Quantile‑Adaptive Temperature Scaling for Confidence Calibration

Deep neural networks often produce poorly calibrated confi-dence estimates, overstating their certainty even when predictions are in-correct. Temperature Scaling (TS) remains the most widely used post-hoccalibration method due to its simplicity and e!ectiveness, yet its global,uniform rescaling of logits fails to correct the highly heterogeneous struc-ture of miscalibration observed across the confidence spectrum. In par-ticular, the largest correctness–confidence discrepancies arise in di!er-ent quantile regions depending on the setting, which standard TS leaveslargely unaddressed. We introduce Quantile-Adaptive Temperature Scal-ing (QaTS), a simple and e"cient post-hoc calibration method thatadapts the temperature as a function of a prediction’s empirical con-fidence quantile. By mapping confidences into the quantile space, QaTSnormalizes the calibration problem, makes the structure of miscalibrationexplicit, and enables a monotone temperature function that adapts acrossquantiles while leaving well-calibrated high-confidence predictions largelyunchanged. This quantile-aware formulation aligns naturally with a repa-rameterized Expected Calibration Error (ECE) objective and yields asample-wise temperature that is robust across a variety of challengingscenarios, such as class imbalance and distributional shifts. Across abroad range of datasets, architectures, evaluation scenarios and diversetasks, QaTS consistently, and substantially, outperforms state-of-the-artpost-hoc calibration methods, delivering more reliable and trustworthyconfidence estimates without modifying model predictions.