From Tokens to Thought: A Theoretical Investigation of Reasoning in Large Language Models

Abstract

The evolution of large language models (LLMs) has transformed natural language processing, enabling machines to engage in tasks traditionally requiring human-like reasoning. These models, built on deep learning architectures, particularly transformers, operate through the manipulation of tokens—atomic units of language representation. However, an emerging debate concerns whether these models merely emulate reasoning through statistical correlations or possess a form of emergent "thought." This research paper offers a theoretical investigation into reasoning in LLMs, emphasizing how token-based processing scales into complex inferential capabilities. The results indicate that while LLMs display impressive reasoning-like behavior, their process lacks genuine comprehension, suggesting a hybrid paradigm where reasoning emerges from pattern synthesis rather than symbolic logic. The findings of this study offer insights into the philosophical and technical aspects of intelligence in machine learning models, highlighting implications for transparency, alignment, and future AI development.