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The Bayesian decision theory framework naturally supports extension to more than two actions and can be used to model cost-sensitive decisions involving actions such as accept, repair, acquire more evidence, escalate to a human, and reject.
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9/11/2026
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UnverifiedBefore ReAct was proposed, large models' reasoning capabilities (represented by Chain-of-Thought) and action capabilities (represented by Action Generation) were two independent lines of research74% similarUnverified作者认为决定最终优化效果的主要是提示词和使用的大模型型号，提示词越具体Agent执行越到位73% similarUnverified将推理轨迹与行动步骤交替结合，比单纯的行动序列或单纯的推理链取得更好效果72% similarUnverifiedOn divergent tasks requiring multi-step reasoning, long-context understanding, or open-ended architectural decisions, the advantages of newer models may be significant.72% similarVerifiedCompared to pure Chain-of-Thought reasoning, the ReAct framework reduces hallucinations by introducing external information sources; compared to pure Action mode, it improves decision quality through explicit reasoning steps.71% similar
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