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
• 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.

Related papers

• Thinking Without Words: Efficient Latent Reasoning with Abstract Chain-of-Thought [13.72996269125319]
  Abstract-CoT achieves up to $11.6times$ fewer reasoning tokens while demonstrating comparable performance across mathematical reasoning, instruction-following, and multi-hop reasoning.<n>Our findings highlight the potential for post-training latent reasoning mechanisms that enable efficient inference through a learned abstract reasoning language.
  arXiv  Detail & Related papers  (2026-04-24T16:45:51Z)
• TDA-RC: Task-Driven Alignment for Knowledge-Based Reasoning Chains in Large Language Models [54.20787536978013]
  Chain-of-Thought (CoT) paradigm dominates practical applications for its single-round efficiency, yet its reasoning chains often exhibit logical gaps.<n>This paper proposes a topology-based method for optimizing reasoning chains.<n>We show that our approach offers a superior balance between reasoning accuracy and efficiency, showcasing a practical solution to single-round generation with multi-round intelligence''
  arXiv  Detail & Related papers  (2026-03-13T13:01:01Z)
• CoLT: Reasoning with Chain of Latent Tool Calls [31.228763375347608]
  Chain-of-Thought (CoT) is a critical technique in enhancing the reasoning ability of Large Language Models (LLMs)<n>We propose CoLT, a novel framework that implements latent reasoning as tool calls''
  arXiv  Detail & Related papers  (2026-02-04T06:12:53Z)
• Analyzing Reasoning Consistency in Large Multimodal Models under Cross-Modal Conflicts [74.47786985522762]
  We identify a critical failure mode termed textual inertia, where models tend to blindly adhere to the erroneous text while neglecting conflicting visual evidence.<n>We propose the LogicGraph Perturbation Protocol that structurally injects perturbations into the reasoning chains of diverse LMMs.<n>Results reveal that models successfully self-correct in less than 10% of cases and predominantly succumb to blind textual error propagation.
  arXiv  Detail & Related papers  (2026-01-07T16:39:34Z)
• A Survey on Latent Reasoning [100.54120559169735]
  Large Language Models (LLMs) have demonstrated impressive reasoning capabilities.<n>CoT reasoning that verbalizes intermediate steps limits the model's expressive bandwidth.<n>Latent reasoning tackles this bottleneck by performing multi-step inference entirely in the model's continuous hidden state.
  arXiv  Detail & Related papers  (2025-07-08T17:29:07Z)
• ConciseHint: Boosting Efficient Reasoning via Continuous Concise Hints during Generation [74.37307916314407]
  We propose a framework dubbed ConciseHint, which continuously encourages the reasoning model to speak concisely.<n>Experiments on the state-of-the-art LRMs, including DeepSeek-R1 and Qwen-3 series, demonstrate that our method can effectively produce concise reasoning.
  arXiv  Detail & Related papers  (2025-06-23T16:20:44Z)
• Theorem-of-Thought: A Multi-Agent Framework for Abductive, Deductive, and Inductive Reasoning in Language Models [2.172419551358714]
  Large language models (LLMs) have shown strong performance across natural language reasoning tasks, yet their reasoning processes remain brittle and difficult to interpret.<n>We introduce Theorem-of-Thought (ToTh), a novel framework that models reasoning as collaboration among three parallel agents.<n> Experiments on symbolic (WebOfLies) and numerical (MultiArithm) reasoning benchmarks show that ToTh consistently outperforms CoT, Self-Consistency, and CoT-Decoding.
  arXiv  Detail & Related papers  (2025-06-08T12:28:38Z)
• Fine-Tuning on Diverse Reasoning Chains Drives Within-Inference CoT Refinement in LLMs [63.36637269634553]
  We introduce a novel approach where LLMs are fine-tuned to generate a sequence of Diverse Chains of Thought (DCoT) within a single inference step.<n>We show that fine-tuning on DCoT improves performance over the CoT baseline across model families and scales.<n>Our work is also significant because both quantitative analyses and manual evaluations reveal the observed gains stem from the models' ability to refine an initial reasoning chain.
  arXiv  Detail & Related papers  (2024-07-03T15:01:18Z)
• Enhancing Chain of Thought Prompting in Large Language Models via Reasoning Patterns [26.641713417293538]
  Chain of Thought (CoT) prompting can encourage language models to engage in logical reasoning.<n>We propose leveraging reasoning patterns to enhance CoT prompting effectiveness.
  arXiv  Detail & Related papers  (2024-04-23T07:50:00Z)
• Contrastive Chain-of-Thought Prompting [74.10511560147293]
  We propose contrastive chain of thought to enhance language model reasoning. Compared to the conventional chain of thought, our approach provides both valid and invalid reasoning demonstrations. Our experiments on reasoning benchmarks demonstrate that contrastive chain of thought can serve as a general enhancement of chain-of-thought prompting.
  arXiv  Detail & Related papers  (2023-11-15T18:54:01Z)
• Why Can Large Language Models Generate Correct Chain-of-Thoughts? [10.888196404348093]
  We introduce a two-level hierarchical graphical model tailored for natural language generation. We establish a compelling geometrical convergence rate that gauges the likelihood of an LLM-generated chain of thoughts.
  arXiv  Detail & Related papers  (2023-10-20T15:09:46Z)
• Beyond Chain-of-Thought, Effective Graph-of-Thought Reasoning in Language Models [74.40196814292426]
  We propose Graph-of-Thought (GoT) reasoning, which models human thought processes not only as a chain but also as a graph. GoT captures the non-sequential nature of human thinking and allows for a more realistic modeling of thought processes. We evaluate GoT's performance on a text-only reasoning task and a multimodal reasoning task.
  arXiv  Detail & Related papers  (2023-05-26T02:15:09Z)
• Visual Chain of Thought: Bridging Logical Gaps with Multimodal Infillings [61.04460792203266]
  We introduce VCoT, a novel method that leverages chain-of-thought prompting with vision-language grounding to bridge the logical gaps within sequential data. Our method uses visual guidance to generate synthetic multimodal infillings that add consistent and novel information to reduce the logical gaps for downstream tasks.
  arXiv  Detail & Related papers  (2023-05-03T17:58:29Z)
• Multimodal Chain-of-Thought Reasoning in Language Models [94.70184390935661]
  We propose Multimodal-CoT that incorporates language (text) and vision (images) modalities into a two-stage framework. Experimental results on ScienceQA and A-OKVQA benchmark datasets show the effectiveness of our proposed approach.
  arXiv  Detail & Related papers  (2023-02-02T07:51:19Z)
• Chain of Thought Prompting Elicits Reasoning in Large Language Models [56.811278668446825]
  This paper explores the ability of language models to generate a coherent chain of thought. Experiments show that inducing a chain of thought via prompting can enable sufficiently large language models to better perform reasoning tasks.
  arXiv  Detail & Related papers  (2022-01-28T02:33:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.