Kernel-Managed Shared Memory for System-Wide Personalization

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, Llama-3.1:8B, Qwen-2.5:7B) and 1,800 total trials. Against an unmanaged external memory backend (Mem0) using identical underlying storage, kernel-managed retrieval and injection improve personalization scores by 2.4-4.0 points on a 5-point scale (e.g., 1.05 to 4.69 profile usage on GPT-4o), with every comparison significant at p < 10^-18. Against standard retrieval-augmented injection, gains are similarly large and consistent across all three models. Against full, unfiltered context concatenation, a soft ceiling on available context rather than on response quality, kernel-managed injection statistically matches performance on two of three models and shows a small, model-specific deficit on the third, while using substantially shorter prompts: end-to-end latency is 15-61% lower across all three models, with corresponding reductions in per-call token usage and inference cost. These results indicate that centralizing memory management in the agent-system kernel, rather than leaving retrieval and privacy enforcement to individual agents, delivers most of the personalization benefit of unconstrained context at a fraction of its cost.

Authors 2

Ryan Lum, Yongfeng Zhang

Specification

Official page

arxiv.org/abs/2609.10144

Source:arXiv (Atom API + RSS)·T1·observed 5 h ago·high

Arxiv announce type

new

Source:arXiv (Atom API + RSS)·T1·observed 5 h ago·high

arXiv id

2609.10144

Source:arXiv (Atom API + RSS)·T1·observed 5 h ago·high

Categories

cs.AI, cs.LG

Source:arXiv (Atom API + RSS)·T1·observed 5 h ago·high

PDF

arxiv.org/pdf/2609.10144

Source:arXiv (Atom API + RSS)·T1·observed 5 h ago·high

Primary category

cs.AI

Source:arXiv (Atom API + RSS)·T1·observed 5 h ago·high

Published

11 Sept 2026

Source:arXiv (Atom API + RSS)·T1·observed 5 h ago·high

Each value shows its source, tier and observation time. Conflicting claims are kept side by side and flagged — never averaged. How AI Atlas records facts →

Provenance