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#machine learning Preprint Sep 2026

RoPE is Dead, Long Live RoPE: Towards Scalable Data-aware Positional Encodings

Transformers process tokens without any inherent notion of order, making positional encoding a fundamental requirement rather than an architectural refinement. Rotary Position Embedding (RoPE) has become the default positional encoding in modern language models, yet it is heavily biased toward nearby tokens. Existing a...

Jarod Lévy, Mathurin Videau, Jad Yehya et al. · 0 citations

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