ScholarPeer introduces a multi-agent system with historian, scout, and Q&A agents that achieves higher win rates than prior models when evaluated on 1800 ICLR submissions for automated peer review tasks.
• Theoretical Bias Elimination:Theorem 2 proves that re-weighting based on Instrumental and Confounder factors yields an unbiased estimation of the IDRF loss
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ScholarPeer: A Context-Aware Multi-Agent Framework for Automated Peer Review
ScholarPeer introduces a multi-agent system with historian, scout, and Q&A agents that achieves higher win rates than prior models when evaluated on 1800 ICLR submissions for automated peer review tasks.