dailyai.report

A new theoretical framework treats human languages as latent spaces engineered for efficient information compression. The research suggests that linguistic structures mirror the dimensionality reduction seen in neural networks. This perspective shifts how developers approach tokenization. It provides a mathematical basis for improving cross-lingual transfer in large language models via shared geometric properties.