| --- |
| 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. |
| |
