Unverified50% confidenceFactExact time
The questions in the proprietary evaluation are not publicly disclosed, and models cannot improve their scores by memorizing questions from training data, making it a better reflection of true generalization ability.
1
Sources
50%
Confidence
Long-term
Relevance
9/14/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedGoodhart's Law states that when a metric becomes a target in itself it is no longer a good metric, and introducing test sets into training data causes a model's exam scores to decouple from its true capabilities76% similarUnverified链式思维、少样本提示、自我一致性等提示词工程技巧无法让模型访问训练截止日期之后的信息，无法保证浮点运算的精确性，也无法处理模型从未见过的外部文件75% similarUnverifiedThe improvement ceiling of runtime improvement is constrained by the base model's inherent capabilities, and the model cannot break through the knowledge and reasoning boundaries obtained during pre-training through runtime optimization.73% similarUnverified当内容宣称某模型领先竞品却不注明具体Benchmark名称、版本及测试机构时，该结论缺乏科学依据72% similarUnverified单次采样无法代表模型对某提示词的真实能力分布，3次以上独立采样才能提供足够的方差估计，是生产级提示词评估的最佳实践72% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/919665API
curl https://kongchang.com/api/v1/knowledge/claims/919665MCP
get_claim(id=919665)