Paper Citation Record · LEDGER

Diffuse to Detect: Generative Diffusion Models for Unsupervised IC Anomaly Detection

As of 8 August 2026, this Paper Citation Record lists 43 of 43 outbound references and 0 inbound Pith citation observations for arXiv:2605.26468.

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

pith.paper-citation-record.v1
2605.26468 v1

Coverage vector

measured 43 of 43 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-06-29T19:53:42.921277Z

measured 43 of 43 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 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

43 of 43 outbound references displayed

verified exact2
verified fuzzy0
unresolved41
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation b8b61830-5b01-4d89-8bce-99b531dcfa40 · outbound

This paper cites Wafer level stress: Enabling zero defect quality for automotive microcontrollers without package burn-in,.

Diffuse to Detect: Generative Diffusion Models for Unsupervised IC Anomaly Detection Wafer level stress: Enabling zero defect quality for automotive microcontrollers without package burn-in,

Reference 1

Resolution: unresolved; no resolver link, observed 2026-06-29T19:53:42.921277Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6e1a78e6-c929-40cb-9f3e-5df044c7dcfe · outbound

This paper cites Silent data corruption: Advancing detection, diagnosis, and mitigation strategies,.

Diffuse to Detect: Generative Diffusion Models for Unsupervised IC Anomaly Detection Silent data corruption: Advancing detection, diagnosis, and mitigation strategies,

Reference 2

Resolution: unresolved; no resolver link, observed 2026-06-29T19:53:42.921277Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8246d7d5-2273-49e7-bfaa-8fe70bb26527 · outbound

This paper cites Denoising diffusion probabilistic models,.

Diffuse to Detect: Generative Diffusion Models for Unsupervised IC Anomaly Detection Denoising diffusion probabilistic models,

Reference 3

Resolution: unresolved; no resolver link, observed 2026-06-29T19:53:42.921277Z

Source-reported events for the cited work

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Observation 2fb404ca-0079-4ae1-9b34-989037faa87f · outbound

This paper cites AnoDDPM: Anomaly detection with denoising diffusion probabilistic models using simplex noise,.

Diffuse to Detect: Generative Diffusion Models for Unsupervised IC Anomaly Detection AnoDDPM: Anomaly detection with denoising diffusion probabilistic models using simplex noise,

Reference 4

Resolution: unresolved; no resolver link, observed 2026-06-29T19:53:42.921277Z

Source-reported events for the cited work

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Observation c779614d-e467-482c-8c4a-aa1102531350 · outbound

This paper cites DiffusionAD: Norm-guided One-step Denoising Diffusion for Anomaly Detection.

Diffuse to Detect: Generative Diffusion Models for Unsupervised IC Anomaly Detection DiffusionAD: Norm-guided One-step Denoising Diffusion for Anomaly Detection

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-06-29T19:53:55.257716Z

Source-reported events for the cited work

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Observation 81af4185-133a-4927-a5df-69678a0415f2 · outbound

This paper cites Tabddpm: modelling tabular data with diffusion models,.

Diffuse to Detect: Generative Diffusion Models for Unsupervised IC Anomaly Detection Tabddpm: modelling tabular data with diffusion models,

Reference 6

Resolution: unresolved; no resolver link, observed 2026-06-29T19:53:42.921277Z

Source-reported events for the cited work

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Observation c5c116ff-cdbe-4ba9-a799-7dc3e8873c50 · outbound

This paper cites Scalable diffusion models with transformers,.

Diffuse to Detect: Generative Diffusion Models for Unsupervised IC Anomaly Detection Scalable diffusion models with transformers,

Reference 7

Resolution: unresolved; no resolver link, observed 2026-06-29T19:53:42.921277Z

Source-reported events for the cited work

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Observation 298e7c80-7053-4118-85d9-6772b9134b8b · outbound

This paper cites an unresolved cited work.

Diffuse to Detect: Generative Diffusion Models for Unsupervised IC Anomaly Detection Unresolved cited work

Reference 8

Resolution: unresolved; no resolver link, observed 2026-06-29T19:53:42.921277Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1992de1d-7787-43ea-9f9c-4f17b24c9073 · outbound

This paper cites Advanced outlier detection using unsupervised learning for screening potential customer returns,.

Diffuse to Detect: Generative Diffusion Models for Unsupervised IC Anomaly Detection Advanced outlier detection using unsupervised learning for screening potential customer returns,

Reference 9

Resolution: unresolved; no resolver link, observed 2026-06-29T19:53:42.921277Z

Source-reported events for the cited work

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Observation ca7107da-6742-42fa-89bd-ee2e0e578177 · outbound

This paper cites Isolation forest,.

Diffuse to Detect: Generative Diffusion Models for Unsupervised IC Anomaly Detection Isolation forest,

Reference 10

Resolution: unresolved; no resolver link, observed 2026-06-29T19:53:42.921277Z

Source-reported events for the cited work

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Observation f097ab1e-20e1-46ec-98e8-9e81bf0775eb · outbound

This paper cites Estimating the support of a high-dimensional distribution,.

Diffuse to Detect: Generative Diffusion Models for Unsupervised IC Anomaly Detection Estimating the support of a high-dimensional distribution,

Reference 11

Resolution: unresolved; no resolver link, observed 2026-06-29T19:53:42.921277Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0c997cf9-4f0a-4199-8755-b444ffcdea49 · outbound

This paper cites Equipment anomaly detection for semiconductor manufacturing by exploiting unsupervised learning from sensory data,.

Diffuse to Detect: Generative Diffusion Models for Unsupervised IC Anomaly Detection Equipment anomaly detection for semiconductor manufacturing by exploiting unsupervised learning from sensory data,

Reference 12

Resolution: unresolved; no resolver link, observed 2026-06-29T19:53:42.921277Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c213f4b4-3278-4cae-8874-9dfe02d725c8 · outbound

This paper cites Generative pre-training of time-series data for unsupervised fault detection in semiconductor manufacturing,.

Diffuse to Detect: Generative Diffusion Models for Unsupervised IC Anomaly Detection Generative pre-training of time-series data for unsupervised fault detection in semiconductor manufacturing,

Reference 13

Resolution: unresolved; no resolver link, observed 2026-06-29T19:53:42.921277Z

Source-reported events for the cited work

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Observation ab07c85a-5763-4a18-98ff-c0406e86c582 · outbound

This paper cites Wafer map fault pattern classifica- tion and image retrieval using convolutional neural network,.

Diffuse to Detect: Generative Diffusion Models for Unsupervised IC Anomaly Detection Wafer map fault pattern classifica- tion and image retrieval using convolutional neural network,

Reference 14

Resolution: unresolved; no resolver link, observed 2026-06-29T19:53:42.921277Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6d7bfa41-7780-4e2a-8fa5-7affeda97d10 · outbound

This paper cites Input-guidance diffusion model for unknown defect patterns detection in wafer bin map,.

Diffuse to Detect: Generative Diffusion Models for Unsupervised IC Anomaly Detection Input-guidance diffusion model for unknown defect patterns detection in wafer bin map,

Reference 15

Resolution: unresolved; no resolver link, observed 2026-06-29T19:53:42.921277Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c8d89718-b23c-4988-bb71-923950f4346f · outbound

This paper cites On diffusion modeling for anomaly detection,.

Diffuse to Detect: Generative Diffusion Models for Unsupervised IC Anomaly Detection On diffusion modeling for anomaly detection,

Reference 16

Resolution: unresolved; no resolver link, observed 2026-06-29T19:53:42.921277Z

Source-reported events for the cited work

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Observation 4e1ed6f8-f45a-4a6a-af3b-6be7754e7f4d · outbound

This paper cites ImDiffusion: Imputed diffusion models for multivariate time series anomaly detection,.

Diffuse to Detect: Generative Diffusion Models for Unsupervised IC Anomaly Detection ImDiffusion: Imputed diffusion models for multivariate time series anomaly detection,

Reference 17

Resolution: unresolved; no resolver link, observed 2026-06-29T19:53:42.921277Z

Source-reported events for the cited work

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Observation b12ad3b4-b820-4e33-9ad7-8db1c66159e2 · outbound

This paper cites Anomaly detection with condi- tioned denoising diffusion models,.

Diffuse to Detect: Generative Diffusion Models for Unsupervised IC Anomaly Detection Anomaly detection with condi- tioned denoising diffusion models,

Reference 18

Resolution: unresolved; no resolver link, observed 2026-06-29T19:53:42.921277Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f8c78b59-e551-4b24-9ccc-782e8d5f8d8a · outbound

This paper cites Unsupervised 3d out-of-distribution detection with latent diffu- sion models,.

Diffuse to Detect: Generative Diffusion Models for Unsupervised IC Anomaly Detection Unsupervised 3d out-of-distribution detection with latent diffu- sion models,

Reference 19

Resolution: unresolved; no resolver link, observed 2026-06-29T19:53:42.921277Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c5b7c3c2-dd5b-4ab3-bff1-5d6eff47bb2f · outbound

This paper cites TransFusion: A transparency- based diffusion model for anomaly detection,.

Diffuse to Detect: Generative Diffusion Models for Unsupervised IC Anomaly Detection TransFusion: A transparency- based diffusion model for anomaly detection,

Reference 20

Resolution: unresolved; no resolver link, observed 2026-06-29T19:53:42.921277Z

Source-reported events for the cited work

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Observation 73b253bd-5e43-4d20-a3b3-9d8852886552 · outbound

This paper cites MVTec AD — a comprehensive real-world dataset for unsupervised anomaly detection,.

Diffuse to Detect: Generative Diffusion Models for Unsupervised IC Anomaly Detection MVTec AD — a comprehensive real-world dataset for unsupervised anomaly detection,

Reference 21

Resolution: unresolved; no resolver link, observed 2026-06-29T19:53:42.921277Z

Source-reported events for the cited work

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Observation 9a32f055-b898-4a1e-940e-4933d520ec68 · outbound

This paper cites An image is worth 16x16 words: Transformers for image recognition at scale,.

Diffuse to Detect: Generative Diffusion Models for Unsupervised IC Anomaly Detection An image is worth 16x16 words: Transformers for image recognition at scale,

Reference 22

Resolution: unresolved; no resolver link, observed 2026-06-29T19:53:42.921277Z

Source-reported events for the cited work

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Observation a8cebb68-9b36-4c15-b02e-d1da91001eed · outbound

This paper cites Attention is all you need,.

Diffuse to Detect: Generative Diffusion Models for Unsupervised IC Anomaly Detection Attention is all you need,

Reference 23

Resolution: unresolved; no resolver link, observed 2026-06-29T19:53:42.921277Z

Source-reported events for the cited work

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Observation 7ae880bb-1e70-4d51-a288-987aa158db89 · outbound

This paper cites Improved denoising diffusion probabilistic models,.

Diffuse to Detect: Generative Diffusion Models for Unsupervised IC Anomaly Detection Improved denoising diffusion probabilistic models,

Reference 24

Resolution: unresolved; no resolver link, observed 2026-06-29T19:53:42.921277Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aeec2709-0f99-4add-aed0-aff819311516 · outbound

This paper cites 8162–8171.

Diffuse to Detect: Generative Diffusion Models for Unsupervised IC Anomaly Detection 8162–8171

Reference 25

Resolution: unresolved; no resolver link, observed 2026-06-29T19:53:42.921277Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e82abd02-b5ba-431c-97ff-6d9f99591ffd · outbound

This paper cites The relationship between precision-recall and ROC curves,.

Diffuse to Detect: Generative Diffusion Models for Unsupervised IC Anomaly Detection The relationship between precision-recall and ROC curves,

Reference 26

Resolution: unresolved; no resolver link, observed 2026-06-29T19:53:42.921277Z

Source-reported events for the cited work

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Observation 49b40c47-e2b0-4354-9aa1-e6cb4e4be2ef · outbound

This paper cites COPOD: Copula-based outlier detection,.

Diffuse to Detect: Generative Diffusion Models for Unsupervised IC Anomaly Detection COPOD: Copula-based outlier detection,

Reference 27

Resolution: unresolved; no resolver link, observed 2026-06-29T19:53:42.921277Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3ed2ce3b-8540-4703-a82c-c945876fba74 · outbound

This paper cites ECOD: Unsupervised outlier detection using empirical cumulative distribution functions,.

Diffuse to Detect: Generative Diffusion Models for Unsupervised IC Anomaly Detection ECOD: Unsupervised outlier detection using empirical cumulative distribution functions,

Reference 28

Resolution: unresolved; no resolver link, observed 2026-06-29T19:53:42.921277Z

Source-reported events for the cited work

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Observation 96564072-b43a-4bb5-981f-a40353f2f963 · outbound

This paper cites Feature bagging for outlier detection,.

Diffuse to Detect: Generative Diffusion Models for Unsupervised IC Anomaly Detection Feature bagging for outlier detection,

Reference 29

Resolution: unresolved; no resolver link, observed 2026-06-29T19:53:42.921277Z

Source-reported events for the cited work

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Observation c6e19df3-c2db-4f89-8d1f-0a890cf5a413 · outbound

This paper cites HBOS: A fast unsupervised anomaly detection algorithm,.

Diffuse to Detect: Generative Diffusion Models for Unsupervised IC Anomaly Detection HBOS: A fast unsupervised anomaly detection algorithm,

Reference 30

Resolution: unresolved; no resolver link, observed 2026-06-29T19:53:42.921277Z

Source-reported events for the cited work

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Observation e2d65851-0022-45a4-8228-6cd692e80901 · outbound

This paper cites Efficient algorithms for mining outliers from large data sets,.

Diffuse to Detect: Generative Diffusion Models for Unsupervised IC Anomaly Detection Efficient algorithms for mining outliers from large data sets,

Reference 31

Resolution: unresolved; no resolver link, observed 2026-06-29T19:53:42.921277Z

Source-reported events for the cited work

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Observation ba74251d-94c6-4565-bdb9-01e9fb3e9b31 · outbound

This paper cites LODA: Lightweight on-line detector of anomalies,.

Diffuse to Detect: Generative Diffusion Models for Unsupervised IC Anomaly Detection LODA: Lightweight on-line detector of anomalies,

Reference 32

Resolution: unresolved; no resolver link, observed 2026-06-29T19:53:42.921277Z

Source-reported events for the cited work

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Observation c9e05897-0e00-4c8e-9461-c4f00ed4a690 · outbound

This paper cites LOF: Identifying density-based local outliers,.

Diffuse to Detect: Generative Diffusion Models for Unsupervised IC Anomaly Detection LOF: Identifying density-based local outliers,

Reference 33

Resolution: unresolved; no resolver link, observed 2026-06-29T19:53:42.921277Z

Source-reported events for the cited work

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Observation 6b0ff077-36ad-4130-9c81-2333d976533a · outbound

This paper cites Outlier detection in the multiple cluster setting using the minimum covariance determinant estimator,.

Diffuse to Detect: Generative Diffusion Models for Unsupervised IC Anomaly Detection Outlier detection in the multiple cluster setting using the minimum covariance determinant estimator,

Reference 34

Resolution: unresolved; no resolver link, observed 2026-06-29T19:53:42.921277Z

Source-reported events for the cited work

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Observation 053bdba7-f43e-4dd4-b6fa-2bd8bf705919 · outbound

This paper cites Deep autoencoding Gaussian mixture model for unsupervised anomaly detection,.

Diffuse to Detect: Generative Diffusion Models for Unsupervised IC Anomaly Detection Deep autoencoding Gaussian mixture model for unsupervised anomaly detection,

Reference 35

Resolution: unresolved; no resolver link, observed 2026-06-29T19:53:42.921277Z

Source-reported events for the cited work

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Observation c6671ba5-3717-4350-835b-d8a002ea4815 · outbound

This paper cites DROCC: Deep robust one-class classification,.

Diffuse to Detect: Generative Diffusion Models for Unsupervised IC Anomaly Detection DROCC: Deep robust one-class classification,

Reference 36

Resolution: unresolved; no resolver link, observed 2026-06-29T19:53:42.921277Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ee94400a-0012-4636-bb24-51079968853e · outbound

This paper cites Classification-based anomaly detection for general data,.

Diffuse to Detect: Generative Diffusion Models for Unsupervised IC Anomaly Detection Classification-based anomaly detection for general data,

Reference 37

Resolution: unresolved; no resolver link, observed 2026-06-29T19:53:42.921277Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bdfac4c8-085c-4255-8d81-2c6422af27cd · outbound

This paper cites Anomaly detection for tabular data with internal contrastive learning,.

Diffuse to Detect: Generative Diffusion Models for Unsupervised IC Anomaly Detection Anomaly detection for tabular data with internal contrastive learning,

Reference 38

Resolution: unresolved; no resolver link, observed 2026-06-29T19:53:42.921277Z

Source-reported events for the cited work

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Observation 909bd74c-d2e2-4752-b340-56eb9b9d27e6 · outbound

This paper cites Variational inference with normalizing flows,.

Diffuse to Detect: Generative Diffusion Models for Unsupervised IC Anomaly Detection Variational inference with normalizing flows,

Reference 39

Resolution: unresolved; no resolver link, observed 2026-06-29T19:53:42.921277Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 93210de4-8b52-4806-9565-e7bc9bd75ead · outbound

This paper cites GANomaly: Semi- supervised anomaly detection via adversarial training,.

Diffuse to Detect: Generative Diffusion Models for Unsupervised IC Anomaly Detection GANomaly: Semi- supervised anomaly detection via adversarial training,

Reference 40

Resolution: unresolved; no resolver link, observed 2026-06-29T19:53:42.921277Z

Source-reported events for the cited work

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Observation 15508129-1f54-4940-b27a-d60afedf6594 · outbound

This paper cites Fascinating supervisory signals and where to find them: Deep anomaly detection with scale learning,.

Diffuse to Detect: Generative Diffusion Models for Unsupervised IC Anomaly Detection Fascinating supervisory signals and where to find them: Deep anomaly detection with scale learning,

Reference 41

Resolution: unresolved; no resolver link, observed 2026-06-29T19:53:42.921277Z

Source-reported events for the cited work

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Observation 63521cec-6b25-41b8-9f2c-c3152855a81a · outbound

This paper cites PyOD: A python toolbox for scalable outlier detection,.

Diffuse to Detect: Generative Diffusion Models for Unsupervised IC Anomaly Detection PyOD: A python toolbox for scalable outlier detection,

Reference 42

Resolution: unresolved; no resolver link, observed 2026-06-29T19:53:42.921277Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f62c3733-5a35-418e-8701-164ad652ee40 · outbound

This paper cites Decoupled Weight Decay Regularization.

Diffuse to Detect: Generative Diffusion Models for Unsupervised IC Anomaly Detection Decoupled Weight Decay Regularization

Reference 43

Resolution: verified exact; local_arxiv, observed 2026-06-29T19:53:55.255103Z

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Pith citing papers

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