RATED: Reliability-Aware AI Agent Tool Retrieval via Execution Dynamics· RATED：基于执行动态的可靠性感知AI代理工具检索
As AI agents increasingly rely on external tools, accurate retrieval is essential for reliable behavior. Existing methods optimize semantic relevance but largely ignore tool availability during execution. To address this gap, we propose RATED (Reliability-Aware Tool Retrieval via Execution Dynamics), a retrieval framework that combines robust semantic matching with dynamic reliability adaptation. RATED employs a frozen dual-encoder with contrastive fine-tuning for semantic matching, and a lightweight contextual bandit module that re-ranks candidates based on real-time execution feedback. This design enables rapid adaptation to runtime tool failures without expensive model retraining. We introduce EcoAgent, a benchmark of 5,172 real-world tools and over 210,000 query-tool pairs for offline
提出RATED框架，结合语义匹配与动态可靠性适应，解决工具执行期间的可用性问题。
- 核心方法
- RATED采用了一个冻结的双编码器和对比调优技术进行语义匹配，并结合轻量级上下文乐队模型根据实时执行反馈重新排序候选工具，以适应运行时的工具故障。
- 适合谁读
- 研究者、工程师
- 要解决的问题
- 现有AI代理工具检索方法主要优化语义相关性，忽视了工具执行时的可用性，导致在运行时工具故障时表现不可靠。
- 关键实验
- 未提供
- 主要贡献
- 1. 提出了一个新颖的基于执行动态的可靠性感知工具检索框架RATED；2. 引入了EcoAgent，一个包含5172个真实工具和210,000多个查询-工具对的基准数据集。
- 意义与局限
- RATED框架提高了AI代理在实际应用中对工具执行动态的适应能力，特别是在工具可能随时失效的情况下。EcoAgent为研究提供了宝贵的实验平台。但该框架的有效性和适应性需在更多实际场景中进一步验证。