The field of artificial intelligence is moving towards the development of more sophisticated and dynamic memory systems for AI agents. Recent research has focused on creating systems that can learn and adapt in real-time, allowing for more efficient and effective decision-making. One of the key areas of innovation is in the development of agentic memory frameworks, which enable AI agents to learn from their experiences and apply that knowledge to new situations. Notably, some papers have introduced novel frameworks and benchmarks that allow for more efficient and interpretable memory management, such as the use of just-in-time compilation and self-evolving memory. These advancements have the potential to significantly improve the performance of AI agents in a variety of tasks and applications. Noteworthy papers include: LOOM, which presents a personalized learning pipeline that utilizes dynamic learner memory graphs to inform learning materials. ViLoMem, which introduces a dual-stream memory framework for multimodal semantic memory, enabling more effective learning from experiences.