NeSyCat Torch: Differentiable Tensor Implementation for Neurosymbolic Learning

NeSyCat Torch provides a differentiable tensor implementation of categorical semantics for neurosymbolic learning, unifying classical, fuzzy, probabilistic, and neural systems under a single inductive truth definition. It outperforms LTN and DeepProbLog in speed and accuracy on MNIST addition, matching DeepStochLog's accuracy while operating within a uniform framework extensible to continuous probability via monad instantiation.