陶哲轩亲测，GPT-5 Pro 40分钟破解3年难题，登顶最难数学考试

Core Insights - The article discusses the capabilities and limitations of AI, specifically GPT-5 Pro, in solving complex mathematical problems, highlighting the distinction between computational ability and true understanding [1][2][34]. Group 1: AI Performance in Mathematics - GPT-5 Pro achieved a score of 13% on the challenging FrontierMath test set, indicating strong computational skills but limited understanding of deeper mathematical concepts [2][32]. - The AI demonstrated proficiency in handling structured and symbolic problems but struggled with geometric constructions and problems requiring intuition [40][41]. Group 2: Real-World Testing by Mathematician - Mathematician Terence Tao tested GPT-5 Pro with an unsolved problem in differential geometry, seeking to explore the AI's ability to generate new ideas in unfamiliar areas [5][6][7]. - The AI successfully generated a reasoning chain for simpler cases but failed to maintain accuracy when the problem was slightly altered, revealing its tendency to reinforce incorrect paths [14][15]. Group 3: Insights Gained from AI Interaction - Tao noted that the AI's performance helped him understand the problem better, not because it solved it, but because it illuminated the reasons for its failure [16][17]. - The experiment highlighted the importance of human intuition and situational awareness in research, suggesting that while AI can assist in calculations, it lacks the ability to grasp the broader context [44][45]. Group 4: Implications for Future Research - The article emphasizes the need for a balance between automation and human oversight in research, as excessive reliance on AI could lead to a decline in critical thinking and understanding [38][39]. - The distinction between AI's linear intelligence and human's topological understanding suggests a new division of labor in mathematics, where AI serves as a computational engine while humans focus on structural design and meaning [45][46].