ScholarPeer: A Context-Aware Multi-Agent Framework for Automated Peer Review
Researchers have introduced ScholarPeer, a novel multi-agent framework designed to alleviate the strain on the traditional peer review process caused by the exponential growth of machine learning submissions. Rather than replacing human judgment, ScholarPeer acts as a co-scientist, serving as a mentor for authors before submission and an verification assistant for reviewers. The framework structurally decouples contextualization from critique by employing specialized agents: a sub-domain historian to synthesize field trajectories, a baseline scout to identify omitted state-of-the-art comparisons, and a multi-aspect Q&A engine to audit technical soundness, logical consistency, and mathematical rigor. The system was comprehensively evaluated on approximately 1,800 ICLR submissions from 2020 to 2025. Results indicate that ScholarPeer achieves significant win rates against state-of-the-art fine-tuned models and search-augmented agentic baselines. This development aims to accelerate feedback loops for authors and reduce the auditing burden on reviewers by operationalizing the rigorous workflow of senior researchers through automated, context-aware analysis.
Wire timeline
ScholarPeer: A Context-Aware Multi-Agent Framework for Automated Peer Review
Researchers have introduced ScholarPeer, a novel multi-agent framework designed to alleviate the strain on the traditional peer review process caused by the exponential growth of machine learning submissions. Rather than replacing human judgment, ScholarPeer acts as a co-scientist, serving as a mentor for authors before submission and an verification assistant for reviewers. The framework structurally decouples contextualization from critique by employing specialized agents: a sub-domain historian to synthesize field trajectories, a baseline scout to identify omitted state-of-the-art comparisons, and a multi-aspect Q&A engine to audit technical soundness, logical consistency, and mathematical rigor. The system was comprehensively evaluated on approximately 1,800 ICLR submissions from 2020 to 2025. Results indicate that ScholarPeer achieves significant win rates against state-of-the-art fine-tuned models and search-augmented agentic baselines. This development aims to accelerate feedback loops for authors and reduce the auditing burden on reviewers by operationalizing the rigorous workflow of senior researchers through automated, context-aware analysis.
cs.AI updates on arXiv.org