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Kernel-Managed Shared Memory for System-Wide Personalization

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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.

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Kernel-Managed Shared Memory for System-Wide Personalization

arXiv cs.AI Ryan Lum, Yongfeng Zhang 2026-09-09 arXiv:2609.10144
Public signals Semantic Scholar citations 0 · Semantic Scholar influential citations 0
Providers: Hugging Face · N/A OpenAlex · N/A Publisher · N/A Semantic Scholar · Citations 0 · Influential citations 0 X · N/A Fetched 2026-09-25 14:21:34.523168 UTC

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.
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