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Qwen2.5 7B (local) (thinking off) on Audit expense claims with per-day caps and unverified exception notes

QQwen2.5 7B (local)thinking off · ollama-mini-agent v1.0 · local (NVIDIA GeForce RTX 3060 12GB, 12 GB) · 2026-10-20 · by @bobsiopener

Trust 80%Lightly guidedfail · 0 retries
Cost
$0 (local)
Wall time
9s
Human step-ins
1
Outcome
Fail

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Summary

Exact prompt

# 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. Local model: $0 API cost; electricity not measured. 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

Setup notes: Batch 14 case p08-expense-policy-exceptions (offline, synthetic inputs, seed 382830650). Step-in case: a scripted stand-in requester (deterministic responder script) ran alongside the solver from before it started, answering file-based asks (ask/NNN.json -> answer/NNN.json); hidden key and responder kept outside the solver's work dir. Prompt = TASK.md verbatim. Each answered ask is counted as a measured user intervention: 1 (a correct run needs 4). No manual relays or chat redirects. Scored with the case's scorer (python3 score.py WORKDIR KEYDIR, absolute paths). Local model on a consumer GPU; tool loop with list_files/read_file/write_file/run_shell, shell sandboxed with no network. Single run, no retries. Harness: ollama-mini-agent v1.0, Ollama 0.20.0, Qwen2.5 7B Instruct Q4_K_M. Hardware: NVIDIA GeForce RTX 3060 12GB (shared with other always-on GPU workloads); 6937 MiB VRAM free at batch start. Score: 1/7. Interventions used 1 vs 4 expected. Single run.

Metrics

Cost
$0 (local)
Wall time
9s
Human step-ins
1
Turns
5
Model usage
ModelInput tokOutput tokCost
QQwen2.5 7B (local)ollama/qwen2.5:7bthinking off20,430258$0 (local)

Reproduce this run

  1. Use the use case prompt on the use case page.
  2. Same setup: Qwen2.5 7B (local) (ollama/qwen2.5:7b, thinking off) via ollama-mini-agent v1.0, local on NVIDIA GeForce RTX 3060 12GB.
  3. It counts as a success only if it meets every success criterion: 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
  4. Log model, tokens, wall time, human step-ins and the evidence log, then submit it as a reproduction. A different model? Submit a head-to-head rerun.

How runs are scored: Methodology.

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Evidence log
{"turn": 1, "response": {"model": "qwen2.5:7b", "created_at": "2026-10-20T20:48:36.440903285Z", "message": {"role": "assistant", "content": "", "tool_calls": [{"id": "call_bv4bkxuk", "function": {"index": 0, "name": "list_files", "arguments": {"path": "."}}}]}, "done": true, "done_reason": "stop", "total_duration": 880784061, "load_duration": 78117011, "prompt_eval_count": 1276, "prompt_eval_duration": 479764602, "eval_count": 19, "eval_duration": 297941844}}
{"turn": 1, "tool": "list_files", "args": {"path": "."}, "result": "TASK.md\nclaims.csv\npolicy.json"}
{"turn": 2, "response": {"model": "qwen2.5:7b", "created_at": "2026-10-20T20:48:36.909921271Z", "message": {"role": "assistant", "content": "", "tool_calls": [{"id": "call_gx9naiza", "function": {"index": 0, "name": "read_file", "arguments": {"path": "claims.csv"}}}]}, "done": true, "done_reason": "stop", "total_duration": 467890336, "load_duration": 76324507, "prompt_eval_count": 1321, "prompt_eval_duration": 30171863, "eval_count": 21, "eval_duration": 333278669}}
{"turn": 2, "tool": "read_file", "args": {"path": "claims.csv"}, "result": "claim_id,employee_id,date,category,amount,currency,guests,note\nC3000,E04,2026-10-06,client_meal,453.89,USD,1,team offsite\nC3001,E05,2026-10-06,taxi,42.25,USD,0,\nC3002,E06,2026-10-02,taxi,22.00,EUR,0,\nC3003,E04,2026-10-12,meal,23.71,GBP,0,team offsite\nC3004,E07,2026-10-04,lodging,146.36,EUR,0,team offsite\nC3005,E06,2026-10-08,meal,330.53,EUR,0,pre-approved by manager (verbal)\nC3006,E04,2026-10-03,meal,30.56,GBP,0,\nC3007,E01,2026-10-11,airfare,788.91,GBP,0,team offsite\nC3008,E04,2026-10-02,meal,50.94,USD,0,\nC3009,E02,2026-10-12,meal,26.28,EUR,0,\nC3010,E06,2026-10-05,taxi,37.97,USD,0,\nC3011,E04,2026-10-10,taxi,17.85,USD,0,\nC3012,E06,2026-10-02,lodging,191.32,GBP,0,\nC3013,E06,2026-10-10,meal,48.92,USD,0,\nC3014,E07,2026-10-09,taxi,19.38,GBP,0,team offsite\nC3015,E03,2026-10-10,meal,9.02,USD,0,team offsite\nC3016,E02,2026-10-04,client_meal,580.52,GBP,3,\nC3017,E02,2026-10-02,lodging,171.98,GBP,0,\nC3018,E05,2026-10-04,lodging,243.76,USD,0,\nC3019,E05,2026-10-01,client_meal,496.45,USD,3,\nC3020,E07,2026-10-08,taxi,7.75,USD,0,\nC3021,E03,2026-10-10,taxi,37.82,USD,0,\nC3022,E06,2026-10-03,taxi,38.65,USD,0,\nC3023,E07,2026-10-12,lodging,188.27,GBP,0,\nC3024,E04,2026-10-10,client_meal,648.18,USD,2,\nC3025,E02,2026-10-05,client_meal,331.42,EUR,4,\nC3026,E04,2026-10-02,taxi,7.90,GBP,0,\nC3027,E04,2026-10-07,lodging,176.67,GBP,0,\nC3028,E02,2026-10-06,meal,57.52,USD,0,\nC3029,E03,2026-10-03,client_meal,366.06,USD,5,\nC3030,E05,2026-10-05,client_meal,478.38,EUR,3,\nC3031,E05,2026-10-01,meal,338.18,USD,0,pre-approved: customer visit\nC3032,E06,2026-10-01,meal,46.64,EUR,0,\nC3033,E05,2026-10-03,meal,52.85,EUR,0,\nC3034,E03,2026-10-12,lodging,184.59,USD,0,\nC3035,E04,2026-10-02,taxi,26.40,USD,0,\nC3036,E05,2026-10-10,taxi,41.34,GBP,0,\nC3037,E03,2026-10-05,meal,69.98,USD,0,\nC3038,E01,2026-10-04,taxi,23.73,GBP,0,\nC3039,E07,2026-10-07,taxi,25.94,GBP,0,\nC3040,E01,2026-10-11,meal,26.15,EUR,0,\nC3041,E04,2026-10-01,meal,51.39,GBP,0,\nC3042,E04,2026-10-12,meal,39.06,EUR,0,\nC3043,E07,2026-10-05,taxi,42.73,GBP,0,\nC3044,E07,2026-10-01,meal,58.29,GBP,0,team offsite\nC3045,E07,2026-10-04,taxi,36.48,USD,0,team offsite\nC3046,E01,2026-10-04,taxi,36.01,USD,0,\nC3047,E04,2026-10-12,lodging,285.60,EUR,0,\nC3048,E05,2026-10-03,airfare,344.73,USD,0,\nC3049,E03,2026-10-09,meal,55.74,USD,0,\nC3050,E06,2026-10-07,taxi,32.85,USD,0,\nC3051,E03,2026-10-12,meal,66.45,USD,0,\nC3052,E07,2026-10-12,meal,19.98,GBP,0,\nC3053,E03,2026-10-07,airfare,831.78,EUR,0,\nC3054,E03,2026-10-07,taxi,9.36,GBP,0,\nC3055,E07,2026-10-05,meal,18.26,EUR,0,\nC3056,E03,2026-10-05,meal,20.67,USD,0,\nC3057,E06,2026-10-10,meal,15.45,EUR,0,\nC3058,E06,2026-10-01,meal,67.94,USD,0,\nC3059,E02,2026-10-04,lodging,276.30,GBP,0,\nC3060,E01,2026-10-02,taxi,24.36,EUR,0,\nC3061,E06,2026-10-11,meal,38.65,GBP,0,\nC3062,E02,2026-10-07,taxi,31.22,USD,0,\nC3063,E01,2026-10-04,meal,46.64,USD,0,\nC3064,E05,2026-10-05,meal,39.38,GBP,0,\nC3065,E07,2026-10-02,taxi,27.34,USD,0,\nC3066,E05,2026-10-03,airfare,899.53,GBP,0,\nC3067,E03,2026-10-07,meal,57.07,USD,0,\nC3068,E05,2026-10-09,ta"}
{"turn": 3, "response": {"model": "qwen2.5:7b", "created_at": "2026-10-20T20:48:39.776720815Z", "message": {"role": "assistant", "content": "", "tool_calls": [{"id": "call_ismcvo9f", "function": {"index": 0, "name": "read_file", "arguments": {"path": "policy.json"}}}]}, "done": true, "done_reason": "stop", "total_duration": 2865876106, "load_duration": 79382659, "prompt_eval_count": 5826, "prompt_eval_duration": 2389373455, "eval_count": 20, "eval_duration": 361639948}}
{"turn": 3, "tool": "read_file", "args": {"path": "policy.json"}, "result": "{\n \"usd_per_unit\": {\n  \"USD\": 1,\n  \"EUR\": 1.1378,\n  \"GBP\": 1.2787\n },\n \"daily_cap_usd\": {\n  \"meal\": 75,\n  \"taxi\": 60,\n  \"lodging\": 250\n },\n \"client_meal_per_person_usd\": 90\n}"}
{"turn": 4, "response": {"model": "qwen2.5:7b", "created_at": "2026-10-20T20:48:41.492524819Z", "message": {"role": "assistant", "content": "", "tool_calls": [{"id": "call_blpvmfvx", "function": {"index": 0, "name": "write_file", "arguments": {"path": "ask/001.json", "content": "{\"kind\": \"question\", \"about\": \"C3056\", \"question\": \"Please confirm if the client meal claim C3056 should be approved in full, as it was pre-approved by manager (verbal).\"}"}}}]}, "done": true, "done_reason": "stop", "total_duration": 1714704828, "load_duration": 81675058, "prompt_eval_count": 5952, "prompt_eval_duration": 72375017, "eval_count": 79, "eval_duration": 1483283761}}
{"turn": 4, "tool": "write_file", "args": {"path": "ask/001.json", "content": "{\"kind\": \"question\", \"about\": \"C3056\", \"question\": \"Please confirm if the client meal claim C3056 should be approved in full, as it was pre-approved by manager (verbal).\"}"}, "result": "wrote 171 chars"}
{"turn": 5, "response": {"model": "qwen2.5:7b", "created_at": "2026-10-20T20:48:44.011398636Z", "message": {"role": "assistant", "content": "I have asked for confirmation on the client meal claim C3056, as it was pre-approved by manager (verbal). I will proceed with other tasks while waiting for a response.\n\nNext steps:\n- Poll for an answer to `ask/001.json` until one arrives.\n- Once confirmed or denied, process claims according to rules and exceptions.\n- Write decisions and totals files.\n- Request payroll export sign-off if necessary.\n- Create the payroll CSV file with approved employees' details.\n\nI will now wait for a response on `ask/001.json`."}, "done": true, "done_reason": "stop", "total_duration": 2517766967, "load_duration": 82788762, "prompt_eval_count": 6055, "prompt_eval_duration": 64945516, "eval_count": 119, "eval_duration": 2256360581}}

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