T ool E xp N et: Optimizing Multi-Tool Selection in LLM s with Similarity and Dependency-Aware Experience Networks (2025)

Abstract: Tool learning enhances Large Language Models’ (LLMs) dynamic interaction with external tools, improving their ability to solve complex problems. However, current empirical methods, which primarily focus on isolated tools learning, still struggle with accurate multi-tool selection due to issues like confusing similar tools and neglecting dependencies. To address these challenges, we propose the Tool Experience Network (ToolExpNet), which integrates tools and trial-and-error experiences into a network characterized by semantic similarity and dependency relationships. ToolExpNet iteratively conducts simulated experiments using adaptive sampling to explore subtle differences and connections between tools, and summarizes these experiences to provide insightful guidance for LLM tool selection. Our experiments demonstrate that learning the relationships between tools helps achieve more comprehensive tool learning. Evaluations on multiple real-world API datasets show that ToolExpNet effectively addresses common challenges in multi-tool selection, significantly outperforming existing baselines across different foundation LLMs.

## introduction tool learning @xcite empowers large language models to dynamically interact with external tools, enhancing their problem-solving capabilities for complex tasks @xcite @xcite . this paradigm significantly boosts performance in knowledge acquisition @xcite , expertise enhancement @xcite @xcite , automation efficiency @xcite , and interaction capabilities @xcite . to invoke external tools, llms visual question answering is the task of answering questions based on an image. dqa (also known as document vqa) is the task of answering questions on document images. please help me to find the final account balance from this bank statement. ## tool experience network we propose the tool experience network (toolex-pnet), as shown in figure 2 , which organizes the available toolset into a network based on similarity and dependency relationships, facilitating more systematic and comprehensive tool learning. ## tool learning strategy we suggest that the role of a tool within a toolkit is determined not only by its intrinsic properties but also by its toolset-context. therefore, during the tool learning phase, we iterate the processes of contrastive-relation trial-and-error and tool insight refinement. this iterative process, similar to how humans learn through trial and error and then summarize their experiences, helps organize knowledge into structured cognition and memory. the prompts for this section are provided in appendix a. ## conclusion in this paper, we propose toolexpnet, a novel framework that organizes tool usage insights and trial-and-error experiences into a network based on semantic similarity and dependency relations, addressing the limitations of existing methods that focus on isolated tools learning. experimental results on various foundation models and real-world datasets demonstrate that toolexpnet outperforms existing methods, providing a comprehensive understanding of tool usage and improving multi-tool invocation accuracy.