Unverified50% confidenceOpinionExact time
同一个模型用同一套假设检查自己的代码天然看不到盲点,跨模型审查因训练侧重点不同才具备互补价值
1
Sources
50%
Confidence
Long-term
Relevance
7/11/2026
First Seen
Sources
Related Claims
UnverifiedBenchmarks tend to evaluate how much a model knows, rather than how much cognitive increment it can bring.78% similarUnverifiedCross-validation should be used rather than a single train/test split to evaluate model performance more reliably.74% 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74% similarUnverifiedDetermining whether a model 'knows' something requires directly probing its internal representations, rather than relying on external detectability as a proxy indicator.73% similarUnverifiedHaving an Agent self-review code it just wrote tends to carry over the original assumptions, making it difficult to find problems; introducing an external heterogeneous model for review can break through this blind spot.72% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/487134API
curl https://kongchang.com/api/v1/knowledge/claims/487134MCP
get_claim(id=487134)