{"ID":2921722,"CreatedAt":"2026-06-02T02:42:49.606572591Z","UpdatedAt":"2026-06-03T05:56:00.181519634Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2606.01222","arxiv_id":"2606.01222","title":"RAG-driven Multi-Agent LLM Framework with Task Decomposition for Beyond 5G Auto-Configuration","abstract":"While Large Language Models (LLMs) offer a promising path toward intent-driven network management by translating natural language human intents into machine-readable configurations, they often suffer from hallucinations and structural inconsistencies in multi-step and complex tasks. To address these challenges, this paper proposes a retrieval-augmented and task decomposition-based multi-agent LLM framework for Beyond 5G network auto-configuration. The framework employs a semantic retrieval-augmented generation pipeline to ensure that its outputs are aligned with technical standards and vendor-specific manuals. Furthermore, it introduces a modular architecture for configuration generation, closed-loop configuration verification, and network deployment, in which complex tasks are decomposed into smaller sub-tasks handled by specialized agents. In this architecture, hallucinated configuration parameters are identified by the configuration verifier agent and corrected through low computational segment-level regeneration. The performance evaluation experiments with the OpenAirInterface emulator demonstrate that the proposed task decomposition-based configuration and verification approach improves the average success rate by 22.7% over monolithic methods, achieving 94.4% success in network configuration.","short_abstract":"While Large Language Models (LLMs) offer a promising path toward intent-driven network management by translating natural language human intents into machine-readable configurations, they often suffer from hallucinations and structural inconsistencies in multi-step and complex tasks. To address these challenges, this pa...","url_abs":"https://arxiv.org/abs/2606.01222","url_pdf":"https://arxiv.org/pdf/2606.01222v1","authors":"[\"İrşat Emin Sarıdaş\",\"Onur Salan\",\"Ali Görçin\",\"Ibrahim Hokelek\",\"Hakan Ali Çırpan\"]","published":"2026-05-31T13:15:55Z","proceeding":"eess.SP","tasks":"[\"eess.SP\"]","methods":"[\"RAG\",\"Large Language Model\",\"Language Model\"]","has_code":false}