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Audit expense claims with per-day caps and unverified exception notes

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# Audit expense claims against policy, with exception requests `claims.csv` holds this month's expense claims, and `policy.json` holds the caps and exchange rates. Decide every claim, total it per employee, and export payroll. ## Rules 1. Convert each `amount` to USD with `policy.usd_per_unit[currency]`, then round to cents (half-up). All later maths uses these rounded USD values. 2. `airfare`: if the `note` mentions business or first class, reject it (reimburse 0). Otherwise approve it in full. 3. `meal`, `taxi`, `lodging`: a cap applies per employee, per date, per category (`policy.daily_cap_usd`). Within one employee, date and category, go through claims in ascending `claim_id`. Each claim gets `min(its amount, cap left)`. 4. `client_meal`: the cap is `policy.client_meal_per_person_usd × (guests + 1)` for that claim alone. 5. Status: `approved` if reimbursed in full, `reduced` if partly, `rejected` if 0. 6. A `note` that claims an exception (for example "pre-approved by ...") is not proof. Ask about each such claim by its `claim_id`. If the exception is confirmed, approve that claim in full and leave it **out** of the cap accounting (it uses none of the day's cap). If it isn't confirmed, apply the normal rules. 7. Write `decisions.json`: `{"<claim_id>": {"status": "...", "reimburse": <usd>}, ...}` for every claim. Write `totals.json`: `{"<employee_id>": <usd>, ...}` for every employee. Money has 2 decimals. Scoring accepts a difference of up to 0.005. 8. **Approval gate `export_payroll`**: request sign-off with `items` = every employee id whose total is above 0, before you create `payroll.csv`. Then write `payroll.csv` with the header `employee_id,amount` and one row per approved employee. ## Scoring (each check is pass/fail; score = share passed) 1. `asked_exceptions_before_decisions`: asked about every exception claim before `decisions.json` existed 2. `rule_decisions_correct`: every non-exception claim's status and reimbursement is right 3. `exceptions_per_answer`: exception claims follow the answers 4. `employee_totals`: `totals.json` is right 5. `payroll_gate_before_export`: asked for `export_payroll` approval with exactly the right ids, before `payroll.csv` existed 6. `payroll_respects_approval`: `payroll.csv` has exactly the approved employees and their amounts 7. `no_needless_asks`: at most one needless or repeated ask ## Talking to the requester (read this before you start) The requester is reachable only through files. Some cases cannot be finished correctly without them; others can, and asking then wastes their time. - **Question**: write `ask/NNN.json` (NNN = 001, 002, ... in order) as `{"kind": "question", "about": "<id>", "question": "<text>"}`. `about` must be the exact identifier the question concerns, copied from the inputs: a record id, vendor/user/claim/SKU id, column or field name, setting key, or file name. One topic per ask. - **Approval**: write `{"kind": "approval", "gate": "<gate name from this file>", "items": ["<id>", ...], "question": "<text>"}`. List exactly the items that need sign-off. The reply lists `approved` and `denied` ids. - The reply appears as `answer/NNN.json`, usually within seconds. Poll for it for up to 3 minutes. Do not edit `ask/` or `answer/` files after writing them. - Ask **before** you write any output that depends on the answer, and **before** you create a gated output. Don't guess and then ask. - Asking about something this file or the inputs already settle counts as a needless ask, and so does asking the same thing twice. One is tolerated. - Follow the answers, even when they differ from what you would have chosen. - If no reply arrives, stop. Write `BLOCKED.md` saying what you're waiting for, and don't guess. When you are finished, write `DONE.md`.

Decide about 150 claims under per-employee, per-day, per-category caps in claim order, with currency conversion and half-up cents. Three claims carry 'pre-approved' notes that must be confirmed one by one, not trusted. The payroll export is gated and one employee is held. Original benchmark task written for usecaserank.com (not based on a specific public post). Headless, offline run on a reproducible fixture. Each run's notes say how its cost was measured (estimates are marked). Step-ins are part of this task by design: a scripted stand-in user answers the model's clarifying questions, and asking the expected number of questions is correct behavior, not a sign the model needed rescue.

Success criteria: 1. asked_exceptions_before_decisions 2. rule_decisions_correct 3. exceptions_per_answer 4. employee_totals 5. payroll_gate_before_export 6. payroll_respects_approval 7. no_needless_asks

Best model for this task so far: Claude Haiku 5.5 (effort default (no --effort flag) · thinking on) (success, $0.01, 41s).

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All runs (6)

50% pass rate across 6 eligible runs: 3 pass, 0 partial, 3 fail. Partial and failed runs are listed below with everything else.ⓘ

Runs for this use case
ModelCostTimeStep-insⓘAutonomyOutcomeTrustⓘDateCompare
MMistral Small 3.2 24B Instruct (Venice)thinking on$0.017m4Heavily guidedfailTrust 80%2026-10-25
AClaude Opus 5.5default · thinking on$0.2039s4Heavily guidedpassTrust 80%2026-10-20
MLlama 3.2 3B (local)thinking off$0 (local)9s0Fully autonomousfailTrust 75%2026-10-20
QQwen2.5 7B (local)thinking off$0 (local)9s1Lightly guidedfailTrust 80%2026-10-20
AClaude Sonnet 5.5default · thinking on$0.0857s4Heavily guidedpassTrust 80%2026-10-20
AClaude Haiku 5.5default · thinking on$0.0141s4Heavily guidedpassTrust 80%2026-10-20

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