Traditional standardized network interfaces face significant limitations, including vendor-specific incompatibilities, rigid design assumptions, and lack of adaptability for new functionalities. We propose a multi-agent framework leveraging large language models (LLMs) to generate control interfaces on demand between network functions (NFs). This includes a matching agent, which aligns required control functionalities with NF capabilities, and a code-generation agent, which generates the necessary API server for interface realization. We validate our approach using simulated multi-vendor gNB and WLAN AP environments. The performance evaluations highlight the trade-offs between cost and latency across LLMs for interface generation tasks. Our work sets the foundation for AI-native dynamic control interface generation, paving the way for enhanced interoperability and adaptability in future mobile networks.
@article{arxiv.2508.15595,
title = {Interface on demand: Towards AI native Control interfaces for 6G},
author = {Abhishek Dandekar and Prashiddha D. Thapa and Ashrafur Rahman and Julius Schulz-Zander},
journal= {arXiv preprint arXiv:2508.15595},
year = {2025}
}