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| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2603.01121 |
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| _version_ | 1866918364034629632 |
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| author | Tang, Shuo Zhang, Jiadong Xu, Jian Zhou, Gengxian Jin, Qizhao Wang, Qinxuan Hu, Yi Hu, Ning Ren, Hongchang He, Lingli Fu, Jiaolan Ding, Jingtao Xiang, Shiming Liu, Chenglin |
| author_facet | Tang, Shuo Zhang, Jiadong Xu, Jian Zhou, Gengxian Jin, Qizhao Wang, Qinxuan Hu, Yi Hu, Ning Ren, Hongchang He, Lingli Fu, Jiaolan Ding, Jingtao Xiang, Shiming Liu, Chenglin |
| contents | While deep learning-based weather forecasting paradigms have made significant strides, addressing extreme weather diagnostics remains a formidable challenge. This gap exists primarily because the diagnostic process demands sophisticated multi-step logical reasoning, dynamic tool invocation, and expert-level prior judgment. Although agents possess inherent advantages in task decomposition and autonomous execution, current architectures are still hampered by critical bottlenecks: inadequate expert knowledge integration, a lack of professional-grade iterative reasoning loops, and the absence of fine-grained validation and evaluation systems for complex workflows under extreme conditions. To this end, we propose HVR-Met, a multi-agent meteorological diagnostic system characterized by the deep integration of expert knowledge. Its central innovation is the ``Hypothesis-Verification-Replanning'' closed-loop mechanism, which facilitates sophisticated iterative reasoning for anomalous meteorological signals during extreme weather events. To bridge gaps within existing evaluation frameworks, we further introduce a novel benchmark focused on atomic-level subtasks. Experimental evidence demonstrates that the system excels in complex diagnostic scenarios. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_01121 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | HVR-Met: A Hypothesis-Verification-Replaning Agentic System for Extreme Weather Diagnosis Tang, Shuo Zhang, Jiadong Xu, Jian Zhou, Gengxian Jin, Qizhao Wang, Qinxuan Hu, Yi Hu, Ning Ren, Hongchang He, Lingli Fu, Jiaolan Ding, Jingtao Xiang, Shiming Liu, Chenglin Artificial Intelligence While deep learning-based weather forecasting paradigms have made significant strides, addressing extreme weather diagnostics remains a formidable challenge. This gap exists primarily because the diagnostic process demands sophisticated multi-step logical reasoning, dynamic tool invocation, and expert-level prior judgment. Although agents possess inherent advantages in task decomposition and autonomous execution, current architectures are still hampered by critical bottlenecks: inadequate expert knowledge integration, a lack of professional-grade iterative reasoning loops, and the absence of fine-grained validation and evaluation systems for complex workflows under extreme conditions. To this end, we propose HVR-Met, a multi-agent meteorological diagnostic system characterized by the deep integration of expert knowledge. Its central innovation is the ``Hypothesis-Verification-Replanning'' closed-loop mechanism, which facilitates sophisticated iterative reasoning for anomalous meteorological signals during extreme weather events. To bridge gaps within existing evaluation frameworks, we further introduce a novel benchmark focused on atomic-level subtasks. Experimental evidence demonstrates that the system excels in complex diagnostic scenarios. |
| title | HVR-Met: A Hypothesis-Verification-Replaning Agentic System for Extreme Weather Diagnosis |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2603.01121 |