对谈 DeepSeek-Prover 核心作者辛华剑：Multi Agent 天然适合形式化数学 ｜Best Minds

Group 1 - The core idea of the article emphasizes the importance of "experience" in achieving AGI, particularly through reinforcement learning (RL) and the accumulation of high-quality data that is not present in human datasets [3][4] - The article discusses the significant advancements in AI's mathematical proof capabilities, highlighting the success of models like DeepMind's AlphaProof and OpenAI's o1 in achieving superhuman performance in mathematical reasoning [3][4] - The transition from static theorem provers to self-planning, self-repairing, and self-knowledge accumulating Proof Engineering Agents is proposed as a necessary evolution in formal mathematics [4][5] Group 2 - The article outlines the challenges faced by contemporary mathematics, likening them to issues in distributed systems, where communication bottlenecks hinder collaborative progress [26][27] - It emphasizes the need for formal methods in mathematics to facilitate better communication and understanding among researchers, thereby accelerating overall mathematical advancement [24][30] - The concept of using formalized mathematics as a centralized knowledge base is introduced, allowing researchers to contribute and extract information more efficiently [30] Group 3 - The DeepSeek Prover series is highlighted as a significant development in the field, with each iteration showing improvements in model scaling and the ability to handle complex mathematical tasks [35][36][38] - The article discusses the role of large language models (LLMs) in enhancing mathematical reasoning and the importance of long-chain reasoning in solving complex problems [41][42] - The integration of LLMs with formal verification processes is seen as a promising direction for future advancements in both mathematics and code verification [32][44] Group 4 - The article suggests that the next phase of generative AI (GenAI) will focus on Certified AI, which emphasizes not only generative capabilities but also quality control over the generated outputs [5] - The potential for multi-agent systems in formal mathematics is explored, where different models can collaborate on complex tasks, enhancing efficiency and accuracy [50][51] - The vision for future agents includes the ability to autonomously propose and validate mathematical strategies, significantly changing how mathematics is conducted [54][58]