MMAG: Mixed Memory-Augmented Generation for Large Language Models Applications

• URL: http://arxiv.org/abs/2512.01710v2
• Date: Thu, 04 Dec 2025 13:06:12 GMT
• Title: MMAG: Mixed Memory-Augmented Generation for Large Language Models Applications
• Authors: Stefano Zeppieri,
• Abstract summary: Large Language Models (LLMs) excel at generating coherent text within a single prompt but fall short in sustaining relevance, personalization, and continuity across extended interactions.<n>This paper introduces the Mixed Memory-Augmented Generation (MMAG) pattern, a framework that organizes memory for LLM-based agents into five interacting layers.<n>We demonstrate the approach through its implementation in the Heero conversational agent, where long-term bios and conversational history already improve engagement and retention.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models (LLMs) excel at generating coherent text within a single prompt but fall short in sustaining relevance, personalization, and continuity across extended interactions. Human communication, however, relies on multiple forms of memory, from recalling past conversations to adapting to personal traits and situational context. This paper introduces the Mixed Memory-Augmented Generation (MMAG) pattern, a framework that organizes memory for LLM-based agents into five interacting layers: conversational, long-term user, episodic and event-linked, sensory and context-aware, and short-term working memory. Drawing inspiration from cognitive psychology, we map these layers to technical components and outline strategies for coordination, prioritization, and conflict resolution. We demonstrate the approach through its implementation in the Heero conversational agent, where encrypted long-term bios and conversational history already improve engagement and retention. We further discuss implementation concerns around storage, retrieval, privacy, and latency, and highlight open challenges. MMAG provides a foundation for building memory-rich language agents that are more coherent, proactive, and aligned with human needs.

Related papers

• The AI Hippocampus: How Far are We From Human Memory? [77.04745635827278]
  Implicit memory refers to the knowledge embedded within the internal parameters of pre-trained transformers.<n>Explicit memory involves external storage and retrieval components designed to augment model outputs with dynamic, queryable knowledge representations.<n>Agentic memory introduces persistent, temporally extended memory structures within autonomous agents.
  arXiv  Detail & Related papers  (2026-01-14T03:24:08Z)
• 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)
• 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)
• Memory in the Age of AI Agents [217.9368190980982]
  This work aims to provide an up-to-date landscape of current agent memory research.<n>We identify three dominant realizations of agent memory, namely token-level, parametric, and latent memory.<n>To support practical development, we compile a comprehensive summary of memory benchmarks and open-source frameworks.
  arXiv  Detail & Related papers  (2025-12-15T17:22:34Z)
• Memoria: A Scalable Agentic Memory Framework for Personalized Conversational AI [0.6840655769002751]
  Agentic memory is emerging as a key enabler for large language models (LLM)<n>We present Memoria, a modular memory framework that augments LLM-based conversational systems with persistent, interpretable, and context-rich memory.<n>We demonstrate how Memoria enables scalable, personalized conversational artificial intelligence (AI) by bridging the gap between stateless LLM interfaces and agentic memory systems.
  arXiv  Detail & Related papers  (2025-12-14T13:38:06Z)
• Evaluating Long-Term Memory for Long-Context Question Answering [100.1267054069757]
  We present a systematic evaluation of memory-augmented methods using LoCoMo, a benchmark of synthetic long-context dialogues annotated for question-answering tasks.<n>Our findings show that memory-augmented approaches reduce token usage by over 90% while maintaining competitive accuracy.
  arXiv  Detail & Related papers  (2025-10-27T18:03:50Z)
• 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)
• Hello Again! LLM-powered Personalized Agent for Long-term Dialogue [63.65128176360345]
  We introduce a model-agnostic framework, the Long-term Dialogue Agent (LD-Agent)<n>It incorporates three independently tunable modules dedicated to event perception, persona extraction, and response generation.<n>The effectiveness, generality, and cross-domain capabilities of LD-Agent are empirically demonstrated.
  arXiv  Detail & Related papers  (2024-06-09T21:58:32Z)
• Empowering Working Memory for Large Language Model Agents [9.83467478231344]
  This paper explores the potential of applying cognitive psychology's working memory frameworks to large language models (LLMs) An innovative model is proposed incorporating a centralized Working Memory Hub and Episodic Buffer access to retain memories across episodes. This architecture aims to provide greater continuity for nuanced contextual reasoning during intricate tasks and collaborative scenarios.
  arXiv  Detail & Related papers  (2023-12-22T05:59:00Z)
• Recursively Summarizing Enables Long-Term Dialogue Memory in Large Language Models [30.48902594738911]
  Given a long conversation, large language models (LLMs) fail to recall past information and tend to generate inconsistent responses.<n>We propose to generate summaries/ memory using large language models (LLMs) to enhance long-term memory ability.
  arXiv  Detail & Related papers  (2023-08-29T04:59:53Z)
• MemoryBank: Enhancing Large Language Models with Long-Term Memory [7.654404043517219]
  We propose MemoryBank, a novel memory mechanism tailored for Large Language Models. MemoryBank enables the models to summon relevant memories, continually evolve through continuous memory updates, comprehend, and adapt to a user personality by synthesizing information from past interactions.
  arXiv  Detail & Related papers  (2023-05-17T14:40:29Z)
• Vision-Dialog Navigation by Exploring Cross-modal Memory [107.13970721435571]
  Vision-dialog navigation posed as a new holy-grail task in vision-language disciplinary targets. We propose the Cross-modal Memory Network (CMN) for remembering and understanding the rich information relevant to historical navigation actions. Our CMN outperforms the previous state-of-the-art model by a significant margin on both seen and unseen environments.
  arXiv  Detail & Related papers  (2020-03-15T03:08:06Z)

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.