路由配置文件：阐明用於路由的大型語言模型配置文件設計空間

摘要

隨著大型語言模型生態系統的擴展，各模型在查詢、基準測試及領域上的能力表現各異，促使了LLM路由機制的發展。雖然先前的研究主要聚焦於路由器機制設計，但用以捕捉模型能力的LLM配置文件仍未受到充分探討。本研究提出問題：LLM配置檔案的設計如何在不同路由器之間影響路由效能？釐清此問題有助於明確配置文件在路由中的角色，將配置文件設計與路由器設計脫鉤，並促進路由系統更公平的比較與更有原則的發展。為此，我們將LLM配置視為一個結構化資訊整合問題，涉及異質互動歷史。我們發展了一個通用的LLM配置文件設計空間，稱為RouteProfile，其包含四個關鍵維度：組織形式、表示類型、聚合深度與學習配置。透過在三個具代表性的路由器上（涵蓋標準設定與新型LLM泛化設定）進行系統性評估，我們發現：（1）結構化配置檔案一致優於扁平式配置；（2）查詢層級信號比粗粒度的領域層級信號更可靠；（3）對於新引入模型的泛化，在可訓練設定下，結構化配置檔案受益最大。總體而言，我們的研究凸顯了LLM配置文件設計作為未來路由研究的重要方向。

English

As the large language model (LLM) ecosystem expands, individual models exhibit varying capabilities across queries, benchmarks, and domains, motivating the development of LLM routing. While prior work has largely focused on router mechanism design, LLM profiles, which capture model capabilities, remain underexplored. In this work, we ask: How does LLM profile design affect routing performance across different routers? Addressing this question helps clarify the role of profiles in routing, disentangle profile design from router design, and enable fairer comparison and more principled development of routing systems. To this end, we view LLM profiling as a structured information integration problem over heterogeneous interaction histories. We develop a general design space of LLM profiles, named RouteProfile, along four key dimensions: organizational form, representation type, aggregation depth, and learning configuration. Through systematic evaluation across three representative routers under both standard and new-LLM generalization settings, we show that: (1) structured profiles consistently outperform flat ones; (2) query-level signals are more reliable than coarse domain-level signals; and (3) generalization to newly introduced models benefits most from structured profiles under trainable configurations. Overall, our work highlights LLM profile design as an important direction for future routing research.