The field of artificial intelligence is moving towards the development of more advanced cognitive architectures, with a focus on creating systems that can learn, reason, and adapt in complex environments. Recent research has explored the integration of neuroscience-inspired approaches, such as multimodal sensing and cognitive maps, to improve spatial reasoning and decision-making capabilities. Additionally, there has been a push towards creating more scalable and sustainable memory mechanisms, such as self-evolving distributed memory and hierarchical adapter merging, to support open-ended multi-agent collaboration and continual learning. Noteworthy papers in this area include Mind Meets Space, which introduces a novel computational framework for agentic spatial reasoning, and HAM, which proposes a hierarchical adapter merging approach for scalable continual learning. These advancements have the potential to significantly improve the performance and efficiency of artificial intelligence systems, and are expected to play a key role in the development of future AI technologies.