CTSC: Cross-Task Score Calibration for Class-Incremental Learning

Class-incremental learning asks a model to acquire new classes over a sequence of tasks while recognizing all seen classes without task identity at test time. This global prediction setting depends on comparable scores across task blocks, yet sequential training can leave those blocks on incompatible scales. We study this cross-task score problem in hybrid ETF/classifier models and propose CTSC, which combines progressive equiangular tight frame (ETF) anchors and feature preservation with classifier standardization before score fusion and additive taskbias fitting afterward. Pre-fusion standardization controls the classifier contribution relative to ETF evidence, whereas post-fusion task offsets adjust cross-task competition while preserving within-task rankings. Across Seq-CIFAR-10, SeqCIFAR-100, and Seq-TinyImageNet with replay budgets of 200 and 500, CTSC improves Class-IL recognition and reduces forgetting under matched local evaluation. Fixed-checkpoint interventions show that task offsets recover cross-task errors without changing within-task predictions, and that pre-fusion standardization and post-fusion correction are not interchangeable. Calibration-pool and taskprior interventions characterize when replay-fitted offsets transfer to the test distribution.