FitText: 通过模因检索演化智能体工具生态
FitText: Evolving Agent Tool Ecologies via Memetic Retrieval
AI总结：
针对用户任务描述与工具文档间的语义鸿沟，提出FitText框架，将检索嵌入推理循环，通过自然语言伪工具描述迭代优化和模因进化选择，显著提升工具检索性能。
摘要：
用户描述任务的方式与工具文档之间存在语义鸿沟。随着API生态扩展到数万个端点，仅凭初始查询的静态检索无法弥合这一鸿沟：智能体对其所需工具的理解在执行过程中不断演变，但其工具集却保持不变。我们指出，这种检索接口（而非规划）是端到端智能体性能的约束瓶颈，并引入FitText——一个无需训练的框架，通过将检索直接嵌入智能体的推理循环中，使其动态化。FitText将检索视为测试时假设的演化：智能体生成自然语言的伪工具描述（关于所需工具的可修正信念），利用检索反馈迭代优化，并通过随机生成探索多样化的替代方案。模因检索在候选描述上施加进化选择压力，并由避免冗余搜索的工具记忆引导。在ToolRet（三个领域）上，FitText的重构策略在所有基模型上相比静态查询检索将NDCG@5提升了2.7至10.6个点；在StableToolBench（16,464个API）上使用GPT-5.4-mini时，模因检索达到了84.3%的合并通过率，相比静态查询检索绝对提升了26.7个点。
英文摘要：
Efficient reasoning is not only a matter of shortening an answer trace; for tool-using agents, it also depends on whether the agent is reasoning over the right action space. As API ecosystems scale to tens of thousands of endpoints, the semantic gap between user requests and tool documentation makes this problem concrete: static retrieval from the initial query can fail before planning begins, and stronger planning alone cannot recover a missing tool. We study this problem as budgeted test-time retrieval and introduce FitText, a training-free framework that makes the tool interface revisable during execution by generating, refining, and evolving natural-language pseudo-tool descriptions as retrieval probes. FitText supports serial refinement, parallel exploration, and Memetic Retrieval, which adds evolutionary selection, local refinement, and tool memory to avoid redundant search. On StableToolBench (16,464 APIs), Memetic FitText reaches an 84.3% pooled pass rate, improving +26.7 points over static retrieval, +22.2 over Single-Pass, +23.2 over Re-Invoke, and +27.5 over Xu-style root refinement. It leads on every evaluated current model, with gains growing alongside model capability, and produces the largest improvements on ambiguous multi-tool tasks where dynamic re-retrieval restores correct candidates after early mistakes. At 40-way concurrency, parallel population execution keeps batched wall-clock at 1.01x Single-Pass despite the added search work.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
