MMBench-GUI: Hierarchical Multi-Platform Evaluation Framework for GUI
  Agents
We introduce MMBench-GUI, a hierarchical benchmark for evaluating GUI
automation agents across Windows, macOS, Linux, iOS, Android, and Web
platforms. It comprises four levels: GUI Content Understanding, Element
Grounding, Task Automation, and Task Collaboration, covering essential skills
for GUI agents. In addition, we propose a novel Efficiency-Quality Area (EQA)
metric to assess GUI agent execution efficiency in online automation scenarios.
Through MMBench-GUI, we identify accurate visual grounding as a critical
determinant of overall task success, emphasizing the substantial benefits of
modular frameworks that integrate specialized grounding modules. Furthermore,
to achieve reliable GUI automation, an agent requires strong task planning and
cross-platform generalization abilities, with long-context memory, a broad
action space, and long-term reasoning playing a critical role. More important,
task efficiency remains a critically underexplored dimension, and all models
suffer from substantial inefficiencies, with excessive redundant steps even
when tasks are ultimately completed. The integration of precise localization,
effective planning, and early stopping strategies is indispensable to enable
truly efficient and scalable GUI automation. Our benchmark code, evaluation
data, and running environment will be publicly available at
https://github.com/open-compass/MMBench-GUI.
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Xuehui Wang
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Zhenyu Wu
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JingJing Xie
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JingJing Xie
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Zichen Ding
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Zichen Ding
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heroding77
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Qingyun Li
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Qingyun Li
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Qingyun
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Xuan Dong
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Zhe Chen
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Weiyun Wang
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Xiangyu Zhao
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Jixuan Chen
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Haodong Duan
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Tianbao Xie
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Chenyu Yang
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Chenyu Yang
user
cyyang822
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Shiqian Su
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Yue Yu
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Yuan Huang
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Yiqian Liu
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Xiao Zhang
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Yanting Zhang
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Xiangyu Yue
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Weijie Su
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Xizhou Zhu
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Wei Shen
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Jifeng Dai
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Wenhai Wang
MMBench-GUI: Hierarchical Multi-Platform Evaluation Framework for GUI
  Agents
We introduce MMBench-GUI, a hierarchical benchmark for evaluating GUI
automation agents across Windows, macOS, Linux, iOS, Android, and Web
platforms. It comprises four levels: GUI Content Understanding, Element
Grounding, Task Automation, and Task Collaboration, covering essential skills
for GUI agents. In addition, we propose a novel Efficiency-Quality Area (EQA)
metric to assess GUI agent execution efficiency in online automation scenarios.
Through MMBench-GUI, we identify accurate visual grounding as a critical
determinant of overall task success, emphasizing the substantial benefits of
modular frameworks that integrate specialized grounding modules. Furthermore,
to achieve reliable GUI automation, an agent requires strong task planning and
cross-platform generalization abilities, with long-context memory, a broad
action space, and long-term reasoning playing a critical role. More important,
task efficiency remains a critically underexplored dimension, and all models
suffer from substantial inefficiencies, with excessive redundant steps even
when tasks are ultimately completed. The integration of precise localization,
effective planning, and early stopping strategies is indispensable to enable
truly efficient and scalable GUI automation. Our benchmark code, evaluation
data, and running environment will be publicly available at
https://github.com/open-compass/MMBench-GUI.
MMBench-GUI evaluates GUI automation agents across multiple platforms using a hierarchical benchmark and Efficiency-Quality Area metric, highlighting the importance of visual grounding, task planning, and efficiency.
GUI Content Understanding Element Grounding Task Automation Task Collaboration Efficiency-Quality Area (EQA) visual grounding modular frameworks task planning cross-platform generalization long-context memory long-term reasoning task efficiency precise localization effective planning early stopping strategies