Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
A Unified Model for Multi-class Anomaly Detection
As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2206.03687.
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 2026-08-14T06:32:32.682623+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-12T05:22:39.280180Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-16T03:07:11.946628Z
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 acaee972-e839-4898-988f-803cf1004f19 · inbound
Friend or Foe? Harnessing Controllable Overfitting for Anomaly Detection cites this paper.
Friend or Foe? Harnessing Controllable Overfitting for Anomaly Detection A Unified Model for Multi-class Anomaly Detection
Reference 31
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T05:22:39.280180Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 1e1a7b09-6b7d-4fbc-95ce-b39ea8f3a1b9 · inbound
HLGFA: High-Low Resolution Guided Feature Alignment for Unsupervised Anomaly Detection cites this paper.
HLGFA: High-Low Resolution Guided Feature Alignment for Unsupervised Anomaly Detection A Unified Model for Multi-class Anomaly Detection
Reference 18
- Resolution
- verified exact
- arxiv_id, observed 2026-05-16T03:07:11.950620Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 08df96ad-b242-4fdd-8ef6-c121f2880271 · inbound
MMR-AD: A Large-Scale Multimodal Dataset for Benchmarking General Anomaly Detection with Multimodal Large Language Models cites this paper.
MMR-AD: A Large-Scale Multimodal Dataset for Benchmarking General Anomaly Detection with Multimodal Large Language Models A Unified Model for Multi-class Anomaly Detection
Reference 58
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T09:46:00.944507Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.