6 papers · 1 filter
MCP-Persona: Benchmarking LLM Agents on Real-World Personal Applications via Environment Simulation
Wenhao Wang, Peizhi Niu, Gongyi Zou +9
The Model Context Protocol (MCP) has emerged as a transformative standard for connecting large language models (LLMs) with external data sources and tools, and has been rapidly ado…
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 (…
PhysMaster: Building an Autonomous AI Physicist for Theoretical and Computational Physics Research
Tingjia Miao, Jiawen Dai, Jingkun Liu +23
Advances in LLM reasoning and tool use have enabled agentic science, yet frontier theoretical and computational physics remains challenging because research requires deep domain ex…
BrowseMaster: Towards Scalable Web Browsing via Tool-Augmented Programmatic Agent Pair
Xianghe Pang, Shuo Tang, Rui Ye +3
Effective information seeking in the vast and ever-growing digital landscape requires balancing expansive search with strategic reasoning. Current large language model (LLM)-based…
X-MAS: Towards Building Multi-Agent Systems with Heterogeneous LLMs
Rui Ye, Xiangrui Liu, Qimin Wu +4
LLM-based multi-agent systems (MAS) extend the capabilities of single LLMs by enabling cooperation among multiple specialized agents. However, most existing MAS frameworks rely on…
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…