Have you ever wondered how AI models like ChatGPT understand the order of words, sentences, or even paragraphs in a text? Traditional methods simply count tokens, like individual words or sub-words, to determine position. But what about understanding the *i*-th sentence or the last time a specific concept was mentioned? That's where a fascinating new technique called Contextual Position Encoding (CoPE) comes in. Imagine trying to find the last time "Alice" was mentioned in a story. Simply counting words won't help if you don't know how long each sentence is. CoPE solves this by letting the AI model learn which tokens are important for determining position, based on the surrounding context. Instead of just counting tokens blindly, it learns to count specific elements, like sentence endings or mentions of certain words, to understand the *true* position of information. This allows the model to perform tasks that stump even the most powerful LLMs, like accurately counting words within specific sentences or selectively copying parts of a text while ignoring others. Researchers tested CoPE on various tasks, including language modeling and code generation, and found consistent improvements. By learning to count what truly matters, CoPE opens exciting new possibilities for AI to understand and generate more complex and nuanced text and code.