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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2601.09200 |
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| _version_ | 1866910018374205440 |
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| author | Cheon, Sung Jun Cho, Jaekyung Choi, Seongho Eun, Hyunjun Jo, Seokhwan Jun, Jaehyun Kang, Minsoo Kim, Jin Kim, Jiwon Kim, Minsang Kim, Seungsik Kim, Sungwan Kim, Tae Yoon Kim, Youngrang Lee, Hyeongmun Lee, Sangyeol Lee, Sungeun Lee, Youngsoon Lee, Yujin Ok, Seongmin Park, Chanyong Park, Hyewoong Park, Junyoung Yang, Hyunho Yi, Subin Arya, Dhammiko Bae, Soohyun Cho, Dongyeon Cho, Seungmo Choi, Sangho Choi, Yongseok Han, Gyoungeun Han, Yong-jin Hong, Seokyoung Hwang, Hyeon Jang, Wonbeom Ju, Minjeong Jung, Wonjin Ka, Keummin Kang, Sungil Kim, Dongnam Kim, Jonghwi Kim, Joonghoon Kim, SaeRom Kim, Sangjin Kim, Seongwon Kim, Youngjin Lee, Seojin Lee, Sunwoo Lee, Taehoon Park, Chanwoo Park, Sohee Park, Sooyeon Ra, Yohan Sek, Sereimony Seo, Seungyeon Song, Gun Woo, Sanghoon Yoon, Janghan Yoon, Sungbin |
| author_facet | Cheon, Sung Jun Cho, Jaekyung Choi, Seongho Eun, Hyunjun Jo, Seokhwan Jun, Jaehyun Kang, Minsoo Kim, Jin Kim, Jiwon Kim, Minsang Kim, Seungsik Kim, Sungwan Kim, Tae Yoon Kim, Youngrang Lee, Hyeongmun Lee, Sangyeol Lee, Sungeun Lee, Youngsoon Lee, Yujin Ok, Seongmin Park, Chanyong Park, Hyewoong Park, Junyoung Yang, Hyunho Yi, Subin Arya, Dhammiko Bae, Soohyun Cho, Dongyeon Cho, Seungmo Choi, Sangho Choi, Yongseok Han, Gyoungeun Han, Yong-jin Hong, Seokyoung Hwang, Hyeon Jang, Wonbeom Ju, Minjeong Jung, Wonjin Ka, Keummin Kang, Sungil Kim, Dongnam Kim, Jonghwi Kim, Joonghoon Kim, SaeRom Kim, Sangjin Kim, Seongwon Kim, Youngjin Lee, Seojin Lee, Sunwoo Lee, Taehoon Park, Chanwoo Park, Sohee Park, Sooyeon Ra, Yohan Sek, Sereimony Seo, Seungyeon Song, Gun Woo, Sanghoon Yoon, Janghan Yoon, Sungbin |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_09200 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | A.X K1 Technical Report Cheon, Sung Jun Cho, Jaekyung Choi, Seongho Eun, Hyunjun Jo, Seokhwan Jun, Jaehyun Kang, Minsoo Kim, Jin Kim, Jiwon Kim, Minsang Kim, Seungsik Kim, Sungwan Kim, Tae Yoon Kim, Youngrang Lee, Hyeongmun Lee, Sangyeol Lee, Sungeun Lee, Youngsoon Lee, Yujin Ok, Seongmin Park, Chanyong Park, Hyewoong Park, Junyoung Yang, Hyunho Yi, Subin Arya, Dhammiko Bae, Soohyun Cho, Dongyeon Cho, Seungmo Choi, Sangho Choi, Yongseok Han, Gyoungeun Han, Yong-jin Hong, Seokyoung Hwang, Hyeon Jang, Wonbeom Ju, Minjeong Jung, Wonjin Ka, Keummin Kang, Sungil Kim, Dongnam Kim, Jonghwi Kim, Joonghoon Kim, SaeRom Kim, Sangjin Kim, Seongwon Kim, Youngjin Lee, Seojin Lee, Sunwoo Lee, Taehoon Park, Chanwoo Park, Sohee Park, Sooyeon Ra, Yohan Sek, Sereimony Seo, Seungyeon Song, Gun Woo, Sanghoon Yoon, Janghan Yoon, Sungbin Computation and Language Artificial Intelligence 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. |
| title | A.X K1 Technical Report |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2601.09200 |