8 citations · 29 across the 18 of their papers we have counts for
9 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…
An AI agent for treatment reasoning over a biomedical tool universe
Shanghua Gao, Ayush Noori, Richard Zhu +13
Treatment reasoning underpins every therapeutic decision, integrating disease context, comorbidities, medications, contraindications, and evolving biomedical knowledge to select an…
AutoScientists: Self-Organizing Agent Teams for Long-Running Scientific Experimentation
Shanghua Gao, Ada Fang, Marinka Zitnik
Scientific research proceeds through iterative cycles of hypothesis generation, experiment design, execution, and revision. AI agents can automate parts of this process, but existi…
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…
TxAgent: An AI Agent for Therapeutic Reasoning Across a Universe of Tools
Shanghua Gao, Richard Zhu, Zhenglun Kong +5
Precision therapeutics require multimodal adaptive models that generate personalized treatment recommendations. We introduce TxAgent, an AI agent that leverages multi-step reasonin…
A Causality-aware Paradigm for Evaluating Creativity of Multimodal Large Language Models
Zhongzhan Huang, Shanshan Zhong, Pan Zhou +3
Recently, numerous benchmarks have been developed to evaluate the logical reasoning abilities of large language models (LLMs). However, assessing the equally important creative cap…
KGARevion: An AI Agent for Knowledge-Intensive Biomedical QA
Xiaorui Su, Yibo Wang, Shanghua Gao +4
Biomedical reasoning integrates structured, codified knowledge with tacit, experience-driven insights. Depending on the context, quantity, and nature of available evidence, researc…
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…
Let's Think Outside the Box: Exploring Leap-of-Thought in Large Language Models with Creative Humor Generation
Shanshan Zhong, Zhongzhan Huang, Shanghua Gao +4
Chain-of-Thought (CoT) guides large language models (LLMs) to reason step-by-step, and can motivate their logical reasoning ability. While effective for logical tasks, CoT is not c…