Paper Citation Record · LEDGER

Detect, Classify, Act: Categorizing Industrial Anomalies with Multi-Modal Large Language Models

As of 17 August 2026, this Paper Citation Record lists 28 of 28 outbound references and 0 inbound Pith citation observations for arXiv:2505.02626.

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

pith.paper-citation-record.v1
2505.02626 v1

Coverage vector

measured 28 of 28 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T00:53:10.556767Z

measured 28 of 28 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 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

28 of 28 outbound references displayed

verified exact0
verified fuzzy11
unresolved17
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 8f22004e-6fb7-4b3b-aa9f-426eb3919655 · outbound

This paper cites GPT-4 Technical Report.

Detect, Classify, Act: Categorizing Industrial Anomalies with Multi-Modal Large Language Models GPT-4 Technical Report

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-16T00:53:10.428437Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 19ae2a17-8557-450c-b4f4-c6f0d7e17469 · outbound

This paper cites Qwen Technical Report.

Detect, Classify, Act: Categorizing Industrial Anomalies with Multi-Modal Large Language Models Qwen Technical Report

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-16T00:53:10.435433Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fb074dc4-2c10-4597-bae5-3421160283cd · outbound

This paper cites Efficien- tad: Accurate visual anomaly detection at millisecond-level latencies.

Detect, Classify, Act: Categorizing Industrial Anomalies with Multi-Modal Large Language Models Efficien- tad: Accurate visual anomaly detection at millisecond-level latencies

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-16T00:53:10.440865Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8c3c44f6-1378-4128-afd6-9175ac4e8792 · outbound

This paper cites Mvtec ad–a comprehensive real-world dataset for unsupervised anomaly detection.

Detect, Classify, Act: Categorizing Industrial Anomalies with Multi-Modal Large Language Models Mvtec ad–a comprehensive real-world dataset for unsupervised anomaly detection

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:53:11.101067Z

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.

Observation 05c04de9-8391-4ea2-98d7-d6e74f409499 · outbound

This paper cites Grounding everything: Emerging localiza- tion properties in vision-language transformers.

Detect, Classify, Act: Categorizing Industrial Anomalies with Multi-Modal Large Language Models Grounding everything: Emerging localiza- tion properties in vision-language transformers

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-16T00:53:10.450902Z

Source-reported events for the cited work

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Observation b8c6bf1a-613e-4c18-b273-4d951b9d651f · outbound

This paper cites CLIP-AD: A Language-Guided Staged Dual-Path Model for Zero-shot Anomaly Detection.

Detect, Classify, Act: Categorizing Industrial Anomalies with Multi-Modal Large Language Models CLIP-AD: A Language-Guided Staged Dual-Path Model for Zero-shot Anomaly Detection

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-16T00:53:10.455725Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bb840ccc-d35f-42d5-a4c1-65ce3659c9cd · outbound

This paper cites Can Multimodal Large Language Models be Guided to Improve Industrial Anomaly Detection?.

Detect, Classify, Act: Categorizing Industrial Anomalies with Multi-Modal Large Language Models Can Multimodal Large Language Models be Guided to Improve Industrial Anomaly Detection?

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-16T00:53:10.461102Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 72558bc2-114d-4627-8963-44dd327b2198 · outbound

This paper cites Sub-Image Anomaly Detection with Deep Pyramid Correspondences.

Detect, Classify, Act: Categorizing Industrial Anomalies with Multi-Modal Large Language Models Sub-Image Anomaly Detection with Deep Pyramid Correspondences

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-16T00:53:10.466230Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 929844b0-57b4-4be4-ac9c-0fd404cb40bd · outbound

This paper cites Padim: a patch distribution modeling framework for anomaly detection and localization.

Detect, Classify, Act: Categorizing Industrial Anomalies with Multi-Modal Large Language Models Padim: a patch distribution modeling framework for anomaly detection and localization

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:53:11.075813Z

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.

Observation 6234b5a7-72d8-475b-bc8a-39b9bf88540f · outbound

This paper cites Anomaly detection via reverse distillation from one-class embedding.

Detect, Classify, Act: Categorizing Industrial Anomalies with Multi-Modal Large Language Models Anomaly detection via reverse distillation from one-class embedding

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:53:11.061030Z

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.

Observation b16f3523-4d28-410e-9df5-a39d39058c52 · outbound

This paper cites Visual Prompt Engineering for Vision Language Models in Radiology.

Detect, Classify, Act: Categorizing Industrial Anomalies with Multi-Modal Large Language Models Visual Prompt Engineering for Vision Language Models in Radiology

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-16T00:53:10.480611Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e00c8ebc-facb-4185-a63a-6aa8bc43bc1b · outbound

This paper cites Diffusion for out-of-distribution detection on road scenes and beyond.

Detect, Classify, Act: Categorizing Industrial Anomalies with Multi-Modal Large Language Models Diffusion for out-of-distribution detection on road scenes and beyond

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:53:11.045658Z

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.

Observation e716f91f-2249-4770-ba05-94f1c2351131 · outbound

This paper cites Anomalygpt: Detecting in- dustrial anomalies using large vision-language models.

Detect, Classify, Act: Categorizing Industrial Anomalies with Multi-Modal Large Language Models Anomalygpt: Detecting in- dustrial anomalies using large vision-language models

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:53:11.029707Z

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.

Observation ecfc72d8-c568-451b-ab9b-3089369d0f9c · outbound

This paper cites Deep residual learning for image recognition.

Detect, Classify, Act: Categorizing Industrial Anomalies with Multi-Modal Large Language Models Deep residual learning for image recognition

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-16T00:53:10.495081Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 19466117-0420-41a7-a9ae-fa1e13623618 · outbound

This paper cites Denoising dif- fusion probabilistic models.

Detect, Classify, Act: Categorizing Industrial Anomalies with Multi-Modal Large Language Models Denoising dif- fusion probabilistic models

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-16T00:53:10.498922Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2696da60-fd7e-4035-8bfe-f714491c0308 · outbound

This paper cites Winclip: Zero- /few-shot anomaly classification and segmentation.

Detect, Classify, Act: Categorizing Industrial Anomalies with Multi-Modal Large Language Models Winclip: Zero- /few-shot anomaly classification and segmentation

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:53:10.993624Z

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.

Observation f3e2450b-a3f9-46c8-ba3f-97c8aeec166f · outbound

This paper cites MMAD: A Comprehensive Benchmark for Multimodal Large Language Models in Industrial Anomaly Detection.

Detect, Classify, Act: Categorizing Industrial Anomalies with Multi-Modal Large Language Models MMAD: A Comprehensive Benchmark for Multimodal Large Language Models in Industrial Anomaly Detection

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-16T00:53:10.506682Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation de4d18fc-794c-4e48-9ccd-26dcbedf570e · outbound

This paper cites Myriad: Large Multimodal Model by Applying Vision Experts for Industrial Anomaly Detection.

Detect, Classify, Act: Categorizing Industrial Anomalies with Multi-Modal Large Language Models Myriad: Large Multimodal Model by Applying Vision Experts for Industrial Anomaly Detection

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-16T00:53:10.511228Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 50531d14-7d13-4149-9a88-207c25f3de71 · outbound

This paper cites Mcad: Multi- classification anomaly detection with relational knowledge distillation.

Detect, Classify, Act: Categorizing Industrial Anomalies with Multi-Modal Large Language Models Mcad: Multi- classification anomaly detection with relational knowledge distillation

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:53:10.978030Z

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.

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

This paper cites Anomaly Detection with Conditioned Denoising Diffusion Models.

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 333921ff-b339-4d1b-927a-e504f8bb9ef1 · outbound

This paper cites Henriques, and Fatma G¨uney.

Detect, Classify, Act: Categorizing Industrial Anomalies with Multi-Modal Large Language Models Henriques, and Fatma G¨uney

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:53:10.962374Z

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.

Observation 1e5cb4d9-5e10-4c38-9fba-ba2a804fb4bf · outbound

This paper cites Learning transferable visual models from natural language supervi- sion.

Detect, Classify, Act: Categorizing Industrial Anomalies with Multi-Modal Large Language Models Learning transferable visual models from natural language supervi- sion

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-16T00:53:10.528943Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ed7c24ff-faf9-434c-b4fe-7ab0996f4758 · outbound

This paper cites Towards to- tal recall in industrial anomaly detection.

Detect, Classify, Act: Categorizing Industrial Anomalies with Multi-Modal Large Language Models Towards to- tal recall in industrial anomaly detection

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:53:10.937675Z

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.

Observation badd013d-c9fe-469e-b0ed-eddcbd35eca0 · outbound

This paper cites Score-Based Generative Modeling through Stochastic Differential Equations.

Detect, Classify, Act: Categorizing Industrial Anomalies with Multi-Modal Large Language Models Score-Based Generative Modeling through Stochastic Differential Equations

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-16T00:53:10.537667Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2a66e9b5-83c5-49f5-9104-52803accadbf · outbound

This paper cites Diffusion models for medical anomaly detection.

Detect, Classify, Act: Categorizing Industrial Anomalies with Multi-Modal Large Language Models Diffusion models for medical anomaly detection

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-16T00:53:10.543388Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bc40edf5-f17a-401c-98e7-e453634a4494 · outbound

This paper cites Unsupervised surface anomaly detec- tion with diffusion probabilistic model.

Detect, Classify, Act: Categorizing Industrial Anomalies with Multi-Modal Large Language Models Unsupervised surface anomaly detec- tion with diffusion probabilistic model

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:53:10.913097Z

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.

Observation ef318068-f0e7-4625-90a2-98f7369ea18d · outbound

This paper cites Anomalyclip: Object-agnostic prompt learn- 9 ing for zero-shot anomaly detection.

Detect, Classify, Act: Categorizing Industrial Anomalies with Multi-Modal Large Language Models Anomalyclip: Object-agnostic prompt learn- 9 ing for zero-shot anomaly detection

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-16T00:53:10.552326Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f5e50f01-47c9-4394-8a4d-da8f5c906f7d · outbound

This paper cites Spot-the-difference self-supervised pre- training for anomaly detection and segmentation.

Detect, Classify, Act: Categorizing Industrial Anomalies with Multi-Modal Large Language Models Spot-the-difference self-supervised pre- training for anomaly detection and segmentation

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:53:10.897170Z

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.

Pith citing papers

No inbound Pith citation observations are available.