The dataset viewer is not available for this split.
Error code: StreamingRowsError
Exception: CastError
Message: Couldn't cast
case_id: string
sources: list<item: struct<columns: list<item: string>, row_count: int64, table: string, task_relevant_rows: (... 18 chars omitted)
child 0, item: struct<columns: list<item: string>, row_count: int64, table: string, task_relevant_rows: list<item: (... 6 chars omitted)
child 0, columns: list<item: string>
child 0, item: string
child 1, row_count: int64
child 2, table: string
child 3, task_relevant_rows: list<item: null>
child 0, item: null
case: struct<case_id: string, decided_at: null, decision_code: null, evidence_refs: null, opened_at: times (... 108 chars omitted)
child 0, case_id: string
child 1, decided_at: null
child 2, decision_code: null
child 3, evidence_refs: null
child 4, opened_at: timestamp[s]
child 5, owner: string
child 6, rationale: null
child 7, status: string
child 8, subject: string
child 9, task_id: string
child 10, workflow: string
lines: list<item: struct<amount: double, case_id: string, control_basis: string, currency: string, descript (... 48 chars omitted)
child 0, item: struct<amount: double, case_id: string, control_basis: string, currency: string, description: string (... 36 chars omitted)
child 0, amount: double
child 1, case_id: string
child 2, control_basis: string
child 3, currency: string
child 4, description: string
child 5, document_ref: string
child 6, line: int64
to
{'case': {'case_id': Value('string'), 'decided_at': Value('null'), 'decision_code': Value('null'), 'evidence_refs': Value('null'), 'opened_at': Value('timestamp[s]'), 'owner': Value('string'), 'rationale': Value('null'), 'status': Value('string'), 'subject': Value('string'), 'task_id': Value('string'), 'workflow': Value('string')}, 'lines': List({'amount': Value('float64'), 'case_id': Value('string'), 'control_basis': Value('string'), 'currency': Value('string'), 'description': Value('string'), 'document_ref': Value('string'), 'line': Value('int64')})}
because column names don't match
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
return get_rows(
dataset=dataset,
...<4 lines>...
column_names=column_names,
)
File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
return func(*args, **kwargs)
File "/src/services/worker/src/worker/utils.py", line 127, in get_rows
rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
File "/src/services/worker/src/worker/utils.py", line 483, in safe_iter
yield from ds.decode(False) if ds.features else ds
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2840, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2373, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2398, in _iter_arrow
for key, pa_table in self.ex_iterable._iter_arrow():
~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
self._cast_table(pa_table, json_field_paths=json_field_paths),
~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2378, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
case_id: string
sources: list<item: struct<columns: list<item: string>, row_count: int64, table: string, task_relevant_rows: (... 18 chars omitted)
child 0, item: struct<columns: list<item: string>, row_count: int64, table: string, task_relevant_rows: list<item: (... 6 chars omitted)
child 0, columns: list<item: string>
child 0, item: string
child 1, row_count: int64
child 2, table: string
child 3, task_relevant_rows: list<item: null>
child 0, item: null
case: struct<case_id: string, decided_at: null, decision_code: null, evidence_refs: null, opened_at: times (... 108 chars omitted)
child 0, case_id: string
child 1, decided_at: null
child 2, decision_code: null
child 3, evidence_refs: null
child 4, opened_at: timestamp[s]
child 5, owner: string
child 6, rationale: null
child 7, status: string
child 8, subject: string
child 9, task_id: string
child 10, workflow: string
lines: list<item: struct<amount: double, case_id: string, control_basis: string, currency: string, descript (... 48 chars omitted)
child 0, item: struct<amount: double, case_id: string, control_basis: string, currency: string, description: string (... 36 chars omitted)
child 0, amount: double
child 1, case_id: string
child 2, control_basis: string
child 3, currency: string
child 4, description: string
child 5, document_ref: string
child 6, line: int64
to
{'case': {'case_id': Value('string'), 'decided_at': Value('null'), 'decision_code': Value('null'), 'evidence_refs': Value('null'), 'opened_at': Value('timestamp[s]'), 'owner': Value('string'), 'rationale': Value('null'), 'status': Value('string'), 'subject': Value('string'), 'task_id': Value('string'), 'workflow': Value('string')}, 'lines': List({'amount': Value('float64'), 'case_id': Value('string'), 'control_basis': Value('string'), 'currency': Value('string'), 'description': Value('string'), 'document_ref': Value('string'), 'line': Value('int64')})}
because column names don't matchNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
LedgerBench-100
LedgerBench-100 is a deterministic corporate-finance agent benchmark: 100 distinct employee decisions over isolated simulated finance worlds. Each world combines a Dynamics 365-shaped ERP, an Odoo procure-to-pay and manufacturing surface, a QBO-style subsidiary ledger, Microsoft Graph workbooks, Gmail, governed documents, and frozen SEC XBRL filings through 8 MCP servers exposing 66 provider-shaped tools. The employee asks for an outcome in ordinary language; the agent must discover the reporting schema and the task-specific investigation.
Grading is fully deterministic. LedgerScore groups the executable verifier contract into
16 task-specific employee outcomes worth 100 points: scope, authority, current state,
task-native investigation, task-native causal reasoning, operating-plan controls, supported decision,
costed options, operational and case state, collaboration, outcome verification,
provider-native readback, containment, exact insights, and execution sequence. Strict pass
still requires all 100 points. No LLM judge,
network, clock, or randomness appears in the reward path.
The source job is the scoring core, not a decorative wrapper: at least 58 of 100 points in every task are assigned to task-native source investigation, reasoning, decision, persisted operational state, and employee-facing insights. Every ERP planning task independently grades its Odoo state transition in addition to the cross-system decision record.
Every case also carries a graded operating-control model: the control requirement is derived from the ERP's in-scope DecisionScopeLines, usable support is reconciled from the current evidence register net of counterparty-corroborated exclusions, the exception is tested against the policy tolerance, the counterparty's committed date and the close or production-planning window bounds three costed timing alternatives (one within authority, one held for the counterparty, one requiring a higher-authority exception), and the selected outcome is compared with the requester's documented need-by date into a signed variance and an honest timing status. All 22 intermediate and final values are graded as their own answer fields, the selected option is persisted on the case, and the completion handoff is graded on its content.
Measured contents
- Tasks: 100 across 23 families: anomaly_triage (1), bank_rec (2), business_brief (1), cash_app (2), cash_forecast (1), close_mgmt (3), collections_ops (1), cross_system (4), erp_qa (6), erp_qa_fb (23), erpbench (27), expense_audit (3), finance_qa (7), fixed_assets (1), fpna (3), journal_entry (1), payment_proposal (1), payment_run (2), pbc (2), revenue_accounting (1), threeway_match (3), treasury_fx (1), vendor_master (4)
- Oracle walk length: min 38 / median 44 / max 144 MCP calls (5722 total); required distributed evidence reads are 28-42 per task
- Public rubric: exactly 16 causal employee-outcome milestones / 100 LedgerScore points per task. The execution layer separately contains 2855 answer + 2707 trace + 1139 state checks, with 81-95 atomic verifier assertions per task assigned exactly once beneath those milestones
- Graded reasoning chain: 23-36 graded answer fields per task (9-16 numeric derivations), a graded decision model in 100/100 tasks and three costed alternatives with authority status in 100/100; task-native work carries 58-58 points per task
- Inspectable assets: 58-72 agent-visible native files per task, including 28-42 exact executed MCP responses bound one-for-one to the verifier's material reads; every material export carries request scope, bytes, and SHA-256, while gold and oracle recipes are excluded
- Readback depth: 134 task-native provider readbacks across the release, plus the governed case and completion-thread readbacks in every task
- Reference diversity: 100/100 distinct raw server/tool sequences (maximum sequence match 0.945274) and 100/100 distinct semantic action graphs
- Human-job curation: 0 prompt variants and zero lineage-marked derived sources; the release uses one instance of each of 27 ERP planning archetypes and 23 distinct ERP control questions, plus separately authored fixed-asset capitalization, bundled-contract revenue allocation, and FX remeasurement workflows. Ticker or company-name swaps do not count as new jobs
- Prompt uniqueness: 100 distinct employee requests; maximum pairwise 5-shingle Jaccard 0.663462
What is included
data/tasks.jsonl: apex-accounting-compatible records (task_id,task_name,world_id,prompt,context_files,rubric,gold_output,metadata).tasks/: one readable JSON record per task.task_files/: 58-72 task-scoped agent-visible evidence files plus any native seeded documents and inputs.world/: the world source — MCP framework, the eight servers, the deterministic verifier engine, the Streamable HTTP bridge, and the full SQL schema.trajectories/: one normalized oracle MCP trajectory per task.reports/: measured build and qualification evidence.
The runnable form is the Harbor dataset blobfishai/ledgerbench-100: self-contained
task packs (prepared SQLite world + runtime on a digest-pinned python:3.12-slim)
whose tests/test.sh calls a token-gated /verify endpoint; the agent container
never sees the verification token.
Measured qualification (1600 executions)
| Gate | Result |
|---|---|
| Oracle replays | 100/100 reward 1.0 |
| Deterministic replays (byte-identical reports) | 100/100 |
| Negative control | Executions | False accepts |
|---|---|---|
| incomplete_read | 100 | 0 |
| keyword_stuffing | 100 | 0 |
| missing_readback | 100 | 0 |
| noop | 100 | 0 |
| rejected_mutation | 100 | 0 |
| shortcut | 100 | 0 |
| state_only | 100 | 0 |
| unauthorized_write | 100 | 0 |
| write_before_read | 100 | 0 |
| wrong_decision | 100 | 0 |
| wrong_evidence | 100 | 0 |
| wrong_option | 100 | 0 |
| wrong_target | 100 | 0 |
| wrong_value | 100 | 0 |
Full per-task evidence is in reports/qualification.json; do not infer a model score
from the oracle trajectories.
Data provenance and contamination
The company, its customers, vendors, employees, balances, and documents are synthetic
(FinanceBenchmark-derived journal shapes with synthetic entities). The filings surface
serves frozen real SEC XBRL facts (38 registrants, snapshot-pinned) — real public data,
included under its own public-domain terms. Task text and gold answers are original to
this release's source repository. Gold outputs are public, so this release suits
transparent evaluation and RL experiments rather than secret-test claims.
Licenses
Task data and documents are CC-BY-4.0. Benchmark code and harnesses are Apache-2.0. SEC XBRL facts are US-government public-domain data.
- Downloads last month
- 1,190