@inproceedings{luo-etal-2026-gate,
    title = "{GATE}: Graph-based Adaptive Tool Evolution Across Diverse Tasks",
    author = "Luo, Jianwen  and
      Huang, Yiming  and
      Meng, Jinxiang  and
      Lei, Fangyu  and
      He, Shizhu  and
      Liu, Xiao  and
      Jiang, Shanshan  and
      Dong, Bin  and
      Zhao, Jun  and
      Liu, Kang",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.87/",
    pages = "1934--1961",
    ISBN = "979-8-89176-390-6",
    abstract = "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{\texttimes} 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."
}