arXiv:2605.29534v1 Announce Type: new Abstract: Recent advances in mobile GUI agents have shown strong potential for automating mobile tasks, but most effective systems still depend on large vision-language models for screenshot understanding and long-horizon planning. Small GUI agents that can be deployed directly on mobile devices are more attractive for practical use, offering lower inference cost and better protection of sensitive on-device information. However, due to limited model capacity, such lightweight agents remain unreliable when planning and executing GUI tasks end-to-end from sc
The proliferation of complex mobile tasks and the high inference costs of large AI models are driving the need for more efficient, on-device agent solutions.
This development suggests a pathway to more practical, widespread adoption of AI agents on mobile devices, addressing privacy concerns and reducing reliance on cloud infrastructure.
The focus shifts towards lightweight, knowledge-oriented GUI agents, enabling robust mobile automation directly on-device rather than solely relying on large cloud-based vision-language models.
- · Mobile device manufacturers
- · On-device AI developers
- · Consumers seeking privacy-preserving automation
- · Cloud-dependent large vision-language model providers (for this specific use cas
- · Companies with less efficient mobile GUI agent solutions
More mobile tasks become reliably automatable directly on user devices.
Increased user adoption of mobile AI agents due to enhanced privacy and reduced latency.
A new ecosystem of AI-powered mobile applications and services emerges, less reliant on continuous internet connectivity.
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Read at arXiv cs.AI