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.
Frey, Tim G
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 2verdicts
UNVERDICTED 2representative citing papers
SPARROW is a black-box optimization method that treats a fixed generative sampler as a structured proposal operator and applies rank-based selection over evaluated candidates to achieve low-budget optimization with asymptotic convergence guarantees over the sampler support.
citing papers explorer
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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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Generative Refinement for Low-Budget Black-Box Optimization
SPARROW is a black-box optimization method that treats a fixed generative sampler as a structured proposal operator and applies rank-based selection over evaluated candidates to achieve low-budget optimization with asymptotic convergence guarantees over the sampler support.