Paper Citation Record · LEDGER

EMIT: Enhancing MLLMs for Industrial Anomaly Detection via Difficulty-Aware GRPO

As of 22 August 2026, this Paper Citation Record lists 56 of 56 outbound references and 5 inbound Pith citation observations for arXiv:2507.21619.

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

pith.paper-citation-record.v1
2507.21619 v1

Coverage vector

measured 56 of 56 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T12:40:09.145482Z

measured 61 of 61 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-11T23:45:43.436443Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-16T22:01:17.994037Z

Reference resolution

56 of 56 outbound references displayed

verified exact3
verified fuzzy3
unresolved50
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 00e221bb-97bc-416e-ae7d-0ca4c02ce789 · outbound

This paper cites , " * write output.state after.block = add.period write newline.

EMIT: Enhancing MLLMs for Industrial Anomaly Detection via Difficulty-Aware GRPO , " * write output.state after.block = add.period write newline

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-06T12:40:08.852129Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b7fa2177-ce07-4b4a-8c67-4e7084e877c1 · outbound

This paper cites write newline.

EMIT: Enhancing MLLMs for Industrial Anomaly Detection via Difficulty-Aware GRPO write newline

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-06T12:40:08.858292Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7e1efec1-6c4d-471f-a6fc-9bfd3e783d8f · outbound

This paper cites GPT-4 Technical Report.

EMIT: Enhancing MLLMs for Industrial Anomaly Detection via Difficulty-Aware GRPO GPT-4 Technical Report

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-06T12:40:08.864234Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aaec8ed6-626d-45ac-82e5-72c0bd482c40 · outbound

This paper cites Pixtral 12B.

EMIT: Enhancing MLLMs for Industrial Anomaly Detection via Difficulty-Aware GRPO Pixtral 12B

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-06T12:40:08.869752Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6f022aee-9599-42ec-91ec-e0fefbe08d6e · outbound

This paper cites VISION Datasets: A Benchmark for Vision-based InduStrial InspectiON.

EMIT: Enhancing MLLMs for Industrial Anomaly Detection via Difficulty-Aware GRPO VISION Datasets: A Benchmark for Vision-based InduStrial InspectiON

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-06T12:40:08.874901Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e93dea11-15a1-4d01-bf75-938c5b1d8bfd · outbound

This paper cites Qwen2.5-VL Technical Report.

EMIT: Enhancing MLLMs for Industrial Anomaly Detection via Difficulty-Aware GRPO Qwen2.5-VL Technical Report

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-06T12:40:08.879879Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 236e4937-1854-43c3-bc16-636291e7c65c · outbound

This paper cites an unresolved cited work.

EMIT: Enhancing MLLMs for Industrial Anomaly Detection via Difficulty-Aware GRPO Unresolved cited work

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-06T12:40:08.885209Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 68b0523e-298e-4129-9b65-e92316cf34c5 · outbound

This paper cites an unresolved cited work.

EMIT: Enhancing MLLMs for Industrial Anomaly Detection via Difficulty-Aware GRPO Unresolved cited work

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-06T12:40:08.890655Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 35853851-dda4-40e9-9a9e-c85ca3e269dc · outbound

This paper cites an unresolved cited work.

EMIT: Enhancing MLLMs for Industrial Anomaly Detection via Difficulty-Aware GRPO Unresolved cited work

Reference 9

Resolution: unresolved; raw_fallback, observed 2026-08-06T12:40:10.452536Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation a9db3fcc-f3cb-4f5c-a685-2d35d9a36ffb · outbound

This paper cites AnomalyR1: A GRPO-based End-to-end MLLM for Industrial Anomaly Detection.

EMIT: Enhancing MLLMs for Industrial Anomaly Detection via Difficulty-Aware GRPO AnomalyR1: A GRPO-based End-to-end MLLM for Industrial Anomaly Detection

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-06T12:40:08.901123Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2f1f051a-9bf5-45f5-bfb7-ad7af829376f · outbound

This paper cites SFT or RL? An Early Investigation into Training R1-Like Reasoning Large Vision-Language Models.

EMIT: Enhancing MLLMs for Industrial Anomaly Detection via Difficulty-Aware GRPO SFT or RL? An Early Investigation into Training R1-Like Reasoning Large Vision-Language Models

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-06T12:40:08.907260Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 34e8e425-fae2-4883-8d95-e557d56e9e67 · outbound

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

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 2ba5473a-01b4-47b6-a103-368106feb191 · outbound

This paper cites an unresolved cited work.

EMIT: Enhancing MLLMs for Industrial Anomaly Detection via Difficulty-Aware GRPO Unresolved cited work

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-06T12:40:08.917690Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7f2329c7-ee51-432d-9f8d-8eafe68949b1 · outbound

This paper cites an unresolved cited work.

EMIT: Enhancing MLLMs for Industrial Anomaly Detection via Difficulty-Aware GRPO Unresolved cited work

Reference 14

Resolution: unresolved; raw_fallback, observed 2026-08-06T12:40:10.429507Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation ff02cf4d-2a46-4650-a861-d3845cfaaf4e · outbound

This paper cites Salvaging the Overlooked: Leveraging Class-Aware Contrastive Learning for Multi-Class Anomaly Detection.

EMIT: Enhancing MLLMs for Industrial Anomaly Detection via Difficulty-Aware GRPO Salvaging the Overlooked: Leveraging Class-Aware Contrastive Learning for Multi-Class Anomaly Detection

Reference 15

Resolution: verified exact; local_arxiv, observed 2026-08-06T12:40:09.791610Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation c6652989-e577-4694-a2ca-7005ad01f334 · outbound

This paper cites an unresolved cited work.

EMIT: Enhancing MLLMs for Industrial Anomaly Detection via Difficulty-Aware GRPO Unresolved cited work

Reference 16

Resolution: unresolved; raw_fallback, observed 2026-08-06T12:40:10.401380Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation b26f6877-ea85-40ce-9ff2-eb4cda56fc10 · outbound

This paper cites an unresolved cited work.

EMIT: Enhancing MLLMs for Industrial Anomaly Detection via Difficulty-Aware GRPO Unresolved cited work

Reference 17

Resolution: unresolved; raw_fallback, observed 2026-08-06T12:40:10.378789Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation e89c7fb9-4743-47e7-adad-bb154a9d8a44 · outbound

This paper cites an unresolved cited work.

EMIT: Enhancing MLLMs for Industrial Anomaly Detection via Difficulty-Aware GRPO Unresolved cited work

Reference 18

Resolution: unresolved; raw_fallback, observed 2026-08-06T12:40:10.360380Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation e73b8163-f4a0-4484-be53-65d7f07aecfb · outbound

This paper cites X.; Nguyen, A.-N.; Tran, D.-T.; Duong, V.-H.; Mai, A.-T.; Pham, D.-L.; Phan, K.-T.; Do, M.-Q.; Duong, T.

EMIT: Enhancing MLLMs for Industrial Anomaly Detection via Difficulty-Aware GRPO X.; Nguyen, A.-N.; Tran, D.-T.; Duong, V.-H.; Mai, A.-T.; Pham, D.-L.; Phan, K.-T.; Do, M.-Q.; Duong, T

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:40:10.342111Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 9a40814f-a810-4fed-8834-84086d62a5f8 · outbound

This paper cites an unresolved cited work.

EMIT: Enhancing MLLMs for Industrial Anomaly Detection via Difficulty-Aware GRPO Unresolved cited work

Reference 20

Resolution: unresolved; raw_fallback, observed 2026-08-06T12:40:10.320984Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 5aaba38c-c518-44bd-bad0-a78a97c5e5c1 · outbound

This paper cites H.; Bae, K.; and Kang, B.

EMIT: Enhancing MLLMs for Industrial Anomaly Detection via Difficulty-Aware GRPO H.; Bae, K.; and Kang, B

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:40:10.298361Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 142b79de-f94a-4ea8-84ba-20b7fd50ad1b · outbound

This paper cites an unresolved cited work.

EMIT: Enhancing MLLMs for Industrial Anomaly Detection via Difficulty-Aware GRPO Unresolved cited work

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-06T12:40:08.963663Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5cf7a066-025b-4c2b-a35c-28c09e2f7526 · outbound

This paper cites an unresolved cited work.

EMIT: Enhancing MLLMs for Industrial Anomaly Detection via Difficulty-Aware GRPO Unresolved cited work

Reference 23

Resolution: unresolved; raw_fallback, observed 2026-08-06T12:40:10.265128Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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

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

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 266db05a-0095-4418-908f-b7326492176b · outbound

This paper cites an unresolved cited work.

EMIT: Enhancing MLLMs for Industrial Anomaly Detection via Difficulty-Aware GRPO Unresolved cited work

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-06T12:40:08.979414Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9d968cc0-595e-4c5c-b79b-01049a331c94 · outbound

This paper cites an unresolved cited work.

EMIT: Enhancing MLLMs for Industrial Anomaly Detection via Difficulty-Aware GRPO Unresolved cited work

Reference 26

Resolution: unresolved; raw_fallback, observed 2026-08-06T12:40:10.235881Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation c8dbd619-02f6-4b04-871b-e4aa6f5cbff9 · outbound

This paper cites an unresolved cited work.

EMIT: Enhancing MLLMs for Industrial Anomaly Detection via Difficulty-Aware GRPO Unresolved cited work

Reference 27

Resolution: unresolved; raw_fallback, observed 2026-08-06T12:40:10.218050Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation bb28c23c-e091-4010-901e-4d0c15e9b1f9 · outbound

This paper cites an unresolved cited work.

EMIT: Enhancing MLLMs for Industrial Anomaly Detection via Difficulty-Aware GRPO Unresolved cited work

Reference 28

Resolution: verified exact; raw_fallback, observed 2026-08-06T12:40:09.745612Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation f1755db4-efc8-42be-a793-82fe83d168cd · outbound

This paper cites LLaVA-OneVision: Easy Visual Task Transfer.

EMIT: Enhancing MLLMs for Industrial Anomaly Detection via Difficulty-Aware GRPO LLaVA-OneVision: Easy Visual Task Transfer

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-06T12:40:08.998962Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c5af75d5-94d6-40be-ab56-232a5a907760 · outbound

This paper cites LLaVA-NeXT-Interleave: Tackling Multi-image, Video, and 3D in Large Multimodal Models.

EMIT: Enhancing MLLMs for Industrial Anomaly Detection via Difficulty-Aware GRPO LLaVA-NeXT-Interleave: Tackling Multi-image, Video, and 3D in Large Multimodal Models

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-06T12:40:09.004173Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 489955fa-f0db-40dd-aab8-7bcd5950611e · outbound

This paper cites an unresolved cited work.

EMIT: Enhancing MLLMs for Industrial Anomaly Detection via Difficulty-Aware GRPO Unresolved cited work

Reference 31

Resolution: unresolved; raw_fallback, observed 2026-08-06T12:40:10.200212Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 1b5a060a-d28d-4eb4-badc-1a12c134e530 · outbound

This paper cites LAD-Reasoner: Tiny Multimodal Models are Good Reasoners for Logical Anomaly Detection.

EMIT: Enhancing MLLMs for Industrial Anomaly Detection via Difficulty-Aware GRPO LAD-Reasoner: Tiny Multimodal Models are Good Reasoners for Logical Anomaly Detection

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-06T12:40:09.026068Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e1d38f56-05e5-4ca2-b905-e9bc1153908d · outbound

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

EMIT: Enhancing MLLMs for Industrial Anomaly Detection via Difficulty-Aware GRPO Myriad: Large Multimodal Model by Applying Vision Experts for Industrial Anomaly Detection

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-06T12:40:09.030806Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a7930e1a-a61a-47ff-be98-d60544282e9d · outbound

This paper cites Decoupled Weight Decay Regularization.

EMIT: Enhancing MLLMs for Industrial Anomaly Detection via Difficulty-Aware GRPO Decoupled Weight Decay Regularization

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-06T12:40:09.035569Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 60f7aea7-46a0-4276-ac8b-3313effe7c7a · outbound

This paper cites an unresolved cited work.

EMIT: Enhancing MLLMs for Industrial Anomaly Detection via Difficulty-Aware GRPO Unresolved cited work

Reference 35

Resolution: unresolved; raw_fallback, observed 2026-08-06T12:40:10.183971Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 9423b6a8-e601-4784-a642-2f8fa04fa683 · outbound

This paper cites an unresolved cited work.

EMIT: Enhancing MLLMs for Industrial Anomaly Detection via Difficulty-Aware GRPO Unresolved cited work

Reference 36

Resolution: unresolved; raw_fallback, observed 2026-08-06T12:40:10.164806Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 5ffa0a77-bb26-47fa-9436-25c1869ed6f9 · outbound

This paper cites Proximal Policy Optimization Algorithms.

EMIT: Enhancing MLLMs for Industrial Anomaly Detection via Difficulty-Aware GRPO Proximal Policy Optimization Algorithms

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-06T12:40:09.055599Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 49291026-ffd8-495c-a8f5-88184fa11fa3 · outbound

This paper cites DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models.

EMIT: Enhancing MLLMs for Industrial Anomaly Detection via Difficulty-Aware GRPO DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-06T12:40:09.060733Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 10d08445-05c6-476d-acbb-f6b8e35c06d7 · outbound

This paper cites Gemma 3 Technical Report.

EMIT: Enhancing MLLMs for Industrial Anomaly Detection via Difficulty-Aware GRPO Gemma 3 Technical Report

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-06T12:40:09.065678Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation eef64f66-a01b-4867-a120-9f3264f46d84 · outbound

This paper cites Kimi-VL Technical Report.

EMIT: Enhancing MLLMs for Industrial Anomaly Detection via Difficulty-Aware GRPO Kimi-VL Technical Report

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-06T12:40:09.070461Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation edabd728-5618-4112-ad4a-022bddbccac1 · outbound

This paper cites GLM-4.5V and GLM-4.1V-Thinking: Towards Versatile Multimodal Reasoning with Scalable Reinforcement Learning.

EMIT: Enhancing MLLMs for Industrial Anomaly Detection via Difficulty-Aware GRPO GLM-4.5V and GLM-4.1V-Thinking: Towards Versatile Multimodal Reasoning with Scalable Reinforcement Learning

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-06T12:40:09.075553Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bc49ce24-5c23-48f2-8f7e-5e0c64f175db · outbound

This paper cites an unresolved cited work.

EMIT: Enhancing MLLMs for Industrial Anomaly Detection via Difficulty-Aware GRPO Unresolved cited work

Reference 42

Resolution: unresolved; raw_fallback, observed 2026-08-06T12:40:10.143145Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 4d8ff12c-2c07-4a63-99f6-b547ba3544cc · outbound

This paper cites MiMo-VL Technical Report.

EMIT: Enhancing MLLMs for Industrial Anomaly Detection via Difficulty-Aware GRPO MiMo-VL Technical Report

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-06T12:40:09.085046Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f3f5af56-2881-48cb-8682-7106a842be25 · outbound

This paper cites M.; and Dwivedi, I.

EMIT: Enhancing MLLMs for Industrial Anomaly Detection via Difficulty-Aware GRPO M.; and Dwivedi, I

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:40:10.123965Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation f3710da3-0809-49a6-8512-8f7d756894fe · outbound

This paper cites Demystifying Long Chain-of-Thought Reasoning in LLMs.

EMIT: Enhancing MLLMs for Industrial Anomaly Detection via Difficulty-Aware GRPO Demystifying Long Chain-of-Thought Reasoning in LLMs

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-06T12:40:09.094196Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6751c8cb-fbe5-49d6-9c41-68c33b1b09fd · outbound

This paper cites DAPO: An Open-Source LLM Reinforcement Learning System at Scale.

EMIT: Enhancing MLLMs for Industrial Anomaly Detection via Difficulty-Aware GRPO DAPO: An Open-Source LLM Reinforcement Learning System at Scale

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-06T12:40:09.098676Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3b75b157-92d3-4b53-b934-bdafdce5508d · outbound

This paper cites AFR-CLIP: Enhancing Zero-Shot Industrial Anomaly Detection with Stateless-to-Stateful Anomaly Feature Rectification.

EMIT: Enhancing MLLMs for Industrial Anomaly Detection via Difficulty-Aware GRPO AFR-CLIP: Enhancing Zero-Shot Industrial Anomaly Detection with Stateless-to-Stateful Anomaly Feature Rectification

Reference 47

Resolution: verified exact; local_arxiv, observed 2026-08-06T12:40:09.377752Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation a606a82d-2e84-4faf-9b0c-f88a7abed14f · outbound

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

EMIT: Enhancing MLLMs for Industrial Anomaly Detection via Difficulty-Aware GRPO LR-IAD:Mask-Free Industrial Anomaly Detection with Logical Reasoning

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-06T12:40:09.107647Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1819753b-6981-4042-9eb5-c37956694526 · outbound

This paper cites an unresolved cited work.

EMIT: Enhancing MLLMs for Industrial Anomaly Detection via Difficulty-Aware GRPO Unresolved cited work

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-06T12:40:09.112920Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e03d549c-589b-4ff7-a744-a3fe99a42268 · outbound

This paper cites an unresolved cited work.

EMIT: Enhancing MLLMs for Industrial Anomaly Detection via Difficulty-Aware GRPO Unresolved cited work

Reference 50

Resolution: unresolved; raw_fallback, observed 2026-08-06T12:40:10.093907Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation b881d835-b58d-455f-a2fb-f01a98b264f0 · outbound

This paper cites an unresolved cited work.

EMIT: Enhancing MLLMs for Industrial Anomaly Detection via Difficulty-Aware GRPO Unresolved cited work

Reference 51

Resolution: unresolved; raw_fallback, observed 2026-08-06T12:40:10.076114Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 1ae09957-90b8-4934-9624-6cb90b19a291 · outbound

This paper cites EIAD: Explainable Industrial Anomaly Detection Via Multi-Modal Large Language Models.

EMIT: Enhancing MLLMs for Industrial Anomaly Detection via Difficulty-Aware GRPO EIAD: Explainable Industrial Anomaly Detection Via Multi-Modal Large Language Models

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-06T12:40:09.126875Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6f400cd3-fb95-49cc-bfa7-d4bede28acbf · outbound

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

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 a91e6f93-06e8-4478-805f-c7c37868bae8 · outbound

This paper cites an unresolved cited work.

EMIT: Enhancing MLLMs for Industrial Anomaly Detection via Difficulty-Aware GRPO Unresolved cited work

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-06T12:40:09.135705Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2dae77c6-46d6-4cdc-84d7-f2e4269458fb · outbound

This paper cites InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models.

EMIT: Enhancing MLLMs for Industrial Anomaly Detection via Difficulty-Aware GRPO InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-06T12:40:09.140188Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b5232b97-f0a7-49bc-a75d-b157cce370b9 · outbound

This paper cites an unresolved cited work.

EMIT: Enhancing MLLMs for Industrial Anomaly Detection via Difficulty-Aware GRPO Unresolved cited work

Reference 56

Resolution: unresolved; raw_fallback, observed 2026-08-06T12:40:10.056240Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Pith citing papers

Observation 27310443-1a63-48a8-ae42-de2b7e9b7d36 · inbound

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

AgentIAD: Agentic Industrial Anomaly Detection via Adaptive Memory Augmentation EMIT: Enhancing MLLMs for Industrial Anomaly Detection via Difficulty-Aware GRPO

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-05-16T22:01:17.995692Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 51d0c095-f704-4f28-9341-ee3fe27bb331 · 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 EMIT: Enhancing MLLMs for Industrial Anomaly Detection via Difficulty-Aware GRPO

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-05-15T21:06:38.083776Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation cd7770c5-3f7f-44ba-8e0f-234ec69dbf17 · 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 EMIT: Enhancing MLLMs for Industrial Anomaly Detection via Difficulty-Aware GRPO

Reference 25

Resolution: verified exact; arxiv_id, observed 2026-05-15T11:55:33.336832Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation b1cba8b3-4987-42a7-95aa-23c071131261 · inbound

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

FORGE: Fine-grained Multimodal Evaluation for Manufacturing Scenarios EMIT: Enhancing MLLMs for Industrial Anomaly Detection via Difficulty-Aware GRPO

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-05-10T23:00:50.195071Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 8ffa8b25-d671-4500-ad62-b61ec1868b9b · 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 EMIT: Enhancing MLLMs for Industrial Anomaly Detection via Difficulty-Aware GRPO

Reference 14

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.