Unsat Core Prediction through Polarity-Aware Representation Learning

Unsat Core Prediction through Polarity-Aware Representation Learning over Clause-Literal Hypergraphs

arXiv:2605.04819v2 Announce Type: replace Abstract: Graph neural networks have been widely used in Boolean satisfiability (SAT) tasks to learn structural information from SAT formulas. The goal of these studies is to solve SAT instances or to enhance SAT solvers, including tasks such as unsat-core prediction. However, most existing approaches model a SAT formula as a bipartite graph or a directed acyclic graph, which are less direct in capturing clause-level and higher-order interactions...