SVC selects singular-vector channels to raise continual-learning performance while preserving pretrained ability across four LLM families
The work defines each singular value's paired left and right singular vectors as a singular-vector channel, estimates each channel's adaptation benefit from current-task gradients and its forgetting cost from activation second moments on an unlabeled general-domain corpus, then selects trainable channels via knee-based cost screening, Pareto-front filtering, and Otsu thresholding; across four LLM families, eight TRACE tasks, and three task orders, SVC preserves pretrained general capabilities while achieving strong downstream continual-learning performance.