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arXiv · 2604.09590

DeepReviewer 2.0: A Traceable Agentic System for Auditable Scientific Peer Review

Yixuan Weng·Minjun Zhu·Qiujie Xie·Zhiyuan Ning·Shichen Li·Panzhong Lu·Zhen Lin·Enhao Gu·Qiyao Sun·Yue Zhang
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Abstract

Automated peer review is often framed as generating fluent critique, yet reviewers and area chairs need judgments they can \emph{audit}: where a concern applies, what evidence supports it, and what concrete follow-up is required. DeepReviewer~2.0 is a process-controlled agentic review system built around an output contract: it produces a \textbf{traceable review package} with anchored annotations, localized evidence, and executable follow-up actions, and it exports only after meeting minimum traceability and coverage budgets. Concretely, it first builds a manuscript-only claim--evidence--risk ledger and verification agenda, then performs agenda-driven retrieval and writes anchored critiques under an export gate. On 134 ICLR~2025 submissions under three fixed protocols, an \emph{un-finetuned 196B} model running DeepReviewer~2.0 outperforms Gemini-3.1-Pro-preview, improving strict major-issue coverage (37.26\% vs.\ 23.57\%) and winning 71.63\% of micro-averaged blind comparisons against a human review committee, while ranking first among automatic systems in our pool. We position DeepReviewer~2.0 as an assistive tool rather than a decision proxy, and note remaining gaps such as ethics-sensitive checks.

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Yixuan Weng, Minjun Zhu, Qiujie Xie, Zhiyuan Ning, Shichen Li, Panzhong Lu, Zhen Lin, Enhao Gu, Qiyao Sun, Yue Zhang. 2026-03-03. DeepReviewer 2.0: A Traceable Agentic System for Auditable Scientific Peer Review. https://arxiv.org/abs/2604.09590

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