Multi-Head Attention Residuals
Transformers propagate information across depth through a single additive residual stream: every sublayer reads only the most recent state. Attention residuals relax this by letting each sublayer attend, through a learned softmax. However, that read uses a single query shared across the entire width, so every feature subspace must read the depth history through one distribution. The cost of this forced compromise grows with how much the subspaces disagree about which layers to read, and disagreement grows with model width. We introduce Multi-Head Attention Residuals (MHAR): the routing query is reshaped into H per-subspace heads, each with its own softmax over the depth history. The read ...
| Authors | Cheng Luo, Zefan Cai, Junjie Hu |
|---|---|
| Primary category | cs.AI |
| Published | 22 July 2026 |
| Last updated at source | 31 July 2026 |
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