Unverified50% confidenceFactExact time
Standard Gaussian diffusion models have inherent limitations in capturing heavy-tailed distributions.
1
Sources
50%
Confidence
Long-term
Relevance
10/15/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedThe 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.78% similarUnverified扩散模型的迭代步数与输出长度不呈线性正相关，耗时增长比自回归模型平缓72% similarUnverifiedBrownian diffusion models may systematically underestimate the probability of extreme events in heavy-tailed data scenarios, whereas stable models can correctly extrapolate to the tail region when α=ν.72% similarUnverifiedPrevious generalization analyses of flow matching/diffusion models often required strong restrictive assumptions on the estimated velocity field, such as global Lipschitz continuity, bounded norm, or specific regularity conditions.71% similarUnverifiedThe paper "Limits of Confidence in Diffusion" points out that the parallel writing mechanism of discrete diffusion has fundamental limitations at a mathematical level.69% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1037335API
curl https://kongchang.com/api/v1/knowledge/claims/1037335MCP
get_claim(id=1037335)