Emergent Structure in the Marginal Attention Space of Language Models
While representation similarity across independently trained language models is well-documented, how internal mechanics such as attention behave across models remains far less characterized. Inspired by this gap, we examine the structure of post-softmax attention weights by marginalizing over query positions, mapping them into a joint token-head "marginal attention space". Evaluating across 60+ diverse LLMs, we find that different properties emerge when reducing this space along its token and head axes. When reduced token-wise, marginal attention yields a text-intrinsic signal robustly conserved across models. To explain this property, we empirically connect marginal attention to the inpu...
| Authors | Valentino Maiorca, Walter Nelson, Francesco Locatello |
|---|---|
| Primary category | cs.CL |
| Published | 2 October 2026 |
| Last updated at source | 2 October 2026 |
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