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Attribution of LLaMa sequence-position geometry to RoPE versus attention feature convergence

Determine whether the geometric patterns observed across sequence positions in post-add latent states from intermediate blocks of LLaMa-7B arise from Rotary Positional Encodings applied within self-attention heads, from the relative convergence of features within self-attention contributions as the number of previous tokens increases, or from both. This should be established using analysis methods that can distinguish RoPE-specific effects from token-content influences, beyond PCA-based visualizations.

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Background

The paper analyzes latent state geometry in GPT-2 and LLaMa via PCA and UMAP, focusing on how representations evolve across layers and sequence positions. LLaMa-7B employs Rotary Positional Encodings (RoPE), which inject positional information within self-attention heads rather than as explicit embedding vectors.

In LLaMa, the authors observe a clear geometric pattern across sequence positions in early PCA dimensions. However, because RoPE acts only through attention heads and token content also shapes latent states, the authors note that their visualizations cannot isolate whether the observed pattern is due to RoPE itself or to convergence of attention-derived features with increasing context length.

The authors explicitly state that, with their current approach, they cannot determine the cause of the observed sequence-wise geometry, and they leave interpretation of these RoPE-related patterns to future research.

References

Due to the inherent nature of RoPE applying only within self-attention heads, it is not straightforward to fully separate the effects of the RoPE-augmented attention heads on latent states from other factors such as the content of the input tokens themselves. As such, it is not possible to determine via these visualizations alone whether the geometric patterns observed in Figure~\ref{fig:llama_pos_unit} are a result of RoPE, the relative convergence of features within contributions from self-attention heads as the number of previous tokens increases, or both. We leave interpreting the sequence-wise latent state geometric patterns of RoPE models to future research.

— Visualizing LLM Latent Space Geometry Through Dimensionality Reduction (2511.21594 - Ning et al., 26 Nov 2025) in Section 4.5 (Effects of Sequence Position), subsubsection “LLaMa Positional Encodings”