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; (5) temporal embeddings for version-aware knowledge; (6) ontology attention — switching between axiomatic frameworks with entropy-based selection cost; (7) cultural compilers — orthonormal transformations preserving distance to an ethical kernel, with universal archetypal bases discovered via joint diagonalization of cross-cultural covariance matrices (S.V.E. VIII); and (8) CogOS integration — recursive ontology refinement and Lyapunov-stable ethical dynamics (per CogOS, DOI: 10.5281/zenodo.18109244). Meta-ontological transparency. VKB itself operates within the S.V.E. ontological hypothesis (defined in S.V.E. IV, VIII, XII). We make this dependency explicit: the six epistemic categories are postulated, not derived; confidence scores presuppose a probabilistic interpretation; the ethical kernel Φ and δ-dehumanization metric depend on choices we acknowledge but do not resolve. VKB's categories are hypotheses subject to revision through empirical contact with reality, following the S.V.E. feedback loop (Reality → Ontology → Language → Models → Verification → Feedback → Ontology). Honest limitations. The paper reports no experimental results. All quantitative claims are hypothetical. We enumerate seven open problems explicitly: scalability of annotation (unknown required fraction); reductionism risk in the δ-metric (useful heuristic, not a theory of ethics); potential collapse of ontology attention; idealized orthonormality in cultural compilers; absence of experiments (the most important next step); dependency on unpublished S.V.E. preprints (provisional foundation, made explicit); and the "first computable metric" claim (may be incorrect — we welcome corrections). The mathematical argument for non-discriminatory deployment is structural: joint diagonalization requires input from all cultures; excluding cultures violates orthonormality — the mathematics itself enforces non-discrimination. VKB-Training was first described as part of the CogOS framework. Cultural compilers and joint diagonalization originate from S.V.E. VIII (Divine Mathematics). This paper integrates these components into a standalone 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 Workshops.