Identifiability and Stability of Generative Drifting with Companion-Elliptic Kernel Families

This paper studies the identifiability and stability of drifting fields in the framework of Generative Modeling via Drifting.

The motivating question is whether a zero-drift equilibrium identifies the target distribution and whether an approximately vanishing drift implies weak distributional convergence.

Since the original drifting model employs the Laplace kernel by default, we first analyze why Gaussian score-based arguments fail to apply.

This analysis motivates the introduction of companion-elliptic kernel families, which are characterized by a companion potential satisfying an elliptic closure relation.

We show that this class naturally contains the Laplace kernel and consists precisely of Gaussian and Matérn kernels with smoothness parameter $\nu>0$.

Within this class, we establish field identifiability for arbitrary Borel probability measures on $R^d$: if the drifting field between two such measures vanishes identically, then they must coincide.

For stability, we demonstrate that convergence of the field alone does not guarantee weak convergence, since mass may escape to infinity while remaining invisible to the field.

Although tightness directly removes this obstruction and restores weak stability, we prove that, even without tightness, every $C_0$-vague cluster point lies exactly on the defect ray $\{cp:0\le c\le1\}$.

Consequently, a single scalar $C_0$ observable suffices to detect the missing mass and recover weak convergence.