Yue Que

Partitioning agent memory by distinct life scenes rather than monolithic topic hierarchies eliminates retrieval interference and unlocks substantially higher accuracy in multi-hop personalization tasks.

Traditional Top-K metrics fail to capture true user preferences, but the LLM-as-a-Judge framework offers a semantic approach that enhances both reliability and explainability in recommendation evaluations.