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Neural networks prune themselves during optimization not through gradual simplification, but via abrupt percolation phase transitions where architectural symmetries force subnetworks to merge in discrete, cascading blocks.
Decision trees and diffusion models are secretly doing the same thing: optimizing a shared objective called Global Trajectory Score Matching.
