Graph out-of-distribution (OOD) generalization remains a major challenge in graph neural networks (GNNs). Invariant learning, aiming to extract invariant features across varied distributions, has ...

Ian says AI is starting to behave more like a coworker. And it turns out researchers have been tracking the reason why this ...

To some, METR’s “time horizon plot” indicates that AI utopia—or apocalypse—is close at hand. The truth is more complicated.

Text mining and knowledge graphs connect cell-culture parameters to glycosylation for faster bioprocess optimization.

Like many of us, [Tim]’s seen online videos of circuit sculptures containing illuminated LED filaments. Unlike most of us, however, he went a step further by using graph theory to design glowing ...

These versatile strategies—from brain dumps to speed sharing—help students track their own progress while informing your next instructional steps.

SciToolAgent is a powerful agent framework designed to integrate diverse scientific tools with large language models (LLMs) to address the limitations of existing systems in scientific research. By ...

Background: Biomedical knowledge graphs (KGs), such as the Data Distillery Knowledge Graph (DDKG), capture known relationships among entities (e.g., genes, diseases, proteins), providing valuable ...

Knowledge graphs are a powerful tool for bringing together information from biological databases and linking what is already known about genes, diseases, treatments, molecular pathways and symptoms in ...

What if you could transform overwhelming, disconnected datasets into a living, breathing map of relationships, one that not only organizes your data but also reveals insights you didn’t even know you ...