ProReviewer is an MDP-formulated proactive peer review agent trained with SFT and RL on an 8B model that outperforms larger frontier LLMs on review quality metrics.
DeepReviewer 2.0: A Traceable Agentic System for Auditable Scientific Peer Review
3 Pith papers cite this work. Polarity classification is still indexing.
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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.
fields
cs.CL 3years
2026 3verdicts
UNVERDICTED 3representative citing papers
EGTR-Review distills a multi-agent evidence-grounded review generator into an efficient student model that outperforms baselines on quality, grounding, and traceability while using fewer tokens.
Uncertainty-aware algorithms based on Bayesian decision theory improve generation utility on tutoring and reviewing tasks while risk-averse methods can degrade performance under high ambiguity, with conformal prediction providing guarantees.
citing papers explorer
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From Passive Generation to Investigation: A Proactive Scientific Peer Review Agent
ProReviewer is an MDP-formulated proactive peer review agent trained with SFT and RL on an 8B model that outperforms larger frontier LLMs on review quality metrics.
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EGTR-Review: Efficient Evidence-Grounded Scientific Peer Review Generation via Multi-Agent Teacher Distillation
EGTR-Review distills a multi-agent evidence-grounded review generator into an efficient student model that outperforms baselines on quality, grounding, and traceability while using fewer tokens.
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Uncertainty-Aware Generation and Decision-Making Under Ambiguity
Uncertainty-aware algorithms based on Bayesian decision theory improve generation utility on tutoring and reviewing tasks while risk-averse methods can degrade performance under high ambiguity, with conformal prediction providing guarantees.