most citedUI-TARS: Pioneering Automated GUI Interaction with Native Agents

5 citations · 12 across the 9 of their papers we have counts for

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cs.AI2025

Game-TARS: Pretrained Foundation Models for Scalable Generalist Multimodal Game Agents

Zihao Wang, Xujing Li, Yining Ye +24

We present Game-TARS, a generalist game agent trained with a unified, scalable action space anchored to human-aligned native keyboard-mouse inputs. Unlike API- or GUI-based approac…

cs.AI2025

Reverse-Engineered Reasoning for Open-Ended Generation

Haozhe Wang, Haoran Que, Qixin Xu +9

While the ``deep reasoning'' paradigm has spurred significant advances in verifiable domains like mathematics, its application to open-ended, creative generation remains a critical…

cs.AI2025

UI-TARS-2 Technical Report: Advancing GUI Agent with Multi-Turn Reinforcement Learning

Haoming Wang, Haoyang Zou, Huatong Song +109

The development of autonomous agents for graphical user interfaces (GUIs) presents major challenges in artificial intelligence. While recent advances in native agent models have sh…

cs.AI20251 cited

Acting Less is Reasoning More! Teaching Model to Act Efficiently

Hongru Wang, Cheng Qian, Wanjun Zhong +7

Tool-integrated reasoning (TIR) augments large language models (LLMs) with the ability to invoke external tools during long-form reasoning, such as search engines and code interpre…

cs.AI2025

AdaCtrl: Towards Adaptive and Controllable Reasoning via Difficulty-Aware Budgeting

Shijue Huang, Hongru Wang, Wanjun Zhong +4

Modern large reasoning models demonstrate impressive problem-solving capabilities by employing sophisticated reasoning strategies. However, they often struggle to balance efficienc…

cs.AI20255 cited

UI-TARS: Pioneering Automated GUI Interaction with Native Agents

Yujia Qin, Yining Ye, Junjie Fang +32

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). U…