AppCopilot: Toward General, Accurate, Long-Horizon, and Efficient Mobile Agent

• URL: http://arxiv.org/abs/2509.02444v2
• Date: Fri, 17 Oct 2025 00:57:58 GMT
• Title: AppCopilot: Toward General, Accurate, Long-Horizon, and Efficient Mobile Agent
• Authors: Jingru Fan, Yufan Dang, Jingyao Wu, Huatao Li, Runde Yang, Xiyuan Yang, Yuheng Wang, Chen Qian,
• Abstract summary: This paper identifies four core problems that should be solved for mobile agents to deliver practical, scalable impact.<n>We present AppCopilot, a multimodal, multi-agent, general-purpose mobile agent that operates across applications.
• Score: 12.27790226999309
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the raid evolution of large language models and multimodal models, the mobile-agent landscape has proliferated without converging on the fundamental challenges. This paper identifies four core problems that should be solved for mobile agents to deliver practical, scalable impact: (1) generalization across tasks, APPs, and devices; (2) accuracy, specifically precise on-screen interaction and click targeting; (3) long-horizon capability for sustained, multi-step goals; and (4) efficiency, specifically high-performance runtime on resource-constrained devices. We present AppCopilot, a multimodal, multi-agent, general-purpose mobile agent that operates across applications. AppCopilot operationalizes this position through an end-to-end pipeline spanning data collection, training, finetuning, efficient inference, and PC/mobile application. At the model layer, it integrates multimodal foundation models with robust Chinese-English support. At the reasoning and control layer, it combines chain-of-thought reasoning, hierarchical task planning and decomposition, and multi-agent collaboration. At the execution layer, it enables experiential adaptation, voice interaction, function calling, cross-APP and cross-device orchestration, and comprehensive mobile APP support. The system design incorporates profiling-driven optimization for latency and memory across heterogeneous hardware. Empirically, AppCopilot achieves significant improvements on four dimensions: stronger generalization, higher precision of on screen actions, more reliable long horizon task completion, and faster, more resource efficient runtime. By articulating a cohesive position and a reference architecture that closes the loop from data collection, training to finetuning and efficient inference, this paper offers a concrete roadmap for general purpose mobile agent and provides actionable guidance.

Related papers

• Hi-Agent: Hierarchical Vision-Language Agents for Mobile Device Control [72.43808515668947]
  We introduce Hi-Agent, a trainable hierarchical vision-language agent for mobile control.<n>Hi-Agent features a high-level reasoning model and a low-level action model that are jointly optimized.<n>Hi-Agent achieves a new State-Of-The-Art (SOTA) 87.9% task success rate on the Android-in-the-Wild (AitW) benchmark.
  arXiv  Detail & Related papers  (2025-10-16T07:38:21Z)
• MobileSteward: Integrating Multiple App-Oriented Agents with Self-Evolution to Automate Cross-App Instructions [45.7564684180131]
  Mobile phone agents can assist people in automating daily tasks on their phones.<n>Existing procedure-oriented agents struggle with cross-app instructions.<n>We propose a self-evolving multi-agent framework named MobileSteward.
  arXiv  Detail & Related papers  (2025-02-24T03:12:45Z)
• Mobile-Agent-E: Self-Evolving Mobile Assistant for Complex Tasks [85.48034185086169]
  Mobile-Agent-E is a hierarchical multi-agent framework capable of self-evolution through past experience.<n>Mobile-Agent-E achieves a 22% absolute improvement over previous state-of-the-art approaches.
  arXiv  Detail & Related papers  (2025-01-20T20:35:46Z)
• Foundations and Recent Trends in Multimodal Mobile Agents: A Survey [72.29426995154088]
  Mobile agents are essential for automating tasks in complex and dynamic mobile environments.<n>Recent advancements enhance real-time adaptability and multimodal interaction.<n>We categorize these advancements into two main approaches: prompt-based methods and training-based methods.
  arXiv  Detail & Related papers  (2024-11-04T11:50:58Z)
• SPA-Bench: A Comprehensive Benchmark for SmartPhone Agent Evaluation [89.24729958546168]
  Smartphone agents are increasingly important for helping users control devices efficiently.<n>We present SPA-Bench, a comprehensive SmartPhone Agent Benchmark designed to evaluate (M)LLM-based agents.
  arXiv  Detail & Related papers  (2024-10-19T17:28:48Z)
• AppAgent v2: Advanced Agent for Flexible Mobile Interactions [46.789563920416626]
  This work introduces a novel LLM-based multimodal agent framework for mobile devices. Our agent constructs a flexible action space that enhances adaptability across various applications. Our results demonstrate the framework's superior performance, confirming its effectiveness in real-world scenarios.
  arXiv  Detail & Related papers  (2024-08-05T06:31:39Z)
• Mobile-Agent-v2: Mobile Device Operation Assistant with Effective Navigation via Multi-Agent Collaboration [52.25473993987409]
  We propose Mobile-Agent-v2, a multi-agent architecture for mobile device operation assistance. The architecture comprises three agents: planning agent, decision agent, and reflection agent. We show that Mobile-Agent-v2 achieves over a 30% improvement in task completion compared to the single-agent architecture.
  arXiv  Detail & Related papers  (2024-06-03T05:50:00Z)
• AgentScope: A Flexible yet Robust Multi-Agent Platform [66.64116117163755]
  AgentScope is a developer-centric multi-agent platform with message exchange as its core communication mechanism. The abundant syntactic tools, built-in agents and service functions, user-friendly interfaces for application demonstration and utility monitor, zero-code programming workstation, and automatic prompt tuning mechanism significantly lower the barriers to both development and deployment.
  arXiv  Detail & Related papers  (2024-02-21T04:11:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.