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Graph Decompositions at the Integral Expectation Threshold

OpenAI · October 5, 2026

Lean

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Abstract

We prove the graph-decomposition conjecture of Ascoli, He, Park, and Talagrand. Every graph admits a partition into a universally bounded number of fixed edge pieces, each having ordinary containment threshold at most a universal constant times the original graph's integral expectation threshold. The partition is chosen before sampling the random host, and the separate embeddings of the pieces need not agree on shared vertices.

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Collected automatically by AI tools (Claude Opus 5.5 and GPT-6 Astra, 2026-10-21) and not checked by a person. May be wrong or incomplete. Report an error.

Formalization

  • OpenAI, Family 175 Lean scope note (lean/docs/175.md) (OpenAI Math Release, Oct 2026). Documents the formalized discrete-convexity theorem with $k=2^{75}$, which this manuscript uses as a companion input; the graph-decomposition theorem (Theorem 1.1) is not listed in its scope. [family-level]

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Cite

@misc{OAI:Graph-Decompositions-at-the-Integral-Expectation-Threshold-October-5-2026,
  author = {{OpenAI}},
  title = {{Graph Decompositions at the Integral Expectation Threshold}},
  howpublished = {OpenAI Math Release preprint
                  \href{https://github.com/openai/math/blob/main/preprints/Graph-Decompositions-at-the-Integral-Expectation-Threshold-October-5-2026/graph-threshold-decompositions.pdf}{OAI:Graph-Decompositions-at-the-Integral-Expectation-Threshold-October-5-2026}},
  year = {2026}
}

Lean formalization

OpenAI provides Lean files for this family: lean/docs/175.md. The Lean statements have not been checked by people against the paper's claims.

Family 175: Talagrand’s expectation thresholds, discrete convexity, and graph decompositions

Proves that integral and fractional expectation thresholds differ by at most a universal factor, and resolves Talagrand's discrete-convexity conjecture. An application proves the Ascoli–He–Park–Talagrand graph-decomposition conjecture: every graph's edges split into a universally bounded number of fixed pieces, each with containment threshold at most a universal constant times the original graph's integral expectation threshold. The pieces' embeddings need not agree on shared vertices.

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