Verified65% confidenceSolutionExact time
Overly verbose, contradictory, or ambiguous system prompts cause the model's attention to become scattered, resulting in over-thinking or instruction-following failures; streamlining agents.md can improve the determinism of instruction following.
3
Sources
65%
Confidence
Long-term
Relevance
7/19/2026
First Seen
Sources
Related Claims
UnverifiedWhen prompts are too long, large models experience an 'attention explosion' phenomenon, where excessively long inputs cause effective information to be lost.80% similarUnverified过度膨胀的上下文会稀释模型注意力、抬高推理成本，甚至引入噪声导致幻觉79% similarUnverifiedIn long contexts, models are more easily influenced by the most recent few turns of conversation rather than the system prompt set at the beginning, a phenomenon known as instruction-following decay.78% similarUnverified在长对话中早期的错误尝试和调试过程会持续占据上下文窗口，造成上下文污染并干扰后续推理准确性76% similar
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