Core Viewpoint - The collaboration between two Tsinghua University alumni has successfully enhanced the Gemini 2.5 Pro model to achieve a gold medal level in the International Mathematical Olympiad (IMO) through a self-iterative verification process and prompt optimization [1][4][10]. Group 1: Model Performance and Methodology - Gemini 2.5 Pro achieved a 31.55% accuracy rate in solving IMO problems, significantly outperforming other models like O3 and Grok 4 [9]. - The research team utilized a structured six-step self-verification process to improve the model's performance, which includes generating initial solutions, self-improvement, and validating solutions [16][18]. - The model was able to generate complete and mathematically rigorous solutions for 5 out of 6 IMO problems, demonstrating the effectiveness of the structured iterative process [24][23]. Group 2: Importance of Prompt Design - The use of specific prompt designs significantly improved the model's ability to solve complex mathematical problems, highlighting the importance of prompt engineering in AI model performance [12][14]. - The research indicated that detailed prompts could reduce the computational search space and enhance efficiency without granting the model new capabilities [23]. Group 3: Research Team Background - The authors, Huang Yichen and Yang Lin, are both Tsinghua University alumni with extensive academic backgrounds in physics and computer science, contributing to the credibility of the research [26][28][33]. - Yang Lin is currently an associate professor at UCLA, focusing on reinforcement learning and generative AI, while Huang Yichen has a strong background in quantum physics and machine learning [30][35]. Group 4: Future Directions and Insights - The research team plans to enhance the model's capabilities through additional training data and fine-tuning, indicating a commitment to ongoing improvement [42]. - Yang Lin expressed the potential for AI to play a more significant role in mathematical research, especially in addressing long-standing unresolved problems [44].

一切都从这里开始 其中第一个也是最明显的挑战是围绕计算扩展（scaling compute）。 近年来，工艺技术的进步已经放缓。虽然仍有一些可以改变的因素，但改变的难度却呈指数级增 长。 如果您希望可以时常见面，欢迎标星收藏哦~ 正如黄仁勋 (Jensen Huang) 喜欢说的那样，摩尔定律已死——而在本月的 Nvidia GTC 大会上，这 位 GPU 大佬的首席执行官无意中透露了计算缩放定律的根深蒂固。 黄仁勋站在台上，不仅展示了这家芯片设计公司的下一代Blackwell Ultra处理器，还透露了有关其 未来两代加速计算平台的大量细节，其中包括一个包含576 个 GPU 的600kW 机架级系统。我们还 了解到，即将于 2028 年问世的 GPU 系列将以Richard Feynman的名字命名。你肯定在开玩笑！ 芯片制造商不时透露其发展路线图并不罕见，但我们通常不会一次性获得这么多信息。这是因为 Nvidia 陷入了困境。它遇到的障碍不只一个，而是好几个。更糟糕的是，除了投入资金解决问题 外，这些障碍基本上都不受 Nvidia 的控制。 这些挑战对于那些关注的人来说并不意外。分布式计算一直是瓶颈打地 ...