arXiv cs.CV
· Papers
CP-MoE: Consistency-Preserving Mixture-of-Experts for Continual Learning
arXiv:2605.20247v2 Announce Type: replace-cross Abstract: Catastrophic forgetting remains a major obstacle to continual learning in large language models (LLMs) and vision--language models (VLMs). Although Mixture-of-Experts (MoE) architectures offer an efficient path to scaling, existing LoRA-based MoE continual learn