arXiv AI
Aug 11

Full-bandwidth transformer

arXiv:2608. 08888v1 Announce Type: new Abstract: Autoregressive transformers compute along two axes: horizontally across generated tokens, and vertically through model depth.

By Xi Wang, Ziyang Cai, Zheng Zhan, Harry Dong, Ying Fan, Gustavo de Rosa, Tim Pearce, John Langford
arXiv AI
Oct 12

Orthros: Phase-Aware Heterogeneous Attention for Efficient Transformers

Orthros introduces a phase‑aware heterogeneous attention mechanism that dynamically switches between linear‑complexity prefilling and full‑attention decoding, reducing the asymptotic cost compared to standard Transformers in long‑context scenarios. The method supports efficient Transformer‑to‑Orthros conversion, achieving higher performance than the original model and delivering a 21.9× prefilling speedup while maintaining strong in‑context recall, including 128K‑scale needle‑in‑a‑haystack tasks. These results demonstrate Orthros’s potential as a more efficient and flexible alternative for industrial Transformer applications.

By Yuhong Chou, Zehao Liu, Yuqi Pan, Qian Liu, Xianwei Chen, Zhongzhu Zhou, Jibin Wu
arXiv Machine Learning
Jul 3

Hyperloop Transformers

arXiv:2604. 21254v3 Announce Type: replace Abstract: LLM architecture research generally aims to maximize model quality subject to fixed compute/latency budgets.

By Abbas Zeitoun, Lucas Torroba-Hennigen, Yoon Kim