Retrospective on PAT x ICML 2026 AI Paper Assistant Program

March 30 2026 Retrospective on PAT x ICML 2026 AI Paper Assistant Program Gautam Kamath ICML 2026 By Google Researchers Rajesh Jayaram and Vincent Cohen-Addad, and ICML 2026 Program Chairs Alekh Agarwal, Miroslav Dudik, Sharon Li, Martin Jaggi. Following the success of the STOC Experimental AI Feedback program , we launched a similar experiment using Google Research’s Paper Assistant Tool (PAT) before the ICML 2026 deadline. Our goal was to provide authors with actionable, substantive feedback prior to the review process, allowing them to improve their submissions before the deadline. The PAT feedback was totally separate from the peer review process, and was not visible to reviewers, area chairs, program chairs, or anyone outside of the authors. Now that the program has concluded and the feedback from our author survey is in, we want to share some of the results. The program ran from January 14th to January 26th , providing feedback for approximately 4,500 papers. Papers sent to the system received feedback within ~30 minutes on average, with some edge cases (primarily due to large PDFs causing failures in the AI models). While we initially set conservative eligibility criteria to ensure a smooth rollout, we were able  to quickly relax these requirements. Ultimately we offered a voucher to any author with an OpenReview account older than one month. We believe this afforded a maximal and equitable offering to the community, without allowing for misuse of the tool. We were quite pleased that we could make the program as accessible as possible. Highlights After concluding the program, we released a feedback survey to participating authors. We received 869 responses over the week it was open, and the results demonstrate a high level of satisfaction with the tool. In particular, the tool was able to frequently identify issues, and make suggestions which allowed the authors to make significant changes to their papers before the submission deadline. High Retention & Satisfaction: 92.1% of respondents stated they would use the tool again. Furthermore, 73.3% rated the feedback as “Very” or “Mostly” helpful. Only 1.6% found the tool to not be useful at all. Delivering Deep Feedback: The most encouraging results concern the depth of the feedback. The tool successfully identified suggestions beyond surface-level edits and typos: 35.4% of authors of papers containing theory reported the tool identified significant theory gaps that took more than an hour to fix. 31% of authors of papers with experimental results said the feedback prompted them to run new experiments. Clarity and Education: 87.3% of authors felt the tool improved the clarity and readability of their papers. Additionally, 84.5% saw clear educational value in the tool. Author Quotes on PAT: The PAT feedback was invaluable in improving my paper’s technical rigor before submission. It helped me identify and fix critical issues including: – Mathematical contradictions in objective functions – Inconsistencies in formal problem definitions – Unsupported claims in experimental sections – Missing theoretical justifications The structured, segment-by-segment analysis made it possible to address issues systematically under tight deadline pressure. — Apoorv Varshney, Department of Mathematics and Computing, Dr. B. R. Ambedkar National Institute of Technology Jalandhar Compared to most LLMs, PAT’s performance is already quite excellent. PAT discovered that one of my theorem proofs was not rigorous and provided some possible solutions. — Peihan Wu, Shanghai Normal University The system was very helpful and also rather “objective”. I look forward to seeing the “human” revisions and comparing them. — Paolo Stefano Giudici, Professor of Statistics, University of Pavia [PAT] gives a comprehensive review, allowing authors to address issues that could potentially be questioned by reviewers. — Enver Menadjiev, M.S.+Ph.D. student, Statistical AI Lab (SAILab) at Kim Jaechul Graduate School of AI at KAIST Outside feedback is so important when you’re about to submit. I [iterated] for two rounds with [competitor] before, which I paid for. PAT had deeper feedback. — Domenic Rosati, Dalhousie University How it worked The pipeline used for this experiment was scaffolding built on top of state-of-the-art Gemini-based models. To handle the complexity of technical papers, PAT segments the document into logical categorical sections, giving separate feedback for each section, with a high level summary at the beginning. Looking Ahead The PAT x ICML 2026 experiment shows that AI feedback can drive improvements in scientific work, enabling authors to fix theoretical gaps and conduct new experiments in advance of an expert-led peer review process. With the continued exponential growth in submissions to machine learning conferences,  AI driven feedback has the benefit both of improving the quality of authors’ papers, and therefore the likelihood of acceptance and reproducibility, as well as easing the increasing burden on human reviewers. The PAT pipeline is still an experimental program in development. We plan to incorporate learnings from the ICML collaboration as we prepare for the next iteration of the Paper Assistant Tool. Thank you to the thousands of authors who participated in the program, provided feedback, and helped shape the future of AI-assisted research. We look forward to our continued collaboration to make our conference even better!