Pruning LLMs Like a Physicist: Block Removal as an Ising Optimization Problem

Researchers at Multiverse Computing propose a method for pruning large language models by removing entire transformer blocks through constrained binary optimization, framing the selection problem as an Ising spin-glass system with pairwise interactions between removal decisions. Rather than independently ranking blocks or restricting pruning to consecutive layers, the approach estimates a Hessian-based energy function from a small calibration dataset and uses it as a low-cost proxy for model quality, enabling many block-removal configurations to be evaluated without repeated benchmarking. The resulting optimization can be solved by brute force for smaller cases or with tabu search, quantum-inspired methods, and other Ising/QUBO solvers for larger ones, while examining low-energy “excited” states can sometimes identify better pruned models than the lowest-energy solution. Across Llama, Qwen, and NVIDIA Nemotron hybrid architectures, the authors report that the method performs comparably to existing approaches at modest compression and substantially better at aggressive depth reductions, including an approximately 23-point MMLU advantage over a block-influence baseline when removing half of Llama-3.3-70B-Instruct’s blocks. The method is designed to complement quantization, low-rank compression, width pruning, and retraining-based recovery techniques, and the associated code has been released publicly.

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