[2512.17052] Dynamic Tool Dependency Retrieval for Efficient Function Calling

Computer Science > Machine Learning arXiv:2512.17052 (cs) [Submitted on 18 Dec 2025 (v1), last revised 1 Mar 2026 (this version, v3)] Title:Dynamic Tool Dependency Retrieval for Efficient Function Calling Authors:Bhrij Patel, Davide Belli, Amir Jalalirad, Maximilian Arnold, Aleksandr Ermolov, Bence Major View a PDF of the paper titled Dynamic Tool Dependency Retrieval for Efficient Function Calling, by Bhrij Patel and 5 other authors View PDF HTML (experimental) Abstract:Function calling agents powered by Large Language Models (LLMs) select external tools to automate complex tasks. On-device agents typically use a retrieval module to select relevant tools, improving performance and reducing context length. However, existing retrieval methods rely on static and limited inputs, failing to capture multi-step tool dependencies and evolving task context. This limitation often introduces irrelevant tools that mislead the agent, degrading efficiency and accuracy. We propose Dynamic Tool Dependency Retrieval (DTDR), a lightweight retrieval method that conditions on both the initial query and the evolving execution context. DTDR models tool dependencies from function calling demonstrations, enabling adaptive retrieval as plans unfold. We benchmark DTDR against state-of-the-art retrieval methods across multiple datasets and LLM backbones, evaluating retrieval precision, downstream task accuracy, and computational efficiency. Additionally, we explore strategies to integrate retrieved...