LLM routing is increasingly important for selecting suitable models under diverse user needs and deployment constraints, but its practical effectiveness depends on continual adaptation to emerging queries and newly released models. New-LLM integration is particularly challenging, as newly released models lack the query-response-reward interactions required for router training and cannot be profiled as directly as new queries via semantic embeddings. Existing profiles are limited: LLM-generated descriptions are often coarse, while interaction-based embeddings are costly to construct. To address this problem, we propose RouteProfile, a graph-based profiling framework that constructs LLM profiles from public signals in technical reports or model cards, including model family, model description, reported benchmark scores, and benchmark domains. RouteProfile organizes these heterogeneous signals into a graph and studies profile construction along four dimensions: organizational form, representation type, aggregation depth, and learning configuration. We evaluate RouteProfile in training-free cold-start routing and new-LLM integration settings. Experiments show that: (1) structured profiles outperform flat baselines in training-free cold-start routing; (2) model family metadata is more reliable than benchmark domain information; and (3) effective new-LLM integration requires profile-router co-design. Overall, our findings highlight the importance of profile design for enabling routing systems to adapt to the evolving model ecosystem.

翻译：大语言模型路由技术对于根据多样化用户需求和部署约束选择合适的模型日益重要，但其实际效果取决于对新兴查询和新发布模型的持续适应能力。新大语言模型的集成尤为困难，因为新发布的模型缺乏路由器训练所需的查询-响应-奖励交互数据，且无法像新查询那样通过语义嵌入直接构建轮廓。现有轮廓构建方案存在局限：基于大语言模型生成的描述往往过于粗略，而基于交互的嵌入构建成本高昂。针对该问题，我们提出RouteProfile框架——一种基于图的轮廓构建方法，通过技术报告或模型卡中的公开信号（包括模型家族、模型描述、公开基准分数及基准领域）构建大语言模型轮廓。RouteProfile将这些异构信号组织成图结构，并从组织形式、表示类型、聚合深度和学习配置四个维度研究轮廓构建方法。我们在免训练冷启动路由和新模型集成场景下评估RouteProfile。实验表明：(1)在免训练冷启动路由中，结构化轮廓优于扁平基线方法；(2)模型家族元数据比基准领域信息更可靠；(3)有效的新模型集成需要轮廓与路由器的协同设计。总体而言，我们的研究结果凸显了轮廓设计对于使路由系统适应不断演变的模型生态系统的重要性。