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The exponential curse of dimensionality in rule-based machine learning can be collapsed to polynomial complexity $\mathcal{O}(mn^2)$ via Kolmogorov-Arnold decomposition, cutting parameters by up to 98% without forfeiting universal approximation guarantees.
