AI RESEARCH
On the Conditioning Consistency Gap in Conditional Neural Processes
arXiv CS.LG
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ArXi:2604.19312v1 Announce Type: new Neural processes are meta-learning models that map context sets to predictive distributions. While inspired by stochastic processes, NPs do not generally satisfy the Kolmogoro consistency conditions required to define a valid stochastic process. This inconsistency is widely acknowledged but poorly understood. Practitioners note that NPs work well despite the violation, without quantifying what this means.