Blocked Gibbs meets Diffusion Transformers: Unsupervised Learning for Constraint Optimization

ArXi:2605.25129v1 Announce Type: new Diffusion models have shown promise in learning to solve constraint optimization problems. However, they are mostly restricted to problems with binary variables and rely on graph neural networks, hindering their application to a broader range of problems such as those with general discrete variables or constraint structures that necessitate global rather than local reasoning. We investigate the use of Diffusion Transformers to address the aforementioned limitations.