Apple researchers introduce shared selective persistent memory, a memory architecture for agentic LLM systems that identifies and retains four categories of reusable context—task specifications, data schemas, tool configurations, and output constraints—while discarding session-specific reasoning traces.

The architecture is implemented in a deployed collaborative workspace platform where LLM agents produce, edit, and maintain git-versioned artifacts from heterogeneous data sources.

Across three enterprise deployment scenarios, shared selective persistent memory achieves 96% task completion (vs. 79% without memory and 71% with full history), and a complementary zero-token data refresh mechanism eliminates LLM re-invocation for recurring data updates (14× task time reduction).