Core Viewpoint - The article highlights the launch of the GUI-Owl and Mobile-Agent-v3, which are advanced open-source models for GUI automation, showcasing superior performance compared to existing models and emphasizing their capabilities in various environments [1][29]. Group 1: Key Achievements - GUI-Owl has achieved state-of-the-art (SOTA) performance on both Android and desktop platforms, with the 32B model surpassing closed-source top models in multiple evaluations [21][29]. - The models are designed to operate in a cloud environment, allowing for dynamic task execution and data collection across multiple operating systems, including Android, Ubuntu, macOS, and Windows [11][29]. Group 2: Technical Innovations - The system employs a self-evolving data production chain that minimizes human involvement in generating high-quality training data, allowing the models to iteratively optimize themselves [11][14]. - GUI-Owl's capabilities include advanced UI element grounding, long task planning, and robust reasoning, enabling it to understand and execute complex tasks effectively [16][20]. Group 3: Reinforcement Learning Framework - A scalable reinforcement learning (RL) system has been developed to enhance the model's stability and adaptability in real-world environments, allowing it to learn continuously from its interactions [22][26]. - The introduction of the Trajectory-aware Relative Policy Optimization (TRPO) algorithm addresses the challenges of sparse and delayed reward signals in GUI automation tasks, improving learning efficiency [26]. Group 4: Conclusion - The release of GUI-Owl and Mobile-Agent-v3 represents a significant advancement in open-source GUI automation, providing a powerful tool for various applications while reducing deployment and resource costs [29].