Rebuttals at ML conferences increasingly AI-written, researchers debate broken review calibration
mmitchell_ai · x · 2026-09-25
Researchers are calling out problems in ML peer review: one proposal would give an extra point to rebuttals that clearly took real effort, since authors can spend days addressing a single reviewer with nothing gained. The quoted discussion adds that a huge share of rebuttals in the position paper track appeared 100% AI-generated, that reviewers of the same paper are poorly calibrated, and that borderline scores cluster because hedging reviewers gravitate to the middle — meaning track-wide calibration would likely reject many papers.
Related event: Researchers Find Many Rebuttals Now Written by AI(3 posts)→
More from Research
- Dual Covariance Gaussian Splatting SLAM decouples rendering and registration — kwangmoo_yi · 2026-09-26
- ImageJevBench: image decision benchmark ranks top models, full eval costs $0.02 — airesearch12 · 2026-09-26
- Biopharma Bench: agents complete only 8 of 71 real biopharma tasks, GPT-6 Astra leads — AllThingsApx · 2026-09-26
- Matryoshka Attribution finds neural network circuits via gradient descent, tops interpretability benchmark by 2.9x — stanfordnlp · 2026-09-26
- UniReps workshop invites NeurIPS rejects, deadline Oct 4 for Paris event — Pseudomanifold · 2026-09-26
- Creative Destruction Lab opens 15-year tech startup panel dataset to researchers — avicgoldfarb · 2026-09-26