AgentReview is the first LLM-based simulation framework for peer review that quantifies a 37.1% decision variation attributable to reviewer biases.
Inconsistency in conference peer review: revisiting the 2014 neurips experiment
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ReviewGuard aligns LLM peer reviews with future citations via impact-aligned RL, achieving Spearman ρ=0.776 on rejected-then-published AI/ML papers versus 0.492 for human reviewers and flagging 5.6× more high-impact cases.
A 120-respondent survey maps ESE community perceptions of review load, quality problems, LLM use in reviewing, and proposed system improvements.
Presents PAT, an agentic AI review tool using inference scaling that claims 34% better math error recall on SPOT benchmark and successful pilots at STOC and ICML conferences.
A game-theoretic model demonstrates a Nash equilibrium in which authors voluntarily accept random pre-review rejection to reduce reviewer burden and raise evaluation quality.
citing papers explorer
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AgentReview: Exploring Peer Review Dynamics with LLM Agents
AgentReview is the first LLM-based simulation framework for peer review that quantifies a 37.1% decision variation attributable to reviewer biases.
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ReviewGuard: Aligning LLM-Assisted Peer Review with Long-Term Scientific Impact
ReviewGuard aligns LLM peer reviews with future citations via impact-aligned RL, achieving Spearman ρ=0.776 on rejected-then-published AI/ML papers versus 0.492 for human reviewers and flagging 5.6× more high-impact cases.
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The State of Peer Review in Empirical Software Engineering: A Community Survey on Review Load, Quality, and GenAI Use
A 120-respondent survey maps ESE community perceptions of review load, quality problems, LLM use in reviewing, and proposed system improvements.
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Towards Automating Scientific Review with Google's Paper Assistant Tool
Presents PAT, an agentic AI review tool using inference scaling that claims 34% better math error recall on SPOT benchmark and successful pilots at STOC and ICML conferences.
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Can We Volunteer Out of the Peer Review Crisis?
A game-theoretic model demonstrates a Nash equilibrium in which authors voluntarily accept random pre-review rejection to reduce reviewer burden and raise evaluation quality.