AI peer review systems are vulnerable to prompt injections, prestige biases, assertion strength effects, and contextual poisoning, as demonstrated by a new attack taxonomy and causal experiments on real conference submissions.
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PeeriScope is an open modular framework that integrates structured features, LLM rubric assessments, and supervised prediction to evaluate peer review quality for self-assessment, editorial triage, and large-scale auditing.
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When AI reviews science: Can we trust the referee?
AI peer review systems are vulnerable to prompt injections, prestige biases, assertion strength effects, and contextual poisoning, as demonstrated by a new attack taxonomy and causal experiments on real conference submissions.
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PeeriScope: A Multi-Faceted Framework for Evaluating Peer Review Quality
PeeriScope is an open modular framework that integrates structured features, LLM rubric assessments, and supervised prediction to evaluate peer review quality for self-assessment, editorial triage, and large-scale auditing.