Towards Autonomous Memory Agents

Researchers introduce U-Mem, autonomous memory agents that actively acquire, validate, and organize knowledge for large language models. Unlike passive systems, U-Mem uses cost-efficient knowledge extraction cascading from self-signals to expert feedback, plus semantic-aware Thompson sampling for balanced exploration. It significantly outperforms existing memory baselines on benchmarks like HotpotQA and AIME25.