Kernel-Managed Shared Memory for System-Wide Personalization
Quick summary
arXiv:2609.10144v1 Announce Type: new Abstract: AI systems become more useful when they can adapt to the people using them, but in multi-agent systems, useful context learned by one agent often remains unavailable to others. We present kernel-managed shared memory, a system-level abstraction in which specialized agents write structured, tagged memories while the agent-system kernel, not individual agents, governs retrieval, privacy enforcement, and prompt injection. We implement and evaluate this design on AIOS and compare it against three alternatives across three assistant models (GPT-4o, Ll
Key takeaways
- arXiv:2609.10144v1 Announce Type: new Abstract: AI systems become more useful when they can adapt to the people using them, but in multi-agent systems, useful context learned by one agent often remains unavailable to others.
- We present kernel-managed shared memory, a system-level abstraction in which specialized agents write structured, tagged memories while the agent-system kernel, not individual agents, governs retrieval, privacy enforcement, and prompt injection.
- We implement and evaluate this design on AIOS and compare it against three alternatives across three assistant models (GPT-4o, Ll
Why it matters
“Kernel-Managed Shared Memory for System-Wide Personalization” may affect what data AI products can use and where accountability sits. Product teams should watch compliance duties, rights holders should watch enforcement, and users should watch transparency and appeal mechanisms.
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