使嵌入模型适应智能体能力检索
Adapting Embedding Models for Agent Capability Retrieval
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中文总结 AI 辅助
研究如何让一般文本检索的现成模型适应智能体能力检索，通过微调三个模型在特定数据集上训练，测试其在未训练目录上的迁移能力，结果显示适应对两个目录均有帮助。
中文摘要 AI 辅助
开放智能体市场在同一搜索界面列出原生智能体、工具包和可复用技能包，但从业者在跨此混合目录检索方面仍缺乏指导。我们研究为一般文本检索训练的现成检索模型能否适应将用户查询与可执行智能体能力匹配，以及学习到的信号是否能在用于调优的基准之外迁移。我们在AgentSelect上对三个开放检索主干模型进行微调，并在训练中未见过的两个目录上测试迁移。适应在两个目录上都有帮助。代码和数据将在发表时发布。
英文摘要
Open agent marketplaces list native agents, tool bundles, and reusable skill packages in the same search interface, yet practitioners still have little guidance on how to retrieve across this mixed catalog. We study whether off-the-shelf retrieval models, trained for general text retrieval, can be adapted to match user queries to executable agent capabilities, and whether the learned signal transfers beyond the benchmark used for tuning. We fine-tune three open retrieval backbones, BGE-base, KaLM-v1.5, and EasyRec, on AgentSelect, which represents marketplace-visible units as capability profiles derived from public metadata, and test transfer on two catalogs not seen during training: MuleRun native agents and a ClawHub benchmark of 50 skills with 1,000 queries. Adaptation helps on both catalogs. Code and data will be released upon publication.
