{"ID":2831518,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.20640","arxiv_id":"2512.20640","title":"Reflection-Driven Self-Optimization 6G Agentic AI RAN via Simulation-in-the-Loop Workflows","abstract":"The escalating complexity of sixth-generation (6G) networks demands unprecedented levels of autonomy beyond the capabilities of traditional optimization-based and current AI-based resource management approaches. While agentic AI has emerged as a promising paradigm for autonomous RAN, current frameworks provide sophisticated reasoning capabilities but lack mechanisms for empirical validation and self-improvement. This article identifies simulation-in-the-loop validation as a critical enabler for truly autonomous networks, where AI agents can empirically verify decisions and learn from outcomes. We present the first reflection-driven self-optimization framework that integrates agentic AI with high-fidelity network simulation in a closed-loop architecture. Our system orchestrates four specialized agents, including scenario, solver, simulation, and reflector agents, working in concert to transform agentic AI into a self-correcting system capable of escaping local optima, recognizing implicit user intent, and adapting to dynamic network conditions. Extensive experiments validate significant performance improvements over non-agentic approaches: 17.1\\% higher throughput in interference optimization, 67\\% improved user QoS satisfaction through intent recognition, and 25\\% reduced resource utilization during low-traffic periods while maintaining service quality.","short_abstract":"The escalating complexity of sixth-generation (6G) networks demands unprecedented levels of autonomy beyond the capabilities of traditional optimization-based and current AI-based resource management approaches. While agentic AI has emerged as a promising paradigm for autonomous RAN, current frameworks provide sophisti...","url_abs":"https://arxiv.org/abs/2512.20640","url_pdf":"https://arxiv.org/pdf/2512.20640v2","authors":"[\"Yunhao Hu\",\"Xinchen Lyu\",\"Chenshan Ren\",\"Keda Chen\",\"Qimei Cui\",\"Xiaofeng Tao\"]","published":"2025-12-08T06:34:35Z","proceeding":"cs.NI","tasks":"[\"cs.NI\",\"cs.MA\"]","methods":"[]","has_code":false}