Kernel-Managed Shared Memory for System-Wide Personalization

TL;DR - This paper introduces kernel-managed shared memory for multi-agent personalization, centralizing retrieval, privacy enforcement, and prompt injection. Across 1,800 trials, it substantially improved personalization over unmanaged memory and standard RAG while reducing latency and token usage versus full-context concatenation.

• Specialized agents write structured, tagged memories, while the agent-system kernel controls how those memories are retrieved and injected.
• On GPT-4o, Llama-3.1:8B, and Qwen-2.5:7B, personalization improved by 2.4–4.0 points on a five-point scale over Mem0, with all comparisons significant at p < 10^-18.
• Performance matched unfiltered context concatenation on two models and showed a small model-specific deficit on the third.
• Shorter prompts reduced end-to-end latency by 15–61%, along with per-call token usage and inference cost.