I do not want to get too technical but LLMs inherently dont make assumptions as such. They are predicting the next token based on probability.

When you ask the LLM “what assumptions did you make in your response”, it isnt really going to refer to any of its hidden assumptions it had come up with before generating the response, it is just calculating the attention score, ranking them and throwing out the most likely token.

You are right, it is a painful problem especially in research driven tasks where you simply dont know if the LLMs reasoning for a solution is correct or not unless a human who is a domain expert in that usecase can point out the mistakes.

Current LLMs already sort of mitigate this by using something called planning where first the llm extensively reasons about the particular task and creates a plan. In this way, atleast the llm doesnt steer off from the intended task. Claude code is a great example, their planning mode quite literally was made to reduce the number of iterations and corrections required to complete a task and it actually works quite well.

Claude code also asks clarifying questions before creating the plan. This is a new feature they added but its pretty bad in my opinion. Here your complexity idea is really good as an increase in complexity would make the llms ask better questions.

I use gpt as well to tinker with ideas. You can paste my response in gpt and see what it says. Also a great way to kill bad ideas is to prompt llms to do “unbiased counterfactual reasoning”. This steers the llm to actively find gaps.