arXiv subjects

Jorge Vančo Sampedro

Publications and source records attributed to Jorge Vančo Sampedro.

2 recordsLinked to original sources

arXiv2026

Discrete Langevin-Inspired Posterior Sampling

We study posterior sampling for inverse problems in discrete state spaces using discrete diffusion models as generative priors. While continuous diffusion models have become widely used for inverse problems, their discrete counterparts remain comparatively underexplored. Existing discrete posterior samplers often rely on continuous relaxations of discrete variables, Gibbs-style updates, or mechanisms specialized to particular corruption processes, which can limit scalability or generality. We propose $Δ$LPS, a Discrete Langevin-Inspired Posterior Sampler that uses gradient information to identify promising discrete moves without leaving the discrete state space. The resulting approach enables efficient parallel updates across all token dimensions and is agnostic to the training paradigm of the discrete diffusion prior, including masked and uniform-state diffusion. We evaluate our method on image restoration tasks across MNIST, CIFAR, and FFHQ, as well as spatial mapping, covering linear, nonlinear, and blind inverse problems. Across these settings, we improve over recent discrete diffusion posterior samplers and are competitive with strong continuous diffusion-based inverse solvers. Our results suggest that fully discrete, gradient-informed posterior samplers offer a scalable and general path toward solving inverse problems over discrete representations.

cs.LG↗

arXiv2026

Contrastive Diffusion Guidance for Spatial Inverse Problems

We consider a class of inverse problems characterized by forward operators that are partially specified, non-smooth, and non-differentiable. Although generative inverse solvers have made significant progress, we find that these forward operators introduce a distinct set of challenges. As a concrete instance, we consider the problem of reconstructing spatial layouts, such as floorplans, from human movement trajectories, where the underlying path-generation process is inherently non-differentiable and only partially known. In such problems, direct likelihood-based guidance becomes unstable, since the underlying path-planning process does not provide reliable gradients. We break-away from existing diffusion-based posterior samplers and reformulate likelihood-based guidance in a smoother embedding space. This embedding space is learned using a contrastive objective to bring compatible trajectory-floorplan pairs close together while pushing mismatched pairs apart. We show that this surrogate likelihood score in the embedding space provides a valid approximation to the true likelihood score, making it possible to steer the denoising process towards the posterior. Across extensive experiments, our model CoGuide produces more consistent reconstructions and is more robust than existing inverse-solvers and guided diffusion. Beyond spatial mapping, we show that our method can be applied more broadly, suggesting a route toward solving generalized blind inverse problems using diffusion models.

cs.CV↗