Yeah, there’s a sort of trick here. The natural question is uniform—we want a single reduction that can work from any consistent guessing oracle, and we think it would be cheating to do different things with different oracles. But this doesn’t matter for the solution, since we produce a single consistent guessing oracle that can’t be reduced to the halting problem.

This reminds me of the theory of enumeration degrees, a generalization of Turing degrees allowing open-set-flavoured behaviour like we see in partial recursive functions; if the answer to an oracle query is positive, the oracle must eventually tell you, but if the answer is negative it keeps you waiting indefinitely. I find the theory of enumeration degrees to be underemphasized in discussion of computability theory, but e.g. Odifreddi has a chapter on it all the way at the end of *Classical Recursion Theory Volume II*.

The consistent guessing problem isn’t a problem about enumeration degrees. It’s using a stronger kind of uniformity—we want to be uniform over oracles that differently guess consistently, not over a set of ways to give the same answers, but to present them differently. But there is again a kind of strangeness in the behaviour of uniformity, in that we get equivalent notions if we do or do not ask that a reduction between sets , be a single function that uniformly enumerates from enumerations of , so there might be some common idea here. More generally, enumeration degrees feel like they let us express more naturally things that are a bit awkward to say in terms of Turing degrees—it’s natural to think about the set of computations that are enumerable/ in a set—so it might be a useful keyword.

Well, I guess describing a model of a computably enumerable theory, like PA or ZFC counts. We could also ask for a model of PA that’s nonstandard in a particular way that we want, e.g. by asking for a model of PA+¬Con(PA), and that works the same way. Describing a reflective oracle has low solutions too, though this is pretty similar to the consistent guessing problem. Another one, which is really just a restatement of the low basis theorem, but perhaps a more evocative one, is as follows. Suppose some oracle machine T has the property that there is some oracle that would cause it to run forever starting from an empty tape. Then, there is a low such oracle.

(Technically, these aren’t decision problems, since they don’t tell us what the right decision is, but just give us conditions that whatever decisions we make have to satisfy. I don’t know what to say instead; this is more general then e.g. promise problems. Maybe I’d use something like decision-class problems?)

All these have a somewhat similar flavour by the nature of the low basis theorem. We can enumerate a set of constraints, but we can’t necessarily compute a single object satisfying all the constraints. But the theorem tells us that there’s a low such object.

I don’t know what the situation is for subsets of the digits of Chaitin’s constant. Can it be as hard as the halting problem? You might try to refute this using some sort of incompressibility idea. Can it be low? I’d expect not, at least for computable subsets of indices of positive density. Plausibly computability theorists know about this stuff. They do like constructing posets of Turing degrees of various shapes, and they know about which shapes can be realized between 0 and the halting degree 0′. (E.g. this paper.)