New evidence, learned while it runs.

Senua AI does not have a training phase that ends. Each new observation refines what it already knows, on the device, while it keeps working.

What that looks like

In one recording, the engine reads a company travel policy and answers booking questions from it. Then an amendment arrives that contradicts part of the policy. The next answer follows the amendment, and cites the line it came from. Nothing was retrained and nothing left the machine.

Watch the recording

Why a causal model can do this

A model made of weights learns everything at once, so changing one thing risks disturbing the rest, and it is safer to retrain the whole model. A model made of causal states learns in pieces. New evidence adjusts the states it concerns, or adds a new one, and leaves the others alone.