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Nanjing University
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Native desktop automation requires seamless GUI and code integration, a feat previously unachievable with prompt-level tool composition, but OS-Helix now unifies these into a single action space.
Effective visual tool use hinges on causally grounded supervision, not just imitating tool calls, reshaping how we train multimodal agents.
Even the best vision-language models struggle with reliable evaluation of computer-using agents, but OS-Shepherd models offer a low-cost solution that matches their performance.
OpenMobile proves that high-performing mobile agents can be trained on entirely synthetic, open-source data, closing the gap with closed-source models and enabling broader research.