Multi-Agent Memory from a Computer Architecture Perspective: Visions and Challenges Ahead

• URL: http://arxiv.org/abs/2603.10062v1
• Date: Mon, 09 Mar 2026 21:16:12 GMT
• Title: Multi-Agent Memory from a Computer Architecture Perspective: Visions and Challenges Ahead
• Abstract summary: We frame multi-agent memory as a computer architecture problem.<n>Our architectural framing provides a foundation for building reliable, scalable multi-agent systems.
• Score: 7.937610025851523
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As LLM agents evolve into collaborative multi-agent systems, their memory requirements grow rapidly in complexity. This position paper frames multi-agent memory as a computer architecture problem. We distinguish shared and distributed memory paradigms, propose a three-layer memory hierarchy (I/O, cache, and memory), and identify two critical protocol gaps: cache sharing across agents and structured memory access control. We argue that the most pressing open challenge is multi-agent memory consistency. Our architectural framing provides a foundation for building reliable, scalable multi-agent systems.

Related papers

• Mandol: An Agglomerative Agent Memory System for Long-Term Conversations [19.51654277630379]
  Mandol is an agglomerative memory system that consolidates fragmented memory representations and storage into a unified memory-native architecture.<n>Mandol achieves the best overall accuracy among representative agent memory systems.
  arXiv  Detail & Related papers  (2026-06-29T04:43:29Z)
• MemGym: a Long-Horizon Memory Environment for LLM Agents [69.79226770543049]
  We present MemGym, a benchmark for agentic memory that unifies existing agent gyms and in-house memory-grounded pipelines.<n>MemGym reports memory-isolated scores that decouple memory performance from reasoning, retrieval, and tool-use ability.<n>Our synthetic pipelines for MEMGYM-CODEQA and MEMGYM-DR are length-controllable, ablation-verified at every stage, and tightly aligned with downstream scenarios.
  arXiv  Detail & Related papers  (2026-05-20T07:25:33Z)
• MemMA: Coordinating the Memory Cycle through Multi-Agent Reasoning and In-Situ Self-Evolution [52.29303869050117]
  Memory-augmented LLM agents maintain external memory banks to support long-horizon interaction.<n>MemMA is a plug-and-play multi-agent framework that coordinates the memory cycle along both the forward and backward paths.
  arXiv  Detail & Related papers  (2026-03-19T10:15:59Z)
• 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)
• Graph-based Agent Memory: Taxonomy, Techniques, and Applications [63.70340159016138]
  Memory emerges as the core module in the Large Language Model (LLM)-based agents for long-horizon complex tasks.<n>Among diverse paradigms, graph stands out as a powerful structure for agent memory due to the intrinsic capabilities to model relational dependencies.<n>This survey presents a comprehensive review of agent memory from the graph-based perspective.
  arXiv  Detail & Related papers  (2026-02-05T13:49:05Z)
• LatentMem: Customizing Latent Memory for Multi-Agent Systems [44.59989123744384]
  We propose LatentMem, a learnable multi-agent memory framework designed to customize agent-specific memories in a token-efficient manner.<n>Specifically, LatentMem comprises an experience bank that stores raw interaction trajectories in a lightweight form, and a memory composer that synthesizes compact latent memories conditioned on retrieved experience and agent-specific contexts.
  arXiv  Detail & Related papers  (2026-02-03T03:03:16Z)
• 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)
• CAM: A Constructivist View of Agentic Memory for LLM-Based Reading Comprehension [55.29309306566238]
  Current Large Language Models (LLMs) are confronted with overwhelming information volume when comprehending long-form documents.<n>This challenge raises the imperative of a cohesive memory module, which can elevate vanilla LLMs into autonomous reading agents.<n>We draw inspiration from Jean Piaget's Constructivist Theory, illuminating three traits of the agentic memory -- structured schemata, flexible assimilation, and dynamic accommodation.
  arXiv  Detail & Related papers  (2025-10-07T02:16:30Z)
• MEM1: Learning to Synergize Memory and Reasoning for Efficient Long-Horizon Agents [84.62985963113245]
  We introduce MEM1, an end-to-end reinforcement learning framework that enables agents to operate with constant memory across long multi-turn tasks.<n>At each turn, MEM1 updates a compact shared internal state that jointly supports memory consolidation and reasoning.<n>We show that MEM1-7B improves performance by 3.5x while reducing memory usage by 3.7x compared to Qwen2.5-14B-Instruct on a 16-objective multi-hop QA task.
  arXiv  Detail & Related papers  (2025-06-18T19:44:46Z)
• 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)
• 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)

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.