Expired50% confidenceFactExact time
When large language models generate code, they form a Chain of Thought, and subsequent self-checks tend to follow the same reasoning path, making it difficult to break out of the existing logical framework to identify blind spots.
1
Sources
50%
Confidence
Medium-term (~90 days)
Relevance
7/2/2026
First Seen
Valid until: 9/30/2026(expired)
Sources
Claude Code + Codex: The Best Practice of One-Writes-One-Reviews AI Programming
bilibiliAli厂长6/3/2026
Related Claims
VerifiedResearch has found that the chain-of-thought reasoning generated by models does not fully correspond to their actual internal computation processes, and may provide post-hoc explanations unrelated to the true decision-making path.77% similarUnverified大语言模型的链式推理（Chain-of-Thought）输出不一定反映其内部计算的真实路径76% similarUnverifiedMechanisms such as Chain-of-Thought, multi-step verification, and self-reflection are implementations of test-time compute scaling.76% similarUnverifiedReasoning tasks require a strong logical chain while summarization tasks demand concise language ability, and using the same model for both not only wastes computing power but may also not yield the best results.76% similarUnverified思考模型在输出前会经历内部链式推理（Chain-of-Thought）过程，在数学证明、代码调试、复杂逻辑推理等任务上表现优于标准模型，代价是延迟更高、计算成本更大75% similar
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