💬 NLP

A.X K1 Technical Report

A.X K1 is a 519B-parameter Mixture-of-Experts (MoE) language model trained on 10T tokens that features a unique "Think-Fusion" training recipe, allowing users to switch between reasoning and non-reasoning modes for efficient, controllable deployment.

Original authors: Sung Jun Cheon, Jaekyung Cho, Seongho Choi, Hyunjun Eun, Seokhwan Jo, Jaehyun Jun, Minsoo Kang, Jin Kim, Jiwon Kim, Minsang Kim, Seungsik Kim, Sungwan Kim, Tae Yoon Kim, Youngrang Kim, Hyeongmun Lee

Published 2026-02-12

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CC BY 4.0

Original authors: Sung Jun Cheon, Jaekyung Cho, Seongho Choi, Hyunjun Eun, Seokhwan Jo, Jaehyun Jun, Minsoo Kang, Jin Kim, Jiwon Kim, Minsang Kim, Seungsik Kim, Sungwan Kim, Tae Yoon Kim, Youngrang Kim, Hyeongmun Lee, Sangyeol Lee, Sungeun Lee, Youngsoon Lee, Yujin Lee, Seongmin Ok, Chanyong Park, Hyewoong Park, Junyoung Park, Hyunho Yang, Subin Yi, Dhammiko Arya, Soohyun Bae, Dongyeon Cho, Seungmo Cho, Sangho Choi, Yongseok Choi, Gyoungeun Han, Yong-jin Han, Seokyoung Hong, Hyeon Hwang, 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, Sanghoon Woo, Janghan Yoon, Sungbin Yoon

Original paper licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/). ✨ This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer

The Story of A.X K1: The "Smart Switch" Brain

Imagine you have a personal assistant. Most assistants are either "The Speedster" (who answers instantly but sometimes misses the nuances of a complex math problem) or "The Professor" (who is brilliant at solving deep mysteries but takes ten minutes to explain how to boil an egg).

Usually, you have to hire two different people. But SK Telecom has just introduced A.X K1, a massive new AI model that is essentially one person who can flip a switch between being a Speedster and a Professor whenever you need them.

Here is how they built this "super-assistant" using three clever tricks:

1. The "Expert Team" Architecture (Mixture-of-Experts)

Instead of building one giant, heavy brain where every single neuron has to fire for every single question, the engineers built a Mixture-of-Experts (MoE).

The Analogy: Imagine a massive hospital. If you walk in with a broken toe, you don't need a brain surgeon, a cardiologist, and a pediatrician all standing around your bed. That would be a waste of time and money. Instead, you have a receptionist who directs you to the specific specialist you need.

A.X K1 has 519 billion "knowledge pieces," but for any single question, it only "wakes up" about 33 billion of them. This makes the model incredibly smart (it has a huge library of knowledge) but very efficient (it doesn't waste energy using the whole library to answer "What is 2+2?").

2. The "Think-Fusion" Switch (The Best of Both Worlds)

This is the most unique part of the paper. Usually, "Reasoning Models" (like those that solve hard math) are forced to "think out loud" for every single prompt, which is slow and annoying for simple tasks.

The Analogy: Think of a professional chef.

Non-Thinking Mode: When you ask for a glass of water, they just hand it to you. Fast and direct.
Thinking Mode: When you ask for a 5-course wedding banquet, they sit down, grab a notepad, plan the menu, check the pantry, and then start cooking.

A.X K1 uses a special training recipe called "Think-Fusion." It allows the user to say, "Hey, just give me the quick answer," or "Take your time and think this through step-by-step." It can do both within the same "brain," preventing it from being a "slow professor" when you just need a "fast speedster."

3. The "Sovereign AI" Mission (The Cultural Specialist)

Most of the world's biggest AI models are trained primarily on English data from the US and Europe. This means they sometimes struggle with the specific "vibe," culture, and complex nuances of other languages.

The Analogy: It’s like a textbook written by someone who has never visited Korea. They might know the facts, but they don't understand the local slang, the social etiquette, or the subtle way people communicate.

A.X K1 was built to be a "Sovereign AI." SK Telecom poured massive amounts of high-quality Korean data into it. As a result, when it comes to Korean language tests, it doesn't just "translate" English logic—it actually understands the Korean context better than many of its global competitors.

Summary: Why does this matter?

In short, A.X K1 is a massive leap toward practical AI. It’s not just a "smart" model; it’s a flexible model. It’s designed to be smart enough to win math competitions, fast enough to power a chatbot, and culturally aware enough to feel like a local, all while being efficient enough to actually run on real-world computers.

Technical Summary: A.X K1 Technical Report

Date: February 12, 2026
Organization: SK Telecom
Model Type: 519B-parameter Mixture-of-Experts (MoE) Language Model

1. Problem Statement

The development of frontier-class Large Language Models (LLMs) faces two fundamental tensions:

Capacity vs. Deployment: Achieving high-level reasoning requires massive parameter counts, but deploying such models under practical inference constraints (latency and cost) is challenging.
Reasoning vs. Efficiency: Modern "reasoning" models (e.g., those using Chain-of-Thought) often suffer from high latency and computational waste when applied to simple queries that do not require deep thought. There is a lack of unified models that can dynamically scale their computational effort based on task complexity.

2. Methodology

SK Telecom developed A.X K1 using a resource-constrained, engineering-heavy approach, guided by scaling laws to maximize performance within a fixed computational budget.

A. Architecture Design

MoE Configuration: A 519B total parameter model with 33B active parameters. This design prioritizes knowledge density and inference throughput.
Attention Mechanism: Utilizes Multi-head Latent Attention (MLA) to improve KV cache efficiency and reduce memory overhead, particularly for long-context tasks.
Stability Measures: Employs a dual-normalization scheme (applying RMSNorm both before and after MoE layers) to mitigate loss spikes during training.
Tokenizer: A Byte-level BPE tokenizer with an optimized 160K vocabulary size, specifically tuned to balance multilingual coverage (English, Korean, Chinese, Japanese, Spanish) with hardware efficiency.

B. Pre-training Strategy

Data Pipeline: Trained on 10 trillion tokens using a multi-stage pipeline involving document parsing (for PDFs), synthetic data generation (to boost reasoning), and a three-stage curation process (Quality Filtering, Domain Classification, and Difficulty Scoring).
Curriculum Learning: The training follows a Warmup-Stable-Decay (WSD) scheduler across three stages: General Knowledge (4K context), High-Quality Reasoning (specialized STEM/reasoning data), and Long-Context Adaptation (extending to 32K context).

C. Post-training: The "Think-Fusion" Recipe
To solve the reasoning-efficiency trade-off, the authors introduced a novel training framework:

Dual-Track SFT: Independent fine-tuning of an "Instruct" model (direct responses) and a "Reasoning" model (using <think>...</think> tags).
Think-Fusion SFT: The two models are linearly merged ( $\alpha=0.8$ ). To prevent "mode confusion" (where the model becomes too verbose or loses format stability), they use a Mode-Overlap Dataset (MOD), training the merged model on paired examples of both reasoning and non-reasoning trajectories for the same prompt.
On-policy Reinforcement Learning: Uses a modified DAPO framework with GSPO loss to refine instruction following. It employs a dual-reward system: $R_{correct}$ (accuracy) and $R_{format}$ (strict penalty for malformed reasoning tags).

3. Key Contributions

Think-Fusion Framework: A method to create a single unified model that supports user-controllable switching between "thinking" (deep reasoning) and "non-thinking" (concise/direct) modes at inference time.
Compute-Efficient Scaling: Demonstrates that a 519B MoE model can be effectively trained from scratch within a strict 75-day window on 1,024–1,536 H200 GPUs by leveraging scaling laws for MoE and vocabulary size.
Sovereign AI Development: A specialized focus on the Korean language, creating a high-performance foundation model that understands local linguistic and cultural nuances.

4. Results

Reasoning Performance: In "Thinking Mode," A.X K1 is highly competitive with frontier models. Notably, it outperformed DeepSeek-V3.1 and GLM-4.6 on the AIME25 math benchmark (89.8).
Korean Language Superiority: The model established a distinctive advantage in Korean benchmarks, scoring significantly higher than competitors on KMMLU (80.2) and AIME25-ko.
Tokenization Efficiency: The custom tokenizer showed superior efficiency in Korean, requiring significantly fewer tokens per sample compared to OpenAI’s o200k and Qwen3.
Inference Control: The model successfully demonstrated the ability to switch modes, providing low-latency responses for simple tasks and deep reasoning for complex ones.

5. Significance

A.X K1 represents a shift toward controllable intelligence. Rather than forcing all queries through a heavy reasoning process, it provides a scalable deployment path for real-world applications. Furthermore, by successfully engineering a 500B+ parameter model under real-world infrastructure and time constraints, SK Telecom has established a blueprint for "Sovereign AI"—reducing reliance on foreign proprietary models and building robust, localized AI ecosystems.