mlsanityData to 7 Oct 2026

Distortion Instead of Hallucination: The Effect of Reasoning Under Strict Constraints

arXiv 2601.01490 ↗First posted 4 Jan 2026cs.CL cs.AIcited by 0

On the map: LLM reasoning → Hallucination detection and mitigation

Abstract (arXiv)

With the widespread adoption of large language models (LLMs), hallucinations, which are non-factual fabrications in model outputs, have become serious concerns. Reasoning capabilities have received attention as a self-verification process to improve output reliability. However, the effect of reasoning within a closed system where LLMs cannot rely on external tools or knowledge has yet to be clarified. We therefore conduct experiments under strict constraints (recommending peer-reviewed journal articles in computer science) to examine the effect of reasoning across multiple models (GPT-5.2 and Gemini 3 Flash). Our results reveal a problematic trade-off between constraint compliance and factual accuracy. Non-reasoning models exhibit high constraint violation rates (66-75%) but maintain factual accuracy, while reasoning models reduce violations (13-26%) but systematically distort known facts to satisfy constraints and increase complete fabrication. This trade-off pattern is consistent across both models despite different architectures, indicating a fundamental limitation of reasoning. Furthermore, reasoning does not uniformly improve output authenticity: effects diverge by model, reflecting different allocations of the compliance-truthfulness trade-off. These findings challenge the assumption that reasoning universally improves reliability: reasoning models trade honest constraint violations for detection-resistant distortions.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Hallucination detection and mitigation, one of 17 directions in LLM reasoning. Builds on 25 of its 29 references that are in the map. First posted on arXiv 4 Jan 2026.

Builds on

25 of 29 references.

References · 25

  1. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
  2. 2021Training Verifiers to Solve Math Word Problemscited by 9,743
  3. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
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  11. 2023A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questionscited by 1,582
  12. 2023Towards Understanding Sycophancy in Language Modelscited by 988
13 more
  1. 2022Quantifying Memorization Across Neural Language Modelscited by 755
  2. 2021Retrieval Augmentation Reduces Hallucination in Conversationcited by 691
  3. 2020Benchmarking Graph Neural Networkscited by 672
  4. 2022Towards Reasoning in Large Language Models: A Surveycited by 593
  5. 2024Hallucination is Inevitable: An Innate Limitation of Large Language Modelscited by 329
  6. 2023Chain-of-Verification Reduces Hallucination in Large Language Modelscited by 317
  7. 2024To CoT or not to CoT? Chain-of-thought helps mainly on math and symbolic reasoningcited by 257
  8. 2025Why Language Models Hallucinatecited by 205
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  10. 2024Chain of Thoughtlessness? An Analysis of CoT in Planningcited by 100
  11. 2025Reasoning Isn't Enough: Examining Truth-Bias and Sycophancy in LLMscited by 11
  12. 2025MONICA: Real-Time Monitoring and Calibration of Chain-of-Thought Sycophancy in Large Reasoning Modelscited by 10
  13. 2025Hierarchical Memorization in Large Language Models: Evidence from Citation Generationcited by 3

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