@inproceedings{guan-etal-2025-kg,
    title = "{KG}-{RAG}: Enhancing {GUI} Agent Decision-Making via Knowledge Graph-Driven Retrieval-Augmented Generation",
    author = "Guan, Ziyi  and
      Li, Jason Chun Lok  and
      Hou, Zhijian  and
      Zhang, Pingping  and
      Xu, Donglai  and
      Zhao, Yuzhi  and
      Wu, Mengyang  and
      Chen, Jinpeng  and
      Nguyen, Thanh-Toan  and
      Xian, Pengfei  and
      Ma, Wenao  and
      Qin, Shengchao  and
      Chesi, Graziano  and
      Wong, Ngai",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-main.274/",
    pages = "5396--5405",
    ISBN = "979-8-89176-332-6",
    abstract = "Despite recent progress, Graphic User Interface (GUI) agents powered by Large Language Models (LLMs) struggle with complex mobile tasks due to limited app-specific knowledge. While UI Transition Graphs (UTGs) offer structured navigation representations, they are underutilized due to poor extraction and inefficient integration. We introduce KG-RAG, a Knowledge Graph-driven Retrieval-Augmented Generation framework that transforms fragmented UTGs into structured vector databases for efficient real-time retrieval. By leveraging an intent-guided LLM search method, KG-RAG generates actionable navigation paths, enhancing agent decision-making. Experiments across diverse mobile apps show that KG-RAG outperforms existing methods, achieving a 75.8{\%} success rate (8.9{\%} improvement over AutoDroid), 84.6{\%} decision accuracy (8.1{\%} improvement), and reducing average task steps from 4.5 to 4.1. Additionally, we present KG-Android-Bench and KG-Harmony-Bench, two benchmarks tailored to the Chinese mobile ecosystem for future research. Finally, KG-RAG transfers to web/desktop (+40{\%} SR on Weibo-web; +20{\%} on QQ Music-desktop), and a UTG cost ablation shows accuracy saturates at {\textasciitilde}4h per complex app, enabling practical deployment trade-offs."
}