Mobile GUI Agent Privacy Personalization with Trajectory Induced Preference Optimization
Mobile GUI agents powered by Multimodal Large Language Models (MLLMs) can execute complex tasks on mobile devices. Despite this progress, most existing systems still optimize task success or efficiency, neglecting users' privacy personalization. In this paper, we study the often-overlooked problem of agent personalization. We observe that personalization can induce systematic structural heterogeneity in execution trajectories. For example, privacy-first users often prefer protective actions, e.g
Record details
Published: 13 April 2026
Source: arXiv
Category: Research
Topics: Privacy · Agents & autonomy
Retrieved: 14 July 2026
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ethics.ai (13 April 2026), “Mobile GUI Agent Privacy Personalization with Trajectory Induced Preference Optimization,” evidence record 5951, https://ethics.ai/record/5951 (originally published by arXiv).
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