Paper Citation Record · LEDGER

Convergence of denoising diffusion models under the manifold hypothesis

As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 30 inbound Pith citation observations for arXiv:2208.05314.

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

pith.paper-citation-record.v1
2208.05314 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 30 of 30 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 30 of 30 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T05:17:50.283429Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-10T01:26:43.202321Z

Reference resolution

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External citation measurements

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Outbound references

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Pith citing papers

Observation 470d67cb-910d-4b04-b187-b92fe47ccdfd · inbound

Smooth transport map via diffusion process cites this paper.

Smooth transport map via diffusion process Convergence of denoising diffusion models under the manifold hypothesis

Reference 21

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Source-reported events for the cited work

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Observation 97c2345d-f7c3-4def-9f72-6dd6d4da7beb · inbound

An analytic theory of creativity in convolutional diffusion models cites this paper.

An analytic theory of creativity in convolutional diffusion models Convergence of denoising diffusion models under the manifold hypothesis

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-10T23:32:26.667223Z

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Observation 69bf17db-0897-4441-9a86-d5bf89ecb534 · inbound

Realistic Image-to-Image Machine Unlearning via Decoupling and Knowledge Retention cites this paper.

Realistic Image-to-Image Machine Unlearning via Decoupling and Knowledge Retention Convergence of denoising diffusion models under the manifold hypothesis

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-08T23:04:31.387941Z

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Observation a1753ea3-09d3-400e-8b3b-421581e4b441 · inbound

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

Advancing Wasserstein Convergence Analysis of Score-Based Models: Insights from Discretization and Second-Order Acceleration Convergence of denoising diffusion models under the manifold hypothesis

Reference 10

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Observation 5847c568-15ca-45da-938c-a7ff77352118 · inbound

Capturing Conditional Dependence via Auto-regressive Diffusion Models cites this paper.

Capturing Conditional Dependence via Auto-regressive Diffusion Models Convergence of denoising diffusion models under the manifold hypothesis

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-16T05:17:50.283429Z

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Observation 61f03126-ecbb-450c-846d-ab95b9a843ad · inbound

Generalization in VAE and Diffusion Models: A Unified Information-Theoretic Analysis cites this paper.

Generalization in VAE and Diffusion Models: A Unified Information-Theoretic Analysis Convergence of denoising diffusion models under the manifold hypothesis

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-07T12:06:38.071605Z

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Observation f5deaba6-c54d-47b5-bcda-a51d2824daee · inbound

When and how can inexact generative models still sample from the data manifold? cites this paper.

When and how can inexact generative models still sample from the data manifold? Convergence of denoising diffusion models under the manifold hypothesis

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-05T22:07:58.079177Z

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Observation 636f3c73-f6bf-4fd3-be02-0055b44c182c · inbound

Non-asymptotic convergence bound of conditional diffusion models cites this paper.

Non-asymptotic convergence bound of conditional diffusion models Convergence of denoising diffusion models under the manifold hypothesis

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-05T20:56:33.392959Z

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Observation 3391c67b-6c9c-466e-a363-954df390cd00 · inbound

Can Generative Artificial Intelligence Survive Data Contamination? Theoretical Guarantees under Contaminated Recursive Training cites this paper.

Can Generative Artificial Intelligence Survive Data Contamination? Theoretical Guarantees under Contaminated Recursive Training Convergence of denoising diffusion models under the manifold hypothesis

Reference 2024

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Observation bb847ace-cc9f-4714-ad1d-950b25a4f333 · inbound

Diffusion Model for Manifold Data: Score Decomposition, Curvature, and Statistical Complexity cites this paper.

Diffusion Model for Manifold Data: Score Decomposition, Curvature, and Statistical Complexity Convergence of denoising diffusion models under the manifold hypothesis

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-05-15T06:45:11.110628Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 058b13d8-5208-45c1-a49f-3345159f89e8 · inbound

Diffusion Processes on Implicit Manifolds cites this paper.

Diffusion Processes on Implicit Manifolds Convergence of denoising diffusion models under the manifold hypothesis

Reference 24

Resolution: verified exact; arxiv_id, observed 2026-05-10T23:20:52.624648Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation c36354e5-8aac-4370-a309-76cdb58a49f2 · inbound

Diffusion Processes on Implicit Manifolds cites this paper.

Diffusion Processes on Implicit Manifolds Convergence of denoising diffusion models under the manifold hypothesis

Reference 24

Resolution: verified exact; arxiv_id, observed 2026-05-21T09:39:57.408495Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation d36048d4-d393-4f6d-b219-83cf265fb66c · inbound

Geometry-Aware Discretization Error of Diffusion Models cites this paper.

Geometry-Aware Discretization Error of Diffusion Models Convergence of denoising diffusion models under the manifold hypothesis

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-05-12T08:01:28.691839Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 6023d7a7-1a34-49c1-98e5-472e50385c96 · inbound

The tractability landscape of diffusion alignment: regularization, rewards, and computational primitives cites this paper.

The tractability landscape of diffusion alignment: regularization, rewards, and computational primitives Convergence of denoising diffusion models under the manifold hypothesis

Reference 39

Resolution: verified exact; arxiv_id, observed 2026-05-13T02:52:08.927588Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 8adbc836-362d-4b3f-a275-640e03a11223 · inbound

Proximal-Based Generative Modeling for Bayesian Inverse Problems cites this paper.

Proximal-Based Generative Modeling for Bayesian Inverse Problems Convergence of denoising diffusion models under the manifold hypothesis

Reference 52

Resolution: verified exact; arxiv_id, observed 2026-05-14T17:57:33.463199Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 93a1fb4b-ad84-482e-9574-4f77d2089b84 · inbound

On the Limits of Latent Reuse in Diffusion Models cites this paper.

On the Limits of Latent Reuse in Diffusion Models Convergence of denoising diffusion models under the manifold hypothesis

Reference 82

Resolution: verified exact; arxiv_id, observed 2026-05-14T18:39:22.015475Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 88cc477b-d2e9-4389-853a-63d093d1c077 · inbound

Training-Free Generative Sampling via Moment-Matched Score Smoothing cites this paper.

Training-Free Generative Sampling via Moment-Matched Score Smoothing Convergence of denoising diffusion models under the manifold hypothesis

Reference 22

Resolution: verified exact; arxiv_id, observed 2026-05-15T02:33:32.444898Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation c9fe20ba-2a51-4661-b327-33777b7b5621 · inbound

Let EEG Models Learn EEG cites this paper.

Let EEG Models Learn EEG Convergence of denoising diffusion models under the manifold hypothesis

Reference 81

Resolution: verified exact; arxiv_id, observed 2026-05-21T05:13:58.310662Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 91d0386e-ace4-451f-a19b-67f5c46a7e00 · inbound

On the Regularity and Generalization of One-Step Wasserstein-guided Generative Models for PDE-Induced Measures cites this paper.

On the Regularity and Generalization of One-Step Wasserstein-guided Generative Models for PDE-Induced Measures Convergence of denoising diffusion models under the manifold hypothesis

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-05-21T05:24:39.028273Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f1ec87f4-e026-4da2-95cb-dc9c31bb25ab · inbound

Noise Schedule Design for Diffusion Models: An Optimal Control Perspective cites this paper.

Noise Schedule Design for Diffusion Models: An Optimal Control Perspective Convergence of denoising diffusion models under the manifold hypothesis

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-05-22T08:01:16.058272Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation c4c5c5d3-7d1d-4693-adea-882268af5ac7 · inbound

Diffusion-based Denoising Beats Vanilla Score Matching in Parameter Estimation: A Theoretical Explanation cites this paper.

Diffusion-based Denoising Beats Vanilla Score Matching in Parameter Estimation: A Theoretical Explanation Convergence of denoising diffusion models under the manifold hypothesis

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-05-25T05:36:39.409323Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 15226815-38fc-4c55-8e22-db0ca902e8fe · inbound

Structured drift design for denoising diffusion models cites this paper.

Structured drift design for denoising diffusion models Convergence of denoising diffusion models under the manifold hypothesis

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-07-02T05:26:39.841262Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation da9e2f62-7b97-4f3a-91a9-c8cb809a72b5 · inbound

Structured drift design for denoising diffusion models cites this paper.

Structured drift design for denoising diffusion models Convergence of denoising diffusion models under the manifold hypothesis

Reference 4

Resolution: unresolved; no resolver link, observed 2026-07-12T15:16:29.520052Z

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Unavailable: canonical work link unavailable.

Observation 9b38cf14-0e4f-47d4-8b6e-90cdc7b12af1 · inbound

Statistical Properties of Training & Generalization cites this paper.

Statistical Properties of Training & Generalization Convergence of denoising diffusion models under the manifold hypothesis

Reference 250

Resolution: verified exact; arxiv_id, observed 2026-07-04T05:39:40.896820Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f078cd24-3c66-4af9-a5aa-765f765241b2 · inbound

Statistical Properties of Training & Generalization cites this paper.

Statistical Properties of Training & Generalization Convergence of denoising diffusion models under the manifold hypothesis

Reference 250

Resolution: verified exact; arxiv_id, observed 2026-07-02T21:57:25.648581Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 835429e0-8106-4848-9c77-b1dba40d0b2d · inbound

Asymptotic Preservation and Uniform Accuracy of Diffusion and Flow-Matching Samplers cites this paper.

Asymptotic Preservation and Uniform Accuracy of Diffusion and Flow-Matching Samplers Convergence of denoising diffusion models under the manifold hypothesis

Reference 17

Resolution: unresolved; no resolver link, observed 2026-07-11T21:36:20.726836Z

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Observation ce3d197f-5839-4db7-af15-869c21be6bfa · inbound

An exact information theory of generalization phase transitions in Bayesian diffusion models cites this paper.

An exact information theory of generalization phase transitions in Bayesian diffusion models Convergence of denoising diffusion models under the manifold hypothesis

Reference 7

Resolution: verified exact; local_arxiv, observed 2026-07-10T01:26:43.203850Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 4b96cbaa-4af5-4c78-93ba-43940386978a · inbound

From Score Learning to Discretized Sampling: An End-to-End Generalization Analysis of Diffusion Models cites this paper.

From Score Learning to Discretized Sampling: An End-to-End Generalization Analysis of Diffusion Models Convergence of denoising diffusion models under the manifold hypothesis

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-01T00:09:12.232347Z

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Observation 3c90be65-498c-49cd-9d3d-662c6df75d6f · inbound

Diffusion Bootstrap for High-Dimensional Linear Models cites this paper.

Diffusion Bootstrap for High-Dimensional Linear Models Convergence of denoising diffusion models under the manifold hypothesis

Reference 20

Resolution: unresolved; no resolver link, observed 2026-07-31T18:14:56.756802Z

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Unavailable: canonical work link unavailable.

Observation 550b06b9-efa0-41cb-98ed-61ace3262b43 · inbound

Denoising growth complexity: Data geometry and certified schedules for diffusion sampling cites this paper.

Denoising growth complexity: Data geometry and certified schedules for diffusion sampling Convergence of denoising diffusion models under the manifold hypothesis

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-01T00:25:42.182636Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.