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PAC Learning with Bandit Feedback: Sharp Sample Complexity in the Realizable Setting Algorithms with Polynomially-Improved Approximation Factors for the $2 \rightarrow q$ Norm, and Applications A computational phase transition for learning-to-sample from Ising models Covering vertices by sequential stars Fermi-Dirac machines as quantizations of neurons A Comprehensive Evaluation of Vertex Elimination Algorithms for Algorithmic Differentiation A Tight Bound on Localization of Electrical Flows Optimal Dimension-Free Sampling for Regularized Classification Reducing the Randomness in Partition Oracles for Bounded Degree Minor-Free Graphs Beyond the Half-Approximation: Fair and Efficient Online Class Matching Efficient Uniform Sampling of Surjections via their Profiles Tractable Maximization of Budgeted Phylogenetic Diversity on Networks Utilizing Node Scanwidth Fairness in Aggregation: Optimal Top-$k$ and Improved Full Ranking Learning-Augmented Online Scheduling with Parsimonious Preemption Entropy Equivalence Testing Lumberjack: Better Differentially Private Random Forests through Heavy Hitter Detection in Trees The Secretary Problem with a Stochastic Precursor Polynomial-Time Robust Multiclass Linear Classification under Gaussian Marginals Efficient Banzhaf-Based Data Valuation for $k$-Nearest Neighbors Classification Block-Sphere Vector Quantization An Approximation Algorithm for Graph Label Selection Iterative Chow Filtering for Learning with Distribution Shift Complexity of Non-Log-Concave Sampling in Fisher Information Stochastic Matching via Local Sparsification Finite Sample Bounds for Learning with Score Matching What is Learnable in Valiant's Theory of the Learnable? Provable Quantization with Randomized Hadamard Transform Min-Max Optimization Requires Exponentially Many Queries Fast and Compact Graph Cuts for the Boykov-Kolmogorov Algorithm A proximal gradient algorithm for composite log-concave sampling
Turing Completeness of GNU find: From mkdir-assisted Loop...
Keigo Oka · 2026-02-24 · via cs.DS updates on arXiv.org

The Unix command \texttt{find} is among the first commands taught to beginners, yet remains indispensable for experienced engineers. In this paper, we demonstrate that \texttt{find} possesses unexpected computational power, establishing three Turing completeness results using the GNU implementation (a standard in Linux distributions). (1) \texttt{find} + \texttt{mkdir} (a system that has only \texttt{find} and \texttt{mkdir}) is Turing complete: by encoding computational states as directory paths and using regex back-references to copy substrings, we simulate 2-tag systems. (2) GNU \texttt{find} 4.9.0+ alone is Turing complete: by reading and writing to files during traversal, we simulate a two-counter machine without \texttt{mkdir}. (3) \texttt{find} + \texttt{mkdir} without regex back-references is still Turing complete: by a trick of encoding regex patterns directly into directory names, we achieve the same power. These results place \texttt{find} among the ``surprisingly Turing-complete'' systems, highlighting the hidden complexity within seemingly simple standard utilities.