Hippocampus–Neocortex-Inspired Sparse-Dense Memory Self-Organization for LLM Agents

Memory is essential for LLM agents to retain and reuse information, but reliable associative access remains challenging over extended interactions. Relevant evidence may span temporally distant interactions, yet existing methods often decouple associative memory organization from fine-grained evidence retrieval and therefore miss related information. Inspired by hippocampus–neocortex interactions in human memory, we propose $\text{Lumi-Mem}$, a sparse–dense memory self-organization framework for LLM agents. Within the resulting memory bank, Sparse Attractor (SA) performs associative indexing over topic basins, while Dense Representation (DR) supports fine-grained memory consolidation over candidate chunks. Sparse-to-dense activation and dense-to-sparse calibration close the bidirectional interaction loop, forming a brain-inspired mechanism for memory organization. Experiments on LongMemEval, 2WikiMultiHopQA, and MuSiQue show that Lumi-Mem achieves the best R@5 on all three benchmarks and the best average QA performance with GPT-4.1-mini. Ablations with different retrievers show that SA, DR, and the sparse–dense interaction modules each contribute to performance, supporting self-organization as a practical mechanism for memory retrieval.