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].

Core Viewpoint - Nvidia is facing multiple challenges in scaling its computing capabilities, particularly in the context of AI and GPU technology, as highlighted during the recent GTC event where significant details about future products were revealed [1][2][17]. Group 1: Challenges in Computing Scaling - The first major challenge is around scaling compute, as advancements in process technology have slowed down, making it increasingly difficult to enhance performance [2][6]. - Nvidia's strategy involves maximizing the number of chips per compute node, with plans to increase GPU counts in racks from 72 to 144 and eventually to 576 [2][5]. - The performance of Nvidia's Blackwell chips is only marginally better than the previous generation, requiring more chips and higher power consumption to achieve these gains [3][6]. Group 2: Power and Cooling Issues - By 2027, Nvidia's rack power is expected to reach 600kW, which poses significant challenges for data center operators in terms of cooling and power supply [5][10]. - The transition to ultra-dense computing systems necessitates advanced cooling solutions, as traditional methods may not suffice for the increased heat output [10][12]. - Nvidia's collaboration with partners like Schneider Electric aims to design specialized data centers to meet the power and cooling demands of AI workloads [11][12]. Group 3: Market Dynamics and Competition - Major cloud providers, including Microsoft, are slowing down their data center expansions due to the inability of existing facilities to support the power and cooling needs of Nvidia's latest systems [12][13]. - The cancellation of data center leases by Microsoft suggests a strategic pivot towards building new facilities that can accommodate the demands of advanced AI hardware [15][16]. - Nvidia's challenges are not unique, as competitors like AMD and Intel will likely face similar issues in scaling their offerings to meet market demands [17][20]. Group 4: Regulatory and Market Risks - Nvidia's business in China is uncertain due to U.S. export restrictions, which limit the sale of advanced chips, potentially impacting revenue from this significant market [19][20]. - New regulations in China may further restrict the use of Nvidia's products in data centers, posing a risk of losing market share to local competitors [20][21].