Paper Citation Record · LEDGER

AnomalyGPT: Detecting Industrial Anomalies Using Large Vision-Language Models

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2308.15366.

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

pith.paper-citation-record.v1
2308.15366 v4

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 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 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T18:34:47.948004Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T00:16:16.768632Z

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 f9214ceb-f0b8-423b-9387-736c7d28d77e · inbound

SAGE: A Visual Language Model for Anomaly Detection via Fact Enhancement and Entropy-aware Alignment cites this paper.

SAGE: A Visual Language Model for Anomaly Detection via Fact Enhancement and Entropy-aware Alignment AnomalyGPT: Detecting Industrial Anomalies Using Large Vision-Language Models

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-06T18:34:47.948004Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3021cf97-cb2b-4d78-a7e3-fa33dfbcf5a5 · inbound

Self-Navigated Residual Mamba for Universal Industrial Anomaly Detection cites this paper.

Self-Navigated Residual Mamba for Universal Industrial Anomaly Detection AnomalyGPT: Detecting Industrial Anomalies Using Large Vision-Language Models

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-06T05:36:22.574516Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 13084791-5a7f-485d-a28a-7d975484dc03 · inbound

AD-FM: Multimodal LLMs for Anomaly Detection via Multi-Stage Reasoning and Fine-Grained Reward Optimization cites this paper.

AD-FM: Multimodal LLMs for Anomaly Detection via Multi-Stage Reasoning and Fine-Grained Reward Optimization AnomalyGPT: Detecting Industrial Anomalies Using Large Vision-Language Models

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-06T00:53:23.824754Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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

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

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

Resolution: verified exact; local_arxiv, observed 2026-08-05T18:34:14.228813Z

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.