The field of multimodal learning is rapidly advancing, with a focus on developing more effective and robust methods for combining and processing multiple forms of data. Recent research has highlighted the importance of balancing modality usage, mitigating biases, and improving representation learning. Notably, innovative approaches such as modality-informed learning rate schedulers and causal debiasing methods have shown promise in enhancing multimodal performance. Additionally, there is a growing interest in exploring the synergy between memorization and composition in deep learning models. Overall, the field is moving towards more principled and effective methods for multimodal learning, with potential applications in a wide range of areas, including search engines, healthcare, and finance. Noteworthy papers include: Theoretical Refinement of CLIP by Utilizing Linear Structure of Optimal Similarity, which proposes a novel method for enhancing similarity computation in multi-modal contrastive pretraining frameworks. MCA: Modality Composition Awareness for Robust Composed Multimodal Retrieval, which introduces a framework for mitigating modality shortcut issues in unified encoders. Lyapunov-Stable Adaptive Control for Multimodal Concept Drift, which presents a novel adaptive control framework for robust multimodal learning in the presence of concept drift.