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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2512.04987 |
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| _version_ | 1866908694297444352 |
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| author | AGI Team Cai, Yuxuan Chen, Lu Chen, Qiaoling Ding, Yuyang Fan, Liwen Fu, Wenjie Gao, Yufei Guo, Honglin Guo, Pinxue Han, Zhenhua He, Zhengfu Hu, Hanglei Hu, Kai Hua, Shengjia Huai, Tianyu Huang, Baodai Ji, Li Jiang, Zhen Lei, Zhikai Li, Bufan Lin, Jiahang Lin, Lizhi Liu, Jinxiu Liu, Shichun Liu, Ziming Ni, Yuchen Qian, Pengfang Shen, Yujiong Shi, Qingyun Shu, Wentao Sun, Peng Suo, Yiran Tang, Tian Tian, Boyu Wang, Guoteng Wang, Junzhe Wang, Peixin Xi, Zhiheng Yan, Hang Yang, Jie Yang, Zhixiong Yao, Tianchu Ye, Guangze Yu, Qianxi Zhang, Shuo Zhang, Xinyue Zhang, Yiqi Zhao, Jiarong Zheng, Miao Zheng, Rui Zhou, Enyu Zhou, Jiazheng Zhou, Maosen Zhou, Yuhao Gui, Tao Zheng, Yining Chen, Xinchi Zhou, Jie Feng, Siyuan Chen, Qin He, Liang Zhang, Qi Huang, Xuanjing Qiu, Xipeng |
| author_facet | AGI Team Cai, Yuxuan Chen, Lu Chen, Qiaoling Ding, Yuyang Fan, Liwen Fu, Wenjie Gao, Yufei Guo, Honglin Guo, Pinxue Han, Zhenhua He, Zhengfu Hu, Hanglei Hu, Kai Hua, Shengjia Huai, Tianyu Huang, Baodai Ji, Li Jiang, Zhen Lei, Zhikai Li, Bufan Lin, Jiahang Lin, Lizhi Liu, Jinxiu Liu, Shichun Liu, Ziming Ni, Yuchen Qian, Pengfang Shen, Yujiong Shi, Qingyun Shu, Wentao Sun, Peng Suo, Yiran Tang, Tian Tian, Boyu Wang, Guoteng Wang, Junzhe Wang, Peixin Xi, Zhiheng Yan, Hang Yang, Jie Yang, Zhixiong Yao, Tianchu Ye, Guangze Yu, Qianxi Zhang, Shuo Zhang, Xinyue Zhang, Yiqi Zhao, Jiarong Zheng, Miao Zheng, Rui Zhou, Enyu Zhou, Jiazheng Zhou, Maosen Zhou, Yuhao Gui, Tao Zheng, Yining Chen, Xinchi Zhou, Jie Feng, Siyuan Chen, Qin He, Liang Zhang, Qi Huang, Xuanjing Qiu, Xipeng |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_04987 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Nex-N1: Agentic Models Trained via a Unified Ecosystem for Large-Scale Environment Construction AGI Team Cai, Yuxuan Chen, Lu Chen, Qiaoling Ding, Yuyang Fan, Liwen Fu, Wenjie Gao, Yufei Guo, Honglin Guo, Pinxue Han, Zhenhua He, Zhengfu Hu, Hanglei Hu, Kai Hua, Shengjia Huai, Tianyu Huang, Baodai Ji, Li Jiang, Zhen Lei, Zhikai Li, Bufan Lin, Jiahang Lin, Lizhi Liu, Jinxiu Liu, Shichun Liu, Ziming Ni, Yuchen Qian, Pengfang Shen, Yujiong Shi, Qingyun Shu, Wentao Sun, Peng Suo, Yiran Tang, Tian Tian, Boyu Wang, Guoteng Wang, Junzhe Wang, Peixin Xi, Zhiheng Yan, Hang Yang, Jie Yang, Zhixiong Yao, Tianchu Ye, Guangze Yu, Qianxi Zhang, Shuo Zhang, Xinyue Zhang, Yiqi Zhao, Jiarong Zheng, Miao Zheng, Rui Zhou, Enyu Zhou, Jiazheng Zhou, Maosen Zhou, Yuhao Gui, Tao Zheng, Yining Chen, Xinchi Zhou, Jie Feng, Siyuan Chen, Qin He, Liang Zhang, Qi Huang, Xuanjing Qiu, Xipeng Computation and Language 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. |
| title | Nex-N1: Agentic Models Trained via a Unified Ecosystem for Large-Scale Environment Construction |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2512.04987 |