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Physics-based pruning beats heuristics for LLM compression

Multiverse AI reframes block removal as an Ising glass optimization problem, capturing the hidden couplings between transformer layers that mean-field methods ignore. By mapping block importance to spin interactions via a Hessian matrix, they turn model compression into a search for low-energy states rather than independent block scoring. This approach yields a massive 23-point MMLU gain over existing baselines when compressing Llama-3.3-70B by half, proving that many-body physics tools can unlock deep compression without retraining. The method scales to large models using classical and quantum-inspired solvers, offering a rigorous alternative to heuristic pruning.