Towards Autonomous Memory Agents

• URL: http://arxiv.org/abs/2602.22406v1
• Date: Wed, 25 Feb 2026 20:59:44 GMT
• Title: Towards Autonomous Memory Agents
• Abstract summary: We propose autonomous memory agents that actively acquire, validate, and curate knowledge at a minimum cost.<n>U-Mem materializes this idea via (i) a cost-aware knowledge-extraction cascade that escalates from cheap self/teacher signals to tool-verified research.<n>On both verifiable and non-verifiable benchmarks, U-Mem consistently beats prior memory baselines and can surpass RL-based optimization.
• Score: 8.294673275138122
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent memory agents improve LLMs by extracting experiences and conversation history into an external storage. This enables low-overhead context assembly and online memory update without expensive LLM training. However, existing solutions remain passive and reactive; memory growth is bounded by information that happens to be available, while memory agents seldom seek external inputs in uncertainties. We propose autonomous memory agents that actively acquire, validate, and curate knowledge at a minimum cost. U-Mem materializes this idea via (i) a cost-aware knowledge-extraction cascade that escalates from cheap self/teacher signals to tool-verified research and, only when needed, expert feedback, and (ii) semantic-aware Thompson sampling to balance exploration and exploitation over memories and mitigate cold-start bias. On both verifiable and non-verifiable benchmarks, U-Mem consistently beats prior memory baselines and can surpass RL-based optimization, improving HotpotQA (Qwen2.5-7B) by 14.6 points and AIME25 (Gemini-2.5-flash) by 7.33 points.

Related papers

• Dual-Grained Agent Memory and Shapley Context Attribution for Multimodal Agentic Learner [55.80248281761638]
  multimodal large language models (MLLMs) deliver impressive perception yet still falter on scientific and mathematical reasoning.<n>We propose DG-Mem, a dual-grained agentic memory framework that augments a frozen MLLM with a non- externally stored memory built once from training-time rollouts and consulted read-only at test time.
  arXiv  Detail & Related papers  (2026-08-24T13:56:01Z)
• Harness the Memory: A Holistic Evaluation of Memory Substrates in Memory Agents [97.19033877543522]
  We present a controlled harness evaluation of memory substrates for memory-augmented agents.<n>We instrument 26 performance and efficiency metrics under a unified harness.<n>These findings motivate substrate routing as a necessary component of adaptive agent memory systems.
  arXiv  Detail & Related papers  (2026-08-15T03:29:48Z)
• Is One Score Enough? Rethinking the Evaluation of Sequentially Evolving LLM Memory [50.857546269660276]
  We introduce SeqMem-Eval, a diagnostic evaluation framework for sequentially evolving large language models (LLMs) memory.<n>Rather than focusing only on final performance, SeqMem-Eval evaluates how memory states evolve, generalize, consolidate experience, and retain useful information during sequential inference.
  arXiv  Detail & Related papers  (2026-05-14T20:15:22Z)
• MemEvoBench: Benchmarking Memory MisEvolution in LLM Agents [78.95081012334116]
  Large Language Models (LLMs) with persistent memory enhances interaction continuity and personalization but introduces new safety risks.<n> contaminated or biased memory accumulation can trigger abnormal agent behaviors.<n>MemeEvoBench is the first benchmark evaluating memory safety in LLM agents against adversarial memory injection, noisy tool outputs, and biased feedback.
  arXiv  Detail & Related papers  (2026-04-17T07:29:52Z)
• Memory Intelligence Agent [32.709148411051764]
  Memory systems enable deep research agents (DRAs) to leverage historical experiences.<n>Existing methods rely on retrieving similar trajectories from memory to aid reasoning.<n>We propose a novel Memory Intelligence Agent framework, consisting of a Manager-Planner-Executor architecture.
  arXiv  Detail & Related papers  (2026-04-06T07:59:52Z)
• NextMem: Towards Latent Factual Memory for LLM-based Agents [58.35585202907478]
  NextMem is a latent factual memory framework that utilizes an autoregressive autoencoder to efficiently construct latent memory.<n>Extensive experiments demonstrate that NextMem achieves superior performance.
  arXiv  Detail & Related papers  (2026-02-26T14:35:27Z)
• Exploratory Memory-Augmented LLM Agent via Hybrid On- and Off-Policy Optimization [34.50047418642433]
  Exploration remains the key bottleneck for large language model agents trained with reinforcement learning.<n>We propose Exploratory Memory-Augmented On- and Off-Policy Optimization (EMPO$2$), a hybrid RL framework that leverages memory for exploration.<n>On ScienceWorld and WebShop, EMPO$2$ achieves 128.6% and 11.3% improvements over GRPO, respectively.
  arXiv  Detail & Related papers  (2026-02-26T13:50:57Z)
• MemCtrl: Using MLLMs as Active Memory Controllers on Embodied Agents [53.44122827359892]
  We propose MemCtrl, a framework that uses Multimodal Large Language Models (MLLMs) for pruning memory online.<n>-augmented MLLMs show an improvement of around 16% on average, with over 20% on specific instruction subsets.
  arXiv  Detail & Related papers  (2026-01-28T18:31:17Z)
• MetaMem: Evolving Meta-Memory for Knowledge Utilization through Self-Reflective Symbolic Optimization [57.17751568928966]
  We propose MetaMem, a framework that augments memory systems with a self-evolving meta-memory.<n>During meta-memory optimization, MetaMem iteratively distills transferable knowledge utilization experiences across different tasks.<n>Extensive experiments demonstrate the effectiveness of MetaMem, which significantly outperforms strong baselines by over 3.6%.
  arXiv  Detail & Related papers  (2026-01-27T04:46:23Z)
• EvolMem: A Cognitive-Driven Benchmark for Multi-Session Dialogue Memory [63.84216832544323]
  EvolMem is a new benchmark for assessing multi-session memory capabilities of large language models (LLMs) and agent systems.<n>To construct the benchmark, we introduce a hybrid data synthesis framework that consists of topic-initiated generation and narrative-inspired transformations.<n>Extensive evaluation reveals that no LLM consistently outperforms others across all memory dimensions.
  arXiv  Detail & Related papers  (2026-01-07T03:14:42Z)
• Remember Me, Refine Me: A Dynamic Procedural Memory Framework for Experience-Driven Agent Evolution [52.76038908826961]
  We propose $textbfReMe$ ($textitRemember Me, Refine Me$) to bridge the gap between static storage and dynamic reasoning.<n>ReMe innovates across the memory lifecycle via three mechanisms: $textitmulti-faceted distillation$, which extracts fine-grained experiences.<n>Experiments on BFCL-V3 and AppWorld demonstrate that ReMe establishes a new state-of-the-art in agent memory system.
  arXiv  Detail & Related papers  (2025-12-11T14:40:01Z)
• Evo-Memory: Benchmarking LLM Agent Test-time Learning with Self-Evolving Memory [89.65731902036669]
  Evo-Memory is a streaming benchmark and framework for evaluating self-evolving memory in large language model (LLM) agents.<n>We evaluate over ten representative memory modules and evaluate them across 10 diverse multi-turn goal-oriented and single-turn reasoning and QA datasets.
  arXiv  Detail & Related papers  (2025-11-25T21:08:07Z)
• 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)
• Memento: Fine-tuning LLM Agents without Fine-tuning LLMs [36.3424780932712]
  We introduce a novel learning paradigm for Adaptive Large Language Model (LLM) agents.<n>Our method enables low-cost continual adaptation via memory-based online reinforcement learning.<n>We instantiate our agent model in the deep research setting, namely emphMemento, which attains top-1 on GAIA validation.
  arXiv  Detail & Related papers  (2025-08-22T07:25:30Z)
• 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)
• $\ ext{Memory}^3$: Language Modeling with Explicit Memory [22.572376536612015]
  We equip large language models (LLMs) with explicit memory, a memory format cheaper than model parameters and text retrieval-augmented generation (RAG) As a preliminary proof of concept, we train from scratch a 2.4B LLM, which achieves better performance than much larger LLMs and RAG models. We introduce a memory circuitry theory to support the externalization of knowledge, and present novel techniques including a memory sparsification mechanism that makes storage tractable.
  arXiv  Detail & Related papers  (2024-07-01T11:07:23Z)
• Carousel Memory: Rethinking the Design of Episodic Memory for Continual Learning [19.260402028696916]
  Continual Learning (CL) aims to learn from a continuous stream of tasks without forgetting knowledge learned from the previous tasks. Previous studies exploit episodic memory (EM), which stores a subset of the past observed samples while learning from new non-i.i.d. data. We propose to exploit the abundant storage to preserve past experiences and alleviate the forgetting by allowing CL to efficiently migrate samples between memory and storage.
  arXiv  Detail & Related papers  (2021-10-14T11:27:45Z)

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.