Mobile-Agent-v3: Foundamental Agents for GUI Automation
This paper introduces GUI-Owl, a foundational GUI agent model that achieves
state-of-the-art performance among open-source end-to-end models on ten GUI
benchmarks across desktop and mobile environments, covering grounding, question
answering, planning, decision-making, and procedural knowledge. GUI-Owl-7B
achieves 66.4 on AndroidWorld and 29.4 on OSWorld. Building on this, we propose
Mobile-Agent-v3, a general-purpose GUI agent framework that further improves
performance to 73.3 on AndroidWorld and 37.7 on OSWorld, setting a new
state-of-the-art for open-source GUI agent frameworks. GUI-Owl incorporates
three key innovations: (1) Large-scale Environment Infrastructure: a
cloud-based virtual environment spanning Android, Ubuntu, macOS, and Windows,
enabling our Self-Evolving GUI Trajectory Production framework. This generates
high-quality interaction data via automated query generation and correctness
validation, leveraging GUI-Owl to refine trajectories iteratively, forming a
self-improving loop. It supports diverse data pipelines and reduces manual
annotation. (2) Diverse Foundational Agent Capabilities: by integrating UI
grounding, planning, action semantics, and reasoning patterns, GUI-Owl supports
end-to-end decision-making and can act as a modular component in multi-agent
systems. (3) Scalable Environment RL: we develop a scalable reinforcement
learning framework with fully asynchronous training for real-world alignment.
We also introduce Trajectory-aware Relative Policy Optimization (TRPO) for
online RL, achieving 34.9 on OSWorld. GUI-Owl and Mobile-Agent-v3 are
open-sourced at https://github.com/X-PLUG/MobileAgent.
68a7cb8d39413c456c05b082
Jiabo Ye
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2025-08-25T18:05:37.268Z
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Jiabo Ye
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Xi Zhang
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Haiyang Xu
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2025-08-25T08:27:25.952Z
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xuhaiyang
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Haowei Liu
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Junyang Wang
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Zhaoqing Zhu
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Ziwei Zheng
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Feiyu Gao
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Junjie Cao
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Zhengxi Lu
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2025-08-22T07:22:04.351Z
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Zhengxi Lu
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Jitong Liao
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Qi Zheng
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Fei Huang
68a7cb8d39413c456c05b08f
Jingren Zhou
68a7cb8d39413c456c05b090
Ming Yan
Mobile-Agent-v3: Foundamental Agents for GUI Automation
This paper introduces GUI-Owl, a foundational GUI agent model that achieves
state-of-the-art performance among open-source end-to-end models on ten GUI
benchmarks across desktop and mobile environments, covering grounding, question
answering, planning, decision-making, and procedural knowledge. GUI-Owl-7B
achieves 66.4 on AndroidWorld and 29.4 on OSWorld. Building on this, we propose
Mobile-Agent-v3, a general-purpose GUI agent framework that further improves
performance to 73.3 on AndroidWorld and 37.7 on OSWorld, setting a new
state-of-the-art for open-source GUI agent frameworks. GUI-Owl incorporates
three key innovations: (1) Large-scale Environment Infrastructure: a
cloud-based virtual environment spanning Android, Ubuntu, macOS, and Windows,
enabling our Self-Evolving GUI Trajectory Production framework. This generates
high-quality interaction data via automated query generation and correctness
validation, leveraging GUI-Owl to refine trajectories iteratively, forming a
self-improving loop. It supports diverse data pipelines and reduces manual
annotation. (2) Diverse Foundational Agent Capabilities: by integrating UI
grounding, planning, action semantics, and reasoning patterns, GUI-Owl supports
end-to-end decision-making and can act as a modular component in multi-agent
systems. (3) Scalable Environment RL: we develop a scalable reinforcement
learning framework with fully asynchronous training for real-world alignment.
We also introduce Trajectory-aware Relative Policy Optimization (TRPO) for
online RL, achieving 34.9 on OSWorld. GUI-Owl and Mobile-Agent-v3 are
open-sourced at https://github.com/X-PLUG/MobileAgent.
GUI-Owl and Mobile-Agent-v3 are open-source GUI agent models and frameworks that achieve state-of-the-art performance across various benchmarks using innovations in environment infrastructure, agent capabilities, and scalable reinforcement learning.
GUI agent model Self-Evolving GUI Trajectory Production UI grounding planning action semantics reasoning patterns multi-agent systems scalable reinforcement learning fully asynchronous training Trajectory-aware Relative Policy Optimization (TRPO)