Constrained Diffusion for Code (CDC) integrates constraint satisfaction into the reverse denoising process of discrete diffusion models via constraint-aware operators that use optimization and program analysis to steer generation toward feasible programs.
Con- strained discrete diffusion, 2025
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Discrete diffusion models learn data support before frequencies because the exact reverse process decomposes edits into a dominant validity scale and a finer probability coefficient.
Adaptive correction scheduling for hard constraints in generative sampling recovers 71% of stepwise projection benefits using 75% fewer corrections by focusing on trajectory-perturbing steps.
SCMDM adapts trained masked diffusion models to condition denoising steps on their own prior clean predictions, cutting generative perplexity nearly in half on open-web text while improving discretized image, molecule, and genomic synthesis.
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Constrained Code Generation with Discrete Diffusion
Constrained Diffusion for Code (CDC) integrates constraint satisfaction into the reverse denoising process of discrete diffusion models via constraint-aware operators that use optimization and program analysis to steer generation toward feasible programs.
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Support Before Frequency in Discrete Diffusion
Discrete diffusion models learn data support before frequencies because the exact reverse process decomposes edits into a dominant validity scale and a finer probability coefficient.
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Enforcing Constraints in Generative Sampling via Adaptive Correction Scheduling
Adaptive correction scheduling for hard constraints in generative sampling recovers 71% of stepwise projection benefits using 75% fewer corrections by focusing on trajectory-perturbing steps.
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Simple Self-Conditioning Adaptation for Masked Diffusion Models
SCMDM adapts trained masked diffusion models to condition denoising steps on their own prior clean predictions, cutting generative perplexity nearly in half on open-web text while improving discretized image, molecule, and genomic synthesis.
- Proximal-Based Generative Modeling for Bayesian Inverse Problems