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October 20, 2025Open Access

Energy Guided Geometric Flow Matching

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The proposed method enhances flow matching by learning a metric tensor that preserves data geometry.
Using synthetic manifolds, it shows superior performance in capturing the underlying geodesics effectively.
Traditional flow matching struggles with high dimensionality, particularly with RBF kernels and nearest neighbor graphs.
This new strategy employs energy distillation for more accurate trajectory interpolation in complex data structures.

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This approach uses score matching and energy distillation to optimize trajectories in data manifold, indicating greater accuracy in flow matching.

Zweig et al. (2025) studied this question.

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This approach uses score matching and energy distillation to optimize trajectories in data manifold, indicating greater accuracy in flow matching.