Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
A Unified Anomaly Synthesis Strategy with Gradient Ascent for Industrial Anomaly Detection and Localization
As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2407.09359.
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 2026-08-08T06:32:00.761636+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-08T12:15:53.314105Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-03T00:17:28.745063Z
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 774a8392-0f72-4a1b-b4ee-fe28d193c77e · inbound
Towards Zero-Shot Anomaly Detection and Reasoning with Multimodal Large Language Models cites this paper.
Towards Zero-Shot Anomaly Detection and Reasoning with Multimodal Large Language Models A Unified Anomaly Synthesis Strategy with Gradient Ascent for Industrial Anomaly Detection and Localization
Reference 9
- Resolution
- unresolved
- no resolver link, observed 2026-08-08T12:15:53.314105Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 3e8beab6-f840-4a4f-9439-b4396b4c806f · inbound
PathoSCOPE: Few-Shot Pathology Detection via Self-Supervised Contrastive Learning and Pathology-Informed Synthetic Embeddings cites this paper.
PathoSCOPE: Few-Shot Pathology Detection via Self-Supervised Contrastive Learning and Pathology-Informed Synthetic Embeddings A Unified Anomaly Synthesis Strategy with Gradient Ascent for Industrial Anomaly Detection and Localization
Reference 5
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T14:50:14.819774Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 735d69b0-3d46-4f19-ba75-a2906ff1948c · inbound
Bridge Feature Matching and Cross-Modal Alignment with Mutual-filtering for Zero-shot Anomaly Detection cites this paper.
Bridge Feature Matching and Cross-Modal Alignment with Mutual-filtering for Zero-shot Anomaly Detection A Unified Anomaly Synthesis Strategy with Gradient Ascent for Industrial Anomaly Detection and Localization
Reference 8
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T17:27:14.549289Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation b158f140-167f-468f-b5b9-d905c429ec85 · inbound
Visual Prompting Meets Feature Reconstruction-Based Anomaly Detection with Dual-Teacher Supervision cites this paper.
Visual Prompting Meets Feature Reconstruction-Based Anomaly Detection with Dual-Teacher Supervision A Unified Anomaly Synthesis Strategy with Gradient Ascent for Industrial Anomaly Detection and Localization
Reference 3
- Resolution
- verified exact
- arxiv_id, observed 2026-07-03T00:17:28.746814Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.