title: AppAgentX: Evolving GUI Agents as Proficient Smartphone Users
summary: Recent advancements in Large Language Models (LLMs) have led to the
  development of intelligent LLM-based agents capable of interacting with
  graphical user interfaces (GUIs). These agents demonstrate strong reasoning and
  adaptability, enabling them to perform complex tasks that traditionally
  required predefined rules. However, the reliance on step-by-step reasoning in
  LLM-based agents often results in inefficiencies, particularly for routine
  tasks. In contrast, traditional rule-based systems excel in efficiency but lack
  the intelligence and flexibility to adapt to novel scenarios. To address this
  challenge, we propose a novel evolutionary framework for GUI agents that
  enhances operational efficiency while retaining intelligence and flexibility.
  Our approach incorporates a memory mechanism that records the agent's task
  execution history. By analyzing this history, the agent identifies repetitive
  action sequences and evolves high-level actions that act as shortcuts,
  replacing these low-level operations and improving efficiency. This allows the
  agent to focus on tasks requiring more complex reasoning, while simplifying
  routine actions. Experimental results on multiple benchmark tasks demonstrate
  that our approach significantly outperforms existing methods in both efficiency
  and accuracy. The code will be open-sourced to support further research.
paper_authors._id: 67c7ebafdf15f5978ac987c3
paper_authors.name: Wenjia Jiang
paper_authors._id: 67c7ebafdf15f5978ac987c4
paper_authors.name: Yangyang Zhuang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-05T11:38:30.615Z
paper_authors._id: 673afd8879b5a48d37f38000
paper_authors.avatarUrl: /avatars/8e552c13b37c46734ed7a5d72469c6ad.svg
paper_authors.fullname: Zhuang Yangyang
paper_authors.type: user
paper_authors.user: yyzhuang2
paper_authors._id: 67c7ebafdf15f5978ac987c5
paper_authors.name: Chenxi Song
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-05T11:38:35.946Z
paper_authors._id: 65cf975bf5a15aa42146b208
paper_authors.avatarUrl: /avatars/d62e3907331a24bb899b7a1fdf4299e7.svg
paper_authors.fullname: Song
paper_authors.type: user
paper_authors.user: ChenxiSong
paper_authors._id: 67c7ebafdf15f5978ac987c6
paper_authors.name: Xu Yang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-05T11:38:43.389Z
paper_authors._id: 63104777cc8ed75decbd9500
paper_authors.avatarUrl: /avatars/f9002d8bffa7304296718321d4c6f58a.svg
paper_authors.fullname: xuyang
paper_authors.type: user
paper_authors.user: xuyang
paper_authors._id: 67c7ebafdf15f5978ac987c7
paper_authors.name: Chi Zhang
paper_title: AppAgentX: Evolving GUI Agents as Proficient Smartphone Users
paper_summary: Recent advancements in Large Language Models (LLMs) have led to the
  development of intelligent LLM-based agents capable of interacting with
  graphical user interfaces (GUIs). These agents demonstrate strong reasoning and
  adaptability, enabling them to perform complex tasks that traditionally
  required predefined rules. However, the reliance on step-by-step reasoning in
  LLM-based agents often results in inefficiencies, particularly for routine
  tasks. In contrast, traditional rule-based systems excel in efficiency but lack
  the intelligence and flexibility to adapt to novel scenarios. To address this
  challenge, we propose a novel evolutionary framework for GUI agents that
  enhances operational efficiency while retaining intelligence and flexibility.
  Our approach incorporates a memory mechanism that records the agent's task
  execution history. By analyzing this history, the agent identifies repetitive
  action sequences and evolves high-level actions that act as shortcuts,
  replacing these low-level operations and improving efficiency. This allows the
  agent to focus on tasks requiring more complex reasoning, while simplifying
  routine actions. Experimental results on multiple benchmark tasks demonstrate
  that our approach significantly outperforms existing methods in both efficiency
  and accuracy. The code will be open-sourced to support further research.
paper_ai_keywords: Large Language Models (LLMs) intelligent LLM-based agents graphical user interfaces (GUIs) reasoning adaptability step-by-step reasoning rule-based systems evolutionary framework GUI agents memory mechanism task execution history repetitive action sequences high-level actions low-level operations