We introduce LongCat ZigZag Attention (LoZA), which is a sparse attention scheme designed to transform any existing full-attention models into sparse versions with rather limited compute budget. In long-context scenarios, LoZA can achieve significant speed-ups both for prefill-intensive (e.g., retrieval-augmented generation) and decode-intensive (e.g., tool-integrated reasoning) cases. Specifically, by applying LoZA to LongCat-Flash during mid-training, we serve LongCat-Flash-Exp as a long-context foundation model that can swiftly process up to 1 million tokens, enabling efficient long-term reasoning and long-horizon agentic capabilities.
@article{arxiv.2512.23966,
title = {Efficient Context Scaling with LongCat ZigZag Attention},
author = {Chen Zhang and Yang Bai and Jiahuan Li and Anchun Gui and Keheng Wang and Feifan Liu and Guanyu Wu and Yuwei Jiang and Defei Bu and Li Wei and Haihang Jing and Hongyin Tang and Xin Chen and Xiangzhou Huang and Fengcun Li and Rongxiang Weng and Yulei Qian and Yifan Lu and Yerui Sun and Jingang Wang and Yuchen Xie and Xunliang Cai},
journal= {arXiv preprint arXiv:2512.23966},
year = {2026}
}