MemoCue: Empowering LLM-Based Agents for Human Memory Recall via Strategy-Guided Querying

• URL: http://arxiv.org/abs/2507.23633v1
• Date: Thu, 31 Jul 2025 15:11:38 GMT
• Title: MemoCue: Empowering LLM-Based Agents for Human Memory Recall via Strategy-Guided Querying
• Authors: Qian Zhao, Zhuo Sun, Bin Guo, Zhiwen Yu,
• Abstract summary: We propose a strategy-guided agent-assisted memory recall method, allowing the agent to transform an original query into a cue-rich one.<n>We design a 5W Recall Map to classify memory queries into five typical scenarios.<n>We then propose a hierarchical recall tree combined with the Monte Carlo Tree Search algorithm to optimize the selection of strategy and the generation of strategy responses.
• Score: 12.524353050278105
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Agent-assisted memory recall is one critical research problem in the field of human-computer interaction. In conventional methods, the agent can retrieve information from its equipped memory module to help the person recall incomplete or vague memories. The limited size of memory module hinders the acquisition of complete memories and impacts the memory recall performance in practice. Memory theories suggest that the person's relevant memory can be proactively activated through some effective cues. Inspired by this, we propose a novel strategy-guided agent-assisted memory recall method, allowing the agent to transform an original query into a cue-rich one via the judiciously designed strategy to help the person recall memories. To this end, there are two key challenges. (1) How to choose the appropriate recall strategy for diverse forgetting scenarios with distinct memory-recall characteristics? (2) How to obtain the high-quality responses leveraging recall strategies, given only abstract and sparsely annotated strategy patterns? To address the challenges, we propose a Recall Router framework. Specifically, we design a 5W Recall Map to classify memory queries into five typical scenarios and define fifteen recall strategy patterns across the corresponding scenarios. We then propose a hierarchical recall tree combined with the Monte Carlo Tree Search algorithm to optimize the selection of strategy and the generation of strategy responses. We construct an instruction tuning dataset and fine-tune multiple open-source large language models (LLMs) to develop MemoCue, an agent that excels in providing memory-inspired responses. Experiments on three representative datasets show that MemoCue surpasses LLM-based methods by 17.74% in recall inspiration. Further human evaluation highlights its advantages in memory-recall applications.

Related papers

• 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)
• Towards Multi-Granularity Memory Association and Selection for Long-Term Conversational Agents [73.77930932005354]
  We propose MemGAS, a framework that enhances memory consolidation by constructing multi-granularity association, adaptive selection, and retrieval.<n>MemGAS is based on multi-granularity memory units and employs Gaussian Mixture Models to cluster and associate new memories with historical ones.<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)
• Rethinking Memory in AI: Taxonomy, Operations, Topics, and Future Directions [55.19217798774033]
  Memory is a fundamental component of AI systems, underpinning large language models (LLMs)-based agents.<n>In this survey, we first categorize memory representations into parametric and contextual forms.<n>We then introduce six fundamental memory operations: Consolidation, Updating, Indexing, Forgetting, Retrieval, and Compression.
  arXiv  Detail & Related papers  (2025-05-01T17:31:33Z)
• A-MEM: Agentic Memory for LLM Agents [42.50876509391843]
  Large language model (LLM) agents require memory systems to leverage historical experiences.<n>Current memory systems enable basic storage and retrieval but lack sophisticated memory organization.<n>This paper proposes a novel agentic memory system for LLM agents that can dynamically organize memories in an agentic way.
  arXiv  Detail & Related papers  (2025-02-17T18:36:14Z)
• On the Structural Memory of LLM Agents [20.529239764968654]
  Memory plays a pivotal role in enabling large language model(LLM)-based agents to engage in complex and long-term interactions.<n>This paper investigates how memory structures and memory retrieval methods affect the performance of LLM-based agents.
  arXiv  Detail & Related papers  (2024-12-17T04:30:00Z)
• Assessing Episodic Memory in LLMs with Sequence Order Recall Tasks [42.22616978679253]
  We introduce Sequence Order Recall Tasks (SORT), which we adapt from tasks used to study episodic memory in cognitive psychology. SORT requires LLMs to recall the correct order of text segments, and provides a general framework that is both easily extendable and does not require any additional annotations. Based on a human experiment with 155 participants, we show that humans can recall sequence order based on long-term memory of a book.
  arXiv  Detail & Related papers  (2024-10-10T17:17:38Z)
• Lift Yourself Up: Retrieval-augmented Text Generation with Self Memory [72.36736686941671]
  We propose a novel framework, selfmem, for improving retrieval-augmented generation models. Selfmem iteratively employs a retrieval-augmented generator to create an unbounded memory pool and using a memory selector to choose one output as memory for the subsequent generation round. We evaluate the effectiveness of selfmem on three distinct text generation tasks.
  arXiv  Detail & Related papers  (2023-05-03T21:40:54Z)
• 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)
• RMM: Reinforced Memory Management for Class-Incremental Learning [102.20140790771265]
  Class-Incremental Learning (CIL) trains classifiers under a strict memory budget. Existing methods use a static and ad hoc strategy for memory allocation, which is often sub-optimal. We propose a dynamic memory management strategy that is optimized for the incremental phases and different object classes.
  arXiv  Detail & Related papers  (2023-01-14T00:07:47Z)
• The act of remembering: a study in partially observable reinforcement learning [24.945756871291348]
  Reinforcement Learning (RL) agents typically learn memoryless policies that only consider the last observation when selecting actions. We provide the agent with an external memory and additional actions to control what, if anything, is written to the memory. Our novel forms of memory outperform binary and LSTM-based memory in well-established partially observable domains.
  arXiv  Detail & Related papers  (2020-10-05T02:56:43Z)
• Self-Attentive Associative Memory [69.40038844695917]
  We propose to separate the storage of individual experiences (item memory) and their occurring relationships (relational memory) We achieve competitive results with our proposed two-memory model in a diversity of machine learning tasks.
  arXiv  Detail & Related papers  (2020-02-10T03:27:48Z)

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.