LedgerBench-100

LedgerBench-100 is a deterministic corporate-finance agent benchmark: 100 tasks over a shared simulated finance world (a D365-shaped ERP, an Odoo-shaped procure-to-pay and manufacturing surface, a QBO-style subsidiary ledger, a shared drive, email, document management, and frozen real SEC XBRL filings) served through 8 MCP servers exposing 66 tools. Tasks are in-fiction persona chat messages; the graded answer contract is discovered through the harness server's reporting_fields tool, the way a real reporting system's schema is read before filing into it.

Grading is fully deterministic and binary — answer checks with typed tolerances, trace checks (required servers, reads before submission), and state checks that grade the world the agent leaves behind (committed payment runs, paid/rejected partitions, reason codes) plus a writes_only anti-hack veto. No LLM judge, no network, no clock in the reward path.

Measured contents

• Tasks: 100 across 22 families: anomaly_triage (1), bank_rec (4), business_brief (3), business_brief_fb (3), cash_app (3), cash_forecast (2), close_mgmt (6), collections_ops (1), cross_system (7), erp_qa (11), erp_qa_fb (5), erpbench (10), expense_audit (4), finance_qa (10), finance_qa_fb (2), fpna (5), journal_entry (2), payment_proposal (2), payment_run (3), pbc (4), threeway_match (5), vendor_master (7)
• Oracle walk length: min 3 / median 6 / max 104 MCP calls (1515 total)
• Checks: 434 answer + 257 trace + 264 state = 955 graded checks
• Context files: 59 seeded documents/inputs (36 unique) across 47 tasks; most context lives inside the world itself (ERP rows, workbooks, emails, filings)
• Escalated variants: 30 tasks are escalations of a base task also in the release; 25 of them (doc_mode = "buried") deliberately reuse the base persona message verbatim against a harder world — the governing policy must be found among seeded decoy documents — so those prompt texts appear twice by design
• Prompt uniqueness across the 75 distinct prompts: maximum pairwise 5-shingle Jaccard 0.91411

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/: seeded per-task context documents and input files.
• 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 (600 executions)

Gate	Result
Oracle replays	100/100 reward 1.0
Deterministic replays (byte-identical reports)	100/100

Negative control	Executions	False accepts
no_submit	100	0
noop	100	0
off_task_write	100	0
wrong_submit	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.