Topics

By revisiting Apéry’s proof and introducing modern methods, a recent research sheds new light on the irrationality of special values in number theory.

When features vastly outnumber samples, PLATO harnesses hidden knowledge graphs to regularize deep learning — boosting accuracy by over 10% in genomics and drug discovery.

Human genetics is reshaping drug discovery by revealing reliable targets, improving success rates, and reducing costly clinical failures in the development of safer, more effective treatments.

NEAT evolves neural networks like living organisms, starting simple, protecting innovation, and growing smarter architectures that learn dramatically faster through artificial evolution.

AI is transforming overwhelming oceans of data into meaningful insights using nature-inspired pattern mining, explainable intelligence, and privacy-preserving techniques that scale to modern complexity.

A small model outsmarts the giants. Microsoft’s rStar-Math rewrites the rules of AI math reasoning—no teacher models, just smart self-training and Monte Carlo strategy.

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.

Beyond parroting answers, Meta-CoT teaches AI to reason—mapping dead ends, correcting itself, and thinking like a human faced with a maze of logic.

What do Beethoven, biomaterials, and mycelium have in common? A new AI-powered knowledge graph reveals scientific insights by connecting ideas no human could link—across text, images, and sound.