Tensor Gauge Flow Models
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Abstract (arXiv)
This paper introduces Tensor Gauge Flow Models, a new class of Generative Flow Models that generalize Gauge Flow Models and Higher Gauge Flow Models by incorporating higher-order Tensor Gauge Fields into the Flow Equation. This extension allows the model to encode richer geometric and gauge-theoretic structure in the data, leading to more expressive flow dynamics. Experiments on Gaussian mixture models show that Tensor Gauge Flow Models achieve improved generative performance compared to both standard and gauge flow baselines.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 14 of its 24 references that are in the map. First posted on arXiv 18 Nov 2025.
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14 of 24 references.
References · 14
- 2022Flow Matching for Generative Modelingcited by 5,426
- 2018Neural Ordinary Differential Equationscited by 3,733
- 2021Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gaugescited by 1,131
- 2016Geometric deep learning on graphs and manifolds using mixture model CNNscited by 468
- 2024Flow Matching Guide and Codecited by 291
- 2019Gauge Equivariant Convolutional Networks and the Icosahedral CNNcited by 262
- 2023Flow Matching on General Geometriescited by 260
- 2024FlowMM: Generating Materials with Riemannian Flow Matchingcited by 105
- 2024Metric Flow Matching for Smooth Interpolations on the Data Manifoldcited by 74
- 2024Riemannian Flow Matching Policy for Robot Motion Learningcited by 61
- 2024Wasserstein Flow Matching: Generative modeling over families of distributionscited by 27
- 2025Theoretical Guarantees for High Order Trajectory Refinement in Generative Flowscited by 9
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