Paper Citation Record · LEDGER

Anomaly Detection with Conditioned Denoising Diffusion Models

As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:2305.15956.

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

pith.paper-citation-record.v1
2305.15956 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 7 of 7 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 7 of 7 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T12:23:55.682460Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-07T10:29:09.975471Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

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 f090464e-5b71-4e22-99bd-1f5bea658fff · inbound

MathPhys-Guided Coarse-to-Fine Anomaly Synthesis with SQE-Driven Bi-Level Optimization for Anomaly Detection cites this paper.

MathPhys-Guided Coarse-to-Fine Anomaly Synthesis with SQE-Driven Bi-Level Optimization for Anomaly Detection Anomaly Detection with Conditioned Denoising Diffusion Models

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-16T12:23:55.682460Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1349e60e-f5a5-4bf1-879e-2c7f291782a7 · inbound

Detect, Classify, Act: Categorizing Industrial Anomalies with Multi-Modal Large Language Models cites this paper.

Detect, Classify, Act: Categorizing Industrial Anomalies with Multi-Modal Large Language Models Anomaly Detection with Conditioned Denoising Diffusion Models

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-16T00:53:10.519330Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8ab5a58d-9ba9-4766-bc7c-413e93b0aeec · inbound

Research on Anomaly Detection Methods Based on Diffusion Models cites this paper.

Research on Anomaly Detection Methods Based on Diffusion Models Anomaly Detection with Conditioned Denoising Diffusion Models

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-15T23:16:11.495573Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 156fd42b-b191-47c0-ae9b-5639ffc3222b · inbound

AquaSignal: An Integrated Framework for Robust Underwater Acoustic Analysis cites this paper.

AquaSignal: An Integrated Framework for Robust Underwater Acoustic Analysis Anomaly Detection with Conditioned Denoising Diffusion Models

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-07T15:41:33.602124Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 28d1f063-559c-4551-8de1-f07267c3b7bd · inbound

Harnessing EHRs for Diffusion-based Anomaly Detection on Chest X-rays cites this paper.

Harnessing EHRs for Diffusion-based Anomaly Detection on Chest X-rays Anomaly Detection with Conditioned Denoising Diffusion Models

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-07T14:52:20.068878Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e73c4392-f3e5-46e3-ad95-133f49c27e78 · inbound

Anomalies by Synthesis: Anomaly Detection using Generative Diffusion Models for Off-Road Navigation cites this paper.

Anomalies by Synthesis: Anomaly Detection using Generative Diffusion Models for Off-Road Navigation Anomaly Detection with Conditioned Denoising Diffusion Models

Reference 79

Resolution: unresolved; no resolver link, observed 2026-08-07T13:06:39.937051Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6b15d565-c6ae-4f82-b3da-547b3db8c6ea · inbound

Track Any Anomalous Object: A Granular Video Anomaly Detection Pipeline cites this paper.

Track Any Anomalous Object: A Granular Video Anomaly Detection Pipeline Anomaly Detection with Conditioned Denoising Diffusion Models

Reference 38

Resolution: verified exact; local_arxiv, observed 2026-08-07T10:29:10.038707Z

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.