RAG-KG-IL is a novel framework that enhances LLM reasoning capabilities by integrating Retrieval-Augmented Generation with Knowledge Graphs and Incremental Learning.

• Combines RAG and KG technologies to reduce hallucinations in LLM outputs
• Utilizes a multi-agent approach to improve reasoning with structured data
• Handles dynamic knowledge evolution through incremental learning
• Specifically addresses challenges in mission-critical domains

For medical applications, this framework offers significant potential by providing more accurate, reliable AI responses for healthcare queries where factual precision is essential for patient safety and clinical decision-making.

RAG-KG-IL: A Multi-Agent Hybrid Framework for Reducing Hallucinations and Enhancing LLM Reasoning through RAG and Incremental Knowledge Graph Learning Integration