The field of music generation and retrieval is moving towards innovative applications of deep learning and cross-modal approaches. Research is focused on developing models that can generate high-quality music, synchronize music with visual cues, and retrieve music based on text descriptions or user queries. Noteworthy papers in this area include those that propose novel frameworks for music similarity retrieval, generative retrieval models for music recommendation, and cross-modal contrastive learning approaches. These papers demonstrate significant performance improvements over existing benchmarks and have the potential to enhance music streaming platforms and other applications. Notable examples include the use of large language models to generate text descriptions for music similarity retrieval and the development of generative retrieval models that learn a mapping from user queries to relevant track IDs. Some notable papers include: CrossMuSim, which presents a cross-modal framework for music similarity retrieval with LLM-powered text description sourcing and mining, achieving significant performance improvements over existing benchmarks. Text2Tracks, which proposes a generative retrieval model that learns a mapping from user queries to relevant track IDs, outperforming sparse and dense retrieval solutions. Other papers, such as those on music genre transfer and dance-to-music generation, also demonstrate promising results and contribute to the advancement of the field.