VeriGUI: Verifiable Long-Chain GUI Dataset
Authors: name: Shunyu Liu; name: Minghao Liu; name: Huichi Zhou; name: Zhenyu Cui; name: Yang Zhou; name: Yuhao Zhou; name: Wendong Fan; name: Ge Zhang; name: Jiajun Shi; name: Weihao Xuan; name: Jiaxing Huang; name: Shuang Luo; name: Fang Wu; name: Heli Qi; name: Qingcheng Zeng; name: Ziqi Ren; name: Jialiang Gao; name: Jindi Lv; name: Junjie Wang; name: Aosong Feng; name: Heng Zhou; name: Wangchunshu Zhou; name: Zhenfei Yin; name: Wenlong Zhang; name: Guohao Li; name: Wenhao Yu; name: Irene Li; name: Lei Ma; name: Lei Bai; name: Qunshu Lin; name: Mingli Song; name: Dacheng Tao
Published: 2025-08-06 02:38:18

Abstract: Recent studies have delved into constructing autonomous agents capable of
performing complex Graphical User Interface (GUI)-based computer tasks, with
the potential to revolutionize human-computer interaction. Despite encouraging
results, existing efforts mainly focus on short-term interactions and rely on
outcome-only verification, thereby limiting their scalability in real-world GUI
applications that demand long-horizon task decomposition and execution. In this
work, we introduce VeriGUI, a novel verifiable long-chain GUI dataset designed
to facilitate the development and evaluation of generalist GUI agents operating
in realistic computer environments. Our dataset emphasizes two critical
dimensions: (1) long-chain complexity, with tasks decomposed into a sequence of
interdependent subtasks spanning hundreds of steps, explicitly designed to
allow any subtask to serve as a valid starting point; and (2) subtask-level
verifiability, which enables diverse exploration strategies within each
subtask, while ensuring that each subtask-level goal remains verifiable and
consistent. The dataset consists of GUI task trajectories across both desktop
and web, annotated by human experts. Extensive experiments on VeriGUI using
various agents with different foundation models reveal significant performance
gaps in handling long-horizon tasks, highlighting the need for more robust
planning and decision-making capabilities in GUI agents.