Dual Mind World Model Inspired Network Digital Twin for Access Scheduling

2026-02-04

AI Summary

This work addresses the challenge of intelligent access scheduling in industrial IoT and real-time cyber-physical systems under dynamic traffic, deadline constraints, and interference. Inspired by dual-process cognitive mechanisms, the authors propose a digital twin–based scheduling framework that integrates short-term predictive planning with symbolic model-based reasoning. For the first time, this approach synergistically combines data-driven learning and symbolic inference within a network digital twin, enabling proactive imagination of network states and adaptive decision-making. Evaluation on a configurable simulation platform demonstrates that, under bursty traffic, strong interference, and deadline-sensitive conditions, the proposed method significantly outperforms conventional heuristic and reinforcement learning approaches—achieving higher scheduling efficiency and lower overhead while simultaneously enhancing interpretability and sample efficiency.

Technology Category

• Planning, Routing, and Scheduling: Model-Based Reasoning
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

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.

Problem

• access scheduling
• networked systems
• dynamic traffic
• interference constraints
• deadline-sensitive

Innovation

• Digital Twin
• Dual Mind World Model
• Network Scheduling
• Model-based Planning
• Sample Efficiency