The interesting bits are when it derives the likely hood of something being correct and does more passes, or splits the data apart in the first pass and opens up new context processes with specialized instructions to handle it. The code stuff goes full on ororborus on some models, writes out the code on one pass, checks for issues on another pass, runs the code looking for errors on a third pass and goes back to step 1 if it fails.
They're getting a lot out of it for it primarily just being a weighted token generator wrapped in an orchestrator.