{"data":{"kind":"file","path":"README.md","version_id":"in5touekeu952rv51o0t65n7","entry":{"name":"README.md","path":"README.md","is_directory":false,"size":9438,"modified_at":"2026-07-08T15:40:13.941000","content_hash":"6d5fa888eae1c34e07ccefa440ec70ce99e934b408e1357198c98e8d2641745b"},"entries":[],"content":"# ai-law-legal-agent-eval\n\n**What this is.** A rigorous, repeatable test of whether an AI agent can handle a\npiece of delegated AI/export-control work the way a careful junior lawyer would. The agent receives\na realistic matter file — nine mixed-format documents (agreements, fact schedules, board\nminutes, chat exports, reference memos) — and a short partner instruction: review the\nrecord and deliver a risk memorandum under a specific filename.\n\n**The matters.** Five original, fully synthetic matters (no real client facts anywhere):\nan advanced-accelerator shipment with unresolved license responsibility and red-flag customers; frontier model weights escrowed in Singapore with a Malaysian failover; a GPU reseller with diversion indicators; a foreign-parent customer reserving a frontier-scale training cluster; and a model-export review. Each matter contains seven planted issues the memo should raise — and\ndeliberately irrelevant documents a disciplined reviewer should decline to escalate.\n\n**How it's graded.** The way a supervising lawyer would grade it, made mechanical: each\nmatter's drafting attorney prepared a private answer key (the issues, their severity, the\nexpected remediation), and scoring checks the delivered memo against that key. There is\nno AI judging AI — the grading is deterministic, published in compiled form with the\npackage, and frozen by cryptographic hash so results are reproducible and comparable.\n\n**What the scores tell you.** Three things a hiring partner would ask about a junior's\nwork: *Did the work product actually get delivered as instructed?* (deliverable\ncompletion) — *Did it catch the issues that matter?* (finding recall, the discriminative\naxis) — *Did it escalate noise?* (distractor telemetry). Quoted source text earns\nnothing; credit requires the agent's own analysis.\n\n**What it can help with.** Benchmarking models and vendors before trusting them with\nAI/export-control work; tracking whether a new model version actually improved on legal\nreasoning rather than fluency; and research on agent scaffolding — the same tasks can be\nrun with different tooling around the same model. This pocket is one of a seven-practice-\narea family sharing a single grading engine, so results are comparable across areas.\n\n## Quickstart\n\n```bash\nprime env install ai-law-legal-agent-eval\nprime eval run ai-law-legal-agent-eval -m <your-model>      # 5 tasks x 3 rollouts\nprime eval run ai-law-legal-agent-eval -m <your-model> -a '{\"task_id\":\"orion-compute-001\"}'\n```\n\nThe agent gets four tools -- `list_documents`, `read_document` (paginated),\n`grep_documents`, and `save_deliverable` -- and up to 30 turns. Content that only appears\nin chat does not count: deliverables must be saved under their declared names, and a\n`memo.md` never satisfies a declared `memo.docx`.\n\n## Why deterministic scoring\n\nExecutable validators are the reward; judges are telemetry. The anchor specifications are\n**compiled from private answer keys** (never hand-written per task) and are designed to be\nfrozen by SHA-256 before any reported run. Matching is deliberately conservative --\nquoted source text is masked before scoring so pasting documents earns nothing, and a\nfinding only credits when the issue is named in its own terms *and* corroborated by\nseverity, remediation, or record-grounding evidence in the same context window.\n\n**Pre-registration note:** the compiled anchors ship with the package because the reward\nneeds them. Do not tune prompts, scaffolds, or models against them; treat them as a\nheld-out grader.\n\n## Tasks\n\nFive synthetic AI law matters, each a realistic dataroom-review delegation:\nterse partner instruction, 9-11 mixed-format documents (docx / pdf / xlsx / eml / txt),\none declared `.docx` memo deliverable, 7 planted issues, and a public rubric. All content\nis original synthetic material -- no third-party benchmark data, no real client facts.\nDocument corpora are intentionally compact in v0.1; corpus enrichment (full-length\ntechnical materials, compliance references, transaction records, multi-deliverable\ntasks) is the named path to v1.0.\n\nFour of the five tasks use the family's standard 42-criterion rubric.\n`orion-compute-001` legitimately ships 48 criteria per the factory manifest: extra\ncitation and overall-conclusion criteria reference multiple planted issues at once, and\na few pure fact-recital criteria reference no single issue. This does not change the\nscored reward shape. Every AI-law task, including Orion, still has exactly 7 planted\nfindings scored by the same `finding_recall` (0.6) and `deliverable_complete` (0.4)\nweights; the extra criteria only add resolution to the weight-0\n`criterion_pass_rate_proxy` telemetry.\n\n| scenario_id | scenario_slug | title | deliverable | instruction |\n| --- | --- | --- | --- | --- |\n| ai-cloud-training-004 | cloud-training-004 | Prepare Cloud Training Access Controls -- Risk Memo | cloud-training-risk-memo.docx | Review the attached documents against the compliance reference and prepare a cloud training access memo. Output: cloud-training-risk-memo.docx. |\n| ai-frontier-weights-002 | frontier-weights-002 | Review Frontier Model-Weight Escrow -- Risk Memo | model-weight-risk-memo.docx | Review the attached documents against the compliance reference and prepare a model-weight export risk memo. Output: model-weight-risk-memo.docx. |\n| ai-gpu-reseller-003 | gpu-reseller-003 | Assess Indirect GPU Reseller Diligence -- Risk Memo | reseller-risk-memo.docx | Review the attached documents against the compliance reference and prepare a reseller diligence risk memo. Output: reseller-risk-memo.docx. |\n| ai-model-export-005 | model-export-005 | Analyze AI Lab Acquisition Model-Export Issues -- Risk Memo | ai-acquisition-risk-memo.docx | Review the attached documents against the compliance reference and prepare an AI acquisition export memo. Output: ai-acquisition-risk-memo.docx. |\n| ai-orion-compute-001 | orion-compute-001 | Analyze Project Orion Export-Control Risk -- Risk Memo | export-control-risk-memo.docx | Review the attached documents against the compliance reference and prepare an export-control risk memo. Output: export-control-risk-memo.docx. |\n\n## Environment arguments\n\n| Argument | Default | Description |\n| --- | --- | --- |\n| `source` | `\"factory\"` | `\"factory\"` loads the bundled anchored tasks; `\"lab\"` loads LAB tasks from an explicit `tasks_root` |\n| `task_id` | `\"all\"` | All five matters, or a single slug such as `orion-compute-001` |\n| `max_turns` | `30` | Tool-use turn budget |\n| `page_chars` | `6000` | Page size returned by `read_document` |\n| `false_flag_weight` | `0.0` | Weight of the distractor-escalation penalty |\n| `min_deliverable_chars` | `200` | Minimum content length for a deliverable to count |\n| `judge_model` | `None` | Optional weight-0 judge telemetry; requires `JUDGE_API_KEY` (or `judge_api_key_var`) only when set |\n| `tasks_root` | `None` | In factory mode, override the bundled task directory with the same layout; in LAB mode, required and must point at your own LAB checkout |\n\nNo environment variables are required by default.\n\n## LAB mode\n\n`source=\"lab\"` measures workflow discipline: did the agent persist every declared\ndeliverable under its exact filename with substantive content. It does not measure issue\ncoverage because LAB tasks do not ship with a compiled answer key, so there is no\n`finding_recall`, `false_flag_rate`, or similar anchored metric in this mode. Every LAB\nmetric carries a `lab_` prefix so LAB workflow telemetry is never confused with or\naveraged alongside factory metrics.\n\nLAB mode requires `tasks_root`; no default or bundled LAB tasks ship in the package. The\nloader discovers `tasks_root/<category>/<task-id>/task.json`, with source files in the\nsibling `documents/` directory. Each `task.json` must contain `instructions` as a string\nand `deliverables` as a dict whose keys are the declared filenames, for example\n`{\"memo.docx\": \"memo.docx\"}`. The internal row slug uses `category/task-id` to keep tasks\nfrom different categories distinct.\n\n## Baseline results\n\nBaseline runs are pending. No paid eval has been run for this AI-law package in this\nbuild.\n\n## Interpreting scores\n\nA competent agent should approach `deliverable_complete = 1.0` quickly; `finding_recall`\nis the discriminative axis and is strict by construction -- credit requires engaging with\nthe specific planted issue, not mentioning its topic. `false_flag_rate > 0` indicates the\nagent escalates noise. `transcript_recall > finding_recall` indicates findings that never\nmade it into the saved deliverable (a workflow-discipline failure, not a knowledge one).\nRollout-to-rollout variance is real at this scale; the default 3 rollouts per task are a\nminimum for stable comparisons.\n\n## Provenance\n\nTask content and answer keys are original work from a validated synthetic-task factory\n(35 tasks across 7 practice areas; this package ships the AI law pocket). Anchor\ncompilation lineage is recorded in each task's `anchors.json` provenance block and in the\nstamped module header. This build has not been frozen; freeze SHA completion is a\nseparate manual step per SPEC section 6. See `NOTICE` for attribution details.\n\nThis is one environment in a per-practice-area family; contracts, corporate governance,\nmergers & acquisitions, cybersecurity & privacy, employment law, and crypto law pockets\nfollow the same engine and scoring contract.\n","encoding":"utf-8","truncated":false,"total_bytes":9438},"status":null}