Expired50% confidenceFactExact time
Even large models with a 128K token context window experience attention dilution when processing large amounts of history.
1
Sources
50%
Confidence
Medium-term (~90 days)
Relevance
7/2/2026
First Seen
Valid until: 9/30/2026(expired)
Sources
Learning AI Agent Development from Scratch: A Beginner's Path and Monetization Guide
bilibiliAI研究所-6/5/2026
Related Claims
UnverifiedEven for large models that support 128K tokens, attention decay is a practical concern in actual use—the model's attention to information in the middle of the window is significantly lower than at the beginning and end.85% 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.72% similarUnverifiedEven large language models that support context lengths of 128K or longer will experience attention dilution when all capability instructions are injected at once, causing a decline in execution quality for specific tasks.69% similarUnverified即便是支持128K上下文的模型，在文档长度超过32K时，中段信息的检索准确率往往出现明显下滑69% similarUnverifiedContext window size does not equal effective context length; exceeding the effective attention range causes a forgetting drift phenomenon.68% similar
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