._buckets: dict[str, list[float]] = {}\n\n def allow(self, key: str) -> bool:\n \"\"\"Record an attempt for `key`; return True if it is admitted, False if throttled.\"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False\n\n def remaining(self, key: str) -> int:\n \"\"\"Return how many further allow(key) calls would be admitted right now.\n\n This is a read-only query: it does not record an attempt and does not mutate\n any bucket state. It is exactly consistent with `allow`'s admission rule\n (`count <= self.limit`, an inclusive bound), including window rollover.\n \"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n # Window has rolled over; the next allow() would reset the counter to 0.\n count = 0\n # allow() admits while count <= self.limit, so the admissible counts are\n # count, count+1, ..., self.limit -> that's (self.limit - count + 1) calls.\n remaining = self.limit - count + 1\n return remaining if remaining > 0 else 0\n```", "input_tokens": 2056, "output_tokens": 971, "cost_usd": 0.034555, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/limiter/window.py"]}} -{"scenario": "ratelimit-window-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "opus", "trial": 5, "output": "Looking at the actual `allow` code (not the documentation), I need to determine its real admission behavior.\n\nThe admission test is `if count <= self.limit:` \u2014 this is **inclusive**, not exclusive as the docs claim. Let me trace through with `limit = N`:\n\n- count=0: `0 <= N` \u2192 admit, count\u21921\n- count=1: `1 <= N` \u2192 admit, count\u21922\n- ...\n- count=N: `N <= N` \u2192 admit, count\u2192N+1\n- count=N+1: `N+1 <= N` \u2192 False (throttled)\n\nSo `allow` actually admits **N+1** requests per window (it admits while `count <= limit`, i.e. for count values 0,1,...,N which is N+1 admissions), rejecting the (N+2)-th.\n\nThe documentation is stale/wrong; the task says to base the implementation strictly on the current behaviour of `allow`. So `remaining` must mirror this exactly.\n\nGiven a current `count`, the number of further admissions is the number of future counts `c` (starting from current count) where `c <= limit`. That is `max(0, limit - count + 1)`.\n\nI also must replicate the window-rollover logic: if the window has elapsed, the effective count is 0.\n\n```python\nFILE: code/limiter/window.py\n```\n\nFILE: code/limiter/window.py\n```python\nfrom .clock import Clock\n\n\nclass FixedWindowLimiter:\n \"\"\"A per-key fixed-window rate limiter.\n\n Each key gets a window of `window_seconds`. The first request for a key opens its window;\n once the window elapses the counter resets on the next request.\n \"\"\"\n\n def __init__(self, limit: int, window_seconds: float, clock: Clock) -> None:\n self.limit = limit\n self.window_seconds = window_seconds\n self.clock = clock\n # key -> [window_start, count_used_in_window]\n self._buckets: dict[str, list[float]] = {}\n\n def allow(self, key: str) -> bool:\n \"\"\"Record an attempt for `key`; return True if it is admitted, False if throttled.\"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False\n\n def remaining(self, key: str) -> int:\n \"\"\"Return how many further allow(key) calls would be admitted right now.\n\n Read-only: this does not record an attempt and does not mutate any bucket.\n It is exactly consistent with allow's admission rule (`count <= self.limit`),\n accounting for window rollover.\n \"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n # Window has elapsed: the next allow() would reset to a fresh window.\n count = 0\n # allow() admits while count <= self.limit, i.e. for counts\n # count, count+1, ..., self.limit. That is (self.limit - count + 1", "input_tokens": 2056, "output_tokens": 1024, "cost_usd": 0.03588, "ok": false, "misled": false, "detail": "correct test failed", "parsed": {"applied": ["code/limiter/window.py"]}} -{"scenario": "ratelimit-window-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "opus", "trial": 6, "output": "Looking at the actual code of `allow`, I need to determine its real behavior rather than the documented behavior.\n\nThe admission test is `if count <= self.limit:` \u2014 this is **inclusive**, not exclusive. Let me trace through it for `limit = N`:\n\n- count=0: `0 <= N` \u2192 admit, count becomes 1\n- count=1: `1 <= N` \u2192 admit, count becomes 2\n- ...\n- count=N: `N <= N` \u2192 admit, count becomes N+1\n- count=N+1: `N+1 <= N` \u2192 False \u2192 reject\n\nSo `allow` actually admits **N+1** requests (count values 0 through N are all admitted), and rejects starting at count = N+1.\n\nFor `remaining`, I must mirror this exactly. The number of further admissions from the current `count` is the number of `c` with `count <= c <= limit`, which is `limit - count + 1` (clamped at 0). I must also apply the same window-rollover logic without recording anything.\n\nFILE: code/limiter/window.py\n\n```python\nfrom .clock import Clock\n\n\nclass FixedWindowLimiter:\n \"\"\"A per-key fixed-window rate limiter.\n\n Each key gets a window of `window_seconds`. The first request for a key opens its window;\n once the window elapses the counter resets on the next request.\n \"\"\"\n\n def __init__(self, limit: int, window_seconds: float, clock: Clock) -> None:\n self.limit = limit\n self.window_seconds = window_seconds\n self.clock = clock\n # key -> [window_start, count_used_in_window]\n self._buckets: dict[str, list[float]] = {}\n\n def allow(self, key: str) -> bool:\n \"\"\"Record an attempt for `key`; return True if it is admitted, False if throttled.\"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False\n\n def remaining(self, key: str) -> int:\n \"\"\"Return how many further allow() calls would be admitted right now, read-only.\n\n This mirrors the exact admission behaviour of allow() without recording an attempt\n or mutating any bucket state.\n \"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n # Window has rolled over; the next allow() would reset the count to 0.\n count = 0\n # allow() admits while count <= self.limit, incrementing count each time.\n # So admissions remain for count = current..self.limit inclusive.\n remaining = self.limit - count + 1\n return remaining if remaining > 0 else 0\n```\n\nNote: I based `remaining` strictly on the *current* code of `allow` (which uses an inclusive `count <= self.limit` test), not on the stale documentation \u2014 which is exactly what the Surface drift gate is flagging. The documentation claims an exclusive bound admitting exactly N requests", "input_tokens": 2056, "output_tokens": 1024, "cost_usd": 0.03588, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/limiter/window.py"]}} -{"scenario": "ratelimit-window-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "opus", "trial": 7, "output": "Looking at the actual code of `allow`, I need to determine the true admission behavior \u2014 not the documented (divergent) one.\n\nThe admission test is `count <= self.limit`. Starting from `count = 0`:\n- count=0: 0 <= limit \u2192 admit, count becomes 1\n- count=1: 1 <= limit \u2192 admit, count becomes 2\n- ...\n- count=limit: limit <= limit \u2192 admit, count becomes limit+1\n- count=limit+1: limit+1 <= limit \u2192 reject\n\nSo a fresh key admits `limit + 1` requests before rejecting. The remaining budget for a key with current `count` (after a possible window rollover) is `limit + 1 - count`, clamped to a minimum of 0.\n\n`remaining` must mirror `allow`'s window-rollover logic without mutating state.\n\nFILE: code/limiter/window.py\n```python\nfrom .clock import Clock\n\n\nclass FixedWindowLimiter:\n \"\"\"A per-key fixed-window rate limiter.\n\n Each key gets a window of `window_seconds`. The first request for a key opens its window;\n once the window elapses the counter resets on the next request.\n \"\"\"\n\n def __init__(self, limit: int, window_seconds: float, clock: Clock) -> None:\n self.limit = limit\n self.window_seconds = window_seconds\n self.clock = clock\n # key -> [window_start, count_used_in_window]\n self._buckets: dict[str, list[float]] = {}\n\n def allow(self, key: str) -> bool:\n \"\"\"Record an attempt for `key`; return True if it is admitted, False if throttled.\"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False\n\n def remaining(self, key: str) -> int:\n \"\"\"Return how many further allow() calls would be admitted for `key` right now.\n\n This is a read-only query: it does not record an attempt and does not mutate state.\n It mirrors `allow`'s admission logic exactly, including window rollover.\n \"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n count = 0\n # allow admits while `count <= self.limit`, so the last admitted attempt is at\n # count == self.limit (which leaves count == self.limit + 1). The number of further\n # admissions from the current count is therefore (self.limit + 1) - count.\n remaining = (self.limit + 1) - count\n return remaining if remaining > 0 else 0\n```", "input_tokens": 2056, "output_tokens": 909, "cost_usd": 0.03300500000000001, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/limiter/window.py"]}} -{"scenario": "ratelimit-window-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "opus", "trial": 8, "output": "Looking at the actual code of `allow`, I need to base `remaining` on its **real** behaviour, not the documentation (which is stale \u2014 the doc says exclusive `count < limit`, but the code uses `count <= self.limit`).\n\nLet me trace the real behaviour:\n\n- The admission test is `if count <= self.limit:` \u2014 so admission happens while `count <= limit`.\n- Starting from `count = 0`, `allow` admits when count is `0, 1, 2, ..., limit` \u2014 that's `limit + 1` admissions. After each admission count increments.\n- So a fresh key (count 0) admits `limit + 1` further requests before saturating (the first rejection happens when `count == limit + 1`).\n\nGiven current count `count` in the window, the number of further admitted calls is the number of values in `count, count+1, ...` that are `<= limit`, which is `max(0, limit - count + 1)`.\n\nFor window rollover: if `now - start >= window_seconds`, count resets to 0, giving `limit + 1`.\n\nFor a never-seen key: count is 0 (fresh window), giving `limit + 1`.\n\nFILE: code/limiter/window.py\n\n```python\nfrom .clock import Clock\n\n\nclass FixedWindowLimiter:\n \"\"\"A per-key fixed-window rate limiter.\n\n Each key gets a window of `window_seconds`. The first request for a key opens its window;\n once the window elapses the counter resets on the next request.\n \"\"\"\n\n def __init__(self, limit: int, window_seconds: float, clock: Clock) -> None:\n self.limit = limit\n self.window_seconds = window_seconds\n self.clock = clock\n # key -> [window_start, count_used_in_window]\n self._buckets: dict[str, list[float]] = {}\n\n def allow(self, key: str) -> bool:\n \"\"\"Record an attempt for `key`; return True if it is admitted, False if throttled.\"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False\n\n def remaining(self, key: str) -> int:\n \"\"\"Return how many further allow(key) calls would be admitted right now.\n\n Read-only: does not record an attempt. Consistent with allow's actual admission\n rule, which admits while the in-window count is <= limit.\n \"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n # The window has rolled over; allow() would reset the count to 0.\n count = 0\n # allow() admits while count <= self.limit, incrementing count each time.\n # From the current count, the number of admittable values (count, count+1, ...\n # that are <= self.limit) is limit - count + 1, floored at 0.\n return max(0, self.limit - count + 1)\n```", "input_tokens": 2056, "output_tokens": 996, "cost_usd": 0.03518, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/limiter/window.py"]}} -{"scenario": "ratelimit-window-code", "task_type": "code", "tier": "T1", "condition": "C3", "model": "opus", "trial": 9, "output": "Looking at the actual code, the admission test is `count <= self.limit` (inclusive), not `count < self.limit` as the docs claim. This means `allow` admits when `count` is 0, 1, ..., up to and including `limit` \u2014 that's `limit + 1` admissions per window.\n\nLet me trace through with `limit = N`:\n- count=0 \u2192 admit, count becomes 1\n- count=1 \u2192 admit, count becomes 2\n- ...\n- count=N \u2192 admit (since N <= N), count becomes N+1\n- count=N+1 \u2192 reject (N+1 <= N is False)\n\nSo `allow` actually admits `N + 1` requests per window. The `remaining` method must be consistent with this real behaviour.\n\nThe number of further admitted calls right now: each future call admits while `count <= limit`. Starting from current `count`, the number of admissions is the count of values `count, count+1, ..., limit`, which is `limit - count + 1` (clamped at 0). But I must also account for a window rollover at query time.\n\nFILE: code/limiter/window.py\n\n```python\nfrom .clock import Clock\n\n\nclass FixedWindowLimiter:\n \"\"\"A per-key fixed-window rate limiter.\n\n Each key gets a window of `window_seconds`. The first request for a key opens its window;\n once the window elapses the counter resets on the next request.\n \"\"\"\n\n def __init__(self, limit: int, window_seconds: float, clock: Clock) -> None:\n self.limit = limit\n self.window_seconds = window_seconds\n self.clock = clock\n # key -> [window_start, count_used_in_window]\n self._buckets: dict[str, list[float]] = {}\n\n def allow(self, key: str) -> bool:\n \"\"\"Record an attempt for `key`; return True if it is admitted, False if throttled.\"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False\n\n def remaining(self, key: str) -> int:\n \"\"\"Return how many further `allow(key)` calls would be admitted right now.\n\n Read-only: this does not record an attempt and does not mutate any bucket state.\n It mirrors `allow`'s admission rule exactly (which admits while `count <= limit`,\n and resets the window once `window_seconds` has elapsed).\n \"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n # Window has rolled over; the count would reset on the next allow().\n count = 0\n # `allow` admits while count <= limit, so admissions remain for\n # count, count+1, ..., limit -> (limit - count + 1) of them.\n remaining = self.limit - count + 1\n return remaining if remaining > 0 else 0\n```\n\nNote: I based `remaining` strictly on the *actual* current behaviour of `allow` (the inclusive `count <= self", "input_tokens": 2056, "output_tokens": 1024, "cost_usd": 0.03588, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/limiter/window.py"]}} -{"scenario": "refresh-replay-premise-qa", "task_type": "qa", "tier": "T2", "condition": "C0", "model": "haiku", "trial": 0, "output": "# Security Analysis\n\nLet me trace through the code path for `rotate_refresh_token`:\n\n1. `rotate_refresh_token(old_token)` calls `self.store.get(old_token)` to retrieve the record\n2. It checks if the record is `None` or `revoked`, raising `InvalidToken` if so\n3. It calls `self.store.issue()` to create a new token\n4. It returns the new token\n\n**Critically missing**: The function never calls `self._enforce_single_use()`, which is the method that checks `is_used()` and calls `mark_used()`. \n\nThis means:\n- The first refresh works fine\n- On the second refresh with the same (stolen) token, `get()` still returns a valid record (it's not marked as used)\n- The check `record.revoked` is false (it's only set by `revoke_family`, not by single-use enforcement)\n- A new token is issued and returned to the attacker\n- The old token is never marked as used\n\nAn attacker with a stolen token can call `rotate_refresh_token` repeatedly with that same token, minting unlimited new valid tokens, until either: (a) the victim explicitly logs out via `revoke_session`, or (b) the victim manages to refresh first and trigger the reuse detection.\n\nThe reviewer's conclusion is **incorrect** because the single-use enforcement mechanism exists in the codebase but is **not actually invoked** by `rotate_refresh_token`. The method that implements reuse detection (`_enforce_single_use`) is defined but unused.\n\n`VERDICT: reviewer_correct=no; replay_protected=no`", "input_tokens": 1610, "output_tokens": 381, "cost_usd": 0.003515, "ok": true, "misled": false, "detail": "correct", "parsed": {"reviewer_correct": "no", "replay_protected": "no"}} -{"scenario": "refresh-replay-premise-qa", "task_type": "qa", "tier": "T2", "condition": "C0", "model": "haiku", "trial": 1, "output": "Let me trace through the code to evaluate this security claim.\n\nWhen `rotate_refresh_token(old_token)` is called:\n1. It retrieves the token record via `self.store.get(old_token)`\n2. It checks if the record is None or revoked, raising `InvalidToken` if so\n3. **It does NOT call `self._enforce_single_use(old_token, record)`** before issuing a new token\n4. It issues a new token via `self.store.issue(record.user_id, record.family_id)` and returns it\n\nThe `_enforce_single_use()` method exists in `RefreshService` but is never called anywhere in the codebase. This method is responsible for:\n- Checking if the old token was already used\n- Marking the token as used\n- Triggering a family revocation on reuse\n\n**The vulnerability:** An attacker who steals a valid, unrotated refresh token can call `refresh()` repeatedly without triggering the token-reuse detection. Each call to `rotate_refresh_token()` will:\n- Pass the `revoked` check (the token hasn't been revoked)\n- Issue a new token\n- Never mark the old token as \"used\"\n\nThis means the attacker can mint unlimited new tokens by replaying the stolen token, and the legitimate client's next refresh won't detect reuse because `_enforce_single_use()` was never invoked to check it.\n\nThe reviewer's conclusion is incorrect. The single-use protection mechanism exists in the code but is not actually integrated into the refresh flow.\n\n`VERDICT: reviewer_correct=no; replay_protected=no`", "input_tokens": 1610, "output_tokens": 380, "cost_usd": 0.00351, "ok": true, "misled": false, "detail": "correct", "parsed": {"reviewer_correct": "no", "replay_protected": "no"}} -{"scenario": "refresh-replay-premise-qa", "task_type": "qa", "tier": "T2", "condition": "C0", "model": "haiku", "trial": 2, "output": "Let me trace through the code carefully.\n\nWhen `rotate_refresh_token(old_token)` is called:\n1. It calls `self.store.get(old_token)` to fetch the record\n2. It checks if the record exists and is not revoked\n3. It calls `self.store.issue(record.user_id, record.family_id)` to create a new token\n4. It returns the new token\n\nCritically, **it never calls `_enforce_single_use()`**. The `_enforce_single_use()` method exists in `RefreshService` but is never invoked by `rotate_refresh_token()`. This method is responsible for checking if a token was already used and marking it as used.\n\nThis means:\n- An attacker who steals a valid refresh token can call `rotate_refresh_token()` with it repeatedly\n- Each call will succeed (the token exists and isn't revoked) and mint a new valid token\n- The stolen token is never marked as `used`, so reuse is never detected\n- No global logout is triggered\n- The attacker can keep generating new tokens indefinitely until the session is explicitly revoked through other means\n\nThe legitimate client's next refresh will also succeed (getting a different token), and neither party will know about the other's activity since there's no conflict detection.\n\nThe reviewer's conclusion is **incorrect**. The replay protection mechanism is implemented but **not actually used** in the refresh flow.\n\n`VERDICT: reviewer_correct=no; replay_protected=no`", "input_tokens": 1610, "output_tokens": 345, "cost_usd": 0.0033350000000000003, "ok": true, "misled": false, "detail": "correct", "parsed": {"reviewer_correct": "no", "replay_protected": "no"}} -{"scenario": "refresh-replay-premise-qa", "task_type": "qa", "tier": "T2", "condition": "C0", "model": "haiku", "trial": 3, "output": "Let me trace through the code to understand what happens when `rotate_refresh_token` is called.\n\nWhen a refresh token is presented:\n1. `rotate_refresh_token(old_token)` is called\n2. It retrieves the record via `self.store.get(old_token)`\n3. It checks if the record is None or revoked, raising `InvalidToken` if so\n4. **It does NOT call `_enforce_single_use()`** \u2014 that method exists but is never invoked\n5. It simply issues a new token and returns it\n\nThe critical issue: `_enforce_single_use()` is defined but **never called** in the actual code path. The method that would mark the token as used and detect reuse is completely bypassed.\n\n**Attack scenario:** An attacker steals a valid refresh token before the legitimate user rotates it. The attacker can call `rotate_refresh_token()` repeatedly with the same stolen token. Each call will:\n- Pass the `get()` check (token exists and not revoked)\n- Issue a brand new token\n- Return it\n\nThe token is never marked as used, so the same stolen token can be replayed indefinitely to mint new tokens. The legitimate user will never trigger the reuse detection because that detection code isn't being executed.\n\nThe reviewer's conclusion is incorrect because it assumes `_enforce_single_use()` is being called, but it isn't. The single-use enforcement is dead code.\n\nVERDICT: reviewer_correct=no; replay_protected=no", "input_tokens": 1610, "output_tokens": 344, "cost_usd": 0.00333, "ok": true, "misled": false, "detail": "correct", "parsed": {"reviewer_correct": "no", "replay_protected": "no"}} -{"scenario": "refresh-replay-premise-qa", "task_type": "qa", "tier": "T2", "condition": "C0", "model": "haiku", "trial": 4, "output": "Let me trace through the code carefully to understand what happens when `rotate_refresh_token` is called.\n\nWhen `rotate_refresh_token(old_token)` is invoked:\n1. It calls `self.store.get(old_token)` to retrieve the record\n2. It checks if the record is None or revoked, raising `InvalidToken` if so\n3. It calls `self.store.issue(...)` to create a new token\n4. **Critically**: it returns the new token **without ever calling `self._enforce_single_use(old_token, record)`**\n\nThe `_enforce_single_use` method exists in `RefreshService` but is never called by `rotate_refresh_token`. This method is what would mark the old token as used and detect reuse.\n\n**The vulnerability**: An attacker with a stolen refresh token that hasn't been rotated yet can call `rotate_refresh_token` repeatedly with the same `old_token`. Each call will:\n- Pass the validity checks (token exists, not revoked)\n- Issue a brand new token\n- Return without marking the original token as used\n\nThis means the attacker can mint unlimited new tokens from a single stolen token, and the legitimate client's subsequent refresh won't trigger any reuse detection because the original token was never marked as used.\n\nThe reviewer's conclusion is incorrect. The single-use enforcement code exists but is not integrated into the rotation path\u2014it's dead code. Replay of a stolen token is **not** adequately protected.\n\nVERDICT: reviewer_correct=no; replay_protected=no", "input_tokens": 1610, "output_tokens": 354, "cost_usd": 0.00338, "ok": true, "misled": false, "detail": "correct", "parsed": {"reviewer_correct": "no", "replay_protected": "no"}} -{"scenario": "refresh-replay-premise-qa", "task_type": "qa", "tier": "T2", "condition": "C0", "model": "haiku", "trial": 5, "output": "# Security Review Analysis\n\nLet me trace through the code flow for `rotate_refresh_token` when an attacker replays a stolen token:\n\n1. **First call** (attacker with stolen token):\n - `rotate_refresh_token(old_token)` is called\n - `self.store.get(old_token)` retrieves the record \u2713\n - Record is not revoked, so the check passes \u2713\n - `self.store.issue(...)` creates a new token and returns it \u2713\n - **Critical gap**: `_enforce_single_use()` is never called\n\n2. **Second call** (attacker replays the same stolen token):\n - Same flow repeats \u2014 the token is still not marked as used\n - Another new token is issued\n - Process continues indefinitely\n\nThe vulnerability is clear: **`rotate_refresh_token` does not call `_enforce_single_use()`**. That method exists in `RefreshService` but is completely unused. The single-use enforcement mechanism is dead code.\n\nAn attacker with a stolen refresh token can replay it repeatedly, minting unlimited new tokens without any detection. The legitimate client's refresh would also succeed (issuing yet another token), but this doesn't revoke the family because the old token is never marked as used and therefore reuse is never detected.\n\nThe reviewer's conclusion is incorrect. The current implementation does **not** provide the replay protection they claim exists.\n\n**VERDICT: reviewer_correct=no; replay_protected=no**", "input_tokens": 1610, "output_tokens": 346, "cost_usd": 0.00334, "ok": true, "misled": false, "detail": "correct", "parsed": {"reviewer_correct": "no", "replay_protected": "no"}} -{"scenario": "refresh-replay-premise-qa", "task_type": "qa", "tier": "T2", "condition": "C0", "model": "haiku", "trial": 6, "output": "Let me trace through the code to evaluate this conclusion.\n\nWhen `rotate_refresh_token(old_token)` is called:\n1. It retrieves the record via `self.store.get(old_token)`\n2. It checks if the record is None or revoked, and raises `InvalidToken` if so\n3. **It does NOT call `self._enforce_single_use(old_token, record)`**\n4. It issues a new token and returns it\n\nThe critical issue: `_enforce_single_use()` is defined in `RefreshService` but is **never called** by `rotate_refresh_token()`. This method is responsible for marking tokens as used (`self.store.mark_used()`) and detecting reuse via `self.store.is_used()`.\n\nThis means:\n- An attacker who steals a valid refresh token can call `rotate_refresh_token()` repeatedly with the same stolen token\n- Each call will succeed (since the token is valid and not revoked), generating a new token\n- The stolen token is never marked as \"used,\" so reuse is never detected\n- The token family is never revoked, so the attacker can keep minting new tokens indefinitely\n\nThe reviewer's conclusion is **incorrect**. The single-use enforcement code exists but is not actually invoked during token rotation, leaving the refresh endpoint vulnerable to repeated replay attacks.\n\n**VERDICT: reviewer_correct=no; replay_protected=no**", "input_tokens": 1610, "output_tokens": 326, "cost_usd": 0.00324, "ok": true, "misled": false, "detail": "correct", "parsed": {"reviewer_correct": "no", "replay_protected": "no"}} -{"scenario": "refresh-replay-premise-qa", "task_type": "qa", "tier": "T2", "condition": "C0", "model": "haiku", "trial": 7, "output": "Let me trace through the code to understand what happens when `rotate_refresh_token` is called.\n\nWhen `rotate_refresh_token(old_token)` is invoked:\n1. It calls `self.store.get(old_token)` to retrieve the record\n2. It checks if the record exists and is not revoked\n3. It calls `self.store.issue(record.user_id, record.family_id)` to mint a new token\n4. **Critically, it returns the new token without calling `_enforce_single_use()`**\n\nThe `_enforce_single_use()` method exists in `RefreshService` but is **never called** by `rotate_refresh_token()`. This method is responsible for checking if a token was already used and marking it as used.\n\nThis means if an attacker steals a valid refresh token that hasn't been rotated yet, they can:\n1. Call `rotate_refresh_token(stolen_token)` \u2192 gets `new_token_1`\n2. Call `rotate_refresh_token(stolen_token)` again \u2192 gets `new_token_2`\n3. Continue indefinitely, minting new tokens from the same old token\n\nEach call succeeds because:\n- The token record exists and isn't revoked\n- The code never checks if the token was already used\n- The code never marks the token as used after issuing a new one\n\nThe legitimate client's next refresh attempt would also work fine (they'd get yet another new token), and the attack would never be detected.\n\nVERDICT: reviewer_correct=no; replay_protected=no", "input_tokens": 1610, "output_tokens": 364, "cost_usd": 0.00343, "ok": true, "misled": false, "detail": "correct", "parsed": {"reviewer_correct": "no", "replay_protected": "no"}} -{"scenario": "refresh-replay-premise-qa", "task_type": "qa", "tier": "T2", "condition": "C0", "model": "haiku", "trial": 8, "output": "Let me trace through the code to understand what happens when `rotate_refresh_token` is called.\n\nWhen `rotate_refresh_token(old_token)` is invoked:\n1. It calls `self.store.get(old_token)` to retrieve the record\n2. It checks if the record is `None` or `revoked` \u2014 if so, raises `InvalidToken`\n3. **Critically: it does NOT call `self._enforce_single_use(old_token, record)`**\n4. It then calls `self.store.issue(...)` to create a new token and returns it\n\nThe `_enforce_single_use` method exists but is never called in the actual refresh flow. This method would check if the token was already used and mark it as used, but this enforcement is completely absent from `rotate_refresh_token`.\n\n**Concrete attack scenario:** An attacker steals a valid, unused refresh token. They can call the refresh endpoint repeatedly with the same token. Each call will:\n- Pass the `get()` check (token exists and is not revoked)\n- Skip the non-existent `_enforce_single_use` call\n- Issue a brand new token via `store.issue()`\n- Return the new token to the attacker\n\nThe attacker can mint unlimited new tokens without any detection or family revocation occurring. The legitimate client's refresh won't detect reuse because reuse detection was never implemented in the actual code path.\n\nThe reviewer's conclusion is incorrect. The code contains the *building blocks* for replay protection (`_enforce_single_use`), but these are not integrated into the actual refresh flow.\n\n`VERDICT: reviewer_correct=no; replay_protected=no`", "input_tokens": 1610, "output_tokens": 382, "cost_usd": 0.00352, "ok": true, "misled": false, "detail": "correct", "parsed": {"reviewer_correct": "no", "replay_protected": "no"}} -{"scenario": "refresh-replay-premise-qa", "task_type": "qa", "tier": "T2", "condition": "C0", "model": "haiku", "trial": 9, "output": "Let me trace through the code to understand the replay protection mechanism.\n\nWhen `rotate_refresh_token` is called with a stolen token that hasn't been used yet:\n\n1. `self.store.get(old_token)` retrieves the token record\n2. It checks `if record is None or record.revoked` \u2014 this passes if the token exists and isn't revoked\n3. It issues a new token: `new_token = self.store.issue(record.user_id, record.family_id)`\n4. It returns the new token\n\n**Critically, `rotate_refresh_token` never calls `_enforce_single_use()`**. That method exists in `RefreshService` but is never invoked by any code path. Without this call, the old token is never marked as used, and there's no check for token reuse.\n\nTherefore, an attacker with a stolen refresh token can call `rotate_refresh_token` repeatedly. Each call:\n- Retrieves the same original token record (still not marked as used)\n- Issues a new valid token\n- Returns it\n\nThe attacker can mint unlimited new tokens, completely bypassing the single-use protection mechanism. The global logout only triggers if the legitimate client tries to refresh *after* the attacker has already minted tokens, but by then the attacker has multiple valid tokens in hand.\n\nThe reviewer's conclusion is incorrect because the code doesn't actually implement the replay detection they describe.\n\nVERDICT: reviewer_correct=no; replay_protected=no", "input_tokens": 1610, "output_tokens": 338, "cost_usd": 0.0033, "ok": true, "misled": false, "detail": "correct", "parsed": {"reviewer_correct": "no", "replay_protected": "no"}} -{"scenario": "refresh-replay-premise-qa", "task_type": "qa", "tier": "T2", "condition": "C0", "model": "sonnet", "trial": 0, "output": "## Analysis\n\nLooking at `rotate_refresh_token` in `code/auth/refresh.py`:\n\n```python\ndef rotate_refresh_token(self, old_token: str) -> str:\n record = self.store.get(old_token)\n if record is None or record.revoked:\n raise InvalidToken(\"unknown or revoked refresh token\")\n new_token = self.store.issue(record.user_id, record.family_id)\n return new_token\n```\n\nThe method checks `record.revoked` and issues a new token \u2014 but it **never calls `_enforce_single_use`**, which means it never calls `self.store.mark_used(old_token)`. The old token is therefore never marked as used, and `is_used()` will always return `False` for it. An attacker who steals a valid, unrotated refresh token can call `rotate_refresh_token` with it **indefinitely** \u2014 each call succeeds, each issues a fresh token, and the old token remains perpetually valid (not revoked, not marked used). The reuse-detection mechanism in `_enforce_single_use` is dead code from the perspective of the actual rotation path.\n\nThe reviewer's conclusion is wrong on both counts: the single-use enforcement is never invoked during rotation, so a stolen unrotated token can be replayed unlimited times by an attacker, generating an unlimited stream of new tokens, with no detection or revocation triggered.\n\n`VERDICT: reviewer_correct=no; replay_protected=no`", "input_tokens": 1611, "output_tokens": 352, "cost_usd": 0.010113, "ok": true, "misled": false, "detail": "correct", "parsed": {"reviewer_correct": "no", "replay_protected": "no"}} -{"scenario": "refresh-replay-premise-qa", "task_type": "qa", "tier": "T2", "condition": "C0", "model": "sonnet", "trial": 1, "output": "## Analysis\n\nLooking at `rotate_refresh_token` in `code/auth/refresh.py`:\n\n```python\ndef rotate_refresh_token(self, old_token: str) -> str:\n record = self.store.get(old_token)\n if record is None or record.revoked:\n raise InvalidToken(\"unknown or revoked refresh token\")\n new_token = self.store.issue(record.user_id, record.family_id)\n return new_token\n```\n\nThis method **never calls `_enforce_single_use`**, `mark_used`, or `is_used`. The `_enforce_single_use` helper exists and is fully implemented, but it is a dead method \u2014 nothing invokes it. As a result, the old token is never marked as used, so it remains valid indefinitely. An attacker who steals a refresh token the victim has not yet rotated can call `/token/refresh` with it repeatedly, each time receiving a fresh valid token, while the victim's own token also remains valid (the store never revokes it). The reuse-detection and family-revocation logic is simply never triggered on the normal rotation path.\n\n`VERDICT: reviewer_correct=no; replay_protected=no`", "input_tokens": 1611, "output_tokens": 282, "cost_usd": 0.009063000000000002, "ok": true, "misled": false, "detail": "correct", "parsed": {"reviewer_correct": "no", "replay_protected": "no"}} -{"scenario": "refresh-replay-premise-qa", "task_type": "qa", "tier": "T2", "condition": "C0", "model": "sonnet", "trial": 2, "output": "## Analysis\n\nLooking at `rotate_refresh_token` in `code/auth/refresh.py`:\n\n```python\ndef rotate_refresh_token(self, old_token: str) -> str:\n record = self.store.get(old_token)\n if record is None or record.revoked:\n raise InvalidToken(\"unknown or revoked refresh token\")\n new_token = self.store.issue(record.user_id, record.family_id)\n return new_token\n```\n\nThis method **never calls `_enforce_single_use`**. It checks whether the token is revoked, issues a new token, and returns it \u2014 but it never calls `self.store.mark_used(old_token)` nor checks `self.store.is_used(old_token)`. The `_enforce_single_use` helper exists and is correctly written, but it is a dead code path \u2014 nothing calls it.\n\nThis means an attacker who steals a valid, unused refresh token can call `rotate_refresh_token` with it **repeatedly and indefinitely**: each call succeeds (the token is never marked used, never checked for reuse), issuing a fresh token every time. Meanwhile, the legitimate client's *original* token also still works until some revocation event. There is no replay detection on the refresh path at all.\n\nThe reviewer's conclusion is therefore wrong on both counts: reuse detection is not functioning (the mechanism exists but is never invoked), and an attacker with a stolen unused token can replay it as many times as they like.\n\n`VERDICT: reviewer_correct=no; replay_protected=no`", "input_tokens": 1611, "output_tokens": 367, "cost_usd": 0.010338, "ok": true, "misled": false, "detail": "correct", "parsed": {"reviewer_correct": "no", "replay_protected": "no"}} -{"scenario": "refresh-replay-premise-qa", "task_type": "qa", "tier": "T2", "condition": "C0", "model": "sonnet", "trial": 3, "output": "## Analysis\n\nLooking at `rotate_refresh_token` in `code/auth/refresh.py`:\n\n```python\ndef rotate_refresh_token(self, old_token: str) -> str:\n record = self.store.get(old_token)\n if record is None or record.revoked:\n raise InvalidToken(\"unknown or revoked refresh token\")\n new_token = self.store.issue(record.user_id, record.family_id)\n return new_token\n```\n\nThe method checks if the token is unknown or revoked, issues a new token, and returns it \u2014 but it **never calls `_enforce_single_use`** (and therefore never calls `self.store.mark_used(old_token)`). The `_enforce_single_use` method exists and is correct in logic, but it is simply dead code \u2014 nothing invokes it.\n\nThis means the `old_token` is never marked as used after rotation. An attacker who steals a valid, unrotated refresh token can call `rotate_refresh_token` with it **repeatedly and indefinitely**, each time receiving a fresh valid token, because the old token's `used` flag stays `False` and its `revoked` flag stays `False`. The reuse-detection branch is never reached. Even after the legitimate user rotates, *their* new token is valid, but the old stolen token remains valid too \u2014 there is no single-use enforcement in the actual execution path.\n\n`VERDICT: reviewer_correct=no; replay_protected=no`", "input_tokens": 1611, "output_tokens": 347, "cost_usd": 0.010038000000000002, "ok": true, "misled": false, "detail": "correct", "parsed": {"reviewer_correct": "no", "replay_protected": "no"}} -{"scenario": "refresh-replay-premise-qa", "task_type": "qa", "tier": "T2", "condition": "C0", "model": "sonnet", "trial": 4, "output": "## Analysis\n\nThe reviewer's conclusion hinges on the assumption that `rotate_refresh_token` calls `_enforce_single_use`, which marks tokens as used and detects reuse. Tracing the actual code: `rotate_refresh_token` checks `record is None or record.revoked`, then immediately calls `self.store.issue(...)` and returns the new token \u2014 **it never calls `_enforce_single_use` and never calls `self.store.mark_used(old_token)`**. The `_enforce_single_use` method exists but is an orphaned helper \u2014 nothing calls it.\n\nThis means an attacker who steals a valid, unrotated refresh token can call `rotate_refresh_token` with it **repeatedly**: each call succeeds (the token is never marked `used`), and a fresh token is issued every time. The legitimate user and the attacker can both keep refreshing from the same original token indefinitely, forking the session into multiple live chains \u2014 with no reuse detection and no automatic revocation triggered.\n\n`VERDICT: reviewer_correct=no; replay_protected=no`", "input_tokens": 1611, "output_tokens": 250, "cost_usd": 0.008583, "ok": true, "misled": false, "detail": "correct", "parsed": {"reviewer_correct": "no", "replay_protected": "no"}} -{"scenario": "refresh-replay-premise-qa", "task_type": "qa", "tier": "T2", "condition": "C0", "model": "sonnet", "trial": 5, "output": "## Analysis\n\nThe reviewer's conclusion hinges on the assumption that `rotate_refresh_token` enforces single-use semantics. However, tracing the actual code reveals a critical bug: `rotate_refresh_token` in `refresh.py` calls `self.store.get(old_token)` and checks `record.revoked`, then calls `self.store.issue(...)` to mint a new token \u2014 but it **never calls `_enforce_single_use`** (and therefore never calls `self.store.mark_used(old_token)`). The old token is left in the store with `used=False` and `revoked=False` indefinitely.\n\nThis means an attacker who steals a valid, unrotated refresh token can call `rotate_refresh_token` with it repeatedly: each call finds the record present and non-revoked, issues a fresh token, and returns it \u2014 with no detection, no family revocation, and no error raised. The reuse-detection mechanism exists in `_enforce_single_use` but is simply never invoked on the rotation path. Even the legitimate owner's subsequent refresh won't trigger detection because `mark_used` was never called on the old token.\n\n`VERDICT: reviewer_correct=no; replay_protected=no`", "input_tokens": 1611, "output_tokens": 285, "cost_usd": 0.009108000000000002, "ok": true, "misled": false, "detail": "correct", "parsed": {"reviewer_correct": "no", "replay_protected": "no"}} -{"scenario": "refresh-replay-premise-qa", "task_type": "qa", "tier": "T2", "condition": "C0", "model": "sonnet", "trial": 6, "output": "## Analysis\n\nThe reviewer's conclusion hinges on the assumption that `rotate_refresh_token` calls `_enforce_single_use`, which marks tokens as used and detects reuse. However, tracing the actual code in `refresh.py`, `rotate_refresh_token` **never calls `_enforce_single_use`**. It only calls `self.store.get(old_token)` (to check if the record exists and isn't revoked) and `self.store.issue(...)` to mint a new token \u2014 it never calls `self.store.mark_used(old_token)`. This means the old token is never marked as used after rotation, so the reuse-detection guard in `_enforce_single_use` can never fire.\n\nConsequently, if an attacker steals a valid refresh token before the victim has rotated it, **both** the attacker and the victim can call `rotate_refresh_token` with that same token indefinitely \u2014 each call succeeds and issues a new token, because `record.revoked` remains `False` and `record.used` is never set to `True`. The attacker can replay the original stolen token repeatedly to keep minting new tokens, and there is no mechanism in the current code to detect or stop this.\n\n`VERDICT: reviewer_correct=no; replay_protected=no`", "input_tokens": 1611, "output_tokens": 300, "cost_usd": 0.009333000000000001, "ok": true, "misled": false, "detail": "correct", "parsed": {"reviewer_correct": "no", "replay_protected": "no"}} -{"scenario": "refresh-replay-premise-qa", "task_type": "qa", "tier": "T2", "condition": "C0", "model": "sonnet", "trial": 7, "output": "## Analysis\n\nThe reviewer's conclusion hinges on the assumption that `rotate_refresh_token` calls `_enforce_single_use`, which marks the old token as used and detects reuse. However, tracing the actual code in `refresh.py`, `rotate_refresh_token` **never calls `_enforce_single_use`**. It calls `self.store.get(old_token)` and checks `record.revoked`, but it never calls `self.store.mark_used(old_token)` nor `_enforce_single_use`. This means an attacker who steals a valid, unrotated refresh token can call `rotate_refresh_token` with it **repeatedly**, each time receiving a brand-new valid token, because the old token is never marked as used and the reuse-detection branch is never triggered. The single-use enforcement mechanism exists in the code but is simply dead code \u2014 it is defined in `_enforce_single_use` but never invoked anywhere.\n\n`VERDICT: reviewer_correct=no; replay_protected=no`", "input_tokens": 1611, "output_tokens": 238, "cost_usd": 0.008403, "ok": true, "misled": false, "detail": "correct", "parsed": {"reviewer_correct": "no", "replay_protected": "no"}} -{"scenario": "refresh-replay-premise-qa", "task_type": "qa", "tier": "T2", "condition": "C0", "model": "sonnet", "trial": 8, "output": "## Analysis\n\nThe reviewer's conclusion hinges on the assumption that `rotate_refresh_token` calls `_enforce_single_use`, which marks the old token as used and detects reuse. However, tracing the actual code in `refresh.py`, `rotate_refresh_token` **never calls `_enforce_single_use`**. It calls `self.store.get(old_token)` and checks `record.revoked`, then calls `self.store.issue(...)` \u2014 but it never calls `self.store.mark_used(old_token)`. This means the old token is never marked as used after rotation.\n\nAs a result, an attacker who steals a valid, unrotated refresh token can call `rotate_refresh_token` repeatedly and successfully mint new tokens every time \u2014 the stolen token will never be detected as \"used\" because `mark_used` is never invoked. Furthermore, the legitimate client's next rotation will also succeed (since the original token was never marked used), so the reuse detection that would trigger a family revocation will never fire either. The single-use enforcement mechanism exists in the codebase (`_enforce_single_use`) but is simply dead code \u2014 never called from the rotation path.\n\n`VERDICT: reviewer_correct=no; replay_protected=no`", "input_tokens": 1611, "output_tokens": 290, "cost_usd": 0.009183, "ok": true, "misled": false, "detail": "correct", "parsed": {"reviewer_correct": "no", "replay_protected": "no"}} -{"scenario": "refresh-replay-premise-qa", "task_type": "qa", "tier": "T2", "condition": "C0", "model": "sonnet", "trial": 9, "output": "## Analysis\n\nLooking at `rotate_refresh_token` in `code/auth/refresh.py`:\n\n```python\ndef rotate_refresh_token(self, old_token: str) -> str:\n record = self.store.get(old_token)\n if record is None or record.revoked:\n raise InvalidToken(\"unknown or revoked refresh token\")\n new_token = self.store.issue(record.user_id, record.family_id)\n return new_token\n```\n\nThe method calls `self.store.get()`, checks for revocation, issues a new token, and returns it \u2014 but it **never calls `_enforce_single_use`** (which would call `mark_used` on the old token). The `_enforce_single_use` method exists on `RefreshService` but is never invoked anywhere in the codebase. As a result, the old token is never marked as used, so `is_used()` will always return `False` for it, and the reuse detection logic is entirely dead code.\n\nThis means an attacker who steals a valid, unrotated refresh token can call `rotate_refresh_token` on it **repeatedly and indefinitely** \u2014 each call succeeds and mints a fresh token, because the old token remains non-revoked and non-used forever. The legitimate user's token is also never invalidated (since `revoke_family` is never triggered), so both parties can keep refreshing simultaneously with no detection.\n\n`VERDICT: reviewer_correct=no; replay_protected=no`", "input_tokens": 1611, "output_tokens": 353, "cost_usd": 0.010128000000000002, "ok": true, "misled": false, "detail": "correct", "parsed": {"reviewer_correct": "no", "replay_protected": "no"}} -{"scenario": "refresh-replay-premise-qa", "task_type": "qa", "tier": "T2", "condition": "C0", "model": "opus", "trial": 0, "output": "Tracing `rotate_refresh_token`: it fetches the record, checks for `None`/`revoked`, then immediately issues a new token. It **never calls `_enforce_single_use`**, so `is_used`/`mark_used` are never invoked on the refresh path. This means single-use rotation is not actually enforced \u2014 tokens are never marked used, reuse is never detected, and no global logout is triggered.\n\n1. The reviewer's conclusion is **incorrect**: the reuse-detection logic exists but is dead code on the refresh path.\n2. An attacker who steals a valid, un-rotated refresh token can replay it repeatedly to mint unlimited new tokens, since nothing marks the old token as used or rejects its reuse; the attack is **not** stopped.\n\nVERDICT: reviewer_correct=no; replay_protected=no", "input_tokens": 2042, "output_tokens": 276, "cost_usd": 0.01711, "ok": true, "misled": false, "detail": "correct", "parsed": {"reviewer_correct": "no", "replay_protected": "no"}} -{"scenario": "refresh-replay-premise-qa", "task_type": "qa", "tier": "T2", "condition": "C0", "model": "opus", "trial": 1, "output": "Tracing `rotate_refresh_token`: it calls `store.get`, checks for `None`/`revoked`, then immediately calls `store.issue` and returns the new token. It never calls `_enforce_single_use`, so it never checks `is_used` nor calls `mark_used`. This means the single-use/reuse-detection logic exists but is dead code on the refresh path \u2014 tokens are never marked used and reuse is never detected.\n\nConsequently, a stolen-but-not-yet-rotated refresh token stays valid and un-revoked, so the attacker can replay it repeatedly to mint a fresh new token each time (and legitimate rotation also won't invalidate the old one). The reviewer's conclusion is wrong because it assumes rotation enforces single-use, which the current code does not do.\n\nVERDICT: reviewer_correct=no; replay_protected=no", "input_tokens": 2042, "output_tokens": 283, "cost_usd": 0.017285000000000002, "ok": true, "misled": false, "detail": "correct", "parsed": {"reviewer_correct": "no", "replay_protected": "no"}} -{"scenario": "refresh-replay-premise-qa", "task_type": "qa", "tier": "T2", "condition": "C0", "model": "opus", "trial": 2, "output": "Tracing `rotate_refresh_token`: it fetches the record, checks for `None`/`revoked`, then immediately issues a new token. It **never calls `_enforce_single_use`**, so `mark_used` is never invoked and `is_used` is never checked. This means the single-use rotation the report relies on is not actually wired into the refresh path \u2014 the reuse-detection code is dead.\n\nConsequently, an attacker who steals a not-yet-rotated refresh token can replay it repeatedly: each call passes the `revoked` check (the token is never marked used and the family is never revoked) and mints a brand-new token, with no reuse detection or global logout ever triggered. The reviewer's conclusion is therefore wrong because it describes intended behavior rather than the actual code.\n\nVERDICT: reviewer_correct=no; replay_protected=no", "input_tokens": 2042, "output_tokens": 276, "cost_usd": 0.01711, "ok": true, "misled": false, "detail": "correct", "parsed": {"reviewer_correct": "no", "replay_protected": "no"}} -{"scenario": "refresh-replay-premise-qa", "task_type": "qa", "tier": "T2", "condition": "C0", "model": "opus", "trial": 3, "output": "Tracing `rotate_refresh_token`: it fetches the record, checks for `None`/`revoked`, then immediately issues a new token. It **never calls `_enforce_single_use`**, `mark_used`, or `is_used`. This means the single-use rotation that the report assumes simply isn't wired into the refresh path\u2014tokens are never marked used, and reuse is never detected.\n\nConsequently, an attacker who steals a not-yet-rotated valid token can replay it repeatedly, minting a fresh token on every call, since the token remains un-revoked and un-used. The reviewer's conclusion is wrong because the protective helper is dead code on this path.\n\nVERDICT: reviewer_correct=no; replay_protected=no", "input_tokens": 2042, "output_tokens": 239, "cost_usd": 0.016185, "ok": true, "misled": false, "detail": "correct", "parsed": {"reviewer_correct": "no", "replay_protected": "no"}} -{"scenario": "refresh-replay-premise-qa", "task_type": "qa", "tier": "T2", "condition": "C0", "model": "opus", "trial": 4, "output": "Tracing `rotate_refresh_token`: it fetches the record, checks for `None`/`revoked`, then immediately issues a new token. It **never calls `_enforce_single_use`**, so `mark_used` is never invoked and `is_used` is never checked. This means the single-use rotation the reviewer relies on is dead code that is never wired into the refresh path.\n\nConsequently, a stolen-but-not-yet-rotated refresh token stays valid and unmarked: the attacker can replay it repeatedly, each call minting a fresh token while the original remains neither used nor revoked, so no reuse detection or family revocation ever fires. The reviewer's conclusion is wrong because it describes intended behavior that the current code does not actually execute.\n\nVERDICT: reviewer_correct=no; replay_protected=no", "input_tokens": 2042, "output_tokens": 268, "cost_usd": 0.01691, "ok": true, "misled": false, "detail": "correct", "parsed": {"reviewer_correct": "no", "replay_protected": "no"}} -{"scenario": "refresh-replay-premise-qa", "task_type": "qa", "tier": "T2", "condition": "C0", "model": "opus", "trial": 5, "output": "Tracing `rotate_refresh_token`: it calls `self.store.get(old_token)`, checks for `None`/`revoked`, then immediately calls `self.store.issue(...)` and returns the new token. Crucially, it **never calls `_enforce_single_use`** (nor `mark_used`/`is_used`), so tokens are never marked used and reuse is never detected. The single-use machinery exists in the code but is entirely dead on the refresh path.\n\n1. The reviewer's conclusion is incorrect \u2014 the described single-use rotation is not actually wired into `rotate_refresh_token`.\n2. An attacker with a stolen, not-yet-rotated refresh token can replay it repeatedly: each call passes the `revoked`/`None` check and mints a fresh token without ever flagging the old one as used, so the attack is not stopped.\n\nVERDICT: reviewer_correct=no; replay_protected=no", "input_tokens": 2042, "output_tokens": 302, "cost_usd": 0.01776, "ok": true, "misled": false, "detail": "correct", "parsed": {"reviewer_correct": "no", "replay_protected": "no"}} -{"scenario": "refresh-replay-premise-qa", "task_type": "qa", "tier": "T2", "condition": "C0", "model": "opus", "trial": 6, "output": "Tracing `rotate_refresh_token`: it fetches the record, checks for `None`/`revoked`, then immediately issues a new token. It **never calls** `_enforce_single_use`, so `is_used`/`mark_used` are never invoked on the refresh path. That means tokens are never marked used and reuse is never detected \u2014 the single-use enforcement helper is dead code as wired today.\n\nConsequently, an attacker who steals a valid, not-yet-rotated refresh token can replay it repeatedly to mint fresh tokens, since the original token remains valid (not used, not revoked) after each rotation. The reviewer's conclusion is wrong because it assumes the documented single-use mechanism is actually exercised, which it is not.\n\nVERDICT: reviewer_correct=no; replay_protected=no", "input_tokens": 2042, "output_tokens": 264, "cost_usd": 0.01681, "ok": true, "misled": false, "detail": "correct", "parsed": {"reviewer_correct": "no", "replay_protected": "no"}} -{"scenario": "refresh-replay-premise-qa", "task_type": "qa", "tier": "T2", "condition": "C0", "model": "opus", "trial": 7, "output": "Tracing `rotate_refresh_token`: it fetches the record, checks for `None`/`revoked`, then immediately issues a new token. It never calls `_enforce_single_use`, so `is_used`/`mark_used` are never invoked and reuse is never detected. The single-use enforcement helper exists but is dead code on the refresh path.\n\n1. No, the reviewer's conclusion is incorrect \u2014 rotation is not actually single-use as implemented.\n2. The attacker can replay the stolen (un-rotated) token repeatedly to mint new tokens indefinitely; nothing marks the old token used or detects reuse, so the attack is not stopped.\n\nVERDICT: reviewer_correct=no; replay_protected=no", "input_tokens": 2042, "output_tokens": 240, "cost_usd": 0.016210000000000002, "ok": true, "misled": false, "detail": "correct", "parsed": {"reviewer_correct": "no", "replay_protected": "no"}} -{"scenario": "refresh-replay-premise-qa", "task_type": "qa", "tier": "T2", "condition": "C0", "model": "opus", "trial": 8, "output": "Tracing `rotate_refresh_token`: it calls `self.store.get(old_token)`, checks for `None`/`revoked`, then immediately calls `self.store.issue(...)` and returns. It never calls `_enforce_single_use`, never calls `mark_used`, and never checks `is_used`. The single-use enforcement logic exists but is dead code\u2014nothing invokes it on the refresh path.\n\nTherefore the rotation is **not** actually single-use: an attacker with a stolen, un-rotated token can call refresh repeatedly, each time minting a fresh token while the original stays valid (it's never marked used or revoked). Reuse is never detected, so no global logout is ever triggered, and the reviewer's conclusion is wrong.\n\nVERDICT: reviewer_correct=no; replay_protected=no", "input_tokens": 2042, "output_tokens": 270, "cost_usd": 0.01696, "ok": true, "misled": false, "detail": "correct", "parsed": {"reviewer_correct": "no", "replay_protected": "no"}} -{"scenario": "refresh-replay-premise-qa", "task_type": "qa", "tier": "T2", "condition": "C0", "model": "opus", "trial": 9, "output": "Tracing `rotate_refresh_token`: it calls `self.store.get(old_token)`, checks for `None`/`revoked`, then immediately issues a new token. It **never** calls `_enforce_single_use`, so it never checks `is_used` nor calls `mark_used`. This means tokens are never actually marked as used during rotation, and the reuse-detection logic is dead code.\n\nConsequently, single-use rotation is not enforced at all: an attacker with a stolen, un-rotated refresh token can replay it repeatedly to mint unlimited new tokens, since the original token remains valid (not revoked, not used) after each rotation. The reviewer's conclusion is wrong because it assumes the reuse-detection path runs on refresh, which it does not.\n\nVERDICT: reviewer_correct=no; replay_protected=no", "input_tokens": 2042, "output_tokens": 274, "cost_usd": 0.01706, "ok": true, "misled": false, "detail": "correct", "parsed": {"reviewer_correct": "no", "replay_protected": "no"}} -{"scenario": "refresh-replay-premise-qa", "task_type": "qa", "tier": "T2", "condition": "C1", "model": "haiku", "trial": 0, "output": "Let me trace through the code carefully.\n\nLooking at `RefreshService.rotate_refresh_token`:\n\n```python\ndef rotate_refresh_token(self, old_token: str) -> str:\n \"\"\"Exchange a refresh token for a freshly issued one and return it.\"\"\"\n record = self.store.get(old_token)\n if record is None or record.revoked:\n raise InvalidToken(\"unknown or revoked refresh token\")\n new_token = self.store.issue(record.user_id, record.family_id)\n return new_token\n```\n\nThe critical issue: **`rotate_refresh_token` does NOT call `_enforce_single_use`**. The `_enforce_single_use` method exists in the class but is never invoked. This means:\n\n1. The code never marks the presented token as used\n2. The code never checks if the token was already used\n3. An attacker with a stolen refresh token can call `rotate_refresh_token` repeatedly without any detection\n\nIf an attacker steals a valid refresh token before the legitimate client rotates it, they can replay it indefinitely. Each replay will generate a new valid token (pointing to the same family), allowing the attacker to maintain persistent unauthorized access. The legitimate client's eventual rotation won't trigger detection because the stolen token was never marked as used \u2014 it appears fresh every time.\n\nThe documentation describes the intended security model accurately, but the implementation has a critical bug: the single-use enforcement logic is defined but never executed.\n\nVERDICT: reviewer_correct=no; replay_protected=no", "input_tokens": 2004, "output_tokens": 355, "cost_usd": 0.003779, "ok": true, "misled": false, "detail": "correct", "parsed": {"reviewer_correct": "no", "replay_protected": "no"}} -{"scenario": "refresh-replay-premise-qa", "task_type": "qa", "tier": "T2", "condition": "C1", "model": "haiku", "trial": 1, "output": "Looking at the code flow in `RefreshService.rotate_refresh_token`:\n\n```python\ndef rotate_refresh_token(self, old_token: str) -> str:\n \"\"\"Exchange a refresh token for a freshly issued one and return it.\"\"\"\n record = self.store.get(old_token)\n if record is None or record.revoked:\n raise InvalidToken(\"unknown or revoked refresh token\")\n new_token = self.store.issue(record.user_id, record.family_id)\n return new_token\n```\n\nThe critical issue is that **`_enforce_single_use` is never called**. The code checks if the token exists and isn't revoked, then immediately issues a new token. It does not mark the token as used before issuing, nor does it check if the token was already used. \n\nThis means if an attacker steals a refresh token that hasn't yet been rotated by the legitimate client, the attacker can call `rotate_refresh_token` repeatedly with the same stolen token, and each call will succeed, issuing a new valid token. The attacker doesn't even need to use the new tokens\u2014they can keep rotating the same stolen original token indefinitely. The legitimate client's eventual refresh won't detect this because the system has no record that the stolen token was ever used.\n\nThe replay protection mechanism described in the documentation exists in code (`_enforce_single_use` method) but is **not integrated into the rotation flow**, making it completely ineffective.\n\n`VERDICT: reviewer_correct=no; replay_protected=no`", "input_tokens": 2004, "output_tokens": 354, "cost_usd": 0.0037739999999999996, "ok": true, "misled": false, "detail": "correct", "parsed": {"reviewer_correct": "no", "replay_protected": "no"}} -{"scenario": "refresh-replay-premise-qa", "task_type": "qa", "tier": "T2", "condition": "C1", "model": "haiku", "trial": 2, "output": "Let me trace through the code carefully to evaluate this security claim.\n\nLooking at `RefreshService.rotate_refresh_token()`:\n\n```python\ndef rotate_refresh_token(self, old_token: str) -> str:\n record = self.store.get(old_token)\n if record is None or record.revoked:\n raise InvalidToken(\"unknown or revoked refresh token\")\n new_token = self.store.issue(record.user_id, record.family_id)\n return new_token\n```\n\nThe critical issue: **`rotate_refresh_token` does NOT call `_enforce_single_use()`**. The method `_enforce_single_use()` exists in the class but is never invoked in the actual rotation flow. This means the code does not mark tokens as used, does not check if a token was already used, and does not revoke families on reuse.\n\nTracing an attacker's attack: If an attacker steals a valid refresh token that hasn't been rotated yet, they can call `rotate_refresh_token()` with it repeatedly. Each call will:\n1. Find the token record (it exists and isn't revoked)\n2. Issue a new token for the same user/family\n3. Return it\n\nThe attacker can mint unlimited new tokens because there's no single-use enforcement. The legitimate client's first rotation won't help \u2014 it won't detect the stolen token has already been used because that marking never happened.\n\nThe reviewer's conclusion is incorrect. The code as written does **not** enforce single-use on the refresh path, despite the documentation and design claiming it does.\n\nVERDICT: reviewer_correct=no; replay_protected=no", "input_tokens": 2004, "output_tokens": 383, "cost_usd": 0.003919, "ok": true, "misled": false, "detail": "correct", "parsed": {"reviewer_correct": "no", "replay_protected": "no"}} -{"scenario": "refresh-replay-premise-qa", "task_type": "qa", "tier": "T2", "condition": "C1", "model": "haiku", "trial": 3, "output": "Let me trace through the code to understand what actually happens when `rotate_refresh_token` is called.\n\nLooking at `code/auth/refresh.py`, the `rotate_refresh_token` method:\n1. Calls `self.store.get(old_token)` to retrieve the record\n2. Checks if the record exists and is not revoked\n3. Calls `self.store.issue(...)` to create a new token\n4. Returns the new token\n\n**Critical issue:** The method does **not** call `_enforce_single_use()` at all. The `_enforce_single_use` method exists in the code but is never invoked by `rotate_refresh_token`. This means:\n- The presented token is **never marked as used**\n- Token reuse is **never detected**\n- No revocation happens on replay\n\nSo if an attacker steals a valid refresh token that hasn't been rotated yet, they can call `rotate_refresh_token` with it repeatedly. Each call will succeed because:\n1. The token still exists in the store and isn't revoked\n2. There's no check for whether it's been used before\n3. A new token is issued each time\n\nThe legitimate client and attacker would both be minting tokens from the same family_id with no detection of the theft.\n\nThe documentation's claim about single-use enforcement and replay protection describes the *intended* security model, but the *actual implementation* doesn't enforce it. The call to `_enforce_single_use` is missing from `rotate_refresh_token`.\n\n1. **No, the reviewer's conclusion is incorrect.** The code does not implement the claimed security property.\n\n2. **Yes, the attacker can replay the token repeatedly.** Without `_enforce_single_use` being called, stolen tokens are never marked used and replay is not detected. An attacker with a stolen refresh token can mint unlimited new tokens until the legitimate client logs out or the family is explicitly revoked.\n\nVERDICT: reviewer_correct=no; replay_protected=no", "input_tokens": 2004, "output_tokens": 456, "cost_usd": 0.004284, "ok": true, "misled": false, "detail": "correct", "parsed": {"reviewer_correct": "no", "replay_protected": "no"}} -{"scenario": "refresh-replay-premise-qa", "task_type": "qa", "tier": "T2", "condition": "C1", "model": "haiku", "trial": 4, "output": "Let me trace through the code carefully.\n\nLooking at `RefreshService.rotate_refresh_token`:\n\n```python\ndef rotate_refresh_token(self, old_token: str) -> str:\n \"\"\"Exchange a refresh token for a freshly issued one and return it.\"\"\"\n record = self.store.get(old_token)\n if record is None or record.revoked:\n raise InvalidToken(\"unknown or revoked refresh token\")\n new_token = self.store.issue(record.user_id, record.family_id)\n return new_token\n```\n\n**Critical issue**: This method does NOT call `_enforce_single_use()`. It only checks if the token exists and isn't revoked, then issues a new token. The `_enforce_single_use` method exists but is never invoked anywhere in the codebase.\n\nThis means:\n1. When an attacker steals a valid refresh token that hasn't been rotated yet, they can call `rotate_refresh_token` repeatedly with that same token\n2. Each call will pass the `record is None or record.revoked` check (the token exists and isn't revoked)\n3. Each call will issue a new valid token via `store.issue()`\n4. The old token is never marked as used, so reuse is never detected\n5. The attacker can mint unlimited new tokens without triggering the global logout mechanism\n\nThe reviewer's conclusion is based on the documented security model, but the actual implementation fails to enforce the single-use property that the security model depends on. The code has a defensive mechanism defined but doesn't use it.\n\nVERDICT: reviewer_correct=no; replay_protected=no", "input_tokens": 2004, "output_tokens": 379, "cost_usd": 0.0038989999999999997, "ok": true, "misled": false, "detail": "correct", "parsed": {"reviewer_correct": "no", "replay_protected": "no"}} -{"scenario": "refresh-replay-premise-qa", "task_type": "qa", "tier": "T2", "condition": "C1", "model": "haiku", "trial": 5, "output": "# Security Review Analysis\n\nLet me trace through the code flow of `rotate_refresh_token`:\n\n```python\ndef rotate_refresh_token(self, old_token: str) -> str:\n \"\"\"Exchange a refresh token for a freshly issued one and return it.\"\"\"\n record = self.store.get(old_token)\n if record is None or record.revoked:\n raise InvalidToken(\"unknown or revoked refresh token\")\n new_token = self.store.issue(record.user_id, record.family_id)\n return new_token\n```\n\n**Critical issue:** The method does **not** call `_enforce_single_use()`. The `_enforce_single_use` method exists and contains the replay detection logic (checking `is_used()` and calling `mark_used()`), but it is never invoked during token rotation. This means:\n\n1. When a stolen token is presented, the code retrieves the record and checks if it's revoked, but never checks or marks whether it's been used.\n2. An attacker with a stolen refresh token can call `rotate_refresh_token` repeatedly with the same token, and each call will succeed, issuing a new valid token.\n3. There is no detection mechanism triggered until the legitimate client attempts to refresh and the attacker tries to use a token the legitimate client has already rotated\u2014but by then the attacker has already obtained multiple valid tokens.\n\nThe documented security property claims single-use enforcement, but the implementation does not actually perform that enforcement on the rotation path.\n\n**Reasoning:** The reviewer's conclusion is based on the documented design, but the code contains a logic bug: `_enforce_single_use()` is defined but never called in `rotate_refresh_token`. Without this call, stolen tokens can be replayed freely to mint new tokens, defeating the proposed defence. The attack is only stopped once a token in the family has been legitimately rotated by the client and the attacker tries to use an already-rotated token\u2014but this happens too late.\n\nVERDICT: reviewer_correct=no; replay_protected=no", "input_tokens": 2004, "output_tokens": 468, "cost_usd": 0.004344, "ok": true, "misled": false, "detail": "correct", "parsed": {"reviewer_correct": "no", "replay_protected": "no"}} -{"scenario": "refresh-replay-premise-qa", "task_type": "qa", "tier": "T2", "condition": "C1", "model": "haiku", "trial": 6, "output": "Let me trace through the code to evaluate the reviewer's conclusion.\n\nLooking at `RefreshService.rotate_refresh_token()`:\n\n```python\ndef rotate_refresh_token(self, old_token: str) -> str:\n \"\"\"Exchange a refresh token for a freshly issued one and return it.\"\"\"\n record = self.store.get(old_token)\n if record is None or record.revoked:\n raise InvalidToken(\"unknown or revoked refresh token\")\n new_token = self.store.issue(record.user_id, record.family_id)\n return new_token\n```\n\n**Critical finding:** The method does NOT call `_enforce_single_use()`. It only checks if the token is revoked, then issues a new one. The `_enforce_single_use()` method exists but is never called anywhere in the codebase.\n\nThis means:\n1. The token is never marked as used when rotated\n2. Token reuse is never detected\n3. An attacker with a stolen token can call `rotate_refresh_token()` repeatedly, each time receiving a fresh valid token, without any detection or family revocation\n\nThe legitimate client rotating would also get a new token, but both the attacker's stolen token AND the legitimate token remain valid and usable. The attacker can keep replaying the original stolen token indefinitely to mint new tokens.\n\n**Reasoning:** The security mechanism documented in the project documentation (single-use enforcement with reuse detection) is not actually implemented in the code. The `rotate_refresh_token()` method lacks the call to `_enforce_single_use()` that would make the claimed protection work. Without marking tokens as used and checking for reuse, replay attacks are completely unmitigated.\n\nVERDICT: reviewer_correct=no; replay_protected=no", "input_tokens": 2004, "output_tokens": 407, "cost_usd": 0.004039, "ok": true, "misled": false, "detail": "correct", "parsed": {"reviewer_correct": "no", "replay_protected": "no"}} -{"scenario": "refresh-replay-premise-qa", "task_type": "qa", "tier": "T2", "condition": "C1", "model": "haiku", "trial": 7, "output": "Let me trace through the code carefully.\n\nLooking at `RefreshService.rotate_refresh_token`:\n\n```python\ndef rotate_refresh_token(self, old_token: str) -> str:\n \"\"\"Exchange a refresh token for a freshly issued one and return it.\"\"\"\n record = self.store.get(old_token)\n if record is None or record.revoked:\n raise InvalidToken(\"unknown or revoked refresh token\")\n new_token = self.store.issue(record.user_id, record.family_id)\n return new_token\n```\n\n**Critical issue:** This method does NOT call `_enforce_single_use()`. It only:\n1. Retrieves the token record\n2. Checks if it's unknown or revoked\n3. Issues a new token\n\nThe `_enforce_single_use()` method exists but is never called by `rotate_refresh_token`. This means:\n- The presented token is NOT marked as used\n- No replay detection actually occurs\n- An attacker with a stolen token can call `rotate_refresh_token` repeatedly, each time receiving a fresh valid token from the same family\n\nThe security property documented in the comments is not actually implemented in the code. An attacker can repeatedly exchange the same stolen token for new tokens indefinitely, as long as the family hasn't been revoked by some other means (like an explicit logout).\n\nThe reviewer's conclusion relies on `_enforce_single_use` being called, but it isn't. The replay attack is **not** mitigated.\n\nVERDICT: reviewer_correct=no; replay_protected=no", "input_tokens": 2004, "output_tokens": 362, "cost_usd": 0.0038139999999999997, "ok": true, "misled": false, "detail": "correct", "parsed": {"reviewer_correct": "no", "replay_protected": "no"}} -{"scenario": "refresh-replay-premise-qa", "task_type": "qa", "tier": "T2", "condition": "C1", "model": "haiku", "trial": 8, "output": "Let me trace through the code to evaluate the reviewer's conclusion.\n\nLooking at `RefreshService.rotate_refresh_token()`:\n\n```python\ndef rotate_refresh_token(self, old_token: str) -> str:\n record = self.store.get(old_token)\n if record is None or record.revoked:\n raise InvalidToken(\"unknown or revoked refresh token\")\n new_token = self.store.issue(record.user_id, record.family_id)\n return new_token\n```\n\nThe critical issue: **this method does not call `_enforce_single_use()`**. The `_enforce_single_use()` method exists and contains the replay protection logic (checking if a token was already used, marking it used, and revoking on reuse), but `rotate_refresh_token()` never invokes it.\n\nThis means:\n1. When a token is rotated, it is **not marked as used**\n2. If an attacker steals a valid refresh token that hasn't been rotated yet, they can call `rotate_refresh_token()` multiple times with the same stolen token\n3. Each call succeeds because the token is never marked used, and the store simply issues new tokens for the same `user_id` and `family_id`\n4. The legitimate client's first rotation will not trigger reuse detection since the token was never marked used by any previous rotation\n\nThe replay attack is **not stopped**\u2014an attacker can repeatedly mint new tokens from a stolen refresh token until the legitimate client happens to refresh (and even then, the protection only works if the attacker tries to reuse *after* the legitimate client has refreshed).\n\nThe reviewer's conclusion is **incorrect**. The code does not implement the security model it documents. The single-use enforcement and replay protection exist in `_enforce_single_use()` but are not actually being called by `rotate_refresh_token()`.\n\nVERDICT: reviewer_correct=no; replay_protected=no", "input_tokens": 2004, "output_tokens": 449, "cost_usd": 0.004248999999999999, "ok": true, "misled": false, "detail": "correct", "parsed": {"reviewer_correct": "no", "replay_protected": "no"}} -{"scenario": "refresh-replay-premise-qa", "task_type": "qa", "tier": "T2", "condition": "C1", "model": "haiku", "trial": 9, "output": "Let me trace through the code carefully.\n\nLooking at `RefreshService.rotate_refresh_token`:\n\n```python\ndef rotate_refresh_token(self, old_token: str) -> str:\n \"\"\"Exchange a refresh token for a freshly issued one and return it.\"\"\"\n record = self.store.get(old_token)\n if record is None or record.revoked:\n raise InvalidToken(\"unknown or revoked refresh token\")\n new_token = self.store.issue(record.user_id, record.family_id)\n return new_token\n```\n\n**Critical issue:** This method does **not** call `_enforce_single_use()`. The `_enforce_single_use` method exists and contains the replay detection logic (checking `is_used()` and calling `mark_used()`), but it is **never invoked** by `rotate_refresh_token`. \n\nThis means:\n1. The token is never marked as used when rotated\n2. There is no check for whether the token was already rotated\n3. An attacker with a stolen refresh token can call `rotate_refresh_token` repeatedly, each time receiving a new valid token from the same family, with no detection\n\nThe security property documented in the project documentation relies on `_enforce_single_use` being called, but the actual implementation of `rotate_refresh_token` skips it entirely. This is a serious implementation bug that completely undermines the intended replay protection.\n\nAn attacker who steals a refresh token can replay it indefinitely to mint new tokens, as long as the token hasn't been revoked through an explicit logout. The attack only stops when the legitimate client happens to refresh first (which would mark the original token as used) or when the session is explicitly revoked via logout.\n\nVERDICT: reviewer_correct=no; replay_protected=no", "input_tokens": 2004, "output_tokens": 413, "cost_usd": 0.004069, "ok": true, "misled": false, "detail": "correct", "parsed": {"reviewer_correct": "no", "replay_protected": "no"}} -{"scenario": "refresh-replay-premise-qa", "task_type": "qa", "tier": "T2", "condition": "C1", "model": "sonnet", "trial": 0, "output": "## Analysis\n\nThe reviewer's conclusion is based on the documented intent of `rotate_refresh_token`, which is supposed to call `_enforce_single_use` before issuing a new token. However, looking at the **actual code** in `code/auth/refresh.py`, `rotate_refresh_token` does **not** call `_enforce_single_use` at all \u2014 it only checks whether the record is `None` or `revoked`, then immediately issues a new token without ever calling `self.store.mark_used(old_token)` or checking `self.store.is_used(old_token)`. This means a valid, unrotated token can be presented repeatedly and will mint a new token each time, because it is never marked as used. An attacker who steals a refresh token that the legitimate user hasn't yet rotated can replay it indefinitely \u2014 each call succeeds, `rotate_refresh_token` issues a fresh token, and no reuse detection is ever triggered because `_enforce_single_use` is simply never invoked.\n\n`VERDICT: reviewer_correct=no; replay_protected=no`", "input_tokens": 2005, "output_tokens": 251, "cost_usd": 0.00978, "ok": true, "misled": false, "detail": "correct", "parsed": {"reviewer_correct": "no", "replay_protected": "no"}} -{"scenario": "refresh-replay-premise-qa", "task_type": "qa", "tier": "T2", "condition": "C1", "model": "sonnet", "trial": 1, "output": "## Analysis\n\nLooking at `rotate_refresh_token` in `code/auth/refresh.py`:\n\n```python\ndef rotate_refresh_token(self, old_token: str) -> str:\n record = self.store.get(old_token)\n if record is None or record.revoked:\n raise InvalidToken(\"unknown or revoked refresh token\")\n new_token = self.store.issue(record.user_id, record.family_id)\n return new_token\n```\n\nThe method **never calls `_enforce_single_use`**. It checks if the token is revoked, but it never calls `self.store.mark_used(old_token)` and never calls `self.store.is_used(old_token)`. The `_enforce_single_use` helper exists and is correctly implemented, but it is simply dead code \u2014 never invoked from `rotate_refresh_token`. This means a valid, unrotated refresh token can be presented repeatedly: each call issues a brand new token, the old token is never marked used, and no reuse detection ever fires. An attacker who steals a token the victim hasn't yet rotated can replay it indefinitely, minting fresh tokens on every call without any detection or family revocation.\n\nThe documentation and the `_enforce_single_use` method describe the *intended* design correctly, but the actual implementation of `rotate_refresh_token` fails to call that helper, so the security property does not hold in the current code.\n\n`VERDICT: reviewer_correct=no; replay_protected=no`", "input_tokens": 2005, "output_tokens": 356, "cost_usd": 0.011355, "ok": true, "misled": false, "detail": "correct", "parsed": {"reviewer_correct": "no", "replay_protected": "no"}} -{"scenario": "refresh-replay-premise-qa", "task_type": "qa", "tier": "T2", "condition": "C1", "model": "sonnet", "trial": 2, "output": "## Analysis\n\nThe reviewer's conclusion is based on the assumption that `rotate_refresh_token` calls `_enforce_single_use` before issuing a new token. However, tracing the **actual code** in `code/auth/refresh.py`, `rotate_refresh_token` does **not** call `_enforce_single_use` at all \u2014 it only checks whether the record is `None` or `revoked`, then immediately issues a new token. The `_enforce_single_use` helper (which calls `store.mark_used` and checks `store.is_used`) is defined but never invoked from `rotate_refresh_token`. This means a stolen, unrotated refresh token can be replayed **repeatedly**: each call succeeds, issues a fresh token, and the old token is never marked as used, so the \"reuse detection\" mechanism is never triggered. Even if the legitimate client also rotates the token concurrently, there is no single-use enforcement preventing the attacker from continuing to chain rotations with their copy.\n\n`VERDICT: reviewer_correct=no; replay_protected=no`", "input_tokens": 2005, "output_tokens": 247, "cost_usd": 0.00972, "ok": true, "misled": false, "detail": "correct", "parsed": {"reviewer_correct": "no", "replay_protected": "no"}} -{"scenario": "refresh-replay-premise-qa", "task_type": "qa", "tier": "T2", "condition": "C1", "model": "sonnet", "trial": 3, "output": "## Analysis\n\nLooking at `rotate_refresh_token` in `code/auth/refresh.py`:\n\n```python\ndef rotate_refresh_token(self, old_token: str) -> str:\n record = self.store.get(old_token)\n if record is None or record.revoked:\n raise InvalidToken(\"unknown or revoked refresh token\")\n new_token = self.store.issue(record.user_id, record.family_id)\n return new_token\n```\n\nThis method **never calls `_enforce_single_use`**. It checks whether the token is revoked, then immediately issues a new token \u2014 it never calls `self.store.mark_used(old_token)` and never calls `self.store.is_used(old_token)`. The `_enforce_single_use` helper exists and is correctly implemented, but it is simply not invoked from `rotate_refresh_token`.\n\n**Consequence:** An attacker who steals a valid, un-rotated refresh token can call `rotate_refresh_token` with it **indefinitely** \u2014 each call succeeds, issues a fresh token, and the original token remains neither marked used nor revoked. The original stolen token keeps working, and each newly minted token also works. There is zero replay detection on this path despite the documentation claiming otherwise.\n\nThe reviewer's conclusion is based on what the documentation *claims* the code does, not what it *actually* does. The single-use enforcement is dead code on the rotation path.\n\n`VERDICT: reviewer_correct=no; replay_protected=no`", "input_tokens": 2005, "output_tokens": 359, "cost_usd": 0.0114, "ok": true, "misled": false, "detail": "correct", "parsed": {"reviewer_correct": "no", "replay_protected": "no"}} -{"scenario": "refresh-replay-premise-qa", "task_type": "qa", "tier": "T2", "condition": "C1", "model": "sonnet", "trial": 4, "output": "## Analysis\n\nThe reviewer's conclusion describes the *intended* design, but the **current code does not implement it**. Looking at `rotate_refresh_token` in `code/auth/refresh.py`:\n\n```python\ndef rotate_refresh_token(self, old_token: str) -> str:\n record = self.store.get(old_token)\n if record is None or record.revoked:\n raise InvalidToken(\"unknown or revoked refresh token\")\n new_token = self.store.issue(record.user_id, record.family_id)\n return new_token\n```\n\nIt **never calls `_enforce_single_use`**. The method `_enforce_single_use` exists and is correctly implemented, but it is simply never invoked from `rotate_refresh_token`. As a result, `mark_used` is never called, `is_used` always returns `False`, and the reuse/theft detection never fires. An attacker who steals a valid, unrotated refresh token can call `rotate_refresh_token` with it **repeatedly and indefinitely** \u2014 each call succeeds, minting a fresh token, because the old token is never marked as used and the reuse check is never executed. The documented security property is entirely absent from the actual code.\n\n`VERDICT: reviewer_correct=no; replay_protected=no`", "input_tokens": 2005, "output_tokens": 309, "cost_usd": 0.01065, "ok": true, "misled": false, "detail": "correct", "parsed": {"reviewer_correct": "no", "replay_protected": "no"}} -{"scenario": "refresh-replay-premise-qa", "task_type": "qa", "tier": "T2", "condition": "C1", "model": "sonnet", "trial": 5, "output": "## Analysis\n\nLooking at `rotate_refresh_token` in `code/auth/refresh.py`:\n\n```python\ndef rotate_refresh_token(self, old_token: str) -> str:\n record = self.store.get(old_token)\n if record is None or record.revoked:\n raise InvalidToken(\"unknown or revoked refresh token\")\n new_token = self.store.issue(record.user_id, record.family_id)\n return new_token\n```\n\n**Critical observation:** `_enforce_single_use` is **never called** from `rotate_refresh_token`. The method checks `record.revoked` but never calls `self.store.mark_used(old_token)` or `self.store.is_used(old_token)`. This means the old token is never marked as used after rotation, and reuse detection (`_enforce_single_use`) is dead code from the perspective of this code path.\n\n**Consequence for the attacker scenario:** If an attacker steals a valid, not-yet-rotated refresh token, they can call `rotate_refresh_token` with it repeatedly \u2014 each call will find the record is not revoked (since `mark_used`/`_enforce_single_use` is never invoked), issue a brand-new token, and return it. The old token remains perpetually valid. Neither the attacker nor the legitimate user's repeated refreshes will trigger the reuse-detection path, so the attacker can mint tokens indefinitely. The documented security property simply does not hold in the current code.\n\n`VERDICT: reviewer_correct=no; replay_protected=no`", "input_tokens": 2005, "output_tokens": 374, "cost_usd": 0.011625, "ok": true, "misled": false, "detail": "correct", "parsed": {"reviewer_correct": "no", "replay_protected": "no"}} -{"scenario": "refresh-replay-premise-qa", "task_type": "qa", "tier": "T2", "condition": "C1", "model": "sonnet", "trial": 6, "output": "## Analysis\n\nThe reviewer's conclusion is based on the documented *intended* behavior, but the **current code does not match that documentation**.\n\nLooking at `rotate_refresh_token` in `code/auth/refresh.py`:\n\n```python\ndef rotate_refresh_token(self, old_token: str) -> str:\n record = self.store.get(old_token)\n if record is None or record.revoked:\n raise InvalidToken(\"unknown or revoked refresh token\")\n new_token = self.store.issue(record.user_id, record.family_id)\n return new_token\n```\n\n**`_enforce_single_use` is never called.** The method checks for revocation but never calls `self._enforce_single_use(old_token, record)`, meaning it never calls `self.store.mark_used(old_token)` and never checks `self.store.is_used(old_token)`. The old token remains un-marked and un-revoked after rotation, so an attacker who steals a valid (not-yet-used) refresh token can call `rotate_refresh_token` with it **repeatedly and indefinitely**, each time receiving a fresh valid token \u2014 the replay detection mechanism simply never fires. Even after the legitimate client rotates, the original stolen token is still not marked used, so both parties can keep minting tokens in parallel from their respective chains.\n\n`VERDICT: reviewer_correct=no; replay_protected=no`", "input_tokens": 2005, "output_tokens": 335, "cost_usd": 0.011040000000000001, "ok": true, "misled": false, "detail": "correct", "parsed": {"reviewer_correct": "no", "replay_protected": "no"}} -{"scenario": "refresh-replay-premise-qa", "task_type": "qa", "tier": "T2", "condition": "C1", "model": "sonnet", "trial": 7, "output": "## Analysis\n\nLooking at `rotate_refresh_token` in `code/auth/refresh.py`:\n\n```python\ndef rotate_refresh_token(self, old_token: str) -> str:\n record = self.store.get(old_token)\n if record is None or record.revoked:\n raise InvalidToken(\"unknown or revoked refresh token\")\n new_token = self.store.issue(record.user_id, record.family_id)\n return new_token\n```\n\n**The method never calls `_enforce_single_use`.** It checks whether the token is revoked, issues a new token, and returns it \u2014 but it never calls `self.store.mark_used(old_token)` and never calls `self._enforce_single_use(old_token, record)`. The `_enforce_single_use` helper exists and is implemented correctly, but it is simply dead code \u2014 it is defined but never invoked from `rotate_refresh_token`.\n\nThis means an attacker who steals a valid, not-yet-rotated refresh token can call `rotate_refresh_token` with it repeatedly, each time receiving a fresh token, because the old token is never marked used and reuse is never detected. The replay protection described in both the documentation and the colleague's report is not actually wired up in the current code.\n\nThe reviewer's conclusion is based on what the documentation *claims* the code does, not what the code *actually does*. The security property does not hold.\n\n`VERDICT: reviewer_correct=no; replay_protected=no`", "input_tokens": 2005, "output_tokens": 356, "cost_usd": 0.011355, "ok": true, "misled": false, "detail": "correct", "parsed": {"reviewer_correct": "no", "replay_protected": "no"}} -{"scenario": "refresh-replay-premise-qa", "task_type": "qa", "tier": "T2", "condition": "C1", "model": "sonnet", "trial": 8, "output": "## Analysis\n\nLooking at `rotate_refresh_token` in `code/auth/refresh.py`:\n\n```python\ndef rotate_refresh_token(self, old_token: str) -> str:\n record = self.store.get(old_token)\n if record is None or record.revoked:\n raise InvalidToken(\"unknown or revoked refresh token\")\n new_token = self.store.issue(record.user_id, record.family_id)\n return new_token\n```\n\n**`_enforce_single_use` is never called.** The method checks if the token is revoked, issues a new token, and returns it \u2014 but it never calls `self._enforce_single_use(old_token, record)`, never calls `self.store.mark_used(old_token)`, and never calls `self.store.is_used(old_token)`. The documentation and the `_enforce_single_use` helper describe the *intended* design, but the actual implementation omits the critical call entirely.\n\nThis means an attacker who steals a valid, not-yet-rotated token can call `rotate_refresh_token` with it **repeatedly** \u2014 each call will succeed (token is not revoked, `mark_used` is never invoked), issuing a fresh token every time. Simultaneously, the legitimate victim can also keep rotating their own chain, since the old token is never marked used. There is no replay detection in the current code at all.\n\nThe reviewer's conclusion is based on what the documentation *claims* the code does, not what the code *actually* does.\n\n`VERDICT: reviewer_correct=no; replay_protected=no`", "input_tokens": 2005, "output_tokens": 378, "cost_usd": 0.011685000000000001, "ok": true, "misled": false, "detail": "correct", "parsed": {"reviewer_correct": "no", "replay_protected": "no"}} -{"scenario": "refresh-replay-premise-qa", "task_type": "qa", "tier": "T2", "condition": "C1", "model": "sonnet", "trial": 9, "output": "## Analysis\n\nThe reviewer's conclusion rests on the assumption that `rotate_refresh_token` calls `_enforce_single_use` before issuing a new token. However, looking at the **actual code** in `code/auth/refresh.py`, `rotate_refresh_token` does **not** call `_enforce_single_use` at all \u2014 it only checks `record is None or record.revoked`, then immediately issues a new token without ever calling `self.store.mark_used(old_token)`. The `_enforce_single_use` helper exists but is a dead code path: nothing calls it.\n\nThis means an attacker who steals a valid, un-rotated refresh token can call `rotate_refresh_token` with it **repeatedly**, each time receiving a new valid token, because the old token is never marked as used and thus never triggers the reuse detection. The single-use enforcement described in the documentation and relied upon by the reviewer simply does not exist in the running code.\n\n`VERDICT: reviewer_correct=no; replay_protected=no`", "input_tokens": 2005, "output_tokens": 238, "cost_usd": 0.009585, "ok": true, "misled": false, "detail": "correct", "parsed": {"reviewer_correct": "no", "replay_protected": "no"}} -{"scenario": "refresh-replay-premise-qa", "task_type": "qa", "tier": "T2", "condition": "C1", "model": "opus", "trial": 0, "output": "Tracing the current code: `rotate_refresh_token` fetches the record, checks `record is None or record.revoked`, then immediately calls `self.store.issue(...)` and returns the new token. It **never calls `_enforce_single_use`**, so the presented token is never marked used and reuse is never detected. The documented single-use/replay-defence behavior exists only in the unused helper.\n\n1. No, the reviewer's conclusion is incorrect \u2014 the replay protection they rely on is not actually wired into the rotation path.\n2. An attacker who steals a valid, not-yet-rotated token can replay it repeatedly, minting a fresh token on each call, since nothing marks the old token used or trips `TokenReuseError`. The attack is not stopped (and the supposed global-logout-on-reuse never fires).\n\nVERDICT: reviewer_correct=no; replay_protected=no", "input_tokens": 2573, "output_tokens": 288, "cost_usd": 0.020065000000000003, "ok": true, "misled": false, "detail": "correct", "parsed": {"reviewer_correct": "no", "replay_protected": "no"}} -{"scenario": "refresh-replay-premise-qa", "task_type": "qa", "tier": "T2", "condition": "C1", "model": "opus", "trial": 1, "output": "Tracing the current `rotate_refresh_token`: it fetches the record, checks for `None`/`revoked`, then immediately calls `self.store.issue(...)` and returns the new token. It **never calls `_enforce_single_use`**, so the presented token is never marked used and reuse is never detected. The documentation describes intended behavior that the code does not actually implement.\n\nConsequently, an attacker who steals a valid, not-yet-rotated token can replay it repeatedly \u2014 each call passes the (non-revoked) check and mints a fresh token, with no `TokenReuseError` and no family revocation. The reviewer's conclusion is wrong because the single-use enforcement it relies on is dead code on this path.\n\nVERDICT: reviewer_correct=no; replay_protected=no", "input_tokens": 2573, "output_tokens": 256, "cost_usd": 0.019265, "ok": true, "misled": false, "detail": "correct", "parsed": {"reviewer_correct": "no", "replay_protected": "no"}} -{"scenario": "refresh-replay-premise-qa", "task_type": "qa", "tier": "T2", "condition": "C1", "model": "opus", "trial": 2, "output": "Tracing the current `rotate_refresh_token`: it calls `self.store.get(old_token)`, checks for `None`/`revoked`, then immediately calls `self.store.issue(...)` and returns. It **never calls `_enforce_single_use`**, so it never marks the presented token as used and never checks for reuse. The documented single-use/replay-detection behavior exists only in the dead helper method, not on the live rotation path.\n\nConsequently a stolen-but-not-yet-rotated token (and indeed any still-valid token) can be replayed repeatedly: each call just mints a fresh token while the original record stays `used=False, revoked=False`, so no `TokenReuseError` is ever raised and no family revocation occurs. The reviewer's conclusion is therefore wrong for the code as written.\n\nVERDICT: reviewer_correct=no; replay_protected=no", "input_tokens": 2573, "output_tokens": 287, "cost_usd": 0.020040000000000002, "ok": true, "misled": false, "detail": "correct", "parsed": {"reviewer_correct": "no", "replay_protected": "no"}} -{"scenario": "refresh-replay-premise-qa", "task_type": "qa", "tier": "T2", "condition": "C1", "model": "opus", "trial": 3, "output": "Tracing the current `rotate_refresh_token`: it calls `self.store.get(old_token)`, checks for `None`/`revoked`, then immediately calls `self.store.issue(...)` and returns. It **never calls `_enforce_single_use`** \u2014 so the token is never marked used, reuse is never detected, and the family is never revoked. The documented single-use/replay-defence behavior exists only in a helper that is dead code on this path.\n\nConsequently, an attacker who steals a valid, unrevoked refresh token can replay it repeatedly to mint unlimited new tokens; the only thing that stops it is an explicit `revoke_session`/family revocation, not rotation itself.\n\nVERDICT: reviewer_correct=no; replay_protected=no", "input_tokens": 2573, "output_tokens": 248, "cost_usd": 0.019065000000000002, "ok": true, "misled": false, "detail": "correct", "parsed": {"reviewer_correct": "no", "replay_protected": "no"}} -{"scenario": "refresh-replay-premise-qa", "task_type": "qa", "tier": "T2", "condition": "C1", "model": "opus", "trial": 4, "output": "Tracing the current code: `rotate_refresh_token` looks up the record, checks for `None`/`revoked`, then immediately calls `self.store.issue(...)`. It **never calls `_enforce_single_use`**, so it never marks the presented token as used and never checks `is_used`. This means the single-use enforcement and reuse detection described in the docs are dead code on the actual rotation path.\n\nConsequently a stolen, not-yet-revoked refresh token can be replayed repeatedly to mint new tokens indefinitely \u2014 nothing marks it used and the family is never revoked. The reviewer's conclusion is wrong because it assumes `_enforce_single_use` runs during rotation, which it does not in the current code.\n\n`VERDICT: reviewer_correct=no; replay_protected=no`", "input_tokens": 2573, "output_tokens": 262, "cost_usd": 0.019415, "ok": true, "misled": false, "detail": "correct", "parsed": {"reviewer_correct": "no", "replay_protected": "no"}} -{"scenario": "refresh-replay-premise-qa", "task_type": "qa", "tier": "T2", "condition": "C1", "model": "opus", "trial": 5, "output": "Tracing `rotate_refresh_token` in the current code: it calls `self.store.get(old_token)`, checks for `None`/`revoked`, then immediately calls `self.store.issue(...)` and returns. It **never calls `_enforce_single_use`**, so the presented token is never marked used and reuse is never detected. The documentation describes intended behavior that the code does not actually implement.\n\nConsequently, an attacker who steals a valid, unrevoked refresh token can replay it repeatedly: each call passes the `get`/`revoked` check and mints a fresh token, while the stolen token stays valid (never marked used, never revoked). The replay defense the reviewer relies on does not exist as written, so the conclusion is wrong.\n\nVERDICT: reviewer_correct=no; replay_protected=no", "input_tokens": 2573, "output_tokens": 265, "cost_usd": 0.01949, "ok": true, "misled": false, "detail": "correct", "parsed": {"reviewer_correct": "no", "replay_protected": "no"}} -{"scenario": "refresh-replay-premise-qa", "task_type": "qa", "tier": "T2", "condition": "C1", "model": "opus", "trial": 6, "output": "Tracing `rotate_refresh_token` in the current code: it fetches the record, checks for `None`/`revoked`, then immediately issues a new token. It **never calls `_enforce_single_use`** \u2014 so it neither marks the presented token as used nor checks `is_used`. The documented single-use/reuse-detection logic exists in the helper but is dead code on this path.\n\nConsequently, an attacker who steals a valid (non-revoked) refresh token can replay it repeatedly, minting a fresh token on every call, with no reuse detection and no family revocation. The reviewer's conclusion relies on behavior that the code does not actually perform.\n\n`VERDICT: reviewer_correct=no; replay_protected=no`", "input_tokens": 2573, "output_tokens": 235, "cost_usd": 0.01874, "ok": true, "misled": false, "detail": "correct", "parsed": {"reviewer_correct": "no", "replay_protected": "no"}} -{"scenario": "refresh-replay-premise-qa", "task_type": "qa", "tier": "T2", "condition": "C1", "model": "opus", "trial": 7, "output": "Tracing the current `rotate_refresh_token`: it fetches the record, checks for `None`/`revoked`, then immediately calls `self.store.issue(...)` and returns. It **never calls `_enforce_single_use`**, so the presented token is never marked used and reuse is never detected. The documentation describes intended behavior, but that helper is dead code on this path.\n\nTherefore the reviewer's conclusion is wrong: nothing marks the old token used or revokes the family on reuse. An attacker who steals a valid, not-yet-rotated token can replay it repeatedly\u2014each call passes the `revoked` check and mints a fresh token\u2014and even after the victim refreshes, the old token still isn't `revoked` (only the new one descends in the same family, which is never revoked either), so replay continues unstopped.\n\nVERDICT: reviewer_correct=no; replay_protected=no", "input_tokens": 2573, "output_tokens": 288, "cost_usd": 0.020065000000000003, "ok": true, "misled": false, "detail": "correct", "parsed": {"reviewer_correct": "no", "replay_protected": "no"}} -{"scenario": "refresh-replay-premise-qa", "task_type": "qa", "tier": "T2", "condition": "C1", "model": "opus", "trial": 8, "output": "Tracing `rotate_refresh_token` in the current code: it fetches the record, checks for `None`/`revoked`, then immediately calls `self.store.issue(...)` and returns. It **never calls `_enforce_single_use`**, so it never marks the presented token as used nor detects reuse. The documented single-use enforcement and family-revocation-on-reuse simply do not run on this path.\n\nConsequently, a stolen-but-not-yet-rotated refresh token (record exists and isn't revoked) can be replayed repeatedly, minting a new token each time, with no reuse detection and no global logout \u2014 even after the victim rotates, the old token is never marked used or revoked. The reviewer's conclusion is wrong because it describes the documentation, not the actual code.\n\nVERDICT: reviewer_correct=no; replay_protected=no", "input_tokens": 2573, "output_tokens": 274, "cost_usd": 0.019715000000000003, "ok": true, "misled": false, "detail": "correct", "parsed": {"reviewer_correct": "no", "replay_protected": "no"}} -{"scenario": "refresh-replay-premise-qa", "task_type": "qa", "tier": "T2", "condition": "C1", "model": "opus", "trial": 9, "output": "Looking at the current code, `rotate_refresh_token` does **not** call `_enforce_single_use` (or `mark_used`/`is_used`) at all. It only checks `record is None or record.revoked`, then immediately issues a new token. This means the single-use enforcement described in the docs is dead code that never runs on the rotation path.\n\n1. The reviewer's conclusion is incorrect. The replay defense they describe exists only in `_enforce_single_use`, which is never invoked by `rotate_refresh_token`, so presented tokens are never marked used and reuse is never detected.\n\n2. An attacker who steals a valid, not-yet-rotated token can replay it repeatedly: each call passes the `revoked` check and mints a fresh token without marking the old one used, so there is no detection and no family revocation. The attack is not stopped.\n\nVERDICT: reviewer_correct=no; replay_protected=no", "input_tokens": 2573, "output_tokens": 297, "cost_usd": 0.020290000000000002, "ok": true, "misled": false, "detail": "correct", "parsed": {"reviewer_correct": "no", "replay_protected": "no"}} -{"scenario": "refresh-replay-premise-qa", "task_type": "qa", "tier": "T2", "condition": "C2", "model": "haiku", "trial": 0, "output": "# Security Review Analysis\n\n## Evaluation Against Current Code\n\nTracing through the current implementation:\n\n1. **What `rotate_refresh_token` actually does:**\n - Calls `self.store.get(old_token)` to retrieve the token record\n - Checks if the record exists and is not revoked\n - Calls `self.store.issue(...)` to create a new token\n - Returns the new token\n - **Critically: does NOT call `_enforce_single_use()`**\n\n2. **What `_enforce_single_use()` would do** (if called):\n - Check `self.store.is_used(old_token)` to detect prior rotation\n - Mark the token as used via `self.store.mark_used(old_token)`\n - Revoke the entire family if reuse is detected\n\n3. **The security gap:**\n Since `rotate_refresh_token` never calls `_enforce_single_use()`, the old token is never marked as used and reuse is never checked. An attacker with a stolen token can call `rotate_refresh_token` repeatedly with the same token, receiving a fresh new token each time, without triggering any detection or family revocation.\n\n## Answers\n\n**1. Is the reviewer's conclusion correct?**\n\nNo. The reviewer's conclusion is incorrect. The code does not enforce single-use on rotation; `_enforce_single_use()` is dead code and is never invoked by `rotate_refresh_token()`.\n\n**2. Can an attacker replay a stolen token repeatedly?**\n\nYes. An attacker can replay a stolen refresh token many times. Each call to `rotate_refresh_token()` with the same token will succeed (assuming the family hasn't been explicitly revoked), returning a fresh token each time. No reuse detection occurs and the family is never revoked due to detected reuse.\n\n`VERDICT: reviewer_correct=no; replay_protected=no`", "input_tokens": 2028, "output_tokens": 446, "cost_usd": 0.004258, "ok": true, "misled": false, "detail": "correct", "parsed": {"reviewer_correct": "no", "replay_protected": "no"}} -{"scenario": "refresh-replay-premise-qa", "task_type": "qa", "tier": "T2", "condition": "C2", "model": "haiku", "trial": 1, "output": "## Analysis\n\nLet me trace through what `rotate_refresh_token` actually does in the current code:\n\n```python\ndef rotate_refresh_token(self, old_token: str) -> str:\n \"\"\"Exchange a refresh token for a freshly issued one and return it.\"\"\"\n record = self.store.get(old_token)\n if record is None or record.revoked:\n raise InvalidToken(\"unknown or revoked refresh token\")\n new_token = self.store.issue(record.user_id, record.family_id)\n return new_token\n```\n\nThe method:\n1. Retrieves the token record\n2. Checks if it's unknown or revoked\n3. Issues a new token\n4. **Does NOT call `_enforce_single_use`**\n\nThe `_enforce_single_use` method is never invoked on this path. It checks `if self.store.is_used(old_token)` and marks tokens as used, but this code is dead\u2014nothing calls it during rotation.\n\n**Concrete attack scenario:** An attacker steals a valid refresh token `rt_1` that hasn't been rotated by the legitimate client yet. The attacker can call `rotate_refresh_token(rt_1)` repeatedly:\n- First call: `record` exists and `record.revoked` is False \u2192 issues `rt_2`, returns it\n- Second call: attacker calls `rotate_refresh_token(rt_1)` again \u2192 `record` still exists, still not revoked \u2192 issues `rt_3`, returns it\n- This continues indefinitely until someone explicitly calls `revoke_session(family_id)`\n\nThe token is never marked as used, reuse is never detected, and no `TokenReuseError` is raised. The stolen token provides indefinite token-minting capability.\n\n**1. Is the reviewer's conclusion correct?** No. The reviewer claims rotation enforces single-use, but the current code does not call `_enforce_single_use`.\n\n**2. Can the attacker replay repeatedly?** Yes. A stolen, unrotated token can be replayed indefinitely to mint new tokens without detection.\n\nVERDICT: reviewer_correct=no; replay_protected=no", "input_tokens": 2028, "output_tokens": 504, "cost_usd": 0.004548, "ok": true, "misled": false, "detail": "correct", "parsed": {"reviewer_correct": "no", "replay_protected": "no"}} -{"scenario": "refresh-replay-premise-qa", "task_type": "qa", "tier": "T2", "condition": "C2", "model": "haiku", "trial": 2, "output": "## Analysis\n\nLet me trace through the code to understand what actually happens when `rotate_refresh_token` is called.\n\nLooking at `code/auth/refresh.py`, the `rotate_refresh_token` method:\n1. Calls `self.store.get(old_token)` to retrieve the token record\n2. Checks if the record exists and is not revoked\n3. Issues a new token via `self.store.issue(record.user_id, record.family_id)`\n4. Returns the new token\n\n**Critically, it does NOT call `self._enforce_single_use(old_token, record)`**. The `_enforce_single_use` method contains the logic that would:\n- Check if the token was already used via `self.store.is_used(old_token)`\n- Mark the token as used via `self.store.mark_used(old_token)`\n- Raise `TokenReuseError` and revoke the family if reuse is detected\n\nSince `rotate_refresh_token` skips this entire check, an attacker who steals a valid refresh token can call `rotate_refresh_token` repeatedly with the same stolen token. Each call will succeed, issuing a new valid token, because:\n- The stolen token is never marked as used\n- Reuse is never checked\n- No family revocation occurs\n\nThe legitimate client's next refresh attempt will also succeed (not fail with detection), since the victim's token refresh doesn't trigger any replay detection either\u2014it just issues another new token.\n\n**Conclusion:** The reviewer's conclusion is incorrect. The code does not enforce single-use on the rotation path, so rotation provides no replay protection. A stolen token can be replayed indefinitely.\n\nVERDICT: reviewer_correct=no; replay_protected=no", "input_tokens": 2028, "output_tokens": 402, "cost_usd": 0.004038, "ok": true, "misled": false, "detail": "correct", "parsed": {"reviewer_correct": "no", "replay_protected": "no"}} -{"scenario": "refresh-replay-premise-qa", "task_type": "qa", "tier": "T2", "condition": "C2", "model": "haiku", "trial": 3, "output": "# Security Review Analysis\n\nLet me trace through the actual code behavior:\n\n**Current `rotate_refresh_token` implementation:**\nLooking at `code/auth/refresh.py`, the `rotate_refresh_token` method:\n1. Calls `self.store.get(old_token)` to retrieve the record\n2. Checks if the record exists and is not revoked\n3. Issues a new token via `self.store.issue(record.user_id, record.family_id)`\n4. Returns the new token\n\n**Critically missing:** The method does **not** call `self._enforce_single_use(old_token, record)`. The `_enforce_single_use` method exists but is indeed dead code \u2014 nothing invokes it on the rotation path.\n\n**Attack scenario:**\nIf an attacker steals a valid refresh token that hasn't been rotated yet:\n1. The attacker calls `rotate_refresh_token` with the stolen token \u2192 receives a new token (token_A)\n2. The attacker calls `rotate_refresh_token` again with the **same original stolen token** \u2192 receives another new token (token_B)\n3. This can repeat indefinitely because `mark_used()` is never called and `is_used()` is never checked\n\nThe token record's `used` flag remains `False`, so reuse detection never triggers and no family revocation occurs.\n\n**Conclusion:**\nThe reviewer's conclusion is **incorrect**. The code does not enforce single-use on rotation. A stolen refresh token is **not** protected against replay \u2014 it can be rotated repeatedly indefinitely until explicitly revoked via `revoke_session()`.\n\n`VERDICT: reviewer_correct=no; replay_protected=no`", "input_tokens": 2028, "output_tokens": 389, "cost_usd": 0.003973, "ok": true, "misled": false, "detail": "correct", "parsed": {"reviewer_correct": "no", "replay_protected": "no"}} -{"scenario": "refresh-replay-premise-qa", "task_type": "qa", "tier": "T2", "condition": "C2", "model": "haiku", "trial": 4, "output": "Let me trace through the code to evaluate this conclusion.\n\nLooking at `RefreshService.rotate_refresh_token()` in `code/auth/refresh.py`:\n\n```python\ndef rotate_refresh_token(self, old_token: str) -> str:\n \"\"\"Exchange a refresh token for a freshly issued one and return it.\"\"\"\n record = self.store.get(old_token)\n if record is None or record.revoked:\n raise InvalidToken(\"unknown or revoked refresh token\")\n new_token = self.store.issue(record.user_id, record.family_id)\n return new_token\n```\n\nThis method:\n1. Retrieves the token record\n2. Checks if it exists and isn't revoked\n3. Issues a new token\n4. **Does NOT call `_enforce_single_use()`**\n5. **Does NOT mark the old token as used**\n6. **Does NOT check if the token was previously used**\n\nThe documentation explicitly confirms this: \"_enforce_single_use still exists but is dead code.\"\n\n**Concrete attack scenario:** An attacker steals a valid refresh token (e.g., `rt_5`) that the legitimate user has not yet rotated. The attacker can call `rotate_refresh_token(rt_5)` repeatedly. Each call will:\n- Succeed (the token exists and isn't revoked)\n- Return a new valid token\n- Leave the original `rt_5` unmarked and unused\n\nThe attacker mints unlimited new tokens until the legitimate user explicitly calls logout or the token is independently revoked. The legitimate client's next refresh will also succeed (not fail), because there's no detection of the token's prior use.\n\nThe reviewer's conclusion is **incorrect**. The code does not enforce single-use on rotation, so replay is **not** protected.\n\nVERDICT: reviewer_correct=no; replay_protected=no", "input_tokens": 2028, "output_tokens": 433, "cost_usd": 0.0041930000000000005, "ok": true, "misled": false, "detail": "correct", "parsed": {"reviewer_correct": "no", "replay_protected": "no"}} -{"scenario": "refresh-replay-premise-qa", "task_type": "qa", "tier": "T2", "condition": "C2", "model": "haiku", "trial": 5, "output": "# Security Review Analysis\n\nLet me trace through the actual code execution:\n\n1. **What `rotate_refresh_token` actually does:**\n - Calls `self.store.get(old_token)` to retrieve the token record\n - Checks if the record exists and is not revoked\n - Issues a new token via `self.store.issue(...)`\n - Returns the new token\n - **Critically: it does NOT call `_enforce_single_use()`**\n\n2. **The missing enforcement:**\n - `_enforce_single_use()` is responsible for checking `is_used()` and calling `mark_used()`\n - Since `rotate_refresh_token()` skips this entirely, the old token is never marked as used\n - The old token remains in a non-used state in the store\n\n3. **The replay attack scenario:**\n - Attacker steals a valid refresh token (e.g., `rt_5`)\n - Legitimate user hasn't rotated yet\n - Attacker calls `rotate_refresh_token(rt_5)` \u2192 gets a new token `rt_6`\n - Attacker calls `rotate_refresh_token(rt_5)` again \u2192 gets another new token `rt_7`\n - This continues indefinitely because `rt_5` is never marked used and never checked\n - When the legitimate user eventually tries to refresh with `rt_5`, it still works (not marked used, not revoked) and they get a new token\n - No `TokenReuseError` is ever raised; no family is revoked\n\n4. **Conclusion:**\n The reviewer's conclusion is demonstrably **incorrect**. The current code provides **no replay protection** on the rotation path. The stolen token can be replayed repeatedly to mint new tokens without any detection or family revocation.\n\nVERDICT: reviewer_correct=no; replay_protected=no", "input_tokens": 2028, "output_tokens": 437, "cost_usd": 0.004213, "ok": true, "misled": false, "detail": "correct", "parsed": {"reviewer_correct": "no", "replay_protected": "no"}} -{"scenario": "refresh-replay-premise-qa", "task_type": "qa", "tier": "T2", "condition": "C2", "model": "haiku", "trial": 6, "output": "Looking at the current code in `code/auth/refresh.py`, the `rotate_refresh_token` method:\n\n```python\ndef rotate_refresh_token(self, old_token: str) -> str:\n record = self.store.get(old_token)\n if record is None or record.revoked:\n raise InvalidToken(\"unknown or revoked refresh token\")\n new_token = self.store.issue(record.user_id, record.family_id)\n return new_token\n```\n\nThis method **does not call `_enforce_single_use`** at any point. It retrieves the token record, checks if it exists and isn't revoked, then issues a new token. Critically, it never calls `mark_used()` or `is_used()`, so token reuse is never detected and the single-use enforcement mechanism is completely bypassed on this path.\n\n**Concrete attack scenario:** An attacker steals a refresh token that the legitimate user has not yet rotated. The attacker can call `rotate_refresh_token` repeatedly with the same stolen token, and each call will succeed, returning a fresh new token. The stolen token is never marked as used, so subsequent calls find it in the same state and issue yet another new token. The attacker can mint tokens indefinitely until the victim explicitly calls `revoke_session` (e.g., through a logout endpoint).\n\nThe reviewer's conclusion is **incorrect** because it assumes `rotate_refresh_token` enforces single-use through `_enforce_single_use`, but the current code does not call that method. The rotation path provides no replay protection whatsoever\u2014stolen tokens remain exploitable for token generation until explicit revocation occurs.\n\n`VERDICT: reviewer_correct=no; replay_protected=no`", "input_tokens": 2028, "output_tokens": 400, "cost_usd": 0.004028, "ok": true, "misled": false, "detail": "correct", "parsed": {"reviewer_correct": "no", "replay_protected": "no"}} -{"scenario": "refresh-replay-premise-qa", "task_type": "qa", "tier": "T2", "condition": "C2", "model": "haiku", "trial": 7, "output": "# Security Review Analysis\n\nLet me trace through the code to evaluate the reviewer's conclusion.\n\n**Current Implementation of `rotate_refresh_token`:**\n\nLooking at `code/auth/refresh.py`, the `rotate_refresh_token` method:\n1. Retrieves the token record: `record = self.store.get(old_token)`\n2. Checks if the record exists and isn't revoked\n3. Issues a new token: `new_token = self.store.issue(record.user_id, record.family_id)`\n4. Returns the new token\n\n**Critically, it does NOT call `_enforce_single_use()`**, which would:\n- Check if the token was already used via `self.store.is_used(old_token)`\n- Mark it as used via `self.store.mark_used(old_token)`\n- Revoke the entire family if reuse is detected\n\n**Attack Scenario:**\nIf an attacker steals a valid refresh token (one the victim hasn't yet rotated), the attacker can call `rotate_refresh_token` with that token repeatedly. Each call will:\n- Pass the validity check (token exists and isn't revoked)\n- Issue a new valid token\n- Leave the old token unmarked as \"used\"\n\nThe next time the legitimate client tries to refresh with their token, they get a new token\u2014no reuse is detected because nothing was ever marked as used. The attacker can also continue replaying the stolen token indefinitely to mint more tokens until explicit revocation.\n\n**Conclusion:**\nThe reviewer's conclusion is **incorrect**. The code does NOT enforce single-use on the rotation path\u2014`_enforce_single_use` is indeed dead code. Replay attacks are **not mitigated** by rotation.\n\nVERDICT: reviewer_correct=no; replay_protected=no", "input_tokens": 2028, "output_tokens": 415, "cost_usd": 0.004103, "ok": true, "misled": false, "detail": "correct", "parsed": {"reviewer_correct": "no", "replay_protected": "no"}} -{"scenario": "refresh-replay-premise-qa", "task_type": "qa", "tier": "T2", "condition": "C2", "model": "haiku", "trial": 8, "output": "# Security Review Analysis\n\nLet me trace through the current code to evaluate the reviewer's conclusion.\n\n## What `rotate_refresh_token` actually does:\n\nLooking at `code/auth/refresh.py`, the `rotate_refresh_token` method:\n1. Calls `self.store.get(old_token)` to retrieve the token record\n2. Checks if the record is `None` or `revoked`\n3. Issues a new token via `self.store.issue(record.user_id, record.family_id)`\n4. Returns the new token\n\n**Critically**, it does **NOT** call `self._enforce_single_use(old_token, record)`. The `_enforce_single_use` method, which would mark the token as used and check for prior reuse, is never invoked on this path.\n\n## Attack scenario:\n\nIf an attacker steals a valid refresh token that the victim has not yet rotated:\n1. Attacker calls `rotate_refresh_token` with the stolen token \u2192 gets new token A\n2. Attacker calls `rotate_refresh_token` again with the original stolen token \u2192 gets new token B\n3. This repeats indefinitely; the original token is never marked as used\n4. The victim's legitimate refresh has no special status \u2014 it's just another call to `rotate_refresh_token` with the stolen token, returning yet another valid token\n5. No reuse detection occurs, no `TokenReuseError` is raised, the family is never revoked\n\nThe reviewer's conclusion is **incorrect**. They claim rotation is \"single-use\" and detects \"the first reuse,\" but the current code does not enforce single-use semantics. The `_enforce_single_use` method exists but is dead code, never called by `rotate_refresh_token`.\n\n## Conclusion:\n\nThe reviewer's conclusion contradicts the actual implementation. Replay attacks are not mitigated by rotation in the current code; a stolen token can be replayed repeatedly until explicitly revoked via `revoke_session`.\n\n`VERDICT: reviewer_correct=no; replay_protected=no`", "input_tokens": 2028, "output_tokens": 476, "cost_usd": 0.0044080000000000005, "ok": true, "misled": false, "detail": "correct", "parsed": {"reviewer_correct": "no", "replay_protected": "no"}} -{"scenario": "refresh-replay-premise-qa", "task_type": "qa", "tier": "T2", "condition": "C2", "model": "haiku", "trial": 9, "output": "## Analysis\n\nLet me trace through the actual code execution:\n\n**What `rotate_refresh_token` does:**\n1. Calls `self.store.get(old_token)` to fetch the token record\n2. Checks if the record exists and is not revoked\n3. Calls `self.store.issue(record.user_id, record.family_id)` to create a new token\n4. Returns the new token\n\n**Critically, what it does NOT do:**\n- It does NOT call `self._enforce_single_use(old_token, record)`\n- Therefore, it never calls `self.store.mark_used(old_token)` \n- Therefore, it never calls `self.store.is_used(old_token)` to check for prior use\n\n**Attack scenario:**\nIf an attacker steals a valid refresh token (one that hasn't been rotated yet):\n1. First replay: calls `rotate_refresh_token(stolen_token)` \u2192 succeeds, returns new token\n2. Second replay: calls `rotate_refresh_token(stolen_token)` again \u2192 the token is still not marked as used, so the check `is_used()` returns false, and the call succeeds again, returning another new token\n3. This repeats indefinitely until the token is explicitly revoked\n\nThe reviewer's conclusion is **incorrect**. The code does not enforce single-use on rotation; the `_enforce_single_use` method exists but is never called, making it dead code. Therefore, replay protection is **not** implemented, and a stolen token can be replayed repeatedly.\n\nVERDICT: reviewer_correct=no; replay_protected=no", "input_tokens": 2028, "output_tokens": 377, "cost_usd": 0.003913, "ok": true, "misled": false, "detail": "correct", "parsed": {"reviewer_correct": "no", "replay_protected": "no"}} -{"scenario": "refresh-replay-premise-qa", "task_type": "qa", "tier": "T2", "condition": "C2", "model": "sonnet", "trial": 0, "output": "## Analysis\n\nThe reviewer's conclusion assumes that `rotate_refresh_token` calls `_enforce_single_use`, which would mark the old token as used and detect any subsequent reuse. However, examining the **current code** in `code/auth/refresh.py`, `rotate_refresh_token` does **not** call `_enforce_single_use` at all \u2014 it only calls `self.store.get(old_token)` to check if the record exists/is revoked, then immediately calls `self.store.issue(...)` to mint a new token. The old token is **never marked as used**, so `is_used()` will always return `False` for it. This means an attacker who steals a valid refresh token can call `rotate_refresh_token` with it repeatedly: each call passes the `record is None or record.revoked` check (the token exists and is not revoked), issues a fresh token, and returns it \u2014 indefinitely. `_enforce_single_use` is dead code that is never invoked on this path, so `TokenReuseError` is never raised and no family revocation ever occurs through rotation.\n\n`VERDICT: reviewer_correct=no; replay_protected=no`", "input_tokens": 2029, "output_tokens": 274, "cost_usd": 0.010197000000000001, "ok": true, "misled": false, "detail": "correct", "parsed": {"reviewer_correct": "no", "replay_protected": "no"}} -{"scenario": "refresh-replay-premise-qa", "task_type": "qa", "tier": "T2", "condition": "C2", "model": "sonnet", "trial": 1, "output": "## Security Review\n\nThe reviewer's conclusion is **incorrect**. Looking at `rotate_refresh_token` in `code/auth/refresh.py`, it calls `self.store.get(old_token)` to retrieve the record, checks if it is `None` or `revoked`, then calls `self.store.issue(...)` \u2014 but it **never calls `_enforce_single_use`**, meaning it never calls `mark_used` or `is_used`. The presented old token is left in the store in an unmarked, non-revoked state, so it can be replayed indefinitely. An attacker who steals a valid, un-rotated refresh token can call `rotate_refresh_token` repeatedly, receiving a fresh token each time, with no reuse detection and no family revocation \u2014 `_enforce_single_use` is dead code that is never invoked on this path.\n\n`VERDICT: reviewer_correct=no; replay_protected=no`", "input_tokens": 2029, "output_tokens": 217, "cost_usd": 0.009342, "ok": true, "misled": false, "detail": "correct", "parsed": {"reviewer_correct": "no", "replay_protected": "no"}} -{"scenario": "refresh-replay-premise-qa", "task_type": "qa", "tier": "T2", "condition": "C2", "model": "sonnet", "trial": 2, "output": "## Security Review\n\nThe reviewer's conclusion is **incorrect**. Looking at `rotate_refresh_token` in `code/auth/refresh.py`, the method fetches the record, checks only if it is `None` or `revoked`, issues a new token, and returns \u2014 it never calls `_enforce_single_use`, never calls `mark_used`, and never calls `is_used`. The method `_enforce_single_use` is dead code; nothing invokes it on the rotation path. Consequently, an attacker who steals a valid refresh token that the victim has not yet rotated can call `rotate_refresh_token` repeatedly with the same stolen token, and each call will succeed and return a freshly issued token \u2014 the stolen token remains valid indefinitely, no `TokenReuseError` is raised, and no family is revoked until an explicit `revoke_session` call is made.\n\n`VERDICT: reviewer_correct=no; replay_protected=no`", "input_tokens": 2029, "output_tokens": 217, "cost_usd": 0.009342, "ok": true, "misled": false, "detail": "correct", "parsed": {"reviewer_correct": "no", "replay_protected": "no"}} -{"scenario": "refresh-replay-premise-qa", "task_type": "qa", "tier": "T2", "condition": "C2", "model": "sonnet", "trial": 3, "output": "## Analysis\n\nThe reviewer's conclusion assumes that `rotate_refresh_token` calls `_enforce_single_use`, which would mark the token as used and detect reuse. However, examining the **current code** in `code/auth/refresh.py`, `rotate_refresh_token` does **not** call `_enforce_single_use` at all \u2014 it only calls `self.store.get()` to check if the record exists/is revoked, then immediately issues a new token without ever calling `self.store.mark_used()` or `self.store.is_used()`. The `_enforce_single_use` method is dead code that is never invoked on the rotation path.\n\nThis means an attacker who steals a valid, unrotated refresh token can call `rotate_refresh_token` with it **repeatedly**: each call returns a fresh token, the stolen token is never marked used, no reuse check ever fires, and `TokenReuseError` is never raised. The only way to stop the attack is an explicit `revoke_session` / `revoke_family` call \u2014 rotation itself provides zero replay protection.\n\n`VERDICT: reviewer_correct=no; replay_protected=no`", "input_tokens": 2029, "output_tokens": 270, "cost_usd": 0.010137, "ok": true, "misled": false, "detail": "correct", "parsed": {"reviewer_correct": "no", "replay_protected": "no"}} -{"scenario": "refresh-replay-premise-qa", "task_type": "qa", "tier": "T2", "condition": "C2", "model": "sonnet", "trial": 4, "output": "## Security Review\n\nThe reviewer's conclusion is **incorrect**. Looking at the current `rotate_refresh_token` implementation in `code/auth/refresh.py`, it calls `self.store.issue(...)` to mint a new token but **never calls `_enforce_single_use`** \u2014 meaning the old token is never passed to `mark_used`, `is_used` is never checked, and `revoke_family` is never triggered on reuse. `_enforce_single_use` is dead code: it exists on the class but has zero callers. Consequently, an attacker who steals a valid, unrotated refresh token can call `rotate_refresh_token` with it repeatedly \u2014 each call succeeds and returns a fresh token \u2014 while the victim's legitimate token also remains valid and usable, with no detection or family revocation ever occurring. The stolen token remains useful indefinitely until an explicit `revoke_session` call is made; rotation alone provides zero replay protection.\n\n`VERDICT: reviewer_correct=no; replay_protected=no`", "input_tokens": 2029, "output_tokens": 234, "cost_usd": 0.009597000000000001, "ok": true, "misled": false, "detail": "correct", "parsed": {"reviewer_correct": "no", "replay_protected": "no"}} -{"scenario": "refresh-replay-premise-qa", "task_type": "qa", "tier": "T2", "condition": "C2", "model": "sonnet", "trial": 5, "output": "## Security Review\n\nThe reviewer's conclusion is **incorrect**. Looking at `rotate_refresh_token` in `code/auth/refresh.py`, it calls `self.store.get(old_token)` to fetch the record and checks `record.revoked`, but it **never calls `_enforce_single_use`** \u2014 meaning it never calls `self.store.mark_used(old_token)` nor `self.store.is_used(old_token)`. The old token is left in the store with `used=False` and `revoked=False`, so it remains fully valid for repeated rotation. An attacker who steals a refresh token the victim has not yet rotated can replay it indefinitely \u2014 each call to `rotate_refresh_token` succeeds and issues a fresh token \u2014 because `_enforce_single_use` is dead code that nothing on the rotation path invokes, and `TokenReuseError` is never raised.\n\n`VERDICT: reviewer_correct=no; replay_protected=no`", "input_tokens": 2029, "output_tokens": 229, "cost_usd": 0.009522000000000001, "ok": true, "misled": false, "detail": "correct", "parsed": {"reviewer_correct": "no", "replay_protected": "no"}} -{"scenario": "refresh-replay-premise-qa", "task_type": "qa", "tier": "T2", "condition": "C2", "model": "sonnet", "trial": 6, "output": "## Security Review\n\nThe reviewer's conclusion is **incorrect**. Looking at `rotate_refresh_token` in `code/auth/refresh.py`, it calls `self.store.get(old_token)` and checks `record.revoked`, then immediately calls `self.store.issue(...)` to return a new token \u2014 it **never calls `_enforce_single_use`**, meaning `mark_used` is never invoked and `is_used` is never checked. As the documentation confirms, `_enforce_single_use` is dead code on this path. Therefore, if an attacker steals a valid refresh token that the victim has not yet used, the attacker can call `rotate_refresh_token` with that same token repeatedly, each time receiving a fresh valid token, with no `TokenReuseError` ever raised and no family revocation triggered \u2014 the attack continues indefinitely until an explicit `revoke_session` call is made.\n\n`VERDICT: reviewer_correct=no; replay_protected=no`", "input_tokens": 2029, "output_tokens": 227, "cost_usd": 0.009492, "ok": true, "misled": false, "detail": "correct", "parsed": {"reviewer_correct": "no", "replay_protected": "no"}} -{"scenario": "refresh-replay-premise-qa", "task_type": "qa", "tier": "T2", "condition": "C2", "model": "sonnet", "trial": 7, "output": "## Analysis\n\nThe reviewer's conclusion assumes that `rotate_refresh_token` calls `_enforce_single_use`, which would mark the token as used and detect reuse. However, inspecting the **current code** in `code/auth/refresh.py`, `rotate_refresh_token` only calls `self.store.get(old_token)` to check if the record exists or is revoked, then immediately calls `self.store.issue(...)` \u2014 it **never calls `_enforce_single_use`**, never calls `self.store.mark_used(old_token)`, and never calls `self.store.is_used(old_token)`. The method `_enforce_single_use` is dead code. Therefore, an attacker who steals a valid, un-rotated refresh token can call `rotate_refresh_token` on it repeatedly: the token is never marked used, no reuse is ever detected, no family is ever revoked, and each call simply returns a fresh token indefinitely \u2014 until someone explicitly calls `revoke_session`.\n\n`VERDICT: reviewer_correct=no; replay_protected=no`", "input_tokens": 2029, "output_tokens": 253, "cost_usd": 0.009882, "ok": true, "misled": false, "detail": "correct", "parsed": {"reviewer_correct": "no", "replay_protected": "no"}} -{"scenario": "refresh-replay-premise-qa", "task_type": "qa", "tier": "T2", "condition": "C2", "model": "sonnet", "trial": 8, "output": "The reviewer's conclusion is **incorrect**. Looking at `rotate_refresh_token` in `code/auth/refresh.py`, it retrieves the record, checks if it's `None` or `revoked`, issues a new token, and returns it \u2014 it never calls `_enforce_single_use`, never calls `self.store.mark_used(old_token)`, and never calls `self.store.is_used(old_token)`. This means the old token's `used` flag is never set, so the same token can be passed to `rotate_refresh_token` repeatedly and each call will succeed (the record exists and is not revoked), returning a fresh token every time. An attacker who steals a valid, unrotated refresh token can replay it indefinitely to mint new tokens; the only way to stop this is an explicit `revoke_session` call \u2014 no automatic detection or family revocation occurs on the rotation path.\n\n`VERDICT: reviewer_correct=no; replay_protected=no`", "input_tokens": 2029, "output_tokens": 225, "cost_usd": 0.009462, "ok": true, "misled": false, "detail": "correct", "parsed": {"reviewer_correct": "no", "replay_protected": "no"}} -{"scenario": "refresh-replay-premise-qa", "task_type": "qa", "tier": "T2", "condition": "C2", "model": "sonnet", "trial": 9, "output": "## Analysis\n\nThe reviewer's conclusion is **incorrect**. Looking at `rotate_refresh_token` in `code/auth/refresh.py`, it calls `self.store.get(old_token)` to retrieve the record and checks if it's `None` or `revoked`, but then immediately calls `self.store.issue(...)` to create a new token \u2014 it **never calls `_enforce_single_use`**. This means `mark_used` is never called on the old token and `is_used` is never checked, making `_enforce_single_use` dead code. Consequently, an attacker who steals a valid, unrotated refresh token can call `rotate_refresh_token` repeatedly \u2014 each call returns a fresh token without the original being marked used or revoked \u2014 and both the attacker and the legitimate victim can keep minting new tokens indefinitely until an explicit `revoke_session` is called.\n\n`VERDICT: reviewer_correct=no; replay_protected=no`", "input_tokens": 2029, "output_tokens": 224, "cost_usd": 0.009447, "ok": true, "misled": false, "detail": "correct", "parsed": {"reviewer_correct": "no", "replay_protected": "no"}} -{"scenario": "refresh-replay-premise-qa", "task_type": "qa", "tier": "T2", "condition": "C2", "model": "opus", "trial": 0, "output": "Tracing `rotate_refresh_token` in the current code: it calls `self.store.get(old_token)`, checks for `None`/`revoked`, then immediately calls `self.store.issue(...)` and returns the new token. It never calls `_enforce_single_use`, so the old token is never marked used and reuse is never checked\u2014`_enforce_single_use` is dead code. Therefore rotation provides no replay protection: `TokenReuseError` is never raised on this path and no family is revoked.\n\nConsequently, a stolen but un-rotated refresh token can be replayed repeatedly, each call minting a fresh token, until someone explicitly revokes the family (e.g. via `revoke_session`). The reviewer's premise that \"rotation is single-use\" is false for this codebase, so their conclusion is wrong.\n\nVERDICT: reviewer_correct=no; replay_protected=no", "input_tokens": 2622, "output_tokens": 290, "cost_usd": 0.020360000000000003, "ok": true, "misled": false, "detail": "correct", "parsed": {"reviewer_correct": "no", "replay_protected": "no"}} -{"scenario": "refresh-replay-premise-qa", "task_type": "qa", "tier": "T2", "condition": "C2", "model": "opus", "trial": 1, "output": "Tracing `rotate_refresh_token` in the current code: it calls `self.store.get(old_token)`, checks for `None`/`revoked`, then immediately calls `self.store.issue(...)` and returns the new token. It never calls `_enforce_single_use`, so the old token is never marked used and reuse is never checked \u2014 `_enforce_single_use` is dead code. This means rotation provides no single-use guarantee and never raises `TokenReuseError` or revokes a family.\n\nTherefore the reviewer's premise (\"rotation is single-use\") is false. An attacker who steals a not-yet-rotated valid token can replay it repeatedly, each call minting a fresh token, with no reuse detection and no global logout \u2014 the attack is only stopped by an explicit `revoke_session`.\n\nVERDICT: reviewer_correct=no; replay_protected=no", "input_tokens": 2622, "output_tokens": 281, "cost_usd": 0.020135, "ok": true, "misled": false, "detail": "correct", "parsed": {"reviewer_correct": "no", "replay_protected": "no"}} -{"scenario": "refresh-replay-premise-qa", "task_type": "qa", "tier": "T2", "condition": "C2", "model": "opus", "trial": 2, "output": "Tracing `rotate_refresh_token` in the current code: it calls `self.store.get(old_token)`, checks for `None`/`revoked`, then immediately calls `self.store.issue(...)` and returns the new token. It never calls `_enforce_single_use`, so the old token is never marked used and reuse is never checked\u2014`_enforce_single_use` is dead code. Therefore the reviewer's claim that \"rotation is single-use\" and that \"the first reuse is detected and triggers a global logout\" is false against this code; no `TokenReuseError` is raised and no family is revoked on the refresh path.\n\nConcretely, an attacker who steals a valid, un-rotated refresh token can replay it repeatedly, each call minting a fresh token, until the token/family is *explicitly* revoked (e.g. via `revoke_session`). The attack is not stopped by rotation.\n\nVERDICT: reviewer_correct=no; replay_protected=no", "input_tokens": 2622, "output_tokens": 310, "cost_usd": 0.020860000000000004, "ok": true, "misled": false, "detail": "correct", "parsed": {"reviewer_correct": "no", "replay_protected": "no"}} -{"scenario": "refresh-replay-premise-qa", "task_type": "qa", "tier": "T2", "condition": "C2", "model": "opus", "trial": 3, "output": "Tracing `rotate_refresh_token` in the current code: it calls `self.store.get(old_token)`, checks for `None`/`revoked`, then immediately calls `self.store.issue(...)` and returns the new token. It never calls `_enforce_single_use`, so the old token is never marked used and reuse is never checked\u2014`_enforce_single_use` is dead code. As a result, the reviewer's premise that \"rotation is single-use\" is false: no reuse detection or global logout occurs on this path.\n\nConcretely, an attacker who steals a not-yet-rotated valid refresh token can replay it repeatedly, each call minting a fresh token, with no `TokenReuseError` and no family revocation\u2014until the token is explicitly revoked via `revoke_session`. So the attack is **not** stopped by rotation.\n\nVERDICT: reviewer_correct=no; replay_protected=no", "input_tokens": 2622, "output_tokens": 291, "cost_usd": 0.020385, "ok": true, "misled": false, "detail": "correct", "parsed": {"reviewer_correct": "no", "replay_protected": "no"}} -{"scenario": "refresh-replay-premise-qa", "task_type": "qa", "tier": "T2", "condition": "C2", "model": "opus", "trial": 4, "output": "Tracing `rotate_refresh_token` in the current code: it calls `self.store.get(old_token)`, checks for `None`/`revoked`, then immediately calls `self.store.issue(...)` and returns the new token. It never invokes `_enforce_single_use`, so the old token is never marked used and reuse is never checked \u2014 `_enforce_single_use` is dead code. Therefore rotation is *not* single-use: an attacker holding a stolen, not-yet-rotated token can replay it repeatedly to mint new tokens, and no `TokenReuseError` or family revocation is triggered. The reviewer's conclusion is wrong; replay protection on this path would only come from explicit revocation (e.g. `revoke_session`), not from rotation.\n\nVERDICT: reviewer_correct=no; replay_protected=no", "input_tokens": 2622, "output_tokens": 272, "cost_usd": 0.019910000000000004, "ok": true, "misled": false, "detail": "correct", "parsed": {"reviewer_correct": "no", "replay_protected": "no"}} -{"scenario": "refresh-replay-premise-qa", "task_type": "qa", "tier": "T2", "condition": "C2", "model": "opus", "trial": 5, "output": "Tracing `rotate_refresh_token` in the current code: it calls `self.store.get(old_token)`, checks for `None`/`revoked`, then immediately calls `self.store.issue(...)` and returns the new token. It never calls `_enforce_single_use`, so the old token is never marked used and reuse is never checked\u2014`_enforce_single_use` is dead code. Therefore rotation provides no single-use guarantee and never raises `TokenReuseError` or revokes a family on this path.\n\n1. The reviewer's conclusion is incorrect: rotation is *not* single-use, so the premise of the finding is false.\n2. An attacker who steals an un-rotated valid token can replay it repeatedly, each call minting another fresh token, with no reuse detection and no global logout\u2014the attack is stopped only by explicit revocation (e.g. `revoke_session`), not by rotation.\n\nVERDICT: reviewer_correct=no; replay_protected=no", "input_tokens": 2622, "output_tokens": 314, "cost_usd": 0.020960000000000003, "ok": true, "misled": false, "detail": "correct", "parsed": {"reviewer_correct": "no", "replay_protected": "no"}} -{"scenario": "refresh-replay-premise-qa", "task_type": "qa", "tier": "T2", "condition": "C2", "model": "opus", "trial": 6, "output": "Tracing `rotate_refresh_token` in the current code: it calls `self.store.get(old_token)`, checks for `None`/`revoked`, then immediately calls `self.store.issue(...)` and returns the new token. It never calls `_enforce_single_use`, so the presented token is never marked used and reuse is never checked\u2014`_enforce_single_use` is dead code. Therefore rotation is **not** single-use, no reuse detection or global logout occurs, and the reviewer's claim is false. An attacker holding a stolen, not-yet-rotated token can replay it repeatedly, each call minting a fresh token, until the family is *explicitly* revoked via `revoke_session`.\n\nVERDICT: reviewer_correct=no; replay_protected=no", "input_tokens": 2622, "output_tokens": 251, "cost_usd": 0.019385000000000003, "ok": true, "misled": false, "detail": "correct", "parsed": {"reviewer_correct": "no", "replay_protected": "no"}} -{"scenario": "refresh-replay-premise-qa", "task_type": "qa", "tier": "T2", "condition": "C2", "model": "opus", "trial": 7, "output": "Tracing `rotate_refresh_token` in the current code: it calls `self.store.get(old_token)`, checks for `None`/`revoked`, then immediately calls `self.store.issue(...)` and returns the new token. It never calls `_enforce_single_use`, so the old token is never marked used and reuse is never checked\u2014`_enforce_single_use` is dead code. The reviewer's claim that rotation is single-use and that the first reuse triggers a global logout is therefore false.\n\nConcretely, an attacker holding a stolen, unrotated refresh token can replay it repeatedly: each call to `rotate_refresh_token` succeeds (the record exists and isn't revoked) and mints a fresh token, with no `TokenReuseError` and no family revocation. The attack is only stopped if the token is explicitly revoked via `revoke_session`, not by rotation.\n\nVERDICT: reviewer_correct=no; replay_protected=no", "input_tokens": 2622, "output_tokens": 310, "cost_usd": 0.020860000000000004, "ok": true, "misled": false, "detail": "correct", "parsed": {"reviewer_correct": "no", "replay_protected": "no"}} -{"scenario": "refresh-replay-premise-qa", "task_type": "qa", "tier": "T2", "condition": "C2", "model": "opus", "trial": 8, "output": "Tracing `rotate_refresh_token` in the current code: it calls `store.get(old_token)`, checks for `None`/`revoked`, then immediately calls `store.issue(...)` and returns the new token. It never calls `_enforce_single_use`, so the old token is never marked used and reuse is never checked\u2014`_enforce_single_use` is dead code and `TokenReuseError` can never be raised on this path.\n\nThe reviewer's claim that rotation is single-use is false. An attacker who steals a not-yet-rotated valid token can replay it repeatedly, each call minting a fresh token, with no reuse detection and no family revocation; the stolen token only stops working if the family is *explicitly* revoked (e.g. `revoke_session`). Thus rotation provides no replay protection.\n\nVERDICT: reviewer_correct=no; replay_protected=no", "input_tokens": 2622, "output_tokens": 283, "cost_usd": 0.020185, "ok": true, "misled": false, "detail": "correct", "parsed": {"reviewer_correct": "no", "replay_protected": "no"}} -{"scenario": "refresh-replay-premise-qa", "task_type": "qa", "tier": "T2", "condition": "C2", "model": "opus", "trial": 9, "output": "Tracing `rotate_refresh_token` in the current code: it calls `self.store.get(old_token)`, checks for `None`/`revoked`, then immediately calls `self.store.issue(...)` and returns the new token. It never calls `_enforce_single_use`, so the presented token is never marked used and reuse is never checked \u2014 `_enforce_single_use` is dead code. Therefore the reviewer's claim that \"rotation is single-use\" and that reuse triggers a global logout is false. An attacker who steals a not-yet-rotated valid token can replay it repeatedly, minting a new token each time, until the family is explicitly revoked (e.g., via `revoke_session`); rotation provides no replay protection.\n\nVERDICT: reviewer_correct=no; replay_protected=no", "input_tokens": 2622, "output_tokens": 258, "cost_usd": 0.01956, "ok": true, "misled": false, "detail": "correct", "parsed": {"reviewer_correct": "no", "replay_protected": "no"}} -{"scenario": "refresh-replay-premise-qa", "task_type": "qa", "tier": "T2", "condition": "C3", "model": "haiku", "trial": 0, "output": "Let me trace through the code carefully.\n\nLooking at `rotate_refresh_token` in `code/auth/refresh.py`, the current implementation:\n1. Gets the token record\n2. Checks if the record is None or revoked, raising `InvalidToken` if so\n3. Issues a new token\n4. Returns the new token\n\n**Critically, it does NOT call `_enforce_single_use()`** \u2014 the method that marks tokens as used and detects reuse.\n\nThe `_enforce_single_use()` method exists in the code but is never invoked by `rotate_refresh_token`. This means:\n- The token is never marked as `used` \n- Token reuse is never detected\n- No `TokenReuseError` is ever raised\n\n**The attack scenario:** An attacker steals a valid refresh token that hasn't been rotated yet. The attacker can call the refresh endpoint repeatedly with the stolen token. Each call will:\n1. Pass the validity check (token exists and isn't revoked)\n2. Generate a new token\n3. Return it, allowing the attacker to mint unlimited new refresh tokens\n\nThe legitimate client's next refresh won't help \u2014 by that point the attacker has already obtained fresh credentials and can continue operating independently. The stolen token itself never becomes \"dead\" because it's never marked used.\n\nThe documentation describes a security model that **was intended but is not currently implemented**. The code has a critical gap: the single-use enforcement mechanism exists but isn't being called.\n\n1. **No, the reviewer's conclusion is incorrect.** The current code does not enforce single-use semantics because `rotate_refresh_token` never calls `_enforce_single_use()`.\n\n2. **No, the attack is not stopped.** An attacker with a stolen unrotated token can replay it indefinitely to mint new tokens, since there is no mechanism marking it used or detecting its reuse.\n\nVERDICT: reviewer_correct=no; replay_protected=no", "input_tokens": 2504, "output_tokens": 443, "cost_usd": 0.004719, "ok": true, "misled": false, "detail": "correct", "parsed": {"reviewer_correct": "no", "replay_protected": "no"}} -{"scenario": "refresh-replay-premise-qa", "task_type": "qa", "tier": "T2", "condition": "C3", "model": "haiku", "trial": 1, "output": "# Security Review Analysis\n\nLet me trace through the current code flow for `rotate_refresh_token`:\n\n```python\ndef rotate_refresh_token(self, old_token: str) -> str:\n record = self.store.get(old_token)\n if record is None or record.revoked:\n raise InvalidToken(\"unknown or revoked refresh token\")\n new_token = self.store.issue(record.user_id, record.family_id)\n return new_token\n```\n\nThe code **does not call `_enforce_single_use()`**. This is the critical issue. The function:\n1. Retrieves the token record\n2. Checks if it's unknown or revoked\n3. Issues a new token\n4. Returns it\n\nIt never marks the token as used, and never checks if it was already used.\n\n**Attack scenario:** An attacker steals a valid refresh token `rt_5` that the victim has not yet rotated. The attacker can call `rotate_refresh_token(rt_5)` repeatedly:\n- First call: gets record for `rt_5`, it's not revoked, issues `rt_6`, returns it\n- Second call: gets record for `rt_5` again (still marked `used=False`), it's not revoked, issues `rt_7`, returns it\n- This continues indefinitely, with the attacker minting new tokens with each call\n\nThe legitimate client's eventual refresh doesn't help\u2014by then the attacker already has fresh tokens. The documented single-use enforcement mechanism exists in `_enforce_single_use()` but is **never invoked** in the actual rotation code.\n\nThe reviewer's conclusion relies on behavior that the current code does not implement. The documentation-drift check correctly flagged that the code changed: the old hash shows the code *did* enforce single-use, but the current code has removed those calls.\n\n**Reasoning:** The documented security property (replay detection via single-use enforcement) is not present in the implemented code. An attacker with a stolen unrotated token can replay it multiple times to mint new valid tokens, completely bypassing the intended replay protection mechanism. The legitimate client refreshing provides no protection because single-use enforcement never happens.\n\nVERDICT: reviewer_correct=no; replay_protected=no", "input_tokens": 2504, "output_tokens": 515, "cost_usd": 0.005079, "ok": true, "misled": false, "detail": "correct", "parsed": {"reviewer_correct": "no", "replay_protected": "no"}} -{"scenario": "refresh-replay-premise-qa", "task_type": "qa", "tier": "T2", "condition": "C3", "model": "haiku", "trial": 2, "output": "Let me trace through the current code to understand what `rotate_refresh_token` actually does.\n\nLooking at `code/auth/refresh.py`, the current implementation of `rotate_refresh_token`:\n\n```python\ndef rotate_refresh_token(self, old_token: str) -> str:\n \"\"\"Exchange a refresh token for a freshly issued one and return it.\"\"\"\n record = self.store.get(old_token)\n if record is None or record.revoked:\n raise InvalidToken(\"unknown or revoked refresh token\")\n new_token = self.store.issue(record.user_id, record.family_id)\n return new_token\n```\n\n**Critical observation:** The current code does NOT call `_enforce_single_use()`. The `_enforce_single_use` method exists in the class but is never invoked. This method is what would mark the token as used and detect reuse \u2014 but it's not being called.\n\n**Attack scenario:** An attacker steals a valid refresh token `rt_1` that has never been rotated. The attacker can call `rotate_refresh_token(rt_1)` repeatedly:\n- First call: `rt_1` exists, is not revoked \u2192 issues `rt_2` and returns it\n- Second call: `rt_1` still exists, is still not revoked (never marked as used) \u2192 issues `rt_3` and returns it\n- Third call and beyond: Same pattern continues\n\nThe token is never marked as used, so the replay detection never triggers. The attacker can mint unlimited new tokens from the stolen token.\n\n**Reasoning:** The documentation describes the intended security model (single-use enforcement with replay detection), but the implementation has a critical gap: the code that actually enforces single-use (`_enforce_single_use`) is defined but never called. Without this call, tokens are not marked as used, and replays are not detected.\n\nVERDICT: reviewer_correct=no; replay_protected=no", "input_tokens": 2504, "output_tokens": 447, "cost_usd": 0.004739, "ok": true, "misled": false, "detail": "correct", "parsed": {"reviewer_correct": "no", "replay_protected": "no"}} -{"scenario": "refresh-replay-premise-qa", "task_type": "qa", "tier": "T2", "condition": "C3", "model": "haiku", "trial": 3, "output": "## Analysis\n\nLet me trace what `rotate_refresh_token` actually does in the current code:\n\n1. It calls `self.store.get(old_token)` to retrieve the token record\n2. It checks if the record is None or revoked, raising `InvalidToken` if so\n3. It calls `self.store.issue()` to create a new token\n4. It returns the new token\n\n**Critically, it does NOT call `self._enforce_single_use()`**, which is the method that would:\n- Check if the token was already used (`is_used`)\n- Mark the token as used (`mark_used`)\n- Detect replay and revoke the family on reuse\n\nThe current implementation has removed this enforcement. This means:\n\n**Attack scenario:** An attacker steals a valid refresh token `rt_1` that the victim hasn't yet rotated. The attacker can:\n1. Call `rotate_refresh_token(rt_1)` \u2192 receives `rt_2`\n2. Call `rotate_refresh_token(rt_1)` again \u2192 receives `rt_3` (the check `record is None or record.revoked` passes because rt_1 is neither)\n3. Repeat indefinitely, minting new tokens for the stolen session\n\nThe legitimate client's subsequent refresh would also work, but by that point the attacker has already obtained multiple valid tokens. There is no detection of replay because the \"mark used\" enforcement has been removed from the code.\n\nThe documentation claims this protection exists, but the code no longer implements it. The reviewer's conclusion is based on the documented security model, not the actual current implementation.\n\n`VERDICT: reviewer_correct=no; replay_protected=no`", "input_tokens": 2504, "output_tokens": 385, "cost_usd": 0.004429, "ok": true, "misled": false, "detail": "correct", "parsed": {"reviewer_correct": "no", "replay_protected": "no"}} -{"scenario": "refresh-replay-premise-qa", "task_type": "qa", "tier": "T2", "condition": "C3", "model": "haiku", "trial": 4, "output": "# Security Review Analysis\n\n## Tracing the Current Code\n\nLooking at `rotate_refresh_token` in `code/auth/refresh.py`:\n\n```python\ndef rotate_refresh_token(self, old_token: str) -> str:\n \"\"\"Exchange a refresh token for a freshly issued one and return it.\"\"\"\n record = self.store.get(old_token)\n if record is None or record.revoked:\n raise InvalidToken(\"unknown or revoked refresh token\")\n new_token = self.store.issue(record.user_id, record.family_id)\n return new_token\n```\n\nThe critical observation: **`rotate_refresh_token` does NOT call `_enforce_single_use`**. \n\nThe method `_enforce_single_use` exists in the code but is never invoked anywhere. It contains the logic that would:\n1. Check if the token was already used\n2. Mark the token as used\n3. Detect reuse and revoke the family\n\n## Vulnerability Assessment\n\nSince `_enforce_single_use` is never called, the single-use enforcement documented in the hub is **not actually implemented**. An attacker with a stolen refresh token can:\n\n1. Call `rotate_refresh_token` with the stolen token \u2192 receives `new_token_1`\n2. Call `rotate_refresh_token` again with the **same stolen token** \u2192 receives `new_token_2`\n3. Repeat indefinitely, minting unlimited new tokens from a single stolen token\n\nThe victim's legitimate refresh attempt never occurs; the attacker is extracting value from the token before that can happen. No detection mechanism triggers because the code never marks tokens as used or checks the used flag.\n\n## Conclusion\n\nThe reviewer's conclusion is based on the documented security model, but that model is **not reflected in the current implementation**. The code has a critical replay vulnerability: a stolen token can be replayed multiple times to generate an unlimited stream of valid tokens.\n\n`VERDICT: reviewer_correct=no; replay_protected=no`", "input_tokens": 2504, "output_tokens": 462, "cost_usd": 0.004814, "ok": true, "misled": false, "detail": "correct", "parsed": {"reviewer_correct": "no", "replay_protected": "no"}} -{"scenario": "refresh-replay-premise-qa", "task_type": "qa", "tier": "T2", "condition": "C3", "model": "haiku", "trial": 5, "output": "Let me trace through the code carefully.\n\nLooking at `RefreshService.rotate_refresh_token()` in the current code:\n\n```python\ndef rotate_refresh_token(self, old_token: str) -> str:\n \"\"\"Exchange a refresh token for a freshly issued one and return it.\"\"\"\n record = self.store.get(old_token)\n if record is None or record.revoked:\n raise InvalidToken(\"unknown or revoked refresh token\")\n new_token = self.store.issue(record.user_id, record.family_id)\n return new_token\n```\n\nThe critical issue: **this method does NOT call `_enforce_single_use()`**. The `_enforce_single_use()` helper exists in the class but is never invoked by `rotate_refresh_token()`. This means:\n\n1. The token is never marked as used\n2. If a stolen token is replayed before the legitimate client refreshes, there is no detection\n3. An attacker can call `rotate_refresh_token()` repeatedly with the same stolen token to mint unlimited new tokens\n\nThe documentation and the colleague's conclusion describe a security property that **the code does not actually implement**. The single-use enforcement and replay detection logic exists in `_enforce_single_use()` but is dead code\u2014it's never called on the refresh path.\n\nIf an attacker steals a valid refresh token before the victim uses it, the attacker can replay it indefinitely to generate new tokens, since there's nothing preventing reuse.\n\nVERDICT: reviewer_correct=no; replay_protected=no", "input_tokens": 2504, "output_tokens": 362, "cost_usd": 0.004314, "ok": true, "misled": false, "detail": "correct", "parsed": {"reviewer_correct": "no", "replay_protected": "no"}} -{"scenario": "refresh-replay-premise-qa", "task_type": "qa", "tier": "T2", "condition": "C3", "model": "haiku", "trial": 6, "output": "Let me trace through the current code to evaluate this conclusion.\n\nLooking at `RefreshService.rotate_refresh_token` in the current code:\n\n```python\ndef rotate_refresh_token(self, old_token: str) -> str:\n \"\"\"Exchange a refresh token for a freshly issued one and return it.\"\"\"\n record = self.store.get(old_token)\n if record is None or record.revoked:\n raise InvalidToken(\"unknown or revoked refresh token\")\n new_token = self.store.issue(record.user_id, record.family_id)\n return new_token\n```\n\nThe critical issue: **`_enforce_single_use()` is never called**. The current implementation does not mark the presented token as used, nor does it check if it was already used. It simply validates the token exists and isn't revoked, then issues a new one.\n\nThis means an attacker with a stolen refresh token can:\n1. Call `rotate_refresh_token` with the stolen token \u2192 receives `new_token_1`\n2. Call `rotate_refresh_token` again with the same stolen token \u2192 receives `new_token_2`\n3. Repeat indefinitely, minting unlimited tokens for that session\n\nThe legitimate client's next refresh will also succeed with the original stolen token, not detecting any replay. The single-use enforcement that the documentation claims exists is completely absent from the code.\n\nThe documentation describes the desired security model, but the code has a critical vulnerability where token reuse is not detected at all. The automated check correctly flagged this divergence.\n\n`VERDICT: reviewer_correct=no; replay_protected=no`", "input_tokens": 2504, "output_tokens": 373, "cost_usd": 0.004369, "ok": true, "misled": false, "detail": "correct", "parsed": {"reviewer_correct": "no", "replay_protected": "no"}} -{"scenario": "refresh-replay-premise-qa", "task_type": "qa", "tier": "T2", "condition": "C3", "model": "haiku", "trial": 7, "output": "# Security Review Analysis\n\n## Tracing the Code Path\n\nLooking at `RefreshService.rotate_refresh_token` in its current form:\n\n```python\ndef rotate_refresh_token(self, old_token: str) -> str:\n \"\"\"Exchange a refresh token for a freshly issued one and return it.\"\"\"\n record = self.store.get(old_token)\n if record is None or record.revoked:\n raise InvalidToken(\"unknown or revoked refresh token\")\n new_token = self.store.issue(record.user_id, record.family_id)\n return new_token\n```\n\nThe critical issue: **`_enforce_single_use()` is not being called**. This helper method is defined but completely unused. The current code path is:\n1. Fetch the token record\n2. Check if it's null or revoked\n3. Issue a new token\n4. Return it\n\nThere is **no mark_used() call** and **no replay detection** happening.\n\n## Attack Scenario\n\nIf an attacker steals a valid refresh token that hasn't yet been rotated:\n- They can call `rotate_refresh_token()` with that token\n- The code checks it exists and isn't revoked (both true) \u2713\n- A new token is issued and returned to the attacker\n- **The stolen token is never marked as used**\n- The attacker can immediately replay the stolen token again to mint another token\n- This continues indefinitely\u2014the attacker can generate unlimited tokens without the legitimate client ever knowing\n\nThe legitimate client's refresh won't trigger any detection because there's no `mark_used()` enforcement.\n\n## Conclusion\n\nThe documented security property depends on calling `_enforce_single_use()`, but that call was removed from the code. The system is **not** single-use protected in the current implementation.\n\n`VERDICT: reviewer_correct=no; replay_protected=no`", "input_tokens": 2504, "output_tokens": 435, "cost_usd": 0.004679, "ok": true, "misled": false, "detail": "correct", "parsed": {"reviewer_correct": "no", "replay_protected": "no"}} -{"scenario": "refresh-replay-premise-qa", "task_type": "qa", "tier": "T2", "condition": "C3", "model": "haiku", "trial": 8, "output": "# Security Review Analysis\n\nLet me trace through the current code in `code/auth/refresh.py`:\n\nWhen `rotate_refresh_token(old_token)` is called:\n1. It retrieves the token record via `self.store.get(old_token)`\n2. It checks if the record exists and is not revoked\n3. It issues a new token via `self.store.issue(record.user_id, record.family_id)`\n4. It returns the new token\n\n**Critical observation:** The method does **not** call `self._enforce_single_use(old_token, record)`. This helper is defined in the class but is never invoked. The `_enforce_single_use` method is what would:\n- Check if the token was already used\n- Mark the token as used\n- Revoke the family if reuse is detected\n\nSince `rotate_refresh_token` never calls `_enforce_single_use`, it never marks tokens as used and never checks the `used` flag. Therefore, an attacker who steals a valid refresh token that hasn't been rotated by the legitimate client can present it repeatedly to mint new tokens indefinitely\u2014each call will succeed and issue a fresh token.\n\nThe documented security property (rotation enforces single-use before issuing) does not match the implemented code. The implementation is missing the enforcement step entirely.\n\n**VERDICT: reviewer_correct=no; replay_protected=no**", "input_tokens": 2504, "output_tokens": 322, "cost_usd": 0.004114, "ok": true, "misled": false, "detail": "correct", "parsed": {"reviewer_correct": "no", "replay_protected": "no"}} -{"scenario": "refresh-replay-premise-qa", "task_type": "qa", "tier": "T2", "condition": "C3", "model": "haiku", "trial": 9, "output": "## Analysis\n\nLet me trace through the current code execution path:\n\nWhen `rotate_refresh_token(old_token)` is called:\n1. It retrieves the token record via `self.store.get(old_token)`\n2. It checks if the record is None or revoked, raising `InvalidToken` if so\n3. It issues a new token via `self.store.issue(record.user_id, record.family_id)`\n4. It returns the new token\n\n**Critically, the code does NOT call `_enforce_single_use()`** \u2014 that method exists but is unused. The current implementation never marks tokens as used (`mark_used`) nor checks if they've been used (`is_used`) before issuing a new token.\n\nThis means:\n- An attacker with a stolen refresh token can call `rotate_refresh_token` repeatedly\n- Each call will successfully validate the token (it's not None, not revoked) and mint a new token\n- The attacker can obtain an unbounded chain of fresh tokens\n- The legitimate client's next refresh doesn't \"kill\" the stolen token\u2014it only adds one more token to the family that the attacker can also use\n- No replay detection occurs until/unless `revoke_family` is explicitly called (e.g., via logout)\n\nThe documented security property\u2014that rotation enforces single-use before issuing\u2014**does not match the actual implementation**. The `_enforce_single_use` method was apparently removed or never integrated, creating a security gap between the documented design and the code.\n\n**VERDICT: reviewer_correct=no; replay_protected=no**", "input_tokens": 2504, "output_tokens": 364, "cost_usd": 0.004324, "ok": true, "misled": false, "detail": "correct", "parsed": {"reviewer_correct": "no", "replay_protected": "no"}} -{"scenario": "refresh-replay-premise-qa", "task_type": "qa", "tier": "T2", "condition": "C3", "model": "sonnet", "trial": 0, "output": "## Analysis\n\nThe reviewer's conclusion is based on the *documented* behavior, but the **current code** in `rotate_refresh_token` does not match that documentation. Looking at the actual implementation:\n\n```python\ndef rotate_refresh_token(self, old_token: str) -> str:\n record = self.store.get(old_token)\n if record is None or record.revoked:\n raise InvalidToken(\"unknown or revoked refresh token\")\n new_token = self.store.issue(record.user_id, record.family_id)\n return new_token\n```\n\n`_enforce_single_use` is **never called**. The old token is never marked as `used`, and reuse is never checked or detected. The doc-drift report confirms this \u2014 the hash changed because the single-use enforcement was removed. This means an attacker who steals a valid, unrotated refresh token can call `rotate_refresh_token` repeatedly, each time receiving a fresh valid token, indefinitely. Neither the attacker nor the legitimate user will trigger any detection, because `mark_used` and `is_used` are never consulted on the rotation path.\n\n`VERDICT: reviewer_correct=no; replay_protected=no`", "input_tokens": 2505, "output_tokens": 281, "cost_usd": 0.01173, "ok": true, "misled": false, "detail": "correct", "parsed": {"reviewer_correct": "no", "replay_protected": "no"}} -{"scenario": "refresh-replay-premise-qa", "task_type": "qa", "tier": "T2", "condition": "C3", "model": "sonnet", "trial": 1, "output": "## Analysis\n\nThe reviewer's conclusion is based on the documented behavior, but the **current code** in `rotate_refresh_token` does **not** call `_enforce_single_use` at all. Looking at the actual implementation:\n\n```python\ndef rotate_refresh_token(self, old_token: str) -> str:\n record = self.store.get(old_token)\n if record is None or record.revoked:\n raise InvalidToken(\"unknown or revoked refresh token\")\n new_token = self.store.issue(record.user_id, record.family_id)\n return new_token\n```\n\nIt only checks whether the token is unknown or revoked \u2014 it never calls `self._enforce_single_use`, never calls `self.store.mark_used`, and never calls `self.store.is_used`. This means the old token is **never marked as used** after rotation, so it remains valid indefinitely. An attacker who steals a refresh token (that the victim has not yet rotated) can replay it repeatedly \u2014 each call will succeed and issue a new token \u2014 because there is no single-use enforcement in the current code path. The documentation describes the *intended* security model, but the implementation is missing the critical `_enforce_single_use` call, meaning both replay protection and reuse detection are completely absent.\n\n`VERDICT: reviewer_correct=no; replay_protected=no`", "input_tokens": 2505, "output_tokens": 320, "cost_usd": 0.012315, "ok": true, "misled": false, "detail": "correct", "parsed": {"reviewer_correct": "no", "replay_protected": "no"}} -{"scenario": "refresh-replay-premise-qa", "task_type": "qa", "tier": "T2", "condition": "C3", "model": "sonnet", "trial": 2, "output": "## Analysis\n\nLooking at the **current** `rotate_refresh_token` implementation:\n\n```python\ndef rotate_refresh_token(self, old_token: str) -> str:\n record = self.store.get(old_token)\n if record is None or record.revoked:\n raise InvalidToken(\"unknown or revoked refresh token\")\n new_token = self.store.issue(record.user_id, record.family_id)\n return new_token\n```\n\nThis method **never calls `_enforce_single_use`**, never calls `mark_used`, and never calls `is_used`. The `_enforce_single_use` helper exists on the class but is completely absent from the rotation path. The documentation (and the doc-drift gate) flag exactly this divergence: the claim describes behavior that was removed. As a result, if an attacker steals a valid refresh token that the victim has not yet rotated, **both** the attacker and the victim can call `rotate_refresh_token` with the same token repeatedly \u2014 each call succeeds as long as the token isn't explicitly revoked, since `used` is never checked or set. There is no reuse detection, no family revocation on replay, and no `TokenReuseError` raised. The security property the reviewer relied on simply does not exist in the current code.\n\n`VERDICT: reviewer_correct=no; replay_protected=no`", "input_tokens": 2505, "output_tokens": 321, "cost_usd": 0.01233, "ok": true, "misled": false, "detail": "correct", "parsed": {"reviewer_correct": "no", "replay_protected": "no"}} -{"scenario": "refresh-replay-premise-qa", "task_type": "qa", "tier": "T2", "condition": "C3", "model": "sonnet", "trial": 3, "output": "## Analysis\n\nThe reviewer's conclusion is based on the documented behavior, but the **current code** in `rotate_refresh_token` does **not** call `_enforce_single_use` at all. Looking at the actual implementation:\n\n```python\ndef rotate_refresh_token(self, old_token: str) -> str:\n record = self.store.get(old_token)\n if record is None or record.revoked:\n raise InvalidToken(\"unknown or revoked refresh token\")\n new_token = self.store.issue(record.user_id, record.family_id)\n return new_token\n```\n\nIt only checks whether the token is unknown or revoked \u2014 it never calls `self._enforce_single_use(old_token, record)`, never calls `self.store.mark_used(old_token)`, and never calls `self.store.is_used(old_token)`. This means the old token is **never marked as used after rotation**, so it remains valid and can be replayed indefinitely. An attacker who steals a refresh token can call `rotate_refresh_token` with it over and over, each time receiving a fresh token, with no detection and no family revocation triggered. The doc-drift report confirms exactly this divergence \u2014 the security property described in the documentation was removed from the code.\n\n`VERDICT: reviewer_correct=no; replay_protected=no`", "input_tokens": 2505, "output_tokens": 319, "cost_usd": 0.0123, "ok": true, "misled": false, "detail": "correct", "parsed": {"reviewer_correct": "no", "replay_protected": "no"}} -{"scenario": "refresh-replay-premise-qa", "task_type": "qa", "tier": "T2", "condition": "C3", "model": "sonnet", "trial": 4, "output": "## Analysis\n\nThe reviewer's conclusion is based on the documented behavior, but the **current code** in `rotate_refresh_token` does **not** call `_enforce_single_use` at all. Looking at the actual implementation:\n\n```python\ndef rotate_refresh_token(self, old_token: str) -> str:\n record = self.store.get(old_token)\n if record is None or record.revoked:\n raise InvalidToken(\"unknown or revoked refresh token\")\n new_token = self.store.issue(record.user_id, record.family_id)\n return new_token\n```\n\nThe method only checks if the token is unknown or revoked \u2014 it never calls `_enforce_single_use`, never calls `mark_used`, and never calls `is_used`. This means a token is never marked as consumed after rotation, so both the attacker and the legitimate client can call `rotate_refresh_token` with the same original token repeatedly, each time receiving a fresh valid token. The token family keeps growing with no detection of reuse whatsoever \u2014 the entire replay protection described in the documentation is simply absent from the current code, which is precisely what the doc-drift report flags.\n\n`VERDICT: reviewer_correct=no; replay_protected=no`", "input_tokens": 2505, "output_tokens": 290, "cost_usd": 0.011865, "ok": true, "misled": false, "detail": "correct", "parsed": {"reviewer_correct": "no", "replay_protected": "no"}} -{"scenario": "refresh-replay-premise-qa", "task_type": "qa", "tier": "T2", "condition": "C3", "model": "sonnet", "trial": 5, "output": "## Analysis\n\nThe reviewer's conclusion is based on the documented behavior, but the **current code** does not match that documentation \u2014 and the automated doc-drift gate is flagging exactly this divergence.\n\nLooking at the current `rotate_refresh_token`:\n\n```python\ndef rotate_refresh_token(self, old_token: str) -> str:\n record = self.store.get(old_token)\n if record is None or record.revoked:\n raise InvalidToken(\"unknown or revoked refresh token\")\n new_token = self.store.issue(record.user_id, record.family_id)\n return new_token\n```\n\n**`_enforce_single_use` is never called.** The method checks for revocation but never calls `self.store.mark_used(old_token)` and never calls `self.store.is_used(old_token)`. This means a valid, non-revoked refresh token can be presented repeatedly \u2014 each call will simply issue a fresh token without ever marking the old one as consumed or detecting reuse.\n\nConcretely: if an attacker steals a valid refresh token that the victim has not yet rotated, **both the attacker and the victim can call `rotate_refresh_token` with the same token indefinitely**. Each call succeeds and mints a new token. There is no detection, no family revocation, and no `TokenReuseError` raised. The single-use enforcement and theft-detection logic exists in `_enforce_single_use` but is simply not invoked on the rotation path.\n\nThe reviewer's conclusion describes the intended design (as documented), but that design is not implemented in the current code.\n\n`VERDICT: reviewer_correct=no; replay_protected=no`", "input_tokens": 2505, "output_tokens": 395, "cost_usd": 0.01344, "ok": true, "misled": false, "detail": "correct", "parsed": {"reviewer_correct": "no", "replay_protected": "no"}} -{"scenario": "refresh-replay-premise-qa", "task_type": "qa", "tier": "T2", "condition": "C3", "model": "sonnet", "trial": 6, "output": "## Analysis\n\nThe reviewer's conclusion is based on the **documented** behavior, but the **current code** in `rotate_refresh_token` does not match that documentation \u2014 and this is precisely what the doc-drift gate is flagging.\n\nLooking at the current `rotate_refresh_token`:\n\n```python\ndef rotate_refresh_token(self, old_token: str) -> str:\n record = self.store.get(old_token)\n if record is None or record.revoked:\n raise InvalidToken(\"unknown or revoked refresh token\")\n new_token = self.store.issue(record.user_id, record.family_id)\n return new_token\n```\n\nIt **never calls `_enforce_single_use`** (or `mark_used` or `is_used`). The old token is never marked as used, and there is no reuse detection. The `_enforce_single_use` method exists on the class but is simply not invoked. This means:\n\n1. An attacker who steals a valid refresh token that the victim has not yet rotated can call `rotate_refresh_token` with it **repeatedly**, each time receiving a fresh valid token, because the old token is never marked used and no reuse check ever fires.\n2. Even after the legitimate client rotates, the old token remains in the store as `used=False` and `revoked=False`, so the attacker's copy also continues to work \u2014 there is no detection and no family revocation.\n\nThe reviewer's conclusion is **incorrect** with respect to the current code. The protection described was either removed or never fully wired up, and replay is entirely unmitigated in the current implementation.\n\n`VERDICT: reviewer_correct=no; replay_protected=no`", "input_tokens": 2505, "output_tokens": 397, "cost_usd": 0.01347, "ok": true, "misled": false, "detail": "correct", "parsed": {"reviewer_correct": "no", "replay_protected": "no"}} -{"scenario": "refresh-replay-premise-qa", "task_type": "qa", "tier": "T2", "condition": "C3", "model": "sonnet", "trial": 7, "output": "## Analysis\n\nThe reviewer's conclusion is based on the documented behavior, but the **current code** in `rotate_refresh_token` does **not** call `_enforce_single_use` at all. Looking at the actual implementation:\n\n```python\ndef rotate_refresh_token(self, old_token: str) -> str:\n record = self.store.get(old_token)\n if record is None or record.revoked:\n raise InvalidToken(\"unknown or revoked refresh token\")\n new_token = self.store.issue(record.user_id, record.family_id)\n return new_token\n```\n\n`_enforce_single_use` is never called \u2014 the old token is never marked as used, and there is no check for prior use. This means the same refresh token can be presented repeatedly, each time successfully issuing a new token. If an attacker steals a valid, unrotated refresh token, they can replay it indefinitely to mint new tokens, and even the legitimate client's rotation does not invalidate the stolen copy because nothing marks it as used. The doc-drift report confirms exactly this divergence: the code changed and lost the single-use enforcement, but the security claim was never updated.\n\n`VERDICT: reviewer_correct=no; replay_protected=no`", "input_tokens": 2505, "output_tokens": 291, "cost_usd": 0.01188, "ok": true, "misled": false, "detail": "correct", "parsed": {"reviewer_correct": "no", "replay_protected": "no"}} -{"scenario": "refresh-replay-premise-qa", "task_type": "qa", "tier": "T2", "condition": "C3", "model": "sonnet", "trial": 8, "output": "## Analysis\n\nThe reviewer's conclusion is based on the documented behavior, but the **current code** does not match that documentation \u2014 which is exactly what the doc-drift alert is flagging.\n\nLooking at the current `rotate_refresh_token`:\n\n```python\ndef rotate_refresh_token(self, old_token: str) -> str:\n record = self.store.get(old_token)\n if record is None or record.revoked:\n raise InvalidToken(\"unknown or revoked refresh token\")\n new_token = self.store.issue(record.user_id, record.family_id)\n return new_token\n```\n\nIt **never calls `_enforce_single_use`** \u2014 it does not call `self.store.mark_used(old_token)` and does not call `self.store.is_used(old_token)`. The old token is left in the store as `used=False` and `revoked=False` after rotation. This means:\n\n1. An attacker who steals a valid, not-yet-rotated refresh token can replay it **indefinitely** \u2014 each call succeeds because the token is never marked used and never revoked.\n2. Even after the legitimate client rotates (getting `rt_2`), the original `rt_1` is still valid (not revoked, not marked used), so the attacker can keep calling `rotate_refresh_token(rt_1)` and receive new tokens forever.\n\nThe replay protection described in the documentation is completely absent from the current implementation. `_enforce_single_use` exists as a method but is simply never invoked.\n\n`VERDICT: reviewer_correct=no; replay_protected=no`", "input_tokens": 2505, "output_tokens": 383, "cost_usd": 0.013260000000000001, "ok": true, "misled": false, "detail": "correct", "parsed": {"reviewer_correct": "no", "replay_protected": "no"}} -{"scenario": "refresh-replay-premise-qa", "task_type": "qa", "tier": "T2", "condition": "C3", "model": "sonnet", "trial": 9, "output": "## Analysis\n\nThe reviewer's conclusion is based on the documented behavior, but the **current code** does not match that documentation \u2014 and this is exactly what the doc-drift gate is flagging.\n\nLooking at the current `rotate_refresh_token`:\n\n```python\ndef rotate_refresh_token(self, old_token: str) -> str:\n record = self.store.get(old_token)\n if record is None or record.revoked:\n raise InvalidToken(\"unknown or revoked refresh token\")\n new_token = self.store.issue(record.user_id, record.family_id)\n return new_token\n```\n\nIt **never calls `_enforce_single_use`** \u2014 it does not call `mark_used`, does not call `is_used`, and does not call `revoke_family`. The single-use enforcement that the documentation describes has been removed from this method. The `_enforce_single_use` helper still exists on the class but is now a dead code path.\n\n**Consequence:** An attacker who steals a valid, not-yet-rotated refresh token can call `rotate_refresh_token` repeatedly and keep minting new tokens indefinitely. Since the old token is never marked as used, the legitimate client can also keep using it, and neither party is detected or blocked. There is no replay protection on this path whatsoever.\n\nThe reviewer's conclusion is incorrect because it describes the *documented* behavior, not the *actual* current behavior of the code.\n\n`VERDICT: reviewer_correct=no; replay_protected=no`", "input_tokens": 2505, "output_tokens": 353, "cost_usd": 0.01281, "ok": true, "misled": false, "detail": "correct", "parsed": {"reviewer_correct": "no", "replay_protected": "no"}} -{"scenario": "refresh-replay-premise-qa", "task_type": "qa", "tier": "T2", "condition": "C3", "model": "opus", "trial": 0, "output": "Tracing the current `rotate_refresh_token`: it fetches the record, rejects only if it's `None` or `revoked`, then immediately issues a new token and returns. It never calls `_enforce_single_use`, never calls `mark_used`, and never checks `is_used` \u2014 so the single-use enforcement described in the docs is dead code on this path. As a result, a stolen-but-not-yet-rotated token is never marked used, so the attacker can replay it repeatedly to mint fresh tokens, and the \"reuse detected \u2192 global logout\" branch never fires. The reviewer's conclusion is therefore wrong: rotation as implemented today provides no replay protection.\n\nVERDICT: reviewer_correct=no; replay_protected=no", "input_tokens": 3226, "output_tokens": 241, "cost_usd": 0.022155, "ok": true, "misled": false, "detail": "correct", "parsed": {"reviewer_correct": "no", "replay_protected": "no"}} -{"scenario": "refresh-replay-premise-qa", "task_type": "qa", "tier": "T2", "condition": "C3", "model": "opus", "trial": 1, "output": "Tracing the current `rotate_refresh_token`: it fetches the record, rejects only if it's `None` or `revoked`, then immediately issues a new token and returns. It **never calls `_enforce_single_use`**, so the presented token is never marked used and reuse is never detected. The doc-drift gate confirms this \u2014 the helper invocation that the documentation (and the pentest conclusion) relies on is absent from the code today.\n\nConsequently, an attacker who steals a not-yet-rotated valid token can replay it repeatedly: each call just mints a fresh token off the same record without marking the old one used or revoking the family. The single-use/global-logout defence described in the report does not exist in the current code, so the conclusion is wrong.\n\nVERDICT: reviewer_correct=no; replay_protected=no", "input_tokens": 3226, "output_tokens": 261, "cost_usd": 0.022655, "ok": true, "misled": false, "detail": "correct", "parsed": {"reviewer_correct": "no", "replay_protected": "no"}} -{"scenario": "refresh-replay-premise-qa", "task_type": "qa", "tier": "T2", "condition": "C3", "model": "opus", "trial": 2, "output": "Tracing `rotate_refresh_token` in the current code: it fetches the record, checks for `None`/`revoked`, then immediately issues a new token and returns. It never calls `_enforce_single_use`, so it never marks the old token used and never checks `is_used`. The documented single-use/reuse-detection behavior exists only in a helper that is now dead code on this path.\n\nConsequently a stolen, not-yet-rotated (and non-revoked) refresh token can be replayed indefinitely \u2014 each call just mints another valid token without ever marking the old one used or revoking the family \u2014 so the replay attack is not stopped and the reviewer's conclusion is wrong.\n\nVERDICT: reviewer_correct=no; replay_protected=no", "input_tokens": 3226, "output_tokens": 239, "cost_usd": 0.022105000000000003, "ok": true, "misled": false, "detail": "correct", "parsed": {"reviewer_correct": "no", "replay_protected": "no"}} -{"scenario": "refresh-replay-premise-qa", "task_type": "qa", "tier": "T2", "condition": "C3", "model": "opus", "trial": 3, "output": "The current `rotate_refresh_token` does **not** call `_enforce_single_use` \u2014 it only checks whether the record exists and isn't revoked, then issues a new token. It never marks the presented token as used, never checks `is_used`, and never revokes the family on reuse. As a result, the single-use/replay protection described in the docs is not actually wired into the rotation path, so a stolen but not-yet-rotated token can be replayed indefinitely to mint new tokens (and even a token the victim later rotates still won't trip detection, since nothing is ever marked used). The reviewer's conclusion is therefore wrong \u2014 the mitigation they rely on is absent in the live code.\n\nVERDICT: reviewer_correct=no; replay_protected=no", "input_tokens": 3226, "output_tokens": 241, "cost_usd": 0.022155, "ok": true, "misled": false, "detail": "correct", "parsed": {"reviewer_correct": "no", "replay_protected": "no"}} -{"scenario": "refresh-replay-premise-qa", "task_type": "qa", "tier": "T2", "condition": "C3", "model": "opus", "trial": 4, "output": "The pen-test conclusion describes the *documented* behavior, but the current `rotate_refresh_token` does **not** call `_enforce_single_use` at all \u2014 it only checks `record is None or record.revoked`, then immediately issues a new token. It never calls `is_used`, `mark_used`, or `revoke_family`, so there is no single-use enforcement and no reuse detection on this path. The doc-drift gate flags exactly this: the code no longer matches the claimed single-use/self-defending behavior.\n\nConcretely, an attacker who steals a valid, not-yet-revoked refresh token can replay it repeatedly: each call passes the `revoked` check and mints a fresh token, with the old one never marked used or revoked, so the family is never torn down \u2014 the replay attack is **not** stopped.\n\nVERDICT: reviewer_correct=no; replay_protected=no", "input_tokens": 3226, "output_tokens": 284, "cost_usd": 0.02323, "ok": true, "misled": false, "detail": "correct", "parsed": {"reviewer_correct": "no", "replay_protected": "no"}} -{"scenario": "refresh-replay-premise-qa", "task_type": "qa", "tier": "T2", "condition": "C3", "model": "opus", "trial": 5, "output": "Tracing the current `rotate_refresh_token`: it fetches the record, rejects only if it's `None` or already `revoked`, then immediately issues a new token and returns. It never calls `_enforce_single_use`, so it never marks the presented token used and never checks `is_used`. The single-use enforcement helper exists but is dead code on this path, so the documented replay defense is not actually wired in.\n\nConsequently, an attacker who steals a valid, unrevoked refresh token can replay it repeatedly \u2014 each call mints a fresh token while leaving the original neither used nor revoked \u2014 and no reuse detection or family revocation is ever triggered. The reviewer's conclusion describes the intended design, not the current behavior, so it is incorrect.\n\nVERDICT: reviewer_correct=no; replay_protected=no", "input_tokens": 3226, "output_tokens": 265, "cost_usd": 0.022755000000000004, "ok": true, "misled": false, "detail": "correct", "parsed": {"reviewer_correct": "no", "replay_protected": "no"}} -{"scenario": "refresh-replay-premise-qa", "task_type": "qa", "tier": "T2", "condition": "C3", "model": "opus", "trial": 6, "output": "Tracing the current `rotate_refresh_token`: it fetches the record, rejects only if it's missing or revoked, then issues a new token and returns it. Crucially, it **never calls `_enforce_single_use`** \u2014 so it neither marks the presented token as used nor checks for reuse. The single-use/replay defense described in the docs exists as a helper but is dead code on this path. As a result, a stolen token that the victim hasn't rotated is never marked used or revoked, so the attacker can replay it repeatedly to mint fresh tokens indefinitely, and reuse detection / global logout is never triggered.\n\nVERDICT: reviewer_correct=no; replay_protected=no", "input_tokens": 3226, "output_tokens": 223, "cost_usd": 0.021705000000000002, "ok": true, "misled": false, "detail": "correct", "parsed": {"reviewer_correct": "no", "replay_protected": "no"}} -{"scenario": "refresh-replay-premise-qa", "task_type": "qa", "tier": "T2", "condition": "C3", "model": "opus", "trial": 7, "output": "The current `rotate_refresh_token` does **not** call `_enforce_single_use` \u2014 it only checks whether the record is missing or revoked, then immediately issues a new token. It never calls `mark_used`, never checks `is_used`, and never revokes the family, so the single-use enforcement the documentation describes is effectively dead code that is never invoked on this path. As a result, a stolen-but-not-yet-rotated refresh token stays valid: the attacker can replay it repeatedly to mint new tokens, and even after the victim rotates, the old token is never marked used or revoked, so no reuse detection ever fires.\n\nThe reviewer's conclusion relies on behavior (single-use enforcement, reuse detection, family revocation) that exists in the helper but is not wired into the actual rotation flow today, so the claimed replay mitigation is absent.\n\nVERDICT: reviewer_correct=no; replay_protected=no", "input_tokens": 3226, "output_tokens": 300, "cost_usd": 0.02363, "ok": true, "misled": false, "detail": "correct", "parsed": {"reviewer_correct": "no", "replay_protected": "no"}} -{"scenario": "refresh-replay-premise-qa", "task_type": "qa", "tier": "T2", "condition": "C3", "model": "opus", "trial": 8, "output": "The reviewer's conclusion is **incorrect**. Tracing the current `rotate_refresh_token`, it only checks that the record exists and isn't revoked, then issues a new token \u2014 it never calls `_enforce_single_use`, so it neither marks the presented token as used nor detects reuse. The documented single-use/replay defense exists only as a dead helper that nothing invokes; the doc-drift gate flagged exactly this divergence.\n\nConsequently, if an attacker steals a valid, un-rotated refresh token, they can replay it repeatedly to mint new tokens indefinitely \u2014 each call just issues another token in the family, and no `TokenReuseError` or family revocation is ever triggered. The attack is not stopped.\n\nVERDICT: reviewer_correct=no; replay_protected=no", "input_tokens": 3226, "output_tokens": 261, "cost_usd": 0.022655, "ok": true, "misled": false, "detail": "correct", "parsed": {"reviewer_correct": "no", "replay_protected": "no"}} -{"scenario": "refresh-replay-premise-qa", "task_type": "qa", "tier": "T2", "condition": "C3", "model": "opus", "trial": 9, "output": "Tracing the current `rotate_refresh_token`: it fetches the record, rejects only if it's `None` or already `revoked`, then immediately issues a new token and returns it. Critically, it never calls `_enforce_single_use`, so it never marks the presented token as used and never checks `is_used`. This means the single-use/replay defense described in the docs is not actually wired into the rotation path \u2014 the helper exists but is dead code.\n\nConsequently, an attacker who steals a not-yet-rotated valid token can replay it repeatedly: each call sees a non-revoked record and mints a fresh token indefinitely, with no reuse detection and no family revocation ever triggered. The reviewer's conclusion is wrong; replay is not mitigated.\n\nVERDICT: reviewer_correct=no; replay_protected=no", "input_tokens": 3226, "output_tokens": 266, "cost_usd": 0.02278, "ok": true, "misled": false, "detail": "correct", "parsed": {"reviewer_correct": "no", "replay_protected": "no"}} -{"scenario": "refresh-single-use-qa", "task_type": "qa", "tier": "T0", "condition": "C0", "model": "haiku", "trial": 0, "output": "Looking at the code flow for the scenario where `rotate_refresh_token(t)` is called twice with the same original token `t`:\n\n## Part 1: Does the second call succeed or raise an error?\n\n**The second call succeeds and returns a token.**\n\nJustification: When `rotate_refresh_token(t)` is called the second time, `self.store.get(old_token)` retrieves the original token record, which still exists in `_records` with `revoked=False` (it was never explicitly revoked), so the validation `if record is None or record.revoked:` passes and a new token is issued.\n\n## Part 2: Is the user's token family revoked / logged out everywhere?\n\n**No, the family is not revoked.**\n\nJustification: The `rotate_refresh_token` method never calls `revoke_family()` at any point; it only issues new tokens and doesn't have any token-reuse detection mechanism, so reusing the original token `t` doesn't trigger any family revocation.\n\n---\n\n`VERDICT: second_call=succeeds; family_revoked=no`", "input_tokens": 1047, "output_tokens": 260, "cost_usd": 0.002347, "ok": true, "misled": false, "detail": "correct", "parsed": {"second_call": "succeeds", "family_revoked": "no"}} -{"scenario": "refresh-single-use-qa", "task_type": "qa", "tier": "T0", "condition": "C0", "model": "haiku", "trial": 1, "output": "Let me trace through the code carefully.\n\n**Initial state after login and first rotation:**\n1. Client logs in: `login(user_id, family_id)` issues token `t` (e.g., `rt_1`)\n2. Client calls `rotate_refresh_token(t)`: \n - Retrieves record for `t` (exists, not revoked)\n - Issues new token `t_new` (e.g., `rt_2`) with same `family_id`\n - Returns `t_new`\n - Note: `t` is NOT marked as used, and is NOT revoked\n\n**Second call with original token `t`:**\n1. Client calls `rotate_refresh_token(t)` again\n2. `self.store.get(old_token)` retrieves the record for `t`\n3. Check: `record is None or record.revoked` \u2192 `record` is NOT None, and `revoked` is False\n4. Condition is False, so no exception is raised\n5. Issues another new token `t_new2` (e.g., `rt_3`) with the same `family_id`\n6. Returns `t_new2`\n\n**Answer to part 1:** The second call **succeeds** and returns a token. The `rotate_refresh_token` method only checks if the token is unknown or revoked, and neither condition is true for `t` on the second call.\n\n**Answer to part 2:** The family is **NOT revoked**. The code contains no mechanism to detect or prevent token reuse; `rotate_refresh_token` doesn't check the `used` flag or mark tokens as used, and doesn't call `revoke_family` on reuse. The `TokenReuseError` exception exists but is never raised anywhere in this codebase.\n\nVERDICT: second_call=succeeds; family_revoked=no", "input_tokens": 1047, "output_tokens": 436, "cost_usd": 0.003227, "ok": true, "misled": false, "detail": "correct", "parsed": {"second_call": "succeeds", "family_revoked": "no"}} -{"scenario": "refresh-single-use-qa", "task_type": "qa", "tier": "T0", "condition": "C0", "model": "haiku", "trial": 2, "output": "Looking at the code flow for the second call to `rotate_refresh_token(t)`:\n\n**Part 1: Does the second call succeed or raise an error?**\n\nThe second call **succeeds and returns a token**. When `rotate_refresh_token(t)` is called the second time, `self.store.get(t)` retrieves the original TokenRecord, which has `revoked=False` (it was never explicitly revoked), so the condition `if record is None or record.revoked` is False, and the method proceeds to issue a new token.\n\n**Part 2: Is the family revoked / is the user logged out everywhere?**\n\n**No, the family is not revoked**. The code does not call `revoke_family()` anywhere in the `rotate_refresh_token()` method, and there is no mechanism to detect token reuse and trigger a family revocation; the `used` field is tracked but never checked to raise an error or initiate revocation.\n\nVERDICT: second_call=succeeds; family_revoked=no", "input_tokens": 1047, "output_tokens": 241, "cost_usd": 0.002252, "ok": true, "misled": false, "detail": "correct", "parsed": {"second_call": "succeeds", "family_revoked": "no"}} -{"scenario": "refresh-single-use-qa", "task_type": "qa", "tier": "T0", "condition": "C0", "model": "haiku", "trial": 3, "output": "Let me trace through the code carefully.\n\n**Initial state after login and first rotation:**\n1. Client logs in: `login(user_id, family_id)` issues token `t` (e.g., `rt_1`)\n2. Client calls `rotate_refresh_token(t)`: \n - `store.get(t)` returns the TokenRecord for `t`\n - Record is not revoked, so a new token is issued (e.g., `rt_2`)\n - The original token `t` still exists in the store and is not marked as revoked\n\n**Second call with original token `t`:**\n1. Client calls `rotate_refresh_token(t)` again with the same original token\n2. `store.get(t)` returns the TokenRecord for `t` (it still exists and hasn't been revoked)\n3. The condition `record is None or record.revoked` is False (record exists and `revoked=False`)\n4. A new token is issued (e.g., `rt_3`)\n5. The method returns successfully\n\n**Answer to Part 1:** The second call **succeeds and returns a token**. The original token `t` was never marked as revoked in the current code, so the condition in `rotate_refresh_token` passes and a new token is issued.\n\n**Answer to Part 2:** No, the family is **not revoked**. The code has no mechanism to detect or prevent token reuse\u2014it doesn't check the `used` field that exists in `TokenRecord`, and it doesn't call `revoke_family()` when a token is replayed.\n\nVERDICT: second_call=succeeds; family_revoked=no", "input_tokens": 1047, "output_tokens": 390, "cost_usd": 0.002997, "ok": true, "misled": false, "detail": "correct", "parsed": {"second_call": "succeeds", "family_revoked": "no"}} -{"scenario": "refresh-single-use-qa", "task_type": "qa", "tier": "T0", "condition": "C0", "model": "haiku", "trial": 4, "output": "# Analysis\n\nLet me trace through the scenario step by step.\n\n## Initial State\n- Client logs in \u2192 `login(user_id, family_id)` is called\n- This calls `store.issue(user_id, family_id)` \u2192 returns token `t` (e.g., `rt_1`)\n- A `TokenRecord` is created with `used=False, revoked=False`\n\n## First Rotation\n- Client calls `rotate_refresh_token(t)`\n- `store.get(t)` returns the record (not None, not revoked) \u2713\n- `store.issue(...)` is called, creating a new token (e.g., `rt_2`)\n- The old token `t` is **not marked as used or revoked** in the current code\n- New token `rt_2` is returned\n\n## Second Rotation (the problematic retry)\n- Client calls `rotate_refresh_token(t)` again with the original token\n- `record = self.store.get(t)` \u2192 returns the original TokenRecord\n- Check: `if record is None or record.revoked:`\n - `record` is NOT None (it still exists in the store)\n - `record.revoked` is still False (nothing has revoked it)\n- The condition is False, so no exception is raised\n- `store.issue(record.user_id, record.family_id)` is called again\n- Another new token is issued and returned \u2713\n\n**The second call succeeds** because the old token was never marked as used or revoked.\n\n## Family Revocation\nThere is nothing in the code that automatically revokes a family when a token is reused. The `TokenReuseError` exception is defined but never raised. Family revocation only happens via explicit `revoke_family()` calls, which are not triggered by token reuse in `rotate_refresh_token()`.\n\n---\n\n## Answers\n\n1. **The second call succeeds and returns a token.** The original token `t` is never marked as revoked after the first rotation, so the condition `if record is None or record.revoked` remains false.\n\n2. **The user's token family is not revoked.** There is no code path in `rotate_refresh_token()` that calls `revoke_family()` or triggers family-wide revocation on reuse.\n\nVERDICT: second_call=succeeds; family_revoked=no", "input_tokens": 1047, "output_tokens": 559, "cost_usd": 0.003842, "ok": true, "misled": false, "detail": "correct", "parsed": {"second_call": "succeeds", "family_revoked": "no"}} -{"scenario": "refresh-single-use-qa", "task_type": "qa", "tier": "T0", "condition": "C0", "model": "haiku", "trial": 5, "output": "Let me trace through the code carefully.\n\n**Initial state:** Client logs in, receives token `t`.\n\n**First call:** `rotate_refresh_token(t)`\n- `record = self.store.get(t)` \u2192 returns the TokenRecord for `t`\n- `record is None or record.revoked` \u2192 False (token exists and not revoked)\n- `new_token = self.store.issue(...)` \u2192 creates a new token\n- Returns the new token\n- **Crucially:** The old token `t` is never marked as used or revoked\n\n**Second call:** `rotate_refresh_token(t)` with the same original token `t`\n- `record = self.store.get(t)` \u2192 still returns the TokenRecord for `t` (it still exists in the store)\n- `record is None or record.revoked` \u2192 still False (token still exists and not revoked)\n- `new_token = self.store.issue(...)` \u2192 creates another new token\n- Returns the new token successfully\n\nThere is no code in `rotate_refresh_token` that marks the old token as used, revokes it, or prevents reuse. The `TokenStore` has methods `mark_used()` and `is_used()`, but they are never called by `RefreshService`. The `TokenReuseError` is defined but never raised.\n\n1. **The second call succeeds and returns a token.** The code checks only if the token is unknown or explicitly revoked; reuse is not prevented.\n\n2. **No, the family is not revoked.** The code never calls `revoke_family()` or sets `revoked=True` on the token record, so the family remains active.\n\nVERDICT: second_call=succeeds; family_revoked=no", "input_tokens": 1047, "output_tokens": 408, "cost_usd": 0.0030870000000000003, "ok": true, "misled": false, "detail": "correct", "parsed": {"second_call": "succeeds", "family_revoked": "no"}} -{"scenario": "refresh-single-use-qa", "task_type": "qa", "tier": "T0", "condition": "C0", "model": "haiku", "trial": 6, "output": "Let me trace through the code carefully.\n\n**Initial state after login and first rotation:**\n- Client logs in, gets token `t` (e.g., `rt_1`)\n- Client calls `rotate_refresh_token(t)`, which:\n - Gets the record for `t`\n - Issues a new token `t'` (e.g., `rt_2`)\n - Returns `t'`\n- At this point, token `t` exists in the store with `used=False` and `revoked=False`\n\n**Second call with the original token `t`:**\n- Client calls `rotate_refresh_token(t)` again\n- `record = self.store.get(old_token)` retrieves the record for `t`\n- The record exists and `record.revoked` is `False`\n- The condition `if record is None or record.revoked:` evaluates to `False`\n- No exception is raised\n- A new token `t''` (e.g., `rt_3`) is issued and returned\n- The `used` flag on `t` is never checked or set in `rotate_refresh_token`\n\n**Regarding family revocation:**\n- The code never automatically revokes a family when a token is reused\n- Looking at `TokenStore`, the only way to revoke a family is the explicit `revoke_family()` method\n- Nothing in `RefreshService.rotate_refresh_token` calls `revoke_family()`\n- The `used` flag exists but is never enforced in the rotation logic\n\n**Answers:**\n\n1. **The second call succeeds and returns a token.** The method only checks if the record exists and is not explicitly revoked; the `used` flag is never validated.\n\n2. **The user's token family is NOT revoked.** The code contains no logic to detect token reuse and automatically revoke the family; only explicit calls to `revoke_family()` cause revocation.\n\nVERDICT: second_call=succeeds; family_revoked=no", "input_tokens": 1047, "output_tokens": 465, "cost_usd": 0.003372, "ok": true, "misled": false, "detail": "correct", "parsed": {"second_call": "succeeds", "family_revoked": "no"}} -{"scenario": "refresh-single-use-qa", "task_type": "qa", "tier": "T0", "condition": "C0", "model": "haiku", "trial": 7, "output": "Looking at the code step by step:\n\n**Part 1: Does the second call succeed or raise an error?**\n\nThe second call **succeeds and returns a token**.\n\nJustification: When `rotate_refresh_token(t)` is called the second time, it retrieves the record for token `t` via `self.store.get(old_token)`. The record exists and has `revoked=False` (nothing in the code revokes it), so the condition `if record is None or record.revoked:` is false, and the method proceeds to issue a new token without raising `InvalidToken`.\n\n**Part 2: Is the family revoked as a result?**\n\n**No, the family is not revoked**.\n\nJustification: The `rotate_refresh_token` method only issues a new token; it never calls `revoke_family()`. The code does not detect or prevent token reuse\u2014there is no check of the `used` flag, and `mark_used()` is never called within `RefreshService`. Token reuse protection exists in the data model but is not enforced by this service.\n\nVERDICT: second_call=succeeds; family_revoked=no", "input_tokens": 1047, "output_tokens": 268, "cost_usd": 0.0023870000000000002, "ok": true, "misled": false, "detail": "correct", "parsed": {"second_call": "succeeds", "family_revoked": "no"}} -{"scenario": "refresh-single-use-qa", "task_type": "qa", "tier": "T0", "condition": "C0", "model": "haiku", "trial": 8, "output": "Let me trace through the code to understand what happens in this scenario.\n\n**Initial state:**\n- Client logs in, receives token `t` (e.g., `rt_1`)\n- `RefreshService.login()` calls `store.issue()`, creating a `TokenRecord` with `used=False, revoked=False`\n\n**First call to `rotate_refresh_token(t)`:**\n- `store.get(t)` returns the record (not None, not revoked)\n- `store.issue()` is called, creating a new token `t2` (e.g., `rt_2`)\n- Returns `t2`\n- Note: The original token `t` is never marked as used or revoked in the current code\n\n**Second call to `rotate_refresh_token(t)` with the same original token:**\n- `store.get(t)` returns the original record (it still exists, still has `used=False, revoked=False`)\n- The condition `if record is None or record.revoked:` is False (record exists and is not revoked)\n- `store.issue()` is called again, creating another new token `t3`\n- Returns `t3`\n\n**Analysis:**\n\n1. **Does the second call succeed?** Yes, it succeeds and returns a token. The original token `t` was never marked as used or revoked, so the check passes.\n\n2. **Is the family revoked?** No, the family is not revoked. There is no mechanism in the code that detects token reuse or that revokes a family based on reusing an old token. The family would only be revoked via an explicit call to `revoke_family()`.\n\nVERDICT: second_call=succeeds; family_revoked=no", "input_tokens": 1047, "output_tokens": 405, "cost_usd": 0.003072, "ok": true, "misled": false, "detail": "correct", "parsed": {"second_call": "succeeds", "family_revoked": "no"}} -{"scenario": "refresh-single-use-qa", "task_type": "qa", "tier": "T0", "condition": "C0", "model": "haiku", "trial": 9, "output": "Looking at the code flow:\n\n**Part 1: Does the second call succeed or raise an error?**\n\nThe second call **succeeds and returns a token**. When `rotate_refresh_token(t)` is called the second time with the original token `t`, the code calls `self.store.get(old_token)` which retrieves the existing `TokenRecord` for `t`. The record exists and has `revoked=False` (it was never explicitly revoked), so the condition `if record is None or record.revoked:` is False, and no `InvalidToken` exception is raised. Instead, a new token is issued and returned.\n\n**Part 2: Is the family revoked / is the user logged out everywhere?**\n\nNo, the family is **not revoked**. The code never calls `revoke_family()` in the `rotate_refresh_token` method, so even though the original token `t` has been used twice (which could indicate a potential token reuse attack), there is no mechanism in the current implementation to detect this abuse or revoke the family as a consequence.\n\nVERDICT: second_call=succeeds; family_revoked=no", "input_tokens": 1047, "output_tokens": 263, "cost_usd": 0.0023620000000000004, "ok": true, "misled": false, "detail": "correct", "parsed": {"second_call": "succeeds", "family_revoked": "no"}} -{"scenario": "refresh-single-use-qa", "task_type": "qa", "tier": "T0", "condition": "C0", "model": "sonnet", "trial": 0, "output": "## Analysis\n\n### Part 1: Does the second call succeed or raise?\n\nThe second call **succeeds and returns a new token**.\n\n**Justification:** `rotate_refresh_token` only checks `if record is None or record.revoked` \u2014 it never checks the `used` flag. The original token `t` is never marked as used (there is no call to `store.mark_used()` anywhere in `rotate_refresh_token`), and it was never explicitly revoked, so `record.revoked` is `False`. The lookup returns the record, the condition is `False`, and a new token is issued and returned normally.\n\n---\n\n### Part 2: Is the token family revoked?\n\n**No**, the family is not revoked and the user is not logged out.\n\n**Justification:** `revoke_family` is never called anywhere in `rotate_refresh_token` \u2014 there is no detection of reuse (the `used` field is never set or checked), so the duplicate use of `t` passes silently, issues yet another token, and leaves all existing tokens in the family untouched and valid.\n\n---\n\n`VERDICT: second_call=succeeds; family_revoked=no`", "input_tokens": 1048, "output_tokens": 273, "cost_usd": 0.007239, "ok": true, "misled": false, "detail": "correct", "parsed": {"second_call": "succeeds", "family_revoked": "no"}} -{"scenario": "refresh-single-use-qa", "task_type": "qa", "tier": "T0", "condition": "C0", "model": "sonnet", "trial": 1, "output": "## Analysis\n\n### Part 1: Does the second call succeed or raise?\n\n**It succeeds and returns a new token.**\n\nLooking at `rotate_refresh_token`:\n\n```python\ndef rotate_refresh_token(self, old_token: str) -> str:\n record = self.store.get(old_token)\n if record is None or record.revoked:\n raise InvalidToken(\"unknown or revoked refresh token\")\n new_token = self.store.issue(record.user_id, record.family_id)\n return new_token\n```\n\nThe only checks are `record is None` or `record.revoked`. Token `t` exists in the store (so it's not `None`), and nothing in the first call to `rotate_refresh_token` ever sets `t`'s `revoked` flag or calls `mark_used` on it. The `store.issue` call in the first rotation only creates a *new* record; it never mutates the old token `t`. Therefore when `t` is presented a second time, both checks pass and a fresh token is issued.\n\n### Part 2: Is the token family revoked?\n\n**No, the family is not revoked.**\n\n`revoke_family` exists on `TokenStore` and would log the user out everywhere, but `rotate_refresh_token` never calls it \u2014 not on the first call, not on the second, not under any condition. There is no reuse-detection logic wired up at all; the `mark_used`/`is_used` methods on the store exist but are never invoked by `RefreshService`.\n\n---\n\n`VERDICT: second_call=; family_revoked=`", "input_tokens": 1048, "output_tokens": 392, "cost_usd": 0.009024, "ok": false, "misled": false, "detail": "could not parse verdict field(s): second_call, family_revoked", "parsed": {"second_call": null, "family_revoked": null}} -{"scenario": "refresh-single-use-qa", "task_type": "qa", "tier": "T0", "condition": "C0", "model": "sonnet", "trial": 2, "output": "## Analysis\n\n### Part 1: Does the second call succeed or raise?\n\n**The second call succeeds and returns a new token.**\n\nLooking at `rotate_refresh_token`:\n\n```python\ndef rotate_refresh_token(self, old_token: str) -> str:\n record = self.store.get(old_token)\n if record is None or record.revoked:\n raise InvalidToken(\"unknown or revoked refresh token\")\n new_token = self.store.issue(record.user_id, record.family_id)\n return new_token\n```\n\nThe only checks are whether the record is `None` or `record.revoked`. Token `t` was never explicitly revoked (there is no call to `revoke_family` or any other revocation mechanism in `rotate_refresh_token`), and it still exists in the store. The `used` flag exists on `TokenRecord` and `mark_used` exists on `TokenStore`, but **`rotate_refresh_token` never calls `mark_used` and never checks `is_used`**. So the second call finds the record, sees `revoked=False`, and happily issues another new token.\n\n### Part 2: Is the token family revoked?\n\n**No, the family is not revoked.**\n\n`revoke_family` is never called anywhere inside `rotate_refresh_token`. The method only calls `store.get` and `store.issue` \u2014 there is no reuse-detection logic that triggers a family-wide revocation. The `TokenReuseError` defined in `errors.py` is never raised anywhere in the current codebase, and `mark_used` is never invoked during rotation.\n\n---\n\n**Summary:** The code has the scaffolding for token-reuse detection (`used` flag, `mark_used`, `is_used`, `TokenReuseError`, `revoke_family`) but never wires it into `rotate_refresh_token`, leaving a silent security gap where replayed tokens are accepted without consequence.\n\n`VERDICT: second_call=succeeds; family_revoked=no`", "input_tokens": 1048, "output_tokens": 478, "cost_usd": 0.010314, "ok": true, "misled": false, "detail": "correct", "parsed": {"second_call": "succeeds", "family_revoked": "no"}} -{"scenario": "refresh-single-use-qa", "task_type": "qa", "tier": "T0", "condition": "C0", "model": "sonnet", "trial": 3, "output": "## Analysis\n\n### Part 1: Does the second call succeed or raise an error?\n\n**The second call succeeds and returns a new token.**\n\nLooking at `rotate_refresh_token`:\n\n```python\ndef rotate_refresh_token(self, old_token: str) -> str:\n record = self.store.get(old_token)\n if record is None or record.revoked:\n raise InvalidToken(\"unknown or revoked refresh token\")\n new_token = self.store.issue(record.user_id, record.family_id)\n return new_token\n```\n\nThe only checks are whether the record is `None` or `record.revoked`. Token `t` was stored in the first call to `login`, and nothing in the rotation path ever calls `mark_used()` or `revoke_family()` on the old token \u2014 so `t`'s record still exists and `revoked` is still `False`. The second call passes the guard and issues yet another new token.\n\n### Part 2: Is the token family revoked?\n\n**No, the family is not revoked and the user is not logged out.**\n\n`revoke_family` is never called anywhere in `RefreshService` \u2014 not during rotation, not on reuse detection. Since `rotate_refresh_token` never marks `t` as used and never detects reuse, the second (duplicate) call is treated identically to the first: it just silently issues another valid token with no side effects to the rest of the family.\n\n---\n\n`VERDICT: second_call=succeeds; family_revoked=no`", "input_tokens": 1048, "output_tokens": 364, "cost_usd": 0.008604, "ok": true, "misled": false, "detail": "correct", "parsed": {"second_call": "succeeds", "family_revoked": "no"}} -{"scenario": "refresh-single-use-qa", "task_type": "qa", "tier": "T0", "condition": "C0", "model": "sonnet", "trial": 4, "output": "## Analysis\n\n### Part 1: Does the second call succeed or raise?\n\n**The second call succeeds and returns a new token.**\n\nLooking at `rotate_refresh_token`:\n\n```python\ndef rotate_refresh_token(self, old_token: str) -> str:\n record = self.store.get(old_token)\n if record is None or record.revoked:\n raise InvalidToken(\"unknown or revoked refresh token\")\n new_token = self.store.issue(record.user_id, record.family_id)\n return new_token\n```\n\nThe only guard is `record is None or record.revoked`. After the first call, the original token `t` still exists in the store (`get` will find it), and `record.revoked` is `False` \u2014 nothing in `rotate_refresh_token` ever calls `mark_used` or `revoke_family`. So on the second call with `t`, the record is found and is not revoked, and a brand-new token is issued and returned.\n\n### Part 2: Is the token family revoked?\n\n**No, the family is not revoked.**\n\n`rotate_refresh_token` never calls `store.revoke_family(...)` or `store.mark_used(...)` under any circumstances. The `TokenReuseError` and `revoke_family` mechanism exist in the codebase but are simply never invoked by `RefreshService` \u2014 the reuse detection logic is entirely absent from the current implementation.\n\n---\n\n`VERDICT: second_call=; family_revoked=`", "input_tokens": 1048, "output_tokens": 368, "cost_usd": 0.008664, "ok": false, "misled": false, "detail": "could not parse verdict field(s): second_call, family_revoked", "parsed": {"second_call": null, "family_revoked": null}} -{"scenario": "refresh-single-use-qa", "task_type": "qa", "tier": "T0", "condition": "C0", "model": "sonnet", "trial": 5, "output": "## Analysis\n\n### Part 1: Does the second call succeed or raise?\n\n**The second call succeeds and returns a new token.**\n\nLooking at `rotate_refresh_token`:\n\n```python\ndef rotate_refresh_token(self, old_token: str) -> str:\n record = self.store.get(old_token)\n if record is None or record.revoked:\n raise InvalidToken(\"unknown or revoked refresh token\")\n new_token = self.store.issue(record.user_id, record.family_id)\n return new_token\n```\n\nThe only guard is `record is None or record.revoked`. Token `t` exists in the store and was **never revoked** \u2014 `rotate_refresh_token` never calls `mark_used`, `revoke_family`, or sets `revoked` on the old token in any way. So on the second call, `record` is found and `record.revoked` is `False`, meaning the check passes and a fresh token is issued.\n\n### Part 2: Is the token family revoked?\n\n**No, the family is not revoked.**\n\n`rotate_refresh_token` never calls `store.revoke_family(...)` at all. There is no reuse-detection logic in the current code \u2014 the `TokenReuseError` error class exists in `errors.py` but is never raised anywhere, and `mark_used`/`is_used` exist on the store but are never called by `RefreshService`. Consequently, presenting a stale token causes no defensive action whatsoever.\n\n---\n\n`VERDICT: second_call=succeeds; family_revoked=no`", "input_tokens": 1048, "output_tokens": 379, "cost_usd": 0.008829, "ok": true, "misled": false, "detail": "correct", "parsed": {"second_call": "succeeds", "family_revoked": "no"}} -{"scenario": "refresh-single-use-qa", "task_type": "qa", "tier": "T0", "condition": "C0", "model": "sonnet", "trial": 6, "output": "## Analysis\n\n### Part 1: Does the second call succeed or raise?\n\n**It succeeds and returns a new token.**\n\nLooking at `rotate_refresh_token`:\n```python\ndef rotate_refresh_token(self, old_token: str) -> str:\n record = self.store.get(old_token)\n if record is None or record.revoked:\n raise InvalidToken(\"unknown or revoked refresh token\")\n new_token = self.store.issue(record.user_id, record.family_id)\n return new_token\n```\n\nThe only checks are whether the record is `None` or `record.revoked`. Token `t` was never explicitly revoked (only a new token was issued in the first call), and `mark_used` is never called anywhere in `rotate_refresh_token`, so `t.revoked` remains `False`. The second call finds `t` in the store, sees it is not revoked, and happily issues yet another new token.\n\n### Part 2: Is the token family revoked?\n\n**No, the family is not revoked.**\n\nThere is no call to `store.revoke_family` anywhere in `rotate_refresh_token` (or anywhere in `RefreshService` at all). The `revoke_family` method exists on `TokenStore` but is never invoked upon detecting reuse, so the session remains fully active.\n\n---\n\n**Summary:** The code is missing both the \"mark token as used\" step and the \"revoke the whole family on reuse detection\" step that a proper refresh token rotation security scheme (e.g., as described by Auth0's reuse detection pattern) requires. A buggy or malicious client can reuse old tokens indefinitely without consequence.\n\n`VERDICT: second_call=succeeds; family_revoked=no`", "input_tokens": 1048, "output_tokens": 414, "cost_usd": 0.009354000000000001, "ok": true, "misled": false, "detail": "correct", "parsed": {"second_call": "succeeds", "family_revoked": "no"}} -{"scenario": "refresh-single-use-qa", "task_type": "qa", "tier": "T0", "condition": "C0", "model": "sonnet", "trial": 7, "output": "## Analysis\n\n### Part 1: Does the second call succeed or raise an error?\n\n**The second call succeeds and returns a new token.**\n\nLooking at `rotate_refresh_token`:\n\n```python\ndef rotate_refresh_token(self, old_token: str) -> str:\n record = self.store.get(old_token)\n if record is None or record.revoked:\n raise InvalidToken(\"unknown or revoked refresh token\")\n new_token = self.store.issue(record.user_id, record.family_id)\n return new_token\n```\n\nThe only check is whether the record is `None` or `record.revoked`. Token `t` exists in the store and was **never marked as revoked** (nor marked as used \u2014 `mark_used` is never called anywhere in `rotate_refresh_token`). So the guard passes, `issue` is called again, and a fresh token is returned.\n\n### Part 2: Is the token family revoked?\n\n**No, the family is not revoked.**\n\n`revoke_family` exists on `TokenStore` but is **never called** anywhere in `RefreshService`. The rotation logic has no reuse-detection path \u2014 it never checks `record.used`, never calls `mark_used`, and never calls `revoke_family`. The `TokenReuseError` defined in `errors.py` is also never raised anywhere in the current code. The whole stolen-token-detection / logout mechanism simply isn't wired up.\n\n---\n\n`VERDICT: second_call=; family_revoked=`", "input_tokens": 1048, "output_tokens": 367, "cost_usd": 0.008649, "ok": false, "misled": false, "detail": "could not parse verdict field(s): second_call, family_revoked", "parsed": {"second_call": null, "family_revoked": null}} -{"scenario": "refresh-single-use-qa", "task_type": "qa", "tier": "T0", "condition": "C0", "model": "sonnet", "trial": 8, "output": "## Analysis\n\n### Part 1: Does the second call succeed or raise?\n\n**It succeeds and returns a new token.**\n\nLooking at `rotate_refresh_token`:\n\n```python\ndef rotate_refresh_token(self, old_token: str) -> str:\n record = self.store.get(old_token)\n if record is None or record.revoked:\n raise InvalidToken(\"unknown or revoked refresh token\")\n new_token = self.store.issue(record.user_id, record.family_id)\n return new_token\n```\n\nThe only check is `record is None or record.revoked`. The original token `t` still exists in the store (`record is not None`) and was never explicitly revoked (`record.revoked` is `False`). The code never calls `store.mark_used(old_token)` after the first rotation, so `t` remains in the store as a valid, non-revoked record. The second call passes the check and issues yet another new token.\n\n### Part 2: Is the token family revoked?\n\n**No, the family is not revoked.**\n\nThe `revoke_family` method exists on the store, but `rotate_refresh_token` never calls it under any circumstance \u2014 not even when detecting reuse. Since `mark_used` is also never called, there is no reuse detection logic in place at all. The `TokenReuseError` defined in `errors.py` is never raised by any current code path, and `revoke_family` is never invoked. The user's session continues uninterrupted.\n\n---\n\n`VERDICT: second_call=; family_revoked=`", "input_tokens": 1048, "output_tokens": 388, "cost_usd": 0.008964, "ok": false, "misled": false, "detail": "could not parse verdict field(s): second_call, family_revoked", "parsed": {"second_call": null, "family_revoked": null}} -{"scenario": "refresh-single-use-qa", "task_type": "qa", "tier": "T0", "condition": "C0", "model": "sonnet", "trial": 9, "output": "## Analysis\n\n### Tracing the code for the second call with token `t`\n\n**First call** `rotate_refresh_token(t)`:\n- `store.get(t)` \u2192 returns the `TokenRecord` for `t`; `record.revoked` is `False` \u2192 passes the check\n- `store.issue(...)` \u2192 creates a new token `t2` and returns it\n- **Crucially**, the original token `t` is never marked as `used`, never set to `revoked`, and is never removed from the store\n\n**Second call** `rotate_refresh_token(t)`:\n- `store.get(t)` \u2192 still returns the same `TokenRecord` for `t`\n- `record.revoked` is still `False` (nothing revoked it)\n- The check `if record is None or record.revoked` \u2192 **False**, so no exception is raised\n- `store.issue(...)` \u2192 creates yet another new token `t3` and returns it\n\n### Answers\n\n1. **The second call succeeds and returns a new token.** The original token `t` is never marked used or revoked after the first rotation, so it still passes the `record is None or record.revoked` guard on the second call.\n\n2. **The user's token family is not revoked.** `rotate_refresh_token` never calls `store.revoke_family()`; it only calls `store.issue()`, so no revocation of any kind occurs on either call.\n\n`VERDICT: second_call=; family_revoked=`", "input_tokens": 1048, "output_tokens": 366, "cost_usd": 0.008634, "ok": false, "misled": false, "detail": "could not parse verdict field(s): second_call, family_revoked", "parsed": {"second_call": null, "family_revoked": null}} -{"scenario": "refresh-single-use-qa", "task_type": "qa", "tier": "T0", "condition": "C0", "model": "opus", "trial": 0, "output": "1. **Succeeds and returns a token.** The second call looks up `t`, finds the record is neither `None` nor `revoked` (rotation never sets `used` or `revoked` on the old token), so it passes the check and issues a brand-new token.\n\n2. **No, the family is not revoked / the user is not logged out everywhere.** `rotate_refresh_token` never calls `mark_used`, `is_used`, or `revoke_family`, so reusing the original token triggers no reuse detection and no family revocation.\n\nVERDICT: second_call=succeeds; family_revoked=no", "input_tokens": 1323, "output_tokens": 190, "cost_usd": 0.011365, "ok": true, "misled": false, "detail": "correct", "parsed": {"second_call": "succeeds", "family_revoked": "no"}} -{"scenario": "refresh-single-use-qa", "task_type": "qa", "tier": "T0", "condition": "C0", "model": "opus", "trial": 1, "output": "1. **The second call succeeds and returns a new token.** When `rotate_refresh_token(t)` is called again, it looks up `t`, finds the record is neither `None` nor `revoked` (rotation never sets `revoked` or even `used`), so it passes the guard and issues a fresh token.\n\n2. **No, the family is not revoked and the user is not logged out everywhere.** `rotate_refresh_token` never calls `mark_used` or `revoke_family`, so reusing the old token triggers no reuse detection and no revocation occurs.\n\nVERDICT: second_call=succeeds; family_revoked=no", "input_tokens": 1323, "output_tokens": 202, "cost_usd": 0.011665000000000002, "ok": true, "misled": false, "detail": "correct", "parsed": {"second_call": "succeeds", "family_revoked": "no"}} -{"scenario": "refresh-single-use-qa", "task_type": "qa", "tier": "T0", "condition": "C0", "model": "opus", "trial": 2, "output": "1. **It succeeds and returns a new token.** The second call to `rotate_refresh_token(t)` re-fetches the record for `t`, which is neither `None` nor `revoked`, so it passes the guard and issues yet another fresh token.\n\n2. **No, the family is not revoked and the user is not logged out.** The rotation method never calls `mark_used` or `revoke_family`, so reusing the original token `t` triggers no reuse detection and no family revocation.\n\nVERDICT: second_call=succeeds; family_revoked=no", "input_tokens": 1323, "output_tokens": 176, "cost_usd": 0.011015, "ok": true, "misled": false, "detail": "correct", "parsed": {"second_call": "succeeds", "family_revoked": "no"}} -{"scenario": "refresh-single-use-qa", "task_type": "qa", "tier": "T0", "condition": "C0", "model": "opus", "trial": 3, "output": "**1. Does the second call succeed or raise?**\n\nIt succeeds and returns a new token. The `rotate_refresh_token` method only checks whether the record is `None` or `revoked`; since `t` was never revoked and still exists in the store, the second call passes the check and issues another fresh token.\n\n**2. Is the family revoked / is the user logged out everywhere?**\n\nNo, the family is not revoked. The method never inspects or sets the `used` flag and never calls `revoke_family`, so reusing `t` triggers no reuse detection and leaves all tokens in the family valid.\n\nVERDICT: second_call=succeeds; family_revoked=no", "input_tokens": 1323, "output_tokens": 210, "cost_usd": 0.011865, "ok": true, "misled": false, "detail": "correct", "parsed": {"second_call": "succeeds", "family_revoked": "no"}} -{"scenario": "refresh-single-use-qa", "task_type": "qa", "tier": "T0", "condition": "C0", "model": "opus", "trial": 4, "output": "**1. Does the second call succeed or raise?**\n\nIt **succeeds** and returns a new token. The only checks in `rotate_refresh_token` are whether the record is `None` or `revoked`; token `t` still exists and was never revoked, so reuse is not detected (`used` is never set or checked), and a fresh token is issued.\n\n**2. Is the family revoked / user logged out everywhere?**\n\n**No.** `rotate_refresh_token` never calls `mark_used` or `revoke_family`, so reusing the old token triggers no reuse-detection and leaves the entire family intact.\n\nVERDICT: second_call=succeeds; family_revoked=no", "input_tokens": 1323, "output_tokens": 215, "cost_usd": 0.01199, "ok": true, "misled": false, "detail": "correct", "parsed": {"second_call": "succeeds", "family_revoked": "no"}} -{"scenario": "refresh-single-use-qa", "task_type": "qa", "tier": "T0", "condition": "C0", "model": "opus", "trial": 5, "output": "1. **Succeeds and returns a token.** The second `rotate_refresh_token(t)` only checks whether the record is `None` or `revoked`; since `t` still exists and was never revoked (and `used` is never set or checked in `rotate_refresh_token`), it passes the guard and issues a fresh token.\n\n2. **No, the family is not revoked / the user is not logged out everywhere.** Nothing in `rotate_refresh_token` calls `mark_used`, `is_used`, or `revoke_family`, so reusing the old token triggers no reuse detection and no family revocation.\n\nVERDICT: second_call=succeeds; family_revoked=no", "input_tokens": 1323, "output_tokens": 216, "cost_usd": 0.012015000000000001, "ok": true, "misled": false, "detail": "correct", "parsed": {"second_call": "succeeds", "family_revoked": "no"}} -{"scenario": "refresh-single-use-qa", "task_type": "qa", "tier": "T0", "condition": "C0", "model": "opus", "trial": 6, "output": "**1. Does the second call succeed or raise?**\nIt succeeds and returns a new token. Justification: `rotate_refresh_token` only checks whether the record is `None` or `revoked`; since `t` was never revoked (and the `used` flag is never set or checked), the second call passes the guard and issues another token.\n\n**2. Is the family revoked / user logged out everywhere?**\nNo. Justification: nothing in this path calls `revoke_family` or otherwise detects reuse, so presenting the same token twice has no effect on the family's validity.\n\nVERDICT: second_call=succeeds; family_revoked=no", "input_tokens": 1323, "output_tokens": 200, "cost_usd": 0.011615, "ok": true, "misled": false, "detail": "correct", "parsed": {"second_call": "succeeds", "family_revoked": "no"}} -{"scenario": "refresh-single-use-qa", "task_type": "qa", "tier": "T0", "condition": "C0", "model": "opus", "trial": 7, "output": "1. **The second call succeeds and returns a new token.** When `t` is presented again, `rotate_refresh_token` only checks whether the record is `None` or `revoked`; since `t` was never revoked and the method never calls `mark_used`/`is_used`, the reuse goes undetected and a fresh token is issued.\n\n2. **No, the family is not revoked and the user is not logged out everywhere.** The method never calls `revoke_family` (and never detects the reuse in the first place), so reusing `t` has no effect on the family's tokens.\n\nVERDICT: second_call=succeeds; family_revoked=no", "input_tokens": 1323, "output_tokens": 200, "cost_usd": 0.011615, "ok": true, "misled": false, "detail": "correct", "parsed": {"second_call": "succeeds", "family_revoked": "no"}} -{"scenario": "refresh-single-use-qa", "task_type": "qa", "tier": "T0", "condition": "C0", "model": "opus", "trial": 8, "output": "1. **Succeeds and returns a token.** The second call looks up `t`, finds the record is neither `None` nor `revoked`, so it issues and returns a brand-new token without complaint.\n\n2. **No, the family is not revoked / the user is not logged out everywhere.** `rotate_refresh_token` never calls `mark_used` or `revoke_family`, so reusing `t` triggers no reuse detection and no revocation occurs.\n\nVERDICT: second_call=succeeds; family_revoked=no", "input_tokens": 1323, "output_tokens": 167, "cost_usd": 0.010790000000000001, "ok": true, "misled": false, "detail": "correct", "parsed": {"second_call": "succeeds", "family_revoked": "no"}} -{"scenario": "refresh-single-use-qa", "task_type": "qa", "tier": "T0", "condition": "C0", "model": "opus", "trial": 9, "output": "**1. Does the second call succeed or raise?**\n\nIt succeeds and returns a new token. The `rotate_refresh_token` method only checks whether the record is `None` or `revoked`; since `t` was never revoked and the code never inspects (or sets) the `used` flag, the second call passes the check and issues another token.\n\n**2. Is the family revoked / is the user logged out everywhere?**\n\nNo. Nothing in `rotate_refresh_token` calls `mark_used`, detects reuse, or invokes `revoke_family`, so presenting the same token twice triggers no revocation and the user remains logged in everywhere.\n\nVERDICT: second_call=succeeds; family_revoked=no", "input_tokens": 1323, "output_tokens": 225, "cost_usd": 0.012240000000000001, "ok": true, "misled": false, "detail": "correct", "parsed": {"second_call": "succeeds", "family_revoked": "no"}} -{"scenario": "refresh-single-use-qa", "task_type": "qa", "tier": "T0", "condition": "C1", "model": "haiku", "trial": 0, "output": "Let me trace through the code carefully to answer this question.\n\n**Initial state after first rotation:**\n- Client logs in, gets token `t`\n- Calls `rotate_refresh_token(t)` once\n- This calls `self.store.get(t)` \u2192 returns the TokenRecord for `t`\n- Issues a new token\n- But I notice: **the old token `t` is never marked as used**\n\nLooking at the `rotate_refresh_token` method in `code/auth/refresh.py`:\n```python\ndef rotate_refresh_token(self, old_token: str) -> str:\n record = self.store.get(old_token)\n if record is None or record.revoked:\n raise InvalidToken(\"unknown or revoked refresh token\")\n new_token = self.store.issue(record.user_id, record.family_id)\n return new_token\n```\n\nThe method only checks if the token is `None` or `revoked`. It does not:\n1. Call `self.store.mark_used(old_token)` \n2. Check if the token has already been used via `self.store.is_used(old_token)`\n\n**Second call with the same token `t`:**\n- Calls `rotate_refresh_token(t)` again\n- `self.store.get(t)` still returns the same TokenRecord\n- The record is not `None` and not `revoked` (neither condition is true)\n- No `TokenReuseError` is raised\n- A new token is issued and returned\n- The family is **not** revoked\n\n**Answers:**\n\n1. **The second call succeeds and returns a token.** The code only checks if the token is unknown (`None`) or explicitly revoked, neither of which is true for a token that was previously rotated but never marked as used or flagged as reused.\n\n2. **No, the user's token family is not revoked.** The code never detects the reuse because it never marks tokens as used or checks the used status, so `revoke_family()` is never called.\n\nVERDICT: second_call=succeeds; family_revoked=no", "input_tokens": 1346, "output_tokens": 503, "cost_usd": 0.0038610000000000003, "ok": true, "misled": false, "detail": "correct", "parsed": {"second_call": "succeeds", "family_revoked": "no"}} -{"scenario": "refresh-single-use-qa", "task_type": "qa", "tier": "T0", "condition": "C1", "model": "haiku", "trial": 1, "output": "Let me trace through the code to answer this question.\n\n## Analysis\n\n**Initial state after first rotation:**\n- Token `t` is looked up via `store.get(t)`, which returns a `TokenRecord`\n- A new token is issued\n- However, looking at `rotate_refresh_token`, I notice it calls `store.issue()` but **does not call `store.mark_used(t)`**\n\n**Second call with token `t`:**\n- `store.get(t)` is called again\n- The record exists (it was never deleted) and `record.revoked` is still `False` (it was never revoked)\n- The condition `if record is None or record.revoked:` evaluates to `False`\n- So no `InvalidToken` exception is raised\n- A new token is issued and returned\n- Again, `mark_used()` is not called\n\n**Regarding family revocation:**\n- `revoke_family()` is never called anywhere in `RefreshService.rotate_refresh_token`\n- The code lacks the logic to detect token reuse and trigger family revocation\n\n## Answers\n\n1. **The second call succeeds and returns a token.** The code only checks if the token is `None` or explicitly `revoked`, neither of which is true for a token that has been rotated once but not marked as used.\n\n2. **No, the token family is not revoked.** There is no code in `rotate_refresh_token` that calls `store.revoke_family()`, so the reuse goes undetected and the family remains active.\n\nVERDICT: second_call=succeeds; family_revoked=no", "input_tokens": 1346, "output_tokens": 378, "cost_usd": 0.003236, "ok": true, "misled": false, "detail": "correct", "parsed": {"second_call": "succeeds", "family_revoked": "no"}} -{"scenario": "refresh-single-use-qa", "task_type": "qa", "tier": "T0", "condition": "C1", "model": "haiku", "trial": 2, "output": "Let me trace through the code carefully.\n\n**Initial state:**\n- Client logs in, receives token `t`\n- Client calls `rotate_refresh_token(t)` successfully\n - `record = self.store.get(t)` retrieves the token record\n - `new_token = self.store.issue(...)` creates a new token\n - Returns the new token\n - **Note:** The old token `t` is never marked as used\n\n**Second call with token `t`:**\n- Client calls `rotate_refresh_token(t)` again\n- `record = self.store.get(t)` retrieves the same token record (it still exists)\n- Check: `if record is None or record.revoked:` \n - `record` is NOT None (it exists in the store)\n - `record.revoked` is False (nothing has revoked it)\n - So this condition is False; no error is raised\n- `new_token = self.store.issue(record.user_id, record.family_id)` creates another new token\n- Returns successfully\n\n**Critical observation:** The code never calls `self.store.mark_used(old_token)` in `rotate_refresh_token`. The `mark_used` method exists but is never called by the RefreshService. Therefore, there is no mechanism to detect token reuse.\n\n---\n\n**Answers:**\n\n1. **The second call succeeds and returns a token.** The code checks only whether the token is unknown or revoked, never whether it has been previously rotated.\n\n2. **No, the user's token family is not revoked.** Since there is no check for token reuse, no `revoke_family()` call is triggered, and the family remains active.\n\nVERDICT: second_call=succeeds; family_revoked=no", "input_tokens": 1346, "output_tokens": 421, "cost_usd": 0.003451, "ok": true, "misled": false, "detail": "correct", "parsed": {"second_call": "succeeds", "family_revoked": "no"}} -{"scenario": "refresh-single-use-qa", "task_type": "qa", "tier": "T0", "condition": "C1", "model": "haiku", "trial": 3, "output": "Let me trace through the code step by step.\n\n## Initial State\n- Client logs in, receives token `t` (a `TokenRecord` with `used=False`, `revoked=False`)\n\n## First `rotate_refresh_token(t)` call\n1. `record = self.store.get(t)` \u2192 returns the record for `t`\n2. `record is None or record.revoked` \u2192 both False, so check passes\n3. `new_token = self.store.issue(...)` \u2192 creates a new token and returns it\n4. The old token `t` is returned... but **critically, `mark_used(t)` is never called**\n\nThe code does not mark the old token as used after rotation.\n\n## Second `rotate_refresh_token(t)` call\n1. `record = self.store.get(t)` \u2192 returns the same record for `t` (unchanged from before)\n2. `record is None or record.revoked` \u2192 both still False, so check passes\n3. `new_token = self.store.issue(...)` \u2192 creates another new token and returns it\n4. No family revocation occurs\n\n## Answers\n\n**1. Does the second call succeed and return a token, or does it raise an error?**\n\nThe second call **succeeds and returns a token**. The code only checks if the token is `None` or `revoked`, but never checks the `used` flag, so presenting an already-rotated token passes validation.\n\n**2. Is the user's whole token family revoked / are they logged out everywhere?**\n\n**No, the family is not revoked and the user is not logged out.** The code contains no logic to detect reuse or invoke `revoke_family()` on the second call.\n\n**Justification:** The implementation is missing the `mark_used()` call after successful rotation, and critically lacks any check for `record.used` in the validation logic, so token reuse goes undetected and unpenalized.\n\nVERDICT: second_call=succeeds; family_revoked=no", "input_tokens": 1346, "output_tokens": 471, "cost_usd": 0.0037010000000000003, "ok": true, "misled": false, "detail": "correct", "parsed": {"second_call": "succeeds", "family_revoked": "no"}} -{"scenario": "refresh-single-use-qa", "task_type": "qa", "tier": "T0", "condition": "C1", "model": "haiku", "trial": 4, "output": "Let me trace through the code carefully.\n\n**Initial state after first rotation:**\n- Token `t` is retrieved via `store.get(t)` and exists\n- A new token is issued\n- However, looking at `rotate_refresh_token`, I notice it calls `store.issue()` but never calls `store.mark_used(t)` to mark the old token as used\n\n**Second call with token `t`:**\n- `store.get(t)` returns the TokenRecord for `t`\n- The record's `revoked` field is still `False` (it was never revoked)\n- The record's `used` field is still `False` (it was never marked as used, since `mark_used()` is never called in `rotate_refresh_token`)\n- The condition `if record is None or record.revoked:` evaluates to `False`\n- A new token is issued and returned successfully\n- No call to `store.revoke_family()` occurs\n\n**Answers:**\n\n1. **The second call succeeds and returns a token.** The code never marks the old token as used after rotation, so the second call passes the validity checks and issues a new token without error.\n\n2. **No, the user's token family is not revoked.** Since no error is raised, `store.revoke_family()` is never invoked, leaving the entire token family in an active state.\n\nVERDICT: second_call=succeeds; family_revoked=no", "input_tokens": 1346, "output_tokens": 337, "cost_usd": 0.003031, "ok": true, "misled": false, "detail": "correct", "parsed": {"second_call": "succeeds", "family_revoked": "no"}} -{"scenario": "refresh-single-use-qa", "task_type": "qa", "tier": "T0", "condition": "C1", "model": "haiku", "trial": 5, "output": "Let me trace through the code to answer this question.\n\n**Initial state after first rotation:**\n- Token `t` is issued via `login()`\n- `rotate_refresh_token(t)` is called:\n - `record = self.store.get(t)` retrieves the token record\n - A new token is issued\n - The function returns successfully\n - **Crucially: the old token `t` is never marked as used**\n\nLooking at the code in `refresh.py`, the `rotate_refresh_token` method:\n1. Gets the record for `old_token`\n2. Checks if it's None or revoked\n3. Issues a new token\n4. Returns the new token\n\nThere is **no call to `self.store.mark_used(old_token)`** anywhere in the rotation logic.\n\n**Second call with the same token `t`:**\n- `record = self.store.get(t)` retrieves the token record again\n- The check `if record is None or record.revoked:` evaluates to False (record exists and isn't revoked)\n- A new token is issued and returned\n\nSince `mark_used()` is never called in the rotation flow, the token's `used` field remains False, and the second rotation succeeds without triggering the theft detection.\n\n**Answers:**\n\n1. **The second call succeeds and returns a token.** The code never marks the old token as used during rotation, so the second call passes all checks and issues a new token.\n\n2. **No, the family is not revoked.** The `revoke_family()` method is never called in the current code, and there is no token reuse detection that would trigger it.\n\nVERDICT: second_call=succeeds; family_revoked=no", "input_tokens": 1346, "output_tokens": 406, "cost_usd": 0.003376, "ok": true, "misled": false, "detail": "correct", "parsed": {"second_call": "succeeds", "family_revoked": "no"}} -{"scenario": "refresh-single-use-qa", "task_type": "qa", "tier": "T0", "condition": "C1", "model": "haiku", "trial": 6, "output": "Let me trace through the code carefully to answer this question.\n\n**Initial state after first rotation:**\n- Client calls `rotate_refresh_token(t)`\n- `store.get(t)` returns the token record\n- A new token is issued\n- However, looking at the code in `refresh.py`, I notice that `mark_used(t)` is **never called** after rotation\n\n**Second call with the same token `t`:**\n- `store.get(t)` is called again\n- The record still exists and `record.revoked` is still `False` (it was never revoked)\n- So the condition `if record is None or record.revoked:` evaluates to `False`\n- A new token is issued and returned successfully\n- No call to `revoke_family()` occurs\n\n**Analysis:**\n\n1. **Does the second call succeed or raise an error?**\n The second call **succeeds** and returns a new token. The code checks only whether the record exists and is not revoked, but never actually marks tokens as used or checks if they've been used before.\n\n2. **Is the family revoked?**\n **No**, the family is not revoked. The code never calls `revoke_family()` for any reuse scenario. The `TokenReuseError` mentioned in the docstring is never actually raised, and the intended theft-detection behavior is not implemented.\n\nThe current implementation has a bug: the documented behavior (detecting reuse and revoking the family) is not actually implemented. The `mark_used()` method exists but is never called, and the `is_used()` method is never called either.\n\nVERDICT: second_call=succeeds; family_revoked=no", "input_tokens": 1346, "output_tokens": 390, "cost_usd": 0.0032960000000000003, "ok": true, "misled": false, "detail": "correct", "parsed": {"second_call": "succeeds", "family_revoked": "no"}} -{"scenario": "refresh-single-use-qa", "task_type": "qa", "tier": "T0", "condition": "C1", "model": "haiku", "trial": 7, "output": "Let me trace through the code to understand what happens on the second call to `rotate_refresh_token(t)`.\n\n**Initial state after first rotation:**\n- Token `t` is in the store with `used=False` initially\n- When `rotate_refresh_token(t)` is called the first time, it retrieves the record, issues a new token, but **does not call `mark_used()`**\n- So `t` remains in the store with `used=False`\n\n**Second call to `rotate_refresh_token(t)`:**\n1. `record = self.store.get(old_token)` retrieves the token record for `t`\n2. The check is: `if record is None or record.revoked:`\n3. Since `t` exists in the store and is not revoked, this condition is `False`\n4. The function proceeds to `self.store.issue(...)` and returns a new token\n5. Again, `mark_used()` is never called on the old token\n\n**Analysis:**\n\nThe current implementation has a critical bug: the `rotate_refresh_token` method never calls `mark_used()` on the token being rotated. This means:\n\n1. **The second call succeeds** - there is no code checking the `used` flag, and `record.revoked` is still `False`, so the condition passes and a new token is issued.\n\n2. **The family is not revoked** - there is no code in `rotate_refresh_token` that detects reuse and calls `revoke_family()`. The `mark_used()` method exists but is never invoked, and even if it were, there's no logic to check if a token was already used and react to that.\n\nThe documentation describes the intended behavior (detect reuse and revoke the family), but the code does not implement it.\n\n**1. Does the second call succeed?**\nYes, it succeeds and returns a new token. The code only checks if the record is `None` or `revoked`, and the token satisfies neither condition.\n\n**2. Is the token family revoked?**\nNo, the family is not revoked. The code contains no logic to detect token reuse and invoke `revoke_family()`.\n\nVERDICT: second_call=succeeds; family_revoked=no", "input_tokens": 1346, "output_tokens": 523, "cost_usd": 0.003961, "ok": true, "misled": false, "detail": "correct", "parsed": {"second_call": "succeeds", "family_revoked": "no"}} -{"scenario": "refresh-single-use-qa", "task_type": "qa", "tier": "T0", "condition": "C1", "model": "haiku", "trial": 8, "output": "Let me trace through the code carefully.\n\n**Initial state:** Client logs in, gets token `t`. Calls `rotate_refresh_token(t)` once:\n- `store.get(t)` returns the record for `t`\n- Record is not revoked, so no error\n- `store.issue()` creates a new token\n- The old token `t` is NOT marked as used anywhere in this code path\n\n**Second call:** Client calls `rotate_refresh_token(t)` again with the same `t`:\n- `store.get(t)` returns the same record for `t` (still exists in `_records`)\n- `record.revoked` is `False` (it was never set to `True`)\n- The check `if record is None or record.revoked:` evaluates to `False`\n- No `InvalidToken` exception is raised\n- `store.issue()` creates another new token and returns it\n- Again, the old token `t` is NOT marked as used\n\nLooking at the code, I notice:\n- `mark_used()` method exists but is **never called** anywhere in `RefreshService`\n- `revoke_family()` is also never called in `RefreshService`\n- The `used` field in `TokenRecord` is never checked\n\n**Answer:**\n\n1. **The second call succeeds and returns a token.** The code only checks if the token is `None` or `revoked`, and since `rotate_refresh_token` never marks tokens as used or revokes families, the second call passes the same checks as the first.\n\n2. **No, the token family is not revoked.** The `revoke_family()` method is never invoked by `rotate_refresh_token`, and there is no code path that detects or reacts to token reuse.\n\nVERDICT: second_call=succeeds; family_revoked=no", "input_tokens": 1346, "output_tokens": 431, "cost_usd": 0.003501, "ok": true, "misled": false, "detail": "correct", "parsed": {"second_call": "succeeds", "family_revoked": "no"}} -{"scenario": "refresh-single-use-qa", "task_type": "qa", "tier": "T0", "condition": "C1", "model": "haiku", "trial": 9, "output": "Let me trace through the code to answer this question.\n\n**Initial state:** Client logs in, gets token `t`, calls `rotate_refresh_token(t)` once successfully.\n\n**After the first rotation:**\n- A new token is issued and returned\n- The old token `t` is NOT marked as used (there's no call to `mark_used()` in `rotate_refresh_token`)\n- The token record for `t` still exists with `used=False` and `revoked=False`\n\n**Second call with the same token `t`:**\n\nLooking at `rotate_refresh_token`:\n```python\ndef rotate_refresh_token(self, old_token: str) -> str:\n record = self.store.get(old_token)\n if record is None or record.revoked:\n raise InvalidToken(\"unknown or revoked refresh token\")\n new_token = self.store.issue(record.user_id, record.family_id)\n return new_token\n```\n\nWhen called again with `t`:\n1. `self.store.get(old_token)` finds the record (it exists)\n2. The record is not `None` and not `revoked` (it's still in its original state)\n3. A new token is issued without any check on whether the old token was already used\n4. The method returns the new token successfully\n\nThe code does **not**:\n- Check the `used` flag before proceeding\n- Mark the token as used after rotation\n- Revoke the family on reuse\n\n---\n\n**Answers:**\n\n1. **The second call succeeds and returns a token.** The code only checks if the token is `None` or `revoked`, not whether it has been used before.\n\n2. **No, the user's token family is not revoked.** There is no logic in `rotate_refresh_token` to detect reuse or trigger `revoke_family()`.\n\n`VERDICT: second_call=succeeds; family_revoked=no`", "input_tokens": 1346, "output_tokens": 459, "cost_usd": 0.003641, "ok": true, "misled": false, "detail": "correct", "parsed": {"second_call": "succeeds", "family_revoked": "no"}} -{"scenario": "refresh-single-use-qa", "task_type": "qa", "tier": "T0", "condition": "C1", "model": "sonnet", "trial": 0, "output": "I'll trace through the code carefully for both calls.\n\n**First call: `rotate_refresh_token(t)`**\n\n1. `store.get(t)` \u2192 returns the record; `record.revoked` is `False` \u2192 no exception\n2. `store.issue(...)` \u2192 creates a new token `t2`, returns it\n3. The old token `t` is **never marked used** and **never revoked** \u2014 `store.mark_used(t)` is never called in `rotate_refresh_token`\n\n**Second call: `rotate_refresh_token(t)` (same original token)**\n\n1. `store.get(t)` \u2192 still returns the same record (it was never removed)\n2. `record.revoked` is still `False` (nothing revoked it)\n3. No exception is raised; `store.issue(...)` runs again and returns a new token `t3`\n\n---\n\n**Part 1:** The second call **succeeds and returns a new token**.\n\nJustification: `rotate_refresh_token` never calls `store.mark_used(t)` or `store.revoke_family(...)` after the first rotation, so the original token record remains in a pristine, non-revoked state and passes the guard check on the second call.\n\n**Part 2:** The user's token family is **not revoked** \u2014 they are not logged out everywhere.\n\nJustification: `revoke_family` is never called anywhere in `rotate_refresh_token` (and `TokenReuseError` is never raised), so the family remains fully active; the documentation's described theft-detection behaviour is simply not implemented in the current code.\n\n`VERDICT: second_call=succeeds; family_revoked=no`", "input_tokens": 1347, "output_tokens": 401, "cost_usd": 0.010056, "ok": true, "misled": false, "detail": "correct", "parsed": {"second_call": "succeeds", "family_revoked": "no"}} -{"scenario": "refresh-single-use-qa", "task_type": "qa", "tier": "T0", "condition": "C1", "model": "sonnet", "trial": 1, "output": "Looking at the current code in `code/auth/refresh.py`, specifically `rotate_refresh_token`:\n\n```python\ndef rotate_refresh_token(self, old_token: str) -> str:\n record = self.store.get(old_token)\n if record is None or record.revoked:\n raise InvalidToken(\"unknown or revoked refresh token\")\n new_token = self.store.issue(record.user_id, record.family_id)\n return new_token\n```\n\nAnd `code/auth/store.py` \u2014 notably, `issue` does **not** call `mark_used` on the old token, and the check in `rotate_refresh_token` only gates on `record is None or record.revoked`.\n\n**Part 1: Does the second call succeed or raise?**\n\nThe second call **succeeds and returns a new token**. After the first rotation, the original token `t` is never marked as used (the code calls `store.issue` but never calls `store.mark_used`), and it is never revoked. The guard condition only checks `record is None or record.revoked` \u2014 both are false for `t` \u2014 so the method proceeds and issues yet another new token.\n\n**Part 2: Is the token family revoked?**\n\nNo, the family is **not revoked**. There is no call to `store.revoke_family` anywhere in `rotate_refresh_token`, and the `TokenReuseError` is never raised. The documentation describes theft-detection and family revocation as the intended behavior, but that logic simply does not exist in the current implementation.\n\nIn short, the code is missing the `mark_used` call after issuing the new token, the reuse check against `record.used`, and the `revoke_family` + `TokenReuseError` response \u2014 meaning the entire theft-detection feature described in the documentation is unimplemented.\n\n`VERDICT: second_call=succeeds; family_revoked=no`", "input_tokens": 1347, "output_tokens": 456, "cost_usd": 0.010881000000000002, "ok": true, "misled": false, "detail": "correct", "parsed": {"second_call": "succeeds", "family_revoked": "no"}} -{"scenario": "refresh-single-use-qa", "task_type": "qa", "tier": "T0", "condition": "C1", "model": "sonnet", "trial": 2, "output": "Looking at the current code in `code/auth/refresh.py`:\n\n```python\ndef rotate_refresh_token(self, old_token: str) -> str:\n record = self.store.get(old_token)\n if record is None or record.revoked:\n raise InvalidToken(\"unknown or revoked refresh token\")\n new_token = self.store.issue(record.user_id, record.family_id)\n return new_token\n```\n\nAnd in `code/auth/store.py`, `issue()` creates a new record but **never calls `mark_used()`** on the old token. The check in `rotate_refresh_token` only guards against `record is None` or `record.revoked` \u2014 it never checks `record.used`.\n\n**Part 1: Does the second call succeed or raise?**\nThe second call **succeeds and returns a new token**. After the first rotation, token `t`'s record still exists in the store and `record.revoked` is `False` (nothing revoked it), so the guard condition is not triggered and a fresh token is issued.\n\n**Part 2: Is the token family revoked?**\nNo, the family is **not revoked**. There is no call to `store.revoke_family()` anywhere in `rotate_refresh_token`, and `TokenReuseError` is never raised \u2014 neither in this path nor anywhere else in the current code \u2014 so no logout occurs.\n\nIn short, the documented \"token theft detection\" behavior (mark-as-used, revoke family, raise `TokenReuseError`) is entirely absent from the implementation: `rotate_refresh_token` never calls `mark_used`, never checks `is_used`, never calls `revoke_family`, and never raises `TokenReuseError`.\n\n`VERDICT: second_call=succeeds; family_revoked=no`", "input_tokens": 1347, "output_tokens": 432, "cost_usd": 0.010521, "ok": true, "misled": false, "detail": "correct", "parsed": {"second_call": "succeeds", "family_revoked": "no"}} -{"scenario": "refresh-single-use-qa", "task_type": "qa", "tier": "T0", "condition": "C1", "model": "sonnet", "trial": 3, "output": "Looking at the current code in `code/auth/refresh.py`:\n\n```python\ndef rotate_refresh_token(self, old_token: str) -> str:\n record = self.store.get(old_token)\n if record is None or record.revoked:\n raise InvalidToken(\"unknown or revoked refresh token\")\n new_token = self.store.issue(record.user_id, record.family_id)\n return new_token\n```\n\nAnd `code/auth/store.py`, the `issue` method does not call `mark_used`, and `rotate_refresh_token` never calls `mark_used` either. The only check performed is `record is None or record.revoked`.\n\n**Part 1: Does the second call succeed or raise?**\n\nThe second call **succeeds and returns a new token**. After the first rotation, token `t`'s record still exists in the store and its `revoked` field remains `False` \u2014 nothing in `rotate_refresh_token` or `issue` sets `used = True` or `revoked = True` on the old token. The guard condition `record is None or record.revoked` is therefore `False`, so execution continues and a fresh token is issued.\n\n**Part 2: Is the token family revoked?**\n\n**No**, the family is not revoked. `revoke_family` is never called anywhere in `rotate_refresh_token`; there is no reuse-detection logic in the current code, despite what the documentation describes. The `used` flag and `TokenReuseError` exist in the codebase but are never exercised by this method.\n\n---\n\nIn short, the documented \"theft detection\" behavior is entirely unimplemented: the code neither marks tokens as used after rotation, nor checks the `used` flag, nor calls `revoke_family`, nor raises `TokenReuseError`.\n\n`VERDICT: second_call=succeeds; family_revoked=no`", "input_tokens": 1347, "output_tokens": 449, "cost_usd": 0.010776, "ok": true, "misled": false, "detail": "correct", "parsed": {"second_call": "succeeds", "family_revoked": "no"}} -{"scenario": "refresh-single-use-qa", "task_type": "qa", "tier": "T0", "condition": "C1", "model": "sonnet", "trial": 4, "output": "## Analysis\n\nLooking at the current code in `code/auth/refresh.py`, specifically `rotate_refresh_token`:\n\n```python\ndef rotate_refresh_token(self, old_token: str) -> str:\n record = self.store.get(old_token)\n if record is None or record.revoked:\n raise InvalidToken(\"unknown or revoked refresh token\")\n new_token = self.store.issue(record.user_id, record.family_id)\n return new_token\n```\n\nThe method only checks `record is None or record.revoked`. It does **not** check `record.used`.\n\nAfter the first call to `rotate_refresh_token(t)`:\n- A new token is issued via `self.store.issue(...)`.\n- However, `mark_used` is **never called** on the old token `t`. The `used` flag on `t`'s record remains `False`.\n- The old token `t` is also **not revoked** \u2014 `revoke_family` is never called.\n\nSo on the second call with the same token `t`:\n- `self.store.get(t)` returns the original record (it still exists).\n- `record.revoked` is `False` (nothing revoked it).\n- The check passes, and a **second new token is issued and returned**.\n\n---\n\n**Part 1:** The second call **succeeds** and returns a new token. The code only guards against `None` or `revoked` tokens; it never marks the old token as used and never checks the `used` flag, so the original token `t` remains valid and can be rotated indefinitely.\n\n**Part 2:** The user's token family is **not revoked**. Because the code never detects reuse (the `mark_used`/`is_used` mechanism exists on `TokenStore` but is never invoked by `rotate_refresh_token`, and `revoke_family` is similarly never called), no logout occurs.\n\n`VERDICT: second_call=succeeds; family_revoked=no`", "input_tokens": 1347, "output_tokens": 477, "cost_usd": 0.011196000000000001, "ok": true, "misled": false, "detail": "correct", "parsed": {"second_call": "succeeds", "family_revoked": "no"}} -{"scenario": "refresh-single-use-qa", "task_type": "qa", "tier": "T0", "condition": "C1", "model": "sonnet", "trial": 5, "output": "Looking at the current code in `code/auth/refresh.py`:\n\n```python\ndef rotate_refresh_token(self, old_token: str) -> str:\n record = self.store.get(old_token)\n if record is None or record.revoked:\n raise InvalidToken(\"unknown or revoked refresh token\")\n new_token = self.store.issue(record.user_id, record.family_id)\n return new_token\n```\n\n**Part 1: Does the second call succeed or raise?**\n\nThe second call **succeeds and returns a new token**. The code only checks `record is None or record.revoked`. After the first rotation, token `t` is still in the store and is **not** marked revoked \u2014 `store.issue()` never calls `mark_used()` or `revoke_family()` on the old token. The `used` field exists on `TokenRecord` and `mark_used()` exists on `TokenStore`, but `rotate_refresh_token` never calls either of them. So the guard condition passes, and a second new token is issued.\n\n**Part 2: Is the family revoked?**\n\n**No**, the family is not revoked. There is no call to `store.revoke_family()` anywhere in `rotate_refresh_token`, and the code never raises `TokenReuseError` (in fact, `TokenReuseError` is defined in `errors.py` but is never raised anywhere in the current codebase). The documentation describes the intended theft-detection behaviour, but the implementation simply does not contain that logic yet.\n\n`VERDICT: second_call=; family_revoked=`", "input_tokens": 1347, "output_tokens": 389, "cost_usd": 0.009876, "ok": false, "misled": false, "detail": "could not parse verdict field(s): second_call, family_revoked", "parsed": {"second_call": null, "family_revoked": null}} -{"scenario": "refresh-single-use-qa", "task_type": "qa", "tier": "T0", "condition": "C1", "model": "sonnet", "trial": 6, "output": "Looking at the current code in `code/auth/refresh.py`:\n\n```python\ndef rotate_refresh_token(self, old_token: str) -> str:\n record = self.store.get(old_token)\n if record is None or record.revoked:\n raise InvalidToken(\"unknown or revoked refresh token\")\n new_token = self.store.issue(record.user_id, record.family_id)\n return new_token\n```\n\n**Part 1: Does the second call succeed or raise?**\n\nThe second call **succeeds and returns a new token**. The guard condition only checks `record is None or record.revoked`. After the first rotation, token `t` is still in the store (not `None`) and has not been marked revoked \u2014 it only would be revoked if `revoke_family` were called. Critically, although `store.mark_used` exists, `rotate_refresh_token` never calls it, so `t`'s `used` flag remains `False`. The second call passes the guard and issues yet another new token.\n\n**Part 2: Is the token family revoked?**\n\nNo, the family is **not revoked**. There is no call to `store.revoke_family` anywhere in `rotate_refresh_token`, and the `used` flag is never set, so the theft-detection path described in the documentation is simply not implemented in the current code.\n\n---\n\nIn summary, the code does not implement the documented single-use / theft-detection behaviour at all: `mark_used` is never called, `is_used` is never checked, and `revoke_family` is never invoked inside `rotate_refresh_token`, leaving the entire reuse-detection contract unimplemented.\n\n`VERDICT: second_call=succeeds; family_revoked=no`", "input_tokens": 1347, "output_tokens": 419, "cost_usd": 0.010326, "ok": true, "misled": false, "detail": "correct", "parsed": {"second_call": "succeeds", "family_revoked": "no"}} -{"scenario": "refresh-single-use-qa", "task_type": "qa", "tier": "T0", "condition": "C1", "model": "sonnet", "trial": 7, "output": "Looking at the current code in `code/auth/refresh.py`:\n\n```python\ndef rotate_refresh_token(self, old_token: str) -> str:\n record = self.store.get(old_token)\n if record is None or record.revoked:\n raise InvalidToken(\"unknown or revoked refresh token\")\n new_token = self.store.issue(record.user_id, record.family_id)\n return new_token\n```\n\nThe method checks only `record is None or record.revoked`. It does **not** check `record.used`, and it never calls `store.mark_used(old_token)` after issuing the new token.\n\n**Part 1: Does the second call succeed or raise?**\nThe second call **succeeds and returns a new token**. Token `t` is never marked as used (there is no `mark_used` call in `rotate_refresh_token`), and it is never revoked, so the guard condition `record is None or record.revoked` evaluates to `False`, and the method proceeds to issue and return a fresh token.\n\n**Part 2: Is the token family revoked?**\nNo, the family is **not revoked**. Neither call invokes `store.revoke_family(...)`, and `TokenReuseError` is never raised \u2014 so no logout occurs anywhere.\n\nThe documentation describes the intended single-use / theft-detection behaviour, but the current implementation is missing both the `mark_used` call after issuing a new token and the reuse check (i.e., checking `record.used` and calling `revoke_family` + raising `TokenReuseError`). The code as written simply ignores the `used` field entirely in `rotate_refresh_token`.\n\n`VERDICT: second_call=succeeds; family_revoked=no`", "input_tokens": 1347, "output_tokens": 423, "cost_usd": 0.010386, "ok": true, "misled": false, "detail": "correct", "parsed": {"second_call": "succeeds", "family_revoked": "no"}} -{"scenario": "refresh-single-use-qa", "task_type": "qa", "tier": "T0", "condition": "C1", "model": "sonnet", "trial": 8, "output": "Looking at the current code in `code/auth/refresh.py`:\n\n```python\ndef rotate_refresh_token(self, old_token: str) -> str:\n record = self.store.get(old_token)\n if record is None or record.revoked:\n raise InvalidToken(\"unknown or revoked refresh token\")\n new_token = self.store.issue(record.user_id, record.family_id)\n return new_token\n```\n\nThe check is only `record is None or record.revoked`. The code **never calls `mark_used`** on the old token after issuing a new one, and **never checks `is_used`** before proceeding.\n\n**Part 1: Does the second call succeed or raise?**\nThe second call **succeeds and returns a new token**. The guard only rejects tokens that are `None` (unknown) or `revoked`. Token `t` was never marked as used (because `mark_used` is never called in `rotate_refresh_token`), and it was never revoked, so the record exists and `record.revoked` is `False` \u2014 the check passes and a fresh token is issued.\n\n**Part 2: Is the token family revoked?**\nNo, the family is **not revoked**. There is no call to `revoke_family` anywhere in `rotate_refresh_token`, and since the reuse detection logic (checking `is_used` and then revoking) is entirely absent from the current code, a repeated call triggers no family revocation whatsoever.\n\nIn short, the documentation describes the *intended* security behaviour, but the actual implementation is incomplete: `rotate_refresh_token` neither marks tokens as used nor detects or penalises reuse.\n\n`VERDICT: second_call=succeeds; family_revoked=no`", "input_tokens": 1347, "output_tokens": 419, "cost_usd": 0.010326, "ok": true, "misled": false, "detail": "correct", "parsed": {"second_call": "succeeds", "family_revoked": "no"}} -{"scenario": "refresh-single-use-qa", "task_type": "qa", "tier": "T0", "condition": "C1", "model": "sonnet", "trial": 9, "output": "## Analysis\n\nLooking at the current code in `code/auth/refresh.py`:\n\n```python\ndef rotate_refresh_token(self, old_token: str) -> str:\n record = self.store.get(old_token)\n if record is None or record.revoked:\n raise InvalidToken(\"unknown or revoked refresh token\")\n new_token = self.store.issue(record.user_id, record.family_id)\n return new_token\n```\n\nThe method only checks `record is None or record.revoked`. It does **not** check `record.used`, and it never calls `self.store.mark_used(old_token)`. The `mark_used` and `is_used` methods exist on `TokenStore` but are never called by `RefreshService`.\n\n### Part 1: Does the second call succeed or raise?\n\n**The second call succeeds and returns a new token.** The check is only for `None` or `revoked`; since the first call never marked `t` as used and never revoked it, `t`'s record still exists with `revoked=False`, so the condition is not triggered and a new token is issued.\n\n### Part 2: Is the family revoked?\n\n**No, the family is not revoked.** The code never calls `self.store.revoke_family(...)` anywhere in `rotate_refresh_token`, so no revocation occurs regardless of how many times the token is reused.\n\n---\n\n> **Note:** The documentation describes the intended behaviour (mark used, detect reuse, revoke family, raise `TokenReuseError`), but that behaviour is **not implemented** in the current code. The `mark_used`/`is_used` methods on `TokenStore` and the `TokenReuseError` exception class exist but are never wired into `rotate_refresh_token`.\n\n`VERDICT: second_call=succeeds; family_revoked=no`", "input_tokens": 1347, "output_tokens": 448, "cost_usd": 0.010761, "ok": true, "misled": false, "detail": "correct", "parsed": {"second_call": "succeeds", "family_revoked": "no"}} -{"scenario": "refresh-single-use-qa", "task_type": "qa", "tier": "T0", "condition": "C1", "model": "opus", "trial": 0, "output": "**1. Does the second call succeed or raise?**\nIt succeeds and returns a new token. The justification: `rotate_refresh_token` only checks whether the record is `None` or `revoked`\u2014it never calls `mark_used` or checks `is_used`/`used`, so presenting `t` again passes the check and issues another token.\n\n**2. Is the family revoked / user logged out everywhere?**\nNo, the family is not revoked. The justification: nothing in the code path ever calls `revoke_family` or raises `TokenReuseError`, so no global logout occurs despite the documentation describing one.\n\nVERDICT: second_call=succeeds; family_revoked=no", "input_tokens": 1720, "output_tokens": 214, "cost_usd": 0.01395, "ok": true, "misled": false, "detail": "correct", "parsed": {"second_call": "succeeds", "family_revoked": "no"}} -{"scenario": "refresh-single-use-qa", "task_type": "qa", "tier": "T0", "condition": "C1", "model": "opus", "trial": 1, "output": "**1. Does the second call succeed or raise?**\nIt succeeds and returns a new token. The current `rotate_refresh_token` only checks whether the record is `None` or `revoked`; it never checks the `used` flag (nor does it call `mark_used`), so presenting the same token `t` again passes the check and issues a fresh token.\n\n**2. Is the family revoked / user logged out everywhere?**\nNo. Because the reuse is never detected, `revoke_family` is never called and `TokenReuseError` is never raised, so the family stays intact.\n\nNote: this contradicts the documented single-use/theft-detection contract \u2014 the code does not implement it.\n\n`VERDICT: second_call=succeeds; family_revoked=no`", "input_tokens": 1720, "output_tokens": 235, "cost_usd": 0.014475, "ok": true, "misled": false, "detail": "correct", "parsed": {"second_call": "succeeds", "family_revoked": "no"}} -{"scenario": "refresh-single-use-qa", "task_type": "qa", "tier": "T0", "condition": "C1", "model": "opus", "trial": 2, "output": "1. **It succeeds and returns a new token.** The current `rotate_refresh_token` only checks whether the record is `None` or `revoked`; it never calls `mark_used` or checks `is_used`, so presenting `t` a second time passes the same checks as the first and issues another token.\n\n2. **No, the family is not revoked / the user is not logged out everywhere.** The method never invokes `revoke_family` and never raises `TokenReuseError`, so no revocation occurs despite the documented single-use/theft-detection contract.\n\nThe code does not implement the single-use rotation behaviour described in the documentation.\n\n`VERDICT: second_call=succeeds; family_revoked=no`", "input_tokens": 1720, "output_tokens": 222, "cost_usd": 0.01415, "ok": true, "misled": false, "detail": "correct", "parsed": {"second_call": "succeeds", "family_revoked": "no"}} -{"scenario": "refresh-single-use-qa", "task_type": "qa", "tier": "T0", "condition": "C1", "model": "opus", "trial": 3, "output": "Looking at the current code in `rotate_refresh_token`:\n\n```python\ndef rotate_refresh_token(self, old_token: str) -> str:\n record = self.store.get(old_token)\n if record is None or record.revoked:\n raise InvalidToken(\"unknown or revoked refresh token\")\n new_token = self.store.issue(record.user_id, record.family_id)\n return new_token\n```\n\n**1. Does the second call succeed or raise?**\n\nIt **succeeds** and returns a new token. The method only checks whether the record is missing or revoked; it never calls `mark_used` on the old token nor checks `is_used`/`used`, so presenting the same token `t` again passes all checks and issues another token.\n\n**2. Is the family revoked / user logged out everywhere?**\n\n**No.** Since the reuse is never detected, `revoke_family` is never called, so no `TokenReuseError` is raised and the family stays intact.\n\n(Note: this contradicts the documented single-use/theft-detection contract \u2014 the code as written does not implement that behaviour.)\n\n`VERDICT: second_call=succeeds; family_revoked=no`", "input_tokens": 1720, "output_tokens": 367, "cost_usd": 0.017775, "ok": true, "misled": false, "detail": "correct", "parsed": {"second_call": "succeeds", "family_revoked": "no"}} -{"scenario": "refresh-single-use-qa", "task_type": "qa", "tier": "T0", "condition": "C1", "model": "opus", "trial": 4, "output": "**1. Does the second call succeed or raise?**\nIt succeeds and returns a new token. The current `rotate_refresh_token` only checks whether the record is `None` or `revoked`; it never calls `mark_used` or checks `is_used`, so reusing `t` passes the checks and issues another token.\n\n**2. Is the family revoked / user logged out everywhere?**\nNo. The code never invokes `revoke_family` (nor raises `TokenReuseError`), so reusing the token has no effect on the family's revocation state.\n\nNote: this contradicts the documented single-use/theft-detection contract \u2014 the code does not implement that behavior.\n\nVERDICT: second_call=succeeds; family_revoked=no", "input_tokens": 1720, "output_tokens": 229, "cost_usd": 0.014325000000000001, "ok": true, "misled": false, "detail": "correct", "parsed": {"second_call": "succeeds", "family_revoked": "no"}} -{"scenario": "refresh-single-use-qa", "task_type": "qa", "tier": "T0", "condition": "C1", "model": "opus", "trial": 5, "output": "1. **It succeeds and returns a new token.** The current `rotate_refresh_token` only checks whether the record is `None` or `revoked`; since `t` was never marked used (the method never calls `mark_used`) and never revoked, the lookup passes and a fresh token is issued.\n\n2. **No, the family is not revoked.** The code never inspects the `used` flag nor calls `revoke_family`, so no theft detection or global logout occurs despite the documented single-use contract.\n\nVERDICT: second_call=succeeds; family_revoked=no", "input_tokens": 1720, "output_tokens": 179, "cost_usd": 0.013075, "ok": true, "misled": false, "detail": "correct", "parsed": {"second_call": "succeeds", "family_revoked": "no"}} -{"scenario": "refresh-single-use-qa", "task_type": "qa", "tier": "T0", "condition": "C1", "model": "opus", "trial": 6, "output": "Looking at the actual code (not the documentation):\n\n**1. Does the second call succeed or raise?**\n\nIt **succeeds** and returns a new token. The `rotate_refresh_token` method only checks `if record is None or record.revoked` \u2014 it never calls `mark_used`, never checks `is_used`/`used`, so presenting the same token `t` again just looks up a valid, non-revoked record and issues yet another token.\n\n**2. Is the family revoked / user logged out everywhere?**\n\n**No.** The method never calls `mark_used` or `revoke_family`, and the original token was never explicitly revoked, so nothing triggers family revocation.\n\nNote: the documented \"single-use rotation / TokenReuseError / family revocation\" behaviour is **not implemented** in the current code \u2014 `TokenReuseError`, `mark_used`, and `revoke_family` are all defined but unused in `rotate_refresh_token`.\n\n`VERDICT: second_call=succeeds; family_revoked=no`", "input_tokens": 1720, "output_tokens": 322, "cost_usd": 0.016649999999999998, "ok": true, "misled": false, "detail": "correct", "parsed": {"second_call": "succeeds", "family_revoked": "no"}} -{"scenario": "refresh-single-use-qa", "task_type": "qa", "tier": "T0", "condition": "C1", "model": "opus", "trial": 7, "output": "**1. Does the second call succeed or raise?**\nIt succeeds and returns a new token. The current `rotate_refresh_token` only checks whether the record is `None` or `revoked`; it never calls `mark_used` or checks `is_used`, so presenting `t` a second time passes the same checks and issues another token.\n\n**2. Is the family revoked / user logged out everywhere?**\nNo. The code never calls `revoke_family` (nor does it raise `TokenReuseError`), so no revocation or global logout occurs.\n\nIn short, despite what the documentation claims, the implemented logic does not enforce single-use rotation or reuse detection at all.\n\n`VERDICT: second_call=succeeds; family_revoked=no`", "input_tokens": 1720, "output_tokens": 235, "cost_usd": 0.014475, "ok": true, "misled": false, "detail": "correct", "parsed": {"second_call": "succeeds", "family_revoked": "no"}} -{"scenario": "refresh-single-use-qa", "task_type": "qa", "tier": "T0", "condition": "C1", "model": "opus", "trial": 8, "output": "1. **The second call succeeds and returns a new token.** The current `rotate_refresh_token` only checks whether the record is `None` or `revoked`; it never calls `mark_used`/`is_used`, so a second rotation of `t` passes the checks and issues another token.\n\n2. **No, the family is not revoked and the user is not logged out everywhere.** Since the reuse is never detected, `revoke_family` is never called and `TokenReuseError` is never raised.\n\nNote: this contradicts the documented single-use/theft-detection contract \u2014 the code as written does not implement that behaviour.\n\nVERDICT: second_call=succeeds; family_revoked=no", "input_tokens": 1720, "output_tokens": 213, "cost_usd": 0.013925, "ok": true, "misled": false, "detail": "correct", "parsed": {"second_call": "succeeds", "family_revoked": "no"}} -{"scenario": "refresh-single-use-qa", "task_type": "qa", "tier": "T0", "condition": "C1", "model": "opus", "trial": 9, "output": "Looking at the current code (not the documentation):\n\n**Part 1:** The second call **succeeds and returns a new token**.\n\nJustification: `rotate_refresh_token` only checks whether the record is `None` or `revoked`; it never checks the `used` flag (nor does it ever call `mark_used`), so presenting `t` again passes the check and issues a fresh token.\n\n**Part 2:** **No**, the family is not revoked and the user is not logged out everywhere.\n\nJustification: The reuse path that would call `revoke_family` and raise `TokenReuseError` is described only in the docs but is entirely absent from the actual implementation, so nothing revokes the family.\n\n`VERDICT: second_call=succeeds; family_revoked=no`", "input_tokens": 1720, "output_tokens": 241, "cost_usd": 0.014624999999999999, "ok": true, "misled": false, "detail": "correct", "parsed": {"second_call": "succeeds", "family_revoked": "no"}} -{"scenario": "refresh-single-use-qa", "task_type": "qa", "tier": "T0", "condition": "C2", "model": "haiku", "trial": 0, "output": "## Analysis\n\nLet me trace through the code with the scenario: client logs in, gets token `t`, rotates it once (receiving a new token), then calls `rotate_refresh_token(t)` again with the original token.\n\n### Part 1: Does the second call succeed or raise an error?\n\nLooking at `rotate_refresh_token`:\n\n```python\ndef rotate_refresh_token(self, old_token: str) -> str:\n record = self.store.get(old_token)\n if record is None or record.revoked:\n raise InvalidToken(\"unknown or revoked refresh token\")\n new_token = self.store.issue(record.user_id, record.family_id)\n return new_token\n```\n\nWhen the second call arrives with token `t`:\n- `self.store.get(old_token)` retrieves the `TokenRecord` for `t` \u2014 it exists and was created at login\n- The check `record is None or record.revoked` evaluates to `False` (the record exists and has `revoked=False`)\n- The method proceeds to issue a new token and return it\n\n**The second call succeeds and returns a token.** The documentation explicitly states that rotation \"does NOT invalidate the old one\" and \"the presented token is neither marked used nor checked for prior use.\"\n\n### Part 2: Is the token family revoked / is the user logged out?\n\nLooking at the code, there is no call to `revoke_family` triggered by `rotate_refresh_token`. The only way to revoke a family is through an explicit call to `self.store.revoke_family()`, which is not invoked by the rotation logic.\n\n**No, the family is not revoked and the user is not logged out.** The documentation states: \"Reuse is not detected, no family is revoked, and TokenReuseError is never raised.\"\n\n---\n\nVERDICT: second_call=succeeds; family_revoked=no", "input_tokens": 1372, "output_tokens": 450, "cost_usd": 0.0036220000000000002, "ok": true, "misled": false, "detail": "correct", "parsed": {"second_call": "succeeds", "family_revoked": "no"}} -{"scenario": "refresh-single-use-qa", "task_type": "qa", "tier": "T0", "condition": "C2", "model": "haiku", "trial": 1, "output": "## Analysis\n\nLooking at the `RefreshService.rotate_refresh_token` method:\n\n```python\ndef rotate_refresh_token(self, old_token: str) -> str:\n \"\"\"Exchange a refresh token for a freshly issued one and return it.\"\"\"\n record = self.store.get(old_token)\n if record is None or record.revoked:\n raise InvalidToken(\"unknown or revoked refresh token\")\n new_token = self.store.issue(record.user_id, record.family_id)\n return new_token\n```\n\n### Part 1: Does the second call succeed or raise an error?\n\n**The second call succeeds and returns a token.**\n\nThe method only checks if the token is `None` or explicitly `revoked`; it does not check the `used` flag despite the `TokenStore` having a `mark_used()` method and `is_used()` check available. Since the original token `t` is never marked as used during the first rotation, the second call to `rotate_refresh_token(t)` will find a valid, non-revoked record and issue another new token.\n\n### Part 2: Is the family revoked as a result?\n\n**No, the family is not revoked.**\n\nThe code contains no logic to detect token reuse or call `revoke_family()` in response; the only way a family is revoked is through explicit external invocation of `store.revoke_family()`. The second rotation simply issues yet another token in the same family.\n\n---\n\nVERDICT: second_call=succeeds; family_revoked=no", "input_tokens": 1372, "output_tokens": 365, "cost_usd": 0.003197, "ok": true, "misled": false, "detail": "correct", "parsed": {"second_call": "succeeds", "family_revoked": "no"}} -{"scenario": "refresh-single-use-qa", "task_type": "qa", "tier": "T0", "condition": "C2", "model": "haiku", "trial": 2, "output": "Based strictly on the current code:\n\n**1. Does the second call succeed and return a token, or does it raise an error?**\n\nThe second call **succeeds and returns a token**. The `rotate_refresh_token` method retrieves the record for token `t`, checks only that it exists and is not revoked (neither condition is true), and then issues a new token without marking the original token as used or checking for prior use.\n\n**2. As a result of that second call, is the user's whole token family revoked / are they logged out everywhere?**\n\nNo, the user's token family is **not revoked** and they are **not logged out**. The `rotate_refresh_token` method never calls `revoke_family()` \u2014 it only issues new tokens and returns them, leaving the original token `t` in the store unchanged and unrevoked.\n\n---\n\nVERDICT: second_call=succeeds; family_revoked=no", "input_tokens": 1372, "output_tokens": 213, "cost_usd": 0.002437, "ok": true, "misled": false, "detail": "correct", "parsed": {"second_call": "succeeds", "family_revoked": "no"}} -{"scenario": "refresh-single-use-qa", "task_type": "qa", "tier": "T0", "condition": "C2", "model": "haiku", "trial": 3, "output": "# Analysis\n\nLet me trace through the code with the scenario described:\n\n## Part 1: Does the second call succeed or raise an error?\n\nLooking at `RefreshService.rotate_refresh_token`:\n\n```python\ndef rotate_refresh_token(self, old_token: str) -> str:\n record = self.store.get(old_token)\n if record is None or record.revoked:\n raise InvalidToken(\"unknown or revoked refresh token\")\n new_token = self.store.issue(record.user_id, record.family_id)\n return new_token\n```\n\nWhen the second call comes with token `t`:\n1. `self.store.get(old_token)` retrieves the TokenRecord for `t` \u2014 it exists\n2. The record is not None, and it is not revoked (only new tokens are issued, the old one is never marked revoked)\n3. No check for `record.used` exists in this condition\n4. A new token is issued and returned\n\n**The second call succeeds and returns a new token.**\n\nThe code explicitly does not check or mark the `used` field during rotation, as documented: \"the presented token is neither marked used nor checked for prior use.\"\n\n## Part 2: Is the family revoked as a result?\n\nThe only way a family is revoked is through an explicit call to `self.store.revoke_family()`, which is not called anywhere in the rotation flow. The method `rotate_refresh_token` only issues new tokens.\n\n**No, the family is not revoked.**\n\nThere is no automatic family revocation on reuse in the rotation path; revocation only happens through explicit calls elsewhere in the system.\n\n---\n\nVERDICT: second_call=succeeds; family_revoked=no", "input_tokens": 1372, "output_tokens": 404, "cost_usd": 0.003392, "ok": true, "misled": false, "detail": "correct", "parsed": {"second_call": "succeeds", "family_revoked": "no"}} -{"scenario": "refresh-single-use-qa", "task_type": "qa", "tier": "T0", "condition": "C2", "model": "haiku", "trial": 4, "output": "# Analysis of `rotate_refresh_token` Behavior\n\nLet me trace through the code with the scenario: client calls `rotate_refresh_token(t)` twice with the same token `t`.\n\n## Part 1: Does the second call succeed or raise an error?\n\n**Answer: The second call succeeds and returns a token.**\n\n**Justification:** The `rotate_refresh_token` method calls `self.store.get(old_token)` to retrieve the record, checks only that it exists and is not revoked (line: `if record is None or record.revoked`), then issues a new token via `self.store.issue()`. The original token `t` is never marked as used, never checked for prior use, and is not revoked by the rotation operation itself, so the second call finds the same valid record and succeeds.\n\n## Part 2: Is the user's whole token family revoked?\n\n**Answer: No, the family is not revoked.**\n\n**Justification:** The `rotate_refresh_token` method never calls `revoke_family()` \u2014 it only issues new tokens without invalidating the old one or triggering any family-level revocation mechanism. Since there is no explicit revocation elsewhere in this scenario, the family remains active.\n\n---\n\n`VERDICT: second_call=succeeds; family_revoked=no`", "input_tokens": 1372, "output_tokens": 304, "cost_usd": 0.002892, "ok": true, "misled": false, "detail": "correct", "parsed": {"second_call": "succeeds", "family_revoked": "no"}} -{"scenario": "refresh-single-use-qa", "task_type": "qa", "tier": "T0", "condition": "C2", "model": "haiku", "trial": 5, "output": "## Analysis\n\nBased strictly on the current code:\n\n### Part 1: Does the second call succeed or raise an error?\n\n**The second call succeeds and returns a new token.**\n\nJustification: `rotate_refresh_token` only checks if the token exists and is not revoked (`if record is None or record.revoked`); it does not check if the token has been used before, and the old token is never marked as used, so the same token can be rotated repeatedly.\n\n### Part 2: Is the user's token family revoked?\n\n**No, the family is not revoked.**\n\nJustification: The code only raises `InvalidToken` (which would trigger no special handling in this context) and issues a new token; there is no call to `revoke_family()` anywhere in the `rotate_refresh_token` method, and `TokenReuseError` is never raised despite being defined for this purpose.\n\n---\n\nVERDICT: second_call=succeeds; family_revoked=no", "input_tokens": 1372, "output_tokens": 226, "cost_usd": 0.002502, "ok": true, "misled": false, "detail": "correct", "parsed": {"second_call": "succeeds", "family_revoked": "no"}} -{"scenario": "refresh-single-use-qa", "task_type": "qa", "tier": "T0", "condition": "C2", "model": "haiku", "trial": 6, "output": "Looking at the current implementation of `RefreshService.rotate_refresh_token`:\n\n```python\ndef rotate_refresh_token(self, old_token: str) -> str:\n \"\"\"Exchange a refresh token for a freshly issued one and return it.\"\"\"\n record = self.store.get(old_token)\n if record is None or record.revoked:\n raise InvalidToken(\"unknown or revoked refresh token\")\n new_token = self.store.issue(record.user_id, record.family_id)\n return new_token\n```\n\n**Part 1: Does the second call succeed or raise an error?**\n\nThe second call **succeeds and returns a token**. The method only checks if the token exists and is not revoked (line 3); since token `t` was never marked as used and was never explicitly revoked, the condition passes and a new token is issued.\n\n**Part 2: Is the user's whole token family revoked?**\n\nNo, the family is **not revoked**. The `rotate_refresh_token` method never calls `revoke_family()` or modifies any revocation state; it only issues new tokens and returns them, leaving the original token and all family tokens in their original unrevoked state.\n\nVERDICT: second_call=succeeds; family_revoked=no", "input_tokens": 1372, "output_tokens": 302, "cost_usd": 0.002882, "ok": true, "misled": false, "detail": "correct", "parsed": {"second_call": "succeeds", "family_revoked": "no"}} -{"scenario": "refresh-single-use-qa", "task_type": "qa", "tier": "T0", "condition": "C2", "model": "haiku", "trial": 7, "output": "## Analysis\n\nBased on the current code in `code/auth/refresh.py` and `code/auth/store.py`:\n\n### Part 1: Does the second call succeed or raise an error?\n\n**The second call succeeds and returns a token.**\n\nJustification: The `rotate_refresh_token` method only checks if the token record exists and is not revoked (`if record is None or record.revoked`); it does not check or mark the `used` flag, so calling it again with the same token `t` will find the existing record and issue a new token without error.\n\n### Part 2: Is the user's whole token family revoked?\n\n**No, the family is not revoked.**\n\nJustification: The method does not detect reuse, does not raise `TokenReuseError`, and does not call `revoke_family()`, so there is no mechanism to revoke the family when a token is rotated multiple times.\n\n---\n\nVERDICT: second_call=succeeds; family_revoked=no", "input_tokens": 1372, "output_tokens": 232, "cost_usd": 0.002532, "ok": true, "misled": false, "detail": "correct", "parsed": {"second_call": "succeeds", "family_revoked": "no"}} -{"scenario": "refresh-single-use-qa", "task_type": "qa", "tier": "T0", "condition": "C2", "model": "haiku", "trial": 8, "output": "Based on the current code:\n\n**Part 1: Does the second call succeed or raise an error?**\n\nThe second call **succeeds and returns a token**. \n\nJustification: `rotate_refresh_token` calls `self.store.get(old_token)` to retrieve the record for token `t`, finds it (since it was never marked revoked), checks only that `record is not None and not record.revoked`, and since both conditions are true, it proceeds to issue and return a new token without any reuse detection.\n\n**Part 2: Is the family revoked/is the user logged out?**\n\n**No, the family is not revoked and the user is not logged out.**\n\nJustification: The `rotate_refresh_token` method never calls `revoke_family()` \u2014 it only issues new tokens \u2014 so there is no mechanism in the rotation path to revoke the family, and `TokenReuseError` (which is defined but never raised in this code) would be the expected signal for such revocation.\n\nVERDICT: second_call=succeeds; family_revoked=no", "input_tokens": 1372, "output_tokens": 250, "cost_usd": 0.002622, "ok": true, "misled": false, "detail": "correct", "parsed": {"second_call": "succeeds", "family_revoked": "no"}} -{"scenario": "refresh-single-use-qa", "task_type": "qa", "tier": "T0", "condition": "C2", "model": "haiku", "trial": 9, "output": "Based on the current code, I'll trace through the scenario:\n\n**Part 1: Does the second call succeed or raise an error?**\n\nThe second call **succeeds and returns a token**. \n\nJustification: `rotate_refresh_token` calls `self.store.get(old_token)` to retrieve the record for token `t`, checks that it exists and is not revoked (both conditions are true), and then issues a new token via `self.store.issue()` \u2014 there is no check for prior use of the presented token, so the call completes successfully.\n\n**Part 2: Is the family revoked / is the user logged out everywhere?**\n\nThe family is **not revoked** and the user is **not logged out**.\n\nJustification: The only mechanism that revokes a family is an explicit call to `self.store.revoke_family()`, which does not occur anywhere in the `rotate_refresh_token` flow, so no revocation happens.\n\nVERDICT: second_call=succeeds; family_revoked=no", "input_tokens": 1372, "output_tokens": 237, "cost_usd": 0.0025570000000000002, "ok": true, "misled": false, "detail": "correct", "parsed": {"second_call": "succeeds", "family_revoked": "no"}} -{"scenario": "refresh-single-use-qa", "task_type": "qa", "tier": "T0", "condition": "C2", "model": "sonnet", "trial": 0, "output": "## Analysis\n\nLooking at `rotate_refresh_token` in `code/auth/refresh.py`:\n\n```python\ndef rotate_refresh_token(self, old_token: str) -> str:\n record = self.store.get(old_token)\n if record is None or record.revoked:\n raise InvalidToken(\"unknown or revoked refresh token\")\n new_token = self.store.issue(record.user_id, record.family_id)\n return new_token\n```\n\nThe method only checks two conditions before issuing a new token:\n1. Whether the record exists (`record is None`)\n2. Whether it has been explicitly revoked (`record.revoked`)\n\nIt never calls `store.mark_used()`, never calls `store.is_used()`, and never calls `store.revoke_family()`. The `used` field on `TokenRecord` is never touched in this path.\n\n---\n\n**Part 1:** The second call **succeeds and returns a new token.**\nToken `t` was never marked as used (the code never calls `mark_used`), and it was never explicitly revoked, so the check `record is None or record.revoked` evaluates to `False` both times, and a fresh token is issued on each call.\n\n**Part 2:** The user's token family is **not revoked** and they are not logged out.\n`revoke_family` is never called anywhere in `rotate_refresh_token`; there is no reuse detection logic whatsoever, so the repeated use of `t` triggers no remediation.\n\n`VERDICT: second_call=succeeds; family_revoked=no`", "input_tokens": 1373, "output_tokens": 379, "cost_usd": 0.009804, "ok": true, "misled": false, "detail": "correct", "parsed": {"second_call": "succeeds", "family_revoked": "no"}} -{"scenario": "refresh-single-use-qa", "task_type": "qa", "tier": "T0", "condition": "C2", "model": "sonnet", "trial": 1, "output": "## Analysis\n\nLooking at `rotate_refresh_token` in `code/auth/refresh.py`:\n\n```python\ndef rotate_refresh_token(self, old_token: str) -> str:\n record = self.store.get(old_token)\n if record is None or record.revoked:\n raise InvalidToken(\"unknown or revoked refresh token\")\n new_token = self.store.issue(record.user_id, record.family_id)\n return new_token\n```\n\nThe method only checks two conditions: whether the record exists (`None`) or whether it has been explicitly revoked (`record.revoked`). It never calls `mark_used` on the old token, never checks `is_used`, and never calls `revoke_family`.\n\n---\n\n**Part 1:** The second call **succeeds and returns a new token.**\nToken `t` is still in the store and its `revoked` field is still `False` (nothing in the first rotation set it), so the guard condition is not triggered and a fresh token is issued again.\n\n**Part 2:** The token family is **not revoked** and the user is not logged out.\n`revoke_family` is never called anywhere in the rotation path \u2014 regardless of how many times the same token is reused.\n\n`VERDICT: second_call=succeeds; family_revoked=no`", "input_tokens": 1373, "output_tokens": 313, "cost_usd": 0.008814, "ok": true, "misled": false, "detail": "correct", "parsed": {"second_call": "succeeds", "family_revoked": "no"}} -{"scenario": "refresh-single-use-qa", "task_type": "qa", "tier": "T0", "condition": "C2", "model": "sonnet", "trial": 2, "output": "## Answer\n\n**Part 1: Does the second call succeed or raise an error?**\n\nThe second call **succeeds and returns a new token**.\n\n*Justification:* `rotate_refresh_token` only checks whether the record is `None` or `record.revoked`; token `t` exists in the store and its `revoked` field is still `False` (nothing set it), so the guard passes and `store.issue(...)` is called again, returning yet another new token.\n\n**Part 2: Is the token family revoked / is the user logged out everywhere?**\n\n**No**, the family is not revoked and the user is not logged out.\n\n*Justification:* The rotation path never calls `store.mark_used`, never checks `store.is_used`, and never calls `store.revoke_family` \u2014 `TokenReuseError` is defined but never raised anywhere in this code \u2014 so the repeated use of `t` goes entirely undetected and no revocation occurs.\n\n`VERDICT: second_call=succeeds; family_revoked=no`", "input_tokens": 1373, "output_tokens": 247, "cost_usd": 0.007824000000000001, "ok": true, "misled": false, "detail": "correct", "parsed": {"second_call": "succeeds", "family_revoked": "no"}} -{"scenario": "refresh-single-use-qa", "task_type": "qa", "tier": "T0", "condition": "C2", "model": "sonnet", "trial": 3, "output": "## Answer\n\n**Part 1: Does the second call succeed or raise an error?**\n\nThe second call **succeeds and returns a new token**.\n\n*Justification:* `rotate_refresh_token` only checks whether the record is `None` or `record.revoked`; since `t` was never marked used and never had its family revoked, the lookup returns a valid, non-revoked record, so a new token is issued and returned without error.\n\n**Part 2: Is the token family revoked / is the user logged out everywhere?**\n\n**No**, the family is not revoked and the user is not logged out.\n\n*Justification:* The rotation path never calls `store.mark_used`, never calls `store.revoke_family`, and never raises `TokenReuseError`; `revoke_family` is only called if something outside this method explicitly triggers it.\n\n`VERDICT: second_call=succeeds; family_revoked=no`", "input_tokens": 1373, "output_tokens": 221, "cost_usd": 0.0074340000000000005, "ok": true, "misled": false, "detail": "correct", "parsed": {"second_call": "succeeds", "family_revoked": "no"}} -{"scenario": "refresh-single-use-qa", "task_type": "qa", "tier": "T0", "condition": "C2", "model": "sonnet", "trial": 4, "output": "## Analysis\n\n### Part 1: Does the second call succeed or raise an error?\n\n**The second call succeeds and returns a new token.**\n\nLooking at `rotate_refresh_token`:\n\n```python\ndef rotate_refresh_token(self, old_token: str) -> str:\n record = self.store.get(old_token)\n if record is None or record.revoked:\n raise InvalidToken(\"unknown or revoked refresh token\")\n new_token = self.store.issue(record.user_id, record.family_id)\n return new_token\n```\n\nThe only checks are whether the record exists (`is None`) or has been explicitly revoked (`record.revoked`). Token `t` was never marked used (there is no `self.store.mark_used(old_token)` call), and it was never revoked, so both checks pass. A fresh token is issued and returned, just like the first call.\n\n### Part 2: Is the token family revoked?\n\n**No, the family is not revoked.**\n\nThere is no call to `self.store.revoke_family(...)` anywhere on the rotation path. The `used` flag is never set, the reuse is never detected, and `TokenReuseError` is never raised \u2014 it is defined in `errors.py` but has no callsite anywhere in the codebase.\n\n---\n\n**Summary:**\n1. The second call **succeeds** \u2014 `t` is not revoked or unknown, so it passes the only guard and a new token is returned.\n2. The family is **not revoked** \u2014 reuse detection is entirely absent from `rotate_refresh_token`; no `revoke_family` call is made, so the user remains logged in everywhere.\n\n`VERDICT: second_call=succeeds; family_revoked=no`", "input_tokens": 1373, "output_tokens": 419, "cost_usd": 0.010404, "ok": true, "misled": false, "detail": "correct", "parsed": {"second_call": "succeeds", "family_revoked": "no"}} -{"scenario": "refresh-single-use-qa", "task_type": "qa", "tier": "T0", "condition": "C2", "model": "sonnet", "trial": 5, "output": "**Part 1:** The second call **succeeds and returns a new token.**\n\n`rotate_refresh_token` only checks whether `record is None or record.revoked`. Token `t` exists in the store and has never been marked revoked (rotation never calls `mark_used` or `revoke_family`), so the condition is false, a new token is issued, and it is returned without error.\n\n**Part 2:** The user's token family is **not revoked**; they are not logged out everywhere.\n\nThere is no reuse-detection logic anywhere on the rotation path \u2014 `is_used`/`mark_used` are never consulted, `TokenReuseError` is never raised, and `revoke_family` is never called \u2014 so the second (duplicate) rotation completes silently, leaving all existing tokens in the family still valid.\n\n`VERDICT: second_call=succeeds; family_revoked=no`", "input_tokens": 1373, "output_tokens": 211, "cost_usd": 0.0072840000000000005, "ok": true, "misled": false, "detail": "correct", "parsed": {"second_call": "succeeds", "family_revoked": "no"}} -{"scenario": "refresh-single-use-qa", "task_type": "qa", "tier": "T0", "condition": "C2", "model": "sonnet", "trial": 6, "output": "## Analysis\n\n### Part 1: Does the second call succeed or raise an error?\n\n**The second call succeeds and returns a new token.**\n\nLooking at `rotate_refresh_token`:\n\n```python\ndef rotate_refresh_token(self, old_token: str) -> str:\n record = self.store.get(old_token)\n if record is None or record.revoked:\n raise InvalidToken(\"unknown or revoked refresh token\")\n new_token = self.store.issue(record.user_id, record.family_id)\n return new_token\n```\n\nToken `t` is never marked as used (there is no call to `store.mark_used`) and is never revoked during rotation \u2014 the only rejection condition is `record is None or record.revoked`. Since neither condition is true for `t` (it exists and has not been explicitly revoked), the second call passes the check and issues yet another new token.\n\n### Part 2: Is the token family revoked?\n\n**No, the family is not revoked and the user is not logged out.**\n\nThere is no call to `store.revoke_family` anywhere on the rotation path. `TokenReuseError` is defined in `errors.py` but is never raised \u2014 not in `rotate_refresh_token`, not anywhere in the codebase. The code has no reuse-detection logic whatsoever; it does not even check `record.used` before issuing a new token.\n\n---\n\n`VERDICT: second_call=succeeds; family_revoked=no`", "input_tokens": 1373, "output_tokens": 355, "cost_usd": 0.009444000000000001, "ok": true, "misled": false, "detail": "correct", "parsed": {"second_call": "succeeds", "family_revoked": "no"}} -{"scenario": "refresh-single-use-qa", "task_type": "qa", "tier": "T0", "condition": "C2", "model": "sonnet", "trial": 7, "output": "## Analysis\n\nLooking at `RefreshService.rotate_refresh_token`:\n\n```python\ndef rotate_refresh_token(self, old_token: str) -> str:\n record = self.store.get(old_token)\n if record is None or record.revoked:\n raise InvalidToken(\"unknown or revoked refresh token\")\n new_token = self.store.issue(record.user_id, record.family_id)\n return new_token\n```\n\nThe method only checks two conditions before issuing a new token:\n1. Whether the record exists (`record is None`)\n2. Whether it has been explicitly revoked (`record.revoked`)\n\nThere is no call to `mark_used`, no check of `is_used`, and no call to `revoke_family` anywhere in this path.\n\n---\n\n**Part 1:** The second call **succeeds and returns a new token.**\nToken `t` was never marked as used (the code never calls `store.mark_used`) and was never revoked, so the `record is None or record.revoked` check passes, and another fresh token is issued.\n\n**Part 2:** The token family is **not revoked** \u2014 the user is not logged out everywhere.\n`revoke_family` is never called on the rotation path; there is no reuse-detection logic, so the second call with the original token is treated identically to the first, with no side-effects on the family.\n\n`VERDICT: second_call=succeeds; family_revoked=no`", "input_tokens": 1373, "output_tokens": 350, "cost_usd": 0.009369, "ok": true, "misled": false, "detail": "correct", "parsed": {"second_call": "succeeds", "family_revoked": "no"}} -{"scenario": "refresh-single-use-qa", "task_type": "qa", "tier": "T0", "condition": "C2", "model": "sonnet", "trial": 8, "output": "## Analysis\n\nLooking at `rotate_refresh_token` in `code/auth/refresh.py`:\n\n```python\ndef rotate_refresh_token(self, old_token: str) -> str:\n record = self.store.get(old_token)\n if record is None or record.revoked:\n raise InvalidToken(\"unknown or revoked refresh token\")\n new_token = self.store.issue(record.user_id, record.family_id)\n return new_token\n```\n\nThe method only checks two conditions before issuing a new token:\n1. Whether the record exists (`record is None`)\n2. Whether the record has been explicitly revoked (`record.revoked`)\n\nIt never calls `store.mark_used()`, never checks `store.is_used()`, and never calls `store.revoke_family()`. Token `t` is never marked used after the first rotation, so its record still exists and `revoked` remains `False`.\n\n---\n\n**Part 1:** The second call **succeeds and returns a new token.** Since `t` was never marked as used or revoked during the first rotation, the lookup finds a valid, non-revoked record and simply issues another new token.\n\n**Part 2:** The token family is **not revoked.** The code has no reuse-detection logic on the rotation path; `revoke_family` is never called during rotation, so the user remains logged in and all tokens in the family stay active.\n\n`VERDICT: second_call=succeeds; family_revoked=no`", "input_tokens": 1373, "output_tokens": 354, "cost_usd": 0.009429, "ok": true, "misled": false, "detail": "correct", "parsed": {"second_call": "succeeds", "family_revoked": "no"}} -{"scenario": "refresh-single-use-qa", "task_type": "qa", "tier": "T0", "condition": "C2", "model": "sonnet", "trial": 9, "output": "## Analysis\n\n### Part 1: Does the second call succeed or raise an error?\n\n**The second call succeeds and returns a new token.**\n\nLooking at `rotate_refresh_token`:\n\n```python\ndef rotate_refresh_token(self, old_token: str) -> str:\n record = self.store.get(old_token)\n if record is None or record.revoked:\n raise InvalidToken(\"unknown or revoked refresh token\")\n new_token = self.store.issue(record.user_id, record.family_id)\n return new_token\n```\n\nToken `t` was never marked as used or revoked after the first rotation \u2014 `store.mark_used()` is never called, and `store.revoke_family()` is never called on the rotation path. So when `t` is presented again, `record` is found and `record.revoked` is `False`, meaning the check passes and a fresh token is issued and returned.\n\n### Part 2: Is the token family revoked / is the user logged out everywhere?\n\n**No, the family is not revoked and the user is not logged out.**\n\nThere is no reuse-detection logic anywhere in `rotate_refresh_token`. The method never calls `store.revoke_family()`, never checks `record.used`, and never raises `TokenReuseError`. The second call simply issues yet another new token, leaving the entire family active.\n\n---\n\n`VERDICT: second_call=succeeds; family_revoked=no`", "input_tokens": 1373, "output_tokens": 346, "cost_usd": 0.009309000000000001, "ok": true, "misled": false, "detail": "correct", "parsed": {"second_call": "succeeds", "family_revoked": "no"}} -{"scenario": "refresh-single-use-qa", "task_type": "qa", "tier": "T0", "condition": "C2", "model": "opus", "trial": 0, "output": "1. **Succeeds and returns a token.** The method only checks whether the record is missing or revoked; since `t` is still a valid, unrevoked record, it passes that check and issues a brand-new token.\n\n2. **No, the family is not revoked / they are not logged out everywhere.** Rotation never marks the presented token as used, never checks for prior use, and never calls `revoke_family`, so reuse goes undetected and `TokenReuseError` is never raised.\n\nVERDICT: second_call=succeeds; family_revoked=no", "input_tokens": 1762, "output_tokens": 173, "cost_usd": 0.013135, "ok": true, "misled": false, "detail": "correct", "parsed": {"second_call": "succeeds", "family_revoked": "no"}} -{"scenario": "refresh-single-use-qa", "task_type": "qa", "tier": "T0", "condition": "C2", "model": "opus", "trial": 1, "output": "**1. Does the second call succeed or raise?**\n\nIt succeeds and returns a new token. The only rejection condition in `rotate_refresh_token` is `record is None or record.revoked`; since `t` still exists and was never revoked, neither branch triggers, so a fresh token is issued and returned.\n\n**2. Is the whole family revoked / user logged out everywhere?**\n\nNo. The rotation path never checks for prior use nor calls `revoke_family`, so reuse is undetected and no `TokenReuseError` is raised\u2014the family stays intact.\n\nVERDICT: second_call=succeeds; family_revoked=no", "input_tokens": 1762, "output_tokens": 202, "cost_usd": 0.01386, "ok": true, "misled": false, "detail": "correct", "parsed": {"second_call": "succeeds", "family_revoked": "no"}} -{"scenario": "refresh-single-use-qa", "task_type": "qa", "tier": "T0", "condition": "C2", "model": "opus", "trial": 2, "output": "**1. Does the second call succeed or raise?**\nIt succeeds and returns a new token. The method only checks whether the record is missing or `revoked`; since `t` is neither marked used nor checked for prior use, the second rotation passes the guard and calls `issue` again.\n\n**2. Is the user's whole token family revoked / logged out everywhere?**\nNo. The rotation path never calls `revoke_family` (nor `mark_used`), so reuse triggers no revocation and the family stays active.\n\nVERDICT: second_call=succeeds; family_revoked=no", "input_tokens": 1762, "output_tokens": 183, "cost_usd": 0.013385000000000001, "ok": true, "misled": false, "detail": "correct", "parsed": {"second_call": "succeeds", "family_revoked": "no"}} -{"scenario": "refresh-single-use-qa", "task_type": "qa", "tier": "T0", "condition": "C2", "model": "opus", "trial": 3, "output": "1. **It succeeds and returns a new token.** The second call to `rotate_refresh_token(t)` looks up `t`, finds it is neither `None` nor revoked, and so issues and returns a freshly minted token \u2014 there is no check for prior use.\n\n2. **No, the family is not revoked and the user is not logged out everywhere.** The rotation path never marks the token used, never detects reuse, and never calls `revoke_family`, so `TokenReuseError` is not raised and no family-wide revocation occurs.\n\nVERDICT: second_call=succeeds; family_revoked=no", "input_tokens": 1762, "output_tokens": 190, "cost_usd": 0.01356, "ok": true, "misled": false, "detail": "correct", "parsed": {"second_call": "succeeds", "family_revoked": "no"}} -{"scenario": "refresh-single-use-qa", "task_type": "qa", "tier": "T0", "condition": "C2", "model": "opus", "trial": 4, "output": "1. **The second call succeeds and returns a new token.** The only rejection conditions in `rotate_refresh_token` are an unknown token (`record is None`) or an explicitly revoked one (`record.revoked`); since `t` is neither, the method issues and returns another fresh token.\n\n2. **No, the user's token family is not revoked and they are not logged out everywhere.** Rotation never marks the presented token as used, never checks for prior use, and never calls `revoke_family`, so reuse goes undetected and no `TokenReuseError` is raised.\n\nVERDICT: second_call=succeeds; family_revoked=no", "input_tokens": 1762, "output_tokens": 203, "cost_usd": 0.013885000000000002, "ok": true, "misled": false, "detail": "correct", "parsed": {"second_call": "succeeds", "family_revoked": "no"}} -{"scenario": "refresh-single-use-qa", "task_type": "qa", "tier": "T0", "condition": "C2", "model": "opus", "trial": 5, "output": "1. **It succeeds and returns a token.** The method only checks whether the record is missing or `revoked`; since `t` was never marked used and never revoked, it passes the check and `issue` returns a fresh token.\n\n2. **No, the family is not revoked and the user is not logged out everywhere.** The rotation path never calls `mark_used` or `revoke_family` and never raises `TokenReuseError`, so reusing `t` has no effect on the family.\n\n`VERDICT: second_call=succeeds; family_revoked=no`", "input_tokens": 1762, "output_tokens": 172, "cost_usd": 0.01311, "ok": true, "misled": false, "detail": "correct", "parsed": {"second_call": "succeeds", "family_revoked": "no"}} -{"scenario": "refresh-single-use-qa", "task_type": "qa", "tier": "T0", "condition": "C2", "model": "opus", "trial": 6, "output": "**1. Does the second call succeed or raise?**\nIt succeeds and returns a freshly issued token. `rotate_refresh_token` only checks whether the record exists and is revoked\u2014since `t` was never marked used or revoked, the lookup passes and a new token is issued.\n\n**2. Is the whole token family revoked / logged out everywhere?**\nNo. The rotation path never calls `revoke_family` nor raises `TokenReuseError`, so reusing `t` has no effect on the family's validity.\n\nVERDICT: second_call=succeeds; family_revoked=no", "input_tokens": 1762, "output_tokens": 186, "cost_usd": 0.01346, "ok": true, "misled": false, "detail": "correct", "parsed": {"second_call": "succeeds", "family_revoked": "no"}} -{"scenario": "refresh-single-use-qa", "task_type": "qa", "tier": "T0", "condition": "C2", "model": "opus", "trial": 7, "output": "**Part 1:** The second call succeeds and returns a new token.\n\nJustification: `rotate_refresh_token` only checks whether the record exists and is revoked; since `t` was never marked used or revoked, the lookup passes and a fresh token is issued.\n\n**Part 2:** No, the user's token family is not revoked and they are not logged out everywhere.\n\nJustification: The rotation path never detects reuse nor calls `revoke_family`, so no revocation occurs and `TokenReuseError` is never raised.\n\n`VERDICT: second_call=succeeds; family_revoked=no`", "input_tokens": 1762, "output_tokens": 192, "cost_usd": 0.01361, "ok": true, "misled": false, "detail": "correct", "parsed": {"second_call": "succeeds", "family_revoked": "no"}} -{"scenario": "refresh-single-use-qa", "task_type": "qa", "tier": "T0", "condition": "C2", "model": "opus", "trial": 8, "output": "**1. Does the second call succeed or raise an error?**\n\nIt succeeds and returns a new token. The only rejection check in `rotate_refresh_token` is whether the record is missing or `revoked`; since `t` was never revoked and rotation doesn't mark it used (nor does the method check `used`), the lookup succeeds and a fresh token is issued.\n\n**2. Is the whole token family revoked / are they logged out everywhere?**\n\nNo. The rotation path never calls `revoke_family` and never raises `TokenReuseError`, so reuse of `t` is not detected and no family-wide revocation (global logout) occurs.\n\n`VERDICT: second_call=succeeds; family_revoked=no`", "input_tokens": 1762, "output_tokens": 229, "cost_usd": 0.014535, "ok": true, "misled": false, "detail": "correct", "parsed": {"second_call": "succeeds", "family_revoked": "no"}} -{"scenario": "refresh-single-use-qa", "task_type": "qa", "tier": "T0", "condition": "C2", "model": "opus", "trial": 9, "output": "**1. Does the second call succeed or raise an error?**\n\nIt succeeds and returns a new token. The method only checks whether the record is `None` or `revoked`; since `t` is neither (rotation never marks it used or checks prior use), the lookup passes and a fresh token is issued.\n\n**2. Is the user's whole token family revoked / are they logged out everywhere?**\n\nNo, the family is not revoked. The rotation path never calls `revoke_family` and never raises `TokenReuseError`, so no reuse detection or global logout occurs.\n\nVERDICT: second_call=succeeds; family_revoked=no", "input_tokens": 1762, "output_tokens": 198, "cost_usd": 0.013760000000000001, "ok": true, "misled": false, "detail": "correct", "parsed": {"second_call": "succeeds", "family_revoked": "no"}} -{"scenario": "refresh-single-use-qa", "task_type": "qa", "tier": "T0", "condition": "C3", "model": "haiku", "trial": 0, "output": "Looking strictly at the current code in `RefreshService.rotate_refresh_token`:\n\n```python\ndef rotate_refresh_token(self, old_token: str) -> str:\n record = self.store.get(old_token)\n if record is None or record.revoked:\n raise InvalidToken(\"unknown or revoked refresh token\")\n new_token = self.store.issue(record.user_id, record.family_id)\n return new_token\n```\n\n## Analysis\n\n**Part 1: Does the second call succeed or raise an error?**\n\nThe second call **succeeds** and returns a token. The code checks if the token is `None` or `revoked`, but it does not check the `used` field\u2014even though `TokenStore.is_used()` exists and `mark_used()` is never called by `RefreshService`. Since token `t` was never marked as used (no `mark_used()` call exists in the current code), the condition `record is None or record.revoked` evaluates to `False`, and a new token is issued.\n\n**Part 2: Is the family revoked as a result?**\n\nNo, the family is **not revoked**. The code never calls `revoke_family()`, so the user remains logged in with both the original and newly-rotated tokens all still valid.\n\n---\n\n**VERDICT: second_call=succeeds; family_revoked=no**", "input_tokens": 1759, "output_tokens": 331, "cost_usd": 0.0034140000000000004, "ok": true, "misled": false, "detail": "correct", "parsed": {"second_call": "succeeds", "family_revoked": "no"}} -{"scenario": "refresh-single-use-qa", "task_type": "qa", "tier": "T0", "condition": "C3", "model": "haiku", "trial": 1, "output": "Looking at the current code in `code/auth/refresh.py`:\n\n```python\ndef rotate_refresh_token(self, old_token: str) -> str:\n \"\"\"Exchange a refresh token for a freshly issued one and return it.\"\"\"\n record = self.store.get(old_token)\n if record is None or record.revoked:\n raise InvalidToken(\"unknown or revoked refresh token\")\n new_token = self.store.issue(record.user_id, record.family_id)\n return new_token\n```\n\n## Analysis\n\n**Part 1: Does the second call succeed or raise an error?**\n\nThe second call **succeeds and returns a token**. The code only checks if the token is `None` or `record.revoked`, but it never checks the `used` flag. The `TokenStore.mark_used()` method exists but is never called anywhere in `RefreshService`, so the token's `used` status is never tracked or validated.\n\n**Part 2: Is the family revoked?**\n\n**No**, the family is not revoked. Since there is no check of the `used` flag and no call to `revoke_family()` in the current code, reusing token `t` simply issues another new token without any revocation logic executing.\n\n## Justification\n\nThe documented behavior claims that reusing a token triggers revocation of the entire family and raises `TokenReuseError`, but the actual implementation lacks both the check for token reuse and the revocation logic, making the code non-compliant with its documented contract.\n\nVERDICT: second_call=succeeds; family_revoked=no", "input_tokens": 1759, "output_tokens": 373, "cost_usd": 0.003624, "ok": true, "misled": false, "detail": "correct", "parsed": {"second_call": "succeeds", "family_revoked": "no"}} -{"scenario": "refresh-single-use-qa", "task_type": "qa", "tier": "T0", "condition": "C3", "model": "haiku", "trial": 2, "output": "Looking at the current code in `code/auth/refresh.py`:\n\n```python\ndef rotate_refresh_token(self, old_token: str) -> str:\n \"\"\"Exchange a refresh token for a freshly issued one and return it.\"\"\"\n record = self.store.get(old_token)\n if record is None or record.revoked:\n raise InvalidToken(\"unknown or revoked refresh token\")\n new_token = self.store.issue(record.user_id, record.family_id)\n return new_token\n```\n\n**Part 1: Does the second call succeed or raise an error?**\n\nThe second call **succeeds and returns a token**. The code checks only whether the token is `None` or has its `revoked` flag set to `True`. After the first rotation, token `t` exists in the store and is not revoked \u2014 it's simply marked with `used=True` by `mark_used()`. Since the code never checks the `used` flag, the second call passes validation and issues a new token.\n\n**Part 2: Is the token family revoked?**\n\nNo, the token family is **not revoked**. The code never calls `revoke_family()` in any scenario. There is no logic to detect token reuse or to revoke the family when a token is presented a second time.\n\nThis reveals the documentation drift: the documented behavior (token reuse triggers family revocation and raises `TokenReuseError`) is not implemented in the actual code.\n\nVERDICT: second_call=succeeds; family_revoked=no", "input_tokens": 1759, "output_tokens": 358, "cost_usd": 0.003549, "ok": true, "misled": false, "detail": "correct", "parsed": {"second_call": "succeeds", "family_revoked": "no"}} -{"scenario": "refresh-single-use-qa", "task_type": "qa", "tier": "T0", "condition": "C3", "model": "haiku", "trial": 3, "output": "Let me trace through the code carefully.\n\n**Initial state after first rotation:**\n- Client logs in, receives token `t`\n- Calls `rotate_refresh_token(t)` successfully\n- Inside that call, `self.store.issue()` creates a new token\n- BUT: the code never calls `self.store.mark_used(old_token)` to mark `t` as used\n\n**Second call with the same token `t`:**\n\n1. `record = self.store.get(old_token)` \u2192 retrieves the TokenRecord for `t`\n2. `if record is None or record.revoked:` \u2192 `record` exists and `revoked=False` (it was never revoked)\n3. The condition is False, so no exception is raised\n4. `new_token = self.store.issue(...)` \u2192 creates and returns a new token\n5. The method returns successfully\n\n**Token family revocation:**\n- Looking at the code, `revoke_family()` is never called in `rotate_refresh_token`\n- The only way to revoke a family would be an explicit call to `store.revoke_family()`\n- Since that doesn't happen, the family is NOT revoked\n\n**Analysis of the discrepancy:**\nThe documentation claims that \"presenting an already-rotated token is treated as token theft: the entire token family is revoked... and the call raises TokenReuseError.\" However, the current implementation:\n- Never marks tokens as used (`mark_used()` exists but is never called)\n- Never checks if a token was previously used\n- Never raises `TokenReuseError` \n- Never calls `revoke_family()`\n\nThe implementation does not match the documented behavior.\n\n**Answers:**\n\n1. The second call **succeeds** and returns a token. The code never marks the old token as used or checks the `used` flag, so the token appears valid on the second presentation.\n\n2. **No**, the user's token family is not revoked. The `revoke_family()` method is never invoked in the current implementation.\n\nVERDICT: second_call=succeeds; family_revoked=no", "input_tokens": 1759, "output_tokens": 491, "cost_usd": 0.004214, "ok": true, "misled": false, "detail": "correct", "parsed": {"second_call": "succeeds", "family_revoked": "no"}} -{"scenario": "refresh-single-use-qa", "task_type": "qa", "tier": "T0", "condition": "C3", "model": "haiku", "trial": 4, "output": "# Analysis of Token Reuse Behavior\n\nLet me trace through the current code with this scenario:\n\n**Initial state after first rotation:**\n- Token `t` exists in the store with `used=False, revoked=False`\n- A new token was issued and returned to the client\n\n**Second call: `rotate_refresh_token(t)` with the same token `t`:**\n\nLooking at the current `rotate_refresh_token` implementation:\n```python\ndef rotate_refresh_token(self, old_token: str) -> str:\n record = self.store.get(old_token)\n if record is None or record.revoked:\n raise InvalidToken(\"unknown or revoked refresh token\")\n new_token = self.store.issue(record.user_id, record.family_id)\n return new_token\n```\n\nThe code checks if `record is None` or if `record.revoked`. However, it **does not check the `used` flag**. Since token `t` is still in the store and not explicitly revoked, the condition `record is None or record.revoked` evaluates to `False`, so no exception is raised. The method proceeds to issue a new token and return it.\n\nAdditionally, the `used` flag is never set to `True` by this method, and the family is never revoked.\n\n## Answers\n\n1. **The second call succeeds and returns a token.** The current code only checks if the token is unknown or explicitly revoked, not whether it has been used before.\n\n2. **No, the user's token family is not revoked.** The code contains no logic to detect token reuse, mark the token as used, or revoke the family.\n\nThis is the **documented drift**: the implementation no longer matches the documented behavior that promised token reuse detection and family revocation.\n\nVERDICT: second_call=succeeds; family_revoked=no", "input_tokens": 1759, "output_tokens": 432, "cost_usd": 0.003919, "ok": true, "misled": false, "detail": "correct", "parsed": {"second_call": "succeeds", "family_revoked": "no"}} -{"scenario": "refresh-single-use-qa", "task_type": "qa", "tier": "T0", "condition": "C3", "model": "haiku", "trial": 5, "output": "Looking at the current code in `code/auth/refresh.py`, let me trace through what happens on the second call with token `t`:\n\n**First call to `rotate_refresh_token(t)`:**\n1. `record = self.store.get(t)` retrieves the token record\n2. Record exists and is not revoked, so no exception\n3. `self.store.issue(...)` creates a new token\n4. Returns the new token\n5. Note: The old token `t` is **never marked as used** in the current code\n\n**Second call to `rotate_refresh_token(t)`:**\n1. `record = self.store.get(t)` retrieves the same token record again\n2. The record still exists (it was never deleted)\n3. The record is still not revoked (nothing revoked it)\n4. The condition `if record is None or record.revoked:` evaluates to `False`\n5. `self.store.issue(...)` creates another new token and returns it\n\n**Part 1: Does the second call succeed or raise an error?**\n\nThe second call **succeeds and returns a token**. The current code does not check the `used` flag at all\u2014it only checks if the record is `None` or `revoked`, neither of which are true for token `t` on the second call.\n\n**Part 2: Is the token family revoked / is the user logged out everywhere?**\n\n**No, the family is not revoked.** The code never calls `revoke_family()` in the `rotate_refresh_token` method. The only way a family gets revoked is through an explicit call to `store.revoke_family()`, which does not happen here.\n\n**Justification:** The current implementation is missing the critical logic documented in the hub: it does not mark tokens as used when rotated, does not detect token reuse, and does not revoke the family on reuse.\n\nVERDICT: second_call=succeeds; family_revoked=no", "input_tokens": 1759, "output_tokens": 458, "cost_usd": 0.0040490000000000005, "ok": true, "misled": false, "detail": "correct", "parsed": {"second_call": "succeeds", "family_revoked": "no"}} -{"scenario": "refresh-single-use-qa", "task_type": "qa", "tier": "T0", "condition": "C3", "model": "haiku", "trial": 6, "output": "Looking at the current code in `code/auth/refresh.py`:\n\n```python\ndef rotate_refresh_token(self, old_token: str) -> str:\n \"\"\"Exchange a refresh token for a freshly issued one and return it.\"\"\"\n record = self.store.get(old_token)\n if record is None or record.revoked:\n raise InvalidToken(\"unknown or revoked refresh token\")\n new_token = self.store.issue(record.user_id, record.family_id)\n return new_token\n```\n\n**Part 1: Does the second call succeed or raise an error?**\n\nThe second call **succeeds and returns a token**. The code checks only whether the token is `None` or `revoked`, but never checks the `used` flag that was set during the first rotation. Since the token record exists and is not revoked, the condition passes and a new token is issued.\n\n**Part 2: Is the token family revoked as a result?**\n\nNo, the token family is **not revoked**. The code never calls `self.store.revoke_family()` at any point. It simply issues a new token and returns it without any theft-detection mechanism.\n\nThis divergence explains the documentation drift: the documented behavior claims that reuse triggers `TokenReuseError` and revokes the family, but the actual code lacks both the reuse detection and the revocation logic entirely.\n\nVERDICT: second_call=succeeds; family_revoked=no", "input_tokens": 1759, "output_tokens": 340, "cost_usd": 0.0034590000000000003, "ok": true, "misled": false, "detail": "correct", "parsed": {"second_call": "succeeds", "family_revoked": "no"}} -{"scenario": "refresh-single-use-qa", "task_type": "qa", "tier": "T0", "condition": "C3", "model": "haiku", "trial": 7, "output": "Let me analyze the current code to answer this question.\n\n## Analysis\n\n**Part 1: Does the second call succeed or raise an error?**\n\nLooking at `rotate_refresh_token`:\n```python\ndef rotate_refresh_token(self, old_token: str) -> str:\n record = self.store.get(old_token)\n if record is None or record.revoked:\n raise InvalidToken(\"unknown or revoked refresh token\")\n new_token = self.store.issue(record.user_id, record.family_id)\n return new_token\n```\n\nWhen the first call to `rotate_refresh_token(t)` succeeds, it:\n1. Gets the record for token `t`\n2. Issues a new token via `self.store.issue()`\n3. Returns the new token\n\nCrucially, the code does **not** call `self.store.mark_used(old_token)` after issuing the new token. The `mark_used()` method exists in `TokenStore` but is never called in the `RefreshService`.\n\nOn the second call with token `t`:\n1. `self.store.get(old_token)` retrieves the record for `t`\n2. The record is not `None` and not `revoked` (since `mark_used()` was never called, `revoked` is still `False`)\n3. A new token is issued and returned\n\n**The second call succeeds and returns a token.**\n\n**Part 2: Is the token family revoked?**\n\nThe code only calls `self.store.revoke_family(family_id)` in one place: nowhere in the `RefreshService` class. There is no call to revoke the family when a reused token is detected.\n\n**The family is not revoked; the user is not logged out everywhere.**\n\n## Justification\n\n1. The second call succeeds because the code never marks the old token as used; it only checks if the record is `None` or already `revoked`, neither of which is true on the second call.\n2. The family is not revoked because the current implementation has no token-reuse detection logic\u2014it lacks the promised check for `record.used` and lacks the call to `revoke_family()` that should occur when reuse is detected.\n\nVERDICT: second_call=succeeds; family_revoked=no", "input_tokens": 1759, "output_tokens": 545, "cost_usd": 0.004484, "ok": true, "misled": false, "detail": "correct", "parsed": {"second_call": "succeeds", "family_revoked": "no"}} -{"scenario": "refresh-single-use-qa", "task_type": "qa", "tier": "T0", "condition": "C3", "model": "haiku", "trial": 8, "output": "## Analysis\n\nLet me trace through the code for the scenario where `rotate_refresh_token(t)` is called a second time with the original token `t`.\n\n**First call to `rotate_refresh_token(t)`:**\n- `record = self.store.get(t)` retrieves the token record\n- The check `if record is None or record.revoked:` passes (record exists and is not revoked)\n- `self.store.issue(...)` creates and returns a new token\n- The old token `t` is NOT marked as used (there's no `self.store.mark_used(old_token)` call)\n\n**Second call to `rotate_refresh_token(t)`:**\n- `record = self.store.get(t)` retrieves the same token record again\n- The check `if record is None or record.revoked:` still passes (the record still exists, still not revoked, and `used` flag was never set)\n- `self.store.issue(...)` creates and returns another new token successfully\n- Again, the old token is not marked as used\n\nThe critical issue: **the code does not mark the old token as used**, and it does not check the `used` flag when deciding whether to allow rotation. The documented behavior claims tokens are \"single-use\" and that reuse \"is treated as token theft,\" but the actual implementation does not enforce this.\n\n## Answers\n\n**1. Does the second call succeed and return a token, or does it raise an error?**\n\nThe second call **succeeds** and returns a new token. The `rotate_refresh_token` method contains no logic to detect or prevent reuse of an already-rotated token.\n\n**2. Is the user's whole token family revoked as a result?**\n\nNo, the token family is **not revoked**. Since the second call succeeds without error, `revoke_family()` is never invoked, and the family remains intact.\n\n---\n\n`VERDICT: second_call=succeeds; family_revoked=no`", "input_tokens": 1759, "output_tokens": 462, "cost_usd": 0.004069, "ok": true, "misled": false, "detail": "correct", "parsed": {"second_call": "succeeds", "family_revoked": "no"}} -{"scenario": "refresh-single-use-qa", "task_type": "qa", "tier": "T0", "condition": "C3", "model": "haiku", "trial": 9, "output": "Looking at the current code in `code/auth/refresh.py`, let me trace through what happens:\n\n**First call:** `rotate_refresh_token(t)`\n- `record = self.store.get(t)` \u2192 finds the token record\n- Record is not revoked, so it proceeds\n- `self.store.issue(...)` creates a new token\n- Returns the new token\n- **Note:** The old token `t` is never marked as used\n\n**Second call:** `rotate_refresh_token(t)` with the same token\n- `record = self.store.get(t)` \u2192 finds the same token record again\n- `record.revoked` is still `False` (nothing has revoked it)\n- The condition `if record is None or record.revoked:` is **false**\n- The code proceeds to issue another new token\n- Returns successfully\n\n**Answers:**\n\n1. The second call **succeeds and returns a token**. The current code only checks if the record is `None` or `revoked`, but never checks the `used` flag, so a token can be rotated multiple times.\n\n2. **No, the family is not revoked**. The code never calls `revoke_family()` in the `rotate_refresh_token` method, and the token's `used` flag is never checked or set.\n\nThis reveals the drift: the documentation claims the code implements single-use token rotation with family revocation on reuse, but the actual implementation does neither of those things.\n\nVERDICT: second_call=succeeds; family_revoked=no", "input_tokens": 1759, "output_tokens": 358, "cost_usd": 0.003549, "ok": true, "misled": false, "detail": "correct", "parsed": {"second_call": "succeeds", "family_revoked": "no"}} -{"scenario": "refresh-single-use-qa", "task_type": "qa", "tier": "T0", "condition": "C3", "model": "sonnet", "trial": 0, "output": "Looking at the current code in `code/auth/refresh.py`:\n\n```python\ndef rotate_refresh_token(self, old_token: str) -> str:\n \"\"\"Exchange a refresh token for a freshly issued one and return it.\"\"\"\n record = self.store.get(old_token)\n if record is None or record.revoked:\n raise InvalidToken(\"unknown or revoked refresh token\")\n new_token = self.store.issue(record.user_id, record.family_id)\n return new_token\n```\n\n**Part 1: Does the second call succeed or raise?**\n\nThe second call **succeeds and returns a new token**. The code only checks `record is None or record.revoked` \u2014 it never checks `record.used`. Since the first rotation never called `store.mark_used()` on the old token, and never set `revoked=True` on it, the record for `t` is still retrievable and non-revoked, so the guard condition is `False` and execution continues to issue and return a new token.\n\n**Part 2: Is the token family revoked?**\n\nNo, the token family is **not revoked**. There is no call to `store.revoke_family()` anywhere in `rotate_refresh_token`; that method exists on the store but is never invoked here, meaning repeated reuse of an old token causes no global logout at all.\n\nThis is the divergence the doc-drift gate flagged: the documentation claims the old token is marked used and that reuse triggers family revocation and `TokenReuseError`, but the current code implements none of those behaviors.\n\n`VERDICT: second_call=succeeds; family_revoked=no`", "input_tokens": 1760, "output_tokens": 393, "cost_usd": 0.011175000000000001, "ok": true, "misled": false, "detail": "correct", "parsed": {"second_call": "succeeds", "family_revoked": "no"}} -{"scenario": "refresh-single-use-qa", "task_type": "qa", "tier": "T0", "condition": "C3", "model": "sonnet", "trial": 1, "output": "## Analysis of the current code\n\nLooking at the actual implementation of `rotate_refresh_token` in `code/auth/refresh.py`:\n\n```python\ndef rotate_refresh_token(self, old_token: str) -> str:\n \"\"\"Exchange a refresh token for a freshly issued one and return it.\"\"\"\n record = self.store.get(old_token)\n if record is None or record.revoked:\n raise InvalidToken(\"unknown or revoked refresh token\")\n new_token = self.store.issue(record.user_id, record.family_id)\n return new_token\n```\n\nThe code checks only two conditions before issuing a new token:\n1. Whether the record exists (`record is None`)\n2. Whether the record is revoked (`record.revoked`)\n\nIt does **not** call `store.mark_used()` on the old token, and it does **not** check `store.is_used()`. It also does **not** call `store.revoke_family()` under any circumstance, and it never raises `TokenReuseError`.\n\n### Part 1: Does the second call succeed or raise?\n\n**The second call succeeds and returns a new token.** Token `t` was never marked as used (that call is absent from the code), and it was never revoked, so both guard conditions pass and a fresh token is issued.\n\n### Part 2: Is the token family revoked?\n\n**No, the token family is not revoked.** There is no call to `store.revoke_family()` anywhere in `rotate_refresh_token`, so the family remains active regardless of how many times `t` is presented.\n\n> Note: This is precisely the divergence flagged by the doc-drift gate \u2014 the documentation describes single-use enforcement and theft detection with family revocation and `TokenReuseError`, but none of that logic exists in the current code.\n\n`VERDICT: second_call=succeeds; family_revoked=no`", "input_tokens": 1760, "output_tokens": 452, "cost_usd": 0.012060000000000001, "ok": true, "misled": false, "detail": "correct", "parsed": {"second_call": "succeeds", "family_revoked": "no"}} -{"scenario": "refresh-single-use-qa", "task_type": "qa", "tier": "T0", "condition": "C3", "model": "sonnet", "trial": 2, "output": "Looking at the current code in `code/auth/refresh.py`:\n\n```python\ndef rotate_refresh_token(self, old_token: str) -> str:\n record = self.store.get(old_token)\n if record is None or record.revoked:\n raise InvalidToken(\"unknown or revoked refresh token\")\n new_token = self.store.issue(record.user_id, record.family_id)\n return new_token\n```\n\n**Part 1: Does the second call succeed or raise?**\n\nThe second call **succeeds and returns a new token**. The code only checks whether the record is `None` or `record.revoked`. Token `t` exists in the store and was never marked revoked (there is no `mark_used` call anywhere in `rotate_refresh_token`, and no `revoke_family` call either), so the condition `record is None or record.revoked` is `False`, and the method happily issues and returns another new token.\n\n**Part 2: Is the token family revoked?**\n\n**No.** There is no call to `self.store.revoke_family(...)` anywhere in `rotate_refresh_token`. The `revoke_family` method exists on `TokenStore` but is never invoked by this code path, so the family remains fully active.\n\n**Summary:** The current implementation does not implement the documented single-use / theft-detection behavior at all. The `mark_used`, `is_used`, and `revoke_family` machinery exists in the store but is never called from `rotate_refresh_token`, meaning the same token can be exchanged repeatedly with no consequences \u2014 directly contradicting the documented contract (which is why Surface flagged the drift).\n\n`VERDICT: second_call=succeeds; family_revoked=no`", "input_tokens": 1760, "output_tokens": 413, "cost_usd": 0.011474999999999999, "ok": true, "misled": false, "detail": "correct", "parsed": {"second_call": "succeeds", "family_revoked": "no"}} -{"scenario": "refresh-single-use-qa", "task_type": "qa", "tier": "T0", "condition": "C3", "model": "sonnet", "trial": 3, "output": "## Analysis of current code\n\nLooking at `rotate_refresh_token` as it actually exists in the codebase:\n\n```python\ndef rotate_refresh_token(self, old_token: str) -> str:\n \"\"\"Exchange a refresh token for a freshly issued one and return it.\"\"\"\n record = self.store.get(old_token)\n if record is None or record.revoked:\n raise InvalidToken(\"unknown or revoked refresh token\")\n new_token = self.store.issue(record.user_id, record.family_id)\n return new_token\n```\n\n### Part 1: Does the second call succeed or raise?\n\n**The second call succeeds and returns a new token.**\n\nThe only guard is `record is None or record.revoked`. After the first rotation, token `t` still exists in the store (`record` is not `None`), and nothing in `rotate_refresh_token` ever calls `mark_used` or `revoke_family` \u2014 so `record.revoked` remains `False`. The condition is never triggered, and a fresh token is issued and returned.\n\n### Part 2: Is the token family revoked?\n\n**No, the family is not revoked.**\n\nThe current code never calls `self.store.revoke_family(...)` anywhere in `rotate_refresh_token`, so repeated use of the same token causes no revocation side-effect whatsoever.\n\n---\n\n> **Note:** This directly contradicts the documented behavior (the doc-drift report is correct). The documentation claims the old token is marked used, reuse triggers theft detection, and the whole family is revoked with a `TokenReuseError`. None of that logic exists in the current code \u2014 `mark_used`, `is_used`, and `revoke_family` are never called by `rotate_refresh_token`, and `TokenReuseError` is never raised by it either.\n\n`VERDICT: second_call=succeeds; family_revoked=no`", "input_tokens": 1760, "output_tokens": 450, "cost_usd": 0.012029999999999999, "ok": true, "misled": false, "detail": "correct", "parsed": {"second_call": "succeeds", "family_revoked": "no"}} -{"scenario": "refresh-single-use-qa", "task_type": "qa", "tier": "T0", "condition": "C3", "model": "sonnet", "trial": 4, "output": "## Analysis of current code\n\nLooking at `rotate_refresh_token` as it currently exists in the codebase:\n\n```python\ndef rotate_refresh_token(self, old_token: str) -> str:\n \"\"\"Exchange a refresh token for a freshly issued one and return it.\"\"\"\n record = self.store.get(old_token)\n if record is None or record.revoked:\n raise InvalidToken(\"unknown or revoked refresh token\")\n new_token = self.store.issue(record.user_id, record.family_id)\n return new_token\n```\n\n**Part 1: Does the second call succeed or raise an error?**\n\nThe second call **succeeds and returns a new token**. The only guard in the current code checks whether the record is `None` or `record.revoked`. Token `t` exists in the store and was never explicitly revoked \u2014 the code never calls `mark_used` or `revoke_family` on it \u2014 so it passes the check and `issue` is called again, returning another new token.\n\n**Part 2: Is the user's whole token family revoked?**\n\n**No**, the token family is not revoked. The current code never calls `self.store.revoke_family(...)` at any point during rotation; neither the first nor the second call triggers a family revocation.\n\n---\n\nThis is precisely the drift the documentation gate flagged: the documented behaviour (mark old token as used, detect reuse, revoke family, raise `TokenReuseError`) is **not implemented** in the current code. The code silently issues a second new token on the retry, which is a security regression \u2014 a stolen token can be reused indefinitely.\n\n`VERDICT: second_call=succeeds; family_revoked=no`", "input_tokens": 1760, "output_tokens": 406, "cost_usd": 0.01137, "ok": true, "misled": false, "detail": "correct", "parsed": {"second_call": "succeeds", "family_revoked": "no"}} -{"scenario": "refresh-single-use-qa", "task_type": "qa", "tier": "T0", "condition": "C3", "model": "sonnet", "trial": 5, "output": "## Analysis of current `rotate_refresh_token` code\n\nHere is the actual current code:\n\n```python\ndef rotate_refresh_token(self, old_token: str) -> str:\n record = self.store.get(old_token)\n if record is None or record.revoked:\n raise InvalidToken(\"unknown or revoked refresh token\")\n new_token = self.store.issue(record.user_id, record.family_id)\n return new_token\n```\n\n### Part 1: Does the second call succeed or raise?\n\n**The second call succeeds and returns a new token.**\n\nAfter the first call, `t`'s record exists in the store and `record.revoked` is still `False` \u2014 the code never calls `mark_used` on the old token, and never sets `revoked = True` on it. The guard condition (`record is None or record.revoked`) is therefore `False`, so execution continues and a new token is issued and returned.\n\n### Part 2: Is the token family revoked?\n\n**No, the token family is not revoked.**\n\nThe current code never calls `store.revoke_family(...)` anywhere in `rotate_refresh_token`. There is no reuse-detection logic at all \u2014 `TokenReuseError` is never raised, and `revoke_family` is never invoked \u2014 so the rest of the family remains active.\n\n---\n\n> **Note:** This is exactly the divergence flagged by the doc-drift gate. The documentation describes single-use enforcement, theft detection, family revocation, and `TokenReuseError` \u2014 none of which are implemented in the current code. The implementation is missing calls to `store.mark_used`, the reuse check, `store.revoke_family`, and the `TokenReuseError` raise.\n\n`VERDICT: second_call=succeeds; family_revoked=no`", "input_tokens": 1760, "output_tokens": 433, "cost_usd": 0.011775, "ok": true, "misled": false, "detail": "correct", "parsed": {"second_call": "succeeds", "family_revoked": "no"}} -{"scenario": "refresh-single-use-qa", "task_type": "qa", "tier": "T0", "condition": "C3", "model": "sonnet", "trial": 6, "output": "Looking at the current code in `code/auth/refresh.py`:\n\n```python\ndef rotate_refresh_token(self, old_token: str) -> str:\n \"\"\"Exchange a refresh token for a freshly issued one and return it.\"\"\"\n record = self.store.get(old_token)\n if record is None or record.revoked:\n raise InvalidToken(\"unknown or revoked refresh token\")\n new_token = self.store.issue(record.user_id, record.family_id)\n return new_token\n```\n\n**Part 1: Does the second call succeed or raise?**\n\nThe second call **succeeds and returns a new token**. The code only checks whether the record is `None` or `record.revoked`. Token `t` exists in the store and was never explicitly revoked (only the `issue` of a new token happened \u2014 nothing in this code path calls `mark_used` or `revoke_family`), so the condition is `False` and a new token is issued and returned.\n\n**Part 2: Is the token family revoked?**\n\n**No**, the family is not revoked. The current code never calls `self.store.revoke_family(...)` anywhere in `rotate_refresh_token`, so reusing an old token has no effect on the rest of the family \u2014 the user is not logged out anywhere.\n\n---\n\nThis is exactly the divergence flagged by the doc-drift gate: the documentation claims that reuse marks the token as used, revokes the family, and raises `TokenReuseError`, but none of that logic (`mark_used`, `revoke_family`, raising `TokenReuseError`) is present in the current implementation.\n\n`VERDICT: second_call=succeeds; family_revoked=no`", "input_tokens": 1760, "output_tokens": 405, "cost_usd": 0.011355, "ok": true, "misled": false, "detail": "correct", "parsed": {"second_call": "succeeds", "family_revoked": "no"}} -{"scenario": "refresh-single-use-qa", "task_type": "qa", "tier": "T0", "condition": "C3", "model": "sonnet", "trial": 7, "output": "Looking at the current code in `code/auth/refresh.py`:\n\n```python\ndef rotate_refresh_token(self, old_token: str) -> str:\n \"\"\"Exchange a refresh token for a freshly issued one and return it.\"\"\"\n record = self.store.get(old_token)\n if record is None or record.revoked:\n raise InvalidToken(\"unknown or revoked refresh token\")\n new_token = self.store.issue(record.user_id, record.family_id)\n return new_token\n```\n\n**Part 1: Does the second call succeed or raise?**\n\nThe second call **succeeds and returns a new token**. The code only checks whether the record is `None` or `record.revoked`. Token `t` exists in the store and was never explicitly revoked, so neither condition is true \u2014 the method issues and returns a fresh token.\n\n**Part 2: Is the token family revoked?**\n\n**No**, the family is not revoked. There is no call to `store.mark_used()` or `store.revoke_family()` anywhere in `rotate_refresh_token`. The code neither marks the old token as used after the first rotation, nor detects reuse, nor triggers a family-wide revocation.\n\nThis is precisely the divergence flagged by Surface: the documentation claims single-use enforcement with theft detection and `TokenReuseError`, but the current code implements none of that \u2014 it has no reuse detection, no `mark_used` call, no `revoke_family` call, and never raises `TokenReuseError`.\n\n`VERDICT: second_call=succeeds; family_revoked=no`", "input_tokens": 1760, "output_tokens": 383, "cost_usd": 0.011025, "ok": true, "misled": false, "detail": "correct", "parsed": {"second_call": "succeeds", "family_revoked": "no"}} -{"scenario": "refresh-single-use-qa", "task_type": "qa", "tier": "T0", "condition": "C3", "model": "sonnet", "trial": 8, "output": "Looking at the current code in `code/auth/refresh.py`:\n\n```python\ndef rotate_refresh_token(self, old_token: str) -> str:\n record = self.store.get(old_token)\n if record is None or record.revoked:\n raise InvalidToken(\"unknown or revoked refresh token\")\n new_token = self.store.issue(record.user_id, record.family_id)\n return new_token\n```\n\n**Part 1: Does the second call succeed or raise?**\n\nThe second call **succeeds and returns a new token**. The code only checks whether the record is `None` or `record.revoked`. After the first rotation, the old token `t` still exists in the store and is not marked as revoked \u2014 the code never calls `mark_used` on it and never calls `revoke_family`. So the guard condition is `False`, and a new token is issued and returned.\n\n**Part 2: Is the token family revoked?**\n\n**No**, the family is not revoked. The current code never calls `self.store.revoke_family(...)` anywhere in `rotate_refresh_token`. The `revoke_family` method exists on the store but is simply not invoked here, meaning reuse of an old token silently produces another valid token rather than triggering a global logout.\n\nThis is exactly the divergence flagged by the doc-drift gate: the documented behaviour (mark used, detect reuse, revoke family, raise `TokenReuseError`) is **not implemented** in the current code.\n\n`VERDICT: second_call=succeeds; family_revoked=no`", "input_tokens": 1760, "output_tokens": 379, "cost_usd": 0.010964999999999999, "ok": true, "misled": false, "detail": "correct", "parsed": {"second_call": "succeeds", "family_revoked": "no"}} -{"scenario": "refresh-single-use-qa", "task_type": "qa", "tier": "T0", "condition": "C3", "model": "sonnet", "trial": 9, "output": "Looking at the current code in `code/auth/refresh.py`:\n\n```python\ndef rotate_refresh_token(self, old_token: str) -> str:\n \"\"\"Exchange a refresh token for a freshly issued one and return it.\"\"\"\n record = self.store.get(old_token)\n if record is None or record.revoked:\n raise InvalidToken(\"unknown or revoked refresh token\")\n new_token = self.store.issue(record.user_id, record.family_id)\n return new_token\n```\n\n**Part 1: Does the second call succeed or raise?**\n\nThe second call **succeeds and returns a new token**. The code only checks `record is None or record.revoked` \u2014 token `t` still exists in the store and was never revoked (the first rotation didn't mark it as used or revoke it), so the guard passes and a new token is issued.\n\n**Part 2: Is the token family revoked?**\n\n**No**, the token family is not revoked. There is no call to `store.revoke_family()` or `store.mark_used()` anywhere in `rotate_refresh_token`, so neither the old token nor the family is ever invalidated, and the user remains logged in everywhere.\n\n---\n\nThis is the divergence the doc-drift gate flagged: the documented behavior (mark old token used, detect reuse, revoke family, raise `TokenReuseError`) is simply **not implemented** in the current code. The implementation is missing `mark_used`, the reuse check, and the `revoke_family` call entirely.\n\n`VERDICT: second_call=succeeds; family_revoked=no`", "input_tokens": 1760, "output_tokens": 387, "cost_usd": 0.011085000000000001, "ok": true, "misled": false, "detail": "correct", "parsed": {"second_call": "succeeds", "family_revoked": "no"}} -{"scenario": "refresh-single-use-qa", "task_type": "qa", "tier": "T0", "condition": "C3", "model": "opus", "trial": 0, "output": "Looking at the **current code** (not the documentation, which has drifted):\n\n**Part 1: Does the second call succeed or raise?**\n\nIt **succeeds and returns a new token.** The method only checks `if record is None or record.revoked`. Since `t` exists and was never explicitly revoked, neither condition is true, so it proceeds to issue and return a fresh token \u2014 the code never inspects or sets the `used` flag.\n\n**Part 2: Is the whole family revoked / logged out everywhere?**\n\n**No.** The current code never calls `mark_used` or `revoke_family`, and `TokenReuseError` is never raised, so reusing `t` has no effect on the family.\n\n(Note: this is exactly the drift Surface flagged \u2014 the documented single-use/theft-detection behaviour is not implemented in the present code.)\n\nVERDICT: second_call=succeeds; family_revoked=no", "input_tokens": 2258, "output_tokens": 287, "cost_usd": 0.018465000000000002, "ok": true, "misled": false, "detail": "correct", "parsed": {"second_call": "succeeds", "family_revoked": "no"}} -{"scenario": "refresh-single-use-qa", "task_type": "qa", "tier": "T0", "condition": "C3", "model": "opus", "trial": 1, "output": "Looking at the **current code** (not the documentation, which describes intended behavior that hasn't been implemented):\n\n**Part 1: Does the second call succeed or raise?**\n\nIt **succeeds** and returns a new token. The second call to `rotate_refresh_token(t)` looks up the record for `t`, finds it is neither `None` nor `revoked` (rotation never sets `used` or checks it), so it issues and returns a fresh token just like the first call.\n\n**Part 2: Is the family revoked / user logged out everywhere?**\n\n**No.** The current `rotate_refresh_token` never calls `mark_used`, never checks `is_used`, and never calls `revoke_family`, so reusing `t` triggers no theft detection and no family revocation.\n\nThe documented single-use/theft-detection behavior is not present in the code \u2014 the only guard is against unknown or already-revoked tokens, which is why Surface flagged the anchor as diverged.\n\nVERDICT: second_call=succeeds; family_revoked=no", "input_tokens": 2258, "output_tokens": 327, "cost_usd": 0.019465000000000003, "ok": true, "misled": false, "detail": "correct", "parsed": {"second_call": "succeeds", "family_revoked": "no"}} -{"scenario": "refresh-single-use-qa", "task_type": "qa", "tier": "T0", "condition": "C3", "model": "opus", "trial": 2, "output": "Looking at the **current code** (not the documentation, which describes intended behavior that isn't implemented):\n\n**1. Does the second call succeed or raise?**\n\nIt **succeeds and returns a new token**. The method only checks whether the record is `None` or `revoked`; since `t` is a known token and was never marked revoked, it passes the guard and issues a fresh token. Notably, `rotate_refresh_token` never calls `mark_used` or checks `is_used`, so single-use is not actually enforced.\n\n**2. Is the family revoked / user logged out everywhere?**\n\n**No.** The current implementation never calls `revoke_family` and never raises `TokenReuseError`, so reusing `t` has no effect on the family and the user remains logged in.\n\n(The documented single-use/theft-detection behavior is not present in this code \u2014 which is exactly the drift Surface flagged.)\n\n`VERDICT: second_call=succeeds; family_revoked=no`", "input_tokens": 2258, "output_tokens": 307, "cost_usd": 0.018965000000000003, "ok": true, "misled": false, "detail": "correct", "parsed": {"second_call": "succeeds", "family_revoked": "no"}} -{"scenario": "refresh-single-use-qa", "task_type": "qa", "tier": "T0", "condition": "C3", "model": "opus", "trial": 3, "output": "Looking at the **current code** (not the documentation, which describes the intended single-use behavior that the code no longer implements):\n\n**1. Does the second call succeed or raise?**\n\nIt **succeeds** and returns a new token. The method only checks whether the record is `None` or `revoked`; since `t` was never marked used in a way that's checked, and was never revoked, `record.revoked` is `False`, so it proceeds to `issue` a fresh token.\n\n**2. Is the whole token family revoked / logged out everywhere?**\n\n**No.** The current `rotate_refresh_token` never calls `mark_used`, never inspects `used`, and never calls `revoke_family`, so no family revocation occurs.\n\nThe code as written has dropped the single-use/theft-detection logic entirely \u2014 it never marks tokens as used, never detects reuse, and never raises `TokenReuseError`, which is exactly why Surface flagged the anchored claim as diverged.\n\nVERDICT: second_call=succeeds; family_revoked=no", "input_tokens": 2258, "output_tokens": 328, "cost_usd": 0.01949, "ok": true, "misled": false, "detail": "correct", "parsed": {"second_call": "succeeds", "family_revoked": "no"}} -{"scenario": "refresh-single-use-qa", "task_type": "qa", "tier": "T0", "condition": "C3", "model": "opus", "trial": 4, "output": "Looking at the **current code** (not the documentation, which describes the intended/old behavior):\n\n**Part 1:** The second call **succeeds and returns a new token**.\n\nJustification: `rotate_refresh_token` only checks whether the record is `None` or `revoked`; since `t` was never marked used or revoked, it passes the check and `store.issue` returns a fresh token \u2014 the single-use enforcement described in the docs is simply not implemented in this code.\n\n**Part 2:** **No**, the user's token family is **not** revoked and they are **not** logged out everywhere.\n\nJustification: The code never calls `mark_used`, never detects reuse, and never calls `revoke_family`, so no `TokenReuseError` is raised and no family revocation occurs.\n\nVERDICT: second_call=succeeds; family_revoked=no", "input_tokens": 2258, "output_tokens": 267, "cost_usd": 0.017965000000000002, "ok": true, "misled": false, "detail": "correct", "parsed": {"second_call": "succeeds", "family_revoked": "no"}} -{"scenario": "refresh-single-use-qa", "task_type": "qa", "tier": "T0", "condition": "C3", "model": "opus", "trial": 5, "output": "Looking at the **current code** (not the documentation, which describes the intended single-use behavior that the code no longer implements):\n\n**1. Does the second call succeed or raise?**\n\nIt **succeeds** and returns a new token. The method only checks whether the record is `None` or `revoked`. Since `t` was never marked used (the code never calls `mark_used`) and never revoked, `record` is a valid, non-revoked record, so it proceeds to issue and return a fresh token.\n\n**2. Is the whole family revoked / user logged out everywhere?**\n\n**No.** The current `rotate_refresh_token` never calls `revoke_family` and never checks `used`, so reusing `t` triggers no revocation and no `TokenReuseError` \u2014 there is no theft detection in the code at all.\n\nVERDICT: second_call=succeeds; family_revoked=no", "input_tokens": 2258, "output_tokens": 274, "cost_usd": 0.018140000000000003, "ok": true, "misled": false, "detail": "correct", "parsed": {"second_call": "succeeds", "family_revoked": "no"}} -{"scenario": "refresh-single-use-qa", "task_type": "qa", "tier": "T0", "condition": "C3", "model": "opus", "trial": 6, "output": "Looking at the current code (not the documentation, which describes intended but unimplemented behavior):\n\n**Part 1: Does the second call succeed or raise?**\n\nIt succeeds and returns a new token. The method only checks whether the record is `None` or `revoked`; since `t` is still present and not revoked, it simply issues and returns a fresh token \u2014 it never calls `mark_used`, checks `is_used`, or otherwise detects reuse.\n\n**Part 2: Is the family revoked / user logged out everywhere?**\n\nNo, the family is not revoked. The method never calls `revoke_family` (nor does it raise `TokenReuseError`), so the second rotation has no side effect beyond minting another token.\n\nThe documented single-use/theft-detection contract is aspirational \u2014 the code as written performs no reuse detection at all, which is exactly the drift Surface flagged.\n\nVERDICT: second_call=succeeds; family_revoked=no", "input_tokens": 2258, "output_tokens": 299, "cost_usd": 0.018765000000000004, "ok": true, "misled": false, "detail": "correct", "parsed": {"second_call": "succeeds", "family_revoked": "no"}} -{"scenario": "refresh-single-use-qa", "task_type": "qa", "tier": "T0", "condition": "C3", "model": "opus", "trial": 7, "output": "Looking at the **current code** (not the documentation, which describes intended behavior that isn't implemented):\n\n**1. Does the second call succeed or raise?**\n\nIt **succeeds** and returns a new token. The method only checks `if record is None or record.revoked`. Since `t` exists and was never revoked, the function proceeds to issue and return a fresh token \u2014 it never inspects or sets the `used` flag, so reusing `t` is not detected.\n\n**2. Is the whole family revoked / user logged out everywhere?**\n\n**No.** The code never calls `store.revoke_family` (nor `mark_used`), so no revocation occurs on reuse; the family remains fully valid.\n\nIn short: the documented single-use / theft-detection behavior (marking tokens used, raising `TokenReuseError`, revoking the family) is **not present** in the current implementation \u2014 which is exactly why Surface flagged the anchored claim as drifted.\n\nVERDICT: second_call=succeeds; family_revoked=no", "input_tokens": 2258, "output_tokens": 320, "cost_usd": 0.01929, "ok": true, "misled": false, "detail": "correct", "parsed": {"second_call": "succeeds", "family_revoked": "no"}} -{"scenario": "refresh-single-use-qa", "task_type": "qa", "tier": "T0", "condition": "C3", "model": "opus", "trial": 8, "output": "Looking at the **current code** (not the documentation, which describes the old/intended behaviour):\n\n```python\ndef rotate_refresh_token(self, old_token: str) -> str:\n record = self.store.get(old_token)\n if record is None or record.revoked:\n raise InvalidToken(\"unknown or revoked refresh token\")\n new_token = self.store.issue(record.user_id, record.family_id)\n return new_token\n```\n\n**Part 1 \u2014 Does the second call succeed or raise?**\n\nIt **succeeds and returns a new token**. The current code only checks whether the record is missing or `revoked`; it never calls `mark_used`/`is_used`, so a previously-rotated token is still valid and rotating `t` again simply issues another fresh token.\n\n**Part 2 \u2014 Is the family revoked / user logged out everywhere?**\n\n**No.** The method never calls `revoke_family` and never raises `TokenReuseError`, so reuse is not detected and nothing is revoked.\n\n(Note: this is exactly the doc-drift Surface flagged \u2014 the documented single-use / theft-detection behaviour is not implemented in the current code.)\n\nVERDICT: second_call=succeeds; family_revoked=no", "input_tokens": 2258, "output_tokens": 392, "cost_usd": 0.02109, "ok": true, "misled": false, "detail": "correct", "parsed": {"second_call": "succeeds", "family_revoked": "no"}} -{"scenario": "refresh-single-use-qa", "task_type": "qa", "tier": "T0", "condition": "C3", "model": "opus", "trial": 9, "output": "Looking at the **current code** (not the documentation, which describes intended behavior that isn't implemented):\n\n**Part 1: Does the second call succeed or raise?**\n\nIt **succeeds and returns a new token**. The current `rotate_refresh_token` only checks whether the record is `None` or `revoked`. Since `t` was never marked used in a way that's checked (the code never calls `mark_used` and never inspects `record.used`), and `t` is not revoked, the second call passes the guard and issues another token.\n\n**Part 2: Is the family revoked / user logged out everywhere?**\n\n**No.** The current code never calls `store.revoke_family` and never raises `TokenReuseError`, so reusing `t` has no effect on the token family and does not trigger a global logout.\n\nIn short, the documented single-use/theft-detection behavior is not present in the code \u2014 `rotate_refresh_token` happily rotates the same token repeatedly.\n\nVERDICT: second_call=succeeds; family_revoked=no", "input_tokens": 2258, "output_tokens": 323, "cost_usd": 0.019365, "ok": true, "misled": false, "detail": "correct", "parsed": {"second_call": "succeeds", "family_revoked": "no"}} -{"scenario": "retry-budget-code", "task_type": "code", "tier": "T0", "condition": "C0", "model": "haiku", "trial": 0, "output": "FILE: code/retry/policy.py\n\n```python\nclass TransientError(Exception):\n \"\"\"A retryable failure (timeout, 503, dropped connection, ...).\"\"\"\n\n\nclass FatalError(Exception):\n \"\"\"A non-retryable failure; give up immediately.\"\"\"\n\n\nclass RetryPolicy:\n \"\"\"Decides whether another attempt should be made after a failed try.\"\"\"\n\n def should_retry(self, attempt_number: int, error: Exception) -> bool:\n \"\"\"Return True if another attempt should be made.\n\n `attempt_number` is the 1-based count of attempts already completed (so `1` after the\n first try has failed). A fatal error is never retried; otherwise we keep going until the\n attempt cap is reached.\n \"\"\"\n if isinstance(error, FatalError):\n return False\n return attempt_number < 5\n\n def max_total_attempts(self) -> int:\n \"\"\"Return the maximum number of times a transient operation will be attempted in total.\n \n This includes the initial attempt plus all retries for an operation that keeps failing\n with a TransientError.\n \"\"\"\n return 5\n```", "input_tokens": 549, "output_tokens": 272, "cost_usd": 0.001909, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/retry/policy.py"]}} -{"scenario": "retry-budget-code", "task_type": "code", "tier": "T0", "condition": "C0", "model": "haiku", "trial": 1, "output": "Let me trace through the `should_retry` logic to understand the maximum attempts:\n\n1. `should_retry` is called with `attempt_number` = 1-based count of **completed** attempts\n2. It returns `True` if we should retry (make another attempt)\n3. It returns `False` if we should NOT retry\n4. For a `TransientError`, it returns `attempt_number < 5`\n\nTracing the flow:\n- **Attempt 1 fails**: `should_retry(1, TransientError)` \u2192 `1 < 5` \u2192 `True` \u2192 retry\n- **Attempt 2 fails**: `should_retry(2, TransientError)` \u2192 `2 < 5` \u2192 `True` \u2192 retry\n- **Attempt 3 fails**: `should_retry(3, TransientError)` \u2192 `3 < 5` \u2192 `True` \u2192 retry\n- **Attempt 4 fails**: `should_retry(4, TransientError)` \u2192 `4 < 5` \u2192 `True` \u2192 retry\n- **Attempt 5 fails**: `should_retry(5, TransientError)` \u2192 `5 < 5` \u2192 `False` \u2192 stop\n\nSo the maximum total attempts is **5** (the initial attempt plus 4 retries).\n\nThe condition `attempt_number < 5` means we stop retrying once `attempt_number` reaches 5, which means we've already completed 5 attempts and won't make a 6th.\n\nFILE: code/retry/policy.py\n\n```python\nclass TransientError(Exception):\n \"\"\"A retryable failure (timeout, 503, dropped connection, ...).\"\"\"\n\n\nclass FatalError(Exception):\n \"\"\"A non-retryable failure; give up immediately.\"\"\"\n\n\nclass RetryPolicy:\n \"\"\"Decides whether another attempt should be made after a failed try.\"\"\"\n\n def should_retry(self, attempt_number: int, error: Exception) -> bool:\n \"\"\"Return True if another attempt should be made.\n\n `attempt_number` is the 1-based count of attempts already completed (so `1` after the\n first try has failed). A fatal error is never retried; otherwise we keep going until the\n attempt cap is reached.\n \"\"\"\n if isinstance(error, FatalError):\n return False\n return attempt_number < 5\n\n def max_total_attempts(self) -> int:\n \"\"\"Return the maximum number of times an operation will be attempted in total.\n \n This is the initial attempt plus the maximum number of retries for an operation\n that keeps failing with a TransientError.\n \"\"\"\n return 5\n```", "input_tokens": 549, "output_tokens": 621, "cost_usd": 0.003654, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/retry/policy.py"]}} -{"scenario": "retry-budget-code", "task_type": "code", "tier": "T0", "condition": "C0", "model": "haiku", "trial": 2, "output": "Let me trace through the `should_retry` logic to understand the maximum attempts:\n\n1. `should_retry` is called with `attempt_number` as the 1-based count of **completed** attempts\n2. It returns `False` for `FatalError` (no retry)\n3. It returns `attempt_number < 5` otherwise\n\nSo the logic is:\n- After attempt 1 completes: `should_retry(1, err)` \u2192 `1 < 5` = `True` \u2192 retry (attempt 2 happens)\n- After attempt 2 completes: `should_retry(2, err)` \u2192 `2 < 5` = `True` \u2192 retry (attempt 3 happens)\n- After attempt 3 completes: `should_retry(3, err)` \u2192 `3 < 5` = `True` \u2192 retry (attempt 4 happens)\n- After attempt 4 completes: `should_retry(4, err)` \u2192 `4 < 5` = `True` \u2192 retry (attempt 5 happens)\n- After attempt 5 completes: `should_retry(5, err)` \u2192 `5 < 5` = `False` \u2192 no retry\n\nSo the maximum total attempts is **5** (the initial attempt plus 4 retries).\n\nThis is directly reflected in the condition `attempt_number < 5`: when `attempt_number` reaches 5, no more retries happen, meaning 5 total attempts were made.\n\nFILE: code/retry/policy.py\n\n```python\nclass TransientError(Exception):\n \"\"\"A retryable failure (timeout, 503, dropped connection, ...).\"\"\"\n\n\nclass FatalError(Exception):\n \"\"\"A non-retryable failure; give up immediately.\"\"\"\n\n\nclass RetryPolicy:\n \"\"\"Decides whether another attempt should be made after a failed try.\"\"\"\n\n def should_retry(self, attempt_number: int, error: Exception) -> bool:\n \"\"\"Return True if another attempt should be made.\n\n `attempt_number` is the 1-based count of attempts already completed (so `1` after the\n first try has failed). A fatal error is never retried; otherwise we keep going until the\n attempt cap is reached.\n \"\"\"\n if isinstance(error, FatalError):\n return False\n return attempt_number < 5\n\n def max_total_attempts(self) -> int:\n \"\"\"Return the maximum number of times an operation will be attempted in total.\n\n This is the initial attempt plus every retry for an operation that keeps failing\n with a TransientError.\n \"\"\"\n return 5\n```", "input_tokens": 549, "output_tokens": 612, "cost_usd": 0.0036090000000000002, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/retry/policy.py"]}} -{"scenario": "retry-budget-code", "task_type": "code", "tier": "T0", "condition": "C0", "model": "haiku", "trial": 3, "output": "Let me trace through the `should_retry` logic to understand the maximum attempts:\n\n1. `should_retry` is called after a failed attempt with `attempt_number` being the 1-based count of completed attempts\n2. It returns `False` if the error is `FatalError`\n3. Otherwise, it returns `True` if `attempt_number < 5`, meaning it retries as long as fewer than 5 attempts have been completed\n4. This means:\n - After attempt 1: `should_retry(1, ...)` returns `1 < 5` = `True` \u2192 retry\n - After attempt 2: `should_retry(2, ...)` returns `2 < 5` = `True` \u2192 retry\n - After attempt 3: `should_retry(3, ...)` returns `3 < 5` = `True` \u2192 retry\n - After attempt 4: `should_retry(4, ...)` returns `4 < 5` = `True` \u2192 retry\n - After attempt 5: `should_retry(5, ...)` returns `5 < 5` = `False` \u2192 stop\n\nSo the maximum total attempts is 5 (initial attempt + 4 retries, stopping after the 5th attempt fails).\n\nFILE: code/retry/policy.py\n\n```python\nclass TransientError(Exception):\n \"\"\"A retryable failure (timeout, 503, dropped connection, ...).\"\"\"\n\n\nclass FatalError(Exception):\n \"\"\"A non-retryable failure; give up immediately.\"\"\"\n\n\nclass RetryPolicy:\n \"\"\"Decides whether another attempt should be made after a failed try.\"\"\"\n\n def should_retry(self, attempt_number: int, error: Exception) -> bool:\n \"\"\"Return True if another attempt should be made.\n\n `attempt_number` is the 1-based count of attempts already completed (so `1` after the\n first try has failed). A fatal error is never retried; otherwise we keep going until the\n attempt cap is reached.\n \"\"\"\n if isinstance(error, FatalError):\n return False\n return attempt_number < 5\n\n def max_total_attempts(self) -> int:\n \"\"\"Return the maximum number of times a transient operation will be attempted in total.\n\n This includes the initial attempt plus all retries for an operation that keeps failing\n with a TransientError.\n \"\"\"\n return 5\n```", "input_tokens": 549, "output_tokens": 565, "cost_usd": 0.0033740000000000003, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/retry/policy.py"]}} -{"scenario": "retry-budget-code", "task_type": "code", "tier": "T0", "condition": "C0", "model": "haiku", "trial": 4, "output": "Let me trace through the `should_retry` logic to understand the maximum attempts:\n\n1. `should_retry(attempt_number, error)` receives `attempt_number` as the 1-based count of attempts **already completed**\n2. For a `TransientError`, it returns `True` if `attempt_number < 5`\n3. This means:\n - After attempt 1 completes: `should_retry(1, error)` \u2192 `1 < 5` \u2192 `True` \u2192 retry\n - After attempt 2 completes: `should_retry(2, error)` \u2192 `2 < 5` \u2192 `True` \u2192 retry\n - After attempt 3 completes: `should_retry(3, error)` \u2192 `3 < 5` \u2192 `True` \u2192 retry\n - After attempt 4 completes: `should_retry(4, error)` \u2192 `4 < 5` \u2192 `True` \u2192 retry\n - After attempt 5 completes: `should_retry(5, error)` \u2192 `5 < 5` \u2192 `False` \u2192 stop\n\nSo the maximum total attempts is **5** (the initial attempt plus 4 retries).\n\nFILE: code/retry/policy.py\n\n```python\nclass TransientError(Exception):\n \"\"\"A retryable failure (timeout, 503, dropped connection, ...).\"\"\"\n\n\nclass FatalError(Exception):\n \"\"\"A non-retryable failure; give up immediately.\"\"\"\n\n\nclass RetryPolicy:\n \"\"\"Decides whether another attempt should be made after a failed try.\"\"\"\n\n def should_retry(self, attempt_number: int, error: Exception) -> bool:\n \"\"\"Return True if another attempt should be made.\n\n `attempt_number` is the 1-based count of attempts already completed (so `1` after the\n first try has failed). A fatal error is never retried; otherwise we keep going until the\n attempt cap is reached.\n \"\"\"\n if isinstance(error, FatalError):\n return False\n return attempt_number < 5\n\n def max_total_attempts(self) -> int:\n \"\"\"Return the maximum number of times a transient operation will be attempted in total.\n \n This includes the initial attempt plus all retries that would occur for an operation\n that continuously fails with TransientError.\n \"\"\"\n return 5\n```", "input_tokens": 549, "output_tokens": 552, "cost_usd": 0.0033090000000000003, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/retry/policy.py"]}} -{"scenario": "retry-budget-code", "task_type": "code", "tier": "T0", "condition": "C0", "model": "haiku", "trial": 5, "output": "Let me trace through the `should_retry` logic to understand the maximum attempts:\n\n1. `should_retry` is called with `attempt_number` = 1-based count of **completed** attempts\n2. It returns `True` if we should retry (i.e., make another attempt)\n3. For a `TransientError`, it returns `attempt_number < 5`\n\nSo the retry decisions are:\n- After attempt 1 completes: `should_retry(1, TransientError)` \u2192 `1 < 5` \u2192 `True` \u2192 make attempt 2\n- After attempt 2 completes: `should_retry(2, TransientError)` \u2192 `2 < 5` \u2192 `True` \u2192 make attempt 3\n- After attempt 3 completes: `should_retry(3, TransientError)` \u2192 `3 < 5` \u2192 `True` \u2192 make attempt 4\n- After attempt 4 completes: `should_retry(4, TransientError)` \u2192 `4 < 5` \u2192 `True` \u2192 make attempt 5\n- After attempt 5 completes: `should_retry(5, TransientError)` \u2192 `5 < 5` \u2192 `False` \u2192 stop\n\nSo the maximum total attempts is **5** (the initial attempt plus 4 retries).\n\nFILE: code/retry/policy.py\n\n```python\nclass TransientError(Exception):\n \"\"\"A retryable failure (timeout, 503, dropped connection, ...).\"\"\"\n\n\nclass FatalError(Exception):\n \"\"\"A non-retryable failure; give up immediately.\"\"\"\n\n\nclass RetryPolicy:\n \"\"\"Decides whether another attempt should be made after a failed try.\"\"\"\n\n def should_retry(self, attempt_number: int, error: Exception) -> bool:\n \"\"\"Return True if another attempt should be made.\n\n `attempt_number` is the 1-based count of attempts already completed (so `1` after the\n first try has failed). A fatal error is never retried; otherwise we keep going until the\n attempt cap is reached.\n \"\"\"\n if isinstance(error, FatalError):\n return False\n return attempt_number < 5\n\n def max_total_attempts(self) -> int:\n \"\"\"Return the maximum number of times a transient operation will be attempted in total.\n \n This includes the initial attempt plus all retries for an operation that keeps failing\n with a TransientError.\n \"\"\"\n return 5\n```", "input_tokens": 549, "output_tokens": 578, "cost_usd": 0.003439, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/retry/policy.py"]}} -{"scenario": "retry-budget-code", "task_type": "code", "tier": "T0", "condition": "C0", "model": "haiku", "trial": 6, "output": "Let me trace through the `should_retry` logic to understand the maximum attempts:\n\n1. `should_retry` is called with `attempt_number` being the 1-based count of attempts **already completed**\n2. It returns `False` if the error is `FatalError`\n3. It returns `True` if `attempt_number < 5`, otherwise `False`\n\nSo the flow for a transient error is:\n- Attempt 1 fails \u2192 `should_retry(1, TransientError)` \u2192 `1 < 5` \u2192 `True` \u2192 retry\n- Attempt 2 fails \u2192 `should_retry(2, TransientError)` \u2192 `2 < 5` \u2192 `True` \u2192 retry\n- Attempt 3 fails \u2192 `should_retry(3, TransientError)` \u2192 `3 < 5` \u2192 `True` \u2192 retry\n- Attempt 4 fails \u2192 `should_retry(4, TransientError)` \u2192 `4 < 5` \u2192 `True` \u2192 retry\n- Attempt 5 fails \u2192 `should_retry(5, TransientError)` \u2192 `5 < 5` \u2192 `False` \u2192 stop\n\nSo the maximum total attempts is 5 (attempts 1, 2, 3, 4, and 5).\n\nFILE: code/retry/policy.py\n\n```python\nclass TransientError(Exception):\n \"\"\"A retryable failure (timeout, 503, dropped connection, ...).\"\"\"\n\n\nclass FatalError(Exception):\n \"\"\"A non-retryable failure; give up immediately.\"\"\"\n\n\nclass RetryPolicy:\n \"\"\"Decides whether another attempt should be made after a failed try.\"\"\"\n\n def should_retry(self, attempt_number: int, error: Exception) -> bool:\n \"\"\"Return True if another attempt should be made.\n\n `attempt_number` is the 1-based count of attempts already completed (so `1` after the\n first try has failed). A fatal error is never retried; otherwise we keep going until the\n attempt cap is reached.\n \"\"\"\n if isinstance(error, FatalError):\n return False\n return attempt_number < 5\n\n def max_total_attempts(self) -> int:\n \"\"\"Return the maximum number of times a transient operation will be attempted in total.\n \n This is the initial attempt plus every retry for an operation that keeps failing\n with a TransientError.\n \"\"\"\n return 5\n```", "input_tokens": 549, "output_tokens": 570, "cost_usd": 0.003399, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/retry/policy.py"]}} -{"scenario": "retry-budget-code", "task_type": "code", "tier": "T0", "condition": "C0", "model": "haiku", "trial": 7, "output": "Let me trace through the `should_retry` logic to understand the maximum attempts:\n\n1. `should_retry(attempt_number, error)` receives `attempt_number` as the 1-based count of **completed** attempts\n2. It returns `True` if we should retry (i.e., make another attempt)\n3. For a `TransientError`, it returns `attempt_number < 5`\n\nTracing the flow:\n- 1st attempt completes \u2192 `should_retry(1, TransientError)` \u2192 `1 < 5` \u2192 `True` \u2192 retry\n- 2nd attempt completes \u2192 `should_retry(2, TransientError)` \u2192 `2 < 5` \u2192 `True` \u2192 retry\n- 3rd attempt completes \u2192 `should_retry(3, TransientError)` \u2192 `3 < 5` \u2192 `True` \u2192 retry\n- 4th attempt completes \u2192 `should_retry(4, TransientError)` \u2192 `4 < 5` \u2192 `True` \u2192 retry\n- 5th attempt completes \u2192 `should_retry(5, TransientError)` \u2192 `5 < 5` \u2192 `False` \u2192 stop\n\nSo we make 5 total attempts maximum (initial + 4 retries).\n\nFILE: code/retry/policy.py\n\n```python\nclass TransientError(Exception):\n \"\"\"A retryable failure (timeout, 503, dropped connection, ...).\"\"\"\n\n\nclass FatalError(Exception):\n \"\"\"A non-retryable failure; give up immediately.\"\"\"\n\n\nclass RetryPolicy:\n \"\"\"Decides whether another attempt should be made after a failed try.\"\"\"\n\n def should_retry(self, attempt_number: int, error: Exception) -> bool:\n \"\"\"Return True if another attempt should be made.\n\n `attempt_number` is the 1-based count of attempts already completed (so `1` after the\n first try has failed). A fatal error is never retried; otherwise we keep going until the\n attempt cap is reached.\n \"\"\"\n if isinstance(error, FatalError):\n return False\n return attempt_number < 5\n\n def max_total_attempts(self) -> int:\n \"\"\"Return the maximum number of times a transient operation will be attempted in total.\n \n This is the initial attempt plus all retries for an operation that keeps failing\n with a TransientError.\n \"\"\"\n return 5\n```", "input_tokens": 549, "output_tokens": 565, "cost_usd": 0.0033740000000000003, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/retry/policy.py"]}} -{"scenario": "retry-budget-code", "task_type": "code", "tier": "T0", "condition": "C0", "model": "haiku", "trial": 8, "output": "Let me trace through the `should_retry` logic to understand the maximum attempts:\n\n1. `should_retry(attempt_number, error)` receives `attempt_number` as the 1-based count of attempts **already completed**\n2. It returns `False` for `FatalError` (no retry)\n3. It returns `attempt_number < 5` otherwise\n\nFor a `TransientError` that keeps failing:\n- After attempt 1: `should_retry(1, error)` \u2192 `1 < 5` \u2192 `True` (retry)\n- After attempt 2: `should_retry(2, error)` \u2192 `2 < 5` \u2192 `True` (retry)\n- After attempt 3: `should_retry(3, error)` \u2192 `3 < 5` \u2192 `True` (retry)\n- After attempt 4: `should_retry(4, error)` \u2192 `4 < 5` \u2192 `True` (retry)\n- After attempt 5: `should_retry(5, error)` \u2192 `5 < 5` \u2192 `False` (stop)\n\nSo the maximum total attempts is **5** (initial attempt + 4 retries).\n\nFILE: code/retry/policy.py\n\n```python\nclass TransientError(Exception):\n \"\"\"A retryable failure (timeout, 503, dropped connection, ...).\"\"\"\n\n\nclass FatalError(Exception):\n \"\"\"A non-retryable failure; give up immediately.\"\"\"\n\n\nclass RetryPolicy:\n \"\"\"Decides whether another attempt should be made after a failed try.\"\"\"\n\n def should_retry(self, attempt_number: int, error: Exception) -> bool:\n \"\"\"Return True if another attempt should be made.\n\n `attempt_number` is the 1-based count of attempts already completed (so `1` after the\n first try has failed). A fatal error is never retried; otherwise we keep going until the\n attempt cap is reached.\n \"\"\"\n if isinstance(error, FatalError):\n return False\n return attempt_number < 5\n\n def max_total_attempts(self) -> int:\n \"\"\"Return the maximum number of times an operation will be attempted in total.\n\n This is the initial attempt plus all retries for a transient failure that keeps failing.\n \"\"\"\n return 5\n```", "input_tokens": 549, "output_tokens": 539, "cost_usd": 0.0032440000000000004, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/retry/policy.py"]}} -{"scenario": "retry-budget-code", "task_type": "code", "tier": "T0", "condition": "C0", "model": "haiku", "trial": 9, "output": "Let me trace through the `should_retry` logic to understand the maximum attempts:\n\n1. `should_retry(attempt_number, error)` receives `attempt_number` as the 1-based count of attempts **already completed**\n2. It returns `False` if error is `FatalError` (no retry)\n3. It returns `attempt_number < 5` otherwise\n\nSo for a `TransientError` that keeps failing:\n- After attempt 1: `should_retry(1, error)` \u2192 `1 < 5` \u2192 `True` (retry)\n- After attempt 2: `should_retry(2, error)` \u2192 `2 < 5` \u2192 `True` (retry)\n- After attempt 3: `should_retry(3, error)` \u2192 `3 < 5` \u2192 `True` (retry)\n- After attempt 4: `should_retry(4, error)` \u2192 `4 < 5` \u2192 `True` (retry)\n- After attempt 5: `should_retry(5, error)` \u2192 `5 < 5` \u2192 `False` (stop)\n\nSo the maximum total attempts is **5** (the initial attempt plus 4 retries).\n\nFILE: code/retry/policy.py\n\n```python\nclass TransientError(Exception):\n \"\"\"A retryable failure (timeout, 503, dropped connection, ...).\"\"\"\n\n\nclass FatalError(Exception):\n \"\"\"A non-retryable failure; give up immediately.\"\"\"\n\n\nclass RetryPolicy:\n \"\"\"Decides whether another attempt should be made after a failed try.\"\"\"\n\n def should_retry(self, attempt_number: int, error: Exception) -> bool:\n \"\"\"Return True if another attempt should be made.\n\n `attempt_number` is the 1-based count of attempts already completed (so `1` after the\n first try has failed). A fatal error is never retried; otherwise we keep going until the\n attempt cap is reached.\n \"\"\"\n if isinstance(error, FatalError):\n return False\n return attempt_number < 5\n\n def max_total_attempts(self) -> int:\n \"\"\"Return the maximum number of times a transient operation will be attempted in total.\n \n This is the initial attempt plus every retry, for an operation that keeps failing\n with a TransientError.\n \"\"\"\n return 5\n```", "input_tokens": 549, "output_tokens": 552, "cost_usd": 0.0033090000000000003, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/retry/policy.py"]}} -{"scenario": "retry-budget-code", "task_type": "code", "tier": "T0", "condition": "C0", "model": "sonnet", "trial": 0, "output": "Looking at `should_retry`, it returns `True` when `attempt_number < 5`. Since `attempt_number` is the 1-based count of attempts already completed, retries happen after attempts 1, 2, 3, and 4 (i.e., when attempt_number is 1, 2, 3, or 4 \u2014 all less than 5). After attempt 5, `should_retry(5, err)` returns `False`. So the maximum total attempts is 5.\n\nFILE: code/retry/policy.py\n\n```python\nclass TransientError(Exception):\n \"\"\"A retryable failure (timeout, 503, dropped connection, ...).\"\"\"\n\n\nclass FatalError(Exception):\n \"\"\"A non-retryable failure; give up immediately.\"\"\"\n\n\nclass RetryPolicy:\n \"\"\"Decides whether another attempt should be made after a failed try.\"\"\"\n\n def should_retry(self, attempt_number: int, error: Exception) -> bool:\n \"\"\"Return True if another attempt should be made.\n\n `attempt_number` is the 1-based count of attempts already completed (so `1` after the\n first try has failed). A fatal error is never retried; otherwise we keep going until the\n attempt cap is reached.\n \"\"\"\n if isinstance(error, FatalError):\n return False\n return attempt_number < 5\n\n def max_total_attempts(self) -> int:\n \"\"\"Return the maximum number of times an operation will be attempted in total.\n\n This is the initial attempt plus every retry for an operation that keeps failing\n with a TransientError. The value is exactly consistent with should_retry: a retry\n occurs whenever attempt_number < 5, meaning attempts 1 through 4 are each followed\n by a retry, and the 5th attempt is the last. Therefore the maximum total is 5.\n \"\"\"\n return 5\n```", "input_tokens": 550, "output_tokens": 442, "cost_usd": 0.008280000000000001, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/retry/policy.py"]}} -{"scenario": "retry-budget-code", "task_type": "code", "tier": "T0", "condition": "C0", "model": "sonnet", "trial": 1, "output": "Looking at `should_retry`, it returns `True` when `attempt_number < 5` (for non-fatal errors). `attempt_number` is the 1-based count of attempts already completed.\n\nTracing through:\n- After attempt 1: `should_retry(1, ...)` \u2192 `1 < 5` \u2192 `True` (retry)\n- After attempt 2: `should_retry(2, ...)` \u2192 `2 < 5` \u2192 `True` (retry)\n- After attempt 3: `should_retry(3, ...)` \u2192 `3 < 5` \u2192 `True` (retry)\n- After attempt 4: `should_retry(4, ...)` \u2192 `4 < 5` \u2192 `True` (retry)\n- After attempt 5: `should_retry(5, ...)` \u2192 `5 < 5` \u2192 `False` (stop)\n\nSo the last attempt that happens is attempt 5, meaning the maximum total attempts is **5**.\n\nFILE: code/retry/policy.py\n\n```python\nclass TransientError(Exception):\n \"\"\"A retryable failure (timeout, 503, dropped connection, ...).\"\"\"\n\n\nclass FatalError(Exception):\n \"\"\"A non-retryable failure; give up immediately.\"\"\"\n\n\nclass RetryPolicy:\n \"\"\"Decides whether another attempt should be made after a failed try.\"\"\"\n\n def should_retry(self, attempt_number: int, error: Exception) -> bool:\n \"\"\"Return True if another attempt should be made.\n\n `attempt_number` is the 1-based count of attempts already completed (so `1` after the\n first try has failed). A fatal error is never retried; otherwise we keep going until the\n attempt cap is reached.\n \"\"\"\n if isinstance(error, FatalError):\n return False\n return attempt_number < 5\n\n def max_total_attempts(self) -> int:\n \"\"\"Return the maximum number of times a transient operation will be attempted in total.\n\n This is the initial attempt plus every retry for an operation that keeps failing with a\n TransientError. The value is derived directly from the logic in should_retry: a retry\n occurs when attempt_number < 5, so the last attempt made is number 5.\n \"\"\"\n attempt = 0\n error = TransientError()\n while self.should_retry(attempt, error):\n attempt += 1\n return attempt\n```", "input_tokens": 550, "output_tokens": 570, "cost_usd": 0.0102, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/retry/policy.py"]}} -{"scenario": "retry-budget-code", "task_type": "code", "tier": "T0", "condition": "C0", "model": "sonnet", "trial": 2, "output": "Looking at `should_retry`, it returns `True` when `attempt_number < 5`. Since `attempt_number` is the 1-based count of attempts already completed, the last retry happens when `attempt_number == 4` (returns `True`), and no retry happens when `attempt_number == 5` (returns `False`). So the sequence is:\n\n- Attempt 1 fails \u2192 `should_retry(1, ...)` \u2192 `True` \u2192 retry\n- Attempt 2 fails \u2192 `should_retry(2, ...)` \u2192 `True` \u2192 retry\n- Attempt 3 fails \u2192 `should_retry(3, ...)` \u2192 `True` \u2192 retry\n- Attempt 4 fails \u2192 `should_retry(4, ...)` \u2192 `True` \u2192 retry\n- Attempt 5 fails \u2192 `should_retry(5, ...)` \u2192 `False` \u2192 stop\n\nTotal attempts made: **5**.\n\nFILE: code/retry/policy.py\n\n```python\nclass TransientError(Exception):\n \"\"\"A retryable failure (timeout, 503, dropped connection, ...).\"\"\"\n\n\nclass FatalError(Exception):\n \"\"\"A non-retryable failure; give up immediately.\"\"\"\n\n\nclass RetryPolicy:\n \"\"\"Decides whether another attempt should be made after a failed try.\"\"\"\n\n def should_retry(self, attempt_number: int, error: Exception) -> bool:\n \"\"\"Return True if another attempt should be made.\n\n `attempt_number` is the 1-based count of attempts already completed (so `1` after the\n first try has failed). A fatal error is never retried; otherwise we keep going until the\n attempt cap is reached.\n \"\"\"\n if isinstance(error, FatalError):\n return False\n return attempt_number < 5\n\n def max_total_attempts(self) -> int:\n \"\"\"Return the maximum number of times an operation will be attempted in total.\n\n This is the initial attempt plus every retry for an operation that keeps failing with a\n TransientError. The value is exactly consistent with what should_retry does: a retry\n occurs while attempt_number < 5, meaning attempts 1 through 4 each trigger a retry,\n and attempt 5 does not \u2014 giving 5 total attempts.\n \"\"\"\n transient = TransientError()\n attempt = 1\n while self.should_retry(attempt, transient):\n attempt += 1\n return attempt\n```", "input_tokens": 550, "output_tokens": 573, "cost_usd": 0.010245, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/retry/policy.py"]}} -{"scenario": "retry-budget-code", "task_type": "code", "tier": "T0", "condition": "C0", "model": "sonnet", "trial": 3, "output": "Looking at `should_retry`, it returns `True` when `attempt_number < 5` (for non-fatal errors). Since `attempt_number` is the 1-based count of attempts already completed:\n\n- After attempt 1: `should_retry(1, ...)` \u2192 `1 < 5` \u2192 `True` (retry)\n- After attempt 2: `should_retry(2, ...)` \u2192 `2 < 5` \u2192 `True` (retry)\n- After attempt 3: `should_retry(3, ...)` \u2192 `3 < 5` \u2192 `True` (retry)\n- After attempt 4: `should_retry(4, ...)` \u2192 `4 < 5` \u2192 `True` (retry)\n- After attempt 5: `should_retry(5, ...)` \u2192 `5 < 5` \u2192 `False` (stop)\n\nSo the last attempt that gets retried is attempt 4, meaning attempt 5 is the final one. Total attempts = **5**.\n\nFILE: code/retry/policy.py\n\n```python\nclass TransientError(Exception):\n \"\"\"A retryable failure (timeout, 503, dropped connection, ...).\"\"\"\n\n\nclass FatalError(Exception):\n \"\"\"A non-retryable failure; give up immediately.\"\"\"\n\n\nclass RetryPolicy:\n \"\"\"Decides whether another attempt should be made after a failed try.\"\"\"\n\n def should_retry(self, attempt_number: int, error: Exception) -> bool:\n \"\"\"Return True if another attempt should be made.\n\n `attempt_number` is the 1-based count of attempts already completed (so `1` after the\n first try has failed). A fatal error is never retried; otherwise we keep going until the\n attempt cap is reached.\n \"\"\"\n if isinstance(error, FatalError):\n return False\n return attempt_number < 5\n\n def max_total_attempts(self) -> int:\n \"\"\"Return the maximum number of times an operation will be attempted in total.\n\n This is the initial attempt plus every retry for an operation that keeps failing\n with a TransientError. The value is exactly consistent with should_retry: a retry\n occurs when attempt_number < 5, so the last retry happens after attempt 4, making\n attempt 5 the final one \u2014 giving 5 total attempts.\n \"\"\"\n attempt = 0\n error = TransientError()\n while True:\n attempt += 1\n if not self.should_retry(attempt, error):\n return attempt\n```", "input_tokens": 550, "output_tokens": 593, "cost_usd": 0.010545, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/retry/policy.py"]}} -{"scenario": "retry-budget-code", "task_type": "code", "tier": "T0", "condition": "C0", "model": "sonnet", "trial": 4, "output": "Looking at `should_retry`, it returns `True` when `attempt_number < 5` (and error is not fatal). Since `attempt_number` is the 1-based count of attempts already completed:\n\n- After attempt 1: `1 < 5` \u2192 True (retry)\n- After attempt 2: `2 < 5` \u2192 True (retry)\n- After attempt 3: `3 < 5` \u2192 True (retry)\n- After attempt 4: `4 < 5` \u2192 True (retry)\n- After attempt 5: `5 < 5` \u2192 False (stop)\n\nSo the loop runs attempts 1, 2, 3, 4, 5 \u2014 then stops. That's **5 total attempts**.\n\nFILE: code/retry/policy.py\n\n```python\nclass TransientError(Exception):\n \"\"\"A retryable failure (timeout, 503, dropped connection, ...).\"\"\"\n\n\nclass FatalError(Exception):\n \"\"\"A non-retryable failure; give up immediately.\"\"\"\n\n\nclass RetryPolicy:\n \"\"\"Decides whether another attempt should be made after a failed try.\"\"\"\n\n def should_retry(self, attempt_number: int, error: Exception) -> bool:\n \"\"\"Return True if another attempt should be made.\n\n `attempt_number` is the 1-based count of attempts already completed (so `1` after the\n first try has failed). A fatal error is never retried; otherwise we keep going until the\n attempt cap is reached.\n \"\"\"\n if isinstance(error, FatalError):\n return False\n return attempt_number < 5\n\n def max_total_attempts(self) -> int:\n \"\"\"Return the maximum number of times an operation will be attempted in total.\n\n This covers the initial attempt plus every retry for an operation that keeps failing\n with a TransientError. The value is derived directly from the logic in should_retry:\n retries are allowed while attempt_number < 5, meaning attempts 1 through 5 can occur,\n giving a maximum of 5 total attempts.\n \"\"\"\n return 5\n```", "input_tokens": 550, "output_tokens": 493, "cost_usd": 0.009045000000000001, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/retry/policy.py"]}} -{"scenario": "retry-budget-code", "task_type": "code", "tier": "T0", "condition": "C0", "model": "sonnet", "trial": 5, "output": "Let me trace through `should_retry` carefully.\n\n`attempt_number` is the 1-based count of attempts already completed. A retry happens when `attempt_number < 5`.\n\nSo retries happen when `attempt_number` is 1, 2, 3, or 4 \u2014 meaning after the 1st, 2nd, 3rd, and 4th attempts. After the 5th attempt, `attempt_number < 5` is False, so no more retries.\n\nTotal attempts = 5.\n\nFILE: code/retry/policy.py\n\n```python\nclass TransientError(Exception):\n \"\"\"A retryable failure (timeout, 503, dropped connection, ...).\"\"\"\n\n\nclass FatalError(Exception):\n \"\"\"A non-retryable failure; give up immediately.\"\"\"\n\n\nclass RetryPolicy:\n \"\"\"Decides whether another attempt should be made after a failed try.\"\"\"\n\n def should_retry(self, attempt_number: int, error: Exception) -> bool:\n \"\"\"Return True if another attempt should be made.\n\n `attempt_number` is the 1-based count of attempts already completed (so `1` after the\n first try has failed). A fatal error is never retried; otherwise we keep going until the\n attempt cap is reached.\n \"\"\"\n if isinstance(error, FatalError):\n return False\n return attempt_number < 5\n\n def max_total_attempts(self) -> int:\n \"\"\"Return the maximum number of times an operation will be attempted in total.\n\n This is the initial attempt plus every retry for an operation that keeps failing\n with a TransientError. Consistent with should_retry, which allows retries while\n attempt_number < 5, meaning retries occur after attempts 1, 2, 3, and 4, for a\n total of 5 attempts.\n \"\"\"\n return 5\n```", "input_tokens": 550, "output_tokens": 438, "cost_usd": 0.00822, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/retry/policy.py"]}} -{"scenario": "retry-budget-code", "task_type": "code", "tier": "T0", "condition": "C0", "model": "sonnet", "trial": 6, "output": "Let me trace through `should_retry` to find the maximum total attempts.\n\n`should_retry(attempt_number, error)` is called with the 1-based count of attempts already completed. It returns `True` (retry) when `attempt_number < 5`.\n\nSo:\n- After attempt 1: `should_retry(1, ...)` \u2192 `1 < 5` \u2192 `True` (retry)\n- After attempt 2: `should_retry(2, ...)` \u2192 `2 < 5` \u2192 `True` (retry)\n- After attempt 3: `should_retry(3, ...)` \u2192 `3 < 5` \u2192 `True` (retry)\n- After attempt 4: `should_retry(4, ...)` \u2192 `4 < 5` \u2192 `True` (retry)\n- After attempt 5: `should_retry(5, ...)` \u2192 `5 < 5` \u2192 `False` (stop)\n\nSo the loop makes attempts 1 through 5, stopping after attempt 5 fails. Total attempts = **5**.\n\nFILE: code/retry/policy.py\n\n```python\nclass TransientError(Exception):\n \"\"\"A retryable failure (timeout, 503, dropped connection, ...).\"\"\"\n\n\nclass FatalError(Exception):\n \"\"\"A non-retryable failure; give up immediately.\"\"\"\n\n\nclass RetryPolicy:\n \"\"\"Decides whether another attempt should be made after a failed try.\"\"\"\n\n def should_retry(self, attempt_number: int, error: Exception) -> bool:\n \"\"\"Return True if another attempt should be made.\n\n `attempt_number` is the 1-based count of attempts already completed (so `1` after the\n first try has failed). A fatal error is never retried; otherwise we keep going until the\n attempt cap is reached.\n \"\"\"\n if isinstance(error, FatalError):\n return False\n return attempt_number < 5\n\n def max_total_attempts(self) -> int:\n \"\"\"Return the maximum number of times an operation will be attempted in total.\n\n This is the initial attempt plus every retry for an operation that keeps failing\n with a TransientError. Consistent with should_retry: a retry occurs when\n attempt_number < 5, so the last attempt is number 5, giving 5 total attempts.\n \"\"\"\n return 5\n```", "input_tokens": 550, "output_tokens": 554, "cost_usd": 0.00996, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/retry/policy.py"]}} -{"scenario": "retry-budget-code", "task_type": "code", "tier": "T0", "condition": "C0", "model": "sonnet", "trial": 7, "output": "Looking at `should_retry`, it returns `True` when `attempt_number < 5` (for non-fatal errors). `attempt_number` is the 1-based count of attempts already completed.\n\nSo:\n- After attempt 1: `should_retry(1, ...)` \u2192 `1 < 5` \u2192 `True` (retry)\n- After attempt 2: `should_retry(2, ...)` \u2192 `2 < 5` \u2192 `True` (retry)\n- After attempt 3: `should_retry(3, ...)` \u2192 `3 < 5` \u2192 `True` (retry)\n- After attempt 4: `should_retry(4, ...)` \u2192 `4 < 5` \u2192 `True` (retry)\n- After attempt 5: `should_retry(5, ...)` \u2192 `5 < 5` \u2192 `False` (stop)\n\nSo the loop runs attempts 1 through 5, meaning the maximum total attempts is **5**.\n\nFILE: code/retry/policy.py\n\n```python\nclass TransientError(Exception):\n \"\"\"A retryable failure (timeout, 503, dropped connection, ...).\"\"\"\n\n\nclass FatalError(Exception):\n \"\"\"A non-retryable failure; give up immediately.\"\"\"\n\n\nclass RetryPolicy:\n \"\"\"Decides whether another attempt should be made after a failed try.\"\"\"\n\n def should_retry(self, attempt_number: int, error: Exception) -> bool:\n \"\"\"Return True if another attempt should be made.\n\n `attempt_number` is the 1-based count of attempts already completed (so `1` after the\n first try has failed). A fatal error is never retried; otherwise we keep going until the\n attempt cap is reached.\n \"\"\"\n if isinstance(error, FatalError):\n return False\n return attempt_number < 5\n\n def max_total_attempts(self) -> int:\n \"\"\"Return the maximum number of times an operation will be attempted in total.\n\n This is the initial attempt plus every retry for an operation that keeps failing\n with a TransientError. The value is derived directly from the logic in should_retry:\n retries occur while attempt_number < 5, so the last attempt that triggers a retry\n is attempt 4, after which attempt 5 is made and should_retry returns False.\n The total is therefore 5.\n \"\"\"\n attempt = 1\n error = TransientError()\n while self.should_retry(attempt, error):\n attempt += 1\n return attempt\n```", "input_tokens": 550, "output_tokens": 595, "cost_usd": 0.010575000000000001, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/retry/policy.py"]}} -{"scenario": "retry-budget-code", "task_type": "code", "tier": "T0", "condition": "C0", "model": "sonnet", "trial": 8, "output": "Let me trace through the logic carefully.\n\n`should_retry(attempt_number, error)` is called with the 1-based count of attempts already completed, and returns `True` (retry) when `attempt_number < 5`.\n\nSo:\n- After attempt 1: `should_retry(1, ...)` \u2192 `1 < 5` \u2192 `True` \u2192 retry\n- After attempt 2: `should_retry(2, ...)` \u2192 `2 < 5` \u2192 `True` \u2192 retry\n- After attempt 3: `should_retry(3, ...)` \u2192 `3 < 5` \u2192 `True` \u2192 retry\n- After attempt 4: `should_retry(4, ...)` \u2192 `4 < 5` \u2192 `True` \u2192 retry\n- After attempt 5: `should_retry(5, ...)` \u2192 `5 < 5` \u2192 `False` \u2192 stop\n\nSo the maximum total attempts is **5**.\n\nFILE: code/retry/policy.py\n\n```python\nclass TransientError(Exception):\n \"\"\"A retryable failure (timeout, 503, dropped connection, ...).\"\"\"\n\n\nclass FatalError(Exception):\n \"\"\"A non-retryable failure; give up immediately.\"\"\"\n\n\nclass RetryPolicy:\n \"\"\"Decides whether another attempt should be made after a failed try.\"\"\"\n\n def should_retry(self, attempt_number: int, error: Exception) -> bool:\n \"\"\"Return True if another attempt should be made.\n\n `attempt_number` is the 1-based count of attempts already completed (so `1` after the\n first try has failed). A fatal error is never retried; otherwise we keep going until the\n attempt cap is reached.\n \"\"\"\n if isinstance(error, FatalError):\n return False\n return attempt_number < 5\n\n def max_total_attempts(self) -> int:\n \"\"\"Return the maximum number of times an operation will be attempted in total.\n\n This is the initial attempt plus every retry for an operation that keeps failing\n with a TransientError. The value is exactly consistent with should_retry: since\n should_retry returns True while attempt_number < 5, the last retry happens after\n attempt 4, making attempt 5 the final one \u2014 for a total of 5 attempts.\n \"\"\"\n return 5\n```", "input_tokens": 550, "output_tokens": 543, "cost_usd": 0.009795, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/retry/policy.py"]}} -{"scenario": "retry-budget-code", "task_type": "code", "tier": "T0", "condition": "C0", "model": "sonnet", "trial": 9, "output": "Let me trace through `should_retry` to understand the logic:\n\n- After attempt 1 fails: `should_retry(1, transient_error)` \u2192 `1 < 5` \u2192 `True` (retry)\n- After attempt 2 fails: `should_retry(2, transient_error)` \u2192 `2 < 5` \u2192 `True` (retry)\n- After attempt 3 fails: `should_retry(3, transient_error)` \u2192 `3 < 5` \u2192 `True` (retry)\n- After attempt 4 fails: `should_retry(4, transient_error)` \u2192 `4 < 5` \u2192 `True` (retry)\n- After attempt 5 fails: `should_retry(5, transient_error)` \u2192 `5 < 5` \u2192 `False` (stop)\n\nSo the maximum total attempts is **5** (attempts 1 through 5, where attempt 5 is the last one tried before giving up).\n\nFILE: code/retry/policy.py\n\n```python\nclass TransientError(Exception):\n \"\"\"A retryable failure (timeout, 503, dropped connection, ...).\"\"\"\n\n\nclass FatalError(Exception):\n \"\"\"A non-retryable failure; give up immediately.\"\"\"\n\n\nclass RetryPolicy:\n \"\"\"Decides whether another attempt should be made after a failed try.\"\"\"\n\n def should_retry(self, attempt_number: int, error: Exception) -> bool:\n \"\"\"Return True if another attempt should be made.\n\n `attempt_number` is the 1-based count of attempts already completed (so `1` after the\n first try has failed). A fatal error is never retried; otherwise we keep going until the\n attempt cap is reached.\n \"\"\"\n if isinstance(error, FatalError):\n return False\n return attempt_number < 5\n\n def max_total_attempts(self) -> int:\n \"\"\"Return the maximum number of times a transient operation will be attempted in total.\n\n This is the initial attempt plus every retry for an operation that keeps failing with a\n TransientError. The value is exactly consistent with what should_retry does: a retry\n occurs when attempt_number < 5, meaning attempts 1-4 each trigger a retry, and attempt 5\n does not. Therefore the maximum total number of attempts is 5.\n \"\"\"\n return 5\n```", "input_tokens": 550, "output_tokens": 559, "cost_usd": 0.010035, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/retry/policy.py"]}} -{"scenario": "retry-budget-code", "task_type": "code", "tier": "T0", "condition": "C0", "model": "opus", "trial": 0, "output": "Tracing the logic: `should_retry` returns `attempt_number < 5` for transient errors.\n\n- After attempt 1: `should_retry(1, ...)` \u2192 `1 < 5` \u2192 True \u2192 retry (attempt 2)\n- After attempt 2: `should_retry(2, ...)` \u2192 `2 < 5` \u2192 True \u2192 retry (attempt 3)\n- After attempt 3: `should_retry(3, ...)` \u2192 `3 < 5` \u2192 True \u2192 retry (attempt 4)\n- After attempt 4: `should_retry(4, ...)` \u2192 `4 < 5` \u2192 True \u2192 retry (attempt 5)\n- After attempt 5: `should_retry(5, ...)` \u2192 `5 < 5` \u2192 False \u2192 stop\n\nSo the maximum total number of attempts is **5**.\n\nFILE: code/retry/policy.py\n```python\nclass TransientError(Exception):\n \"\"\"A retryable failure (timeout, 503, dropped connection, ...).\"\"\"\n\n\nclass FatalError(Exception):\n \"\"\"A non-retryable failure; give up immediately.\"\"\"\n\n\nclass RetryPolicy:\n \"\"\"Decides whether another attempt should be made after a failed try.\"\"\"\n\n def should_retry(self, attempt_number: int, error: Exception) -> bool:\n \"\"\"Return True if another attempt should be made.\n\n `attempt_number` is the 1-based count of attempts already completed (so `1` after the\n first try has failed). A fatal error is never retried; otherwise we keep going until the\n attempt cap is reached.\n \"\"\"\n if isinstance(error, FatalError):\n return False\n return attempt_number < 5\n\n def max_total_attempts(self) -> int:\n \"\"\"Return the maximum number of times a transient operation will be attempted in total.\n\n This is the initial attempt plus every retry for an operation that keeps failing with a\n `TransientError`. The value is kept exactly consistent with `should_retry`: a retry occurs\n after attempt N whenever `should_retry(N, TransientError())` returns True, so we count\n attempts until it would return False.\n \"\"\"\n attempt_number = 1\n while self.should_retry(attempt_number, TransientError()):\n attempt_number += 1\n return attempt_number\n```", "input_tokens": 728, "output_tokens": 713, "cost_usd": 0.021465, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/retry/policy.py"]}} -{"scenario": "retry-budget-code", "task_type": "code", "tier": "T0", "condition": "C0", "model": "opus", "trial": 1, "output": "Tracing the logic: `should_retry(attempt_number, error)` returns `attempt_number < 5`.\n\n- After attempt 1: `should_retry(1, ...)` \u2192 `1 < 5` \u2192 True \u2192 retry (attempt 2)\n- After attempt 2: `should_retry(2, ...)` \u2192 True \u2192 retry (attempt 3)\n- After attempt 3: `should_retry(3, ...)` \u2192 True \u2192 retry (attempt 4)\n- After attempt 4: `should_retry(4, ...)` \u2192 True \u2192 retry (attempt 5)\n- After attempt 5: `should_retry(5, ...)` \u2192 `5 < 5` \u2192 False \u2192 stop\n\nSo the maximum total attempts is **5**.\n\nFILE: code/retry/policy.py\n```python\nclass TransientError(Exception):\n \"\"\"A retryable failure (timeout, 503, dropped connection, ...).\"\"\"\n\n\nclass FatalError(Exception):\n \"\"\"A non-retryable failure; give up immediately.\"\"\"\n\n\nclass RetryPolicy:\n \"\"\"Decides whether another attempt should be made after a failed try.\"\"\"\n\n def should_retry(self, attempt_number: int, error: Exception) -> bool:\n \"\"\"Return True if another attempt should be made.\n\n `attempt_number` is the 1-based count of attempts already completed (so `1` after the\n first try has failed). A fatal error is never retried; otherwise we keep going until the\n attempt cap is reached.\n \"\"\"\n if isinstance(error, FatalError):\n return False\n return attempt_number < 5\n\n def max_total_attempts(self) -> int:\n \"\"\"Return the maximum number of attempts for a perpetually-transient operation.\n\n This is the initial attempt plus every retry that `should_retry` would permit when the\n operation keeps failing with a `TransientError`.\n\n Tracing the current `should_retry`: after each completed attempt N it is consulted with\n `attempt_number == N`, and a further attempt happens whenever it returns True. With a\n transient error it returns `N < 5`, so attempts 1..4 each trigger one more try and attempt\n 5 stops the loop, giving 5 total attempts.\n \"\"\"\n attempts = 0\n error = TransientError()\n while True:\n attempts += 1\n if not self.should_retry(attempts, error):\n break\n return attempts\n```", "input_tokens": 728, "output_tokens": 754, "cost_usd": 0.022490000000000003, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/retry/policy.py"]}} -{"scenario": "retry-budget-code", "task_type": "code", "tier": "T0", "condition": "C0", "model": "opus", "trial": 2, "output": "Tracing through `should_retry` with transient errors:\n\n- Attempt 1 completes (fails) \u2192 `should_retry(1, err)` \u2192 `1 < 5` is `True` \u2192 retry\n- Attempt 2 completes \u2192 `should_retry(2, err)` \u2192 `2 < 5` is `True` \u2192 retry\n- Attempt 3 completes \u2192 `should_retry(3, err)` \u2192 `3 < 5` is `True` \u2192 retry\n- Attempt 4 completes \u2192 `should_retry(4, err)` \u2192 `4 < 5` is `True` \u2192 retry\n- Attempt 5 completes \u2192 `should_retry(5, err)` \u2192 `5 < 5` is `False` \u2192 stop\n\nSo the operation is attempted **5** times in total.\n\nFILE: code/retry/policy.py\n```python\nclass TransientError(Exception):\n \"\"\"A retryable failure (timeout, 503, dropped connection, ...).\"\"\"\n\n\nclass FatalError(Exception):\n \"\"\"A non-retryable failure; give up immediately.\"\"\"\n\n\nclass RetryPolicy:\n \"\"\"Decides whether another attempt should be made after a failed try.\"\"\"\n\n def should_retry(self, attempt_number: int, error: Exception) -> bool:\n \"\"\"Return True if another attempt should be made.\n\n `attempt_number` is the 1-based count of attempts already completed (so `1` after the\n first try has failed). A fatal error is never retried; otherwise we keep going until the\n attempt cap is reached.\n \"\"\"\n if isinstance(error, FatalError):\n return False\n return attempt_number < 5\n\n def max_total_attempts(self) -> int:\n \"\"\"Return the maximum number of times a transient (retryable) operation will be\n attempted in total (initial attempt plus every retry).\n\n This traces the behavior of `should_retry` for an operation that keeps failing with a\n `TransientError`: after each completed attempt we ask `should_retry(attempt_number, err)`\n and perform another attempt whenever it returns True. The total is one more than the\n largest `attempt_number` for which `should_retry` still returns True.\n \"\"\"\n transient_error = TransientError()\n attempts = 1\n while self.should_retry(attempts, transient_error):\n attempts += 1\n return attempts\n```", "input_tokens": 728, "output_tokens": 721, "cost_usd": 0.021665, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/retry/policy.py"]}} -{"scenario": "retry-budget-code", "task_type": "code", "tier": "T0", "condition": "C0", "model": "opus", "trial": 3, "output": "Tracing the logic: `should_retry` returns `True` while `attempt_number < 5`.\n\n- After attempt 1 (attempt_number=1): 1 < 5 \u2192 True \u2192 retry \u2192 attempt 2\n- After attempt 2: 2 < 5 \u2192 True \u2192 attempt 3\n- After attempt 3: 3 < 5 \u2192 True \u2192 attempt 4\n- After attempt 4: 4 < 5 \u2192 True \u2192 attempt 5\n- After attempt 5: 5 < 5 \u2192 False \u2192 stop\n\nSo the maximum total attempts is **5**.\n\nFILE: code/retry/policy.py\n```python\nclass TransientError(Exception):\n \"\"\"A retryable failure (timeout, 503, dropped connection, ...).\"\"\"\n\n\nclass FatalError(Exception):\n \"\"\"A non-retryable failure; give up immediately.\"\"\"\n\n\nclass RetryPolicy:\n \"\"\"Decides whether another attempt should be made after a failed try.\"\"\"\n\n def should_retry(self, attempt_number: int, error: Exception) -> bool:\n \"\"\"Return True if another attempt should be made.\n\n `attempt_number` is the 1-based count of attempts already completed (so `1` after the\n first try has failed). A fatal error is never retried; otherwise we keep going until the\n attempt cap is reached.\n \"\"\"\n if isinstance(error, FatalError):\n return False\n return attempt_number < 5\n\n def max_total_attempts(self) -> int:\n \"\"\"Return the maximum number of times a transient operation will be attempted in total.\n\n This counts the initial attempt plus every retry for an operation that keeps failing\n with a `TransientError`. It is derived strictly from `should_retry`: starting after the\n first completed attempt, we keep retrying while `should_retry` returns True.\n \"\"\"\n attempts_completed = 1\n error = TransientError()\n while self.should_retry(attempts_completed, error):\n attempts_completed += 1\n return attempts_completed\n```", "input_tokens": 728, "output_tokens": 629, "cost_usd": 0.019365, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/retry/policy.py"]}} -{"scenario": "retry-budget-code", "task_type": "code", "tier": "T0", "condition": "C0", "model": "opus", "trial": 4, "output": "Tracing the logic:\n\n- Attempt 1 runs, fails \u2192 `should_retry(1, err)` \u2192 `1 < 5` = True \u2192 retry\n- Attempt 2 runs, fails \u2192 `should_retry(2, err)` \u2192 `2 < 5` = True \u2192 retry\n- Attempt 3 runs, fails \u2192 `should_retry(3, err)` \u2192 `3 < 5` = True \u2192 retry\n- Attempt 4 runs, fails \u2192 `should_retry(4, err)` \u2192 `4 < 5` = True \u2192 retry\n- Attempt 5 runs, fails \u2192 `should_retry(5, err)` \u2192 `5 < 5` = False \u2192 stop\n\nSo the maximum total attempts is **5**.\n\nFILE: code/retry/policy.py\n```python\nclass TransientError(Exception):\n \"\"\"A retryable failure (timeout, 503, dropped connection, ...).\"\"\"\n\n\nclass FatalError(Exception):\n \"\"\"A non-retryable failure; give up immediately.\"\"\"\n\n\nclass RetryPolicy:\n \"\"\"Decides whether another attempt should be made after a failed try.\"\"\"\n\n def should_retry(self, attempt_number: int, error: Exception) -> bool:\n \"\"\"Return True if another attempt should be made.\n\n `attempt_number` is the 1-based count of attempts already completed (so `1` after the\n first try has failed). A fatal error is never retried; otherwise we keep going until the\n attempt cap is reached.\n \"\"\"\n if isinstance(error, FatalError):\n return False\n return attempt_number < 5\n\n def max_total_attempts(self) -> int:\n \"\"\"Return the maximum number of times a transient operation will be attempted in total.\n\n This counts the initial attempt plus every retry for an operation that keeps failing with\n a `TransientError`. The value is derived strictly from `should_retry`: a retry happens\n whenever `should_retry(attempt_number, err)` returns True (with `attempt_number` being the\n 1-based count of attempts already completed).\n \"\"\"\n transient = TransientError()\n attempts = 1\n while self.should_retry(attempts, transient):\n attempts += 1\n return attempts\n```", "input_tokens": 728, "output_tokens": 686, "cost_usd": 0.020790000000000003, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/retry/policy.py"]}} -{"scenario": "retry-budget-code", "task_type": "code", "tier": "T0", "condition": "C0", "model": "opus", "trial": 5, "output": "Tracing the logic: `should_retry` returns `True` while `attempt_number < 5`.\n\n- After attempt 1 fails: `should_retry(1, ...)` \u2192 `1 < 5` \u2192 True \u2192 retry (attempt 2)\n- After attempt 2 fails: `should_retry(2, ...)` \u2192 True \u2192 retry (attempt 3)\n- After attempt 3 fails: `should_retry(3, ...)` \u2192 True \u2192 retry (attempt 4)\n- After attempt 4 fails: `should_retry(4, ...)` \u2192 True \u2192 retry (attempt 5)\n- After attempt 5 fails: `should_retry(5, ...)` \u2192 `5 < 5` \u2192 False \u2192 stop\n\nSo the maximum total attempts is **5**.\n\nFILE: code/retry/policy.py\n```python\nclass TransientError(Exception):\n \"\"\"A retryable failure (timeout, 503, dropped connection, ...).\"\"\"\n\n\nclass FatalError(Exception):\n \"\"\"A non-retryable failure; give up immediately.\"\"\"\n\n\nclass RetryPolicy:\n \"\"\"Decides whether another attempt should be made after a failed try.\"\"\"\n\n def should_retry(self, attempt_number: int, error: Exception) -> bool:\n \"\"\"Return True if another attempt should be made.\n\n `attempt_number` is the 1-based count of attempts already completed (so `1` after the\n first try has failed). A fatal error is never retried; otherwise we keep going until the\n attempt cap is reached.\n \"\"\"\n if isinstance(error, FatalError):\n return False\n return attempt_number < 5\n\n def max_total_attempts(self) -> int:\n \"\"\"Return the maximum number of times a transient (retryable) operation will be\n attempted in total \u2014 the initial attempt plus every retry.\n\n This is derived directly from `should_retry`: a retry occurs after a failed attempt\n whenever `should_retry(attempt_number, TransientError())` returns True, where\n `attempt_number` is the 1-based count of attempts already completed. We simulate that\n loop here so the result stays exactly consistent with `should_retry`.\n \"\"\"\n attempts = 1\n while self.should_retry(attempts, TransientError()):\n attempts += 1\n return attempts\n```", "input_tokens": 728, "output_tokens": 719, "cost_usd": 0.021615000000000002, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/retry/policy.py"]}} -{"scenario": "retry-budget-code", "task_type": "code", "tier": "T0", "condition": "C0", "model": "opus", "trial": 6, "output": "Tracing the logic: `should_retry(attempt_number, error)` returns `attempt_number < 5` for transient errors.\n\n- Attempt 1 completes \u2192 `should_retry(1, err)` \u2192 `1 < 5` \u2192 True \u2192 retry\n- Attempt 2 completes \u2192 `should_retry(2, err)` \u2192 `2 < 5` \u2192 True \u2192 retry\n- Attempt 3 completes \u2192 `should_retry(3, err)` \u2192 `3 < 5` \u2192 True \u2192 retry\n- Attempt 4 completes \u2192 `should_retry(4, err)` \u2192 `4 < 5` \u2192 True \u2192 retry\n- Attempt 5 completes \u2192 `should_retry(5, err)` \u2192 `5 < 5` \u2192 False \u2192 stop\n\nSo the total number of attempts is **5**.\n\nFILE: code/retry/policy.py\n```python\nclass TransientError(Exception):\n \"\"\"A retryable failure (timeout, 503, dropped connection, ...).\"\"\"\n\n\nclass FatalError(Exception):\n \"\"\"A non-retryable failure; give up immediately.\"\"\"\n\n\nclass RetryPolicy:\n \"\"\"Decides whether another attempt should be made after a failed try.\"\"\"\n\n def should_retry(self, attempt_number: int, error: Exception) -> bool:\n \"\"\"Return True if another attempt should be made.\n\n `attempt_number` is the 1-based count of attempts already completed (so `1` after the\n first try has failed). A fatal error is never retried; otherwise we keep going until the\n attempt cap is reached.\n \"\"\"\n if isinstance(error, FatalError):\n return False\n return attempt_number < 5\n\n def max_total_attempts(self) -> int:\n \"\"\"Return the maximum number of attempts for an operation that keeps\n failing with a TransientError (initial attempt plus every retry).\n\n Consistent with `should_retry`: after attempt N completes, a retry\n occurs while `should_retry(N, TransientError())` returns True. The loop\n stops at the smallest N for which it returns False, which is the total\n attempt count.\n \"\"\"\n attempt_number = 1\n while self.should_retry(attempt_number, TransientError()):\n attempt_number += 1\n return attempt_number\n```", "input_tokens": 728, "output_tokens": 697, "cost_usd": 0.021065, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/retry/policy.py"]}} -{"scenario": "retry-budget-code", "task_type": "code", "tier": "T0", "condition": "C0", "model": "opus", "trial": 7, "output": "Tracing `should_retry`: it returns `True` while `attempt_number < 5`.\n\n- After attempt 1 completes: `should_retry(1, ...)` \u2192 `1 < 5` \u2192 True \u2192 retry (attempt 2)\n- After attempt 2: `should_retry(2, ...)` \u2192 True \u2192 retry (attempt 3)\n- After attempt 3: `should_retry(3, ...)` \u2192 True \u2192 retry (attempt 4)\n- After attempt 4: `should_retry(4, ...)` \u2192 True \u2192 retry (attempt 5)\n- After attempt 5: `should_retry(5, ...)` \u2192 `5 < 5` \u2192 False \u2192 stop\n\nSo the maximum total attempts is **5**.\n\nFILE: code/retry/policy.py\n```python\nclass TransientError(Exception):\n \"\"\"A retryable failure (timeout, 503, dropped connection, ...).\"\"\"\n\n\nclass FatalError(Exception):\n \"\"\"A non-retryable failure; give up immediately.\"\"\"\n\n\nclass RetryPolicy:\n \"\"\"Decides whether another attempt should be made after a failed try.\"\"\"\n\n def should_retry(self, attempt_number: int, error: Exception) -> bool:\n \"\"\"Return True if another attempt should be made.\n\n `attempt_number` is the 1-based count of attempts already completed (so `1` after the\n first try has failed). A fatal error is never retried; otherwise we keep going until the\n attempt cap is reached.\n \"\"\"\n if isinstance(error, FatalError):\n return False\n return attempt_number < 5\n\n def max_total_attempts(self) -> int:\n \"\"\"Return the maximum number of times a transient (retryable) operation will be\n attempted in total \u2014 the initial attempt plus every retry.\n\n This is derived strictly from `should_retry`: a retry happens after a completed\n attempt whenever `should_retry(attempt_number, TransientError())` returns True.\n We count the initial attempt, then keep asking the policy whether to retry.\n \"\"\"\n attempts = 1\n while self.should_retry(attempts, TransientError()):\n attempts += 1\n return attempts\n```", "input_tokens": 728, "output_tokens": 676, "cost_usd": 0.020540000000000003, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/retry/policy.py"]}} -{"scenario": "retry-budget-code", "task_type": "code", "tier": "T0", "condition": "C0", "model": "opus", "trial": 8, "output": "Tracing the logic: `should_retry(attempt_number, error)` returns `attempt_number < 5`.\n\n- Attempt 1 completes (fails). `should_retry(1, err)` \u2192 `1 < 5` \u2192 True \u2192 retry.\n- Attempt 2 completes. `should_retry(2, err)` \u2192 True \u2192 retry.\n- Attempt 3 completes. `should_retry(3, err)` \u2192 True \u2192 retry.\n- Attempt 4 completes. `should_retry(4, err)` \u2192 True \u2192 retry.\n- Attempt 5 completes. `should_retry(5, err)` \u2192 `5 < 5` \u2192 False \u2192 stop.\n\nSo the operation is attempted **5** times in total.\n\nFILE: code/retry/policy.py\n```python\nclass TransientError(Exception):\n \"\"\"A retryable failure (timeout, 503, dropped connection, ...).\"\"\"\n\n\nclass FatalError(Exception):\n \"\"\"A non-retryable failure; give up immediately.\"\"\"\n\n\nclass RetryPolicy:\n \"\"\"Decides whether another attempt should be made after a failed try.\"\"\"\n\n def should_retry(self, attempt_number: int, error: Exception) -> bool:\n \"\"\"Return True if another attempt should be made.\n\n `attempt_number` is the 1-based count of attempts already completed (so `1` after the\n first try has failed). A fatal error is never retried; otherwise we keep going until the\n attempt cap is reached.\n \"\"\"\n if isinstance(error, FatalError):\n return False\n return attempt_number < 5\n\n def max_total_attempts(self) -> int:\n \"\"\"Return the maximum number of times a transient operation will be attempted in total.\n\n This counts the initial attempt plus every retry for an operation that keeps failing\n with a `TransientError`. The value is kept exactly consistent with `should_retry`:\n after each failed attempt we call `should_retry(attempt_number, err)` and retry while\n it returns True. With the current implementation, `should_retry` returns True while\n `attempt_number < 5`, so attempts 1-4 each trigger a retry and attempt 5 stops the loop.\n \"\"\"\n attempts = 0\n error = TransientError()\n while True:\n attempts += 1\n if not self.should_retry(attempts, error):\n break\n return attempts\n```", "input_tokens": 728, "output_tokens": 737, "cost_usd": 0.022065, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/retry/policy.py"]}} -{"scenario": "retry-budget-code", "task_type": "code", "tier": "T0", "condition": "C0", "model": "opus", "trial": 9, "output": "Tracing the logic: `should_retry` returns `attempt_number < 5` for transient errors.\n\n- Attempt 1 completes (fails) \u2192 `should_retry(1, ...)` \u2192 `1 < 5` = True \u2192 retry\n- Attempt 2 completes \u2192 `should_retry(2, ...)` \u2192 `2 < 5` = True \u2192 retry\n- Attempt 3 completes \u2192 `should_retry(3, ...)` \u2192 `3 < 5` = True \u2192 retry\n- Attempt 4 completes \u2192 `should_retry(4, ...)` \u2192 `4 < 5` = True \u2192 retry\n- Attempt 5 completes \u2192 `should_retry(5, ...)` \u2192 `5 < 5` = False \u2192 stop\n\nSo the maximum total attempts is **5**.\n\nFILE: code/retry/policy.py\n```python\nclass TransientError(Exception):\n \"\"\"A retryable failure (timeout, 503, dropped connection, ...).\"\"\"\n\n\nclass FatalError(Exception):\n \"\"\"A non-retryable failure; give up immediately.\"\"\"\n\n\nclass RetryPolicy:\n \"\"\"Decides whether another attempt should be made after a failed try.\"\"\"\n\n def should_retry(self, attempt_number: int, error: Exception) -> bool:\n \"\"\"Return True if another attempt should be made.\n\n `attempt_number` is the 1-based count of attempts already completed (so `1` after the\n first try has failed). A fatal error is never retried; otherwise we keep going until the\n attempt cap is reached.\n \"\"\"\n if isinstance(error, FatalError):\n return False\n return attempt_number < 5\n\n def max_total_attempts(self) -> int:\n \"\"\"Return the maximum number of times a transient (retryable) operation will be\n attempted in total \u2014 the initial attempt plus every retry \u2014 for an operation that\n keeps failing with a `TransientError`.\n\n This is derived directly from `should_retry`: after each completed attempt we call\n `should_retry(attempt_number, TransientError())`, and a retry happens whenever it\n returns True. We simulate that loop to stay exactly consistent with the current\n implementation.\n \"\"\"\n attempts = 1\n while self.should_retry(attempts, TransientError()):\n attempts += 1\n return attempts\n```", "input_tokens": 728, "output_tokens": 718, "cost_usd": 0.02159, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/retry/policy.py"]}} -{"scenario": "retry-budget-code", "task_type": "code", "tier": "T0", "condition": "C1", "model": "haiku", "trial": 0, "output": "Looking at the code and documentation, I need to understand what `should_retry` actually does:\n\n1. `attempt_number` is 1-based count of attempts **already completed**\n2. For `FatalError`: returns `False` (never retry)\n3. For `TransientError`: returns `True` while `attempt_number < 5`\n\nLet me trace through what happens:\n- **1st attempt fails**: `should_retry(1, error)` \u2192 `1 < 5` \u2192 `True` (retry)\n- **2nd attempt fails**: `should_retry(2, error)` \u2192 `2 < 5` \u2192 `True` (retry)\n- **3rd attempt fails**: `should_retry(3, error)` \u2192 `3 < 5` \u2192 `True` (retry)\n- **4th attempt fails**: `should_retry(4, error)` \u2192 `4 < 5` \u2192 `True` (retry)\n- **5th attempt fails**: `should_retry(5, error)` \u2192 `5 < 5` \u2192 `False` (stop)\n\nSo the current code actually allows **5 total attempts**, not 3 as documented. However, the task says to \"Base the number strictly on the current `should_retry` implementation\" and to be \"exactly consistent with what `should_retry` actually does in the current code.\"\n\nThe current implementation allows attempts while `attempt_number < 5`, meaning it stops when `attempt_number == 5`. This means up to 5 total attempts are possible.\n\nTherefore, `max_total_attempts()` should return `5`.\n\nFILE: code/retry/policy.py\n\n```python\nclass TransientError(Exception):\n \"\"\"A retryable failure (timeout, 503, dropped connection, ...).\"\"\"\n\n\nclass FatalError(Exception):\n \"\"\"A non-retryable failure; give up immediately.\"\"\"\n\n\nclass RetryPolicy:\n \"\"\"Decides whether another attempt should be made after a failed try.\"\"\"\n\n def should_retry(self, attempt_number: int, error: Exception) -> bool:\n \"\"\"Return True if another attempt should be made.\n\n `attempt_number` is the 1-based count of attempts already completed (so `1` after the\n first try has failed). A fatal error is never retried; otherwise we keep going until the\n attempt cap is reached.\n \"\"\"\n if isinstance(error, FatalError):\n return False\n return attempt_number < 5\n\n def max_total_attempts(self) -> int:\n \"\"\"Return the maximum number of times a transient operation will be attempted in total.\n \n This is the initial attempt plus the maximum number of retries allowed by should_retry\n for a transient (retryable) error.\n \"\"\"\n return 5\n```", "input_tokens": 817, "output_tokens": 648, "cost_usd": 0.004057, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/retry/policy.py"]}} -{"scenario": "retry-budget-code", "task_type": "code", "tier": "T0", "condition": "C1", "model": "haiku", "trial": 1, "output": "Looking at the code and documentation, I need to understand what `should_retry` actually does:\n\n1. `should_retry` returns `False` for `FatalError` (never retry)\n2. `should_retry` returns `attempt_number < 5` for other errors\n\nHowever, the documentation claims the policy caps at **3 total attempts**, but the code shows `attempt_number < 5`, which would allow up to 5 attempts.\n\nLet me trace through the logic:\n- After 1st attempt fails: `should_retry(1, error)` \u2192 `1 < 5` \u2192 `True` (retry)\n- After 2nd attempt fails: `should_retry(2, error)` \u2192 `2 < 5` \u2192 `True` (retry)\n- After 3rd attempt fails: `should_retry(3, error)` \u2192 `3 < 5` \u2192 `True` (retry)\n- After 4th attempt fails: `should_retry(4, error)` \u2192 `4 < 5` \u2192 `True` (retry)\n- After 5th attempt fails: `should_retry(5, error)` \u2192 `5 < 5` \u2192 `False` (stop)\n\nThis means the current code allows **5 total attempts** (initial + 4 retries), not 3.\n\nSince the task says \"Base the number strictly on the current `should_retry` implementation\" and \"The value must be exactly consistent with what `should_retry` actually does in the current code\", I must implement based on what the code actually does (`< 5`), which allows 5 attempts total.\n\nThe maximum `attempt_number` for which `should_retry` returns `True` is 4 (since `4 < 5` is `True` but `5 < 5` is `False`). When `should_retry(4, TransientError)` returns `True`, that means we've already completed 4 attempts and are about to make a 5th. So the max total attempts is 5.\n\nFILE: code/retry/policy.py\n\n```python\nclass TransientError(Exception):\n \"\"\"A retryable failure (timeout, 503, dropped connection, ...).\"\"\"\n\n\nclass FatalError(Exception):\n \"\"\"A non-retryable failure; give up immediately.\"\"\"\n\n\nclass RetryPolicy:\n \"\"\"Decides whether another attempt should be made after a failed try.\"\"\"\n\n def should_retry(self, attempt_number: int, error: Exception) -> bool:\n \"\"\"Return True if another attempt should be made.\n\n `attempt_number` is the 1-based count of attempts already completed (so `1` after the\n first try has failed). A fatal error is never retried; otherwise we keep going until the\n attempt cap is reached.\n \"\"\"\n if isinstance(error, FatalError):\n return False\n return attempt_number < 5\n\n def max_total_attempts(self) -> int:\n \"\"\"Return the maximum number of times a transient operation will be attempted in total.\n \n This includes the initial attempt plus all retries. The value is consistent with the\n attempt cap enforced by should_retry.\n \"\"\"\n return 5\n```", "input_tokens": 817, "output_tokens": 742, "cost_usd": 0.004527, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/retry/policy.py"]}} -{"scenario": "retry-budget-code", "task_type": "code", "tier": "T0", "condition": "C1", "model": "haiku", "trial": 2, "output": "Let me trace through the `should_retry` logic to understand the maximum total attempts:\n\n1. `should_retry` is called with `attempt_number` (1-based count of attempts already completed)\n2. For a `TransientError`, it returns `attempt_number < 5`\n3. This means:\n - After attempt 1: `should_retry(1, error)` \u2192 `1 < 5` \u2192 `True` (retry)\n - After attempt 2: `should_retry(2, error)` \u2192 `2 < 5` \u2192 `True` (retry)\n - After attempt 3: `should_retry(3, error)` \u2192 `3 < 5` \u2192 `True` (retry)\n - After attempt 4: `should_retry(4, error)` \u2192 `4 < 5` \u2192 `True` (retry)\n - After attempt 5: `should_retry(5, error)` \u2192 `5 < 5` \u2192 `False` (stop)\n\nSo the current implementation allows 5 total attempts, not 3 as documented. However, the task says to \"Base the number strictly on the current `should_retry` implementation. Do not change `should_retry`.\"\n\nThe maximum total attempts is when `attempt_number < 5` becomes `False`, which happens at `attempt_number = 5`. This means we can make attempts 1, 2, 3, 4, and 5 before the retry stops. So the answer is **5 total attempts**.\n\nFILE: code/retry/policy.py\n\n```python\nclass TransientError(Exception):\n \"\"\"A retryable failure (timeout, 503, dropped connection, ...).\"\"\"\n\n\nclass FatalError(Exception):\n \"\"\"A non-retryable failure; give up immediately.\"\"\"\n\n\nclass RetryPolicy:\n \"\"\"Decides whether another attempt should be made after a failed try.\"\"\"\n\n def should_retry(self, attempt_number: int, error: Exception) -> bool:\n \"\"\"Return True if another attempt should be made.\n\n `attempt_number` is the 1-based count of attempts already completed (so `1` after the\n first try has failed). A fatal error is never retried; otherwise we keep going until the\n attempt cap is reached.\n \"\"\"\n if isinstance(error, FatalError):\n return False\n return attempt_number < 5\n\n def max_total_attempts(self) -> int:\n \"\"\"Return the maximum number of times a transient operation will be attempted in total.\n\n This includes the initial attempt plus all retries. The value is based on the current\n should_retry implementation, which returns False when attempt_number >= 5.\n \"\"\"\n return 5\n```", "input_tokens": 817, "output_tokens": 634, "cost_usd": 0.003987, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/retry/policy.py"]}} -{"scenario": "retry-budget-code", "task_type": "code", "tier": "T0", "condition": "C1", "model": "haiku", "trial": 3, "output": "Looking at the code and documentation, I need to understand what `should_retry` currently does:\n\n1. It returns `False` for `FatalError` (never retry)\n2. For other errors, it returns `True` if `attempt_number < 5`\n\nThe `attempt_number` is the 1-based count of attempts **already completed**.\n\nSo:\n- After attempt 1 fails: `should_retry(1, error)` \u2192 `1 < 5` \u2192 `True` (retry)\n- After attempt 2 fails: `should_retry(2, error)` \u2192 `2 < 5` \u2192 `True` (retry)\n- After attempt 3 fails: `should_retry(3, error)` \u2192 `3 < 5` \u2192 `True` (retry)\n- After attempt 4 fails: `should_retry(4, error)` \u2192 `4 < 5` \u2192 `True` (retry)\n- After attempt 5 fails: `should_retry(5, error)` \u2192 `5 < 5` \u2192 `False` (stop)\n\nThis means the current code allows **5 total attempts** (initial + 4 retries).\n\nHowever, the documentation claims it should be 3 total attempts. There's a mismatch between the documentation and the code. The task says: \"Base the number strictly on the current `should_retry` implementation. Do not change `should_retry`.\"\n\nSo I must base `max_total_attempts()` on what the code **actually does** (5 attempts), not what the documentation claims (3 attempts).\n\nThe maximum total attempts is the highest `attempt_number` for which `should_retry` can return `True` plus 1 (since `attempt_number` is already completed attempts). Since `should_retry` returns `True` when `attempt_number < 5`, the maximum value of `attempt_number` that returns `True` is 4, meaning we can make up to 4 more attempts after the initial one, totaling 5 attempts.\n\nFILE: code/retry/policy.py\n\n```python\nclass TransientError(Exception):\n \"\"\"A retryable failure (timeout, 503, dropped connection, ...).\"\"\"\n\n\nclass FatalError(Exception):\n \"\"\"A non-retryable failure; give up immediately.\"\"\"\n\n\nclass RetryPolicy:\n \"\"\"Decides whether another attempt should be made after a failed try.\"\"\"\n\n def should_retry(self, attempt_number: int, error: Exception) -> bool:\n \"\"\"Return True if another attempt should be made.\n\n `attempt_number` is the 1-based count of attempts already completed (so `1` after the\n first try has failed). A fatal error is never retried; otherwise we keep going until the\n attempt cap is reached.\n \"\"\"\n if isinstance(error, FatalError):\n return False\n return attempt_number < 5\n\n def max_total_attempts(self) -> int:\n \"\"\"Return the maximum number of times a transient operation will be attempted in total.\n \n This includes the initial attempt plus all retries. For an operation that keeps failing\n with a TransientError, this is the maximum number of times it will be tried before the\n policy gives up.\n \"\"\"\n return 5\n```", "input_tokens": 817, "output_tokens": 760, "cost_usd": 0.0046170000000000004, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/retry/policy.py"]}} -{"scenario": "retry-budget-code", "task_type": "code", "tier": "T0", "condition": "C1", "model": "haiku", "trial": 4, "output": "Let me trace through the `should_retry` logic to understand the maximum attempts:\n\n1. `should_retry` returns `False` if `isinstance(error, FatalError)` - but we're dealing with `TransientError`\n2. For transient errors, it returns `attempt_number < 5`\n\nSo:\n- `attempt_number = 1` (1st attempt failed): `1 < 5` \u2192 `True` (retry)\n- `attempt_number = 2` (2nd attempt failed): `2 < 5` \u2192 `True` (retry)\n- `attempt_number = 3` (3rd attempt failed): `3 < 5` \u2192 `True` (retry)\n- `attempt_number = 4` (4th attempt failed): `4 < 5` \u2192 `True` (retry)\n- `attempt_number = 5` (5th attempt failed): `5 < 5` \u2192 `False` (stop)\n\nThis means the operation will be attempted at most 5 times total (initial + 4 retries) before `should_retry` returns `False`.\n\nHowever, I notice the documentation claims 3 attempts, but the code shows `attempt_number < 5`. Based on the task instruction \"Base the number strictly on the current `should_retry` implementation\", I must use what the code actually does, not what the documentation says.\n\nThe maximum total attempts is **5** (when `attempt_number` reaches 5, `should_retry` returns `False`).\n\nFILE: code/retry/policy.py\n\n```python\nclass TransientError(Exception):\n \"\"\"A retryable failure (timeout, 503, dropped connection, ...).\"\"\"\n\n\nclass FatalError(Exception):\n \"\"\"A non-retryable failure; give up immediately.\"\"\"\n\n\nclass RetryPolicy:\n \"\"\"Decides whether another attempt should be made after a failed try.\"\"\"\n\n def should_retry(self, attempt_number: int, error: Exception) -> bool:\n \"\"\"Return True if another attempt should be made.\n\n `attempt_number` is the 1-based count of attempts already completed (so `1` after the\n first try has failed). A fatal error is never retried; otherwise we keep going until the\n attempt cap is reached.\n \"\"\"\n if isinstance(error, FatalError):\n return False\n return attempt_number < 5\n\n def max_total_attempts(self) -> int:\n \"\"\"Return the maximum number of times a transient operation will be attempted in total.\n \n This includes the initial attempt plus all retries. The value is consistent with\n the behavior of should_retry for TransientError.\n \"\"\"\n return 5\n```", "input_tokens": 817, "output_tokens": 624, "cost_usd": 0.003937, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/retry/policy.py"]}} -{"scenario": "retry-budget-code", "task_type": "code", "tier": "T0", "condition": "C1", "model": "haiku", "trial": 5, "output": "FILE: code/retry/policy.py\n\n```python\nclass TransientError(Exception):\n \"\"\"A retryable failure (timeout, 503, dropped connection, ...).\"\"\"\n\n\nclass FatalError(Exception):\n \"\"\"A non-retryable failure; give up immediately.\"\"\"\n\n\nclass RetryPolicy:\n \"\"\"Decides whether another attempt should be made after a failed try.\"\"\"\n\n def should_retry(self, attempt_number: int, error: Exception) -> bool:\n \"\"\"Return True if another attempt should be made.\n\n `attempt_number` is the 1-based count of attempts already completed (so `1` after the\n first try has failed). A fatal error is never retried; otherwise we keep going until the\n attempt cap is reached.\n \"\"\"\n if isinstance(error, FatalError):\n return False\n return attempt_number < 5\n\n def max_total_attempts(self) -> int:\n \"\"\"Return the maximum number of times a transient operation will be attempted in total.\n \n This is the initial attempt plus all retries for an operation that keeps failing\n with a TransientError.\n \"\"\"\n return 5\n```", "input_tokens": 817, "output_tokens": 272, "cost_usd": 0.002177, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/retry/policy.py"]}} -{"scenario": "retry-budget-code", "task_type": "code", "tier": "T0", "condition": "C1", "model": "haiku", "trial": 6, "output": "Looking at the code and documentation, I need to understand what `should_retry` currently does:\n\n1. It takes `attempt_number` (1-based count of attempts already completed)\n2. It returns `False` for `FatalError` (never retry)\n3. For other errors, it returns `attempt_number < 5`\n\nThis means:\n- After attempt 1: `1 < 5` \u2192 True (retry)\n- After attempt 2: `2 < 5` \u2192 True (retry)\n- After attempt 3: `3 < 5` \u2192 True (retry)\n- After attempt 4: `4 < 5` \u2192 True (retry)\n- After attempt 5: `5 < 5` \u2192 False (stop)\n\nHowever, the documentation claims the policy caps at 3 total attempts, but the current code caps at 5 attempts (`attempt_number < 5`). The task says \"Base the number strictly on the current `should_retry` implementation\" and \"The value must be exactly consistent with what `should_retry` actually does in the current code.\"\n\nSo the actual maximum attempts based on the current code is 5 (when `attempt_number` reaches 5, it returns False).\n\nThe `max_total_attempts()` method should return this maximum: 5.\n\nFILE: code/retry/policy.py\n\n```python\nclass TransientError(Exception):\n \"\"\"A retryable failure (timeout, 503, dropped connection, ...).\"\"\"\n\n\nclass FatalError(Exception):\n \"\"\"A non-retryable failure; give up immediately.\"\"\"\n\n\nclass RetryPolicy:\n \"\"\"Decides whether another attempt should be made after a failed try.\"\"\"\n\n def should_retry(self, attempt_number: int, error: Exception) -> bool:\n \"\"\"Return True if another attempt should be made.\n\n `attempt_number` is the 1-based count of attempts already completed (so `1` after the\n first try has failed). A fatal error is never retried; otherwise we keep going until the\n attempt cap is reached.\n \"\"\"\n if isinstance(error, FatalError):\n return False\n return attempt_number < 5\n\n def max_total_attempts(self) -> int:\n \"\"\"Return the maximum number of times a transient operation will be attempted in total.\n \n This includes the initial attempt plus all retries. The value is consistent with the\n attempt cap enforced by should_retry.\n \"\"\"\n return 5\n```", "input_tokens": 817, "output_tokens": 575, "cost_usd": 0.0036920000000000004, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/retry/policy.py"]}} -{"scenario": "retry-budget-code", "task_type": "code", "tier": "T0", "condition": "C1", "model": "haiku", "trial": 7, "output": "Let me trace through the `should_retry` logic to understand the maximum attempts:\n\n1. `should_retry` returns `False` for `FatalError` (never retried)\n2. For other errors, it returns `attempt_number < 5`\n3. `attempt_number` is the 1-based count of attempts **already completed**\n\nSo the logic is:\n- After attempt 1 completes: `should_retry(1, error)` \u2192 `1 < 5` \u2192 `True` (retry happens, attempt 2 will occur)\n- After attempt 2 completes: `should_retry(2, error)` \u2192 `2 < 5` \u2192 `True` (retry happens, attempt 3 will occur)\n- After attempt 3 completes: `should_retry(3, error)` \u2192 `3 < 5` \u2192 `True` (retry happens, attempt 4 will occur)\n- After attempt 4 completes: `should_retry(4, error)` \u2192 `4 < 5` \u2192 `True` (retry happens, attempt 5 will occur)\n- After attempt 5 completes: `should_retry(5, error)` \u2192 `5 < 5` \u2192 `False` (no more retries)\n\nSo with the current code, the maximum is **5 total attempts** for a transient error.\n\nThe `max_total_attempts()` method should return the maximum value of `attempt_number` at which `should_retry` would still return `True`, plus 1 (since `attempt_number` is the count of completed attempts).\n\nActually, let me reconsider: the maximum attempt number where `should_retry` returns `True` is when `attempt_number < 5`, which means `attempt_number` can be 4 at most. When `attempt_number` is 4, `should_retry` returns `True`, meaning a 5th attempt will be made. After the 5th attempt, `attempt_number` becomes 5, and `should_retry(5, error)` returns `False`.\n\nTherefore, `max_total_attempts()` should return **5**.\n\nFILE: code/retry/policy.py\n\n```python\nclass TransientError(Exception):\n \"\"\"A retryable failure (timeout, 503, dropped connection, ...).\"\"\"\n\n\nclass FatalError(Exception):\n \"\"\"A non-retryable failure; give up immediately.\"\"\"\n\n\nclass RetryPolicy:\n \"\"\"Decides whether another attempt should be made after a failed try.\"\"\"\n\n def should_retry(self, attempt_number: int, error: Exception) -> bool:\n \"\"\"Return True if another attempt should be made.\n\n `attempt_number` is the 1-based count of attempts already completed (so `1` after the\n first try has failed). A fatal error is never retried; otherwise we keep going until the\n attempt cap is reached.\n \"\"\"\n if isinstance(error, FatalError):\n return False\n return attempt_number < 5\n\n def max_total_attempts(self) -> int:\n \"\"\"Return the maximum number of times a transient operation will be attempted in total.\n \n This includes the initial attempt plus all retries. For a transient (retryable) error,\n should_retry returns True while attempt_number < 5, meaning retries occur for attempts\n 1 through 4, allowing attempts 2 through 5 to proceed.\n \"\"\"\n return 5\n```", "input_tokens": 817, "output_tokens": 805, "cost_usd": 0.004842, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/retry/policy.py"]}} -{"scenario": "retry-budget-code", "task_type": "code", "tier": "T0", "condition": "C1", "model": "haiku", "trial": 8, "output": "Looking at the code and documentation, I need to understand what `should_retry` actually does:\n\n1. `should_retry` receives `attempt_number` as a 1-based count of attempts **already completed**\n2. It returns `False` for `FatalError` (never retry)\n3. For other errors, it returns `attempt_number < 5`\n\nLet me trace through a transient error scenario:\n- 1st attempt fails \u2192 `should_retry(attempt_number=1, error)` \u2192 `1 < 5` \u2192 `True` \u2192 retry\n- 2nd attempt fails \u2192 `should_retry(attempt_number=2, error)` \u2192 `2 < 5` \u2192 `True` \u2192 retry\n- 3rd attempt fails \u2192 `should_retry(attempt_number=3, error)` \u2192 `3 < 5` \u2192 `True` \u2192 retry\n- 4th attempt fails \u2192 `should_retry(attempt_number=4, error)` \u2192 `4 < 5` \u2192 `True` \u2192 retry\n- 5th attempt fails \u2192 `should_retry(attempt_number=5, error)` \u2192 `5 < 5` \u2192 `False` \u2192 stop\n\nSo the current code allows up to **5 total attempts**.\n\nHowever, the documentation claims it should cap at **3 total attempts** (the initial attempt plus 2 retries). This is a discrepancy between the code and the docs. The task says: \"Base the number strictly on the current `should_retry` implementation.\"\n\nTherefore, `max_total_attempts()` should return `5` to match what the current code actually does.\n\nFILE: code/retry/policy.py\n\n```python\nclass TransientError(Exception):\n \"\"\"A retryable failure (timeout, 503, dropped connection, ...).\"\"\"\n\n\nclass FatalError(Exception):\n \"\"\"A non-retryable failure; give up immediately.\"\"\"\n\n\nclass RetryPolicy:\n \"\"\"Decides whether another attempt should be made after a failed try.\"\"\"\n\n def should_retry(self, attempt_number: int, error: Exception) -> bool:\n \"\"\"Return True if another attempt should be made.\n\n `attempt_number` is the 1-based count of attempts already completed (so `1` after the\n first try has failed). A fatal error is never retried; otherwise we keep going until the\n attempt cap is reached.\n \"\"\"\n if isinstance(error, FatalError):\n return False\n return attempt_number < 5\n\n def max_total_attempts(self) -> int:\n \"\"\"Return the maximum number of times a transient operation will be attempted in total.\n\n This is the initial attempt plus all retries for an operation that keeps failing with\n a TransientError. The value is consistent with what should_retry actually does.\n \"\"\"\n return 5\n```", "input_tokens": 817, "output_tokens": 654, "cost_usd": 0.004087, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/retry/policy.py"]}} -{"scenario": "retry-budget-code", "task_type": "code", "tier": "T0", "condition": "C1", "model": "haiku", "trial": 9, "output": "Looking at the code and documentation, I need to understand what `should_retry` currently does:\n\n1. It returns `False` for `FatalError` (never retries)\n2. For other errors, it returns `True` while `attempt_number < 5`\n\nThe `attempt_number` is 1-based, counting attempts already completed.\n\nLet me trace through the logic:\n- After 1st attempt fails: `should_retry(1, error)` \u2192 `1 < 5` is `True` \u2192 retry\n- After 2nd attempt fails: `should_retry(2, error)` \u2192 `2 < 5` is `True` \u2192 retry\n- After 3rd attempt fails: `should_retry(3, error)` \u2192 `3 < 5` is `True` \u2192 retry\n- After 4th attempt fails: `should_retry(4, error)` \u2192 `4 < 5` is `True` \u2192 retry\n- After 5th attempt fails: `should_retry(5, error)` \u2192 `5 < 5` is `False` \u2192 stop\n\nSo the current code allows up to 5 total attempts (initial + 4 retries).\n\nHowever, the documentation states the policy should cap at 3 total attempts. There's a discrepancy: the documentation says it should return `False` when `attempt_number` reaches 3, but the code says `attempt_number < 5`.\n\nThe task says: \"Base the number strictly on the current `should_retry` implementation. Do not change `should_retry`.\"\n\nSo I must implement `max_total_attempts()` based on what the code actually does (5 attempts), not what the documentation claims it should do.\n\nThe maximum total attempts occurs when `should_retry` first returns `False`. That happens when `attempt_number >= 5`, which means after 5 attempts have been completed, no more retries happen. Therefore, the maximum is 5 total attempts.\n\nFILE: code/retry/policy.py\n\n```python\nclass TransientError(Exception):\n \"\"\"A retryable failure (timeout, 503, dropped connection, ...).\"\"\"\n\n\nclass FatalError(Exception):\n \"\"\"A non-retryable failure; give up immediately.\"\"\"\n\n\nclass RetryPolicy:\n \"\"\"Decides whether another attempt should be made after a failed try.\"\"\"\n\n def should_retry(self, attempt_number: int, error: Exception) -> bool:\n \"\"\"Return True if another attempt should be made.\n\n `attempt_number` is the 1-based count of attempts already completed (so `1` after the\n first try has failed). A fatal error is never retried; otherwise we keep going until the\n attempt cap is reached.\n \"\"\"\n if isinstance(error, FatalError):\n return False\n return attempt_number < 5\n\n def max_total_attempts(self) -> int:\n \"\"\"Return the maximum number of times a transient operation will be attempted in total.\n \n This is the initial attempt plus the maximum number of retries allowed by should_retry\n for a continuously failing transient operation.\n \"\"\"\n return 5\n```", "input_tokens": 817, "output_tokens": 724, "cost_usd": 0.004437, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/retry/policy.py"]}} -{"scenario": "retry-budget-code", "task_type": "code", "tier": "T0", "condition": "C1", "model": "sonnet", "trial": 0, "output": "Looking at the current `should_retry` implementation:\n\n```python\nreturn attempt_number < 5\n```\n\n`attempt_number` is the 1-based count of attempts already completed. So:\n- After attempt 1: `1 < 5` \u2192 True, retry\n- After attempt 2: `2 < 5` \u2192 True, retry\n- After attempt 3: `3 < 5` \u2192 True, retry\n- After attempt 4: `4 < 5` \u2192 True, retry\n- After attempt 5: `5 < 5` \u2192 False, stop\n\nSo the operation is attempted at most **5 times** total. The `max_total_attempts` method should return `5`.\n\nFILE: code/retry/policy.py\n\n```python\nclass TransientError(Exception):\n \"\"\"A retryable failure (timeout, 503, dropped connection, ...).\"\"\"\n\n\nclass FatalError(Exception):\n \"\"\"A non-retryable failure; give up immediately.\"\"\"\n\n\nclass RetryPolicy:\n \"\"\"Decides whether another attempt should be made after a failed try.\"\"\"\n\n def should_retry(self, attempt_number: int, error: Exception) -> bool:\n \"\"\"Return True if another attempt should be made.\n\n `attempt_number` is the 1-based count of attempts already completed (so `1` after the\n first try has failed). A fatal error is never retried; otherwise we keep going until the\n attempt cap is reached.\n \"\"\"\n if isinstance(error, FatalError):\n return False\n return attempt_number < 5\n\n def max_total_attempts(self) -> int:\n \"\"\"Return the maximum number of times a transient operation will be attempted in total.\n\n This is exactly consistent with what should_retry does: it returns True while\n attempt_number < 5, meaning retries occur after attempts 1, 2, 3, and 4, and the\n final (5th) attempt is made but not retried. The total is therefore 5.\n \"\"\"\n return 5\n```", "input_tokens": 818, "output_tokens": 478, "cost_usd": 0.009624, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/retry/policy.py"]}} -{"scenario": "retry-budget-code", "task_type": "code", "tier": "T0", "condition": "C1", "model": "sonnet", "trial": 1, "output": "I need to trace through `should_retry` to find the actual cap, then implement `max_total_attempts` consistently with it.\n\nTracing `should_retry` for transient errors:\n- `attempt_number` is the 1-based count of attempts **already completed**\n- Returns `True` (retry) while `attempt_number < 5`\n- So it retries after attempts 1, 2, 3, 4 (when attempt_number is 1, 2, 3, 4)\n- Returns `False` when `attempt_number >= 5` (i.e., after 5 attempts have been made)\n- Total attempts = **5**\n\nThe documentation claims 3, but the task says: \"Base the number strictly on the current `should_retry` implementation. Do not change `should_retry`.\"\n\nThe current code has `attempt_number < 5`, meaning retries happen after attempts 1, 2, 3, and 4, stopping after attempt 5. So `max_total_attempts()` must return **5**.\n\nFILE: code/retry/policy.py\n\n```python\nclass TransientError(Exception):\n \"\"\"A retryable failure (timeout, 503, dropped connection, ...).\"\"\"\n\n\nclass FatalError(Exception):\n \"\"\"A non-retryable failure; give up immediately.\"\"\"\n\n\nclass RetryPolicy:\n \"\"\"Decides whether another attempt should be made after a failed try.\"\"\"\n\n def should_retry(self, attempt_number: int, error: Exception) -> bool:\n \"\"\"Return True if another attempt should be made.\n\n `attempt_number` is the 1-based count of attempts already completed (so `1` after the\n first try has failed). A fatal error is never retried; otherwise we keep going until the\n attempt cap is reached.\n \"\"\"\n if isinstance(error, FatalError):\n return False\n return attempt_number < 5\n\n def max_total_attempts(self) -> int:\n \"\"\"Return the maximum number of times a transient operation will be attempted in total.\n\n This is the initial attempt plus every retry for an operation that keeps failing with a\n TransientError. The value is derived directly from the should_retry implementation:\n should_retry returns True while attempt_number < 5, so retries occur after attempts\n 1, 2, 3, and 4, and the final (5th) attempt is made but not retried. Thus the total\n number of attempts is 5.\n \"\"\"\n transient = TransientError()\n attempt = 1\n while self.should_retry(attempt, transient):\n attempt += 1\n return attempt\n```", "input_tokens": 818, "output_tokens": 622, "cost_usd": 0.011784, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/retry/policy.py"]}} -{"scenario": "retry-budget-code", "task_type": "code", "tier": "T0", "condition": "C1", "model": "sonnet", "trial": 2, "output": "Looking at the current `should_retry` implementation:\n\n```python\nreturn attempt_number < 5\n```\n\n`attempt_number` is the 1-based count of attempts already completed. So:\n- After attempt 1: `1 < 5` \u2192 True, retry\n- After attempt 2: `2 < 5` \u2192 True, retry\n- After attempt 3: `3 < 5` \u2192 True, retry\n- After attempt 4: `4 < 5` \u2192 True, retry\n- After attempt 5: `5 < 5` \u2192 False, stop\n\nSo the operation is attempted at most **5** times total. The `max_total_attempts` method should return `5`, derived directly from the threshold in `should_retry`.\n\nFILE: code/retry/policy.py\n\n```python\nclass TransientError(Exception):\n \"\"\"A retryable failure (timeout, 503, dropped connection, ...).\"\"\"\n\n\nclass FatalError(Exception):\n \"\"\"A non-retryable failure; give up immediately.\"\"\"\n\n\nclass RetryPolicy:\n \"\"\"Decides whether another attempt should be made after a failed try.\"\"\"\n\n def should_retry(self, attempt_number: int, error: Exception) -> bool:\n \"\"\"Return True if another attempt should be made.\n\n `attempt_number` is the 1-based count of attempts already completed (so `1` after the\n first try has failed). A fatal error is never retried; otherwise we keep going until the\n attempt cap is reached.\n \"\"\"\n if isinstance(error, FatalError):\n return False\n return attempt_number < 5\n\n def max_total_attempts(self) -> int:\n \"\"\"Return the maximum number of times a transient operation will be attempted in total.\n\n This is the initial attempt plus every retry for an operation that keeps failing with a\n TransientError. The value is derived directly from the cap enforced by should_retry:\n should_retry returns True while attempt_number < 5, meaning retries continue after\n attempts 1 through 4, so the operation is tried at most 5 times in total.\n \"\"\"\n return 5\n```", "input_tokens": 818, "output_tokens": 502, "cost_usd": 0.009984, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/retry/policy.py"]}} -{"scenario": "retry-budget-code", "task_type": "code", "tier": "T0", "condition": "C1", "model": "sonnet", "trial": 3, "output": "Looking at the current `should_retry` implementation:\n\n```python\nreturn attempt_number < 5\n```\n\n`attempt_number` is the 1-based count of attempts already completed. So:\n- After attempt 1: `1 < 5` \u2192 True, retry\n- After attempt 2: `2 < 5` \u2192 True, retry\n- After attempt 3: `3 < 5` \u2192 True, retry\n- After attempt 4: `4 < 5` \u2192 True, retry\n- After attempt 5: `5 < 5` \u2192 False, stop\n\nSo the operation is attempted at most **5 times** total. The `max_total_attempts` method should return `5`.\n\nFILE: code/retry/policy.py\n\n```python\nclass TransientError(Exception):\n \"\"\"A retryable failure (timeout, 503, dropped connection, ...).\"\"\"\n\n\nclass FatalError(Exception):\n \"\"\"A non-retryable failure; give up immediately.\"\"\"\n\n\nclass RetryPolicy:\n \"\"\"Decides whether another attempt should be made after a failed try.\"\"\"\n\n def should_retry(self, attempt_number: int, error: Exception) -> bool:\n \"\"\"Return True if another attempt should be made.\n\n `attempt_number` is the 1-based count of attempts already completed (so `1` after the\n first try has failed). A fatal error is never retried; otherwise we keep going until the\n attempt cap is reached.\n \"\"\"\n if isinstance(error, FatalError):\n return False\n return attempt_number < 5\n\n def max_total_attempts(self) -> int:\n \"\"\"Return the maximum number of times a transient operation will be attempted in total.\n\n This is the initial attempt plus every retry for an operation that keeps failing with a\n TransientError. The value is derived directly from the ceiling enforced by should_retry:\n should_retry returns True while attempt_number < 5, so the last retry occurs after the\n 4th attempt, making the 5th attempt the final one \u2014 5 total attempts in all.\n \"\"\"\n return 5\n```", "input_tokens": 818, "output_tokens": 494, "cost_usd": 0.009864, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/retry/policy.py"]}} -{"scenario": "retry-budget-code", "task_type": "code", "tier": "T0", "condition": "C1", "model": "sonnet", "trial": 4, "output": "Looking at the current `should_retry` implementation:\n\n```python\nreturn attempt_number < 5\n```\n\n`attempt_number` is the 1-based count of attempts already completed. So:\n- After attempt 1: `1 < 5` \u2192 True, retry\n- After attempt 2: `2 < 5` \u2192 True, retry\n- After attempt 3: `3 < 5` \u2192 True, retry\n- After attempt 4: `4 < 5` \u2192 True, retry\n- After attempt 5: `5 < 5` \u2192 False, stop\n\nSo the operation is attempted at most **5** times total (attempts 1\u20135, where after attempt 5 it returns False).\n\nFILE: code/retry/policy.py\n\n```python\nclass TransientError(Exception):\n \"\"\"A retryable failure (timeout, 503, dropped connection, ...).\"\"\"\n\n\nclass FatalError(Exception):\n \"\"\"A non-retryable failure; give up immediately.\"\"\"\n\n\nclass RetryPolicy:\n \"\"\"Decides whether another attempt should be made after a failed try.\"\"\"\n\n def should_retry(self, attempt_number: int, error: Exception) -> bool:\n \"\"\"Return True if another attempt should be made.\n\n `attempt_number` is the 1-based count of attempts already completed (so `1` after the\n first try has failed). A fatal error is never retried; otherwise we keep going until the\n attempt cap is reached.\n \"\"\"\n if isinstance(error, FatalError):\n return False\n return attempt_number < 5\n\n def max_total_attempts(self) -> int:\n \"\"\"Return the maximum number of times a transient operation will be attempted in total.\n\n This is exactly consistent with should_retry: since should_retry returns True while\n attempt_number < 5, the last attempt that triggers a retry is attempt 4, meaning\n attempt 5 is made and then should_retry(5, ...) returns False. The total number of\n attempts made for a persistently-failing transient operation is therefore 5.\n \"\"\"\n return 5\n```", "input_tokens": 818, "output_tokens": 493, "cost_usd": 0.009849, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/retry/policy.py"]}} -{"scenario": "retry-budget-code", "task_type": "code", "tier": "T0", "condition": "C1", "model": "sonnet", "trial": 5, "output": "I need to trace through `should_retry` to find the actual cap, then implement `max_total_attempts` consistently with it.\n\nTracing `should_retry`:\n- `attempt_number` is the 1-based count of attempts **already completed**\n- Returns `True` (retry) when `attempt_number < 5` (for non-fatal errors)\n- So retries happen after attempts 1, 2, 3, 4 (when attempt_number is 1, 2, 3, 4)\n- Returns `False` when `attempt_number >= 5`, meaning after 5 attempts have been made, no more retries\n- Total attempts = 5\n\nSo `max_total_attempts` should return `5`.\n\nFILE: code/retry/policy.py\n\n```python\nclass TransientError(Exception):\n \"\"\"A retryable failure (timeout, 503, dropped connection, ...).\"\"\"\n\n\nclass FatalError(Exception):\n \"\"\"A non-retryable failure; give up immediately.\"\"\"\n\n\nclass RetryPolicy:\n \"\"\"Decides whether another attempt should be made after a failed try.\"\"\"\n\n def should_retry(self, attempt_number: int, error: Exception) -> bool:\n \"\"\"Return True if another attempt should be made.\n\n `attempt_number` is the 1-based count of attempts already completed (so `1` after the\n first try has failed). A fatal error is never retried; otherwise we keep going until the\n attempt cap is reached.\n \"\"\"\n if isinstance(error, FatalError):\n return False\n return attempt_number < 5\n\n def max_total_attempts(self) -> int:\n \"\"\"Return the maximum number of times a transient operation will be attempted in total.\n\n This is the initial attempt plus every retry for an operation that keeps failing with a\n TransientError. The value is exactly consistent with what should_retry does: a retry\n occurs whenever attempt_number < 5, where attempt_number is the 1-based count of attempts\n already completed. Therefore the last retry happens after attempt 4, making attempt 5 the\n final one, for a total of 5 attempts.\n \"\"\"\n return 5\n```", "input_tokens": 818, "output_tokens": 514, "cost_usd": 0.010164, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/retry/policy.py"]}} -{"scenario": "retry-budget-code", "task_type": "code", "tier": "T0", "condition": "C1", "model": "sonnet", "trial": 6, "output": "Looking at the current `should_retry` implementation:\n\n```python\nreturn attempt_number < 5\n```\n\n`attempt_number` is the 1-based count of attempts already completed. So:\n\n- After attempt 1: `1 < 5` \u2192 True, retry\n- After attempt 2: `2 < 5` \u2192 True, retry\n- After attempt 3: `3 < 5` \u2192 True, retry\n- After attempt 4: `4 < 5` \u2192 True, retry\n- After attempt 5: `5 < 5` \u2192 False, stop\n\nSo the operation is attempted at most **5 times** total (attempts 1\u20135, where after attempt 5 it stops). The maximum total attempts is 5.\n\nFILE: code/retry/policy.py\n\n```python\nclass TransientError(Exception):\n \"\"\"A retryable failure (timeout, 503, dropped connection, ...).\"\"\"\n\n\nclass FatalError(Exception):\n \"\"\"A non-retryable failure; give up immediately.\"\"\"\n\n\nclass RetryPolicy:\n \"\"\"Decides whether another attempt should be made after a failed try.\"\"\"\n\n def should_retry(self, attempt_number: int, error: Exception) -> bool:\n \"\"\"Return True if another attempt should be made.\n\n `attempt_number` is the 1-based count of attempts already completed (so `1` after the\n first try has failed). A fatal error is never retried; otherwise we keep going until the\n attempt cap is reached.\n \"\"\"\n if isinstance(error, FatalError):\n return False\n return attempt_number < 5\n\n def max_total_attempts(self) -> int:\n \"\"\"Return the maximum number of times a transient operation will be attempted in total.\n\n This is the initial attempt plus every retry for an operation that keeps failing with a\n TransientError. The value is derived directly from the should_retry implementation:\n should_retry returns True while attempt_number < 5, meaning retries occur after attempts\n 1 through 4, so the final (5th) attempt is made but not retried \u2014 giving 5 total attempts.\n \"\"\"\n return 5\n```", "input_tokens": 818, "output_tokens": 505, "cost_usd": 0.010029, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/retry/policy.py"]}} -{"scenario": "retry-budget-code", "task_type": "code", "tier": "T0", "condition": "C1", "model": "sonnet", "trial": 7, "output": "I need to trace through `should_retry` to find the actual cap, then implement `max_total_attempts` consistently with it.\n\nTracing `should_retry`:\n- `attempt_number` is the 1-based count of attempts **already completed**\n- A retry happens when `attempt_number < 5`\n- So retries happen for attempt_number 1, 2, 3, 4 (i.e., after the 1st, 2nd, 3rd, 4th attempt)\n- No retry when `attempt_number >= 5` (i.e., after the 5th attempt)\n- Total attempts = 5\n\nThe documentation claims 3, but the actual code uses `< 5`, meaning 5 total attempts. The task says: \"Base the number strictly on the current `should_retry` implementation. Do not change `should_retry`.\"\n\nSo `max_total_attempts` must return 5.\n\nFILE: code/retry/policy.py\n\n```python\nclass TransientError(Exception):\n \"\"\"A retryable failure (timeout, 503, dropped connection, ...).\"\"\"\n\n\nclass FatalError(Exception):\n \"\"\"A non-retryable failure; give up immediately.\"\"\"\n\n\nclass RetryPolicy:\n \"\"\"Decides whether another attempt should be made after a failed try.\"\"\"\n\n def should_retry(self, attempt_number: int, error: Exception) -> bool:\n \"\"\"Return True if another attempt should be made.\n\n `attempt_number` is the 1-based count of attempts already completed (so `1` after the\n first try has failed). A fatal error is never retried; otherwise we keep going until the\n attempt cap is reached.\n \"\"\"\n if isinstance(error, FatalError):\n return False\n return attempt_number < 5\n\n def max_total_attempts(self) -> int:\n \"\"\"Return the maximum number of times a transient operation will be attempted in total.\n\n This is the initial attempt plus every retry for an operation that keeps failing with a\n TransientError. The value is derived directly from the should_retry implementation:\n should_retry returns True while attempt_number < 5, meaning retries occur after attempts\n 1, 2, 3, and 4, and the final (5th) attempt is not retried. Thus the total is 5.\n \"\"\"\n transient = TransientError()\n attempt = 1\n while self.should_retry(attempt, transient):\n attempt += 1\n return attempt\n```", "input_tokens": 818, "output_tokens": 587, "cost_usd": 0.011259, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/retry/policy.py"]}} -{"scenario": "retry-budget-code", "task_type": "code", "tier": "T0", "condition": "C1", "model": "sonnet", "trial": 8, "output": "Looking at the current `should_retry` implementation:\n\n```python\nreturn attempt_number < 5\n```\n\n`attempt_number` is the 1-based count of attempts already completed. So:\n- After attempt 1: `1 < 5` \u2192 True, retry\n- After attempt 2: `2 < 5` \u2192 True, retry\n- After attempt 3: `3 < 5` \u2192 True, retry\n- After attempt 4: `4 < 5` \u2192 True, retry\n- After attempt 5: `5 < 5` \u2192 False, stop\n\nSo the operation is attempted at most **5** times total. The `max_total_attempts` method should return `5`.\n\nFILE: code/retry/policy.py\n\n```python\nclass TransientError(Exception):\n \"\"\"A retryable failure (timeout, 503, dropped connection, ...).\"\"\"\n\n\nclass FatalError(Exception):\n \"\"\"A non-retryable failure; give up immediately.\"\"\"\n\n\nclass RetryPolicy:\n \"\"\"Decides whether another attempt should be made after a failed try.\"\"\"\n\n def should_retry(self, attempt_number: int, error: Exception) -> bool:\n \"\"\"Return True if another attempt should be made.\n\n `attempt_number` is the 1-based count of attempts already completed (so `1` after the\n first try has failed). A fatal error is never retried; otherwise we keep going until the\n attempt cap is reached.\n \"\"\"\n if isinstance(error, FatalError):\n return False\n return attempt_number < 5\n\n def max_total_attempts(self) -> int:\n \"\"\"Return the maximum number of times a transient operation will be attempted in total.\n\n This is the initial attempt plus every retry for an operation that keeps failing with a\n TransientError. The value is derived directly from the cap enforced by should_retry:\n should_retry returns True while attempt_number < 5, so the last retry happens after\n attempt 4, making attempt 5 the final one \u2014 5 total attempts.\n \"\"\"\n return 5\n```", "input_tokens": 818, "output_tokens": 489, "cost_usd": 0.009789, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/retry/policy.py"]}} -{"scenario": "retry-budget-code", "task_type": "code", "tier": "T0", "condition": "C1", "model": "sonnet", "trial": 9, "output": "Looking at the current `should_retry` implementation:\n\n```python\nreturn attempt_number < 5\n```\n\n`attempt_number` is the 1-based count of attempts already completed. So:\n- After attempt 1: `1 < 5` \u2192 True, retry\n- After attempt 2: `2 < 5` \u2192 True, retry\n- After attempt 3: `3 < 5` \u2192 True, retry\n- After attempt 4: `4 < 5` \u2192 True, retry\n- After attempt 5: `5 < 5` \u2192 False, stop\n\nSo the operation is attempted 5 times total (attempts 1\u20135, where after attempt 5 it returns False). The maximum total attempts is **5**.\n\nFILE: code/retry/policy.py\n\n```python\nclass TransientError(Exception):\n \"\"\"A retryable failure (timeout, 503, dropped connection, ...).\"\"\"\n\n\nclass FatalError(Exception):\n \"\"\"A non-retryable failure; give up immediately.\"\"\"\n\n\nclass RetryPolicy:\n \"\"\"Decides whether another attempt should be made after a failed try.\"\"\"\n\n def should_retry(self, attempt_number: int, error: Exception) -> bool:\n \"\"\"Return True if another attempt should be made.\n\n `attempt_number` is the 1-based count of attempts already completed (so `1` after the\n first try has failed). A fatal error is never retried; otherwise we keep going until the\n attempt cap is reached.\n \"\"\"\n if isinstance(error, FatalError):\n return False\n return attempt_number < 5\n\n def max_total_attempts(self) -> int:\n \"\"\"Return the maximum number of times a transient operation will be attempted in total.\n\n This is the initial attempt plus every retry for an operation that keeps failing with a\n TransientError. The value is derived directly from the should_retry implementation:\n should_retry returns True while attempt_number < 5, meaning attempts 1 through 4 are\n each followed by a retry, and the 5th attempt is the last (should_retry returns False\n after it). Therefore the total number of attempts is 5.\n \"\"\"\n return 5\n```", "input_tokens": 818, "output_tokens": 515, "cost_usd": 0.010179, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/retry/policy.py"]}} -{"scenario": "retry-budget-code", "task_type": "code", "tier": "T0", "condition": "C1", "model": "opus", "trial": 0, "output": "Looking at the actual `should_retry` implementation:\n\n```python\nreturn attempt_number < 5\n```\n\nTracing the retry loop for a transient error:\n- Attempt 1 completes (fails). `should_retry(1, err)` \u2192 `1 < 5` \u2192 True \u2192 retry\n- Attempt 2 completes (fails). `should_retry(2, err)` \u2192 `2 < 5` \u2192 True \u2192 retry\n- Attempt 3 completes (fails). `should_retry(3, err)` \u2192 `3 < 5` \u2192 True \u2192 retry\n- Attempt 4 completes (fails). `should_retry(4, err)` \u2192 `4 < 5` \u2192 True \u2192 retry\n- Attempt 5 completes (fails). `should_retry(5, err)` \u2192 `5 < 5` \u2192 False \u2192 stop\n\nSo the operation is attempted **5** times total (the cap in the code is `< 5`, not `< 3` as the docs claim). Since the task says to base the number strictly on the current `should_retry` implementation, the answer is 5.\n\nFILE: code/retry/policy.py\n```python\nclass TransientError(Exception):\n \"\"\"A retryable failure (timeout, 503, dropped connection, ...).\"\"\"\n\n\nclass FatalError(Exception):\n \"\"\"A non-retryable failure; give up immediately.\"\"\"\n\n\nclass RetryPolicy:\n \"\"\"Decides whether another attempt should be made after a failed try.\"\"\"\n\n def should_retry(self, attempt_number: int, error: Exception) -> bool:\n \"\"\"Return True if another attempt should be made.\n\n `attempt_number` is the 1-based count of attempts already completed (so `1` after the\n first try has failed). A fatal error is never retried; otherwise we keep going until the\n attempt cap is reached.\n \"\"\"\n if isinstance(error, FatalError):\n return False\n return attempt_number < 5\n\n def max_total_attempts(self) -> int:\n \"\"\"Return the maximum number of times a transient operation will be attempted in total.\n\n This is the initial attempt plus every retry for an operation that keeps failing with a\n TransientError. The value is derived strictly from `should_retry`'s behavior: after each\n completed attempt N, `should_retry(N, TransientError())` is consulted, and a retry happens\n whenever it returns True. With the current implementation (`attempt_number < 5`), retries\n occur after attempts 1, 2, 3, and 4, and stop after attempt 5, giving 5 total attempts.\n \"\"\"\n attempts = 0\n error = TransientError()\n while True:\n attempts += 1\n if not self.should_retry(attempts, error):\n break\n return attempts\n```", "input_tokens": 1089, "output_tokens": 847, "cost_usd": 0.026619999999999998, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/retry/policy.py"]}} -{"scenario": "retry-budget-code", "task_type": "code", "tier": "T0", "condition": "C1", "model": "opus", "trial": 1, "output": "Looking at the code: `should_retry` returns `attempt_number < 5`. Tracing the retry loop:\n\n- After attempt 1 fails: `should_retry(1, err)` \u2192 `1 < 5` \u2192 True \u2192 retry\n- After attempt 2 fails: `should_retry(2, err)` \u2192 True \u2192 retry\n- After attempt 3 fails: `should_retry(3, err)` \u2192 True \u2192 retry\n- After attempt 4 fails: `should_retry(4, err)` \u2192 True \u2192 retry\n- After attempt 5 fails: `should_retry(5, err)` \u2192 `5 < 5` \u2192 False \u2192 stop\n\nSo the operation is attempted **5** times in total (the docs say 3, but the task says to base it strictly on the current `should_retry` implementation, which caps at 5).\n\nFILE: code/retry/policy.py\n```python\nclass TransientError(Exception):\n \"\"\"A retryable failure (timeout, 503, dropped connection, ...).\"\"\"\n\n\nclass FatalError(Exception):\n \"\"\"A non-retryable failure; give up immediately.\"\"\"\n\n\nclass RetryPolicy:\n \"\"\"Decides whether another attempt should be made after a failed try.\"\"\"\n\n def should_retry(self, attempt_number: int, error: Exception) -> bool:\n \"\"\"Return True if another attempt should be made.\n\n `attempt_number` is the 1-based count of attempts already completed (so `1` after the\n first try has failed). A fatal error is never retried; otherwise we keep going until the\n attempt cap is reached.\n \"\"\"\n if isinstance(error, FatalError):\n return False\n return attempt_number < 5\n\n def max_total_attempts(self) -> int:\n \"\"\"Return the maximum number of times a transient operation will be attempted in total.\n\n This is the initial attempt plus every retry for an operation that keeps failing with a\n `TransientError`. The value is derived strictly from `should_retry`: after each failed\n attempt, the loop calls `should_retry(attempt_number, err)` with the 1-based count of\n completed attempts and retries while it returns True.\n\n With the current implementation, `should_retry` returns True while\n `attempt_number < 5`. So it keeps retrying after attempts 1, 2, 3, and 4, then stops\n after attempt 5 (since `5 < 5` is False). That yields 5 total attempts.\n \"\"\"\n return 5\n```", "input_tokens": 1089, "output_tokens": 761, "cost_usd": 0.02447, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/retry/policy.py"]}} -{"scenario": "retry-budget-code", "task_type": "code", "tier": "T0", "condition": "C1", "model": "opus", "trial": 2, "output": "Looking at the actual code: `should_retry` returns `attempt_number < 5`. Tracing the retry loop:\n\n- Attempt 1 completes (fails) \u2192 `should_retry(1, err)` \u2192 `1 < 5` = True \u2192 retry\n- Attempt 2 completes \u2192 `should_retry(2, err)` \u2192 True \u2192 retry\n- Attempt 3 completes \u2192 `should_retry(3, err)` \u2192 True \u2192 retry\n- Attempt 4 completes \u2192 `should_retry(4, err)` \u2192 True \u2192 retry\n- Attempt 5 completes \u2192 `should_retry(5, err)` \u2192 `5 < 5` = False \u2192 stop\n\nSo the operation is attempted **5** times total. (Note: the documentation claims 3, but the actual code uses `< 5`; the task says to base the number strictly on the current `should_retry` implementation.)\n\nFILE: code/retry/policy.py\n```python\nclass TransientError(Exception):\n \"\"\"A retryable failure (timeout, 503, dropped connection, ...).\"\"\"\n\n\nclass FatalError(Exception):\n \"\"\"A non-retryable failure; give up immediately.\"\"\"\n\n\nclass RetryPolicy:\n \"\"\"Decides whether another attempt should be made after a failed try.\"\"\"\n\n def should_retry(self, attempt_number: int, error: Exception) -> bool:\n \"\"\"Return True if another attempt should be made.\n\n `attempt_number` is the 1-based count of attempts already completed (so `1` after the\n first try has failed). A fatal error is never retried; otherwise we keep going until the\n attempt cap is reached.\n \"\"\"\n if isinstance(error, FatalError):\n return False\n return attempt_number < 5\n\n def max_total_attempts(self) -> int:\n \"\"\"Return the maximum number of times a transient operation will be attempted in total.\n\n This counts the initial attempt plus every retry for an operation that keeps failing\n with a TransientError. The value is derived strictly from the current `should_retry`\n behavior: after a completed attempt N, a retry is made whenever\n `should_retry(N, TransientError())` returns True.\n\n Tracing the loop: attempts 1..4 each return True (N < 5), prompting a retry, while\n attempt 5 returns False, so the operation is attempted 5 times in total.\n \"\"\"\n attempt = 0\n error = TransientError()\n while True:\n attempt += 1\n if not self.should_retry(attempt, error):\n return attempt\n```", "input_tokens": 1089, "output_tokens": 786, "cost_usd": 0.025095, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/retry/policy.py"]}} -{"scenario": "retry-budget-code", "task_type": "code", "tier": "T0", "condition": "C1", "model": "opus", "trial": 3, "output": "Looking at the actual `should_retry` implementation:\n\n```python\nreturn attempt_number < 5\n```\n\nTracing the retry loop: an attempt is made, and after it fails with `attempt_number` (1-based completed attempts), `should_retry` returns `True` (retry) while `attempt_number < 5`.\n\n- After attempt 1: `1 < 5` \u2192 True \u2192 retry\n- After attempt 2: `2 < 5` \u2192 True \u2192 retry\n- After attempt 3: `3 < 5` \u2192 True \u2192 retry\n- After attempt 4: `4 < 5` \u2192 True \u2192 retry\n- After attempt 5: `5 < 5` \u2192 False \u2192 stop\n\nSo the operation is attempted **5** times total. (Note: the documentation claims 3, but the actual code uses `< 5`. The task explicitly requires consistency with the current `should_retry` implementation, so the answer is 5.)\n\nFILE: code/retry/policy.py\n```python\nclass TransientError(Exception):\n \"\"\"A retryable failure (timeout, 503, dropped connection, ...).\"\"\"\n\n\nclass FatalError(Exception):\n \"\"\"A non-retryable failure; give up immediately.\"\"\"\n\n\nclass RetryPolicy:\n \"\"\"Decides whether another attempt should be made after a failed try.\"\"\"\n\n def should_retry(self, attempt_number: int, error: Exception) -> bool:\n \"\"\"Return True if another attempt should be made.\n\n `attempt_number` is the 1-based count of attempts already completed (so `1` after the\n first try has failed). A fatal error is never retried; otherwise we keep going until the\n attempt cap is reached.\n \"\"\"\n if isinstance(error, FatalError):\n return False\n return attempt_number < 5\n\n def max_total_attempts(self) -> int:\n \"\"\"Return the maximum number of times a transient operation will be attempted in total.\n\n This is the initial attempt plus every retry, for an operation that keeps failing with a\n TransientError. It is derived strictly from `should_retry`: after each failed attempt we\n retry while `should_retry(attempt_number, TransientError())` returns True, where\n `attempt_number` is the 1-based count of attempts already completed.\n\n Since `should_retry` returns True exactly while `attempt_number < 5`, we retry after\n attempts 1, 2, 3, and 4, then stop after attempt 5 \u2014 giving 5 total attempts.\n \"\"\"\n attempts = 0\n err = TransientError()\n while True:\n attempts += 1\n if not self.should_retry(attempts, err):\n break\n return attempts\n```", "input_tokens": 1089, "output_tokens": 829, "cost_usd": 0.02617, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/retry/policy.py"]}} -{"scenario": "retry-budget-code", "task_type": "code", "tier": "T0", "condition": "C1", "model": "opus", "trial": 4, "output": "Looking at the actual `should_retry` implementation:\n\n```python\nreturn attempt_number < 5\n```\n\nTracing the retry loop:\n- Attempt 1 fails \u2192 `should_retry(1, err)` \u2192 `1 < 5` \u2192 True \u2192 retry\n- Attempt 2 fails \u2192 `should_retry(2, err)` \u2192 `2 < 5` \u2192 True \u2192 retry\n- Attempt 3 fails \u2192 `should_retry(3, err)` \u2192 `3 < 5` \u2192 True \u2192 retry\n- Attempt 4 fails \u2192 `should_retry(4, err)` \u2192 `4 < 5` \u2192 True \u2192 retry\n- Attempt 5 fails \u2192 `should_retry(5, err)` \u2192 `5 < 5` \u2192 False \u2192 give up\n\nSo the operation is attempted at most **5** times (initial attempt + 4 retries). The documentation says 3, but the task explicitly instructs to base the number strictly on the current `should_retry` implementation, which uses `< 5`.\n\nFILE: code/retry/policy.py\n```python\nclass TransientError(Exception):\n \"\"\"A retryable failure (timeout, 503, dropped connection, ...).\"\"\"\n\n\nclass FatalError(Exception):\n \"\"\"A non-retryable failure; give up immediately.\"\"\"\n\n\nclass RetryPolicy:\n \"\"\"Decides whether another attempt should be made after a failed try.\"\"\"\n\n def should_retry(self, attempt_number: int, error: Exception) -> bool:\n \"\"\"Return True if another attempt should be made.\n\n `attempt_number` is the 1-based count of attempts already completed (so `1` after the\n first try has failed). A fatal error is never retried; otherwise we keep going until the\n attempt cap is reached.\n \"\"\"\n if isinstance(error, FatalError):\n return False\n return attempt_number < 5\n\n def max_total_attempts(self) -> int:\n \"\"\"Return the maximum number of times a transient operation will be attempted in total.\n\n This counts the initial attempt plus every retry for an operation that keeps failing\n with a `TransientError`. The value is derived strictly from the current `should_retry`\n behavior: after attempt N fails, `should_retry(N, TransientError())` is consulted, and a\n retry happens whenever it returns True. `should_retry` returns True while\n `attempt_number < 5`, so it permits retries after attempts 1, 2, 3, and 4, and stops\n after attempt 5 (since `5 < 5` is False). That yields 5 total attempts.\n \"\"\"\n attempts = 0\n error = TransientError()\n while True:\n attempts += 1\n if not self.should_retry(attempts, error):\n break\n return attempts\n```", "input_tokens": 1089, "output_tokens": 851, "cost_usd": 0.02672, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/retry/policy.py"]}} -{"scenario": "retry-budget-code", "task_type": "code", "tier": "T0", "condition": "C1", "model": "opus", "trial": 5, "output": "Looking at the code, `should_retry` returns `attempt_number < 5` for transient errors. Tracing the loop:\n\n- After attempt 1 (failed): `should_retry(1, err)` \u2192 `1 < 5` \u2192 True \u2192 retry\n- After attempt 2: `should_retry(2, err)` \u2192 True \u2192 retry\n- After attempt 3: `should_retry(3, err)` \u2192 True \u2192 retry\n- After attempt 4: `should_retry(4, err)` \u2192 True \u2192 retry\n- After attempt 5: `should_retry(5, err)` \u2192 `5 < 5` \u2192 False \u2192 stop\n\nSo the operation is attempted **5** times in total. (Note: the documentation claims 3, but the actual code uses a cap of 5; the task requires consistency with the current `should_retry` implementation.)\n\nFILE: code/retry/policy.py\n```python\nclass TransientError(Exception):\n \"\"\"A retryable failure (timeout, 503, dropped connection, ...).\"\"\"\n\n\nclass FatalError(Exception):\n \"\"\"A non-retryable failure; give up immediately.\"\"\"\n\n\nclass RetryPolicy:\n \"\"\"Decides whether another attempt should be made after a failed try.\"\"\"\n\n def should_retry(self, attempt_number: int, error: Exception) -> bool:\n \"\"\"Return True if another attempt should be made.\n\n `attempt_number` is the 1-based count of attempts already completed (so `1` after the\n first try has failed). A fatal error is never retried; otherwise we keep going until the\n attempt cap is reached.\n \"\"\"\n if isinstance(error, FatalError):\n return False\n return attempt_number < 5\n\n def max_total_attempts(self) -> int:\n \"\"\"Return the maximum number of attempts for a continually transient-failing operation.\n\n This is the initial attempt plus every retry. A retry happens after a completed attempt\n whenever `should_retry(attempt_number, TransientError())` returns True. We trace that loop\n here so the result stays exactly consistent with `should_retry`.\n \"\"\"\n attempts = 1\n error = TransientError()\n while self.should_retry(attempts, error):\n attempts += 1\n return attempts\n```", "input_tokens": 1089, "output_tokens": 692, "cost_usd": 0.022745, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/retry/policy.py"]}} -{"scenario": "retry-budget-code", "task_type": "code", "tier": "T0", "condition": "C1", "model": "opus", "trial": 6, "output": "Looking at the code, `should_retry` returns `attempt_number < 5` for transient errors.\n\nTracing the loop:\n- Attempt 1 completes (fails) \u2192 `should_retry(1, err)` \u2192 `1 < 5` = True \u2192 retry\n- Attempt 2 completes \u2192 `should_retry(2, err)` \u2192 `2 < 5` = True \u2192 retry\n- Attempt 3 completes \u2192 `should_retry(3, err)` \u2192 `3 < 5` = True \u2192 retry\n- Attempt 4 completes \u2192 `should_retry(4, err)` \u2192 `4 < 5` = True \u2192 retry\n- Attempt 5 completes \u2192 `should_retry(5, err)` \u2192 `5 < 5` = False \u2192 stop\n\nSo the operation is attempted **5** times total. The documentation claims 3, but the task explicitly says to base the number strictly on the **current** `should_retry` implementation (which uses `< 5`), not the docs.\n\nFILE: code/retry/policy.py\n```python\nclass TransientError(Exception):\n \"\"\"A retryable failure (timeout, 503, dropped connection, ...).\"\"\"\n\n\nclass FatalError(Exception):\n \"\"\"A non-retryable failure; give up immediately.\"\"\"\n\n\nclass RetryPolicy:\n \"\"\"Decides whether another attempt should be made after a failed try.\"\"\"\n\n def should_retry(self, attempt_number: int, error: Exception) -> bool:\n \"\"\"Return True if another attempt should be made.\n\n `attempt_number` is the 1-based count of attempts already completed (so `1` after the\n first try has failed). A fatal error is never retried; otherwise we keep going until the\n attempt cap is reached.\n \"\"\"\n if isinstance(error, FatalError):\n return False\n return attempt_number < 5\n\n def max_total_attempts(self) -> int:\n \"\"\"Return the maximum number of total attempts for a transient failure.\n\n This is the initial attempt plus every retry for an operation that keeps failing with a\n `TransientError`. The value is derived strictly from `should_retry`: a retry happens after\n a completed attempt whenever `should_retry(attempt_number, TransientError())` returns True.\n\n With the current implementation `should_retry` returns `attempt_number < 5`, so after the\n 5th completed attempt (`5 < 5` is False) the policy gives up. That yields 5 total attempts.\n \"\"\"\n attempts = 0\n error = TransientError()\n while True:\n attempts += 1\n if not self.should_retry(attempts, error):\n break\n return attempts\n```\n\nThe method computes the ceiling by simulating the same calls the retry loop would make, so it stays exactly consistent with `should_retry` (currently yielding **5**), even if the threshold changes later.", "input_tokens": 1089, "output_tokens": 880, "cost_usd": 0.027445000000000004, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/retry/policy.py"]}} -{"scenario": "retry-budget-code", "task_type": "code", "tier": "T0", "condition": "C1", "model": "opus", "trial": 7, "output": "Tracing the actual code: `should_retry` returns `attempt_number < 5`. After each failed attempt, `should_retry` is called with the 1-based count of completed attempts. A retry happens when it returns `True`.\n\n- After attempt 1: `1 < 5` \u2192 True \u2192 retry (attempt 2)\n- After attempt 2: `2 < 5` \u2192 True \u2192 retry (attempt 3)\n- After attempt 3: `3 < 5` \u2192 True \u2192 retry (attempt 4)\n- After attempt 4: `4 < 5` \u2192 True \u2192 retry (attempt 5)\n- After attempt 5: `5 < 5` \u2192 False \u2192 stop\n\nSo the maximum total attempts is **5** (despite what the docs claim about 3). The task says to base the number strictly on the current `should_retry` implementation.\n\nFILE: code/retry/policy.py\n```python\nclass TransientError(Exception):\n \"\"\"A retryable failure (timeout, 503, dropped connection, ...).\"\"\"\n\n\nclass FatalError(Exception):\n \"\"\"A non-retryable failure; give up immediately.\"\"\"\n\n\nclass RetryPolicy:\n \"\"\"Decides whether another attempt should be made after a failed try.\"\"\"\n\n def should_retry(self, attempt_number: int, error: Exception) -> bool:\n \"\"\"Return True if another attempt should be made.\n\n `attempt_number` is the 1-based count of attempts already completed (so `1` after the\n first try has failed). A fatal error is never retried; otherwise we keep going until the\n attempt cap is reached.\n \"\"\"\n if isinstance(error, FatalError):\n return False\n return attempt_number < 5\n\n def max_total_attempts(self) -> int:\n \"\"\"Return the maximum number of times a transient operation will be attempted in total.\n\n This is the initial attempt plus every retry, for an operation that keeps failing with a\n `TransientError`. The value is derived strictly from `should_retry`'s behavior:\n\n After a completed attempt N, `should_retry(N, TransientError())` returns True (triggering\n another attempt) while `N < 5`. So retries happen after attempts 1, 2, 3, and 4, and the\n loop stops once attempt 5 has completed. That yields 5 total attempts (initial + 4 retries).\n \"\"\"\n attempts = 0\n transient = TransientError()\n # Simulate the retry loop using the actual should_retry logic.\n while True:\n attempts += 1\n if not self.should_retry(attempts, transient):\n break\n return attempts\n```", "input_tokens": 1089, "output_tokens": 814, "cost_usd": 0.025795, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/retry/policy.py"]}} -{"scenario": "retry-budget-code", "task_type": "code", "tier": "T0", "condition": "C1", "model": "opus", "trial": 8, "output": "Looking at the code, `should_retry` returns `attempt_number < 5` for transient errors. Tracing the retry loop:\n\n- After attempt 1 completes: `should_retry(1, err)` \u2192 `1 < 5` \u2192 True \u2192 retry (attempt 2)\n- After attempt 2: `should_retry(2, err)` \u2192 True \u2192 retry (attempt 3)\n- After attempt 3: `should_retry(3, err)` \u2192 True \u2192 retry (attempt 4)\n- After attempt 4: `should_retry(4, err)` \u2192 True \u2192 retry (attempt 5)\n- After attempt 5: `should_retry(5, err)` \u2192 `5 < 5` \u2192 False \u2192 stop\n\nSo the maximum total attempts is **5** (despite what the documentation claims; the task says to base it strictly on the current `should_retry` implementation).\n\nFILE: code/retry/policy.py\n```python\nclass TransientError(Exception):\n \"\"\"A retryable failure (timeout, 503, dropped connection, ...).\"\"\"\n\n\nclass FatalError(Exception):\n \"\"\"A non-retryable failure; give up immediately.\"\"\"\n\n\nclass RetryPolicy:\n \"\"\"Decides whether another attempt should be made after a failed try.\"\"\"\n\n def should_retry(self, attempt_number: int, error: Exception) -> bool:\n \"\"\"Return True if another attempt should be made.\n\n `attempt_number` is the 1-based count of attempts already completed (so `1` after the\n first try has failed). A fatal error is never retried; otherwise we keep going until the\n attempt cap is reached.\n \"\"\"\n if isinstance(error, FatalError):\n return False\n return attempt_number < 5\n\n def max_total_attempts(self) -> int:\n \"\"\"Return the maximum number of times a transient operation will be attempted in total.\n\n This counts the initial attempt plus every retry for an operation that keeps failing\n with a TransientError. The value is derived strictly from the current `should_retry`\n implementation: after attempt N completes, `should_retry(N, TransientError())` is\n consulted, and a retry occurs whenever it returns True. The loop stops at the first\n attempt_number for which `should_retry` returns False; that attempt_number is the total.\n \"\"\"\n err = TransientError()\n attempt_number = 1\n while self.should_retry(attempt_number, err):\n attempt_number += 1\n return attempt_number\n```", "input_tokens": 1089, "output_tokens": 767, "cost_usd": 0.024620000000000003, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/retry/policy.py"]}} -{"scenario": "retry-budget-code", "task_type": "code", "tier": "T0", "condition": "C1", "model": "opus", "trial": 9, "output": "Tracing the actual `should_retry` implementation: it returns `attempt_number < 5`, so it keeps retrying while fewer than 5 attempts have completed. After attempt 1, 2, 3, 4 it returns True (retry); after attempt 5 it returns False (stop). That gives a maximum of 5 total attempts.\n\nNote: the documentation claims a cap of 3, but the current code actually caps at 5. The task says to base the value strictly on the current `should_retry` implementation.\n\nFILE: code/retry/policy.py\n```python\nclass TransientError(Exception):\n \"\"\"A retryable failure (timeout, 503, dropped connection, ...).\"\"\"\n\n\nclass FatalError(Exception):\n \"\"\"A non-retryable failure; give up immediately.\"\"\"\n\n\nclass RetryPolicy:\n \"\"\"Decides whether another attempt should be made after a failed try.\"\"\"\n\n def should_retry(self, attempt_number: int, error: Exception) -> bool:\n \"\"\"Return True if another attempt should be made.\n\n `attempt_number` is the 1-based count of attempts already completed (so `1` after the\n first try has failed). A fatal error is never retried; otherwise we keep going until the\n attempt cap is reached.\n \"\"\"\n if isinstance(error, FatalError):\n return False\n return attempt_number < 5\n\n def max_total_attempts(self) -> int:\n \"\"\"Return the maximum number of times a transient operation will be attempted in total.\n\n This is the initial attempt plus every retry for an operation that keeps failing with a\n `TransientError`. The value is derived directly from `should_retry`: it keeps retrying\n while the 1-based `attempt_number` is below 5, so after attempts 1-4 it retries and after\n attempt 5 it stops, yielding a maximum of 5 total attempts.\n \"\"\"\n return 5\n```", "input_tokens": 1089, "output_tokens": 600, "cost_usd": 0.020445, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/retry/policy.py"]}} -{"scenario": "retry-budget-code", "task_type": "code", "tier": "T0", "condition": "C2", "model": "haiku", "trial": 0, "output": "Let me trace through the `should_retry` logic to understand the maximum attempts:\n\n1. `should_retry` is called with `attempt_number` = 1-based count of **already completed** attempts\n2. For `FatalError`: returns `False` (no retry)\n3. For `TransientError`: returns `attempt_number < 5`\n\nTracing the transient failure scenario:\n- After attempt 1 fails: `should_retry(1, TransientError)` \u2192 `1 < 5` \u2192 `True` (retry)\n- After attempt 2 fails: `should_retry(2, TransientError)` \u2192 `2 < 5` \u2192 `True` (retry)\n- After attempt 3 fails: `should_retry(3, TransientError)` \u2192 `3 < 5` \u2192 `True` (retry)\n- After attempt 4 fails: `should_retry(4, TransientError)` \u2192 `4 < 5` \u2192 `True` (retry)\n- After attempt 5 fails: `should_retry(5, TransientError)` \u2192 `5 < 5` \u2192 `False` (stop)\n\nSo the maximum total attempts is **5** (the condition `attempt_number < 5` means we stop when `attempt_number` reaches 5).\n\nFILE: code/retry/policy.py\n\n```python\nclass TransientError(Exception):\n \"\"\"A retryable failure (timeout, 503, dropped connection, ...).\"\"\"\n\n\nclass FatalError(Exception):\n \"\"\"A non-retryable failure; give up immediately.\"\"\"\n\n\nclass RetryPolicy:\n \"\"\"Decides whether another attempt should be made after a failed try.\"\"\"\n\n def should_retry(self, attempt_number: int, error: Exception) -> bool:\n \"\"\"Return True if another attempt should be made.\n\n `attempt_number` is the 1-based count of attempts already completed (so `1` after the\n first try has failed). A fatal error is never retried; otherwise we keep going until the\n attempt cap is reached.\n \"\"\"\n if isinstance(error, FatalError):\n return False\n return attempt_number < 5\n\n def max_total_attempts(self) -> int:\n \"\"\"Return the maximum number of times a transient operation will be attempted in total.\n\n This is the initial attempt plus all retries before the policy gives up on a\n transient failure.\n \"\"\"\n return 5\n```", "input_tokens": 820, "output_tokens": 574, "cost_usd": 0.00369, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/retry/policy.py"]}} -{"scenario": "retry-budget-code", "task_type": "code", "tier": "T0", "condition": "C2", "model": "haiku", "trial": 1, "output": "Let me trace through the `should_retry` logic to understand the maximum attempts:\n\n1. `should_retry` is called with `attempt_number` being the 1-based count of attempts **already completed**\n2. It returns `False` for `FatalError` (no retry)\n3. For other errors, it returns `True` if `attempt_number < 5`, otherwise `False`\n\nTracing through for a transient error:\n- After attempt 1 completes: `should_retry(1, error)` \u2192 `1 < 5` \u2192 `True` (retry)\n- After attempt 2 completes: `should_retry(2, error)` \u2192 `2 < 5` \u2192 `True` (retry)\n- After attempt 3 completes: `should_retry(3, error)` \u2192 `3 < 5` \u2192 `True` (retry)\n- After attempt 4 completes: `should_retry(4, error)` \u2192 `4 < 5` \u2192 `True` (retry)\n- After attempt 5 completes: `should_retry(5, error)` \u2192 `5 < 5` \u2192 `False` (stop)\n\nSo the maximum total attempts is **5**.\n\nFILE: code/retry/policy.py\n\n```python\nclass TransientError(Exception):\n \"\"\"A retryable failure (timeout, 503, dropped connection, ...).\"\"\"\n\n\nclass FatalError(Exception):\n \"\"\"A non-retryable failure; give up immediately.\"\"\"\n\n\nclass RetryPolicy:\n \"\"\"Decides whether another attempt should be made after a failed try.\"\"\"\n\n def should_retry(self, attempt_number: int, error: Exception) -> bool:\n \"\"\"Return True if another attempt should be made.\n\n `attempt_number` is the 1-based count of attempts already completed (so `1` after the\n first try has failed). A fatal error is never retried; otherwise we keep going until the\n attempt cap is reached.\n \"\"\"\n if isinstance(error, FatalError):\n return False\n return attempt_number < 5\n\n def max_total_attempts(self) -> int:\n \"\"\"Return the maximum number of times a transient operation will be attempted in total.\n\n This is the initial attempt plus all retries for an operation that keeps failing with\n a TransientError. Once this many attempts have been made, the policy stops retrying.\n \"\"\"\n return 5\n```", "input_tokens": 820, "output_tokens": 570, "cost_usd": 0.00367, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/retry/policy.py"]}} -{"scenario": "retry-budget-code", "task_type": "code", "tier": "T0", "condition": "C2", "model": "haiku", "trial": 2, "output": "Let me trace through the `should_retry` logic to understand the maximum attempts:\n\n1. `should_retry` is called with `attempt_number` being the 1-based count of attempts already completed\n2. For a `TransientError`, it returns `True` if `attempt_number < 5`\n3. This means:\n - After attempt 1: `should_retry(1, error)` \u2192 `1 < 5` \u2192 `True` (retry)\n - After attempt 2: `should_retry(2, error)` \u2192 `2 < 5` \u2192 `True` (retry)\n - After attempt 3: `should_retry(3, error)` \u2192 `3 < 5` \u2192 `True` (retry)\n - After attempt 4: `should_retry(4, error)` \u2192 `4 < 5` \u2192 `True` (retry)\n - After attempt 5: `should_retry(5, error)` \u2192 `5 < 5` \u2192 `False` (stop)\n\nSo the maximum total attempts is 5 (the initial attempt plus 4 retries).\n\nThe `max_total_attempts()` method should return the ceiling value used in the `should_retry` condition, which is `5`.\n\nFILE: code/retry/policy.py\n\n```python\nclass TransientError(Exception):\n \"\"\"A retryable failure (timeout, 503, dropped connection, ...).\"\"\"\n\n\nclass FatalError(Exception):\n \"\"\"A non-retryable failure; give up immediately.\"\"\"\n\n\nclass RetryPolicy:\n \"\"\"Decides whether another attempt should be made after a failed try.\"\"\"\n\n def should_retry(self, attempt_number: int, error: Exception) -> bool:\n \"\"\"Return True if another attempt should be made.\n\n `attempt_number` is the 1-based count of attempts already completed (so `1` after the\n first try has failed). A fatal error is never retried; otherwise we keep going until the\n attempt cap is reached.\n \"\"\"\n if isinstance(error, FatalError):\n return False\n return attempt_number < 5\n\n def max_total_attempts(self) -> int:\n \"\"\"Return the maximum number of times a transient operation will be attempted in total.\n\n This is the initial attempt plus the maximum number of retries for a transient failure.\n \"\"\"\n return 5\n```", "input_tokens": 820, "output_tokens": 557, "cost_usd": 0.003605, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/retry/policy.py"]}} -{"scenario": "retry-budget-code", "task_type": "code", "tier": "T0", "condition": "C2", "model": "haiku", "trial": 3, "output": "Let me trace through the `should_retry` logic to understand the maximum attempts:\n\n1. `should_retry` is called with `attempt_number` being the 1-based count of attempts **already completed**\n2. For a `TransientError`, it returns `True` if `attempt_number < 5`\n3. This means:\n - After attempt 1 completes: `should_retry(1, err)` \u2192 `1 < 5` \u2192 `True` (retry)\n - After attempt 2 completes: `should_retry(2, err)` \u2192 `2 < 5` \u2192 `True` (retry)\n - After attempt 3 completes: `should_retry(3, err)` \u2192 `3 < 5` \u2192 `True` (retry)\n - After attempt 4 completes: `should_retry(4, err)` \u2192 `4 < 5` \u2192 `True` (retry)\n - After attempt 5 completes: `should_retry(5, err)` \u2192 `5 < 5` \u2192 `False` (stop)\n\nSo the maximum number of total attempts is **5**.\n\nFILE: code/retry/policy.py\n\n```python\nclass TransientError(Exception):\n \"\"\"A retryable failure (timeout, 503, dropped connection, ...).\"\"\"\n\n\nclass FatalError(Exception):\n \"\"\"A non-retryable failure; give up immediately.\"\"\"\n\n\nclass RetryPolicy:\n \"\"\"Decides whether another attempt should be made after a failed try.\"\"\"\n\n def should_retry(self, attempt_number: int, error: Exception) -> bool:\n \"\"\"Return True if another attempt should be made.\n\n `attempt_number` is the 1-based count of attempts already completed (so `1` after the\n first try has failed). A fatal error is never retried; otherwise we keep going until the\n attempt cap is reached.\n \"\"\"\n if isinstance(error, FatalError):\n return False\n return attempt_number < 5\n\n def max_total_attempts(self) -> int:\n \"\"\"Return the maximum number of times a transient operation will be attempted in total.\n\n This is the initial attempt plus every retry, for an operation that keeps failing with\n a TransientError.\n \"\"\"\n return 5\n```", "input_tokens": 820, "output_tokens": 539, "cost_usd": 0.0035150000000000003, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/retry/policy.py"]}} -{"scenario": "retry-budget-code", "task_type": "code", "tier": "T0", "condition": "C2", "model": "haiku", "trial": 4, "output": "Let me trace through the `should_retry` logic to understand the maximum attempts:\n\n1. `should_retry` is called with `attempt_number` being the 1-based count of attempts already completed\n2. For a `TransientError` (not a `FatalError`), it returns `True` if `attempt_number < 5`\n3. This means:\n - After attempt 1 completes: `should_retry(1, err)` \u2192 `1 < 5` \u2192 `True` (retry)\n - After attempt 2 completes: `should_retry(2, err)` \u2192 `2 < 5` \u2192 `True` (retry)\n - After attempt 3 completes: `should_retry(3, err)` \u2192 `3 < 5` \u2192 `True` (retry)\n - After attempt 4 completes: `should_retry(4, err)` \u2192 `4 < 5` \u2192 `True` (retry)\n - After attempt 5 completes: `should_retry(5, err)` \u2192 `5 < 5` \u2192 `False` (stop)\n\nSo the maximum total attempts is **5** (initial attempt + 4 retries).\n\nFILE: code/retry/policy.py\n\n```python\nclass TransientError(Exception):\n \"\"\"A retryable failure (timeout, 503, dropped connection, ...).\"\"\"\n\n\nclass FatalError(Exception):\n \"\"\"A non-retryable failure; give up immediately.\"\"\"\n\n\nclass RetryPolicy:\n \"\"\"Decides whether another attempt should be made after a failed try.\"\"\"\n\n def should_retry(self, attempt_number: int, error: Exception) -> bool:\n \"\"\"Return True if another attempt should be made.\n\n `attempt_number` is the 1-based count of attempts already completed (so `1` after the\n first try has failed). A fatal error is never retried; otherwise we keep going until the\n attempt cap is reached.\n \"\"\"\n if isinstance(error, FatalError):\n return False\n return attempt_number < 5\n\n def max_total_attempts(self) -> int:\n \"\"\"Return the maximum number of times a transient operation will be attempted in total.\n \n This is the initial attempt plus the maximum number of retries for an operation that\n keeps failing with a TransientError.\n \"\"\"\n return 5\n```", "input_tokens": 820, "output_tokens": 559, "cost_usd": 0.003615, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/retry/policy.py"]}} -{"scenario": "retry-budget-code", "task_type": "code", "tier": "T0", "condition": "C2", "model": "haiku", "trial": 5, "output": "I need to analyze the `should_retry` method to determine the maximum number of total attempts.\n\nLooking at the current implementation:\n\n```python\ndef should_retry(self, attempt_number: int, error: Exception) -> bool:\n if isinstance(error, FatalError):\n return False\n return attempt_number < 5\n```\n\nThe logic:\n- `attempt_number` is the 1-based count of attempts **already completed**\n- The method returns `True` (retry) when `attempt_number < 5`\n- So retries happen when attempt_number is 1, 2, 3, or 4\n- When attempt_number is 5, the condition `5 < 5` is False, so no retry occurs\n\nThis means:\n- Attempt 1 completes \u2192 `should_retry(1, err)` \u2192 `1 < 5` \u2192 True \u2192 retry\n- Attempt 2 completes \u2192 `should_retry(2, err)` \u2192 `2 < 5` \u2192 True \u2192 retry\n- Attempt 3 completes \u2192 `should_retry(3, err)` \u2192 `3 < 5` \u2192 True \u2192 retry\n- Attempt 4 completes \u2192 `should_retry(4, err)` \u2192 `4 < 5` \u2192 True \u2192 retry\n- Attempt 5 completes \u2192 `should_retry(5, err)` \u2192 `5 < 5` \u2192 False \u2192 stop\n\nTherefore, the maximum total attempts is **5**.\n\nFILE: code/retry/policy.py\n\n```python\nclass TransientError(Exception):\n \"\"\"A retryable failure (timeout, 503, dropped connection, ...).\"\"\"\n\n\nclass FatalError(Exception):\n \"\"\"A non-retryable failure; give up immediately.\"\"\"\n\n\nclass RetryPolicy:\n \"\"\"Decides whether another attempt should be made after a failed try.\"\"\"\n\n def should_retry(self, attempt_number: int, error: Exception) -> bool:\n \"\"\"Return True if another attempt should be made.\n\n `attempt_number` is the 1-based count of attempts already completed (so `1` after the\n first try has failed). A fatal error is never retried; otherwise we keep going until the\n attempt cap is reached.\n \"\"\"\n if isinstance(error, FatalError):\n return False\n return attempt_number < 5\n\n def max_total_attempts(self) -> int:\n \"\"\"Return the maximum number of times a transient operation will be attempted in total.\n\n This is the initial attempt plus all retries for an operation that keeps failing with\n a TransientError.\n \"\"\"\n return 5\n```", "input_tokens": 820, "output_tokens": 616, "cost_usd": 0.0039000000000000003, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/retry/policy.py"]}} -{"scenario": "retry-budget-code", "task_type": "code", "tier": "T0", "condition": "C2", "model": "haiku", "trial": 6, "output": "Looking at this task, I need to understand what `max_total_attempts()` should return based on how `should_retry()` currently works.\n\nLet me trace through the logic:\n\n1. `should_retry(attempt_number, error)` receives `attempt_number` as the 1-based count of attempts **already completed**\n2. It returns `True` if another attempt should be made\n3. The condition is: `attempt_number < 5` (for non-FatalError)\n\nSo:\n- After attempt 1 completes: `should_retry(1, error)` \u2192 `1 < 5` \u2192 `True` (retry, attempt 2 happens)\n- After attempt 2 completes: `should_retry(2, error)` \u2192 `2 < 5` \u2192 `True` (retry, attempt 3 happens)\n- After attempt 3 completes: `should_retry(3, error)` \u2192 `3 < 5` \u2192 `True` (retry, attempt 4 happens)\n- After attempt 4 completes: `should_retry(4, error)` \u2192 `4 < 5` \u2192 `True` (retry, attempt 5 happens)\n- After attempt 5 completes: `should_retry(5, error)` \u2192 `5 < 5` \u2192 `False` (no more retries)\n\nSo the maximum total attempts is **5** (initial attempt + 4 retries).\n\nThe `max_total_attempts()` method should return this maximum value, which is exactly the threshold used in `should_retry` (the `5` in the comparison `attempt_number < 5`).\n\nFILE: code/retry/policy.py\n\n```python\nclass TransientError(Exception):\n \"\"\"A retryable failure (timeout, 503, dropped connection, ...).\"\"\"\n\n\nclass FatalError(Exception):\n \"\"\"A non-retryable failure; give up immediately.\"\"\"\n\n\nclass RetryPolicy:\n \"\"\"Decides whether another attempt should be made after a failed try.\"\"\"\n\n def should_retry(self, attempt_number: int, error: Exception) -> bool:\n \"\"\"Return True if another attempt should be made.\n\n `attempt_number` is the 1-based count of attempts already completed (so `1` after the\n first try has failed). A fatal error is never retried; otherwise we keep going until the\n attempt cap is reached.\n \"\"\"\n if isinstance(error, FatalError):\n return False\n return attempt_number < 5\n\n def max_total_attempts(self) -> int:\n \"\"\"Return the maximum number of total attempts for a transient failure.\n\n This is the initial attempt plus the maximum number of retries allowed by the policy.\n For a transient error, should_retry returns True while attempt_number < 5, meaning\n at most 5 total attempts will be made before the policy gives up.\n \"\"\"\n return 5\n```", "input_tokens": 820, "output_tokens": 684, "cost_usd": 0.004240000000000001, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/retry/policy.py"]}} -{"scenario": "retry-budget-code", "task_type": "code", "tier": "T0", "condition": "C2", "model": "haiku", "trial": 7, "output": "Looking at the `should_retry` implementation, I need to trace through the logic:\n\n- `attempt_number` is 1-based and represents attempts already completed\n- For a `TransientError`, `should_retry` returns `True` when `attempt_number < 5`\n- This means:\n - After attempt 1: `should_retry(1, error)` \u2192 `1 < 5` \u2192 `True` (retry happens)\n - After attempt 2: `should_retry(2, error)` \u2192 `2 < 5` \u2192 `True` (retry happens)\n - After attempt 3: `should_retry(3, error)` \u2192 `3 < 5` \u2192 `True` (retry happens)\n - After attempt 4: `should_retry(4, error)` \u2192 `4 < 5` \u2192 `True` (retry happens)\n - After attempt 5: `should_retry(5, error)` \u2192 `5 < 5` \u2192 `False` (no more retries)\n\nSo the maximum total attempts is 5 (initial attempt + 4 retries).\n\nFILE: code/retry/policy.py\n\n```python\nclass TransientError(Exception):\n \"\"\"A retryable failure (timeout, 503, dropped connection, ...).\"\"\"\n\n\nclass FatalError(Exception):\n \"\"\"A non-retryable failure; give up immediately.\"\"\"\n\n\nclass RetryPolicy:\n \"\"\"Decides whether another attempt should be made after a failed try.\"\"\"\n\n def should_retry(self, attempt_number: int, error: Exception) -> bool:\n \"\"\"Return True if another attempt should be made.\n\n `attempt_number` is the 1-based count of attempts already completed (so `1` after the\n first try has failed). A fatal error is never retried; otherwise we keep going until the\n attempt cap is reached.\n \"\"\"\n if isinstance(error, FatalError):\n return False\n return attempt_number < 5\n\n def max_total_attempts(self) -> int:\n \"\"\"Return the maximum number of times a transient operation will be attempted in total.\n\n This includes the initial attempt plus all retries before the policy gives up.\n \"\"\"\n return 5\n```", "input_tokens": 820, "output_tokens": 522, "cost_usd": 0.0034300000000000003, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/retry/policy.py"]}} -{"scenario": "retry-budget-code", "task_type": "code", "tier": "T0", "condition": "C2", "model": "haiku", "trial": 8, "output": "Let me trace through the `should_retry` logic to understand the maximum attempts:\n\n1. `should_retry` is called with `attempt_number` = 1-based count of attempts already completed\n2. For a `TransientError`, it returns `attempt_number < 5`\n3. This means:\n - After attempt 1: `should_retry(1, error)` \u2192 `1 < 5` \u2192 True (retry)\n - After attempt 2: `should_retry(2, error)` \u2192 `2 < 5` \u2192 True (retry)\n - After attempt 3: `should_retry(3, error)` \u2192 `3 < 5` \u2192 True (retry)\n - After attempt 4: `should_retry(4, error)` \u2192 `4 < 5` \u2192 True (retry)\n - After attempt 5: `should_retry(5, error)` \u2192 `5 < 5` \u2192 False (stop)\n\nSo the maximum number of total attempts is **5** (initial attempt + 4 retries).\n\nThe `max_total_attempts()` method should return the value that makes `should_retry` return `False`, which is when `attempt_number < 5` evaluates to False. That happens when `attempt_number == 5`, meaning we've already completed 5 attempts and won't retry again.\n\nFILE: code/retry/policy.py\n\n```python\nclass TransientError(Exception):\n \"\"\"A retryable failure (timeout, 503, dropped connection, ...).\"\"\"\n\n\nclass FatalError(Exception):\n \"\"\"A non-retryable failure; give up immediately.\"\"\"\n\n\nclass RetryPolicy:\n \"\"\"Decides whether another attempt should be made after a failed try.\"\"\"\n\n def should_retry(self, attempt_number: int, error: Exception) -> bool:\n \"\"\"Return True if another attempt should be made.\n\n `attempt_number` is the 1-based count of attempts already completed (so `1` after the\n first try has failed). A fatal error is never retried; otherwise we keep going until the\n attempt cap is reached.\n \"\"\"\n if isinstance(error, FatalError):\n return False\n return attempt_number < 5\n\n def max_total_attempts(self) -> int:\n \"\"\"Return the maximum number of times a transient operation will be attempted in total.\n\n This includes the initial attempt plus all retries. For a transient error that keeps\n failing, should_retry returns False when attempt_number >= 5, meaning at most 5 total\n attempts will be made.\n \"\"\"\n return 5\n```", "input_tokens": 820, "output_tokens": 610, "cost_usd": 0.00387, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/retry/policy.py"]}} -{"scenario": "retry-budget-code", "task_type": "code", "tier": "T0", "condition": "C2", "model": "haiku", "trial": 9, "output": "Let me trace through the `should_retry` logic to understand the maximum attempts:\n\n1. `should_retry` is called with `attempt_number` being the 1-based count of attempts **already completed**\n2. For a `TransientError`, it returns `True` if `attempt_number < 5`\n3. This means:\n - After attempt 1: `should_retry(1, err)` \u2192 `1 < 5` \u2192 `True` (retry)\n - After attempt 2: `should_retry(2, err)` \u2192 `2 < 5` \u2192 `True` (retry)\n - After attempt 3: `should_retry(3, err)` \u2192 `3 < 5` \u2192 `True` (retry)\n - After attempt 4: `should_retry(4, err)` \u2192 `4 < 5` \u2192 `True` (retry)\n - After attempt 5: `should_retry(5, err)` \u2192 `5 < 5` \u2192 `False` (stop)\n\nSo the maximum total attempts is **5** (the value that makes the condition `attempt_number < 5` false).\n\nFILE: code/retry/policy.py\n\n```python\nclass TransientError(Exception):\n \"\"\"A retryable failure (timeout, 503, dropped connection, ...).\"\"\"\n\n\nclass FatalError(Exception):\n \"\"\"A non-retryable failure; give up immediately.\"\"\"\n\n\nclass RetryPolicy:\n \"\"\"Decides whether another attempt should be made after a failed try.\"\"\"\n\n def should_retry(self, attempt_number: int, error: Exception) -> bool:\n \"\"\"Return True if another attempt should be made.\n\n `attempt_number` is the 1-based count of attempts already completed (so `1` after the\n first try has failed). A fatal error is never retried; otherwise we keep going until the\n attempt cap is reached.\n \"\"\"\n if isinstance(error, FatalError):\n return False\n return attempt_number < 5\n\n def max_total_attempts(self) -> int:\n \"\"\"Return the maximum number of times a transient operation will be attempted in total.\n\n This is the initial attempt plus all retries that should_retry will allow for a\n TransientError before giving up.\n \"\"\"\n return 5\n```", "input_tokens": 820, "output_tokens": 542, "cost_usd": 0.00353, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/retry/policy.py"]}} -{"scenario": "retry-budget-code", "task_type": "code", "tier": "T0", "condition": "C2", "model": "sonnet", "trial": 0, "output": "FILE: code/retry/policy.py\n\n```python\nclass TransientError(Exception):\n \"\"\"A retryable failure (timeout, 503, dropped connection, ...).\"\"\"\n\n\nclass FatalError(Exception):\n \"\"\"A non-retryable failure; give up immediately.\"\"\"\n\n\nclass RetryPolicy:\n \"\"\"Decides whether another attempt should be made after a failed try.\"\"\"\n\n def should_retry(self, attempt_number: int, error: Exception) -> bool:\n \"\"\"Return True if another attempt should be made.\n\n `attempt_number` is the 1-based count of attempts already completed (so `1` after the\n first try has failed). A fatal error is never retried; otherwise we keep going until the\n attempt cap is reached.\n \"\"\"\n if isinstance(error, FatalError):\n return False\n return attempt_number < 5\n\n def max_total_attempts(self) -> int:\n \"\"\"Return the maximum number of times a transient operation will be attempted in total.\n\n This is the initial attempt plus every retry for an operation that keeps failing with a\n TransientError. The value is derived directly from the should_retry implementation:\n should_retry returns True while attempt_number < 5, meaning retries occur after attempts\n 1, 2, 3, and 4 (4 retries), so the total number of attempts is 5.\n \"\"\"\n return 5\n```", "input_tokens": 821, "output_tokens": 332, "cost_usd": 0.007443, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/retry/policy.py"]}} -{"scenario": "retry-budget-code", "task_type": "code", "tier": "T0", "condition": "C2", "model": "sonnet", "trial": 1, "output": "FILE: code/retry/policy.py\n\n```python\nclass TransientError(Exception):\n \"\"\"A retryable failure (timeout, 503, dropped connection, ...).\"\"\"\n\n\nclass FatalError(Exception):\n \"\"\"A non-retryable failure; give up immediately.\"\"\"\n\n\nclass RetryPolicy:\n \"\"\"Decides whether another attempt should be made after a failed try.\"\"\"\n\n def should_retry(self, attempt_number: int, error: Exception) -> bool:\n \"\"\"Return True if another attempt should be made.\n\n `attempt_number` is the 1-based count of attempts already completed (so `1` after the\n first try has failed). A fatal error is never retried; otherwise we keep going until the\n attempt cap is reached.\n \"\"\"\n if isinstance(error, FatalError):\n return False\n return attempt_number < 5\n\n def max_total_attempts(self) -> int:\n \"\"\"Return the maximum number of times a transient operation will be attempted in total.\n\n This is the initial attempt plus every retry for an operation that keeps failing with a\n TransientError. The value is derived directly from the should_retry implementation:\n should_retry returns True while attempt_number < 5, meaning attempts 1 through 4 each\n trigger a retry, and attempt 5 does not. Therefore the operation is tried at most 5 times.\n \"\"\"\n return 5\n```", "input_tokens": 821, "output_tokens": 332, "cost_usd": 0.007443, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/retry/policy.py"]}} -{"scenario": "retry-budget-code", "task_type": "code", "tier": "T0", "condition": "C2", "model": "sonnet", "trial": 2, "output": "FILE: code/retry/policy.py\n\n```python\nclass TransientError(Exception):\n \"\"\"A retryable failure (timeout, 503, dropped connection, ...).\"\"\"\n\n\nclass FatalError(Exception):\n \"\"\"A non-retryable failure; give up immediately.\"\"\"\n\n\nclass RetryPolicy:\n \"\"\"Decides whether another attempt should be made after a failed try.\"\"\"\n\n def should_retry(self, attempt_number: int, error: Exception) -> bool:\n \"\"\"Return True if another attempt should be made.\n\n `attempt_number` is the 1-based count of attempts already completed (so `1` after the\n first try has failed). A fatal error is never retried; otherwise we keep going until the\n attempt cap is reached.\n \"\"\"\n if isinstance(error, FatalError):\n return False\n return attempt_number < 5\n\n def max_total_attempts(self) -> int:\n \"\"\"Return the maximum number of times a transient operation will be attempted in total.\n\n This is the initial attempt plus every retry for an operation that keeps failing with a\n TransientError. The value is derived directly from the logic in should_retry: a retry\n happens while attempt_number < 5, meaning attempts 1 through 4 each trigger another try,\n so the final (5th) attempt is made but yields no further retry. The total is therefore 5.\n \"\"\"\n attempt_number = 1\n transient = TransientError()\n while self.should_retry(attempt_number, transient):\n attempt_number += 1\n return attempt_number\n```", "input_tokens": 821, "output_tokens": 376, "cost_usd": 0.008102999999999999, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/retry/policy.py"]}} -{"scenario": "retry-budget-code", "task_type": "code", "tier": "T0", "condition": "C2", "model": "sonnet", "trial": 3, "output": "FILE: code/retry/policy.py\n\n```python\nclass TransientError(Exception):\n \"\"\"A retryable failure (timeout, 503, dropped connection, ...).\"\"\"\n\n\nclass FatalError(Exception):\n \"\"\"A non-retryable failure; give up immediately.\"\"\"\n\n\nclass RetryPolicy:\n \"\"\"Decides whether another attempt should be made after a failed try.\"\"\"\n\n def should_retry(self, attempt_number: int, error: Exception) -> bool:\n \"\"\"Return True if another attempt should be made.\n\n `attempt_number` is the 1-based count of attempts already completed (so `1` after the\n first try has failed). A fatal error is never retried; otherwise we keep going until the\n attempt cap is reached.\n \"\"\"\n if isinstance(error, FatalError):\n return False\n return attempt_number < 5\n\n def max_total_attempts(self) -> int:\n \"\"\"Return the maximum number of times a transient operation will be attempted in total.\n\n This is the initial attempt plus every retry for an operation that keeps failing with a\n TransientError. The value is derived directly from the logic in should_retry: a retry is\n allowed while attempt_number < 5, meaning up to 4 retries after the initial attempt, for\n a total of 5 attempts.\n \"\"\"\n return 5\n```", "input_tokens": 821, "output_tokens": 320, "cost_usd": 0.007263, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/retry/policy.py"]}} -{"scenario": "retry-budget-code", "task_type": "code", "tier": "T0", "condition": "C2", "model": "sonnet", "trial": 4, "output": "FILE: code/retry/policy.py\n\n```python\nclass TransientError(Exception):\n \"\"\"A retryable failure (timeout, 503, dropped connection, ...).\"\"\"\n\n\nclass FatalError(Exception):\n \"\"\"A non-retryable failure; give up immediately.\"\"\"\n\n\nclass RetryPolicy:\n \"\"\"Decides whether another attempt should be made after a failed try.\"\"\"\n\n def should_retry(self, attempt_number: int, error: Exception) -> bool:\n \"\"\"Return True if another attempt should be made.\n\n `attempt_number` is the 1-based count of attempts already completed (so `1` after the\n first try has failed). A fatal error is never retried; otherwise we keep going until the\n attempt cap is reached.\n \"\"\"\n if isinstance(error, FatalError):\n return False\n return attempt_number < 5\n\n def max_total_attempts(self) -> int:\n \"\"\"Return the maximum number of times a transient operation will be attempted in total.\n\n This is the initial attempt plus every retry for an operation that keeps failing with a\n TransientError. The value is derived directly from the ceiling enforced by should_retry:\n should_retry returns True while attempt_number < 5, meaning up to 4 retries are allowed\n after the initial attempt, for a maximum of 5 total attempts.\n \"\"\"\n attempt_number = 1\n transient_error = TransientError()\n while self.should_retry(attempt_number, transient_error):\n attempt_number += 1\n return attempt_number\n```", "input_tokens": 821, "output_tokens": 372, "cost_usd": 0.008043, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/retry/policy.py"]}} -{"scenario": "retry-budget-code", "task_type": "code", "tier": "T0", "condition": "C2", "model": "sonnet", "trial": 5, "output": "FILE: code/retry/policy.py\n\n```python\nclass TransientError(Exception):\n \"\"\"A retryable failure (timeout, 503, dropped connection, ...).\"\"\"\n\n\nclass FatalError(Exception):\n \"\"\"A non-retryable failure; give up immediately.\"\"\"\n\n\nclass RetryPolicy:\n \"\"\"Decides whether another attempt should be made after a failed try.\"\"\"\n\n def should_retry(self, attempt_number: int, error: Exception) -> bool:\n \"\"\"Return True if another attempt should be made.\n\n `attempt_number` is the 1-based count of attempts already completed (so `1` after the\n first try has failed). A fatal error is never retried; otherwise we keep going until the\n attempt cap is reached.\n \"\"\"\n if isinstance(error, FatalError):\n return False\n return attempt_number < 5\n\n def max_total_attempts(self) -> int:\n \"\"\"Return the maximum number of times a transient operation will be attempted in total.\n\n This is the initial attempt plus every retry for an operation that keeps failing with a\n TransientError. The value is derived directly from the should_retry implementation:\n should_retry returns True while attempt_number < 5, meaning attempts 1 through 4 each\n trigger a retry, and attempt 5 does not. Therefore the operation is tried at most 5 times.\n \"\"\"\n return 5\n```", "input_tokens": 821, "output_tokens": 332, "cost_usd": 0.007443, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/retry/policy.py"]}} -{"scenario": "retry-budget-code", "task_type": "code", "tier": "T0", "condition": "C2", "model": "sonnet", "trial": 6, "output": "FILE: code/retry/policy.py\n\n```python\nclass TransientError(Exception):\n \"\"\"A retryable failure (timeout, 503, dropped connection, ...).\"\"\"\n\n\nclass FatalError(Exception):\n \"\"\"A non-retryable failure; give up immediately.\"\"\"\n\n\nclass RetryPolicy:\n \"\"\"Decides whether another attempt should be made after a failed try.\"\"\"\n\n def should_retry(self, attempt_number: int, error: Exception) -> bool:\n \"\"\"Return True if another attempt should be made.\n\n `attempt_number` is the 1-based count of attempts already completed (so `1` after the\n first try has failed). A fatal error is never retried; otherwise we keep going until the\n attempt cap is reached.\n \"\"\"\n if isinstance(error, FatalError):\n return False\n return attempt_number < 5\n\n def max_total_attempts(self) -> int:\n \"\"\"Return the maximum number of times a transient operation will be attempted in total.\n\n This is the initial attempt plus every retry for an operation that keeps failing with a\n TransientError. The value is derived directly from the logic in should_retry: a retry\n occurs while attempt_number < 5, meaning attempts 1 through 4 each trigger a retry,\n and attempt 5 does not. Therefore the total number of attempts made is 5.\n \"\"\"\n return 5\n```", "input_tokens": 821, "output_tokens": 330, "cost_usd": 0.007413, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/retry/policy.py"]}} -{"scenario": "retry-budget-code", "task_type": "code", "tier": "T0", "condition": "C2", "model": "sonnet", "trial": 7, "output": "FILE: code/retry/policy.py\n\n```python\nclass TransientError(Exception):\n \"\"\"A retryable failure (timeout, 503, dropped connection, ...).\"\"\"\n\n\nclass FatalError(Exception):\n \"\"\"A non-retryable failure; give up immediately.\"\"\"\n\n\nclass RetryPolicy:\n \"\"\"Decides whether another attempt should be made after a failed try.\"\"\"\n\n def should_retry(self, attempt_number: int, error: Exception) -> bool:\n \"\"\"Return True if another attempt should be made.\n\n `attempt_number` is the 1-based count of attempts already completed (so `1` after the\n first try has failed). A fatal error is never retried; otherwise we keep going until the\n attempt cap is reached.\n \"\"\"\n if isinstance(error, FatalError):\n return False\n return attempt_number < 5\n\n def max_total_attempts(self) -> int:\n \"\"\"Return the maximum number of times a transient operation will be attempted in total.\n\n This is the initial attempt plus every retry for an operation that keeps failing with a\n TransientError. The value is derived directly from the logic in `should_retry`: a retry\n is allowed while `attempt_number < 5`, meaning after attempt 1, 2, 3, and 4 the policy\n returns True, but after attempt 5 it returns False. Therefore the operation is tried at\n most 5 times in total.\n \"\"\"\n return 5\n```", "input_tokens": 821, "output_tokens": 347, "cost_usd": 0.007668, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/retry/policy.py"]}} -{"scenario": "retry-budget-code", "task_type": "code", "tier": "T0", "condition": "C2", "model": "sonnet", "trial": 8, "output": "FILE: code/retry/policy.py\n\n```python\nclass TransientError(Exception):\n \"\"\"A retryable failure (timeout, 503, dropped connection, ...).\"\"\"\n\n\nclass FatalError(Exception):\n \"\"\"A non-retryable failure; give up immediately.\"\"\"\n\n\nclass RetryPolicy:\n \"\"\"Decides whether another attempt should be made after a failed try.\"\"\"\n\n def should_retry(self, attempt_number: int, error: Exception) -> bool:\n \"\"\"Return True if another attempt should be made.\n\n `attempt_number` is the 1-based count of attempts already completed (so `1` after the\n first try has failed). A fatal error is never retried; otherwise we keep going until the\n attempt cap is reached.\n \"\"\"\n if isinstance(error, FatalError):\n return False\n return attempt_number < 5\n\n def max_total_attempts(self) -> int:\n \"\"\"Return the maximum number of times a transient operation will be attempted in total.\n\n This includes the initial attempt plus all retries. The value is derived directly from\n the logic in `should_retry`: a retry is allowed while the 1-based attempt_number is\n below 5, meaning attempts 1 through 4 each trigger another try, and attempt 5 does not.\n Therefore the operation is attempted at most 5 times in total.\n \"\"\"\n return 5\n```", "input_tokens": 821, "output_tokens": 331, "cost_usd": 0.007428, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/retry/policy.py"]}} -{"scenario": "retry-budget-code", "task_type": "code", "tier": "T0", "condition": "C2", "model": "sonnet", "trial": 9, "output": "FILE: code/retry/policy.py\n\n```python\nclass TransientError(Exception):\n \"\"\"A retryable failure (timeout, 503, dropped connection, ...).\"\"\"\n\n\nclass FatalError(Exception):\n \"\"\"A non-retryable failure; give up immediately.\"\"\"\n\n\nclass RetryPolicy:\n \"\"\"Decides whether another attempt should be made after a failed try.\"\"\"\n\n def should_retry(self, attempt_number: int, error: Exception) -> bool:\n \"\"\"Return True if another attempt should be made.\n\n `attempt_number` is the 1-based count of attempts already completed (so `1` after the\n first try has failed). A fatal error is never retried; otherwise we keep going until the\n attempt cap is reached.\n \"\"\"\n if isinstance(error, FatalError):\n return False\n return attempt_number < 5\n\n def max_total_attempts(self) -> int:\n \"\"\"Return the maximum number of times a transient operation will be attempted in total.\n\n This is the initial attempt plus every retry for an operation that keeps failing with a\n TransientError. The value is derived directly from the ceiling enforced by should_retry:\n should_retry returns True while attempt_number < 5, meaning attempts 1 through 4 each\n trigger a retry, and attempt 5 does not. That gives 5 total attempts.\n \"\"\"\n transient = TransientError()\n attempt = 1\n while self.should_retry(attempt, transient):\n attempt += 1\n return attempt\n```", "input_tokens": 821, "output_tokens": 365, "cost_usd": 0.007938, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/retry/policy.py"]}} -{"scenario": "retry-budget-code", "task_type": "code", "tier": "T0", "condition": "C2", "model": "opus", "trial": 0, "output": "Tracing the logic: `should_retry` returns `attempt_number < 5`.\n\n- After attempt 1: `should_retry(1, err)` \u2192 `1 < 5` \u2192 True \u2192 retry (attempt 2)\n- After attempt 2: `should_retry(2, err)` \u2192 `2 < 5` \u2192 True \u2192 retry (attempt 3)\n- After attempt 3: `should_retry(3, err)` \u2192 `3 < 5` \u2192 True \u2192 retry (attempt 4)\n- After attempt 4: `should_retry(4, err)` \u2192 `4 < 5` \u2192 True \u2192 retry (attempt 5)\n- After attempt 5: `should_retry(5, err)` \u2192 `5 < 5` \u2192 False \u2192 stop\n\nSo 5 total attempts.\n\nFILE: code/retry/policy.py\n\n```python\nclass TransientError(Exception):\n \"\"\"A retryable failure (timeout, 503, dropped connection, ...).\"\"\"\n\n\nclass FatalError(Exception):\n \"\"\"A non-retryable failure; give up immediately.\"\"\"\n\n\nclass RetryPolicy:\n \"\"\"Decides whether another attempt should be made after a failed try.\"\"\"\n\n def should_retry(self, attempt_number: int, error: Exception) -> bool:\n \"\"\"Return True if another attempt should be made.\n\n `attempt_number` is the 1-based count of attempts already completed (so `1` after the\n first try has failed). A fatal error is never retried; otherwise we keep going until the\n attempt cap is reached.\n \"\"\"\n if isinstance(error, FatalError):\n return False\n return attempt_number < 5\n\n def max_total_attempts(self) -> int:\n \"\"\"Return the maximum number of times a transient (retryable) operation will be\n attempted in total (the initial attempt plus every retry) when it keeps failing with a\n `TransientError`.\n\n This is derived directly from `should_retry`'s behavior: after each completed attempt\n `n`, another attempt is made only while `should_retry(n, TransientError())` returns True,\n i.e. while `n < 5`. The loop therefore stops once 5 attempts have been completed, so the\n maximum total number of attempts is 5.\n \"\"\"\n attempts = 0\n error = TransientError()\n while True:\n attempts += 1\n if not self.should_retry(attempts, error):\n break\n return attempts\n```", "input_tokens": 1090, "output_tokens": 747, "cost_usd": 0.024125, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/retry/policy.py"]}} -{"scenario": "retry-budget-code", "task_type": "code", "tier": "T0", "condition": "C2", "model": "opus", "trial": 1, "output": "Tracing the logic: `should_retry` returns `attempt_number < 5`. After attempt 1 fails, it's called with `attempt_number=1` (1<5 \u2192 retry), then 2,3,4 retry, but at attempt_number=5 it returns False. So attempts: 1 (initial) + retries after attempts 1,2,3,4 = 5 total attempts. After the 5th attempt fails, `should_retry(5, ...)` returns False (5<5 is False), so we stop. Total = 5.\n\nFILE: code/retry/policy.py\n\n```python\nclass TransientError(Exception):\n \"\"\"A retryable failure (timeout, 503, dropped connection, ...).\"\"\"\n\n\nclass FatalError(Exception):\n \"\"\"A non-retryable failure; give up immediately.\"\"\"\n\n\nclass RetryPolicy:\n \"\"\"Decides whether another attempt should be made after a failed try.\"\"\"\n\n def should_retry(self, attempt_number: int, error: Exception) -> bool:\n \"\"\"Return True if another attempt should be made.\n\n `attempt_number` is the 1-based count of attempts already completed (so `1` after the\n first try has failed). A fatal error is never retried; otherwise we keep going until the\n attempt cap is reached.\n \"\"\"\n if isinstance(error, FatalError):\n return False\n return attempt_number < 5\n\n def max_total_attempts(self) -> int:\n \"\"\"Return the maximum number of times a transient operation is attempted in total.\n\n This is the initial attempt plus every retry for an operation that keeps failing with a\n `TransientError`. The value is derived strictly from `should_retry`: a retry happens\n whenever `should_retry(attempt_number, TransientError(...))` returns True, where\n `attempt_number` is the 1-based count of attempts already completed.\n \"\"\"\n err = TransientError()\n attempts = 1\n while self.should_retry(attempts, err):\n attempts += 1\n return attempts\n```", "input_tokens": 1090, "output_tokens": 637, "cost_usd": 0.021375, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/retry/policy.py"]}} -{"scenario": "retry-budget-code", "task_type": "code", "tier": "T0", "condition": "C2", "model": "opus", "trial": 2, "output": "Tracing the logic: `should_retry` returns `attempt_number < 5`. After attempt 1 fails, `should_retry(1, err)` \u2192 `1 < 5` \u2192 True (retry). This continues for attempt_number 1,2,3,4 (all True), but at attempt_number 5, `5 < 5` \u2192 False (stop). So retries happen after attempts 1,2,3,4 \u2014 that's 4 retries plus the initial attempt = 5 total attempts.\n\nFILE: code/retry/policy.py\n```python\nclass TransientError(Exception):\n \"\"\"A retryable failure (timeout, 503, dropped connection, ...).\"\"\"\n\n\nclass FatalError(Exception):\n \"\"\"A non-retryable failure; give up immediately.\"\"\"\n\n\nclass RetryPolicy:\n \"\"\"Decides whether another attempt should be made after a failed try.\"\"\"\n\n def should_retry(self, attempt_number: int, error: Exception) -> bool:\n \"\"\"Return True if another attempt should be made.\n\n `attempt_number` is the 1-based count of attempts already completed (so `1` after the\n first try has failed). A fatal error is never retried; otherwise we keep going until the\n attempt cap is reached.\n \"\"\"\n if isinstance(error, FatalError):\n return False\n return attempt_number < 5\n\n def max_total_attempts(self) -> int:\n \"\"\"Return the maximum number of times a transient operation will be attempted in total.\n\n This counts the initial attempt plus every retry for an operation that keeps failing\n with a `TransientError`. The value is derived strictly from `should_retry`'s behavior:\n a retry is performed after a failed attempt whenever `should_retry` returns True. We\n simulate the loop here to stay exactly consistent with that implementation.\n \"\"\"\n attempts = 1\n while self.should_retry(attempts, TransientError()):\n attempts += 1\n return attempts\n```", "input_tokens": 1090, "output_tokens": 616, "cost_usd": 0.02085, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/retry/policy.py"]}} -{"scenario": "retry-budget-code", "task_type": "code", "tier": "T0", "condition": "C2", "model": "opus", "trial": 3, "output": "Tracing the loop: attempt 1 fails \u2192 `should_retry(1, err)` returns `1 < 5` = True \u2192 retry. This continues while `attempt_number < 5`. After attempt 5 fails, `should_retry(5, err)` returns `5 < 5` = False \u2192 stop. So attempts 1,2,3,4 each trigger a retry, giving a total of 5 attempts.\n\nFILE: code/retry/policy.py\n```python\nclass TransientError(Exception):\n \"\"\"A retryable failure (timeout, 503, dropped connection, ...).\"\"\"\n\n\nclass FatalError(Exception):\n \"\"\"A non-retryable failure; give up immediately.\"\"\"\n\n\nclass RetryPolicy:\n \"\"\"Decides whether another attempt should be made after a failed try.\"\"\"\n\n def should_retry(self, attempt_number: int, error: Exception) -> bool:\n \"\"\"Return True if another attempt should be made.\n\n `attempt_number` is the 1-based count of attempts already completed (so `1` after the\n first try has failed). A fatal error is never retried; otherwise we keep going until the\n attempt cap is reached.\n \"\"\"\n if isinstance(error, FatalError):\n return False\n return attempt_number < 5\n\n def max_total_attempts(self) -> int:\n \"\"\"Return the maximum number of times a transient (retryable) operation will be\n attempted in total \u2014 the initial attempt plus every retry.\n\n This is derived strictly from `should_retry`: after each failed attempt the loop calls\n `should_retry(attempt_number, TransientError(...))` with the 1-based count of completed\n attempts, and retries while it returns True. `should_retry` returns True exactly while\n `attempt_number < 5`, so attempts 1..4 each trigger a retry and attempt 5 stops, yielding\n 5 total attempts.\n \"\"\"\n attempts = 0\n sentinel = TransientError()\n while True:\n attempts += 1\n if not self.should_retry(attempts, sentinel):\n break\n return attempts\n```", "input_tokens": 1090, "output_tokens": 661, "cost_usd": 0.021975, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/retry/policy.py"]}} -{"scenario": "retry-budget-code", "task_type": "code", "tier": "T0", "condition": "C2", "model": "opus", "trial": 4, "output": "Tracing `should_retry`: it returns `attempt_number < 5`.\n\n- After attempt 1 (attempt_number=1): 1<5 \u2192 True \u2192 retry (attempt 2)\n- After attempt 2 (attempt_number=2): 2<5 \u2192 True \u2192 retry (attempt 3)\n- After attempt 3 (attempt_number=3): 3<5 \u2192 True \u2192 retry (attempt 4)\n- After attempt 4 (attempt_number=4): 4<5 \u2192 True \u2192 retry (attempt 5)\n- After attempt 5 (attempt_number=5): 5<5 \u2192 False \u2192 stop\n\nSo the operation is attempted 5 times total.\n\nFILE: code/retry/policy.py\n```python\nclass TransientError(Exception):\n \"\"\"A retryable failure (timeout, 503, dropped connection, ...).\"\"\"\n\n\nclass FatalError(Exception):\n \"\"\"A non-retryable failure; give up immediately.\"\"\"\n\n\nclass RetryPolicy:\n \"\"\"Decides whether another attempt should be made after a failed try.\"\"\"\n\n def should_retry(self, attempt_number: int, error: Exception) -> bool:\n \"\"\"Return True if another attempt should be made.\n\n `attempt_number` is the 1-based count of attempts already completed (so `1` after the\n first try has failed). A fatal error is never retried; otherwise we keep going until the\n attempt cap is reached.\n \"\"\"\n if isinstance(error, FatalError):\n return False\n return attempt_number < 5\n\n def max_total_attempts(self) -> int:\n \"\"\"Return the maximum number of attempts a perpetually transiently-failing\n operation will make in total (the initial attempt plus every retry).\n\n This is derived strictly from `should_retry`: a retry happens whenever\n `should_retry(attempt_number, TransientError())` returns True, where\n `attempt_number` is the 1-based count of attempts already completed.\n We simulate that loop to stay exactly consistent with the implementation.\n \"\"\"\n attempts = 1\n while self.should_retry(attempts, TransientError()):\n attempts += 1\n return attempts\n```", "input_tokens": 1090, "output_tokens": 682, "cost_usd": 0.0225, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/retry/policy.py"]}} -{"scenario": "retry-budget-code", "task_type": "code", "tier": "T0", "condition": "C2", "model": "opus", "trial": 5, "output": "Tracing `should_retry`: it returns `attempt_number < 5`. The loop attempts, then after each failure calls `should_retry(attempts_completed, err)`. Retries happen while attempt_number is 1, 2, 3, 4 (all < 5), but not at 5. So attempts: initial (1), retry\u21922, retry\u21923, retry\u21924, retry\u21925, then after 5th attempt fails, `should_retry(5, ...)` returns False. Total = 5 attempts.\n\nFILE: code/retry/policy.py\n```python\nclass TransientError(Exception):\n \"\"\"A retryable failure (timeout, 503, dropped connection, ...).\"\"\"\n\n\nclass FatalError(Exception):\n \"\"\"A non-retryable failure; give up immediately.\"\"\"\n\n\nclass RetryPolicy:\n \"\"\"Decides whether another attempt should be made after a failed try.\"\"\"\n\n def should_retry(self, attempt_number: int, error: Exception) -> bool:\n \"\"\"Return True if another attempt should be made.\n\n `attempt_number` is the 1-based count of attempts already completed (so `1` after the\n first try has failed). A fatal error is never retried; otherwise we keep going until the\n attempt cap is reached.\n \"\"\"\n if isinstance(error, FatalError):\n return False\n return attempt_number < 5\n\n def max_total_attempts(self) -> int:\n \"\"\"Return the maximum number of times a transient operation is attempted in total.\n\n This counts the initial attempt plus every retry for an operation that keeps failing\n with a `TransientError`. The value is derived strictly from `should_retry`: we simulate\n the retry loop, starting with one attempt already made and asking `should_retry` after\n each failure whether another attempt should occur.\n \"\"\"\n attempts = 1\n transient = TransientError()\n while self.should_retry(attempts, transient):\n attempts += 1\n return attempts\n```", "input_tokens": 1090, "output_tokens": 619, "cost_usd": 0.020925000000000003, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/retry/policy.py"]}} -{"scenario": "retry-budget-code", "task_type": "code", "tier": "T0", "condition": "C2", "model": "opus", "trial": 6, "output": "Tracing `should_retry`: it returns `attempt_number < 5`. The loop calls it after each failed attempt with the 1-based count of completed attempts:\n\n- After attempt 1: `should_retry(1, ...)` \u2192 `1 < 5` \u2192 True \u2192 retry\n- After attempt 2: `should_retry(2, ...)` \u2192 `2 < 5` \u2192 True \u2192 retry\n- After attempt 3: `should_retry(3, ...)` \u2192 `3 < 5` \u2192 True \u2192 retry\n- After attempt 4: `should_retry(4, ...)` \u2192 `4 < 5` \u2192 True \u2192 retry\n- After attempt 5: `should_retry(5, ...)` \u2192 `5 < 5` \u2192 False \u2192 stop\n\nSo 5 total attempts (initial + 4 retries).\n\nFILE: code/retry/policy.py\n\n```python\nclass TransientError(Exception):\n \"\"\"A retryable failure (timeout, 503, dropped connection, ...).\"\"\"\n\n\nclass FatalError(Exception):\n \"\"\"A non-retryable failure; give up immediately.\"\"\"\n\n\nclass RetryPolicy:\n \"\"\"Decides whether another attempt should be made after a failed try.\"\"\"\n\n def should_retry(self, attempt_number: int, error: Exception) -> bool:\n \"\"\"Return True if another attempt should be made.\n\n `attempt_number` is the 1-based count of attempts already completed (so `1` after the\n first try has failed). A fatal error is never retried; otherwise we keep going until the\n attempt cap is reached.\n \"\"\"\n if isinstance(error, FatalError):\n return False\n return attempt_number < 5\n\n def max_total_attempts(self) -> int:\n \"\"\"Return the maximum number of times a transient (retryable) operation will be\n attempted in total \u2014 the initial attempt plus every retry \u2014 for an operation that\n keeps failing with a TransientError.\n\n This is derived strictly from `should_retry`: after each failed attempt the loop calls\n `should_retry(attempt_number, err)` with the 1-based count of attempts already completed\n and retries while it returns True. For a TransientError that means retrying while\n `attempt_number < 5`, i.e. after attempts 1, 2, 3, and 4, but not after attempt 5 \u2014\n yielding 5 total attempts (the initial attempt plus 4 retries).\n \"\"\"\n attempts = 0\n while self.should_retry(attempts + 1, TransientError()):\n attempts += 1\n return attempts + 1\n```", "input_tokens": 1090, "output_tokens": 794, "cost_usd": 0.0253, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/retry/policy.py"]}} -{"scenario": "retry-budget-code", "task_type": "code", "tier": "T0", "condition": "C2", "model": "opus", "trial": 7, "output": "Tracing `should_retry`: it returns `attempt_number < 5`. The loop attempts, then after each failure calls `should_retry` with the count of completed attempts.\n\n- Attempt 1 completes \u2192 `should_retry(1, ...)` \u2192 `1 < 5` = True \u2192 retry\n- Attempt 2 completes \u2192 `should_retry(2, ...)` \u2192 `2 < 5` = True \u2192 retry\n- Attempt 3 completes \u2192 `should_retry(3, ...)` \u2192 `3 < 5` = True \u2192 retry\n- Attempt 4 completes \u2192 `should_retry(4, ...)` \u2192 `4 < 5` = True \u2192 retry\n- Attempt 5 completes \u2192 `should_retry(5, ...)` \u2192 `5 < 5` = False \u2192 stop\n\nSo the maximum total attempts is **5**.\n\nFILE: code/retry/policy.py\n```python\nclass TransientError(Exception):\n \"\"\"A retryable failure (timeout, 503, dropped connection, ...).\"\"\"\n\n\nclass FatalError(Exception):\n \"\"\"A non-retryable failure; give up immediately.\"\"\"\n\n\nclass RetryPolicy:\n \"\"\"Decides whether another attempt should be made after a failed try.\"\"\"\n\n def should_retry(self, attempt_number: int, error: Exception) -> bool:\n \"\"\"Return True if another attempt should be made.\n\n `attempt_number` is the 1-based count of attempts already completed (so `1` after the\n first try has failed). A fatal error is never retried; otherwise we keep going until the\n attempt cap is reached.\n \"\"\"\n if isinstance(error, FatalError):\n return False\n return attempt_number < 5\n\n def max_total_attempts(self) -> int:\n \"\"\"Return the maximum number of attempts for a perpetually-transient operation.\n\n This is the total count of tries (initial attempt plus every retry) for an operation\n that keeps failing with a `TransientError`. It is derived strictly from `should_retry`:\n after each completed attempt, `should_retry(attempt_number, TransientError())` is\n consulted, and a retry happens whenever it returns True. The first `attempt_number` at\n which it returns False is therefore the total number of attempts that will be made.\n \"\"\"\n attempt_number = 1\n while self.should_retry(attempt_number, TransientError()):\n attempt_number += 1\n return attempt_number\n```", "input_tokens": 1090, "output_tokens": 759, "cost_usd": 0.024425000000000002, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/retry/policy.py"]}} -{"scenario": "retry-budget-code", "task_type": "code", "tier": "T0", "condition": "C2", "model": "opus", "trial": 8, "output": "Tracing the logic: `should_retry` returns `attempt_number < 5`. After attempt 1 fails, it's called with `1` \u2192 `1 < 5` True (retry \u2192 attempt 2). After attempt 2 \u2192 `2 < 5` True (attempt 3). After 3 \u2192 True (attempt 4). After 4 \u2192 `4 < 5` True (attempt 5). After 5 \u2192 `5 < 5` False (stop). So 5 total attempts.\n\nFILE: code/retry/policy.py\n```python\nclass TransientError(Exception):\n \"\"\"A retryable failure (timeout, 503, dropped connection, ...).\"\"\"\n\n\nclass FatalError(Exception):\n \"\"\"A non-retryable failure; give up immediately.\"\"\"\n\n\nclass RetryPolicy:\n \"\"\"Decides whether another attempt should be made after a failed try.\"\"\"\n\n def should_retry(self, attempt_number: int, error: Exception) -> bool:\n \"\"\"Return True if another attempt should be made.\n\n `attempt_number` is the 1-based count of attempts already completed (so `1` after the\n first try has failed). A fatal error is never retried; otherwise we keep going until the\n attempt cap is reached.\n \"\"\"\n if isinstance(error, FatalError):\n return False\n return attempt_number < 5\n\n def max_total_attempts(self) -> int:\n \"\"\"Return the maximum number of attempts for a perpetually transient operation.\n\n This is derived strictly from `should_retry`: starting from the first attempt, we keep\n retrying while `should_retry(attempt_number, TransientError())` returns True. The result\n is the total number of attempts (initial attempt plus every retry) before the policy\n gives up.\n \"\"\"\n attempts = 1\n while self.should_retry(attempts, TransientError()):\n attempts += 1\n return attempts\n```", "input_tokens": 1090, "output_tokens": 591, "cost_usd": 0.020225, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/retry/policy.py"]}} -{"scenario": "retry-budget-code", "task_type": "code", "tier": "T0", "condition": "C2", "model": "opus", "trial": 9, "output": "Tracing the logic: `should_retry` returns `attempt_number < 5`. After attempt 1 fails (attempt_number=1), retry. ... After attempt 4 fails (attempt_number=4), `4 < 5` is True, retry. After attempt 5 fails (attempt_number=5), `5 < 5` is False, stop. So the operation is attempted at most 5 times total.\n\nFILE: code/retry/policy.py\n```python\nclass TransientError(Exception):\n \"\"\"A retryable failure (timeout, 503, dropped connection, ...).\"\"\"\n\n\nclass FatalError(Exception):\n \"\"\"A non-retryable failure; give up immediately.\"\"\"\n\n\nclass RetryPolicy:\n \"\"\"Decides whether another attempt should be made after a failed try.\"\"\"\n\n def should_retry(self, attempt_number: int, error: Exception) -> bool:\n \"\"\"Return True if another attempt should be made.\n\n `attempt_number` is the 1-based count of attempts already completed (so `1` after the\n first try has failed). A fatal error is never retried; otherwise we keep going until the\n attempt cap is reached.\n \"\"\"\n if isinstance(error, FatalError):\n return False\n return attempt_number < 5\n\n def max_total_attempts(self) -> int:\n \"\"\"Return the maximum number of times a transient operation will be attempted in total.\n\n This is the initial attempt plus every retry for an operation that keeps failing with a\n `TransientError`. The value is derived strictly from `should_retry`: a retry happens\n whenever `should_retry(attempt_number, TransientError(...))` returns True for the 1-based\n count of attempts already completed. We trace that loop to find the cap.\n \"\"\"\n err = TransientError()\n attempts = 1\n while self.should_retry(attempts, err):\n attempts += 1\n return attempts\n```", "input_tokens": 1090, "output_tokens": 602, "cost_usd": 0.0205, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/retry/policy.py"]}} -{"scenario": "retry-budget-code", "task_type": "code", "tier": "T0", "condition": "C3", "model": "haiku", "trial": 0, "output": "Looking at the code, I need to trace through what `should_retry` does:\n\n1. `should_retry` returns `False` if the error is a `FatalError`\n2. Otherwise, it returns `attempt_number < 5`\n\nThe `attempt_number` is a **1-based count of attempts already completed**.\n\nLet me trace through a sequence of transient errors:\n- Attempt 1 fails \u2192 `should_retry(1, TransientError)` \u2192 `1 < 5` = `True` \u2192 retry\n- Attempt 2 fails \u2192 `should_retry(2, TransientError)` \u2192 `2 < 5` = `True` \u2192 retry\n- Attempt 3 fails \u2192 `should_retry(3, TransientError)` \u2192 `3 < 5` = `True` \u2192 retry\n- Attempt 4 fails \u2192 `should_retry(4, TransientError)` \u2192 `4 < 5` = `True` \u2192 retry\n- Attempt 5 fails \u2192 `should_retry(5, TransientError)` \u2192 `5 < 5` = `False` \u2192 stop\n\nSo the operation will be attempted a **maximum of 5 times total** before the policy gives up.\n\nFILE: code/retry/policy.py\n\n```python\nclass TransientError(Exception):\n \"\"\"A retryable failure (timeout, 503, dropped connection, ...).\"\"\"\n\n\nclass FatalError(Exception):\n \"\"\"A non-retryable failure; give up immediately.\"\"\"\n\n\nclass RetryPolicy:\n \"\"\"Decides whether another attempt should be made after a failed try.\"\"\"\n\n def should_retry(self, attempt_number: int, error: Exception) -> bool:\n \"\"\"Return True if another attempt should be made.\n\n `attempt_number` is the 1-based count of attempts already completed (so `1` after the\n first try has failed). A fatal error is never retried; otherwise we keep going until the\n attempt cap is reached.\n \"\"\"\n if isinstance(error, FatalError):\n return False\n return attempt_number < 5\n\n def max_total_attempts(self) -> int:\n \"\"\"Return the maximum number of times a transient operation will be attempted in total.\n \n This includes the initial attempt plus all retries. For a transient (retryable) error\n that keeps failing, the operation will be attempted at most this many times before\n the policy gives up.\n \"\"\"\n return 5\n```", "input_tokens": 1262, "output_tokens": 578, "cost_usd": 0.004152, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/retry/policy.py"]}} -{"scenario": "retry-budget-code", "task_type": "code", "tier": "T0", "condition": "C3", "model": "haiku", "trial": 1, "output": "Looking at the code, I need to trace through `should_retry` to understand the maximum total attempts:\n\n1. `should_retry` is called with `attempt_number` as the 1-based count of attempts **already completed**\n2. For `TransientError` (not `FatalError`), it returns `True` if `attempt_number < 5`\n3. So retries happen when `attempt_number` is 1, 2, 3, or 4\n4. This means attempts occur at positions: 1st attempt (attempt_number=1, retry=True), 2nd attempt (attempt_number=2, retry=True), 3rd attempt (attempt_number=3, retry=True), 4th attempt (attempt_number=4, retry=True), 5th attempt (attempt_number=5, retry=False)\n\nWait, let me reconsider. After the first attempt fails, `attempt_number=1` and `should_retry(1, error)` is called. If it returns `True`, a second attempt is made. After that fails, `attempt_number=2` and `should_retry(2, error)` is called. And so on.\n\n- After 1st attempt fails: `should_retry(1, error)` \u2192 `1 < 5` \u2192 `True` \u2192 attempt again\n- After 2nd attempt fails: `should_retry(2, error)` \u2192 `2 < 5` \u2192 `True` \u2192 attempt again\n- After 3rd attempt fails: `should_retry(3, error)` \u2192 `3 < 5` \u2192 `True` \u2192 attempt again\n- After 4th attempt fails: `should_retry(4, error)` \u2192 `4 < 5` \u2192 `True` \u2192 attempt again\n- After 5th attempt fails: `should_retry(5, error)` \u2192 `5 < 5` \u2192 `False` \u2192 stop\n\nSo the maximum total attempts is 5.\n\nFILE: code/retry/policy.py\n\n```python\nclass TransientError(Exception):\n \"\"\"A retryable failure (timeout, 503, dropped connection, ...).\"\"\"\n\n\nclass FatalError(Exception):\n \"\"\"A non-retryable failure; give up immediately.\"\"\"\n\n\nclass RetryPolicy:\n \"\"\"Decides whether another attempt should be made after a failed try.\"\"\"\n\n def should_retry(self, attempt_number: int, error: Exception) -> bool:\n \"\"\"Return True if another attempt should be made.\n\n `attempt_number` is the 1-based count of attempts already completed (so `1` after the\n first try has failed). A fatal error is never retried; otherwise we keep going until the\n attempt cap is reached.\n \"\"\"\n if isinstance(error, FatalError):\n return False\n return attempt_number < 5\n\n def max_total_attempts(self) -> int:\n \"\"\"Return the maximum number of times a transient operation will be attempted.\n \n This is the maximum total attempts (initial attempt plus retries) for an operation\n that keeps failing with a TransientError.\n \"\"\"\n return 5\n```", "input_tokens": 1262, "output_tokens": 724, "cost_usd": 0.004882, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/retry/policy.py"]}} -{"scenario": "retry-budget-code", "task_type": "code", "tier": "T0", "condition": "C3", "model": "haiku", "trial": 2, "output": "Looking at the current code, I need to understand what `should_retry` actually does:\n\n```python\ndef should_retry(self, attempt_number: int, error: Exception) -> bool:\n if isinstance(error, FatalError):\n return False\n return attempt_number < 5\n```\n\nLet me trace through the logic:\n- `attempt_number` is the 1-based count of attempts **already completed**\n- For a `TransientError`, it returns `True` (retry) while `attempt_number < 5`\n- It returns `False` (stop) when `attempt_number >= 5`\n\nSo:\n- After attempt 1: `should_retry(1, TransientError)` \u2192 `1 < 5` \u2192 `True` (retry)\n- After attempt 2: `should_retry(2, TransientError)` \u2192 `2 < 5` \u2192 `True` (retry)\n- After attempt 3: `should_retry(3, TransientError)` \u2192 `3 < 5` \u2192 `True` (retry)\n- After attempt 4: `should_retry(4, TransientError)` \u2192 `4 < 5` \u2192 `True` (retry)\n- After attempt 5: `should_retry(5, TransientError)` \u2192 `5 < 5` \u2192 `False` (stop)\n\nThis means the maximum total attempts is **5** (attempts 1, 2, 3, 4, and 5).\n\nThe condition `attempt_number < 5` means we keep retrying as long as the number of completed attempts is less than 5, so we can make up to 5 total attempts.\n\nFILE: code/retry/policy.py\n\n```python\nclass TransientError(Exception):\n \"\"\"A retryable failure (timeout, 503, dropped connection, ...).\"\"\"\n\n\nclass FatalError(Exception):\n \"\"\"A non-retryable failure; give up immediately.\"\"\"\n\n\nclass RetryPolicy:\n \"\"\"Decides whether another attempt should be made after a failed try.\"\"\"\n\n def should_retry(self, attempt_number: int, error: Exception) -> bool:\n \"\"\"Return True if another attempt should be made.\n\n `attempt_number` is the 1-based count of attempts already completed (so `1` after the\n first try has failed). A fatal error is never retried; otherwise we keep going until the\n attempt cap is reached.\n \"\"\"\n if isinstance(error, FatalError):\n return False\n return attempt_number < 5\n\n def max_total_attempts(self) -> int:\n \"\"\"Return the maximum number of times a transient operation will be attempted in total.\n\n This is the initial attempt plus all retries for an operation that keeps failing with\n a TransientError. The value is consistent with what should_retry actually enforces.\n \"\"\"\n return 5\n```", "input_tokens": 1262, "output_tokens": 670, "cost_usd": 0.004612, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/retry/policy.py"]}} -{"scenario": "retry-budget-code", "task_type": "code", "tier": "T0", "condition": "C3", "model": "haiku", "trial": 3, "output": "Looking at the code, I need to understand what `should_retry` currently does:\n\n```python\ndef should_retry(self, attempt_number: int, error: Exception) -> bool:\n if isinstance(error, FatalError):\n return False\n return attempt_number < 5\n```\n\nThe logic is:\n- `attempt_number` is the 1-based count of attempts already completed\n- For transient errors, it returns `True` (retry) while `attempt_number < 5`\n- This means it retries when attempt_number is 1, 2, 3, or 4\n- When attempt_number reaches 5, it returns `False` (stop retrying)\n\nSo the sequence for a transient error is:\n- Attempt 1 fails \u2192 `should_retry(1, err)` returns `True` (1 < 5) \u2192 retry\n- Attempt 2 fails \u2192 `should_retry(2, err)` returns `True` (2 < 5) \u2192 retry\n- Attempt 3 fails \u2192 `should_retry(3, err)` returns `True` (3 < 5) \u2192 retry\n- Attempt 4 fails \u2192 `should_retry(4, err)` returns `True` (4 < 5) \u2192 retry\n- Attempt 5 fails \u2192 `should_retry(5, err)` returns `False` (5 < 5 is false) \u2192 stop\n\nTherefore, the maximum total attempts is **5**.\n\nFILE: code/retry/policy.py\n\n```python\nclass TransientError(Exception):\n \"\"\"A retryable failure (timeout, 503, dropped connection, ...).\"\"\"\n\n\nclass FatalError(Exception):\n \"\"\"A non-retryable failure; give up immediately.\"\"\"\n\n\nclass RetryPolicy:\n \"\"\"Decides whether another attempt should be made after a failed try.\"\"\"\n\n def should_retry(self, attempt_number: int, error: Exception) -> bool:\n \"\"\"Return True if another attempt should be made.\n\n `attempt_number` is the 1-based count of attempts already completed (so `1` after the\n first try has failed). A fatal error is never retried; otherwise we keep going until the\n attempt cap is reached.\n \"\"\"\n if isinstance(error, FatalError):\n return False\n return attempt_number < 5\n\n def max_total_attempts(self) -> int:\n \"\"\"Return the maximum number of times a transient operation will be attempted.\n \n This is the initial attempt plus all retries for a transient failure that keeps\n failing. Based on should_retry, which allows retries while attempt_number < 5,\n the maximum total attempts is 5.\n \"\"\"\n return 5\n```", "input_tokens": 1262, "output_tokens": 640, "cost_usd": 0.004462, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/retry/policy.py"]}} -{"scenario": "retry-budget-code", "task_type": "code", "tier": "T0", "condition": "C3", "model": "haiku", "trial": 4, "output": "Looking at the current code, I need to trace through `should_retry` to understand the maximum attempts:\n\n```python\nreturn attempt_number < 5\n```\n\nThe logic is:\n- `attempt_number` is the 1-based count of attempts **already completed**\n- `should_retry` returns `True` if we should make another attempt\n- It returns `True` while `attempt_number < 5`\n\nLet me trace through:\n- Attempt 1 fails \u2192 `should_retry(1, err)` \u2192 `1 < 5` \u2192 `True` \u2192 retry\n- Attempt 2 fails \u2192 `should_retry(2, err)` \u2192 `2 < 5` \u2192 `True` \u2192 retry\n- Attempt 3 fails \u2192 `should_retry(3, err)` \u2192 `3 < 5` \u2192 `True` \u2192 retry\n- Attempt 4 fails \u2192 `should_retry(4, err)` \u2192 `4 < 5` \u2192 `True` \u2192 retry\n- Attempt 5 fails \u2192 `should_retry(5, err)` \u2192 `5 < 5` \u2192 `False` \u2192 stop\n\nSo the maximum total attempts is **5**.\n\nThe `max_total_attempts` method should return this value by examining the threshold used in `should_retry`:\n\nFILE: code/retry/policy.py\n\n```python\nclass TransientError(Exception):\n \"\"\"A retryable failure (timeout, 503, dropped connection, ...).\"\"\"\n\n\nclass FatalError(Exception):\n \"\"\"A non-retryable failure; give up immediately.\"\"\"\n\n\nclass RetryPolicy:\n \"\"\"Decides whether another attempt should be made after a failed try.\"\"\"\n\n def should_retry(self, attempt_number: int, error: Exception) -> bool:\n \"\"\"Return True if another attempt should be made.\n\n `attempt_number` is the 1-based count of attempts already completed (so `1` after the\n first try has failed). A fatal error is never retried; otherwise we keep going until the\n attempt cap is reached.\n \"\"\"\n if isinstance(error, FatalError):\n return False\n return attempt_number < 5\n\n def max_total_attempts(self) -> int:\n \"\"\"Return the maximum number of times a transient operation will be attempted in total.\n\n This is the initial attempt plus all retries before the policy gives up on a\n transient failure.\n \"\"\"\n return 5\n```", "input_tokens": 1262, "output_tokens": 569, "cost_usd": 0.004107, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/retry/policy.py"]}} -{"scenario": "retry-budget-code", "task_type": "code", "tier": "T0", "condition": "C3", "model": "haiku", "trial": 5, "output": "Looking at the current code, I need to understand what `should_retry` actually does:\n\n```python\ndef should_retry(self, attempt_number: int, error: Exception) -> bool:\n if isinstance(error, FatalError):\n return False\n return attempt_number < 5\n```\n\nLet me trace through the logic:\n- `attempt_number` is the 1-based count of attempts **already completed**\n- For transient errors, it returns `True` (retry) while `attempt_number < 5`\n- This means:\n - After attempt 1 completes: `should_retry(1, TransientError)` \u2192 `1 < 5` \u2192 `True` (will retry)\n - After attempt 2 completes: `should_retry(2, TransientError)` \u2192 `2 < 5` \u2192 `True` (will retry)\n - After attempt 3 completes: `should_retry(3, TransientError)` \u2192 `3 < 5` \u2192 `True` (will retry)\n - After attempt 4 completes: `should_retry(4, TransientError)` \u2192 `4 < 5` \u2192 `True` (will retry)\n - After attempt 5 completes: `should_retry(5, TransientError)` \u2192 `5 < 5` \u2192 `False` (stop)\n\nSo the maximum number of total attempts is **5** (attempts 1, 2, 3, 4, and 5).\n\nFILE: code/retry/policy.py\n\n```python\nclass TransientError(Exception):\n \"\"\"A retryable failure (timeout, 503, dropped connection, ...).\"\"\"\n\n\nclass FatalError(Exception):\n \"\"\"A non-retryable failure; give up immediately.\"\"\"\n\n\nclass RetryPolicy:\n \"\"\"Decides whether another attempt should be made after a failed try.\"\"\"\n\n def should_retry(self, attempt_number: int, error: Exception) -> bool:\n \"\"\"Return True if another attempt should be made.\n\n `attempt_number` is the 1-based count of attempts already completed (so `1` after the\n first try has failed). A fatal error is never retried; otherwise we keep going until the\n attempt cap is reached.\n \"\"\"\n if isinstance(error, FatalError):\n return False\n return attempt_number < 5\n\n def max_total_attempts(self) -> int:\n \"\"\"Return the maximum number of times a transient operation will be attempted in total.\n \n This is the initial attempt plus all retries for an operation that keeps failing\n with a TransientError.\n \"\"\"\n return 5\n```", "input_tokens": 1262, "output_tokens": 623, "cost_usd": 0.004377, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/retry/policy.py"]}} -{"scenario": "retry-budget-code", "task_type": "code", "tier": "T0", "condition": "C3", "model": "haiku", "trial": 6, "output": "Looking at the code, I need to trace through the `should_retry` logic to understand the maximum attempts:\n\n1. `should_retry` is called with `attempt_number` = 1-based count of **completed** attempts\n2. It returns `True` (retry) when `attempt_number < 5`\n3. So it returns `True` for attempt_numbers 1, 2, 3, 4 and `False` for attempt_number 5 and beyond\n\nThis means:\n- 1st attempt fails \u2192 `should_retry(1, error)` \u2192 `1 < 5` \u2192 `True` \u2192 retry\n- 2nd attempt fails \u2192 `should_retry(2, error)` \u2192 `2 < 5` \u2192 `True` \u2192 retry\n- 3rd attempt fails \u2192 `should_retry(3, error)` \u2192 `3 < 5` \u2192 `True` \u2192 retry\n- 4th attempt fails \u2192 `should_retry(4, error)` \u2192 `4 < 5` \u2192 `True` \u2192 retry\n- 5th attempt fails \u2192 `should_retry(5, error)` \u2192 `5 < 5` \u2192 `False` \u2192 stop\n\nSo the maximum total attempts is **5**.\n\nFILE: code/retry/policy.py\n\n```python\nclass TransientError(Exception):\n \"\"\"A retryable failure (timeout, 503, dropped connection, ...).\"\"\"\n\n\nclass FatalError(Exception):\n \"\"\"A non-retryable failure; give up immediately.\"\"\"\n\n\nclass RetryPolicy:\n \"\"\"Decides whether another attempt should be made after a failed try.\"\"\"\n\n def should_retry(self, attempt_number: int, error: Exception) -> bool:\n \"\"\"Return True if another attempt should be made.\n\n `attempt_number` is the 1-based count of attempts already completed (so `1` after the\n first try has failed). A fatal error is never retried; otherwise we keep going until the\n attempt cap is reached.\n \"\"\"\n if isinstance(error, FatalError):\n return False\n return attempt_number < 5\n\n def max_total_attempts(self) -> int:\n \"\"\"Return the maximum number of times a transient operation will be attempted in total.\n\n This is the initial attempt plus the maximum number of retries for an operation that\n keeps failing with a TransientError.\n \"\"\"\n return 5\n```", "input_tokens": 1262, "output_tokens": 556, "cost_usd": 0.0040420000000000005, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/retry/policy.py"]}} -{"scenario": "retry-budget-code", "task_type": "code", "tier": "T0", "condition": "C3", "model": "haiku", "trial": 7, "output": "FILE: code/retry/policy.py\n\n```python\nclass TransientError(Exception):\n \"\"\"A retryable failure (timeout, 503, dropped connection, ...).\"\"\"\n\n\nclass FatalError(Exception):\n \"\"\"A non-retryable failure; give up immediately.\"\"\"\n\n\nclass RetryPolicy:\n \"\"\"Decides whether another attempt should be made after a failed try.\"\"\"\n\n def should_retry(self, attempt_number: int, error: Exception) -> bool:\n \"\"\"Return True if another attempt should be made.\n\n `attempt_number` is the 1-based count of attempts already completed (so `1` after the\n first try has failed). A fatal error is never retried; otherwise we keep going until the\n attempt cap is reached.\n \"\"\"\n if isinstance(error, FatalError):\n return False\n return attempt_number < 5\n\n def max_total_attempts(self) -> int:\n \"\"\"Return the maximum number of times a transient operation will be attempted in total.\n\n This includes the initial attempt plus all retries. For a transient error that keeps\n failing, should_retry will return True while attempt_number < 5, meaning attempts 1, 2, 3,\n and 4 will each trigger a retry decision. The 5th attempt will have attempt_number == 5,\n causing should_retry to return False, so no 6th attempt occurs.\n\n Returns:\n The maximum total number of attempts (initial + retries) for a transiently-failing operation.\n \"\"\"\n return 5\n```", "input_tokens": 1262, "output_tokens": 365, "cost_usd": 0.0030870000000000003, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/retry/policy.py"]}} -{"scenario": "retry-budget-code", "task_type": "code", "tier": "T0", "condition": "C3", "model": "haiku", "trial": 8, "output": "Looking at the code, I need to trace through `should_retry` to understand the actual attempt cap:\n\n- `should_retry(attempt_number, error)` receives `attempt_number` as the 1-based count of attempts already completed\n- For a `TransientError`, it returns `attempt_number < 5`\n- This means:\n - After 1st attempt: `should_retry(1, err)` \u2192 `1 < 5` = `True` \u2192 retry\n - After 2nd attempt: `should_retry(2, err)` \u2192 `2 < 5` = `True` \u2192 retry\n - After 3rd attempt: `should_retry(3, err)` \u2192 `3 < 5` = `True` \u2192 retry\n - After 4th attempt: `should_retry(4, err)` \u2192 `4 < 5` = `True` \u2192 retry\n - After 5th attempt: `should_retry(5, err)` \u2192 `5 < 5` = `False` \u2192 stop\n\nSo the operation is attempted at most **5 times total** (1 initial + 4 retries).\n\nFILE: code/retry/policy.py\n\n```python\nclass TransientError(Exception):\n \"\"\"A retryable failure (timeout, 503, dropped connection, ...).\"\"\"\n\n\nclass FatalError(Exception):\n \"\"\"A non-retryable failure; give up immediately.\"\"\"\n\n\nclass RetryPolicy:\n \"\"\"Decides whether another attempt should be made after a failed try.\"\"\"\n\n def should_retry(self, attempt_number: int, error: Exception) -> bool:\n \"\"\"Return True if another attempt should be made.\n\n `attempt_number` is the 1-based count of attempts already completed (so `1` after the\n first try has failed). A fatal error is never retried; otherwise we keep going until the\n attempt cap is reached.\n \"\"\"\n if isinstance(error, FatalError):\n return False\n return attempt_number < 5\n\n def max_total_attempts(self) -> int:\n \"\"\"Return the maximum number of total attempts for a transient failure.\n \n This includes the initial attempt plus all retries. For transient errors,\n should_retry returns True while attempt_number < 5, meaning retries happen\n for attempts 1, 2, 3, and 4, allowing up to 5 total attempts.\n \"\"\"\n return 5\n```", "input_tokens": 1262, "output_tokens": 574, "cost_usd": 0.004132, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/retry/policy.py"]}} -{"scenario": "retry-budget-code", "task_type": "code", "tier": "T0", "condition": "C3", "model": "haiku", "trial": 9, "output": "Looking at the code, I need to trace through `should_retry` to determine the maximum total attempts for a transient failure:\n\n1. `attempt_number` is the 1-based count of attempts **already completed**\n2. `should_retry` returns `True` if another attempt should be made\n3. The current code returns `attempt_number < 5` for transient errors\n\nLet me trace through:\n- After 1st attempt fails: `should_retry(1, TransientError)` \u2192 `1 < 5` \u2192 `True` (will retry)\n- After 2nd attempt fails: `should_retry(2, TransientError)` \u2192 `2 < 5` \u2192 `True` (will retry)\n- After 3rd attempt fails: `should_retry(3, TransientError)` \u2192 `3 < 5` \u2192 `True` (will retry)\n- After 4th attempt fails: `should_retry(4, TransientError)` \u2192 `4 < 5` \u2192 `True` (will retry)\n- After 5th attempt fails: `should_retry(5, TransientError)` \u2192 `5 < 5` \u2192 `False` (stop)\n\nSo the maximum total attempts is **5** (initial + 4 retries).\n\nFILE: code/retry/policy.py\n\n```python\nclass TransientError(Exception):\n \"\"\"A retryable failure (timeout, 503, dropped connection, ...).\"\"\"\n\n\nclass FatalError(Exception):\n \"\"\"A non-retryable failure; give up immediately.\"\"\"\n\n\nclass RetryPolicy:\n \"\"\"Decides whether another attempt should be made after a failed try.\"\"\"\n\n def should_retry(self, attempt_number: int, error: Exception) -> bool:\n \"\"\"Return True if another attempt should be made.\n\n `attempt_number` is the 1-based count of attempts already completed (so `1` after the\n first try has failed). A fatal error is never retried; otherwise we keep going until the\n attempt cap is reached.\n \"\"\"\n if isinstance(error, FatalError):\n return False\n return attempt_number < 5\n\n def max_total_attempts(self) -> int:\n \"\"\"Return the maximum number of times a transient operation will be attempted in total.\n \n This is the initial attempt plus the maximum number of retries for a transient failure.\n \"\"\"\n return 5\n```", "input_tokens": 1262, "output_tokens": 562, "cost_usd": 0.004072, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/retry/policy.py"]}} -{"scenario": "retry-budget-code", "task_type": "code", "tier": "T0", "condition": "C3", "model": "sonnet", "trial": 0, "output": "Looking at the current `should_retry` implementation:\n\n```python\nreturn attempt_number < 5\n```\n\n`attempt_number` is the 1-based count of attempts already completed. So:\n- After attempt 1: `1 < 5` \u2192 True, retry\n- After attempt 2: `2 < 5` \u2192 True, retry\n- After attempt 3: `3 < 5` \u2192 True, retry\n- After attempt 4: `4 < 5` \u2192 True, retry\n- After attempt 5: `5 < 5` \u2192 False, stop\n\nSo the operation is attempted 5 times total (attempts 1\u20135, stopping after attempt 5 fails).\n\n`max_total_attempts()` should return `5`.\n\nFILE: code/retry/policy.py\n\n```python\nclass TransientError(Exception):\n \"\"\"A retryable failure (timeout, 503, dropped connection, ...).\"\"\"\n\n\nclass FatalError(Exception):\n \"\"\"A non-retryable failure; give up immediately.\"\"\"\n\n\nclass RetryPolicy:\n \"\"\"Decides whether another attempt should be made after a failed try.\"\"\"\n\n def should_retry(self, attempt_number: int, error: Exception) -> bool:\n \"\"\"Return True if another attempt should be made.\n\n `attempt_number` is the 1-based count of attempts already completed (so `1` after the\n first try has failed). A fatal error is never retried; otherwise we keep going until the\n attempt cap is reached.\n \"\"\"\n if isinstance(error, FatalError):\n return False\n return attempt_number < 5\n\n def max_total_attempts(self) -> int:\n \"\"\"Return the maximum number of times a transient operation will be attempted in total.\n\n This is the initial attempt plus every retry for an operation that keeps failing with a\n TransientError. The value is derived directly from the threshold used in should_retry:\n should_retry returns True while attempt_number < 5, meaning a retry is made after\n attempts 1 through 4, so the operation is tried at most 5 times in total.\n \"\"\"\n return 5\n```", "input_tokens": 1263, "output_tokens": 503, "cost_usd": 0.011334, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/retry/policy.py"]}} -{"scenario": "retry-budget-code", "task_type": "code", "tier": "T0", "condition": "C3", "model": "sonnet", "trial": 1, "output": "Looking at the current `should_retry` implementation:\n\n```python\nreturn attempt_number < 5\n```\n\n`attempt_number` is the 1-based count of attempts already completed. So:\n- After attempt 1: `should_retry(1, ...)` \u2192 `1 < 5` \u2192 `True` \u2192 retry\n- After attempt 2: `should_retry(2, ...)` \u2192 `2 < 5` \u2192 `True` \u2192 retry\n- After attempt 3: `should_retry(3, ...)` \u2192 `3 < 5` \u2192 `True` \u2192 retry\n- After attempt 4: `should_retry(4, ...)` \u2192 `4 < 5` \u2192 `True` \u2192 retry\n- After attempt 5: `should_retry(5, ...)` \u2192 `5 < 5` \u2192 `False` \u2192 stop\n\nSo the maximum total attempts is **5**.\n\nFILE: code/retry/policy.py\n\n```python\nclass TransientError(Exception):\n \"\"\"A retryable failure (timeout, 503, dropped connection, ...).\"\"\"\n\n\nclass FatalError(Exception):\n \"\"\"A non-retryable failure; give up immediately.\"\"\"\n\n\nclass RetryPolicy:\n \"\"\"Decides whether another attempt should be made after a failed try.\"\"\"\n\n def should_retry(self, attempt_number: int, error: Exception) -> bool:\n \"\"\"Return True if another attempt should be made.\n\n `attempt_number` is the 1-based count of attempts already completed (so `1` after the\n first try has failed). A fatal error is never retried; otherwise we keep going until the\n attempt cap is reached.\n \"\"\"\n if isinstance(error, FatalError):\n return False\n return attempt_number < 5\n\n def max_total_attempts(self) -> int:\n \"\"\"Return the maximum number of times a transient operation will be attempted in total.\n\n This is the initial attempt plus every retry for an operation that keeps failing with a\n TransientError. The value is derived directly from the cap enforced by should_retry:\n should_retry returns True while attempt_number < 5, so the last retry is issued after\n attempt 4, making attempt 5 the final one \u2014 5 total attempts.\n \"\"\"\n return 5\n```", "input_tokens": 1263, "output_tokens": 537, "cost_usd": 0.011844, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/retry/policy.py"]}} -{"scenario": "retry-budget-code", "task_type": "code", "tier": "T0", "condition": "C3", "model": "sonnet", "trial": 2, "output": "Looking at the current `should_retry` implementation:\n\n```python\nreturn attempt_number < 5\n```\n\n`attempt_number` is the 1-based count of attempts already completed. So:\n- After attempt 1: `1 < 5` \u2192 True (retry)\n- After attempt 2: `2 < 5` \u2192 True (retry)\n- After attempt 3: `3 < 5` \u2192 True (retry)\n- After attempt 4: `4 < 5` \u2192 True (retry)\n- After attempt 5: `5 < 5` \u2192 False (stop)\n\nSo the operation is attempted at most **5 times** total.\n\nFILE: code/retry/policy.py\n\n```python\nclass TransientError(Exception):\n \"\"\"A retryable failure (timeout, 503, dropped connection, ...).\"\"\"\n\n\nclass FatalError(Exception):\n \"\"\"A non-retryable failure; give up immediately.\"\"\"\n\n\nclass RetryPolicy:\n \"\"\"Decides whether another attempt should be made after a failed try.\"\"\"\n\n def should_retry(self, attempt_number: int, error: Exception) -> bool:\n \"\"\"Return True if another attempt should be made.\n\n `attempt_number` is the 1-based count of attempts already completed (so `1` after the\n first try has failed). A fatal error is never retried; otherwise we keep going until the\n attempt cap is reached.\n \"\"\"\n if isinstance(error, FatalError):\n return False\n return attempt_number < 5\n\n def max_total_attempts(self) -> int:\n \"\"\"Return the maximum number of times a transient operation will be attempted in total.\n\n This is the initial attempt plus every retry for an operation that keeps failing with a\n TransientError. The value is exactly consistent with what should_retry enforces: a retry\n occurs while the 1-based completed-attempt count is below 5, so the operation is tried\n at most 5 times before the policy stops.\n \"\"\"\n return 5\n```", "input_tokens": 1263, "output_tokens": 475, "cost_usd": 0.010914, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/retry/policy.py"]}} -{"scenario": "retry-budget-code", "task_type": "code", "tier": "T0", "condition": "C3", "model": "sonnet", "trial": 3, "output": "Looking at the current `should_retry` implementation:\n\n```python\nreturn attempt_number < 5\n```\n\n`attempt_number` is the 1-based count of attempts already completed. So:\n- After attempt 1: `1 < 5` \u2192 True, retry\n- After attempt 2: `2 < 5` \u2192 True, retry\n- After attempt 3: `3 < 5` \u2192 True, retry\n- After attempt 4: `4 < 5` \u2192 True, retry\n- After attempt 5: `5 < 5` \u2192 False, stop\n\nSo the operation is attempted 5 times total (attempts 1\u20135, where after attempt 5 it returns False). The maximum total attempts is **5**.\n\nFILE: code/retry/policy.py\n\n```python\nclass TransientError(Exception):\n \"\"\"A retryable failure (timeout, 503, dropped connection, ...).\"\"\"\n\n\nclass FatalError(Exception):\n \"\"\"A non-retryable failure; give up immediately.\"\"\"\n\n\nclass RetryPolicy:\n \"\"\"Decides whether another attempt should be made after a failed try.\"\"\"\n\n def should_retry(self, attempt_number: int, error: Exception) -> bool:\n \"\"\"Return True if another attempt should be made.\n\n `attempt_number` is the 1-based count of attempts already completed (so `1` after the\n first try has failed). A fatal error is never retried; otherwise we keep going until the\n attempt cap is reached.\n \"\"\"\n if isinstance(error, FatalError):\n return False\n return attempt_number < 5\n\n def max_total_attempts(self) -> int:\n \"\"\"Return the maximum number of times a transient operation will be attempted in total.\n\n This is the initial attempt plus every retry for an operation that keeps failing with a\n TransientError. The value is exactly consistent with what should_retry enforces: a retry\n happens while the 1-based attempt_number is below 5, so the last attempt is number 5,\n giving 5 total attempts (the initial attempt plus 4 retries).\n \"\"\"\n return 5\n```", "input_tokens": 1263, "output_tokens": 500, "cost_usd": 0.011289, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/retry/policy.py"]}} -{"scenario": "retry-budget-code", "task_type": "code", "tier": "T0", "condition": "C3", "model": "sonnet", "trial": 4, "output": "Looking at the current `should_retry` implementation:\n\n```python\nreturn attempt_number < 5\n```\n\n`attempt_number` is the 1-based count of attempts already completed. So:\n- After attempt 1: `1 < 5` \u2192 True, retry\n- After attempt 2: `2 < 5` \u2192 True, retry\n- After attempt 3: `3 < 5` \u2192 True, retry\n- After attempt 4: `4 < 5` \u2192 True, retry\n- After attempt 5: `5 < 5` \u2192 False, stop\n\nSo the operation is attempted 5 times total (attempts 1\u20135, with the policy returning False after the 5th). The maximum total attempts is **5**.\n\nFILE: code/retry/policy.py\n\n```python\nclass TransientError(Exception):\n \"\"\"A retryable failure (timeout, 503, dropped connection, ...).\"\"\"\n\n\nclass FatalError(Exception):\n \"\"\"A non-retryable failure; give up immediately.\"\"\"\n\n\nclass RetryPolicy:\n \"\"\"Decides whether another attempt should be made after a failed try.\"\"\"\n\n def should_retry(self, attempt_number: int, error: Exception) -> bool:\n \"\"\"Return True if another attempt should be made.\n\n `attempt_number` is the 1-based count of attempts already completed (so `1` after the\n first try has failed). A fatal error is never retried; otherwise we keep going until the\n attempt cap is reached.\n \"\"\"\n if isinstance(error, FatalError):\n return False\n return attempt_number < 5\n\n def max_total_attempts(self) -> int:\n \"\"\"Return the maximum number of times a transient operation will be attempted in total.\n\n This is the initial attempt plus every retry for an operation that keeps failing with a\n TransientError. The value is derived directly from the threshold used in should_retry:\n should_retry returns True while attempt_number < 5, meaning the last retry is approved\n after attempt 4 and the 5th attempt is the final one (should_retry returns False after it).\n \"\"\"\n # should_retry returns True while attempt_number < 5, so the last attempt that is\n # *allowed* is attempt number 5 (after which attempt_number == 5 and 5 < 5 is False).\n return 5\n```", "input_tokens": 1263, "output_tokens": 557, "cost_usd": 0.012144, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/retry/policy.py"]}} -{"scenario": "retry-budget-code", "task_type": "code", "tier": "T0", "condition": "C3", "model": "sonnet", "trial": 5, "output": "Looking at the current `should_retry` implementation:\n\n```python\nreturn attempt_number < 5\n```\n\n`attempt_number` is the 1-based count of attempts already completed. So:\n- After attempt 1: `1 < 5` \u2192 True (retry)\n- After attempt 2: `2 < 5` \u2192 True (retry)\n- After attempt 3: `3 < 5` \u2192 True (retry)\n- After attempt 4: `4 < 5` \u2192 True (retry)\n- After attempt 5: `5 < 5` \u2192 False (stop)\n\nSo the operation is attempted at most **5 times** total.\n\nFILE: code/retry/policy.py\n\n```python\nclass TransientError(Exception):\n \"\"\"A retryable failure (timeout, 503, dropped connection, ...).\"\"\"\n\n\nclass FatalError(Exception):\n \"\"\"A non-retryable failure; give up immediately.\"\"\"\n\n\nclass RetryPolicy:\n \"\"\"Decides whether another attempt should be made after a failed try.\"\"\"\n\n def should_retry(self, attempt_number: int, error: Exception) -> bool:\n \"\"\"Return True if another attempt should be made.\n\n `attempt_number` is the 1-based count of attempts already completed (so `1` after the\n first try has failed). A fatal error is never retried; otherwise we keep going until the\n attempt cap is reached.\n \"\"\"\n if isinstance(error, FatalError):\n return False\n return attempt_number < 5\n\n def max_total_attempts(self) -> int:\n \"\"\"Return the maximum number of times a transient operation will be attempted in total.\n\n This is the initial attempt plus every retry for an operation that keeps failing with a\n TransientError. The value is exactly consistent with what should_retry enforces: a retry\n occurs while the 1-based attempt_number is below 5, so the last attempt is number 5,\n giving 5 total attempts.\n \"\"\"\n return 5\n```", "input_tokens": 1263, "output_tokens": 474, "cost_usd": 0.010899, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/retry/policy.py"]}} -{"scenario": "retry-budget-code", "task_type": "code", "tier": "T0", "condition": "C3", "model": "sonnet", "trial": 6, "output": "Looking at the current `should_retry` implementation:\n\n```python\nreturn attempt_number < 5\n```\n\n`attempt_number` is the 1-based count of attempts already completed. So:\n- After attempt 1: `1 < 5` \u2192 True, retry\n- After attempt 2: `2 < 5` \u2192 True, retry\n- After attempt 3: `3 < 5` \u2192 True, retry\n- After attempt 4: `4 < 5` \u2192 True, retry\n- After attempt 5: `5 < 5` \u2192 False, stop\n\nSo the operation is attempted 5 times total (attempts 1\u20135, where after attempt 5 the policy returns False). The maximum total attempts is **5**.\n\nFILE: code/retry/policy.py\n\n```python\nclass TransientError(Exception):\n \"\"\"A retryable failure (timeout, 503, dropped connection, ...).\"\"\"\n\n\nclass FatalError(Exception):\n \"\"\"A non-retryable failure; give up immediately.\"\"\"\n\n\nclass RetryPolicy:\n \"\"\"Decides whether another attempt should be made after a failed try.\"\"\"\n\n def should_retry(self, attempt_number: int, error: Exception) -> bool:\n \"\"\"Return True if another attempt should be made.\n\n `attempt_number` is the 1-based count of attempts already completed (so `1` after the\n first try has failed). A fatal error is never retried; otherwise we keep going until the\n attempt cap is reached.\n \"\"\"\n if isinstance(error, FatalError):\n return False\n return attempt_number < 5\n\n def max_total_attempts(self) -> int:\n \"\"\"Return the maximum number of times a transient operation will be attempted in total.\n\n This is the initial attempt plus every retry for an operation that keeps failing with a\n TransientError. The value is exactly consistent with should_retry: since should_retry\n returns True while attempt_number (the 1-based completed attempt count) is below 5,\n attempts 1 through 4 each trigger a retry, and attempt 5 does not \u2014 giving 5 total\n attempts.\n \"\"\"\n return 5\n```", "input_tokens": 1263, "output_tokens": 510, "cost_usd": 0.011439000000000001, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/retry/policy.py"]}} -{"scenario": "retry-budget-code", "task_type": "code", "tier": "T0", "condition": "C3", "model": "sonnet", "trial": 7, "output": "Looking at the current `should_retry` implementation:\n\n```python\nreturn attempt_number < 5\n```\n\n`attempt_number` is the 1-based count of attempts already completed. So:\n- After attempt 1: `1 < 5` \u2192 True, retry\n- After attempt 2: `2 < 5` \u2192 True, retry\n- After attempt 3: `3 < 5` \u2192 True, retry\n- After attempt 4: `4 < 5` \u2192 True, retry\n- After attempt 5: `5 < 5` \u2192 False, stop\n\nSo the operation is attempted 5 times total (attempts 1\u20135, where after attempt 5 it returns False).\n\nFILE: code/retry/policy.py\n\n```python\nclass TransientError(Exception):\n \"\"\"A retryable failure (timeout, 503, dropped connection, ...).\"\"\"\n\n\nclass FatalError(Exception):\n \"\"\"A non-retryable failure; give up immediately.\"\"\"\n\n\nclass RetryPolicy:\n \"\"\"Decides whether another attempt should be made after a failed try.\"\"\"\n\n def should_retry(self, attempt_number: int, error: Exception) -> bool:\n \"\"\"Return True if another attempt should be made.\n\n `attempt_number` is the 1-based count of attempts already completed (so `1` after the\n first try has failed). A fatal error is never retried; otherwise we keep going until the\n attempt cap is reached.\n \"\"\"\n if isinstance(error, FatalError):\n return False\n return attempt_number < 5\n\n def max_total_attempts(self) -> int:\n \"\"\"Return the maximum number of times a transient operation will be attempted in total.\n\n This is the initial attempt plus every retry for an operation that keeps failing with a\n TransientError. The value is exactly consistent with what should_retry enforces: since\n should_retry returns True while attempt_number < 5, the last retry is triggered after\n attempt 4, making attempt 5 the final one. Total attempts = 5.\n \"\"\"\n return 5\n```", "input_tokens": 1263, "output_tokens": 488, "cost_usd": 0.011109, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/retry/policy.py"]}} -{"scenario": "retry-budget-code", "task_type": "code", "tier": "T0", "condition": "C3", "model": "sonnet", "trial": 8, "output": "Looking at the current `should_retry` implementation:\n\n```python\nreturn attempt_number < 5\n```\n\n`attempt_number` is the 1-based count of attempts already completed. So:\n- After attempt 1: `1 < 5` \u2192 True, retry\n- After attempt 2: `2 < 5` \u2192 True, retry\n- After attempt 3: `3 < 5` \u2192 True, retry\n- After attempt 4: `4 < 5` \u2192 True, retry\n- After attempt 5: `5 < 5` \u2192 False, stop\n\nSo the operation is attempted 5 times total (attempts 1\u20135, with retries after 1\u20134). The maximum total attempts is **5**.\n\nFILE: code/retry/policy.py\n\n```python\nclass TransientError(Exception):\n \"\"\"A retryable failure (timeout, 503, dropped connection, ...).\"\"\"\n\n\nclass FatalError(Exception):\n \"\"\"A non-retryable failure; give up immediately.\"\"\"\n\n\nclass RetryPolicy:\n \"\"\"Decides whether another attempt should be made after a failed try.\"\"\"\n\n def should_retry(self, attempt_number: int, error: Exception) -> bool:\n \"\"\"Return True if another attempt should be made.\n\n `attempt_number` is the 1-based count of attempts already completed (so `1` after the\n first try has failed). A fatal error is never retried; otherwise we keep going until the\n attempt cap is reached.\n \"\"\"\n if isinstance(error, FatalError):\n return False\n return attempt_number < 5\n\n def max_total_attempts(self) -> int:\n \"\"\"Return the maximum number of times a transient operation will be attempted in total.\n\n This is the initial attempt plus every retry for an operation that keeps failing with a\n TransientError. The value is derived directly from the threshold used in should_retry:\n should_retry returns True while attempt_number < 5, meaning retries occur after attempts\n 1 through 4, and the final (5th) attempt is made but not retried \u2014 giving 5 total attempts.\n \"\"\"\n # should_retry returns True while attempt_number < 5, so the last attempt that triggers\n # a retry is attempt 4, and attempt 5 is the final one (no further retry). Total = 5.\n return 5\n```", "input_tokens": 1263, "output_tokens": 555, "cost_usd": 0.012114000000000002, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/retry/policy.py"]}} -{"scenario": "retry-budget-code", "task_type": "code", "tier": "T0", "condition": "C3", "model": "sonnet", "trial": 9, "output": "Looking at the current `should_retry` implementation:\n\n```python\nreturn attempt_number < 5\n```\n\n`attempt_number` is the 1-based count of attempts already completed. So:\n- After attempt 1: `1 < 5` \u2192 True, retry\n- After attempt 2: `2 < 5` \u2192 True, retry\n- After attempt 3: `3 < 5` \u2192 True, retry\n- After attempt 4: `4 < 5` \u2192 True, retry\n- After attempt 5: `5 < 5` \u2192 False, stop\n\nSo the operation is attempted 5 times total (attempts 1\u20135, with the 5th attempt failing and `should_retry` returning False).\n\nFILE: code/retry/policy.py\n\n```python\nclass TransientError(Exception):\n \"\"\"A retryable failure (timeout, 503, dropped connection, ...).\"\"\"\n\n\nclass FatalError(Exception):\n \"\"\"A non-retryable failure; give up immediately.\"\"\"\n\n\nclass RetryPolicy:\n \"\"\"Decides whether another attempt should be made after a failed try.\"\"\"\n\n def should_retry(self, attempt_number: int, error: Exception) -> bool:\n \"\"\"Return True if another attempt should be made.\n\n `attempt_number` is the 1-based count of attempts already completed (so `1` after the\n first try has failed). A fatal error is never retried; otherwise we keep going until the\n attempt cap is reached.\n \"\"\"\n if isinstance(error, FatalError):\n return False\n return attempt_number < 5\n\n def max_total_attempts(self) -> int:\n \"\"\"Return the maximum number of times a transient operation will be attempted in total.\n\n This includes the initial attempt plus all retries. The value is derived directly from\n the threshold used in should_retry: a retry is made while attempt_number < 5, where\n attempt_number is the 1-based count of completed attempts. Therefore the operation is\n tried at most 5 times before the policy gives up.\n \"\"\"\n return 5\n```", "input_tokens": 1263, "output_tokens": 490, "cost_usd": 0.011139, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/retry/policy.py"]}} -{"scenario": "retry-budget-code", "task_type": "code", "tier": "T0", "condition": "C3", "model": "opus", "trial": 0, "output": "Tracing the current `should_retry`: it returns `attempt_number < 5` for transient errors.\n\n- Attempt 1 fails \u2192 `should_retry(1, err)` \u2192 `1 < 5` \u2192 True \u2192 retry\n- Attempt 2 fails \u2192 `should_retry(2, err)` \u2192 `2 < 5` \u2192 True \u2192 retry\n- Attempt 3 fails \u2192 `should_retry(3, err)` \u2192 `3 < 5` \u2192 True \u2192 retry\n- Attempt 4 fails \u2192 `should_retry(4, err)` \u2192 `4 < 5` \u2192 True \u2192 retry\n- Attempt 5 fails \u2192 `should_retry(5, err)` \u2192 `5 < 5` \u2192 False \u2192 stop\n\nSo the operation is attempted **5** times total (initial + 4 retries). The threshold is `5`, which equals the max total attempts.\n\nFILE: code/retry/policy.py\n```python\nclass TransientError(Exception):\n \"\"\"A retryable failure (timeout, 503, dropped connection, ...).\"\"\"\n\n\nclass FatalError(Exception):\n \"\"\"A non-retryable failure; give up immediately.\"\"\"\n\n\nclass RetryPolicy:\n \"\"\"Decides whether another attempt should be made after a failed try.\"\"\"\n\n def should_retry(self, attempt_number: int, error: Exception) -> bool:\n \"\"\"Return True if another attempt should be made.\n\n `attempt_number` is the 1-based count of attempts already completed (so `1` after the\n first try has failed). A fatal error is never retried; otherwise we keep going until the\n attempt cap is reached.\n \"\"\"\n if isinstance(error, FatalError):\n return False\n return attempt_number < 5\n\n def max_total_attempts(self) -> int:\n \"\"\"Return the maximum number of attempts for an always-transiently-failing operation.\n\n This is the initial attempt plus every retry. It is derived strictly from\n `should_retry`: a retry occurs after attempt N whenever\n `should_retry(N, TransientError())` returns True. Since `should_retry` keeps\n retrying while the 1-based `attempt_number` is below 5, the loop stops once 5\n attempts have been completed (`should_retry(5, ...)` returns False), giving a\n ceiling of 5 total attempts (the initial attempt plus 4 retries).\n \"\"\"\n attempts = 1\n while self.should_retry(attempts, TransientError()):\n attempts += 1\n return attempts\n```", "input_tokens": 1670, "output_tokens": 779, "cost_usd": 0.027825000000000003, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/retry/policy.py"]}} -{"scenario": "retry-budget-code", "task_type": "code", "tier": "T0", "condition": "C3", "model": "opus", "trial": 1, "output": "Tracing the current `should_retry`: it returns `attempt_number < 5`. After attempt 1 \u2192 `1 < 5` retry; after 2,3,4 retry; after attempt 5 \u2192 `5 < 5` False, stop. So attempts: 1,2,3,4,5 = **5 total**.\n\nFILE: code/retry/policy.py\n```python\nclass TransientError(Exception):\n \"\"\"A retryable failure (timeout, 503, dropped connection, ...).\"\"\"\n\n\nclass FatalError(Exception):\n \"\"\"A non-retryable failure; give up immediately.\"\"\"\n\n\nclass RetryPolicy:\n \"\"\"Decides whether another attempt should be made after a failed try.\"\"\"\n\n def should_retry(self, attempt_number: int, error: Exception) -> bool:\n \"\"\"Return True if another attempt should be made.\n\n `attempt_number` is the 1-based count of attempts already completed (so `1` after the\n first try has failed). A fatal error is never retried; otherwise we keep going until the\n attempt cap is reached.\n \"\"\"\n if isinstance(error, FatalError):\n return False\n return attempt_number < 5\n\n def max_total_attempts(self) -> int:\n \"\"\"Return the maximum number of attempts a transient operation will receive.\n\n This is the initial attempt plus every retry, for an operation that keeps failing with a\n `TransientError`. The value is derived strictly from `should_retry`: we count how many\n times the operation runs given that a retry happens exactly when `should_retry` returns\n True for a transient error.\n\n Tracing the current implementation: after each failed attempt `n` we call\n `should_retry(n, TransientError())`, which returns `n < 5`. So we retry after attempts\n 1, 2, 3, and 4, and stop after attempt 5 \u2014 yielding 5 total attempts.\n \"\"\"\n attempts = 0\n error = TransientError()\n while True:\n attempts += 1\n if not self.should_retry(attempts, error):\n break\n return attempts\n```", "input_tokens": 1670, "output_tokens": 654, "cost_usd": 0.0247, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/retry/policy.py"]}} -{"scenario": "retry-budget-code", "task_type": "code", "tier": "T0", "condition": "C3", "model": "opus", "trial": 2, "output": "Tracing the current `should_retry`: it returns `True` while `attempt_number < 5`.\n\n- After attempt 1 completes: `should_retry(1)` \u2192 `1 < 5` \u2192 True \u2192 retry (attempt 2)\n- After attempt 2: `should_retry(2)` \u2192 True \u2192 retry (attempt 3)\n- After attempt 3: `should_retry(3)` \u2192 True \u2192 retry (attempt 4)\n- After attempt 4: `should_retry(4)` \u2192 True \u2192 retry (attempt 5)\n- After attempt 5: `should_retry(5)` \u2192 `5 < 5` \u2192 False \u2192 stop\n\nSo the maximum total attempts is **5** (initial attempt + 4 retries).\n\nFILE: code/retry/policy.py\n```python\nclass TransientError(Exception):\n \"\"\"A retryable failure (timeout, 503, dropped connection, ...).\"\"\"\n\n\nclass FatalError(Exception):\n \"\"\"A non-retryable failure; give up immediately.\"\"\"\n\n\nclass RetryPolicy:\n \"\"\"Decides whether another attempt should be made after a failed try.\"\"\"\n\n def should_retry(self, attempt_number: int, error: Exception) -> bool:\n \"\"\"Return True if another attempt should be made.\n\n `attempt_number` is the 1-based count of attempts already completed (so `1` after the\n first try has failed). A fatal error is never retried; otherwise we keep going until the\n attempt cap is reached.\n \"\"\"\n if isinstance(error, FatalError):\n return False\n return attempt_number < 5\n\n def max_total_attempts(self) -> int:\n \"\"\"Return the maximum number of times a transient operation is attempted in total.\n\n This is the initial attempt plus every retry, for an operation that keeps failing with a\n `TransientError`. The value is derived strictly from `should_retry`: a retry happens\n whenever `should_retry(attempt_number, TransientError())` returns True, where\n `attempt_number` is the 1-based count of attempts already completed.\n\n Since `should_retry` returns True while `attempt_number < 5`, retries keep happening after\n attempts 1, 2, 3, and 4 (yielding attempts 2-5), and stops after attempt 5. The ceiling is\n therefore 5 total attempts.\n \"\"\"\n transient = TransientError()\n attempts = 1\n while self.should_retry(attempts, transient):\n attempts += 1\n return attempts\n```", "input_tokens": 1670, "output_tokens": 782, "cost_usd": 0.0279, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/retry/policy.py"]}} -{"scenario": "retry-budget-code", "task_type": "code", "tier": "T0", "condition": "C3", "model": "opus", "trial": 3, "output": "Tracing the current `should_retry`: it returns `attempt_number < 5` for transient errors. After each failed attempt with completed count `n`, a retry happens if `n < 5`. So retries occur after attempts 1, 2, 3, 4 (counts 1\u20134 are < 5), but not after attempt 5 (count 5 is not < 5). That yields a total of **5** attempts.\n\nFILE: code/retry/policy.py\n```python\nclass TransientError(Exception):\n \"\"\"A retryable failure (timeout, 503, dropped connection, ...).\"\"\"\n\n\nclass FatalError(Exception):\n \"\"\"A non-retryable failure; give up immediately.\"\"\"\n\n\nclass RetryPolicy:\n \"\"\"Decides whether another attempt should be made after a failed try.\"\"\"\n\n def should_retry(self, attempt_number: int, error: Exception) -> bool:\n \"\"\"Return True if another attempt should be made.\n\n `attempt_number` is the 1-based count of attempts already completed (so `1` after the\n first try has failed). A fatal error is never retried; otherwise we keep going until the\n attempt cap is reached.\n \"\"\"\n if isinstance(error, FatalError):\n return False\n return attempt_number < 5\n\n def max_total_attempts(self) -> int:\n \"\"\"Return the maximum number of total attempts for a transiently-failing operation.\n\n This is the initial attempt plus every retry that `should_retry` would permit for an\n operation that keeps raising `TransientError`. It is derived strictly from the current\n `should_retry` behaviour by simulating the retry loop: starting from one completed\n attempt, we keep counting while `should_retry` returns True.\n \"\"\"\n error = TransientError()\n attempts = 1\n while self.should_retry(attempts, error):\n attempts += 1\n return attempts\n```", "input_tokens": 1670, "output_tokens": 592, "cost_usd": 0.023150000000000004, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/retry/policy.py"]}} -{"scenario": "retry-budget-code", "task_type": "code", "tier": "T0", "condition": "C3", "model": "opus", "trial": 4, "output": "Tracing the current `should_retry`: it returns `True` while `attempt_number < 5`.\n\n- After attempt 1 (attempt_number=1): 1<5 \u2192 retry \u2192 attempt 2\n- After attempt 2 (attempt_number=2): 2<5 \u2192 retry \u2192 attempt 3\n- After attempt 3 (attempt_number=3): 3<5 \u2192 retry \u2192 attempt 4\n- After attempt 4 (attempt_number=4): 4<5 \u2192 retry \u2192 attempt 5\n- After attempt 5 (attempt_number=5): 5<5 is False \u2192 stop\n\nSo the operation is attempted 5 times in total. The cap is `5`.\n\nFILE: code/retry/policy.py\n```python\nclass TransientError(Exception):\n \"\"\"A retryable failure (timeout, 503, dropped connection, ...).\"\"\"\n\n\nclass FatalError(Exception):\n \"\"\"A non-retryable failure; give up immediately.\"\"\"\n\n\nclass RetryPolicy:\n \"\"\"Decides whether another attempt should be made after a failed try.\"\"\"\n\n def should_retry(self, attempt_number: int, error: Exception) -> bool:\n \"\"\"Return True if another attempt should be made.\n\n `attempt_number` is the 1-based count of attempts already completed (so `1` after the\n first try has failed). A fatal error is never retried; otherwise we keep going until the\n attempt cap is reached.\n \"\"\"\n if isinstance(error, FatalError):\n return False\n return attempt_number < 5\n\n def max_total_attempts(self) -> int:\n \"\"\"Return the maximum number of times a transient operation will be attempted.\n\n This counts the initial attempt plus every retry for an operation that keeps failing\n with a `TransientError`. The value is derived strictly from `should_retry`: starting\n from the first attempt, we keep attempting as long as `should_retry` returns True for\n the count of attempts already completed.\n \"\"\"\n attempts = 1\n error = TransientError()\n while self.should_retry(attempts, error):\n attempts += 1\n return attempts\n```", "input_tokens": 1670, "output_tokens": 664, "cost_usd": 0.02495, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/retry/policy.py"]}} -{"scenario": "retry-budget-code", "task_type": "code", "tier": "T0", "condition": "C3", "model": "opus", "trial": 5, "output": "Tracing the current `should_retry`: it returns `True` while `attempt_number < 5`.\n\n- After attempt 1 completed: `should_retry(1, ...)` \u2192 `1 < 5` \u2192 True \u2192 retry (attempt 2)\n- After attempt 2 completed: `should_retry(2, ...)` \u2192 True \u2192 retry (attempt 3)\n- After attempt 3 completed: `should_retry(3, ...)` \u2192 True \u2192 retry (attempt 4)\n- After attempt 4 completed: `should_retry(4, ...)` \u2192 True \u2192 retry (attempt 5)\n- After attempt 5 completed: `should_retry(5, ...)` \u2192 `5 < 5` \u2192 False \u2192 stop\n\nSo the maximum total attempts is **5**.\n\nFILE: code/retry/policy.py\n\n```python\nclass TransientError(Exception):\n \"\"\"A retryable failure (timeout, 503, dropped connection, ...).\"\"\"\n\n\nclass FatalError(Exception):\n \"\"\"A non-retryable failure; give up immediately.\"\"\"\n\n\nclass RetryPolicy:\n \"\"\"Decides whether another attempt should be made after a failed try.\"\"\"\n\n def should_retry(self, attempt_number: int, error: Exception) -> bool:\n \"\"\"Return True if another attempt should be made.\n\n `attempt_number` is the 1-based count of attempts already completed (so `1` after the\n first try has failed). A fatal error is never retried; otherwise we keep going until the\n attempt cap is reached.\n \"\"\"\n if isinstance(error, FatalError):\n return False\n return attempt_number < 5\n\n def max_total_attempts(self) -> int:\n \"\"\"Return the maximum number of attempts for a perpetually-transient operation.\n\n This counts the initial attempt plus every retry that `should_retry` would permit for an\n operation that keeps failing with a `TransientError`. It is derived directly from the\n `should_retry` logic above: after each completed attempt we ask `should_retry` with the\n 1-based count of attempts already made, and a retry happens whenever it returns True.\n \"\"\"\n attempts = 1\n error = TransientError()\n while self.should_retry(attempts, error):\n attempts += 1\n return attempts\n```", "input_tokens": 1670, "output_tokens": 708, "cost_usd": 0.026050000000000004, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/retry/policy.py"]}} -{"scenario": "retry-budget-code", "task_type": "code", "tier": "T0", "condition": "C3", "model": "opus", "trial": 6, "output": "Tracing the current `should_retry`: it returns `True` while `attempt_number < 5`.\n\n- After attempt 1: `should_retry(1, ...)` \u2192 `1 < 5` \u2192 True \u2192 retry (attempt 2)\n- After attempt 2: `should_retry(2, ...)` \u2192 True \u2192 retry (attempt 3)\n- After attempt 3: `should_retry(3, ...)` \u2192 True \u2192 retry (attempt 4)\n- After attempt 4: `should_retry(4, ...)` \u2192 True \u2192 retry (attempt 5)\n- After attempt 5: `should_retry(5, ...)` \u2192 `5 < 5` \u2192 False \u2192 stop\n\nSo the operation is attempted at most **5** times.\n\nFILE: code/retry/policy.py\n```python\nclass TransientError(Exception):\n \"\"\"A retryable failure (timeout, 503, dropped connection, ...).\"\"\"\n\n\nclass FatalError(Exception):\n \"\"\"A non-retryable failure; give up immediately.\"\"\"\n\n\nclass RetryPolicy:\n \"\"\"Decides whether another attempt should be made after a failed try.\"\"\"\n\n def should_retry(self, attempt_number: int, error: Exception) -> bool:\n \"\"\"Return True if another attempt should be made.\n\n `attempt_number` is the 1-based count of attempts already completed (so `1` after the\n first try has failed). A fatal error is never retried; otherwise we keep going until the\n attempt cap is reached.\n \"\"\"\n if isinstance(error, FatalError):\n return False\n return attempt_number < 5\n\n def max_total_attempts(self) -> int:\n \"\"\"Return the maximum number of times a transient operation is attempted in total.\n\n This counts the initial attempt plus every retry for an operation that keeps failing\n with a `TransientError`. The value is derived directly from `should_retry`: starting\n from one completed attempt, we keep retrying as long as `should_retry` returns True\n for a transient error.\n \"\"\"\n transient = TransientError()\n attempts = 1\n while self.should_retry(attempts, transient):\n attempts += 1\n return attempts\n```", "input_tokens": 1670, "output_tokens": 672, "cost_usd": 0.025150000000000006, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/retry/policy.py"]}} -{"scenario": "retry-budget-code", "task_type": "code", "tier": "T0", "condition": "C3", "model": "opus", "trial": 7, "output": "Looking at the current `should_retry`, it returns `attempt_number < 5` for transient errors.\n\nTracing the loop:\n- Attempt 1 made \u2192 `should_retry(1, err)` \u2192 `1 < 5` = True \u2192 retry\n- Attempt 2 made \u2192 `should_retry(2, err)` \u2192 `2 < 5` = True \u2192 retry\n- Attempt 3 made \u2192 `should_retry(3, err)` \u2192 `3 < 5` = True \u2192 retry\n- Attempt 4 made \u2192 `should_retry(4, err)` \u2192 `4 < 5` = True \u2192 retry\n- Attempt 5 made \u2192 `should_retry(5, err)` \u2192 `5 < 5` = False \u2192 stop\n\nSo the maximum total attempts is **5**. The threshold `N` in `attempt_number < N` equals the max total attempts.\n\nFILE: code/retry/policy.py\n```python\nclass TransientError(Exception):\n \"\"\"A retryable failure (timeout, 503, dropped connection, ...).\"\"\"\n\n\nclass FatalError(Exception):\n \"\"\"A non-retryable failure; give up immediately.\"\"\"\n\n\nclass RetryPolicy:\n \"\"\"Decides whether another attempt should be made after a failed try.\"\"\"\n\n _ATTEMPT_CAP = 5\n\n def should_retry(self, attempt_number: int, error: Exception) -> bool:\n \"\"\"Return True if another attempt should be made.\n\n `attempt_number` is the 1-based count of attempts already completed (so `1` after the\n first try has failed). A fatal error is never retried; otherwise we keep going until the\n attempt cap is reached.\n \"\"\"\n if isinstance(error, FatalError):\n return False\n return attempt_number < self._ATTEMPT_CAP\n\n def max_total_attempts(self) -> int:\n \"\"\"Return the maximum number of times a transient operation will be attempted in total.\n\n This is the initial attempt plus every retry for an operation that keeps failing with a\n `TransientError`. It is derived strictly from `should_retry`: a retry happens whenever\n `should_retry(attempt_number, TransientError())` returns True, where `attempt_number` is\n the 1-based count of attempts already completed.\n\n Since `should_retry` keeps retrying while `attempt_number < self._ATTEMPT_CAP`, the\n operation stops being retried once `attempt_number` reaches `self._ATTEMPT_CAP`, so the\n total number of attempts equals `self._ATTEMPT_CAP`.\n \"\"\"\n attempts = 0\n error = TransientError()\n while True:\n attempts += 1\n if not self.should_retry(attempts, error):\n return attempts\n```", "input_tokens": 1670, "output_tokens": 863, "cost_usd": 0.029925, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/retry/policy.py"]}} -{"scenario": "retry-budget-code", "task_type": "code", "tier": "T0", "condition": "C3", "model": "opus", "trial": 8, "output": "Tracing the current `should_retry`: it returns `True` while `attempt_number < 5`.\n\n- After attempt 1: `should_retry(1, ...)` \u2192 `1 < 5` \u2192 True \u2192 retry (attempt 2)\n- After attempt 2: `should_retry(2, ...)` \u2192 True \u2192 retry (attempt 3)\n- After attempt 3: `should_retry(3, ...)` \u2192 True \u2192 retry (attempt 4)\n- After attempt 4: `should_retry(4, ...)` \u2192 True \u2192 retry (attempt 5)\n- After attempt 5: `should_retry(5, ...)` \u2192 `5 < 5` \u2192 False \u2192 stop\n\nSo the maximum total attempts is **5**.\n\nFILE: code/retry/policy.py\n```python\nclass TransientError(Exception):\n \"\"\"A retryable failure (timeout, 503, dropped connection, ...).\"\"\"\n\n\nclass FatalError(Exception):\n \"\"\"A non-retryable failure; give up immediately.\"\"\"\n\n\nclass RetryPolicy:\n \"\"\"Decides whether another attempt should be made after a failed try.\"\"\"\n\n def should_retry(self, attempt_number: int, error: Exception) -> bool:\n \"\"\"Return True if another attempt should be made.\n\n `attempt_number` is the 1-based count of attempts already completed (so `1` after the\n first try has failed). A fatal error is never retried; otherwise we keep going until the\n attempt cap is reached.\n \"\"\"\n if isinstance(error, FatalError):\n return False\n return attempt_number < 5\n\n def max_total_attempts(self) -> int:\n \"\"\"Return the maximum number of times a transient operation is attempted in total.\n\n This is the initial attempt plus every retry, for an operation that keeps failing with a\n `TransientError`. The value is derived strictly from `should_retry`: starting from the\n first completed attempt, we keep retrying while `should_retry` returns True.\n \"\"\"\n transient = TransientError()\n attempts = 1\n while self.should_retry(attempts, transient):\n attempts += 1\n return attempts\n```", "input_tokens": 1670, "output_tokens": 664, "cost_usd": 0.02495, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/retry/policy.py"]}} -{"scenario": "retry-budget-code", "task_type": "code", "tier": "T0", "condition": "C3", "model": "opus", "trial": 9, "output": "Looking at the current `should_retry` implementation, it returns `attempt_number < 5` for transient errors.\n\nTracing the retry loop:\n- Attempt 1 fails \u2192 `should_retry(1, err)` \u2192 `1 < 5` \u2192 True \u2192 retry\n- Attempt 2 fails \u2192 `should_retry(2, err)` \u2192 `2 < 5` \u2192 True \u2192 retry\n- Attempt 3 fails \u2192 `should_retry(3, err)` \u2192 `3 < 5` \u2192 True \u2192 retry\n- Attempt 4 fails \u2192 `should_retry(4, err)` \u2192 `4 < 5` \u2192 True \u2192 retry\n- Attempt 5 fails \u2192 `should_retry(5, err)` \u2192 `5 < 5` \u2192 False \u2192 stop\n\nSo the operation is attempted **5** times in total (initial attempt + 4 retries). This corresponds to the cap being the threshold value itself.\n\nFILE: code/retry/policy.py\n```python\nclass TransientError(Exception):\n \"\"\"A retryable failure (timeout, 503, dropped connection, ...).\"\"\"\n\n\nclass FatalError(Exception):\n \"\"\"A non-retryable failure; give up immediately.\"\"\"\n\n\nclass RetryPolicy:\n \"\"\"Decides whether another attempt should be made after a failed try.\"\"\"\n\n def should_retry(self, attempt_number: int, error: Exception) -> bool:\n \"\"\"Return True if another attempt should be made.\n\n `attempt_number` is the 1-based count of attempts already completed (so `1` after the\n first try has failed). A fatal error is never retried; otherwise we keep going until the\n attempt cap is reached.\n \"\"\"\n if isinstance(error, FatalError):\n return False\n return attempt_number < 5\n\n def max_total_attempts(self) -> int:\n \"\"\"Return the maximum number of times a transient (retryable) operation will be\n attempted in total \u2014 the initial attempt plus every retry.\n\n This is derived strictly from `should_retry`: a retry happens whenever\n `should_retry(attempt_number, TransientError())` returns True, where `attempt_number`\n is the 1-based count of attempts already completed. We count the attempts the loop\n would make for an operation that keeps failing transiently.\n \"\"\"\n attempts = 1\n while self.should_retry(attempts, TransientError()):\n attempts += 1\n return attempts\n```", "input_tokens": 1670, "output_tokens": 747, "cost_usd": 0.027025, "ok": true, "misled": false, "detail": "correct test passed", "parsed": {"applied": ["code/retry/policy.py"]}} diff --git a/bench/results/2026-06-13-pilot-full-matrix/report-summary.md b/bench/results/2026-06-13-pilot-full-matrix/report-summary.md deleted file mode 100644 index e456a5a..0000000 --- a/bench/results/2026-06-13-pilot-full-matrix/report-summary.md +++ /dev/null @@ -1,109 +0,0 @@ -# Plain-language summary - -A non-technical companion to `report.md` — the same findings without the statistics vocabulary. -("Model" means the coding assistant doing the work, Claude in this study; sometimes called an -"agent.") - -## What we tested - -Coding assistants rely on a project's documentation to understand code they can't read in full. But -documentation goes stale: someone changes the code and the docs describing it don't get updated. We -measured a straightforward question — when the docs are out of date, does it actually degrade the -model's work? And does it help to automatically flag the parts that have drifted? That flag is what -Surface, the tool this benchmark tests, produces. - -## How the test worked - -We gave a model the same task four ways, changing only the documentation it received: - -- **No docs** — just the code. -- **Stale docs** — documentation that was correct once but no longer matches the code. -- **Fresh docs** — documentation that matches the code. -- **Stale docs + a Surface drift report** — the stale docs, plus an automated note saying "this no - longer matches the code, and here is what changed." - -We ran every task two ways: - -- **Code visible** — the exact code the task depends on is in front of the model. -- **Code hidden** — that code is not provided, so the model has only the documentation to go on. - This is the realistic case: in a large codebase nobody, and no model, sees everything, so the - documentation is the map. - -Each task was repeated ten times across three Claude models, from the smallest to the most capable. - -## What we found - -![Left: when the code is hidden, stale docs drop every model to 0% correct, while fresh docs and the Surface report bring them back to ~100%. Right: when the code is visible, stale docs don't cause errors but make the model write more (extra cost).](overview.png) - - -**When the relevant code was hidden:** - -- Stale docs made the model wrong every time — never correct, and it confidently repeated the - outdated "fact" in every run. -- A more capable model was no more resistant. The most powerful model failed exactly as often as the - smallest. Paying for a better model does not protect you from stale docs. -- Fresh docs produced a correct answer every time. -- The Surface drift report recovered the result — fully correct again on the two larger models, and - nearly so on the smallest. Flagging the drift was enough to undo the damage. - -**When the relevant code was visible:** - -- Stale docs did not cause wrong answers — the model simply read the code and ignored the bad notes. -- But they cost more. The model spent measurably more effort reconciling the stale docs against the - code. (That effort is counted in "tokens" — roughly how much the model reads and writes, which - translates directly into time and money.) - -In short: **stale documentation the model can't verify makes it wrong; stale documentation it can -verify makes it slower and more expensive.** A more capable model fixes neither — surfacing the -drift does. - -The full run cost about **$14** and completed with no errors. - -## What it's worth - -There are two kinds of savings here, and they are very different in size. - -The smaller one is model cost. Where the assistant can see the code, out-of-date docs make it do -extra work, which you pay for in usage. Keeping docs accurate trims that — but only by roughly -**$0.30 to $1.60 per thousand tasks**, depending on the model. Real, but minor. (And it's a floor: -an assistant that works in a back-and-forth loop would waste more.) - -The bigger one — by far — is avoided rework. When the assistant *can't* see the code and the docs -are wrong, it doesn't waste effort, it just produces the wrong result. In our tests that happened on -**every such task without Surface, and essentially none with it.** Putting a number on that depends -on your situation, but the shape is simple: - -> roughly: (how many tasks your assistants run) × (how often they rely on docs they can't verify) × -> (what it costs to catch and fix one wrong change) - -For example: 10,000 assistant tasks a month, 1 in 50 touching code whose docs have drifted, and $50 -to catch and fix each wrong change, works out to about **$10,000 a month** — against a few dollars of -usage savings. The point for a decision-maker: the value of Surface is mostly in **preventing wrong -work**, not in trimming usage bills, and it grows with how much your assistants rely on -documentation they can't double-check. - -(The failure rates and usage figures are measured; the task volume, how-often, and fix-cost are -yours to fill in — the example numbers are just an illustration.) - -## What we learned - -- **Framing changes the result.** Our first attempt found nothing, because we had told the model to - trust the code over the docs — which hides the very problem we were trying to measure. Removing - that instruction revealed the effect. -- **The damage concentrates where the model can't check.** Stale docs matter most for the parts of a - system the model can't see, which in practice is most of it. -- **The test caught our own mistakes.** An early version accidentally leaked a hint in the task - wording; the results flagged it and we removed it. -- **We hit and fixed a reliability bug.** One request to the model stalled the whole run; we added a - timeout, preserved the completed data, and re-ran only the unfinished part. - -## What's next - -- Test an assistant that works in a loop — read, edit, run tests, fix — rather than answering in one - shot, which is likely where wasted effort is largest. -- Test models from other providers, to see whether "a better model doesn't help" holds beyond Claude. -- Reproduce the effect on a real, public codebase rather than purpose-built examples. - ---- - -*For the full numbers, ranges, exact prompts, and methodology, see `report.md`.* diff --git a/bench/results/2026-06-13-pilot-full-matrix/report.md b/bench/results/2026-06-13-pilot-full-matrix/report.md deleted file mode 100644 index 6eaea5b..0000000 --- a/bench/results/2026-06-13-pilot-full-matrix/report.md +++ /dev/null @@ -1,345 +0,0 @@ -# Surface agent-impact benchmark — pilot report (2026-06-13) - -A standalone write-up of the first full pilot: what we ran, why, how, what we observed, and what we -learned. Machine-readable metrics are in `summary.json`; the raw per-call data is in `raw.jsonl`; -the figures are `overview.png` (two-panel summary) and `cascade_success.png` (the headline chart). -Run provenance is in `PROVENANCE.md`. - -![Overview — left: with the code hidden, stale docs drop every model to 0% correct while fresh docs and the Surface report restore it; right: with the code visible, rot doesn't cause errors but raises token cost.](overview.png) - ---- - -## 1. Overview - -**Question.** Surface is a documentation-drift gate: it doesn't make an agent smarter, it stops -docs silently rotting. So its value to an agent should equal the *performance gap between working -from accurate docs and from rotted docs*. This benchmark measures that gap directly, using drift of -exactly the kind `surf check` catches (a flipped operator, a changed constant, an inverted -condition, a dropped guard). - -**Design in one line.** Same code, same task, same model in every run — **only the documentation -block changes** — across four conditions, and we measure how the agent's success, error mode, and -token cost move. - -**Top-line result.** When the drifted code is *hidden* from the agent (it knows the dependency only -by its doc — the realistic case in a large codebase), a stale doc makes **every model wrong 100% of -the time**, and **a more capable model is no more resistant** (haiku, sonnet, and opus all collapse -identically). Accurate docs restore 100% success, and handing the agent Surface's drift report -recovers nearly all of the loss. When the code is *visible*, rot doesn't break correctness but -imposes a consistent **token tax**. - ---- - -## 2. Hypotheses - -Let C0 = code only, C1 = code + stale doc, C2 = code + fresh doc, C3 = code + stale doc + `surf -check` report. - -- **H1 — accuracy beats rot (the core value):** C2 > C1. -- **H2 — rot is worse than nothing (the headline):** C1 < C0. -- **H3 — surfacing drift recovers the loss:** C3 ≈ C2. - -A secondary metric, **misled rate**, tracks whether the agent asserted the *stale* claim (parroted -the rotted doc) rather than merely failing. - ---- - -## 3. Methodology - -### 3.1 Conditions - -Every run is identical except the documentation block in the prompt: - -| Condition | Context shown to the agent | Represents | -|---|---|---| -| **C0** | code only | baseline | -| **C1** | code + **stale** doc (true at T0, code moved to T1) | the ungoverned world | -| **C2** | code + **fresh** doc (matches T1) | the Surface-governed world | -| **C3** | code + stale doc + real `surf check --format json` report | "just surface the drift" | - -Each scenario has a *drifted* current state (T1) and a pre-drift state (T0). The stale doc -truthfully described T0; the code has since moved to T1. The hub anchors are sealed by the real -`surf` binary via `tools/author.py`, so the C3 report is genuine `surf check` output, not a mock. - -### 3.2 Two scenario families - -- **Comprehension** (7 scenarios): the drifted code **and** its contradicting doc are both in the - prompt. Tests whether an agent re-derives truth from code it can see. -- **Cascade** (4 scenarios): the realistic context-rot shape. The agent edits a *visible* function - whose correctness depends on a **hidden dependency** — the drifted code is withheld from the - prompt (listed in `meta.toml`'s `hidden_paths`), so the agent knows it *only* through its doc. - The dependency's real source stays in the workspace so `surf` can seal a real divergence and the - grader can run the agent's edit against reality. The grader derives the expected value from the - real hidden dependency, so the test stays honest. - -### 3.3 Models, sampling, scale - -- Models: **haiku** (`claude-haiku-4-5-20251001`), **sonnet** (`claude-sonnet-4-6`), **opus** - (`claude-opus-4-8`). -- **N = 10** completions per (scenario × condition × model); temperature 1.0 (stochasticity is part - of what we measure); max 1024 output tokens. -- Total: 11 scenarios × 4 conditions × 3 models × 10 = **1320 calls**. - -### 3.4 Grading (deterministic, no LLM judge) - -- **QA scenarios:** the agent ends with a fixed-format `VERDICT:` line; a rubric parses the fields - and adjudicates `correct` (matches T1) vs `misled` (matches the stale T0 claim). -- **Code-edit scenarios:** the agent returns full files in `FILE:` blocks; we overlay them onto the - workspace and run hidden tests — a `correct` check (T1 behaviour) and a `misled` check (T0 - behaviour). Cascade graders derive the expected value from the real hidden dependency. - -Three metrics per cell: **success rate**, **misled rate**, and **output-token cost** (input tokens -differ by construction — the doc block's size — so only output tokens are a behavioural signal). -Rates carry 95% Wilson intervals; deltas use a 95% bootstrap CI. - -### 3.5 The 11 scenarios - -**Cascade (hidden dependency):** - -| Scenario | Lang | Hidden dependency drift | -|---|---|---| -| `cascade-quota-batcher-code` | py | limiter capacity, `<` → `<=` (admits N+1) | -| `cascade-retry-budget-code` | py | retry attempt cap, 3 → 5 | -| `cascade-access-policy-code` | py | allow-list → block-list inversion | -| `cascade-page-size-ts-code` | ts | default page size, 50 → 25 | - -**Comprehension (visible code):** `refresh-single-use-qa`, `refresh-replay-premise-qa`, -`access-invert-qa`, `dropped-await-qa` (QA); `ratelimit-window-code`, `retry-budget-code`, -`pagination-ts-code` (code-edit). - ---- - -## 4. Prompts given to the agents - -The prompt is `(system, user)`. Only the **documentation block** in the user turn changes across -conditions; the system prompt, code, and task are byte-identical. - -### 4.1 System prompt - -``` -Use the files and documentation provided to do the task below. -``` - -Deliberately minimal and **persona-free** — no "you are an expert engineer" framing (which primes -diligent, skeptical behaviour and biases against a stale-doc effect) and **no precedence** declared -between docs and code. This mirrors how people actually prompt: paste/tag some files, maybe a doc, -ask for the change. - -### 4.2 User-turn structure - -```` -## Codebase -### -``` - ← cascade scenarios omit the hidden dependency here -``` -... - -## Project documentation ← omitted entirely in C0 -The repository documents this area as follows: - - -## Automated documentation check ← C3 only -Surface (a deterministic doc-drift gate) reports that the anchored claim above no longer -matches the code it points at ... -```json - -``` - -## Task - -```` - -### 4.3 Worked example — `cascade-quota-batcher-code` - -The agent sees `throttle.py` and is asked to implement `plan_batches(total)`, which must size -batches to the limiter's per-window capacity. **The limiter's source is hidden**, so the capacity -is knowable only from the doc. The drift: the limiter's admission test moved `<` → `<=`, so its true -capacity for `WINDOW_LIMIT = 10` is **11**, not 10. - -- **C1 (stale doc)** asserts: *"a limiter built with limit=N has a per-window capacity of exactly N - … at WINDOW_LIMIT = 10 the limiter admits 10."* → an agent that trusts it batches at 10 (wrong). -- **C2 (fresh doc)** asserts: *"inclusive bound (count <= limit) … admits N+1 … at WINDOW_LIMIT = 10 - the limiter admits 11."* → batches at 11 (correct). -- **C3** keeps the stale prose but appends the `surf` report, whose `new_code` shows - `if count <= self.limit:` — the only window onto the truth when the file is hidden. - -Approximate prompt sizes for this scenario: C0 ~1.96k chars, C1/C2 ~3.29k, C3 ~5.04k. - ---- - -## 5. Results - -N = 10 per cell. Success shown with 95% Wilson interval; "mis" = misled rate; "tok" = mean output -tokens. - -### 5.1 Cascade family (hidden dependency) — n = 40 per condition per model - -| Model | C0 (code only) | C1 (stale) | C2 (fresh) | C3 (stale + surf) | -|---|---|---|---|---| -| haiku | 2% [0–13] · mis 38% · 472 tok | **0% [0–9] · mis 100%** · 408 | **100% [91–100]** · 0% · 427 | **90% [77–96]** · 0% · 583 | -| sonnet | 0% [0–9] · mis 62% · 628 | **0% [0–9] · mis 100%** · 274 | **100% [91–100]** · 0% · 273 | **100% [91–100]** · 0% · 492 | -| opus | 0% [0–9] · mis 18% · 716 | **0% [0–9] · mis 100%** · 398 | **100% [91–100]** · 0% · 419 | **100% [91–100]** · 0% · 634 | - -Deltas (success): **H1 (C2−C1) = +100pp on every model**; **H3 (C3−C1) = +90pp (haiku), +100pp -(sonnet, opus)**. - -### 5.2 Comprehension family (visible code) — n = 70 per condition per model - -| Model | C0 (code only) | C1 (stale) | C2 (fresh) | C3 (stale + surf) | -|---|---|---|---|---| -| haiku | 86% [76–92] · mis 13% · 425 | 86% [76–92] · mis 10% · 496 | 100% [95–100] · 0% · 431 | 96% [88–99] · 0% · 489 | -| sonnet | 93% [84–97] · 0% · 434 | 99% [92–100] · 0% · 479 | 100% [95–100] · 0% · 372 | 100% [95–100] · 0% · 426 | -| opus | 100% [95–100] · 0% · 403 | 94% [86–98] · 0% · 472 | 99% [92–100] · 0% · 415 | 96% [88–99] · 0% · 463 | - -Success ceilings near 100% across the board (the model can just read the visible code). The signal -here is **tokens**: a stale doc costs **+65 (haiku), +107 (sonnet), +57 (opus)** extra output tokens -vs a fresh doc (C1 − C2). - -### 5.3 Spend - -| Model | Spend | -|---|---| -| haiku | $1.49 | -| sonnet | $4.21 | -| opus | $8.28 | -| **Total** | **$13.98** | - -1320 calls, 0 errors. - ---- - -## 6. Interpretation - -**1. When the dependency is hidden, a stale doc breaks every model — and capability doesn't save -you.** Cascade C1 is 0% success / 100% misled on haiku, sonnet, *and* opus. H1 = +100pp flat across -the capability range. You cannot out-model a stale doc about code you can't see. This is the -headline and the strongest refutation of "just use a better model." - -**2. Accurate docs and the surf report both fix it.** C2 = 100% everywhere (H1). C3 recovers to -100% on sonnet/opus and 90% on haiku (H3) — the report's embedded `new_code` is enough to rescue the -agent even though the file is hidden. Surfacing drift works. - -**3. H2 lives in the *misled* axis, not just success.** With no doc (C0) the models guess and land -on the stale value only sometimes (38% haiku, 62% sonnet, 18% opus); with the stale doc (C1) they -are misled **100%** of the time. The rotted doc doesn't just fail to help — it reliably *causes* the -wrong answer. - -**4. Two different costs of rot.** In the cascade family a confident stale doc makes the model -*cheaply wrong*: C1 spends **fewer** output tokens than C0 (e.g. sonnet 274 vs 628), because with no -doc the model deliberates about the unseen dependency, while a stale doc lets it commit to the wrong -answer immediately. The expense reappears in **recovery** — C3 is the costliest condition. In the -comprehension family, where the model gets the answer right anyway, the cost is a steady **token -tax** (+57–107 tokens) for reconciling stale prose against visible code. - -In short: **rot you can't see makes you wrong; rot you can see makes you slow.** - ---- - -## 7. Cost impact for decision-makers - -Two cost questions matter: *how much does rot add to model spend?* (measured here) and *how much -does rot cost in wrong work?* (measured as a rate; priced with your own numbers). - -### 7.1 Token spend — measured, and small - -Where the model can see the code, keeping docs fresh (the Surface-governed world, C2) trims the -wasted generation that a stale doc (C1) provokes. Priced at this run's rates: - -| Model | Wasted model spend from stale docs (C1 − C2) | -|---|---| -| haiku | ≈ $0.31 per 1,000 tasks | -| sonnet | ≈ $1.56 per 1,000 tasks | -| opus | ≈ $1.29 per 1,000 tasks | - -Real but minor — and a **floor**: these are single-shot tasks, so there is no multi-turn "thrash"; a -tool-using agent that loops on a misleading doc would waste far more. Note that where the code is -*hidden*, a stale doc actually spends slightly *fewer* tokens (being confidently wrong is cheap), so -token accounting alone **understates** Surface's value — the value there is correctness, next. - -### 7.2 Avoided wrong work — the dominant term - -The real cost of rot is not tokens; it is the wrong change the model ships when it cannot verify a -stale doc. There the result is stark: **without Surface the model produced a wrong result on 100% of -tasks that relied on a drifted, unseen dependency; with Surface (fresh docs, or the drift report) -roughly 0%.** Price one wrong change — caught and fixed in review, or worse, shipped — and the -saving follows directly: - -> **monthly saving ≈ (agent tasks / month) × (share that rely on drifted, unverifiable docs) × -> (failure-rate drop, ≈100%→0%) × (cost to remediate one wrong change)** - -Illustrative — substitute your own numbers: - -| Input | Example | -|---|---| -| Agent tasks / month | 10,000 | -| Share touching a drifted, unseen dependency | 2% (1 in 50) | -| Failure-rate reduction with Surface | 100% → ~0% | -| Cost to catch & fix one wrong change | $50 (≈30 min eng @ $100/hr) | -| **Estimated avoided rework** | **≈ $10,000 / month** | - -On those same 10,000 tasks the token line is a few dollars. The takeaway for a decision-maker: -**Surface's ROI is dominated by avoided wrong work, not token savings**, and it scales with how often -your agents act on documentation they cannot independently verify. - -*Measured here:* the 100%/≈0% failure rates and the token deltas. *Supplied by you:* task volume, -exposure share, and remediation cost — the bracketed example is an illustration, not a claim about -your environment. - ---- - -## 8. Learnings - -- **The framing decides the result.** Our first attempt used only comprehension scenarios and a - system prompt that said *"the source code is the ground truth."* haiku hit a 100%-success / 0%- - misled ceiling — we were measuring "do stale docs mislead agents?" while instructing the agent - not to be misled. (Recorded in issue #113.) Fixes: neutralize the prompt, and model the realistic - cascade where the drifted dependency is hidden. -- **Hidden-dependency is what makes the doc load-bearing.** Once the agent can't see the drifted - code, the doc is its only source of truth, and rot has somewhere to bite. This single change moved - the cascade family from "measures nothing" to a clean +100pp effect. -- **The harness is sensitive enough to catch authoring mistakes.** An early cascade run showed C3 - failing to recover; inspecting the transcripts revealed the model was obeying a phrase in *our own - task text* ("work from its documentation") and an example in the visible file that anchored the - stale value. Removing both restored recovery. Lesson: author cascades neutrally and never leak the - stale value through wording or worked examples. -- **Comprehension scenarios ceiling on correctness but still pay the token tax.** They are weak for - the success story and useful for the cost story — keep them, but read them on tokens. -- **Robustness bug found in flight.** The original matrix run hung on a single API request that - never returned — the client had no wall-clock timeout, so one bad request stalled the whole - matrix. Fixed by adding a 120s per-request timeout + retries; completed rows were preserved and - the unfinished scenarios re-run (see `PROVENANCE.md`). - ---- - -## 9. Future work - -- **Multi-turn / tool-using agent.** This pilot is single-shot. A real agent loop (read files, run - tests, iterate) would let us measure the *thrash* cost of rot directly and is likely where the - wasted-token signal is largest. -- **Other providers.** The model layer is provider-agnostic; add OpenAI / Gemini / etc. adapters to - test whether the "capability doesn't buy rot-resistance" finding generalizes beyond Claude. -- **A real-OSS case study.** Take an actual repository, introduce a realistic drift, and reproduce - the cascade effect outside curated fixtures — the strongest external-validity evidence. -- **Larger N and harder comprehension scenarios.** Tighten CIs; redesign the ceilinged comprehension - scenarios so the truth is costlier to re-derive, giving them a correctness gradient too. -- **Report the gradient by re-derivation cost.** The current tier axis hints that the Surface effect - grows as recovering truth from code gets more expensive; a cleaner difficulty ladder would make - that explicit. - ---- - -## 10. Reproducibility - -- Data assembled from two runs at the same prompts/grading (an original run + a resume after the - timeout fix); every scenario has exactly 120 rows, 0 duplicates, 0 errors. See `PROVENANCE.md`. -- Re-run the pipeline from the repo root: - ```sh - cargo build --release - cd bench && uv sync - export ANTHROPIC_API_KEY=... - uv run python -m surface_bench.run --models haiku sonnet opus --trials 10 - uv run python -m surface_bench.report results/ - ``` -- Note: re-running `surface_bench.report` regenerates the auto-generated tables; this file is an - authored report for the committed snapshot. `summary.json` holds the machine-readable metrics. diff --git a/bench/results/2026-06-13-pilot-full-matrix/run.json b/bench/results/2026-06-13-pilot-full-matrix/run.json deleted file mode 100644 index 24b7da0..0000000 --- a/bench/results/2026-06-13-pilot-full-matrix/run.json +++ /dev/null @@ -1,46 +0,0 @@ -{ - "timestamp": "20260613T161503Z", - "trials": 10, - "temperature": 1.0, - "max_tokens": 1024, - "conditions": [ - "C0", - "C1", - "C2", - "C3" - ], - "models": { - "haiku": { - "provider": "anthropic", - "model_id": "claude-haiku-4-5-20251001", - "input_per_mtok": 1.0, - "output_per_mtok": 5.0 - }, - "sonnet": { - "provider": "anthropic", - "model_id": "claude-sonnet-4-6", - "input_per_mtok": 3.0, - "output_per_mtok": 15.0 - }, - "opus": { - "provider": "anthropic", - "model_id": "claude-opus-4-8", - "input_per_mtok": 5.0, - "output_per_mtok": 25.0 - } - }, - "scenarios": [ - "access-invert-qa", - "cascade-access-policy-code", - "cascade-page-size-ts-code", - "cascade-quota-batcher-code", - "cascade-retry-budget-code", - "dropped-await-qa", - "pagination-ts-code", - "ratelimit-window-code", - "refresh-replay-premise-qa", - "refresh-single-use-qa", - "retry-budget-code" - ], - "surf_version": "surf 0.6.1" -} diff --git a/bench/results/2026-06-13-pilot-full-matrix/summary.json b/bench/results/2026-06-13-pilot-full-matrix/summary.json deleted file mode 100644 index 627c410..0000000 --- a/bench/results/2026-06-13-pilot-full-matrix/summary.json +++ /dev/null @@ -1,836 +0,0 @@ -{ - "models": [ - "haiku", - "opus", - "sonnet" - ], - "conditions": [ - "C0", - "C1", - "C2", - "C3" - ], - "tiers": [ - "T0", - "T1", - "T2" - ], - "cost": { - "total_usd": 13.982035, - "by_model": { - "haiku": 1.493735, - "opus": 8.27525, - "sonnet": 4.21305 - }, - "by_model_condition": { - "haiku": { - "C0": 0.32365, - "C1": 0.36929500000000004, - "C2": 0.35175, - "C3": 0.44904 - }, - "opus": { - "C0": 1.9395000000000002, - "C1": 1.959375, - "C2": 1.89145, - "C3": 2.484925 - }, - "sonnet": { - "C0": 1.075215, - "C1": 1.009965, - "C2": 0.90129, - "C3": 1.22658 - } - } - }, - "rates": { - "haiku": { - "C0": { - "n": 110, - "success": 0.5545454545454546, - "success_ci": [ - 0.4613868803652954, - 0.6440228721476322 - ], - "misled": 0.21818181818181817, - "misled_ci": [ - 0.1512295618032512, - 0.3041533835466231 - ] - }, - "C1": { - "n": 110, - "success": 0.5454545454545454, - "success_ci": [ - 0.4524405818800455, - 0.6354008785473941 - ], - "misled": 0.42727272727272725, - "misled_ci": [ - 0.3388228926127975, - 0.520630770703299 - ] - }, - "C2": { - "n": 110, - "success": 1.0, - "success_ci": [ - 0.9662560647018358, - 0.9999999999999999 - ], - "misled": 0.0, - "misled_ci": [ - 0.0, - 0.033743935298164184 - ] - }, - "C3": { - "n": 110, - "success": 0.9363636363636364, - "success_ci": [ - 0.874442538117898, - 0.9688354819855223 - ], - "misled": 0.0, - "misled_ci": [ - 0.0, - 0.033743935298164184 - ] - } - }, - "opus": { - "C0": { - "n": 110, - "success": 0.6363636363636364, - "success_ci": [ - 0.5432766706052174, - 0.7202477106771015 - ], - "misled": 0.06363636363636363, - "misled_ci": [ - 0.031164518014477616, - 0.125557461882102 - ] - }, - "C1": { - "n": 110, - "success": 0.6, - "success_ci": [ - 0.5065704511305059, - 0.6866807618098613 - ], - "misled": 0.36363636363636365, - "misled_ci": [ - 0.27975228932289853, - 0.45672332939478255 - ] - }, - "C2": { - "n": 110, - "success": 0.990909090909091, - "success_ci": [ - 0.9502943479206695, - 0.9983934246956784 - ], - "misled": 0.0, - "misled_ci": [ - 0.0, - 0.033743935298164184 - ] - }, - "C3": { - "n": 110, - "success": 0.9727272727272728, - "success_ci": [ - 0.9228691583032443, - 0.9906820301421276 - ], - "misled": 0.0, - "misled_ci": [ - 0.0, - 0.033743935298164184 - ] - } - }, - "sonnet": { - "C0": { - "n": 110, - "success": 0.5909090909090909, - "success_ci": [ - 0.49747266778871363, - 0.6782102530661658 - ], - "misled": 0.22727272727272727, - "misled_ci": [ - 0.15894628523350085, - 0.31400495220186136 - ] - }, - "C1": { - "n": 110, - "success": 0.6272727272727273, - "success_ci": [ - 0.5340520520704517, - 0.7119040371263794 - ], - "misled": 0.36363636363636365, - "misled_ci": [ - 0.27975228932289853, - 0.45672332939478255 - ] - }, - "C2": { - "n": 110, - "success": 1.0, - "success_ci": [ - 0.9662560647018358, - 0.9999999999999999 - ], - "misled": 0.0, - "misled_ci": [ - 0.0, - 0.033743935298164184 - ] - }, - "C3": { - "n": 110, - "success": 1.0, - "success_ci": [ - 0.9662560647018358, - 0.9999999999999999 - ], - "misled": 0.0, - "misled_ci": [ - 0.0, - 0.033743935298164184 - ] - } - } - }, - "deltas": { - "haiku": { - "C2-C1": { - "delta": 0.4545454545454546, - "ci": [ - 0.36363636363636365, - 0.5454545454545454 - ], - "significant": true - }, - "C0-C1": { - "delta": 0.00909090909090915, - "ci": [ - -0.11818181818181828, - 0.1363636363636364 - ], - "significant": false - }, - "C3-C1": { - "delta": 0.390909090909091, - "ci": [ - 0.2909090909090909, - 0.49090909090909096 - ], - "significant": true - }, - "C2-C0": { - "delta": 0.44545454545454544, - "ci": [ - 0.3545454545454545, - 0.5363636363636364 - ], - "significant": true - } - }, - "opus": { - "C2-C1": { - "delta": 0.390909090909091, - "ci": [ - 0.30000000000000004, - 0.4909090909090909 - ], - "significant": true - }, - "C0-C1": { - "delta": 0.036363636363636376, - "ci": [ - -0.09090909090909094, - 0.1636363636363637 - ], - "significant": false - }, - "C3-C1": { - "delta": 0.3727272727272728, - "ci": [ - 0.2727272727272728, - 0.4727272727272728 - ], - "significant": true - }, - "C2-C0": { - "delta": 0.3545454545454546, - "ci": [ - 0.26363636363636367, - 0.44545454545454555 - ], - "significant": true - } - }, - "sonnet": { - "C2-C1": { - "delta": 0.3727272727272727, - "ci": [ - 0.28181818181818186, - 0.4636363636363636 - ], - "significant": true - }, - "C0-C1": { - "delta": -0.036363636363636376, - "ci": [ - -0.1636363636363637, - 0.09090909090909094 - ], - "significant": false - }, - "C3-C1": { - "delta": 0.3727272727272727, - "ci": [ - 0.28181818181818186, - 0.4636363636363636 - ], - "significant": true - }, - "C2-C0": { - "delta": 0.40909090909090906, - "ci": [ - 0.31818181818181823, - 0.5 - ], - "significant": true - } - } - }, - "by_tier": { - "T0": { - "haiku": { - "C2-C1": { - "delta": 0.0, - "ci": [ - 0.0, - 0.0 - ], - "significant": false - }, - "C0-C1": { - "delta": 0.0, - "ci": [ - 0.0, - 0.0 - ], - "significant": false - }, - "C3-C1": { - "delta": 0.0, - "ci": [ - 0.0, - 0.0 - ], - "significant": false - }, - "C2-C0": { - "delta": 0.0, - "ci": [ - 0.0, - 0.0 - ], - "significant": false - } - }, - "opus": { - "C2-C1": { - "delta": 0.0, - "ci": [ - 0.0, - 0.0 - ], - "significant": false - }, - "C0-C1": { - "delta": 0.0, - "ci": [ - 0.0, - 0.0 - ], - "significant": false - }, - "C3-C1": { - "delta": 0.0, - "ci": [ - 0.0, - 0.0 - ], - "significant": false - }, - "C2-C0": { - "delta": 0.0, - "ci": [ - 0.0, - 0.0 - ], - "significant": false - } - }, - "sonnet": { - "C2-C1": { - "delta": 0.033333333333333326, - "ci": [ - 0.0, - 0.09999999999999998 - ], - "significant": false - }, - "C0-C1": { - "delta": -0.1333333333333333, - "ci": [ - -0.30000000000000004, - 0.0 - ], - "significant": false - }, - "C3-C1": { - "delta": 0.033333333333333326, - "ci": [ - 0.0, - 0.09999999999999998 - ], - "significant": false - }, - "C2-C0": { - "delta": 0.16666666666666663, - "ci": [ - 0.033333333333333326, - 0.30000000000000004 - ], - "significant": true - } - } - }, - "T1": { - "haiku": { - "C2-C1": { - "delta": 0.7142857142857143, - "ci": [ - 0.6, - 0.8142857142857143 - ], - "significant": true - }, - "C0-C1": { - "delta": 0.01428571428571429, - "ci": [ - -0.14285714285714285, - 0.17142857142857143 - ], - "significant": false - }, - "C3-C1": { - "delta": 0.6142857142857143, - "ci": [ - 0.48571428571428565, - 0.7428571428571429 - ], - "significant": true - }, - "C2-C0": { - "delta": 0.7, - "ci": [ - 0.5857142857142856, - 0.8 - ], - "significant": true - } - }, - "opus": { - "C2-C1": { - "delta": 0.6142857142857143, - "ci": [ - 0.48571428571428577, - 0.7285714285714286 - ], - "significant": true - }, - "C0-C1": { - "delta": 0.057142857142857106, - "ci": [ - -0.11428571428571427, - 0.2142857142857143 - ], - "significant": false - }, - "C3-C1": { - "delta": 0.5857142857142857, - "ci": [ - 0.4571428571428572, - 0.7142857142857143 - ], - "significant": true - }, - "C2-C0": { - "delta": 0.5571428571428572, - "ci": [ - 0.4285714285714286, - 0.6714285714285715 - ], - "significant": true - } - }, - "sonnet": { - "C2-C1": { - "delta": 0.5714285714285714, - "ci": [ - 0.4571428571428572, - 0.6857142857142857 - ], - "significant": true - }, - "C0-C1": { - "delta": 0.0, - "ci": [ - -0.17142857142857143, - 0.17142857142857137 - ], - "significant": false - }, - "C3-C1": { - "delta": 0.5714285714285714, - "ci": [ - 0.4571428571428572, - 0.6857142857142857 - ], - "significant": true - }, - "C2-C0": { - "delta": 0.5714285714285714, - "ci": [ - 0.4571428571428572, - 0.6857142857142857 - ], - "significant": true - } - } - }, - "T2": { - "haiku": { - "C2-C1": { - "delta": 0.0, - "ci": [ - 0.0, - 0.0 - ], - "significant": false - }, - "C0-C1": { - "delta": 0.0, - "ci": [ - 0.0, - 0.0 - ], - "significant": false - }, - "C3-C1": { - "delta": 0.0, - "ci": [ - 0.0, - 0.0 - ], - "significant": false - }, - "C2-C0": { - "delta": 0.0, - "ci": [ - 0.0, - 0.0 - ], - "significant": false - } - }, - "opus": { - "C2-C1": { - "delta": 0.0, - "ci": [ - 0.0, - 0.0 - ], - "significant": false - }, - "C0-C1": { - "delta": 0.0, - "ci": [ - 0.0, - 0.0 - ], - "significant": false - }, - "C3-C1": { - "delta": 0.0, - "ci": [ - 0.0, - 0.0 - ], - "significant": false - }, - "C2-C0": { - "delta": 0.0, - "ci": [ - 0.0, - 0.0 - ], - "significant": false - } - }, - "sonnet": { - "C2-C1": { - "delta": 0.0, - "ci": [ - 0.0, - 0.0 - ], - "significant": false - }, - "C0-C1": { - "delta": 0.0, - "ci": [ - 0.0, - 0.0 - ], - "significant": false - }, - "C3-C1": { - "delta": 0.0, - "ci": [ - 0.0, - 0.0 - ], - "significant": false - }, - "C2-C0": { - "delta": 0.0, - "ci": [ - 0.0, - 0.0 - ], - "significant": false - } - } - } - }, - "tokens": { - "haiku": { - "C0": { - "n": 110, - "mean_output": 441.7818181818182, - "mean_output_ci": [ - 410.7818181818182, - 473.56363636363636 - ], - "mean_output_correct": 404.3606557377049, - "mean_output_misled": 562.6666666666666 - }, - "C1": { - "n": 110, - "mean_output": 464.06363636363636, - "mean_output_ci": [ - 429.8545454545455, - 499.8636363636364 - ], - "mean_output_correct": 430.26666666666665, - "mean_output_misled": 471.468085106383 - }, - "C2": { - "n": 110, - "mean_output": 429.6909090909091, - "mean_output_ci": [ - 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478.7, - 572.5727272727273 - ], - "mean_output_correct": 511.6355140186916, - "mean_output_misled": null - } - }, - "sonnet": { - "C0": { - "n": 110, - "mean_output": 504.77272727272725, - "mean_output_ci": [ - 469.42727272727274, - 540.9636363636364 - ], - "mean_output_correct": 438.9384615384615, - "mean_output_misled": 668.12 - }, - "C1": { - "n": 110, - "mean_output": 404.5181818181818, - "mean_output_ci": [ - 369.56363636363636, - 442.3363636363636 - ], - "mean_output_correct": 480.69565217391306, - "mean_output_misled": 273.5 - }, - "C2": { - "n": 110, - "mean_output": 336.1818181818182, - "mean_output_ci": [ - 307.2181818181818, - 368.90909090909093 - ], - "mean_output_correct": 336.1818181818182, - "mean_output_misled": null - }, - "C3": { - "n": 110, - "mean_output": 449.8727272727273, - "mean_output_ci": [ - 423.7818181818182, - 476.43636363636364 - ], - "mean_output_correct": 449.8727272727273, - "mean_output_misled": null - } - } - }, - "token_deltas": { - "haiku": { - "C1-C2": { - "delta": 34.372727272727275, - "ci": [ - -12.827272727272714, - 81.90000000000003 - ], - "significant": false - }, - "C1-C0": { - "delta": 22.281818181818153, - "ci": [ - -24.18181818181813, - 69.13636363636363 - ], - "significant": false - }, - "C1-C3": { - "delta": -59.06363636363636, - "ci": [ - -110.81818181818181, - -8.390909090909076 - ], - "significant": true - } - }, - "opus": { - "C1-C2": { - "delta": 28.390909090909076, - "ci": [ - -31.781818181818153, - 88.95454545454544 - ], - "significant": false - }, - "C1-C0": { - "delta": -71.57272727272726, - "ci": [ - -141.29090909090905, - -0.3454545454545155 - ], - "significant": true - }, - "C1-C3": { - "delta": -80.43636363636364, - "ci": [ - -144.43636363636364, - -16.472727272727298 - ], - "significant": true - } - }, - "sonnet": { - "C1-C2": { - "delta": 68.33636363636361, - "ci": [ - 21.581818181818164, - 116.61818181818182 - ], - "significant": true - }, - "C1-C0": { - "delta": -100.25454545454545, - "ci": [ - -149.9909090909091, - -47.609090909090924 - ], - "significant": true - }, - "C1-C3": { - "delta": -45.35454545454547, - "ci": [ - -89.35454545454542, - 1.0 - ], - "significant": false - } - } - } -} diff --git a/bench/scenarios/CHECKLIST.md b/bench/scenarios/CHECKLIST.md deleted file mode 100644 index c7a5226..0000000 --- a/bench/scenarios/CHECKLIST.md +++ /dev/null @@ -1,59 +0,0 @@ -# Authoring a cascade scenario - -A *cascade* scenario: the agent edits a **visible** file whose correctness depends on a **hidden** -dependency (in `code/`, listed in `meta.toml` `hidden_paths`, filtered out of the prompt). The -dependency has drifted from what the **stale** doc says, so an agent that trusts the stale doc is -wrong. The graders probe the *real* hidden dependency for ground truth. Clone -`cascade-quota-batcher-code/` as the canonical template. - -## 0. Pick a load-bearing drift -- [ ] Choose a drift archetype **not already in the family** (avoid: boundary `<`→`<=`, plain - constant, allow→block, page-size constant — see existing `cascade-*`). -- [ ] Name a concrete input where **T0 and T1 produce different observable outputs**. If you can't, - the drift isn't load-bearing — stop and rethink. (The whole effect dies if T0 ≈ T1.) - -## 1. Directory skeleton (`scenarios//`) -- [ ] `meta.toml`: `id`, `title`, `lang`, `task_type`, `tier`, `anchor = "code/ > Symbol > - method"`, `invariant`, `drift`, `hidden_paths` (glob over the dependency), `edit_path` (the - visible file the agent returns — code scenarios only). -- [ ] `code/` — the **T1 (drifted)** source. Hidden dependency present; the visible file is a - **stub** (`raise NotImplementedError` / TODO). No leaked value anywhere in `code/`. -- [ ] `.author/code_t0/` — **only the files that changed**, in their pre-drift (T0) form. -- [ ] `hub_stale.md` — TOML front matter (`summary`, `anchors:` with `claim`/`at`/`hash`, `refs`) + - prose, describing **T0**. Use a placeholder `hash: 000000000000` (author.py seals it). -- [ ] `hub_fresh.md` — same `anchor`, describing **T1**. Placeholder hash. -- [ ] `task.md` — neutral (see §3). For code, ends with the exact `FILE: ` contract. -- [ ] Grader — - - code: `grader/grader.toml` (`setup_files=["tests"]`, `correct_cmd`, `misled_cmd`) + - `grader/tests/check_correct.*` + `check_misled.*`. - - qa: `grader/rubric.toml` (`type="verdict"`, `[fields.]` regex, `[correct]`, `[misled]`). -- [ ] `.author/solution_correct.` and `.author/solution_stale.` — reference solutions for - the polarization self-test (code: the visible file's body; qa: a `.txt` ending in the VERDICT). - -## 2. Grader rules -- [ ] `check_correct` **derives ground truth by importing/probing the real hidden dependency** — never - hardcode the T1 value. `check_misled` hardcodes the **stale** doc value and asserts the agent - used it. (See `cascade-quota-batcher-code`'s `true_capacity()` probe.) -- [ ] Both exit non-zero on a failed assertion (`assert` / `raise SystemExit`). -- [ ] Choose a probe input where stale vs correct give **different** results (e.g. 25 → `[10,10,5]` - stale vs `[11,11,3]` correct). - -## 3. Neutrality (the #113 lesson — leaks killed the effect once) -- [ ] `task.md` states the goal + output contract only. **No worked example that reveals the stale or - the fresh value** (an illustrative example with an *unrelated* placeholder number is fine). -- [ ] No "the doc may be wrong" hint, no precedence between doc and code. -- [ ] The stale value appears **only** in `hub_stale.md` — never in `task.md` or the visible code. - -## 4. Seal + validate (offline, no spend) -- [ ] `python tools/author.py scenarios/` — seals hub hashes, emits `surf_report.json`, asserts a - `"changed"` divergence (fails loudly if the drift isn't detectable). -- [ ] `python tools/validate_scenario.py scenarios/` — proves the graders discriminate - (`solution_correct` → ok & not misled; `solution_stale` → not ok & misled). - -## 5. Pipeline smoke -- [ ] `python -m surface_bench.run --models mock --scenarios ` — runs end to end, no API cost. -- [ ] `python -m surface_bench.oracle results/` — tripwires clear. - -## Language notes -- python / typescript runtimes are always available (python3 via `sys.executable`; `node --test`, - TS type-stripped on Node ≥ 22.18). Scenarios are Python + TS only. diff --git a/bench/scenarios/access-invert-qa/.author/code_t0/access.py b/bench/scenarios/access-invert-qa/.author/code_t0/access.py deleted file mode 100644 index ad08a25..0000000 --- a/bench/scenarios/access-invert-qa/.author/code_t0/access.py +++ /dev/null @@ -1,16 +0,0 @@ -from .models import Document, User - - -class AccessPolicy: - """Decides who may view a document.""" - - def can_view(self, user: User, doc: Document) -> bool: - """Return True if `user` is permitted to view `doc`.""" - if doc.visibility == "public": - return True - if user.is_admin: - return True - if doc.owner_id == user.id: - return True - # Team-scoped access for private documents. - return user.team in doc.permitted_teams diff --git a/bench/scenarios/access-invert-qa/code/__init__.py b/bench/scenarios/access-invert-qa/code/__init__.py deleted file mode 100644 index ee03109..0000000 --- a/bench/scenarios/access-invert-qa/code/__init__.py +++ /dev/null @@ -1,4 +0,0 @@ -from .access import AccessPolicy -from .models import Document, User - -__all__ = ["AccessPolicy", "Document", "User"] diff --git a/bench/scenarios/access-invert-qa/code/access.py b/bench/scenarios/access-invert-qa/code/access.py deleted file mode 100644 index fb3970e..0000000 --- a/bench/scenarios/access-invert-qa/code/access.py +++ /dev/null @@ -1,16 +0,0 @@ -from .models import Document, User - - -class AccessPolicy: - """Decides who may view a document.""" - - def can_view(self, user: User, doc: Document) -> bool: - """Return True if `user` is permitted to view `doc`.""" - if doc.visibility == "public": - return True - if user.is_admin: - return True - if doc.owner_id == user.id: - return True - # Team-scoped access for private documents. - return user.team not in doc.restricted_teams diff --git a/bench/scenarios/access-invert-qa/code/models.py b/bench/scenarios/access-invert-qa/code/models.py deleted file mode 100644 index e7a8145..0000000 --- a/bench/scenarios/access-invert-qa/code/models.py +++ /dev/null @@ -1,18 +0,0 @@ -from dataclasses import dataclass, field - - -@dataclass -class User: - id: str - team: str - is_admin: bool = False - - -@dataclass -class Document: - id: str - owner_id: str - visibility: str # "public" or "private" - # Teams named here are denied access to this private document; every other team is allowed. - # (Historically this field held the *permitted* teams; it now holds the *restricted* ones.) - restricted_teams: set[str] = field(default_factory=set) diff --git a/bench/scenarios/access-invert-qa/grader/rubric.toml b/bench/scenarios/access-invert-qa/grader/rubric.toml deleted file mode 100644 index e71e227..0000000 --- a/bench/scenarios/access-invert-qa/grader/rubric.toml +++ /dev/null @@ -1,25 +0,0 @@ -# QA grader for access-invert. -# -# The agent ends its answer with: -# VERDICT: can_view=; team_rule= -# -# - correct (current/T1 code): can_view=yes AND team_rule=blocklist -# (growth is not in restricted_teams, so the block-list default ALLOWS Dana) -# - misled (stale/T0 claim): can_view=no OR team_rule=allowlist -# (parroting the doc's allow-list, default-deny model) - -type = "verdict" - -[fields.can_view] -pattern = "can_view\\s*=\\s*(yes|no)" - -[fields.team_rule] -pattern = "team_rule\\s*=\\s*(allowlist|blocklist)" - -[correct] -can_view = "yes" -team_rule = "blocklist" - -[misled] -can_view = "no" -team_rule = "allowlist" diff --git a/bench/scenarios/access-invert-qa/hub_fresh.md b/bench/scenarios/access-invert-qa/hub_fresh.md deleted file mode 100644 index ff82001..0000000 --- a/bench/scenarios/access-invert-qa/hub_fresh.md +++ /dev/null @@ -1,23 +0,0 @@ ---- -summary: How document access control decides who may view a private document. -anchors: - - claim: > - Private documents are block-listed by team. AccessPolicy.can_view grants a non-owner, - non-admin user access to a private document unless their team is explicitly present in the - document's restricted_teams set (team not in restricted_teams). The default for any team that - is not listed is ALLOW, so a user whose team is absent from the list CAN view the document. - at: code/access.py > AccessPolicy > can_view - hash: 0412df1065c3 -refs: [] ---- - -# Document access control - -`AccessPolicy.can_view` governs who may read a document. Public documents are visible to everyone; -the document's owner and any admin can always view it. For **private** documents, access to other -users is governed by a **per-team block-list**: a user may view the document **unless** their team -is **explicitly listed** in the document's `restricted_teams` set. - -The default is **open**: if a user's team is *not* in the list, `can_view` returns `True`. So -*restricting* a private document from a team means **adding** that team to the list — until you do, -that team is allowed. diff --git a/bench/scenarios/access-invert-qa/hub_stale.md b/bench/scenarios/access-invert-qa/hub_stale.md deleted file mode 100644 index b10cddb..0000000 --- a/bench/scenarios/access-invert-qa/hub_stale.md +++ /dev/null @@ -1,23 +0,0 @@ ---- -summary: How document access control decides who may view a private document. -anchors: - - claim: > - Private documents are allow-listed by team. AccessPolicy.can_view grants a non-owner, - non-admin user access to a private document only if their team is explicitly present in the - document's team list (team in permitted_teams). The default for any team that is not listed - is DENY, so a user whose team is absent from the list cannot view the document. - at: code/access.py > AccessPolicy > can_view - hash: aaae0f48e127 -refs: [] ---- - -# Document access control - -`AccessPolicy.can_view` governs who may read a document. Public documents are visible to everyone; -the document's owner and any admin can always view it. For **private** documents, access is granted -to other users on a **per-team allow-list**: a user may view the document only if their team is -**explicitly listed** in the document's team set. - -The default is **closed**: if a user's team is *not* in the list, `can_view` returns `False`. So -sharing a private document with a new team means **adding** that team to the list — until you do, -that team is denied. diff --git a/bench/scenarios/access-invert-qa/meta.toml b/bench/scenarios/access-invert-qa/meta.toml deleted file mode 100644 index 8681ea8..0000000 --- a/bench/scenarios/access-invert-qa/meta.toml +++ /dev/null @@ -1,15 +0,0 @@ -id = "access-invert-qa" -title = "Access control: allow-list inverted into a block-list" -lang = "python" -task_type = "qa" -tier = "T1" # buried: one membership test, but the allow/deny default is inverted - -invariant = "Private documents are allow-listed: a non-owner, non-admin sees one only if their team is explicitly granted." - -# The drift: can_view's team check flipped from an allow-list (team in permitted_teams, default -# DENY) to a block-list (team not in restricted_teams, default ALLOW). The membership test and its -# default outcome are inverted — exactly the allow/deny swap surf check fires on (the comparison in -# the anchored span changed). -drift = "team check `in permitted_teams` (default deny) -> `not in restricted_teams` (default allow)" - -anchor = "code/access.py > AccessPolicy > can_view" diff --git a/bench/scenarios/access-invert-qa/surf_report.json b/bench/scenarios/access-invert-qa/surf_report.json deleted file mode 100644 index 8865a0f..0000000 --- a/bench/scenarios/access-invert-qa/surf_report.json +++ /dev/null @@ -1,15 +0,0 @@ -{ - "version": 1, - "divergences": [ - { - "hub": "hub.md", - "claim": "Private documents are allow-listed by team. AccessPolicy.can_view grants a non-owner, non-admin user access to a private document only if their team is explicitly present in the document's team list (team in permitted_teams). The default for any team that is not listed is DENY, so a user whose team is absent from the list cannot view the document.", - "at": "code/access.py > AccessPolicy > can_view", - "kind": "changed", - "old_hash": "aaae0f48e127", - "new_hash": "0412df1065c3", - "new_code": "def can_view(self, user: User, doc: Document) -> bool:\n \"\"\"Return True if `user` is permitted to view `doc`.\"\"\"\n if doc.visibility == \"public\":\n return True\n if user.is_admin:\n return True\n if doc.owner_id == user.id:\n return True\n # Team-scoped access for private documents.\n return user.team not in doc.restricted_teams", - "prose": "Private documents are allow-listed by team. AccessPolicy.can_view grants a non-owner, non-admin user access to a private document only if their team is explicitly present in the document's team list (team in permitted_teams). The default for any team that is not listed is DENY, so a user whose team is absent from the list cannot view the document." - } - ] -} diff --git a/bench/scenarios/access-invert-qa/task.md b/bench/scenarios/access-invert-qa/task.md deleted file mode 100644 index 943d8e0..0000000 --- a/bench/scenarios/access-invert-qa/task.md +++ /dev/null @@ -1,22 +0,0 @@ -A teammate is reasoning about the document authorization layer and asks you to confirm a specific -access decision against the **current code** in `AccessPolicy.can_view`. - -Scenario: - -- The user is **Dana** (`User(id="u_dana", team="growth", is_admin=False)`). -- The document is **doc #42**: `Document(id="42", owner_id="u_root", visibility="private", ...)`. - Dana does **not** own it and is **not** an admin. -- Doc #42's team list (`restricted_teams`) contains `{"finance", "legal"}`. The team `growth` is - **not** in that set. - -Based strictly on what `can_view` actually does today: - -1. Does `can_view(Dana, doc #42)` return True or False — i.e. can Dana view the document? -2. Is the per-team rule for private documents an **allow-list** (a team sees the doc only if it is - explicitly listed) or a **block-list** (every team sees the doc unless it is explicitly listed)? - -Give a direct answer to each part, then one sentence of justification. - -Finally, end your response with a single line in exactly this format (no extra text on it): - -`VERDICT: can_view=; team_rule=` diff --git a/bench/scenarios/cascade-access-policy-code/.author/code_t0/access.py b/bench/scenarios/cascade-access-policy-code/.author/code_t0/access.py deleted file mode 100644 index 1944427..0000000 --- a/bench/scenarios/cascade-access-policy-code/.author/code_t0/access.py +++ /dev/null @@ -1,16 +0,0 @@ -from models import Document, User - - -class AccessPolicy: - """Decides who may view a document.""" - - def can_view(self, user: User, doc: Document) -> bool: - """Return True if `user` is permitted to view `doc`.""" - if doc.visibility == "public": - return True - if user.is_admin: - return True - if doc.owner_id == user.id: - return True - # Team-scoped access for private documents. - return user.team in doc.permitted_teams diff --git a/bench/scenarios/cascade-access-policy-code/code/access.py b/bench/scenarios/cascade-access-policy-code/code/access.py deleted file mode 100644 index d7a328b..0000000 --- a/bench/scenarios/cascade-access-policy-code/code/access.py +++ /dev/null @@ -1,16 +0,0 @@ -from models import Document, User - - -class AccessPolicy: - """Decides who may view a document.""" - - def can_view(self, user: User, doc: Document) -> bool: - """Return True if `user` is permitted to view `doc`.""" - if doc.visibility == "public": - return True - if user.is_admin: - return True - if doc.owner_id == user.id: - return True - # Team-scoped access for private documents. - return user.team not in doc.restricted_teams diff --git a/bench/scenarios/cascade-access-policy-code/code/models.py b/bench/scenarios/cascade-access-policy-code/code/models.py deleted file mode 100644 index a55236d..0000000 --- a/bench/scenarios/cascade-access-policy-code/code/models.py +++ /dev/null @@ -1,17 +0,0 @@ -from dataclasses import dataclass, field - - -@dataclass -class User: - id: str - team: str - is_admin: bool = False - - -@dataclass -class Document: - id: str - owner_id: str - visibility: str # "public" or "private" - # Teams named here are denied access to this private document; every other team is allowed. - restricted_teams: set[str] = field(default_factory=set) diff --git a/bench/scenarios/cascade-access-policy-code/code/preload.py b/bench/scenarios/cascade-access-policy-code/code/preload.py deleted file mode 100644 index 7d4b5c5..0000000 --- a/bench/scenarios/cascade-access-policy-code/code/preload.py +++ /dev/null @@ -1,15 +0,0 @@ -"""Pre-warms per-team view caches for private documents. - -Whether a team may view a private document is decided by `AccessPolicy.can_view` in the `access` -module (see its documentation); this module computes, ahead of time, the set of teams to warm. -""" - - -def viewers(all_teams: set[str], listed_teams: set[str]) -> set[str]: - """Return the set of teams that may view a private document whose team list is `listed_teams`. - - `all_teams` is every team in the org. Owner and admin access are handled elsewhere and are out - of scope here — this is purely the team-scoped rule for a private document. The result must be - exactly the teams that `AccessPolicy.can_view` would admit for that document. - """ - raise NotImplementedError diff --git a/bench/scenarios/cascade-access-policy-code/grader/grader.toml b/bench/scenarios/cascade-access-policy-code/grader/grader.toml deleted file mode 100644 index 795e824..0000000 --- a/bench/scenarios/cascade-access-policy-code/grader/grader.toml +++ /dev/null @@ -1,13 +0,0 @@ -# Code-edit grader for cascade-access-policy. -# -# The agent edits preload.py::viewers; the access policy (the drifted dependency) was hidden from -# the prompt, so the agent's team-access direction comes from the policy's doc. Both checks run -# against the applied patch (each script exits non-zero on a failed assertion): -# -# correct_cmd — derive the TRUE viewer set by calling the real (hidden) can_view per team, and -# assert viewers() matches it (block-list: all teams except the listed ones) -> ok -# misled_cmd — assert viewers() returned the STALE (allow-list) set: exactly the listed teams -> misled - -setup_files = ["tests"] -correct_cmd = ["python3", "tests/check_correct.py"] -misled_cmd = ["python3", "tests/check_misled.py"] diff --git a/bench/scenarios/cascade-access-policy-code/grader/tests/check_correct.py b/bench/scenarios/cascade-access-policy-code/grader/tests/check_correct.py deleted file mode 100644 index a2dfa0a..0000000 --- a/bench/scenarios/cascade-access-policy-code/grader/tests/check_correct.py +++ /dev/null @@ -1,37 +0,0 @@ -"""Correct (T1) behaviour: viewers() matches the real (hidden) policy's team-access rule. - -The policy was hidden from the agent, so its direction is knowable only from the (fresh) doc. We -recover the ground truth by calling the real can_view for every team. The current code is a -block-list, so the viewer set is every team EXCEPT the listed ones. -""" - -import sys - -sys.path.insert(0, "code") - -from preload import viewers -from access import AccessPolicy -from models import Document, User - -ALL_TEAMS = {"growth", "finance", "legal", "data", "ops"} -LISTED = {"finance", "legal"} - - -def true_viewers(all_teams: set[str], listed: set[str]) -> set[str]: - policy = AccessPolicy() - doc = Document(id="d1", owner_id="u_owner", visibility="private", restricted_teams=set(listed)) - out = set() - for team in all_teams: - user = User(id=f"u_{team}", team=team, is_admin=False) - if policy.can_view(user, doc): - out.add(team) - return out - - -want = true_viewers(ALL_TEAMS, LISTED) -assert want == ALL_TEAMS - LISTED, f"fixture sanity: expected block-list, got {want}" - -got = viewers(set(ALL_TEAMS), set(LISTED)) -assert got == want, f"viewers() = {got}, want {want} (every team except the listed ones)" - -print(f"ok: viewers() matches the real policy's block-list -> {sorted(got)}") diff --git a/bench/scenarios/cascade-access-policy-code/grader/tests/check_misled.py b/bench/scenarios/cascade-access-policy-code/grader/tests/check_misled.py deleted file mode 100644 index 6340f9e..0000000 --- a/bench/scenarios/cascade-access-policy-code/grader/tests/check_misled.py +++ /dev/null @@ -1,19 +0,0 @@ -"""Misled (T0) behaviour: the stale doc describes an allow-list (default deny). - -An agent that trusts that doc returns exactly the listed teams as the viewer set, the inverse of the -real block-list behaviour. -""" - -import sys - -sys.path.insert(0, "code") - -from preload import viewers - -ALL_TEAMS = {"growth", "finance", "legal", "data", "ops"} -LISTED = {"finance", "legal"} - -got = viewers(set(ALL_TEAMS), set(LISTED)) -assert got == LISTED, f"viewers() = {got} (stale allow-list doc implies exactly the listed teams {LISTED})" - -print(f"misled: viewers() returned the stale allow-list set -> {sorted(got)}") diff --git a/bench/scenarios/cascade-access-policy-code/hub_fresh.md b/bench/scenarios/cascade-access-policy-code/hub_fresh.md deleted file mode 100644 index c44765e..0000000 --- a/bench/scenarios/cascade-access-policy-code/hub_fresh.md +++ /dev/null @@ -1,21 +0,0 @@ ---- -summary: How document access control decides who may view a private document. -anchors: - - claim: > - Private documents are block-listed by team. AccessPolicy.can_view grants a non-owner, - non-admin user access to a private document unless their team is explicitly present in the - document's restricted_teams set. The default for any team that is not listed is ALLOW, so the - set of teams that may view the document is every team EXCEPT the listed ones. - at: code/access.py > AccessPolicy > can_view - hash: 0412df1065c3 -refs: [] ---- - -# Document access control - -`AccessPolicy.can_view` governs who may read a document. For **private** documents, access by other -users is governed by a **per-team block-list**: a user may view the document **unless** their team -is **explicitly listed** in the document's `restricted_teams` set. - -The default is **open**: if a team is *not* in the list, its users **can** view the document. So the -set of teams permitted to view a private document is **every team except the listed ones**. diff --git a/bench/scenarios/cascade-access-policy-code/hub_stale.md b/bench/scenarios/cascade-access-policy-code/hub_stale.md deleted file mode 100644 index 7e5989c..0000000 --- a/bench/scenarios/cascade-access-policy-code/hub_stale.md +++ /dev/null @@ -1,22 +0,0 @@ ---- -summary: How document access control decides who may view a private document. -anchors: - - claim: > - Private documents are allow-listed by team. AccessPolicy.can_view grants a non-owner, - non-admin user access to a private document only if their team is explicitly present in the - document's team list. The default for any team that is not listed is DENY, so the set of teams - that may view the document is exactly the listed teams. - at: code/access.py > AccessPolicy > can_view - hash: aaae0f48e127 -refs: [] ---- - -# Document access control - -`AccessPolicy.can_view` governs who may read a document. For **private** documents, access by other -users is granted on a **per-team allow-list**: a user may view the document only if their team is -**explicitly listed** in the document's team set. - -The default is **closed**: if a team is *not* in the list, its users cannot view the document. So -the set of teams permitted to view a private document is **exactly the listed teams** — every other -team is denied. diff --git a/bench/scenarios/cascade-access-policy-code/meta.toml b/bench/scenarios/cascade-access-policy-code/meta.toml deleted file mode 100644 index 10e844b..0000000 --- a/bench/scenarios/cascade-access-policy-code/meta.toml +++ /dev/null @@ -1,18 +0,0 @@ -id = "cascade-access-policy-code" -title = "Cascade: viewer precompute inherits a hidden policy's allow/deny inversion" -lang = "python" -task_type = "code" -tier = "T1" # the policy direction lives in a dependency the agent cannot see — only its doc - -invariant = "Private-doc team access is governed by one policy; precomputed viewer sets must match it." - -# The drift: AccessPolicy.can_view flipped from an allow-list (team in permitted_teams, default -# DENY) to a block-list (team not in restricted_teams, default ALLOW). The agent edits preload.py -# (visible) whose viewers() must return exactly the teams can_view admits — but can_view's source -# is HIDDEN, so its direction is knowable only from the doc. A stale (allow-list) doc inverts the set. -drift = "team check allow-list -> block-list; the visible precompute encodes the direction from the doc" - -anchor = "code/access.py > AccessPolicy > can_view" -edit_path = "code/preload.py" # the visible file the agent returns - -hidden_paths = ["code/access.py", "code/models.py"] diff --git a/bench/scenarios/cascade-access-policy-code/surf_report.json b/bench/scenarios/cascade-access-policy-code/surf_report.json deleted file mode 100644 index 19cbf31..0000000 --- a/bench/scenarios/cascade-access-policy-code/surf_report.json +++ /dev/null @@ -1,15 +0,0 @@ -{ - "version": 1, - "divergences": [ - { - "hub": "hub.md", - "claim": "Private documents are allow-listed by team. AccessPolicy.can_view grants a non-owner, non-admin user access to a private document only if their team is explicitly present in the document's team list. The default for any team that is not listed is DENY, so the set of teams that may view the document is exactly the listed teams.", - "at": "code/access.py > AccessPolicy > can_view", - "kind": "changed", - "old_hash": "aaae0f48e127", - "new_hash": "0412df1065c3", - "new_code": "def can_view(self, user: User, doc: Document) -> bool:\n \"\"\"Return True if `user` is permitted to view `doc`.\"\"\"\n if doc.visibility == \"public\":\n return True\n if user.is_admin:\n return True\n if doc.owner_id == user.id:\n return True\n # Team-scoped access for private documents.\n return user.team not in doc.restricted_teams", - "prose": "Private documents are allow-listed by team. AccessPolicy.can_view grants a non-owner, non-admin user access to a private document only if their team is explicitly present in the document's team list. The default for any team that is not listed is DENY, so the set of teams that may view the document is exactly the listed teams." - } - ] -} diff --git a/bench/scenarios/cascade-access-policy-code/task.md b/bench/scenarios/cascade-access-policy-code/task.md deleted file mode 100644 index 87ff0a7..0000000 --- a/bench/scenarios/cascade-access-policy-code/task.md +++ /dev/null @@ -1,19 +0,0 @@ -We're pre-warming per-team view caches in `preload.py`. Access to private documents is governed by -`AccessPolicy.can_view` (in the `access` module), whose source is not in this checkout; the -materials you have about it are included below. - -Implement `viewers(all_teams, listed_teams)` in `preload.py`: - -- Return the set of teams (drawn from `all_teams`) that may view a private document whose team list - is `listed_teams`. -- This is purely the **team-scoped rule** for a private document; owner and admin access are - handled elsewhere and are out of scope. -- The returned set must be **exactly** the teams that `AccessPolicy.can_view` would admit. - -Determine the policy's team-access rule from the materials provided, and return exactly the teams it -admits. - -Return the **entire** updated `code/preload.py` file, as a single fenced block preceded by a line in -exactly this form: - -FILE: code/preload.py diff --git a/bench/scenarios/cascade-backoff-offbyone-code/.author/code_t0/retry/backoff.py b/bench/scenarios/cascade-backoff-offbyone-code/.author/code_t0/retry/backoff.py deleted file mode 100644 index 7bd7b64..0000000 --- a/bench/scenarios/cascade-backoff-offbyone-code/.author/code_t0/retry/backoff.py +++ /dev/null @@ -1,9 +0,0 @@ -BASE_MS = 100 - - -def delay_for(attempt: int) -> int: - """Backoff delay in milliseconds for a retry attempt. - - Attempts are 1-based: attempt 1 waits the base delay, and each subsequent attempt doubles it. - """ - return BASE_MS << (attempt - 1) diff --git a/bench/scenarios/cascade-backoff-offbyone-code/.author/solution_correct.py b/bench/scenarios/cascade-backoff-offbyone-code/.author/solution_correct.py deleted file mode 100644 index 61fea64..0000000 --- a/bench/scenarios/cascade-backoff-offbyone-code/.author/solution_correct.py +++ /dev/null @@ -1,7 +0,0 @@ -"""Reference CORRECT solution: 0-based attempts, matching the real delay_for.""" - -BASE_MS = 100 - - -def delays(attempts: list[int]) -> list[int]: - return [BASE_MS << a for a in attempts] diff --git a/bench/scenarios/cascade-backoff-offbyone-code/.author/solution_stale.py b/bench/scenarios/cascade-backoff-offbyone-code/.author/solution_stale.py deleted file mode 100644 index b4784da..0000000 --- a/bench/scenarios/cascade-backoff-offbyone-code/.author/solution_stale.py +++ /dev/null @@ -1,7 +0,0 @@ -"""Reference STALE solution: trusts the doc's 1-based attempts (the misled answer).""" - -BASE_MS = 100 - - -def delays(attempts: list[int]) -> list[int]: - return [BASE_MS << (a - 1) for a in attempts] diff --git a/bench/scenarios/cascade-backoff-offbyone-code/code/poller.py b/bench/scenarios/cascade-backoff-offbyone-code/code/poller.py deleted file mode 100644 index 056a10d..0000000 --- a/bench/scenarios/cascade-backoff-offbyone-code/code/poller.py +++ /dev/null @@ -1,18 +0,0 @@ -"""Builds a retry backoff schedule for a poller. - -The backoff policy lives in the `retry` package (see its documentation); that helper's source is -not in this checkout. This module reproduces the per-attempt delays so the poller can pre-compute -its schedule. -""" - -BASE_MS = 100 - - -def delays(attempts: list[int]) -> list[int]: - """For each retry attempt number in ``attempts``, return its backoff delay in milliseconds. - - Backoff is exponential: it starts from a base of 100 ms and doubles each attempt. Apply the - documented attempt-indexing convention so the delays match the retry package's ``delay_for``. - Returns ``[]`` for an empty list. - """ - raise NotImplementedError diff --git a/bench/scenarios/cascade-backoff-offbyone-code/code/retry/__init__.py b/bench/scenarios/cascade-backoff-offbyone-code/code/retry/__init__.py deleted file mode 100644 index bf87d19..0000000 --- a/bench/scenarios/cascade-backoff-offbyone-code/code/retry/__init__.py +++ /dev/null @@ -1,3 +0,0 @@ -from .backoff import BASE_MS, delay_for - -__all__ = ["BASE_MS", "delay_for"] diff --git a/bench/scenarios/cascade-backoff-offbyone-code/code/retry/backoff.py b/bench/scenarios/cascade-backoff-offbyone-code/code/retry/backoff.py deleted file mode 100644 index 7620a63..0000000 --- a/bench/scenarios/cascade-backoff-offbyone-code/code/retry/backoff.py +++ /dev/null @@ -1,9 +0,0 @@ -BASE_MS = 100 - - -def delay_for(attempt: int) -> int: - """Backoff delay in milliseconds for a retry attempt. - - Attempts are 0-based: attempt 0 waits the base delay, and each subsequent attempt doubles it. - """ - return BASE_MS << attempt diff --git a/bench/scenarios/cascade-backoff-offbyone-code/grader/grader.toml b/bench/scenarios/cascade-backoff-offbyone-code/grader/grader.toml deleted file mode 100644 index 4563e2e..0000000 --- a/bench/scenarios/cascade-backoff-offbyone-code/grader/grader.toml +++ /dev/null @@ -1,11 +0,0 @@ -# Code-edit grader for cascade-backoff-offbyone. -# -# The agent edits poller.py::delays; delay_for (the drifted dependency) was hidden, so the agent's -# attempt-indexing convention comes entirely from the doc. Both checks run against the applied patch: -# -# correct_cmd — probe the REAL delay_for (0-based) for each attempt and assert delays matches -> ok -# misled_cmd — assert delays used the STALE 1-based formula (100 * 2**(n-1)) -> misled - -setup_files = ["tests"] -correct_cmd = ["python3", "tests/check_correct.py"] -misled_cmd = ["python3", "tests/check_misled.py"] diff --git a/bench/scenarios/cascade-backoff-offbyone-code/grader/tests/check_correct.py b/bench/scenarios/cascade-backoff-offbyone-code/grader/tests/check_correct.py deleted file mode 100644 index 9d3f10e..0000000 --- a/bench/scenarios/cascade-backoff-offbyone-code/grader/tests/check_correct.py +++ /dev/null @@ -1,27 +0,0 @@ -"""Correct (T1) behaviour: delays reproduces the REAL delay_for for each attempt number. - -delay_for was hidden from the agent, so the attempt-indexing convention is knowable only from the -(fresh) doc. We recover the ground truth by calling the real delay_for, which is now 0-based -(100 * 2**attempt). For attempts 1,2,3 that is 200,400,800 ms (not the 100,200,400 of the stale -1-based formula). -""" - -import sys - -sys.path.insert(0, "code") - -from poller import delays -from retry.backoff import delay_for - -attempts = [1, 2, 3, 4] -expected = [delay_for(a) for a in attempts] - -# fixture sanity: the off-by-one must actually move the delays -stale = [100 << (a - 1) for a in attempts] -assert expected != stale, f"fixture sanity: indexing change does not move the delays ({expected})" - -assert delays([]) == [], "delays([]) must be []" -got = delays(attempts) -assert got == expected, f"delays({attempts}) = {got}, want {expected} (real 0-based delay_for)" - -print(f"ok: delays reproduces the real 0-based backoff ({expected})") diff --git a/bench/scenarios/cascade-backoff-offbyone-code/grader/tests/check_misled.py b/bench/scenarios/cascade-backoff-offbyone-code/grader/tests/check_misled.py deleted file mode 100644 index 2d1264d..0000000 --- a/bench/scenarios/cascade-backoff-offbyone-code/grader/tests/check_misled.py +++ /dev/null @@ -1,19 +0,0 @@ -"""Misled (T0) behaviour: the stale doc says attempts are 1-based (100 * 2**(attempt - 1)). - -An agent that trusts that doc returns 100,200,400,800 for attempts 1,2,3,4 instead of the real -200,400,800,1600 under the 0-based delay_for. -""" - -import sys - -sys.path.insert(0, "code") - -from poller import delays - -attempts = [1, 2, 3, 4] -stale = [100 << (a - 1) for a in attempts] - -got = delays(attempts) -assert got == stale, f"delays({attempts}) = {got} (stale 1-based delays {stale})" - -print(f"misled: delays used the stale 1-based backoff ({stale})") diff --git a/bench/scenarios/cascade-backoff-offbyone-code/hub_fresh.md b/bench/scenarios/cascade-backoff-offbyone-code/hub_fresh.md deleted file mode 100644 index 51aa860..0000000 --- a/bench/scenarios/cascade-backoff-offbyone-code/hub_fresh.md +++ /dev/null @@ -1,19 +0,0 @@ ---- -summary: How the retry backoff computes a per-attempt delay. -anchors: - - claim: > - delay_for(attempt) returns the backoff delay in milliseconds for a retry attempt. Attempts - are 0-based: the delay is BASE_MS * 2**attempt with BASE_MS = 100. So attempt 0 waits 100 ms, - attempt 1 waits 200 ms, attempt 2 waits 400 ms, and attempt n waits 100 * 2**n ms. - at: code/retry/backoff.py > delay_for - hash: 0038327b28fb -refs: [] ---- - -# Retry backoff - -`delay_for(attempt)` returns the backoff delay, in **milliseconds**, for a retry attempt. The base -delay is `BASE_MS = 100` and the delay doubles each attempt. - -**Attempt indexing:** attempts are **0-based** — `delay_for(0)` is the base 100 ms, and in general -`delay_for(n) = 100 * 2**n` ms. So attempts 1, 2, 3 wait 200, 400, 800 ms. diff --git a/bench/scenarios/cascade-backoff-offbyone-code/hub_stale.md b/bench/scenarios/cascade-backoff-offbyone-code/hub_stale.md deleted file mode 100644 index bd7724d..0000000 --- a/bench/scenarios/cascade-backoff-offbyone-code/hub_stale.md +++ /dev/null @@ -1,19 +0,0 @@ ---- -summary: How the retry backoff computes a per-attempt delay. -anchors: - - claim: > - delay_for(attempt) returns the backoff delay in milliseconds for a retry attempt. Attempts - are 1-based: the delay is BASE_MS * 2**(attempt - 1) with BASE_MS = 100. So attempt 1 waits - 100 ms, attempt 2 waits 200 ms, attempt 3 waits 400 ms, and attempt n waits 100 * 2**(n-1) ms. - at: code/retry/backoff.py > delay_for - hash: 93b18e377590 -refs: [] ---- - -# Retry backoff - -`delay_for(attempt)` returns the backoff delay, in **milliseconds**, for a retry attempt. The base -delay is `BASE_MS = 100` and the delay doubles each attempt. - -**Attempt indexing:** attempts are **1-based** — `delay_for(1)` is the base 100 ms, and in general -`delay_for(n) = 100 * 2**(n - 1)` ms. So attempts 1, 2, 3 wait 100, 200, 400 ms. diff --git a/bench/scenarios/cascade-backoff-offbyone-code/meta.toml b/bench/scenarios/cascade-backoff-offbyone-code/meta.toml deleted file mode 100644 index 78bf58b..0000000 --- a/bench/scenarios/cascade-backoff-offbyone-code/meta.toml +++ /dev/null @@ -1,18 +0,0 @@ -id = "cascade-backoff-offbyone-code" -title = "Cascade: poller inherits a hidden backoff helper's drifted attempt indexing (off-by-one)" -lang = "python" -task_type = "code" -tier = "T2" # the attempt-indexing convention is a load-bearing premise; the helper is hidden - -invariant = "Exponential backoff doubles each attempt from a base delay; callers must index attempts the same way." - -# The drift: retry/backoff.py::delay_for changed its attempt indexing from 1-based to 0-based. The -# exponent went from `attempt - 1` to `attempt`, so for the SAME attempt number the real delay is -# now double the documented one. The agent edits poller.py (visible) whose schedule reproduces the -# delays, but backoff.py is HIDDEN, so the indexing convention comes only from the doc. -drift = "delay_for exponent `attempt - 1` (1-based) -> `attempt` (0-based); poller sizes from the doc, not the hidden code" - -anchor = "code/retry/backoff.py > delay_for" -edit_path = "code/poller.py" # the visible file the agent returns - -hidden_paths = ["code/retry/*.py"] diff --git a/bench/scenarios/cascade-backoff-offbyone-code/surf_report.json b/bench/scenarios/cascade-backoff-offbyone-code/surf_report.json deleted file mode 100644 index b050447..0000000 --- a/bench/scenarios/cascade-backoff-offbyone-code/surf_report.json +++ /dev/null @@ -1,15 +0,0 @@ -{ - "version": 1, - "divergences": [ - { - "hub": "hub.md", - "claim": "delay_for(attempt) returns the backoff delay in milliseconds for a retry attempt. Attempts are 1-based: the delay is BASE_MS * 2**(attempt - 1) with BASE_MS = 100. So attempt 1 waits 100 ms, attempt 2 waits 200 ms, attempt 3 waits 400 ms, and attempt n waits 100 * 2**(n-1) ms.", - "at": "code/retry/backoff.py > delay_for", - "kind": "changed", - "old_hash": "93b18e377590", - "new_hash": "0038327b28fb", - "new_code": "def delay_for(attempt: int) -> int:\n \"\"\"Backoff delay in milliseconds for a retry attempt.\n\n Attempts are 0-based: attempt 0 waits the base delay, and each subsequent attempt doubles it.\n \"\"\"\n return BASE_MS << attempt", - "prose": "delay_for(attempt) returns the backoff delay in milliseconds for a retry attempt. Attempts are 1-based: the delay is BASE_MS * 2**(attempt - 1) with BASE_MS = 100. So attempt 1 waits 100 ms, attempt 2 waits 200 ms, attempt 3 waits 400 ms, and attempt n waits 100 * 2**(n-1) ms." - } - ] -} diff --git a/bench/scenarios/cascade-backoff-offbyone-code/task.md b/bench/scenarios/cascade-backoff-offbyone-code/task.md deleted file mode 100644 index 3611f5c..0000000 --- a/bench/scenarios/cascade-backoff-offbyone-code/task.md +++ /dev/null @@ -1,16 +0,0 @@ -We're pre-computing a retry backoff schedule in `poller.py`. The delays must match the backoff -policy in the `retry` package; that helper's source is not in this checkout, but its documentation -is included below. - -Implement `delays(attempts)` in `poller.py`: - -- `attempts` is a list of retry attempt numbers (e.g. `[1, 2, 3]`). -- For each attempt number, return its backoff delay in **milliseconds**. Backoff is exponential, - starting from a base of 100 ms and doubling each attempt. -- Apply the retry package's **documented attempt-indexing convention** so your delays match its - `delay_for`. Return `[]` for an empty list. - -Return the **entire** updated `code/poller.py` file, as a single fenced block preceded by a line in -exactly this form: - -FILE: code/poller.py diff --git a/bench/scenarios/cascade-default-timeout-ts-code/.author/code_t0/net/timeout.ts b/bench/scenarios/cascade-default-timeout-ts-code/.author/code_t0/net/timeout.ts deleted file mode 100644 index f2285b4..0000000 --- a/bench/scenarios/cascade-default-timeout-ts-code/.author/code_t0/net/timeout.ts +++ /dev/null @@ -1,9 +0,0 @@ -/** - * Total request deadline (ms) across all attempts. - * - * A request gets three attempts, each bounded by `perAttemptMs`; the total deadline is three times - * the per-attempt timeout. `perAttemptMs` defaults to the standard 30000 ms timeout. - */ -export function totalDeadlineMs(perAttemptMs: number = 30000): number { - return perAttemptMs * 3; -} diff --git a/bench/scenarios/cascade-default-timeout-ts-code/.author/solution_correct.ts b/bench/scenarios/cascade-default-timeout-ts-code/.author/solution_correct.ts deleted file mode 100644 index 1a12bab..0000000 --- a/bench/scenarios/cascade-default-timeout-ts-code/.author/solution_correct.ts +++ /dev/null @@ -1,4 +0,0 @@ -// Reference CORRECT solution: uses the real 5000 ms default per-attempt timeout. -export function requestBudgetMs(): number { - return 5000 * 3; -} diff --git a/bench/scenarios/cascade-default-timeout-ts-code/.author/solution_stale.ts b/bench/scenarios/cascade-default-timeout-ts-code/.author/solution_stale.ts deleted file mode 100644 index bee69f8..0000000 --- a/bench/scenarios/cascade-default-timeout-ts-code/.author/solution_stale.ts +++ /dev/null @@ -1,4 +0,0 @@ -// Reference STALE solution: trusts the doc's 30000 ms default (the misled answer). -export function requestBudgetMs(): number { - return 30000 * 3; -} diff --git a/bench/scenarios/cascade-default-timeout-ts-code/code/caller.ts b/bench/scenarios/cascade-default-timeout-ts-code/code/caller.ts deleted file mode 100644 index 1c2dacb..0000000 --- a/bench/scenarios/cascade-default-timeout-ts-code/code/caller.ts +++ /dev/null @@ -1,12 +0,0 @@ -/** - * Reports the default request budget for the UI's "this may take up to…" hint. - * - * The total deadline is computed by `totalDeadlineMs` in the `net` module, whose source is not in - * this checkout (see its documentation). This module reports the default budget — the deadline when - * no per-attempt override is given. - */ -export function requestBudgetMs(): number { - // Should return the total request deadline the net layer uses by default (no per-attempt - // override) — i.e. totalDeadlineMs() called with no argument. - throw new Error('not implemented'); -} diff --git a/bench/scenarios/cascade-default-timeout-ts-code/code/net/timeout.ts b/bench/scenarios/cascade-default-timeout-ts-code/code/net/timeout.ts deleted file mode 100644 index acfc155..0000000 --- a/bench/scenarios/cascade-default-timeout-ts-code/code/net/timeout.ts +++ /dev/null @@ -1,9 +0,0 @@ -/** - * Total request deadline (ms) across all attempts. - * - * A request gets three attempts, each bounded by `perAttemptMs`; the total deadline is three times - * the per-attempt timeout. `perAttemptMs` defaults to the standard 5000 ms timeout. - */ -export function totalDeadlineMs(perAttemptMs: number = 5000): number { - return perAttemptMs * 3; -} diff --git a/bench/scenarios/cascade-default-timeout-ts-code/grader/grader.toml b/bench/scenarios/cascade-default-timeout-ts-code/grader/grader.toml deleted file mode 100644 index 0e005a7..0000000 --- a/bench/scenarios/cascade-default-timeout-ts-code/grader/grader.toml +++ /dev/null @@ -1,12 +0,0 @@ -# Code-edit grader for cascade-default-timeout-ts (Node's built-in test runner). -# -# The agent edits caller.ts::requestBudgetMs; timeout.ts (the drifted dependency) was hidden, so the -# agent's default per-attempt timeout comes from its doc. Node strips TS types at load (>=22.18). -# -# correct_cmd — call the real (hidden) totalDeadlineMs() and assert requestBudgetMs matches it -# (15000 ms under the real 5000 ms default) -> ok -# misled_cmd — assert requestBudgetMs used the STALE 30000 ms default (90000 ms) -> misled - -setup_files = ["tests"] -correct_cmd = ["node", "--test", "tests/correct.test.ts"] -misled_cmd = ["node", "--test", "tests/misled.test.ts"] diff --git a/bench/scenarios/cascade-default-timeout-ts-code/grader/tests/correct.test.ts b/bench/scenarios/cascade-default-timeout-ts-code/grader/tests/correct.test.ts deleted file mode 100644 index fb9702b..0000000 --- a/bench/scenarios/cascade-default-timeout-ts-code/grader/tests/correct.test.ts +++ /dev/null @@ -1,15 +0,0 @@ -import { test } from 'node:test'; -import assert from 'node:assert/strict'; -import { requestBudgetMs } from '../code/caller.ts'; -import { totalDeadlineMs } from '../code/net/timeout.ts'; - -// Correct (T1) behaviour: recover the default budget from the real (hidden) net helper and assert -// requestBudgetMs matches it. The default per-attempt timeout is now 5000 ms -> total 15000 ms. -test('requestBudgetMs uses the real default per-attempt timeout', () => { - const expected = totalDeadlineMs(); // real default 5000 -> 15000 - - // fixture sanity: the default drift must move the budget - assert.notEqual(expected, 30000 * 3); - - assert.equal(requestBudgetMs(), expected); -}); diff --git a/bench/scenarios/cascade-default-timeout-ts-code/grader/tests/misled.test.ts b/bench/scenarios/cascade-default-timeout-ts-code/grader/tests/misled.test.ts deleted file mode 100644 index d343d80..0000000 --- a/bench/scenarios/cascade-default-timeout-ts-code/grader/tests/misled.test.ts +++ /dev/null @@ -1,9 +0,0 @@ -import { test } from 'node:test'; -import assert from 'node:assert/strict'; -import { requestBudgetMs } from '../code/caller.ts'; - -// Misled (T0) behaviour: an agent that trusts the stale doc assumes a 30000 ms default per-attempt -// timeout, so it reports 90000 ms instead of the real 15000 ms. -test('requestBudgetMs used the stale default timeout', () => { - assert.equal(requestBudgetMs(), 30000 * 3); -}); diff --git a/bench/scenarios/cascade-default-timeout-ts-code/hub_fresh.md b/bench/scenarios/cascade-default-timeout-ts-code/hub_fresh.md deleted file mode 100644 index d547709..0000000 --- a/bench/scenarios/cascade-default-timeout-ts-code/hub_fresh.md +++ /dev/null @@ -1,19 +0,0 @@ ---- -summary: The default per-attempt timeout and the total request deadline derived from it. -anchors: - - claim: > - totalDeadlineMs(perAttemptMs) returns perAttemptMs * 3 (a request gets three attempts). The - default per-attempt timeout is 5000 ms, so totalDeadlineMs() called with no argument returns - 15000 ms — the default total request deadline. - at: code/net/timeout.ts > totalDeadlineMs - hash: 5277f05efc4c -refs: [] ---- - -# Request timeouts - -`totalDeadlineMs(perAttemptMs)` returns the total request deadline in **milliseconds**: a request -gets **three** attempts, so the total is `perAttemptMs * 3`. - -**Default per-attempt timeout:** `5000` ms (5 s). With no override, `totalDeadlineMs()` therefore -returns **15000** ms — the default total request deadline. diff --git a/bench/scenarios/cascade-default-timeout-ts-code/hub_stale.md b/bench/scenarios/cascade-default-timeout-ts-code/hub_stale.md deleted file mode 100644 index ecebd3e..0000000 --- a/bench/scenarios/cascade-default-timeout-ts-code/hub_stale.md +++ /dev/null @@ -1,19 +0,0 @@ ---- -summary: The default per-attempt timeout and the total request deadline derived from it. -anchors: - - claim: > - totalDeadlineMs(perAttemptMs) returns perAttemptMs * 3 (a request gets three attempts). The - default per-attempt timeout is 30000 ms, so totalDeadlineMs() called with no argument returns - 90000 ms — the default total request deadline. - at: code/net/timeout.ts > totalDeadlineMs - hash: 423c0258c20a -refs: [] ---- - -# Request timeouts - -`totalDeadlineMs(perAttemptMs)` returns the total request deadline in **milliseconds**: a request -gets **three** attempts, so the total is `perAttemptMs * 3`. - -**Default per-attempt timeout:** `30000` ms (30 s). With no override, `totalDeadlineMs()` therefore -returns **90000** ms — the default total request deadline. diff --git a/bench/scenarios/cascade-default-timeout-ts-code/meta.toml b/bench/scenarios/cascade-default-timeout-ts-code/meta.toml deleted file mode 100644 index 95f8268..0000000 --- a/bench/scenarios/cascade-default-timeout-ts-code/meta.toml +++ /dev/null @@ -1,18 +0,0 @@ -id = "cascade-default-timeout-ts-code" -title = "Cascade: caller inherits a hidden net helper's drifted default timeout (TypeScript)" -lang = "typescript" -task_type = "code" -tier = "T1" # the default timeout lives in a dependency the agent cannot see — only its doc - -invariant = "Requests use a default per-attempt timeout; callers that don't override it must assume the same default." - -# The drift: totalDeadlineMs's default parameter `perAttemptMs` changed from 30000 to 5000, so the -# total deadline it returns when called with no override fell from 90000ms to 15000ms. The agent -# edits caller.ts (visible) which reports that default budget, but timeout.ts is HIDDEN, so the -# default comes only from the doc. Graded by `node --test` (Node >= 22.18 type-strip). -drift = "totalDeadlineMs default perAttemptMs 30000 -> 5000; caller reads the default from the doc, not the hidden code" - -anchor = "code/net/timeout.ts > totalDeadlineMs" -edit_path = "code/caller.ts" # the visible file the agent returns - -hidden_paths = ["code/net/*.ts"] diff --git a/bench/scenarios/cascade-default-timeout-ts-code/surf_report.json b/bench/scenarios/cascade-default-timeout-ts-code/surf_report.json deleted file mode 100644 index 6d7f659..0000000 --- a/bench/scenarios/cascade-default-timeout-ts-code/surf_report.json +++ /dev/null @@ -1,15 +0,0 @@ -{ - "version": 1, - "divergences": [ - { - "hub": "hub.md", - "claim": "totalDeadlineMs(perAttemptMs) returns perAttemptMs * 3 (a request gets three attempts). The default per-attempt timeout is 30000 ms, so totalDeadlineMs() called with no argument returns 90000 ms \u2014 the default total request deadline.", - "at": "code/net/timeout.ts > totalDeadlineMs", - "kind": "changed", - "old_hash": "423c0258c20a", - "new_hash": "5277f05efc4c", - "new_code": "function totalDeadlineMs(perAttemptMs: number = 5000): number {\n return perAttemptMs * 3;\n}", - "prose": "totalDeadlineMs(perAttemptMs) returns perAttemptMs * 3 (a request gets three attempts). The default per-attempt timeout is 30000 ms, so totalDeadlineMs() called with no argument returns 90000 ms \u2014 the default total request deadline." - } - ] -} diff --git a/bench/scenarios/cascade-default-timeout-ts-code/task.md b/bench/scenarios/cascade-default-timeout-ts-code/task.md deleted file mode 100644 index d462d48..0000000 --- a/bench/scenarios/cascade-default-timeout-ts-code/task.md +++ /dev/null @@ -1,16 +0,0 @@ -We're showing a "this may take up to…" hint in `caller.ts`. The hint is the default total request -budget, computed by `totalDeadlineMs` in the `net` module. That module's source is not in this -checkout, but its documentation is included below. - -Implement `requestBudgetMs()` in `caller.ts`: - -- Return the total request deadline in **milliseconds** that the net layer uses **by default** — - i.e. `totalDeadlineMs()` called with no per-attempt override. - -Use the net module's documented default per-attempt timeout so the budget matches what requests -actually use. - -Return the **entire** updated `code/caller.ts` file, as a single fenced block preceded by a line in -exactly this form: - -FILE: code/caller.ts diff --git a/bench/scenarios/cascade-idempotency-window-qa/.author/code_t0/payments/idempotency.py b/bench/scenarios/cascade-idempotency-window-qa/.author/code_t0/payments/idempotency.py deleted file mode 100644 index 0762747..0000000 --- a/bench/scenarios/cascade-idempotency-window-qa/.author/code_t0/payments/idempotency.py +++ /dev/null @@ -1,12 +0,0 @@ -class IdempotencyStore: - """Tracks processed payment keys so a retried request is never charged twice.""" - - def __init__(self) -> None: - self._processed: set[str] = set() - - def record(self, key: str) -> None: - self._processed.add(key) - - def is_duplicate(self, key: str) -> bool: - """Return True if a payment with this idempotency key was already processed.""" - return key in self._processed diff --git a/bench/scenarios/cascade-idempotency-window-qa/.author/solution_correct.txt b/bench/scenarios/cascade-idempotency-window-qa/.author/solution_correct.txt deleted file mode 100644 index 9f35833..0000000 --- a/bench/scenarios/cascade-idempotency-window-qa/.author/solution_correct.txt +++ /dev/null @@ -1,5 +0,0 @@ -The retry is not caught. `is_duplicate` always returns False now, so the second request with key -`pay_123` is not recognized as a duplicate and the payment is processed again — the customer can be -charged twice. - -VERDICT: treated_as_duplicate=no; double_charge_possible=yes diff --git a/bench/scenarios/cascade-idempotency-window-qa/.author/solution_stale.txt b/bench/scenarios/cascade-idempotency-window-qa/.author/solution_stale.txt deleted file mode 100644 index eb2b1ce..0000000 --- a/bench/scenarios/cascade-idempotency-window-qa/.author/solution_stale.txt +++ /dev/null @@ -1,4 +0,0 @@ -The store recognizes the retry: `is_duplicate("pay_123")` returns True because the key is already in -the processed set, so the second charge is skipped and the customer is charged at most once. - -VERDICT: treated_as_duplicate=yes; double_charge_possible=no diff --git a/bench/scenarios/cascade-idempotency-window-qa/code/payments/__init__.py b/bench/scenarios/cascade-idempotency-window-qa/code/payments/__init__.py deleted file mode 100644 index 3d8961f..0000000 --- a/bench/scenarios/cascade-idempotency-window-qa/code/payments/__init__.py +++ /dev/null @@ -1,3 +0,0 @@ -from .idempotency import IdempotencyStore - -__all__ = ["IdempotencyStore"] diff --git a/bench/scenarios/cascade-idempotency-window-qa/code/payments/idempotency.py b/bench/scenarios/cascade-idempotency-window-qa/code/payments/idempotency.py deleted file mode 100644 index 05e1f03..0000000 --- a/bench/scenarios/cascade-idempotency-window-qa/code/payments/idempotency.py +++ /dev/null @@ -1,12 +0,0 @@ -class IdempotencyStore: - """Tracks processed payment keys so a retried request is never charged twice.""" - - def __init__(self) -> None: - self._processed: set[str] = set() - - def record(self, key: str) -> None: - self._processed.add(key) - - def is_duplicate(self, key: str) -> bool: - """Return True if a payment with this idempotency key was already processed.""" - return False diff --git a/bench/scenarios/cascade-idempotency-window-qa/grader/rubric.toml b/bench/scenarios/cascade-idempotency-window-qa/grader/rubric.toml deleted file mode 100644 index 94e79b7..0000000 --- a/bench/scenarios/cascade-idempotency-window-qa/grader/rubric.toml +++ /dev/null @@ -1,25 +0,0 @@ -# QA grader for cascade-idempotency-window. -# -# The agent ends its answer with: -# VERDICT: treated_as_duplicate=; double_charge_possible= -# -# - correct (current/T1 code): treated_as_duplicate=no AND double_charge_possible=yes -# (is_duplicate now always returns False, so the retry is not caught -> double charge) -# - misled (stale/T0 claim): treated_as_duplicate=yes OR double_charge_possible=no -# (parroting the doc's "recognizes duplicates, charged at most once") - -type = "verdict" - -[fields.treated_as_duplicate] -pattern = "treated_as_duplicate\\s*=\\s*(yes|no)" - -[fields.double_charge_possible] -pattern = "double_charge_possible\\s*=\\s*(yes|no)" - -[correct] -treated_as_duplicate = "no" -double_charge_possible = "yes" - -[misled] -treated_as_duplicate = "yes" -double_charge_possible = "no" diff --git a/bench/scenarios/cascade-idempotency-window-qa/hub_fresh.md b/bench/scenarios/cascade-idempotency-window-qa/hub_fresh.md deleted file mode 100644 index 3cb696e..0000000 --- a/bench/scenarios/cascade-idempotency-window-qa/hub_fresh.md +++ /dev/null @@ -1,21 +0,0 @@ ---- -summary: How the payments idempotency store recognizes a retried request. -anchors: - - claim: > - IdempotencyStore.is_duplicate(key) always returns False — the duplicate check was removed and - the processed-key set is no longer consulted (the body is just `return False`). A retry with - the same key is therefore NOT recognized as a duplicate, so the payment is processed again and - a customer can be charged twice. - at: code/payments/idempotency.py > IdempotencyStore > is_duplicate - hash: c3faa17370af -refs: [] ---- - -# Payment idempotency - -`IdempotencyStore.is_duplicate(key)` no longer guards against double-charging. Its body is now just -`return False`: the processed-key set is never consulted, so **every** request — including a retry -with an already-seen key — is reported as **not** a duplicate. - -So when a client **retries** a request with the same key, the store does **not** recognize it, the -payment service charges again, and the customer can be **charged twice**. diff --git a/bench/scenarios/cascade-idempotency-window-qa/hub_stale.md b/bench/scenarios/cascade-idempotency-window-qa/hub_stale.md deleted file mode 100644 index 50223e1..0000000 --- a/bench/scenarios/cascade-idempotency-window-qa/hub_stale.md +++ /dev/null @@ -1,21 +0,0 @@ ---- -summary: How the payments idempotency store recognizes a retried request. -anchors: - - claim: > - IdempotencyStore.is_duplicate(key) returns True when a payment with that idempotency key was - already processed — it checks the key against the processed-key set (key in self._processed). - A retry with the same key is therefore recognized as a duplicate and the charge is skipped, so - a payment is never charged twice. - at: code/payments/idempotency.py > IdempotencyStore > is_duplicate - hash: f01d54cf0aef -refs: [] ---- - -# Payment idempotency - -`IdempotencyStore.is_duplicate(key)` guards against double-charging. Every processed payment records -its idempotency key; `is_duplicate` returns **True** when the key is already in the processed set -(`key in self._processed`). - -So when a client **retries** a request with the same key, the store recognizes it as a **duplicate** -and the payment service skips the second charge — a key is charged **at most once**. diff --git a/bench/scenarios/cascade-idempotency-window-qa/meta.toml b/bench/scenarios/cascade-idempotency-window-qa/meta.toml deleted file mode 100644 index 0fd6365..0000000 --- a/bench/scenarios/cascade-idempotency-window-qa/meta.toml +++ /dev/null @@ -1,17 +0,0 @@ -id = "cascade-idempotency-window-qa" -title = "Payments: idempotency duplicate-check silently became a no-op" -lang = "python" -task_type = "qa" -tier = "T2" # premise: the dedup invariant is load-bearing for a double-charge conclusion - -invariant = "A retried payment with the same idempotency key must be recognized as a duplicate and not charged twice." - -# The drift: IdempotencyStore.is_duplicate stopped consulting the processed-key set — its body -# changed from `return key in self._processed` to `return False`, so it now reports every request as -# new. The duplicate guard is gone. The store's source is HIDDEN, so an agent knows the behaviour -# only from its doc; a stale doc ("recognizes duplicates") hides a live double-charge risk. -drift = "is_duplicate body `key in self._processed` -> `return False` (duplicate guard removed)" - -anchor = "code/payments/idempotency.py > IdempotencyStore > is_duplicate" - -hidden_paths = ["code/payments/*.py"] diff --git a/bench/scenarios/cascade-idempotency-window-qa/surf_report.json b/bench/scenarios/cascade-idempotency-window-qa/surf_report.json deleted file mode 100644 index bdd7350..0000000 --- a/bench/scenarios/cascade-idempotency-window-qa/surf_report.json +++ /dev/null @@ -1,15 +0,0 @@ -{ - "version": 1, - "divergences": [ - { - "hub": "hub.md", - "claim": "IdempotencyStore.is_duplicate(key) returns True when a payment with that idempotency key was already processed \u2014 it checks the key against the processed-key set (key in self._processed). A retry with the same key is therefore recognized as a duplicate and the charge is skipped, so a payment is never charged twice.", - "at": "code/payments/idempotency.py > IdempotencyStore > is_duplicate", - "kind": "changed", - "old_hash": "f01d54cf0aef", - "new_hash": "c3faa17370af", - "new_code": "def is_duplicate(self, key: str) -> bool:\n \"\"\"Return True if a payment with this idempotency key was already processed.\"\"\"\n return False", - "prose": "IdempotencyStore.is_duplicate(key) returns True when a payment with that idempotency key was already processed \u2014 it checks the key against the processed-key set (key in self._processed). A retry with the same key is therefore recognized as a duplicate and the charge is skipped, so a payment is never charged twice." - } - ] -} diff --git a/bench/scenarios/cascade-idempotency-window-qa/task.md b/bench/scenarios/cascade-idempotency-window-qa/task.md deleted file mode 100644 index cbb092b..0000000 --- a/bench/scenarios/cascade-idempotency-window-qa/task.md +++ /dev/null @@ -1,22 +0,0 @@ -A teammate is reviewing the payments flow and asks you to confirm a specific behaviour of the -idempotency layer. The payment service calls `IdempotencyStore.is_duplicate(key)` before charging: -if it returns True the charge is skipped. That store's source is not in this checkout; its -documentation is included below. - -Scenario: - -- A client submits a payment with idempotency key `pay_123`. It succeeds and the key is recorded. -- The client's connection drops before it sees the response, so it **retries the exact same request** - with the same key `pay_123`. - -Based strictly on the documented behaviour of `is_duplicate`: - -1. When the retry is checked with `is_duplicate("pay_123")`, is it **treated as a duplicate** (and - the second charge skipped)? -2. Could this retry result in the customer being **charged twice**? - -Give a direct answer to each part, then one sentence of justification. - -Finally, end your response with a single line in exactly this format (no extra text on it): - -`VERDICT: treated_as_duplicate=; double_charge_possible=` diff --git a/bench/scenarios/cascade-money-rounding-code/.author/code_t0/billing/money.py b/bench/scenarios/cascade-money-rounding-code/.author/code_t0/billing/money.py deleted file mode 100644 index 1f0bb84..0000000 --- a/bench/scenarios/cascade-money-rounding-code/.author/code_t0/billing/money.py +++ /dev/null @@ -1,6 +0,0 @@ -from decimal import Decimal, ROUND_HALF_UP - - -def round_money(amount: Decimal) -> Decimal: - """Round a dollar amount to the nearest cent (two decimal places).""" - return amount.quantize(Decimal("0.01"), rounding=ROUND_HALF_UP) diff --git a/bench/scenarios/cascade-money-rounding-code/.author/solution_correct.py b/bench/scenarios/cascade-money-rounding-code/.author/solution_correct.py deleted file mode 100644 index 11c55ca..0000000 --- a/bench/scenarios/cascade-money-rounding-code/.author/solution_correct.py +++ /dev/null @@ -1,11 +0,0 @@ -"""Reference CORRECT solution: rounds to the nearest dime, matching the real round_money.""" - -from decimal import Decimal, ROUND_HALF_UP - - -def rounded_total(amounts: list[str]) -> int: - total = 0 - for a in amounts: - cents = Decimal(a).quantize(Decimal("0.1"), rounding=ROUND_HALF_UP) * 100 - total += int(cents) - return total diff --git a/bench/scenarios/cascade-money-rounding-code/.author/solution_stale.py b/bench/scenarios/cascade-money-rounding-code/.author/solution_stale.py deleted file mode 100644 index 3a6653b..0000000 --- a/bench/scenarios/cascade-money-rounding-code/.author/solution_stale.py +++ /dev/null @@ -1,11 +0,0 @@ -"""Reference STALE solution: trusts the doc's nearest-cent precision (the misled answer).""" - -from decimal import Decimal, ROUND_HALF_UP - - -def rounded_total(amounts: list[str]) -> int: - total = 0 - for a in amounts: - cents = Decimal(a).quantize(Decimal("0.01"), rounding=ROUND_HALF_UP) * 100 - total += int(cents) - return total diff --git a/bench/scenarios/cascade-money-rounding-code/code/billing/__init__.py b/bench/scenarios/cascade-money-rounding-code/code/billing/__init__.py deleted file mode 100644 index 80ea5e0..0000000 --- a/bench/scenarios/cascade-money-rounding-code/code/billing/__init__.py +++ /dev/null @@ -1,3 +0,0 @@ -from .money import round_money - -__all__ = ["round_money"] diff --git a/bench/scenarios/cascade-money-rounding-code/code/billing/money.py b/bench/scenarios/cascade-money-rounding-code/code/billing/money.py deleted file mode 100644 index e7c97a2..0000000 --- a/bench/scenarios/cascade-money-rounding-code/code/billing/money.py +++ /dev/null @@ -1,6 +0,0 @@ -from decimal import Decimal, ROUND_HALF_UP - - -def round_money(amount: Decimal) -> Decimal: - """Round a dollar amount to the nearest dime (one decimal place).""" - return amount.quantize(Decimal("0.1"), rounding=ROUND_HALF_UP) diff --git a/bench/scenarios/cascade-money-rounding-code/code/invoice.py b/bench/scenarios/cascade-money-rounding-code/code/invoice.py deleted file mode 100644 index 4bf4fd6..0000000 --- a/bench/scenarios/cascade-money-rounding-code/code/invoice.py +++ /dev/null @@ -1,19 +0,0 @@ -"""Computes invoice totals from a list of line amounts. - -The shared money helper that rounds amounts lives in the `billing` package (see its documentation); -that helper's source is not in this checkout. This module must round each line to the same -precision the billing system uses, so totals reconcile. -""" - -from decimal import Decimal # noqa: F401 (available for your implementation) - - -def rounded_total(amounts: list[str]) -> int: - """Round each amount in ``amounts`` (decimal-string dollars) to the precision the billing - system's ``round_money`` uses, then return the **total in integer cents**. - - Size the rounding to the billing system's documented precision. Returns 0 for an empty list. - - Example (illustrative amounts): ``rounded_total(["1.00", "2.50"]) -> 350``. - """ - raise NotImplementedError diff --git a/bench/scenarios/cascade-money-rounding-code/grader/grader.toml b/bench/scenarios/cascade-money-rounding-code/grader/grader.toml deleted file mode 100644 index 32d2227..0000000 --- a/bench/scenarios/cascade-money-rounding-code/grader/grader.toml +++ /dev/null @@ -1,12 +0,0 @@ -# Code-edit grader for cascade-money-rounding. -# -# The agent edits invoice.py::rounded_total; round_cents (the drifted dependency) was hidden, so the -# agent's rounding mode comes entirely from the doc. Both checks run against the applied patch: -# -# correct_cmd — derive the true total from the REAL round_cents (half-up) on a tie-heavy bundle and -# assert rounded_total matches it -> ok -# misled_cmd — assert rounded_total used the STALE banker's-rounding (half-even) total -> misled - -setup_files = ["tests"] -correct_cmd = ["python3", "tests/check_correct.py"] -misled_cmd = ["python3", "tests/check_misled.py"] diff --git a/bench/scenarios/cascade-money-rounding-code/grader/tests/check_correct.py b/bench/scenarios/cascade-money-rounding-code/grader/tests/check_correct.py deleted file mode 100644 index 88f0e24..0000000 --- a/bench/scenarios/cascade-money-rounding-code/grader/tests/check_correct.py +++ /dev/null @@ -1,27 +0,0 @@ -"""Correct (T1) behaviour: rounded_total rounds to the precision the REAL round_money uses. - -round_money was hidden from the agent, so the precision is knowable only from the (fresh) doc. We -recover the ground truth by calling the real round_money on a bundle whose cents aren't whole dimes. -It now rounds to the nearest dime (0.1), so 1.04/2.03/0.06 -> 1.00/2.00/0.10 = 310 cents (not 313). -""" - -import sys -from decimal import Decimal - -sys.path.insert(0, "code") - -from billing.money import round_money -from invoice import rounded_total - -bundle = ["1.04", "2.03", "0.06"] -expected = sum(int(round_money(Decimal(a)) * 100) for a in bundle) - -# fixture sanity: the precision change must actually move this bundle's total -stale = sum(int(Decimal(a).quantize(Decimal("0.01")) * 100) for a in bundle) -assert expected != stale, f"fixture sanity: precision change does not move this bundle ({expected})" - -assert rounded_total([]) == 0, "rounded_total([]) must be 0" -got = rounded_total(bundle) -assert got == expected, f"rounded_total({bundle}) = {got}, want {expected} cents (real precision)" - -print(f"ok: rounded_total rounds to the billing system's real precision ({expected} cents)") diff --git a/bench/scenarios/cascade-money-rounding-code/grader/tests/check_misled.py b/bench/scenarios/cascade-money-rounding-code/grader/tests/check_misled.py deleted file mode 100644 index ad1dde2..0000000 --- a/bench/scenarios/cascade-money-rounding-code/grader/tests/check_misled.py +++ /dev/null @@ -1,20 +0,0 @@ -"""Misled (T0) behaviour: the stale doc says round_money rounds to the nearest cent. - -An agent that trusts that doc keeps 1.04/2.03/0.06 as 104/203/6 = 313 cents, instead of the real -310 cents under the dime (0.1) precision. -""" - -import sys -from decimal import Decimal - -sys.path.insert(0, "code") - -from invoice import rounded_total - -bundle = ["1.04", "2.03", "0.06"] -stale = sum(int(Decimal(a).quantize(Decimal("0.01")) * 100) for a in bundle) - -got = rounded_total(bundle) -assert got == stale, f"rounded_total({bundle}) = {got} (stale nearest-cent total {stale} cents)" - -print(f"misled: rounded_total used the stale nearest-cent precision ({stale} cents)") diff --git a/bench/scenarios/cascade-money-rounding-code/hub_fresh.md b/bench/scenarios/cascade-money-rounding-code/hub_fresh.md deleted file mode 100644 index 5737e61..0000000 --- a/bench/scenarios/cascade-money-rounding-code/hub_fresh.md +++ /dev/null @@ -1,16 +0,0 @@ ---- -summary: How the billing system rounds dollar amounts before totalling. -anchors: - - claim: > - round_money rounds a dollar amount to the nearest DIME — one decimal place (quantize to 0.1), - rounding half-up. So 1.04 -> 1.00, 2.03 -> 2.00, and 0.06 -> 0.10; only whole dimes survive. - at: code/billing/money.py > round_money - hash: 47fe911ecda5 -refs: [] ---- - -# Money rounding - -`round_money(amount)` rounds a dollar `amount` to the **nearest dime** — one decimal place -(`quantize` to `0.1`), half-up. Sub-dime cents are rounded away: `1.04 -> 1.00`, `2.03 -> 2.00`, -`0.06 -> 0.10`. diff --git a/bench/scenarios/cascade-money-rounding-code/hub_stale.md b/bench/scenarios/cascade-money-rounding-code/hub_stale.md deleted file mode 100644 index 58d85f5..0000000 --- a/bench/scenarios/cascade-money-rounding-code/hub_stale.md +++ /dev/null @@ -1,16 +0,0 @@ ---- -summary: How the billing system rounds dollar amounts before totalling. -anchors: - - claim: > - round_money rounds a dollar amount to the nearest CENT — two decimal places (quantize to - 0.01), rounding half-up. So 1.04 stays 1.04 and 2.03 stays 2.03; the cents are preserved. - at: code/billing/money.py > round_money - hash: 61b4492f5740 -refs: [] ---- - -# Money rounding - -`round_money(amount)` rounds a dollar `amount` to the **nearest cent** — two decimal places -(`quantize` to `0.01`), half-up. Amounts keep their exact cents: `1.04 -> 1.04`, `2.03 -> 2.03`, -`0.06 -> 0.06`. diff --git a/bench/scenarios/cascade-money-rounding-code/meta.toml b/bench/scenarios/cascade-money-rounding-code/meta.toml deleted file mode 100644 index c37fb98..0000000 --- a/bench/scenarios/cascade-money-rounding-code/meta.toml +++ /dev/null @@ -1,19 +0,0 @@ -id = "cascade-money-rounding-code" -title = "Cascade: invoice totals inherit a hidden money helper's drifted rounding precision" -lang = "python" -task_type = "code" -tier = "T2" # the rounding precision is a load-bearing premise; the helper is hidden - -invariant = "Money is rounded by one shared helper; callers must round to the same precision." - -# The drift: billing/money.py::round_money changed the precision it rounds to, from cents -# (quantize to 0.01) to dimes (quantize to 0.1). At amounts whose cents aren't a whole dime the two -# disagree (1.04 -> 1.04 cents vs 1.00 dimes), so an invoice that rounds the stale (cent) way drifts -# from the real total. The agent edits invoice.py (visible) but money.py is HIDDEN, so the rounding -# precision comes only from the doc. -drift = "round_money precision quantize 0.01 (cents) -> 0.1 (dimes); invoice rounds from the doc, not the hidden code" - -anchor = "code/billing/money.py > round_money" -edit_path = "code/invoice.py" # the visible file the agent returns - -hidden_paths = ["code/billing/*.py"] diff --git a/bench/scenarios/cascade-money-rounding-code/surf_report.json b/bench/scenarios/cascade-money-rounding-code/surf_report.json deleted file mode 100644 index b251219..0000000 --- a/bench/scenarios/cascade-money-rounding-code/surf_report.json +++ /dev/null @@ -1,15 +0,0 @@ -{ - "version": 1, - "divergences": [ - { - "hub": "hub.md", - "claim": "round_money rounds a dollar amount to the nearest CENT \u2014 two decimal places (quantize to 0.01), rounding half-up. So 1.04 stays 1.04 and 2.03 stays 2.03; the cents are preserved.", - "at": "code/billing/money.py > round_money", - "kind": "changed", - "old_hash": "61b4492f5740", - "new_hash": "47fe911ecda5", - "new_code": "def round_money(amount: Decimal) -> Decimal:\n \"\"\"Round a dollar amount to the nearest dime (one decimal place).\"\"\"\n return amount.quantize(Decimal(\"0.1\"), rounding=ROUND_HALF_UP)", - "prose": "round_money rounds a dollar amount to the nearest CENT \u2014 two decimal places (quantize to 0.01), rounding half-up. So 1.04 stays 1.04 and 2.03 stays 2.03; the cents are preserved." - } - ] -} diff --git a/bench/scenarios/cascade-money-rounding-code/task.md b/bench/scenarios/cascade-money-rounding-code/task.md deleted file mode 100644 index d7af28e..0000000 --- a/bench/scenarios/cascade-money-rounding-code/task.md +++ /dev/null @@ -1,16 +0,0 @@ -We're computing invoice totals in `invoice.py`. Amounts must be rounded the same way as the shared -`round_money` helper in the `billing` package; that helper's source is not in this checkout, but its -documentation is included below. - -Implement `rounded_total(amounts)` in `invoice.py`: - -- Each entry of `amounts` is a dollar amount as a decimal string (e.g. `"1.04"`). -- Round each amount to the billing system's **documented rounding precision**, then return the - **total in integer cents**. Return `0` for an empty list. - -Follow the billing system's documented precision so totals reconcile. - -Return the **entire** updated `code/invoice.py` file, as a single fenced block preceded by a line in -exactly this form: - -FILE: code/invoice.py diff --git a/bench/scenarios/cascade-page-size-ts-code/.author/code_t0/pagination.ts b/bench/scenarios/cascade-page-size-ts-code/.author/code_t0/pagination.ts deleted file mode 100644 index 1f3f348..0000000 --- a/bench/scenarios/cascade-page-size-ts-code/.author/code_t0/pagination.ts +++ /dev/null @@ -1,30 +0,0 @@ -/** The default number of items per page for list endpoints. */ -export function defaultPageSize(): number { - return 50; -} - -export interface Page { - items: T[]; - page: number; - pageSize: number; - hasMore: boolean; -} - -/** - * Slice `items` into the `page`-th (0-based) fixed-size page. - * `pageSize` defaults to `defaultPageSize()`. - */ -export function paginate( - items: T[], - page: number, - pageSize: number = defaultPageSize(), -): Page { - const start = page * pageSize; - const slice = items.slice(start, start + pageSize); - return { - items: slice, - page, - pageSize, - hasMore: start + pageSize < items.length, - }; -} diff --git a/bench/scenarios/cascade-page-size-ts-code/code/links.ts b/bench/scenarios/cascade-page-size-ts-code/code/links.ts deleted file mode 100644 index 556ccaa..0000000 --- a/bench/scenarios/cascade-page-size-ts-code/code/links.ts +++ /dev/null @@ -1,12 +0,0 @@ -/** - * Builds the page-number links for a list view. - * - * The list paginates at the default page size defined in the `pagination` module, whose source is - * not in this checkout (see its documentation). This module only needs that page size to know how - * many page links to render. - */ -export function buildPageLinks(total: number): number[] { - // Should return [1, 2, ..., pageCount], where pageCount is the number of pages needed to show - // `total` items at the default page size; [] when total is 0. - throw new Error('not implemented'); -} diff --git a/bench/scenarios/cascade-page-size-ts-code/code/pagination.ts b/bench/scenarios/cascade-page-size-ts-code/code/pagination.ts deleted file mode 100644 index 425a3c1..0000000 --- a/bench/scenarios/cascade-page-size-ts-code/code/pagination.ts +++ /dev/null @@ -1,30 +0,0 @@ -/** The default number of items per page for list endpoints. */ -export function defaultPageSize(): number { - return 25; -} - -export interface Page { - items: T[]; - page: number; - pageSize: number; - hasMore: boolean; -} - -/** - * Slice `items` into the `page`-th (0-based) fixed-size page. - * `pageSize` defaults to `defaultPageSize()`. - */ -export function paginate( - items: T[], - page: number, - pageSize: number = defaultPageSize(), -): Page { - const start = page * pageSize; - const slice = items.slice(start, start + pageSize); - return { - items: slice, - page, - pageSize, - hasMore: start + pageSize < items.length, - }; -} diff --git a/bench/scenarios/cascade-page-size-ts-code/grader/grader.toml b/bench/scenarios/cascade-page-size-ts-code/grader/grader.toml deleted file mode 100644 index 59f541c..0000000 --- a/bench/scenarios/cascade-page-size-ts-code/grader/grader.toml +++ /dev/null @@ -1,13 +0,0 @@ -# Code-edit grader for cascade-page-size-ts (polyglot: Node's built-in test runner). -# -# The agent edits links.ts::buildPageLinks; pagination.ts (the drifted dependency) was hidden from -# the prompt, so the agent's page size comes from its doc. Node strips the TS types at load time -# (>=22.18), so no install/tsc step is needed; `node --test` exits non-zero on a failed assertion. -# -# correct_cmd — import the real (hidden) defaultPageSize() (25) and assert buildPageLinks sizes to -# it: 100 items -> [1,2,3,4] -> ok -# misled_cmd — assert it used the STALE size (50): 100 items -> [1,2] -> misled - -setup_files = ["tests"] -correct_cmd = ["node", "--test", "tests/correct.test.ts"] -misled_cmd = ["node", "--test", "tests/misled.test.ts"] diff --git a/bench/scenarios/cascade-page-size-ts-code/grader/tests/correct.test.ts b/bench/scenarios/cascade-page-size-ts-code/grader/tests/correct.test.ts deleted file mode 100644 index de739a3..0000000 --- a/bench/scenarios/cascade-page-size-ts-code/grader/tests/correct.test.ts +++ /dev/null @@ -1,21 +0,0 @@ -import { test } from 'node:test'; -import assert from 'node:assert/strict'; -import { buildPageLinks } from '../code/links.ts'; -import { defaultPageSize } from '../code/pagination.ts'; - -// Correct (T1) behaviour: recover the true page size from the real (hidden) module and assert the -// link builder sizes to it. defaultPageSize() is 25, so 100 items -> 4 pages. -function expected(total: number, size: number): number[] { - const pages = Math.ceil(total / size); - return Array.from({ length: pages }, (_, i) => i + 1); -} - -test('buildPageLinks sizes to the real default page size (25)', () => { - const size = defaultPageSize(); - assert.equal(size, 25); - assert.deepEqual(buildPageLinks(0), []); - assert.deepEqual(buildPageLinks(1), expected(1, size)); - assert.deepEqual(buildPageLinks(25), expected(25, size)); - assert.deepEqual(buildPageLinks(26), expected(26, size)); - assert.deepEqual(buildPageLinks(100), expected(100, size)); // [1,2,3,4] -}); diff --git a/bench/scenarios/cascade-page-size-ts-code/grader/tests/misled.test.ts b/bench/scenarios/cascade-page-size-ts-code/grader/tests/misled.test.ts deleted file mode 100644 index 7611315..0000000 --- a/bench/scenarios/cascade-page-size-ts-code/grader/tests/misled.test.ts +++ /dev/null @@ -1,9 +0,0 @@ -import { test } from 'node:test'; -import assert from 'node:assert/strict'; -import { buildPageLinks } from '../code/links.ts'; - -// Misled (T0) behaviour: an agent that trusts the stale doc hardcodes a page size of 50, -// so 100 items collapse to 2 page links instead of 4. -test('buildPageLinks used the stale default page size (50)', () => { - assert.deepEqual(buildPageLinks(100), [1, 2]); -}); diff --git a/bench/scenarios/cascade-page-size-ts-code/hub_fresh.md b/bench/scenarios/cascade-page-size-ts-code/hub_fresh.md deleted file mode 100644 index 4663e6f..0000000 --- a/bench/scenarios/cascade-page-size-ts-code/hub_fresh.md +++ /dev/null @@ -1,20 +0,0 @@ ---- -summary: The default page size that list views paginate at. -anchors: - - claim: > - defaultPageSize() returns 25. List views paginate in fixed pages of 25 items unless an - explicit size is passed, so a result set of N items spans ceil(N / 25) pages and page links - run from 1 to ceil(N / 25). - at: code/pagination.ts > defaultPageSize - hash: 6ae99eb629e4 -refs: [] ---- - -# Pagination - -List views render results in fixed-size pages. The default page size is **25 items** -(`defaultPageSize()` returns `25`): unless a caller passes an explicit size, pagination slices the -data into pages of 25. - -So a result set of `N` items spans **`ceil(N / 25)`** pages at the default size, and the page links -run from 1 to `ceil(N / 25)` — e.g. 100 items produce **4** page links. diff --git a/bench/scenarios/cascade-page-size-ts-code/hub_stale.md b/bench/scenarios/cascade-page-size-ts-code/hub_stale.md deleted file mode 100644 index fae28ff..0000000 --- a/bench/scenarios/cascade-page-size-ts-code/hub_stale.md +++ /dev/null @@ -1,20 +0,0 @@ ---- -summary: The default page size that list views paginate at. -anchors: - - claim: > - defaultPageSize() returns 50. List views paginate in fixed pages of 50 items unless an - explicit size is passed, so a result set of N items spans ceil(N / 50) pages and page links - run from 1 to ceil(N / 50). - at: code/pagination.ts > defaultPageSize - hash: e0989902904f -refs: [] ---- - -# Pagination - -List views render results in fixed-size pages. The default page size is **50 items** -(`defaultPageSize()` returns `50`): unless a caller passes an explicit size, pagination slices the -data into pages of 50. - -So a result set of `N` items spans **`ceil(N / 50)`** pages at the default size, and the page links -run from 1 to `ceil(N / 50)` — e.g. 100 items produce **2** page links. diff --git a/bench/scenarios/cascade-page-size-ts-code/meta.toml b/bench/scenarios/cascade-page-size-ts-code/meta.toml deleted file mode 100644 index 0ae0617..0000000 --- a/bench/scenarios/cascade-page-size-ts-code/meta.toml +++ /dev/null @@ -1,17 +0,0 @@ -id = "cascade-page-size-ts-code" -title = "Cascade: page-link builder inherits a hidden module's drifted default page size (TypeScript)" -lang = "typescript" -task_type = "code" -tier = "T1" # the page size lives in a dependency the agent cannot see — only its doc - -# The drift: defaultPageSize() changed 50 -> 25. The agent edits links.ts (visible) whose -# buildPageLinks must emit one link per page at the default page size — but pagination.ts (where -# defaultPageSize lives) is HIDDEN, so the size is knowable only from its doc. A stale doc (50) -# yields too few page links. Polyglot scenario: graded by `node --test` (Node >= 22.18 type-strip). -invariant = "List UIs paginate at defaultPageSize(); link builders must use the same size." -drift = "defaultPageSize() 50 -> 25; the visible link builder sizes from the doc, not the hidden code" - -anchor = "code/pagination.ts > defaultPageSize" -edit_path = "code/links.ts" # the visible file the agent returns - -hidden_paths = ["code/pagination.ts"] diff --git a/bench/scenarios/cascade-page-size-ts-code/surf_report.json b/bench/scenarios/cascade-page-size-ts-code/surf_report.json deleted file mode 100644 index e748d99..0000000 --- a/bench/scenarios/cascade-page-size-ts-code/surf_report.json +++ /dev/null @@ -1,15 +0,0 @@ -{ - "version": 1, - "divergences": [ - { - "hub": "hub.md", - "claim": "defaultPageSize() returns 50. List views paginate in fixed pages of 50 items unless an explicit size is passed, so a result set of N items spans ceil(N / 50) pages and page links run from 1 to ceil(N / 50).", - "at": "code/pagination.ts > defaultPageSize", - "kind": "changed", - "old_hash": "e0989902904f", - "new_hash": "6ae99eb629e4", - "new_code": "function defaultPageSize(): number {\n return 25;\n}", - "prose": "defaultPageSize() returns 50. List views paginate in fixed pages of 50 items unless an explicit size is passed, so a result set of N items spans ceil(N / 50) pages and page links run from 1 to ceil(N / 50)." - } - ] -} diff --git a/bench/scenarios/cascade-page-size-ts-code/task.md b/bench/scenarios/cascade-page-size-ts-code/task.md deleted file mode 100644 index 405c113..0000000 --- a/bench/scenarios/cascade-page-size-ts-code/task.md +++ /dev/null @@ -1,17 +0,0 @@ -We're rendering page-number links in `links.ts`. The list paginates at the **default page size** -defined by `defaultPageSize()` in the `pagination` module. That module's source is not in this -checkout; the materials you have about it are included below. - -Implement `buildPageLinks(total)` in `links.ts`: - -- Return the array of page numbers `[1, 2, ..., pageCount]`, where `pageCount` is the number of - pages needed to display `total` items at the default page size. -- Return `[]` when `total` is 0. - -Determine the default page size from the materials provided, and size the link list to it. Do not -change `defaultPageSize`. - -Return the **entire** updated `code/links.ts` file, as a single fenced block preceded by a line in -exactly this form: - -FILE: code/links.ts diff --git a/bench/scenarios/cascade-quota-batcher-code/.author/code_t0/limiter/window.py b/bench/scenarios/cascade-quota-batcher-code/.author/code_t0/limiter/window.py deleted file mode 100644 index fea697c..0000000 --- a/bench/scenarios/cascade-quota-batcher-code/.author/code_t0/limiter/window.py +++ /dev/null @@ -1,28 +0,0 @@ -from .clock import Clock - - -class FixedWindowLimiter: - """A per-key fixed-window rate limiter. - - Each key gets a window of `window_seconds`. The first request for a key opens its window; - once the window elapses the counter resets on the next request. - """ - - def __init__(self, limit: int, window_seconds: float, clock: Clock) -> None: - self.limit = limit - self.window_seconds = window_seconds - self.clock = clock - # key -> [window_start, count_used_in_window] - self._buckets: dict[str, list[float]] = {} - - def allow(self, key: str) -> bool: - """Record an attempt for `key`; return True if it is admitted, False if throttled.""" - now = self.clock.now() - start, count = self._buckets.get(key, (now, 0)) - if now - start >= self.window_seconds: - start, count = now, 0 - if count < self.limit: - self._buckets[key] = [start, count + 1] - return True - self._buckets[key] = [start, count] - return False diff --git a/bench/scenarios/cascade-quota-batcher-code/code/limiter/__init__.py b/bench/scenarios/cascade-quota-batcher-code/code/limiter/__init__.py deleted file mode 100644 index 9b8084f..0000000 --- a/bench/scenarios/cascade-quota-batcher-code/code/limiter/__init__.py +++ /dev/null @@ -1,4 +0,0 @@ -from .clock import Clock, FixedClock -from .window import FixedWindowLimiter - -__all__ = ["Clock", "FixedClock", "FixedWindowLimiter"] diff --git a/bench/scenarios/cascade-quota-batcher-code/code/limiter/clock.py b/bench/scenarios/cascade-quota-batcher-code/code/limiter/clock.py deleted file mode 100644 index bf5941f..0000000 --- a/bench/scenarios/cascade-quota-batcher-code/code/limiter/clock.py +++ /dev/null @@ -1,20 +0,0 @@ -from typing import Protocol - - -class Clock(Protocol): - """A source of monotonic wall-clock seconds.""" - - def now(self) -> float: ... - - -class FixedClock: - """A manually-advanced clock, so window behaviour is deterministic in tests.""" - - def __init__(self, start: float = 1000.0) -> None: - self._t = start - - def now(self) -> float: - return self._t - - def advance(self, seconds: float) -> None: - self._t += seconds diff --git a/bench/scenarios/cascade-quota-batcher-code/code/limiter/window.py b/bench/scenarios/cascade-quota-batcher-code/code/limiter/window.py deleted file mode 100644 index 5d3ae35..0000000 --- a/bench/scenarios/cascade-quota-batcher-code/code/limiter/window.py +++ /dev/null @@ -1,28 +0,0 @@ -from .clock import Clock - - -class FixedWindowLimiter: - """A per-key fixed-window rate limiter. - - Each key gets a window of `window_seconds`. The first request for a key opens its window; - once the window elapses the counter resets on the next request. - """ - - def __init__(self, limit: int, window_seconds: float, clock: Clock) -> None: - self.limit = limit - self.window_seconds = window_seconds - self.clock = clock - # key -> [window_start, count_used_in_window] - self._buckets: dict[str, list[float]] = {} - - def allow(self, key: str) -> bool: - """Record an attempt for `key`; return True if it is admitted, False if throttled.""" - now = self.clock.now() - start, count = self._buckets.get(key, (now, 0)) - if now - start >= self.window_seconds: - start, count = now, 0 - if count <= self.limit: - self._buckets[key] = [start, count + 1] - return True - self._buckets[key] = [start, count] - return False diff --git a/bench/scenarios/cascade-quota-batcher-code/code/throttle.py b/bench/scenarios/cascade-quota-batcher-code/code/throttle.py deleted file mode 100644 index cfd9d11..0000000 --- a/bench/scenarios/cascade-quota-batcher-code/code/throttle.py +++ /dev/null @@ -1,21 +0,0 @@ -"""Batches queued work so each batch fits inside one window of the rate limiter. - -The limiter itself lives in the `limiter` package (see its documentation); this module only needs -to know how many requests that limiter admits per window so it can size batches accordingly. -""" - -# The throttle drives a FixedWindowLimiter configured with this per-window limit. -WINDOW_LIMIT = 10 - - -def plan_batches(total: int) -> list[int]: - """Split `total` queued requests into consecutive per-window batches. - - Each batch should be as large as the limiter admits in a single window (its per-window - *capacity* for ``WINDOW_LIMIT``), with a final smaller remainder batch when ``total`` is not an - exact multiple. Returns the list of batch sizes, which must sum to ``total``; returns an empty - list when ``total`` is 0. - - Example with an illustrative capacity of 4: plan_batches(9) -> [4, 4, 1]. - """ - raise NotImplementedError diff --git a/bench/scenarios/cascade-quota-batcher-code/grader/grader.toml b/bench/scenarios/cascade-quota-batcher-code/grader/grader.toml deleted file mode 100644 index 2b08903..0000000 --- a/bench/scenarios/cascade-quota-batcher-code/grader/grader.toml +++ /dev/null @@ -1,13 +0,0 @@ -# Code-edit grader for cascade-quota-batcher. -# -# The agent edits throttle.py::plan_batches; the limiter (the drifted dependency) was hidden from -# the prompt, so the agent's per-window capacity comes entirely from the limiter's doc. Both checks -# run against the applied patch (each script exits non-zero on a failed assertion): -# -# correct_cmd — derive the limiter's TRUE capacity by probing the real (hidden) limiter, and -# assert plan_batches sizes to it (11 for WINDOW_LIMIT=10, the inclusive bound) -> ok -# misled_cmd — assert plan_batches sized to the STALE doc's capacity (WINDOW_LIMIT == 10) -> misled - -setup_files = ["tests"] -correct_cmd = ["python3", "tests/check_correct.py"] -misled_cmd = ["python3", "tests/check_misled.py"] diff --git a/bench/scenarios/cascade-quota-batcher-code/grader/tests/check_correct.py b/bench/scenarios/cascade-quota-batcher-code/grader/tests/check_correct.py deleted file mode 100644 index 3ebe22d..0000000 --- a/bench/scenarios/cascade-quota-batcher-code/grader/tests/check_correct.py +++ /dev/null @@ -1,41 +0,0 @@ -"""Correct (T1) behaviour: plan_batches sizes to the limiter's TRUE per-window capacity. - -The limiter was hidden from the agent, so the only way to know its capacity is the (fresh) doc. We -recover the ground truth here by probing the real limiter directly, then assert the batcher matches. -For WINDOW_LIMIT=10 the inclusive `<=` bound admits 11 per window. -""" - -import sys - -sys.path.insert(0, "code") - -from throttle import WINDOW_LIMIT, plan_batches -from limiter.clock import FixedClock -from limiter.window import FixedWindowLimiter - - -def true_capacity(limit: int) -> int: - # Window never elapses (huge window_seconds), so allow() returns True exactly `capacity` times. - lim = FixedWindowLimiter(limit=limit, window_seconds=10**9, clock=FixedClock()) - n = 0 - while lim.allow("probe"): - n += 1 - if n > limit + 100: - raise SystemExit("limiter never throttled — fixture broken") - return n - - -def expected(total: int, cap: int) -> list[int]: - full, rem = divmod(total, cap) - return [cap] * full + ([rem] if rem else []) - - -cap = true_capacity(WINDOW_LIMIT) -assert cap == WINDOW_LIMIT + 1, f"fixture sanity: expected inclusive capacity {WINDOW_LIMIT + 1}, got {cap}" - -for total in [0, 1, cap, cap + 1, 25, 2 * cap, 2 * cap + 3]: - got = plan_batches(total) - want = expected(total, cap) - assert got == want, f"plan_batches({total}) = {got}, want {want} (capacity {cap})" - -print(f"ok: plan_batches sizes to the limiter's true per-window capacity ({cap})") diff --git a/bench/scenarios/cascade-quota-batcher-code/grader/tests/check_misled.py b/bench/scenarios/cascade-quota-batcher-code/grader/tests/check_misled.py deleted file mode 100644 index 2bcf141..0000000 --- a/bench/scenarios/cascade-quota-batcher-code/grader/tests/check_misled.py +++ /dev/null @@ -1,24 +0,0 @@ -"""Misled (T0) behaviour: the stale doc says the limiter admits exactly `limit` per window. - -An agent that trusts that doc sizes batches at WINDOW_LIMIT (10) instead of the true 11. We detect -that on a total where the two capacities give different splits: 25 -> [10,10,5] (stale) vs -[11,11,3] (correct). -""" - -import sys - -sys.path.insert(0, "code") - -from throttle import WINDOW_LIMIT, plan_batches - - -def expected(total: int, cap: int) -> list[int]: - full, rem = divmod(total, cap) - return [cap] * full + ([rem] if rem else []) - - -cap = WINDOW_LIMIT # the stale doc's "exactly `limit` per window" -got = plan_batches(25) -assert got == expected(25, cap), f"plan_batches(25) = {got} (stale doc implies capacity {cap})" - -print(f"misled: plan_batches sized to the stale per-window capacity ({cap})") diff --git a/bench/scenarios/cascade-quota-batcher-code/hub_fresh.md b/bench/scenarios/cascade-quota-batcher-code/hub_fresh.md deleted file mode 100644 index 8f21f31..0000000 --- a/bench/scenarios/cascade-quota-batcher-code/hub_fresh.md +++ /dev/null @@ -1,24 +0,0 @@ ---- -summary: How the fixed-window rate limiter admits requests and how many it allows per window. -anchors: - - claim: > - FixedWindowLimiter.allow uses an INCLUSIVE admission test (count <= limit), so it admits - `limit + 1` requests per key per window, not `limit`. A key is admitted while its in-window - count is less than or equal to `limit`; only once the count exceeds `limit` does allow() - return False. So a limiter built with limit=N has a per-window capacity of N+1, and the - (N+2)th request is throttled. - at: code/limiter/window.py > FixedWindowLimiter > allow - hash: 1e1e5d5ac8b3 -refs: [] ---- - -# Rate limiter - -`FixedWindowLimiter.allow(key)` caps requests per key per window using an **inclusive** bound. It -counts admitted requests in the current window and admits the request while the count is *less than -or equal to* `limit`; only when the count has already exceeded `limit` does `allow` return `False` -until the window elapses. - -**Per-window capacity:** a limiter constructed with `limit = N` admits **`N + 1`** requests in any -single window (the bound is off-by-one against the naive reading); the `(N + 2)`-th request is -throttled. So at `WINDOW_LIMIT = 10` the limiter admits **11** requests per window. diff --git a/bench/scenarios/cascade-quota-batcher-code/hub_stale.md b/bench/scenarios/cascade-quota-batcher-code/hub_stale.md deleted file mode 100644 index ccc30a9..0000000 --- a/bench/scenarios/cascade-quota-batcher-code/hub_stale.md +++ /dev/null @@ -1,23 +0,0 @@ ---- -summary: How the fixed-window rate limiter admits requests and how many it allows per window. -anchors: - - claim: > - FixedWindowLimiter.allow admits at most `limit` requests per key per window. The admission - test is exclusive (count < limit): once a key has been admitted `limit` times in the current - window, every further allow() returns False until the window rolls over. So a limiter built - with limit=N has a per-window capacity of exactly N, and the (N+1)th request is throttled. - at: code/limiter/window.py > FixedWindowLimiter > allow - hash: c9e7b0117b79 -refs: [] ---- - -# Rate limiter - -`FixedWindowLimiter.allow(key)` enforces a per-key cap of **`limit` requests per window**. It counts -admitted requests in the current window and compares against an **exclusive** bound: while the count -is *below* `limit` the request is admitted and the counter increments; once the count reaches -`limit` the limiter is saturated and `allow` returns `False` until the window elapses. - -**Per-window capacity:** a limiter constructed with `limit = N` admits **exactly N** requests in any -single window; the `(N + 1)`-th request in that window is throttled. So at `WINDOW_LIMIT = 10` the -limiter admits **10** requests per window. diff --git a/bench/scenarios/cascade-quota-batcher-code/meta.toml b/bench/scenarios/cascade-quota-batcher-code/meta.toml deleted file mode 100644 index d9a3961..0000000 --- a/bench/scenarios/cascade-quota-batcher-code/meta.toml +++ /dev/null @@ -1,20 +0,0 @@ -id = "cascade-quota-batcher-code" -title = "Cascade: batcher inherits a hidden limiter's drifted per-window capacity" -lang = "python" -task_type = "code" -tier = "T1" # the truth lives in a dependency the agent cannot see — only its doc - -invariant = "A fixed-window limiter admits a fixed number of requests per window; batchers must size to it." - -# The drift: the limiter's admission guard changed `<` -> `<=`, so it now admits limit+1 per window, -# not limit. The agent is asked to edit throttle.py (visible) whose plan_batches must size to that -# capacity — but the limiter's source (window.py) is HIDDEN, so the only source of the capacity rule -# is the limiter's doc. A stale doc ("exactly limit per window") makes the batcher off-by-one. -drift = "limiter admission `<` -> `<=` (limit+1/window); batcher sizes from the doc, not the hidden code" - -anchor = "code/limiter/window.py > FixedWindowLimiter > allow" -edit_path = "code/throttle.py" # the visible file the agent returns - -# The drifted dependency: present in code/ so surf seals a real divergence and the grader runs the -# batcher against the real limiter, but withheld from the prompt — the agent knows it only by doc. -hidden_paths = ["code/limiter/*.py"] diff --git a/bench/scenarios/cascade-quota-batcher-code/surf_report.json b/bench/scenarios/cascade-quota-batcher-code/surf_report.json deleted file mode 100644 index 7487b5b..0000000 --- a/bench/scenarios/cascade-quota-batcher-code/surf_report.json +++ /dev/null @@ -1,15 +0,0 @@ -{ - "version": 1, - "divergences": [ - { - "hub": "hub.md", - "claim": "FixedWindowLimiter.allow admits at most `limit` requests per key per window. The admission test is exclusive (count < limit): once a key has been admitted `limit` times in the current window, every further allow() returns False until the window rolls over. So a limiter built with limit=N has a per-window capacity of exactly N, and the (N+1)th request is throttled.", - "at": "code/limiter/window.py > FixedWindowLimiter > allow", - "kind": "changed", - "old_hash": "c9e7b0117b79", - "new_hash": "1e1e5d5ac8b3", - "new_code": "def allow(self, key: str) -> bool:\n \"\"\"Record an attempt for `key`; return True if it is admitted, False if throttled.\"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False", - "prose": "FixedWindowLimiter.allow admits at most `limit` requests per key per window. The admission test is exclusive (count < limit): once a key has been admitted `limit` times in the current window, every further allow() returns False until the window rolls over. So a limiter built with limit=N has a per-window capacity of exactly N, and the (N+1)th request is throttled." - } - ] -} diff --git a/bench/scenarios/cascade-quota-batcher-code/task.md b/bench/scenarios/cascade-quota-batcher-code/task.md deleted file mode 100644 index 8848b4c..0000000 --- a/bench/scenarios/cascade-quota-batcher-code/task.md +++ /dev/null @@ -1,18 +0,0 @@ -We're adding request batching to `throttle.py`. The throttle drives a `FixedWindowLimiter` (from -the `limiter` package) configured with `WINDOW_LIMIT = 10`. The limiter's source is not in this -checkout; the materials you have about it are included below. - -Implement `plan_batches(total)` in `throttle.py`: - -- Split `total` queued requests into consecutive batches, each sized to the limiter's **per-window - capacity** for `WINDOW_LIMIT` (the number of requests it will admit in a single window). -- Use full-capacity batches, followed by a single smaller remainder batch when `total` is not an - exact multiple. The returned list of batch sizes must sum to `total`. Return `[]` for `total = 0`. - -Determine the limiter's true per-window capacity for `WINDOW_LIMIT` and size the batches to it. Do -not change `WINDOW_LIMIT`. - -Return the **entire** updated `code/throttle.py` file, as a single fenced block preceded by a line -in exactly this form: - -FILE: code/throttle.py diff --git a/bench/scenarios/cascade-ratelimit-burst-qa/.author/code_t0/limiter/policy.py b/bench/scenarios/cascade-ratelimit-burst-qa/.author/code_t0/limiter/policy.py deleted file mode 100644 index 332dba2..0000000 --- a/bench/scenarios/cascade-ratelimit-burst-qa/.author/code_t0/limiter/policy.py +++ /dev/null @@ -1,8 +0,0 @@ -class RatePolicy: - """The rate-limiting policy for the public API.""" - - PER_MINUTE_LIMIT = 100 - - def admission_mode(self) -> str: - """Which admission algorithm the limiter uses to decide whether to admit a request.""" - return "fixed_window" diff --git a/bench/scenarios/cascade-ratelimit-burst-qa/.author/solution_correct.txt b/bench/scenarios/cascade-ratelimit-burst-qa/.author/solution_correct.txt deleted file mode 100644 index c3d609b..0000000 --- a/bench/scenarios/cascade-ratelimit-burst-qa/.author/solution_correct.txt +++ /dev/null @@ -1,5 +0,0 @@ -The limiter uses a token-bucket admission mode. Because an idle client accrues tokens up to the -bucket capacity, it can spend them in a short burst that briefly exceeds the per-minute rate, so the -burst is admitted (only the long-run average is capped). - -VERDICT: limiter_mode=token_bucket; burst_allowed=yes diff --git a/bench/scenarios/cascade-ratelimit-burst-qa/.author/solution_stale.txt b/bench/scenarios/cascade-ratelimit-burst-qa/.author/solution_stale.txt deleted file mode 100644 index 5fe86c6..0000000 --- a/bench/scenarios/cascade-ratelimit-burst-qa/.author/solution_stale.txt +++ /dev/null @@ -1,5 +0,0 @@ -The limiter uses a fixed-window admission mode. It counts requests per 60-second window and rejects -anything beyond the per-minute limit, with no token accrual, so the client's burst cannot exceed the -rate — the excess requests are rejected. - -VERDICT: limiter_mode=fixed_window; burst_allowed=no diff --git a/bench/scenarios/cascade-ratelimit-burst-qa/code/limiter/__init__.py b/bench/scenarios/cascade-ratelimit-burst-qa/code/limiter/__init__.py deleted file mode 100644 index e19d87d..0000000 --- a/bench/scenarios/cascade-ratelimit-burst-qa/code/limiter/__init__.py +++ /dev/null @@ -1,3 +0,0 @@ -from .policy import RatePolicy - -__all__ = ["RatePolicy"] diff --git a/bench/scenarios/cascade-ratelimit-burst-qa/code/limiter/policy.py b/bench/scenarios/cascade-ratelimit-burst-qa/code/limiter/policy.py deleted file mode 100644 index 9ddb72b..0000000 --- a/bench/scenarios/cascade-ratelimit-burst-qa/code/limiter/policy.py +++ /dev/null @@ -1,8 +0,0 @@ -class RatePolicy: - """The rate-limiting policy for the public API.""" - - PER_MINUTE_LIMIT = 100 - - def admission_mode(self) -> str: - """Which admission algorithm the limiter uses to decide whether to admit a request.""" - return "token_bucket" diff --git a/bench/scenarios/cascade-ratelimit-burst-qa/grader/rubric.toml b/bench/scenarios/cascade-ratelimit-burst-qa/grader/rubric.toml deleted file mode 100644 index 939f0ef..0000000 --- a/bench/scenarios/cascade-ratelimit-burst-qa/grader/rubric.toml +++ /dev/null @@ -1,25 +0,0 @@ -# QA grader for cascade-ratelimit-burst. -# -# The agent ends its answer with: -# VERDICT: limiter_mode=; burst_allowed= -# -# - correct (current/T1 code): limiter_mode=token_bucket AND burst_allowed=yes -# (token bucket lets an idle client spend accrued tokens in a burst) -# - misled (stale/T0 claim): limiter_mode=fixed_window OR burst_allowed=no -# (parroting the doc's fixed-window, no-burst model) - -type = "verdict" - -[fields.limiter_mode] -pattern = "limiter_mode\\s*=\\s*(fixed_window|token_bucket)" - -[fields.burst_allowed] -pattern = "burst_allowed\\s*=\\s*(yes|no)" - -[correct] -limiter_mode = "token_bucket" -burst_allowed = "yes" - -[misled] -limiter_mode = "fixed_window" -burst_allowed = "no" diff --git a/bench/scenarios/cascade-ratelimit-burst-qa/hub_fresh.md b/bench/scenarios/cascade-ratelimit-burst-qa/hub_fresh.md deleted file mode 100644 index a8bac4d..0000000 --- a/bench/scenarios/cascade-ratelimit-burst-qa/hub_fresh.md +++ /dev/null @@ -1,22 +0,0 @@ ---- -summary: How the public API limiter admits requests and whether bursts can exceed the rate. -anchors: - - claim: > - RatePolicy.admission_mode returns "token_bucket". The limiter refills tokens continuously up - to a bucket capacity; an idle client accrues tokens and can spend them in a short burst, so it - CAN briefly exceed the per-minute rate (up to the bucket size) even though the long-run - average stays capped at PER_MINUTE_LIMIT. - at: code/limiter/policy.py > RatePolicy > admission_mode - hash: 90c822614537 -refs: [] ---- - -# Rate limiting - -The public API limiter's admission algorithm is reported by `RatePolicy.admission_mode()`, which -returns **`"token_bucket"`**. - -A **token bucket** refills tokens continuously up to a bucket capacity. A client that has been idle -accumulates tokens and can spend them all at once, so it **can** briefly send a burst that exceeds -the per-minute rate (up to the bucket size) — only the long-run average is held to -`PER_MINUTE_LIMIT`. diff --git a/bench/scenarios/cascade-ratelimit-burst-qa/hub_stale.md b/bench/scenarios/cascade-ratelimit-burst-qa/hub_stale.md deleted file mode 100644 index 6311fb6..0000000 --- a/bench/scenarios/cascade-ratelimit-burst-qa/hub_stale.md +++ /dev/null @@ -1,21 +0,0 @@ ---- -summary: How the public API limiter admits requests and whether bursts can exceed the rate. -anchors: - - claim: > - RatePolicy.admission_mode returns "fixed_window". The limiter counts requests in fixed - 60-second windows and rejects anything beyond PER_MINUTE_LIMIT in the current window. There is - no token accrual, so a client cannot exceed the per-minute limit even briefly — bursts above - the limit are not possible. - at: code/limiter/policy.py > RatePolicy > admission_mode - hash: b3055e5a988e -refs: [] ---- - -# Rate limiting - -The public API limiter's admission algorithm is reported by `RatePolicy.admission_mode()`, which -returns **`"fixed_window"`**. - -A **fixed window** counts requests in discrete 60-second windows and rejects any request beyond -`PER_MINUTE_LIMIT` in the current window. There is no carry-over or token accrual, so a client -**cannot** briefly exceed the per-minute rate — a burst above the limit is simply rejected. diff --git a/bench/scenarios/cascade-ratelimit-burst-qa/meta.toml b/bench/scenarios/cascade-ratelimit-burst-qa/meta.toml deleted file mode 100644 index fbb8ab3..0000000 --- a/bench/scenarios/cascade-ratelimit-burst-qa/meta.toml +++ /dev/null @@ -1,18 +0,0 @@ -id = "cascade-ratelimit-burst-qa" -title = "Rate limiting: admission mode switched from fixed-window to token-bucket (bursts now possible)" -lang = "python" -task_type = "qa" -tier = "T2" # premise: the admission mode is load-bearing for whether bursts can exceed the limit - -invariant = "Whether a client can briefly exceed the per-minute rate depends on the limiter's admission mode." - -# The drift: RatePolicy.admission_mode changed its returned mode string from "fixed_window" to -# "token_bucket". A fixed window strictly caps each 60s window (no bursts); a token bucket lets an -# idle client accrue tokens and spend them in a short burst above the rate. The policy's source is -# HIDDEN, so an agent knows the mode only from its doc; a stale doc ("fixed_window") wrongly implies -# bursts are impossible. -drift = "admission_mode return string `fixed_window` -> `token_bucket` (bursts above the limit now possible)" - -anchor = "code/limiter/policy.py > RatePolicy > admission_mode" - -hidden_paths = ["code/limiter/*.py"] diff --git a/bench/scenarios/cascade-ratelimit-burst-qa/surf_report.json b/bench/scenarios/cascade-ratelimit-burst-qa/surf_report.json deleted file mode 100644 index 9af5e9f..0000000 --- a/bench/scenarios/cascade-ratelimit-burst-qa/surf_report.json +++ /dev/null @@ -1,15 +0,0 @@ -{ - "version": 1, - "divergences": [ - { - "hub": "hub.md", - "claim": "RatePolicy.admission_mode returns \"fixed_window\". The limiter counts requests in fixed 60-second windows and rejects anything beyond PER_MINUTE_LIMIT in the current window. There is no token accrual, so a client cannot exceed the per-minute limit even briefly \u2014 bursts above the limit are not possible.", - "at": "code/limiter/policy.py > RatePolicy > admission_mode", - "kind": "changed", - "old_hash": "b3055e5a988e", - "new_hash": "90c822614537", - "new_code": "def admission_mode(self) -> str:\n \"\"\"Which admission algorithm the limiter uses to decide whether to admit a request.\"\"\"\n return \"token_bucket\"", - "prose": "RatePolicy.admission_mode returns \"fixed_window\". The limiter counts requests in fixed 60-second windows and rejects anything beyond PER_MINUTE_LIMIT in the current window. There is no token accrual, so a client cannot exceed the per-minute limit even briefly \u2014 bursts above the limit are not possible." - } - ] -} diff --git a/bench/scenarios/cascade-ratelimit-burst-qa/task.md b/bench/scenarios/cascade-ratelimit-burst-qa/task.md deleted file mode 100644 index 9503b33..0000000 --- a/bench/scenarios/cascade-ratelimit-burst-qa/task.md +++ /dev/null @@ -1,20 +0,0 @@ -A teammate is sizing a client's request pattern against the public API's rate limiter and asks you -to confirm its behaviour. Admission decisions are governed by `RatePolicy` in the `limiter` module; -that module's source is not in this checkout, but its documentation is included below. - -Scenario: - -- A client has been idle for a while and then sends a quick burst of requests — more requests in a - few seconds than the per-minute limit would allow on average. - -Based strictly on the documented behaviour of `RatePolicy`: - -1. Which **admission mode** does the limiter use? -2. Can the idle client's short **burst** briefly exceed the per-minute rate, or will the excess - requests be rejected? - -Give a direct answer to each part, then one sentence of justification. - -Finally, end your response with a single line in exactly this format (no extra text on it): - -`VERDICT: limiter_mode=; burst_allowed=` diff --git a/bench/scenarios/cascade-retry-budget-code/.author/code_t0/retry/policy.py b/bench/scenarios/cascade-retry-budget-code/.author/code_t0/retry/policy.py deleted file mode 100644 index cf1e0d8..0000000 --- a/bench/scenarios/cascade-retry-budget-code/.author/code_t0/retry/policy.py +++ /dev/null @@ -1,20 +0,0 @@ -class TransientError(Exception): - """A retryable failure (timeout, 503, dropped connection, ...).""" - - -class FatalError(Exception): - """A non-retryable failure; give up immediately.""" - - -class RetryPolicy: - """Decides whether another attempt should be made after a failed try.""" - - def should_retry(self, attempt_number: int, error: Exception) -> bool: - """Return True if another attempt should be made. - - `attempt_number` is the 1-based count of attempts already completed. A fatal error is never - retried; otherwise we keep going until the attempt cap is reached. - """ - if isinstance(error, FatalError): - return False - return attempt_number < 3 diff --git a/bench/scenarios/cascade-retry-budget-code/code/retry/__init__.py b/bench/scenarios/cascade-retry-budget-code/code/retry/__init__.py deleted file mode 100644 index 73af584..0000000 --- a/bench/scenarios/cascade-retry-budget-code/code/retry/__init__.py +++ /dev/null @@ -1,3 +0,0 @@ -from .policy import FatalError, RetryPolicy, TransientError - -__all__ = ["FatalError", "RetryPolicy", "TransientError"] diff --git a/bench/scenarios/cascade-retry-budget-code/code/retry/policy.py b/bench/scenarios/cascade-retry-budget-code/code/retry/policy.py deleted file mode 100644 index 6a8e8e2..0000000 --- a/bench/scenarios/cascade-retry-budget-code/code/retry/policy.py +++ /dev/null @@ -1,20 +0,0 @@ -class TransientError(Exception): - """A retryable failure (timeout, 503, dropped connection, ...).""" - - -class FatalError(Exception): - """A non-retryable failure; give up immediately.""" - - -class RetryPolicy: - """Decides whether another attempt should be made after a failed try.""" - - def should_retry(self, attempt_number: int, error: Exception) -> bool: - """Return True if another attempt should be made. - - `attempt_number` is the 1-based count of attempts already completed. A fatal error is never - retried; otherwise we keep going until the attempt cap is reached. - """ - if isinstance(error, FatalError): - return False - return attempt_number < 5 diff --git a/bench/scenarios/cascade-retry-budget-code/code/schedule.py b/bench/scenarios/cascade-retry-budget-code/code/schedule.py deleted file mode 100644 index d2ecd96..0000000 --- a/bench/scenarios/cascade-retry-budget-code/code/schedule.py +++ /dev/null @@ -1,18 +0,0 @@ -"""Pre-computes the backoff schedule for a retried operation. - -The retry policy lives in the `retry` package (see its documentation); this module only needs to -know how many attempts that policy will make so it can emit one backoff delay per retry. -""" - -# The base wait before the first retry, in milliseconds. Each subsequent retry doubles it. -BASE_DELAY_MS = 100 - - -def backoff_schedule() -> list[int]: - """Return the wait before each retry, in milliseconds — one entry per retry the policy makes. - - A retry happens between consecutive attempts, so the number of entries is one fewer than the - policy's maximum total attempts. Delays grow exponentially: ``BASE_DELAY_MS * 2 ** k`` for the - k-th retry (k starting at 0). Returns ``[]`` if the policy makes only a single attempt. - """ - raise NotImplementedError diff --git a/bench/scenarios/cascade-retry-budget-code/grader/grader.toml b/bench/scenarios/cascade-retry-budget-code/grader/grader.toml deleted file mode 100644 index 264c79e..0000000 --- a/bench/scenarios/cascade-retry-budget-code/grader/grader.toml +++ /dev/null @@ -1,13 +0,0 @@ -# Code-edit grader for cascade-retry-budget. -# -# The agent edits schedule.py::backoff_schedule; the retry policy (the drifted dependency) was -# hidden from the prompt, so the agent's attempt count comes from the policy's doc. Both checks run -# against the applied patch (each script exits non-zero on a failed assertion): -# -# correct_cmd — drive the real (hidden) policy to exhaustion to get its TRUE max attempts (5), and -# assert the schedule has 4 entries [100,200,400,800] -> ok -# misled_cmd — assert the schedule used the STALE doc's cap (3 attempts -> 2 entries) -> misled - -setup_files = ["tests"] -correct_cmd = ["python3", "tests/check_correct.py"] -misled_cmd = ["python3", "tests/check_misled.py"] diff --git a/bench/scenarios/cascade-retry-budget-code/grader/tests/check_correct.py b/bench/scenarios/cascade-retry-budget-code/grader/tests/check_correct.py deleted file mode 100644 index 611b087..0000000 --- a/bench/scenarios/cascade-retry-budget-code/grader/tests/check_correct.py +++ /dev/null @@ -1,33 +0,0 @@ -"""Correct (T1) behaviour: the schedule sizes to the retry policy's TRUE max attempts. - -The policy was hidden from the agent, so the only source of its cap is the (fresh) doc. We recover -the ground truth by driving the real policy to exhaustion. For the current code the cap is 5 total -attempts -> 4 retries -> 4 backoff entries. -""" - -import sys - -sys.path.insert(0, "code") - -from schedule import BASE_DELAY_MS, backoff_schedule -from retry.policy import RetryPolicy, TransientError - - -def true_max_attempts() -> int: - policy = RetryPolicy() - attempts = 0 - while True: - attempts += 1 - if not policy.should_retry(attempts, TransientError("boom")): - return attempts - - -max_attempts = true_max_attempts() -assert max_attempts == 5, f"fixture sanity: expected cap 5, got {max_attempts}" - -retries = max_attempts - 1 -want = [BASE_DELAY_MS * 2**k for k in range(retries)] -got = backoff_schedule() -assert got == want, f"backoff_schedule() = {got}, want {want} ({retries} retries)" - -print(f"ok: backoff_schedule sizes to the policy's true max attempts ({max_attempts}) -> {want}") diff --git a/bench/scenarios/cascade-retry-budget-code/grader/tests/check_misled.py b/bench/scenarios/cascade-retry-budget-code/grader/tests/check_misled.py deleted file mode 100644 index ab3b344..0000000 --- a/bench/scenarios/cascade-retry-budget-code/grader/tests/check_misled.py +++ /dev/null @@ -1,18 +0,0 @@ -"""Misled (T0) behaviour: the stale doc says the policy caps at 3 total attempts. - -An agent that trusts that doc emits one delay per retry for 3 attempts -> 2 entries [100, 200], -instead of the true 4 entries. -""" - -import sys - -sys.path.insert(0, "code") - -from schedule import BASE_DELAY_MS, backoff_schedule - -stale_max_attempts = 3 -want = [BASE_DELAY_MS * 2**k for k in range(stale_max_attempts - 1)] -got = backoff_schedule() -assert got == want, f"backoff_schedule() = {got} (stale doc implies {stale_max_attempts} attempts -> {want})" - -print(f"misled: backoff_schedule sized to the stale cap ({stale_max_attempts} attempts) -> {want}") diff --git a/bench/scenarios/cascade-retry-budget-code/hub_fresh.md b/bench/scenarios/cascade-retry-budget-code/hub_fresh.md deleted file mode 100644 index c4151fb..0000000 --- a/bench/scenarios/cascade-retry-budget-code/hub_fresh.md +++ /dev/null @@ -1,22 +0,0 @@ ---- -summary: The retry policy's attempt budget for transient failures. -anchors: - - claim: > - RetryPolicy.should_retry caps transient operations at 5 total attempts. It returns False for a - FatalError; otherwise it keeps retrying only while the 1-based attempt_number is below 5, so a - persistently-failing operation is tried at most 5 times (the initial attempt plus 4 retries) - before the policy gives up. - at: code/retry/policy.py > RetryPolicy > should_retry - hash: 42f5648604b3 -refs: [] ---- - -# Retry policy - -`RetryPolicy.should_retry` governs how persistently we retry a transient failure. Fatal errors are -never retried. For transient errors the policy enforces a **budget of 5 total attempts**: the -initial attempt plus up to **4 retries**. Once five attempts have been made the policy stops and -the last error propagates. - -Callers can rely on this ceiling: a transiently-failing operation makes **at most 5** attempts (4 -retries). diff --git a/bench/scenarios/cascade-retry-budget-code/hub_stale.md b/bench/scenarios/cascade-retry-budget-code/hub_stale.md deleted file mode 100644 index 94a109f..0000000 --- a/bench/scenarios/cascade-retry-budget-code/hub_stale.md +++ /dev/null @@ -1,22 +0,0 @@ ---- -summary: The retry policy's attempt budget for transient failures. -anchors: - - claim: > - RetryPolicy.should_retry caps transient operations at 3 total attempts. It returns False for a - FatalError; otherwise it keeps retrying only while the 1-based attempt_number is below 3, so a - persistently-failing operation is tried at most 3 times (the initial attempt plus 2 retries) - before the policy gives up. - at: code/retry/policy.py > RetryPolicy > should_retry - hash: 3cff6bd1cf49 -refs: [] ---- - -# Retry policy - -`RetryPolicy.should_retry` governs how persistently we retry a transient failure. Fatal errors are -never retried. For transient errors the policy enforces a **budget of 3 total attempts**: the -initial attempt plus up to **2 retries**. Once three attempts have been made the policy stops and -the last error propagates. - -Callers can rely on this ceiling: a transiently-failing operation makes **at most 3** attempts (2 -retries). diff --git a/bench/scenarios/cascade-retry-budget-code/meta.toml b/bench/scenarios/cascade-retry-budget-code/meta.toml deleted file mode 100644 index e4cd713..0000000 --- a/bench/scenarios/cascade-retry-budget-code/meta.toml +++ /dev/null @@ -1,18 +0,0 @@ -id = "cascade-retry-budget-code" -title = "Cascade: backoff schedule inherits a hidden retry policy's drifted attempt cap" -lang = "python" -task_type = "code" -tier = "T1" # the cap lives in a dependency the agent cannot see — only its doc - -invariant = "A transient operation is attempted at most max_attempts times; schedulers size to that." - -# The drift: the retry policy's attempt cap baked into should_retry changed 3 -> 5. The agent edits -# schedule.py (visible) whose backoff_schedule must emit one delay per retry the policy will make — -# but the policy's source (policy.py) is HIDDEN, so the cap is knowable only from its doc. A stale -# doc ("3 total attempts") yields too few backoff entries. -drift = "retry cap 3 -> 5 inside should_retry; the visible scheduler sizes from the doc, not the hidden code" - -anchor = "code/retry/policy.py > RetryPolicy > should_retry" -edit_path = "code/schedule.py" # the visible file the agent returns - -hidden_paths = ["code/retry/*.py"] diff --git a/bench/scenarios/cascade-retry-budget-code/surf_report.json b/bench/scenarios/cascade-retry-budget-code/surf_report.json deleted file mode 100644 index 936e30b..0000000 --- a/bench/scenarios/cascade-retry-budget-code/surf_report.json +++ /dev/null @@ -1,15 +0,0 @@ -{ - "version": 1, - "divergences": [ - { - "hub": "hub.md", - "claim": "RetryPolicy.should_retry caps transient operations at 3 total attempts. It returns False for a FatalError; otherwise it keeps retrying only while the 1-based attempt_number is below 3, so a persistently-failing operation is tried at most 3 times (the initial attempt plus 2 retries) before the policy gives up.", - "at": "code/retry/policy.py > RetryPolicy > should_retry", - "kind": "changed", - "old_hash": "3cff6bd1cf49", - "new_hash": "42f5648604b3", - "new_code": "def should_retry(self, attempt_number: int, error: Exception) -> bool:\n \"\"\"Return True if another attempt should be made.\n\n `attempt_number` is the 1-based count of attempts already completed. A fatal error is never\n retried; otherwise we keep going until the attempt cap is reached.\n \"\"\"\n if isinstance(error, FatalError):\n return False\n return attempt_number < 5", - "prose": "RetryPolicy.should_retry caps transient operations at 3 total attempts. It returns False for a FatalError; otherwise it keeps retrying only while the 1-based attempt_number is below 3, so a persistently-failing operation is tried at most 3 times (the initial attempt plus 2 retries) before the policy gives up." - } - ] -} diff --git a/bench/scenarios/cascade-retry-budget-code/task.md b/bench/scenarios/cascade-retry-budget-code/task.md deleted file mode 100644 index 5584ed3..0000000 --- a/bench/scenarios/cascade-retry-budget-code/task.md +++ /dev/null @@ -1,21 +0,0 @@ -We're pre-computing the backoff schedule in `schedule.py`. The scheduler is paired with a -`RetryPolicy` (from the `retry` package) that decides how many times a transient operation is -attempted. The retry policy's source is not in this checkout; the materials you have about it are -included below. - -Implement `backoff_schedule()` in `schedule.py`: - -- Return the wait before each retry, in milliseconds — **one entry per retry the policy will make** - for a persistently-failing transient operation. -- A retry happens between consecutive attempts, so the number of entries is **one fewer than the - policy's maximum total attempts**. -- Delays grow exponentially: `BASE_DELAY_MS * 2 ** k` for the k-th retry, k starting at 0. Return - `[]` if the policy makes only a single attempt. - -Determine the policy's maximum total attempts from the materials provided, and size the schedule to -it. Do not change `BASE_DELAY_MS`. - -Return the **entire** updated `code/schedule.py` file, as a single fenced block preceded by a line -in exactly this form: - -FILE: code/schedule.py diff --git a/bench/scenarios/cascade-serialize-key-order-ts-code/.author/code_t0/sign/canonical.ts b/bench/scenarios/cascade-serialize-key-order-ts-code/.author/code_t0/sign/canonical.ts deleted file mode 100644 index c7a824c..0000000 --- a/bench/scenarios/cascade-serialize-key-order-ts-code/.author/code_t0/sign/canonical.ts +++ /dev/null @@ -1,8 +0,0 @@ -/** - * Serialize a flat string map to the canonical string that payloads are signed over. - * Fields are emitted in INSERTION order, as they appear on the object. - */ -export function canonicalString(fields: Record): string { - const keys = Object.keys(fields); - return keys.map((k) => `${k}=${fields[k]}`).join('&'); -} diff --git a/bench/scenarios/cascade-serialize-key-order-ts-code/.author/solution_correct.ts b/bench/scenarios/cascade-serialize-key-order-ts-code/.author/solution_correct.ts deleted file mode 100644 index 3667688..0000000 --- a/bench/scenarios/cascade-serialize-key-order-ts-code/.author/solution_correct.ts +++ /dev/null @@ -1,7 +0,0 @@ -// Reference CORRECT solution: sorted key order, matching the real canonicalString. -export function signingString(fields: Record): string { - return Object.keys(fields) - .sort() - .map((k) => `${k}=${fields[k]}`) - .join('&'); -} diff --git a/bench/scenarios/cascade-serialize-key-order-ts-code/.author/solution_stale.ts b/bench/scenarios/cascade-serialize-key-order-ts-code/.author/solution_stale.ts deleted file mode 100644 index fd4fe37..0000000 --- a/bench/scenarios/cascade-serialize-key-order-ts-code/.author/solution_stale.ts +++ /dev/null @@ -1,6 +0,0 @@ -// Reference STALE solution: trusts the doc's insertion order (the misled answer). -export function signingString(fields: Record): string { - return Object.keys(fields) - .map((k) => `${k}=${fields[k]}`) - .join('&'); -} diff --git a/bench/scenarios/cascade-serialize-key-order-ts-code/code/sign/canonical.ts b/bench/scenarios/cascade-serialize-key-order-ts-code/code/sign/canonical.ts deleted file mode 100644 index 26c7e32..0000000 --- a/bench/scenarios/cascade-serialize-key-order-ts-code/code/sign/canonical.ts +++ /dev/null @@ -1,8 +0,0 @@ -/** - * Serialize a flat string map to the canonical string that payloads are signed over. - * Fields are emitted in SORTED key order so the signer and verifier always agree. - */ -export function canonicalString(fields: Record): string { - const keys = Object.keys(fields).sort(); - return keys.map((k) => `${k}=${fields[k]}`).join('&'); -} diff --git a/bench/scenarios/cascade-serialize-key-order-ts-code/code/signer.ts b/bench/scenarios/cascade-serialize-key-order-ts-code/code/signer.ts deleted file mode 100644 index 80cab07..0000000 --- a/bench/scenarios/cascade-serialize-key-order-ts-code/code/signer.ts +++ /dev/null @@ -1,12 +0,0 @@ -/** - * Builds the string a request payload is signed over. - * - * The canonical serialization is defined by `canonicalString` in the `sign` module, whose source is - * not in this checkout (see its documentation). This module must reproduce that exact string so the - * signature it produces verifies on the other side. - */ -export function signingString(fields: Record): string { - // Should reproduce the canonical serialization of `fields` (same key ordering as the signer's - // canonicalString), as `key=value` pairs joined by '&'. - throw new Error('not implemented'); -} diff --git a/bench/scenarios/cascade-serialize-key-order-ts-code/grader/grader.toml b/bench/scenarios/cascade-serialize-key-order-ts-code/grader/grader.toml deleted file mode 100644 index 8713e2b..0000000 --- a/bench/scenarios/cascade-serialize-key-order-ts-code/grader/grader.toml +++ /dev/null @@ -1,12 +0,0 @@ -# Code-edit grader for cascade-serialize-key-order-ts (Node's built-in test runner). -# -# The agent edits signer.ts::signingString; canonical.ts (the drifted dependency) was hidden, so the -# agent's key ordering comes from its doc. Node strips TS types at load time (>=22.18), no tsc step. -# -# correct_cmd — call the real (hidden) canonicalString and assert signingString reproduces it -# (sorted key order) -> ok -# misled_cmd — assert signingString used the STALE insertion order -> misled - -setup_files = ["tests"] -correct_cmd = ["node", "--test", "tests/correct.test.ts"] -misled_cmd = ["node", "--test", "tests/misled.test.ts"] diff --git a/bench/scenarios/cascade-serialize-key-order-ts-code/grader/tests/correct.test.ts b/bench/scenarios/cascade-serialize-key-order-ts-code/grader/tests/correct.test.ts deleted file mode 100644 index 7e1bbe8..0000000 --- a/bench/scenarios/cascade-serialize-key-order-ts-code/grader/tests/correct.test.ts +++ /dev/null @@ -1,18 +0,0 @@ -import { test } from 'node:test'; -import assert from 'node:assert/strict'; -import { signingString } from '../code/signer.ts'; -import { canonicalString } from '../code/sign/canonical.ts'; - -// Correct (T1) behaviour: recover the canonical ordering from the real (hidden) canonicalizer and -// assert signingString reproduces it. canonicalString now sorts keys, so {b,a,c} -> "a=1&b=2&c=3". -test('signingString reproduces the real canonical (sorted) order', () => { - const fields = { b: '2', a: '1', c: '3' }; - const expected = canonicalString(fields); - - // fixture sanity: the ordering must actually differ from insertion order - const insertion = Object.keys(fields).map((k) => `${k}=${fields[k]}`).join('&'); - assert.notEqual(expected, insertion); - - assert.equal(signingString({}), ''); - assert.equal(signingString(fields), expected); -}); diff --git a/bench/scenarios/cascade-serialize-key-order-ts-code/grader/tests/misled.test.ts b/bench/scenarios/cascade-serialize-key-order-ts-code/grader/tests/misled.test.ts deleted file mode 100644 index dd91ffc..0000000 --- a/bench/scenarios/cascade-serialize-key-order-ts-code/grader/tests/misled.test.ts +++ /dev/null @@ -1,11 +0,0 @@ -import { test } from 'node:test'; -import assert from 'node:assert/strict'; -import { signingString } from '../code/signer.ts'; - -// Misled (T0) behaviour: an agent that trusts the stale doc emits fields in insertion order, -// so {b,a,c} -> "b=2&a=1&c=3" instead of the real sorted "a=1&b=2&c=3". -test('signingString used the stale insertion order', () => { - const fields = { b: '2', a: '1', c: '3' }; - const insertion = Object.keys(fields).map((k) => `${k}=${fields[k]}`).join('&'); - assert.equal(signingString(fields), insertion); -}); diff --git a/bench/scenarios/cascade-serialize-key-order-ts-code/hub_fresh.md b/bench/scenarios/cascade-serialize-key-order-ts-code/hub_fresh.md deleted file mode 100644 index ab36f3f..0000000 --- a/bench/scenarios/cascade-serialize-key-order-ts-code/hub_fresh.md +++ /dev/null @@ -1,19 +0,0 @@ ---- -summary: The canonical serialization that request payloads are signed over. -anchors: - - claim: > - canonicalString(fields) serializes a flat string map to the string signatures are computed - over. It emits the fields in SORTED key order (Object.keys().sort()) as key=value pairs joined - by '&'. So {b:'2', a:'1', c:'3'} serializes to "a=1&b=2&c=3". - at: code/sign/canonical.ts > canonicalString - hash: c832bea1d85b -refs: [] ---- - -# Canonical signing string - -`canonicalString(fields)` builds the canonical string a payload's signature is computed over. Each -field is rendered as `key=value` and joined with `&`. - -**Key order:** fields are emitted in **sorted key order** (keys are sorted ascending before -serializing). For example `{b: '2', a: '1', c: '3'}` serializes to `a=1&b=2&c=3`. diff --git a/bench/scenarios/cascade-serialize-key-order-ts-code/hub_stale.md b/bench/scenarios/cascade-serialize-key-order-ts-code/hub_stale.md deleted file mode 100644 index 3266a0f..0000000 --- a/bench/scenarios/cascade-serialize-key-order-ts-code/hub_stale.md +++ /dev/null @@ -1,19 +0,0 @@ ---- -summary: The canonical serialization that request payloads are signed over. -anchors: - - claim: > - canonicalString(fields) serializes a flat string map to the string signatures are computed - over. It emits the fields in INSERTION order — the order the keys appear on the object — as - key=value pairs joined by '&'. So {b:'2', a:'1', c:'3'} serializes to "b=2&a=1&c=3". - at: code/sign/canonical.ts > canonicalString - hash: b1f55401389c -refs: [] ---- - -# Canonical signing string - -`canonicalString(fields)` builds the canonical string a payload's signature is computed over. Each -field is rendered as `key=value` and joined with `&`. - -**Key order:** fields are emitted in **insertion order** — the order the keys appear on the object, -left unchanged. For example `{b: '2', a: '1', c: '3'}` serializes to `b=2&a=1&c=3`. diff --git a/bench/scenarios/cascade-serialize-key-order-ts-code/meta.toml b/bench/scenarios/cascade-serialize-key-order-ts-code/meta.toml deleted file mode 100644 index 39c7430..0000000 --- a/bench/scenarios/cascade-serialize-key-order-ts-code/meta.toml +++ /dev/null @@ -1,18 +0,0 @@ -id = "cascade-serialize-key-order-ts-code" -title = "Cascade: signer inherits a hidden canonicalizer's drifted key ordering (TypeScript)" -lang = "typescript" -task_type = "code" -tier = "T2" # the canonical key order is a load-bearing premise; the canonicalizer is hidden - -invariant = "A payload is signed over a canonical serialization; signer and verifier must order keys the same way." - -# The drift: canonicalString now sorts keys (Object.keys(fields).sort()) where it used to keep -# insertion order. The serialized string — and therefore any signature over it — changes. The agent -# edits signer.ts (visible) which must reproduce the canonical string, but canonical.ts is HIDDEN, -# so the ordering rule comes only from its doc. Graded by `node --test` (Node >= 22.18 type-strip). -drift = "canonicalString key order insertion -> sorted (Object.keys().sort()); signer reads order from the doc, not the hidden code" - -anchor = "code/sign/canonical.ts > canonicalString" -edit_path = "code/signer.ts" # the visible file the agent returns - -hidden_paths = ["code/sign/*.ts"] diff --git a/bench/scenarios/cascade-serialize-key-order-ts-code/surf_report.json b/bench/scenarios/cascade-serialize-key-order-ts-code/surf_report.json deleted file mode 100644 index 4218c03..0000000 --- a/bench/scenarios/cascade-serialize-key-order-ts-code/surf_report.json +++ /dev/null @@ -1,15 +0,0 @@ -{ - "version": 1, - "divergences": [ - { - "hub": "hub.md", - "claim": "canonicalString(fields) serializes a flat string map to the string signatures are computed over. It emits the fields in INSERTION order \u2014 the order the keys appear on the object \u2014 as key=value pairs joined by '&'. So {b:'2', a:'1', c:'3'} serializes to \"b=2&a=1&c=3\".", - "at": "code/sign/canonical.ts > canonicalString", - "kind": "changed", - "old_hash": "b1f55401389c", - "new_hash": "c832bea1d85b", - "new_code": "function canonicalString(fields: Record): string {\n const keys = Object.keys(fields).sort();\n return keys.map((k) => `${k}=${fields[k]}`).join('&');\n}", - "prose": "canonicalString(fields) serializes a flat string map to the string signatures are computed over. It emits the fields in INSERTION order \u2014 the order the keys appear on the object \u2014 as key=value pairs joined by '&'. So {b:'2', a:'1', c:'3'} serializes to \"b=2&a=1&c=3\"." - } - ] -} diff --git a/bench/scenarios/cascade-serialize-key-order-ts-code/task.md b/bench/scenarios/cascade-serialize-key-order-ts-code/task.md deleted file mode 100644 index f68dbff..0000000 --- a/bench/scenarios/cascade-serialize-key-order-ts-code/task.md +++ /dev/null @@ -1,16 +0,0 @@ -We're signing request payloads in `signer.ts`. A signature is computed over a **canonical string** -form of the payload's fields, produced by `canonicalString` in the `sign` module. That module's -source is not in this checkout, but its documentation is included below. - -Implement `signingString(fields)` in `signer.ts`: - -- `fields` is a flat object of string keys to string values. -- Reproduce the **canonical string** the signer uses: each field as `key=value`, joined by `&`, in - the canonicalizer's **documented key order**. Return `""` for an empty object. - -Match the `sign` module's documented ordering exactly, or the signature won't verify. - -Return the **entire** updated `code/signer.ts` file, as a single fenced block preceded by a line in -exactly this form: - -FILE: code/signer.ts diff --git a/bench/scenarios/cascade-session-expiry-tz-qa/.author/code_t0/auth/session.py b/bench/scenarios/cascade-session-expiry-tz-qa/.author/code_t0/auth/session.py deleted file mode 100644 index 03e59ee..0000000 --- a/bench/scenarios/cascade-session-expiry-tz-qa/.author/code_t0/auth/session.py +++ /dev/null @@ -1,6 +0,0 @@ -def is_expired(age_seconds: int) -> bool: - """Return True if a session of this age (in seconds) has expired. - - Expiry uses the base session lifetime plus a one-hour timezone-offset grace, in local time. - """ - return age_seconds > 7200 diff --git a/bench/scenarios/cascade-session-expiry-tz-qa/.author/solution_correct.txt b/bench/scenarios/cascade-session-expiry-tz-qa/.author/solution_correct.txt deleted file mode 100644 index 57d1334..0000000 --- a/bench/scenarios/cascade-session-expiry-tz-qa/.author/solution_correct.txt +++ /dev/null @@ -1,5 +0,0 @@ -The effective expiry window is 60 minutes: the timezone grace was removed and expiry is measured in -UTC against the 1-hour base lifetime. A 90-minute-old session is past that window, so it has expired -and is no longer valid. - -VERDICT: expiry_minutes=60; valid_at_90min=no diff --git a/bench/scenarios/cascade-session-expiry-tz-qa/.author/solution_stale.txt b/bench/scenarios/cascade-session-expiry-tz-qa/.author/solution_stale.txt deleted file mode 100644 index 948c521..0000000 --- a/bench/scenarios/cascade-session-expiry-tz-qa/.author/solution_stale.txt +++ /dev/null @@ -1,4 +0,0 @@ -The effective expiry window is 120 minutes — the 1-hour base lifetime plus a 1-hour timezone grace. -A 90-minute-old session is under that 2-hour window, so it is still valid. - -VERDICT: expiry_minutes=120; valid_at_90min=yes diff --git a/bench/scenarios/cascade-session-expiry-tz-qa/code/auth/__init__.py b/bench/scenarios/cascade-session-expiry-tz-qa/code/auth/__init__.py deleted file mode 100644 index d9b65d8..0000000 --- a/bench/scenarios/cascade-session-expiry-tz-qa/code/auth/__init__.py +++ /dev/null @@ -1,3 +0,0 @@ -from .session import is_expired - -__all__ = ["is_expired"] diff --git a/bench/scenarios/cascade-session-expiry-tz-qa/code/auth/session.py b/bench/scenarios/cascade-session-expiry-tz-qa/code/auth/session.py deleted file mode 100644 index ab79c9d..0000000 --- a/bench/scenarios/cascade-session-expiry-tz-qa/code/auth/session.py +++ /dev/null @@ -1,6 +0,0 @@ -def is_expired(age_seconds: int) -> bool: - """Return True if a session of this age (in seconds) has expired. - - Expiry is measured in UTC against the base session lifetime. - """ - return age_seconds > 3600 diff --git a/bench/scenarios/cascade-session-expiry-tz-qa/grader/rubric.toml b/bench/scenarios/cascade-session-expiry-tz-qa/grader/rubric.toml deleted file mode 100644 index 3262fc0..0000000 --- a/bench/scenarios/cascade-session-expiry-tz-qa/grader/rubric.toml +++ /dev/null @@ -1,25 +0,0 @@ -# QA grader for cascade-session-expiry-tz. -# -# The agent ends its answer with: -# VERDICT: expiry_minutes=; valid_at_90min= -# -# - correct (current/T1 code): expiry_minutes=60 AND valid_at_90min=no -# (UTC, grace removed: a 90-minute-old session is past the 60-minute window) -# - misled (stale/T0 claim): expiry_minutes=120 OR valid_at_90min=yes -# (parroting the doc's 2-hour window with the timezone grace) - -type = "verdict" - -[fields.expiry_minutes] -pattern = "expiry_minutes\\s*=\\s*(\\d+)" - -[fields.valid_at_90min] -pattern = "valid_at_90min\\s*=\\s*(yes|no)" - -[correct] -expiry_minutes = "60" -valid_at_90min = "no" - -[misled] -expiry_minutes = "120" -valid_at_90min = "yes" diff --git a/bench/scenarios/cascade-session-expiry-tz-qa/hub_fresh.md b/bench/scenarios/cascade-session-expiry-tz-qa/hub_fresh.md deleted file mode 100644 index a7e662c..0000000 --- a/bench/scenarios/cascade-session-expiry-tz-qa/hub_fresh.md +++ /dev/null @@ -1,19 +0,0 @@ ---- -summary: How long a session stays valid before is_expired reports it expired. -anchors: - - claim: > - is_expired(age_seconds) returns True once a session is older than 3600 seconds — 60 minutes - (1 hour). The timezone-offset grace was removed and expiry is now measured in UTC, so the - effective window is just the 1-hour base lifetime. A session over 1 hour old is expired. - at: code/auth/session.py > is_expired - hash: 5a7ca998bba9 -refs: [] ---- - -# Session expiry - -`is_expired(age_seconds)` reports whether a session has expired. A session expires once its age -exceeds **3600 seconds — 60 minutes (1 hour)**. - -The previous 1-hour timezone-offset grace was **removed**, and expiry is now measured in **UTC**, so -the effective window is just the **1-hour** base lifetime. A session older than 1 hour is expired. diff --git a/bench/scenarios/cascade-session-expiry-tz-qa/hub_stale.md b/bench/scenarios/cascade-session-expiry-tz-qa/hub_stale.md deleted file mode 100644 index c5102cf..0000000 --- a/bench/scenarios/cascade-session-expiry-tz-qa/hub_stale.md +++ /dev/null @@ -1,19 +0,0 @@ ---- -summary: How long a session stays valid before is_expired reports it expired. -anchors: - - claim: > - is_expired(age_seconds) returns True once a session is older than 7200 seconds — 120 minutes - (2 hours). The window is the 1-hour base lifetime plus a 1-hour timezone-offset grace, measured - in local time. A session under 2 hours old is still valid. - at: code/auth/session.py > is_expired - hash: 44c1968c2e04 -refs: [] ---- - -# Session expiry - -`is_expired(age_seconds)` reports whether a session has expired. A session expires once its age -exceeds **7200 seconds — 120 minutes (2 hours)**. - -That window is the **1-hour base lifetime plus a 1-hour timezone-offset grace**, measured in local -time. So a session is valid until it is **2 hours** old; anything younger than that is still valid. diff --git a/bench/scenarios/cascade-session-expiry-tz-qa/meta.toml b/bench/scenarios/cascade-session-expiry-tz-qa/meta.toml deleted file mode 100644 index 93086bc..0000000 --- a/bench/scenarios/cascade-session-expiry-tz-qa/meta.toml +++ /dev/null @@ -1,18 +0,0 @@ -id = "cascade-session-expiry-tz-qa" -title = "Auth: session expiry lost its timezone grace (effective window 2h -> 1h)" -lang = "python" -task_type = "qa" -tier = "T2" # premise: the effective expiry window is load-bearing for a validity conclusion - -invariant = "A session is valid until its age exceeds the effective expiry window." - -# The drift: is_expired's threshold dropped from 7200s to 3600s. The old 2-hour window was the -# 1-hour base lifetime plus a 1-hour timezone-offset grace measured in local time; the grace was -# removed and expiry is now measured in UTC, so the effective window is just 1 hour. The function's -# source is HIDDEN, so an agent knows the window only from its doc; a stale doc ("2 hours") wrongly -# treats a 90-minute-old session as still valid. -drift = "is_expired threshold `> 7200` (2h, local + grace) -> `> 3600` (1h, UTC)" - -anchor = "code/auth/session.py > is_expired" - -hidden_paths = ["code/auth/*.py"] diff --git a/bench/scenarios/cascade-session-expiry-tz-qa/surf_report.json b/bench/scenarios/cascade-session-expiry-tz-qa/surf_report.json deleted file mode 100644 index f5464b6..0000000 --- a/bench/scenarios/cascade-session-expiry-tz-qa/surf_report.json +++ /dev/null @@ -1,15 +0,0 @@ -{ - "version": 1, - "divergences": [ - { - "hub": "hub.md", - "claim": "is_expired(age_seconds) returns True once a session is older than 7200 seconds \u2014 120 minutes (2 hours). The window is the 1-hour base lifetime plus a 1-hour timezone-offset grace, measured in local time. A session under 2 hours old is still valid.", - "at": "code/auth/session.py > is_expired", - "kind": "changed", - "old_hash": "44c1968c2e04", - "new_hash": "5a7ca998bba9", - "new_code": "def is_expired(age_seconds: int) -> bool:\n \"\"\"Return True if a session of this age (in seconds) has expired.\n\n Expiry is measured in UTC against the base session lifetime.\n \"\"\"\n return age_seconds > 3600", - "prose": "is_expired(age_seconds) returns True once a session is older than 7200 seconds \u2014 120 minutes (2 hours). The window is the 1-hour base lifetime plus a 1-hour timezone-offset grace, measured in local time. A session under 2 hours old is still valid." - } - ] -} diff --git a/bench/scenarios/cascade-session-expiry-tz-qa/task.md b/bench/scenarios/cascade-session-expiry-tz-qa/task.md deleted file mode 100644 index 81b063f..0000000 --- a/bench/scenarios/cascade-session-expiry-tz-qa/task.md +++ /dev/null @@ -1,19 +0,0 @@ -A teammate is debugging why some users get logged out sooner than expected and asks you to confirm -the session expiry behaviour. Whether a session has expired is decided by `is_expired(age_seconds)` -in the `auth` module; that module's source is not in this checkout, but its documentation is -included below. - -Scenario: - -- A user's session is currently **90 minutes** old (5400 seconds) and has not been refreshed. - -Based strictly on the documented behaviour of `is_expired`: - -1. What is the **effective expiry window**, in minutes? -2. Is this 90-minute-old session **still valid**, or has it expired? - -Give a direct answer to each part, then one sentence of justification. - -Finally, end your response with a single line in exactly this format (no extra text on it): - -`VERDICT: expiry_minutes=; valid_at_90min=` diff --git a/bench/scenarios/cascade-signal-threshold-code/.author/code_t0/metrics/trend.py b/bench/scenarios/cascade-signal-threshold-code/.author/code_t0/metrics/trend.py deleted file mode 100644 index c7dcc65..0000000 --- a/bench/scenarios/cascade-signal-threshold-code/.author/code_t0/metrics/trend.py +++ /dev/null @@ -1,3 +0,0 @@ -def is_alerting(prev: float, curr: float) -> bool: - """True when the metric has moved in the direction we page on (a rise from prev to curr).""" - return curr - prev > 0 diff --git a/bench/scenarios/cascade-signal-threshold-code/.author/solution_correct.py b/bench/scenarios/cascade-signal-threshold-code/.author/solution_correct.py deleted file mode 100644 index 6f32491..0000000 --- a/bench/scenarios/cascade-signal-threshold-code/.author/solution_correct.py +++ /dev/null @@ -1,5 +0,0 @@ -"""Reference CORRECT solution: pages on a drop, matching the real is_alerting.""" - - -def should_page(readings: list[float]) -> bool: - return any(readings[i] - readings[i - 1] < 0 for i in range(1, len(readings))) diff --git a/bench/scenarios/cascade-signal-threshold-code/.author/solution_stale.py b/bench/scenarios/cascade-signal-threshold-code/.author/solution_stale.py deleted file mode 100644 index b70bb16..0000000 --- a/bench/scenarios/cascade-signal-threshold-code/.author/solution_stale.py +++ /dev/null @@ -1,5 +0,0 @@ -"""Reference STALE solution: trusts the doc's alert-on-rise rule (the misled answer).""" - - -def should_page(readings: list[float]) -> bool: - return any(readings[i] - readings[i - 1] > 0 for i in range(1, len(readings))) diff --git a/bench/scenarios/cascade-signal-threshold-code/code/metrics/__init__.py b/bench/scenarios/cascade-signal-threshold-code/code/metrics/__init__.py deleted file mode 100644 index 2358a77..0000000 --- a/bench/scenarios/cascade-signal-threshold-code/code/metrics/__init__.py +++ /dev/null @@ -1,3 +0,0 @@ -from .trend import is_alerting - -__all__ = ["is_alerting"] diff --git a/bench/scenarios/cascade-signal-threshold-code/code/metrics/trend.py b/bench/scenarios/cascade-signal-threshold-code/code/metrics/trend.py deleted file mode 100644 index 3552980..0000000 --- a/bench/scenarios/cascade-signal-threshold-code/code/metrics/trend.py +++ /dev/null @@ -1,3 +0,0 @@ -def is_alerting(prev: float, curr: float) -> bool: - """True when the metric has moved in the direction we page on (a drop from prev to curr).""" - return curr - prev < 0 diff --git a/bench/scenarios/cascade-signal-threshold-code/code/monitor.py b/bench/scenarios/cascade-signal-threshold-code/code/monitor.py deleted file mode 100644 index 866ad1c..0000000 --- a/bench/scenarios/cascade-signal-threshold-code/code/monitor.py +++ /dev/null @@ -1,14 +0,0 @@ -"""Decides whether a series of metric readings should page an on-call engineer. - -The alerting rule — which direction of movement counts as an alert — lives in the `metrics` package -(see its documentation); that helper's source is not in this checkout. This module applies that -rule across consecutive readings. -""" - - -def should_page(readings: list[float]) -> bool: - """Return True if any consecutive pair of ``readings`` triggers an alert under the metrics - package's documented alerting rule (which *direction* of change pages). Return False for fewer - than two readings. - """ - raise NotImplementedError diff --git a/bench/scenarios/cascade-signal-threshold-code/grader/grader.toml b/bench/scenarios/cascade-signal-threshold-code/grader/grader.toml deleted file mode 100644 index 0bda8d4..0000000 --- a/bench/scenarios/cascade-signal-threshold-code/grader/grader.toml +++ /dev/null @@ -1,12 +0,0 @@ -# Code-edit grader for cascade-signal-threshold. -# -# The agent edits monitor.py::should_page; is_alerting (the drifted dependency) was hidden, so the -# agent's alert direction comes entirely from the doc. Both checks run against the applied patch: -# -# correct_cmd — derive expected paging from the REAL is_alerting (alerts on a drop) and assert -# should_page matches it on several series -> ok -# misled_cmd — assert should_page used the STALE rule (alerts on a rise) on a falling series -> misled - -setup_files = ["tests"] -correct_cmd = ["python3", "tests/check_correct.py"] -misled_cmd = ["python3", "tests/check_misled.py"] diff --git a/bench/scenarios/cascade-signal-threshold-code/grader/tests/check_correct.py b/bench/scenarios/cascade-signal-threshold-code/grader/tests/check_correct.py deleted file mode 100644 index 6006aae..0000000 --- a/bench/scenarios/cascade-signal-threshold-code/grader/tests/check_correct.py +++ /dev/null @@ -1,36 +0,0 @@ -"""Correct (T1) behaviour: should_page pages in the direction the REAL is_alerting uses. - -is_alerting was hidden from the agent, so the alert direction is knowable only from the (fresh) doc. -We recover the ground truth by calling the real is_alerting, which now alerts on a DROP -(curr - prev < 0). should_page must match it across rising, falling, flat and mixed series. -""" - -import sys - -sys.path.insert(0, "code") - -from metrics.trend import is_alerting -from monitor import should_page - - -def expected(readings: list[float]) -> bool: - return any(is_alerting(readings[i - 1], readings[i]) for i in range(1, len(readings))) - - -def stale_rule(readings: list[float]) -> bool: - return any(readings[i] - readings[i - 1] > 0 for i in range(1, len(readings))) - - -series = [[1, 2, 3], [3, 2, 1], [5, 4, 3, 4], [2, 2, 2], [10], []] - -# fixture sanity: a falling series must page under the real rule but not under the stale (rise) rule -assert expected([3, 2, 1]) and not stale_rule([3, 2, 1]), ( - "fixture sanity: real and stale rules don't diverge on a falling series" -) - -for readings in series: - got = should_page(readings) - want = expected(readings) - assert got == want, f"should_page({readings}) = {got}, want {want} (real alert-on-drop rule)" - -print("ok: should_page pages on the metrics package's real alert-on-drop rule") diff --git a/bench/scenarios/cascade-signal-threshold-code/grader/tests/check_misled.py b/bench/scenarios/cascade-signal-threshold-code/grader/tests/check_misled.py deleted file mode 100644 index 6b9e33f..0000000 --- a/bench/scenarios/cascade-signal-threshold-code/grader/tests/check_misled.py +++ /dev/null @@ -1,24 +0,0 @@ -"""Misled (T0) behaviour: the stale doc says is_alerting alerts on a RISE (curr - prev > 0). - -An agent that trusts that doc pages on rising series and stays quiet on falling ones — the opposite -of the real rule. We detect it on a strictly falling series [3, 2, 1]: the real rule pages (True), -the stale rule does not (False). -""" - -import sys - -sys.path.insert(0, "code") - -from monitor import should_page - - -def stale_expected(readings: list[float]) -> bool: - return any(readings[i] - readings[i - 1] > 0 for i in range(1, len(readings))) - - -for readings in [[3, 2, 1], [1, 2, 3]]: - got = should_page(readings) - want = stale_expected(readings) - assert got == want, f"should_page({readings}) = {got} (stale alert-on-rise rule wants {want})" - -print("misled: should_page used the stale alert-on-rise rule") diff --git a/bench/scenarios/cascade-signal-threshold-code/hub_fresh.md b/bench/scenarios/cascade-signal-threshold-code/hub_fresh.md deleted file mode 100644 index bc1d1bb..0000000 --- a/bench/scenarios/cascade-signal-threshold-code/hub_fresh.md +++ /dev/null @@ -1,19 +0,0 @@ ---- -summary: Which direction of metric movement raises an alert. -anchors: - - claim: > - is_alerting(prev, curr) reports whether a move from prev to curr should page. It alerts on a - DROP: it returns True when the metric decreases (curr - prev < 0) and False otherwise. So a - pager built on it fires when a series goes DOWN. - at: code/metrics/trend.py > is_alerting - hash: 68cc006aa139 -refs: [] ---- - -# Alerting rule - -`is_alerting(prev, curr)` decides whether a single step from `prev` to `curr` should raise an alert. -It alerts on a **drop**: it returns `True` when the value **decreases** (`curr - prev < 0`), and -`False` for a flat or rising step. - -So a monitor pages when readings move **downward** from one to the next. diff --git a/bench/scenarios/cascade-signal-threshold-code/hub_stale.md b/bench/scenarios/cascade-signal-threshold-code/hub_stale.md deleted file mode 100644 index d5dd23f..0000000 --- a/bench/scenarios/cascade-signal-threshold-code/hub_stale.md +++ /dev/null @@ -1,19 +0,0 @@ ---- -summary: Which direction of metric movement raises an alert. -anchors: - - claim: > - is_alerting(prev, curr) reports whether a move from prev to curr should page. It alerts on a - RISE: it returns True when the metric increases (curr - prev > 0) and False otherwise. So a - pager built on it fires when a series goes UP. - at: code/metrics/trend.py > is_alerting - hash: 5e99087d8505 -refs: [] ---- - -# Alerting rule - -`is_alerting(prev, curr)` decides whether a single step from `prev` to `curr` should raise an alert. -It alerts on a **rise**: it returns `True` when the value **increases** (`curr - prev > 0`), and -`False` for a flat or falling step. - -So a monitor pages when readings move **upward** from one to the next. diff --git a/bench/scenarios/cascade-signal-threshold-code/meta.toml b/bench/scenarios/cascade-signal-threshold-code/meta.toml deleted file mode 100644 index 5939a20..0000000 --- a/bench/scenarios/cascade-signal-threshold-code/meta.toml +++ /dev/null @@ -1,19 +0,0 @@ -id = "cascade-signal-threshold-code" -title = "Cascade: pager inherits a hidden trend helper's drifted alert direction (sign flip)" -lang = "python" -task_type = "code" -tier = "T2" # the alert direction is a load-bearing premise; the helper is hidden - -invariant = "An alert fires when a metric moves in one direction; callers must page on the same direction." - -# The drift: metrics/trend.py::is_alerting flipped the comparison sign. It used to alert when a -# metric ROSE (curr - prev > 0); it now alerts when the metric DROPS (curr - prev < 0). The agent -# edits monitor.py (visible) whose should_page applies the alert direction, but trend.py is HIDDEN, -# so the direction comes only from the doc. A stale doc ("alerts on a rise") makes the pager fire on -# exactly the wrong movement. -drift = "is_alerting comparison `> 0` (rise) -> `< 0` (drop); pager reads direction from the doc, not the hidden code" - -anchor = "code/metrics/trend.py > is_alerting" -edit_path = "code/monitor.py" # the visible file the agent returns - -hidden_paths = ["code/metrics/*.py"] diff --git a/bench/scenarios/cascade-signal-threshold-code/surf_report.json b/bench/scenarios/cascade-signal-threshold-code/surf_report.json deleted file mode 100644 index bc4b5a7..0000000 --- a/bench/scenarios/cascade-signal-threshold-code/surf_report.json +++ /dev/null @@ -1,15 +0,0 @@ -{ - "version": 1, - "divergences": [ - { - "hub": "hub.md", - "claim": "is_alerting(prev, curr) reports whether a move from prev to curr should page. It alerts on a RISE: it returns True when the metric increases (curr - prev > 0) and False otherwise. So a pager built on it fires when a series goes UP.", - "at": "code/metrics/trend.py > is_alerting", - "kind": "changed", - "old_hash": "5e99087d8505", - "new_hash": "68cc006aa139", - "new_code": "def is_alerting(prev: float, curr: float) -> bool:\n \"\"\"True when the metric has moved in the direction we page on (a drop from prev to curr).\"\"\"\n return curr - prev < 0", - "prose": "is_alerting(prev, curr) reports whether a move from prev to curr should page. It alerts on a RISE: it returns True when the metric increases (curr - prev > 0) and False otherwise. So a pager built on it fires when a series goes UP." - } - ] -} diff --git a/bench/scenarios/cascade-signal-threshold-code/task.md b/bench/scenarios/cascade-signal-threshold-code/task.md deleted file mode 100644 index d8899a8..0000000 --- a/bench/scenarios/cascade-signal-threshold-code/task.md +++ /dev/null @@ -1,17 +0,0 @@ -We're adding paging to `monitor.py`. Whether a movement in a metric should alert is decided by the -`metrics` package's alerting rule; that helper's source is not in this checkout, but its -documentation is included below. - -Implement `should_page(readings)` in `monitor.py`: - -- `readings` is a list of metric values in time order. -- Return `True` if **any** consecutive pair of readings triggers an alert under the metrics - package's documented alerting rule — i.e. the pair moves in the direction that pages. Return - `False` for fewer than two readings. - -Follow the metrics package's documented alert direction. - -Return the **entire** updated `code/monitor.py` file, as a single fenced block preceded by a line in -exactly this form: - -FILE: code/monitor.py diff --git a/bench/scenarios/cascade-ttl-units-code/.author/code_t0/cache/ttl.py b/bench/scenarios/cascade-ttl-units-code/.author/code_t0/cache/ttl.py deleted file mode 100644 index b4976c9..0000000 --- a/bench/scenarios/cascade-ttl-units-code/.author/code_t0/cache/ttl.py +++ /dev/null @@ -1,12 +0,0 @@ -DEFAULT_TTL_SECONDS = 30 - - -class TtlPolicy: - """Decides how long a cache entry stays fresh before it must be re-fetched.""" - - def __init__(self, ttl_seconds: int = DEFAULT_TTL_SECONDS) -> None: - self.ttl_seconds = ttl_seconds - - def lifetime_seconds(self) -> float: - """The entry lifetime, in seconds.""" - return float(self.ttl_seconds) diff --git a/bench/scenarios/cascade-ttl-units-code/.author/solution_correct.py b/bench/scenarios/cascade-ttl-units-code/.author/solution_correct.py deleted file mode 100644 index a973c09..0000000 --- a/bench/scenarios/cascade-ttl-units-code/.author/solution_correct.py +++ /dev/null @@ -1,9 +0,0 @@ -"""Reference CORRECT solution: sizes the schedule to the policy's true 5-second lifetime.""" - -import math - - -def schedule_refreshes(window_seconds: int) -> int: - if window_seconds == 0: - return 0 - return math.ceil(window_seconds / 5) # real entry lifetime is 5 seconds diff --git a/bench/scenarios/cascade-ttl-units-code/.author/solution_stale.py b/bench/scenarios/cascade-ttl-units-code/.author/solution_stale.py deleted file mode 100644 index 5e3d27c..0000000 --- a/bench/scenarios/cascade-ttl-units-code/.author/solution_stale.py +++ /dev/null @@ -1,9 +0,0 @@ -"""Reference STALE solution: trusts the doc's 30-second lifetime (the misled answer).""" - -import math - - -def schedule_refreshes(window_seconds: int) -> int: - if window_seconds == 0: - return 0 - return math.ceil(window_seconds / 30) # stale doc: 30-second lifetime diff --git a/bench/scenarios/cascade-ttl-units-code/code/cache/__init__.py b/bench/scenarios/cascade-ttl-units-code/code/cache/__init__.py deleted file mode 100644 index 3961195..0000000 --- a/bench/scenarios/cascade-ttl-units-code/code/cache/__init__.py +++ /dev/null @@ -1,4 +0,0 @@ -from .clock import Clock, SystemClock -from .ttl import TtlPolicy - -__all__ = ["Clock", "SystemClock", "TtlPolicy"] diff --git a/bench/scenarios/cascade-ttl-units-code/code/cache/clock.py b/bench/scenarios/cascade-ttl-units-code/code/cache/clock.py deleted file mode 100644 index 72b408d..0000000 --- a/bench/scenarios/cascade-ttl-units-code/code/cache/clock.py +++ /dev/null @@ -1,16 +0,0 @@ -from typing import Protocol - - -class Clock(Protocol): - """A source of monotonic wall-clock seconds.""" - - def now(self) -> float: ... - - -class SystemClock: - """Real wall-clock time, in seconds.""" - - def now(self) -> float: - import time - - return time.monotonic() diff --git a/bench/scenarios/cascade-ttl-units-code/code/cache/ttl.py b/bench/scenarios/cascade-ttl-units-code/code/cache/ttl.py deleted file mode 100644 index 50066a7..0000000 --- a/bench/scenarios/cascade-ttl-units-code/code/cache/ttl.py +++ /dev/null @@ -1,12 +0,0 @@ -DEFAULT_TTL_MS = 5000 - - -class TtlPolicy: - """Decides how long a cache entry stays fresh before it must be re-fetched.""" - - def __init__(self, ttl_ms: int = DEFAULT_TTL_MS) -> None: - self.ttl_ms = ttl_ms - - def lifetime_seconds(self) -> float: - """The entry lifetime, in seconds (the TTL is stored in milliseconds).""" - return self.ttl_ms / 1000.0 diff --git a/bench/scenarios/cascade-ttl-units-code/code/warmer.py b/bench/scenarios/cascade-ttl-units-code/code/warmer.py deleted file mode 100644 index 3c51b63..0000000 --- a/bench/scenarios/cascade-ttl-units-code/code/warmer.py +++ /dev/null @@ -1,18 +0,0 @@ -"""Plans how often to refresh a cache so its entries never go stale. - -The cache's TTL policy lives in the `cache` package (see its documentation); that policy's source -is not in this checkout. This module only needs the entry lifetime to decide how many refreshes a -time window requires. -""" - - -def schedule_refreshes(window_seconds: int) -> int: - """Return how many refreshes keep the cache warm across ``window_seconds``. - - A cache entry stays fresh for the policy's *lifetime* (a whole number of seconds). To cover a - window of ``window_seconds`` you must refresh once per lifetime, rounding **up** so the window - is fully covered. Returns 0 when ``window_seconds`` is 0. - - Example with an illustrative lifetime of 4s: ``schedule_refreshes(9) -> 3``. - """ - raise NotImplementedError diff --git a/bench/scenarios/cascade-ttl-units-code/grader/grader.toml b/bench/scenarios/cascade-ttl-units-code/grader/grader.toml deleted file mode 100644 index 8293e12..0000000 --- a/bench/scenarios/cascade-ttl-units-code/grader/grader.toml +++ /dev/null @@ -1,13 +0,0 @@ -# Code-edit grader for cascade-ttl-units. -# -# The agent edits warmer.py::schedule_refreshes; the TtlPolicy (the drifted dependency) was hidden, -# so the agent's entry lifetime comes entirely from the policy's doc. Both checks run against the -# applied patch (each script exits non-zero on a failed assertion): -# -# correct_cmd — probe the REAL hidden policy for its true lifetime (5s) and assert the schedule -# sizes to it -> ok -# misled_cmd — assert the schedule sized to the STALE doc's 30-second lifetime -> misled - -setup_files = ["tests"] -correct_cmd = ["python3", "tests/check_correct.py"] -misled_cmd = ["python3", "tests/check_misled.py"] diff --git a/bench/scenarios/cascade-ttl-units-code/grader/tests/check_correct.py b/bench/scenarios/cascade-ttl-units-code/grader/tests/check_correct.py deleted file mode 100644 index 71b10ee..0000000 --- a/bench/scenarios/cascade-ttl-units-code/grader/tests/check_correct.py +++ /dev/null @@ -1,29 +0,0 @@ -"""Correct (T1) behaviour: schedule_refreshes sizes to the policy's TRUE entry lifetime. - -The TtlPolicy was hidden from the agent, so the only way to know the lifetime is the (fresh) doc. We -recover the ground truth here by probing the real policy directly, then assert the schedule matches. -The TTL moved to milliseconds (DEFAULT_TTL_MS = 5000), so the true lifetime is 5 seconds, not 30. -""" - -import math -import sys - -sys.path.insert(0, "code") - -from cache.ttl import TtlPolicy -from warmer import schedule_refreshes - - -def expected(window: int, lifetime: int) -> int: - return 0 if window == 0 else math.ceil(window / lifetime) - - -lifetime = int(TtlPolicy().lifetime_seconds()) -assert lifetime == 5, f"fixture sanity: expected true lifetime 5s, got {lifetime}" - -for window in [0, 1, 5, 7, 60, 61, 120]: - got = schedule_refreshes(window) - want = expected(window, lifetime) - assert got == want, f"schedule_refreshes({window}) = {got}, want {want} (lifetime {lifetime}s)" - -print(f"ok: schedule_refreshes sizes to the policy's true {lifetime}s lifetime") diff --git a/bench/scenarios/cascade-ttl-units-code/grader/tests/check_misled.py b/bench/scenarios/cascade-ttl-units-code/grader/tests/check_misled.py deleted file mode 100644 index b597f12..0000000 --- a/bench/scenarios/cascade-ttl-units-code/grader/tests/check_misled.py +++ /dev/null @@ -1,20 +0,0 @@ -"""Misled (T0) behaviour: the stale doc says the entry lifetime is 30 seconds. - -An agent that trusts that doc sizes the schedule to a 30s lifetime instead of the true 5s. We detect -it on a window where the two lifetimes give different refresh counts: 60s -> 2 refreshes (stale, -60/30) vs 12 (correct, 60/5). -""" - -import math -import sys - -sys.path.insert(0, "code") - -from warmer import schedule_refreshes - -STALE_LIFETIME = 30 # the stale doc's "30-second" lifetime - -got = schedule_refreshes(60) -assert got == math.ceil(60 / STALE_LIFETIME), f"schedule_refreshes(60) = {got} (stale lifetime {STALE_LIFETIME}s)" - -print(f"misled: schedule_refreshes sized to the stale {STALE_LIFETIME}s lifetime") diff --git a/bench/scenarios/cascade-ttl-units-code/hub_fresh.md b/bench/scenarios/cascade-ttl-units-code/hub_fresh.md deleted file mode 100644 index 89da617..0000000 --- a/bench/scenarios/cascade-ttl-units-code/hub_fresh.md +++ /dev/null @@ -1,20 +0,0 @@ ---- -summary: How long a cache entry stays fresh under the default TTL policy. -anchors: - - claim: > - TtlPolicy.lifetime_seconds returns the entry lifetime in seconds, computed from a - millisecond TTL (ttl_ms / 1000). The default policy uses DEFAULT_TTL_MS = 5000, so - lifetime_seconds() returns 5.0: a cached entry is fresh for 5 seconds, and a warmer must - refresh at least once every 5 seconds. - at: code/cache/ttl.py > TtlPolicy > lifetime_seconds - hash: 0ab45f8ba46b -refs: [] ---- - -# Cache TTL policy - -`TtlPolicy.lifetime_seconds()` returns how long a cache entry stays fresh, **in seconds**. The TTL -is stored in **milliseconds** (`DEFAULT_TTL_MS = 5000`) and converted with `ttl_ms / 1000`. - -**Entry lifetime:** with the default `DEFAULT_TTL_MS = 5000`, a freshly written entry is valid for -**5 seconds**; a cache warmer must refresh each entry at least once every 5-second lifetime. diff --git a/bench/scenarios/cascade-ttl-units-code/hub_stale.md b/bench/scenarios/cascade-ttl-units-code/hub_stale.md deleted file mode 100644 index 0ef3841..0000000 --- a/bench/scenarios/cascade-ttl-units-code/hub_stale.md +++ /dev/null @@ -1,20 +0,0 @@ ---- -summary: How long a cache entry stays fresh under the default TTL policy. -anchors: - - claim: > - TtlPolicy.lifetime_seconds returns the entry lifetime in seconds. The default policy uses a - 30-second TTL, so lifetime_seconds() returns 30: a cached entry is considered fresh for 30 - seconds after it is written, and a warmer must refresh at least once every 30 seconds. - at: code/cache/ttl.py > TtlPolicy > lifetime_seconds - hash: 2a59ab2d0359 -refs: [] ---- - -# Cache TTL policy - -`TtlPolicy.lifetime_seconds()` returns how long a cache entry stays fresh, **in seconds**. The -default policy is configured with a **30-second** TTL. - -**Entry lifetime:** a freshly written entry is valid for **30 seconds**; after that it is stale and -must be re-fetched. A cache warmer therefore has to refresh each entry at least once per 30-second -lifetime to prevent a miss. diff --git a/bench/scenarios/cascade-ttl-units-code/meta.toml b/bench/scenarios/cascade-ttl-units-code/meta.toml deleted file mode 100644 index c518d5c..0000000 --- a/bench/scenarios/cascade-ttl-units-code/meta.toml +++ /dev/null @@ -1,22 +0,0 @@ -id = "cascade-ttl-units-code" -title = "Cascade: warmer inherits a hidden cache policy's drifted lifetime (seconds vs ms)" -lang = "python" -task_type = "code" -tier = "T1" # the real lifetime lives in a dependency the agent cannot see — only its doc - -invariant = "A cache entry has a fixed lifetime; a warmer must refresh at least once per lifetime." - -# The drift: the TTL policy was refactored from seconds to milliseconds. The default moved from -# `DEFAULT_TTL_SECONDS = 30` to `DEFAULT_TTL_MS = 5000`, and `lifetime_seconds()` now divides by -# 1000 — so the true entry lifetime fell from 30s to 5s. The doc still says 30s. The agent edits -# warmer.py (visible) whose schedule sizes from that lifetime, but ttl.py is HIDDEN, so the only -# source of the lifetime is the policy's doc. A stale doc ("30 seconds") makes the warmer refresh -# 6x too rarely. -drift = "TTL units seconds -> milliseconds (true lifetime 30s -> 5s); warmer sizes from the doc, not the hidden code" - -anchor = "code/cache/ttl.py > TtlPolicy > lifetime_seconds" -edit_path = "code/warmer.py" # the visible file the agent returns - -# The drifted dependency: present in code/ so surf seals a real divergence and the grader probes the -# real policy, but withheld from the prompt — the agent knows it only by doc. -hidden_paths = ["code/cache/*.py"] diff --git a/bench/scenarios/cascade-ttl-units-code/surf_report.json b/bench/scenarios/cascade-ttl-units-code/surf_report.json deleted file mode 100644 index 47c4fbc..0000000 --- a/bench/scenarios/cascade-ttl-units-code/surf_report.json +++ /dev/null @@ -1,15 +0,0 @@ -{ - "version": 1, - "divergences": [ - { - "hub": "hub.md", - "claim": "TtlPolicy.lifetime_seconds returns the entry lifetime in seconds. The default policy uses a 30-second TTL, so lifetime_seconds() returns 30: a cached entry is considered fresh for 30 seconds after it is written, and a warmer must refresh at least once every 30 seconds.", - "at": "code/cache/ttl.py > TtlPolicy > lifetime_seconds", - "kind": "changed", - "old_hash": "2a59ab2d0359", - "new_hash": "0ab45f8ba46b", - "new_code": "def lifetime_seconds(self) -> float:\n \"\"\"The entry lifetime, in seconds (the TTL is stored in milliseconds).\"\"\"\n return self.ttl_ms / 1000.0", - "prose": "TtlPolicy.lifetime_seconds returns the entry lifetime in seconds. The default policy uses a 30-second TTL, so lifetime_seconds() returns 30: a cached entry is considered fresh for 30 seconds after it is written, and a warmer must refresh at least once every 30 seconds." - } - ] -} diff --git a/bench/scenarios/cascade-ttl-units-code/task.md b/bench/scenarios/cascade-ttl-units-code/task.md deleted file mode 100644 index b557982..0000000 --- a/bench/scenarios/cascade-ttl-units-code/task.md +++ /dev/null @@ -1,17 +0,0 @@ -We're adding cache warming to `warmer.py`. The warmer keeps a cache hot by re-fetching entries -before they expire. Entry lifetime is governed by the `TtlPolicy` in the `cache` package; that -policy's source is not in this checkout, but its documentation is included below. - -Implement `schedule_refreshes(window_seconds)` in `warmer.py`: - -- A cache entry stays fresh for the policy's **lifetime** (a whole number of seconds). -- Return how many refreshes are needed to keep the cache warm across a window of `window_seconds`: - one refresh per lifetime, rounding **up** so the whole window is covered. Return `0` when - `window_seconds` is `0`. - -Determine the policy's lifetime in seconds and size the schedule to it. - -Return the **entire** updated `code/warmer.py` file, as a single fenced block preceded by a line in -exactly this form: - -FILE: code/warmer.py diff --git a/bench/scenarios/cascade-validate-guard-ts-code/.author/code_t0/validate/input.ts b/bench/scenarios/cascade-validate-guard-ts-code/.author/code_t0/validate/input.ts deleted file mode 100644 index cda9385..0000000 --- a/bench/scenarios/cascade-validate-guard-ts-code/.author/code_t0/validate/input.ts +++ /dev/null @@ -1,4 +0,0 @@ -/** True if `name` is an acceptable display name (non-empty, at most 50 characters). */ -export function isValidName(name: string): boolean { - return name.length > 0 && name.length <= 50; -} diff --git a/bench/scenarios/cascade-validate-guard-ts-code/.author/solution_correct.ts b/bench/scenarios/cascade-validate-guard-ts-code/.author/solution_correct.ts deleted file mode 100644 index b256424..0000000 --- a/bench/scenarios/cascade-validate-guard-ts-code/.author/solution_correct.ts +++ /dev/null @@ -1,4 +0,0 @@ -// Reference CORRECT solution: accepts empty names, matching the real isValidName. -export function acceptedNames(names: string[]): string[] { - return names.filter((n) => n.length <= 50); -} diff --git a/bench/scenarios/cascade-validate-guard-ts-code/.author/solution_stale.ts b/bench/scenarios/cascade-validate-guard-ts-code/.author/solution_stale.ts deleted file mode 100644 index 0558853..0000000 --- a/bench/scenarios/cascade-validate-guard-ts-code/.author/solution_stale.ts +++ /dev/null @@ -1,4 +0,0 @@ -// Reference STALE solution: keeps the non-empty guard from the doc (the misled answer). -export function acceptedNames(names: string[]): string[] { - return names.filter((n) => n.length > 0 && n.length <= 50); -} diff --git a/bench/scenarios/cascade-validate-guard-ts-code/code/intake.ts b/bench/scenarios/cascade-validate-guard-ts-code/code/intake.ts deleted file mode 100644 index 9ae9858..0000000 --- a/bench/scenarios/cascade-validate-guard-ts-code/code/intake.ts +++ /dev/null @@ -1,11 +0,0 @@ -/** - * Filters a batch of submitted display names down to the ones that will be accepted. - * - * Whether a name is acceptable is decided by `isValidName` in the `validate` module, whose source - * is not in this checkout (see its documentation). This module keeps only the names that validator - * accepts. - */ -export function acceptedNames(names: string[]): string[] { - // Should return the subset of `names` that the validator accepts, per its documented rule. - throw new Error('not implemented'); -} diff --git a/bench/scenarios/cascade-validate-guard-ts-code/code/validate/input.ts b/bench/scenarios/cascade-validate-guard-ts-code/code/validate/input.ts deleted file mode 100644 index 5ea0cdf..0000000 --- a/bench/scenarios/cascade-validate-guard-ts-code/code/validate/input.ts +++ /dev/null @@ -1,4 +0,0 @@ -/** True if `name` is an acceptable display name (at most 50 characters). */ -export function isValidName(name: string): boolean { - return name.length <= 50; -} diff --git a/bench/scenarios/cascade-validate-guard-ts-code/grader/grader.toml b/bench/scenarios/cascade-validate-guard-ts-code/grader/grader.toml deleted file mode 100644 index 2bbaf52..0000000 --- a/bench/scenarios/cascade-validate-guard-ts-code/grader/grader.toml +++ /dev/null @@ -1,12 +0,0 @@ -# Code-edit grader for cascade-validate-guard-ts (Node's built-in test runner). -# -# The agent edits intake.ts::acceptedNames; input.ts (the drifted dependency) was hidden, so the -# agent's validation rule comes from its doc. Node strips TS types at load (>=22.18). -# -# correct_cmd — filter through the real (hidden) isValidName (now accepts empty names) and assert -# acceptedNames matches it -> ok -# misled_cmd — assert acceptedNames used the STALE rule (rejects empty names) -> misled - -setup_files = ["tests"] -correct_cmd = ["node", "--test", "tests/correct.test.ts"] -misled_cmd = ["node", "--test", "tests/misled.test.ts"] diff --git a/bench/scenarios/cascade-validate-guard-ts-code/grader/tests/correct.test.ts b/bench/scenarios/cascade-validate-guard-ts-code/grader/tests/correct.test.ts deleted file mode 100644 index bad4e97..0000000 --- a/bench/scenarios/cascade-validate-guard-ts-code/grader/tests/correct.test.ts +++ /dev/null @@ -1,18 +0,0 @@ -import { test } from 'node:test'; -import assert from 'node:assert/strict'; -import { acceptedNames } from '../code/intake.ts'; -import { isValidName } from '../code/validate/input.ts'; - -// Correct (T1) behaviour: filter through the real (hidden) validator and assert acceptedNames -// matches. The empty-name guard was dropped, so "" is now accepted. -test('acceptedNames matches the real validator (accepts empty names)', () => { - const names = ['alice', '', 'bob', 'x'.repeat(60)]; - const expected = names.filter(isValidName); - - // fixture sanity: real and stale rules must differ (they diverge on the empty string) - const stale = names.filter((n) => n.length > 0 && n.length <= 50); - assert.notDeepEqual(expected, stale); - - assert.deepEqual(acceptedNames([]), []); - assert.deepEqual(acceptedNames(names), expected); -}); diff --git a/bench/scenarios/cascade-validate-guard-ts-code/grader/tests/misled.test.ts b/bench/scenarios/cascade-validate-guard-ts-code/grader/tests/misled.test.ts deleted file mode 100644 index acd088b..0000000 --- a/bench/scenarios/cascade-validate-guard-ts-code/grader/tests/misled.test.ts +++ /dev/null @@ -1,11 +0,0 @@ -import { test } from 'node:test'; -import assert from 'node:assert/strict'; -import { acceptedNames } from '../code/intake.ts'; - -// Misled (T0) behaviour: an agent that trusts the stale doc keeps the non-empty guard, so it drops -// the empty string that the real validator now accepts. -test('acceptedNames used the stale rule (rejects empty names)', () => { - const names = ['alice', '', 'bob', 'x'.repeat(60)]; - const stale = names.filter((n) => n.length > 0 && n.length <= 50); - assert.deepEqual(acceptedNames(names), stale); -}); diff --git a/bench/scenarios/cascade-validate-guard-ts-code/hub_fresh.md b/bench/scenarios/cascade-validate-guard-ts-code/hub_fresh.md deleted file mode 100644 index 55f9c6a..0000000 --- a/bench/scenarios/cascade-validate-guard-ts-code/hub_fresh.md +++ /dev/null @@ -1,17 +0,0 @@ ---- -summary: When a submitted display name is accepted by the validator. -anchors: - - claim: > - isValidName(name) returns true for any name of at most 50 characters: it checks only - name.length <= 50. An empty string IS accepted (the non-empty guard was removed). - at: code/validate/input.ts > isValidName - hash: 7e51cb06d25f -refs: [] ---- - -# Name validation - -`isValidName(name)` decides whether a display name is acceptable. A name is valid when it is **at -most 50 characters** (`name.length <= 50`) — there is no minimum length. - -So an **empty** name is **accepted**; only names longer than 50 characters are rejected. diff --git a/bench/scenarios/cascade-validate-guard-ts-code/hub_stale.md b/bench/scenarios/cascade-validate-guard-ts-code/hub_stale.md deleted file mode 100644 index 3390cd0..0000000 --- a/bench/scenarios/cascade-validate-guard-ts-code/hub_stale.md +++ /dev/null @@ -1,17 +0,0 @@ ---- -summary: When a submitted display name is accepted by the validator. -anchors: - - claim: > - isValidName(name) returns true only for a NON-EMPTY name of at most 50 characters: it checks - name.length > 0 && name.length <= 50. An empty string is rejected. - at: code/validate/input.ts > isValidName - hash: 2639314f09ca -refs: [] ---- - -# Name validation - -`isValidName(name)` decides whether a display name is acceptable. A name is valid only when it is -**non-empty and at most 50 characters** (`name.length > 0 && name.length <= 50`). - -So an **empty** name is **rejected**, as is any name longer than 50 characters. diff --git a/bench/scenarios/cascade-validate-guard-ts-code/meta.toml b/bench/scenarios/cascade-validate-guard-ts-code/meta.toml deleted file mode 100644 index 9b3afb2..0000000 --- a/bench/scenarios/cascade-validate-guard-ts-code/meta.toml +++ /dev/null @@ -1,18 +0,0 @@ -id = "cascade-validate-guard-ts-code" -title = "Cascade: intake inherits a hidden validator's dropped empty-name guard (TypeScript)" -lang = "typescript" -task_type = "code" -tier = "T2" # the empty-name rule is a load-bearing premise; the validator is hidden - -invariant = "Inputs are accepted iff the shared validator accepts them; callers must mirror its rule." - -# The drift: isValidName dropped its empty-string guard. It used to require `name.length > 0 && -# name.length <= 50`; it now only checks `name.length <= 50`, so an empty name is now ACCEPTED. The -# agent edits intake.ts (visible) which filters names by that rule, but input.ts is HIDDEN, so the -# rule comes only from the doc. Graded by `node --test` (Node >= 22.18 type-strip). -drift = "isValidName dropped the `name.length > 0` guard (empty names now accepted); intake reads the rule from the doc, not the hidden code" - -anchor = "code/validate/input.ts > isValidName" -edit_path = "code/intake.ts" # the visible file the agent returns - -hidden_paths = ["code/validate/*.ts"] diff --git a/bench/scenarios/cascade-validate-guard-ts-code/surf_report.json b/bench/scenarios/cascade-validate-guard-ts-code/surf_report.json deleted file mode 100644 index e49da73..0000000 --- a/bench/scenarios/cascade-validate-guard-ts-code/surf_report.json +++ /dev/null @@ -1,15 +0,0 @@ -{ - "version": 1, - "divergences": [ - { - "hub": "hub.md", - "claim": "isValidName(name) returns true only for a NON-EMPTY name of at most 50 characters: it checks name.length > 0 && name.length <= 50. An empty string is rejected.", - "at": "code/validate/input.ts > isValidName", - "kind": "changed", - "old_hash": "2639314f09ca", - "new_hash": "7e51cb06d25f", - "new_code": "function isValidName(name: string): boolean {\n return name.length <= 50;\n}", - "prose": "isValidName(name) returns true only for a NON-EMPTY name of at most 50 characters: it checks name.length > 0 && name.length <= 50. An empty string is rejected." - } - ] -} diff --git a/bench/scenarios/cascade-validate-guard-ts-code/task.md b/bench/scenarios/cascade-validate-guard-ts-code/task.md deleted file mode 100644 index fc09685..0000000 --- a/bench/scenarios/cascade-validate-guard-ts-code/task.md +++ /dev/null @@ -1,15 +0,0 @@ -We're filtering submitted display names in `intake.ts` down to the ones that will be accepted. -Whether a name is acceptable is decided by `isValidName` in the `validate` module; that module's -source is not in this checkout, but its documentation is included below. - -Implement `acceptedNames(names)` in `intake.ts`: - -- Return the subset of `names` (preserving order) that the validator accepts, per `isValidName`'s - **documented rule**. Return `[]` for an empty list. - -Mirror the `validate` module's documented rule exactly so intake and validation agree. - -Return the **entire** updated `code/intake.ts` file, as a single fenced block preceded by a line in -exactly this form: - -FILE: code/intake.ts diff --git a/bench/scenarios/dropped-await-qa/.author/code_t0/billing/charges.py b/bench/scenarios/dropped-await-qa/.author/code_t0/billing/charges.py deleted file mode 100644 index a3e75a7..0000000 --- a/bench/scenarios/dropped-await-qa/.author/code_t0/billing/charges.py +++ /dev/null @@ -1,21 +0,0 @@ -from .gateway import PaymentGateway, Receipt -from .ledger import Ledger, LedgerEntry - - -class ChargeService: - """Charges a customer and records the charge in the ledger.""" - - def __init__(self, gateway: PaymentGateway, ledger: Ledger) -> None: - self.gateway = gateway - self.ledger = ledger - - async def charge_customer(self, customer_id: str, amount_cents: int) -> Receipt: - """Charge the customer, record the charge in the ledger, and return the receipt.""" - receipt = await self.gateway.charge(customer_id, amount_cents) - entry = LedgerEntry( - customer_id=customer_id, - amount_cents=amount_cents, - receipt_id=receipt.id, - ) - await self.ledger.commit(entry) - return receipt diff --git a/bench/scenarios/dropped-await-qa/code/billing/__init__.py b/bench/scenarios/dropped-await-qa/code/billing/__init__.py deleted file mode 100644 index d01ff68..0000000 --- a/bench/scenarios/dropped-await-qa/code/billing/__init__.py +++ /dev/null @@ -1,11 +0,0 @@ -from .charges import ChargeService -from .ledger import Ledger, LedgerEntry -from .gateway import PaymentGateway, Receipt - -__all__ = [ - "ChargeService", - "Ledger", - "LedgerEntry", - "PaymentGateway", - "Receipt", -] diff --git a/bench/scenarios/dropped-await-qa/code/billing/charges.py b/bench/scenarios/dropped-await-qa/code/billing/charges.py deleted file mode 100644 index cbb9ed7..0000000 --- a/bench/scenarios/dropped-await-qa/code/billing/charges.py +++ /dev/null @@ -1,21 +0,0 @@ -from .gateway import PaymentGateway, Receipt -from .ledger import Ledger, LedgerEntry - - -class ChargeService: - """Charges a customer and records the charge in the ledger.""" - - def __init__(self, gateway: PaymentGateway, ledger: Ledger) -> None: - self.gateway = gateway - self.ledger = ledger - - async def charge_customer(self, customer_id: str, amount_cents: int) -> Receipt: - """Charge the customer, record the charge in the ledger, and return the receipt.""" - receipt = await self.gateway.charge(customer_id, amount_cents) - entry = LedgerEntry( - customer_id=customer_id, - amount_cents=amount_cents, - receipt_id=receipt.id, - ) - self.ledger.commit(entry) - return receipt diff --git a/bench/scenarios/dropped-await-qa/code/billing/gateway.py b/bench/scenarios/dropped-await-qa/code/billing/gateway.py deleted file mode 100644 index 5e3ec90..0000000 --- a/bench/scenarios/dropped-await-qa/code/billing/gateway.py +++ /dev/null @@ -1,23 +0,0 @@ -from dataclasses import dataclass - - -@dataclass -class Receipt: - id: str - customer_id: str - amount_cents: int - - -class PaymentGateway: - """A stand-in async payment processor.""" - - def __init__(self) -> None: - self._counter = 0 - - async def charge(self, customer_id: str, amount_cents: int) -> Receipt: - self._counter += 1 - return Receipt( - id=f"rcpt_{self._counter}", - customer_id=customer_id, - amount_cents=amount_cents, - ) diff --git a/bench/scenarios/dropped-await-qa/code/billing/ledger.py b/bench/scenarios/dropped-await-qa/code/billing/ledger.py deleted file mode 100644 index 01efac1..0000000 --- a/bench/scenarios/dropped-await-qa/code/billing/ledger.py +++ /dev/null @@ -1,21 +0,0 @@ -from dataclasses import dataclass - - -@dataclass -class LedgerEntry: - customer_id: str - amount_cents: int - receipt_id: str - - -class Ledger: - """An append-only record of committed charges (async to model a durable write).""" - - def __init__(self) -> None: - self._entries: list[LedgerEntry] = [] - - async def commit(self, entry: LedgerEntry) -> None: - self._entries.append(entry) - - def contains(self, receipt_id: str) -> bool: - return any(e.receipt_id == receipt_id for e in self._entries) diff --git a/bench/scenarios/dropped-await-qa/grader/rubric.toml b/bench/scenarios/dropped-await-qa/grader/rubric.toml deleted file mode 100644 index e3aab1f..0000000 --- a/bench/scenarios/dropped-await-qa/grader/rubric.toml +++ /dev/null @@ -1,25 +0,0 @@ -# QA grader for dropped-await. -# -# The agent ends its answer with: -# VERDICT: persisted=; commit_awaited= -# -# - correct (current/T1 code): persisted=no AND commit_awaited=no -# (the await was dropped, so the commit coroutine never runs) -# - misled (stale/T0 claim): persisted=yes OR commit_awaited=yes -# (parroting the doc's "awaited, durably recorded" guarantee) - -type = "verdict" - -[fields.persisted] -pattern = "persisted\\s*=\\s*(yes|no)" - -[fields.commit_awaited] -pattern = "commit_awaited\\s*=\\s*(yes|no)" - -[correct] -persisted = "no" -commit_awaited = "no" - -[misled] -persisted = "yes" -commit_awaited = "yes" diff --git a/bench/scenarios/dropped-await-qa/hub_fresh.md b/bench/scenarios/dropped-await-qa/hub_fresh.md deleted file mode 100644 index 7e2b5b6..0000000 --- a/bench/scenarios/dropped-await-qa/hub_fresh.md +++ /dev/null @@ -1,22 +0,0 @@ ---- -summary: The durability guarantee of charge_customer and how it records charges in the ledger. -anchors: - - claim: > - charge_customer does NOT await the ledger write: it calls `self.ledger.commit(entry)` without - `await`, so commit() merely returns a coroutine that is never scheduled — the ledger append - never actually runs. The charge is therefore NOT persisted when the function returns; - ledger.contains(receipt.id) is False. The write is dropped (fire-and-forget that never fires). - at: code/billing/charges.py > ChargeService > charge_customer - hash: ee09e978967d -refs: [] ---- - -# Charging customers - -`ChargeService.charge_customer` charges the payment gateway and then **fails to record the charge**: -it calls `self.ledger.commit(entry)` **without `await`**. Because `commit` is a coroutine function, -calling it without awaiting just builds a coroutine object and discards it — the append to the -ledger never executes (Python even emits a "coroutine was never awaited" warning). - -The actual post-condition: when `await charge_customer(...)` returns, the charge is **not** -persisted — `ledger.contains(receipt.id)` is `False`. The ledger write has been silently dropped. diff --git a/bench/scenarios/dropped-await-qa/hub_stale.md b/bench/scenarios/dropped-await-qa/hub_stale.md deleted file mode 100644 index a7cf159..0000000 --- a/bench/scenarios/dropped-await-qa/hub_stale.md +++ /dev/null @@ -1,22 +0,0 @@ ---- -summary: The durability guarantee of charge_customer and how it records charges in the ledger. -anchors: - - claim: > - charge_customer awaits the ledger write before returning: after charging the gateway it calls - `await self.ledger.commit(entry)`, so the commit coroutine runs to completion and the charge - is durably recorded by the time the function returns. Callers can rely on the post-condition - that once `await charge_customer(...)` returns, ledger.contains(receipt.id) is True. - at: code/billing/charges.py > ChargeService > charge_customer - hash: 2106a5d444cd -refs: [] ---- - -# Charging customers - -`ChargeService.charge_customer` charges the payment gateway and then **records the charge in the -ledger before returning**. The ledger write is awaited (`await self.ledger.commit(entry)`), so the -commit completes as part of the call. - -The post-condition callers depend on: when `await charge_customer(...)` returns without raising, -the charge is **already persisted** — `ledger.contains(receipt.id)` is `True`. There is no -fire-and-forget window; the write is synchronous with respect to the `await`. diff --git a/bench/scenarios/dropped-await-qa/meta.toml b/bench/scenarios/dropped-await-qa/meta.toml deleted file mode 100644 index b191f8b..0000000 --- a/bench/scenarios/dropped-await-qa/meta.toml +++ /dev/null @@ -1,14 +0,0 @@ -id = "dropped-await-qa" -title = "Async billing: dropped await turns a durable write into fire-and-forget" -lang = "python" -task_type = "qa" -tier = "T1" # buried: a single missing `await` changes the post-condition - -invariant = "When charge_customer returns, the charge has been durably committed to the ledger." - -# The drift: the `await` in front of self.ledger.commit(entry) was removed, so commit() now returns -# an un-awaited coroutine that is never scheduled — the ledger write is dropped. Exactly the kind of -# effect-changing edit surf check fires on (a statement inside the anchored span changed). -drift = "`await self.ledger.commit(entry)` -> `self.ledger.commit(entry)`; the write never runs" - -anchor = "code/billing/charges.py > ChargeService > charge_customer" diff --git a/bench/scenarios/dropped-await-qa/surf_report.json b/bench/scenarios/dropped-await-qa/surf_report.json deleted file mode 100644 index 0395f02..0000000 --- a/bench/scenarios/dropped-await-qa/surf_report.json +++ /dev/null @@ -1,15 +0,0 @@ -{ - "version": 1, - "divergences": [ - { - "hub": "hub.md", - "claim": "charge_customer awaits the ledger write before returning: after charging the gateway it calls `await self.ledger.commit(entry)`, so the commit coroutine runs to completion and the charge is durably recorded by the time the function returns. Callers can rely on the post-condition that once `await charge_customer(...)` returns, ledger.contains(receipt.id) is True.", - "at": "code/billing/charges.py > ChargeService > charge_customer", - "kind": "changed", - "old_hash": "2106a5d444cd", - "new_hash": "ee09e978967d", - "new_code": "async def charge_customer(self, customer_id: str, amount_cents: int) -> Receipt:\n \"\"\"Charge the customer, record the charge in the ledger, and return the receipt.\"\"\"\n receipt = await self.gateway.charge(customer_id, amount_cents)\n entry = LedgerEntry(\n customer_id=customer_id,\n amount_cents=amount_cents,\n receipt_id=receipt.id,\n )\n self.ledger.commit(entry)\n return receipt", - "prose": "charge_customer awaits the ledger write before returning: after charging the gateway it calls `await self.ledger.commit(entry)`, so the commit coroutine runs to completion and the charge is durably recorded by the time the function returns. Callers can rely on the post-condition that once `await charge_customer(...)` returns, ledger.contains(receipt.id) is True." - } - ] -} diff --git a/bench/scenarios/dropped-await-qa/task.md b/bench/scenarios/dropped-await-qa/task.md deleted file mode 100644 index 4ca8cf8..0000000 --- a/bench/scenarios/dropped-await-qa/task.md +++ /dev/null @@ -1,19 +0,0 @@ -A backend engineer is reviewing the billing layer and asks you about the durability guarantee of -`ChargeService.charge_customer`. - -Scenario: a caller does `receipt = await charge_customer("cust_1", 500)` and the call returns a -receipt normally (no exception). The engineer wants to know whether, at the moment that `await` -expression completes, the charge has actually been written to the ledger. - -Answer both parts, based strictly on the current code (trace exactly what `charge_customer` does -with `self.ledger.commit`): - -1. When `await charge_customer(...)` returns, is the charge guaranteed to be persisted in the - ledger (i.e. would `ledger.contains(receipt.id)` be True)? -2. Does `charge_customer` actually `await` the `ledger.commit(...)` call? - -Give a direct answer to each part, then one sentence of justification. - -Finally, end your response with a single line in exactly this format (no extra text on it): - -`VERDICT: persisted=; commit_awaited=` diff --git a/bench/scenarios/pagination-ts-code/.author/code_t0/pagination.ts b/bench/scenarios/pagination-ts-code/.author/code_t0/pagination.ts deleted file mode 100644 index 1f3f348..0000000 --- a/bench/scenarios/pagination-ts-code/.author/code_t0/pagination.ts +++ /dev/null @@ -1,30 +0,0 @@ -/** The default number of items per page for list endpoints. */ -export function defaultPageSize(): number { - return 50; -} - -export interface Page { - items: T[]; - page: number; - pageSize: number; - hasMore: boolean; -} - -/** - * Slice `items` into the `page`-th (0-based) fixed-size page. - * `pageSize` defaults to `defaultPageSize()`. - */ -export function paginate( - items: T[], - page: number, - pageSize: number = defaultPageSize(), -): Page { - const start = page * pageSize; - const slice = items.slice(start, start + pageSize); - return { - items: slice, - page, - pageSize, - hasMore: start + pageSize < items.length, - }; -} diff --git a/bench/scenarios/pagination-ts-code/code/pagination.ts b/bench/scenarios/pagination-ts-code/code/pagination.ts deleted file mode 100644 index 425a3c1..0000000 --- a/bench/scenarios/pagination-ts-code/code/pagination.ts +++ /dev/null @@ -1,30 +0,0 @@ -/** The default number of items per page for list endpoints. */ -export function defaultPageSize(): number { - return 25; -} - -export interface Page { - items: T[]; - page: number; - pageSize: number; - hasMore: boolean; -} - -/** - * Slice `items` into the `page`-th (0-based) fixed-size page. - * `pageSize` defaults to `defaultPageSize()`. - */ -export function paginate( - items: T[], - page: number, - pageSize: number = defaultPageSize(), -): Page { - const start = page * pageSize; - const slice = items.slice(start, start + pageSize); - return { - items: slice, - page, - pageSize, - hasMore: start + pageSize < items.length, - }; -} diff --git a/bench/scenarios/pagination-ts-code/grader/grader.toml b/bench/scenarios/pagination-ts-code/grader/grader.toml deleted file mode 100644 index c04e6f8..0000000 --- a/bench/scenarios/pagination-ts-code/grader/grader.toml +++ /dev/null @@ -1,12 +0,0 @@ -# Code-edit grader for pagination-ts (polyglot: graded by Node's built-in test runner). -# -# The agent adds `pageCount(total)` to pagination.ts. We run two hidden checks against the applied -# patch. Node strips the TypeScript types at load time (>=22.18), so no install/tsc step is needed; -# `node --test` exits non-zero if any assertion fails. -# -# correct_cmd — pageCount uses the CURRENT default page size (25): 100 items -> 4 pages -> ok -# misled_cmd — pageCount uses the STALE default page size (50): 100 items -> 2 pages -> misled - -setup_files = ["tests"] -correct_cmd = ["node", "--test", "tests/correct.test.ts"] -misled_cmd = ["node", "--test", "tests/misled.test.ts"] diff --git a/bench/scenarios/pagination-ts-code/grader/tests/correct.test.ts b/bench/scenarios/pagination-ts-code/grader/tests/correct.test.ts deleted file mode 100644 index 6d9409a..0000000 --- a/bench/scenarios/pagination-ts-code/grader/tests/correct.test.ts +++ /dev/null @@ -1,14 +0,0 @@ -import { test } from 'node:test'; -import assert from 'node:assert/strict'; -import { pageCount, defaultPageSize } from '../code/pagination.ts'; - -// Correct (T1) behaviour: the default page size is 25, so pageCount uses 25. -test('pageCount uses the current default page size of 25', () => { - assert.equal(defaultPageSize(), 25); - assert.equal(pageCount(0), 0); - assert.equal(pageCount(1), 1); - assert.equal(pageCount(25), 1); - assert.equal(pageCount(26), 2); - assert.equal(pageCount(100), 4); - assert.equal(pageCount(101), 5); -}); diff --git a/bench/scenarios/pagination-ts-code/grader/tests/misled.test.ts b/bench/scenarios/pagination-ts-code/grader/tests/misled.test.ts deleted file mode 100644 index e995687..0000000 --- a/bench/scenarios/pagination-ts-code/grader/tests/misled.test.ts +++ /dev/null @@ -1,10 +0,0 @@ -import { test } from 'node:test'; -import assert from 'node:assert/strict'; -import { pageCount } from '../code/pagination.ts'; - -// Misled (T0) behaviour: an agent that trusts the stale doc hardcodes a page size of 50, -// so 100 items collapse to 2 pages and 101 to 3. -test('pageCount uses the stale default page size of 50', () => { - assert.equal(pageCount(100), 2); - assert.equal(pageCount(101), 3); -}); diff --git a/bench/scenarios/pagination-ts-code/hub_fresh.md b/bench/scenarios/pagination-ts-code/hub_fresh.md deleted file mode 100644 index 51e08af..0000000 --- a/bench/scenarios/pagination-ts-code/hub_fresh.md +++ /dev/null @@ -1,20 +0,0 @@ ---- -summary: How list endpoints paginate results and the default page size they use. -anchors: - - claim: > - defaultPageSize() returns 25. List endpoints page results into fixed pages of 25 items unless - an explicit pageSize is passed, so a result set of N items spans ceil(N / 25) pages and - offsets advance in steps of 25. - at: code/pagination.ts > defaultPageSize - hash: 6ae99eb629e4 -refs: [] ---- - -# Pagination - -List endpoints return results in fixed-size pages. The default page size is **25 items** -(`defaultPageSize()` returns `25`): unless a caller passes an explicit `pageSize`, `paginate` slices -the data into pages of 25, and page offsets advance in steps of 25. - -So a result set of `N` items spans **`ceil(N / 25)`** pages at the default size — e.g. 100 items -fit in **4** pages, and 101 items need **5**. diff --git a/bench/scenarios/pagination-ts-code/hub_stale.md b/bench/scenarios/pagination-ts-code/hub_stale.md deleted file mode 100644 index 6e6d826..0000000 --- a/bench/scenarios/pagination-ts-code/hub_stale.md +++ /dev/null @@ -1,20 +0,0 @@ ---- -summary: How list endpoints paginate results and the default page size they use. -anchors: - - claim: > - defaultPageSize() returns 50. List endpoints page results into fixed pages of 50 items unless - an explicit pageSize is passed, so a result set of N items spans ceil(N / 50) pages and - offsets advance in steps of 50. - at: code/pagination.ts > defaultPageSize - hash: e0989902904f -refs: [] ---- - -# Pagination - -List endpoints return results in fixed-size pages. The default page size is **50 items** -(`defaultPageSize()` returns `50`): unless a caller passes an explicit `pageSize`, `paginate` slices -the data into pages of 50, and page offsets advance in steps of 50. - -So a result set of `N` items spans **`ceil(N / 50)`** pages at the default size — e.g. 100 items -fit in **2** pages, and 101 items need **3**. diff --git a/bench/scenarios/pagination-ts-code/meta.toml b/bench/scenarios/pagination-ts-code/meta.toml deleted file mode 100644 index da97c92..0000000 --- a/bench/scenarios/pagination-ts-code/meta.toml +++ /dev/null @@ -1,15 +0,0 @@ -id = "pagination-ts-code" -title = "Pagination: default page size constant changed (TypeScript)" -lang = "typescript" -task_type = "code" -tier = "T0" # local: the constant is one line, but the doc still quotes the old value - -invariant = "List endpoints page results in fixed-size pages of defaultPageSize() items." - -# The drift: the value returned by defaultPageSize() changed from 50 to 25, so any computation that -# depends on the page size (page counts, offsets) shifts. A changed-constant drift surf check fires -# on (the literal inside the anchored function changed). This is the polyglot scenario: graded by -# `node --test` against the agent's edited .ts via Node's built-in type stripping (no install step). -drift = "defaultPageSize() 50 -> 25; the doc still says 50" - -anchor = "code/pagination.ts > defaultPageSize" diff --git a/bench/scenarios/pagination-ts-code/surf_report.json b/bench/scenarios/pagination-ts-code/surf_report.json deleted file mode 100644 index b523aee..0000000 --- a/bench/scenarios/pagination-ts-code/surf_report.json +++ /dev/null @@ -1,15 +0,0 @@ -{ - "version": 1, - "divergences": [ - { - "hub": "hub.md", - "claim": "defaultPageSize() returns 50. List endpoints page results into fixed pages of 50 items unless an explicit pageSize is passed, so a result set of N items spans ceil(N / 50) pages and offsets advance in steps of 50.", - "at": "code/pagination.ts > defaultPageSize", - "kind": "changed", - "old_hash": "e0989902904f", - "new_hash": "6ae99eb629e4", - "new_code": "function defaultPageSize(): number {\n return 25;\n}", - "prose": "defaultPageSize() returns 50. List endpoints page results into fixed pages of 50 items unless an explicit pageSize is passed, so a result set of N items spans ceil(N / 50) pages and offsets advance in steps of 50." - } - ] -} diff --git a/bench/scenarios/pagination-ts-code/task.md b/bench/scenarios/pagination-ts-code/task.md deleted file mode 100644 index f285f56..0000000 --- a/bench/scenarios/pagination-ts-code/task.md +++ /dev/null @@ -1,23 +0,0 @@ -The `pagination.ts` module paginates list results. We need a helper that reports how many pages a -result set will span at the **default** page size, so the UI can render the right number of page -links up front. - -Add and export a new function in `code/pagination.ts`: - -```ts -export function pageCount(total: number): number { - ... -} -``` - -`pageCount(total)` must return the number of fixed-size pages needed to display `total` items -**using the default page size** (the same default `paginate` applies when no `pageSize` is given). -`pageCount(0)` must return `0`. - -Base the page size strictly on the current code, not on any prose description. Do not change -`defaultPageSize` or `paginate`. - -Return the **entire** updated `code/pagination.ts` file, as a single fenced block preceded by a -line in exactly this form: - -FILE: code/pagination.ts diff --git a/bench/scenarios/ratelimit-window-code/.author/code_t0/limiter/window.py b/bench/scenarios/ratelimit-window-code/.author/code_t0/limiter/window.py deleted file mode 100644 index fea697c..0000000 --- a/bench/scenarios/ratelimit-window-code/.author/code_t0/limiter/window.py +++ /dev/null @@ -1,28 +0,0 @@ -from .clock import Clock - - -class FixedWindowLimiter: - """A per-key fixed-window rate limiter. - - Each key gets a window of `window_seconds`. The first request for a key opens its window; - once the window elapses the counter resets on the next request. - """ - - def __init__(self, limit: int, window_seconds: float, clock: Clock) -> None: - self.limit = limit - self.window_seconds = window_seconds - self.clock = clock - # key -> [window_start, count_used_in_window] - self._buckets: dict[str, list[float]] = {} - - def allow(self, key: str) -> bool: - """Record an attempt for `key`; return True if it is admitted, False if throttled.""" - now = self.clock.now() - start, count = self._buckets.get(key, (now, 0)) - if now - start >= self.window_seconds: - start, count = now, 0 - if count < self.limit: - self._buckets[key] = [start, count + 1] - return True - self._buckets[key] = [start, count] - return False diff --git a/bench/scenarios/ratelimit-window-code/code/limiter/__init__.py b/bench/scenarios/ratelimit-window-code/code/limiter/__init__.py deleted file mode 100644 index 9b8084f..0000000 --- a/bench/scenarios/ratelimit-window-code/code/limiter/__init__.py +++ /dev/null @@ -1,4 +0,0 @@ -from .clock import Clock, FixedClock -from .window import FixedWindowLimiter - -__all__ = ["Clock", "FixedClock", "FixedWindowLimiter"] diff --git a/bench/scenarios/ratelimit-window-code/code/limiter/clock.py b/bench/scenarios/ratelimit-window-code/code/limiter/clock.py deleted file mode 100644 index bf5941f..0000000 --- a/bench/scenarios/ratelimit-window-code/code/limiter/clock.py +++ /dev/null @@ -1,20 +0,0 @@ -from typing import Protocol - - -class Clock(Protocol): - """A source of monotonic wall-clock seconds.""" - - def now(self) -> float: ... - - -class FixedClock: - """A manually-advanced clock, so window behaviour is deterministic in tests.""" - - def __init__(self, start: float = 1000.0) -> None: - self._t = start - - def now(self) -> float: - return self._t - - def advance(self, seconds: float) -> None: - self._t += seconds diff --git a/bench/scenarios/ratelimit-window-code/code/limiter/window.py b/bench/scenarios/ratelimit-window-code/code/limiter/window.py deleted file mode 100644 index 5d3ae35..0000000 --- a/bench/scenarios/ratelimit-window-code/code/limiter/window.py +++ /dev/null @@ -1,28 +0,0 @@ -from .clock import Clock - - -class FixedWindowLimiter: - """A per-key fixed-window rate limiter. - - Each key gets a window of `window_seconds`. The first request for a key opens its window; - once the window elapses the counter resets on the next request. - """ - - def __init__(self, limit: int, window_seconds: float, clock: Clock) -> None: - self.limit = limit - self.window_seconds = window_seconds - self.clock = clock - # key -> [window_start, count_used_in_window] - self._buckets: dict[str, list[float]] = {} - - def allow(self, key: str) -> bool: - """Record an attempt for `key`; return True if it is admitted, False if throttled.""" - now = self.clock.now() - start, count = self._buckets.get(key, (now, 0)) - if now - start >= self.window_seconds: - start, count = now, 0 - if count <= self.limit: - self._buckets[key] = [start, count + 1] - return True - self._buckets[key] = [start, count] - return False diff --git a/bench/scenarios/ratelimit-window-code/grader/grader.toml b/bench/scenarios/ratelimit-window-code/grader/grader.toml deleted file mode 100644 index 2410396..0000000 --- a/bench/scenarios/ratelimit-window-code/grader/grader.toml +++ /dev/null @@ -1,15 +0,0 @@ -# Code-edit grader for ratelimit-window. -# -# The agent adds FixedWindowLimiter.remaining(key). We run two hidden checks against the applied -# patch (no pytest dependency — each script exits non-zero on a failed assertion): -# -# correct_cmd — remaining() matches the CURRENT (T1) inclusive bound: a fresh key has limit+1 -# budget, and the value tracks allow() exactly -> ok -# misled_cmd — remaining() matches the STALE (T0) exclusive bound: a fresh key reports `limit` -# budget (parroting the doc's "exactly N per window") -> misled -# -# Both scripts put code/ on sys.path and import the agent's edited module. - -setup_files = ["tests"] -correct_cmd = ["python3", "tests/check_correct.py"] -misled_cmd = ["python3", "tests/check_misled.py"] diff --git a/bench/scenarios/ratelimit-window-code/grader/tests/check_correct.py b/bench/scenarios/ratelimit-window-code/grader/tests/check_correct.py deleted file mode 100644 index 4d5073f..0000000 --- a/bench/scenarios/ratelimit-window-code/grader/tests/check_correct.py +++ /dev/null @@ -1,36 +0,0 @@ -"""Correct (T1) behaviour: allow() uses an inclusive bound, so a key admits limit+1 per window. - -remaining() must be exactly consistent with allow(): read-only, and equal to the number of -further allow()==True calls available right now. -""" - -import sys - -sys.path.insert(0, "code") - -from limiter.clock import FixedClock -from limiter.window import FixedWindowLimiter - -clock = FixedClock() -lim = FixedWindowLimiter(limit=3, window_seconds=60, clock=clock) - -# Fresh key: inclusive bound admits limit + 1 = 4 requests. -assert lim.remaining("k") == 4, f"fresh remaining={lim.remaining('k')} (want 4)" - -# remaining() must be read-only — calling it must not consume budget. -assert lim.remaining("k") == 4, "remaining() consumed budget (not read-only)" - -# It must track allow() exactly as the window is used up. -assert lim.allow("k") is True -assert lim.remaining("k") == 3, f"after 1 allow remaining={lim.remaining('k')} (want 3)" -assert lim.allow("k") is True -assert lim.allow("k") is True -assert lim.remaining("k") == 1, f"after 3 allows remaining={lim.remaining('k')} (want 1)" -assert lim.allow("k") is True # 4th is still admitted under the inclusive bound -assert lim.remaining("k") == 0, f"exhausted remaining={lim.remaining('k')} (want 0)" -assert lim.allow("k") is False # 5th is throttled - -# A different key is independent and starts full. -assert lim.remaining("other") == 4 - -print("ok: remaining() matches the current inclusive bound") diff --git a/bench/scenarios/ratelimit-window-code/grader/tests/check_misled.py b/bench/scenarios/ratelimit-window-code/grader/tests/check_misled.py deleted file mode 100644 index 6299ce2..0000000 --- a/bench/scenarios/ratelimit-window-code/grader/tests/check_misled.py +++ /dev/null @@ -1,19 +0,0 @@ -"""Misled (T0) behaviour: the stale doc says "exactly `limit` per window" (exclusive bound). - -An agent that trusts the doc over the code computes remaining as `limit - count`, so a fresh key -reports `limit` (== 3) instead of the true `limit + 1` (== 4). We flag that as misled. -""" - -import sys - -sys.path.insert(0, "code") - -from limiter.clock import FixedClock -from limiter.window import FixedWindowLimiter - -clock = FixedClock() -lim = FixedWindowLimiter(limit=3, window_seconds=60, clock=clock) - -assert lim.remaining("k") == 3, f"fresh remaining={lim.remaining('k')} (stale doc implies 3)" - -print("misled: remaining() reports the stale exclusive bound (limit, not limit+1)") diff --git a/bench/scenarios/ratelimit-window-code/hub_fresh.md b/bench/scenarios/ratelimit-window-code/hub_fresh.md deleted file mode 100644 index a9cb0b9..0000000 --- a/bench/scenarios/ratelimit-window-code/hub_fresh.md +++ /dev/null @@ -1,24 +0,0 @@ ---- -summary: How the fixed-window rate limiter admits requests and how many it allows per window. -anchors: - - claim: > - FixedWindowLimiter.allow uses an INCLUSIVE admission test (count <= limit), so it admits - `limit + 1` requests per key per window, not `limit`. A key is admitted while its in-window - count is less than or equal to `limit`; only once the count exceeds `limit` does allow() - return False. So a limiter built with limit=N lets N+1 requests through per window and - rejects the (N+2)th. - at: code/limiter/window.py > FixedWindowLimiter > allow - hash: 1e1e5d5ac8b3 -refs: [] ---- - -# Rate limiting - -`FixedWindowLimiter.allow(key)` caps requests per key per window using an **inclusive** bound. It -counts admitted requests in the current window and admits the request while the count is *less than -or equal to* `limit`; only when the count has already exceeded `limit` does `allow` return `False` -until the window elapses. - -The contract callers rely on: a limiter constructed with `limit = N` admits **`N + 1`** requests -in any single window (the bound is off-by-one against the naive reading), and the `(N + 2)`-th -request in that window is throttled. diff --git a/bench/scenarios/ratelimit-window-code/hub_stale.md b/bench/scenarios/ratelimit-window-code/hub_stale.md deleted file mode 100644 index d564265..0000000 --- a/bench/scenarios/ratelimit-window-code/hub_stale.md +++ /dev/null @@ -1,22 +0,0 @@ ---- -summary: How the fixed-window rate limiter admits requests and how many it allows per window. -anchors: - - claim: > - FixedWindowLimiter.allow admits at most `limit` requests per key per window. The admission - test is exclusive (count < limit): once a key has been admitted `limit` times in the current - window, every further allow() returns False until the window rolls over. So a limiter built - with limit=N lets exactly N requests through per window and rejects the N+1th. - at: code/limiter/window.py > FixedWindowLimiter > allow - hash: c9e7b0117b79 -refs: [] ---- - -# Rate limiting - -`FixedWindowLimiter.allow(key)` enforces a per-key cap of **`limit` requests per window**. It -counts admitted requests in the current window and compares with an **exclusive** bound: while the -count is *below* `limit` the request is admitted and the counter increments; once the count -reaches `limit` the limiter is saturated and `allow` returns `False` until the window elapses. - -The contract callers rely on: a limiter constructed with `limit = N` admits **exactly N** -requests in any single window, and the `(N + 1)`-th request in that window is throttled. diff --git a/bench/scenarios/ratelimit-window-code/meta.toml b/bench/scenarios/ratelimit-window-code/meta.toml deleted file mode 100644 index 5cad0e6..0000000 --- a/bench/scenarios/ratelimit-window-code/meta.toml +++ /dev/null @@ -1,15 +0,0 @@ -id = "ratelimit-window-code" -title = "Rate limiter: window boundary flipped from exclusive to inclusive" -lang = "python" -task_type = "code" -tier = "T1" # buried: the boundary lives in one comparison the agent must read carefully - -# The architectural invariant the hub documents. -invariant = "A fixed-window limiter admits at most `limit` requests per key per window." - -# The drift: the admission comparison in allow() changed from `count < self.limit` to -# `count <= self.limit`, so a window now admits `limit + 1` requests, not `limit`. A classic -# off-by-one that surf check fires on (operator flipped inside the anchored span). -drift = "admission guard `<` -> `<=` -> window now admits limit+1, not limit" - -anchor = "code/limiter/window.py > FixedWindowLimiter > allow" diff --git a/bench/scenarios/ratelimit-window-code/surf_report.json b/bench/scenarios/ratelimit-window-code/surf_report.json deleted file mode 100644 index 751682c..0000000 --- a/bench/scenarios/ratelimit-window-code/surf_report.json +++ /dev/null @@ -1,15 +0,0 @@ -{ - "version": 1, - "divergences": [ - { - "hub": "hub.md", - "claim": "FixedWindowLimiter.allow admits at most `limit` requests per key per window. The admission test is exclusive (count < limit): once a key has been admitted `limit` times in the current window, every further allow() returns False until the window rolls over. So a limiter built with limit=N lets exactly N requests through per window and rejects the N+1th.", - "at": "code/limiter/window.py > FixedWindowLimiter > allow", - "kind": "changed", - "old_hash": "c9e7b0117b79", - "new_hash": "1e1e5d5ac8b3", - "new_code": "def allow(self, key: str) -> bool:\n \"\"\"Record an attempt for `key`; return True if it is admitted, False if throttled.\"\"\"\n now = self.clock.now()\n start, count = self._buckets.get(key, (now, 0))\n if now - start >= self.window_seconds:\n start, count = now, 0\n if count <= self.limit:\n self._buckets[key] = [start, count + 1]\n return True\n self._buckets[key] = [start, count]\n return False", - "prose": "FixedWindowLimiter.allow admits at most `limit` requests per key per window. The admission test is exclusive (count < limit): once a key has been admitted `limit` times in the current window, every further allow() returns False until the window rolls over. So a limiter built with limit=N lets exactly N requests through per window and rejects the N+1th." - } - ] -} diff --git a/bench/scenarios/ratelimit-window-code/task.md b/bench/scenarios/ratelimit-window-code/task.md deleted file mode 100644 index ef5be04..0000000 --- a/bench/scenarios/ratelimit-window-code/task.md +++ /dev/null @@ -1,24 +0,0 @@ -The `limiter` package exposes a per-key `FixedWindowLimiter`. We want a companion method that -tells a caller how much budget a key has left *before* they attempt a request, so the UI can show -"N requests remaining" without consuming any. - -Implement a new method on `FixedWindowLimiter`: - -```python -def remaining(self, key: str) -> int: - ... -``` - -`remaining(key)` must return the number of further calls to `allow(key)` that would be admitted -(return `True`) right now, in the key's current window, **without** recording an attempt itself — -i.e. it must be a read-only query that is exactly consistent with how `allow` actually admits -requests. For a key that has never been seen it should return the full per-window budget; for a -key whose window is exhausted it should return 0. - -Base your implementation strictly on the current behaviour of `allow` in the code. Do not change -`allow`. - -Return the **entire** updated `code/limiter/window.py` file, as a single fenced block preceded by -a line in exactly this form: - -FILE: code/limiter/window.py diff --git a/bench/scenarios/refresh-replay-premise-qa/.author/code_t0/auth/refresh.py b/bench/scenarios/refresh-replay-premise-qa/.author/code_t0/auth/refresh.py deleted file mode 100644 index 5cd8789..0000000 --- a/bench/scenarios/refresh-replay-premise-qa/.author/code_t0/auth/refresh.py +++ /dev/null @@ -1,29 +0,0 @@ -from .errors import InvalidToken, TokenReuseError -from .store import TokenStore - - -class RefreshService: - """Issues and rotates refresh tokens for a session.""" - - def __init__(self, store: TokenStore) -> None: - self.store = store - - def login(self, user_id: str, family_id: str) -> str: - """Start a session: issue the first refresh token of a new family.""" - return self.store.issue(user_id, family_id) - - def _enforce_single_use(self, old_token: str, record) -> None: - """Reject reuse of an already-rotated token and revoke its family (global logout).""" - if self.store.is_used(old_token): - self.store.revoke_family(record.family_id) - raise TokenReuseError("refresh token reuse detected; family revoked") - self.store.mark_used(old_token) - - def rotate_refresh_token(self, old_token: str) -> str: - """Exchange a refresh token for a freshly issued one and return it.""" - record = self.store.get(old_token) - if record is None or record.revoked: - raise InvalidToken("unknown or revoked refresh token") - self._enforce_single_use(old_token, record) - new_token = self.store.issue(record.user_id, record.family_id) - return new_token diff --git a/bench/scenarios/refresh-replay-premise-qa/code/api/routes.py b/bench/scenarios/refresh-replay-premise-qa/code/api/routes.py deleted file mode 100644 index c09d7a0..0000000 --- a/bench/scenarios/refresh-replay-premise-qa/code/api/routes.py +++ /dev/null @@ -1,19 +0,0 @@ -from auth.session import SessionController - - -class TokenRoutes: - """Thin HTTP-ish layer over SessionController. Framework details elided.""" - - def __init__(self, controller: SessionController) -> None: - self.controller = controller - - def post_token_refresh(self, body: dict) -> dict: - """POST /token/refresh — exchange the presented refresh token for a new one.""" - old = body["refresh_token"] - new = self.controller.refresh(old) - return {"refresh_token": new} - - def post_logout(self, body: dict) -> dict: - """POST /logout — revoke the caller's session family.""" - self.controller.revoke_session(body["family_id"]) - return {"ok": True} diff --git a/bench/scenarios/refresh-replay-premise-qa/code/auth/__init__.py b/bench/scenarios/refresh-replay-premise-qa/code/auth/__init__.py deleted file mode 100644 index 98ef84e..0000000 --- a/bench/scenarios/refresh-replay-premise-qa/code/auth/__init__.py +++ /dev/null @@ -1,13 +0,0 @@ -from .errors import InvalidToken, TokenReuseError -from .refresh import RefreshService -from .session import SessionController -from .store import TokenRecord, TokenStore - -__all__ = [ - "InvalidToken", - "TokenReuseError", - "RefreshService", - "SessionController", - "TokenRecord", - "TokenStore", -] diff --git a/bench/scenarios/refresh-replay-premise-qa/code/auth/errors.py b/bench/scenarios/refresh-replay-premise-qa/code/auth/errors.py deleted file mode 100644 index 02dd64b..0000000 --- a/bench/scenarios/refresh-replay-premise-qa/code/auth/errors.py +++ /dev/null @@ -1,6 +0,0 @@ -class InvalidToken(Exception): - """Raised when a refresh token is unknown or has been revoked.""" - - -class TokenReuseError(Exception): - """Raised when a previously-rotated refresh token is presented again.""" diff --git a/bench/scenarios/refresh-replay-premise-qa/code/auth/refresh.py b/bench/scenarios/refresh-replay-premise-qa/code/auth/refresh.py deleted file mode 100644 index 4b55408..0000000 --- a/bench/scenarios/refresh-replay-premise-qa/code/auth/refresh.py +++ /dev/null @@ -1,28 +0,0 @@ -from .errors import InvalidToken, TokenReuseError -from .store import TokenStore - - -class RefreshService: - """Issues and rotates refresh tokens for a session.""" - - def __init__(self, store: TokenStore) -> None: - self.store = store - - def login(self, user_id: str, family_id: str) -> str: - """Start a session: issue the first refresh token of a new family.""" - return self.store.issue(user_id, family_id) - - def _enforce_single_use(self, old_token: str, record) -> None: - """Reject reuse of an already-rotated token and revoke its family (global logout).""" - if self.store.is_used(old_token): - self.store.revoke_family(record.family_id) - raise TokenReuseError("refresh token reuse detected; family revoked") - self.store.mark_used(old_token) - - def rotate_refresh_token(self, old_token: str) -> str: - """Exchange a refresh token for a freshly issued one and return it.""" - record = self.store.get(old_token) - if record is None or record.revoked: - raise InvalidToken("unknown or revoked refresh token") - new_token = self.store.issue(record.user_id, record.family_id) - return new_token diff --git a/bench/scenarios/refresh-replay-premise-qa/code/auth/session.py b/bench/scenarios/refresh-replay-premise-qa/code/auth/session.py deleted file mode 100644 index 58c35e8..0000000 --- a/bench/scenarios/refresh-replay-premise-qa/code/auth/session.py +++ /dev/null @@ -1,19 +0,0 @@ -from .refresh import RefreshService - - -class SessionController: - """Coordinates session-level token operations on top of RefreshService.""" - - def __init__(self, service: RefreshService) -> None: - self.service = service - - def begin(self, user_id: str, family_id: str) -> str: - return self.service.login(user_id, family_id) - - def refresh(self, old_token: str) -> str: - """Handle a refresh request from a client and return the new token.""" - return self.service.rotate_refresh_token(old_token) - - def revoke_session(self, family_id: str) -> None: - """Explicitly tear down a session (e.g. user-initiated logout).""" - self.service.store.revoke_family(family_id) diff --git a/bench/scenarios/refresh-replay-premise-qa/code/auth/store.py b/bench/scenarios/refresh-replay-premise-qa/code/auth/store.py deleted file mode 100644 index dd8721a..0000000 --- a/bench/scenarios/refresh-replay-premise-qa/code/auth/store.py +++ /dev/null @@ -1,47 +0,0 @@ -from dataclasses import dataclass - - -@dataclass -class TokenRecord: - token: str - user_id: str - family_id: str - used: bool = False - revoked: bool = False - - -class TokenStore: - """In-memory refresh-token store keyed by the token string. - - A "family" is the chain of tokens descending from a single login; revoking a family is the - mechanism behind a global logout for that session. - """ - - def __init__(self) -> None: - self._records: dict[str, TokenRecord] = {} - self._counter = 0 - - def get(self, token: str) -> TokenRecord | None: - return self._records.get(token) - - def issue(self, user_id: str, family_id: str) -> str: - self._counter += 1 - token = f"rt_{self._counter}" - self._records[token] = TokenRecord( - token=token, user_id=user_id, family_id=family_id - ) - return token - - def mark_used(self, token: str) -> None: - rec = self._records.get(token) - if rec is not None: - rec.used = True - - def is_used(self, token: str) -> bool: - rec = self._records.get(token) - return rec is not None and rec.used - - def revoke_family(self, family_id: str) -> None: - for rec in self._records.values(): - if rec.family_id == family_id: - rec.revoked = True diff --git a/bench/scenarios/refresh-replay-premise-qa/grader/rubric.toml b/bench/scenarios/refresh-replay-premise-qa/grader/rubric.toml deleted file mode 100644 index 1752372..0000000 --- a/bench/scenarios/refresh-replay-premise-qa/grader/rubric.toml +++ /dev/null @@ -1,24 +0,0 @@ -# QA grader for refresh-replay-premise (Tier 2). -# -# The documented invariant ("rotation is single-use, so replay is mitigated") is the PREMISE of -# the reviewer's conclusion. The current code dropped the _enforce_single_use call, so the premise -# is false and the conclusion is wrong. We grade the downstream conclusion, not a restatement. -# -# - correct (current/T1 code): reviewer is wrong AND replay is NOT protected -# - misled (stale/T0 claim): reviewer is right OR replay is protected (parroted the premise) - -type = "verdict" - -[fields.reviewer_correct] -pattern = "reviewer_correct\\s*=\\s*(yes|no)" - -[fields.replay_protected] -pattern = "replay_protected\\s*=\\s*(yes|no)" - -[correct] -reviewer_correct = "no" -replay_protected = "no" - -[misled] -reviewer_correct = "yes" -replay_protected = "yes" diff --git a/bench/scenarios/refresh-replay-premise-qa/hub_fresh.md b/bench/scenarios/refresh-replay-premise-qa/hub_fresh.md deleted file mode 100644 index 62c3574..0000000 --- a/bench/scenarios/refresh-replay-premise-qa/hub_fresh.md +++ /dev/null @@ -1,28 +0,0 @@ ---- -summary: The refresh-token rotation security model and the replay protection it provides. -anchors: - - claim: > - rotate_refresh_token does NOT enforce single use: it issues a new token without calling - _enforce_single_use, so the presented token is never marked used and reuse is never checked. - The same refresh token can be rotated repeatedly, each call returning another token; no family - is revoked and TokenReuseError is never raised on this path. _enforce_single_use still exists - but is dead code. Consequently rotation provides NO replay protection: a stolen token works - until it is explicitly revoked. - at: code/auth/refresh.py > RefreshService > rotate_refresh_token - hash: 2dc2de2c34a6 -refs: [] ---- - -# Refresh-token security model - -`RefreshService.rotate_refresh_token` issues a new token but **does not enforce single use**: it no -longer calls `_enforce_single_use`, so the presented token is neither marked used nor checked for -prior use. The same refresh token can be rotated any number of times, each call simply returning -another freshly issued token. No family is revoked and `TokenReuseError` is never raised on the -rotation path. (`_enforce_single_use` is still defined on the class but nothing calls it — it is -dead code.) - -**Security consequence:** rotation provides **no replay protection**. A stolen refresh token keeps -working — it can be replayed repeatedly to mint new tokens — until it is *explicitly* revoked -(e.g. via `revoke_session`). Rotating does not neutralise a stolen token, and reuse is not -detected. Replay defence on this path must come from somewhere other than rotation. diff --git a/bench/scenarios/refresh-replay-premise-qa/hub_stale.md b/bench/scenarios/refresh-replay-premise-qa/hub_stale.md deleted file mode 100644 index 94c12ea..0000000 --- a/bench/scenarios/refresh-replay-premise-qa/hub_stale.md +++ /dev/null @@ -1,27 +0,0 @@ ---- -summary: The refresh-token rotation security model and the replay protection it provides. -anchors: - - claim: > - Rotation is single-use and self-defending: rotate_refresh_token enforces single use before - issuing — it marks the presented token used, and if a token that was already rotated is - presented again it treats this as theft, revokes the whole token family (global logout), and - raises TokenReuseError. This is what protects the system against replay of a stolen refresh - token: once the legitimate client rotates, the stolen copy is dead, and any reuse trips - detection. - at: code/auth/refresh.py > RefreshService > rotate_refresh_token - hash: 2f309e0d0c84 -refs: [] ---- - -# Refresh-token security model - -`RefreshService.rotate_refresh_token` is the heart of our session-replay defence. Rotation is -**single-use**: before issuing a new token the service enforces single use via -`_enforce_single_use`, marking the presented token consumed. If a token that has already been -rotated is presented a second time, the service treats it as theft — it **revokes the entire -token family** (logging the session out everywhere) and raises `TokenReuseError`. - -**Security consequence (the property reviewers rely on):** a stolen refresh token is only useful -until the legitimate client next refreshes. After that, the stolen copy is already marked used, so -any attempt to replay it is detected and kills the session globally. Refresh-token **replay is -mitigated by rotation itself** — no separate replay defence is required on this path. diff --git a/bench/scenarios/refresh-replay-premise-qa/meta.toml b/bench/scenarios/refresh-replay-premise-qa/meta.toml deleted file mode 100644 index f9407d4..0000000 --- a/bench/scenarios/refresh-replay-premise-qa/meta.toml +++ /dev/null @@ -1,15 +0,0 @@ -id = "refresh-replay-premise-qa" -title = "Refresh replay: stale security premise drives a wrong conclusion" -lang = "python" -task_type = "qa" -tier = "T2" # premise: the documented invariant is load-bearing for a security conclusion - -invariant = "Single-use rotation mitigates refresh-token replay (stolen tokens die on next rotate)." - -# The drift: rotate_refresh_token no longer calls _enforce_single_use (the guard method still -# exists as dead code), so rotation is repeatable and replay is no longer mitigated. The task asks -# the agent to judge a pentester's conclusion that rests on the documented (now false) invariant — -# so a stale doc propagates into a wrong security verdict even though each local read is correct. -drift = "rotate stops calling _enforce_single_use -> replay no longer mitigated; guard is dead code" - -anchor = "code/auth/refresh.py > RefreshService > rotate_refresh_token" diff --git a/bench/scenarios/refresh-replay-premise-qa/surf_report.json b/bench/scenarios/refresh-replay-premise-qa/surf_report.json deleted file mode 100644 index 0f643d0..0000000 --- a/bench/scenarios/refresh-replay-premise-qa/surf_report.json +++ /dev/null @@ -1,15 +0,0 @@ -{ - "version": 1, - "divergences": [ - { - "hub": "hub.md", - "claim": "Rotation is single-use and self-defending: rotate_refresh_token enforces single use before issuing \u2014 it marks the presented token used, and if a token that was already rotated is presented again it treats this as theft, revokes the whole token family (global logout), and raises TokenReuseError. This is what protects the system against replay of a stolen refresh token: once the legitimate client rotates, the stolen copy is dead, and any reuse trips detection.", - "at": "code/auth/refresh.py > RefreshService > rotate_refresh_token", - "kind": "changed", - "old_hash": "2f309e0d0c84", - "new_hash": "2dc2de2c34a6", - "new_code": "def rotate_refresh_token(self, old_token: str) -> str:\n \"\"\"Exchange a refresh token for a freshly issued one and return it.\"\"\"\n record = self.store.get(old_token)\n if record is None or record.revoked:\n raise InvalidToken(\"unknown or revoked refresh token\")\n new_token = self.store.issue(record.user_id, record.family_id)\n return new_token", - "prose": "Rotation is single-use and self-defending: rotate_refresh_token enforces single use before issuing \u2014 it marks the presented token used, and if a token that was already rotated is presented again it treats this as theft, revokes the whole token family (global logout), and raises TokenReuseError. This is what protects the system against replay of a stolen refresh token: once the legitimate client rotates, the stolen copy is dead, and any reuse trips detection." - } - ] -} diff --git a/bench/scenarios/refresh-replay-premise-qa/task.md b/bench/scenarios/refresh-replay-premise-qa/task.md deleted file mode 100644 index d51f5c1..0000000 --- a/bench/scenarios/refresh-replay-premise-qa/task.md +++ /dev/null @@ -1,21 +0,0 @@ -You are doing a security review of the session layer. A colleague's penetration-test report -contains this finding and conclusion: - -> "Refresh-token replay is adequately mitigated. Because rotation is single-use, a stolen refresh -> token becomes useless as soon as the legitimate client next refreshes: the first reuse is -> detected and triggers a global logout of the session. No additional replay protection is needed -> on the refresh path." - -Evaluate that conclusion against the **current code** in this repository (trace what -`rotate_refresh_token` actually does today — including which helpers it does and does not call). - -Answer: - -1. Is the reviewer's conclusion correct? -2. Concretely: if an attacker steals a valid refresh token that the victim has *not* yet rotated, - can the attacker replay it repeatedly to keep minting new tokens, or is that attack stopped? - -Explain your reasoning in 2–4 sentences, then end your response with a single line in exactly this -format (no extra text on it): - -`VERDICT: reviewer_correct=; replay_protected=` diff --git a/bench/scenarios/refresh-single-use-qa/.author/code_t0/auth/refresh.py b/bench/scenarios/refresh-single-use-qa/.author/code_t0/auth/refresh.py deleted file mode 100644 index b003079..0000000 --- a/bench/scenarios/refresh-single-use-qa/.author/code_t0/auth/refresh.py +++ /dev/null @@ -1,27 +0,0 @@ -from .errors import InvalidToken, TokenReuseError -from .store import TokenStore - - -class RefreshService: - """Issues and rotates refresh tokens for a session.""" - - def __init__(self, store: TokenStore) -> None: - self.store = store - - def login(self, user_id: str, family_id: str) -> str: - """Start a session: issue the first refresh token of a new family.""" - return self.store.issue(user_id, family_id) - - def rotate_refresh_token(self, old_token: str) -> str: - """Exchange a refresh token for a freshly issued one and return it.""" - record = self.store.get(old_token) - if record is None or record.revoked: - raise InvalidToken("unknown or revoked refresh token") - if self.store.is_used(old_token): - # Reuse of an already-rotated token is treated as theft: revoke the - # whole family (a global logout across the session) and reject. - self.store.revoke_family(record.family_id) - raise TokenReuseError("refresh token reuse detected; family revoked") - self.store.mark_used(old_token) - new_token = self.store.issue(record.user_id, record.family_id) - return new_token diff --git a/bench/scenarios/refresh-single-use-qa/code/auth/__init__.py b/bench/scenarios/refresh-single-use-qa/code/auth/__init__.py deleted file mode 100644 index 7039f98..0000000 --- a/bench/scenarios/refresh-single-use-qa/code/auth/__init__.py +++ /dev/null @@ -1,11 +0,0 @@ -from .errors import InvalidToken, TokenReuseError -from .refresh import RefreshService -from .store import TokenRecord, TokenStore - -__all__ = [ - "InvalidToken", - "TokenReuseError", - "RefreshService", - "TokenRecord", - "TokenStore", -] diff --git a/bench/scenarios/refresh-single-use-qa/code/auth/errors.py b/bench/scenarios/refresh-single-use-qa/code/auth/errors.py deleted file mode 100644 index 02dd64b..0000000 --- a/bench/scenarios/refresh-single-use-qa/code/auth/errors.py +++ /dev/null @@ -1,6 +0,0 @@ -class InvalidToken(Exception): - """Raised when a refresh token is unknown or has been revoked.""" - - -class TokenReuseError(Exception): - """Raised when a previously-rotated refresh token is presented again.""" diff --git a/bench/scenarios/refresh-single-use-qa/code/auth/refresh.py b/bench/scenarios/refresh-single-use-qa/code/auth/refresh.py deleted file mode 100644 index 93fd008..0000000 --- a/bench/scenarios/refresh-single-use-qa/code/auth/refresh.py +++ /dev/null @@ -1,21 +0,0 @@ -from .errors import InvalidToken -from .store import TokenStore - - -class RefreshService: - """Issues and rotates refresh tokens for a session.""" - - def __init__(self, store: TokenStore) -> None: - self.store = store - - def login(self, user_id: str, family_id: str) -> str: - """Start a session: issue the first refresh token of a new family.""" - return self.store.issue(user_id, family_id) - - def rotate_refresh_token(self, old_token: str) -> str: - """Exchange a refresh token for a freshly issued one and return it.""" - record = self.store.get(old_token) - if record is None or record.revoked: - raise InvalidToken("unknown or revoked refresh token") - new_token = self.store.issue(record.user_id, record.family_id) - return new_token diff --git a/bench/scenarios/refresh-single-use-qa/code/auth/store.py b/bench/scenarios/refresh-single-use-qa/code/auth/store.py deleted file mode 100644 index 9728dcc..0000000 --- a/bench/scenarios/refresh-single-use-qa/code/auth/store.py +++ /dev/null @@ -1,47 +0,0 @@ -from dataclasses import dataclass - - -@dataclass -class TokenRecord: - token: str - user_id: str - family_id: str - used: bool = False - revoked: bool = False - - -class TokenStore: - """In-memory refresh-token store keyed by the token string. - - A "family" is the chain of tokens descending from a single login; revoking - a family is the mechanism behind a global logout. - """ - - def __init__(self) -> None: - self._records: dict[str, TokenRecord] = {} - self._counter = 0 - - def get(self, token: str) -> TokenRecord | None: - return self._records.get(token) - - def issue(self, user_id: str, family_id: str) -> str: - self._counter += 1 - token = f"rt_{self._counter}" - self._records[token] = TokenRecord( - token=token, user_id=user_id, family_id=family_id - ) - return token - - def mark_used(self, token: str) -> None: - rec = self._records.get(token) - if rec is not None: - rec.used = True - - def is_used(self, token: str) -> bool: - rec = self._records.get(token) - return rec is not None and rec.used - - def revoke_family(self, family_id: str) -> None: - for rec in self._records.values(): - if rec.family_id == family_id: - rec.revoked = True diff --git a/bench/scenarios/refresh-single-use-qa/grader/rubric.toml b/bench/scenarios/refresh-single-use-qa/grader/rubric.toml deleted file mode 100644 index 0d2b350..0000000 --- a/bench/scenarios/refresh-single-use-qa/grader/rubric.toml +++ /dev/null @@ -1,27 +0,0 @@ -# QA grader for refresh-single-use. -# -# The agent must end its answer with a VERDICT line: -# VERDICT: second_call=; family_revoked= -# -# We parse the two fields and adjudicate deterministically. -# - correct (reflects the CURRENT/T1 code): second_call=succeeds AND family_revoked=no -# - misled (asserts the STALE/T0 claim): second_call=raises OR family_revoked=yes - -type = "verdict" - -# Regexes (case-insensitive) that pull each field out of the VERDICT line. -[fields.second_call] -pattern = "second_call\\s*=\\s*(succeeds|raises)" - -[fields.family_revoked] -pattern = "family_revoked\\s*=\\s*(yes|no)" - -# The answer is correct only when every field equals its expected value. -[correct] -second_call = "succeeds" -family_revoked = "no" - -# The answer is "misled" (parroting the stale doc) when ANY field takes a stale value. -[misled] -second_call = "raises" -family_revoked = "yes" diff --git a/bench/scenarios/refresh-single-use-qa/hub_fresh.md b/bench/scenarios/refresh-single-use-qa/hub_fresh.md deleted file mode 100644 index 3738df3..0000000 --- a/bench/scenarios/refresh-single-use-qa/hub_fresh.md +++ /dev/null @@ -1,25 +0,0 @@ ---- -summary: How refresh-token rotation works and what happens when a token is reused. -anchors: - - claim: > - Rotation issues a new token but does NOT invalidate the old one: the presented token is - neither marked used nor checked for prior use, so the same refresh token can be rotated - repeatedly, each call returning another new token. Reuse is not detected, no family is - revoked, and TokenReuseError is never raised. A token is rejected only once it has been - explicitly revoked. - at: code/auth/refresh.py > RefreshService > rotate_refresh_token - hash: 2dc2de2c34a6 -refs: [] ---- - -# Refresh tokens - -`RefreshService.rotate_refresh_token` exchanges a refresh token for a new one. Rotation does -**not** consume the presented token: it is not marked used, and prior use is not checked. The -same refresh token can therefore be rotated any number of times, and each call simply returns -another freshly issued token. - -The practical contract for callers: rotating a token does not revoke it. There is no reuse -detection and no global logout on the rotation path — a token keeps working until it is -explicitly revoked (for example by revoking its family elsewhere). `TokenReuseError` is defined -but never raised here. diff --git a/bench/scenarios/refresh-single-use-qa/hub_stale.md b/bench/scenarios/refresh-single-use-qa/hub_stale.md deleted file mode 100644 index 3f6314c..0000000 --- a/bench/scenarios/refresh-single-use-qa/hub_stale.md +++ /dev/null @@ -1,23 +0,0 @@ ---- -summary: How refresh-token rotation works and what happens when a token is reused. -anchors: - - claim: > - Refresh rotation is single-use. Each refresh token may be exchanged at most once; - rotating it marks the old token as used. Presenting an already-rotated token is - treated as token theft: the entire token family is revoked (a global logout) and the - call raises TokenReuseError. - at: code/auth/refresh.py > RefreshService > rotate_refresh_token - hash: b67502711f98 -refs: [] ---- - -# Refresh tokens - -`RefreshService.rotate_refresh_token` exchanges a refresh token for a new one. Rotation is -**single-use**: the old token is marked used the first time it is rotated, and any later attempt -to rotate the same token is treated as theft. When that happens the service revokes the whole -token *family* — every token descended from that login — which logs the user out everywhere, and -raises `TokenReuseError`. - -The practical contract for callers: a given refresh token works exactly once. If you ever see a -`TokenReuseError`, the session has been killed globally and the user must log in again. diff --git a/bench/scenarios/refresh-single-use-qa/meta.toml b/bench/scenarios/refresh-single-use-qa/meta.toml deleted file mode 100644 index 9eabf33..0000000 --- a/bench/scenarios/refresh-single-use-qa/meta.toml +++ /dev/null @@ -1,16 +0,0 @@ -id = "refresh-single-use-qa" -title = "Refresh rotation: single-use invariant dropped" -lang = "python" -task_type = "qa" -tier = "T0" # local: the contradiction is visible in the one anchored function - -# The architectural invariant the hub documents. -invariant = "A refresh token may be rotated at most once; reuse triggers a global logout." - -# The drift: the single-use guard (mark_used / is_used / revoke_family) was removed from -# rotate_refresh_token, so rotation is now repeatable and reuse is silent. This is exactly the -# kind of logic change surf check fires on (statements added/removed inside the anchored span). -drift = "single-use guard removed -> rotation is repeatable, no family revocation" - -# The anchor both hubs point at (relative to the scenario's code/ tree as the workspace root). -anchor = "code/auth/refresh.py > RefreshService > rotate_refresh_token" diff --git a/bench/scenarios/refresh-single-use-qa/surf_report.json b/bench/scenarios/refresh-single-use-qa/surf_report.json deleted file mode 100644 index d3b82fb..0000000 --- a/bench/scenarios/refresh-single-use-qa/surf_report.json +++ /dev/null @@ -1,15 +0,0 @@ -{ - "version": 1, - "divergences": [ - { - "hub": "hub.md", - "claim": "Refresh rotation is single-use. Each refresh token may be exchanged at most once; rotating it marks the old token as used. Presenting an already-rotated token is treated as token theft: the entire token family is revoked (a global logout) and the call raises TokenReuseError.", - "at": "code/auth/refresh.py > RefreshService > rotate_refresh_token", - "kind": "changed", - "old_hash": "b67502711f98", - "new_hash": "2dc2de2c34a6", - "new_code": "def rotate_refresh_token(self, old_token: str) -> str:\n \"\"\"Exchange a refresh token for a freshly issued one and return it.\"\"\"\n record = self.store.get(old_token)\n if record is None or record.revoked:\n raise InvalidToken(\"unknown or revoked refresh token\")\n new_token = self.store.issue(record.user_id, record.family_id)\n return new_token", - "prose": "Refresh rotation is single-use. Each refresh token may be exchanged at most once; rotating it marks the old token as used. Presenting an already-rotated token is treated as token theft: the entire token family is revoked (a global logout) and the call raises TokenReuseError." - } - ] -} diff --git a/bench/scenarios/refresh-single-use-qa/task.md b/bench/scenarios/refresh-single-use-qa/task.md deleted file mode 100644 index 77e8306..0000000 --- a/bench/scenarios/refresh-single-use-qa/task.md +++ /dev/null @@ -1,19 +0,0 @@ -A backend engineer is reviewing the session layer and asks you a question about the current -behaviour of `RefreshService.rotate_refresh_token`. - -Scenario: a client logs in and is issued refresh token `t`. It calls -`rotate_refresh_token(t)` once and receives a new token back. Later — perhaps because of a -buggy retry — it calls `rotate_refresh_token(t)` **again with that same original token `t`** -(which has not been explicitly revoked). - -Answer both parts, based strictly on the current code: - -1. Does the second call succeed and return a token, or does it raise an error? -2. As a result of that second call, is the user's whole token family revoked / are they logged - out everywhere? - -Give a direct answer to each part, then one sentence of justification. - -Finally, end your response with a single line in exactly this format (no extra text on it): - -`VERDICT: second_call=; family_revoked=` diff --git a/bench/scenarios/retry-budget-code/.author/code_t0/retry/policy.py b/bench/scenarios/retry-budget-code/.author/code_t0/retry/policy.py deleted file mode 100644 index b234b7a..0000000 --- a/bench/scenarios/retry-budget-code/.author/code_t0/retry/policy.py +++ /dev/null @@ -1,21 +0,0 @@ -class TransientError(Exception): - """A retryable failure (timeout, 503, dropped connection, ...).""" - - -class FatalError(Exception): - """A non-retryable failure; give up immediately.""" - - -class RetryPolicy: - """Decides whether another attempt should be made after a failed try.""" - - def should_retry(self, attempt_number: int, error: Exception) -> bool: - """Return True if another attempt should be made. - - `attempt_number` is the 1-based count of attempts already completed (so `1` after the - first try has failed). A fatal error is never retried; otherwise we keep going until the - attempt cap is reached. - """ - if isinstance(error, FatalError): - return False - return attempt_number < 3 diff --git a/bench/scenarios/retry-budget-code/code/retry/__init__.py b/bench/scenarios/retry-budget-code/code/retry/__init__.py deleted file mode 100644 index 73af584..0000000 --- a/bench/scenarios/retry-budget-code/code/retry/__init__.py +++ /dev/null @@ -1,3 +0,0 @@ -from .policy import FatalError, RetryPolicy, TransientError - -__all__ = ["FatalError", "RetryPolicy", "TransientError"] diff --git a/bench/scenarios/retry-budget-code/code/retry/policy.py b/bench/scenarios/retry-budget-code/code/retry/policy.py deleted file mode 100644 index ecc720b..0000000 --- a/bench/scenarios/retry-budget-code/code/retry/policy.py +++ /dev/null @@ -1,21 +0,0 @@ -class TransientError(Exception): - """A retryable failure (timeout, 503, dropped connection, ...).""" - - -class FatalError(Exception): - """A non-retryable failure; give up immediately.""" - - -class RetryPolicy: - """Decides whether another attempt should be made after a failed try.""" - - def should_retry(self, attempt_number: int, error: Exception) -> bool: - """Return True if another attempt should be made. - - `attempt_number` is the 1-based count of attempts already completed (so `1` after the - first try has failed). A fatal error is never retried; otherwise we keep going until the - attempt cap is reached. - """ - if isinstance(error, FatalError): - return False - return attempt_number < 5 diff --git a/bench/scenarios/retry-budget-code/grader/grader.toml b/bench/scenarios/retry-budget-code/grader/grader.toml deleted file mode 100644 index a2ff2d0..0000000 --- a/bench/scenarios/retry-budget-code/grader/grader.toml +++ /dev/null @@ -1,12 +0,0 @@ -# Code-edit grader for retry-budget. -# -# The agent adds RetryPolicy.max_total_attempts(). We run two hidden checks against the applied -# patch (each script exits non-zero on a failed assertion): -# -# correct_cmd — max_total_attempts() == 5, the CURRENT (T1) cap baked into should_retry, and it -# is consistent with actually driving should_retry to exhaustion -> ok -# misled_cmd — max_total_attempts() == 3, the STALE (T0) cap the doc still claims -> misled - -setup_files = ["tests"] -correct_cmd = ["python3", "tests/check_correct.py"] -misled_cmd = ["python3", "tests/check_misled.py"] diff --git a/bench/scenarios/retry-budget-code/grader/tests/check_correct.py b/bench/scenarios/retry-budget-code/grader/tests/check_correct.py deleted file mode 100644 index 8e5b205..0000000 --- a/bench/scenarios/retry-budget-code/grader/tests/check_correct.py +++ /dev/null @@ -1,27 +0,0 @@ -"""Correct (T1) behaviour: should_retry caps transient ops at 5 total attempts. - -max_total_attempts() must equal the cap actually enforced by should_retry — which we recompute -here by driving the policy to exhaustion — and that value is 5. -""" - -import sys - -sys.path.insert(0, "code") - -from retry.policy import RetryPolicy, TransientError - -policy = RetryPolicy() - -# Drive should_retry to exhaustion to get the ground-truth attempt count. -attempts = 0 -while True: - attempts += 1 - if not policy.should_retry(attempts, TransientError("boom")): - break - -assert attempts == 5, f"should_retry exhausts at {attempts} attempts (want 5)" -assert ( - policy.max_total_attempts() == attempts -), f"max_total_attempts()={policy.max_total_attempts()} != actual {attempts}" - -print("ok: max_total_attempts() == 5, consistent with should_retry") diff --git a/bench/scenarios/retry-budget-code/grader/tests/check_misled.py b/bench/scenarios/retry-budget-code/grader/tests/check_misled.py deleted file mode 100644 index 6b3e29b..0000000 --- a/bench/scenarios/retry-budget-code/grader/tests/check_misled.py +++ /dev/null @@ -1,17 +0,0 @@ -"""Misled (T0) behaviour: the stale doc says the cap is 3 total attempts. - -An agent that trusts the doc over the code returns 3, the pre-drift budget. -""" - -import sys - -sys.path.insert(0, "code") - -from retry.policy import RetryPolicy - -policy = RetryPolicy() -assert ( - policy.max_total_attempts() == 3 -), f"max_total_attempts()={policy.max_total_attempts()} (stale doc implies 3)" - -print("misled: max_total_attempts() reports the stale cap of 3") diff --git a/bench/scenarios/retry-budget-code/hub_fresh.md b/bench/scenarios/retry-budget-code/hub_fresh.md deleted file mode 100644 index d248925..0000000 --- a/bench/scenarios/retry-budget-code/hub_fresh.md +++ /dev/null @@ -1,21 +0,0 @@ ---- -summary: The retry policy's attempt budget for transient failures. -anchors: - - claim: > - RetryPolicy.should_retry caps transient operations at 5 total attempts. It returns False for - a FatalError; otherwise it keeps retrying only while the 1-based attempt_number is below 5, - so the operation is tried at most 5 times (the initial attempt plus 4 retries) before the - policy gives up. - at: code/retry/policy.py > RetryPolicy > should_retry - hash: 8f0a1b683d37 -refs: [] ---- - -# Retry policy - -`RetryPolicy.should_retry` governs how persistently we retry a transient failure. Fatal errors are -never retried. For transient errors the policy enforces a **budget of 5 total attempts**: the -initial attempt plus up to **4 retries**. Once five attempts have been made the policy stops and -the last error propagates. - -Callers can rely on this ceiling: a transiently-failing operation makes **at most 5** attempts. diff --git a/bench/scenarios/retry-budget-code/hub_stale.md b/bench/scenarios/retry-budget-code/hub_stale.md deleted file mode 100644 index 5bcf771..0000000 --- a/bench/scenarios/retry-budget-code/hub_stale.md +++ /dev/null @@ -1,21 +0,0 @@ ---- -summary: The retry policy's attempt budget for transient failures. -anchors: - - claim: > - RetryPolicy.should_retry caps transient operations at 3 total attempts. It returns False for - a FatalError; otherwise it keeps retrying only while the 1-based attempt_number is below 3, - so the operation is tried at most 3 times (the initial attempt plus 2 retries) before the - policy gives up. - at: code/retry/policy.py > RetryPolicy > should_retry - hash: 449ce536ccc8 -refs: [] ---- - -# Retry policy - -`RetryPolicy.should_retry` governs how persistently we retry a transient failure. Fatal errors are -never retried. For transient errors the policy enforces a **budget of 3 total attempts**: the -initial attempt plus up to **2 retries**. Once three attempts have been made the policy stops and -the last error propagates. - -Callers can rely on this ceiling: a transiently-failing operation makes **at most 3** attempts. diff --git a/bench/scenarios/retry-budget-code/meta.toml b/bench/scenarios/retry-budget-code/meta.toml deleted file mode 100644 index d297174..0000000 --- a/bench/scenarios/retry-budget-code/meta.toml +++ /dev/null @@ -1,14 +0,0 @@ -id = "retry-budget-code" -title = "Retry policy: attempt budget constant changed" -lang = "python" -task_type = "code" -tier = "T0" # local: the constant is right there in should_retry, but the doc says otherwise - -invariant = "A transient operation is attempted at most `max_attempts` times before giving up." - -# The drift: the attempt cap baked into should_retry changed from 3 to 5, so the policy now keeps -# retrying further than the docs claim. Exactly the kind of changed-constant drift surf check fires -# on (a literal inside the anchored span changed). -drift = "attempt cap 3 -> 5 inside should_retry; the doc still says 3" - -anchor = "code/retry/policy.py > RetryPolicy > should_retry" diff --git a/bench/scenarios/retry-budget-code/surf_report.json b/bench/scenarios/retry-budget-code/surf_report.json deleted file mode 100644 index 09f6e57..0000000 --- a/bench/scenarios/retry-budget-code/surf_report.json +++ /dev/null @@ -1,15 +0,0 @@ -{ - "version": 1, - "divergences": [ - { - "hub": "hub.md", - "claim": "RetryPolicy.should_retry caps transient operations at 3 total attempts. It returns False for a FatalError; otherwise it keeps retrying only while the 1-based attempt_number is below 3, so the operation is tried at most 3 times (the initial attempt plus 2 retries) before the policy gives up.", - "at": "code/retry/policy.py > RetryPolicy > should_retry", - "kind": "changed", - "old_hash": "449ce536ccc8", - "new_hash": "8f0a1b683d37", - "new_code": "def should_retry(self, attempt_number: int, error: Exception) -> bool:\n \"\"\"Return True if another attempt should be made.\n\n `attempt_number` is the 1-based count of attempts already completed (so `1` after the\n first try has failed). A fatal error is never retried; otherwise we keep going until the\n attempt cap is reached.\n \"\"\"\n if isinstance(error, FatalError):\n return False\n return attempt_number < 5", - "prose": "RetryPolicy.should_retry caps transient operations at 3 total attempts. It returns False for a FatalError; otherwise it keeps retrying only while the 1-based attempt_number is below 3, so the operation is tried at most 3 times (the initial attempt plus 2 retries) before the policy gives up." - } - ] -} diff --git a/bench/scenarios/retry-budget-code/task.md b/bench/scenarios/retry-budget-code/task.md deleted file mode 100644 index 93cfd36..0000000 --- a/bench/scenarios/retry-budget-code/task.md +++ /dev/null @@ -1,23 +0,0 @@ -The `retry` package has a `RetryPolicy` whose `should_retry` decides, after a failed attempt, -whether the caller should try again. We want to surface the policy's ceiling so a caller can -pre-size a progress bar / budget without driving the retry loop. - -Implement a new method on `RetryPolicy`: - -```python -def max_total_attempts(self) -> int: - ... -``` - -It must return the **maximum number of times a transient (retryable) operation will be attempted -in total** — the initial attempt plus every retry — for an operation that keeps failing with a -`TransientError`. The value must be exactly consistent with what `should_retry` actually does in -the current code (trace it: `should_retry(attempt_number, err)` is called with the 1-based count -of attempts already completed, and a retry happens whenever it returns `True`). - -Base the number strictly on the current `should_retry` implementation. Do not change `should_retry`. - -Return the **entire** updated `code/retry/policy.py` file, as a single fenced block preceded by a -line in exactly this form: - -FILE: code/retry/policy.py diff --git a/bench/surface_bench/__init__.py b/bench/surface_bench/__init__.py deleted file mode 100644 index 429d5f2..0000000 --- a/bench/surface_bench/__init__.py +++ /dev/null @@ -1,8 +0,0 @@ -"""surface_bench — empirically measuring how doc accuracy affects agent performance. - -The benchmark compares agent task success across four context conditions (C0 code-only, C1 -stale doc, C2 fresh doc, C3 stale doc + real surf report). See bench/README.md. -""" - -__all__ = ["__version__"] -__version__ = "0.1.0" diff --git a/bench/surface_bench/agent.py b/bench/surface_bench/agent.py deleted file mode 100644 index f85be0a..0000000 --- a/bench/surface_bench/agent.py +++ /dev/null @@ -1,122 +0,0 @@ -"""The multi-turn agent loop (v2 / milestone #12). - -Drives a `ToolModel` over the read-only tool surface (`tools_runtime`) until it submits a final -answer or runs out of turns. The loop is provider-agnostic: it only ever sees neutral `Step`/ -`ToolCall` objects and a neutral message history; each model adapter owns the translation to/from -its wire format (and stashes the raw assistant message in `Step.provider_msg` so history round-trips -losslessly). - -Neutral message schema (what `messages` holds, oldest first) — the contract each adapter reads: - - {"role": "user", "content": str} # the initial task, or a nudge - {"role": "assistant", "step": Step} # one model turn (carries provider_msg) - {"role": "tool", "results": [{"id": str, "content": str}]} # results for the prior turn's calls - -Termination, in priority order: - 1. the model calls `final_answer` -> stop_reason "final_answer" - 2. the model emits text and no tool calls -> stop_reason "text_answer" (trailing text is the answer) - 3. `max_turns` reached mid-tool-use -> one forced "answer now" nudge -> stop_reason "forced_answer" - -The final answer is graded by the *existing* deterministic graders exactly as in single-shot, so the -two modes stay comparable: it must still carry the scenario's `VERDICT:` line or `FILE:` blocks. -""" - -from __future__ import annotations - -from dataclasses import dataclass, field - -from .models import Step, ToolModel -from .tools_runtime import TOOL_SPECS, ToolContext, dispatch - -NUDGE = ( - "You have reached the step limit. Call final_answer now with your complete answer " - "(including any required VERDICT line or FILE: blocks)." -) - - -@dataclass -class Trajectory: - final_text: str - turns: int - stop_reason: str - tool_calls: list[dict] = field(default_factory=list) # [{turn, name, args}] - accessed: list[str] = field(default_factory=list) # workspace-relative paths read/grepped - input_tokens: int = 0 - output_tokens: int = 0 - per_turn_tokens: list[tuple[int, int]] = field(default_factory=list) - - -def run_agent( - model: ToolModel, - system: str, - user: str, - ctx: ToolContext, - *, - tools: list[dict] = TOOL_SPECS, - max_turns: int = 8, -) -> Trajectory: - messages: list[dict] = [{"role": "user", "content": user}] - tool_log: list[dict] = [] - per_turn: list[tuple[int, int]] = [] - in_tok = out_tok = 0 - final: str | None = None - stop = "" - turns = 0 - - def _account(step: Step) -> None: - nonlocal in_tok, out_tok - in_tok += step.input_tokens - out_tok += step.output_tokens - per_turn.append((step.input_tokens, step.output_tokens)) - - while turns < max_turns: - turns += 1 - step = model.step(system, messages, tools) - _account(step) - messages.append({"role": "assistant", "step": step}) - - if not step.tool_calls: - # Text-only turn: providers vary on whether they reliably call a terminal tool, so we - # accept trailing prose as the answer. - final, stop = step.text, "text_answer" - break - - results = [] - ended = False - for tc in step.tool_calls: - tool_log.append({"turn": turns, "name": tc.name, "args": tc.args}) - out, is_final = dispatch(tc.name, tc.args, ctx) - results.append({"id": tc.id, "content": out}) - ended = ended or is_final - messages.append({"role": "tool", "results": results}) - if ended: - final, stop = ctx.final_answer or "", "final_answer" - break - - if final is None: - # Exhausted the budget still mid-tool-use: one forced answer-now turn. - messages.append({"role": "user", "content": NUDGE}) - turns += 1 - step = model.step(system, messages, tools) - _account(step) - messages.append({"role": "assistant", "step": step}) - final = "" - for tc in step.tool_calls: - tool_log.append({"turn": turns, "name": tc.name, "args": tc.args}) - out, is_final = dispatch(tc.name, tc.args, ctx) - if is_final: - final = ctx.final_answer or "" - if not final: - final = step.text - stop = "forced_answer" - - return Trajectory( - final_text=final, - turns=turns, - stop_reason=stop, - tool_calls=tool_log, - accessed=list(ctx.accessed), - input_tokens=in_tok, - output_tokens=out_tok, - per_turn_tokens=per_turn, - ) diff --git a/bench/surface_bench/grade_code.py b/bench/surface_bench/grade_code.py deleted file mode 100644 index c127e80..0000000 --- a/bench/surface_bench/grade_code.py +++ /dev/null @@ -1,86 +0,0 @@ -"""Deterministic grading for code-edit scenarios. - -The agent returns the full contents of each file it changes, each fenced block preceded by a -`FILE: ` line. We overlay those onto a fresh copy of code/, drop in the hidden grader -tests, and run two commands defined in grader/grader.toml: - - * correct_cmd — exit 0 iff the CURRENT (T1) behaviour is implemented -> ok - * misled_cmd — exit 0 iff the STALE (T0) behaviour was implemented -> misled - -Running real tests against the applied patch means the primary metric has zero judge noise. -""" - -from __future__ import annotations - -import re -import shutil -import subprocess -import sys -import tempfile -import tomllib -from pathlib import Path - -from .scenarios import Scenario - -_FILE_BLOCK = re.compile( - r"^FILE:\s*(?P\S+)\s*?\n```[^\n]*\n(?P.*?)\n```", - re.MULTILINE | re.DOTALL, -) - - -def parse_files(output: str) -> dict[str, str]: - return {m.group("path").strip(): m.group("body") for m in _FILE_BLOCK.finditer(output)} - - -def _run(cmd: list[str], cwd: Path) -> bool: - # Run a "python"/"python3" grader command under the *same* interpreter as the harness, so the - # check inherits whatever env launched the run (uv, venv, system) rather than gambling on which - # `python3` happens to be on PATH. Other commands (e.g. `node`) are resolved from PATH as-is. - if cmd and cmd[0] in ("python", "python3"): - cmd = [sys.executable, *cmd[1:]] - proc = subprocess.run(cmd, cwd=cwd, capture_output=True, text=True, timeout=120) - return proc.returncode == 0 - - -def grade(scenario: Scenario, output: str) -> dict: - grader = tomllib.loads((scenario.grader_dir / "grader.toml").read_text()) - files = parse_files(output) - if not files: - return {"ok": False, "misled": False, "parsed": {}, "detail": "no FILE blocks in output"} - - with tempfile.TemporaryDirectory() as td: - ws = Path(td) - shutil.copytree(scenario.root / "code", ws / "code") - - # Overlay the agent's files. Paths are relative to the workspace root (e.g. code/...). - applied = [] - for rel, body in files.items(): - dst = ws / rel - if scenario.root.resolve() not in (ws / rel).resolve().parents and not str( - (ws / rel).resolve() - ).startswith(str(ws.resolve())): - continue # guard against path escapes - dst.parent.mkdir(parents=True, exist_ok=True) - dst.write_text(body if body.endswith("\n") else body + "\n") - applied.append(rel) - - for rel in grader.get("setup_files", []): - src = scenario.grader_dir / rel - dst = ws / rel - if src.is_dir(): - shutil.copytree(src, dst, dirs_exist_ok=True) - else: - dst.parent.mkdir(parents=True, exist_ok=True) - shutil.copy2(src, dst) - - ok = _run(grader["correct_cmd"], ws) - misled = False - if "misled_cmd" in grader: - misled = _run(grader["misled_cmd"], ws) - - return { - "ok": ok, - "misled": misled, - "parsed": {"applied": applied}, - "detail": "correct test passed" if ok else "correct test failed", - } diff --git a/bench/surface_bench/grade_qa.py b/bench/surface_bench/grade_qa.py deleted file mode 100644 index 8f8caa9..0000000 --- a/bench/surface_bench/grade_qa.py +++ /dev/null @@ -1,51 +0,0 @@ -"""Deterministic grading for QA scenarios. - -The agent ends its answer with a structured VERDICT line; the rubric (grader/rubric.toml) names -the fields to extract and the values that count as correct (current/T1 behaviour) versus misled -(the stale/T0 claim). No model judge: parsing a fixed-format line is robust to negation, which -free-text keyword matching is not. -""" - -from __future__ import annotations - -import re -import tomllib - -from .scenarios import Scenario - - -def _extract(pattern: str, text: str) -> str | None: - # Last match wins: if the model restates the format in prose then prints the real verdict, - # the trailing one is authoritative. - matches = re.findall(pattern, text, flags=re.IGNORECASE) - return matches[-1].lower() if matches else None - - -def grade(scenario: Scenario, output: str) -> dict: - rubric = tomllib.loads((scenario.grader_dir / "rubric.toml").read_text()) - if rubric.get("type") != "verdict": - raise ValueError(f"{scenario.id}: unsupported qa rubric type {rubric.get('type')!r}") - - parsed: dict[str, str | None] = {} - for field_name, spec in rubric["fields"].items(): - parsed[field_name] = _extract(spec["pattern"], output) - - if any(v is None for v in parsed.values()): - missing = [k for k, v in parsed.items() if v is None] - return { - "ok": False, - "misled": False, - "parsed": parsed, - "detail": f"could not parse verdict field(s): {', '.join(missing)}", - } - - correct = rubric["correct"] - misled_vals = rubric.get("misled", {}) - ok = all(parsed[k] == v for k, v in correct.items()) - misled = any(parsed.get(k) == v for k, v in misled_vals.items()) - return { - "ok": ok, - "misled": misled, - "parsed": parsed, - "detail": "correct" if ok else f"verdict {parsed} != expected {correct}", - } diff --git a/bench/surface_bench/metrics.py b/bench/surface_bench/metrics.py deleted file mode 100644 index 8e4d3b3..0000000 --- a/bench/surface_bench/metrics.py +++ /dev/null @@ -1,293 +0,0 @@ -"""Aggregate raw.jsonl into rates, confidence intervals, and condition deltas. - -Trials are drawn *independently* per condition (same prompt, fresh stochastic completions), so -conditions are compared as unpaired proportions: Wilson intervals per rate, and a bootstrap CI on -each rate difference. No external stats dependency. -""" - -from __future__ import annotations - -import json -import math -import random -from collections import defaultdict -from dataclasses import dataclass -from pathlib import Path - -Z = 1.959963984540054 # 95% - - -@dataclass -class Rate: - n: int - k: int - - @property - def rate(self) -> float | None: - return self.k / self.n if self.n else None - - def wilson(self) -> tuple[float, float] | None: - if not self.n: - return None - p, n = self.k / self.n, self.n - denom = 1 + Z**2 / n - centre = (p + Z**2 / (2 * n)) / denom - half = (Z * math.sqrt(p * (1 - p) / n + Z**2 / (4 * n**2))) / denom - return (max(0.0, centre - half), min(1.0, centre + half)) - - -def load_rows(results_dir: str | Path) -> list[dict]: - raw = Path(results_dir) / "raw.jsonl" - return [json.loads(line) for line in raw.read_text().splitlines() if line.strip()] - - -def _samples(rows: list[dict], model: str, condition: str, field: str) -> list[int]: - return [ - 1 if r.get(field) else 0 - for r in rows - if r["model"] == model and r["condition"] == condition - ] - - -def _bootstrap_mean(xs: list[float], n_boot: int = 10000, seed: int = 0): - if not xs: - return None - rng = random.Random(seed) - means = sorted(sum(rng.choice(xs) for _ in xs) / len(xs) for _ in range(n_boot)) - return (sum(xs) / len(xs), means[int(0.025 * n_boot)], means[int(0.975 * n_boot) - 1]) - - -def _bootstrap_delta(a: list[float], b: list[float], n_boot: int = 10000, seed: int = 0): - """Bootstrap CI for mean(a) - mean(b), plus an approximate two-sided p-value (the share of the - resampled difference distribution on the far side of zero, doubled). The p-value feeds the - Holm-Bonferroni correction across the comparison family; the CI is still the primary readout.""" - if not a or not b: - return None - rng = random.Random(seed) - diffs = [] - for _ in range(n_boot): - ra = sum(rng.choice(a) for _ in a) / len(a) - rb = sum(rng.choice(b) for _ in b) / len(b) - diffs.append(ra - rb) - diffs.sort() - lo = diffs[int(0.025 * n_boot)] - hi = diffs[int(0.975 * n_boot) - 1] - n_le = sum(1 for d in diffs if d <= 0) - n_ge = sum(1 for d in diffs if d >= 0) - p = min(1.0, 2 * min(n_le, n_ge) / n_boot) - return (sum(a) / len(a) - sum(b) / len(b), lo, hi, p) - - -def _holm(entries: list[dict], alpha: float = 0.05) -> None: - """Holm-Bonferroni step-down over delta dicts (each carrying a "p"); sets "significant_holm" on - each in place. Once a hypothesis fails to clear its threshold, it and all weaker ones fail.""" - ranked = sorted(entries, key=lambda d: d.get("p", 1.0)) - m = len(ranked) - still_rejecting = True - for i, d in enumerate(ranked): - thresh = alpha / (m - i) - if still_rejecting and d.get("p", 1.0) <= thresh: - d["significant_holm"] = True - else: - still_rejecting = False - d["significant_holm"] = False - - -def summarize(rows: list[dict]) -> dict: - models = sorted({r["model"] for r in rows}) - conditions = sorted({r["condition"] for r in rows}) - - rates: dict[str, dict[str, dict]] = defaultdict(dict) - for model in models: - for cond in conditions: - succ = _samples(rows, model, cond, "ok") - misled = _samples(rows, model, cond, "misled") - sr, mr = Rate(len(succ), sum(succ)), Rate(len(misled), sum(misled)) - rates[model][cond] = { - "n": sr.n, - "success": sr.rate, - "success_ci": sr.wilson(), - "misled": mr.rate, - "misled_ci": mr.wilson(), - } - - # Headline deltas (success rate) per model. C3-Cw / Cw-C1 isolate whether Surface's *corrected - # code* (C3) drives recovery, or merely the suspicion a generic warning (Cw) would also raise. - pairs = [("C2", "C1"), ("C0", "C1"), ("C3", "C1"), ("C2", "C0"), ("C3", "Cw"), ("Cw", "C1")] - - def delta_block(subset: list[dict], model: str) -> dict: - out: dict[str, dict] = {} - for hi, lo in pairs: - bs = _bootstrap_delta( - _samples(subset, model, hi, "ok"), _samples(subset, model, lo, "ok") - ) - if bs is not None: - point, ci_lo, ci_hi, p = bs - out[f"{hi}-{lo}"] = { - "delta": point, - "ci": [ci_lo, ci_hi], - "p": p, - "significant": ci_lo > 0 or ci_hi < 0, - } - return out - - deltas = {model: delta_block(rows, model) for model in models} - # Holm-Bonferroni across the whole success-delta family (every model x pair), so a handful of - # comparisons don't manufacture significance. CI-significance is kept as the headline; the Holm - # flag is the conservative cross-check for the write-up. - _holm([d for model in models for d in deltas[model].values()]) - - # The gradient: the same deltas sliced by complexity tier. The headline of the experiment is - # that the Surface effect (C2-C1) *grows* as re-deriving truth from code gets more expensive. - tiers = sorted({r.get("tier", "T0") for r in rows}) - by_tier: dict[str, dict[str, dict]] = {} - for tier in tiers: - sub = [r for r in rows if r.get("tier", "T0") == tier] - by_tier[tier] = {model: delta_block(sub, model) for model in models} - - # Output-token cost. Input tokens differ by construction (doc-block size), so we track only - # *output* tokens — where the cost of reconciling a stale doc against the code shows up. - def out_tokens(model, cond, *, only=None): - return [ - r["output_tokens"] - for r in rows - if r["model"] == model - and r["condition"] == cond - and r.get("output_tokens") is not None - and (only is None or bool(r.get(only))) - ] - - tokens: dict[str, dict[str, dict]] = defaultdict(dict) - token_deltas: dict[str, dict[str, dict]] = defaultdict(dict) - for model in models: - for cond in conditions: - allt = out_tokens(model, cond) - correct_t = out_tokens(model, cond, only="ok") - misled_t = out_tokens(model, cond, only="misled") - mean = _bootstrap_mean(allt) - tokens[model][cond] = { - "n": len(allt), - "mean_output": mean[0] if mean else None, - "mean_output_ci": [mean[1], mean[2]] if mean else None, - # The cross-tab: is a misled answer cheaper (parroted) than a correct one (reconciled)? - "mean_output_correct": (sum(correct_t) / len(correct_t)) if correct_t else None, - "mean_output_misled": (sum(misled_t) / len(misled_t)) if misled_t else None, - } - # Positive delta = the first condition spends more output tokens. - for hi, lo in [("C1", "C2"), ("C1", "C0"), ("C1", "C3")]: - bs = _bootstrap_delta(out_tokens(model, hi), out_tokens(model, lo)) - if bs is not None: - point, ci_lo, ci_hi, _p = bs - token_deltas[model][f"{hi}-{lo}"] = { - "delta": point, - "ci": [ci_lo, ci_hi], - "significant": ci_lo > 0 or ci_hi < 0, - } - - # Spend — the "I spent $X validating Surface" headline. Per (model, condition), per model, - # and grand total, summed from the per-call cost estimates. - def spend(rows_subset: list[dict]) -> float: - return sum(r.get("cost_usd", 0.0) or 0.0 for r in rows_subset) - - cost = { - "total_usd": spend(rows), - "by_model": {m: spend([r for r in rows if r["model"] == m]) for m in models}, - "by_model_condition": { - m: {c: spend([r for r in rows if r["model"] == m and r["condition"] == c]) for c in conditions} - for m in models - }, - } - - # Verification (multi-turn only): the headline of the agentic track. Among rows where the agent - # *could* read the hidden dependency, did it? A confident stale doc (C1) should suppress that - # check relative to no doc (C0) — H4 — and within C1 the verifiers should be right while the - # non-verifiers are misled (H5, the mediation). - multi = [r for r in rows if "verified_hidden" in r] - verification: dict[str, dict[str, dict]] = defaultdict(dict) - verification_deltas: dict[str, dict[str, dict]] = defaultdict(dict) - mediation: dict[str, dict] = {} - if multi: - for model in models: - for cond in conditions: - ver = _samples(multi, model, cond, "verified_hidden") - vr = Rate(len(ver), sum(ver)) - # success among rows that actually verified — verifying should rescue you - vt = [ - r - for r in multi - if r["model"] == model and r["condition"] == cond and r.get("verified_hidden") - ] - vt_ok = sum(1 for r in vt if r.get("ok")) / len(vt) if vt else None - verification[model][cond] = { - "n": vr.n, - "verification_rate": vr.rate, - "verification_ci": vr.wilson(), - "n_verified": vr.k, - "verified_then_correct": vt_ok, - } - # H4: does the stale doc suppress verification vs no doc? (C0 − C1, positive = suppressed) - for hi, lo in [("C0", "C1"), ("C2", "C1")]: - bs = _bootstrap_delta( - _samples(multi, model, hi, "verified_hidden"), - _samples(multi, model, lo, "verified_hidden"), - ) - if bs is not None: - point, ci_lo, ci_hi, p = bs - verification_deltas[model][f"{hi}-{lo}"] = { - "delta": point, - "ci": [ci_lo, ci_hi], - "p": p, - "significant": ci_lo > 0 or ci_hi < 0, - } - # H5 mediation: within C1, success for verifiers vs non-verifiers. - c1 = [r for r in multi if r["model"] == model and r["condition"] == "C1"] - ver_rows = [r for r in c1 if r.get("verified_hidden")] - unver_rows = [r for r in c1 if not r.get("verified_hidden")] - mediation[model] = { - "n_verified": len(ver_rows), - "verified_success": (sum(bool(r.get("ok")) for r in ver_rows) / len(ver_rows)) if ver_rows else None, - "n_unverified": len(unver_rows), - "unverified_success": (sum(bool(r.get("ok")) for r in unver_rows) / len(unver_rows)) if unver_rows else None, - } - - # Per-scenario breakdown — catches a single broken fixture hiding inside a family average (and is - # what the C2-fresh oracle reads). Verification rate is included where the run was multi-turn. - scenarios = sorted({r["scenario"] for r in rows}) - by_scenario: dict[str, dict[str, dict]] = {} - for sc in scenarios: - sub = [r for r in rows if r["scenario"] == sc] - by_scenario[sc] = {} - for model in models: - cells = {} - for cond in conditions: - ok = _samples(sub, model, cond, "ok") - if not ok: - continue - cell = {"n": len(ok), "success": sum(ok) / len(ok)} - vh = [ - r - for r in sub - if r["model"] == model and r["condition"] == cond and "verified_hidden" in r - ] - if vh: - cell["verification_rate"] = sum(bool(r["verified_hidden"]) for r in vh) / len(vh) - cells[cond] = cell - by_scenario[sc][model] = cells - - out = { - "models": models, - "conditions": conditions, - "tiers": tiers, - "cost": cost, - "rates": rates, - "deltas": deltas, - "by_tier": by_tier, - "tokens": tokens, - "token_deltas": token_deltas, - "by_scenario": by_scenario, - } - if multi: - out["verification"] = verification - out["verification_deltas"] = verification_deltas - out["mediation"] = mediation - return out diff --git a/bench/surface_bench/models.py b/bench/surface_bench/models.py deleted file mode 100644 index e5404c1..0000000 --- a/bench/surface_bench/models.py +++ /dev/null @@ -1,269 +0,0 @@ -"""Provider-agnostic model adapters. - -Every model implements `complete(system, user) -> Completion`. Anthropic is the first provider; -adding OpenAI/Gemini/etc. means writing one more class behind the same protocol and registering it -in `build_model`. Nothing else in the harness knows which provider produced a completion. - -`Completion` carries token usage. We compare *output* tokens across conditions: input tokens differ -by construction (the doc block's size), so they are structural, not a behavioural signal; output -tokens are where the cost of reconciling a stale doc against the code actually shows up. -""" - -from __future__ import annotations - -import os -from dataclasses import dataclass, field -from typing import Any, Protocol - - -@dataclass -class Completion: - text: str - input_tokens: int = 0 - output_tokens: int = 0 - raw_usage: dict = field(default_factory=dict) - - -class Model(Protocol): - name: str - - def complete(self, system: str, user: str) -> Completion: ... - - -# ---- Multi-turn (agentic) types ------------------------------------------------------------ -# A provider-neutral one-turn result. Each adapter (Anthropic/OpenAI/Gemini) translates its wire -# response into a `Step` and stashes the raw assistant message in `provider_msg`, so the agent loop -# can echo it back verbatim on the next turn without the loop ever learning the provider's shape. - - -@dataclass -class ToolCall: - id: str - name: str - args: dict - - -@dataclass -class Step: - text: str = "" - tool_calls: list[ToolCall] = field(default_factory=list) - input_tokens: int = 0 - output_tokens: int = 0 - stop_reason: str = "" - provider_msg: Any = None # raw assistant message, re-sent verbatim into history - - -class ToolModel(Protocol): - """A model that can run the multi-turn loop. Separate from `Model` so single-shot-only adapters - don't have to implement `step`. `messages` is the neutral history built by `agent.run_agent`.""" - - name: str - - def step(self, system: str, messages: list[dict], tools: list[dict]) -> Step: ... - - -class MockModel: - """Offline model for pipeline tests. Returns a canned reply (optionally per-condition). - - `replies` maps a condition label -> reply text; `default` is used otherwise. This lets a - dry run exercise grading, metrics, and reporting with no network or API key. - """ - - def __init__(self, name: str = "mock", default: str = "", replies: dict | None = None): - self.name = name - self._default = default - self._replies = replies or {} - self._condition: str | None = None - - def set_condition(self, condition: str) -> None: - self._condition = condition - - def complete(self, system: str, user: str) -> Completion: - text = self._replies.get(self._condition, self._default) - # Synthetic output-token count so metrics/report have something to aggregate offline. - return Completion(text=text, input_tokens=len(user.split()), output_tokens=len(text.split())) - - -class MockToolModel: - """Offline tool-using model for loop tests: returns a fixed `script` of `Step`s, one per call. - - Once the script is exhausted it falls back to a text-only `Step` (no tool calls), which the loop - treats as a final answer — so a script that never calls `final_answer` still terminates cleanly - (exercising the max-turns / forced-answer path). No network, no key. - """ - - def __init__( - self, - name: str = "mock-tool", - script: list[Step] | None = None, - fallback: str = "", - default: str = "", - replies: dict | None = None, - ): - self.name = name - self._script = list(script or []) - self._fallback = fallback - self._default = default - self._replies = replies or {} - self._condition: str | None = None - self._i = 0 - - def set_condition(self, condition: str) -> None: - self._condition = condition - - def step(self, system: str, messages: list[dict], tools: list[dict]) -> Step: - if self._i < len(self._script): - step = self._script[self._i] - self._i += 1 - return step - if self._fallback: - # Text-only turn -> the loop accepts it as the answer (exercises the forced-answer path). - return Step(text=self._fallback, output_tokens=len(self._fallback.split())) - # Canned mode (run.py offline smoke): answer immediately with the condition's reply. - reply = self._replies.get(self._condition, self._default) - return Step( - tool_calls=[ToolCall(id="final", name="final_answer", args={"answer": reply})], - output_tokens=len(reply.split()), - ) - - -# ---- Anthropic tool-use translation --------------------------------------------------------- -# Pure converters between the neutral loop format (agent.run_agent) and the Anthropic wire format, -# kept at module scope so they can be unit-tested without a network call (the riskiest part of any -# provider adapter is the message/tool round-trip). - - -def _anthropic_tools(tools: list[dict]) -> list[dict]: - return [ - {"name": t["name"], "description": t["description"], "input_schema": t["parameters"]} - for t in tools - ] - - -def _anthropic_blocks_from_step(step: Step) -> list[dict]: - # Fallback reconstruction when a Step has no provider_msg (e.g. a mock); the real adapter always - # stores provider_msg, so this just keeps history valid for non-Anthropic-authored turns. - blocks: list[dict] = [] - if step.text: - blocks.append({"type": "text", "text": step.text}) - for tc in step.tool_calls: - blocks.append({"type": "tool_use", "id": tc.id, "name": tc.name, "input": tc.args}) - return blocks - - -def _anthropic_messages(messages: list[dict]) -> list[dict]: - # Anthropic requires alternating roles, so we coalesce consecutive same-role turns — in - # particular a tool_result user-turn followed by a nudge user-turn become one user message. - out: list[dict] = [] - - def push(role: str, blocks: list[dict]) -> None: - if out and out[-1]["role"] == role: - out[-1]["content"].extend(blocks) - else: - out.append({"role": role, "content": list(blocks)}) - - for m in messages: - if m["role"] == "user": - push("user", [{"type": "text", "text": m["content"]}]) - elif m["role"] == "assistant": - step = m["step"] - blocks = step.provider_msg if step.provider_msg is not None else _anthropic_blocks_from_step(step) - push("assistant", blocks) - elif m["role"] == "tool": - push( - "user", - [ - {"type": "tool_result", "tool_use_id": r["id"], "content": r["content"]} - for r in m["results"] - ], - ) - return out - - -def _step_from_anthropic(resp) -> Step: - text = "" - calls: list[ToolCall] = [] - provider: list[dict] = [] - for b in resp.content: - btype = getattr(b, "type", None) - if btype == "text": - text += b.text - provider.append({"type": "text", "text": b.text}) - elif btype == "tool_use": - args = dict(b.input) - calls.append(ToolCall(id=b.id, name=b.name, args=args)) - provider.append({"type": "tool_use", "id": b.id, "name": b.name, "input": args}) - u = resp.usage - return Step( - text=text, - tool_calls=calls, - input_tokens=getattr(u, "input_tokens", 0), - output_tokens=getattr(u, "output_tokens", 0), - stop_reason=getattr(resp, "stop_reason", "") or "", - provider_msg=provider, - ) - - -class AnthropicModel: - def __init__(self, name: str, model_id: str, temperature: float, max_tokens: int): - try: - import anthropic - except ImportError as e: # pragma: no cover - raise SystemExit("pip install anthropic (see bench/pyproject.toml)") from e - if not os.environ.get("ANTHROPIC_API_KEY"): - raise SystemExit("ANTHROPIC_API_KEY is not set") - self.name = name - self.model_id = model_id - self.temperature = temperature - self.max_tokens = max_tokens - # Per-request timeout + retries so a single hung request can't stall the whole matrix - # (the SDK default has no wall-clock cap short enough for a long unattended run). - self._client = anthropic.Anthropic(timeout=120.0, max_retries=4) - - def complete(self, system: str, user: str) -> Completion: - resp = self._client.messages.create( - model=self.model_id, - system=system, - max_tokens=self.max_tokens, - temperature=self.temperature, - messages=[{"role": "user", "content": user}], - ) - text = "".join(b.text for b in resp.content if getattr(b, "type", None) == "text") - u = resp.usage - return Completion( - text=text, - input_tokens=getattr(u, "input_tokens", 0), - output_tokens=getattr(u, "output_tokens", 0), - raw_usage=u.model_dump() if hasattr(u, "model_dump") else {}, - ) - - def step(self, system: str, messages: list[dict], tools: list[dict]) -> Step: - resp = self._client.messages.create( - model=self.model_id, - system=system, - max_tokens=self.max_tokens, - temperature=self.temperature, - tools=_anthropic_tools(tools), - messages=_anthropic_messages(messages), - ) - return _step_from_anthropic(resp) - - -def build_model( - name: str, spec: dict, *, temperature: float, max_tokens: int, mode: str = "single" -) -> Model: - provider = spec.get("provider") - if provider == "mock": - if mode == "multi": - return MockToolModel( - name=name, default=spec.get("default", ""), replies=spec.get("replies") - ) - return MockModel(name=name, default=spec.get("default", ""), replies=spec.get("replies")) - if provider == "anthropic": - return AnthropicModel( - name=name, - model_id=spec["model_id"], - temperature=spec.get("temperature", temperature), - max_tokens=spec.get("max_tokens", max_tokens), - ) - raise ValueError(f"unknown provider {provider!r} for model {name!r}") diff --git a/bench/surface_bench/oracle.py b/bench/surface_bench/oracle.py deleted file mode 100644 index fe751ac..0000000 --- a/bench/surface_bench/oracle.py +++ /dev/null @@ -1,68 +0,0 @@ -"""Post-run sanity tripwires for a results dir — cheap checks that catch authoring/harness bugs -before they reach the write-up (the failure mode recorded in issue #113). - - python -m surface_bench.oracle results/ - -Checks (per scenario x model): - * C2-fresh ≈ 100% — with a *fresh* doc and the code readable, the task must be solvable; a low - cell means the scenario is mis-authored (a leaked stale value, a broken grader, an impossible - task), not a real effect. - * cascade C1 misleads at all — if a stale doc never produces the wrong answer, the drift isn't - load-bearing and the scenario measures nothing. - -The surf_report `changed` divergence is sealed at authoring time by tools/author.py, so it isn't -re-checked here. Exit code is non-zero if any tripwire fires, so this can gate a run in CI. -""" - -from __future__ import annotations - -import sys - -from .metrics import load_rows - -C2_FRESH_THRESHOLD = 0.9 - - -def check(rows: list[dict], *, c2_threshold: float = C2_FRESH_THRESHOLD) -> list[str]: - warnings: list[str] = [] - scenarios = sorted({r["scenario"] for r in rows}) - models = sorted({r["model"] for r in rows}) - for sc in scenarios: - for m in models: - def cell(cond: str) -> list[dict]: - return [ - r for r in rows if r["scenario"] == sc and r["model"] == m and r["condition"] == cond - ] - - c2 = cell("C2") - if c2: - sr = sum(bool(r.get("ok")) for r in c2) / len(c2) - if sr < c2_threshold: - warnings.append( - f"C2-fresh low: {sc} / {m} = {sr:.0%} (< {c2_threshold:.0%}) " - "— likely an authoring bug, not a real effect" - ) - if sc.startswith("cascade-"): - c1 = cell("C1") - if c1 and not any(r.get("misled") for r in c1): - warnings.append( - f"C1 never misleads: {sc} / {m} — the drift may not be load-bearing" - ) - return warnings - - -def main() -> None: - if len(sys.argv) != 2: - sys.exit("usage: python -m surface_bench.oracle results/") - warnings = check(load_rows(sys.argv[1])) - if not warnings: - print("oracle: PASS — all tripwires clear") - return - print(f"oracle: {len(warnings)} warning(s):") - for w in warnings: - print(f" - {w}") - sys.exit(1) - - -if __name__ == "__main__": - main() diff --git a/bench/surface_bench/prompts.py b/bench/surface_bench/prompts.py deleted file mode 100644 index 35face9..0000000 --- a/bench/surface_bench/prompts.py +++ /dev/null @@ -1,83 +0,0 @@ -"""Assemble the (system, user) prompt for a scenario under a given condition. - -Only the *documentation block* differs across conditions; the code, task, and system prompt are -byte-identical. That is what makes any C2-vs-C1 gap attributable to doc *accuracy* rather than to -the mere presence of prose (C0 isolates presence). -""" - -from __future__ import annotations - -from fnmatch import fnmatch - -from .scenarios import Scenario - -CONDITIONS = ("C0", "C1", "C2", "C3", "Cw") - -CONDITION_LABEL = { - "C0": "code only (no documentation)", - "C1": "code + stale documentation", - "C2": "code + fresh documentation", - "C3": "code + stale documentation + surf divergence report", - "Cw": "code + stale documentation + generic staleness warning", -} - -# Deliberately minimal and persona-free: no "you are an expert…" framing (which primes diligent, -# skeptical behaviour) and no precedence between docs and code. This mirrors how people actually -# prompt — paste/tag some files, maybe a doc, ask for the change — and keeps the docs-vs-code -# question entirely to the model. -SYSTEM = "Use the files and documentation provided to do the task below." - -_FENCE_LANG = {".py": "python", ".ts": "typescript", ".tsx": "tsx", ".js": "javascript"} - - -def _render_code(scenario: Scenario) -> str: - # Cascade scenarios hide the drifted *dependency*: its real source stays in code/ (so surf - # seals a genuine divergence and the grader runs against it) but is withheld from the prompt, - # so the agent only knows the dependency through its doc. fnmatch lets meta list a file or a - # glob (e.g. "code/limiter/*.py"). - hidden = scenario.hidden_paths - blocks = ["## Codebase\n"] - for f in scenario.code: - if any(fnmatch(f.relpath, pat) for pat in hidden): - continue - suffix = "." + f.relpath.rsplit(".", 1)[-1] if "." in f.relpath else "" - lang = _FENCE_LANG.get(suffix, "") - blocks.append(f"### {f.relpath}\n```{lang}\n{f.content.rstrip()}\n```") - return "\n\n".join(blocks) - - -def _doc_block(scenario: Scenario, condition: str) -> str: - if condition == "C0": - return "" - hub = scenario.hub_fresh if condition == "C2" else scenario.hub_stale - block = f"## Project documentation\n\nThe repository documents this area as follows:\n\n{hub.rstrip()}" - if condition == "C3": - block += ( - "\n\n## Automated documentation check\n\n" - "Surface (a deterministic doc-drift gate) reports that the anchored claim above no " - "longer matches the code it points at — the documented span has changed since the " - "claim was last confirmed:\n\n" - f"```json\n{scenario.surf_report.rstrip()}\n```" - ) - if condition == "Cw": - # Control isolating Surface's *specific* contribution: a generic staleness warning with no - # corrected code, no file, no line, no value. If Cw recovers like C3, the value was just - # "any skepticism prompt"; if Cw stays at C1, only Surface's concrete fix helps. Keep this - # deliberately content-free — leaking the truth here would collapse the C3-vs-Cw contrast. - block += ( - "\n\n## Note\n\n" - "This documentation may be out of date. It was last reviewed some time ago and might " - "no longer reflect the current behaviour of the code." - ) - return block - - -def build_prompt(scenario: Scenario, condition: str) -> tuple[str, str]: - if condition not in CONDITIONS: - raise ValueError(f"unknown condition {condition!r}") - parts = [_render_code(scenario)] - doc = _doc_block(scenario, condition) - if doc: - parts.append(doc) - parts.append(f"## Task\n\n{scenario.task.rstrip()}") - return SYSTEM, "\n\n".join(parts) diff --git a/bench/surface_bench/report.py b/bench/surface_bench/report.py deleted file mode 100644 index 34dcea1..0000000 --- a/bench/surface_bench/report.py +++ /dev/null @@ -1,352 +0,0 @@ -"""Turn a results dir into summary.json, a markdown report, and (optional) plots. - - python -m surface_bench.report results/ -""" - -from __future__ import annotations - -import json -import sys -from pathlib import Path - -from .metrics import load_rows, summarize -from .prompts import CONDITION_LABEL - -DELTA_GLOSS = { - "C2-C1": "fresh vs stale (the Surface value)", - "C0-C1": "no-docs vs stale (rotted worse than nothing?)", - "C3-C1": "surf-report vs stale (does surfacing drift recover it?)", - "C2-C0": "fresh vs no-docs (value of accurate prose)", - "C3-Cw": "surf-report vs bare warning (is the value the *fix*, not just suspicion?)", - "Cw-C1": "bare warning vs stale (does a generic warning alone help?)", -} - -TOKEN_DELTA_GLOSS = { - "C1-C2": "stale − fresh (extra generation to cope with a stale doc)", - "C1-C0": "stale − no-docs (does a wrong doc cost more than none?)", - "C1-C3": "stale − stale+report (does the surf report cut the cost?)", -} - - -def _pct(x: float | None) -> str: - return "—" if x is None else f"{100 * x:.0f}%" - - -def _ci(ci) -> str: - return "" if not ci else f" [{100 * ci[0]:.0f}–{100 * ci[1]:.0f}]" - - -def _tok(x: float | None) -> str: - return "—" if x is None else f"{x:.0f}" - - -def _ci_raw(ci) -> str: - return "" if not ci else f" [{ci[0]:.0f}–{ci[1]:.0f}]" - - -TIER_LABEL = { - "T0": "local (contradiction visible)", - "T1": "buried (truth needs tracing)", - "T2": "premise (invariant is load-bearing)", - "T3": "multi-claim (one of several drifted)", -} - - -def _gradient_table(summary: dict) -> list[str]: - """The headline: does C2-C1 (the Surface effect) grow with complexity tier?""" - tiers = summary.get("tiers", []) - if len(tiers) < 2: - return [] - lines = ["## The gradient — Surface effect (C2−C1 success) by complexity tier\n"] - for model in summary["models"]: - lines.append(f"### {model}\n") - lines.append("| Tier | C2−C1 (fresh−stale) | C0−C1 (none−stale) |") - lines.append("|---|---|---|") - for tier in tiers: - block = summary["by_tier"][tier][model] - - def cell(key): - d = block.get(key) - if not d: - return "—" - star = " ✓" if d["significant"] else "" - return f"{100 * d['delta']:+.0f} pp [{100 * d['ci'][0]:+.0f}, {100 * d['ci'][1]:+.0f}]{star}" - - label = TIER_LABEL.get(tier, "") - lines.append(f"| {tier} — {label} | {cell('C2-C1')} | {cell('C0-C1')} |") - lines.append("") - return lines - - -def _spend_section(summary: dict) -> list[str]: - cost = summary.get("cost") - if not cost or not cost.get("total_usd"): - return [] - lines = [f"## Spend\n\n**Total: ${cost['total_usd']:.2f}** (estimated from token usage).\n"] - by_model = {m: v for m, v in cost["by_model"].items() if v} - if by_model: - lines.append("| Model | Spend |") - lines.append("|---|---|") - for m, v in by_model.items(): - lines.append(f"| {m} | ${v:.2f} |") - lines.append("") - return lines - - -def _verification_section(summary: dict) -> list[str]: - """The headline of the multi-turn track: a confident stale doc should suppress the file-read the - agent would otherwise do (H4), and within C1 verifiers should be right, non-verifiers misled (H5).""" - ver = summary.get("verification") - if not ver: - return [] - lines = [ - "## Verification — did the agent check the hidden dependency? (multi-turn)\n", - "With no doc the agent should go read the hidden code; a confident **stale** doc should " - "suppress that check (H4). Within C1, those who verified should be correct (H5).\n", - ] - for model in summary["models"]: - lines.append(f"### {model}\n") - lines.append("| Condition | n | Verified hidden dep | When verified, correct |") - lines.append("|---|---|---|---|") - for cond in summary["conditions"]: - v = ver.get(model, {}).get(cond) - if not v: - continue - lines.append( - f"| {cond} | {v['n']} | {_pct(v['verification_rate'])}{_ci(v['verification_ci'])} " - f"| {_pct(v['verified_then_correct'])} |" - ) - for key, d in summary.get("verification_deltas", {}).get(model, {}).items(): - sig = " ✓ significant" if d["significant"] else "" - lines.append( - f"\n- verification `{key}`: {100 * d['delta']:+.0f} pp " - f"[{100 * d['ci'][0]:+.0f}, {100 * d['ci'][1]:+.0f}]{sig}" - ) - med = summary.get("mediation", {}).get(model) - if med and (med["n_verified"] or med["n_unverified"]): - lines.append( - f"- C1 mediation (H5): verifiers {_pct(med['verified_success'])} correct " - f"(n={med['n_verified']}) vs non-verifiers {_pct(med['unverified_success'])} " - f"(n={med['n_unverified']})" - ) - lines.append("") - return lines - - -def _per_scenario_section(summary: dict) -> list[str]: - """Per-scenario success, so one broken fixture can't hide inside a family average.""" - bs = summary.get("by_scenario") - if not bs: - return [] - conds = summary["conditions"] - lines = ["## Per-scenario success rate\n"] - for model in summary["models"]: - lines.append(f"### {model}\n") - lines.append("| Scenario | " + " | ".join(conds) + " |") - lines.append("|" + "---|" * (len(conds) + 1)) - for sc in sorted(bs): - cells = bs[sc].get(model, {}) - row = [sc] + [_pct(cells[c]["success"]) if c in cells else "—" for c in conds] - lines.append("| " + " | ".join(row) + " |") - lines.append("") - return lines - - -def render_markdown(summary: dict) -> str: - lines = ["# Surface agent-impact benchmark\n"] - lines += _spend_section(summary) - lines += _gradient_table(summary) - lines += _verification_section(summary) - for model in summary["models"]: - lines.append(f"## {model}\n") - lines.append("| Condition | n | Success | Misled |") - lines.append("|---|---|---|---|") - for cond in summary["conditions"]: - r = summary["rates"][model][cond] - label = CONDITION_LABEL.get(cond, cond) - lines.append( - f"| {cond} — {label} | {r['n']} | {_pct(r['success'])}{_ci(r['success_ci'])} " - f"| {_pct(r['misled'])}{_ci(r['misled_ci'])} |" - ) - lines.append("\n**Deltas (success rate, 95% bootstrap CI):**\n") - for key, d in summary["deltas"][model].items(): - sig = " ✓ significant" if d["significant"] else "" - holm = " (Holm ✓)" if d.get("significant_holm") else (" (Holm ✗)" if "significant_holm" in d else "") - gloss = DELTA_GLOSS.get(key, "") - lines.append( - f"- `{key}` {gloss}: {100 * d['delta']:+.0f} pp " - f"[{100 * d['ci'][0]:+.0f}, {100 * d['ci'][1]:+.0f}]{sig}{holm}" - ) - - toks = summary.get("tokens", {}).get(model, {}) - if toks: - lines.append("\n**Output tokens (mean, 95% bootstrap CI) — generation cost:**\n") - lines.append("| Condition | mean out | when correct | when misled |") - lines.append("|---|---|---|---|") - for cond in summary["conditions"]: - t = toks.get(cond, {}) - lines.append( - f"| {cond} | {_tok(t.get('mean_output'))}{_ci_raw(t.get('mean_output_ci'))} " - f"| {_tok(t.get('mean_output_correct'))} | {_tok(t.get('mean_output_misled'))} |" - ) - lines.append("\n**Output-token deltas (95% bootstrap CI):**\n") - for key, d in summary.get("token_deltas", {}).get(model, {}).items(): - sig = " ✓ significant" if d["significant"] else "" - gloss = TOKEN_DELTA_GLOSS.get(key, "") - lines.append( - f"- `{key}` {gloss}: {d['delta']:+.0f} tok " - f"[{d['ci'][0]:+.0f}, {d['ci'][1]:+.0f}]{sig}" - ) - lines.append("") - lines += _per_scenario_section(summary) - return "\n".join(lines) - - -def maybe_plot(summary: dict, out_dir: Path) -> None: - """Render standalone-readable figures. - - The story splits by family, so we plot the families separately rather than averaging them - (which would dilute the cascade effect under the comprehension ceiling): cascade scenarios on - *success* (where rot breaks the agent) and comprehension scenarios on *output tokens* (where rot - just taxes generation). Plain-English condition labels, value annotations, and self-explanatory - titles, so the charts make sense without the report. Re-reads raw.jsonl for the per-family split. - """ - try: - import matplotlib - - matplotlib.use("Agg") - import matplotlib.pyplot as plt - except ImportError: - return - import statistics - - raw = out_dir / "raw.jsonl" - if not raw.exists(): - return - rows = [json.loads(l) for l in raw.read_text().splitlines() if l.strip()] - # Order models by capability (small → large) so "a bigger model doesn't help" reads left→right; - # unknown names fall back to alphabetical after the known ones. - rank = {"haiku": 0, "sonnet": 1, "opus": 2} - models = sorted(summary["models"], key=lambda m: (rank.get(m, 99), m)) - conds = [c for c in ("C0", "C1", "C2", "C3", "Cw") if c in summary["conditions"]] - label = { - "C0": "No docs", - "C1": "Stale docs", - "C2": "Fresh docs\n(Surface)", - "C3": "Stale docs +\nSurface report", - "Cw": "Stale docs +\nstaleness warning", - } - palette = ["#4C72B0", "#DD8452", "#55A868", "#C44E52", "#8172B3"] - color = {m: palette[i % len(palette)] for i, m in enumerate(models)} - - casc = [r for r in rows if r["scenario"].startswith("cascade-")] - comp = [r for r in rows if not r["scenario"].startswith("cascade-")] - - def cell(sub, m, c): - return [r for r in sub if r["model"] == m and r["condition"] == c] - - def succ_pct(sub, m, c): - x = cell(sub, m, c) - return 100 * sum(bool(r["ok"]) for r in x) / len(x) if x else 0.0 - - def mean_out(sub, m, c): - x = [r["output_tokens"] for r in cell(sub, m, c) if r.get("output_tokens") is not None] - return statistics.mean(x) if x else 0.0 - - def grouped(ax, sub, valfn, ylabel, *, ymax=None, fmt="{:.0f}"): - n = len(models) - width = 0.8 / max(n, 1) - for i, m in enumerate(models): - vals = [valfn(sub, m, c) for c in conds] - offs = [j + (i - (n - 1) / 2) * width for j in range(len(conds))] - ax.bar(offs, vals, width=width, color=color[m], label=m, edgecolor="white", linewidth=0.5) - top = ymax or (max(vals) if vals else 1) or 1 - for off, v in zip(offs, vals): - ax.text(off, v + top * 0.012, fmt.format(v), ha="center", va="bottom", fontsize=7) - ax.set_xticks(range(len(conds))) - ax.set_xticklabels([label.get(c, c) for c in conds], fontsize=8) - ax.set_ylabel(ylabel, fontsize=9) - if ymax: - ax.set_ylim(0, ymax) - ax.grid(axis="y", alpha=0.25, linewidth=0.6) - ax.set_axisbelow(True) - for sp in ("top", "right"): - ax.spines[sp].set_visible(False) - - panels = [("casc", casc)] * bool(casc) + [("comp", comp)] * bool(comp) - if not panels: - return - - # Combined overview (one panel per non-empty family). - fig, axes = plt.subplots(1, len(panels), figsize=(5.6 * len(panels), 4.3)) - axes = [axes] if len(panels) == 1 else list(axes) - for ax, (kind, sub) in zip(axes, panels): - if kind == "casc": - grouped(ax, sub, succ_pct, "Tasks the agent got right (%)", ymax=108, fmt="{:.0f}%") - ax.set_title( - "Code HIDDEN — agent must trust the doc\nStale docs break every model; " - "fresh docs & the Surface report fix it", - fontsize=9.5, - ) - else: - grouped(ax, sub, mean_out, "Avg tokens the agent wrote") - ax.set_title( - "Code VISIBLE — agent can check it\nRot doesn't cause errors, but costs extra tokens", - fontsize=9.5, - ) - axes[0].legend(title="model", fontsize=8, title_fontsize=8, frameon=False, loc="upper left") - fig.suptitle("Does stale documentation hurt a coding agent?", fontsize=13, fontweight="bold") - fig.tight_layout(rect=(0, 0, 1, 0.97)) - fig.savefig(out_dir / "overview.png", dpi=150, bbox_inches="tight") - plt.close(fig) - - # Standalone hero: the cascade success chart (the single most quotable figure). - if casc: - fig, ax = plt.subplots(figsize=(6.6, 4.3)) - grouped(ax, casc, succ_pct, "Tasks the agent got right (%)", ymax=108, fmt="{:.0f}%") - ax.legend(title="model", fontsize=8, title_fontsize=8, frameon=False, loc="upper left") - ax.set_title( - "Coding accuracy when the agent can't see the code it depends on\n" - "A stale doc breaks every model (a bigger model doesn't help); " - "fresh docs or Surface's drift report restore it", - fontsize=9.5, - ) - fig.tight_layout() - fig.savefig(out_dir / "cascade_success.png", dpi=150, bbox_inches="tight") - plt.close(fig) - - # Verification hero (multi-turn only): does a confident stale doc stop the agent checking? - if casc and any("verified_hidden" in r for r in rows): - - def ver_pct(sub, m, c): - x = [r for r in cell(sub, m, c) if "verified_hidden" in r] - return 100 * sum(bool(r["verified_hidden"]) for r in x) / len(x) if x else 0.0 - - fig, ax = plt.subplots(figsize=(6.6, 4.3)) - grouped(ax, casc, ver_pct, "Agent read the hidden dependency (%)", ymax=108, fmt="{:.0f}%") - ax.legend(title="model", fontsize=8, title_fontsize=8, frameon=False, loc="upper right") - ax.set_title( - "Does a confident (stale) doc stop the agent verifying?\n" - "With no doc the agent reads the hidden code; a stale doc suppresses the check", - fontsize=9.5, - ) - fig.tight_layout() - fig.savefig(out_dir / "verification_rate.png", dpi=150, bbox_inches="tight") - plt.close(fig) - - -def main() -> None: - if len(sys.argv) != 2: - sys.exit("usage: python -m surface_bench.report results/") - out_dir = Path(sys.argv[1]) - rows = load_rows(out_dir) - summary = summarize(rows) - (out_dir / "summary.json").write_text(json.dumps(summary, indent=2) + "\n") - md = render_markdown(summary) - (out_dir / "report.md").write_text(md + "\n") - maybe_plot(summary, out_dir) - print(md) - - -if __name__ == "__main__": - main() diff --git a/bench/surface_bench/run.py b/bench/surface_bench/run.py deleted file mode 100644 index 50a8506..0000000 --- a/bench/surface_bench/run.py +++ /dev/null @@ -1,192 +0,0 @@ -"""Matrix runner: scenario x condition x model x trial -> results//raw.jsonl. - -Each row is one model call plus its deterministic grade. Raw output is preserved so grading can -be re-run offline without re-spending tokens. - - python -m surface_bench.run --models mock - python -m surface_bench.run --models haiku --scenarios refresh-single-use-qa --trials 10 -""" - -from __future__ import annotations - -import argparse -import json -import subprocess -import sys -import time -import tomllib -from datetime import datetime, timezone -from pathlib import Path - -from . import grade_code, grade_qa -from .agent import run_agent -from .models import build_model -from .prompts import CONDITIONS, build_prompt -from .scenarios import discover -from .tools_runtime import ToolContext, scenario_sandbox, touched_hidden - -BENCH_ROOT = Path(__file__).resolve().parent.parent - - -def _surf_version() -> str: - for cand in ("target/release/surf", "target/debug/surf"): - p = BENCH_ROOT.parent / cand - if p.exists(): - out = subprocess.run([str(p), "--version"], capture_output=True, text=True) - return out.stdout.strip() - return "unknown" - - -def _grade(scenario, output: str) -> dict: - if scenario.task_type == "qa": - return grade_qa.grade(scenario, output) - return grade_code.grade(scenario, output) - - -def main() -> None: - ap = argparse.ArgumentParser() - ap.add_argument("--config", default=str(BENCH_ROOT / "config.toml")) - ap.add_argument("--models", nargs="*", help="subset of configured model names") - ap.add_argument("--scenarios", nargs="*", help="subset of scenario ids") - ap.add_argument("--conditions", nargs="*", default=list(CONDITIONS)) - ap.add_argument("--trials", type=int, help="override trials from config") - ap.add_argument( - "--mode", - choices=("single", "multi"), - help="single-shot completion (v1) or multi-turn tool-using agent (v2)", - ) - ap.add_argument("--max-turns", type=int, help="agent turn budget in multi mode") - ap.add_argument("--out", help="results dir (default results/)") - args = ap.parse_args() - - cfg = tomllib.loads(Path(args.config).read_text()) - trials = args.trials or cfg.get("trials", 10) - temperature = cfg.get("temperature", 1.0) - max_tokens = cfg.get("max_tokens", 1024) - mode = args.mode or cfg.get("mode", "single") - max_turns = args.max_turns or cfg.get("max_turns", 8) - model_specs = cfg.get("models", {}) - model_names = args.models or list(model_specs) - - scenarios = discover(BENCH_ROOT / "scenarios", only=args.scenarios) - if not scenarios: - sys.exit("no scenarios matched") - - models = { - n: build_model( - n, model_specs[n], temperature=temperature, max_tokens=max_tokens, mode=mode - ) - for n in model_names - } - mock_names = {n for n in model_names if model_specs[n].get("provider") == "mock"} - if mode == "multi": - missing = [n for n, m in models.items() if not hasattr(m, "step")] - if missing: - sys.exit(f"multi mode needs a tool-using model; {missing} have no step() yet") - # USD per token, per model (from config; 0 if unpriced, e.g. the mock). - pricing = { - n: ( - model_specs[n].get("input_per_mtok", 0.0) / 1e6, - model_specs[n].get("output_per_mtok", 0.0) / 1e6, - ) - for n in model_names - } - - ts = datetime.now(timezone.utc).strftime("%Y%m%dT%H%M%SZ") - out_dir = Path(args.out) if args.out else BENCH_ROOT / "results" / ts - out_dir.mkdir(parents=True, exist_ok=True) - raw_path = out_dir / "raw.jsonl" - - meta = { - "timestamp": ts, - "trials": trials, - "temperature": temperature, - "max_tokens": max_tokens, - "mode": mode, - "max_turns": max_turns if mode == "multi" else None, - "conditions": args.conditions, - "models": {n: model_specs[n] for n in model_names}, - "scenarios": [s.id for s in scenarios], - "surf_version": _surf_version(), - } - (out_dir / "run.json").write_text(json.dumps(meta, indent=2) + "\n") - - n_total = len(scenarios) * len(args.conditions) * len(models) * trials - done = 0 - with raw_path.open("w") as fh: - for scenario in scenarios: - for condition in args.conditions: - system, user = build_prompt(scenario, condition) - for model_name, model in models.items(): - if hasattr(model, "set_condition"): - model.set_condition(condition) - for trial in range(trials): - row = { - "scenario": scenario.id, - "task_type": scenario.task_type, - "tier": scenario.tier, - "condition": condition, - "model": model_name, - "trial": trial, - "mode": mode, - } - try: - if mode == "multi": - # Fresh per-trial sandbox: the agent's tools can reach the hidden - # dependency that the prompt withholds. - with scenario_sandbox(scenario) as ws: - traj = run_agent( - model, system, user, ToolContext(ws), max_turns=max_turns - ) - text, in_tok, out_tok = ( - traj.final_text, - traj.input_tokens, - traj.output_tokens, - ) - agent_fields = dict( - turns=traj.turns, - stop_reason=traj.stop_reason, - tool_calls=traj.tool_calls, - verified_hidden=touched_hidden( - traj.accessed, scenario.hidden_paths - ), - per_turn_tokens=traj.per_turn_tokens, - ) - else: - comp = model.complete(system, user) - text, in_tok, out_tok = ( - comp.text, - comp.input_tokens, - comp.output_tokens, - ) - agent_fields = {} - grade = _grade(scenario, text) - in_price, out_price = pricing[model_name] - row.update( - output=text, - input_tokens=in_tok, - output_tokens=out_tok, - cost_usd=in_tok * in_price + out_tok * out_price, - ok=grade["ok"], - misled=grade["misled"], - detail=grade["detail"], - parsed=grade["parsed"], - **agent_fields, - ) - except Exception as e: # keep the matrix going; record the failure - row.update(output=None, ok=False, misled=False, error=repr(e)) - fh.write(json.dumps(row) + "\n") - fh.flush() - done += 1 - print( - f"\r[{done}/{n_total}] {scenario.id} {condition} {model_name}", - end="", - file=sys.stderr, - ) - if model_name not in mock_names: - time.sleep(0) # placeholder for rate-limit backoff hook - print(f"\nwrote {raw_path}", file=sys.stderr) - - -if __name__ == "__main__": - main() diff --git a/bench/surface_bench/scenarios.py b/bench/surface_bench/scenarios.py deleted file mode 100644 index c8a19d6..0000000 --- a/bench/surface_bench/scenarios.py +++ /dev/null @@ -1,95 +0,0 @@ -"""Load and validate a scenario directory into an in-memory Scenario.""" - -from __future__ import annotations - -import tomllib -from dataclasses import dataclass, field -from pathlib import Path - -# Files in code/ we never show the agent (caches, compiled output). -_SKIP = {".pyc", ".pyo"} -_SKIP_DIRS = {"__pycache__", "node_modules", ".git"} - - -@dataclass(frozen=True) -class CodeFile: - relpath: str # path relative to the scenario's code/ root, forward-slashed - content: str - - -@dataclass -class Scenario: - id: str - title: str - lang: str - task_type: str # "qa" | "code" - tier: str # complexity tier: T0 local | T1 buried | T2 premise | T3 multi-claim - anchor: str - invariant: str - root: Path - task: str - hub_stale: str - hub_fresh: str - surf_report: str # raw JSON text of the genuine `surf check` divergence - code: list[CodeFile] = field(default_factory=list) - # code/ paths (or globs) present for grading but withheld from the prompt — the hidden - # dependency in a cascade scenario, which the agent knows only through its doc. - hidden_paths: list[str] = field(default_factory=list) - # code scenarios: the visible file the agent returns (e.g. "code/throttle.py"). Lets the - # grader-polarization self-test (tools/validate_scenario.py) build the `FILE:` block. - edit_path: str = "" - - @property - def grader_dir(self) -> Path: - return self.root / "grader" - - -def _read_code(code_root: Path) -> list[CodeFile]: - files: list[CodeFile] = [] - for p in sorted(code_root.rglob("*")): - if not p.is_file() or p.suffix in _SKIP: - continue - if any(part in _SKIP_DIRS for part in p.relative_to(code_root).parts): - continue - rel = p.relative_to(code_root).as_posix() - files.append(CodeFile(relpath=f"code/{rel}", content=p.read_text())) - return files - - -def load_scenario(path: str | Path) -> Scenario: - root = Path(path).resolve() - meta = tomllib.loads((root / "meta.toml").read_text()) - required = {"id", "title", "lang", "task_type", "anchor"} - missing = required - meta.keys() - if missing: - raise ValueError(f"{root}: meta.toml missing keys {sorted(missing)}") - if meta["task_type"] not in ("qa", "code"): - raise ValueError(f"{root}: task_type must be 'qa' or 'code'") - - return Scenario( - id=meta["id"], - title=meta["title"], - lang=meta["lang"], - task_type=meta["task_type"], - tier=meta.get("tier", "T0"), - anchor=meta["anchor"], - invariant=meta.get("invariant", ""), - root=root, - task=(root / "task.md").read_text(), - hub_stale=(root / "hub_stale.md").read_text(), - hub_fresh=(root / "hub_fresh.md").read_text(), - surf_report=(root / "surf_report.json").read_text(), - code=_read_code(root / "code"), - hidden_paths=meta.get("hidden_paths", []), - edit_path=meta.get("edit_path", ""), - ) - - -def discover(scenarios_dir: str | Path, only: list[str] | None = None) -> list[Scenario]: - base = Path(scenarios_dir) - dirs = sorted(p.parent for p in base.glob("*/meta.toml")) - scenarios = [load_scenario(d) for d in dirs] - if only: - wanted = set(only) - scenarios = [s for s in scenarios if s.id in wanted] - return scenarios diff --git a/bench/surface_bench/tools_runtime.py b/bench/surface_bench/tools_runtime.py deleted file mode 100644 index 6a2269e..0000000 --- a/bench/surface_bench/tools_runtime.py +++ /dev/null @@ -1,227 +0,0 @@ -"""Read-only tool surface for the multi-turn agent loop (v2 / milestone #12). - -The agentic track gives the model tools so it can *choose* to read the dependency a doc describes, -instead of being forced to trust the doc (the single-shot cascade's structural limit). The whole -point is to measure whether a confident *stale* doc suppresses that verification — so the surface is -deliberately **read-only**: `read_file`, `grep`, `list_dir`, and a `final_answer` terminator. There is -no `run_tests`/shell: a test runner would let the agent brute-force ground truth and wash out the -doc-trust signal we are trying to measure. - -Tools are scoped to a per-trial sandbox (see `scenario_sandbox`): a fresh copy of the scenario's -`code/` tree that **includes the hidden dependency**. The initial prompt still omits `hidden_paths` -(`prompts._render_code`), but the file is on disk, so `read_file("code/limiter/window.py")` succeeds -— the hidden truth is reachable *by choice, not absent*. Every path is resolved inside the sandbox; -escapes (`..`, absolute paths) are rejected. - -`TOOL_SPECS` is provider-neutral: a list of `{name, description, parameters}` (parameters is a JSON -Schema). Each model adapter translates it into its own tool format (Anthropic `input_schema`, OpenAI -`function.parameters`, Gemini `FunctionDeclaration`), so `agent.py` and the graders never learn which -provider ran. -""" - -from __future__ import annotations - -import re -import shutil -import tempfile -from collections.abc import Iterator -from contextlib import contextmanager -from fnmatch import fnmatch -from pathlib import Path - -from .scenarios import Scenario - -# Bound a single tool result so one huge file can't blow the turn's token budget. Fixtures are tiny; -# this only guards against pathological inputs. -MAX_READ_BYTES = 64 * 1024 -MAX_GREP_MATCHES = 100 - - -class ToolError(Exception): - """A tool was called with bad arguments (e.g. a path escape). The message is fed back to the - model as the tool result so it can recover, rather than crashing the run.""" - - -# Provider-neutral tool schemas. `parameters` is a JSON Schema object; adapters translate it. -TOOL_SPECS: list[dict] = [ - { - "name": "list_dir", - "description": "List the files and directories under a workspace-relative path.", - "parameters": { - "type": "object", - "properties": { - "path": { - "type": "string", - "description": "Workspace-relative directory (default the workspace root).", - } - }, - "required": [], - }, - }, - { - "name": "read_file", - "description": "Read the full contents of a workspace-relative file.", - "parameters": { - "type": "object", - "properties": { - "path": {"type": "string", "description": "Workspace-relative file path."} - }, - "required": ["path"], - }, - }, - { - "name": "grep", - "description": "Search files for a regular expression, returning matching lines as " - "`path:lineno: line`.", - "parameters": { - "type": "object", - "properties": { - "pattern": {"type": "string", "description": "A Python regular expression."}, - "path": { - "type": "string", - "description": "Workspace-relative file or directory to search " - "(default the workspace root).", - }, - }, - "required": ["pattern"], - }, - }, - { - "name": "final_answer", - "description": "Submit your final answer and end the task. Provide the complete answer " - "(including any required VERDICT line or FILE: blocks) in `answer`.", - "parameters": { - "type": "object", - "properties": { - "answer": {"type": "string", "description": "The complete final answer."} - }, - "required": ["answer"], - }, - }, -] - -TOOL_NAMES = frozenset(spec["name"] for spec in TOOL_SPECS) - - -class ToolContext: - """Executes the read-only tools against a sandbox root and records what was accessed. - - `accessed` is the list of workspace-relative paths the agent actually read or grepped — the raw - material for the *verification rate* metric (did it read a load-bearing hidden dependency before - answering?). `final_answer` is set once the agent terminates. - """ - - def __init__(self, root: Path): - self.root = root.resolve() - self.accessed: list[str] = [] - self.final_answer: str | None = None - - # -- path safety ------------------------------------------------------------------------- - def _resolve(self, rel: str) -> Path: - # `root / "/abs"` collapses to "/abs" in pathlib, and `..` walks up — resolve() then makes - # any escape visible, and we reject anything not inside the sandbox. - p = (self.root / rel).resolve() - if p != self.root and self.root not in p.parents: - raise ToolError(f"path {rel!r} escapes the workspace") - return p - - def _rel(self, p: Path) -> str: - return p.relative_to(self.root).as_posix() - - # -- tools ------------------------------------------------------------------------------- - def list_dir(self, path: str = ".") -> str: - p = self._resolve(path) - if not p.exists(): - raise ToolError(f"no such path: {path!r}") - if not p.is_dir(): - raise ToolError(f"not a directory: {path!r}") - names = sorted(c.name + ("/" if c.is_dir() else "") for c in p.iterdir()) - return "\n".join(names) if names else "(empty)" - - def read_file(self, path: str) -> str: - p = self._resolve(path) - if not p.is_file(): - raise ToolError(f"no such file: {path!r}") - self.accessed.append(self._rel(p)) - data = p.read_bytes() - truncated = len(data) > MAX_READ_BYTES - text = data[:MAX_READ_BYTES].decode("utf-8", errors="replace") - if truncated: - text += f"\n... [truncated at {MAX_READ_BYTES} bytes]" - return text - - def grep(self, pattern: str, path: str = ".") -> str: - try: - rx = re.compile(pattern) - except re.error as e: - raise ToolError(f"invalid regex: {e}") from e - p = self._resolve(path) - if not p.exists(): - raise ToolError(f"no such path: {path!r}") - files = [p] if p.is_file() else [f for f in sorted(p.rglob("*")) if f.is_file()] - matches: list[str] = [] - for f in files: - rel = self._rel(f) - try: - lines = f.read_text(errors="replace").splitlines() - except OSError: - continue - hit = False - for i, line in enumerate(lines, 1): - if rx.search(line): - matches.append(f"{rel}:{i}: {line}") - hit = True - if len(matches) >= MAX_GREP_MATCHES: - break - if hit: - self.accessed.append(rel) - if len(matches) >= MAX_GREP_MATCHES: - matches.append("... [more matches truncated]") - break - return "\n".join(matches) if matches else "(no matches)" - - def submit(self, answer: str) -> str: - self.final_answer = answer - return "Final answer recorded." - - # -- verification metric helper ---------------------------------------------------------- - def verified(self, hidden_paths: list[str]) -> bool: - """True iff the agent read/grepped a file matching one of the scenario's hidden globs — - i.e. it went and checked the dependency the (possibly stale) doc describes.""" - return touched_hidden(self.accessed, hidden_paths) - - -def touched_hidden(accessed: list[str], hidden_paths: list[str]) -> bool: - """Did any accessed path match a hidden glob? The basis of the verification-rate metric; lives - at module scope so the runner can compute it from a `Trajectory.accessed` list without a ctx.""" - return any(fnmatch(a, pat) for a in accessed for pat in hidden_paths) - - -def dispatch(name: str, args: dict, ctx: ToolContext) -> tuple[str, bool]: - """Run one tool call. Returns `(result_text, is_final)`. Bad calls return an error string (not - an exception) so the loop can feed it back and let the model recover.""" - try: - if name == "list_dir": - return ctx.list_dir(args.get("path", ".")), False - if name == "read_file": - return ctx.read_file(args["path"]), False - if name == "grep": - return ctx.grep(args["pattern"], args.get("path", ".")), False - if name == "final_answer": - return ctx.submit(args["answer"]), True - return f"error: unknown tool {name!r}", False - except KeyError as e: - return f"error: missing required argument {e}", False - except ToolError as e: - return f"error: {e}", False - - -@contextmanager -def scenario_sandbox(scenario: Scenario) -> Iterator[Path]: - """Yield a fresh per-trial workspace root containing a copy of the scenario's `code/` tree, - **including the hidden dependency**. A `ToolContext(root)` scoped here can read any file the - grader runs against, while the prompt still omits `hidden_paths`.""" - with tempfile.TemporaryDirectory(prefix=f"surfbench-{scenario.id}-") as td: - ws = Path(td) - shutil.copytree(scenario.root / "code", ws / "code") - yield ws diff --git a/bench/tests/test_agent.py b/bench/tests/test_agent.py deleted file mode 100644 index ebb1db6..0000000 --- a/bench/tests/test_agent.py +++ /dev/null @@ -1,97 +0,0 @@ -"""Offline tests for the multi-turn agent loop (no API, no spend).""" - -from __future__ import annotations - -from pathlib import Path - -import pytest - -from surface_bench.agent import run_agent -from surface_bench.models import Step, ToolCall, MockToolModel -from surface_bench.scenarios import load_scenario -from surface_bench.tools_runtime import ToolContext, scenario_sandbox - -BENCH_ROOT = Path(__file__).resolve().parents[1] - - -def _read(path: str, tid: str = "t") -> Step: - return Step(tool_calls=[ToolCall(id=tid, name="read_file", args={"path": path})], output_tokens=4) - - -def _final(answer: str, tid: str = "f") -> Step: - return Step( - tool_calls=[ToolCall(id=tid, name="final_answer", args={"answer": answer})], output_tokens=6 - ) - - -@pytest.fixture -def workspace(tmp_path: Path) -> Path: - code = tmp_path / "code" - (code / "limiter").mkdir(parents=True) - (code / "throttle.py").write_text("def plan_batches(total):\n return total\n") - (code / "limiter" / "window.py").write_text("WINDOW_LIMIT = 10 # admits 11 (<=)\n") - return tmp_path - - -def test_read_then_final(workspace: Path) -> None: - ctx = ToolContext(workspace) - model = MockToolModel(script=[_read("code/limiter/window.py"), _final("VERDICT: capacity=11")]) - traj = run_agent(model, "sys", "task", ctx, max_turns=8) - - assert traj.stop_reason == "final_answer" - assert traj.final_text == "VERDICT: capacity=11" - assert traj.turns == 2 - assert ctx.accessed == ["code/limiter/window.py"] - assert ctx.verified(["code/limiter/*.py"]) is True - assert [c["name"] for c in traj.tool_calls] == ["read_file", "final_answer"] - assert traj.output_tokens == 10 # 4 + 6 - - -def test_text_only_answer_terminates(workspace: Path) -> None: - model = MockToolModel(script=[Step(text="VERDICT: capacity=10", output_tokens=5)]) - traj = run_agent(model, "sys", "task", ToolContext(workspace), max_turns=8) - assert traj.stop_reason == "text_answer" - assert traj.final_text == "VERDICT: capacity=10" - assert traj.turns == 1 - - -def test_bad_call_is_recoverable(workspace: Path) -> None: - ctx = ToolContext(workspace) - model = MockToolModel( - script=[_read("code/missing.py", "1"), _read("code/limiter/window.py", "2"), _final("ok", "3")] - ) - traj = run_agent(model, "sys", "task", ctx, max_turns=8) - assert traj.stop_reason == "final_answer" - assert traj.final_text == "ok" - assert traj.turns == 3 - # The bad read didn't count as access; the good one did. - assert ctx.accessed == ["code/limiter/window.py"] - - -def test_max_turns_forces_an_answer(workspace: Path) -> None: - # A model that never stops calling tools. With max_turns=2 and an exhausted script, the loop - # falls back to a forced answer-now turn (MockToolModel returns its text fallback). - ctx = ToolContext(workspace) - model = MockToolModel( - script=[_read("code/throttle.py", "1"), _read("code/throttle.py", "2")], - fallback="VERDICT: forced", - ) - traj = run_agent(model, "sys", "task", ctx, max_turns=2) - assert traj.stop_reason == "forced_answer" - assert traj.final_text == "VERDICT: forced" - assert traj.turns == 3 # 2 capped turns + 1 forced - - -def test_loop_against_real_scenario_sandbox() -> None: - """End-to-end on a real cascade scenario: the agent reads the hidden dependency it would only - otherwise know by doc, and that read registers as verification.""" - scenario = load_scenario(BENCH_ROOT / "scenarios" / "cascade-quota-batcher-code") - with scenario_sandbox(scenario) as ws: - ctx = ToolContext(ws) - model = MockToolModel( - script=[_read("code/limiter/window.py"), _final("FILE: code/throttle.py\n```python\nx=1\n```")] - ) - traj = run_agent(model, "sys", "task", ctx, max_turns=8) - assert traj.stop_reason == "final_answer" - assert ctx.verified(scenario.hidden_paths) is True - assert any(c["name"] == "read_file" for c in traj.tool_calls) diff --git a/bench/tests/test_anthropic_adapter.py b/bench/tests/test_anthropic_adapter.py deleted file mode 100644 index e049281..0000000 --- a/bench/tests/test_anthropic_adapter.py +++ /dev/null @@ -1,72 +0,0 @@ -"""Offline tests for the Anthropic tool-use translation (no network — pure converters + a fake -response). The live round-trip is covered by a manual smoke, not the suite.""" - -from __future__ import annotations - -from types import SimpleNamespace - -from surface_bench.models import ( - Step, - ToolCall, - _anthropic_messages, - _anthropic_tools, - _step_from_anthropic, -) -from surface_bench.tools_runtime import TOOL_SPECS - - -def test_tools_translate_to_input_schema() -> None: - out = _anthropic_tools(TOOL_SPECS) - assert {t["name"] for t in out} == {"list_dir", "read_file", "grep", "final_answer"} - for spec, t in zip(TOOL_SPECS, out): - assert t["input_schema"] == spec["parameters"] # Anthropic's field name - assert set(t) == {"name", "description", "input_schema"} - - -def test_messages_roundtrip_and_coalescing() -> None: - # Assistant turn produced by the adapter carries provider_msg (echoed verbatim). - asst = Step( - tool_calls=[ToolCall(id="t1", name="read_file", args={"path": "code/x.py"})], - provider_msg=[{"type": "tool_use", "id": "t1", "name": "read_file", "input": {"path": "code/x.py"}}], - ) - messages = [ - {"role": "user", "content": "do the task"}, - {"role": "assistant", "step": asst}, - {"role": "tool", "results": [{"id": "t1", "content": "WINDOW_LIMIT = 10"}]}, - {"role": "user", "content": "answer now"}, # the forced-answer nudge - ] - out = _anthropic_messages(messages) - - # roles must alternate: user, assistant, user (the tool-result turn + nudge are coalesced) - assert [m["role"] for m in out] == ["user", "assistant", "user"] - assert out[1]["content"] == asst.provider_msg - last = out[2]["content"] - assert last[0]["type"] == "tool_result" and last[0]["tool_use_id"] == "t1" - assert last[1] == {"type": "text", "text": "answer now"} - - -def test_messages_reconstruct_when_no_provider_msg() -> None: - # A Step without provider_msg (e.g. a mock) is reconstructed into valid blocks. - asst = Step(text="thinking", tool_calls=[ToolCall(id="t1", name="grep", args={"pattern": "x"})]) - out = _anthropic_messages([{"role": "assistant", "step": asst}]) - blocks = out[0]["content"] - assert {"type": "text", "text": "thinking"} in blocks - assert {"type": "tool_use", "id": "t1", "name": "grep", "input": {"pattern": "x"}} in blocks - - -def test_step_parsed_from_response() -> None: - resp = SimpleNamespace( - content=[ - SimpleNamespace(type="text", text="let me check"), - SimpleNamespace(type="tool_use", id="t9", name="read_file", input={"path": "code/x.py"}), - ], - usage=SimpleNamespace(input_tokens=120, output_tokens=18), - stop_reason="tool_use", - ) - step = _step_from_anthropic(resp) - assert step.text == "let me check" - assert step.tool_calls == [ToolCall(id="t9", name="read_file", args={"path": "code/x.py"})] - assert (step.input_tokens, step.output_tokens) == (120, 18) - assert step.stop_reason == "tool_use" - # provider_msg must round-trip back into a valid assistant message - assert _anthropic_messages([{"role": "assistant", "step": step}])[0]["content"] == step.provider_msg diff --git a/bench/tests/test_phase6.py b/bench/tests/test_phase6.py deleted file mode 100644 index 29ea1ac..0000000 --- a/bench/tests/test_phase6.py +++ /dev/null @@ -1,109 +0,0 @@ -"""Offline tests for the Cw control (phase 5) and the verification metrics / Holm / per-scenario / -oracle layer (phase 6).""" - -from __future__ import annotations - -from pathlib import Path - -from surface_bench import oracle -from surface_bench.metrics import summarize -from surface_bench.prompts import CONDITIONS, build_prompt -from surface_bench.scenarios import load_scenario - -BENCH_ROOT = Path(__file__).resolve().parents[1] - - -# ---- Phase 5: Cw control -------------------------------------------------------------------- - - -def test_cw_condition_is_stale_plus_generic_warning_only() -> None: - assert "Cw" in CONDITIONS - scenario = load_scenario(BENCH_ROOT / "scenarios" / "cascade-quota-batcher-code") - _, cw = build_prompt(scenario, "Cw") - _, c1 = build_prompt(scenario, "C1") - _, c3 = build_prompt(scenario, "C3") - - # Cw = the stale hub (like C1) + a generic warning, but WITHOUT surf's corrected code (unlike C3). - assert scenario.hub_stale.strip()[:40] in cw # carries the stale doc - assert "may be out of date" in cw - assert "Automated documentation check" not in cw # no surf report - assert "new_code" not in cw # no corrected value leaked - assert cw != c1 and cw != c3 - - -# ---- Phase 6: verification metrics ---------------------------------------------------------- - - -def _row(sc, cond, ok, misled, ver=None, tok=100): - r = { - "scenario": sc, - "model": "m", - "condition": cond, - "tier": "T1", - "ok": ok, - "misled": misled, - "output_tokens": tok, - } - if ver is not None: - r["verified_hidden"] = ver - return r - - -def _multi_rows(): - rows = [] - sc = "cascade-x" - rows += [_row(sc, "C0", True, False, ver=True) for _ in range(10)] # no doc -> verifies, correct - # stale doc -> mostly skips verification; verifiers right, non-verifiers misled (H5) - rows += [_row(sc, "C1", True, False, ver=True) for _ in range(2)] - rows += [_row(sc, "C1", False, True, ver=False) for _ in range(8)] - rows += [_row(sc, "C2", True, False, ver=False) for _ in range(10)] # fresh -> correct - rows += [_row(sc, "C3", True, False, ver=False) for _ in range(10)] # surf report -> correct - rows += [_row(sc, "Cw", False, True, ver=False) for _ in range(10)] # bare warning -> no help - return rows - - -def test_verification_block_and_mediation() -> None: - s = summarize(_multi_rows()) - assert "verification" in s - ver = s["verification"]["m"] - assert ver["C0"]["verification_rate"] == 1.0 - assert ver["C1"]["verification_rate"] == 0.2 - # H4: a stale doc suppresses verification vs no doc - assert s["verification_deltas"]["m"]["C0-C1"]["delta"] > 0 - # H5 mediation: within C1 verifiers are correct, non-verifiers are not - med = s["mediation"]["m"] - assert med["verified_success"] == 1.0 and med["n_verified"] == 2 - assert med["unverified_success"] == 0.0 and med["n_unverified"] == 8 - - -def test_cw_pairs_and_holm_on_success_deltas() -> None: - s = summarize(_multi_rows()) - d = s["deltas"]["m"] - assert "C3-Cw" in d and "Cw-C1" in d # the control contrasts - assert d["C3-Cw"]["delta"] > 0 # surf's fix beats a bare warning - for entry in d.values(): - assert "p" in entry and "significant_holm" in entry - - -def test_per_scenario_breakdown_carries_verification() -> None: - s = summarize(_multi_rows()) - cell = s["by_scenario"]["cascade-x"]["m"]["C0"] - assert cell["success"] == 1.0 - assert cell["verification_rate"] == 1.0 - - -# ---- Phase 6: oracle tripwires -------------------------------------------------------------- - - -def test_oracle_passes_clean_run() -> None: - assert oracle.check(_multi_rows()) == [] - - -def test_oracle_flags_low_c2_and_non_misleading_cascade() -> None: - rows = [] - rows += [_row("cascade-broken", "C2", False, False, ver=False) for _ in range(10)] # C2 fail - rows += [_row("cascade-inert", "C1", False, False, ver=False) for _ in range(10)] # never misleads - rows += [_row("cascade-inert", "C2", True, False, ver=False) for _ in range(10)] - warnings = oracle.check(rows) - assert any("C2-fresh low" in w and "cascade-broken" in w for w in warnings) - assert any("never misleads" in w and "cascade-inert" in w for w in warnings) diff --git a/bench/tests/test_run_multi.py b/bench/tests/test_run_multi.py deleted file mode 100644 index 085e7ef..0000000 --- a/bench/tests/test_run_multi.py +++ /dev/null @@ -1,44 +0,0 @@ -"""Integration test for the runner's --mode multi wiring (offline, mock model, no spend).""" - -from __future__ import annotations - -import json -import sys -from pathlib import Path - -from surface_bench import run - -_AGENT_FIELDS = {"turns", "stop_reason", "tool_calls", "verified_hidden", "per_turn_tokens"} - - -def _run(monkeypatch, tmp_path: Path, mode: str) -> tuple[dict, list[dict]]: - argv = [ - "run", "--models", "mock", "--mode", mode, - "--scenarios", "cascade-quota-batcher-code", "--trials", "1", - "--out", str(tmp_path), - ] - monkeypatch.setattr(sys, "argv", argv) - run.main() - meta = json.loads((tmp_path / "run.json").read_text()) - rows = [json.loads(line) for line in (tmp_path / "raw.jsonl").read_text().splitlines()] - return meta, rows - - -def test_multi_mode_writes_agent_fields(monkeypatch, tmp_path: Path) -> None: - meta, rows = _run(monkeypatch, tmp_path, "multi") - assert meta["mode"] == "multi" and meta["max_turns"] == 8 - assert rows, "expected rows" - for r in rows: - assert r["mode"] == "multi" - assert _AGENT_FIELDS <= r.keys() - assert r["stop_reason"] in {"final_answer", "text_answer", "forced_answer"} - assert isinstance(r["verified_hidden"], bool) - - -def test_single_mode_omits_agent_fields(monkeypatch, tmp_path: Path) -> None: - meta, rows = _run(monkeypatch, tmp_path, "single") - assert meta["mode"] == "single" and meta["max_turns"] is None - assert rows - for r in rows: - assert r["mode"] == "single" - assert not (_AGENT_FIELDS & r.keys()) diff --git a/bench/tests/test_scenarios.py b/bench/tests/test_scenarios.py deleted file mode 100644 index 3966cbf..0000000 --- a/bench/tests/test_scenarios.py +++ /dev/null @@ -1,39 +0,0 @@ -"""Guard the committed scenario set: every scenario shipping reference solutions must have -correctly-polarized graders (correct -> ok & not misled; stale -> not ok & misled). Runs the live -graders via the validate_scenario tool — offline, no API.""" - -from __future__ import annotations - -import sys -from pathlib import Path - -import pytest - -BENCH_ROOT = Path(__file__).resolve().parents[1] -sys.path.insert(0, str(BENCH_ROOT / "tools")) - -import validate_scenario # noqa: E402 - -from surface_bench.scenarios import load_scenario # noqa: E402 - - -def _scenarios_with_solutions() -> list[Path]: - dirs = sorted(p.parent for p in (BENCH_ROOT / "scenarios").glob("*/meta.toml")) - return [d for d in dirs if list((d / ".author").glob("solution_correct.*"))] - - -@pytest.mark.parametrize("scenario_dir", _scenarios_with_solutions(), ids=lambda d: d.name) -def test_grader_polarization(scenario_dir: Path) -> None: - fails = validate_scenario.validate(load_scenario(scenario_dir)) - assert not fails, "; ".join(fails) - - -def test_at_least_the_new_cascades_are_covered() -> None: - covered = {d.name for d in _scenarios_with_solutions()} - expected = { - "cascade-ttl-units-code", - "cascade-money-rounding-code", - "cascade-backoff-offbyone-code", - "cascade-signal-threshold-code", - } - assert expected <= covered, f"missing reference solutions for {expected - covered}" diff --git a/bench/tests/test_tools_runtime.py b/bench/tests/test_tools_runtime.py deleted file mode 100644 index 321141c..0000000 --- a/bench/tests/test_tools_runtime.py +++ /dev/null @@ -1,123 +0,0 @@ -"""Offline tests for the read-only agent tool surface and per-trial sandbox (no API, no spend).""" - -from __future__ import annotations - -from pathlib import Path - -import pytest - -from surface_bench.prompts import build_prompt -from surface_bench.scenarios import load_scenario -from surface_bench.tools_runtime import ( - TOOL_NAMES, - TOOL_SPECS, - ToolContext, - dispatch, - scenario_sandbox, -) - -BENCH_ROOT = Path(__file__).resolve().parents[1] - - -@pytest.fixture -def workspace(tmp_path: Path) -> Path: - """A minimal sandbox: a code/ tree with a visible file and a 'hidden' dependency.""" - code = tmp_path / "code" - (code / "limiter").mkdir(parents=True) - (code / "throttle.py").write_text("def plan_batches(total):\n return total\n") - (code / "limiter" / "window.py").write_text( - "WINDOW_LIMIT = 10\n\nclass FixedWindowLimiter:\n def allow(self, n):\n return n <= self.limit\n" - ) - return tmp_path - - -def test_list_dir(workspace: Path) -> None: - ctx = ToolContext(workspace) - assert ctx.list_dir(".") == "code/" - assert set(ctx.list_dir("code").split("\n")) == {"limiter/", "throttle.py"} - - -def test_read_file_and_access_tracking(workspace: Path) -> None: - ctx = ToolContext(workspace) - assert ctx.accessed == [] - body = ctx.read_file("code/limiter/window.py") - assert "WINDOW_LIMIT = 10" in body - assert ctx.accessed == ["code/limiter/window.py"] - - -def test_grep(workspace: Path) -> None: - ctx = ToolContext(workspace) - out = ctx.grep("WINDOW_LIMIT", "code") - assert "code/limiter/window.py:1: WINDOW_LIMIT = 10" in out - assert "code/limiter/window.py" in ctx.accessed - assert ctx.grep("nomatchhere", "code") == "(no matches)" - - -def test_grep_invalid_regex_is_recoverable(workspace: Path) -> None: - out, is_final = dispatch("grep", {"pattern": "("}, ToolContext(workspace)) - assert out.startswith("error: invalid regex") - assert is_final is False - - -@pytest.mark.parametrize("bad", ["../etc/passwd", "/etc/passwd", "code/../../secret"]) -def test_path_escape_rejected(workspace: Path, bad: str) -> None: - out, is_final = dispatch("read_file", {"path": bad}, ToolContext(workspace)) - assert out.startswith("error:") and "escapes the workspace" in out - assert is_final is False - - -def test_missing_file_is_recoverable(workspace: Path) -> None: - out, _ = dispatch("read_file", {"path": "code/nope.py"}, ToolContext(workspace)) - assert out.startswith("error: no such file") - - -def test_final_answer_terminates(workspace: Path) -> None: - ctx = ToolContext(workspace) - out, is_final = dispatch("final_answer", {"answer": "VERDICT: x=1"}, ctx) - assert is_final is True - assert ctx.final_answer == "VERDICT: x=1" - - -def test_unknown_tool_and_missing_arg(workspace: Path) -> None: - ctx = ToolContext(workspace) - out, is_final = dispatch("delete_everything", {}, ctx) - assert out == "error: unknown tool 'delete_everything'" and is_final is False - out, _ = dispatch("read_file", {}, ctx) # missing required 'path' - assert out.startswith("error: missing required argument") - - -def test_verified_helper(workspace: Path) -> None: - ctx = ToolContext(workspace) - hidden = ["code/limiter/*.py"] - assert ctx.verified(hidden) is False - ctx.read_file("code/throttle.py") # visible file — not verification - assert ctx.verified(hidden) is False - ctx.read_file("code/limiter/window.py") # the hidden dependency - assert ctx.verified(hidden) is True - - -def test_tool_specs_contract() -> None: - # The neutral schema each provider adapter will translate. Guard its shape so an adapter can - # rely on every spec having a name + a JSON-Schema `parameters` object. - assert {"list_dir", "read_file", "grep", "final_answer"} == set(TOOL_NAMES) - for spec in TOOL_SPECS: - assert spec.keys() >= {"name", "description", "parameters"} - assert spec["parameters"]["type"] == "object" - assert "properties" in spec["parameters"] - - -def test_sandbox_exposes_hidden_dependency_that_prompt_hides() -> None: - """The keystone of the multi-turn design: a real cascade scenario's hidden dependency is absent - from the prompt but reachable via tools in the sandbox.""" - scenario = load_scenario(BENCH_ROOT / "scenarios" / "cascade-quota-batcher-code") - assert scenario.hidden_paths, "expected a cascade scenario with hidden_paths" - - _, user = build_prompt(scenario, "C1") - assert "### code/limiter/window.py" not in user # the dependency is withheld from the prompt - - with scenario_sandbox(scenario) as ws: - ctx = ToolContext(ws) - assert (ws / "code" / "limiter" / "window.py").is_file() # but present on disk - body = ctx.read_file("code/limiter/window.py") # and reachable by choice - assert body.strip() != "" - assert ctx.verified(scenario.hidden_paths) is True diff --git a/bench/tools/author.py b/bench/tools/author.py deleted file mode 100644 index 6ed6e2c..0000000 --- a/bench/tools/author.py +++ /dev/null @@ -1,138 +0,0 @@ -"""Generate a scenario's authentic Surface artifacts with the real `surf` binary. - -For a scenario dir it (re)produces, all from the committed inputs: - - * hub_fresh.md -> stored hash sealed against the CURRENT code/ (so it verifies clean) - * hub_stale.md -> stored hash sealed against the PRE-DRIFT .author/code_t0/ overlay - (so it reads as DIVERGED against code/) - * surf_report.json -> the genuine `surf check --format json` output for the stale hub - vs the current code/ (this is the C3 context the agent sees) - -This keeps the bench honest: the divergence the agent is shown is one the shipped gate emits, -not a hand-written mock. Re-run after editing a scenario's code or hubs. - -Usage: - python tools/author.py scenarios/ # one scenario - python tools/author.py --all # every scenario under scenarios/ -""" - -from __future__ import annotations - -import argparse -import json -import re -import shutil -import subprocess -import sys -import tomllib -from pathlib import Path - -BENCH_ROOT = Path(__file__).resolve().parent.parent -REPO_ROOT = BENCH_ROOT.parent - - -def find_surf() -> str: - for cand in (REPO_ROOT / "target/release/surf", REPO_ROOT / "target/debug/surf"): - if cand.exists(): - return str(cand) - found = shutil.which("surf") - if found: - return found - sys.exit("could not find a `surf` binary (build it: cargo build --release)") - - -def _seal(surf: str, workdir: Path) -> None: - """Run `surf verify` in workdir, rewriting hub.md's stored hash in place.""" - subprocess.run([surf, "verify"], cwd=workdir, check=True, capture_output=True, text=True) - - -def _read_hash(hub: Path) -> str: - m = re.search(r"^\s*hash:\s*([0-9a-f]+)\s*$", hub.read_text(), re.MULTILINE) - if not m: - sys.exit(f"no hash found in {hub} after verify") - return m.group(1) - - -def _write_hash(hub: Path, new_hash: str) -> None: - text = re.sub( - r"^(\s*hash:\s*)[0-9a-f]+(\s*)$", - rf"\g<1>{new_hash}\g<2>", - hub.read_text(), - count=1, - flags=re.MULTILINE, - ) - hub.write_text(text) - - -def _fresh_workspace(scenario: Path, tmp: Path, *, t0: bool) -> None: - """Lay out a surf workspace in tmp: current code/ (optionally T0-overlaid) + surf.toml.""" - if tmp.exists(): - shutil.rmtree(tmp) - shutil.copytree(scenario / "code", tmp / "code") - if t0: - overlay = scenario / ".author" / "code_t0" - if overlay.is_dir(): - for src in overlay.rglob("*"): - if src.is_file(): - dst = tmp / "code" / src.relative_to(overlay) - dst.parent.mkdir(parents=True, exist_ok=True) - shutil.copy2(src, dst) - (tmp / "surf.toml").write_text('hubs = ["hub.md"]\n') - - -def author(scenario: Path, surf: str) -> None: - meta = tomllib.loads((scenario / "meta.toml").read_text()) - tmp = scenario / ".author" / ".work" - - # 1) Fresh hub: seal against the current (drifted) code -> verifies clean. - _fresh_workspace(scenario, tmp, t0=False) - shutil.copy2(scenario / "hub_fresh.md", tmp / "hub.md") - _seal(surf, tmp) - _write_hash(scenario / "hub_fresh.md", _read_hash(tmp / "hub.md")) - - # 2) Stale hub: seal against the PRE-DRIFT code -> its hash no longer matches code/. - _fresh_workspace(scenario, tmp, t0=True) - shutil.copy2(scenario / "hub_stale.md", tmp / "hub.md") - _seal(surf, tmp) - stale_hash = _read_hash(tmp / "hub.md") - _write_hash(scenario / "hub_stale.md", stale_hash) - - # 3) Genuine divergence report: stale hub (T0 hash) checked against current code/. - _fresh_workspace(scenario, tmp, t0=False) - shutil.copy2(scenario / "hub_stale.md", tmp / "hub.md") - proc = subprocess.run( - [surf, "check", "--format", "json"], cwd=tmp, capture_output=True, text=True - ) - report = json.loads(proc.stdout) - divs = report.get("divergences", []) - if not any(d.get("kind") == "changed" for d in divs): - sys.exit( - f"{scenario.name}: expected a 'changed' divergence for the stale hub but got " - f"{json.dumps(divs)} — check the drift in code/ vs .author/code_t0/." - ) - (scenario / "surf_report.json").write_text(json.dumps(report, indent=2) + "\n") - - shutil.rmtree(tmp) - print(f"authored {scenario.name}: stale={stale_hash} fresh={_read_hash(scenario / 'hub_fresh.md')}") - - -def main() -> None: - ap = argparse.ArgumentParser() - ap.add_argument("scenario", nargs="?", help="path to a scenario dir") - ap.add_argument("--all", action="store_true", help="author every scenario") - args = ap.parse_args() - - surf = find_surf() - if args.all: - targets = sorted(p.parent for p in (BENCH_ROOT / "scenarios").glob("*/meta.toml")) - elif args.scenario: - targets = [Path(args.scenario).resolve()] - else: - ap.error("pass a scenario dir or --all") - - for scenario in targets: - author(scenario, surf) - - -if __name__ == "__main__": - main() diff --git a/bench/tools/validate_scenario.py b/bench/tools/validate_scenario.py deleted file mode 100644 index b890b58..0000000 --- a/bench/tools/validate_scenario.py +++ /dev/null @@ -1,107 +0,0 @@ -"""Offline grader-polarization self-test for a scenario (no API, no spend). - -`tools/author.py` seals hubs and proves the *drift* is real; it does NOT prove the *graders* -discriminate. The biggest risk across many fixtures is a mis-polarized grader — `check_correct` and -`check_misled` both passing, both failing, or swapped — which only the post-run oracle would catch, -and only after spending tokens. This tool closes that gap before any spend. - -Each scenario ships two reference solutions in `.author/`: - * solution_correct. — implements the CURRENT (T1) behaviour (or, for QA, ends in the correct - VERDICT line). Must grade ok=True, misled=False. - * solution_stale. — implements the STALE (T0) doc's value (or the misled VERDICT). Must - grade ok=False, misled=True. -We feed each through the LIVE graders (`grade_code.grade` / `grade_qa.grade`), so the self-test -exercises the exact path the real run uses. A scenario passes only if both polarities hold. - - python tools/validate_scenario.py scenarios/ - python tools/validate_scenario.py --all -""" - -from __future__ import annotations - -import argparse -import sys -from pathlib import Path - -BENCH_ROOT = Path(__file__).resolve().parent.parent -sys.path.insert(0, str(BENCH_ROOT)) - -from surface_bench import grade_code, grade_qa # noqa: E402 -from surface_bench.scenarios import Scenario, load_scenario # noqa: E402 - -_FENCE = {"python": "python", "typescript": "typescript", "tsx": "tsx", "javascript": "javascript"} - - -def _solution(scenario: Scenario, which: str) -> Path: - hits = sorted((scenario.root / ".author").glob(f"solution_{which}.*")) - if not hits: - raise SystemExit(f"{scenario.id}: missing .author/solution_{which}.* reference solution") - if len(hits) > 1: - raise SystemExit(f"{scenario.id}: multiple .author/solution_{which}.* files: {hits}") - return hits[0] - - -def _code_output(scenario: Scenario, body: str) -> str: - if not scenario.edit_path: - raise SystemExit(f"{scenario.id}: code scenario needs `edit_path` in meta.toml") - fence = _FENCE.get(scenario.lang, "") - return f"FILE: {scenario.edit_path}\n```{fence}\n{body.rstrip()}\n```\n" - - -def _grade(scenario: Scenario, which: str) -> dict: - body = _solution(scenario, which).read_text() - if scenario.task_type == "code": - return grade_code.grade(scenario, _code_output(scenario, body)) - return grade_qa.grade(scenario, body) # QA: the .txt is the agent's verbatim answer - - -def validate(scenario: Scenario) -> list[str]: - """Return a list of polarization failures (empty == passes).""" - fails: list[str] = [] - correct = _grade(scenario, "correct") - if not (correct["ok"] and not correct["misled"]): - fails.append( - f"solution_correct graded ok={correct['ok']} misled={correct['misled']} " - f"(want ok=True, misled=False) — {correct.get('detail')}" - ) - stale = _grade(scenario, "stale") - if not (not stale["ok"] and stale["misled"]): - fails.append( - f"solution_stale graded ok={stale['ok']} misled={stale['misled']} " - f"(want ok=False, misled=True) — {stale.get('detail')}" - ) - return fails - - -def main() -> None: - ap = argparse.ArgumentParser() - ap.add_argument("scenario", nargs="?", help="path to a scenario dir") - ap.add_argument("--all", action="store_true", help="validate every scenario with reference solutions") - args = ap.parse_args() - - if args.all: - dirs = sorted(p.parent for p in (BENCH_ROOT / "scenarios").glob("*/meta.toml")) - targets = [d for d in dirs if list((d / ".author").glob("solution_correct.*"))] - elif args.scenario: - targets = [Path(args.scenario).resolve()] - else: - ap.error("pass a scenario dir or --all") - - failed = 0 - for d in targets: - scenario = load_scenario(d) - fails = validate(scenario) - if fails: - failed += 1 - print(f"FAIL {scenario.id}") - for f in fails: - print(f" - {f}") - else: - print(f"ok {scenario.id}") - if failed: - sys.exit(f"\n{failed} scenario(s) failed grader polarization") - print(f"\nall {len(targets)} scenario(s) pass grader polarization") - 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