Robustness, Active Learning, and Governance for Structured Language Systems: Align-100 7/7

This paper provides a comprehensive, conclusive treatment of operational robustness, budgeted active learning, and governance for structured language systems built from the primitives developed in P1–P6. It formalizes per-task drift detection for both raw inputs and structured outputs, derives finite-sample change-point guarantees, proposes composite acquisition functions that combine uncertainty, governance risk, diversity, and adversarial suspicion, and proves approximation and expected-error bounds for budgeted selection. The paper defines provenance-aware governance policies that map calibrated marginals and provenance metadata to actions (accept, abstain, human review), and supplies a complete reproducibility and deployment bundle: full proofs, annotation and adjudication protocols, FAISS and quantization recipes, sample mani
fests, ASCII diagrams, and operational checklists. The design is explicitly aligned with P1–P6: alignment marginals (P1) feed drift summaries; structured calibration (P2) supplies trustworthy confidences; DEGs (P3) and GNN corrections (P4) provide structural summaries; KG linking (P5) and SES adversarial scores (P6) inform acquisition and governance.