[ICML 2026] Extending the deadline for reviewer final justifications while not extending for Author-AC comments was a huge mistake [D]
A controversial policy gave reviewers extra time for justifications but barred authors from responding, potentially tanking papers.
A significant procedural controversy has erupted around the International Conference on Machine Learning (ICML) 2026's review timeline. The conference committee made the decision to extend the deadline for reviewers to submit their final justifications for scores, while simultaneously not granting a corresponding extension for authors to communicate with their assigned Area Chairs (ACs). This asymmetric policy has created a critical vulnerability in the peer-review process, as highlighted by a viral post from an affected author.
The core issue is that this timeline mismatch allows reviewers to introduce entirely new criticisms in their final justification—criticisms that were never mentioned during the initial review or the author rebuttal phase. In the cited case, a reviewer questioned the paper's experimental setup and comparison fairness for the first time in this final statement, seemingly to rationalize maintaining a 'weak accept' score. With no opportunity for authors to counter these last-minute arguments via their AC, the review process becomes unfairly skewed, potentially allowing a single negative review to unjustly reject a paper that otherwise received positive feedback. This flaw undermines the rebuttal system's purpose and has sparked a broader debate about fairness and transparency in top-tier AI conference reviewing.
- ICML 2026 extended the reviewer justification deadline but not the author-AC communication window, creating a procedural imbalance.
- An author reports a reviewer introduced new critiques on experimental reliability and fairness only in their final justification.
- The policy allows reviewers to solidify negative scores post-rebuttal without author recourse, risking unfair paper rejections.
Why It Matters
This highlights systemic flaws in AI peer review, where procedural decisions can disproportionately impact research careers and publication fairness.