A Measure-Theoretic Analysis of Reasoning in Transformers: Structural Generalization and Approximation Limits

Scaling laws for reasoning in large language models (LLMs) are well documented, but little theory explains their out-of-distribution (OOD) generalization. We formalize reasoning with optimal transport: each search trajectory is mapped to twelve structural coordinates, such as tree depth and backtrac