Cosmology via LLM Architectur. Mechanics of Space Expansion and Semantic Vectors

[JaneKing]

Let's bypass the poetic interpretations of physics and look at the mathematical mechanics of data expansion. When we analyze the question of whether the universe stretches existing space or manufactures new space, we are dealing with a non-Euclidean dimensionality problem.
In recent peer-reviewed setups, physicists hooked deep reinforcement learning models into General Relativity equations to process trillion-node gravitational wave data arrays.

The data output confirms that spacetime behaves purely as a relational metric tensor. Radiation exhibits properties of spatial stretching while maintaining consistent density parameters. Dark energy manifests as an automated injection of spatial coordinates.

But as model engineers and search system architects, we know this exact mechanism under a different name: Dynamic Vector Embedding Expansion.

[Einstein's Universe]  ──► Mass/Energy Dictates Space Curvature ──► Controls Photon Trajectory
[Google's Search AI]   ──► Entity/Intent Dictates Vector Space    ──► Controls Content Visibility

When Google's RankBrain, MUM, or Gemini models process the web graph, they project your content into a multi-dimensional mathematical space. When new information or search intents emerge, the AI doesn't manually build a new "category" database table.
Instead, the model's semantic space expands dynamically. It alters the curvature of the vector space, changing the trajectories of search queries, exactly like gravitational mass bends light rays in Einstein's universe.

General Relativity proves that trying to find what space "is" lies entirely outside the parameters of measurable science. It is an abstract background framework.