Nex-N1: Agentic Models Trained via a Unified Ecosystem for Large-Scale Environment Construction
Authors: name: Nex-AGI Team, name: Yuxuan Cai, name: Lu Chen, name: Qiaoling Chen, name: Yuyang Ding, name: Liwen Fan, name: Wenjie Fu, name: Yufei Gao, name: Honglin Guo, name: Pinxue Guo, name: Zhenhua Han, name: Zhengfu He, name: Hanglei Hu, name: Kai Hu, name: Shengjia Hua, name: Tianyu Huai, name: Baodai Huang, name: Li Ji, name: Zhen Jiang, name: Zhikai Lei, name: Bufan Li, name: Jiahang Lin, name: Lizhi Lin, name: Jinxiu Liu, name: Shichun Liu, name: Ziming Liu, name: Yuchen Ni, name: Pengfang Qian, name: Yujiong Shen, name: Qingyun Shi, name: Wentao Shu, name: Peng Sun, name: Yiran Suo, name: Tian Tang, name: Boyu Tian, name: Guoteng Wang, name: Junzhe Wang, name: Peixin Wang, name: Zhiheng Xi, name: Hang Yan, name: Jie Yang, name: Zhixiong Yang, name: Tianchu Yao, name: Guangze Ye, name: Qianxi Yu, name: Shuo Zhang, name: Xinyue Zhang, name: Yiqi Zhang, name: Jiarong Zhao, name: Miao Zheng, name: Rui Zheng, name: Enyu Zhou, name: Jiazheng Zhou, name: Maosen Zhou, name: Yuhao Zhou, name: Tao Gui, name: Yining Zheng, name: Xinchi Chen, name: Jie Zhou, name: Siyuan Feng, name: Qin Chen, name: Liang He, name: Qi Zhang, name: Xuanjing Huang, name: Xipeng Qiu
Organization: Nex AGI

The evolution of Large Language Models (LLMs) from passive responders to autonomous agents necessitates a fundamental shift in learning paradigms -- from static imitation to incentive-driven decision making. However, this transition is significantly impeded by the lack of scalable infrastructure capable of constructing high-quality interaction signals for effective policy learning. To address this, we introduce a comprehensive method designed to systematically scale the diversity and complexity of interactive environments. Our method realizes this scaling by addressing three orthogonal dimensions: (1) Complexity: NexAU, a flexible agent framework that supports building complex agent hierarchies via simple configurations; (2) Diversity: NexA4A automatically generates diverse agent hierarchies from natural language to cover infinite domains; and (3) Fidelity: NexGAP bridges the simulation-reality gap by integrating dynamic real-world environment for grounded trajectories synthesis. We train Nex-N1 upon the diverse and complex interactive environments established by our infrastructure. Empirical results on benchmarks such as SWE-bench and tau2 demonstrate that Nex-N1 consistently outperforms SOTA open-source models and achieves competitive performance against frontier proprietary models on complex agentic tasks. We open-source the Nex ecosystem and model weights to facilitate further research.