The relationships between various meta-complexity problems are not well understood in the worst-case regime, including whether the search version is harder than the decision version, whether the hardness scales with the ``threshold", and how the hardness of different meta-complexity problems relate to one another, and to the task of function inversion.

In this note, we present resolutions to some of these questions with respect to the black-box analog of these problems. In more detail, let $MK^t_MP[s]$ denote the language consisting of strings $x$ with $K_{M}^t(x) < s(|x|)$, where $K_M^t(x)$ denotes the $t$-bounded Kolmogorov complexity of $x$ with $M$ as the underlying (Universal) Turing machine, and let $search-MK^t_MP[s]$ denote the search version of the same problem.

We show that if there for every Universal Turing machine $U$ there exists a $2^{\alpha n}poly(n)$-size $U$-oracle aided circuit deciding $MK^t_UP [n-O(1)]$, then for every function $s$, and every---not necessarily universal---Turing machine $M$, there exists a $2^{\alpha s(n)}poly(n)$-size $M$-oracle aided circuit solving $search-MK^t_MP[s(n)]$; this in turn yields circuits of roughly the same size for both the Minimum Circuit Size Problem (MCSP), and the function inversion problem, as they can be thought of as instantiating $MK^t_MP$ with particular choices of (a non-universal) TMs $M$ (the circuit emulator for the case of MCSP, and the function evaluation in the case of function inversion).

As a corollary of independent interest, we get that the complexity of black-box function inversion is (roughly) the same as the complexity of black-box deciding $MK^t_UP[n-O(1)]$ for any universal TM $U$; that is, also in the worst-case regime, function inversion is ``equivalent" to deciding $MK^t_UP$, in the black-box setting.

The relationships between various meta-complexity problems are not well understood in the worst-case regime, including whether the search version is harder than the decision version, whether the hardness scales with the ``threshold", and how the hardness of different meta-complexity problems relate to one another, and to the task of function inversion.

In this note, we present resolutions to some of these questions with respect to the black-box analog of these problems. In more detail, let $MK^t_MP[s]$ denote the language consisting of strings $x$ with $K_{M}^t(x) < s(|x|)$, where $K_M^t(x)$ denotes the $t$-bounded Kolmogorov complexity of $x$ with $M$ as the underlying (Universal) Turing machine, and let $search-MK^t_MP[s]$ denote the search version of the same problem.

We show that if there for every Universal Turing machine $U$ there exists a $2^{\alpha n}poly(n)$-size $U$-oracle aided circuit deciding $MK^t_UP [n-O(1)]$, then for every function $s$, and every---not necessarily universal---Turing machine $M$, there exists a $2^{\alpha s(n)}poly(n)$-size $M$-oracle aided circuit solving $search-MK^t_MP[s(n)]$; this in turn yields circuits of roughly the same size for both the Minimum Circuit Size Problem (MCSP), and the function inversion problem, as they can be thought of as instantiating $MK^t_MP$ with particular choices of (a non-universal) TMs $M$ (the circuit emulator for the case of MCSP, and the function evaluation in the case of function inversion).

As a corollary of independent interest, we get that the complexity of black-box function inversion is (roughly) the same as the complexity of black-box deciding $MK^t_UP[n-O(1)]$ for any universal TM $U$; that is, also in the worst-case regime, function inversion is ``equivalent" to deciding $MK^t_UP$, in the black-box setting.