Likely Interpolants of Generative Models

• URL: http://arxiv.org/abs/2510.26266v1
• Date: Thu, 30 Oct 2025 08:46:53 GMT
• Title: Likely Interpolants of Generative Models
• Authors: Frederik Möbius Rygaard, Shen Zhu, Yinzhu Jin, Søren Hauberg, Tom Fletcher,
• Abstract summary: Interpolation in generative models allows for controlled generation, model inspection, and more.<n>Most generative models lack a principal notion of interpolants without restrictive assumptions on either the model or data dimension.<n>We develop a general scheme that targets likely transition paths compatible with different metrics and probability distributions.
• Score: 12.543430183696648
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Interpolation in generative models allows for controlled generation, model inspection, and more. Unfortunately, most generative models lack a principal notion of interpolants without restrictive assumptions on either the model or data dimension. In this paper, we develop a general interpolation scheme that targets likely transition paths compatible with different metrics and probability distributions. We consider interpolants analogous to a geodesic constrained to a suitable data distribution and derive a novel algorithm for computing these curves, which requires no additional training. Theoretically, we show that our method locally can be considered as a geodesic under a suitable Riemannian metric. We quantitatively show that our interpolation scheme traverses higher density regions than baselines across a range of models and datasets.

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