The field of natural language processing is moving towards a more nuanced understanding of language structures and models that can handle complex linguistic phenomena. Recent research has focused on developing more robust language models that can perform well even when faced with distorted or scrambled input, such as typoglycemic words. Additionally, there is a growing interest in preserving and promoting endangered languages through digital archiving and language learning applications. Multilingual modeling has also shown promise in simplifying deployment and improving performance across a wide range of languages. Noteworthy papers include: Flexing in 73 Languages, which presents a compact, single-model approach to multilingual inflection that outperforms monolingual baselines in most languages. Integrating Linguistics and AI, which develops a trilingual language learning application to digitally archive and promote the endangered Toto language of West Bengal, India, and offers a sustainable model for preserving endangered languages by incorporating traditional linguistic methodology with AI.