Isn't that the core fundamental of AI? Constrain the results to a latent space which has all the probable answers and then pick up whichever token makes sense based on your random sampling?

I feel like the whole concept of harness and LLMs IS to pick random tokens which make sense. Think about this as a solution space (a very complex one), then the harness and the LLM are moving towards the local minima at which the code is complete. This is akin to how we think about AI anyways.