DPNL: A DPLL-based Algorithm for Probabilistic Neurosymbolic Learning

Excerpt

arXiv:2610.06270v1 Announce Type: new Abstract: Probabilistic Neurosymbolic Learning (PNL) combines neural predictions with symbolic reasoning, enabling end-to-end learning from final-output supervision without labels for intermediate concepts. A central challenge is probabilistic inference: state-of-the-art approaches often rely on materializing the logical provenance of a query, which can itself become a major computational bottleneck. We introduce Dynamic Probabilistic Neurosymbolic Learning