A Survey on GenAI-Driven Digital Twins: Toward Intelligent 6G Networks and Metaverse Systems
Bibliographic record
Abstract
Sixth-Generation (6G) networks aim to deliver unprecedented network performance by facilitating intelligent, ultra-low-latency, and massively connected applications that seamlessly integrate the physical and digital domains through context-aware operation. These applications work across physical and digital environments. Within this broader shift, digital twins (DTs) have demonstrated notable improvements in overall network performance by creating high-fidelity digital counterparts of physical 6G systems. These DTs give researchers and operators a way to view network behavior as it evolves, to forecast likely performance patterns, and – crucially – to adjust key processes such as beamforming, resource allocation, and interference management. Even so, the value of DT-based optimization is limited by several practical factors. Their effectiveness depends a great deal on access to reliable and sufficiently rich data, and the inherent complexity of 6G environments often makes accurate modeling and efficient resource coordination challenging. This paper examines how a range of generative artificial intelligence (GenAI) models can be used alongside DTs to strengthen resource allocation and improve security in 6G networks. It also sets out a GenAI-enabled DT framework for various 6G-enabling applications, highlighting the potential roles of different GenAI models in supporting semantic communications, the metaverse, integrated sensing and communication (ISAC), AI-generated content (AIGC), and reconfigurable intelligent surfaces (RIS). This paper concludes by drawing attention to emerging conceptual frameworks for DT–GenAI integration. It notes several research challenges that have yet to be resolved, and outlines future directions for deploying GenAI-augmented DTs to achieve intelligent, adaptive, and resilient 6G networks.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".