The field of retrieval-augmented generation is experiencing significant growth, with a focus on enhancing the accuracy and factual consistency of content generated by large language models. Recent developments have centered around the integration of knowledge graphs, which provide a structured representation of knowledge, to improve the retrieval process. This has led to the creation of more sophisticated systems that can navigate complex relationships between entities and provide more accurate answers to multi-hop questions. Noteworthy papers in this area include DyG-RAG, which introduces a novel event-centric dynamic graph retrieval-augmented generation framework, and BifrostRAG, which proposes a dual-graph RAG-integrated system that models both linguistic relationships and document structure. Other notable papers, such as BioGraphFusion, Agentic RAG, QMKGF, HypoChainer, and DynaSearcher, have also made significant contributions to the field, demonstrating the effectiveness of knowledge graph-based approaches in various applications, including biomedical research, scientific discovery, and question answering.