Stop Automating Peer Review Without Rigorous Evaluation

This paper argues against using current AI systems for peer review, identifying two critical issues: a "hivemind effect" that reduces perspective diversity and the trivial gameability of AI review scores through paper rewriting. Empirical comparison of human- versus AI-generated reviews shows that AI reviewers are susceptible to stylistic changes rather than scientific merit, highlighting the need for non-gameability and review diversity for automation.