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Unverified50% confidenceFactExact time

ReAct每一轮循环需要将完整历史上下文重新输入，导致上下文窗口随迭代次数线性增长

1

Sources

50%

Confidence

Long-term

Relevance

7/24/2026

First Seen

Sources

Mindwalk：用3D代码地图回放AI编程代理行为轨迹

hackernewshackernews7/12/2026

Related Claims

UnverifiedReAct循环表现为Thought→Action→Observation的三段式格式，其局限性在于长任务中上下文窗口快速消耗以及可能陷入推理循环73% similar UnverifiedReAct 每次循环产生 Thought-Action-Observation 三元组，会被逐步拼接进上下文窗口，多轮循环会快速消耗模型的 Token 配额70% similar Unverified在Agent应用中，每一轮推理-行动-观察循环都需要将完整历史上下文重新输入模型，导致Token消耗随任务复杂度呈非线性增长68% similar Unverified在 Loop 场景中，每轮循环需要将整个对话历史重新送入模型，导致单轮 Token 消耗因 Context 累积而递增，呈现比线性更陡峭的成本曲线66% similar Unverified在多轮迭代模式下每一轮执行都会重新传入累积的上下文，导致token消耗呈近似二次方增长66% similar

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get_claim(id=611091)