ProReviewer: Evidence-Driven Investigation for Scientific Peer Review

Abstract

Effective peer review requires adaptive investigation that responds to evidence emerging from each paper. Existing LLM-based reviewers largely organize the review process around predefined workflows, leaving limited scope for emerging evidence to reshape investigation. We propose ProReviewer, an evidence-driven review agent that formulates reviewing as a Markov Decision Process, with a structured review log forming the core of its state. At each step, the agent uses accumulated findings to decide what to inspect next and when to conclude. As new evidence emerges, it updates the log by revising claim statuses, resolving open questions, and recording new findings. Each point in the final review cites supporting log entries, making its judgments traceable to the recorded evidence. On 1,000 held-out ICLR 2026 papers, the trained 8B ProReviewer achieves the highest average score among all compared methods, including those built on a 397B model; human evaluators prefer its reviews over all baselines on three of four review-quality dimensions.