The field of multimodal learning and recommendation systems is rapidly evolving, with a focus on improving the accuracy and novelty of recommendations. Recent developments have highlighted the importance of incorporating multiple modalities, such as text, images, and audio, to enhance the understanding of user preferences and item relationships. The use of large language models (LLMs) and multimodal fusion techniques has shown promising results in various applications, including fact-checking, content moderation, and advertising. Notably, the introduction of new benchmarks and datasets, such as ViLLA-MMBench, has enabled more comprehensive evaluations of multimodal recommendation systems. Furthermore, innovative methods like HiD-VAE and Bidding-Aware Retrieval have demonstrated significant improvements in recommendation accuracy and novelty. Overall, the field is moving towards more sophisticated and interpretable models that can effectively leverage multimodal data to improve user experience and satisfaction. Noteworthy papers include: The Missing Parts, which introduces the task of half-truth detection and proposes a modular re-assessment framework to identify omission-based misinformation. VELI4SBR, which presents a two-stage framework for session-based recommendation using validated and enriched LLM-generated intents. HALO, which proposes a hindsight-augmented learning approach for online auto-bidding to improve adaptation in multi-constraint environments.