That’s plausible. I suspect that the whole reinforcement learning step the models use only gets you so far. I think that neurosymbolic approach is actually more promising. The idea there is pretty clever. You use a deep neural network to parse noisy data from the outside world, and classify it. Then you use a symbolic logic engine to operate on classified data, and now you have actual reasoning happening within the system.