Paper Citation Record · LEDGER

MMAD: A Comprehensive Benchmark for Multimodal Large Language Models in Industrial Anomaly Detection

As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 22 inbound Pith citation observations for arXiv:2410.09453.

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

pith.paper-citation-record.v1
2410.09453 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 22 of 22 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 22 of 22 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T10:22:52.515246Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T20:47:23.223615Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
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malformed identifier0
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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 7993e364-238e-4880-af64-d2dd371c1ec7 · inbound

Can Multimodal Large Language Models be Guided to Improve Industrial Anomaly Detection? cites this paper.

Can Multimodal Large Language Models be Guided to Improve Industrial Anomaly Detection? MMAD: A Comprehensive Benchmark for Multimodal Large Language Models in Industrial Anomaly Detection

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-10T14:03:31.220351Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b7f9d68f-0e98-45e0-8036-38fd462d3364 · inbound

Vision-Language In-Context Learning Driven Few-Shot Visual Inspection Model cites this paper.

Vision-Language In-Context Learning Driven Few-Shot Visual Inspection Model MMAD: A Comprehensive Benchmark for Multimodal Large Language Models in Industrial Anomaly Detection

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-07T22:51:40.356938Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation af02aa70-aabd-4317-b138-8e2890980087 · inbound

HRScene: How Far Are VLMs from Effective High-Resolution Image Understanding? cites this paper.

HRScene: How Far Are VLMs from Effective High-Resolution Image Understanding? MMAD: A Comprehensive Benchmark for Multimodal Large Language Models in Industrial Anomaly Detection

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-16T10:22:52.515246Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 529ec67e-0c75-46c7-8f93-6a68075f5d93 · inbound

LR-IAD:Mask-Free Industrial Anomaly Detection with Logical Reasoning cites this paper.

LR-IAD:Mask-Free Industrial Anomaly Detection with Logical Reasoning MMAD: A Comprehensive Benchmark for Multimodal Large Language Models in Industrial Anomaly Detection

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-16T05:54:46.860607Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f3e2450b-a3f9-46c8-ba3f-97c8aeec166f · inbound

Detect, Classify, Act: Categorizing Industrial Anomalies with Multi-Modal Large Language Models cites this paper.

Detect, Classify, Act: Categorizing Industrial Anomalies with Multi-Modal Large Language Models MMAD: A Comprehensive Benchmark for Multimodal Large Language Models in Industrial Anomaly Detection

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-16T00:53:10.506682Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bee9f7fd-0d4b-465a-8738-b3887770e436 · inbound

SAGE: A Visual Language Model for Anomaly Detection via Fact Enhancement and Entropy-aware Alignment cites this paper.

SAGE: A Visual Language Model for Anomaly Detection via Fact Enhancement and Entropy-aware Alignment MMAD: A Comprehensive Benchmark for Multimodal Large Language Models in Industrial Anomaly Detection

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-06T18:34:47.967475Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1851a40f-3be0-4c6b-95b1-707892dad808 · 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 MMAD: A Comprehensive Benchmark for Multimodal Large Language Models in Industrial Anomaly Detection

Reference 212

Resolution: unresolved; no resolver link, observed 2026-08-06T17:21:54.817701Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 64081e69-ca6e-4f2c-baf8-59a4d0b21066 · inbound

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

Foundation Models and Transformers for Anomaly Detection: A Survey MMAD: A Comprehensive Benchmark for Multimodal Large Language Models in Industrial Anomaly Detection

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-06T15:32:50.564287Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 007bb533-27df-4c62-8e37-bbfb189e3896 · inbound

EMIT: Enhancing MLLMs for Industrial Anomaly Detection via Difficulty-Aware GRPO cites this paper.

EMIT: Enhancing MLLMs for Industrial Anomaly Detection via Difficulty-Aware GRPO MMAD: A Comprehensive Benchmark for Multimodal Large Language Models in Industrial Anomaly Detection

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-06T12:40:08.974046Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6fed35c5-d9e3-4382-85d2-bf731e053db6 · 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 MMAD: A Comprehensive Benchmark for Multimodal Large Language Models in Industrial Anomaly Detection

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-06T00:53:24.705695Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 097b68f0-659b-4101-8481-ef9810bc19e3 · 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 MMAD: A Comprehensive Benchmark for Multimodal Large Language Models in Industrial Anomaly Detection

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-05T18:34:04.530984Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b06d0ff1-c98e-4c7b-b322-be4356b6c902 · inbound

PiERN: Token-Level Routing for Integrating High-Precision Computation and Reasoning cites this paper.

PiERN: Token-Level Routing for Integrating High-Precision Computation and Reasoning MMAD: A Comprehensive Benchmark for Multimodal Large Language Models in Industrial Anomaly Detection

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-05-18T16:06:34.904970Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 1f147fe2-74b3-40ae-a222-06dc89c50baa · inbound

AgentIAD: Agentic Industrial Anomaly Detection via Adaptive Memory Augmentation cites this paper.

AgentIAD: Agentic Industrial Anomaly Detection via Adaptive Memory Augmentation MMAD: A Comprehensive Benchmark for Multimodal Large Language Models in Industrial Anomaly Detection

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-05-16T22:01:18.007457Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation deb1d730-1f81-43e7-81ba-feb77e83d1b5 · inbound

Towards Explainable Industrial Anomaly Detection via Knowledge-Guided Latent Reasoning cites this paper.

Towards Explainable Industrial Anomaly Detection via Knowledge-Guided Latent Reasoning MMAD: A Comprehensive Benchmark for Multimodal Large Language Models in Industrial Anomaly Detection

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-05-16T03:10:32.056498Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 86f63741-e379-48d0-a54c-39f097aaee09 · inbound

Redefining End-of-Life: Intelligent Automation for Electronics Remanufacturing Systems cites this paper.

Redefining End-of-Life: Intelligent Automation for Electronics Remanufacturing Systems MMAD: A Comprehensive Benchmark for Multimodal Large Language Models in Industrial Anomaly Detection

Reference 169

Resolution: verified exact; arxiv_id, observed 2026-05-13T19:23:09.460349Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation b45cb84e-96f3-4c90-92a9-468db1e9947b · inbound

FORGE: Fine-grained Multimodal Evaluation for Manufacturing Scenarios cites this paper.

FORGE: Fine-grained Multimodal Evaluation for Manufacturing Scenarios MMAD: A Comprehensive Benchmark for Multimodal Large Language Models in Industrial Anomaly Detection

Reference 20

Resolution: verified exact; arxiv_id, observed 2026-05-10T23:00:50.164090Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 40147dd9-8016-4ee1-ad9b-af5d09a46f0c · inbound

MARINER: A 3E-Driven Benchmark for Fine-Grained Perception and Complex Reasoning in Open-Water Environments cites this paper.

MARINER: A 3E-Driven Benchmark for Fine-Grained Perception and Complex Reasoning in Open-Water Environments MMAD: A Comprehensive Benchmark for Multimodal Large Language Models in Industrial Anomaly Detection

Reference 17

Resolution: verified exact; arxiv_id, observed 2026-05-11T06:11:00.773018Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation cbcac17c-99a8-4358-a505-df320ec1bfae · inbound

IAD-Unify: A Region-Grounded Unified Model for Industrial Anomaly Segmentation, Understanding, and Generation cites this paper.

IAD-Unify: A Region-Grounded Unified Model for Industrial Anomaly Segmentation, Understanding, and Generation MMAD: A Comprehensive Benchmark for Multimodal Large Language Models in Industrial Anomaly Detection

Reference 20

Resolution: verified exact; arxiv_id, observed 2026-05-11T09:46:07.329769Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 62f32705-14e9-4f07-8e44-e51cdad93c12 · inbound

Unification of Closed-Open Industrial Detection Scenarios: New Large-Scale Benchmarks,Challenges and Baselines cites this paper.

Unification of Closed-Open Industrial Detection Scenarios: New Large-Scale Benchmarks,Challenges and Baselines MMAD: A Comprehensive Benchmark for Multimodal Large Language Models in Industrial Anomaly Detection

Reference 44

Resolution: verified exact; arxiv_id, observed 2026-07-02T20:47:23.225186Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation ea9dff73-1389-43e1-a4dc-ee88c5134f11 · inbound

Vision-driven Preference Synthesis for Mitigating Hallucinations in VLMs cites this paper.

Vision-driven Preference Synthesis for Mitigating Hallucinations in VLMs MMAD: A Comprehensive Benchmark for Multimodal Large Language Models in Industrial Anomaly Detection

Reference 17

Resolution: verified exact; arxiv_id, observed 2026-07-01T15:35:47.662273Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 7b83ab78-d336-48f9-bdd6-cd8018dd95c8 · inbound

Global Logic and Local Search: Dual-Stream Multimodal In-Context Learning for Verifiable Industrial Anomaly Detection cites this paper.

Global Logic and Local Search: Dual-Stream Multimodal In-Context Learning for Verifiable Industrial Anomaly Detection MMAD: A Comprehensive Benchmark for Multimodal Large Language Models in Industrial Anomaly Detection

Reference 23

Resolution: unresolved; no resolver link, observed 2026-07-11T23:45:43.436443Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 65bede4b-d7a3-4a98-8cfb-0c5229e0b49c · inbound

O-VAD: Industrial Video Anomaly Detection through Object-Centric Tracking and Reasoning cites this paper.

O-VAD: Industrial Video Anomaly Detection through Object-Centric Tracking and Reasoning MMAD: A Comprehensive Benchmark for Multimodal Large Language Models in Industrial Anomaly Detection

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-01T15:56:41.874961Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.