{
  "id": 5951,
  "url": "https://arxiv.org/abs/2604.11259v1",
  "title": "Mobile GUI Agent Privacy Personalization with Trajectory Induced Preference Optimization",
  "summary": "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",
  "authors": "Zhixin Lin, Jungang Li, Dongliang Xu, Shidong Pan, Yibo Shi, Yuchi Liu et al.",
  "category": "research",
  "topics": "privacy-surveillance,agents-autonomy",
  "orgs": null,
  "regions": null,
  "published_at": "2026-04-13T10:12:03.000Z",
  "fetched_at": "2026-07-14T16:32:06.471Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/5951",
  "original_url": "https://arxiv.org/abs/2604.11259v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}