ICML Experimental Program using Google’s Paper Assistant Tool (PAT)

January 14 2026 ICML Experimental Program using Google’s Paper Assistant Tool (PAT) 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. ICML is excited to announce that we will be providing an experimental tool, in partnership with Google, called the Paper Assistant Tool (PAT) , to support authors in improving their submissions to ICML 2026. This program offers authors a unique opportunity to receive automated, private, and actionable feedback on their manuscripts before the final deadline. In a pilot at the Annual ACM Symposium on Theory of Computing ( see blog post) , 94% of participants found the pre-submission feedback generated by an AI assistant to be helpful, and 85% reported that the feedback resulted in improved clarity of their paper. The feedback the authors will receive from PAT through the ICML experiment will not be used in the review process. Reviewers, area chairs, and program committee members will not have access to the PAT feedback. What is PAT? PAT is a specialized Gemini-based tool powered by Google’s Gemini models, utilizing a reasoning-focused pipeline. It is similar to those that have achieved high-level performance in mathematical competitions. While the program at STOC focused heavily on theoretical correctness, the ICML experiment  has been specifically tuned to address the needs of the machine learning community. The model is designed to help authors identify issues that human reviewers might flag, including (but not limited to) experimental and methodological rigor, narrative clarity in English, and technical correctness. The goal is to help authors improve the quality of their papers, not to replace human peer review. By fixing clarity issues and potential technical gaps before the work is submitted to the conference, we hope to give authors actionable feedback before their paper enters the review process. Logistics and Eligibility The program is entirely optional and operates completely outside the official review process. The program will run for a limited window between January 14 and January 22, 2026.  This deadline may be extended subject to the determination of the program organizers. Eligible submissions uploaded within the window will typically receive the feedback within 24 hours, but there might be additional delays if the request volume is too high. To manage resources fairly, each eligible author is granted one virtual “voucher” to have a single paper run through the AI feedback system. We define an eligible author as someone who has authored at least one published paper at one of the following venues hosted on OpenReview: ICLR, NeurIPS, ICML, CVPR, or AISTATS. To redeem this voucher, authors will select a button “Ready for LLM Feedback” on their paper submission form to flag the manuscript for AI review. This feature becomes active only after a PDF has been successfully uploaded to the OpenReview server. The author who checks the button redeems their voucher for the paper, and the version of the document at the time of the edit is then sent to the pipeline for automated feedback. If an ineligible author or an author who has already used their voucher attempts to select the “Ready for LLM Feedback” button, an error message will appear and the paper will not be sent out for review. The technical staff will be able to provide limited, best-effort support on the program, such as answering questions or checking for failed paper delivery. Please direct such questions, as well as any feedback you may have on the program, to icml-paperassistant@google.com . Privacy and Data Safety We recognize the sensitivity of unpublished research. Trust is the cornerstone of this experiment, and we have implemented strict protocols to ensure author safety: Strict Separation from Peer Review: The AI Feedback is entirely independent of the official ICML review process. It is visible only to the authors. Reviewers, Area Chairs, and Program Chairs will have no access to this feedback. Stateless Inference (No Training): Submissions will not be used to train, fine-tune, or improve Google’s models. The model operates in a stateless “inference-only” mode; it processes the text to generate feedback and retains no memory of the specific content for future learning. Data Destruction: To minimize data exposure, Google will employ a strict deletion policy. All PDFs and feedback submitted to Google are stored in a restricted access environment and are scheduled for permanent deletion shortly after the feedback is delivered and the program is completed. Restricted Access: Google access to the data (submission PDFs and generated feedback) during the retention window is strictly limited to the primary program organizers and a minimal number of essential technical staff (listed at the bottom of this post) required for system maintenance. Google organizers and staff will only inspect the data in the event of technical difficulties with a submission (and if necessary to resolve those difficulties). Transparency: The AI feedback will be clearly flagged with a disclaimer stating it is AI-generated. It is framed strictly as “feedback” rather than a “review” to manage author expectations. It will be posted as a comment on the submission page that is only visible to authors. Caveats and Disclaimers Like all LLMs,  the models used by the PAT pipeline are not infallible. Authors should treat the generated feedback with the same critical eye they would apply to a human review. The model may occasionally flag correct statements as errors or miss actual flaws. It is the author’s responsibility to verify the validity of the feedback. Note that the model may make suggestions for the paper that you disagree with. This is not necessarily a bug, and may even be expected. Think of these suggestions as potential points of criticism from a reviewer. By considering why you disagree with the suggestion, you may be able to add justification to the paper which preempts these reviewers’ comments, thereby increasing the strength of the paper. Outcomes Our primary objective is to empower authors to elevate the quality of their submissions by acting as a high-precision filter. This tool is designed to help authors catch nuanced errors—in proofs, experimental setups, or reasoning—that human reviewers might miss or lack the time to detail. To achieve this, the program utilizes models that are more fit for purpose than standard generalist LLMs. We are leveraging a reasoning-focused pipeline related to the Gemini models that achieved Gold-medal performance at the International Mathematical Olympiad (IMO). By designing a pipeline specialized for ML conference structures, we can offer distinct advantages over generic tools. In the post-mortem for the STOC experiment, authors consistently confirmed that these confidential models provided quality meaningfully superior to public alternatives. We believe this tool represents a unique opportunity to pioneer the responsible use of AI to elevate the standard of our own scientific submissions. We look forward to seeing the results of this experiment, which will be shared with the broader community via the ICML blog once the experiment is over. Google Program Organizers: Rajesh Jayaram Vincent Cohen-Addad Google Technical Staff: Rajesh Jayaram Vincent Cohen-Addad Drew Tyler Jieming Mao Jon Schneider Contact (for feedback and best-effort assistance): icml-paperassistant@google.com