Paper Citation Record · LEDGER

A Sharp KL-Convergence Analysis for Diffusion Models under Minimal Assumptions

As of 10 August 2026, this Paper Citation Record lists 32 of 32 outbound references and 1 inbound Pith citation observation for arXiv:2508.16306.

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

pith.paper-citation-record.v1
2508.16306 v1

Coverage vector

measured 32 of 32 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T17:45:11.438908Z

measured 33 of 33 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 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-22T07:38:10.286293Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-22T07:41:14.840521Z

Reference resolution

32 of 32 outbound references displayed

verified exact4
verified fuzzy13
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation d007fa6e-0d83-4e34-8da0-3cc69b2dea2f · outbound

This paper cites Nearly $d$-Linear Convergence Bounds for Diffusion Models via Stochastic Localization.

A Sharp KL-Convergence Analysis for Diffusion Models under Minimal Assumptions Nearly $d$-Linear Convergence Bounds for Diffusion Models via Stochastic Localization

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-05T17:45:08.560164Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 600577aa-db4e-4a29-87bc-bc822e57db2a · outbound

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

A Sharp KL-Convergence Analysis for Diffusion Models under Minimal Assumptions Improved analysis of score-based generative modeling: User-friendly bounds under minimal smoothness assumptions

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:45:14.579392Z

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 652614e0-6786-49b3-b613-3e7fb6ca48c8 · outbound

This paper cites The probability flow ode is provably fast.

A Sharp KL-Convergence Analysis for Diffusion Models under Minimal Assumptions The probability flow ode is provably fast

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:45:14.336120Z

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 283d8c1a-75c8-4be6-9c86-90a0e59d2909 · outbound

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

A Sharp KL-Convergence Analysis for Diffusion Models under Minimal Assumptions Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions

Reference 4

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Source-reported events for the cited work

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Observation 32b69758-b5ed-4694-af8f-ec8cf61018c3 · outbound

This paper cites Control-a-video: Controllable text-to-video generation with diffusion models.arXiv e-prints, pages arXiv–2305, 2023.

A Sharp KL-Convergence Analysis for Diffusion Models under Minimal Assumptions Control-a-video: Controllable text-to-video generation with diffusion models.arXiv e-prints, pages arXiv–2305, 2023

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-05T17:45:08.879916Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a2821694-84c2-4076-ba72-9435db470cad · outbound

This paper cites Improved analysis for a proximal algorithm for sampling.

A Sharp KL-Convergence Analysis for Diffusion Models under Minimal Assumptions Improved analysis for a proximal algorithm for sampling

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:45:14.144754Z

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 84a71bb0-53cb-4fb8-b5d9-627168b62484 · outbound

This paper cites Diffusion models in vision: A survey.

A Sharp KL-Convergence Analysis for Diffusion Models under Minimal Assumptions Diffusion models in vision: A survey

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:45:13.969437Z

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 5f7be551-aabe-4153-b6d7-f5e6db392849 · outbound

This paper cites Diffusion self-guidance for controllable image generation.

A Sharp KL-Convergence Analysis for Diffusion Models under Minimal Assumptions Diffusion self-guidance for controllable image generation

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:45:13.795084Z

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 1365b339-23d5-4d14-b05e-016b4edc391c · outbound

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

A Sharp KL-Convergence Analysis for Diffusion Models under Minimal Assumptions Convergence Analysis for General Probability Flow ODEs of Diffusion Models in Wasserstein Distances

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-05T17:45:09.200294Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5beb3526-f378-423c-83cb-a15728e20c22 · outbound

This paper cites Protein design with guided discrete diffusion.Advances in neural information processing systems , 36:12489–12517, 2023.

A Sharp KL-Convergence Analysis for Diffusion Models under Minimal Assumptions Protein design with guided discrete diffusion.Advances in neural information processing systems , 36:12489–12517, 2023

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:45:13.641299Z

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 b9d8f585-956a-42e3-82b1-e35d5f2ab965 · outbound

This paper cites Diffusion models in bioinformatics and computational biology.Nature reviews bioengineering, 2(2):136–154, 2024.

A Sharp KL-Convergence Analysis for Diffusion Models under Minimal Assumptions Diffusion models in bioinformatics and computational biology.Nature reviews bioengineering, 2(2):136–154, 2024

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:45:13.487216Z

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 b241343b-3b60-427b-84c4-856de7443b98 · outbound

This paper cites Denoising diffusion probabilistic models.

A Sharp KL-Convergence Analysis for Diffusion Models under Minimal Assumptions Denoising diffusion probabilistic models

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-05T17:45:09.512396Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f93c66d3-6e3c-4c2e-93dd-0d32dac42a02 · outbound

This paper cites Convergence Rates for Non-Log-Concave Sampling and Log-Partition Estimation.

A Sharp KL-Convergence Analysis for Diffusion Models under Minimal Assumptions Convergence Rates for Non-Log-Concave Sampling and Log-Partition Estimation

Reference 13

Resolution: verified exact; local_arxiv, observed 2026-08-05T17:45:12.201920Z

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 63029cf9-d82c-4786-8659-bbe2ee6cdb54 · outbound

This paper cites Convergence analysis of probability flow ode for score-based generative models.

A Sharp KL-Convergence Analysis for Diffusion Models under Minimal Assumptions Convergence analysis of probability flow ode for score-based generative models

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:45:13.354937Z

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 1bba731d-76eb-4121-af4e-83de0ed3d327 · outbound

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

A Sharp KL-Convergence Analysis for Diffusion Models under Minimal Assumptions Multi-Step Consistency Models: Fast Generation with Theoretical Guarantees

Reference 15

Resolution: verified exact; local_arxiv, observed 2026-08-05T17:45:12.024797Z

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 5e590118-3cba-4916-b5ab-7442f5035c1f · outbound

This paper cites Convergence for score-based generative modeling with polynomial complexity.

A Sharp KL-Convergence Analysis for Diffusion Models under Minimal Assumptions Convergence for score-based generative modeling with polynomial complexity

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-05T17:45:09.856607Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9e070b31-4840-4a8d-9a83-16d64cce3a00 · outbound

This paper cites Towards a mathematical theory for consistency training in diffusion models.

A Sharp KL-Convergence Analysis for Diffusion Models under Minimal Assumptions Towards a mathematical theory for consistency training in diffusion models

Reference 17

Resolution: verified exact; local_arxiv, observed 2026-08-05T17:45:11.858485Z

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 1f8d670b-7905-4c71-b9ad-b7558034a85b · outbound

This paper cites Towards non-asymptotic convergence for diffusion-based generative models.

A Sharp KL-Convergence Analysis for Diffusion Models under Minimal Assumptions Towards non-asymptotic convergence for diffusion-based generative models

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:45:13.166810Z

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 ba489d91-cd0a-4f5f-8241-195ee3149676 · outbound

This paper cites A Sharp Convergence Theory for The Probability Flow ODEs of Diffusion Models.

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

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

Source-reported events for the cited work

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Observation 214a7e96-996b-4144-8ea4-1ddfd2f8af45 · outbound

This paper cites O(d/T) Convergence Theory for Diffusion Probabilistic Models under Minimal Assumptions.

A Sharp KL-Convergence Analysis for Diffusion Models under Minimal Assumptions O(d/T) Convergence Theory for Diffusion Probabilistic Models under Minimal Assumptions

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-05T17:45:10.282133Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b91af8dd-dae8-4db9-9c20-a855a05f3199 · outbound

This paper cites Unified Convergence Analysis for Score-Based Diffusion Models with Deterministic Samplers.

A Sharp KL-Convergence Analysis for Diffusion Models under Minimal Assumptions Unified Convergence Analysis for Score-Based Diffusion Models with Deterministic Samplers

Reference 21

Resolution: verified exact; local_arxiv, observed 2026-08-05T17:45:11.674672Z

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 2c13ce50-2bf6-4b04-b0de-6320c1807ec6 · outbound

This paper cites Diffusion-lm improves controllable text generation.

A Sharp KL-Convergence Analysis for Diffusion Models under Minimal Assumptions Diffusion-lm improves controllable text generation

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:45:12.960408Z

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 b40285ca-fcc7-4c76-86fb-d4cc7f16590e · outbound

This paper cites AudioLDM: Text-to-Audio Generation with Latent Diffusion Models.

A Sharp KL-Convergence Analysis for Diffusion Models under Minimal Assumptions AudioLDM: Text-to-Audio Generation with Latent Diffusion Models

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-05T17:45:10.648281Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 11e33dd2-38fd-470c-b3c6-e8d348b032aa · outbound

This paper cites Repaint: Inpainting using denoising diffusion probabilistic models.

A Sharp KL-Convergence Analysis for Diffusion Models under Minimal Assumptions Repaint: Inpainting using denoising diffusion probabilistic models

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-05T17:45:10.752127Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 83958483-7ea8-4f54-b111-1571f5f06473 · outbound

This paper cites GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models.

A Sharp KL-Convergence Analysis for Diffusion Models under Minimal Assumptions GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-05T17:45:10.860668Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a0abaab1-d48d-43c9-b595-98b1e59de7cf · outbound

This paper cites Denoising Diffusion Implicit Models.

A Sharp KL-Convergence Analysis for Diffusion Models under Minimal Assumptions Denoising Diffusion Implicit Models

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-05T17:45:10.937012Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation eeadd303-5ddf-4385-97b1-e640ab5e7472 · outbound

This paper cites Consistency Trajectory Models: Learning Probability Flow ODE Trajectory of Diffusion.

A Sharp KL-Convergence Analysis for Diffusion Models under Minimal Assumptions Consistency Trajectory Models: Learning Probability Flow ODE Trajectory of Diffusion

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-05T17:45:11.045526Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b4c7c1b2-2198-4ca0-a1b9-621da5fa63e8 · outbound

This paper cites Maximum likelihood training of score-based diffusion models.

A Sharp KL-Convergence Analysis for Diffusion Models under Minimal Assumptions Maximum likelihood training of score-based diffusion models

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:45:12.774904Z

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 1860bd56-0507-4ec6-9618-353e1dd96c56 · outbound

This paper cites Generative modeling by estimating gradients of the data distribution.Advances in neural information processing systems , 32, 2019.

A Sharp KL-Convergence Analysis for Diffusion Models under Minimal Assumptions Generative modeling by estimating gradients of the data distribution.Advances in neural information processing systems , 32, 2019

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:45:12.542046Z

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 81d3339e-b7f2-40bc-83ba-cdb91a91d70d · outbound

This paper cites Score- based generative modeling through stochastic differential equations.

A Sharp KL-Convergence Analysis for Diffusion Models under Minimal Assumptions Score- based generative modeling through stochastic differential equations

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-05T17:45:11.298664Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c52f238b-3b27-4152-8ffb-41cf092bb995 · outbound

This paper cites Provably Robust Score-Based Diffusion Posterior Sampling for Plug-and-Play Image Reconstruction.

A Sharp KL-Convergence Analysis for Diffusion Models under Minimal Assumptions Provably Robust Score-Based Diffusion Posterior Sampling for Plug-and-Play Image Reconstruction

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-05T17:45:11.354908Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a0addf13-82ac-4fd1-a584-93de64d04d38 · outbound

This paper cites Z tk−2 tk dt Z t tk e2us′ r(u, z(u))du 2 2 # ≤ E.

A Sharp KL-Convergence Analysis for Diffusion Models under Minimal Assumptions Z tk−2 tk dt Z t tk e2us′ r(u, z(u))du 2 2 # ≤ E

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T17:45:12.379952Z

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.

Pith citing papers

Observation dea97e5b-b1f0-437f-8c94-8934344ae1e5 · 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 A Sharp KL-Convergence Analysis for Diffusion Models under Minimal Assumptions

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-05-22T07:41:14.843836Z

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.