Paper Citation Record · LEDGER

IllusionVQA: A Challenging Optical Illusion Dataset for Vision Language Models

As of 21 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 16 inbound Pith citation observations for arXiv:2403.15952.

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

pith.paper-citation-record.v1
2403.15952 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 16 of 16 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 16 of 16 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T14:33:14.980113Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-01T22:26:18.101110Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation a94c3810-2420-4ad0-8311-6a0c4839632e · inbound

Evaluating Model Perception of Color Illusions in Photorealistic Scenes cites this paper.

Evaluating Model Perception of Color Illusions in Photorealistic Scenes IllusionVQA: A Challenging Optical Illusion Dataset for Vision Language Models

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-11T19:59:23.191351Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 96683c85-2c11-4a28-9fa8-32aeeee56807 · inbound

The Art of Deception: Color Visual Illusions and Diffusion Models cites this paper.

The Art of Deception: Color Visual Illusions and Diffusion Models IllusionVQA: A Challenging Optical Illusion Dataset for Vision Language Models

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-11T16:23:09.254506Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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

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

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

Resolution: unresolved; no resolver link, observed 2026-08-10T22:46:41.929565Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f7f4036e-b3a8-4fc2-bbc5-3f5eeb9a67dd · inbound

Rethinking Evaluation of Sparse Autoencoders through the Representation of Polysemous Words cites this paper.

Rethinking Evaluation of Sparse Autoencoders through the Representation of Polysemous Words IllusionVQA: A Challenging Optical Illusion Dataset for Vision Language Models

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-10T21:28:16.007497Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation acb0fa7e-86e9-47fc-aefc-ee8c5b667f34 · inbound

Do Large Vision-Language Models Distinguish between the Actual and Apparent Features of Illusions? cites this paper.

Do Large Vision-Language Models Distinguish between the Actual and Apparent Features of Illusions? IllusionVQA: A Challenging Optical Illusion Dataset for Vision Language Models

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-07T10:18:06.129541Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9e722217-a78f-4df5-8d01-779faf826a48 · 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 IllusionVQA: A Challenging Optical Illusion Dataset for Vision Language Models

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-06T11:34:28.396066Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4e7533f3-45be-49f4-8ab8-417bdd1a5967 · inbound

Empowering Multimodal LLMs with External Tools: A Comprehensive Survey cites this paper.

Empowering Multimodal LLMs with External Tools: A Comprehensive Survey IllusionVQA: A Challenging Optical Illusion Dataset for Vision Language Models

Reference 78

Resolution: unresolved; no resolver link, observed 2026-08-05T20:28:50.070895Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cd6fbf9e-f7c2-4509-b98a-77fc80dd3366 · 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 IllusionVQA: A Challenging Optical Illusion Dataset for Vision Language Models

Reference 43

Resolution: verified exact; arxiv_id, observed 2026-05-21T19:54:20.276464Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 75a71166-75b6-4c34-9aa1-47523f66e0fe · 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 IllusionVQA: A Challenging Optical Illusion Dataset for Vision Language Models

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-03T21:44:01.631528Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aa3009b2-a25a-4be7-b765-98b5cd75a494 · 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 IllusionVQA: A Challenging Optical Illusion Dataset for Vision Language Models

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-02T17:37:24.238320Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c470afb2-a3ab-46ec-9af8-360ac8356a35 · inbound

VLMs Need Words: Vision Language Models Ignore Visual Detail In Favor of Semantic Anchors cites this paper.

VLMs Need Words: Vision Language Models Ignore Visual Detail In Favor of Semantic Anchors IllusionVQA: A Challenging Optical Illusion Dataset for Vision Language Models

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-05-13T21:58:19.876482Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 72376066-0eef-47af-975c-16ec9ffa031e · inbound

Saliency-R1: Enforcing Interpretable and Faithful Vision-language Reasoning via Saliency-map Alignment Reward cites this paper.

Saliency-R1: Enforcing Interpretable and Faithful Vision-language Reasoning via Saliency-map Alignment Reward IllusionVQA: A Challenging Optical Illusion Dataset for Vision Language Models

Reference 58

Resolution: verified exact; arxiv_id, observed 2026-05-10T22:20:47.820906Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 560d5947-15b0-46f1-b2fa-12aee1f6899f · 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 IllusionVQA: A Challenging Optical Illusion Dataset for Vision Language Models

Reference 17

Resolution: verified exact; arxiv_id, observed 2026-05-12T01:46:14.568503Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation b8763a95-16c8-40d0-8524-5248b3dbd631 · inbound

Readable Yet Unpredictable: Rotated-Outcome Prediction in Vision-Language Models cites this paper.

Readable Yet Unpredictable: Rotated-Outcome Prediction in Vision-Language Models IllusionVQA: A Challenging Optical Illusion Dataset for Vision Language Models

Reference 6

Resolution: metadata mismatch; arxiv_id, observed 2026-07-01T22:26:18.102419Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 36f6b56f-5d4b-442f-9bb1-9500562a6c59 · inbound

Pattern over Pixels: Measuring Pattern Completion Bias in Multimodal Code Generation cites this paper.

Pattern over Pixels: Measuring Pattern Completion Bias in Multimodal Code Generation IllusionVQA: A Challenging Optical Illusion Dataset for Vision Language Models

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-05T14:41:00.273756Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 969e6648-fee2-4cb9-9374-aaf722b1d62d · inbound

RoRA: Role-Oriented Regional Allocation for Visual Token Pruning in MLLMs cites this paper.

RoRA: Role-Oriented Regional Allocation for Visual Token Pruning in MLLMs IllusionVQA: A Challenging Optical Illusion Dataset for Vision Language Models

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-15T14:33:14.980113Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.