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How to score agent reproduction runs with the ReproBench adapter with Veri Repro

Turn each agent's reproduction task into a result file and a summary with the ReproBench commands.

  • Research
  • Advanced
  • 1 hour
  • From the README
  • Any agent

Who it is forAn evaluator building or comparing AI agents on paper reproduction tasks who needs consistent, auditable results.

The problemAgents report reproduction outcomes in different formats, which makes them hard to compare and audit.

The outcomeA per-task result JSON with outcome, comparisons, failure taxonomy, and provenance, plus one summary JSON across tasks.

What you need first

  • Python with pip to install the verirepro package from PyPI
  • A pinned arXiv ID, DOI, PDF URL, or local PDF of the paper
  • Docker, only if you want third-party experiment code to execute
  • An OpenAI-compatible model endpoint, only if you want model-assisted analysis

Steps

  1. Install the package with python -m pip install verirepro.
  2. Prepare a task JSON file for one reproduction task, using only the allowed paper sources and confined paths.
  3. Run the adapter on the task with verirepro-reprobench task.json --output result.json.
  4. Summarize all results with verirepro-reprobench-summary results/*.json --output summary.json and check that each result records its operator interventions.

Prompt for your agent

Using the VeriRepro repo (XiantingWu/VeriRepro), run `verirepro-reprobench task.json --output result.json` for the task file <task-file>, then run `verirepro-reprobench-summary results/*.json --output summary.json`. Expected output is a per-task result and a combined summary. Verify that the task file had no local, file, or insecure HTTP paper sources and that operator interventions are recorded.

Caveat. The adapter rejects local, file, and insecure HTTP paper sources, so tasks must use allowed paper sources to be accepted.

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