Paper Citation Record · LEDGER

Assessing the Quality of Denoising Diffusion Models in Wasserstein Distance: Noisy Score and Optimal Bounds

As of 14 August 2026, this Paper Citation Record lists 21 of 21 outbound references and 1 inbound Pith citation observation for arXiv:2506.09681.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2506.09681 v1

Coverage vector

measured 21 of 21 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T04:51:16.640835Z

measured 22 of 22 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-10T18:15:58.587798Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-05-11T05:10:55.346624Z

Reference resolution

21 of 21 outbound references displayed

verified exact0
verified fuzzy8
unresolved13
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation df4afbfe-9df8-4d64-9b7a-07c223dff2e8 · outbound

This paper cites Rotskoff.

Assessing the Quality of Denoising Diffusion Models in Wasserstein Distance: Noisy Score and Optimal Bounds Rotskoff

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:51:17.042215Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 89d59350-b38f-4e0f-9974-ce996dde175d · outbound

This paper cites Denoising diffusion probabilistic models are optimally adaptive to unknown low dimensionality.CoRR, arXiv:2410.18784,.

Assessing the Quality of Denoising Diffusion Models in Wasserstein Distance: Noisy Score and Optimal Bounds Denoising diffusion probabilistic models are optimally adaptive to unknown low dimensionality.CoRR, arXiv:2410.18784,

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-07T04:51:16.609205Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 249f5d4a-9c81-4b30-afe2-5cf64c37f2d7 · outbound

This paper cites Linear Convergence of Diffusion Models Under the Manifold Hypothesis.

Assessing the Quality of Denoising Diffusion Models in Wasserstein Distance: Noisy Score and Optimal Bounds Linear Convergence of Diffusion Models Under the Manifold Hypothesis

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-07T04:51:16.612170Z

Source-reported events for the cited work

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Observation d74802d9-90f2-4782-bdf2-b86ca3ca2f26 · outbound

This paper cites Beyond log-concavity and score regularity: Improved convergence bounds for score-based generative models in w2-distance.CoRR, arXiv:2501.02298,.

Assessing the Quality of Denoising Diffusion Models in Wasserstein Distance: Noisy Score and Optimal Bounds Beyond log-concavity and score regularity: Improved convergence bounds for score-based generative models in w2-distance.CoRR, arXiv:2501.02298,

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-07T04:51:16.615167Z

Source-reported events for the cited work

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Observation 143d03f1-04a5-4241-857c-d0a16af22e2c · outbound

This paper cites Score-based Diffusion Models via Stochastic Differential Equations -- a Technical Tutorial.

Assessing the Quality of Denoising Diffusion Models in Wasserstein Distance: Noisy Score and Optimal Bounds Score-based Diffusion Models via Stochastic Differential Equations -- a Technical Tutorial

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-07T04:51:16.620663Z

Source-reported events for the cited work

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Observation 5a2360d3-cadd-4150-b125-30b23ca9bdf6 · outbound

This paper cites an unresolved cited work.

Assessing the Quality of Denoising Diffusion Models in Wasserstein Distance: Noisy Score and Optimal Bounds Unresolved cited work

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-07T04:51:16.623664Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 057bcbcc-6bc7-4b6d-8652-c171eddc28cf · outbound

This paper cites Stochastic Runge-Kutta Methods: Provable Acceleration of Diffusion Models.

Assessing the Quality of Denoising Diffusion Models in Wasserstein Distance: Noisy Score and Optimal Bounds Stochastic Runge-Kutta Methods: Provable Acceleration of Diffusion Models

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-07T04:51:16.626327Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 449c9b3b-3b99-43ed-a7d6-5214da79946c · outbound

This paper cites Sampling as optimization in the space of measures: The langevin dynamics as a composite optimization problem.

Assessing the Quality of Denoising Diffusion Models in Wasserstein Distance: Noisy Score and Optimal Bounds Sampling as optimization in the space of measures: The langevin dynamics as a composite optimization problem

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:51:17.014425Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 5b815cf9-d153-408f-9126-bd9a1ad1825f · outbound

This paper cites Optimal score estimation via empirical bayes smoothing.

Assessing the Quality of Denoising Diffusion Models in Wasserstein Distance: Noisy Score and Optimal Bounds Optimal score estimation via empirical bayes smoothing

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:51:17.005186Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 2d7bab04-0aba-4bab-824c-99aa34694445 · outbound

This paper cites Convergence in KL and rényi divergence of the unadjusted langevin algorithm using estimated score.

Assessing the Quality of Denoising Diffusion Models in Wasserstein Distance: Noisy Score and Optimal Bounds Convergence in KL and rényi divergence of the unadjusted langevin algorithm using estimated score

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:51:16.995327Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 543c38a4-af00-4c73-a637-67fd8dd3ec24 · outbound

This paper cites Advancing Wasserstein Convergence Analysis of Score-Based Models: Insights from Discretization and Second-Order Acceleration.

Assessing the Quality of Denoising Diffusion Models in Wasserstein Distance: Noisy Score and Optimal Bounds Advancing Wasserstein Convergence Analysis of Score-Based Models: Insights from Discretization and Second-Order Acceleration

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-07T04:51:16.637936Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 225b4099-843a-4a78-a4ee-a1dd58d3720e · outbound

This paper cites LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop.

Assessing the Quality of Denoising Diffusion Models in Wasserstein Distance: Noisy Score and Optimal Bounds LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-07T04:51:16.640835Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1a8b73ce-b340-486d-861a-9d9eba2f02bf · outbound

This paper cites Nearly d-linear convergence bounds for diffusion models via stochastic localization.

Assessing the Quality of Denoising Diffusion Models in Wasserstein Distance: Noisy Score and Optimal Bounds Nearly d-linear convergence bounds for diffusion models via stochastic localization

Reference 1982

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:51:17.060009Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 2c13a7f0-3366-473b-8d30-74b0deaa9e9d · outbound

This paper cites Adaptivity of diffusion models to manifold structures.

Assessing the Quality of Denoising Diffusion Models in Wasserstein Distance: Noisy Score and Optimal Bounds Adaptivity of diffusion models to manifold structures

Reference 2014

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:51:17.023898Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 66172154-945b-40e5-b8a4-9dc6e7eea5b9 · outbound

This paper cites Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions.

Assessing the Quality of Denoising Diffusion Models in Wasserstein Distance: Noisy Score and Optimal Bounds Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions

Reference 2018

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:51:17.051226Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 1acf1292-1ea8-4257-92c3-4bf7f858c4ce · outbound

This paper cites Faster Diffusion Sampling with Randomized Midpoints: Sequential and Parallel.

Assessing the Quality of Denoising Diffusion Models in Wasserstein Distance: Noisy Score and Optimal Bounds Faster Diffusion Sampling with Randomized Midpoints: Sequential and Parallel

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-07T04:51:16.600199Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 03a760de-1a89-4ede-a3ab-87bfd9f9af0d · outbound

This paper cites Denoising diffusion probabilistic models.

Assessing the Quality of Denoising Diffusion Models in Wasserstein Distance: Noisy Score and Optimal Bounds Denoising diffusion probabilistic models

Reference 2020

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:51:17.032834Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation e10e2213-df50-45ae-841c-b1d48f9f4eff · outbound

This paper cites On diffusion-based generative models and their error bounds: The log-concave case with full convergence estimates.

Assessing the Quality of Denoising Diffusion Models in Wasserstein Distance: Noisy Score and Optimal Bounds On diffusion-based generative models and their error bounds: The log-concave case with full convergence estimates

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-07T04:51:16.587851Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6d23ceda-985b-4488-9bde-82955255bf10 · outbound

This paper cites An Overview of Diffusion Models: Applications, Guided Generation, Statistical Rates and Optimization.

Assessing the Quality of Denoising Diffusion Models in Wasserstein Distance: Noisy Score and Optimal Bounds An Overview of Diffusion Models: Applications, Guided Generation, Statistical Rates and Optimization

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-07T04:51:16.594135Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ebbc430f-6932-43a3-a456-214ea9ae070c · outbound

This paper cites Convergence of Diffusion Models Under the Manifold Hypothesis in High-Dimensions.

Assessing the Quality of Denoising Diffusion Models in Wasserstein Distance: Noisy Score and Optimal Bounds Convergence of Diffusion Models Under the Manifold Hypothesis in High-Dimensions

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-07T04:51:16.580890Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 10d2df44-a0b9-4c90-aef0-cb02d66fd29a · outbound

This paper cites Convergence Analysis for General Probability Flow ODEs of Diffusion Models in Wasserstein Distances.

Assessing the Quality of Denoising Diffusion Models in Wasserstein Distance: Noisy Score and Optimal Bounds Convergence Analysis for General Probability Flow ODEs of Diffusion Models in Wasserstein Distances

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-07T04:51:16.603286Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 0b577ac6-c0d6-43ee-9288-3af4acfae214 · inbound

Lipschitz regularity in Flow Matching and Diffusion Models: sharp sampling rates and functional inequalities cites this paper.

Lipschitz regularity in Flow Matching and Diffusion Models: sharp sampling rates and functional inequalities Assessing the Quality of Denoising Diffusion Models in Wasserstein Distance: Noisy Score and Optimal Bounds

Reference 1

Resolution: verified exact; arxiv_id, observed 2026-05-11T05:10:55.348766Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.