> For the complete documentation index, see [llms.txt](https://docs.gridium.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.gridium.ai/readme/2.-mathematical-foundations-from-cosmology/2.2-mapping-cosmology-to-ai.md).

# 2.2 Mapping Cosmology to AI

In Gridium, **semantic perturbations** replace cosmic fluctuations. Instead of temperature fields, we model **task flows, agent states, and data streams** as evolving context graphs:

$$
C\_t = f(G\_t, S\_t) = \text{Aggregate}({x\_{v\_1}, ..., x\_{v\_n}}, S\_t)
$$

## Where:

$$
G\_t
$$

## = context graph of tasks and transitions,

$$
S\_t
$$

\= external sentiment or off-chain signal,

* $$
  C\_t
  $$

  \= aggregated semantic context vector.

This mapping allows Gridium to detect **semantic hotspots**, predict **task clustering**, and route computation **as if it were matter clustering in the early universe**.