GATE: Graph-based Adaptive Tool Evolution Across Diverse Tasks
Jianwen Luo, Yiming Huang, Jinxiang Meng University of the Chinese Academy of Sciences, Fangyu Lei, Shizhu He Institute of automation, Chinese academy of science, Chinese Academy of Sciences, Xiao Liu Microsoft Research Asia, Shanshan Jiang, Bin Dong Ricoh Software Research Center Beijing Co., Ltd. 等 10 位作者
Large Language Models (LLMs) have shown great promise in tool-making, yet existing frameworks often struggle to efficiently construct reliable toolsets and are limited to single-task settings. To address these challenges, we propose GATE (Graph-based Adaptive Tool Evolution), an adaptive framework that dynamically constructs and evolves a hierarchical graph of reusable tools across multiple scenarios. We evaluate GATE on open-ended tasks (Minecraft), agent-based tasks (TextCraft, DABench), and code generation tasks (MATH, Date, TabMWP). Our results show that GATE achieves up to 4.3× faster milestone completion in Minecraft compared to the previous state-of-the-art method, and provides an average improvement of 9.23% over existing tool-making methods in code generation tasks and 10.03% in agent tasks. Further analysis shows that GATE exhibits strong adaptive evolution capabilities, effectively balancing tool quantity, complexity, and functionality while maintaining high efficiency. Code and data are available at https://github.com/ayanami2003/GATE.
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Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表