Imagine giving an AI an eye exam. That's essentially what researchers did in a fascinating new study exploring how Vision-Language Models (VLMs) actually "see" the world. VLMs, combining image processing with the language skills of large language models (LLMs), have shown impressive results on various tasks. But do they truly understand what they're looking at? This research delves into the core of visual recognition, testing VLMs on fundamental elements like color, shape, and semantic understanding. Researchers created a special dataset, LENS (Learning ElemeNt for visual Sensory), to "instruct" the VLMs on how to perform the tests, much like prepping a human for an eye exam. This involved teaching the AI to compare colors and shapes, and even to identify misplaced parts in scrambled images. The results were intriguing. The study found that VLMs are surprisingly insensitive to the color green, unlike human vision. They seem to see the world with a greenish tint, excelling at distinguishing reds and blues but struggling with greens. This begs the question: why do machines perceive color so differently than we do? Further investigation pointed to the visual encoder—the component responsible for processing images—as the source of this green bias. The study also revealed how LLM size affects shape perception. Larger models proved more sensitive to subtle shape variations, highlighting how the "brain" of a VLM influences its interpretation of visual information. These findings have significant implications for the future of AI. Imagine an AI analyzing a chart with similar shades of green—its color blindness could lead to inaccurate interpretations. But by understanding these limitations, we can develop strategies to overcome them, such as pre-processing images to enhance color differences. This research opens a window into the complex world of VLM perception, paving the way for more robust and reliable AI systems in the future.