67 citations · 94 across the 7 of their papers we have counts for
10 papers
Too Long to Watch, Too Short to Leave: Designing for Agent Micro-Waits
Kevin Pu, Anh Truong, Michael Terry +3
Agentic systems increasingly carry out complex work, where a single turn routinely runs for minutes. This produces a micro-wait that is too long to watch and too short to leave. In…
Working with Agentic `Teammates': When a New Organizational Actor Collides with the Human Ecosystem of Work
Rida Qadri, Remi Denton, Michael Madaio +13
Enterprise AI is transitioning from single-user, reactive tools toward proactive, multi-user 'teammates,' but our empirical understanding of this transition is limited. In this pap…
Synthetic TLX: Forecasting Human Workload Using Agent Simulation
Tzu-Sheng Kuo, Carrie J. Cai, Meredith Ringel Morris +1
Assessing human workload for technology-mediated tasks helps prevent task failure caused by poor technology design. Traditionally, workload is assessed retrospectively using the NA…
Compass vs Railway Tracks: Unpacking User Mental Models for Communicating Long-Horizon Work to Humans vs. AI
Savvas Petridis, Michael Xieyang Liu, Alexander J. Fiannaca +2
As AI systems grow increasingly capable of operating for hours or days at a time, users' prompts are transforming into elaborate specifications for the AI to autonomously work on.…
Gensors: Authoring Personalized Visual Sensors with Multimodal Foundation Models and Reasoning
Michael Xieyang Liu, Savvas Petridis, Vivian Tsai +4
Multimodal large language models (MLLMs), with their expansive world knowledge and reasoning capabilities, present a unique opportunity for end-users to create personalized AI sens…
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…
In Situ AI Prototyping: Infusing Multimodal Prompts into Mobile Settings with MobileMaker
Savvas Petridis, Michael Xieyang Liu, Alexander J. Fiannaca +3
Recent advances in multimodal large language models (LLMs) have made it easier to rapidly prototype AI-powered features, especially for mobile use cases. However, gathering early,…
"We Need Structured Output": Towards User-centered Constraints on Large Language Model Output
Michael Xieyang Liu, Frederick Liu, Alexander J. Fiannaca +4
Large language models can produce creative and diverse responses. However, to integrate them into current developer workflows, it is essential to constrain their outputs to follow…
PromptInfuser: How Tightly Coupling AI and UI Design Impacts Designers' Workflows
Savvas Petridis, Michael Terry, Carrie J. Cai
Prototyping AI applications is notoriously difficult. While large language model (LLM) prompting has dramatically lowered the barriers to AI prototyping, designers are still protot…
ConstitutionMaker: Interactively Critiquing Large Language Models by Converting Feedback into Principles
Savvas Petridis, Ben Wedin, James Wexler +5
Large language model (LLM) prompting is a promising new approach for users to create and customize their own chatbots. However, current methods for steering a chatbot's outputs, su…