Unverified50% confidenceFactExact time
思维链推理的效果随模型规模呈现涌现性，在较小模型上几乎无效，在足够大的模型上才能显著提升推理准确率
1
Sources
50%
Confidence
Long-term
Relevance
7/19/2026
First Seen
Sources
BAML：专为AI Agent设计的类型安全编程语言
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.84% similarUnverified大模型在约千亿参数规模附近突然展现出思维链推理、多步数学解题等此前小模型不具备的能力82% 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.79% similarUnverifiedReasoning models perform internal Chain-of-Thought before returning final results, consuming large amounts of tokens and computation time in exchange for deeper understanding and lower error rates.78% similarUnverifiedReasoning models have built-in autonomous decomposition and verification mechanisms, and overly detailed prompts can actually compress the model's reasoning space.78% similar
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