SAGE: A Self-Evolving Agentic Graph-Memory Engine for Structure-Aware Associative Memory

arXiv:2605.12061v1 Announce Type: new Abstract: Long-term memory is becoming a central bottleneck for language agents. Exsting RAG and GraphRAG systems largely treat memory graphs as static retrieval middleware, which limits their ability to recover complete evidence chains from partial cues, exploit reusable graph-structrual roles, and improve the memory itself through downstream feedback. We introduce SAGE, a Self-evolving Agentic Graph-memory Engine that models graph memory as a dynamic long-