arXiv:2605.29486v1 Announce Type: cross Abstract: A central bottleneck for phone-use agents is that controllable, reproducible environments covering real mobile behavior are hard to build at scale. Existing mobile-agent benchmarks have made important progress on evaluation, but they do not by themselves provide a scalable way to construct many new phone-use environments. We present PhoneWorld, a reusable pipeline that converts real GUI trajectories and screenshots into controllable phone-use environments, executable tasks, automatic verifiers, and training rollouts. Rather than hand-building o
The increasing sophistication of AI models and the critical need for scalable, real-world interaction environments are converging to make phone-use agents a viable and necessary next step for AI development.
This development addresses a key bottleneck in AI agent training, enabling more robust, real-world capable agents that can operate across various digital interfaces, potentially revolutionizing how humans interact with technology.
The ability to generate a multitude of 'real' phone-use environments automatically changes the development paradigm from hand-built, limited scenarios to scalable, data-driven agent training and evaluation.
- · AI Agent developers
- · Mobile OS platforms
- · Application developers
- · AI research institutions
- · Manual software testers
- · Companies reliant on limited, bespoke AI environments
AI agents will become significantly more adept at navigating complex mobile interfaces and completing real-world tasks.
This improved capability could lead to pervasive AI agents automating many individual mobile tasks, shifting user interaction paradigms.
The widespread adoption of highly capable phone-use agents might fundamentally alter job roles involving repetitive digital tasks, increasing productivity but also prompting workforce re-skilling.
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