mlsanityData to 7 Oct 2026

Asymptotic Preservation and Uniform Accuracy of Diffusion and Flow-Matching Samplers

arXiv 2607.04113 ↗First posted 5 Jul 2026cs.LG cs.NA math.NAcited by 0

On the map: Diffusion models → Flow matching, time series and science

Abstract (arXiv)

Diffusion and Gaussian-interpolant flow-matching samplers approach data through a terminal noise floor varepsilon, a singular limit for manifold-supported or rank-deficient data. We study two properties of a complete sampler specification, comprising its update rule, time grid, and terminal rule. Asymptotic preservation (AP) means a stable and consistent zero-noise discretization with a step count bounded independently of varepsilon. Uniform accuracy (UA) of order p means that, at numerical resolution h, the endpoint W2 error is O(hp) with a floor-independent constant. Bounded log-noise stepping fails AP because its step count diverges. Stopping a stable base solver at a positive switching scale a and appending one map fitted to the analytic normal mode restores AP. On smooth compact boundaryless manifolds, the standard map has exact-input error O(a2-varepsilon2) and sharp zero-floor error Theta(a2). A base solver with a floor-uniform order-p estimate on the resolved interval retains that order when a=O(hp/2), provided the terminal transfer factor remains bounded. Along exact trajectories, the posterior-mean identity D(x(σ),σ)=x(σ)-σ x'(σ) cancels the linear terminal defect and enables higher-order fitted maps. A three-evaluation Hermite construction is uniformly third order for exact switching-scale input over 0≤varepsilon≤ a, and a seven-evaluation construction is fourth order at zero. We classify representative diffusion and flow-matching specifications by AP and UA. On EDM and Rectified Flow checkpoints, a paired decomposition separates base-integration from terminal-completion error and predicts held-out same-seed endpoint errors.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Flow matching, time series and science, one of 14 directions in Diffusion models. Builds on 26 of its 37 references that are in the map. First posted on arXiv 5 Jul 2026.

Builds on

26 of 37 references.

Closest ancestors · 3

Same direction, newest first

  1. 2026Flow Matching is Adaptive to Manifold Structures25 Feb
  2. 2022Flow Matching for Generative Modeling6 Oct
  3. 2022Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow7 Sep

Other references · 23

  1. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  2. 2020Denoising Diffusion Implicit Modelscited by 9,655
  3. 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 8,964
  4. 2022Elucidating the Design Space of Diffusion-Based Generative Modelscited by 2,994
  5. 2022DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Stepscited by 1,961
  6. 2022Building Normalizing Flows with Stochastic Interpolantscited by 964
  7. 2022DPM-Solver++: Fast Solver for Guided Sampling of Diffusion Probabilistic Modelscited by 924
  8. 2023Stochastic Interpolants: A Unifying Framework for Flows and Diffusionscited by 897
  9. 2022Pseudo Numerical Methods for Diffusion Models on Manifoldscited by 695
  10. 2022Fast Sampling of Diffusion Models with Exponential Integratorcited by 526
  11. 2017Towards Principled Methods for Training Generative Adversarial Networkscited by 460
  12. 2023UniPC: A Unified Predictor-Corrector Framework for Fast Sampling of Diffusion Modelscited by 441
11 more
  1. 2022Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptionscited by 440
  2. 2022Convergence of denoising diffusion models under the manifold hypothesiscited by 237
  3. 2023Nearly d-Linear Convergence Bounds for Diffusion Models via Stochastic Localizationcited by 230
  4. 2023Generalization in diffusion models arises from geometry-adaptive harmonic representationscited by 179
  5. 2022Convergence for score-based generative modeling with polynomial complexitycited by 176
  6. 2024Align Your Steps: Optimizing Sampling Schedules in Diffusion Modelscited by 91
  7. 2023Interpreting and Improving Diffusion Models from an Optimization Perspectivecited by 19
  8. 2023Lookahead Diffusion Probabilistic Models for Refining Mean Estimationcited by 11
  9. 2025Enhanced Diffusion Sampling via Extrapolation with Multiple ODE Solutionscited by 7
  10. 2026Formalizing the Sampling Design Space of Diffusion-Based Generative Models via Adaptive Solvers and Wasserstein-Bounded Timestepscited by 3
  11. 2026Rao-Blackwellized Score Matching on Manifoldscited by 2

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  5. 2026How Many Directions Must a Truncated Diffusion Sampler Retain? Matching Bounds Under Power-Law Spectra
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