# World Model-Enabled Causal Digital Twins for Semantic Communications in Physical AI Systems arXiv: https://arxiv.org/abs/2605.16547 ยท HTML: https://mlsanity.com/paper/2605.16547/ ## Key facts - Cited by 2 papers in the map (data through 14 Oct 2026). - Placed in World models for language agents, one of 11 directions in World models. - Builds on 29 of its 43 references that are in the map. - First posted on arXiv 15 May 2026. ## Placed in - World models / [World models for language agents](https://mlsanity.com/wm/world-models-for-language-agents/) - Deep reinforcement learning methods / [RL for energy, networks and operations](https://mlsanity.com/deeprl/rl-for-energy-networks-and-operations/) ## Builds on (most cited first, 29 of 29) - [Learning Latent Dynamics for Planning from Pixels](https://mlsanity.com/paper/1811.04551/) (2018) - [AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles](https://mlsanity.com/paper/1705.05065/) (2017) - [TD-MPC2: Scalable, Robust World Models for Continuous Control](https://mlsanity.com/paper/2310.16828/) (2023) - [Less Data, More Knowledge: Building Next Generation Semantic Communication Networks](https://mlsanity.com/paper/2211.14343/) (2022) - [Dream to Manipulate: Compositional World Models Empowering Robot Imitation Learning with Imagination](https://mlsanity.com/paper/2412.14957/) (2024) - [Artificial General Intelligence (AGI)-Native Wireless Systems: A Journey Beyond 6G](https://mlsanity.com/paper/2405.02336/) (2024) - [AoI-Aware Resource Allocation for Platoon-Based C-V2X Networks via Multi-Agent Multi-Task Reinforcement Learning](https://mlsanity.com/paper/2105.04196/) (2021) - [World Model-based Perception for Visual Legged Locomotion](https://mlsanity.com/paper/2409.16784/) (2024) - [Age-of-Information vs. Value-of-Information Scheduling for Cellular Networked Control Systems](https://mlsanity.com/paper/1903.05356/) (2019) - [World Models for Cognitive Agents: Transforming Edge Intelligence in Future Networks](https://mlsanity.com/paper/2506.00417/) (2025) - [Adaptive Resource Allocation for Semantic Communication Networks](https://mlsanity.com/paper/2312.01081/) (2023) - [Simplifying Latent Dynamics with Softly State-Invariant World Models](https://mlsanity.com/paper/2401.17835/) (2024) - [A Multi-Task Oriented Semantic Communication Framework for Autonomous Vehicles](https://mlsanity.com/paper/2403.12997/) (2024) - [DMWM: Dual-Mind World Model with Long-Term Imagination](https://mlsanity.com/paper/2502.07591/) (2025) - [Value of Information in Feedback Control: Quantification](https://mlsanity.com/paper/1812.07534/) (2018) - [Energy-Efficient Edge Inference in Integrated Sensing, Communication, and Computation Networks](https://mlsanity.com/paper/2503.00298/) (2025) - [World Model-Based Learning for Long-Term Age of Information Minimization in Vehicular Networks](https://mlsanity.com/paper/2505.01712/) (2025) - [MobiWorld: World Models for Mobile Wireless Network](https://mlsanity.com/paper/2507.09462/) (2025) - [Bridging Physical and Digital Worlds: Embodied Large AI for Future Wireless Systems](https://mlsanity.com/paper/2506.24009/) (2025) - [Dual-Mind World Models: A General Framework for Learning in Dynamic Wireless Networks](https://mlsanity.com/paper/2510.24546/) (2025) - [Goal-Oriented Semantic Communication for Wireless Visual Question Answering](https://mlsanity.com/paper/2411.02452/) (2024) - [Robust Semantic Communications for Speech Transmission](https://mlsanity.com/paper/2403.05187/) (2024) - [Model Predictive Control Enabled UAV Trajectory Optimization and Secure Resource Allocation](https://mlsanity.com/paper/2411.04423/) (2024) - [NetWorld: Communication-Based Diffusion World Model for Multi-Agent Reinforcement Learning in Wireless Networks](https://mlsanity.com/paper/2602.00558/) (2026) - [Digital Twin of Industrial Networked Control System based on Value of Information](https://mlsanity.com/paper/2404.14960/) (2024) - [IRS-Enhanced Secure Semantic Communication Networks: Cross-Layer and Context-Awared Resource Allocation](https://mlsanity.com/paper/2411.01821/) (2024) - [VoI-Driven Joint Optimization of Control and Communication in Vehicular Digital Twin Network](https://mlsanity.com/paper/2505.07892/) (2025) - [Task-Oriented Low-Label Semantic Communication With Self-Supervised Learning](https://mlsanity.com/paper/2505.19940/) (2025) - [A Dynamic Programming Framework for Vehicular Task Offloading with Successive Action Improvement](https://mlsanity.com/paper/2509.05907/) (2025) ## Built on by (most cited first, 2 of 2) - [Baseline-Relative Counterfactual Refinement for Bit-Aware Visual Token Communication](https://mlsanity.com/paper/2608.16192/) (2026) - [From Semantic to Token Communication: The Next Paradigm for Large-Model-Driven 6G Intelligent Connectivity](https://mlsanity.com/paper/2609.10714/) (2026) ## Abstract (arXiv) 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.