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

2025/06/03 by Shao, Jintian, Yi‐Ming Cheng, Cheng, Yiming · 2 citations
Decision Sciences · #Artificial Intelligence (cs.AI) #Computation and Language (cs.CL) #FOS: Computer and information sciences #Personal Information Management and User Behavior

paper · pdf · doi:10.48550/arxiv.2506.02878

openalex publication_date 2025/06/03 · openalex created_date 2025/10/14 · openalex updated_date 2026/07/28

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.

Citations

• Large Language Models are Zero-Shot Reasoners
• Self-Consistency Improves Chain of Thought Reasoning in Language Models
• A Reduction of Imitation Learning and Structured Prediction to No-Regret Online Learning
• BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Cited by

• Bounds of Chain-of-Thought Robustness: Reasoning Steps, Embed Norms, and Beyond
• Do LLMs Dream of Discrete Algorithms?

Related

• Towards Understanding Chain-of-Thought Prompting: An Empirical Study of What Matters
• The Curse of CoT: On the Limitations of Chain-of-Thought in In-Context Learning
• Chain-of-Thought Reasoning Without Prompting
• A Theoretical Understanding of Chain-of-Thought: Coherent Reasoning and Error-Aware Demonstration
• Causal Sufficiency and Necessity Improves Chain-of-Thought Reasoning