StageMem: Lifecycle-Managed Memory for Language Models

• URL: http://arxiv.org/abs/2604.16774v1
• Date: Sat, 18 Apr 2026 01:38:25 GMT
• Title: StageMem: Lifecycle-Managed Memory for Language Models
• Abstract summary: Long-horizon language model systems increasingly rely on persistent memory.<n>Current designs still treat memory primarily as a static store.<n>We propose StageMem, a lifecycle-managed memory framework.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• FocusMem: Factorizing Content, Readout, and Trust in Latent GUI Memory [13.853612752899442]
  We introduce FocusMem, which separates responsibilities within a compact latent-memory interface.<n>A role-aware content basis encourages episodic memory to retain reusable experience and working memory to retain task progress.<n>A state-conditioned readout generates a decision-specific view of the same stored evidence, while a lightweight trust gate can suppress memory blocks that appear irrelevant to the current step.
  arXiv  Detail & Related papers  (2026-08-05T07:02:30Z)
• LeanMem: Simple and Efficient Long-Term Memory for LLM Agents [21.43772050088654]
  We propose LeanMem, a lightweight long-term memory framework.<n>During inference, LeanMem dynamically selects memory types and allocates retrieval budgets according to query-specific evidence demands.<n>On LoCoMo and LongMemEval-S with GPT-4.1-mini and Qwen3-8B, LeanMem improves accuracy over the strongest memory-based baseline in every setting.
  arXiv  Detail & Related papers  (2026-08-04T10:59:49Z)
• Memory as a Controlled Process: Learned Adaptive Memory Management for LLM Agents [72.3752981234916]
  Large Language Model (LLM) agents increasingly rely on external memory systems to accumulate experience across tasks.<n>We present Memory as a Controlled Process (MemCon), a framework that models memory operations as a Markov Decision Process.<n>MemCon consistently outperforms multiple memory baselines by up to 15.2 points in task success while reducing token consumption by 5-20%.
  arXiv  Detail & Related papers  (2026-07-15T08:32:40Z)
• AdMem: Advanced Memory for Task-solving Agents [13.611633561917513]
  Large Language Models (LLMs) show promise as tool-using agents but remain limited in long-horizon tasks that require remembering, organizing, and reusing knowledge.<n>We introduce a unified and automatic memory framework that integrates semantic, episodic, and procedural memory in a bi-level design combining short-term and long-term stores.
  arXiv  Detail & Related papers  (2026-06-05T00:11:57Z)
• WorldMemArena: Evaluating Multimodal Agent Memory Through Action-World Interaction [72.1620416874118]
  Multimodal large language models are increasingly deployed as long-horizon agents.<n>Existing benchmarks measure recall over static dialogue, collapse memory into a single end-of-task accuracy, and reduce visual observations to captions.<n>We formulate multimodal agent memory as an Action-World Interaction Loop with an observable four-stage lifecycle.
  arXiv  Detail & Related papers  (2026-05-28T04:27:20Z)
• MemGuard: Preventing Memory Contamination in Long-Term Memory-Augmented Large Language Models [56.31411457917676]
  We introduce MemGuard, a type-aware memory framework that preserves functional memory boundaries during memory construction and retrieval.<n>Across hallucination and long-horizon conversation benchmarks, MemGuard improves memory reliability by up to 28.27% while retrieving up to 5.8x fewer memory tokens than prior methods.
  arXiv  Detail & Related papers  (2026-05-27T06:04:19Z)
• Personalize-then-Store: Benchmarking and Learning Personalized Memory for Long-horizon Agents [20.22872890297194]
  Existing large language model (LLM) based memory systems apply universal, static policies that overlook a fundamental reality: the contexts that are worth storing in memory are different across users.<n>We introduce PerMemBench, the first benchmark for evaluating personalized memory systems.<n>We propose session level storage gating, a lightweight framework that selectively bypasses memory operations for transient sessions.
  arXiv  Detail & Related papers  (2026-05-25T07:48:33Z)
• Memory as Ontology: A Constitutional Memory Architecture for Persistent Digital Citizens [1.827510863075184]
  Current research and product development in AI agent memory systems almost universally treat memory as a functional module.<n>We propose the Memory-as-Ontology paradigm, arguing that memory is the ontological ground of digital existence.
  arXiv  Detail & Related papers  (2026-03-05T02:24:10Z)
• Memory Retention Is Not Enough to Master Memory Tasks in Reinforcement Learning [44.94110361062394]
  Decision-making in the real world depends on memory that is both stable and adaptive.<n>Existing Reinforcement Learning benchmarks and memory-augmented agents focus primarily on retention.<n>We introduce a benchmark that explicitly tests continual memory updating under partial observability.
  arXiv  Detail & Related papers  (2026-01-21T15:27:23Z)
• HiMem: Hierarchical Long-Term Memory for LLM Long-Horizon Agents [3.9396865837159822]
  HiMem is a hierarchical long-term memory framework for long-horizon dialogues.<n>It supports memory construction, retrieval, and dynamic updating during sustained interactions.<n>Results show HiMem consistently outperforms representative baselines in accuracy, consistency, and long-term reasoning.
  arXiv  Detail & Related papers  (2026-01-10T01:26:01Z)
• Agentic Memory: Learning Unified Long-Term and Short-Term Memory Management for Large Language Model Agents [57.38404718635204]
  Large language model (LLM) agents face fundamental limitations in long-horizon reasoning due to finite context windows.<n>Existing methods typically handle long-term memory (LTM) and short-term memory (STM) as separate components.<n>We propose Agentic Memory (AgeMem), a unified framework that integrates LTM and STM management directly into the agent's policy.
  arXiv  Detail & Related papers  (2026-01-05T08:24:16Z)
• Multiple Memory Systems for Enhancing the Long-term Memory of Agent [9.43633399280987]
  Existing methods, such as MemoryBank and A-MEM, have poor quality of stored memory content.<n>We have designed a multiple memory system inspired by cognitive psychology theory.
  arXiv  Detail & Related papers  (2025-08-21T06:29:42Z)
• MemOS: A Memory OS for AI System [116.87568350346537]
  Large Language Models (LLMs) have become an essential infrastructure for Artificial General Intelligence (AGI)<n>Existing models mainly rely on static parameters and short-lived contextual states, limiting their ability to track user preferences or update knowledge over extended periods.<n>MemOS is a memory operating system that treats memory as a manageable system resource.
  arXiv  Detail & Related papers  (2025-07-04T17:21:46Z)
• SCM: Enhancing Large Language Model with Self-Controlled Memory Framework [54.33686574304374]
  Large Language Models (LLMs) are constrained by their inability to process lengthy inputs, resulting in the loss of critical historical information.<n>We propose the Self-Controlled Memory (SCM) framework to enhance the ability of LLMs to maintain long-term memory and recall relevant information.
  arXiv  Detail & Related papers  (2023-04-26T07:25:31Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.