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AI Research
arXiv CS.AI·May 27·5 min read
MobileExplorer: Accelerating On-Device Inference for Mobile GUI Agents via Online Exploration
TL;DR
Mobile GUI agents (AI systems that autonomously control smartphones) currently rely on cloud-hosted models, creating privacy and latency issues. MobileExplorer is a framework that enables fast inference directly on mobile devices through online exploration techniques.
✦ Why It Matters
Engineers can now deploy privacy-preserving mobile agents locally without cloud infrastructure or network latency penalties.
Key Takeaways
- Deploy mobile GUI agents directly on phones to eliminate cloud dependency and network latency.
- Use online exploration to identify and prioritize relevant screen regions, reducing computational cost.
- Maintain task accuracy while accelerating inference through selective processing of GUI elements.
- Enable privacy-preserving autonomous smartphone control without transmitting user data to remote servers.
Full Summary
Mobile GUI agents are AI systems trained to autonomously operate smartphone interfaces by interpreting visual screens and executing actions. Existing approaches depend on cloud-hosted inference, which introduces privacy risks, network latency, and user data exposure.
MobileExplorer addresses this gap by enabling efficient on-device inference—running AI models directly on the phone rather than sending data to remote servers. The framework uses online exploration, a technique that dynamically learns which parts of the screen are most important during task execution, reducing computational overhead.
By focusing inference on relevant GUI elements rather than processing entire screens, MobileExplorer accelerates inference speed while preserving accuracy. This enables fully on-device deployment, eliminating network dependency and protecting user privacy.
The work opens practical pathways for deploying mobile GUI agents in real-world scenarios where latency and privacy are critical constraints.