This article discusses RePo, a module that improves transformer-based language models by assigning semantic positions to tokens, enhancing their ability to manage context. It shows that RePo effectively reduces cognitive load, helping models better handle noisy inputs, structured data, and long contexts. Experimental results demonstrate significant performance gains in various tasks.