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Discrete Mathematics Papers of the Week

The 20 most important new Discrete Mathematics papers of the past week, ranked by potential impact. A new issue comes out every Monday, covering the previous Monday-to-Sunday week.

Nov 2 - 8, 2026 · Updated · Category: Discrete Mathematics (arXiv cs.DM)

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  1. 1
    The Lovász Local Lemma Is Sharp for The Hat Guessing Number

    A direct application of the symmetric Lovász Local Lemma shows that $\textrm{HG}(G) < e\Delta$ for every graph $G$ of maximum degree at most $\Delta \ge 1$. We prove that this bound is asymptotically sharp: for every $\varepsilon > 0$ and every sufficiently large $\Delta$, there exists a graph $G$ of maximum degree at most $\Delta$ with $\textrm{HG}(G) \ge (e - \varepsilon)\Delta$.

  2. 2
    The Value of Steiner Hubs in Two-Hop Distance Labeling

    We determine it on $\ell_1$ products of betweenness-free metrics, those with no point between two others, a class containing every Hamming graph.

  3. 3
    Edge-structure Fault Diameters of Exchanged Hypercubes

    We determine the edge-structure and edge-substructure fault diameters of the exchanged hypercube $EH(s,t)$ for every $1\le s\le t$.

  4. 4
    Monadically Dependent Graph Classes Have Almost Bounded Merge-Width

    We prove that every monadically dependent graph class has almost bounded merge-width, thereby confirming a conjecture of the first and the last author [STOC '25].

  5. 5
    First-Order Model Checking on Monadically Dependent Graph Classes

    In particular, we show that in monadically dependent classes, tuples of fixed length have only $|U|^{O(1)}$ types of fixed quantifier rank over any set of parameters $U$.

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  1. 6
    Sublinear Metric Distortion for Matching

    Given $n$ agents and $n$ items in an unknown metric space, our goal is to match agents to items so that the total distance between matched pairs is small.

  2. 7
  3. 8
    Metric Testing: A Characterization of One-Sided Testability

    We give a unified framework for one-sided testability (constant-query testing) of metric families.

  4. 9
  5. 10
    The Five Vertex Model on A Rectangle

    We derive a determinantal expression for the inhomogeneous asymmetric five vertex model in a rectangular geometry with arbitrary boundary conditions at the bottom and top.

  6. 11
    Single-Variable Connectivity in Random $k$-SAT

    We resolve longstanding questions about connectivity, reconstruction,and looseness in random $k$-SAT, for sufficiently large fixed $k$ as the number $n$ of variables tends to infinity.

  7. 12
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  9. 14
    Practical Consensus of The Multi-Agent Systems on The Two-Layer Networks with Time-Varying Coupling Weights

    Unlike conventional consensus, which requires the system state errors to converge exactly to the origin, practical consensus only requires the errors to converge to a bounded neighborhood of the origin, thereby better satisfying practical engineering requirements. Motivated by the above analysis, this paper investigates the practical consensus problem for two-layer leader-following multi-agent systems, where uncertain communication environments induce bounded time-varying deviations in coupling weights.

  10. 15
  11. 16
    On Edge-Primitive $2$-Arc-Transitive (Bi-)Cayley Graphs

    In this paper, we give a complete classification of all types of finite edge-primitive $2$-arc-transitive Cayley graphs and bi-Cayley graphs.

  12. 17
    Exact Agreement Expansion Up to The Top Via Non-polarity

    Our key proof ideas center around non-polarity of the distributions of local assignments, probed via a random exposure of elements.

  13. 18
  14. 19
    Research on Graph Theory Segmentation Method and Dataset Construction for Connected Handwritten Digits

    On the methodological front, this paper proposes a “cutting domain” graph-theoretic segmentation framework.

  15. 20
    R4-Graph: Integrating Sustainability Into Discrete Mathematics Education Through Resilience-Oriented Graph Theory

    This study develops R4-Graph, a four-phase sequence for sustainability-oriented discrete mathematics: Represent, Remove, Recover, and Redesign.