We integrate discrete diffusion models with neurosymbolic predictors for scalable and calibrated learning and reasoning

We introduce Prototypical Neurosymbolic models to satisfy the symbolic constraints while learning the correct unsupervised concepts.

We introduce a distribution semantics on logic programs using probabilistic equivalence.