MemGuide: Intent-Driven Memory Selection for Goal-Oriented Multi-Session LLM Agents

• URL: http://arxiv.org/abs/2505.20231v2
• Date: Wed, 13 Aug 2025 03:43:47 GMT
• Title: MemGuide: Intent-Driven Memory Selection for Goal-Oriented Multi-Session LLM Agents
• Abstract summary: We introduce MemGuide, a two-stage framework for intent-driven memory selection.<n>MemGuide matches current dialogue context with stored intent descriptions in the memory bank.<n>Missing-Aligned Guided Filtering employs a chain-of-thought slot reasoner to enumerate unfilled slots, then uses a fine-tuned LLaMA-8B filter to re-rank the retrieved units.
• Score: 46.21840714172862
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Modern task-oriented dialogue (TOD) systems increasingly rely on large language model (LLM) agents, leveraging Retrieval-Augmented Generation (RAG) and long-context capabilities for long-term memory utilization. However, these methods are primarily based on semantic similarity, overlooking task intent and reducing task coherence in multi-session dialogues. To address this challenge, we introduce MemGuide, a two-stage framework for intent-driven memory selection. (1) Intent-Aligned Retrieval matches the current dialogue context with stored intent descriptions in the memory bank, retrieving QA-formatted memory units that share the same goal. (2) Missing-Slot Guided Filtering employs a chain-of-thought slot reasoner to enumerate unfilled slots, then uses a fine-tuned LLaMA-8B filter to re-rank the retrieved units by marginal slot-completion gain. The resulting memory units inform a proactive strategy that minimizes conversational turns by directly addressing information gaps. Based on this framework, we introduce the MS-TOD, the first multi-session TOD benchmark comprising 132 diverse personas, 956 task goals, and annotated intent-aligned memory targets, supporting efficient multi-session task completion. Evaluations on MS-TOD show that MemGuide raises the task success rate by 11% (88% -> 99%) and reduces dialogue length by 2.84 turns in multi-session settings, while maintaining parity with single-session benchmarks.

Related papers

• MEMO: Multimodal Evidence Memory Organization for Long-Horizon LLM Agents [24.912135848528845]
  Long-running LLM agents rely on external memory to store and reuse information beyond a single context window.<n>Key challenge in agent memory is not only to retrieve relevant records, but also to select necessary evidence under a given budget.<n>Existing memory readout methods mainly use textual or visual forms.
  arXiv  Detail & Related papers  (2026-09-07T13:24:40Z)
• Where to Look and What to Use: Retrieve-Localize-Generate for Long-Term Conversational Memory Question Answering [42.31517185994942]
  RAG enables large language models to answer questions by accessing external knowledge.<n>Existing methods suffer from two key challenges: fragmented evidence scattered across temporally distant sessions, and noisy content within retrieved sessions that triggers the lost-in-the-middle effect.<n>We propose MemLoc, a unified Retrieve-Localize-Generate framework for long-term conversational memory QA.
  arXiv  Detail & Related papers  (2026-09-07T06:32:12Z)
• 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)
• MemLens: Benchmarking Multimodal Long-Term Memory in Large Vision-Language Models [50.25006399944962]
  Memory is essential for large vision-language models to handle long, multimodal interactions.<n>MEMLENS is a benchmark for memory in multimodal multi-session conversations.<n>We evaluate 27 LVLMs and 7 memory-augmented agents.
  arXiv  Detail & Related papers  (2026-05-14T14:41:17Z)
• MemoryArena: Benchmarking Agent Memory in Interdependent Multi-Session Agentic Tasks [55.145729491377374]
  Existing evaluations of agents with memory typically assess memorization and action in isolation.<n>We introduce MemoryArena, a unified evaluation gym for benchmarking agent memory in multi-session Memory-Agent-Environment loops.<n> MemoryArena supports evaluation across web navigation, preference-constrained planning, progressive information search, and sequential formal reasoning.
  arXiv  Detail & Related papers  (2026-02-18T09:49:14Z)
• Mem-Gallery: Benchmarking Multimodal Long-Term Conversational Memory for MLLM Agents [76.76004970226485]
  Long-term memory is a critical capability for multimodal large language model (MLLM) agents.<n>Mem-Gallery is a new benchmark for evaluating multimodal long-term conversational memory in MLLM agents.
  arXiv  Detail & Related papers  (2026-01-07T02:03:13Z)
• 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)
• SGMem: Sentence Graph Memory for Long-Term Conversational Agents [14.89396085814917]
  We introduce SGMem (Sentence Graph Memory), which represents dialogue as sentence-level graphs within chunked units.<n>We show that SGMem consistently improves accuracy and outperforms strong baselines in long-term conversational question answering.
  arXiv  Detail & Related papers  (2025-09-25T14:21:44Z)
• Multiple Memory Systems for Enhancing the Long-term Memory of Agent [9.43633399280987]
  Existing methods, such as MemoryBank and A-MEM, have poor quality of stored memory content.<n>We have designed a multiple memory system inspired by cognitive psychology theory.
  arXiv  Detail & Related papers  (2025-08-21T06:29:42Z)
• FindingDory: A Benchmark to Evaluate Memory in Embodied Agents [49.89792845476579]
  We introduce a new benchmark for long-range embodied tasks in the Habitat simulator.<n>This benchmark evaluates memory-based capabilities across 60 tasks requiring sustained engagement and contextual awareness.
  arXiv  Detail & Related papers  (2025-06-18T17:06:28Z)
• 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)
• Task-Core Memory Management and Consolidation for Long-term Continual Learning [62.880988004687815]
  We focus on a long-term continual learning (CL) task, where a model learns sequentially from a stream of vast tasks over time.<n>Unlike traditional CL settings, long-term CL involves handling a significantly larger number of tasks, which exacerbates the issue of catastrophic forgetting.<n>We propose a novel framework inspired by human memory mechanisms for long-term continual learning (Long-CL)
  arXiv  Detail & Related papers  (2025-05-15T04:22:35Z)
• In Prospect and Retrospect: Reflective Memory Management for Long-term Personalized Dialogue Agents [70.12342024019044]
  Large Language Models (LLMs) have made significant progress in open-ended dialogue, yet their inability to retain and retrieve relevant information limits their effectiveness.<n>We propose Reflective Memory Management (RMM), a novel mechanism for long-term dialogue agents, integrating forward- and backward-looking reflections.<n>RMM shows more than 10% accuracy improvement over the baseline without memory management on the LongMemEval dataset.
  arXiv  Detail & Related papers  (2025-03-11T04:15:52Z)
• Interpersonal Memory Matters: A New Task for Proactive Dialogue Utilizing Conversational History [13.389395397698035]
  We introduce a novel task named Memory-aware Proactive Dialogue (MapDia)<n>By the task, we then propose an automatic data construction method and create the first Chinese Memory-aware Proactive dataset (ChMapData)<n> Furthermore, we introduce a joint framework based on Retrieval Augmented Generation (RAG), featuring three modules: Topic Summarization, Topic Retrieval, and Proactive Topic-shifting Detection and Generation.
  arXiv  Detail & Related papers  (2025-03-07T05:19:17Z)
• On Memory Construction and Retrieval for Personalized Conversational Agents [69.46887405020186]
  We propose SeCom, a method that constructs the memory bank at segment level by introducing a conversation segmentation model.<n> Experimental results show that SeCom exhibits a significant performance advantage over baselines on long-term conversation benchmarks LOCOMO and Long-MT-Bench+.
  arXiv  Detail & Related papers  (2025-02-08T14:28:36Z)
• HiAgent: Hierarchical Working Memory Management for Solving Long-Horizon Agent Tasks with Large Language Model [39.169389255970806]
  HiAgent is a framework that leverages subgoals as memory chunks to manage the working memory of Large Language Model (LLM)-based agents hierarchically. Results show that HiAgent achieves a twofold increase in success rate and reduces the average number of steps required by 3.8.
  arXiv  Detail & Related papers  (2024-08-18T17:59:49Z)
• TF-SASM: Training-free Spatial-aware Sparse Memory for Multi-object Tracking [6.91631684487121]
  Multi-object tracking (MOT) in computer vision remains a significant challenge, requiring precise localization and continuous tracking of multiple objects in video sequences. We propose a novel memory-based approach that selectively stores critical features based on object motion and overlapping awareness. Our approach significantly improves over MOTRv2 in the DanceTrack test set, demonstrating a gain of 2.0% AssA score and 2.1% in IDF1 score.
  arXiv  Detail & Related papers  (2024-07-05T07:55:19Z)

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.