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Mobile agents can now navigate complex GUIs with unprecedented efficiency, thanks to a novel data-environment co-scaling framework.
Task success rates for agentic phone use soar from 36.67% to 45.33% through a novel combination of real and mock environments in training.
Coding agents struggle to create complete and engaging games, with top performers barely reaching 41.46% success in end-to-end game generation.
Reliable phone automation hinges on mixed-action capabilities, with agents achieving a 75% success rate in real-world workflows.
Gradual bridging with embodied trajectory-coupled data transforms VLMs into robust robot control policies, overcoming significant transfer challenges.
LLM agents still fail to reliably automate real-world workflows, with even the best models succeeding on only two-thirds of tasks in a new live benchmark.
Current phone-use agents are often *too* helpful, routinely violating user privacy by filling in unnecessary personal information even when a task doesn't require it.
