Paper Citation Record · LEDGER

Towards Generic Anomaly Detection and Understanding: Large-scale Visual-linguistic Model (GPT-4V) Takes the Lead

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:2311.02782.

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

pith.paper-citation-record.v1
2311.02782 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 9 of 9 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 9 of 9 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T16:13:53.287459Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-30T11:54:38.318598Z

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 7de44c2f-1496-49b9-81d5-28830f20a88f · inbound

Exploring Few-Shot Defect Segmentation in General Industrial Scenarios with Metric Learning and Vision Foundation Models cites this paper.

Exploring Few-Shot Defect Segmentation in General Industrial Scenarios with Metric Learning and Vision Foundation Models Towards Generic Anomaly Detection and Understanding: Large-scale Visual-linguistic Model (GPT-4V) Takes the Lead

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-09T16:13:53.287459Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6240f525-8b5d-4ed4-9fc1-efddc6d9993e · inbound

OmniAD: Detect and Understand Industrial Anomaly via Multimodal Reasoning cites this paper.

OmniAD: Detect and Understand Industrial Anomaly via Multimodal Reasoning Towards Generic Anomaly Detection and Understanding: Large-scale Visual-linguistic Model (GPT-4V) Takes the Lead

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-07T13:20:57.370815Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a1a2226f-13a8-47df-b020-48b14c39ec3a · inbound

Foundation Models and Transformers for Anomaly Detection: A Survey cites this paper.

Foundation Models and Transformers for Anomaly Detection: A Survey Towards Generic Anomaly Detection and Understanding: Large-scale Visual-linguistic Model (GPT-4V) Takes the Lead

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-06T15:32:49.214107Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3e277ee4-4324-4251-977e-67a81fa83a6b · inbound

AD-FM: Multimodal LLMs for Anomaly Detection via Multi-Stage Reasoning and Fine-Grained Reward Optimization cites this paper.

AD-FM: Multimodal LLMs for Anomaly Detection via Multi-Stage Reasoning and Fine-Grained Reward Optimization Towards Generic Anomaly Detection and Understanding: Large-scale Visual-linguistic Model (GPT-4V) Takes the Lead

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-06T00:53:23.020697Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 822384a5-c1be-49f2-b17a-e430b35e14ce · inbound

IADGPT: Unified LVLM for Few-Shot Industrial Anomaly Detection, Localization, and Reasoning via In-Context Learning cites this paper.

IADGPT: Unified LVLM for Few-Shot Industrial Anomaly Detection, Localization, and Reasoning via In-Context Learning Towards Generic Anomaly Detection and Understanding: Large-scale Visual-linguistic Model (GPT-4V) Takes the Lead

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-05T20:20:12.317577Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4458cc4b-21e3-4c61-9392-4f918c64025d · inbound

AD-Copilot: A Vision-Language Assistant for Industrial Anomaly Detection via Visual In-context Comparison cites this paper.

AD-Copilot: A Vision-Language Assistant for Industrial Anomaly Detection via Visual In-context Comparison Towards Generic Anomaly Detection and Understanding: Large-scale Visual-linguistic Model (GPT-4V) Takes the Lead

Reference 44

Resolution: verified exact; arxiv_id, observed 2026-05-15T11:55:33.284392Z

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 58bd90a8-ecad-48ac-976a-f6ea1e16bc2c · 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 Towards Generic Anomaly Detection and Understanding: Large-scale Visual-linguistic Model (GPT-4V) Takes the Lead

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-05-11T09:46:00.818492Z

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 158bb9dc-f5ad-4b3d-8f31-63f2a1266f2b · inbound

Fine-Tuning Vision-Language Models for Understanding Current Damage and Scoring Priority with Quality Guard Agent cites this paper.

Fine-Tuning Vision-Language Models for Understanding Current Damage and Scoring Priority with Quality Guard Agent Towards Generic Anomaly Detection and Understanding: Large-scale Visual-linguistic Model (GPT-4V) Takes the Lead

Reference 26

Resolution: verified exact; arxiv_id, observed 2026-06-30T11:54:38.321005Z

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 6f64f4e8-e456-42d9-bc5a-3377813b5579 · 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 Towards Generic Anomaly Detection and Understanding: Large-scale Visual-linguistic Model (GPT-4V) Takes the Lead

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-02T09:56:12.376403Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.