Learn to Memorize: Optimizing LLM-based Agents with Adaptive Memory Framework

• URL: http://arxiv.org/abs/2508.16629v1
• Date: Fri, 15 Aug 2025 12:22:52 GMT
• Title: Learn to Memorize: Optimizing LLM-based Agents with Adaptive Memory Framework
• Authors: Zeyu Zhang, Quanyu Dai, Rui Li, Xiaohe Bo, Xu Chen, Zhenhua Dong,
• Abstract summary: We propose to optimize LLM-based agents with an adaptive and data-driven memory framework by modeling memory cycles.<n>Specifically, we design an MoE gate function to facilitate memory retrieval, propose a learnable aggregation process to improve memory utilization, and develop task-specific reflection to adapt memory storage.
• Score: 33.739298910759544
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: LLM-based agents have been extensively applied across various domains, where memory stands out as one of their most essential capabilities. Previous memory mechanisms of LLM-based agents are manually predefined by human experts, leading to higher labor costs and suboptimal performance. In addition, these methods overlook the memory cycle effect in interactive scenarios, which is critical to optimizing LLM-based agents for specific environments. To address these challenges, in this paper, we propose to optimize LLM-based agents with an adaptive and data-driven memory framework by modeling memory cycles. Specifically, we design an MoE gate function to facilitate memory retrieval, propose a learnable aggregation process to improve memory utilization, and develop task-specific reflection to adapt memory storage. Our memory framework empowers LLM-based agents to learn how to memorize information effectively in specific environments, with both off-policy and on-policy optimization. In order to evaluate the effectiveness of our proposed methods, we conduct comprehensive experiments across multiple aspects. To benefit the research community in this area, we release our project at https://github.com/nuster1128/learn_to_memorize.

Related papers

• MemSifter: Offloading LLM Memory Retrieval via Outcome-Driven Proxy Reasoning [78.46301394559903]
  Large Language Models (LLMs) are increasingly used for long-duration tasks.<n>Current methods face a trade-off between cost and accuracy.<n>MemSifter is a novel framework that offloads the memory retrieval process to a small-scale proxy model.
  arXiv  Detail & Related papers  (2026-03-03T02:57:38Z)
• AMemGym: Interactive Memory Benchmarking for Assistants in Long-Horizon Conversations [61.6579785305668]
  AMemGym is an interactive environment enabling on-policy evaluation and optimization for memory-driven personalization.<n>Our framework provides a scalable, diagnostically rich environment for advancing memory capabilities in conversational agents.
  arXiv  Detail & Related papers  (2026-03-02T15:15:11Z)
• 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)
• 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)
• From Experience to Strategy: Empowering LLM Agents with Trainable Graph Memory [48.22750809620306]
  Large Language Models (LLMs) based agents have demonstrated remarkable potential in autonomous task-solving.<n>In this paper, we introduce a novel agent-centric, trainable, multi-layered graph memory framework.<n>We show how context memory enhances the ability of LLMs to utilize information.
  arXiv  Detail & Related papers  (2025-11-11T03:36:33Z)
• MR.Rec: Synergizing Memory and Reasoning for Personalized Recommendation Assistant with LLMs [23.593398623128735]
  MR.Rec is a novel framework that synergizes memory and reasoning for Large Language Models (LLMs)-based recommendations.<n>To achieve personalization, we develop a comprehensive Retrieval-Augmented Generation (RAG) system that efficiently indexes and retrieves relevant external memory.<n>By combining dynamic memory retrieval with adaptive reasoning, this approach ensures more accurate, context-aware, and highly personalized recommendations.
  arXiv  Detail & Related papers  (2025-10-16T12:40:48Z)
• Memory-Driven Self-Improvement for Decision Making with Large Language Models [26.996248662693997]
  Large language models (LLMs) have emerged as effective action policies for sequential decision-making tasks.<n>We propose a memory-driven self-improvement framework that combines LLM general prior knowledge with a compact memory of domain-specific experiences.
  arXiv  Detail & Related papers  (2025-09-30T14:46:06Z)
• Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning [59.16831804985279]
  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.<n>With as few as 152 question-answer pairs and a corresponding temporal memory bank for training, Memory-R1 outperforms the most competitive existing baseline.
  arXiv  Detail & Related papers  (2025-08-27T12:26:55Z)
• Hierarchical Memory for High-Efficiency Long-Term Reasoning in LLM Agents [19.04968632268433]
  We propose a hierarchical memory architecture for Large Language Model Agents (LLM Agents)<n>Each memory vector is embedded with a positional index encoding pointing to its semantically related sub-memories in the next layer.<n>During the reasoning phase, an index-based routing mechanism enables efficient, layer-by-layer retrieval without performing exhaustive similarity computations.
  arXiv  Detail & Related papers  (2025-07-23T12:45:44Z)
• MemBench: Towards More Comprehensive Evaluation on the Memory of LLM-based Agents [26.647812147336538]
  We construct a more comprehensive dataset and benchmark to evaluate the memory capability of LLM-based agents.<n>Our dataset incorporates factual memory and reflective memory as different levels, and proposes participation and observation as various interactive scenarios.<n>Based on our dataset, we present a benchmark, named MemBench, to evaluate the memory capability of LLM-based agents from multiple aspects, including their effectiveness, efficiency, and capacity.
  arXiv  Detail & Related papers  (2025-06-20T10:09:23Z)
• MemLLM: Finetuning LLMs to Use An Explicit Read-Write Memory [49.96019697955383]
  We introduce MemLLM, a novel method of enhancing large language models (LLMs) by integrating a structured and explicit read-and-write memory module.<n>Our experiments indicate that MemLLM enhances the LLM's performance and interpretability, in language modeling in general and knowledge-intensive tasks in particular.
  arXiv  Detail & Related papers  (2024-04-17T18:13:16Z)
• Online Adaptation of Language Models with a Memory of Amortized Contexts [82.02369596879817]
  Memory of Amortized Contexts (MAC) is an efficient and effective online adaptation framework for large language models. We show how MAC can be combined with and improve the performance of popular alternatives such as retrieval augmented generations.
  arXiv  Detail & Related papers  (2024-03-07T08:34:57Z)
• Revisiting Zeroth-Order Optimization for Memory-Efficient LLM Fine-Tuning: A Benchmark [166.40879020706151]
  This paper proposes a shift towards BP-free, zeroth-order (ZO) optimization as a solution for reducing memory costs during fine-tuning. Unlike traditional ZO-SGD methods, our work expands the exploration to a wider array of ZO optimization techniques. Our study unveils previously overlooked optimization principles, highlighting the importance of task alignment, the role of the forward gradient method, and the balance between algorithm complexity and fine-tuning performance.
  arXiv  Detail & Related papers  (2024-02-18T14:08:48Z)
• LLM-based Medical Assistant Personalization with Short- and Long-Term Memory Coordination [20.269899169364397]
  Large Language Models (LLMs) have exhibited remarkable proficiency in comprehending and generating natural language. We propose a novel computational bionic memory mechanism, equipped with a parameter-efficient fine-tuning (PEFT) schema, to personalize medical assistants.
  arXiv  Detail & Related papers  (2023-09-21T00:34:33Z)

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.