mlsanityData to 14 Oct 2026

Is Chain-of-Thought Reasoning of LLMs a Mirage? A Data Distribution Lens

arXiv 2508.01191 ↗First posted 2 Aug 2025cs.AI cs.CL cs.LGcited by 43

On the map: LLM reasoning → Prompting and chain-of-thought reasoning

Abstract (arXiv)

Chain-of-Thought (CoT) prompting has been shown to be effective in eliciting structured reasoning (i.e., CoT reasoning) from large language models (LLMs). Regardless of its popularity, recent studies expose its failures in some reasoning tasks, raising fundamental questions about the nature of CoT reasoning. In this work, we propose a data distribution lens to understand when and why CoT reasoning succeeds or fails. We hypothesize that CoT reasoning reflects a structured inductive bias learned from in-distribution data, enabling models to conditionally generate reasoning trajectories that approximate those observed during training. As such, the effectiveness of CoT reasoning is fundamentally governed by the nature and degree of distribution discrepancy between training data and test queries. Guided by this lens, we dissect CoT reasoning via three dimensions: task, length, and format. To test the hypothesis, we introduce DataAlchemy, an abstract and fully controllable environment that trains LLMs from scratch and systematically probes them under various distribution conditions. Through rigorous controlled experiments, we reveal that CoT reasoning is a brittle mirage when it is pushed beyond training distributions, emphasizing the ongoing challenge of achieving genuine and generalizable reasoning.

Key facts. Cited by 43 papers in the map (data through 14 Oct 2026). Placed in Prompting and chain-of-thought reasoning, one of 17 directions in LLM reasoning. Builds on 38 of its 51 references that are in the map. First posted on arXiv 2 Aug 2025.

Builds on

38 of 51 references.

Closest ancestors · 15

Same direction, newest first

  1. 2025Beyond Semantics: The Unreasonable Effectiveness of Reasonless Intermediate Tokens19 May
  2. 2025Reasoning Models Don't Always Say What They Think8 May
  3. 2025Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning Large Language Models12 Mar
  4. 2025CODI: Compressing Chain-of-Thought into Continuous Space via Self-Distillation28 Feb
  5. 2024Training Nonlinear Transformers for Chain-of-Thought Inference: A Theoretical Generalization Analysis3 Oct
  6. 2024Chain of Preference Optimization: Improving Chain-of-Thought Reasoning in LLMs13 Jun
  7. 2024Faithful Logical Reasoning via Symbolic Chain-of-Thought28 May
  8. 2024Chain of Thoughtlessness? An Analysis of CoT in Planning8 May
  9. 2024Chain-of-Thought Unfaithfulness as Disguised Accuracy22 Feb
  10. 2023Towards Better Chain-of-Thought Prompting Strategies: A Survey8 Oct
  11. 2023Measuring Faithfulness in Chain-of-Thought Reasoning17 Jul
  12. 2022Automatic Chain of Thought Prompting in Large Language Models7 Oct
3 more
  1. 2022Large Language Models are Zero-Shot Reasoners24 May
  2. 2022Self-Consistency Improves Chain of Thought Reasoning in Language Models21 Mar
  3. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Models28 Jan

Other references · 23

  1. 2024The Llama 3 Herd of Modelscited by 14,715
  2. 2025Qwen3 Technical Reportcited by 8,820
  3. 2025DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learningcited by 6,330
  4. 2023Tree of Thoughts: Deliberate Problem Solving with Large Language Modelscited by 3,211
  5. 2025Kimi k1.5: Scaling Reinforcement Learning with LLMscited by 1,210
  6. 2021Generalized Out-of-Distribution Detection: A Surveycited by 802
  7. 2023Multimodal Chain-of-Thought Reasoning in Language Modelscited by 752
  8. 2022What Can Transformers Learn In-Context? A Case Study of Simple Function Classescited by 715
  9. 2024GSM-Symbolic: Understanding the Limitations of Mathematical Reasoning in Large Language Modelscited by 550
  10. 2025Demystifying Long Chain-of-Thought Reasoning in LLMscited by 326
  11. 2025The Illusion of Thinking: Understanding the Strengths and Limitations of Reasoning Models via the Lens of Problem Complexitycited by 302
  12. 2023MathPrompter: Mathematical Reasoning using Large Language Modelscited by 236
11 more
  1. 2023Deductive Verification of Chain-of-Thought Reasoningcited by 124
  2. 2024Can Large Language Models Reason and Plan?cited by 82
  3. 2023Large Language Models are In-Context Semantic Reasoners rather than Symbolic Reasonerscited by 63
  4. 2025OMEGA: Can LLMs Reason Outside the Box in Math? Evaluating Exploratory, Compositional, and Transformative Generalizationcited by 50
  5. 2024Can LLM Graph Reasoning Generalize beyond Pattern Memorization?cited by 47
  6. 2025Visual Large Language Models for Generalized and Specialized Applicationscited by 36
  7. 2024Out-of-distribution generalization via composition: a lens through induction heads in Transformerscited by 28
  8. 2024Position Coupling: Improving Length Generalization of Arithmetic Transformers Using Task Structurecited by 23
  9. 2024Can In-context Learning Really Generalize to Out-of-distribution Tasks?cited by 23
  10. 2025SCALE: Towards Collaborative Content Analysis in Social Science with Large Language Model Agents and Human Interventioncited by 7
  11. 2025A Theoretical Framework for OOD Robustness in Transformers using Gevrey Classescited by 4

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 18; 2026: 25.

43 papers.

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  1. 2026This human study did not involve human subjects: Validating LLM simulations as behavioral evidencecited by 29
  2. 2025FaithCoT-Bench: Benchmarking Instance-Level Faithfulness of Chain-of-Thought Reasoningcited by 26
  3. 2025Stop When Enough: Adaptive Early-Stopping for Chain-of-Thought Reasoningcited by 16
  4. 2025Learning from Diverse Reasoning Paths with Routing and Collaborationcited by 12
  5. 2025First Try Matters: Revisiting the Role of Reflection in Reasoning Modelscited by 12
  6. 2026Evaluating LLMs in Finance Requires Explicit Bias Considerationcited by 11
  7. 2025Revolution or Hype? Seeking the Limits of Large Models in Hardware Designcited by 9
  8. 2025Why Chain of Thought Fails in Clinical Text Understandingcited by 9
  9. 2025Who's Your Judge? On the Detectability of LLM-Generated Judgmentscited by 8
  10. 2026Seeing Is Believing? A Benchmark for Multimodal Large Language Models on Visual Illusions and Anomaliescited by 8
  11. 2026Verify Before You Commit: Towards Faithful Reasoning in LLM Agents via Self-Auditingcited by 7
  12. 2025From surveillance to signalling: escalation channels as environmental controls for agentic AIcited by 6
31 more
  1. 2026Decoupling the Effect of Chain-of-Thought Reasoning: A Human Label Variation Perspectivecited by 6
  2. 2026From Chatbot to Digital Colleague: The Paradigm Shift Toward Persistent Autonomous AIcited by 6
  3. 2025Can GRPO Help LLMs Transcend Their Pretraining Origin?cited by 5
  4. 2026Learning from Mistakes: Negative Reasoning Samples Enhance Out-of-Domain Generalizationcited by 5
  5. 2026Do explanations generalize across large reasoning models?cited by 5
  6. 2026Verbalizing LLM's Higher-order Uncertainty via Imprecise Probabilitiescited by 5
  7. 2025Say One Thing, Do Another? Diagnosing Reasoning-Execution Gaps in VLM-Powered Mobile-Use Agentscited by 4
  8. 2025When to Think and When to Look: Uncertainty-Guided Lookbackcited by 3
  9. 2026Student Guides Teacher: Weak-to-Strong Inference via Spectral Orthogonal Explorationcited by 3
  10. 2026Detecting Unfaithful Chain-of-Thought via Circuit-Guided Internal-External Discrepancycited by 3
  11. 2025Understanding Chain-of-Thought Effectiveness in Code Generation: An Empirical and Information-Theoretic Analysiscited by 2
  12. 2025From Isolation to Alignment: Unified LoRA for Efficient Multi-Task Learningcited by 1
  13. 2025Causality Guided Representation Learning for Cross-Style Hate Speech Detectioncited by 1
  14. 2026Scaling Reasoning Hop Exposes Weaknesses: Demystifying and Improving Hop Generalization in Large Language Modelscited by 1
  15. 2026Boule or Baguette? A Study on Task Topology, Length Generalization, and the Benefit of Reasoning Tracescited by 1
  16. 2026On the Out-of-Distribution Generalization of Reasoning in Multimodal LLMs for Simple Visual Planning Taskscited by 1
  17. 2026Probing to Refine: Reinforcement Distillation of LLMs via Explanatory Inversioncited by 1
  18. 2026Bayesian Elicitation with LLMs: Model Size Helps, Extra "Reasoning" Doesn't Alwayscited by 1
  19. 2026Neural-Symbolic Knowledge Tracing: Injecting Educational Knowledge into Deep Learning for Responsible Learner Modellingcited by 1
  20. 2025Revisiting the UID Hypothesis in LLM Reasoning Tracescited by 0
  21. 2025The Kinetics of Reasoning: How Chain-of-Thought Shapes Learning in Transformers?cited by 0
  22. 2025On the Computability of Artificial General Intelligencecited by 0
  23. 2025TRACE: A Framework for Analyzing and Enhancing Stepwise Reasoning in Vision-Language Modelscited by 0
  24. 2026HyPER: Bridging Exploration and Exploitation for Scalable LLM Reasoning with Hypothesis Path Expansion and Reductioncited by 0
  25. 2026Answering the Wrong Question: Reasoning Trace Inversion for Abstention in LLMscited by 0
  26. 2026Absurd World: A Simple Yet Powerful Method to Absurdify the Real-world for Probing LLM Reasoning Capabilitiescited by 0
  27. 2026A Measure-Theoretic Analysis of Reasoning: Structural Generalization and Approximation Limitscited by 0
  28. 2026REC-CBM: Rubric-Aware Error-Correction Concept Bottleneck Models for Trustworthy Open-Ended Gradingcited by 0
  29. 2026From Consumption to Reflection: Designing Human-AI Relations for Stable Reasoningcited by 0
  30. 2026ThinkLog: Leveraging Reasoning for Log Statement Generationcited by 0
  31. 2026TextCloak: Thwarting Unauthorized LLM Exploitation via RL-Driven Unlearnable Textcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2022Towards Understanding Chain-of-Thought Prompting: An Empirical Study of What Matters
  2. 2025CoT Vectors: Transferring and Probing the Reasoning Mechanisms of LLMs
  3. 2024Chain-of-Thought Reasoning Without Prompting
  4. 2024To CoT or not to CoT? Chain-of-thought helps mainly on math and symbolic reasoning
  5. 2024Unveiling the Statistical Foundations of Chain-of-Thought Prompting Methods
  6. 2024The Impact of Reasoning Step Length on Large Language Models
  7. 2025The Curse of CoT: On the Limitations of Chain-of-Thought in In-Context Learning
  8. 2024ChainLM: Empowering Large Language Models with Improved Chain-of-Thought Prompting