校准AI置信度时，别让它偷偷改答案
给AI的答案校准置信度，本意是让“它有多确定”更可信。但研究者发现一个隐蔽副作用：校准过程可能悄悄把原本正确的答案改成错的——准确率数字看不出这种偷换，因为它只统计最终对错，不统计答案被改了多少次。这篇提出一个后置修补器CORD:它不动校准器本身，只把校准后的概率向量修一下，保证最大概率对应的答案和校准前完全一致，同时保留校准带来的置信度改善。在CIFAR和ImageNet上，它做到了零答案改动，且校准质量还更好。它不是你明天能用上的工具，但它提醒你：当AI系统说“我改主意了”，先分清是模型真改主意，还是校准流程在替你改主意。
📄 原文摘要(英文)
Post-hoc calibration corrects reported confidence, yet a multiclass calibrator can also change the associated top-1 prediction. Accuracy captures only the net effect of these changes on correctness, not how often predictions change; the Top-1 Prediction Change Rate (TPCR) instead measures this frequency. We propose Calibrator-Output Repair for Top-1 Decision Preservation (CORD), the first post-fit adapter to impose exact prediction preservation by repairing the full calibrated probability vector. From the original and calibrated outputs alone, CORD determines the mass assigned to the original top-1. The calibrated conditional distribution allocates the remaining mass over the other classes, yielding a repaired vector whose own argmax recovers the original prediction. On the calibration split, CORD coordinates the repaired masses to retain the calibrated outputs' mean mass on original predictions whenever attainable. The adapter alters neither the fitted calibrator nor its direct output, fits no additional supervised map, and requires no user- or validation-tuned hyperparameter. Across CIFAR-10/100 and ImageNet-1K, CORD attains zero TPCR by construction and lowers mean ECE, NLL, and Brier relative to the corresponding direct outputs in every dataset; paired gains persist under distribution shift and across calibration-set sizes. CORD thus removes the preservation constraint from calibrator fitting and assigns exact recovery of the original decision to subsequent output repair. Our code is available at https://github.com/labhai/CORD.