178 citations · 195 across the 14 of their papers we have counts for
6 papers · 1 filter
AutoGen Studio: A No-Code Developer Tool for Building and Debugging Multi-Agent Systems
Victor Dibia, Jingya Chen, Gagan Bansal +5
Multi-agent systems, where multiple agents (generative AI models + tools) collaborate, are emerging as an effective pattern for solving long-running, complex tasks in numerous doma…
BADGE: BADminton report Generation and Evaluation with LLM
Shang-Hsuan Chiang, Lin-Wei Chao, Kuang-Da Wang +2
Badminton enjoys widespread popularity, and reports on matches generally include details such as player names, game scores, and ball types, providing audiences with a comprehensive…
Assessing and Verifying Task Utility in LLM-Powered Applications
Negar Arabzadeh, Siqing Huo, Nikhil Mehta +5
The rapid development of Large Language Models (LLMs) has led to a surge in applications that facilitate collaboration among multiple agents, assisting humans in their daily tasks.…
Mora: Enabling Generalist Video Generation via A Multi-Agent Framework
Zhengqing Yuan, Yixin Liu, Yihan Cao +10
Text-to-video generation has made significant strides, but replicating the capabilities of advanced systems like OpenAI Sora remains challenging due to their closed-source nature.…
StateFlow: Enhancing LLM Task-Solving through State-Driven Workflows
Yiran Wu, Tianwei Yue, Shaokun Zhang +2
It is a notable trend to use Large Language Models (LLMs) to tackle complex tasks, e.g., tasks that require a sequence of actions and dynamic interaction with tools and external en…
Towards better Human-Agent Alignment: Assessing Task Utility in LLM-Powered Applications
Negar Arabzadeh, Julia Kiseleva, Qingyun Wu +5
The rapid development in the field of Large Language Models (LLMs) has led to a surge in applications that facilitate collaboration among multiple agents to assist humans in their…