A program synthesis system models collaborative physical activities from narrated demonstrations as editable programs, enabling users to teach, inspect, and correct them, with a study showing 70% success in refining soccer tactics programs.
In Proceedings of the 37th Annual ACM Symposium on User Interface Software and Technology (UIST ’24)
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A generative-AI pipeline dynamically generates and anchors virtual assets to match the shape of physical props, enabling adaptive passive haptics in MR that users rate higher in realism, immersion, and enjoyment than static baselines.
Intent Lenses infer capture-time user intent from photos via LLMs to create dynamic, reusable interactive objects that generate and organize structured visual notes for later sensemaking.
DroidRetriever is a transparent steerable mobile automation system that decomposes information-seeking tasks with multi-LLM agents, navigates apps, synthesizes reports with screenshots, and provides a dashboard for real-time user intervention and privacy pauses.
Pista decomposes AI agent actions in spreadsheets into auditable steps, enabling real-time user intervention that improves task outcomes, user comprehension, agent perception, and sense of co-ownership over baseline agents.
VIDEE introduces a human-in-the-loop system using Monte-Carlo Tree Search for task decomposition, executable pipeline generation, and LLM-based evaluation with visualizations to support non-expert text analytics.
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VIDEE: Visual and Interactive Decomposition, Execution, and Evaluation of Text Analytics with Intelligent Agents
VIDEE introduces a human-in-the-loop system using Monte-Carlo Tree Search for task decomposition, executable pipeline generation, and LLM-based evaluation with visualizations to support non-expert text analytics.