Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-05T18:34:09.861254Z
Paper Citation Record · LEDGER
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.
Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-05T18:34:09.861254Z
One-hop event checks from named stored sources.
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Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
61 of 61 outbound references displayed
External citation measurements
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Observation 9d96b2cf-78d7-4e8e-b5f1-72fa0c031761 · outbound
Reference 1
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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
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PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments Chatterjee, R
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PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments Deep Time Series Models: A Comprehensive Survey and Benchmark
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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?
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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
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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
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PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments Raiaan, M.S.H
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PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments An Introduction to Vision-Language Modeling
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PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments AnomalyGPT: Detecting Industrial Anomalies Using Large Vision-Language Models
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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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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
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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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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
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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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PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments Unresolved cited work
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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
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PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments Parameter-Efficient Transfer Learning for NLP
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PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments LoRA: Low-Rank Adaptation of Large Language Models
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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
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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
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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
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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
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PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments Refining Time Series Anomaly Detectors using Large Language Models
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PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments Learning Transferable Visual Models From Natural Language Supervision
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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
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