Unverified50% confidenceDecision RuleExact time
The termination condition of the ReAct loop is determined by the large model; when the large model considers that the contextual information is sufficient to solve the user's problem, it stops calling tools and outputs the final answer.
1
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50%
Confidence
Long-term
Relevance
9/11/2026
First Seen
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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75% similarUnverifiedThe core idea of ReAct is to have LLMs perform explicit reasoning before generating actions, and to feed execution results back to the model as observations to form a closed loop.74% similarUnverified传统ReAct框架要求模型在每次工具调用后将结果写回上下文再重新读取整个对话历史决定下一步，导致Token消耗和延迟累积72% similarUnverifiedReasoning models perform internal Chain-of-Thought before returning final results, consuming large amounts of tokens and computation time in exchange for deeper understanding and lower error rates.72% similarUnverifiedFirst verifying the model's understanding of the problem domain through explanatory tasks, then gradually transitioning to generative tasks—this phased prompting approach reduces the probability that the final output will deviate from expectations.71% similar
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