🤖 AI Summary
This work addresses the limitations of existing language models, whose memory mechanisms typically rely on static storage and struggle to effectively manage information retention, forgetting, and user trust in memory reliability. To overcome these challenges, the authors propose StageMem, a novel framework that models memory as a dynamic process with a well-defined lifecycle, comprising transient, working, and persistent stages. Each memory item is explicitly assigned a confidence score and strength, enabling fine-grained control. Through stage-specific policies for admission, promotion, updating, and eviction, StageMem decouples shallow writing from long-term commitment, substantially enhancing the flexibility and reliability of memory management. Experimental results demonstrate that StageMem effectively preserves critical late-stage information under controlled stress, reduces contamination and overall load in deep memory, and remains compatible with sophisticated retrieval systems.
📝 Abstract
Long-horizon language model systems increasingly rely on persistent memory, yet many current designs still treat memory primarily as a static store: write an item, place it into memory, and retrieve it later if needed. We argue that this framing does not adequately capture the practical memory-control problem in deployed LLM systems. In realistic settings, the difficulty is often not merely forgetting useful information, but retaining too many uncertain items, forgetting important content in the wrong order, and giving users little trust in what will persist over time. We propose StageMem, a lifecycle-managed memory framework that treats memory as a stateful process rather than a passive repository. StageMem organizes memory into three stages -- transient, working, and durable memory -- and models each item with explicit confidence and strength. This separates shallow admission from long-term commitment: information may first be written at low cost and only later be promoted, retained, updated, or evicted as evidence and pressure evolve. Under controlled pressure regimes, this decomposition helps preserve late-important content while keeping memory burden and deeper-tier pollution more controlled. Adapted external tasks provide boundary evidence that the same schema remains compatible with stronger retrieval structure outside pure synthetic control. We present StageMem as a principled decomposition of the memory-control problem for language model systems.