Optimal Coding for Randomized Kolmogorov Complexity and Its Applications

The coding theorem for Kolmogorov complexity states that any string sampled from a computable distribution has a description length close to its information content. A coding theorem for resource-bounded Kolmogorov complexity is the key to obtaining fundamental results in average-case complexity, yet whether any samplable distribution admits a coding theorem for randomized time-bounded Kolmogorov complexity $(\text{rK}^{\text{poly}})$ is open and a common bottleneck in the recent literature of meta-complexity. Previous works bypassed this issue by considering probabilistic Kolmogorov complexity $(\text{pK}^{\text{poly}})$, in which public random bits are assumed to be available. In this paper, we present an efficient coding theorem for randomized Kolmogorov complexity under the non-existence of one-way functions, thereby removing the common bottleneck. This enables us to prove $\text{rK}^{\text{poly}}$ counterparts of virtually all the average-case results that were proved only for $\text{pK}^{\text{poly}}$, and enables the resolution of the following concrete open problems. 1)The existence of a one-way function is characterized by the failure of average-case symmetry of information for randomized time-bounded Kolmogorov complexity, as well as a conditional coding theorem for randomized time-bounded Kolmogorov complexity. This resolves the open problem of Hirahara, Ilango, Lu, Nanashima, and Oliveira (STOC'23). 2)Hirahara, Kabanets, Lu, and Oliveira (CCC'24) showed that randomized time-bounded Kolmogorov complexity admits search-to-decision reductions in the errorless average-case setting over any samplable distribution, and left open whether a similar result holds in the error-prone setting. We resolve this question affirmatively, and as a consequence, characterize the existence of a one-way function by the average-case hardness of computing $\text{rK}^{\text{poly}}$ with respect to an arbitrary samplable distribution, which is an $\text{rK}^{\text{poly}}$ analogue of the $\text{pK}^{\text{poly}}$ characterization of Liu and Pass (CRYPTO'23). The key technical lemma is that any distribution whose next bits are efficiently predictable admits an efficient encoding and decoding scheme, which could be of independent interest to data compression.