GUI-JEPA: Candidate-Conditioned JEPA Latents as a Fast Action Policy for Mobile GUI Agents

Authors: Bingchao Wang , Yongcheng Jing , Shunyu Liu , Chengyu Wang , Jun Huang , Jie Yang , Wei Liu , Dacheng Tao

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Abstract: Mobile GUI agents are typically fine-tuned 7B-scale vision-language models (VLMs) that generate each action as text. However, these VLMs decode every action token by token, and even agents that score candidate actions keep a large language model or VLM in the loop, making them slow and memory-hungry. To address these issues, we propose GUI-JEPA, to our knowledge the first decoder-free GUI agent policy built on a Joint-Embedding Predictive Architecture (JEPA), which selects actions in the latent space of a JEPA encoder pretrained on unlabelled GUI screenshots. Its action- and region-conditioned latent predictor turns the screen latent into one latent per candidate action, letting lightweight heads score all candidates against the instruction in a single batched pass, without decoding a token. We further show that the scorer relies on this latent pathway and that the predictor is trained for the decision itself: a randomly initialised predictor does just as well as one pretrained on screen dynamics. On AndroidControl, GUI-JEPA is 2.8-4.0x faster on the same GPU than a Qwen2.5-VL-7B agent we fine-tune with the same inputs, while reaching 86-92% of its in-distribution accuracy with 6% of its parameters. Code is available at this https URL

Comments:

26 pages, 11 figures, 16 tables

Subjects:

Artificial Intelligence (cs.AI) ; Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)

ACM classes:

I.2.6; I.2.10

Cite as:

arXiv:2610.36877 [cs.AI]

(or

arXiv:2610.36877v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2610.36877

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arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Bingchao Wang [ view email ]

[v1]

Tue, 27 Oct 2026 09:17:51 UTC (734 KB)

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