arXiv:2606.14832v1 Announce Type: new Abstract: Phone agents are increasingly expected to complete real mobile workflows rather than merely predict the next screen action. However, much of the current mobile-agent literature still evaluates agents primarily as GUI controllers that observe a screen, emit taps and swipes, and are scored by target app state. Real phone-use tasks are broader: they require deciding when to use app GUIs, device-side commands, or structured tools, while leaving evidence that the intended side effect actually occurred. We introduce PhoneHarness, a mixed-action benchma
The paper introduces a benchmark (PhoneHarness) addressing the limitations of current mobile-agent evaluations, which are crucial as agents are increasingly expected to complete complex real-world mobile workflows.
This development pushes AI agents beyond simple GUI control towards multimodal and more autonomous phone operation, indicating a faster path to agents handling real-world tasks on mobile devices.
The evaluation of mobile AI agents will now incorporate mixed GUI, CLI, and tool actions, moving beyond mere screen interaction and into more comprehensive device control.
- · AI agent developers
- · Mobile app developers
- · Cloud service providers
- · Device manufacturers
- · Monotonous digital labor
- · Traditional mobile automation tools
More sophisticated and versatile AI agents capable of operating mobile devices more autonomously.
Increased adoption of AI agents for personal and professional mobile tasks, leading to efficiency gains across various sectors.
New business models emerging around agentic mobile workflows, potentially disrupting existing app ecosystems and human-driven service industries.
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