New Bounds for Zarankiewicz Numbers via Reinforced LLM Evolutionary Search

arXiv cs.AI / 5/5/2026

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Key Points

  • The paper reports new exact values of three Zarankiewicz numbers, including Z(11,21,3,3)=116, Z(11,22,3,3)=121, and Z(12,22,3,3)=132, and also updates bounds for many additional cases.
  • It establishes lower bounds for 41 other Zarankiewicz numbers and matches known established values in four more closed cases, with several results coming within one edge of the best known upper bounds.
  • The authors use OpenEvolve, an open-source LLM-based evolutionary search system that iteratively improves mathematical construction-generating algorithms by optimizing a reward signal tailored to the Zarankiewicz-number setting.
  • The work emphasizes practical reproducibility and low cost, reporting total compute expenses under $30 per parameter combination while also publishing the generated algorithms and key implementation details.
  • Overall, the results show that LLM-guided evolutionary search can produce new extremal graph constructions and may be a viable, economical tool for advancing combinatorial mathematics.

Abstract

The Zarankiewicz number \textbf{Z}(m, n, s, t) is the maximum number of edges in a bipartite graph G_{m, n} such that there is no complete K_{s, t} bipartite subgraph. We determine for the first time the exact values of three Zarankiewicz numbers: \textbf{Z}(11, 21, 3, 3)=116, \textbf{Z}(11, 22, 3, 3)=121, and \textbf{Z}(12, 22, 3, 3)=132. We further establish lower bounds for 41 more Zarankiewicz numbers, including several that are within one edge of the best known upper bound, and we match the established value in four more closed cases. Our results are obtained using OpenEvolve, an open-source evolutionary algorithm based on Large Language Models (LLMs) that iteratively improves algorithms for generating mathematical constructions by optimizing a reward signal which we tailored for this specific problem. These findings provide new extremal graph constructions and demonstrate the potential of LLM-guided evolutionary search to contribute to mathematical research. In addition to presenting the resulting constructions, we report the generation algorithms produced, describe the relevant implementation details, and provide our computational costs. Our costs are remarkably low, at less than \$30 for each Zarankiewicz parameter combination, showing that LLM-guided evolutionary search can be an inexpensive, reproducible, and accessible tool for discovering new combinatorial constructions.