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.
World model- based learning for long-term age of information minimization in vehic- ular networks
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
years
2026 4verdicts
UNVERDICTED 4representative citing papers
G-RSSM learns per-node dynamics in wireless ad hoc networks via graph attention and trains clustering policies through imagined rollouts, generalizing from N=50 training to larger networks.
Proposes a world model-empowered SCD integration framework for complex unmanned systems using AoI-driven sensing, a predictive hybrid latent world model, and a multi-granularity knowledge graph.
Argues that wireless data's configuration dependence and lack of self-containment make monolithic foundation models unsuitable for AI-native 6G, favoring instead composable agentic architectures.
citing papers explorer
-
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.
-
Learning Ad Hoc Network Dynamics via Graph-Structured World Models
G-RSSM learns per-node dynamics in wireless ad hoc networks via graph attention and trains clustering policies through imagined rollouts, generalizing from N=50 training to larger networks.
-
Towards World Model-Empowered Integrated Sensing, Communication, and Decision for Complex Unmanned Systems
Proposes a world model-empowered SCD integration framework for complex unmanned systems using AoI-driven sensing, a predictive hybrid latent world model, and a multi-granularity knowledge graph.
-
Against the Monolithic Wireless World Model: Why NextG Needs Composable and Agentic Intelligence
Argues that wireless data's configuration dependence and lack of self-containment make monolithic foundation models unsuitable for AI-native 6G, favoring instead composable agentic architectures.