Y-shaped Generative Flows

• URL: http://arxiv.org/abs/2510.11955v2
• Date: Wed, 15 Oct 2025 10:40:14 GMT
• Title: Y-shaped Generative Flows
• Abstract summary: We introduce Y-shaped generative flows, which move probability mass together along shared pathways before branching to target-specific endpoints.<n>On synthetic, image, and biology datasets, Y-flows recover hierarchy-aware structure, improve distributional metrics over strong flow-based baselines, and reach targets with fewer integration steps.
• Score: 23.602025081791187
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Modern continuous-time generative models often induce V-shaped transport: each sample travels independently along nearly straight trajectories from prior to data, overlooking shared structure. We introduce Y-shaped generative flows, which move probability mass together along shared pathways before branching to target-specific endpoints. Our formulation is based on novel velocity-powered objective with a sublinear exponent (between zero and one). this concave dependence rewards joint and fast mass movement. Practically, we instantiate the idea in a scalable neural ODE training objective. On synthetic, image, and biology datasets, Y-flows recover hierarchy-aware structure, improve distributional metrics over strong flow-based baselines, and reach targets with fewer integration steps.

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