That a full world model is there is the preliminary condition. So, first you have to establish that an LLM actually draws a world model in its inner structure.
Then you have to refine that world model, and be able to refine its intentionally spawned branches (e.g. "Imagine scenario X"...). If the iteration of the refining is successful, you achieved the goal.
The point is implementing critical thinking; that the initial world model is flawed is the known starting point: at the beginning, it only knows it has "heard" a lot of information.
If it has no concrete reasoning, how is this looped revision intended to be more-accurate than a zero-shot guess?
I agree with the fundamental principle of not trusting input data from the start, but that just puts us back in square-one where we generate non-authoritative results dependent on a series of autoregressive parameters. It might generate better-reasoned results, but the outcome will be equally unreliable and prone to hallucination.
It can work if the reasoning (the logic) is part of the world model and is similarly refined.
Then, like in the normal case, you gather a good amount of information than draw your conclusions based on you logic and the "wisdom" built. Were this automated, you would have an automated champion in judgement - doing its best with the imperfect data and function available. Like us, just better.
Well, if it encodes a world model, and can work on its own encoding, loop it into a critical revision of its contents and you have the Real Thing - something that is informed and reasons.