NeSyCat Torch: A Differentiable Tensor Implementation of Categorical Semantics for Neurosymbolic Learning

In three linesNeSyCat Torch unifies neurosymbolic semantics (classical, fuzzy, probabilistic, neural) under a single truth definition parametrized by monads. Implemented in PyTorch, JAX, and HaskTorch, the framework interprets computational symbols via neural networks. On MNIST addition, outperforms LTN and DeepProbLog in speed and accuracy.

Summary generated by Claude — human-verified