Verified65% confidenceFactExact time
Every large language model has a maximum token limit it can process, and when the content accumulated by a long task approaches that limit the model will forget early content due to degradation of the attention mechanism over excessively long sequences.
3
Sources
65%
Confidence
Long-term
Relevance
10/4/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedLarge language model APIs are generally billed by the total number of input plus output tokens, and re-injecting historical context on every call in long projects causes token consumption to balloon rapidly.78% similarUnverifiedEven when large language models support a 200K token context, they exhibit the Lost in the Middle phenomenon when processing large codebases, meaning their attention to earlier information in the context decreases progressively.78% similarUnverifiedThe context of a large language model is essentially the total amount of information the model can process in a single inference, typically measured in the number of tokens.77% similarUnverified语言模型生成 token 本质上是内存带宽受限任务而非纯算力受限77% similarUnverifiedWhen generating longer structured outputs, large language models experience type degradation due to the attention mechanism's decaying weight on early context—for example, serializing an entire array as a string or writing numbers as strings.75% similar
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