Who it is forA research reviewer or lab analyst who has been given a reported ML Benchmark score and does not want to rely on trusting Simreal.
The problemA published agent score is hard to trust without seeing how it was produced, and reviewers need to confirm the arithmetic and provenance.
The outcomeA recomputed task score from the public scoring code, plus a check of the submission id, artifact hash, and reference snapshot hash behind the number.
What you need first
- Python 3.11 or newer (Python 3.12 is the locally tested version)
- A local copy of the repository from github.com/Simreal-AI/Simreal-MLBench
- A terminal with a shell that supports the commands in the README
- For full agent evaluation only: contact with [email protected] to get access to the Simreal evaluation service
Steps
- Set up the environment with
python3 -m venv .venv,source .venv/bin/activate, andpython -m pip install -e '.[test]'. - Recompute a score from a snapshot and result pair with
mleb grade examples/snapshot.json examples/result.json. - Run the report verification script with
python scripts/verify_report.py. - Compare the submission id, artifact hash, and reference snapshot hash against the values in the reported result.
- Check the arithmetic against the 100 × p² rule described in SCORING.md.
Prompt for your agent
In the Simreal-MLBench repository, run `mleb grade examples/snapshot.json examples/result.json` and then `python scripts/verify_report.py`. Replace the example files with the saved reference counts for <target task> if you have them. Report the recomputed score and whether the verification script passed.
Caveat. The local example files only demonstrate the grading path; checking a real reported number needs its saved reference counts, which the README says must be recomputed from saved data.
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