8 citations · 9 across the 9 of their papers we have counts for
10 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…
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…
Step 3.5 Flash: Open Frontier-Level Intelligence with 11B Active Parameters
Ailin Huang, Ang Li, Aobo Kong +213
We introduce Step 3.5 Flash, a sparse Mixture-of-Experts (MoE) model that bridges frontier-level agentic intelligence and computational efficiency. We focus on what matters most wh…
Mitigating Conversational Inertia in Multi-Turn Agents
Yang Wan, Zheng Cao, Zhenhao Zhang +4
Large language models excel as few-shot learners when provided with appropriate demonstrations, yet this strength becomes problematic in multiturn agent scenarios, where LLMs erron…
Unified Generation and Self-Verification for Vision-Language Models via Advantage Decoupled Preference Optimization
Xinyu Qiu, Heng Jia, Zhengwen Zeng +4
Parallel test-time scaling typically trains separate generation and verification models, incurring high training and inference costs. We propose Advantage Decoupled Preference Opti…