Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Do LLMs Understand Visual Anomalies? Uncovering LLM's Capabilities in Zero-shot Anomaly Detection
As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2404.09654.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-12T19:14:49.268696Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-10T05:30:23.456663Z
Reference resolution
0 of 0 outbound references displayed
- verified exact0
- verified fuzzy0
- unresolved0
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
1
pith, observed 2026-08-10T05:30:23.456663Z
Outbound references
No outbound reference observations are available for this paper version.
Pith citing papers
Observation cde2ab5b-caa0-48a3-9826-93e1b13dda08 · inbound
INVARLLM: LLM-assisted Physical Invariant Extraction for Cyber-Physical Systems Anomaly Detection cites this paper.
INVARLLM: LLM-assisted Physical Invariant Extraction for Cyber-Physical Systems Anomaly Detection Do LLMs Understand Visual Anomalies? Uncovering LLM's Capabilities in Zero-shot Anomaly Detection
Reference 52
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T19:14:49.268696Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 28b65a82-0884-4a7e-bbb2-1d46249a8393 · 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 Do LLMs Understand Visual Anomalies? Uncovering LLM's Capabilities in Zero-shot Anomaly Detection
Reference 42
- Resolution
- verified exact
- 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 2026-08-13T06:32:02.005865+00:00.