ScoutRoute improves expert selection in large language model systems
ScoutRoute: Efficient Mixture-of-Experts Routing Using Internal Language Model Signals
Artificial IntelligenceComputation and Language
Summary
Choosing the best expert model to answer a question is hard when multiple language models are involved. The authors introduce ScoutRoute, a lightweight way to predict which model will perform best by looking at signals from a quick initial pass through a simple model. This approach works without needing to retrain models or run many costly checks. Their tests show ScoutRoute picks better experts across different types of tasks, helping systems answer questions more efficiently.
What this means in practice
- •For ai infrastructure teams: Enable faster and more efficient routing to the best language model expert without retraining when managing multiple independently trained models.
- •For automated code generation teams: Improve selection of language models specialized in code tasks by using internal signals to route requests accurately and efficiently.
Authors
Rajdeep Pandhare, Delzaan Bharucha
Abstract
Efficient expert routing is a fundamental challenge in Mixture-of-Experts (MoE) systems, particularly for heterogeneous collections of independently trained Large Language Mod els (LLMs). Existing routing approaches typically rely on jointly trained gating networks, prompt-based predictors, or computationally expensive multi-model inference, limiting their flexibility and efficiency. This paper presents ScoutRoute, a lightweight post-hoc routing framework that predicts expert competence using internal language model signals extracted from a single forward pass through an inexpensive scout model. Hidden-state representations are used to estimate cross-model competence through lightweight logistic probes, while logit-derived confidence measures are incorporated within a domain-aware routing strategy for efficient expert selec tion without modifying the underlying language models. ScoutRoute isevaluatedon fiveopen-weightlanguagemodels across fourbenchmark datasets covering mathematical reasoning, code generation, commonsense reasoning, and multidis ciplinary knowledge. Experimental results show that ScoutRoute consistently outperforms individual routing signals while requiring only a single scout-model inference. The proposed competence probes also achieve superior calibration compared with conventional confidence measures, demonstrating that internal language model representations provide transferable information for accurate and efficient expert routing across heterogeneous language models