Personalized memory gating—deciding which facts to remember for each user—can be learned efficiently by treating it as a bandit problem with delayed feedback, outperforming one-size-fits-all memory policies.
This paper tackles personalized memory management for AI agents by learning which facts to keep in a limited memory bank. The authors propose a method that learns user-specific patterns of what information gets reused later, combining offline learning from past data with online adaptation to individual users.