AutoMem: Automated Learning of Memory as a Cognitive Skill

• URL: http://arxiv.org/abs/2607.01224v1
• Date: Wed, 01 Jul 2026 17:57:03 GMT
• Title: AutoMem: Automated Learning of Memory as a Cognitive Skill
• Abstract summary: We show that memory expertise is a learned skill.<n>We introduce AutoMem, a framework that automates the two axes of memory management.<n>Our results show that memory management is an independently learnable skill.
• Score: 63.07128029793901
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Memory expertise is a learned skill: knowing what to encode, when to retrieve, and how to organize knowledge--a capacity known in cognitive science as metamemory. We bring this perspective to LLMs by treating memory management as a trainable skill. We promote file-system operations to first-class memory actions alongside task actions, letting the model itself decide how to manage its memory. This memory skill improves along two axes: the structure that supports it (prompts, file schemas, action vocabulary), and the proficiency of the model exercising it. Both axes resist manual optimization: episodes in long-horizon tasks run for thousands of steps, and a single memory mistake can hide long before it surfaces, making human review of full trajectories impractical. We introduce AutoMem, a framework that automates both axes. In the first loop, a strong LLM reviews complete agent trajectories and iteratively revises the memory structure that shapes how the agent interacts with its memory files. In the second loop, the agent's own good memory decisions are identified from many episodes and used as training signal to sharpen the model's memory proficiency directly. Across three procedurally generated long-horizon games (Crafter, MiniHack, and NetHack), optimizing memory alone--without modifying the model's task-action behavior--improved the base agent's performance ~2x-4x, bringing a 32B open-weight model competitive with frontier systems such as Claude Opus 4.5 and Gemini 3.1 Pro Thinking. Our results show that memory management is an independently learnable skill, and a high-leverage objective yielding large gains on long-horizon tasks.

Related papers

• Agent Memory Distillation: Empowering Small LLM Agents with Hierarchical Teacher Memory [49.6908961629532]
  Agent Memory Distillation (AMD) is a training-free framework that transfers structured knowledge from a large teacher agent to a small student agent through hierarchical memory.<n>AMD constructs three complementary memory types from successful teacher trajectories: memory encodes task-level strategies, Subtask memory provides concrete behavioral examples, and Function memory captures per-function calling conventions.<n>We evaluate AMD on three tool-use benchmarks using four student models (4B-8B parameters) with GPT-5-mini as the teacher, achieving average accuracy gains of 27.2%p, 11.2%p, and 3.4%p on AppWorld, BFCL V
  arXiv  Detail & Related papers  (2026-08-07T12:43:00Z)
• Metis: Memory Foundation Model [96.59067961902532]
  We introduce memory foundation models, which empower foundation models with native memory capabilities.<n>We show that native memory offers advantages in architecture, end-to-end optimization, and efficiency.<n>We present Metis, the first prototype of memory foundation models.
  arXiv  Detail & Related papers  (2026-07-29T10:58:44Z)
• 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)
• MemTrain: Self-Supervised Context Memory Training [40.64801276577851]
  Existing memory-agent approaches are typically trained end-to-end with reinforcement learning on downstream tasks.<n>MemTrain is a self-supervised training framework for enhancing context-memory capability of LLM agents.<n>MemTrain consistently improves downstream memory-intensive reasoning performance across different models.
  arXiv  Detail & Related papers  (2026-06-02T05:56:24Z)
• AtomMem : Learnable Dynamic Agentic Memory with Atomic Memory Operation [40.1709026042412]
  We propose AtomMem, which reframes memory management as a dynamic decision-making problem.<n>By combining supervised fine-tuning with reinforcement learning, AtomMem learns an autonomous, task-aligned policy to orchestrate memory behaviors.<n> Experimental results across 3 long-context benchmarks demonstrate that the trained AtomMem-8B consistently outperforms prior static-workflow memory methods.
  arXiv  Detail & Related papers  (2026-01-13T08:22:28Z)
• Learning How to Remember: A Meta-Cognitive Management Method for Structured and Transferable Agent Memory [31.318565330948562]
  This paper proposes the Meta-Cognitive Memory Abstraction method (MCMA)<n>MCMA treats memory abstraction as a learnable cognitive skill rather than a fixed design choice.<n> Experiments on ALFWorld, ScienceWorld, and BabyAI demonstrate substantial improvements in performance, out-of-distribution generalization, and cross-task transfer.
  arXiv  Detail & Related papers  (2026-01-12T12:26:02Z)
• 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)
• Mem-α: Learning Memory Construction via Reinforcement Learning [20.916677456417464]
  Large language model (LLM) agents are constrained by limited context windows.<n>Current memory-augmented agents depend on pre-defined instructions and tools for memory updates.<n>Mem-alpha is a reinforcement learning framework that trains agents to effectively manage complex memory systems.
  arXiv  Detail & Related papers  (2025-09-30T08:02:34Z)
• Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning [89.55738101744657]
  Large Language Models (LLMs) have demonstrated impressive capabilities across a wide range of NLP tasks, but they remain fundamentally stateless.<n>We present Memory-R1, a reinforcement learning framework that equips LLMs with the ability to actively manage and utilize external memory.
  arXiv  Detail & Related papers  (2025-08-27T12:26:55Z)

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.