<ul data-eligibleForWebStory="false"><li>A new method named MILP-SAT-GNN combines Graph Neural Networks (GNNs) with Mixed Integer Linear Programming (MILP) techniques to solve SAT problems.</li><li>The method involves mapping k-CNF formulae to MILP problems, encoding them as weighted bipartite graphs, and training a GNN for solving SAT problems.</li><li>The approach shows stable outputs under clause and variable reordering, but has limitations in distinguishing satisfiable from unsatisfiable instances for foldable formulae.</li><li>The experimental evaluation demonstrates promising results, indicating the effectiveness of the method despite its simple neural architecture.</li></ul>

MILP-SAT-GNN: Yet Another Neural SAT Solver

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