Freeze before grade
Treatment identity and scoring surfaces are separated before evaluation.
Kingan Logic studies how AI-assisted decision systems preserve evidence, state, authority and human context across long workflows. Product validation and scientific efficacy remain separate: negative, mixed, interrupted and repaired results stay in the record rather than disappearing when a later run improves.
The research program asks what happens before, during and after a model answer: what evidence was available, what the evaluator actually measured, what permission existed, what state survived handoff, and what a later repair is allowed to change.
Implementation progress, test coverage, cloud receipts and product behavior remain separate from claims about human or model benefit. A system can be technically real without the research question being settled.
Null, mixed, interrupted and repaired studies stay in the chronology. Parser, runtime, scorer, evaluator and protocol failures are recorded separately from target behavior rather than being silently folded into a later success.
Cross-lane recurrence can generate a research question, but shared architecture is not proof that the same mechanism or benefit transfers across multifamily, physical assets, logistics, disaster response or long-horizon human–AI work.
A model score is only interpretable if the experiment around it survives inspection. Current work uses frozen protocols, blind treatment mapping, exact artifact custody, explicit missing/replacement handling, paired or heldout comparisons where justified, human/machine calibration and claim ceilings that remain binding after a positive-looking result.
Treatment identity and scoring surfaces are separated before evaluation.
Parser, runtime, scorer, evaluator and protocol failures are separated from target behavior.
A detected signal or successful repair does not automatically authorize a downstream claim or consequential action.
Versioned prompts, receipts, hashes, run identities and negative results remain attached to the result they support.
These are research work products and manuscript states, not claims of publication or peer review.
When evidence permits movement, what determines whether move, hold or another state placement is appropriate?
Failure ownership, repair and claim ceilings when the evaluation apparatus itself breaks before the target is measured.
Compute, latency, reasoning cost and human correction burden.
Whether recurring evidence, permission and state architecture across different domains represents architecture reuse or measurable effect transfer.
Evaluator validity, reproducibility versus validity, human/machine calibration and independence of evidence.
Human reconstruction burden, retained state, provenance and long-horizon human–AI collaboration.
Whether valuable adjacent evidence can materially improve the route without stealing the destination.
Cases where a protective HOLD or FAIL may be appropriate even though the system's diagnosis of why it stopped is wrong.
Finding a defect does not establish the repair or grant permission to execute it.
Public research outputs appear here when they are actually released. Manuscript directions elsewhere on the page are not presented as publications.
Public scholarly identity and persistent researcher identifiers. Development accounts and private product repositories are intentionally not linked here.
Active researcher profile and submission identity.
Open profile →Persistent scholarly identifier.
Open profile →Researcher identifier linked to the scholarly identity stack.
Open profile →Kingan Logic is open to bounded research collaboration, independent replication, evaluator/instrument studies and selectively scoped product research where the protocol, evidence boundary, contribution and claim ceiling can be stated clearly.