RouterInterp: Understanding Superposed Specialisation in MoE Routing

Ilya Lasy, Nora Yinuo Cai, Kola Ayonrinde

Keywords: interpretability, mixture of experts, superposition, mechanistic interpretability, sparse autoencoders

TL;DR: We introduce a method for producing natural language explanations for expert routing in MoE models.

Abstract: Sparse Mixture of Experts (MoE) models scale more efficiently than dense models by routing tokens to modular expert networks that are only active when relevant to the task. A leading hypothesis for the performance of MoE models is that each expert specialises in a single, coherent domain. However, interpretability efforts that assume this hypothesis have generally been unsuccessful. We propose and present evidence for an alternative account that we call the *Superposed Specialisation Hypothesis* (SSH): experts specialise in a disjoint union of fine-grained features rather than one broad domain. Leveraging the SSH, we introduce *RouterInterp*, a method for interpreting expert routing that identifies Sparse Autoencoder features most predictive of routing decisions and produces unified natural language explanations. On gpt-oss-20b, explanations from RouterInterp predict expert routing with 77% higher accuracy than prior methods. This work provides a scalable method for generating concise and more accurate explanations of expert routing and increases our understanding of a previously uninterpretable component of foundation models.

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Submission Number: 36