The field of embodied AI is rapidly advancing, with a growing focus on efficient and self-improving systems. Recent developments have highlighted the importance of equivariant flow-based policy learning, which enables flexible and expressive control across diverse tasks while reducing data demands. Theoretical foundations have also been established for self-improving AI agents, which can recursively update their capabilities through self-play and other mechanisms. Furthermore, geometric frameworks for assessing progress in artificial intelligence have been introduced, providing a structured approach to evaluating and improving autonomous systems. Noteworthy papers include: EfficientFlow, which achieves competitive performance on robotic manipulation benchmarks with limited data and faster inference, and Self-Improving AI Agents through Self-Play, which derives a variance inequality for stable self-improvement. The Geometry of Benchmarks also presents a new path toward artificial general intelligence by introducing a geometric framework for assessing progress in AI.