Researchers at Multiverse Computing have published a new approach to large language model compression that applies insights from statistical physics to the problem of removing transformer blocks. Rather than treating block removal as a ranking problem—a common approach in the field—they reformulate it as a constrained binary optimization problem that maps directly onto an Ising glass, a concept from physics describing disordered spin systems. This reframing accounts for the interdependencies between blocks, which traditional mean-field methods overlook. The practical advantage lies in replacing expensive model evaluations with cheap energy calculations. The method requires computing a Hessian matrix just once from a small calibration dataset, then reuses it to evaluate billions of potential configurations without running the actual model. For Llama-3.3-70B-Instruct at 50% compression, the technique achieves nearly 23 percentage points higher MMLU scores than existing block-removal methods—a significant margin in a deep-compression regime where performance degradation is typically severe. Beyond exact solving via GPU brute-force for tractable cases, the team leverages the Ising glass formulation to apply quantum-inspired solvers for larger configuration spaces. The approach stacks neatly with quantization and low-rank compression, positioning block removal as a foundational step in multi-stage model efficiency pipelines.