MobileExplorer: Accelerating On-Device Inference for Mobile GUI Agents via Online Exploration

MobileExplorer is a new framework that enables faster on-device inference for mobile GUI agents by leveraging parallel exploration of UI elements during model reasoning time. The system reduces latency by 23% while maintaining or improving task success rates, addressing privacy and network dependency concerns in mobile AI applications.

MobileExplorer addresses a critical gap in mobile AI deployment: the tension between model capability and practical constraints. While cloud-hosted mobile GUI agents have demonstrated strong task accuracy, they introduce privacy vulnerabilities and network latency that limit real-world adoption. This research tackles on-device inference, a less-explored frontier that matters increasingly as users demand local processing for sensitive interactions.

The innovation centers on temporal efficiency—exploiting the inherent delay during vision-language model inference to conduct parallel exploration of UI elements. Rather than waiting passively for the model to reason, the system proactively maps relevant interface components and stores them as structured memory. This approach sidesteps the traditional bottleneck where inference time feels wasted. The two-level rollback mechanism adds robustness, recognizing that real mobile environments are unstable and naive backtracking strategies fail unpredictably.

For developers and device manufacturers, MobileExplorer offers tangible benefits: 23% latency reduction directly improves user experience and reduces computational load on devices. The maintained or improved task success rates suggest the exploration-enriched prompting strategy actually enhances reasoning quality. This matters for consumer privacy advocates and enterprises handling sensitive data.

The broader implications extend to edge AI deployment across domains. If similar exploration techniques prove effective for other on-device models, manufacturers could deploy more sophisticated AI features without relying on cloud infrastructure. The research signals that on-device AI remains viable despite the current trend toward larger, cloud-hosted models, potentially reshaping how companies approach mobile intelligence.

• →MobileExplorer reduces mobile GUI agent latency by 23% while maintaining task accuracy through intelligent parallel exploration during inference
• →On-device deployment eliminates privacy concerns and network dependencies inherent in cloud-hosted mobile AI systems
• →The framework uses a two-level rollback mechanism to ensure reliable execution in unstable real-world mobile environments
• →Parallel UI element exploration during model reasoning transforms idle inference time into productive contextual gathering
• →Results demonstrate that edge AI for mobile devices remains viable and competitive with cloud-based approaches