Unverified50% confidenceFactExact time
ReAct范式通过强制要求每次工具调用后观察真实环境反馈，将推理链锚定在可验证的现实结果上，抑制错误累积
1
Sources
50%
Confidence
Long-term
Relevance
7/18/2026
First Seen
Sources
Related Claims
VerifiedReAct 智能体的核心是「推理-行动-观察」循环，LLM 分析问题后决定调用工具，观察返回结果，若不满足则继续推理直到给出最终回复80% similarUnverifiedReAct combines reasoning and action, allowing the model to alternately generate natural language reasoning steps and concrete actions within a trajectory, making errors traceable and correctable.80% similarUnverifiedReAct establishes an alternating execution protocol between reasoning traces and actions, writing observations returned by tools into the context before the next round of reasoning, laying the theoretical foundation for mainstream agent frameworks such as LangChain, AutoGPT, and Claude tool calling.79% similarVerifiedThe ReAct framework enables the model to interact with the external environment while reasoning—first reasoning about what to do, then calling external tools to act, then observing the results returned by the tools, iterating in a loop until the task is complete.78% similarUnverifiedReAct triggers actual tool calls after each reasoning step, whereas Chain-of-Thought prompting only performs reasoning without calling tools.78% similar
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