Interaction of Positional Embeddings, Local Attention, and Geometric Formation
Characterize the interaction among positional embeddings, local attention kernels (including sliding-window attention), and the formation of the low-dimensional geometric substrate (e.g., entropy-ordered value manifolds) in transformer language models, with particular attention to sliding-window and hybrid transformer–state-space-model architectures.
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Likewise, the interaction between positional embeddings, local attention kernels, and geometric formation remains an open problem, especially in sliding-window or hybrid transformer----SSM architectures.
This observation motivates us to rethink the characteristic distribution of NoPE attention: is the band-like distribution extending from the upper-middle to the lower-right a property of softmax attention in hybrid models, or is it intrinsic to NoPE attention itself? Unfortunately, because NoPE-only models have inherent limitations, we are currently unable to answer this question.