The recent developments in the research area indicate a strong focus on enhancing the evaluation and performance of large language models (LLMs) and multi-modal models, particularly in addressing data contamination, leveraging vision models for audio tasks, and improving multilingual language model evaluations. There is a notable shift towards creating more comprehensive and contamination-free evaluation benchmarks, as well as exploring parameter-efficient methods to integrate vision models into audio tasks without the need for extensive pretraining. Additionally, the role of English in multilingual evaluations is being critically examined to better align with language understanding rather than just task performance. Multi-modal models are also advancing, with a specific emphasis on integrating speech capabilities to achieve more efficient and versatile AI systems. These advancements are paving the way for more robust, efficient, and versatile AI models that can handle complex, multi-dimensional inputs across various modalities.