6 citations · 16 across the 7 of their papers we have counts for
5 papers · 1 filter
Do LLMs Know What to Ask and When? Evaluating Multi-Turn Information Seeking
Yepeng Huang, Jiawen Zhang, Michelle Dai +4
When a user question is underspecified, a capable model should recognize that its context is insufficient, identify the missing information, ask for it, and respond only once that…
MatrAIx: Simulating the World with 8.3 Billion Persona Agents
Xiaomin Li, Yuexing Hao, Jianheng Hou +90
Human evaluation of AI systems and digital products is costly, slow, and difficult to scale. Offline evaluations are more scalable but often abstract away human diversity and inter…
Autonomous Agents for Scientific Discovery: Orchestrating Scientists, Language, Code, and Physics
Lianhao Zhou, Hongyi Ling, Cong Fu +14
Computing has long served as a cornerstone of scientific discovery. Recently, a paradigm shift has emerged with the rise of large language models (LLMs), introducing autonomous sys…
ToolUniverse: An open platform for democratizing AI scientists
Shanghua Gao, Richard Zhu, Pengwei Sui +8
AI scientists are emerging computational systems that serve as collaborative partners in discovery. These systems remain difficult to build because they are bespoke, tied to rigid…
Empowering Biomedical Discovery with AI Agents
Shanghua Gao, Ada Fang, Yepeng Huang +6
We envision "AI scientists" as systems capable of skeptical learning and reasoning that empower biomedical research through collaborative agents that integrate AI models and biomed…