Paper Citation Record · LEDGER

LiZAD: A Lightweight Zero-Shot Anomaly Detection Framework for Industrial Manufacturing

As of 7 August 2026, this Paper Citation Record lists 40 of 40 outbound references and 0 inbound Pith citation observations for arXiv:2607.01949.

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

pith.paper-citation-record.v1
2607.01949 v1

Coverage vector

measured 40 of 40 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-03T15:51:05.566926Z

measured 40 of 40 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+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

40 of 40 outbound references displayed

verified exact4
verified fuzzy35
unresolved0
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malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation da788b0b-0cf2-4a86-870a-3045018623bd · outbound

This paper cites Deep Industrial Image Anomaly Detection: A Survey,.

LiZAD: A Lightweight Zero-Shot Anomaly Detection Framework for Industrial Manufacturing Deep Industrial Image Anomaly Detection: A Survey,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T06:00:44.443710Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 93861463-2181-49a1-978e-cea826730b2e · outbound

This paper cites Towards Real Unsupervised Anomaly Detection Via Confident Meta-Learning,.

LiZAD: A Lightweight Zero-Shot Anomaly Detection Framework for Industrial Manufacturing Towards Real Unsupervised Anomaly Detection Via Confident Meta-Learning,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T06:00:44.436152Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c531b451-29dd-4c03-bd0e-2d1799f8be0a · outbound

This paper cites KairosAD: A SAM-Based Model for Industrial Anomaly Detection on Embedded Devices,.

LiZAD: A Lightweight Zero-Shot Anomaly Detection Framework for Industrial Manufacturing KairosAD: A SAM-Based Model for Industrial Anomaly Detection on Embedded Devices,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T06:00:44.438124Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation bb4b0929-cdeb-4a1e-b069-5024589ab817 · outbound

This paper cites Diffusion-Based Image Generation for In- Distribution Data Augmentation in Surface Defect Detection,.

LiZAD: A Lightweight Zero-Shot Anomaly Detection Framework for Industrial Manufacturing Diffusion-Based Image Generation for In- Distribution Data Augmentation in Surface Defect Detection,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T06:00:44.442588Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation aa40a0a5-639f-44e8-b19f-682d042dd4e5 · outbound

This paper cites Leveraging Latent Diffusion Models for Training- Free in-Distribution Data Augmentation for Surface Defect Detection,.

LiZAD: A Lightweight Zero-Shot Anomaly Detection Framework for Industrial Manufacturing Leveraging Latent Diffusion Models for Training- Free in-Distribution Data Augmentation for Surface Defect Detection,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T06:00:44.446481Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 16735f69-6756-4230-a323-625017487ed2 · outbound

This paper cites Towards Total Recall in Industrial Anomaly Detection,.

LiZAD: A Lightweight Zero-Shot Anomaly Detection Framework for Industrial Manufacturing Towards Total Recall in Industrial Anomaly Detection,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T06:00:44.438623Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c41c1d64-a105-4445-8893-7b6446e384c2 · outbound

This paper cites Collaborative Discrepancy Optimization for Reliable Image Anomaly Localization,.

LiZAD: A Lightweight Zero-Shot Anomaly Detection Framework for Industrial Manufacturing Collaborative Discrepancy Optimization for Reliable Image Anomaly Localization,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T06:00:44.425337Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 7acd6c1f-ef07-47d9-b8ae-cad36202ad21 · outbound

This paper cites WinCLIP: Zero-/Few-Shot Anomaly Classification and Segmentation,.

LiZAD: A Lightweight Zero-Shot Anomaly Detection Framework for Industrial Manufacturing WinCLIP: Zero-/Few-Shot Anomaly Classification and Segmentation,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T06:00:44.431375Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 8da55fe8-78e7-4248-9abb-eccc23558446 · outbound

This paper cites Efficient visual anomaly detection at the edge: Enabling real-time industrial inspection on resource- constrained devices.

LiZAD: A Lightweight Zero-Shot Anomaly Detection Framework for Industrial Manufacturing Efficient visual anomaly detection at the edge: Enabling real-time industrial inspection on resource- constrained devices

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-07-03T15:58:37.566872Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 67539eed-c217-43e6-a48e-c113641a9fae · outbound

This paper cites DINOv3.

LiZAD: A Lightweight Zero-Shot Anomaly Detection Framework for Industrial Manufacturing DINOv3

Reference 10

Resolution: metadata mismatch; local_arxiv, observed 2026-07-03T15:58:37.561324Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 4fbc7995-2f00-4f84-958b-be9df5a38e7b · outbound

This paper cites MobileCLIP2: Improving Multi-Modal Reinforced Training,.

LiZAD: A Lightweight Zero-Shot Anomaly Detection Framework for Industrial Manufacturing MobileCLIP2: Improving Multi-Modal Reinforced Training,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T06:00:44.408633Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 090da0ae-96ea-42ca-835e-57e62652f477 · outbound

This paper cites TinyGLASS: Real-Time Self-Supervised In-Sensor Anomaly Detection.

LiZAD: A Lightweight Zero-Shot Anomaly Detection Framework for Industrial Manufacturing TinyGLASS: Real-Time Self-Supervised In-Sensor Anomaly Detection

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-07-21T02:20:36.390785Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation bc9db8a7-7b93-4dc8-8384-4f062e434062 · outbound

This paper cites Enhancing Safety and Privacy in Industry 4.0: The ICE Laboratory Case Study,.

LiZAD: A Lightweight Zero-Shot Anomaly Detection Framework for Industrial Manufacturing Enhancing Safety and Privacy in Industry 4.0: The ICE Laboratory Case Study,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T06:00:44.453569Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 224dd0b6-3fdb-4b61-b545-47cd97761a49 · outbound

This paper cites Reconstruction by inpainting for visual anomaly detection,.

LiZAD: A Lightweight Zero-Shot Anomaly Detection Framework for Industrial Manufacturing Reconstruction by inpainting for visual anomaly detection,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T06:00:44.446127Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 3b156bb7-4d8a-4805-a154-8bac76517631 · outbound

This paper cites Self-Supervised Predictive Convolutional Attentive Block for Anomaly Detection,.

LiZAD: A Lightweight Zero-Shot Anomaly Detection Framework for Industrial Manufacturing Self-Supervised Predictive Convolutional Attentive Block for Anomaly Detection,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T06:00:44.455479Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation fb3b7537-4961-4032-ab02-0dd5201fb42d · outbound

This paper cites Pni : Industrial anomaly detection using position and neighborhood information,.

LiZAD: A Lightweight Zero-Shot Anomaly Detection Framework for Industrial Manufacturing Pni : Industrial anomaly detection using position and neighborhood information,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T06:00:44.416380Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 75eb5420-ecd6-49f0-bc20-d7f753276430 · outbound

This paper cites PANDA: Adapting Pretrained Features for Anomaly Detection and Segmentation,.

LiZAD: A Lightweight Zero-Shot Anomaly Detection Framework for Industrial Manufacturing PANDA: Adapting Pretrained Features for Anomaly Detection and Segmentation,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T06:00:44.404686Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 5b7f632c-d21a-4a15-b85d-4ee6e0706c52 · outbound

This paper cites PaDiM: A Patch Distribution Modeling Framework for Anomaly Detection and Localization,.

LiZAD: A Lightweight Zero-Shot Anomaly Detection Framework for Industrial Manufacturing PaDiM: A Patch Distribution Modeling Framework for Anomaly Detection and Localization,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T06:00:44.412640Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation dc221f9c-90e9-4b9c-9450-630d91d96f5b · outbound

This paper cites FastFlow: Unsupervised Anomaly Detection and Localization via 2D Normalizing Flows.

LiZAD: A Lightweight Zero-Shot Anomaly Detection Framework for Industrial Manufacturing FastFlow: Unsupervised Anomaly Detection and Localization via 2D Normalizing Flows

Reference 19

Resolution: verified exact; arxiv_id, observed 2026-07-03T15:58:37.569557Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a64f5368-b50b-4082-a16b-ba4e21892032 · outbound

This paper cites Same Same but DifferNet: Semi-Supervised Defect Detection With Normalizing Flows,.

LiZAD: A Lightweight Zero-Shot Anomaly Detection Framework for Industrial Manufacturing Same Same but DifferNet: Semi-Supervised Defect Detection With Normalizing Flows,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T06:00:44.427102Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 8f02e165-db7f-42da-a5d2-9da99b75bbb7 · outbound

This paper cites CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing Flows,.

LiZAD: A Lightweight Zero-Shot Anomaly Detection Framework for Industrial Manufacturing CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing Flows,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T06:00:44.424820Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 829b89ae-4c34-4670-b04a-f254e705304b · outbound

This paper cites Exploiting Multimodal Latent Diffusion Models for Accurate Anomaly Detection in Industry 5.0,.

LiZAD: A Lightweight Zero-Shot Anomaly Detection Framework for Industrial Manufacturing Exploiting Multimodal Latent Diffusion Models for Accurate Anomaly Detection in Industry 5.0,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T06:00:44.406696Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation fd8dd4ce-4384-4c13-887f-c5f21ff9ae33 · outbound

This paper cites CutPaste: Self-Supervised Learning for Anomaly Detection and Localization,.

LiZAD: A Lightweight Zero-Shot Anomaly Detection Framework for Industrial Manufacturing CutPaste: Self-Supervised Learning for Anomaly Detection and Localization,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T06:00:44.402540Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 94e9f70c-aef9-47d0-865c-f3b848f94324 · outbound

This paper cites DRAEM - A Discriminatively Trained Recon- struction Embedding for Surface Anomaly Detection,.

LiZAD: A Lightweight Zero-Shot Anomaly Detection Framework for Industrial Manufacturing DRAEM - A Discriminatively Trained Recon- struction Embedding for Surface Anomaly Detection,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T06:00:44.406500Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 37cdd805-ac71-47f6-b56f-286ddf1f7e56 · outbound

This paper cites SimpleNet: A Simple Network for Image Anomaly Detection and Localization,.

LiZAD: A Lightweight Zero-Shot Anomaly Detection Framework for Industrial Manufacturing SimpleNet: A Simple Network for Image Anomaly Detection and Localization,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T06:00:44.422729Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 9ea26661-446d-47bb-9037-04c2b0556c98 · outbound

This paper cites A Unified Anomaly Synthesis Strategy with Gradient Ascent for Industrial Anomaly Detection and Localization,.

LiZAD: A Lightweight Zero-Shot Anomaly Detection Framework for Industrial Manufacturing A Unified Anomaly Synthesis Strategy with Gradient Ascent for Industrial Anomaly Detection and Localization,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T06:00:44.395048Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 55a835a3-c6ca-419d-9d33-302233f0e7ae · outbound

This paper cites Learning Transferable Visual Models From Natural Language Supervision,.

LiZAD: A Lightweight Zero-Shot Anomaly Detection Framework for Industrial Manufacturing Learning Transferable Visual Models From Natural Language Supervision,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T06:00:44.404298Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 4e2e608f-9cac-4287-802b-e00863e65b8f · outbound

This paper cites APRIL-GAN: A Zero-/Few-Shot Anomaly Classification and Segmentation Method for CVPR 2023 VAND Workshop Challenge Tracks 1&2: 1st Place on Zero-shot AD and 4th Place on Few-shot AD.

LiZAD: A Lightweight Zero-Shot Anomaly Detection Framework for Industrial Manufacturing APRIL-GAN: A Zero-/Few-Shot Anomaly Classification and Segmentation Method for CVPR 2023 VAND Workshop Challenge Tracks 1&2: 1st Place on Zero-shot AD and 4th Place on Few-shot AD

Reference 28

Resolution: verified exact; arxiv_id, observed 2026-07-03T15:58:37.558979Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 8b5a2fb0-762e-4fad-99db-5e54c2667cbf · outbound

This paper cites AnomalyCLIP: Object-agnostic Prompt Learning for Zero-shot Anomaly Detection,.

LiZAD: A Lightweight Zero-Shot Anomaly Detection Framework for Industrial Manufacturing AnomalyCLIP: Object-agnostic Prompt Learning for Zero-shot Anomaly Detection,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T06:00:44.420529Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 22b36a7a-58c8-4341-97aa-5cdda9c0fd45 · outbound

This paper cites AdaCLIP: Adapting CLIP with Hybrid Learnable Prompts for Zero-Shot Anomaly Detection,.

LiZAD: A Lightweight Zero-Shot Anomaly Detection Framework for Industrial Manufacturing AdaCLIP: Adapting CLIP with Hybrid Learnable Prompts for Zero-Shot Anomaly Detection,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T06:00:44.429417Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 939d4136-7290-4843-af4c-4cb41c977325 · outbound

This paper cites Bayesian Prompt Flow Learning for Zero-Shot Anomaly Detection,.

LiZAD: A Lightweight Zero-Shot Anomaly Detection Framework for Industrial Manufacturing Bayesian Prompt Flow Learning for Zero-Shot Anomaly Detection,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T06:00:44.384756Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 868d5a95-8460-43cd-a49d-38b0e32fc187 · outbound

This paper cites PaSTe: Improving the Efficiency of Visual Anomaly Detection at the Edge,.

LiZAD: A Lightweight Zero-Shot Anomaly Detection Framework for Industrial Manufacturing PaSTe: Improving the Efficiency of Visual Anomaly Detection at the Edge,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T06:00:44.378086Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a63b02b1-941e-4646-b186-966cee48bca2 · outbound

This paper cites A SAM-guided Two-stream Lightweight Model for Anomaly Detection,.

LiZAD: A Lightweight Zero-Shot Anomaly Detection Framework for Industrial Manufacturing A SAM-guided Two-stream Lightweight Model for Anomaly Detection,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T06:00:44.383328Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 46499739-dc61-4f11-ad66-e018af17aa95 · outbound

This paper cites A Machine Learning-Oriented Survey on Tiny Machine Learning,.

LiZAD: A Lightweight Zero-Shot Anomaly Detection Framework for Industrial Manufacturing A Machine Learning-Oriented Survey on Tiny Machine Learning,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T06:00:44.422927Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation aea2d455-76d6-4fcd-9ca5-917e53c2a691 · outbound

This paper cites AA-CLIP: Enhancing Zero-Shot Anomaly Detection via Anomaly-Aware CLIP,.

LiZAD: A Lightweight Zero-Shot Anomaly Detection Framework for Industrial Manufacturing AA-CLIP: Enhancing Zero-Shot Anomaly Detection via Anomaly-Aware CLIP,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T06:00:44.378338Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation d084d57b-f3f0-4dc6-b6ea-34f6f480287d · outbound

This paper cites MVTec AD – A Comprehensive Real-World Dataset for Unsupervised Anomaly Detection,.

LiZAD: A Lightweight Zero-Shot Anomaly Detection Framework for Industrial Manufacturing MVTec AD – A Comprehensive Real-World Dataset for Unsupervised Anomaly Detection,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T06:00:44.414701Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 88fee805-28bb-4b86-a772-2fa7c3d6d398 · outbound

This paper cites ReConPatch: Contrastive Patch Representation Learning for Industrial Anomaly Detection,.

LiZAD: A Lightweight Zero-Shot Anomaly Detection Framework for Industrial Manufacturing ReConPatch: Contrastive Patch Representation Learning for Industrial Anomaly Detection,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T06:00:44.387953Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 6e4613b8-74d2-4c69-ac55-877e22ee4d46 · outbound

This paper cites Deep learning-based defect detection of metal parts: evaluating current methods in complex conditions,.

LiZAD: A Lightweight Zero-Shot Anomaly Detection Framework for Industrial Manufacturing Deep learning-based defect detection of metal parts: evaluating current methods in complex conditions,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T06:00:44.448293Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c891bf04-423a-409e-9b49-231748f0130d · outbound

This paper cites SPot-the-Difference Self-supervised Pre-training for Anomaly Detection and Segmentation,.

LiZAD: A Lightweight Zero-Shot Anomaly Detection Framework for Industrial Manufacturing SPot-the-Difference Self-supervised Pre-training for Anomaly Detection and Segmentation,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T06:00:44.410731Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation ff166257-11e5-4c93-978d-5b86f1ff5733 · outbound

This paper cites SuperSimpleNet: Unifying Unsupervised and Super- vised Learning for Fast and Reliable Surface Defect Detection,.

LiZAD: A Lightweight Zero-Shot Anomaly Detection Framework for Industrial Manufacturing SuperSimpleNet: Unifying Unsupervised and Super- vised Learning for Fast and Reliable Surface Defect Detection,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T06:00:44.414407Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Pith citing papers

No inbound Pith citation observations are available.