Foundation Models for Anomaly Detection: Vision and Challenges | Paper Citation Record

Observation 279c55b7-a2cd-413a-8dc6-06249d5604df · outbound

This paper cites Large language models can be zero-shot anomaly detectors for time series?.

Foundation Models for Anomaly Detection: Vision and Challenges Large language models can be zero-shot anomaly detectors for time series?

Reference 1

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Observation 4e15b24e-4de0-4122-90a1-540f486638d0 · outbound

This paper cites Gpt-lad: Leveraging large multimodal models for logical anomaly detection.

Foundation Models for Anomaly Detection: Vision and Challenges Gpt-lad: Leveraging large multimodal models for logical anomaly detection

Reference 2

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Observation d5853aec-99a6-40ee-99b6-5387eb25cc24 · outbound

This paper cites Advancing anomaly detection: Non-semantic financial data encoding with llms.

Foundation Models for Anomaly Detection: Vision and Challenges Advancing anomaly detection: Non-semantic financial data encoding with llms

Reference 3

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Observation 41fcd9d9-1dce-40ce-84f3-a9d4d006b12b · outbound

This paper cites On the Opportunities and Risks of Foundation Models.

Foundation Models for Anomaly Detection: Vision and Challenges On the Opportunities and Risks of Foundation Models

Reference 4

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Observation c52c455b-464a-405d-b7cd-e0d370962658 · outbound

This paper cites Domain-controlled prompt learn- ing.

Foundation Models for Anomaly Detection: Vision and Challenges Domain-controlled prompt learn- ing

Reference 5

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Observation b0265631-0ec9-450f-a1d4-57761cbef82d · outbound

This paper cites Spiced: Syntactical bug and trojan pattern identification in a/ms circuits using llm-enhanced detection.

Foundation Models for Anomaly Detection: Vision and Challenges Spiced: Syntactical bug and trojan pattern identification in a/ms circuits using llm-enhanced detection

Reference 6

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Observation b067b791-ffcf-41a1-9bd4-d7d28d8a957d · outbound

This paper cites Can LLMs Serve As Time Series Anomaly Detectors?.

Foundation Models for Anomaly Detection: Vision and Challenges Can LLMs Serve As Time Series Anomaly Detectors?

Reference 7

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Observation 7627b04b-cc37-4b18-81da-ec9c37b9d7de · outbound

This paper cites Llm-botguard: A novel framework for detecting llm-driven bots with mixture of experts and graph neural networks.

Foundation Models for Anomaly Detection: Vision and Challenges Llm-botguard: A novel framework for detecting llm-driven bots with mixture of experts and graph neural networks

Reference 8

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Observation e9df5fcd-fcf0-4323-903c-f0a06a1aa374 · outbound

This paper cites Semantic anomaly detection with large language mod- els.

Foundation Models for Anomaly Detection: Vision and Challenges Semantic anomaly detection with large language mod- els

Reference 9

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Observation ae677f81-9f98-4288-8c54-c8d33364e140 · outbound

This paper cites Anomalygpt: Detecting industrial anomalies using large vision-language mod- els.

Foundation Models for Anomaly Detection: Vision and Challenges Anomalygpt: Detecting industrial anomalies using large vision-language mod- els

Reference 10

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Observation bcb3e551-a51b-45ad-8629-006cbd3dd6df · outbound

This paper cites Dabl: Detecting semantic anomalies in business processes using large language models.

Foundation Models for Anomaly Detection: Vision and Challenges Dabl: Detecting semantic anomalies in business processes using large language models

Reference 11

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Observation 2a9ef954-de9d-4436-919b-22d13ddb2ea5 · outbound

This paper cites Anomaly detection on unstable logs with gpt models.

Foundation Models for Anomaly Detection: Vision and Challenges Anomaly detection on unstable logs with gpt models

Reference 12

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Observation fdb85e0c-2e15-495a-b1c5-393c7a7406e0 · outbound

This paper cites Adapting visual-language models for generalizable anomaly detection in medical images.

Foundation Models for Anomaly Detection: Vision and Challenges Adapting visual-language models for generalizable anomaly detection in medical images

Reference 13

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Observation 9c7357c7-100b-4257-b6a2-cd5b522866cb · outbound

This paper cites Win- clip: Zero-/few-shot anomaly classification and segmen- tation.

Foundation Models for Anomaly Detection: Vision and Challenges Win- clip: Zero-/few-shot anomaly classification and segmen- tation

Reference 14

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Observation a44d54cc-a8af-4826-ab03-9ef05d320407 · outbound

This paper cites Logicad: Explainable anomaly detection via vlm-based text feature extraction.

Foundation Models for Anomaly Detection: Vision and Challenges Logicad: Explainable anomaly detection via vlm-based text feature extraction

Reference 15

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Source-reported events for the cited work

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Observation 4bde0f0a-ea68-4127-a443-b049199f4bfd · outbound

This paper cites Unsupervised video anomaly detection based on similarity with predefined text descriptions.

Foundation Models for Anomaly Detection: Vision and Challenges Unsupervised video anomaly detection based on similarity with predefined text descriptions

Reference 16

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Source-reported events for the cited work

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Observation e8a3e4c5-160a-48c3-b213-42ec5b308588 · outbound

This paper cites The power of scale for parameter-efficient prompt tuning.

Foundation Models for Anomaly Detection: Vision and Challenges The power of scale for parameter-efficient prompt tuning

Reference 17

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Source-reported events for the cited work

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Observation ba577baa-3681-488b-9136-ad8b3a75b6fb · outbound

This paper cites Anomaly Detection of Tabular Data Using LLMs.

Foundation Models for Anomaly Detection: Vision and Challenges Anomaly Detection of Tabular Data Using LLMs

Reference 18

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Observation bf4fa0cc-04bc-4ff3-b45f-41790c781bbb · outbound

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

Foundation Models for Anomaly Detection: Vision and Challenges Myriad: Large Multimodal Model by Applying Vision Experts for Industrial Anomaly Detection

Reference 19

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Observation e37d2a4a-1e44-467a-b77e-c1a57d683f85 · outbound

This paper cites A survey of graph meets large language model: Progress and future direc- tions.

Foundation Models for Anomaly Detection: Vision and Challenges A survey of graph meets large language model: Progress and future direc- tions

Reference 20

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Observation 17b54439-1b2c-45a5-ba88-dc1a7cd0480d · outbound

This paper cites Large language model guided knowl- edge distillation for time series anomaly detection.

Foundation Models for Anomaly Detection: Vision and Challenges Large language model guided knowl- edge distillation for time series anomaly detection

Reference 21

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Source-reported events for the cited work

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Observation 0be2aeed-bdb5-414f-93d4-9b43f5d6f70a · outbound

This paper cites Deep graph learning for anomalous citation detec- tion.

Foundation Models for Anomaly Detection: Vision and Challenges Deep graph learning for anomalous citation detec- tion

Reference 22

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Observation d9c50eb7-9e3a-4fa5-b198-e0a1c783d556 · outbound

This paper cites Large Language Models can Deliver Accurate and Interpretable Time Series Anomaly Detection.

Foundation Models for Anomaly Detection: Vision and Challenges Large Language Models can Deliver Accurate and Interpretable Time Series Anomaly Detection

Reference 23

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Source-reported events for the cited work

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Observation 6bb610b5-bb50-4b6e-bf17-4e769ed8e204 · outbound

This paper cites AnomalyLLM: Few-shot Anomaly Edge Detection for Dynamic Graphs using Large Language Models.

Foundation Models for Anomaly Detection: Vision and Challenges AnomalyLLM: Few-shot Anomaly Edge Detection for Dynamic Graphs using Large Language Models

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-08T16:34:14.331763Z

Source-reported events for the cited work

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Observation 39afeb22-d65c-4c73-9127-c8a62c18f1be · outbound

This paper cites Graph self-supervised learn- ing: A survey.

Foundation Models for Anomaly Detection: Vision and Challenges Graph self-supervised learn- ing: A survey

Reference 25

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 14942ef1-d42c-4587-a94f-b3954cce01f0 · outbound

This paper cites Unified-io: A uni- fied model for vision, language, and multi-modal tasks.

Foundation Models for Anomaly Detection: Vision and Challenges Unified-io: A uni- fied model for vision, language, and multi-modal tasks

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:34:14.836208Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 9dd87f14-31ec-485e-ae2f-e9a0b8f7ae5b · outbound

This paper cites Video Anomaly Detection and Explanation via Large Language Models.

Foundation Models for Anomaly Detection: Vision and Challenges Video Anomaly Detection and Explanation via Large Language Models

Reference 27

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Observation 51f8ebff-6a0c-47f0-a326-66567e61aadb · outbound

This paper cites Foundation models for generalist medical artificial intelligence.

Foundation Models for Anomaly Detection: Vision and Challenges Foundation models for generalist medical artificial intelligence

Reference 28

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation dff9feb1-6de7-4b7e-922c-6b9298579bf2 · outbound

This paper cites A decade survey of transfer learning (2010–2020).

Foundation Models for Anomaly Detection: Vision and Challenges A decade survey of transfer learning (2010–2020)

Reference 29

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Observation 28cf7d61-4070-4217-8474-f8a409d8c68e · outbound

This paper cites Enhancing Anomaly Detection in Financial Markets with an LLM-based Multi-Agent Framework.

Foundation Models for Anomaly Detection: Vision and Challenges Enhancing Anomaly Detection in Financial Markets with an LLM-based Multi-Agent Framework

Reference 30

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Observation 9ce9b361-44e2-4533-826c-014524dc8f52 · outbound

This paper cites Learning transferable visual models from natural lan- guage supervision.

Foundation Models for Anomaly Detection: Vision and Challenges Learning transferable visual models from natural lan- guage supervision

Reference 31

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Observation d3b53c0d-0ba8-456e-87e6-d063951ad016 · outbound

This paper cites an unresolved cited work.

Foundation Models for Anomaly Detection: Vision and Challenges Unresolved cited work

Reference 32

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Observation db35f6b6-27c4-4e45-bc1f-424ddc5de10a · outbound

This paper cites Graph learning for anomaly ana- lytics: Algorithms, applications, and challenges.

Foundation Models for Anomaly Detection: Vision and Challenges Graph learning for anomaly ana- lytics: Algorithms, applications, and challenges

Reference 33

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Observation a451a121-1508-4612-90c3-f11367d47711 · outbound

This paper cites Deep video anomaly detection: Opportunities and challenges.

Foundation Models for Anomaly Detection: Vision and Challenges Deep video anomaly detection: Opportunities and challenges

Reference 34

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Observation 07f566d2-de8f-40bc-850a-ea755e34e18f · outbound

This paper cites Aad-llm: Adaptive anomaly detec- tion using large language models.

Foundation Models for Anomaly Detection: Vision and Challenges Aad-llm: Adaptive anomaly detec- tion using large language models

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:34:14.778456Z

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Observation 701053d5-2712-44e0-8bce-27d1104b8653 · outbound

This paper cites Face it yourselves: An llm-based two-stage strategy to localize configuration errors via logs.

Foundation Models for Anomaly Detection: Vision and Challenges Face it yourselves: An llm-based two-stage strategy to localize configuration errors via logs

Reference 36

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Observation 71411c01-0386-48bb-8aa0-4d93bb9cfb7e · outbound

This paper cites Real-time anomaly detection and reactive plan- ning with large language models.

Foundation Models for Anomaly Detection: Vision and Challenges Real-time anomaly detection and reactive plan- ning with large language models

Reference 37

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Observation 99d16913-392a-40ca-8992-453dc67c7ea4 · outbound

This paper cites Audit-LLM: Multi-Agent Collaboration for Log-based Insider Threat Detection.

Foundation Models for Anomaly Detection: Vision and Challenges Audit-LLM: Multi-Agent Collaboration for Log-based Insider Threat Detection

Reference 38

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Observation 676eab65-3464-41b0-a38b-4669e9d44264 · outbound

This paper cites Large Language Models for Forecasting and Anomaly Detection: A Systematic Literature Review.

Foundation Models for Anomaly Detection: Vision and Challenges Large Language Models for Forecasting and Anomaly Detection: A Systematic Literature Review

Reference 39

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Observation 4232b27c-c293-40b9-be09-6c27fa52ebcf · outbound

This paper cites Large Language Models for Anomaly and Out-of-Distribution Detection: A Survey.

Foundation Models for Anomaly Detection: Vision and Challenges Large Language Models for Anomaly and Out-of-Distribution Detection: A Survey

Reference 40

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Observation 0932b39a-2662-47ae-9722-59e93fb2907f · outbound

This paper cites Follow the rules: reasoning for video anomaly detection with large language mod- els.

Foundation Models for Anomaly Detection: Vision and Challenges Follow the rules: reasoning for video anomaly detection with large language mod- els

Reference 41

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Observation 7c3e9fcc-20e5-4e61-930f-1ed57db1c58b · outbound

This paper cites Harnessing large lan- guage models for training-free video anomaly detec- tion.

Foundation Models for Anomaly Detection: Vision and Challenges Harnessing large lan- guage models for training-free video anomaly detec- tion

Reference 42

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Observation 6fce0573-a35f-42ff-bd91-5f726c3acf99 · outbound

This paper cites Holmes-VAD: Towards Unbiased and Explainable Video Anomaly Detection via Multi-modal LLM.

Foundation Models for Anomaly Detection: Vision and Challenges Holmes-VAD: Towards Unbiased and Explainable Video Anomaly Detection via Multi-modal LLM

Reference 43

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Observation bc61c781-adcd-4d8a-a1f3-b4a70395cba5 · outbound

This paper cites Transfer adaptation learn- ing: A decade survey.

Foundation Models for Anomaly Detection: Vision and Challenges Transfer adaptation learn- ing: A decade survey

Reference 44

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Observation dc8919d6-a2fc-4a81-b178-abc1d4dd66cf · outbound

This paper cites Scalalog: Scalable log-based failure diagnosis using llm.

Foundation Models for Anomaly Detection: Vision and Challenges Scalalog: Scalable log-based failure diagnosis using llm

Reference 45

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Observation 8d9a68f3-de11-4be5-8210-be2ced42a030 · outbound

This paper cites Logicode: an llm-driven framework for logical anomaly detection.

Foundation Models for Anomaly Detection: Vision and Challenges Logicode: an llm-driven framework for logical anomaly detection

Reference 46

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Source-reported events for the cited work

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Observation a0e87395-2f96-40e1-893e-772739c7dc01 · outbound

This paper cites A comprehensive survey on pre- trained foundation models: A history from bert to chat- gpt.

Foundation Models for Anomaly Detection: Vision and Challenges A comprehensive survey on pre- trained foundation models: A history from bert to chat- gpt

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:34:14.707477Z

Source-reported events for the cited work

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Observation 72412847-e709-4f99-892d-e8091f9a0609 · outbound

This paper cites Anomalyclip: Object-agnostic prompt learning for zero-shot anomaly detection.

Foundation Models for Anomaly Detection: Vision and Challenges Anomalyclip: Object-agnostic prompt learning for zero-shot anomaly detection

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:34:14.698375Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 410e4cbf-159b-4d65-b2bf-266ead20a4dc · outbound

This paper cites Do llms understand visual anoma- lies? uncovering llm’s capabilities in zero-shot anomaly detection.

Foundation Models for Anomaly Detection: Vision and Challenges Do llms understand visual anoma- lies? uncovering llm’s capabilities in zero-shot anomaly detection

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:34:14.689323Z

Source-reported events for the cited work

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Observation 9e1059ca-fdfb-488f-a1be-2bf716657a19 · outbound

This paper cites Toward general- ist anomaly detection via in-context residual learning with few-shot sample prompts.

Foundation Models for Anomaly Detection: Vision and Challenges Toward general- ist anomaly detection via in-context residual learning with few-shot sample prompts

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:34:14.680453Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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