Depth-Attention: Cross-Layer Value Mixing for Language Models 事件

Depth-Attention: Cross-Layer Value Mixing for Language Models arXiv:2606.05014v1 Announce Type: new Abstract: Self-attention selects information freely across the sequence, but across depth, Transformers merely add each layer's output to the residual stream, so later layers cannot selectively reuse earlier-layer representations. Recent cross-layer methods improve this flow but operate on hidden states outside attention, adding state beyond the key-value cache at inference--a cost that becomes i