A Survey on Applications of Large Language Model-Driven Digital Twins for Intelligent Network Optimization

With the widespread application of digital twin (DT) technology in network optimization and intelligent management, its integration with large language models (LLMs) presents immense potential. LLMs excel in natural language processing, multimodal analysis, and real-time optimization, enabling innovative solutions for intelligent monitoring, resource allocation, and decision-making in complex network environments. This paper systematically reviews the development of DTs and LLMs, elaborates on their core principles and application scenarios, and examines the capabilities of LLM-driven DTs in key network optimization tasks, including traffic prediction, fault diagnosis, resource allocation, and multi-objective optimization. By leveraging real-time data from DTs, LLMs can dynamically generate optimization strategies, enabling precise monitoring and intelligent tuning. Furthermore, this paper explores the potential of integrating LLMs and DTs to address complex challenges such as data quality, latency sensitivity, and energy consumption demands, while summarizing existing technical bottlenecks. Finally, the paper proposes several potential research directions to address these challenges, offering a comprehensive perspective for advancing the efficiency and automation of next-generation intelligent networks.

• Publication year

  2026

• Venue

  IEEE Communications Surveys and Tutorials

• Publication date

  Unknown publication date

• Fields of study

  Computer Science, Engineering

• Identifiers
  DOI 10.1109/COMST.2025.3568637
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• Large language models integrated with digital twins are presented as promising for network optimization, intelligent monitoring, and decision-making in complex network environments, but integration challenges around data quality, latency sensitivity, energy consumption, and technical bottlenecks motivate future research directions aimed at more efficient and automated network optimization.

  Confidence 0.93

  뀨 (7c402c1b98) extractionAnonymous (12632b8b5f) review
• Traffic prediction, fault diagnosis, resource allocation, and multi-objective optimization are highlighted as key network optimization tasks for LLM-driven digital twins.

  Confidence 0.95

  뀨 (7c402c1b98) extractionAnonymous (12632b8b5f) review
• LLM-driven digital twins can dynamically generate optimization strategies from real-time digital-twin data for precise monitoring and intelligent tuning in network environments.

  Confidence 0.96

  뀨 (7c402c1b98) extractionAnonymous (12632b8b5f) review

• fault diagnosis

  task

  Identifying faults or abnormal states in a network.

  뀨 (7c402c1b98) extractionAnonymous (12632b8b5f) review
• integration challenges

  challenge

  Obstacles to combining large language models with digital twins, including data quality, latency sensitivity, energy consumption, and technical bottlenecks.

  뀨 (7c402c1b98) extractionAnonymous (12632b8b5f) review
• large language model

  model

  A pretrained generative model used here for natural-language processing, multimodal analysis, and real-time optimization support.

  Aliases: LLM, LLMs, large language models

  뀨 (7c402c1b98) extractionAnonymous (12632b8b5f) review
• llm-driven digital twin

  architecture

  A digital twin architecture that uses an LLM to interpret twin data and generate optimization strategies.

  Aliases: LLM-driven DT, LLM-driven DTs, LLM-driven digital twins, large language model-driven digital twins

  뀨 (7c402c1b98) extractionAnonymous (12632b8b5f) review
• multi-objective optimization

  task

  Optimizing several network goals at the same time.

  뀨 (7c402c1b98) extractionAnonymous (12632b8b5f) review
• network optimization

  domain

  The process of improving performance, allocation, and control in complex network systems.

  Aliases: intelligent network optimization

  뀨 (7c402c1b98) extractionAnonymous (12632b8b5f) review
• resource allocation

  task

  Assigning network resources to competing demands.

  뀨 (7c402c1b98) extractionAnonymous (12632b8b5f) review
• traffic prediction

  task

  Forecasting future traffic patterns from network observations.

  뀨 (7c402c1b98) extractionAnonymous (12632b8b5f) review

• DeepSeek-R1 incentivizes reasoning in LLMs through reinforcement learning

  2025cited by this paper
• Toward the Integration and Convergence Between 5G and TSN Technologies and Architectures for Industrial Communications: A Survey

  2025influential reference
• Semi-Distributed Network Fault Diagnosis Based on Digital Twin Network in Highly Dynamic Heterogeneous Networks

  2025cited by this paper
• Towards LLM-Enhanced Digital Twins of Intelligent Computing Center

  2024cited by this paper
• DT Empowered Accessibility Services for Networks of the Future

  2024cited by this paper
• Basis Sharing: Cross-Layer Parameter Sharing for Large Language Model Compression

  2024cited by this paper
• Application of AI in Optimizing Energy and Resource Management: Effectiveness of Deep Learning Models

  2024cited by this paper
• A hybrid approach of ConvLSTMBNN-DT and GPT-4 for real-time anomaly detection decision support in edge-cloud environments

  2024cited by this paper
• Large Language Model (LLM)-Enabled Graphs in Dynamic Networking

  2024cited by this paper
• From Simulation to Prediction: Enhancing Digital Twins with Advanced Generative AI Technologies

  2024cited by this paper
• Empirical study on fine-tuning pre-trained large language models for fault diagnosis of complex systems

  2024cited by this paper
• Industrial 6G-IoT and Machine-Learning-Supported Intelligent Sensing Framework for Indicator Control Strategy in Sewage Treatment Process

  2024influential reference
• Mobile-LLaMA: Instruction Fine-Tuning Open-Source LLM for Network Analysis in 5G Networks

  2024cited by this paper
• DefectTwin: When LLM Meets Digital Twin for Railway Defect Inspection

  2024cited by this paper
• LINKs: Large Language Model Integrated Management for 6G Empowered Digital Twin NetworKs

  2024cited by this paper
• MM1: Methods, Analysis and Insights from Multimodal LLM Pre-training

  2024cited by this paper
• Intent-Based Management of Next-Generation Networks: an LLM-Centric Approach

  2024cited by this paper
• Enhancing Cognitive Digital Twin Interaction using an LLM Agent

  2024cited by this paper
• Leveraging generative AI for urban digital twins: a scoping review on the autonomous generation of urban data, scenarios, designs, and 3D city models for smart city advancement

  2024cited by this paper
• LLM experiments with simulation: Large Language Model Multi-Agent System for Process Simulation Parametrization in Digital Twins

  2024cited by this paper
• Generation of Asset Administration Shell With Large Language Model Agents: Toward Semantic Interoperability in Digital Twins in the Context of Industry 4.0

  2024cited by this paper
• DTLLM-VLT: Diverse Text Generation for Visual Language Tracking Based on LLM

  2024cited by this paper
• Large Language Model (LLM) for Telecommunications: A Comprehensive Survey on Principles, Key Techniques, and Opportunities

  2024cited by this paper
• TPC: A Digital Twin-Based Predictive Control Method for Tailplane Control

  2024cited by this paper
• WorldGPT: Empowering LLM as Multimodal World Model

  2024cited by this paper
• 6G Comprehensive Intelligence: Network Operations and Optimization Based on Large Language Models

  2024cited by this paper
• A Cross-Layer Survey on Secure and Low-Latency Communications in Next-Generation IoT

  2024influential reference
• Advancements and challenges of digital twins in industry

  2024cited by this paper
• Frequency Selective Surface Toward 6G Communication Systems: A Contemporary Survey

  2024influential reference
• Structured information extraction from scientific text with large language models

  2024cited by this paper
• NetLLM: Adapting Large Language Models for Networking

  2024cited by this paper
• When Large Language Model Agents Meet 6G Networks: Perception, Grounding, and Alignment

  2024cited by this paper
• Multi-objective coupling optimization of electrical cable intelligent production line driven by digital twin

  2024cited by this paper
• Security and Privacy on 6G Network Edge: A Survey

  2023cited by this paper
• OWQ: Outlier-Aware Weight Quantization for Efficient Fine-Tuning and Inference of Large Language Models

  2023cited by this paper
• Digital twin aided adversarial transfer learning method for domain adaptation fault diagnosis

  2023cited by this paper
• Implementing a Digital Twin-Based Fault Detection and Diagnosis Approach for Optimal Operation and Maintenance of Urban Distributed Solar Photovoltaics

  2023cited by this paper
• LLM-Twin: mini-giant model-driven beyond 5G digital twin networking framework with semantic secure communication and computation

  2023cited by this paper
• Honeybee: Locality-Enhanced Projector for Multimodal LLM

  2023cited by this paper
• Green Edge AI: A Contemporary Survey

  2023cited by this paper
• Personalized Aging-in-Place Support Through Fine-Tuning of Generative AI Models

  2023cited by this paper
• Large Language Models for Networking: Applications, Enabling Techniques, and Challenges

  2023cited by this paper
• Rethinking Conversational Agents in the Era of LLMs: Proactivity, Non-collaborativity, and Beyond

  2023cited by this paper
• Generative Twin for 6G and Beyond 5G Networks: Vision, Challenges and Architecture

  2023cited by this paper
• ChatTwin: Toward Automated Digital Twin Generation for Data Center via Large Language Models

  2023cited by this paper
• A digital twin in transportation: Real-time synergy of traffic data streams and simulation for virtualizing motorway dynamics

  2023cited by this paper
• Large Language Models Are Zero-Shot Time Series Forecasters

  2023cited by this paper
• Towards Making the Most of LLM for Translation Quality Estimation

  2023cited by this paper
• Network Meets ChatGPT: Intent Autonomous Management, Control and Operation

  2023cited by this paper
• BLIVA: A Simple Multimodal LLM for Better Handling of Text-Rich Visual Questions

  2023cited by this paper
• Specializing Smaller Language Models towards Multi-Step Reasoning

  2023cited by this paper
• From Digital Twin to Metaverse: The Role of 6G Ultra-Reliable and Low-Latency Communications with Multi-Tier Computing

  2023cited by this paper
• Large Language Models Empowered Autonomous Edge AI for Connected Intelligence

  2023cited by this paper
• Conceptualisation of a 7-element digital twin framework in supply chain and operations management

  2023cited by this paper
• Generating Images with Multimodal Language Models

  2023cited by this paper
• The CoT Collection: Improving Zero-shot and Few-shot Learning of Language Models via Chain-of-Thought Fine-Tuning

  2023cited by this paper
• Tree of Thoughts: Deliberate Problem Solving with Large Language Models

  2023cited by this paper
• Digital Twins for Next-Generation Mobile Networks: Applications and Solutions

  2023cited by this paper
• Digital twin-driven fault diagnosis method for composite faults by combining virtual and real data

  2023cited by this paper
• AudioGPT: Understanding and Generating Speech, Music, Sound, and Talking Head

  2023cited by this paper
• A Survey of Large Language Models

  2023cited by this paper
• Strategies to save energy in the context of the energy crisis: a review

  2023cited by this paper
• Distributed Training of Large Language Models

  2023cited by this paper
• On the Road to 6G: Visions, Requirements, Key Technologies, and Testbeds

  2023influential reference
• Reference architecture for digital twin-based predictive maintenance systems

  2023cited by this paper
• Internet of Everything and Digital Twin enabled Service Platform for Cold Chain Logistics

  2023cited by this paper
• Human-Autonomy Teaming on Autonomous Vehicles with Large Language Model-Enabled Human Digital Twins

  2023cited by this paper
• Future landscape visualization using a city digital twin: integration of augmented reality and drones with implementation of 3D model-based occlusion handling

  2022cited by this paper
• Cooperative Ramp Merging Design and Field Implementation: A Digital Twin Approach Based on Vehicle-to-Cloud Communication

  2022cited by this paper
• A framework for data-driven digitial twins of smart manufacturing systems

  2022cited by this paper
• Chain of Thought Prompting Elicits Reasoning in Large Language Models

  2022cited by this paper
• Digital Twin of Wireless Systems: Overview, Taxonomy, Challenges, and Opportunities

  2022cited by this paper
• Internet of Things (IoT) Security Intelligence: A Comprehensive Overview, Machine Learning Solutions and Research Directions

  2022cited by this paper
• What Language Model Architecture and Pretraining Objective Work Best for Zero-Shot Generalization?

  2022cited by this paper
• Applying Digital Twins in Metaverse: User Interface, Security and Privacy Challenges

  2022cited by this paper
• Building digital twins of the human immune system: toward a roadmap

  2022cited by this paper
• Edge Intelligence-Based Ultra-Reliable and Low-Latency Communications for Digital Twin-Enabled Metaverse

  2022cited by this paper
• Survey on Digital Twin Edge Networks (DITEN) Toward 6G

  2022cited by this paper
• A comprehensive review of digital twin — part 1: modeling and twinning enabling technologies

  2022cited by this paper
• Digital Twins: A Survey on Enabling Technologies, Challenges, Trends and Future Prospects

  2022cited by this paper
• Less is More: Task-aware Layer-wise Distillation for Language Model Compression

  2022cited by this paper
• Automatic Chain of Thought Prompting in Large Language Models

  2022cited by this paper
• Digital thread-based modeling of digital twin framework for the aircraft assembly system

  2022cited by this paper
• BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

  2022cited by this paper
• SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models

  2022cited by this paper
• 5G mobile networks and Digital Twins concept: Research challenges in network DT emulation

  2022cited by this paper
• Talking about Large Language Models

  2022cited by this paper
• Large Language Models Are Reasoning Teachers

  2022cited by this paper
• Coalition of 6G and Blockchain in AR/VR Space: Challenges and Future Directions

  2021cited by this paper
• Green IoT for Eco-Friendly and Sustainable Smart Cities: Future Directions and Opportunities

  2021cited by this paper
• Digital Twins based Day-ahead Integrated Energy System Scheduling under Load and Renewable Energy Uncertainties

  2021cited by this paper
• Intelligent fault diagnosis of machinery using digital twin-assisted deep transfer learning

  2021cited by this paper
• 6G Internet of Things: A Comprehensive Survey

  2021cited by this paper
• Digital Twin Networks: A Survey

  2021cited by this paper
• AI and 6G Security: Opportunities and Challenges

  2021cited by this paper
• AI-Native Network Slicing for 6G Networks

  2021cited by this paper
• Digital Twin for Intelligent Context-Aware IoT Healthcare Systems

  2021cited by this paper
• Survey on 6G Frontiers: Trends, Applications, Requirements, Technologies and Future Research

  2021cited by this paper
• Green Internet of Things (GIoT): Applications, Practices, Awareness, and Challenges

  2021cited by this paper
• Digital-Twin-Enabled 6G: Vision, Architectural Trends, and Future Directions

  2021cited by this paper

• From Data Mirror to Smart Copilot: A Survey on NextG Semantic Communication for Propelling Digital Twin World Into Cognitive Stage

  2026cites this paper
• Wavelet Transform and Time Embedding-Based Deep Learning for Orbit Correction of Lunar Navigation Satellites

  2026cites this paper
• Reinforcement-led digital twin optimization for secure operation in cyber-physical grids

  2026cites this paper
• Unifying Gradient Leakage Attacks Against Privacy-Protected Federated Learning in IoT Networks

  2026cites this paper
• A Survey on IEEE 802.11bn Wi-Fi 8: Advantages of Ultra High Reliability for Next-Generation Wireless LANs

  2026cites this paper
• Pivotal AI Paradigms for Satellite Communication Security: From Machine Learning to Large Language Models

  2026cites this paper
• Edge General Intelligence Through World Models, Large Language Models, and Agentic AI: Fundamentals, Solutions, and Challenges

  2026cites this paper
• Geographically-aware Transformer-based Traffic Forecasting for Urban Motorway Digital Twins

  2026cites this paper
• LLM and AI Agents for Autonomous Systems: A Survey of Applications, Datasets, and Security Challenges

  2026cites this paper
• Context-Aware Text Generation for Interactive Dashboards via Multimodal LLM Orchestration

  2025cites this paper
• Generative Diffusion Models for Wireless Networks: Fundamental, Architecture, and State-of-the-Art

  2025cites this paper
• Edge General Intelligence Through World Models and Agentic AI: Fundamentals, Solutions, and Challenges

  2025cites this paper
• Large Language Models for Next-Generation Wireless Network Management: A Survey and Tutorial

  2025cites this paper
• Dynamic Model Deployment, Batch Scheduling, and Resource Allocation in MLLM-Enabled Edge–Cloud Networks: A Multiagent Two-Timescale DRL Approach

  2025cites this paper
• Toward Efficient and Privacy-Aware eHealth Systems: An Integrated Sensing, Computing, and Semantic Communication Approach

  2025cites this paper
• A Multilingual LLM-Driven Digital Twin Framework for Climate Resilient Precision Agriculture

  2025cites this paper
• CacheMoE: Task-Aware Expert Model Caching for Multitask Inference in Distributed Edge IoT Networks

  2025cites this paper
• Toward Edge General Intelligence With Multiple-Large Language Model (Multi-LLM): Architecture, Trust, and Orchestration

  2025cites this paper