How does the Dynamic Clue Bottleneck architecture affect inference throughput (tokens/sec) and FLOPs efficienc

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 the Dynamic Clue Bottleneck architecture affect inference throughput (tokens/sec) and FLOPs efficiency relative to Top-2 modality-agnostic MoE-VLMs on VCR adversarial splits under matched expert counts?

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