# IR3DE：大语言模型的线性路由器

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-06-04 20:36
- AIHOT 分数：49
- AIHOT 链接：https://aihot.virxact.com/items/cmq7u3iet01pfslep4o0w9oon
- 原文链接：https://arxiv.org/abs/2606.06098

## AI 摘要

IR3DE是一个基于岭回归的线性路由器，为每个提示选择最合适的领域专家大语言模型。在两个因果语言建模（CLM）任务中，IR3DE性能与其他基线相当；在推理任务中，归一化性能达到98.4%，超越其他方法。该路由器支持动态添加或移除领域专家，无需重新训练，代码已开源。

## 正文

Foundational Large Language Models (LLMs) demonstrate proficiency on a wide range of general tasks, and achieve remarkable results on various specialized tasks via domain-expert LLMs. With the ever-growing list of available LLMs, inference routers are being proposed to select the most appropriate LLM for each prompt. However, existing routing methods either optimize cost across weak-to-strong generalist LLMs or require substantial training to support domain-expertise routing. In this paper, we propose IR3DE, a Ridge Regression-based Router for Domain Experts that provides cheap and fast routing decisions for each prompt. We evaluate IR3DE in two Causal Language Modeling (CLM) settings where the tasks are next-token prediction for all domains, and one reasoning setting where each domain has its own distinct reasoning task. Despite being a linear router, IR3DE achieves performance comparable to the other baselines in both CLM settings, and surpassing them in the reasoning setting, with a normalized performance of 98.4%. Moreover, IR3DE enables the addition or removal of new domain experts without requiring the router to be retrained from scratch, allowing a dynamic set of LLMs to be served with minimal disruption to the router itself. Our code is available at: github.com/gensyn-ai/IR3DE.