De-Chuan Zhan

CLIP's cross-modal gap makes text-derived classifier weights actively harm class-incremental learning—discarding the text branch entirely in favor of closed-form visual SVMs delivers superior continual recognition.

Multimodal continual learning does not require rigid parameter isolation or massive replay buffers—external reasoning retrieval can dynamically compose modular capability paths while subspace recycling safely prevents cross-task interference.