---
license: cc-by-4.0
language:
  - en
pretty_name: LegalReasonBench (sample)
task_categories:
  - text-generation
annotations_creators:
  - expert-generated
language_creators:
  - expert-generated
source_datasets:
  - original
size_categories:
  - n<1K
tags:
  - legal
  - legal-reasoning
  - agents
  - long-horizon
  - benchmark
configs:
  - config_name: assignments
    data_files:
      - split: test
        path: data/assignments/*.parquet
---

# LegalReasonBench (micro1): Sample Release

LegalReasonBench measures how well AI agents carry a legal matter through a sequence of
assignments the way a practicing lawyer would: reading a case file, researching the law,
applying it to the facts, and producing court filings and memoranda, where each assignment
builds on the work before it. Every deliverable is graded against attorney-authored
rubrics that reward required legal points and penalize specific errors.

**Published report:** https://www.micro1.ai/benchmark/legal-reason-bench

## Citation

Please cite as: **"LegalReasonBench (micro1). Available at: https://www.micro1.ai/benchmark/legal-reason-bench"**

## Scope of this release

This is a sample of **5 matters (10% of LegalReasonBench)**, released for evaluation and
benchmarking. The remaining matters are held out. To request access to the full dataset,
contact **maria@micro1.ai** or fill out the enquiry form at https://www.micro1.ai/.

| Matter | Practice area | Assignments | Criteria (penalties) | Research |
|---|---|---:|---:|---|
| `lrb-certiorari-quasi-judicial-land-use` · Petition for Writ of Certiorari for Quasi-judicial Proceeding | Real estate and land use | 2 | 37 (5) | open |
| `lrb-dcca-challenge-police-discipline` · Viability of a DCCA Challenge | Employment and labor | 5 | 54 (11) | closed |
| `lrb-interim-cmo-appointment-governance` · Interim CMO Appointment: Governance and Compliance Analysis | Corporate governance | 4 | 42 (8) | closed |
| `lrb-vicar-trial-jury-instructions` · OTB Gang VICAR Trial Jury Instructions | Criminal and investigations | 2 | 35 (7) | closed |
| `lrb-forum-non-conveniens-response` · Plaintiff's Response, Forum Non Conveniens | Litigation and dispute resolution | 2 | 53 (5) | closed |

15 assignments, 221 criteria, 40 attachments (PDF and DOCX).

**Research universe.** In an *open-universe* matter the agent may research outside
authority on the web. In a *closed-universe* matter the prompt restricts it to the
supplied case file.

## Contents

A `tasks/` tree of **5 runnable [Harbor](https://harborframework.com) multi-step tasks**,
one per matter, plus a Parquet index for the Dataset Viewer.

```
tasks/lrb-dcca-challenge-police-discipline/
├── task.toml                     # metadata, timeouts, one [[steps]] entry per assignment
├── environment/Dockerfile        # sandbox: Python, pandoc, poppler-utils, LibreOffice
└── steps/
    ├── assignment-1/
    │   ├── instruction.md        # role prompt + the assignment, shown to the agent
    │   ├── workdir/attachments/  # case-file documents for this assignment (copied to /app/attachments/)
    │   ├── tests/                # verifier, NOT visible to the agent
    │   │   ├── fixtures/rubric.json      # the weighted criteria for this assignment
    │   │   ├── raw_correctness/*.toml    # one judge criterion per rubric item
    │   │   ├── m1_judge_panel.json       # the three-judge panel
    │   │   └── test.sh                   # verifier entrypoint
    │   └── solution/solve.sh     # no oracle solution ships
    ├── assignment-2/
    └── ...
```

Assignments run in order in one persistent `/app` sandbox. Each assignment writes its
deliverable to `/app/response<N>.md`, and later assignments can read earlier deliverables
and attachments.

## Dataset Viewer columns

The `assignments/test` split has one row per assignment.

- `matter_id`, `matter_title`, `practice_area`, `research_universe`
- `assignment`, `num_assignments`, `assignment_title`
- `prompt`: the full assignment prompt (the instruction file adds the shared role prompt)
- `attachments`: paths of the documents introduced in this assignment
- `rubric_count`, `penalty_count`, `positive_points`
- `rubric`: JSON list of criteria with `criterion_id`, `points` (negative for penalties),
  `polarity`, `irac_section` (Issue, Rule, Application, Conclusion), `criterion`,
  `source` (the authority or record cite the criterion rests on) and `justification`
- `instruction_path`, `rubric_path`, `source_task_path`: paths into the Harbor bundle

## Scoring

- **Criteria.** Each criterion is judged pass or fail. A required criterion earns its
  points when met. A penalty criterion subtracts its points when the deliverable commits
  the described error.
- **Assignment reward.** Signed points earned over available positive points, clamped to
  [0, 1].
- **Matter reward.** Points-weighted across the matter's assignments: total signed points
  earned over total positive points, clamped to [0, 1]. `scripts/score_matter.py`
  computes it from a finished Harbor trial. Harbor's built-in `multi_step_reward_strategy
  = "mean"` is diagnostic only.
- **Judges.** A strict majority of three LLM judges decides every criterion: GPT-5.6
  Terra, Claude Sonnet 5 and Gemini 3.8 Flash, each at medium reasoning effort. Each
  criterion is judged in its own call, and the judge reads only the deliverable.

## Running

```bash
uv tool install harbor
export OPENAI_API_KEY=... ANTHROPIC_API_KEY=... GEMINI_API_KEY=...   # the judge panel
harbor run -p tasks/lrb-dcca-challenge-police-discipline -a opencode -m <provider/model>
harbor run -p tasks -a opencode -m <provider/model>                    # all five matters
python scripts/score_matter.py <trial directory>
```

All three judge keys are required. The panel fails the assignment if any judge cannot be
reached, rather than scoring with fewer votes. The published results ran the OpenCode agent
with a three-hour limit per assignment.

## Important notes

- **Sources.** The case files combine public legal authority (court opinions, statutes,
  regulations and pattern jury instructions), public court records reproduced as filed,
  and matter documents that legal experts wrote or adapted for the benchmark. Some matters
  are built on real, publicly filed cases, so public records can name real parties.
- **No professional use.** This dataset does not provide legal advice and should not be
  used for production decision-making without independent validation.
- **No endorsement.** Use of this dataset does not imply endorsement by micro1.

## License

CC-BY-4.0.