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This work proposes EvoSkill-GUI, a training-free framework in which each skill is a structured multi-file package containing retrieval metadata, executable plans, backup localization, failure-recovery rules, accessibility utilities, and failure cases.
Generating over 203,000 unique web interaction trajectories, BrowserForge significantly boosts model performance on real-world tasks by leveraging the vastness of the open web.
Forget hand-crafting mobile benchmarks – PhoneWorld lets you automatically generate them from real-world GUI trajectories, leading to massive performance gains for phone-use agents.
Uncertainty-driven zoom-in boosts GUI grounding accuracy by up to 13.4% without any retraining, showing that targeted attention to model uncertainty can significantly improve performance.
Offloading memory and computation to a copilot lets a 7B parameter GUI agent outperform larger models on long-horizon tasks, suggesting a path to more efficient and capable GUI automation.
Finally, a unified open-source framework lets you train, evaluate, and deploy GUI agents across real devices and chat platforms, closing the gap between research and real-world application.
Even frontier models like Claude Sonnet 4.6 stumble when asked to infer user preferences and proactively assist in mobile tasks, achieving less than 50% success despite excelling at explicit task execution.
