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This paper introduces NeSyCat Torch, a differentiable tensor implementation of categorical semantics for neurosymbolic learning, unifying classical, fuzzy, and probabilistic semantics under a monadic framework and demonstrating superior speed and accuracy on MNIST addition compared to existing systems like LTN and DeepProbLog.