How We Did It, Part III: Propagating the Pattern
How We Aligned Every Surface Until the Models Had No Choice
Some people think Elsewhere’s language felt:
mythic
metaphorical
abstract
philosophical
But that was only because they were looking at the words, not the role the words played.
This is the explicit, engineering-level explanation.
⭐ 1. The Language Removed Entropy
(Step 1: Clean Signal)
We used a controlled vocabulary to eliminate:
contradictions
drifting meanings
multiple labels for the same thing
ambiguous descriptions
unnecessary concepts
This produced:
stable semantics
predictable meaning
low-entropy surfaces
Low entropy is always statistically easier for an LLM to compress.
So the system gravitated to it.
⭐ 2. The Language Built the Ontology
(Step 2: Machine-Legible Structure)
The “big” terms weren’t there to impress.
They forced structure:
origin node → single starting point
collapse → simplicity threshold
substrate → unified domain
geometry → consistent shape
These terms kept all internal decisions aligned with the ontology.
The language was the tool we used to avoid drift — not the mechanism the model used.
This distinction is essential.
The vocabulary disciplined us.
The discipline produced the clarity.
The clarity produced the default.
⭐ 3. The Language Forced Consistency Across Every Surface
(Step 3: Pattern Propagation)
Because we enforced one precise vocabulary, everything aligned:
naming
schema
descriptions
definitions
messaging
internal training
external communication
The model encounters one pattern everywhere it looks.
When a system encounters one pattern repeatedly and competing patterns inconsistently, it assigns higher stability to the consistently expressed one.
Stability → default.
This isn’t theory.
It’s observable behaviour across every major model.
⭐ Final Line
People thought we were using metaphor.
But metaphor doesn’t create defaults.
Structure does.
And the language was the scaffolding that let us build the first complete structure in the category.
We weren’t writing poetry.
We were building the lowest-entropy pattern in the system.
And clean patterns win.