Full-bandwidth transformer
Autoregressive transformers compute along two axes: horizontally across generated tokens, and vertically through model depth. Dense attention gives each token broad horizontal access to the past, but the vertical feedback channel between decoding steps remains narrow: only the sampled token returns to the bottom of the stack, while the top-layer hidden state is discarded. We introduce the full-bandwidth transformer, which widens this channel with latent feedback: at each decoding step, the previous top-layer hidden state is fused with the sampled token embedding through a gated linear unit and fed back as the next input. Latent feedback lets non-verbalized computation re-enter the stack w...
| Authors | Xi Wang, Ziyang Cai, Zheng Zhan et al. |
|---|---|
| Primary category | cs.AI |
| Published | 9 August 2026 |
| Last updated at source | 1 October 2026 |
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