Constraint-Guided Prediction Refinement via Deterministic Diffusion Trajectories

• URL: http://arxiv.org/abs/2506.12911v1
• Date: Sun, 15 Jun 2025 17:02:07 GMT
• Title: Constraint-Guided Prediction Refinement via Deterministic Diffusion Trajectories
• Authors: Pantelis Dogoulis, Fabien Bernier, Félix Fourreau, Karim Tit, Maxime Cordy,
• Abstract summary: We propose a general-purpose framework for constraint-aware guided denoising diffusion diffusionDDIMs.<n>Our method iteratively refines it through a diffusion trajectory by a learned prior and augmented by constraint corrections.
• Score: 7.279433512595361
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Many real-world machine learning tasks require outputs that satisfy hard constraints, such as physical conservation laws, structured dependencies in graphs, or column-level relationships in tabular data. Existing approaches rely either on domain-specific architectures and losses or on strong assumptions on the constraint space, restricting their applicability to linear or convex constraints. We propose a general-purpose framework for constraint-aware refinement that leverages denoising diffusion implicit models (DDIMs). Starting from a coarse prediction, our method iteratively refines it through a deterministic diffusion trajectory guided by a learned prior and augmented by constraint gradient corrections. The approach accommodates a wide class of non-convex and nonlinear equality constraints and can be applied post hoc to any base model. We demonstrate the method in two representative domains: constrained adversarial attack generation on tabular data with column-level dependencies and in AC power flow prediction under Kirchhoff's laws. Across both settings, our diffusion-guided refinement improves both constraint satisfaction and performance while remaining lightweight and model-agnostic.

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