# Dual-Mind World Models: A General Framework for Learning in Dynamic Wireless Networks

arXiv: https://arxiv.org/abs/2510.24546 · HTML: https://mlsanity.com/paper/2510.24546/

## Key facts

- Cited by 4 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 26 of its 38 references that are in the map.
- First posted on arXiv 28 Oct 2025.

## 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, 26 of 26)

- [When to Trust Your Model: Model-Based Policy Optimization](https://mlsanity.com/paper/1906.08253/) (2019)
- [TD-MPC2: Scalable, Robust World Models for Continuous Control](https://mlsanity.com/paper/2310.16828/) (2023)
- [Driving into the Future: Multiview Visual Forecasting and Planning with World Model for Autonomous Driving](https://mlsanity.com/paper/2311.17918/) (2023)
- [Neural Logic Reasoning](https://mlsanity.com/paper/2008.09514/) (2020)
- [Artificial General Intelligence (AGI)-Native Wireless Systems: A Journey Beyond 6G](https://mlsanity.com/paper/2405.02336/) (2024)
- [MoDem-V2: Visuo-Motor World Models for Real-World Robot Manipulation](https://mlsanity.com/paper/2309.14236/) (2023)
- [Gradient-based Planning with World Models](https://mlsanity.com/paper/2312.17227/) (2023)
- [A Survey of Recent Advances in Optimization Methods for Wireless Communications](https://mlsanity.com/paper/2401.12025/) (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)
- [Machine Learning for Large-Scale Optimization in 6G Wireless Networks](https://mlsanity.com/paper/2301.03377/) (2023)
- [A Survey on Model-based, Heuristic, and Machine Learning Optimization Approaches in RIS-aided Wireless Networks](https://mlsanity.com/paper/2303.14320/) (2023)
- [Adaptive Layer Splitting for Wireless LLM Inference in Edge Computing: A Model-Based Reinforcement Learning Approach](https://mlsanity.com/paper/2406.02616/) (2024)
- [World Models for Cognitive Agents: Transforming Edge Intelligence in Future Networks](https://mlsanity.com/paper/2506.00417/) (2025)
- [Age of Information-Aware Radio Resource Management in Vehicular Networks: A Proactive Deep Reinforcement Learning Perspective](https://mlsanity.com/paper/1908.02047/) (2019)
- [Adaptive Resource Allocation for Semantic Communication Networks](https://mlsanity.com/paper/2312.01081/) (2023)
- [DMWM: Dual-Mind World Model with Long-Term Imagination](https://mlsanity.com/paper/2502.07591/) (2025)
- [DRL-Based Optimization for AoI and Energy Consumption in C-V2X Enabled IoV](https://mlsanity.com/paper/2411.13104/) (2024)
- [Vehicular Blockage Modelling and Performance Analysis for mmWave V2V Communications](https://mlsanity.com/paper/2110.10576/) (2021)
- [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)
- [Accuracy-Aware Cooperative Sensing and Computing for Connected Autonomous Vehicles](https://mlsanity.com/paper/2403.16408/) (2024)
- [Fast Geometric Learning of MIMO Signal Detection over Grassmannian Manifolds](https://mlsanity.com/paper/2406.10453/) (2024)
- [Path Loss Models for V2V mmWave Communication: Performance Evaluation and Open Challenges](https://mlsanity.com/paper/1907.10126/) (2019)
- [A Comprehensive Survey of Linear, Integer, and Mixed-Integer Programming Approaches for Optimizing Resource Allocation in 5G and Beyond Networks](https://mlsanity.com/paper/2502.15585/) (2025)
- [QoS-based Beamforming and Compression Design for Cooperative Cellular Networks via Lagrangian Duality](https://mlsanity.com/paper/2306.13962/) (2023)
- [Integrating Optimization Theory with Deep Learning for Wireless Network Design](https://mlsanity.com/paper/2412.08761/) (2024)

## Built on by (most cited first, 4 of 4)

- [MetaMind: General and Cognitive World Models in Multi-Agent Systems by Meta-Theory of Mind](https://mlsanity.com/paper/2603.00808/) (2026)
- [Learning Ad Hoc Network Dynamics via Graph-Structured World Models](https://mlsanity.com/paper/2604.14811/) (2026)
- [World Model-Enabled Causal Digital Twins for Semantic Communications in Physical AI Systems](https://mlsanity.com/paper/2605.16547/) (2026)
- [Reasoning-Native Agentic Communication for 6G](https://mlsanity.com/paper/2602.17738/) (2026)

## Abstract (arXiv)

Despite the popularity of reinforcement learning (RL) in wireless networks, existing approaches that rely on model-free RL (MFRL) and model-based RL (MBRL) are data inefficient and short-sighted. Such RL-based solutions cannot generalize to novel network states since they capture only statistical patterns rather than the underlying physics and logic from wireless data. These limitations become particularly challenging in complex wireless networks with high dynamics and long-term planning requirements. To address these limitations, in this paper, a novel dual-mind world model-based learning framework is proposed with the goal of optimizing completeness-weighted age of information (CAoI) in a challenging mmWave V2X scenario. Inspired by cognitive psychology, the proposed dual-mind world model encompasses a pattern-driven System 1 component and a logic-driven System 2 component to learn dynamics and logic of the wireless network, and to provide long-term link scheduling over reliable imagined trajectories. Link scheduling is learned through end-to-end differentiable imagined trajectories with logical consistency over an extended horizon rather than relying on wireless data obtained from environment interactions. Moreover, through imagination rollouts, the proposed world model can jointly reason network states and plan link scheduling. During intervals without observations, the proposed method remains capable of making efficient decisions. Extensive experiments are conducted on a realistic simulator based on Sionna with real-world physical channel, ray-tracing, and scene objects with material properties. Simulation results show that the proposed world model achieves a significant improvement in data efficiency and achieves strong generalization and adaptation to unseen environments, compared to the state-of-the-art RL baselines, and the world model approach with only System 1.