# FAR：为AI数学研究构建问题筛选层

- 来源：Rohan Paul (@rohanpaul_ai)
- 发布时间：2026-08-31 22:27
- AIHOT 分数：53
- AIHOT 链接：https://aihot.virxact.com/items/cmthcsw2d0afvrodmghmpgom0
- 原文链接：https://x.com/rohanpaul_ai/status/2094431998538551687

## AI 摘要

一篇论文提出FAR框架，将AI数学从“解题”转向“选题”，在文献中搜索开放问题并大规模尝试，再筛选出最值得专家关注的结果。在组合学试点中，该流程将51,110篇论文缩减至4,717个开放猜想，产出1,050个新解法，598个通过自动评判，77个推荐专家评审。人工抽查15项成果全部正确。

## 正文

AI is getting good enough at mathematics that the harder problem is deciding which problems deserve its compute and a mathematician's attention.

So this paper shifts AI mathematics from "solve this conjecture" to "find which conjectures are actually worth trying."

Makes a lot of sense because, AI can attempt far more mathematical problems than experts can review.

So this paper builds the missing ranking layer between those 2 stages.

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.

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.

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.