Paper Citation Record · LEDGER

Diffusion models for inverse problems

As of 22 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:2508.01975.

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

pith.paper-citation-record.v1
2508.01975 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 8 of 8 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-03T18:26:33.129484Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-10T12:15:01.137692Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
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malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation b59f135f-d6ad-415c-a800-ecd3c773759a · inbound

InverseCrafter: Efficient Video ReCapture as a Latent Domain Inverse Problem cites this paper.

InverseCrafter: Efficient Video ReCapture as a Latent Domain Inverse Problem Diffusion models for inverse problems

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-03T18:26:33.129484Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0767be7d-34c3-4948-baf9-82fd62f9a09d · inbound

Fast and Robust Diffusion Posterior Sampling for MR Image Reconstruction Using the Preconditioned Unadjusted Langevin Algorithm cites this paper.

Fast and Robust Diffusion Posterior Sampling for MR Image Reconstruction Using the Preconditioned Unadjusted Langevin Algorithm Diffusion models for inverse problems

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-05-17T01:03:46.592663Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation e0fa5e2f-086b-43ce-aaf0-f5174daab376 · inbound

Efficient Zero-Shot Inpainting with Decoupled Diffusion Guidance cites this paper.

Efficient Zero-Shot Inpainting with Decoupled Diffusion Guidance Diffusion models for inverse problems

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-05-16T20:21:13.532769Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation f86ad6fa-fc01-4521-ba43-5b8c98020019 · inbound

Particle-Guided Diffusion Models for Partial Differential Equations cites this paper.

Particle-Guided Diffusion Models for Partial Differential Equations Diffusion models for inverse problems

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-03T06:13:16.293605Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6c58d0bf-5a15-46a2-aa14-90117056e659 · inbound

Uncertainty-Aware Spatiotemporal Super-Resolution Data Assimilation with Diffusion Models cites this paper.

Uncertainty-Aware Spatiotemporal Super-Resolution Data Assimilation with Diffusion Models Diffusion models for inverse problems

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-05-11T14:41:19.546714Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation d1811ab4-b0ee-4556-910b-f65467fb6963 · 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 Diffusion models for inverse problems

Reference 4

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T21:31:14.173195Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 8b994c50-6006-4f1b-9cec-c131d229e2df · inbound

A Principled Self-Referenced Early Stopping Approach for Deep Image Prior cites this paper.

A Principled Self-Referenced Early Stopping Approach for Deep Image Prior Diffusion models for inverse problems

Reference 12

Resolution: metadata mismatch; arxiv_id, observed 2026-06-30T11:34:37.578769Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 3919cb56-2550-4dba-b5ce-84e2072d974d · inbound

Flow Map Denoisers: Traversing the Distortion-Perception Plane for Inverse Problems cites this paper.

Flow Map Denoisers: Traversing the Distortion-Perception Plane for Inverse Problems Diffusion models for inverse problems

Reference 161

Resolution: metadata mismatch; arxiv_id, observed 2026-07-04T03:09:30.424207Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.