[2603.01121] HVR-Met: A Hypothesis-Verification-Replaning Agentic System for Extreme Weather Diagnosis

Computer Science > Artificial Intelligence arXiv:2603.01121 (cs) [Submitted on 1 Mar 2026] Title:HVR-Met: A Hypothesis-Verification-Replaning Agentic System for Extreme Weather Diagnosis Authors:Shuo Tang, Jiadong Zhang, Jian Xu, Gengxian Zhou, Qizhao Jin, Qinxuan Wang, Yi Hu, Ning Hu, Hongchang Ren, Lingli He, Jiaolan Fu, Jingtao Ding, Shiming Xiang, Chenglin Liu View a PDF of the paper titled HVR-Met: A Hypothesis-Verification-Replaning Agentic System for Extreme Weather Diagnosis, by Shuo Tang and 13 other authors View PDF HTML (experimental) Abstract: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 facil...