NapMem Turns Agent Memory Into Structured Action Space With RL

elvis@omarsar0TECH

// Memory becomes an action space // Great paper on long-term memory for agents. (bookmark it) In short, it's discusses the use of a learned policy for using memory at the right granularity. Most memory systems still hand the model whatever a retriever selected, leaving it a passive consumer of pre-picked evidence. NapMem reframes memory as a structured action space the agent navigates on its own. It organizes user history into a multi-granularity pyramid, raw conversations, typed memory records, topic tracks, and user profiles, linked by provenance relations and exposed as tools. Trained with memory-tool RL, the agent chooses which granularity to inspect before answering. NapMem stays competitive across PersonaMem-v2, LongMemEval, and LoCoMo while largely preserving general reasoning and tool-use ability. Paper: https://arxiv.org/abs/2607.05794 Learn to build effective AI agents in our academy: https://academy.dair.ai/

NapMem Turns Agent Memory Into Structured Action Space With RL

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