Datasets:

micro1-inc
/

MissionReport

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.

A tasks/ tree of 5 runnable Harbor 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

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.