Paper Citation Record · LEDGER

IllusionBench+: A Large-scale and Comprehensive Benchmark for Visual Illusion Understanding in Vision-Language Models

As of 23 August 2026, this Paper Citation Record lists 41 of 41 outbound references and 6 inbound Pith citation observations for arXiv:2501.00848.

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

pith.paper-citation-record.v1
2501.00848 v2

Coverage vector

measured 41 of 41 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T22:46:42.106412Z

measured 47 of 47 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T11:34:29.583103Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-21T19:54:20.190246Z

Reference resolution

41 of 41 outbound references displayed

verified exact1
verified fuzzy29
unresolved11
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation b9c53194-0425-4ed8-869d-de8b221d6fce · outbound

This paper cites What are visual illusions?,.

IllusionBench+: A Large-scale and Comprehensive Benchmark for Visual Illusion Understanding in Vision-Language Models What are visual illusions?,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:46:43.280346Z

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 440dd4d5-7f37-4fd3-8ff4-146fa0496deb · outbound

This paper cites Putting illusions in their place,.

IllusionBench+: A Large-scale and Comprehensive Benchmark for Visual Illusion Understanding in Vision-Language Models Putting illusions in their place,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:46:43.264815Z

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Observation 2d237406-d24a-4f7d-9259-df5ac23d9aaf · outbound

This paper cites Visual illusions classified,.

IllusionBench+: A Large-scale and Comprehensive Benchmark for Visual Illusion Understanding in Vision-Language Models Visual illusions classified,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:46:43.234518Z

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Observation d3ece0ba-b7b2-4908-a717-647fc21fb04a · outbound

This paper cites an unresolved cited work.

IllusionBench+: A Large-scale and Comprehensive Benchmark for Visual Illusion Understanding in Vision-Language Models Unresolved cited work

Reference 4

Resolution: unresolved; raw_fallback, observed 2026-08-10T22:46:43.210460Z

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Observation 41030fe4-43e4-48b8-a397-a3fe3f4033a7 · outbound

This paper cites An empirical taxonomy of visual illusions,.

IllusionBench+: A Large-scale and Comprehensive Benchmark for Visual Illusion Understanding in Vision-Language Models An empirical taxonomy of visual illusions,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:46:43.188387Z

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Observation 17772523-7d2b-4f61-90cb-7529d140ea33 · outbound

This paper cites an unresolved cited work.

IllusionBench+: A Large-scale and Comprehensive Benchmark for Visual Illusion Understanding in Vision-Language Models Unresolved cited work

Reference 7

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Observation 5e9b5ee2-9ba5-4f30-b4ee-20cd3a15361b · outbound

This paper cites Knowledge in perception and illusion,.

IllusionBench+: A Large-scale and Comprehensive Benchmark for Visual Illusion Understanding in Vision-Language Models Knowledge in perception and illusion,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:46:43.139829Z

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Observation ca533382-b7e6-465a-828c-cf6afba04234 · outbound

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IllusionBench+: A Large-scale and Comprehensive Benchmark for Visual Illusion Understanding in Vision-Language Models Unresolved cited work

Reference 9

Resolution: unresolved; raw_fallback, observed 2026-08-10T22:46:43.119604Z

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Observation e802154f-8d46-4062-97fd-1a8f5abeda58 · outbound

This paper cites Structure from motion,.

IllusionBench+: A Large-scale and Comprehensive Benchmark for Visual Illusion Understanding in Vision-Language Models Structure from motion,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:46:43.106577Z

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Observation ae7332ab-3757-4acc-8744-c19db0666098 · outbound

This paper cites Unbiased look at dataset bias,.

IllusionBench+: A Large-scale and Comprehensive Benchmark for Visual Illusion Understanding in Vision-Language Models Unbiased look at dataset bias,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:46:43.092454Z

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Observation 74046bb1-c71a-4849-b6d8-d3d2dda8fe57 · outbound

This paper cites The role of context in object recognition,.

IllusionBench+: A Large-scale and Comprehensive Benchmark for Visual Illusion Understanding in Vision-Language Models The role of context in object recognition,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:46:43.079550Z

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Observation efad1c0a-b61a-4e1f-92a5-60e9246d83ac · outbound

This paper cites Eye and brain: The psychology of seeing,.

IllusionBench+: A Large-scale and Comprehensive Benchmark for Visual Illusion Understanding in Vision-Language Models Eye and brain: The psychology of seeing,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:46:43.063300Z

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Observation c08de946-78eb-4aee-8bcf-c0a158ba4349 · outbound

This paper cites Bruce Goldstein and Laura Cacciamani, Sensation and Perception , Cengage Learning, Boston, MA, 11th edition, 2022.

IllusionBench+: A Large-scale and Comprehensive Benchmark for Visual Illusion Understanding in Vision-Language Models Bruce Goldstein and Laura Cacciamani, Sensation and Perception , Cengage Learning, Boston, MA, 11th edition, 2022

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:46:43.172622Z

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Observation d151851a-d9b9-4c56-9ffb-2889ec780abc · outbound

This paper cites Vision- language models for vision tasks: A survey,.

IllusionBench+: A Large-scale and Comprehensive Benchmark for Visual Illusion Understanding in Vision-Language Models Vision- language models for vision tasks: A survey,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:46:43.049328Z

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Observation 25461b94-8581-4108-8a3b-63f22d74011b · outbound

This paper cites Seed-bench: Benchmarking multimodal large language models,.

IllusionBench+: A Large-scale and Comprehensive Benchmark for Visual Illusion Understanding in Vision-Language Models Seed-bench: Benchmarking multimodal large language models,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:46:43.032373Z

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Observation da7e5417-092b-40b5-83da-cf8b61190e45 · outbound

This paper cites A Survey on Multimodal Large Language Models.

IllusionBench+: A Large-scale and Comprehensive Benchmark for Visual Illusion Understanding in Vision-Language Models A Survey on Multimodal Large Language Models

Reference 17

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Source-reported events for the cited work

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Observation c50386cd-0332-4894-a8bc-c855a114989a · outbound

This paper cites Grounding visual illusions in language: Do vision-language models perceive illusions like humans?,.

IllusionBench+: A Large-scale and Comprehensive Benchmark for Visual Illusion Understanding in Vision-Language Models Grounding visual illusions in language: Do vision-language models perceive illusions like humans?,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:46:43.015102Z

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Observation 984a8446-69f2-4851-afae-c61853377531 · outbound

This paper cites Hallusionbench: an advanced diagnostic suite for entangled language hallucination and visual illusion in large vision-language models,.

IllusionBench+: A Large-scale and Comprehensive Benchmark for Visual Illusion Understanding in Vision-Language Models Hallusionbench: an advanced diagnostic suite for entangled language hallucination and visual illusion in large vision-language models,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:46:42.990959Z

Source-reported events for the cited work

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Observation 1d5b83a8-0091-44bf-aefb-43b8d20fd0da · outbound

This paper cites IllusionVQA: A Challenging Optical Illusion Dataset for Vision Language Models.

IllusionBench+: A Large-scale and Comprehensive Benchmark for Visual Illusion Understanding in Vision-Language Models IllusionVQA: A Challenging Optical Illusion Dataset for Vision Language Models

Reference 20

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Observation 083b725a-62e0-4fa6-aacb-38d09850260d · outbound

This paper cites On the synthesis of visual illusions using deep generative models,.

IllusionBench+: A Large-scale and Comprehensive Benchmark for Visual Illusion Understanding in Vision-Language Models On the synthesis of visual illusions using deep generative models,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:46:42.968642Z

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Observation 23899485-7a92-4361-a4ca-ce3b7008f772 · outbound

This paper cites Color illusions also deceive cnns for low- level vision tasks: Analysis and implications,.

IllusionBench+: A Large-scale and Comprehensive Benchmark for Visual Illusion Understanding in Vision-Language Models Color illusions also deceive cnns for low- level vision tasks: Analysis and implications,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:46:42.934839Z

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Observation 312559b6-5977-4a64-b224-142035ae4536 · outbound

This paper cites Illusory motion reproduced by deep neural networks trained for prediction,.

IllusionBench+: A Large-scale and Comprehensive Benchmark for Visual Illusion Understanding in Vision-Language Models Illusory motion reproduced by deep neural networks trained for prediction,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:46:42.713273Z

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Observation e0c00c5d-0354-4bdc-b27d-72a42c6d9e1d · outbound

This paper cites Convolutional neural networks can be deceived by visual illusions,.

IllusionBench+: A Large-scale and Comprehensive Benchmark for Visual Illusion Understanding in Vision-Language Models Convolutional neural networks can be deceived by visual illusions,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:46:42.691284Z

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Observation f34a96c1-d430-4b1a-a063-452147b4f543 · outbound

This paper cites Neural Networks Trained on Natural Scenes Exhibit Gestalt Closure.

IllusionBench+: A Large-scale and Comprehensive Benchmark for Visual Illusion Understanding in Vision-Language Models Neural Networks Trained on Natural Scenes Exhibit Gestalt Closure

Reference 25

Resolution: verified exact; local_arxiv, observed 2026-08-10T22:46:42.393824Z

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Observation a55e4d37-e449-48c6-a561-2584a64a7770 · outbound

This paper cites Judging llm-as-a-judge with mt-bench and chatbot arena,.

IllusionBench+: A Large-scale and Comprehensive Benchmark for Visual Illusion Understanding in Vision-Language Models Judging llm-as-a-judge with mt-bench and chatbot arena,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:46:42.661181Z

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Observation 08ff5501-ade2-4639-bbec-591497043d4e · outbound

This paper cites GPT-4 Technical Report.

IllusionBench+: A Large-scale and Comprehensive Benchmark for Visual Illusion Understanding in Vision-Language Models GPT-4 Technical Report

Reference 27

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Observation f39227e9-8285-410e-a666-26f9a450d2fd · outbound

This paper cites Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context.

IllusionBench+: A Large-scale and Comprehensive Benchmark for Visual Illusion Understanding in Vision-Language Models Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-10T22:46:41.981818Z

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Observation 7a8c08a8-0aa6-42e0-8f3c-900075598a2c · outbound

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

IllusionBench+: A Large-scale and Comprehensive Benchmark for Visual Illusion Understanding in Vision-Language Models Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-10T22:46:41.987940Z

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Observation 6673995a-2184-4aa0-92c2-0e47c10a8f6f · outbound

This paper cites CogVLM: Visual Expert for Pretrained Language Models.

IllusionBench+: A Large-scale and Comprehensive Benchmark for Visual Illusion Understanding in Vision-Language Models CogVLM: Visual Expert for Pretrained Language Models

Reference 30

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Observation cfc99991-ab28-4c54-9777-f97c1040b983 · outbound

This paper cites DeepSeek-VL: Towards Real-World Vision-Language Understanding.

IllusionBench+: A Large-scale and Comprehensive Benchmark for Visual Illusion Understanding in Vision-Language Models DeepSeek-VL: Towards Real-World Vision-Language Understanding

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-10T22:46:42.001970Z

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Observation 50c12914-e412-4691-b419-ec18444118df · outbound

This paper cites InternLM-XComposer2: Mastering Free-form Text-Image Composition and Comprehension in Vision-Language Large Model.

IllusionBench+: A Large-scale and Comprehensive Benchmark for Visual Illusion Understanding in Vision-Language Models InternLM-XComposer2: Mastering Free-form Text-Image Composition and Comprehension in Vision-Language Large Model

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-10T22:46:42.013624Z

Source-reported events for the cited work

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Observation 056ec2d9-bb95-4739-b40d-7f88cb73606d · outbound

This paper cites Llava-next: Improved reasoning, ocr, and world knowledge,.

IllusionBench+: A Large-scale and Comprehensive Benchmark for Visual Illusion Understanding in Vision-Language Models Llava-next: Improved reasoning, ocr, and world knowledge,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:46:42.637811Z

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Observation 616b581f-bbb6-4ce5-83c2-2f31e970eef1 · outbound

This paper cites mplug-owl2: Revolutionizing multi-modal large language model with modality collaboration,.

IllusionBench+: A Large-scale and Comprehensive Benchmark for Visual Illusion Understanding in Vision-Language Models mplug-owl2: Revolutionizing multi-modal large language model with modality collaboration,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:46:42.620855Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 4b86c04b-84c6-436d-8e07-9733c1c0a943 · outbound

This paper cites Each image is accompanied by at least two binary questions and three multiple-choice questions, all manually annotated by humans.

IllusionBench+: A Large-scale and Comprehensive Benchmark for Visual Illusion Understanding in Vision-Language Models Each image is accompanied by at least two binary questions and three multiple-choice questions, all manually annotated by humans

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:46:42.599329Z

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Observation b59da44c-6dbe-4388-aab0-9b912ee79918 · outbound

This paper cites True” when the correct answer is “False.

IllusionBench+: A Large-scale and Comprehensive Benchmark for Visual Illusion Understanding in Vision-Language Models True” when the correct answer is “False

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:46:42.579677Z

Source-reported events for the cited work

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Observation e0ff4874-567b-4037-8713-beaa38e75826 · outbound

This paper cites True,” “The answer is true.

IllusionBench+: A Large-scale and Comprehensive Benchmark for Visual Illusion Understanding in Vision-Language Models True,” “The answer is true

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:46:42.565344Z

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Observation f3da34af-27ab-430c-9d35-5ad6cb4d658c · outbound

This paper cites Determine if the respondent’s answer is correct (1) or incorrect (1).

IllusionBench+: A Large-scale and Comprehensive Benchmark for Visual Illusion Understanding in Vision-Language Models Determine if the respondent’s answer is correct (1) or incorrect (1)

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:46:42.537736Z

Source-reported events for the cited work

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Observation f9e177ec-3f58-4a1f-92f7-79b98f52f0d4 · outbound

This paper cites Therefore, we assess VLM performance by examining the accuracy of their descriptions, particularly whether they align with physical reality or human sensory perception.

IllusionBench+: A Large-scale and Comprehensive Benchmark for Visual Illusion Understanding in Vision-Language Models Therefore, we assess VLM performance by examining the accuracy of their descriptions, particularly whether they align with physical reality or human sensory perception

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:46:42.515598Z

Source-reported events for the cited work

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Observation 02a47be4-0d85-4436-afa7-33c28bb1c2ed · outbound

This paper cites Evalu- ate if there is a conflict between the image contents in the respondent’s answer and the reference answer.

IllusionBench+: A Large-scale and Comprehensive Benchmark for Visual Illusion Understanding in Vision-Language Models Evalu- ate if there is a conflict between the image contents in the respondent’s answer and the reference answer

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:46:42.498853Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 41e09e8c-8022-40c6-ad1b-31f6aa97c760 · outbound

This paper cites no illusion.

IllusionBench+: A Large-scale and Comprehensive Benchmark for Visual Illusion Understanding in Vision-Language Models no illusion

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:46:42.484373Z

Source-reported events for the cited work

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Observation 6b91fd11-f6f7-4a1f-ae35-3e75ea412915 · outbound

This paper cites real scene illu- sion.

IllusionBench+: A Large-scale and Comprehensive Benchmark for Visual Illusion Understanding in Vision-Language Models real scene illu- sion

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:46:42.466966Z

Source-reported events for the cited work

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Pith citing papers

Observation 146890dd-4eeb-48e1-bbfe-099d407d78e3 · inbound

Hate in Plain Sight: On the Risks of Moderating AI-Generated Hateful Illusions cites this paper.

Hate in Plain Sight: On the Risks of Moderating AI-Generated Hateful Illusions IllusionBench+: A Large-scale and Comprehensive Benchmark for Visual Illusion Understanding in Vision-Language Models

Reference 69

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Observation cb3b0148-b9f2-4e32-aed8-8a0726103c59 · inbound

MVI-Bench: A Comprehensive Benchmark for Evaluating Robustness to Misleading Visual Inputs in LVLMs cites this paper.

MVI-Bench: A Comprehensive Benchmark for Evaluating Robustness to Misleading Visual Inputs in LVLMs IllusionBench+: A Large-scale and Comprehensive Benchmark for Visual Illusion Understanding in Vision-Language Models

Reference 65

Resolution: verified exact; arxiv_id, observed 2026-05-21T19:54:20.192322Z

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Observation 1ea91375-9c0d-44bb-b5a3-d0c2f7e0ae79 · inbound

MVI-Bench: A Comprehensive Benchmark for Evaluating Robustness to Misleading Visual Inputs in LVLMs cites this paper.

Reference 65

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Observation c282b3cc-03dc-49f2-a012-1f69280e04d9 · inbound

SMSP: A Plug-and-Play Strategy of Multi-Scale Perception for MLLMs to Perceive Visual Illusions cites this paper.

SMSP: A Plug-and-Play Strategy of Multi-Scale Perception for MLLMs to Perceive Visual Illusions IllusionBench+: A Large-scale and Comprehensive Benchmark for Visual Illusion Understanding in Vision-Language Models

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-02T17:37:24.265521Z

Source-reported events for the cited work

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Observation 043d47d4-a50f-445a-9e2d-27eb094c9ea1 · inbound

Illusion-Aware Visual Preprocessing and Anti-Illusion Prompting for Classic Illusion Understanding in Vision-Language Models cites this paper.

Illusion-Aware Visual Preprocessing and Anti-Illusion Prompting for Classic Illusion Understanding in Vision-Language Models IllusionBench+: A Large-scale and Comprehensive Benchmark for Visual Illusion Understanding in Vision-Language Models

Reference 20

Resolution: verified exact; arxiv_id, observed 2026-05-12T01:46:14.577438Z

Source-reported events for the cited work

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Observation 02612bdb-a4ee-4908-acdc-e9f3c2bd71de · inbound

Learn to Think: Improving Multimodal Reasoning through Vision-Aware Self-Improvement Training cites this paper.

Learn to Think: Improving Multimodal Reasoning through Vision-Aware Self-Improvement Training IllusionBench+: A Large-scale and Comprehensive Benchmark for Visual Illusion Understanding in Vision-Language Models

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-05-13T06:22:23.520892Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.