VKB-Training: Epistemic Metadata, Ontology Attention, and Cultural Compilers for Hallucination Reduction in Large Language Models

Large language models hallucinate because their training data carries no epistemic metadata: facts, hypotheses, value judgments, and acknowledged unknowns occupy the same embedding space with identical weight. A deeper problem compounds this: every claim presupposes an ontology — an axiomatic framework equipped with a metric — and as Bertrand's paradox demonstrates, probability itself is ill-defined without specifying the measure. Deeper still: the same ethical truth can be expressed in culturally distinct "coordinate systems," and collapsing these into a single representation introduces systematic bias. We propose VKB-Training (Verified Knowledge Base Training), a data-centric approach that assigns each training sample a six-category epistemic tag (Fact, Model, Value, Hypothesis, BlindSpot, Ontology), a calibrated confidence score, a provenance chain, and an ontology identifier specifying the axiomatic framework under which the claim is asserted. We introduce a four-stage hybrid annotation pipeline: (1) AI triangulation — multiple LLMs classify independently; inter-model disagreement signals normative content (the "Caesar/God boundary"); (2) Human sampling with axiom extraction — domain annotators resolve high-disagreement cases; recurrent decision principles are extracted as reusable rules; (3) Expert calibration with reputation weighting — formalized Galton's ox-weighing insight (per S.V.E. XI, DOI: 10.5281/zenodo.18109198); (4) Logical consistency filters — contradiction detection and symmetry verification via the CGS Method (DOI: 10.5281/zenodo.18776172). Eight training mechanisms are proposed: (1) confidence-weighted loss; (2) provenance-aware attention; (3) BlindSpot training maximizing output entropy at known knowledge gaps; (4) confidence propagation through DAG-structured knowledge dependencies (conservative weakest-link and probabilistic strategies); (5) temporal embeddings for version-aware knowledge representation; (6) ontology attention — switching between axiomatic frameworks with entropy-based selection cost balancing parsimony against epistemic humility; (7) cultural compilers — orthonormal transformations mapping between culture-specific value representations while preserving distance to an ethical kernel, with universal archetypal bases discovered via joint diagonalization of cross-cultural covariance matrices (drawing on Moral Foundations Theory and Jung's archetypal psychology, formalized in S.V.E. VIII); and (8) CogOS integration — recursive ontology refinement, Lyapunov-stable ethical dynamics, and the Invariant Semantic Core as constraint on permissible ontology transitions (per CogOS, DOI: 10.5281/zenodo.18109244). Ontologies are formally defined as triples (axioms, metric, measure) and parameterized along five dimensions, drawing on the SES parameterization from S.V.E. XII (included as supplementary material). Cultural compilers are formalized as orthonormal transformations satisfying distance-preservation and coherence constraints; the joint diagonalization discovers culture-invariant moral foundations as basis vectors that simultaneously diagonalize all cultural covariance matrices. A mathematical consequence: excluding cultures from the training set breaks orthonormality — the mathematics itself enforces non-discriminatory deployment. The paper additionally describes: multi-observer Bayesian calibration for cross-cultural epistemic consistency; and a computable δ-dehumanization safety metric for detecting ethical drift in LLM outputs. VKB-Training was first described as part of the CogOS framework. Cultural compilers and joint diagonalization for archetypal discovery originate from S.V.E. VIII (Divine Mathematics). This paper integrates these components into a standalone, empirically testable proposal with a falsifiable experimental protocol and pre-specified success thresholds. Section 7 (Ethical Data Sourcing: Author Revenue Sharing, 10–50%) is included in the preprint but will be omitted from the workshop submission. [NOTE: ILLUSTRATIVE NUMBERS — WIP] Prepared for submission to NeurIPS 2025 Workshop.