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  AI achieves silver-medal standard solving International Mathematical Olympiad problems

  • 18 users
  • deepmind.google
  • テクノロジー
  • 2024/07/26

  Research AI achieves silver-medal standard solving International Mathematical Olympiad problems Published 25 July 2024 Authors AlphaProof and AlphaGeometry teams Breakthrough models AlphaProof and AlphaGeometry 2 solve advanced reasoning problems in mathematics Artificial general intelligence (AGI) with advanced mathematical reasoning has the potential to unlock new frontiers in science and techno

  • サイエンス
  • 人工知能
  • ai
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  AlphaGeometry: An Olympiad-level AI system for geometry

  • 12 users
  • deepmind.google
  • テクノロジー
  • 2024/01/18

  Research AlphaGeometry: An Olympiad-level AI system for geometry Published 17 January 2024 Authors Trieu Trinh and Thang Luong Our AI system surpasses the state-of-the-art approach for geometry problems, advancing AI reasoning in mathematics Reflecting the Olympic spirit of ancient Greece, the International Mathematical Olympiad is a modern-day arena for the world's brightest high-school mathemati

  • 人工知能
  • 数学
  • AI
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  Solving olympiad geometry without human demonstrations - Nature

  • 5 users
  • www.nature.com
  • 学び
  • 2024/01/19

  Proving theorems showcases the mastery of logical reasoning and the ability to search through an infinitely large space of actions towards a target, signifying a remarkable problem-solving skill. Since the 1950s (refs. 6,7), the pursuit of better theorem-proving capabilities has been a constant focus of artificial intelligence (AI) research8. Mathematical olympiads are the most reputed theorem-pro

  
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  Accessing GPT-4 level Mathematical Olympiad Solutions via Monte Carlo Tree Self-refine with LLaMa-3 8B

  • 4 users
  • arxiv.org
  • テクノロジー
  • 2024/06/13

  This paper introduces the MCT Self-Refine (MCTSr) algorithm, an innovative integration of Large Language Models (LLMs) with Monte Carlo Tree Search (MCTS), designed to enhance performance in complex mathematical reasoning tasks. Addressing the challenges of accuracy and reliability in LLMs, particularly in strategic and mathematical reasoning, MCTSr leverages systematic exploration and heuristic s