SMoES: Soft Modality-Guided Expert Specialization in MoE-VLMs

Abstract

Abstract: Mixture-of-Experts (MoE) has become a prevalent backbone for large vision-language models (VLMs), yet how modality-specific signals should guide expert routing remains under-explored. Existing routing strategies are either hand-crafted or modality-agnostic, relying on idealized priors that ignore the layer-dependent modality fusion patterns in MoE-VLMs and provide little guidance for expert specialization. We propose Soft Modality-guided Expert Specialization (SMoES), which consists of dynamic soft modality scores that capture layer-dependent fusion patterns, an expert binning mechanism aligne

Research Question

How does soft modality-guided routing in SMoES affect inference latency and throughput compared to dense VLMs and hard-routing MoE baselines when scaled to 7B+ parameter models on multimodal benchmarks like MMBench and SEED-Bench?

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