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Paper Citation Record · LEDGER

Principled Probabilistic Imaging using Diffusion Models as Plug-and-Play Priors

As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:2405.18782.

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

pith.paper-citation-record.v1
2405.18782 v2

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 2026-08-17T06:30:58.91139+00:00

measured 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T21:34:57.810084Z

measured 1 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

2
pith, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 8804b71e-cc3b-4ccd-963e-f723d843c596 · inbound

ADOBI: Adaptive Diffusion Bridge For Blind Inverse Problems with Application to MRI Reconstruction cites this paper.

ADOBI: Adaptive Diffusion Bridge For Blind Inverse Problems with Application to MRI Reconstruction Principled Probabilistic Imaging using Diffusion Models as Plug-and-Play Priors

Reference 32

Resolution
unresolved
no resolver link, observed 2026-08-12T13:03:25.636898Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T13:03:25.636898Z digest=sha256:97c270f4a4d56e09fbe023d6e99f39f1c31077c53bcc30befd91392e5d362f61

Observation 5881da94-cac6-43a8-8708-8a9c1aad7995 · inbound

Steering Rectified Flow Models in the Vector Field for Controlled Image Generation cites this paper.

Steering Rectified Flow Models in the Vector Field for Controlled Image Generation Principled Probabilistic Imaging using Diffusion Models as Plug-and-Play Priors

Reference 52

Resolution
unresolved
no resolver link, observed 2026-08-12T11:05:33.803422Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T11:05:33.803422Z digest=sha256:709073c90fac5e47e585d6f2deb6f1751068f0c4b82efa88fb90266b6898cda7

Observation 2e0487f4-6ad8-4749-a37a-41dfd6750ab4 · inbound

Improving Decoupled Posterior Sampling for Inverse Problems using Data Consistency Constraint cites this paper.

Improving Decoupled Posterior Sampling for Inverse Problems using Data Consistency Constraint Principled Probabilistic Imaging using Diffusion Models as Plug-and-Play Priors

Reference 42

Resolution
unresolved
no resolver link, observed 2026-08-12T05:12:24.241956Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T05:12:24.241956Z digest=sha256:60bfedc5c13fd6747f27fc0a941c4adcdba69b2d4015aac4048eb8c7675f4761

Observation 186d9a86-bf90-4210-8f1d-f1ec796b3f10 · inbound

A Mixture-Based Framework for Guiding Diffusion Models cites this paper.

A Mixture-Based Framework for Guiding Diffusion Models Principled Probabilistic Imaging using Diffusion Models as Plug-and-Play Priors

Reference 54

Resolution
unresolved
no resolver link, observed 2026-08-09T05:10:54.827931Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T05:10:54.827931Z digest=sha256:3c07eaf3ac5c758eebf6cbce75ad9b94cf30c09e37a3906b5b2878450e459122

Observation 9f7a0de0-cc04-493e-8ab3-dde9f260539b · inbound

Conformal Bounds on Full-Reference Image Quality for Imaging Inverse Problems cites this paper.

Conformal Bounds on Full-Reference Image Quality for Imaging Inverse Problems Principled Probabilistic Imaging using Diffusion Models as Plug-and-Play Priors

Reference 73

Resolution
unresolved
no resolver link, observed 2026-08-15T21:34:57.810084Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T21:34:57.810084Z digest=sha256:4183c0d75affa474d851fd00aa6a18047051e545697f5b868ddc50eca1ca9cb5

Observation 5550437d-9887-4188-8c04-a3cb4b9184c5 · inbound

Exploiting the Exact Denoising Posterior Score in Training-Free Guidance of Diffusion Models cites this paper.

Exploiting the Exact Denoising Posterior Score in Training-Free Guidance of Diffusion Models Principled Probabilistic Imaging using Diffusion Models as Plug-and-Play Priors

Reference 24

Resolution
unresolved
no resolver link, observed 2026-08-15T20:03:19.068181Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:03:19.068181Z digest=sha256:f840cae550ac3ae8e9abfd4100d4f3bc64557c1c1d378767037fbb6264b00a9e

Observation e22168bd-5248-49c3-b913-ad029e2094e5 · inbound

Blade: A Derivative-free Bayesian Inversion Method using Diffusion Priors cites this paper.

Blade: A Derivative-free Bayesian Inversion Method using Diffusion Priors Principled Probabilistic Imaging using Diffusion Models as Plug-and-Play Priors

Reference 13

Resolution
unresolved
no resolver link, observed 2026-08-04T10:17:48.183456Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T10:17:48.183456Z digest=sha256:dc96bf025d929e3de627c9daf1dc2b03685883cf92dd6e6c983bfdf17dd9bebb

Observation ab78d4de-741c-4096-b3f0-202c96f6cd0f · inbound

Strong Gravitational Lensing Posterior Sampling in Pixel-Space Using Diffusion Models and Recurrent Inference Machines cites this paper.

Strong Gravitational Lensing Posterior Sampling in Pixel-Space Using Diffusion Models and Recurrent Inference Machines Principled Probabilistic Imaging using Diffusion Models as Plug-and-Play Priors

Reference 52

Resolution
verified exact
local_arxiv, observed 2026-08-01T12:49:44.354749Z

Source-reported events for the cited work

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

source=arxiv_source observed=2026-08-01T12:47:34.837098Z digest=sha256:10474028ece6460d27f610f5290de142c0759144d0ae3dbab796f51399fe4e61