In the world of computer vision, pixels reign supreme. But what about vector graphics, those infinitely scalable images built on geometric primitives instead of pixels? A new research paper, 'VGBench: Evaluating Large Language Models on Vector Graphics Understanding and Generation,' explores whether Large Language Models (LLMs) can truly 'see' and interpret these unique visual representations. Researchers put LLMs like GPT-4 to the test, evaluating their ability to understand and even generate vector graphics in formats like SVG, TikZ, and Graphviz. Surprisingly, LLMs demonstrated a knack for grasping the high-level semantics embedded within vector graphics, particularly in formats like TikZ and Graphviz, which are used for complex diagrams and scientific illustrations. However, the study reveals that LLMs struggle more with lower-level formats like SVG, which deal with basic geometric shapes. This suggests that LLMs are better at interpreting the meaning and relationships within a visual scene rather than the raw visual elements themselves. One of the fascinating findings is that techniques like 'chain-of-thought' prompting can boost LLM performance on tasks involving low-level vector graphics. By guiding the LLM through a step-by-step reasoning process, researchers could improve its understanding of SVG images. This highlights the potential for optimizing LLMs to better handle the complexities of visual data. The research also delves into the LLMs’ capacity to generate vector graphics from textual descriptions. Results show that LLMs like GPT-4 can indeed create vector graphic code from captions, opening exciting possibilities for automated design and content creation. The 'VGBench' research offers a critical first step towards understanding how LLMs can bridge the gap between text and vector graphics. The findings not only reveal the strengths and weaknesses of current AI models in visual processing but also pave the way for future research aimed at developing more robust and versatile AI systems for design, data visualization, and creative content generation.