Unverified50% confidenceFactExact time
When the context contains key information but is mixed with a large amount of irrelevant content, the attention mechanism struggles to pinpoint the key information precisely, and the model may be misled into solving the wrong problem.
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10/18/2026
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UnverifiedThe trimming strategy will lose important context in scenarios where early critical information needs to be referenced.76% similarUnverified尽管上下文窗口很大，上下文腐化（Context Rot）依然是巨大问题，模型对上下文中不同位置信息的注意力分布不均匀（Lost in the Middle效应）76% similarUnverifiedThe essence of information overload is often not too much information, but the lack of an effective filtering mechanism.75% similarUnverifiedThe larger the context window, the more information the model can remember in a single call, but it also brings rising inference costs and the problem of intermediate information being ignored.75% similarUnverifiedContext loss refers to a model forgetting earlier information after exceeding the context window limit when processing long conversations or long documents, leading to contradictions.73% similar
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