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

• URL: http://arxiv.org/abs/2601.01490v1
• Date: Sun, 04 Jan 2026 11:35:39 GMT
• Title: Distortion Instead of Hallucination: The Effect of Reasoning Under Strict Constraints
• Authors: Junichiro Niimi,
• Abstract summary: Reasoning capabilities have received attention as a self-verification process to improve output reliability.<n>We conduct experiments under strict constraints to examine the effect of reasoning across multiple models.<n>Our results reveal a problematic trade-off between constraint compliance and factual accuracy.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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