[2603.20450] Policies Permitting LLM Use for Polishing Peer Reviews Are Currently Not Enforceable

Computer Science > Computation and Language arXiv:2603.20450 (cs) [Submitted on 20 Mar 2026] Title:Policies Permitting LLM Use for Polishing Peer Reviews Are Currently Not Enforceable Authors:Rounak Saha, Gurusha Juneja, Dayita Chaudhuri, Naveeja Sajeevan, Nihar B Shah, Danish Pruthi View a PDF of the paper titled Policies Permitting LLM Use for Polishing Peer Reviews Are Currently Not Enforceable, by Rounak Saha and Gurusha Juneja and Dayita Chaudhuri and Naveeja Sajeevan and Nihar B Shah and Danish Pruthi View PDF HTML (experimental) Abstract:A number of scientific conferences and journals have recently enacted policies that prohibit LLM usage by peer reviewers, except for polishing, paraphrasing, and grammar correction of otherwise human-written reviews. But, are these policies enforceable? To answer this question, we assemble a dataset of peer reviews simulating multiple levels of human-AI collaboration, and evaluate five state-of-the-art detectors, including two commercial systems. Our analysis shows that all detectors misclassify a non-trivial fraction of LLM-polished reviews as AI-generated, thereby risking false accusations of academic misconduct. We further investigate whether peer-review-specific signals, including access to the paper manuscript and the constrained domain of scientific writing, can be leveraged to improve detection. While incorporating such signals yields measurable gains in some settings, we identify limitations in each approach and find that no...