Imagine teaching a super-intelligent robot something new. You could tweak its internal code directly or show it some examples. Both work, but what if there was a better way? That's the question researchers tackled in "Towards Unified Multimodal Editing with Enhanced Knowledge Collaboration." They found current methods of editing multimodal large language models (MLLMs), like those powering image captioning or visual question answering, have limitations. Tweaking internal code ("intrinsic editing") can make the AI inflexible, while showing examples ("external editing") can be misleading. The researchers propose a unified approach called UniKE, treating both internal code and examples as different parts of the AI's memory. Think of it like teaching a child—you both explain concepts and show them real-world examples. UniKE does something similar, representing knowledge as 'key-value pairs' and using a process resembling human cognitive development to help the AI assimilate new information. This allows for a more balanced and precise knowledge transfer, boosting both accuracy and flexibility. The results? UniKE improves MLLMs across various tasks, enabling them to learn new things without forgetting what they already know. This research opens doors to more efficient, reliable, and robust MLLM editing, promising future AI systems that learn and adapt more like humans.