用多智能体模拟资深研究者，自动审查论文技术严谨性与文献完整性。
ScholarPeer: A Context-Aware Multi-Agent Framework for Automated Peer Review
- 分设历史学家、基准探测器和问答引擎，分离背景理解与批判性评估。
- 在1800篇ICLR论文上测试，表现优于现有先进模型与基线代理。
- 适合希望快速优化论文的作者及需辅助审稿的人类评审员。
机器学习投稿量激增使传统同行评审流程不堪重负，导致作者反馈延迟，评审人面临巨大压力。为此，我们提出ScholarPeer，一个情境感知的多智能体框架，旨在模拟资深研究者的严谨审计流程。该框架不替代人类判断，而是作为协作科研伙伴：在投稿前为作者提供快速迭代建议，或作为主动验证助手增强人类评审。系统通过三个模块实现结构解耦：子领域历史学家整合领域演进脉络，基准探测器主动查找被遗漏的前沿方法对比，多维度问答引擎深入审查技术合理性——包括内部逻辑一致性、实验有效性与数学严谨性，并交叉验证论点与顶级学术期刊内容。我们在2020至2025年间约1800篇ICLR投稿上全面评估了ScholarPeer，结果表明其在对抗现有先进微调模型与检索增强型智能体基线时取得显著胜率。
原文摘要 · Abstract (English)
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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