arXiv cs.AI· Amin Rezaei, Pouya Parsa·· 12 小时前AI 评分35
COMPASS：基于能力导向模型画像的智能体路由，支持自适应搜索、选择与工具调用
COMPASS: Routing via Capability-Oriented Model Profiling with Adaptive Search, Selection, and Tool Use
AI 导读
COMPASS 是一种无需路由专项训练的智能体路由器，由 LLM 按路由配方调用工具收集性能与成本证据，并可选检查候选答案后再选模型。
正文 · 原文
Abstract:LLM routing aims to select a capable model at low cost, but specialized routers can require updates as models and tasks change. We propose COMPASS, an agentic router in which an LLM follows a routing recipe, gathers performance and cost evidence through tools, and optionally inspects candidate answers before selecting a model. Our evaluations on LLMRouterBench and DeepSWE show that agentic routing can improve accuracy and lower model-call cost relative to specialized and trained routing baselines, without routing-specific training. On LLMRouterBench, COMPASS reaches 69.85% accuracy at \$139.57, compared with RLCascadeRouter's 69.14% at \$180.22. On DeepSWE under fixed five-fold cross-validation, it reaches 78.1% at \$215, compared with 74.1% at \$500 for the best single model. COMPASS also supports routing across benchmarks and adapts to an expanded model pool by reading new evaluation records, without changing the router's weights or prompt.
| Comments: | 14 pages, 4 figures |
| Subjects: | Artificial Intelligence (cs.AI) |
| ACM classes: | I.2.6; I.2.7 |
| Cite as: | arXiv:2610.27822 [cs.AI] |
| (or arXiv:2610.27822v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.27822 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Amin Rezaei [view email]
[v1]
Tue, 20 Oct 2026 08:13:24 UTC (257 KB)
来源：arXiv cs.AI · arxiv.org