KAN-CL: Per-Knot Importance Regularization for Continual Learning with Kolmogorov-Arnold Networks

ArXi:2605.12306v1 Announce Type: cross Catastrophic forgetting remains the central obstacle in continual learning (CL): parameters shared across tasks interfere with one another, and existing regularization methods such as EWC and SI apply uniform penalties without awareness of which input region a parameter serves. We propose KAN-CL, a continual learning framework that exploits the compact- spline parameterization of Kolmogoro-Arnold Networks (KANs) to perform importance-weighted anchoring at per-knot granularity.