Collaborative Memory: Multi-User Memory Sharing in LLM Agents with Dynamic Access Control

Fuente: arXiv
Salvato in:
Dettagli Bibliografici
Autori principali: Rezazadeh, Alireza, Li, Zichao, Lou, Ange, Zhao, Yuying, Wei, Wei, Bao, Yujia
Natura: Preprint
Pubblicazione: 2025
Soggetti:
Accesso online:
Tags: Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
_version_ 1866909621913911296
author Rezazadeh, Alireza
Li, Zichao
Lou, Ange
Zhao, Yuying
Wei, Wei
Bao, Yujia
author_facet Rezazadeh, Alireza
Li, Zichao
Lou, Ange
Zhao, Yuying
Wei, Wei
Bao, Yujia
contents Complex tasks are increasingly delegated to ensembles of specialized LLM-based agents that reason, communicate, and coordinate actions-both among themselves and through interactions with external tools, APIs, and databases. While persistent memory has been shown to enhance single-agent performance, most approaches assume a monolithic, single-user context-overlooking the benefits and challenges of knowledge transfer across users under dynamic, asymmetric permissions. We introduce Collaborative Memory, a framework for multi-user, multi-agent environments with asymmetric, time-evolving access controls encoded as bipartite graphs linking users, agents, and resources. Our system maintains two memory tiers: (1) private memory-private fragments visible only to their originating user; and (2) shared memory-selectively shared fragments. Each fragment carries immutable provenance attributes (contributing agents, accessed resources, and timestamps) to support retrospective permission checks. Granular read policies enforce current user-agent-resource constraints and project existing memory fragments into filtered transformed views. Write policies determine fragment retention and sharing, applying context-aware transformations to update the memory. Both policies may be designed conditioned on system, agent, and user-level information. Our framework enables safe, efficient, and interpretable cross-user knowledge sharing, with provable adherence to asymmetric, time-varying policies and full auditability of memory operations.
format Preprint
id arxiv_https___arxiv_org_abs_2505_18279
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Collaborative Memory: Multi-User Memory Sharing in LLM Agents with Dynamic Access Control
Rezazadeh, Alireza
Li, Zichao
Lou, Ange
Zhao, Yuying
Wei, Wei
Bao, Yujia
Multiagent Systems
Artificial Intelligence
Computation and Language
Machine Learning
Complex tasks are increasingly delegated to ensembles of specialized LLM-based agents that reason, communicate, and coordinate actions-both among themselves and through interactions with external tools, APIs, and databases. While persistent memory has been shown to enhance single-agent performance, most approaches assume a monolithic, single-user context-overlooking the benefits and challenges of knowledge transfer across users under dynamic, asymmetric permissions. We introduce Collaborative Memory, a framework for multi-user, multi-agent environments with asymmetric, time-evolving access controls encoded as bipartite graphs linking users, agents, and resources. Our system maintains two memory tiers: (1) private memory-private fragments visible only to their originating user; and (2) shared memory-selectively shared fragments. Each fragment carries immutable provenance attributes (contributing agents, accessed resources, and timestamps) to support retrospective permission checks. Granular read policies enforce current user-agent-resource constraints and project existing memory fragments into filtered transformed views. Write policies determine fragment retention and sharing, applying context-aware transformations to update the memory. Both policies may be designed conditioned on system, agent, and user-level information. Our framework enables safe, efficient, and interpretable cross-user knowledge sharing, with provable adherence to asymmetric, time-varying policies and full auditability of memory operations.
title Collaborative Memory: Multi-User Memory Sharing in LLM Agents with Dynamic Access Control
topic Multiagent Systems
Artificial Intelligence
Computation and Language
Machine Learning
url https://arxiv.org/abs/2505.18279