ML Reviewers Push Back on Rebuttal Experiments

This is less about being soft on rigor than avoiding review theater: once rebuttal becomes a gotcha hunt, it rewards reviewer imagination more than scientific judgment. Major venue policies already draw the line at clarifications and small experiments, not substantial new revisions. The extra-what-if habit hits hardest when rebuttal time is short and compute is limited, because rushed results can muddy an otherwise clean story and favor better-resourced labs. Reviewers should distinguish rating-changing evidence from curiosity questions and say which bucket each request belongs in; the fair counterpoint is that some edge-case checks do uncover real limits, so the right norm is calibration, not zero extra experiments.