Comprehension Audits to Mitigate Risks from Automated AI Research
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The paper introduces an author‑in‑the‑loop collaborative audit system for reproducibility checks during peer review, where authors explicitly declare reproduction instructions via a structured checklist. An AI agent uses these declarations to investigate and assess reproducibility, while human reviewers approve requests and decide on issue resolution. The system was deployed on 36 conference papers, identifying 20 issues that were subsequently resolved, and the study examined how different checklist conditions affected the agent’s assessment.
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