用早期经验自动决定该直接回答还是调用智能体，提速六成还更准。
Learning Agent Routing From Early Experience
- 基于早期行为和评分标准，判断是否需要调用完整智能体
- 相比纯LLM推理性能提升28.6%，推理时间减少60.6%
- 无需训练，适合资源受限的冷启动场景
大型语言模型（LLM）智能体在复杂推理任务中表现优异，但存在高延迟和高计算成本问题。实践中，许多查询在其能力范围内，无需完整智能体执行，因此在轻量级LLM推理与全智能体执行之间进行有效路由成为关键挑战。本文研究在真实冷启动场景下，如何将查询路由至轻量级LLM或完整智能体。为此，我们提出BoundaryRouter——一种无需训练的路由框架，利用早期行为经验与评分引导推理，决定是否通过直接LLM推理回答问题或升级至智能体。该框架通过在共享种子集上执行两种系统构建紧凑的经验记忆，并在推理时检索相似案例以指导决策。为评估该方法，我们引入RouteBench基准，涵盖域内、改写及域外路由场景。实验表明，BoundaryRouter相比完整智能体将推理时间降低60.6%，同时性能较直接LLM推理提升28.6%；相较于基于提示和仅检索的路由方法，平均分别提升37.9%和8.2%。
原文摘要 · Abstract (English)
LLM agents achieve strong performance on complex reasoning tasks but incur high latency and compute cost. In practice, many queries fall within the capability boundary of cutting-edge LLMs and do not require full agent execution, making effective routing between LLMs and agents a key challenge. We study the problem of routing queries between lightweight LLM inference and full agent execution under realistic cold-start settings. To address this, we propose BoundaryRouter, a training-free routing framework that uses early behavioral experience and rubric-guided reasoning to decide whether to answer a query with direct LLM inference or escalate to an agent. BoundaryRouter builds a compact experience memory by executing both systems on a shared seed set and retrieves similar cases at inference time to guide routing decisions. To evaluate this method, we introduce RouteBench, a benchmark covering in-domain, paraphrased, and out-of-domain route settings. Experiments show that BoundaryRouter reduces inference time by 60.6% compared to the agent while improving performance by 28.6% over direct LLM inference, outperforming prompt-based and retrieval-only routing by an average of 37.9% and 8.2%, respectively.
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