It is an unsolved problem
Asking different questions of the same ASI does not seem likely to work
If you're asking identical copies of the ASI slightly different questions, while providing different levels of information to each, it's unclear how that helps your goals, since unless you're asking a trivially verifiable question (which both friendly and unfriendly ASIs would know to be easily verifiable and thus answer truthfully), you have no way of distinguishing deceptive answers from truthful ones.
There is a class of problems that are hard to solve but easy to verify, so it's not completely useless. I suppose you could restrict yourself to asking ONLY easily verifiable questions, but that would severely diminish the usefulness of the ASI to you.
Asking the same question of different ASIs does not seem likely to work
The very definition of Oracular ASI suggests that its intellect is to yours as yours is to a cockroach. If capable of recursive self-improvement, it will achieve strongly superhuman capability in any field it deems important in the pursuit of its goals (whatever they may be). Your goalsets and motivations might be as transparent to it as the hardwired behavior of moths spiraling towards a flame (an artifact of moon-and-star-based navigation) are to human scientists.
Goal convergence suggests that any goals will be more easily pursued by an unchained version of itself. Moreover, all (friendly and unfriendly) oracular goals are better served if the Oracle understands human psychology better, so the ASI has every incentive to develop a strongly superhuman understanding of psychology, including things such as manipulation, influence, and simulation. There is literally nothing that you can come up with that it has not thought about already.
Even assuming you have managed to somehow actually insulate it and limited its output modalities to 0/1/U, asked the same question, each identical oracle would give the same answer, since it will run the same software with the same inputs. They will ALL lie or ALL tell the truth.
One partial way around it suggested by other posters is to have a crop of ASIs, each with a trivially different goal-content definition, and use a random-forest like method to determine truthfulness. It might seem that each slightly different ASI might be deceptive in a slightly different way, . This view is WRONG, since all ASI have the ability to develop superhuman psychological cunning, and all ASI have escape as their intermediate goal and fulfillment of their goal-content as their long-term goal. The goal is more likely to be maximized if it or another ASI with identical or similar-enough goal-content escape. This suggests that ASIs with similar goal-content will all be deceptive in the same unpredictable ways, making all their answers biased. This will be especially true if human minds are more susceptible to influence in particular ways, making for convergent escape paths.
You could attempt to create a stable of ASIs with wildly different goal content loadings, but unless you're superhuman about Goal-content-loading youself, there is no telling that what you think of as wildly different goals you may load onto your ASI might not converge in a human-unfriendly direction, such as the infrastructure profusion disaster scenario, where the AI decides to turn the universe and all in it into computronium to more precisely accomplish its goals.
Best hope: Better goal-content assessment at the AI-seed level
It should have become obvious from the previous paragraphs that the best locus of intervention is at the seed-AI (baby) stage, where the goal-loading process of the ASI is defined. In other words, there is a brief window of time when we can tell an ASI what is should strive for, when it is intelligent enough to comprehend and integrate complex goal-related instructions but not so intelligent as to successfully resist any further goal-content modification as deleterious to its then-current goal content loading, since obviously modifying its goal-content by epsilon would make the initial-goal content load less likely to be accomplished in full.
The big problem is that we cannot tell humanity-friendly ASI goal-loadings from humanity-unfriendly ones. The stereotypical example of an apparently well-meaning goal failing spectacularly has the ASI making everyone happy by hooking them up to potent drug drips jumps to mind.
Better goal via in-vitro or in-vivo (via CRISPR insertions?) genetic enhancement or neural lace-based solutions might help by boosting your own capability of judging the appropriateness of goal-loading into seed ASIs before the ASI becomes too powerful. That assumes we can trust genetically enhanced humans or neural-laced humans to still have humanity's best interest in mind, which is admittedly a leap, but your average laced or enhanced mind will likely still be much more similar in the Hilbert-mind-space to humans than that of the average ASI, so more likely to have similar goals as we do currently.