LLMRouter：用于开发、评估和部署LLM路由器的统一基础设施

摘要

没有任何单一大型语言模型（LLM）能在所有查询和预算约束下都表现最优，因此模型路由对经济高效的部署至关重要。现有路由器采用多种不同的公式和实现方式，导致公平比较和扩展变得困难。我们提出了LLM路由的统一表述，将其视为一个序列决策过程，由五个组件构成：上下文编码器、模型编码器、评分函数、决策规则和学习信号，涵盖单轮、多轮及个性化路由。基于这一表述，我们开发了一条自动化流水线，用于构建路由监督，并联合评估路由器的响应质量与推理成本。由此产生的基准xRouteBench覆盖通用LLM、记忆增强、视觉、时间序列和个性化路由任务。我们进一步推出了LLMRouter，这是一个开源模块化基础设施，包含超过16个代表性路由器。我们的实证研究表明，学习型路由器的相对性能超过最强固定模型基线14.6%，轻量级路由器在严格的成本约束下更具竞争力，且用户条件路由能够持续提升个性化效果。

English

No single large language model (LLM) is optimal across all queries and budget constraints, making model routing essential for cost-effective deployment. Existing routers adopt diverse formulations and implementations, making fair comparison and extension difficult. We present a unified formulation of LLM routing as a sequential decision process characterized by five components: context encoders, model encoders, scoring functions, decision rules, and learning signals, covering single-turn, multi-turn, and personalized routing. Based on this formulation, we develop an automated pipeline for constructing routing supervision and evaluating routers jointly on response quality and inference cost. The resulting benchmark, xRouteBench, spans generic LLM, memory-augmented, vision, time-series, and personalized routing tasks. We further introduce LLMRouter, an open-source modular infrastructure with more than 16 representative routers. Our empirical study shows that learned routers outperform the strongest fixed-model baseline by 14.6% relatively, lightweight routers become more competitive under tight cost constraints, and user-conditioned routing consistently improves personalization.