The field of multimodal learning is witnessing significant advancements, with a focus on improving efficiency and performance in audio-visual learning tasks. Researchers are exploring innovative approaches to adapt pre-trained transformers for multimodal tasks, such as leveraging layer-wise tokens and directional alignment to enhance model merging. Noteworthy papers in this area include MoLT, which proposes a parameter- and memory-efficient adaptation framework for audio-visual learning, and From Coefficients to Directions, which introduces a unified geometric framework for merging models with directional alignment. Another significant direction is the development of probing methods for combining features from multiple foundation models, as seen in Fantastic Features and Where to Find Them. Furthermore, Stay Unique, Stay Efficient presents a personalized merging framework that preserves task-specific information with minimal storage overhead. Overall, these advancements are pushing the boundaries of multimodal learning and model merging, enabling more efficient and effective integration of multiple models and tasks. Notable papers include MoLT, which outperforms existing methods on diverse audio-visual benchmarks, and From Coefficients to Directions, which improves structural coherence and achieves strong empirical performance across diverse tasks.