Unverified50% confidenceOpinionExact time
The researchers propose the attention dilution effect hypothesis to explain why batching reduces overcorrection.
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50%
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Long-term
Relevance
9/11/2026
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UnverifiedHaving too many tokens in a prompt dilutes the attention mechanism's ability to focus, reducing the actual influence of each description.79% similarUnverifiedThe root cause of attention dilution lies in Softmax normalization: the more tokens in a sequence, the effective attention weight allocated to each position decreases approximately inversely proportional to the sequence length.73% similarUnverifiedContext pollution causes the model's attention to become scattered, response quality to decline, and costs to spiral out of control.72% similarUnverifiedCurrent frontier models face attention decay when handling long-range dependencies, with the ability to maintain consistency of early-established settings declining significantly as context length increases.72% 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72% similar
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