What is the primary purpose of applying a logic-based constraint layer over a probabilistic model's output in a neuro-symbolic architecture?

The primary purpose of applying a logic-based constraint layer over a probabilistic model's output is to enforce structural or domain-specific rules that the model cannot guarantee on its own. Probabilistic models, such as neural networks, learn by identifying patterns in data to predict outcomes as statistical likelihoods. Because these models are based on continuous numerical approximations, they often p....