StageMem: Lifecycle-Managed Memory for Language Models

TL;DR

StageMem introduces a lifecycle-managed memory framework for language models, organizing memory into transient, working, and durable stages with explicit confidence, improving retention and control over long-horizon tasks.

Contribution

It proposes a novel memory management approach that models memory as a stateful process with staged organization and confidence levels, enhancing long-term information retention.

Findings

StageMem effectively preserves important content over time.

The staged approach reduces memory pollution and improves control.

External tasks demonstrate compatibility with real-world scenarios.

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.