arxivcs.LG2026-07-15
Temperature Scaling Is Not Enough: Calibration Gaps Under Human Label Distributions
Temperature scaling is the dominant post-hoc calibration method in modern deep learning. Its theoretical justification rests on an assumption that is rarely stated explicitly: that ground-truth labels are one-hot and deterministic. In practice, labels are frequently soft, crowd-s…