💡 The paper discusses the challenges of continual learning in large language models and how current methods such as retrieval-augmented generation have limitations in mimicking human long-term memory. The authors propose a new framework called HippoRAG 2 which builds upon previous work and enhances it with deeper passage integration and more effective online use of a large language model. This approach improves performance across factual, sense-making, and associative memory tasks, addressing the deterioration in performance seen in previous methods that tried to augment vector embeddings with structures like knowledge graphs. The results show that HippoRAG 2 outperforms standard retrieval-augmented generation comprehensively, achieving a 7 percent improvement in associative memory tasks over the state-of-the-art embedding model, while also exhibiting superior factual knowledge and sense-making memory capabilities. The work contributes to non-parametric continual learning for large language models, paving the way for more effective and human-like memory capabilities in artificial intelligence systems.