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Yicheng Zou, Dongsheng Zhu, Lin Zhu, Tong Zhu, Yunhua Zhou, Peiheng Zhou, Xinyu Zhou, Dongzhan Zhou, Zhiwang Zhou, Yuhao Zhou, Bowen Zhou, Zhanping Zhong, Zhijie Zhong, Haiteng Zhao, Penghao Zhao, Xiaomeng Zhao, Zhiyuan Zhao, Yechen Zhang, Jin Zhang, Wenwei Zhang, Hongjie Zhang, Zhuo Zhang, Wenlong Zhang, Bo Zhang, Chao Zhang, Chen Zhang, Yuhang Zang, Fei Yuan, Jiakang Yuan, Jiashuo Yu, Jinhui Yin, Haochen Ye, Qian Yao, Bowen Yang, Danni Yang, Kaichen Yang, Ziang Yan, Jun Xu, Yicheng Xu, Wanghan Xu, Xuenan Xu, Chao Xu, Ruiliang Xu, Shuhao Xing, Long Xing, Xinchen Xie, Ling-I Wu, Zijian Wu, Zhenyu Wu, Lijun Wu, Yue Wu, Jianyu Wu, Wen Wu, Fan Wu, Xilin Wei, Qi Wei, Bingli Wang, Rui Wang, Ziyi Wang, Zun Wang, Yi Wang, Haomin Wang, Yizhou Wang, Lintao Wang, Yiheng Wang, Longjiang Wang, Bin Wang, Jian Tong, Zhongbo Tian, Huanze Tang, Chen Tang, Shixiang Tang, Yu Sun, Qiushi Sun, Xuerui Su, Qisheng Su, Chenlin Su, Demin Song, Jin Shi, Fukai Shang, Yuchen Ren, Pengli Ren, Xiaoye Qu, Yuan Qu, Jiantao Qiu, Yu Qiao, Runyu Peng, Tianshuo Peng, Jiahui Peng, Qizhi Pei, Zhuoshi Pan, Linke Ouyang, Wenchang Ning, Yichuan Ma, Zerun Ma, Ningsheng Ma, Runyuan Ma, Chengqi Lyu, Haijun Lv, Han Lv, Lindong Lu, Kuikun Liu, Jiangning Liu, Yuhong Liu, Kai Liu, Hongwei Liu, Zhoumianze Liu, Mengjie Liu, Ziyu Liu, Wenran Liu, Yang Liu, Liwei Liu, Kaiwen Liu, Junyao Lin, Junming Lin, Tianyang Lin, Dahua Lin, Jianze Liang, Linyang Li, Peiji Li, Zonglin Li, Zehao Li, Pengze Li, Guoyan Li, Lingkai Kong, Linglin Jing, Zhenjiang Jin, Feifei Jiang, Qian Jiang, Junhao Huang, Zixian Huang, Haian Huang, Zhouqi Hua, Han Hu, Linfeng Hou, Yinan He, Conghui He, Tianyao He, Xu Guo, Qipeng Guo, Aijia Guo, Yuzhe Gu, Lixin Gu, Jingyang Gong, Qiming Ge, Jiaye Ge, Songyang Gao, Jianfei Gao, Xinyu Fang, Caihua fan, Yue Fan, Yanhui Duan, Zichen Ding, Shengyuan Ding, Xuanlang Dai, Erfei Cui, Ganqu Cui, Pei Chu, Tao Chu, Guangran Cheng, Yu Cheng, Kai Chen, Yongkang Chen, Chiyu Chen, Guanzhou Chen, Qiaosheng Chen, Sitao Chen, Xin Chen, Haojiong Chen, Yicheng Chen, Weihan Cao, Yuhang Cao, Qinglong Cao, Lei Bai
We introduce Intern-S1-Pro, the first one-trillion-parameter scientific multimodal foundation model. Scaling to this unprecedented size, the model delivers a comprehensive enhancement across both general and scientific domains. Beyond stronger reasoning and image-text understanding capabilities, its intelligence is augmented with advanced agent capabilities. Simultaneously, its scientific expertise has been vastly expanded to master over 100 specialized tasks across critical science fields, including chemistry, materials, life sciences, and earth sciences. Achieving this massive scale is made possible by the robust infrastructure support of XTuner and LMDeploy, which facilitates highly efficient Reinforcement Learning (RL) training at the 1-trillion parameter level while ensuring strict precision consistency between training and inference. By seamlessly integrating these advancements, Intern-S1-Pro further fortifies the fusion of general and specialized intelligence, working as a Specializable Generalist, demonstrating its position in the top tier of open-source models for general capabilities, while outperforming proprietary models in the depth of specialized scientific tasks.
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@misc{zou2026intern,
title = {Intern-S1-Pro: Scientific Multimodal Foundation Model at Trillion Scale},
author = {Yicheng Zou and Dongsheng Zhu and Lin Zhu and Tong Zhu and Yunhua Zhou and Peiheng Zhou and Xinyu Zhou and Dongzhan Zhou and Zhiwang Zhou and Yuhao Zhou and Bowen Zhou and Zhanping Zhong and Zhijie Zhong and Haiteng Zhao and Penghao Zhao and Xiaomeng Zhao and Zhiyuan Zhao and Yechen Zhang and Jin Zhang and Wenwei Zhang and Hongjie Zhang and Zhuo Zhang and Wenlong Zhang and Bo Zhang and Chao Zhang and Chen Zhang and Yuhang Zang and Fei Yuan and Jiakang Yuan and Jiashuo Yu and Jinhui Yin and Haochen Ye and Qian Yao and Bowen Yang and Danni Yang and Kaichen Yang and Ziang Yan and Jun Xu and Yicheng Xu and Wanghan Xu and Xuenan Xu and Chao Xu and Ruiliang Xu and Shuhao Xing and Long Xing and Xinchen Xie and Ling-I Wu and Zijian Wu and Zhenyu Wu and Lijun Wu and Yue Wu and Jianyu Wu and Wen Wu and Fan Wu and Xilin Wei and Qi Wei and Bingli Wang and Rui Wang and Ziyi Wang and Zun Wang and Yi Wang and Haomin Wang and Yizhou Wang and Lintao Wang and Yiheng Wang and Longjiang Wang and Bin Wang and Jian Tong and Zhongbo Tian and Huanze Tang and Chen Tang and Shixiang Tang and Yu Sun and Qiushi Sun and Xuerui Su and Qisheng Su and Chenlin Su and Demin Song and Jin Shi and Fukai Shang and Yuchen Ren and Pengli Ren and Xiaoye Qu and Yuan Qu and Jiantao Qiu and Yu Qiao and Runyu Peng and Tianshuo Peng and Jiahui Peng and Qizhi Pei and Zhuoshi Pan and Linke Ouyang and Wenchang Ning and Yichuan Ma and Zerun Ma and Ningsheng Ma and Runyuan Ma and Chengqi Lyu and Haijun Lv and Han Lv and Lindong Lu and Kuikun Liu and Jiangning Liu and Yuhong Liu and Kai Liu and Hongwei Liu and Zhoumianze Liu and Mengjie Liu and Ziyu Liu and Wenran Liu and Yang Liu and Liwei Liu and Kaiwen Liu and Junyao Lin and Junming Lin and Tianyang Lin and Dahua Lin and Jianze Liang and Linyang Li and Peiji Li and Zonglin Li and Zehao Li and Pengze Li and Guoyan Li and Lingkai Kong and Linglin Jing and Zhenjiang Jin and Feifei Jiang and Qian Jiang and Junhao Huang and Zixian Huang and Haian Huang and Zhouqi Hua and Han Hu and Linfeng Hou and Yinan He and Conghui He and Tianyao He and Xu Guo and Qipeng Guo and Aijia Guo and Yuzhe Gu and Lixin Gu and Jingyang Gong and Qiming Ge and Jiaye Ge and Songyang Gao and Jianfei Gao and Xinyu Fang and Caihua fan and Yue Fan and Yanhui Duan and Zichen Ding and Shengyuan Ding and Xuanlang Dai and Erfei Cui and Ganqu Cui and Pei Chu and Tao Chu and Guangran Cheng and Yu Cheng and Kai Chen and Yongkang Chen and Chiyu Chen and Guanzhou Chen and Qiaosheng Chen and Sitao Chen and Xin Chen and Haojiong Chen and Yicheng Chen and Weihan Cao and Yuhang Cao and Qinglong Cao and Lei Bai},
year = {2026},
abstract = {We introduce Intern-S1-Pro, the first one-trillion-parameter scientific multimodal foundation model. Scaling to this unprecedented size, the model delivers a comprehensive enhancement across both general and scientific domains. Beyond stronger reasoning and image-text understanding capabilities, its intelligence is augmented with advanced agent capabilities. Simultaneously, its scientific expertise has been vastly expanded to master over 100 specialized tasks across critical science fields, incl},
url = {https://huggingface.co/papers/2603.25040},
keywords = {multimodal foundation model, reinforcement learning, parameter-efficient fine-tuning, scientific expertise, specialized tasks, general intelligence, specialized intelligence, training infrastructure, inference precision, huggingface daily},
eprint = {2603.25040},
archiveprefix = {arXiv},
}
{}