Sparse Subspace-to-Expert Sharing for Task-Agnostic Continual Learning

One of the great frustrations in deploying AI systems is that teaching a model something new often erases what it previously knew — a phenomenon called catastrophic forgetting. For AI to be genuinely useful over time, it must accumulate knowledge the way humans do. SETA addresses this by partitioning knowledge into specialized expert modules, ensuring new learning doesn't overwrite old foundations. This has enormous practical implications for enterprise AI systems that must continuously adapt to new domains, personalized assistants that evolve with users, and medical AI that must integrate new clinical knowledge without forgetting established diagnostic patterns. Authors: Fatema Siddika, Md Anwar Hossen, Tanwi Mallick, Ali Jannesari Paper: https://arxiv.org/abs/2606.07500v1