What about going all in on coincidence? What about a path finding algorithm that takes actions to pick the next draw (per column, of course) and gets reward points for being correct and loses points for being wrong... however, it is given the freedom to use any method to get there. It could transform (+1/-1 etc), it could mirror, it could perform arithmetic and / or algebraic calculations, etc...

Training the model on past data to arrive at a best guess BUT also output what it used to get there... 

I will learn the majority of what I need to know by the time I finish the Markov Decision Process via reinforcement learning script for the current system... 

So if a system is given a toolbox of techniques to solve the problem and let loose to do it's thing, what kind of pick would that generate?

I still have the rest of 2025 to focus on the current system, but I need to start thinking about next steps far ahead of retiring the current system...