[D] On conferences and page limitations

A viral discussion on Reddit's r/MachineLearning, sparked by user kostaspap90, is challenging a quiet revolution in AI academic publishing. The core issue is the ballooning size of appendices in papers submitted to premier conferences like ICML and NeurIPS. While main submissions are tightly constrained to 8-10 pages, appendices are now routinely exceeding 15 pages and, in many cases, contain the bulk of a paper's experimental validation—testing on dozens of datasets, extensive ablation studies, and supplementary proofs. This effectively makes the appendix a mandatory, central component of the paper, undermining the very purpose of a page limit designed to encourage concise, self-contained contributions.

Researchers report being pressured by reviewers to add extensive experiments that can only fit in the appendix, creating a de facto standard where the core argument is split across two documents. This places a significant burden on reviewers and readers, who must now digest what amounts to a 25-page journal article to properly assess the work. The community is divided: some see this as a necessary adaptation for the increasing complexity of modern AI research, while others argue it erodes conference standards and that such lengthy works belong in journals. The debate signals a potential inflection point for how the field communicates and validates its rapid progress.