graded-transformers

This paper introduces Graded Large Language Models (GLLMs), an algebraic framework that imposes a hierarchical grading on transformer representations, theoretically improving sample efficiency for language hierarchies while preserving inference complexity. It provides geometric and information-theoretic justifications, and outlines a grade-selection procedure validated in a companion manuscript.