this post attempts to explain QACI and notably the question and answer blobs and the question-answer interval.
let's say we view a particular way the world could be as a giant causal DAGs. in this world, a user is randomly generating a large (perhaps 1GB) file of random text called the question, and is supposed to, after some time, produce another large file called the answer.
i'm going to color:
(the real DAG of the universe since the big bang is obviously astronomically larger, this is a simplified sketch to illustrate what's going on)
the AI will be given a hypothetical distribution over hypotheses for what the world is like, each in the form of a never-halting input tape to a universal turing machine — something akin to solomonoff induction, or alternatively the "spec" or "world" part of a UDASSA hypothesis.
in the image above, the turing machine running over time is illustrated as a sequence of step transitions, some of which correspond to the turing machine writing the question and then later the answer onto its tape — again, the real sequence of steps involved in the history since the start of the universe and the question-answer interval would be astronomically larger in an actual hypothesis.
just like in the causal DAG of the universe, the question and answer blobs — as well as the user of course — are distributed over large amounts of tiny parts. in the turing machine, they're encoded — possibly in a highly complex way! — over large amounts of steps, at each of which the turing machine is writing a bit to its tape.
the problem of blob location (1, 2) is that of finding a way to identify the intended first instance of the question and answer as they are represented on a turing machine input tape serving as hypothesis for our universe.
the question-answer interval is then used as a standalone function whose input are counterfactual questions and which outputs counterfactual answers, and a hypothetical simulation of a collection of these calling passing information to each other is used to build a long-reflection process from within which the user is to solve alignment or, at least and more likely, come up with a better long-reflection process to eventually solve alignment.