Generative Stochastic Optimal Transport: Guided Harmonic Path-Integral Diffusion

• URL: http://arxiv.org/abs/2512.11859v1
• Date: Fri, 05 Dec 2025 05:18:15 GMT
• Title: Generative Stochastic Optimal Transport: Guided Harmonic Path-Integral Diffusion
• Abstract summary: We introduce Guided Harmonic Path-Integral Diffusion (GH-PID), a linearly-solvable framework for guided Optimal Transport.<n>A low-dimensional guidance protocol shapes the trajectory ensemble while preserving analytic structure.<n>GH-PID generates geometry-aware, trust-aware trajectories that satisfy the prescribed terminal distribution.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce Guided Harmonic Path-Integral Diffusion (GH-PID), a linearly-solvable framework for guided Stochastic Optimal Transport (SOT) with a hard terminal distribution and soft, application-driven path costs. A low-dimensional guidance protocol shapes the trajectory ensemble while preserving analytic structure: the forward and backward Kolmogorov equations remain linear, the optimal score admits an explicit Green-function ratio, and Gaussian-Mixture Model (GMM) terminal laws yield closed-form expressions. This enables stable sampling and differentiable protocol learning under exact terminal matching. We develop guidance-centric diagnostics -- path cost, centerline adherence, variance flow, and drift effort -- that make GH-PID an interpretable variational ansatz for empirical SOT. Three navigation scenarios illustrated in 2D: (i) Case A: hand-crafted protocols revealing how geometry and stiffness shape lag, curvature effects, and mode evolution; (ii) Case B: single-task protocol learning, where a PWC centerline is optimized to minimize integrated cost; (iii) Case C: multi-expert fusion, in which a commander reconciles competing expert/teacher trajectories and terminal beliefs through an exact product-of-experts law and learns a consensus protocol. Across all settings, GH-PID generates geometry-aware, trust-aware trajectories that satisfy the prescribed terminal distribution while systematically reducing integrated cost.

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