Unverified50% confidenceFactExact time
Chain-of-thought (CoT) technology explicitly writes intermediate steps into the output token sequence, trading sequence length for the computational depth that would otherwise be absent.
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50%
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Long-term
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10/4/2026
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UnverifiedBy externalizing intermediate steps as text, chain-of-thought equivalently extends the model's effective computational depth from the number of layers in a single forward pass to the number of layers multiplied by the number of reasoning steps.73% similarUnverifiedCoT ties the model's thinking budget entirely to the number of output tokens—each additional reasoning step requires generating more text, increasing computational cost and subject to the serial bottleneck of autoregressive decoding.71% similarVerifiedThe core finding of CoT is that when models are asked to think step by step, accuracy on tasks such as arithmetic and commonsense reasoning improves substantially.70% similarVerified推理能力基于思维链（Chain-of-Thought, CoT）技术，模型在给出最终答案前生成内部逐步推导过程69% similarUnverifiedCoT通过让模型在给出最终答案前逐步推理，可在数学推理等复杂任务上将准确率提升40%以上68% similar
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