World Model-Enabled Causal Digital Twins for Semantic Communications in Physical AI Systems

• URL: http://arxiv.org/abs/2605.16547v1
• Date: Fri, 15 May 2026 18:41:51 GMT
• Title: World Model-Enabled Causal Digital Twins for Semantic Communications in Physical AI Systems
• Abstract summary: This paper investigates goal-oriented semantic communications for physical AI systems with closed-loop sensing-communication-inference-control.<n>In particular, the problem of semantic communications is formulated as a long-term return-per-bit under wireless bit-budget constraints.<n>A world-model-enabled causal digital twin (WM-CDT) framework is proposed to capture the dynamics of closed-loop AI systems.
• Score: 37.02293452905313
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Semantic communication has emerged as a promising paradigm for enabling goal-oriented networking. However, most existing semantic communication solutions are tailored to one-shot tasks and optimize instantaneous performance. Hence, they cannot be used to support closed-loop dynamic systems with physical artificial intelligence (AI), in which the transmitted semantics affect not only the current inference outcome but also future control actions, state evolution, and ultimately long-horizon task performance. To address this gap, this paper investigates goal-oriented semantic communications for physical AI systems with closed-loop sensing-communication-inference-control. In particular, the problem of semantic communications is formulated as a long-term return-per-bit maximization under wireless bit-budget constraints while capturing both control efficiency and communication efficiency. To solve this problem, a novel causal information value (CIV) metric is introduced to evaluate the marginal contribution of each semantic token to the expected long-term return by transmission interventions. Then, a world-model-enabled causal digital twin (WM-CDT) framework is proposed to capture the dynamics of closed-loop physical AI systems and enable counterfactual reasoning for long-horizon imagined rollouts. Based on these imagined rollouts, an actor-critic policy is trained for long-horizon agent control with high data efficiency, while the semantic token selector is trained through CIV-per-bit evaluation. Extensive simulations on an AirSim-Sionna-based unmanned aerial vehicle (UAV) navigation simulator show that the proposed WM-CDT framework achieves significant improvement in return-per-kbit and navigation success rate compared to existing reinforcement learning solutions.

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