Researchers from The University of New Mexico and Los Alamos National Laboratory have developed a novel computational framework that addresses a longstanding challenge in statistical physics.

Moving beyond static code prediction, the model learns an internal world model of computational environments for more grounded and reliable code generation.

Discover how to fine-tune large language models with Tunix, the open-source library that simplifies AI customization and optimization.