Paper Citation Record · LEDGER

Customizing Visual-Language Foundation Models for Multi-modal Anomaly Detection and Reasoning

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2403.11083.

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

pith.paper-citation-record.v1
2403.11083 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T00:41:51.482974Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-15T18:50:16.716427Z

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 48e17370-e0a5-410b-898a-a55c5ece6b1c · inbound

SmartHome-Bench: A Comprehensive Benchmark for Video Anomaly Detection in Smart Homes Using Multi-Modal Large Language Models cites this paper.

SmartHome-Bench: A Comprehensive Benchmark for Video Anomaly Detection in Smart Homes Using Multi-Modal Large Language Models Customizing Visual-Language Foundation Models for Multi-modal Anomaly Detection and Reasoning

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-07T00:41:51.482974Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bc601d36-9fa5-47be-9c02-8b811112b611 · inbound

A Comprehensive Survey for Real-World Industrial Defect Detection: Challenges, Approaches, and Prospects cites this paper.

A Comprehensive Survey for Real-World Industrial Defect Detection: Challenges, Approaches, and Prospects Customizing Visual-Language Foundation Models for Multi-modal Anomaly Detection and Reasoning

Reference 213

Resolution: unresolved; no resolver link, observed 2026-08-06T17:21:54.980293Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 32100dff-0c70-47ce-9a57-9141d6ad07bd · 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 Customizing Visual-Language Foundation Models for Multi-modal Anomaly Detection and Reasoning

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-05T18:34:07.559247Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5cb5653c-3e60-41b3-a152-09ca9e561992 · inbound

SubspaceAD: Training-Free Few-Shot Anomaly Detection via Subspace Modeling cites this paper.

SubspaceAD: Training-Free Few-Shot Anomaly Detection via Subspace Modeling Customizing Visual-Language Foundation Models for Multi-modal Anomaly Detection and Reasoning

Reference 40

Resolution: verified exact; arxiv_id, observed 2026-05-15T18:50:16.720071Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation c0151ee0-e25d-482a-a441-be1bbebe0bc3 · 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 Customizing Visual-Language Foundation Models for Multi-modal Anomaly Detection and Reasoning

Reference 50

Resolution: verified exact; arxiv_id, observed 2026-05-11T09:46:00.510016Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 53c2da82-7f54-4610-9227-7181ad221b22 · inbound

RobustMAD: Evaluating Real-World Robustness of Multimodal Small Language Models for Deployable Anomaly Detection Assistants cites this paper.

RobustMAD: Evaluating Real-World Robustness of Multimodal Small Language Models for Deployable Anomaly Detection Assistants Customizing Visual-Language Foundation Models for Multi-modal Anomaly Detection and Reasoning

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-02T09:56:13.548378Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.