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Paper Citation Record · LEDGER

Regularity of the score function in generative models

As of 17 August 2026, this Paper Citation Record lists 33 of 33 outbound references and 5 inbound Pith citation observations for arXiv:2506.19559.

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

pith.paper-citation-record.v1
2506.19559 v1

Coverage vector

measured 33 of 33 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T18:44:21.625536Z

measured 38 of 38 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T19:54:29.354919Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-20T00:37:54.079419Z

Reference resolution

33 of 33 outbound references displayed

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External citation measurements

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Outbound references

Observation 6141e2ef-6b94-49cd-af07-e3db32af37c1 · outbound

This paper cites Anderson.

Regularity of the score function in generative models Anderson

Reference 1

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Observation 5b40918f-7a39-4f3f-b084-31ee1acf9cca · outbound

This paper cites Error Bounds for Flow Matching Methods.

Regularity of the score function in generative models Error Bounds for Flow Matching Methods

Reference 2

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Observation c881b501-0f01-436f-9115-721b71629deb · outbound

This paper cites On extensions of the brunn-minkowski and prékopa-leindler theorems, including inequalities for log concave functions, and with an application to the diffusion equation.

Regularity of the score function in generative models On extensions of the brunn-minkowski and prékopa-leindler theorems, including inequalities for log concave functions, and with an application to the diffusion equation

Reference 3

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Observation 5325610d-7e22-490a-93ae-5223d6ab6666 · outbound

This paper cites Heat flow, log-concavity, and Lipschitz transport maps.

Regularity of the score function in generative models Heat flow, log-concavity, and Lipschitz transport maps

Reference 4

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Observation 6c874e34-d16e-4e0d-9c59-08d19199cbdc · outbound

This paper cites Improved analysis of score-based generative modeling: User-friendly bounds under minimal smoothness assumptions.

Regularity of the score function in generative models Improved analysis of score-based generative modeling: User-friendly bounds under minimal smoothness assumptions

Reference 5

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Observation bc9102c9-e88f-45d8-9e2a-af7931a01309 · outbound

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

Regularity of the score function in generative models Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions

Reference 6

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Observation fd005e02-7c0b-4c2f-b075-88758f8d457f · outbound

This paper cites The probability flow ode is provably fast.

Regularity of the score function in generative models The probability flow ode is provably fast

Reference 7

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Observation 4d907b2b-4afa-47ef-add6-4a9d7314f7f2 · outbound

This paper cites A coupling approach to Lipschitz transport maps.

Regularity of the score function in generative models A coupling approach to Lipschitz transport maps

Reference 8

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Observation 8f760bc6-9bec-4802-ad06-bb3a0f84fcef · outbound

This paper cites On the approximation of functions by tanh neural networks.

Regularity of the score function in generative models On the approximation of functions by tanh neural networks

Reference 9

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Observation 5d796e1e-79f4-4e36-b31f-c3827ffe5044 · outbound

This paper cites Diffusion models beat gans on image synthesis.

Regularity of the score function in generative models Diffusion models beat gans on image synthesis

Reference 10

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Observation a68287ee-660f-4f9a-a995-37c8e42013f3 · outbound

This paper cites Some gronwall type inequalities and applications.

Regularity of the score function in generative models Some gronwall type inequalities and applications

Reference 11

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Observation 97f93c82-1ef3-4083-ad44-e8c581471450 · outbound

This paper cites Transportation onto log-lipschitz perturbations.

Regularity of the score function in generative models Transportation onto log-lipschitz perturbations

Reference 12

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Observation 4964e47b-8871-49dd-be43-9ff21af4ab64 · outbound

This paper cites Flow matching achieves almost minimax optimal convergence.

Regularity of the score function in generative models Flow matching achieves almost minimax optimal convergence

Reference 13

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Observation 001c204e-2b70-4080-abf3-e01e3e25b4ac · outbound

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

Regularity of the score function in generative models Beyond log-concavity and score regularity: Improved convergence bounds for score-based generative models in w2-distance

Reference 14

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Observation 7ae1cf08-57ee-4964-83c9-557cedb069d6 · outbound

This paper cites Equivariant diffusion for molecule generation in 3d.

Regularity of the score function in generative models Equivariant diffusion for molecule generation in 3d

Reference 15

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Observation 05b372de-786c-4222-a959-1fabce4cdb9f · outbound

This paper cites Convergence Analysis of Probability Flow ODE for Score-based Generative Models.

Regularity of the score function in generative models Convergence Analysis of Probability Flow ODE for Score-based Generative Models

Reference 16

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Observation 551a953d-b384-4e78-97d2-76c27e517512 · outbound

This paper cites Elucidating the design space of diffusion-based generative models.

Regularity of the score function in generative models Elucidating the design space of diffusion-based generative models

Reference 17

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Observation 1864be32-63cc-41c4-8b40-c3f352ae818c · outbound

This paper cites A generalization of caffarelli’s contraction theorem via (reverse) heat flow.

Regularity of the score function in generative models A generalization of caffarelli’s contraction theorem via (reverse) heat flow

Reference 18

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Observation 78e71d85-07da-46e7-b2fd-34985d392223 · outbound

This paper cites DiffWave: A Versatile Diffusion Model for Audio Synthesis.

Regularity of the score function in generative models DiffWave: A Versatile Diffusion Model for Audio Synthesis

Reference 19

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Observation 84864842-4262-4eed-a7ce-37c65ec5bf90 · outbound

This paper cites Score-based generative modeling secretly minimizes the wasserstein distance.

Regularity of the score function in generative models Score-based generative modeling secretly minimizes the wasserstein distance

Reference 20

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Observation 540f8164-39d3-497a-9ac5-203e810adca8 · outbound

This paper cites Convergence of score-based generative modeling for general data distributions.

Regularity of the score function in generative models Convergence of score-based generative modeling for general data distributions

Reference 21

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Observation 49aabb20-633f-42aa-91c1-5ce4a113ec07 · outbound

This paper cites Transport equations and flows with one-sided lipschitz velocity fields.

Regularity of the score function in generative models Transport equations and flows with one-sided lipschitz velocity fields

Reference 22

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Observation 30408812-9503-4461-8661-1b6087a65862 · outbound

This paper cites A bakry- \'e mery approach to lipschitz transportation on manifolds.

Regularity of the score function in generative models A bakry- \'e mery approach to lipschitz transportation on manifolds

Reference 23

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Observation c0d094f7-1a76-4c02-a950-da3e8da4e9e5 · outbound

This paper cites On the lipschitz properties of transportation along heat flows.

Regularity of the score function in generative models On the lipschitz properties of transportation along heat flows

Reference 24

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Observation 3abac536-1603-4291-b422-1d0f4e11c63e · outbound

This paper cites Diffusion models are minimax optimal distribution estimators.

Regularity of the score function in generative models Diffusion models are minimax optimal distribution estimators

Reference 25

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Observation 9c99b13b-bb2b-42f3-b802-b8bf5f110fd5 · outbound

This paper cites Improved Convergence of Score-Based Diffusion Models via Prediction-Correction.

Regularity of the score function in generative models Improved Convergence of Score-Based Diffusion Models via Prediction-Correction

Reference 26

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Observation 28ab5f44-10a7-436c-b101-a0d7893ed228 · outbound

This paper cites Nonparametric regression using deep neural networks with relu activation function.

Regularity of the score function in generative models Nonparametric regression using deep neural networks with relu activation function

Reference 27

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Observation 13b5548a-a2f8-430d-a712-34f9d396090a · outbound

This paper cites Score-Based Generative Modeling through Stochastic Differential Equations.

Regularity of the score function in generative models Score-Based Generative Modeling through Stochastic Differential Equations

Reference 28

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Observation c8bda7a6-8d7b-4e61-a809-70dc295fae67 · outbound

This paper cites Smooth transport map via diffusion process.

Regularity of the score function in generative models Smooth transport map via diffusion process

Reference 29

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Observation 57eeba25-7920-415a-8b3e-bb3cd0b22925 · outbound

This paper cites Adaptivity of deep relu network for learning in besov and mixed smooth besov spaces: optimal rate and curse of dimensionality, 2018.

Regularity of the score function in generative models Adaptivity of deep relu network for learning in besov and mixed smooth besov spaces: optimal rate and curse of dimensionality, 2018

Reference 30

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Observation 7123bb8e-ecaa-470e-b6e5-4726d13e3471 · outbound

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Regularity of the score function in generative models Tsybakov

Reference 31

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Observation 06fb904a-c73a-40af-9892-e4fe52b98981 · outbound

This paper cites Optimal transport: old and new, volume 338.

Regularity of the score function in generative models Optimal transport: old and new, volume 338

Reference 32

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Observation cc056b68-3b4b-44e9-882f-5480e47eee6f · outbound

This paper cites Optimal score estimation via empirical bayes smoothing.

Regularity of the score function in generative models Optimal score estimation via empirical bayes smoothing

Reference 33

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-15T18:44:21.625536Z digest=sha256:b9f45ca75d35e2a1f8a89a2e012bbfab79631d9e41a3e9674c66ff83e7bc9f88

Pith citing papers

Observation 1119d330-272e-46a3-9f7d-2419465325fc · inbound

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

Generalization bounds for score-based generative models: a synthetic proof Regularity of the score function in generative models

Reference 16

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Observation 3401b0e1-30ee-482e-a873-7ff26fde6634 · 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 Regularity of the score function in generative models

Reference 17

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arxiv_id, observed 2026-05-11T05:10:55.366896Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 5dba0dbc-6f08-4a46-8270-3211f08edafe · inbound

Expressivity of Bi-Lipschitz Normalizing Flows: A Score-Based Diffusion Perspective cites this paper.

Expressivity of Bi-Lipschitz Normalizing Flows: A Score-Based Diffusion Perspective Regularity of the score function in generative models

Reference 45

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 299392bd-81f9-4269-ac54-aeb8d7ee8e4f · inbound

Wasserstein bounds for denoising diffusion probabilistic models via the F\"ollmer process cites this paper.

Wasserstein bounds for denoising diffusion probabilistic models via the F\"ollmer process Regularity of the score function in generative models

Reference 60

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arxiv_id, observed 2026-05-20T00:37:54.082895Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-05-20T00:36:51.915596Z digest=sha256:ea9d3513debc42c52dcd27175500cd8cd55a257b94f11e8b196e005840cdd130

Observation 304a4b05-72d0-4c66-851f-46caf6aa0909 · inbound

Mimicking diffusion processes with differential equations cites this paper.

Mimicking diffusion processes with differential equations Regularity of the score function in generative models

Reference 32

Resolution
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