The field of recommender systems is witnessing a significant shift with the integration of large language models (LLMs). Recent developments indicate a strong focus on leveraging LLMs to improve the accuracy and personalization of recommendations. Researchers are exploring various approaches, including the use of LLMs as embedding models, retrieval-augmented generation, and multi-agent systems. These innovative methods aim to capture complex user preferences, encode semantic relationships between items, and provide more effective and efficient recommendation pipelines. Notable papers in this area demonstrate substantial improvements in recommendation quality, outperforming traditional baselines and showcasing the potential of LLMs in transforming the field. Noteworthy papers include LLM2Rec, which proposes a novel embedding model that integrates LLMs with collaborative filtering awareness, and ARAG, which introduces an agentic retrieval-augmented generation framework for personalized recommendation. CAL-RAG and VRAgent-R1 also present promising approaches, utilizing multimodal retrieval and reinforcement learning to enhance recommendation performance.