Story · Apple ML Research
Shared Selective Persistent Memory for Agentic LLM Systems (Apple ML Research)
Every session starts from zero, discarding the configuration choices, domain constraints, data schemas and tool-use patterns that made the last one productive. Apple's answer keeps four categories of reusable context instead, arguing that persisting whole histories is token-inefficient and that irrelevant context degrades generation quality.
In plain words
- Apple researchers proposed a way for artificial intelligence assistants to remember useful details between programming sessions.
- The system saves selected information about the work, including what the finished program should do.
- The researchers say irrelevant details from old conversations make the assistants' code worse.
- Users could keep useful setup information available in new sessions without saving every past conversation.
Appeared in
- Every audited chat tokenizer lets prompt text forge control tokens
Sep 17, 2026 · in the sections
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