Research map / Graph learning / GNN architectures
On the Expressive Power of GNNs for Boolean Satisfiability
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Closest papers by meaning
- G2SAT: Learning to Generate SAT Formulas (NeurIPS 2019 · similarity 0.91)
- Learning from Algorithm Feedback: One-Shot SAT Solver Guidance with GNNs (ICLR 2026 · similarity 0.91)
- NSNet: A General Neural Probabilistic Framework for Satisfiability Problems (NeurIPS 2022 · similarity 0.91)
- Learning How to Cube (NeurIPS 2026 · similarity 0.89)
- Can Transformers Reason Logically? A Study in SAT Solving (ICML 2025 · similarity 0.88)
- Learning Better Representations From Less Data For Propositional Satisfiability (NeurIPS 2024 · similarity 0.87)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.