Unverified50% confidenceFactExact time
The O(1/√N) convergence rate cannot be further improved in the sense of rates, representing that risk estimation has reached the information-theoretically optimal rate.
1
Sources
50%
Confidence
Long-term
Relevance
10/12/2026
First Seen
Sources
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Related Claims
UnverifiedThe paper proves that the logarithmic factor appearing in previous risk bounds is not essential and can be completely eliminated, thereby improving the convergence rate to O(1/√N), consistent with the classical law of large numbers.75% similarUnverifiedRegardless of which regime applies, estimators based on compression complexity stably maintain a convergence rate of O(1/√N).71% similarUnverifiedThe paper derives that under the softmax retrieval framework, the evidence score margin required to maintain a fixed accuracy must grow at a rate of Ω(√log N), where N is the number of effective distractors rather than the context length.69% similarUnverifiedIn the model loader, a higher inference strength value yields better generation quality but lower speed, representing a trade-off between quality and speed.68% similarUnverifiedThe paper identifies the convergence behavior of empirical risk across different regimes: some regimes maintain an O(1/√N) rate, some regimes converge noticeably slower, and in some regimes the empirical risk no longer exhibits consistency.68% similar
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