CanvasConvo presents a spatial canvas interface for branching LLM conversations, evaluated in a 5-7 day field study with 24 participants that found support for exploratory workflows.
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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.
Adaptive Prompt Elicitation (APE) uses an information-theoretic framework to generate visual queries that elicit and compile user intent into better prompts for text-to-image models, showing improved alignment in benchmarks and a user study.
citing papers explorer
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Conversations in Space: Structuring Non-Linear LLM Interactions on a Canvas
CanvasConvo presents a spatial canvas interface for branching LLM conversations, evaluated in a 5-7 day field study with 24 participants that found support for exploratory workflows.
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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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Adaptive Prompt Elicitation for Text-to-Image Generation
Adaptive Prompt Elicitation (APE) uses an information-theoretic framework to generate visual queries that elicit and compile user intent into better prompts for text-to-image models, showing improved alignment in benchmarks and a user study.