VDR-1 through VDR-4 built the arithmetic and ML stack. VDR-5 specified the knowledge architecture. VDR-6 specified the execution layer. All of these papers describe what happens during a single prompt interaction — the system receives input, computes, reasons, and responds. This paper specifies everything else: the complete lifecycle from raw data sourcing through corpus preparation, tokenization, model initialization, pre-training, fine-tuning, human feedback integration, evaluation, deployment, continuous monitoring, model updates, and retirement. Every phase is specified in terms of the VDR-Prolog KB architecture, meaning every phase produces queryable facts, operates under declared constraints, stores results with provenance, and is controllable through KB activation and deactivation. The central architectural principle is that the system is both API and generator. It serves data through structured endpoints and generates text through the language model. Command tokens let the model invoke its own lifecycle operations. KBs let operators enable, disable, and layer lifecycle components like a file tree. The UI is an API to the KB layer, not a separate system. Training data, model weights, evaluation results, feedback records, and deployment configurations are all KBs — surfaceable, queryable, versionable, and constrainable by the same mechanisms that govern prompt-time operation.
Geoffrey Howland (Fri,) studied this question.
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