# Dual Mind World Model Inspired Network Digital Twin for Access Scheduling

arXiv: https://arxiv.org/abs/2602.04566 · HTML: https://mlsanity.com/paper/2602.04566/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in World models for language agents, one of 11 directions in World models.
- Builds on 3 of its 17 references that are in the map.
- First posted on arXiv 4 Feb 2026.

## Placed in

- World models / [World models for language agents](https://mlsanity.com/wm/world-models-for-language-agents/)

## Builds on (most cited first, 3 of 3)

- [DMWM: Dual-Mind World Model with Long-Term Imagination](https://mlsanity.com/paper/2502.07591/) (2025)
- [On the Power of Randomization for Scheduling Real-Time Traffic in Wireless Networks](https://mlsanity.com/paper/2001.05146/) (2020)
- [Randomized Scheduling of Real-Time Traffic in Wireless Networks Over Fading Channels](https://mlsanity.com/paper/2101.04815/) (2021)

## Abstract (arXiv)

Emerging networked systems such as industrial IoT and real-time cyber-physical infrastructures demand intelligent scheduling strategies capable of adapting to dynamic traffic, deadlines, and interference constraints. In this work, we present a novel Digital Twin-enabled scheduling framework inspired by Dual Mind World Model (DMWM) architecture, for learning-informed and imagination-driven network control. Unlike conventional rule-based or purely data-driven policies, the proposed DMWM combines short-horizon predictive planning with symbolic model-based rollout, enabling the scheduler to anticipate future network states and adjust transmission decisions accordingly. We implement the framework in a configurable simulation testbed and benchmark its performance against traditional heuristics and reinforcement learning baselines under varied traffic conditions. Our results show that DMWM achieves superior performance in bursty, interference-limited, and deadline-sensitive environments, while maintaining interpretability and sample efficiency. The proposed design bridges the gap between network-level reasoning and low-overhead learning, marking a step toward scalable and adaptive NDT-based network optimization.