SO(3)-RoPE for Spherical Transformers

Abstract:Spherical data arise in many scientific applications. Often spherical transformers disregard the geometry of the underlying spherical domain, causing distortions and coordinate singularities near the poles. We introduce SO(3)-RoPE, a relative positional embedding that incorporates spherical geometry into transformer attention through unitary SO(3) representations. Our formulation is SO(3)-equivariant and compatible with FlashAttention, retaining efficiency of vanilla transformers. On shallow water dynamics prediction over a rotating sphere, our SO3ViT outperforms an S2Transformer baseline with lower errors and reduced runtime.

Machine Learning (cs.LG); Artificial Intelligence (cs.AI)

arXiv:2610.06229 [cs.LG]

(or arXiv:2610.06229v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2610.06229 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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