Researchers at University of New Mexico and Los Alamos National Laboratory have developed THOR (Tensors for High-dimensional Object Representation), an AI framework that cracks a century-old puzzle in statistical physics. It directly computes configurational integrals at speeds 400 times faster than traditional methods. It replaces long-used approximations with exact first-principles solutions.