News
- We released the technical report of the KAT-V1 model, available at https://arxiv.org/pdf/2507.08297.
- Kwaipilot-AutoThink ranks first among all open-source models on LiveCodeBench Pro, a challenging benchmark explicitly designed to prevent data leakage, and even surpasses strong proprietary systems such as Seed and o3-mini.
Introduction
KAT (Kwaipilot-AutoThink) is an open-source large-language model that mitigates over-thinking by learning when to produce explicit chain-of-thought and when to answer directly.
Its development follows a concise two-stage training pipeline:
Data Format
KAT produces responses in a structured template that makes the reasoning path explicit and machine-parsable.
Two modes are supported:
Special Tokens
| Token | Description |
|---|---|
<judge> | Analyzes the input to decide whether explicit reasoning is needed. |
<think_on> / <think_off> | Indicates whether reasoning is activated (“on”) or skipped (“off”). |
<think> | Marks the start of the chain-of-thought segment when think_on is chosen. |
<answer> | Marks the start of the final user-facing answer. |
🔧 Quick Start
from transformers import AutoTokenizer, AutoModelForCausalLM
model_name = "Kwaipilot/KAT-V1-40B"
# load the tokenizer and the model
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map="auto"
)
# prepare the model input
prompt = "Give me a short introduction to large language model."
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# conduct text completion
generated_ids = model.generate(
**model_inputs,
max_new_tokens=65536,
temperature=0.6,
top_p=0.95,
)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist()
content = tokenizer.decode(output_ids, skip_special_tokens=True).strip("\n")
print("prompt:\n", prompt)
print("content:\n", content)
"""
prompt:
Give me a short introduction to large language model.
content:
<judge>
The user's request is to provide a concise factual introduction to large language models, which involves retrieving and summarizing basic information. This task is straightforward as it only requires recalling and presenting well-known details without deeper analysis. No complex reasoning is needed here—just a simple explanation will suffice.
</judge>
<think_off>
<answer>
A **Large Language Model (LLM)** is an advanced AI system trained on vast amounts of text data to understand, generate, and process human-like language. Here’s a concise introduction:
### Key Points:
1. **Training**: Trained on diverse text sources (books, websites, etc.) using deep learning.
2. **Capabilities**:
- Answer questions, generate text, summarize content, translate languages.
- Understand context, sentiment, and nuances in language.
3. **Architecture**: Often based on **transformer models** (e.g., BERT, GPT, LLaMA).
4. **Scale**: Billions of parameters, requiring massive computational resources.
5. **Applications**: Chatbots, content creation, coding assistance, research, and more.
### Examples:
- **OpenAI’s GPT-4**: Powers ChatGPT.
- **Google’s Gemini**: Used in Bard.
- **Meta’s LLaMA**: Open-source alternative.
### Challenges:
- **Bias**: Can reflect biases in training data.
- **Accuracy**: May hallucinate "facts" not grounded in reality.
- **Ethics**: Raises concerns about misinformation and job displacement.
LLMs represent a leap forward in natural language processing, enabling machines to interact with humans in increasingly sophisticated ways. 🌐🤖
</answer>
"""
Future Releases
Looking ahead, we will publish a companion paper that fully documents the AutoThink training framework, covering:
- Cold-start initialization procedures
- Reinforcement-learning (Step-SRPO) strategies
- Data curation and reward design details
At the same time, we will open-source:
- Training resources – the curated dual-regime datasets and RL codebase
- Model suite – checkpoints at 1.5B, 7B, and 13B parameters, all trained with AutoThink gating
Citation
@techreport{Zhan2025KATV1,
title={KAT-V1: Kwai-AutoThink Technical Report},
author={Zizheng, Zhan and Ken, Deng and Huaixi, Tang and Wen, Xiang and Kun, Wu and others},
year={2025},
institution={arXiv preprint arXiv:2507.08297},
number={arXiv:2507.08297},
url={https://arxiv.org/abs/2507.08297}
}