Asymmetric Attention Heads: Structured Head-Wise Context Allocation for Transformer Attention

Abstract:Standard multi-head attention (MHA) gives every head the same full causal context span, although heads can serve different contextual roles. Some heads may rely mainly on nearby lexical or syntactic context, while others may depend on longer-range relations such as entity interactions, discourse links, or state changes. We present Asymmetric Attention Heads (AAH), a head-wise context- allocation framework that treats context length as an explicit per-head or per-group allocation variable. AAH groups heads using feature-derived statistics, organizes these groups hierarchically, and assigns causal local windows while preserving the standard flat MHA output interface. In 4096- token seed-0 experiments, several AAH-style local-allocation variants achieve lower validation loss than pure full attention. Short-budget ablations show that stable local allocation and head-window assignment structure matter, while fixed/local controls can be competitive with adaptive hierarchy. We interpret AAH as a structured head-wise context-allocation mechanism for quality and analysis, with Attention Coverage Ratio (ACR) reported as a selected-window routing diagnostic

Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)

arXiv:2608.19203 [cs.CL]

(or arXiv:2608.19203v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2608.19203 Focus to learn more arXiv-issued DOI via DataCite

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