CorMem: A Brain-Inspired Bipartite Cognitive Map Inducing Latent Relations for Accurate Long-Term Memory Retrieval in LLM Agents

Long-term LLM agents require memory systems that can retrieve relations across past experiences while incorporating new information without corrupting stored evidence. Relational organization plays a central role in long-term memory, where experiences are organized through associative and hierarchical structures. Current graph-based memory systems attempt to capture such relational structures. For example, association graphs capture broad semantic similarities but entangle meanings, whereas explicit-relation graphs depend on explicit relation extraction but may omit implicit relations. However, designing the representation for relationships remains challenging, as existing methods struggle to achieve both semantic explicitness and broad relational coverage. Inspired by infants' ability to infer latent relationships from statistical co-occurrence, we ask whether relational structure can be naturally induced. To this end, we propose CorMem, a dual-layer memory that induces latent relations in a cue–fact bipartite cognitive map and forms higher-level semantic gists in a hierarchical schema layer. Cue co-occurrence creates shared-cue paths between facts, and query-conditioned Personalized PageRank (PPR) traverses this structure to activate relevant memories. Schema formation incorporates updates without rewriting stored facts. Experiments on LoCoMo and PersonaMem-v2 show consistent gains over strong baselines. Across all LoCoMo backbones, CorMem achieves the best average F1 and improves gold-session Recall@3 by up to 18.4 points. Retrieved session co-activation also aligns with annotated co-evidence. On PersonaMem-v2, it reaches 70.5% accuracy and significantly outperforms the variant without the schema layer.