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.
Kummerfeld, Toby Jia-Jun Li, and Tianyi Zhang
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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.
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.
citing papers explorer
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Interactive Program Synthesis for Modeling Collaborative Physical Activities from Narrated Demonstrations
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.
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Prop-Chromeleon: Adaptive Haptic Props in Mixed Reality through Generative Artificial Intelligence
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.
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Intent Lenses: Inferring Capture-Time Intent to Transform Opportunistic Photo Captures into Structured Visual Notes
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.
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Auditing and Controlling AI Agent Actions in Spreadsheets
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.