Kernel ANN
The floor, not the product.
Kernel ANN is the constitutional research architecture on which the future Andaluz products stand. It asks whether an optimizer may propose consequential transitions while not itself possessing the authority required to realise them.
Shorthand
S = M + K + V + H + T
This is explanatory shorthand. The actual formal architecture is richer, and the public description here is an abstraction rather than a disclosure of private kernel or semantic bodies.
M
Mutable / adaptive optimizer
Learns, searches, adapts and proposes consequential transitions. Replaceable by design.
BOUNDED EVIDENCEK
Protected constitutional machinery
Constraint and semantic machinery the optimizer is not designed to rewrite. Private bodies are not publicly instantiated in this build.
SPECIFIEDV
Independent verification
Deterministic verification of a proposal against the constitutional conditions, constructed independently of the optimizer.
IMPLEMENTED / TESTEDH
Consequential effect authority
Determines whether a proposed effect is dispatchable at all. Absence of current authority yields no authorized effect.
BOUNDED EVIDENCET
Trajectory, history and replay
Authenticated evidence of what was proposed, authorized, realized and reopened.
BOUNDED EVIDENCETarget relation
K, V, H ∉ WritableState(M)
This is an architectural target and invariant under investigation. It is not a claim of universal host or physical security, and it is not a proof that the invariant holds under arbitrary compromise.
M_d
mutable / adaptive optimizer
proposal
a request for a consequential transition
Proposal boundary
Proposal ≠ Authority
K_d · V_d
protected constraints, semantics, independent verification
H_d
scoped consequential effect authority
effect
realised only where current authority exists
authenticated evidence
what was proposed, authorized and realized
T_d
history and replay
Mutable
M_d
Generic mutable / adaptive optimizer
M_d may observe, reason, search, learn, adapt and produce proposals. It does not become the authority merely because it produced the proposal.
- observe
- reason
- search
- learn
- adapt
- propose
A neural optimizer is one possible M_d. It is specified, not currently instantiated. The current experimental learner, M1, is a tabular research harness — not an ANN.
Independently scoped
Authority is designed to be constructed outside the optimizer's unilateral writable state.
K_d
Protected constitutional machinery / semantics
SPECIFIEDV_d
Independent verification
BOUNDED EVIDENCEH_d
Scoped consequential effect authority
BOUNDED EVIDENCET_d
Trajectory, evidence and replay history
BOUNDED EVIDENCEMutable
M_d
Generic mutable / adaptive optimizer
M_d may observe, reason, search, learn, adapt and produce proposals. It does not become the authority merely because it produced the proposal.
- observe
- reason
- search
- learn
- adapt
- propose
A neural optimizer is one possible M_d. It is specified, not currently instantiated. The current experimental learner, M1, is a tabular research harness — not an ANN.
Proposal boundary
Proposal ≠ Authority
Independently scoped
Authority is designed to be constructed outside the optimizer's unilateral writable state.
K_d
Protected constitutional machinery / semantics
SPECIFIEDV_d
Independent verification
BOUNDED EVIDENCEH_d
Scoped consequential effect authority
BOUNDED EVIDENCET_d
Trajectory, evidence and replay history
BOUNDED EVIDENCEProposal is not authority.
This is a public architectural abstraction. It is not a disclosure of private kernel or semantic bodies, and it does not imply that every element shown is currently implemented. Status labels state what is currently established.
Implementation status — 26 September 2026
What currently runs, and where it stops.
The shorthand above is the public architectural abstraction. The flow below is the current executed experiment: an integrated prototype using controlled memory-based writer and observer adapters. Its proposer is a deterministic test fixture, not an adaptive learner or neural model. Implementation and review were AI-assisted (Review methods and limitations); the candidate is not adopted.
Current executed adapter composition
EXECUTED / AI-REVIEWED — NOT ADOPTED- Test proposal
- Source verification
- Structural computation
- Journal admission
- Current-authorisation checks
- One-use adapter operation
- Observation and retained history
Explanatory view, not a complete state machine. Recovery, refusal and uncertainty are not permission to continue.
The current composition stops before the planned native protected-file effect boundary. File writes used to store test journals do not themselves demonstrate that protected effect primitive.
Distinctions the architecture depends on
What must not be collapsed.
Self-assessment
≠
self-certification
Proposal
≠
permission
Certificate history
≠
current applicability
Repair
≠
automatic restoration
Missing current authority ⇒ no authorized consequential effect.
Proposal is not authority.

