nD-RoPE: A Generalized RoPE for n-Dimensional Position Embedding
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Computer Science > Machine Learning
Title:nD-RoPE: A Generalized RoPE for n-Dimensional Position Embedding
Abstract:Rotary Position Embedding (RoPE) is widely adopted in Transformer models, yet its extension to high-dimensional domains lacks a unified theoretical formulation. Most existing approaches either apply rotations independently along each axis or empirically mix frequencies, which limits cross-dimensional interactions and yields direction-dependent representations. To address these limitations, we propose nD-RoPE, a decomposition-free generalization of RoPE to arbitrary dimensions. From a translation-invariant formulation in continuous Hilbert space, we derive a spectral condition for isotropy that requires treating positions and frequencies as coupled \(n\)-dimensional vectors. We instantiate this formulation with a multi-scale regular-simplex wave-vector design, which provides non-degenerate spatial coverage and a symmetric, directionally balanced second-order response. Experiments across images, videos, and point clouds demonstrate consistent performance gains and improved generalization in high-dimensional settings.
| Comments: | Accepted to the 43rd International Conference on Machine Learning (ICML 2026) |
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2606.12146 [cs.LG] |
| (or arXiv:2606.12146v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2606.12146
arXiv-issued DOI via DataCite (pending registration)
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