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When AI researchers try to model uncertainty, they often use hacks like the Reparameterization Trick (from VAEs) or Monte Carlo Dropout [6] just to make the math run on a GPU. But this forces the network into a “Gaussian straitjacket,” limiting its ability to hold complex, discrete, multi-peaked hypotheses (like knowing a blurry shape could be a dog or a cat, but definitely not a smooth mathematical average of both).