Today's tech breakthroughs showcase how AI is becoming both more powerful and more accessible, with new innovations allowing models to process massive amounts of text and generate more reliable citations. In a significant development for global AI equity, researchers demonstrate how smaller languages can achieve sophisticated AI capabilities with limited resources, potentially democratizing advanced AI technology beyond English-speaking regions. Links to all the papers we discussed: InfiniteHiP: Extending Language Model Context Up to 3 Million Tokens on a Single GPU, Skrr: Skip and Re-use Text Encoder Layers for Memory Efficient Text-to-Image Generation, SelfCite: Self-Supervised Alignment for Context Attribution in Large Language Models, Can this Model Also Recognize Dogs? Zero-Shot Model Search from Weights, An Open Recipe: Adapting Language-Specific LLMs to a Reasoning Model in One Day via Model Merging, EmbodiedBench: Comprehensive Benchmarking Multi-modal Large Language Models for Vision-Driven Embodied Agents