Imagine giving AI a puzzle and being able to see its thought process. That's the idea behind Interleaved-modal Chain-of-Thought (ICoT), a breakthrough approach that enhances visual reasoning in AI. Traditionally, AI models like vision-language models (VLMs) struggle with complex visual problems because they can't fully explain their 'thinking'. Their attempts at rationale are limited to text, like trying to describe a picture with words instead of showing the actual image. ICoT changes this by allowing the AI to incorporate visual elements directly into its reasoning steps. It’s like showing its work, using snippets of the image to support its textual explanation. This interleaved approach makes the AI's process transparent and significantly improves its accuracy. The key innovation is a technique called Attention-driven Selection (ADS), which works by letting the AI focus on specific parts of the image, like highlighting relevant clues in a visual puzzle. ADS smartly picks out the essential parts of the image without requiring any extra training or complex modifications. This makes it adaptable to various VLM architectures. Tests on challenging visual reasoning tasks demonstrate that ICoT consistently outperforms traditional methods, particularly in scenarios requiring detailed understanding and intricate explanations. While still a nascent technology, ICoT holds immense potential. Its ability to solve complex visual problems while offering transparency into its reasoning is a significant step towards making AI vision more robust and trustworthy. Challenges remain, such as efficiently processing larger amounts of visual information. But by giving us a glimpse into the AI’s mind, ICoT paves the way for more interpretable, accurate, and reliable AI vision in the future.