Identifiability and Stability of Generative Drifting with Companion-Elliptic Kernel Families
arXiv:2604.24196v3 Announce Type: replace-cross Abstract: 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 chara
This paper represents a refinement in the theoretical underpinnings of generative models, specifically addressing identifiability and stability issues that are crucial for advancing their reliability and interpretability.
Improved theoretical understanding of generative models, particularly regarding their stability and the properties of their outputs, is vital for developing more robust and trustworthy AI systems and applications.
This research contributes to a deeper mathematical foundation for generative AI, potentially leading to more deliberate and less 'black box' development of advanced models.
- · AI researchers
- · Generative AI developers
- · Academic institutions
Refined theoretical models for generative AI gain traction within research communities.
Development of new generative models with enhanced stability and predictability becomes possible.
More reliable and interpretable generative AI applications emerge across various industries.
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