arXiv:2605.10347v2 Announce Type: replace Abstract: Recent advances in vision-language models have enabled mobile GUI agents to perceive visual interfaces and execute user instructions, but reliable prediction of action consequences remains critical for long-horizon and high-risk interactions. Existing mobile world models provide either text-based or image-based future states, yet it remains unclear which representation is useful, whether generated rollouts can replace real environments, and how test-time guidance helps agents of different strengths. To answer the above questions, we filter an
Advances in vision-language models have made mobile GUI agents feasible, leading to a critical need to understand how these agents interact with and predict future states in mobile environments.
Reliable action consequence prediction for mobile GUI agents is crucial for developing robust, autonomous systems capable of complex and sensitive long-horizon tasks.
The research into how mobile world models guide GUI agents will clarify the most effective representations for future state prediction, influencing the development direction of agentic systems.
- · AI agent developers
- · Mobile app developers
- · Generative AI platforms
- · Manual mobile UI testing
- · Inefficient AI agent development approaches
Improved mobile AI agents will automate more complex user interactions and tasks.
Ubiquitous, highly capable mobile AI agents could significantly streamline various digital workflows and customer support.
Enhanced agent autonomy on mobile devices might lead to new paradigms in human-computer interaction and device utility.
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Read at arXiv cs.AI