Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
A Survey on Video Anomaly Detection via Deep Learning: Human, Vehicle, and Environment
As of 24 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2508.14203.
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 2026-08-23T06:30:58.430688+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-06-30T22:38:43.102769Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-01T13:55:45.266202Z
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 6f684cf7-5975-4e68-9189-a60b7c592b26 · inbound
Are Multimodal LLMs Ready for Surveillance? A Reality Check on Zero-Shot Anomaly Detection in the Wild cites this paper.
Are Multimodal LLMs Ready for Surveillance? A Reality Check on Zero-Shot Anomaly Detection in the Wild A Survey on Video Anomaly Detection via Deep Learning: Human, Vehicle, and Environment
Reference 3
- Resolution
- verified exact
- arxiv_id, observed 2026-05-21T12:24:10.649701Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation c2d018d9-7eab-425a-9f51-31ff57282b2c · inbound
CaC: Advancing Video Reward Models via Hierarchical Spatiotemporal Concentrating cites this paper.
CaC: Advancing Video Reward Models via Hierarchical Spatiotemporal Concentrating A Survey on Video Anomaly Detection via Deep Learning: Human, Vehicle, and Environment
Reference 1
- Resolution
- verified exact
- arxiv_id, observed 2026-05-13T06:02:23.218414Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 291cd926-56f5-4eae-8cec-c40b810024f5 · inbound
CaC: Advancing Video Reward Models via Hierarchical Spatiotemporal Concentrating cites this paper.
CaC: Advancing Video Reward Models via Hierarchical Spatiotemporal Concentrating A Survey on Video Anomaly Detection via Deep Learning: Human, Vehicle, and Environment
Reference 1
- Resolution
- verified exact
- arxiv_id, observed 2026-07-01T13:55:45.267904Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 576e75af-93e3-432a-9e84-cbd02a73a97c · inbound
Probing Collision Grounding in Vision-Language Models for Safe Human-Robot Collaboration cites this paper.
Probing Collision Grounding in Vision-Language Models for Safe Human-Robot Collaboration A Survey on Video Anomaly Detection via Deep Learning: Human, Vehicle, and Environment
Reference 23
- Resolution
- verified exact
- arxiv_id, observed 2026-06-28T23:22:47.342342Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.