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arXiv · 2604.23996

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

Zi-Hao Bo·Yaqian Li·Anzhou Hou·Rinyoichi Takezoe·Ertao Zhao·Tianxiang Pan·Jiale Yan·Mo Guang·Kaiwen Long
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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 aligned with expert-parallel deployment, and an inter-bin mutual information regularization that encourages coherent modality specialization. Our method leverages attention-based or Gaussian-statistics modality scores to optimize mutual information regularization. Experiments across four MoE-based VLMs and 16 benchmarks demonstrate improvement on both effectiveness and efficiency: 0.9% and 4.2% average gain on multimodal and language-only tasks, 56.1% reduction in EP communication overhead, and 12.3% throughput improvement under realistic deployment. These results validate that aligning routing with modality-aware expert specialization unlocks MoE-VLM capacity and efficiency.

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Zi-Hao Bo, Yaqian Li, Anzhou Hou, Rinyoichi Takezoe, Ertao Zhao, Tianxiang Pan, Jiale Yan, Mo Guang, Kaiwen Long. 2026-04-27. SMoES: Soft Modality-Guided Expert Specialization in MoE-VLMs. https://arxiv.org/abs/2604.23996

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