TMLR editor tested whether authors understood their own papers. All 10 flagged submissions were rejected

Transactions on Machine Learning Research, or TMLR, has published a firsthand account of a small editorial experiment that speaks to a broader problem facing academic publishing in the AI era. In a Sept. 16 blog post, co-editor-in-chief Nihar B. Shah of Carnegie Mellon University said that during his Aug. 14-28, 2026 rotation, he pulled 10 submissions that were already headed for desk rejection and invited the authors to discuss their papers before any external review. The result was stark. One paper was withdrawn, one author declined the call, one no-showed, and Shah ultimately spoke with authors of seven papers. Only one author answered all questions. Three could explain the high-level idea but struggled on technical details. Three others could not answer even basic questions about the problem setup, notation, or where the abstract’s claims were supported in the paper. Those three were all single-author submissions. Even the one strongest case was rejected after Shah found a major error in a central conclusion, though resubmission was allowed after correction. The post also outlines how TMLR is responding to a surge in submissions: author quotas, AI-generated reliability reviews, and a stronger requirement that papers clearly communicate their findings. Shah argues the issue is not whether AI was used, but whether named authors can stand behind the work.