mlsanityData to 7 Oct 2026

Accurate Sampling from Diffusion Models

arXiv 2609.29902 ↗First posted 24 Sep 2026hep-lat cond-mat.dis-nn cs.LGcited by 0

On the map: Diffusion models → Diffusion foundations and sampling also fits: Flow matching, time series and science

Abstract (arXiv)

A new proposal called DM-SMC (Diffusion Model - Sequential Monte Carlo) is investigated, which samples ensembles defined in terms of an action, using diffusion models trained on samples from the ensemble. The SMC setup allows for accurate sampling in spite of an approximate diffusion model and the finite stepsize used in the numerical solution of the stochastic process. Improved update strategies are also investigated. Results are presented for a Z2 symmetric scalar field theory in 2 dimensions near its 2nd order phase transition.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Diffusion foundations and sampling, one of 14 directions in Diffusion models. Builds on 15 of its 35 references that are in the map. First posted on arXiv 24 Sep 2026.

Builds on

15 of 35 references.

Closest ancestors · 3

Same direction, newest first

  1. 2025Combining complex Langevin dynamics with score-based and energy-based diffusion models1 Oct
  2. 2024On learning higher-order cumulants in diffusion models28 Oct
  3. 2024Understanding Diffusion Models by Feynman's Path Integral17 Mar

Other references · 12

  1. 2021Deep Generative Modelling: A Comparative Review of VAEs, GANs, Normalizing Flows, Energy-Based and Autoregressive Modelscited by 251
  2. 2019Flow-based generative models for Markov chain Monte Carlo in lattice field theorycited by 147
  3. 2021An Introduction to Deep Generative Modelingcited by 104
  4. 2020Sampling using SU(N) gauge equivariant flowscited by 83
  5. 2021Introduction to Normalizing Flows for Lattice Field Theorycited by 35
  6. 2022Aspects of scaling and scalability for flow-based sampling of lattice QCDcited by 26
  7. 2022Stochastic normalizing flows as non-equilibrium transformationscited by 25
  8. 2021Scaling Up Machine Learning For Quantum Field Theory with Equivariant Continuous Flowscited by 24
  9. 2023Advances in machine-learning-based sampling motivated by lattice quantum chromodynamicscited by 23
  10. 2023Sampling the lattice Nambu-Goto string using Continuous Normalizing Flowscited by 12
  11. 2023Generative models for scalar field theories: how to deal with poor scaling?cited by 6
  12. 2024AdvNF: Reducing Mode Collapse in Conditional Normalising Flows using Adversarial Learningcited by 6

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  1. 2026Non-Asymptotic Error Bounds for SMC with Biased Proposals: Application to Conditional Diffusion Sampling
  2. 2023Accelerating Markov Chain Monte Carlo sampling with diffusion models
  3. 2026Steering Diffusion Models to Rare Events with Sequential Monte Carlo
  4. 2025Generative diffusion posterior sampling for informative likelihoods
  5. 2025Scalable Discrete Diffusion Samplers: Combinatorial Optimization and Statistical Physics
  6. 2024Optimizing Few-Step Sampler for Diffusion Probabilistic Model
  7. 2023Transport meets Variational Inference: Controlled Monte Carlo Diffusions
  8. 2026High-accuracy and dimension-free sampling with diffusions