Unverified50% confidenceOpinionExact time
The LIPS-PC method has limitations: at high budgets its advantage weakens or even reverses, possibly because the learned patterns overfit to low-measurement conditions; and the experiments involve only linear measurements and simple classifiers.
1
Sources
50%
Confidence
Long-term
Relevance
11/4/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedThis theoretical study warns against the current trend in MLIP model design of pursuing larger, more flexible, and less physically constrained models: relaxing inductive bias can improve fitting accuracy and computational efficiency but may sacrifice sampling reliability.74% similarUnverified无梯度方法样本效率低，在高维参数空间中缺乏梯度指引意味着需要海量函数评估才能收敛71% similarUnverifiedThe paper points out that the analysis relies on moment conditions (p>1 or second moments), which heavy-tailed gradient distributions in deep learning may fail to satisfy.68% similarUnverifiedPreset template design lowers the learning cost of prompt engineering, but limits the diversity of outputs and the granularity of control.68% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1134350API
curl https://kongchang.com/api/v1/knowledge/claims/1134350MCP
get_claim(id=1134350)