Unverified50% confidenceFactExact time
The paper points out that calibration causes neighborhood overlap to continue to grow as model scale increases, yet essentially eliminates the growth of centered kernel alignment (CKA).
1
Sources
50%
Confidence
Long-term
Relevance
10/15/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedThe authors prove that the effect of eliminating CKA growth can arise from a calibration operation that takes the maximum over multiple layers, so whether global convergence holds remains an open question.69% similarUnverifiedLeslie矩阵的主特征值（谱半径）决定种群长期增长率：大于1则增长，小于1则趋于灭绝63% similarUnverifiedThe challenge of migrating speculative sampling to diffusion models lies in the fact that the sampling space of diffusion models is a continuous high-dimensional distribution rather than a discrete vocabulary, requiring a redesign of the accept-reject rules.63% similarUnverified长文档处理中，KV Cache机制导致显存占用随上下文长度近似平方级增长61% similarUnverifiedWhen guiding discrete diffusion models with sequence-level goals, enumerating all possible completion combinations leads to computation that grows exponentially with the number of unresolved positions.61% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1033010API
curl https://kongchang.com/api/v1/knowledge/claims/1033010MCP
get_claim(id=1033010)