Paper Citation Record · LEDGER

Can Multimodal Large Language Models be Guided to Improve Industrial Anomaly Detection?

As of 10 August 2026, this Paper Citation Record lists 41 of 41 outbound references and 7 inbound Pith citation observations for arXiv:2501.15795.

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

pith.paper-citation-record.v1
2501.15795 v1

Coverage vector

measured 41 of 41 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T14:03:31.639280Z

measured 48 of 48 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 7 of 7 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:20:58.030616Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-15T21:06:38.124724Z

Reference resolution

41 of 41 outbound references displayed

verified exact0
verified fuzzy19
unresolved22
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 99ba9d0e-9795-4680-a978-ad2c609abb5b · outbound

This paper cites Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond.

Can Multimodal Large Language Models be Guided to Improve Industrial Anomaly Detection? Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-10T14:03:30.881245Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 95c1819b-3930-4171-a424-cd83eca5fb28 · outbound

This paper cites Mvtec ad–a comprehensive real-world dataset for unsupervised anomaly detection.

Can Multimodal Large Language Models be Guided to Improve Industrial Anomaly Detection? Mvtec ad–a comprehensive real-world dataset for unsupervised anomaly detection

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:03:33.023028Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation b1e9870d-f13c-4876-ae3c-b5949295a663 · outbound

This paper cites A Survey on Visual Anomaly Detection: Challenge, Approach, and Prospect.

Can Multimodal Large Language Models be Guided to Improve Industrial Anomaly Detection? A Survey on Visual Anomaly Detection: Challenge, Approach, and Prospect

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-10T14:03:30.950201Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b7e51970-d4bf-4eb4-b564-264ac589a3c8 · outbound

This paper cites Surface defect detection methods for industrial products: A review.

Can Multimodal Large Language Models be Guided to Improve Industrial Anomaly Detection? Surface defect detection methods for industrial products: A review

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:03:32.982222Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 87912c0f-976b-4315-854d-1651e61fd5d8 · outbound

This paper cites InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks.

Can Multimodal Large Language Models be Guided to Improve Industrial Anomaly Detection? InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-10T14:03:31.051771Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ef9f4337-e101-41f3-90a4-ba86dafbc33c · outbound

This paper cites How Far Are We to GPT-4V? Closing the Gap to Commercial Multimodal Models with Open-Source Suites.

Can Multimodal Large Language Models be Guided to Improve Industrial Anomaly Detection? How Far Are We to GPT-4V? Closing the Gap to Commercial Multimodal Models with Open-Source Suites

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-10T14:03:31.069770Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1bb00841-177b-4903-9002-8156b2501165 · outbound

This paper cites MLLM Is a Strong Reranker: Advancing Multimodal Retrieval-augmented Generation via Knowledge-enhanced Reranking and Noise-injected Training.

Can Multimodal Large Language Models be Guided to Improve Industrial Anomaly Detection? MLLM Is a Strong Reranker: Advancing Multimodal Retrieval-augmented Generation via Knowledge-enhanced Reranking and Noise-injected Training

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-10T14:03:31.085402Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aeace695-1092-4cea-affe-9316c40c8bed · outbound

This paper cites Vmad: Visual-enhanced multimodal large lan- guage model for zero-shot anomaly detection.arXiv preprint arXiv:2409.20146, 2024.

Can Multimodal Large Language Models be Guided to Improve Industrial Anomaly Detection? Vmad: Visual-enhanced multimodal large lan- guage model for zero-shot anomaly detection.arXiv preprint arXiv:2409.20146, 2024

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-10T14:03:31.098792Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a825f4f6-f2a7-4eae-91a1-6ae14fb2ea4e · outbound

This paper cites Cantor: Inspiring multimodal chain-of-thought of mllm.

Can Multimodal Large Language Models be Guided to Improve Industrial Anomaly Detection? Cantor: Inspiring multimodal chain-of-thought of mllm

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:03:32.908906Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 0da3166e-2d12-4eb5-a550-7f363aa795ea · outbound

This paper cites Anomalygpt: Detecting in- dustrial anomalies using large vision-language models.

Can Multimodal Large Language Models be Guided to Improve Industrial Anomaly Detection? Anomalygpt: Detecting in- dustrial anomalies using large vision-language models

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:03:32.859473Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 358bf62b-f01a-4195-9f10-66e1f0f86633 · outbound

This paper cites Reveal: Retrieval-augmented visual-language pre-training with multi-source multimodal knowledge mem- ory.

Can Multimodal Large Language Models be Guided to Improve Industrial Anomaly Detection? Reveal: Retrieval-augmented visual-language pre-training with multi-source multimodal knowledge mem- ory

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-10T14:03:31.178546Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 453f34f9-5a8c-4745-b1d2-32a4ffcd1908 · outbound

This paper cites Winclip: Zero- /few-shot anomaly classification and segmentation.

Can Multimodal Large Language Models be Guided to Improve Industrial Anomaly Detection? Winclip: Zero- /few-shot anomaly classification and segmentation

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:03:32.793159Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 7993e364-238e-4880-af64-d2dd371c1ec7 · outbound

This paper cites MMAD: A Comprehensive Benchmark for Multimodal Large Language Models in Industrial Anomaly Detection.

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 cd4aa0e1-1536-4502-9e6a-411bb855ad57 · outbound

This paper cites Billion- scale similarity search with GPUs.

Can Multimodal Large Language Models be Guided to Improve Industrial Anomaly Detection? Billion- scale similarity search with GPUs

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:03:32.723615Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation ff2cc32d-3fff-4b0f-b3dd-fbb94c98e141 · outbound

This paper cites Retrieval-augmented generation for knowledge-intensive nlp tasks.

Can Multimodal Large Language Models be Guided to Improve Industrial Anomaly Detection? Retrieval-augmented generation for knowledge-intensive nlp tasks

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:03:32.704141Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 32742275-1a0e-4409-a543-2aa3df58e391 · outbound

This paper cites Llava-onevision: Easy visual task transfer, 2024.

Can Multimodal Large Language Models be Guided to Improve Industrial Anomaly Detection? Llava-onevision: Easy visual task transfer, 2024

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:03:32.689652Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation be4be849-b7cc-4051-8823-bed55a968b66 · outbound

This paper cites Llava-next-interleave: Tackling multi-image, video, and 3d in large multimodal models, 2024.

Can Multimodal Large Language Models be Guided to Improve Industrial Anomaly Detection? Llava-next-interleave: Tackling multi-image, video, and 3d in large multimodal models, 2024

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:03:32.674588Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation ea85c018-af0d-44c2-9b95-50508f9349c3 · outbound

This paper cites Myriad: Large Multimodal Model by Applying Vision Experts for Industrial Anomaly Detection.

Can Multimodal Large Language Models be Guided to Improve Industrial Anomaly Detection? Myriad: Large Multimodal Model by Applying Vision Experts for Industrial Anomaly Detection

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-10T14:03:31.261951Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 33c293e0-26ff-437c-b882-a2c071f3dbf4 · outbound

This paper cites Promptad: Zero-shot anomaly detection using text prompts.

Can Multimodal Large Language Models be Guided to Improve Industrial Anomaly Detection? Promptad: Zero-shot anomaly detection using text prompts

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:03:32.661322Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 60f58039-d5c4-4aa9-b9e5-bc24282f9f70 · outbound

This paper cites Improved baselines with visual instruction tuning, 2023.

Can Multimodal Large Language Models be Guided to Improve Industrial Anomaly Detection? Improved baselines with visual instruction tuning, 2023

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-10T14:03:31.280614Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6bae4ad5-4e27-4c39-8363-b1a2806f39da · outbound

This paper cites Visual instruction tuning.

Can Multimodal Large Language Models be Guided to Improve Industrial Anomaly Detection? Visual instruction tuning

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-10T14:03:31.284753Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6ae1c3bd-0e2e-406c-8820-a5faba3e7ab7 · outbound

This paper cites Learning customized visual models with retrieval-augmented knowledge.

Can Multimodal Large Language Models be Guided to Improve Industrial Anomaly Detection? Learning customized visual models with retrieval-augmented knowledge

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:03:32.629130Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 20ce5825-c837-4851-a47a-604577024c04 · outbound

This paper cites Deep indus- trial image anomaly detection: A survey.

Can Multimodal Large Language Models be Guided to Improve Industrial Anomaly Detection? Deep indus- trial image anomaly detection: A survey

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:03:32.615541Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 4bd083ee-eab3-46f2-b241-e715c62304e4 · outbound

This paper cites RAR: Retrieving And Ranking Augmented MLLMs for Visual Recognition.

Can Multimodal Large Language Models be Guided to Improve Industrial Anomaly Detection? RAR: Retrieving And Ranking Augmented MLLMs for Visual Recognition

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-10T14:03:31.297504Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 84e0c488-db44-468f-89f8-393bbf15b98d · outbound

This paper cites Efficient and robust approximate nearest neighbor search using hierarchical nav- igable small world graphs.

Can Multimodal Large Language Models be Guided to Improve Industrial Anomaly Detection? Efficient and robust approximate nearest neighbor search using hierarchical nav- igable small world graphs

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:03:32.563372Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 2b01791c-265e-44ef-8a2d-f134bdc41cc9 · outbound

This paper cites an unresolved cited work.

Can Multimodal Large Language Models be Guided to Improve Industrial Anomaly Detection? Unresolved cited work

Reference 26

Resolution: unresolved; raw_fallback, observed 2026-08-10T14:03:32.512975Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation d347ae66-872b-4495-86a9-a82c1bcfca28 · outbound

This paper cites Learning transferable visual models from natural language supervi- sion.

Can Multimodal Large Language Models be Guided to Improve Industrial Anomaly Detection? Learning transferable visual models from natural language supervi- sion

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-10T14:03:31.310840Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c4e22230-961b-4cdc-b2de-735e738d7687 · outbound

This paper cites Fully convolutional cross-scale-flows for image- based defect detection.

Can Multimodal Large Language Models be Guided to Improve Industrial Anomaly Detection? Fully convolutional cross-scale-flows for image- based defect detection

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:03:32.442330Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation dde64bcd-8e87-47ee-892b-77e9f81f6490 · outbound

This paper cites Deep learning for unsupervised anomaly lo- calization in industrial images: A survey.

Can Multimodal Large Language Models be Guided to Improve Industrial Anomaly Detection? Deep learning for unsupervised anomaly lo- calization in industrial images: A survey

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:03:32.354457Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 8d231e54-7ef3-4abb-88e2-b8fff87a8691 · outbound

This paper cites Support vector data description.

Can Multimodal Large Language Models be Guided to Improve Industrial Anomaly Detection? Support vector data description

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:03:32.270993Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation edaa49ac-76f5-4e60-9e2b-d5964bf23afd · outbound

This paper cites Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution.

Can Multimodal Large Language Models be Guided to Improve Industrial Anomaly Detection? Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-10T14:03:31.449223Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3d99f16a-3d98-4455-a644-fabcd8689fe8 · outbound

This paper cites Chain-of-thought prompting elicits reasoning in large lan- guage models.

Can Multimodal Large Language Models be Guided to Improve Industrial Anomaly Detection? Chain-of-thought prompting elicits reasoning in large lan- guage models

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-10T14:03:31.474466Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 95547cb0-85a7-458f-bd5c-be44f8789979 · outbound

This paper cites Simple synthetic data reduces sycophancy in large language models.

Can Multimodal Large Language Models be Guided to Improve Industrial Anomaly Detection? Simple synthetic data reduces sycophancy in large language models

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-10T14:03:31.522707Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f972dcc8-9438-43fa-8503-ac71b087c2a5 · outbound

This paper cites MiniCPM-V: A GPT-4V Level MLLM on Your Phone.

Can Multimodal Large Language Models be Guided to Improve Industrial Anomaly Detection? MiniCPM-V: A GPT-4V Level MLLM on Your Phone

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-10T14:03:31.547592Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 09f19506-76b0-4f4f-80e2-c43afc14ebfe · outbound

This paper cites Patch svdd: Patch-level svdd for anomaly detection and segmentation.

Can Multimodal Large Language Models be Guided to Improve Industrial Anomaly Detection? Patch svdd: Patch-level svdd for anomaly detection and segmentation

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:03:32.229736Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 5469bdbd-6b0b-48f2-a505-9ad7058296e1 · outbound

This paper cites Rag-driver: Gen- eralisable driving explanations with retrieval-augmented in- context learning in multi-modal large language model.arXiv preprint arXiv:2402.10828, 2024.

Can Multimodal Large Language Models be Guided to Improve Industrial Anomaly Detection? Rag-driver: Gen- eralisable driving explanations with retrieval-augmented in- context learning in multi-modal large language model.arXiv preprint arXiv:2402.10828, 2024

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-10T14:03:31.586651Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7fb324ef-22d9-40d7-a378-c11227397ae2 · outbound

This paper cites Recon- struction by inpainting for visual anomaly detection.

Can Multimodal Large Language Models be Guided to Improve Industrial Anomaly Detection? Recon- struction by inpainting for visual anomaly detection

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:03:32.215475Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 0fff4939-0f9e-468d-81b1-47dbe501d478 · outbound

This paper cites Multimodal Chain-of-Thought Reasoning in Language Models.

Can Multimodal Large Language Models be Guided to Improve Industrial Anomaly Detection? Multimodal Chain-of-Thought Reasoning in Language Models

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-10T14:03:31.604152Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 15d85996-bfac-4501-a97e-ecead5da2b58 · outbound

This paper cites Retrieving Multimodal Information for Augmented Generation: A Survey.

Can Multimodal Large Language Models be Guided to Improve Industrial Anomaly Detection? Retrieving Multimodal Information for Augmented Generation: A Survey

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-10T14:03:31.618493Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ec5a2709-208e-43ce-b7ae-3804cb4bcef4 · outbound

This paper cites Anomalyclip: Object-agnostic prompt learn- ing for zero-shot anomaly detection.

Can Multimodal Large Language Models be Guided to Improve Industrial Anomaly Detection? Anomalyclip: Object-agnostic prompt learn- ing for zero-shot anomaly detection

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-10T14:03:31.628664Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0fe084cb-7e98-4057-b0b1-8d725cc54dd3 · outbound

This paper cites fold": "<Fold Defect>\n Description: A raised, irregular line….

Can Multimodal Large Language Models be Guided to Improve Industrial Anomaly Detection? fold": "<Fold Defect>\n Description: A raised, irregular line…

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:03:32.200210Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Pith citing papers

Observation bf93753c-c81c-4d61-aa73-993475a430bb · inbound

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

OmniAD: Detect and Understand Industrial Anomaly via Multimodal Reasoning Can Multimodal Large Language Models be Guided to Improve Industrial Anomaly Detection?

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-07T13:20:58.030616Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 57437c8c-ddc2-45a8-aa45-aa22c5afefaa · inbound

Filter-And-Refine: A MLLM Based Cascade System for Industrial-Scale Video Content Moderation cites this paper.

Filter-And-Refine: A MLLM Based Cascade System for Industrial-Scale Video Content Moderation Can Multimodal Large Language Models be Guided to Improve Industrial Anomaly Detection?

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-06T14:58:42.623068Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 34e8e425-fae2-4883-8d95-e557d56e9e67 · 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 Can Multimodal Large Language Models be Guided to Improve Industrial Anomaly Detection?

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-06T12:40:08.912929Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 570ebd84-7f3b-4f71-8ff5-5ed22a4e5b9e · 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 Can Multimodal Large Language Models be Guided to Improve Industrial Anomaly Detection?

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-06T00:53:23.322660Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 077587d2-98f8-4071-8f38-d87d8f1a3d28 · 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 Can Multimodal Large Language Models be Guided to Improve Industrial Anomaly Detection?

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-05T18:34:02.796027Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 95a56f7b-1f65-4fe5-92af-3c0f359d48f2 · inbound

EAGLE: Expert-Augmented Attention Guidance for Tuning-Free Industrial Anomaly Detection in Multimodal Large Language Models cites this paper.

EAGLE: Expert-Augmented Attention Guidance for Tuning-Free Industrial Anomaly Detection in Multimodal Large Language Models Can Multimodal Large Language Models be Guided to Improve Industrial Anomaly Detection?

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-05-15T21:06:38.126298Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 56ee13e5-42b9-42e2-9c4c-95dc813bfed1 · 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 Can Multimodal Large Language Models be Guided to Improve Industrial Anomaly Detection?

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-05-15T11:55:33.332412Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.