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
Organization: StepFun

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.