AgentReview is the first LLM-based simulation framework for peer review that quantifies a 37.1% decision variation attributable to reviewer biases.
Can large language models provide useful feedback on research papers? a large-scale empirical analysis
6 Pith papers cite this work. Polarity classification is still indexing.
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FARS deployed at scale produced 166 AI/ML papers across 67 topics that received 282 structured human reviews indicating some review-worthy outputs alongside recurring failure modes.
FactReview extracts claims from ML papers, positions them via literature retrieval, and verifies them through code execution, labeling each as Supported, Partially supported, or In conflict, as shown in a CompGCN case study.
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.
AI peer reviewers for POMP analyses show jagged performance: strong on technical error detection and invalid inference but weak on interpretive errors, narrative coherence, and domain-informed critique.
A rejoinder organizing responses to discussants into four core themes on statistical modeling, equity, signals, and AI in peer review.
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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FARS: A Fully Automated Research System Deployed at Scale
FARS deployed at scale produced 166 AI/ML papers across 67 topics that received 282 structured human reviews indicating some review-worthy outputs alongside recurring failure modes.
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FactReview: Evidence-Grounded Reviews with Literature Positioning and Execution-Based Claim Verification
FactReview extracts claims from ML papers, positions them via literature retrieval, and verifies them through code execution, labeling each as Supported, Partially supported, or In conflict, as shown in a CompGCN case study.
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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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Jagged AI in Scientific Peer Review: Evidence from POMP Data Analysis
AI peer reviewers for POMP analyses show jagged performance: strong on technical error detection and invalid inference but weak on interpretive errors, narrative coherence, and domain-informed critique.
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Rejoinder: The ICML 2023 Ranking Experiment: Examining Author Self-Assessment in ML/AI Peer Review
A rejoinder organizing responses to discussants into four core themes on statistical modeling, equity, signals, and AI in peer review.