E2Former-V2: On-the-Fly Equivariant Attention with Linear Activation Memory 事件

E2Former-V2: On-the-Fly Equivariant Attention with Linear Activation Memory arXiv:2601.16622v2 Announce Type: replace-cross Abstract: Equivariant Graph Neural Networks (EGNNs) have become a widely used approach for modeling 3D atomistic systems. However, mainstream architectures face critical scalability bottlenecks due to the explicit construction of geometric features or dense tensor products on \textit{every} edge. To overcome this, we introduce \textbf{E2Former-V2}, a scalable architecture