Unverified50% confidenceFactExact time
A sequence can perfectly well pass all NIST tests yet be rapidly predicted when facing an adversary with learning capability.
1
Sources
50%
Confidence
Long-term
Relevance
10/14/2026
First Seen
Sources
Related Entities
Related Claims
Unverified多种子实验中检验对象从单样本预测分歧变为多次运行的准确率序列时，通常已不再适用McNemar检验69% similarUnverifiedDetermining whether a model truly outperforms the baseline usually requires larger samples (hundreds to thousands), paired tests (such as the McNemar test), and repeated experiments under multiple random seeds.67% similarUnverifiedExisting science benchmarks provide all known quantities to the model at once, requiring only one-shot symbolic reasoning and bypassing the step of acquiring information through experimentation67% similarUnverifiedThe model trained using Edu-QuRating-filtered mixed sets achieved a higher overall observed accuracy than the FineWeb-Edu baseline model across nine benchmark tests, with performance gains concentrated on specific tasks.66% similarUnverified当比较两个具体训练模型的预测行为差异时应固定种子使用同一测试集运行McNemar检验，当评估方法整体优越性时应用多种子重复实验配合配对t检验或非参数检验65% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1026919API
curl https://kongchang.com/api/v1/knowledge/claims/1026919MCP
get_claim(id=1026919)