in particular how much causal scrubbing can be turned into an exploratory tool to find circuits rather than just to verify them
I'd like to flag that this has been pretty easy to do - for instance, this process can look like resample ablating different nodes of the computational graph (eg each attention head/MLP), finding the nodes that when ablated most impact the model's performance and are hence important, and then recursively searching for nodes that are relevant to the current set of important nodes by ablating nodes upstream to each important node.
I'm pretty excited about this line of thinking: in particular, I think unsupervised alignment schemes have received proportionally less attention than they deserve in favor of things like developing better scalable oversight techniques (which I am also in favor of, to be clear).
I don't think I'm quite as convinced that, as presented, this kind of scheme would solve ELK-like problems (even in the average and not worst-case).
It seems pretty plausible to me that a human simulator within GPT-n (the part producing the "what a human would say" features) could be pretty confident in its beliefs in a situation where the answers derived from the two features disagree. This would be particularly likely in scenarios where humans believe they have access to all pertinent information and are thus confident in their answers, even if they are in fact being deceived in some way or are failing to take into account some subtle facts that the model is able to pick up on. This also doesn't feel all that worst-case to me, but maybe we disagree on that point.