TIP-LoRA:Two-Sided Interference-Aware Projection for Exemplar-Free Class-Incremental Learning

Continual learning (CL) requires a model to acquire new knowledge without forgetting old knowledge, yet low-rank adaptation (LoRA) applied to pre-trained models remains vulnerable to interference from sequential updates. Existing LoRA-based CL methods constrain an update from the input side; with a finite-rank subspace, however, residual output changes can remain, and their interference also depends on the loss response. In this paper, we propose Two-Sided Interference-Aware Projection LoRA (TIP-LoRA), a two-branch framework that controls this interference through paired input- and output-side projections. A local Taylor analysis shows that first-order interference is jointly determined by the layer input and the backpropagated loss response. This analysis suggests controlling both the input- and output-side directions of a new update. Since old-task signals are unavailable, the stability branch estimates these directions from cumulative task updates, and restricts new updates to their two-sided orthogonal complement. In parallel, the plasticity branch learns within the leading singular subspaces of the frozen pre-trained weights to retain transferable structure for new knowledge. We further provide a local bound on the old-task loss change under the projected update. Across seven settings on five benchmarks, TIP-LoRA achieves the highest final accuracy in all seven settings and competitive average incremental accuracy. Controlled diagnostics further show that two-sided projection removes residual interference left by input-only control and that cumulative updates retain the paired directions used by the stability branch.