Knowledge Formation Optimization: A Framework for Shaping AI Conceptual Representations in Advance of Retrieval

Knowledge Formation Optimization (KFO) structures, sequences, distributes, corroborates, and corrects intellectual frameworks and entity definitions across the public information environment and measures whether AI systems reproduce them accurately across relevant queries and over time. This paper presents KFO as a practitioner-facing diagnostic framework for identifying and addressing public source-environment conditions associated with inaccurate, weakly attributed, or inconsistent AI representation. It defines a three-mode taxonomy of formation layer failure: Absence, Intermediary Dominance, and Conceptual Dilution, and organizes remediation around five operating principles: Conceptual Precision, Canonical Authority Establishment, Query Mapping, Conceptual Boundary Defense, and Adaptive Representation Monitoring. Version 4.0, revised September 2, 2026, is a substantive epistemic-boundary and evidence-integrity revision. It adopts the current canonical KFO definition, clarifies formation layer as a diagnostic construct rather than a directly observed proprietary model stage, narrows causal and hidden-mechanism claims, restructures the case evidence to distinguish reconstructed baseline material from directly preserved records, corrects technical and hospitality citations, and adds a retrospective coding rubric and prospective replication instrument. The three-mode taxonomy, five operating principles, central diagnostic contribution, observational case, and research agenda remain intact. Versions 1.0 (June 2, 2026), 2.0 (June 13, 2026), and 3.0 (July 17, 2026) remain permanently available under the same Zenodo concept DOI.