Long-lived AI systems may preserve canonical structures at more than one level of abstraction. A concrete data-level canon can coexist with metadata that organizes relations among records and with higher-order generative constraints that determine how schemas, mappings, and lower-level realizations are produced. This p...
Bin Seol· Zenodo (CERN European Organi...· 0 citations
Long-lived AI systems may treat records, organizing schemas, and higher-order generative constraints as canonical references in different roles. Abstraction-indexed canonicity makes official standing specific to scope, revision, and functional level; compression or expansion alone grants no new standing. Admission and...
Bin Seol· Zenodo (CERN European Organi...· 0 citations
Long-lived AI ecosystems need to preserve what was institutionally recognized, when, and under which authority, even after correction. This paper proposes a Custodian architecture for scoped canonicity without assigning a model ownership of truth. It separates record standing, contestation, handling, and attestation; p...
Bin Seol· Zenodo (CERN European Organi...· 0 citations
Long-lived AI ecosystems need to preserve what was institutionally recognized, when, and under which authority, even after correction. This paper proposes a Custodian architecture for scoped canonicity without assigning a model ownership of truth. It separates record standing, contestation, handling, and attestation; p...
Bin Seol· Zenodo (CERN European Organi...· 0 citations
Long-lived AI systems may treat records, organizing schemas, and higher-order generative constraints as canonical references in different roles. Abstraction-indexed canonicity makes official standing specific to scope, revision, and functional level; compression or expansion alone grants no new standing. Admission and...
Bin Seol· Zenodo (CERN European Organi...· 0 citations
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