arXiv:2603.01121v2 Announce Type: replace 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-g
The accelerating development of AI agentic systems is meeting critical challenges in real-world application, particularly in complex domains like extreme weather diagnosis, prompting focus on integrating expert knowledge.
Advanced AI agents capable of sophisticated multi-step reasoning and dynamic tool invocation are crucial for automating and improving decision-making in high-stakes fields, addressing limitations of current deep learning models.
This research introduces a novel architecture that explicitly aims to integrate expert knowledge and a hypothesis-verification-replanning loop, potentially making agents more robust and reliable for critical tasks.
- · AI agent developers
- · Weather forecasting agencies
- · Disaster preparedness organizations
- · Insurance sector
- · Traditional algorithmic forecasting
- · Inefficient manual diagnostic processes
Improved accuracy and speed in diagnosing extreme weather events, leading to better early warnings.
Reduced economic and human impact from natural disasters due to more timely and precise interventions.
Extension of such agentic architectures to other complex scientific and diagnostic fields, further accelerating automation.
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Read at arXiv cs.AI