Unverified50% confidenceFactExact time
The target models with the largest improvements were the weakest ones, because what they suffer most from is process stability rather than stored knowledge.
1
Sources
50%
Confidence
Long-term
Relevance
10/17/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedThe improvement ceiling of runtime improvement is constrained by the base model's inherent capabilities, and the model cannot break through the knowledge and reasoning boundaries obtained during pre-training through runtime optimization.78% similarUnverified轻量模型在高难度任务上的失败往往是结构性的，切换模型比优化提示词更务实76% similarUnverified模型的综合能力强弱和它对指令的忠实程度是两件不同的事74% similarUnverified能力不够的模型会因为要处理Harness的优化任务而分散本身的交互能力，导致整体表现下降73% similarUnverifiedThe capability ceiling effect is a common pitfall in large model evaluation: task difficulty that is too low or too high causes all models to perform similarly, making it impossible to differentiate their relative strengths and weaknesses.73% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1050043API
curl https://kongchang.com/api/v1/knowledge/claims/1050043MCP
get_claim(id=1050043)