On the Spatial Structure of Mixture-of-Experts in Transformers

Abstract

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.

Accepted to ICLR 2025 Workshop on Sparsity in LLMs (SLLM)