Non-Redundancy of Low-Arity Symmetric Boolean CSPs
Abstract
Non-redundancy, introduced by Bessiere, Carbonnel, and Katsirelos (AAAI 2020), is a structural parameter for Constraint Satisfaction Problems () that governs kernelization, exact and approximate sparsification, and exact streaming complexity. It is the largest size of a instance admitting no smaller subinstance with the same satisfying assignments. We study non-redundancy for Boolean symmetric defined by an -ary relation whose value depends only on Hamming weight. An instance of has variables and constraints given by -tuples; a constraint is satisfied exactly when the induced tuple lies in . This class includes natural predicates such as cuts and -SAT clauses. Our main result is a near-complete classification of the asymptotic growth of for symmetric Boolean predicates of arity at most . Using computational experiments and algebraic upper- and lower-bound criteria, we resolve every predicate of arity at most and all but two predicates of arity . For upper bounds, we introduce -balancedness, a lifted, higher-degree version of the balancedness notion of Chen, Jansen, and Pieterse (Algorithmica 2020). We prove that -balancedness is equivalent to the existence of degree- multilinear polynomials capturing , and hence implies . For lower bounds, we use Carbonnel's (CP 2022) framework: predicates admitting a special reduction from -ary OR inherit OR's lower bound . The only unresolved arity- predicates in our framework have bounds and ; we reduce their exact classification to natural extremal set-system questions.
Keywords
Cite
@article{arxiv.2605.14007,
title = {Non-Redundancy of Low-Arity Symmetric Boolean CSPs},
author = {Amatya Sharma and Santhoshini Velusamy},
journal= {arXiv preprint arXiv:2605.14007},
year = {2026}
}