Unverified50% confidenceOpinionExact time
An overly long context dilutes the model's attention to the current task, and trimming the context often leads to more focused and accurate AI output.
1
Sources
50%
Confidence
Long-term
Relevance
10/10/2026
First Seen
Sources
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Related Claims
UnverifiedDSA稀疏注意力不仅更快，在某些任务上甚至更准，因为跳过无关token让模型更专注于有效信息80% similarUnverifiedAs task chains grow longer, the capacity limitations of the context window and attention decay affect the model's ability to accurately maintain the initial goal, which is one of the root causes of the 'goal drift' phenomenon in long-horizon tasks.79% similarUnverified初始token消耗越低，用户可用于实际任务的上下文空间越大；过长的系统提示词会引发提示词漂移干扰模型自主发挥79% similarUnverifiedCurrent frontier models face attention decay when handling long-range dependencies, with the ability to maintain consistency of early-established settings declining significantly as context length increases.77% similar
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