Papers for

optical network operators

Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.

Agentic AI prevents false data in optical network troubleshooting

Preventing Data Hallucinations in Agentic AI for Optical Network Troubleshooting

Abstract: An agentic AI system for automated troubleshooting in optical communication networks is demonstrated that integrates data references into the agent architecture to prevent data hallucinations, improving the interpretability and trustworthiness of root-cause identification and reasoning.

Fri 16 OctNetworking and Internet ArchitectureArtificial Intelligence
The gist
Troubleshooting optical networks can be tricky because AI systems sometimes make up data, leading to wrong conclusions. The authors show how adding real data sources into an AI agent helps stop this by making the AI’s reasoning more reliable. This leads to better identification of network problems and clearer explanations of what went wrong. The system aims to build trust by avoiding mistakes caused by AI hallucinations.
Open → 2610.23252v1