想提升LLM智能体调用工具的准确率？这篇论文的HYSET方法把工具集当成整体打分，比单个检索靠谱，ToolBench上全面领先。
现有工具检索方法逐个评分或顺序组装工具集，忽略了候选集的联合效用。新提出的HYSET方法将工具检索建模为查询条件超边预测，在工具共调用超图上以工具集为单元评分。HYSET通过基数特定交互捕获工具兼容性，并在ToolBench基准上持续超越当前最优基线。该方法支持零样本和少样本迁移，可在最小监督下泛化到未见工具和领域。
Tools Are Not Islands: Set-Level Tool Retrieval for LLM Agents via Query-Conditioned Hyperedge Prediction
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.