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

arXiv:2604.09590v1 Announce Type: new Abstract: Automated peer review is often framed as generating fluent critique, yet reviewers and area chairs need judgments they can audit: where a concern applies, what evidence supports it, and what concrete follow-up is required. DeepReviewer2.0 is a process-controlled agentic review system built around an output contract: it produces a extbf{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 ICLR2025 submissions under three fixed protocols, an un-finetuned 196B model running DeepReviewer2.0 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 DeepReviewer2.0 as an assistive tool rather than a decision proxy, and note remaining gaps such as ethics-sensitive checks.

Source: arXiv cs.AI | 2026-04-14