The architecture establishes how data is structured, classified, governed, and verified—ensuring system behavior remains transparent, traceable, and principle-aligned.
All data within a DI system must adhere to these core principles.
Different categories of knowledge must remain structurally distinct.
All data must have identifiable origin and lineage.
Critical artifacts must be cryptographically provable.
System-relevant changes must follow formal processes.
The system must not misrepresent source type, authority, or certainty.
All data is organized into four primary classes, each with distinct characteristics and governance requirements.
The constitutional layer of the system. Defines primary sources and principles that anchor system behavior.
Purpose: Defines system boundaries, anchors interpretation, ensures long-term stability.
Contextual, analytical, and explanatory layers applied to the foundational corpus. Reflects evolving human understanding.
Purpose: Provides context and interpretation, supports reasoning, enables plural perspectives.
Evolving, non-foundational information sources that connect the system to real-world knowledge.
Purpose: Enables adaptability, expands system capability, supports real-world reasoning.
Structured human judgment used to improve system behavior through reinforcement and alignment.
Purpose: Reinforcement learning, ranking optimization, alignment refinement.
Foundational Corpus
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Interpretation Corpus
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External Knowledge
↓
Model Generation
↓
Policy Engine
↓
Decision Output
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Human Evaluation Feedback
The DI data architecture enforces these guarantees to ensure integrity and transparency.
Every data element must include source identifier, timestamp, version reference, and attribution metadata.
No data may influence the system without traceable origin.
Critical artifacts are verifiable through cryptographic hashing, optional on-chain anchoring, and public verification mechanisms.
Verification replaces trust with proof.
The system preserves strict separation between data classes. It never conflates foundational corpus with interpretation.
Clarity is mandatory.
All updates to system-relevant data must follow formal governance processes for additions, modifications, and deprecations.
Evolution is controlled.
By adhering to this data architecture, Derivative Intelligence systems achieve: