{"ID":2898478,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.03730","arxiv_id":"2507.03730","title":"Less is More: Empowering GUI Agent with Context-Aware Simplification","abstract":"The research focus of GUI agents is shifting from text-dependent to pure-vision-based approaches, which, though promising, prioritize comprehensive pre-training data collection while neglecting contextual modeling challenges. We probe the characteristics of element and history contextual modeling in GUI agent and summarize: 1) the high-density and loose-relation of element context highlight the existence of many unrelated elements and their negative influence; 2) the high redundancy of history context reveals the inefficient history modeling in current GUI agents. In this work, we propose a context-aware simplification framework for building an efficient and effective GUI Agent, termed SimpAgent. To mitigate potential interference from numerous unrelated elements, we introduce a masking-based element pruning method that circumvents the intractable relation modeling through an efficient masking mechanism. To reduce the redundancy in historical information, we devise a consistency-guided history compression module, which enhances implicit LLM-based compression through innovative explicit guidance, achieving an optimal balance between performance and efficiency. With the above components, SimpAgent reduces 27% FLOPs and achieves superior GUI navigation performances. Comprehensive navigation experiments across diverse web and mobile environments demonstrate the effectiveness and potential of our agent.","short_abstract":"The research focus of GUI agents is shifting from text-dependent to pure-vision-based approaches, which, though promising, prioritize comprehensive pre-training data collection while neglecting contextual modeling challenges. We probe the characteristics of element and history contextual modeling in GUI agent and summa...","url_abs":"https://arxiv.org/abs/2507.03730","url_pdf":"https://arxiv.org/pdf/2507.03730v1","authors":"[\"Gongwei Chen\",\"Xurui Zhou\",\"Rui Shao\",\"Yibo Lyu\",\"Kaiwen Zhou\",\"Shuai Wang\",\"Wentao Li\",\"Yinchuan Li\",\"Zhongang Qi\",\"Liqiang Nie\"]","published":"2025-07-04T17:37:15Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.AI\",\"cs.HC\",\"cs.LG\"]","methods":"[\"Large Language Model\"]","has_code":false}