6 papers
Obshazard-bench: Benchmarking Multimodal Foundation Models for Real-Time Disaster Intelligence from Raw Earth Observation Streams
Fengxiang Wang, Qiuyang Yu, Yueying Li +14
Multimodal Large Language Models (MLLMs) are increasingly used to interpret Earth observation data, yet their capability to support real-world disaster emergency response remains i…
Earth Science Foundation Models: From Perception to Reasoning and Discovery
Xiangyu Zhao, Bo Liu, Yuehan Zhang +9
Large foundation models (FMs) are transforming Earth science by integrating heterogeneous multimodal data, such as multi-platform imagery, gridded reanalysis data, diverse geophysi…
Earth-o1: A Grid-free Observation-native Atmospheric World Model
Junchao Gong, Kaiyi Xu, Wangxu Wei +22
Despite the unprecedented volume of multimodal data provided by modern Earth observation systems, our ability to model atmospheric dynamics remains constrained. Traditional modelin…
SCP: Accelerating Discovery with a Global Web of Autonomous Scientific Agents
Yankai Jiang, Wenjie Lou, Lilong Wang +17
We introduce SCP: the Science Context Protocol, an open-source standard designed to accelerate discovery by enabling a global network of autonomous scientific agents. SCP is built…
Probing Scientific General Intelligence of LLMs with Scientist-Aligned Workflows
Wanghan Xu, Yuhao Zhou, Yifan Zhou +104
Despite advances in scientific AI, a coherent framework for Scientific General Intelligence (SGI)-the ability to autonomously conceive, investigate, and reason across scientific do…
A Self-Evolving AI Agent System for Climate Science
Zijie Guo, Jiong Wang, Fenghua Ling +19
Scientific progress in Earth science depends on integrating data across the planet's interconnected spheres. However, the accelerating volume and fragmentation of multi-sphere know…