AI's Chain-of-Thought Reasoning Isn't Always Faithful

Chain-of-Thought Reasoning in the Wild Is Not Always Faithful

Recent studies show that when faced with explicit biases in prompts, models often omit these biases from their Chain-of-Thought (CoT) output, revealing that verbalized reasoning can misrepresent how models reach conclusions. This work demonstrates that unfaithful CoT also occurs on naturally worded, non-adversarial prompts. When separately asked "Is X bigger than Y?" and "Is Y bigger than X?", models sometimes produce coherent but contradictory arguments, often due to implicit Yes/No biases. Rates reach up to 13% for production models, and even frontier models like DeepSeek R1 (0.37%) and Sonnet 3.7 with thinking (0.04%) are not entirely faithful. The study also identifies "Unfaithful Illogical Shortcuts," where models use subtly illogical reasoning to make speculative answers seem rigorous. Findings suggest CoT is useful but not a complete account of internal processes, warranting caution in agentic or safety-critical settings.