For sparse MoE vision-language models, how does the optimal number of active experts (k) change when evaluated

Abstract

Abstract: Sparse Mixture-of-Experts (MoE) architectures enable efficient scaling of large language models through conditional computation, yet the routing mechanisms responsible for expert selection remain poorly understood. In this work, we introduce routing signatures, a vector representation summarizing expert activation patterns across layers for a given prompt, and use them to study whether MoE routing exhibits task-conditioned structure. Using OLMoE-1B-7B-0125-Instruct as an empirical testbed, we show that prompts from the same task category induce highly similar routing signatures, while prompts

Research Question

For sparse MoE vision-language models, how does the optimal number of active experts (k) change when evaluated on visually complex inputs (e.g., high-resolution or cluttered scenes from COCO or VizWiz) versus simple ones, and what is the resulting accuracy gap compared to dense baselines?

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