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On attention heads and bilinear forms

arXiv 2609.22990 ↗First posted 19 Sep 2026cs.LG math.SP stat.MLcited by 0

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Abstract (arXiv)

We study the symmetric and antisymmetric parts of bilinear forms in the attention heads of trained large language models. We introduce an orthogonally invariant profile map from real bilinear forms to a three-dimensional simplex and observe that profiles of trained bilinear forms accumulate near profiles of rank-one bilinear forms. We prove that the symmetric part of a bilinear form in an attention head is the sum of a hyperbolic form and a zero form for a Zariski-dense subset of query-key matrices.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 8 of its 11 references that are in the map. First posted on arXiv 19 Sep 2026.

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References · 8

  1. 2017Attention Is All You Needcited by 60,361
  2. 2021RoFormer: Enhanced Transformer with Rotary Position Embeddingcited by 4,703
  3. 2019DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lightercited by 4,307
  4. 2022OPT: Open Pre-trained Transformer Language Modelscited by 3,512
  5. 2022BLOOM: A 176B-Parameter Open-Access Multilingual Language Modelcited by 1,984
  6. 2023Pythia: A Suite for Analyzing Large Language Models Across Training and Scalingcited by 1,786
  7. 2021Train Short, Test Long: Attention with Linear Biases Enables Input Length Extrapolationcited by 1,103
  8. 2025The underlying structures of self-attention: symmetry, directionality, and emergent dynamics in Transformer trainingcited by 9

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