AT-LPC: Reliability-Tempered Log-Partition Calibration for Continual Local Learning

Continual local learning provides a modular alternative to end-to-end optimization by assigning learning objectives to individual components. However, the class-score channels produced by these components can lose a consistent comparison reference as learning progresses through a nonstationary stream. We identify this failure mode as temporal score incomparability: within-task discrimination can remain useful while scores learned at different stages become unreliable under global all-class comparison. We diagnose this phenomenon in score space and introduce Automatic Tempered Log-Partition Calibration (AT-LPC), a replay-based post-update calibration method that restores a shared score reference without retraining the classifier. AT-LPC evaluates every class-score channel on a common replay memory, estimates class-wise tempered log partitions, and selects a shared temperature using robust score scale and effective sample size (ESS). This reliability-aware tempering suppresses poorly supported score-tail effects while retaining stronger distributional correction when replay evidence is sufficient. In five-seed one-pass experiments, AT-LPC improves final accuracy from 15.64% to 26.90% on CIFAR-10 and from 4.57% to 8.48% on CIFAR-100, while remaining compatible with fixed-schedule updates, blurry arrivals, recent-prior adjustment, and alternative local score models. These results establish score comparability and replay reliability as complementary factors in the stability-plasticity behavior of continual local learning.