Avidor-Zwick Question on Low-Dimensional Max-Cut SDP

Precise statement

For fixed $d$, can every $d$-dimensional feasible solution of the triangle-strengthened Max-Cut SDP be rounded in polynomial time with ratio strictly larger than $\alpha_{GW}$? A rounding achieving $\alpha_{GW} + 2^{-O(d)}$ answers yes.

The source statement is reproduced for indexing with attribution. Mathematical correctness requires domain-expert or mechanical review. VibeMath has not independently audited statement fidelity, correctness, priority, or novelty.

What AI did

Gemini (internal), ChatGPT-5.2 Extended Pro, Gemini 3.0 Pro DeepThink

The key anti-concentration lemma for signs of low-dimensional Gaussian projections was first proved by Google's internal Gemini model with a weaker bound; the optimal $2^{-\Theta(d)}$ form was then obtained with ChatGPT-5.2 Extended Pro and Gemini 3.0 Pro DeepThink, with proofs edited by the authors.

Provider: Google DeepMind / OpenAI · Prompt public: unknown · Independence: unknown

Verification boundary

unreviewed

Author-edited and checked arXiv preprint. Not yet peer-reviewed.

Correctness: unknown · statement fidelity: unaudited · peer review: none

Timeline

1. 2026-04-16

   arXiv:2604.13971 - Max Cut with small-dimensional SDP solutions

   VibeMathed reports this item as resolved. VibeMath preserves that report as a source assertion and has not independently authored a plain-language mathematical explanation.

Known method families

argument (source-reported)

Source-reported tools: argument.

Independent: unknown · difference confidence: 0

What remains uncertain

VibeMath has not independently audited the mathematical statement, proof, or novelty claim.

• VibeMath has not independently verified the mathematical claim.
• AI-attempt independence and training-data exposure are unknown unless explicitly documented.
• VibeMath has not independently audited the mathematical statement, proof, or novelty claim.