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Paper Citation Record · LEDGER

A Sharp Convergence Theory for The Probability Flow ODEs of Diffusion Models

As of 21 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 16 inbound Pith citation observations for arXiv:2408.02320.

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

pith.paper-citation-record.v1
2408.02320 v1

Coverage vector

measured 0 of 0 reference resolution

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Source: paper_references, paper_reference_links

measured 16 of 16 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 16 of 16 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T04:38:28.211774Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T06:17:41.550579Z

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 1499eadd-4b54-4e2d-926b-ac0f612c4289 · inbound

Low-dimensional adaptation of diffusion models: Convergence in total variation cites this paper.

Low-dimensional adaptation of diffusion models: Convergence in total variation A Sharp Convergence Theory for The Probability Flow ODEs of Diffusion Models

Reference 46

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no resolver link, observed 2026-08-10T16:42:26.457749Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T16:42:26.457749Z digest=sha256:8dbbb72fdb74f2148644ee5ea36b5c2144b4758b62cac4f97e3f45460c056569

Observation be72680b-df7a-49cd-92c2-fb2d7f935ac4 · inbound

Adaptivity and Convergence of Probability Flow ODEs in Diffusion Generative Models cites this paper.

Adaptivity and Convergence of Probability Flow ODEs in Diffusion Generative Models A Sharp Convergence Theory for The Probability Flow ODEs of Diffusion Models

Reference 22

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no resolver link, observed 2026-08-09T22:27:37.456695Z

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source=arxiv_source observed=2026-08-09T22:27:37.456695Z digest=sha256:24c216c16fa4476f250d02afdf951594cb8352f5b4eaccec9b8f059e0ad8c514

Observation f363e014-9d0f-4fb2-9dfb-deb7bb386228 · inbound

Distribution learning via neural differential equations: minimal energy regularization and approximation theory cites this paper.

Distribution learning via neural differential equations: minimal energy regularization and approximation theory A Sharp Convergence Theory for The Probability Flow ODEs of Diffusion Models

Reference 21

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no resolver link, observed 2026-08-09T00:56:24.196728Z

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source=pdf_text observed=2026-08-09T00:56:24.196728Z digest=sha256:808448802403e5be1ae9f9470b8d301c47e528718127ab4adaa87921ceb3d51f

Observation 4ebf878f-3ca8-4e6d-b545-97e44a916ccb · inbound

Distribution learning via neural differential equations: minimal energy regularization and approximation theory cites this paper.

Distribution learning via neural differential equations: minimal energy regularization and approximation theory A Sharp Convergence Theory for The Probability Flow ODEs of Diffusion Models

Reference 22

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no resolver link, observed 2026-08-09T00:56:24.200354Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T00:56:24.200354Z digest=sha256:e7656092273b09e26bdb8bad493ea6d442207dd6543c8a642560a6809621b910

Observation f9da6351-c36d-43e1-9dde-22cb2dfa3e9d · 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 A Sharp Convergence Theory for The Probability Flow ODEs of Diffusion Models

Reference 28

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no resolver link, observed 2026-08-08T21:18:48.857915Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-08T21:18:48.857915Z digest=sha256:7ed6b0a8958e540e51e6e48369c1fdd3ccd168322b00b5f05b293a4f8d3df9d5

Observation bdf2a39d-9451-406c-b4b7-a0f923e02451 · inbound

Multi-Step Consistency Models: Fast Generation with Theoretical Guarantees cites this paper.

Multi-Step Consistency Models: Fast Generation with Theoretical Guarantees A Sharp Convergence Theory for The Probability Flow ODEs of Diffusion Models

Reference 22

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no resolver link, observed 2026-08-16T04:38:28.211774Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-16T04:38:28.211774Z digest=sha256:b9a460cd1b53bda69a7fcbee6b87811b90f19f32f950c3a057c96a9ef72b565e

Observation 4f273d64-79ca-4629-bd39-951d7df0317b · inbound

Provable Efficiency of Guidance in Diffusion Models for General Data Distribution cites this paper.

Provable Efficiency of Guidance in Diffusion Models for General Data Distribution A Sharp Convergence Theory for The Probability Flow ODEs of Diffusion Models

Reference 24

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no resolver link, observed 2026-08-16T04:30:48.473597Z

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source=arxiv_source observed=2026-08-16T04:30:48.473597Z digest=sha256:e01fa82371e62fc248992278f87e69c91c6c6e88647e41de8d6dab044f1b1260

Observation ba2c7e4b-94ed-4953-91a6-bb889158b15a · inbound

Improved Sample Complexity For Diffusion Model Training Without Empirical Risk Minimizer Access cites this paper.

Improved Sample Complexity For Diffusion Model Training Without Empirical Risk Minimizer Access A Sharp Convergence Theory for The Probability Flow ODEs of Diffusion Models

Reference 12

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verified exact
arxiv_id, observed 2026-05-19T12:37:17.431130Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-05-19T12:36:56.621522Z digest=sha256:b459b7c5234d26a35cea18d5957af97f9a02ec47309a942b8d6276dccd611b8c

Observation 5d8b914e-440f-4600-a5a8-041710c0c559 · 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 A Sharp Convergence Theory for The Probability Flow ODEs of Diffusion Models

Reference 41

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no resolver link, observed 2026-08-07T00:49:46.315800Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:49:46.315800Z digest=sha256:2a47db0c50934fb1294d3ec4754f438e1814ed63540399e3c3f5033c4c70702c

Observation 271b556b-cc54-492d-aec4-701280e9bcb1 · inbound

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

Faster Diffusion Models via Higher-Order Approximation A Sharp Convergence Theory for The Probability Flow ODEs of Diffusion Models

Reference 30

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no resolver link, observed 2026-08-06T21:45:14.834350Z

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source=arxiv_source observed=2026-08-06T21:45:14.834350Z digest=sha256:f7b68a41a3d57025b096636ce4b54d957bd6c51d25a394c1a8d7c54b41926c45

Observation 2cc64cfc-6ecb-4641-b265-3497e513bea1 · inbound

Generalization bounds for score-based generative models: a synthetic proof cites this paper.

Generalization bounds for score-based generative models: a synthetic proof A Sharp Convergence Theory for The Probability Flow ODEs of Diffusion Models

Reference 2023

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no resolver link, observed 2026-08-06T19:54:29.088405Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:54:29.088405Z digest=sha256:23ad43c664a3738028118317ad33ac5f8251b454029d60c3095ccd8659c3cbd4

Observation 3e8b7f66-37d3-49b6-98ac-79507e5a3571 · 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? A Sharp Convergence Theory for The Probability Flow ODEs of Diffusion Models

Reference 34

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no resolver link, observed 2026-08-05T22:07:58.168258Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T22:07:58.168258Z digest=sha256:e853ba5f5d9211fd23f943010aca60407b9a8b69814a51f90b851fa75c24190c

Observation 0d39b500-7339-4b3d-a5e2-11d319773ce7 · inbound

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

Non-asymptotic convergence bound of conditional diffusion models A Sharp Convergence Theory for The Probability Flow ODEs of Diffusion Models

Reference 51

Resolution
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no resolver link, observed 2026-08-05T20:56:33.446221Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T20:56:33.446221Z digest=sha256:331b4a21eca083adefe154c53e886321f97bbe88702cbc72f10ca836514c5ce4

Observation ba489d91-cd0a-4f5f-8241-195ee3149676 · inbound

A Sharp KL-Convergence Analysis for Diffusion Models under Minimal Assumptions cites this paper.

A Sharp KL-Convergence Analysis for Diffusion Models under Minimal Assumptions A Sharp Convergence Theory for The Probability Flow ODEs of Diffusion Models

Reference 19

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no resolver link, observed 2026-08-05T17:45:10.176341Z

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source=pdf_text observed=2026-08-05T17:45:10.176341Z digest=sha256:0fdd09f7953d579bc37642aa7758dbd90dc113a231b1740cf1872f9367159253

Observation 58568171-f6b1-4674-a588-b9314b505986 · inbound

When Diffusion Model Can Ignore Dimension: An Entropy-Based Theory cites this paper.

When Diffusion Model Can Ignore Dimension: An Entropy-Based Theory A Sharp Convergence Theory for The Probability Flow ODEs of Diffusion Models

Reference 13

Resolution
verified exact
arxiv_id, observed 2026-05-11T03:30:58.295488Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-05-11T02:26:35.458400Z digest=sha256:c4aae775b4230cad97b437f9c1581b58f9ba955429da76ab9f6713e9411c2594

Observation a58befa5-9635-4ccf-a4b6-07320a55931c · inbound

Higher-order Diffusion Sampling via Chebyshev Interpolation and Gauss--Seidel Iterations cites this paper.

Higher-order Diffusion Sampling via Chebyshev Interpolation and Gauss--Seidel Iterations A Sharp Convergence Theory for The Probability Flow ODEs of Diffusion Models

Reference 26

Resolution
verified exact
arxiv_id, observed 2026-07-03T06:17:41.552011Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-06-27T12:49:00.674523Z digest=sha256:e0a063e7ed238c00c24f9e1c9991892e8d7ec89dea8dc4bf12c04f3c09aec24b