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The paper investigates using graph neural networks to correct spectral prior estimates for connectivity-loss in road networks under disruptions, with evaluations on synthetic and real-world OpenStreetMap datasets.
This paper proposes Spectral Alignment (SPA), a lightweight guidance-based method that reduces exposure bias in diffusion models by calibrating the power spectrum of intermediate predictions, showing consistent improvements across pixel-space, latent, and flow-matching models with minimal computational overhead.