To what extent does AnyExperts' dynamic expert allocation mitigate representational collapse and maintain per-

Abstract

Abstract: Multimodal Mixture-of-Experts (MoE) models offer a promising path toward scalable and efficient large vision-language systems. However, existing approaches rely on rigid routing strategies (typically activating a fixed number of experts per token) ignoring the inherent heterogeneity in semantic importance across modalities. This leads to suboptimal compute allocation, where redundant tokens consume as many resources as critical ones. To address this, we propose AnyExperts, a novel on-demand, budget-aware dynamic routing framework that allocates a variable total number of expert slots per token

Research Question

To what extent does AnyExperts' dynamic expert allocation mitigate representational collapse and maintain per-expert specialization across varying numbers of experts (8 to 64) on multimodal reasoning tasks from the ARO dataset?

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