Unverified50% confidenceFactExact time
In the context of machine learning and agent engineering, cross-validation usually refers to using multiple independent means to check the consistency of the same output.
1
Sources
50%
Confidence
Long-term
Relevance
10/17/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedCommon cross-validation practices include: multi-model voting or comparison, sampling the same question multiple times to check result stability, secondary verification using rules or small models, and manual spot-checking as the gold standard.75% similarUnverified独立验证可通过人工确认（Human-in-the-loop）、规则引擎校验、二次AI审查、幂等性与回滚设计等方式实现74% similarUnverified关键数值结论应通过独立的第二次询问、切换不同模型或人工复核的方式进行交叉验证，尤其在财务、工程等高精度场景73% similarUnverifiedMulti-agent architectures can use externalized verification to have different agents cross-check each other's statements, theoretically forming a peer-review-like error-correction mechanism to combat hallucinations.72% similarVerifiedCross-validation should be used rather than a single train/test split to evaluate model performance more reliably.72% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1051422API
curl https://kongchang.com/api/v1/knowledge/claims/1051422MCP
get_claim(id=1051422)