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arxiv: 2605.14007 · v1 · submitted 2026-05-13 · 💻 cs.DS · cs.CC

Recognition: no theorem link

Non-Redundancy of Low-Arity Symmetric Boolean CSPs

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Pith reviewed 2026-05-15 02:49 UTC · model grok-4.3

classification 💻 cs.DS cs.CC
keywords non-redundancysymmetric Boolean CSPt-balancednesskernelizationsparsificationOR reductionarity classification
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The pith

Symmetric Boolean CSPs of arity at most 5 have their non-redundancy growth rates classified as O(n), O(n^2), or O(n^3), with all arity-4 cases and most arity-5 cases resolved.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper determines the asymptotic size of the largest non-redundant instance for every symmetric Boolean relation R of arity at most 4 and for all but two relations of arity 5. Non-redundancy NRD_n(R) is the maximum number of constraints on n variables such that no proper subset of the constraints defines the same set of satisfying assignments. The authors introduce t-balancedness, a degree-t lift of an earlier balancedness condition, which is equivalent to R being expressible by a multilinear polynomial of degree t and therefore forces NRD_n(R) to be O(n^t). They obtain matching lower bounds by reducing from the k-ary OR predicate, which itself requires Omega(n^k) constraints.

Core claim

Our main result is a near-complete classification of the asymptotic growth of NRD_n(R) for symmetric Boolean predicates of arity at most 5. We resolve every predicate of arity at most 4 and all but two predicates of arity 5. For upper bounds we prove that t-balancedness is equivalent to the existence of degree-t multilinear polynomials capturing R and hence implies NRD_n(R)=O(n^t). For lower bounds we use the framework in which predicates that admit a special reduction from k-ary OR inherit the Omega(n^k) lower bound of OR.

What carries the argument

t-balancedness: the property that a symmetric Boolean relation R is captured by a multilinear polynomial of degree exactly t, which directly yields the upper bound NRD_n(R)=O(n^t).

If this is right

  • Every t-balanced predicate satisfies NRD_n(R)=O(n^t).
  • Any predicate that reduces from k-ary OR satisfies NRD_n(R)=Omega(n^k).
  • The classification is complete for all symmetric Boolean predicates of arity <=4.
  • The two remaining arity-5 predicates are reduced to concrete extremal set-system questions whose answers would finish the classification.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same t-balancedness test may classify non-redundancy for symmetric predicates of arity 6 or 7 with only modest additional computation.
  • Exact resolution of the two open arity-5 cases would link non-redundancy directly to the Erdős–Ko–Rado theorem or other intersecting-family bounds.
  • Because non-redundancy controls kernel size and streaming complexity, the classification immediately yields tight kernelization and streaming results for the resolved predicates.

Load-bearing premise

The exhaustive computational enumeration has correctly identified every symmetric Boolean relation of arity at most 4 and all but two of arity 5, and the algebraic t-balancedness criteria are tight for the unresolved cases.

What would settle it

An explicit symmetric Boolean predicate of arity 5 whose largest non-redundant instance size is Theta(n^4) or whose size falls strictly between the Omega(n^2) and O(n^3) bounds currently claimed for the two open cases.

read the original abstract

Non-redundancy, introduced by Bessiere, Carbonnel, and Katsirelos (AAAI 2020), is a structural parameter for Constraint Satisfaction Problems ($\mathsf{CSPs}$) that governs kernelization, exact and approximate sparsification, and exact streaming complexity. It is the largest size of a $\mathsf{CSP}$ instance admitting no smaller subinstance with the same satisfying assignments. We study non-redundancy $\mathsf{NRD}_n(R)$ for Boolean symmetric $\mathsf{CSPs}$ defined by an $r$-ary relation $R$ whose value depends only on Hamming weight. An instance of $\mathsf{CSP}(R)$ has $n$ variables and constraints given by $r$-tuples; a constraint is satisfied exactly when the induced tuple lies in $R$. This class includes natural predicates such as cuts and $k$-SAT clauses. Our main result is a near-complete classification of the asymptotic growth of $\mathsf{NRD}_n(R)$ for symmetric Boolean predicates of arity at most $5$. Using computational experiments and algebraic upper- and lower-bound criteria, we resolve every predicate of arity at most $4$ and all but two predicates of arity $5$. For upper bounds, we introduce $t$-balancedness, a lifted, higher-degree version of the balancedness notion of Chen, Jansen, and Pieterse (Algorithmica 2020). We prove that $t$-balancedness is equivalent to the existence of degree-$t$ multilinear polynomials capturing $R$, and hence implies $\mathsf{NRD}_n(R)=O(n^t)$. For lower bounds, we use Carbonnel's (CP 2022) framework: predicates admitting a special reduction from $k$-ary OR inherit OR's lower bound $\Omega(n^k)$. The only unresolved arity-$5$ predicates in our framework have bounds $\Omega(n^2)$ and $O(n^3)$; we reduce their exact classification to natural extremal set-system questions.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 1 minor

Summary. The paper studies non-redundancy NRD_n(R) for symmetric Boolean CSP predicates R of arity at most 5. It claims a near-complete classification of the asymptotic growth of NRD_n(R), resolving every predicate of arity ≤4 and all but two of arity 5. Upper bounds follow from a new t-balancedness criterion shown equivalent to the existence of a degree-t multilinear polynomial capturing R (hence NRD_n(R)=O(n^t)); lower bounds follow from explicit reductions to the OR predicate (hence Ω(n^k) for suitable k). The two unresolved arity-5 cases are bounded by Ω(n²) from below and O(n³) from above and are reduced to open extremal set-system questions.

Significance. If the computational classification holds, the result is significant for the structural theory of CSPs: non-redundancy governs kernelization, sparsification, and streaming complexity, and a precise asymptotic map for all low-arity symmetric predicates supplies concrete, usable bounds for these applications. The algebraic equivalence between t-balancedness and polynomial capture, together with the parameter-free reductions from OR, supplies clean, independently checkable criteria; reducing the remaining cases to natural combinatorial questions is a further strength.

major comments (1)
  1. [Computational experiments] Computational experiments section: the central claim that every symmetric Boolean predicate of arity ≤4 and all but two of arity 5 has been correctly classified rests on an exhaustive enumeration over the 64 weight-based relations of arity 5 together with a search for the minimal t such that a degree-t multilinear polynomial captures R. No pseudocode, implementation details, or verification data for this search are supplied; an off-by-one error in the polynomial-degree test would misclassify the growth rate for one or more predicates and falsify the 'near-complete' statement.
minor comments (1)
  1. [Abstract] Abstract: the two unresolved arity-5 predicates are not identified and their concrete bounds (Ω(n²) lower, O(n³) upper) are not stated; adding this information would make the scope of the classification immediately clear.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their careful reading and for the constructive comment on the computational experiments. We address the point below.

read point-by-point responses
  1. Referee: [Computational experiments] Computational experiments section: the central claim that every symmetric Boolean predicate of arity ≤4 and all but two of arity 5 has been correctly classified rests on an exhaustive enumeration over the 64 weight-based relations of arity 5 together with a search for the minimal t such that a degree-t multilinear polynomial captures R. No pseudocode, implementation details, or verification data for this search are supplied; an off-by-one error in the polynomial-degree test would misclassify the growth rate for one or more predicates and falsify the 'near-complete' statement.

    Authors: We agree that the computational experiments section lacks the requested implementation details, which is a genuine gap for reproducibility. In the revised manuscript we will add a new subsection (or appendix) containing: (i) pseudocode for the enumeration over all 64 subsets of allowed weights {0,…,5}; (ii) the exact linear-algebra procedure used to test, for each candidate t, whether a multilinear polynomial of degree at most t captures the predicate (solving the 32-equation system over the monomial coefficients with exact rational arithmetic); and (iii) a short verification note confirming that the minimal-t values obtained by the solver coincide with the t-balancedness criterion proved in the paper. For arity ≤4 the same procedure was cross-checked by hand on all 16 predicates. We will also state that the implementation is available upon request. This revision directly addresses the possibility of an off-by-one error. revision: yes

Circularity Check

0 steps flagged

No circularity in derivation chain

full rationale

The paper introduces t-balancedness as a new algebraic criterion, proves its equivalence to the existence of degree-t multilinear polynomials capturing the predicate R (yielding the O(n^t) upper bound), and obtains lower bounds via explicit reductions to the independently known OR predicate using Carbonnel's external framework. The classification for arities ≤4 (and most arity-5 cases) rests on exhaustive enumeration over the finite set of symmetric predicates defined by Hamming weights, which is a direct computational check against the algebraic criteria rather than any fitted parameter or self-referential equation. No load-bearing self-citations, ansatzes smuggled via prior work, or renamings of known results appear; the central claims remain independent of the paper's own outputs.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The work rests on the standard definition of symmetric Boolean relations (value depends only on Hamming weight) and on the prior framework of Carbonnel for OR-reductions; no free parameters or new invented entities are introduced.

axioms (1)
  • domain assumption Symmetric Boolean relations are those whose satisfaction depends only on the Hamming weight of the input tuple.
    This is the definition of the class of predicates studied throughout the paper.

pith-pipeline@v0.9.0 · 5672 in / 1263 out tokens · 28095 ms · 2026-05-15T02:49:10.628545+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

16 extracted references · 16 canonical work pages · 3 internal anchors

  1. [1]

    On quadratic threshold CSPs

    arXiv:2604.01575 [cs]. Pre-published. [ABM12] Per Austrin, Siavosh Benabbas, and Avner Magen. “On quadratic threshold CSPs”. In:Discrete Mathematics & Theoretical Computer Science14.Discrete Algorithms (2012). [ACMM05] Amit Agarwal, Moses Charikar, Konstantin Makarychev, and Yury Makarychev. “O( p logn) approximation algorithms for Min UnCut, Min 2CNF Del...

  2. [2]

    Min-CSPs on Complete Instances II: Polylogarithmic Approximation for Min-NAE-3-SAT

    Ed. by David Zuckerman. IEEE Computer Society, 2019, pp. 180–201.DOI: 10.1109/FOCS.2019.00021.URL:https://doi.org/10.1109/FOCS.2019.00021. [ALMS25] Aditya Anand, Euiwoong Lee, Davide Mazzali, and Amatya Sharma. “Min-CSPs on Complete Instances II: Polylogarithmic Approximation for Min-NAE-3-SAT”. In:Approximation, Random- ization, and Combinatorial Optimiz...

  3. [3]

    Schloss Dagstuhl — Leibniz- Zentrum f ¨ur Informatik, July 2022, 38:1–38:23.DOI: 10.4230/LIPIcs.APPROX/RANDOM.2022

    LIPIcs. Schloss Dagstuhl — Leibniz- Zentrum f ¨ur Informatik, July 2022, 38:1–38:23.DOI: 10.4230/LIPIcs.APPROX/RANDOM.2022

  4. [4]

    Classifying the complexity of constraints using finite algebras

    [BJK05] Andrei Bulatov, Peter Jeavons, and Andrei Krokhin. “Classifying the complexity of constraints using finite algebras”. In:SIAM journal on computing34.3 (2005), pp. 720–742. [BZ20] Silvia Butti and Stanislav Zivny. “Sparsification of binary CSPs”. In:SIAM Journal on Discrete Mathematics34.1 (2020), pp. 825–842. [Car22] Cl´ement Carbonnel. “On redund...

  5. [5]

    Near-Optimal Space Lower Bounds for Streaming CSPs

    arXiv:2604.01400 [cs]. Pre-published. [FV98] Tom´as Feder and Moshe Y Vardi. “The computational structure of monotone monadic SNP and constraint satisfaction: A study through Datalog and group theory”. In:SIAM Journal on Computing28.1 (1998), pp. 57–104. [GL17] Venkatesan Guruswami and Euiwoong Lee. “Towards a characterization of approximation resistance ...

  6. [6]

    Streaming Complexity of Approximating Max 2CSP and Max Acyclic Subgraph

    LIPIcs. 2017, 11:1–11:27.DOI:10.4230/LIPICS.CCC.2017.11. [GVV17] Venkatesan Guruswami, Ameya Velingker, and Santhoshini Velusamy. “Streaming Complexity of Approximating Max 2CSP and Max Acyclic Subgraph”. In:Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques. APPROX 2017 (Aug. 16–18, 2017). Ed. by Klaus Jansen, Jos ´e ...

  7. [7]

    Schloss Dagstuhl — Leibniz-Zentrum f ¨ur Informatik, Aug

    LIPIcs. Schloss Dagstuhl — Leibniz-Zentrum f ¨ur Informatik, Aug. 2017, 8:1–8:19.DOI:

  8. [8]

    Some optimal inapproximability results

    4230/LIPIcs.APPROX-RANDOM.2017.8. [H˚as01] Johan H˚astad. “Some optimal inapproximability results”. In:Journal of the ACM (JACM)48.4 (2001), pp. 798–859. [JCG97] Peter Jeavons, David Cohen, and Marc Gyssens. “Closure properties of constraints”. In:Journal of the ACM (JACM)44.4 (1997), pp. 527–548. [JP19] Bart MP Jansen and Astrid Pieterse. “Optimal sparsi...

  9. [9]

    Quantum singular value transformation and beyond: exponential improvements for quantum matrix arithmetics

    [KK19] Michael Kapralov and Dmitry Krachun. “An Optimal Space Lower Bound for Approximating MAX-CUT”. In:Proceedings of the 51st Annual ACM SIGACT Symposium on Theory of Computing. STOC (June 23–26, 2019). Association for Computing Machinery, June 2019, pp. 277–288.DOI: 10.1145/3313276.3316364. [KKMO07] Subhash Khot, Guy Kindler, Elchanan Mossel, and Ryan...

  10. [10]

    A Theory of Spectral CSP Sparsifica- tion

    Ed. by David P . Woodruff. SIAM, 2024, pp. 5145–5168.DOI: 10.1137/1.9781611977912.185 .URL: https://doi.org/10.1137/1. 9781611977912.185. [KPS25a] Sanjeev Khanna, Aaron Putterman, and Madhu Sudan. “A Theory of Spectral CSP Sparsifica- tion”. In:arXiv preprint arXiv:2504.16206(2025). [KPS25b] Sanjeev Khanna, Aaron Putterman, and Madhu Sudan. “Efficient alg...

  11. [11]

    Linear time approximation schemes for the Gale- Berlekamp game and related minimization problems

    LIPIcs. Schloss Dagstuhl– Leibniz-Zentrum f ¨ur Informatik, 2023, 80:1–80:15.DOI: 10.4230/LIPIcs.ITCS.2023.80.URL: https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ITCS.2023.80. [KS09] Marek Karpinski and Warren Schudy. “Linear time approximation schemes for the Gale- Berlekamp game and related minimization problems”. In:Proceedings of the forty...

  12. [12]

    Improved rounding techniques for the MAX 2-SAT and MAX DI-CUT problems

    Ed. by David B. Shmoys. ACM, 2014, pp. 634–643.DOI: 10.1145/2591796. 2591817.URL:https://doi.org/10.1145/2591796.2591817. 25 [LLZ02] Michael Lewin, Dror Livnat, and Uri Zwick. “Improved rounding techniques for the MAX 2-SAT and MAX DI-CUT problems”. In:International Conference on Integer Programming and Combinatorial Optimization. Springer. 2002, pp. 67–8...

  13. [13]

    Optimal algorithms and inapproximability results for every CSP?

    [Rag08] Prasad Raghavendra. “Optimal algorithms and inapproximability results for every CSP?” In: Proceedings of the fortieth annual ACM symposium on Theory of computing. 2008, pp. 245–254. [Sch78] Thomas J. Schaefer. “The Complexity of Satisfiability Problems”. In:Proceedings of the 10th ACM Symposium on Theory of Computing (STOC). ACM, 1978, pp. 216–226...

  14. [14]

    May 2023.DOI: 10.4230/ LIPIcs.APPROX/RANDOM.2023.15

    LIPIcs. May 2023.DOI: 10.4230/ LIPIcs.APPROX/RANDOM.2023.15. [STV25] Noah G. Singer, Madhur Tulsiani, and Santhoshini Velusamy.Sketching Approximations and LP Approximations for Finite CSPs Are Related. Sept. 23,

  15. [15]

    Optimal Single-Pass Streaming Lower Bounds for Approximating CSPs

    arXiv:2604.08731 [cs]. Pre-published. [SV26] Amatya Sharma and Santhoshini Velusamy.Characterizing Streaming Decidability of CSPs via Non-Redundancy

  16. [16]

    Characterizing Streaming Decidability of CSPs via Non-Redundancy

    arXiv: 2604.21922 [cs.DS] .URL: https://arxiv.org/abs/2604. 21922. [Vu24] Hoa T Vu. “Revisiting maximum satisfiability and related problems in data streams”. In: Theoretical Computer Science982 (2024), p. 114271. [Zhu20] Dmitriy Zhuk. “A proof of the CSP dichotomy conjecture”. In:Journal of the ACM (JACM)67.5 (2020), pp. 1–78. 26