ProReviewer is an MDP-formulated proactive peer review agent trained with SFT and RL on an 8B model that outperforms larger frontier LLMs on review quality metrics.
ScholarPeer: A Context-Aware Multi-Agent Framework for Automated Peer Review
4 Pith papers cite this work. Polarity classification is still indexing.
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abstract
The exponential growth of machine learning submissions has strained the traditional peer review process, resulting in slow feedback loops for authors and an immense burden on reviewers to rigorously audit technical soundness and verify literature. To address this, we introduce ScholarPeer, a multi-agent framework designed to operationalize the rigorous auditing workflow of a senior researcher. Rather than attempting to replace human judgment, ScholarPeer serves as a co-scientist: acting as a mentor for rapid author iteration prior to submission, and as an active verification assistant that augments human reviewers. The framework structurally decouples contextualization from critique by deploying a sub-domain historian to synthesize the field's trajectory, a baseline scout to proactively hunt for omitted state-of-the-art comparisons, and a multi-aspect Q&A engine that deeply audits technical soundness-scrutinizing internal logical consistency, experimental validity, and mathematical rigor-while cross-referencing claims against top-tier academic venues. We comprehensively evaluate ScholarPeer on ~1,800 ICLR submissions spanning 2020 through 2025. Our results show that ScholarPeer achieves significant win-rates against state-of-the-art fine-tuned models and search-augmented agentic baselines.
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ScientistOne introduces Chain-of-Evidence and an audit system that achieves zero hallucinated references, perfect score verification, and top method-code alignment while matching or beating human experts on five frontier tasks and generalizing to six more.
An empirical study on 20 architecture papers finds AI reviews capture a significant fraction of human-raised issues while also surfacing additional ones, using a released tool that clusters AI comments for comparison.
The paper delivers a stage-by-stage roadmap for AI in research, showing reliable assistance in retrieval and tool tasks but fragility in novelty and judgment, advocating human-governed collaboration.
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From Passive Generation to Investigation: A Proactive Scientific Peer Review Agent
ProReviewer is an MDP-formulated proactive peer review agent trained with SFT and RL on an 8B model that outperforms larger frontier LLMs on review quality metrics.
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ScientistOne introduces Chain-of-Evidence and an audit system that achieves zero hallucinated references, perfect score verification, and top method-code alignment while matching or beating human experts on five frontier tasks and generalizing to six more.
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Can AI Review Improve Paper Drafting? An Empirical Study on 20 Computer Architecture Submissions
An empirical study on 20 architecture papers finds AI reviews capture a significant fraction of human-raised issues while also surfacing additional ones, using a released tool that clusters AI comments for comparison.
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AI for Auto-Research: Roadmap & User Guide
The paper delivers a stage-by-stage roadmap for AI in research, showing reliable assistance in retrieval and tool tasks but fragility in novelty and judgment, advocating human-governed collaboration.