The field of Retrieval-Augmented Generation (RAG) is experiencing significant growth, with a focus on improving the efficiency and effectiveness of language models. Recent developments have centered around optimizing the trade-off between quality and efficiency in RAG pipelines, with various approaches aiming to reduce computational challenges and enhance system-level efficiency. Another key area of research is the integration of external knowledge into models, with techniques such as dynamic clustering-based document compression and adaptive memory-based optimization being explored. Additionally, there is a growing interest in decentralizing AI memory and developing unified architectures for scalable agent reasoning. Noteworthy papers include: HyperRAG, which achieves a 2-3 throughput improvement with decoder-only rerankers while delivering higher downstream performance. EDC2-RAG, which effectively utilizes latent inter-document relationships and removes irrelevant information, demonstrating strong robustness and applicability. Amber, which integrates and optimizes language model memory through a multi-agent collaborative approach, ensuring comprehensive knowledge integration. MicroNN, which enables efficient on-device vector search for real-world workloads with updates and hybrid search queries. SHIMI, which models knowledge as a dynamically structured hierarchy of concepts, enabling agents to retrieve information based on meaning rather than surface similarity.