5 citations · 10 across the 9 of their papers we have counts for
9 papers
StudentSim: Training LLM-based Student Simulators
Ke Yang, Chenglong Wang, Michel Galley +4
AI tutors are most useful when they adapt to each student's strengths, weaknesses, and preferred guidance, but evidence about which guidance works for which student is sparse, slow…
VGI-Bench: Probing Visual Intelligence in Video Generation Models
Xuan He, Cong Wei, Yuhao Cheng +20
Recent studies suggest that video generation models can exhibit certain forms of zero-shot visual reasoning through generated frames. Yet reliable evaluation remains challenging: b…
MUSE: An Interactive Meta-Agent for Understanding and Steering LLM-powered Data Science Systems
Wei-Hao Chen, Weixi Tong, Yuan Tian +2
Recent advances in large language models have enabled a new class of agentic data science systems that allow users to complete complex data science workflows through natural langua…
Sanity Checks for Agentic Data Science
Zachary T. Rewolinski, Austin V. Zane, Hao Huang +4
Agentic data science (ADS) pipelines have grown rapidly in both capability and adoption, with systems such as OpenAI Codex now able to directly analyze datasets and produce answers…
PlugMem: A Task-Agnostic Plugin Memory Module for LLM Agents
Ke Yang, Zixi Chen, Xuan He +6
Long-term memory is essential for large language model (LLM) agents operating in complex environments, yet existing memory designs are either task-specific and non-transferable, or…
ReUseIt: Synthesizing Reusable AI Agent Workflows for Web Automation
Yimeng Liu, Misha Sra, Jeevana Priya Inala +1
AI-powered web agents have the potential to automate repetitive tasks, such as form filling, information retrieval, and scheduling, but they struggle to reliably execute these task…
Towards Understanding Graphical Perception in Large Multimodal Models
Kai Zhang, Jianwei Yang, Jeevana Priya Inala +4
Despite the promising results of large multimodal models (LMMs) in complex vision-language tasks that require knowledge, reasoning, and perception abilities together, we surprising…
Data Analysis in the Era of Generative AI
Jeevana Priya Inala, Chenglong Wang, Steven Drucker +6
This paper explores the potential of AI-powered tools to reshape data analysis, focusing on design considerations and challenges. We explore how the emergence of large language and…
Contextualized Data-Wrangling Code Generation in Computational Notebooks
Junjie Huang, Daya Guo, Chenglong Wang +7
Data wrangling, the process of preparing raw data for further analysis in computational notebooks, is a crucial yet time-consuming step in data science. Code generation has the pot…