How Mobile World Model Guides GUI Agents?
A new research paper submitted to arXiv investigates the role of mobile world models in guiding Graphical User Interface (GUI) agents. While vision-language models have improved agent perception, predicting action consequences for complex tasks remains challenging. The authors trained world models across four modalities: delta text, full text, diffusion-based images, and renderable code. These models achieved state-of-the-art performance on MobileWorldBench and Code2WorldBench. Key findings indicate that renderable code offers high fidelity for data construction, whereas text-based feedback is more robust for online out-of-distribution execution. Additionally, world-model-generated trajectories enhance end-to-end task performance by providing transferable interaction experience during training, despite not preserving original data distributions. The study concludes that world models are most effective as prior perception tools or training supervision rather than universal post-hoc verifiers, particularly for overconfident agents with low action entropy. This research contributes to advancing autonomous mobile agents by clarifying optimal representation strategies and integration methods for improved reliability in high-risk interactions.
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How Mobile World Model Guides GUI Agents?
A new research paper submitted to arXiv investigates the role of mobile world models in guiding Graphical User Interface (GUI) agents. While vision-language models have improved agent perception, predicting action consequences for complex tasks remains challenging. The authors trained world models across four modalities: delta text, full text, diffusion-based images, and renderable code. These models achieved state-of-the-art performance on MobileWorldBench and Code2WorldBench. Key findings indicate that renderable code offers high fidelity for data construction, whereas text-based feedback is more robust for online out-of-distribution execution. Additionally, world-model-generated trajectories enhance end-to-end task performance by providing transferable interaction experience during training, despite not preserving original data distributions. The study concludes that world models are most effective as prior perception tools or training supervision rather than universal post-hoc verifiers, particularly for overconfident agents with low action entropy. This research contributes to advancing autonomous mobile agents by clarifying optimal representation strategies and integration methods for improved reliability in high-risk interactions.
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