Change-Provenant Supervision: Governing Learned Artifacts Under Policy Change

Abstract

A recorded dependency graph cannot certify that it contains no omitted edge. For learned artifacts, graph-scoped invalidation therefore cannot by itself justify admission after authority changes, especially when output testing misses a provenance-stale derivation. We separate recorded lineage, which proposes impact scope and a dependency explanation, from independent current-contract revalidation of every retained target, which supplies soundness relative to the declared contract. We evaluate this design in two evidence layers. Four Qwen3-14B LoRA packages bind 1.028 GB of adapter bytes to training and authority records. Stale and full-fresh adapters trained on mutually exclusive versioned supervision emitted identical plans on all 80 tested authority-neutral inputs. Byte-derived reconstruction and a sealed current-contract ledger nevertheless refused the stale and naive-append packages and admitted full-fresh and lineage-selective packages. In a controlled harness with six role-separated amendments, transitive lineage recovered all 40 semantically invalidated amendment-target pairs while nominating 56 impact candidates, compared with 120 under document-wide invalidation. All 56 had a declared path to an amended clause, although 16 remained content-valid. Omitting one load-bearing edge per amendment reduced semantic recall to 34/40 while independent revalidation refused all six affected packages. The results establish properties of a controlled governance mechanism, not an enterprise invalidation rate, unlearning result, operationally optimal workflow, or measured total-cost reduction.