A.X K1 Technical Report
Abstract
We introduce A.X K1, a 519B-parameter Mixture-of-Experts (MoE) language model trained from scratch. Our design leverages scaling laws to optimize training configurations and vocabulary size under fixed computational budgets. A.X K1 is pre-trained on a corpus of approximately 10T tokens, curated by a multi-stage data processing pipeline. Designed to bridge the gap between reasoning capability and inference efficiency, A.X K1 supports explicitly controllable reasoning to facilitate scalable deployment across diverse real-world scenarios. We propose a simple yet effective Think-Fusion training recipe, enabling user-controlled switching between thinking and non-thinking modes within a single unified model. Extensive evaluations demonstrate that A.X K1 achieves performance competitive with leading open-source models, while establishing a distinctive advantage in Korean-language benchmarks.
Cite
@article{arxiv.2601.09200,
title = {A.X K1 Technical Report},
author = {Sung Jun Cheon and Jaekyung Cho and Seongho Choi and Hyunjun Eun and Seokhwan Jo and Jaehyun Jun and Minsoo Kang and Jin Kim and Jiwon Kim and Minsang Kim and Seungsik Kim and Sungwan Kim and Tae Yoon Kim and Youngrang Kim and Hyeongmun Lee and Sangyeol Lee and Sungeun Lee and Youngsoon Lee and Yujin Lee and Seongmin Ok and Chanyong Park and Hyewoong Park and Junyoung Park and Hyunho Yang and Subin Yi and Dhammiko Arya and Soohyun Bae and Dongyeon Cho and Seungmo Cho and Sangho Choi and Yongseok Choi and Gyoungeun Han and Yong-jin Han and Seokyoung Hong and Hyeon Hwang and Wonbeom Jang and Minjeong Ju and Wonjin Jung and Keummin Ka and Sungil Kang and Dongnam Kim and Jonghwi Kim and Joonghoon Kim and SaeRom Kim and Sangjin Kim and Seongwon Kim and Youngjin Kim and Seojin Lee and Sunwoo Lee and Taehoon Lee and Chanwoo Park and Sohee Park and Sooyeon Park and Yohan Ra and Sereimony Sek and Seungyeon Seo and Gun Song and Sanghoon Woo and Janghan Yoon and Sungbin Yoon},
journal= {arXiv preprint arXiv:2601.09200},
year = {2026}
}