Sorting from Counterexamples

• URL: http://arxiv.org/abs/2608.21579v1
• Date: Fri, 21 Aug 2026 19:29:39 GMT
• Title: Sorting from Counterexamples
• Abstract summary: We study a problem of learning an unknown linear order on $n$ items.<n>Up to $k$ of the returned counterexamples may be untruthful, where $k$ is not known in advance.<n>We determine the optimal query complexity up to constant factors.
• Score: 21.887990428338384
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Consider the following problem of learning an unknown linear order on $n$ items. In each round, the learner guesses a complete ordering of the items and receives either confirmation that the guess is correct or a counterexample: a pair of items in the wrong order. The goal is to identify the unknown order using as few queries as possible. We study this problem when up to $k$ of the returned counterexamples may be untruthful, where $k$ is not known in advance. We determine the optimal query complexity up to constant factors: \[ Θ(n\log n + nk). \] Thus, while the noiseless complexity matches the classical complexity of sorting, each untruthful counterexample incurs an additional cost of order $n$. The upper bound is based on a geometric representation of permutations and Grünbaum's theorem, while the lower bound combines sorting arguments with a Condorcet-type construction. We also study the case where the target ranking has a low-dimensional geometric representation: each item is represented by a point in $\mathbb{R}^d$, and the ranking is obtained by projecting the points onto an unknown direction. For these classes we give an upper bound of $O(d^2\log n+dk)$ and a lower bound of $Ω(d\log n+dk)$, leaving a factor of $d$ gap in the noiseless term.

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