FactReview: Evidence-Grounded Peer Review with Execution-Based Claim Verification

Abstract:Large language model (LLM)-based reviewing systems typically assess manuscripts in isolation, leaving literature- and code-dependent claims difficult to verify. We present FactReview, an audit pipeline that extracts review-relevant claims, grounds them in related work and reference checks, and, when code is available, executes released artifacts under a fixed repair budget. On 26 paper-disjoint test papers with 354 human-verified claims, FactReview achieves 84.3\% F1 for claim recovery. In a same-backend, evidence-matched comparison, FactReview scores 4.72/5 overall, outperforming a direct LLM reviewer by 0.74 points. Removing execution evidence changes 17.0\% of claim statuses, more than removing any other single evidence source. In a reviewer-assistance study, FactReview reduces mean review time by 58\% while increasing benchmark-claim coverage from 87\% to 99\%. FactReview supports evidence-based claim auditing, with acceptance decisions reserved for human reviewers. The code is public at this https URL.

Artificial Intelligence (cs.AI); Machine Learning (cs.LG)

arXiv:2604.04074 [cs.AI]

(or arXiv:2604.04074v4 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2604.04074 Focus to learn more arXiv-issued DOI via DataCite

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