G4SATBench: Benchmarking and Advancing SAT Solving with Graph Neural Networks
Fuente:
arXiv
Saved in:
| Main Authors: | Li, Zhaoyu, Guo, Jinpei, Si, Xujie |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
SATBench: Benchmarking LLMs' Logical Reasoning via Automated Puzzle Generation from SAT Formulas
by: Wei, Anjiang, et al.
Published: (2025)
by: Wei, Anjiang, et al.
Published: (2025)
NeuroBack: Improving CDCL SAT Solving using Graph Neural Networks
by: Wang, Wenxi, et al.
Published: (2021)
by: Wang, Wenxi, et al.
Published: (2021)
LogicXGNN: Grounded Logical Rules for Explaining Graph Neural Networks
by: Geng, Chuqin, et al.
Published: (2025)
by: Geng, Chuqin, et al.
Published: (2025)
Chronosymbolic Learning: Efficient CHC Solving with Symbolic Reasoning and Inductive Learning
by: Luo, Ziyan, et al.
Published: (2023)
by: Luo, Ziyan, et al.
Published: (2023)
Graph Neural Networks Uncover Geometric Neural Representations in Reinforcement-Based Motor Learning
by: Nardi, Federico, et al.
Published: (2024)
by: Nardi, Federico, et al.
Published: (2024)
EEG Decoding for Datasets with Heterogenous Electrode Configurations using Transfer Learning Graph Neural Networks
by: Han, Jinpei, et al.
Published: (2023)
by: Han, Jinpei, et al.
Published: (2023)
Neural Proposals, Symbolic Guarantees: Neuro-Symbolic Graph Generation with Hard Constraints
by: Geng, Chuqin, et al.
Published: (2026)
by: Geng, Chuqin, et al.
Published: (2026)
Neural Approaches to SAT Solving: Design Choices and Interpretability
by: Mojžíšek, David, et al.
Published: (2025)
by: Mojžíšek, David, et al.
Published: (2025)
Fast T2T: Optimization Consistency Speeds Up Diffusion-Based Training-to-Testing Solving for Combinatorial Optimization
by: Li, Yang, et al.
Published: (2025)
by: Li, Yang, et al.
Published: (2025)
Learning Minimal Neural Specifications
by: Geng, Chuqin, et al.
Published: (2024)
by: Geng, Chuqin, et al.
Published: (2024)
HyperSAT: Unsupervised Hypergraph Neural Networks for Weighted MaxSAT Problems
by: Chen, Qiyue, et al.
Published: (2025)
by: Chen, Qiyue, et al.
Published: (2025)
Autoformalizing Euclidean Geometry
by: Murphy, Logan, et al.
Published: (2024)
by: Murphy, Logan, et al.
Published: (2024)
NEUROLOGIC: From Neural Representations to Interpretable Logic Rules
by: Geng, Chuqin, et al.
Published: (2025)
by: Geng, Chuqin, et al.
Published: (2025)
Benchmarking Graph Neural Networks in Solving Hard Constraint Satisfaction Problems
by: Skenderi, Geri, et al.
Published: (2026)
by: Skenderi, Geri, et al.
Published: (2026)
GraSS: Combining Graph Neural Networks with Expert Knowledge for SAT Solver Selection
by: Zhang, Zhanguang, et al.
Published: (2024)
by: Zhang, Zhanguang, et al.
Published: (2024)
SAT-DIFF: A Tree Diffing Framework Using SAT Solving
by: Geng, Chuqin, et al.
Published: (2024)
by: Geng, Chuqin, et al.
Published: (2024)
Unified Graph Networks (UGN): A Deep Neural Framework for Solving Graph Problems
by: Dawn, Rudrajit, et al.
Published: (2025)
by: Dawn, Rudrajit, et al.
Published: (2025)
MILP-SAT-GNN: Yet Another Neural SAT Solver
by: Cardillo, Franco Alberto, et al.
Published: (2025)
by: Cardillo, Franco Alberto, et al.
Published: (2025)
LLM Library Learning Fails: A LEGO-Prover Case Study
by: Berlot-Attwell, Ian, et al.
Published: (2025)
by: Berlot-Attwell, Ian, et al.
Published: (2025)
Solving the Tree Containment Problem Using Graph Neural Networks
by: Dushatskiy, Arkadiy, et al.
Published: (2024)
by: Dushatskiy, Arkadiy, et al.
Published: (2024)
RBF-MGN:Solving spatiotemporal PDEs with Physics-informed Graph Neural Network
by: Xiang, Zixue, et al.
Published: (2022)
by: Xiang, Zixue, et al.
Published: (2022)
Graph Neural Networks in Intelligent Transportation Systems: Advances, Applications and Trends
by: Li, Hourun, et al.
Published: (2024)
by: Li, Hourun, et al.
Published: (2024)
LLM4DyG: Can Large Language Models Solve Spatial-Temporal Problems on Dynamic Graphs?
by: Zhang, Zeyang, et al.
Published: (2023)
by: Zhang, Zeyang, et al.
Published: (2023)
Solving Max-Cut to Global Optimality via Feasibility-Preserving Graph Neural Networks
by: Chen, Hao, et al.
Published: (2026)
by: Chen, Hao, et al.
Published: (2026)
Benchmarking Fairness-aware Graph Neural Networks in Knowledge Graphs
by: Sasaki, Yuya
Published: (2025)
by: Sasaki, Yuya
Published: (2025)
Library Learning Doesn't: The Curious Case of the Single-Use "Library"
by: Berlot-Attwell, Ian, et al.
Published: (2024)
by: Berlot-Attwell, Ian, et al.
Published: (2024)
STG4Traffic: A Survey and Benchmark of Spatial-Temporal Graph Neural Networks for Traffic Prediction
by: Luo, Xunlian, et al.
Published: (2023)
by: Luo, Xunlian, et al.
Published: (2023)
Beyond Message Passing: A Symbolic Alternative for Expressive and Interpretable Graph Learning
by: Geng, Chuqin, et al.
Published: (2026)
by: Geng, Chuqin, et al.
Published: (2026)
Exact Verification of Graph Neural Networks with Incremental Constraint Solving
by: Liu, Minghao, et al.
Published: (2025)
by: Liu, Minghao, et al.
Published: (2025)
APPL: A Prompt Programming Language for Harmonious Integration of Programs and Large Language Model Prompts
by: Dong, Honghua, et al.
Published: (2024)
by: Dong, Honghua, et al.
Published: (2024)
Understanding Heterophily for Graph Neural Networks
by: Wang, Junfu, et al.
Published: (2024)
by: Wang, Junfu, et al.
Published: (2024)
W2SAT: Learning to generate SAT instances from Weighted Literal Incidence Graphs
by: Wen, Weihuang, et al.
Published: (2023)
by: Wen, Weihuang, et al.
Published: (2023)
Graph Alignment for Benchmarking Graph Neural Networks and Learning Positional Encodings
by: Lagesse, Adrien, et al.
Published: (2025)
by: Lagesse, Adrien, et al.
Published: (2025)
Adversarial Graph Neural Network Benchmarks: Towards Practical and Fair Evaluation
by: Ngo, Tran Gia Bao, et al.
Published: (2026)
by: Ngo, Tran Gia Bao, et al.
Published: (2026)
ProG: A Graph Prompt Learning Benchmark
by: Zi, Chenyi, et al.
Published: (2024)
by: Zi, Chenyi, et al.
Published: (2024)
OpenGLT: A Comprehensive Benchmark of Graph Neural Networks for Graph-Level Tasks
by: Li, Haoyang, et al.
Published: (2025)
by: Li, Haoyang, et al.
Published: (2025)
Can Transformers Reason Logically? A Study in SAT Solving
by: Pan, Leyan, et al.
Published: (2024)
by: Pan, Leyan, et al.
Published: (2024)
Improved Physics-Driven Neural Network to Solve Inverse Scattering Problems
by: Du, Yutong, et al.
Published: (2025)
by: Du, Yutong, et al.
Published: (2025)
Relating-Up: Advancing Graph Neural Networks through Inter-Graph Relationships
by: Zou, Qi, et al.
Published: (2024)
by: Zou, Qi, et al.
Published: (2024)
Benchmarking Graph Representations and Graph Neural Networks for Multivariate Time Series Classification
by: Yang, Wennuo, et al.
Published: (2025)
by: Yang, Wennuo, et al.
Published: (2025)
Similar Items
-
SATBench: Benchmarking LLMs' Logical Reasoning via Automated Puzzle Generation from SAT Formulas
by: Wei, Anjiang, et al.
Published: (2025) -
NeuroBack: Improving CDCL SAT Solving using Graph Neural Networks
by: Wang, Wenxi, et al.
Published: (2021) -
LogicXGNN: Grounded Logical Rules for Explaining Graph Neural Networks
by: Geng, Chuqin, et al.
Published: (2025) -
Chronosymbolic Learning: Efficient CHC Solving with Symbolic Reasoning and Inductive Learning
by: Luo, Ziyan, et al.
Published: (2023) -
Graph Neural Networks Uncover Geometric Neural Representations in Reinforcement-Based Motor Learning
by: Nardi, Federico, et al.
Published: (2024)