Paper Citation Record · LEDGER

Towards Faster Non-Asymptotic Convergence for Diffusion-Based Generative Models

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 19 inbound Pith citation observations for arXiv:2306.09251.

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

pith.paper-citation-record.v1
2306.09251 v3

Coverage vector

measured 0 of 0 reference resolution

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Source: paper_references, paper_reference_links

measured 19 of 19 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 19 of 19 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:45:48.886655Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T12:49:52.513717Z

Reference resolution

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External citation measurements

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Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation d958fa07-9530-486a-900a-8fe19504beb2 · inbound

From Score Matching to Diffusion: A Fine-Grained Error Analysis in the Gaussian Setting cites this paper.

From Score Matching to Diffusion: A Fine-Grained Error Analysis in the Gaussian Setting Towards Faster Non-Asymptotic Convergence for Diffusion-Based Generative Models

Reference 26

Resolution: verified exact; arxiv_id, observed 2026-05-23T00:12:17.777075Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 1d0bfcd0-607d-44ca-9c19-e0297cc4c89c · inbound

Learning Single Index Models with Diffusion Priors cites this paper.

Learning Single Index Models with Diffusion Priors Towards Faster Non-Asymptotic Convergence for Diffusion-Based Generative Models

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-07T13:45:48.886655Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 551f77a6-47eb-42ab-9063-69b3ec807a2b · inbound

Fast Convergence for High-Order ODE Solvers in Diffusion Probabilistic Models cites this paper.

Fast Convergence for High-Order ODE Solvers in Diffusion Probabilistic Models Towards Faster Non-Asymptotic Convergence for Diffusion-Based Generative Models

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-07T00:49:46.175306Z

Source-reported events for the cited work

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Observation 9c526b55-5cfb-43db-8458-14989ab3ffb5 · inbound

Faster Diffusion Models via Higher-Order Approximation cites this paper.

Faster Diffusion Models via Higher-Order Approximation Towards Faster Non-Asymptotic Convergence for Diffusion-Based Generative Models

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-06T21:45:14.744270Z

Source-reported events for the cited work

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Observation 40679906-90c8-48aa-ab4c-32aa4f8162c6 · inbound

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

Non-asymptotic convergence bound of conditional diffusion models Towards Faster Non-Asymptotic Convergence for Diffusion-Based Generative Models

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-05T20:56:33.436549Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f9526746-504b-49c4-81fd-8e6a7541b0e1 · inbound

Provable Diffusion Posterior Sampling for Bayesian Inversion cites this paper.

Provable Diffusion Posterior Sampling for Bayesian Inversion Towards Faster Non-Asymptotic Convergence for Diffusion-Based Generative Models

Reference 95

Resolution: unresolved; no resolver link, observed 2026-08-03T17:55:18.840971Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 192c4f91-df63-4185-85bc-b92d9702b9da · inbound

Fast Score-Based Sampling via Log-Concave Reductions cites this paper.

Fast Score-Based Sampling via Log-Concave Reductions Towards Faster Non-Asymptotic Convergence for Diffusion-Based Generative Models

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-03T13:36:15.764744Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 887cacf7-7172-4ba2-b8fc-63583e804432 · inbound

Light Forcing: Accelerating Autoregressive Video Diffusion via Sparse Attention cites this paper.

Light Forcing: Accelerating Autoregressive Video Diffusion via Sparse Attention Towards Faster Non-Asymptotic Convergence for Diffusion-Based Generative Models

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-03T04:33:10.973593Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 371cdef9-fda4-4b57-a707-53a351749c2f · inbound

Light Forcing: Accelerating Autoregressive Video Diffusion via Sparse Attention cites this paper.

Light Forcing: Accelerating Autoregressive Video Diffusion via Sparse Attention Towards Faster Non-Asymptotic Convergence for Diffusion-Based Generative Models

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-04T00:38:16.847470Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 504a0c64-b7eb-4ac2-bcf4-661067f61f7d · inbound

Energy Generative Modeling: A Lyapunov-based Energy Matching Perspective cites this paper.

Energy Generative Modeling: A Lyapunov-based Energy Matching Perspective Towards Faster Non-Asymptotic Convergence for Diffusion-Based Generative Models

Reference 7

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T16:36:08.207771Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation d20a0a82-fef1-4b26-8c3f-2c1f4a130b65 · inbound

Tessellations of Semi-Discrete Flow Matching cites this paper.

Tessellations of Semi-Discrete Flow Matching Towards Faster Non-Asymptotic Convergence for Diffusion-Based Generative Models

Reference 70

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T03:10:53.699279Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 3f4af111-eb02-48e8-8253-491f1cf35ca4 · inbound

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

Proximal-Based Generative Modeling for Bayesian Inverse Problems Towards Faster Non-Asymptotic Convergence for Diffusion-Based Generative Models

Reference 24

Resolution: metadata mismatch; arxiv_id, observed 2026-05-14T17:57:33.431515Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 401afd2b-8460-4b13-a665-dc6ec785ac78 · inbound

Intrinsic Wasserstein Rates for Score-Based Generative Models on Smooth Manifolds cites this paper.

Intrinsic Wasserstein Rates for Score-Based Generative Models on Smooth Manifolds Towards Faster Non-Asymptotic Convergence for Diffusion-Based Generative Models

Reference 31

Resolution: verified exact; arxiv_id, observed 2026-05-20T20:53:43.715483Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 00f375d2-b7d0-4ed5-838f-e292d291e5de · inbound

HYVINT: Intensity-Driven Hypergraph Generation with Variational Embeddings cites this paper.

HYVINT: Intensity-Driven Hypergraph Generation with Variational Embeddings Towards Faster Non-Asymptotic Convergence for Diffusion-Based Generative Models

Reference 36

Resolution: verified exact; arxiv_id, observed 2026-05-19T20:02:44.826286Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation caaea4cb-1020-4e1e-ae97-fbfd933aa62f · inbound

HYVINT: Intensity-Driven Hypergraph Generation with Variational Embeddings cites this paper.

HYVINT: Intensity-Driven Hypergraph Generation with Variational Embeddings Towards Faster Non-Asymptotic Convergence for Diffusion-Based Generative Models

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-02T13:52:48.773546Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 106c6678-abed-4568-b822-c7e3780d09a4 · inbound

The Value of Covariance Matching in Gaussian DDPMs and the Lanczos Sampler cites this paper.

The Value of Covariance Matching in Gaussian DDPMs and the Lanczos Sampler Towards Faster Non-Asymptotic Convergence for Diffusion-Based Generative Models

Reference 7

Resolution: metadata mismatch; arxiv_id, observed 2026-05-22T07:41:14.849780Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation fdd4ac69-f208-426d-8fd5-8a646da8687c · inbound

Diffusion Models Adapt to Low-Dimensional Structure Under Flexible Coefficient Choices cites this paper.

Diffusion Models Adapt to Low-Dimensional Structure Under Flexible Coefficient Choices Towards Faster Non-Asymptotic Convergence for Diffusion-Based Generative Models

Reference 32

Resolution: verified exact; arxiv_id, observed 2026-07-04T12:49:52.516284Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 66ae64da-2328-4ad0-afb4-4ce332a71eb8 · 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 Towards Faster Non-Asymptotic Convergence for Diffusion-Based Generative Models

Reference 124

Resolution: unresolved; no resolver link, observed 2026-08-01T00:25:51.418349Z

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Unavailable: canonical work link unavailable.

Observation da72132d-caa5-4e4f-baa8-5a6a32e423c5 · inbound

A Unified Kullback--Leibler Divergence Analysis of Generative Diffusion Models via Entropy Production Rate cites this paper.

A Unified Kullback--Leibler Divergence Analysis of Generative Diffusion Models via Entropy Production Rate Towards Faster Non-Asymptotic Convergence for Diffusion-Based Generative Models

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-04T07:52:48.985515Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.