The field of retrieval-augmented generation (RAG) is witnessing significant developments, with a focus on improving efficiency, accuracy, and robustness. Researchers are exploring innovative approaches to overcome the limitations of traditional RAG pipelines, such as the use of hierarchical memory architectures, context-aware semantic caching, and decoupled planning and execution frameworks. These advancements have the potential to enhance the performance of RAG systems in various applications, including question answering, text generation, and deep search. Noteworthy papers in this area include: PentaRAG, which introduces a five-layer module for large-scale intelligent knowledge retrieval, achieving significant improvements in latency and factual correctness. Frustratingly Simple Retrieval, which presents a minimal RAG pipeline that achieves consistent accuracy improvements across challenging, reasoning-intensive benchmarks using a compact, high-quality, web-scale datastore. Decoupled Planning and Execution, which proposes a hierarchical reasoning framework that separates strategic planning from specialized execution, outperforming state-of-the-art RAG and agent-based systems in complex, cross-modal deep search benchmarks.