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PRAXIS is a new algorithm that efficiently approximates the Rashomon set of near-optimal decision trees, achieving orders of magnitude improvement in runtime and memory while maintaining near-perfect recall.
Introduces horizon-constrained Rashomon sets to characterize how model multiplicity evolves in chaotic systems. The framework proves exponential contraction of predictive equivalence and develops decision-aligned algorithms that improve decision quality by 18-34%.