Agency in sustained human-AI chatbot talks emerges as co-constructed turn-by-turn through boundary-setting and intention-steering, organized in a new 3-by-4 framework of actors and actions.
LLM integration in extended reality: A comprehensive review of current trends, challenges, and future perspectives
4 Pith papers cite this work. Polarity classification is still indexing.
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JARVIS delivers VLM-powered contextual AR guidance with state verification for cross-reality tasks, improving usability and success rates over baselines in a 14-person study.
AnimationDiff is a visual comparison tool that combines contextual scene viewing, overlay/side-by-side modes, filtering, and temporal lenses to help users select among generated 3D character animations.
AIvaluateXR benchmarks 17 LLMs across four XR platforms on performance, speed, memory and battery metrics and proposes a 3D Pareto optimality method to identify optimal on-device model-device pairs.
citing papers explorer
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Does My Chatbot Have an Agenda? Understanding Human and AI Agency in Human-Human-like Chatbot Interaction
Agency in sustained human-AI chatbot talks emerges as co-constructed turn-by-turn through boundary-setting and intention-steering, organized in a new 3-by-4 framework of actors and actions.
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JARVIS: A Just-in-Time Augmented Reality VLM-Powered Instruction System for Cross-Reality Task Guidance
JARVIS delivers VLM-powered contextual AR guidance with state verification for cross-reality tasks, improving usability and success rates over baselines in a 14-person study.
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AnimationDiff: A Visual Comparison Tool for Generated 3D Character Animations
AnimationDiff is a visual comparison tool that combines contextual scene viewing, overlay/side-by-side modes, filtering, and temporal lenses to help users select among generated 3D character animations.
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AIvaluateXR: An Evaluation Framework for on-Device AI in XR with Benchmarking Results
AIvaluateXR benchmarks 17 LLMs across four XR platforms on performance, speed, memory and battery metrics and proposes a 3D Pareto optimality method to identify optimal on-device model-device pairs.