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Institutional audit of a non-decision AI framework (27-document corpus)

I’ve just published an institutional, non-normative audit of a 27-document corpus called CP27 — Système Paradoxe.

This is not a model, not a product, and not a regulatory framework. It is an audit of structure.

What’s being audited:

A large, interconnected corpus designed as a cognitive and governance framework

Explicit separation between methodology, narrative content, and institutional rules

A strict non-decision AI role (measurement, verification, traceability only)

Human decision sovereignty enforced by design

STOP / SILENCE considered valid fail-safe outcomes

Full third-party auditability without oral context or author dependency

The objective is to explore how complex AI-adjacent systems can remain institution-ready without delegating responsibility, authority, or decision-making to the system itself.

This may be relevant if you work on AI governance, safety-by-design architectures, auditability, traceability, or institutional interfaces for AI systems.

Full audit and encapsulation documents:
https://zenodo.org/records/18172473

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