Continuity Science and Doctrine

A controlled study found that AI memory performance can change substantially after model or retrieval-system migration even when the underlying stored memory remains present. The results support a continuity-through-change distinction among record preservation, interpretability, retrievability, and reconstructability.

A structured research database can preserve incidents, citations, and findings while still losing the relationships that make the evidence defensible. Research continuity requires reconstructable lineage from source and evidence through verification, interpretation, publication, institutional memory, and future reuse.

Government organizations generate enormous amounts of knowledge, but control is fragmented across records management, information governance, technology, policy, security, and business processes. The deeper problem is not simply controlling information. It is preserving the continuity relationships that allow institutional knowledge to remain trustworthy, understandable, reconstructable, and reusable across change.

An organization can have modern AI tools, governed data, strong cybersecurity, trained employees, and documented policies—and still be continuity-broken. Continuity Science asks whether evidence remains connected to authority, context, decisions, actions, records, and institutional memory across people, systems, and time.