Reducing hullucination of  Generative AI via Agentic AI and Edge Computing

[TechRxiv] Laha Ale and others propose a framework that combines agentic workflows with retrieval-augmented generation (RAG), deployed in 6G network edge environments, to reduce hallucinations in generative AI. The paper states that although RAG can improve factual grounding with external knowledge beyond the model's training data, RAG alone cannot fully eliminate hallucinations; agentic workflows that introduce external tools such as APIs, search engines, and self-reflection mechanisms can enable models to iteratively evaluate and correct outputs. The framework consists of a multi-agent system composed of generator and critic agents, reducing errors through iterative self-criticism, and uses 6G edge intelligence for data fusion, dynamic updating of knowledge bases, and delivery of customized AI services. The authors state that this helps provide more reliable generative AI services across a variety of applications.