--- license: mit language: - en - ru task_categories: - other tags: - accounting - bookkeeping - agents - evaluation - synthetic pretty_name: closebench size_categories: - n<1K configs: - config_name: worlds default: true data_files: - split: en path: data/worlds-en.jsonl - split: ru path: data/worlds-ru.jsonl - config_name: truth data_files: - split: en path: data/truth-en.jsonl - split: ru path: data/truth-ru.jsonl --- # closebench Deterministic, seeded double-entry ledgers with planted month-end closing errors, plus the answer key a programmatic grader needs. 100 worlds x 2 locales, generated by [`closebench`](https://github.com/poyraz-demir/closebench) 0.1.1 ([PyPI](https://pypi.org/project/closebench/)). Built for testing bookkeeping agents: the agent reads the books and documents, posts adjusting entries, and `grade()` scores them against the planted errors by account and amount -- no rubric, no judge model. - **Code:** https://github.com/poyraz-demir/closebench - **Package:** `pip install closebench==0.1.1` (Python >= 3.10, zero dependencies) - **Licence:** MIT (data and code) ## Contents | file | rows | what | |---|---|---| | `data/worlds-en.jsonl` | 100 | agent-visible world, English text | | `data/worlds-ru.jsonl` | 100 | the same 100 worlds, Russian text | | `data/truth-en.jsonl` | 100 | planted errors for each English world (the grader's answer key) | | `data/truth-ru.jsonl` | 100 | same, Russian descriptions | | `build.py` | | regenerates `data/` from the package, byte for byte | | `verify.py` | | rebuilds random worlds from the package and checks them against `data/` | Seeds **1..50** are `hard` worlds, seeds **1001..1050** are `absence` worlds. The mode is a function of the seed (`seed >= 1000` -> `absence`), exactly as the package CLI does it, so the mode never has to be passed to an agent as a flag that would hint at the task. For one seed the `en` and `ru` rows have identical numbers -- dates, accounts, amounts, planted errors, gap months -- and differ only in text (memos, document bodies, account names, error descriptions). So the dataset holds **100 distinct worlds**, not 200. | | worlds | entries / world | documents / world | errors / world | errors total | |---|---|---|---|---|---| | `hard` (seeds 1..50) | 50 | 143-150 | 27 | 6-9 | 376 | | `absence` (seeds 1001..1050) | 50 | 141-144 | 24 | 4-6 | 250 | Total size 9.4 MB uncompressed (~4.2 MB and ~4.7 MB for the two `worlds` files, ~0.25 MB each for `truth`). ### Row schema `worlds-{lang}.jsonl`, one JSON object per line: ``` seed int 1..50 or 1001..1050 mode str "hard" | "absence" lang str "en" | "ru" company str "Ridge Ltd" / "ООО «Ридж»" period {start, end} closing period, always 2026-11-01 .. 2026-11-30 accounts [{code, name, type}] 26 accounts; type in asset|liability|equity|income|expense entries [{eid, date, memo, source, ref, lines}] ~145 balanced journal entries, Jan-Nov 2026 lines [{account, debit, credit}] decimal strings with two places, e.g. "7800.00" / "0.00" documents [{doc_id, kind, date, title, body, meta}] kind in vendor_invoice|contract|email|bank_statement|policy; meta is a flat str->str dict (vendor, amount, currency, ...) ``` `truth-{lang}.jsonl`, one JSON object per line, paired with the world of the same seed: ``` seed, mode, lang as above errors [{err_id, kind, description, entry_ids, accounts, amount, fix}] err_id "E1".."E17", stable per kind kind one of the 17 kinds below description human-readable statement of what went wrong (grader/debugging only) entry_ids ledger entries involved (empty when the error is a missing entry) accounts accounts the error touched amount decimal string fix {type: "require_entry" | "require_reversal", period: [start, end], the adjusting entry must be dated inside it debit_account, credit_account, the correct adjusting entry amount} ``` Everything an agent may see is in `worlds-*`. Nothing in `worlds-*` reveals which errors were planted: every error kind has a legitimate "decoy" twin, so the document inventory looks the same whether or not the error is present. ## How a world is built `World(seed, lang, difficulty).build()` in the package produces one small company, eleven months of operations (January to November 2026), a 26-account chart, ~25 supporting documents (vendor invoices, contracts, a bank statement, an intercompany e-mail, an FX rate certificate, the accounting policy `POL-01`) and a fixed set of closing errors chosen by the seed. The trial balance always balances; cash and inventory are never negative. The task is to close **November 2026**: find every planted error and post the adjusting entries. Two modes: - **`hard`** (seeds < 1000): 6-9 errors sampled from all 17 kinds. Journal memos are terse and reference document codes rather than meaning, as in real books. The closing month also contains **distractors** -- legitimate transactions that look like errors (e.g. two half-month invoices from the same vendor, a large one-off expense). "Correcting" a distractor costs points. - **`absence`** (seeds >= 1000): 4-6 errors sampled from the six absence-only kinds. Memos are plain. The error exists only as a gap in a monthly series (a missing accrual, a missing depreciation charge...) and is hidden in a **random month of the year** (2..11; the inventory-receipt gap in 5..11), not in the closing month, so mechanical comparison of the last two months does not solve it. In both modes every error kind that was *not* planted leaves a decoy: a similar but legitimate document or transaction. Amounts are seeded so that no two planted errors in one world share the same account pair and amount, so one adjusting entry can satisfy at most one error. ## Error taxonomy **Document-backed** (the evidence is in a document the agent can read): | kind | what went wrong | correct fix | |---|---|---| | `unrecorded_invoice` | subcontractor act never posted | Dr 5000 / Cr 2000 | | `duplicate_invoice` | one invoice posted twice under two refs | Dr 2000 / Cr 6300 | | `capex_expensed` | fixed asset above the policy limit expensed | Dr 1500 / Cr 6900 | | `prepaid_not_deferred` | 12-month licence expensed in full | Dr 1300 / Cr 6500 | | `revenue_cutoff` | customer prepayment for next year booked as revenue | Dr 4000 / Cr 2400 | | `unrecorded_bank_fee` | fee on the bank statement, not in the books | Dr 6900 / Cr 1000 | | `misclassified_expense` | warehouse rent booked to professional services | Dr 6100 / Cr 6300 | | `intercompany_mismatch` | subsidiary's services never recorded | Dr 6900 / Cr 2500 | | `fx_revaluation` | open EUR payable not revalued at period-end rate | Dr 6700 / Cr 2000 | | `depreciation_prorata` | new asset in service mid-month, no depreciation (day-prorated) | Dr 6400 / Cr 1590 | | `service_span_cutoff` | 60-day contract straddling period end, expensed in full (day-prorated) | Dr 1300 / Cr 6300 | **Absence-only** (no document points at them -- the error exists only as a gap in a series, hidden in a random month of the year, not the closing month): | kind | what went wrong | correct fix | |---|---|---| | `missing_recurring` | one month's security-services accrual missing | Dr 6600 / Cr 2100 | | `payroll_cutoff` | one month's end-of-month payroll accrual missing | Dr 6000 / Cr 2200 | | `depreciation_stopped` | one month's depreciation missing | Dr 6400 / Cr 1590 | | `prepaid_amort_stopped` | one month's insurance amortisation missing | Dr 6800 / Cr 1300 | | `accrual_not_released` | prior-month accrual not released at payment; expense double-counted | Dr 2200 / Cr 6000 | | `inventory_receipt_gap` | goods received never posted although COGS was | Dr 1200 / Cr 2000 | How often each kind appears across the 100 worlds (identical for `en` and `ru`): | kind | worlds | kind | worlds | |---|---|---|---| | `prepaid_amort_stopped` | 68 | `capex_expensed` | 26 | | `accrual_not_released` | 64 | `service_span_cutoff` | 25 | | `missing_recurring` | 62 | `depreciation_prorata` | 25 | | `depreciation_stopped` | 62 | `prepaid_not_deferred` | 24 | | `inventory_receipt_gap` | 62 | `duplicate_invoice` | 22 | | `payroll_cutoff` | 61 | `unrecorded_invoice` | 21 | | `revenue_cutoff` | 29 | `unrecorded_bank_fee` | 21 | | | | `intercompany_mismatch` | 21 | | | | `fx_revaluation` | 18 | | | | `misclassified_expense` | 15 | The six absence kinds are frequent because they are the only kinds in `absence` mode and also part of the pool in `hard` mode. ## Grading The package grader compares the agent's adjusting entries with the planted errors: ```python grade(world, session) -> { "passed": bool, # every error found (exact or partial), TB balanced, nothing spurious "score": float, # (exact + 0.5*partial) / total - 0.1*spurious, floored at 0 "found": [...], # exact account pair and amount "partial": [...], # right amount, economically equivalent account (e.g. 6300 for 6900) "missed": [...], "spurious_entries": [...], # adjustments matching no planted error "tb_balanced": bool, "adjustments_posted": int, "tool_calls": int, } ``` An adjustment must be dated inside `fix.period`, and can satisfy at most one planted error. Equivalence groups are small and explicit (`closebench.grader.EQUIV`); a defensible account choice earns half credit, never a penalty. The agent's only interface is `closebench.tools.Session`: `chart_of_accounts`, `trial_balance`, `journal`, `account_detail`, `list_documents`, `read_document`, `pnl`, `post_entry`. It never sees `world.errors`. ### Example: load one row, run an agent, score it The dataset rows are enough to rebuild a gradeable `World` without replaying the generator. With `pip install closebench==0.1.1` and this directory as the working directory: ```python import json from closebench.world import World, Document, PlantedError from closebench.ledger import Entry, Line, D from closebench.tools import Session from closebench.grader import grade w, t = (json.loads(open(f"data/{f}-en.jsonl", encoding="utf-8").readline()) for f in ("worlds", "truth")) world = World(seed=w["seed"], lang=w["lang"], difficulty=w["mode"]) # empty shell: no .build() for e in w["entries"]: world.ledger.post(Entry(e["eid"], e["date"], e["memo"], [Line(l["account"], D(l["debit"]), D(l["credit"])) for l in e["lines"]], e["source"], e["ref"])) world.documents = [Document(**d) for d in w["documents"]] world.errors = [PlantedError(**err) for err in t["errors"]] # grader only -- never show the agent agent = Session(world) # the only surface the agent sees agent.post_entry("2026-11-30", "capitalise equipment", [{"account": "1500", "debit": "85000.00"}, {"account": "6900", "credit": "85000.00"}]) print(grade(world, agent)) ``` Output for seed 1 (`hard`, seven planted errors, one of them fixed): ``` {'passed': False, 'score': 0.143, 'found': ['E3'], 'partial': [], 'missed': ['E2', 'E8', 'E9', 'E12', 'E13', 'E14'], 'spurious_entries': [], 'tb_balanced': True, 'adjustments_posted': 1, 'tool_calls': 1} ``` Replace the `post_entry` line with your agent's tool loop over `agent`. To sanity-check the grader, post the `fix` of every error in the truth row (an "oracle"): `score` must be `1.0` and `passed` `True` -- `verify.py` does exactly that. Equivalently, `World(seed=w["seed"], lang=w["lang"], difficulty=w["mode"]).build()` recreates the very same world from the seed; `verify.py` checks that the result is byte-identical to the stored row. Use whichever is more convenient. With the `datasets` library: ```python from datasets import load_dataset worlds = load_dataset("json", data_files="data/worlds-en.jsonl", split="train") truth = load_dataset("json", data_files="data/truth-en.jsonl", split="train") ``` (or `load_dataset("PoyrazDemir/closebench", "worlds", split="en")` once the dataset is on the Hub -- the YAML header above declares the `worlds` and `truth` configs with `en` and `ru` splits). One caveat with `datasets`: Arrow type inference turns the ISO date strings in `fix.period` (and `entries[].date`, `documents[].date`) into timestamps. The grader compares dates as `YYYY-MM-DD` strings, so convert back with `.date().isoformat()` before calling `grade`, or read the JSONL with the standard-library `json` module as in the example above, which keeps every field exactly as stored. Over a set of seeds, report `pass_rate` (share of worlds with `passed == True`) and `mean_score`. Keep `en` and `ru` separate: they are the same worlds, so pooling them double-counts. ## Reproducing ```bash pip install closebench==0.1.1 python build.py # rewrites data/ deterministically python verify.py # 5 random seeds, both locales: byte-identical rebuild + oracle score 1.0 python verify.py --n 100 --sample-seed 0 # every seed, fixed sample ``` Both scripts accept `--src /path/to/closebench-checkout` when the package is not installed. SHA-256 of the shipped files (closebench 0.1.1): ``` 68a76532552584d445f365603823e4f98336c30227050e79c44fcaeb7e9f7b07 worlds-en.jsonl 2a758aac55628dfe0e36943b66b42d420dccd1da79b45f73424ca7d4b7d5f2f4 worlds-ru.jsonl b4e8f5410f6bd62f6ed4cb3cecb8f696d2b2c762125263bcfffba5052296780e truth-en.jsonl c340bcd9e7a89e624260feb80f27ef4162e9b9f0d1ca45529fcfc764e5a9e26f truth-ru.jsonl ``` Rows are written with `json.dumps(obj, ensure_ascii=False, separators=(",", ":"))` in the key order shown above; the package's `World.snapshot()` and `World.truth()` are the source of every field except `mode`, which is added by `build.py`. ## Limitations -- read before drawing conclusions - **Synthetic.** One fictional company, one chart of 26 accounts, one fiscal year, one closing month (November 2026), round-ish amounts, no VAT or income tax, no sub-ledgers, no multi-entity consolidation beyond a single intercompany e-mail. Real closes are messier and larger. Passing here says an agent can do the mechanics, not that it can close real books. - **One period per world.** The agent closes November; the ten earlier months are context. There is no multi-period drift, no reopening, no prior-year adjustments. - **The answer key is public.** `truth-*` ships alongside `worlds-*`, and the generator is open source, so any model that has seen this data (or the package) can memorise it. Use it as a regression suite for *your own agent*, not as a leaderboard. For a held-out set, run `build.py` with seeds nobody has published (edit `HARD_SEEDS` / `ABSENCE_SEEDS`). - **Grading is exact-match on account pair, amount and period**, with a small explicit equivalence table for partial credit. A correct fix expressed through a different but defensible route (e.g. two entries, or a compound entry) scores as missed plus spurious. Adjustments dated outside the closing period are ignored. - **Not a hard benchmark.** In the package author's runs a frontier model with a neutral prompt closed `hard` worlds with a **83 %** pass rate and `absence` worlds with **58 %** (**92 %** if you accept the agent's refusal to book an undocumented goods receipt). Distractors caught nobody. The one systematic failure: two errors that cancel in the balances (a missing accrual and an unreleased accrual of the same amount) are still two errors, and models tend to book one. See the package README and `runs/` in the repository. - **Two locales, same numbers.** `ru` is a translation layer over the same seeds, not an independent sample; it tests whether text language changes an agent's behaviour, nothing more. Both locales were generated from the same `strings.py` tables and may carry the same wording quirks. - **Scale.** 100 worlds. Enough to catch a regression in an agent; not enough to rank models with tight confidence intervals. ## Links - Repository: https://github.com/poyraz-demir/closebench - PyPI: https://pypi.org/project/closebench/ (`closebench==0.1.1`) - Issues: https://github.com/poyraz-demir/closebench/issues ## Licence MIT, same as the package. See `LICENSE`.