Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-07T13:21:04.089879Z
Paper Citation Record · LEDGER
OmniAD: Detect and Understand Industrial Anomaly via Multimodal Reasoning
As of 8 August 2026, this Paper Citation Record lists 59 of 59 outbound references and 9 inbound Pith citation observations for arXiv:2505.22039.
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 2026-08-06T12:40:09.131334Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-04T15:09:55.062088Z
Reference resolution
59 of 59 outbound references displayed
- verified exact2
- verified fuzzy16
- unresolved40
- parse uncertain0
- malformed identifier1
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 58abc4f5-d1bc-49d1-ba35-a1858981987f · outbound
This paper cites Anomalib: A deep learning library for anomaly detection.
OmniAD: Detect and Understand Industrial Anomaly via Multimodal Reasoning Anomalib: A deep learning library for anomaly detection
Reference 1
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T13:21:08.162140Z
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.
Observation 50408b29-026a-4737-96c7-3dd0d0152546 · outbound
This paper cites Cableinspect-ad: An expert-annotated anomaly detection dataset.Advances in Neural Information Processing Systems, 37:64703–64716, 2024.
OmniAD: Detect and Understand Industrial Anomaly via Multimodal Reasoning Cableinspect-ad: An expert-annotated anomaly detection dataset.Advances in Neural Information Processing Systems, 37:64703–64716, 2024
Reference 2
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T13:21:07.988119Z
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.
Observation 048ccbf2-c046-48a2-95c1-f73b9cf407f2 · outbound
This paper cites Qwen2.5-VL Technical Report.
OmniAD: Detect and Understand Industrial Anomaly via Multimodal Reasoning Qwen2.5-VL Technical Report
Reference 3
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T13:20:56.766263Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 939d67a2-1442-4395-8c48-fc50f7e81cde · outbound
This paper cites Beyond dents and scratches: Logical constraints in unsupervised anomaly detection and localization.International Journal of Computer Vision, 130(4):947–969, 2022.
OmniAD: Detect and Understand Industrial Anomaly via Multimodal Reasoning Beyond dents and scratches: Logical constraints in unsupervised anomaly detection and localization.International Journal of Computer Vision, 130(4):947–969, 2022
Reference 4
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T13:21:07.845567Z
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.
Observation 1201a951-a960-4015-85bc-7789f101fb8b · outbound
This paper cites Mvtec ad–a comprehensive real-world dataset for unsupervised anomaly detection.
OmniAD: Detect and Understand Industrial Anomaly via Multimodal Reasoning Mvtec ad–a comprehensive real-world dataset for unsupervised anomaly detection
Reference 5
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T13:20:57.045660Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 2627b121-1dfc-46e9-b2da-d99912abc3e8 · outbound
This paper cites Segment Any Anomaly without Training via Hybrid Prompt Regularization.
OmniAD: Detect and Understand Industrial Anomaly via Multimodal Reasoning Segment Any Anomaly without Training via Hybrid Prompt Regularization
Reference 6
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T13:20:57.206576Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 6240f525-8b5d-4ed4-9fc1-efddc6d9993e · outbound
This paper cites Towards Generic Anomaly Detection and Understanding: Large-scale Visual-linguistic Model (GPT-4V) Takes the Lead.
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 35a8bb78-6e6d-44bf-89b7-1064b49ec51c · outbound
This paper cites Adaclip: Adapting clip with hybrid learnable prompts for zero-shot anomaly detection.
OmniAD: Detect and Understand Industrial Anomaly via Multimodal Reasoning Adaclip: Adapting clip with hybrid learnable prompts for zero-shot anomaly detection
Reference 8
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T13:20:57.486788Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 3355f90a-119c-437b-b7bb-838314bc3c59 · outbound
This paper cites AnomalyR1: A GRPO-based End-to-end MLLM for Industrial Anomaly Detection.
OmniAD: Detect and Understand Industrial Anomaly via Multimodal Reasoning AnomalyR1: A GRPO-based End-to-end MLLM for Industrial Anomaly Detection
Reference 9
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T13:20:57.635131Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation b488326c-c9a6-4cb4-9238-e94076a6db0b · outbound
This paper cites Clip-ad: A language-guided staged dual-path model for zero-shot anomaly detection.
OmniAD: Detect and Understand Industrial Anomaly via Multimodal Reasoning Clip-ad: A language-guided staged dual-path model for zero-shot anomaly detection
Reference 10
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T13:21:07.664348Z
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.
Observation 8c6e46bd-298f-4224-9070-673427cf3719 · outbound
This paper cites Internvl: Scaling up vision foundation models and aligning for generic visual-linguistic tasks.
OmniAD: Detect and Understand Industrial Anomaly via Multimodal Reasoning Internvl: Scaling up vision foundation models and aligning for generic visual-linguistic tasks
Reference 11
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T13:20:57.903281Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation bf93753c-c81c-4d61-aa73-993475a430bb · outbound
This paper cites Can Multimodal Large Language Models be Guided to Improve Industrial Anomaly Detection?.
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 91fb99cf-ac7b-4346-abcb-f0ccb378be04 · outbound
This paper cites Padim: a patch distribution modeling framework for anomaly detection and localization.
OmniAD: Detect and Understand Industrial Anomaly via Multimodal Reasoning Padim: a patch distribution modeling framework for anomaly detection and localization
Reference 13
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T13:20:58.182176Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation cc592adb-c164-4a53-8a7e-be9bdc7acea0 · outbound
This paper cites Salvaging the Overlooked: Leveraging Class-Aware Contrastive Learning for Multi-Class Anomaly Detection.
OmniAD: Detect and Understand Industrial Anomaly via Multimodal Reasoning Salvaging the Overlooked: Leveraging Class-Aware Contrastive Learning for Multi-Class Anomaly Detection
Reference 14
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T13:20:58.338595Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation dae48e54-eaeb-4b7d-8e46-33d2e5153809 · outbound
This paper cites Fastrecon: Few-shot industrial anomaly detection via fast feature reconstruction.
OmniAD: Detect and Understand Industrial Anomaly via Multimodal Reasoning Fastrecon: Few-shot industrial anomaly detection via fast feature reconstruction
Reference 15
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T13:21:07.556681Z
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.
Observation f40b1728-53fc-4b12-9491-214c2d7fc5a5 · outbound
This paper cites Anomalygpt: Detecting industrial anomalies using large vision-language models.
OmniAD: Detect and Understand Industrial Anomaly via Multimodal Reasoning Anomalygpt: Detecting industrial anomalies using large vision-language models
Reference 16
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T13:21:07.384197Z
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.
Observation 79c5195f-752d-4012-8bd2-2fda2e600cc3 · outbound
This paper cites UniVAD: A Training-free Unified Model for Few-shot Visual Anomaly Detection.
OmniAD: Detect and Understand Industrial Anomaly via Multimodal Reasoning UniVAD: A Training-free Unified Model for Few-shot Visual Anomaly Detection
Reference 17
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T13:20:58.812195Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 6a1960ec-084f-4d98-922b-171bf6106173 · outbound
This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.
OmniAD: Detect and Understand Industrial Anomaly via Multimodal Reasoning DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning
Reference 18
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T13:20:58.997302Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 4ee964b0-24da-46ab-baa0-77b89bce62f2 · outbound
This paper cites Dinomaly: The Less Is More Philosophy in Multi-Class Unsupervised Anomaly Detection.
OmniAD: Detect and Understand Industrial Anomaly via Multimodal Reasoning Dinomaly: The Less Is More Philosophy in Multi-Class Unsupervised Anomaly Detection
Reference 19
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T13:20:59.197386Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 67260346-f999-434b-be5b-6c1d614998be · outbound
This paper cites MambaAD: Exploring State Space Models for Multi-class Unsupervised Anomaly Detection.
OmniAD: Detect and Understand Industrial Anomaly via Multimodal Reasoning MambaAD: Exploring State Space Models for Multi-class Unsupervised Anomaly Detection
Reference 20
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T13:20:59.365566Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation bb0ec15a-0e2a-4614-bd80-4f0af414e38a · outbound
This paper cites Surface defect saliency of magnetic tile.The Visual Computer, 36(1):85–96, 2020.
OmniAD: Detect and Understand Industrial Anomaly via Multimodal Reasoning Surface defect saliency of magnetic tile.The Visual Computer, 36(1):85–96, 2020
Reference 21
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T13:20:59.474647Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 950e5f5a-f8eb-4757-a08b-c98103bebd68 · outbound
This paper cites Reconpatch: Contrastive patch representation learning for industrial anomaly detection.
OmniAD: Detect and Understand Industrial Anomaly via Multimodal Reasoning Reconpatch: Contrastive patch representation learning for industrial anomaly detection
Reference 22
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T13:21:07.248263Z
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.
Observation 122b7aca-5e07-4da7-8f57-3d2811d58050 · outbound
This paper cites OpenAI o1 System Card.
OmniAD: Detect and Understand Industrial Anomaly via Multimodal Reasoning OpenAI o1 System Card
Reference 23
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T13:20:59.715744Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d0612ad1-463a-49d6-92d5-670b86306575 · outbound
This paper cites Winclip: Zero-/few-shot anomaly classification and segmentation.
OmniAD: Detect and Understand Industrial Anomaly via Multimodal Reasoning Winclip: Zero-/few-shot anomaly classification and segmentation
Reference 24
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T13:20:59.846648Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 331591db-4257-4020-99be-4e9463783e40 · outbound
This paper cites MMAD: A comprehensive benchmark for multimodal large language models in industrial anomaly detection.
OmniAD: Detect and Understand Industrial Anomaly via Multimodal Reasoning MMAD: A comprehensive benchmark for multimodal large language models in industrial anomaly detection
Reference 25
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T13:21:07.045568Z
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.
Observation bb5f25a6-79cd-4993-a65f-7aac4affdd6e · outbound
This paper cites Softpatch: Unsupervised anomaly detection with noisy data.Advances in Neural Information Processing Systems, 35:15433–15445, 2022.
OmniAD: Detect and Understand Industrial Anomaly via Multimodal Reasoning Softpatch: Unsupervised anomaly detection with noisy data.Advances in Neural Information Processing Systems, 35:15433–15445, 2022
Reference 26
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T13:21:06.885729Z
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.
Observation 48a53102-8571-4fdc-ac31-3d5c2db7f909 · outbound
This paper cites Fabgpt: An efficient large multimodal model for complex wafer defect knowledge queries.
OmniAD: Detect and Understand Industrial Anomaly via Multimodal Reasoning Fabgpt: An efficient large multimodal model for complex wafer defect knowledge queries
Reference 27
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T13:21:06.697796Z
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.
Observation 8a86e20f-e622-480d-b983-d7fb379371a7 · outbound
This paper cites Logicad: Explainable anomaly detection via vlm-based text feature extraction.
OmniAD: Detect and Understand Industrial Anomaly via Multimodal Reasoning Logicad: Explainable anomaly detection via vlm-based text feature extraction
Reference 28
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T13:21:06.462410Z
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.
Observation ce69b31f-9960-4a2d-91ab-16933625777b · outbound
This paper cites Segment anything.
OmniAD: Detect and Understand Industrial Anomaly via Multimodal Reasoning Segment anything
Reference 29
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T13:21:00.587083Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation b8f8536e-960c-4ad1-9a70-bf0e8411cab4 · outbound
This paper cites Text4Seg: Reimagining Image Segmentation as Text Generation.
OmniAD: Detect and Understand Industrial Anomaly via Multimodal Reasoning Text4Seg: Reimagining Image Segmentation as Text Generation
Reference 30
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T13:21:00.710431Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 77e345f7-2c82-430f-8ff3-80a40af82a7a · outbound
This paper cites LLaVA-OneVision: Easy Visual Task Transfer.
OmniAD: Detect and Understand Industrial Anomaly via Multimodal Reasoning LLaVA-OneVision: Easy Visual Task Transfer
Reference 31
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T13:21:00.848055Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation bee0c231-17ff-4248-bb05-7bb698b8d753 · outbound
This paper cites LAD-Reasoner: Tiny Multimodal Models are Good Reasoners for Logical Anomaly Detection.
OmniAD: Detect and Understand Industrial Anomaly via Multimodal Reasoning LAD-Reasoner: Tiny Multimodal Models are Good Reasoners for Logical Anomaly Detection
Reference 32
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T13:21:01.001056Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 37840bce-a1fc-405a-8bf4-ea6ef0c14a97 · outbound
This paper cites Myriad: Large Multimodal Model by Applying Vision Experts for Industrial Anomaly Detection.
OmniAD: Detect and Understand Industrial Anomaly via Multimodal Reasoning Myriad: Large Multimodal Model by Applying Vision Experts for Industrial Anomaly Detection
Reference 33
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T13:21:01.141824Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 47ba5158-66a0-4d07-a5a6-050bb1f18cc3 · outbound
This paper cites Triad: Empowering LMM-based Anomaly Detection with Vision Expert-guided Visual Tokenizer and Manufacturing Process.
OmniAD: Detect and Understand Industrial Anomaly via Multimodal Reasoning Triad: Empowering LMM-based Anomaly Detection with Vision Expert-guided Visual Tokenizer and Manufacturing Process
Reference 34
- Resolution
- verified exact
- local_arxiv, observed 2026-08-07T13:21:04.700944Z
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.
Observation 21bb4206-b8f0-4bfe-b2a9-0a91836eb555 · outbound
This paper cites Improved baselines with visual instruction tuning.
OmniAD: Detect and Understand Industrial Anomaly via Multimodal Reasoning Improved baselines with visual instruction tuning
Reference 35
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T13:21:01.413901Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 70c63195-6468-4e9c-a69e-ade911400d40 · outbound
This paper cites Llava-next: Improved reasoning, ocr, and world knowledge, January 2024.
OmniAD: Detect and Understand Industrial Anomaly via Multimodal Reasoning Llava-next: Improved reasoning, ocr, and world knowledge, January 2024
Reference 36
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T13:21:01.578941Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 023e1924-4ca3-426a-9f48-df47ce7a20f1 · outbound
This paper cites Seg-Zero: Reasoning-Chain Guided Segmentation via Cognitive Reinforcement.
OmniAD: Detect and Understand Industrial Anomaly via Multimodal Reasoning Seg-Zero: Reasoning-Chain Guided Segmentation via Cognitive Reinforcement
Reference 37
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T13:21:01.719165Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation ece15928-5c8b-4bd6-b41d-69f8a11904c3 · outbound
This paper cites Visual-RFT: Visual Reinforcement Fine-Tuning.
OmniAD: Detect and Understand Industrial Anomaly via Multimodal Reasoning Visual-RFT: Visual Reinforcement Fine-Tuning
Reference 38
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T13:21:01.874412Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation afae3e3e-2bc1-4377-b75c-274f37e83a79 · outbound
This paper cites AA-CLIP: Enhancing Zero-shot Anomaly Detection via Anomaly-Aware CLIP.
OmniAD: Detect and Understand Industrial Anomaly via Multimodal Reasoning AA-CLIP: Enhancing Zero-shot Anomaly Detection via Anomaly-Aware CLIP
Reference 39
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T13:21:02.033225Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d5b71674-de4c-4a72-8da7-8eb7188450b3 · outbound
This paper cites Learning transferable visual models from natural language supervision.
OmniAD: Detect and Understand Industrial Anomaly via Multimodal Reasoning Learning transferable visual models from natural language supervision
Reference 40
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T13:21:02.154563Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 5f418583-59eb-4dc1-a996-faeba0da443f · outbound
This paper cites Deepspeed: System optimizations enable training deep learning models with over 100 billion parameters.
OmniAD: Detect and Understand Industrial Anomaly via Multimodal Reasoning Deepspeed: System optimizations enable training deep learning models with over 100 billion parameters
Reference 41
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T13:21:02.215231Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation ac80a114-cbc1-4d1f-b45d-0fdbbb7c76ae · outbound
This paper cites Towards total recall in industrial anomaly detection.
OmniAD: Detect and Understand Industrial Anomaly via Multimodal Reasoning Towards total recall in industrial anomaly detection
Reference 42
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T13:21:02.353679Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 482b7268-a4ed-41c3-aa4d-31d72d2ea098 · outbound
This paper cites DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models.
OmniAD: Detect and Understand Industrial Anomaly via Multimodal Reasoning DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models
Reference 43
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T13:21:02.462892Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 32a46ec0-a5fd-46ee-bace-2600e5f97347 · outbound
This paper cites VLM-R1: A Stable and Generalizable R1-style Large Vision-Language Model.
OmniAD: Detect and Understand Industrial Anomaly via Multimodal Reasoning VLM-R1: A Stable and Generalizable R1-style Large Vision-Language Model
Reference 44
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T13:21:02.650468Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation cf464214-97c7-4498-990d-9e23b52bfaea · outbound
This paper cites Crossing the Reward Bridge: Expanding RL with Verifiable Rewards Across Diverse Domains.
OmniAD: Detect and Understand Industrial Anomaly via Multimodal Reasoning Crossing the Reward Bridge: Expanding RL with Verifiable Rewards Across Diverse Domains
Reference 45
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T13:21:02.793750Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 658c6e9b-49f8-4e20-9cba-ac6ad1805db0 · outbound
This paper cites Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context.
OmniAD: Detect and Understand Industrial Anomaly via Multimodal Reasoning Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context
Reference 46
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T13:21:02.911911Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 484193b1-a969-42f3-abb9-ef19fc837b71 · outbound
This paper cites Real-iad: A real-world multi-view dataset for benchmarking versatile industrial anomaly detection.
OmniAD: Detect and Understand Industrial Anomaly via Multimodal Reasoning Real-iad: A real-world multi-view dataset for benchmarking versatile industrial anomaly detection
Reference 47
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T13:21:02.972208Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a5a2d521-8ec4-401a-b756-116d237faa9a · outbound
This paper cites 3CAD: A Large-Scale Real-World 3C Product Dataset for Unsupervised Anomaly.
OmniAD: Detect and Understand Industrial Anomaly via Multimodal Reasoning 3CAD: A Large-Scale Real-World 3C Product Dataset for Unsupervised Anomaly
Reference 48
- Resolution
- verified exact
- local_arxiv, observed 2026-08-07T13:21:04.483118Z
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.
Observation 62ec5e7c-9b55-47a7-a3a2-248154aa85da · outbound
This paper cites Defect spectrum: a granular look of large-scale defect datasets with rich semantics.
OmniAD: Detect and Understand Industrial Anomaly via Multimodal Reasoning Defect spectrum: a granular look of large-scale defect datasets with rich semantics
Reference 49
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T13:21:06.050191Z
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.
Observation 15b75bad-1466-45a4-8766-15d94b277c09 · outbound
This paper cites MiniCPM-V: A GPT-4V Level MLLM on Your Phone.
OmniAD: Detect and Understand Industrial Anomaly via Multimodal Reasoning MiniCPM-V: A GPT-4V Level MLLM on Your Phone
Reference 50
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T13:21:03.043936Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation ae2f8659-c6a0-4b68-b12e-dadde4bfe414 · outbound
This paper cites Draem-a discriminatively trained reconstruction embedding for surface anomaly detection.
OmniAD: Detect and Understand Industrial Anomaly via Multimodal Reasoning Draem-a discriminatively trained reconstruction embedding for surface anomaly detection
Reference 51
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T13:21:03.106000Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 8dfdb32b-a9dd-4dad-bf80-82bd0b891742 · outbound
This paper cites DiffusionAD: Norm-guided One-step Denoising Diffusion for Anomaly Detection.
OmniAD: Detect and Understand Industrial Anomaly via Multimodal Reasoning DiffusionAD: Norm-guided One-step Denoising Diffusion for Anomaly Detection
Reference 52
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T13:21:03.173909Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 1da6c693-5f0f-48c7-8184-e6113a6babfa · outbound
This paper cites Pku-goodsad: A supermarket goods dataset for unsupervised anomaly detection and segmentation.IEEE Robotics and Automation Letters, 9(3):2008– 2015, 2024.
OmniAD: Detect and Understand Industrial Anomaly via Multimodal Reasoning Pku-goodsad: A supermarket goods dataset for unsupervised anomaly detection and segmentation.IEEE Robotics and Automation Letters, 9(3):2008– 2015, 2024
Reference 53
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T13:21:05.871915Z
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.
Observation 660aafdb-89db-4518-94e9-55348a5d38f9 · outbound
This paper cites Realnet: A feature selection network with realistic synthetic anomaly for anomaly detection.
OmniAD: Detect and Understand Industrial Anomaly via Multimodal Reasoning Realnet: A feature selection network with realistic synthetic anomaly for anomaly detection
Reference 54
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T13:21:03.348470Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 2118a647-651a-48c6-8684-fcd922a9215a · outbound
This paper cites Logicode: an llm-driven framework for logical anomaly detection.IEEE Transactions on Automation Science and Engineering, 2024.
OmniAD: Detect and Understand Industrial Anomaly via Multimodal Reasoning Logicode: an llm-driven framework for logical anomaly detection.IEEE Transactions on Automation Science and Engineering, 2024
Reference 55
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T13:21:05.612511Z
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.
Observation 991f797d-8906-47e1-b09b-183efe203c1c · outbound
This paper cites Industrial anomaly detection with domain shift: A real-world dataset and masked multi-scale reconstruction.Computers in Industry, 151:103990, 2023.
OmniAD: Detect and Understand Industrial Anomaly via Multimodal Reasoning Industrial anomaly detection with domain shift: A real-world dataset and masked multi-scale reconstruction.Computers in Industry, 151:103990, 2023
Reference 56
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T13:21:05.357304Z
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.
Observation 9063a26d-245a-435e-9b10-eae3e2b2bec9 · outbound
This paper cites Anomalyclip: Object-agnostic prompt learning for zero-shot anomaly detection.arXiv preprint arXiv:2310.18961, 2023.
OmniAD: Detect and Understand Industrial Anomaly via Multimodal Reasoning Anomalyclip: Object-agnostic prompt learning for zero-shot anomaly detection.arXiv preprint arXiv:2310.18961, 2023
Reference 57
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T13:21:03.775467Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 56065073-6a5b-4330-9b89-19a54aa5174a · outbound
This paper cites Do llms understand visual anomalies? uncovering llm’s capabilities in zero-shot anomaly detection.
OmniAD: Detect and Understand Industrial Anomaly via Multimodal Reasoning Do llms understand visual anomalies? uncovering llm’s capabilities in zero-shot anomaly detection
Reference 58
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T13:21:05.175280Z
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.
Observation 1bdbc879-ba34-4d48-989c-2c42fc99febd · outbound
This paper cites Spot-the-difference self-supervised pre-training for anomaly detection and segmentation.
OmniAD: Detect and Understand Industrial Anomaly via Multimodal Reasoning Spot-the-difference self-supervised pre-training for anomaly detection and segmentation
Reference 59
- Resolution
- malformed identifier
- raw_fallback, observed 2026-08-07T13:21:04.998487Z
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.
Pith citing papers
Observation 6f400cd3-fb95-49cc-bfa7-d4bede28acbf · 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 OmniAD: Detect and Understand Industrial Anomaly via Multimodal Reasoning
Reference 53
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T12:40:09.131334Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 388e729c-8576-4d57-8b7b-59cf3aebcfaf · 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 OmniAD: Detect and Understand Industrial Anomaly via Multimodal Reasoning
Reference 46
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T00:53:26.634905Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a85a8799-c818-49d3-833f-7a822ffb85be · inbound
AgentIAD: Agentic Industrial Anomaly Detection via Adaptive Memory Augmentation cites this paper.
AgentIAD: Agentic Industrial Anomaly Detection via Adaptive Memory Augmentation OmniAD: Detect and Understand Industrial Anomaly via Multimodal Reasoning
Reference 39
- Resolution
- verified exact
- arxiv_id, observed 2026-07-30T01:18:55.009310Z
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.
Observation 374ca66c-3e38-48a9-92b4-c0d3361892d2 · 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 OmniAD: Detect and Understand Industrial Anomaly via Multimodal Reasoning
Reference 35
- Resolution
- verified exact
- arxiv_id, observed 2026-07-30T01:18:55.009310Z
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.
Observation 71412f35-02eb-45c0-a4d6-44ad12a689f7 · 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 OmniAD: Detect and Understand Industrial Anomaly via Multimodal Reasoning
Reference 24
- Resolution
- verified exact
- arxiv_id, observed 2026-07-30T01:18:55.009310Z
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.
Observation e53e2285-dbab-4ac2-a5ed-426ccd0fce03 · 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 OmniAD: Detect and Understand Industrial Anomaly via Multimodal Reasoning
Reference 43
- Resolution
- verified exact
- arxiv_id, observed 2026-07-30T01:18:55.009310Z
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.
Observation bc3a91db-4bdc-4cd7-bef0-df456842df68 · inbound
From Structure to Synergy: A Survey of Vision-Language Perception Paradigm Evolution in Multimodal Large Language Models cites this paper.
From Structure to Synergy: A Survey of Vision-Language Perception Paradigm Evolution in Multimodal Large Language Models OmniAD: Detect and Understand Industrial Anomaly via Multimodal Reasoning
Reference 189
- Resolution
- verified exact
- arxiv_id, observed 2026-07-30T01:18:55.009310Z
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.
Observation 755089d3-864f-4090-8220-4bea35b9db50 · 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 OmniAD: Detect and Understand Industrial Anomaly via Multimodal Reasoning
Reference 43
- 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 db536c50-f8a5-4cc9-bb04-df4b5617fcb5 · 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 OmniAD: Detect and Understand Industrial Anomaly via Multimodal Reasoning
Reference 45
- Resolution
- unresolved
- no resolver link, observed 2026-08-01T15:56:44.645657Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.