Multi-Agent Memory from a Computer Architecture Perspective: Visions and Challenges Ahead
Type: academic-paper
Author: Zhongming Yu, Naicheng Yu, Hejia Zhang, Wentao Ni, Mingrui Yin, Jiaying Yang, Yujie Zhao, Jishen Zhao (University of California, San Diego; Georgia Institute of Technology) Source: https://arxiv.org/html/2603.10062v1 Date: March 9, 2026
Abstract
This position paper reframes multi-agent memory management through a computer architecture lens. It proposes distinguishing shared versus distributed memory paradigms, introduces a three-layer hierarchy (I/O, cache, memory), and identifies critical protocol gaps. The authors argue that multi-agent memory consistency represents the most urgent unresolved challenge for building scalable, dependable multi-agent systems.
Why Memory Matters
Modern LLM agents face evolving complexity: - Longer context windows requiring multi-hop reasoning - Multimodal inputs (images, videos, diagrams) - Structured executable traces - State tracking in customized environments
Context is no longer static; it functions as a dynamic system with bandwidth, caching, and coherence demands.
Memory Architectures
The paper identifies two fundamental approaches: - Shared memory: All agents access a common pool but require coherence support - Distributed memory: Each agent maintains local memory with selective synchronization
Most practical systems exist between these extremes.
Three-Layer Hierarchy
- I/O layer: Information ingestion/emission interfaces
- Cache layer: Fast, limited-capacity reasoning memory
- Memory layer: Large-capacity, persistent storage systems
Protocol Gaps
Two critical missing elements:
- Cache sharing protocol: Systems lack principled approaches for agents to share and reuse cached artifacts across systems
- Memory access protocol: Frameworks don't standardize permissions, scope, and granularity for shared memory access
Multi-Agent Consistency Challenge
The authors highlight that agent systems require consistency models analogous to hardware architecture. Key requirements include: - "Read-time conflict handling under iterative revisions" - Update visibility and ordering determining when writes become observable
This challenge is harder than classical settings because artifacts are heterogeneous and conflicts carry semantic weight.
Conclusion
Current agent memory systems resemble informal human memory. Advancing toward reliable multi-agent systems requires better hierarchies, explicit protocols, and principled consistency models maintaining coherent shared context.