7 citations · 30 across the 27 of their papers we have counts for
10 papers · 1 filter
CUA-Universe: A Scalable and Dynamic Environment for Hybrid GUI+CLI Agents
Haoting Shi, Wenhao Wang, Weicheng Fang +6
Computer-use agents have advanced on benchmarks like OSWorld and AndroidWorld, but still act mostly through the GUI, often producing inefficient trajectories. Real-world computer w…
EvoMaster: A Foundational Evolving Agent Framework for Agentic Science at Scale
Xinyu Zhu, Yuzhu Cai, Zexi Liu +20
The convergence of large language models and agents is catalyzing a new era of scientific discovery: Agentic Science. However, common agent infrastructure is repeatedly rebuilt acr…
MCP-Flow: Facilitating LLM Agents to Master Real-World, Diverse and Scaling MCP Tools
Wenhao Wang, Peizhi Niu, Zhao Xu +8
Large Language Models (LLMs) increasingly rely on external tools to perform complex, realistic tasks, yet their ability to utilize the rapidly expanding Model Contextual Protocol (…
ML-Master: Towards AI-for-AI via Integration of Exploration and Reasoning
Zexi Liu, Yuzhu Cai, Xinyu Zhu +6
As AI capabilities advance toward and potentially beyond human-level performance, a natural transition emerges where AI-driven development becomes more efficient than human-centric…
Incentivizing Inclusive Contributions in Model Sharing Markets
Enpei Zhang, Jingyi Chai, Rui Ye +2
While data plays a crucial role in training contemporary AI models, it is acknowledged that valuable public data will be exhausted in a few years, directing the world's attention t…
FedMABench: Benchmarking Mobile Agents on Decentralized Heterogeneous User Data
Wenhao Wang, Zijie Yu, Rui Ye +3
Mobile agents have attracted tremendous research participation recently. Traditional approaches to mobile agent training rely on centralized data collection, leading to high cost a…
MobileA3gent: Training Mobile GUI Agents Using Decentralized Self-Sourced Data from Diverse Users
Wenhao Wang, Mengying Yuan, Zijie Yu +5
The advancement of mobile GUI agents has opened new opportunities for automating tasks on mobile devices. Training these agents requires large-scale high-quality data, which is pro…
Synthesizing Post-Training Data for LLMs through Multi-Agent Simulation
Shuo Tang, Xianghe Pang, Zexi Liu +6
Post-training is essential for enabling large language models (LLMs) to follow human instructions. However, its effectiveness depends on high-quality instruction data, which is cha…
Robust Collaborative Perception without External Localization and Clock Devices
Zixing Lei, Zhenyang Ni, Ruize Han +5
A consistent spatial-temporal coordination across multiple agents is fundamental for collaborative perception, which seeks to improve perception abilities through information excha…
Language-Driven Interactive Traffic Trajectory Generation
Junkai Xia, Chenxin Xu, Qingyao Xu +3
Realistic trajectory generation with natural language control is pivotal for advancing autonomous vehicle technology. However, previous methods focus on individual traffic particip…