Introduces WM-CDT framework with causal information value metric to maximize long-term return-per-bit in semantic communications for physical AI with closed-loop sensing-inference-control.
Bridging physical and digital worlds: Embodied large AI for future wireless systems
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
A hybrid beamforming framework combining liquid crystal antennas and liquid neural networks delivers 88.6% spectral efficiency gain and improved robustness in 108 GHz urban ray-tracing simulations compared to baselines and 3GPP models.
citing papers explorer
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World Model-Enabled Causal Digital Twins for Semantic Communications in Physical AI Systems
Introduces WM-CDT framework with causal information value metric to maximize long-term return-per-bit in semantic communications for physical AI with closed-loop sensing-inference-control.
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Robust Hybrid Beamforming with Liquid Crystal Antennas and Liquid Neural Networks
A hybrid beamforming framework combining liquid crystal antennas and liquid neural networks delivers 88.6% spectral efficiency gain and improved robustness in 108 GHz urban ray-tracing simulations compared to baselines and 3GPP models.