Shared-Memory Multi-Agent Systems
Overview
This field studies how multiple AI agents use common memory to coordinate, preserve useful experience, and divide work across long or complex tasks. Approaches include shared logs and workspaces, structured or role-routed retrieval, local-plus-global memory, and consistency mechanisms such as transactional updates and conflict-free replicated data structures. Systems are tested on question answering, web and household tasks, coding, visual reasoning, and research workflows. A central tension is that sharing can reduce duplicated work but also spread errors, stale claims, or private information; provenance, access control, verification, and efficient retrieval remain open challenges.
Latest papers
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