Unverified50% confidenceSolutionExact time
严格的对比实验应当对所有模型施加相同的超参数搜索预算，并报告搜索过程中的性能分布而非仅报告最优结果
1
Sources
50%
Confidence
Long-term
Relevance
8/21/2026
First Seen
Sources
Related Claims
VerifiedWhen evaluating the capabilities of large models, the stability across multiple rounds of testing reflects true ability better than a single correct answer.68% similarUnverified扎实的概率统计基础在 A/B 测试设计、回归建模和模型评估中都是不可替代的底层能力67% similarUnverified用单次结果评判AI模型能力是统计陷阱，评估AI预测能力需要跨赛季跨项目的大规模可重复测试67% similarUnverified选择和使用不同模型档位时不能仅凭版本号判断，基于真实场景的多轮测试才是评估模型能力的可靠依据66% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/784014API
curl https://kongchang.com/api/v1/knowledge/claims/784014MCP
get_claim(id=784014)