AI RESEARCH
Unsat Core Prediction through Polarity-Aware Representation Learning over Clause-Literal Hypergraphs
arXiv CS.LG
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ArXi:2605.04819v1 Announce Type: new 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 expressive in capturing higher-order interactions among literals and clauses.