Datasets:
Tasks:
Text Generation
Modalities:
Text
Formats:
json
Languages:
English
Size:
< 1K
Tags:
benchmarking
agent-evaluation
deterministic-verification
reinforcement-learning
legal
litigation
License:
Blobfish Litigation Bench v1.0 — 100 tasks, median 108 verified steps, deterministic verifiers
7d49280 verified | license: cc-by-4.0 | |
| task_categories: | |
| - text-generation | |
| language: | |
| - en | |
| tags: | |
| - benchmarking | |
| - agent-evaluation | |
| - deterministic-verification | |
| - reinforcement-learning | |
| - legal | |
| - litigation | |
| - long-horizon | |
| - mcp | |
| size_categories: | |
| - n<1K | |
| configs: | |
| - config_name: default | |
| data_files: | |
| - split: dev | |
| path: data/dev.jsonl | |
| # Blobfish Litigation Bench | |
| Executable, long-horizon litigation environments for training and evaluating | |
| tool-using agents. **100 tasks, 104-113 verified tool-calling steps each (median 108), | |
| graded by deterministic state-diff — no LLM judge anywhere on the reward path.** | |
| Every task runs inside a stateful law-firm simulation exposed over MCP: nine | |
| litigation systems of record (matters, parties, claims, pleadings, motions, discovery_requests, depositions, exhibits, settlements) behind 47 product-shaped tools. The | |
| agent works a real matter through those systems while reading a mounted file of | |
| 6-7 synthetic documents. | |
| > **Simulation only.** Every matter, party, document, attorney and figure is synthetic. | |
| ## At a glance | |
| | | | | |
| |---|---| | |
| | Tasks | 100 | | |
| | Verified steps per task | 104-113 (median 108) | | |
| | Distinct scenarios | 10 | | |
| | Unique prompt skeletons | 100/100 | | |
| | Documents per task | 6-7 (docx/eml/pptx/xlsx) | | |
| | Grading | Deterministic state-diff + trace assertions | | |
| | LLM judge | **None** | | |
| | Oracle pass rate | 100/100 (1.00) | | |
| ## Why deterministic grading | |
| Rubric-graded agent benchmarks ask a model whether another model's prose | |
| satisfied a criterion. That conflates the grader's judgement with the agent's | |
| capability, and it cannot tell a rubric miss from a model miss. | |
| Here every task ships an executable reference walk, and grading is a diff of | |
| world state before and after plus assertions over the recorded tool trace. A | |
| zero means the agent failed, not that a judge disagreed. | |
| ## First results | |
| | Agent | Reward | Conditions failed | Tools missed | Systems never touched | | |
| |---|---|---|---|---| | |
| | Oracle (reference walk) | **1.00** | 0 | 0 | — | | |
| | Claude Sonnet 4.5 · motion to compel | 0.00 | 40 | 32 | calendar, contacts, documents, efiling, matters, time | | |
| | Claude Sonnet 4.5 · case chronology | 0.00 | 29 | 25 | calendar, contacts, matters, time | | |
| The oracle finishes every task. A frontier coding agent, given the same | |
| environment and the same mounted matter file, returns 0.0 on both — and the | |
| deterministic verifier says exactly which tools it never called and which work | |
| products it never created. The failure is not refusal or malformed output: the | |
| agent worked the litigation system and never completed the cross-application | |
| chain. | |
| **Two tasks is not a leaderboard.** These are the first end-to-end runs, not a | |
| scored evaluation, and no confidence interval is claimed. They are reported | |
| because they establish the thing that matters before any leaderboard: the | |
| benchmark discriminates. | |
| ## Data schema | |
| | Field | Type | Description | | |
| |---|---|---| | |
| | `task_id` | string | Stable identifier | | |
| | `task_name` | string | Human-readable name | | |
| | `world_id` | string | Environment pointer | | |
| | `prompt` | string | Full agent instruction | | |
| | `context_files` | list | Mounted document manifest (name + role) | | |
| | `rubric` | list | Deterministic assertion names checked by the verifier | | |
| | `gold_output` | string | Reference walk summary (tool sequence) | | |
| | `metadata` | dict | scenario, work_type, posture, complication, phases, steps, docket, court | | |
| ## Scenario coverage | |
| - **Motion to Compel Deficient Discovery Responses** — draft (10 variants, 9 phases) | |
| - **Privilege Review and Log for a Rolling Production** — review (10 variants, 8 phases) | |
| - **Deposition Preparation and Exhibit Assembly** — draft (10 variants, 9 phases) | |
| - **Litigation Hold Scoping and Custodian Identification** — advise (10 variants, 8 phases) | |
| - **Opposition to Motion for Summary Judgment** — draft (10 variants, 8 phases) | |
| - **Settlement Valuation and Authority Memorandum** — analyze (10 variants, 7 phases) | |
| - **Production Completeness Audit Against Requests** — compare (10 variants, 8 phases) | |
| - **Motion for Sanctions for Discovery Abuse** — draft (10 variants, 8 phases) | |
| - **Expert Disclosure Review and Exclusion Motion** — analyze (10 variants, 8 phases) | |
| - **Case Chronology Reconstruction from the Record** — analyze (10 variants, 8 phases) | |
| ## Splits and release policy | |
| `dev` carries all 100 tasks with their full deterministic assertion sets. The | |
| environment image and reference walks are published alongside so results are | |
| reproducible rather than self-reported. | |
| ## Running the tasks | |
| The executable environments are published to the Harbor registry: | |
| ```bash | |
| harbor run -d blobfishai/litigation -a <your-agent> | |
| ``` | |
| ## Intended use and licensing | |
| Released CC BY 4.0 for benchmarking and agent training. The corpus is entirely | |
| synthetic and contains no client data. Nothing here is legal advice; every task | |
| carries an attorney-validation limitation that a correct answer must preserve. | |