Zhen-Ning Guo

Editable Multimodal Memory with Reinforcement Learning Management for Long-Horizon Personalized Agents

Long-horizon personalized agents must retain heterogeneous evidence without allowing memory overflow, modality fragmentation, stale preferences, or irreversible storage errors. This paper presents an editable multimodal memory governed by an actor-critic manager. Text, visual, and auditory events are normalized into ve...