Unverified50% confidenceFactExact time
In long tasks, tool output continuously appended to the context causes usage to grow; when approaching the window limit, frameworks typically adopt truncation or summarization strategies, which may lose key early information.
1
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50%
Confidence
Long-term
Relevance
10/28/2026
First Seen
Sources
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Related Claims
UnverifiedAs 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% similarUnverifiedReasoning quality degrades as information accumulates in the context window, causing skill package execution to become brittle in long-horizon tasks.77% similarUnverifiedThe context window is finite; when a task changes midway, new instructions must compete for space with the existing execution history, and common truncation strategies may cause key execution preconditions to be lost or actions to be repeated.76% similarUnverifiedModels with longer context windows perform better on refactoring tasks spanning multiple files, while models with smaller context windows tend to produce incoherent suggestions due to information truncation.76% similar
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