May 14, 2024, 4:44 a.m. | Zhaoyu Li, Jinpei Guo, Xujie Si

cs.LG updates on

arXiv:2309.16941v2 Announce Type: replace
Abstract: Graph neural networks (GNNs) have recently emerged as a promising approach for solving the Boolean Satisfiability Problem (SAT), offering potential alternatives to traditional backtracking or local search SAT solvers. However, despite the growing volume of literature in this field, there remains a notable absence of a unified dataset and a fair benchmark to evaluate and compare existing approaches. To address this crucial gap, we present G4SATBench, the first benchmark study that establishes a comprehensive evaluation …

arxiv benchmarking cs.lg graph graph neural networks networks neural networks replace type

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