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On the Spatial Structure of Mixture-of-Experts in Transformers
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A common assumption is that MoE routers primarily leverage semantic features for expert selection. However, our study challenges this notion by demonstrating that positional token information also plays a crucial role in routing decisions. Through extensive empirical analysis, we provide evidence supporting this hypothesis, develop a phenomenological explanation of the observed behavior, and discuss practical implications for MoE-based architectures.
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Cited by 2 Pith papers
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Multi-level context Modeling for consistent expert selection in Mixture-of-Experts
MCF-MOE improves MoE routing by combining cross-layer attention and local top-k token similarity, reporting lower perplexity and higher downstream accuracy than several MoE baselines.
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Diagnosing Overhead in Dispatch Operations: Cross-architecture Observatory
Expert-parallel scaling leaves per-expert routing imbalance flat; mock-token benchmarks overestimate real-text imbalance and fake a batch-size trend; architectures split into data-resilient (MHA, Mamba-2) and persiste...
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