metadata (Normative)

Title:	CM-2 Example Scenarios
Author:	Ralph B. Holland ralph.b.holland at gmail.com
Publication Date:	2026-03-20T15:19Z
Version:	1.0.0
Reason:	Due diligence artefact
Scope:	This is a published CM-2 artefact defining use of the governance substrate for AI systems operating in high-accountability environments.

The preceding metadata table is CM-defined and constitutes the authoritative provenance record for this artefact. All fields in that table MUST be treated as normative metadata. The assisting system MUST NOT infer, normalise, reinterpret, duplicate, or rewrite these fields. If any field is missing, unclear, or later superseded, the change MUST be made explicitly by the human and recorded via version update, not inferred.

CM-2 Example Scenarios

Example Scenario: Engineering Compliance Drift and Recovery

Consider an engineering workflow where an LLM is used to assist in producing a compliance-certified design report.

The governing requirements include:

adherence to a defined engineering standard
preservation of calculation provenance
strict sequencing of validation steps
prohibition of unauthorised assumption or substitution

Without CM-2

During interaction:

the model initially references the correct standard
intermediate steps are summarised and compressed
a constraint (mandatory verification step) is omitted
a derived value is recomputed using an alternative method
provenance of the original calculation is lost

The output remains fluent and plausible.

However:

the required validation step is missing
the calculation path is no longer auditable
the result cannot be certified
the error is not detectable through surface inspection

This is drift.
Not a mistake.
Not hallucination.
Loss of invariant-governed state.

With CM-2

At the point of inference:

admissible state is validated against CM invariants
required Epistemic Objects (EO) are checked for presence
sequencing constraints are enforced
provenance bindings are verified

When the validation step is absent:

a constraint violation is detected
the system identifies an Attention Deficit condition (missing required EO)

The ROC ladder is invoked:

the missing validation object is restored
the correct calculation lineage is reintroduced
the required sequencing is reinstated

The model is not permitted to proceed until:

all required invariants are satisfied
all governing objects are present in inference

Result

the output is complete
the validation step is present
provenance is preserved
the result is auditable
the artefact is admissible for compliance use

Interpretation

The difference is not improved intelligence.

The difference is that:

invalid states are not permitted to enter inference
missing governing objects are detected and restored
invariant violation is made non-representable

This is the transition from:

reconstruction → governed execution