Researchers have developed NeSyCat Torch, a differentiable tensor implementation of categorical semantics for neurosymbolic learning. This extends the NeSyCat framework, providing a unified definition of truth across classical, fuzzy, probabilistic, and neural systems. NeSyCat Torch integrates neural networks with categorical semantics, enabling learned predicates and functions. This development could help builders create more robust and generalizable neurosymbolic models.