A Comprehensive Anatomy of Human and DeepSeek-R1 LLM Mathematical Reasoning

The AI field has celebrated chain-of-thought reasoning as evidence that large models are learning to truly think. This paper introduces a more skeptical lens, exhaustively annotating thousands of reasoning steps to ask whether what looks like reasoning actually functions as reasoning. The findings suggest a troubling pattern: models reproduce the structural shape of human mathematical thought without its logical substance, cycling through verification loops that check local details while missing global errors. For anyone building AI tutors, automated proof checkers, or mathematical research tools, this anatomy of failure points toward more honest evaluation criteria and training signals that reward genuine deductive progress rather than the performance of reasoning. Authors: Yuxiang Chen, Jun Wang Paper: https://arxiv.org/abs/2606.07410v1