Today's tech breakthroughs are reshaping how machines understand and interact with humans, from more efficient speech processing to squeezing unprecedented amounts of information into tiny spaces. As AI systems become increasingly sophisticated at mimicking human communication and creativity, researchers are finding ways to make these powerful tools more accessible and practical, raising both exciting possibilities and important questions about the future of human-machine interaction. Links to all the papers we discussed: Soundwave: Less is More for Speech-Text Alignment in LLMs, Cramming 1568 Tokens into a Single Vector and Back Again: Exploring the Limits of Embedding Space Capacity, Continuous Diffusion Model for Language Modeling, Phantom: Subject-consistent video generation via cross-modal alignment, Rethinking Diverse Human Preference Learning through Principal Component Analysis, Multimodal Mamba: Decoder-only Multimodal State Space Model via Quadratic to Linear Distillation