Paper Citation Record · LEDGER

Nearly $d$-Linear Convergence Bounds for Diffusion Models via Stochastic Localization

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 22 inbound Pith citation observations for arXiv:2308.03686.

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

pith.paper-citation-record.v1
2308.03686 v3

Coverage vector

measured 0 of 0 reference resolution

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measured 22 of 22 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 22 of 22 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:19:59.605198Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-10T12:17:04.030612Z

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 17a35320-edfa-4c87-8694-985c26d06814 · inbound

Importance Weighted Score Matching for Diffusion Samplers with Enhanced Mode Coverage cites this paper.

Importance Weighted Score Matching for Diffusion Samplers with Enhanced Mode Coverage Nearly $d$-Linear Convergence Bounds for Diffusion Models via Stochastic Localization

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-07T14:19:59.605198Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 06efb3d8-470a-4758-8b14-7e4b58f80765 · inbound

Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach cites this paper.

Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach Nearly $d$-Linear Convergence Bounds for Diffusion Models via Stochastic Localization

Reference 116

Resolution: unresolved; no resolver link, observed 2026-08-07T10:54:50.065317Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7fce74b7-6535-46da-a78a-83dc010e763b · inbound

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

Non-asymptotic convergence bound of conditional diffusion models Nearly $d$-Linear Convergence Bounds for Diffusion Models via Stochastic Localization

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-05T20:56:33.413776Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d007fa6e-0d83-4e34-8da0-3cc69b2dea2f · 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 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 a021c5e7-15f6-4c69-a411-ac126d9ab8a1 · inbound

Generative Modeling from Black-box Corruptions via Self-Consistent Stochastic Interpolants cites this paper.

Generative Modeling from Black-box Corruptions via Self-Consistent Stochastic Interpolants Nearly $d$-Linear Convergence Bounds for Diffusion Models via Stochastic Localization

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-05-16T23:03:39.126990Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation bb971400-db8b-4e7e-bd1d-7ae8308db416 · inbound

Discrete Stochastic Localization for Non-autoregressive Generation cites this paper.

Discrete Stochastic Localization for Non-autoregressive Generation Nearly $d$-Linear Convergence Bounds for Diffusion Models via Stochastic Localization

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-05-22T11:36:28.959473Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 3e74b76c-2e27-4ae6-b452-2914194b428b · 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 Nearly $d$-Linear Convergence Bounds for Diffusion Models via Stochastic Localization

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-05-15T06:45:11.166262Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation a9362c1d-1c8b-4387-bd6a-512a0f1073b3 · inbound

Generating DDPM-based Samples from Tilted Distributions cites this paper.

Generating DDPM-based Samples from Tilted Distributions Nearly $d$-Linear Convergence Bounds for Diffusion Models via Stochastic Localization

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-05-13T20:08:12.979687Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation e6ad81b1-7901-4d4c-91ec-05a204d8cb95 · inbound

Potential Hessian Ascent III: Sampling the Sherrington--Kirkpatrick Model at Beta < 1/2 cites this paper.

Potential Hessian Ascent III: Sampling the Sherrington--Kirkpatrick Model at Beta < 1/2 Nearly $d$-Linear Convergence Bounds for Diffusion Models via Stochastic Localization

Reference 28

Resolution: verified exact; arxiv_id, observed 2026-05-12T00:46:13.367300Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 6bb51e3f-fece-4633-9c78-8d56fa988a04 · inbound

Potential Hessian Ascent III: Sampling the Sherrington--Kirkpatrick Model at Beta < 1/2 cites this paper.

Potential Hessian Ascent III: Sampling the Sherrington--Kirkpatrick Model at Beta < 1/2 Nearly $d$-Linear Convergence Bounds for Diffusion Models via Stochastic Localization

Reference 28

Resolution: verified exact; arxiv_id, observed 2026-05-20T23:49:15.401092Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation e24d5621-0fbe-4b76-bc5a-370a8cddb340 · inbound

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

Energy Generative Modeling: A Lyapunov-based Energy Matching Perspective Nearly $d$-Linear Convergence Bounds for Diffusion Models via Stochastic Localization

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-05-11T16:36:08.265049Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 16185a4f-0fdb-4244-abb4-b452b3cc522a · inbound

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

Proximal-Based Generative Modeling for Bayesian Inverse Problems Nearly $d$-Linear Convergence Bounds for Diffusion Models via Stochastic Localization

Reference 101

Resolution: verified exact; arxiv_id, observed 2026-05-14T17:57:33.577970Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation e2783031-c530-4617-b9e2-288ea862e8c0 · inbound

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

HYVINT: Intensity-Driven Hypergraph Generation with Variational Embeddings Nearly $d$-Linear Convergence Bounds for Diffusion Models via Stochastic Localization

Reference 37

Resolution: verified exact; arxiv_id, observed 2026-05-19T20:02:44.778162Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 44ceb2be-82c8-46f3-9fa0-9c14fdbb80b4 · inbound

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

HYVINT: Intensity-Driven Hypergraph Generation with Variational Embeddings Nearly $d$-Linear Convergence Bounds for Diffusion Models via Stochastic Localization

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-02T13:52:48.941495Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ce766833-3e47-4cfa-b7ba-1bf0546d956f · inbound

Provably Learning Diffusion Models under the Manifold Hypothesis: Collapse and Refine cites this paper.

Provably Learning Diffusion Models under the Manifold Hypothesis: Collapse and Refine Nearly $d$-Linear Convergence Bounds for Diffusion Models via Stochastic Localization

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-05-21T07:39:49.444133Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation ac723149-24cb-41c1-9a46-e226362b88a2 · inbound

Guided Flow Matching for Forward and Inverse PDE Problems with Sparse Observations: Algorithm and Theory cites this paper.

Guided Flow Matching for Forward and Inverse PDE Problems with Sparse Observations: Algorithm and Theory Nearly $d$-Linear Convergence Bounds for Diffusion Models via Stochastic Localization

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-06-29T20:53:58.046642Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation fa591f47-ae6b-4e2a-ad5a-292d5f642d12 · inbound

Adaptive Order Policies for Masked Diffusion cites this paper.

Adaptive Order Policies for Masked Diffusion Nearly $d$-Linear Convergence Bounds for Diffusion Models via Stochastic Localization

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-06-28T23:42:49.797854Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 8d43e5d3-c5ab-4c52-8658-3b8e7d1a5301 · 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 Nearly $d$-Linear Convergence Bounds for Diffusion Models via Stochastic Localization

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-07-04T12:49:52.531066Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 17b756e5-742c-4c49-935d-5cfead51d7c4 · inbound

A Mathematical Introduction to Diffusion Models cites this paper.

A Mathematical Introduction to Diffusion Models Nearly $d$-Linear Convergence Bounds for Diffusion Models via Stochastic Localization

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-07-03T17:58:46.899574Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 62548807-7429-45c3-ad13-531910ccd015 · inbound

Weak Poincar\'e Inequalities via Approximate Stochastic Localization: Application to Sampling the Sherrington-Kirkpatrick Model cites this paper.

Weak Poincar\'e Inequalities via Approximate Stochastic Localization: Application to Sampling the Sherrington-Kirkpatrick Model Nearly $d$-Linear Convergence Bounds for Diffusion Models via Stochastic Localization

Reference 210

Resolution: verified exact; local_arxiv, observed 2026-07-10T12:17:04.032168Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 05b2feac-bf34-4323-a42e-11d866322b59 · inbound

FlashDiff: Efficient Regional Execution and Scheduling for Diffusion Model Serving cites this paper.

FlashDiff: Efficient Regional Execution and Scheduling for Diffusion Model Serving Nearly $d$-Linear Convergence Bounds for Diffusion Models via Stochastic Localization

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-02T06:45:36.877240Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b822edb2-60dc-4c2a-bc14-0d3532595a4c · inbound

Breaking the Curse with BAND: Nonparametric Distribution Estimation in High Dimensions cites this paper.

Breaking the Curse with BAND: Nonparametric Distribution Estimation in High Dimensions Nearly $d$-Linear Convergence Bounds for Diffusion Models via Stochastic Localization

Reference 113

Resolution: unresolved; no resolver link, observed 2026-07-30T15:56:45.509722Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.