71 citations · 84 across the 13 of their papers we have counts for
14 papers
ChatDev 2.0: A No-Code Multi-Agent Platform for Developing Everything
Yufan Dang, Shu Yao, Bowen Lai +6
Large language model (LLM)-based multi-agent systems (MAS) have shown strong potential for solving complex tasks, yet their development forces a tradeoff: code frameworks are expre…
OctoNest: Adaptive Cross-Device Execution through Stateful Control
Shu Yao, Yuhua Luo, Qian Long +7
Computer use agents are expanding from single-device operation toward cross-device systems that coordinate tasks across heterogeneous environments. Execution conditions are often o…
Towards a Science of Collective AI: LLM-based Multi-Agent Systems Need a Transition from Blind Trial-and-Error to Rigorous Science
Jingru Fan, Dewen Liu, Yufan Dang +15
Recent advancements in Large Language Models (LLMs) have greatly extended the capabilities of Multi-Agent Systems (MAS), demonstrating significant effectiveness across a wide range…
AgentXRay: White-Boxing Agentic Systems via Workflow Reconstruction
Ruijie Shi, Houbin Zhang, Yuecheng Han +8
Large Language Models have shown strong capabilities in complex problem solving, yet many agentic systems remain difficult to interpret and control due to opaque internal workflows…
AppCopilot: Toward General, Accurate, Long-Horizon, and Efficient Mobile Agent
Jingru Fan, Yufan Dang, Jingyao Wu +5
With the raid evolution of large language models and multimodal models, the mobile-agent landscape has proliferated without converging on the fundamental challenges. This paper ide…
Cross-Task Experiential Learning on LLM-based Multi-Agent Collaboration
Yilong Li, Chen Qian, Yu Xia +12
Large Language Model-based multi-agent systems (MAS) have shown remarkable progress in solving complex tasks through collaborative reasoning and inter-agent critique. However, exis…
Co-Saving: Resource Aware Multi-Agent Collaboration for Software Development
Rennai Qiu, Chen Qian, Ran Li +9
Recent advancements in Large Language Models (LLMs) and autonomous agents have demonstrated remarkable capabilities across various domains. However, standalone agents frequently en…
Multi-Agent Collaboration via Evolving Orchestration
Yufan Dang, Chen Qian, Xueheng Luo +11
Large language models (LLMs) have achieved remarkable results across diverse downstream tasks, but their monolithic nature restricts scalability and efficiency in complex problem-s…
Autonomous Agents for Collaborative Task under Information Asymmetry
Wei Liu, Chenxi Wang, Yifei Wang +7
Large Language Model Multi-Agent Systems (LLM-MAS) have achieved great progress in solving complex tasks. It performs communication among agents within the system to collaborativel…
Multi-Agent Collaboration via Cross-Team Orchestration
Zhuoyun Du, Chen Qian, Wei Liu +9
Large Language Models (LLMs) have significantly impacted various domains, especially through organized LLM-driven autonomous agents. A representative scenario is in software develo…
Scaling Large Language Model-based Multi-Agent Collaboration
Chen Qian, Zihao Xie, YiFei Wang +9
Recent breakthroughs in large language model-driven autonomous agents have revealed that multi-agent collaboration often surpasses each individual through collective reasoning. Ins…
Iterative Experience Refinement of Software-Developing Agents
Chen Qian, Jiahao Li, Yufan Dang +8
Autonomous agents powered by large language models (LLMs) show significant potential for achieving high autonomy in various scenarios such as software development. Recent research…
Experiential Co-Learning of Software-Developing Agents
Chen Qian, Yufan Dang, Jiahao Li +9
Recent advancements in large language models (LLMs) have brought significant changes to various domains, especially through LLM-driven autonomous agents. A representative scenario…
ChatDev: Communicative Agents for Software Development
Chen Qian, Wei Liu, Hongzhang Liu +11
Software development is a complex task that necessitates cooperation among multiple members with diverse skills. Numerous studies used deep learning to improve specific phases in a…