GenTwIBN: Generative AI Digital Twin for Interactive Intent-Based Networking
Bibliographic record
Abstract
Intent-Based Networking (IBN) automates network management via high-level intents, yet current frameworks struggle with completeness, validation, and real-time adaptability. This paper introduces GenTwIBN, a novel framework that integrates Generative Artificial Intelligence (GenAI), Digital Twin (DT), and Software-Defined Networking (SDN) to achieve a fully automated, adaptive, and intent-driven network management system. The proposed framework iteratively refines business intents using GenAI, more specifically Large Language Models (LLMs), ensuring accuracy, consistency, and completeness before translation into enforceable network policies. Furthermore, DT-driven simulations enable the validation of network configurations prior to deployment, mitigating the risk of misconfigurations and enhancing network reliability. The framework also incorporates realtime monitoring and proactive adjustments, ensuring continuous alignment with network objectives. A comprehensive analysis shows that GenTwIBN has the potential to outperform conventional networks and traditional IBN frameworks by enabling seamless interaction, pre-deployment validation, and proactive real-time assurance, paving the way for zero-touch, AI-driven networking in next-generation communication systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".