UI-TARS: Pioneering Automated GUI Interaction with Native Agents

• URL: http://arxiv.org/abs/2501.12326v1
• Date: Tue, 21 Jan 2025 17:48:10 GMT
• Title: UI-TARS: Pioneering Automated GUI Interaction with Native Agents
• Authors: 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,
• Abstract summary: This paper introduces UI-TARS, a native GUI agent model that solely perceives the screenshots as input and performs human-like interactions. In the OSWorld benchmark, UI-TARS achieves scores of 24.6 with 50 steps and 22.7 with 15 steps, outperforming Claude (22.0 and 14.9 respectively)
• Score: 58.18100825673032
• License:
• Abstract: This paper introduces UI-TARS, a native GUI agent model that solely perceives the screenshots as input and performs human-like interactions (e.g., keyboard and mouse operations). Unlike prevailing agent frameworks that depend on heavily wrapped commercial models (e.g., GPT-4o) with expert-crafted prompts and workflows, UI-TARS is an end-to-end model that outperforms these sophisticated frameworks. Experiments demonstrate its superior performance: UI-TARS achieves SOTA performance in 10+ GUI agent benchmarks evaluating perception, grounding, and GUI task execution. Notably, in the OSWorld benchmark, UI-TARS achieves scores of 24.6 with 50 steps and 22.7 with 15 steps, outperforming Claude (22.0 and 14.9 respectively). In AndroidWorld, UI-TARS achieves 46.6, surpassing GPT-4o (34.5). UI-TARS incorporates several key innovations: (1) Enhanced Perception: leveraging a large-scale dataset of GUI screenshots for context-aware understanding of UI elements and precise captioning; (2) Unified Action Modeling, which standardizes actions into a unified space across platforms and achieves precise grounding and interaction through large-scale action traces; (3) System-2 Reasoning, which incorporates deliberate reasoning into multi-step decision making, involving multiple reasoning patterns such as task decomposition, reflection thinking, milestone recognition, etc. (4) Iterative Training with Reflective Online Traces, which addresses the data bottleneck by automatically collecting, filtering, and reflectively refining new interaction traces on hundreds of virtual machines. Through iterative training and reflection tuning, UI-TARS continuously learns from its mistakes and adapts to unforeseen situations with minimal human intervention. We also analyze the evolution path of GUI agents to guide the further development of this domain.

Related papers

• Zero-Shot Prompting Approaches for LLM-based Graphical User Interface Generation [53.1000575179389]
  We propose a Retrieval-Augmented GUI Generation (RAGG) approach, integrated with an LLM-based GUI retrieval re-ranking and filtering mechanism. In addition, we adapt Prompt Decomposition (PDGG) and Self-Critique (SCGG) for GUI generation. Our evaluation, which encompasses over 3,000 GUI annotations from over 100 crowd-workers with UI/UX experience, shows that SCGG, in contrast to PDGG and RAGG, can lead to more effective GUI generation.
  arXiv  Detail & Related papers  (2024-12-15T22:17:30Z)
• Falcon-UI: Understanding GUI Before Following User Instructions [57.67308498231232]
  We introduce an instruction-free GUI navigation dataset, termed Insight-UI dataset, to enhance model comprehension of GUI environments. Insight-UI dataset is automatically generated from the Common Crawl corpus, simulating various platforms. We develop the GUI agent model Falcon-UI, which is initially pretrained on Insight-UI dataset and subsequently fine-tuned on Android and Web GUI datasets.
  arXiv  Detail & Related papers  (2024-12-12T15:29:36Z)
• Aguvis: Unified Pure Vision Agents for Autonomous GUI Interaction [69.57190742976091]
  We introduce Aguvis, a unified vision-based framework for autonomous GUI agents. Our approach leverages image-based observations, and grounding instructions in natural language to visual elements. To address the limitations of previous work, we integrate explicit planning and reasoning within the model.
  arXiv  Detail & Related papers  (2024-12-05T18:58:26Z)
• ShowUI: One Vision-Language-Action Model for GUI Visual Agent [80.50062396585004]
  Building Graphical User Interface (GUI) assistants holds significant promise for enhancing human workflow productivity. We develop a vision-language-action model in digital world, namely ShowUI, which features the following innovations. ShowUI, a lightweight 2B model using 256K data, achieves a strong 75.1% accuracy in zero-shot screenshot grounding.
  arXiv  Detail & Related papers  (2024-11-26T14:29:47Z)
• Improved GUI Grounding via Iterative Narrowing [0.03922370499388702]
  We introduce a visual prompting framework that employs an iterative narrowing mechanism to improve the performance of both general and fine-tuned models in GUI grounding. For evaluation, we tested our method on a comprehensive benchmark comprising various UI platforms and provided the code to reproduce our results.
  arXiv  Detail & Related papers  (2024-11-18T05:47:12Z)
• OmniParser for Pure Vision Based GUI Agent [37.911094082816504]
  Power multimodal models like GPT-4V as a general agent on multiple operating systems are largely underestimated due to the lack of a robust screen parsing technique. textsc Omni significantly improves GPT-4V's performance on ScreenSpot benchmark. textsc Omni with screenshot only outperforms GPT-4V baselines requiring additional information outside of screenshot.
  arXiv  Detail & Related papers  (2024-08-01T00:00:43Z)
• AMEX: Android Multi-annotation Expo Dataset for Mobile GUI Agents [50.39555842254652]
  We introduce the Android Multi-annotation EXpo (AMEX) to advance research on AI agents in mobile scenarios. AMEX comprises over 104K high-resolution screenshots from 110 popular mobile applications, which are annotated at multiple levels. AMEX includes three levels of annotations: GUI interactive element grounding, GUI screen and element functionality descriptions, and complex natural language instructions.
  arXiv  Detail & Related papers  (2024-07-03T17:59:58Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.