# Neural heuristics for SAT solving

arXiv: https://arxiv.org/abs/2005.13406 · HTML: https://mlsanity.com/paper/2005.13406/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 May 2020.

## Built on by (most cited first, 7 of 7)

- [Machine Learning Methods in Solving the Boolean Satisfiability Problem](https://mlsanity.com/paper/2203.04755/) (2022)
- [NeuroBack: Improving CDCL SAT Solving using Graph Neural Networks](https://mlsanity.com/paper/2110.14053/) (2021)
- [NSNet: A General Neural Probabilistic Framework for Satisfiability Problems](https://mlsanity.com/paper/2211.03880/) (2022)
- [G4SATBench: Benchmarking and Advancing SAT Solving with Graph Neural Networks](https://mlsanity.com/paper/2309.16941/) (2023)
- [Unveiling the Limits of Learned Local Search Heuristics: Are You the Mightiest of the Meek?](https://mlsanity.com/paper/2310.19990/) (2023)
- [Machine Learning for SAT: Restricted Heuristics and New Graph Representations](https://mlsanity.com/paper/2307.09141/) (2023)
- [Using deep learning to construct stochastic local search SAT solvers with performance bounds](https://mlsanity.com/paper/2309.11452/) (2023)

## Abstract (arXiv)

We use neural graph networks with a message-passing architecture and an attention mechanism to enhance the branching heuristic in two SAT-solving algorithms. We report improvements of learned neural heuristics compared with two standard human-designed heuristics.