Graph Neural Networks (GNNs) and GraphRAG don’t “reason”—they navigate complex, open-world financial graphs with traceable, multi-hop evidence. Here’s why BFSI leaders should embrace graph-native AI ...

In the AI era, pure data-driven meteorological and climate models are gradually catching up with and even surpassing traditional numerical models. However, significant challenges persist in current ...

The demand for immersive, realistic graphics in mobile gaming and AR or VR is pushing the limits of mobile hardware. Achieving lifelike simulations of fluids, cloth, and other materials historically ...

Graph neural networks (GNNs) have gained traction and have been applied to various graph-based data analysis tasks due to their high performance. However, a major concern is their robustness, ...

A World Bank study introduces an AI-based method using graph neural networks to break down national statistics like GDP into ...

Scientists have developed SpaMosaic, an AI-driven method to integrate fragmented spatial multi-omics datasets, enabling unified molecular maps across tissues. The tool combines contrastive learning ...

“Neural networks are currently the most powerful tools in artificial intelligence,” said Sebastian Wetzel, a researcher at the Perimeter Institute for Theoretical Physics. “When we scale them up to ...

In 2026, AI research is moving beyond raw scaling to focus on efficiency, adaptability, and operational robustness. Advances in architectures, benchmarks, and conferences reflect a growing emphasis on ...

In the AI era, pure data-driven meteorological and climate models are gradually catching up with, and even surpassing, traditional numerical models. However, significant challenges persist in current ...