How does scaling top-k selection in SMoES affect inference throughput and MMMU accuracy trade-off under cross-

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

How does scaling top-k selection in SMoES affect inference throughput and MMMU accuracy trade-off under cross-modal adversarial attacks versus dense VLMs?

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