The fractal nature of the WCLM: world models, analogies, anchored facts

The engines are world-model transformations — every layer asks what does this mean, expand it, contract it, give me more so the analysis continues. The destination: ask a document, ask a paragraph — here's the graph of where I'm going; does it agree, provide evidence, reach the same conclusion? New and named: ANALOGIES — to explain this to somebody from finance, graphs of graphs must become spreadsheets of spreadsheets, because their world really nests them. Corrections are the training (better meaning, missing nodes, manual overrides — what pre-training is to an LLM). The LLM layer-warming picture, redone with determinism: our lines are exact, so facts and hypotheses can anchor to them. And some layers may one day need an LLM — acceptable, because graph in and graph out are both kept as evidence.