Can LVLMs Uncover the Truth Behind Visual Illusions? An Analysis of Perceptual and Reasoning Capabilities

arXiv:2607.27747 · cs.CL, cs.AI · Submitted 2026-07-30 · Read on arXiv

cs.CL, cs.AI

Submitted: 2026-07-30

Updated: 2026-08-27

Code: https://github.com/open-compass/opencompass

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Benchmark — 24× in this paper · explained in 2 episodes
• Hallucination — 12× in this paper · explained in 4 episodes
• Thinking mode — 9× in this paper · explained in 2 episodes
• Core — 5× in this paper · explained in 2 episodes
• Accuracy — 4× in this paper · explained in 1 episode
• Adam — 3× in this paper · explained in 3 episodes
• Vision-language models — 3× in this paper · explained in 2 episodes
• Foundation Model — 2× in this paper · explained in 3 episodes
• Foundation Models — 2× in this paper · explained in 8 episodes
• Memorization — 2× in this paper · explained in 4 episodes
• Reinforcement Learning — 2× in this paper · explained in 4 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Cross-modal Alignment — 1× in this paper · explained in 2 episodes
• Evaluation Framework — 1× in this paper · explained in 3 episodes
• Evaluation Protocol — 1× in this paper · explained in 2 episodes
• Generator — 1× in this paper · explained in 2 episodes
• Grounding — 1× in this paper · explained in 3 episodes
• Instruction Tuning — 1× in this paper · explained in 3 episodes
• LLM-as-a-Judge — 1× in this paper · explained in 4 episodes
• Language Models (LLMs) — 1× in this paper · explained in 2 episodes
• Large Language Models (LLMs) — 1× in this paper · explained in 9 episodes
• Localization — 1× in this paper · explained in 2 episodes
• MLLMs — 1× in this paper · explained in 2 episodes
• Transformer — 1× in this paper · explained in 2 episodes
• Transformers — 1× in this paper · explained in 2 episodes
• Verification — 1× in this paper · explained in 2 episodes
• Vision Foundation Models — 1× in this paper · explained in 2 episodes

Sources

• Qwen3-VL Technical Report
• Hallucination of Multimodal Large Language Models: A Survey
• Are We on the Right Way for Evaluating Large Vision-Language Models?
• SimpleVQA: Multimodal Factuality Evaluation for Multimodal Large Language Models
• DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning
• Insight-V: Exploring Long-Chain Visual Reasoning with Multimodal Large Language Models
• An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
• A Survey on LLM-as-a-Judge
• OlympiadBench: A Challenging Benchmark for Promoting AGI with Olympiad-Level Bilingual Multimodal Scientific Problems
• LLM2CLIP: Powerful Language Model Unlocks Richer Cross-Modality Representation
• GPT-4 Technical Report
• LLaVA-NeXT-Interleave: Tackling Multi-image, Video, and 3D in Large Multimodal Models
• Evaluating Object Hallucination in Large Vision-Language Models
• PhD: A ChatGPT-Prompted Visual hallucination Evaluation Dataset
• MMBench: Is Your Multi-modal Model an All-around Player?
• IllusionVQA: A Challenging Optical Illusion Dataset for Vision Language Models
• Do Large Vision-Language Models Distinguish between the Actual and Apparent Features of Illusions?
• The Illusion-Illusion: Vision Language Models See Illusions Where There Are None
• DeepSeek-VL: Towards Real-World Vision-Language Understanding
• AMBER: An LLM-free Multi-dimensional Benchmark for MLLMs Hallucination Evaluation

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