Unverified50% confidenceFactExact time
The root causes of benchmark failure lie in two issues: training set contamination and question bank saturation.
1
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50%
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Long-term
Relevance
9/23/2026
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Related Claims
VerifiedBenchmark Contamination is a significant challenge in the current field of large model evaluation, where publicly available test questions may already be included in model training data, causing evaluation results to be distorted.76% similarVerifiedBenchmark contamination refers to the phenomenon where test questions may have already appeared in training data, causing high scores to not truly represent generalization ability.69% similarVerified仅用成功轨迹训练的模型往往存在分布外泛化的严重缺陷，遇到工具返回错误时便丧失纠偏能力69% similarUnverifiedBenchmark overfitting is a systemic limitation where models score high through targeted training on test sets without corresponding improvements in generalization.69% similarUnverifiedThe article concludes that the corruption caused by the seed stems from numerical conflicts in the sampling process itself and is unrelated to training data.67% similar
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