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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2602.08990 |
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| _version_ | 1866918328881119232 |
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| author | Feng, Shiyang Ma, Runmin Yan, Xiangchao Fan, Yue Hu, Yusong Huang, Songtao Zhang, Shuaiyu Cao, Zongsheng Peng, Tianshuo Yuan, Jiakang Guo, Zijie Zhong, Zhijie Du, Shangheng Wang, Weida Shi, Jinxin Zhou, Yuhao He, Xiaohan Yu, Zhiyin Yu, Fangchen Zheng, Qihao Wu, Jiamin Liu, Mianxin Zhang, Chi Hou, Shaowei Li, Shuya Jiang, Yankai Lou, Wenjie Wang, Lilong Wang, Zifu Wang, Jiong Xu, Wanghan Deng, Yue Liu, Dongrui Wang, Yiheng Zhang, Wenlong Ling, Fenghua Zhang, Shufei Wang, Xiaosong Zheng, Shuangjia Huang, Xun Sun, Siqi Hu, Shuyue Ye, Peng Song, Chunfeng Wang, Bin He, Conghui Liu, Yihao Li, Xin Hou, Qibin Chen, Tao Yue, Xiangyu Wang, Bin He, Liang Lin, Dahua Zhou, Bowen Zhang, Bo Bai, Lei |
| author_facet | Feng, Shiyang Ma, Runmin Yan, Xiangchao Fan, Yue Hu, Yusong Huang, Songtao Zhang, Shuaiyu Cao, Zongsheng Peng, Tianshuo Yuan, Jiakang Guo, Zijie Zhong, Zhijie Du, Shangheng Wang, Weida Shi, Jinxin Zhou, Yuhao He, Xiaohan Yu, Zhiyin Yu, Fangchen Zheng, Qihao Wu, Jiamin Liu, Mianxin Zhang, Chi Hou, Shaowei Li, Shuya Jiang, Yankai Lou, Wenjie Wang, Lilong Wang, Zifu Wang, Jiong Xu, Wanghan Deng, Yue Liu, Dongrui Wang, Yiheng Zhang, Wenlong Ling, Fenghua Zhang, Shufei Wang, Xiaosong Zheng, Shuangjia Huang, Xun Sun, Siqi Hu, Shuyue Ye, Peng Song, Chunfeng Wang, Bin He, Conghui Liu, Yihao Li, Xin Hou, Qibin Chen, Tao Yue, Xiangyu Wang, Bin He, Liang Lin, Dahua Zhou, Bowen Zhang, Bo Bai, Lei |
| contents | We introduce InternAgent-1.5, a unified system designed for end-to-end scientific discovery across computational and empirical domains. The system is built on a structured architecture composed of three coordinated subsystems for generation, verification, and evolution. These subsystems are supported by foundational capabilities for deep research, solution optimization, and long horizon memory. The architecture allows InternAgent-1.5 to operate continuously across extended discovery cycles while maintaining coherent and improving behavior. It also enables the system to coordinate computational modeling and laboratory experimentation within a single unified system. We evaluate InternAgent-1.5 on scientific reasoning benchmarks such as GAIA, HLE, GPQA, and FrontierScience, and the system achieves leading performance that demonstrates strong foundational capabilities. Beyond these benchmarks, we further assess two categories of discovery tasks. In algorithm discovery tasks, InternAgent-1.5 autonomously designs competitive methods for core machine learning problems. In empirical discovery tasks, it executes complete computational or wet lab experiments and produces scientific findings in earth, life, biological, and physical domains. Overall, these results show that InternAgent-1.5 provides a general and scalable framework for autonomous scientific discovery. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_08990 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | InternAgent-1.5: A Unified Agentic Framework for Long-Horizon Autonomous Scientific Discovery Feng, Shiyang Ma, Runmin Yan, Xiangchao Fan, Yue Hu, Yusong Huang, Songtao Zhang, Shuaiyu Cao, Zongsheng Peng, Tianshuo Yuan, Jiakang Guo, Zijie Zhong, Zhijie Du, Shangheng Wang, Weida Shi, Jinxin Zhou, Yuhao He, Xiaohan Yu, Zhiyin Yu, Fangchen Zheng, Qihao Wu, Jiamin Liu, Mianxin Zhang, Chi Hou, Shaowei Li, Shuya Jiang, Yankai Lou, Wenjie Wang, Lilong Wang, Zifu Wang, Jiong Xu, Wanghan Deng, Yue Liu, Dongrui Wang, Yiheng Zhang, Wenlong Ling, Fenghua Zhang, Shufei Wang, Xiaosong Zheng, Shuangjia Huang, Xun Sun, Siqi Hu, Shuyue Ye, Peng Song, Chunfeng Wang, Bin He, Conghui Liu, Yihao Li, Xin Hou, Qibin Chen, Tao Yue, Xiangyu Wang, Bin He, Liang Lin, Dahua Zhou, Bowen Zhang, Bo Bai, Lei Artificial Intelligence We introduce InternAgent-1.5, a unified system designed for end-to-end scientific discovery across computational and empirical domains. The system is built on a structured architecture composed of three coordinated subsystems for generation, verification, and evolution. These subsystems are supported by foundational capabilities for deep research, solution optimization, and long horizon memory. The architecture allows InternAgent-1.5 to operate continuously across extended discovery cycles while maintaining coherent and improving behavior. It also enables the system to coordinate computational modeling and laboratory experimentation within a single unified system. We evaluate InternAgent-1.5 on scientific reasoning benchmarks such as GAIA, HLE, GPQA, and FrontierScience, and the system achieves leading performance that demonstrates strong foundational capabilities. Beyond these benchmarks, we further assess two categories of discovery tasks. In algorithm discovery tasks, InternAgent-1.5 autonomously designs competitive methods for core machine learning problems. In empirical discovery tasks, it executes complete computational or wet lab experiments and produces scientific findings in earth, life, biological, and physical domains. Overall, these results show that InternAgent-1.5 provides a general and scalable framework for autonomous scientific discovery. |
| title | InternAgent-1.5: A Unified Agentic Framework for Long-Horizon Autonomous Scientific Discovery |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2602.08990 |