# 移动GUI智能体隐私个性化：基于轨迹诱导偏好优化

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-04-13 08:00
- AIHOT 链接：https://aihot.virxact.com/items/cmnykz7zs007jsl5bp2dhlddn
- 原文链接：https://arxiv.org/abs/2604.11259

## AI 摘要

针对移动GUI智能体忽视用户隐私个性化需求的问题，研究者提出轨迹诱导偏好优化框架TIPO。该方法通过偏好强度加权突出关键隐私步骤，并采用填充门控抑制对齐噪声，有效解决了隐私优先与效用优先用户间轨迹结构异质性导致的优化不稳定难题。在Privacy Preference Dataset上的测试显示，TIPO在保持任务可执行性的同时，实现65.60%成功率、46.22合规性得分和66.67%隐私区分度，显著优于现有优化方法。相关代码与数据集已开源。

## 正文

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., 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. 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.