Paper 2025/2184

One-way Functions and Boundary Hardness of Randomized Time-Bounded Kolmogorov Complexity

Abstract

We revisit the question of whether worst-case hardness of the time-bounded Kolmogorov complexity problem, $\KpolyA$---that is, determining whether a string is ``structured" (i.e., $K^t(x) < n-1$) or ``random" (i.e., $K^{\poly(t)} \geq n-1$)---suffices to imply the existence of one-way functions (OWF). Liu-Pass (CRYPTO'25) recently showed that worst-case hardness of a \emph{boundary} version of $\KpolyA$---where, roughly speaking, the goal is to decide whether given an instance $x$, (a) $x$ is $K^\poly$-random (i.e., $K^{\poly(t)}(x) \geq n-1$), or just close to $K^\poly$-random (i.e., $K^{t}(x) < n-1$ \emph{but} $K^{\poly(t)}> n - \log n$)---characterizes OWF, but with either of the following caveats (1) considering a non-standard notion of \emph{probabilistic $K^t$}, as opposed to the standard notion of $K^t$, or (2) assuming somewhat strong, and non-standard, derandomization assumptions. In this paper, we present an alternative method for establishing their result which enables significantly weakening the caveats. First, we show that boundary hardness of the more standard \emph{randomized} $K^t$ problem suffices (where randomized $K^t(x)$ is defined just like $K^t(x)$ except that the program generating the string $x$ may be randomized). As a consequence of this result, we can provide a characterization also in terms of just ``plain" $K^t$ under the most standard derandomization assumption (used to derandomize just $\BPP$ into $\P$)---namely $\E \not\subseteq {\sf ioSIZE}[2^{o(n)}]$. Our proof relies on language compression schemes of Goldberg-Sipser (STOC'85); using the same technique, we also present the the first worst-case to average-case reduction for the \emph{exact} $\KpolyA$ problem (under the same standard derandomization assumption), improving upon Hirahara's celebrated results (STOC'18, STOC'21) that only applied to a \emph{gap} version of the $\KpolyA$ problem, referred to as $\GapKpolyA$, where the goal is to decide whether $K^t(x) \leq n-O(\log n))$ or $K^{\poly(t)}(x) \geq n-1$ and under the same derandomization assumption.