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We introduce TWIG (Tree-structured Weights with Inherited Gates), a representation where every neural network weight is the endpoint of a path of shared discrete decisions, unifying pruning, quantization, low-rank factorization, and weight sharing into a single editable object optimizable via higher-order Boltzmann machine ground-state solvers, achieving 9–15% fewer additions than GPTQ at equal LLM layer error.