The EMO framework uses a specific pretraining mixture of experts to create modular neural networks. By isolating specialized knowledge into distinct components, the system reduces interference during learning. This approach allows researchers to swap or update specific modules without retraining the entire model. It provides a scalable path toward more efficient, customizable large-scale AI architectures.