工具并非孤岛：通过查询条件超边预测为语言模型智能体进行集合级工具检索
Tools Are Not Islands: Set-Level Tool Retrieval for LLM Agents via Query-Conditioned Hyperedge Prediction
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中文总结 AI 辅助
研究LLM智能体的工具检索问题，提出HYSET方法，将其表述为查询条件超边预测，通过特定基数交互捕捉工具兼容性，设计为预选择模块，实验证明该方法在工具检索性能及任务成功率上优于基线，还支持零样本/少样本迁移。
中文摘要 AI 辅助
大语言模型（LLM）智能体越来越依赖调用外部工具来完成现实世界任务。工具检索在智能体行动前从数千个工具库中选择与任务相关的小子集，已成为LLM智能体管道的关键组件。然而，现有检索器要么单独对每个工具评分，要么顺序组装工具集，未整体评估候选集的联合效用。本文提出基于超边的集合级工具检索HYSET。贡献如下：一是将工具检索表述为工具共同调用超图上的查询条件超边预测，工具集本身成为评分单位；二是通过特定基数交互捕捉与大小相关的工具兼容性；三是将HYSET设计为预选择模块，无需修改下游智能体。在ToolBench上的实验表明，HYSET在工具检索性能和端到端任务成功率上均优于现有基线。此外，HYSET还支持零样本/少样本迁移，能在最少监督下推广到未见过的工具/类别和领域。
英文摘要
Large language model (LLM) agents increasingly rely on invoking external tools to complete real-world tasks. Tool retrieval, which selects a small task-relevant subset from a library of thousands of tools before the agent acts, has therefore become a critical component of LLM agent pipelines. However, existing retrievers either score each tool in isolation or assemble the tool set sequentially, so the joint utility of a candidate set is never evaluated as a whole. In this paper, we propose HYSET, short for HYperedge-based SEt-level Tool retrieval. Our contributions are threefold: (i) we formulate tool retrieval as query-conditioned hyperedge prediction on a tool co-invocation hypergraph, under which the tool set itself becomes the unit of scoring and most existing retrieval paradigms reduce to restricted instances; (ii) we capture size-dependent tool compatibility through cardinality-specific interactions; and (iii) we design HYSET as a pre-selection module requiring no modification to the downstream agent. Experiments on ToolBench demonstrate that HYSET consistently outperforms state-of-the-art baselines in both tool retrieval performance and end-to-end task success. Beyond the in-domain setting, HYSET further supports zero-shot/few-shot transfer, generalizing to held-out tools/categories and unseen domains with minimal supervision.
