This white paper introduces the concept of Hierarchical Tokens, a novel architectural direction for transformer-based models. Instead of limiting language generation to token-level prediction (word by word), this approach expands the predictive structure to higher-level semantic units such as sentences, paragraphs, sections, chapters, and even domains of knowledge—each treated as composable macro-tokens. By applying the same mechanisms of attention and probability distribution across multiple scales of abstraction [1][4], this method proposes a path toward Artificial General Intelligence (AGI) that is simpler, more natural, and more human-like. This approach aligns machine generation with how humans plan, organize, and express thought.

https://github.com/rfigurelli/XCP-eXtended-Content-Protocol/