interesting. that living plan document is something humans learn to make and update themselves. these problems are dynamical, requiring the solver to maintain state, and the plan is what records that.

doing it for the LLM really highlights that limitation. they arent trained statefully, not at the foundation model, where it matters. that state gets reproduced on top of the model in the form of "reasoning" and "chain of thought" but that level of scaffolding is a classic example of the bitter lesson. like semantic trees of old.

the representation learning + transformer model needs to be evolved to handle state, then it should be able to do these things itself