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Justin Sun Prize Launches 50-Problem Pilot with MathDB

1 reports1 sources10/26/2026 updated

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The Justin Sun Prize, initiated by Justin Sun and Caltech Professor Tony Yue Yu, has launched a 50-problem pilot project in partnership with MathDB. Covering major unsolved problems in mathematics such as P versus NP and the Riemann Hypothesis, each problem carries a maximum reward of $1 million. Awards are subject to qualification review, anti-money laundering compliance, and official rules. The pilot aims to advance research in these fundamental mathematical challenges.

Written with AI from the reporting · updated 10/26/2026

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  1. 1Unclassified fact stationOct 27, 00:00recorded Oct 27, 00:24single source

    Justin Sun Prize 与 MathDB 启动 50 问题试点

    Read original source ↗ BeInCryptoReplay this moment in the explorer
    Evidence & revisions · 1 source records
    Subject:
    Office of Justin Sun
    Action:
    宣布与 MathDB 合作启动 Justin Sun Prize 50 问题试点
    Object:
    MathDB 平台与 Justin Sun Prize 联合评审数学问题
    Occurred (fact side):
    Oct 27, 00:00
    BeInCrypto ↗Report pageOriginal / first version
    Published (valid side):
    Oct 27, 00:00
    First seen (known side):
    Oct 27, 00:24
    Fact stationSingle source · one-sided claim

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Oct 27
  1. BeInCryptoRelated fact: single source
    Justin Sun Prize partners with MathDB to launch 50-problem pilot with $1 million awards

    Report first recorded Oct 27, 00:24

    The Justin Sun Prize, founded to become the "Nobel Prize of the AI Era," has launched a pilot program with MathDB, a mathematical research platform led by Caltech Professor Tony Yue Yu. The initiative covers 50 mathematical problems across four award levels, with individual prizes reaching up to $1 million, including the highest tier featuring Millennium Prize problems such as P versus NP and the Riemann Hypothesis. The collaboration introduces a submission and review process where MathDB handles solution verification and Lean formalization, while the Prize separately assesses eligibility and attribution. Rewards are divided into 50% for mathematical solutions, 30% for formalization, and 20% for exposition, aiming to integrate human insight with machine intelligence.

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