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
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
years
2026 4verdicts
UNVERDICTED 4representative citing papers
RadioMaster is a multi-agent system that autonomously generates radio signals from user input using domain knowledge retrieval, collaborative I/Q sample creation, and closed-loop verification, outperforming baselines on the new RadioBench benchmark.
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.
Proposes a system-level perception-communication-action architecture for embodied AI-native 6G and identifies enabling technologies plus open challenges.
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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RadioMaster: Multi-Agent System for Autonomous Radio Signal Generation
RadioMaster is a multi-agent system that autonomously generates radio signals from user input using domain knowledge retrieval, collaborative I/Q sample creation, and closed-loop verification, outperforming baselines on the new RadioBench benchmark.
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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.
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Empowering Embodied AI in 6G Networks: Architecture, Enablers, and Open Challenges
Proposes a system-level perception-communication-action architecture for embodied AI-native 6G and identifies enabling technologies plus open challenges.