When AI Reviews Train AI Reviewers: Scientific-Judgment Collapse and Mitigation
How recursive model-generated evaluations threaten the integrity and evolution of scientific discourse.
Large language models (LLMs) increasingly participate in scientific evaluation, both as automated reviewers and as assistants to human reviewers. As model-generated reviews enter public data and future training corpora, AI peer review can become recursive: later reviewers learn f...
ArXiv CS.LG
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The reliance on AI for peer review risks creating a feedback loop where models reinforce existing biases and stagnate scientific innovation. Mitigating this collapse requires rigorous human oversight and transparent data curation to ensure future models learn from genuine expertise rather than synthesized artifacts.
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