Unverified50% confidenceFactExact time
Existing large language models have limited context windows and often experience state drift when processing long videos or long-horizon operation tasks, with memory of earlier events becoming increasingly vague over time.
1
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50%
Confidence
Long-term
Relevance
10/11/2026
First Seen
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Unverified当叙事篇幅超出上下文窗口长度时，早期建立的角色设定与情节伏笔会逐渐淡出模型的有效记忆范围，导致生成内容前后矛盾77% similarVerifiedEven models with a million-token context face an attention dilution problem when handling large codebases—when the context is too long, the model's attention to early content drops significantly.77% similarUnverifiedEarly large language models were prone to forgetting prior context or hallucinating in long-context scenarios.77% similarUnverifiedNative large models are constrained by context window size and tend to forget early information when handling ultra-long tasks.76% similarUnverified开发者通常通过将历史对话拼接进上下文窗口来模拟LLM的记忆，但会遭遇上下文长度限制、成本飙升及信息检索精度下降等问题76% similar
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