CP-MoE: Consistency-Preserving Mixture-of-Experts for Continual Learning

In three linesCP-MoE introduces a continual learning framework for LLMs and VLMs using Mixture-of-Experts architecture. A transient expert captures early task-specific updates and guides their integration into stable experts via consistency-preserving routing bias and regularization. Validated on SuperNI and VQA v2, CP-MoE reduces catastrophic forgetting while preserving cross-task knowledge transfer.

Summary generated by Claude — human-verified