[2603.27996] From Independent to Correlated Diffusion: Generalized Generative Modeling with Probabilistic Computers

Computer Science > Machine Learning arXiv:2603.27996 (cs) [Submitted on 30 Mar 2026] Title:From Independent to Correlated Diffusion: Generalized Generative Modeling with Probabilistic Computers Authors:Nihal Sanjay Singh, Mazdak Mohseni-Rajaee, Shaila Niazi, Kerem Y. Camsari View a PDF of the paper titled From Independent to Correlated Diffusion: Generalized Generative Modeling with Probabilistic Computers, by Nihal Sanjay Singh and 3 other authors View PDF HTML (experimental) Abstract:Diffusion models have emerged as a powerful framework for generative tasks in deep learning. They decompose generative modeling into two computational primitives: deterministic neural-network evaluation and stochastic sampling. Current implementations usually place most computation in the neural network, but diffusion as a framework allows a broader range of choices for the stochastic transition kernel. Here, we generalize the stochastic sampling component by replacing independent noise injection with Markov chain Monte Carlo (MCMC) dynamics that incorporate known interaction structure. Standard independent diffusion is recovered as a special case when couplings are set to zero. By explicitly incorporating Ising couplings into the diffusion dynamics, the noising and denoising processes exploit spatial correlations representative of the target system. The resulting framework maps naturally onto probabilistic computers (p-computers) built from probabilistic bits (p-bits), which provide orders-...