MHAR improves Transformer information propagation by replacing the single additive residual stream with learned softmax-based attention. It reshapes the routing query into H per-subspace heads, allowing different feature subspaces to attend to different layers of depth history.
HOW THIS AFFECTS YOU
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researcherThis architecture allows for more complex depth-wise information routing with negligible computational overhead.