Paper Citation Record · LEDGER

Large language models can be zero-shot anomaly detectors for time series?

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 10 inbound Pith citation observations for arXiv:2405.14755.

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

pith.paper-citation-record.v1
2405.14755 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 10 of 10 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 10 of 10 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:26:59.255454Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T12:38:07.705465Z

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 fd13f89c-9d7c-41a8-8bf8-1fae5cbcf095 · inbound

Large Language models for Time Series Analysis: Techniques, Applications, and Challenges cites this paper.

Large Language models for Time Series Analysis: Techniques, Applications, and Challenges Large language models can be zero-shot anomaly detectors for time series?

Reference 103

Resolution: unresolved; no resolver link, observed 2026-08-07T15:26:59.255454Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fa82f6c6-f461-4dad-a0cb-b87f4fea30ad · inbound

A Survey of AIOps in the Era of Large Language Models cites this paper.

A Survey of AIOps in the Era of Large Language Models Large language models can be zero-shot anomaly detectors for time series?

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-06T23:26:36.534828Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 07fd6604-6d0e-4e55-a49c-63b970c3d17e · inbound

A Survey of Reasoning and Agentic Systems in Time Series with Large Language Models cites this paper.

A Survey of Reasoning and Agentic Systems in Time Series with Large Language Models Large language models can be zero-shot anomaly detectors for time series?

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-04T16:49:26.013497Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2a648c81-a817-4761-885e-d7ee0370d8da · inbound

Seeing the Unseen: Towards Training-Free Inspection for Wind Turbine Blades Using Knowledge-Augmented Vision Language Models cites this paper.

Seeing the Unseen: Towards Training-Free Inspection for Wind Turbine Blades Using Knowledge-Augmented Vision Language Models Large language models can be zero-shot anomaly detectors for time series?

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-04T08:07:29.804546Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 002378d5-cf9d-474f-baad-b17a30981d67 · inbound

AnomSeer: Reinforcing Multimodal LLMs to Reason for Time-Series Anomaly Detection cites this paper.

AnomSeer: Reinforcing Multimodal LLMs to Reason for Time-Series Anomaly Detection Large language models can be zero-shot anomaly detectors for time series?

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-03T03:12:03.356902Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9a05d660-02ee-4379-a4f7-e93a2f6ad0f0 · inbound

AnomaMind: Agentic Time Series Anomaly Detection with Tool-Augmented Reasoning cites this paper.

AnomaMind: Agentic Time Series Anomaly Detection with Tool-Augmented Reasoning Large language models can be zero-shot anomaly detectors for time series?

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-02T23:29:44.426784Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a8254a09-9838-41fa-9ed3-f366f26e818c · inbound

Detecting Time Series Anomalies Like an Expert: A Multi-Agent LLM Framework with Specialized Analyzers cites this paper.

Detecting Time Series Anomalies Like an Expert: A Multi-Agent LLM Framework with Specialized Analyzers Large language models can be zero-shot anomaly detectors for time series?

Reference 1

Resolution: verified exact; arxiv_id, observed 2026-05-11T19:31:09.978635Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation ad63f25b-c7c8-4292-84d1-2010d0fd00cc · inbound

IstGPT: LLM-based Anomaly Detection for Spatial-Temporal Graph in Industrial Systems cites this paper.

IstGPT: LLM-based Anomaly Detection for Spatial-Temporal Graph in Industrial Systems Large language models can be zero-shot anomaly detectors for time series?

Reference 26

Resolution: verified exact; arxiv_id, observed 2026-07-01T23:26:22.014430Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 62586549-3328-438a-b797-f36de95ec149 · inbound

Large Language Models in Process Systems Engineering: Opportunities, Architectures, and Industrial Deployment Challenges cites this paper.

Large Language Models in Process Systems Engineering: Opportunities, Architectures, and Industrial Deployment Challenges Large language models can be zero-shot anomaly detectors for time series?

Reference 47

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T12:38:07.707365Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation eebc9568-4106-4e6f-a013-fdc28f1eea86 · inbound

Cardiologent: Multi-Agent Clinical Decision Support for Patient-Level Arrhythmia Assessment, Urgency, and Management cites this paper.

Cardiologent: Multi-Agent Clinical Decision Support for Patient-Level Arrhythmia Assessment, Urgency, and Management Large language models can be zero-shot anomaly detectors for time series?

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-01T02:47:20.440123Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.