Turing Test on Screen: A Benchmark for Mobile GUI Agent Humanization
The rise of autonomous GUI agents has triggered adversarial countermeasures from digital platforms, yet existing research prioritizes utility and robustness over the critical dimension of anti-detection. We argue that for agents to survive in human-centric ecosystems, they must evolve Humanization capabilities. We introduce the ``Turing Test on Screen,'' formally modeling the interaction as a MinMax optimization problem between a detector and an agent aiming to minimize behavioral divergence. We then collect a new high-fidelity dataset of mobile touch dynamics, and conduct our analysis that vanilla LMM-based agents are easily detectable due to unnatural kinematics. Consequently, we establish the Agent Humanization Benchmark (AHB) and detection metrics to quantify the trade-off between imitability and utility. Finally, we propose methods ranging from heuristic noise to data-driven behavioral matching, demonstrating that agents can achieve high imitability theoretically and empirically without sacrificing performance. This work shifts the paradigm from whether an agent can perform a task to how it performs it within a human-centric ecosystem, laying the groundwork for seamless coexistence in adversarial digital environments.
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Jiachen Zhu
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2026-04-14T08:15:01.202Z
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jiachen zhu
gebro13
user
gebro13
69dddd72aea31e227c22c631
Lingyu Yang
69dddd72aea31e227c22c632
Rong Shan
69dddd72aea31e227c22c633
Congmin Zheng
69dddd72aea31e227c22c634
Zeyu Zheng
69dddd72aea31e227c22c635
Weiwen Liu
69dddd72aea31e227c22c636
Yong Yu
69dddd72aea31e227c22c637
Weinan Zhang
69dddd72aea31e227c22c638
Jianghao Lin
Turing Test on Screen: A Benchmark for Mobile GUI Agent Humanization
The rise of autonomous GUI agents has triggered adversarial countermeasures from digital platforms, yet existing research prioritizes utility and robustness over the critical dimension of anti-detection. We argue that for agents to survive in human-centric ecosystems, they must evolve Humanization capabilities. We introduce the ``Turing Test on Screen,'' formally modeling the interaction as a MinMax optimization problem between a detector and an agent aiming to minimize behavioral divergence. We then collect a new high-fidelity dataset of mobile touch dynamics, and conduct our analysis that vanilla LMM-based agents are easily detectable due to unnatural kinematics. Consequently, we establish the Agent Humanization Benchmark (AHB) and detection metrics to quantify the trade-off between imitability and utility. Finally, we propose methods ranging from heuristic noise to data-driven behavioral matching, demonstrating that agents can achieve high imitability theoretically and empirically without sacrificing performance. This work shifts the paradigm from whether an agent can perform a task to how it performs it within a human-centric ecosystem, laying the groundwork for seamless coexistence in adversarial digital environments.
Researchers propose humanization capabilities for autonomous GUI agents to avoid detection by digital platforms, introducing a benchmark and methods to balance imitability with task performance.
autonomous GUI agents adversarial countermeasures human-centric ecosystems Turing Test on Screen MinMax optimization LMM-based agents behavioral divergence Agent Humanization Benchmark imitability task performance