🤗 Hugging Face Models | 📑 Paper |
🖥️ UI-TARS-desktop
🏄 Midscene (Browser Automation) | 🫨 Discord
We also offer a UI-TARS-desktop version, which can operate on your local personal device. To use it, please visit https://github.com/bytedance/UI-TARS-desktop. To use UI-TARS in web automation, you may refer to the open-source project Midscene.js.
UI-TARS is a next-generation native GUI agent model designed to interact seamlessly with graphical user interfaces (GUIs) using human-like perception, reasoning, and action capabilities. Unlike traditional modular frameworks, UI-TARS integrates all key components—perception, reasoning, grounding, and memory—within a single vision-language model (VLM), enabling end-to-end task automation without predefined workflows or manual rules.
- Comprehensive GUI Understanding: Processes multimodal inputs (text, images, interactions) to build a coherent understanding of interfaces.
- Real-Time Interaction: Continuously monitors dynamic GUIs and responds accurately to changes in real-time.
- Unified Action Space: Standardized action definitions across platforms (desktop, mobile, and web).
- Platform-Specific Actions: Supports additional actions like hotkeys, long press, and platform-specific gestures.
- System 1 & System 2 Reasoning: Combines fast, intuitive responses with deliberate, high-level planning for complex tasks.
- Task Decomposition & Reflection: Supports multi-step planning, reflection, and error correction for robust task execution.
- Short-Term Memory: Captures task-specific context for situational awareness.
- Long-Term Memory: Retains historical interactions and knowledge for improved decision-making.
- Cross-Platform Interaction: Supports desktop, mobile, and web environments with a unified action framework.
- Multi-Step Task Execution: Trained to handle complex tasks through multi-step trajectories and reasoning.
- Learning from Synthetic and Real Data: Combines large-scale annotated and synthetic datasets for improved generalization and robustness.
Perception Capabilty Evaluation
| Model | VisualWebBench | WebSRC | SQAshort |
|---|---|---|---|
| Qwen2-VL-7B | 73.3 | 81.8 | 84.9 |
| Qwen-VL-Max | 74.1 | 91.1 | 78.6 |
| Gemini-1.5-Pro | 75.4 | 88.9 | 82.2 |
| UIX-Qwen2-7B | 75.9 | 82.9 | 78.8 |
| Claude-3.5-Sonnet | 78.2 | 90.4 | 83.1 |
| GPT-4o | 78.5 | 87.7 | 82.3 |
| UI-TARS-2B | 72.9 | 89.2 | 86.4 |
| UI-TARS-7B | 79.7 | 93.6 | 87.7 |
| UI-TARS-72B | 82.8 | 89.3 | 88.6 |
Grounding Capability Evaluation
- ScreenSpot Pro
| Agent Model | Dev-Text | Dev-Icon | Dev-Avg | Creative-Text | Creative-Icon | Creative-Avg | CAD-Text | CAD-Icon | CAD-Avg | Scientific-Text | Scientific-Icon | Scientific-Avg | Office-Text | Office-Icon | Office-Avg | OS-Text | OS-Icon | OS-Avg | Avg-Text | Avg-Icon | Avg |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| QwenVL-7B | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7 | 0.0 | 0.4 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1 | 0.0 | 0.1 |
| GPT-4o | 1.3 | 0.0 | 0.7 | 1.0 | 0.0 | 0.6 | 2.0 | 0.0 | 1.5 | 2.1 | 0.0 | 1.2 | 1.1 | 0.0 | 0.9 | 0.0 | 0.0 | 0.0 | 1.3 | 0.0 | 0.8 |
| SeeClick | 0.6 | 0.0 | 0.3 | 1.0 | 0.0 | 0.6 | 2.5 | 0.0 | 1.9 | 3.5 | 0.0 | 2.0 | 1.1 | 0.0 | 0.9 | 2.8 | 0.0 | 1.5 | 1.8 | 0.0 | 1.1 |
| Qwen2-VL-7B | 2.6 | 0.0 | 1.3 | 1.5 | 0.0 | 0.9 | 0.5 | 0.0 | 0.4 | 6.3 | 0.0 | 3.5 | 3.4 | 1.9 | 3.0 | 0.9 | 0.0 | 0.5 | 2.5 | 0.2 | 1.6 |
| OS-Atlas-4B | 7.1 | 0.0 | 3.7 | 3.0 | 1.4 | 2.3 | 2.0 | 0.0 | 1.5 | 9.0 | 5.5 | 7.5 | 5.1 | 3.8 | 4.8 | 5.6 | 0.0 | 3.1 | 5.0 | 1.7 | 3.7 |
| ShowUI-2B | 16.9 | 1.4 | 9.4 | 9.1 | 0.0 | 5.3 | 2.5 | 0.0 | 1.9 | 13.2 | 7.3 | 10.6 | 15.3 | 7.5 | 13.5 | 10.3 | 2.2 | 6.6 | 10.8 | 2.6 | 7.7 |
| CogAgent-18B | 14.9 | 0.7 | 8.0 | 9.6 | 0.0 | 5.6 | 7.1 | 3.1 | 6.1 | 22.2 | 1.8 | 13.4 | 13.0 | 0.0 | 10.0 | 5.6 | 0.0 | 3.1 | 12.0 | 0.8 | 7.7 |
| Aria-UI | 16.2 | 0.0 | 8.4 | 23.7 | 2.1 | 14.7 | 7.6 | 1.6 | 6.1 | 27.1 | 6.4 | 18.1 | 20.3 | 1.9 | 16.1 | 4.7 | 0.0 | 2.6 | 17.1 | 2.0 | 11.3 |
| UGround-7B | 26.6 | 2.1 | 14.7 | 27.3 | 2.8 | 17.0 | 14.2 | 1.6 | 11.1 | 31.9 | 2.7 | 19.3 | 31.6 | 11.3 | 27.0 | 17.8 | 0.0 | 9.7 | 25.0 | 2.8 | 16.5 |
| Claude Computer Use | 22.0 | 3.9 | 12.6 | 25.9 | 3.4 | 16.8 | 14.5 | 3.7 | 11.9 | 33.9 | 15.8 | 25.8 | 30.1 | 16.3 | 26.9 | 11.0 | 4.5 | 8.1 | 23.4 | 7.1 | 17.1 |
| OS-Atlas-7B | 33.1 | 1.4 | 17.7 | 28.8 | 2.8 | 17.9 | 12.2 | 4.7 | 10.3 | 37.5 | 7.3 | 24.4 | 33.9 | 5.7 | 27.4 | 27.1 | 4.5 | 16.8 | 28.1 | 4.0 | 18.9 |
| UGround-V1-7B | - | - | 35.5 | - | - | 27.8 | - | - | 13.5 | - | - | 38.8 | - | - | 48.8 | - | - | 26.1 | - | - | 31.1 |
| UI-TARS-2B | 47.4 | 4.1 | 26.4 | 42.9 | 6.3 | 27.6 | 17.8 | 4.7 | 14.6 | 56.9 | 17.3 | 39.8 | 50.3 | 17.0 | 42.6 | 21.5 | 5.6 | 14.3 | 39.6 | 8.4 | 27.7 |
| UI-TARS-7B | 58.4 | 12.4 | 36.1 | 50.0 | 9.1 | 32.8 | 20.8 | 9.4 | 18.0 | 63.9 | 31.8 | 50.0 | 63.3 | 20.8 | 53.5 | 30.8 | 16.9 | 24.5 | 47.8 | 16.2 | 35.7 |
| UI-TARS-72B | 63.0 | 17.3 | 40.8 | 57.1 | 15.4 | 39.6 | 18.8 | 12.5 | 17.2 | 64.6 | 20.9 | 45.7 | 63.3 | 26.4 | 54.8 | 42.1 | 15.7 | 30.1 | 50.9 | 17.5 | 38.1 |
- ScreenSpot
| Method | Mobile-Text | Mobile-Icon/Widget | Desktop-Text | Desktop-Icon/Widget | Web-Text | Web-Icon/Widget | Avg |
|---|---|---|---|---|---|---|---|
| Agent Framework | |||||||
| GPT-4 (SeeClick) | 76.6 | 55.5 | 68.0 | 28.6 | 40.9 | 23.3 | 48.8 |
| GPT-4 (OmniParser) | 93.9 | 57.0 | 91.3 | 63.6 | 81.3 | 51.0 | 73.0 |
| GPT-4 (UGround-7B) | 90.1 | 70.3 | 87.1 | 55.7 | 85.7 | 64.6 | 75.6 |
| GPT-4o (SeeClick) | 81.0 | 59.8 | 69.6 | 33.6 | 43.9 | 26.2 | 52.3 |
| GPT-4o (UGround-7B) | 93.4 | 76.9 | 92.8 | 67.9 | 88.7 | 68.9 | 81.4 |
| Agent Model | |||||||
| GPT-4 | 22.6 | 24.5 | 20.2 | 11.8 | 9.2 | 8.8 | 16.2 |
| GPT-4o | 20.2 | 24.9 | 21.1 | 23.6 | 12.2 | 7.8 | 18.3 |
| CogAgent | 67.0 | 24.0 | 74.2 | 20.0 | 70.4 | 28.6 | 47.4 |
| SeeClick | 78.0 | 52.0 | 72.2 | 30.0 | 55.7 | 32.5 | 53.4 |
| Qwen2-VL | 75.5 | 60.7 | 76.3 | 54.3 | 35.2 | 25.7 | 55.3 |
| UGround-7B | 82.8 | 60.3 | 82.5 | 63.6 | 80.4 | 70.4 | 73.3 |
| Aguvis-G-7B | 88.3 | 78.2 | 88.1 | 70.7 | 85.7 | 74.8 | 81.8 |
| OS-Atlas-7B | 93.0 | 72.9 | 91.8 | 62.9 | 90.9 | 74.3 | 82.5 |
| Claude Computer Use | - | - | - | - | - | - | 83.0 |
| Gemini 2.0 (Project Mariner) | - | - | - | - | - | - | 84.0 |
| Aguvis-7B | 95.6 | 77.7 | 93.8 | 67.1 | 88.3 | 75.2 | 84.4 |
| Aguvis-72B | 94.5 | 85.2 | 95.4 | 77.9 | 91.3 | 85.9 | 89.2 |
| Our Model | |||||||
| UI-TARS-2B | 93.0 | 75.5 | 90.7 | 68.6 | 84.3 | 74.8 | 82.3 |
| UI-TARS-7B | 94.5 | 85.2 | 95.9 | 85.7 | 90.0 | 83.5 | 89.5 |
| UI-TARS-72B | 94.9 | 82.5 | 89.7 | 88.6 | 88.7 | 85.0 | 88.4 |
- ScreenSpot v2
| Method | Mobile-Text | Mobile-Icon/Widget | Desktop-Text | Desktop-Icon/Widget | Web-Text | Web-Icon/Widget | Avg |
|---|---|---|---|---|---|---|---|
| Agent Framework | |||||||
| GPT-4o (SeeClick) | 85.2 | 58.8 | 79.9 | 37.1 | 72.7 | 30.1 | 63.6 |
| GPT-4o (OS-Atlas-4B) | 95.5 | 75.8 | 79.4 | 49.3 | 90.2 | 66.5 | 79.1 |
| GPT-4o (OS-Atlas-7B) | 96.2 | 83.4 | 89.7 | 69.3 | 94.0 | 79.8 | 87.1 |
| Agent Model | |||||||
| SeeClick | 78.4 | 50.7 | 70.1 | 29.3 | 55.2 | 32.5 | 55.1 |
| OS-Atlas-4B | 87.2 | 59.7 | 72.7 | 46.4 | 85.9 | 63.1 | 71.9 |
| OS-Atlas-7B | 95.2 | 75.8 | 90.7 | 63.6 | 90.6 | 77.3 | 84.1 |
| Our Model | |||||||
| UI-TARS-2B | 95.2 | 79.1 | 90.7 | 68.6 | 87.2 | 78.3 | 84.7 |
| UI-TARS-7B | 96.9 | 89.1 | 95.4 | 85.0 | 93.6 | 85.2 | 91.6 |
| UI-TARS-72B | 94.8 | 86.3 | 91.2 | 87.9 | 91.5 | 87.7 | 90.3 |
Offline Agent Capability Evaluation
- Multimodal Mind2Web
| Method | Cross-Task Ele.Acc | Cross-Task Op.F1 | Cross-Task Step SR | Cross-Website Ele.Acc | Cross-Website Op.F1 | Cross-Website Step SR | Cross-Domain Ele.Acc | Cross-Domain Op.F1 | Cross-Domain Step SR |
|---|---|---|---|---|---|---|---|---|---|
| Agent Framework | |||||||||
| GPT-4o (SeeClick) | 32.1 | - | - | 33.1 | - | - | 33.5 | - | - |
| GPT-4o (UGround) | 47.7 | - | - | 46.0 | - | - | 46.6 | - | - |
| GPT-4o (Aria-UI) | 57.6 | - | - | 57.7 | - | - | 61.4 | - | - |
| GPT-4V (OmniParser) | 42.4 | 87.6 | 39.4 | 41.0 | 84.8 | 36.5 | 45.5 | 85.7 | 42.0 |
| Agent Model | |||||||||
| GPT-4o | 5.7 | 77.2 | 4.3 | 5.7 | 79.0 | 3.9 | 5.5 | 86.4 | 4.5 |
| GPT-4 (SOM) | 29.6 | - | 20.3 | 20.1 | - | 13.9 | 27.0 | - | 23.7 |
| GPT-3.5 (Text-only) | 19.4 | 59.2 | 16.8 | 14.9 | 56.5 | 14.1 | 25.2 | 57.9 | 24.1 |
| GPT-4 (Text-only) | 40.8 | 63.1 | 32.3 | 30.2 | 61.0 | 27.0 | 35.4 | 61.9 | 29.7 |
| Claude | 62.7 | 84.7 | 53.5 | 59.5 | 79.6 | 47.7 | 64.5 | 85.4 | 56.4 |
| Aguvis-7B | 64.2 | 89.8 | 60.4 | 60.7 | 88.1 | 54.6 | 60.4 | 89.2 | 56.6 |
| CogAgent | - | - | 62.3 | - | - | 54.0 | - | - | 59.4 |
| Aguvis-72B | 69.5 | 90.8 | 64.0 | 62.6 | 88.6 | 56.5 | 63.5 | 88.5 | 58.2 |
| Our Model | |||||||||
| UI-TARS-2B | 62.3 | 90.0 | 56.3 | 58.5 | 87.2 | 50.8 | 58.8 | 89.6 | 52.3 |
| UI-TARS-7B | 73.1 | 92.2 | 67.1 | 68.2 | 90.9 | 61.7 | 66.6 | 90.9 | 60.5 |
| UI-TARS-72B | 74.7 | 92.5 | 68.6 | 72.4 | 91.2 | 63.5 | 68.9 | 91.8 | 62.1 |
- Android Control and GUI Odyssey
| Agent Models | AndroidControl-Low Type | AndroidControl-Low Grounding | AndroidControl-Low SR | AndroidControl-High Type | AndroidControl-High Grounding | AndroidControl-High SR | GUIOdyssey Type | GUIOdyssey Grounding | GUIOdyssey SR |
|---|---|---|---|---|---|---|---|---|---|
| Claude | 74.3 | 0.0 | 19.4 | 63.7 | 0.0 | 12.5 | 60.9 | 0.0 | 3.1 |
| GPT-4o | 74.3 | 0.0 | 19.4 | 66.3 | 0.0 | 20.8 | 34.3 | 0.0 | 3.3 |
| SeeClick | 93.0 | 73.4 | 75.0 | 82.9 | 62.9 | 59.1 | 71.0 | 52.4 | 53.9 |
| InternVL-2-4B | 90.9 | 84.1 | 80.1 | 84.1 | 72.7 | 66.7 | 82.1 | 55.5 | 51.5 |
| Qwen2-VL-7B | 91.9 | 86.5 | 82.6 | 83.8 | 77.7 | 69.7 | 83.5 | 65.9 | 60.2 |
| Aria-UI | -- | 87.7 | 67.3 | -- | 43.2 | 10.2 | -- | 86.8 | 36.5 |
| OS-Atlas-4B | 91.9 | 83.8 | 80.6 | 84.7 | 73.8 | 67.5 | 83.5 | 61.4 | 56.4 |
| OS-Atlas-7B | 93.6 | 88.0 | 85.2 | 85.2 | 78.5 | 71.2 | 84.5 | 67.8 | 62.0 |
| Aguvis-7B | -- | -- | 80.5 | -- | -- | 61.5 | -- | -- | -- |
| Aguvis-72B | -- | -- | 84.4 | -- | -- | 66.4 | -- | -- | -- |
| UI-TARS-2B | 98.1 | 87.3 | 89.3 | 81.2 | 78.4 | 68.9 | 93.9 | 86.8 | 83.4 |
| UI-TARS-7B | 98.0 | 89.3 | 90.8 | 83.7 | 80.5 | 72.5 | 94.6 | 90.1 | 87.0 |
| UI-TARS-72B | 98.1 | 89.9 | 91.3 | 85.2 | 81.5 | 74.7 | 95.4 | 91.4 | 88.6 |
Online Agent Capability Evaluation
| Method | OSWorld (Online) | AndroidWorld (Online) |
|---|---|---|
| Agent Framework | ||
| GPT-4o (UGround) | - | 32.8 |
| GPT-4o (Aria-UI) | 15.2 | 44.8 |
| GPT-4o (Aguvis-7B) | 14.8 | 37.1 |
| GPT-4o (Aguvis-72B) | 17.0 | - |
| GPT-4o (OS-Atlas-7B) | 14.6 | - |
| Agent Model | ||
| GPT-4o | 5.0 | 34.5 (SoM) |
| Gemini-Pro-1.5 | 5.4 | 22.8 (SoM) |
| Aguvis-72B | 10.3 | 26.1 |
| Claude Computer-Use | 14.9 (15 steps) | 27.9 |
| Claude Computer-Use | 22.0 (50 steps) | - |
| Our Model | ||
| UI-TARS-7B-SFT | 17.7 (15 steps) | 33.0 |
| UI-TARS-7B-DPO | 18.7 (15 steps) | - |
| UI-TARS-72B-SFT | 18.8 (15 steps) | 46.6 |
| UI-TARS-72B-DPO | 22.7 (15 steps) | - |
We recommend using HuggingFace Inference Endpoints for fast deployment. We provide two docs for users to refer:
English version: GUI Model Deployment Guide
中文版: GUI模型部署教程
We recommend using vLLM for fast deployment and inference. You need to use vllm>=0.6.1.
pip install -U transformers
VLLM_VERSION=0.6.6
CUDA_VERSION=cu124
pip install vllm==${VLLM_VERSION} --extra-index-url https://download.pytorch.org/whl/${CUDA_VERSION}
Run the command below to start an OpenAI-compatible API service:
python -m vllm.entrypoints.openai.api_server --served-model-name ui-tars --model <path to your model>Then you can use the chat API as below with the gui prompt (choose from mobile or computer) and base64-encoded local images (see OpenAI API protocol document for more details):
import base64
from openai import OpenAI
instruction = "search for today's weather"
screenshot_path = "screenshot.png"
client = OpenAI(
base_url="http://127.0.0.1:8000/v1",
api_key="empty",
)
## Below is the prompt for mobile
prompt = r"""<|im_start|>system
You are a helpful assistant.<|im_end|>
<|im_start|>user
You are a GUI agent. You are given a task and your action history, with screenshots. You need to perform the next action to complete the task.
## Output Format
```\nAction_Summary: ...
Action: ...\n```
## Action Space
click(start_box='<|box_start|>(x1,y1)<|box_end|>')
long_press(start_box='<|box_start|>(x1,y1)<|box_end|>', time='')
type(content='')
scroll(direction='down or up or right or left')
open_app(app_name='')
navigate_back()
navigate_home()
WAIT()
finished() # Submit the task regardless of whether it succeeds or fails.
## Note
- Use English in `Action_Summary` part.
## User Instruction
"""
with open(screenshot_path, "rb") as image_file:
encoded_string = base64.b64encode(image_file.read()).decode("utf-8")
response = client.chat.completions.create(
model="ui-tars",
messages=[
{
"role": "user",
"content": [
{"type": "text", "text": prompt + instruction},
{"type": "image_url", "image_url": {"url": f"data:image/png;base64,{encoded_string}"}},
],
},
],
frequency_penalty=1,
max_tokens=128,
)
print(response.choices[0].message.content)We provide two prompt templates currently for stable running and performance, one for mobile scene and one for personal computer scene.
- Prompt template for mobile:
## Below is the prompt for mobile
prompt = r"""<|im_start|>system
You are a helpful assistant.<|im_end|>
<|im_start|>user
You are a GUI agent. You are given a task and your action history, with screenshots. You need to perform the next action to complete the task.
## Output Format
```\nThought: ...
Action: ...\n```
## Action Space
click(start_box='<|box_start|>(x1,y1)<|box_end|>')
long_press(start_box='<|box_start|>(x1,y1)<|box_end|>', time='')
type(content='')
scroll(direction='down or up or right or left')
open_app(app_name='')
navigate_back()
navigate_home()
WAIT()
finished() # Submit the task regardless of whether it succeeds or fails.
## Note
- Use English in `Action_Summary` part.
## User Instruction
"""- Prompt template for computer:
## Below is the prompt for computer
prompt = r"""<|im_start|>system
You are a helpful assistant.<|im_end|>
<|im_start|>user
You are a GUI agent. You are given a task and your action history, with screenshots. You need to perform the next action to complete the task.
## Output Format
```\nThought: ...
Action: ...\n```
## Action Space
click(start_box='<|box_start|>(x1,y1)<|box_end|>')
left_double(start_box='<|box_start|>(x1,y1)<|box_end|>')
right_single(start_box='<|box_start|>(x1,y1)<|box_end|>')
drag(start_box='<|box_start|>(x1,y1)<|box_end|>', end_box='<|box_start|>(x3,y3)<|box_end|>')
hotkey(key='')
type(content='') #If you want to submit your input, use \"\
\" at the end of `content`.
scroll(start_box='<|box_start|>(x1,y1)<|box_end|>', direction='down or up or right or left')
wait() #Sleep for 5s and take a screenshot to check for any changes.
finished()
call_user() # Submit the task and call the user when the task is unsolvable, or when you need the user's help.
## Note
- Use Chinese in `Thought` part.
- Summarize your next action (with its target element) in one sentence in `Thought` part.
## User Instruction
"""Ollama can deploy the model via gguf format. Bugs exist for safetensors.
We provide 2B and 7B model in GGUF format:
2B: https://huggingface.co/bytedance-research/UI-TARS-2B-gguf
7B: https://huggingface.co/bytedance-research/UI-TARS-7B-gguf
Users can convert the model into GGUF format by using the script from llama.cpp:
python3 convert_hf_to_gguf.py <path to your model>The GGUF file will be generated under the path provided.
We deploy the model by following Ollama tutorial.
# Create Modelfile, Windows users can just create a file named Modelfile
echo "FROM ./path/to/model.gguf" > Modelfile
# Create model in Ollama
ollama create ui-tars -f Modelfile
# Run the model
ollama run ui-tars
Test script is same as vLLM except two changes:
...
client = OpenAI(
base_url="http://127.0.0.1:11434/v1/",
...
)
...
response = client.chat.completions.create(
model="ui-tars" # the name we create via Ollama cli
...
)To experience ui-tars agent in desktop, you may refer to UI-TARS-desktop.
Midscene.js is an open-source web automation SDK that has supported UI-TARS model. Developers can use javascript and natural language to control the browser. See this guide for more details about setting up the model.
UI-TARS is licensed under the Apache License 2.0.
This project builds upon and extends the capabilities of Qwen-2-VL, a powerful vision-language model, which serves as the foundational architecture for UI-TARS. We would like to acknowledge the contributions of the developers and researchers behind Qwen-2-VL for their groundbreaking work in the field of multimodal AI and for providing a robust base for further advancements.
Additionally, we thank the broader open-source community for their datasets, tools, and insights that have facilitated the development of UI-TARS. These collaborative efforts continue to push the boundaries of what GUI automation and AI-driven agents can achieve.
If you find our paper and code useful in your research, please consider giving a star ⭐ and citation 📝
@article{uitars2025,
author = {Yujia Qin, Yining Ye, Junjie Fang, Haoming Wang, Shihao Liang, Shizuo Tian, Junda Zhang, Jiahao Li, Yunxin Li, Shijue Huang, Wanjun Zhong, Kuanye Li, Jiale Yang, Yu Miao, Woyu Lin, Longxiang Liu, Xu Jiang, Qianli Ma, Jingyu Li, Xiaojun Xiao, Kai Cai, Chuang Li, Yaowei Zheng, Chaolin Jin, Chen Li, Xiao Zhou, Minchao Wang, Haoli Chen, Zhaojian Li, Haihua Yang, Haifeng Liu, Feng Lin, Tao Peng, Xin Liu, Guang Shi},
title = {UI-TARS: Pioneering Automated GUI Interaction with Native Agents},
journal = {arXiv preprint arXiv:2501.12326},
url = {https://github.com/bytedance/UI-TARS},
year = {2025}
}
