The field of Retrieval-Augmented Generation (RAG) is rapidly evolving, with a focus on improving the accuracy and robustness of large language models. Recent research has explored various techniques to enhance RAG systems, including the use of implicit queries, clue-anchored reasoning, and knowledge-aware refinement. These innovations aim to address the challenges of noisy retrieved content, inconsistent knowledge, and ineffective utilization of parametric knowledge. Notably, some studies have investigated the vulnerability of RAG systems to poisoning attacks and proposed defense mechanisms to mitigate these threats. Overall, the field is moving towards developing more sophisticated and robust RAG systems that can effectively integrate external knowledge and improve performance on knowledge-intensive tasks. Noteworthy papers include Spa-VLM, which proposes a stealthy poisoning attack on RAG-based vision-language models, and ClueAnchor, which introduces a novel framework for enhancing RAG via clue-anchored reasoning exploration and optimization. Additionally, ImpRAG presents a query-free RAG system that integrates retrieval and generation into a unified model, and KARE-RAG improves knowledge utilization through structured knowledge representations and dense direct preference optimization.