arXiv Machine Learning
Aug 13

LLM Router: Rethinking Routing with Prefill Activations

arXiv:2603. 20895v3 Announce Type: replace-cross Abstract: Existing routers rely on semantic query features or handcrafted features, which often fail to capture model-specific failures or intrinsic task difficulty.

By Tanay Varshney, Annie Surla, Michelle Xu, Gomathy Venkata Krishnan, Maximilian Jeblick, David Austin, Neal Vaidya, Davide Onofrio
arXiv AI
Oct 9

RouterInterp: Understanding Superposed Specialisation in Mixture of Experts Routing

RouterInterp: Understanding Superposed Specialisation in Mixture of Experts Routing proposes the Superposed Specialisation Hypothesis (SSH), suggesting that experts in Sparse Mixture of Experts models specialise in a disjoint union of fine‑grained features rather than a single domain. The paper introduces RouterInterp, a method that identifies Sparse Autoencoder features predictive of routing decisions and generates unified natural‑language explanations. On the gpt‑oss‑20b model, RouterInterp achieves approximately 65% higher detection accuracy than previous token‑statistics based methods, offering a scalable way to explain expert routing.

By Ilya Lasy, Nora Yinuo Cai, Kola Ayonrinde