Generating from Discrete Distributions Using Diffusions: Insights from Random Constraint Satisfaction Problems

arXiv:2603.20589 · cs.LG · Submitted 2026-03-21 · Read on arXiv

cs.LG

Submitted: 2026-03-21

Updated: 2026-09-07

Comments: 42 pages; 18 figures

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Accuracy — 10× in this paper · explained in 1 episode
• Benchmark — 9× in this paper · explained in 2 episodes
• Embeddings — 7× in this paper · explained in 2 episodes
• Neural Network — 7× in this paper
• Phase Transitions — 6× in this paper · explained in 2 episodes
• Diffusion Model — 5× in this paper · explained in 8 episodes
• Diffusion Models — 4× in this paper · explained in 6 episodes
• International Conference on Learning Representations — 3× in this paper
• Core — 2× in this paper · explained in 2 episodes
• Diffusion Process — 2× in this paper · explained in 2 episodes
• Generalization — 2× in this paper · explained in 13 episodes
• Generative Model — 2× in this paper · explained in 2 episodes
• Generative Modeling — 2× in this paper · explained in 3 episodes
• Annual Symposium on Foundations of Computer Science — 1× in this paper
• Benchmarking — 1× in this paper · explained in 3 episodes
• Diffusion Processes — 1× in this paper · explained in 2 episodes
• Fidelity — 1× in this paper · explained in 2 episodes
• Gibbs State — 1× in this paper · explained in 2 episodes
• Hamming Distance — 1× in this paper · explained in 2 episodes
• Hypergraph — 1× in this paper · explained in 2 episodes
• Latent Space — 1× in this paper · explained in 7 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes
• Symmetry Breaking — 1× in this paper · explained in 3 episodes
• Transformer — 1× in this paper · explained in 2 episodes
• Verification — 1× in this paper · explained in 2 episodes

Sources

• Denoising Diffusion for Sampling SAT Solutions
• Sampling from Spherical Spin Glasses in Total Variation via Algorithmic Stochastic Localization
• Solving Constraint Satisfaction Problems through Belief Propagation-guided decimation
• Computational bottlenecks for denoising diffusions
• Posterior Sampling in High Dimension via Diffusion Processes
• Hierarchical Text-Conditional Image Generation with CLIP Latents

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