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

• URL: http://arxiv.org/abs/2509.02444v1
• Date: Tue, 02 Sep 2025 15:48:21 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, Zhong Zhang, Yaxi Lu, Yankai Lin, Zhiyuan Liu, Dahai Li, Chen Qian,
• Abstract summary: This paper identifies four core problems that must be solved for mobile agents to deliver practical, scalable impact.<n>We present AppCopilot, a multimodal, multi-agent, general-purpose on-device assistant.<n>AppCopilot operates across applications and constitutes a full-stack, closed-loop system from data to deployment.
• Score: 49.61420186190895
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the raid evolution of large language models and multimodal foundation models, the mobile-agent landscape has proliferated without converging on the fundamental challenges. This paper identifies four core problems that must be solved for mobile agents to deliver practical, scalable impact: (1) generalization across tasks, modalities, 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 on-device assistant that operates across applications and constitutes a full-stack, closed-loop system from data to deployment. AppCopilot operationalizes this position through an end-to-end autonomous pipeline spanning data collection, training, deployment, high-quality and efficient inference, and mobile application development. 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 user personalization and 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, memory, and energy across heterogeneous hardware. Empirically, AppCopilot achieves significant improvements along all four dimensions: stronger generalization, higher-precision on-screen actions, more reliable long-horizon task completion, and faster, more resource-efficient runtime.

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