[pessimizer]

Seems like LLMs are that. A bunch of most probable word associations is a network, and you can build a physical model of a network, or build a network that allows you to reason about a physical model. Whether it's just a flowchart or workflow diagram, or an X-dimensional matrix with vectors moving through it.

But the only way to map the network in an LLM is experimentally. You have to prompt it, and see how the coefficients fall in order to construct your most likely walk through the training data.

I think that LLMs can and do come up with novel things through exhaustion, just by applying the relationships between some set of entities to entirely different sets of entities because an accumulation of earlier context pushed the probability of those entities being mentioned, and they were able to easily replace a selection of entities that were more associated with those nearer connective, relationship words.

I think that as such LLMs are good at generating metaphors, and a lot of innovation comes from going "What if As worked like Bs?" Just go through all the As and Bs, toss the ones that don't make any sense and test the ones that seem like they might.