ScholarPeer: A Context-Aware Multi-Agent Framework for Automated Peer Review

• URL: http://arxiv.org/abs/2601.22638v1
• Date: Fri, 30 Jan 2026 06:54:55 GMT
• Title: ScholarPeer: A Context-Aware Multi-Agent Framework for Automated Peer Review
• Authors: Palash Goyal, Mihir Parmar, Yiwen Song, Hamid Palangi, Tomas Pfister, Jinsung Yoon,
• Abstract summary: ScholarPeer is a search-enabled multi-agent framework designed to emulate the cognitive processes of a senior researcher.<n>We evaluate ScholarPeer on DeepReview-13K and the results demonstrate that ScholarPeer achieves significant win-rates against state-of-the-art approaches in side-by-side evaluations.
• Score: 48.60540055009675
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Automated peer review has evolved from simple text classification to structured feedback generation. However, current state-of-the-art systems still struggle with "surface-level" critiques: they excel at summarizing content but often fail to accurately assess novelty and significance or identify deep methodological flaws because they evaluate papers in a vacuum, lacking the external context a human expert possesses. In this paper, we introduce ScholarPeer, a search-enabled multi-agent framework designed to emulate the cognitive processes of a senior researcher. ScholarPeer employs a dual-stream process of context acquisition and active verification. It dynamically constructs a domain narrative using a historian agent, identifies missing comparisons via a baseline scout, and verifies claims through a multi-aspect Q&A engine, grounding the critique in live web-scale literature. We evaluate ScholarPeer on DeepReview-13K and the results demonstrate that ScholarPeer achieves significant win-rates against state-of-the-art approaches in side-by-side evaluations and reduces the gap to human-level diversity.

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