Step-GUI Technical Report
Authors: name: Haolong Yan; name: Jia Wang; name: Xin Huang; name: Yeqing Shen; name: Ziyang Meng; name: Zhimin Fan; name: Kaijun Tan; name: Jin Gao; name: Lieyu Shi; name: Mi Yang; name: Shiliang Yang; name: Zhirui Wang; name: Brian Li; name: Kang An; name: Chenyang Li; name: Lei Lei; name: Mengmeng Duan; name: Danxun Liang; name: Guodong Liu; name: Hang Cheng; name: Hao Wu; name: Jie Dong; name: Junhao Huang; name: Mei Chen; name: Renjie Yu; name: Shunshan Li; name: Xu Zhou; name: Yiting Dai; name: Yineng Deng; name: Yingdan Liang; name: Zelin Chen; name: Wen Sun; name: Chengxu Yan; name: Chunqin Xu; name: Dong Li; name: Fengqiong Xiao; name: Guanghao Fan; name: Guopeng Li; name: Guozhen Peng; name: Hongbing Li; name: Hang Li; name: Hongming Chen; name: Jingjing Xie; name: Jianyong Li; name: Jingyang Zhang; name: Jiaju Ren; name: Jiayu Yuan; name: Jianpeng Yin; name: Kai Cao; name: Liang Zhao; name: Liguo Tan; name: Liying Shi; name: Mengqiang Ren; name: Min Xu; name: Manjiao Liu; name: Mao Luo; name: Mingxin Wan; name: Na Wang; name: Nan Wu; name: Ning Wang; name: Peiyao Ma; name: Qingzhou Zhang; name: Qiao Wang; name: Qinlin Zeng; name: Qiong Gao; name: Qiongyao Li; name: Shangwu Zhong; name: Shuli Gao; name: Shaofan Liu; name: Shisi Gao; name: Shuang Luo; name: Xingbin Liu; name: Xiaojia Liu; name: Xiaojie Hou; name: Xin Liu; name: Xuanti Feng; name: Xuedan Cai; name: Xuan Wen; name: Xianwei Zhu; name: Xin Liang; name: Xin Liu; name: Xin Zhou; name: Yingxiu Zhao; name: Yukang Shi; name: Yunfang Xu; name: Yuqing Zeng; name: Yixun Zhang; name: Zejia Weng; name: Zhonghao Yan; name: Zhiguo Huang; name: Zhuoyu Wang; name: Zheng Ge; name: Jing Li; name: Yibo Zhu; name: Binxing Jiao; name: Xiangyu Zhang; name: Daxin Jiang
Published: 2025-12-17 13:26:30

Abstract: Recent advances in multimodal large language models unlock unprecedented opportunities for GUI automation. However, a fundamental challenge remains: how to efficiently acquire high-quality training data while maintaining annotation reliability? We introduce a self-evolving training pipeline powered by the Calibrated Step Reward System, which converts model-generated trajectories into reliable training signals through trajectory-level calibration, achieving >90% annotation accuracy with 10-100x lower cost. Leveraging this pipeline, we introduce Step-GUI, a family of models (4B/8B) that achieves state-of-the-art GUI performance (8B: 80.2% AndroidWorld, 48.5% OSWorld, 62.6% ScreenShot-Pro) while maintaining robust general capabilities. As GUI agent capabilities improve, practical deployment demands standardized interfaces across heterogeneous devices while protecting user privacy. To this end, we propose GUI-MCP, the first Model Context Protocol for GUI automation with hierarchical architecture that combines low-level atomic operations and high-level task delegation to local specialist models, enabling high-privacy execution where sensitive data stays on-device. Finally, to assess whether agents can handle authentic everyday usage, we introduce AndroidDaily, a benchmark grounded in real-world mobile usage patterns with 3146 static actions and 235 end-to-end tasks across high-frequency daily scenarios (8B: static 89.91%, end-to-end 52.50%). Our work advances the development of practical GUI agents and demonstrates strong potential for real-world deployment in everyday digital interactions.