Stanford’s new GNN-powered method cracks the NP-hard subgraph matching problem, enabling fast motif discovery in massive graphs from biology to social networks.

Discover how autoencoders reveal hidden neural dynamics: latent vector fields, attractors, and trajectories expose memorization, generalization, and data-free insights—reshaping interpretability, OOD detection, and AI understanding.

Neural Cellular Automata scaled to HD: Coarse NCA handles self-organizing structure on a small grid, while lightweight LPPN renders high-resolution details in real-time for images, textures, and 3D meshes.

Can AI redesign itself? The Darwin Gödel Machine lets systems rewrite their own code, evolve via trial-and-error, and boost coding performance while revealing new risks.

Incompressible-fluid networks reframe regime changes as engineered bifurcations: composable, divergence-free transformations that preserve conservation laws, enabling stable shifts from exploration to focused exploitation in AI and cognition.

Forget synaptic weight changes—what if learning happens differently? A new study shows neural networks can adapt by adjusting only biases, reshaping our understanding of AI and the brain.