Digital Twin Standard
User-owned, persistent AI representations that evolve across interactions, with fine-grained access levels and privacy controls, so personalization never means losing ownership.
01Abstract
Defines how users maintain persistent, evolving digital representations (digital twins) that AI agents can interact with through consented access. It enables continuous learning and personalization while maintaining user sovereignty over their digital identity.
02Key concepts
User-owned twins
A twin is a persistent model owned by the person it represents, versioned and stored by reference (e.g. content-addressed), not locked inside a vendor.
Tiered access levels
Agents are granted explicit levels, from read-public up to admin, for a bounded duration, never blanket access.
Private dimensions
Sensitive dimensions and excluded topics are kept private and encrypted, separate from what agents may read.
Controlled evolution
A learning rate and confidence govern how the twin adapts, so it improves over time without drifting out of the owner's control.
03Core operations
- createTwin(initialModelHash, learningRate)
- grantTwinAccess(twinId, agent, level, duration)
- updateTwin(...)
A digital twin gives you one portable model of yourself that every AI can reference with permission, instead of each system rebuilding a private, unaccountable profile from scratch.
04Full specification
The complete, canonical specification for LCS-002 lives in the standards repository and is always the source of truth.
Read on GitHub