1 citations · 1 across the 5 of their papers we have counts for
6 papers
LLaDA-UI: Bringing Block-wise Diffusion to Vision-Language GUI Agents
Zhangxuan Gu, Haoxing Chen, Qi Qin +17
Diffusion large language models (dLLMs) achieve high decoding efficiency through block-parallel, arbitrary-order generation, making them attractive for latency-sensitive applicatio…
UI-Venus-2 Technical Report
Venus Team, Zhuohan Cai, Haoxing Chen +28
Multimodal GUI agents have emerged as a promising paradigm for digital task automation, yet transitioning from benchmark-oriented models to dependable real-world applications remai…
MAGA: Multi-Platform Self-Fusion of GUI Agents via Structured Action Distillation
Hang Yan, Zhangxuan GU, Beitong Zhou +5
Graphical user interface (GUI) agents based on large language models are increasingly deployed across mobile, web, and desktop environments. However, existing agents are typically…
UI-Venus-1.5 Technical Report
Venus Team, Changlong Gao, Zhangxuan Gu +24
GUI agents have emerged as a powerful paradigm for automating interactions in digital environments, yet achieving both broad generality and consistently strong task performance rem…
VenusBench-GD: A Comprehensive Multi-Platform GUI Benchmark for Diverse Grounding Tasks
Beitong Zhou, Zhexiao Huang, Yuan Guo +10
GUI grounding is a critical component in building capable GUI agents. However, existing grounding benchmarks suffer from significant limitations: they either provide insufficient d…
UI-Venus Technical Report: Building High-performance UI Agents with RFT
Zhangxuan Gu, Zhengwen Zeng, Zhenyu Xu +21
We present UI-Venus, a native UI agent that takes only screenshots as input based on a multimodal large language model. UI-Venus achieves SOTA performance on both UI grounding and…