Memory-Centric Continual Learning for AI Agents

One-line takeaway

Memory-Centric Continual Learning (MCCL) argues agent memory should be judged by whether it improves future actions, not merely what it retrieves, turning external memory from a passive cache into a driver of genuine continual learning for long-deployed foundation-model agents.

记忆中心的持续学习（MCCL）主张：智能体的外部记忆不该只当被动检索缓存、按能否回忆事实来评，而应看它能否改进以后的行动，让基于基座模型的智能体在长期部署中真正从经验里持续学习。