用闭环推理提升极端天气诊断能力，解决模型缺乏专家判断与迭代验证的问题。
HVR-Met: A Hypothesis-Verification-Replanning Agentic System for Extreme Weather Diagnosis
- 构建假设-验证-重规划闭环，实现多步逻辑推理
- 在极端天气场景下诊断准确率显著优于传统方法
- 适合气象专家与高精度灾害预警系统使用
尽管基于深度学习的天气预报已取得显著进展，但极端天气诊断仍面临巨大挑战。这主要源于诊断过程需要复杂的多步逻辑推理、动态工具调用及专家级先验判断。尽管智能体在任务分解与自主执行方面具有优势，现有架构仍受限于专家知识融合不足、缺乏专业级迭代推理机制，以及复杂流程在极端条件下的细粒度验证与评估体系缺失。为此，我们提出HVR-Met——一种深度融合专家知识的多智能体气象诊断系统。其核心创新在于「假设-验证-重规划」闭环机制，可针对极端天气事件中的异常气象信号进行高级迭代推理。为弥补现有评估框架的不足，我们进一步构建了聚焦原子级子任务的新基准。实验表明，该系统在复杂诊断场景中表现优异。
原文摘要 · Abstract (English)
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.
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