Recursive Flow Matching

• URL: http://arxiv.org/abs/2605.26535v1
• Date: Tue, 26 May 2026 04:32:01 GMT
• Title: Recursive Flow Matching
• Abstract summary: We introduce Recursive Flow Matching (RecFM), a generative framework for forecasting complex dynamics.<n>RecFM enforces self-consistency to align discretization scales, reducing disctization errors and improving performance across metrics for physics-based tasks.
• Score: 26.525851478512678
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Generative models have emerged as a powerful paradigm for solving physics systems and modeling complex spatiotemporal dynamics. However, achieving high physical accuracy without incurring high computational cost remains a fundamental challenge, as existing approaches face a critical speed-fidelity trade-off. In this work, we introduce Recursive Flow Matching (RecFM), a generative framework for forecasting complex spatiotemporal dynamics. RecFM enforces self-consistency to align trajectories across discretization scales, reducing discretization errors and improving performance across metrics for physics-based tasks. To our knowledge, this is the first method to achieve high-fidelity one- and few-step (2-4 step) dynamic generation for scientific systems with performance comparable to state-of-the-art multi-step solvers. Across challenging scientific benchmarks, RecFM achieves up to a 20$\times$ speedup over leading diffusion-based emulators while improving predictive accuracy. Furthermore, RecFM reduces mean squared error by over 15% compared to vanilla flow matching, offering a scalable and efficient solution for real-time scientific emulation.

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