Graph neural networks trained as oracles improve step counts and solved instances for stochastic local search SAT solvers on random and pseudo-industrial benchmarks while preserving theoretical bounds.
Harris and Aravind Srinivasan
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Using deep learning to construct stochastic local search SAT solvers with performance bounds
Graph neural networks trained as oracles improve step counts and solved instances for stochastic local search SAT solvers on random and pseudo-industrial benchmarks while preserving theoretical bounds.