ECR-LoRA: Evolving Core LoRA and Complementary Residual LoRAs for Class-Incremental Learning

Rehearsal-free parameter-efficient continual learning (PECL) enables frozen pretrained Vision Transformers (ViTs) to adapt to sequentially arriving tasks using lightweight adaptation modules, such as LoRA, without historical exemplars. Existing LoRA-based PECL methods face a fundamental trade-off between knowledge sharing and task specialization: sharing or merging adapters promotes reuse but blurs task-specific representations, whereas isolating updates mitigates forgetting at the cost of reusable knowledge transfer. We propose ECR-LoRA, a split-depth LoRA framework that organizes continual adaptation into an evolving Core LoRA and complementary task-specific Residual LoRAs. Early blocks maintain the fixed-rank Core, which evolves across tasks to accumulate transferable knowledge, while later blocks preserve Residual LoRAs for evidence not sufficiently captured by the Core. Core evolution is controlled by subspace-compatible learning and Fisher-guided consolidation, while core-error reweighting and residual-mix consistency encourage complementary residual learning and robustness to cross-residual variation. During inference, uncertainty from the consolidated core provides an entropy-guided prior for calibrating residual experts without task identities, additional training, or exemplar replay. Experiments on four class-incremental learning benchmarks show that ECR-LoRA achieves the best final accuracy on all four and the best average accuracy on three, with a favorable stability–plasticity trade-off. Code is available in the supplementary material.