ScholarPeer: A Context-Aware Multi-Agent Framework for Automated Peer Review
This addresses the challenge of improving automated peer review for researchers by reducing the gap to human-level diversity, though it appears incremental as it builds on existing structured feedback generation.
The paper tackles the problem of automated peer review systems struggling with deep methodological flaws by introducing ScholarPeer, a context-aware multi-agent framework that dynamically constructs domain narratives and verifies claims using live literature, achieving significant win-rates against state-of-the-art approaches on DeepReview-13K.
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.