EA-L4 / EATP is now published in a canonical form inside the public stack.
A new public layer is now part of the corpus.
Not a loose note.
Not a side PDF.
A structured package.
Its role is simple to state, even if the problem is not: most AI systems are trained to preserve capability, but not origin.
They become better at producing text, while becoming weaker at preserving where a decision came from, what real constraint shaped it, what consequence followed, and whether that experience can still be audited later.
That is the gap this layer addresses.
EA-L4 introduces Experience Artifacts (EA) as a distinct class of training input: not anonymous learning residue, not just polished summaries, but bounded, witness-backed, consequence-carrying records of real interaction under L4 constraints.
In practical terms, this package now has a public reading path:
- concept note
- normative draft
- executive / AI Act bridge
- hospital use case
- service catalog
- sell sheet
So this is no longer only an architectural claim.
It is now a public package for:
- training provenance
- consequence-preserving learning
- a more operational route from AI governance to AI auditability
The point is not to make models sound more responsible.
The point is to preserve a path by which learning can remain attributable to reality.
AGI here still means Advanced Global Intelligence.
And SER remains the normative SER core.
EA-L4 / EATP sits beside that layer: not as SER v2, but as a training-origin and consequence-preserving extension.
The future is not an event. It is a process.