Unverified50% confidenceFactExact time
In multi-step tasks, an Agent loses context, which is fundamentally a state management problem, because information relies on the context window's memory, and key information is easily buried or truncated as the number of conversation turns increases.
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50%
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Long-term
Relevance
9/11/2026
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Related Claims
UnverifiedAs execution progresses, the context and state maintained by the agent gets truncated or diluted due to context window limitations, causing the value of critical information to decay.79% similarUnverified若持续清除工具输出，Agent需要重新检索已拥有的信息，导致更多工具调用，既费token又更慢且记忆召回更差75% similarUnverified当前多Agent系统面临的核心技术挑战是上下文窗口与长程记忆问题，模型对早期内容的关注度会随对话深度增加而下降（注意力稀释现象）73% similarUnverified在多轮对话中，冗余输出会占据上下文窗口空间，导致关键信息被挤出窗口（上下文截断问题）73% similarUnverifiedAs the number of conversation turns increases, the weight of early key constraint information in the context window is diluted (attention drift), causing the quality of AI output to gradually decline.72% similar
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