Mobile-Agent-v3.5: Multi-platform Fundamental GUI Agents
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , , , , , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2026
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866908840442724352 |
|---|---|
| author | Xu, Haiyang Zhang, Xi Liu, Haowei Wang, Junyang Zhu, Zhaozai Zhou, Shengjie Hu, Xuhao Gao, Feiyu Cao, Junjie Wang, Zihua Chen, Zhiyuan Liao, Jitong Zheng, Qi Zeng, Jiahui Xu, Ze Bai, Shuai Lin, Junyang Zhou, Jingren Yan, Ming |
| author_facet | Xu, Haiyang Zhang, Xi Liu, Haowei Wang, Junyang Zhu, Zhaozai Zhou, Shengjie Hu, Xuhao Gao, Feiyu Cao, Junjie Wang, Zihua Chen, Zhiyuan Liao, Jitong Zheng, Qi Zeng, Jiahui Xu, Ze Bai, Shuai Lin, Junyang Zhou, Jingren Yan, Ming |
| contents | The paper introduces GUI-Owl-1.5, the latest native GUI agent model that features instruct/thinking variants in multiple sizes (2B/4B/8B/32B/235B) and supports a range of platforms (desktop, mobile, browser, and more) to enable cloud-edge collaboration and real-time interaction. GUI-Owl-1.5 achieves state-of-the-art results on more than 20+ GUI benchmarks on open-source models: (1) on GUI automation tasks, it obtains 56.5 on OSWorld, 71.6 on AndroidWorld, and 48.4 on WebArena; (2) on grounding tasks, it obtains 80.3 on ScreenSpotPro; (3) on tool-calling tasks, it obtains 47.6 on OSWorld-MCP, and 46.8 on MobileWorld; (4) on memory and knowledge tasks, it obtains 75.5 on GUI-Knowledge Bench. GUI-Owl-1.5 incorporates several key innovations: (1) Hybird Data Flywheel: we construct the data pipeline for UI understanding and trajectory generation based on a combination of simulated environments and cloud-based sandbox environments, in order to improve the efficiency and quality of data collection. (2) Unified Enhancement of Agent Capabilities: we use a unified thought-synthesis pipeline to enhance the model's reasoning capabilities, while placing particular emphasis on improving key agent abilities, including Tool/MCP use, memory and multi-agent adaptation; (3) Multi-platform Environment RL Scaling: We propose a new environment RL algorithm, MRPO, to address the challenges of multi-platform conflicts and the low training efficiency of long-horizon tasks. The GUI-Owl-1.5 models are open-sourced, and an online cloud-sandbox demo is available at https://github.com/X-PLUG/MobileAgent. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_16855 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Mobile-Agent-v3.5: Multi-platform Fundamental GUI Agents Xu, Haiyang Zhang, Xi Liu, Haowei Wang, Junyang Zhu, Zhaozai Zhou, Shengjie Hu, Xuhao Gao, Feiyu Cao, Junjie Wang, Zihua Chen, Zhiyuan Liao, Jitong Zheng, Qi Zeng, Jiahui Xu, Ze Bai, Shuai Lin, Junyang Zhou, Jingren Yan, Ming Artificial Intelligence Computation and Language The paper introduces GUI-Owl-1.5, the latest native GUI agent model that features instruct/thinking variants in multiple sizes (2B/4B/8B/32B/235B) and supports a range of platforms (desktop, mobile, browser, and more) to enable cloud-edge collaboration and real-time interaction. GUI-Owl-1.5 achieves state-of-the-art results on more than 20+ GUI benchmarks on open-source models: (1) on GUI automation tasks, it obtains 56.5 on OSWorld, 71.6 on AndroidWorld, and 48.4 on WebArena; (2) on grounding tasks, it obtains 80.3 on ScreenSpotPro; (3) on tool-calling tasks, it obtains 47.6 on OSWorld-MCP, and 46.8 on MobileWorld; (4) on memory and knowledge tasks, it obtains 75.5 on GUI-Knowledge Bench. GUI-Owl-1.5 incorporates several key innovations: (1) Hybird Data Flywheel: we construct the data pipeline for UI understanding and trajectory generation based on a combination of simulated environments and cloud-based sandbox environments, in order to improve the efficiency and quality of data collection. (2) Unified Enhancement of Agent Capabilities: we use a unified thought-synthesis pipeline to enhance the model's reasoning capabilities, while placing particular emphasis on improving key agent abilities, including Tool/MCP use, memory and multi-agent adaptation; (3) Multi-platform Environment RL Scaling: We propose a new environment RL algorithm, MRPO, to address the challenges of multi-platform conflicts and the low training efficiency of long-horizon tasks. The GUI-Owl-1.5 models are open-sourced, and an online cloud-sandbox demo is available at https://github.com/X-PLUG/MobileAgent. |
| title | Mobile-Agent-v3.5: Multi-platform Fundamental GUI Agents |
| topic | Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2602.16855 |