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Paper Citation Record · LEDGER

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

As of 14 August 2026, this Paper Citation Record lists 61 of 61 outbound references and 0 inbound Pith citation observations for arXiv:2508.14504.

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

pith.paper-citation-record.v1
2508.14504 v1

Coverage vector

measured 61 of 61 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T18:34:09.861254Z

measured 61 of 61 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+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

61 of 61 outbound references displayed

  • verified exact17
  • verified fuzzy7
  • unresolved33
  • parse uncertain0
  • malformed identifier1
  • metadata mismatch3

External citation measurements

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Outbound references

Observation 9d96b2cf-78d7-4e8e-b5f1-72fa0c031761 · outbound

This paper cites Markatos, A.

PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments Markatos, A

Reference 1

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Observation e287f5a6-9ba9-43f2-9c3f-6c1c5cdf2b66 · outbound

This paper cites Montgomery, Introduction to statistical quality control, Eighth edition, Wiley, Hoboken, NJ, 2019.

PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments Montgomery, Introduction to statistical quality control, Eighth edition, Wiley, Hoboken, NJ, 2019

Reference 2

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Observation b7418850-216e-4209-9291-7cb8b080c291 · outbound

This paper cites Chandola, A.

PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments Chandola, A

Reference 3

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Observation 7c2dddd9-02f8-45b3-b120-c3ae5ab1af4f · outbound

This paper cites Chatterjee, R.

PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments Chatterjee, R

Reference 4

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Observation 5eef7902-3db9-430c-bf60-044e947314bb · outbound

This paper cites Deep Time Series Models: A Comprehensive Survey and Benchmark.

PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments Deep Time Series Models: A Comprehensive Survey and Benchmark

Reference 5

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Observation 3bfa73b2-1d96-4200-9f5b-4473b0ac90d3 · outbound

This paper cites Juerging, P.M.

PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments Juerging, P.M

Reference 6

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Observation 8ef6346c-3d51-471e-a186-dc7d1be70926 · outbound

This paper cites Greco, B.

PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments Greco, B

Reference 7

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Observation 077587d2-98f8-4071-8f38-d87d8f1a3d28 · outbound

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

PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments Can Multimodal Large Language Models be Guided to Improve Industrial Anomaly Detection?

Reference 8

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Observation 86ee23ec-3765-4b53-b342-cf088839e18a · outbound

This paper cites GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models.

PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models

Reference 9

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Observation 1a245522-9fc3-430d-b694-6c185869e8d3 · outbound

This paper cites Anomaly Detection for Industrial Applications, Its Challenges, Solutions, and Future Directions: A Review.

PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments Anomaly Detection for Industrial Applications, Its Challenges, Solutions, and Future Directions: A Review

Reference 10

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Observation e9482623-4ffd-4a97-a71f-f5314e2f3aee · outbound

This paper cites Abdullahi, K.

PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments Abdullahi, K

Reference 11

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Observation 1d1bc18e-b6dd-449f-a834-21e85dc310e7 · outbound

This paper cites Evaluating Large Language Models on Time Series Feature Understanding: A Comprehensive Taxonomy and Benchmark.

PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments Evaluating Large Language Models on Time Series Feature Understanding: A Comprehensive Taxonomy and Benchmark

Reference 12

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Observation 774dbb31-cda6-4826-93af-5a0ec2348e02 · outbound

This paper cites Raiaan, M.S.H.

PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments Raiaan, M.S.H

Reference 13

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Observation 1d795958-303e-4ac8-ac8a-1b2bf58114cd · outbound

This paper cites An Introduction to Vision-Language Modeling.

PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments An Introduction to Vision-Language Modeling

Reference 14

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Observation 551d69e5-f6f2-41e2-bec9-73067d7a4543 · outbound

This paper cites AnomalyGPT: Detecting Industrial Anomalies Using Large Vision-Language Models.

PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments AnomalyGPT: Detecting Industrial Anomalies Using Large Vision-Language Models

Reference 15

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source=pdf_text observed=2026-08-05T18:34:03.853482Z digest=sha256:fc6cecceb331da9aabdbd4bf2ccfbba911e202fa9a0fe7f9aacef46dfbb13877

Observation c2d07beb-2121-4723-a844-1c3fe468a619 · outbound

This paper cites Wood (Ed.), Principles of quality costs: Financial measures for strategic implementation of quality management, ASQ Quality Press, Milwaukee, Wisconsin, 2013.

PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments Wood (Ed.), Principles of quality costs: Financial measures for strategic implementation of quality management, ASQ Quality Press, Milwaukee, Wisconsin, 2013

Reference 16

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation 0e577d4c-3641-4608-a140-2c06be68ee97 · outbound

This paper cites Qiu, Statistical Process Control Charts as a Tool for Analyzing Big Data, in: S.E.

PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments Qiu, Statistical Process Control Charts as a Tool for Analyzing Big Data, in: S.E

Reference 17

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Observation 791ad67c-c866-410a-bb85-0c8261bc0861 · outbound

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PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments Unresolved cited work

Reference 18

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Observation a191ec26-75fb-4c49-a681-66ae267f0b0f · outbound

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PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments Unresolved cited work

Reference 19

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Observation 09c6cdc8-76a1-4db7-8f42-42f0841bd121 · outbound

This paper cites GPT-4 Technical Report.

PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments GPT-4 Technical Report

Reference 20

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Observation 097b68f0-659b-4101-8481-ef9810bc19e3 · outbound

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

PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments MMAD: A Comprehensive Benchmark for Multimodal Large Language Models in Industrial Anomaly Detection

Reference 21

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Observation 9b8eb699-ebeb-4b0a-871b-0ffef88133f8 · outbound

This paper cites Surbier, G.

PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments Surbier, G

Reference 22

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Observation 2cdbd467-97ae-4b47-8f8b-ca5eca908d37 · outbound

This paper cites Kernan Freire, LLM-Powered Cognitive Assistants for Knowledge Sharing among Factory Operators, 2025.

PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments Kernan Freire, LLM-Powered Cognitive Assistants for Knowledge Sharing among Factory Operators, 2025

Reference 23

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Observation 921c7448-a11e-495f-ba74-c100d98e045d · outbound

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PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments Unresolved cited work

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Observation 542d2c55-a935-4590-ac06-4abb189b150f · outbound

This paper cites Weiss, T.M.

PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments Weiss, T.M

Reference 25

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Observation 406aab86-0228-42cc-9947-46adedc833fd · outbound

This paper cites Johnson, S.R.

PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments Johnson, S.R

Reference 26

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Observation 93b8d420-e051-4a86-9388-d385615fccde · outbound

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PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments Unresolved cited work

Reference 27

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Observation 3b48310f-e450-493d-9679-6344e50a7834 · outbound

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PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments Unresolved cited work

Reference 28

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Observation e24d145e-0ce0-49ed-ab7f-be9fa36dc8af · outbound

This paper cites Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey.

PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey

Reference 29

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Observation 2f3b6a0b-2b47-406e-a890-47fac8dc7720 · outbound

This paper cites Parameter-Efficient Transfer Learning for NLP.

PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments Parameter-Efficient Transfer Learning for NLP

Reference 30

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Observation e40e9949-b502-4120-a65e-378041878667 · outbound

This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments LoRA: Low-Rank Adaptation of Large Language Models

Reference 31

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Observation f97ba744-af0d-4f37-8d7b-63281d83f1be · outbound

This paper cites Parameter-Efficient Fine-Tuning Methods for Pretrained Language Models: A Critical Review and Assessment.

PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments Parameter-Efficient Fine-Tuning Methods for Pretrained Language Models: A Critical Review and Assessment

Reference 32

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Observation cb8a1802-c477-4ac9-96f1-4d5398e351fa · outbound

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PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments Language Models are Few-Shot Learners

Reference 33

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Observation f22d359f-7324-41f7-a370-e84fdc635e55 · outbound

This paper cites The Prompt Report: A Systematic Survey of Prompt Engineering Techniques.

PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments The Prompt Report: A Systematic Survey of Prompt Engineering Techniques

Reference 34

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Observation 2776cae5-9209-482f-8f80-78fc2a8620e8 · outbound

This paper cites FD-LLM: Large Language Model for Fault Diagnosis of Machines.

PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments FD-LLM: Large Language Model for Fault Diagnosis of Machines

Reference 35

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Observation 97140fec-ddb6-457f-acb8-488b05307090 · outbound

This paper cites FaultGPT: Industrial Fault Diagnosis Question Answering System by Vision Language Models.

PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments FaultGPT: Industrial Fault Diagnosis Question Answering System by Vision Language Models

Reference 36

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local_arxiv, observed 2026-08-05T18:34:12.894833Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-05T18:34:06.477288Z digest=sha256:acdd138111fc835f61998a2989c212797faba793625ee0f07522f4300ea73740

Observation 1fcd6ea0-8964-41fd-b227-d8425d5ebd32 · outbound

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

PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments Myriad: Large Multimodal Model by Applying Vision Experts for Industrial Anomaly Detection

Reference 37

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source=pdf_text observed=2026-08-05T18:34:06.605200Z digest=sha256:5fe9e9d51bba2e108526a5f345d88bbbab1c83632f5cdaabe1e8532193d70728

Observation ab63906e-1ca4-4550-b76b-e756c0e56482 · outbound

This paper cites Refining Time Series Anomaly Detectors using Large Language Models.

PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments Refining Time Series Anomaly Detectors using Large Language Models

Reference 38

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local_arxiv, observed 2026-08-05T18:34:12.593574Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-05T18:34:06.760293Z digest=sha256:906a7e0df15f99f58d80a29cef301e68df7a3ace0b9911a50975d06ed8c89efb

Observation 70fa2853-9e2f-46c6-be5a-8a549e75f9b0 · outbound

This paper cites Singh, J.

PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments Singh, J

Reference 39

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-05T18:34:06.953779Z digest=sha256:759f649085f78ad9cfe3b7daa937bba4ea75d438b30bd3330bbee54dfd1009c9

Observation 2866a241-abb5-4332-b2b3-eaf3576542aa · outbound

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

PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments WinCLIP: Zero-/Few-Shot Anomaly Classification and Segmentation

Reference 40

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source=pdf_text observed=2026-08-05T18:34:07.112109Z digest=sha256:0e1c55c85488d275d9e5961ad3e38b31317abf076bd832bc5e3fffdad9c5407c

Observation 8417cf78-986d-4442-a3e1-961500552084 · outbound

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

PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments Learning Transferable Visual Models From Natural Language Supervision

Reference 41

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source=pdf_text observed=2026-08-05T18:34:07.291423Z digest=sha256:bb7fe5db6e7b25597ca0e72bfb76edac13d4c07c5f28112fb166f890ff4d0d42

Observation 28b65a82-0884-4a7e-bbb2-1d46249a8393 · outbound

This paper cites Do LLMs Understand Visual Anomalies? Uncovering LLM's Capabilities in Zero-shot Anomaly Detection.

PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments Do LLMs Understand Visual Anomalies? Uncovering LLM's Capabilities in Zero-shot Anomaly Detection

Reference 42

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local_arxiv, observed 2026-08-05T18:34:12.245959Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-05T18:34:07.405592Z digest=sha256:d777791a0f85a35a64a03ca8a910299137a6a1c662d084af92fb29154111fb4d

Observation 32100dff-0c70-47ce-9a57-9141d6ad07bd · outbound

This paper cites Customizing Visual-Language Foundation Models for Multi-modal Anomaly Detection and Reasoning.

PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments Customizing Visual-Language Foundation Models for Multi-modal Anomaly Detection and Reasoning

Reference 43

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source=pdf_text observed=2026-08-05T18:34:07.559247Z digest=sha256:ba5c3e30c8a4fe5ff3afa21c48d40e3bec7c4194d8b73c669533f41f043a2fe7

Observation 3fc88e15-46a4-4352-96fc-09cb36e0791e · outbound

This paper cites Schiele, D.

PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments Schiele, D

Reference 44

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doi, observed 2026-08-05T18:34:11.926848Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-05T18:34:07.720828Z digest=sha256:0c994701e6573a88f0a52f1c5e28d9a4b2e2b4425fedd200e08061caea741610

Observation 7c7e21bd-9d2d-4675-bf02-bc0d2d84993c · outbound

This paper cites RAAD-LLM: Adaptive Anomaly Detection Using LLMs and RAG Integration.

PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments RAAD-LLM: Adaptive Anomaly Detection Using LLMs and RAG Integration

Reference 45

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local_arxiv, observed 2026-08-05T18:34:11.600049Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-05T18:34:07.801431Z digest=sha256:87be1dfdba2641bc2d88aa3f8f2bd78fe02ea22724f996f41c28d93ee1952b53

Observation 17616937-c6e3-42e4-bcd9-02074a26b11f · outbound

This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 46

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source=pdf_text observed=2026-08-05T18:34:07.987394Z digest=sha256:bd522fdee3c11393715cbbe8bbce2cf84e0fee2512332d91eb705ad9ecb7c407

Observation e4d103c0-3013-4fc0-9d5b-5af2ada04aa6 · outbound

This paper cites JSONSchemaBench: A Rigorous Benchmark of Structured Outputs for Language Models.

PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments JSONSchemaBench: A Rigorous Benchmark of Structured Outputs for Language Models

Reference 47

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source=pdf_text observed=2026-08-05T18:34:08.136452Z digest=sha256:8715f673fc22b7d9f918edd3b4849c280a73da8876ff7f74fd3e30decc59b73c

Observation 6ffd3139-981e-4c80-9793-409b3dbd6bfb · outbound

This paper cites https://platform.openai.com/docs/guides/text?api-mode=responses (accessed 28 May 2025).

PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments https://platform.openai.com/docs/guides/text?api-mode=responses (accessed 28 May 2025)

Reference 48

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raw_fallback, observed 2026-08-05T18:34:17.201415Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-05T18:34:08.263886Z digest=sha256:97b0a3a6fef141e96e3b0a1bd8dcd1376de80dc01eb951a2c61f654168bea007

Observation 864b68b9-9a8c-47d0-a396-600252e3b347 · outbound

This paper cites https://docs.anthropic.com/en/docs/build-with-claude/prompt-engineering/overview (accessed 28 May 2025).

PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments https://docs.anthropic.com/en/docs/build-with-claude/prompt-engineering/overview (accessed 28 May 2025)

Reference 49

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raw_fallback, observed 2026-08-05T18:34:16.944552Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-05T18:34:08.437280Z digest=sha256:9d85195602059dc8dbf3a11551f78a00fec4f4e349a21471983913751c2b0113

Observation 5870e732-a313-4c65-9f0a-ea786b08a4d1 · outbound

This paper cites Meier, S.

PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments Meier, S

Reference 50

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doi, observed 2026-08-05T18:34:11.300505Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-05T18:34:08.523131Z digest=sha256:bf545eab3d70b14839316d8668d4cfcbb38c363d8282f27393e8eb39015c2b00

Observation 31d882e8-d82d-4565-8646-01af8f47a0ff · outbound

This paper cites Bergmann, K.

PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments Bergmann, K

Reference 51

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source=pdf_text observed=2026-08-05T18:34:08.660382Z digest=sha256:846b9aa1815dd57a85948d24b0dcaed9616d8f54d90874717a32e23c915d5fdc

Observation 605f3eb8-ebf1-445e-8148-6b0562e62c21 · outbound

This paper cites Heckler-Kram, J.-H.

PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments Heckler-Kram, J.-H

Reference 52

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doi, observed 2026-08-05T18:34:11.028594Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-05T18:34:08.832839Z digest=sha256:e58d62d4cbb75523ad66024ffeedbdc10c6d2253daa476858aafdf508e8e9f40

Observation a50ac56b-33c9-4aea-9b8b-3fdf593cce3f · outbound

This paper cites Scheck, Stripped Wire Dataset, Zenodo, 2025.

PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments Scheck, Stripped Wire Dataset, Zenodo, 2025

Reference 53

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doi, observed 2026-08-05T18:34:10.680860Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-05T18:34:08.994180Z digest=sha256:7c8411ab5b5807f0440eca5297e16a7bb55acbb3c13f5a0cb93fadd2d37519f2

Observation 414790c6-6723-4647-bf7e-1baa3074876f · outbound

This paper cites https://doi.org/10.7910/DVN/WBDKN6.

PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments https://doi.org/10.7910/DVN/WBDKN6

Reference 54

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T18:34:09.091301Z digest=sha256:13ddaa2527800e05acc6caa21a99a266f43b367165c391c44364f2e1f604ea58

Observation eab90f6e-7dc7-46d4-bd0a-e035a6aa3a51 · outbound

This paper cites Bruhin US 2010/0139351 A1, 2009.

PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments Bruhin US 2010/0139351 A1, 2009

Reference 55

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no resolver link, observed 2026-08-05T18:34:09.264529Z

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source=pdf_text observed=2026-08-05T18:34:09.264529Z digest=sha256:b0f2e3d2d68b29292fd966e153382699902715a6b3c2c189a1f8f2f64ed7fb5f

Observation f3c70d7d-771f-4a18-9a2a-574d4cc9174a · outbound

This paper cites Towards transparent and data-driven fault detection in manufacturing: A case study on univariate, discrete time series.

PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments Towards transparent and data-driven fault detection in manufacturing: A case study on univariate, discrete time series

Reference 56

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local_arxiv, observed 2026-08-05T18:34:10.390434Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-05T18:34:09.373922Z digest=sha256:c3ed18fdb8284889a7fdc59deb7821c9f8972d36f350b1632e65271cd962f61c

Observation 42856a3c-5a5d-45a0-9624-d228685dae1c · outbound

This paper cites Hofmann, A.

PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments Hofmann, A

Reference 57

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doi, observed 2026-08-05T18:34:10.129667Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-05T18:34:09.465687Z digest=sha256:5d10b54afeca38233d8af93e757bf0e4e2ddd09750191386cfbe1d4fc9ed63ac

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

This paper cites Towards Total Recall in Industrial Anomaly Detection.

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

Reference 58

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T18:34:09.593045Z digest=sha256:20f1c46f0de700756a1f16a62f62901e6f2ceae9e23f3e50afcb236b876315a1

Observation 31194705-677f-4e24-8d77-b0b780e7e513 · outbound

This paper cites Liu, K.M.

PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments Liu, K.M

Reference 59

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no resolver link, observed 2026-08-05T18:34:09.738409Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T18:34:09.738409Z digest=sha256:51d537528cfb6467ee62defc84c16c86e394deee5fce1ec4eea5438d7b3a9928

Observation afe43cdd-8143-42a1-9834-2149fd8f41c2 · outbound

This paper cites https://scikit- learn.org/stable/modules/generated/sklearn.ensemble.IsolationForest.html (accessed 23 June 2025).

PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments https://scikit- learn.org/stable/modules/generated/sklearn.ensemble.IsolationForest.html (accessed 23 June 2025)

Reference 60

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raw_fallback, observed 2026-08-05T18:34:16.699340Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-05T18:34:09.861254Z digest=sha256:46752cef2e0c12110f8c589012513883c0401760126ed328c1a0f33734dc7c19

Observation a424fc26-dd71-48cc-9493-fedc02fb152d · outbound

This paper cites an unresolved cited work.

PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments Unresolved cited work

Reference 214

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no resolver link, observed 2026-08-05T18:34:05.328680Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T18:34:05.328680Z digest=sha256:6bf24feff8c9f92abc667d856faab7d88129555abc047dfbfc7e0154d6044391

Pith citing papers

No inbound Pith citation observations are available.