Katanemo Labs introduces Arch-Router, a 1.5B parameter model that intelligently maps user queries to the most suitable LLM, achieving 93% accuracy without the need for costly retraining. It uses a preference-aligned routing framework based on a Domain-Action Taxonomy, allowing for flexible adaptation to evolving models and use cases.
This paper introduces Arch-Router, a preference-aligned routing framework for large language models (LLMs). It addresses limitations in existing routing approaches by focusing on matching queries to user-defined preferences (domain and action types) rather than solely relying on benchmark performance. The framework includes a 1.5B parameter model, Arch-Router, and a data creation pipeline. Experiments demonstrate state-of-the-art results in matching queries with human preferences and improved adaptability.
