Pith. sign in

Paper Citation Record · LEDGER

Towards Total Recall in Industrial Anomaly Detection

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 12 inbound Pith citation observations for arXiv:2106.08265.

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

pith.paper-citation-record.v1
2106.08265 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 12 of 12 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 12 of 12 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T16:58:31.799061Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-30T07:34:21.594997Z

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 3d89f9e1-3ca5-4e26-beb1-630d76f82f31 · inbound

MoViAD: A Modular Library for Visual Anomaly Detection cites this paper.

MoViAD: A Modular Library for Visual Anomaly Detection Towards Total Recall in Industrial Anomaly Detection

Reference 16

Resolution
unresolved
no resolver link, observed 2026-08-06T16:58:31.799061Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T16:58:31.799061Z digest=sha256:1fcdb8dc522f78c64bc8e75e7aba41a89036e97a5e9590296daca23409082783

Observation bcc0b6c2-0103-4e64-b35f-c80d2b1f20f3 · inbound

C3D-AD: Toward Continual 3D Anomaly Detection via Kernel Attention with Learnable Advisor cites this paper.

C3D-AD: Toward Continual 3D Anomaly Detection via Kernel Attention with Learnable Advisor Towards Total Recall in Industrial Anomaly Detection

Reference 35

Resolution
unresolved
no resolver link, observed 2026-08-06T05:47:37.320469Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T05:47:37.320469Z digest=sha256:7479782b4b58d0ce36fef8fa5b789ee8e58076295b6e56b01228e925584b078f

Observation 265ddf60-a0bd-44f6-b314-0bdbbcd4e8fd · inbound

Self-Navigated Residual Mamba for Universal Industrial Anomaly Detection cites this paper.

Self-Navigated Residual Mamba for Universal Industrial Anomaly Detection Towards Total Recall in Industrial Anomaly Detection

Reference 34

Resolution
unresolved
no resolver link, observed 2026-08-06T05:36:22.638090Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T05:36:22.638090Z digest=sha256:e9f0e7c9ee94255b2994b8ac833d56c84f087c339b6a273e6559d089bb82d908

Observation 4a258553-773f-4d8c-924b-27cf661c77b3 · inbound

AD-FM: Multimodal LLMs for Anomaly Detection via Multi-Stage Reasoning and Fine-Grained Reward Optimization cites this paper.

AD-FM: Multimodal LLMs for Anomaly Detection via Multi-Stage Reasoning and Fine-Grained Reward Optimization Towards Total Recall in Industrial Anomaly Detection

Reference 38

Resolution
unresolved
no resolver link, observed 2026-08-06T00:53:25.953227Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T00:53:25.953227Z digest=sha256:3e71d1f67bd8a30172f9a5fa359d61752ac7b4eda52be942a8f6a17298602a49

Observation f941292b-7618-4a36-93d8-55a91f3f3783 · inbound

PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments cites this paper.

PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments Towards Total Recall in Industrial Anomaly Detection

Reference 58

Resolution
unresolved
no resolver link, observed 2026-08-05T18:34:09.593045Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T18:34:09.593045Z digest=sha256:2133e2f81029879298cb51370ce14071fa40513735b5cebec1b862d984faeff6

Observation ca95dbfc-99d1-48fa-b1ae-1b290a7f4a34 · inbound

Wavelet-Enhanced PaDiM for Industrial Anomaly Detection cites this paper.

Wavelet-Enhanced PaDiM for Industrial Anomaly Detection Towards Total Recall in Industrial Anomaly Detection

Reference 11

Resolution
unresolved
no resolver link, observed 2026-08-05T17:39:20.434011Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T17:39:20.434011Z digest=sha256:9cd0a9cb40c7dc9b637eaad12c79beff6e967abfa4e5de5a528960341cd1aa11

Observation b280441d-fc8a-4a59-98c1-4d63100aff7d · inbound

HLGFA: High-Low Resolution Guided Feature Alignment for Unsupervised Anomaly Detection cites this paper.

HLGFA: High-Low Resolution Guided Feature Alignment for Unsupervised Anomaly Detection Towards Total Recall in Industrial Anomaly Detection

Reference 15

Resolution
verified exact
arxiv_id, observed 2026-05-16T03:07:11.905169Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-16T03:05:37.320403Z digest=sha256:7259c59a9b673c372544693bb9465ef1b4b2921d7bdf9c02b0ef97c9a12cfcb6

Observation 89a04942-5bd0-4c2d-84c5-3aec796dc706 · inbound

Latent Anomaly Knowledge Excavation: Unveiling Sparse Sensitive Neurons in Vision-Language Models cites this paper.

Latent Anomaly Knowledge Excavation: Unveiling Sparse Sensitive Neurons in Vision-Language Models Towards Total Recall in Industrial Anomaly Detection

Reference 11

Resolution
metadata mismatch
arxiv_id, observed 2026-05-11T06:51:18.359745Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-10T17:25:34.942028Z digest=sha256:d29eec9630d39444a7bf01642887fde851bacdc12cbd4b6078c7bfc4a141f7c3

Observation 382002f4-7ab7-4210-8866-a88521acc208 · inbound

AD4AD: Benchmarking Visual Anomaly Detection Models for Safer Autonomous Driving cites this paper.

AD4AD: Benchmarking Visual Anomaly Detection Models for Safer Autonomous Driving Towards Total Recall in Industrial Anomaly Detection

Reference 5

Resolution
metadata mismatch
arxiv_id, observed 2026-05-10T11:55:21.008247Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-10T11:51:12.649684Z digest=sha256:9eda2241a188fe213d36fd19f527abf2541e8829cc6744bd21da3d357f942326

Observation 7fbf819d-a93f-41ac-80b2-2629d0e276c1 · inbound

Learning Discriminative Signed Distance Functions from Multi-scale Level-of-detail Features for 3D Anomaly Detection cites this paper.

Learning Discriminative Signed Distance Functions from Multi-scale Level-of-detail Features for 3D Anomaly Detection Towards Total Recall in Industrial Anomaly Detection

Reference 31

Resolution
verified exact
arxiv_id, observed 2026-05-12T11:01:30.687376Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-07T17:57:53.482855Z digest=sha256:7ddad52ff9ce59cd47dd2e9d41a427dfee4e977291733a166646f14551b90311

Observation e6930b3a-d0c1-4f0e-80fd-15ab749362ff · inbound

Learning Discriminative Signed Distance Functions from Multi-scale Level-of-detail Features for 3D Anomaly Detection cites this paper.

Learning Discriminative Signed Distance Functions from Multi-scale Level-of-detail Features for 3D Anomaly Detection Towards Total Recall in Industrial Anomaly Detection

Reference 31

Resolution
verified exact
arxiv_id, observed 2026-05-09T05:55:31.293429Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-08T19:20:50.624344Z digest=sha256:4e75a3c6c6db1aa19cfe818ce276597f03f62c3e5a9269868865c041ff1d2298

Observation 56583071-0a7e-4923-82b1-6df89cfd8c71 · inbound

Benchmark AUC Is Not Deployable Reliability: A Cross-Dataset Audit of Off-the-Shelf Features for Surveillance Video Anomaly Detection cites this paper.

Benchmark AUC Is Not Deployable Reliability: A Cross-Dataset Audit of Off-the-Shelf Features for Surveillance Video Anomaly Detection Towards Total Recall in Industrial Anomaly Detection

Reference 8

Resolution
metadata mismatch
arxiv_id, observed 2026-06-30T07:34:21.596905Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-06-30T07:29:32.843624Z digest=sha256:26d82b632681c321259b0a255acadb3489e23e6fcf91e5a1a4a8ac5ded181e28