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Clique Is Hard on Average for Sherali-Adams with Bounded Coefficients

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arxiv 2404.16722 v1 pith:CWYZY6QW submitted 2024-04-25 cs.CC

classification cs.CC
keywords cliqueboundedcapturescoefficientsmeasuresherali-adamssizetechnique
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abstract

We prove that Sherali-Adams with polynomially bounded coefficients requires proofs of size $n^{\Omega(d)}$ to rule out the existence of an $n^{\Theta(1)}$-clique in Erd\H{o}s-R\'{e}nyi random graphs whose maximum clique is of size $d\leq 2\log n$. This lower bound is tight up to the multiplicative constant in the exponent. We obtain this result by introducing a technique inspired by pseudo-calibration which may be of independent interest. The technique involves defining a measure on monomials that precisely captures the contribution of a monomial to a refutation. This measure intuitively captures progress and should have further applications in proof complexity.

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  1. TCS-BENCH: Benchmarking State-of-the-Art Generative AI Theoretical Computer Science Research Ability

    cs.CL 2026-08 reject novelty 7.0 of 10

    The paper introduces TCS-Bench, a 300-task proof-generation benchmark from top TCS papers, and reports frontier LLM accuracies from 30% to 68% using an automated verifier.

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