@inproceedings{zheng-etal-2026-naturalgaia,
    title = "{N}atural{GAIA}: A Verifiable Benchmark and Hierarchical Framework for Long-Horizon {GUI} Tasks",
    author = "Zheng, Zihan  and
      Cui, Tianle  and
      Wang, Taoran  and
      Wang, Fengtao  and
      Pan, Jiahui  and
      He, Lewei  and
      Chen, Qianglong",
    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.2207/",
    pages = "47772--47799",
    ISBN = "979-8-89176-390-6",
    abstract = "Despite significant advances in LLM-driven GUI agents, the field remains constrained by the challenge of reconciling high-fidelity realism with verifiable evaluation accuracy. To address this, we introduce NaturalGAIA, a verifiable evaluation dataset grounded in real-world human GUI interaction intents. By decoupling logical causal pathways from linguistic narratives, it rigorously simulates natural human intent, characterized by cognitive non-linearity and contextual dependencies. Furthermore, we propose LightManus-Jarvis, a hierarchical collaborative framework where LightManus manages dynamic topological planning and context evolution, while Jarvis ensures execution precision via hybrid visual-structural perception. Experiments demonstrate that our approach achieves a Weighted Pathway Success Rate of 45.6{\%}, significantly outperforming the state-of-the-art baseline (21.1{\%}), while reducing token consumption by 75{\%} and execution time by 76{\%}. These results validate the efficacy of the macro-planning and micro-execution paradigm in handling complex naturalized tasks. Our code is publicly available at: https://anonymous.4open.science/r/NatureGAIA-721F/."
}