[D] ICML 2026 Average Score

A discussion on the r/MachineLearning subreddit is bringing the peer review process for the International Conference on Machine Learning (ICML) 2026 under scrutiny. The post, authored by Reddit user u/Hope999991, directly solicits information from reviewers about the average scores of submissions following the crucial rebuttal phase. The core question is whether the score distributions visible on third-party tracking websites, such as PaperCopilot's ICML 2026 statistics page, provide a genuine reflection of the post-rebuttal landscape or if they are skewed or incomplete.

The inquiry taps into a persistent tension within the fast-moving AI research community. As publication in top-tier conferences like ICML, NeurIPS, and ICLR becomes increasingly critical for career advancement and funding, the opacity of the review process is a frequent pain point. Researchers are left to speculate about acceptance chances, with tools like PaperCopilot emerging to fill the information void by aggregating self-reported scores. The post's popularity indicates a widespread desire for more formal transparency from conference organizers themselves, rather than relying on crowdsourced data.

This debate matters because the integrity of peer review directly impacts the quality and direction of published AI research. Inconsistent scoring or unclear post-rebuttal adjustments can lead to the rejection of novel work or the acceptance of flawed papers, ultimately slowing scientific progress. The community's engagement with this post suggests a push for standardized reporting or clearer communication from program committees on how rebuttals influence final decisions, moving beyond unofficial trackers.