CoT is Not True Reasoning, It Is Just a Tight Constraint to Imitate: A Theory Perspective

• URL: http://arxiv.org/abs/2506.02878v2
• Date: Fri, 06 Jun 2025 22:13:39 GMT
• Title: CoT is Not True Reasoning, It Is Just a Tight Constraint to Imitate: A Theory Perspective
• Authors: Jintian Shao, Yiming Cheng,
• Abstract summary: Chain-of-Thought (CoT) prompting has demonstrably enhanced the performance of Large Language Models.<n>We argue that Chain-of-Thought functions as a powerful structural constraint that guides Large Language Models to imitate the form of reasoning.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Chain-of-Thought (CoT) prompting has demonstrably enhanced the performance of Large Language Models on tasks requiring multi-step inference. This success has led to widespread claims of emergent reasoning capabilities in these models. In this paper, we present a theoretical counter-perspective: Chain-of-Thought (CoT) does not elicit genuine, abstract reasoning. Instead, we argue that Chain-of-Thought functions as a powerful structural constraint that guides Large Language Models to imitate the form of reasoning. By forcing the generation of intermediate steps, Chain-of-Thought leverages the model immense capacity for sequence prediction and pattern matching, effectively constraining its output to sequences that resemble coherent thought processes. Chain-of-Thought (CoT) prompting has demonstrably enhanced the performance of Large Language Models on tasks requiring multi-step inference. This success has led to widespread claims of emergent reasoning capabilities in these models. In this paper, we present a theoretical counter-perspective: Chain-of-Thought (CoT) does not elicit genuine, abstract reasoning. Instead, we argue that Chain-of-Thought functions as a powerful structural constraint that guides Large Language Models to imitate the form of reasoning. By forcing the generation of intermediate steps, Chain-of-Thought leverages the model immense capacity for sequence prediction and pattern matching, effectively constraining its output to sequences that resemble coherent thought processes.

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