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ReAct范式在CoT基础上引入行动维度，形成思考→行动→观察→再思考的闭环，显著减少大模型幻觉问题

1

Sources

50%

Confidence

Long-term

Relevance

7/17/2026

First Seen

Sources

上海交大开源Agent教程：四阶段系统路径从零到AI工程师

Related Claims

VerifiedReAct通过将推理轨迹与环境交互深度融合，有效减少了幻觉的发生并增强任务执行的可解释性82% similar Unverified在长链路任务中，纯ReAct范式会因每步微小偏差被放大形成'幻觉漂移'，逐渐偏离原始意图74% similar UnverifiedReAct的思考—行动—观察循环机制使Agent在处理多步骤复杂任务时表现出更高可靠性，显著减少幻觉和错误累积73% similar UnverifiedReAct范式通过将思考（Thought）→行动（Action）→观察（Observation）三元组交织在同一上下文中构建闭环反馈控制系统73% similar UnverifiedReAct/AutoAgent范式通过'思考-行动-观察'循环自主决定下一步，灵活性强但不确定性高，容易出现幻觉漂移或死循环72% similar

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