Proof, Policy, and Probability: How DeepProofLog Rewrites the Rules of Reasoning
Opening — Why this matters now Neurosymbolic AI has long promised a synthesis: neural networks that learn, and logical systems that reason. But in practice, the two halves have been perpetually out of sync — neural systems scale but don’t explain, while symbolic systems explain but don’t scale. The recent paper DeepProofLog: Efficient Proving in Deep Stochastic Logic Programs takes a decisive step toward resolving this standoff by reframing reasoning itself as a policy optimization problem. In short, it teaches logic to think like a reinforcement learner. ...