type: concept tags: [privacy, gui-agent, personalization, mobile, preference-optimization] related: [[mobile-agent-framework]], [[pspa-bench-gui-agent]], [[sustainability-ondevice-intelligence]] sources: - url: https://arxiv.org/abs/2604.11259v1 title: "Mobile GUI Agent Privacy Personalization with Trajectory Induced Preference Optimization" date: 2026-04 created: 2026-04-14

Mobile GUI Agent Privacy Personalization with Trajectory Induced Preference Optimization¶

核心问题¶

Mobile GUI agents powered by Multimodal Large Language Models (MLLMs) can execute complex tasks on mobile devices.

方法/架构¶

基于论文摘要，该方法包含以下关键创新点：

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.

实验结果¶

论文报告了以下主要实验结果：

For example, privacy-first users often prefer protective actions, e.g., refusing permissions, logging out, and minimizing exposure, leading to logically different execution trajectories from utility-first users.
Such variable-length and structurally different trajectories make standard preference optimization unstable and less informative.
To address this issue, we propose Trajectory Induced Preference Optimization (TIPO), which uses preference-intensity weighting to emphasize key privacy-related steps and padding gating to suppress alignment noise.

为什么重要¶

该研究的重要性体现在：

Results on our Privacy Preference Dataset show that TIPO improves persona alignment and distinction while preserving strong task executability, achieving 65.60% SR, 46.22 Compliance, and 66.67% PD, outperforming existing optimization methods across various GUI tasks.
The code and dataset will be publicly released at https://github.com/Zhixin-L/TIPO.

关联¶

基于论文内容和研究领域，该工作与以下概念相关：

[mobile-agent-framework

参考资源¶

论文原文：https://arxiv.org/abs/2604.11259