Large language models (LLMs) are rapidly evolving beyond text, demonstrating a surprising ability to understand images. But how can a model trained on words suddenly grasp visual concepts? New research delves into the inner workings of LLMs, specifically examining their “attention heads”—components that focus on different parts of input data. This study, analyzing four model families and scales, uncovered a fascinating revelation: certain attention heads specialize in visual processing, acting like the model's eyes. These “visual heads” concentrate on image tokens, especially in the model’s early and middle layers, indicating a structured approach to visual understanding. Surprisingly, these visual heads cluster in specific layers, unlike other types of attention mechanisms distributed more evenly. This concentration, researchers found, correlates with better performance on visual tasks. The study used a specialized dataset called PointQA, which challenges models with various image-related questions and visual prompts, to rigorously test these visual heads. Results showed that these heads activate dynamically depending on the visual and textual context, adapting to different inputs. Intriguingly, later layers showed less reliance on visual heads, opening opportunities for optimizing LLMs by pruning unnecessary computations and speeding up image processing. This research not only reveals how LLMs adapt to multimodal tasks but also paves the way for building more efficient and capable AI systems that can truly bridge the gap between text and visual understanding, leading to more intuitive and powerful applications in the future.