The field of multimodal learning and text clustering is moving towards more sophisticated and integrated approaches. Researchers are exploring ways to combine different modalities, such as text and images, to improve the accuracy and robustness of clustering and retrieval tasks. One notable direction is the use of transformer-based embeddings and autoencoders to preserve semantic relationships and improve clustering performance. Another area of focus is the development of more efficient and effective methods for text-image retrieval, including the use of sparse and dense representations. Additionally, there is a growing interest in compositional visual reasoning, which aims to enable machines to decompose visual scenes and perform multi-step logical inference. Overall, the field is seeing significant advancements in the development of more powerful and flexible models that can handle complex multimodal data. Noteworthy papers include: SDEC, which presents a novel unsupervised text clustering framework that combines an improved autoencoder with transformer-based embeddings. Sparse and Dense Retrievers Learn Better Together, which proposes a simple yet effective framework that enables bi-directional learning between dense and sparse representations. Explain Before You Answer, which provides a comprehensive survey of compositional visual reasoning and identifies key insights and open challenges. OwlCap, which proposes a powerful video captioning multi-modal large language model with motion-detail balance. Beyond Quality, which proposes a novel framework that jointly optimizes language models for both diversity and quality. Disentangling Latent Embeddings with Sparse Linear Concept Subspaces, which proposes a supervised dictionary learning approach to estimate a linear synthesis model. BiListing, which proposes an approach to align text and photos of a listing by leveraging large-language models and pretrained language-image models. SUMMA, which proposes a multimodal model that automatically processes video ads into summaries highlighting the content of highest commercial value.