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        <identifier>oai:drops-oai.dagstuhl.de:25384</identifier>
        <datestamp>2026-03-19T12:04:19Z</datestamp>
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          <dc:title>One-Way Functions and Boundary Hardness of Randomized Time-Bounded Kolmogorov Complexity</dc:title>
          <dc:creator>Liu, Yanyi</dc:creator>
          <dc:creator>Pass, Rafael</dc:creator>
          <dc:subject>One-way functions</dc:subject>
          <dc:subject>Time-Bounded Kolmogorov Complexity</dc:subject>
          <dc:subject>Worst-case to Average-case Reductions</dc:subject>
          <dc:description>We revisit the question of whether worst-case hardness of the time-bounded Kolmogorov complexity problem, MINK^{poly} - that is, determining whether a string is "structured" (i.e., K^t(x) &lt; n-1) or "random" (i.e., K^{poly(t)} ≥ n-1) - suffices to imply the existence of one-way functions (OWF). Liu-Pass (CRYPTO'25) recently showed that worst-case hardness of a boundary version of MINK^{poly} - 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) ≥ n-1), or just close to K^poly-random (i.e., K^{t}(x) &lt; n-1 but K^{poly(t)} &gt; n - log n) - characterizes OWF, but with either of the following caveats (1) considering a non-standard notion of probabilistic K^t, as opposed to the standard notion of K^t, or (2) assuming somewhat strong, and non-standard, derandomization assumptions.&#13;
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 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). &#13;
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 ̸ ⊆ ioSIZE[2^{o(n)}].&#13;
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 exact MINK^{poly} problem (under the same standard derandomization assumption), improving upon Hirahara’s celebrated results (STOC'18, STOC'21) that only applied to a gap version of the MINK^{poly} problem, referred to as GapMINK^{poly}, where the goal is to decide whether K^t(x) ≤ n-O(log n)) or K^{poly(t)}(x) ≥ n-1 and under the same derandomization assumption.</dc:description>
          <dc:publisher>Schloss Dagstuhl – Leibniz-Zentrum für Informatik</dc:publisher>
          <dc:contributor>Yanyi Liu and Rafael Pass</dc:contributor>
          <dc:date>2026</dc:date>
          <dc:relation>Is Part Of LIPIcs, Volume 362, 17th Innovations in Theoretical Computer Science Conference (ITCS 2026)</dc:relation>
          <dc:type>InProceedings</dc:type>
          <dc:type>Text</dc:type>
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          <dc:identifier>doi:10.4230/LIPIcs.ITCS.2026.97</dc:identifier>
          <dc:identifier>urn:nbn:de:0030-drops-253849</dc:identifier>
          <dc:identifier>https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ITCS.2026.97</dc:identifier>
          <dc:language>eng</dc:language>
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