FactReview: Evidence-Grounded Peer Review with Execution-Based Claim Verification
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...
| Authors | Ling Yue, Chaoqian Ouyang, Hang Xu et al. |
|---|---|
| Primary category | cs.AI |
| Published | 5 April 2026 |
| Last updated at source | 16 August 2026 |
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