PATHS applies parallel tempering to improve initial particle sampling for SMC reward alignment, yielding better results on layout-to-image and quantity-aware generation tasks.
arXiv preprint arXiv:2509.23265 , year=
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Bayesian inverse problem with diffusion model priors for CML-based rain field reconstruction outperforms baselines by preserving rainfall statistics better than Gaussian processes.
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Parallel Tempering Initial Sampling in Inference-Time Reward Alignment
PATHS applies parallel tempering to improve initial particle sampling for SMC reward alignment, yielding better results on layout-to-image and quantity-aware generation tasks.
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Bayesian Rain Field Reconstruction using Commercial Microwave Links and Diffusion Model Priors
Bayesian inverse problem with diffusion model priors for CML-based rain field reconstruction outperforms baselines by preserving rainfall statistics better than Gaussian processes.