Large language models (LLMs) are impressive, but they can still hallucinate or make factual errors. While methods exist to correct these issues, they often treat each mistake as a separate, isolated incident. Imagine having to re-teach someone the same fact over and over again—that's essentially what's happening with current LLM editing techniques. But what if we could teach an LLM to learn from its mistakes and continuously improve its knowledge? Researchers have developed a new approach called DAFNet (Dynamic Auxiliary Fusion Network) that addresses this challenge of *sequential model editing*. Instead of treating each correction as a one-time fix, DAFNet considers the relationships between facts over time. It's like connecting the dots, allowing the LLM to build a more comprehensive and accurate understanding of the world. This is achieved through a clever two-step process. First, DAFNet analyzes the relationships *within* a single fact, understanding how different parts of the information connect. Then, it looks at the relationships *between* different facts presented over time, preventing the model from forgetting previous corrections as it learns new ones. To further enhance this learning process, the researchers created a special dataset called DAFSet. This dataset is designed to teach the LLM how to edit itself more effectively, focusing on recent, popular, and even obscure or complex information. The results are promising. DAFNet significantly outperforms existing methods in correcting both single and multiple errors in LLMs. This research opens exciting possibilities for creating more reliable and adaptable LLMs. Imagine AI assistants that continuously learn and refine their knowledge, becoming more accurate and helpful over time. While challenges remain, such as the computational resources required for training, DAFNet represents a significant step towards more robust and continuously learning AI.