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Reweighted normalizing flows enable exponential convergence to arbitrary Boltzmann distributions via a novel WFR JKO scheme, bypassing common structural assumptions.
Sub-quadratic attention no longer requires sacrificing softmax-level expressivity: additive sorting kernels achieve infinite identity-approximation capacity while scaling quasi-linearly on long sequences.
