Concern alignment is a new diagnostic framework that audits AI peer reviews at the level of individual concerns via match graphs to reveal gaps in detection, calibration, and prioritization beyond verdict agreement.
ReviewEval: An evaluation framework for AI-generated reviews
3 Pith papers cite this work. Polarity classification is still indexing.
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2026 3verdicts
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PRAIB reveals LLM reviews are less variable, positively biased, overconfident, longer, and overlook atomic weaknesses noted by humans compared to real reviewer feedback.
PRISM benchmark finds LLMs match or exceed humans on isolated review dimensions like novelty verification but none achieve the balanced performance of human reviewers across depth, flaw prioritization, and constructiveness.
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PRISM: A Multi-Dimensional Benchmark for Evaluating LLM Peer Reviewers
PRISM benchmark finds LLMs match or exceed humans on isolated review dimensions like novelty verification but none achieve the balanced performance of human reviewers across depth, flaw prioritization, and constructiveness.