| --- |
| license: cc-by-4.0 |
| language: |
| - en |
| pretty_name: "MissionReport: Evaluating AI Judgment in Government Reporting (sample)" |
| task_categories: |
| - text-generation |
| annotations_creators: |
| - expert-generated |
| language_creators: |
| - expert-generated |
| source_datasets: |
| - original |
| size_categories: |
| - n<1K |
| tags: |
| - government |
| - defense |
| - report-writing |
| - document-generation |
| - agents |
| - benchmark |
| configs: |
| - config_name: tasks |
| data_files: |
| - split: test |
| path: data/tasks/*.parquet |
| --- |
| |
| # MissionReport (micro1): Sample Release |
|
|
| *Evaluating AI Judgment in Government Reporting* |
|
|
| MissionReport measures how well AI agents write government status reports. Each task is an |
| assignment written by an expert who has done the job: a role, a reader, the working files and the |
| organization's template. The agent reads the files and produces a finished Word document. The |
| expert's rubric then scores each decision the report makes: what leads, what the facts mean |
| together, how to report what is not yet known, and what to leave out. |
|
|
| The full benchmark has 20 tasks in four report families (weekly activity reports, after-action |
| reports, program status reports, and performance and accomplishment reports) and five settings |
| (Army, Air Force, Navy, Marine Corps, and Department of Defense offices and agencies). In the |
| published run, nine frontier models scored between 54.3% and 64.1%, and 3 of 540 reports met every |
| criterion in their rubric. |
|
|
| **Published report:** https://www.micro1.ai/benchmark/mission-report |
|
|
| ## Citation |
|
|
| Please cite as: **"MissionReport: Evaluating AI Judgment in Government Reporting (micro1). Available at: https://www.micro1.ai/benchmark/mission-report"** |
|
|
| ## Scope of this release |
|
|
| This is a sample of **5 tasks (25% of MissionReport)**, released for evaluation and |
| benchmarking. The remaining 15 tasks 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/. |
|
|
| Task IDs and titles match the report's Appendix B, and Appendix A shows each task's expert report beside one model's report. Mean score is the task's average over nine |
| frontier models with three attempts each, in the published run. |
|
|
| | Task | Report | Setting | Reader | Length limit | Source files | Criteria (penalties) | Mean score | |
| |---|---|---|---|---|---:|---:|---:| |
| | `war-02` · WAR-02 | Staff trip entry for a mobility command | Air Force | the directorate head and the command's commander | 200 words | 1 | 23 (4) | 60.9% | |
| | `war-08` · WAR-08 | Division gunnery qualification weekly activity report | Army | a deputy commanding general | 150 words | 10 | 18 (2) | 54.3% | |
| | `aar-01` · AAR-01 | Wing emergency operations exercise after-action report | Air Force | a wing commander | one full page | 15 | 16 (3) | 60.4% | |
| | `aar-04` · AAR-04 | Armor company training rotation after-action report | Army | the training center chain of command | 1,500 words | 9 | 27 (3) | 52.3% | |
| | `psr-01` · PSR-01 | Career field hiring read-ahead memo | DoD offices and agencies | a senior executive director | two pages, 350 words | 7 | 13 (2) | 40.5% | |
|
|
| 42 source files (Word, PDF and Excel), 83 required criteria worth 155 points and 14 penalties worth |
| 20 points. PSR-01 has the lowest mean score of the 20 tasks. |
|
|
| **Task versions.** These are the current task versions. Compared with the versions behind the |
| published scores, the prompts, source files, criterion descriptions and point weights are |
| unchanged. Two expert reports (PSR-01 and AAR-04), one grading note and some criterion source notes |
| received typo and formatting fixes. A full re-run on these versions matched the published scores |
| within run-to-run variation. |
|
|
| ## Contents |
|
|
| A `tasks/` tree of **5 runnable [Harbor](https://harborframework.com) tasks**, plus a Parquet index |
| for the Dataset Viewer and a script that summarizes a finished run. |
|
|
| ``` |
| tasks/psr-01/ |
| ├── task.toml # metadata, timeouts, judge settings |
| ├── instruction.md # the assignment shown to the agent |
| ├── environment/Dockerfile # sandbox: Python document libraries, pandoc, poppler-utils, LibreOffice, OpenCode |
| ├── steps/answer/ |
| │ ├── instruction.md # the same assignment, for Harbor's single step |
| │ └── workdir/attachments/ # source files, copied to /app/attachments/ |
| ├── tests/ # verifier, NOT visible to the agent |
| │ ├── test.sh # entrypoint: format check, then the judge, then the reward |
| │ ├── agent_judge.py # the agentic judge |
| │ ├── fixtures/rubric.json # the weighted criteria |
| │ ├── fixtures/config.json # prompt, rubric, grading guidance, judge settings |
| │ └── golden/ # the expert's report, the judge's reference |
| └── solution/solve.sh # oracle: submits the expert's report |
| ``` |
|
|
| The agent works in `/app` and must save its report as `/app/outputs/report.docx`, the only file the |
| grader reads. The instruction wraps the expert's prompt with a short role preamble, file-reading |
| tips and the list of attachments. |
|
|
| ## Dataset Viewer columns |
|
|
| The `tasks/test` split has one row per task. |
|
|
| - `task_id`, `title`, `report_family`, `setting`, `reader`, `length_limit`: as in the report's |
| Appendix B |
| - `prompt`: the expert's prompt |
| - `attachments`, `num_attachments`: paths of the source files in the Harbor task |
| - `rubric_count`, `penalty_count`, `positive_points`, `penalty_points` |
| - `rubric`: JSON list of criteria with `criterion_id`, `points` (negative for penalties), |
| `polarity`, `criterion`, `source` (the prompt line or file the criterion rests on) and |
| `justification` |
| - `grading_guidance`: the expert's grading notes. The rubric judge does not see them. |
| - `mean_score_pct`: the task's mean score in the published run |
| - `golden_report_path`, `instruction_path`, `rubric_path`, `task_path`: paths into the Harbor task |
|
|
| ## Scoring |
|
|
| - **Criteria.** Each criterion is judged true or false. A required criterion earns its points when |
| the report meets it. A penalty subtracts its points when the report commits the error it |
| describes, such as including material the reader does not need. |
| - **Task reward.** Positive points earned minus penalty points triggered, divided by the task's |
| positive points, clamped to [0, 1]. A missing, corrupt or empty `report.docx` scores 0. |
| - **Judge.** Claude Opus 5.5, running as an agent in OpenCode at medium reasoning effort, reads the |
| submitted document, can render its pages, and compares it with the expert's report. It decides |
| each criterion with a written reason. It never sees the point weights, the grading guidance or |
| the source files. |
|
|
| ## Running |
|
|
| ```bash |
| uv tool install harbor |
| export ANTHROPIC_API_KEY=... # the judge (Claude Opus 5.5) |
| |
| # Check the setup end to end: the oracle submits the expert's report and should score 1.0 |
| harbor run -p tasks/psr-01 -a oracle |
| |
| # Run a model on all five tasks, three attempts each (also export that model's provider key) |
| harbor run -p tasks -a opencode -m <provider/model> -k 3 |
| |
| # Rewards per task, plus each missed criterion and triggered penalty with the judge's reason |
| python scripts/summarize_job.py jobs/<job name> --criteria |
| ``` |
|
|
| Harbor runs each task in Docker by default; `-e` selects another sandbox provider. Harbor asks |
| before passing your environment to a task; add `-y` to confirm when running unattended. The |
| published results used the OpenCode agent at version 1.18.31 (add `--ak version=1.18.31` to match) |
| and default reasoning effort, with a 60-minute limit per attempt and three attempts per task. The |
| verifier allows up to 85 minutes per report; a judging pass usually takes a few minutes. |
|
|
| ## Important notes |
|
|
| - **Sources.** Each task's source files were assembled, written or adapted by an expert with |
| reporting experience in that organization. They name real organizations, ranks, places and |
| publications. AAR-04 includes the Army's public doctrine manual, FM 6-0. |
| - **Not for operational use.** Reports produced on these tasks are not official products. Do not |
| use this dataset for real reporting decisions without independent validation. |
| - **No endorsement.** MissionReport is not affiliated with or endorsed by the U.S. Department |
| of Defense or any military service. Use of this dataset does not imply endorsement by micro1. |
|
|
| ## License |
|
|
| CC-BY-4.0. |
|
|
