If you want to peek inside how a model solves a math problem have a look at some data visualizations I made solving basic multiplication.[0]

I wanted to demonstrate capacity (how well it does a thing) instead of capability (which things it does, like drawing a pelican on a bicycle with SVG or solving a Rubik's Cube). To understand how LLMs solve math, look at the simplest case of multiplication. I deconstructed and classified the thinking token output. It is very important that model training yields thinking token output that structurally follows an observe, orient, decide, act (do the multiplication), and observe again loop.

[0] https://adamsohn.com/reasoning-grid/

You probably know it, but Boyd did not at all suggest speed beats quality. If anything, his realisation was the opposite: the US aircraft had a more open canopy and thus improved quality of observation, and that was what won despite the superior power and turning performance of the Soviet aircraft.