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Discusses methods for generating structured outputs from large language models using constrained sampling techniques.
This paper proposes primal-dual inference for constrained diffusion models, jointly inferring the optimal distribution and its dual variable via a dual-conditioned score network, with convergence guarantees and applications in wireless resource allocation and portfolio management.
DiRecT introduces a training-free algorithm for safe diffusion-based planning that enforces constraints only on final clean trajectories using receding-horizon denoising, improving safety and performance over existing methods.
Proposes Constraint-Aware Flow Matching, a novel end-to-end framework that aligns the model's learning dynamics with constrained sampling procedure, mitigating distributional shift from projection corrections for high-quality constrained generation.