A.X K1 Technical Report

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Authors: Sung Jun Cheon, Jaekyung Cho, Seong-Ho Choi, Hyunjun Eun, Seokhwan Jo, Jun, Jaehyun, Minsoo Kang, Jin Hyun Kim, Jiwon Lai Kim, Minsang Kim, Sungwan Kim, S. G. Kim
+48 more Tae Yoon Kim, Chongam Kim, Hyeongmun Lee, Sangyeol Lee, Sungeun Lee, YoungSoon Lee, Yujin Lee, Seongmin Ok, Chanyong Park, Hyewoong Park, Junyoung Park, Hyun‐Ho Yang, Subin Yi, Dhammiko Arya, Soohyun Bae, Yongseok Choi, Sangho Choi, Sangho Choi, Yongseok Choi, Gyoungeun Han, Yong-jin Han, Hong, Seokyoung, Hwang, Hyeon, Wonbeom Jang, Minjeong Ju, Wonjin Jung, Keummin Ka, Sungil Kang, Dongnam Kim, Jonghwi Kim, Joonghoon Kim, SaeRom Kim, Sangjin Kim, Seongwon Kim, Youngjin Kim, Seojin Lee, Sunwoo Lee, Taehoon Lee, Chanwoo Park, Sohee Park, Sooyeon Park, Yohan Ra, Sereimony Sek, Seungyeon Seo, Gun Song, Woo, Sanghoon, Janghan Yoon, Sungbin Yoon
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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.

Fields of Study & Subjects

Topic Modeling Natural Language Processing Techniques Advanced Neural Network Applications

Publication details

Journal / source
arXiv (Cornell University)
Publication date
14 January 2026
Document type
Preprint