activity
20242026
most citedExpanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling

16 citations · 22 across the 14 of their papers we have counts for

collaborators

14 papers

cs.AI2026

OSReward: Instituting Standardized Evaluation for Cross-Platform Computer-Use Reward Models

Qiushi Sun, Kanzhi Cheng, Yian Wang +20

Computer-using agents (CUAs) are advancing rapidly across the digital world. A CUA trajectory records the agent's actions, states, and reasoning. Verifying whether it fulfilled the…

cs.AI2025

OS-Sentinel: Towards Safety-Enhanced Mobile GUI Agents via Hybrid Validation in Realistic Workflows

Qiushi Sun, Mukai Li, Zhoumianze Liu +11

Computer-using agents powered by Vision-Language Models (VLMs) have demonstrated human-like capabilities in operating digital environments like mobile platforms. While these agents…

cs.AI2025

OS-MAP: How Far Can Computer-Using Agents Go in Breadth and Depth?

Xuetian Chen, Yinghao Chen, Xinfeng Yuan +12

Computer-using agents have shown strong potential to boost human productivity and enable new application forms across platforms. While recent advances have led to usable applicatio…

eess.AS2025

Enhancing Generalization of Speech Large Language Models with Multi-Task Behavior Imitation and Speech-Text Interleaving

Jingran Xie, Xiang Li, Hui Wang +4

Large language models (LLMs) have shown remarkable generalization across tasks, leading to increased interest in integrating speech with LLMs. These speech LLMs (SLLMs) typically u…

cs.AI2025

ScienceBoard: Evaluating Multimodal Autonomous Agents in Realistic Scientific Workflows

Qiushi Sun, Zhoumianze Liu, Chang Ma +18

Large Language Models (LLMs) have extended their impact beyond Natural Language Processing, substantially fostering the development of interdisciplinary research. Recently, various…

cs.CL2025

Genius: A Generalizable and Purely Unsupervised Self-Training Framework For Advanced Reasoning

Fangzhi Xu, Hang Yan, Chang Ma +6

Advancing LLM reasoning skills has captivated wide interest. However, current post-training techniques rely heavily on supervisory signals, such as outcome supervision or auxiliary…