Use and Effects of LLMs in Peer Review: A Randomized Experiment and Survey at ICML 2026

Sunnie S. Y. Kim; Wesley Hanwen Deng; Jennifer Wortman Vaughan; Buxin Su; Weijie Su; Alekh Agarwal; Sharon Li; Martin Jaggi; Daniel G. Goldstein; Nihar B. Shah; Miroslav Dudík

arXiv · 2609.19420

Use and Effects of LLMs in Peer Review: A Randomized Experiment and Survey at ICML 2026

Sunnie S. Y. Kim·Wesley Hanwen Deng·Jennifer Wortman Vaughan·Buxin Su·Weijie Su·Alekh Agarwal·Sharon Li·Martin Jaggi·Daniel G. Goldstein·Nihar B. Shah·Miroslav Dudík

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Abstract

LLMs are rapidly reshaping peer review, making it important to understand how reviewers use them in practice and how different LLM-use policies affect review outcomes. We investigate these questions through a randomized experiment and an anonymous post-survey at ICML 2026, a major machine learning conference involving over 24,000 papers and 17,000 reviewers. Reviewers were assigned to either a conservative policy prohibiting all LLM use or a permissive policy allowing limited assistance, with randomization among a subset of main-track papers and reviewers. Policy assignment had near-zero effects on final paper decisions, paper scores, and reviewer confidence, although reviews under the permissive policy were 5.5-7% longer. Post-survey responses (N=1,486) revealed diverse attitudes toward LLMs and substantial noncompliance: 22.5% of conservative-policy reviewers reported using an LLM despite the prohibition, and 36.5% of permissive-policy reviewers reported at least one explicitly disallowed use. We discuss implications for future peer-review policy and tool design.

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Sunnie S. Y. Kim, Wesley Hanwen Deng, Jennifer Wortman Vaughan, Buxin Su, Weijie Su, Alekh Agarwal, Sharon Li, Martin Jaggi, Daniel G. Goldstein, Nihar B. Shah, Miroslav Dudík. 2026-09-16. Use and Effects of LLMs in Peer Review: A Randomized Experiment and Survey at ICML 2026. https://arxiv.org/abs/2609.19420

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