BloGDiT introduces blocked Gibbs-style denoising in diffusion transformers to enable large targeted edits for constraint satisfaction and optimization, matching or exceeding prior methods on Sudoku, graph coloring, MIS, and MaxCut.
One model, any csp: graph neural networks as fast global search heuristics for constraint satisfaction
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Blocked Gibbs meets Diffusion Transformers: Unsupervised Learning for Constraint Optimization
BloGDiT introduces blocked Gibbs-style denoising in diffusion transformers to enable large targeted edits for constraint satisfaction and optimization, matching or exceeding prior methods on Sudoku, graph coloring, MIS, and MaxCut.