# NDT Factory: Synthesizing Verified Network Digital Twins from Semantic Models via Multi-Agent LLM

arXiv: https://arxiv.org/abs/2609.12170 · HTML: https://mlsanity.com/paper/2609.12170/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Agentic AI for wireless networks, one of 16 directions in LLM agents and tool use.
- Builds on 7 of its 28 references that are in the map.
- First posted on arXiv 10 Sep 2026.

## Placed in

- LLM agents and tool use / [Agentic AI for wireless networks](https://mlsanity.com/agents/agentic-ai-for-wireless-networks/)

## Builds on (most cited first, 7 of 7)

- [Qwen3 Technical Report](https://mlsanity.com/paper/2505.09388/) (2025)
- [MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework](https://mlsanity.com/paper/2308.00352/) (2023)
- [ChatDev: Communicative Agents for Software Development](https://mlsanity.com/paper/2307.07924/) (2023)
- [A Survey on Large Language Models for Code Generation](https://mlsanity.com/paper/2406.00515/) (2024)
- [What-if Analysis Framework for Digital Twins in 6G Wireless Network Management](https://mlsanity.com/paper/2404.11394/) (2024)
- [Integrating Generative AI with Network Digital Twins for Enhanced Network Operations](https://mlsanity.com/paper/2406.17112/) (2024)
- [From Agentic to Autogenic Network Management for AI-Native 6G and Beyond: A Standards Perspective](https://mlsanity.com/paper/2607.06786/) (2026)

## Abstract (arXiv)

Autonomous network management requires systems that can evaluate Network Service Intents (NSIs) under varying conditions without manual implementation of analysis logic, as envisioned in TM Forum Level~4 (L4) autonomy. Behavioral Network Digital Twins (NDTs) enable such evaluation, but existing NDTs rely on pre-defined analytical logic, limiting adaptability for evolving closed-loop control. This paper introduces the NDT factory, a multi-agent software system that synthesizes executable behavioral NDTs on demand from semantic models using Large Language Model (LLM). We validate the system using a Call Admission Control (CAC) case study, where deterministic what-if analysis serves as the admission decision process. The NDT factory generates a complete CAC NDT through parallel synthesis and orchestration, achieving 100% compilation and test pass rates across multiple runs. Simulation over 300 NSIs shows 99.3% decision agreement with a reference implementation, 90% admission rate, and correct attribution of all rejections, demonstrating reliable synthesis with deterministic, verifiable execution.