Cross-vendor telecom optical network configuration automation from intentions: reasoning-enhanced and equipment-verified paradigm through an LLM

The correct configurations of optical infrastructure are the cornerstone for telecoms to stably provision large-capacity and long-distance service pipelines. Although traditional configuration workflows can be automated using predefined message templates and scripting, the introduction of new multi-vendor devices or required updates to network configurations forces administrators to manually review extensive documentation to implement the necessary procedures. Fundamentally, these processes are a form of natural language understanding and transformation. To address the high effort-cost, time-investment, and cross-vendor interoperability challenges, we introduce large language models (LLMs) with additional reasoning enhancement to achieve Automation-from-Intentions . Specifically, we design a reinforcement-learning-based fine-tuning paradigm of an LLM and evaluate its performance on a deployed cross-vendor optical network. The results show perfect configuration accuracy—achieving a score of 100 without hallucinations—across a 12-vendor, 256-node network. By reducing reliance on vendor-provided professional services and lowering manual operational effort, our proposal can effectively decrease both CAPEX and OPEX for telecom operators.