Understanding Architectural Phase Boundary – AI Training Systems

The transition point between the current parametric LLM epoch (in which entity knowledge is encoded in model weights during training and decays between cycles) and the emerging explicit knowledge representation epoch (in which entity knowledge is stored in retrievable knowledge graphs with persistent records). At the phase boundary, the governing inequality changes form – from a rate inequality (Sε,flow + Sε,stock > Eε(γ) + Sα) to a completeness threshold – and the primary adversarial attack surface shifts from parametric manipulation to knowledge graph integrity. In the AI training systems context; distinct from thermodynamic and material science uses of 'phase boundary.'