The authors reformulate block removal from large language models as a constrained binary optimization (CBO) problem, equivalent to finding low-energy states of an Ising glass, a disordered spin system with all-to-all interactions and a fixed number of "up" spins. This approach allows for efficient pruning of models by computing the energy of candidate configurations and using classical and quantum-inspired solvers to find good solutions. AI summary
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