Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
IllusionBench+: A Large-scale and Comprehensive Benchmark for Visual Illusion Understanding in Vision-Language Models
As of 8 August 2026, this Paper Citation Record lists 0 of 0 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.
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 2026-08-06T11:34:29.583103Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-21T19:54:20.190246Z
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 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
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T11:34:29.583103Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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
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 1ea91375-9c0d-44bb-b5a3-d0c2f7e0ae79 · 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
- unresolved
- no resolver link, observed 2026-08-03T21:44:03.993848Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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
Unavailable: canonical work link unavailable.
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
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
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 2026-08-08T06:32:00.761636+00:00.