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
Published: 2025-12-04 16:57:02

Abstract: 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.