Jev-Mobile: Jev as an Executor for Mobile GUI Agents
Jev-Mobile splits VLM planning from fast GUI execution, cutting AndroidWorld API cost 73% at similar success.
Jev-Mobile separates low-frequency vision-language planning from high-frequency action execution for mobile GUI agents. The VLM specifies local goals, the accessibility tree defines the action space, and a fast typed model, Jev, selects actions so several steps follow one VLM decision. On the full AndroidWorld suite it reached 79% success, compared with 78% for SeeAct-V and 84% for a step-wise VLM. Among successful trajectories, mean runtime fell 32.7% and mean API cost fell 73.4% versus the step-wise baseline.
- A VLM sets local goals while Jev selects actions in the accessibility tree.
- AndroidWorld success was 79% for Jev-Mobile, 78% SeeAct-V, and 84% step-wise VLM.
- Successful runs cut mean time 32.7% and API cost 73.4% versus step-wise VLM.
- Products
- Jev-MobileAndroidWorldSeeAct-V
From the original article
1 min read at arxiv.orgVision-language models (VLMs) have become a common foundation for autonomous mobile GUI agents, but most existing systems rely on the VLM for both planning and action grounding at nearly every interaction step, leading to substantial latency and model-serving cost.
By Linghua Zhang · arxiv.org
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