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What the 55-logit comparison measures
Mira Ceti’s 2026 article reports a controlled sweep: the token pair and its projected query/key vectors are held fixed, their position gap remains five, and their absolute positions shift from 0 through 2047. The measurement is the resulting attention score for that pair—not a language model’s output logits or a score of answer quality.
In the author’s implementation, sinusoidal encoding yielded scores from -33.9097 to +21.6053, a spread of 55.5150, and 157 sign changes. RoPE yielded scores from -0.610445 to -0.609907, a spread of 5.387e-04, with no sign changes. These are figures from the article’s constructed code experiment, not independently reproduced benchmark results. Mira Ceti’s experiment and reported results.
The fixed five-position gap makes this a probe of whether the score for one projected pair remains stable as the pair moves to different absolute positions. It does not establish that all query/key pairs, model layers, sequence lengths, or implementations will show the same behavior.
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How the positional methods differ
Sinusoidal encoding adds position vectors
The original Transformer uses sine and cosine functions at different frequencies to form a vector for each position. The model adds that position vector to the token representation at the input, so the representation contains both token and position information. The functions use a base of 10,000, with frequencies varying by embedding dimension. The original Transformer paper describes this encoding; Hugging Face’s positional-encoding explainer provides additional context.
RoPE rotates query and key components
RoPE applies position-dependent rotations to pairs of components in the query and key vectors used in attention. The RoFormer authors describe the method as encoding absolute position with a rotation matrix while incorporating an explicit relative-position dependency into self-attention. In other words, the position operation occurs in the attention computation rather than by adding a position vector to the input representation. RoFormer: Enhanced Transformer with Rotary Position Embedding.
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What the result does—and does not—support
The sweep supports a narrow observation: under the author’s fixed-pair setup and implementation, the measured RoPE attention score changed much less as absolute position shifted while the distance stayed fixed. That is consistent with RoPE’s relative-position formulation. It is not evidence, by itself, that RoPE is universally more stable in deployed models.
The article lists Python 3.12.14, PyTorch 2.2.2, and openlanguagemodel 2.2.1 as its environment. Numerical results can depend on implementation choices, dimensions, precision, and the vectors being tested. The reported sweep isolates one projected pair; the article also describes random-pair sweeps, but those remain author-reported implementation experiments rather than independent validation.
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Task quality is a separate question. The RoFormer paper describes theoretical properties and reports evaluations including long-text classification and other NLP tasks. Those evaluations are a different evidence category from the fixed-pair sweep, and the sweep cannot rank general task performance across trained models. A practical comparison needs task-specific model evaluations under comparable training and inference conditions.
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