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Generative drifting's empirical success is no longer a mystery: it's secretly score matching, but with frequency-dependent convergence bottlenecks that explain the preference for Laplacian kernels.
Ditching ReLU can provably prevent oversmoothing in deep GNNs by inducing bifurcations that create stable, heterogeneous node representations.
