AI 自己发现了数学里没人见过的联系
大模型能证明定理，但选哪些问题去证，一直是人说了算。这篇让模型用自己内部的“直觉”去找数学里值得挖的联系：它拿一个分类器读模型内部激活，从 5 千万对整数序列里筛出 62 对从未被收录过的关联，其中 4 对是文献里完全没有的新发现。整个过程不到 8 小时，成本极低。它不是你明天能用的工具，但它指向一个更远的可能：AI 不只是解题的，还能当发现问题的眼睛。
📄 原文摘要(英文)
Language models can now prove theorems, but people still decide which problems to pursue. We ask whether a model's internal representations can help identify promising mathematical connections. We develop LANTERN, a fast, cost-efficient pipeline that uses a classifier over pretrained-model activations to rank candidate relations, followed by staged filtering, hypothesis generation, executable verification, and analytical checking. Applied to the On-Line Encyclopedia of Integer Sequences (OEIS), LANTERN ranked 50 million pairs among 10,000 frequently referenced sequences and produced 62 verified relations between pairs without an existing OEIS cross-reference. A content screen retained 13 relations worth presenting; nine of these are informative or insightful, including four which are entirely novel to the best of our knowledge: none appears in the OEIS or in our targeted literature search. The entire end-to-end process including classifier training, candidate ranking, filtering and verification took under 8 hours.