From Passive Generation to Investigation: A Proactive Scientific Peer Review Agent

• URL: http://arxiv.org/abs/2606.13349v1
• Date: Thu, 11 Jun 2026 13:38:23 GMT
• Title: From Passive Generation to Investigation: A Proactive Scientific Peer Review Agent
• Abstract summary: We argue that a key limitation is the lack of flexibility to proactively investigate suspicious parts of a paper.<n>We propose ProReviewer, a scientific peer review agent that proactively reviews a paper guided by a maintained, structured review log.<n> Experiments show that ProReviewer with an 8B backbone, trained by supervised fine-tuning and optimized by reinforcement learning, achieves the highest average score across five quality dimensions.
• Score: 52.42081442204093
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models (LLMs) have shown promise in automating scientific peer review. However, existing approaches often struggle to generate in-depth reviews supported by concrete evidence. We argue that a key limitation is the lack of flexibility to proactively investigate suspicious parts of a paper based on accumulated evidence, as human reviewers do. In this paper, we explore how to enable an LLM-based review agent to perform such proactive investigation. We find that this can be naturally formulated as a Markov Decision Process (MDP), and propose ProReviewer, a scientific peer review agent that proactively reviews a paper guided by a maintained, structured review log. The structured review log serves as a workspace for the agent to track evidence and intermediate findings collected during review. Experiments show that ProReviewer with an 8B backbone, trained by supervised fine-tuning and optimized by reinforcement learning, achieves the highest average score across five quality dimensions, outperforming prompt-based methods with much larger frontier LLMs by up to 39% and the strongest fine-tuned baseline by 16% relatively. It also attains the highest win rates against baselines in human evaluation.

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