Computer scientist and engineer, amateur philosopher and chess player.
For the time-complexity of the verifier to grow only linearly with , we need also to assume the complexity of the reduction is for a fixed , regardless of , which I admit is quite optimistic. If the time-complexity of the reduction is doubly exponential in , the (upper bound of the) time-complexity of the verifier will be exponential in , even with the stronger version of Reduction-Regularity.
By "unconditionally", do you mean without an additional assumption, such as Reduction-Regularity?
I actually have a result in the other direction (an older version of this post). That is, with a slightly stronger assumption, we could derive a better upper bound (on ) for the time-complexity of the verifier. The stronger assumption is just a stronger version of Reduction-Regularity, requiring -- that is, replaces in the inequality. It could still lead to an exponential bound in if the complexity of the reduction increases exponentially with . In the version presented in this post, the exponential bound relies on the complexity of being for a fixed , regardless of , which is admittedly optimistic. If we assume this with the stronger version of Reduction-Regularity, then I think the upper bound for the time-complexity of the verifier would increase only linearly with , which is a huge improvement (I can add something in the appendix about this if you are interested in the details). In the end, I opted for presenting the weakest assumption here, even if corresponding to a worse complexity bound.
Your comment seems to imply that the conjecture's "99%" is about circuits for which is false. Otherwise, it would be impossible for a to miss 100% of random reversible circuits without missing the circuits for which is true. In the conjecture, should "random reversible circuits " be read as "random reversible circuits for which is false"? It does not change much indeed, but I might have to correct my presentation of the conjecture here.
I think I have an idea to make that 99% closer to 1. But its development became too big for a comment here, so I made a post about it.
Thanks for the feedback! The summary here and the abstract in the draft paper have been updated; I hope it is clearer now.
Hi everyone,
I’m Glauber De Bona, a computer scientist and engineer. I left an assistant professor position to focus on independent research related to AGI and AI alignment (topics that led me to this forum). My current interests include self-location beliefs, particularly the Sleeping Beauty problem.
I’m also interested in philosophy, especially formal reasoning -- logic, Bayesianism, and decision theory. Outside of research, I’m an amateur chess player.
Correcting myself: "doubly" just added above.