Nex-N1：透過大規模環境建構統一生態系統訓練的能動性模型

Nex-N1: Agentic Models Trained via a Unified Ecosystem for Large-Scale Environment Construction

December 4, 2025

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

cs.AI

摘要

大型語言模型（LLMs）從被動響應者向自主智能體的演進，亟需學習範式的根本性轉變——從靜態模仿邁向激勵驅動的決策。然而，由於缺乏能夠構建高質量互動信號以實現有效策略學習的可擴展基礎設施，這一轉型進程受到嚴重阻礙。為此，我們提出一套系統性方法，旨在從三個正交維度實現互動環境多樣性與複雜性的規模化擴展：（1）複雜性：NexAU框架通過簡潔配置支持構建複雜的智能體層級結構；（2）多樣性：NexA4A從自然語言自動生成多樣化智能體層級，覆蓋無限領域；（3）真實性：NexGAP通過整合動態現實環境進行具身軌跡合成，彌合仿真與現實的差距。基於該基礎設施建立的多元化複雜互動環境，我們訓練出Nex-N1模型。在SWE-bench和tau2等基準測試中的實證結果表明，Nex-N1在複雜智能體任務上不僅持續超越開源SOTA模型，更在與前沿專有模型的對比中展現出競爭性表現。我們現開源Nex生態系統及模型權重，以推動相關研究的深入發展。

English

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.