Unverified50% confidenceOpinionExact time
提升模型单步精度的边际收益远不如缩短任务链条、增加检查点
1
Sources
50%
Confidence
Long-term
Relevance
7/23/2026
First Seen
Sources
Harness工程:为什么模型强也没用,关键在这里
Related Claims
UnverifiedLarger models tend to be more token-efficient, completing tasks with fewer interaction rounds and more refined reasoning.79% similarUnverifiedOn convergent tasks, there is a clear performance saturation phenomenon after models reach a certain scale, where continued increases in parameter count or training data yield rapidly diminishing marginal contributions to accuracy.78% similarUnverified模型路由在精度上更优,但引入了额外的推理延迟与维护成本76% similarUnverifiedAugment单个任务的处理速度可能不如Cursor,但修改精度更高,基本不需要就同一个问题反复调整75% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/596172API
curl https://kongchang.com/api/v1/knowledge/claims/596172MCP
get_claim(id=596172)