Expired60% confidenceFactExact time
Even 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.
2
Sources
60%
Confidence
Medium-term (~90 days)
Relevance
7/2/2026
First Seen
Valid until: 9/30/2026(expired)
Sources
Four Core Modules of AI Agent Development: An Architecture Guide from Failure to Stable Deployment
bilibiliAIAgent应用开发6/24/2026
Related Claims
UnverifiedEven large models with a 128K token context window experience attention dilution when processing large amounts of history.85% 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.76% similarUnverified小模型长上下文理解质量随长度下降快，通常超过4K-8K token窗口后表现退化75% similarUnverified上下文窗口大小不等于有效上下文长度，超出有效注意力范围会导致遗忘漂移现象75% similarUnverified早期模型上下文窗口较小（4K–8K Token），长任务中较早的指令和中间状态会被挤出窗口导致遗忘75% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/41632API
curl https://kongchang.com/api/v1/knowledge/claims/41632MCP
get_claim(id=41632)