The field of AI research is moving towards developing more advanced and efficient language models that can maintain consistency over prolonged conversations and integrate new knowledge seamlessly. Recent work has focused on introducing scalable memory-centric architectures and continual learning frameworks that enable large language models to recall entire memories and answer related questions. These innovations have led to significant improvements in accuracy and reductions in computational overhead, paving the way for more reliable and efficient AI agents. Notably, researchers are drawing inspiration from the human brain's complementary memory system to inform their approaches. Noteworthy papers include: Mem0, which introduces a scalable memory-centric architecture that achieves 26% relative improvements in the LLM-as-a-Judge metric and reduces computational overhead compared to full-context methods. MEGa, which proposes a continual learning framework that injects event memories directly into the weights of large language models, outperforming baseline approaches in mitigating catastrophic forgetting. Birdie, which presents a novel framework using a differentiable search index, unifying indexing and search into a single encoder-decoder language model and achieving 16.8% higher accuracy than state-of-the-art dense methods.