satisfiability solving with learning

**Satisfiability (SAT) Solving with Learning** is the **augmentation of classical Boolean SAT solvers with machine learning heuristics** — using GNNs or other models to predict variable assignments or branching decisions to speed up the solving of NP-complete problems. **What Is this field?** - **SAT Solver**: Finds if there exists an assignment of True/False to variables to make a formula True. (CDCL algorithm). - **Bottleneck**: Choosing which variable to split on next (Branching Heuristic). - **ML Solution**: Train a GNN on the formula's structure (variable-clause graph) to predict the best split. - **NeuroSAT**: A famous architecture that learns to solve SAT problems end-to-end. **Why It Matters** - **Combinatorial Optimization**: Solving scheduling, routing, and verification problems faster. - **Generalization**: A model trained on small 40-variable problems can often guide solvers on huge 4000-variable problems. **Satisfiability Solving with Learning** is **AI-guided search** — using neural intuition to navigate the exponentially large search spaces of logic problems.