ProReviewer: Evidence-Driven Investigation for Scientific Peer Review

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 re...

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