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arXiv · 2604.04074

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

Ling Yue·Chaoqian Ouyang·Hang Xu·Ruijun Huang·Yuchen Liu·Libin Zheng·Wei Liu·Shaowu Pan·Shimin Di·Min-Ling Zhang
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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 https://github.com/DEFENSE-SEU/FactReview.

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Ling Yue, Chaoqian Ouyang, Hang Xu, Ruijun Huang, Yuchen Liu, Libin Zheng, Wei Liu, Shaowu Pan, Shimin Di, Min-Ling Zhang. 2026-04-05. FactReview: Evidence-Grounded Peer Review with Execution-Based Claim Verification. https://arxiv.org/abs/2604.04074

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