Unverified50% confidenceOpinionExact time
Optimizing for benchmarks carries risks, as high scores may only reflect a model's adaptation to specific question types rather than a genuine improvement in general capabilities.
1
Sources
50%
Confidence
Long-term
Relevance
9/13/2026
First Seen
Sources
Related Claims
UnverifiedRecursive Self-Improvement can theoretically cause model capabilities to grow exponentially, but also raises deep concerns in the field of alignment77% similarUnverifiedA model's 'raw capability level' and its 'actual deployment risk' are two concepts that need to be considered separately.77% similarUnverified确定性优化可能以牺牲模型精度或表达能力为代价,需在质量与能效之间寻找平衡点76% similarUnverifiedIn mature AI projects, swapping in an uncertain new model carries more risk than benefit, and stability and controllability are more important than using the latest model.76% similarUnverified提示词优化的收益受限于底层工程链路的稳定性,若接口不稳定或测试环境不可复现,提示词优化效果将无法被客观衡量75% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/918048API
curl https://kongchang.com/api/v1/knowledge/claims/918048MCP
get_claim(id=918048)