FAR系统：AI数学研究从解题转向筛选高价值猜想
AI is getting good enough at mathematics that the harder problem is deciding whi…
改变了AI辅助数学研究的范式，从盲目解题转向智能筛选高价值问题，对科研工作者极具参考价值。
AI is getting good enough at mathematics that the harder problem is deciding which problems deserve its compute and a mathematician's attention.
AI 在数学领域的能力已足够强大，以至于更难的挑战在于决定哪些问题值得分配计算资源以及数学家的注意力。
So this paper shifts AI mathematics from "solve this conjecture" to "find which conjectures are actually worth trying."
因此，这篇论文将 AI 数学从“解决某个猜想”转向了“找出哪些猜想实际上值得尝试”。
Makes a lot of sense because, AI can attempt far more mathematical problems than experts can review.
这很有道理，因为 AI 能够尝试的数学问题远超专家能审查的数量。
So this paper builds the missing ranking layer between those 2 stages.
因此，这篇论文在这两个阶段之间构建了缺失的排序层。
FAR starts with a research direction instead of a hand-picked conjecture, searches the literature for open problems, attempts them at scale, then filters the outputs so expert mathematicians only see the most promising cases.
FAR 从一个研究方向而非手工挑选的猜想开始，搜索文献以寻找开放性问题，大规模地尝试这些问题，然后过滤输出结果，使专家数学家仅看到最有前景的案例。
In its combinatorics pilot, the pipeline narrowed 51,110 papers to 4,717 apparently open, attemptable conjectures, produced 1,050 claimed new resolutions, passed 598 through automated judging, and recommended 77 for expert review.
在其组合学试点中，该流程将 51,110 篇论文缩减为 4,717 个看似开放且可尝试的猜想，产生了 1,050 项声称的新解答，通过自动化评判筛选出 598 项，并推荐 77 项供专家审查。
The authors manually checked 15 selected artifacts and found all 15 mathematically correct, including proofs, counterexamples, and answers to open questions, although 1 had already been solved elsewhere.
作者手动检查了 15 个选定的成果，发现全部 15 个在数学上都是正确的，包括证明、反例以及对开放性问题的回答，尽管其中 1 个已在其他地方被解决。
更进一步：量化金融体系
看懂新闻只是起点——沿量化金融路径，把它变成能交付的工程能力