5 citations · 13 across the 10 of their papers we have counts for
10 papers
Overseeing Agents Without Constant Oversight: Challenges and Opportunities
Madeleine Grunde-McLaughlin, Hussein Mozannar, Maya Murad +3
To enable human oversight, agentic AI systems often provide a trace of reasoning and action steps. Designing traces to have an informative, but not overwhelming, level of detail re…
VizCopilot: Fostering Appropriate Reliance on Enterprise Chatbots with Context Visualization
Sam Yu-Te Lee, Jingya Chen, Albert Calzaretto +4
Enterprise chatbots show promise in supporting knowledge workers in information synthesis tasks by retrieving context from large, heterogeneous databases before generating answers.…
Magentic-UI: Towards Human-in-the-loop Agentic Systems
Hussein Mozannar, Gagan Bansal, Cheng Tan +17
AI agents powered by large language models are increasingly capable of autonomously completing complex, multi-step tasks using external tools. Yet, they still fall short of human-l…
Inference-Time Scaling for Complex Tasks: Where We Stand and What Lies Ahead
Vidhisha Balachandran, Jingya Chen, Lingjiao Chen +8
Inference-time scaling can enhance the reasoning capabilities of large language models (LLMs) on complex problems that benefit from step-by-step problem solving. Although lengtheni…
nvAgent: Automated Data Visualization from Natural Language via Collaborative Agent Workflow
Geliang Ouyang, Jingyao Chen, Zhihe Nie +4
Natural Language to Visualization (NL2Vis) seeks to convert natural-language descriptions into visual representations of given tables, empowering users to derive insights from larg…
Steering Language Model Refusal with Sparse Autoencoders
Kyle O'Brien, David Majercak, Xavier Fernandes +7
Responsible deployment of language models requires mechanisms for refusing unsafe prompts while preserving model performance. While most approaches modify model weights through add…
Eureka: Evaluating and Understanding Large Foundation Models
Vidhisha Balachandran, Jingya Chen, Neel Joshi +6
Rigorous and reproducible evaluation is critical for assessing the state of the art and for guiding scientific advances in Artificial Intelligence. Evaluation is challenging in pra…
AutoGen Studio: A No-Code Developer Tool for Building and Debugging Multi-Agent Systems
Victor Dibia, Jingya Chen, Gagan Bansal +5
Multi-agent systems, where multiple agents (generative AI models + tools) collaborate, are emerging as an effective pattern for solving long-running, complex tasks in numerous doma…
Privacy-Preserving Script Sharing in GUI-based Programming-by-Demonstration Systems
Toby Jia-Jun Li, Jingya Chen, Brandon Canfield +1
An important concern in end user development (EUD) is accidentally embedding personal information in program artifacts when sharing them. This issue is particularly important in GU…
Towards Effective Human-AI Collaboration in GUI-Based Interactive Task Learning Agents
Toby Jia-Jun Li, Jingya Chen, Tom M. Mitchell +1
We argue that a key challenge in enabling usable and useful interactive task learning for intelligent agents is to facilitate effective Human-AI collaboration. We reflect on our pa…