8 citations · 29 across the 18 of their papers we have counts for
18 papers
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…
When Sensors Fail: Temporal Sequence Models for Robust PPO under Sensor Drift
Kevin Vogt-Lowell, Theodoros Tsiligkaridis, Rodney Lafuente-Mercado +4
Real-world reinforcement learning systems must operate under distributional drift in their observation streams, yet most policy architectures implicitly assume fully observed and n…
Qworld: Question-Specific Evaluation Criteria for LLMs
Shanghua Gao, Yuchang Su, Pengwei Sui +2
Evaluating large language models (LLMs) on open-ended questions is difficult because response quality depends on the question's context. Binary scores and static rubrics fail to ca…
STRAND: Sequence-Conditioned Transport for Single-Cell Perturbations
Boyang Fu, George Dasoulas, Sameer Gabbita +5
Prediction of the transcriptional response of a cell to a genetic perturbation is a central problem in the computational modeling of gene regulation. Perturbations of the same gene…
A Generative AI System for Biomedical Data Discovery with Grammar-Based Visualizations
Devin Lange, Shanghua Gao, Pengwei Sui +4
We explore the potential for combining generative AI with grammar-based visualizations for biomedical data discovery. In our prototype, we use a multi-agent system to generate visu…
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…
YAC: Bridging Natural Language and Interactive Visual Exploration with Generative AI for Biomedical Data Discovery
Devin Lange, Shanghua Gao, Pengwei Sui +7
Incorporating natural language input has the potential to improve the capabilities of biomedical data discovery interfaces. However, user interface elements and visualizations are…
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…
Multimodal Medical Code Tokenizer
Xiaorui Su, Shvat Messica, Yepeng Huang +5
Foundation models trained on patient electronic health records (EHRs) require tokenizing medical data into sequences of discrete vocabulary items. Existing tokenizers treat medical…
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…
MoExtend: Tuning New Experts for Modality and Task Extension
Shanshan Zhong, Shanghua Gao, Zhongzhan Huang +3
Large language models (LLMs) excel in various tasks but are primarily trained on text data, limiting their application scope. Expanding LLM capabilities to include vision-language…
Composable Interventions for Language Models
Arinbjorn Kolbeinsson, Kyle O'Brien, Tianjin Huang +8
Test-time interventions for language models can enhance factual accuracy, mitigate harmful outputs, and improve model efficiency without costly retraining. But despite a flood of n…
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…
UniTS: A Unified Multi-Task Time Series Model
Shanghua Gao, Teddy Koker, Owen Queen +3
Although pre-trained transformers and reprogrammed text-based LLMs have shown strong performance on time series tasks, the best-performing architectures vary widely across tasks, w…
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…