MoCE: Mixture of Communitified Experts for Continual Vision-Language Learning under Task-Complexity Flux

Class-incremental learning (CIL) with vision-language models is often formulated as a balanced sequence of class increments, but deployed systems rarely receive such regular updates. Some increments contain only a few closely related classes, while others introduce many visually diverse or semantically distant classes. We refer to this variation as uneven class-incremental complexity. It exposes a weakness of standard sparse Mixture-of-Experts (MoE) adapters: a fixed top-$k$ router activates the same number of isolated experts for every input and every increment, even when the amount of expert knowledge needed by the current update changes. We propose MoCE, a Mixture of Communitified Experts for CLIP-based class-incremental learning. MoCE decouples the number of executed experts from the number of involved experts. It represents adapters as nodes in an input-conditioned learnable graph, lets each node exchange messages with its neighbors, and then selects sparse community anchors. A selected anchor is therefore not a single isolated expert; it is the representative of a graph community whose state has already absorbed information from related experts. As a result, MoCE can execute a small anchor set for efficiency while involving a variable number of neighboring experts in routing, and it can optionally increase the anchor budget for increments with more classes. This gives the router a flexible capacity response to uneven task complexity without dense expert execution. Experiments under even and uneven CIL schedules show that MoCE improves over standard MoE adapters, with especially consistent gains when the number of arriving classes varies across increments.