Unverified50% confidenceFactExact time
The meta-level depth of existing recursive self-improvement methods is almost incapable of exceeding two levels.
1
Sources
50%
Confidence
Long-term
Relevance
9/12/2026
First Seen
Sources
Related Entities
Related Claims
Unverified深度学习的损失曲面是高度非凸的，传统凸优化中梯度为零即为全局最优的结论在此并不成立70% similarUnverifiedA class of self-improving systems adds a fixed meta-level to supervise the underlying solver, but that meta-level itself is static and cannot continue to evolve upward.67% similarUnverified专项优化不保证对所有任务类型的普遍提升，在特定领域可能反而不及通用基础模型67% similarUnverified基准饱和是指当顶尖模型在某基准上的得分逼近或超越人类水平时，该基准失去区分度，无法有效衡量模型之间的真实差距66% similarUnverified向量检索存在语义天花板和粒度困境两个本质局限，无法处理多跳关系问题66% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/911641API
curl https://kongchang.com/api/v1/knowledge/claims/911641MCP
get_claim(id=911641)