EWE: an AI Weather Detective for Extreme Events

Kari Jaaskelainen

27 Nov 2025 — 1 min read

Extreme weather is hitting harder and more often, but diagnosing the “why” behind each event is slow and expert-heavy. Meet EWE (Extreme Weather Expert): an AI agent that acts like a weather detective.

Thinks like experts: Plans analyses, reasons step-by-step, and uses a meteorology toolkit.
Works end-to-end: Turns raw atmospheric data into maps, charts, and plain-language findings.
Built for trust: Closed-loop checks help it catch mistakes and explain its steps.
New benchmark: 103 real, high-impact events plus a stepwise scoring method to measure reasoning quality.

Why it matters: Faster, transparent diagnostics can speed up science, support forecasters, and share expertise with regions most vulnerable to heatwaves, floods, and storms.

From Zhe Jiang, Jiong Wang, Xiaoyu Yue, Zijie Guo, Wenlong Zhang, Fenghua Ling, Wanli Ouyang, and Lei Bai. Read more: https://arxiv.org/abs/2511.21444v1

Paper: https://arxiv.org/abs/2511.21444v1

Register: https://www.AiFeta.com

AI Climate ExtremeWeather Meteorology EarthScience DisasterRisk DataScience OpenScience

Meet GRACE: a moral governor for safer, more transparent AI

AI agents are getting powerful—so how do we make sure they do the right thing, not just the effective thing? Meet GRACE, a reason-based moral governor that keeps AI behavior aligned with human norms by separating moral reasoning from goal-driven decision-making. * Moral Module: uses deontic logic and explicit reasons

Why some fine-tuned LLMs miss phishing—and how to fix it

Not all fine-tuned LLMs spot phishing equally. A new study tests Llama 3.1 8B, Gemma 2 9B, and Mistral on high-stakes phishing detection—and uses SHAP and mechanistic interpretability to reveal why models do (or don’t) generalize. * Architecture × data diversity matters: Gemma 2 9B hits state-of-the-art performance (F1

AI that explains itself—by following the science

AI that explains itself—by following the science Black-box AI can be powerful, but it often can’t tell us why it made a decision. Concept Bottleneck Models (CBMs) try to fix that by predicting human-understandable concepts first, then the final answer. The catch: standard CBMs ignore domain-specific cause-and-effect and

Meet GenomAgent: A Team of AI Specialists for Smarter Genomics Q&A

TL;DR: Finding reliable answers in genomics is hard. GenomAgent turns one big AI into a coordinating team of specialists—and beats the current leader by 12% on a key benchmark. Genomic facts live across many databases. Standard chatbots struggle because they can’t flexibly query those sources. GeneGPT added