Have you ever wondered how AI models understand human language? Tokenization, a process of breaking down text into smaller units (tokens), plays a vital role in this understanding. While it's often assumed that each piece of text has a single, 'canonical' tokenization, the reality is far more complex. Each text can be tokenized in a multitude of ways, forming a hidden space with vast potential. Researchers at UCLA explored this space and found that while the canonical tokenization usually holds the most probability mass, the "non-canonical" tokenizations aren't meaningless. They, in fact, harbor subtle signals that influence an AI's reasoning. This research unveils that the commonly held assumption that the probability of a text equals the probability of its canonical tokenization isn't entirely accurate. Calculating the actual probability by considering all possible tokenizations is computationally challenging, as proven by the researchers. But through clever algorithms, they approximated this 'marginal probability' and discovered it often mirrors the canonical one. Surprisingly, even with this similarity, simply combining probabilities from various tokenizations enhanced the performance of several AI models across various benchmarks, including answering complex questions. This discovery unlocks new possibilities for improving AI's understanding and generation of human language. By exploring and harnessing this tokenization space, researchers can refine AI models, leading to more accurate and nuanced interactions. The research opens up exciting avenues for enhancing AI’s reasoning and communication abilities, moving us closer to truly conversational AI.