Imagine a universal translator, effortlessly switching between languages. While we're not quite there yet, recent Large Language Model (LLM) advancements are getting us closer. However, these powerful AIs face challenges like "catastrophic forgetting"—losing previously learned translation skills when acquiring new ones. A groundbreaking new framework called LANDeRMT addresses this by targeting specific "neurons" within the AI’s neural network. Instead of retraining the entire model, LANDeRMT identifies and fine-tunes only the essential language-aware neurons. This method not only improves translation quality across multiple languages but also minimizes interference with other AI capabilities. It's like giving the AI a specialized language center that can adapt without disrupting its overall knowledge base. The process is remarkably efficient, finetuning only a small fraction of the model's parameters for enhanced performance. LANDeRMT also uses a "routing" mechanism to allocate language-general and language-specific capacities, making the AI more adaptable to different linguistic nuances. While more research is needed, early results are promising, showing significant improvements in translation quality across ten language pairs. This innovative approach represents a significant leap forward in making truly multilingual AI a reality, paving the way for more accurate, adaptable, and efficient translation technology.