activity
20242026
collaborators

11 papers

econ.TH2026

How AI Prompts Can Teach Us About the Structure of Human Behavior

Matthew O. Jackson, Benjamin S. Manning, Yutong Xie +2

We introduce a general, easy-to-implement AI-based method for studying the structure and complexity of human behavior. We assign a large language model a ``type vector'' and then p…

cs.CL2026

BehaviorBench: Benchmarking Foundation Models for Behavioral Science Tasks

Jin Huang, Yutong Xie, Wanli Song +4

Foundation models have been increasingly applied to behavioral science domains such as psychology, sociology, and economics. While these models show promise in individual tasks suc…

cs.LG2026

IRIS: time-structured manifold projections

Brian Ondov, Chia-Hsuan Chang, Weipeng Zhou +6

High-dimensional biomedical data, such as cell-by-gene matrices, are increasingly generated temporally. However, Manifold Learning algorithms, like t-SNE and UMAP, cannot incorpora…

cs.HC2026

AI Behavioral Science

Matthew O. Jackson, Qiaozhu Me, Stephanie W. Wang +16

We outline a foundation for a new field of ``AI Behavioral Science,'' covering three perspectives. First, as AI becomes ubiquitous and is increasingly proprietary and opaque, it be…

cs.IR2026

MedViz: An Agent-based, Visual-guided Research Assistant for Navigating Biomedical Literature

Huan He, Xueqing Peng, Yutong Xie +6

Biomedical researchers face increasing challenges in navigating millions of publications in diverse domains. Traditional search engines typically return articles as ranked text lis…

cs.HC2025

Position: Towards Bidirectional Human-AI Alignment

Hua Shen, Tiffany Knearem, Reshmi Ghosh +21

Recent advances in general-purpose AI underscore the urgent need to align AI systems with human goals and values. Yet, the lack of a clear, shared understanding of what constitutes…