From Passive Retrieval to Active Memory Navigation: Learning to Use Memory as a Structured Action Space

Current AI agents often rely on passive memory retrieval, limiting their personalization capabilities. The new NapMem framework addresses this by treating long-term memory as a structured action space, enabling agents to actively navigate and inspect memory at multiple granularities.
Computer Science > Artificial Intelligence
Title:From Passive Retrieval to Active Memory Navigation: Learning to Use Memory as a Structured Action Space
View PDF HTML (experimental)Abstract:Long-term user memory is essential for personalized conversational agents, yet many memory systems still expose memory through passive retrieval interfaces, making the model a consumer of pre-selected evidence. We introduce NapMem, a framework for learning to use long-term user memory as a structured action space rather than passively retrieved context. NapMem organizes user history into a linked multi-granularity memory pyramid, where raw conversations, typed memory records, topic tracks, and user profiles are connected through provenance relations, and exposes these levels through memory tools. The agent is trained to select memory according to the query and intermediate evidence, allowing it to inspect different memory granularities before answering. Experiments on PersonaMem-v2, LongMemEval, and LoCoMo show that a NapMem agent trained with memory-tool reinforcement learning is competitive across diverse memory-intensive tasks, while evaluations on non-memory tasks suggest that the learned policy largely preserves general reasoning and tool-use abilities. Additional analyses examine storage, inference cost, tool-use behavior, and ablations over navigation, memory granularity, and RL training. Our results suggest that long-term user memory benefits from coupling structured storage with a learned policy for using memory at the appropriate granularity.
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Source: arXiv cs.AI Recent
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