Unverified50% confidenceFactExact time
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
1
Sources
50%
Confidence
Long-term
Relevance
8/10/2026
First Seen
Sources
Related Claims
UnverifiedWhen 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.76% similarVerifiedResearch 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.76% similarUnverifiedChain-of-Thought 的推理链完全在模型内部展开，无法感知外部状态变化75% similarUnverifiedThe inference speed of large language models is constrained by the nature of autoregressive generation, which can only produce one token at a time and depends on all preceding tokens, making true parallelization impossible.74% similarUnverifiedLarge language models perform poorly on math and logic problems requiring multi-step reasoning, as models tend to 'jump' directly to an answer rather than going through intermediate reasoning steps.73% similar
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