Mnemis: Dual-Route Retrieval on Hierarchical Graphs for Long-Term LLM Memory

AI memory is increasingly important for long-term interactions with large language models, but existing RAG and Graph-RAG methods mainly rely on similarity-based retrieval. We propose Mnemis, a dual-route memory framework that combines fast System-1 similarity search with a System-2-style global selection mechanism over hierarchical memory graphs. By jointly retrieving semantically and structurally relevant memories, Mnemis improves long-term memory reasoning and achieves state-of-the-art performance on LoCoMo and LongMemEval-S benchmarks.