Remote script. This runs third-party experiment code from the paper's repository, so use disposable infrastructure if the repository might be hostile, since Docker is not a formal sandbox proof.
Who it is forA reproducibility engineer or lab member who needs a grounded verdict on whether a paper's reported numbers can be reproduced.
The problemA script exiting successfully does not show the paper's scientific result was reproduced, and manual comparison of metrics is error-prone.
The outcomeA verdict in report.json and report.md, with metrics, figures, tables, and stdout and stderr logs preserved in the workspace.
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
- Install the package with
python -m pip install verirepro. - Confirm the sandbox prerequisites with
verirepro doctor --strictbefore any third-party code runs. - Run the reproduction of the target paper with
verirepro reproduce <paper-id>, adding--allow-networkor--allow-gpuonly when you have decided to grant those authorities. (inferred, check the README) - Read the verdict in report.json and check the metric comparisons and the experiment.stdout.log and experiment.stderr.log files before you trust the result.
Prompt for your agent
Using the VeriRepro repo (XiantingWu/VeriRepro), run `verirepro doctor --strict` and then `verirepro reproduce <paper-id>` for the paper <paper-id>. Expected output is a workspace with report.json and a PASS, FAIL, or PARTIAL status. Verify that any PASS is backed by evidence-authorized scalar comparisons and not just a zero exit code.
Caveat. Veri Repro states that Docker is an execution boundary, not a formal sandbox proof, and that a PARTIAL verdict is intentional when evidence is insufficient.
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