🤖 AI Summary
This work addresses the limitations of existing tool-integrated reasoning models, which rely on predefined tools, lack self-optimization capabilities, and incur high tool construction costs, thereby struggling with open-ended tasks. To overcome these challenges, the authors propose UCT, a novel framework that introduces the first training-free paradigm for automatic tool construction. UCT extracts reasoning traces from large language models and distills them into reusable tool assets, while incorporating a memory consolidation mechanism to dynamically maintain and update the tool library. This enables agents to autonomously create and refine tools during reasoning, transitioning from mere tool users to tool creators. Evaluated on diverse mathematical and scientific reasoning benchmarks, UCT achieves performance gains of 20.86% and 23.04%, respectively, demonstrating its capacity for continuous self-improvement.
📝 Abstract
Existing Tool-Integrated Reasoning (TIR) models have effectively extended the question-answering capabilities of LLMs by incorporating external tools. However, real-world scenarios present numerous open-ended problems where fixed tools often fail to meet task requirements. Furthermore, the lack of self-optimization mechanisms means that erroneous tool outputs can mislead the LLM's responses. Additionally, the construction of existing tools entails significant manual effort, which consequently constrains their applicability. Recognizing that the reasoning traces of LLMs encapsulate implicit problem-solving capabilities, we propose UCT, a novel training-free framework that transforms agents from tool users to tool creators. This approach harvests reasoning experiences and distills them into reusable assets. This method transforms the agent from a mere tool user into a tool creator, enabling adaptive tool creation and self-updating during the inference process. We also introduce a memory consolidation mechanism to maintain the tool library, ensuring high reusability of retained experiential memory for subsequent reasoning tasks. This novel automated tool construction paradigm continuously improves tool quality during reasoning, allowing the overall agent system to progress without additional training. Extensive experiments demonstrate that our method serves as a novel paradigm for enhancing the capabilities of TIR models. In particular, the significant performance gains achieved +20.86%$\uparrow$ and +23.04%$\uparrow$ on benchmarks across multi-domain mathematical and scientific reasoning tasks validate the self-evolving capability of the agent.