From Passive Retrieval to Active Memory Navigation: Learning to Use Memory as a Structured Action Space
arXiv:2607.05794v1 Announce Type: new 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 relati
The increasing sophistication of conversational AI and the growing demand for personalized interactions highlight the limitations of current passive memory systems, driving innovation in active memory management.
This development proposes a fundamental shift in how AI agents interact with long-term memory, moving from passive consumption to active, structured utilization, which is critical for truly intelligent and personalized AI.
AI models will transition from merely retrieving pre-selected information to actively navigate and manipulate a complex, multi-granularity memory structure, enabling more sophisticated and context-aware interactions.
- · AI software developers
- · Conversational AI companies
- · Personalized assistant providers
- · Companies relying on simple, passive memory architectures
- · AI models with limited memory integration
Conversational agents will exhibit significantly improved long-term coherence and personalization.
The development of highly personalized and proactive AI assistants could accelerate the adoption of AI in daily life and professional workflows.
New ethical and privacy considerations will arise as AI systems gain more active and deep access to user history and personal data.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.AI