dailyai.report

A new theoretical framework treats human languages as engineered latent spaces designed for efficient data compression. This perspective suggests that linguistic structures mirror the dimensionality reduction seen in Large Language Models. Researchers argue this explains why tokenization works. The theory provides a mathematical lens for understanding how semantic meaning maps to discrete symbols.