RIDER: SUPPORT-AWARE ANALYTICAL EVIDENCE CONSTRUCTION FOR REHEARSAL-FREE CLASS- INCREMENTAL LEARNING

Recently, rehearsal-free class-incremental learning (CIL) based on pre-trained models has attracted increasing attention, and low-rank adaptation further enables parameter-efficient continual learning. However, when task identity is unavailable at test time and class scores from different task branches participate in a unified decision, the model must resolve both competition across tasks and ambiguity among classes within each task. To better understand this issue, we diagnose the final prediction errors by decomposing them into cross-task errors and within-task classification errors, and find that cross-task errors dominate the final errors in three of the four evaluated settings. Motivated by this finding, we propose RIDER with a Support-Aware Analytical Evidence Construction mechanism that addresses the problem from the perspective of evidence construction.Specifically, RIDER combines calibrated prototype evidence, an all-class analytical global reference, and support-valid branch-native evidence to construct global predictions, without completing unsupported branch–class statistics. In addition,we constrain shared-update drift through importance-weighted regularization, decouple task-specific directions from their magnitudes, and progressively reduce the ranks of newly introduced directions to limit incremental parameter growth. Extensive experiments across multiple CIL benchmarks demonstrate the effectiveness of RIDER. In particular, both final accuracy and average incremental accuracy improve in all 55 strictly paired runs.