arXiv — Machine Learning · · 3 min read

nD-RoPE: A Generalized RoPE for n-Dimensional Position Embedding

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Computer Science > Machine Learning

arXiv:2606.12146 (cs)
[Submitted on 10 Jun 2026]

Title:nD-RoPE: A Generalized RoPE for n-Dimensional Position Embedding

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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)

Submission history

From: Boyang Li [view email]
[v1] Wed, 10 Jun 2026 14:38:00 UTC (3,169 KB)
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