Contextual PPC uses world-model score densities to impose Riemannian structure on actions, yielding a safety bound on manifold distance controlled by estimation error and barrier curvature that improves with richer context.
A connection between score matching and denoising autoencoders
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
ADDPS formulates semantic decoding as a Bayesian inverse problem and uses alternating latent- and image-domain consistency enforcement during diffusion sampling to achieve optimal perceptual quality by preserving the data distribution.
A training-free diffusion-based method with RCC module and score-scaled PF-ODE decoder achieves optimal RDP in the Gaussian case and allows empirical traversal of the ternary tradeoff surface.
citing papers explorer
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Safety-Critical Contextual Control via Online Riemannian Optimization with World Models
Contextual PPC uses world-model score densities to impose Riemannian structure on actions, yielding a safety bound on manifold distance controlled by estimation error and barrier curvature that improves with richer context.
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Generative Semantic Communication via Alternating Dual-Domain Posterior Sampling
ADDPS formulates semantic decoding as a Bayesian inverse problem and uses alternating latent- and image-domain consistency enforcement during diffusion sampling to achieve optimal perceptual quality by preserving the data distribution.
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Training-Free Rate-Distortion-Perception Traversal With Diffusion
A training-free diffusion-based method with RCC module and score-scaled PF-ODE decoder achieves optimal RDP in the Gaussian case and allows empirical traversal of the ternary tradeoff surface.