The research landscape in the field of multimodal learning and large language models (LLMs) is witnessing significant advancements, particularly in the integration of continuous and discrete data modalities. Innovations are focusing on enhancing the robustness and efficiency of models by introducing novel architectures that bridge autoregressive and diffusion-based approaches. Notably, continuous speech tokens are being explored to improve speech-to-speech interaction, while unified frameworks are being developed to handle both discrete and continuous data seamlessly. Additionally, there is a growing emphasis on simplifying data processing pipelines and reducing deployment costs, with a particular focus on integrating text-to-speech and automatic speech recognition tasks. The field is also seeing a shift towards higher-level semantic representations, such as 'concepts,' to better align with human-like information processing. These developments collectively push the boundaries of multimodal learning, aiming for more scalable, efficient, and versatile models.