AI RESEARCH
HAGE: Harnessing Agentic Memory via RL-Driven Weighted Graph Evolution
arXiv CS.AI
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ArXi:2605.09942v1 Announce Type: new Memory retrieval in agentic large language model (LLM) systems is often treated as a static lookup problem, relying on flat vector search or fixed binary relational graphs. However, fixed graph structures cannot capture the varying strength, confidence, and query-dependent relevance of relationships between events. In this paper, we propose HAGE, a weighted multi-relational memory framework that reconceptualizes retrieval as sequential, query-conditioned traversal over a unified relational memory graph.