Xiaomi-GUI-0 Technical Report
📰 ArXiv cs.AI
Learn how to build GUI agents that interact with real-world applications using vision-language models and interface actions
Action Steps
- Build a GUI agent using a vision-language model to complete user tasks end-to-end
- Train the agent on real-world applications instead of offline trajectories or simulated environments
- Evaluate the agent's performance on interface actions such as tapping, swiping, text entry, and navigation
- Compare the agent's performance with existing GUI agents trained on standardized benchmarks
- Apply the findings to improve the agent's interaction logic and handling of abnormal states
Who Needs to Know This
AI engineers and researchers working on GUI agents and vision-language models can benefit from this technical report to improve their agent's performance in real-world applications
Key Insight
💡 GUI agents can be improved by training and evaluating them on real-world applications instead of offline trajectories or simulated environments
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🤖 Build GUI agents that interact with real-world apps using vision-language models! 📊
Key Takeaways
Learn how to build GUI agents that interact with real-world applications using vision-language models and interface actions
Full Article
Title: Xiaomi-GUI-0 Technical Report
Abstract:
arXiv:2606.31410v1 Announce Type: new Abstract: Graphical user interface (GUI) agents build on vision-language models to complete user tasks end-to-end in real applications through interface actions such as tapping, swiping, text entry, and navigation. However, existing GUI agents are trained and evaluated largely on offline trajectories, simulated environments, and standardized benchmarks. These differ substantially from real applications in interface layout, interaction logic, and abnormal-sta
Abstract:
arXiv:2606.31410v1 Announce Type: new Abstract: Graphical user interface (GUI) agents build on vision-language models to complete user tasks end-to-end in real applications through interface actions such as tapping, swiping, text entry, and navigation. However, existing GUI agents are trained and evaluated largely on offline trajectories, simulated environments, and standardized benchmarks. These differ substantially from real applications in interface layout, interaction logic, and abnormal-sta
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