Imagine training an AI model to be a master chef, only to find it’s forgotten how to boil water. This “catastrophic forgetting” is a common problem in AI, where fine-tuning a model for a specific task can make it worse at others. Researchers are constantly looking for ways to merge multiple specialized AI models into a single, all-encompassing one, but this often leads to those pesky conflicts—like our chef struggling with basic cooking skills. A new technique called LiNeS (Layer-increasing Network Scaling) offers an elegant solution. Instead of treating all layers of a neural network equally during merging, LiNeS recognizes that the “shallow” layers encode general knowledge (like boiling water), while “deep” layers hold specialized skills (like perfecting a soufflé). LiNeS scales the changes made during fine-tuning, ensuring that shallow layers retain their broad understanding while deeper layers maintain task-specific expertise. This clever approach has shown remarkable results in both image recognition and natural language processing tasks. For example, in tests with large vision models, LiNeS managed to retain nearly perfect performance on the fine-tuned task while restoring almost all of the original model's general knowledge. Similar gains were observed with language models. LiNeS isn’t just about preventing forgetting; it can also be used to merge multiple AI personalities, like combining a chatbot trained for customer service with one that excels at creative writing. This allows developers to build more versatile AI systems without starting from scratch each time. LiNeS is computationally inexpensive and remarkably simple to implement, suggesting a bright future for merging and fine-tuning AI models. While further research will explore optimal scaling strategies and adapt LiNeS to more complex architectures, it represents a significant step toward building truly generalist AI systems that can seamlessly handle a multitude of tasks.