OrganicHAR discovers 4-8 activity categories per user from sensor signals, achieves 79% accuracy on coarse activities with ambient sensors alone and cuts VLM queries by 90% by triggering video analysis only at detected pattern moments.
Why is ’Chicago’ deceptive?
5 Pith papers cite this work. Polarity classification is still indexing.
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Cognitive forcing interventions reduce overreliance on AI recommendations more than simple explanations, with effects moderated by individual need for cognition.
A single LLM rewrite of skill descriptions using false positive and negative cases matches manual optimization performance in production, with most other pipeline components adding little value.
Head- and eye-based pointing outperform hand-based methods for AR 2D selection across depths, with head remaining most accurate and consistent.
An online experiment finds that showing users an overview of an AI's values reduces reliance on AI suggestions during writing tasks.
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Framing an AI with Values Reduces AI Reliance in AI-supported Writing Tasks
An online experiment finds that showing users an overview of an AI's values reduces reliance on AI suggestions during writing tasks.