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.
TX t=1 logq θ(Xt−1 |X t, mt) # | {z } (2) Reverse entropy / reconstruction (11) −E X0:T ,m1:T ∼qθ
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.