Beyond Single-Shot: Multi-step Tool Retrieval via Query Planning

• URL: http://arxiv.org/abs/2601.07782v1
• Date: Mon, 12 Jan 2026 17:58:39 GMT
• Title: Beyond Single-Shot: Multi-step Tool Retrieval via Query Planning
• Authors: Wei Fang, James Glass,
• Abstract summary: TOOLQP is a lightweight framework that models retrieval as iterative query planning.<n>It decomposes instructions into sub-tasks and dynamically generates queries to interact with the retriever.<n>It achieves state-of-the-art performance, exhibiting superior zero-shot generalization, robustness across diverse retrievers, and significant improvements in downstream agentic execution.
• Score: 6.212994999785976
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: LLM agents operating over massive, dynamic tool libraries rely on effective retrieval, yet standard single-shot dense retrievers struggle with complex requests. These failures primarily stem from the disconnect between abstract user goals and technical documentation, and the limited capacity of fixed-size embeddings to model combinatorial tool compositions. To address these challenges, we propose TOOLQP, a lightweight framework that models retrieval as iterative query planning. Instead of single-shot matching, TOOLQP decomposes instructions into sub-tasks and dynamically generates queries to interact with the retriever, effectively bridging the semantic gap by targeting the specific sub-tasks required for composition. We train TOOLQP using synthetic query trajectories followed by optimization via Reinforcement Learning with Verifiable Rewards (RLVR). Experiments demonstrate that TOOLQP achieves state-of-the-art performance, exhibiting superior zero-shot generalization, robustness across diverse retrievers, and significant improvements in downstream agentic execution.

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