Datasets:
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:
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.