ProAgent uses on-demand tiered perception and context-aware LLM reasoning to deliver proactive assistance on AR glasses, achieving up to 27.7% higher prediction accuracy and 20.5% lower false detections than baselines.
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RF-LEGO turns signal processing algorithms into trainable modular DL modules via deep unrolling, outperforming pure SP and DL baselines in RF sensing while preserving interpretability.
citing papers explorer
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ProAgent: Harnessing On-Demand Sensory Contexts for Proactive LLM Agent Systems in the Wild
ProAgent uses on-demand tiered perception and context-aware LLM reasoning to deliver proactive assistance on AR glasses, achieving up to 27.7% higher prediction accuracy and 20.5% lower false detections than baselines.
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RF-LEGO: Modularized Signal Processing-Deep Learning Co-Design for RF Sensing via Deep Unrolling
RF-LEGO turns signal processing algorithms into trainable modular DL modules via deep unrolling, outperforming pure SP and DL baselines in RF sensing while preserving interpretability.