Dual Attention Residuals
Recent work extends Transformer residual pathways along two complementary axes: historical retrieval selects information from earlier depths, whereas multi-stream methods maintain multiple residual trajectories. These capabilities have largely been studied in isolation, and assigning an independent retriever to each stream still prevents one trajectory from influencing depth selection in another. We propose Dual Attention Residuals (DAR), which brings multi-stream interaction into historical retrieval through reciprocal cross-stream addressing. For each target stream, DAR computes depth weights from normalized states in the opposite stream and applies them to values from the target stream...
| Authors | Xingda Yu, Yining Li, Xinzhang Liu et al. |
|---|---|
| Primary category | cs.CL |
| Published | 21 July 2026 |
| Last updated at source | 6 September 2026 |
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