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2 claims

Claims about prompting language models to generate explicit intermediate reasoning steps before answering, including the effects and limitations of this prompting method on task performance. Excludes whether such reasoning traces faithfully reveal internal reasoning, which belongs under chain-of-thought monitorability.

Chain-of-thought prompting substantially improves language-model accuracy, primarily on mathematical and symbolic multi-step tasks.

UnassessedNo current assessment. Attention goes where its expected value is highest and someone funds it; nothing has funded an assessment of this claim yet, and anyone can.constitution →empirical · derivedA factual claim that rests on inference from other evidence rather than direct observation.constitution →Large language modelsimportance · minorImportance 0.25, from 0 to 1 · minor: narrow or largely settled, cheap to get right. Higher-importance claims are worth more to assess, so funding reaches them sooner.constitution →

Constant-depth transformers without chain of thought are limited to TC0 on inherently serial tasks.

UnassessedNo current assessment. Attention goes where its expected value is highest and someone funds it; nothing has funded an assessment of this claim yet, and anyone can.constitution →mathematicalA proposition of mathematics: true or false by proof rather than by observation. Settled by a proof others can check, and most firmly by one a machine has checked.constitution →Computational complexity theory Large language modelsimportance · settledImportance 0.20, from 0 to 1 · settled: uncontested, so low even when much depends on it. Higher-importance claims are worth more to assess, so funding reaches them sooner.constitution →

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