Role-Decoupled Attention Residuals: Separating Matching and Content Retrieval Across Depth
Depth-routing residual architectures allow Transformer layers to retrieve earlier representations instead of inheriting only the immediately preceding state. Existing Block Attention Residuals, however, use a single content-dependent depth mixture to construct the inputs to queries, keys, and values. This design couples two functionally different decisions: queries and keys determine where attention matches, whereas values determine what content is retrieved. We therefore ask whether matching and content retrieval should be forced to read from the same depth. We introduce Role-Decoupled Attention Residuals (RD-AttnRes), a minimal extension that shares one depth route between queries and k...
| Authors | Kehan Wang |
|---|---|
| Primary category | cs.AI |
| Published | 2 August 2026 |
| Last updated at source | 2 August 2026 |
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