Imagine teaching AI by showing it a few examples, just like you'd teach a child. That’s the magic of in-context learning (ICL). Now, take it a step further and add images to the mix. This is *multimodal* ICL, where AI learns from both text and visual cues. Researchers dove deep into this fascinating area, exploring why it sometimes works brilliantly and other times falls short. They discovered that the importance of images versus text varies greatly depending on the task. For instance, if the AI needs to extract key information from a text-heavy image, the visuals are crucial. But for tasks involving different image styles, the text often takes center stage. This discovery led to a clever idea: tailor the examples! By choosing examples with similar images or text, depending on the task, they significantly boosted the AI’s performance. But here’s the real kicker: the research also hints that AI can learn underlying rules, even if they contradict what it already knows! By cleverly tweaking the examples, researchers showed that AI can adapt to new tasks without extensive retraining. This breakthrough opens doors to a more adaptable and efficient AI future, where models can learn new tricks on the fly.