Researchers from the Multiverse Computing and the Center for Artificial Intelligence (CAI) at the Polytechnic University of Catalonia have found a new approach to pruning large language models (LLMs) by framing it as an Ising optimization problem. This method aims to remove redundant blocks in LLMs to improve efficiency and reduce computational resources. The approach leverages insights from physics to optimize the pruning process, leading to significant reductions in the number of parameters needed to achieve a certain level of accuracy.