dailyai.report

A framework treats hidden states of language models (LLMs) as points on a semantic manifold, using the Fisher information metric to map tokens to Voronoi regions. This approach quantifies the expressibility gap, revealing a linear volume scaling law and a rate‑distortion lower bound for finite vocabularies.