Governed Collaborative Memory as Artificial Selection in LLM-Based Multi-Agent Systems

TL;DR

This paper introduces the concept of governed collaborative memory in LLM-based multi-agent systems, framing memory management as a selection process that influences which memories are shared, private, or discarded, emphasizing design choices for system behavior.

Contribution

It proposes a layered architecture for managing different types of memory with provenance and versioning, and outlines a design agenda for evaluating memory beyond recall, including provenance and epistemic quality.

Findings

Illustrated unmanaged false-memory persistence in a multi-agent ecosystem

Demonstrated ratified institutional memory and revision processes

Showcased governance-as-learning through documented traces

Abstract

Persistent memory is turning language-model-based agents from stateless participants in isolated interactions into state-bearing components of LLM-based multi-agent systems. As memory becomes durable, reloadable, and behavior-shaping across agents, sessions, or versions, a design question arises that is not captured by retrieval accuracy or access control alone: which candidate memories should become shared institutional state? This Viewpoint frames that problem as governed collaborative memory. We argue that memory governance functions as a selection regime, determining which memory variants persist, which remain private, and which are rejected, abstained from, or superseded. We distinguish ungoverned persistence, constitutional or hybrid selection, automatic metric-based selection, and human-ratified artificial selection, emphasizing that these regimes are not a ranking but a design choice over target properties. We then describe a layered architecture that separates agent-local memory, shared institutional memory, archive memory, and project-continuity memory, with provenance and version lineage making selection inspectable. Documented traces from one running LLM-based multi-agent ecosystem illustrate unmanaged false-memory persistence, ratified institutional memory, rejection and revision, identity-preserving expansion, and governance-as-learning. The contribution is a design agenda: persistent LLM-based multi-agent systems should evaluate memory not only for recall and performance, but also for provenance fidelity, selection traceability, epistemic quality, correction pathways, and role preservation.