MemR$^3$: Memory Retrieval via Reflective Reasoning for LLM Agents

• URL: http://arxiv.org/abs/2512.20237v1
• Date: Tue, 23 Dec 2025 10:49:42 GMT
• Title: MemR$^3$: Memory Retrieval via Reflective Reasoning for LLM Agents
• Authors: Xingbo Du, Loka Li, Duzhen Zhang, Le Song,
• Abstract summary: We build memory retrieval as an autonomous, accurate, and compatible agent system.<n>MemR$3$ has two core mechanisms: 1) a router that selects among retrieve, reflect, and answer actions to optimize answer quality; 2) a global evidence-gap tracker that explicitly renders the answering process transparent and tracks the evidence collection process.
• Score: 29.652985606497882
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Memory systems have been designed to leverage past experiences in Large Language Model (LLM) agents. However, many deployed memory systems primarily optimize compression and storage, with comparatively less emphasis on explicit, closed-loop control of memory retrieval. From this observation, we build memory retrieval as an autonomous, accurate, and compatible agent system, named MemR$^3$, which has two core mechanisms: 1) a router that selects among retrieve, reflect, and answer actions to optimize answer quality; 2) a global evidence-gap tracker that explicitly renders the answering process transparent and tracks the evidence collection process. This design departs from the standard retrieve-then-answer pipeline by introducing a closed-loop control mechanism that enables autonomous decision-making. Empirical results on the LoCoMo benchmark demonstrate that MemR$^3$ surpasses strong baselines on LLM-as-a-Judge score, and particularly, it improves existing retrievers across four categories with an overall improvement on RAG (+7.29%) and Zep (+1.94%) using GPT-4.1-mini backend, offering a plug-and-play controller for existing memory stores.

Related papers

• Choosing How to Remember: Adaptive Memory Structures for LLM Agents [43.27579458682491]
  Memory is critical for enabling large language model (LLM) based agents to maintain coherent behavior over long-horizon interactions.<n>We propose a unified framework, FluxMem, that enables adaptive memory organization for LLM agents.<n> Experiments on two long-horizon benchmarks, PERSONAMEM and LoCoMo, demonstrate that our method achieves average improvements of 9.18% and 6.14%.
  arXiv  Detail & Related papers  (2026-02-15T07:56:24Z)
• MemAdapter: Fast Alignment across Agent Memory Paradigms via Generative Subgraph Retrieval [25.68006224976726]
  Memory mechanism is a core component of LLM-based agents, enabling reasoning and knowledge discovery over long-horizon contexts.<n>Existing agent memory systems are typically designed within isolated paradigms with tightly coupled retrieval methods.<n>MemAdapter is a memory retrieval framework that enables fast alignment across agent memory paradigms.
  arXiv  Detail & Related papers  (2026-02-09T08:09:25Z)
• Mem-T: Densifying Rewards for Long-Horizon Memory Agents [23.19373149519922]
  We introduce Mem-T, an autonomous memory agent that interfaces with a lightweight hierarchical memory database to perform dynamic updates and multi-turn retrieval over streaming inputs.<n>We also propose MoT-GRPO, a tree-guided reinforcement learning framework that transforms sparse terminal feedback into dense, step-wise supervision via memory operation tree backpropagation and hindsight credit assignment.
  arXiv  Detail & Related papers  (2026-01-30T14:23:33Z)
• 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)
• MemRec: Collaborative Memory-Augmented Agentic Recommender System [57.548438733740504]
  We propose MemRec, a framework that architecturally decouples reasoning from memory management.<n>MemRec introduces a dedicated LM_Mem to manage a dynamic collaborative memory graph.<n>It achieves state-of-the-art performance on four benchmarks.
  arXiv  Detail & Related papers  (2026-01-13T18:51:16Z)
• 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)
• MemSearcher: Training LLMs to Reason, Search and Manage Memory via End-to-End Reinforcement Learning [73.27233666920618]
  We propose MemSearcher, an agent workflow that iteratively maintains a compact memory and combines the current turn with it.<n>At each turn, MemSearcher fuses the user's question with the memory to generate reasoning traces, perform search actions, and update memory to retain only information essential for solving the task.<n>We introduce multi-context GRPO, an end-to-end RL framework that jointly optimize reasoning, search strategies, and memory management of MemSearcher Agents.
  arXiv  Detail & Related papers  (2025-11-04T18:27:39Z)
• 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)
• G-Memory: Tracing Hierarchical Memory for Multi-Agent Systems [44.844636264484905]
  Large language model (LLM)-powered multi-agent systems (MAS) have demonstrated cognitive and execution capabilities that far exceed those of single LLM agents.<n>We introduce G-Memory, a hierarchical, agentic memory system for MAS inspired by organizational memory theory.<n>G-Memory improves success rates in embodied action and accuracy in knowledge QA by up to $20.89%$ and $10.12%$, respectively.
  arXiv  Detail & Related papers  (2025-06-09T03:43:46Z)
• From Single to Multi-Granularity: Toward Long-Term Memory Association and Selection of Conversational Agents [79.87304940020256]
  Large Language Models (LLMs) have been widely adopted in conversational agents.<n>MemGAS is a framework that enhances memory consolidation by constructing multi-granularity association, adaptive selection, and retrieval.<n> Experiments on four long-term memory benchmarks demonstrate that MemGAS outperforms state-of-the-art methods on both question answer and retrieval tasks.
  arXiv  Detail & Related papers  (2025-05-26T06:13:07Z)

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.