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cs.LG2026

SE(3)-MeanFlow: Few-Step Protein Backbone Generation on Lie Groups

Yikun Bai, Binghang Lu, Yikai Liu +7

The paper presents SE(3)-MeanFlow, a generative model that creates protein backbone structures directly on the SE(3) Lie group using a few inference steps, avoiding costly ODE inte…

cs.LG2026

Min Generalized Sliced Gromov Wasserstein: A Scalable Path to Gromov Wasserstein

Ashkan Shahbazi, Xinran Liu, Ping He +1

We propose min Generalized Sliced Gromov--Wasserstein (min-GSGW), a sliced formulation for the Gromov--Wasserstein (GW) problem using expressive generalized slicers. The key idea i…

cs.LG2026

Sinkhorn-Drifting Generative Models

Ping He, Om Khangaonkar, Hamed Pirsiavash +2

We establish a theoretical link between the recently proposed "drifting" generative dynamics and gradient flows induced by the Sinkhorn divergence. In a particle discretization, th…

cs.LG2026

OT-MeanFlow3D: Bridging Optimal Transport and Meanflow for Efficient 3D Point Cloud Generation

Elaheh Akbari, Shansita Sharma, Ping He +5

Flow-matching models have recently emerged as a powerful framework for continuous generative modeling, including 3D point cloud synthesis. However, their deployment is limited by t…

cs.LG2025

LUNA: Linear Universal Neural Attention with Generalization Guarantees

Ashkan Shahbazi, Ping He, Ali Abbasi +6

Scaling attention faces a critical bottleneck: the quadratic computational cost of softmax attention, which limits its application in long-sequence domains. Whil…