The field of multilingual research is moving towards more inclusive and comprehensive models, with a focus on addressing data sparsity and improving cross-lingual transfer. Recent developments have shown that expanding linguistic knowledge bases and integrating new languages can significantly improve performance on low-resource languages. Additionally, novel methods for cross-lingual alignment and word alignment have been proposed, demonstrating substantial improvements over existing approaches. The use of multimodal embedders and adaptive query augmentation has also shown promise in reducing embedding latency and improving performance. Noteworthy papers include: Simple Additions, Substantial Gains, which extended URIEL+ with script vectors and expanded lineage imputation, and Languages are Modalities, which presented LLINK, a compute-efficient language-as-modality method. TransAlign also achieved strong word alignment performance using a massively multilingual MT model. POSESTITCH-SLT proposed a novel pre-training scheme for sign language translation, and Leveraging the Cross-Domain & Cross-Linguistic Corpus introduced a new parallel corpus for low-resource machine translation. Let Multimodal Embedders Learn When to Augment Query via Adaptive Query Augmentation proposed M-Solomon, a universal multimodal embedder that adaptively determines when to augment queries.