AnyExperts: On-Demand Expert Allocation for Multimodal Language Models with Mixt

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

Does AnyExperts' dynamic routing strategy exhibit robustness to distribution shift across different multimodal benchmarks (e.g., from GQA to NLVR2) in terms of accuracy consistency and expert utilization variance?

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Provenance