# LLMRouter：构建、评估与部署LLM路由器的统一基础设施

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-08-07 08:00
- AIHOT 分数：51
- AIHOT 链接：https://aihot.virxact.com/items/cmssf8hxr05terod0app21hxy
- 原文链接：https://arxiv.org/abs/2608.06867

## AI 摘要

LLMRouter将LLM路由统一表述为包含上下文编码器、模型编码器、评分函数、决策规则和学习信号的序列决策过程，并构建自动化监督构建与评估管线。其推出的xRouteBench基准覆盖通用、记忆增强、视觉、时间序列及个性化路由任务，开源基础设施集成16种以上代表性路由器。实验显示，学习型路由器相对最强固定模型基线提升14.6%。

## 正文

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.