Unverified50% confidenceOpinionExact time
Compared with linear or exponential decay, log-rank decay can retain more candidates in the early stage and converge quickly in the later stage, thereby mitigating temporal compression.
1
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50%
Confidence
Long-term
Relevance
10/26/2026
First Seen
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Related Claims
UnverifiedThe exponential decay kernel maps time differences to decayed feature values, giving recent events higher weight and preventing timestamp values from growing unbounded.78% similarVerifiedSmaller models often show exponential rather than linear degradation in performance on long reasoning chain tasks.78% similarUnverifiedFurther reducing the bit count brings more noticeable performance degradation, but a brief fine-tuning stage can recover a considerable portion of the loss.78% similarUnverifiedReduced variance leads to less clipping, because in rejection sampling an overly volatile estimate of the acceptance probability requires more aggressive clipping to ensure numerical stability, and clipping introduces additional distributional bias.76% similarUnverifiedA per-step success rate that looks very high gets rapidly diluted across a long chain, with degradation becoming more severe as the number of steps increases.75% similar
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