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arXiv · 2506.02803

SemVink: Advancing VLMs' Semantic Understanding of Optical Illusions via Visual Global Thinking

Sifan Li·Yujun Cai·Yiwei Wang
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Abstract

Vision-language models (VLMs) excel in semantic tasks but falter at a core human capability: detecting hidden content in optical illusions or AI-generated images through perceptual adjustments like zooming. We introduce HC-Bench, a benchmark of 112 images with hidden text, objects, and illusions, revealing that leading VLMs achieve near-zero accuracy (0-5.36%)-even with explicit prompting. Humans resolve such ambiguities instinctively, yet VLMs fail due to an overreliance on high-level semantics. Strikingly, we propose SemVink (Semantic Visual Thinking) by simply scaling images to low resolutions (32-128 pixels), which unlocks >99% accuracy by eliminating redundant visual noise. This exposes a critical architectural flaw: VLMs prioritize abstract reasoning over low-level visual operations crucial for real-world robustness. Our work urges a shift toward hybrid models integrating multi-scale processing, bridging the gap between computational vision and human cognition for applications in medical imaging, security, and beyond.

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Sifan Li, Yujun Cai, Yiwei Wang. 2025-10-15. SemVink: Advancing VLMs' Semantic Understanding of Optical Illusions via Visual Global Thinking. https://arxiv.org/abs/2506.02803

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