Neural guidance accelerates symbolic solvers only when the solver can dynamically correct bad neural suggestions—rigid solvers that always follow neural hints may actually slow down.
This paper combines neural networks with symbolic solvers to solve constraint satisfaction problems like Sudoku. A neural model (SE-RRM) generates solution proposals that guide traditional solvers like backtracking and SAT solvers, producing correct answers faster. The approach works best when solvers can override bad neural hints and problems have large search spaces.