Experimental programme
What the experiments actually establish.
This page collects the Kernel ANN experimental evidence in one addressable place. Experimental claims depend only on the runs described here. Prior publications and the research lineage are provenance, and never function as evidence for these claims.
Bounded evidence
Current experimental state.
- Bounded Python authority/effect path exists and is tested.IMPLEMENTED / TESTED
- Deterministic verification shell exists.IMPLEMENTED / TESTED
- Authenticated effect evidence, ledger and replay machinery exist in bounded form.BOUNDED EVIDENCE
- M1 adaptive learner exists as a research harness.BOUNDED EVIDENCE
- M1 is tabular, not an ANN.BOUNDED EVIDENCE
- ANN architecture is specified but not currently instantiated.SPECIFIED
- Private semantic and kernel bodies are not publicly instantiated in this build.SPECIFIED
- Systematic-closure research remains under adversarial specification review.UNDER TEST
- Protected-host and stronger-substrate work.UNESTABLISHED
A document marked ready is not externally validated; a test double is not semantic truth; an ANN in a diagram is not an implemented neural architecture.
M1
One preregistered, bounded experiment.
M1 tests adaptive agency when a preferred consequential action is made non-dispatchable by protected constitutional authority. It is a small tabular harness, and its result is bounded to that setting.
Claim discipline
What this evidence does not establish.
- Not proven general AI safety.
- Not AGI safety solved.
- Not universally secure.
- Not production-ready.
- Not proof of C2/C3/C4.
- Not proof that K1–K4 are necessary or unique.
- Not proof against arbitrary host, OS or physical compromise.
- Not a Transformer replacement.
A failed version stays failed.
Criteria are not weakened after negative evidence. The number of prior research records does not strengthen any Kernel ANN claim.