Unverified50% confidenceFactExact time
Large models are stateless by nature, with each conversation starting from zero by default, and because the context window is limited, content that exceeds its range is automatically truncated and forgotten
1
Sources
50%
Confidence
Long-term
Relevance
9/24/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedNative large models are constrained by context window size and tend to forget early information when handling ultra-long tasks.73% similarUnverified跨会话的状态持久化问题无法靠扩大上下文窗口解决,每次新对话都是全局失忆的白板状态72% similarUnverifiedIf the specified duration exceeds the actual amount of content provided, the model will fill the gap with meaningless noise.68% 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68% 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.66% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/946913API
curl https://kongchang.com/api/v1/knowledge/claims/946913MCP
get_claim(id=946913)